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Search Fundamentals of Biochemistry In the last section, we studied how the cell surface GPCRs indirectly activate membrane-bound enzymes adenylyl cyclase and phospholipase C (the GPCRs) and how RTKs become active enzymes (kinases) themselves on ligand-induced dimerization. We will now explore what happens next in signaling by examining the products of these activated enzymes and how they continue the signaling process. The products of adenylyl cyclase (cAMP) and phospholipase C (diacylglycerol - DAG - and IP3) are second messengers. These activate key protein kinases in the cell. The products of the activated RTKs are phosphoproteins (including itself) that are phosphorylated on tyrosines by the RTK. Target proteins bind to the autophosphorylated RTKs through SH2 domains on the target protein. First, we will explore protein kinases and their mechanism in general. The Kinome There are 518 different protein kinases (about 1.7% of the human genome) and as a group, they are the key players in most eukaryotic signaling pathways. Collectively they are called the kinome. They help regulate every aspect of cell life including metabolism, cell growth, differentiation, and division, as well as programmed cell death. In humans, most (388) are Ser/Thr kinases. 90 are Tyr kinases and 40 are classified as atypical. In this section, we will focus on the AGC kinases (60 in total) which include Protein Kinase A (PKA), Protein Kinase C (PKC), and also Protein Kinase B (PKB, better known as AKT. We'll wait for another section to explore Protein Kinase G (PKG) which is activated by cGMP, not AMP. We'll also study the receptor Tyr kinases (RTKs) Luckily kinases can be grouped into families based on structure and mechanism. The ones directly activated by the second messengers cAMP (Protein Kinase A) and DAG (Protein Kinase C) are members of one family of kinases the AGC Protein Kinase family. The clustered families of protein kinases are shown in Figure \(1\). Figure \(1\): The family of protein kinases. https://peerj.com/articles/126/. Illustration reproduced courtesy of Cell Signaling Technology, Inc. (www.cellsignal.com) Luckily, most of the kinases, which catalyze the phosphorylation of the OH-containing side chains (Tyr, Ser, and Thr) of proteins have similar catalytic sites. Given that phosphorylation of proteins often acts as an on/off switch for protein activity and function, it makes great sense that the catalytic site of protein kinases is not available for substrate binding and/or catalysis in the off state. Protein kinases have catalytic and activation loops and conformation changes on activation of protein kinases usually involve movement of the activation loop and rearrangement of the catalytic residues. These conformational changes are often initiated by phosphorylation by another upstream active protein kinase of key groups in the activation loop! Figure \(2\) shows another representation of the different families of protein kinase and their common structural features. Pane (A) shows the nine eukaryotic and one atypical protein kinase groups with their numbers of kinases, structures, and inhibitors (shown in black) as well as mutations (in red) and SNPs (in green). Here, the atypical protein kinases are defined as all non-eukaryotic protein kinases including also the nonprotein kinase-like kinases. The middle of Panel (B) shows the activation and catalytic loops and other common features found in the active sites of protein kinases. The left and right parts of Panel (B) show two different kinases. The protein kinase domain of epidermal growth factor receptor (EGFR) is shown on the left and the Ser/Thr protein kinase domain of mTOR (mammalian or mechanistic Target Of Rapamycin), a protein we will explore in a separate section, is shown on the right. AGC Kinases These serine/threonine kinases are activated by the second messengers cAMP (A Kinase), cGMP (G kinase, which we will see later), and by diacylglycerol (DAG) formed by activation of phospholipase C. Figure \(3\) shows the generic structure of active kinase domains (including tyrosine kinases) A generic kinase has an N-terminal and C-terminal lobe between which the substrates ATP and the target protein containing the key serine, threonine, or tyrosine side chain bind. In the active conformation, Glu 91 (E91) in the αC-helix in the N-lobe forms a salt-bridge (ion-ion interaction) with Lys72 to position it so it can stabilize the binding of the ATP through ion-dipole and H-bond interactions. In the active state, Thr 197 (T197) in the activation loop has been phosphorylated (represented by pT197) by an upstream kinase allowing a less flexible conformation of the loop. This facilitates optimal repositioning of active site side chains in the catalytic loop allowing substrate access and catalysis. Asp 166 (D166) in the catalytic loop acts as a general base in abstracting a proton from the target Ser, Thr or Tyr. Arginine 165 (R165) in the catalytic loop forms a salt bridge to phosphotyrosine 197 (pT197), stabilizing the catalysis-competent conformation of the activation loop. Aspartic acid 184 (D184) plays a key role in stabilizing the Mg2+ that itself stabilized the negative charges on the phosphates of ATP and in the developing transition state. Most kinases are also classified as RD kinases since they have a key arginine (R)- aspartic acid (D) sequence in the catalytic loop. The normal protein substrate for an AGC kinase would be a protein with a Ser or Thr projecting into the active site for phosphorylation by bound ATP. The normal sequence for phosphorylation in protein targets of PKA is Arg-Arg-X-Ser/Thr-X (or more fully as XR(R/K)X(S/T)B where B is a hydrophobic amino acid). Again we will focus on three AGC kinases here, PKA, PKC, and AKT (PKB), and defer our discussion of PKG to another section. cAMP-dependent Protein Kinase A - PKA Before we study PKA, the formation of the second messenger cAMP is presented in review in Figure \(4\). PKA contains a kinase domain with short N- and C-terminal extensions, making it a prototypical and simple model for study. In the absence of the second messenger cAMP, this prototypic AGC kinase exists as a holo-heterotetramer (or dimer of a heterodimer). It contains two catalytic kinase subunits (C) and two regulatory subunits (R). cAMP produced upon GPCRs activation of adenylyl cyclase binds to the regulatory subunits, causing them to dissociate from the heterotetramer, freeing the catalytic subunits for activity. This is illustrated in Figure \(5\). The cAMP serves as an allosteric activator of protein kinase A. Figure \(6\) shows the actual structures of the protein kinase A RIIb tetrameric holoenzyme (3TNP) The inhibited catalytic subunits are shown in red and the two regulatory subunits are shown in cyan. Figure \(7\) shows only one catalytic subunit of protein kinase A as it goes from the more closed inactive holoenzyme (tetramer, 3tnp) containing the regulatory subunits (not shown), to the more open apo-form (monomer, 1J3HA), without the regulatory subunit. The conformational change opens the free catalytic subunit to the substrate (protein and ATP) binding. Side chains required for MgATP binding and phosphoryl transfer are pre-formed in the apo form but some changes still occur on the binding of substrate. Figure \(8\) shows an interactive iCn3D model of the mouse catalytic subunit of cAMP-dependent protein kinase complexed with MnATP and a peptide inhibitor (1ATP) Zoom into the model to see each of the labeled residues. All of the features shown in Figure \(3\) are displayed in this model. The activation loop is shown in dark blue and the catalytic loop is shown in red. The peptide inhibitor is shown in cyan. The normal target motif for phosphorylation by Protein Kinase A (Arg-Arg-X-Ser/Thr-X) has been replaced in this model by Arg-Arg-X-Ala-X. Why does this make the peptide an inhibitor instead of a substrate? Many primary signals activate adenylate cyclase through GPCR and produce cAMP as a second messenger. These include corticotrophin, dopamine, epinephrine (β-adrenergic), follicle-stimulating hormone, glucagon, many odorants, prostaglandins E1and E2, and many tastants. All of these would activate protein kinase A. Some enzymes regulated by cAMP-dependent phosphorylation by PKA are shown in Table \(1\) below Enzyme Pathway Glycogen Synthase Glycogen synthesis Phosphorylase Kinase Glycogen breakdown Pyruvate Kinase Glycolysis Pyruvate Dehydrogenase Pyruvate to acetyl-CoA Hormone-sensitive Lipase Triacylglycerol breakdown Tyrosine Hydroxylase Synthesis of DOPA, dopamine, norepinephrine Histone H1 Nucleosome formation with DNA Histone H2B Nucleosome formation with DNA Protein phosphatase 1 Inhibitor 1 Regulation of protein dephosphorylation CREB cAMP regulation of gene expression PKA consensus sequence XR(R/K)X(S/T)B (B = hydrophobic amino acid) Table \(1\): Proteins phosphorylated by Protein Kinase A. An example of how epinephrine (a flight/fight hormone) can lead to the breakdown of glycogen (your main carbohydrate reserves in muscle and liver) is shown in Figure \(9\). A cascade of events starts with the binding of the hormone to its receptor, followed by the activation of adenylate cyclase and the formation of the second messenger cAMP. This activates PKA, which phosphorylates and activates the enzyme phosphorylase kinase. Following the naming convention we discussed earlier, phosphorylase kinase is a protein kinase whose target enzyme is another enzyme called glycogen phosphorylase, which is NOT a kinase. When active, glycogen phosphorylase uses inorganic phosphate (Pi) as a nucleophile in a phosphorolysis reaction to cleave glucose from the end of glycogen polymers forming glucose-1-phosphate. This is the first step in the mobilization of glycogen as an energy reserve. No wonder it is so tightly regulated by this complicated  signaling pathway. The second messengers DAG and IP3 and their activation of Protein Kinase C The activation of protein kinase C (PKC, a Ser/Thr kinase member of the ACG protein kinase family) is very similar to that of protein kinase A. To start, an extracellular signal molecule binds to a GPCR receptor (again with no intrinsic enzyme activity), causing a conformational change in the receptor that propagates through the membrane to its intracellular domain. That then activates the exchange of GTP for GDP in the alpha subunit of the bound heterotrimeric G protein, which contains the special Gαq subunit. The Gαq subunit dissociates and binds to the membrane protein enzyme phospholipase C (not adenylyl cyclase). Once activated, it cleaves the phospho-head group from the membrane phosphatidyl inositol - 4,5-bisphosphate (PIP2) into two, second messengers - diacylglycerol and inositol trisphosphate (IP3). These products are shown in Figure \(10\). The formation of the second messengers IP3 and DAG is presented n Figure \(11\). Note that phospholipase C is a peripheral membrane protein bound to the inner leaflet of the cell membrane. Diacylglycerol binds to and activates protein kinase C (PKC). The IP3 binds to ligand-gated receptor/Ca2+ channels on internal membranes, leading to an influx of calcium ions into the cytoplasm. The released calcium ions also activate PKC. These steps are illustrated in Figure \(12\) in more detail below. The activation of the peripheral membrane phospholipase C (PLC) is very similar to that of the integral membrane protein adenylyl cyclase, both of which produce second messengers. Figure \(12\): GPCR activation of phospholipase C, generation of second messengers IP3, DAG, and Ca2+ ions, and downstream activation of Protein Kinase C You might also expect the regulation of the activation of protein kinase C (PKC) activity to be similar to the regulation of the activation of protein kinase A (PKA). It is, but with a major difference. In contrast to the structure of PKA, which cycles between an inactive R2C2 (R is the regulatory and C the catalytic subunit) and an active separated form, PKC is a single chain that has a regulatory, domain, and catalytic domain as shown in Figure \(13\). Variants of PKC are also shown. Protein kinase C was named "C" because of its activation by Ca2+ ions, but you could also remember it because it requires the upstream activation of phospholipase C. There are more than 500 PKCs divided into 15 subgroups and their downstream functions lead to gene expression. They all have 4 common domains, C1-C4. C1 and C2 are the functional regulatory domain and C3 and C4 are in the overall catalytic domain. They have the following activities. • C1 binds diacylglycerol (DAG) and phorbol esters (commonly known activators or PKC) • C2 binds Ca2+, another second messenger, which activates the protein; Novel PKCs use DAG and phosphatidyl ethanolamine but not Ca2+ to activate the protein, while atypical ones use only DAG. • C3 binds ATP • C4 binds target proteins for phosphorylation. The structure of the phorbol 12-myristate 13-acetate, which activates PKC, is shown in Figure \(14\). In a novel way to keep the protein inactive, it contains a small peptide sequence (a pseudosubstrate) that mimics the amino acids around the target phosphorylation site (with the target Ser/Thr). This binds in the active site in the inactive form of PKC like a competitive inhibitor and prevents PKC activity. PKCα is found in the cytoplasm, cell membrane, nucleus, and mitochondria. Its activation by DAG suggests it becomes localized to membrane surfaces. The protein is conformationally very flexible (note the hinge domain in Figure \(13\)) so it has been difficult to get detailed structures. Figure \(15\) shows an interactive iCn3D model of the PKC (alpha)-C2 domain complexed with Ca2+ and PtdIns(4,5)P2 (IP3) (PDBID 3GPE). The hydrogen bonds and ion-ion interaction between the C2 domain and IP3 are detailed and labeled. Protein kinase C is targeted to the membrane (as shown in Figure \(12\)), but it lacks the Pleckstrin Homology (PH) which most phospholipase Cs use to interact with PIP2 in the membrane. Before activation of PKC by DAG and Ca2+, it must be phosphorylated sequentially. Before these phosphorylations, PKC loosely binds to the membrane with the activation loop open to phosphorylation by kinases. These are the subsequent steps: 1. phosphorylation of Thr500 (PKC βII)) in the activation loop of PKC by an upstream kinase PDK1 (a kinase which also phosphorylates other AGC kinases such as AKT discussed below). This kinase can bind to the exposed activation loop 2. autophosphorylation at the C-terminus of PKC, which leads to conformational positioning of side chains needed for catalysis and substrate binding, and access to the substrate binding site PKC engages in a very dynamic cycle. It starts as the inactive cytoplasmic form that is autoinhibited by its pseudosubstrate sequence. It then moves to the membrane where the autoinhibition is relieved. All of this requires flexibility which makes it difficult to determine its structure. Figure \(16\) shows how the inactive cytoplasmic form of PKC becomes activated at the cell membrane. The regulatory domain, which contains the C1 (DAG binding) and C2 (Ca2+) domains binds to the membrane freeing and activating the kinase domain on the release of the bound internal pseudosubstrate. Figure \(17\) the optimal consensus sequence flanking both sides of the phosphorylation site in target proteins (based on model peptides) and the sequence of the internal pseudosubstrate motif for several PKCs. Boxed amino acids show structural similarity between the target and internal pseudosubstrate. Position 0 indicates the serine that is phosphorylated in the target. Note that it is replaced with alanine in the pseudosubstrate. Also, note the abundance of positively charged side chains. Figure \(16\) also shows that phosphorylation of key side chains facilitates the activation of the enzyme. The upstream kinase 3-phosphoinositide-dependent protein kinase 1 (PDPK1 or PDK1) phosphorylates the key Ser/Thr in the activation loop as we discussed above. PDK1 is a "master" Ser/Thr kinase which phosphorylates and activates many proteins, including PKA, PKC, and protein kinase B (which we will explore below). Figure \(18\) shows an interactive iCn3D model of the Protein Kinase C beta II (3PFQ) The C2 domain (magenta)has two bound Ca2+ ions (gray spheres) and interacts with the bottom leaflet of the cell membrane. The C1 domain (purple) has two bound Zn2+ ions. The N lobe of the kinase domain is shown in green and the C lobe is shown in brown. A nonhydrolyzable ATP analog, AMPPNP (ANP), is shown in spacefill between the N and C lobes. There is an additional NFD helix preceding the C-terminal tail which can adopt two positions, one which confers low activity and the other high PKC activity. The Phe629 in this region is out of the active site in the low activity form and in it and interacting with the adenine of ATP in the high activity form. Some signals that activate phospholipase C and make IP3 and diacylglycerol include acetylcholine (a different class than the type located at the neuromuscular junction that we discussed in the last chapter section), angiotensin II, glutamate, histamine, oxytocin, platelet-derived growth factor, vasopressin, gonadotropin-releasing hormone, and thyrotropin-releasing hormone. In the inactive form of PKC, the arginine-rich basic autoinhibitory pseudosubstrate interacts with acidic side chains in the substrate binding site.An acidic patch in the substrate-binding site (Figure 6.2). When PKC is activated by phosphorylation of the regulatory domain, it phosphorylates arginine-rich sites in protein substrates. This also releases pseudosubstrates from some target inactive protein kinases, which allows them to become active kinases in turn. PKC physiological substrates include receptors, cytoskeleton proteins, protein kinases, proteases, and nuclear proteins The Ca2+ ions also act as second messengers. The calcium ions bind to the calcium-modulatory protein, calmodulin, which binds to and activates the calmodulin-dependent kinase (CAM-PK), which we will discuss later. Some kinases regulated by calcium and calmodulin include myosin light chain kinase, PI-3 kinase, and CAM-dependent kinases. Ca/CAM also regulates other proteins which include: adenylate cyclase (brain), Ca-dependent Na channel, cAMP phosphodiesterase, calcineurin (phosphoprotein phosphatase 2B), cAMP gated olfactory channels, NO synthase, and plasma membrane Ca/ATPase. Protein Phosphorylation by activated Receptor Tyrosine Kinases (RTKs) Figure \(19\)s shows the dimeric structure of RTKs driven by extracellular signal binding. Table \(2\) below shows the classification of RTKs into classes and families. Table \(2\):EGFR: epidermal growth factor receptor; InsR: insulin receptor; PDGFR: platelet-derived growth factor receptor; VEGFR: vascular endothelial growth factor receptor; FGFR:fibroblast growth factor receptor; CCK: colon carcinoma kinase; NGFR, nerve growth factor receptor; HGFR: hepatocyte growth factor receptor; EphR: ephrin receptor; Axl: from the Greek word anex-elekto, or uncontrolled, a Tyro3 protein tyrosine kinase; TIE:tyrosine kinase receptor in endothelial cells; RYK: receptor related to tyrosine kinases; DDR: discoidin domain receptor; Ret: rearranged during transfection; ROS: RPTK, expressed in some epithelial cell types; LTK: leukocyte tyrosine kinase; ROR: receptor orphan; MuSK: muscle-specific kinase; LMR: Lemur. Sareshma Sudhesh Dev et al. Front. Pharmacol., 15 November 2021 | https://doi.org/10.3389/fphar.2021.772510. Creative Commons Attribution License (CC BY). We introduced the activation of RTKs in the previous section and indicated that ligand-induced dimerization led to their activation. Let's expand on that now. As in the case of PKC, the intracellular kinase domains of RTKs are inhibited by specific structures in their chains (cis-autoinhibited). These include the activation loop but in addition C-terminal sequences and the sequence region linking the C-terminal domain with the transmembrane domain. This is called the juxtamembrane region. All of these must be phosphorylated for the activation of kinase activity. The autoinhibition is released on ligand binding and dimerization. The kinase domains can also be allosterically activated by the kinase domain of the dimer. Each domain phosphorylates the cytoplasmic domain of the other, so it's called trans-phosphorylation (i.e. a kinase domain on one monomer does not autophosphorylate itself, which would be called cis-phosphorylation). The now active kinase domains recruit target proteins containing SH2 domains (which bind p-Tyr peptides/proteins) in a fashion that propagates signaling. These include proteins involved in other signaling pathways including MAPK and phosphoinositide-3-kinase (PI3K)/Akt pathways which we will discuss later. A particular phospholipase Cγ (PLC-γ) is also activated by an RTK. Cancers can arise if the RTK signaling pathways, which control cell growth and division, are overactive. Figure \(20\) shows four different mechanisms for the expression and/or activation of RTKs that could lead to cancer. We will focus on the epidermal growth factor receptor (EGFR) for most of the remaining discussion. One of the most interesting questions is how ligand binding in the extracellular domain of the biotopic integral membrane proteins leads to an intracellular signal in the cytoplasmic domain. It's difficult to imagine such an activation propagating through a single transmembrane helix. We'll discuss how ligand-promoted dimerization of the receptor appears to be the main mechanism of activation of RTKs. Figure \(21\) shows the domain structure of three different RTKs, including the insulin receptor (IR) family. The insulin receptor is also synthesized as a single chain but undergoes proteolysis and interchain disulfide bond formation to give the "dimeric" structure shown below. The EGFR is a member of the ErbB family which consists of 4 members: EGFR (also called Erb1 or HER1), ErbB2 (HER2), ErbB3 (HER3), and ErbB4 (HER4). The name ErbB arises from the avian ERythroBlastosis oncogene B). They are also called HER after the Human Epidermal growth factor Receptor). The HER2 gene is often dysregulated in breast cancer. Members of the ErbB family have unique numbers and positions of tyrosine in the C-terminal kinase domains. EGFR has 20, of which 12 are phosphorylated. The EGFR is also a bit unique in that it has only one tyrosine in the activation loop that is phosphorylated but the tyrosine itself is not required for kinase activity. Although we suggested earlier that RTKs are activated on dimerization, studies show that RTKs However, an increasing number of studies demonstrate that RTKs exist as inactive dimers in the absence of the ligand. As shown in Figure \(21\), ErbB receptors have 4 extracellular domains, a transmembrane domain, the juxtamembrane region (about 40 residues), the cytoplasmic kinase domain and a C-terminal extension that gets autophosphorylated and which binds downstream target protein through their SH2 domains. Extracellular domains I ( L1) and III/L2 have β-helix solenoid secondary motifs that bind the ligand. Domains II/CR1 and IV/CR2 are cysteine-rich disulfide bonds. Some fraction (>80% through cross-linking studies) of the ErbB receptors exist as dimers at the cell surface in the absence of ligand, a finding in contrast to the older view that ligand binding is required for dimerization The structure of the extracellular domains of the free ErbB and ligand-bound EGFR receptors show conformational changes that are required for dimer formation. In the absence of the ligand, a "tether" arm, denoted by an open triangle in domain IV in Figure \(22\), is close to a buried "dimerization" arm (asterisk *) in domain II of the extracellular regions of EGFR, ErbB3, and ErbB4, effectively inhibiting dimerization. When the ligand is bound, domains I and III interact, freeing the dimerization domains to interact (** in EGFR). These features are illustrated in Figure \(22\). Figure \(12\): Schematic representations of the structures of the extracellular regions of the ErbB family. EGFR, ErbB3, and ErbB4 adopt the tethered conformation in the absence of ligand, while ErbB2 adopts an extended, or untethered, conformation that resembles the ligand-activated, dimerization-competent EGFR protomer in the ligand-bound form of the EGFR dimer, shown at the right. The ‘dimerization arm’ and ‘tethering arm’ are shown by an asterisk * and an open triangle, respectively. Ligands are shown in red. Domains I–IV correspond to the domains shown in Figure \(21\). Not drawn to scale. Ichiro N. Maruyama 2014 Jun; 3(2): 304–330. doi: 10.3390/cells3020304' (http://creativecommons.org/licenses/by/3.0/). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4092861/ In addition, the tether arms on both monomers now interact in the dimer. Note that the ligand binding region is not involved in dimer formation and is not in the dimer interface, as it is with other RTKs where the ligand is involved in direct contact in the dimer interface. Mutations in the II/IV domains that inhibit their interactions do not lead to receptor activation so ligand binding is still required. ErbB2 exists in an extended conformation (no ligand is known for it) so it is free to interact with another ErbB chain in a heterodimer. Structural studies now suggest that EGFR kinase dimer has a symmetrical inactive conformation in which the activation loop is packed and occluding the active site. In addition, it has an asymmetrical active one in which the activation loop is unpacked and the active site is open. How is this transmitted across the subunits? It appears that the C-lobe of the "activator/donor" kinase interacts with the N-lobe of the adjacent "receiver/acceptor" kinase which activates it through a conformational change. When the ligand binds, the inactive dimer dissociates, and the asymmetric active dimer results. Two models have been proposed for ligand-gated activation of the Erb dimers: the dimerization and rotation/twist models, as shown in Figure \(23\). • A. Dimerization model: Ligand binding to the I and III extracellular domains of a monomer lead to dimerization by causing the release of the tether arm between I and III, allowing the dimerization arms to become free and interact with each other. This causes the kinases domains to adopt the active state • B. Rotation/Twist model: The receptor is already a dimer in the unliganded state with the extracellular region untethered and the intracellular kinase domains in an inactive symmetric state. Ligand binding causes the two dimerization arms to extend, causing a twist in the transmembrane segment. This causes the kinase domains to adopt the active asymmetric state with the activator kinase domain of one monomer activating the receiver kinase domain of the second monomer in the dimer, with each forming an extended conformation. Figure \(24\) shows an interactive iCn3D model of the intracellular dimeric EGFR kinase domains in complex with an ATP analog-peptide conjugate (2GS6) The two EGFR kinase domains are shown in cyan and magenta. The ATP analogs in each domain (spacefill) are thiophosphoric acid O-((adenosyl-phospho)phospho_-S-acetamidyldiester. The peptide substrates (green stick) are 13-mers with a tyrosine (sticks, labeled Y, minus the OHs) connected to the ATP analog. Figure \(25\) shows an interactive iCn3D model of a single EGFR kinase domain showing N and C terminal lobes in complex with an ATP analog-peptide conjugate (2GS6) The ATP part of the ATP-peptide conjugate is sandwiched between N-lobe (green) and the C-lobe (brown) just as in PKA. The ATP in the peptide conjugate is shown in spacefill while the peptide is shown in gray. The tyrosine (Y) linked to the peptide is labeled. Figure \(26\) shows an interactive iCn3D model of showing the similarity in structure between the PKA kinase domain (1J3H A chain) and the EGFR kinase domain (2GS6) The gray structure by itself is the second kinase domain of the EGFR dimer. The superimposed chains are on the other side. Red indicated identical residue and blue nonconserved in the structural alignment. Zoom in on the aligned sequences in blue and red to show how similar the kinase domains are. In the next chapter section, we will explore the next downstream effects in signaling, mediated by the second messengers cAMP, DAG, and IP3 and the substrates phosphorylated by the ligand-active receptor tyrosine kinases. EGFR and HER2 in breast cancer The HER2 receptor can form homo or heterodimers with single chain ErbB1-ErbB4 (same as HER1-HER4). HER receptors exist as both monomers and dimers, either homo- or heterodimers. Rapid dimerization of HER1, HER3, or HER4 occurs if they form heterodimers with HER2. In addition, any ErbB dimer with HER2 leads to strong intracellular signaling compared to other HER heterodimers. Ligand binding to HERI, HER3, or HER4 induces rapid receptor dimerization, with a marked preference for HER2 as a dimer partner. Since noncancer cells have very little HER2, correspondingly few heterodimers of HER2 around found. If HER2 is overexpressed as in HER2+ breast cancer cells, more heterodimers are found, and anomalously-high levels of signaling occur. This resulted in a poorer prognosis for HER2+ breast cancers in the past. A revolution in breast cancer therapy has changed that situation. Humanized antibodies (human antibodies made in mice cells) to HER2, called trastuzumab (Herceptin) are now used in therapy. Since the antibodies are derived from human genes, they are not targeted as foreign by the immune system. In early-stage HER2+ breast cancers, the antibody trastuzumab (Herceptin) is administered intravenously periodically for one year. The antibody selectively binds to HER2 on breast cancer cells. Once bound, the Fc portion of the bound antibody signals the immune system, leading to the recruitment of immune cells and modulators to the tumor cell, leading to its destruction. If the cancers are in a later stage or if tumor cells are found in lymph nodes, a variant of the anti-HER2 antibody can be given in which a chemotherapeutic drug is covalently attached to the antibody. The structure of the drug Ado-trastuzumab emtansine (T-DM1), trade name Kaycyla, is shown in Figure \(27\). When the antibody-drug complex binds to the receptor on cancer cells, it is taken up into the cell. The antibody is degraded and the toxic drug is released into the cell sparing all other types of cells (only ones with the receptor on it are targeted). The released drug binds to microtubules comprising part of the internal cytoskeleton of the cell and prevents changes necessary for cell division. HER2 has a very low level of expression in noncancer cells so side effects can occur. However, these are much less server than traditional chemotherapy, which targets all dividing cells. The antibody acts as a homing device bringing the attached chemotherapy predominantly to tumor cells. It can be likened to a smart bomb or a cruise missile guided to just one target. A better image of DMI and the linker connecting to the antibody is shown in Figure \(28\). The toxic chemotherapy drug is chemically linked to the antibody through a non-reducible thioether linker, N-succinimidyl-4-(N-maleimidomethyl) cyclohexane-1-carboxylate (SMCC). Figure \(29\) shows a mechanism for the actual cross-linking reaction. The reaction of the maytansine derivative through its free thiol and the human antibody (trastuzumab) through a free amine should be readily understandable base on the reactions present in Chapter 2. The final linker after the departure of the N-hydroxysuccinimide is designated MCC. The C-MET receptor, illustrated as an example in Figure 19, is an RTK involved in cell proliferation and survival, and as such, alterations in its expression can lead to cancer. Its mature form results from selective proteolysis by furin. Its physiological ligand is hepatocyte growth factor (HGF), a multisubunit protein in its mature form. It also binds a naturally occurring smaller splicing variant of HGF called NK1. The structure of C-MET bound to both HGF and NK1 has been solved. Binding of either lead to dimerization of the C-MET receptor, as illustrated in the cartoon representation of Figure \(30\). The glycosaminoglycan heparin enhances c-MET activation by HGF. This is illustrated in Figure 30 above. Heparin binds between the N domain of HGF I and the IPT1 domain of c-MET II, facilitating the interactions between the two domains. In addition, a long enough heparin chain could bridge both HGF I and II, further strengthening the full complex. Structural representations of the c-MET:HGF asymmetric dimer are shown in cartoon form in Figure \(31\). Panel (a) shows the domain structures of human c-MET and HGF. The proteolytic cleavage site of c-MET is located between Arg307 and Ser308. The proteolytic cleavage site of HGF is located between Arg494 and Val495. C-MET927-LZ and full-length HGF were used for structural determination in this study. The dash boxes indicate the domains that were unsolved in cryo-EM maps. Panel (b) shows the 3D reconstruction of the 2:2 c-MET/HGF holo-complex and the corresponding ribbon representation of this complex fitted into the cryo-EM map at 4.8 Å resolution, shown in two orthogonal views. Panel (c) shows the ribbon representation of the c-MET/HGF holo-complex shown in two orthogonal views Figure \(32\): Shows the structure of the c-MET/NK1 symmetrical dimer. Nuclear RTKs What makes signal transduction so complicated yet interesting is the unexpected. It turns out that some RTKs (EGFR, VEGFR, FGFR, IR, and NGFR have been found in the nucleus. It's experimentally easy to localize proteins in cells using immunofluorescence microscopy. It's hard to determine their functions. ErbB-2 is one that is also found in the nucleus. Kinase inhibitors blocked the expression of ErbB-2 in the nucleus suggesting that its kinase activity is required for it to translocate to the nucleus. The structure of the membrane forms of ErbB-2, EGFR, and ErbB-3 appear to be the same as the structure of the nuclear forms. The carboxy-terminal ends of EGFR and ErbB-4 can activate gene transcription as measured by the expression of luciferase (a fluorescent protein) reporter proteins. Hence they appear to act as transcription factors that bind DNA to promote gene expression. For example, nuclear EGFR increases the expression of cyclin D1 which drives progression through the cell cycle. ErbB-2 appears to activate transcription from the promoter of the gene for cyclooxygenase 2 (COX-2). The protein COX-2 leads to the synthesis of inflammatory prostaglandins. It also increases blood vessel growth and is dysregulated in tumors. Since proteomic analysis of these growth factor receptors shows no DNA binding motifs or domains, they must promote gene transcription through binding to other protein transcription factors in the nucleus. Back to AGC Kinase - AKT (Protein Kinase B) We've just explored the: • activation of Protein Kinase A (an AGC Kinase) through GPCR signaling, activation of the integral membrane protein adenylyl cyclase, production of the second messenger cAMP, which binds to inactive PKA and leads to its activation • activation of Protein Kinase C, (an AGC Kinase) again through GPCR signaling, which leads to activation of the peripheral membrane protein phospholipase C, production of the second messengers DAG and IP3 from PIP2, and activation of PKC at the membrane. • activation of receptor tyrosine kinases (RTKs) leading to autophosphorylation of the cytoplasmic kinase domain, followed by recruitment of downstream signaling proteins through binding the phosphorylated RTKs through the downstream protein's SH2 domain Now let's explore another AGC kinase called AKT or protein kinase B (PKB) that links signaling through RTKs to phosphoinositol-related signaling in a fashion similar to the link between phospholipase C and Protein Kinase C activities. In the process, we will introduce in this section our first nonprotein kinase involved in signaling. It's a lipid kinase called phosphoinositide 3-kinase (P13K) and it's very important. Since Protein Kinase B is usually referred to as AKT, we will stick with that abbreviation. As with other AGC kinases, AKT is a Ser/Thr protein kinase. There are three variants, AKT1, AKT2, and AKT3. These are involved in metabolism, growth, and proliferation so they are key players in signaling. It is a key player in the uptake of glucose into cells as it regulates the translocation of the glucose transporter SLC2A4/GLUT4 to the cell surface in response to insulin. You would expect aberrant expression of these would lead to cancer. The abbreviation AKT appears to derive from "a serine/threonine protein kinase encoded by the oncogene in the transforming retrovirus isolated from the thymoma cell line AKT-8, which is derived from the Stock A Strain k AKR". As with Protein Kinase C as phospholipase C, AKT is recruited to the inner leaflet of membranes. Recruitment is mediated by its binding through its pleckstrin homology (PH) domain to phosphatidylinositol (3,4,5)-trisphosphate, a modified form of PIP2, abbreviated either as PtdIns(3,4,5)P3 or more simply as PIP3. Note that PIP2 has 3 phosphate groups while PIP3 has 4.) The mechanism of membrane recruitment is similar to that of phospholipase C, which also binds membrane PIP2 through its pleckstrin homology (PH). (This is in contrast to PKC which is recruited through its C1 and C2 domains.) PIP3 is generated in the membrane from PIP2 by the enzyme phosphoinositide 3-kinase (P13K), a lipid kinase, whose own activation occurs through stimulation of insulin and growth factors receptor tyrosine kinases (RTKs). Class 1 PI3K has a regulatory/adapter subunit (p85) and a 110 kDa catalytic subunit (p110). The regulatory subunit has SH2 domains which recruit it to autophosphorylated RTKs. Figure \(33\) shows how membrane PIP2 can be converted to the second messengers DAG and IP3 by phospholipase C, or to PIP3 by the enzyme phosphoinositide 3-kinase (P13K), which is a lipid kinase. The binding of AKT (PKB) to inner leaflet PIP3 through its PH domain causes a conformational change that activates AKT for phosphorylation by phosphoinositide-dependent kinase 1 (PDK1) a membrane protein kinase. Once activated. AKT dissociates from the membrane and acts enzymatically in the cytosol and nucleus. The overall activation of AKT is shown in Figure \(34\). . The blunt arrows in the figure above show inhibition by the enzymes indicated. These (PTEN, PP2A, and PHLPP 1/2) are phosphatases. • PTEN is lipid phosphatase (phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase), which removes a phosphate from the lipid PIP3. • PP2A and PHLPP 1/2 are protein phosphatase that removes phosphates added by the kinase PDK1 in the kinase domain and mTORC2 in the C-terminal domain. Figure \(35\) shows an interactive iCn3D model of AKT bound to a novel allosteric inhibitor is shown below (3o96). The PH domain (at N-terminus, bind to PIP3) is shown in magenta, the N-lobe in green, and the C-lobe in brown. The activation loop is in dark blue and the catalytic loop is in red. The blue activation loop is shown in two parts as the interior part, which contains T305 (equivalent to T197 in AGC kinases without a PH domain) is too disordered to resolve. Table \(1\) shows the numbering of key amino acids and features of generic AGC kinase and the corresponding numbers in AKT. They are different since AKT has an N-terminal PH domain. Table \(1\) SITE Generic Akt (+108) N lobe K72 K180 N lobe E91 E199 C lobe, cat loop R165 R273 C lobe, cat loop D166 D274 C lobe, act loop D184 D292 C lobe, act loop T197 T308* missing in this structure) Approx Cat Loop 163-179 271V—287H Approx Act loop Start DFG (292-294) to APE 184-200 292-308 308Tmiss toAPE end 319 The allosteric inhibitor shown in the structure above is especially interesting in that it requires both the PH domain as well as the kinase domains for its effect. Figure \(36\) shows an interactive iCn3D model of the structural overlap between the inactive form (shown above, 3o96 containing a bound allosteric inhibitor) with an active form of AKT(3cqw), which has a bound substrate (from glycogen synthase kinase-3 beta, yellow spacefill). The bound decapeptide substrate (GRPRTTSFAE) in the active form becomes phosphorylated on the Ser chain by active AKT. The N-lobe is shown in cyan, while the catalytic loop is in red and the activation loop is in blue. By pressing the "a" key you can toggle between the inactive 3o96 form and the active 3cqw forms. Note the large change in the blue activation loop. Figure \(37\): below shows a series of coupled equilibria reactions that regulate the activity of AKT1. The top part of the figure shows the cytoplasmic, nonmembrane-bound form of the enzyme. The PH domain is shown in orange. The top-right figure shows the inactive N- and C-lobes of the kinase loosely interacting with an "out" (or away) conformation of the PH domain with respect to the kinase domains. In the presence of the allosteric inhibitor (green hexagon, green bound ligand), the kinase domains tightly interact with the PH domain in the "in" conformation. The bottom three structures show AKT bound to the membrane through the interaction of the PH domain with PIP3 (purple). The bottom right kinase domains are identical in representation to the two in the top part of the figure, showing that they are inactive. Only when bound to the membrane is AKT phosphorylated on Thr 308 (red in the bottom middle figure), which activates the enzyme. The bottom left and middle structures show the yellow kinase domains in a different conformation (3cqw), both of which are phosphorylated (red). The active form can bind ATP and protein substrates for phosphorylation. It can also bind ATP analogs which would competitively inhibit the active form of the enzyme by occupying the ATP binding site. The activation loop in the inhibited form is missing part of its sequence which reflects its disorder. In this state, the loops partially occludes substrate interactions. On phosphorylation of Ser 308 in the activation loop, the loop adopts a different conformation which allows less restricted access to the active. The loop itself on phosphorylation has more local ordering as it shifts away from the active site as seen in the iCn3D model above. Another change decreases inhibitory noncovalent interactions of activation loop amino acids with catalytic residues, which increases catalytic efficiency. In summary, these two types of changes in the activation loop lead to more access by substrates and enhanced catalysis of bound substrates. Additional regulation of the kinase occurs through the PH domain which adds additional conditions on Akt conformational changes and subsequent activity. The PH domain "appears to lock the kinase in an inactive conformation and the kinase domain disrupts the phospholipid binding site of the PH domain". Figure \(38\) shows an interactive iCn3D model of the separate AKT Pleckstrin Homology (PH) domain bound to the inner member through just the head group of PIP3 (inositol (1,3,4,5)-tetrakisphosphate) (1unq). Waters H bonded to the ligands is not shown. Figure \(38\): AKT Pleckstrin Homology (PH) domain bound to inositol (1,3,4,5)-tetrakisphosphate (1unq) (Copyright; author via source). 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textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.03%3A_The_Next_step_-_The_Kinome_and_Activation_of_Kinases_at_the_Cell_Membrane.txt

Search Fundamentals of Biochemistry Intracellular signaling from activated PKA and PKC We discussed the basics of the activation of the protein kinase A holoenzyme (R4C4) by cAMP binding to the regulatory subunit, which frees the catalytic subunit C for activity. Likewise, we discussed the activation of PKC at the cell membrane by DAG, Ca2+ ions, and phosphorylation of key Ser/Thr in the protein. Where in the cell are the downstream protein targets of activated PKA and PKC? This is a much simpler question for RTKs since downstream signaling proteins come to them through the interaction of their SH2 domains with the autophosphorylated RTKs. For activated PKA and PKC, it turns out that their location is controlled by scaffolding proteins, which bind them either before their activation or after. Let's discuss a particularly important scaffolding protein, the A-kinase-anchoring protein (AKAP). There are 13 classes of these containing 50 different members. These proteins bind PKA through its regulatory subunit, where cAMP can mediate the activation of the holoenzyme (R4C4). In addition, AKAPs can bind other signaling proteins including PKC and phosphatases, the latter of which in turn counter-regulate signaling by phosphoproteins. For example, the bound phosphatases can dephosphorylate PKC to deactivate it as well as other downstream phosphoproteins. AKAPs can also bind phosphodiesterase, the enzyme that converts cAMP to AMP, returning signaling to baseline levels. AKAPs localize key signaling enzymes to sites where biologically appropriate protein targets are localized. In addition, they decrease indiscriminate phosphorylation of other off-target proteins elsewhere in the cell. They may also allosterically regulate the activity of bound signaling proteins. There are at least 50 A-kinase anchoring proteins or A-kinase anchor proteins (AKAPs) that bind the regulatory subunit of protein kinase A (PKA) and localize PKA to specific sites in the cell. By binding multiple signaling enzymes at specific sites, they integrate signaling pathways mediated by cAMP (for example) with others mediated by PKC (again for example). Here are some examples of AKAPs in humans (from UniProt). Note that one (12) also binds PKC • 1, mitochondrial: Binds to type I and II regulatory subunits of protein kinase A and anchors them to the cytoplasmic face of the mitochondrial outer membrane; • 6: Binds to type II regulatory subunits of protein kinase A and anchors/targets them to the nuclear membrane or sarcoplasmic reticulum; • 7 isoforms alpha and beta: Targets the cAMP-dependent protein kinase (PKA) to the plasma membrane, and permits functional coupling to the L-type calcium channel; • 7 isoform gamma: targets cAMP-dependent protein kinase (PKA) to the cellular membrane or cytoskeletal structures; • 8: Acts as an anchor for a PKA-signaling complex onto mitotic chromosomes, which is required for the maintenance of chromosomes in a condensed form throughout mitosis; • 8-like: Required for cell cycle G2/M transition and histone deacetylation during mitosis and recruitment of signaling enzymes into the nucleus; • 9: assembles several protein kinases and phosphatases on the centrosome and Golgi apparatus; • 12: Anchoring protein that mediates the subcellular compartmentation of protein kinase A (PKA) and protein kinase C (PKC) • 17A: Splice factor regulating alternative splice site selection for certain mRNA precursors. Mediates the regulation of pre-mRNA splicing in a PKA-dependent manner. Figure \(1\) illustrates the localization/binding of signaling enzyme (PKA, PKA substrates, PDE, other kinases) to AKAPs. Note that some AKAPs can also bind PKA substrates, facilitating their phosphorylation and minimizing the phosphorylation of the wrong targets. AKAPs use an amphiphilic helix to interact with the R2 regulatory dimer of the PKA. Some AKAPs bind to just one of the regulatory subunits. Note that some AKAPs can also bind PKA substrates, facilitating their phosphorylation and minimizing the phosphorylation of the wrong targets. Figure \(2\) shows specific AKAP complexes in the heart that could be targeted for drug therapies. Panel (A): Disruption of the AKAP18γ/δ-PLB (another phospholipase involved in signaling) interaction prevents PLB phosphorylation on Ser16 and dislocation from SERCA2 (Sarcoplasmic/endoplasmic reticulum calcium ATPase 2). This inhibits SERCA2 activation and consequently Ca2+ uptake into the sarcoplasmic reticulum Panel (B): Disruption of the nesprin-1α /mAKAP interaction promotes AKAP/PKA complex dissociation from the perinuclear membrane and might be a strategy to reduce hypertrophy. Nesprin 1 is a protein that forms a linking network between organelles and the actin cytoskeleton to maintain the subcellular spatial organization. Panel (C): Disruption of the connexin 43-ezrin interaction could prevent PKA-mediated phosphorylation increasing inter-cardiomyocyte conductivity which could be cardioprotective following myocardial infarction damage. Connexin is a gap junction protein. Ezrin is involved in the connections of major cytoskeletal structures to the plasma membrane. To add to the complexity of PKA activation and signaling, there are different forms of the regulatory subunits of the holoenzyme PKA. These include RIalpha (RIA), RIbeta (RIB), RIIalpha (RIIA), and RIIbeta (RIIB). They have different affinities for cAMP, the catalytic subunits Cs, and different AKAPs. Figure \(3\) shows an interactive iCn3D model of the amphiphilic anchoring peptide AKAP-IS for AKAP binding to the docking and dimerization (D/D) domain of the RIIalpha regulatory subunit of PKA (2IZX) The brown represents the RII dimer D/D domains of the regulatory subunit. The anchoring peptide AKAP-IS is shown in gray. In both, the side chains involved in binding of the peptide to the regulatory subunit domains are shown as sticks and colored coded based on the hydrophobicity of the side chains. Green indicates the most hydrophobic. Rotate the model carefully to differentiate the side chains and note that the hydrophobic face of the peptide is interacting with hydrophobic side chains projecting into a groove made by the two RII dimer D/D domains. Polar side chains in AKAP help target the correct isoform of the R subunit. In addition to binding to some AKAP scaffolds, PKC also binds to Receptors for Activated C-Kinases (RACKs), heat shock proteins (HSP), importing, and annexins (AnxA1, A2, A5, and A6). The interactions of activated PKC with RACK1 and downstream events are shown in Figure \(4\). The insect protein BR-C (Broad Complex) has a DNA binding domain (two zinc fingers) domain for the activation of gene transcription and a BTB) domain that allows binding to RACK1. On binding the PKC:RACK1 complex, BR-C is phosphorylated at Ser373 and Thr406, after which it is translocated into the nucleus where it binds DNA and activates gene transcription The binding of PKC to RACK1 stabilizes PKC for the phosphorylation of targets. PKC binds to RACK through its C2 regulatory domains. Binding may be to specific forms of PKC including unphosphorylated, inactive, and activated phosphorylated forms, as well as to specific isozymes of PKC. RACK1 may also recruit PKC to the ribosome and it inhibits the activity of SRC kinases which we will discuss later. PKC activity occurs in many cellular locations, including the cell membrane, nucleus, Golgi apparatus, mitochondria, and cytosol. RACKS also bind and recruit other signaling proteins including PLCγ, Src, and integrins. In addition to interactions of PKC with RACK through the C2 domain, PKC can localize through its C1 domain. Structure of RACK1 RACK1 (317 amino acids) has a very interesting structure. It is a member of a family of over 100 proteins that have tryptophan-aspartate (WD) repeats that are 44-60 amino acids and ending in WD or a variant. It is homologous to the beta subunit of the heterodimeric Gαβγ signaling protein. RACK 1 interacts with proteins through a 7-bladed propeller structure that allow the binding of proteins with SH2 domains, plextrin homology (PH) domains, and C2 domains (PKCs). Figure \(5\) shows the WD repeats in human RACK1. Note that the N-terminal end of the WD repeat often is glycine-histidine (GH). Figure \(6\) a model for the interaction of RACK1 and PKC-βII. Figure \(6\): Model for PKC-βII and RACK1 interaction. Adams et al. Cell Communication and Signaling 2011, 9:22 http://www.biosignaling.com/content/9/1/22. Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0) Panel (A) shows the resting state with no interaction between RACK1 and inactive PKCbII. Panel (B) shows how activation of PKC-βII leads to its interaction with RACK1. Substrate binding and phosphorylation follow. R is a receptor and L is its ligand. Figure \(7\) shows how RACK1 can translate into the nucleus after ligand (L) gated activation of GPCRs (R) through adenylyl cyclase production of cAM and activation of Protein Kinase A. Figure \(7\)Model for cAMP/PKA-mediated nuclear translocation of RACK1. Adams et al. Cell Communication and Signaling 2011, 9:22 http://www.biosignaling.com/content/9/1/22. Creative Commons Attribution License. (http://creativecommons.org/licenses/by/2.0) Panel (A) shows the resting state of RACK, which forms homodimers and heterodimers with the homologous Gβ subunit of the Gαβγ complex. Panel (B) shows how activation of PKA leads to dissociation of RACK which then can translate into the nucleus, where it leads to increased transcription of brain-derived neurotrophic factor (BDNF). Figure \(8\) shows an interactive iCn3D model of a the human Rack1 (4AOW) color coded as in Figure \(5\). Downstream signaling from activated receptor tyrosine kinases To review once again, when receptor tyrosine kinases get activated by binding a primary messenger such as a growth factor, the receptors dimerize, activating their cytoplasmic kinase domains. The activated RTK then autophosphorylates itself. The phosphorylated intracellular domain provides a docking site for other cellular "adaptor" proteins that contain an SH2 domain. The bound adaptor protein binds other signaling molecules within the cell leading to downstream propagation of the signal. Figure \(8\) shows some RTKs and downstream signaling events. We have already discussed the activation of the lipid kinase phosphoinositide 3-kinase (P13K) which leads to the activation of Akt (PKB). In the rest of this section, we will focus on the next step after the activation of RTKs. We focus on the epidermal growth factor receptor (EGFR, ErbB1) again. 1. Downstream signaling from the epidermal growth factor receptor. Figure \(9\) shows events associated with the activation of EGFR. Once bound and activated by binding of growth factors protein signals, the intracellular domains of the now dimeric EGFR autophosphorylates themselves on selected tyrosine side chains. This then recruits a protein called Growth factor receptor-bound protein 2 (GRB2), which has an SH2 (Sarc Homology 2) domain that binds phosphotyrosine motifs in proteins. GRB2 acts as an adaptor protein in that in addition to the SH2 domain, it has two SH3 (Sarc Homology 3) domains that bind proline-rich domains on other signaling proteins, including the protein Son of sevenless homolog (SOS). GRB2 does not have enzymatic activity. The adaptor protein GRB2 binds through its SH3 domain to the protein SOS, which then interacts with the protein Ras. This last member in the recruited complex is named because it causes Rat Sarcomas. There are many variants of these proteins but three are key in humans, HRas, KRas, and NRas. Ras is perhaps the key member of the family of intracellular small G proteins that bind GDP/GTP and are activated on the exchange of GTP for GDP. The proteins also have intrinsic GTPase activity as is found in the Gα protein of the heterotrimeric Gαβγ protein. So what does SOS do? The SOS bound to RAS facilitates the exchange of GTP for GDP on Ras, maintaining it in an active state. SOS is a member of another fascinating class of small proteins that catalyze the exchange of GTP for GDP. The family of GTP/GDP exchange proteins is called Guanine nucleotide Exchange Factors (GEFs). We will explore this in the next section. The EGFR -GRB2-SOS-Ras complex in the figure above looks somewhat similar to the structure of a GPCR-heterotrimeric G protein GαGβGγ complex, where Gα is also a GTP/GDP exchange protein with intrinsic GTPase activity. When the ligand binds to the GPCR, a conformational change ensues which facilitates the exchange of GTP for GDP on the Gα protein, activating intracellular signaling. Once RAS is activated (bound to GTP), it binds and activates key kinases in the cell, including the lipid kinase PI3K and RAF, a kinase shown in Figure \(8\). Control of RAS activity is critical in signaling. It is one of the most commonly mutated proteins in cancer cells. Mutations that inhibit the intrinsic GTPase activity keep the protein active, leading to unregulated cell growth, proliferation, and differentiation, hallmarks of cancer cells. The domain structures of GRB2 and SOS are shown in Figure \(10\). The proline-rich domain (motif) is not shown in the figure. GRB2 domain structure (green SH3 domains) SOS1 domain structure Figure \(10\): Domain structure of GRB2 and SOS1 (proline rich domain not shown in SOS) Note As we discussed in Chapter xx (domains), new genes encoding proteins with new functionalities can be created by duplicating and adding gene segments for different domains in a preexisting gene. As we have seen with the SH2 and SH3 domains, signaling proteins often share common domains. Table \(1\) below, adapted from the excellent book Cell Signaling by Lim, Mayer, and Pawson, shows some common domains found in signaling proteins. Domains in Signaling Molecules Domain Binding Target Cellular Process Example protein Pdb file (examples) Find your own in the PDB Bromo Acetyl-Lys Chromatin reg. BRD4 2YYN C1 diacylglycerol Plasma membrane recruitment Raf-1 3OMV C2 Phospholipid (Ca-dependent) Membrane targeting, vesicle trafficking PRKCA 3IW4 CARD Homotypic Interactions apoptosis CRADD 3CRD Chromo Methyl-Lys Chromo reg, gene transcription CBX1 3F2U Death (DD) Homotypic inter. Apoptosis Fas 3EZQ DED Homotypic inter. Apoptosis Caspase 8 1F9E DEP Memb, GPCRs Sig trans, protein trafficking Dsh human disheveled 2 2REY GRIP Arf/Art G prot Golgi traffic Golgin-97 (Golga5) 1R4A PDZ C-term peptide motifs Diverse, scaffolding PSD-95 Or discs large homolog 4 1L6O PH Phospholipids Membrane recruirtment Akt 1O6L 3CQW PTB Phospho-Y Y kinase signaling Shc 1 SHC-transforming protein 1 1UEF 1irs RGS GTP binding pocket of Galpha Sig trans RGS4 1EZT SH2 Phospho-Y pY-signaling Src 4U5W SH3 Pro-rich sequence Diverse, cytoskeleton Src 2PTK TIR Homo/Heterotypic Cytokine and immune TLR4 3VQ2 TRAF TNF signaling Cell survival TRAF-1 3ZJB VHL hydroxyPro ubiquitinylation VHL 1VCB Figure \(11\) shows an interactive iCn3D model of the GRB2-SH2 domain in complex with a high affinity phosphopeptide KPFpYVNVEF (1BMB) Grb2 exists in both a monomeric state which can bind SOS, and a dimeric state which can't. You would expect the equilibrium between the monomer and dimeric form would be highly regulated. When a phospho-tyrosine ligand is bound to Grb2 through its SH2 domain, the dimer dissociated. This also occurs on phosphorylation of tyrosine 160 (Y160) on Grb2, a post-translational modification found in human prostate, colon, and breast cancers. Figure \(12\) shows an interactive iCn3D model of the GRB2 N-terminal SH3 domain complexed with a ten-residue proline-rich peptide (1135 Ac-VPPPVPPRRR-NH2) derived from SOS (1GBQ) We will explore interactive structures of the small G protein Ras with the guanine nucleotide exchange factor SOS in the next chapter section. After activation of Ras through GTP exchange for GDP, the GTP-Ras protein binds to and activates the kinase Raf. We will continue our exploration of that later in this section. 2. Downstream signaling from the insulin receptor. When insulin binds to the receptor tyrosine kinase (RTK), it phosphorylates itself, which then leads to the binding of other proteins to the activated receptor and their phosphorylation. These lead to more intracellular signaling and ultimately changes in gene transcription. We'll focus on a specific adaptor protein, the Insulin Receptor Substrate 1, IRS1, a "scaffolding protein", which leads to the movement of the glucose transport protein GLUT4 to the cell surface, allowing glucose uptake. These activities are shown schematically in Figure \(13\). Human IRS1 has two domains, a PH domain for binding to membranes through phosphorylated IP2 derivatives and an IRS/PTB domain which binds phosphotyrosines on proteins. The PTB and the SH2 domains are the most common domains for binding phosphotyrosines on proteins. PTB binds the NPXY, where X is a pTyr. We show a more detailed view in Figure \(14\), in part, to review many of the signaling proteins we have seen before, including PI3K and PDK1. Likewise, another review of PIP2 derivatives is warranted. After phosphorylation by the activated insulin receptor protein tyrosine kinase, IRS-1 binds phosphatidylinositol 3-kinase (PI3K) that causes phosphorylation of the 3'OH on phosphatidyl inositol (PI) in the inner leaflet of the membrane to form PI(3)P. PI3K is a member of a family of kinases that phosphorylates PIP2. The metabolic pathway centered on pI3K is one of the most mutated in human cancers. PI(3)P in turn recruits to the membrane other inactive kinases, phosphoinositide-dependent kinase 1, PDK1, and Akt, also known as PKB. Figure \(15\) shows phosphorylated phosphatidylinositol derivatives. On binding of PI(3)P, PDK1 becomes an active kinase, which phosphorylates and activates Akt. Akt kinases are major Ser/Thr protein kinase that phosphorylates proteins involved in a host of cell activities, including regulation of glucose transport, cell proliferation, and death. In the insulin signaling pathway, active (phosphorylated) Akt leads to the movement of the GLUT4 protein from intracellular endosomal vesicles to the cell surface, which offers a quicker way to import glucose into the cell than if Akt activated GLUT 4 gene expression. PDK1 phosphorylation of Akt2-T309 is required for insulin-stimulated Glut4 translocation. If T309 is mutated to A309 or if PDK1 is inhibited, GLUT 4 is not translocated to the cell membrane. Figure \(16\) shows an interactive iCn3D model of the activated insulin receptor tyrosine kinase in complex with peptide substrate and ATP analog (1IR3)) The dimeric form of the kinase is shown. ANP is shown in spacefill. The peptide substrate containing the interior tyrosine (stick) for phosphorylation is shown in blue. Three key tyrosines in the activation loop that are autophosphorylated (Tyr1158, Tyr1162, and Tyr1163) are shown and labeled in the right-hand monomer. As with a protein kinase, the activation loop of the insulin receptor kinase domain is phosphorylated and the resulting conformational change allows ATP and target protein access. The activation loop gets phosphorylated on Tyr1158, Tyr1162, and Tyr1163 with Y1163 being key. Downstream signaling from Cytokine activated Receptors- The JAK/STAT pathway. Now we explore two signaling pathways mediated by the adaptor protein JAK and STAT. These are abbreviations for the Janus Kinase (JAK) and the Signal Transducer and Activator of Transcription (STAT). These play key roles in embryonic development, stem cell maintenance, hematopoiesis (formation of blood cells), and immune cell signaling. . This pathway is stimulated by cytokines and interleukins, protein modulators released by immune cells, as well as growth factors. In general, there are five groups of cytokines: • tumor necrosis factor alpha (TNF-alpha) • Interleukin-1 family members (IL-1_ • Transforming growth factor-beta (TGF-bet) • those that signal through RTKs (such as macrophage colony-stimulating factor (M-CSF) • Chemokines • cytokines that signal through JAK/STAT In contrast to RTKs, which have kinase domains activated on receptor dimerization, cytokine receptors that work through JAK/STAT do NOT have kinase domains. On cytokine binding to their target cytokine receptor, the now-activated receptors activate the prebound inactive Janus kinase through conformational changes. The kinase domains autophosphorylate themselves in a trans fashion. The active kinase then phosphorylates the cytoplasmic tails of the cytokine receptors. This trigger further signal transduction reactions mediated by the binding of target signaling proteins to the cytoplasmic region of the phosphorylated cytokine receptor. Just to reiterate, the cytokine receptor is NOT a RTK with latent kinase activity. Instead, it becomes phosphorylated by the bound and cytokine-activated JAK. A portion of the pathway is illustrated in Figure \(17\). Panel (A1) shows that the kinase JAK is bound constitutively in an inactive state to the cytokine receptor cytoplasmic region, not through its SH2 domain, but through its FERM domain (Panel B). The figure implies the cytokine receptor is dimeric in the absence of a ligand. On cytokine binding, conformational changes and repositioning of the bound JAK activates its kinase domain, which phosphorylated the C-terminal tails of the cytokine receptor. STAT monomers, through their SH2 domains, bind to the phosphorylated cytokine receptor where they get phosphorylated by the active JAK. The phospho-STAT monomers form a dimer, dissociate from the complex, and translocate to the nucleus where they act as transcription factors. the Janus kinase is named after Janus, the two-faced Roman god of beginnings, endings, and duality since JAK has two nearly identical JH kinase domains. One has kinase activity while the other inhibits the first. Panels (B) shows the domain structure of JAK. The FERM domain facilitates JAK:cytokine receptor binding. The JH2 pseudokinase domain regulates the kinase activity of the JH1 kinase domain. Ps show site on JAK necessary for activation Panel (C) shows the domain structure of STAT. The SH2 domain binds phosphorylated tyrosines. The carboxy terminus transactivation domain is required for full transcriptional activation. P marks the conserved tyrosine residue whose phosphorylation is essential for STAT activation. The pseudokinase domain of JAK interacts with the kinase domain on the same chain and prevents its activity in the inactive monomer and dimer. Cytokine binding to the cytokine receptor induces a conformational change that promotes the interactions of the pseudokinase domain on one JAK monomer with the same domain on another, promoting dimerization and freeing the kinase domains for activity. Figure \(18\) shows the pseudokinase domain (orange) interactions in the active JAK dimer. Figure \(18\): The pseudokinase domain (orange) interactions in the active JAK dimer. The kinase domain of each dimer is shown in red and the pseudokinase domain is shown in orange. The activation loop (cyan) and ADP (spacefill, CPK colors) are shown in the red kinase domains. The orange pseudokinase domain has a bound adenosine (not ADP) that is shown spacefill and CPK color. However, it lacks the DFG motif required for catalysis. The phenylalanine cluster (F635, 657, 574) are labeled. The structure is actually for a mutant that has the V657F mutation that promotes dimerization and JAK activity. Hence the V657F mutation is oncogenic. JAK activity can be inhibited by the protein suppressor of cytokine signaling (SOCS). Transcription of the protein is activated by STAT, and the SOCS protein in a feedback inhibition loop binds to JAKs and also to IFNGR1, which inhibit JAK activity. Figure \(19\) shows an interactive iCn3D model of the structure of the inhibitory protein SOCS1 in complex with JAK1 kinase domain (6C7Y) The N-lobe of the JAK1 kinase domain is shown in cyan and the C-lobe in magenta. ADP (sticks) and Mg2+ (green) are shown in the interface between the lobes. SOCS1 is shown in gray except for the kinase inhibitor region which is shown as spheres and CPK colors. It binds in the substrate binding grove and prevents substrate access. Activated JAK activity and signaling do not stop simply with the activation of STAT. In addition to stimulating signaling through phosphorylated dimers of STAT, cytokines also activate other signaling pathways through the same receptors. Examples include the PI3K pathway described in an earlier section and also the MAPK pathways, described in detail later in this section. Both the PI3K and MAPK pathways are activated by binding the cytokine IL6 to its cytokine receptor. The mechanism for PI3K activation is a bit unclear. The MAPK pathway is activated by a phosphatase called SHP2 for Src homology region 2 domain‐containing phosphatase 1. This protein binds to pTyr759 on the IL-6 receptor. As binding of the cytokine activates the prebound JAK, it also activates SHP2, ultimately activating signaling through Ras (a small G protein), which activates RAF, a kinase. Figure \(20\) shows these three combined signaling pathways for a cytokine receptor: STAT, Ras, and PI3K activations. The cytokine receptor induces activation of JAKs after cytokine stimulation following the phosphorylation of STATs. Furthermore, phosphorylated STATs undergo dimerization and translocate to the nucleus to activate target gene transcription. SOCS, suppressors of cytokine signaling; PI3K, phosphatidyl inositol 3 kinase; Akt, protein kinase B; FOXO, Forkhead box protein O; mTOR, mammalian Target Of Rapamycin. Figure \(21\) shows an interactive iCn3D model of the Crystal structure of a tyrosine phosphorylated STAT-1 dimer bound to DNA (1BF5) The dimer chains are shown in brown and gold except for those colored-coded by secondary structure (helix red, sheet yellow). The backbone of the nucleotides is shown in spacefill cyan and gray. Zoom in to see noncovalent interactions between the bound DNA and protein monomers. Two phospho-tyrosines labeled pTR701 are shown as well. The DNA binding domain of the STAT dimer has an immunoglobulin fold and forms a "C-shaped clamp" around the DNA. The domains colored by secondary structure are SH2 domains, with each recognizing and binding to the phosphorylated Tyr 701 (labeled pTR701), a very interesting use of the SH2 domain. Figure \(22\) shows an interactive iCn3D model of the active Janus Kinase (JAK) dimer complexed with the intracellular domains (spacefill) of the interferon lambda(a cytokine) receptor (7T6F). Figure \(22\): Active Janus Kinase (JAK) dimer complexed with the intracellular domain of the interferon lambda(a cytokine) receptor (7T6F). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...9s3VBNMxsk7WM7 Domain/protein Coloring • Red: PTKc-JAK-rpt2 kinase catalytic domain • Orange: PTK-JAK1-rpt1 pseudokinase domain • Yellow: FERM F2 • Magenta: FERM C-JAK1 • Cyan: SH2 • Brown: FERM F1 • Navy Blue and Blue spacefill: cytoplasmic domains of the cytokine receptor interferon lambda receptor 1 dimer • The gold large sphere represents interferon bound to the extracellular domain of the interferon lambda receptor 1 dimer Figure \(23\) shows a model combining the active Janus kinase (JAK) containing the intracellular domain of the interferon alpha receptor 1 dimer (navy and blue spacefill) with AlphaFold models of the extracellular and transmembrane domain of the interferon-alpha receptor ((Q8IU57)) Figure \(23\): AlphaFold model of Interferon lambda receptor 1 Extracellular Domain and transmembrane domain (Q8IU57) with Active Janus Kinase (JAK) dimer complexed with cytokine receptor intracellular domain (7T6F) Again, to reiterate as we did above, the cytokine receptor (in the figure above the interferon lambda receptor) is NOT a RTK with latent kinase activity. Instead, it becomes phosphorylated by the bound and cytokine-activated JAK which is resident in the cytoplasm. Here is a link to a second iCn3D model which shows the phenylalanine cluster that promotes pseudokinase domain interactions in the active JAK dimer. Downstream Intracellular signaling through Src Family Kinases - Membrane-associated non-receptor tyrosine protein kinases Another family of intracellular protein kinases - the Src family - are often activated on upstream activation of many different types of receptors including GPCRs, RTKs, cytokine receptors, as well as integrins and adhesion receptors that we explore in a later chapter section. We have already touched on them when we discussed proteins containing the src homology domains SH2 and SH3. Src, an intracellular Tyr kinase (MW 60,000), is the founding member of this family of protein kinases. Src is activated when it binds through its own SH2 domain to a phosphorylated membrane receptor. Src has many names including Proto-oncogene tyrosine-protein kinase Src, proto-oncogene c-Src, pp60c-src, and p60-Sr. These membrane-associated, non-receptor tyrosine kinases regulate cell proliferation, differentiation, apoptosis, migration, metabolism, and cytoskeleton organization. They are one of the major classes of intracellular kinases which are activated after upstream activation of the membrane receptors mentioned above (GPCRs, RTKs, cytokine receptors, as well as integrins and adhesion receptors). They in turn activate further downstream protein kinases by phosphorylation. They even phosphorylate the upstream membranes which led to their activation. There are 10 members in the Src family: Src, Frk, Lck, Lyn, Blk, Hck, Fyn, Yrk, Fgr, and Yes. They all share the same expanded domain structure shown in Figure \(24\). They have an SH4 N-terminal region that can be post-translationally modified with fatty acids (myristoylation and palmitoylation), which can anchor it to the membrane. The spacer SH2-SH3 spacer is intrinsically disordered and differs in sequence among members of the Src family. Two key phosphorylation sites (Tyr416 and Tyr527) are important. In inactive Src, Try 527 is phosphorylated, allowing it to bind to the SH2 domain. The SH2-kinase linker also binds to the SH3 domain. This occludes the active site region and prevents the phosphorylation of Try 416 in the "activation loop" of the SH1 kinase domain. When Try 527 is dephosphorylated, a conformation change ensues the opens the binding site allowing autophosphorylation of Tyr 416 and its activation. Hsu et al, Cancers 12(6):1361. DOI: 10.3390/cancers12061361. CC BY 4.0 Figure \(25\) shows an interactive iCn3D model of the Human tyrosine-protein kinase C-Src in complex with AMP-PNP (2SRC) pY527 is shown in stick and labeled. It binds to the SH2 domain (151-248) shown in cyan. The SH3 domain is in magenta and the kinase domain is shown in gray. The activation loop in the kinase domain is shown in red with the pY416 needed for activation shown in stick and labeled. ANP is shown in spacefill. We have now seen the structure of many kinase domains. Figure \(26\) shows an interactive iCn3D model of the alignment of human c-Src (452 amino acids) (2SRC) and Erk2 (362 amino acids) also called MAPK1 (2Y9Q), a protein kinase will be explored at the end of this section Red shown conserved sequences, blue is aligned (but not conserved), and gray is nonaligned. Use the "a" key to toggle between the states. After much discussion of the binding of p-Tyrosine side chains to the SH2 domain, we now present Figure \(27\), which shows an interactive iCn3D model of a phosphotyrosine peptide bound to the SH2 domain of Fer tyrosine kinase (6KC4) The SH2 domain is shown in gray. The phosphopeptide (DEpYENVD) is shown in cyan with the labeled pY in sticks. The side chains on the SH2 domains interacting with the pY are shown in stick and labeled. We are about to explore the last but incredibly important downstream kinases activated in signal transduction cascades, the mitogen activate protein kinase (MAPK) cascade. It could also be called the Erk cascade. Before we do that, we present parts of three pathways mediated by activated RTKs to refresh your minds and also because the more you see key players in the pathway, the more you start to remember the names, structural features, and function of the signaling molecules. Figure \(28\) offers a quick and abbreviated look at signaling through activated RTKs that proceed through the adaptor protein and the GEF SOS, leading to the activation of Ras, a key small G protein. Ras in turn activates a protein (MAPKKK) in the MAPK cascade. Figure \(28\): Simplified cartoon showing the activation of the MAPK cascade protein MAPKKK. Cordover E, Minden A. Signaling pathways downstream to receptor tyrosine kinases: targets for cancer treatment. J Cancer Metastasis Treat 2020;6:45. http://dx.doi.org/10.20517/2394-4722.2020.101. Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), Figure \(29\) shows an abbreviated version of the activation of the lipid kinase PI3K and through the activation of PDK1 and AKT, the activation of two major kinase complexes, mTORC1, and mTORC2, which we will explore in its a separate chapter section. Finally, Figure \(30\) show the combined activation of both the MAPK (ERK) cascade pathway and the mTOR complex through GPCR signaling using the adaptor protein Grb2. Downstream signaling through the Mitogen-Activated Protein Kinase Cascade Active upstream kinases like PKA, PKC, and RTKs phosphorylate target proteins and in doing so change their activities. The usual protein targets are kinases, which become active on phosphorylation. They in turn activate other kinases, resulting in a complex cascade and amplification of the original signal. Often the end product of such cascade is a phosphorylated transcription factor that can alter gene expression. Perhaps the most described of these cascades is the Mitogen-Activated Protein Kinase (MAPK) pathway. Mitogens are chemical species that lead to mitosis (cell division). The MAPK system has three layers and a confusing nomenclature (until you are used to it). The end (downstream) product of the cascade is the enzyme mitogen-activated protein kinase, abbreviated MAPK. It can be phosphorylated several times to produce MAPKP or MAPKPP where the last Ps in the abbreviation signifies the number of added phosphate groups. The kinase immediately upstream that phosphorylates MAPK is abbreviated MAPKK (for mitogen-activated protein kinase kinase) or MAP2K. MAPKK (MAP2K) is activated by yet another upstream kinase called MAPKKK or MAP3K. If these are also targets of another upstream unrelated kinase, they would be abbreviated MAP3KPP for example. Now, of course, there are families of the MAPK cascade enzyme, each with its own name. Figure \(31\) shows the names of four different mammalian MAP3Ks leading to the activation of 5 different MAP2Ks which lead to the activation of 4 different MAPKs. Some of these enzymes are so widely discussed in textbooks and journal articles that it is good to remember them specifically with their alternative names. These include the MAP3K enzymes Raf (Rapidly Accelerated Fibrosarcoma) and MEK, and the MAPK enzymes ERK (Extracellular Related Kinase) and JNK (c-Jun N-terminal Kinase) Figure \(32\) shows another representation of the MAPK cascade with some different enzyme names and added phosphates shown in circles. Figure \(32\): Another representation of the MAPK cascade. Journal of Cardiovascular Development and Disease 6(3):27. 2019. 10.3390/jcdd6030027. CC BY 4.0 One way to organize a seminar on a complex topic is to use these 3 steps: tell your audience what you will tell them, tell them, and then tell them what you told them. Following that advice, we present in Figure \(33\) an integrated view of signaling, starting from the membrane and moving inward to three enzymes in the MAPK kinase cascade, RAF (a MAP3K), MEK (a MAP2K) and ERK (a MAPK). Upstream signaling to the MAPK cascade comes RTKs, GPCRs, and Ca2+ signaling, which we will discuss later. The sequence of events is: • binding of an external signal to membrane receptor and activation of receptor kinase • phosphorylation of receptor kinase and interaction with an activator GTP binding protein such as Ras • binding of activated G-protein to and activation of a mitogen-activated protein kinase kinase kinase (MAPKKK) • MKKK phosphorylates and activates another kinase, MAPKK • MKK phosphorylates and activates mitogen-activated protein kinase, MAPK • MAPK phosphorylates inactive transcription factors (or other proteins) and activates them. Unfortunately (from a naming point of view) when the activated proteins are themselves protein kinases, they are called mitogen-activated protein kinase activated protein kinases (MAPKAPK) There are seven types of MAPKs, four conventional and three atypical. Four typical ones are described in the table below. Activator GTP binding protein Ras:GTP MAPKKK or MAPK3 Raf-1A/B c-Mos MEKK1-4 DLK MLK2 MEKK1-4 DLK MLK2 MEKK2/3 Tpl-2 MAPKK or MAPK2 MEK1,2 MEK4,7 MEK3,6 MEK5 MAPK or MAK ERK1,2 JNK1-3 p38 ERK5 MAPKAPK RSK 1-4 MNK2 MSK 1,2 MK2,3 MSK1,2 MK2,3 RSK1-4 An eventual Protein Target c-Jun c-Jun MAP Kinase System from Cell Signaling Structural, these proteins are similar in size and domain structure as we have seen for other kinases. Figure \(34\) shows an interactive iCn3D model of the alignment of MEK 1 (4U7Z) and ERK2 (5NHJ) We now present multiple visual images of the activation of the MAPK cascade. Figure \(35\) shows two. Details: Figure \(35\): Two visual representations of the activation of the MAPK cascade through to activation of gene transcription. As with protein kinase A and protein kinase C, signaling efficiency and specificity with minimal phosphorylation of wrong targets occurs when multiple signaling partners are scaffolded. This is also true of the MAPK cascade trio of the kinase. Figure \(36\) shows the role of scaffolds KSR and Ste5 in MAPK signaling. Computational Analyses - MAPK Cascade You might think that the interconnected reactions of the MAPK cascade are complicated. However, as presented in the figures above, it only consists of 3 enzymes, MAPKKK (MAPK3 or MK3), MAPKK (MAPK2 or MK2), and MAPK. We added complexity by converting the actual enzymes from an inactive state to an active state by phosphorylation. In reality, this pathway is simple compared to the complete signal transduction pathways they are part of, and very simple compared to whole catabolic and anabolic pathways that we will see in Part Two of this book. We have discussed kinetics in earlier chapters and have shown how it can be used to more fully understand an enzyme and its regulation/control. We need to extend such kinetic analyses to whole pathways as well. We can do that using the VCell. Let's look at two different models of the MAPK cascade using Vcell. One particularly interesting feature is the regulation of the pathway. We will see in the next section on metabolism that pathways are often regulated by the end product of the pathway. This makes sense since if that end product is abundant, it would make biological sense for that product to inhibit the first enzyme in the pathway to avoid making more of the ultimate end product. Of course, that inhibition would be relieved as the concentration of the end product falls. Hence there is a temporal sense to the inhibition. Let's look run two Vcell models for the MAPK Cascade, one with no end product inhibition and one with inhibition of the first step. Since we dealing with linked kinases, the inhibition of the first enzyme (MAP3K=MKKK) and hence the first reaction (MKKK ↔ MKKK_P) is not mediated by a chemical product of the last enzyme (MAPK_PP) but by phosphorylation of the first enzyme (MKKK) by the last (MAPK_PP). MAPK Cascade - Model 1: No feedback inhibition of the MAPK cascade by inhibition of the first step (MKKK ↔MKKK_P) by the "end product" of the cascade (MAPK__PP) try quick MODEL MAPK Cascade - no feedback inhibition Initial Condition - See simulation results Select Load [model name] below Interactive Element Select Plot to change Y axis min/max, then Reset and Play  |  Select Slider to change which constants are displayed.  For this model, select Vm, Km, Ki and I |  Select About  for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health.  Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington.  Funded by NIH/NIGMS (RO1-GM123032-04) The various concentration vs time curves in the output graph should make "intuitive" sense. There are no surprises! Now let's add a twist. What if the last active enzyme in the pathway, MAPK that is doubly phosphorylated (MAPK_PP), the "final product" of the cascade, can, in a feedback reaction, inhibit the very first reaction of the cascade, MKKK → MKKK_P through an inhibiting phosphorylation. Run the simulation in Vcell to find out! MODEL MAPK Cascade - With feedback inhibition Initial Conditions:  See simulation results Select Load [model name] below Interactive Element Select Plot to change Y axis min/max, then Reset and Play  |  Select Slider to change which constants are displayed.  For this model, select Vm, Km, Ki and I |  Select About  for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health.  Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington.  Funded by NIH/NIGMS (RO1-GM123032-04) The various concentration vs time curves in the output graph should make "intuitive" sense. There are no surprises! Now let's add a twist. What if the last active enzyme in the pathway, MAPK that is doubly phosphorylated (MAPK_PP), the "final product" of the cascade, can, in a feedback reaction, inhibit the very first reaction of the cascade, MKKK → MKKK_P through an inhibiting phosphorylation. Run the simulation in Vcell to find out! MAPK Cascade - Model 2: Feedback inhibition of the MAPK cascade by inhibition of the first step (MKKK ↔MKKK_P) by the "end product" of the cascade (MAPK__PP) Kholodenko2000 - Ultrasensitivity and negative feedback bring oscillations in the MAPK cascade. https://www.ebi.ac.uk/biomodels/BIOMD0000000010. Based on Kholodenko BN. Negative feedback and ultrasensitivity can bring about oscillations in the mitogen-activated protein kinase cascades. Eur J Biochem. 2000 Mar;267(6):1583-8. doi: 10.1046/j.1432-1327.2000.01197.x. PMID: 10712587. There is utterly no way to have predicted, using intuition or simple mathematical analyses, the oscillatory changes in the concentration of all the enzymes and their phosphorylated variants. Of course, the oscillating concentrations depend on the chosen initial concentrations and on the rate constants. When enzymes are linked sequentially in signal transduction pathways, their actions are amplified by the preceding enzyme. If the first kinase (MK3) activates a 1000 molecules of the second kinase (MK2), and each of these activates 1000 of the last kinase (MAPK), the net effect of activating the first kinase is a million-fold amplification of the last! This causes this three-kinase pathway to be ultrasensitive to small changes to the first enzyme. Add other activating and inhibiting agents and the net activities of the pathway components become even more complicated. Just the simple feedback inhibition by the last enzyme of the first enzyme in this cascade can bring about the oscillation shown in Vcell model 2. Depending on the concentrations and rate constants used in the model, the oscillations can last from minutes to hours. These oscillations can produce waves of phosphoproteins that propagate through the cytoplasm of the cell. Here is a simplified animation of the MAPK cascade that shows changes in MKKK (red dots) and MAPK_PP) blue dots with no feedback inhibition (left, Model 1) and feedback inhibition (right, Model 2) in the cascade. (Animations produced by Shraddha Nayak and Hui Lui.) Model 1: No feedback inhibition of the MAPK cascade Model 2: Feedback inhibition of the MAPK cascade You might expect similar oscillatory behavior in proteins (cyclins and cyclin-dependent protein kinases) controlling the movement of cells through the cell cycle. We will see that in a subsequent section.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.04%3A__The_next_step_-_Downsteam_intracellular_signaling.txt

Search Fundamentals of Biochemistry G proteins: Cellular Switch for Kinases In the preceding chapter sections, we discussed two types of small G proteins, Gα, part of the heterotrimeric Gαβγ complex linked to GPCR signaling, and Ras (H, K, and N). Both bind GTP and GDP and have GTPase activity. When bound to GTP they are active while the GDP bound form is inactive. What a perfect molecular switch to turn on signaling and with a built-in off switch (the GTPase activity). It turns out that his simple on/off switch is too simple. For example, a single mutation that inhibits the GTPase activity would leave the protein on continually which could (and does) lead to unregulated growth and tumor formation. Two new sets of proteins that regulate the on-off activity of small G proteins are found abundantly in cells: • GTPase activating protein or GAPs: As the name implies they enhance the GTPase activity of the small G proteins, which would decrease G protein signaling; • Guanine nucleotide exchange proteins or GEFs: These lead to the dissociation of bound GDP and its replacement with GTP, which would increase G protein signaling. Mammalian cells contain 3 variants of Ras: H, K, and N. They all bind GDP/GTP and have GTPase activity. Ras is targeted to the cell membrane through the post-translational addition of a hydrophobic farnesyl group. When activated by binding to GTP, it can bind to and activate a protein called Raf-1, which on binding becomes an active tyrosine kinase. Ras has intrinsic GTPase activity, so eventually, active Ras will deactivate itself. Ras is just one member of a large superfamily of small G proteins, which all have GTPase activity. However, they are poor GTPases, so they need help to autocatalytically cleave the GTP to GDP. GAPs and GEFs evolved to regulate their activity by modulating the balance of bound GTP (an active form of the protein) and GDP (an inactive form of the small G protein). Before describing these proteins, we need to have a better understanding of the family of small G proteins. Small G proteins Small G proteins in the superfamily have a common 20 K molecular weight catalytic (GTPase) domain with 5 alpha helices, 6 beta strands, and connecting loops. The small G proteins are "active" in the GTP-bound form. Hydrolysis of GTP to GDP causes the protein to become inactive. Figure \(1\) shows the domain structures of small G proteins. G boxes of the G domain are highlighted with orange boxes. The hypervariable region, including a polybasic region and a CAAX motif, is highlighted with pink boxes. The P-loop, switch I, and switch II are shown as bars colored green, red, and blue, respectively. The bottom structures show H-Ras bound to GDP and GTP. The P-loop, switch I, and switch II are colored green, red, and blue, respectively. Figure \(2\) shows an interactive iCn3D model of human KRAS G12C mutant covalently bound to AMG 510, a covalent inhibitor (6OIM). This mutation flips the Ras switch so it is permanently on. The coloring coding is the same as in the above Figure 1: • P-Loop (same as G1) is Green • Switch 1 is red • Swithch 2 is blue • Polybasic region is pink • The GAAX motif was not observed (low electron density in the crystal structure) GDP is shown in colored sticks.  The covalent inhibitor, AMG 510, is shown in spacefill and labeled MOV. It is covalently attached to the Cys 12 in the mutated version. The G12C human KRAS mutation is found in about 13% or patients with non-small cell lung cancer (NSCLC) with is any type of epithelial lung cancer that is not small cell lung cancer.  NSCLCs include squamous cell carcinoma, large cell carcinoma, and adenocarcinoma.  The G12C mutation causes the protein to be "stuck" in the active conformation, which leads to continued activation of signaling pathways leading to cell proliferation, a trait of cancer cells.  Before the discoveryof AMG 510, Ras was considered "undrugable" since it was devote of obvious pockets that could inhibit its activity.  The G12C mutation replaces glycine with cysteine, a potent nucleophile that can react with the covalent inhibitor and inhibit the always-switched on G12C mutant in the absence of the inhibitor. A new generation of inhibitors attempts to affect different aspects of its activity, including its localization to the membrane, the binding of different effectors, and nucletodide exchange. Figure \(3\) shows these key structural features of Ras (with different color coding). Important parts of Ras necessary for GTP binding include the phosphate-binding (P loop), residues 10 to 16 (dark blue trace below), switch regions I (30 to 37, light blue trace) and II (60 to 76, green trace), which are flexible loops which sandwich GTP. Figure \(4\) is an animation showing structural differences between the GTP bound form (blue, pdb id 5p21) and GDP form (red, pdb id 4q21) of the H-Ras protein. One helix and nearby loops are perturbed. There are about 150 members of the human Ras superfamily as shown in Figure \(5\) and Table 1 below. Table \(1\) shows common Ras superfamily functions Ras regulation of gene expression, cell proliferation, survival, and differentiation Rho regulation of actin cytoskeleton, cell shape, and movement, cell interactions with the extracellular matrix Rab vesicle trafficking, endocytosis, secretion Arf vesicle trafficking, endocytosis, secretion, microtubule assembly Ran nuclear cytoplasm transport, mitotic spindle We have discussed Ran before as a mediator of protein movement across the nuclear membrane in Chapter 11.5. It's mainly in the GDP-bound form in the cytoplasm and the GTP-bound form in the nucleus. Switches between a cytoplasmic GDP- and a nuclear GTP-bound state by nucleotide exchange and GTP hydrolysis. Nuclear import receptors with bound cargo protein containing a nuclear import signal bind RAN-GTP in the nucleus, leading to the release of the importin and the cargo protein. In contrast, cargo proteins with a nuclear export signal bind exportins and RAN-GTP in the nucleus and move into the cytoplasm, where the RAN-bound GTP is hydrolyzed on binding a RAN-GAP. This cycle is illustrated in Figure \(6\). Small G proteins are a fundamental form of molecular switch. They are simply too important to not be regulated. Probably the most common mutation in human cancer cells involved a single amino acid change in Ras (H, K, and N form). If the GTPase activity is inhibited with mutation, the protein may be constitutively active. Such a Ras mutation is found in almost 90% of pancreatic cancers. Hence researchers have been trying to design drugs that inhibit its GTPase activity. This has proven difficult since it has very few targetable pockets that could bind a drug. Regulation of small G proteins: GAPs and GEFs Given the critical importance of small G proteins, it makes biological sense that their on/off activity would be exquisitely regulated. Indeed, they are. Two families of protein have evolved to regulate them by determining whether GTP or GDP is bound to the protein (leading to an active, and inactive small G protein respectively). One family, GTPase activating proteins (GAPs) facilitate the hydrolysis of bound GTP, leading to the inhibition of the protein. The other proteins are GTP exchange proteins (GEFs), which facilitate the exchange of GTP for GDP, activating the protein. The activity of Ras GAPs and GEFs, as well as various proteins interacting with Ras, are depicted in Figure \(7\). Figure \(7\): The activity of Ras GAPs and GEFs, as well as various proteins interacting with Ras It may seem crazy but the number of GEFs and GAPs is greater than the number of G proteins with which they interact. As shown in Figure \(4\), there are 20 Rho G proteins but about 80 GEFs and 70 GAPs for them. This number presumably allows greater control of the specificity of the reactions controlled by the Rho G protein. GAPs - GTPase Activating Proteins The hydrolysis of the gamma phosphate of GTP by water in Ras proceeds by a pentavalent transition state with two axial and three equatorial ligands to the P. Developing charge in the transition state would usually be stabilized by catalytic residues in the catalytic domain of Ras. However, Ras is a poor GTPase. That's where GAP comes in. In the Ras/GAP complex, GAP positions its Arg 789 on the GAP in a position to stabilize the transition state for Ras-bound GTP cleavage. This Arg 789 is almost in the same position as Arg 178 in the Galpha inhibitor subunit of a heterotrimeric G protein which inhibits GPCR signaling. Both of these arginines have similar catalytic functions. Figure \(8\) shows an interactive iCn3D model of Ras-GAP complex (1WQ1) Ras is shown in secondary structure colors, while GAP is shown in gray. GDP-AlF3, a GTP analog, is shown in color spacefill. Arginine 789 in GAP is shown in spacefill with CPK colors and labeled R789. It is a clear position to stabilize the bound GTP in the complex and its cleavage transition state. GEFs - GTP Exchange Factor Once bound to Ras, GDP dissociates very slowly. Values of 10-5 sec-1 have been reported for the first order rate constant of the dissociation of GDP from a small G protein. Assuming that the diffusion is controlled on the rate constant for the complex, the KD for the G protein:GDP complex would be 0.1 pM and the half-life would be 0.8 days, similar to the lac repressor:DNA operator complex. Hence the protein, when bound to GDP, is essentially locked in the off position. What if it needs to be reactivated quickly? How can the rate at which GDP dissociates be increased so that GTP could replace it? If it were to dissociate, GTP could quickly replace it since from an equilibrium point of view, Ras and other small G proteins would favor GTP binding since its concentration in the cell is higher. One could envision several ways to change the rate at which GDP dissociates. In organic chemistry, a favorite student response to many questions is to evoke steric effects. In biochemistry, the analog is often conformational changes. How could you change the conformation of Ras such that it might favor GTP binding? That could occur by ligand binding or more likely by a post-translation modification such as phosphorylation as part of a signaling process. It turns out that for the case of small G proteins, another mechanism is evoked: the binding of another protein, a GTP Exchange Factor or GEF, which promotes GTP exchange for the bound GDP. If the Ras:GEF:GDP complex has a 10,000 increase in koff for GTP, the half-life of the bound GDP is 7 seconds. There are 80 GEFs in the human genome. If you think about it, in GPCR coupled signaling, the ligand-bound GPCR is a GEF for the Gαsubunit of the heterotrimeric Gαβγ protein. The crystal structure of a Ras GEF, SOS, in complex with Ras allows a detailed understanding of the mechanism. SOS, a cytoplasmic protein, is recruited to the cell membrane where active Ras is found, tethered to the membrane with a hydrophobic farnesyl attachment.  Figure \(9\) shows an interactive iCn3D model of Ras and SOS (a GEF) complex (1bkd). The actual biological unit (functional structure) is a hetero 8-mer (A4B4) with C4 symmetry. The iCn3D model shows just a heterodimer for clarity. Ras is shown in cyan and SOS in dark blue. SOS as a GEF affects nucleotide binding to SOS in two essential ways. An alpha helix from SOS displaces Switch 1 (amino acids 30-38, shown in red) in Ras, which opens the binding site for the guanine nucleotides open. Additional conformational changes in Switch II (59–72, green) in Ras and interference from the side chains form the SOS alpha helix interfere with the binding of the phosphates on the bound nucleotide. This promotes the dissociation of the bound nucleotide and Mg2+. Now GTP can preferentially rebind. How? Before we answer that question, let's explore the conformational differences just in the structure of Ras in the Ras:GDP complex (4q21) and Ras in the Ras:SOS (1bkd) complex. These differences are shown in Figure \(10\). The green structure is the Ras:GDP. The cyan structure is Ras without bound GDP but bound to SOS. Note the large shift in Switch 1 in the Ras structure from the Ras:SOS complex. This leaves a "gaping" hole from which GDP can "escape". Now how do the opening of the active site and release of bound GDP facilitate GTP binding? Once bound just to Ras, GDP dissociates very slowly. Values of 10-5 sec-1 have been reported for the first-order rate constant of the dissociation of GDP from a small G protein. Assuming that the on-rate for complex formation is diffusion-controlled, the KD for the G protein:GDP complex would be 0.1 pM and the half-life would be 0.8 days, similar to the lac repressor:DNA operator complex. Hence the protein, when bound to GDP, is essentially locked in the off position. The conformational changes on RAS binding to SOS open up the active site, allowing GDP dissociation. GTP can now replace it since from an equilibrium point of view, Ras and other small G proteins favor GTP binding since the concentration of GTP in the cell is higher than that of GDP. In addition, some additional noncovalent interactions with the extra phosphate on GTP probably help. Figure \(11\) shows a cartoon showing the changes in Ras on GEF binding as illustrated in these coupled chemical equilibria: GEFloose:Ras:GDPtight ↔ GEFtight:Ras:GDPloose ↔ GEFtight:Ras

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.05%3A_Small_G_proteins_GAPs_and_GEFs.txt

Search Fundamentals of Biochemistry Introduction Protein kinases phosphorylate proteins in a process that can either activate or inhibit the target protein's activities. To control signaling processes, the activities altered by protein phosphorylation can be readily reversed by dephosphorylation of the Ser-, Thr- or Tyr-phosphoesters by simple hydrolysis. These reactions are catalyzed by protein phosphatases. Some of these phosphatases also cleave phosphates from lipids as well. There are three main families of phosphatases, phospho-Tyr phosphatases (PTP), the phospho-Ser/Thr phosphatases, and those that cleave both. Of all phosphorylation sites, most (86%) are on serine, 12% involve threonine and about 2% are on tyrosine. They can also be categorized by molecular sizes, inhibitors, divalent cation requirements, etc. In contrast to kinases which differ in the structure of their catalytic domains, many protein phosphatases (PPs, also abbreviated Ppp for Protein phosphatases) gain specificity by binding protein cofactors which facilitate translocation and binding to specific phosphoproteins. The active phosphatase hence often consists of a complex of the phosphatase catalytic subunit and a regulatory subunit. Regulatory subunits for Tyr phosphatases may contain a SH2 domain allowing binding of the binary complex to autophosphorylated membrane receptor Tyr kinases. We'll consider examples of all four families. They recognize target proteins through protein:protein interactions and specific binding site motifs. There are over 10,000 pSer and p-Thr sites on target proteins, so targeting specific sites must be quite nuanced. Serine/Threonine Phosphatases Important Ser/Thr phosphatases (PPs for Protein Phosphatases) include: • Protein phosphatase 1 (PP-1 or Ppp1) - This is the most abundant PPP in humans. Different regulatory subunits target this to the liver glycogen particles (GL subunit), striated muscle glycogen, and sarcoplasmic reticulum (GM subunit) or smooth muscle fibers (M subunit). It is also present in the nucleus where it is presumably involved in the regulation of transcription factors. It is also involved in RNA splicing and signaling at neural synapses. • Protein phosphatase 2A (PP-2A or Ppp2) - is a trimer with catalytic, regulatory, and scaffolding (also regulatory) structural subunits. It is found mainly in the cytoplasm and is involved in a myriad of cellular processes. • Protein phosphatase 2B (PP-2B or Ppp3) - also called calcineurin or Ca2+/Calmodulin dependent protein phosphatase - It consists of a catalytic subunit (calcineurin A) and a regulatory, calcium-binding subunit (calcineurin B). It is inhibited by the complex of the immunosuppressant cyclosporin and FK506 with immunophilins. PP2B regulates PKA and PKC • (PP2C) - The catalytic subunits of PP1, 2A, and 2B share a great deal of amino acid homology, and based on this homology, belong to one family. PP2C belongs to another family. PPs are often categorized into three other families including, phosphoprotein phosphatases (PPPs) and metal-dependent protein phosphatases (PPMs). There are about 30 catalytic PP subunits (many fold fewer than Ser/Thr Kinases). They gain specificity by binding numerous modulatory regulatory subunits. As with other proteins, the names given to the proteins when discovered often do not reflect an organization scheme that would name different members based on structural similarities. PP-1, 2A, and 2B are better named Ppp1, Ppp2, and Ppp3 which denote members of the Protein PP (PPP) family. PP-2C would be named Ppm1 as the first member of the PPN family. All PPPs have three short sequence motifs that bind divalent cations. In addition, older names for the given PPs referred to both the catalytic subunit and the dimer with the regulatory subunits. For clarity, the name of the catalytic protein phosphatase 1 is PP1c, and its regulatory subunits as RIPPOs, regulatory interactors of protein phosphatase one. Protein Phosphate-1 (PP-1): PP-1 is involved in many signaling pathways that control cell division, protein synthesis, etc. It catalyzes most serine–threonine dephosphorylation in cells. It is perhaps best known for its regulation of glycogen mobilization. Insulin signals the well-fed state in healthy people and promotes glucose uptake through the GPCR insulin receptor which we discussed in Chapter 12.4. Under these condition excess glucose is used to elongate glycogen, our main carbohydrate energy storage polymer. In contrast, the starving or low energy state is signaled by the hormone glucagon. You would expect that signaling pathways activated in the presence of insulin would promote glycogen synthesis and inhibit glycogen breakdown. PP-1 is a key factor in the regulation of both processes: • Insulin activation of glycogen synthesis - PP1 dephosphorylates glycogen synthase (the enzyme that synthesizes glycogen) and in the process ACTIVATES it. • Insulin inhibition of glycogen breakdown - PPI dephosphorylates two key enzymes involved in glycogen breakdown, phosphorylase kinase and glycogen phosphorylase a (with a pSer14), and in the process INHIBITS them. PP1c interacts with many different regulatory subunits (RIPPOs) forming unique heterodimers. The regulatory subunits also bind potential substrates for PP1c and help localize the enzyme. at sites. The regulatory subunit involved in the PP1c effect on glycogen is called the glycogen-targeting subunit, GM. There are 7 such regulatory subunits involved in glycogen metabolism. GM (RGL) is expressed in muscles and GL in the liver. All of the regulatory subunits (unfortunately called G-subunits) have a conserved RVxF amino acid sequence which interacts with specific sites on the catalytic subunit typically distal to the active phosphatase site. Binding through the RVxF sequence does not affect the active site of the PP-1c. The binding of the regulatory subunit to the PP-1c can also occur outside of the canonical RVxF sequence. The regulatory subunits also have starch binding domain (SBD), also called the carbohydrate-binding module (CBM21). The subunits are often highly disordered until they are bound. Figure \(1\) shows an interactive iCn3D model of protein phosphatase 1 (PP1) bound to the muscle glycogen-targeting subunit (Gm) and microcystin (6DNO) and the toxin microcystin. PP-1 alpha catalytic chain is shown in gray. Its active site, where it binds Ser/Thr phosphorylated proteins is shown in magenta. Bound in that site is the toxin microcystin. The small chain shown in cyan is protein phosphatase 1 regulatory subunit 3A. PP-1 binds to its regulatory sequence to a 65RVxF68 sequence common on many regulatory PP-1 subunits. This subunit contains a serine 67 which is phosphorylation by protein kinase A. This inhibits the binding of the catalytic and regulatory subunits. (PKA) of the “x” residue in the GM RVxF motif, Ser67GM, inhibits PP1 binding (16). Val79 and Lys80 on GM also form a motif that binds a corresponding pocket in the catalytic subunit. Note the pi-cation (red) and pi-stack (blue) interactions from Phe 68 of the GM 65RVSF68 motif of the regulatory subunit to the PPI catalytic subunit. Microcystin is 7-mer peptide ring that has 5 noncanonical amino acids and 2 regular ones. A covalent bond from Cys273 of PP1 (labeled in the above iCn3D model) to the methyl-dehydroalanine (Mdha) of the toxin forms. Microcystins are produced by toxic cyanobacteria and are very toxic and lethal, especially to animals including humans that drink water contaminated with the cyanobacteria. They will pose a greater threat in a warming world from climate change. They also bind to PP-2A. Another example of a regulatory subunit for PP1 is the PP1 nuclear target subunit (PNUTS). The activity of the complex in the nucleus regulates the phosphorylation state o many proteins involved in the cell cycle including p53, a tumor suppressor in many tumors. It also regulated chromatin structure and RNA processing. As with the muscle glycogen-targeting subunit (Gm), PNUTS is intrinsically disordered when not bound to PP1 and is very extended when bound. The catalytic subunits of PP1c and PP2A have an acidic, hydrophobic, and C-terminal grove. PNUTS binds one of the substrate groves at an arginine subsite, but not the active site. So far we can say the specificity of the binding of PPP is determined to some degree by the regulatory subunits. We turn to the contributions of the catalytic specificity of PP-1c when we compare it to PP-2Ac below. Mechanism of PP1-A and PP-2A and B The catalytical subunits of PP-1 and PP-2 are very homologous with nearly identical key active site side chains. The site contains two Mn2+ ions very near each other as shown in Figure \(2\). The figure crudely shows each Mn2+ is octahedrally coordinated to side chains and one water oxygen for each metal ion. Water 2 (or more likely OH-) and an aspartate D92 oxygen bridge the two ions. The phosphate of the target Ser-OPO32- or Thr-OPO32-, or an inhibitor such as tungstate, also bridges the metal ion. Figure \(3\) shows an interactive iCn3D model of Human Protein Phosphatase 1 Active Site Residue (4mov) which shows the same active site residues The metals singly or in combination probably reduce the pKa of bound water to produce the deprotonated hydroxide, which engages in an SN2 attack on the phosphate. Hence the metal ions act as an electrostatic catalyst. The subtle differences in the active site and the three groups contribute to the specificity of the PPPs. Comparison of the catalytic subunits of PP1A the PP2A As mentioned above, substrate specificity is altered by subtle changes in the active site and three groves of PPPs. Figure \(4\) shows the acidic groves for PP1c and PP2Ac. The color is the electrostatic surface potential with red indicating negative and blue (notably absent) positive. The acidic groove is stronger in PP1c. The asterixis * shows the catalytic cleft containing two Mn2+ ions. Hoermann et al. Nature Communication | (2020) 11:3583 | https://doi.org/10.1038/s41467-020-17334-x. Creative Commons Attribution. 4.0 International License, http://creativecommons.org/licenses/by/4.0/. The negative acidic grove is highly enriched in negatively charged side chains. Figure \(5\) shows the actual amino acids contributing to the negative electrostatic potential in aligned PP1c and PP2Ac. The orange spheres show the location of acidic side chains. PP1c is shown in blue/black and PP2Ac in red/gray. The stronger acidic groove in PP1Ac gives it great preferences for pSer/pThr-protein targets with basic motifs than PP2Ac in a fashion that is independent of the bound regulator subunit. In contrast, PP2A needs to interact with regulatory subunits with more acidic composition to target basic motifs in protein targets. These features for PP1c and PP2Ac are compared in Figure \(6\). Panel (d) shows that the holoenzyme for PP1 has a great preference for positive basic motifs than the holoenzyme for PP2A, which needs to associate with a negatively charged regulatory subunit for activity towards target proteins with basic motifs. Panel (e) also shows that the catalytic subunits of both PP1 and PP2A prefer p-Thr protein targets compared to p-Velocity vs substrate graphs show these effects as well. e Both, PP1 and PP2A holoenzymes have a preference for pT due to higher catalytic efficiency of their respective catalytic subunits towards pT over pS. Hoermann et al. ibid Figure \(7\) shows an interactive iCn3D model of the protein phosphatase 2A catalytic subunit in complex with a larger regulatory subunit and bound to the phosphatase inhibitor and tumor promoter okadaic acid (2IE4). The toxin, found in sponges and shellfish, is produced by dinoflagellates. The regulatory subunit is shown in secondary structure color. This scaffolding protein is shaped like a horseshoe. The phosphatase inhibitor okadaic acids are shown in spacefill bound to the catalytic subunit shown in gray. The side chains of the catalytic subunit interacting with the 2 Mn2+ ions are shown in CPK-colored sticks (zoom in to see them). On binding the catalytic subunit, the scaffolding regulatory subunit is quite flexible and adaptable in interacting with other proteins. Protein Phosphate 2B: Calcineurin (CN) Calcineurin (CN), or PP2B, is depended on Ca2+. It is involved in the development, immune signaling, and heart function. It consists of a catalytic site (CNA) and a calcium-binding regulatory subunit CNB so it is another example of PPP heterodimers. CNA has a catalytic domain and domains that bind CNB (the regulatory subunit), calmodulin (CAM, a calcium-binding protein that we will explore more in the next chapter section), and an autoinhibitory domain that blocks the active site. On Ca2+ release from internal organelles, the ion binds to both CNB and also CAM. These events cause conformational changes that release the bound autoinhibitor. As with the other phosphatases, much effort has been made to determine how CN interacts with specific pSer- and p-Thr sites on targets. We'll focus on one, the integral membrane Na+/H exchanger 1 (NHE1). This protein is itself regulated by Ca2+ ions and by phosphorylation by kinases we have previously studied, the MAPK ERK1/2 and the JNK kinase. Erk2 phosphorylates NHE1 at 6 Ser/Thr side chains in the recognition sequences name [S/T]P11. Several different phosphatases, including CN, can regulate NHE1 activity through direct dephosphorylation. CN binds short linear motifs (SLiMs) named PxIxIT and the LxVP that are found in interacting partners including regulatory subunits as well as inhibitors and substrates. As we saw above, the regulatory subunits of PP1A and PP2A are highly disordered. Likewise, SLiMs are on intrinsically disordered regions as well as interacting proteins. • PxIxIT binds to the catalytic domain of CNA22. It also enables interaction between CN and NHE1. • LxVP binds to a cleft between the CNA and CNB, which is only available in the active form of the protein. CN doesn't dephosphorylate multiple nearby p-Ser side chains of NHE1 (pS363, pS723, and pS726) since they are close to the NHE1-PxIxIT interaction sit,e which sterically restricts their binding to the active site. However the 3 other phospo S/T sites on NHE1 (pS771, pS785, and the actual target site (pT779) are far enough away from the NHE1's PxIxIT site so they can interact with the CN active site. Making the T779S mutation shows that dephosphorylation of their phosphorylated version shows a faster rate with pT779 and a slower, yet reasonable rate for pS779. Therefore other specificity factors are in play. A newly discovered very short 4-amino acid site motif in NHE1 including pS779 appears to be a source of selectivity. This TxxP motif in NHE1 is 779TPAP782. Such short recognition motifs are different than the selection of substrates by PP1 which involved multiple domain binding interactions and steric restrictions imposed by them. Figure \(8\) shows a model of the structure of the NHE1 exchanger (left panel a) and the calcineurin CNA/CNB complex. jd Figure \(8\): Docking motifs mediate the interaction of NHE1ct with CN The motifs present in NHE1 (LxVP and PxIxIT) in the intrinsically disordered tail of NHE are indicated in the left panel and their corresponding binding sites in the CNA dimer are shown in corresponding colors. The calmodulin binding site is also shown. Erk2 phosphorylation sites in NHE1 are shown are listed in panel C along with the consensus motif sequences (PxIxIT in purple, LxVP in orange, and TRAP (uncolored)). Hendus Altenburger et al. Nature Communication (2019) 10:3489 | https://doi.org/10.1038/s41467-019-11391-7Creative Commons Attribution 4.0 International License. http://creativecommons.org/ licenses/by/4.0/. Figure \(9\) shows an interactive iCn3D model of calcineurin (PP2B) complex bound to a peptide from the Na+ /H+ -exchanger 1 (6NUC) Protein Phosphatase 2C The protein phosphatase 2C (PP2C) is a member of a family of metal-dependent protein phosphatases sometimes abbreviated as PPMs. (Of course, the other phosphatases we discussed above are also metal-dependent.) The required Mg2+ or Mn2+. PP2C is a monomeric enzyme with at least four isoforms in humans. In humans, there are at least 17 members. One is unfortunately named protein phosphatase 1D but also referred to as PPM1D, PP2Cδ, or Wip1). It is involved in heterochromatin silencing and the cycle. Mutations in its gene can accordingly give rise to tumors. PP2Cs in humans have a binuclear metal cluster which reduces the pKa of water, producing OH- for SN2 attack on the phosphorus in the pSer or pThr in target phosphorylated proteins. Figure \(10\) describes binding interactions around the binuclear site in PP2Cα. Figure \(10\): Binding interactions around the binuclear site in PP2Cα. after Pan et al. Sci Rep 5, 8560 (2015). https://doi.org/10.1038/srep08560htt...cles/srep08560 The metal binding site with many water molecules Interactions with the metals is very different than the PP1 and PP2 active site shown in Figure \(2\). If Cd is bound at the M1 site, the activity of the enzyme is blocked so it is required for catalysis. Making the mutations affecting M2 (D38A and D38K) suggests that M2 is involved in binding the phosphate of the substrate, and also stabilizes the transition state and the leaving group in the reaction. H62 probably acts as a general acid catalyst. Not all PPCs require both metal ions. The plant hormone abscisic acid regulates stress responses in plants. When it binds to a particular receptor called PYL1 (alternative name PYR1-like protein 1), the receptor interacts with a PP-2C called ABI1 (also called Absisic acid-insensitive 1). Figure \(11\) shows an interactive iCn3D model of the ternary complex of Abscisic acid, PYL1 and ABI1 (phospholipase 2C) (3KDJ) The catalytic subunit, which in contrast to PP1, PP2A, and PP2B, has only 1 Mn2+ ion, is shown in gray with amino acids side chains interacting with the metal ion labeled. The cyan subunit is the receptor of abscisic acid, which is shown in spacefill. Protein Tyrosine Phosphatases Protein Tyr phosphatases (PTPs) consist of receptor-like (transmembrane) and intracellular Tyr phosphatases. They more resemble tyrosine kinases in their complexity than the Ser/Thr phosphatases. There are about 100 PTPs in the genome, a number similar to the number of protein tyrosine kinases. PTPs have an active site Cys in a CX5R-(S/T) motif with an active site Cys nucleophile and an Arg in the phosphate binding (P) loop. Some examples we will discuss include: • Low molecular weight PTPase - These have roles in the metabolism and differentiation of cells. They have a molecular weight of 18,000 and have an active site CX5R-(S/T) motif, where the C (Cys) is an active site nucleophile. • PTP1B - dephosphorylates many cell surface receptors (insulin, EGF, PDGF) that have been phosphorylated on Tyr residues. Its main activity seems to dephosphorylate nascent receptors in the endoplasmic reticulum before they get to the final cell membrane destination. • Tyrosine phosphatase nonreceptor type 11, ptpn11, commonly called SHP2 Figure \(12\) shows the protein tyrosine phosphatase (PTP) superfamily. In contrast to the active site of the Ser/Thr phosphatases like PP-1, PP-2A and PP-2B, the active sites of protein tyrosine phosphatases (PTPs) do not have a bimetal ion cluster in the active site. Rather they all have an active site cysteine that acts as a nucleophilic catalyst in the hydrolysis of the p-Try phosphoester bond. The active site PTP domain is found in all of the proteins, so all use similar catalytic mechanisms shown in Figure \(13\). The phosphotyrosine side chain of the target phosphoprotein binds in the phosphate-binding P-loop (H/VCxxxxxRS/T), which contains the nucleophilic Cys 12 and Arg 18 that stabilizes the charge on the phosphate. The Asp in the WPD loop positioned across from the nucleophilic Cys 12 acts as a general acid. Since the active site is nearly identical in the PTPs, it has been hard to design drugs that bind to the active site but that are also selective for specific PTPs. Low molecular weight protein tyrosine phosphatase - LMW-PTP This protein tyrosine phosphatase is the simplest of all in structure. It has the phosphate-binding P-loop (12CxxxxxR18) with the nucleophilic Cys 12 and Arg 18 that stabilizes the charge on the phosphate. It does not have the conserved WPD loop but deploys Asp 129 across from Cys 12 as a general acid. This enzyme exists as two main isozymes, A and B. Figure \(14\) shows an interactive iCn3D model of human low molecular weight protein tyrosine phosphatase bound to sulfate (1xww) The active site is deep as shown in Figure \(15\). The sulfate, a mimetic for the phosphate on the p-Try protein target, is deeply buried. The CPK-colored surface (green red, blue) around the sulfate are the side chains of Tyrosine 131 and 132 as well as Trp 49. Y131 and Y132 are part of a loop containing Asp 129, the general acid. This loop is analogous to the WPD loop. The three aromatic amino acids on top of the pocket make it deep enough that pSer and pThr side chains can't reach the active site nucleophile Cys 12. Trp 49 is also in a variable loop (34 amino acids, shown as an orange surface) that differentiates two of the major isozymes, the A and B forms, and contributes to substrate binding specificity. Tyrosine-protein phosphatase non-receptor type 1, also known as PTPN1 or PTP1B PTP1B PTP-1B regulates the endoplasmic reticulum unfolded protein response and is involved in insulin JAK/STAT and HER2 (ErbB2) signaling. It has a full-length form (MW 50,000) and a C-terminal shortened form (37,000) Given the difficulty in targeting the active site which is common in all PTPs, efforts have concentrated on the development of allosteric inhibitors that bind to exosites removed from the active site. An example is trodusquemine (MSI-1436) used in the treatment of obesity and type 2 diabetes. It binds much more tightly to the full-length form. Figure \(16\) shows an interactive iCn3D model of the human Protein Tyrosine Phosphatase 1B (1-301) in complex with the inhibitor OTA (5K9W). The P-loop is shown in magenta, the WPD loop in cyan, and the substrate binding loop (SBL), which allows entry of pTyr but not pSer and pThr, in blue. The key side chain in the P-loop (Cys 215 and Arg 221) as well as the catalytic general acid (Asp 181) are shown in sticks and labeled. The inhibitor is shown in spacefill, and CPK colors. The movement of the WPD loop is rate-limiting for the hydrolysis of P-Tyr esters. On binding, the WPD starts to close, and in the process Arg 221 moves to form salt bridges with the phosphate. Full closure of the WPD follows, which positions Asp 181 for general acid catalysis. Key interactions of PTP-1B phosphoprotein in insulin and cytokine signaling, are shown in Figure \(17\).: Figure \(17\): Protein tyrosine phosphatase 1B (PTP1B) and its effects on signaling. Maja Köhn ACS Cent. Sci. 2020, 6, 4, 467–477. Publication Date: March 13, 2020. https://doi.org/10.1021/acscentsci.9b00909. This is an open-access article published under an ACS Author Choice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes. Panel (A, left) shows how PTP1B dephosphorylates the insulin receptor and the insulin receptor substrate (IRS), which we have explored in a previous section. Panel (A, right) show its activity in the JAK/STAT pathway, which we have all seen previously. One cytokine receptor that it regulates is the leptin receptor. The hormone leptin, released from fat cells (adipocytes) is a key regulator of lipid metabolism. Pane B shows the structures of key inhibitors of PTP-1B. Protein tyrosine phosphatase nonreceptor type 11 (ptpn11) also known as SHP2 (SH2-domain containing phosphatase-2) This is an example of another phosphatase in which a mutation leads to cancer. It is downstream and activated by most receptor tyrosine kinases (RTKs) involved in the activation of the MAPK pathway with its ultimate links into the nucleus and activation of gene transcription. Figure \(18\) shows an interactive iCn3D model of Non-receptor Protein Tyrosine Phosphatase SHP2 in Complex with Allosteric Inhibitor Pyrazolo-pyrimidinone 5 (6MDB) The phosphatase domain is shown in gray. The N- and C-terminal SH2 domains are shown in green and blue, respectively. The allosteric inhibitor is shown in spacefill and CPK colors. The P-loop in the catalytic domain is shown in red with the Cys 459 (active site nucleophile) and R465 (stabilizer of phosphate in the complex) shown in sticks, CPK colors, and labeled. The bound inhibitor is especially interesting as it binds at an allosteric site. As mentioned above, it is very difficult to design specific inhibitors that target just one PTP given their common active sites and mechanisms. Figure \(19\) shows multiple features of SHP2. Figure \(19\):. SH2-domain containing phosphatase-2 SHP2. Köhn ibid. Panel (A) shows how SHP2 recruited to phosphorylated RTKs activates the MAPK pathway. The dotted line indicates multiple steps. Upon receptor activation, SHP2 is recruited in different ways to activate the MAPK pathway. Panel (B) shows that in the inactive state, the N-terminal SH2 domain (green) blocks access to the active site. When the N- and C-terminal domains bind pY residues in a single pY-protein, two pY-proteins, or pY residues on its C-terminal tail, a conformational change ensues opening the active site (5EHR). Panel (C) shows how another allosteric inhibitor keeps the protein in a closed state (5EHR). Panel (D) shows how SHP2 can decrease T-cell responses through the MHC:Tcell receptor (TCR) complex. Tumor cells express a ligand called PD-L1, which binds to the PD1 receptor on the T cell surface. After binding, SHP2 is recruited to PD1, decreasing T cell activation. This is not a good thing since it inhibits the immune response to the cancer cell. Dual Specificity Phosphatases (DUSPs) Another important phosphatase is phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTEN (phosphatase and tensin homolog). Dual-specificity protein phosphatase hydrolyzes pTyr- as well as pSer- and pThr- phosphoesters in target proteins. They don't require divalent metal cations and are closer in structure to protein tyrosine phosphatases. They have an active site cysteine in a P-loop also containing arginine. In addition, they are lipid phosphatases, removing phosphate from the inositol ring from phosphatidyl inositol derivatives. These both impact many signaling pathways. Its activity as a lipid phosphatase makes it a tumor suppressor protein as it inhibits the PI3K-AKT/PKB signaling pathway by dephosphorylating phosphoinositides. Hence it modulates both AKT and mTor pathways. The domain structure of PTEN is shown in Figure \(20\). The C2 domain enables phospholipid binding. Multiple post-translational modification sites are indicated. The PEST motif is sequence rich in proline (P), glutamic acid (E), serine (S), and threonine (T) and bounded by positively charged amino acids (Lys, Arg, or His) that act as signals for protein degradation. The PDZ domain, often found at the C-terminal of signaling proteins, acts as a scaffolding site for interaction with other signaling proteins. In the next chapter section, we will consider redox signaling, for which PTEN is a great example. Disulfide formation (in a more oxidizing environment) between the nucleophilic Cys 124 and a nearby Cys 71 (figure above) inhibits PTEN phosphatase activity. Figure \(21\) shows an interactive iCn3D model of an AlphaFold computational model of full-length human PTEN (Uniprot P60484). The phosphatase (PTPase) domain is shown in blue and the C2 domain is in orange. The P-loop is in red with the active site Cys 124 and R130 in colored sticks and labeled. The backbone of the highly extended intrinsically disordered C-terminus region is shown in gray. It contains the clustered residues Ser 380, Thr 382, Thr 383, and Ser 385 (shown in colored sticks and labeled) that are sites for phosphorylation by activated kinases. iFigure \(22\) shows key molecules dephosphorylated by PTEN, including the lipid PIP3, and Thr 308 and Ser 473 on AKT. When PTEN dephosphorylates Akt1, it inhibits AKT activity and effectively antagonizes the main PTEN-PIP3-PDK1-Akt pathway. PTEN is considered a tumor suppressor for this reason. Mutations in PTEN hence are associated with cancer.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.06%3A_Phosphatases.txt

Search Fundamentals of Biochemistry The following is adapted directly and modified from Sharma et al. Biomedicines 2021, 9(9), 1077; https://doi.org/10.3390/biomedicines9091077. Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Introduction Ca2+ is central to numerous cellular processes and functions. Its chemical features, including a low hydration energy, high polarizability, relative flexibility of coordination sites and bond length, and large concentration gradient across cellular membranes (100 nM intracellular to 2 mM extracellular) due to low intracellular levels make it the ion of choice at the core of cellular signaling in prokaryotes and eukaryotes alike. In studying calcium ion signaling we will focus on four key areas: • Buffering of intracellular Ca2+ ion concentrations: Basal low levels must be maintained, which allows transient increases to act as signals. Ca2+ ions hence are no different from other second messengers like cAMP, for example. What matters is the rise from a basal level to a threshold concentration level that allows binding to signaling proteins and subsequent signal transmission. • Storage of intracellular Ca2+: Calcium ions are stored in organelles such as ER, mitochondria, and lysosome. The ions must be released in the presence of specific signals, then returned to the storage organelle to maintain basal Ca2+ levels. • Signaling pathways activated by Ca2+ ion: We have seen many pathways simulated by a rise in second messengers and by phosphorylation of lipid and protein molecules in interconnected pathways. We will return to several pathways we have previously studied to see how they integrate with Ca2+ in signaling processes. • Ca2+ binding proteins and their binding partners in signaling pathways: We will focus on one key Ca2+ binding protein, calmodulin (CAM), and the kinase that it activates, Ca2+/CAM protein kinases or CAMKs. The next two sections are adapted and modified from Sharma et al. Biomedicines 2021, 9(9), 1077; https://doi.org/10.3390/biomedicines9091077. Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Buffering of intracellular Ca2+ on concentrations Calcium ions, like the hydronium ion, must be buffered in cells otherwise its potential as a signaling agent would be compromised. The mechanisms adopted by cells for intracellular Ca2+ buffering involve sequestration by special proteins as shown in Figure $1$. Intracellular Ca2+ levels are managed through binding to special proteins or sequestration within different cellular compartments. The three main ways by which intracellular Ca2+ is buffered are depicted in Figure 1. These include soluble or unbound proteins that are found in the cytosol or inside organelles, membrane proteins (generally Ca2+ channels, ATP-driven pumps (SERCA or PMCA), and ion exchangers (NCX), inside organelles like endoplasmic reticulum (ER), mitochondria, acidic vesicles (mainly lysosomes and Golgi bodies) or organelle junctions (endoplasmic reticulum-plasma membrane (ER-PM), endoplasmic reticulum-mitochondria, or endoplasmic reticulum-lysosomes). The major players regulating inter-organellar Ca2+ transfer are IP3R (inositol-3,4,5-triphosphate receptor), NCX (sodium-Ca2+ exchanger), ORAI1/CRACM1 (Ca2+ release activated modulator 1), PMCA, (plasma-membrane Ca2+ ATPase), SERCA (sarco-endoplasmic reticulum Ca2+ ATPase), STIM1 (stromal interaction molecule 1), SOCE (store-operated Ca2+ entry), TPC1/2 (two-pore channel), TRP (transient receptor potential) and VDAC (voltage-dependent anion channel). We will discuss some below. These proteins are involved in sequestering cytosolic Ca2+ upon sensing an increase in its levels and participate in relaying the associated cellular messages. Other proteins that work as intracellular Ca2+ buffers exist in the lipid bilayers, plasma membranes, or organelle membranes, like pumps or transporters. Apart from these proteins, intracellular Ca2+ is regulated by inter-organellar transport and the influx of Ca2+ ions from extracellular space. Storage of intracellular Ca2+ - Proteins Soluble and Unbound Intracellular Proteins: Calmodulin, Calbindin, and Calretinin Nonmembrane-associated proteins inside a cell can act as both Ca2+ sensors and buffers. Most of these proteins have EF-hand motif(s) that allows Ca2+ ions to bind and trigger changes in protein folding, influencing downstream or linked cellular pathways. Calmodulin (CaM) is one of the best-studied and ubiquitously expressed Ca2+-sensing proteins known to play a key role in intracellular Ca2+ homeostasis. It is a prototype for intracellular Ca2+ sensors. It has a 148 amino acid structure with two Ca2+-binding sites in two separate lobes, each with two EF-hand motifs. The lobes are connected in the holo (Ca2+-bound form that binds other proteins. N- and C-termini alpha-helices with a Ca2+ coordination loop in between providing affinity for Ca2+ ion docking and sequestration. The ability of CaM to transmit a change in free intracellular Ca2+ levels into a signal depends on the conformational flexibility of the Ca2+-dependent (apo) form. CaM can exist in a Ca2+-free closed conformational state (Apo-CaM), a semi-open (Ca2-CaM), or an open state (Holo-CaM or Ca4-CaM) after Ca2+-binding as shown in Figure $2$. Figure $3$ shows an interactive iCn3D model of Holo-calmodulin with 4 bound Ca2+ ions (1CLL) Side chains interacting with one Ca2+ ion in an EF-hand is shown in the model. The central helix is colored based on hydrophobicity with green indicating more hydrophobic. This amphiphilic helix can bind target proteins in this region through nonpolar interactions. Figure $4$ shows the change in conformation going from the apo form (without Ca2+) to the holo-form with the fully-formed central helix connecting to the two lobes of the "dumbbell". Differential Ca2+ binding to the two lobes of CaM makes fast buffering of a wide range of free intracellular Ca2+ possible for this protein. The presence of methionine residues in its lobes and the plasticity of the central linker in its structure also provides CaM with properties to function as an adaptor protein in intracellular Ca2+ signaling. CaM can bind to several targets or effector molecules over a variable distance and in multiple orientations to mediate change in intracellular Ca2+ signaling. Some major effector proteins that are regulated by CaM binding and are relevant for Ca2+ homeostasis include EGFR, PI3K, and connexins. CaM is required for spatial and temporal regulation of [Ca2+]as evident by its role in modulation (activation or inactivation) of Ca2+ pumps (such as PMCA and SERCA) and Ca2+ channels (such as CaV1.3, TRPV5 and 6, ORAI). CaM also acts via serine/threonine kinases known as Calmodulin-activated Kinases (CaMKs) to influence cellular processes like proliferation (for example, centrosome duplication at G1/S or anaphase to metaphase transition via CaMKII). We will discuss those in detail below. Integral Membrane protein molecular buffers: SERCA, PMCA, NCX, and TRP Integral membrane protein Ca2+ buffers primarily translocate free Ca2+ between domains and organelles. These mainly comprise ion exchangers, channels, and ATP-driven pumps. SERCA, Sarcoendoplasmic Reticulum Ca2+ ATPase, is an ATP-dependent ion pump known to significantly maintain free cytosolic Ca2+ concentration via actively pumping the ion into the endoplasmic reticulum (or sarcoplasmic reticulum in muscle cell). They share a general structure that includes 10-pass transmembrane helices and three cytoplasmic domain lobes as shown in Figure $5$ P-type Ca2+-ATPases also exist within the plasma membrane and maintain cytosolic Ca2+ levels by transferring them into the extracellular space. The Plasma Membrane Ca2+ ATPases (PMCAs) can PMCAs transport one Ca2+ ion per ATP molecule which differs from the two Ca2+ ions per ATP molecule stoichiometry of SERCA. The general structure of such Ca2+ transporters comprises 10 transmembrane segments with large cytosolic loops TM 1–2 and TM 3–4, a cytosolic N- and C-termini tails are shown in Figure $6$, panel A below. The cytoplasmic region of PMCA (left) has three loop structures with binding sites for signaling molecules like CAM. They also have phosphorylation sites for additional regulation. The C-terminal tail contains additional CAM sites as well as a PKA site. It has a PDZ domain that can anchor the protein to cytoskeletal components. Differential RNA processing lead to variations in the amino acid sequence in this region and hence binding specificity. The binding of CaM reverses auto-inhibition of the pump due to conformational shifts which displace C-tail from cytosolic loops. Other means of autoinhibition reversal include phosphorylation of C-tail (Ser/Thr residues) by protein kinase A or C, proteolytic cleavage of C-tail, or dimerization via the C-terminus. Transient Receptor Potential (TRP) channels have a similar function in neurons, epithelial and immune cells. The Mammalian TRP channel superfamily is composed of 28 family members belonging to six subfamilies—TRPC (Canonical), TRPA (Ankyrin), TRPM (Melastatin), TRPV (Vanilloid), TRPP (Polycystin), and TRPML (Mucopilin)—that differ in their sensitivity to various sensory stimulations and affinity for cations (including Ca2+ ions) sequestration. Commonly, TRP family members share a structure with six transmembrane domains, intracellular N- and C-termini, and a pore-forming TM 5–6 loop. The cytoplasmic C-terminus of each subunit is a site for protein interaction and post-translational modification. The C-tail of these channels can have PDZ protein binding domains (TRPV and C), sites for interaction with G-proteins (Gq/11)/calmodulin/PLCβ, ADP-ribose binding (NUDIX; TRPM2), or PLC-interacting kinase (PLIK; TRPM6 and 7) domain. TRP channels act as activators, integrators, as well as downstream effectors of Ca2+ signaling at the plasma membrane and in intracellular compartments. Many members of the TRPC subfamily are activated by DAG (diacylglycerol) which is produced by PLC β- or γ-mediated cleavage of phosphatidylinositol 4,5-bisphosphate (PIP2) after the ligand binding at GPCRs or RTKs. TRPP1/2, TRPA1, TRPM8, and TRPV1-4 are all expressed on the ER membrane. At this site, PLC-independent activation of the TRP channels (such as TRPV1) is suggested to induce ER Ca2+ release via inositol triphosphate receptor (IP3R) which further triggers bulk entry of extracellular Ca2+ into the cell. On the flip side, cytosolic Ca2+ regulates the activity of TRP channels in response to physiological stimuli. This regulatory effect is usually through CaM binding (inhibition of TRPV5, TRPV6, and sensitization of TRPV3) and indirectly through CaM-binding kinase II (CaMKII). These activities are shown in Figure $7$. Storage of intracellular Ca2+ ions - organelles Endoplasmic Reticulum: STIM, ORAI, IP3Rs, and TRPC1 in SOCE and SOCIC Ca2+ Entry Models The ER serves as the largest and most dynamic organelle reservoir for intracellular Ca2+ and is therefore central to many signaling processes for protein synthesis, folding, and post-translational modifications. In contrast to the cytosol, ER Ca2+ ion levels can range from 100 uM to 1 mM based on the cell type. ER, and other intracellular organelles buffer excessive cytosolic Ca2+ by both housing Ca2+-binding proteins (example: calreticulin in ER) and via active transport (example: SERCA pumps in ER). • Depletion of Ca2+ from the ER lumen actuates an indirect mode of Ca2+ entry into the organelle which is termed Store-Operated Ca2+ Entry (SOCE) or Ca2+ Release Activated Ca2+ (CRAC) entry; it is activated when plasma membrane receptors like PLC-coupled GPCRs (but not voltage-gated channels) trigger Ca2+ ion release from the organelle. • Exhaustion of the intraluminal ER Ca2+ ion store following such prolonged release is then sensed by STIM (Stromal Interaction Molecule) tethered to the ER membrane and subsequently relayed to the CRAC channels on the plasma membrane. Figure $8$ shows the domain structures of STIM 1/2, ORAI 1-3, and IP3Rs (panel A) and the mechanism of Ca2+ influx into the cell (B). IP3 Receptors (IP3Rs), on stimulation by IP3, open and allow Ca2+ influx from the organelle lumen into the cytoplasm. After activation of IP3Rs on the ER membrane by ligand IP3 and cytosolic Ca2+ from activation of phospholipase C, STIM dimers are activated once the luminal Ca2+ concentration drops below basal levels. These receptors provide intracellular Ca2+ ions for downstream Ca2+ signaling including NFAT-mediated transcription. The red semi-circle in the ER represents high luminal Ca2+ levels, the pink semi-circle is for moderately low Ca2+ ion concentration, and the pale semi-circle indicates extremely low Ca2+ concentration. CC, coiled-coil; NFAT, nuclear factor of activated T-cells; SAM, sterile alpha motif; SOAR, STIM1 Orai1-activating region; TM, transmembrane. Mitochondria and Acidic Vesicles (Mainly Lysosomes) Mitochondria also play a critical role in maintaining Ca2+ ion levels in the cytosol and endoplasmic reticulum. They are found mostly aggregated around the nucleus and store similar levels of intracellular Ca2+ as the cytosol (0.1 μM). Electrochemical proton gradient or membrane potential (Ψmt = −150 to −180 mV) and close association to the ER are the two key factors responsible for Ca2+ uptake in mitochondria. The free movement of small molecules (less than 5 kDa) from the outer mitochondrial membrane (OMM) into the inner mitochondrial space and their impermeability across the latter generates a high electrochemical proton gradient for ATP synthesis. This gradient simultaneously draws Ca2+ ions from the cytosol. Transfer of Ca2+ ions from ER to mitochondria occurs at specialized microdomains or contact sites known as Mitochondrial Associated Membranes (MAMs). These are characterized by the ER and OMM apposed at 10–25 nm from each other and are strewn with a cluster of channels, transporters, exchangers, and tethering proteins for facilitating Ca2+ ion transfer. IP3Rs localized at the ER side of the MAMs release Ca2+ ions that gate voltage-dependent anion channels (VDACs) located on the OMM. VDACs (1, 2, and 3) are 30 kDa polypeptides having a 19-strand beta-barrel structure that regulates the flux of metabolites (polyvalent anions like ADP and ATP) across the outer mitochondria membranes. These channels transport cations including Ca2+ more readily than anions like chloride. Due to voltage-dependent electrostatic gating, the ion selectivity and flux across VDACs change between open and closed states. Figure $9$ shows couped mitochondrial and lysosomal effects on intracellular Ca2+ signaling. Primary components of Ca2+ signaling at the mitochondrial associated membranes (MAMs) include IP3R3 on the endoplasmic reticulum, VDAC1 on the outer mitochondrial membrane, and MCU complex on the inner mitochondrial membrane [151,154,156,161]. Transport of Ca2+ ions from ER to mitochondria plays a crucial role in cellular metabolism (autophagy), cell survival (during unfolded protein response and impinging cell death signals), lipid production, and distribution. F While IP3 acts as the dominant Ca2+-mobilizing messenger, cADPR (cyclic ADP-ribose) and NAADP (nicotinic acid adenine dinucleotide phosphate) are also known to modulate intracellular Ca2+ stores. cADPR evokes Ca2+ ion release from ER by acting on ryanodine receptors (RyR; counterpart of IP3R in myocytes and co-expressed in some other cell types). NAADP releases Ca2+ from acidic and/or secretory vesicles such as lysosomes and endosomes. In most mammalian cells, lysosomes comprise ~5 percent of the cell volume and store similar levels of intracellular Ca2+ (0.5 mM) as the ER. Due to their relatively smaller size than ER, lysosomes release nearly undetectable amounts of intracellular Ca2+ in response to NAADP trigger Signaling pathways activated by Ca2+ ion It is daunting to both readers and writers to introduce a myriad of new signaling pathways. Instead will show how Ca2+ signaling fits into other pathways we have already discussed. A summary showing how Ca2+ signaling integrates with other pathways is shown in Figure $10$. An Overview of Calcium Signaling Pathway Ca2+ signaling, as described above, requires ion buffer, organelle storage, and Ca2+ protein pumps and channels. The concentration (amplitude) and frequency of Ca2+ release affect signaling. Figure $10$ shows the importance of upstream signaling through GPCRs, phospholipase C, RTKs, and IP3/DAGs. Ca2+ also enters the nucleus vs IP3 receptors (IP3Rs) and ryanodine recetors (RYR). An important family of cytoplasmic transcription factors, the Nuclear factor of activated T-cells (NFAT), which are important in immune responses and development of muscle and nervous systems, are activated in calcium ion signaling. As we described above, Ca2+ release from the ER is sensed by integral ER membrane proteins called STIMs. These bind Ca2+ ions as a buffering system, but if most of the ER calcium is depleted, the STIM-bound Ca2+ ions are also released. This lead to their self-association and ultimate activation or ORAI1, part of the CRAC complex in the cell membranes which allow extracellular Ca2+ ions to flow into the cell in a process called store-operated calcium entry (SOCE). Sufficient calcium now accumulates in the cell to activate the transcription factor NFAT through dephosphoylation by calcineurin (PP2B), also abbreviated CaN in Figure $10$. NFAt then translocates into the nucleus and activates gene transcription. Also, it has been shown that nuclear calcium ions directly can activate the cAMP response element binding protein (CREB), a transcription factor that activates gene transcription. In addition the CAM:CAMKII complex can translocate into the nucleus. Calcium signals also activate ERK1/2-MAPK cascade. Ca2+ binding proteins and their binding partners in signaling pathways We have already described the key calcium-binding protein, calmodulin. On binding Ca2+, it undergoes a profound conformational change that allows it to interact with a family of key signaling kinases called CAM and Ca2+/CAM-Dependent Protein Kinases (CAMKs). Ca2+/calmodulin-dependent protein kinase is a key signaling protein activated by Ca2+ through the binding of calmodulin to CAMK. Activated CAMK is a Ser/Thr kinase. There are many types of CAMKs. We will focus on multifunctional CAMKs that can phosphorylate multiple target proteins. These are important in learning and memory, metabolism, and gene transcription. As with other kinases, they have catalytic and regulatory domains. Some like CAMK II have association domains that allow the formation of CAMKII multimers. In addition, they must have a CAM binding domain. As with all kinases, the CAMKs must be able to switch from an inactive to an active form. CAMKI has a catalytic, substrate-binding domain and an autoinhibitory domain that blocks the active site. CAMKI is activated by an upstream kinase CAMKK (a naming system similar to the MAPK cascade) on the binding of Ca2+ to CAM. It helps regulate transcription, the cell cycle, hormone production, cell differentiation, actin filament organization, and neurite outgrowth. It is found in the cytoplasm and nucleus. We will focus our attention on CAMKII, which has four isoforms (α, β, γ, and δ). It is activated by the binding of Ca2+/CAM which promotes autophosphorylation. After that, it is active in the absence of CAM. It is important in learning and memory and synapse formation in neurons and the regulation of sarcoplasmic reticulum Ca2+ transport in skeletal muscles. They have an N-terminal catalytic domain and a C-terminal association domain that facilitates multimer formation into large holoenzymes with 12 or 14 CAMK monomeric subunits (a homomer or heteromer). These two domains are separated by a linker/regulatory domain that has a CAM binding site, an autoinhibitory region, and key Ser and Thr side chains that are targets for phosphorylation. Figure $11$ shows the domain structure of the CAMKII monomer (a), the overall structure of a homododecamer (b), and the mechanism for activation of kinase activity (c). T253, T286, and T305/306 are targets of autophosphorylation. M281/282 are also sites for oxidative modification. The C-terminal association domain allows multimer formation. It has a variable region that differentiates CaMKII subtypes. Panel (b) shows the multimer that forms on interactions of multiple association domains on different CAMKIIs. Panel (c) shows binding of CAM promotes the phosphorylation of key residues including T286 (through autophosphorylation). In the absence of Ca2+/CAM T286 amino acid forms interactions with the catalytic domain to maintain the inactive conformation. The regulatory domain effectively autoinhibits the kinase domain. On the formation of the Ca2+/CAM/CAMKII complex, a conformation change ensues that frees the catalytic domain from autoinhibition and exposes the active site. Each subunit in the dodecamer is activated separately. T286 is now free to be "autophosphorylated" by an adjacent active subunit. Once phosphorylated, pT286 prevents the rebinding of the autoinhibitory region to the catalytic domain, even when CAM dissociates. At this point, CAMKII is active in the absence of Ca2+/CAM. Phosphorylation of T286 also regulates its binding to target proteins for their phosphorylation. In addition, CAMKII can autophosphorylate T254 and T306 with further effects on activity. T306 is only autophosphorylated after CAM dissociates and the enzyme is autonomously active. Dephosphorylation by PP1 and PP2A returns the enzyme to an inactive state. Figure $12$ shows an interactive iCn3D model of a single subunit of human Ca2+ Calmodulin- Dependent Kinase II Holoenzyme (3SOA) The catalytic domain is shown in orange, the association domain in green, and the linker domain in light cyan. The spacefill light cyan site in the linker domain is the CAM binding site. The side chains of Thr 253 and Thr 286, sites for phosphorylation, are shown in spacefill CPK colors and labeled. The side chains of Cys 280 and Met 281, the site for redox regulation, are shown in spacefill CPK colors and labeled. Bosutinib (shown in ball and stick) is a small molecule BCR-ABL and src tyrosine kinase inhibitor used for the treatment of chronic myelogenous leukemia. It is bound in the ATP binding site of the catalytic subunit. Finally, the activation loop is shown in red. Figure $13$ shows an interactive iCn3D model of Human Ca2+ Calmodulin- Dependent Kinase II Holoenzyme (3SOA) The response of most protein kinases we have studied depends on the concentration (amplitude) of a binding ligand (like Ca2+). The CAMKII dodecamer also responds to the frequency of Ca2+ waves or spikes as it is released from intracellular organelles. This is important in neurons where CAMKII phosphorylates ion channels that control and regulate neuron response. If the frequency of Ca2+ spikes reaches a certain threshold level, the enzyme no longer depends on Ca2+. Modeling of Ca2+ signaling Model for signal-induced Ca2+ oscillations and their frequency encoding through protein phosphorylation. HVJ: In response to hormones and neurotransmitters, cyclic intracellular oscillations/spikes (as a function of time) and spatial waves of cytoplasmic Ca2+ arise.  The Ca2+ ions derive from the opening of cell membrane channels but also, more importantly, from the release and recapture of the ions from intracellular compartments such as the endoplasmic reticulum (ER).  Signaling through membrane GPCRs can lead to the activation of phospholipase C and the formation of inositol 1,4,5-trisphosphate (IP3 or InsP3) as a key second messenger.  IP3 can interact with the IP3 receptor on ER membranes, leading to the release of intracellular stores of Ca2+ into the cytoplasm in ways that leads to oscillations in its concentration.   These processes are shown in Figure $14$ below. Figure $14$: Ca2+ oscillations in response to inositol trisphosphate (IP3) increase, with and without Ca2+ influx from extracellular space.  Catacuzzeno L, Franciolini F. Role of KCa3.1 Channels in Modulating Ca2+ Oscillations during Glioblastoma Cell Migration and Invasion. Int J Mol Sci. 2018 Sep 29;19(10):2970. doi: 10.3390/ijms19102970. PMID: 30274242; PMCID: PMC6213908.  Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Panel (A) bottom shows a drawing illustrating the hormone-based production of IP3 that activates the IP3 receptor to release Ca2+ from the endoplasmic reticulum (ER). The biphasic effects of cytosolic Ca2+ on IP3 receptor gating (the basic mechanism for Ca2+ oscillations), whereby Ca2+ modulates positively the receptor at low [Ca2+] but negatively at high [Ca2+], is also illustrated. Top, Ca2+ oscillations as produced from the schematics below. Note the decaying trend of Ca2+ spikes due to the absence of Ca2+ influx from extracellular space; Panel (B) shows  Ca2+ influx apparatus from extracellular spaces through ER-depletion by activated Orai channels on the plasma membrane (bottom), which generates sustained Ca2+ oscillations (top). Ca2+ levels fall through Ca2+ pumps on the sarco/endoplasmic reticulum (SERCA), which moves the ion into the SR/ER or the plasma membrane (PMCA) that then moves it to the outside of the cell.  The SERCA and PMCA Ca2+ pumps as well as the ER STIM protein are not shown in Figure 14 above.  The STIM1 protein is involved in what is known as store-operated Ca2+ entry (SOCE).  This allows the influx of Ca2+ influx after intracellular stores are depleted.  The STIM1 protein has an EF-hand so it binds and acts as a sensor for Ca2+.  When Ca2+ is depleted, the STIM1 protein moves from the ER to the cell membrane and activates ORA1, a subunit of the Ca2+ release-activated Ca2+ (CRAC) channel to promote Ca2+  into the cell. In a variety of cells, hormonal or neurotransmitter signals elicit a train of intracellular Ca2+ spikes. The analysis of a minimal model based on Ca2+ -induced Ca2+ release from intracellular stores shows how sustained oscillations of cytosolic Ca2+ may develop as a result of a rise in inositol 1,4,5-trisphosphate (InsP3) triggered by external stimulation. This rise elicits the release of a certain amount of Ca2+ from an InsP3-sensitive intracellular store. The subsequent rise in cytosolic Ca2+ in turn triggers the release of Ca2+ from a second store insensitive to InsP3. The model shows how signal-induced Ca2+ oscillations might be effectively encoded in terms of their frequency through the phosphorylation of a cellular substrate by a protein kinase activated by cytosolic Ca2+. The release of intracellular Ca2+ release by IP3 can be broken down into 3 steps: β • agonist binding to GPCRs to activate the Phospholipase C pathway to produce the second messengers IP3 and DAG; • IP3 induces some Ca2+ release from intracellular stores in the SR/ER.  This can be called the Primer Step characterized by a rate V1 to produce cytosolic Ca2+ of concentration [Z] that prime the next step. This step has a rate of v1β. • released Ca2+induces more Ca2+ release, a self-amplification positive feedback step, which causes the Ca2+ spike. A simplified view of this model is shown in Figure $15$ below. Figure $15$: Schematic representation of the one-pool model based on CICR with Ca2+-stimulated degradation of IP3. Lloyd, C.M., Lawson, J.L., Hunter, P.J. and Nielsen, P.F. The CellML Model Repository. Bioinformatics. 2008 September;24(18):2122-2123 (accessed 4/19/23; 5:40 am EDT).  Creative Commons Attribution 3.0 Unported License. A simple model can be constructed to account for cytosolic Ca2+ fluxes.  The steps include • IP3 causes a triggering a constant flux of Ca2+ into the cytosol, v1β ,to produce cytosolic [Ca2+] = Z (the primer) • cytosolic Ca2+ flows into an IP3-insensitive sequestered pool (concentration Y) with rate v2 to keep low levels of cytosolic Ca2+ • Spikes arise when the sequestered pool Y releases Ca2+ back into the cytosol at a rate v3 in a process that is activated by cytosolic Ca2+ Goldbeter at al (Proc. Natl. Acad. Sci. 87, 1461-1465 (1990) constructed a mathematical model to account for the oscillations/spikes  In addition to the parameters mentioned above, they included 3 more: • vo, describing the influx of extracellular Ca2+ into the cytosol and k describing the efflux of cytosolic Ca2+ from the cell.  These are controlled by Ca2+ pumps SERCA, PMCA, et al as described above. • kf, which describes the passive leak of Y into Z. The Ca2+ oscillations then are based on a self-amplified release of Ca2+ from intracellular stores. Here are their equations: • $dZ/dt=v_{0}+v_{1}beta - v_{2}+v_{3}+k_{f}Y-kZ$ • $dY/dt=v_{2}-v_{3}+v_{3}-k_{f}Y$ • $v_{2}=V_{m2}\frac{Z^{n}}{K^{n}_{2}+Z^{n}}$ • $v_{3}=V_{m3}\frac{Y^{m}}{K^{m}_{R}+Y^{m}}.\frac{Z^{p}}{K^{p}_{A}+Z^{p}}$ We'll use Vcell to plot the following species: • Species Z: Concentration (uM) of cytosolic Ca2+ • Species Y: Concentration (uM) of Ca2+ stored in the InsP3-insensitive pool. The model is based on code from EBI-Biomodels: https://www.ebi.ac.uk/biomodels/BIOMD0000000098. MODEL Calcium ion oscillation in the cell without oscillations in IP3 Here are the adjustable parameters: • v0: influx of Ca2+ into the cell, uM/s. • v1: influx of Ca2into the cell from the InsP3 receptor, uM/s. • beta: saturation function of the receptor for InsP3 , unitless. • Vm2: maximum rate of Ca2+ pumping into the intracellular InsP3-insensitive store,  uM/s. • Vm3: maximum rate of Ca2+ released from the intracellular store, uM/s; • K2: Threshold constant for Ca2+ pumping, uM. • n: Hill function cooperativity coefficient for Ca2+ pumping into the store, unitless. • m: Hill function cooperativity coefficient for Ca2+ pumping from the store, unitless. • Ka: threshold constant for activation, uM. • kf: efflux of cytosolic Ca2+ from the cell, 1/s. • k:  influx of extracellular Ca2+ into the cytosol, 1/s. • p denotes the degree of cooperativity of the activation process, unitless. Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play  |  Select Slider to change which constants are displayed |  Select About  for software information. Move the sliders to change the constants and see changes in the displayed graph in real time. After loading the GoldBeter model, select the 'Start' button below to simulate the model. Adjust the parameter sliders below the plot to see how they affect Ca2+ concentrations (Z, Y). The simulator only displays twelve parameters at a time. To choose others, select the 'Slider' button on the side and chose up to twelve parameters to adjust. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health.  Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington.  Funded by NIH/NIGMS (RO1-GM123032-04) Adjust the parameter sliders below the plot to see how they affect Ca2+ concentrations (Z, Y). The simulator only displays twelve parameters at a time. To choose others, hit the 'Slider' button on the side and chose up to twelve parameters to adjust. Only two variables, Y and Z, and some intra-connections are required to generate the oscillations, which do NOT required oscillation in the second messenger, IP3. One can imagine that cytosolic Ca2+ oscillations might also elicit oscillatory activity of protein kinases activated by it. Questions: • Does v0 change the oscillation frequency? • What other parameters affect the frequency? • How does Vm2 affect the oscillations?

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.07%3A_Calcium_Signaling.txt

Search Fundamentals of Biochemistry Much of this material is derived from Friebe et al. cGMP: a unique 2nd messenger molecule – recent developments in cGMP research and development. Naunyn-Schmiedeberg's Archives of Pharmacology volume 393, pages 287–302 (2020). Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/ Introduction We have considered many signal transduction pathways, starting with an extracellular signal, a primary messenger, that initiates signaling when it binds to a receptor (GPCR, RTK, Cytokine Receptor, etc). It elicits a conformation change in the receptor, which is transmitted to an intracellular domain, from where it can propagate and transmits a signal intracellularly through secondary messengers and phosphorylation of signaling proteins. Some primary messengers, however, actually pass through the cell either passively or through membrane carriers, so there is no need to generate a second messenger. We will consider two signals that translocate through the cell membrane, gases such as nitric oxide (NO), and steroid hormones (which we will discuss in a future section). NO produced within a cell activates the formation of an intracellular second messenger, cyclic GMP (analogous to cAMP). cGMP in turn activates our final member of the AGC Ser/Thr protein kinase family, protein kinase G (PKG). Intracellular NO has an unexpected role on adjacent cells. Given its small size and its nonpolar nature, it can diffuse out of the cell where it is produced and into an adjacent cell, where it can also initiate signaling. Some call this retrograde signaling. Before we consider cGMP and PKG, let's see how nitric oxide (NO) is produced. NO formation NO is synthesized by the enzyme nitric oxide synthase (NOS). There are three isoforms in mammals, neuronal (NOS1 or nNOS), inducible (NOS2 or iNOS), and endothelial (NOS3 or eNO2). Each is a homodimer with a complex domain structure, including a • N-terminal oxidase (NO_synthase) domain that binds heme • calmodulin binding site between the N and C terminal domains • C-terminal reductase domain containing the FMN (Flavodoxin_1) subdomain (which contains an autoinhibitory helix) and FAD/NADPH subdomain. Ca2+ ion activates the enzyme through the binding of Ca2+/CAM. A more detailed description of the domain structures of human NOS is shown in Figure \(1\). The dimeric molecular weight of the neuronal NOS1 (321K) is greater than for iNOS (206) and eNOS (266) as it has a N-terminal PDZ domain. neuronal NOS 1 (MW 321) purple NADPH binding inducible (MW 206K) endothelial (MW 266) yellow NADPH binding Figure \(1\): Domain structure of human nitric oxide synthases NOS catalyzes the conversion of the free amino acid arginine to citrulline and NO, as shown in the chemical equation in Figure \(2\). The structures of the three enzymes with or without bound CAM are similar. Linkers between the domains and subdomains allow flexibility. Figure \(3\) shows the flow of electrons from NADPH into the reductase (NADPH/FAD subdomain to the FMN subdomain), and on to the NOS synthase domain containing the heme. Two monooxygenase reactions occur in NOS synthase (oxidative domain) in which electrons are funneled into the heme and bound dioxygen (O2) leading to the formation of water and the final product, NO. Electron transfer only occurs within the dimer when calmodulin is bound. However, iNOS is active even at basal Ca2+ concentrations. Figure \(3\) shows an interactive iCn3D model of the structure of human neuronal nitric oxide synthase (with its PDZ domain) predicted by AlphaFold (P29475). In the iCn3D model, orient the protein as shown in the figure above. The dark blue (left) is the PDZ domain, and cyan is the oxidase (NOS synthase) domain that contains the heme (which is not shown in AlphaFold models). The spacefill CPK color shown in the cyan domain is active site residues interacting with the heme (not shown). The orange domain is the FMN (Flavodoxin_1) subdomain in the reductase domain. The magenta (far right) shows the FAD/NADPH subdomains of the reductase domain. The yellow spacefill shows the NAD binding pocket and the white spacefill the FAD binding pocket. The structures of amino acids 129-304 between the PDZ domain and the oxidase domain are not predicted with any certainty. Crystal structures are available for the oxidase domain alone. The spacefill CPK-colored helix (amino acid 730-754) represents a helical peptide region that binds to calmodulin. NO that is synthesized in the cells can signal there or diffuse to another cell and signal there. Figure \(11\) shows how NO synthesized in vascular epithelial cells that line blood vessels can move into the nearby muscle cells and initiate signaling through soluble guanylyl cyclase there, leading to vasodilation and a lowering of blood pressure. Endothelial nitric oxide synthase (eNOS) located in the vascular endothelium forms NO from plasma arginine. Two substrates, O2 and NADPH, are required along with the cofactors tetrahydrobiopterin (BH4), FAD, and flavin mononucleotide (FMN). NO diffuses into smooth muscle cells and activates soluble guanylyl cyclase (sGC), increasing cGMP production. cGMP subsequently activates protein kinase G (PKG), resulting in decreased [Ca2+] by these mechanisms: • inhibition of voltage-dependent calcium channels (VDCC), reducing calcium influx; • activation of plasma membrane calcium ATPases (PMCA), increasing ATP-dependent calcium efflux; • inhibition of inositol triphosphate receptors (IP3R), reducing calcium release from the sarcoplasmic reticulum (SR) to the cytoplasm; • activation of sarcoplasmic calcium ATPases (SERCA), increasing the ATP-dependent sequestration of calcium from the cytoplasm to the SR. Decreased [Ca2+] mediates smooth muscle relaxation via the activation of myosin light chain kinase and the inhibition of myosin light chain phosphatase (not shown in the figure), resulting in vasodilation. Figure \(13\) shows retrograde diffusion of NO from an activated post-synaptic neuron back to the presynaptic neuron that excited it on the release of the neurotransmitter glutamate. NO is synthesized by post-synaptic cell nNOS after it's activated by Ca2+ inflow and binding to CAM (not shown). NO with the post-synaptic neuron binds to guanylyl cyclase to produce the second messenger cGMP which can directly activate other channels, protein kinase G, or phosphodiesterases (PDEs) Synaptic glutamate release activates postsynaptic NMDA and AMPA receptors (NMDAR, AMPAR) leading to Ca2+-induced nNOS activation. NO will diffuse back to the presynaptic cell and activate sGC to produce cGMP, which has many signaling roles including affecting presynaptic neurotransmitter release. cGMP directly targets several ion channels in the post-synaptic cell. As we saw in the previous chapter, many ion channels are voltage-gated. However, ion channels can be regulated directly by ions (ex Ca2+, Na+) as well as by cyclic nucleotides such as cAMP and cGMP. The later channels are called cyclic nucleotide-gated (CNG) channels. NO in the post-synaptic cell also associates with CAPON, a nNOS binding protein, leading to downstream MAP kinase cascade. Carbon Monoxide Everyone knows that carbon monoxide (CO) in high doses is lethal as it binds to heme Fe2+ in hemoglobin and myoglobin with a higher affinity than O2. Hence it may come as a surprise to you that endogenous CO is a signaling molecule, which now in retrospect might make sense given its similarity in chemical structure to NO. CO is produced through heme oxygenase (HOs). CO can act as a signaling molecule in neural, cardiovascular, respiratory, gastrointestinal, immune, and reproductive systems. In contrast to the lethal effects of inhaling exogenous CO from incomplete combustion, endogenous CO has anti-inflammatory and antioxidant effects. It can also act to dilate the vasculature system. Other gases like H2S also are signaling agents. cGMP formation The second (or third) messenger cyclic guanosine monophosphate (cGMP) is synthesized after activation of the enzyme guanylyl cyclase (GC) by nitric oxide. cGMP has many signaling effects in cells, some of which were outlined above. The cytoplasmic soluble GC (sGC) is activated by NO. The membrane-associated "particulate" GC (pGC) form is activated on the binding of natriuretic peptides (NPs) to natriuretic peptide receptors, which are NP-activated integral membrane guanylyl cyclase. The peptide hormones (ANP secreted by the atria and BNP secreted by ventricles) decrease blood pressure. The membrane form does not require NO for activation Figure \(14\) shows the conversion of GTP to cGMP. Synthesis of cGMP from soluble GC is activated by NO or molecules like nitrates that can be metabolized to NO. These molecules are called NO donors. Since NO causes vasodilation, they are used to treat angina and hypertension. A class of drugs called stimulators (for example riociguat), increases cGMP production from sGC in the absence and synergistically in the presence of NO. They are also used to treat hypertension. Another class of drugs called activators can activate sGC even if heme is oxidized or even missing without upstream NO signaling. They are effective even if the heme is oxidized or lost from the NOS catalytic domain. We saw that cAMP is cleaved to AMP by phosphodiesterase. Likewise, phosphodiesterases (PDEs) cleave cGMP to GMP to attenuate signaling through cGMP. Selective drugs targeting PDE are available. These include sildenafil for the treatment of pulmonary hypertension and erectile dysfunction and tadalafil for benign prostatic hyperplasia (BPH). Pathways for activation of guanylyl cyclase activity (sGC and pGC) are shown in Figure \(15\). Soluble guanylyl cyclase (cGC) structure and function The soluble form of GC is a heterodimer of α and β subunits. The domain structure of guanylyl cyclase is shown in Figure \(16\). It appears that when NO binds to the heme group, a twist in the coiled-coil domain leads to its extension, which leads to the activation of the catalytic domain. Simulators likely cause similar conformational changes initiated by their binding to the top part of the CC domain. Figure \(17\) shows an interactive iCn3D model of the human soluble guanylate cyclase in the riociguat (stimulator) and NO-bound state (7D9R) The A chain is dark gray and the B chain is light gray. The guanylate cyclase domain contributed to by each monomer is shown in cyan. A phosphonate GTP analog (labeled G2P) is shown in spacefill CPK color bound in the guanylyl cylase domain (cyan). The heme (HEM) and the stimulator riociguat (GZO) are shown in the HNOBA (Heme NO Binding Associated) domain and shown in spacefill CPK colors. The conformation change of A chain of the bent inactive form of guanylyl cyclase (6JT1) to the active extended form (6JT2) is shown in Figure \(18\). The guanylyl cyclase domain is at the top of the figure. The conformational change is somewhat reminiscent of the change in apo-calmodulin on the binding of Ca2+ ions, although the central helix is fully intact in the inactive and active forms of guanylyl cyclase. Figure \(19\) shows how oxidation of the heme iron and S-nitrosation of the protein that occurs in the presence of reactive oxygen species (ROS) and reactive nitrogen species (RNS) leads to heme loss and inactivation of sGC. pGC structure and function The other source of cytosolic cGMP is particulate guanylyl cyclase (pGC). These are integral membrane protein receptors for natriuretic peptides (NPs), which when bound to the receptor activate the cytoplasmic guanylyl cyclase domain of the receptor. In effect, they are ligand (NPs)-gated receptor enzymes. The peptide hormones (ANP secreted by the atria and BNP secreted by ventricles) decrease blood pressure. There are seven variants of pGC (A-G) found in mammals. GC-A (also called NPR-A or NPR1) and GC-B (NPR-B or NPR2), are both receptors for natriuretic peptides. The domain structure of NPR-A is shown in Figure \(20\). Figure \(21\) shows an interactive iCn3D model of the human atrial natriuretic peptide receptor1 AlphaFold predicted model (P16066) Orient the model as shown in the figure above. The coloring coding in the model is as follows: • green: Ligand (ANP)-binding domain of the type A natriuretic peptide receptor (NPR-A); • magenta: PK-GC pseudokinase domain; • cyan: cyclase domain; • rainbow helix: HNOBA domain is found to be associated with the HNOB domain and pfam00211 in soluble cyclases and signaling proteins. The HNOB domain is predicted to function as a heme-dependent sensor for gaseous ligands rainbow; • gray helix: transmembrane segment amino acids 474-494. Note the protein is an integral membrane protein that passes through the membrane using a single alpha helix (474-494). The N-terminal domains above it and the C-terminal domains below it would orient like a typical single-pass membrane protein in the presence of a bilayer. Protein Kinase G (PKG) To briefly review, NO production leads to the production of cGMP. cGMP can directly bind to and regulate membrane ion channels. In alignment with the basic paradigm of signaling described throughout this chapter, we will now discuss how it activates Protein Kinase G (PKG) a member of the Ser/Thr Protein Kinase AGC family. The are two mammalian genes for PKG1 and PKG2. Both are homodimers. PKG1 acts in the cytoplasm while PKG2 becomes tethered to the membrane by N-terminal myristoylation. Figure \(22\) shows the domain structure of PKG-I, which is similar to PKG2. Red indicates the two nonidentical cGMP binding domains. Green is the N-terminal coiled-coil dimerization domain, which inhibits kinase activity in the absence of cGMP. On binding of cGMP, autoinhibition of the catalytic domain by the N-terminal domain is relieved. The binding of cGMP to the regulatory domain induces a conformational change which stops the inhibition of the catalytic core by the N-terminus and allows the phosphorylation of self (autophosphorylation) and then substrate proteins. Whereas PKG-I is predominantly localized in the cytoplasm, PKG-II is anchored to the plasma membrane by N-terminal myristoylation. PKG1 is involved in modulating Ca2+ activity, platelet activation, smooth muscle contraction, gene expression as well as neural function. PKG2 helps regulate bone growth, intestinal secretion, and synaptic plasticity. It also regulates gene expression and activates the MAPK cascade in bone cells. Figure \(23\) shows an interactive iCn3D model of the predicted structure of Human cGMP-dependent protein kinase 2 (AlphaFold, Q13237). The cyan domain is the STK (Ser/Th Kinase) domain. The magenta and purple domains are the CAP-Ed domains. The spacefill side chain structures within them represent the cGMP (or with lower affinity cAMP) binding sites. The CPK-colored sticks in the cyan kinase domain are the amino acid side chains in the active site where ATP binds. The orange backbone represents the least confident part of the predicted structure. The black spacefill is the N-terminal Gly (after removal of Met) which is myristoylated, allowing targeting of the modified PKG2 to the cell membrane.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.08%3A_Receptor_Guanylyl_Cyclases_cGMP_and_Protein_Kinase_G.txt

Search Fundamentals of Biochemistry Introduction to Neural Signaling Ion channels are membrane proteins that allow the flow of normally impermeant ions across the hydrophobic "sea" of cell or organelle membranes. The channel proteins can be constitutively open or can be "gated" open (or closed) by signals that affect the conformation of the protein. The signals can be ligands (in the form of hormones or neurotransmitters), post-translational modifications (mostly phosphorylation, dephosphorylation), or more difficult-to-understand events (change in transmembrane potential, pressure, or temperature). Gated channels are perhaps best studied through their role in neural signaling. Neurochemistry is one of the most explosive areas of biological research. Scientists are now starting to unravel the molecular bases for memory, cognition, emotion, and behavior. The next decades will bring a greater understanding of brain chemistry and along with it the potential to alter human mood, and memory, and to treat mental illnesses such as schizophrenia much more effectively. The human brain, with about 86 billion neurons. Compare this with some estimates for the number of stars in the Milky Way galaxy (about 100 billion, a number derived from luminosity and mass measurements). Now imagine that each neuron can form connections - synapses - with 1000 to 10,000 other neurons. Throw in another 86 billion brain glial cells and you have one of the most complex structures in the universe. This section will explore the biology and chemistry of neurons. We will discuss two kinds of neurons - those that interact with muscles at the neuromuscular junction and those that interact with other neurons in the central nervous system. Neurons consist of a single, nucleated cell body with multiple signal-receiving outgrowths (dendrites) and multiple-signal sending outgrowths (axons) which end in a terminal button. These interact through the synapse with dendrites on other neurons. These characteristics are shown in Figure \(1\). A presynaptic neuron can stimulate an adjacent postsynaptic neuron by releasing a neurotransmitter into the synapse between the cells, which binds to a receptor in the membrane of the post-synaptic cell, stimulating the cell, as shown in Figure \(2\). Figure \(2\): The synapse. https://cnx.org/contents/[email protected]:fEI3C8Ot@10/Preface..Creative Commons Attribution 4.0 International license. We will discuss the events which cause the post-synaptic cell to "fire", but we will not discuss the immediate events which lead to the release of neurotransmitters by the presynaptic neuron. Neurons (as do all cells) have a transmembrane potential across the membrane. Transient changes in the membrane potential are associated with neuron activation or inhibition. This arises in part due to the imbalance of ions across the membrane which was established by the Na+-K+-ATPase in the membrane. This electrogenic antiporter transfers 3 Na ions out of the cytoplasm for every 2 K ions it transports in. Likewise Cl- has a much higher level outside the cell. Membrane potentials are determined not only by the size of the ion gradients across the membrane but also by the differential permeability of membranes to ions. As we saw previously, synthetic bilayer membranes are not very permeable to ions, as shown in Table \(1\) below. Ion permeability of phosphatidyl serine vesicles ION PERMEABILITY (cm/s) sodium <1.6 x 10-13 (lowest) potassium <9 x 10-13 chloride <1.5 x 10-11 (highest) Table \(1\): Ion permeability of phosphatidyl serine vesicles Sodium would be expected to have a lower permeability than potassium since it has a higher charge density. It also is the largest in effective size due to the larger hydration sphere around the ion arising from its higher charge density. Chloride would be expected to have the highest permeability since it has the lowest charge density (due to the repulsion of the electron cloud in the negative ion). Intracellular charged proteins (which are mostly negative) are not permeable and help in creating the negative charge imbalance across the membrane. Much work has been done on the giant axon of the squid, which has uniquely high intracellular potassium (400 mM compared to 20 mM outside) and high extracellular fluid sodium (440 mM vs, 50 mM inside) and chloride (560 mM vs 52 mM inside). Mammalian cell concentrations are much lower, but the relative size of the gradient is about the same. Typical ion concentrations and permeabilities for mammalian membranes are shown in Table \(2\) below. T Ion Cell (mM) Blood (mM) Permeability (cm/s) potassium 140 5 5 x 10-7 sodium 5-15 145 5 x 10-9 chloride 4 110 1 x 10-8 X- (negative macromolecules) 138 9 0 Table \(2\): Typical ion concentrations and permeabilities for mammalian membranes How can we account for the markedly greater permeabilities of ions (1000x to 1,000,000 x) in mammalian cell membranes compared to synthetic lipid vesicles? Previously, we showed that glucose has a greater permeability through red blood cell membranes than through synthetic liposomes because of a membrane receptor that allows facilitated diffusion across the membrane and down a concentration gradient. The same thing is true of ion permeabilities in intact biological membranes. These membranes have several types of selective ion channels (non-gated - always open, and gated - open only after specific conformational changes). The non-gated channels dramatically increase the permeability of membranes to ions, as the glucose transport protein increased the permeability to glucose. It turns out that this differential permeability contributes to the transmembrane potential. Ion channels in nerves and muscles can move ions across the membrane at a rate of up to 109/s, which is comparable to kcat for the best enzymes. If we envision channels as pores, how can we account for the selectivity of the channel to specific ions? A larger pore should admit any ion less than a maximal size for the pore, so it is hard to imagine the nature of the selectivity filter. Because of this, many people discounted the ideas of channels in favor of a transporter, which would bind the ion selectivity and then, through conformational changes, move the ion across (much like the Na/K ATPase we discussed in the previous guide). This model could not, however, account for the incredible rates of ion flux across the membrane. Selectivity can be accounted for by a channel that contains a narrow opening that acts as an ion sieve. The ion loses most of its hydration sphere and would form specific interactions with amino acid side chains in the pore region. Such an interaction would be transient and not too tight since the ion must pass through the membrane. As we will see later, these ion channels: • pass ions down a concentration gradient in a thermodynamically favorable process • are specific for certain ions (although a few are less selective and will pass Na, K, Ca, and Mg ions) • allow ion flow through either ungated or gated channels., • saturate with increasing ion concentration (even though as concentration increases, the ions have a greater thermodynamic drive to pass through the channel). This is consistent with the binding of the ion at a selectivity filter in the narrow part of the pore. The KD for the interaction is usually in the mM range and indicates weak binding with large dissociation rate constants (koff). Transmembrane Potentials Several questions arise about the distribution of ions and the magnitude of the transmembrane potential. 1. How are the ion gradients established? 2. How does the transmembrane ion distribution contribute to the membrane potential? 3. How can the resting electrochemical potential and the ion distribution be maintained? The answer to these questions will be illustrated using studies on two types of brain cells, glial cells (which function as protectors, scavengers, and feeders for brain neurons) and neurons. Both types of cells have transmembrane potentials. Glial Cells The transmembrane ion gradients for ions can be established by different mechanisms. One uses ion-specific ATPases (P-type ion transporters), such as we discussed with the Na/K ATPase. This transporter ejects 3 sodium ions from the inside of the cell for every 2 potassium ions it transports in, all against a concentration gradient. Since it is an electrogenic antiporter, it helps generate the potential. Additionally, specific ion channels also contribute (as described below) to the transmembrane gradients and potentials. The harder question is how the ion distribution contributes to the membrane potential. Two things must occur for a membrane potential to exist: First, there must be a concentration gradient of charged ions (for example, sodium, potassium, or chloride) across the membrane. Second, the membrane must be differentially permeable to different ions. If the membrane were completely impermeable to ions, then no movement of ions across the membrane could occur, and no membrane potential would arise. If, however, membranes are differentially permeable to the ions, an electrical potential across the membrane can arise. Remember, synthetic bilayers are quite impermeable to ions, given the hydrophobicity of the internal part of the bilayer. Likewise, it is quite impermeable to glucose. It turns out that glial cells appear to have only a non-gated potassium channel, which allows the outward flow of potassium ions down the concentration gradient. The inside will then have a net negative charge since impermeable anions remain. The chemical potential gradient causes this outward flow of potassium ions. As more ions leave, the inside gets more negative, and a transmembrane potential develops which resists further efflux of potassium. Eventually, they balance, and the net efflux of potassium stops. The resting transmembrane potential reaches around -75 mV which is exactly the value obtained from the equations we will derive below. Since glial cells appear to only express a non-gated (or leakage) potassium channel, their resting potential is equal to the potassium equilibrium potential. Figure \(3\) shows hypothetical transmembrane potentials (Ψ), and the electrical and chemical potentials (ΔG) for K+ loaded vesicles with and without a non-gated K+ channel As we have shown previously, very little K+ efflux is required to develop a transmembrane potential. At equilibrium, the K+IN is simply shown as < 0.1 M while K+OUT is shown as > 0.1 M Here is a PhET simulation showing the differences between non-gated (leakage, always open) and gated channels (opened in response to stimuli like ligands, transmembrane potential, mechanical forces, etc). Neurotransmitter Activation of Neurons What happens when a neurotransmitter binds to a receptor on the post-synaptic cell? We will study two examples. The first is the simplest: binding of the neurotransmitter acetylcholine, released by a motor neuron, to its receptor on muscle. This region is called the neuromuscular junction. The binding of acetylcholine will lead to a transient depolarization of the muscle cell. Next, we will discuss the interaction of a neurotransmitter with a post-synaptic neuron in the central nervous system. This is a much more complex system. Their differences are described below: In neurons interacting with muscles: • Most muscle fibers are innervated by only one neuron - a motor neuron • Neurotransmitter release at the neuromuscular junction leads only to muscle excitation, not inhibition. • All fibers are excited by the same neurotransmitter - acetylcholine. In the central nervous system, life is more complicated: • Stimuli are received from hundreds to thousands of different neurons. • Nerves receive both excitatory and inhibitory stimuli from neurotransmitters • Different kinds of receptors are present to receive stimuli, which control the activity of different kinds of channels. • The ion channels in neurons are gated by a variety of mechanisms in addition to changes in membrane potential, including gating by heat, cold, stretch, or covalent modification. • Most nerve cells have a resting potential of about -65 mV compared to -90 mV for a muscle cell. What happens when a neurotransmitter binds to the receptor on the post-synaptic cell? A depolarization occurs (mediated by conformational changes in the transmitter-receptor complex), raising the membrane potential from the resting equilibrium level. What happens next depends on the identity of the post-synaptic cell. In the muscle cell, the rising potential caused by the binding of acetylcholine ultimately leads to muscle contraction by opening intracellular organelle membrane calcium channels. In a neuron, the rising potential triggers an action potential by opening voltage-gated sodium channels. The potential rises to about + 35 mV but does not reach the Na ion equilibrium potential, because the high positive potential opens a voltage-gated potassium channel. The potential then falls until it reaches the K ion equilibrium potential when the cells are hyperpolarized. It slowly then relaxes back to the resting potential of -60 mV. This wave of changes in potential sweeps down the post-synaptic cell membrane and is the basis for the "firing" of the neuron. A plot of transmembrane voltage changes vs time for a typical action potential is shown in Figure \(4\). Figure \(5\) shows the actual changes in ion permeability in various phases of the action potential (replace figure, seek permission) mine Figure \(5\): Na+ and K+ permeability during the action potential Figure \(6\) shows an animation of a neuron firing showing all the key players. (produced by PhET, University of Colorado, Boulder). Proteins of the Neural Synapse We must now account for the rise and fall of the membrane potential to a variety of neurotransmitters, including the cholinergic transmitters (ex. acetylcholine), catecholamines (dopamine, epinephrine, norepinephrine), amino acid derivatives (ex. Glu, Asp, N-methyl-D-Asp, Gly, gamma-amino-butyric acid -GABA), and peptides (endorphins, enkephalins). We will consider five membrane proteins as shown below in Figure \(7\). Many of these we have explored in detail in Chapter 11.3: Diffusion Across a Membrane - Channels, so we will focus on just one new one, the chloride channel, in the following section. Na+-K+-ATPase: It transports both sodium and potassium ions against a concentration gradient using ATP as an energy source. The protein is a sodium-dependent ATPase. Without this protein, the membrane potential could not be maintained since the sodium and potassium gradient would collapse. It also contributes to the potential since it is an electrogenic antiporter. (In addition, we have seen that ungated potassium and sodium channels are also present.) Neurotransmitter receptor: The receptors we will consider here are typically ligand-gated ion channels. Once the ligand binds, a conformational change occurs in the protein, allowing a flow of ions down a concentration gradient. Depending on the nature of the ion, the channel either initiates depolarization (when Na+ enters from the outside and raises ΔΨ) or inhibits depolarization (when Cl- enters from the outside and lowers ΔΨ). When chloride channels open, they hyperpolarize the transmembrane potential. Stimulatory neurotransmitters (like glutamate) lead to depolarization of the membrane, while inhibitory neurotransmitters (like gamma-aminobutyric acid) lead to hyperpolarization of the membrane (making the potential more negative). Na+ channel (voltage-gated): When the ligand-gated channel depolarizes the membrane to some threshold value, sodium channels undergo a conformational change and open allowing Na+ ions to flood into the cell, raising the potential to a positive approx. 33 mV (a value lower than the equilibrium sodium potential). This membrane protein is a voltage-gated channel, not a ligand-gated one. Two potent neurotoxins, tetrodotoxin (from Pufferfish) and saxitoxin bind to the channel and act as antagonists (inhibit the activity of the receptor by blocking sodium influx). Their structures are shown in Figure \(8\). The guanidino group of the tetrodotoxin appears to bind with high affinity to the entrance of the channel that interacts with the hydrated sodium ion. Affinity chromatography using tetrodotoxin beads has been used to purify the protein. Figure \(9\) shows the relative sizes of ions used to probe size requirements for the channel. the hydrated K+ ion can not pass through. Depolarization of the membrane potential may result in an outward movement and rotation of the positively charged helixes containing Lys and Arg side chains which had presumably formed salt bridges (ion-ion interactions) with negatively charged side chains within the protein. Depolarization of the membrane results in breaking a few salt bridges, and effectively leads to the movement of 1 or 2 charges on the helix through the membrane. Work occurs when charges are moved through an electric field. Work is also related to the ΔG for the system, which is also dependent on the ratio of the open and closed form of the channel. Other voltage-gated ion channels (for potassium and chloride) have a similar membrane topology and an S4 voltage-sensor helix. K+ channel (voltage-gated): When the membrane potential reaches around +25 mV or so, the K+ channel, a voltage-gated membrane protein, alters its conformation, allowing K+ efflux from the cells, lowering the potential until it reaches the potassium equilibrium potential. It slowly relaxes back to the cell resting potential of about -60 mV. Cl- channel: If these channels (typically ligand-gated) are open, they will hyperpolarize the cell and make it more difficult to fire. The selectivity filter is composed of many stacked rings of oxygens that can interact with a dehydrated K ion but not with a dehydrated Na ion which can not approach close enough to form significant interactions. Surrounding the filter are twelve aromatic amino acids which constrain the size of the pore opening. The interactions of the filter O's with the K ion make up for the energetically disfavored dehydration of the ion. The filter contains two K ions which repel each other, assisting in the vectorial discharge of the ions through the membrane. These ions must form weak interactions with the selectivity filter. The actual pore is mostly hydrophobic, which facilitates the flow-through of the ions. The central cavity of the pore can hold some water molecules in addition to the K ions which helps stabilize the ion in the middle of the pore. Inhibitory Neurotransmitters The main inhibitory neurotransmitters are GABA (gamma-aminobutyric acid), which is made from glutamic acid through decarboxylation of the α-C group, and glycine. They bind to transmitter-gated chloride channels, which when open hyperpolarize (make more negative) the transmembrane potential. Benzodiazepines (like Valium and Librium - anti-anxiety and muscle-relaxing agents) and barbituates (like phenobarbital-hypnotics) bind at allosteric sites on the GABA receptor and potentiate the binding of each other and GABA. This receptor is also affected by alcohol and anesthetics. Let's focus on the GABAA channels (also called the GABA(A) receptor or GABA(A)R) since it binds so many interesting pharmaceutical drugs. Figure \(10\) shows an interactive iCn3D model of the human full-length heteromeric α1-β3-γ2L GABA(A)R in complex with picrotoxin, GABA, and megabody Mb38 (6huj) derived from cryo-EM (loads slowly). This structure is a Type-A γ-aminobutyric (GABA A ) receptor. It consists of two alpha chains (orange), two beta chains (magenta), and one gamma chain (brown). Two GABAs (spacefill, CPK colors) are bound between the orange and magenta subunits (between the α and β chains). Picrotoxin is shown in spacefill CPK colors in the central pore (bottom), near the cytoplasmic end (blue bilayer dummy atoms). The extracellular domain (above the red) has glycans shown in SNFG cartoon form and shown in Figure \(11\). The structure of the receptor bound to many pharmacological agents has been solved. The structure of GABA (the agonist), bicuculline (a competitive antagonist), the benzodiazepines alprazolam (Valium) and diazepam (Xanax), a channel blocker (picrotoxin), ethanol, and Ro-15-4513 (an ethanol antagonist) are shown in Figure \(12\). (12\): Structures of GABA-R binding molecules -GABA (the agonist), bicuculline (a competitive antagonist), the benzodiazepines alprazolam (Valium) and diazepam (Xanax), a channel blocker (picrotoxin), ethanol, and Ro-15-4513 (an ethanol antagonist) The benzodiazepine diazepam (Xanax) binds at an allosteric site and promotes GABA binding, so it is considered an "indirect agonist”. It is also an anxiolytic (a drug used to reduce anxiety) and an anticonvulsant. Ethanol activates the inhibitory GABA channel. Too much ethanol leads to cells hyperpolarized cells so neural responses are greatly inhibited, possibly leading to death. Ethanol acts synergistically with benzodiazepine, which makes this combination so lethal. Figure \(13\) shows an interactive iCn3D model of the human full-length heteromeric α1-β3-γ2L GABA(A)R in complex with diazepam (Valium), GABA (6HUP) (loads slowly). https://structure.ncbi.nlm.nih.gov/i...fTZMqePnpdwTy8 The structure shows two alpha chains (orange), two beta chains (magenta), and one gamma chain (gray). Two GABAs (spacefill, CPK colors) are bound between the orange and magenta subunits (between the α and β chains). Three valiums (spacefill, CPK) are shown in the structure. One is bound to an allosteric site between the orange (alpha) and gray (gamma) chains. Two others are bound within the bilayer, this time between the alpha (orange) and beta chains (magenta) chains. Different benzodiazepines appear to bind in slightly different sites, and binding elicits their specific effects. A drug, Ro-15-4513, was developed in the 1980s that antagonizes the effect of ethanol. It has a complicated pharmacology. It is considered a benzodiazepine “partial inverse agonist”. It has no effect by itself. It reverses the anticonvulsant effects of benzodiazepines, and blocks the Cl- effects of ethanol so it is an inverse agonist of GABA-mediated Cl- flux. Hence it antagonizes the effect of benzodiazepines and alcohol. If given to intoxicated mice, they act normally! Such drugs have not reached the market as their use poses significant ethical issues. The effect of pharmacological agents on the GABA receptor In the “voltmeters” below, draw an arrow indicating if the transmembrane potential becomes more negative or more positive for the conditions given. Answer Add texts here. Do not delete this text first. Metabotropic Neural Receptors Some signaling molecules, whose effects are regulated by kinases (β-adrenergic and some olfactory signals by PKA and acetylcholine by PKC for example), are neurotransmitters. In all the examples presented previously, the neurotransmitters gate the inactive ion channels directly. These types of membrane receptors are classified as ionotropic. Typical examples of neurotransmitter-gated ion channels are the acetylcholine receptor in neuromuscular junctions and the Glu, Gly, and GABA receptors in the central nervous system. These receptors are multimeric proteins. Receptors with direct gating of ion flow are fast, with activities that last milliseconds, and are used in eliciting behavioral responses. However, ion channels can also be gated indirectly when the neurotransmitter binds to its receptor and leads to downstream events which subsequently open an ion channel that is distinct from the receptor. In this case, the occupied receptor communicates to an ion channel indirectly through a G protein for example. Examples of this indirect gating of ion channels include the serotonin, adrenergic, and dopamine receptors in the brain. These receptors are classic single-protein serpentine GPR receptors with 7 transmembrane helices and intracellular domains that can interact with G proteins as described above to increase second messenger levels (cAMP, DAG) in the neuron. The receptors are classified as metabotropic since they must activate a series of metabolic steps before ion channels are open. The second messengers can either activate kinases in the cell, which phosphorylate ion channels to either open or close them, or can bind directly to the channel and modulate its activity through an allosteric conformational change. In some cases, the G protein directly acts on the ion channel. These different ways are described in Figure \(14\). In contrast to direct gating, receptors that indirectly gate ion channels have activities that are slow and last seconds to minutes. These receptors are usually involved in modulating behavior by changing the excitability of neurons and the strength of neural connections, hence modulating learning and memory. These changes can occur in many ways, summarized below: Phosphorylating ion channels: Receptors that act through a second messenger system can change ion channel activity by activating kinases that phosphorylate the channels. This may: • open the channel normally closed at the resting potential and produce an effect like gating. • close a channel usually opens at the resting potential (such as non-gated K channels which when closed would depolarize the cell and make it more excitable). Gα interaction with ion channels: • the Ga subunit of the G protein interacts with K channels after stimulation of the CNS Acetylcholine receptor, opening the channel and hyperpolarizing the cell Second messenger interaction with ion channels: • cGMP opens cation channels in retinal cells after activation of the photoreceptor by photons • cAMP opens cation channels in olfactory cells after activation of the olfactory receptor by odorants. Second messenger effects on proteins other than ion channels (usually different receptors): • the β-adrenergic receptors are phosphorylated by PKA and PKC (activated by stimulation of a different neurotransmitter receptor linked through a G protein to produce increased levels of second messengers cAMP and diacylglycerol). When phosphorylated, the β-adrenergic receptor, itself activated through G protein) can't bind Gs. This attenuates the response of the β-adrenergic receptor to its neurotransmitter which leads to desensitization to that signal. Second messengers regulate gene expression: • cAMP-activated PKA can phosphorylate an inactive transcription factor in the cell, which then can bind to a section of DNA called the cAMP Response Element (CRE), which is upstream of certain genes, leading to the transcription of the genes. The transcription factor is called CREB for cAMP Response Element Binding protein. Example: tyrosine hydroxylase (a monooxygenase) is involved in the synthesis of epinephrine and norepinephrine. The activity of this protein is increased when it is phosphorylated by PKA. Hence its activity can be increased quickly by this modification of the already present protein. If an animal is subjected to severe or long-term stress (cold or immobilization), presynaptic cells with norepinephrine will be stimulated to release the neurotransmitter. This requires continual synthesis of the neurotransmitter by the presynaptic cell. The increase in the synthesis of this neurotransmitter is caused by the presynaptic cell being stimulated by another neuron, which leads to increased levels of cAMP, and ultimately activation of CREB which increases transcription of the hydroxylase gene. Caffeine Caffeine produces a state of arousal in the central nervous system. High levels appear to block the binding of an inhibitory neurotransmitter, adenosine, to the A2A adenosine receptor. In the absence of caffeine, adenosine levels rise during the day, which promotes interaction with its receptor, leading to increased sleepiness and lack of concentration. When adenosine binds normally to its receptors, it activates the adenylate cyclase cascade, which activates PKA, leading to changes in the phosphorylation state of many proteins inside the cell, including protein phosphatase (2A). These changes inhibit neural firing. Caffeine blocks these changes. Hallucinogenic drugs Illicit drugs like LSD, psilocybin, and ecstasy can produce hallucinations as they have profound effects on consciousness and perception of self and reality. Recent clinical studies have shown that under tightly controlled conditions and doses, these drugs might have significant therapeutic effects in the treatment of mental health issues such as depression and post-traumatic stress syndrome. Hence their mechanisms of action have been the source of many studies. All of these drugs bind to the human serotonin 2A receptor (5-HT2AR), a metabotropic GPCR receptor of serotonin (5-hydroxytryptamine). When serotonin binds, it leads to GPCR signaling, partly through beta-arrestins. These are adapter proteins that form complexes with ligand-bound and activated GPCRs and GPCR protein kinases. Structures show that LSD binds to the orthosteric binding site (i.e. the active site, not an allosteric site) for serotonin. The study also found that serotonin and psilocin binding extends into an adjacent site called the extended binding pocket (EBP). These differences suggest that it should be possible to design agonists that have therapeutic, but not hallucinogenic properties. Serotonin binding does not cause hallucinations. A drug, IHCH-7086, in mouse studies seems to not provoke hallucinations but appeared to have antidepressive effects (based on observations of mouse behaviors like twitching and freezing). The structures of serotonin (5HT) and psychotropic drugs that bind the 5HT2AR are shown in Figure \(15\). Figure \(15\): Structures of serotonin (5HT) and psychotropic drugs that bind the 5HT2AR Figure \(16\) shows a model of the serotonin receptor (5HT-2A) with each of the bound drugs. Note the different occupancy of the orthosteric and extended binding pockets. The GPCRs have been aligned for each of the poses. The ligands are represented in spacefill in a single color, as listed below: • red: serotonin (5HT), the physiological agonist, 7WC4 • yellow: psilocin, 7WC5 LSD, 7WC6 • magenta: LSD, 7WC6 • cyan: IHCH-7086 An alternative way to explain the fact that serotonin does not cause hallucinations and why the other agonists do is that in addition to binding plasma membrane GPCRs, the agonist might have additional effects that are associated with hallucinations by binding intracellular membrane GPCRs. The cerebral cortex plays a key role in what it means to be human.  It is involved in higher-level processing involving thought, learning, emotion, personality, language, and memory.  As such its dysregulation is involved in mental health conditions such as depression et al. in which synaptic plasticity is decreased as evidenced by a reduction in dendritic structure require for synaptic connections.  The serotonin (5HT) reuptake inhibitors (SSRIs) used to treat depression appear to increase neural plasticity and connections over time (so they don't give immediate responses).  Hallucinogens that target the 5-HT2A receptor (5-HT2AR) also promote neuroplasticity.  However, neuron growth in cortical cultures is not affected by serotonin.  What is the mechanism that allows various combinations of neuron plasticity and hallucinations all from the same receptor? One factor that determines these differential effects is that serotonin (5HT) is more polar and cannot readily enter cells, while the HT2AR agonists that are hallucinogenic are less polar (more lipophilic) and could potentially enter cells where they might elicit hallucinogenic effects.  Intracellular hallucinogens would bind internal 5-HT2AR receptors, which are found in cortical neurons on the Golgi and other organelles that are more acidic than the cytoplasm or extracellular environment.  (In fact, in cortical neurons, the main location of the 5-HT2ARs is intracellular.) This might lead to more prolonged retention of intracellular hallucinogens and longer signaling (LSD has a profound hallucinogenic effect that lasts for 10 hours or more) leading to neuronal growth as well. If serotonin is given to wild-type mice, no hallucinations occur, as evidenced by the lack of a head twitch response (HTR).  However, if given to mutant mice who expressed serotonin transporter (SERT) on cortical neurons, the HTR response was observed. The import of serotonin led to neuroplasticity.  It could be that serotonin is not the physiological ligand for intracellular HT2ARs.  It might also imply that as with the case with endocannabinoids, we have endogenous psychedelics.  The subtle difference in the binding of serotonin and the hallucinogens in Figure 16 might have little to do with their tendency to produce hallucinations. Recent Updates:  LSD binding to the BDNF receptor TrkB   06/09/23 Much is still not known about how antidepressants work and what causes delays in their therapeutic effects.  Increased synaptic connections through expanded neuroplasticity appears to be required for their therapeutic effects.  The major action of drugs like Prozac, a serotonin reuptake inhibitor, is through blocking the serotonin transporter (SERT), increasing extracellular levels of serotonin in the neural synapse.  Tricyclic anti-depressants as well as monoamine oxidase inhibitors (MAOI), also increases monoamine neurotransmitters in the synapse with delayed therapeutic effects (the monomaines act quickly however).  Perhaps these agents bind other receptors! In fact, they do.  The binding of both typical and fast antidepressants also occurs in the transmembrane domain of tyrosine kinase receptor 2 (TRKB), the brain-derived neurotrophic factor (BDNF) receptor, which is linked to increases in neuroplasticity. The receptor also binds cholesterol which modulates its activity.  The antidepressants binding site is formed on dimerization of their transmembrane domains.  Mutations in the transmembrane region block the efficacy of the antidepressants. LSD and other psychedelics also produce fast and long-lasting antidepressant effects promoted by increases in neuroplasticity.  Studies have shown that LSD and psilocin bind to slightly overlapping sites in the transmembrane domain of the BDNF receptor as antidepressants, but with a 1000-fold higher affinity.  If the LSD binding site on the serotonin 2A receptor (5-HT2A) is blocked, LSD still has antidepressant and increased neuroplasticity effects. Figure \(17\) shows the interaction of LSD and the metabolite of psilocybin, psilocin (PSI), with the TRKB receptor. Figure \(17\): Characterization of the psychedelics binding site in the TrkB TMD.  Moliner, R., Girych, M., Brunello, C.A. et al. Psychedelics promote plasticity by directly binding to . BDNF receptor TrkB. Nat Neurosci 26, 1032–1041 (2023). https://doi.org/10.1038/s41593-023-01316-5.  Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. Panel ac show representative MD snapshots showing the binding pocket for LSD (purple) (a) and PSI (green) (c) in the extracellular-facing crevice of the TrkB TMD dimer (gray). Side chains (yellow) of relevant binding site residues are displayed. A structural model of full-length TrkB dimer (gray) embedded in a lipid membrane is shown with bound BDNF (blue) and LSD (purple) (b). Panel d shows in silico binding free energy estimations for fluoxetine, LSD and PSI. Each free energy estimate (ΔG, circles) and its statistical error (SE, bars) were estimated from a separate set of FEP simulations (n = 1). Dissociation constants are given as a range with upper and lower bounds converted from ΔG − SE and ΔG + SE, respectively. Panel e,f show chemical structures of LSD (e) and PSI (f) with atom numbers annotated. Panel i shows the distributions of TMD dimer C-terminal distance and shows that LSD and PSI stabilize the cross-shaped conformation of TrkB favorable for receptor activation in a 40 mol% CHOL membrane. Lines represent the mean distribution, and bands represent the standard errors (n = 10 independent simulations). TMD conformations corresponding to indicated C-terminal distances and drug-bound states are shown in the inset. Figure \(18\) shows the different binding modes of LSD and Fluoxetine (Prozac), a selective serotonin reuptake inhibitor (SSRI) for TrkB.  Fluoxetine is used to treat depression, obsessive-compulsive disorder (OCD), bulimia nervosa, and panic disorder. Figure \(18\): Different TrkB binding modes of LSD and fluoxetine. Moliner et al., ibid. Panels a,b, show representative snapshots of atomistic MD simulations showing the front (a) and back (b) views of the binding pockets for LSD (purple) and fluoxetine (yellow) in the extracellular-facing crevice of TrkB TMD dimers. Side chains of relevant binding site residues are displayed. Superimposed structures of TrkB optimally bound to LSD or fluoxetine reveal that, while some residues involved in binding are shared (Y433 and V437), the binding modes are different. Fluoxetine binds at a site deeper within the dimer, locking the TMD dimers in a more open cross-shaped conformation (distance between the center of mass L451–L453 Cα atoms of each monomer ~20 Å). In contrast, LSD binds closer to the N-terminus of the TrkB TMD and establishes more stable interactions with the dimer: a hydrogen bond between the oxygen atom of the diethylamide group of LSD and the Y433 residue of one monomer, and pi-stacking of the aromatic backbone of the drug with the Y433 residue of the second monomer, locking the TMD dimer in a tighter cross-shaped conformation (L451–L453 Cα distance ~17 Å) compared with fluoxetine. Drugs are shown in van der Waals representation. Oxygen, nitrogen, and hydrogen atoms are shown in red, blue, and white, respectively.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.09%3A_Gated_Ion_Channels_-_Neural_Signaling.txt

Search Fundamentals of Biochemistry This section was derived almost completely, with some modifications and additions, from the following source: Mezu-Ndubuisi, O.J., Maheshwari, A. The role of integrins in inflammation and angiogenesis. Pediatr Res 89, 1619–1626 (2021). https://doi.org/10.1038/s41390-020-01177-9. https://doi.org/10.1038/s41390-020-01177-9. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. Consult the original article for specific references. Introduction Integrins link the extracellular matrix to the intracellular cytoskeleton through a single transmembrane alpha-helical segment. They work with growth factor receptors to regulate cell survival, cell migration, and cell division. They contain a very large extracellular domain comprising most of the protein and a very small intracellular domain. Integrins are heterodimeric transmembrane cell adhesion molecules made up of alpha (α) and beta (β) subunits arranged in numerous dimeric pairings. These complexes have varying affinities to extracellular ligands. Integrins regulate cellular growth, proliferation, migration, signaling, and cytokine activation and release and thereby play important roles in cell proliferation and migration, apoptosis, and tissue repair, as well as in all processes critical to inflammation, infection, and angiogenesis. Integrins are a family of ubiquitous αβ heterodimeric receptors that exist in multiple conformations and interact with a diverse group of ligands. These molecules mediate interactions between cells and these cells with the extracellular matrix (ECM) and thereby serve a critical role in signaling and homeostasis. By facilitating dynamic linkages between the intracellular actin cytoskeleton and the ECM, integrins also transduce both external and internal mechanochemical cues and bi-directional signaling across the plasma membrane. Integrins are involved in a diverse range of body processes, including cellular survival, inflammation, immunity, infection, thrombosis, angiogenesis, and malignancy. In this review, we highlight the structure and function of integrins; the mechanisms involved in integrin activation and signaling; their role in inflammation, infection, and angiogenesis; and discuss current advances in integrin-targeted therapies. Understanding the factors that regulate integrin structure, function, and signaling would enable the identification of new therapeutic targets. Structure of integrins In mammals, the family of integrins is comprised of 24 αβ pairs of heterodimeric transmembrane adhesion receptors and cell-surface proteins. These pairings are known to involve 18 α and 8 β subunits as shown in Figure \(1\). With respect to ligand specificity, integrins are generally classified as collagen-binding integrins (α1β1, α2β1, α10β1, and α11β1), Arg-Gly-Asp (RGD)-recognizing integrins (α5β1, αVβ1, αVβ3, αVβ5, αVβ6, αVβ8, and αIIbβ3), laminin-binding integrins (α3β1, α6β1, α7β1, and α6β4), and leukocyte integrins (αLβ2, αMβ2, αXβ2, and αDβ2). The right-hand side of Figure \(1\) shows that the β2 integrin subunit (CD18) can pair with one of the four α subunits (αL-CD11a, αM-CD11b, αX-CD11c, and αD-CD11d), forming leukocyte function-associated antigen-1, Mac1/CR3 (macrophage-1 antigen, complement receptor 3), 150.95/CR4 (complement receptor 4), and CD18/CD11d, respectively. The structure of the heterodimers and their non-covalent associations are shown in Figure \(2\). Each subunit consists of one large multi-domain extracellular segment, one transmembrane helix, and a short cytoplasmic tail. The extracellular region interacts with extracellular matrix (ECM) ligands and is composed of about 1104 residues in the α subunit and 778 residues in the β subunits and shorter cytoplasmic domains with 30–50 residues. The short cytoplasmic tails are composed of 20–70 amino acids and mediate interactions with intracellular cytoskeletal and signaling proteins. In response to intracellular or extracellular stimuli, integrin activation occurs by ligand binding or by the changes in the cytoplasmic domains, resulting in elongation and separation of the legs. Integrins appear in a closed or “bent” conformation on resting cells and display a low binding affinity for ligands rendering them inactive to ligand binding or signal transduction; while once activated, the integrin shape extends to an open conformation leading to a high affinity. In a closed conformation, integrins show low ligand-binding affinity, partly due to the bend in the center of the α and β subunits, which brings the ligand-binding site within 5 nm of the cell surface. However, when the conformation is open, the two subunits straighten with increased integrin affinity for the ligand. The initial binding of extracellular ligands causes separation of the cytoplasmic domains, allowing interaction with signal transduction and cytoskeletal molecules during outside-in signaling, while separation of the cytoplasmic domains by talin and other activators activates the head to enable ligand binding during inside-out signaling. The αβ pairings of integrin subunits dictate the specificity of the integrin to a particular ligand, modulate the formation of intracellular adhesion complexes, and regulate downstream signaling. Six α (α1–6) and seven β (β1–7) subunits are known to form several unique αβ subunit associations, as shown in Figure \(1\). Interestingly, the earliest discovered integrins, lymphocyte function-associated antigen 1 (integrin αLβ2) and macrophage antigen 1 (integrin αMβ2), derive their specificity from specific α subunits, but these share the same β subunit.5 Integrin α subunit family The integrin α subunits carry a 200 amino acid “inserted” domain, the I-domain (αI). When present on an integrin, the αI domain is an exclusive ligand-binding site. αI integrins have 13 extracellular domains in 2 subunits, which interact with a variety of ligands. The I-domains are seen in 6 out of the 15 integrin α subunits. Integrin alpha-1/beta-1 is a receptor for laminin and collagen. It recognizes the proline-hydroxylated sequence G-F-P-G-E-R in collagen. the human α1 subunit (1179 amino acids) has the domain structure shown in Figure \(3\): The green represents the Van Willibrand Factor Type A domain. The middle pinkish domains are FG-GAP extracellular domains. They are repeated up to 7 times in alpha integrins. The reddish domain at the very C-terminus is the transmembrane helix domain (1142-1164). This membrane protein is very different than those we have seen before as it has just a 15 amino acid C-terminal tail exposed in the cytoplasm. Figure \(4\) shows an interactive iCn3D model of the predicted structure of human Integrin alpha-1 (AlphaFold, P56199). The color coding is gray spacefill for the C-terminal transmembrane helix, yellow spacefill for the inhibitor binding pocket and magenta for the collagen-binding site Integrin β subunit family In humans, integrin β subunits have a cytoplasmic tail that has <75 amino acids in length, except the β4 tail which is about 1000 amino acids long (includes four fibronectin type III repeats). The integrin β tails have one or two NPxY/F motifs (x refers to any amino acid) that recognize protein modules, phosphotyrosine-binding domains, that are involved in several signaling and cytoskeletal proteins at the cytoplasmic face of the plasma membrane through phosphorylation of the tyrosine (Y) in the NPxY/F motif. The integrin β subunit family includes β1–7, which bind the α subunits in different combinations. The most frequently seen β subunit integrin heterodimers are β1. Although β2 integrins show functional overlap, the corresponding α subunit defines its individual functional properties. The β2/CD18 chain has also received attention because of its involvement in several inflammatory receptors such as αLβ2, lymphocyte function-associated antigen-1 (LFA-1), and the αMβ2, Mac-1, complement receptor 3 (CR3). In these β2 integrins, the α subunits bind specific ligands such as the intercellular adhesion molecules (ICAMs). The non-I-domain α subunits in other integrins, such as the laminin-binding α3, α6, and α7, and others that recognize the arginine (R), glycine (G), aspartic acid (D) (RGD) motif (αV, α8, α5, and αIIb), are also closely related to each other. • The α subunit of each integrin is the primary determinant of its extracellular ligand specificity. • The β chain binds acidic residues in ICAMs and cytoplasmic adapters such as paxillin, talins, and kindlins to facilitate cellular adhesions with the ECM. Integrins interact with the actin cytoskeleton through the talin- and kindlin-binding motifs present in the cytoplasmic domains of their β subunits. Characteristics of specific integrin heterodimers Integrin αβ heterodimers are divided into four classes (leukocyte, collagen-binding, Arg-Gly-Asp (RGD)-binding, and laminin-binding integrins (as shown in Figure \(1\), based on evolutionary associations, ligand specificity, and restricted expression on white blood cells (β2 and β7 integrins). • Leucocyte integrins have a common β2 chain that is linked to CD-18 and binds receptors such as ICAM and plasma proteins such as complement components C3b and C4b. • Collagen-binding integrins have a common β1 chain that binds various α chains in integrins α1β1, α2β1, α10β1, and α11β1. The α2β1 integrin binds its primary ligand, collagen, and chondroadherin, a matrix protein. • The RGD-binding integrins have a common αV chain or β1 chain. The RGD peptide motif was first discovered in fibronectin but was later found in several other ECM proteins, such as fibronectin, osteopontin, vitronectin, von Willebrand factor (VWF), and laminin. Among the 24 human integrin subtypes known to date, eight integrin dimers recognize the tripeptide RGD motif within ECM proteins, namely: αVβ1, αVβ3, αVβ5, αVβ6, αVβ8, α5β1, α8β1, and αIIbβ3. Laminin-binding integrins (α3β1, α6β1, α7β1, and α6β4) mediate the adhesion of cells to basement membranes in various tissues. The α4β1, α9β1, and α4β7 integrin family binds fibronectin in an RGD-independent manner. Figure \(5\) shows an interactive iCn3D model of the structure of α6β1 integrin in complex with laminin-511 (7CEC) The cyan chain is integrin α6 and the magenta chain is integrin β1. The laminin chains are α (brown), β (orange), and γ (red). Note that each of the laminin subunits interacts with the α6β1 integrin. Two carboxylates on the C-terminal region of the laminin γ chain interact with the metal ion-dependent binding sites on the integrin beta subunit and an Asp 189 in the alpha subunit. Integrin–ligand binding and consequent activation The structure and function of integrins are complex. Integrins bind numerous extracellular ligands, intracellular signaling molecules, and the cytoskeleton in a bivalent-cation-dependent manner with varying specificities. Integrins also have many states with multiple conformations and affinities. Mechanism of integrin-ligand binding and conformational states Integrins bind cell-surface ligands to promote cellular interactions with the ECM and with other cells in the transduction of complex signals that modulate many cellular processes, such as adhesion, migration, and differentiation. These soluble, ECM, or cell surface-bound ligands may include growth factors, structural constituents of the ECM, proteases, cytokines, plasma proteins, microbial pathogens, or receptors specific to immune cells. The affinity and avidity of a ligand may change actively by inside-out signaling in specific pathways. Ligand affinity may vary with the strength of interaction and dissociation of a monovalent protein and its ligand, where ligand avidity refers to its ability to form multiple combinations of bonds. Integrins exist primarily in three conformational states: bent–closed (inactive; the predominant state), extended–closed (active; low affinity or intermediate state), and extended–open (active; high affinity). The affinity of integrins to various inhibitory and stimulatory ligands is modulated by bivalent cations, which induce a range of conformational changes in integrins ranging from a folded, inactive, and low-affinity state to a high-affinity conformation as shown in Figure \(2\). These conformational changes in the extracellular domains of integrins modulate both ligand binding and downstream cellular signaling. Integrin activation The activation of integrins increases the affinity of these molecules to extracellular ligands. Integrin tail domains play a critical role in these steps, and any genetic mutations in these parts of integrins can disrupt downstream intracellular signaling. Integrin-mediated signaling across cell membranes is typically bi-directional and termed “outside-in” and “inside-out” signaling. When integrins interact with ECM ligands, a conformational change allows adherence to downstream adaptor molecules in the cell-membrane plane. Once clustered, integrins can recruit and activate kinases such as Src family kinases, focal adhesion, and scaffold molecules such as the adaptor protein p130CRK-associated substrate/breast cancer anti-estrogen resistance 1 (p130CAS/BCAR1). These integrin-associated complexes include discrete active and inactive integrin organizations, which can activate unique signaling pathways. The extracellular domains of integrins are known to undergo a diverse range of conformational changes that alter the ligand-binding domains. In the cytoplasmic tails of integrins, α-helices are seen as heterodimers, and the β-strands often bind intracellular proteins, such as talin or filamin. The cytoplasmic tail may undergo several specific conformational changes to bind a range of other signal transducers. This section is derived from Mechanobiology. https://www.mechanobio.info/what-is-mechanosignaling/what-is-the-extracellular-matrix-and-the-basal-lamina/what-is-integrin/how-is-integrin-activated/. Creative Commons Attribution-NonCommercial 4.0 International License. Integrin can be activated from two directions, from the inside by the regulated binding of proteins to the cytoplasmic tails, and from the outside by multivalent ligand binding. In either case, talin binding to the integrin β tails is an essential and the final common step. Though the two processes are conceptually separate, they are mutually cooperative i.e one can lead to the other. Some structural studies done with force application to mimic ligand/intracellular protein suggested that the combined action of these two events favors the transition from the closed, low affinity to an open, high-affinity conformation of integrin. Activation leads to bidirectional signaling crucial in a variety of anchorage-dependent events such as adhesion, cell spreading, migration, polarity, and organization of the ECM leading to physiological changes. Figure \(6\) shows the different states of the integrin dimer in inside-out and outside-in signaling. (A) shows integrin in a low affinity, inactive, bent, conformation. (B1) and (B2) show inside-out integrin activation by cytoplasmic proteins or outside-in integrin activation via extracellular matrix (ECM) ligands, both of which lead to the complete extension of the extracellular domains. (C) shows high affinity and active integrin characterized by separation of the cytoplasmic leg domains. Inside-out signaling Signals received by other receptors foster the binding of talin and kindlin to the cytoplasmic end of the integrin β subunit, at sites of actin polymerization. Substantial information on signaling pathway leading activation is available for integrin αIIbβ3. Talin binds to integrin β-tail via the F3 phospho-tyrosine binding (PTB) domain, a unique interaction with the membrane-proximal (MP) region of the integrin (NPxY motif). This permits competition between a conserved lysine on talin and an aspartic acid on integrin α essential for α/β salt bridge disruption and sufficient for integrin activation. Addition interactions through the basic patches in the FERM subdomain F2 help to orient the β-subunit to promote spatial separation of the cytoplasmic domains. Kindlin is also an essential co-activator of integrin and binds to a membrane distal NxxY motif on β-integrin via its FERM F3 subdomain. A preceding threonine patch on integrins β1 and β3 that gets phosphorylated and a tryptophan on kindlin F3 are also required for binding. However, kindlins are not known to activate integrins on their own but may render integrin-specific effects. Outside-in signaling Ligand binding to the external domain causes conformational changes that increase ligand affinity, modify protein-interaction sites in the cytoplasmic domains, and thence the resulting signals. Besides conformational changes that extend integrin dimers, multivalent ligand binding leads to the clustering of integrins, which in turn activates the Src family of kinases (SFKs) by autophosphorylation. SFKs phosphorylate tyrosines of the integrin cytoplasmic domain (NPxY motifs) and other proteins leading to 1. control of ligand binding strength 2. alteration of binding with signaling molecules (kinases, GTPases, and adaptors), that constitute dynamic adhesion structures such as focal adhesions and podosomes Nevertheless, whether clustering triggers outside-in signaling to facilitate integrin activation or occurs after integrin activation is uncertain. David G. Menter, Raymond N. DuBois, "Prostaglandins in Cancer Cell Adhesion, Migration, and Invasion", International Journal of Cell Biology, vol. 2012, Article ID 723419, 21 pages, 2012. https://doi.org/10.1155/2012/723419. Creative Commons Attribution License, Let's look at another more detailed representation of integrin states. Each αβ dimer recognizes a different intercellular adhesion molecule (ICAM), ligand, or protein substrate in the basement membrane or extracellular matrix. The α subunit dictates the ligand specificity by a seven-bladed β-propeller head domain connected to a leg support structure made of a "thigh", a "calf"-1, a "calf"-2, a transmembrane, and a cytoplasmic domain. The β subunit interacts with the cell cytoskeleton and contains an N-terminal plexin-semaphorin-integrin (PSI) domain, a hybrid domain, a βI domain, four cysteine-rich epidermal growth factor (EGF) repeats, a transmembrane, and a cytoplasmic domain. In many cases, the N-terminal β-I domain of a β subunit inserts into the 7-bladed β-propeller domain of an α subunit (α1, α2, α10, α11, αL, αM, αX, and αD) to form a bulbous-binding headpiece complex. The formation of integrin receptor complexes depends on divalent cation (i.e., Ca2+, Mn2+, Mg2+) that bind to metal-ion-dependent adhesion site (MIDAS) motifs in the α subunits and adjacent to MIDAS (ADMIDAS) motifs in β subunits found in the N-terminus of these receptors. Together they joined α and β subunit termini form an N-terminal headpiece. These detailed features of the integrin dimer structure are shown in Figure \(7\). Three conformation states exist for α and β subunit complexes. (1) The unliganded conformation has a closed headpiece and a bent receptor structure with the EGF domains of the β-subunit touching the calf-1-calf-2 domains of the α-subunit. (2) The headpiece remains closed, but structural changes in the β-subunit EGF domains cause a separation from the calf-1-calf-2 domains of the α-subunits causing an extended structure. (3) Conformational changes in the β 6-α 7 loops expose the ligand-binding site along with a complete separation of the β-subunit from the calf-1-calf-2 domains in the α-subunit. These conformational changes engage the specific integrin headpiece with its ligand" Figure \(8\) shows an interactive iCn3D model of the headpiece of integrin αIIbβ3 in the headpiece extended and open conformation (3FCU) The αIIb part of the headpiece is shown colored based on the secondary structure with the yellow sheets comprising the beta propellor secondary structure motif. The gray chain is the β3 chain. The side chains of the β3 chain forming the binding interface between the αIIb and β3 chains are displayed as colored sticks. The three close metal ions (2 Ca2+ and 1 Mg2+) are important for ligand binding with the Mg2+ involved in coordination to acidic side chains of integrin ligands. These metal ions are present before ligand binding. The RGD binding motif of some integrin ligands binds through their aspartate to the Mg2+. Without bound Mg2+, acidic side chains around the site would interfere with binding. It appears that lateral forces are most important in activating integrins. This is in contrast to tensile forces which act along the length of the receptor. Tensile forces appear to stabilize the closed, extended low-affinity form, while lateral forces at the beta subunit, a mimic for moving cytoskeletal filaments, stabilized the open, extended, high-affinity form. This links conformational allosteric changes to cytoskeletal changes. The mechanism for activation is hence mechanochemical. Similar to conventional cell surface signal transducing receptors, integrins bind ligands and transmit information in an “outside-in signaling” as shown in the top panel of Figure \(9\). “Outside-in signaling” behavior typically involves the engagement of integrins with the extracellular matrix or ICAM surface receptors. When external factors bind to exposed ligand binding sites on integrins this results in conformational changes described in the previous section. Most ECM proteins exhibit multivalent or recurrent molecular patterns, which trigger integrin clustering. As cells engage the repetitive patterns in the ECM, these events occur simultaneously thereby activating intracellular signals. The myriad of different extracellular signals that cells encounter in their microenvironment mediates cell polarity, cytoskeletal structure, adhesion, migration, invasion, gene expression, cell survival, and proliferation. The “outside-in” binding of ECM ligands to cell surface integrins stimulates conformational changes that activate focal adhesion kinase (FAK). FAK then is autophosphorylated on Tyrosine 397 near the catalytic domain, which binds Src. FAK contains a central kinase domain bordered by FERM (protein 4.1, ezrin, radixin, and moesin homology) domain at the N-terminus and a focal adhesion targeting (FAT) sequence at the C-terminus. Activated Src interacts with human enhancer of filamentation1 (HEF1) and p130 CRK-associated substrate (p130CAS) scaffold proteins that help to positively regulate Src-FAK-Crk interactions with Rac. FAK also activates (PKL/Git2)-β-Pix complexes and β-pix then serves either as an exchange factor for Cdc42 or a scaffold protein to promote signaling via Rac and p21-activated protein kinases (PAK). FAK also interacts with actin-related proteins (ARP2 and ARP3) which are regulated by the Wiskott-Aldrich Syndrome Protein (WASP). ARP2/ARP3 initiates the polymerization of new actin filaments. FAK also influences actin contraction and polarization through another GTPase protein, Rho. The regulation of Rho GTPase hydrolysis of GTP (active) to GDP (inactive) form occurs through the opposing activities of guanine nucleotide exchange factor (GEFs). The GTPase regulator associated with FAK (GRAF) and p190RhoGAP blocks actin cytoskeleton changes. In contrast, PDZRhoGEF and p190RhoGEF both serve to activate Rho. “Outside-in signaling” transfers integrin-mediated external signals to the inside of cells.“Inside-out signaling” depends on talin and kindlin. Both talin and kindlin contain FERM (4.1/ezrin/radixin/moesin) domains and a highly conserved C-terminal F3 domains. Talins bind β integrin, actin through the C-terminus, and also vinculin. Kindlins bind integrins, the cell membrane, and various actin adaptor proteins like migfilin, or integrin-linked kinase (ILK). Talin activation occurs through G-protein-coupled receptors that increase cytoplasmic Ca2+ and diacylglycerol. This activates GEF function in conjunction with Ras-proximate-1/Ras-related-protein-1-(Rap1-) GTPase. Rap1 then binds to the Rap1-GTP-interacting adaptor molecule (RIAM). RIAM recruits talin to the membrane and the α and β integrin cytoplasmic domains. Kindlin interacts with β integrin cytoplasmic domain stabilizing the activated state of the integrin complex. “Inside-out signaling” strengthens adhesive contacts and the appropriate force necessary for integrin-mediated cell migration, invasion, ECM remodeling, and matrix assembly. In the case of “outside-in signaling” initiated by ECM proteins, a single ligand-binding event can trigger integrin activation, but repetitive regularly spaced molecular patterns provide a more effective stimulus [122, 123]. This type of mechanoreception has been explored using nanopatterned molecular printing techniques that form regular cRGDfK patch spacings on a polyethylene glycol background matrix [122–125]. These adhesion-dependent sensory mechanisms lead to signal transduction inside the cell by the activation of multiple pathways. Focal adhesions are often formed as a result of cell interactions with the ECM substrata, which initiate signal transduction via kinase cascades and other mechanisms. Integrins in inflammation and infection In the resting state, β2 integrins are expressed specifically on leucocyte receptors. During inflammation, the inflammatory cytokines activate these integrins and promote cellular adherence to the counter-receptors such as ICAMs and promote phagocytosis and cytotoxic killing. Integrin receptors on leukocytes, such as the macrophage-1 antigen (Mac-1, also known as CR3, αMβ2, CD11b/CD18) interact with platelet antigens such as the glycoprotein Ibα (GPIbα) during inflammation. Integrins bind to the pro-domain of transforming growth factor (TGF)-β1 to activate it and promote its secretion. The pro-TGF-βs are biosynthesized and stored in tissues in latent forms, and integrins αVβ6 and αVβ8 can uniquely bind and activate pro-TGF-β1 and pro-TGF-β3. The αVβ6 integrin is known to specifically bind the RGDLXXL/I motif in TGF-β1 and TGF-β3. β2 integrins promote the recruitment of leukocytes to the sites of inflammation by promoting the adhesion of circulating leukocytes to vascular endothelium, transendothelial migration, the formation of immunological synapses in leucocytes, and inflammatory signaling in involved cells. β integrins on the leukocyte surface are also involved in the tethering, rolling, and adhesion of leukocytes to activated endothelial cells. β2 integrins can also initiate intracellular signaling pathways in macrophages and neutrophils and stimulate cytokine secretion from these cells either directly or in synergy with Toll-like receptors (TLRs). Integrins may also integrate the impact of the epidermal growth factor receptor, platelet-derived growth factor receptor, insulin receptor, met receptor superfamily (hepatocyte growth factor receptor), and the vascular endothelial growth factor receptor (VEGFR) in inflammatory cells. β2 integrins are important regulators of adhesion, leukocyte recruitment, and immunological signaling. These integrins mediate adhesive interactions between myeloid cells, endothelial cells, antigen-presenting cells, T cells, and the ECM. L-selectin, the CCR7 chemokine receptor, interacts with specific carbohydrate epitopes on the endothelium and promotes leukocyte rolling and transmigration through the vascular endothelium. Leukocyte rolling induces a rapid, although a transient, increase in the affinity of the β1 and β2 integrins to the endothelial ligands. Conformational changes in the structure of the inserted (I) domain of the αL subunit of LFA-1 enhance firm leukocyte adhesion under shear flow.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.10%3A_Integrins-_Bidirectional_Cell_Adhesion_Receptors.txt

Search Fundamentals of Biochemistry Introduction We will now consider signaling by steroid hormones. These hormones are derivatives of cholesterol, which is found in membrane bilayers and lipoproteins. They are mostly nonpolar. Steroid hormones can affect signaling in two major ways: • through binding to membrane receptors, which when occupied affect signaling through the myriad of ways we discussed throughout this chapter. These effects would be rapid. • through binding to cytoplasmic receptors after they diffuse into the cell through passive and active means. This signaling is hence similar to retrograde signaling by nitric oxide, which can passively diffuse out of a cell and enter an adjacent cell to effect signaling in that cell. If the steroid primary messenger is in the cell, it most often can enter the nucleus and regulate gene transcription. Binding to cytoplasmic receptors account for most of the biological effects of steroid hormones. Since transcription is involved, the pathways elicit a slower response. We will briefly discuss the first type of signaling (binding to membrane receptors) mostly by presenting figures which describe their signaling pathways. Then, we will focus on steroid hormone activation of gene transcription. There are many classes of steroid hormones. These are illustrated in Figure \(1\) along with their overall synthetic pathway. Figure \(1\): Diagram of the pathways of human steroidogenesis. WikiJournal of Medicine 1 (1). DOI:10.15347/wjm/2014.005. ISSN 20018762. The overall families include progestogens and estrogens (female sex hormones), androgens (male sex hormones), glucocorticoids (like cortisol which affects immune and metabolic systems), and mineral corticoids (which affect salt/water balance). We will mostly use the estrogens in the section as a prototypical example because of their involvement in breast cancer and epidermal growth factor receptors. Steroid hormone signaling through binding to membrane receptors Most of this subsection is taken from and adapted from the following source:  Masi et al. Cells 2021, 10(11), 2999; https://doi.org/10.3390/cells10112999. Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Most people think of steroid signaling, it is through their nuclear effects on gene transcription (the predominant signaling effect). Signaling at the cell membrane is an emerging area of interest. We will present steroid signaling at the cell nucleus in four figures with their associated captions. We present these lesser-known effects of steroid signaling first since they utilize many of the pathways we have already studied. The figures will also give you a short review of some canonical pathways, which is always good in a field so complicated as signal transduction. The figures focus on the signaling effects in cancer. Figure \(2\) shows membrane signaling through androgen receptors. Figure \(3\) shows membrane signaling through estrogen receptors A tamoxifen (a selective estrogen receptor modulator used in breast cancer therapy) binding site has been found in NavMs voltage-gated sodium channels. Figure \(4\) show membrane signaling through membrane progesterone receptors Figure \(5\) shows membrane-associated progesterone receptor effects from progesterone signaling. Steroid hormone signaling through binding to cytoplasmic receptors and activation of gene transcription Now we will explore the major effects of steroid hormones on signaling, in which steroids enter the cell, bind to cytoplasmic receptors, and then translocate into the nucleus where they regulate gene expression. The naming of steroid receptors can be confusing since it is important to differentiate steroid receptors that are resident in the cell membrane and those that move to the nucleus. An added complexity arises from the factor that some nuclear receptors can be covalently modified with a fatty acid (palmitoylated) and targeted to the cell membrane. Estrogen receptors targeted to the membrane can then act independently of their nuclear transcriptional activity. We will primarily focus on estrogen effects in breast cancer (most diagnosed cancer) that are mediated through the nuclear steroid receptors, which belong to the nuclear receptor superfamily. Tamoxifen, a commonly used drug in the treatment of breast cancer is an estrogen receptor antagonist (also called an estrogen receptor modulator—SERM). Molecular Function of Steroid Receptors—Common Features Steroid receptors (SR) consist of four main domains, the C-terminal ligand-binding domain (LBD), the DNA-binding domain (DBD), the hinge region, and amino-terminal domain (NTD). Each SR contains also two motifs called activation function 1 and 2 (AF1 and AF2) within NTD and LBD, respectively, and are crucial for the regulation of gene transcription. Two zinc fingers are located in the DBD. Figure \(6\): The domains are often labeled A-F. The N-terminal domain (NTD) is also called the A/B domain. It can also bind DNA and can weakly activate transcription in the absence of hormones. The C domain is the DNA binding domain (DBD) containing Zn fingers, which bind to the steroid response element in promoters of key genes. The D domain is the hinge domain, and The E domain binds hormones (like estrogen) and protein regulators and when bound can activate gene transcription. The last domain (F) varies in length and its function is not completely clear. The specific DNA structure for the estrogen receptor from Pfam is shown in Figure \(7\). The green is the N-terminal Oest_recep domain (NTD, A/B). The red zf-C4 is the DNA binding domain which has two Zn fingers that bind DNA (DBD, C). The blue Hormone_Receptor is the ligand (estrogen) binding domain (LBD, domain E). The yellow is the C-terminal domain (F). These two genes for the estrogen receptor encoding ERα and ERβ. The transcriptionally active form is a dimer that forms on the binding of estrogens. The dimer then translocates to the nucleus and activates transcription at ERE sites. The ERα dimer promotes estrogen-dependent growth while the ERβ dimer inhibits it. Heterodimers can form which seem to reduce the proliferative effects of ERα. Both ERα and ERβ can be expressed in Figure \(8\) shows an interactive iCn3D model of the human estrogen receptor computational model (P03372, AlphaFold) • red spacefill: N-terminal Met • green backbone trace: N-terminal domain disordered, which can bind DNA • magenta backbone trace: DNA binding domain with Zn fingers. The Cys side chains of the Zn fingers are shown in sticks, CPK colors, and labeled. • blue: Hormone_Receptor is the ligand (estrogen) binding domain (LBD, domain E) • yellow: C-terminal tail (domain) • Cyan spacefill: C terminal Val There are no full-length crystal structures of ER dimer. Most available structures are for the estrogen-binding domain. It's really useful to see the full predicted structure to see how all the domains are connected, but perhaps more interestingly, the extended regions of disordered structures, which you should imagine adopting specific structures on interaction with key signaling partners. Figure \(9\) shows an interactive iCn3D model of the ligand binding domain of human estrogen receptor ERα bound to the antagonist tamoxifen (3ERT). The ligand binding domains (one for each of the ERα monomers) are shown in magenta and cyan. The antagonist Tamoxifen, one bound in each of the ligand binding domains, is shown in sticks, CPK color. The amino acids comprising the binding site for tamoxifen, are shown in stick, and CPK colors and labeled in the magenta subunit. The CPK-colored spheres show the binding site on the ligand binding domain for other binding proteins called corepressors or coactivators (not shown, discussed below). Figure \(10\) shows the structures of estrogens and selective estrogen receptor modulators (SERMs). The iCn3D model for the human estrogen receptor ERα bound to the antagonist tamoxifen (3ERT) showed binding sites for other proteins called coactivators or corepressors. The ER-estrogen complex, after binding to DNA, can also bind a protein coactivator, which activates transcription. Likewise binding of a corepressor to the DNA-bound complex inhibits transcriptional activity. Tamoxifen binding to the ERα monomer leads to dimerization and DNA binding. The DNA-bound dimer can then bind either a corepressor (the usual case for tamoxifen binding to ER in the breast), leading to inhibition of DNA transcription (i.e. tamoxifen antagonizes ER transcriptional function), or a coactivator which stimulates gene transcription. A cartoon diagram illustrating the role of ER coactivators and corepressors is shown in Figure \(11\). When a SERM binds to the estrogen receptor, the receptor adopts a unique conformation that allows dimerization and interaction with estrogen response elements (EREs) of the target genes. The unique conformational change induced by the binding of the SERM may result in a distinct pattern of cofactor recruitment. Before steroid binding, most steroid receptors are found in the cytoplasm bound to heat shock proteins like Hsp90. Phosphorylation of the Hsp:SR complex leads to dissociation of the Hsp, followed by dimerization and translocation into the nucleus. In some cases, hormone binding occurs in the nucleus. Figure \(12\) shows an interactive iCn3D model of the estrogen receptor DNA-binding domain bound to DNA (1HCQ). The backbones of the dsDNA are shown in spacefill cyan and magenta. The DNA bases are shown in CPK colors. The two chains of the DNA binding domain of the estrogen receptor are shown in gray and gold. Zn2+ ions are shown as brown spheres. The coordinating Cys side chains in the gray DNA binding domain are shown in sticks, CPK colors, and labeled "C". The amino acid side chains from the gold DNA binding domain that interact with DNA are shown in sticks CPK colors. There are two types of ways that steroid hormones activate gene transcription, a direct and an indirect method Direct (classical): The DNA binding domain (containing the Zn fingers binds) of the dimer bind to the target hormone response element (HRE) or for steroids the steroid response element (SRE) sequences in the promoter site of specific genes under steroid hormone control. As seen in the iCn3D above, one of the two Zn fingers on each of the hormone receptors binds to the target site in the major groove of DNA. The other Zn fingers are involved in the dimerization of the hormone receptor. The SRE contains two 6-base pair repeats separated by 3 base pairs. The DNA sequence shown in the iCn3D above is CCAPGGTCA. The general consensus sequence for steroid hormones is 5′-GGTACAnnnTGTTCT-3′. The ER binds to 5′-GGTCAnnnTGACC-3′. Note that the complementary strand sequence is 5'-GGTCAnnTGACC so the sequence is a palindrome (the complementary strand has the same sequence going in the opposite direction. After binding the hormone:receptor dimer to DNA, coregulators bind. These modify histones and remodel the DNA to facilitate or inhibit transcription. Indirect: In this method, the steroid receptors bridge other DNA-bound transcription factors without the steroid hormone binding to its response element. The direct and indirect methods for steroid hormone effects on transcription are shown in Figure \(13\). (1) Translation of a SR and binding of Hsp70. (2) Hsp70 to Hsp90 transition. (3) Ligand binding, Hsp90 dissociation, and dimerization. (4) Nuclear translocation. (5) Transcriptional action: induction (5a, 5c) or inhibition (5b, 5d) of target gene expression, performed either in the classical mechanism involving SRE-binding (5a, 5b) or by tethering other TFs (5c, 5d). (6) Ligand dissociation followed by disassembly of the transcriptional complex and SR binding to a molecular chaperone. (7) Rebinding of the ligand. (8) Ubiquitination. (9) Proteasomal degradation. SR—steroid receptor, SH—steroid hormone, Hsp 70—heat shock protein 70, Hsp90—heat shock protein 90, SRE—steroid response element, CoA—coactivators, CoR—corepressors, HAT—histone acetyltransferase, HDAC—histone deacetylase, TF—transcription factor, TFRE—transcription factor response element, Ub—ubiquitin. Although histone acetyltransferases (HATs) and histone deacetylases (HDACs) are classified as coregulators, here they are shown separately to emphasize their role. Illustration created using elements from Servier Medical Art https://smart.servier.com/, reproduced under Creative Commons Attribution 3.0 Unported License https://creativecommons.org/licenses/by/3.0/. Hormone dissociation leads to steroid hormone dissociation from DNA by chaperone proteins. The Hsp90-SR complex can reenter the cycle. The steroid hormone receptor can be targeted by proteolysis by the proteasome by ubiquitination in either the nucleus or the cytoplasm. Modulation of ER function by phosphorylation No pathways stand in isolation, so it should be no surprise the estrogen receptor (again used as an example) is regulated by post-translational modification, especially by phosphorylation. Phosphorylation can be ligand dependent or independent. Multiple kinases are involved in the phosphorylation of the N-terminal region. An especially important one occurs at Ser 118 at a cyclin-dependent kinase (involved in cell cycling) when the receptor is bound to estradiol. Ser 118 also gets phosphorylation through epidermal growth factor signaling by MAPK. This may increase cell proliferation in breast cancers even in the absence of estrogen. Figure \(14\) shows the activation of ER by phosphorylation through growth factor and cytokine signaling pathways. Figure \(14\): Activation of ER by phosphorylation induced by growth factor and cytokine signaling pathways. Siersbæk et al , 2018 Sep 1; 32(17-18): 1141–1154. doi:10.1101/gad.316646.118. Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.11%3A_Signaling_by_Steroid_Hormones.txt

Search Fundamentals of Biochemistry Introduction: mTOR and AMPK Imagine when you were in high school, you weighed 135 pounds (61 kg). Let's say it's 50 years later and you still weighed 135 pounds. In the intervening years, think of how much food and liquid you consumed. In 2011, the USA Food and Drug Administration indicated that the average American consumed about 2000 pounds (907 kg) each year, including liquids other than drinking water. (Compare this to 365 kg for someone in Somalia!) So over 50 years you would have consumed 100,000 lbs (45,400 kg) of food or 740 times your body weight. These numbers are essentially unchanged even if you gained one pound a year for a total of 50 pounds. These figures suggest that we have an elaborate system that regulates how much we eat and how much weight we gain or lose. There are obvious times in our lives when we are growing and actively gaining body mass. Incoming food is not only processed into energy but also into net protein, lipid, carbohydrate, and nucleic acid synthesis. This system has become dysregulated in an ever-increasing number of people with type II diabetes and obesity throughout the world. Obvious candidates for regulators in general of net body weight and in specific of protein and lipid synthesis are the nutrients we consume and store. Many systemic hormones and neurotransmitters are involved in hunger, satiety, and eating behavior. This chapter will not focus on those but rather on mechanisms of nutrient signaling pathways in growth which requires new protein, lipid, and nucleic acid synthesis for cell growth and division. Likewise, it will not focus on nutrient signaling through hexosamines and UDP-GlcNAc. A key player in these signaling pathways is mTORC (mammalian or mechanistic Target Of Rapamycin Complex). A key protein in this multiprotein complex is mTOR, a Ser/Thr kinase that regulates cell growth, division, protein synthesis, RNA synthesis (transcription), and even autophagy (the major process whereby cells die and their contents are recycled for use). There are two physiologically relevant complexes of mTOR, mTORC1, and mTORC2. These two complexes have been called the master regulators of metabolic and growth processes. • mTORC1 activates protein, lipid, and nucleotide synthesis, all required for cell growth and division; it is inhibited by rapamycin. For activation, it needs two obvious conditions: energy and growth factors. In addition, it needs amino acids. • mTORC2 activates many processes through phosphorylation; it is not inhibited by rapamycin. What is Rapamycin? It sounds like an antibiotic but it is an antifungal agent produced by certain bacteria as a defense against fungal (eukaryotic) pathogens. It blocks cell division in fungi by stopping cell growth. The cell cycle consists of the following general sequential steps: (Go-G1)→S →G2→M→ G1. Gap 0 (Go) is a quiescent phase outside of the cycle. In G1, cells are growing and preparing for DNA synthesis which occurs in the S phase. After DNA replication/synthesis (S phase), cells grow again and prepare for mitotic cell division (M phase). Rapamycin traps fungal cells in the G1 phase. It also traps mammalian cells, and in particular immune lymphocytes in G1 as well, preventing lymphocytes from dividing. Hence rapamycin has been used to prevent rejection of transplanted tissue as it suppresses the immune system. Rapamycin inhibits mTORC1. That it inhibits mTOR is consistent with its immunosuppressive (antigrowth and antiproliferative) effects Another key player regulated by mTOR is the energy sensor AMP Protein Kinase (AMPK). We will discuss both below. mTOR Inhibition by Rapamycin The structure of the mTORC1 complex is complex itself, in part, since some of its components were studied and named before their roles in the mTORC1 complex were elucidated. Investigators were interested in the molecular target(s) of rapamycin. Yeast (a fungus) was an easy organism to study using genetic techniques. Three genes were found that when mutated inhibited the effect of the inhibitor rapamycin (i.e. so that rapamycin did not inhibit mTOR). Two of these were the Ser/Thr kinases mTOR1 and mTOR2. The other was an analog of a protein found in humans (FK Binding Protein 12 - FKBP12). The family of FK binding proteins act as protein chaperones and have Pro-X peptidyl-prolyl isomerase activity (PPI). FKBP12 specifically binds a drug (FK506 also called Tacrolimus) that is an immunosuppressant (do you see the general link with immune cell division and growth?). The binary complex of FK506 and FKBP12 binds a third protein, a phosphatase called calcineurin, and blocks its phosphatase activity and signal transduction required for T cell activation and proliferation. This activity of FKBP12 strangely does not require its PPI activity. Now back to mTORC1. Here are the known components of the core complex: • mTOR, a Ser/Thr kinase; • Raptor, a Regulatory-associated protein of mTOR; modulates the specificity of the kinase; • mLST8, the mammalian lethal with SEC13 protein 8 (also called Mammalian Lethal With SEC13 Protein 8 e In addition, other proteins associate with the core complex • PRAS40, a proline-rich Akt substrate of Akt • DEPTOR, DEP Domain Containing MTOR-Interacting Protein (where DEP is Disheveled, Egl-10 and Pleckstrin domain found in these 3 proteins and others involved in G-protein signaling) • FKBP, which binds rapamycin. mTORC2 is not sensitive to rapamycin. Instead of RAPTOR, it has a protein called RICTOR (rapamycin-insensitive companion of TOR). Figure $1$ shows an interactive iCn3D model of the human mTORC1 containing mTOR, Raptor and mLST8 bound to FK506 binding protein (FKBP)-rapamycin complex (5FLC) Figure $1$: Human mTORC1 containing mTOR, Raptor and mLST8 bound to FK506 binding protein (FKBP)-rapamycin complex (5FLC). Click the image for a popup or use this external link https://structure.ncbi.nlm.nih.gov/i...iSuYwaymutw867 The complex acts physiologically as a dimer with rotational C2 symmetry. The static image in Figure $1$ is color coded as below. The iCn3D image is similarly. • chains B ,F ,1-4: pieces of mTOR (mol ID 1-3) as a homodimer - Red; • chains A and E: Raptor (mol id 4) orange; • chains D and H: LST8 (mol id 5) green; • chains C and G: FKBP (mol id 6) light blue; • rapamycin in C and G, yellow spacefill. Raptor has been likened to tape as it interacts with the two mTOR subunits, holding them together into a larger, donut-like structure, and stabilizing the dimer. How does rapamycin inhibit mTOR? You have studied many kinds of inhibitors (competitive, uncompetitive, mixed, or noncompetitive inhibition) in which the inhibitor binds to either free E or the ES complex. The figure above suggests that rapamycin binds at the interface of the mTOR kinase (red) and FKBP. Presumably the RAP:FKBP complex binds to mTOR. Figure $2$ shows how rapamycin (shown in sticks) is sandwiched between mTOR (red) and FKBP (light blue). This offers clues as to how it inhibits mTOR. The rapamycin:FKBP complex is an allosteric inhibitor with its effect dependent on both substrate and post-translational phosphorylation. Right across from the light blue FKBP protein is the green LST8 protein (see above). In between these is a cleft which is the active site. One could imagine that RAP:FKBP binding to mTOR might interfere with substrate binding in mTORC1 but not in mTORC2. RAPTOR in mTORC1 probably helps recruit substrates and hence is involved in the determination of substrate specificity. RAPTOR interacts with a short section called the TOR signaling (TOS) motif in mTORC1 substrates. The part of RAPTOR that binds TOS in substrates is at the base of the mTORC1 active site, probably narrowing it further as it provides exquisite substrate selectivity. mTOR Protein Kinase Structure and Activity How does the structure of such an important kinase (mTOR) differ from other kinases? Remember that there are 388 S/T Kinases, 90 Y Kinases, and 40 atypical protein kinases in the human genome. The generic structure is shown below with ATP and substrate binding between N- and C-terminal lobes. The C lobe has a catalytic lobe which contains an Asp side chain acting as a general base in nucleophilic attack on the gamma P of ATP. A disordered activation loop in the C lobe often prevents substrate binding to the enzyme and keeps the kinase in an inactive state. On phosphorylation of the activation loop or elsewhere, or substrate binding, conformational changes lead to movement of the activation loop away from the active site, activating the kinase activity. The structure of generic kinases is shown in Figure $3$. Compared to generic protein kinases, mTOR has several insertions (about 200 amino acids) into the protein sequence and these must be involved in the determination of its specificity toward protein substrates. The C-terminal domain structure of the mTOR kinase is shown in Figure $4$. The FAT domain precedes the mTOR kinase domain. The FRB is inserted into the N lobe of the kinase domain whereas the LBE and FATC are inserted into the C lobe of the kinase domain. Figure $5$ shows an interactive iCn3D model of the mTOR with the LST8 protein bound through the kinase LBE domain and with bound AGS, a nonhydrolyzable ATP analog (4JSP). Key regions are: • FAT domain: 1385-2000 • N Lobe: 2003-2240 • C Lobe: 2241-2549 • FRB domain (magenta) inserted into N lobe: 2021-2118 • LBE domain (medium blue) inserted into C lobe: 2259 (ILL) to 2296 (TAG) • Catalytic Loop (red): 2337 (GDR) to 2344 (SNL) • Activation loop (dark blue): 2357 (DFG) to 2379 (FRL) • FATC domain (purple) inserted in C lobe: 2519 (LDV) to 2549 (PFW) Now imagine the FKBP:RAMP complex binding to the FRB domain in the figure above and you can easily imagine how RAMP could inhibit a large protein substrate from binding. A close-up of the active site of the kinase showing bound AGS, the catalytic loop (red) containing the general base Asp 2388, and the activation loop is shown in Figure $6$. The mTOR kinase seems to be always primed for catalysis since the Asp 2388 is in a position to act as a general base. The FRB domain seems to be involved in directing substrate access (such as S6K1) and hence in controlling substrate specificity. Binding of FKBP:RAMP to the FRB domain would prevent substrate binding. Important substrates for each complex are shown below. mTORC1: Eukaryotic Translation Initiation Factor 4E Binding Protein 1 (EIF4EBP1): This protein inhibits translation by binding eukaryotic translation initiation factor 4EIF (eIF4E). In the absence of active IF4EBP1, eIF4E is part of a complex that recruits 40S ribosomal subunits to the 5' end of mRNAs, which allows the initiation of translation. The binding protein inhibits complex assembly and represses translation. Active mTORC1 phosphorylates the binding protein in a variety of conditions (UV irradiation and insulin) which leads to dissociation of the binding protein which allows eIF4E to initiate translation. Ribosomal S6 kinase 1 (RPS6KB1 aka S6K1): This is a Ser/Thr kinase involved in proliferation, protein synthesis, cell growth, and cell proliferation. It phosphorylates eIF4B. In nutrient depletion (non-growth conditions), it forms a complex with the EIF3 translation initiation complex which inhibits translation. Under growth conditions, it is phosphorylated by mTORC1, causing its dissociation from the EIF3 complex and activation of translation. The active form then phosphorylates and activates several substrates in the pre-initiation complex, including the EIF2B complex and the cap-binding complex component EIF4B. In the presence of amino acids, both EIF4EBP1 and S6K1 are phosphorylated. If amino acids are depleted, they are dephosphorylated. Lipin 1 (LPIN1): This is a phosphatase that converts phosphatidic acid to diacylglycerol in triglyceride synthesis. Interestingly, it is also a transcriptional coactivator with PPARs (peroxisome proliferator-activated receptors) to modulate genes involved in lipid synthesis. A summary figure of mTORC1 signaling is shown in Figure $7$. mTORC2: Akt (also known as Protein Kinase B): This is a Ser/Thr kinase that is involved in the regulation of metabolism, proliferation, cell survival, growth, and angiogenesis. It has a notable role in the movement of the glucose GLUT4 transporter to the cell membrane in response to insulin signaling. Akt also interacts with mTORC1. Serum/Glucocorticoid Regulated Kinase 1 (SGK1): This serine/threonine protein kinase is involved in cellular stress response. It activates certain potassium, sodium, and chloride channels. It also activates membrane transporters, enzymes, and transcription factors. Its effects regulate neuronal activity, cell growth, proliferation, survival, migration, and apoptosis. Protein Kinase C alpha (PRKCA): This is a Ser/Thr kinase involved in cell adhesion, proliferation, differentiation, and migration. Rho and Rac: These are small G-protein involved in cytoskeletal structure and cell cycle. Figure $8$ shows a more complete pathway of activation, regulation, and activity of both mTORC1 and mTORC2. Figure $\PageIndex{x}$ below is used with courtesy of Cell Signaling Technologies (www.cellsignal.com). This chapter section will mostly focus on mTORC1. Regulation of mTORC1 by Leucine mTORC1 is a key regulator of protein synthesis but that begs the question as to how it determines that protein synthesis is required. How does it sense that?  Rregulators of mTORC1 might be amino acids in cells, but who would have thought that the master regulator would be leucine, a simple branched chain hydrophobic amino acid. It would be nice if free leucine bound directly to mTORC1, but it's not that simple. Rather, it binds to a "leucine" receptor, sestrin 2 (SESN2). Figure $9$ shows the binding interactions between Leu (spacefill) and key side chains in sestrin 2 (5dj4). The Leu is rather buried, which suggests a conformational change ensues on binding to the protein. Saxton et al (2016) describe three types of sestrin2 side chains involved in the interaction: Lid: Thr374, Thr377, and Thr386 form H bonds with the Leu amine and carboxyl group. Leucine is represented as a stick model (orange). Latch: Tyr375 and His86 form hydrogen bonds to the Leu. Note that these residues are distal in the chain and are probably pulled together during the conformational changes which occur after binding to form a latch to sequester the bound Leu. Floor: F447 and W444 which interact with the nonpolar side chain of Leu. Figure $10$ shows an interactive iCn3D model of Leucine-bound Sestrin2 (5DJ4). What happens after leucine binds? It's a complicated but understandable process described below in words and images. But first a quick review. Kinases must be regulated to be turned on and off at the right time. They are often regulated by phosphorylation, as mTOR is. In addition, they can be regulated by binding proteins as mTOR is (by Raptor, FKBP, etc). They can also be regulated by small G proteins (like Ras) which are active when bound to GTP and inactive when bound to GDP. Of course, whether small G proteins have GTP bound depends in part if they interact with GAPs (GTPase activating protein which inactivates small G proteins) or GEFs (which facilitate the exchange of GDP for GTP and activate them). Such a master regulator of growth as mTORC1 is regulated by all of these, in addition to the presence of abundant leucine. In the absence of leucine, sestrin 2 is bound to a protein called GATOR2 (GTPase-activating protein - GAP - activity toward Rags 2). The binding of leucine to sestrin 2 causes the dissociation of GATOR2. This is shown in Figure $11$. Free GATOR2 is a GAP that regulated mTORC1. Specifically, it regulates the activity of a heterodimer of small GTP binding proteins, RagA/B:RagC/D (see pathway above) which are associated with the outer leaflet of the lysosome. There they interact with a membrane protein, SLC38A9, and a protein that regulates the Rag proteins, which of course is named Ragulator. Active RagA/B:RagC/D recruits mTORC1, presumably through the Raptor subunit) from the cytoplasm to the lysosome membrane. Small G proteins like Ras, when activated by exchanging bound GDP for GTP, can interact with and activate kinases (like the Raf kinase for Ras). When mTORC1 binds to active RagA/B:RagC/D, it becomes activated. We often think of activating a protein by ligand binding, which promotes a conformational change, or by post-translational modification, which can provide a binding interaction or conformational change to activate the protein. Another way is to inhibit an inhibitor of a protein, as shown in Figure $12$.: Y inhibits Z as denoted by the blunt blunt-ended. If X inhibits Y, then Y can't inhibit Z, which is now active. This is analogous to the quote that "the enemy of my enemy is my friend", which has been attributed to Kautilya (from India) in the 4th century BCE. Leucine binding to sestrin 2 leads to free GATOR, which activates mTORC1 by blocking downstream inhibitors. Figure $13$ (after Buel and Blenis, 2016) shows the interactions from an activation (arrow) or inhibition (blunt arrow) perspective. The figure above shows the involvement of multiple proteins in the lysosome membrane that are involved in mTORC1 activation. There is yet another way that the RagA/B and RagC/D proteins are regulated (other than by the GATOR GAP activity. The main one appears to be Ragulator, which is a GEF for the Rag proteins. Here is a summary of the components of this lysosomal membrane recruitment center for mTORC1. • Ragulator (what a great name) binds and recruits the small G proteins Rag to the lysosome membrane where Ragulator acts as a GEF for RagA/B • SLC38A9 is a weak amino acid transporter in the lysosome membrane, with a preference towards polar amino acids. More likely it is yet another sensor of amino acids, particularly of arginine, which has a high concentration in the lysosome. The protein has a high Km for the transport of Arg. It has a Ragulator binding domain and is hence part of the complex that recruits mTORC1 to the lysosome • vacuolar adenosine triphosphatase (v-ATPase): function unclear These interactions, which involve multiple activations and inhibitions, are difficult to follow even with a diagram. The actions of small G proteins can be especially difficult to understand since the G protein is biologically INACTIVE in its GDP-bound form towards its target binding protein. This occurs when the GTPase activity of the G protein is ACTIVE. The arrows and blunt end arrows in the figure above represent the activity of the protein toward its target protein. Here are two alternative ways to make sense of the interactions: - Stepping backward from Rag A/B, Gator 1 (a GAP) inhibits the ACTIVITY of the protein Rag A/B as it acts as a GAP to leave Rag A/B in the inactive GDP-bound state. Paradoxically this occurs as the inherent GTPase activity of the protein is activated as described above). Free Gator 2 (also a GAP) appears to inhibit the GAP activity of Gator 1 (through an unknown mechanism), thereby increasing the amount of GTP-bound Rag A/B, which then can activate mTORC1. Free Gator 2 does this only if Sestrin 2 is bound to Leu which allows the Gator 2 to dissociate from the inactive sestrin 2:Gator 2 complex. - The diagram above shows that in the absence of leucine, three blunt end (inhibition) arrows occur between Sestrin 2 and Rag A/B. One blunt arrow denotes inhibition, two activation (inhibition of inhibition), and hence three net inhibition Hence in the absence of Leu (when Sestrin is bound to Gator 2, Rag A/B is inhibited in its ability to activate mTORC1 as Rag A/B is in the GDP-bound state. However, free leucine unblocks the inhibitor action of sestrin 2 as Gator 2 is now free and active on its own. Amino acids (especially arginine, which is abundant) in the lumen of the lysosome activate, through the v-ATPase and SLC38A9, the GEF activity of Ragulator. When Rag A/B has sufficient GTP, some conformational changes must ensue to allow mTORC1 recruitment to the lysosomal membrane. Regulation of mTORC1 by Energy Availability - AMP Kinase Believe it or not, another small G protein with GTPase activity, Rheb (Ras homolog enriched in the brain), is involved in both mTORC1 recruitment to the lysosomal membrane and activation of mTOR. This interaction is also shown in the figure above. Mostly, Rheb is involved in the activation of the kinase activity of the mTORC1 complex and specifically the phosphorylation by mTOR of the substrates S6K1 and EIF4EBP1. In the presence of growth factors, Rheb is localized to the membrane by a lipid anchor (a farnesyl group). The mTORC1 kinase-activating activity of Rheb stands in contrast to the role of the Rag G proteins which appears to be chiefly recruitment. How is the small G protein Rheb regulated? Of course, by its interaction with yet another GAP, named the tuberous sclerosis complex (TSC). In the absence of growth factors, TSC binds to Rheb and, acting as a GAP, promotes GTP hydrolysis. This inactivates Rheb, inhibiting mTOR kinase activity. How then is Rheb regulated? One way is through phosphorylation by AMP Kinase (AMPK), an enzyme that is itself regulated by the energy level of the cells. AMPK phosphorylates and activates the TSC, which, acting as a GAP, inactivates the small G protein Rheb complex (TSC complex). Sestrins 1 and 2 may also regulate AMPK. Let's look at the energy sensor of the cell in more detail. AMP Kinase is one of the cell's major fuel sensors and also in mammals responds systemically to hormone and nutrient levels. The enzyme is a heterotrimeric protein consisting of an alpha (catalytic), beta (regulatory), and gamma (regulatory) subunit that binds AMP, ADP, and ATP. Cellular ATP levels are determined in part by the enzyme adenylate kinase which helps interconvert adenine nucleotide (AXPs) as shown in the following equilibrium: Adenylate Kinase: ADP + ADP ↔ ATP + AMP, Keq = 0.44 In red blood cells, the concentrations of ATP, ADP, and AMP are approximately 1850 uM, 145 uM, and 5 uM. Even in cells that use lots of ATP (muscle for example), ATP never falls by much. Using the values above and simple general chemistry, an 8% drop in ATP would lead, through the action of adenylate kinase, to an ATP concentration of about 1710 uM and an AMP concentration of 20 uM. This value for AMP is still much lower than ADP and ATP. However, this change represents a 4 fold increase in AMP which, even with the low actual concentration of AMP, leads to the activation of AMPK. Another "normalized" indicator of cell energy status (or "charge") is the Energy Charge, EC. It is defined by an equation that gives a value from 0-1 where 0 indicates that all AXPs are in the AMP form and 1 where only ATP is present. The numerator of the equation of EC below represents the number of moles of phosphoanhydride linkages in the AXP pool (two for each ATP and one for ADP) and the denominator is the number of moles of AXPs (mass balance). The 1/2 term allows the bracketed term to equal 1 when only ATP exists and 0 when only AMP exists. The EC values of cells are regulated to remain around 0.85. \mathrm{EC}=\frac{1}{2}\left[\frac{2 \mathrm{ATP}+\mathrm{ADP}}{\mathrm{ATP}+\mathrm{ADP}+\mathrm{AMP}}\right] Before we explore the mechanism of energy sensing by AMPK, let's look at the domain structure of the three subunits of AMPK. They are shown in Figure $14$. Figure $14$: Domain structure of the alpha, beta, and gamma subunits of AMPK. Kim et al. Experimental & Molecular Medicine (2016) 48, e224. https://www.nature.com/articles/emm201616.pdf. a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. http:// creativecommons.org/licenses/by-nc-sa/4.0/ The mammalian α1/α2 and β1/β2 isoforms are very similar, and their characteristic features are shown. AMPKα subunits: KD, kinase domain containing Thr-172 for the activation by upstream kinases; AID, autoinhibitory domain; two α-RIM, regulatory subunit interacting motifs triggering the conformational changes in response to AMP binding to the AMPKγ subunit; α-CTD, C-terminal domain binding to the β-subunit. AMPKβ subunit: CBM, carbohydrate-binding module, in which Ser108 is important for the action of some direct AMPK activators, such as thienopyridone (A-769662) and salicylate; β-CTD, C-terminal domain containing α-subunit-binding site and immediately followed by the domain for γ-subunit interaction. AMPKγ subunit: three γ-subunit isoforms have variable N-terminal domains (NTDs); four CBS, cystathione-β-synthase domain, which forms two Bateman domains that create four adenosine nucleotide-binding sites (Sites 1–4). Site 2 appears to be always empty and Site 4 has a tightly bound AMP, whereas Sites 1 and 3 represent the regulatory sites that bind AMP, ADP, or ATP in competition. How AMPK detects this exponential but still small molar increase in AMP is interesting, especially given the much higher concentrations of ADP and ATP. AMPK contains four binding sites that can bind AMP, ADP, and ATP (AXPs) in the regulatory subunit (gamma). This is in addition to the binding of ATP and ADP at the active site in the catalytic subunit (alpha). What binds depends on the Kd for binding of different AXPs as well as their concentrations. Bound AMP seems to have three effects on AMPK. When bound to the gamma subunit, AMP • increases phosphorylation of Thr 172 in an "activation" loop in the catalytic alpha subunit by an upstream kinase which increases the kinase activity of AMPK by 100-200 fold. Phosphorylation is essential for the activity of the enzyme; • inhibits dephosphorylation of Thr 172 which is perhaps the major way that AMP enhances the kinase activity of the catalytic subunit. ADP binding also inhibits dephosphorylation as shown by studies that show that the binding of ADP and the dephosphorylation of the phospho-AMPK have the same ADP concentration dependency; • allosterically activates ten-fold the kinase activity of the catalytic alpha subunit (a secondary effect). ADP has no such effect. These effects are altered by the markedly higher concentrations of ATP which counteracts all these effects, enhancing the Energy Charge sensor activity of this enzyme. The gamma regulatory subunit has 4 binding sites for AXP. Crystal structures show site 2 is empty, site 4 is always bound to AMP, and sites 1 and 3 can bind AMP, ADP, or ATP. Site 1, which mediates the allosteric effects on AMPK binds all AXPs with similar affinity. This appears paradoxical since given the high energy charge, one would expect ATP and ADP to out-compete AMP for binding. However, it was found that the Mg2+ -ATP complex has marked lower affinity for the site, allowing both AMP and ADP, which under cellular conditions are mostly not bound to Mg2+ while ATP is, to out-compete Mg2+ -ATP for binding. Site 3 binds AMP and ADP with a 30-fold lower affinity but on binding protects p-AMPK from dephosphorylation of Thr 172. Figure $15$ shows an interactive iCn3D model of human AMPK (a2b2g1) in complex with a small molecule activator SC4 (6B2E) SC4 or similar molecules may be important drugs to target AMPK and be useful in the treatment of insulin resistance and Type II diabetes. SC4 activates α2 complexes and glucose uptake into muscle cells. Some would call this type of drug an importagog, as it increases the uptake of important metabolites into cells. The alpha catalytic kinase subunit is shown in gray with key catalytic residues and phosphorylated Thr and Ser shown in sticks, CPK coloring, and labeled. The beta subunit is shown in cyan. It appears to be two chains since the connecting section is not ordered in the crystal structure. The gamma subunit with bound AMP (spacefill, CPK colors, labeled) is shown in magenta. What effect does activated AMPK have on the cell? Active AMPK has an amazing number of effects (see figure below). It activates liver glycolysis (by phosphorylating phosphofructokinase 2 which forms F2,6-BP, an activator of PFK) and inhibits by phosphorylation enzymes involved in fatty acid synthesis (acetyl-CoA carboxylase), glycogen synthesis (glycogen synthase) and cholesterol synthesis (HMG-CoA reductase). Yeast AMPK has recently been shown to be also controlled by acetylation of the equivalent beta subunit (Sip2). Acetylation increases its interaction with the alpha catalytic subunit (Snf1) which decreases its kinase activity. This decreases the phosphorylation of downstream kinases (including an analog of Akt1 called Sch9) which slows the growth and increases longevity. Normal aging is associated with decreased acetylation of Sip2. Figure $16$ shows how the many signaling pathways we have studied interact with AMPK Figure $17$ shows a more complete pathway of activation, regulation, and activity of AMPK. The illustration is used with courtesy of Cell Signaling Technologies (www.cellsignal.com). It also shows the effect of AMPK on the master regulator of protein, lipid, and nucleic acids synthesis, mTOR. Synthesis of these molecules is necessary for cell growth and proliferation, two activities that cells do not engage in when AMP levels are high, which signifies an energy-depleted state. Regulation of mTORC1 by Insulin and Growth Factors mTORC1 is regulated by local factors (amino acids, energy state) and systems factors (growth factors). This list is growing daily. The following have been shown to lead to mTORC1 activation including small molecules such as amino acids, ATP (through AMP kinase), oxygen, and glucose, and larger ones such as insulin, other growth factors, cytokines (immune growth factors and regulators), oncogenes (which promote cell proliferation) and some infectious agents. Other molecules or processes inhibit mTORC1, including tumor suppressors and stress. mTORC1 promotes mRNA and protein synthesis as described above but also nucleotide and lipid synthesis, which is not described in detail above. In addition, it promotes aerobic glycolysis (Warburg effect), to supply not energy but intermediates for biosynthesis, as well as the pentose pathway, which forms NADPH for reductive biosynthesis and ribose for nucleic acid synthesis. Let's look at 2 specific external hormones, insulin and epidermal growth factor (EGF), and how they affect mTORC1 activity; Insulin: Insulin binding to its receptor leads to the activation through phosphorylation of the kinase Akt (aka Protein Kinase B) after upstream phosphorylation of membrane phosphoinositides in the membrane and activation of phosphoinositide-dependent kinase 1, PDK1. Atk, as shown in the signaling figure for AMPK, phosphorylates TSC2, the GAP for Rheb. The arrows on the AMPK kinase figure above are not consistent with our previous use of arrows. In the figure from CST, arrows show that both AMPK and Akt phosphorylate TSC2. The phosphorylated TSC is shown to inhibit Rheb, the small G protein. This would not make physiological sense. Phosphorylation of TSC2 by AMPK (signaling energy depletion) activates the TSC2 GAP protein which would inhibit RheB (the G protein) and hence inhibit mTORC1. In contrast, phosphorylation of TSC2 by Akt (signaling the abundance of glucose) leads to the inhibition of the GAP activity of TSC2. That would keep Rheb in the active, GTP-bound form, which leads to the activation of the bound mTORC1. A more complete description of the pathway where insulin binds to its receptor (an insulin-gated receptor tyrosine kinase) and leads to activation of mTORC1 through Akt is shown in Figure $18$. MAPK Cascade: EGF binds its receptor, activating it as a receptor tyrosine kinase. Typical of other receptor kinases, it activates the mitogen activate protein kinase system. This process is mediated by Ras (a small G protein) activator of Raf (a mitogen-activated protein kinase kinase kinase or MAP3K). Active Raf phosphorylates and activates MEK (a MAPK2) which activates ERK (a MAPK). Erk phosphorylates mTORC1 directly, which activates it. It also phosphorylates TSC2/TSC1, which inhibits this GAP protein, leading indirectly to the activation of the small G protein Rheb, which also activates mTORC1. These steps are shown in Figure $19$. A summary showing the kinases that activate or inhibit TSC2/1 is shown in Figure $20$. We tend to concentrate on our favorite protein and confer it with special status as critically important in a pathway. One could pick the GAP protein TSC2 as especially important in regulating the activity of mTORC1. Figure $20$: kinases that activate or inhibit TSC2/1 The figure above shows two additional proteins. One is REDD1 (not a kinase), which activates the GAP protein TSC2, leading to the inhibition of the small G protein Rheb, and hence the inhibition of mTORC1. REDD1 (regulated in development and DNA damage responses 1) is also called DDIT4 (DNA-Damage-Inducible Transcript 4). It is a gene whose expression is activated during hypoxia by hypoxia-inducible factor-1 and also during DNA damage. Hypoxia alters metabolism very quickly. The protein is degraded by the proteasome after it is targeted for degradation by the post-translational addition of ubiquitin. This suggests yet another way to regulate the activity of mTORC1. The other is IKK beta, also known as IKBKB ( Inhibitor Of Kappa Light Polypeptide Gene Enhancer In B-Cells, Kinase Beta). It is a kinase that phosphorylates and inhibits TSC2 which inhibits Rheb, leading to the activation of mTORC1. This kinase is activated by many stimuli including inflammation (mediated by cytokines), bacterial or viral infections, and DNA damage. It phosphorylates a bound inhibitor of NF-kappa beta. This allows ubiquitinylation of the inhibitor, targeting it for proteasomal degradation. The free NFKB can then enter the nuclease and alter the transcription of genes involved in the immune response and hence promote proliferation. Under these conditions, one would expect the activation of mTORC1. A final note: Less is known about how lipids regulate mTORC1. Two possible lipid signaling molecules, phosphatidic acid, and phosphatidyl inositol -3-phosphate are probably involved. The enzyme that makes them, phospholipase D and phosphoinositide 3-kinase (from the PIK3C3 gene), also known as VPS34 (for vacuolar protein sorting from yeast), are found in phagosomal and lysosomal vesicles and are involved in their processing, seem to be involved in mTOR signaling. Obesity and people with high-fat diets have elevated mTOR activity.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.12%3A_mTOR_and_Nutrient_Signaling.txt

Search Fundamentals of Biochemistry This section is an integration of materials as referenced, with significant modifications and additions. Aleem and Arceci. Targeting cell cycle regulators in hematologic malignancies. Article in Frontiers in Cell and Developmental Biology 2015. DOI: 10.3389/fcell.2015.00016. Creative Commons Attribution 4.0 International Introduction For a cell to undergo successful division, it has to perform four key tasks in a highly ordered fashion. First, there is a preparatory synthetic phase (G1) that results in increased cell size in anticipation of DNA replication (S phase). Cells then proceed through (G2-phase) to prepare to equally segregate duplicated DNA (M phase) and finally divide into two equal daughter cells. From G1 a cell can also exit the cell cycle and enter a state of quiescence (G0), undergo differentiation, or re-enter the cell cycle to proliferate in response to mitogenic signals. The core molecular machinery controlling the mammalian cell cycle consists of a family of serine/threonine protein kinases called cyclin-dependent kinases (CDKs). These are catalytic subunits, which are activated in most cases by association with cyclin regulatory subunits. The activity of CDK/cyclin complexes is further regulated by CDK inhibitors (CKIs), phosphorylation and dephosphorylation, ubiquitin-mediated degradation, transcriptional regulation, substrate recognition, and subcellular localization. The family of CDKs/cyclins/CKIs contains more than 30 members. They are implicated in essential cellular functions such as transcription, DNA damage repair, epigenetic regulation, metabolism, proteolytic degradation, stem cell self-renewal, neuronal functions, and spermatogenesis. Figure $1$ shows the cell cycle and the involvement of CDKs/cyclins at key points. CDK3/cyclin C drives cell cycle entry from G0. CDK4/6/cyclin D complexes initiate phosphorylation of the retinoblastoma protein (pRb) and they sequester p21Cip1 and p27kip1 (not shown), which are both inhibitors of CDK2, thus promoting the activation of CDK2/cyclin E complex. In late G1, the CDK2/cyclin E complex completes phosphorylation and inactivation of pRb, which releases the E2F transcription factors and the G1/S transition takes place. DNA replication takes place in the S phase. CDK2/cyclin A complex regulates progression through the S phase and CDK1/cyclin A complex through the G2 phase in preparation for mitosis (M). Mitosis is initiated by CDK1/cyclin B complex (which will model at the end of this section). The activity of CDK1/cyclin B is tightly regulated by activating phosphorylation by the CDK-activating kinase CAK (a heterodimer of cyclin H-CDK7-MAT1) and inhibitory phosphorylations by Wee1 and Myt1 on Tyr15 and Thr14 (not shown). Some specific CDK4/CDK6 pharmacological inhibitors are also shown CDKs with Direct Functions in Cell Cycle Regulation The classical CDKs that directly regulate the mammalian cell cycle in complexes with cyclin subunits include CDK3, CDK4, CDK6, CDK2, and CDK1. CDK3 promotes cell cycle entry from quiescence in association with cyclin C. CDK8 has also been suggested to play a role in cell cycle entry from G0 and in the G1/S transition. In its simplest model, the mammalian cell cycle proceeds as follows: • In early G1, CDK4/CDK6 in complex with cyclin D receive mitogenic signals that result in activation of cell cycle entry, as shown in Figure $1$. Key signaling events include the initiation of retinoblastoma protein (pRb) phosphorylation and the sequestration of p21Cip1 and p27kip1, which are both inhibitors of CDK2, thus promoting the activation of CDK2/ cyclin E complex. In late G1, CDK2 in complex with cyclin E completes the phosphorylation and hence inactivation of pRb, which in turn releases the E2F transcription factors. E2F promotes transcription of cyclin E which is necessary for the G1/S transition. • Progression through the S phase is mediated by CDK2/cyclin A complex. • Mitosis is then initiated by CDK1/cyclin B complexes. We will model the regulation of CDK1 later in this section. CDK1/cyclin A complexes contribute to the preparation for mitosis in the G2 phase. The activity of CDK1/cyclin B is tightly regulated by activating phosphorylation by the CDK-activating kinase (CAK) (a heterodimer of cyclin H and CDK7) and inhibitory phosphorylations by WEE-1 and Myt1 on Tyr15 and Thr14. Mitosis starts after WEE-1 is degraded and CDC25C phosphatase releases the inhibitory phosphorylation on CDK1/cyclin B. The cyclins are also expressed in a coordinated fashion throughout the cell cycle. The cyclin expression cycle is shown in Figure $2$. The timing of expression is consistent with the explanations above. A graph showing multiple progressions through the cell cycle is shown in Figure $3$. Figure $3$: Sustained oscillations of the CDK network in mammalian cells. Gérard and Goldbeter, Front. Physiol., 02 November 2012 Sec. Systems Biology Archive. https://doi.org/10.3389/fphys.2012.00413. Creative Commons Attribution License The time evolution of cyclin D/Cdk4–6 (in black), cyclin E/Cdk2 (in blue), cyclin A/Cdk2 (in green), and cyclin B/Cdk1 (in red) is shown in the presence of a suprathreshold level of growth factor. Cyclin D/Cdk4–6 is the total active form of the kinases, which is composed of cyclin D/Cdk4–6 and also the complex formed by cyclin D/Cdk4–6 and p21/p27. Note again the oscillatory nature of the cyclin B/Cdk1 complex, which will explore at the end of this section. CDKs with Transcriptional and Other Functions In addition to their direct role in the mitotic cell cycle regulation, some classical CDK/cyclin complexes have essential functions in meiosis, such as CDK2, in transcription and/or DNA repair. Other CDKs act by activating the classical CDKs, such as CDK7/cyclin H (CAK) and the related CDK20, also known as cell cycle-related kinase (CCRK). Some CDKs function mainly in influencing transcription by phosphorylating the carboxy-terminal domain (CTD) of ribonucleic acid (RNA) polymerase II (RNA pol II). This phosphorylation also serves as a platform for RNA processing and chromatin regulation. CDKs that have important transcriptional roles include CDK7/cyclin H/MAT1 complex, a component of the basal transcription factor, TFIIH, and facilitate transcriptional initiation. CDK8/cyclin C, in addition to its role in transcription, is also involved in the Wnt/β-catenin pathway and inhibition of lipogenesis. Cyclin C can recruit CDK8 or CDK19 to the CDK8 module of the Mediator complex, which can function as a positive or negative regulator of transcription by RNA pol II. CDK3/cyclin C also plays a role in NHEJ-mediated DNA damage repair. While CDK9 in complex with cyclin T forms the phospho-transcription elongation factor b (p-TEFb) and promotes transcriptional elongation, CDK9 also functions in the DNA damage response when complexed with cyclin K. CDK10/cyclin M phosphorylates the Ets2 transcription factor and positively controls its degradation by the proteasome. Ets2 plays a key role in cancer and development. CDK11/cyclin L controls the assembly of the RNA pol II mediator complex. CDK12 and CDK13 in complex with cyclin K control RNA pol II transcription, and CDK12/cyclin K controls DNA damage response. Structure of CDKs–cyclins Open Biology. Wood and Jane A. Endicott (2018) https://doi.org/10.1098/rsob.180112. Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, The structures of inactive cyclin-free kinases are very similar but vary at the N-terminal and C-terminal ends. Figure $4$ shows an interactive iCn3D model of the prototypical active human cyclin-dependent kinase 2 with a bound ATP (1HCK). The model shows that CDK2 has structural features shown in all the kinases we have studied previously: • a smaller N-terminal lobe (light cyan) and larger C-terminal lobe (light magenta) in between which ATP binds (along with Mg2+). • the C-helix (residues 45 – 55, purple), which contains a conserved Glu. It forms an interaction with and helps position a key Lys in the active, which facilitates ATP binding and transition state stabilization; • hinge (residues 80 – 84, yellow), • activation loop (residues 145– 172, red), which contains T160 (sticks, CPK colors, labeled) that becomes phosphorylated on activation by yet another kinase called CDK-activation kinase (CAK). When T160 is phosphorylated, the kinase binds to cyclin A. The loop starts and ends with the conserved residue DFG and APE, respectively. • Not highlighted in the model is a conserved conformationally flexible glycine-rich region (residue 12-16) with the motif GXGXXG In the inactive form, the N-terminal end of the activation loop has a short alpha helix that prevents the C-helix from adopting the correct position for catalysis. Activation requires movement of the C-helix allowing the Glu in the C-helix to position the active site Lys. CDK2–cyclin A activation The binding of cyclin A to CDK2 activates it through the repositioning of the C-helix and the activation loop. When CDK2 is phosphorylated and bound to cyclin A, there is a large shift in the C-helix allowing the interaction of the C-helix Glu with the active site Lys. First, let's look at the structure of cyclins. Each cyclin has a unique sequence and structural features that allow them to interact with specific CDKs and associated proteins. However they all have a conserved "cyclin box" structure containing about 100 amino acids. Figure $5$ shows an interactive iCn3D model of bovine cyclin A (1VIN). Cyclin A has two linked cyclin box folds, each containing around 100 amino acids and comprised of five helices. They interact with the more disordered parts of unphosphorylated CDK2, which result in some low levels of activity. Cyclin binding causes a large movement of the C-helix enabling the Glu -- Lys interaction. Phosphorylation of T160 leads to the repositioning of the activation loop. Figure $6$ shows an interactive iCn3D model of Phosphorylated cyclin-dependent kinase 2 bound to cyclin A (1JST) CDK2 is shown in cyan and cyclin A in gray. Here are some structural features represented in the model. • the C-helix (residues 45 – 55, purple), • activation loop (residues 145– 172, red), which contains pT160 (sticks, CPK colors, labeled) • the catalytic "triad" Lys33, Glu51, and Asp145 Figure $7$ shows an animation of structural changes in just CDK2 when "apo"-CDK2 (without bound cyclin A, pdbID 1HCK) binds cyclin A (1JST). Gray represents the structure of CDK2 in the absence of cyclin A. The structure of just CDK2 in the presence of cyclin A is shown in magenta. Note that large shifts in the C-helix (purple) and activation loop (red) on binding cyclin A. Cyclin partners of CDK1 and CDK2 CDK1 is the closest member of the CDK family to CDK2 and for which structures of the cyclin-free and authentic cyclin-bound forms can also be compared. Depending on cyclin availability and concentration, CDK2 can bind cyclin A, B (if CDK1 expression is knocked down), and E (see Figure $1$. The binding interface between CDK2 and the cyclins is quite large compared to the interface between CDK1 and cyclins. Three large aromatic side cyclin side chains (Y170, Y177, and Y258) are conserved in the binding interface. In cyclin E, the corresponding amino acids are smaller (N112, I119, and L202). The binding interface between CDK1 and the cyclins is smaller so it appears that it might preferentially interact with cyclins A and B to gain binding affinity through the more robust interactions with the aromatic groups in the interface in the CDK2: Cyclin A and CDK2:cyclin B complexes. A comparison of the CDK1–cyclin B and CDK2–cyclin A/B/E structures also highlights the potential for these closely related CDKs to be differentially regulated by reversible phosphorylation. The antagonistic activities of Wee1/Myt1 kinases and Cdc25 phosphatases regulate the phosphorylation status of the CDK glycine-rich loop (defined by the GXGXXG motif, residues 11–16 in CDK2). The structure of CDK2–cyclin A phosphorylated on Y15 illustrates how phosphorylation promotes a glycine loop structure that antagonizes both peptide substrate binding and the ATP conformation required for catalysis. The flexibility of the glycine-rich loop is compatible with a model in which the phosphorylated Y15 side chain is solvent exposed and accessible to both kinases and phosphatases. CDK1 is also regulated by active-site phosphorylation, and the conserved nature of the structure in this region suggests that the mechanism of inhibition is also conserved. CDK substrate recognition Local and distal sequence motifs must be used to confer specificity to the binding of specific cyclins and other proteins to specific CDKs. One interesting example is provided by examining the structure of a phospho-CDK2 Cyclin A in complex with a peptide substrate derived from the protein CDC6. Figure $8$ shows an interactive iCn3D model of Phospho-CDK2:Cyclin A complex with a peptide containing both the substrate and recruitment sites of CDC6 (2CCI) The color coding is the same as the models above: • CDK2 is shown in cyan and cyclin A in gray. • the C-helix (residues 45 – 55, purple), • activation loop (residues 145– 172, red), which contains pT160 (sticks, CPK colors, labeled) • the catalytic "triad" Lys33, Glu51, and Asp145 The 30 amino acids peptide (numbers 67-96) shown in gold is a substrate for phosphorylation by the CDK2:cyclin A complex. It derives from an actual biological substrate in the protein cell division control protein 6 homolog, also called CDC6-related protein. It is involved in a checkpoint control of the cell cycle that "checks" that DNA replication is completed before mitosis. It is discontinuous in the model since part of the bound peptide is intrinsically disordered and not observed in the crystal structure. • The 1st fragment of the CDC6 peptide (67-73) contains the binding motif sequence S/T)PX(K/R) (the CDC6 substrate has the sequence 70Ser-Pro-Arg-Lys). Ser 70 is the target amino for phosphorylation by CDK2:cyclin A. • The second fragment seen in the model (amino acid 85-96) contains another binding motif, RXL (in this peptide RRL), which acts to recruit cyclin A. This binds to the sequence MRAIL (210-214) in cyclin A. What is so interesting is that this second binding site on cyclin A for its target protein is so far away from the active site of the CDK2:cyclin A. These kinds of interactions work to determine the specificity for CDKs and the binding cyclin partners. CDKs 7, 9, 12, and 13 phosphorylate the RNA polymerase carboxy-terminal domain (CTD). The sequence of the CTD is unusual, composed of 52 heptad repeats in humans, with the consensus sequence YSPTSPS. Extracted from cells, CTD residues S2 and S5 are the most abundantly phosphorylated serine residues, while S7 is phosphorylated to a lesser extent. CDK7 has been shown to predominantly phosphorylate S5 and S7, CDK9 to have activity towards all three serines, and CDK12 and CDK13 to predominantly phosphorylate S2. CDKs form complexes not only with target protein substrates but other proteins which can serve as scaffolding anchors that bind both the CDK and the cyclin. Figure $9$ shows an interactive iCn3D model of human CDK-activating kinase (CAK), a complex composed of cyclin-dependent kinase (CDK) 7, cyclin H, and the scaffolding protein MAT1 (6xbz). The gray protein is CDK7, the cyan is cyclin H and the orange MAT1. The purple again represents the C-helix of the CDK, and the red is the activation loop. The catalytic triad side chains in the active site of the CDK are shown in CPK-colored sticks. Also shown is phospho-Ser in the activation loop. The CDK activating kinase (CAK) shown above phosphorylates the target S/T in the activation loop (which is also called the T-loop) in CDKs, activating the kinase. In addition, it regulates the initiation of transcription by phosphorylating the YSPTSPS repeats in the C-terminus of RNA polymerase II subunit RPB1. There are 15 consecutive repeats in the sequence as well as others dispersed in the C-terminal domain. We already mentioned the motif RXL found in cyclin binding proteins that recruit them to cyclins (through, for example, their interaction with MRAIL (210-214) in cyclin A. Likewise short motifs in cyclins are used to bind to proteins that increase CDK activity or decrease it. A number of cyclin-encoded protein-binding sites or short peptide motifs have been structurally characterized. A well-characterized example is the recycling of the cyclin RXL recruitment site that is exploited to either enhance or inhibit CDK activity. Alternatively, short motifs encoded within the cyclin sequence can be used both to dock cyclins to substrates to enhance CDK activity and alternatively to localize them to CDK regulators frequently resulting in a loss of CDK activity. Members of the p27KIP1/p21CIP1 cyclin-dependent kinase inhibitor (CKI) family share an RXL motif with RXL-containing substrates and compete with them for CDK–cyclin association. The INK (inhibitors of CDK) family of CKIs selectively inhibits CDK4 or CDK6 and, through an allosteric mechanism, disfavors CDK–cyclin binding [15]. Their tandem ankyrin repeat structures exemplified bCy CDK6–p19INK4d bind in the vicinity of the CDK hinge on the interface opposite to the surface remodeled upon cyclin association. Modeling the Cell Cycle - Oscillations Lastly, we will focus on mathematical models that show how the oscillatory behavior of CDK1/Cyclin B arises (remember than CDKs become active on binding to a cyclin). When CDK1 is activated, the cell is driven into mitosis. It is driven out of mitosis by the activation of the anaphase-promoting (APC) complex, which contains APC-Cdc20, an E3 ubiquityl ligase. Yeast Cdc20 is an activator protein that regulates the ubiquitin ligase activity of APC by binding at the right time in the cell cycle to B cyclins that contain a D box motif. This recruits the B cyclin:CDK1 to APC which ubiquitinates the cyclin B, leading to its degradation by the proteasome. Figure $10$ shows the levels of both cyclin B/CDK1 (red) and cyclin A/CDK2 (green) with time. Now in your mind, imagine another curve on the graph showing similar oscillations of activated APC, only frameshifted a bit in time so that the active APC trails that of active cyclin B/CDK1. When cyclin B/CDK1 is at iitsactive peak, active APC is already beginning its rise. Figure $10$: The switch to sustained oscillations of cyclin A/Cdk2 (in green) and cyclin B/Cdk1 (in red) is shown following the overexpression of AP1. Gérard and Goldbeter, ibid. The dissociation constant, KD, for CDK1 and cyclin B is about 28 nM, which represents high affinity binding. Ubiquitinylation of cyclin B and its degradation allows for the freeing of CDK1 and inhibition of its activity. The oscillatory behavior in activity occurs only on the overexpression of yet another protein, AP1. Let's explore in detail a model proposed by Ferrell et al that accounts for the 25 min oscillatory behavior of CDK1 in Xenopus (frog) eggs. The eggs are very large and perhaps because of their size have different constraints on their cell cle cycle. For example, cells can enter mitosis before the completion of DNA synthesis. The players which regulate its activity are shown below in Figure $11$. Figure $11$: Proteins that help regulate the activity of CDK1 in Xenopus eggs. • Cyclin: binds to and activates CDKs; active CDK1 drives cells into mitosis. • APC: anaphase-promoting complex; active APC drives cell out of mitosis with its E3 ubiquitin ligase activity, modifying cyclin and targeting it for proteolysis; • Wee1: nuclear Ser/Thr kinase • Cdc25: cell cycle division phosphatase that activates cyclin-CDK1. Our goal in this discussion is not only to model the actual oscillatory behavior of CDK1 but also to show you how models are built and tested. When modeling enzyme inhibition data, it is important to fit the data to many models (reversible competitive, uncompetitive, mixed, noncompetitive) to find out which best fits the data. Typically one starts with the simplest possible model and then advances to more complicated models until the data with the best statistical fit to the data is found. Other examples, which are more relevant here, involve fitting binding and kinetic data using equations that give hyperbolic (for saturation binding and simple Michaelis-Menten kinetics) and sigmoidal fits (for cooperative binding and allosteric enzymes). So let's start with the simplest model that might lead to oscillatory activity of CDK1. In this section, active CDK1 will be designed as CDK1*. Model 1: One-step process with negative feedback - CDK1* inhibits its own activation (for example by activating APC and hence ubiquitinylation of cyclin) We saw in the Vcell models for the MAP kinase cascade that feedback phosphorylation of the first enzyme in the cascade (MAPKKK) by the last enzyme in the cascade, MAPKPP, leads to oscillations in enzyme activity. Could it explain the oscillations in the activity of CPK1 in Xenopus eggs? Let's make the following set of assumptions, which all be understandable from the material presented in previous chapters: 1. CDK1* is inhibited by APC* (active APC), and to make it simpler, APC* can be expressed as a simple function of CDK1* so there is just one species that vary in the flux equation; 2. CDK1 activation occurs on rapid high-affinity binding of cyclin, which is synthesized at a constant rate a1; 3. The rate of CDK1 activation to produce CDK1* is given by mass action = rate activation - rate inactivation; 4. APC is activated to APC "instantaneously" by CDK1* so APC* is a very sensitive “cooperative” function of CDK1* which can replace the APC*. For this type of "instantaneous (or highly cooperative effect), we will use the Hill equation which gives a sharp, sensitive, cooperative rise in complex instead of a simple formation of a complex between CDK1* and APC. We explored this property of the Hill equation in Chapter section 5.3.5 on Mathematical Analysis of Cooperative Binding. Here is the Hill expression commonly use to empirically fit the fractional saturation of a species Y=\frac{L^n}{K_D^n+L^n} It's a bit different than the Hill equation we saw for modeling the cooperative binding of O2 to hemoglobin (Chapter 5.3.5) since the KD term is also raised to the power n (which is not in the actual Hill equation). However, we did see that for oxygen binding to hemoglobin, \mathrm{K}_{\mathrm{D}}=\mathrm{P}_{50}^{\mathrm{n}} Hence the empirical expression used in fitting Model 1 is completely in accord with the Hill treatment of cooperativity. Again we use the Hill equation when modeling binding and kinetic data that show significant sensitivity to conditions. It gives yet another parameter to help fit the data and to test models. Two different representations of a reaction diagram showing Model 1 are shown in Figure $12$. Figure $12$: Two different representations of a reaction diagram for Model 1 - Activity of CDK1. The representation on the right is from Vcell. The one on the left shows that CDK1 can inhibit itself. Model 1 and its associated assumptions lead to the following differential equation that can easily be solved numerically in Vcell. All of the VCell outputs shown below were obtained from Vcell models kindly provided by Leslie Loew. v=\frac{d C D K 1^*}{d t}=a_1-b_1\left(C D K 1^*\right)\left(A P C^*\right)=a_1-b_1\left(C D K 1^*\right)\left(\frac{C D K 1^{* n 1}}{K_1^{n 1}+C D K 1^{* n 1}}\right) This rate equation has two terms (assumption 3). The first is the rate that CDK1* forms (a constant a1 defined by the rate of cyclin synthesis) and the rate at which it is degraded by APC*. The constant b1 in the second term can be thought of as a second-order rate constant for the interaction of CDK1* and APC*, a process that inactivates CDK1*. The [APC*] in the middle equation is replaced with the Hill equation for the effective fractional saturation concentration of APC (see assumption 4 of Model 1 described above) in the right-hand side. \left(A P C^*\right)=\left(\frac{C D K 1^{* n 1}}{K_1^{n 1}+C D K 1^{* n 1}}\right) We will define the activity of the system as the rate at which CDK1* forms. \text { Activity }=\frac{d C D K 1^*}{d t} Now let's see if changing the Hill coefficient n1 can cause oscillations in CDK1*. MODEL CDK - Model 1 Initial Conditions Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play  |  Select Slider to change which constants are displayed |  Select About  for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health.  Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington.  Funded by NIH/NIGMS (RO1-GM123032-04) Note that all the graphs plateau quickly at which point the CDK1* activity is constant. The graph (gray) of the curve with the highest value of the Hill coefficient (n1=24) is linear and then abruptly plateaus. The slope of the velocity curve over the entire linear part of the n1=24 graph curve is 0.1, which is the value set for the rate of activation of CDK1. Then suddenly at around 4 seconds, an "almost infinitely cooperative" shift to a constant rate of formation occurs arising from an abruptly reached rate of inactivation of CDK1* by the APC complex. These graphs do not show oscillations. Now let's see the graph with no feedback inhibition, much as we did with the MAPK cascade in Chapter 12.4. The easiest way to do that is to set b1, the "second" order rate constant for the interaction of CDK1* and APC* in the model to 0. The graph is shown in Figure $13$. Figure $13$: Activity of CDK1* in Model 1 in the absence of feedback inhibition. The activity of CPK1* continually increases. When feedback inhibition is added, the curve "bends" to a plateau, but it does not start to decrease and shows no signs of oscillations. Time to move on to a more complex model! Model 2: Two-species model with activation and inhibition- This model is more complicated and shows 2 species (CDK1 and APC) both of which are activated and inhibited. We need 2 mass action differential equations, one for each. Figure $14$: Figure $14$: Two different representations of a reaction diagram of Model 2 The equation for dCDK1*/dt is the same as in Model 1, as is repeated below. v=\frac{d C D K 1^*}{d t}=a_1-b_1\left(C D K 1^*\right)\left(A P C^*\right)=a_1-b_1\left(C D K 1^*\right)\left(\frac{C D K 1^{* n 1}}{K_1^{n 1}+C D K 1^{* n 1}}\right) Likewise, the equation for dAPC*/dt consists of two terms, one for its activation and one for its inhibition. v=\frac{d A P C^*}{d t}=k_f A P C-k_r A P C^* Assume that kf, the rate constant of the activation of APC, is equal to a constant a2 times a Hill function of CDK1*, and kr, the rate constant for the inactivation of APC*, is simply b2. Then the equation becomes v=\frac{d A P C^*}{d t}=a_2\left(\frac{C D K 1^{* n 2}}{K_2^{n 2}+C D K 1^{* n 2}}\right) A P C-b_2 A P C^* Let's look at the output graphs for the following initial condition: • CDK1* = 0 uM • APC = 1 uM • APC* = 0 uM MODEL CDK Model 2 See equations in text. Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play  |  Select Slider to change which constants are displayed |  Select About  for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health.  Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington.  Funded by NIH/NIGMS (RO1-GM123032-04) Figure $15$ shows statics graphs of just CDK1* activity vs time for n1 values of 1, 4, 8, and 24 for Model 2. Figure $15$: Graphs of just CDK1* activity vs time in Model 2 for n1 values of 1, 4, 8, and 24. Wow! By simply adding an additional species to the model and a second differential equation for it, we see the first signs of oscillatory behavior in the activity of CDK1*. The output at higher n1 values is best described as damped oscillations. Now let's try a final third model. Model 3: Three species model with activation and inhibition- This model contains the enzyme Plk1 (Polo-like kinase 1, also called serine/threonine-protein kinase 10-A), along with APC and CDK1. These three species are all activated and inhibited. Assume that Plk1 is activated by CDK1 and that it also helps activate APC. Following the arrows in the left part of the figure below shows that it acts as an "intermediary" between CDK1 and APC. Two different representations of a reaction diagram of Model 3 are shown in Figure $16$. Figure $16$: Two different representations of a reaction diagram of Model 3 We have 3 species, so we need three differential equations, as shown below. v=\frac{d C D K 1^*}{d t}=a_1-b_1\left(C D K 1^*\right)\left(\frac{A P C^{* n 1}}{K_1^{n 1}+A P C^{* n 1}}\right) v=\frac{d P l k 1^*}{d t}=a_2\left(1-P l k 1^*\right)\left(\frac{C D K 1^{* n 2}}{K_2^{n 2}+C D K 1^{* n 2}}-b_2 C D K 1^*\right. and v=\frac{d A P C^*}{d t}=a_3\left(1-A P C^*\right)\left(\frac{P l k 1^{* n 3}}{K_3^{n 3}+P l k 1^{* n 3}}-b_3 A P C^*\right. The equation for activation of CDK1 is the same as in Models 1 and 2. The equation for the activation of APC is similar to Model 2 with kf modeled as a Hill function of Plk1star, which activates APC The equation for the activation of Plk1 is similar to Models 2 and 3 with kf modeled as a Hill function of CDK1star which activates Plk1 Although you probably can't write these differential equations by yourself, hopefully, you can see that they make sense. MODEL CDK Model 3 Initial values Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play  |  Select Slider to change which constants are displayed |  Select About  for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health.  Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington.  Funded by NIH/NIGMS (RO1-GM123032-04) Figure $17$ shows graphs of just CDK1* activity vs time for n1 values of 1, 4 and 8 for Model 3 Figure $17$: Graphs of CDK1* activity vs time for n1 values of 1, 4 and 8 Finally, we observe oscillatory behavior in the activity of CDK1*, but only for higher values of the Hill coefficient (n1 = 4 and 8). Other models can produce oscillation, but this one seems perhaps most comprehensible to students who have studied mass action equations along with Hill binding and kinetic equations. The three-component system described in Model 3 is of course embedded in a large pathway of inputs and outputs so other factors most likely affect the oscillatory behavior.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.13%3A_Regulation_of_the_Cell_Cycle_by_Protein_Kinases.txt

Search Fundamentals of Biochemistry Introduction We have discussed often how cell signaling might go awry and lead to cancer. However, there are signaling systems that lead to cell death. There are many ways in which cells can die. We'll discuss not "accidental" cell death but one, apoptosis, that is programmed into the genome and highly regulated. Figure \(1\) shows how normal cell proliferation and growth can be modulated by two classes of genes, oncogenes that cause proliferation, and tumor suppressor genes that inhibit it. Apoptosis involves chromatin aggregation and cleavage, the concentration of cell material, and apoptosis body formation. Mutations to aberrantly activate oncogenes or inhibit the expression of tumor suppressor genes lead to cancer. These cells would ideally undergo programmed cell death or apoptosis. As with control of proliferation, some genes promote apoptosis as well as anti-apoptotic genes which inhibit programmed cell death. Dysregulation of these can also cause cancer. Apoptosis is an important mechanism to kill viral-infected cells. However, this can go too far. For example, T helper cells (TH) infected with the HIV virus die. However, the collapse of the population of these cells is in part attributed to apoptosis. As we learn more about programmed cell death, it is clear that apoptosis is not the only way the genome is programmed to cause cell death. These other ways include: autophagy - This is a catabolic pathway in which intracellular proteins, protein complexes, and organelles are collected into large autophagosomes in which incorporated lysosomes and their degradative enzymes reprocess damaged or unneeded cell material. It is a highly programmed process, which if dysregulated, could lead to cell death. necroptosis: Infections and toxins are known to cause necrosis, which is a "passive" form of cell death. In contrast, programmed necrosis is called necroptosis. Overview of apoptosis. Apoptosis consists of 4 steps: • the decision to activate the pathway; • the actual "suicide" of the cell; • engulfment of the cell remains by specialized immune cells called phagocytes; • degradation of the engulfed cell. The actual steps in cell death require: • condensing the cell nucleus and breaking it into pieces • condensing and fragmenting of cytoplasm into membrane-bound apoptotic bodies; and • breaking chromosomes into fragments containing multiple numbers of nucleosomes (a nucleosome ladder) To commit suicide must be an extremely important cellular decision. Hence you would expect this process to be regulated and highly complicated. When would it be advantageous to the organism to want a cell to kill itself (or be told to kill itself)? Cell death would be used to: • "sculpt" an organism during development such as during embryo development, metamorphosis, and tissue atrophy • regulate the total number of cells. • defend and remove unwanted or dangerous cells like tumor cells, virally infected cells, or immune cells that recognize self (which could lead to autoimmune disease). Unregulated apoptosis could exacerbate or cause diseases such as: • AIDS, in which T helper cell numbers plummet. Part of the dramatic decline in these cells might be caused by health T helper cells being tricked into committing suicide; • neurodegenerative diseases like Alzheimer's; • ischemic stroke, when restricted blood flow to certain regions of the brain can lead to neural death through increased apoptosis' • cancer, in which tumor cells lose their ability to undergo apoptosis; • autoimmune disease, in which self-reactive immune cells trick normal body cells to kill themselves; • viral disease; Apoptosis does not require new transcription or translation, suggesting that the molecular machinery required for cell death lay dormant in the cell, and just requires appropriate activation. What "signals" induce apoptosis? Signals can be extracellular: • a hormone (such as thyroxine that causes apoptosis in tadpole tails • a lack of a "survival" signal (which inhibits apoptosis) such as a growth factor • a cell:cell contact from an adjacent cell Signals can be intracellular: • ionizing radiation • virus infection • oxidative damage from free radicals Apoptosis Much of this section was derived directly from the following reference, with modifications and additions. Fox, J., MacFarlane, M. Targeting cell death signaling in cancer: minimizing ‘Collateral damage’. Br J Cancer 115, 5–11 (2016). https://doi.org/10.1038/bjc.2016.111. Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ There are two apoptotic pathways in cells: • The extrinsic pathway: extracellular apoptotic ligands bind to membrane death receptors, leading to the assembly of the death-inducing signaling complex (DISC). Similar to the inflammatory response we have already discussed in Chapter 5, two specific cysteine-aspartic proteases (caspases) are activated, caspases 8 and 10. These activate other caspases in an amplification of the process. • The intrinsic pathway: intracellular signals such as damaged DNA or proteins are sensed by Bcl-2 proteins on the outer membrane of mitochondria. The BcL-2 (B-cell lymphoma-2) family of proteins all have Bcl homology domains. Their functions are carried out at the outer mitochondrial membrane. Some members of this family are antiapoptotic (Bcl-2, Bcl-xL, Mcl-1, Bcl-w, A1/Bfl-1, and Bcl-B/Bcl2L10), while others are proapoptotic (Bid, Bim, Puma, Noxa, Bad, Bmf, Hrk, Bik Bax, Bak, and Bok/Mtd). Apoptosis leads to activation of Bax/Bak, which initiate mitochondria degradation, starting with damage to the outer membrane and release of pro-apoptotic proteins like the inner membrane space protein cytochrome C into the cytoplasm. This leads to the assembly of the apoptosome and activation of caspases 9 and 13. Again this is very similar to the formation and activity of the inflammasome which we saw in Chapter 5. An overview of the extrinsic and intrinsic apoptotic pathways is shown in Figure \(2\). We will explore some of the proteins involved in the section below. The extrinsic death receptor pathway is activated by death receptor ligands, including FasL, TNF-α, DR3, DR4, and DR5 or TRAIL, etc. FasL is an integral membrane protein found in cells. In addition, there are soluble versions of it. The binding of FasL to Fas, an integral membrane protein, initiates the recruitment of FADD, TRADD, and caspase-8 to form the DISC complex, which in turn activates caspase-8 and downstream caspases. The binding of tissue necrosis factor alpha (TNF-α) to its receptor, TNFR1 (a Fas protein), initiates the recruitment of TRADD, RIP, TRAF2/5, and cIAP1/2 to form complex I, which activates NF-κB and JNK pathways and increases the transcription of pro-survival genes. However, the modification of RIP or degradation of cIAP1/2 can lead to the disassociation of complex I. TRADD and RIP then associate with FADD and caspase-8 to form complex II, the so-called death complex. The intrinsic death receptor pathway is initiated by the BH3-only protein, BCL-2 homology 3 (BH3-only), under intracellular stress such as DNA damage. The BH3-only proteins activate apoptosis by binding and neutralizing the pro-survival proteins, allowing Bax/Bak to homo-oligomerize and permeabilize the mitochondria. For example, BH3-only protein can inactivate Bcl-2 and prevent Bcl-2 from effectively neutralizing Bax and Bak, leading to the activation of Bax and Bak. The activated Bax and Bak on the mitochondrial membrane alter its permeability, depolarizes the membrane, and leads to the release of cytochrome c and Smac, normally found in the inner membrane space, from mitochondria. Figure \(3\) shows how monomeric BAK can form an altered dimeric form in the presence of detergent. The extended left-hand helix on the right-hand side is color-coded to show nonpolar residue (orange) and the polar/charged amino acids in gray. That same section of the protein is shown in cyan in the monomeric protein to the right. One can easily imagine how the apparent amphiphilic helices of the BAK dimer could bind to the outer mitochondrial membrane and alter its structure. Cytoplasmic cytochrome c associates with Apaf-1 and caspase-9 to form the apoptosome, which activates caspase-9 and downstream executing caspases. Smac can regulate apoptosis by inhibiting the inhibitor of apoptosis proteins (IAPs)." Zhou and Li. Chapter 9, Apoptosis in Polycystic Kidney Disease: From Pathogenesis to Treatment. License: This open-access article is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) Another diagram of the extrinsic and intrinsic apoptotic pathways that show more detail on the domain structures of some key protein and the "executioner" caspases is shown in Figure \(4\). Extrinsic pathway: The first step is an association of death receptors with their cognate ligands, which leads to the recruitment of adaptor molecules, including FAS-associated death domain protein (FADD), and then caspase 8. Caspase 8 cleaves and activate caspase 3 and caspase 7 and can proteolytically activate BH3-only protein BH3-interacting domain death agonist (BID). Proteolytically activated BID (tBID) promotes mitochondrial membrane permeabilization through the activation of the assembly of BAX-BAK channels and represents the main link between the extrinsic and intrinsic pathways. Now let's look more closely at the ligands that activate the extrinsic pathway, as shown in Figure \(5\). Soluble Fas and soluble FasL bind to the respective ligands inhibiting activation of the pathway. FLIP inhibits the activation of caspase-8 and is thus a major anti-apoptotic protein. Volpe E et al. (2016) Fas–Fas Ligand: Checkpoint of T Cell Functions in Multiple Sclerosis. Front. Immunol. 7:382. doi: 10.3389/fimmu.2016.00382. Creative Commons Attribution License (CCBY). Now we are in a position to examine the actual structure of some key components of the extrinsic pathway. Active human apoptosome with procaspase-9 (5JUY) Figure \(6\) shows an interactive iCn3D model of the active human apoptosome with procaspase-9 (5JUY) Each of the 7 different subunits of the apoptotic protease-activating factors (Apaf-1) is shown in a different color. The seven yellow subunits are cytochrome Cs. The 4 red subunits underneath the disk plane of the other subunits are the zymogen procaspase 9s. The small spacefill CPK color ligands are 2'-deoxyadenosine 5'-triphosphate. The Apaf-1:pc9 pairs, interacting through their CARD domains, form a spiral underneath the disk. Apaf-1 can be considered an adaptor protein with an N-terminal caspase activation and recruitment domain (CARD), followed by a nucleotide-binding and oligomerization domain (NOD, also known as NB-ARC). Figure \(7\) shows the domain structure of caspase 9 and Apaf-1. Caspase 9 domain structure Apaf-1 Figure \(7\): domain structure of caspase 9 and Apaf-1. The presence of CARD domains in both allows their mutual binding and the assembly of the full apoptosome. An AlphaFold model of the Cas 9 zymogen Figure \(8\) shows an interactive iCn3D model of human Cas 9 AlphaFold model (P55211) The green is the CARD domain and the salmon is the caspase (peptidase_C14) domain. Procaspase 9 is cleaved at Asp 315 (sticks, CPK colors, labeled) into two chains for activation. The activated Cas 9 has two key active site residues, His 237 and the catalytic nucleophile C287 (sticks, CPK colors, labeled). Phosphorylation at Thr-125 by MAPK1/ERK2 blocks procaspase activation by proteolysis. to block caspase-9 processing Apaf-1 Oligomeric Apaf-1 mediates the cytochrome c-dependent autocatalytic activation of pro-caspase-9 (Apaf-3), leading to the activation of caspase-3 and apoptosis Figure \(9\) shows an interactive iCn3D model of Human Apaf-1 AlphaFold model (O14727) Domain colors: • The green is the N-terminal CARD domain • light red NB-ARC (nucleotide-binding and oligomerization domain - NOD) • purple is Apaf • yellow is WD40, gold the C-terminal WD40. Again the model above does not show the actual structure since the intrinsically disordered regions are not more structured. The CARD domain of Cas 9 inhibits the catalytic domain of Cas 9. When the CARD domain of Cas 9 interacts with the CARD domain of Apaf-1, the autoinhibition is removed. In addition, the Apaf-1 stimulates the catalytic activity of the protease. Before assembly into the apoptosome, Apaf-1 is monomeric and in an inactive dATP or ATP conformation. When cytochrome C is released into the cytoplasm, it binds to the WD domains, facilitating a dATP/ATP-cleavage associated conformation change in the Apaf-1, which in the presence of heat shock protein 70 (Hsp) folds to form which leads to the assembly of the active apoptosome. Fas - Tumor necrosis factor receptor superfamily member 6 - P25445 Figure \(10\) shows an interactive iCn3D model of Fas-Tumor necrosis factor receptor superfamily member 6 AlphaFold model (P25445) The green is the N-terminal TNFR/NGFR domain that is highly enriched in Cys (spacefill, color CPK) in disulfide bonds. The gray spheres are the transmembrane helix. The Red shows the Death Domain. The death domains are common protein:protein binding domains that serve as adaptors or scaffolds. They can form homo- or heterodimers with other proteins containing the domain which is a part of the CARD domain, DED (Death Effector Domain), and PYRIN. Human FasL and a soluble Fas Receptor DcR Figure \(11\) shows an interactive iCn3D model of the complex of Human FasL and Its Decoy Fas Receptor DcR (4MSV) The three gray subunits are soluble decoy receptor (DcR) versions of the Fas TNFR/NGFR domain, which again is highly enriched in Cys. It is structurally very similar to its typical membrane receptor ligand Fas (tumor necrosis factor receptor superfamily member 6 - P25445) DcR is a secreted member of the TNF family and disrupts apoptosis, which can allow tumors to survive. Fas and FADD death domain interactions Figure \(12\) shows an interactive iCn3D model of two Fas death domains bound to two FADD death domains (3EZQ) tetrameric arrangement of four FADD death domains bound to four Fas death domain Two Fas death domains are shown in a different shade of gray. The two FADD death domains are shown in a different shade of magenta. Each domain consists of six alpha-helical bundles. Interaction between the dark and light grays Fas death domains and between the light gray Fas and light magenta FADD death domains are shown with side chains in stick with CPK colors. The Fas-FADD-procaspase 8 complex is collectively called the Death Inducing Signaling Complex (DISC). The Fas-FADD interactions lead to the binding of capase 8 and the completion of the DISC. The actual disc appears to contain 4 FADD death domains bound to 4 Fas death domains. Conformational changes allow both FADD:Fas and Fas:Fas interactions, some of which are weak but when formed switch on the activity of the building complex. The need for 4 monomers probably prevents accidental assembly which would be deadly to the cell. Mechanism and regulation of apoptosis Caspases Characterization of apoptotic mechanisms and cellular players started with the study of C. elegans, a roundworm. The mature worm has about 1000 cells. During development, 131 cells die. Two mutations were found in which the 131 cells did not die. These mutations were called ced3 and ced4 (ced stands for cell death). The sequence of ced 3 was very homologous to a protein called interleukin converting enzyme (ICE) which is required for proteolytic activation of the precursor to interleukin 1, a protein hormone released by certain immune cells during activation and which can promote inflammation. This suggested that proteolysis was required for apoptosis. Subsequent studies show that a whole family of proteases (about 10 in humans) called caspases (ICE has been renamed caspase 1) are required for programmed cell death. These proteases are found in the cell in an inactive form which must undergo limited proteolysis for activation. These caspases form a cascade of proteases which are activated in this process. They are endoproteases that have an active site Cys (C) and cleave at the C-terminal side of Asp residues (asp) and hence are known as caspases - cys containing-asp specific proteases). ICE is not normally involved in apoptosis, but its artificial activation in cultured mammalian cells can lead to it. Each caspase had the same sequence as they are designed to cleave, so it became evident that they probably cleave each other in an activation cascade mechanism, similar to the coagulation protease cascade of activation of precursors (zymogens) of serine proteases which activate the next in the series. Two series of caspases seem to be involved. One set initiates the process of caspase activation. Just as in the clotting system, the question of what activates the first caspase appeared problematic until investigators found that the initiator caspase can be activated if they aggregate to a critical concentration. This could occur by binding a suicide signal molecule at the cell surface. Conformational changes in the receptor can lead to aggregation of surface receptor molecules with concomitant aggregation of intracellular caspases which interact with the aggregated receptors. Intracellular signals How might intracellular activators of apoptosis (like radiation or reactive oxygen species) work? Research indicated the involvement of mitochondria in the apoptotic pathway. Believe it or not, cytochrome C, the heme protein which acts as a water-soluble mobile carrier of electrons in mitochondrial oxidative phosphorylation, shuttling electrons through cytochrome C oxidase or complex IV, leaks out of the intermembrane space and binds to a cytoplasmic protein called Apaf-1 for apoptotic protease activating factor-1. This then activates an initiator caspase-9 in the cytoplasm. These proteins seem to leak out of mitochondria after a collapse of the electrochemical potential across the inner membrane. The potential collapses as a consequence of the opening of a channel called a nonspecific inner membrane permeability transition pore, composed of both an inner membrane protein (adenine nucleotide translocator - ant) and an outer membrane protein (porin, the voltage-gated anion channel - VDAC). These proteins act together, probably at sites where the inner and outer membranes are in contact. This channel passes anything smaller than molecular weight 1500. Collapsing the proton gradient uncouples oxidation and phosphorylation in the mitochondria. Changes in ionic strength cause a swelling of the matrix. Since the inner membrane is highly convoluted and has a much greater surface area than the outer membrane, swelling of the matrix leads to a rupture of the outer membrane, spilling the inner membrane space proteins (cytochrome C and Apaf-1) into the cytoplasm. What causes all these changes in the mitochondria? Several interrelated events appear to be involved: 1. disruption of ox-phos. and electron transport, caused by irradiation and certain second messengers such as ceramide. 2. changes in cell redox potential and generation of reactive oxygen species (ROS). 3. DNA damage (caused by radiation, ROS, etc). A protein called p53 is often expressed in cells with DNA damage. Expression of this protein results in inhibition of cell division, or apoptosis, both of which would keep the damaged cell from becoming a tumor cell. Hence the p53 gene is a tumor suppressor gene. It is inactivated by mutation in approximately 50% of all human tumor cells studied. p53 can induce gene expression. Of the 14 different genes whose expression is significantly altered by p53, many seem to be used by cells to generate or respond to oxidative stress. Cells undergo p53 apoptosis through oxidative damage. 4. increases in intracellular calcium ions through signal transduction. Caspase targets: Apoptosis involves: 1. condensing of the cell nucleus and breaking into pieces 2. condensing and fragmenting of cytoplasm into membrane-bound apoptotic bodies 3. breaking chromosomes into fragments containing multiple numbers of nucleosomes (a nucleosome ladder) How does caspase activation lead to these events? A protein has been uncovered that when cleaved by a caspase leads to a nuclear breakup. The target protein is usually bound to another protein, a DNA endonuclease. When the target protein is cleaved, the DNase is free to migrate to the nucleus and begin the execution. Membrane changes in apoptosis occur when caspase 3 cleaves gelsolin, a protein involved in maintaining cell morphology. The cleaved gelsolin cleaves actin filaments inside the cell. Another protein is necessary to form apoptotic bodies: a kinase named p21-activated kinase 2 (PAK-2). This kinase is activated by caspase-3 by limited proteolysis. Caspases also cleave beta-amyloid precursor protein which might generate more beta-amyloid protein, causing neural cell death in Alzheimer patients. Controlling Apoptosis It should be clear that cells keep tight control of the caspases. Two players which appear to inhibit apoptosis are the mitochondrial proteins Bcl-2 and Bcl-X, which can block the release of cytochrome C from the mitochondria. The Bcl family of proteins has a hydrophobic tail and binds to the outside surface of mitochondria and other organelles like the nucleus and endoplasmic reticulum. These proteins seem to be able to form ion channels in liposomes. So far 15 members of this family (related to ced-9 of C. elegans) have been discovered in humans. Bcl-2 can also bind to Apaf-1 (mentioned above) and inhibit its activation of initiator caspase-9. Bcl-2 is regulated by changes in the expression of the Bcl-2 gene, by post-translational phosphorylation by kinases, or by cleavage by caspases. Overexpression of Bcl-2 can cause a cell to become a tumor cell. Other members of the family, BAX and BAD bind to mitochondria and facilitate apoptosis by stimulating cytochrome C release. In addition, other proteins called IAPs (inhibitors of apoptosis) can inhibit caspase or other apoptotic proteins. Some viruses make the protease to keep their host cells viable. Cell Membrane Events Cells can be instructed to undergo apoptosis through cell surface interactions with other cells which are often immune cells. One of the jobs of the immune cell is to destroy an altered cell (for example a virally-infected cell or a tumor cell). Immune cells themselves must also die after they are activated in an immune response. Activated lymphocytes (like cytotoxic T cells or natural killer cells) can target and kill cells using several ways which can involve apoptosis. In one, an activated lymphocyte binds to a target cell (like a virally infected cell) and secretes perforin, a protein that assembles in the target cell membrane to form a transmembrane channel. Other proteins released by the activated lymphocyte can enter the target cell through the pore and initiate apoptosis. One such protein that enters, granzyme B is a protease that activates caspases in the target cell. Target cells that express a specific membrane protein called CD95 (also called Fas) are also targeted for apoptosis. This protein receptor, a member of the tumor necrosis factor receptor (TNFR) binds to a membrane protein-ligand on the surface of an activated lymphocyte called CD95 Ligand - CD95L- (also called the Fas ligand). On binding, the CD95 (Fas) receptors on the target membrane aggregate after conformation changes. An adapter protein in the cell, FADD (Fas-associated death domain) binds to the aggregated cytoplasmic domain (the death domain) of CD95 (Fas) and recruits inactive caspase-8 to the site, where their concentration increases. This leads to the activation of the caspases. This mechanism is used to get rid of activated lymphocytes after they have finished their work. Activated immune cells start expressing Fas a few days after activation, targeting them for elimination. Some cells which have been stressed express both Fas and Fas ligands and kill themselves. Various cells express CD95 (Fas), but CD95L (Fas-Ligand) is expressed predominately by activated T cells. Cell surface events also can inhibit apoptosis. The binding of "survival" factors (like growth factors) to cell surface receptors can shut off apoptotic pathways in the cells. Some survival factor receptors are coupled to PI-3-kinase (phosphoinositol-3-kinase) through the G protein ras (p21) which is targeted to the cell membrane by post-translational addition of a hydrophobic anchor. The activated kinase produces PI-3,4-P2 and PI-3,4,5-P3, which activates Akt, a Ser/Thr protein kinase. This activated kinase phosphorylates the proapoptotic-protein BAD, which then becomes inactive. In addition, active Akt phosphorylates procaspase, which in its phosphorylated form will not interact with cytochrome C, hence inhibiting apoptosis. The endpoint of apoptosis is the engulfment of the fragmented cell by a phagocytic cell (such as a macrophage). In a recent article (Nature, 405, pg 85, 2000), it was shown that the activity of phagocytes could be inhibited stereospecifically by the addition of phosphatidyl serine (PS) to the mixture, but not by other negative phospholipids. If you remember from our description of lipids, PS is found exclusively in the inner leaflet of red blood cells). The investigators cloned a gene from the phagocytic cell for a receptor that recognizes PS. When added to ordinary T and B lymphocytes (immune cells), these cells could also take up apoptotic cells. The gene is homologous to genes in Drosophila (fruit fly) and C. elegans (roundworm) suggesting that it is conserved in nature. The message: when cells undergo apoptosis, PS, normally found only in the inner leaflet, is exposed to the outside. It can then bind to receptors on phagocytic cells to complete the process of apoptosis. Therapeutics Figure \(13\) shows points of therapeutic intervention in the intrinsic and extrinsic apoptotic signaling pathways. Intrinsic and extrinsic apoptotic signaling pathways and points of therapeutic intervention. Apoptosis can be initiated by signals originating from either the plasma membrane via death receptor ligation (extrinsic pathway) or at the mitochondria (intrinsic pathway). Stimulation of the extrinsic pathway by TRAIL results in TRAIL receptor (TRAIL-R) aggregation and formation of the DISC, in which pro-caspase 8 becomes activated and initiates apoptosis by direct cleavage of downstream effector caspases. The addition of either agonistic TRAIL-R1/R2 antibodies or recombinant human TRAIL (rhTRAIL) has been used to trigger the extrinsic pathway for therapy. The intrinsic pathway is regulated by the BCL-2 family of proteins, which regulate pore formation in the outer mitochondrial membrane and the release of apoptogenic factors such as cytochrome c or SMAC from the mitochondria. The release of cytochrome c into the cytosol triggers caspase 9 activations through the formation of the cytochrome c/Apaf-1/caspase 9-containing apoptosome complex. SMAC promotes caspase activation through the neutralizing the inhibitory effect of IAPs. The intrinsic pathway has been targeted for therapy either by blocking the inhibitory action of the pro-survival BCL-2 family proteins with BH3 mimetics or by inhibiting the anti-apoptotic action of IAPs with SMAC mimetics. The extrinsic and intrinsic pathways are interconnected, for example, by BID, a BH3 domain-containing protein of the BCL-2 family, which upon cleavage by caspase 8 triggers intrinsic apoptosis, thereby further amplifying the signal from the extrinsic pathway. The entire pathway Now we can present detailed pathways showing apoptosis in its complexity. Trace the interconnections in the different views. Figure \(14\): View 1 Figure \(15\) presents a second view

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.14%3A_Programmed_Cell_Death.txt

Search Fundamentals of Biochemistry The main organization of this section derives from Bacterial transmembrane signaling systems and their engineering for biosensing. Jung et al :25 April 2018https://doi.org/10.1098/rsob.180023. Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/. Significant content from the source has been integrated into the section. Introduction Bacteria constantly interact with their surroundings. They identify and actively acquire nutrient resources, sense and respond to environmental stresses, and exchange information with other cells, while commensals and pathogens adapt their lifestyles for survival in their hosts. The cytoplasmic (inner) membrane of bacterial cells separates the cytoplasm from the outer world. Therefore, all information from the outside must be transferred across this interface, which contains various sensors that carry out this function. Bacteria use three major types of signaling systems: membrane-integrated one-component systems (for example -ToxR-like receptors), two-component systems consisting of a receptor histidine kinase and a response regulator, and extracytoplasmic function (ECF) sigma factors. These are shown in Figure \(1\). The one-component signaling family ToxR (named after the main regulator of virulence in Vibrio cholerae) is the simplest. They have a periplasmic sensor domain, a single transmembrane helix, and an intracellular winged helix-turn-helix DNA-binding domain. The family is named after the main regulator of virulence in Vibrio cholerae, ToxR. In two-component systems, the membrane-integrated histidine kinase generally acts as a sensor for various stimuli and is also responsible for information transfer across the membrane. This process usually results in the autophosphorylation of the protein and the phosphoryl group is subsequently transferred to a specific soluble response regulator which usually acts as a transcription factor (see Figure \(1\)). The number of histidine kinase/response regulator systems varies widely between bacterial species, ranging from 30 in Escherichia coli and 36 in Bacillus subtilis to 132 in Myxococcus xanthus. In chemotactic systems, a soluble histidine kinase perceives the signal(s) conveyed by membrane-integrated chemoreceptors and transduces this information via phosphorylation/protein–proteins interaction to the flagellar motor. The ECF sigma factors are small regulatory proteins that bind to RNA polymerase and stimulate the transcription of specific genes. Many bacteria, particularly those with more complex genomes, contain multiple ECF sigma factors, and these regulators often outnumber all other types of sigma factors. Little is known about the roles or the regulatory mechanisms employed by the majority of ECF sigma factors. Most of them are co-expressed with one or more negative regulators. Often, these regulators include a transmembrane protein that functions as an anti-sigma factor, which binds and inhibits the cognate sigma factor. Let's look at three examples. One-component system: pH sensor CadC pH in E. Coli is regulated by a series of Cad proteins. CadA is a cytoplasmic decarboxylase, which converts lysine to cadaverine, while CadB is a membrane-integrated lysine/cadaverine antiporter. CadC acts as a homodimeric one-component regulator. Together, their activities lead to an increase in both internal and external pH, which favors the survival of E. coli under moderate acid stress and helps to maintain pH homeostasis. Their activities are shown in Figure \(2\). CadC is the regulator of the cadBA operon encoding the lysine decarboxylase CadA and the lysine/cadaverine antiporter CadB. Under non-inducing conditions, the lysine-specific transporter LysP inhibits CadC. When cells are exposed to low pH in the presence of lysine, the interaction between LysP and CadC is weakened, rendering CadC susceptible to protonation and transcriptional activation. The end-product of decarboxylation, cadaverine, binds to CadC and thereby inactivates this receptor. CadC is activated by two stimuli, low pH (less than 6.8) and the presence of external lysine (greater than 0.5 mM), which are perceived by different mechanisms. The periplasmic domain of CadC directly senses a decrease in pH. It has two distinct subdomains: the N-terminal subdomain comprises a mixture of β-sheets and α-helices, and the C-terminal subdomain consists of a bundle of 11 α-helices. A patch of acidic amino acids (D198, D200, E461, E468, D471) detect changes in the external pH through protonation changes, altering their charges and noncovalent interactions between the subdomains/monomers. This in turn leads to dimer formation of the periplasmic domain, triggering receptor activation. Figure \(3\) shows an interactive iCn3D model of a Transcriptional activator CadC One Component Model - AlphaFold Model (P23890) The gold represents the N-terminal CadC DNA binding domain. The green (hydrophobic) is the transmembrane helix and the gray is the outside periplasmic domain containing the acidic side chains (D198, D200, E461, E468, D471). The N term Met is magenta spacefill and the C-terminal Ser is cyan spacefill. Again, the long intrinsically disordered region between the DNA binding domain and the transmembrane domain would have a more defined structure in the actual membrane-bound form and probably form additional interactions with other molecules. It also presumably leads to a conformation change in the DNA binding domain which leads to its interaction with the target DNA. CadC senses external lysine only in interaction with the lysine-specific permease LysP. In addition, the products of lysine decarboxylation, CO2, and cadaverine, act as feedback inhibitors on CadC. Cadaverine binds to the periplasmic domain of CadC, thereby switching off cadBA transcription. Two Component System Two-component Histine kinase systems have (in general) two main components • A HK sensor protein binds a ligand in a receptor binding domain leading to the transfer of a gamma-phosphate from ATP to a His by the kinase domain. This component is often called a transmitter. The conserved His located in the H box. • a separate response regulator (or effector) protein containing a reactive Asp which receives the phosphate from p-Histidine. The conserved Asp (D) is located in the D box. This activates the response regulator protein. This component is often called the receiver. It may also transfer it to another His in a phospho-relay system. Histidine kinase/response regulator systems are the most commons in bacterial signaling across the membrane. In contrast to the myriad of serine/threonine (S/T) and less abundant tyrosine (Y) kinases that dominate signaling in mammals, the histidine kinase predominates in bacterial signaling. Before we present more detail on the two-component system, let's looks at protein kinases in general and see what is different about histidine kinases. ATP is a donor of a gamma-phosphate in both S/T/Y and H kinases. However, their products are very different energetically. pS, pT, and pY of the O-phosphoproteome are all phosphoesters, which are not high energy compared to their hydrolysis products. (Remember, there is no such thing as a "high energy" bond.) In contrast, pHis, a member of the N-phosphoproteome (along with pLys and pArg), is not a phosphoester but more analogous to the mixed anhydride of a carboxylic acid as in the case of phosphorylated aspartic acid. In pHis and pAsp, there is an electronegative N (in pHis) and O (in pAsp) bridging two atoms which are each connected to another atom by double bonds. This type of structure, which allows for bridging resonance between the center N (in pHis) and O (in pAsp) is also high energy compared to its hydrolysis products. Hence the phosphorylation of His by ATP is not as energetically favored as the phosphorylation of Ser, Thr, or Tyr since it produces another high-energy molecule (with respect to its hydrolysis products. Since the pHis is also considered high energy compared to its hydrolysis products, it can act as a phosphate donor to another receiving group. That could be water in a simple hydrolysis reaction or, if sheltered from water in an active site of an enzyme or receptor, to a carboxylate receiver like Asp to form another high energy mixed anhydride which is isoenergetic with the pHis. This is the process that occurs in the two-component His kinase signaling pathways in bacteria. Figure \(4\) compares Ser, Thr, Tyr, and His kinase reaction and their products. The more common mammalian S/T/Y kinases are shown at the top and the histidine kinase at the bottom. Note that phosphorylation of His can occur at either nitrogen to produce either τ- or π-pHis. In the two-component system, instead of water being the receiver of the phosphate from the pHis (hydrolysis), the receiver is an Asp or another His in the same His Kinase receptor or in another receiver protein (shown in gray and its phosphorylated blue form in Figure \(9\). You can imagine the phosphate on the original pHis jumping to a receiver, which then donates it to another receiver in the signaling process in a relay process. Proteins like the receptor His-kinase proteins in two-component systems have multiple domains with different functions. It's really helpful to present domain structure diagrams to help in understanding the protein's structure and activities. At the same time, the domain structures determine by various bioinformatic programs vary. Nevertheless is it useful to see multiple representations of domain structure, especially if they are shown in conjunction with actual structures. The first component of the two-component signaling system is the receptor His-kinase which can be viewed as a stimulus-activated kinase (much like receptor tyrosine kinases - RTKs). The second component is the response regulator protein, which is typically a second protein. Each of these in turn has its own domain structure. For example, the periplasmic sensing domain regulates the kinase domain of the receptor His-kinase (component one). The phosphate from the p-His in the first component is transferred to an Asp in the second component. The domain structures and phospho-transfer are illustrated in Figure \(\PageIndex{5\) below. Panel (A) shows a prototypical two-component pathway. The transmembrane sensor HK (component one) and a cytoplasmic response regulator (RR) protein (component two) are shown. (Note: the actual protein is a dimer in the membrane.) The transmembrane segments are labeled TM1 and TM2. N, G1, F, and G2 are conserved sequence motifs in the ATP-binding domain. HK catalyzes ATP-dependent autophosphorylation of a specific conserved His residue within the HK dimerization domain. The phosphoryl group (P) is then transferred to a specific aspartate residue (D) at the conserved RR domain (component two). Phosphorylation of this domain usually triggers an associated (or downstream) effector domain, which ultimately produces a specific cellular response. Panel(B) shows a multi-component phospho-relay system that often involves a variant of HK with an additional internal C-terminal RR domain. In these complex systems, at least two His–Asp phosphoryl transfer events occur, typically involving a His-containing phosphotransfer protein (HPT) operating as a His-phosphorylated intermediate. In most prokaryotic systems, the response is directly carried by the RRD which functions as a transcription factor. Two-component systems also exist in some eukaryotes. They often interact with other downstream signaling pathways such as the MAPK system. However, in eukaryotic systems, the TCS are placed at the start of the pathways and establish an interface with more conventional signaling strategies such as mitogen-activated protein (MAP) kinase and cyclic nucleotide cascades The domain structure shown in Figure \(10\) doesn't show the actual orientation of the proteins in a membrane sytem. Orientation is important since the sensing domain of the HK receptor must be in the environment of the stimuli. Stimuli can be encountered in the periplasmic (equivalent to extracellular) region, in the transmembrane region and in the cytoplasm. Variants of the HisK receptors exist that recognize stimuli in each of these locations as shown in Figure \(6\). Most histidine kinases sense extracellular signals (left-hand structure). All have their cytoplasmic transmitter domains which contain the pHis. As mentioned previously, the histidine kinases are dimeric, so when activate they phosphorylate a His on the other monomer (transphosphorylation). In addition to the H box with the reactive histidine, they also have N, G1, F, and G2 boxes. The H box is also involved in dimerization The transmitter domain can be further divided into two parts: the H-box is involved in dimerization and obviously in phosphotransfer. The figure also shows the CA domain (HK-type ATPase catalytic or HATPase_c), also known as the catalytic and ATP-binding (CA) domain (Figure 1.3-3) The histidine kinase senses a variety of stimuli in its sensory domain. The stimuli can be generally grouped into organic (e.g. dicarboxylates, citrate, etc), ions (e.g. Mg2+, H+, K+), gaseous ligands (e.g. O2, N2), and physical changes (osmolarity/turgor, light, and temperature). Stimuli are "sensed" by a variety of different characterized folds. Some common sensing domains are PAS (Per-ARNT-Sim), CHASE (cyclase/histidine kinase-associated sensing extracellular), four-helix bundle (4HB), and NIT (nitrate and nitrite-sensing) classes. We will focus on one particular His kinase system, histidine kinase KdpD. Histidine kinase KdpD and the regulation of K+ ion concentration It is often difficult to identify the primary stimulus for a receptor, as exemplified by the histidine kinase KdpD which, together with the response regulator KdpE, controls the expression of a high-affinity K+-uptake system in many bacteria. K+ is the most abundant cation in all living cells, especially in bacteria it is crucial for the regulation of cell turgor and intracellular pH and the activation of several enzymes. To ensure a sufficient supply of K+, most bacteria have more than one K+-uptake system. For example, E. coli has at least three such systems, the constitutively expressed systems Trk and Kup, and the inducible high-affinity K+-uptake system KdpFABC. The genes kdpF, kdpA, kdpB, and kdpC form an operon that codes for four inner membrane proteins. The kdp operon is induced when E. coli is grown under K+ limitation, lacks the major K+ transporter Trk or has an increased need for K+ when under hyperosmotic stress. Under all these conditions, the membrane-integrated histidine kinase KdpD autophosphorylates and transfers the phosphoryl group to the cytoplasmic transcriptional (response) regulator KdpE, resulting in the induction of the kdp operon, as shown in Figure \(7\). What is amazing is that KdpD has not only kinase activity but also phosphatase activity towards phosphorylated KdpE, which switches the signaling cascade off. It is a bifunctional enzyme/receptor. A single substitution (T677A) in the C-terminal domain results in no phosphatase activity. Hence KdpD can be thought of as a bifunctional receptor acting as both kinase and phosphatase to regulate gene expression. The bifunctional receptor histidine kinase KdpD acts as both an autokinase (including phosphotransferase) and phosphatase for the response regulator KdpE. Phosphorylated KdpE activates the expression of the genes encoding the high-affinity K+ transporter KdpFABC. KdpD autokinase activity depends on the external K+ concentration, and the phosphatase activity is influenced by the internal K+ concentration. K+ ions don't move through a channel in KdpD but through the KdpFABC. The cartoon in Figure \(7\), as with all cartoons, can be misleading with respect to scale. Figure \(8\) shows an interactive iCn3D model of the actual membrane domain of E. coli histidine kinase receptor KdpD (2KSF) 4 helices space the membrane. Hence both the N-terminal and C-terminal domains are actually in the cytoplasm, not one in the periplasmic space and one in the cytoplasm as you would infer from Figure \(7\). The actual periplasmic (outsithe de of cell" domain) consists of only about 6 amino acids. Hence it most clearly is represented by the middle model in Figure \(6\). Mutations of key residues in the periplasmic loop region (P466A, T469A,, L470A and V472A) drastically affect K+ recognition. Actually how it "senses" periplasmic K+ ions is not clear. Domain representation We present three different domain diagrams for KdpD in Figure \(9\), not to confuse readers, but to show the utility of mulrepresentationstation they are likely to encounter in reading the literature. Moscoso et al. Journal of Bacteriology. 198 (2016) http://dx.doi.org/10.1128 /JB.00480-15. Creative Commons Attribution 3.0 Unported license B. Pfam C. Dutta et al. JBC. 296, 100771 (2021). DOI:https://doi.org/10.1016/j.jbc.2021.100771. Creative Commons Attribution (CC BY 4.0) Figure \(9\): Multiple representations of the domain structure of the histidine kinase KdpD In panel A, the 4 boxes btw 360 and 500 are 4 transmembrane helices, which would not be evident in a simple cartoon as in Figure \(7\). In panel B, the domains are shown as follows: • Green: K+ channel His kinase sensor domain 21-230; • Red: 4 transmembrane helices 402-508; the helices are lumped together in the red domain representation; • Blue: GAF domain 527-644; • Yellow and Purple combo: HK domain; • Yellow: HisKinase A phosphoacceptor domain 663-730 which contains the His acceptor, in effect the substrate of the kinase domain; • Purple: His Kinase/HSP 90 is like ATPase 773-883. The domain structure in panel C is the most detailed and also has the domain structure of the receiver (response regulator). Again the periplasmic domain which senses K+ consists of only a few amino acids, 424-427 and 467-474. Neither representations A nor B show that the major N-terminal and C-terminal halves of the protein are in the cytoplasm. Panel C shows more information about the domains, their function, and their orientation in the intracellular environment. It turns out there is also a sensor for intracellular K+ions, which is depicted in Figure \(7\). A central question is how histidine kinase KdpD responds to changes in K+ concentrations. Both the kinase and phosphatase activities are regulated by K+. • When periplasmic (extracellular) K+ is > 5 mM (high), the ion appears to bind to the small extracellular loops (see Figure \(8\)), which inhibits the autokinase activity. Under the same conditions the intracellular C-terminal tail senses K+ and activates the phosphatase activity, which cleaves its pHis. These combined effects inhibit high-affinity K+ transport. • When periplasmic K+ becomes low, kinase activity is activated and the protein is autophosphorylated, ultimately leading to the activation of the gene for the high-affinity K+ transporter KdpFABC. As long as intracellular K+ levels are high, the phosphatase is active. When intracellular K+ levels drop sufficiently, the phosphatase becomes inhibited, which further simulations the transcription of both high-affinity K+ transporter KdpFABC. Hence the histidine kinase KdpD system is regulated by both periplasmic and cytoplasmic K+ ions. Yet another signal regulates the KdpD His Kinase receptor two-component signal. What has been conspicuously absent from this discussion about signaling in bacteria is the involvement of second messengers like cAMP (which activates Protein Kinase A and some membrane proteins). There does appear to be one major second messenger in bacteria - cytoplasmic di-AMP (c-di-AMP), whose structure is shown in Figure \(10\). It binds in the N-terminal region of KdpD His Kinase receptor protein "sensor" domain region to a specific domain called the Universal stress protein (USP) domain as shown in panels A and C of Figure \(9\). Figure \(11\) shows an interactive iCn3D model of the Staphylococcus aureus universal stress protein (USP) domain of KdpD histidine kinase in complex with second messenger cyclic diadenosine phosphate (c-di-AMP) (7JI4) "Dual sensing thus emerges as a highly optimized regulation strategy. The key advantage of this strategy is that it confers on cells the ability to directly sense changes in both the supply of and demand for the limiting resource. It is, in fact, analogous to strategies that are widely used in control engineering, e.g. modern heating systems work with both exterior and interior thermometers to ensure constant room temperature." Escherichia coli nitrate/nitrite sensor kinase NarQ Let's examine another TCS protein, the Escherichia coli nitrate/nitrite sensor kinase NarQ, to see how the binding of a ligand to the periplasmic domain might transmit a signal so far into the cell through the plasma membrane. We won't discuss the His Kinases that lack transmembrane regions (about 1/4). The sensor domain hence is mostly in the periplasm, followed by the transmembrane domain (see Figure \(8\)), followed often by a cytoplasmic HAMP domain, with a four-helical parallel coiled-coil. The HAMP domain transmits the signal to downstream signaling domains like Dhp in the protein. Nitrate/nitrite is sensed by two different two-component systems, sensing systems NarX-NarL and NarQ-NarP, which regulate anaerobic respiration. NarQ phosphorylates two different proteins, NarL and NarP in the presence of nitrate or nitrite and dephosphorylates both proteins in the absence of ligands. Both NarQ (and NarX) have seven domains: a four-helical periplasmic sensor domain, TM bundle, HAMP domain, so-called signaling helix, (S-helix), GAF-like domain, dimerization and histidine phosphotransfer domain, and, finally, catalytic kinase domain, as shown in Figure \(12\). Pane (a) shows the architecture of NarQ. Note that the functional protein is homodimeric. Approximate domain boundaries, according to InterPro [17], are TM1, residues 14–34; sensor, 39–146; TM2, 147–167; HAMP, 172–227; S-helix, 228–246; GAF-like, 247–360; DHp, 361–425; CA, 424–560. Panel (b) shows the overall structure of the sensor-TM-HAMP fragment of the R50S mutant (which allowed crystallization). The position of Ser50 is highlighted with spheres. The backbone structure is identical to that of the WT protein. Panel (c) shows the structure of the ligand (nitrate) -binding site in the WT protein. Panel (d) shows the structure of the ligand-binding pocket in the R50S mutant. Asp133 is reoriented towards Ser50. 2Fo−Fc electron density maps are contoured at the level of 1.2 × r.m.s. Putative water molecules are shown as red spheres. Gushchin et al. Int. J. Mol. Sci. 2020, 21, 3110; doi:10.3390/ijms21093110. Creative Commons Attribution. (CC BY) license. (http://creativecommons.org/licenses/by/4.0/). Although the sequence identity between NarQ and NarX is ~28%, their ligand binding sites—membrane-proximal parts of the sensor domain’s helices H1 called P boxes—are very well conserved: 14 out of 15 amino acids (residues 42–56 in NarQ) are identical, and the differing ones, Ile 45 in NarQ and Lys49 in NarX, are responsible for the differentiation between nitrate and nitrite. It appears that the ligand-induced conformational change in the ligand-binding site is helical rotation, which results in diagonal scissoring of the sensor domain helices, leading to the change in the secondary structure of the sensor-TM linker and, eventually, piston-like shifts of the transmembrane α-helices. Nitrate causes changes in the transmembrane region when the apo (nitrate free) and holo (nitrate bound) state structures of NarQ are compared. On binding of nitrate, the induced conformation changes in NarQ have been described as a "combination of changes in the lateral arrangement of the TM helices and piston-like shifts of the helices in the direction perpendicular to the membrane plane." This results in either symmetric or asymmetric changes and scissoring of the transmembrane helices (based on two different crystal structures of the holo-form. A "piston-like" movement of the helices is observed on both holo-forms. Results show that the binding of ligand to NarQ causes a piston-like displacement of the TM helices, which is accompanied by extensive symmetric or asymmetric rearrangements and scissoring of the TM helices. The rearrangements are different in the two presented holo-state structures, but the piston-like displacement is perfectly conserved. Thus, the latter appears to be a more robust mechanism of TM signal transduction. Figure \(13\) shows an interactive iCn3D model of a fragment of nitrate/nitrite sensor histidine kinase NarQ (mutant R50K) in the symmetric holo state (5IJI) A homodimer (cyan and magenta) is shown with the N-terminus and C-terminus on the cytoplasmic side. Each monomer passes through the membrane with an alpha-helical domain twice. Nitrate is shown in spacefill in the extracellular domain outside of the outer leaflet (red spheres) of the membrane. Figure \(14\) shows the conformational transition going from the symmetric apo state (5JEQ) magenta to the symmetric holo state (IJI) (cyan) with bound nitrate (not shown). Conformational changes in the HAMP domain seem to amplify and convert the piston-like conformational changes in the transmembrane domain. Note the splaying out to the helices at the bottom (cytoplasmic end) in the apo form. Phototaxic Photoreceptors We have discussed two-component systems that have a His-Kinase receptor transmitter protein. There are two other major types of bacterial receptors, chemoreceptors (involved in chemotaxis) and photoreceptors, involved in phototaxis. These often have similar modular domain structures. Chemotaxis receptors are, like the His Kinase receptor, dimers with extracellular domains that bind the chemotactic signal. The photoreceptors appear to be active as a trimer of dimers. The basic dimeric structure contains the microbial light sensor rhodopsin which contains the chromophore opsin, and its transducer Htr. In the halobacteria N. pharaonis, (archaeal, not a bacterial cell) the proteins are sensory rhodopsin II (NpSRII) with its transducer (NpHtrII), mediates negative phototaxis in halobacteria N. pharaonis. Microbial rhodopsins are phototransducing proteins with a conjugated chromophore retinal, covalently attached to the protein opsin through a Schiff base (imine) linkage. The holoprotein (opsin with the attached retinal) is called rhodopsin. Retinal is derived from beta-carotene. The structures of animal and microbial retinals are shown in Figure \(15\). When light of the correct wavelength is absorbed, an electron in a pi molecular orbital in retinal is promoted to a pi antibonding molecular orbital, breaking a 2 electron pi bond in the structure at a certain site in the isoprenoid chain, allowing rotation around the now single bond. The final result after the electrons return to the ground state is photoisomerization of the trans 13-14 and cis 11-12 bonds in microbial and animal retinal, respectively, to their respective cis 13-14 and trans 11-12 configuration. This conformational change in the bound retinal induces a conformational change in the protein opsin, leading to signaling. Figure \(16\) shows an interactive iCn3D model of one monomeric of bacteria rhodopsin (1C3W) containing retinal attached through a Schiff base to Lys 216. The covalently attached retinal is shown in gray spacefill. The protein opsin is a membrane protein that spans it with seven helices. Hence it is very similar to a GPCR. In the phototaxic receptor, when light is absorbed by rhodopsin (NpSRII), the resulting conformational change in the protein causes conformational changes in the transducer protein NpHtrII associated with it, leading to signaling through a two-component signal system. (The chemotaxis response to chemical signals occurs through a similar but ligand-induced process.) Figure \(17\) shows the structure of the Archaeal photoreceptor complex. A-G, TM1, and TM2 are the transmembrane helices. The cytoplasmic part of NpHtrII consists of two HAMP domains (HAMP1 and HAMP2) connected by an α-helical linker (Inter-HAMP) and the kinase control module. Primes denote symmetry mates of the complex. Ishchenko, A., Round, E., Borshchevskiy, V. et al. New Insights on Signal Propagation by Sensory Rhodopsin II/Transducer Complex. Sci Rep 7, 41811 (2017). https://doi.org/10.1038/srep41811. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/ Figure \(17\) shows a more details representation of both the transmitter and receiver in the Archeal two-component phototransduction system and how it leads to phototaxis. Figure \(17\): Signal transduction pathway in case of the two-component phototaxis system of Natronomonas pharaonis5 and domain architecture of membrane chemo- and photoreceptors of TCS. Ryzhykau, Y.L., Orekhov, P.S., Rulev, M.I. et al. Molecular model of a sensor of the two-component signaling system. Sci Rep 11, 10774 (2021). https://doi.org/10.1038/s41598-021-89613-6. Creative Commons Attribution 4.0 International License, http://creativecommons.org/licenses/by/4.0/. Pane (A) shows Light activated sensory rhodopsin II (NpSRII) induces conformational and/or dynamical changes in the transducer (NpHtrII), which are converted by two HAMP domains and conveyed along the 200 Å long transducer to the tip region. Activated by the transducer histidine kinase CheA (bound to the adapter protein CheW) undergoes auto-phosphorylation and further transfers the phosphate group to the response regulators CheY or CheB. CheY affects the rotational bias of the flagellar motor, while the methylesterase CheB along with the methyltransferase CheR controls the adaptation mechanism. Panel (B) shows cartoon representations of the chemoreceptor dimer (Tar and Tsr in complex with kinases) from E. coli and of the photosensor dimer of the complex of the sensory rhodopsin II with its cognate transducer NpHtrII and kinases from N. pharaonis.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.15%3A_Signaling_in_Microorganisms.txt

Search Fundamentals of Biochemistry Introduction Plants are obviously comprised of cells. Hence they must engage in cell signaling within and between cells. It is beyond the scope of this book to give a detailed description of cell signaling in plants. Instead, we focus on 5 key classic plant hormones, auxins, cytokinins, ethylene, gibberellins, and abscisic acid, which are produced by leaves, flowers, shoots, roots, or fruit, and see how they initiate signaling in plants. Finally, we would be remiss if we didn't include the profound signaling in plants initiated by light. Most of this section comes directly from a series of sources, with modifications and additions (mostly molecule models). Auxins (3-indolebutyric acids derivatives) are regulators of growth and development and are found in actively growing parts of the plant (root, shoot, leaves) but mostly in the cell stem. Auxins facilitate the bending of plants toward the light, for example. They work in conjunction with other hormones like cytokinins. When auxins are higher than cytokinins, roots will form, while the opposite produces shoots. Auxins facilitate the elongation of cells, while cytokinins promote cell division and growth as well as wound repair. Gibberellins also are plant growth regulators and facilitate cell elongation. They also help in germination, elongation of the stem, fruit ripening, and flowering. Abscisic acid affects seed development and maturation and helps plants tolerate environmental or biotic stresses. It also inhibits growth and metabolism. Ethylene affects fruit ripening, organ abscission, and growth by restricting cell elongation. We will focus on the hormones, their protein receptors, and how the hormone:receptor complex initiates some key events in the cell. Auxin Much of this section derives from Kou et al. Appl. Sci. 2022, 12(3), 1360; https://doi.org/10.3390/app12031360. Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Auxins, the first plant hormones discovered, regulate plant growth and development. The most common auxin is 3-indole acetic acid. Figure \(1\) shows the structures of naturally occurring auxins. Figure \(2\) shows an interactive iCn3D model of auxin bound to its receptor, TIR1 ubiquitin ligase (2P1Q) Figure \(2\): Auxin bound to its receptor TIR1 ubiquitin ligase (2P1Q) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...6xSkEZ3aJA4gT6 Auxin (IAA) is shown in spacefill CPK colors along with an unexpected binding cofactor, inositol hexakisphosphate (IHP), shown in spacefill CPK colors. The peptide shown in light brown sticks is part of the protein Auxin-responsive protein IAA7, a member of a class of proteins called AUX/IAAs. These are short-lived transcriptional factors that function as repressors of early auxin response genes at low auxin concentrations. The magenta subunit, TIR1 (transport inhibitor response 1), is part of the larger TIR1 complex, the SCF(TIR1) E3 ubiquitin ligase, of which only TIR1 is shown. Its mere name suggests that it is involved in the ubiquitinylation of a key protein involved in auxin activity, which will be targeted for proteolysis. That protein is the repressor protein IAA7 (an AUX/IAA protein). Auxin binds in a hydrophobic pocket, which accounts for the binding of the other largely hydrophobic auxins shown in Figure \(1\). Note however that Arg 401 that forms a salt bridge (ion-ion interaction) with the carboxylate of 3-indole acetic acid Figure \(3\) shows an interactive iCn3D model of auxin bound in the hydrophobic pocket of its receptor, TIR1 ubiquitin ligase (2P1Q). The green color represents nonpolar side chains. The brown side chains, Trp and Pro, are from the auxin-responsive protein IAA7 peptide. To reiterate, the protein IAA7 is a member of a class of proteins called AUX/IAAs which repress auxin activity. The IAA7 peptide packs over the auxin in the binding pocket. Now we can see how auxin function to regulate gene transcription. First, we must introduce another protein family, the auxin response factors (ARFs). These are transcription factors that bind to a key DNA sequence, the auxin response element (AuxRE) in promoter sequences of auxin-activated genes. Once bound they can either activate or repress transcription from target genes. Auxin binds its receptor TIR1 enabling the binding of an AUX/IAAs (like IAA7) repressor and the binding of the complex to ARF. The TIR1 ubiquitin ligase activity of the complex ubiquitinylates the bound AUX/IAAs (like IAA7) repressor, targeting it for degradation, freeing the ARF to become active in the regulation of gene transcription in the nucleus. ARFs are structurally similar, with most members containing three regions: DBD (DNA-binding domain), MR (middle region), and PB1 (Phox and Bem 1). Figure \(4\) shows a model of how auxin affects ARF transcriptional activity. At low concentrations of auxin, the AUX/IAA repressor binds to the ARF transcription factor through their PB1 domains. The PB1 (Phox and Bem1) domain is about 80 amino acids in length. It acts as a protein binding module allowing heterodimerization or homo-oligomerization with proteins have also contained the PB1 domain. The dimer of AUX/IAA and ARF recruits the co-repressor TPL (TOPLESS) to inhibit the ARF activity and the expression of auxin-responsive genes. When the concentration of auxin is increased, Aux/IAA binds to the SCF TIR1/AFB complex and is ubiquitinated and then degraded by 26S protease. The ARF transcription factors are released to activate the transcription of downstream genes. DBD, DNA-binding domain; MR, middle region; PB1, Phox, and Bem 1. • At low concentrations of auxin, the AUX/IAA repressor binds to the ARF transcription factor and forms a dimer that recruits the co-repressor TPL (TOPLESS) to inhibit the ARF activity and the expression of auxin-responsive genes; • When the concentration of auxin is increased, Aux/IAA binds to the auxin:SCF TIR1/AFB complex (remember that the auxin receptor is the TIR1 component of the complex) and is ubiquitinated by the TIR1, which is also a ubiquitin ligase; • The ubiquitinated AUX/IAA protein is degraded by proteolysis by the 26S protease, allowing the ARF to become an active transcription factors It appears that the MR domain of ARF determines whether it activates or inhibits transcription. If it is rich in proline, serine, and threonine, it acts as an inhibitor. If it is enriched in glutamine and leucine it acts as an activator. Some reports show that Aux/IAA and ARFs can form not only dimers but also larger complexes (oligomers), noting that oligomerization of Aux/IAA proteins may be essential for the inhibition of ARF proteins and only sufficient amounts of Aux/IAA proteins can exert the inhibitory effect of ARF proteins. Figure \(5\) shows an interactive iCn3D model of the DNA binding domain of arabidopsis thaliana auxin response factor 1 (ARF1) in complex with auxin response element-like sequence ER7 (4LDX) The ARF protein must translocate to the nucleus to regulate gene transcription. ARF7 and ARF19 have been shown to form micron-sized aggregates in the cytoplasm. These have low responses to auxin. Aggregation occurs through PB1 domain interactions between ARFs as well as through intrinsically disordered regions. Mutation of a single lysine in the PB1 prevents aggregation and leads to morphological changes in the plant. This shows the importance of regulating not only transcription but also the translocation of proteins to the nucleus. Figure \(6\) reviews the activation of ARFs and some of the genes affected by ARF. Auxin promotes the formation of the TIR1/AFB Auxin/Indole-3-acetic acid inducible (Aux/IAA) co-receptor to promote the ubiquitylation and subsequent degradation of the Aux/IAA repressor. Aux/IAA degradation relieves repression of auxin response factor (ARF) transcription factors, allowing for auxin-responsive gene expression. One of the transcript families upregulated by auxin is the SAUR family. The small auxin up RNA (SAUR) proteins encoded by these transcripts have been suggested to play roles in multiple processes, one of which is interaction with and inhibition of members of the PP2C.D family of phosphatases, which act to regulate H+-ATPase activity. Further, indole-3-butyric acid response 5 (IBR5) and mitogen-activated protein kinase 12 (MPK12) have been implicated in regulating auxin-responsive gene transcription; this regulation is not through destabilization of the Aux/IAA repressors, suggesting a yet-to-be-discovered mechanism of regulating auxin-responsive gene expression. F Cytokinins (CKs) and Ethylene (ET) Much of this material derives from Bidon et al. Cells 2020, 9, 2526; doi:10.3390/cells9112526 . Creative Commons Attribution (CC BY) license. (http://creativecommons.org/licenses/by/4.0/). Cytokinins (CKs) and ethylene (ET) are among the most ancient organic chemicals on Earth. The structure of a representative cytokinin (kinetin) and ethylene are shown in Figure \(7\). A wide range of organisms including plants, algae, fungi, amoebae, and bacteria use these substances as signaling molecules to regulate cellular processes. Because of their ancestral origin and ubiquitous occurrence, CKs and ET are also considered to be ideal molecules for inter-kingdom communication. Their signal transduction pathways were first determined in plants and are related to the two-component systems of bacteria (which we explored in a previous section), using histidine kinases as primary sensors. CKs share a common structure of N6-substituted adenine (see Figure \(7\)), with biological activities defined by the N6-substituents (isoprenoids or aromatic groups). They were originally described as the major hormones regulating cell division but are also implicated in the control of morphogenesis and embryogenesis and inhibition of senescence. Conversely, ET is a simple gas, often referred to as the senescence hormone in plants, acting to stimulate the senescence of leaves and petals as well as the ripening of fruits. Both CK and ET are also well known to orchestrate plant responses to many types of biotic and abiotic stresses. Signaling pathways in plants are related to the two-component systems typically described in prokaryotes. CKs and ET are perceived by two types of membrane-bound histidine kinase receptors, CRE1 and ETR1 as shown in Figure \(8\). Panel A shows the cytokinin signaling pathway. CKs in Arabidopsis primarily are recognized by dimerized receptors such as the CRE1 receptor via the cyclase/histidine kinase-associated sensing extracellular (CHASE) domain. CRE1 then auto-phosphorylates (histidine kinase (HK) activity) and immediately transfers its phosphate group to the conserved histidine of a protein belonging to the histidine-containing phosphotransfer (HPt) family. This small protein then acts as a cytoplasm-to-nucleus shuttle and in turn phosphorylates a type B response regulator, which, when activated, positively regulates the transcription of response genes to the CK signal. Panel (B) shows the ET signaling pathway. Ethylene molecules are detected by ethylene receptors (labeled ETR1) with ethylene binding to the three transmembrane helices (shown in sky blue). The binding of ET to the dimerized ETR1 receptor downregulates its activity. In the absence of ET, ETR1 activates the serine/threonine kinase CTR1. The CTR1 protein then phosphorylates the EIN2 protein located in the ER membrane, leading to the proteolysis of EIN2. In the presence of ET, ETR1 activity is reduced, leading to less CTR1 activity; this leads to lower phosphorylation and accumulation of EIN2 protein and subsequent activation of the EIN3 and related transcription factors. EIN3 then positively regulates the transcription of ET signal response genes. Panel (C) shows the domain structure of the Arabidopsis ET (ETR1) and CK (CRE1) receptors. Mechanistically, the two pathways use fundamentally different families of downstream modules. It is now known that bacteria also use CK and ET signaling, as described in Figure \(9\). Let's look in more detail at CRE1, cytokinin response 1, the main cytokinin receptor in plants. Different computational programs often show different domain structures. Figure \(10\) shows the domain structure determined by Pfam. Uniprot describes this domain structure, color-coded as in Figure \(11\) • 131-149: transmembrane • 200-382: Green Chase (Cyclases/Histidine kinases Associated Sensory Extracellular) • 420-443 transmembrane • 472-537: Red His Kinase A Phosphoaccepter domain • 584-760 Blue HK kinase, DNAgyrase, HSP-like ATPase • 786-920: Yellow Reg REsp 1 • 946-1071: Yellow Reg Regulator receiver domain Figure \(11\) shows an interactive iCn3D model of Histidine kinase 4 - Cytokinin receptor 1 (CRE) from Arabidopsis thaliana (AlphaFold model - Q9C5U0). The coloring matches the Pfam domains shown in Figure \(10\). Figure \(12\) shows an interactive iCn3D model of Histidine kinase 4 - Cytokinin receptor 1 (CRE) from Arabidopsis thaliana (AlphaFold model) - Domain organization (Q9C5U0) that clearly shows the extracellular and intracellular domains. The N-terminal methionine is in cyan spacefill and the C-terminal Ser is in spacefill. Two transmembrane helices are shown in dark gray spacefill (125-145) and light gray spacefill (430-450). These connect the extracellular domain (cyan, 146-429) and the two cytoplasmic domains (magenta 1-124, which is mostly disordered in the model, and 451-1080). This model does not reflect the relative disposition of the protein in the actual structure, but clearly shows the extracellular and cytoplasmic domains. The extracellular domain (cyan, 146-429) is the CHASE domain. Here are the step involved in cytokinin signaling through its receptor (shown in Figure \(8\) : • the cytokinin binds to the CHASE domain • the receptor autophosphorylates a His in the HK domain • a phosphotransfer from the pHis to an Asp in the Yellow Reg (Regulator) Receiver domain • a phosphotransfer from pAsp to the His in the histidine-containing phosphotransfer protein (HPt) • a final transfer from pHIs to an Asp in a response regulator (RR) The MAPK cascade is activated in the cytokinin signaling pathway. Phosphorylated pRR can also regulate target gene transcription. Type-A RRs are negative regulators of cytokinin signaling. It also acts with phytochromes (discussed at the end of this section) to regulate red light signaling. Cytokinin receptors can bind synthetical chemicals that act as defoliants and herbicides. Intermolecular interactions in the cytokinin signaling pathway leading to transcriptional effects are illustrated in Figure \(13\). Gibberellin Much of this material derives from Hedden, P., Sponsel, V. A Century of Gibberellin Research. J Plant Growth Regul 34, 740–760 (2015). https://doi.org/10.1007/s00344-015-9546-1. https://doi.org/10.1007/s00344-015-9546-1. Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/) Gibberellin controls growth and development pathways in plants and fungi. They act in plants by removing growth limitation by promoting the degradation of the growth-inhibiting DELLA proteins which contain the Asp-Glu-Leu-Leu-Ala (DELLA) motif. The name gibberellin derives from the fungus Gibberella fujikuroi. There are many types of gibberellins, which are all diterpenoids. The structures of the main bioactive GAs in plants, GA1 and GA4, are shown below in Figure \(14\). Gibberellin initiates signaling by binding to the nuclear gibberellin receptor. One such receptor is the Gibberellin Insensitive Dwarf1 (GID1). When bound, it leads to the proteolysis of another protein bound to it called a DELLA protein (an example is GAI), a transcriptional regulator that inhibits growth. The control of transcriptional activity by gibberellins is hence reminiscent of that of auxins. Figure \(15\) shows an interactive iCn3D model of the gibberellin(GA3)- active gibberellin receptor GID1L1 bound to the DELLA domain of GAI (2ZSH). The gibberellin receptor is gray and associated with the inner leaflet (blue) of the membrane. The plant hormone gibberellin A3 is bound to the receptor. Amino acid side chains in the receptor involved in the interactions with gibberellin A3 are shown in sticks, colored CPK. The DELLA protein GAI RAG protein is shown in cyan. The 3 amino acid motifs within it (DELLA, cyan spacefill; and VHYNP and LExLE, both magenta spacefill) are also shown. As with the auxin receptor, GID11A binds gibberellin in a deep pocket, which is covered by an N-terminal helix of the receptor. That helix recognizes and binds to the DELLA sequence in the DELLA transcription regulator protein. Figure \(16\) shows the effects of a mutation that leads to deficiencies in gibberellin 1-3 (right-hand side). The mechanism by which GAs promote growth is shown in Figure \(17\). Binding of bioactive GA results in a conformational change in the GID1 receptor that promotes interaction with DELLA proteins. Recruitment of an F-box protein initiates ubiquitination of DELLA by an SCF E3 ubiquitin ligase targeting the DELLA for proteasomal degradation. Loss of DELLA relieves growth repression and suppresses other DELLA-mediated responses Abscisic acid Much of this material derives from Hewage et al. (2020). Advanced Science. https://doi.org/10.1002/advs.202001265. This is an open-access article under the terms of the Creative Commons Attribution License. The phytohormone abscisic acid (ABA) is the best-known stress signaling molecule in plants As such it will be key as plants struggle to adapt to climate change. Its structure is shown in Figure \(18\). ABA protects land plants from biotic and abiotic stresses. ABA receptors proteins (PYLs) contain a conserved pyrabactin resistance/pyrabactin resistance-like/regulatory domain (PYR/PYL/RCAR) that binds ABA and triggers a cascade of signaling events. ABA has significant roles throughout a plant's life cycle. From the single-celled zygotic stage to the mature multicellular plant, plant developmental stages involve ABA. ABA allows germination only under optimum conditions and inhibits growth under stress conditions. The adult plant as well as the seedling experience biotic and abiotic stressors that vary in severity and persistence. ABA allows that plant to survive by inducing both short-term and long-term stress responses, including rapid and reversible stomatal closure, long-term growth inhibition, dormancy, senescence, and abscission. ABA is therefore both a developmental and a stress-signaling molecule with diverse roles, as shown in Figure \(19\). ABA signaling in drought Let's look at a specific example of ABA signaling in the presence of drought, stress that will expand as the world's climate changes due to the combustion of fossil fuels. Insufficient levels of soil water can result in an imbalance of water between the cells and the outer environment. A resulting change in the cellular electrolyte content affects metabolism, resulting in an osmotic imbalance or stress. The osmotic stress thus leads to the accumulation of ABA in cells and triggers ABA signaling. The cellular pool of ABA is dramatically increased during drought. Biosynthesis, catabolism, conjugation, and transportation of ABA are coordinated to increase ABA levels. ABA rapidly regulates plant water levels by controlling stomata. Stomata, which are microscopic pores controlled by two highly differentiated epidermal cells (guard cells), have the primary role in regulating gas exchange between the air and plant. Open stomata allow CO2 to diffuse into the leaf mesophyll and reach the sites of photosynthesis. They also allow water vapor to exit from the plant interior to the atmosphere. By allowing transpirational water loss, stomata allow the cooling of the plant and the managing of the interior water levels. Thus, stomata are essential regulators that connected the plant interior to the outside environment. Increases in osmotic pressure in guard cells lead to water uptake and then to cell expansion; as the cells expand, the pore opens because of differential thickenings of guard cell walls. Stomatal movements are regulated by numerous environmental signals such as light, plant growth regulators, pathogens, drought, cold, and nutrient status. Stomatal movement is the quickest response to ABA signaling. Therefore, the core ABA signaling is essential for guard cell function. The involvement of ABA signaling events in guard cell function is summarized in Figure \(20\). In the absence of ABA, (left) ABA receptors (PYLs) are in ligand-free form. H+ATPase action pumps H+ ions outside of the plasma membrane. The SnRK2 protein kinases and the S-type anion channel SLAC1 are kept dephosphorylated by PP2Cs. The dephosphorylation state of SLAC1 prevents the nonspecific activation of S-type anion channels. In the presence of ABA (right) PYLs bind to and inhibit PP2Cs. ABA inhibits H+ATPase activity, blocking the H+ pumping outside. The Ca2+-independent protein kinases (SnRK2s) are released from PP2C inhibition and activated by auto-phosphorylation. Ca2+-permeable cation (ICa) channels are released from PP2C-mediated inhibition, causing increases of ABA-responsive Ca2+ in cytosol leading to activate CPKs. The activated SnRK2s and CPKs phosphorylate SLAC1 The SnRK2.6/OST1 protein kinase phosphorylates and activates the R-type anion channel ALMT12/QUAC1. The K+ ions are effluxed via voltage-dependent outward K+ (K+out) channel GORK, causing a guard cell turgor decrease leading to stomatal closure. PYLs: ABA receptors; ABA: abscisic acid; PP2C: protein phosphatase 2C proteins; OST1: open stomata 1/SnRK 2.6 protein kinase; Ca/CPK: Ca2+/calcium dependent protein kinases; ICa2+: plasma membrane nonselective cation channel permeable to Ca2+ SLAC1: slow anion channel-associated 1 (SLAC1); QUAC: aluminum-activated malate transporter 12/quickly activating anion channel 1 (ALMT12/QUAC1); GORK: guard cell outward rectifying K+ channel (GORK); KAT1: K+ activated 1 potassium ion channel; A−: anions; K+: potassium ions. The ABA receptor core complex There are three main phases of ABA signaling: ABA synthesis/metabolism, long-distance transport, and ABA binding to its receptor. Downstream signaling ensues. through transcriptional activators/repressors and plasma membrane-located channel proteins. The main components of the core ABA signaling pathway are shown in Figure \(21\). In the absence of ABA (A above), SnRK2 kinases are dephosphorylated by protein phosphatase 2C (PP2Cs). In the presence of ABA (B above) PP2Cs are inhibited by the complexes PYLs-ABA. Thus, the SnRK2 kinases are released and make a cascade of downstream transcription factors, NADPH transporters, and ion channels phosphorylate the transcription factors that induce ABA-responsive gene transcription, and ion channels act on the guard cells to bring about transpirational control. ABA receptors (PYLs) bind ABA, PP2C, and protein kinases. The ABA:PYL complex binds PP2Cs, leading to conformational changes in the active sites of PP2Cs that inhibits the phosphatase. This in turn leads to the release of downstream protein kinases (SnRK2s) from PP2C-mediated inhibition. The SnRK2s undergo autophosphorylation to activate a series of ion channels, NADPH oxidases, and transcription factors via phosphorylation. This activates both short-term and long-term ABA responses such as stomatal closure and upregulation of ABA-dependent gene expression. MAPKKKs (MAPK3s) also activate SnRK2.6 by phosphorylating a specific site during salinity stress. ABA binding regulates a double-negative regulatory system, in which the ABA receptor (PYLs) act as ABA receptors, PP2Cs as negative-regulatory coreceptors, and SnRK2s as negative regulators. In addition to the regulation by SnRK2 and PP2Cs, several post-translational modifications also regulate ABA signaling. Phosphorylation, dephosphorylation, ubiquitination, farnesylation, and sumoylation have been found to modulate ABA signaling by targeting core components (PYLs or PP2Cs) or other interacting proteins downstream. ABA Receptors (PYLs) PYLs are soluble proteins, and among the 14 PYLs in Arabidopsis, 13 functions as ABA receptors. All PYLs are known to share a dominant helix-grip structure. This characteristic motif consists of a seven-stranded antiparallel β-pleated sheet, which is flanked by two α helices. The β-pleated sheets enfold a long carboxy-terminal α-helix of PYLs. The apo-PYLs contain a sufficiently large hydrophobic pocket of 543˚A between the C-terminal helix and β sheet. The size of this pocket is estimated to be 480˚A in the ABA-bound state. The 23 pocket residues are highly conserved and are more hydrophobic than the other parts of PYLs. The interactions of ABA and PYL2 are shown in Figure \(22\). Figure \(22\): The binding mode of ABA and PYL2 (PDBID: 3KDI) in A) 3D and B) 2D (redrawn).[211] In the 3D structure, the cartoon of PYL2 is colored in white (A). The important residues and ABA are shown in sticks with blue and yellow colors respectively. The H-bonds are marked with red dotted lines. Figure \(23\) shows an interactive iCn3D model of Abscisic acid bound to the Abscisic Acid Receptor (PYL2) (pdbid: 3KDI) In the absence of ABA, the apo-PYL2 has a pocket surrounded by four surface loops. When ABA binds, one loop (CL2) closes onto the pocket, forming a PP2C binding site for the phosphatase ABI1 and ABI2. This blocks the active site of the phosphatase. Figure \(24\) shows an interactive iCn3D model of ABA-bound PYL1 and the Protein Phosphatase 2C ABI1 (pdbid 3kdj) The PP2C phosphatase (ABI1) is shown in gray with its active site highlighted in green spacefill. The ABA receptor PYL1 is shown in cyan with ABA shown in sticks, colored CPK. The CL2 loop of the ABA-bound PYL1 receptor is shown in red spacefill. It projects into the PP2C active site, inhibiting its activity. Light Signaling through Phytochromes Much of this material derives from Liu, Y., Jafari, F. & Wang, H. Integration of light and hormone signaling pathways in the regulation of plant shade avoidance syndrome. aBIOTECH 2, 131–145 (2021). https://doi.org/10.1007/s42994-021-00038-1. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. get rid of this Plants deal with competing plants in regions of high plant density by sensing changes in the intensity and wavelengths of light. Signaling leaves to responses (stem elongation, reduced branching, early flowering, etc) called shade avoidance syndrome (SAS). A photosensory system initiates signaling that alters gene transcription. In the SAS in plants in a large canopy, the upper leaves used red and blue light for photosynthesis. Multiple photoreceptors are used. Some transcription factors are also sensitive to light. For example, PIF3, a transcription factor, binds to light-responsive genes only when it binds to another transcription factor called Pr. Pr is resident in the cytoplasm but moves to the nucleus after altering conformation on absorbing red light. Legris, M., Ince, Y.Ç. & Fankhauser, C. Molecular mechanisms underlying phytochrome-controlled morphogenesis in plants. Nat Commun 10, 5219 (2019). https://doi.org/10.1038/s41467-019-13045-0. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. do Phytochromes are present in bacteria, cyanobacteria, fungi, algae, and land plants. We will focus mostly on phytochromes in Arabidopsis. In land plants, phytochromes are red and far-red light receptors that exist in two forms. They are synthesized in the inactive Pr state, which upon light absorption converts to the active Pfr conformation. Pfr is inactivated upon far-red (FR) light absorption or through thermal relaxation, which depends on temperature. Phytochromes act as dimers, resulting in three possible phytochrome species: Pr–Pr, Pfr–Pr, and Pfr–Pfr. Pr and Pfr have different absorption maxima, but due to overlapping spectra both conformers are always present in the light while only prolonged darkness returns all phytochrome to Pr. Given that phytochrome responses depend on the proportion of Pfr conformers, signaling is influenced by a combination of light quantity, color, and temperature. These features of phytochromes are summarized in Figure  \(25\). Panel a shows factors that control phytochrome activity. Phytochromes exist in two conformations, Pr and Pfr, the latter being the active form. They exist as dimers so three species can be found. Each monomer can be activated by red light (R) and inactivated by far-red light (FR) or by thermal reversion, a process that depends on temperature (T). At least in the case of phyB, Pfr in heterodimers reverts much faster than that in homodimers, allowing phyB to perceive temperature both during the day and during the night. Panel b shows plant phytochrome absorption spectra of the Pr and Pfr conformations. In dark-adapted seedlings, phytochromes are in the Pr form. Upon a saturating R pulse, due to overlapping absorption spectra of Pr and Pfr, only 87% of Pfr is achieved. Panel c shows action spectra for phyA and phyB in the control of hypocotyl elongation. Fluence rate (number of particles passing per unit time) response curves are measured at different wavelengths and fluence rate that leads to 40% inhibition compared with dark control is determined. To specifically determine action spectra for phyA and phyB, for phyB the curve was performed with phyB-GFP/phyAphyB seedlings, and for phyA using phyB-5 seedlings. Values are relative to the response obtained at the most efficient wavelength in each case Plant phytochrome structure Plant phytochromes are dimeric, each monomer consisting of ~1150 amino acids. The chromophore, a linear tetrapyrrole named phytochromobilin (PΦB), whose structure is shown in Figure \(26\), is attached to the protein. Figure \(26\): Structure of phytochromobilin (PΦB) The domain structure of phytochromes is shown in Figure \(27\). The apoprotein can be divided into the N-terminal PSM, which consists of the N-terminal extension (NTE), for which structural information remains scarce, and three structurally related domains Period/Arnt/SIM (PAS), cGMP phosphodiesterase/adenylyl cyclase/FhlA (GAF), and a phytochrome-specific domain (PHY) and a C-terminal module (CTM) comprising two PAS domains and a histidine kinase-related domain (HKRD). The chromophore is bound covalently to a conserved cysteine in the GAF domain, which has intrinsic chromophore lyase activity. Light perception triggers a Z to E isomerization around the C15–C16 double bond of PΦB, which leads to a cascade of structural modifications in the protein. Figure \(28\) shows an interactive iCn3D model of phytochrome (Deinococcus) Pfr form in the Photoactivated State (5C5K) The protein is shown in its active dimeric state. One chain is shown in secondary structure colors and the other in cyan. The chromophore (heme derivatives) are shown in spacefill in both subunits. Side chains surrounding the chromophore are shown in colored sticks in the cyan chain. At first glance, the presence of the histidine kinase-related domain (HKRD) would seem to suggest that phytochromes transduce their signal through the C-terminal module (CTM). Although many bacterial and cyanobacterial phytochromes have a C-terminal histidine kinase domain and act as light-regulated histidine kinases, plant phytochromes are not histidine kinases, and their role as Ser/Thr kinases remains contentious. The photosensory module (PSM) fused to a nuclear localization signal and a dimerization sequence is sufficient to restore most phyB functions, pointing to key signaling functions of the PSM. Major The major of the plant phytochrome CTM are dimerization, nuclear import, and localization to sub-nuclear structures known as photobodies. However, it was recently shown that the C-terminal part of phyB also engages in light-regulated interactions and regulation of PIF activity. Moreover, the activity of the CTM is controlled by post-translational modification with SUMOylation limiting the ability of active phyB to interact with downstream signaling targets thereby limiting light responses. In addition, the CTM modulates active (Pfr) phytochrome levels with the HKRD inhibiting the Pr–Pfr photoconversion while the PAS–PAS promotes thermal reversion. Hence, while the division of plant phytochromes into PSM and a CTM helps describe the molecule, both parts of the photoreceptor contribute to the regulation of active Pfr levels and downstream signaling activities. Figure \(29\) shows a simplified mechanism for phytochrome control of transcription factors in different light environments. Panel a shows the response below the soil surface during growth in partial or a complete absence of light (called etiolated growth). For simplicity, we consider that phytochromes remain inactive (Pr) below the soil surface, which results in the accumulation of transcription factors PIFs, EIN3, and ARFs and subsequent induction of etiolation and auxin response genes. The COP1/SPA ubiquitin E3 ligase accumulates in dark and leads to proteasome-mediated degradation of HY5, a transcription factor that suppresses the expression of genes required for etiolation and induces expression of genes required for de-etiolation. Pane b shows changes that occur when light intensity increases (de-etiolation). Light perception activates phytochromes (Pfr) which promote de-etiolation by directly inhibiting PIFs and EIN3, and indirectly inhibiting ARFs by stabilizing Aux/IAA proteins. The Pfr form of either phyA or phyB interacts with SPA proteins, resulting in the inhibition of COP1/SPA. This results in the stabilization of HY5 leading to the induction of de-etiolation-related gene expression and repression of etiolation genes. Pane c shows de-etiolated plant in response to shade (reduced R/FR). Low R/FR in shade reduces the fraction of active phytochrome (Pfr/Ptot). PIFs accumulate and induce growth-promoting gene expression. In addition, PIFs induce a negative feedback loop exemplified by HFR1 expression. HFR1 (and other HLH proteins) binds to PIFs forming non-DNA-binding heterodimers. COP1/SPA is also involved in this loop by leading HFR1 to proteasome-mediated degradation. Arrows indicate positive regulation, blunt-ended arrows indicate negative regulation and dotted-lined arrows indicate nucleo-cytoplasmatic relocalization Figure \(30\) summarizes how phytochromes affect transcription. Panel a (top to bottom) shows sequential steps by which Pfr inhibits PIFs. Top: PfrA interacts with PIF1 and PIF3 while PfrB interacts with PIF1–PIF8. Middle left: for PIF1, 3, and 4 phytochrome inhibits DNA binding. Middle right: Interaction with Pfr leads to the phosphorylation of PIFs. Many kinases have been found to phosphorylate PIFs (see text) with PPKs phosphorylating PIFs in response to light. Bottom: after light-induced phosphorylation, PIF3 is degraded by LRBs and EBFs with phyB co-degradation occurring in the LRB-mediated process (left, center), phosphorylated PIF7 interacts with 14-3-3 proteins and remains in the cytoplasm (right). Panel b shows other mechanisms of transcriptional control by phytochromes. Left: PfrA and PfrB interact with SPA and inhibit the COP1/SPA complex. Center: PfrB interacts with EIN3 to promote ERF-mediated EIN3 degradation. Right: PfrA and PfrB interact with Aux/IAA to prevent their degradation by SCFTIR1/AFB. Panel c shows Patterns of PIF abundance depending on the developmental state and growth conditions. In etiolated seedlings PIFs accumulate to high levels, promoting etiolated growth. Upon light exposure, PIFs are rapidly degraded in a phytochrome-dependent manner, with half-lives of ~5 min for PIF1 and PIF5, and ~10 min for PIF3 and PIF4 (left). In contrast, in light-grown seedlings PIFs are under strong transcriptional control, allowing accumulation of the protein even in conditions when phytochrome activity is predicted to be high (right), SD (short days), LD (long days)

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.16%3A_Signaling_in_Plants.txt

Search Fundamentals of Biochemistry This chapter section is taken in entirety from Genovese et al Front. Cell. Neurosci., 08 October 2021 | https://doi.org/10.3389/fncel.2021.761416. Creative Commons Attribution License (CC BY) Sensory Transduction in Photoreceptors and Olfactory Sensory Neurons Photoreceptors and olfactory sensory neurons (OSNs) have highly specialized structures that enable them to capture their respective stimuli of light and odorant ligands. Both photoreceptors and OSNs have evolved highly specific abilities to detect and discriminate light wavelengths or odors. They use intricate transduction mechanisms to convert sensory stimuli into electrical signals. Their transduction cascades not only can greatly amplify the signal but also to enhance the signal to noise, enabling these cells to detect and distinguish minute stimuli within very noisy background conditions. Such transduction mechanisms provide for modulation at multiple steps to adapt the sensory neurons to different background stimulation and optimize the capture of useful information about the surrounding world. In this review, we summarize some of the key structural and functional features of vertebrate rod and cone photoreceptors and of OSNs, and the molecular mechanisms that underlie their function. While describing the features of both cell types, we emphasize the similarities and differences between photoreceptors and OSNs and the unique features of each cell type that make them perfectly suited to perform their function. Signal Detection in Photoreceptors and Olfactory Sensory Neurons’ Specialized Cilia Vertebrate rod and cone photoreceptors as well as OSNs are ciliary neurons (Figure 1) with specialized cilia where the initial detection of the sensory stimulus takes place to activate a sensory transduction cascade. Rods and cones have a single cilium that has evolved to accommodate a stack of ~1,000 membrane disks where the visual pigment is expressed at a very high 3–5 mM concentration (Figure 1A; Palczewski, 2006). In the case of rods, the disks are enveloped by the plasma membrane, whereas in cones the disks are formed by invaginations of the plasma membrane. As light enters the eye and reaches the retina, it travels along the length of the rod and cone outer segments. The orientation of the elongated outer segments along the light path, together with the high density of visual pigment in their disks results in ~50% probability that an incident photon is absorbed by a visual pigment molecule (Bowmaker and Dartnall, 1980). In the case of OSNs (Figure 1B), odorant ligands are detected in the ~20 cilia protruding from each dendritic knob which are immersed in the mucus layer covering the olfactory epithelium. The olfactory cilia, which are motile in amphibians but not in rodents, are only about 0.1–0.2 μm thin but can reach up to 100 μm in length depending on the species (Kleene and Gesteland, 1981; Ukhanov et al., 2021). While this greatly increases the surface membrane area available to incorporate olfactory receptor (OR) proteins to detect odorants, it also greatly reduces the ciliary volume with potentially detrimental effects (see below). Figure 1. Photoreceptors and olfactory sensory neurons (OSNs). (A) Simplified schematic representation of a rod and a cone in the retina. Photoreceptors are polarized neurons with a specialized morphology optimized to detect light stimuli. The outer segments of both rods and cones are modified sensory cilia, containing membrane disks organized in a stack. In the case of rods, the outer segment has a slim rod-like structure in which the disks are enclosed by the plasma membrane. The outer segment of the cones has a stocky conical-shaped structure, in which the disks are constituted by invaginations of the plasma membrane. The outer segment does not contain any proteins of the cell translation machinery, which are mostly localized in the inner segment, including the endoplasmic reticulum, Golgi, and mitochondria. Outer and inner segments are connected by the connecting cilium, while distal to the inner segment is the cell body containing the nucleus, followed by the axon and synaptic termini that extend into the outer plexiform layer where they synapse with the second-order neurons. When the light enters the eye, after reaching the retina, it travels along the length of the rod and cone inner segment until finally reaching the outer segments. (B) Simplified schematic of an OSN in the olfactory epithelium. OSNs are ciliated bipolar neurons, their apical dendrites extend to the surface of the epithelium terminating with a spherical structure called a dendritic knob, from which the sensory cilia enter the mucus layer. The ciliary membrane contains the olfactory receptors (ORs) necessary to detect different odorants. Distal from the knob is the cell body of the OSN with its nucleus, followed by a long axon that projects to the olfactory bulb, where it synapses with the second-order neurons. Images created with BioRender.com. Electrophysiological Approaches to Record Light- and Odorant-Induced Responses The similar morphological structure of rods, cones, and OSNs, with a ciliary part able to detect the respective stimuli and an adjacent cell body, allows similar electrophysiological approaches to recording stimulus-induced responses in these cell types. The cell body of a photoreceptor or an OSN can be sucked into the tip of a recording pipette by using a loose-patch (or suction pipette) recording configuration (Baylor et al., 1979; Lowe and Gold, 1991). This leaves the outer segment of photoreceptors or the olfactory cilia exposed and accessible to bath solution changes, e.g., the application of pharmacological agents or odorants, in the case of OSNs. Suction pipette recordings can be performed from isolated sensory neurons, as shown in Figures 2A,B,D (respectively, a salamander rod, salamander cone, and salamander OSN) but also from dissected retina tissue, as in the case of the outer segment of a mouse rod drawn in the recording electrode from a piece of the retina (Figure 2C). This recording configuration measures the transduction current entering the photoreceptor outer segment or olfactory cilia, and leaving via the cell body. A fundamental difference between photoreceptors’ and OSNs’ responses to stimuli lies in their polarity. In the absence of light, rods, and cones are kept depolarized by a standing inward current of approximately 20–40 pA for amphibian cones and rods, and 7–15 pA for mouse photoreceptors. This depolarizing current is gradually suppressed upon light stimulation until, for sufficiently high light intensities, it is reduced to zero (Figures 3A, B, mouse rod, and cone responses, respectively), leading to photoreceptor hyperpolarization. Similar to rods, but differently from cones, the OSNs show comparatively little spontaneous activity in absence of stimuli (Reisert, 2010; Connelly et al., 2013). Different OSNs show varying levels of spontaneous basal activity determined by the constitutive activity of their ORs (Reisert, 2010; Connelly et al., 2013). In the presence of odorants, OSNs generate an inward receptor current which leads to depolarization, and the generation of action potentials (Firestein and Werblin, 1989; Kurahashi, 1989; Reisert and Matthews, 1999). This receptor current is odorant concentration-depend and increases progressively with increasing stimulation until it eventually saturates at high odorant concentrations. Responses recorded from OSNs expressing different olfactory receptors can generate fairly different response amplitudes when stimulated with their respective agonists (Figures 3D, E: responses recorded from mouse OSNs that express the mOR-EG or the M71 olfactory receptor, which are activated by the ligands eugenol and acetophenone, respectively). The hyperpolarization and signals carried by graded potentials in photoreceptors vs. depolarization and signals carried by action potentials in OSNs represent another fundamental difference between these two types of sensory neurons. These topics and the differences in synaptic structure and transmission between photoreceptors and OSNs go beyond the focus of this review and are discussed in an excellent recent review on this topic (Lankford et al., 2020). Sensitivity of Photoreceptors and Olfactory Sensory Neurons In part due to their unique structure, photoreceptors, and, to a lesser extent, OSNs have achieved exquisite sensitivity that optimizes the detection of stimuli within the respective sensory organs. In addition, both sensory receptors use a transduction cascade to amplify the signal (see below). As a result, rod photoreceptors can reliably detect single photons (Baylor et al., 1979), enabling humans to perceive light with as few as six photons detected by adjacent rods (Hecht et al., 1942). This renders rods perfectly suited for dim light vision, with a dynamic range spanning lights from a dark cloudy night to sunrise (Fain et al., 2010). Cones are ~100-fold less sensitive than rods, making them suited for daytime light conditions. Figure 3C compares the intensity-response function of mouse rods and cones, demonstrating the much lower sensitivity of cones compared to rods. Most OSNs respond to odor concentrations in the low micromolar range (Bozza et al., 2002; Grosmaitre et al., 2009; Saito et al., 2009; Lee et al., 2011; Dibattista and Reisert, 2016), but they can also reach exquisite sensitivity and are capable of detecting odors at the nanomolar concentration range. Picomolar sensitivity is reached by a subset of OSNs that express receptors specialized in detecting amines, the trace-amine-associated receptors (Zhang et al., 2013). In comparison to rods, OSNs do not reach such high sensitivity, and cannot be activated by a single odorant molecule but instead require around 30 odorant binding events to begin firing action potentials reliably (Bhandawat et al., 2010). The detection of odorants in the olfactory epithelium can be further enhanced by the expression of a wider number of different OR genes, more than 350 in humans and 1,000 in mice (Malnic, 2007), with overlapping response profiles to odorants. A larger number of OSNs, particularly in species relying heavily on their sense of smell, may enhance further the detection of odorants. For instance, the human olfactory epithelium covers ~3–4 cm2 and contains approximately 5–6 million OSNs while in the case of dogs, the area of the olfactory epithelium is 18–150 cm2 and contains 150–300 million OSNs (Lippi and Heaney, 2020). Detection of Stimuli In both photoreceptors and OSNs, the detection of stimuli is mediated by G protein-coupled receptors. In photoreceptors, this function is achieved by rod and cone visual pigments, which consist of a protein, opsin, covalently attached to the visual chromophore, typically 11-cis-retinal (Ebrey and Koutalos, 2001). The chromophore serves as a reverse agonist, keeping the receptor molecule in the inactive ground state (Crouch et al., 1996). Absorption of a photon by 11-cis-retinal triggers its conformational change to all-trans-retinal, which, in turn, results in the rearrangement of the opsin transmembrane helices and switch of the visual pigment molecule into its active state. We discussed bacterial rhodopsin in an earlier section. We present it here again to show and note its similarity to animal light-sensing proteins. Review: Retinal, Opsin and Light Transduction in Bacteria Microbial rhodopsins are phototransducing proteins with a conjugated chromophore retinal, covalently attached to the protein opsin through a Schiff base (imine) linkage. The holoprotein (opsin with the attached retinal) is called rhodopsin. Retinal is derived from beta-carotene. The structures of animal and microbial retinals are shown below. When light of the correct wavelength is absorbed, an electron in a pi molecular orbital in retinal is promoted to a pi antibonding molecular orbital, breaking a 2 electron pi bond in the structure at a certain site in the isoprenoid chain, allowing rotation around the now single bond. The final result after the electrons return to the ground state is photoisomerization of the trans 13-14 and cis 11-12 bonds in microbial and animal retinal, respectively, to their respective cis 13-14 and trans 11-12 configuration. This conformational change in the bound retinal induces a conformational change in the protein opsin, leading to signaling. The figure below shows an interactive iCn3D model of one monomeric of bacteria rhodopsin (1C3W) containing retinal attached through a Schiff base to Lys 216. Bacterial rhodopsin (1C3W). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...9HzvfdFvb9kcc6 The covalently attached retinal is shown in gray spacefill. The protein opsin is a membrane protein that spans it with seven helices. Hence it has very similar to a GPCR. The activated visual pigment then binds to a G protein, transducin, activating it. The activation of transducin triggers the transduction cascade that ultimately generates the cellular response (Pugh and Lamb, 1993). Eventually, the all-trans-retinal chromophore is released from opsin after the covalent Schiff base between them is hydrolyzed, leaving behind chromophore-free opsin (Saari, 2016). Notably, without chromophore, opsin has residual activity, and in sufficient quantities can produce steady activation of the photoreceptors, similar to steady background light, thus modulating the sensitivity of photoreceptors (Fain et al., 1996). This process is known as bleaching adaptation, indicating the production of free opsin after the photoactivation of the visual pigment and dissociation of the visual chromophore. Unlike in photoreceptors, where the ligand, a light-sensitive reverse agonist, is covalently attached to opsin, in olfaction, the ligands are dissolved in the mucus covering the surface of the olfactory epithelium and come into direct contact with the OR proteins expressed in the OSN ciliary membrane. This results in the activation of the receptor protein that, in turn, is transduced downstream to a G protein to trigger a transduction cascade resulting in the cellular response. The binding of the ligand to the receptor protein is noncovalent and rapidly reversible. ORs, like other G protein-coupled receptors, do display antagonism, inverse, and partial agonism, leading to suppressed responses to their agonists, a reduction in basal activity in the absence of stimulation or suppression of the maximal response (Firestein et al., 1993; Oka et al., 2004; Reisert, 2010). Discrimination Between Stimuli The spectral sensitivity of individual rod and cone photoreceptors is dictated by the absorption properties of their visual pigments. Typically, each photoreceptor type expresses only one type of opsin; in the case of the human retina, rods express rod opsin, whereas cones express long wavelength (LW, red), middle wavelength (MW, green), or short wavelength (SW, blue) opsin (Nathans, 1987). When bound to the chromophore, the amino acid structure of each opsin determines the optical properties of the resulting visual pigment and the spectral sensitivity of the photoreceptors expressing it. As a result, species existing in environments with characteristic light distribution, such as deep-sea fish, have visual pigments that have evolved to optimize their spectral sensitivity (Hope et al., 1997). A second factor controlling the optical properties of the visual pigment is the structure of the visual chromophore. Most species, including mice and humans, use 11-cis-retinal, a derivative of Vitamin A, also known as A1. However, some amphibians and fish also use 3,4-dehydro 11-cis retinal, also known as A2. This chromophore has an extra conjugated double bond in its structure, which shifts the absorption spectrum of A2 visual pigments to longer wavelengths compared to the A1 visual pigment embedded in the same opsin molecule (Corbo, 2021). Some aquatic and amphibian species use the A1/A2 chromophores to shift their spectral sensitivity from murky waters dominated by longer wavelengths of light to seawater and air, dominated by shorter wavelength lights (Bridges, 1964). One notable example includes the toad, where the retina is populated by A1 visual pigment in its ventral section, receiving light from above the surface of the water, and by A2 visual pigment in its dorsal section, receiving light from below the surface of the water (Reuter et al., 1971). A shift in the chromophore can also occur during the lifetime of the animal as its environment changes, such as the A2 to A1 shift in salamanders as they metamorphose from the larval (aquatic) to the adult (terrestrial) stage (Ala-Laurila et al., 2007), or the A2 to A1 shift in Atlantic salmon during migration from sea to freshwater (Beatty, 1966). Similar to photoreceptors, the ligand specificity of the OSNs is also dictated by the expression of OR genes in their cilia. As photoreceptors, each OSN expresses generally only one receptor gene so that its ligand specificity is determined by the structure of the OR expressed in that particular cell. However, photoreceptors typically use no more than five opsin genes to cover the visible spectrum, while OSNs can use hundreds, in the case of humans, to thousand and more, for rodents and dogs, OR genes to cover the odor space (Malnic, 2007). The same OR can be activated by multiple odorants with different sensitivities, and a given odorant can activate different ORs with different half-maximal concentrations (Buck, 1996; Ache, 2020). This generates a complex mosaic of ORs and odorants response pairs. Figure 3F compares the dose responses of OSNs expressing either the mOR-EG or the M71 OR to eugenol and acetophenone, respectively. In this case, mOR-EG OSNs display higher sensitivity to their agonist compared to M71 OSNs. However, this does not preclude the possibility that the M71 OR is more sensitive to another ligand resulting in a more left-shifted dose-response relation than the one seen with acetophenone. Conversely, the dose-response relation of M71 OSNs to benzaldehyde is approximately 10-fold right-shifted compared to acetophenone (Bozza et al., 2002). Determining the ligand specificity of ORs is an ongoing endeavor (Abaffy et al., 2006; Saito et al., 2009; Kurian et al., 2021). Due to the large number and diversity of OR genes, as well as the nearly endless number of odorant molecules, understanding the overall mechanisms that control their ligand binding affinity and specificity remains a challenge. Receptor modeling approaches to understand and predict OR–odorant molecule interactions can provide valuable insights but are somewhat hampered by the lack of a crystal structure of any vertebrate OR. The rhodopsin structure is often used as a guide and homology model to predict the structure of ORs (Katada et al., 2005; Bavan et al., 2014). Sensory Transduction Activation In both photoreceptors and OSNs, the detection of stimuli by their respective G protein-coupled receptors is converted into electrical signals via the activation of a G protein-coupled to a second messenger transduction cascade. The two pathways, though distinct, share an amazing level of similarity (Figure 4). Thus, in both cases, the second messenger is a cyclic nucleotide, cGMP in photoreceptors (Pugh and Lamb, 1990) and cAMP in OSNs (Sklar et al., 1987; Bakalyar and Reed, 1990). As a result, the activation of both transduction cascades results in a rapid shift in the equilibrium between synthesis and hydrolysis of the respective cyclic nucleotide, which is then sensed by the cyclic nucleotide-gated (CNG) transduction channels in the plasma membrane of the photoreceptor outer segment or olfactory cilium. Figure 4. Activation of the transduction cascade in rod photoreceptors and OSNs. (A) Schematic representation of phototransduction cascade in rods. Abbreviations: rhodopsin (Rh), Tα, β, and γ subunits of the retinal G protein, transducin (T), guanosine-5′-triphosphate (GTP), guanosine-5′-diphosphate (GDP), phosphodiesterase (PDE), guanosine monophosphate (GMP), and cyclic guanosine monophosphate (cGMP), and cyclic nucleotide-gated (CNG) channel. (B) Schematic representation of the olfactory transduction cascade in OSNs. Abbreviations: Olfactory receptor (OR), guanosine-5′-triphosphate (GTP), guanosine-5′-diphosphate (GDP), Gαolf, β, and γ, subunits of the olfactory G protein; adenylyl cyclase 3 (AC3), adenosine-5′-triphosphate (ATP), cyclic adenosine monophosphate (cAMP), cyclic nucleotide-gated (CNG) channel; Ca2+-activated Cl channel anoctamin 2 (ANO2). Images created with BioRender.com. In the case of photoreceptors, the photoactivated visual pigment binds to and activates the trimeric G protein transducin (T) (Figure 4A), causing the exchange of GDP for GTP on its α-subunit, which is part of the Gαt protein family. Following the subsequent dissociation of the α-subunit (Tα) from its β/γ complex (Tβγ), Tα then binds to the cGMP phosphodiesterase (PDE) complex, relieving the inhibition of its catalytic α- and β-subunits by its inhibitory γ-subunits (Ebrey and Koutalos, 2001; Burns and Arshavsky, 2005). All these transduction proteins are embedded in or tethered to the disc membranes inside rods or are contained in the cell membrane of cones. As a result of their activation, the hydrolysis of free cGMP in the outer segment is upregulated, causing its rapid decline, and partial or complete closure of the cGMP-gated channels expressed in the rod and cone cell membrane (Luo et al., 2008). The closure of the CNG channels leads to the reduction of the inward transduction current, followed by the hyperpolarization of the cells, and a reduction of neurotransmitter release to second-order neurons within the retina. Inversely, in the absence of light, the opening of CNG channels and the resulting inward transduction current is sustained by the continuous cGMP production by guanylyl cyclase (GC). Similarly, in OSNs (Figure 4B), the ligand-activated OR proteins bind to the G protein Golf, causing its dissociation into active Gαolf and olfactory β- and γ-subunit, Gβγolf. In contrast to transducin, however, Gαolf is part of the Gαs protein family and binds to adenylyl cyclase 3 (AC3), activating it. As a result, the synthesis of cAMP in the olfactory cilia is upregulated, causing its rapid increase and the opening of cAMP-gated channels (Kleene, 2008; Su et al., 2009; Boccaccio et al., 2021). While both photoreceptors and OSNs use CNG transduction channels, their respective channels have different subunit compositions (Bradley et al., 2005). Rods and cones express heterotetramers consisting of the main A1 and A3 and the modulatory B1a and B3 subunits in 3:1 and 2:2 stoichiometries respectively. The olfactory CNG channel is a heterotetramer consisting of two units of the main A2 subunits and one each of the modulatory A4 and B1b subunits. Interestingly, the rod and the olfactory CNG channels express different splice variants of the same B1 subunit. In OSNs, the initial inward Na+ and Ca2+ current generated by the opening of the CNG channel raises ciliary Ca2+ and opens a secondary ion channel, the Ca2+-activated Cl channel Anoctamin 2. A high intraciliary Cl maintained by the Na+/K+/2Cl cotransporter 1 ensures a Cl efflux which further depolarizes the OSNs (Dibattista et al., 2017; Boccaccio et al., 2021). This depolarization triggers the generation of action potentials which further propagate along the axons, inducing glutamate release at synapses with the second-order neurons in the olfactory bulb (Murphy et al., 2004). In photoreceptors, the transduction cascade upon stimulation does not ultimately generate action potentials in the receptor cell, but only a graded receptor potential that directly causes a change in neurotransmitter release. Amplification As for any other sensory modality, proper amplification of the signal is required to detect small stimuli and the resulting high sensory sensitivity is critical for the survival and propagation of the species. Nature has reached the highest physically possible sensitivity in the case of rod photoreceptors that can produce a detectable electrical response to the absorption of a single photon. This impressive feat is achieved by employing a transduction cascade that allows tremendous amplification of the signal. During the ~50 ms active lifetime, a single photoactivated rhodopsin molecule activates ~20 transducins, producing an immediate 20-fold amplification (Burns and Pugh, 2010). The following activation of PDE by transducin does not directly produce amplification as each transducin has to bind to a PDE molecule to activate it. However, once activated, each PDE enzyme can hydrolyze thousands of cGMP molecules. Lastly, as the binding of cGMP to the CNG transduction channels is cooperative, a slight change in cGMP levels can reduce the number of cGMP molecules bound to the channel from 3 to 2. This results in channel closure and a sharp reduction in the transduction current, further enhancing the detection of photostimulation. Despite the similarities in the transduction cascades of rods and cones, the amplification in cone photoreceptors is substantially lower as a result of fine-tuning at several of the phototransduction steps (Yau, 1994; Kawamura and Tachibanaki, 2008). Interestingly, even though rod and cone visual pigments activate transducin with similar efficiencies, the lower thermal stability of the cone visual pigment results in higher intrinsic activity in cones compared to rods in darkness (Kefalov et al., 2003), effectively desensitizing the cones and shifting their function towards brighter daytime light conditions (see Figure 3C). Curiously, the activation of Golf by the OR molecule does not result in amplification. Indeed, the dwell time of the odorant ligand on the OR appears to be very short and on a millisecond timescale (Bhandawat et al., 2005). As a consequence, on average, this results in the activation of less than one G protein per activated receptor. As such, in contrast to phototransduction, where the lifetime of the activated rhodopsin greatly influences the response size and kinetics, in OSNs the response depends more prominently on the coupling efficacy of downstream transduction components while the odorant presence keeps the OR activated. To compensate for the lack of initial amplification at the G protein level, OSNs employ a secondary amplification step on top of the cAMP transduction cascade. The activation of AC3 by Golf results in the synthesis of most likely hundreds of cAMP molecules, the opening of the CNG channels which is followed by a unique secondary amplification based on excitatory Ca2+-activated Cl channels in the cilia (Figure 4B). The Cl current carries up to 80% of the overall transduction current (Dibattista et al., 2017). Physiological experiments with pharmacological and genetic modulation of the Cl conductance indicates that the Cl channels serve to set the length of the action potential train generated in response to odorant stimulation (Pietra et al., 2016) and to promote recognition of novel odorants (Pietra et al., 2016; Neureither et al., 2017). A puzzling aspect of the secondary amplification step is why Cl is the charge-carrying ion and not Na+, which could be achieved easily by increasing the expression level and/or the ion permeation and conductance of the olfactory CNG channel. Recent theoretical work hinted at two main advantages of Cl, instead of Na+, as the charge carrier. As the external environment of cilia is the nasal mucus, currents will depend on the ion concentration in the mucus, which can be unstable. A current that depends on the intracellular ion concentration, as is the case for Cl but not for Na+, is much less dependent on the mucosal ion concentration. For instance, this could become an issue in the case of a cold with a runny nose or during swimming, when the mucus becomes diluted. The second advantage results from the “compromise” to increase the ciliary surface area, at the expense of having a very small ciliary volume, in the femtoliter range. In such small volumes, even small ionic currents can lead to large changes in ion concentration and osmotic pressures. If the main charge carrier was Na+ this would lead to a large increase (tens of mM). This would cause a large increase in osmotic pressure and also would prevent Ca2+ clearance via the olfactory Na+/Ca2+, K+ exchanger (see below) with greatly deleterious effects. In contrast, high intracellular Cl is maintained throughout the OSN so that its local depletion in the cilia upon ligand activation is rapidly reversed by diffusion from the cell soma. Both these issues do not exist for photoreceptors as they are embedded in the interstitial fluid of the eye and photoreceptors are sufficiently large and their transduction currents are sufficiently small that ion concentration changes due to changes in transduction currents are relatively small (Reisert and Reingruber, 2019). Nevertheless, rod photoreceptors undergo osmotically-driven length changes upon light activation, an effect that is mitigated by the translocation of G protein subunits into the cytosol (Zhang et al., 2017). Receptor and G Protein Inactivation Timely and effective transduction inactivation is critical for allowing sensory neurons to continue to detect stimuli with high temporal resolution. Equally important is to extract behaviorally relevant information from the presented stimuli. In both photoreceptors and OSNs, all active transduction components need to be turned off and the level of cyclic nucleotides within the cells needs to be restored to the resting level before the sensory cell can be reset to the inactive state and become ready for subsequent activation (Figure 5). In the case of photoreceptors, the identity of the step determining the overall kinetics of the photoresponse inactivation was the subject of intense research and debate over several decades. As the visual chromophore ligand is covalently attached to opsin, inactivation of the visual pigment could potentially be extremely slow. Indeed, if left on its own, the active state of rhodopsin decays with a time constant of ~50 s (Imai et al., 2007). Its inactivation in photoreceptors is a two-step process, involving phosphorylation of the rhodopsin C-terminus by rhodopsin kinase (GRK1) which partially quenches its activity, followed by the binding of arrestin1, which completely inactivates the visual pigment (Figure 5A). Though the decay of the active state of cone pigment is significantly faster at ~2 s (Fu et al., 2008), this is still clearly too slow to enable the timely termination and reset of phototransduction. Thus, in both rods and cones, the visual pigments are inactivated by phosphorylation by rhodopsin kinase and the subsequent binding of arrestin long before they would decay spontaneously (Makino et al., 2003). The effective time constant of rod visual pigment inactivation is ~50 ms (Krispel et al., 2006). The slowest step in the inactivation of rod phototransduction turned out to be the hydrolysis of GTP which shuts off Tα, a reaction driven by the transducin GTPase activity and enhanced by a GTPase (GTPase activating protein, GAP) complex consisting of Gβ5 and the membrane anchoring protein R9AP (Arshavsky and Wensel, 2013). Inactivation of transducin results in its release from PDE, allowing the two PDE γ inhibitory subunits to resume their inhibition on the two catalytic subunits (α and β) of this enzyme. The kinetics of this reaction determines the overall kinetics of response inactivation in rod photoreceptors. In contrast, work from amphibian cones suggests that in cones the photoresponse duration is Ca2+-dependent and involves the quenching of the cone visual pigment (Matthews and Sampath, 2010). In OSNs, the inactivation by phosphorylation and arrestin are potentially not needed for the timely shutoff of the olfactory transduction cascade, due to the extremely short lifetime of the active ligand-bound receptor molecule. Early biochemical experiments suggested that OR phosphorylation does control cAMP kinetics (Dawson et al., 1993; Schleicher et al., 1993; Peppel et al., 1997), but it seems to play little, if any, role in the control of odorant-response kinetics for one particular OR, mOR-EG (Kato et al., 2014). It remains to be established whether this applies to all ORs, or whether a subset of ORs is subject to phosphorylation and inactivation. β-arrestin interacts with ORs, mediating internalization during prolonged stimulation and altering adaptation to repetitive odor stimuli (Mashukova et al., 2006). Experiments on isolated human and rat OSNs suggested a role for protein kinases A (PKA) and C (PKC) in the termination of the olfactory response. Ca2+ imaging showed that the inhibition of PKA and PKC increases intracellular Ca2+ responses in the presence of odorant mixtures, and blocks their termination after odorant stimulation ceases. While the inhibition of both PKA and PKC modulated the odor-induced intracellular Ca2+ increase in the human OSNs, only PKC and not PKA affected the Ca2+ response to odorants in rat OSNs, suggesting differences among species in the termination of the olfactory response (Gomez et al., 2000). The control of the lifetime of the olfactory G protein seems to be more complex and less well-understood compared to phototransduction. Ric-8B (resistant to inhibitors of cholinesterase-8B) has been identified as a GTP exchange factor (GEF) expressed in OSNs, which facilitates the exchange of GDP for GTP on Gαolf and its activation. Unusually, Ric-8B not only interacts with the G protein α-subunit but also with γ13, the olfactory γ-subunit. In a heterologous system, Ric-8B co-expression with olfactory transduction components can greatly increase cAMP production, suggesting that it could indeed modulate olfactory transduction (Von Dannecker et al., 2005; Kerr et al., 2008). A knockout mouse for Ric-8B displays impaired olfactory behavior, and, surprisingly, greatly reduced odorant responses. Ric-8B is localized primarily in the cell body and the dendritic knob of OSNs. Ric-8B knockout OSNs are devoid of Gαolf (Machado et al., 2017), suggesting that this gene is needed for the stable expression of Gαolf, and excludes addressing its potential role as a GEF in the odorant response. The Ric-8B knockout mice also display higher OSN cell death. Regulators of G protein signaling (RGS) are GAPs that modulate the lifetime of an activated G protein as described above. RGS2, instead of functioning as a GAP, directly inhibits AC3 to control the size of the odorant response (Sinnarajah et al., 2001). However, inconsistent and contradictory data on RGS2 and RGS3 expression and their roles in OSNs suggest that more research is needed (Norlin and Berghard, 2001; Kanageswaran et al., 2015; Saraiva et al., 2015). Adaptation Adaptation plays a critical role in the capacity of our sensory neurons to remain able to detect stimuli above the background in a complex and rapidly changing environment. For instance, in constant light conditions, the dynamic range for both rods and cones is only 100-fold, spanning a range from threshold stimulation to saturation (Figure 3C). However, as a result of light adaptation, photoreceptors can shift their functional range over a very wide span of light conditions, ranging from cloudy night to sunrise for rods, and starry night to bright sunny day for cones (Weale, 1961). Thus, using the adaptation of individual photoreceptors, the visual system can remain responsive to stimuli over a wide range of light conditions. In contrast, the ability of OSNs to adapt is rather limited even at modest levels of background odorant (Reisert and Matthews, 1999). Nevertheless, increasing concentrations of the same odorant can recruit less sensitive ORs, and therefore less sensitive OSNs, preserving its perception at higher concentrations and ensuring to report the presence of that odorant to the brain. In both types of sensory neurons, adaptation is mediated by a change in Ca2+ upon stimulation. This change is sensed by several Ca2+-binding proteins that trigger a negative feedback on the vision and olfaction transduction cascades by modulating several of their steps. In the outer segments of rods and cones and olfactory cilia, Ca2+ levels are controlled by the balance of influx via the CNG channels, whose current is carried in part by Ca2+, and efflux via Na+/Ca2+, K+ exchangers (NCKXs) that use the electrochemical gradient for Na+ and K+ to extrude Ca2+ (Figure 5; Yau and Nakatani, 1984). In rods (Figure 5A), this task is accomplished by rod-specific NCKX1, whereas cones employ two separate exchangers, NCKX2 and NCKX4 (Vinberg et al., 2017). At rest, both in darkness and in steady-state light, the influx of Ca2+ is matched with its extrusion and, as a result, the level of free Ca2+ in the outer segments is maintained constant. Upon photostimulation, the transduction cascade is activated, resulting in the depletion of cGMP, closure of CNG channels, and reduction in the influx of Ca2+ into the outer segments. However, Ca2+ extrusion by the Na+/Ca2+, and K+ exchangers carry on for at least a while and, as a result, the level of Ca2+ in the outer segments declines. Direct Ca2+ measurements in amphibian photoreceptors indicate a dynamic range from 670 to 30 nM in rods (Sampath et al., 1998) and 400–5 nM in cones (Sampath et al., 1999), in darkness and bright light, respectively. The light-driven decline in Ca2+ causes its release from several Ca2+-binding proteins. The dominant Ca2+-dependent feedback mechanism in both rods and cones controls the synthesis of cGMP by membrane-bound GC via a pair of GC-activating proteins (GCAPs)—GCAP1 and GCAP2. When Ca2+ in the outer segments is high, Ca2+-bound GCAPs bind to and partially inhibit the activity of GC. Upon photoactivation and the decline in Ca2+, GCAPs become Ca2+-free and release from GC, resulting in the upregulation of cGMP synthesis which restores the dark current after photostimulation and modulates the activation of the transduction cascade in the presence of background light (Dizhoor, 2000; Sakurai et al., 2011). Another mechanism by which Ca2+ modulates phototransduction involves the Ca2+-binding protein recoverin. As GCAPs, recoverin is a member of the EF-hands protein family, and when bound to Ca2+ in darkness, it inhibits rhodopsin kinase, thus slowing down the inactivation of the visual pigment (Makino et al., 2004; Sakurai et al., 2015). When the photoreceptors are activated and Ca2+ declines, it is released from recoverin, which in turn dissociates from rhodopsin kinase and relieves its inhibition. This enhances the phosphorylation of visual pigments and accelerates their inactivation, effectively reducing the activation of the transduction cascade by the background light. Finally, direct modulation of the CNG channels has also been suggested. However, in the case of rods, such modulation appears to play a marginal, at best, role (Koutalos and Yau, 1996) and is not mediated by the Ca2+-binding protein calmodulin (Chen et al., 2010). In zebrafish cones, the modulation of the CNG channels appears to play a more substantial role and is mediated by the Ca2+-binding protein CNG-modulin (Korenbrot et al., 2013). It is still unclear whether the mammalian homolog of CNG modulin, EML1 plays a similar role in mammalian cones. Adaptation in OSNs is less well understood compared to phototransduction. Early data, mostly of biochemical nature or obtained from heterologously-expressed proteins of interest, suggested three main molecular targets for adaptation. All three of them are mediated by the Ca2+ influx during the odorant response: Ca2+/calmodulin-mediated desensitization of the olfactory CNG channel to close the channel even in the presence of high cAMP (Chen and Yau, 1994); phosphorylation via CaM-kinase 2 of AC3 to reduce the rate of cAMP production (Wei et al., 1996, 1998); and Ca2+-mediated upregulation of phosphodiesterase 1C, which is expressed in olfactory cilia, and is assumed to degrade cAMP to AMP to terminate the response (Borisy et al., 1992). Follow-up experiments using recordings from OSNs all seem to indicate that none of these mechanisms plays as prominently or as originally thought role in transduction (Reisert and Zhao, 2011). A mouse with a mutation in the CNGB1b channel subunit that entirely prevents desensitization by Ca2+ surprisingly displays normal olfactory adaptation but instead shows a delayed response termination, suggesting that Ca2+/calmodulin-mediated desensitization of the CNG channel speeds up response termination (Song et al., 2008). A mouse model that carries a mutation in AC3 that prevents phosphorylation does not show a discernable phenotype of the olfactory response (Cygnar et al., 2012), although it might be possible that other, unknown phosphorylation sites in AC3 might be important. Finally, a knockout mouse for PCE1C has no deficits in response termination but instead shows much-reduced response amplitudes for unclear reasons (Cygnar and Zhao, 2009). This begs the obvious question as to what the role of PDE1C might be and what might actual to cAMP that is generated during the odorant response. For the latter, an interesting option is that cAMP diffuses out of the cilia into the cell body as a means to reduce ciliary cAMP, allowing OSNs to recover from stimulation (Cygnar and Zhao, 2009). One aspect that is reasonably understood is NCKX4, the Ca2+ exchanger in OSNs that is required to lower intraciliary Ca2+ during and after odorant stimulation, allowing the transduction cascade to recover from adaptation (Reisert and Matthews, 1998; Stephan et al., 2012). Diseases Disorders affecting photoreceptors are among the leading causes of blindness in the human population. One of the prevalent visual disorders, called retinitis pigmentosa, is a complex disease caused by a wide range of mutations in photoreceptors. Many of these mutations affect the expression, structure, and function of the rod's visual pigment (Athanasiou et al., 2018). Because of the very high expression of opsin in the outer segments of rods, this protein plays not only a functional role but is also critical for the proper formation of the outer segment itself. As a result, mutations affecting the expression, folding, or targeting of opsin to the rod outer segments, cause gradual degeneration of the rods. Other genes implicated in rod dysfunction and degeneration include those for phosphodiesterase (e.g., rd1, rd10; Chang et al., 2002), the CNG channels A and B subunits (channelopathies; Michalakis et al., 2018), GC, and GCAPs (Olshevskaya et al., 2002). Another diverse set of visual disorders is caused by abnormal chromophore production or supply to photoreceptors, which limits the ability to detect light and can also lead to degeneration (Ku and Pennesi, 2020). Notably, the efficiency of the visual system to produce chromophore seems to decline with age, which may result in poor rod function in dim light even in normally aging adults. It is also an early indicator of age-related macular degeneration, a devastating blinding disorder that affects the function of cones in the central retina responsible for acute vision and color discrimination (Jackson et al., 2002). Interestingly, rods and cones seem to coexist synergistically in the retina, and diseases caused by rod-specific mutations that result in rod degeneration, eventually lead to the loss of cones and central vision as well. Thus, considerable efforts are currently focused on developing methods for preserving rods even when they are not functional, as a way of protecting daytime cone-driven vision. Because the eye is a relatively accessible organ, novel therapeutic approaches for vision protection and restoration have led the field, with successful examples of gene therapy and stem cell therapy in experimental and clinical trial phases (Ovando-Roche et al., 2017). Compared to vision, in olfactory transduction, very few mutations in transduction components are known that lead to deleterious effects. Several aspects might account for this. Mutations causing a partial reduction of olfaction might go unnoticed in the human population as very little systematic olfactory testing is done. OSNs regenerate throughout life and only have a lifespan of a few weeks. Hence any slow degeneration as those seen in photoreceptors might not manifest in that time window. In a screen of families with congenital anosmia, no potentially causative mutations were found in three main transduction proteins (Gαolf, CNGA2, AC3), with these genes also being under purifying selection (Feldmesser et al., 2007). An interesting exception is patients suffering from retinitis pigmentosa, which is caused by mutations in the gene encoding the CNGB1 subunit expressed in both rods and OSNs. Those patients, identified because of their visual function decline, were found to be hyposmic or anosmic when tested for their olfactory ability (Charbel Issa et al., 2018). If congenital anosmia is considered to be a relatively rare and little-understood condition, more known and frequently detected are specific anosmias, which manifest in the inability to detect certain odorants (Keller et al., 2007; Trimmer et al., 2019). Broadly speaking, this is the olfactory equivalent of color blindness and is caused by known OR mutations. Arguably, the most common causes of smell loss are events that lead to the destruction of the olfactory epithelium and/or the olfactory nerves connecting it to the central nervous system (CNS). These events include head or face trauma, inhalation of toxic chemicals, or viral infection (such as SARS-CoV2), and, neurodegenerative diseases such as Alzheimer’s and Parkinson’s disease (Attems et al., 2015; Cooper et al., 2020). In the former, the origin of the smell disorder can be tracked down to the periphery, the olfactory epithelium. In the case of neurodegenerative diseases, it has been thought that olfactory dysfunction originates centrally in the CNS, but it is becoming clearer now that peripheral olfaction can be affected in these cases as well, although the respective mechanisms have not been fully elucidated.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.17%3A_Signal_Transduction_-_Vision_and_Olfaction.txt

Search Fundamentals of Biochemistry This chapter section is taken in entirety from Ahmad and Dalziel. Front. Pharmacol., 30 November 2020 | https://doi.org/10.3389/fphar.2020.587664. Creative Commons Attribution License (CC BY) G Protein-Coupled Receptors in Taste Physiology and Pharmacology Heterotrimeric G protein-coupled receptors (GPCRs) comprise the largest receptor family in mammals and are responsible for the regulation of most physiological functions. Besides mediating the sensory modalities of olfaction and vision, GPCRs also transduce signals for three basic taste qualities of sweet, umami (savory taste), and bitter, as well as the flavor sensation kokumi. Taste GPCRs reside in specialized taste receptor cells (TRCs) within taste buds. Type I taste GPCRs (TAS1R) form heterodimeric complexes that function as sweet (TAS1R2/TAS1R3) or umami (TAS1R1/TAS1R3) taste receptors, whereas Type II are monomeric bitter taste receptors or kokumi/calcium-sensing receptors. Sweet, umami, and kokumi receptors share structural similarities in containing multiple agonist binding sites with pronounced selectivity while most bitter receptors contain a single binding site that is broadly tuned to a diverse array of bitter ligands in a non-selective manner. Tastant binding to the receptor activates downstream secondary messenger pathways leading to depolarization and increased intracellular calcium in TRCs, that in turn innervates the gustatory cortex in the brain. Despite recent advances in our understanding of the relationship between agonist binding and the conformational changes required for receptor activation, several major challenges and questions remain in taste GPCR biology that is discussed in the present review. In recent years, intensive integrative approaches combining heterologous expression, mutagenesis, and homology modeling have together provided insight regarding agonist binding site locations and molecular mechanisms of orthosteric and allosteric modulation. In addition, studies based on transgenic mice, utilizing either global or conditional knock-out strategies have provided insights to taste receptor signal transduction mechanisms and their roles in physiology. However, the need for more functional studies in a physiological context is apparent and would be enhanced by a crystallized structure of taste receptors for a more complete picture of their pharmacological mechanisms. Introduction G protein-coupled receptors (GPCRs) are the largest and the most diverse group of membrane receptors in eukaryotes. They are activated by a wide variety of ligands in the form of light energy, lipids, sugars, peptides, and proteins (Billington and Penn, 2003; Schoneberg et al., 2004; Lundstrom, 2009) which convey information from the outside environment into the cell to mediate their corresponding functional responses. The conformational changes of GPCRs upon ligand binding initiate a series of biochemical reactions within the cell. These intracellular reactions regulate sensory functions of smell, taste, and vision, and a wide variety of physiological processes such as secretion, neurotransmission, metabolism, cellular differentiation, inflammation, and immune responses (Lagerström and Schiöth, 2008; Rosenbaum et al., 2009; Venkatakrishnan et al., 2013; Ahmad et al., 2015). Taste is one of the most important sensations in human life, enabling us to perceive different tastes from the diverse range of food available in nature, and is a major determinant of our ingestion decisions. The anatomical units of taste detection are taste receptor cells (TRCs) that are assembled into taste buds distributed across different papillae of the tongue and palate epithelium. Taste processing is first achieved at the level of TRCs that are activated by specific tastants. They transmit information via sensory afferent fibers to the gustatory cortex in the brain for taste perception (Figure 1). Three different morphologic subtypes of TRCs in taste buds sense the different tastes we perceive. Type I glial-like cells detect salty taste while type II cells expressing GPCRs detect sweet, umami, and bitter tastes. Type III cells sense sour stimuli (Janssen and Depoortere, 2013). FIGURE 1. A schematic diagram shows taste signal transmission between the tongue and brain. Taste buds present in different papillae in the tongue and palate contain taste receptor cells (TRC) which contain taste G protein-coupled receptors (GPCRs). The left side shows how afferent nerves transmit a signal to the gustatory cortex in the brain via cranial/glossopharyngeal nerves. The right side shows taste buds with taste TRCs and a simplified signal transduction pathway of taste receptors where taste GPCRs are activated by a tastant that in turn recruits a specific G protein that further induces intracellular calcium release (created with BioRender.com). Sweet and umami stimuli are transduced by Type 1 taste GPCRs while the bitter taste is sensed by Type 2 taste GPCRs (Figure 2; Table 1). The more recently described kokumi sensation is mediated by another GPCR, the calcium-sensing receptor (CaSR) (Figure 2; Table 1). Taste GPCRs are activated by specific taste ligands present in foods and recruit G proteins to activate downstream signaling effectors (Figure 3). TABLE 1. Taste GPCRs classification and their downstream signaling regulators. FIGURE 3. Schematic representation of signal transduction pathway of sweet, umami, bitter, and kokumi-calcium sensing receptors (CaSR) in taste receptor cells on the tongue. Ligand-induced stimulation of the sweet (TAS1R2/TAS1R3), umami (TAS1R1/TAS1R3), bitter receptors (TAS2Rs) and kokumi sensation expressed in type II taste cells within taste bud activate a trimeric G protein composed of α-gustducin (Gα-gust) in sweet, umami, bitter and Gα-q/11 in kokumi-receptor and a complex consisting of Gβγ proteins. The released Gβγ-complex activates phospholipase C isoform β2 (PLCβ2) which then induces the production of inositol 1,4,5-trisphosphate (IP3) and diacylglycerol (DAG); the second messenger IP3, in turn, activates the IP3 receptor (IP3R), an intracellular ion channel that allows Ca2+ release from the intracellular endoplasmic reticulum (ER store). An increase in intracellular Ca2+ then activates the complex of transient receptor potential cation channel subfamily M member 4 and 5 (TRPM4/5) that are plasma membrane-localized sodium-selective channels which leads to depolarization and subsequent activation of voltage-gated sodium channels (VGSC). The combined action of increased Ca2+ and membrane depolarization activates the complex of calcium homeostasis modulator 1 and 3(CALHM1/3) channel and pannexin1 channels, thus resulting in the release of the neurotransmitter ATP. Increased ATP, in turn, activates P2X ionotropic purinergic receptors 2 and 3 (P2X2/P2X3) on the afferent cranial nerve generating an action potential that subsequently signals to the gustatory cortex for sensory perception. Besides well-known taste GPCR pathways, connecting proteins semaphorin 7A (Sem 7A) and 3A (Sem 3A) are depicted in close contact with sweet and bitter receptors as they provide instructive signals that fine-tune to sweet or bitter ganglion neurons, respectively. VFT, venus flytrap domain; CRD, cystine-rich domain; ECD, extracellular domain. (created with BioRender.com). In this review, we will first explore the basic architecture of the gustatory sensory system and its peripheral signal transmission. Then we will discuss taste GPCR signal transduction mechanisms for the different taste modalities, their molecular structure, and the conformational changes that occur following orthosteric/allosteric binding of endogenous and food-derived ligands. Taste Buds and Neural Transmission In mammals, taste buds on the tongue comprise 50–100 elongated epithelial cells and a small number of proliferative basal cells (Sullivan et al., 2010). Ultrastructural studies and patterns of gene expression with cell function reveal three distinct anatomical types of TRCs within each taste bud: Type I, Type II and Type III (Murray, 1986) (refer to Figure 2; Table 2). TABLE 2. Summary of taste receptor cell characteristics. Type II TRCs express either sweet, umami, or bitter taste receptors at their cell surface. These receptors share some commonality with their signal transduction mechanisms that are intrinsic to TRCs. Taste GPCRs (sweet, umami, and bitter) couple to heterotrimeric G proteins that include Gα-gustducin, Gβ3, and Gγ13 (Huang et al., 1999) and initiate a series of signal transduction cascades involving activation of phospholipase C-β2 (PLCB2), production of inositol-1,4,5-triophosphate (IP3), and IP3-dependent Ca2+ release from the endoplasmic reticulum (ER) via the IP3 receptor (IP3R). The increased intracellular [Ca2+]i then activates the transient receptor potential cation channel subfamily M member 4 and 5 (TRPM4/5) in the basolateral plasma membrane, leading to membrane depolarization that triggers Na+ action potential firing, and depolarization-induced release of ATP. In turn, ATP acts as the primary neurotransmitter stimulating purinergic receptors 2 and 3 (P2X2 and P2X3) on afferent cranial nerves whose activation triggers an action potential that subsequently activates the gustatory cortex in the brain (McLaughlin et al., 1992; Wong et al., 1996; Margolskee, 2002). α-gustducin is a distinct G protein selectively expressed in ∼30% of type II TRCs and shares 80% identity with retinal protein α-transducin (McLaughlin et al., 1992) and is a key contributor to signal transduction for sweet and bitter taste receptors (McLaughlin et al., 1992; Wong et al., 1996; Margolskee, 2002). An important aspect of taste transduction is how ATP signaling is conducted. Recent studies have discovered that calcium homeostasis modulators 1 and 3 (CALHM1/3) are enriched in type II TRCs where they interact and form a functional complex. Their genetic deletion abolishes responses to sweet, bitter, and umami tastes, supporting the requirement of the CALHM1/3 complex as an ATP release channel for the GPCRs mediated tastes (Taruno et al., 2013; Ma et al., 2018). New information has provided insight into how specific taste qualities are fine-tuned to recognize their partner ganglionic neurons in the brain. Lee et al. (2017) discovered semaphorin proteins, 7A and 3A as the physical links between sweet and bitter TRCs, respectively, and their partner ganglion neurons in the brain. It remains to be determined what physical links exist between umami TRCs and their corresponding neurons in the brain. Delineating the underlying molecular basis for this interaction would provide a further understanding of purinergic transmission in the taste system. In addition, whether these mechanisms are relevant for kokumi sensation has not yet been investigated, despite CaSR having distinct expression in TRCs and significant functional synergy with other prominent taste qualities (sweet, umami, and salty). Moreover, there is still debate regarding the recognition of kokumi as a sixth taste entity, consequently, the calcium-sensing receptor (CaSR) is not yet included in the nomenclature for any subtypes of taste GPCRs, although it would best fit with Type 1 taste receptors. Type 1 Taste G Protein-Coupled Receptors (Sweet and Umami) The type 1 taste receptors (TAS1Rs) belong to the class C GPCRs, which possess a large N-terminal extracellular domain (ECD) fused to the heptahelical seven transmembrane domain (TMD). The ECD is further divided into two ligand-binding domains (LBD1 and LBD2), having a bi-lobed structure called a Venus flytrap domain (VFT) due to its resemblance to this shape (Hoon et al., 1999). Except for GABAB receptors, a cysteine-rich domain (CRD) connects the VFT to the TMD (Leach and Gregory, 2017). In contrast to other receptors from this class C of GPCRs, such as the metabotropic glutamate receptor (mGluR) or γ-aminobutyric acid type B receptors (GABABRs) which function as homo- or heterodimers, respectively (Jones et al., 1998; Kaupmann et al., 1998; White et al., 1998; Kunishima et al., 2000), the TAS1Rs function as obligatory heterodimers. The distinct expression pattern of TAS1R1 and TAS1R2 in different subsets of murine cells led to the idea that they could detect two different taste profiles. However, following the discovery of the TAS1R3 subtype, it was clear that when TAS1R1 heterodimerizes with TAS1R2, the receptor detects sweet taste substances (Nelson et al., 2001; Ohkuri et al., 2009; Kim et al., 2017). On the other hand, if heterodimerized with TAS1R3 (TAS1R1/TAS1R3), it is responsible for umami or amino acid taste detection (Li et al., 2002; Nelson et al., 2002). Please refer to figure 4A for the basic structure of sweet and umami receptors. Figure \(\PageIndex{x}\) shows an interactive iCn3D model of Taste receptor type 1 member 2 (TAS1R2) AlphaFold model (uniprot Q8TE23) The gray is the predicted transmembrane helices. The cyan is the intracellular domain. The blue is the extracellular domain. The predicted model of the structure has high confidence except for the yellow/orange at the distal end of the extracellular domain. Key residues in the ligand binding domain are shown as sticks, CPK colors, and labeled. Sweet Taste Signal Transduction Mechanisms The TAS1R2/TAS1R3 receptor recognizes a wide variety of sweet substances including natural sugars, artificial sweeteners, amino acids, and proteins (Li et al., 2002; Xu et al., 2004; Jiang et al., 2005a; Jiang et al., 2005c) (Table 3). This was demonstrated in studies using heterologous expression systems as well as knockout mice for TAS1R2 and/or TAS1R3 subtypes that showed a blunted response to sugars, sweeteners, and D-amino acids, confirming the TAS1R2/TAS1R3 heterodimer as the main sweet taste receptor in vivo (Li et al., 2002; Zhao et al., 2003; Xu et al., 2004). TABLE 3 | Agonists of sweet taste receptors along with their EC50 values. Agonists Nature Binding pocket EC50 (mM) References Sucrose Natural carbohydrate VFT (TAS1R2 and TAS1R3) 62 (Li et al., 2002; Servant et al., 2010; Zhang et al., 2010; Zhang et al., 2003) Aspertame Peptide VFT (TAS1R2) 0.75 (Li et al., 2002; Liu et al., 2011; Masuda et al., 2012) Neotame Peptide VFT (TAS1R2) 5 (Li et al., 2002; Masuda et al., 2012) Cyclamate Sulfamate TMD (TAS1R3) 3.1 (Xu et al., 2004; Jiang et al., 2005c) Brazzein Protein CRD (TAS1R3) 0.08 (Li et al., 2002; Jiang et al., 2004; Ide, et al., 2009; Masuda et al., 2012) Thaumatin Protein CRD (TAS1R3) 0.005 Masuda et al., 2012; Jiang et al., 2004 Monellin Protein VFT (TAS1R3), VFT (TAS1R2) 0.01 Koizumi et al., 2007; Jiang et al., 2004 Neoculin Protein VFT (TAS1R2) 0.001 (Jiang et al., 2004; Koizumi et al., 2007) Saccharin N sulfonyl amide VFT (TAS1R2) 0.19 (Li et al., 2002; Masuda et al., 2012; DuBois, 2016) Suosan, cyanosuasan Arylurea VFT (TAS1R2) ND (Tinti and Nofre, 1991; Du Bois, 2016) SC-45647 Guanidinoacetic acid VFT (TAS1R2) 0.3 (DuBois, 1995; Sanematsu et al., 2014) Sucralose Halogenated carbohydrate VFT (TAS1R2 and TAS1R3) 0.06 (Li et al., 2002; Masuda et al., 2012) Acesulfame K Sulfamate ester VFT (TAS1R2) 0.54 (Li et al., 2002; Masuda et al., 2012) Perillartine Oxime, ethoxyphenyl urea, alkoxyaryl urea, TMD (TAS1R2) 15 (Li et al., 2002; Servant et al., 2010) Dulcin Ethoxyphenyl urea TMD (TAS1R2) 0.01 (Servant et al., 2010) S819 Alkoxyaryl urea TMD (TAS1R2) 0.025 (Zhang et al., 2008) D-tryptophan Amino acid VFT (TAS1R2) 2.09 (Li et al., 2002; Masuda et al., 2012) Xylitol, sorbitol Polyols VFT (TAS1R2) ND (Mahalapbutr et al., 2019) Maltotriose, acarbose Oligosaccharide, pseudotetrasaccharide ND ND (Pullicin et al., 2017; Pullicin et al., 2019) Where VFT, venus ytrap domain; TMD, transmembrane domain; ND, not determined. TABLE 3. Agonists of sweet taste receptors along with their EC50 values. The sweet receptor couples to heterotrimeric Gα-gustducin which include Gβ3 and Gγ13, as mice lacking Gα-gustducin, showed a reduced response to sweet substances either natural or artificial (McLaughlin et al., 1992; Wong et al., 1996; Margolskee, 2002). Moreover, a point mutation in the C-terminal region of gustducin (G352P) (critical for its receptor interaction) results in the loss of its ability to activate taste GPCRs while keeping other functions intact. Further, G352P acts as a dominant negative to block heterotrimeric G protein interaction with taste receptors and disrupts the responses to sweet and bitter compounds in both wild-type (WT) and null mice (Ruiz-Avila et al., 2001). In addition, the G352 mutant further reduces any residual sweet/bitter taste responses of the null mice by acting as a “βγ sink” to bind all unbound βγ-subunits and remove them from the viable pool of G protein heterotrimers available to the receptor (Ruiz-Avila et al., 2001). These observations confirm the essential requirement of Gα-gustducin in sweet and bitter taste transduction. In addition to the Gα-gustducin pathway, sweet taste transduction occurs via two additional signaling pathways involving different secondary messengers. The first one involves cAMP and the second one involves IP3. Normally, sugars elevate the level of cAMP, while sweeteners stimulate IP3 production (Tonosaki and Funakoshi, 1988; Uchida and Sato, 1997). Sucrose or other sugars bind to either TAS1R2 or TAS1R3 and recruit Gαs protein that leads to increased cAMP levels which initiate the influx of cations through ion channels. Alternatively, cAMP activates protein kinase A that leads to TRC cell depolarization resulting in an influx of calcium ions and neurotransmitter release (Avenet et al., 1988; Tonosaki and Funakoshi, 1988; Margolskee, 2002). Sweetener binding to the TAS1R2/TAS1R3 heterodimer recruits Gα-gustducin proteins that stimulate PLCβ2 which in turn hydrolyzes phosphatidylinositol 4,5-bisphosphate (PIP2) to diacylglycerol (DAG) and IP3 (Margolskee, 2002; Chandrashekar et al., 2006). IP3R3 (Hisatsune et al., 2007) induced Ca2+ release from ER stores (Figure 3) activates TRPM5 (Zhao et al., 2003; Hisatsune et al., 2007; Dutta Banik et al., 2018) that leads to an action potential (Yoshida et al., 2005; Yoshida et al., 2006) and subsequent release of neurotransmitters. Interestingly, Dutta Banik et al. (2018) confirmed that TRPM4 also mediates taste signaling independent of TRPM5, and knocking out both channel proteins (TRPM4/5) abolishes the sweet, umami, and bitter taste response completely. This revealed another layer of complexity to sweet signal transmission. This in-depth mechanistic research has increased our understanding of sweet and bitter receptors and presents a challenge to dissect the taste signal transmission pathways for umami and kokumi as well. Structural, Molecular, and Conformational Changes of Sweet Receptor Since the sweet taste receptor has not yet been crystallized, determining the structure of the sweetener binding site and mechanism of activation has been a challenge. Based on homology with other class C GPCRs (mGluRs and GABABRs), multiple studies propose similar activation mechanisms for the sweet receptor (Kunishima et al., 2000; Tsuchiya et al., 2002; Jingami et al., 2003; Muto et al., 2007; Perez-Aguilar et al., 2019). The many different sweet agonists and their diverse binding sites across receptor domains (VFT, TMD, and CRD) (Table 3) may explain its complex yet broadly tuned nature. For example, a single residue in VFT (I60) of TAS1R3 of the TAS1R2/TAS1R3 heteromer is required for a saccharin preference in inbred mouse strains (Max et al., 2001; Reed et al., 2004). Several studies utilizing homology and computational modeling based on the crystal structure of mGluR and GABABRs have predicted structural and functional aspects of orthosteric and allosteric binding sites for the sweet receptor (Kim et al., 2017; Cheron et al., 2019; Park et al., 2019). They reported that both VFT regions undergo ligand-dependent conformational changes and intersubunit interactions between ECDs that further stabilize heterodimer formation for subsequent downstream signaling (Perez-Aguilar et al., 2019). The binding of orthosteric agonists to VFT of TAS1R2 leads to major conformational changes that form a TMD6/TMD6 interface between TMDs of TAS1R2 and TAS1R3, which is consistent with the activation process observed biophysically on the mGluR2 homodimer. The initial role of the bound agonist is to pull the bottom part of VFT3 (VFT of TAS1R3) toward the bottom part of VFT2 (VFT of TAS1R2) to transmit this movement from VFT2 (where agonists bind) through the VFT3 and the CRD3 (VFT and CRD of TAS1R3) to the TMD3 (TMD of TAS1R3). This facilitates G protein coupling and downstream signaling. The CRDs are crucial in this streamlined relay of structural changes where disulfide bonds provide rigidity to the CRD and amplify the mechanical constraints that help in attaining an active conformation (Cheron et al., 2019). This is empirically supported by a study in which a single mutation (A537P) in the CRD of TAS1R3 abolished the response to all sweeteners, indicating that the CRD3 must couple ligand binding in VFT2 to the conformational changes required in TMD3 for receptor activation. Trafficking and cell surface expression are also crucial factors for sweet taste transduction. Molecular modeling with mutagenesis scanning revealed specific regions consisting of hydrophobic residues in ECD (site II; at the tip of CRD) and TMD regions (site IV; includes TMD6 and the cytoplasmic base of TMD5) of the TAS1R2 subunit to be important for dimerization with TAS1R3. Moreover, the CRD region and ECL2 domain of the transmembrane region seems to be important for surface co-expression of the TAS1R2/TAS1R3 dimer. In particular, the cytosolic C-terminus portion of the CRD region of TAS1R2 needs to be properly folded for coexpression and trafficking (Park et al., 2019). This reflects the difficulty in expressing these receptors at consistent levels in mammalian cell lines (Li et al., 2002; Shimizu et al., 2014). Positive Allosteric Modulation of Sweet Receptor Class C GPCRs pose an ideal target for allosteric modulation either positive (PAM) or negative (NAM). PAMs show little or no agonist activity on their own but significantly enhance agonist activity. Sweet taste is a major target of the food industry globally and non-caloric sweeteners are highly sought to exploit a huge commercial market. In a first comprehensive high throughput screen by Servant et al. (2010), novel PAMs (SE1, SE2, SE3; Table 4) for the sweet heteromer were reported that were not sweet on their own but significantly enhanced the sweetness of sucralose or sucrose. Agonist binding to the VFT region of TAS1R2 facilitates a closed conformation which constitutes an active state of the sweet receptor, while its open conformation represents an inactive state. Molecular modeling and mutagenesis studies revealed that these PAMs follow a similar mode of binding as that reported for umami PAMs (IMP and GMP). They bind near the opening of the binding pocket of the VFT region adjacent to their agonists, through Van der Waals and hydrogen bonding interactions, and utilize several critical residues for their activity. Although these residues are not in direct contact with any receptor-bound sweetener, mutation of some of them (K65, Y103, L279, D307, and R383) diminishes the response to sweeteners suggesting that these residues normally stabilize the closed conformation. The initial closing of the VFT region by agonist binding and further stabilization of the closed conformation by subsequent binding of SE modulators occurs in two steps. First, by interacting with the ECD region of TAS1R2, and second, by strengthening the hydrophobic interactions between the two lobes of ECD and lowering the free energy needed for their closure (Zhang et al., 2010). TABLE 4 | Sweet taste receptor’s positive allosteric regulators with concentration (used in cell-based assays in studies) and negative allosteric modulators with their IC50 values. Positive allosteric modulators (PAMs) Nature Binding pocket Conc. (mM) References SE1, SE2, SE3 Undisclosed VFT (TAS1R2) 0.05 (Servant et al., 2010; Zhang et al., 2010) Neohesperidin dihydrochalcone (NHDC) Flavonoid TMD (TAS1R3) 0.25 (Jiang et al., 2005c; Winnig et al., 2007) Unnatural tripeptides (several) Biaryl derivative tripeptides ND 2 – 20 Yamada et al., 2019 Sodium, cholesterol Cation, lipid TMD (TAS1R2) ND Perez-Aguilar et al., 2019 NAMs IC50 (mM) Lactisole Carboxylic acid salt TMD (TAS1R3) 0.041 (Jiang et al., 2005c) (2-(2,4-dichlorophenoxy)propionic acid) Carboxylic acid salt TMD (TAS1R3) 0.006 (Nakagita et al., 2019) Gymnemic acid Triterpenoid glycoside TMD (TAS1R3) 6.9 (Sanematsu et al., 2014) Clofibric acid Herbicide TMD (TAS1R3) 1.4 (Maillet et al., 2009; Kochem and Breslin, 2017) Amiloride Diuretic TMD (TAS1R2) 0.87 (Imada et al., 2010; Zhao et al., 2018) Umami compounds: MSG, Glu-Glu, Glu-Asp Peptides VFT (TAS1R2) ND (Shim et al., 2015) Where VFT, Venus ytrap domain; TMD, transmembrane domain; ND, not determined. TABLE 4. Sweet taste receptor’s positive allosteric regulators with concentration (used in cell-based assays in studies) and negative allosteric modulators with their IC50 values. Using a high throughput chemical screening approach and heterologous expression of the TAS1R2/TAS1R3 heteromer, several unnatural tripeptides with a novel core biaryl structure were found as potential sweet enhancers (Yamada et al., 2019). This study divided the potential molecule into three parts namely, “head and linker” which together are essential for its sweet enhancer activity, while the “tail” determines the level of activity. This approach provided some useful inputs toward the synthesis of potent PAMs. Firstly, an amine incorporated at the α-position of carbonyl moiety in the tail structure interacts with the TAS1R2 subunit thereby increasing allosteric activity. Secondly, additional hydrophobic substitutions in the tail structure provided an increased allosteric activity to the molecule. Lastly, the distance between the head and linker and the insertion of an amide bond is crucial for its synthesis. Although their binding characteristics and allosteric mechanisms are not yet known, these observations provide a starting point to identify and synthesize new sweet PAMs in the future. Small molecule PAMs can also bind to the transmembrane domain in class C GPCRs, in contrast to agonist which binds to the extracellular domain (Urwyler, 2011). For example, the flavonoid sweetener, neohesperidin dihydrochalcone (NHDC) binds to TMD regions to enhance the agonist-induced sweet response. It interacts with a receptor binding pocket in the TMD of TAS1R3 and requires seventeen critical residues in TMDs and extracellular loop 2 for its allosteric activity (Winnig et al., 2007). These residues also contribute to cyclamate and lactisole binding sites. Among seventeen residues, eight alter receptor activation by NHDC (Q6373.29, S6403.32, H6413.33, Y6994.60, W7756.48, F7786.51, L7826.55, and C8017.39) and influence lactisole mediated inhibition. Similarly, nine of the seventeen residues (Q6373.29, H6413.33, H721ex2, S7265.39, F7305.43, W7756.48, F7786.51, L7826.55, and C8017.39) mediate activation by cyclamate, while six (Q6373.29, H6413.33, W7756.48, F7786.51, L7826.55, and C8017.39) influence receptor inhibition by lactisole as well as receptor activation by cyclamate [superscript refers to the nomenclature suggested for class C GPCRs by Pin et al. (2003) where first number denotes TMD region and the second number denotes residue position from the most conserved residue]. Notably, three critical residues in TMD6 (W7756.48, F7786.51, L7826.55) and one in TMD7 (C8017.39) of TAS1R3 were found crucial for allosteric binding, as their mutation to alanine altered the receptor's sensitivity to NHDC and cyclamate, as well as to the inhibitor lactisole (Winnig et al., 2005). Therefore, TMD6 and TMD7α helices of TAS1R3 are integral to allosteric modulation of the sweet receptor, implicating them in TAS1R2 and TAS1R3 subunit interactions and indicating an important role for this structural region in the conformational changes involved in receptor activation. Furthermore, these residues are conserved across mammalian species (Cheron et al., 2019). Negative Allosteric Modulation of Sweet Receptor Like PAMs, negative allosteric modulators (NAM) such as lactisole and gymnemic acid bind to the TMD region of TAS1R3 and inhibit sweet substance-induced responses. Lactisole, an aralkyl carboxylic acid not only inhibits sweet but also the umami receptor response in humans and presents a rare opportunity to study the structural cross-talk between these two taste qualities. Using heterologous expression and mutagenesis, Jiang et al. (2005b) reported that lactisole's sweet inhibition might be mediated by its binding to TMD3, TMD5, and TMD6 of TAS1R3 and induce a conformation change which restricts the movement required to stabilize the active state. Residues A7335.46 in TMD5, L7987.36 in TMD7, and R790ex3 in extracellular loop 3 were found to be crucially important for sensitivity to lactisole in humans (Jiang et al., 2005b). These observations were confirmed in a recent study where 2-(2,4-dichlorophenoxy)propionic acid (2,4-DP) was found to be a more potent antagonist and utilize the same residues as well as four additional ones (H6413.37, H7345.43, F7786.53, and Q7947.32) in binding to TAS1R3. Moreover, the (S)- isomer of both compounds was found to be more strongly bound to the TMD of TAS1R3 and be a more effective inhibitor [lactisole; (S)-lactisole IC50, 20 µM while (R)- lactisole exerted no inhibition at this concentration.; 2,4-DP: (S)-isomer was 10-fold more effective than (R)-2,4DP]. The (S)- lactisole isomer interacts with the TMD via its carboxyl group and stabilizes in only one orientation in the binding pocket that does not allow for very strong binding. In contrast, (S)-2,4- DP binds through two moieties simultaneously, a carboxyl group and an aromatic ring with two Cl groups and stabilizes in several different orientations through hydrophobic interactions that allow stronger binding, resulting in stronger negative allosteric modulation (Nakagita et al., 2019). These observations provide information about the relevance of structural modification in NAM compounds that could affect their interaction with the receptor. Although TMDs of TAS1R3 are the most likely regions responsible for allosteric modulation, TMDs and VFT regions of TAS1R2 cannot be ruled out completely. For example, the diuretic amiloride binds to TAS1R2 (TMD3, TMD5, TMD7) and inhibits the sweet response in a species-dependent manner (Zhao et al., 2018). Further, the umami compound [monosodium glutamate (MSG)] and peptides (Glu-Asp, Glu-Glu) bind to the VFT region of TAS1R2 and inhibit the sweet-induced response (Shim et al., 2015). These observations suggest that both subunits are important for the allosteric activity of TAS1R2/TAS1R3 and further structural studies are required to design novel sweet allosteric modulators. Umami Taste Signal Transduction Mechanisms In contrast to four well-known basic human tastes (sweet, bitter, salty, and sour), umami or ‘savory taste’ is relatively recent and was introduced in early 2000 by Kikuna Ikeda (Ikeda, 2002) as a new seasoning element in food. The main stimulus for the umami taste is the amino acid, L-glutamate present in the diet mainly in the form of MSG (Roper, 2007). Glutamate was first extracted from konbu/kombu (dried kelp of Fucus vesiculosus) and described as having a “unique taste” and “very different from other tastes”. The terminology “umami” comes from the Japanese word “umai” meaning “delicious.” Moreover, the taste of umami is also produced by food such as mushrooms and soy sauce that contain amino acids (L-aspartate), peptides, and synthetic ingredients similar to glutamate and some organic acids (Roper, 2007; Kinnamon, 2009) (Table 5). TABLE 5 | Umami receptor agonists with their EC50 values and other pharmacological properties. Agonist Nature EC50 (mM) Binding pocket References L-amino acids (glutamate, aspartate, alanine, serine, asparagine, arginine, histidine, threonine, glutamine) L-theanine Amino acids Amino acid (plant origin) 3 (glutamate), ND for others ND VFT (TAS1R1) VFT (TAS1R1) (Li et al., 2002; Nelson et al., 2002; Zhang et al., 2008; Toda et al., 2013) (Narukawa et al., 2014) VFT, venus ytrap domain; ND, not determined. TABLE 5. Umami receptor agonists with their EC50 values and other pharmacological properties. The umami receptor (TAS1R1/TAS1R3) is a heteromeric member of the class C GPCRs, whereas most other receptors of this class exist as homodimers (Nelson et al., 2002; Temussi, 2009; Leach and Gregory, 2017). TAS1R1/TAS1R3 is the predominant umami taste receptor (Zhao et al., 2003; Behrens and Meyerhof, 2011) and the TAS1R1 subtype is critical for sensing umami taste as its deletion abolished the response to umami taste stimuli (Mouritsen and Khandelia, 2012). However, TAS1R1/TAS1R3 is not the only receptor capable of detecting umami ligands (Chaudhari et al., 2000; Kunishima et al., 2000; Li et al., 2002; Nelson et al., 2002). Studies using heterologous expression, afferent nerve recordings, and behavioral experiments have confirmed that metabotropic glutamate receptors 1, and 4 (taste-mGluR1 and taste-mGluR4) also sense umami stimuli (Chaudhari et al., 2000; Kunishima et al., 2000; Li et al., 2002; Nelson et al., 2002). Notably, TAS1R3 knock-out mice show a strongly diminished response to glutamate and sweet stimuli (Damak et al., 2003) and taste cells isolated from these mice respond to IMP and glutamate which is abolished in presence of mGluR antagonists (Pal Chaudhry et al., 2016). TAS1R1/TAS1R3 is not only activated by glutamate but this activation is strongly enhanced in the presence of 5′-ribonucleotides, (inosine 5′ monophosphate; IMP) a response that is a hallmark of umami taste (Rifkin and Bartoshuk, 1980). The main transduction components following the activation of TAS1R1/TAS1R3 are similar to those for sweet taste (Zhang et al., 2003), i.e., α-gustducin (and γ13/β1 or β3), PLCβ2, IP3R, and TRPM4/5. Cyclic nucleotides may also contribute to the transduction of umami taste in TRCs. When taste tissue is stimulated with umami, its cyclic AMP level is decreased (Abaffy et al., 2003). However, the consequence of decreased cAMP in TRCs has not yet been fully elucidated. Both α-transducin and α-gustducin are involved in umami taste signal transduction, as mice lacking the gene for one of these proteins showed a reduced response to this taste (He et al., 2004; Leach and Gregory, 2017). In the taste palate fungiform papillae, α-gustducin and α-transducin activate PDE that reduces cAMP levels. Ligand binding to the TAS1R1/TAS1R3 heterodimer releases Gβγ subunits to stimulate PLCβ2, which hydrolyzes PIP2 to DAG and IP3 (Kinnamon, 2009). IP3 then activates IP3R3 which results in the release of calcium ions from intracellular compartments (Clapp et al., 2001; Leach and Gregory, 2017) (Figure 3). Calcium ions activate TRPM5 and TRPM4 channels, leading to an influx of sodium ions, subsequent cell membrane depolarization, and finally release of ATP, which activates ionotropic purinergic receptors located in sensory fibers (Perez et al., 2002; Sugita, 2006). This pathway was confirmed when mice devoid of TRPM5, TRPM4, PLCβ2, and IP3R3 showed a reduced response to umami taste perception following glutamate stimuli (Damak et al., 2006; Kinnamon, 2009; Eddy et al., 2012). Structural, Molecular, and Conformational Changes of Umami Receptor In the last decade, several in-depth modeling and mutagenesis approaches have improved structural and molecular understanding of the umami receptor. The VFT regions of both subunits of TAS1R1/TAS1R3 comprise orthosteric and allosteric ligand binding sites for umami stimuli. Mutagenesis and molecular modeling studies reveal that the cognate agonist glutamate binds in the VFT region of the TAS1R1 subunit of TAS1R1/TAS1R3 and stabilizes the closed active receptor conformation. Moreover, four residues in the TAS1R1 VFT region (S172, D192, Y220, and E301) showed no detectable response to glutamate when they were mutated to alanine suggesting that they are critical for glutamate binding. The glutamate binding and stabilization of the closed conformation of TAS1R1, activates the downstream signaling pathway, while TAS1R3 remains in an open (inactive) conformation. Therefore, closure of the VFT is the key event that sensitizes umami taste receptor signal transduction (Lopez Cascales et al., 2010). Apart from glutamate, other L amino acids were also found to elicit functional responses by binding to the corresponding VFT region of TAS1R1. Six residues that contributed to the acidic amino acid agonist (L-glutamate and L-alanine) responses have been identified (S148, R151, A170, E174, A302, and D435). Allosteric Modulation of Umami Receptor Because of significant advancements in understanding and food industry application of umami taste, its allosteric modulators are sought after. Several allosteric umami ligands have been discovered with varying potency, only a few of which have been characterized at the molecular level. The best-characterized umami PAMs, the 5′-ribonucleotides: inosine 5′-monophosphate (IMP) and guanosine 5′-monophosphate (GMP), interact with the VFT region of the TAS1R1 subunit to enhance the glutamate-induced response that is the hallmark of umami taste (Table 6). IMP and GMP binding sites in the VFT are adjacent to that for glutamate binding. The mutation of four residues (H71, R277, S306, and H308) abolished the IMP/GMP-induced glutamate response suggesting their involvement in the allosteric binding of these nucleotides. Structurally, IMP and GMP stabilize the closed form of the TAS1R1 VFT region through electrostatic interactions and coordinate the positively charged residues that act as pincers. The ability of IMP and GMP to interact with the VFT region (as opposed to the TMD region) represents a unique mechanism of positive allosteric regulation within class C GPCRs (Urwyler, 2011). TABLE 6 | Umami receptor allosteric modulators with concentrations used in cell-based assays and other pharmacological properties. Allosteric modulators Nature Conc. (mM) Binding pocket References IMP/GMP Nucleotide 1 VFT (TAS1R1) (Li et al., 2002; Nelson et al., 2002; Zhang et al., 2008) Cyclamate Sodium 8 TMD (TAS1R3) (Xu et al., 2004) cyclohexylsulfamate Methional (3-methylsulfanylpropanal) 0.12 TMD (TAS1R3) (Toda et al., 2018) Lactisole (2-4-methoxyphenoxy propionic acid) Carboxylic acid salt 5 TMD (TAS1R3) (Xu et al., 2004) Clofibric acid (4- chlorophenoxy)-2-methylpropanoic Herbicide acid 4 TMD (TAS1R3) (Maillet et al., 2009; Kochem and Breslin, 2017) acid Where VFT, Venus ytrap domain; TMD, transmembrane domain. TABLE 6. Umami receptor allosteric modulators with concentrations used in cell-based assays and other pharmacological properties. In contrast to IMP and GMP which bind to the TAS1R1 extracellular domain, the well-known flavor compound methional and its analogs bind to the TMD region and allosterically regulate the umami receptor in a species-dependent manner (Toda et al., 2018). Importantly, methional utilizes several distinct residues in different TAS1R1 transmembrane domains (TMD2-7) to act as a PAM in the human umami receptor, yet it behaves as a NAM in the mouse counterpart. This unusual phenomenon provided an opportunity to study the mechanisms of both positive and negative modulation in TAS1R1 simultaneously (Toda et al., 2018). Construction of chimeric receptors between human (h) and mouse (m) and their functional analysis demonstrated that the TMD of TAS1R1 is the key domain for switching the PAM/NAM activities of methional. Point mutation substitutions between these species identified four residues (h/m; F768/L769, N769/H770, S799/T800, and S802/G803) that are collectively required to switch PAM/NAM activities. A similar mode of allosteric regulation and PAM/NAM mode switching has been reported for mGluR5 (Gregory et al., 2013) suggesting this is an unusual and distinct phenomenon of the class C GPCRs. Further, alanine scanning mutagenesis in TAS1R1 of the corresponding residues vital for the activity of other taste inhibitors (sweetener inhibitors; NHDC and cyclamate; sweet and umami taste inhibitor; lactisole) revealed three residues required for PAM (W6974.50 F7285.40 and F7325.44) and a single residue (F6423.40) for NAM. These results suggest that both the PAM and NAM activities of methional are conferred by residues that are distinct from those required for the PAM/NAM switch. Knowing that methional is an important part of food seasoning globally, these observations could help in maximizing its use in enhancing flavors along with amino acids and nucleotides. Despite PAMs being a central focus for umami allosteric modulation, there has also been considerable research on negative allosteric modulation where lactisole emerged as a prominent NAM of the umami receptor, TAS1R2/TAS1R3. Because umami and sweet receptors share the TAS1R3 subunit, findings from studies on sweet receptor lactisole binding are relevant. A comprehensive study on the sweet receptor identified critical residues within the TMD regions (S6403.32, H6413.33 in TMD3 and F7786.51, L7826.55 in TMD6) of TAS1R3 required for lactisole binding pocket and showed a large effect on sensitivity to lactisole (Xu et al., 2004; Jiang et al., 2005b). Because lactisole shares structural similarities with two other classes of compounds: fibrates and phenoxy-herbicides, researchers studied them to search for novel sweet/umami inhibitors (Maillet et al., 2009). The lipid-lowering drug, clofibric acid inhibits the TAS1R3 umami receptor-mediated response both in vitro and in vivo (Table 6). Like lactisole, clofibrate inhibits the umami taste from glutamate by binding with a similar affinity to TAS1R1/TAS1R3. However, its specificity against the umami receptor still needs to be validated alongside other umami taste receptors (mGluR1, mGluR4, or NMDA). TYPE 2 TASTE G PROTEIN-COUPLED RECEPTORS (BITTER RECEPTORS) Type 2 taste GPCRs are represented by bitter taste receptors that have a distinct subset of bitter sensing cells in type II TRCs and notably, 25 bitter taste receptors (TAS2Rs) are reported to be expressed in humans (Chandrashekar et al., 2000; Devillier et al., 2015; Behrens and Meyerhof, 2018). A significant amount of work has been done to explore the diversity among TAS2Rs and their agonists in taste biology (Adler et al., 2000; Behrens and Meyerhof, 2009; Behrens and Meyerhof, 2018). Some TAS2Rs (TAS2R3, TAS2R5, TAS2R13, TAS2R50) are narrowly tuned to structurally similar bitter compounds, whereas others are broadly tuned (TAS2R10, TAS214, TAS2R46), responding to several bitter compounds. Initially, it was believed that each bitter-sensitive type II TRC expressed every TAS2R isoform (Adler et al., 2000) but other studies suggest that TAS2Rs can be expressed differentially, allowing for possible discrimination among bitter compounds (Caicedo and Roper, 2001; Behrens and Meyerhof, 2009; Behrens et al., 2009). Please refer to figure 4B for the basic structure of the bitter receptor. Bitter Taste Signal Transduction Mechanisms The bitter taste is the most complex of all the five basic tastes and protects against the ingestion of toxic substances by eliciting an innate aversive response across species (Chandrashekar et al., 2006; Behrens and Meyerhof, 2018). The TAS2Rs that mediate bitter taste perception are among ∼50 TAS2Rs identified in mammals, and 25 are known to be expressed in humans (Adler et al., 2000; Devillier et al., 2015; Yoshida et al., 2018). The TAS2R family is the most diverse and binds to a wide range of agonists compared with the other taste GPCRs (Jaggupilli et al., 2016) (Supplementary Table 1). TAS2Rs are distinctive among class A GPCRs in that many of them bind agonists with low apparent affinity in the micromolar range, rather than the nanomolar range (Di Pizio et al., 2016). The activation of TAS2Rs by harmless, minute amounts of bitter compounds such as those contained in most vegetables would limit the availability of food resources appearing safe for consumption and therefore could negatively affect survival. Hence, the concentration ranges at which bitter taste receptors are activated are well-balanced to allow species to maintain a healthy diet yet avoid ingestion of spoiled food containing strongly bitter ligands. Hundreds of bitter compounds have been reported to evoke bitterness and activate human bitter receptors in different cell-based assays. These bitter agonists include plant-derived and synthetic compounds such as peptides, alkaloids, and many other substances (Supplementary Table 1). (Pronin et al., 2004; Meyerhof et al., 2010; Iwata et al., 2014). Some TAS2Rs are activated by a wide range of compounds, whereas others show strict specificity for a single bitter compound (Behrens et al., 2009; Sakurai et al., 2010a; Born et al., 2013). Interestingly, TAS2R31, TAS2R43, and TAS2R46 have around 85% sequence homology, but they bind to different agonists (Brockhoff et al., 2010; Jaggupilli et al., 2016), reinforcing the idea that each TAS2R might have a unique ligand-binding pocket. The canonical TAS2R signal transduction cascade signaling molecules shared among bittersweet and umami receptors (Wong et al., 1996; Huang et al., 1999; Mueller et al., 2005), includes the heterotrimeric G protein subunits (Gα-gustducin, Gβ3, and Gγ13), (Ishimaru, 2009; Shi and Zhang, 2009), a phospholipase C (PLCβ2), an inositol trisphosphate receptor (InsP3R), and the TRPM5 ion channel. Upon receptor activation by bitter ligands, the G protein α-gustducin dissociates from its βγ subunits. The latter activates PLCβ2, leading to a release of Ca2+ from IP3-sensitive Ca2+ stores, resulting in Na+ influx through TRPM5 channels. This Na+ influx depolarizes the cells and causes the release of neurotransmitter ATP through gap junction hemichannels or CALHM1 ion channels (Finger et al., 2005; Chaudhari and Roper, 2010; Taruno et al., 2013) (Figure 3). Structural, Molecular, and Conformational Changes of Bitter Receptors Classification of TAS2Rs has always been ambiguous because they were originally considered to be a distinct family (Horn et al., 2003) or grouped with the frizzled receptors (Fredriksson et al., 2003; Jaggupilli et al., 2016), but most recent analyses (Di Pizio et al., 2016) support their classification with Class A GPCRs. The ability of bitter taste receptors to interact with numerous structurally diverse substances compared to other GPCRs is remarkable and includes a wide range of drugs/antibiotics, polyphenols, bacterial metabolites, salts, and metal ions (Supplementary Table 1). Therefore, exploring the criteria for the identification of highly heterogeneous bitter compounds with pronounced selectivity has become a major research area. Some of these studies rely solely on in silico homology/computational modeling (Dai et al., 2011; Tan et al., 2012; Di Pizio et al., 2020; Dunkel et al., 2020) and others on in vitro genetic modification and functional assay systems (Pronin et al., 2004; Nowak et al., 2018; Jaggupilli et al., 2019). As a group of over ∼50 receptor subtypes, TAS2Rs recognize structurally diverse agonists where some are broadly tuned (TAS2R46, TAS2R14, TAS2R10, and TAS2R43) recognize diverse agonists, while others (TAS2R1, TAS2R4, TAS2R7) show strong selectivity and narrow tuning (Liu et al., 2018; Wang et al., 2019). The agonist binding cavity in most bitter GPCRs is located deep within their transmembrane domain (TMD), except TAS2R7 in which it resides on the extracellular surface (Liu et al., 2018). TAS2Rs are also distinct in containing highly conserved TMD regions, with thirteen key residues and two motifs (LXXXR in TMD2 and LXXSL in TMD5) that are absent in class A GPCRs, and may reflect their different activation mechanisms (Singh et al., 2011). LXXSL plays a structural role by stabilizing the helical conformation of TMD5 at the cytoplasmic end and a functional role by interacting with residues in intracellular loop 3 (ICL3) which is important for proper receptor folding and function (Singh et al., 2011). Moreover, mutation of the conserved residues in LXXSL and LXXXR motifs results in protein misfolding and poor surface expression (Singh et al., 2011; Pydi et al., 2014a). The initial study highlighting the structure–activity relationship of bitter taste receptors was performed with receptors belonging to a subfamily of closely related TAS2Rs (Pronin et al., 2004). By physically swapping the extracellular loop 1 (ECL1) between TAS2R43 and TAS2R31, chimeric TAS2R31/TAS2R43 (ECL) gained responsiveness to the compound n-isopropyl-2methyl-5-nitrobenzenesulfonamide (IMNB), whereas the reverse chimera TAS2R31 (ECL)/TAS2R43 lost responsiveness for IMNB. Although this report supports an important contribution of residues located within the transmembrane region of the investigated receptors, the extracellular loops appear to be of importance for agonist selectivity. This empirical finding contrasts with earlier computational studies which predicted the agonist binding site to lie within the helical bundle of TAS2Rs without particular contacts between extracellular loops and docked agonists (Floriano et al., 2006; Miguet et al., 2006). Bitter Receptor Ligand Binding Pocket The emergence of TAS2Rs as the most broadly tuned taste receptors might give the impression that their specific interaction with numerous agonists is because of several binding pockets that accommodate subgroups of bitter compounds. However, structure–function analysis of TAS2Rs (except for TAS2R7) has demonstrated the presence of only a single agonist binding pocket comprising the upper parts of TMD2, TMD3, TMD5, TMD6, and TMD7. The reason for their broad tuning and recognition of such a broad spectrum of agonists might most likely be attributed to the presence of an additional extracellular binding site called a “vestibular site,” in addition to the orthosteric selecting as reported for TAS2R46 (Sandal et al., 2015). This two-site architecture offers more ligand recognition points than a single one and thus might help in selecting the appropriate agonists. Moreover, the presence of the vestibular site may also help to discriminate among the wide spectrum of bitter ligands. Although broadly tuned receptors (TAS2R46, TAS2R31, and TAS2R43) have high homology in amino acid sequence, their agonist profiles only slightly overlap (Kuhn et al., 2004; Brockhoff et al., 2007; Di Pizio and Niv, 2015) which suggests the involvement of key residues at different positions in agonist specificity. Consequently, when strychnine interacting positions in TAS2R46 (residues differ at this position in TAS2R31, TAS2R43) were exchanged between these two receptors not only was the strychnine responsiveness transferred to the recipient receptor (TAS2R31, TAS2R43), but also sensitivity to additional TAS2R46 agonists (absinthin and denatonium). Sensitivity to activation by aristolochic acid was lost in the mutant receptors (Brockhoff et al., 2010). This experimental evidence supports the presence of a common agonist binding pocket and agrees with other studies on TAS2R16, TAS2R14, and TAS2R7 receptors (Sakurai et al., 2010a; Sakurai et al., 2010b; Thomas et al., 2017; Liu et al., 2018; Nowak et al., 2018). Recent studies used homology modeling and mutagenesis to elucidate the nature of the ligand-binding pocket in TAS2R7, TAS2R14, and TAS2R16 receptors (Thomas et al., 2017; Liu et al., 2018; Nowak et al., 2018). They reported that the binding pocket is flexible and wide open to accommodate molecules of diverse sizes and shapes, and thus permits chemical modifications among agonists as well (Thomas et al., 2017; Liu et al., 2018; Nowak et al., 2018). Although the molecular basis for the promiscuity of bitter receptors is attributed to their apparent flexible spacious binding site, future work elucidating the contact points between TAS2Rs binding site residues and its agonists in terms of additional binding locations is required. Bitter Receptors Ligand Binding Domain and Amino Acid Residues A majority of the TAS2R studies are based on molecular modeling, mutagenesis, and heterologous expression systems (Biarnes et al., 2010; Brockhoff et al., 2010; Tan et al., 2012; Nowak et al., 2018; Shaik et al., 2019) suggest that the ligand binding pocket is formed by several key residues in most TMDs (TMD1, TMD2, TMD3, TMD5, TMD6, and TMD7), except for TMD4. Studies show similarities as well as differences regarding residues and positions involved in agonist-receptor interactions. However, most of them agree that besides position N3.36 in TMD3 (superscript as per Ballestros-Weinstein nomenclature for class A GPCRs) (Ballesteros and Weinstein, 1995) and other residues (L3.32, L3.33, and E3.37) in its close proximity, play a role in agonist activation of several broadly tuned TAS2Rs (TAS2R1, TAS2R16, TAS2R30, TAS2R38, TAS2R46) (Pronin et al., 2004; Biarnes et al., 2010; Brockhoff et al., 2010; Sakurai et al., 2010b; Dai et al., 2011). In contrast, for the narrowly tuned TAS2R7, one position in TMD3 (H943.37) and another in TMD7 (E2647.32) were found crucial for metal ion binding (Wang et al., 2019). Mutagenesis and molecular modeling revealed that these two residues contribute to the metal ion binding pocket in TAS2R7. Moreover, metal ions bind distinctively to residues lining the binding pocket and interestingly, the presence of calcium in the assay solution appears to affect the TAS2R7 response to metal ions. It is not clear how calcium affects metal ion binding to TAS2R7, but it might work cooperatively with certain ions and not others. Future studies focusing on structural interactions between the receptor and metal ions will provide further insights into how they activate the receptor. In TMD2, two studies suggest that position N2.61 is critical for binding in TAS2R1 (Singh et al., 2011) and TAS2R46 (Brockhoff et al., 2010). Likewise, in TMD7, position 2657.39 is implicated in binding to TAS2R46 (E265) and TAS2R1 (I263) (Dai et al., 2011). In TMD5, position H5.43 is implicated in binding in TAS2R16 and E5.46 in TAS2R1 (Dai et al., 2011) while, in TMD7, position E7.32 was crucial for metal ion binding (Wang et al., 2019). These residues represent putative contact points for agonist interaction and form a pattern of being spaced one helical turn from each other. Recent mutagenesis studies (Nowak et al., 2018; Di Pizio et al., 2020) performed in broadly tuned TAS2R14 with agonists (aristolochic acid, picrotoxinin, thujone) found several residues in TMDs to be involved in agonist binding. However, in contrast to TAS2R10 (Born et al., 2013) and TAS2R46 (Brockhoff et al., 2007), mutation of TAS2R14 did not result in a complete loss of function for all agonists but a varied reduction in responsiveness or selectivity toward agonists. Among several mutants, only mutation of W89A resulted in a complete loss of responsiveness against picrotoxinin while others showed more subtle agonist selective changes. This indicates that TAS2R14 is not streamlined for the most sensitive detection of selected agonists, but rather tailored to detect numerous diverse agonists, with comparatively lower apparent affinity. The binding characteristics of bacterial acyl homoserine lactones (AHLs) on TAS2Rs (TAS2R4, TAS2R14, and TAS2R20) suggest the presence of a single orthosteric site situated close to the extracellular surface and reinforce the significant role of the extracellular loop structure (ECL2) in TAS2R ligand binding and activation (Jaggupilli et al., 2018). The crucial AHL binding residues in TAS2R4 and TAS2R14 are predominantly located in the ECL2, while in TAS2R20 they are present in TMD3 and TMD7 helices. The ECL2 residues, N165 in TAS2R4, and R160 and K163 in TAS2R14 were found crucial for lactone binding. In contrast, TAS2R20 residues W88 (TMD3) and Q265 (TMD7) are essential for agonist binding (Pydi et al., 2014c; Zhang et al., 2017; Jaggupilli et al., 2018). In addition, the hydrophobic amino acids in the three TAS2Rs are considered important in directing the orientation of the hydrophobic acyl chains of lactones that facilitate receptor activation. The transmembrane domain in GPCRs is composed mainly of hydrophobic amino acids accommodated in the plasma membrane. Therefore, hydrophobic properties of the receptor binding pocket are important for any membrane-accessible agonist. Hydrophobic residues in TMD3 and TMD7 of TAS2R16 are important in forming a wide ligand-binding pocket (Thomas et al., 2017) that accommodates larger ligands like the β-glycosides. By using salicin analogs as TAS2R16 novel agonists (differ structurally from salicin in β-glucoside core constituents), several critical residues were identified that are required for signaling. Interestingly, these were identical to the residues critical for salicin signaling, except for W261, which was not required for activation by the analog 4-NP-β-mannoside. Importantly, all these residues are in the TMD helices or intracellular face of the receptor, consistent with classical GPCR signal transduction. These results suggest that larger ligands bind to the wide binding pocket of TAS2R16 on the extracellular side, and then their signal is transduced via conserved residues on the intracellular side. This can account for the broad spectrum of ligand recognition conferred by TAS2R16. Unlike broadly tuned receptors, narrowly tuned ones like TAS2R7 show two different types of critical residue in ligand binding. The first type includes D86, W170, and S181 which are agonist independent and their mutation significantly reduces the ability of TAS2R7 to bind agonist, while a second group consisting of D65 and W89 are selective for quinine and enhance binding to a specific category of ligand (Liu et al., 2018). Despite the variation in the amino acid type and location important for agonist binding among receptors of the bitter family, for the most part, ligand binding pockets are present on the extracellular surface of TMDs or ECL2. The function of the residues at these binding pockets is dictated by multiple factors that include the type of ligand, the movements in TMDs, and the associated movement of ECL2 to accommodate the ligand. Structure–function studies have identified a conserved KLK/R motif in the intracellular carboxyl-terminal domain of 19 TAS2Rs that is critical for cell surface expression, trafficking, and receptor activation (Upadhyaya et al., 2015; Jaggupilli et al., 2016). Agonist, Antagonist Binding and Modulation of Bitter Receptors In simple pharmacological terms, an antagonist is a ligand that inhibits the biological response induced by an agonist and does not induce any response of its own, while a ligand that reduces the constitutive/basal activity of a GPCR is considered an inverse agonist. An antagonist acts as a competitive inhibitor to block receptor activity. Large numbers of agonists have been identified for bitter receptors, but few antagonists have been found so far (Table 7). Finding an antagonist/inhibitor for bitter taste would not only help in understanding the TAS2R mechanism of signal transduction but have potential use in foods to overcome unwanted bitterness in consumer products. Such bitter blockers have been proposed to increase the palatability of bitter-tasting food and beverages, increase compliance in taking bitter-tasting drugs, especially children’s formulations and reduce or prevent off-target drug effects in extra-oral tissues (Clark et al., 2012) TABLE 7 | Bitter taste receptor inhibitors with their IC50 values and other pharmacological properties. Antagonist Mode of action Bitter receptors Tested agonists IC50 (µM) References GIV3727or 4-(2,2,3-trimethylcyclopentyl) butanoic acid Competitive orthosteric inhibitor 31 acesulfameK 6.4 (Slack et al., 2010) 43 Aristolochic acid 11.33 4 Colchicine 108 40 Cohumulone 6.24 Gamma-aminobutyric acid (GABA) Orthosteric inhibitor 4 Quinine 3.2 (Pydi et al., 2014b) 3β-hydroxydihydrocostunolide (3HDC) ND 46 Absinthin 14.1 (Slack et al., 2010; Brockhoff et al., 2011) Andrographolide 4.9 Denatonium 6.8 Picrotoxinin 4.7 Strychnine 15.3 3-hydroxypelenolide(3HP) ND   Absinthin 57.8 (Brockhoff et al., 2011) Andrographolide 44.5 Denatonium 51.4 Picrotoxinin 22.9 Strychnine 84.9 Probenecid Allosteric inhibitor 16 Salicin 292 (Greene et al., 2011) Sakuranetin ND 31 Saccharin 5.5 (Fletcher et al., 2011) 6-Methoxysakuranetin ND 31 Saccharin 10.2 (Fletcher et al., 2011) Jaceosidin ND 31 Saccharin 11.7 (Fletcher et al., 2011) 6,3′-dimethoxyflavanone ND 39 Epicatechin gallate (ECG) 4075 (Roland et al., 2014) Denatonium 240 6-Methoxyflavanone ND 39 Epicatechin gallate (ECG) 479 (Roland et al., 2014 N,N-bis(carboxymethyl)-l-lysine(BCML) ND 4 Quinine 0.059 (Pydi et al., 2014b) (±) abscisic acid (ABA) ND 4 Quinine 34.4 (Pydi et al., 2015) ND, not determined. TABLE 7. Bitter taste receptor inhibitors with their IC50 values and other pharmacological properties. To date, ∼12 bitter inhibitors have been reported to interact with only 10 TAS2Rs subtypes (Table 5) by binding to transmembrane domains in a similar manner to agonists. GIV3727 (4-(2,2,3-trimethylcyclopentyl) butanoic acid) was the first TAS2R antagonist discovered and to be well characterized structurally (Slack et al., 2010) that acts as an orthosteric competitive antagonist for TAS2R31. It competes with the acesulfame K agonist both in vitro and in vivo. GIV3727 is moderately selective because it inhibits multiple bitter receptors including, TAS2R4, TAS2R40, and TAS2R43. Homology modeling revealed that the -COOH group in GIV3727 is important for ligand-receptor interactions as its replacement with an ester or the corresponding alcohol abolished its antagonist activity. Moreover, a mutagenesis study in TAS2R31 and TAS2R43 revealed residues K2657.39 and R2687.39 in TMD7 to be crucial for its antagonistic activity (Slack et al., 2010). Similarly, another non-selective inhibitor, probenecid (p-(dipropylsulfamoyl) benzoic acid) was found to act as NAM of TAS2R16 activity and inhibits TAS2R38 and TAS2R43 as well (Greene et al., 2011). Two point mutations, P44T and N96T in TMD3 of hTAS2R16 were found to significantly suppress the ability of probenecid to inhibit salicin activity. Hydrophobicity seems important for their pharmacological activity as observed for both probenecid and GIV3727. The sesquiterpene lactone, 3β-hydroxydihydrocostunolide (3HDC) is an interesting bitter blocker as it acts as a competitive antagonist of TAS2R46, TAS2R30, TAS2R40, yet activates TAS2R4, TAS2R10, TAS2R14, and TAS2R31 as an agonist (Brockhoff et al., 2011). Similarly, various flavanones were also noted as antagonists for TAS2R31, and TAS2R39 with varying efficacy. Taken together most of the currently known antagonists are non-selective and there is an urgent need for studies that focus on selective antagonists of major broadly tuned TAS2Rs (such as TAS2R10, TAS2R14, TAS2R16, and TAS2R46). To target bitterness in terms of food industry needs, potential peptide inhibitors from different protein sources such as hen protein hydrolysates (inhibits TAS2R4, TAS2R7, TAS2R14) and beef proteins (inhibits TAS2R4) (Zhang et al., 2018; Xu et al., 2019) are reported to be effective. Several umami glutamyl peptides isolated from soybeans have been found to act as non-competitive allosteric inhibitors of TAS2R16 against the salicin-induced response (Kim et al., 2015). Constitutive Activity of Bitter Receptors A phenomenon in GPCR activity is that of constitutive activity, essentially an active state occurring in the absence of an agonist which has been demonstrated in more than 60 GPCRs (Seifert and Wenzel-Seifert, 2002). It is the production of a second messenger or downstream signaling by a receptor in a ligand-independent manner. The constitutive activity provides another possibility for taste inhibitor discovery using inverse agonists. Inverse agonists can inhibit both agonist-dependent and agonist-independent activity, while antagonists can inhibit only agonist-dependent activity (Chalmers and Behan, 2002). Interestingly, some mutations in GPCRs can lead to constitutive activity and receptors with this characteristic (including constitutively active mutants or CAM) are important tools to investigate new bitter inhibitors. Although constitutive activity has not been observed naturally in TAS2Rs, when induced by mutation these receptors provide a useful means to investigate the relationship between an active receptor conformation and inverse agonist pharmacology. Molecular modeling and functional assays report five CAMs critical residues for TAS2Rs, one in TMD7 (S2857.47) and four others in intracellular loop 3 (H214A, Q216A, V234A, and M237A) (Pydi et al., 2014a; Pydi et al., 2014b). Of the five CAMs, only the TAS2R4 with H214A mutation shows a 10-fold increase in constitutive activity. This histidine residue is highly conserved in most TAS2Rs. Mutation of H214 (H214A) helped in finding two new inverse agonists (GABA and ABA; Table 7) (Pydi et al., 2015). Similar pharmacological approaches can be used to generate mutants of all TAS2Rs to screen for their inverse agonist/bitter taste blockers. However, for better characterization and interpretation of TAS2Rs, future in vivo studies should be performed to understand the functional relevance of these CAMs. At the same time, it is worth noting that the potential presence of endogenous agonists makes it difficult to determine the true constitutive activity of GPCRs including TAS2Rs (Devillier et al., 2015). Kokumi Sensation Signal Transduction In addition to the five basic tastes, sensations beyond these add another dimension to taste perception. One such example is “kokumi” which is distinct from the other five tastes in that it does not have a taste as such but rather induces a sensation of “mouthfulness,” depth, thickness, and aftertaste in the flavors. Although this flavor has been used historically and is well recognized in Japanese cuisine, it was first characterized by Ueda et al. (1990) who isolated a kokumi taste substance from water extracts of garlic and onion and identified, γ-glutamylcysteinylglycine or glutathione (GSH) as the main active ingredient of kokumi flavor (Ueda et al., 1990; Ueda et al., 1997; Dunkel et al., 2007). GSH is abundantly present in food-grade yeast extract and has been used to make foods more flavorsome. Kokumi signal transduction was unknown until CaSR expression was reported in a subpopulation of taste cells in mice and rats that suggested it could function as a taste receptor for calcium and amino acids (San Gabriel et al., 2009; Bystrova et al., 2010). However, its apparent role in kokumi stimuli detection was not confirmed. Ohsu et al. (2010) for the first time reported that kokumi peptides (GSH, γ-Glu-Val-Gly, and various γ-glutamyl peptides; Table 8) signal through CaSR and can synergize with sweet, salty, and umami taste qualities to impart an augmented kokumi sensation, i.e., increased depth of flavor which was further complemented by later studies (Maruyama et al., 2012; Kuroda and Miyamura, 2015). By using heterologous expression systems and human sensory analysis these studies demonstrated that kokumi peptides impart kokumi sensation to sweet, salty, and umami taste via CaSR as the kokumi component was specifically suppressed in the presence of the CaSR-specific NAM NPS-2143. To further validate this idea, Maruyama et al. (2012) identified a distinct population of taste cells expressing CaSR in mouse lingual tissue which did not express either sweet or umami receptors. Notably, these cells are specifically responsive to kokumi substances and elicit a Ca2+ response to focally applied kokumi stimuli in mouse lingual slices. Moreover, this response was inhibited in the presence of NPS-2143. These findings support the idea that CaSR mediates kokumi sensation effects in TRCs TABLE 8 | Kokumi sensation receptor agonists, allosteric modulators with concentrations used in cell-based assays. Ca2+ Orthosteric agonist/cation 1a VFT Mg2+ Orthosteric agonist/cation 10a VFT Gd2+ Orthosteric agonist/cation 0.02a VFT Al2+ Orthosteric agonist/cation 0.5a VFT Sr2+ Orthosteric agonist/cation 0.5a VFT Mn2+ Orthosteric agonist/cation 0.5a VFT Ni2+ Orthosteric agonist/cation 0.5a VFT Ba2+ Orthosteric agonist/cation 0.2a VFT Ca2+ Orthosteric agonist/cation 1a VFT Ca2+ Spermidine Orthosteric agonist/polyamine 0.002a VFT (Nemeth et al., 2018) Neomycin Orthosteric agonist/aminoglycoside antibiotic 0.06a VFT (Katz et al., 1992) Gentamicin Orthosteric agonist/aminoglycoside antibiotic 0.15a VFT Katz et al., 1992) Kanamycin Orthosteric agonist/aminoglycoside antibiotic 0.1 VFT (Katz et al., 1992) Amyloid β-peptides Orthosteric agonist/Peptide 0.001–0.04 (Ye et al., 1997) Poly-Lysine Orthosteric agonist/peptide 0.03 µMa VFT (Brown et al., 1991; Nemeth et al., 2018) Poly L-arginine Orthosteric agonist/peptide 0.004 µMa VFT Brown et al., 1991; Nemeth et al., 2018) Lysozyme Agonist/protein 0.59a ND (Yamamoto et al., 2020) Thaumatin Agonist/protein 0.07a ND (Yamamoto et al., 2020) Aromatic L-amino acids (Trp, Phe, His, Ala, Ser) PAMs 10 VFT (Conigrave et al., 2000; Mun et al., 2004; Geng et al., 2016) Anions (SO42-) NAM 10 VFT (Geng et al., 2016) Cinacalcet PAM/phenylalkylamine 0.051 µMa TMD (Miedlich et al., 2002; Petrel et al., 2004; Nemeth et al., 2004) Calindol PAM/phenylalkylamine 0.31 µMa TMD Miedlich et al., 2002; Petrel et al., 2004) NPS R-568 PAM/phenylalkylamine 0.5 µMa TMD (Miedlich et al., 2002; Petrel et al., 2004) NPS R-467 PAM/phenylalkylamine 0.01 TMD (Miedlich et al., 2002; Petrel et al., 2004) γ-Glu-Val-Gly PAM/Peptide 0.041 µMa (Ohsu et al., 2010) γ-Glu-Cys-Gly (Glutathione) PAM/Peptide 76.5 µMa VFT (Ohsu et al., 2010; Wang et al., 2006 γ-Glu-Ala PAM/Peptide 3.65 µMa ND (Wang et al., 2006; Ohsu et al., 2010) γ -Glu-Val PAM/Peptide 1.34 µMa ND (Wang et al., 2006; Ohsu et al., 2010) γ -Glu-Cys PAM/Peptide 0.45 µMa VFT (Ohsu et al., 2010; Wang et al., 2006) γ -Glu-α-aminobutyryl-Gly (Opthalmic acid) PAM/Peptide 0.018 µMa ND (Ohsu et al., 2010) NPS2143 NAM 0.0003 (IC50) TMD (Gowen et al., 2000; Petrel et al., 2004) Calhex 231 Mixed PAM/NAM 0.1–1 µM (PAM); 3–10 µM (NAM) TMD (Petrel et al., 2003; Petrel et al., 2004; Gregory et al., 2018) Where VFT, venus flytrap domain; TMD, transmembrane domain; ND, not determined. a shows EC50 value. TABLE 8. Kokumi sensation receptor agonists, allosteric modulators with concentrations used in cell-based assays. More recently, kokumi peptides have been found to have an extraoral physiological role in the gastrointestinal tract where they stimulate the secretion of hormones (cholecystokinin and glucagon-like peptide1 by activating CaSR (Depoortere, 2014; Yang et al., 2019). However, future studies with tissue-specific deletion of CaSR in taste buds would help delineate its role in taste physiology. CaSR involvement in taste is a relatively recent discovery, but its central role in extracellular calcium homeostasis in mammals is well recognized (Brown et al., 1993; Brown, 2013). Diverse ligands activate CaSR, including cations (Ca2+ and Gd3+), peptides, polyamines (Brown and MacLeod, 2001), and amino acids (Conigrave et al., 2000; Conigrave and Hampson, 2006) (Table 8). Unlike other taste modalities (sweet, bitter, and umami), CaSR–ligand binding and recruitment of G protein results in the activation of an intricate, amplifying signaling network that initiates numerous intracellular functions. The functional diversity of CaSR results from its ability to activate multiple Gα proteins (Gq/11, Gi/o, G12/13, and Gs) (Magno et al., 2011; Conigrave and Ward, 2013) which subsequently affect multiple signaling pathways related to the pathophysiology of parathyroid hormone secretion, cancer, and metastasis (Kelly et al., 2007; Wettschureck et al., 2007; Mamillapalli et al., 2008). Kokumi substrates activate CaSR and transmit their signal through Gαq/11 proteins which further activate PLCβ that results in the release of intracellular Ca2+ stored through activation of IP3 receptor channels in the ER. Whether the kokumi pathway strictly relies on Gαq/11 protein or can also use Gα-gustducin, like other taste modalities for downstream signaling, is still unknown (Figure 3). The growing number of reports on kokumi flavor signal transduction are shedding light on its potential use as a flavor enhancer. Structural, Molecular, and Conformational Changes of Kokumi Receptor CaSR belongs to the class C GPCR. Within this class, CaSR and metabotropic glutamate receptors (mGluRs) are known to function as disulfide-linked homodimers (Bai et al., 1998; Ward et al., 1998; Pidasheva et al., 2006) (Figure 4A). Structurally, the human CaSR is similar to sweet and umami taste receptors but differs in being a homodimer instead of a heterodimer (Hendy et al., 2013). The ECD of CaSR not only senses nutrients (Ca2+, L-Phe, and polypeptides; Table 8) and allows ligands to modulate CaSR cooperatively, but is also required for its dimerization (Ray et al., 1999; Zhang et al., 2014). The binding of Ca2+ and other ligands to the ECD changes the conformation of the seven transmembrane domains, causing alterations in the intracellular loops and the intracellular domain (ICD), which further trigger downstream signaling pathways (Brown et al., 1975). The ICD is relatively diverse among species and participates in controlling CaSR signaling in multiple ways by modulating receptor expression, trafficking, and desensitization (Gama and Breitwieser, 1998; Ward, 2004; Huang et al., 2006). Homology modeling, mutagenesis, and heterologous expression revealed distinct and closely located binding sites for Ca2+ and aromatic L-amino acids, in VFT and the cleft of the VFT, respectively (Silve et al., Conigrave et al.,2000; Huang et al., 2009). Notably, four putative Ca2+ binding sites of varying affinity have been predicted in the VFT of the CaSR and in which the interaction between site 1 and the other three sites plays a central role in positive cooperativity in sensing Ca2+ (Zhang et al., 2014). Besides Ca2+, aromatic L amino acids (L-Trp, L-Phe) also activate the CaSR by binding adjacent to the VFT region through three serine and one threonine residue (S169/S170/S171/T145). Interestingly, the double mutation T145/S170 was found to selectively impair L amino acid (Phe, Trp, His) sensing of CaSR, while Ca2+ sensing remained intact (Mun et al., 2004; Mun et al., 2005). The recent crystal structure of the entire extracellular domain of CaSR (Geng et al., 2016) identified four novel Ca2+ binding sites in each protomer of the homodimer including one at the homodimer interface which does not correspond to any of the sites reported previously by Huang et al., (2007). It is unclear why these additional calcium-binding sites were not found in earlier studies. This might be due to the different expression systems used, crystallization conditions, and methods of analysis. The conditions of the more recent studies may have stabilized an active conformational state in which these calcium sites become available (Geng et al., 2016). Among these four Ca2+-binding sites, site 4 seems most relevant to receptor activation as it directly participates in the active CaSR conformation. Moreover, a previously reported natural mutation G557E (Hendy et al., 2009) reduced the potency of Ca2+ possibly by affecting backbone conformation, thereby weakening the affinity of Ca2+ for this site. This confirms that a Ca2+ ion at site 4 stabilizes the active conformation of the receptor by facilitating homodimer interactions between the membrane-proximal LBD2 region and CRD of CaSR. The most interesting aspect of Ca2+ and L-amino acid interplay was reported by Zhang et al. (2014) who studied L-Phe binding characteristics by monitoring intracellular [Ca2+]i oscillations in living cells and performing molecular dynamic simulations. Their findings supported a previous observation that the L-Phe binding pocket is adjacent to the Ca2+ binding site 1. Importantly, by binding to this site, L-Phe influences all Ca2+ binding sites in the VFT region and enhances CaSR functional cooperativity through positive heterotropic cooperativity to Ca2+. Moreover, the dynamic communication of L-Phe at its predicted binding site in the hinge region with the Ca2+ binding sites not only influences the adjacent Ca2+ binding site 1 but also globally enhances cooperative activation of the receptor in response to alterations in extracellular Ca2+. The crystal structures (Geng et al., 2016) of the entire ECD region of CaSR in the resting and active conformations have provided additional information about the dynamics between calcium and L-amino acid binding (Geng et al., 2016). Most importantly, by using L-Trp, the study provided direct evidence that L-amino acids are CaSR co-agonists, and they act concertedly with Ca2+ to achieve full receptor activation. Several lines of evidence support this contention: 1) L-Trp binds at the interdomain cleft of the VFT, which is a canonical agonist-binding site for class C GPCRs (Kunishima et al., 2000; Muto et al., 2007; Geng et al., 2016) and shares a common receptor-binding mode with the endogenous agonists (amino acids or their analogs) of mGluR and GABAB receptors, (Kunishima et al., 2000; Tsuchiya et al., 2002; Muto et al., 2007; Geng et al., 2016). 2) L-Trp interacts with both LBD1 and LBD2 in ECD to facilitate its closure, a crucial first step during CaSR activation. In contrast, no Ca2+ ion is found at the putative orthosteric agonist-binding site to induce domain closure. 3) Mutations of L-Trp-binding residues (S147A, S170A, Y218A, and E297K) severely reduced Ca2+ induced IP accumulation and intracellular Ca2+ mobilization (Zhang et al., 2002; Silve et al., 2005), indicating that L-Trp is required for a Ca2+ induced receptor response. Notably, the presence of extracellular Ca2+ above a threshold level is required for amino-acid-mediated CaSR activation, amino acids increase the sensitivity of the receptor toward Ca2+. Taken together, amino acids and Ca2+ ions act jointly to trigger CaSR activation. Knowing that aromatic L-amino acids (Trp, Phe, His) are important tastants in kokumi flavor, CaSR becomes more relevant for taste biology. Moreover, the kokumi tripeptide, glutathione (GSH), and glutamyl peptide are suggested to bind allosterically to CaSR at the same site as L-amino acids (Wang et al., 2006; Broadhead et al., 2011) and enhance its activity in the presence of 0.5–1 mM free calcium, thereby acting as a positive allosteric modulator. In addition, an ECD crystal structure might help to explain the structural and molecular details of the GSH binding pocket such as the nature of critical residues and their binding characteristics. Given recent reports of calcium emerging as a taste modifier, it would be worth investigating how GSH and Ca2+ operate in kokumi human perception. Allosteric Modulation of Calcium-Sensing Receptor Classically CaSR is known to be involved in the pathophysiology of parathyroid and renal-related diseases by sensing calcium ions in the extracellular fluid (Brown, 2007). Research on related therapeutic applications has identified several classes of PAMs and NAMs that modulate CaSR agonist sensitivity. More recently this has been applied to kokumi taste signal transduction. Endogenous Modulators (L-amino Acids, Anions, and Glutathione Analogs) Several studies based on molecular modeling and mutagenesis report L-amino acids (L-Phe, L-Tyr, L-His, and L -Trp) as PAMs because they enhance the Ca2+-induced response of CaSR. Aromatic L-amino acids bind in the VFT domain (Mun et al., 2004) and require a highly conserved five residue binding motif (S147, S170, D190, Y218, and E297) (Conigrave and Hampson, 2006; Geng et al., 2016). Among these residues, E297 was identified through the natural mutation E297K as essential for structural and functional activity (Table 8) (Pollak et al., 1993; Bai et al., 1998; Conigrave et al., 2000; Zhang et al., 2002; Mun et al., 2004). As recently identified NAMs, anions SO42 and PO43 are important modulators of the Ca2+-induced response. They bind in the VFT region and act as moderate NAMs for CaSR activity (Geng et al., 2016; Centeno et al., 2019). Based on anomalous difference maps, four anion-binding sites were identified in the inactive and active CaSR ECD structures. Sites 1 and 3 are located above the interdomain cleft in LBD1, while site 4 lies in the LBD2 region. Sites 1 and 3 appear to stabilize the inactive conformation while site 2, which is present in both active and inactive conformations appears important for receptor function as mutations in its residues (R66H, R69E, and S417L) abolished the Ca2+-induced response. In addition, each protomer structure contains one Ca2+ ion and three SO42 ions which together contribute to the structural integrity of the receptor (Geng et al., 2016). Taken together, anions along with Ca2+ and amino acids are involved in an intricate interplay for CaSR activation to maintain conformational equilibrium between inactive and active states. As positive allosteric modulators, γ glutamyl peptides including glutathione (γGlu-Cys-Gly) and its analogs (Table 8) are predicted to have overlapping binding sites with L-amino acids in the VFT region (Wang et al., 2006; Ohsu et al., 2010; Broadhead et al., 2011). Kokumi peptides that activate CaSR resemble amino acids in having free α-amino and free α-carboxylate groups because they contain both amide bond formation between the γ-carboxylate group of L-glutamate and the α-amino group of its neighboring Cys residue. However, compared to amino acids, glutathione analogs have much larger side chains and are more potent activators of CaSR (Wang et al., 2006). Nonetheless, free sulfhydryl is not required for CaSR activation (Ohsu et al., 2010; Maruyama et al., 2012). The crystal structure of ECD enables mapping of the GSH binding site and investigation into how GSH binding works in synergy with Ca2+ to modulate the kokumi sensation. NPS2143, the sole kokumi NAM identified to date has been reported to inhibit kokumi taste sensation to GSH and its analogs which provides an opportunity to screen for novel kokumi-enhancing molecules in a cell-based assay. Synthetic Drugs as Allosteric Ligands of Calcium-Sensing Receptor Because of its pathophysiological importance, various synthetic PAMs and NAMs of CaSR have been identified and are in clinical use. The allosteric modulation of CaSR by synthetic drugs has been recently reviewed (Hannan et al., 2016; Chavez-Abiega et al., 2020; Leach et al., 2020). Since the 1990’s the terms calcimimetics and calcilytics, have been used for drugs that mimic or antagonize the effect of extracellular Ca2+ on CaSR activity, respectively. Pharmacologically, a calcimimetic activates the CaSR and includes agonists (type I) and allosteric ligands (type II). Most type I calcimimetics are either inorganic or organic polycations (e.g., Mg2+, Gd3+, neomycin), whereas type II calcimimetics are small naturally occurring molecules (aromatic amino acids or GSH) or synthetic drugs and peptides (NPS R-568, cinacalcet). Type II calcimimetics (like aromatic amino acids) bind in the ECD while others (e.g., NPS R-568, NPS R-467) bind in the TMD of the CaSR. Calcilytics are thus small organic molecules that appear to act as NAMs and bind in the TMD of the receptor (Widler, 2011; Nemeth, 2013). Homology modeling and mutational studies show that both PAMs and NAMs have overlapping but non-identical binding sites in TMD and can partially allosterically modulate CaSR activity in the complete absence of the ECD, but their potencies vary among structurally different compounds (Collins et al., 1998; Ma et al., 2011) (Table 8). Several residues reportedly critical for allosteric modulation, W8186.48, F8216.51 (TMD6) and E8377.39, I8417.43 (TMD7), R6803.28, F6843.32, F6883.36 (TMD3) impair calcimimetic and calcilytic induced CaSR signaling (Miedlich et al., 2004; Petrel et al., 2004; Leach et al., 2016). Nevertheless, subtle differences in ligand–receptor interactions drive negative vs. positive modulation of CaSR signaling, by NPS2143 or cinacalcet and NPSR-568, respectively (Miedlich et al., 2004; Leach et al., 2016; Keller et al., 2018). The details of CaSR allosteric modulation by synthetic drugs is out of the scope of the current review, for a comprehensive explanation refers to these studies (Chaves-López et al., 2014; Hannan et al., 2016; Leach et al., 2020). Conclusion Taste GPCR research has advanced rapidly over the past two decades providing a more thorough understanding of receptor molecular pharmacology and signal transduction pathways. Except for the kokumi receptor ECD, high-resolution crystal structures for any taste receptor would be a major step toward designing novel and potent surrogate taste receptor ligands and selective antagonists. This has been a challenge due to low taste GPCR functional heterologous expression, appropriate post-translational modifications, high conformational flexibility, and low detergent stability. However, significant advancements in structural biology technologies of serial femtosecond crystallography using X-ray free-electron lasers and high-resolution cryo-electron microscopy provide promising tools for understanding conformational dynamics and visualizing the process of receptor activation with high spatial and temporal resolution. The physiological relevance of taste GPCRs will be further advanced through in vivo studies to help provide information on potential synergies in taste signal transduction mechanisms, particularly among bitter, umami, sweet, and kokumi receptors.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.18%3A_Signal_Transduction_-_Taste_%28Gustation%29.txt

Search Fundamentals of Biochemistry This chapter section is taken in entirety from: Ion Channels and Thermosensitivity: TRP, TREK, or Both? Lamas et al. Int. J. Mol. Sci. 2019, 20(10), 2371; https://doi.org/10.3390/ijms20102371. Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Introduction Mammals and other animals spend large amounts of energy in maintaining a nearly constant body temperature, irrespective of the temperature of the environment. The mechanisms controlling thermal regulation are complex and often rely on negative feedback, where it is first necessary to determine the body and ambient temperature. The temperature of the environment can be sensed by external receptor cells, mainly located in the skin, whereas body temperature is sensed by internal receptors expressed by cells located in several internal organs. Traditionally, only the skin and core thermoreceptors (spinal cord, hypothalamus) have attracted the attention of researchers, but more recently, some very interesting information has emerged regarding visceral thermal receptors, even in humans. Although a hypothesis conceived many years ago, the terminals of receptor neurons are thought to contain branches of nerve fibers without any apparent structural specialization. Indeed, only recently have we begun to understand the molecular basis of thermoreception by cells. Many biochemical processes like chemical reactions, and physical processes like conformational changes, are extraordinarily dependent on temperature, and although these processes generally occur faster at higher temperatures, the relationships can be very complex [3]. If we consider the nervous system (NS), the effects of temperature on the resting membrane potential (RMP) were the first to be studied, as were its effects on the kinetics and speed of compound and single action potentials, long before the existence of ion channels was demonstrated. All neurons and ion channels are affected by changes in temperature, not least because channel gating is generally a temperature-dependent process. However, only some neurons can be called thermoreceptors and very few ion channel types can be designated as thermosensors. In general, only channels with a temperature coefficient (Q10) ≥2–5 are considered temperature dependent. (Q10 is the ratio of a reaction at two different temperatures that differ by 10o C.  See Chapter 32.11 for more details.) Thermoreceptors are sensitive to changes in temperature rather than to the value of the temperature itself, probably due to their characteristic strong adaptation. These receptors are classified into two groups depending on whether their discharge frequency increases when they are heated or cooled (Figure 1). Based on this classification, it is common to speak of four thermal sensations (cold −10 to 15 °C, cool 16–30 °C, warm 31–42 °C and hot 43–60 °C), whereby cold and hot are potentially noxious and/or painful [11,12]. The modulation of TWIK-related potassium (TREK) channels by temperature has been touched on in several reviews, yet very few have dealt exclusively with this exciting topic. Conversely, after transient receptor potential (TRP) channels sensitive to temperature were discovered, they were studied extensively to understand how thermal stimuli were transduced. Such interest led to the appearance of good reviews covering this issue. In this review, we will focus on the less well-known role of TREK channels in thermosensation, and we will compare the behavior of these channels to that of TRP channels. Other thermosensitive proteins have also been described, like the Na/K ATPase and ENaC channels, or P2X receptors, and while these should also receive attention, we consider this to fall beyond the scope of this review. Indeed, cell thermosensitivity seems to be governed by the interplay of a number of channel types, as reported in hypothalamic neurons. TREK Channels The TWIK-related potassium channel (TREK) subfamily belongs to the two-pore domain potassium channels family (K2P) and is comprised of three members: TREK1, TREK2, and TRAAK (TWIK-related arachidonic acid-activated potassium channel). These are background potassium channels characteristically modulated by several physical and chemical stimuli, such as membrane stretch, pH, unsaturated fatty acids, general anesthetics, and temperature. In general, TREK channels display very weak activity at room temperature and normal pressure, even when overexpressed in heterologous systems. However, their activity increases strongly when a number of different stimuli are applied, including an increase in temperature. From a physiological point of view, it is important to note that at 37 °C, all three members of the TREK subfamily respond to stimuli (pH, membrane stretch, or arachidonic acid), much like they do at room temperature. TREK channels may fulfill a dual role in the transmission of thermal pain. Thus, their strong activation by noxious heat results in an outward current that provokes membrane hyperpolarization and a reduction of thermoreceptor firing, provoking heat-pain relief. Conversely, inhibition of TREK channels by noxious cold should depolarize thermoreceptors and increase their excitability, cooperating in the transduction of noxious cold sensations (see Figure 2). TREK1 Soon after their discovery, it was shown that TREK1 channels are strongly and reversibly activated by an increase in temperature when expressed in heterologous systems (cell lines derived from kidney (COS) cells, oocytes). If we consider that these are mostly voltage-independent channels open at resting potentials, TREK1 channels should function as cold sensors because low temperatures would dampen their activity and depolarize these thermoreceptors (see Figure 2). Many authors have demonstrated that macroscopic TREK1 currents are strongly outwardly rectifying at room temperature. While the outward current is not evident at 12 °C, it is strongly enhanced at 37 °C, and the current progressively increases as the temperature gradually augments. Indeed, the current increases around 7-fold with an increase of 10 °C in the range of 14 to 42 degrees, and importantly, maximal sensitivity (0.9-fold per degree) was reached at nearly physiological temperatures, between 32 and 37 °C. The current induced by heating is also outwardly rectifying, and it reverses at potentials close to the equilibrium potential for potassium (EK). In heterologous systems, the activation of TREK1 by temperature may be reversibly inhibited by cAMP, and this inhibition is suppressed by mutation of the C-terminal region that harbors a phosphorylation site for protein kinase A (PKA). Moreover, chicken embryonic atrial myocytes express TREK-like currents, and they have a resting membrane potential of around −20 mV in culture, which increases to −70 mV when the temperature rises to 35 °C, a change that was ascribed to the activation of TREK1/2 channels. In voltage-clamp, the outward current recorded at +60 mV increased 9-fold. Both TREK1 and TWIK-related arachidonic acid-activated potassium (TRAAK) channels have been proposed to shut down the firing of hippocampal neurons when the temperature rises too high. Figure \(\PageIndex{a}\) shows an interactive iCn3D model of the mouse temperature sensitive K2P2.1 (TREK-1) potassium channels (6W84) The red dots represent the outer leaflet, The gray spheres are potassium ions. The threshold for the activation of slowly conducting C-fibers by noxious heat (30–50 °C) recorded in a skin-nerve preparation decreases in TREK1 KO mice, and the range of activation of these fibers by heat corresponds closely to the range in which TREK1 is activated (30–45 °C). The number of action potentials in response to a heating ramp (30–50 °C) was higher in the KO mice, although the response of C-fibers to a cooling ramp (32–10 °C) was similar in native and KO mice. Indeed, TREK1 KO mice were hypersensitive to thermal pain up to 50 °C but not at higher temperatures (52–56 °C), indicating that TREK1 channels may be important for the perception of low-threshold but not high-threshold thermal stimuli, for which TRP channels may be more important. Accordingly, the proportion of small diameter, cultured, dorsal root ganglia (DRG) neurons that respond to noxious heat (34%) increases in TREK1 KO (64%) and TREK1/TRAAK KO (74%) mice, as does the proportion of heat-responsive C-fibers in nerve-skin preparations from the single and double KO. TREK1 and TRAAK channels may counteract the stimulatory effect of heat-activated TRP channels in pain-transducing fibers when temperatures increase, such that the overall response may reflect a balance of the activity of these two functionally contrasting channel types. The threshold of thermoreceptors should certainly increase in the presence of TREK channels as temperatures increase. Cooling of DRG neurons in culture from 32 to 20 °C induces a depolarization of about 10 mV and often the firing of action potentials, an effect shown to be due to the inhibition of a background potassium current. Accordingly, the inhibition of a native TREK1-like current may underlie the excitation (depolarization and firing) produced by cold in small, cultured, trigeminal ganglion (TG) neurons (see Figure 2). Interestingly, cold induces subthreshold oscillations in cold-sensitive DRG neurons. Transduction seems to be rather complex, involving the dampening of a hyperpolarization-activated cationic current and a permissive role for a slowly inactivating potassium current. Interestingly, the TREK1/TRAAK double KO mutant shows a consistent cool allodynia (pain due to a stimulus that does not normally provoke pain), and oxaliplatin, a cancer therapy that causes peripheral nerve neuropathy, exacerbates cold sensitivity in many patients and animals, inducing allodynia to cool temperatures. Neither the deletion of TREK1 nor TRAAK increases the fraction of small DRG neurons sensitive to noxious cold stimuli (below 20 °C and down to about 10 °C), although the TREK1/TRAAK KO and the triple TREK1/TREK2/TRAAK KO showed a significant increase in such neurons. Similar results were obtained when recording C-fibers in a skin-nerve preparation, in which case the double KO C-fibers fired more strongly than the single TRAAK KO and wild-type fibers. Oxaliplatin also induced hypersensitivity to noxious cold temperatures, while double and triple KO mice but not the TREK1 KO mice are hypersensitive to cold, which is not further affected by oxaliplatin. Hence, the deletion of two of the three TREK channels appears to be sufficient to reach maximal hypersensitization. The neuroprotective agent riluzole induces an analgesic effect against painful cold in normal animals, but also in oxaliplatin-pretreated TREK2 KO and TRAAK KO animals. However, riluzole did not affect pain sensitivity in TREK1 KO animals treated with oxaliplatin, in animals treated with the TREK1 inhibitor spadin, or in untreated TREK1 KO mice or triple KO animals. Similarly, a presumed TREK leak outward current recorded in DRG neurons was inhibited by riluzole and fluoxetine at 22 and 30 °C but not at 14 °C, probably because the current was already inhibited at low temperatures. Together, these experiments suggest that TREK1 channels fulfill an essential role in the perception of noxious cold and that TREK1 and TRAAK channels work together in sensing cold. Cell-attached patches demonstrated that the basal activity of expressed TREK1 channels is insignificant at room temperature, gradually increasing as the temperature rises (17-fold for an increase of 20 °C) and with a threshold around 25 °C. The current activated by temperature also displays outward rectification and reverses around the equilibrium potential for K+ [31], although the single-channel conductance remains unaffected TREK1-like channels naturally expressed in cardiac ventricular myocytes and DRGs and recorded in cell-attached patches, do not open at 24 °C, yet they are very active at 37 °C  Surprisingly, temperature increases fail to modulate TREK1 activity in outside-out and inside-out patches, but under the same conditions, TREK1 is still strongly activated by arachidonic acid TREK1 channels are ideally positioned to act as thermosensors because they are expressed in structures related to thermosensitivity and thermoregulation such as DRGs, the TG, nodose ganglia (NG), or the anterior and preoptic hypothalamus . TREK2 Heterologously expressed TREK2 channels also produce strong outward rectification when recorded in whole-cell configuration at room temperature, which increases greatly at temperatures around 37 °C in several heterologous systems  In COS cells, a small TREK2 current was observed at 0 mV that augmented progressively with a gradual rise in temperature to about 40 °C. Notwithstanding, the response of TREK2 to abrupt changes in temperature was rapid. Importantly, the IVs of the TREK2 current at different temperatures (24 and 37 °C) showed that the effect of temperature was not voltage-dependent: both inward and outward currents increased to the same degree. In this range of temperatures, the current increased 14-fold per 10 °C, indicating a very strong temperature dependence that was even bigger than that of TREK1. Much like TREK1, TREK2 responds to temperature changes around the physiological range, with current activated reasonably well at 37 °C and at resting membrane potential (RMP). Most experiments on TREK channels have been carried out at room temperature and at 0 mV. However, in the future these currents should be investigated using more physiological parameters, around a resting potential and 37 °C, providing a more precise idea of their role in the behavior of central neurons Figure \(\PageIndex{b}\) shows an interactive iCn3D model of the human two-pore domain temperature-sensitive potassium ion channel TREK2 (K2P10.1)(4BW5) Cerebellar granule and DRG neurons expressed native TREK2-like channels with weak activity at 24 °C in cell-attached patches, yet when the temperature increased to 37 or 41 °C they became very active at all voltages (−80 to +80 mV). Moreover, cultured cortical astrocytes have TREK2-like whole-cell outward currents that are strongly enhanced in the temperature range of 23–40 °C. Interestingly, ischemia significantly augmented the outward current provoked by an increase in temperature in these astrocytes In addition, it was recently reported that TREK2 channels contribute about 10 mV to the RMP of DRG neurons at about 30 °C  Furthermore, single TREK2 and triple TREK1/TREK2/TRAAK KO mice were more sensitive to warm temperatures (40–42 °C) when tested with the tail-flick reflex. Using a skin-nerve preparation, it was demonstrated that the proportion of heat-sensitive C-fibers and their activity (the number of action potentials) increased in the TREK2 and triple KO mice when temperatures rose to noxious heat levels (ramped from 30 to 50 °C), whereas the temperature threshold for firing decreased At high temperatures (between 40 and 50 °C), the triple but not the single KO fibers were more active than their wild-type counterparts, indicating that TREK2 regulates C-fiber responses at temperatures below 40 °C, while at higher temperatures other TREK channels participate in these responses. Both KOs suffered hyperalgesia at temperatures around 45 °C, but only the triple KO showed the same behavior above this temperature. The withdrawal latency in the tail immersion test was reduced in both the TREK2 KO and the triple KO mice when innocuous cooling temperatures were tested (20–25 °C). As such, the KOs show enhanced sensitivity to temperatures in the normal range, and similar results were obtained in a temperature preference test. The percentage of C-fibers responding to moderate cold (30–21 °C) was higher in single and triple KOs when compared to those recorded from the nerve-skin preparation of wild-type mice. Interestingly, the cold threshold for C-fiber firing (21 °C) was lower in the triple KO (24 °C) but not in the TREK2 KO (23 °C) mice [63]. Moreover, oxaliplatin induces mice to spend more time on a hot plate (30 °C) than on a cold plate (20–25 °C) when compared to untreated animals, indicating that neuropathic mice have enhanced sensitivity to moderate cold [63]. It has been suggested that TREK2 is implicated in the neuropathic hypersensitivity induced by this drug and indeed, oxaliplatin almost halved the TREK2 mRNA detected in DRG neurons. Generally, the data suggest that TREK2 channels may be essential for the control of the C-fiber response to cold at moderate temperatures. The tail immersion test showed that triple KO mice were hypersensitive to noxious cold temperatures (15–5 °C), while the single TREK2 KO mice behaved much like the wild-type mice. Moreover, very similar results were obtained in the nocifensive dynamic cold plate test. Accordingly, it was suggested that TREK2 may not be important in noxious cold sensitivity but that it might be essential for thermoreception at moderately cool temperatures (25–20 °C). A clear, fast, and reversible increase in activity was also reported for single TREK2 channels in cell-attached patches held at −40 mV when the temperature increased (24 to 37 °C), with a threshold for this increase at 25 °C (from 24 °C) and not affecting the conductance. It should be noted that in these circumstances, the activity of TREK2 single channels was very low at 24 °C. Significantly, neither TASK3 nor TRESK2 showed such dependence on temperature. However, like TREK1, the activity of TREK2 in inside-out patches was not modified by changes in temperature (24 to 42 °C). Finally, it is important to consider that TREK2 channels are expressed strongly in the DRG, TG, and hypothalamus, yet less than TREK1 in the NG. TRAAK Much like the other members of the family, TRAAK currents showed a strong open-channel outward rectification when recorded in whole-cell configuration, and these currents increase strongly when the temperature rises (24 to 42 °C). Moreover, the percentage of small-diameter DRG neurons responding to noxious heat, in culture, is increased in TRAAK and TRAAK/TREK1 KO mice. Consistently in skin-nerve preparations, the percentage of fibers responding to heating (30–50 °C) and the number of action potentials in response to a heating ramp also clearly increases, while the firing threshold is reduced. Notably, TRAAK and TRAAK/TREK1 KO mice suffer heat hyperalgesia when evaluated in the tail immersion test in the 46–50 °C range. Moreover, the double but not the single KO also shows hypersensitivity at higher temperatures (52–56 °C) in the hot plate test. Knock-out of TRAAK did not modify the percentage of DRG neurons in culture that respond to noxious (12 °C) cold. Moreover, in the cold plate assay, TRAAK KO mice behave like wild-type mice, whereas TREK1/TRAAK KO mice are more sensitive to cooling in the 10 to 20 °C range. The activity of single TRAAK channels heterologously expressed in COS cells and recorded in cell-attached patches at −40 mV was very low at 24 °C, yet it increased progressively as the temperature rose from 24 to 37 °C. The threshold for activity was around 30 °C, slightly higher than that reported for TREK1 and TREK2. However, the behavior of TRAAK channels in inside-out patches mimics that of TREK1 and TREK2 such that their activity was not affected by changes in temperature (from 24 to 42 °C). Native TRAAK-like channels in DRG neurons displayed little activity at room temperature, but there was clear activity in all cell-attached patches at 37 °C [36, 48]. Finally, TRAAK channels are clearly expressed in the hypothalamus, TG, and DRG , yet they are only weakly expressed in the NG. Molecular Origin of Thermosensitivity When first discovered, mouse TREK1 was reported to have four transmembrane segments, two pore domains, and a sequence of 370 aa [37]. The activation of heterologously expressed TREK1 currents by increasing temperature is unaffected by the deletion of the cytoplasmic N-terminal region. By contrast, partial deletion of the C-terminal region (Δ103) or replacement of this region with that of TASK1 strongly dampens the activation of TREK1 by heat. The sensitivity of the TREK1 channels to temperature can be eliminated by mutating helix 1 of the pore (G137I), suggesting that temperature affects the TREK1 and TREK2 channels by manipulating the C-type gate. It was suggested that functional coupling between the C-terminal domain and the C-type gate through the M4 segment is crucial for the heat sensitivity of the TREK1 channel. Thus, it is tempting to speculate that increasing the affinity of the C-terminal domain for phospholipids of the inner leaflet would increase the activity of TREK1 by heat, as proposed for other stimuli like stretch, PUFAs, phospholipids, or pH. Conversely, dissociation of this domain from the membrane would result in TREK1 inhibition. Surprisingly, the replacement of the C-terminus of TREK2 with that of TASK3 did not reduce the sensitivity of the channel to changes in temperature in the range of 24 to 37 °C under similar conditions, although it became insensitive to pH and arachidonic acid. Heat enhances the activity of TREK1, TREK2, and TRAAK in whole-cell and cell-attached recordings, yet not in outside- and inside-out patches, indicating that the integrity of the cell, and probably also a second messenger, are necessary for this modulation. The contribution of TREK channels to maintaining the RMP has often been questioned; however, this assertion is mostly based on experiments carried out at room temperature. Thus, new experiments should be performed at physiological temperatures to ascertain the role of these channels on both the RMP and neuronal excitability. TRP Channels Six transient receptor potential (TRP) channels are considered thermosensors, four of them responding to heat and two to cool. Temperature-sensitive TRP channels (Thermo-TRP) are extremely dependent on temperature, showing very high Q10 values (>20). Heat-Sensitive TRP Channels Four TRP subtypes are activated by an increase in temperature (Figure 1). Two of them respond to warm stimuli (TRPV4 Warm >27 °C and TRPV3 Warm >34 °C), and the other two to hot-painful stimuli (TRPV1 Hot >43 °C and TRPV2 Hot >52 °C). TRPV1s are voltage- and temperature-dependent channels that display outward rectification when expressed in human embryonic kidney (HEK) cells and that is strongly enhanced by heating (to 48 °C) and by capsaicin. Figure \(\PageIndex{c}\) shows an interactive iCn3D model of human TRPV1 with capsaicin at 48 degrees Celsius in an open state (7LPE) At room temperature, the current passing through these channels is negligible below 0 mV, but at 42 °C the channel activates more or less between −100 and +50 mV. These cationic channels are ten times more permeable to Ca2+ than to Na+ (PCa/PNa ~10) and are thought to be sensors for noxious heat but not activated by innocuous heat. Indeed, the response to noxious heat in mice lacking TRPV1 (KO) or DRG neurons was clearly weaker, although other channels may also contribute to the perception of noxious thermal stimuli because heat still evokes receptor activation in several preparations. The NG sensory neurons that innervate the lungs produce an inward current in response to an elevation in temperature (from 23 to 41 °C, with a threshold around 35 °C and a Q10 of about 30 in the range of 35–41 °C) as well as membrane depolarization and action potential firing. This response was ascribed to the presence of TRPV1 channels, even though the participation of TRPV2-4 could not be ruled out. We obtained similar results with NG neurons in culture, although these were slightly more complex because a hyperpolarization was observed before the depolarization and firing (unpublished data). It is interesting to note that inflammatory mediators like ATP and bradykinin strongly reduce the threshold of TRPV1 activation (30 °C) such that warm temperatures become painful. TRPV1 is strongly expressed in small-diameter sensory neurons of the DRG, TG, and NG, but also in the hypothalamus, sites where they may exert an important role in thermoreception. TRPV2 is activated at extremely high temperatures (52 °C), although it is not affected by capsaicin and shows an outwardly rectifying IV curve and a PCa/PNa ~3 . This channel has a Q10 of around 100, and it is thought that the temperatures that activate TRPV2 are more harmful than those that activate TRPV1. These channels are strongly expressed by myelinated medium-large diameter DRG neurons (Aδ and Aβ), as well as in the hypothalamus and the NG. TRPV3 channels are activated at warm, close to hot, temperatures (around 34–39 °C, with a Q10 around 6), generating currents with pronounced outward rectification and a PCa/PNa ~12. They are capsaicin-insensitive channels but stimulated by camphor, and they are thought to be involved in thermosensation and thermal nociception. Indeed, it has been suggested that TRPV3 channels contribute more to the speed with which mice select a more comfortable temperature than to the choice of the value of the temperature itself. By contrast, TRPV4 channels are more likely to be involved in choosing the preferred temperature from a non-painful range. Interestingly, it was proposed that TRPV3 channels transmit thermal stimuli through skin keratinocytes, which in turn will transmit this information to sensory endings. TRPV3 channels are expressed in sensory DRG and NG neurons but also in the hypothalamus. Interestingly, they co-localize with TRPV1 in DRG neurons. TRPV4 are cationic (PCa/PNa ~6) channels activated at even lower warm temperatures (around 27 °C, with a Q10 of about 10), generating outwardly rectifying currents and responding dynamically to temperature changes in the physiological range. These channels were proposed to play a role in thermosensation and thermoregulation although some authors were unable to activate these channels by increasing the temperature. Similarly, some behavioral studies reported a reduced response to temperature changes in TRPV4 KO mice, a behavior that was less clear in other studies. Much like TRPV3, the expression of these channels in keratinocytes was proposed to play an important role in the transmission of thermal information, which probably contributed to the controversy generated. The sensitivity of this channel to temperature is lost in excised patches, suggesting that it requires a soluble intracellular factor TRPV4 channels are expressed in DRG, TG, NG, and preoptic/anterior hypothalamic neurons, although in the hypothalamus they seem to be expressed in terminals rather than in the soma, such that their role in body thermoregulation is unclear. TRPM2 (>35 °C), TRPM3 (>40 °C), TRPM4 (>15 °C), and TRPM5 (>15 °C) are channels that can also be activated by warming (Figure 1), yet they have received less attention, probably because it was initially thought that they were not expressed by somatosensory neurons or keratinocytes. TRPM2 is voltage-insensitive, shows a PCa/PNa ~1, activates at 35 °C, and has a Q10 of around 15. TRPM3 is expressed broadly, generating an outwardly rectifying current, having a PCa/PNa between 0.1 and 10, and activating at >40 °C with a Q10 of 7. It is important to say that TRPM3 has been described as part of a triad of TRPs, together with TRPV1 and TRPA1, involved in the transduction of acute noxious heat in mice. The combined ablation of these channels (triple KO) was necessary for the complete reduction of acute noxious sensing; single or double KO combinations resulted in deficits in heat responsiveness, but mice still conserved vigorous withdrawal responses to noxious heat. Heat activation of TRPM2 and TRPM5 was obtained in inside-out patches, suggesting a membrane-delimited mechanism. Interestingly, TRPM2 activation seems to result from the increase in the IV slope while that of TRPM4 and TRPM5 results from a shift of the activation curve to negative potentials. These last two channels are essentially not permeable to calcium. Cold-Sensitive TRP Channels Two TRP channels are activated by decreases in temperature (Figure 1), TRPM8 (<25 °C) activates in the cool range while TRPA1 (<18 °C) senses cold-painful temperatures. Similarly, cool fibers (Aδ and C) have activation thresholds at about 30 °C, and cold fibers (C) have activation thresholds <20 °C. Accordingly, two populations of TG neurons were described in terms of their activation threshold when temperatures decrease: 30 and 20 °C for a low and high threshold, respectively. In general, cold fibers fire continuously at normal skin temperatures and they increase their firing frequency when the skin is cooled down, or they shut down when the skin is warmed. In addition, cold fibers can adapt to small decreases in temperature. TRPM8 channels are voltage-dependent cationic channels that are permeable to Na+, K+, Cs-, and Ca2+ (PCa/PNa ~3). When expressed in HEK cells and recorded in the whole-cell configuration, they show a voltage-dependent outwardly rectifying current that strongly increases upon cooling from 30 to 15 °C or through the application of menthol. Figure \(\PageIndex{d}\) shows an interactive iCn3D model of TRPM8 ion channel in complex with the menthol analog WS-12 and PI(4,5)P2 (6NR2) The methanol analog is shown in spacefill and CPK colors in the membrane bilayers. PI(4,5)P2 is shown in spacefill just below the lower cytoplasmic leaflet. The TRPM8 receptor is a Ca2+ cation channel. Cooling compounds like menthol and WS-12 depend on allosteric interactions and membrane phosphatidylinositol 4,5-bisphosphate (PIP2 ). Importantly, both basal and cold-stimulated currents reverse around 0 mV and were almost negligible below this potential. Cooling CHO cells expressing TRPM8 (in the range of 25 to 15 °C) also induces an increase in intracellular calcium, and the Q10 in the range of 25 to 18 °C is around 24. The effect of temperature is due to an increase in the open probability and a shift in the conductance–voltage relationships along the voltage axis [9]. Similar results were obtained in inside-out macropatch recordings, although the stimulation occurred at lower temperatures, suggesting that the integrity of the cell is important but not indispensable. The role of this channel as a detector of painful cold has been questioned in experiments on KO mice, but it is accepted that it is an important cold sensor in vagal, TG, and especially DRG afferents. It was predicted that cold transduction may require the activation and inhibition of several different ion channels (see Figure 3), such as TRP, TREK, and ENaC channels. If this were the case, TRP channels would probably be more important in the noxious-cold range, whereas TREK channels might participate more strongly in the cool range of temperatures (Figure 1). TRPM8 is expressed in small-diameter DRG and TG neurons, presumably thermoreceptors, yet it seems not to co-localize with TRPV1. TRPA1 is activated by lower temperatures than TRPM8 (<18 °C), and while it would be expected to be involved in cold nociception, this is not that clear. TRPA1 generates an outward rectifying cationic current, both in control conditions and when cold activated (about 10 °C), with similar permeability for Ca2+ and Na+ (PCa/PNa ~1). Cinnamaldehyde can selectively activate currents through this channel in native DRG neurons, as can bradykinin (when co-expressed with BK receptors), strongly suggesting a role in sensing nociceptive stimuli. However, TRPA1 KO mice do not seem to have difficulties in sensing cold stimuli through the skin, while the response of TRPM KO mice to cold is significantly dampened. By contrast, about 50% of NG neurons in culture were activated by cooling (<24 °C), mainly through TRPA1 channel activation (increase in [Ca]i, depolarization, and AP firing). Interestingly, about 10% of the NG neurons responded to cold through a TRPA1- and Ca-independent pathway. TRPA1 often co-localizes with TRPV1, and in fact, this could explain the paradoxical hot sensation experienced with an extremely cold stimulus. Interestingly, most NG neurons sensitive to cold are also sensitive to heat. TRPA1 is expressed in DRG and TGs, while TRPA1 and TRPM8 are not co-expressed in DRG neurons, but they are in TG neurons. In summary, the data available suggest that TRPA1 is the principal ion channel involved in cold sensation in visceral (NG) neurons, while TRPM8 would fulfill the same role in somatic neurons. Molecular Origin of Thermosensitivity The mechanism by which temperature modulates TRP channels is still unclear, yet several hypotheses have been proposed: (1) changes in temperature could produce a ligand that binds to a receptor and affects the channel; (2) changes in temperature could produce a structural change in the channel that provokes its opening; (3) temperature changes could affect the structure of the membrane, causing changes in tension that would, in turn, affect ion channels. Because capsaicin induces burning pain, it has been hypothesized that both capsaicin and heat may use a common mechanism to activate TRPV1 and produce pain. Both stimuli affect excised patches, and in general, it is accepted that TRPV1 is directly activated by noxious heat, so that it can be considered a true heat sensor. The fact that TRPM8 can be activated by cooling in inside-out patches suggests that the mechanism is membrane delimited, also arguing against the participation of a second messenger pathway. Notwithstanding, inhibition of phospholipase C strongly dampened the increase in calcium provoked by cold stimuli in TRPM8-expressing CHO cells. Cooling activates TRPM8-expressed channels by causing a shift in the voltage dependence of activation to negative values, and the same mechanism is responsible for the activation of TRPV1 by heatbut not the activation of TRPM2. It has been proposed that temperature induces large rearrangements of the protein and thus, the existence of a temperature-sensing domain or “temperature sensor” in the structure of TRPM8 channels. Much like for TRPM8, inhibition of phospholipase C strongly reduces the increase in calcium provoked by cold stimuli in TRPA1-expressing CHO cells. Conclusions There are several important differences between the two main types of thermosensor channels that have been reviewed in this article (see Figure 2). First, TREKs are potassium channels with negative reversal potentials, such that their activation would result in a reduction in thermoreceptor activity. By contrast, as the reversal potential of TRPs (cationic channels) is close to 0 mV, their activation will result in increased thermoreceptor excitability. Second, the three TREK channels appear to increase their open probability as temperatures increase, while there are two possible situations in the case of TRPs: one in which its activity increases by increasing the temperature; and another in which activity increases when the temperature decreases (Figure 2). Although the activation of these channels generates opposing effects on thermoreceptors (depolarization versus hyperpolarization), the information available to date regarding the participation of TRP and TREK channels in thermosensitivity strongly suggests that both types of channels collaborate and complement each other to generate the sensations of heat, cold, and thermal pain. In support of this hypothesis, TREK, and TRP channels are very often co-expressed in thermoreceptors and other sensory neurons TRP channels are generally accepted as the primary thermosensors; however, several lines of evidence indicate that other channels are necessary to explain the full plethora of mechanisms involved in thermosensation. TREK2 channels appear to be important in thermoreception at moderate temperatures and sensing innocuous cold but not aversive cold, while TREK1 and TRAAK acting together may be important in sensing painful cold.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.19%3A_Signal_Transduction_-__Temperature.txt

Search Fundamentals of Biochemistry This chapter section is taken in entirety from Fang, XZ., Zhou, T., Xu, JQ. et al. Structure, kinetic properties, and biological function of mechanosensitive Piezo channels. Cell Biosci 11, 13 (2021). https://doi.org/10.1186/s13578-020-00522-z Creative Commons Attribution 4.0 International License, http://creativecommons.org/licenses/by/4.0/. We added iCn3D molecular models. Introduction Mechanotransduction, the process by which mechanical stimuli are converted into electrochemical signals, is essential for various biological processes, including neuronal cell development, pain sensation, and red blood cell volume regulation. As pivotal mechanosensors of in the mechanotransduction process, mechanosensitive (MS) ion channels have been found in organisms from bacteria to mammals. Extensive studies have revealed a variety of ion channels in eukaryotic cells that can sense various forms of mechanical forces (Table 1). These ion channels include transient receptor potential (TRP) channels and voltage-gated Na+, K+, and Ca2+ channels, whose dysfunction may be associated with human genetic diseases. Notably, the MS candidates identified in invertebrates either have no homologs (e.g., TRPN) or no functional conservation (e.g., DEG/ENaC/ASIC) in mammals. Furthermore, most MS candidates (the TRP channel in particular) are activated not only by mechanical stimuli but also by chemicals, temperature, osmolarity, and heat (> 27–34 °C). Defining the molecular details of MS cation channels in mammals is therefore of paramount importance to understand the mechanotransduction process and find potentially novel therapeutic strategies for mechanosensitivity disorders. Channel family Channel isoforms Ref. TRP channels TRPA1 [6] TRPC1 [7] TRPC6 [8] TRPV1 [9] TRPV4 [10] TRPM4 [11] TRPM7 [12] TRPN [13] TRPP2 [14] K + channels Shaker (Kv1.1) [15] Ca2+-activated K+ (BK) [16] TREK1/2 [17] TRAAK [18] HCN2 [19] Na+ channels Nav1.5 [20] Ca2+ channels L-type [21] N-type [22] T-type [23] Cl channels CFTR [24] OSCA protein family ScCSC1, HsCSC1 [25] DEG/ENaC superfamily C.elegans MEC (MEC-4, MEC-10) [26] ASIC [27] Other channels TMC1/2 [28] In 2010, Coste et al. revealed a novel family of mechanically activated (MA) cation channels in eukaryotes consisting of Piezo1 and Piezo2 channels, which have been proposed as the long-sought-after MS ion channels in mammals. The Piezo1 channel is present in nonsensory tissues, with particularly high expression in the lung, bladder, and skin; by contrast, the Piezo2 channel is predominantly present in sensory tissues, such as dorsal root ganglia (DRG) sensory neurons and Merkel cells. Since their discovery, tremendous effort has been made to reveal the structures and biological functions of Piezo 1 and 2. The partial molecular structure of a Piezo channel was determined by cryo-electron microscopy (cryo-EM). Furthermore, Piezo channels have been linked to various pathological and physiological processes, including erythrocyte volume regulation, cell division, and innate immunity. Moreover, Piezo channel mutations are associated with multiple hereditary human diseases, such as autosomal recessive congenital lymphatic dysplasia, hereditary xerocytosis (a rare, autosomal dominant congenital hemolytic anemia characterized by macrocytic stomatocytosis, and decreased red cell osmotic fragility due to a defect in cation permeability), and an autosomal recessive syndrome of muscular atrophy with perinatal respiratory distress. Considerable progress has been made toward characterizing the structural features, physiological significance, and biophysical properties of Piezo proteins. Given the importance of Piezo channels in understanding mechanotransduction processes, this review focuses on their structural details, kinetic properties, and potential functions as mechanosensors. We also briefly review the hereditary diseases caused by mutations in the Piezo genes, which is key to understanding their functions. Structure of Piezo channels Piezo proteins have an uncommonly large predicted size of approximately 2500 amino acids and encompass numerous transmembrane (TM) regions. Subsequent research has revealed that the mouse Piezo1 (mPiezo1) channel is an evolutionarily conserved pore-forming ion channel directly gated by membrane stretch. Several published cryo-EM studies have revealed that mPiezo1 exhibits a three-bladed, propeller-shaped homotrimeric structure that includes a central cap, three peripheral blade-like structures on the extracellular side, three long beams on the intracellular side that bridge the blades to the cap, and a TM region between these features (Fig. 1). Figure 1: Cryo-EM structure of the mPiezo1 channel (adapted from Zhao et al.). a Multiple views of the sharpened map of the trimeric channel with the major domains labeled, with the three subunits colored red, green, and blue. b Cartoon model in which the three subunits are colored red, green, and blue. In the middle panel, the front subunit has been omitted to provide a better view of the curvature of the TMs Structure of the Piezo1 channel Unprecedented 38-TM topology Piezo channels are predicted to possess an unusually large number of TM regions, ranging from 10 to 40. Zhao et al. recently produced high-resolution structures of mouse Piezo1 (mPiezo1), revealing a unique 38-TM topology in each subunit (Fig. 2a, b). The two TM regions (TM37 and TM38) closest to the center of the protein are designated as the inner helix (IH) and outer helix (OH), respectively, and enclose the transmembrane pore of the central pore module. The other 36 TM regions (TM1-36) form a curved blade-like structure with nine repetitive folds containing 4 TM regions each, named transmembrane helical units (THUs) Figure 2: A 38-TM topology model and key functional sites in mPiezo1(adapted from Zhao et al.). a A model showing one subunit with individual THUs and featured structural components. Residues L1342 and L1345 in the beam are indicated by red spheres. b A 38-TM topology model color-coded to match the cartoon model in A Figure \(\PageIndex{a}\) shows an interactive iCn3D model of the mouse mechanosensitive Piezo1 channel (5Z10) (long load time) Central cap Kamajaya and colleagues [48] employed topological prediction modeling and found that residues 2210 to 2457 in Piezo1 form an extracellular loop following the last TM region from the C-terminus, defined as the C-terminal extracellular domain (CED) (Fig. 1). The deletion of residues 2218 to 2453 from the Piezo1 protein abolished expression of the central cap, suggesting that this region trimerizes to form the central cap (Figs. 1 and 3). Further analysis revealed that the central cap consists of the CED in the form of a trimeric complex that encloses an extracellular vestibule (EV) with openings (Fig. 3). Anchor A hairpin structure, referred to as the anchor, connects the OH-IH pair to the C-terminal domain (CTD) plane, which moves the OH-CED-IH-containing region of one subunit into the neighboring subunit in a clockwise direction (Figs. 1 and 2). The anchor is made up of three helices (α1, α2, and α3). Helices α1 and α2 were found to organize into an inverted V-shaped structure, which maintains the integrity of the ion-conducting pore (Fig. 2b). In parallel with the membrane plane, the long α3 helix links to the OH via a lysine-rich anchor-OH linker that interacts with the polar residue-rich α2–3 turn in the anchor and the glutamate-rich region of the CTD. A few mutations in Piezo1 at locations including KKKK (2182-K2185), T2143, T2142 (T2127 in human Piezo1), R2514, E2523, and E2522, which are located in α3 in the anchor, have been reported to cause severe disease. Additionally, SERCA2, a Piezo-interacting protein, suppresses Piezo1 by acting on the anchor-OH linker. These findings support the structural and functional importance of the anchor region. The long intracellular beam On the intracellular surface, Piezo1 contains three beam-like structures 90 nm in length that are organized at a 30° angle relative to the membrane plane (Figs. 1 and 2). Residues H1300-S1362 form the beam structure. The large intracellular THU7-8 loop, which contains approximately 390 residues, might provide the beam with the structural basis for mechanical transmission. Functionally, the three long intracellular beams not only support the whole TM skeleton but also physically bridge the distal THUs to the central ion-conducting pore. When residues 1280 to 1360 (which form this beam structure) were deleted, the resulting mutant protein was absent, suggesting the structural importance of the beam. Highly curved blades The nine peripheral THUs in each subunit form blade-like structures, with each blade twisted clockwise (Fig. 1b). The proximal TM25–TM36 and peripheral TM13-24 interact at a 100° angle, as viewed from 90º relative to the plasma membrane plane, and a 140° angle, as viewed from a line parallel to the plasma membrane plane. Another important feature of the blades is the L-shaped helical structures formed by TM13, TM17, TM21, TM25, and TM29. Both identifiable structural features appear to be ideal not only for mechanosensation but also for the induction of local membrane curvature. Intriguingly, the peripheral TM13-24 appears to be within a highly curved membrane plane, indicating that the Piezo1 channel can curve the membrane in which it resides. This is consistent with past studies implying that Piezo1 ion channels can be regulated by cellular membrane curvature and tension. The ion-conducting pathway As pore-forming ion channels, Piezo proteins contain a trimeric ion-conducting channel made up of residues 2,189 to 2,547, which contain the last two TMs (Fig. 3). The continuous central channel consists of three parts, an EV within the cap region, a transmembrane vestibule (MV) within the membrane, and an intracellular vestibule (IV) underneath the membrane. Both the EV and IV possess an opening that connects to the MVs, which are positioned above and below the membrane. Importantly, DEEED (2393–2397), a patch of negatively charged residues residing in the opening of the extracellular “cap” structure consisting of the CED, is required to ensure efficient ion conduction and determine the selection of cations over anions. Additionally, two critical acidic residues, E2495 and E2496, located at the CTD-constituted IV, may be responsible for divalent calcium ion selectivity, unitary conductance, and pore blockage. Structure of the Piezo2 channel Similar to Piezo1 channels, Piezo2 channels are large membrane proteins consisting of over 2,800 residues. However, the Piezo2 channel and Piezo1 channel share approximately only 42% sequence homology. Recent studies have shown that the overall structure of the Piezo2 channel is very similar to that of Piezo1 in that it forms a three-bladed, propeller-like homotrimeric structure comprising a central ion-conducting pore module and three peripheral blades with 38 TMs. Figure \(\PageIndex{b}\) shows an interactive iCn3D model of the mammalian tactile channel PIEZO2 (6KG7) (long load time) In the Piezo2 channel, the charged residues at the interface between the beam and the CTD are required to ensure the normal mechanosensitivity of the channel. Moreover, single-channel recordings indicated that a previously unrecognized intrinsically disordered domain adjacent to the beam acts as a cytosolic plug that limits ion permeation, possibly by clogging the inner vestibule in both Piezo1 and Piezo2. Furthermore, by structurally comparing the Piezo1 and Piezo2 channels, Wang et al. found that the Piezo2 channel has additional constriction sites at L2743, F2754, and E2757 that might serve as a transmembrane gate controlled by the cap domain. Lever-like mechanotransduction mechanism Based on the unique topological features of the mPiezo1 channel, a lever-like mechanotransduction mechanism to explain its extraordinary mechanosensitivity was proposed (Fig. 4). In the mPiezo1 channel, the curved blades composed of THUs can act as a mechanosensor, while the beam structure, with the residues Ll1342 and Ll1345 acting as a pivot, can act as a lever-like apparatus. Coupling the distal blades and the central pore through the lever-like apparatus converts mechanical force into a force used for cation conduction. Adapted from Ge et al. b A lever-like mechano-gating model in Piezo1. The blue and red dashed arrows indicate input and output forces, respectively Figure 4: Model of the lever-like mechanotransduction model. The curved blades can act as a mechanosensor, while the beam structure, with residues Ll1342 and Ll1345 acting as a pivot, can act as a lever-like apparatus. Coupling of the distal blades and the central pore through the lever-like apparatus converts mechanical force into cation conduction. a Proposed model of the force-induced gating of Piezo channels. The blue and orange models represent the channel in its closed and open states, respectively. Red dashed lines indicate possible ion-conduction pathways. Because the pivot of the lever is positioned closer to the central pore than to the distal blades, the input force is effectively amplified through the lever-like apparatus. Additionally, a large conformational change in the distal blades is converted into a relatively slight opening of the central pore, allowing cation-selective permeation. Kinetics properties of Piezo channels Activation mechanisms of Piezo channels Normal Piezo channel kinetics can be modeled with three states: open, closed, and inactivated; these states have emerged, collectively, as an important mechanism in the Piezo channel function. Studies have proposed that the Piezo1 channel is gated directly by bilayer tension that can be modified by cytoskeletal proteins and linkages to the extracellular matrix (ECM). For example, in overhydrated red blood cells (RBCs), Piezo1-mediated Ca2+ influx activates K+ efflux through the Gardos channel (KCa3.1), which in turn leads to water loss and RBC dehydration. Piezo1 and Piezo2 channels not only exhibit a three-bladed, propeller-shaped trimeric architecture but also can locally deform lipid membranes into a dome-like shape. In addition, changes in the projection area of Piezo channels from closed to open are essential for their mechanosensitivity; this was investigated by calculating the available free energy. Based on these findings, the membrane dome mechanism was proposed and experimentally proved to explain the activation mechanisms of Piezo channels  (Fig. 5). Essentially, the dome shape created by Piezo channels in their closed conformation acts as a potential energy source for MS gating. Under tension, lateral membrane tension flattens the Piezo dome, which increases the energy of the membrane-channel system in proportion to the expansion of the projected area of the dome. Piezo channels then open due to the relative energy difference. This mechanism can account for the highly sensitive mechanical gating of Piezo channels with a cation-selective pore. Although the membrane dome mechanism explains the exquisite mechanosensitivity of Piezo channels, it does not consider the shape of the surrounding membrane. Haselwandter et al. [57] proposed the membrane footprint hypothesis, which states that the Piezo1 channel deforms the shape of the membrane outside the perimeter of the channel such that it exhibits a curved membrane footprint, which amplifies the sensitivity of Piezo1 to changes in the membrane tension. Nevertheless, further experiments are needed to test and refine these hypotheses. Inactivation kinetics of Piezo channels Various types of mechanical stimulation trigger Piezo channel activation and sequentially elicit an MA current with rapid decay, even in the presence of continued stimulation, due to rapid inactivation. Coste et al. first described detailed information about the voltage-dependent inactivation kinetics of Piezo channels, characterized as fast at rather negative membrane potentials and slow at rather positive membrane potentials. Additionally, Piezo1 channel inactivation is relatively slow compared with Piezo2 channel inactivation. Several point mutations in Piezo channels have been reported to slow down the inactivation process, which produces larger cation fluxes and results in various human diseases. Given its demonstrated key role in normal channel function, we next review what is known about the inactivation kinetics of Piezo channels with a focus on the inactivation mechanism. The available information regarding the structures (residues/domains) and human disease-related point mutations have helped to clarify the mechanisms of ion channel inactivation. Currently, six gain-of-function mutations associated with dehydrated hereditary xerocytosis (DHS) have been found to slow the inactivation rate of Piezo channels (Table 2), most of which are clustered at the central core region of the Piezo channel structure. This implies that the pore region, which contains an OH, an IH, an extracellular cap domain, and an intracellular CTD, determines the kinetics of inactivation. Further detailed links between structural domains and inactivation kinetics have been investigated. Wu et al. identified that the distinct inactivation kinetics of Piezo1 and Piezo2 channels and characteristic voltage-dependent inactivation appears to be determined by the C-terminal extracellular domains (cap domain). Two potential inactivation gates within the IH and CTD have been thought to be sufficient for the normal inactivation of the Piezo1 and Piezo2 channels. Recently, three small subdomains within the extracellular cap were shown to individually confer Piezo channel inactivation. These results support the idea that the ion-conducting pore region of Piezo channels is essential for their inactivation properties. Table 2 Mutations in Piezo1 and Piezo2 Associated with Human Diseases: Full-size table Interestingly, a slowly inactivating MS current in mouse embryonic stem cells (mESs) has been described that is dependent on the Piezo1 channel. However, heterologous expression of Piezo1 cDNA from mES cells displays fast inactivation kinetics, indicating that additional regulatory mechanisms other than the amino acid sequence determine the slow kinetics of the Piezo1 channel in mES cells [70]. Recently, sphingomyelinase activity has been revealed to be a crucial determinant of Piezo1 inactivation. Various modulators, such as pH, temperature, divalent ion concentrations, alternative splicing, osmotic swelling, membrane lipid composition, co-expression of other membrane proteins, and G-protein-coupled pathways have also been reported to regulate the Piezo channel kinetics; however, we still know very little about the relationships among these factors and pivotal structural domains. Pharmacological modulators of Piezo channels Despite the relatively recent discovery of Piezos, there has been progress regarding small-molecule modulators of Piezo1. Piezo1 chemical activators, including Yoda1 and Jedi1/2, were able to open Piezo1 ion channels in the absence of mechanical stimulation. Jedi1/2, a novel hydrophilic Piezo1 chemical activator, acts through the peripheral blades and utilizes a peripheral lever-like apparatus consisting of the blades and a beam to gate the central ion-conducting pore whereas Yoda1 acts as a molecular wedge, facilitating force-induced conformational changes, effectively lowering the channel’s mechanical threshold for activation However, the reason why Yoda1 does not efficiently activate the Piezo2 channel is unclear. Specific inhibitors of Piezo1 are in their infancy. As nonspecific inhibitors of the ion pore in stretch-activated ion channels, gadolinium, and ruthenium red have also been shown to block mouse Piezo1 channels with IC50 values of approximately 5 mM. The commonly used toxin inhibitor of mechanosensitive channels, GsMTx4, was also found to inhibit the Piezo1channel, but it might not bind Piezo1 directly, rather acting via modulating local membrane tension near the channel. Dooku1, an analog of Yoda1 without a stimulatory effect, antagonizes Yoda1-evoked activation of Piezo1 and aortic relaxation. The function of Piezo channels Piezo channels are expressed in a wide range of mechanically sensitive cells and allow Ca2+ influx in response to different types of external forces, such as fluid flow, pulling, and ultrasonic forces. The biological function of Piezo channels was recently investigated in a number of studies (Fig. 6). The results of these studies verified the pivotal roles of Piezo channels in mechanotransduction under physiological and pathophysiological conditions. Here, we focus on reviewing the biological function of Piezo channels in several different types of MS tissues and cells. Figure 6: Expression and function of Piezo channels Multiple tissues and cells express Piezo channels, and each of those shown is discussed in this review. a–e demonstrate the vital role of the Piezo1 channel in the CNS, blood vessels, erythrocytes, lungs, gastrointestinal tract, and urinary tract. f–h illustrate the expression of both the Piezo1 channel and Piezo2 channel in articular cartilage, trigeminal ganglia, and dorsal root ganglia. i shows that the Piezo2 channel is expressed in Merkel cells, which are involved in sensing light touch Consult the original article for details on the roles of Piezo channels on the systems above.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/28%3A_Biosignaling_-_Capstone_Volume_I/28.20%3A_Signal_Transduction_-__Pressure.txt

An Overview of Metabolic Pathways - Catabolism Biological cells have a daunting task. They must carry out 1000s of different chemical reactions required to carry out cell function. These reactions can include opposing goals such as energy production and energy storage, macromolecule degradation and synthesis, and breakdown and synthesis of small molecules. All of these reactions are catalyzed by proteins and RNAs enzymes whose activities must be regulated, again through chemical reactions, to avoid a futile and energy wasting scenario of having opposing pathways functioning simultaneously in a cell. Metabolism can be divided into two main parts, catabolism, the degradation of molecules, usually to produce energy or small molecules useful for cell function, and anabolism, the synthesis of larger biomolecules from small precursors. CATBOLISM: Catabolic reactions involve the breakdown of carbohydrates, lipids, proteins, and nucleic acids to produce smaller molecules and biological energy in the form of heat or small thermodynamically reactive molecules like ATP whose further degradation can drive endergonic process such as biosynthesis. Our whole world is reliant on the oxidation of organic hydrocarbons to water and carbon dioxide to produce energy (at the expense of releasing a potent greenhouse gas, CO2). In the biological world, reduced molecules like fatty acids and partially oxidized molecules such as glucose polymers (glycogen, starch), as well as simple sugars, can be partially or fully oxidized to ultimately produce CO2 as well. Energy released from oxidative reactions is used to produce molecules like ATP as well as heat. Oxidative pathways include glycolysis, the tricarboxylic acid cycle (aka Kreb's cycle) and mitochondrial oxidative phosphorylation/electron transport. To fully oxidize carbon in glucose and fatty acids to carbon dioxide requires splitting C-C bonds and the availability of series of oxidizing agents that can perform controlled, step-wise oxidation reactions, analogous to the sequential oxidation of methane, CH4 to methanol (CH3OH), formaldehyde (CH2O) and carbon dixoxide. • Glycolysis: This most primitive of metabolic pathways is found in perhaps all organisms. In glycolysis, glucose (C6H12O6), a 6C molecule, is split (or lysed) into two, 3C carbon molecules, glyceraldehyde-3-phosphate, which are then partially oxidized under anaerobic conditions (without O2) to form two molecules of pyruvate (CH3COCO2-). Instead of the very strong oxidizing agent, O2, a weaker one, NAD+ is used, which is reduced in the process to form NADH. Since none of the carbon atoms is oxidized to the state of CO2, little energy is released compared to the complete oxidation to CO2. This pathway comes to a screeching halt if all cellular NAD+ is converted to NADH as NAD+ is not replenished by the simple act of breathing as is the case with O2 in aerobic oxidation. To prevent the depletion of NAD+ from inhibiting the cycle and to allow the cycle to continue under anaerobic conditions, excess NADH is reconverted to NAD+ when the other product of glycolysis, pyruvate is converted to lactate by the enzyme lactate dehydrogenase. Glycolysis occurs in the cytoplasm of the cell. • Tricarboxylic Acid (Kreb's) Cycle: The TCA cycle is an aerobic pathway which takes place in an intracellular organelle called the mitochondria. It takes pyruvate, the incompletely oxidized product from glycolysis, and finishes the job of oxidizing the 3C atoms all the way to CO2. First the pyruvate moves into the mitochondria where is is oxidized to the 2C molecule acetylCoA with the release of one CO2 by the enzyme pyruvate dehydrogenase. The acetyl-CoA then enters the TCA cycle where two more CO2 are released. As in glycolysis, C-C bonds are cleaved and C is oxidized by NAD+ and another related oxidizing agent, FAD. What is very different about this pathway is that instead of being a series of linear, sequential reactions with one reactant (glucose) and one product (two pryuvates), it is a cyclic pathway. This has significant consequences since if any of the reactants within the pathways becomes depleted, the whole cyclic pathway can slow down and stop. To see how this happens consider the molecule oxaloacetate (OAA) which condenses with acetyl-CoA to form citrate (see diagram below). In this reaction, one OAA is consumed. However, when the cycle returns, one malate is converted to OAA so there is no net loss of OAA, unless OAA is pulled out of the TCA cycle for other reactions, which happens. • Mitochondrial Oxidative Phosphorylation/Electron Transport: The TCA cycle accomplishes what glycolysis didn't, that is the cleavage of all C-C bonds in glucose (in the form of pyruvate and acetyl-CoA, and the complete oxidation of all C atoms to CO2. Yet two problem remains. The pool of oxidizing molecules, NAD+ and FAD get converted to their reduced forms, NADH and FADH2. Unless NAD+ and FAD are regenerated, as was the case in anaerobic conditions when pyruvate gets converted to lacate, the pathway would again come to a grinding halt. In addition, not much ATP is made in the cycle (in the form of a related molecule GTP). Both these problems are resolved as the resulting NADH and FADH2 formed are reoxidized by mitochondrial membrane enzyme complexes which pass electrons from the oxidized NADH and FADH2 to increasingly potent oxidizing agents until they are accepted by the powerful oxidant O2,which is converted reduced to water. The net oxidation of NADH and FADH2 by dioxygen is greatly exergonic, and the energy released by the process drives the synthesis of ATP from ADP and Pi by an mitochondrial enzyme complex, the F0F1ATPase. Feeder Pathways: Other catabolic pathways produce products that can enter glycolysis or the TCA cycle. Two examples are given below. • Complex carbohydrates: In mammals, the major carbohydrate storage molecule is glycogen, a polymer of glucose linked a1-4 with a1-6 branches. The terminal acetal linkages in this highly branched polymer is cleaved sequentially at the ends not through hydrolysis but through phosphorolysis to produce lots of glucose-1-phosphate which can enter glycolysis. • Lipids: Lipids are stored mostly as triacylglycerides in fat cells (adipocytes). When needed for energy, fatty acids are hydrolyzed from the glycerol backbone of the triacylglyceride, and send into cells where they broken down in an oxidative process to form acetyl-CoA with the concomitant production of lots of NADH and FADH2. These can then enter the mitochondrial oxidative phosphorylation/electrons transport system, which produces, under aerobic conditions, lots of ATP. • Proteins: When intracellular proteins get degraded, they from individual amino acids. The amine N is lost as it enters the urea cycle. The rest of some amino acid structures can be ultimately converted to acetyl-CoA or keto acids (like alpha-ketoglutarate- a-KG) that are TCA intermediate. These amino acids are called ketogenic. Alternatively, some amino acids, after deamination, are coveted to pyruvate which can either enter the TCA cycle or in the liver be used to synthesize glucose in an anabolic process. These amino acids are called glucogenic. Chemical reactions such as these can be used to replenish intermediates in the TCA cycle which can become depleted as they are withdraw for other reactions. Anabolic Reactions Anabolic reactions are those that lead to the synthesis of biomolecules. In contrast to the catabolic reactions just discussed (glycolysis, TCA cycle and electron transport/oxidative phosphorylation) which lead to the oxidative degradation of carbohydrates and fatty acids and energy release, anabolic reactions lead to the synthesis of more complex biomolecules including biopolymers (glycogen, proteins, nucleic acids) and complex lipids. Many biosynthetic reactions, including those for fatty acid synthesis, are reductive and hence require reducing agents. Reductive biosynthesis and complex polymer formation require energy input, usually in the form of ATP whose exergonic cleavage is coupled to endergonic biosynthesis. Cells have evolved interesting mechanism so as not to have oxidative degradation reactions (which release energy) proceed at the same time and in the same cell as reductive biosynthesis (which requires energy input). Consider this scenario. You dive into a liver cell and find palmitic acid, a 16C fatty acid. From where did it come? Was it just synthesized by the liver cell or did it just enter the cell from a distant location such as adipocytes (fat cells). Should it be oxidized, which should happen if there is a demand for energy production by the cell, or should the liver cell export it, perhaps to adipocytes, which might happen if there is an excess of energy storage molecules? Cells have devised many ways to distinguish these opposing needs. One is by using a slightly different pool of redox reagents for anabolic and catabolic reactions. Oxidative degradation reactions typically use the redox pair NAD+/NADH (or FAD/FADH2) while reductive biosynthesis often uses phosphorylated variants of NAD+, NADP+/NADPH. In addition, cells often carry out competing reactions in different cellular compartments. Fatty acid oxidation of our example molecule (palmitic acid) occurs in the mitochondrial matrix, while reductive fatty acid synthesis occurs in the cytoplasm of the cell. Fatty acids entering the cell destined for oxidative degradation are transported into the mitochondria by the carnitine transport system. This transport system is inhibited under conditions when fatty acid synthesis is favored. We will discuss the regulation of metabolic pathways in a subsequent section. One of the main methods, as we will see, is to activate or inhibit key enzymes in the pathways under a given set of cellular conditions. The key enzyme in fatty acid synthesis, acetyl-CoA carboxylase, is inhibited when cellular conditions require fatty acid oxidation. The following examples give short descriptions of anabolic pathways. Compare them to the catabolic pathways from the previous section. • Glucose synthesis, better known as Gluconeogenesis: In glycolysis, glucose (C6H12O6), a 6C molecule, is converted to two, 3C molecules (pyruvate) in an oxidative process that requires NAD+ and makes two net ATP molecules. In a few organs, most predominately in the liver, the reverse pathway can take place. The liver does this to provide glucose to the brain when the body is deficient in circulating glucose, for example, under fasting and starving conditions. (The liver under these conditions can get its energy from oxidation of fatty acids). The reactions in gluconeogenesis are the same reactions in glycolysis but run in reverse, with the exception of three glycolytic steps which are essentially irreversible. These three steps have bypass enzymes in the gluconeogenesis pathway. Although the synthesis of glucose is a reductive pathway, it uses NADH instead of NADPH as the redundant as the same enzyme used in glycolysis is simply run in reverse. Gluconeogenesis, which also occurs in the cortex of the kidney, is more than just a simple reversal of glycolysis, however. It can be thought of as the net synthesis of glucose from non-carbohydrate precursors. Pyruvate, as seen in the section on catabolism, can be formed from protein degradation to glucogenic amino acids which can be converted to pyruvate. It can also be formed from triacylglycerides from the 3C molecule glycerol formed and released from adipocytes after hydrolysis of three fatty acids from triacylglycerides. However, in humans, glucose can not be made in net fashion from fatty acids. Fatty acids can be converted to acetyl-CoA by fatty acid oxidation. The resulting acetyl-CoA can not form pyruvate since the enzyme that catalyzes the formation for acetyl-CoA from pyruvate, pyruvate dehydrogenase, is irreversible and there is no bypass reaction known. The acetyl-CoA can enter the TCA cycle but since the pathway is cyclic and proceeds in one direction, it can not form in net fashion oxaloacetate. Although oxaloacetate can be remove from the TCA cycle and be use to form phosphoenolpyuvate, a glycolytic intermediate, one acetyl-CoA condenses with one oxaloacetate to form citrate which leads back to one oxaloacetate. Hence fatty acids can not be converted to glucose and other sugars in a net fashion. • Pentose Phosphate Shunt: This two-part pathway doesn't appear to start as a reductive biosynthetic pathway as the first part is the oxidative conversion of a glycolytic intermediate, glucose-6-phosphate, to ribulose-5-phosphate. The next, nonoxidative branch leads to the formation of ribose-5-phosphate, a key biosynthetic intermediate in nucleic acid synthesis as well as erthyrose-4-phosphate used for biosynthesis of aromatic amino acids . The oxidative branch is important in reductive biosynthesis as it is a major source of the reductant NADPH used in biosynthetic reactions. • Fatty acid and isoprenoid/sterol biosynthesis: Acetyl-CoA is the source of carbon atoms for the synthesis of more complex lipids such as fatty acids, isoprenoids, and sterols. When energy needs in a cell are not high, citrate, the condensation product of oxaloacetate and acetyl-CoA in the TCA cycle, builds up in the mitochondrial matrix. It is then transported by the citrate transporter (an inner mitochondrial membrane protein) to the cytoplasm, where it is cleaved back to oxaloacetate and acetyl-CoA by the cytoplasmic enzyme citrate lyase. The oxaloacetate is returned to the mitochondria by conversion first to malate (reduction reaction using NADH), which can move back into the mitochondria through the malate transporter, or further conversion to pyruate, using the cytosolic malic enzyme, which uses NADP+ to oxidize malate to pyruvate which then enters the mitochondria. The acetyl-CoA formed in the cytoplasm can then be used in reductive biosynthesis using NADPH as the reductant to form fatty acids, isoprenoids, and sterols. The NADPH for the reduction comes from the oxidative branch of the pentose phosphate pathway and from the reaction catalyzed by malic enzyme. The liver cells can still run the glycolytic pathway as the NADH/NAD+ ratio is low in the cytoplasm while NADPH/NADP+ ratio is high. Regulation and Integration None of these pathways exist in isolation. They are connected in a complicated web of interactions. Instead of cursory summaries of how these pathways are all interconnected, we have chosen to present a series of published articles that are freely available for reuse through Creative Common's licenses. They describe organ- and systems-specific metabolisms and emerging understanding of metabolism in disease states. These include:

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/29%3A_Integration_of_Mammalian_Metabolism_-_Capstone_Volume_II/29.01%3A__Overview_of_Metabolism.txt

Han, HS., Kang, G., Kim, J. et al. Regulation of glucose metabolism from a liver-centric perspective. Exp Mol Med 48, e218 (2016). https://doi.org/10.1038/emm.2015.122 This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ Experimental & Molecular Medicine volume 48, pagee218 (2016) Abstract Glucose homeostasis is tightly regulated to meet the energy requirements of the vital organs and maintain an individual’s health. The liver has a major role in the control of glucose homeostasis by controlling various pathways of glucose metabolism, including glycogenesis, glycogenolysis, glycolysis and gluconeogenesis. Both the acute and chronic regulation of the enzymes involved in the pathways are required for the proper functioning of these complex interwoven systems. Allosteric control by various metabolic intermediates, as well as post-translational modifications of these metabolic enzymes constitute the acute control of these pathways, and the controlled expression of the genes encoding these enzymes is critical in mediating the longer-term regulation of these metabolic pathways. Notably, several key transcription factors are shown to be involved in the control of glucose metabolism including glycolysis and gluconeogenesis in the liver. In this review, we would like to illustrate the current understanding of glucose metabolism, with an emphasis on the transcription factors and their regulators that are involved in the chronic control of glucose homeostasis. Overview of glucose metabolism in the liver Under feeding conditions, dietary carbohydrates are digested and processed by various glucosidases in the digestive tract, and the resultant monosaccharides, mainly hexose glucose, are transported into various tissues as a primary fuel for ATP generation.1 In most mammalian tissues, the catabolism of glucose into pyruvate, termed glycolysis, is preserved as a major pathway in eliciting ATP. In tissues with abundant mitochondria, cytosolic pyruvate is transported into the mitochondrial matrix, converted to acetyl-CoA by pyruvate dehydrogenase complex, and incorporated into the tricarboxylic acid cycle in conjunction with oxaloacetate. The cycle generates energy equivalent to ATP (that is, GTP) as well as both NADH and FADH2, which serve as important electron carriers for electron transport chain-oxidative phosphorylation, resulting in the generation of ATP. In some cases, such as red blood cells lacking mitochondria or cells under ischemic conditions, pyruvate is converted into lactate in the cytosol to regenerate the NAD+ that is necessary for the continued generation of ATP by substrate-level phosphorylation via anaerobic glycolysis. Excessive carbohydrates in the liver are first converted into glycogen, a storage form of glucose in animals, by glycogenesis. In addition, in a carbohydrate-rich diet, the excessive carbohydrates are also converted into fatty acids via lipogenesis using the acetyl-CoA generated from glycolysis-driven pyruvate, which is incorporated into very low density lipoproteins for transport to white adipose tissue for the storage.2 The regulation of glycogen metabolism is examined in detail in this section, and the transcriptional control of glycolysis and lipogenesis is delineated in the following section. Under fasting conditions, the liver has a major role in generating glucose as a fuel for other tissues, such as the brain, red blood cells and muscles. Initially, an increase in the pancreatic hormone glucagon initiates the cascade of kinase action (stated below in detail) that releases glucose from the stored glycogen via glycogenolysis.1 Normally, stored glycogen is critical for maintaining glucose homeostasis in mammals during an overnight fasting period. During a longer term fast or starvation, essentially all of the stored glycogen in the liver is depleted (after ~30 h of fasting), and de novo glucose synthesis or gluconeogenesis is responsible for the generation of glucose as a fuel for other tissues. Major non-carbohydrate precursors for gluconeogenesis are lactate, which is transported from peripheral tissues such as skeletal muscles or red blood cells, and glycerol, which is released from the adipose tissues via enhanced lipolysis during fasting. Most of the initial precursors for gluconeogenesis are generated in the mitochondria (except glycerol 3-phosphate via glycerol kinase activity), but the majority of the reaction occurs in the cytosolic part of the cell. The complex regulatory mechanism is delineated in detail in the following section, with an emphasis on the transcriptional control of key regulatory enzyme genes. Regulation of glycogen metabolism in the liver The accumulation of glycogen in the liver during feeding conditions provides a storage form of glucose that can be used in times of reduced food intake (Figure 1). Multiple layers of regulation are required for this process for both the activation of glycogen synthase, which is a key enzyme of glycogenesis (glycogen synthesis), and the inhibition of glycogen phosphorylase, which is a key enzyme of glycogenolysis (glycogen breakdown) in the liver. Glycogen synthase is a major enzyme that facilitates the elongation of glycogen chains by catalyzing the transfer of the glucose residue of UDP-glucose to the non-reducing end of a pre-existing glycogen branch to produce a new α1→4 glycosidic linkage. The regulation of glycogen synthase has been mostly studied using a muscle-specific isoform. In the muscle, glycogen synthase is inactivated via phosphorylation on multiple serine residues by various serine/threonine kinases such as casein kinase-1, protein kinase A (PKA), and glycogen synthase kinase-3 (GSK-3). Most notably, the phosphorylation of glycogen synthase by GSK-3 at serine residues 640, 644 and 648 has been linked to the most important inhibitory post-translational modification for its catalytic activity. Regulation of hepatic glycogen metabolism. Under fasting conditions, glucagon and epinephrine induce cAMP-dependent signaling cascades, leading to the activation of glycogen phosphorylase and glycogenolysis while inhibiting glycogenesis. Conversely, feeding enhances insulin-mediated signaling in the liver, leading to the activation of both PP1 and Akt, thus promoting glycogen synthesis in response to increased glucose uptake in the liver. See the main text for more specific regulatory pathways. cAMP, cyclic AMP. Under fasting conditions, dephosphorylated and active GSK-3 phosphorylate and inactivate glycogen synthase, leading to the inhibition of hepatic glycogen synthesis. On feeding, increased insulin signaling activates Akt in the cell, which in turn phosphorylates and inactivates GSK-3, thus resulting in the activation of glycogen synthase. In addition, increased concentrations of glucose 6-phosphate allosterically activate this enzyme, thus potentiating its catalytic activity under feeding conditions.3, 4 One recent publication argues against the role of GSK-3 in the regulation of the liver-specific isoform of glycogen synthase. In that study, Guinovart et al.5 mutated the corresponding serine residues that are shown to be regulated by GSK-3 in the liver-isoform of glycogen synthase. They found that the mutation of those residues did not affect the overall enzyme activity but that the mutation of serine 7 to alanine, a site that is recognized and regulated by PKA, led to the increased activity of this enzyme. Further study is necessary to determine whether these results can be verified in vivo using animal models such as liver-specific knock-in mice for S7A liver glycogen synthase. The protein phosphatase 1 (PP1) may be responsible for the dephosphorylation and activation of glycogen synthase. Accordingly, both glucose and insulin have been shown to activate PP1 activity, whereas glucagon and epinephrine have been linked to the inhibition of its activity. Glycogen phosphorylase is a major enzyme involved in glycogenolysis (Figure 1). This enzyme catalyzes the reaction of the removal of a glucose residue from the non-reducing end of a glycogen chain, leading to the generation of glucose 1-phosphate.6 Glucose 1-phosphate can be converted into glucose 6-phosphate by phosphoglucomutase, and glucose 6-phosphate can be incorporated into glycolysis or further converted into glucose by glucose 6-phosphatase, depending on the energy status of the organism. Glycogen phosphorylase is active when it is phosphorylated at its serine 14 residue. The phosphorylation of glycogen phosphorylase requires a cascade mechanism of epinephrine and glucagon in the liver. On the activation of Gαs by the binding of hormones to cell surface G protein-coupled receptors (beta adrenergic receptor or glucagon receptor), the intracellular cyclic AMP (cAMP) levels increase via adenylate cyclase, leading to the activation of PKA. PKA is then responsible for the phosphorylation and activation of glycogen phosphorylase kinase, which in turn phosphorylates and activates glycogen phosphorylase to enhance glycogen breakdown. Under feeding conditions, this kinase cascade is inactive due to the lack of secretion of catabolic hormones. In addition, insulin promotes the activation of PP1, which dephosphorylates and inactivates glycogen phosphorylase. In essence, the anabolic hormone insulin promotes glycogenesis and inhibits glycogenolysis via the activation of PP1, leading to the dephosphorylation of glycogen phosphorylase (inactivation) and glycogen synthase (activation), and via the activation of Akt, leading to the phosphorylation of GSK-3 (inactivation) that is unable to phosphorylate and inactivate glycogen synthase. Control of hepatic glycolysis As stated above, glycolysis is critical to the catabolism of glucose in most cells to generate energy. The key rate-limiting enzymes for this pathway include glucokinase (GK, also termed hexokinase IV), which converts glucose into glucose 6-phosphate; phosphofructokinase-1 (PFK-1), which converts fructose 6-bisphosphate into fructose 1,6-bisphosphate; and liver-type pyruvate kinase (L-PK), which converts phosphoenolpyruvate (PEP) into pyruvate in the liver. These enzymes are tightly regulated by allosteric mediators that generally promote the catabolism of glucose in the cell.2, 7, 8, 9 GK is a high Km hexokinase that is present in the liver and the pancreatic beta cells, thus functioning as a glucose sensor for each cell type. Unlike the other hexokinase isotypes, GK activity is not allosterically inhibited by its catalytic product, glucose 6-phosphate in the cell, thus enabling the liver to continuously utilize glucose for glycolysis during conditions of increased glucose availability, such as during feeding conditions. GK is regulated via its interaction with glucokinase regulatory protein (GKRP). In the low intracellular glucose concentration during fasting, the binding of GK and GKRP is enhanced by fructose 6-phosphate, leading to the nuclear localization of this protein complex. Higher concentrations of glucose during feeding compete with fructose 6-phosphate to bind this complex, which promotes the cytosolic localization of GK that is released from GKRP, thus causing the increased production of glucose 6-phosphate in this setting.10 PFK-1 catalyzes the metabolically irreversible step that essentially commits glucose to glycolysis. This enzyme activity is allosterically inhibited by ATP and citrate, which generally indicate a sign of energy abundance. Reciprocally, it is allosterically activated by ADP or AMP, making it more efficient to bring about glycolysis to produce more ATP in the cell. In addition, PFK-1 activity is allosterically activated by fructose 2,6-bisphosphate (F26BP), a non-glycolytic metabolite that is critical for the regulation of glucose metabolism in the liver. F26BP is generated from fructose 6-phosphate by the kinase portion of a bifunctional enzyme that contains both a kinase domain (phosphofructokinase-2, PFK-2) and a phosphatase domain (fructose 2,6-bisphosphatase, F-2,6-Pase). PFK-2 is activated by the insulin-dependent dephosphorylation of a bifunctional enzyme that activates PFK-2 activity and simultaneously inhibits F-2,6-Pase activity to promote the increased F26BP concentration. Glucagon-mediated activation of PKA is shown to be responsible for the phosphorylation and inactivation of the kinase portion of this enzyme.7 Unlike its muscle counterpart, L-PK is also a critical regulatory step in the control of glycolysis in the liver. As in the case of other glycolytic enzymes, L-PK activity is regulated by both allosteric mediators and post-translational modifications. L-PK activity is allosterically activated by fructose 1,6-bisphosphate, an indicator for the active glycolysis. By contrast, its activity is allosterically inhibited by ATP, acetyl-CoA, and long-chain fatty acids, all of which signal an abundant energy supply. Additionally, the amino acid alanine inhibits its activity, as it can be readily converted to pyruvate by a transamination reaction. L-PK is inhibited by PKA following a glucagon-mediated increase in intracellular cAMP during fasting and is activated by insulin-mediated dephosphorylation under feeding conditions.7 In addition to the acute regulation of key regulatory enzymes, glycolysis is regulated by a transcriptional mechanism that is activated during feeding conditions. Two major transcription factors, sterol regulatory element binding protein 1c (SREBP-1c) and carbohydrate response element binding protein (ChREBP), are responsible for the transcriptional activation of not only glycolytic enzyme genes but also the genes involved in fatty acid biosynthesis (such as fatty acid synthase (FAS), acetyl-CoA carboxylase (ACC), and stearoyl-CoA desaturase 1 (SCD1)) and triacylglycerol formation (such as glycerol 3-phosphate acyltransferase (GPAT) and diacylglycerol acyltransferase 2 (DGAT2)), a process that is normally activated by a carbohydrate-rich diet (Figure 2).11 Because these processes are often coordinately regulated, that is activated during feeding and inhibited by fasting, they are sometimes collectively called lipogenesis. Regulation of hepatic glycolysis. Under feeding conditions, increased glucose uptake in hepatocytes promotes glycolysis and lipogenesis to generate triglycerides as storage forms of fuel. This process is transcriptionally regulated by two major transcription factors in the liver, SREBP-1c and ChREBP-Mlx heterodimer, which mediate the insulin and glucose response, respectively. See the main text for more specific regulatory pathways. Full size image SREBPs are the major regulators of lipid metabolism in mammals. They are members of the basic helix-loop-helix leucine zipper (b/HLH/LZ) type transcription factor families comprising SREBP-1a, SREBP-1c, and SREBP-2. SREBP is translated as an endoplasmic reticulum (ER)-bound precursor form that contains the N-terminal transcription factor domain and the C-terminal regulatory domain linked with the central transmembrane domain.12 Within this family of transcription factors, SREBP-2 is linked to the control of cholesterol uptake or biosynthesis in the liver by the transcriptional activation of the genes involved in the pathway including low density lipoprotein receptor (LDLR), 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR), hydroxy-3-methylglutaryl-coenzyme A synthase 1 (HMGCS1), and farnesyl diphosphate synthase (FDPS). SREBP-1c, however, activates the genes encoding the enzymes for lipogenesis (FAS, ACC, SCD1, and DGAT2) as well as GK, which is a first enzyme in the commitment step of glucose utilization in the liver. Indeed, liver-specific SREBP-1c knockout mice showed an impaired activation of lipogenic genes in a high carbohydrate diet, thus confirming the importance of this transcription factor in the regulation of hepatic glycolysis and fatty acid biosynthesis.13 SREBP-1a is not highly expressed in the liver but was shown to be involved in the formation of inflammasomes in response to lipopolysaccharide (LPS) treatment in macrophages by transcriptional activation of Nlrp1.14 The regulation of SREBP-2 and SREBP-1c are quite distinct in the liver. The expression of SREBP-2 is not controlled by sterols, but its proteolytic processing is tightly regulated by intracellular concentrations of cholesterol. It is normally bound in the ER via the interaction of SREBP-cleavage-activating protein (SCAP) and insulin-induced gene protein (INSIG) in the presence of high intracellular cholesterol levels, and the reduction in the cholesterol concentration releases the interaction of SCAP and SREBP-2/INSIG complex, resulting in the translocation of the latter complex into the Golgi apparatus and the liberation of the active SREBP-2 factor by sequential proteolytic cleavages.15 Unlike SREBP-2, SREBP-1c is mainly regulated at the transcription level by insulin. The exact transcription factor that mediates this insulin-dependent signal is not yet clear, although SREBP-1c itself might be involved in the process as part of an auto-regulatory loop. Interestingly, the oxysterol-sensing transcription factor liver X receptor (LXR) is shown to control the transcription of SREBP-1c, suggesting that SREBP-1c and SREBP-2 could be regulated differently in response to cellular cholesterol levels.16 Recent studies have revealed the involvement of various kinases in the control of SREBP-1c activity. In HepG2 cells, PKA was shown to reduce the DNA binding ability of SREBP-1a by the phosphorylation of serine 338 (equivalent of serine 265 for SREBP-1c).17 A report by Bengoechea and Ericsson suggested that GSK-3, a kinase known to reduce glycogen synthesis by targeting glycogen synthase, downregulates SREBP-1 activity via the phosphorylation of the C-terminal residue that promotes the ubiquitin ligase Fbw7-dependent degradation of SREBP-1 proteins.18 In addition, both AMP activated protein kinase (AMPK) and its related kinase salt-inducible kinase (SIK) 1 are involved in the down-regulation of its activity through inhibitory phosphorylation (serine 372 for AMPK, which blocks proteolysis and nuclear localization of SREBP-1c, and serine 329 for SIK1, which directly reduces its transcriptional activity).19, 20 These data suggest that the fine-tuning of SREBP-1c activity is critical to the maintenance of glucose and lipid homeostasis in the liver. The other prominent transcription factor for controlling glycolysis and fatty acid biosynthesis in the liver is ChREBP. ChREBP was initially known as Williams-Beuren syndrome critical region 14 (WBSCR14) and was considered one of the potential genes that instigate Williams-Beuren syndrome. Later, by using a carbohydrate response element (ChoRE) from L-PK, ChREBP was isolated as a bona fide transcription factor for binding ChoRE of glycolytic promoters.21 Indeed, ChREBP is highly expressed in tissues that are active in lipogenesis such as the liver, brown adipocytes, white adipocytes, small intestine, and kidney. As in the case for SREBP, ChREBP belongs to the b/HLH/LZ transcription factor family and forms a heterodimer with another b/HLH/LZ transcription factor Max-like protein X (Mlx) on the glycolytic promoter.22 As in the case for the SREBP-1c, the expression of ChREBP is increased in the liver as a result of a high carbohydrate diet, and the effect was recapitulated in primary hepatocytes with high glucose treatment. A recent report indeed suggested a role for LXR in the transcriptional activation of ChREBP in response to glucose, although the study needs to be further verified because the transcriptional response is shown not only by the treatment of D-glucose, a natural form of glucose present in animals, but also by the treatment of unnatural L-glucose, a form of glucose that is not known to activate lipogenesis in the liver.23 Moreover, studies performed in LXR knockout mice revealed no changes in ChREBP expression in the liver, arguing against the role of LXR in the control of ChREBP.24 Glucose is also shown to regulate ChREBP activity by controlling its nuclear localization. There are three prominent serine/threonine residues that are targeted by serine/threonine kinases. PKA is shown to phosphorylate serine 196, which is critical for cellular localization, and threonine 666, which is critical for its DNA binding ability, whereas AMPK phosphorylate serine 568 dictates its DNA binding ability. All three sites are phosphorylated under fasting conditions by these kinases and are dephosphorylated under feeding by xylulose 5-phosphate (X5P)-mediated activity of protein phosphatase 2A (PP2A).25, 26 However, the current model needs to be further verified due to the contrasting data that have been published regarding the role of these phosphorylations on ChREBP activity. First, high glucose concentrations in primary hepatocytes do not result in decreased cAMP levels or PKA activity, suggesting that other signals might be necessary to mediate the high glucose-dependent nuclear translocation of ChREBP. In addition, a serine to alanine mutant of ChREBP still requires high glucose for its full activity, suggesting that additional actions are necessary to recapitulate the high glucose-mediated activation/nuclear localization of ChREBP in the liver.27, 28 The physiological role of ChREBP in liver glucose metabolism was verified by in vivo studies. ChREBP knockout mice were born in a Mendelian ratio and showed no developmental problems. The knockout animals showed reduced liver triacylglycerol levels together with a reduction in lipogenic gene expression, thus confirming the role of ChREBP in the control of hepatic glycolysis and fatty acid synthesis.29 Interestingly, the compensatory increase in glycogen was observed in the livers of these mice, suggesting that these mice adapted to store more glycogen as a storage form of fuel as opposed to triacylglycerol. In ob/ob mouse liver, increased accumulation of nuclear ChREBP was shown, suggesting that this phenomenon might be causal to the fatty liver phenotype in these mice. Indeed, knockdown of ChREBP in ob/ob mice reduced the rate of lipogenesis with decreased expression of most lipogenic genes.30 Furthermore, the depletion of hepatic ChREBP in ob/ob mice improved hyperglycemia, hyperlipidemia, and hyperinsulinemia, suggesting that regulation of ChREBP might be critical in the control of metabolic disorders in the presence of obesity and insulin resistance. Control of hepatic gluconeogenesis Prolonged fasting or starvation induces de novo glucose synthesis from non-carbohydrate precursors, termed hepatic gluconeogenesis. This process initiates from the conversion of pyruvate to oxaloacetate by pyruvate carboxylase (PC) in the mitochondria and eventually concludes in the conversion into glucose via several enzymatic processes in the cytosol.7, 8, 9 Among the substrates for gluconeogenesis are amino acids, which can be converted into either pyruvate or intermediates of the tricarboxylic acid cycle; lactate, which can be converted into pyruvate by lactate dehydrogenase; and glycerol (from increased lipolysis in the white adipocytes under fasting), which can be converted into dihydroxyacetone phosphate, a gluconeogenic intermediate (a two-step process that is catalyzed by glycerol kinase and glycerol 3-phosphate dehydrogenase). Key regulatory enzymes in that pathway, including glucose 6-phosphatase (G6Pase), fructose 1,6-bisphosphatase (Fbpase1), PC, and phosphoenolpyruvate carboxykinase (PEPCK), are activated under fasting conditions to enhance gluconeogenic flux in that setting. Mitochondrial acetyl-CoA (derived from the increased fatty acid oxidation under fasting) functions as a key allosteric activator of PC, leading to the increased production of oxaloacetate for the gluconeogenesis. In addition, F26BP, which is a key allosteric regulator for glycolysis by activating PFK-1, was shown to inhibit gluconeogenesis via the allosteric inhibition of Fbpase1, which helps reciprocally control gluconeogenesis and glycolysis under different dietary statuses. Because Fbpase2 is activated but PFK-2 is inhibited under fasting, the lack of F26BP enables the activation of Fbpase1 and the increased production of fructose 6-phosphate in gluconeogenesis. The chronic activation of gluconeogenesis is ultimately achieved via transcriptional mechanisms. Major transcriptional factors that are shown to induce gluconeogenic genes include CREB, FoxO1, and several nuclear receptors (Figure 3).31 Regulation of hepatic gluconeogenesis. Under fasting conditions, hepatic gluconeogenesis is enhanced via a decreased concentration of insulin and an increased concentration of insulin counterregulatory hormones such as glucagon. CREB/CRTC2, FoxO1, and a family of nuclear receptors are critical in coordinating the fasting-mediated activation of gluconeogenesis in the liver. See the main text for more specific regulatory pathways. FoxO1, forkhead box O 1 Full size image Under fasting conditions, glucagon and epinephrine can increase the cAMP concentration in the liver via the activation of adenylate cyclase, leading to the activation of PKA and the subsequent induction of CREB via its serine 133 phosphorylation. The phosphorylation event is crucial in the recruitment of histone acetyltransferases (HAT) CBP/p300, leading to the histone H3 and H4 acetylation and the transcriptional activation of target genes.32, 33 CREB-dependent transcription is further enhanced by association with additional transcriptional coactivators CREB regulated transcription coactivators (CRTCs), which are a target for CBP/p300-mediated acetylation, which in turn promotes a tighter association of CREB, CBP/p300, and CRTC on the promoter.34, 35, 36 The role of CREB in the control of hepatic gluconeogenesis has been confirmed by in vivo studies by utilizing albumin promoter-driven ACREB (CREB inhibitor) transgenic mice and siRNA-mediated CREB knockdown mouse models.37, 38 In both mouse models, the inactivation of CREB reduced blood glucose levels and reduced the expression of gluconeogenic genes in mice, showing that CREB is a bona fide physiological transcriptional regulator of hepatic gluconeogenesis in vivo. In contrast, the role for CBP in gluconeogenesis is still controversial. Disruption of CREB-CBP interaction does not appear to affect glucose homeostasis because mice exhibiting a stable expression of mutant CBP that was unable to bind CREB showed normal glycemia.36 Furthermore, mutant mice producing CH1 null products (ΔCH1-a domain that is critical for insulin-mediated depression of CBP activity) displayed normal fasting gluconeogenesis.39 Thus, further studies are required to describe the potential role of HATs in the transcriptional control of CREB activity in this setting. The CRTC family of transcriptional coactivators consists of CRTC1, CRTC2 and CRTC3, which were isolated by the expression library screening as activaters of CREB-dependent transcription.34 CRTC activity is regulated by cellular localization, and the AMPK family of serine/threonine kinases, such as AMPK, SIK1 or SIK2, was shown to be involved in the inhibitory phosphorylation of this factor (serine 171 for CRTC2).40 In addition, the phosphorylation status of CRTC is regulated by a pair of serine/threonine phosphatases (PP2B or PP4) in response to cAMP signaling or calcium concentration in the cell.41, 42 CRTC activity is also further enhanced by O-GlcNAcylation on serine 171 and arginine methylation by protein arginine methyltransferase (PRMT) 6.43, 44 Among the family members, CRTC2 is the prominent isoform in the liver. Recent studies have delineated the role of CRTC2 in the regulation of hepatic gluconeogenesis in vivo. Knockdown of CRTC2 in mice by RNAi reduced blood glucose levels and led to a concomitant repression of gluconeogenic gene expression.36 In addition, CRTC2 knockout mice displayed lower plasma glucose levels and improved glucose tolerance, indeed showing that CRTC2 is crucial in controlling hepatic glucose metabolism in vivo.45 A recent study indicated that CRTC2 could also coactivate other bZIP transcription factors that are implicated in the regulation of glucose homeostasis.46, 47 Further study is required to delineate the potential contributions from other bZIP factors in the control of hepatic gluconeogenesis by using tissue-specific knockout mouse models. The forkhead box O (FoxOs) belongs to a class of forkhead families of transcription, which recognize the AT-rich insulin response element on the promoter.48, 49 Of the four major isoforms in mammals (FoxO1, FoxO3, FoxO4, and FoxO6), FoxO1 is the predominant isoform in the liver. The activity of this protein is also regulated by phosphorylation-dependent subcellular localization, and three major serine and threonine residues (threonine 24, serine 253 and serine 316 for murine FoxO1) are targeted by the insulin/Akt pathway. Following phosphorylation, FoxO1 moves to the cytosol via an association with 14-3-3, where it is degraded by the ubiquitin/proteasome-dependent pathway.50, 51, 52 In addition to phosphorylation, FoxO1 was shown to be regulated by the HAT-dependent acetylation of specific lysine residues (lysine 242, 245 and 262 for murine FoxO1), which also inhibit its transcriptional activity.53 In the liver, FoxO1 regulates hepatic gluconeogenesis via the transcriptional regulation of key genes in the pathway such as PEPCK and G6Pase and is considered a major regulatory point for the insulin-mediated repression of hepatic gluconeogenesis.54 Indeed, mice with liver-specific knockout of FoxO1 showed lower plasma glucose levels that those associated with reduced hepatic glucose output, thus underscoring the physiological significance of this factor in the control of glucose homeostasis in vivo.54, 55 As in the case for CREB, FoxO1 requires transcriptional coactivators for optimal transcriptional activity. Peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PGC-1α), a known coactivator for nuclear receptors, functions as a key transcriptional coactivator for FoxO1 in hepatic gluconeogenesis.56 PGC-1α was originally isolated in brown adipocytes and was shown to control adaptive thermogenesis in response to cold shock in that setting.57 In the liver, the expression of PGC-1α is upregulated under fasting conditions via a CRTC2-CREB-dependent mechanism and is critical in maintaining prolonged gluconeogenesis under starvation by enhancing the transcriptional activity of FoxO1 as a coactivator.38, 57, 58 Indeed, the depletion of hepatic PGC-1α in mice results in lower fasting glucose levels with a concomitant reduction in hepatic gluconeogenesis, thus showing the physiological relevance of this coactivator in the control of glucose homeostasis.59, 60 As is the case for CRTC2, FoxO1 activity is enhanced by arginine methylation by PRMT. In this case, PRMT1 promotes the asymmetric dimethylation of arginine 248 and 250 in FoxO1, which blocks the binding of Akt and the subsequent Akt-mediated phosphorylation of the adjacent serine residue (serine 253), thus enhancing the nuclear localization of FoxO1.61 Consequently, the chromatin occupancy of FoxO1 onto the gluconeogenic promoter and gluconeogenesis itself are increased due to the PRMT1-dependent arginine methylation.62 Acute knockdown of hepatic PRMT1 in mice reduces FoxO1-mediated glucose production, confirming that PRMT1 is crucial in modulating FoxO1 activity and subsequent gluconeogenesis in the physiological context. Nuclear receptors belong to the superfamily of transcription factors that possess two Cys2-His2 type zinc finger motifs as a DNA binding domain as well as both ligand-independent and ligand-dependent transactivation domains.63 The latter activation domain also contains a ligand-binding domain. Nuclear receptors can be classified into one of three subgroups based on their dimer-forming potential. Homodimeric nuclear receptors are also called cytosolic receptors because they reside in the cytosol and associate with molecular chaperones such as heat-shock proteins. On binding to the ligand, they form homodimers and translocate to the nucleus to bind a specific response element termed the hormone response element to elicit the ligand-dependent transcriptional response. Most of the steroid hormone receptors, such as the glucocorticoid receptor (GR), estrogen receptor (ER), and progesterone receptor (PR), belong to this subfamily. By contrast, heterodimeric nuclear receptors reside in the nucleus and are bound to their cognate binding sites together with the universal binding partner retinoid X receptor (RXR). In the absence of the ligands, these factors repress the transcription of target genes in association with transcriptional corepressors such as histone deacetylase or nuclear receptor corepressor (NCoR)/silencing mediator of retinoid and thyroid hormone receptors (SMRT). Ligand binding initiates the conformational changes of these heterodimeric nuclear receptors, which promotes the dissociation of corepressors and the association of coactivators such as CBP/p300, p160 steroid receptor coactivator family, and PGC-1α. Examples of this class of nuclear receptors include members of peroxisome proliferator-activated receptors, LXRs, vitamin D receptors and thyroid hormone receptors. The final subclasses of nuclear receptors are types that function as monomers. They usually lack specific endogenous ligands and are often called orphan nuclear receptors. Some of them also lack DNA binding domain and thus function as transcriptional repressors of various transcription factors, including members of nuclear receptors. They are called atypical orphan nuclear receptors. Among the homodimeric nuclear receptors, the role of GR has been linked to the control of hepatic gluconeogenesis. GR is activated by cortisol, which is released from the adrenal cortex in response to chronic stresses such as prolonged fasting.64, 65 GR was shown to directly bind to the cognate binding sites found in the promoters of gluconeogenic genes such as PEPCK and G6Pase and to enhance transcription of these genes under fasting conditions. The same response elements were also shown to be recognized and regulated by hepatocyte nuclear factor 4 (HNF4), a member of heterodimeric nuclear receptors, which suggests that these nuclear receptors could coordinately function to control hepatic gluconeogenesis in response to fasting.58 In accordance with this idea, the activity of these nuclear receptors can be effectively integrated by the function of transcriptional co-activator PGC-1α. Recently, estrogen-related receptor gamma (ERRγ), a member of monomeric nuclear receptors, was shown to be involved in the regulation of hepatic gluconeogenesis.66, 67 In the liver, ERRγ expression is increased under fasting or by insulin resistance in a CRTC2-CREB-dependent manner. This factor regulates hepatic gluconeogenesis by binding to unique response elements that are distinct from the known nuclear receptor-binding sites in the promoters of PEPCK and G6Pase. Inhibition of ERRγ activity by injecting either RNAi or the inverse agonist GSK5182 effectively reduced hyperglycemia in diabetic mice, suggesting that the control of this factor might potentially be beneficial in the treatment of patients with metabolic diseases. As is the case for other nuclear receptors that control hepatic gluconeogenesis, ERRγ activity is further enhanced by interaction with the transcriptional coactivator PGC-1α, showing that this coactivator functions as a master regulator for the hepatic glucose metabolism. Three members of atypical orphan nuclear receptors, the small heterodimer partner (SHP, also known as NR0B2); the dosage-sensitive sex reversal, adrenal hypoplasia critical region, on chromosome X (DAX-1, also known as NR0B1); and the SHP-interacting leucine zipper protein (SMILE) are implicated in the transcriptional repression of hepatic gluconeogenesis.68, 69, 70 SHP is ubiquitously expressed in mammalian tissues, with the highest expression occurring in the liver. Interestingly, metformin directly activates the transcription of SHP via an AMPK-mediated pathway. SHP directly inhibits cAMP-dependent transcription by binding to CREB, resulting in the reduced association of CREB with CRTC2.71, 72 The adenovirus-mediated overexpression of SHP could effectively reduce blood glucose levels in diabetic mice, thus showing the importance of this pathway in the control of hepatic glucose metabolism. These results provide a dual mechanism for a metformin-AMPK dependent pathway to inhibit hepatic gluconeogenesis at the transcriptional level; an acute regulation of CRTC2 phosphorylation to inhibit the CRTC2-CREB-dependent transcriptional circuit; and a longer-term regulation of gluconeogenic transcription by enhanced SHP expression. Both DAX-1 and SMILE were shown to repress hepatic gluconeogenesis by inhibiting HNF4-dependent transcriptional events.73, 74 SIK1, a member of the AMPK-related kinases, was shown to enhance DAX-1 expression in the liver, whereas Akt was shown to activate the transcription of SMILE to target the HNF4 pathway under feeding conditions. Interestingly, SMILE was shown to directly replace PGC-1α from HNF4 and the gluconeogenic promoters, suggesting that this factor could potentially function as a major transcriptional repressor of hepatic gluconeogenesis in response to insulin signaling. Further study is necessary to fully understand the relative contribution of these nuclear receptors in the control of glucose homeostasis in both physiological conditions and pathological settings. Concluding remarks In this review, we attempted to describe the current understanding of the regulation of glucose metabolism in the mammalian liver. Under feeding conditions, glucose, a major hexose monomer of dietary carbohydrate, is taken up in the liver and oxidized via glycolysis. The excess glucose that is not utilized as an immediate fuel for energy is stored initially as glycogen and is later converted into triacylglycerols via lipogenesis. Glycogenesis is activated via the insulin-Akt-mediated inactivation of GSK-3, leading to the activation of glycogen synthase and the increased glycogen stores in the liver. Insulin is also critical in the activation of PP1, which functions to dephosphorylate and activate glycogen synthase. In addition, PP1 inhibits glycogenolysis via the dephosphorylation/inactivation of glycogen phosphorylase. Glycolysis is controlled by the regulation of three rate-limiting enzymes: GK, PFK-1 and L-PK. The activities of these enzymes are acutely regulated by allosteric regulators such as ATP, AMP, and F26BP but are also controlled at the transcription level. Two prominent transcription factors are SREBP-1c and ChREBP, which regulate not only the aforementioned glycolytic enzyme genes but also the genes encoding enzymes for fatty acid biosynthesis and triacylglycerol synthesis (collectively termed as lipogenesis). The importance of these transcription factors in the control of glycolysis and fatty acid biosynthesis has been verified by knockout mouse studies, as described in the main text. The liver also has a critical role in controlling glucose homeostasis under fasting conditions. Initially, insulin counterregulatory hormones such as glucagon and epinephrine are critical in activating the PKA-driven kinase cascades that promote glycogen phosphorylase and glycogenolysis in the liver, thus enabling this tissue to provide enough fuel for peripheral tissues such as the brain, red blood cells and muscles. Subsequently, these hormones together with adrenal cortisol are crucial in initiating the transcriptional activation of gluconeogenesis such as PC, PEPCK and G6Pase. The major transcription factors involved in the pathway include CREB, FoxO1 and members of nuclear receptors, with aid from transcriptional coactivators such as CRTC, PGC-1α and PRMTs. These adaptive responses are critical for maintaining glucose homeostasis in times of starvation in mammals. Further study is necessary by using liver-specific knockout mice for each regulator of hepatic glucose metabolism to provide better insights into the intricate control mechanisms of glucose homeostasis in mammals. Acknowledgements This work is supported by the National Research Foundation of Korea (grant nos.: NRF-2012M3A9B6055345, NRF-2015R1A5A1009024 and NRF- 2015R1A2A1A01006687), funded by the Ministry of Science, ICT & Future Planning, Republic of Korea, a grant of the Korean Health technology R&D Project (grant no: HI13C1886), Ministry of Health & Welfare, Republic of Korea and a grant from Korea University, Seoul, Republic of Korea. Author information Affiliations 1. Division of Life Sciences, College of Life Sciences & Biotechnology, Korea University, Seoul, 136-713, Korea Corresponding author Correspondence to Seung-Hoi Koo. Ethics declarations Competing interests The authors declare no conflict of interest. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/29%3A_Integration_of_Mammalian_Metabolism_-_Capstone_Volume_II/29.02%3A__Regulation_of_glucose_metabolism_from_a_liver-centric_perspe.txt

Roh, E., Song, D. & Kim, MS. Emerging role of the brain in the homeostatic regulation of energy and glucose metabolism. Exp Mol Med 48, e216 (2016). https://doi.org/10.1038/emm.2016.4​​​​​​ This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ Abstract Accumulated evidence from genetic animal models suggests that the brain, particularly the hypothalamus, has a key role in the homeostatic regulation of energy and glucose metabolism. The brain integrates multiple metabolic inputs from the periphery through nutrients, gut-derived satiety signals and adiposity-related hormones. The brain modulates various aspects of metabolism, such as food intake, energy expenditure, insulin secretion, hepatic glucose production and glucose/fatty acid metabolism in adipose tissue and skeletal muscle. Highly coordinated interactions between the brain and peripheral metabolic organs are critical for the maintenance of energy and glucose homeostasis. Defective crosstalk between the brain and peripheral organs contributes to the development of obesity and type 2 diabetes. Here we comprehensively review the above topics, discussing the main findings related to the role of the brain in the homeostatic regulation of energy and glucose metabolism. Central regulation of energy metabolism In normal individuals, food intake and energy expenditure are tightly regulated by homeostatic mechanisms to maintain energy balance. Substantial evidence indicates that the brain, particularly the hypothalamus, is primarily responsible for the regulation of energy homeostasis.1 The brain monitors changes in the body energy state by sensing alterations in the plasma levels of key metabolic hormones and nutrients. Specialized neuronal networks in the brain coordinate adaptive changes in food intake and energy expenditure in response to altered metabolic conditions (Figure 1).2, 3 Integration of peripheral metabolic signals andthe central nervous system maintains energy homeostasis. The brain integrates metabolic signals from peripheral tissues such as the liver, pancreas, adipose tissue, gut and muscle. Specialized neuronal networks in the brain coordinate adaptive changes in food intake and energy expenditure in response to altered metabolic conditions. Neuropeptide Y/agouti-related protein- and proopiomelanocortin-producing neurons in the hypothalamic arcuate nucleus primarily sense the body energy state. These neurons project to other hypothalamic nuclei and to the nucleus of the solitary tract in the brain stem to control multiple aspects of the homeostatic regulation of energy balance. ARC, arcuate nucleus; CCK, cholecystokinin; GLP-1, glucagon-like peptide-1; IL-6, interleukin-6; PP, pancreatic polypeptide; PVN, paraventricular nucleus; PYY, peptide YY. Full size image Brain regulation of food intake The hypothalamus is considered a key organ in the regulation of food intake. The hypothalamic arcuate nucleus (ARC) is adjacent to the median eminence, one of the circumventricular organs, and surrounds the third cerebroventricle. Thus, hormones and nutrients in the systemic circulation and the cerebrospinal fluid can easily access the ARC. Anatomically, the ARC is considered a hypothalamic area that primarily senses metabolic signals from the periphery via the systemic circulation.4 In the ARC, there are two distinct neuronal populations: one group of neurons produces the orexigenic neuropeptides neuropeptide Y (NPY) and agouti-related peptide (AgRP), whereas the other subset of neurons expresses the anorexigenic neuropeptides proopiomelanocortin (POMC), and cocaine- and amphetamine-regulated transcript. These neurons are the first-order neurons on which peripheral metabolic hormones, including leptin, insulin, ghrelin and nutrients, primarily act.5 The anorexigenic effect of monoamine serotonin is also mediated by the 5HT-2C receptor in POMC neurons.6 POMC neurons project axonal processes to second-order neurons in hypothalamic areas such as the paraventricular nucleus (PVN), ventromedial hypothalamus (VMH) and lateral hypothalamus (LH), and to autonomic preganglionic neurons in the brain stem and spinal cord.7 The anorexigenic neuropeptide α-melanocyte-stimulating hormone (α-MSH) is produced by posttranscriptional processing of POMC and is released from the presynaptic terminals of POMC neurons. Upon binding to the melanocortin-3 and -4 receptors (MC3R and MC4R) on second-order neurons, α-MSH activates catabolic pathways, leading to reduced food intake and increased energy expenditure. Targeted deletion of the MC4R in mice induces hyperphagia, reduces energy expenditure and leads to obesity.8 In humans, MC4R mutations account for ~6% of severe early-onset obesity cases,9 suggesting an important role for the central melanocortin system in the maintenance of normal body weight. The endogenous MC-3/4R antagonist AgRP is released from the terminals of NPY/AgRP-producing neurons to the synaptic space of second-order neurons where it competes with α-MSH for MC3Rs and MC4Rs and antagonizes its effects.10 Selective ablation of NPY/AgRP neurons in young mice results in a significant decrease in food intake and body weight,11 suggesting that these neurons are critical for promoting food intake and preventing weight loss. Administration of NPY stimulates food intake via Y1 or Y5 receptors.12 NPY is required for the rapid stimulation of feeding, whereas AgRP stimulates feeding over a prolonged period.13 PVN neurons synthesize and secrete neuropeptides that have a net catabolic action, including corticotrophin-releasing hormone, thyrotropin-releasing hormone, somatostatin, vasopressin and oxytocin. On the other hand, PVN neurons control sympathetic outflow to peripheral metabolic organs, resulting in increased fatty acid oxidation and lipolysis.14 Destruction of PVN and haploinsufficiency of Sim1, a critical transcriptional factor in the development of PVN, cause hyperphagia and obesity,15 implying a inhibitory role of the PVN in food intake and weight gain. The VMH mainly receives neuronal projections from the ARC and projects their axons to the ARC, dorsomedial nucleus (DMN), LH and brain stem regions. The VMH contains neurons that sense glucose and leptin.16, 17 Moreover, the anorexigenic neuropeptide brain-derived neurotrophic factor is produced in the VMH.18 Destruction of the VMH causes hyperphagia, obesity and hyperglycemia.19 Thus, the VMH is regarded a pivotal area in generating satiety and maintaining glucose homeostasis. The DMN contains a high level of NPY terminals and α-MSH terminals originating from the ARC.20 Destruction of the DMN also results in hyperphagia and obesity.21 In contrast to the PVN, VMH and DMN, destruction of the LH leads to hypophagia and weight loss. Therefore, LH is considered a feeding center. LH contains two neuronal populations producing the orexigenic neuropeptides melanin-concentrating hormone (MCH) and orexin, also called hypocretin. NPY/AgRP- and α-MSH-immunoreactive terminals from ARC neurons are in contact with MCH- and orexin-expressing neurons.22 Orexin-producing neurons are also involved in glucose sensing and the regulation of sleep–awake cycles.23 Alterations in the orexin receptor-2 and orexin genes produce narcolepsy in animal models and humans.24 On the other hand, depletion of MCH or the MCH-1 receptor in mice attenuates weight gain, suggesting that MCH is an endogenous orexigenic molecule.25 The brain stem is another key brain area involved in the regulation of food intake. Satiety signals from the gastrointestinal tract are relayed to the nucleus tractus solitaries (NTS) through the sensory vagus nerve, a major neuronal connection between the gut and brain. Transection of sensory vagal fibers decreases meal size and meal duration, confirming that vagal afferents transfer meal-related signals to the brain.26 Like the ARC, the NTS is anatomically close to the area postrema, another circumventricular organ.27 Therefore, the NTS is perfectly located for receiving both humoral and neural signals. Meanwhile, the NTS receives extensive neuronal projections from the PVN and vice versa,28 indicating that there are intimate communications between the hypothalamus and the brain stem. Like hypothalamic neurons, NTS neurons produce appetite-regulating glucagon-like peptide-1 (GLP-1), NPY and POMC, and sense peripheral metabolic signals.29 For instance, NTS POMC neurons show the signal transducer and activator of transcription 3 (STAT3) activation in response to exogenous leptin.30 Thus, circulating hormones and nutrients may relay metabolic signals to the brain by acting on both the hypothalamus and brain stem. On the other hand, the brain reward system is involved in the control of hedonic feeding, that is, intake of palatable foods. Like other addiction behaviors, the mesolimbic and mesocortical dopaminergic pathways are involved in hedonic feeding. Intake of palatable foods elicits dopamine release in the ventral tegmental area (VTA), which in turn activates the neural pathways from the VTA to the nucleus accumbens via the medial forebrain bundles. Interestingly, hedonic feeding is modulated by metabolic signals. Leptin acts on the dopaminergic neurons in the VTA to suppress feeding.31 Conversely, hedonic feeding can override satiety signals. Mice lacking the D2 receptor are more sensitive to leptin.32 Brain regulation of energy expenditure The brain modulates various processes that consume energy, such as locomotor activity, fatty acid oxidation in the skeletal muscle and thermogenesis.33 Tumor growth factor-α, produced in the suprachiasmatic nucleus in a circadian manner, strongly inhibits locomotor activity by acting on the epidermal growth factor receptors expressed in the hypothalamic subparaventricular zone.34 Orexin-A produced by LH neurons promotes locomotor activity and wakefulness through orexin-1 and orexin-2 receptors.35 A role for orexin in food-seeking behavior in food-deprived conditions has been suggested.36 Leptin stimulates locomotor activity via a mechanism that depends on hypothalamic POMC neurons37. Leptin also enhances fatty acid oxidation in skeletal muscle via both central and peripheral mechanisms.38 Thermogenesis is theprocess that dissipates energy as heat to maintain body temperature. Thermogenesis mainly occurs in brown adipose tissue (BAT).39 Brown fat-like adipocytes, so-called browning of white adipose tissue (WAT), are found in the subcutaneous inguinal WAT under certain circumstances. Cold exposure or intracerebroventricular (ICV) coinjection of insulin and leptin induces WAT browning.40 Induction of WAT browning results in increased energy expenditure and attenuation of diet-induced obesity in mice. Conversely, inhibition of WAT browning by depletion of Prdm16 leads to obesity.39 The brain regulates BAT thermogenesis through modulation of the sympathetic nervous system. Norepinephrine released from sympathetic nervous terminals acts on the β3-adrenergic receptors in adipocytes in the BAT and inguinal fat pads. Activation of adrenergic receptors triggers cyclic-adenosine monophosphate signaling, which in turn increases the expression of uncoupling protein-1 in the mitochondria. BAT thermogenesis is important for maintaining body temperature in response to cold exposure and dissipating excess energy after high-calorie intake. Because metabolic fuel substrates such as glucose and fatty acid are consumed during BAT thermogenesis, BAT thermogenesis can affect body weight and body fat mass.41 In the past, BAT was thought to be present only in human infants. However, 18F-fluorodeoxyglucose positron emission tomography revealed the presence of BAT in the adult humans. Human BAT depots are distributed in the supraclavicular area and in perivascular and periviscus areas (for example, around the heart, airway, gut, liver and adrenal gland) of the chest and abdomen.42 BAT activity, determined by 18F-fluorodeoxyglucose positron emission tomography, is affected by outdoor temperature, age, sex, body mass index and the coexistence of diabetes. Because the amount of BAT is inversely correlated with body mass index, especially in older subjects, a potential role of BAT in adult human metabolism has been suggested.43 In thermogenic regulation, the hypothalamus integrates the sensation of body temperature with efferent sympathetic outflow. Hypothalamic areas such as the prooptic area, VMH, DMN and ARC modulate thermogenic activity by influencing the sympathetic nervous system.44 The prooptic area is an important area in the control of body temperature.45 VMH was the first hypothalamic nucleus to be studied regarding the regulation of BAT activity. The DMN also contains sympathoexcitatory neurons,46 which regulate thermogenic activity.47, 48 BAT thermogenesis is also related to the ARC melanocortin system because α-MSH stimulates BAT activity.49 Hormonal- and nutrient-mediated metabolic signals can influence sympathetic outflow to the BAT. Central administration of leptin, MC3/4R agonist, glucagon and GLP-1 stimulates BAT activity.50, 51 Central administration of insulin either stimulates or inhibits BAT thermogenesis, depending on the insulin dose. Central administration of high doses of insulin increases sympathetic nerve activity in the BAT, whereas low doses of insulin decrease it.52, 53 Food consumption or dietary composition also affects BAT thermogenesis. Although the mechanism of postprandial thermogenesis is unclear, norepinephrine turnover in the BAT is increased after a meal.54 Glucose administration increases thermogenesis, whereas fasting or food restriction inhibits thermogenesis. Low-protein diet and high-fat diet increase BAT activity.55 Peripheral signals modulating energy metabolism Adiposity signals Adiposity signals refer to the peripheral signals that circulate in proportion to the total amount of stored fat and inform the brain about the stored energy state. They modulate energy balance through the regulation of food intake and energy expenditure.2, 56 Insulin is a hormone that was first identified as an adiposity signal.5, 57 Insulin is secreted by β-cells in response to energy flux. Plasma insulin concentrations increase in proportion to the amount of stored fat.58 When insulin is administered directly into the central nervous system, it induces a dose-dependent reduction in food intake and body weight.59, 60 Thus, insulin is thought to signal adiposity to the brain. In hypothalamic neurons, insulin activates the insulin receptor substrate-2 (IRS2)–phosphatidylinositol 3-kinase (PI3K) signaling pathway. Neuronal deletion of insulin receptor and IRS2 results in increased food intake and susceptibility to diet-induced obesity.61, 62 The adipose tissue-derived hormone leptin was discovered by positional cloning of the obesity locus (ob) in 1994.63 Leptin is now considered a representative adiposity signal.64 The receptors activated by leptin are highly expressed in several regions of brain, including the hypothalamus.65 Genetic deficiency in leptin or the long-form leptin receptor (LepRb) is associated with hyperphagia, hypoactivity and obesity.66 Of several brain regions, the ARC is an important area that mediates leptin actions. Injection of leptin directly into the ARC reduces food intake and body weight.67 Leptin also stimulates locomotion through signaling in POMC neurons.37 Consistently, ICV administration of leptin in leptin-deficient (ob/ob) mice attenuates obesity.66 In hypothalamic neurons, leptin provokes several signaling cascades such as the Janus kinase–STAT pathway, IRS–PI3K signaling, the mammalian target of rapamycin–S6 kinase signaling, AMP-activated protein kinase (AMPK) signaling and ERK signaling.68 Of those, STAT3 signaling represents hypothalamic leptin signaling and is frequently used as a marker of leptin signaling activity. Nutrient signals Nutrients such as glucose, fatty acids and amino acids provide information on nutrient availability to the brain. Glucose signals the presence of anenergy supply to the brain, whereas hypoglycemia signals an energy deficit.69 Thus, central administration of glucose and long-chain fatty acids decreases food intake.70 In contrast, ICV administration of the glucose anti-metabolite 2-deoxy-D-glucose increases food intake.71 The malonyl-CoA content in hypothalamic neurons has been suggested to be a fuel gauge.56, 72 Administration of the fatty acid synthase inhibitor C75 induces accumulation of malonyl-CoA in hypothalamic neurons, leading to decreased food intake and body weight.73 Long-chain fatty acyl-CoA (LCFA-CoA) content in hypothalamic neurons also acts as a cellular nutrient sensor. An increased hypothalamic LCFA-CoA level due to ICV long-chain fatty acid (LCFA) administration leads to decreased food intake.70 Hypothalamic inhibition of carnitine palmitoyltransferase-1 inhibits food intake by elevating LCFA-CoA content in hypothalamic neurons.74 Gastrointestinal signals Hormones secreted by the gut in response to a meal provide information on energy intake. Cholecystokinin, peptide YY and GLP-1 released from the gut induce satiety by acting on the vagus nerve or in the brain.75 For example, GLP-1 is secreted from intestinal L-cells after a meal. GLP-1 receptors are prevalent in vagus nerve terminals,76 as well as in the central nucleus of the amygdala, the PVN and ARC of the hypothalamus, and the caudal brain stem.77 Both central and peripheral administration of GLP-1 promotes satiety.78, 79 In contrast, ghrelin is secreted by the stomach during a fast and promotes food intake.80 Signals from other organs Interleukin-6 (IL-6) is synthesized and released from contracting skeletal muscle during exercise. The elevation in the plasma IL-6 concentration during exercise correlates with exercise intensity and duration and the muscle mass recruited.81 IL-6 enters the brain across the blood–brain barrier. IL-6 may mobilize fat from storage sites to provide energy to the muscle. ICV administration of IL-6 stimulates energy expenditure, and mice lacking IL-6 develop mature-onset obesity.82 Hormones secreted from the endocrine pancreas are also involved in energy homeostasis. Insulin and amylin are co-secreted by β-cells. Like insulin, amylin acts as a satiety signal and reduces food intake via amylin receptors in the area postrema. Other brain sites mediating amylin action include the NTS and the lateral parabrachial nucleus.83 Amylin also acts as an adiposity signal because amylin levels are well correlated with body fat content. Glucagon, a counter-regulatory hormone to insulin, is secreted from α-cells. Glucagon reduces meal size by acting on the vagus nerves and stimulates energy expenditure through central and peripheral mechanisms.84 Pancreatic polypeptide is also secreted from the endocrine pancreas. Pancreatic polypeptide regulates gastric motility, pancreatic exocrine secretion and food intake. Systemic administration of pancreatic polypeptide reduces food intake and weight gain.85 The anorectic effect of pancreatic polypeptide is mediated by Y4 receptors in the dorsal vagal complex.86 Brain regulation of glucose metabolism The earliest demonstration of the role of the brain in glucose homeostasis was provided by the physiologist Claude Bernard in 1854. Dr Bernard demonstrated that a puncture in the floor of the fourth ventricle of the rabbit brain resulted in glycosuria.87 In the past few decades, the concept of central regulation of glucose metabolism has been further established by the subsequent discovery of glucose-sensing neurons in the hypothalamus88, 89 and the demonstration of their roles in maintaining normal glucose levels.90 A specialized neuronal population in the brain senses hormones (insulin and leptin) and nutrients (glucose and fatty acids) to regulate glucose homeostasis. The major sites of convergence of these metabolic signals are the hypothalamus and brain stem (Figure 2). Brain regulation of glucose homeostasis. The brain senses peripheral metabolic signals through hormones (insulin, leptin and so on) and nutrients (glucose, free fatty acids and so on) to regulate glucose metabolism. The sites of the convergence of these metabolic signals are the hypothalamus and brain stem. The autonomic nervous system intervenes in the brain and peripheral metabolic organs to modulate pancreatic insulin/glucagon secretion, hepatic glucose production and skeletal muscle glucose uptake. AP, area postrema; ARC, arcuate nucleus; BLM, basolateral medulla; DMN, dorsomedial nucleus; DMNX, dorsal motor nucleus of the vagus; FFA, free fatty acids; LH, lateral hypothalamus; NTS, nucleus of the solitary tract; PNS, parasympathetic nervous system; PVN, paraventricular nucleus; SNS, sympathetic nervous system; VMH, ventromedial hypothalamus. Full size image Neuronal populations controlling glucose metabolism Brain regions related to the control of glucose metabolism contain neurons whose excitability changes with alterations in glucose concentrations in the extracellular fluid. These glucose-sensing neurons are found in the hypothalamic nuclei and brain stem, which are also important areas in the control of energy balance. Glucose-sensing neurons are subgrouped into two types. Glucose-excited neurons are excited when extracellular glucose levels increase. In contrast, glucose-inhibited neurons are activated by a fall in extracellular glucose concentrations.91 Glucose-excited neurons are mostly located in the VMH, the ARC and the PVN,92 whereas glucose-inhibited neurons are distributed in the LH, ARC and PVN.89, 91 Both types of neurons are also located in the dorsal vagal complex in the brain stem, which encompasses the NTS, area postrema and the dorsal motor nucleus of the vagus.93, 94, 95 Peripheral signals affecting brain regulation of glucose metabolism Insulin During the past decade, the brain has been recognized to be a site of insulin action with regard to glucose homeostasis. Obici et al.96 showed that insulin acts on the brain to modulate hepatic glucose metabolism. They showed, by injecting insulin receptor antisense oligonucleotides into the cerebroventricle, that inhibition of central insulin action impaired insulin-mediated suppression of hepatic glucose production (HGP) during hyperinsulinemic clamp studies in rats. They also demonstrated that infusion of insulin into the cerebroventricle suppressed HGP, irrespective of circulating insulin levels. Moreover, central administration of insulin antibodies or inhibitors of the downstream signaling of insulin diminished the ability of insulin to inhibit glucose production.97 The hypothalamic insulin signaling pathway was investigated in subsequent studies. Overexpression of the insulin signaling molecules IRS2 and Akt in the hypothalamus enhances the glucose-lowering effect of insulin in streptozotocin-induced diabetic rats.98 These data support a role for hypothalamic insulin actions in controlling glucose metabolism in peripheral organs. The ATP-sensitive potassium (KATP) channel mediates insulin actions in hypothalamic neurons.99 Activation of neuronal KATP channels by ICV injection of a KATP channel activator (diazoxide) lowers glucose production,100 whereas infusion of a KATP blocker (sulfonylurea) negates the glucose production-lowering effect of centrally and peripherally administered insulin.96, 100 Moreover, mice lacking the sulfonylurea receptor subunit SUR1 of the KATP channel show a diminished response to central insulin action.100 Vagal efferent fibers constitute the brain–liver axis of insulin actions because hepatic vagotomy blocks central insulin actions.100 Interestingly, ICV infusion of insulin increases hepatic IL-6 expression, which leads to the activation of hepatic STAT3 signaling.101 Activated STAT3 inhibits FoxO1 activity and gluconeogenic gene expression in the liver. Collectively, central insulin actions are mediated via neuronal KATP channel–vagus nerve–hepatic IL6/STAT3 signaling, although the detailed mechanisms involved remain to be determined. Leptin Leptin has an important role in the control of glucose metabolism. A lack of leptin (ob/ob mice) or its functional receptor (db/db mice) leads not only to obesity, but also metabolic derangement, including insulin resistance and diabetes.102 Leptin treatment of ob/ob mice improves glucose homeostasis.103, 104 Notably, acute leptin treatment via both systemic and central routes in ob/ob mice restores glucose metabolism independently of changes in food intake and adiposity.105, 106 Consistently, leptin-treated ob/ob mice display a marked reduction in serum glucose and insulin concentrations.107 Leptin treatment in lipodystrophy mice improves insulin resistance and hyperglycemia independently of food intake.108, 109 Thus, leptin regulates glucose homeostasis independently of its anorectic effects. The hypothalamus is a key site of action of leptin-mediated control of glucose metabolism. ICV administration of leptin in the lipodystrophy mice model corrects insulin resistance and improves impaired insulin signaling in the liver. In contrast, peripheral injection of the same dose of leptin did not have a similar effect.110 Acute ICV injection of leptin suppresses glycogenolysis and reduces hepatic insulin resistance induced by high-fat feeding.111 Restoration of leptin signaling in the unilateral ARC by viral gene therapy in leptin receptor-null mice markedly improves hyperinsulinemia and normalizes blood glucose levels, with a mild decrease in body weight and food intake. These data demonstrate that leptin signaling in the ARC is critical for the maintenance of glucose homeostasis.112 Leptin-mediated regulation of glucose metabolism is mediated by hypothalamic STAT3 and PI3K signaling pathways. As in db/db mice, s/s mice with a mutated leptin receptor, which are unable to activate STAT3, exhibit severe hepatic insulin resistance.113 Blockade of leptin-induced STAT3 activation in the hypothalamus abolishes the suppressive effect of leptin on HGP, confirming the importance of leptin-induced STAT3 signaling.113 Conversely, hypothalamic deletion of suppressor of cytokine signaling 3, a negative regulator of STAT3 signaling, enhances leptin sensitivity and improves glucose metabolism.114 On the other hand, reconstitution of leptin receptors in the ARC of leptin-receptor-deficient fak/fak rats improves insulin sensitivity, which is attenuated by ICV infusion of PI3K inhibitor. Consistently, ARC expression of constitutively active Aktin fak/fak rats mimics the effect of restored hypothalamic leptin signaling.115 These findings indicate that PI3K–Akt signaling mediates leptin actions on glucose homeostasis. Glucose Glucose sensing in the hypothalamus is important in glucose homeostasis. Injection of 2-deoxy-D-glucose into the VMH increases plasma glucose levels by elevating plasma glucagon and catecholamine levels.116 Conversely, intra-VMH glucose infusion suppresses counter-regulatory hormonal responses to hypoglycemia.90 The brain stem is also involved in glucoprivic feeding and counter-regulatory hormone secretion during hypoglycemia. Injection of another glucose anti-metabolite, 5-thio-D-glucose, into the NTS and the basolateral medulla, which contain A1/C1 catecholaminergic neurons projecting to the hypothalamic PVN and ARC, induces feeding and glucose responses, as seen in hypoglycemia.117 Similarly, destruction of hindbrain catecholaminergic neurons by immunotoxins blocks 2-deoxy-D-glucose-induced feeding and blood glucose responses.118 The glucose-sensing mechanisms in hypothalamic neurons are similar to those in pancreatic β-cells.119 Glucose signaling in glucose-excited neurons requires glucose uptake via the type 2 glucose transporter, which is followed by glucose phosphorylation by glucokinase, intramitochondrial glucose oxidation, and an increased cellular ATP/ADP ratio. This leads to the closure of ATP-sensitive KATP channels, depolarization of the membrane potential, and influx of Ca2+ through voltage-dependent calcium channels, which stimulate neuronal activity and neurotransmitter release.120 The role of hypothalamic type 2 glucose transporter, glucokinase and KATP channels in sensing hypoglycemia and counter-regulatory hormone responses has been demonstrated.121, 122, 123, 124, 125, 126 How glucose inhibits neuronal activity in glucose-inhibited neurons is unclear. One possibility is that glucose increases the ATP/ADP ratio, which stimulates the Na+/K+-ATPase pump and triggers hyperpolarizing currents.127 Alternatively, glucose-induced activation of ATP-dependent Cl channels may induce hyperpolarization of the plasma membrane.91, 128 AMPK functions as a ‘fuel gauge’ that monitors cellular energy status and provokes adaptive responses to maintain cellular energy levels129, 130. ICV administration of glucose suppresses feeding via inhibition of hypothalamic AMPK activity.131, 132 Hypothalamic AMPK activation is critical for feeding and counter-regulatory responses to hypoglycemia.131 Intra-VMH administration of AICAR (5-aminoimidazole-4-carboxamide ribonucleotide), a chemical AMPK activator, increases HGP without changing the plasma levels of counter-regulatory hormones.133 AMPK activation in the VMH restores reduced counter-regulatory responses induced by repeated hypoglycemia.134 Consistent with these findings, inhibition of hypothalamic AMPK activity attenuates the counter-regulatory response during hypoglycemia.135 Fatty acids LCFA signals nutrient availability to the brain and modulates peripheral glucose metabolism.70 ICV administration of oleic acid suppresses HGP during basal insulin clamping. ICV administration of KATP channel blocker attenuates the inhibitory effect of oleic acid on glucose production, indicating an involvement of brain KATP channels in this process.70 Increased LCFA-CoA levels in hypothalamic neurons suppresses endogenous glucose production.74 Pharmacological inhibition of hypothalamic esterification of fatty acids or surgical resection of the hepatic branch of the vagus nerve increases HGP.136 Therefore, hypothalamic lipid sensing regulates glucose homeostasis via a mechanism involving the esterification of LCFAs to LCFA-CoAs, intact KATP channels and vagal outflow to the liver. Effector pathways in the brain control of glucose metabolism To the liver In rodents, direct action of insulin on the liver is necessary, but is insufficient to inhibit HGP, unless the indirect brain pathway is not fully functional. Restoration of insulin receptor expression in either the liver or brain of insulin receptor-null mice does not completely restore the ability of insulin to inhibit HGP.137 In contrast, restoration of insulin receptor expression in both the brain and liver normalizes insulin actions on HGP.138 Whether neuronal control of HGP is unique to rodents remains uncertain. ICV insulin infusion in the dog augments hepatic glucose uptake and glycogen synthesis without altering HGP,139 indicating that the regulation of gluconeogenesis by brain insulin signaling may differ among species. The basal HGP rate per weight is almost 5–10 times higher in rodents than in dogs and humans.140 Dogs and humans maintain hepatic glycogen storage even after a 42-h fasting.141, 142 In contrast, hepatic glycogen content is significantly depleted in rodents after a relatively short fast, which may be due to higher metabolic rates.143 Therefore, the contribution of the gluconeogenic pathway to HGP may be greater in rodents than in animals with a larger body size.139 Thus, changes in gluconeogenesis may be more easily detected in rodents. To the skeletal muscle Electrical stimulation of VMH neurons and local injection of leptin into the VMH increases glucose uptake in the skeletal muscle of rats independently of circulating insulin levels.144, 145 These effects appears to be mediated by the sympathetic nervous system as they are abolished by blockade of the sympathetic nervous system.146, 147 Consistently, central infusion of leptin improves glucose tolerance and enhances insulin-stimulated Akt phosphorylation in skeletal muscle.148, 149 Activated Akt leads to translocation of the glucose transporter GLUT4 from its sequestered cytoplasmic location to the cell membrane, facilitating glucose uptake150. In the skeletal muscle, AMPK activation is induced by muscle contraction and adrenergic agonist and mediates insulin-independent glucose uptake.151 Leptin activates skeletal muscle AMPK through the hypothalamus and sympathetic nervous system.152 Therefore, leptin may promote glucose uptake to the skeletal muscle through the sympathetic nervous system–muscle AMPK signaling pathway. On the other hand, orexin-producing neurons in the LH are activated by sweet foods. Orexin regulates skeletal muscle glucose uptake through VMH neurons expressing orexin receptors and the sympathetic nervous system.153 To the pancreas The autonomic nervous system controls the secretion of insulin and glucagon in the pancreas. Sympathetic and parasympathetic nerve endings are foundin pancreatic islets.154 Moreover, α- and β-cells express neurotransmitter receptors.155 Both sympathetic and parasympathetic nerve branches can stimulate glucagon secretion. In contrast, parasympathetic branches stimulate insulin secretion, whereas sympathetic branches inhibit it.156 Neurons in the dorsal motor nucleus of the vagus project nerve terminals to the pancreatic ganglions via the vagus nerve, and thus vagus nerves connect the dorsal motor nucleus of the vagus and endocrine pancreas.157 Insulin regulates whole-body glucose metabolism by acting on the brain, and modulating insulin and glucagon secretion. ICV administration of insulin increases pancreatic insulin output, demonstrating that pancreatic β-cells are influenced by insulin-sensitive cells of the brain.158 Moreover, insulin injection into the VMH inhibits glucagon secretion by pancreatic α-cells, indicating that insulin controls glucagon secretion via brain-mediated mechanisms.159 Taken together, the brain, especially the hypothalamus and brain stem, modulates pancreatic insulin and glucagon secretion via the parasympathetic and sympathetic efferent nerves that innervate pancreatic α- and β-cells.160 Dysregulation of energy/glucose metabolism in obesity and diabetes In healthy conditions, energy intake matches energy expenditure to maintain normal body weight. Impaired ability of the brain to maintain energy homeostasis may underlie pathological weight gain and obesity (Figure 3). Several defects in the negative-feedback pathway in energy homeostatic mechanisms have been suggested.3 Defects in the secretion of key metabolic hormones such as insulin and leptin may predispose weight gain. Because leptin primarily acts on hypothalamic neurons to regulate the energy balance, leptin transfer to the brain may be critical for its action.5 Leptin concentrations in the plasma increase in proportion to body mass index, an indicator of fat mass. However, the increase in leptin concentrations in the cerebrospinal fluid of obese individuals is less than that of plasma leptin concentrations.161 Therefore, reduced leptin transport to the brain may be due to reduced action of leptin in obesity. Pathogenesis of obesity and type 2 diabetes due to defective central regulation of energy and glucose homeostasis. Reduced nutrient sensing and impaired insulin and leptin signaling in the hypothalamus may result in a positive energy balance and predispose weight gain, causing insulin resistance in peripheral metabolic organs. Obesity-associated insulin resistance may lead to type 2 diabetes when it is combined with β-cell dysfunction. IRS, insulin receptor substrate; PI3K, phosphatidylinositol 3 kinase; STAT3, signal transducer and activator of transcription 3. Full size image Defective hypothalamic sensing of these hormones favors a positive energy balance because loss of leptin receptors in the hypothalamus leads to obesity in mice.13 Rats with diet-induced obesity have reduced expressions of leptin receptors in the hypothalamus.162 Impaired postreceptor signaling in hypothalamic neurons may also result in pathological weight gain. Disruption of the hypothalamic IRS–PI3K signaling pathway causes resistance to peripheral metabolic signals and leads to obesity.163 Likewise, mice with disrupted neuronal STAT3 signaling develop hyperphagia and obesity.164 In rodents, long-term high-fat feeding reduces the anorectic response and hypothalamic STAT3 activation induced by leptin, which is called leptin resistance.165 Increased hypothalamic expression of suppressor of cytokine signaling 3 has been suggested to be a mechanism of hypothalamic leptin resistance. Ablation of suppressor of cytokine signaling 3 expression in neurons mitigates high-fat diet-induced weight gain and hyperleptinemia and improves glucose tolerance and insulin sensitivity.166 Protein-tyrosine phosphatase 1B, a well-known negative regulator of insulin and leptin signaling,167, 168 has also been suggested to cause leptin and insulin resistance in hypothalamic neurons. Neuronal Protein-tyrosine phosphatase 1B knockout mice are hypersensitive to exogenous leptin and insulin, and display improved glucose tolerance during chronic high-fat feeding.169 Increased IKKβ-NFκB and endoplasmic stress have been found in the hypothalamus of obese rodents and shown to disrupt hypothalamic leptin and insulin signaling.170, 171 However, a recent paper has shown, using a leptin receptor antagonist, that endogenous leptin signaling and actions in high-fat diet-fed obese mice treated are comparable to those of chow diet-fed mice, arguing against the concept of leptin resistance.172 Thus, further studies are needed to clarify the issue of leptin resistance in obese humans and animals. Diabetes mellitus is a metabolic disorder characterized by hyperglycemia that affects ~9% of adults worldwide.173 It results from deficits in pancreatic insulin secretion and insulin signaling/actions in insulin target organs. Experimental evidence suggests that defective metabolic sensing in hypothalamic neurons may lead to dysregulation of glucose homeostasis and diabetes (Figure 3). Hypothalamic insulin–PI3K signaling is markedly impaired in rats with streptozotocin-induced diabetes.98 Pharmacological inhibition of hypothalamic PI3K signaling attenuates the glucose-lowering effect of insulin. Conversely, enhanced hypothalamic PI3K signaling via adenoviral gene therapy potentiates insulin-induced glucose lowering.98 Notably, central insulin actions are blunted by short-term high-fat diet feeding.174 Thus, a fat-rich diet may contribute to the development of diabetes by disrupting insulin signaling in the hypothalamus.174 Concluding remarks This review highlights the role of the brain in the homeostatic regulation of energy and glucose metabolism. The brain detects energy intake by sensing gut hormones released in response to food intake and detecting nutrients in circulating blood. The brain also monitors body energy stores by sensing adiposity-related signals. Information on nutrient availability and stored fat is transferred to specialized neurons in the hypothalamus and brain stem. In the control of the energy balance, outflow pathways from the brain regulate food intake and energy expenditure (thermogenesis or locomotor activity). The brain also has an important role in the maintenance of glucose homeostasis, which is achieved by the modulation of insulin/glucagon secretion in the endocrine pancreas, HGP, and skeletal muscle glucose uptake. The autonomic nervous system constitutes the outflow pathways from the brain to peripheral metabolic organs. Defective crosstalk between the brain and peripheral metabolic organs observed in the obese condition may lead to type 2 diabetes development and obesity progression. Therefore, better understanding of neural mechanisms involved in the regulation of glucose/energy homeostasis will provide us with the opportunity to develop new therapeutics combating obesity and diabetes. Acknowledgements This work was supported by grants from the National Research Foundation (NRF-2014R1A6A3A01057664, NRF-2013M3C7A1056024 for M-SK) and the Asan Institute for Life Sciences (2013-326). Author information Author notes 1. Eun Roh and Do Kyeong Song: These authors contributed equally to this work. Affiliations 1. Appeptite Regulation Laboratory, Asan Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, Korea 2. Department of Medicine, University of Ulsan College of Medicine, Seoul, Korea 3. Division of Endocrinology and Metabolism, Asan Medical Center, Seoul, Korea Corresponding author Correspondence to Min-Seon Kim. Ethics declarations Competing interests The authors declare no conflict of interest. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/29%3A_Integration_of_Mammalian_Metabolism_-_Capstone_Volume_II/29.03%3A_Emerging_role_of_the_brain_in_the_homeostatic_regulation_of_e.txt

Josep M. Argilés, Nefertiti Campos, José M. Lopez-Pedrosa, Ricardo Rueda, Leocadio Rodriguez-Mañas. Journal of the American Medical Directors Association, Volume 17, Issue 9, 2016, Pages 789-796,ISSN 1525-8610,https://doi.org/10.1016/j.jamda.2016.04.019. Under a Creative Commons license Abstract Skeletal muscle is recognized as vital to physical movement, posture, and breathing. In a less known but critically important role, muscle influences energy and protein metabolism throughout the body. Muscle is a primary site for glucose uptake and storage, and it is also a reservoir of amino acids stored as protein. Amino acids are released when supplies are needed elsewhere in the body. These conditions occur with acute and chronic diseases, which decrease dietary intake while increasing metabolic needs. Such metabolic shifts lead to the muscle loss associated with sarcopenia and cachexia, resulting in a variety of adverse health and economic consequences. With loss of skeletal muscle, protein and energy availability is lowered throughout the body. Muscle loss is associated with delayed recovery from illness, slowed wound healing, reduced resting metabolic rate, physical disability, poorer quality of life, and higher health care costs. These adverse effects can be combatted with exercise and nutrition. Studies suggest dietary protein and leucine or its metabolite β-hydroxy β-methylbutyrate (HMB) can improve muscle function, in turn improving functional performance. Considerable evidence shows that use of high-protein oral nutritional supplements (ONS) can help maintain and rebuild muscle mass and strength. We review muscle structure, function, and role in energy and protein balance. We discuss how disease- and age-related malnutrition hamper muscle accretion, ultimately causing whole-body deterioration. Finally, we describe how specialized nutrition and exercise can restore muscle mass, strength, and function, and ultimately reverse the negative health and economic outcomes associated with muscle loss. Keywords Muscle glucose amino acid sarcopenia HMB ONS Skeletal muscle is integral to physical movement, posture, and vital actions, such as chewing, swallowing, and breathing.1, 2 Skeletal muscle also serves as a regulator of interorgan crosstalk for energy and protein metabolism throughout the body, a less recognized but critically important role. As such, skeletal muscle is a key site for glucose uptake and storage.3 Skeletal muscle is likewise a reservoir of amino acids that can support protein synthesis or energy production elsewhere in the body when other sources are depleted.4 This review of muscle metabolism describes how amino acids stored as protein in muscle can be broken down through proteolysis for ultimate use in energy production. Such breakdown occurs when energy demands are high (as with stress-induced hypermetabolism), or when supplies are low (as in severe starvation or longer-term protein energy malnutrition). Both of these states can be hallmarks of many diseases, either directly as a result of disease-related dysregulation of metabolism (such as in the extreme case of cancer-cachexia) or, more subtly, as a result of the general illness-associated loss of appetite. Muscle is therefore crucially important during illness, both for its role in balancing the metabolic needs of other organs and for its reserves of protein for use in energy production. Yet, during illness, the maintenance of muscle mass through exercise and nutrition are often overlooked or difficult to address, and muscle atrophy develops. Even more subtle is aging-related muscle loss, which can dramatically increase morbidity and mortality of otherwise survivable illnesses in the aged. This review also illustrates the consequences of muscle atrophy in aging and illness and proposes steps to combat these challenges. Muscle Basics Muscle Structure and Classification Skeletal muscle comprises the fibrillar proteins myosin (a thick filament) and actin (a thin filament) that interact to cause muscle contraction, a process requiring energy in the form of adenosine triphosphate (ATP). Different muscle types have been classified according to histochemical features, structural protein composition, and major metabolic properties.5, 6 Most commonly, skeletal muscles are referred to as either “slow” or “fast” to reflect speeds of contraction, or the shortening of myosin heavy chain (MHC) protein.6 The velocity of this shortening is dependent on the MHC isoform present; “fast” fiber isoforms MHCIIa and IIb demonstrate a higher shortening velocity than their “slow” fiber MHCI counterparts.6, 7 Classic histochemical staining methods also classify muscle as type I (slow) and type II (fast) based on the myosin ATPase enzyme forms revealed. Recently, these types have been further distinguished based on histology (types I, IC, IIC, IIAC, IIA, IIAB, and IIB).6 Muscle Metabolism and Interorgan Crosstalk Glucose regulation is central to energy balance both within muscle fibers and throughout the body. In the cytoplasm of most cells, glucose undergoes glycolysis to produce the substrate for ATP generation. Muscle fibers are also characterized on the basis of the speed and manner in which they metabolize glucose. The terms “fast” and “slow” can indicate the type of glucose metabolism occurring within the fiber. Slow muscles, which use aerobic metabolism, contain a high density of capillaries and oxidative enzymes that allow a greater resistance to fatigue.7 Fast muscles, which depend on anaerobic metabolism, or glycolysis, can quickly generate ATP and therefore contract more readily. Fast muscles also fatigue sooner than slow fibers, as the conversion of glucose to pyruvate generates less ATP than can be generated by using the rest of central metabolism, ultimately generating CO2. Muscle has the ability to store glucose in the form of glycogen, which facilitates the rapid initiation of energy production for contraction even when glucose is not readily available from the diet. This unique capacity, shared also by the liver and kidneys, makes skeletal muscle an important metabolic organ that helps all organs have access to essential energy substrates during fasting. Furthermore, the amino acids stored in muscle as protein can be broken down as a last resort during times of starvation or extreme energy shortfalls.4 Patterns of glucose utilization throughout the body as a whole reflect feeding status (Figure 1; Table 1). Based on a classic study of the fed state (measurement within 3 hours of eating), researchers estimated that 25% to 35% of an ingested carbohydrate load was quickly extracted from circulation and stored by the liver.3 Of the remaining glucose, approximately 40% was disposed in the muscle and 10% in the kidney.3 The brain used 15% to 20% of post-meal glucose.3 Table 1. Glucose Metabolism in Fasted and Fed States Fed State Fasted State Diet-sourced glucose (exogenous glucose) is absorbed from the intestine to circulate in blood; glucose serves as an energy source in cells throughout the body. Cytoplasmic glucose undergoes glycolysis, in turn producing ATP. Little or no blood glucose from dietary sources; alternative energy sources are needed for function of tissues body-wide. Glucose is primarily taken up by muscle and liver, where it can be used for energy or stored as glycogen (Glycogen synthesis). Glycogen stored in liver, kidney, and muscle is broken down to provide glucose as energy source (Glycogenolysis). Muscle uses glycogen-sourced glucose internally; liver and kidney can supply glucose to circulation. Gluconeogenic substrates are stored in various organs (eg, pyruvate in liver, glycerol in fat, and amino acids in muscle). Endogenous generation of glucose from noncarbohydrate carbon substrates, such as pyruvate, lactate, glycerol, and glucogenic amino acids (Gluconeogenesis); occurs primarily in the liver and muscle, and to a lesser extent in the kidney. In the fasted state (after 14 to 16 h without eating), the liver provides approximately 80% of glucose that is released into circulation. About half of this glucose comes from the breakdown of stored glycogen, and the rest from the metabolism of sources other than carbohydrate or glycogen, including certain amino acids, through a process known as gluconeogenesis.8 Interactions between muscle and liver are largely responsible for regulating carbohydrate metabolism and for achieving energy balance in normal fed and fasted states; the kidneys play a role similar to that of the liver, but to a lesser extent.3, 8 In addition, muscle tissue stores amino acids as protein, and adipose tissue serves as a depot of glycerol and fatty acids. As needed, amino acids and fatty acids can be metabolized to form acetyl coenzyme A for the tricarboxylic acid (TCA) cycle. As glycogen stores become depleted, increasingly more glucose is produced by gluconeogenesis. Gluconeogenesis provides 70% of glucose released into the body 24 hours after eating, and 90% by 48 hours.8 As fasting is prolonged, the kidneys contribute increasingly higher amounts of glucose from gluconeogenesis. Ultimately, amino acids stored in skeletal muscle are metabolized when the need for gluconeogenesis substrate is greatest. Skeletal muscle houses nearly 75% of all protein in the body and constitutes an important contributor to gluconeogenesis in states of drastic depletion. Maintenance of muscle protein content depends on the balance between protein synthesis and degradation.5 Under normal conditions, muscle protein mass gains during the fed state balance losses during the fasted state.4 However, under severe metabolic stress generated by serious illness or injury, muscle protein can become depleted by catabolism, and this can lead to harmful functional limitations. Skeletal muscle proteolysis can provide amino acid substrates for glucose and glycogen formation, notably glutamine and alanine. Alanine is released into circulation and reaches the liver, where it serves as an excellent substrate for gluconeogenesis. Glutamine also has a beneficial role in this process: the carbon skeleton of glutamine is a gluconeogenic precursor that can regulate gluconeogenesis independently of the insulin/glucagon ratio. Therefore, glutamine supplementation may also enhance glycogen synthesis and increase muscle glycogen stores even when insulin levels are low or when insulin resistance is present.9 In summary, dietary glucose is supplied by meals, and glucose is stored as glycogen in liver, kidney, and muscle for metabolic energy functions, as needed (Table 1). At times when glucose supplies are not sufficient to meet energy needs, breakdown of glycogen (glycogenolysis) occurs. When stored glucose products are no longer available, energy is released by breakdown of substrates other than glucose. In this review of muscle metabolism, we emphasize that amino acids stored as protein in muscle can be broken down by way of gluconeogenesis, ultimately entering the TCA cycle for energy production. Such breakdown occurs when energy demands are high, as with stress-induced hypermetabolism of disease, or when supplies are low, as in severe starvation or disease-associated loss of appetite. Such use can become problematic in that it reduces skeletal muscle mass and produces waste nitrogen, which requires further energy to sequester and secrete. Prolonged reliance on these processes can accelerate existing health problems and must be addressed by the health care provider. Muscle Plasticity: Changes in Muscle Mass, Strength, and Function Skeletal muscle is remarkably plastic. It changes continuously in response to calorie and nutrient intake, illness, and physical stress. Changes in adult skeletal muscle also may occur as fiber-type switching, which is influenced by changes in physical activity, loading, nerve stimulation, or hormone and cytokine levels.7, 10, 11, 12 Mechanisms of Muscle Growth and Strength Increase Muscle adapts positively to demands placed on it, such as the increased contractile activity associated with endurance training or the increased loading attributable to strength training. This tremendous plasticity is evident as muscle tissue accretion, specific changes within muscle, and muscle tissue breakdown.1 Muscle growth, or hypertrophy, occurs when protein synthesis within the muscles outpaces protein degradation. This process can be positively regulated by mammalian target of rapamycin (mTOR) signaling induced by insulin after calorie ingestion, by hormones such as testosterone, and by exercise.10 Mechanisms of Muscle Loss in Aging, Inactivity, Sickness, and Frailty This ability of skeletal muscle to change dynamically in response to body conditions is also manifest as changes resulting from injury, illness, or aging. When the metabolic demands placed on muscle outweigh the protein synthesis that occurs from dietary intake and after exercise, muscle mass is lost, metabolic storage products are depleted, and muscle fiber balance changes. Aging may lead to a loss of muscle mass resulting from both the shrinking of muscle fibers (atrophy) and the elimination of fibers altogether (Figure 2).6 This condition is known as primary sarcopenia, the age-related loss of muscle mass and function. Although both fiber types I and II lose mass, aging causes preferential atrophy of type II fibers; the net change is thus from type II to type I fibers, or from fast to slow muscle fibers.6, 13 Because fast muscle fibers mobilize ATP and create tension more readily than slow fibers, this shift can leave older adults without the energy to perform daily tasks.14 This shift to type I slow fibers leads to a corresponding increase in their characteristic oxidative metabolism relative to the glycolytic metabolism that occurs in type II fast fibers. Exacerbating the problems caused by muscle degradation in aging, it is possible that type I oxidative fibers normally experience higher protein turnover (i. protein synthesis and degradation), are less able to grow in size, and have different responses to insufficient nutrient intake, although these fiber-type differences remain poorly understood.7, 15, 16 Beyond aging, muscle wasting is associated with many pathological states and chronic diseases, such as malnutrition, cancer, chronic kidney disease, chronic obstructive pulmonary disease, burns, muscular dystrophies, acquired immunodeficiency syndrome, sepsis, and immune disorders, and forced immobilization and bed rest are devastating to patients who are already challenged by these factors (Figure 2).14, 17 Most of these pathological conditions are associated with variable degrees of local and/or systemic chronic inflammation, which plays a crucial role in the onset of muscle atrophy. Loss of muscle mass is frequently associated with increased production of proinflammatory cytokines. Systemic inflammation is associated with reduced rates of protein synthesis paralleled by enhanced protein breakdown, both accounting for the loss of muscle mass. The effects exerted by proinflammatory cytokines on muscle mass are partially mediated by activating the transcription factor nuclear factor κB (NF-κB).18 The transcriptional activity is regulated by the phosphorylation and consequent degradation of the inhibitor Iκ-Bα, allowing the positive regulation of muscle RING-finger protein-1 (MuRF1) and other atrophy-related genes. Proinflammatory cytokines act on muscle protein metabolism not only by activating catabolic pathways, but also by downregulating the anabolic pathways.19 Elevated tumor necrosis factor-alpha (TNF-α) and interleukin-1 (IL-1) lead to inhibition of the Akt/mTOR signal transduction pathway and a subsequent reduction in protein synthesis. The inflammatory process that takes place during trauma or fractures is controlled and finely regulated. In the short term, it can facilitate complete and efficient reconstruction of muscle fibers through the stimulation of myogenesis. However, chronic inflammation can be deleterious, driving uncontrolled muscle atrophy and affecting contraction ability. Balance between pro- and anti-inflammatory cytokines is well known to be important in regulating physiological muscle protein turnover and myogenesis, and evidence suggesting that inflammation can impair force generation in muscles is also growing.20, 21 As inflammation accelerates muscle catabolism, resting energy expenditure increases and amino acids are released from muscles to serve as substrates for gluconeogenesis in liver and elsewhere in the body (Table 2).22 The efficiency of energy production is low when amino acids are used to generate energy, so muscle is at further risk for breakdown to meet needs.23 In addition, the liver changes metabolic priorities, using amino acids to produce acute phase reactant proteins instead of normal proteins, such as serum albumin, and to support gluconeogenesis. This process continues until the cause of stress has subsided. Thus, when the dietary proteins supplied are inadequate to meet needs, muscle protein is broken down to supply amino acids throughout the body. This reaction releases waste nitrogen, which requires further energy to convert to urea, thereby exacerbating the problem of the energy shortfall.24 Table 2. Major Molecular Pathways Influencing Muscle Accretion Effector Mediator Major Pathway(s) Consequence Mammalian target of rapamycin (mTOR) +Induced by BCAAs, HMB Interacts with protein translation machinery to facilitate initiation and elongation mTOR stimulation by a number of pathways increases protein synthesis Insulinlike growth factor (IGF1) +Stimulated by meal-induced insulin IGF1R → PI3K → AKT → mTOR Reduced IGF1 from decreased eating and/or exercise leads to reduced protein synthesis and to muscle wasting +Stimulated by exercise Myostatin/Activin +Produced by skeletal muscle Activin receptors (ACTRIIA/B) → Smad2/3 –I mTOR Myostatins negatively regulate protein synthesis –Inhibited by Follistatin ACTRIIA/B → FoxO → UPS Inflammatory cytokines (TNFα, IL-1) +Upregulated by illness, injury Cytokine receptors → NFKB, p38, JAK, Caspases, E3 ligases Inflammation leads to apoptosis or autophagy-mediated muscle cell loss –Inhibited by exercise FoxO transcription factors → MAFBX; MURF1 → UPS (ubiquitin-proteasome system) Vitamin D +Levels are increased by diet and sunlight Vitamin D receptor → gene expression or repression in myogenic cells Vitamin D positively influences muscle growth AKT, protein kinase B; BCAA, branched-chain amino acid; FoxO, forkhead box protein O; HMB, β-Hydroxy β-Methylbutyrate; IGF1R, insulinlike growth factor 1 receptor; JAK, janus kinase; MURF1, muscle RING-finger protein-1 p38, mitogen-activated protein kinase; PI3K, phosphatidylinositide 3-kinase; UPS, ubiquitin-proteasome system. Complications Associated With Loss of Muscle As aging and illness lead to muscle breakdown and atrophy, reduced muscle mass leaves patients without a crucial reservoir of amino acids and effector molecules, such as myokines, cytokines released by muscle, to help the body combat illness, infection, and wasting (Figure 3).23, 25, 26, 27 Therefore, muscle atrophy is associated with a wide range of harmful health effects that can be life changing, especially for older people.28, 29, 30, 31, 32, 33, 34, 35, 36 The most relevant condition associated with the presence of sarcopenia in this population is a clinical syndrome called frailty. The most accepted physiological framework explaining frailty and its consequences was proposed by Walston and Fried,37 who described a relationship between sarcopenia and energy imbalance called the “frailty cycle.” This cycle affects multiple systems, especially those susceptible to changes in hormones (mainly sexual hormones, IGF-1, and insulin) and the progressive development of a proinflammatory state.38, 39, 40 Additional biomarkers have recently been identified for roles in frailty, such as those related to endothelial dysfunction or micro RNAs central to the aging process.41, 42 Frailty can be defined as an age-associated biological syndrome characterized by a decreased biological reserve resulting from a decline in multiple physiological systems that leaves the individual at risk for developing poor outcomes (disability, death, and hospitalization) in the presence of stressors.43, 44 The prevalence of frailty in people older than 65 is approximately 10%, increases with age, and is greater in women.45 Frailty is now a recognized clinical medical syndrome that provides a biological framework for understanding vulnerabilities resulting from aging or chronic conditions.44, 46 It is clinically important to detect frailty in those at risk of developing disability. As aging progresses, frailty increases as the prognostic factor for death and incident disability.47, 48 Frailty and its underlying sarcopenia have been shown to predict risk of death, disability, and other adverse outcomes, including muscle mass atrophy, metabolic deterioration, slowed wound or postsurgical healing, and delayed recovery from illness.32, 34, 35 Frailty and the weakness that follows muscle loss lead to higher risk of falls, fractures,30 physical disability,29 need for institutional care,29 reduced quality of life,36 and heightened mortality.29, 33 Early identification of frailty risk provides the opportunity to provide interventions and avoid or delay disability as well as enhance recovery. Loss of muscle associated with disease, injury, disuse, or aging significantly increases the cost of health care.34, 49, 50 Results of a recent study showed that older adults (mean age=70 years) who were very frail spent (Euro) 1917 more on total health costs in an interval of 3 months than did those who were not frail.51 In the United States, the direct cost of cachexia/sarcopenia to health care was reported to be 1.5% of annual total health care expenses.50 Such costs arise from the increased rate of hospitalization, incidence of complications, lengths of stay, and likelihood of readmission.52, 53 In the face of an aging population, the importance of identifying, preventing, and treating muscle loss cannot be overstated. Detection and Treatment of Muscle Loss Who Is at Risk of Muscle Atrophy, and How Do We Identify It? Screening is crucial for predicting risk, and proper, timely intervention can reduce or eliminate the ensuing muscle mass and metabolic atrophy, substantially affecting morbidity, mortality, and cost. Special attention should be paid to the main risk categories: people who are malnourished or at risk of malnutrition for any reason33, 54; frail adults, especially the very old; people who become deconditioned and lose muscle due to age- and disability-related physical inactivity35; those with diseases or conditions with inflammatory components, such as chronic heart failure,55 chronic or acute kidney disease,56 cancer,57, 58, 59 severe infection and sepsis,60 insulin resistance/diabetes,61 intensive care unit–acquired weakness,25 and wound/surgical recovery.34 Reaching an accurate diagnosis of age- or disease-related muscle atrophy is difficult, and a number of criteria have been proposed but have not yet assessed in the clinical setting.14 Nonetheless, specific criteria and measures can be used to diagnose sarcopenia or cachexia.13, 27, 62, 63 Sarcopenia can be diagnosed when a patient has muscle mass that is at least 2 SDs below the relevant population mean and also presents with a low gait (walking) speed. In addition, low muscle strength and general physical performance may be taken into consideration.14 Cachexia can be diagnosed when at least 5% of body weight is lost within 12 months in the presence of underlying illness, and 3 of the following criteria are also met: decreased muscle strength, increased fatigue, anorexia, low fat-free mass index, abnormal biochemistry, increased inflammatory markers C-reactive protein (>5.0 mg/L) or IL-6 (>4.0 pg/mL), anemia (<12 g/dL), or low serum albumin (<3.2 g/dL). Recent research into the molecular adaptations associated with the development of or that result from muscle atrophy and metabolic depletion may lead to the identification of biomarkers and, therefore, improvements in early detection (Table 2). A variety of signaling pathways known to positively influence muscle growth (bone morphogenetic proteins, brain-derived neurotrophic factors, follistatin, and irisin), as well as those known to negatively regulate muscle growth (transforming growth factor β, myostatin, activins, and growth and differentiation factor-15) and factors associated with muscle function and dysfunction (C-terminal agrin fragment and skeletal muscle specific troponin T) may emerge as biomarkers for muscle atrophy in aging and disease.64 To date, there is no universally recognized biomarker for muscle atrophy, but recent research in the field suggests that the combination of several biomarkers may facilitate the adequate diagnosis of muscle atrophy. Identification of such biomarkers and their incorporation into validated testing instruments should allow early identification of muscle atrophy (improving prognosis, and likely reducing cost to health care systems), but may also provide exciting targets for the development of new medications. Nutritional Strategies for Maintaining and Rebuilding Muscle Treatment of patients at risk can prevent or delay onset of muscle atrophy, or even target rebuilding of muscle when muscle atrophy is already evident (Figure 4).65 As a first step, treatment must provide adequate energy so that muscle proteins and their constituent amino acids are spared as an energy source. In addition, high protein intake is vital to treatment of muscle atrophy or for delaying its onset.7, 66, 67, 68, 69 It should be noted that the range of protein needs can vary widely from patient to patient. Because muscle mass may decrease or remain the same (based largely on how much protein synthesis outpaces protein degradation), the most direct way to prevent muscle loss is to ensure that sufficient protein is ingested. Use of high-protein oral nutritional supplements (ONS; ≥20% of total calories as protein) may be beneficial to such patients.70 By definition, the essential amino acids (EAAs) play a central role in protein nutritional status. Some amino acids play roles that are distinct from the traditional one of protein building blocks; many of these have little or nothing to do with protein synthesis, and are thus not included here. However, of central importance to the current discussion are the branched-chain amino acids (BCAAs), especially leucine.65, 71, 72 BCAAs promote protein synthesis in the muscles through a number of pathways.66 In particular, they are now known to have a key role in altering tissue response to a meal, the post-prandial response, especially in muscle, where they signal a reduction in protein breakdown and an increase in protein synthesis, resulting in net accretion of protein in muscle and helping to regulate blood amino acid levels. However, aspects of this postprandial regulation are not as robust in aged muscle, and muscle in hypercatabolic conditions, such as cancer, is challenged and its normal system is overwhelmed. In these cases, a substantial body of research suggests that significantly more of these amino acids are required in the diet to overcome resistance to protein anabolism; very high doses, such as 10 to 15 g of BCAAs, or 3 g or more of leucine per meal, have been studied to combat muscle loss in the elderly,44 although this may be a result of improved protein synthesis that does not lead to muscle mass accretion.73, 74, 75 This resistance to the normal of BCAAs in muscle protein homeostasis has prompted studies into leucine's mechanism of action. These have identified the leucine metabolite β-hydroxy β-methylbutyrate (HMB) as a potent stimulator of protein synthesis as well as an inhibitor of protein breakdown in the extreme case of cachexia.65, 72, 76, 77, 78, 79, 80, 81, 82, 83, 84 A growing body of evidence suggests HMB may help slow, or even reverse, the muscle loss experienced in sarcopenia and improve measures of muscle strength.44, 65, 72, 76, 77, 78, 79, 80, 81, 82, 83, 84 However, dietary leucine does not provide a large amount of HMB: only a small portion, as little as 5%, of catabolized leucine is metabolized into HMB.85 Thus, although dietary leucine itself can lead to a modest stimulation of protein synthesis by producing a small amount of HMB, direct ingestion of HMB more potently affects such signaling, resulting in demonstrable muscle mass accretion.71, 80 Indeed, a vast number of studies have found that supplementation of HMB to the diet may reverse some of the muscle loss seen in sarcopenia and in hypercatabolic disease.65, 72, 83, 86, 87 The overall treatment of muscle atrophy should include dietary supplementation with HMB, although the optimal dosage for each condition is still under investigation.68 In addition to dietary protein, EAAs/BCAAs including leucine, the leucine metabolite HMB, a number of other dietary or supplemental components have been explored for their ability to positively influence muscle mass during sarcopenia. These include creatine monohydrate, a variety of antioxidants, ornithine α-ketoglutarate, omega-3 fatty acids, ursolic acid, and nitrates.68, 88, 89, 90 Given the length of this list, its growing nature, and the difficulty many aged individuals experience ingesting proper calories and nutrients, additional studies will be needed to determine which components are most beneficial to maintaining muscle mass, as well as their optimal doses and administration routes both in isolation and in combination. Physical Activity Is Also Key Nutrition is important and can counteract metabolic alterations induced during periods of significant stress and inflammation; however, sufficient exogenous provisions of protein and energy substrates alone cannot completely eliminate or reverse the deteriorations associated with aging or the deleterious impact inadequate control and regulation of inflammation have on muscle.23, 66, 69 Protein synthesis occurs in muscle fibers following their contraction,91 and physical activity has been shown to induce a number of anabolic signaling pathways.92 Physical activity can likewise reduce degradation of muscle protein.93, 94 Even more, a lack of physical activity increases the resistance of muscle to anabolism, particularly the synthesis of proteins from amino acids.95 An exercise component to muscle atrophy treatment is therefore highly recommended, and exercise also may prevent the onset of sarcopenia later in life, possibly by increasing the presence of type I fibers that are less susceptible to degradation during sarcopenia.7, 25, 66, 89 Although aerobic and other types of exercise are all preferable to a lack of physical activity, resistance exercise in particular has been shown repeatedly to improve rates of protein synthesis and reverse muscle loss.88, 89, 94 This may be attributable to differential effects on muscle fiber types.7 It is therefore recommended that patients with muscle atrophy or at risk of developing muscle atrophy engage a regular exercise program containing both aerobic and anaerobic components, and the importance of appropriate resistance training cannot be overstated. Although this must be tailored to the individual's current physical status, it should also periodically be reviewed and increased to maximize its impact. Although resistance exercise and general physical activity are important to the stimulation of protein synthesis from amino acids in the diet, some aged and ill individuals experiencing extensive muscle atrophy are likely to have difficulty engaging in physical activities because of low energy and other medical complications. Nutrition and some supplements can be used to bolster results of exercise, both preventively and during sarcopenia. For example, bioactive substances known as nutraceuticals that mimic the molecular effects of exercise can induce signaling pathways that are thought to support or even underlie exercise's effects on health and muscle mass accretion. Found in a variety of foods, including some common fruits, green tea, and even red wine, these compounds can be isolated and added to nutritional supplements used in the treatment of muscle atrophy.44 Summary and Conclusions The classic physical functions of skeletal muscle are well known, but skeletal muscle is increasingly recognized as a one of the key regulators of energy and protein metabolism by way of metabolic crosstalk between body organs. Skeletal muscle is the primary site for glucose uptake and storage, and it is likewise a reservoir of amino acids that sustain protein synthesis in all other body sites. When dietary glucose intake decreases or metabolic needs increase, stored glucose is mobilized from liver, while energy is released from fat depots. When these energy supplies are depleted, the muscle reservoir of amino acids stores is tapped, and muscle proteins are broken down to provide amino acids for gluconeogenesis, thereby supplying energy to other parts of the body. Undernutrition and resultant muscle loss (muscle atrophy), as associated with aging and disease, can lead to adverse health and economic consequences. Conditions and diseases that lower dietary intake and increase nutrient needs are associated with catabolism of skeletal muscle, which in turn limits availability of protein and energy throughout the body. Loss of muscle mass, strength, and function has adverse consequences: slowed wound healing and recovery from illness, physical disability (due both to overall reduction of muscle status), as well as selective losses in type I fibers, which are essential for balance recovery (and thus fall prevention), poorer quality of life, and higher health care costs. Nutrition and exercise are key to growth and maintenance of muscle promoting overall health, well-being, and recovery from disease. A wealth of research underscores the importance of a few key dietary components: protein (EAAs/BCAAs in particular), and the leucine metabolite HMB. Others will very likely be added to this list as our knowledge base grows. In addition, physical activity, especially resistance strength training, is essential to the treatment of muscle atrophy. Others will very likely be added to this list as our knowledge base grows. Considerable evidence shows that ONS and enteral feeding formulations can help maintain and rebuild muscle mass and strength. Further studies are needed to show support for functional outcomes, such as ability to perform activities of daily living and maintain or restore independence. Acknowledgments The authors thank Jeffrey H. Baxter and Abby Sauer from ANR&D for their critical review of this article, as well as Cecilia Hofmann, PhD, and Hilary North Scheler, PhD (C Hofmann & Associates, Western Springs, IL), for valuable assistance with efficient compilation of the medical literature and with editing this English-language review article.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/29%3A_Integration_of_Mammalian_Metabolism_-_Capstone_Volume_II/29.04%3A_Skeletal_Muscle_Regulates_Metabolism.txt

Merino, B.; Fernández-Díaz, C.M.; Cózar-Castellano, I.; Perdomo, G. Intestinal Fructose and Glucose Metabolism in Health and Disease. Nutrients 2020, 12, 94. https://doi.org/10.3390/nu12010094 This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Abstract The worldwide epidemics of obesity and diabetes have been linked to increased sugar consumption in humans. Here, we review fructose and glucose metabolism, as well as potential molecular mechanisms by which excessive sugar consumption is associated to metabolic diseases and insulin resistance in humans. To this end, we focus on understanding molecular and cellular mechanisms of fructose and glucose transport and sensing in the intestine, the intracellular signaling effects of dietary sugar metabolism, and its impact on glucose homeostasis in health and disease. Finally, the peripheral and central effects of dietary sugars on the gut–brain axis will be reviewed. 1. Introduction According to the World Health Organization, obesity is the epidemic of the 21th century. About 13% of the world’s adult population is obese [1]. Worldwide, between 1975–2016, the global obesity rate was nearly triplicated, increasing from 1% up to 6%–8% among children and adolescents [1]. As a major public health issue, clinical interventions based on low-fat diets attracted significant interest. However, over decades, the consumption of sugars has risen significatively worldwide, and has been partially associated to the rapid increase in the prevalence of obesity [2]. From the Industrial Revolution, the consumption of sweeteners has increased dramatically, causing a dietary switch in the world population [3]. Most of this increase in sugar consumption is derived from refined or processed fructose, which is obtained from the conversion of glucose from sugar cane and corn through a chemical process developed in 1957 [4,5]. Fructose constitutes a significant portion of the caloric intake in many countries [3]. The average daily consumption of added sugars (13%–17% of daily energy intake), of which about half is fructose, is above the recommended limit of 10% in many countries [6]. Of note, 16% of total energy in children’s diets comes from added sugars [7]. The increase in total fructose intake parallels a decrease in the proportion of dietary fructose coming from fruits, but augmented proportion from fructose-based sweeteners [3]. In the past, fructose was considered sweeter, more soluble, and less gluconeogenic than glucose and sucrose, and was proposed as a substitute for these sugars [8]. Over time, this idea has been reconsidered in view of the impact of high-fructose consumption on whole-body metabolism, and because it is a risk factor for developing obesity and diabetes [9,10,11]. Although fructose and glucose share the same molecular formula (C6H12O6) and caloric value (4 kcal/g), fructose tastes sweeter than glucose (relative to sucrose, which by consensus agreement is equal to one; the sweetness of glucose is 0.75, and fructose is 1.7), and has a lower glycemic index than glucose (23 versus 100, respectively) [12]. In addition, fructose is less satiating than glucose, increasing food intake [13]. As reviewed in detail below, intestinal fructose and glucose absorption are also quite different, because glucose transport is an energy-requiring process mediated by the sodium-glucose co-transporter 1 (SLGT1), whereas fructose moves through a facilitated passive transport mediated by GLUT5 [14]. Furthermore, fructose metabolism has a negligible impact on circulating insulin levels compared to glucose metabolism, which is related to insufficient leptin (the satiety hormone) secretion, and suppression of ghrelin (the hunger-promoting hormone) [15]. 2. Intestinal Fructose Transport and Metabolism: Implications for Health and Disease Whole body fructose homeostasis results from two main processes: Intestinal absorption and clearance, the latter is commonly assumed to be mainly mediated by the liver (~55%–71%) and, to a lesser extent, by kidneys (<20%) [16]. Dietary fructose moves from the intestinal lumen to the circulation through a facilitated passive transport [3] across enterocyte membranes by members of the facilitative glucose transport (GLUT; Slc2a) family [14]. Upon its intestinal absorption, fructose reaches the liver through the hepatic portal vein and undergoes metabolization in hepatocytes [16]. Fructose transport and metabolism has been extensively reviewed (see refs. [3,14,17]). Exhaustive description of fructose hepatic metabolism is out of the scope of this review. Here, we briefly describe the regulation of intestinal fructose transport and transporters and its intracellular metabolism. We focus on revisiting the role of the liver and small intestine in fructose clearance, the relevance of endogenous fructose production in human diseases, and plant extract inhibitors of fructose transporters. 2.1. Intestinal Fructose Transport Fructose uptake into enterocytes is an insulin-independent process [18]. Among the members of the GLUT family able to transport fructose (GLUT5, GLUT8 and GLUT11), GLUT5 (Slc2a5) is primarily responsible for fructose uptake into the enterocyte at the apical side of the membrane, whereas GLUT2 (Slc2a2) moves most of fructose from the cytosol into blood vessels at the basolateral side of the enterocyte [14,19,20,21] (Figure 1). Although GLUT5 belongs to the GLUT family, it only transports fructose without the ability to transport glucose or galactose. Conversely, GLUT2 can transport glucose and galactose in addition to fructose, with an affinity (Km) for fructose more than five-fold higher than that of GLUT5 [22,23]. The main site of GLUT5 expression is the apical membrane of intestinal epithelial cells, although to a much lower extent is also expressed in kidneys, brain, fat, testes, and muscle [24]. However, the physiological relevance of GLUT5 expression in these extraintestinal human tissues is uncertain. On the other hand, GLUT2, in addition to the basolateral membranes of intestinal epithelial cells, is highly expressed in hepatocytes, pancreatic β-cells, and the basolateral membranes of kidney epithelial cells [22]. The Km of GLUT5 for fructose varies depending on the study model and the species used for its assessment. Thus, Burant et al. reported a Km of ~6 mM in Xenopus oocytes expressing the mammalian GLUT5 [19]. In contrast, Kane et al. reported a Km of 11–15 mM using the same expression system for human GLUT5 [25]. Similar values (Km of 11–13 mM) were found for mouse and rabbit GLUT5 transporter expressed in oocytes [26,27]. Finally, Mate et al. reported a Km of ~8–11 mM in ileal brush border membrane vesicles of normotensive Wistar-Kyoto rats and their spontaneously hypertensive rats [28]. Assuming a Km value ranging from 11–15 mM for GLUT5, this Km is similar to that reported for intestinal luminal fructose concentrations (26 mM) in rats fed dietary fructose [29]. On the other hand, the Km of GLUT2 for fructose is ~11–17 mM [22,23]. 2.2. Dietary Fructose Metabolism High concentrations of dietary fructose in foods and drinks lead to elevated intestinal luminal fructose concentrations that are needed for driving the facilitated fructose transport across the enterocyte membrane, and fluctuate around the Km of GLUT5 for fructose [3]. Unlike the high fructose concentration in luminal small intestine, fructose concentrations in systemic circulation are relatively low as a result of intestinal absorption and liver clearance rates. In humans, estimates of fasting systemic blood fructose concentrations are low (<0.05 mM), even in those healthy humans consuming high-fructose or sucrose diets (~0.2–0.5 mM) [30,31,32,33], which is still very low compared to fasting blood glucose levels (5.5 mM). Finally, type 1 and type 2 diabetic patients exhibited 0.016 mM and 0.009–0.013 mM fasting fructose concentrations, respectively [34,35]. The low fructose concentrations in peripheral blood support the notion that the liver and kidneys are much more sensitive to small changes in circulating fructose levels than the small intestine. Nonetheless, it is unclear how hepatocytes or nephrons reabsorb fructose from the sinusoidal capillaries or glomerular filtrates, respectively, containing very low fructose levels. Metabolization of dietary fructose in the small intestine is a process regulated at various steps. In the first step of the classical Hers pathway for fructose metabolism, fructose is mobilized from intestinal lumen into the cytosol of enterocytes by GLUT5, where it is rapidly phosphorylated by the ketohexokinase (KHK, Khk), also known as fructokinase, to fructose-1-phosphate using ATP as a phosphate donor [36]. The Khk gene encodes two isoforms of the enzyme as a result of alternative splicing of the adjacent exons 3A and 3C of the gene leading to the KHK-A and KHK-C isoforms, respectively [37,38]. Studies of expression analysis in several human and rat tissues indicated that only one mRNA variant is expressed in each tissue [38], but the pancreas is an exception to this pattern because, although KHK-C expression predominates, some KHK-A is also expressed. The KHK-C mRNA variant is expressed at high levels in the liver, kidneys, and duodenum, and is considered more physiological than the KHK-A variant because its Km for fructose is <1 mM [39,40]. On the other hand, KHK-A is expressed at a low level in a wide range of tissues including skeletal muscle and adipose tissue [39,40]. KHK-A Km for fructose is 8 mM, suggesting that it poorly phosphorylates fructose at physiological concentrations and that it may have a more important role when fructose intake is excessive [41]. In the second step of the fructose pathway, fructose-1-phosphate is split into glyceraldehyde and dihydroxyacetone phosphate by aldolase B (ALDOB; Aldob) [16]. In the third and final step of the pathway, the triokinase (TKFC; ATP:D-glyceraldehyde 3-phosphotransferase) catalyzes the phosphorylation of glyceraldehyde by ATP to form glyceraldehyde-3-phosphate [16,36]. Both ALDOB and TKFC are highly expressed in the liver, kidneys, and small intestine, relative to other organs [42]. Unlike glycolysis, the catabolism of fructose (fructolysis) bypasses major regulatory steps of glycolysis and gluconeogenesis (i.e., phosphofructokinase and fructose-1,6-bisphosphatase), and it is not regulated by feedback inhibition [16,43]. In addition, fructolysis bypasses the glucose-6-phosphate and fructose-6-phosphate production from the pentose phosphate pathway for de novo synthesis of nucleotides and nucleic acids [44]. Thus, it is plausible that in conditions of excessive fructose consumption, KHK-mediated fructolysis leads to increased glyceraldehyde, dihydroxy-acetone-phosphate, and glyceraldehyde-3-phosphate production, which are the source of gluconeogenic and lipogenic substrates (e.g., pyruvate, lactate, acetyl-CoA, and glycerol-3-phosphate), leading to elevated rates of gluconeogenesis, glycogenesis, and/or lipogenesis. Another consequence of the fructolysis is a rapid ATP and Pi intracellular depletion [45,46]. 2.3. Regulation of GLUT5 In addition to dietary fructose catabolism, metabolism of fructose comprises its biosynthesis from glucose through the polyol pathway [16]. This two-step pathway becomes active when intracellular glucose concentrations are elevated. In the first step, glucose undergoes reduction by NADPH to sorbitol (polyol) by the rate-limiting enzyme in the pathway, the aldose reductase (AR), followed by metabolization of sorbitol into fructose by sorbitol dehydrogenase (SDH) in the presence of NAD+ as a cofactor [16]. Intestinal fructose metabolism is not only important for the metabolic fate of fructose but for the up-regulation of GLUT5, KHK, ALDOB, TKFC, fructose-1,6-bisphosphatease, and glucose-6-phosphate (Figure 1) [47,48]. Thus, it has been extensively shown that chronic or acute fructose exposure increases GLUT5 levels and activity in rodents and human proximal intestine regions [3,17]. The response of GLUT5 to its substrate requires partial or total metabolization of fructose because the nonmetabolizable fructose analog 3-O-methylfructose has a modest effect on GLUT5 expression [49], and blocking intracellular fructose metabolism in the HKH-/- mouse model prevents fructose up-regulation of GLUT5 [47]. Furthermore, these effects of fructose on GLUT5 expression are very specific because fructose, glucose, and nonmetabolizable glucose analogs have similar changes on GLUT2 expression in intestinal cells [49]. The molecular mechanisms underlying fructose-mediated regulation of GLUT5 in enterocytes remain incompletely understood. In rats, fructose-induced cAMP stimulates fructose uptake without affecting transcriptional regulation of Slc2a5 [50], whereas in human Caco-2 cells, fructose increases Slc2a5 mRNA stability mediated by the cAMP pathway [51]. On the other hand, the use of inhibitors or activators of the phosphatidylinositol 3-kinase (PI3K) and/or protein kinase B (PKB) have demonstrated that this signaling pathway mediates the fructose-induced increase in fructose transport without affecting transcriptional regulation of GLUT5 [52]. How does the PI3K/AKT signaling pathway mediate the effects of fructose on GLUT5 upregulation? It is known that Class II PI3Ks control the endocytic trafficking of transporters through the production of phosphatidylinositol 3-phosphate (PtdIns3P). This second messenger is required for Rab11 activation, a small GTPase of the Rab family that coordinates endosome recycling to the plasma membrane [53]. Enterocyte-specific Rab11aΔIEC ablation (Rab11a-KO mouse model) blunted fructose-induced upregulation of GLUT5 in the small intestine, most likely by impairing endosomal trafficking of the fructose transporter towards the apical membrane of the enterocyte [47]. The expression of GLUT5 in the intestine can also be regulated by the carbohydrate response element-binding protein (ChREBP), a liver glucose-responsive basic helix-loop-helix-leucine zipper transcriptional factor [54]. High fructose diet feeding increases intestinal ChREBP protein levels, accompanied by increased fructose transport (GLUT5), fructolytic (fructokinase, ALDOB, TKFC, and lactate dehydrogenase) and gluconeogenic (glucose-6-phosphatae and fructose-1,6-bisphosphatase) gene expression in mice [55]. Conversely, genetic ablation of ChREBP (ChREBP-KO mice) leads to fructose intolerance due to insufficient induction of these genes involved in fructose transport and metabolism [55,56,57,58,59]. The molecular mechanism by which fructose mediates ChREBP-induction of Slc2a5 gene expression involves direct interaction of ChREBP with the promoter of Slc2a5 [55] in mice, whereas ectopic co-expression of ChREBP and its heterodimer partner Max-like protein X (MLX) binds to carbohydrate response elements (ChoREs) and activates Slc2a5 promoter in Caco-2BBE human cells [55]. Further work is required to confirm whether, similarly to glucose, fructose might regulate ChREBP activity by posttranslational modifications such as O-glycosylation, phosphorylation and conformational changes in intestinal cells [57]. Another identified regulatory protein of intestinal fructose transport is the thioredoxin-interacting protein (TXNIP, Txnip), an arrestin-like protein that can bind to thioredoxin protein that regulates cellular metabolism and redox state [60,61]. In response to glucose, the transcriptional complex ChREBP/MLX and MondoA/MLX binds to the ChoRE on the Txnip promoter to induce mRNA expression [62,63]. Glucose-induced TXNIP inhibits glucose transport through interaction with GLUT1 and inducing its internalization through clathrin-coated pits, as well as reducing the expression of GLUT1, whereas energy stress leads to TXNIP degradation through phosphorylation by AMP-dependent protein kinase (AMPK), resulting in increased GLUT1 function and mRNA expression [61,64]. Dotimas et al. demonstrated that TXNIP regulates fructose absorption in the small intestine [65]. Although the precise mechanisms remains elusive, TXNIP is upregulated in response to fructose consumption and co-immunoprecipitates with GLUT2 and GLUT5. It may be possible that the link between fructose transport and TXNIP may be mediated by phosphorylation of the protein mediated by AMPK, similar to what we described above for GLUT1 [65]. The expression of GLUT5 and its activity is also regulated by early development in the intestine of mammalians (i.e., rat, rabbit, and humans). In rats, under normal conditions (suckling and weaning), intestinal fructose transport and GLUT5 mRNA levels are very low due to the fact that maternal milk is fructose-free, unless there is a precocious exposure to luminal intestine fructose signal, which in turn stimulates GLUT5 expression and activity [17]. The mechanism by which fructose increases GLUT5 expression and activity during weaning is complex and involves systemic levels of glucocorticoids, but not thyroxine [17,66,67,68]. In addition, the diurnal rhythm regulates GLUT5 mRNA and protein expression in adult rats, but this regulation is not present in neonates [69]. Independently of fructose uptake, 3–4 h before the onset of peak feeding, GLUT5 levels increase by four-fold. This diurnal rhythm is also accompanied by upregulation of GLUT2 [8]. 2.4. Fructose Metabolism in Human Diseases Major pathways of fructose metabolism are conversion to glucose and lipids [16]. Therefore, excessive fructose intake would result in increased portal fructose concentrations that stimulates endogenous glucose production and lipid synthesis in the liver, which is associated with metabolic syndrome (MetS) [70,71,72], non-alcoholic fatty liver disease (NAFLD) [73,74,75,76,77,78,79,80,81], obesity, and type 2 diabetes mellitus (T2DM) [9,10,11,82,83,84,85,86,87,88]. Although there is mounting epidemiological and experimental evidence linking fructose consumption to metabolic diseases, the relative contribution of fructose to these human diseases remains controversial [87,89,90,91]. 2.5. Revisiting the Role of Liver and Small Intestine in Fructose Clearance Traditionally, the liver has been considered as the main organ that metabolizes fructose before entering systemic circulation [16]. This assumption is based on the following evidences: (1) Intestinal absorption of fructose is primarily driven to the liver through portal circulation; (2) peripheral tissues, such as skeletal muscle, have low capacity for fructose metabolism; and (3) the ketohexokinase isoform KHK-C is expressed at the highest level in the liver relative to extrahepatic tissues, leading to a high capacity for fructose phosphorylation and extraction from the blood. In this way, the liver would prevent high fructose doses to spill over peripheral tissues [16]. The current notion that the liver is the main site of dietary fructose metabolism and clearance has been recently challenged by Jang et al. [92]. They used sophisticated and elegant isotopic tracing techniques and arterio-venous blood sampling to demonstrate that most ingested fructose is metabolized by the small intestine in mice. At low-doses of fructose (<0.5 g kg−1), ~90% of fructose phosphorylation occurs in the jejunum, duodenum, or ileum. Most of this fructose is metabolized in the small intestine, appearing in the portal circulation as glucose and lactate (~60%), and the remaining as fructose (<20%). In contrast, high-doses of fructose (≥1 g kg−1) saturate the absorption and catabolism of fructose in the small intestine, leading to fructose spill-over into the liver (>30%) and the colonic microbiota in mice [92] (Figure 2). This work challenges our current knowledge about the role of the small intestine in dietary fructose metabolism and spurs the notion that the small intestine shields the liver from toxic fructose exposure. However, several questions arise from this work and remain to be fully addressed: (1) A limitation of the study is regarding the dose-response to fructose, which may vary between mice and humans. Humans may saturate the capacity for fructose metabolism in the small intestine at relatively lower doses than mice. It is necessary to understand the associated dose-response pattern in humans. (2) The role of the small intestine in fructose metabolism in mice and humans may have diverged across evolution. In fact, humans have a relative shorter gut and smaller intestinal area than rodents [93]. (3) The long standing view is that the liver and kidneys are the only gluconeogenic organs in humans, but not the small intestine because it does not express glucose-6-phosphatase (G-6-Pase) [16]. This critical issue is important to translate experimental evidences from mice to humans. In this line, one study have shown the expression of G-6-Pase in the small intestine of humans [94], and another one showed some evidence of the existence of a conversion of fructose to glucose in human jejunum [95]. 2.6. Relevance of Endogenous Fructose Production in Human Diseases In addition to exogenous fructose, fructose can be synthesized from glucose through the polyol pathway [16,96], which has drawn attention on the potential role of endogenous fructose production in metabolic diseases. The biosynthetic fructose pathway is constituted by two enzymes; the aldose reductase that converts glucose into sorbitol, and the sorbitol dehydrogenase that converts sorbitol into fructose [16]. Under physiological conditions, this pathway is mostly inactive in the majority of body tissues and organs, which has been associated to lower fasting and postprandial circulating fructose levels [97]. However, this pathway can be activated after ingestion of a drink containing glucose (~30 g) and fructose (~30 g) in healthy individuals. Tracer dilution analysis estimated endogenous fructose production ~ 55 mug kg−1·min−1. This work evidenced, for the first time, the capacity for endogenous fructose production in humans [97]. Further research demonstrated the presence of an active polyol pathway in tissues other than those involved in metabolizing dietary fructose, such as the human brain [98,99,100]. Numerous studies using animal models have linked the polyol pathway to metabolic alterations such as obesity, insulin resistance, diabetes, diabetic nephropathy, chronic kidney disease, acute kidney injury, blood pressure, and MetS [101,102,103,104]. Nonetheless, although the presence of an active polyol pathway has been described in humans, and mounting evidences obtained in animal models of the importance of this pathway in diseases, its significance in human metabolic diseases awaits further confirmation. 2.7. Plant Extracts Inhibitors of Fructose Transporters As described above, multiple studies in humans and animal models have linked fructose consumption with diseases, which has spurred the notion of the potential use of GLUT5 inhibitors for preventing fructose-induced diseases. So far, no potent and specific inhibitors of GLUT5 have been discovered, although phloretin and cytochalasin B are used to inhibit GLUT2 for assessing fructose transport in vitro, whereas GLUT5 is insensitive to both inhibitors [22,25]. In the last decade, plant extracts have been used to screen compounds with inhibitory effects on intestinal GLUT5 transporters. Thus, green tea catechins inhibited D-fructose transport in Xenopus laevis oocytes expressing the mammalian GLUT5. Inhibition of D-fructose transport via GLUT5 was more efficient by catechins containing a gallate group [apparent Ki values between ~113 and ~117 μM for (−)-epigallocatechin-gallate and (−)-epicatechin-gallate, respectively] than by catechins lacking this group [apparent Ki values >500 μM for (−)-epicatechin and (−)-epigallocatechin] [105]. In this line of evidence, it has been shown that chamomile tea and green tea [containing (−)-epigallocatechin gallate (240 mg/g extract), (−)-epigallocatechin (70 mg/g extract), (−)-epicatechin (40 mg/g extract), and (+)-catechin (17 mg/g extract)] effectively inhibited fructose transport through GLUT2 in differentiated Caco-2 cells [106]. In addition, chamomile also inhibits D-fructose transport via GLUT5 in Caco-2 cells and in Xenopus oocytes expressing the mammalian GLUT5 [106]. Likewise, Satsu et al. demonstrated that epicatechin gallate inhibited fructose uptake in Caco-2 cells. Interestingly, this reduction in fructose uptake was not related to changes in the affinity (Km) of GLUT5 for fructose, but with a decrease in the maximal velocity (Vmax) [107]. Furthermore, authors demonstrated that epicatechin gallate suppressed fructose permeation in Caco-2 cells, suggesting that this compound suppressed the transepithelial transport of fructose across epithelial cell monolayers, in addition to its effect on fructose uptake. Lastly, authors reported that similar effects on fructose uptake and permeation were observed with nobiletin, another phytochemical tested in this study [107]. An additional compound extracted from the Chinese blackberry tea (rubusoside) inhibited GLUT5-mediated fructose transport in liposomes reconstituted with human GLUT5 purified from insect cells transduced with baculoviruses [18]. Likewise, astragalin-6-glucoside (a glycosylated derivative of astragalin) inhibited GLUT5-mediated fructose transport in these proteoliposomes [18]. The same group performed a virtual screening (in silico) for potential GLUT5 inhibitors using a 3D inward-facing GLUT5 model against a library of >600,000 chemicals [108]. The ability of the top ranked compounds for inhibiting GLUT5-mediated fructose transport were tested in GLUT5 proteoliposomes, identifying the N-[4-(methylsulfonyl)-2-nitrophenyl]-1,3-benzodioxol-5-amine (MSNBA) as an specific inhibitor, which did not affect the fructose transport of human GLUT2 or the glucose transport of human GLUT1-4 [108]. Additionally, whole-cell systems for high-throughput screening of potential GLUT5 inhibitors and activators have been developed using a yeast strain deficient in fructose uptake [109]. The ability of culinary plant extracts containing phytochemicals to inhibit fructose transport has also been assessed in Caco-2 cells. Lee et al. found that demethoxycurcumin and curcumin from turmeric extracts inhibited fructose transport by GLUT2- and GLUT5-mediated fructose uptake, respectively [110]. Similarly, catechin from guava leaf (Psidium guajava) inhibited GLUT5-mediated fructose uptake, whereas quercetin inhibited both GLUT5- and GLUT2-mediated fructose transport [110]. In addition, the ability of guava leaf and guava fruit extracts to inhibit glucose transport have also been demonstrated by Müller et al. in Caco-2 cells and mice (C57BL/6N) [111]. The effect of these extracts on glucose uptake in Caco-2 cells were related to inhibition of GLUT2, although the effects on fructose uptake were not assessed [111]. More recently, König et al. demonstrated that fruit extracts prepared from guava inhibited intestinal glucose resorption in a clinical trial [112]. The effects of hesperidin, a flavonoid present in orange juice, on fructose uptake in Caco-2 cell monolayers was studied by Kerimi et al. [113]. They showed that hesperidin inhibited fructose uptake in these cells using fructose (130 mM) as the only source of sugars. Of note, the inhibitory effect of hesperidin on fructose uptake was abolished in the presence of other sugars, such as glucose and sucrose, at high concentrations (120 mM and 130 mM, respectively). Using Xenopus laevis oocytes expressing human GLUT2 or GLUT5, they gained insights into the molecular mechanisms by which hesperidin inhibited fructose transport. Thus, hesperidin inhibited the uptake of fructose by GLUT5 expressed in Xenopus oocytes. In addition to its effects on fructose uptake, hesperidin lowered glucose uptake in Caco-2 cells and inhibited GLUT2 and GLUT5 transporters when expressed in Xenopus oocytes. Lastly, in an attempt to reproduce in vivo these previously observed effects of hesperidin, authors conducted three separated human intervention studies on healthy volunteers using orange juice with different amounts of added hesperidin and a control drink containing equivalent amounts of glucose, fructose, and sucrose, and measured the postprandial glycemic response as biomarker for the effect of hesperidin. They observed that the biggest difference in postprandial blood glucose between orange juice and the control drink was when the juice was diluted [113]. The inhibitory effects of other flavonoids, such as apigenin, on fructose uptake have also been investigated by Gauer et al. in Xenopus oocytes. Apigenin, as well as (−)-epigallocatechin gallate, inhibited fructose uptake in oocytes expressing GLUT5 [114]. Finally, acarbose, an α-glucosidase inhibitor that improves insulin sensitivity and decreases postprandial hyperglycemia [115], does not inhibit fructose transport in human Caco-2 cells or in Xenopus oocytes expressing the mammalian GLUT2 and GLUT5 [106]. These results suggest that the effects of acarbose on fructose absorption would be mediated by its well-known effects on attenuating sucrose digestion [116], rather than direct effects on fructose transport across the intestinal epithelium. 3. Intestinal Glucose Transport and Metabolism: Implications for Health and Disease Glucose is the main catabolic and anabolic substrate for the great majority of complex organs that controls energy homeostasis in the body. Glucose homeostasis is the result of three physiological events: Intestinal glucose absorption in the post-prandial state, hepatic glucose production (which accounts for ~90% of endogenous glucose production and is the net balance between gluconeogenesis, glycogenolysis, glycogen synthesis, glycolysis, and other pathways), and extrahepatic glucose usage, mainly by the brain, the skeletal muscle, and the adipose tissue. Glucose controls hormonal secretion in endocrine pancreas (i.e., insulin, glucagon, and somatostatin) [117,118,119] and neuronal signaling involved in glucose homeostasis, feeding regulation, and energy expenditure [120]. 3.1. Intestinal Glucose Transport. Gastric emptying and intestinal glucose absorption determine the glucose appearance rate in the bloodstream after a meal. Intestinal enterocytes are polarized cells responsible for glucose uptake from the intestinal lumen to capillary blood vessels, which is the main mechanism of glucose entrance into the body. Enterocytes express two glucose transporters named sodium-glucose co-transporter 1 (SGLT1; expressed in the brush border membrane) and GLUT2 (localized in the basolateral membrane). SGLT1 couples the transport of one glucose molecule and two sodium ions, which provides the energy to drive glucose accumulation in the enterocyte against its concentration gradient due to the energy stored in the sodium electrochemical potential gradient across the brush border membrane generated by the sodium transport. Sodium is then transported out into the blood vessels by the Na+/K+-ATPase in the basolateral membrane, maintaining the driving force to transport glucose. As a result, glucose accumulates within the enterocyte and diffuse out of the cell through GLUT2 into the blood stream. This process is ATP-dependent [121,122] (Figure 3). Intestinal SGLT1 is a high-affinity (Km ~0.4 mM), low-capacity transporter able to transport glucose or galactose. It is a monomeric integral membrane protein embedded in the lipid bilayer composed by 664 amino acids with 14 transmembrane-spanning regions and it has one glucose binding-site and two sodium binding-sites in the center of the protein. In humans, SGLT1 is encoded by the Slc5a1 gene, and it is highly expressed in the duodenum and skeletal muscle [123,124]. SGLT1 activity varies diurnally to meet fluctuating availability of glucose. The maximal transport capacity occurs when food is anticipated, and it could be regulated by clock genes [125,126]. After energy-dependent glucose uptake via SGLT1, glucose exits the enterocyte passively through GLUT2 located in basolateral membrane. Intestinal GLUT2 is a facilitative glucose uniporter with low glucose affinity (Km ~17 mM), but high transport capacity, located in basolateral membrane of the enterocytes. GLUT2 can also transport galactose, mannose, and fructose (with low affinity), and glucosamine with high affinity (Km ~ 0.8 mM) [127]. 3.2. Regulation of SGLT1 and GLUT2 SGLT1 expression in the intestinal lumen is regulated by dietary carbohydrate content. Thus, luminal glucose, but not intravenous administration of glucose, increases intestinal SGLT1 expression. High-diet glucose feeding increases SGLT1 expression and activity (rat, mouse, and sheep), which is accompanied by increased glucose transport. Similarly, obese mice exhibit increased intestinal glucose transport mediated by augmented SGLT1 transporters, without increased activity [128,129,130,131,132]. In addition to glucose-mediated regulation of SGLT1, phosphorylation by protein kinase A (PKA) and protein kinase C (PKC) regulates its activity. In humans, SGLT1 contains one consensus site for regulation by PKA and five consensus sites for PKC. The number of consensus sites and conserved sequences varies between species (rat, rabbit, and humans) [133,134]. In Chinese hamster ovary (CHO) cells overexpressing human SGLT1, activation of PKA increased the amount of SGLT1 in the membrane [135]. In contrast, stimulation of human embryonic kidney cells expressing human SGLT1 with 8-Br-cAMP (a brominated derivative of cyclic adenosine monophosphate that activates cAMP-dependent protein kinase) significantly reduced glucose transport [136]. On the other hand, PKC activation in the absence of RS1 increases transport capacity of human SGLT1, while in the presence of RS1, glucose transport is decreased [137]. The adipocyte-derived hormone leptin also regulates SGLT1. Although leptin is not required for intestinal SGLT1 expression, hyperleptinemia or leptin administration drastically reduce intestinal SGLT1 expression. The intracellular signaling pathways by which leptin regulates intestinal SGLT1 remain incompletely understood, but may include PKA, PKC, and the leptin receptor isoform b [138,139]. Finally, as in the case of GLUT5, green tea catechins markedly inhibit SGLT1-mediated glucose transport in the small intestine, being more pronounced by catechins containing a gallate group [(−)-epigallocatechin-gallate and (−)-epicatechin-gallate] than by catechins lacking this group [140]. The classical view of intestinal glucose absorption is underlined by the evidence that SGLT1 is in the apical membrane of enterocytes, while GLUT2 is located exclusively in the basolateral membrane, leading to the transepithelial glucose transport from the lumen into the portal circulation. This classical theory explains glucose absorption at low luminal glucose concentrations (≤10 mM) but it fails to explain the marked increase at glucose concentrations that surpass SGLT1 (≥25 mM) transport capacity. GLUT2 levels are also regulated by glucose concentrations in enterocytes. As part of an adaptive physiological mechanism in response to increased luminal glucose concentrations, caloric demand, and glucagon-like peptide 2 (GLP-2); GLUT2 is rapidly and transiently recruited to the brush border membrane of the enterocyte, leading to a three-fold enhancement of glucose transport [141,142] (Figure 3). This adaptive mechanism that is known as the “GLUT2 translocation” theory, which in addition to other theories, such as the “solvent drag” theory, have been proposed to explain the marked increase in glucose absorption in response to high luminal glucose concentrations [143]. Conversely, it has also been demonstrated that in addition to high luminal glucose concentrations, insulin decreases GLUT2 membrane levels as a result of the internalization of GLUT2 from plasma membranes back into intracellular pools, leading to the inhibition of glucose transport [144]. The regulation of intestinal glucose absorption by insulin is probably another physiological mechanism at the enterocyte level by which the hormone limits sugar excursions in the blood circulation during a sugar-rich meal. This evidence raised the idea that insulin resistance may provoke a loss of insulin-mediated control of GLUT2 membrane trafficking, leading to unleash intestinal glucose absorption upon high-sugar diets consumption. Tobin et al. demonstrated that insulin resistance in mice provoked a loss of GLUT2 trafficking control, where GLUT2 levels remain permanently elevated in the brush border membrane and low in the basolateral membrane of the enterocyte [144]. Ait-Omar et al. investigated the relevance of these previously described mechanisms in the small intestine of morbidly obese insulin resistant humans and lean control subjects. They found that GLUT2 was accumulated in apical and/or endosomal membranes of enterocytes in obese subjects. Interpretation of these findings is complex, but authors proposed that permanent apical GLUT2 localization in obese subjects would mediate blood-to-lumen glucose flux during fasting hyperglycemia, leading to glucose secretion into the intestinal lumen. In contrast, after consumption of a sugar-rich meal, permanent apical GLUT2 localization would provide a large glucose uptake from the intestinal lumen to the portal circulation [145]. 3.3.1. Relevance of Glycemic Index and Glycemic Load for T2DM The glycemic response (GR) is the appearance of glucose in blood after a meal. It depends on the amount of glucose absorbed, the rate of glucose entry into circulation, the rate of disappearance due to tissue uptake from circulation, and the regulation of hepatic glucose production [146]. Blood glucose concentrations will rise and fall rapidly or slowly depending on the carbohydrate content of food. The glycemic index (GI) is a tool developed to compare the postprandial responses to constants amounts of different carbohydrate-containing food. It is a useful tool for people with diabetes, providing information on the GR that might be expected when a person consumes the quantity of a food containing a fixed amount of carbohydrates [147]. The glycemic load (GL) concept was introduced as a mean of predicting the GR, considering the GI and the amount of available carbohydrate in a portion of the food eaten [148]. Thus, foods have been classified by GI into low (GI ≤ 55), medium (GI 56–69), and high (GI ≥ 70) categories, and classified by GL as being low (GL ≤ 10), medium (GL 11–19), and high (GL ≥ 20). Since these concepts were introduced, numerous studies have been performed to ascertain how GI and GL relate to health and disease. Of note, the American Diabetes Association (ADA) indicated that current knowledge is insufficient to relate low–GL diet with a reduction on diabetes risk, and that it has not been demonstrated that one method of assessing the relationship between carbohydrate intake and blood glucose response is better than other methods [149]. To shed light into this issue, Livesey et al. performed a review meta-analysis of prospective cohort studies for a comprehensive examination of evidence on the dose-response that links GL to T2DM. The analysis concluded that a GL over a dose range of 100 g/2000 kcal, increases the risk of T2DM by 45%, supporting the notion that GL is an important and underestimated dietary characteristic that contributes to the incidence of T2DM [150]. Greenwood et al., in a systematic review and dose-response meta-analysis of prospective studies, showed that there is a protective effect of low dietary GI and GL and risk of T2DM [151]. In addition, two previous systematic reviews concluded that there is evidence of a positive association between both dietary GI and GL and risk of T2DM [152,153]. In summary, despite the fact that epidemiological studies of GI and GL in relation to diabetes risk have yielded inconsistent results, there is important research in support of significantly positive associations between dietary GI and GL and the risk of T2DM, thus reducing the intake of high-GI foods may bring benefits in diabetes prevention. 3.3.2. Regulation of SGLT1 in Diabetes Mellitus Several studies in rodent models of T2DM and type 1 diabetes mellitus (T1DM) have shown a link between intestinal SGLT1 expression and diabetes. Streptozotozin (STZ)-induced diabetes in mice and rats (STZ; a toxic drug that produces a destruction of pancreatic β-cells causing insulin deficiency and hyperglycemia [154]) produces increased SGLT1 intestinal expression [155]. Likewise, a rat model of T2DM (Otsuka Log-Evans Tokushima Fatty rats) exhibited increased intestinal mRNA expression of SGLT1 associated with impaired glucose tolerance and occurred before the onset of insulin resistance and hyperinsulinemia [156]. Similar results were confirmed in patients with noninsulin-dependent diabetes mellitus where mRNA and protein levels were increased three- to four-fold in brush border membranes of enterocytes in the small intestine [157]. Finally, in morbid obese non-diabetic patients, increased SGLT1 expression in the intestine was found and it correlated with accelerated intestinal absorption [158]. Taken together, these findings are consistent with the concept that SGLT1-mediated glucose absorption in the intestine underlies the rapid post-prandial rise in blood glucose levels observed in obesity and T2DM. This knowledge has prompted the concept that pharmacological inhibition of SGLT1 in the small intestine can lower hyperglycemia by inhibiting glucose absorption and increasing GLP-1. The pharmacological tools that have been used to determine the potential of SGLT1 inhibition include phlorizin (or phloridzin), canagliflozin, LX4211 (or sotagliflozin), LP-925219, KGA-2727, and GSK-1614235 [159]. The use of these inhibitors in rodent models of T2DM and in humans has lent support to this pharmacological approach in the treatment of T2DM, but more studies are needed on long-term safety of SGLT1 inhibition. 4. Peripheral and Central Effects of Dietary Sugars in the Gut–Brain Axis in Health and Disease 4.1. The Gut–Brain Axis In the early 20th century, Ivan Pavlov discovered the existence of a close interaction between the gut and the brain. Pavlov observed in dogs how a stimulus associated with feeding induced vagal-dependent gastric acid secretion [160]. Since then, this interaction has been widely described and is enclosed in the term of “gut–brain axis”, a complex bidirectional communication system that maintains constant crosstalk between the gastrointestinal system and the enteric and central nervous system. This intimate connection involves numerous endocrine, immune, and neuronal pathways [161]. Through this complex system, the gut can send modulating signals to the brain via visceral messages that influence emotional and cognitive brain centers producing different psychobehavioural responses [161]. In the other direction, the brain is able to send orders for proper maintenance of gastrointestinal homeostasis (such as by modulating intestinal motility and mucin production) and can also modulate the immune system (such as by modulating cytokine production by mucosal cells) [161]. The gut–brain axis uses mostly four major information carriers to communicate with each other: Neural messages via vagal and spinal afferent neurons, immune mediators carried by cytokines, endocrine signals carried by gut hormones, and microbiota-related factors that reach the brain directly from the blood stream [162,163]. The integration of all these signals allows the maintenance of a large number of vital functions such as the control of food intake and satiety, the repulsion of harmful foods, and the adaptation of our gastrointestinal system to the environment, giving rise in pathological conditions to the sensation of nausea, pain, or even may result in gastrointestinal dysfunction [164,165]. 4.2. Regulation of the Gut–Brain Axis by Enteroendocrine Cells and Sensing of Intestinal Sugars Enteroendocrine cells (EECs) form the largest endocrine organ in the body and play a key role in regulating nutrients intake and postprandial metabolism. Following a meal, EECs in the small intestine sense luminal and circulating levels of nutrients, and simultaneously are stimulated by prevailing nutrients through multiple nutrient transporters and G protein-coupled receptors (GPCRs), leading to activation of intracellular signaling pathways that produce secretion of peptides and hormones. These hormones enter circulation and act on multiple distant tissues such as the brain, gallbladder, and pancreas, as well as, on neighboring enteric neurons, endothelial cells, and the gastrointestinal epithelium. Thus, the physiological role of the enteroendocrine system in response to ingested glucose and fructose is to detect nutrients in the intestinal lumen, to monitor energy status of the body, and to elaborate an appropriate response, through the production of more than 30 different hormones and neurotransmitters to control postprandial whole-body metabolic homeostasis [166]. EECs are endoderm-derived epithelial cells widely distributed in the villi and crypts, where they are interspersed between non endocrine cells [166]. The intestinal epithelium is in a constant turnover that is replenished from pluripotent stem cells at the base of intestinal crypts and their progenies migrating up the crypt–villus axis [167]. The spatial distribution and differentiation of EECs is regulated by an interplay of the surface protein Notch and three basic helix-loop-helix transcriptional factors (Math1, Neurogenin 3, and NeuroD), among other factors [167,168]. EECs are classified depending on their morphology and position in the gastrointestinal mucosa into “open-type” with a bottle neck shape and an apical prolongation with microvilli facing towards the intestinal lumen or “closed type” that are located close to the basal membrane, do not reach the lumen of the gut, and lack microvilli [169,170,171]. The open-type cells are activated by luminal content through the microvilli, whereas the close type cells are activated by luminal content indirectly through neuronal or humoral pathways. In both cases, hormones and peptides accumulate into cytoplasmatic secretory granules that are released by exocytosis at the basolateral membrane upon chemical, mechanical, or neural stimulation [170,172]. 4.2.1. Fructose-Induced Hormonal Secretion in Intestinal Cells Using specialized organoid cultures enriched in a single intestinal cell type, primarily enterocytes, Paneth or goblet, but not intestinal stem cells, Kishida et al. demonstrated that fructose can be sensed by absorptive enterocytes and secretory goblet and Paneth cells, but not stem cells [173]. In response to fructose there was an increased expression of fructolytic genes without affecting non-fructolytic gene expression. Sensing was independent of Notch, Wnt, and glucose concentrations in the culture medium, but required fructose uptake and metabolism. Stronger responses were found in more mature enterocyte- and goblet-enriched organoids. Of note, the response to fructose in enterocyte organoids was retained upon forced dedifferentiation to reacquire stem cells characteristics [173]. Fructose increases secretion of human peptide tyrosine tyrosine (PYY), cholecystokinin (CCK), neurotensin, and serotonin (5-HT) in EECs subtypes L, I, N, and enterochromaffin cells (EC), respectively [174,175]. Likewise, fructose stimulates secretion of glucagon-like peptide 1 (GLP-1) from L-subtype EECs in humans, rats and mice, but not glucose-dependent insulinotropic polypeptide (GIP; glucose-dependent insulinotropic polypeptide or gastric inhibitory peptide) [174,176]. On the other hand, fructose induced the secretion of GIP from K-subtype EECs in mice [176] but is unaffected or reduced in rats and humans [174,177]. 4.2.2. Glucose-Induced Hormonal Secretion in Intestinal Cells Oral glucose, but not intravenous glucose, leads to a greater stimulation of insulin secretion and modulation of glucagon secretion in the pancreas. This physiological response to glucose is called the incretin effect, which is due to the release of incretin hormones (GIP and GLP-1) from specialized EECs [178,179]. Of the three signals originating in the gut (glucose, incretins, and neutral signals transmitted by the autonomic nervous system) that regulate pancreatic insulin secretion, the incretin effect makes a substantial contribution to maintenance of glucose homeostasis [178,179]. GIP is secreted in response to glucose by K-cells located in the duodenum and upper jejunum. GIP is synthesized as a precursor pro-peptide (pro-GIP), which is cleaved to GIP by posttranslational processing. GLP-1 and GLP-2 are secreted in response to glucose by L-cells in the small and large intestine, with a gradient from low density in the duodenum to high density in the ileum, but also in the colon and rectum [180,181]. The proglucagon gene is cleaved to GLP-1 and GLP-2 by posttranslational processing. The biological active forms of GLP-1 are GLP-1 [7-36 amide] (amidated GLP-1), and GLP-1 [7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37] (glycine-extended GLP-1), which are “truncated” forms in comparison to the originally proposed sequences GLP-1 [1-36 amide] by the N-terminal six amino acids [182,183,184]. In pancreatic α-cells, the same proglucagon gene is processed in a different manner, yielding glucagon and a “major proglucagon fragment that is not further processed to GLP-1 and GLP-2 [185]. Finally, in addition to its role in the regulation of pancreatic insulin secretion, GLP-1 and GLP-2 promotes nutrient absorption [180]. 5-HT is secreted in response to glucose by EC cells located throughout the gastrointestinal tract, and regulates intestinal motility and brain control of appetite [186,187]. 5-HT and GLP-1 activate 5-HT3 and GLP-1 receptors in the intestinal vagus nerve, respectively, leading to vagal reflexes, which in turn slow the subsequent emptying of carbohydrates from the stomach and induce satiation [188,189]. 4.2.3. Intestinal Sweet Sensing and Glycemic Control The gastrointestinal tract is a major determinant of metabolic homeostasis. Sensing of nutrients, and particularly glucose, in the EECs provides feedback signals from the intestine to slow the rate of gastric emptying, limit postprandial glycemic excursions, and induce satiation. Intestinal sweet sensing is regulated by the sweet taste receptor (STR), which has been described on K-cells, L-cells, and EECs in humans. Additionally, STRs have been described in metabolic tissues that sense and respond to carbohydrates, such as hypothalamic neurons, hepatocytes, adipocytes, and β-cells (for review see refs. [190,191,192,193]). STR senses hexose sugars, D-amino acids, sweet proteins, and low-calorie sweeteners. The receptor is comprised of a heterodimer of class C, G-protein coupled receptors T1R2 and T1R3. The mechanisms of sweet taste transduction have been mostly studied in lingual sweet taste cells (this topic is out of the scope of this manuscript, for review see refs. [194,195,196,197]). Briefly, the interaction of sweet tastings with STR initiates the dissociation of the gustducin (the G-protein) into Gα and Gβγ subunits and activation of phospholipase C. Then, intracellular Ca2+ is released from inositol 1,4,5-triphosphate (IP3)-sensitive stores, leading to opening of the melastatine type-5 transient receptor potential cation (TRPM5) channel allowing sodium influx. Increases in intracellular Na+ and Ca2+ levels lead to depolarization of the basolateral membrane, which via 5-HT and ATP-dependent pathways activate intermediary taste cells and nerves involved in lingual sweet taste that convey information centrally to the cortex. Numerous studies in rodents and human cells support the notion that intestinal STR is a glucose sensor on the gut luminal membrane responsible for the regulation of SGLT1 expression and GLP-1 secretion. First, it was demonstrated that T1R2, T1R3, and the α-subunit of gustducin were co-expressed in K- and L-endocrine cells in rodents and humans [198], and to a lesser extend in EC cells containing serotonin in pig intestine [199]. Second, Parker et al. proposed that secretion of GLP-1 by L-cells and GIP by K-cells was through uptake of glucose by SGLT1, suggesting that SGLT1 was likely the mediator of the direct responsiveness of K- and L-cells to luminal glucose [200]. Third, genetic deletion of T1R3 or gustducin in mice abolished the ability of mouse intestine to upregulate SGLT1 expression in response to increased dietary carbohydrate, providing convincing evidence for the involvement of the STR in intestinal sweet transduction [198]. Fourth, genetic deletion of T1R3 and gustducin exhibited deficiencies in secretion of GLP-1 [201]. Fifth, luminal glucose above a threshold results in secretion of GPL-1, GLP-2, and GIP through a signaling pathway involving STR in enteroendocrine cells [198]. These evidences beg an important question: How does glucose activation of STR in EECs cause increased expression of SGLT1 in enterocytes? The communication between EECs and neighboring enterocytes likely resides in the involvement of intermediaries such as GLP-1 and/or GLP-2 and enteric neurons. Thus, GLP-2 receptors are present on enteric neurons [202,203], while enterocytes respond to GLP-2 in an enteric neuron-dependent manner [203]. In addition, GLP-2 upregulates SGLT1 expression [204,205,206], and STR-dependent release of GLP-1 and GLP-2 is detected at higher concentrations in the portal and lymphatic circulation in rodents [204,207]. All these evidences have led to a model of intestinal dietary glucose sensing. Luminal glucose is sensed by STR expressed on the luminal membrane of enteroendocrine cells. Above a threshold level of luminal glucose, the hexose binds to and activates STR, initiated by dissociation of gustducin into Gα and Gβγ subunits, which leads to activation of phospholipase C β2. Then, IP3-sensitive stores release intracellular Ca2+ that opens the TRPM5 channel increasing sodium influx. Intracellular elevation of Ca2+ and Na+ depolarizes the basolateral membrane resulting in the release of GLP-2. GLP-2 binds to its receptor on enteric neurons evoking an action potential that triggers the release of an unknown neuropeptide to the vicinity of neighboring enterocytes. The neuropeptide binds to its receptor located on basolateral membranes of enterocytes leading to a rise in intracellular levels of cAMP, which increases stabilization of the 3′end of Slc5a1 mRNA and ultimately augmented SGLT1 translation and insertion into the apical brush border membrane of the enterocyte (Figure 4). 4.3. Central Effects of Glucose and Fructose Consumption Sugar overconsumption has been associated with detrimental metabolic effects, such as obesity, dyslipidemia, MetS, and impaired insulin sensitivity [71,208,209]. Therefore, it is necessary to understand the specific molecular mechanisms by which dietary sugars cause an addictive eating behavior and how sugar intake affects the gut–brain axis. Herein, we will review the effects of the two main dietary monosaccharides: Glucose and fructose, the latter of which is usually consumed in the form of sucrose disaccharide (50% glucose, 50% fructose) or in the form of high-fructose corn syrup (HFCS) (range 47%–65% fructose, and 53%–35% glucose) [210], the major component of sweetened soft drinks. Appetite control is a complex crosstalk between the periphery and the central nervous system that involves a large number of peptides and hormones [211]. Disturbances in food intake control will ultimately be responsible for large changes in energy balance and different metabolic effects. The appetite regulatory hormones are secreted from peripheral tissues such as the pancreas (e.g., insulin), adipose tissue (e.g., leptin), or the gastrointestinal tract [e.g., ghrelin, CCK, PYY, GLP-1 and GIP], and bind to receptors located in the arcuate nucleus of the hypothalamus, where they inhibit or stimulate appetite or satiety [212]. Many studies have demonstrated that circulating levels of satiety hormones are regulated by the type of sugar consumed. In response to glucose stimuli, a cascade of hormonal secretion is triggered. Thus, glucose produces a repression of the hunger hormone ghrelin (secreted by the stomach), whereas there is a stimulation of the secretion of satiety hormones such as leptin, insulin, GIP, GLP-1, and PYY. However, fructose produces lower repression of ghrelin and a decreased stimulation of satiety hormones (leptin, insulin, GIP, GLP-1, and PYY) than glucose [13,83,212,213,214,215,216,217]. These effects may be related to different explanations such as the lower ratio of intestinal fructose uptake, the lower intestinal levels of GLUT5 compared to the high levels of GLUT2, and also due to the low expression of GLUT5 in pancreatic β-cells leading to decreased insulin release [218,219,220]. Some of these hormones regulated differentially by fructose or glucose convey signals to brain structures. Specifically, there are two neuronal types in the arcuate nucleus that integrate signals from the periphery, acting as metabolic sensors: Neurons co-expressing agouti-related peptide (AgRP) and neuropeptide Y (NPY), whose activation triggers orexigenic effects; and neurons expressing pro-opiomelanocortin (POMC), whose activation triggers anorexigenic effects [221,222,223]. These different types of neurons are sensitive to changes in hormone levels promoting or suppressing food intake. Therefore, the differential effect of dietary sugars on hormonal levels affects neuronal stimulation causing both short-term and long-term central effects in the regulation of food intake and energy homeostasis [224,225]. The low stimulatory capacity of fructose on satiety hormones such as leptin and insulin will lead to low stimulation of POMC neurons and the maintenance of the signal on NPY/AgRP neurons, thus promoting less satiety than glucose, and therefore increased food intake. In the same way, the hypothalamic AMPK functions as a ‘fuel gauge’ to monitor cellular energy status, and its inhibition promotes anorexigenic effect [226]. AMPK activity is inhibited by leptin and insulin. Intracerebroventricular glucose administration in rodents inhibits hypothalamic AMPK activity and suppresses food intake, whereas fructose activates it, thus promoting an orexigenic effect [227,228,229,230] (Figure 5). With the use of new technological advances, it is possible to evaluate the brain activity produced by the intake of different nutrients. In humans, differences in cerebral blood flow have been reported between subjects undergoing glucose and fructose infusions [231], and compared to glucose, fructose causes poor satiety stimulation in specific appetite-regulating regions (e.g., hypothalamus) [13]. It has also been observed that fructose ingestion compared to glucose resulted in a significantly greater incentive value of food cues [232]. These findings suggest that fructose promotes effects on brain activity that affect appetite, probably promoting less satiety than other sugars in humans. In addition to the above-mentioned findings, it has been described that high-fructose intake may affect central appetite regulation by altering specific components of the endocannabinoid system in rats. Fructose consumption has been reported to significantly increase the mRNA expression of the cannabinoid 1 receptor (CB1) [233], and induces an increase in fatty acid amide hydrolase (FAAH) and diacylglycerol lipase (DAG) 1β, but a decrease in DAG1α mRNA [234]. These changes in the endocannabinoid system suggest that fructose consumption may lead to increased hedonic reward for food, thus leading to disturbances in the eating behavior pattern. The consumption of dietary sugars has not only been related to central effects that control appetite and satiety, but also to disturbances in cognitive functions. In rodents, studies have shown that fructose consumption reduced phosphorylation levels of the insulin receptor, leading to impaired brain insulin signaling [235,236], a harmful feature associated with cognitive impairment [237]. Moreover, diminished phosphorylation of cAMP-response element binding and synapsin I, and reduced synaptophysin levels have been observed after fructose intake [236]. Together, these findings indicate that excessive fructose consumption could lead not only to detrimental effects in eating behavior, but also can trigger impaired cognitive function. Further work is required to investigate these evidences. The Fructose Hypothesis In view of the association between fructose consumption in Western diets and MetS, fructose has been suggested as one of etiological factor of MetS. The “fructose hypothesis” proposed that a high amount of fructose consumption is a leading risk factor for the development and progression of MetS, covering obesity, insulin resistance, dyslipidemia, fatty liver, and cardiovascular disease [238,239,240]. Fructose may cause insulin resistance by accumulation of triglycerides in the liver. There are two metabolic pathways to increased hepatic lipid content, i.e., lipogenesis and/or reduced mitochondrial fatty acid oxidation. Hepatic fructolysis leads to increased gluconeogenic sources resulting in elevated rates of lipogenesis [16,45,46]. Hepatic accumulation of toxic intermediary lipid metabolites, such as diacylglycerol (DAG) results in PKCε activation that impairs hepatic insulin signaling through phosphorylation of serine residues on the insulin receptor substrate 1 and 2 (IRS1/2). When hepatic insulin signaling is impaired, gluconeogenesis and glycogenolysis are unleashed, contributing to hyperglycemia and hyperinsulinemia. Under these circumstances, hepatic lipid synthesis is enhanced due to hyperinsulinemia [241,242]. Likewise, reduced fatty acid oxidation leads to hepatic triglycerides accumulation. Of note, Ohashi et al. demonstrated that excessive amounts of fructose consumption lead to epigenetic modifications, such as DNA hypermethylation of promoter regions of peroxisome proliferator-activated receptor alpha (PPARα) and carnitine palmitoyl transferase 1A (CPT1A) that results in lower amounts of mRNA levels [243]. Hepatic triglyceride accumulation results in augmented secretion of very low-density lipoprotein (VLDL) leading to increased lipid uptake in skeletal muscle and peripheral tissues. Similarly to what happens in the liver, intramyocellular lipid accumulation (particularly DAG) activates the PKCθ isoform that phosphorylates and inactivates IRS1 resulting in impaired insulin-stimulated glucose uptake, contributing to hyperglycemia, increased delivery of glucose to the liver, and hyperinsulinemia [241,242]. Fructose-induced hyperuricemia has also been proposed as a causal agent in the etiology of insulin resistance [244,245,246]. This notion arises from the observation that lowering uric acid levels prevents the development of MetS induced by fructose [244,245,246], defective endothelial NO production in mice leads to development of MetS [247], and that uric acid inhibits endothelial NO in in vitro and in vivo [248]. Two mechanisms have been proposed: The first mechanism proposed is that uric acid inhibits endothelial nitric oxide (NO) release, and NO increases blood flow ensuing enhanced insulin delivery and glucose disposal in skeletal muscle and peripheral tissues [249]. The second mechanism states that uric acid promotes inflammation and oxidative stress within the adipocyte [250,251,252]. In addition, uric acid-mediated insulin resistance in the adipose tissue, via the classical mechanisms (i.e., low-grade chronic inflammation mediated by proinflammatory cytokines secreted by the adipocytes, increased lipolysis, and reduced lipogenesis), may result in MetS [241,242]. Additionally, persistent high fructose consumption leads to higher levels of leptin and leptin resistance, which in turn increases food and energy intake [253]. Potential molecular mechanisms underlying leptin resistance may be related to impaired leptin transport across the blood-brain barrier and/or reduced basal levels of phosphorylated signal transducer and activator of transcription 3 (STAT3; a downstream component of the leptin receptor signaling cascade), despite equivalent expression of leptin receptors, in the hypothalamus [253]. However, the fructose hypothesis is not universally accepted. It has been argued that fructose is rarely consumed in its pure form and many published studies have used fructose levels that far exceed dietary composition [254]. Likewise, many animal studies have used extremely high-fructose doses or unusual glucose to fructose ratio that are not representative of actual human diets, which makes it difficult to extrapolate this phenomenon to humans. Therefore, caution in interpreting studies of the effects of fructose on health should be taken into consideration [254]. Another proposed argument to refute the fructose hypothesis is that the causative role of fructose in increasing the risk for the development and progression of MetS is not fully demonstrated. Carefully designed studies should be performed to tease apart the contribution of each risk factor associated to MetS (e.g., obesity, diabetes, or insulin resistance) from fructose, per se [254]. Gut Microbiota, Lipid Metabolism, and Liver Disease The gut microbiota is a complex and dynamic population of microorganisms that, in addition to acting as an immune barrier and protecting against pathogens, plays a crucial role as a metabolic organ itself modulating intestinal permeability, and therefore the nutrient availability [255]. It is generally known that diet exerts a large effect on the gut microbiota, which may affect intestinal permeability and ultimately cause a great metabolic impact [255,256,257,258]. High-fructose or high-glucose diets have been described as an intestinal microbiota modulator that increases inflammation, gut permeability, and metabolic endotoxemia, causing metabolic disturbances such as hepatic lipid accumulation, liver damage, and insulin resistance [258,259]. Likewise, sugar overconsumption also affects lipid metabolism. In obese and overweight subjects, the consumption of glucose-sweetened beverages leads to a lower increase in plasma triglycerides, de novo lipogenesis, and visceral adipose tissue compared to those that consumed fructose-sweetened beverage [71]. However, in rodents, both high-glucose and -fructose diets stimulated similar hepatic lipogenic gene expression [260]. Liver is the principal metabolic organ within the human body and has a major role in regulating carbohydrate metabolism [261]. Many studies point out to the direct implication of high-sugar diets in the development of serious liver diseases, such as NAFLD, hepatic steatosis, liver fibrosis, and dysfunction [262,263,264]. Multiple studies showed that fructose more potently stimulates hepatic de novo lipogenesis than glucose [78,265,266], and the effect is much higher when both monosaccharides were consumed simultaneously [265]. These differences in de novo lipogenesis between both sugars can be explained by differences in their hepatic metabolism. Fructose is directly phosphorylated by fructokinase, bypassing the enzyme phosphofructokinase, a major rate-limiting step in glucose metabolism, providing a larger available substrate for de novo lipogenesis than glucose [261,267]. Regarding the effect of isocaloric diets with different sugar composition, various studies have observed no differences in liver fat content between high-fructose or high-glucose diets [268,269], nor between isocaloric diets with high-fructose corn syrup or sucrose [270]. However, when comparing different doses of fructose in the diet, liver fat content was increased in high-fructose diet, probably associated with increased de novo lipogenesis and reduced whole-body fatty acid oxidation [266,270]. In the same direction as the previous findings, when comparing hypercaloric diets enriched in either fructose or glucose, no significant changes are observed between both diets, suggesting that high-glucose and high-fructose diets provide the same risk for the development of NAFLD [269,271,272]. Taken together, these data would point to the detrimental effect of fructose compared to glucose in terms of hepatic and lipid metabolism. However, there is controversy between different studies, probably due to differences in the doses of sugars administered and their form of administration (oral, intraperitoneal injection, etc.). Many of the above-mentioned studies were performed using supra-doses of fructose in rodents. Since humans typically do not consume fructose as a single sugar, and it is frequently consumed in the form of HFCS, the direct relationship with the real effect of fructose human consumption is not entirely clear. Therefore, more detailed studies on the pattern of sugar consumption in humans should be carried out. 4.5. Impact of Excessive Dietary Sugars Consumption on Incretin Secretion There are many associations reported between high-sugar consumption and the development of pathologies such as diabetes, obesity, and MetS [10,273,274,275]. These associations are mainly due to the current consumption of sugar-sweetened beverages, whose main sweetener is the HFCS. HFCS represents >40% of caloric sweeteners and its consumption has been increased by >1000% between 1970 and 1990. This sugar overconsumption can lead to important changes in the secretion of gut hormones, and therefore, lead to central effects that affect appetite and satiety control. Many authors have focused their studies on the effect that different GIs and GLs have on incretin secretion. Runchey et al. observed that 28-days consumption of a high-GL diet in weight-maintained healthy individuals led to statistically significant increased post-prandial GIP and lower GLP-1 concentrations compared with low-GL diets [276]. However, other authors did not corroborate these findings clearly. One study performed in healthy sedentary women reported that GLP-1 concentrations did not differ significantly following high- or low-GI meals [277]. In the same way, another study in overweight subjects observed no differences in GLP-1 concentrations when comparing consumption of low- and high-GI beverages [278]. Other authors suggest that the rate of small intestinal glucose exposure (i.e., GL) is a major determinant of the magnitude of the incretin effect, since they observed that the incretin effect was stronger when they administered larger intraduodenal glucose load [279]. However, it is necessary to be cautious with this upregulation of incretins in response to high-sugar diets, because it has been described that in diabetic patients, who have increased levels of incretins, a reduced incretin effect is observed, which suggest the development of an “incretin resistance” process [280,281,282,283]. 5. Future Directions During human evolution, ancestral human diets contained low carbohydrate levels and most of the sugars were derived from fruits and honey. In the last century, changes in lifestyle, nutritional habits in the world population, and the abusive use of sweeteners by the food industry have dramatically increased dietary sugar consumption, particularly constituent monomers, such as glucose and fructose, and fructose-based sweeteners. International and national health organizations have called attention into this issue and recommend reductions in sugars consumption due to concerns in their potential role as risk factors for developing human diseases such as obesity and T2DM. In the last decades, the scientific community has made great efforts to understand intestinal sugar absorption, identifying molecular and physiological mechanisms of fructose and glucose sensing and transport. In the case of fructose metabolism, the current notion that fructose is mainly metabolized by the liver has been challenged, and the new paradigm proposes that the small intestine shields the liver from toxic fructose exposure. This provocative view of intestinal fructose metabolism is awaiting confirmation in humans. Similarly, the finding that humans can synthesize fructose by the polyol pathway leaves open the question about the significance of this pathway in human metabolic diseases. In addition, more meta-analysis studies should be performed to clearly demonstrate the causal role of dietary fructose and glucose in developing human metabolic diseases. On the other hand, the role of intestinal glucose metabolism on the etiology of hyperglycemia remains incompletely clarified. Despite studies relating chronic hyperglycemia with impaired glucose transport and metabolism in the small intestine, more studies in humans are required to reveal if chronic hyperglycemia is a cause or consequence of impaired glucose homeostasis in the small intestine. The identification of molecular mechanisms by which glucose and insulin regulate SGLT1 have set this transporter, and its potential role in the physiopathology of hyperglycemia and intestinal insulin resistance, in the spotlight. In this line of thinking, further research is required to demonstrate the efficacy of SGLT1 inhibitors in the treatment of T2DM and obesity. Likewise, it remains to be clarified whether the apical localization of GLUT2 in response to high glucose levels in obese and/or diabetic patients is an adaptive mechanism to protect the body from excessive glucose concentrations, or if it is a consequence of hyperglycemia and insulin resistance. Finally, the differentiated effects of glucose and fructose on eating behavior and impaired cognitive function observed in rodent models are difficult to extrapolate to humans due to the use of extremely high-fructose diets or unusual glucose to fructose ratio. To clarify the causality of fructose in human eating disorders leading to metabolic diseases, it is necessary to develop new research tools and experimental approaches in humans. Author Contributions I.C.-C. and G.P. conceptualized the manuscript. B.M., C.M.F.-D., I.C.-C., and G.P. drafted the manuscript. B.M. and C.M.F.-D. prepared the figures. I.C.-C. and G.P. revised and edited the manuscript. All authors have read and agreed to the published version of the manuscript. Funding This research was funded by the Spanish MINISTERIO DE ECONOMÍA, INDUSTRIA Y COMPETITIVIDAD, grant numbers SAF2016-77871-C2-1-R and SAF2016-77871-C2-2-R to I.C-C. and G.P. respectively; the EFSD European Research Programme on New Targets for Type 2 Diabetes supported by an educational research grant from MSD to I.C-C. and G.P.; the FUNDACIÓN LA-CAIXA Y FUNDACIÓN CAJA DE BURGOS, grant number CAIXA-UBU001 to G.P. Conflicts of Interest The authors declare no conflict of interest.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/29%3A_Integration_of_Mammalian_Metabolism_-_Capstone_Volume_II/29.05%3A_Intestinal_Fructose_and_Glucose_Metabolism_in_Health_and_Dise.txt

Metabolic consequences of obesity and type 2 diabetes: Balancing genes and environment for personalized care Nicolas J. Pillon, Ruth J.F. Loos, Sally M. Marshall, Juleen R. Zierath,. Metabolic consequences of obesity and type 2 diabetes: Balancing genes and environment for personalized care. Cell, Volume 184, Issue 6, 2021, Pages 1530-1544, ISSN 0092-8674, https://doi.org/10.1016/j.cell.2021.02.012. Under a Creative Commons license. Attribution 4.0 International (CC BY 4.0) Summary The prevalence of type 2 diabetes and obesity has risen dramatically for decades and is expected to rise further, secondary to the growing aging, sedentary population. The strain on global health care is projected to be colossal. This review explores the latest work and emerging ideas related to genetic and environmental factors influencing metabolism. Translational research and clinical applications, including the impact of the COVID-19 pandemic, are highlighted. Looking forward, strategies to personalize all aspects of prevention, management and care are necessary to improve health outcomes and reduce the impact of these metabolic diseases. Introduction The COVID-19 pandemic has brought the deleterious health consequences of obesity and type 2 diabetes into sharp focus. Individuals with type 2 diabetes and/or obesity are more likely to have severe disease and to die than are individuals without diabetes (Barron et al., 2020). Fasting glucose level at the time of hospital admission predicts 28-day mortality even in those without a previous diagnosis of diabetes (Wang et al., 2020a). Glycemic control and body mass index along with older age, male sex, socio-economic deprivation, non-white ethnicity, and pre-existing renal and cardiovascular disease all independently increase mortality (Holman et al., 2020). COVID-19 is also a timely reminder that diabetes is not merely a state of glucose dysregulation but a multi-faceted syndrome driven by many medical and social risk factors and associated with pathophysiological changes throughout the body. The World Health Organization estimates that worldwide, 422 million people have diabetes, the majority living in low- and middle-income countries, and most having type 2 diabetes (who.int/health-topics/diabetes). The prevalence has risen dramatically for decades, as the population ages and becomes less active and more overweight (GBD 2019 Risk Factors Collaborators, 2020). Early detection is vital, particularly as long-term complications, such as referable diabetic retinopathy, may be present at diagnosis of type 2 diabetes (Kohner et al., 1998). Many developed countries have systematic screening programs of individuals deemed to be at high risk (American Diabetes Association, 2020). However, there is disagreement as to how to define “high risk” and how to screen (oral glucose tolerance test, fasting glucose or glycated hemoglobin, HbA1c). Glucose-based tests and HbA1c each identify slightly different populations. We do not know if these differences in diagnoses lead to important clinically different outcomes or if they signal slightly different pathological metabolic forms of glucose dysregulation (American Diabetes Association, 2020). The WHO defines overweight and obesity as body mass indexes (BMI) 3 25 and 30 kg/m2, respectively and estimated that 1.9 billion adults were overweight and 650 million obese in 2016 (https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight). Obesity is now regarded as a chronic, progressive disease with remissions and relapses (Bray et al., 2017) and an important driver of the development of diabetes and many of its associated features (GBD 2019 Risk Factors Collaborators, 2020). The deleterious effects of obesity and type 2 diabetes are seen in most, if not all, tissues in the body, with consequences resulting in significantly increased premature morbidity and mortality (GBD 2019 Risk Factors Collaborators, 2020). Social and cultural factors are also extremely important in the development, management, and clinical outcomes of obesity and type 2 diabetes. Despite advances in diabetes care over the recent decades, there remain vast challenges: developing an improved understanding of the heterogeneity of obesity and diabetes, how best to assess risk, to screen, to select individualized treatments and vitally how to engage the relevant populations in these programs. This review explores the genetic and metabolic aspects of diabetes and obesity (Figure 1) and discusses some of the latest work and emerging ideas related to basic biological mechanisms, translational research, and clinical applications. Genetics and metabolism The current obesogenic environment, favoring high-calorie foods and physical inactivity, is a major driver of the growing obesity and diabetes epidemic. However, not everyone exposed to this environment gains weight or develops type 2 diabetes. The way people respond to environmental factors is, at least in part, determined by their genetic predisposition to obesity and type 2 diabetes. Traditionally, the genetic contribution has been quantified by the heritability, which is a population-level estimate of how much of the variation in disease susceptibility is attributable to genetic variation. For obesity and type 2 diabetes, the heritability has been estimated to be moderate-to-high, ranging between 30% and 70% (Elks et al., 2012; Willemsen et al., 2015). The search for contributing genes started in the 1990’s with early success largely confined to monogenic forms of obesity and diabetes. Mutations that segregate in families or occur de novo were found to cause major disruptions in the function of genes in which they are located, providing the first insights in the pathophysiology of body-weight regulation and glucose metabolism (Hattersley and Patel, 2017; van der Klaauw and Farooqi, 2015). The search for genetic variants that contribute to common forms of obesity and diabetes began slowly with candidate gene and genome-wide linkage studies. However, the advent of genome-wide association studies (GWASs) in the mid-2000’s accelerated the pace of gene discovery. GWASs have identified thousands of genetic loci that are robustly associated with complex diseases and traits, including 700 for obesity (Yengo et al., 2018) and at least 400 for type 2 diabetes (Mahajan et al., 2018). From the earliest GWAS, tissue enrichment and pathway analyses for BMI-associated loci have suggested that the central nervous system plays a key role in body weight regulation (Locke et al., 2015). Loci associated with type 2 diabetes act predominantly through the perturbation of insulin secretion, pointing to the importance of beta cell function or mass, whereas few loci affect insulin resistance through an effect on body weight or fat distribution (Barroso and McCarthy, 2019). Despite the success of GWASs, pinpointing the causal gene(s) and variant(s) within each locus remains an ongoing challenge. So far, about 20% of loci associated with type 2 diabetes and a handful of loci associated with obesity have been mapped to the most likely causal variant, whereas the underlying biology of hundreds of additional loci remain to be elucidated (Larder et al., 2017; Mahajan et al., 2018; Rathjen et al., 2017). However, with increasing availability of high-throughput genome-scale technologies for mapping regulatory elements, comprehensive multi-omics databases, advanced computational tools, and the latest genetic engineering and molecular phenotyping approaches, we are poised to accelerate the translation of GWAS loci into meaningful biology in the years ahead. With GWASs, genetic susceptibility to disease can be assessed using polygenic scores. A polygenic score represents an individual’s overall genetic susceptibility to disease and is calculated by summing the number of disease-increasing alleles that were inherited from either parent, weighted by each variant’s effect size observed in a GWAS. Even though each locus has a small effect on disease risk and explains only a fraction of the variation in disease susceptibility, when aggregated in a polygenic score, their contribution can be substantial. Polygenic scores are normally distributed, with most individuals having an average score, and thus an average genetic susceptibility, whereas individuals at the extremes of the distribution have a (very) high or low genetic risk of disease. For example, in the UK Biobank, the average BMI of individuals with a high polygenic score (top decile) is 2.9 kg/m2 (equivalent to 8 kg in body weight) higher and their odds of severe obesity (BMI 3 40 kg/m2) is 4.2-fold higher, compared to those with a lower polygenic score (bottom 9 deciles) (Khera et al., 2019). Similarly, individuals with a very high polygenic score (top 5%) for type 2 diabetes have a 2.75-fold increased risk of disease compared to the remainder of the population (Udler et al., 2019). These observations have fueled expectations that genotype information, including polygenic scores, can soon be used in clinical care for early diagnosis of high-risk individuals, to tailor prevention and treatments strategies, and to improve disease prognostics. In fact, many online direct-to-consumer genomic companies are already informing customers about their risks and predispositions for a range of common diseases and traits based solely on genetic profiling, including for obesity and type 2 diabetes (Figure 2). However, even though the genetic associations observed in GWASs are robust, their ability to predict who will be at a high risk of obesity or type 2 diabetes is still low-to-moderate, and not ready for use in clinical settings (Udler et al., 2019). For example, a recent polygenic score applied to individuals of European ancestry of the UK Biobank explains only 8%–9% of the variation in BMI and is a weak predictor of obesity, with an area under the receiver operating characteristic curve (AUCROC) of 0.64 (Khera et al., 2019). Findings are similar for polygenic scores for type 2 diabetes, with AUCROC of 0.64–0.66 (Udler et al., 2019). The predictive ability of polygenic scores are expected to improve as GWASs increase in sample size and the per-variant effect estimates become more precise, and as algorithms to aggregate millions of genetic variants across the genome improve. Nevertheless, given the importance of socio-demographic, lifestyle and clinical risk factors in the etiology of obesity and type 2 diabetes, it is unlikely that a polygenic score on its own will ever be able to accurately predict obesity or type 2 diabetes. More comprehensive approaches that include a broad spectrum of genetic, demographic, environmental, clinical, and possibly also molecular markers are needed to accurately predict who is at risk of gaining weight and/or developing type 2 diabetes. The vast amount of new genetic information generated by GWASs is being used in sub-typing disease at a population level. Obesity and type 2 diabetes are highly heterogeneous diseases, and the diagnosis of these metabolic diseases is unrefined, based on a single marker (BMI 3 30 kg/m2 and hyperglycemia, respectively). Consequently, individuals with the same diagnosis may differ considerably in disease pathogenesis, clinical presentation, disease course and response to treatments. Subtypes of obesity and type 2 diabetes have been typically based on phenotypic differences and similarities. As the number of GWAS-identified loci continues to increase, subtyping of obesity and type 2 diabetes based on genetic information has become possible. In a recent study, 141 variants previously identified for diabetes and diabetes-related traits were clustered in five groups, based on their association with more than 75 traits (Udler et al., 2018). Variants with a similar association profile cluster in the same group, and the group-specific association profile can inform about the mechanisms underlying a given subtype of type 2 diabetes. For example, two of the five groups identified for diabetes-related traits represent reduced beta-cell function, of which one cluster is characterized by high and the other by low proinsulin levels. The three other groups of variants show features of insulin resistance, of which one group represents obesity-mediated insulin resistance, a second group represents abnormal body fat distribution (“lipodystrophy-like”), and a third group represents disrupted liver lipid metabolism. Genetic risk scores based on variants in each cluster are associated with distinct clinical outcomes (Udler et al., 2018). Further for obesity, genotype information has been used to identify individuals who are predisposed to increased adiposity and, concomitantly, are protected from cardiometabolic outcomes (representing the so-called metabolically healthy obesity phenotype) (Ji et al., 2019). Subtyping of heterogenous diseases, like obesity and type 2 diabetes, is key to precision medicine. Indeed, these more homogeneous subgroups are characterized by distinct underlying biological mechanisms, such that diagnosis and prognosis will be more precise and optimization of treatment more efficient (Chung et al., 2020). As GWASs continue to identify more loci, additional and possibly better-defined clusters may be identified to more accurately represent the heterogenous group of individuals with obesity and type 2 diabetes. As more GWAS loci are being discovered, Mendelian Randomization (MR) becomes an increasingly powerful approach to determine causality between an exposure (e.g., health-related behaviors, biomarkers [e.g., lipid levels, metabolites]) and an outcome (e.g., obesity, type 2 diabetes). Genetic variants that are robustly associated with the exposure are used to randomize a population in individuals with high exposure (i.e., carriers of the risk alleles) and those with low exposure (i.e., carriers of non-risk alleles). If the same genetic variants also associate with the disease outcome, through their association with the exposure, then causality between exposure and disease is inferred. For example, a large-scale MR study examined the causal role of a wide range of possible risk factors for type 2 diabetes, mostly confirming established risk factors, but also revealing new ones (e.g., insomnia) (Yuan and Larsson, 2020). As more GWAS data becomes available for a range of multi-omics biomarkers, MR analyses may reveal novel disease-causing biomarkers, broadening insights in the pathogenesis of obesity and type 2 diabetes. Epigenetic impact on metabolism Beyond genetic risk, the genes we inherit and the environmental factors we are exposed to can interact synergistically to modify our physiology and risk for obesity and type 2 diabetes through epigenetic modifications (Figure 3). Epigenetic modifications are biochemical processes that influence gene activity and expression, and ultimately modify cellular and whole-body physiology, without altering the DNA sequence of an organism’s genome (Barrès and Zierath, 2016). Mechanistically, epigenetic modifications can arise from chemical alterations of nucleosides in the DNA molecule itself by methylation or hydroxymethylation, alterations in chromatin structure or post-translational modifications of histones (i.e., methylation, phosphorylation, acetylation, ubiquitylation, and sumoylation) or RNA-associated gene silencing (Bošković and Rando, 2018). Although epigenetic modifications are generally thought to be fixed during development and maintained over an organism’s lifetime, there is some degree of plasticity in the epigenome, which engenders organismal adaptation to rapid environmental changes. Alterations in nutritional status, food supply, physical activity/exercise, thermal stress, toxins, or other environmental insults can trigger epigenetic modifications and lead to genomic changes in somatic cells within an individual that directly disrupt metabolic homeostasis (Barrès and Zierath, 2016; Bošković and Rando, 2018). These same factors may also modify the physiology of an organism by transgenerational epigenetic inheritance, whereby paternal or maternal environmental exposure can influence metabolism and manifest obesity- or type 2 diabetes-related traits in the offspring. Prenatal undernutrition affects glucose tolerance and risk of diabetes in the offspring, as demonstrated by epidemiological studies of several famines over the past century (Li et al., 2010; Ravelli et al., 1998). In rodents, paternal and maternal diet and exercise influence metabolic and cardiovascular outcomes in offspring over several generations (de Castro Barbosa et al., 2015; Murashov et al., 2016; Stanford et al., 2015). Thus, nutritional status in utero during fetal development may affect the epigenome for several generations, but the molecular transducers remain to be clarified. Additionally, food restriction during childhood, at different growth phases around puberty, also leads to epigenetic changes that influence the risk of cardiovascular and metabolic disease of offspring over several generations (Kaati et al., 2002). Accordingly, epigenetic factors passed on by the gametes may contribute to the global increase in obesity and type 2 diabetes. Thus, an area of emerging interest is the influence of the environment on epigenetic mechanisms, and how this modifies metabolic disease risk. A variety of dietary agents, as well as micronutrients and metabolites synthesized de novo, can serve as substrates or co-factors to influence the epigenome and potentially affect metabolic disease risk in humans, in part by affecting genomic plasticity (Tiffon, 2018). One-carbon metabolism encompasses folate and methionine cycles, which transfer one-carbon moieties and methyl groups for nucleotide synthesis, methylation reactions and reductive metabolism (Newman and Maddocks, 2017). Metabolites including acetyl-coA, AMP, NAD+, and S-adenosylmethionine are required for histone modifications (acetylation, phosphorylation) and methylation of DNA and histones. The extent to which nutritional factors, metabolites, and other co-factors directly modify the epigenome within a generation remains to be fully substantiated in humans. While it is important to stress that type 2 diabetes and obesity are complex multi-factorial, progressive metabolic diseases with diverse etiology, and not simply “lifestyle disorders,” diet and exercise regimes can prevent or delay disease progression. Changes in the concentration of cellular metabolites, nucleotides, or calcium levels in skeletal muscle in response to acute exercise alter DNA methylation or histone modifications and influence gene expression through epigenetic mechanisms (Barrès and Zierath, 2016). In humans, acute exercise alters DNA methylation of the promoters of genes involved in metabolic regulation in skeletal muscle (Barrès et al., 2012; Nitert et al., 2012). Epigenetic modifications have also been observed in skeletal muscle and adipose tissue in humans with obesity and weight loss (Barres et al., 2013; Multhaup et al., 2015). Thus, the impact of environmental exposures and epigenetic influences on the risk for metabolic diseases throughout the lifespan is an important aspect of biology to unravel. Circadian control of metabolism An evolutionarily conserved mechanism by which environmental factors can impact whole-body physiology is through internal biological clocks and the control of circadian rhythms (Young, 2018). Circadian rhythms are driven by cell-autonomous intrinsic clocks that anticipate day/night cycles in order to optimize the physiology and behavior of organisms. Circadian programs are regulated at both the central and peripheral level with the master clock, located in the suprachiasmatic nucleus region of the hypothalamus, acting as conductor to synchronize and direct peripheral oscillators (Young, 2018). Synchronization of these intrinsic circadian clocks can be achieved in response to photic and non-photic zeitgebers (time-givers). The most powerful zeitgeber is light, which synchronizes the central clock. In addition to receiving cues from the central clock, peripheral clocks are synchronized by external zeitgebers, including food intake, temperature, energetic stressors, and drive the expression of a broad network of genes, many of which are involved in metabolic homeostasis (Gabriel and Zierath, 2019). The precise mechanism by which circadian clocks coordinate whole-body homeostatic processes is an area of emerging interest given the importance of external zeitgebers and the regulation of gene programs controlling metabolism and development. One mechanism by which the circadian machinery influences metabolism is through the diurnal patterns of hormone secretion (Gamble et al., 2014). Endocrine organs release a variety of hormones in response to diverse environmental factors including diurnal cycles of light/dark, fasting/feeding, and temperature changes. For example, there are diurnal or circadian patterns of secretion of cortisol, growth hormone, prolactin, thyroid hormone, gonadal steroids, and melatonin related to sleep/wake cycles, whereas metabolic hormones including insulin, leptin, ghrelin, and glucagon vary in response to nutritional cues related to fasting/feeding cycles (Gamble et al., 2014). Many of these hormones including insulin, insulin-like growth factor 1, and glucocorticoids can act as zeitgebers to reset or fine tune the clock (Balsalobre et al., 2000; Crosby et al., 2019). Thus, an intimate relationship between circadian clocks and endocrine systems exists. This relationship is clinically relevant since disruption of the circadian clock is linked to metabolic disease. In humans, long duration of shift work is associated with an increased risk of type 2 diabetes, which is only partly explained by lifestyle factors and BMI (Vimalananda et al., 2015). Epidemiological studies show that disruption of the sleep/wake cycle through extended periods of rotating night shift work is associated with obesity and increased risk of type 2 diabetes (Lin et al., 2009; Pan et al., 2011). Chronic jet lag in mouse models disrupts exergy homeostasis and leptin signaling and leads to circadian dysfunction-induced obesity (Kettner et al., 2015). Similarly, a population-based cohort study indicates that social jet lag, defined as the discrepancy between circadian and social clocks, is associated with increased risk of metabolic syndrome and diabetes/prediabetes (Koopman et al., 2017). Thus, chronobiology has implications for obesity and type 2 diabetes pathogenesis. A basic paradigm of circadian regulation of metabolism is that oscillations of gene expression generate daily rhythms in cellular metabolism (Kim and Lazar, 2020). At the molecular level, circadian rhythms are generated by a cell autonomous and self-sustained transcriptional auto-regulatory feedback loop that is composed of transcriptional activators and their target genes, which rhythmically accumulate and form a repressor complex to inhibit transcriptional activity (Figure 4). Energy, nutrient, and oxygen sensors interact with the circadian clock machinery to control metabolic outputs including mitochondrial function, substrate utilization, insulin sensitivity, and glycemic control (Lamia et al., 2009; Peek et al., 2017; Sato et al., 2019). These sensors monitor oxygen availability and energy stress via hypoxia-inducible factor-1 alpha (HIF1α) and AMP-activated protein kinase (AMPK), respectively. Cells also integrate signals from nutrients and growth factors via mammalian target of rapamycin (mTOR). These energetic sensors not only exhibit circadian rhythmicity, but also regulate components of the core clock machinery through epigenetic modifications, mainly involving histone modifications (Kim and Lazar, 2020). Thus, cross-talk exists between the circadian clock and epigenetic factors that influence the genomic plasticity of organs controlling metabolic homeostasis. In rodents, dysregulation of the intrinsic molecular clock in a variety of tissues leads to obesity, insulin resistance, and altered glucose homeostasis (Rudic et al., 2004; Turek et al., 2005). Nevertheless, the mechanisms underlying disrupted circadian rhythmicity in people with type 2 diabetes are unknown. There is potential to coordinate behavioral changes with the body’s daily rhythm to improve metabolic homeostasis. Timing of exercise training bouts or meals and distribution of calories throughout the day may lead to improved outcomes for people with obesity or type 2 diabetes (Lundell et al., 2020; Savikj et al., 2019). Impact of energetic stressors on the control of metabolism Obesity, diabetes, exercise, and food restriction are energetic stressors that represent major challenges to organismal homeostasis, triggering wide-ranging responses in numerous cells and tissues controlling glucose and energy metabolism. An essential component of an organism’s survival is the ability to sense energy availability and to adapt accordingly. Metabolic flexibility, the ability to shift between fat and glucose oxidation with fasting and feeding, is reduced in individuals with metabolic diseases and contributes to the overall insulin resistance phenotype (Kelley et al., 1992). Skeletal muscle exhibits metabolic flexibility in fuel preference, likely due to its crucial role in hunting and surviving predation, situations requiring movement even if nutrient availability is not optimal (Freese et al., 2017). A body of literature supports the idea that metabolic flexibility can be directly influenced by physical activity, independent of changes in energy balance (Rynders et al., 2018). Physical exercise enhances skeletal muscle insulin sensitivity and improves whole-body glucose metabolism in people with type 2 diabetes (Savikj and Zierath, 2020). However, recent findings, based on stable-isotope tracer and liquid chromatography tandem mass spectrometry, demonstrate that skeletal muscle mitochondrial substrate preference is not altered in insulin resistant rodents and humans, calling into question the central role of metabolic flexibility in the pathogenesis of metabolic diseases (Song et al., 2020). Nevertheless, there is growing appreciation that insulin resistance, obesity, and type 2 diabetes can be avoided or at least delayed by lifestyle intervention strategies, including diet and exercise, which initiate diverse homeostatic responses across multiple organs (Savikj and Zierath, 2020). The concept of “time-restricted feeding” has gained traction as a dietary means to restore metabolic homeostasis, enhance insulin sensitivity, and curb obesity. Time-restricted feeding refers to restricting daily food intake to a few hours, without caloric restriction (Chaix et al., 2014). In rodents, time-restricted feeding synchronizes the feeding/fasting cycle with the central clock, thereby promoting robust circadian and metabolic cycles, which mitigates obesity and metabolic dysfunction (Hatori et al., 2012). Thus, timing of food intake with the molecular circadian clock may fine-tune metabolism. In humans, time-restricted feeding paradigms improve cardiometabolic health in people with obesity or metabolic disease (Cienfuegos et al., 2020; Wilkinson et al., 2020). Short-term time-restricted feeding schedules in men with obesity modulate the diurnal rhythm of lipid and amino acid metabolism, without affecting core clock gene expression in skeletal muscle (Lundell et al., 2020). Furthermore, the timing and type of nutritional intake throughout a day influences carbohydrate metabolism and protein synthesis (Areta et al., 2013). Whether this is dependent upon the release of hormones, metabolites, or thermogenesis warrants further investigation. Moreover, the weight and cardiometabolic benefits achieved with time-restricted feeding schedules may be related to reductions in calorie intake, rather than meal timing. Concordantly, a prospective randomized clinical trial including 116 men and women with overweight or obesity found that modest reductions in weight loss and energy intake from time-restricted eating did not differ from the control group (Lowe et al., 2020), hinting at the possibility that benefits of time-restricted feeding programs are mainly due to reductions in calorie intake. Diet and exercise have a synergistic effect on insulin sensitivity, which may be influenced by altering the timing of the meal or an exercise bout throughout the day. In rodents, there is a time-of-day-dependent effect of acute exercise on the diurnal oscillations of skeletal muscle metabolites and transcripts, with a greater reliance on glycolytic metabolism when exercise is performed during the early active phase of the day (Sato et al., 2019). Moreover, in a preliminary clinical investigation comparing the time-of-day impact of high intensity exercise in men with type 2 diabetes, greater blood glucose control was achieved with afternoon versus morning exercise (Savikj et al., 2019). The oxygen-sensitive transcription factor HIF1α links time-of-day-specific effects of exercise on gene expression and carbohydrate metabolism in mice models (Peek et al., 2017; Sato et al., 2019). This finding has clinical relevance, since intense exercise acutely increases skeletal muscle protein abundance and DNA binding activity of HIF1α in humans (Ameln et al., 2005). Moreover, energetic stressors, such as exercise and hypoxia, increase skeletal muscle glucose uptake in healthy and insulin resistant humans and rodents (Ranheim et al., 1997; Ryder et al., 2000). Thus, perturbing energy, nutrient, and/or oxygen sensors may have a varied response on cellular metabolism depending on the time of day. Collectively, these studies provide evidence that the timing of exercise bouts throughout the day is clinically relevant for the diurnal control of glycemia or systemic metabolism. Adjusting the timing of external cues (i.e., meal/exercise timing) may sustain or amplify circadian clock signals to prevent or mitigate metabolic disease. Thermal tolerance Excess energy can be dissipated in the form of heat, a process that occurs in brown adipose tissue and is stimulated by food intake and cold exposure (Chouchani et al., 2019). Feedback loops involving temperature sensors, thermogenesis, sweating, and the control of blood circulation are tightly regulated to maintain body temperature in humans at ∼37°C. Alterations in ambient temperature trigger acute and chronic changes in whole-body physiology, making climate a major environmental stressor that affects all individuals on the planet. Acute exposure to cold triggers shivering in skeletal muscle, where ATP is used to generate movement and its associated production of heat. Chronic adaptation to cold involves different mechanisms, the main one being activation of brown adipose tissue thermogenesis (Chouchani et al., 2019). Uncoupling protein 1 dissipates the proton gradient in the mitochondria to generate heat instead of ATP. Consequently, oxidative phosphorylation increases to maintain mitochondrial membrane potential. Therefore, exposure to cold temperatures increases the metabolic rate during sleep cycles, as well as diet-induced thermogenesis, thereby increasing total energy expenditure (Chouchani et al., 2019). A rise in ambient temperature above thermoneutrality also increases metabolism by promoting heat dissipation (Chouchani et al., 2019). The processes involved in heat acclimation have been extensively studied in humans and involve an increase in total body water, increased sweat volume and decreased sweat concentration, as well as adaptations of heart rate and skin blood flow (Périard et al., 2015). Mechanisms involved in heat acclimation and associated cardiovascular events are related to increased central heat production and dehydration and the ensuing deleterious consequences on blood pressure and cardiovascular function (Meade et al., 2020). Acute exposure to extreme ambient temperatures, often referred to as “cold stress” or “heat stress,” is associated with an increased risk of cardio-pulmonary mortality (Achebak et al., 2019). In this context, age, weight, obesity, and type 2 diabetes are major risk factors (Hajat et al., 2017; Huang et al., 2012). The mechanisms for increased risk of cardiovascular events secondary to extreme temperatures in people with metabolic diseases are poorly understood. Reduced heat tolerance in obesity might be due to impairments in blood flow and sweat production (Vroman et al., 1983). The reduced sweating ability is possibly linked to a decreased body surface area-to-body mass ratio in a person with obesity as compared to a leaner person. Individuals with type 2 diabetes also exhibit reduced skin blood flow in response to local and whole-body heating, likely due to impaired endothelial function (Meade et al., 2020). However, chronic exposure to mild electrical stimulation with heat shock improves visceral adiposity, glucose homeostasis, and insulin sensitivity in people with type 2 diabetes (Kondo et al., 2014). This paradox suggests that increasing heat tolerance by repeated acute exposures to heat might mitigate heat-induced cardiovascular events in individuals with metabolic diseases. Heat stress from both exercise and environmental factors can increase thermal strain in unacclimated individuals (Figure 5). Acute exercise increases core body temperature and high-intensity exercise can lead to heat illness consisting of symptoms ranging from minor cramps and syncope to major heat stroke, even in highly trained athletes (Charlot et al., 2017). The capacity to dissipate an exercise-induced elevation in body temperature is reduced in people with type 2 diabetes, but this can be overcome by regular exercise training, which is associated with improved heat tolerance (Kenny et al., 2016). Regular exercise training also reduces cardiovascular mortality and improves glucose control in people with type 2 diabetes (Savikj and Zierath, 2020). At a molecular level, exercise training increases heat shock protein abundance, a process that could contribute to the beneficial effects of exercise to enhance insulin sensitivity (Archer et al., 2018). Individuals with obesity or type 2 diabetes exhibit decreased levels of heat shock proteins in skeletal muscle (Chung et al., 2008). This decrease is reversible, and induction of heat shock proteins by mild electrical stimulation with heat shock improves visceral adiposity as well as plasma glucose and insulin levels (Kondo et al., 2014). Regular exposure to thermal stressors, such as exercise or environmental temperature, may improve heat tolerance through overlapping adaptive mechanistic responses (sweat volume and composition, body water, heart rate), thereby improving metabolism and decreasing risk of cardio-pulmonary events in individuals exposed to extreme ambient temperatures. Presently, most of the human population lives under conditions of thermoneutrality, which is made possible due to appropriate clothing and heating systems in homes and workplaces. Reduced energy expenditure, due to the comforts of our modern society and the decline in our prolonged exposure to cold environments, may contribute to the worldwide rise in obesity, although this is difficult to firmly establish. However, there is a clear link between thermal regulation, metabolic diseases, and associated complications. Most of the temperature-related cardio-pulmonary events occur on moderately hot and moderately cold days (Gasparrini et al., 2015), suggesting that steady increases in the average global temperature has the potential to impact the numbers of these events worldwide. The combination of an epidemic of obesity and type 2 diabetes, juxtaposed with an aging population and climate change, may potentially lead to a dramatic increase in cardio-vascular morbimortality. Understanding the molecular basis of heat intolerance in people with obesity or type 2 diabetes could open novel preventative and therapeutic perspectives. Inflammatory responses The stress that temperature, obesity, diabetes, exercise, and food exert on organismal homeostasis triggers activation of the immune system and different states of metabolic inflammation. The immune system is composed of specialized cells present in every organ that protect against a wide variety of insults, including infections, mechanical injuries, and a variety of diseases. The immune response comes in waves, starting with a pro-inflammatory activation and finishing with a resolving anti-inflammatory phase (Feehan and Gilroy, 2019). When the immune system fails to recover after an insult, a chronic inflammatory state occurs, leading to long-term deleterious consequences. This typically happens in obesity, where immune cells infiltrate tissues and lead to chronic low-grade inflammation, associated with increased risk of cardiovascular complications. The association of inflammation with type 2 diabetes and obesity has been extensively studied, as evidenced by the rapid development of the field of “immunometabolism,” which includes the analysis of the complex interactions between metabolic and inflammatory pathways in immune and metabolic tissues (Lee et al., 2018). Obesity and type 2 diabetes are associated with an accumulation of immune cells in key tissues involved in metabolic homeostasis. A link between metabolic diseases and immunology emerged with the detection of macrophage infiltration in adipose tissue, followed by the discovery that lymphocytes, neutrophils, and other specific subtypes of immune cells accumulate not only in adipose tissue but also in skeletal muscle and liver (Hotamisligil, 2017). Even neuroinflammation is part of the systemic inflammatory syndrome in metabolic diseases (Cai, 2013). The accumulation of triglycerides in adipocytes increases adipocyte size (hypertrophy) and number (hyperplasia), resulting in the rapid expansion of adipose tissue, which triggers hypoxia and the production of soluble mediators likely responsible for the attraction of immune cells. The first immune cells reaching adipose tissue are likely attracted to support tissue remodeling in a beneficial manner, but the chronic increase in adipose tissue volume and the establishment of a new obese steady-state leads to increasing lipolysis and circulating free fatty acids, which activate immune cells toward a pro-inflammatory phenotype and promote the establishment of chronic inflammation (Lee et al., 2018). Immune cells also respond to metabolic changes and are susceptible to the deleterious effects of an excessive lipid or glucose accumulation (i.e., “lipotoxicity” or “glucotoxicity”), as well as other metabolism-related danger signals that are released by tissues during metabolic stress (Wang et al., 2020b). The composition and phenotype of circulating immune cells is altered in blood of individuals with obesity, with an increase in CD16+ monocytes, and immune cell activation in response to high concentrations of glucose or fatty acids (Pillon et al., 2016). These findings suggest that the immune system is profoundly affected by whole-body glucose and lipid homeostasis. The mechanisms by which non-adipose tissues establish a state of inflammation is unclear. However, lipotoxicity, including the excessive accumulation of toxic lipid mediators such as ceramides, diacylglycerol, or acylcarnitine, and increased levels of circulating free fatty acids likely play a role. In addition, activated immune cells primed to respond to metabolism-related danger signals can impair whole-body metabolism. There is ample evidence to suggest that inflammation is associated with the development of metabolic diseases and the ensuing complications, but pharmacological targeting of pathways controlling immunometabolism has shown limited benefits for the treatment of metabolic diseases (Pålsson-McDermott and O’Neill, 2020). Perhaps the key to successful clinical intervention will be to identify relevant patient groups early, before the manifestation of a chronic low-grade inflammatory state. Acute exercise, especially intense and/or eccentric exercise, triggers an acute inflammatory response, which is necessary for skeletal muscle repair and adaptations to exercise training. Exercise training has beneficial anti-inflammatory effects (Gleeson et al., 2011). Thus, repeated peaks of inflammation triggered by acute bouts of moderate intensity exercise may be beneficial to reduce long-term basal concentrations of pro-inflammatory mediators. In severely obese individuals, combining exercise and dietary interventions can reduce macrophage infiltration and pro-inflammatory polarization in adipose tissue (Bruun et al., 2006). The anti-inflammatory effects of exercise could be secondary to an increased capacity for fatty acid utilization, as exercise training in people with obesity or type 2 diabetes reduces the level of deleterious lipid species such as DAG, acetylcarnitines, and ceramides in skeletal muscle (Lancaster and Febbraio, 2014). However, exercise training in healthy individuals also improves insulin sensitivity without changes in these lipid species, making the role of intramyocellular lipids on insulin sensitivity ambiguous and perhaps more relevant in an obesity context (Reidy et al., 2020). Unsuspected causes Currently known genetic, lifestyle, and environmental risk factors only partly explain the development of obesity and diabetes. Other yet unknown factors must be at play. A recent example of potential novel causes of diabetes is the high prevalence of extreme hyperglycemia/ketoacidosis in patients not known to have diabetes admitted to hospital with COVID-19 (Rubino et al., 2020). This seems both more common and more severe than has been seen with other infections/serious illnesses, so it may not represent “stress hyperglycemia” or unmasking of pre-existing, undiagnosed diabetes. Instead, these observations may suggest a specific pathological entity. The SARS-CoV-2 spike protein penetrates cell membranes by binding to the angiotensin converting enzyme (ACE) 2 receptor. This receptor is present on pancreatic beta cells (Hamming et al., 2004). Infection may result in acute loss of insulin secretory capacity and/or beta cell destruction (Apicella et al., 2020). ACE2 receptor is also present on adipocytes so that SARS-CoV-2 may also exacerbate chronic inflammation in adipose tissue (Kassir, 2020). The mechanisms whereby widely accepted risk factors such as obesity result in disease may have novel aspects. It is generally assumed that individuals with type 2 diabetes who are not obese have a different pathophysiological cause unrelated to weight. However, this belief has been challenged recently. The concept of a “personal fat threshold” arose from observations that the median BMI in the UK Prospective Diabetes Study was only 28 kg/m2 (Taylor and Holman, 2015) and that reversal of type 2 diabetes by weight loss could be achieved equally successfully in individuals with higher and lower BMI (Lim et al., 2011). The underlying mechanism appears to be lipotoxicity, an individual’s propensity to accumulate liver and pancreas fat, and their susceptibility to the adverse effects of fat accumulation. At any given body weight or BMI, at-risk individuals will accumulate more liver fat and be more susceptible to developing hepatic insulin resistance at any given liver fat content. The subsequent increase in VLDL-TG export from the liver drives fat accumulation in the pancreas and declining insulin secretion, both also dependent on the individual’s susceptibility. Remission of type 2 diabetes by weight loss is accompanied by reduction in liver and pancreatic fat, decreased hepatic VLDL export, and increased insulin secretion (Al-Mrabeh et al., 2020). Conversely, weight re-gain leading to re-emergence of diabetes is associated with increased liver fat export and pancreatic fat, with recurrent pancreatic dysfunction. The importance of these observations underscores the usefulness of weight loss in the management of diabetic individuals even of normal weight. However, diabetes does not remit in everyone following substantial weight loss, so weight loss is not a universal panacea. Further work is needed to establish if this relates to longer duration diabetes, perhaps with irreversible beta cell damage, or to different pathological mechanisms of disease. Bending the curve Weight loss is clearly the key to reducing rates of obesity and type 2 diabetes, with considerable individual and societal benefits. There is a continuum of action required in prevention of obesity and diabetes, and management and care if they develop (Chan et al., 2020). Many intervention programs have demonstrated successful short-term weight loss and reversal of diabetes, but perhaps the bigger challenge is in preventing weight re-gain (Forouhi et al., 2018). There may be a weight “set-point,” at which compensatory hormonal, metabolic and neurochemical mechanisms prevent further weight loss and drive weight regain (Blüher, 2019). However, a significant proportion of individuals who lose a substantial amount of weight, whether by diet or bariatric surgery, do not regain weight over years and maintain the metabolic benefits of the initial weight loss. Thus, weight loss programs must have two parts: an initial phase of weight loss, followed by a weight maintenance program. Obviously, reduction in energy intake by some means is essential for weight loss. Exactly how this is achieved is probably less important than an individual’s ability to adhere to the program long term (Johnston et al., 2014). The benefits of one regimen over another have been debated (Forouhi et al., 2018), but no one size fits all, and many different approaches are needed. Understanding the influence of social and cultural aspects in the development and management of obesity and diabetes is also crucial (Blüher, 2019). Individuals from socially deprived backgrounds are more likely to be at high risk to develop obesity and type 2 diabetes, to have poorer glycemic control, to develop more complications, and to have a greater reduction in life expectancy (Chan et al., 2020). Identifying and overcoming barriers to participation in screening and prevention programs and in diabetes and obesity care are vital. Most programs do not reach individuals from ethnic minorities or low socioeconomic class, who are most at need (Timpel et al., 2019). Involving overweight and obese individuals from a wide diversity of backgrounds in the identification of barriers to adherence and then in the design of weight loss and diabetes prevention/reversal programs is essential to improve engagement. Personalized medicine Although we talk about type 2 diabetes as one disease, this “blanket” diagnosis covers important heterogeneity (Ahlqvist et al., 2018). Only rarely is the heterogeneity obvious and explainable: slim rather than obese, or young age at presentation with a striking family history in monogenic diabetes. On many occasions, individuals with apparently similar phenotypes have very different clinical courses and respond quite differently to glucose lowering agents. Dissecting out particular forms of “type 2 diabetes,” whether by genetic analyses and risk scores or by improving our understanding of the underlying pathophysiological bases to dysglycemia, is currently possible at the population level but remains extremely difficult at an individual level. As a result, the selection of glucose-lowering agents for individuals is a “best guess” approach, far removed from personalized medicine. Personalized medicine is defined simply as the right treatment for the right person at the right time. The recent American Diabetes Association/European Association for the Study of Diabetes consensus report describes the ambition to personalize all aspects of an individual’s diabetes, including precision diagnosis, lifestyle and pharmacological management, and prognosis (Chung et al., 2020). Currently, for a very small number of individuals (for example, those with congenital leptin deficiency [Montague et al., 1997] and GCK-MODY [MODY 2] [Froguel et al., 1992; Hattersley et al., 1992]), precision diagnosis is possible. However, there are major challenges in precision diagnosis for individuals with the polygenic common forms of obesity and type 2 diabetes. Likewise, there are only a small number of examples of precision therapeutics (for example, leptin for management of severe obesity in congenital leptin deficiency [Farooqi et al., 2002] and sulphonylureas rather than insulin for individuals with neonatal diabetes due to mutations in the genes encoding the potassium channel [KCNJ11 and ABCC8] [Pearson et al., 2006]). For many individuals with obesity and type 2 diabetes, we have extremely blunt “precision” tools. For example, analysis of data from participants in the RECORD and ADOPT trials demonstrated that individuals with insulin resistance have a greater sustained fall in HbA1c on thiazolidenediones compared to sulphonylureas (Dennis et al., 2018). Additionally, there are benefits of SGLT2 inhibitors in individuals with high cardiovascular risk and/or renal disease (Lo et al., 2020). Work is beginning to examine possibilities for personalization of lifestyle measures. Grand challenges Prevention of obesity is probably the most important factor in reducing the prevalence of obesity and related metabolic diseases. This will require action at an individual and societal level (Chan et al., 2020). Societal action is necessary in many areas, including changes to road, rail, and cycling transport plans to encourage increased physical activity, as well as negotiations with the food industry (Chan et al., 2020). Governments also need effective communication plans that reach all sections of society (Timpel et al., 2019). Moreover, different strategies are required for different life stages. The lifestyles of almost everyone must change radically, and this must be facilitated by appropriate action by governments and many branches of industry. The challenges to overcome the status quo and vested interests are considerable. There is abundant evidence that many individuals with obesity at high risk of metabolic disease can lose substantial amounts of weight, reversing pre-diabetes and diabetes. A significant proportion then maintain the weight loss and improved metabolic status for years. Weight loss programs are projected to be more effective per quality-adjusted life year and cost-saving over a lifetime compared to standard care in individuals with type 2 diabetes (Xin et al., 2020). The challenge then is to expand and adapt these successful programs so that all individuals can access them and be supported through them. We must work with individuals who find current programs inappropriate for them, identifying barriers to participation and working together to develop practical solutions. Most current weight loss programs center on improving basic diet, with or without advice on exercise (Forouhi et al., 2018). Further incremental benefit may well be obtained by incorporating additional “personalized” measures, perhaps based on genes, occupation, and inflammatory status, such as advice on specific micronutrients, timing of food intake and exercise, and light exposure. However, the challenge will be to ensure that the message does not become so complex that adherence falls. There is a particular challenge for young people (Chan et al., 2020). The WHO estimated in 2016 that world-wide, 340 million children and adolescents aged 5–19 years were overweight or obese and, in 2019, that 38 million children aged <5 years were overweight/obese (https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight). Associated with this, type 2 diabetes is increasingly diagnosed in children, adolescents, and young adults (IDF Diabetes Atlas 9th edition 2019, www.diabetesatlas.org). A recent meta-analysis has demonstrated the greater impact of type 2 diabetes presenting at younger age: each one-year increase in age at diabetes diagnosis was associated with a 4%, 3%, and 5% decreased risk of all-cause mortality, macrovascular, and microvascular disease respectively (Nanayakkara et al., 2020). These changes underscore the need to prevent obesity and/or manage it appropriately in young people. Paralleling the rise in obesity in younger people is the rise in the number of women with hyperglycemia during pregnancy. The IDF estimated that, in 2017, 16% of women with live births had some form of hyperglycemia during pregnancy, and that 86% of them had gestational diabetes (IDF Diabetes Atlas 9th edition 2019, www.diabetesatlas.org). In addition to the immediate maternal and fetal adverse effects of hyperglycemia during pregnancy, many of these women will develop type 2 diabetes in the subsequent 5-10 years. There are also longer-term consequences to the offspring of increased risk of obesity, type 2 diabetes, hypertension, and cardiovascular disease (Catalano and Shankar, 2017). Some of these adverse consequences are now being reported over several generations of offspring, implicating an epigenetic influence (Catalano and Shankar, 2017). Thus, in addition to the immediate management of the index pregnancy, it is extremely important that further studies of the index in women, their children, and potentially subsequent generations are conducted urgently. Low levels of fitness are a risk factor for hospitalizations and all-cause mortality, and predict morbidity after surgical interventions (West et al., 2016). During the COVID-19 pandemic, public health recommendations regarding confinement and closure of recreation areas decreased daily activity in the general population (Sánchez-Sánchez et al., 2020), aggravating already high levels of inactivity in most countries (https://www.who.int/news-room/fact-sheets/detail/physical-activity). In young adults who only developed mild symptoms, COVID-19 decreased the predicted maximal aerobic capacity (Crameri et al., 2020), while persons more severely infected with SARS-CoV-2 exhibited anorexia and skeletal muscle loss, aggravated by long hospital stays, raising the question of whether COVID-19 could be a major cause of cachexia and sarcopenia (Morley et al., 2020). As obesity and type 2 diabetes are risk factors for COVID-19 complications, the underlying inflammatory conditions in combination with impaired skeletal muscle function may contribute to worse outcomes after infection (Guisado-Vasco et al., 2020). Whether exercise can protect against viral infection or influence disease severity is unclear, but the benefits of physical activity to prevent skeletal muscle wasting are important factors for prevention and rehabilitation of people in risks groups. Understanding the molecular mechanisms underlying the beneficial effects of physical exercise as an inflammatory modulator could thus potentially prevent or mitigate complications due to unexpected infections (da Silveira et al., 2021; Krause et al., 2020). On the horizon How do we move forward? Progress will only come if we tackle the problems at both a population and an individual level. Putting into practice what we already know will benefit many individuals (Chan et al., 2020). Incorporating the newer evidence described above—for example, around the timing of eating and exercise—and light exposure, in ways that do not overwhelm people, will bring added benefits. Better personalization of all aspects of prevention, management, and care should help adherence. In-depth large-scale analysis of genetic and environmental factors may help clarify why people respond differently to the whole gamut of care, allow stratification into refined sub-groups with specific risk factors and genetic predispositions, and potentially thus optimize the efficacy of both lifestyle and pharmacological interventions. Ongoing initiatives like the Innovative Medicines Initiative (www.imi.europa.eu) have demonstrated that combining large databases from multiple public and private organizations is possible, generating power to tackle relevant genetic and biomarker questions. Such initiatives, bringing together diverse stakeholders with people with obesity or diabetes, are essential in our efforts to provide personalized, timely, affordable, and equitable access to high-quality health interventions, with the aim of improving health outcomes for all. Acknowledgments N.J.P. was supported by an Individual Fellowship from the Marie Skłodowska-Curie Actions (European Commission, 704978). J.R.Z. was supported from the Swedish Research Council (Vetenskapsrådet) (2015-00165), a Novo Nordisk Foundation Challenge Grant (NNF14OC0011493), and the Novo Nordisk Foundation Center for Basic Metabolic Research at the University of Copenhagen (NNF18CC0034900). R.J.F.L. was supported by the National Institutes of Health (R01DK110113; R01DK107786; R01HL142302; R01 DK124097) and the Novo Nordisk Foundation Center for Basic Metabolic Research at the University of Copenhagen (Alliance 190503). Author contributions N.J.P., R.L., S.M., and J.R.Z. wrote the manuscript. All authors read and approved the final version of the manuscript. Declaration of interests The authors declare no competing interests. J.R.Z. is member of Cell Advisory Board.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/29%3A_Integration_of_Mammalian_Metabolism_-_Capstone_Volume_II/29.06%3A_Metabolic_consequences_of_obesity_and_type_2_diabetes-_Balanc.txt

Yulia K. Denisenko, Oxana Yu Kytikova,Tatyana P. Novgorodtseva, Marina V. Antonyuk, Tatyana A. Gvozdenko,and Tatyana A. Kantur. VJournal of Obesity, Volume 2020 |Article ID 5762395 | https://doi.org/10.1155/2020/5762395 Copyright © 2020 Yulia K. Denisenko et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Metabolic syndrome (MetS) has a worldwide tendency to increase and depends on many components, which explains the complexity of diagnosis, approaches to the prevention, and treatment of this pathology. Insulin resistance (IR) is the crucial cause of the MetS pathogenesis, which develops against the background of abdominal obesity. In light of recent evidence, it has been shown that lipids, especially fatty acids (FAs), are important signaling molecules that regulate the signaling pathways of insulin and inflammatory mediators. On the one hand, the lack of n-3 polyunsaturated fatty acids (PUFAs) in the body leads to impaired molecular mechanisms of glucose transport, the formation of unresolved inflammation. On the other hand, excessive formation of free fatty acids (FFAs) underlies the development of oxidative stress and mitochondrial dysfunction in MetS. Understanding the molecular mechanisms of the participation of FAs and their metabolites in the pathogenesis of MetS will contribute to the development of new diagnostic methods and targeted therapy for this disease. The purpose of this review is to highlight recent advances in the study of the effect of fatty acids as modulators of insulin response and inflammatory process in the pathogenesis and treatment for MetS. 1. Introduction Metabolic syndrome (MetS) is a complex of several disorders (abdominal obesity, hyperglycemia, hypertriglyceridemia, and hypertension), which together dramatically raise the risk of developing atherosclerotic cardiovascular disease, insulin resistance, and diabetes mellitus [1, 2]. Because the prevalence of obesity has doubly increased worldwide over the past 30 years, the prevalence of MetS has markedly boosted in tandem [2–5]. Currently, clinicians and researchers have not identified an optimal treatment for MetS, and consequently, it is critical to identify new ways of approaching this syndrome in order to identify efficacious methods of diagnosing, screening, and treating MetS. Most researchers believe that hyperinsulinemia and/or insulin resistance (IR) is the first link in the chain of clinical-metabolic disturbances of MetS [5–7]. The development of IR is the result of a long chain of pathological events. Lipids play the crucial role in the pathogenesis of IR and the subsequent development of MetS [8–18]. All lipids are no longer considered the same. It is well known that excessive consumption of saturated fats contributes to the development of obesity and related diseases [19]. It has now been shown that high plasma levels of free fatty acids (FFAs), particularly saturated fatty acids (SFAs), may be associated with insulin resistance in obese patients with type 2 diabetes mellitus [17]. The lack of polyunsaturated fatty acids (PUFAs), especially n-3 PUFAs, some phospholipids, and plasmalogens in the cell membrane, is the cause of changes in glucose-insulin homeostasis and the development of inflammation [10, 13, 20–23]. Conversely, multiple investigations have established a connection between inflammation and changes in lipids and their derivatives in the setting of MetS [24–29]. Alteration in the metabolism of fatty acids affects the synthesis of eicosanoids and pro-resolving lipid mediators responsible for immune-metabolic homeostasis [30–33]. Recent studies have further elucidated the role of these metabolites in the contribution to the chronic, low-grade inflammatory state in MetS [34–36]. A comprehensive understanding of the importance of lipids in the pathogenesis of MetS contributes to the development of preventive and targeted lipid-correcting therapy. The aim of the review is to analyse the modern views on the role of lipids, particularly PUFAs and FFAs, in the pathogenesis of MetS. In this review, we summarized the molecular mechanisms of the relationship between fatty acids and glucose transport, inflammatory response, mitochondrial dysfunction, and endoplasmic reticulum stress in the development of MetS. 2. Metabolic Syndrome: Definitions and Criteria Metabolic syndrome (MetS) has become a widely debated scientific, medical, and social problem worldwide. Indeed, the definition of metabolic syndrome is important for clinical practice and deserves serious scientific and medical research. MetS is characterized by the following clinical criteria: abdominal obesity, decreased peripheral tissue sensitivity to insulin, and hyperinsulinemia, which cause metabolic disorders of carbohydrates, lipids, and purines [1, 2]. This combination of metabolic disorders is often found in one person and, thus, significantly increases the risk of cardiovascular disease (CVD), type 2 diabetes mellitus (T2DM), arthritis, chronic kidney disease, schizophrenia, nonalcoholic fatty liver disease (NAFLD), and several types of cancer [37–42]. MetS is characterized by a steadily increasing prevalence [3, 4]. However, its prevalence rates vary depending on the criteria used to determine MetS, genetic component, gender, age, population and area of residence, education, level of physical activity, nutrition, and lifestyle [39]. Approximately one-fourth of world’s adult population have MetS [43, 44]. Urbanization and its associated sedentary lifestyle and surplus nutrition are the root cause of this global epidemic. The determination of MetS uses the criteria of the following medical communities: WHO (World Health Organization), NCEPATP III (National Cholesterol Education Program-Adult Treatment Panel III), AACE (American Association of Clinical Endocrinologists), IDF (International Diabetes Federation), EGIR (European Group for the Study of Insulin Resistance), The International Diabetes Federation (IDF), American Heart Association/National Heart, Lung and Blood Institute (AHA/NLHBI), World Heart Federation (WHF), International Atherosclerosis Society (IAS), and The International Association for the Study of Obesity (IASO) [3]. A guideline was made in 2009 to unify the criteria for the diagnosis of MеtS. According to this guide, three of the five criteria are necessary for diagnosing MetS: (1) waist circumference ≥102 cm for males and ≥88 cm for females (for Asians ≥90 cm for males and ≥80 cm for females); (2) systolic blood pressure ≥130 mmHg or diastolic blood pressure ≥85 mmHg or antihypertensive medication; (3) fasting plasma glucose ≥5.6 mmol/L or on medication for high blood glucose; (4) HDL cholesterol <1.03 mmol/L for males and <1.30 mmol/L for females or on medications for reduced HDL cholesterol; and (5) triglycerides ≥1.7 mmol/L or on medications for elevated triglycerides [3]. Although the exact etiology of the MetS is not clearly understood, insulin resistance (IR) is considered as the principal factor for the pathogenesis of this syndrome [6, 11, 18, 45]. As found by the insulin-modified, frequently sampled intravenous glucose tolerance assay, insulin sensitivity is significantly lower in patients with two or more components of the MetS compared to those with none of these components [2]. Dysregulation of lipid metabolism is considered as an important link in the overall development chain of IR. It is well known that lipids play a critical role in the regulation of energy metabolism, glucose transport, and immune process in many organs and tissues such as the liver, adipose tissue, muscle, heart, and gastrointestinal tract [46]. However, the molecular mechanisms of this regulation remain largely unexplored. The study of lipid metabolism disorders is a promising direction for the development of methods for the effective treatment of this pathology. 3. Polyunsaturated Fatty Acids and Metabolic Syndrome Risk Fatty acids (FAs) play multiple roles in humans and other organisms. Most importantly, FAs are a substantial part of lipids. Fatty acids are either saturated or unsaturated carboxylic acids with carbon chains varying between 2 and 36 carbon atoms. Polyunsaturated fatty acids with an acid end containing the functional carboxylic acid group and a methyl end are also known as omega end. In omega-3 (ω-3 or n-3) and omega-6 (ω-6 or n-6) fatty acids, the first site of desaturation is located after the third and the sixth carbon from the omega end, respectively. Our body cannot synthesize some PUFAs, such as alpha-linolenic acid (18:3n3) and linoleic acid (18:2n6). These essential PUFAs enter our bodies only through diet. The dietary sources of n-3 PUFAs include fish oils rich in eicosapentaenoic acid (20:5n3) and docosahexaenoic acid (22:6n3), whereas the n-6 PUFA linoleic acid is mostly found in plants and vegetable oils [47]. Nowadays, there is growing evidence showing that dietary n-3 PUFAs have a variety of healthy properties such as the reduction of plasma atherogenic lipids and inflammation [16, 21, 25, 41, 47–49]. The associations between n-3 PUFAs and metabolic syndrome risk demonstrate inconsistent results [50]. Several cross-sectional and case-control studies have indicated that plasma/serum n-3 PUFAs were significantly higher in healthy subjects compared with those in patients with MetS, while some studies have suggested opposite and null associations [51]. Meanwhile, Guo et al. showed that higher circulating n-3 PUFAs were significantly associated with decreased MetS risk [52]. A study by Kim et al. demonstrated that in healthy individuals the level of long-chain n-3 PUFAs is positively correlated with insulin sensitivity [53]. A decrease in the level of PUFAs has been established in patients with T2DM and diabetic retinopathy. It was found that the development of insulin resistance is preceded by a reduction of essential n-3 PUFAs in the cell membranes [6]. There are several reasons why n-3 PUFAs are important in the pathogenesis and prevention of MetS. PUFAs perform a structural function, being important components of the cell membrane and determining its physical and chemical properties [10, 16]. The efficiency of glucose transport and expression of many receptors depend on the composition and ratio of PUFAs in the cell membrane [17]. Also, PUFAs are the precursors for inflammatory and pro-resolving lipids mediators’ synthesis [29, 32]. The imbalance between the synthesis of inflammatory and pro-resolving lipids mediators determines the development of chronic inflammation in MetS. Furthermore, we will summarize the main molecular mechanisms underlying the ability of n-3 PUFAs to prevent and/or ameliorate insulin resistance and inflammation in MetS. 3.1. Polyunsaturated Fatty Acids and Glucose Transport The identification of a causal relationship between the composition of fatty acids of cell membranes and MetS pathogenesis significantly contributes to an understanding of the main pathophysiological mechanisms of the disease. Polyunsaturated fatty acids affect the fundamental properties of the cell membrane, including its fluidity, elasticity, receptor expression activity, the functionality of embedded proteins, and signal transmission through lipid rafts, which leads to changes in cell signaling and modification of gene expression [54, 55]. The length and degree of the FA chain unsaturation have a profound effect on the physical and chemical properties of cell membranes [48]. The ratio of polyunsaturated to saturated fatty acids determines the membrane flexibility, which affects the efficiency of glucose transport using insulin-independent glucose transporters (GLUTs) and insulin-dependent GLUT4 [54]. GLUT1 is a monomeric protein with 12 transmembrane helical segments [56]. One molecule GLUT1 covers an area of about 17 molecules of a phosphatidylcholine bilayer with saturated fatty acids (SFAs), which requires a high membrane flexibility for pore formation. GLUT4 is inserted into the membrane of intracellular vesicles, which demands the flexibility of the vesicular membrane. The GLUT4 containing vesicles take part in a fusion process with the cell membrane. The increased flexibility of the membrane provides a smooth bending of the cell membrane bilayer and the fused pores formation [54]. Thus, decreased membrane flexibility causes a reduction in all Class 1 glucose transporters which, in turn, reduces the glucose flux and increases the plasma glucose concentration. Therefore, high membrane flexibility is a crucial factor in glucose transport. Changes in the fatty acid composition of membranes will result in disturbance in the physicochemical properties of the bilayer, such as flexibility and fluidity. Tighter membrane packaging due to increased saturated fatty acids in it leads to a reduction in the capacity for GLUT4 glucose transport [54]. A number of other studies have also revealed that an increase in the level of saturated fatty acids in the cell membrane is associated with a growth in blood glucose level and the development of insulin resistance [13, 14]. The important role of PUFAs in maintaining glucose-insulin homeostasis is confirmed by many studies [13–15, 21, 25, 47, 49, 51, 53]. Comprehensive evidence shows that diet n-3 PUFAs can improve insulin signal transduction in adipocytes, affecting in turn the insulin-stimulated glucose uptake through the regulation of the expression or the translocation of the GLUT4 [11]. In vitro studies have found that adipocytes from n-3 PUFAs-depleted rats had lower basal and insulin-stimulated glucose incorporation, while cultured adipocytes supplemented with fish oil increased levels of GLUT4 and GLUT1 [41]. González-Périz et al. [11] reported that feeding with a marine n-3 PUFAs-enriched diet improved insulin resistance in association with an increased expression of Irs-1 and Glut4mRNA in the adipose tissue of genetically obese ob/ob mice. The above indicates the huge importance of n-3 PUFAs in the development and regulation of components in the MetS, such as insulin resistance and glucose tolerance. 3.2. Polyunsaturated Fatty Acids and Inflammation Numerous studies have suggested that MetS, like its downstream sequelae of atherosclerotic cardiovascular disease and T2DM, is largely an inflammatory disease [24]. A chronic, low-grade inflammatory state caused by obesity leads to metabolic alterations responsible for multiple organ damage [57, 58]. This metabolic dysfunction could determine clinical conditions such as hypertension, hypercholesterinemia, and insulin resistance [40]. The contribution of inflammation to insulin resistance has been widely studied, and immunological changes occurring in various tissues are thought to be etiological factors affecting the development of insulin resistance [58]. A characteristic of obese people is a chronic, low-grade inflammation state promoted by the release of many inflammatory mediators by the adipose tissue and, more importantly, by infiltrating macrophages. PUFAs and their oxidized metabolites are important participants of the inflammatory processes of MetS [13, 15, 27, 28]. Understanding the molecular mechanisms of the participation of PUFAs and their metabolites in the pathogenesis of MetS will contribute to the development of new diagnostic methods and targeted therapy for this disease. 3.2.1. Specialized Pro-Resolving Mediators Inflammation is a complex, multifactorial adaptive process with different periods of development. Inflammation is a natural reaction to harmful irritants, such as bacterial infections, virus infections, and tissue damage. This is a host’s defensive reaction in which immune and endothelial cells and proinflammatory mediators are attracted to eliminate inflammatory agents, clear damaged cells and tissues, and initiate tissue repair. This response, when properly functioning, is self-limiting and leads to the cessation of the inflammatory response and a return to homeostasis, a process called the resolution of inflammation [16, 48]. Resolution of inflammation is now known to be an active process involving the activation of negative feedback mechanisms, such as anti-inflammatory cytokine secretion, reduction in receptor expression, activation of regulatory cells, and production of pro-resolving lipid mediators [57]. However, when acute inflammation is intense or prolonged, the resolution process is not successful, which leads to excessive tissue damage and ultimately resulting in chronic inflammation [16]. Many studies have confirmed that unresolved inflammation is the main mechanism for the pathogenesis of MetS [15, 16, 24, 48, 57, 58]. PUFAs are a source of synthesis of inflammatory and pro-resolving lipid mediators. The major substrate for the synthesis of inflammatory lipid mediators is arachidonic acid (20:4n6) (see Figure 1). The high content of 20:4n6 provides a direct link with inflammation since 20:4n6 released from cell membrane phospholipids acts as a substrate for cyclooxygenase (COX), lipoxygenase (LOX), and cytochrome P450 enzymes [29]. Eicosanoids are important regulators and mediators of acute inflammatory processes and include prostaglandins (PGs), thromboxanes (TBs), and leukotrienes (LTs). Many anti-inflammatory therapies, such as nonsteroidal anti-inflammatory drugs and COX inhibitors, target arachidonic acid metabolism [29, 35, 59, 60]. PUFA pathway and role of lipid mediators in the development and resolution of inflammation. Eicosapentaenoic acid (20:5n3) and docosahexaenoic acid (22:6n3) from the n-3 PUFAs family are a source of synthesizing specialized pro-resolving mediators (SPMs): maresins, resolvins, and protectins (see Figure 1) [32–35]. SPMs are a class of cell compounds generated at a later stage of the inflammation and initiate the resolution of the inflammatory process [33, 34, 61]. SPMs actively facilitate the resolution stage of acute inflammation unlike eicosanoids, which mainly act during the first stage of inflammation. A balanced n-6 : n-3 PUFAs ratio (where 1 : 1 to 2 : 1 is optimal) is important for homeostasis and normal development throughout the lifespan. High n-6 PUFA intake in the Western diet increases the n-6 : n-3 ratio to a range from 10 : 1 to 20 : 1 and may play a role in the pathogenesis of MetS and related diseases [36]. The balance between n-3 PUFAs and n-6 PUFAs determines the path of inflammatory response. The prevalence of n-6 PUFAs and the shortage of n-3 PUFAs may contribute to impaired inflammation resolution [31]. Docosahexaenoic acid- and eicosapentaenoic acid-derived SPMs are identified in the adipose tissue. At the same time, the levels of certain SPMs are markedly reduced with obesity, suggesting adipose SPM deficiency, potentially resulting in unresolved inflammation [36]. Resolvins are synthesized spontaneously from eicosapentaenoic and docosahexaenoic acids during inflammation and thus are designated as E‐series (RvE) and D‐series (RvD), respectively [32]. The anti-inflammatory effect of RvE1 is due to interaction with peroxisome proliferator-activated receptors (PPARs), which are classified as nuclear transcription factors with anti-inflammatory activity. Leukotriene B4 receptor 1 (BLT1) and G protein-coupled receptor, Chemerin Receptor 23 (ChemR23), are receptors for RvE1. RvE2 has a similar biologic effect; it regulates neutrophil chemotaxis and activates phagocytosis and proinflammatory cytokines synthesis [31, 62]. Protectins (PD) are another class of pro‐resolving molecules produced from 22:6n3 during the resolution of inflammation. Protectins are synthesized by a number of cells including brain cells, monocytes, and CD4+ lymphocytes [33]. PD1, the key representative of the protectin family, demonstrates a strong anti-inflammatory and neuroprotective effect. This mediator functioning is based on PPARs interacting and NF-κB blocking [62]. An alternative process for docosahexaenoic acid oxygenation is found in human macrophages and platelets, leading to the synthesis of maresin 1 (MaR1). In addition, 13S, 14S‐epoxy‐maresin, which has important biological activities of its own, is the precursor for maresin 2 (MaR2) [36]. Lipoxins (LXs) are powerful anti-inflammatory bioregulators suppressing inflammation and activating resolution and recovery processes, in particular in MetS [34]. The substrate for LXs synthesis is arachidonic acids. Two members of the LXs family, LXA4 and LXB4, have been well studied [31]. In general, LXs are a branch of the leukotriene family. For example, their production by platelets is catalyzed by 12-LOX through converting LTA4 [32]. Unlike proinflammatory LTs, LXs act as powerful anti-inflammatory bioregulators, suppressing the inflammation and activating the processes of resolution and recovery. The result of their action is the inhibition of chemotaxis and migration of macrophages and neutrophils to the inflammatory focus, blocking of the lipid peroxidation, the activation of NF-κB, and the suppression of the synthesis of proinflammatory cytokines. In addition, LXs are actively involved in functioning of macrophages that are associated with homeostasis restoration processes [32]. There is a considerable amount of evidence regarding the contribution of n‐3 PUFAs to diseases with inflammatory conditions, such as metabolic syndrome [25, 34–36, 47, 49, 51, 53]. It was reported that SPM levels reduced in metabolic syndrome as well as sensitivity to SPM of the adipose tissue [36]. Obesity reduces the levels of PD1, intermediates in the synthesis of D-series resolvins and protectins (17-HDHA), and intermediates in the maresin biosynthesis (14-HDHA) for the adipose tissues from diet- and genetically-induced obese mice [36]. One of the mechanisms resulting in a decrease in the SPM level in obesity is a change in the enzyme activities involved in biosynthesis or conversion of certain SPMs. N‐3 PUFAs supplementation increased the level of SPM in the blood of individuals with obesity and MetS. The effects of n-3 PUFAs are mediated by their ability to interfere with arachidonic acid metabolism and promote the synthesis of SPMs. The supply of n‐3 PUFAs increases the levels of resolvins, enhances resolution, and improves insulin sensitivity in an experiment with fat‐1 mice. In addition, n‐3 PUFAs prevent macrophage increase, adipokine secretion, and insulin resistance induced by a high‐fat diet. Synthetic pro-resolving lipid mediators (17-hydroxy-DHA) or n-3 PUFAs added to the treatment contributed to higher levels of pro-resolving lipid mediators in the adipose tissue, reduced inflammation, and increased insulin sensitivity [31]. N-3 PUFAs increased RvЕ-series levels in patients with MetS but did not affect RvD-series, which requires further studies into the mechanism of n-3 PUFAs influence in MetS. For instance, intraperitoneal administration of 17‐HDHA or RvD1 significantly reduced adipose inflammation and improved the glucose tolerance in diet‐induced obese mice and in db/db mice [63]. Treatment with either RvD1 or RvD2 also reduced the secretion of proinflammatory cytokines including TNF‐α, IL‐1β, and IL‐12 in the adipose tissue [64]. The MaR1 treatment improved insulin sensitivity, determined with an insulin tolerance test. MaR1 also increased adiponectin gene expression and Akt phosphorylation in the adipose tissues and attenuated adipose tissue inflammation in both ob/ob and diet‐induced obese mice [36]. PD1 treatment acutely increased the adiponectin transcripts in adipose tissue explants isolated from ob/ob mice. A potent ability to induce adiponectin expression/secretion has been demonstrated with synthetical RvD1, RvD2, and PD1 and their biosynthetic intermediate, 17‐HDHA [63]. Therefore, one of the pathogenetic mechanisms of the development of MetS is a reduction of the processes of resolving inflammation and the development of chronic, low-grade inflammatory. A decrease in the synthesis of specialized pro-resolving lipid mediators is the basis of the above disorders [65]. Thus, the anti-inflammatory effect of n-3 PUFAs in MetS can be mediated through the regulation of the SPM synthesis. 3.2.2. Toll-Like Receptor 4 The inflammatory process observed in individuals with metabolic syndrome differs from the classical inflammatory response and this type of inflammation characterized by a chronic, low-intensity reaction [58]. The toll-like receptor 4 (TLR4) signaling pathway is acknowledged as one of the main triggers of the obesity-induced inflammatory response [57]. TLR4 plays a significant role in the pathogenesis of inflammation mediated by insulin resistance in MetS [57]. Toll-like receptors, including TLR4, are type 1 transmembrane proteins with three domains: (1) extracellular domain with leucine-rich repeats (LRRs) responsible for ligand recognition; (2) transmembrane domain; and (3) intracellular toll/interleukin-1 receptor (TIR) domain. These provide signal transmission from the cell surface to adapter proteins. TLR4 was the first TLR reported in humans; it is expressed in innate immune cells, including monocytes, macrophages, and dendritic cells, as well as in other cell types, such as adipocytes, enterocytes, and muscle cells. TLR4 is a membrane-associated receptor involved in lipid recognition [66]. TLRs are activated both by the influence of endogenous ligands and by the participation of lipids—cholesterol, SFAs, and oxidized forms of phospholipids [67]. Humans with type I diabetes exhibit greater expression of TLR4 in the cellular membrane in monocytes. Individuals with T2DM show increased cellular membrane levels of TLR4 in blood monocytes, as well as a higher concentration of IL-1, IL-6, IL-8, and TNF in serum. Similarly, TLR4 is more highly expressed in blood mononuclear cells and in the abdominal subcutaneous white adipose tissue of obese and diabetic individuals [57, 68]. Lipids from foods change the expression of TLRs by cells [69]. On the one hand, SFAs activate the TLR4 signaling pathway (see Figure 2). Among the SFAs, lauric acid (12 : 0) and palmitic acid (16 : 0) had the strongest activation capacity through TLR4 [69]. On the other hand, TLRs can be inhibited by PUFAs [70]. Consumption of n-3 PUFAs, particularly 22:6n3, is associated with anti-inflammatory and cardioprotective effects. It is believed that the use of n-3 PUFAs is associated with anti-inflammatory activity due to inhibition of arachidonic acid metabolism [71]. The molecular effect of n-3 PUFAs, especially 20:5n3 and 22:6n3, on inflammatory-response modulation are based on the ability of these PUFAs to inhibit the expression of inflammatory genes, such as COX-2, iNOS, and IL-1 in macrophages [72]. PUFAs of the n-3 family reduce the activation of the NF-κB transcription factor pathway that is induced by various agonists [70]. The role of TLRs and FAs in the signaling mechanisms of inflammation in the adipose tissue and insulin resistance. The saturated fatty acids (SFAs) act as nonmicrobial TLR4 agonists or indirectly promote the TLR4 activation, triggering its inflammatory response and inflammation of the adipose tissue. Inflammatory signaling caused by saturated fatty acids via TLR4/MD-2 inhibits the phosphorylation of the insulin receptor, leading to the development of insulin resistance. GPR120 activation induced by n-3 PUFA leads to a decrease in the activity of IKK-β/NF-κB and JNK/AP-1 signaling pathways, which reduces the expression of proinflammatory genes. The anti-inflammatory properties of PPARs are achieved by inhibiting nuclear factor-kappa B (NF-κB). N-3 PUFAs directly interact with PPARs and modulate the expression of proinflammatory genes. Other mechanisms modulate the inflammatory response by fatty acids based on binding G protein-coupled receptor 120 (GPR120) [66]. GPR120 is a free fatty acid 4 receptor (FFAR4), and GPR120 activation induced by n-3 PUFA leads to β-arrestin 2 recruitment to the plasma membrane where this protein binds to GPR120 (see Figure 2) The GPR120/β-arrestin 2 complex is internalized into the cytoplasmic compartment where this complex binds to the TAK1-binding protein (TAB1). This process impairs the association between TAB1 and the kinase activated by the growth factor beta (TAK1) and, consequently, results in reduced TAK1 activation and decreases the activity of the IKK-β/NF-κB and JNK/AP-1 signaling pathways. The mitigation of TAK-1 activation by n-3 PUFAs leads to the reduced expression of TNF-α and IL-6 genes with proinflammatory actions [17, 57]. One more important molecular mechanism that is associated with the n-3 PUFA effects concerns their capacities to bind to PPARs [62]. Three isoforms of PPARs are known: PPARα (NR1C1), PPARβ/δ (NR1C2), and PPARγ (NR1C3). PPARs are involved in the regulation of inflammatory reactions and lipid metabolism. The anti-inflammatory properties of PPARs are mainly achieved by inhibiting nuclear factor-kappa B (NF-κB) which, in turn, is the proinflammatory nuclear transcription factor [73]. The interactions between PPARs, NF-κB, and toll-like receptors (TLRs) are of great interest. Along with the anti-inflammatory mechanism of action of PPARs, the proinflammatory activity of some isoforms of PPARs is also being studied. For example, PPARγ is considered a mediator of interactions between dendritic and T cells in the development of type 2 (or T2) inflammation [73]. N-3 PUFAs directly interact with PPARs and, therefore, modulate the expression of genes that are involved in lipid metabolism and the inflammatory response [57]. Anti-inflammatory effects of 20:5n3 and 22:6n3 on this signaling pathway can occur due to diminished nicotinamide adenine dinucleotide phosphate (NADPH) oxidase activity, which leads to lower TLR4 recruitment for lipid rafts and TLR4 dimerization [16]. Also, another possible mechanism of action of the n-3 PUFA concerns the capacity of incorporating 22:6n3 into the plasma membrane, which can lead to reduced TLR4 translocation for lipid rafts formation [74, 75]. The variety of molecular mechanisms in lipids and TLR4 signaling pathway interaction indicates the complexity of the pathogenesis of MetS and associated diseases. 3.3. Polyunsaturated Fatty Acids and Plasmalogens Permanent exogenous use of PUFA is a necessary condition for maintaining immune-metabolic homeostasis. The profile of fatty acids that are present in the Western diet consists of a high level of saturated fatty acids and trans fatty acids. While the total consumption of marine and plant n-3 polyunsaturated fatty acids in contemporary society is significantly reduced [7, 76]. Another reason for PUFAs reduction is deterioration in the plasmalogen synthesis [77]. Plasmalogens are a subclass of phospholipids characterized by having a vinyl ether bond linking the fatty aldehyde to the glycerol molecule in the 1-position and a fatty acyl bond in the 2-position. The sn-1 position consists of palmitic acid (16 : 0), stearic acid (18 : 0), or oleic acid (18 : 1) carbon chains, and the head group is usually either ethanolamine or choline. Thus, there are two main types of plasmalogens: ethanolamine plasmalogens and choline plasmalogens. The sn-2 position is generally occupied by PUFAs, specifically arachidonic acid or docosahexaenoic acid [78, 79]. The highest concentrations of plasmalogens are found in the brain, red blood cells, skeletal muscle, and spermatozoa and can represent as much as 18–20% of the total phospholipids in cell membranes [78, 79]. Plasmalogens are either derived from dietary sources and/or are synthesized mainly in the liver and gastrointestinal epithelium. Plasmalogens are not only important structural phospholipids in the cell membranes but they are also reservoirs of secondary messages and mediators of membrane dynamics and involved in membrane fusion, ion transport, cholesterol efflux, membrane-bound enzyme activity, and diffusion of signal-transduction molecules [80]. Secondary deficiency of plasmalogens triggered by their synthesis reduction or their degradation growth is associated with metabolic and inflammatory disorders such as cardiac diseases and diabetes mellitus [77]. The specificity of choline plasmalogens as a sensitive biomarker of an atherogenic state was confirmed. On the one hand, positive correlations of the choline plasmalogen content with serum adiponectin concentration and high-density lipoproteins (HDL), and on the other hand, inverse relationships with waist circumference, including triacylglycerides and low-density lipoproteins (LDL) content, have been identified. Reduced levels of ethanolamine plasmalogens in plasma have been shown to be also closely associated with cardiovascular, metabolic, and cancer diseases [81]. The content of plasmalogens is relatively stable in all lipoprotein fractions. However, the correlation between the levels of choline plasmalogens and HDL is stronger than that between the levels of ethanolamine plasmalogens and HDL. In the study by Pietiläinen et al., a decrease in the level of plasmalogens in adipocyte membranes in obese twins was established compared with metabolically healthy twins. Conversely, plasmalogen levels increase in trained people and dietary patients [82]. At the same time, it was found that the level of plasmalogens increases in the liver of rats receiving a high-fat diet [83]. The adaptation of the phospholipid composition of cells to exogenous lipid changes has been verified [83]. The compensatory response to a decrease in the plasmalogen level is the regulation of the level of phosphatidylethanolamine [84]. However, with plasmalogen deficiency, the total amount of PUFAs in phosphatidylethanolamine remains constant in human fibroblasts and in the brains of mice. Plasmalogens have been noted to play an important role as neuroprotectors and modulators of the signaling mechanisms of cell membranes [85]. Plasmalogens also act as endogenous antioxidants, protecting lipids and lipoproteins from oxidative stress [86]. This can be attributed to the fact that the hydrogen atoms adjacent to the vinyl ether bond are more susceptible to oxidation, protecting PUFAs from it that are found in the sn-2 position of the glycerol residue. Plasmalogen oxidation products are not capable of further initiation of lipid peroxidation processes. Another important function of plasmalogens is their participation in cell metabolism and transmembrane transport of FAs. The presence of PUFAs in the side chains of plasmalogens preconfigures their significant depositing function [77]. Cholesterol esterification depends on the level of plasmalogens. So, for example, the cells characterized by plasmalogen deficiency demonstrated a lower level of esterified cholesterol and a higher level of free and total cholesterol [84]. Therefore, the important role of plasmalogens as modulators of signaling mechanisms in protecting cells from lipid peroxidation and participation in PUFA metabolism has been made clear. However, the exact biological functions of plasmalogens and the underlying molecular mechanisms still remain to be discovered [52, 55]. 4. Free Fatty Acids and Metabolic Syndrome Risk Free fatty acids (FFAs), or nonesterified fatty acids (NEFAs), in circulating plasma are derived from the ingestion of dietary fat or from the triglycerides stored in adipose tissue that are distributed to cells to serve as fuel for muscle contraction and systemic metabolism [87]. As FAs are insoluble in water, they are transported by binding to plasma albumin. FFAs can be taken up from circulating plasma by all mitochondria-containing cells, and they are metabolized by β-oxidation [17]. FFAs carry out many important biological functions in the body, and they are a source of energy, signal molecules, and structural components of cell membranes [17]. FFAs are involved in the pathogenesis of insulin resistance and subsequent development of metabolic syndrome [12, 17, 87]. Chronic energy imbalance can trigger adipocyte hypertrophy, endoplasmic reticulum stress, and mitochondrial dysfunction, which lead to the systemic release of FFAs [17, 88–90]. When plasma FFA levels rise, as occurs in obesity, a lipotoxicity state is induced, which induce activation of different cell responses: oxidative stress, apoptosis, and inflammation [17]. Consequently, FFAs play a highly important role in the association between obesity and insulin resistance. 4.1. FFAs and Mitochondria There is an interesting hypothesis that IR is associated with the development of mitochondrial dysfunction [87, 90]. Lipid degradation occurs in mitochondria. On top of that, the normal functioning of mitochondria provides glucose-stimulated insulin secretion from β-cells of the pancreas. Initially, the theories have suggested that impaired mitochondrial function leads to impaired β-oxidation of lipids, which is accompanied by the accumulation of FFAs in peripheral tissues (lipotoxicity theory) [91]. The accumulation of lipid metabolites brings about the activation of kinases involved in the disruption of insulin signaling at the level of insulin receptor substrate 1 (IRS-1). In skeletal muscles, insulin signaling pathway disorder is accompanied by a decrease in the production of GLUT4 and glucose uptake by cells. In this case, an improvement in insulin sensitivity can be achieved by enhancing the β-oxidation of lipids. This theory was supported by studies that proved an increase in the rate of β-oxidation of lipids to be followed by protection against the development of IR [17]. Nevertheless, the early stages of obesity and IR development are characterized by an increase in β-oxidation of lipids. Besides, an impairment of fat oxidation results in higher insulin production. Therefore, mitochondrial dysfunction in skeletal muscles cannot be the only reason for the development of IR [92]. An alternative explanation of the relationship between mitochondria and insulin resistance is focused on the production of a reactive oxygen species (ROS) by mitochondria as a result of excess accumulation of FFAs in them [92]. Oxidative stress is known to be a pathogenetic component of chronic inflammation development and IR [88]. An oxidized redox environment can induce insulin resistance by directly affecting the protein involved in glucose uptake [89]. On the other hand, changes in redox cell homeostasis have been argued to step up the activity of the serine-/threonine-sensitive stress kinases that inhibit the transmission of insulin signals, inducing the development of IR [93, 94]. Oxidative stress also can stimulate the activation of transcriptional factors, such as nuclear factor-kappa B (NF-κB), activator protein 1 (AP-1), and hypoxia-inducible factor 1 (HIF-1), which promote the synthesis of inflammatory cytokines (IL-1β, IL-6, and TNF-α) (see Figure 3). These inflammatory cytokines contribute to obesity-associated local inflammation and directly induce insulin resistance. Also, chronic prolonged FFAs excess is the cause of pancreatic β-cells dysfunction. In addition, FFAs inhibit insulin gene expression and induce apoptosis in these cells [17]. FFA-induced insulin resistance through endoplasmic reticulum stress and oxidative stress. A high level of FFA induces an increase in the production of ROS by mitochondria and the formation of oxidative stress. ROS stimulates NF-κB, which promotes the synthesis of L-1β, IL-6, and TNF-α. These inflammatory cytokines contribute to obesity-associated local inflammation and directly induce insulin resistance. In response to the enhanced level of FFAs and other nutrients in fats, adipose cells can develop signs of ER stress. A decrease in SERCA expression promotes the development of ER stress. UPR triggers the activation of IRE. Activation of IRE induces interaction with TRAF protein, which stimulations activation of IKKβ and JNK kinases. Its reaction can phosphorylate the IRS, thus blocking insulin signaling. JNK and IKKβ also lead to NF-κB activation and the development of inflammation. Although discussing the role of mitochondrial skeletal muscle dysfunction in the pathogenesis of IR and type 2 diabetes is still underway [93], it is generally accepted that a mitochondrial defect does occur in these diseases. The connection between IR and mitochondrial dysfunction of liver cells, visceral, and subcutaneous adipose tissue has been proved [95]. Moreover, in the mitochondria of individuals suffering from obesity and type 2 diabetes, ATP synthesis is reduced, which correlates with the accumulation of FFAs and inhibition of insulin-stimulated glucose utilization. 4.2. FFAs and Endoplasmic Reticulum Stress Results of numerous studies establish that dysregulation of the endoplasmic reticulum (ER) function contributes to the development of MetS [96, 97]. Mitochondria are known to be both functionally and structurally associated with ER [97]. Obviously, the changes of the structure and function of these organelles can serve as a trigger for the development of metabolic homeostasis disorders [96]. ER is involved in maintaining Ca2+ homeostasis and participates in maturation and expression of membrane and secretion proteins. Cell stress conditions that increase ER demand and entail an overload of its functional capacity cause a series of alterations known as “endoplasmic reticulum stress.” Under these conditions, the ER activates a compensatory mechanism called the “unfolding protein response” (UPR), which attempts to restore the homeostasis of ER functions. With the stressful effects lasting for a long time, ER stress results in cell death (apoptosis) [17, 98]. UPR triggers activation of inositol-requiring endoribonuclease enzyme (IRE) (see Figure 3). The activation of IRE induces interaction with TRAF protein, which stimulations activation of IKKβ and JNK kinases. Its reaction can phosphorylate IRS, thus blocking insulin signaling. In response to the enhanced level of FFAs and other nutrients in fats, adipose cells can develop signs of ER stress [17]. ER stress produces insulin resistance mainly through JNK activation. JNK activity has been detected to be elevated in animal models of obesity, and JNK isoforms 1 and 2 deletion protects mice from insulin resistance induced by a fat-rich diet. Experimental evidence indicates that, on the one hand, JNK phosphorylates serine IRS-1, and on the other, it phosphorylates IKKβ, which leads to NF-κB activation and to inflammation development [96]. Remarkably, that change in expression of sarco/endoplasmic reticulum Ca2+ ATPase (SERCA), which has calcium elimination from the cytosol and returns it to the ER as their function, is associated with ER stress and subsequently with insulin resistance. The treatment of people with diabetes mellitus by rosiglitazone, an antidiabetic drug, increased SERCA expression, thus restoring the pump expression reduction observed in diabetic patients with altered hyperglycemia [17]. This way, the decrease in SERCA expression promotes the development of ER stress, with JNK ensuing activation, which desensitizes the insulin signal, thus generating a state of insulin resistance and contributing to chronic metabolic deterioration. 4.3. FFAs as Ligands for FFAR FFAs serve not only as energy sources but also as natural ligands for a group of orphan G protein-coupled receptors (GPCRs) termed free fatty acid receptors (FFARs) [99]. The GPCR superfamily is the largest one in the human genome and encompasses some subfamilies (Gq, Gi, Gs, and G12/13) [100]. These receptors respond to various ligands and, therefore, are involved in the pathogenesis of many diseases, e.g., MetS, and are the target for more than half of pharmaceutical products [101–106]. There are four main members of FFAR family: FFAR1 (GPR40), FFAR2 (GPR43), FFAR3 (GPR41), and FFAR4 (GPR120 and GPR84) (see Table 1) [75]. Table 1 Family of FFARs and their ligands. FFAR1 expression was revealed in neurons and in pancreas β-cells [99]. FFAR2 and FFAR3 are common in leukocytes and adipose tissues. Besides, FFAR3 is also expressed by pancreas cells, in the sympathetic nervous system and vessel plain muscles [100]. FFAR4 is expressed in adipocytes, the intestinal tract, macrophages, and in the central nervous system [105]. There are other specific receptors for FFA: GPR119 and GRP84. GPR119 is expressed in intestinal endocrine cells and pancreatic β-cells and activates the synthesis of GLP-1 and insulin. GPR84 is expressed in the spleen, thymus, leukocytes, and macrophages [99]. Long- and medium-chain length fatty acids are endogenous ligands for FFAR1, FFAR4, and GPR84. FFAR2 and FFAR3 are activated by short-chain FAs. FFAR2 is capable of binding with Gq and Gi proteins, whereas FFAR3 binds only with Gi. FFAR4 is activated by n-3 or n-6 PUFAs [99]. Thus, each FFAR can act as an FFA sensor with selectivity for a particular FFA carbon chain length derived from food or food-derived metabolites. FFARs have been reported to have physiological functions such as facilitation of insulin and incretin hormone secretion, adipocyte differentiation, anti-inflammatory effects, neuronal responses, and taste preferences [106]. Dysfunction of FFARs underlies the pathogenesis of many metabolic diseases, such as MetS and diabetes mellitus. It has been found that FFAR4 acts as an anti-inflammatory receptor in proinflammatory macrophages and mature adipocytes. Signaling of FFAR4 activated by n-3 PUFAs inhibits TLR signaling and TNF-α-induced inflammatory responses. FFAR4 dysfunction leads to obesity and glucose intolerance in humans and mice [107]. Many results strongly support that FFAR4-mediated anti-inflammatory effects reduce the infiltration of proinflammatory macrophages into the adipose tissue and improve insulin sensitivity [102]. The activation of FFAR1 signaling enhances glucose-stimulated insulin secretion (GSIS) directly via stimulation of insulin secretion from pancreatic β cells and indirectly via the production of incretin hormones. Also, the activation of FFAR1 signaling reduces the expression of inflammatory cytokines such as TNF-α and IL-8. It has been shown that α-linolenic (18:3n3) and oleic (18:1n9) acids improve insulin resistance in obesity and type 2 diabetes [108]. There is some scientific evidence that short-chain fatty acids (SCFAs) are a substantial modulator of MetS inflammation [109]. SCFAs are the end products of metabolic fermentation of dietary fibers by gut microbiota. FFAR2 is a receptor for SCFAs and is expressed in enteroendocrine cells, adipose tissues, and pancreatic β-cells [99]. Dietary fiber intake reduces the risk of obesity, diabetes, inflammatory bowel disease, colon cancer, and cardiovascular disease. SCFAs supplementation with a high-fat diet improved insulin sensitivity and increased energy expenditure in a mouse model of diet-induced obesity [110, 111]. SCFAs are involved in intestinal immune homeostasis due to their role in regulating T cell polarization and differentiation. In human monocytes, SCFAs decrease the production of TNF-α and increase the production of PGE2 [109]. Activation of FFAR2 by SCFAs regulates metabolic disorders, increases energy expenditure, and preferentially enables fat consumption by inhibition of insulin signaling in adipose tissues. The expression of FFAR2 in neutrophils and mononuclear cells regulates intestinal homeostasis and inflammation. In light of this evidence, regulation FFAR2 expression and/or high fiber consumption may be a potential target for therapeutic intervention of MetS. FFAR3 is also a receptor for SCFAs. FFAR3 is widely expressed in enteroendocrine cells, adipose tissues, the peripheral nervous system, peripheral blood mononuclear cells, monocytes, and macrophages [99]. FFAR3 expression in intestinal epithelial cells enhances the synthesis of proinflammatory mediators through extracellular signal-regulated kinase 1/2 and p38 MAPK [100]. Since these pathways help to protect against bacterial infection, FFAR3 can stimulate acute inflammatory reactions in the intestine that have beneficial effects on host homeostasis [112]. Thus, FFAR 3 can exhibit proinflammatory properties. 5. Conclusion The wide phenotypic heterogeneity of MetS and its complex pathogenesis make it difficult to identify a therapeutic target. This syndrome is considered as a cluster of pathogenetically related conditions caused by metabolic disorders and the development of chronic, low-grade inflammation. In this review, we examined the molecular mechanisms of the development of MetS driven by impaired lipid metabolism. PUFAs and FFAs have been shown to play an important role in both the pathogenesis and treatment of MetS. Fatty acids perform structural, energy, signaling, and immunoregulatory functions. These FAs properties underlie the pathogenetic mechanisms of glucose transport disturbance, the development of IR and chronic inflammation, the formation of oxidative stress, and mitochondrial dysfunction in MetS. Correction in lifestyle and nutrition is considered as the main way to minimize complications caused by an imbalance in the body between saturated and polyunsaturated fatty acids. At the same time, there are controversial data about the therapeutic efficacy of dietary n-3 PUFAs in MetS [50]. SPMs have shown potent pro-resolving actions in different disease models, including MetS [61]. SPM-based therapeutics could be one of the most optimistic treatments for MetS. Further studies are needed to detail the mechanisms of FA participation and their oxidized metabolites in the development of inflammation and pathogenesis of MetS. Conflicts of Interest The authors declare that there are no conflicts of interest regarding the publication of this paper. Acknowledgments The study was funded by the Ministry of Education and Science of the Russian Federation. Copyright © 2020 Yulia K. Denisenko et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/29%3A_Integration_of_Mammalian_Metabolism_-_Capstone_Volume_II/29.07%3A__Lipid-Induced_Mechanisms_of_Metabolic_Syndrome.txt

Fundamentals of cancer metabolism 1. Ralph J. DeBerardinis1,* and 2. Navdeep S. Chandel2,* Science Advances 27 May 2016: Vol. 2, no. 5, e1600200. DOI: 10.1126/sciadv.1600200 This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited. Abstract Tumors reprogram pathways of nutrient acquisition and metabolism to meet the bioenergetic, biosynthetic, and redox demands of malignant cells. These reprogrammed activities are now recognized as hallmarks of cancer, and recent work has uncovered remarkable flexibility in the specific pathways activated by tumor cells to support these key functions. In this perspective, we provide a conceptual framework to understand how and why metabolic reprogramming occurs in tumor cells, and the mechanisms linking altered metabolism to tumorigenesis and metastasis. Understanding these concepts will progressively support the development of new strategies to treat human cancer. INTRODUCTION AND OVERARCHING PRINCIPLES Cancer metabolism is one of the oldest areas of research in cancer biology, predating the discovery of oncogenes and tumor suppressors by some 50 years. The field is based on the principle that metabolic activities are altered in cancer cells relative to normal cells, and that these alterations support the acquisition and maintenance of malignant properties. Because some altered metabolic features are observed quite generally across many types of cancer cells, reprogrammed metabolism is considered a hallmark of cancer (1, 2). Precisely how metabolism becomes reprogrammed in cancer cells, whose functions or malignant properties are enabled by these activities, and how to exploit metabolic changes for therapeutic benefit are among the key questions driving research in the field. This review covers several fundamental principles in cancer metabolism, with the goal of introducing non-experts to the concepts motivating ongoing research. With the explosion of research in cancer metabolism over the past decade, no single review can possibly cover it all. The sections below highlight some of the essential, recent papers supporting these core principles. An overarching theme in cancer metabolism is that reprogrammed activities improve cellular fitness to provide a selective advantage during tumorigenesis. Most of the classical examples of reprogrammed activities either support cell survival under stressful conditions or allow cells to grow and proliferate at pathologically elevated levels. Three of these—altered bioenergetics, enhanced biosynthesis, and redox balance—are discussed at length below. It logically flows that if these activities provide benefit to the malignant cell, then some of them might be suitable therapeutic targets. This rendering of cancer metabolism is supported by many examples in which inhibition of an enhanced metabolic activity results in impaired growth of experimental tumors (3, 4). In some cases, the particular metabolic liabilities of cancer cells have been translated into effective therapies in human cancer. Asparaginase, an enzyme that converts the amino acid asparagine to aspartic acid and ammonia, is an essential component of treatment for acute lymphoblastic leukemia (ALL) (5). Because of their high rates of protein synthesis and poor ability to synthesize asparagine de novo, ALL cells require a constant supply of asparagine from the plasma. This supply is essentially eliminated by systemic administration of asparaginase. Ultimately, effective metabolic therapy will require defining the stage of tumor progression in which each pathway provides its benefit to the cancer cell. Some activities first become essential very early in tumorigenesis as the nascent tumor begins to experience nutrient limitations (6). In other cases, altered pathways may be dispensable in primary tumors but essential for metastasis (7, 8). Because new therapeutic targets are nominated from simple experimental models like cultured cells, it will be essential to define their context-specific roles in biologically accurate models of tumor initiation and progression. METABOLIC REPROGRAMMING AND ONCOMETABOLITES IN CANCER Altered metabolic activity supports anabolic growth during nutrient-replete conditions, catabolism to support cell survival during nutrient limitation, and fortification of redox homeostatic systems to counteract the metabolic effects of oncogene activation, tumor suppressor loss, and other stresses (9). Discovery and characterization of reprogrammed activities may provide opportunities to image tumor tissue noninvasively, predict tumor behavior, and prevent tumor progression by blocking essential pathways. It is important to differentiate “metabolic reprogramming” from “oncometabolites,” two terms widely used in the recent cancer metabolism literature (10). We propose that the term metabolic reprogramming be used to describe conventional metabolic pathways whose activities are enhanced or suppressed in tumor cells relative to benign tissues as a consequence of tumorigenic mutations and/or other factors. Oncometabolite is a relatively new term that refers to metabolites whose abundance increases markedly in tumors. We suggest that this term be reserved for metabolites for which (i) there is a clear mechanism connecting a specific mutation in the tumor to accumulation of the metabolite, and (ii) there is compelling evidence for involvement of the metabolite in the development of malignancy. The classical example of a reprogrammed metabolic pathway in cancer is the Warburg effect or aerobic glycolysis (11). Glycolysis is a physiological response to hypoxia in normal tissues, but Otto Warburg in the 1920s observed that tumor slices and ascites cancer cells constitutively take up glucose and produce lactate regardless of oxygen availability, an observation that has been seen in many types of cancer cells and tumors (12). The increase in glycolytic flux allows glycolytic intermediates to supply subsidiary pathways to fulfill the metabolic demands of proliferating cells (11). Like glycolytic intermediates, tricarboxylic acid (TCA) cycle intermediates are also used as precursors for macromolecule synthesis (13). Their utilization in biosynthetic pathways requires that carbon be resupplied to the cycle so that intermediate pools are maintained; pathways that “refill” the cycle are termed anaplerotic pathways, and they generate TCA cycle intermediates that can enter the cycle at sites other than acetyl-CoA (coenzyme A) (14). Two activities that provide anaplerotic fluxes in cancer cells are glutaminolysis, which produces α-ketoglutarate from glutamine, and pyruvate carboxylation, which produces oxaloacetate from glucose/pyruvate. Oxidation of the branched-chain amino acids (BCAAs) isoleucine and valine also provides an anaplerotic flux in some tissues. Despite the incredible genetic and histological heterogeneity of tumors, malignancy seems to involve the common induction of a finite set of pathways to support core functions like anabolism, catabolism, and redox balance (15). The general induction of these pathways may reflect their regulation by signaling pathways that are commonly perturbed in cancer cells (Fig. 1). Normal cells, upon stimulation by growth factors, activate phosphatidylinositol 3-kinase (PI3K) and its downstream pathways AKT and mammalian target of rapamycin (mTOR), thereby promoting a robust anabolic program involving increased glycolytic flux and fatty acid synthesis through activation of hypoxia-inducible factor–1 (HIF-1) and sterol regulatory element–binding protein (SREBP), respectively (16). Tumor cells very frequently contain mutations that allow the PI3K-AKT-mTOR network to achieve high levels of signaling with minimal dependence on extrinsic stimulation by growth factors (17). Many of the best-characterized oncogenes and tumor suppressors reside in the PI3K-AKT-mTOR network, and aberrant activation of this pathway is among the most frequent alterations seen in a diverse set of cancers. Another commonly deregulated pathway in cancer is gain of function of MYC by chromosomal translocations, gene amplification, and single-nucleotide polymorphisms. MYC increases the expression of many genes that support anabolic growth, including transporters and enzymes involved in glycolysis, fatty acid synthesis, glutaminolysis, serine metabolism, and mitochondrial metabolism (18). Oncogenes like Kras, which is frequently mutated in lung, colon, and pancreatic cancers, co-opt the physiological functions of PI3K and MYC pathways to promote tumorigenicity. Aside from oncogenes, tumor suppressors such as the p53 transcription factor can also regulate metabolism (19). The p53 protein–encoding gene TP53 (tumor protein p53) is mutated or deleted in 50% of all human cancers. The tumor-suppressive functions of p53 have been ascribed to execution of DNA repair, cell cycle arrest, senescence, and apoptosis. However, recent studies indicate that p53 tumor-suppressive actions might be independent of these canonical p53 activities but rather dependent on the regulation of metabolism and oxidative stress (20, 21). Loss of p53 increases glycolytic flux to promote anabolism and redox balance, two key processes that promote tumorigenesis (19). A salient feature of many tumors is that they reside in a low-oxygen environment (hypoxia) ranging from 0 to 2% O2 because the tumor cell proliferation rate often exceeds the rate of new blood vessel formation (angiogenesis) (22). The metabolic adaptation to hypoxia is coordinated by HIF-1, which induces metabolic genes involved in increasing glycolytic flux (23). Some tumors display constitutive activation of HIF-1 under normoxic conditions through a variety of mechanisms, including (i) hyperactivation of mTORC1, (ii) loss of von Hippel–Lindau, (iii) accumulation of ROS, and (iv) accumulation of the TCA cycle metabolites succinate or fumarate due to cancer-specific mutations in succinate dehydrogenase (SDH) or fumarate hydratase (FH), respectively (24). The robust coordinated induction of metabolic pathways that support tumorigenesis by combination of deregulation of PI3K-AKT-mTOR signaling pathways, loss of tumor suppressors, and activation of oncogenes alleviates the necessity of having mutations or amplifications in metabolic enzymes per se. Thus, examples of metabolic enzyme deregulation through genetic mutation are rare. One example is the elevated expression of phosphoglycerate dehydrogenase (PHGDH) due to amplification in a fraction of breast cancer and melanoma (25, 26). PHGDH catalyzes the conversion of the glycolytic intermediate 3-phosphoglycerate to 3-phosphohydroxypyruvate in the first step of the serine biosynthesis pathway. Serine metabolism supplies methyl groups to the one-carbon and folate pools contributing to nucleotide synthesis, methylation reactions, and NADPH (reduced nicotinamide adenine dinucleotide phosphate) production (27). Inhibiting serine biosynthesis by silencing PHGDH in cells with high levels of this enzyme results in growth suppression, and the enzyme displays oncogenic properties in gain of function assays (25, 26). The other examples of mutational deregulation of metabolic enzymes are those that generate oncometabolites. The current list of true oncometabolites is short (28). The term is most commonly and appropriately applied to D-2-hydroxyglutarate (D2HG), a reduced form of the TCA cycle intermediate α-ketoglutarate. D2HG is scarce in normal tissues but rises to millimolar concentrations in tumors with mutations in isocitrate dehydrogenase 1 or 2 (IDH1 or IDH2). These mutations occur commonly in gliomas, acute myelogenous leukemias (AMLs), and other types of cancer (2931). D2HG and its relationship to mutant IDH1 and IDH2 have been reviewed extensively elsewhere (32). The most relevant point here is that D2HG production requires a neomorphic enzyme activity imparted to IDH1/IDH2 by specific active-site mutations (33, 34). High levels of D2HG interfere with the function of dioxygenases requiring α-ketoglutarate as a cosubstrate. These include prolyl hydroxylases, cytosine hydroxylases, and histone demethylases, whose inhibition influences gene expression in part via an altered epigenetic state characterized by a failure to express differentiation programs (3541). Thus, although D2HG arises from an alteration of the metabolic network, its role in cancer seems to depend on nonmetabolic effects. Currently, D2HG is being used as a biomarker for disease monitoring, and inhibitors specific to mutants IDH1/IDH2 are in clinical trials for AML and solid tumors. The metabolite 2HG also exists as the enantiomer L-2HG (L2HG), which is not produced by mutant forms of IDH1/IDH2. This metabolite arises from the noncanonical activity of various dehydrogenases, including malate dehydrogenase and lactate dehydrogenase, whose promiscuous behavior reduces α-ketoglutarate to L2HG (4244). L2HG may be oxidized back to α-ketoglutarate by a FAD-linked enzyme, L2HG dehydrogenase (L2HGDH). L2HGDH deficiency, also called L2HG aciduria, is a rare neurometabolic disease of childhood caused by the inheritance of biallelic mutations in the gene encoding L2HGDH (45). Affected children have seizures, mental retardation, white matter abnormalities of the brain, and systemically elevated levels of L2HG. Remarkably, a number of these children have developed malignant brain tumors (46), providing an early clue to the significance of D2HG in IDH1/IDH2-mutant gliomas and raising the question of whether L2HG is also an oncometabolite. L2HG and D2HG exhibit different effects on dioxygenase function (38), suggesting that the sensitivity of a particular tissue to the presence of either metabolite may depend on which dioxygenases are expressed. Recent work has demonstrated modest accumulation of L2HG in cells experiencing hypoxia or electron transport chain (ETC) dysfunction (42, 47) and in human renal cell carcinomas, which frequently display epigenetic silencing of L2HGDH (48). It is unknown whether reducing L2HG levels in these settings will promote cellular differentiation or suppress tumor progression. The principle that oncometabolites exert their effects outside of the conventional metabolic network also applies to the other two molecules that can reasonably be called oncometabolites: fumarate and succinate (28). Both are TCA cycle intermediates found throughout the body, but some tumors accumulate massive levels of fumarate and/or succinate as a consequence of loss-of-function mutations in FH or the SDH complex, respectively (4951). Although these mutations markedly reprogram metabolism by impairing TCA cycle flux, the extent to which fumarate and succinate participate in cancer development likely involves their nonmetabolic functions (28). Like D2HG, evidence indicates that succinate and fumarate interfere with dioxygenase activity, supporting the notion that a general property of oncometabolites is the ability to regulate epigenetics (52, 53). PHGDH overexpression and mutations in IDH1/IDH2, SDH, and FH all alter metabolite levels that control epigenetics (54). Several other metabolites, including acetyl-CoA, α-ketoglutarate, and S-adenosylmethionine also participate in epigenetic reprogramming, and time will tell whether genetically specific alterations of these metabolites in tumors promote tumorigenesis (54). Some metabolites, notably fumarate, covalently bind to sulfhydryl groups in glutathione, proteins, and peptides, altering their function and perhaps accounting for another mechanism by which oncometabolites promote or perpetuate malignant phenotypes (5558). BIOENERGETICS Otto Warburg’s hypothesis that cancer cells take up glucose and generate a substantial amount of lactate in the presence of ambient oxygen due to impaired mitochondrial function led to the widely held misconception that cancer cells rely on glycolysis as their major source of ATP (59, 60). Today, it is clear that cancer cells exhibit aerobic glycolysis due to activation of oncogenes, loss of tumor suppressors, and up-regulation of the PI3K pathway, and that one advantage of high glycolytic rates is the availability of precursors for anabolic pathways (2, 61). Warburg’s observation that tumors display a high rate of glucose consumption has been validated in many human cancers with fluorodeoxyglucose positron emission tomography, which uses a radioactive glucose analog to image glucose uptake in tumors and adjacent normal tissue. Nevertheless, many studies have demonstrated that the great majority of tumor cells have the capacity to produce energy through glucose oxidation (that is, the process by which glucose-derived carbons enter the TCA cycle and are oxidized to CO2, producing ATP through oxidative phosphorylation). Furthermore, limiting glycolytic ATP production by inhibiting the activity of pyruvate kinase fails to prevent tumorigenesis, suggesting that the major role of glycolysis is not to supply ATP (62). Moreover, mitochondrial metabolism is necessary for cancer cell proliferation and tumorigenesis (6365). Thus, despite their high glycolytic rates, most cancer cells generate the majority of ATP through mitochondrial function, with the likely exception of tumors bearing mutations in enzymes involved in mitochondrial respiration (for example, SDH and FH) (66). Nevertheless, cells harboring mutations in FH or SDH still rely on mitochondrial metabolism by metabolically rewiring themselves to provide the necessary TCA cycle intermediates and ROS for cell proliferation (55, 6770). In addition to pyruvate derived from glycolysis, fatty acids and amino acids can supply substrates to the TCA cycle to sustain mitochondrial ATP production in cancer cells (Fig. 2). The breakdown of fatty acids (β-oxidation) in the mitochondria generates acetyl-CoA and the reducing equivalents NADH and FADH2, which are used by the ETC to produce mitochondrial ATP. The amino acid glutamine can generate glutamate and subsequently α-ketoglutarate to fuel the TCA cycle through a series of biochemical reactions termed glutaminolysis (71). Furthermore, the BCAAs isoleucine, valine, and leucine, which are elevated in plasma of patients with pancreatic cancers, can be converted into acetyl-CoA and other organic molecules that also enter the TCA cycle (72). The metabolic flexibility afforded by multiple inputs into the TCA cycle allows cancer cells to adequately respond to the fuels available in the changing microenvironment during the evolution of the tumor (9). A combination of the local tumor microenvironment and oncogenic lesions is likely to dictate the fuel used by mitochondria to sustain tumor growth. Solid tumors contain significant heterogeneity of perfusion, such that many tumor cells reside in nutrient- and oxygen-poor environments. Cancer cells have therefore adapted multiple mechanisms to sustain mitochondrial function for survival. For example, the mitochondrial ETC can function optimally at oxygen levels as low as 0.5% (73). Moreover, hypoxic tumor cells (<2% O2) can continue to cycle and use glutamine as a fuel for oxidative ATP production (7476). Kras-driven pancreatic cancer cells in nutrient-depleted conditions use proteins scavenged from the extracellular space to produce glutamine and other amino acids to fuel the TCA cycle for cell survival and growth (Fig. 2) (77). Furthermore, if pyruvate oxidation to acetyl-CoA is compromised by hypoxia or ETC impairment, glutamine can provide acetyl-CoA as a biosynthetic precursor to sustain tumor growth (69, 78, 79). When tumor cells become nutrient-deprived, they adapt to the microenvironment by decreasing their demand for ATP. The resultant increase in ATP availability maintains an adequate ATP/ADP (adenosine 5´-diphosphate) ratio to drive unfavorable biological reactions. The anabolic kinase mTOR, discussed in greater detail below, drives the energetically demanding growth of tumor cells. This kinase is inhibited when amino acids and oxygen levels are diminished (80). Furthermore, decreased mTOR activity increases autophagic flux. In oncogenic Kras- or Braf-driven non–small-cell lung cancer (NSCLC) cells, autophagy provides an intracellular glutamine supply to sustain mitochondrial function (81, 82). To survive the hypoxic tumor microenvironment, cancer cells also diminish their demand for ATP by decreasing highly demanding ATP-dependent processes, such as running the Na/K-dependent adenosine triphosphatase. If diminishing ATP demand is not sufficient to maintain ATP/ADP ratio, the rise in ADP activates adenylate kinase, a phosphotransferase enzyme that buffers the fall in ATP levels by converting two ADP molecules into adenosine 5´-monophosphate (AMP) and ATP (83). The rise in AMP during nutrient deprivation triggers the activation of AMP kinase (AMPK), which activates catabolic pathways like fatty acid oxidation to stimulate ATP production (84). In conditions of metabolic stress, certain Ras-driven cancer cells scavenge lipids to support ATP production (85). Ovarian cancer cells use fatty acids from neighboring adipocytes to fuel mitochondrial ATP production (86). Thus, there are multiple mechanisms by which cancer cells maintain their ATP/ADP ratio to sustain viability in nutrient- and oxygen-poor environments. BIOSYNTHESIS OF MACROMOLECULES Biosynthetic or anabolic pathways are an essential aspect of cancer metabolism because they enable cells to produce the macromolecules required for replicative cell division and tumor growth. As a general theme, these pathways involve the acquisition of simple nutrients (sugars, essential amino acids, etc.) from the extracellular space, followed by their conversion into biosynthetic intermediates through core metabolic pathways like glycolysis, the PPP, the TCA cycle, and nonessential amino acid synthesis, and finally the assembly of larger and more complex molecules through ATP-dependent processes (Fig. 3). The three macromolecular classes most commonly studied in cancer metabolism are proteins, lipids, and nucleic acids, which comprise approximately 60, 15, and 5% of the dry mass of mammalian cells, respectively. Evidence indicates that biosynthesis of all three classes is under the control of the same signaling pathways that govern cell growth and are activated in cancer via tumorigenic mutations, particularly PI3K-mTOR signaling. Protein biosynthesis is highly regulated and requires access to a full complement of essential and nonessential amino acids. Cancer cells and other cells under the influence of growth factor signaling express surface transporters that allow them to acquire amino acids from the extracellular space (87). This not only provides cells with the raw materials needed for protein synthesis but also allows them to maintain activity of the mTOR signaling system, specifically mTORC1. mTORC1 is stimulated by the presence of amino acids and activates protein synthesis via its effects on translation and ribosome biogenesis (80). Most nonessential amino acids are produced through transamination reactions, which transfer the amino group from glutamate to a ketoacid. Proliferating cancer cells take up glutamine and convert it to glutamate through a variety of deamidation and transamidation reactions, most notably the mitochondrial amidohydrolase glutaminase (71). Together, these enzymes generate a large intracellular glutamate pool available for nonessential amino acid synthesis. Both glutamine uptake and glutaminase activity are stimulated by mTORC1, providing glutamate for transamination reactions and/or maintenance of the TCA cycle, which also contributes to amino acid synthesis. Furthermore, when the intracellular glutamine supply exceeds the cell’s demands, glutamine can be exported in exchange for essential amino acids to stimulate mTORC1 and protein synthesis (88). Thus, growth conditions in which glutamine and essential amino acids are abundant enable mTORC1-mediated activation of protein synthesis. When nutrients are scarce, cells have access to a number of catabolic pathways to degrade macromolecules and resupply key pools of intracellular metabolic intermediates. Protein degradation pathways have been characterized extensively as mechanisms to supply amino acids in cancer cells. Intracellular proteins and other macromolecules can be recycled through autophagy, a highly regulated process through which proteins and organelles are delivered to the lysosome and degraded (89). Autophagy is an essential survival pathway during nutrient or growth factor deprivation, and genetic studies demonstrate that it contributes to some forms of cancer in mice (90, 91). However, because autophagy supplies amino acids through protein degradation, it does not serve as a source of net protein synthesis. It is also potently suppressed by mTORC1. Macropinocytosis allows cells to internalize proteins and other components of the extracellular milieu and deliver them for degradation via the endocytic pathway. Under conditions of nutrient depletion, macropinocytosis supplies both nitrogen and carbon to central metabolic pathways (92). Evidence indicates that extracellular protein degradation, like autophagy, is suppressed by mTORC1 (93). Suppressing pathways of protein degradation may help maximize rates of net protein synthesis when extracellular amino acids are available and mTORC1 is active. Tumor cells rapidly produce fatty acids for membrane biosynthesis, lipidation reactions, and cellular signaling. Fatty acid synthesis requires sources of acetyl-CoA and reducing power in the form of cytosolic NADPH; effective fatty acid synthesis therefore requires integration with other pathways of carbon and redox metabolism. In most cultured cells, glucose is the most prominent acetyl-CoA source for fatty acid synthesis (94, 95). Glutamine and acetate have been demonstrated to provide alternative carbon sources when access to glucose-derived acetyl-CoA is impaired by hypoxia or mitochondrial dysfunction (69, 78, 79, 96). Leucine degradation can also supply acetyl-CoA in some cell lines (97). The relative importance of these nutrients for fatty acid synthesis in vivo is unknown, although early studies suggested that most fatty acyl carbon in experimental tumors is derived from glucose (98, 99). Isotopic tracing experiments designed to assess the cytosolic NADPH pool have recently suggested that most NADPH used for fatty acid synthesis arises from the PPP (100, 101). Transcription of genes involved in fatty acid synthesis is regulated by the SREBP-1 transcription factor (102). SREBP-1 regulates not only the enzymes needed to convert acetyl-CoA into fatty acids but also the enzymes of the PPP and pathways required to convert acetate and glutamine into acetyl-CoA (103). This transcription factor therefore regulates genes encoding proteins that catalyze or facilitate fatty acid synthesis. In lipid-replete conditions, SREBP-1’s transcriptional activity is suppressed by its sequestration in the endoplasmic reticulum. Under conditions of sterol depletion, proteolytic cleavage releases the transcriptionally active domain, which travels to the nucleus and binds to sterol response elements in the promoters of lipogenic genes (104). In cancer cells with constitutively high rates of fatty acid synthesis, additional mechanisms help keep SREBP-1 in a transcriptionally active state. mTORC1 signaling, via its effector S6 kinase (S6K), activates a transcriptional program that includes both SREBP-1 and the related protein SREBP-2, which regulates transcription of genes in sterol biosynthesis (103). Both SREBP-1 and SREBP-2 are required for mTORC1-mediated cell proliferation. The mechanism of SREBP activation by mTORC1 is incompletely understood but involves nuclear entry of the phosphatidic acid phosphatase Lipin-1, which enhances nuclear SREBP abundance and activity on the promoters of lipogenic genes (105). Both fatty acids and lipids can also be acquired from the extracellular space to supply membrane biosynthesis. PI3K signaling activates fatty acid uptake and suppresses fatty acid oxidation, thereby maximizing lipogenesis in proliferating cells under the control of growth factors (106). Lipid uptake may acquire further importance during conditions of metabolic stress, when the ability to meet oncogene-driven demands for biosynthesis is compromised. The ability to scavenge lysophospholipids (lipid intermediates containing a glycerophosphate backbone with one acyl chain) is required for maximal cancer cell growth during hypoxia, which suppresses de novo fatty acid synthesis from glucose (85). Furthermore, in cancer cells with constitutive mTORC1 signaling, hypoxia induces a state of dependence on access to extracellular desaturated fatty acids to maintain endoplasmic reticulum integrity in support of protein biosynthesis (107). Notably, SREBP-1 was first identified as the transcription factor responsible for expression of the low-density lipoprotein receptor (LDLR) (108), implying that enhanced de novo lipogenesis occurs concomitantly with enhanced import of lipids from the extracellular space. These parallel pathways appear to be essential in glioma, where oncogenic activation of epidermal growth factor receptor (EGFR) signaling stimulates SREBP-1 and expression of LDLR (109). These cancer cells are highly sensitive to inhibitors of fatty acid and cholesterol biosynthesis. Inhibition of the EGFR-PI3K signaling axis but not of mTORC1 suppresses nuclear translocation of SREBP-1 in glioma cells with oncogenic EGFR, suggesting an alternate, mTORC1-independent mode of SREBP-1 activation in glioma cells (109). This transcriptional program includes LDLR expression and induces reliance on cholesterol uptake to maintain the intracellular pool (110). Impairing intracellular cholesterol availability by activating liver X receptor induced cell death both in culture and in vivo, suggesting a pharmacological approach to silence lipogenic programs in glioma (110). Purine and pyrimidine nucleotides are required for synthesis of RNA and DNA. De novo biosynthesis of nucleotides is complex, requiring input from several pathways in a coordinated fashion. The phosphoribosylamine backbone of these molecules is produced from ribose-5-phosphate, an intermediate of the PPP, and an amide donation reaction using glutamine as a substrate (111). The purine and pyrimidine bases are constructed from various nonessential amino acids and methyl groups donated from the one-carbon/folate pool. The TCA cycle contributes oxaloacetate, which is transaminated to aspartate, an intermediate required to synthesize both purine and pyrimidine bases. Conversion of ribonucleotides to deoxynucleotides by ribonucleotide reductase requires a source of NADPH. Well-characterized mechanisms of feedback inhibition exist to prevent excessive accumulation of nucleotides, and mutations interrupting these mechanisms can produce pathological accumulation of nucleotide intermediates (for example, precipitation of uric acid crystals in gout). Clearly, nucleotide biosynthesis is a targetable vulnerability in cancer cells because nucleoside analogs and antifolates have been a mainstay of chemotherapeutic regimens for decades (112). However, relatively little is known about how oncogenic signaling interfaces with nucleotide biosynthesis. It is likely that the effects of numerous signaling pathways on glucose and amino acid metabolism influence the availability of precursors for purines and pyrimidines. In the case of mTORC1, evidence points to a distinct mechanism by which activation of the signaling pathway enables nucleotide biosynthesis. The mTORC1 effector ribosomal S6K1 phosphorylates the trifunctional enzyme CAD (carbamoyl-phosphate synthetase 2, aspartate transcarbamoylase, dihydroorotase), which catalyzes the first three steps of pyrimidine synthesis (113). Phosphorylation on CAD S1859 is required for mTORC1-dependent stimulation of pyrimidine biosynthesis (113). Additional work is needed to determine how other aspects of de novo nucleotide synthesis, purine and pyrimidine salvage pathways, and accessory activities like folate metabolism are regulated in cancer cells in support of cell proliferation. REDOX BALANCE ROS are intracellular chemical species that contain oxygen and include the superoxide anion (O2), hydrogen peroxide (H2O2), and the hydroxyl radical (OH·) (114). The mitochondria and cytosolic NADPH oxidases (NOXs) produce O2 from the one-electron reduction of oxygen (115). O2 is converted into H2O2 by the enzymatic activity of superoxide dismutase 1 or 2, which are localized to the cytosol or mitochondrial matrix, respectively. H2O2 is subsequently detoxified to water by the enzymatic activity of mitochondrial and cytosolic peroxiredoxins (PRXs), which, as a consequence, undergo H2O2-mediated oxidation of their active-site cysteines (116). Thioredoxin (TXN), thioredoxin reductase (TrxR), and the reducing equivalent NADPH reduce oxidized PRXs to complete the catalytic cycle (117). Glutathione peroxidases (GPXs) can also convert H2O2 to water in the mitochondrial matrix and cytosol through H2O2-mediated oxidation of reduced glutathione (GSH) (118). Glutathione reductase (GR) and NADPH reduce oxidized glutathione (GSSG) back to GSH. Additionally, catalase, an abundant antioxidant in peroxisomes, can detoxify H2O2 to water without any cofactors. However, in the presence of ferrous or cuprous ions, H2O2 can become OH· and quickly cause the oxidation of lipids, proteins, and DNA, resulting in cellular damage. NADPH is required to maintain multiple antioxidant defense systems. The cytosol has multiple sources of NADPH generation, including the oxidative PPP, malic enzyme 1, IDH1, and one-carbon metabolism. NADPH generation in the mitochondria, in part, is controlled by one-carbon metabolism and IDH2. Historically, ROS have been thought of as lethal metabolic by-products of cellular respiration and protein folding. However, studies over the past two decades have unveiled a previously underappreciated role of ROS in cellular signaling. Low levels of ROS, particularly H2O2, can reversibly oxidize the cysteine residues of proteins to positively regulate cell proliferation and cellular adaptation to metabolic stress (119). As H2O2 levels increase, however, cell death signaling pathways are initiated, and H2O2 is converted to OH·, which can directly damage DNA, proteins, and lipids. Cancer cells have an increased rate of spatially localized mitochondria- and NOX-dependent ROS production compared to normal cells. This allows for the proximal activation of signaling pathways [PI3K and mitogen-activated protein kinase/extracellular signal–regulated kinase (MAPK/ERK)] and transcription factors [HIF and nuclear factor κB (NF-κB)] necessary for tumorigenesis. The cancer cell–specific increased rate of spatially localized ROS production is due to a combination of oncogenic lesions and the tumor microenvironment. For example, the activation of oncogenes, PI3K signaling pathway induction, and hypoxia (low-oxygen levels) stimulate the increased rate of ROS production from the mitochondria and NOXs in cancer cells (120122). Thus, mitochondria-targeted antioxidants and NOX inhibitors can prevent cancer cell proliferation, hypoxic activation of HIF, tumorigenesis, and metastasis (64, 123125). The increased localized ROS in cancer cells, which activates signaling pathways and transcription factors to promote tumorigenesis, needs to be buffered from reaching levels of ROS that incur cellular damage by the increased expression of antioxidant proteins (126). Thus, cancer cells have higher levels of ROS scavenging enzymes than normal cells, preventing ROS-mediated activation of death-inducing pathways like c-Jun N-terminal kinase (JNK) and p38 MAPK and oxidation of lipids, proteins, and DNA, resulting in irreversible damage and cell death. One mechanism by which cancer cells increase their antioxidant capacity is by activating the transcription factor nuclear factor (erythroid-derived 2)–related factor-2 (NRF2) (127). Specifically, NRF2 is activated following disruption of the interaction of NRF2 with its binding partner Kelch-like ECH-associated protein 1 (KEAP1). Critical cysteine residues within KEAP1 can undergo oxidation, succination, and glutathionylation, thereby inhibiting the KEAP1-NRF2 interaction, leading to the proteasomal degradation of NRF2. Additionally, NRF2 activation can occur independently of KEAP1 (128). Once activated, NRF2 induces the transcription of many antioxidant proteins including GPXs and TXNs as well as enzymes involved in GSH synthesis and cysteine import through the cystine/glutamate antiporter. Furthermore, to maintain the antioxidant capacity of GPXs and TXNs, NADPH is required. NRF2 plays an important role in activating enzymes that increase cytosolic NADPH levels. NRF2 also regulates the serine biosynthesis pathway, generating NADPH in the mitochondria, which is critical for redox balance under hypoxic conditions (129, 130). Therefore, inactivating NRF2 or disabling antioxidant proteins in cancer cells would allow for the accumulation of excessive amounts of ROS to levels that initiate toxicity and reduce tumorigenesis (128, 131, 132). During tumorigenesis and metastasis, redox homeostasis is required (Fig. 4). An emerging model of redox balance is that as a tumor initiates, the metabolic activity of cancer cells is increased, resulting in an increase in ROS production and subsequent activation of signaling pathways that support cancer cell proliferation, survival, and metabolic adaptation (126). Accordingly, to prevent toxic levels of ROS, tumor cells increase their antioxidant capacity to allow for cancer progression (133). The harsh tumor microenvironment increases ROS levels due to hypoxia, and the low glucose levels limit flux through the cytosolic oxidative PPP, thus decreasing cytosolic NADPH levels. Cells in these nutrient-deprived conditions activate AMPK to increase NADPH levels by stimulating PPP-dependent NADPH and diminishing anabolic pathways, such as lipid synthesis, that require high levels of NADPH (134, 135). ROS-dependent signaling and increased mitochondrial respiration are also necessary for tumor metastasis (124, 136). However, when tumor cells detach from a matrix, they encounter high levels of ROS that incur cellular damage and require activation of adaptive ROS-mitigating pathways to survive and grow (137, 138). The ability to up-regulate antioxidant proteins and increase flux through NADPH-producing metabolic pathways enables distant metastasis to occur (8). These findings suggest that perhaps disabling antioxidant capacity in cancer cells to raise ROS levels might be beneficial in preventing metastasis. TARGETING METABOLISM FOR CANCER THERAPY There are a few things to consider when determining what makes a good metabolic target for cancer therapy. First, inhibition of some metabolic enzymes is likely to be systemically toxic because of their physiological functions in normal tissues (139). The feasibility of targeting these pathways therapeutically depends on whether systemic blockade of the pathway can be tolerated. Normal proliferating cells, such as immune cells and stem cells, also reprogram their metabolism in a manner similar to cancer cells (140, 141). Metabolic inhibitors should likely not interfere with the adaptive immune system. Nevertheless, there are excellent examples of pathways whose reprogramming does provide an adequate therapeutic window in cancer. Enhanced nucleotide and DNA synthesis in tumor cells is targeted by antifolates (methotrexate, pemetrexed, and others) (112). Although these drugs do produce toxicity in normal proliferative tissues like the intestinal epithelium and bone marrow, they are essential components of highly successful chemotherapeutic regimens. Thus, it is critical to elucidate in normal cells any toxic effects of metabolic enzyme inhibition. To circumvent this challenge, one approach is to target a metabolic enzyme in a deregulated pathway specific to cancer cells. To date, many of the genetic and pharmacologic interventions on metabolic enzymes have been conducted using human cancer cells subcutaneously injected into athymic mice. Therefore, it will be important for future experiments to not only use patient-derived xenograft (PDX) models but also make use of genetically engineered mouse cancer models and syngeneic mouse models that have intact immune systems, especially given the promising results from immunotherapy. An emerging theme is that cancer cells display metabolic plasticity and can alter their metabolic profile during the course of tumorigenesis and metastasis. Thus, it is conceivable that cancer cells could develop resistance to inhibition of a particular metabolic pathway by expressing alternate protein isoforms or up-regulating compensatory pathways. Therefore, a rational cancer therapeutic strategy should involve targeting multiple metabolic pathways simultaneously or targeting a particular metabolic pathway in combination with therapies against oncogenic or signaling pathways. Here, we highlight a few promising metabolic targets in glycolytic, one-carbon, mitochondrial, and redox metabolism. Glycolysis was an early attractive target for cancer therapy given the clinical observation that many tumors exhibit a significant increase in glucose uptake compared with adjacent normal tissue (112). LDH-A, a metabolic enzyme that converts pyruvate (the final product of glycolysis) to lactate, was identified as the first metabolic target of the oncogene MYC (142). Genetic or pharmacologic inhibition of LDH-A has been shown to diminish MYC-driven tumors in xenograft models (143, 144). Furthermore, recent studies indicate that inhibition of LDH-A leads to the regression of established tumors in genetically engineered mouse models of NSCLC without systemic toxicity (145). Genetic ablation of LDH-A also delays the progression of myeloid leukemia (146). Thus, the increased expression of LDH-A, specifically in MYC-mutant cancer cells, may prove to be an attractive target. Another potential therapeutic target is the glycolytic protein HK2. Many tumor cells overexpress HK2, and preclinical mouse models of genetically engineered NSCLC and breast cancer demonstrate that HK2 inhibition delays tumor progression (3). Furthermore, systemic HK2 deletion in mice does not cause adverse physiological consequences. However, the effect of LDH-A and HK2 on the adaptive immune system is currently unknown. Lactate has been shown to inhibit cytotoxic T cells; thus, LDH-A inhibition may cooperate with immune checkpoint inhibitors to unleash host inflammatory T cells that will specifically attack tumor cells (147). Lactate can also reprogram macrophages to promote tumorigenesis (148). Thus, it may be efficacious to target LDH-A or HK2 in highly glycolytic tumors that overexpress these proteins. Another potential glucose-dependent target is PHGDH, an enzyme in the de novo serine synthesis pathway. High levels of PHGDH have been found in a subset of human melanoma and breast cancers, and these cancer cells require PHGDH for their growth in vitro (25, 26). Serine starvation in mice diminishes tumorigenicity of p53-null cancers (149). De novo synthesis or exogenous uptake of serine can enter the mitochondria where SHMT2 converts it into glycine to generate folate intermediates (101, 150). In many cancer types, SHMT2 expression is elevated and correlates with a poor prognosis. Furthermore, the transcription factors MYC and HIF induce SHMT2 under hypoxia to promote survival (130, 151). Currently, it is not known whether targeting PHGDH, SHMT2, or other enzymes in the one-carbon metabolism pathway would be effective in delaying or regressing tumor progression in genetically engineered, PDX, or syngeneic mouse models of cancer without incurring systemic toxicity. However, given the importance of one-carbon metabolism in supporting the anabolic needs of cancer cells and its up-regulation in cancer cells, it is likely that this pathway is needed for in vivo tumor progression (152). Mitochondrial metabolism has also emerged as a key target for cancer therapy, in part, due to the revelation that the antidiabetic drug metformin is an anticancer agent (153). Numerous epidemiological studies first suggested that diabetic patients taking metformin, to control their blood glucose levels, were less likely to develop cancer and had an improved survival rate if cancer was already present (154). Laboratory-based studies have also provided evidence that metformin may serve as an anticancer agent (155157). Biochemists recognized that metformin reversibly inhibits mitochondrial complex I (158160). Recent studies indicate that metformin acts as an anticancer agent by inhibiting mitochondrial ETC complex I (161). Specifically, metformin inhibits mitochondrial ATP production, inducing cancer cell death when glycolytic ATP levels diminish as a result of limited glucose availability. Metformin also inhibits the biosynthetic capacity of the mitochondria to generate macromolecules (lipids, amino acids, and nucleotides) within cancer cells (162). The remarkable safety profile of metformin is due to its uptake by organic cation transporters (OCTs), which are only present in a few tissues, such as the liver and kidney (163). Certain tumor cells also express OCTs to allow the uptake of metformin (164). However, in the absence of OCTs, tumors would not accumulate metformin to inhibit mitochondrial complex I. Ongoing clinical trials using metformin as an anticancer agent should assess the expression levels of OCTs to identify the tumors with highest expression, which are likely to be susceptible to metformin. Moreover, it is not clear whether the current antidiabetic dosing of metformin used in clinical trials allows for metformin accumulation to levels necessary to inhibit mitochondrial complex I in tumors. Thus, it is possible that metformin at doses higher than those currently used for diabetes might be more efficacious without causing toxicity. Like metformin, the biguanide phenformin exhibits anticancer properties by inhibiting mitochondrial complex I (165). In contrast to metformin, phenformin is readily transported into tumor cells and has been withdrawn from human use in most parts of the world due to its clinical increase in the incidence of lactic acidosis. However, it is worth considering phenformin as a possible cancer therapy because lactic acidosis can be monitored. Biguanide sensitivity can be improved in mice starved for serine or in tumors that have lost p53 or LKB1 (155, 166, 167). Thus, biguanides, and possibly other mitochondrial complex I inhibitors, may be effective anticancer agents. Another potential therapeutic strategy to inhibit mitochondrial metabolism in certain tumors would be to use autophagy or glutaminase inhibitors. Autophagy provides amino acids, such as glutamine, that fuel the TCA cycle in NSCLC and pancreatic cancers, and short-term autophagy inhibition has been shown to decrease tumor progression without incurring systemic toxicity in mouse models of NSCLC (168, 169). Some tumors are addicted to using glutamine to support TCA cycle metabolism even in the absence of autophagy; thus, glutaminase inhibitors can reduce tumor burden in these models (4, 75, 170). An alternative approach is to target acetate metabolism. Although a major function of the mitochondria is to provide acetyl-CoA to the cell, cancer cells can also use acetate to support cell growth and survival during metabolic stress (hypoxia or nutrient deprivation) (96, 171). The cytosolic enzyme acetyl-CoA synthase 2 (ACCS2), which converts acetate to acetyl-CoA, is dispensable for normal development; thus, ACCS2 is a promising target of acetate metabolism. ACCS2 knockout mice do not display overt pathologies, but genetic loss of ACCS2 reduces tumor burden in models of hepatocellular carcinoma (171). Human glioblastomas can oxidize acetate and may be sensitive to inhibitors of this process (172). Thus, targeting metabolism with inhibitors of autophagy, acetate metabolism, and other pathways that supply key metabolic intermediates may be efficacious in some contexts. Because mitochondrial inhibitors are unlikely to be effective cancer therapies as single agents, combination therapy is likely the best approach. For example, the use of metformin with the current clinical PI3K inhibitors, which reduce glucose uptake and glycolysis (173), is one approach that would impair both sources of ATP within cells. Targeted therapies against oncogenes such as KRAS, BRAF, and NOTCH1 kill a large majority of cancer cells but ultimately yield resistant cells that exhibit an increased sensitivity to inhibitors that impair mitochondrial metabolism (174176). Cancer-initiating cells also have increased sensitivity to mitochondrial inhibitors, adding further evidence that inhibiting mitochondrial metabolism may suppress tumor recurrence (177, 178). Furthermore, to counterbalance the increased production of ROS encountered during tumorigenesis and metastasis, cancer cells increase their antioxidant capacity (126). Thus, an additional therapeutic approach is to target redox metabolism, that is, selectively disable the antioxidant capacity of cancer cells causing ROS levels to rise and induce cancer cell death (133). The reducing equivalent NADPH is required to maintain multiple antioxidant defense systems. The cytosol has multiple sources of NADPH generation, including the oxidative PPP, malic enzyme 1, IDH1, and one-carbon metabolism. By contrast, NADPH generation in the mitochondria is controlled in part by one-carbon metabolism and IDH2. Many of these NADPH-generating systems are critical for normal cell survival and function. Nevertheless, there are two NADPH-generating systems that may serve as potential therapeutic targets. It is estimated that 400 million people worldwide are deficient in G6PDH, an enzyme in the oxidative PPP that converts NADP+ to NADPH. However, certain tumors rely on this pathway as a major source of cytosolic NADPH; therefore, it may be therapeutic to disable this pathway and induce oxidative stress and diminish tumor growth. Moreover, RNA profiling of metabolic enzymes identified the mitochondrial one-carbon metabolism protein MTHFD2, which can generate NADPH, as being highly expressed in 19 different cancer types but not in normal adult proliferating cells (152). Loss of MTHFD2 in cancer cells increases ROS levels and sensitizes the cells to oxidant-induced cell death in vitro. An interesting approach to depleting NADPH levels and increasing ROS is to administer high doses of vitamin C (ascorbate). Vitamin C is imported into cells through sodium-dependent vitamin C transporters, whereas the oxidized form of vitamin C, dehydroascorbate (DHA), is imported into cells through glucose transporters such as GLUT1 (179). When the cell takes up DHA, it is reduced back to vitamin C by glutathione (GSH), which consequently becomes GSSG. Subsequently, GSSG is converted back to GSH by NADPH-dependent GR. Because the blood is an oxidizing environment, vitamin C becomes oxidized to DHA before being taken up by the cell. Thus, high doses of vitamin C diminish the tumorigenesis of colorectal tumors that harbor oncogenic KRAS mutations and express high levels of GLUT1 by depleting the NADPH and GSH pools and consequently increasing ROS levels to induce cancer cell death (179, 180). Vitamin C administered at high doses intravenously is safe in humans and, in conjunction with conventional paclitaxel-carboplatin therapy, demonstrated a benefit in a small number of patients (181). Additional strategies to diminish GSH include the administration of buthionine sulfoximine, an irreversible inhibitor of γ-glutamylcysteine synthetase, which can be safely administered to humans and is efficacious in preclinical tumor models (182). Moreover, glutathione is a tripeptide consisting of cysteine, glutamate, and glycine. Thus, decreasing glutamate levels using glutaminase inhibitors or diminishing cysteine levels by preventing extracellular cystine (two linked cysteine molecules) uptake can also raise ROS levels in cancer cells to induce cell death. An important consideration is that normal stem cells are sensitive to ROS levels; thus, it is important to stratify patients on the basis of their expression levels of a particular antioxidant protein or antioxidant pathway. It is critical to determine which antioxidant pathways are likely up-regulated as a result of the high rate of ROS production within cancer cells. Many cancer types use the NRF2 pathway to maintain redox balance; therefore, targeting this pathway may provide a viable therapeutic opportunity (128). Additionally, superoxide dismutase 1 (SOD1) is overexpressed in NSCLC, and its inhibition kills human NSCLC cells and decreases the tumor burden in mouse models of NSCLC (183). Because NRF2 and SOD1 knockout mice develop normally, short-term inhibition of these pathways might be an effective way to kill cancer cells. TECHNOLOGIES ENABLING DISCOVERY IN CANCER METABOLISM Many recent advances in our understanding of cancer metabolism have been propelled by advanced technologies to detect metabolites and metabolic activities (184). A key concept is that quantifying metabolites (that is, metabolomics) is a more distinct form of metabolic analysis than measuring the activities of metabolic pathways [that is, metabolic flux analysis (185)]. Although these two approaches can provide complementary types of information, they are not interchangeable. One cannot infer metabolic activity from changes in metabolite levels, and altered metabolic fluxes may or may not cause changes in metabolite levels (186). Both of these approaches have provided important recent insights into cancer metabolism, and using the two techniques together provides the most complete assessment of metabolic phenotypes. Metabolomics experiments seek to characterize and quantify the metabolites in a biological sample, usually by nuclear magnetic resonance (NMR) or, more commonly, mass spectrometry. Depending on the methods of extraction, separation, and detection, metabolomics experiments may focus on particular classes of metabolites or provide a comprehensive analysis of as many metabolites as possible. Targeted approaches typically detect a few dozen to a few hundred molecules, whereas untargeted analyses may detect more than 1000. Detecting alterations of metabolite levels in cancer can be extremely valuable. The massive accumulation of D2HG in IDH1-mutant gliomas was initially discovered through a metabolomics approach (33). Because altered metabolite levels can be detected noninvasively using 1H magnetic resonance spectroscopy (MRS), perturbed metabolite levels discovered through metabolomics can sometimes be translated into clinical diagnostic techniques. Elevated levels of lactate, choline, glycine, and other metabolites are detected by MRS in glioma. More recently, MRS techniques have been developed to monitor specific metabolic states programmed by tumor-specific mutations in metabolic enzymes. Applications include elevated 2HG in IDH1/IDH2-mutated gliomas (187) and elevated succinate in SDH-deficient paragangliomas (188). Metabolic flux studies use isotope tracers like 13C, 15N, and 2H to track flow through metabolic pathways. Typically, a nutrient of interest is labeled by an isotope (for example, 13C-glucose) and supplied to cancer cells in the culture medium. Metabolites extracted from the culture are analyzed for isotope enrichment using mass spectrometry or NMR. The extent and distribution of labeling within informative metabolites encode information about which pathways are active in the cells. Incorporating additional data (for example, definitive rates of nutrient consumption, waste secretion, and biomass production) allows quantitative fluxes to be determined across a metabolic network. Isotope tracing studies provide information about metabolic alterations in cancer cells that cannot be detected by metabolite levels alone. For example, hypoxia and mutations in the ETC induce a restructuring of the TCA cycle in which many of the intermediates are produced in the reverse order from the conventional form of the cycle. The key reaction in this pathway involves the reductive carboxylation of α-ketoglutarate to isocitrate in a NADPH-dependent carboxylation reaction catalyzed by IDH1 and/or IDH2. Although metabolomics experiments can detect altered levels of TCA cycle metabolites in cells using the reductive carboxylation pathway or in cells with deficiencies in pyruvate import into mitochondria, the marked restructuring of the cycle is apparent only through isotope tracing experiments, particularly experiments using 13C-glutamine as the tracer (69, 78, 79, 189191). An example of the use of isotope tracers to identify metabolic liabilities involves the surprising discovery that a significant fraction of cellular NADPH, particularly in the mitochondria, is produced through folate metabolism (100, 101). These studies involved a sophisticated combination of 13C and 2H tracers, coupled with quantitative measurements of metabolic flux. Several recent studies have begun to use stable isotopes to investigate metabolism in intact tumors. Because these isotopes do not undergo radioactive decay, they are safe for administration to animals and human subjects. Systemic administration of 13C-labeled nutrients through either boluses or continuous infusions has been shown to generate substantial labeling of glycolytic and TCA cycle intermediates in tumors. In mice bearing orthotopic transplants of high-grade human gliomas, continuous infusion of 13C-glucose was demonstrated to produce steady-state labeling of metabolites from the TCA cycle within the tumor, enabling the assessment of several metabolic pathways (192). Here, tumors with diverse oncogenotypes oxidized glucose-derived pyruvate in the mitochondria and synthesized glutamine from glucose carbon. In contrast to most cultured glioma cell lines, these tumors did not demonstrate significant levels of 13C-glutamine oxidation in vivo, and primary cell lines derived from the tumors did not require glutamine for survival or proliferation. In another study, metabolism of 13C-glucose and 13C-glutamine in autochthonous models of MYC- or MET-driven tumorigenesis revealed that metabolic phenotypes depend not only on the tumor’s genetic driver but also on the tissue or origin. MYC but not MET stimulated glutamine catabolism in liver tumors, whereas MYC-driven lung tumors expressed glutamine synthetase and accumulated glutamine (193). Thus, in vivo isotope tracing can detect metabolic activities of intact tumors and characterize some of the factors that specify the metabolic phenotype. Administration of 13C-labeled nutrients has also proven to be valuable in human cancer (172, 194197). Fan et al. (196) used 13C-glucose to demonstrate that human non–small cell lung tumors metabolize glucose through glycolysis and the TCA cycle concurrently, with metabolites from both pathways demonstrating higher levels of labeling in tumors relative to adjacent lung tissue. In a subsequent study, these investigators demonstrated that the anaplerotic enzyme pyruvate carboxylase (PC) was highly expressed in lung tumors and contributed to 13C labeling in TCA cycle intermediates (195). Enhanced glucose oxidation involving both PC and pyruvate dehydrogenase (PDH) was demonstrated in a separate cohort of non–small cell lung tumors, in which formal analysis of metabolic fluxes was used to complement measurements of 13C labeling (197). An important conclusion from these studies, and from a similar study in mice bearing KRAS-driven tumors (198), is that non–small cell lung tumors demonstrate higher levels of both glycolysis and glucose oxidation relative to adjacent, benign lung. This finding sharply contrasts with the frequently invoked “switch” from oxidative metabolism to glycolysis in malignant tissue, commonly used to explain the Warburg effect (Fig. 5A). Rather, the data support a model in which the amplitude of both pathways is increased simultaneously, perhaps through increased substrate delivery and enzyme expression in tumor cells (Fig. 5B). It is also significant that human tumors exhibit substantial heterogeneity of metabolic phenotypes, both between tumors and even within distinct regions of the same tumor (197). The extent of glucose-dependent labeling of TCA cycle intermediates is predicted by noninvasive assessment of tumor perfusion by magnetic resonance imaging, providing an approach to identify areas of regional metabolic heterogeneity in human cancer (197). Metabolomics and metabolic flux analysis can be integrated with functional genomics to identify and understand metabolic vulnerabilities in cancer cells. This approach has produced several good examples of screens that identified potential therapeutic targets while stimulating entirely new lines of investigation in cancer cell biology. For example, the serine biosynthetic enzyme PHGDH was first identified as a metabolic vulnerability in breast cancer cells through a large-scale in vivo short hairpin RNA screen targeting thousands of metabolic enzymes (25). PHGDH is frequently amplified at the genomic level in breast tumors and melanomas and exhibits oncogene-like features in cell culture (25, 26). Subsequent work on serine biosynthesis, much of it involving metabolomics and metabolic flux analysis, has uncovered novel functions and liabilities of this pathway in cancer cell growth and stress resistance (129, 150, 151). Combining functional screens with metabolic analysis can also identify context-specific vulnerabilities that may be therapeutically actionable. A CRISPR (clustered regularly interspaced short palindromic repeats)–based loss-of-function screen identified GOT1, the cytosolic aspartate aminotransferase, as conditionally essential for survival during treatment with the ETC inhibitor phenformin (199). Isotope labeling then demonstrated that ETC blockade caused the direction of this enzyme to reverse from aspartate consumption in untreated cells to aspartate synthesis during ETC blockade (200). In addition to the discovery of synthetic lethality between ETC and GOT1 inhibition, these studies led to the novel biological concept that a major function of the ETC in proliferating cells is to support the synthesis of aspartate for nucleotide and protein synthesis (199, 200). CONCLUSIONS AND CURRENT CHALLENGES Substantial progress has been made in the past decade toward understanding the mechanisms, biological consequences, and liabilities associated with metabolic reprogramming in cancer. Several common themes have emerged from this research (Box 1). First, metabolic reprogramming is essential for the biology of malignant cells, particularly their ability to survive and grow by using conventional metabolic pathways to produce energy, synthesize biosynthetic precursors, and maintain redox balance. Second, metabolic reprogramming is the result of mutations in oncogenes and tumor suppressors, leading to activation of PI3K and mTORC1 signaling pathways and transcriptional networks involving HIFs, MYC, and SREBP-1. Third, alterations in metabolite levels can affect cellular signaling, epigenetics, and gene expression through posttranslational modifications such as acetylation, methylation, and thiol oxidation. Fourth, taken together, studies on cultured cells have demonstrated a remarkable diversity of anabolic and catabolic pathways in cancer, with induction of autophagy and utilization of extracellular lipids and proteins complementing the classical pathways like glycolysis and glutaminolysis. We have exited the period when cancer metabolism could be considered synonymous with the Warburg effect. Box 1 Key Principles and Lessons Learned Reprogrammed metabolic pathways are essential for cancer cell survival and growth. Frequently reprogrammed activities include those that allow tumor cells to take up abundant nutrients and use them to produce ATP, generate biosynthetic precursors and macromolecules, and tolerate stresses associated with malignancy (for example, redox stress and hypoxia). An emerging class of reprogrammed pathways includes those allowing cancer cells to tolerate nutrient depletion by catabolizing macromolecules from inside or outside the cell (for example, autophagy, macropinocytosis, and lipid scavenging). Reprogramming may be regulated intrinsically by tumorigenic mutations in cancer cells or extrinsically by influences of the microenvironment. Oncometabolites (for example, 2HG) accumulate as a consequence of genetic changes within a tumor and contribute to the molecular process of malignant transformation. Many metabolites exert their biological effects outside of the classical metabolic network, affecting signal transduction, epigenetics, and other functions. New approaches to assess metabolism in living tumors in humans and mice may improve our ability to understand how metabolic reprogramming is regulated and which altered pathways hold opportunities to improve care of cancer patients. Several challenges will likely shape research over the next decade. First, the studies cited above were performed primarily in cancer cell lines rather than intact tumors. These straightforward experimental models have been highly informative about the molecular mechanisms of metabolic reprogramming, particularly those linking aberrant signaling to altered metabolic fluxes. But it is challenging (perhaps impossible) to model an accurate tumor microenvironment in culture. Direct analysis of metabolic fluxes in intact tumors should begin to play a more prominent role in the field and may prove essential in determining precisely how to deploy metabolic inhibitors in clinical trials. Along these lines, it is remarkable that some tumor cell metabolic vulnerabilities observed in vivo are absent from cultured cell models (198) and that metabolic phenotypes are inconsistent even across single solid tumors in patients (197). Developing rational therapeutic strategies will be aided by learning how to derive metabolic information efficiently and comprehensively from both preclinical and clinical models of intact tumor growth. A further challenge for these in vivo studies will be to develop analytical or computational approaches to deconvolute the distinct metabolic phenotypes of discrete cell types (cancer cells, cancer-associated fibroblasts, lymphocytes, and endothelial cells) within solid tumors. This may allow us to understand the metabolic cooperativity among populations of cells within a tumor and whether metabolic reprogramming of stromal cells provides therapeutic opportunities. Second, by far the best recent candidate for a targetable, tumor-specific metabolic activity is the neomorphic function of mutant IDH1/IDH2. This has stimulated intense interest in finding other metabolic alterations for which the therapeutic window may be wide enough for real clinical opportunities. Third, although we have learned a great deal about the metabolic pathways that support cancer cell proliferation, we know much less about the metabolism that supports survival of nonproliferating tumor cells, which constitute the bulk of the malignant cells in most solid tumors. Along these lines, the metabolism of tumor-initiating cells/cancer stem cells is just now beginning to be investigated, and it will be of major interest to devise strategies to target metabolism in these cells. Finally, we still know relatively little about metabolic interactions between tumor and host. This area has the potential for enormous impact on public health. It is clear that obesity and diabetes, both of which are reaching epidemic proportions in the developed world, increase cancer risk, but we lack insight into how to break these links. 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Pierce, 11. D. Y. Gui, 12. L. B. Sullivan, 13. T. M. Wasylenko, 14. L. Subbaraj, 15. C. R. Chin, 16. G. Stephanopolous, 17. B. T. Mott, 18. T. Jacks, 19. C. B. Clish, 20. M. G. Vander Heiden , Environment impacts the metabolic dependencies of ras-driven non-small cell lung cancer. Cell Metab. 23, 517–528 (2016). 1. K. Birsoy, 2. T. Wang, 3. W. W. Chen, 4. E. Freikman, 5. M. Abu-Remaileh, 6. D. M. Sabatini , An essential role of the mitochondrial electron transport chain in cell proliferation is to enable aspartate synthesis. Cell 162, 540–551 (2015). 1. L. B. Sullivan, 2. D. Y. Gui, 3. A. M. Hosios, 4. L. N. Bush, 5. E. Freinkman, 6. M. G. Vander Hediden , Supporting aspartate biosynthesis is an essential function of respiration in proliferating cells. Cell 162, 552–563 (2015). Acknowledgments: We are grateful to J. Schaffer for illustrating the figures. Funding: This work was supported by NIH grants RO1 CA12306708 (to N.S.C.) and RO1 CA157996 to (R.J.D.). Author contributions: N.S.C. wrote the abstract, bioenergetics, redox, and targeting metabolism for cancer therapy sections. R.J.D. wrote the introduction, biosynthesis, technology, Box 1, and conclusion sections. N.S.C. and R.J.D. both wrote the metabolic reprogramming and oncometabolites sections. Competing interests: R.J.D. is on the Advisory Boards of Agios Pharmaceuticals and Peloton Therapeutics. N.S.C. declares no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/29%3A_Integration_of_Mammalian_Metabolism_-_Capstone_Volume_II/29.08%3A_Fundamentals_of_cancer_metabolism.txt

and . Immunometabolism: Cellular Metabolism Turns Immune Regulator. Journal of Biological Chemistry. MINIREVIEWS| VOLUME 291, ISSUE 1, P1-10, JANUARY 01, 2016. https://www.jbc.org/article/S0021-92...233-5/fulltext Abstract Immune cells are highly dynamic in terms of their growth, proliferation, and effector functions as they respond to immunological challenges. Different immune cells can adopt distinct metabolic configurations that allow the cell to balance its requirements for energy, molecular biosynthesis, and longevity. However, in addition to facilitating immune cell responses, it is now becoming clear that cellular metabolism has direct roles in regulating immune cell function. This review article describes the distinct metabolic signatures of key immune cells, explains how these metabolic setups facilitate immune function, and discusses the emerging evidence that intracellular metabolism has an integral role in controlling immune responses. Metabolic Challenges Facing Immune Cells During the course of an immune response, immune cells can traverse multiple tissues containing diverse conditions of nutrient and oxygen availability. Additionally, in response to activation, immune cells often dramatically change their functional activities; a lymphocyte transforms from a relatively inert cell to a cell engaging in robust growth and proliferation, often producing large amounts of effector molecules such as cytokines. These microenvironmental and functional alterations represent significant metabolic stresses that are efficiently managed by immune cells due their ability to dynamically reprogram their cellular metabolism. Inflammatory Microenvironments Most normal tissue is well vascularized and replete with nutrients and oxygen. However, during an immune response, conditions in the local immune microenvironment can often be significantly less accommodating due to competition for nutrients. For example, tumor cells have a prodigious appetite for glucose and other nutrients. As a result, the microenvironment within solid tumors can become depleted of glucose, resulting in decreased rates of glycolysis in tumor infiltrating lymphocytes (, , ). Bacterial infections can also compete for nutrients with immune cells. Infection with Staphylococcus aureus, a common human pathogen, can result in localized tissue hypoxia due to elevated levels of oxygen consumption by the invading bacteria. As glucose is a key fuel for this bacteria, the levels of glucose available to immune cells will also be reduced (). Viral infection can also result in a decrease in the amount of glucose that is available to infiltrating immune cells; viruses can reprogram infected cells to up-regulate glucose uptake and metabolism to facilitate viral replication (, , ). Additionally, various cells at sites of inflammation can release enzymes that consume nutrients in the local microenvironment, including arginase and indoleamine-2,3-dioxygenase, which deplete arginine and tryptophan, respectively (). Inflammatory sites can also become hypoxic due to the pronounced influx of inflammatory cells such as neutrophils and monocytes (). Dynamic Changes in Cellular Function Immune activation is accompanied by substantial changes in cellular activities, such as those accompanying T cell activation. Naïve T cells are long-lived, relatively inert, exhibit low levels of cellular biosynthesis, and primarily require ATP to meet cellular demands (Fig. 1A). Following activation, T cells undergo substantial changes in function and engage in robust cellular growth and rapid cellular proliferation (). Essential in supporting these cellular activities is the provision of sufficient biomolecules (amino acids, nucleotides, lipids) for the biosynthesis of new cellular components. Therefore, in activated T cells, the objectives of cellular metabolism have shifted from primarily generating ATP to the generation of sufficient ATP plus large amounts of biomolecules for the generation of biomass (). Therefore, immune cells adapt their cellular metabolism to accommodate altered functional outputs. FIGURE 1.Configuring metabolism to match immune cell function. A, ATP is the key molecule that provides energy for cellular processes. Maintaining cellular ATP levels is essential for bioenergetic homeostasis and cell survival. Glucose, a key fuel source for mammalian cells, can be metabolized via two integrated metabolic pathways, glycolysis and OxPhos, that efficiently convert this simple sugar glucose into ATP. Glycolysis converts glucose to pyruvate through a series of enzymatic steps that occur in the cytosol, generating two molecules of ATP. Following its transportation into the mitochondria, pyruvate is further metabolized to CO2 by the Krebs cycle, which drives OxPhos and the translocation of protons across the mitochondrial inner membrane. The resulting proton gradient drives the enzyme ATP synthase, converting ADP to ATP, generating up to 34 ATP per molecule of glucose. In addition to the breakdown of glucose via glycolysis, cells have the ability to metabolize alternative substrates, such as lipids and glutamine, which feed into the Krebs cycle and drive OxPhos. Fatty acid β-oxidation and glutaminolysis replenish the Krebs cycle intermediates acetyl-CoA and α-ketoglutarate, respectively, thereby fueling OxPhos and the efficient generation of cellular ATP. B, aerobic glycolysis supports biosynthetic processes of the cell as it allows the uptake of larger amounts of glucose and the maintenance of elevated glycolytic flux. Glycolytic intermediates are then diverted into various pathways for the synthesis of biomolecules that support biosynthetic processes. For instance, glucose 6-phosphate (G6P), generated by the first step in glycolysis, can feed into the pentose phosphate pathway (PPP) to support nucleotide synthesis. This pathway also generates NADPH, a cofactor that is essential for various biosynthetic processes including lipid synthesis. Glucose can also be converted into cytoplasmic acetyl-CoA via citrate in the Krebs cycle for the production of cholesterol and fatty acids for lipid synthesis. Other glycolytic intermediates can also be converted into biomolecules used for protein and lipid synthesis. During aerobic glycolysis, a significant proportion of pyruvate is also converted to lactate and secreted from the cell. Although aerobic glycolysis is an inefficient way to generate ATP (generating only two ATP molecules per glucose) due to the high rates of flux through the pathway, the rate of ATP production can be sufficient to maintain energy homeostasis even when mitochondrial ATP synthesis is impaired. Alternative fuels including glutamine feed into the Krebs cycle and can also supply biomolecules for biosynthetic processes under certain conditions. Configuring Metabolism for Biosynthesis, Inflammation, and Longevity Aerobic Glycolysis for Cellular Biosynthesis A common feature of pro-inflammatory immune cells is that they adopt a distinct metabolic signature termed “aerobic glycolysis” to support cellular biosynthetic processes: that is, glucose metabolized to lactate in the presence of abundant oxygen (Fig. 1B). Aerobic glycolysis is adopted by cells engaging in robust growth and proliferation because it provides the biosynthetic precursors that are essential for the synthesis of nucleotides, amino acids, and lipids (). Many intermediates of the glycolytic pathway act as a source of carbon that feeds into a range of biosynthetic pathways (Fig. 1B). Therefore, for cells engaged in aerobic glycolysis, the function of glucose is not just as a fuel to generate energy but also as a source of carbon that can be used for biosynthetic purposes (). Hence, aerobic glycolysis provides immune cells with the components needed to facilitate proliferation and the synthesis of inflammatory molecules. Metabolic reprogramming to aerobic glycolysis has advantages beyond enhanced biosynthetic capacity. This metabolic signature allows cells to adapt and survive as they encounter metabolically restrictive conditions, such as hypoxia. Although hypoxia prevents efficient ATP synthesis through OxPhos (Fig 2), high rates of glycolysis can generate enough ATP to maintain energy homeostasis. Glycolytic reprogramming involves increased expression of glucose transporters, especially Glut1, that facilitates elevated glucose uptake and enables immune cells to compete for glucose in nutrient restrictive environments (). Immune cells also have a degree of metabolic plasticity in response to limiting glucose availability. For instance, when glucose levels are low, effector T cells have the ability to adapt and increase glutamine uptake and glutaminolysis to support cellular metabolism (). Aerobic Glycolysis in Activated Lymphocytes Upon stimulation through antigen or cytokine receptors, lymphocytes increase the rates of both glycolysis and OxPhos (Fig. 2) (). Although glucose is an essential fuel during T cell activation, glutamine is also important, and effector T cell differentiation is impaired when the supply of glutamine is disrupted (, , ). T cells that differentiate into effector subsets maintain aerobic glycolysis in response to various cytokines (). In contrast, FoxP3+ regulatory T cells (Tregs) switch to low levels of glycolysis and preferentially use oxidative metabolism (). However, another type of regulatory T cell, FoxP3 regulatory T cells (Tr1), maintains elevated glycolysis similar to effector T cells (). Although many of the functions of Tr1 cells overlap with those of Tregs, others are unique to Tr1 cells including granzyme/perforin-mediated cytolysis of target cells. Therefore, perhaps the distinct metabolic characteristics of these regulatory cells reflect the different mechanisms through which they regulate T cell responses. Similarly, B lymphocytes and NK cells also increase rates of glycolysis and OxPhos in response to various stimuli (,, ,). However, as metabolic analyses of B lymphocytes have all been performed using in vitro stimulated splenic B cells, the metabolic profile of distinct B cell subsets is currently unknown. Similarly, the metabolic signatures of distinct NK subsets, or indeed other innate lymphoid cells, also remain to be characterized. FIGURE 2.Distinct metabolic configurations of different immune cell subsets. Blue panels represent cells with oxidative metabolism, and red panels represent cells with glycolytic metabolism. A, naïve T cells use glucose and glutamine and OxPhos. B, effector lymphocytes and Tr1 regulatory T cells have high rates of both glycolysis and OxPhos, metabolize glucose to lactate, and use metabolic intermediates to support biosynthetic processes. Nuc, nucleotides; FA, fatty acids; AA, amino acids; Lac, Lactate. C, memory T cells use glucose to generate mitochondrial citrate, which is exported into the cytosol to support lipid synthesis. These de novo synthesized fatty acids are used with imported glycerol to generate and store TAGs. OxPhos is fueled by acetyl-CoA generated following β-oxidation of these TAGs. FAO, fatty acid oxidation; FAS, fatty acid synthesis. D, FoxP3+ regulatory T cells use exogenously derived fatty acids metabolized by β-oxidation to support OxPhos. E, M1 macrophages and mature DC engage aerobic glycolysis for ATP synthesis and to support biosynthesis while also inactivating OxPhos. F, M2 macrophage metabolism is characterized by fatty acid β-oxidation and OxPhos. β-Oxidation is fueled by lipids that are scavenged from the external microenvironment and also by lipids generated by de novo fatty acid synthesis. G, neutrophils are highly glycolytic with few functional mitochondrial and very low rates of OxPhos. Although the exact molecular mechanisms controlling glycolytic metabolism are not universal for all lymphocyte subsets, it is clear that mammalian target of rapamycin (mTOR) has a fundamental role (, ). mTOR complex 1 (mTORC1) activity is essential for the initial induction of glycolysis in T cells and is also required to maintain aerobic glycolysis in effector T cells subsets (, , ). The data also suggest that mTORC1 has an important role for cytokine-induced glycolysis in NK cells (). A number of transcription factors are involved in glycolytic reprogramming of T cells including both hypoxia-inducible factor (HIF1α) and c-Myc (, , ). In B cells, c-Myc but not HIF1α is important for the glycolytic response (). HIF1α and c-Myc directly bind the promoters of an array of genes, notably those of glycolytic enzymes and glucose transporters. Aerobic Glycolysis in Myeloid Cells Unlike lymphocytes, mature myeloid cells tend to be non-proliferative and so have substantially different metabolic requirements. Activated M1 macrophages, dendritic cells (DC), and granulocytes are all highly glycolytic with little or no flux through OxPhos (,, , , ,). In activated M1 macrophages and DC, OxPhos is inactivated following inducible NOS-dependent nitric oxide production, which directly inhibits oxidative phosphorylation (, ). In these cells, the Krebs cycle is no longer cycling, which allows the repurposing of Krebs cycle enzymes to generate molecules that are important for proinflammatory functions (, ). M1 macrophages generate high levels of the Krebs cycle metabolite succinate, which can lead to increased HIF1α activity and sustained IL1β production (). Levels of citrate are also elevated and are used to generate the antimicrobial metabolite itaconic acid that inhibits the growth of bacteria such as Salmonella enterica and Mycobacterium tuberculosis (, ). The metabolic changes following DC activation occur in two phases and result in a metabolic switch from fatty acid β-oxidation and OxPhos to glycolysis (). An initial increase in glycolysis occurs within minutes of DC activation to support de novo lipid biosynthesis, facilitating the expansion of endoplasmic reticulum and Golgi apparatus and increasing the biosynthetic capacity that is essential for mature DC function (). Over the course of 18 h, activated DC sustain elevated glycolysis and inactivate OxPhos (). This metabolic shift is important in regulating DC-induced T cell responses, in part due to the fact that it impacts upon DC lifespan and thus the duration over which DC can activate T cells (, ). The metabolism of granulocytes is best characterized for neutrophils, which rely almost entirely on glycolysis and exhibit very low levels of OxPhos (, ,, ). Neutrophil effector functions, including the formation of neutrophil extracellular traps, require mTORC1/HIF1α signaling and glucose metabolism (, , , ,). Although the metabolism of other granulocytes such as basophils and eosinophils remains poorly characterized, there is some evidence that these cells are also glycolytic and rely upon metabolic regulators such as HIF1α to maintain glycolysis and normal function (). For instance, HIF1α accumulation upon basophil activation was shown to be required for VEGF and IL4 production (). Oxidative Cellular Metabolism in Naïve Lymphocytes and Memory T Cells As previously mentioned, naïve lymphocytes are relatively inert cells with limited biosynthetic demands, and so ATP alone is relatively sufficient to sustain these cells. Given that these cells reside in well oxygenated tissues, oxidative metabolism is a consistent and efficient way to meet cellular metabolic demands. Memory cells generated during the course of an immune response share many of the same characteristics of naïve lymphocytes; they are long-lived, relatively inert cells with limited biosynthetic demands. As nothing is known regarding the metabolism of memory B cells, only memory T cells will be considered here. The key distinction between naïve and memory T cells is the rapid recall responses characteristic of memory T cells when compared with primary T cell responses. Although both naïve and memory T cells adopt oxidative metabolism, there are key differences in the metabolic configurations of these cells that contribute to rapid memory T cell recall responses. Memory T cells predominantly use fatty acid β-oxidation to generate acetyl-CoA to fuel OxPhos () (Fig. 2). β-Oxidation is an efficient method for generating ATP with each fatty acid molecule generating significantly more ATP (about 106 ATP/molecule of palmitate) when compared with one molecule of glucose (about 36 ATP/molecule of glucose). Indeed, fatty acid oxidation is essential for rapid memory T cell responses (). Interestingly, these fatty acids are not taken up from the surrounding microenvironment, but rather memory T cells use glucose and glycolysis to generate citrate for de novo fatty acid synthesis and the generation and storage of triacylglycerides (TAGs) (, ). These endogenously derived TAGs are then broken down by β-oxidation in the mitochondria to generate acetyl-CoA to fuel OxPhos (). From a bioenergetics standpoint, this would seem like an inefficient mechanism to fuel OxPhos as fatty acid synthesis utilizes both ATP and NADPH. Nonetheless, this seemingly futile cycle of fatty acid synthesis and fatty acid oxidation is important for memory T cell survival (, ). This approach may be taken by memory T cells, for which long term survival is of utmost importance, as glucose levels are stringently controlled in the blood, making glucose a more dependable fuel source than fatty acids, whose levels can vary in different tissues. Another advantage of this cycle of fatty acid synthesis and oxidation may be that it allows the cell to concurrently engage both glycolysis and OxPhos, thus maintaining the machinery required for rapid induction of metabolic flux through these pathways upon antigen recognition and so facilitating rapid functional responses. Indeed, memory T cells can induce rates of glycolysis much more rapidly and robustly than naïve T cells (, ). Oxidative Cellular Metabolism in Cells with Significant Biosynthetic Output FoxP3+ Tregs also primarily engage in oxidative metabolism, but in contrast to naïve lymphocytes and memory T cells, FoxP3+ Tregs are not inert cells and are in fact producing relatively large quantities of biomolecules (, ). Tregs make immunosuppressive cytokines IL10 and TGFβ and can also engage in cellular proliferation in response to IL2. In this respect, M2 macrophages are similar to FoxP3+ Tregs; M2 macrophages engage in oxidative metabolism and yet have significant biosynthetic outputs. M2 macrophages have roles in tissue repair and secrete anti-inflammatory cytokines, growth factors, and factors involved in tissue remodeling (). Tregs and M2 macrophages oxidize both glucose and fatty acids in the mitochondria to sustain OxPhos (, ,, ,). In contrast to memory T cells, Tregs fuel β-oxidation and the Krebs cycle using exogenously derived fatty acids. Meanwhile, in M2 macrophages, there is evidence that both exogenously derived lipids scavenged from the microenvironment and de novo synthesized lipids fuel β-oxidation and OxPhos (). It is likely that Tregs and M2 macrophages use glutamine metabolites to sustain cellular biosynthetic processes (Fig. 1) (). Indeed, M2 macrophages have increased glutamine metabolism when compared with M1 macrophages (). Additionally, given that M2 macrophages are professional scavengers of apoptotic debris, it is tempting to speculate that M2 macrophages sustain cellular biosynthesis using biomolecules scavenged from the surrounding microenvironment (, ). Oxidative Metabolism Supports Immune Cell Longevity Controlling the longevity of immune cells is an important aspect of a healthy immune system. For example, a long lifespan (years) is essential for naïve and memory T cells to maintain functional primary and recall T cell responses. In contrast, it is crucial that upon resolution of a viral infection, the large population of CTL undergoes apoptosis as these effector T cells have the potential to cause significant immunopathology (). Therefore, CTL have a short lifespan of days to weeks. Similarly, differences in lifespan are apparent in different subsets of macrophages. M1 macrophages are short-lived and are a key component of the innate immune system that forms the first line of defense occurring within hours to days of an immunological challenge. In contrast, M2 macrophages are longer-lived as they have important roles within the resolution phase and in tissue repair and remodeling. Strikingly, the cellular metabolic signature of an immune cell corresponds to the longevity of the cell; aerobic glycolysis is characteristic of short-lived immune cells, whereas oxidative metabolism is characteristic of long-lived cells (Fig. 2). It is perhaps unsurprising that OxPhos is important for longevity in immune cells given the importance of mitochondrial membrane potential in controlling the induction of apoptosis. Certainly, in activated DC, preserving OxPhos results in an increased cellular lifespan (). Moreover, in macrophages, switching cellular metabolism from glycolysis to oxidative metabolism promotes a shift from short-lived M1 macrophages to longer-lived M2 macrophages (). In addition, manipulating glycolytic versus oxidative metabolism impacts upon the formation of long-lived memory T cells; inhibiting glycolysis promotes memory T cell formation, whereas inhibiting fatty acid oxidation-dependent OxPhos represses memory T cell formation (, ). These reports are consistent with a number of other studies that also support the notion that promoting oxidative phosphorylation enhances cell survival and lifespan (, ,). On the other hand, there are also numerous reports on a variety of cell types showing that manipulating glycolytic metabolism has profound impacts upon cellular viability (,, , ,). Growth factors that promote elevated levels of cellular glycolysis also have the consequence of making that cell highly dependent on continued growth factor signaling and glycolysis for survival (). This provides an elegant mechanism for terminating effector T cell responses. For instance, glycolytic metabolism in CD8+ CTL is sustained by IL2, and upon IL2 withdrawal, as will occur upon resolution of a viral infection, glycolytic metabolism is rapidly lost and the CTL will die (, ). Metabolic Control of Immune Cell Function Metabolic Enzymes or Regulators Controlling Immune Cell Function Cellular metabolism is crucial for facilitating immune cell functions, but in addition, there is emerging evidence that metabolic enzymes and regulators can also have a direct role in controlling immune cell functions. For instance, in CD4 T cells, GAPDH has been described to bind to the 3′-UTR of IFNγ and IL2 mRNA and inhibit translation (). This function of GAPDH is perhaps unsurprising due to the numerous reports describing RNA binding activities for GAPDH over the past two decades (, ). Indeed, in myeloid cells, GAPDH is a component of the IFNγ-activated inhibitor of translation (GAIT) complex that binds defined 3′-UTR elements within a family of inflammatory mRNAs and suppresses their translation (). Importantly, GAPDH functions in glycolysis and mRNA binding are likely to be mutually exclusive so that in glycolytic cells, GAPDH is preferentially engaged in glycolysis, and thus the translation of IFNγ and IL2 mRNA is unconstrained. This mechanism provides a direct link between rates of glycolysis and the expression of important immunological effector molecules. Intriguingly, it appears that many other metabolic enzymes can bind to mRNA molecules including numerous glycolytic enzymes, Krebs cycle enzymes, and enzymes involved in other metabolic pathways (). Although the specific mRNA transcripts that these metabolic enzymes bind to still have to be identified, this study highlights the abundant potential for cellular metabolism to directly impact upon cellular functions. Various metabolic regulators that evolved to control cellular metabolic pathways have since acquired roles in directly controlling immune cell function. The glycolytic regulator HIF1α also promotes the expression of IL1β in M1 macrophages and programmed death ligand-1 (PD-L1), a ligand for the immune checkpoint receptor PD-1, on various myeloid cells (, ). The aryl-hydrocarbon receptor (AhR), which together with HIF1α controls glycolytic metabolism in Tr1 regulatory T cells, also directly regulates T cell responses. AhR promotes Th17 differentiation, while inhibiting Treg differentiation, and is required for the production of the Th17 cytokines IL17 and IL22 (, , ). Additionally, AhR is important for Tr1 regulatory T cell differentiation, directly promoting the expression of IL10 and IL21 (, ). The transcription factor sterol regulatory element-binding protein (Srebp), a central regulator fatty acid and cholesterol synthesis, has dual roles in controlling T cell metabolism and directly controlling genes required for immune function. CD8+ T cells lacking Srebp activity fail to undergo metabolic reprogramming and blastogenesis and do not mount a functional T cell response (). In CD4+ T cells, the Srebp1c isoform is involved in Th17 differentiation and directly binds to the IL17 promotor to inhibit AhR-induced IL17 expression (). Moreover, the Srebp1a isoform is required for pro-inflammatory functions in myeloid cells, including IL1β production, as it promotes the expression of a key component of the inflammasome, Nlrp1 (). Therefore, there is growing evidence that multiple important regulators of cellular metabolism have additional functions in directly controlling immune responses. Metabolites Controlling Immune Cell Function Distinct metabolic configurations will result in different levels of metabolites that can directly impact upon cellular function. It has recently been shown that the glycolytic intermediate phosphoenolpyruvate is important in sustaining T cell receptor (TCR) signaling and T cell effector functions. Phosphoenolpyruvate inhibits Ca2+ re-uptake into the endoplasmic reticulum, thus sustaining nuclear factor of activated T-cells (NFAT) signaling (). Mitochondrial reactive oxygen species generated as a side product of OxPhos are also important for optimal TCR signal transduction. T cells that cannot produce mitochondrial reactive oxygen species fail to activate nuclear NFAT, produce IL2, or engage in proliferative expansion (). In M1 macrophages, the levels of Krebs cycle metabolites are substantially altered, leading to dramatically elevated levels of succinate, the stabilization of HIF1α, and prolonged production of IL1β (, ). Succinate can stabilize HIF1α by inhibiting the α-ketoglutarate-dependent prolyl-hydroxylases responsible for tagging HIF1α for proteasomal degradation (, , ). Indeed, succinate can inhibit other α-ketoglutarate-dependent enzymes that can impact upon immune cells due their roles in controlling cellular epigenetics, namely TET2 DNA hydroxylates and Jumonji C (JmjC) domain-containing histone demethylases (discussed further below) (, ). Succinate can act as a signaling molecule that acts through the receptor SUCNR1 and can also be used as a substrate for the post-translational modification of proteins (that is, succinylation) (). Succinate acting through SUCNR1 impacts upon DC functions and also induces DC chemotaxis to enhance DC-induced T cell responses (). Numerous metabolic enzymes are succinylated on lysine residues, but at present, it is not clear whether this modification impacts upon the regulation of immune responses (). Citrate levels are also elevated in M1 macrophages, and this metabolite is important for the production of various proinflammatory molecules: nitric oxide, reactive oxygen species, and prostaglandins (, ). Cellular metabolites are also important substrates for various enzymes involved in the epigenetic control of gene expression via covalent modification of DNA and histones. Given that the distinct metabolic configurations that characterize immune cells result in different levels of these cellular metabolites, it follows that the epigenetic control of gene expression will differ in parallel with differences in metabolism. For example, TET family enzymes, which oxidize methylcytosine, leading to DNA demethylation, and JmjC domain-containing histone demethylases both require α-ketoglutarate as a substrate and are both inhibited by succinate (Fig. 3). Indeed, TET2 has recently been shown to regulate the expression of IFNγ, IL17a, and IL10 in Th1 and Th17 cells (). Jmjd3 has been shown to be of particular importance in controlling gene expression in LPS-stimulated macrophages (). Acetylation of histones is another post-translational modification that impacts on DNA structure and gene expression. Acetylation of histones by histone acetyl transferases (HATs) requires acetyl-CoA, which is supplied via the export of mitochondrial citrate (Fig. 3). Indeed, there is evidence in yeast that the concentration of acetyl-CoA is important for histone acetylation (). Histone acetylation levels are also controlled by the rate of deacetylation. The activity of sirtuin histone deacetylases is linked to cellular metabolism as these deacetylases are sensitive to the ratio of oxidized NAD+ to reduced NADH, which is affected by the balance of glycolysis and OxPhos (). Oxidized NAD+ is an essential substrate for sirtuins, whereas reduced NADH acts to inhibit sirtuin activity (Fig. 3) (). In fact, sirtuins can also deacetylate targets other than histones, which are important in immune regulation. For example, Sirt1 deacetylates FoxP3 to inhibit Treg responses and RORγt to promote Th17 responses (,, ,). Additionally, sirtuins can also have a negative impact upon inflammatory responses, in part through inhibition of NFκB activity (, ). Although there are numerous studies suggesting that cellular metabolism impacts upon epigenetic programming of immune cells to affect immune cell fate and function, the best evidence of this comes from a study of trained immunity in macrophages. Cheng et al. () elegantly demonstrated that mTORC1/HIF1α-stimulated glycolysis is required for changes in the epigenome of human or murine myeloid cells that provides enhanced nonspecific protection from secondary infections. Therefore, it is clear that metabolites can impact directly on immune cell function, and it is likely that further examples of this will be revealed as the field of immunometabolism progresses. FIGURE 3.Links between cellular metabolism and epigenetic modifications. Histone deacetylation by sirtuin (SIRT) family members requires NAD+ as a substrate, and the activity of these enzymes is inhibited by NADH. The balance of oxidized NAD+ and reduced NADH is affected by levels of glycolysis and OxPhos. Methylation of DNA and histones is controlled by the rates of methylation and demethylation. The activities of JmjC domain-containing histone demethylases and the TET2 hydroxylase lead to histone and DNA demethylation, respectively, and can be regulated by Krebs cycle intermediates α-ketoglutarate (α-KG) and succinate (Succ). α-Ketoglutarate is a substrate for these enzymes, and succinate acts as an inhibitor. NAM, nicotinamide; HDM, histone demethylase; SAM, S-adenosylmethionine. Immune Metabolism Relays External Signals to Regulate Immune Cell Function The data now support an important role for cellular metabolism in controlling the function of immune cells. Given that metabolic regulators and pathways are acutely sensitive to external levels of nutrients, oxygen, and growth factors, cellular metabolism represents a means to relay information from the local microenvironment to modulate immune cell function accordingly. Nutrients such as glucose, glutamine, and fatty acids that directly supply metabolic pathways also regulate the activity of important regulators of immune metabolism and function including mTORC1, HIF1α, and Srebp. Other nutrients are important for providing the substrates for enzymes that impact upon immune cell function. For example, methionine, which is an essential amino acid and so must be imported into the cell, is used to generate S-adenosylmethionine for epigenetic methylation of DNA and histones. Although most studies have focused on how activating immune receptors affect cellular metabolism, it is now becoming apparent that ligation of inhibitory receptors also alters metabolic pathways. Recent research has demonstrated that ligation of the inhibitory receptors PD-1 and CTLA-4 expressed on human CD4 T cells has pronounced effects on cellular metabolism, inhibiting aerobic glycolysis, and in the case of PD-1, promoting fatty acid oxidation (). These data suggest that the inhibitory actions of these receptors may be mediated, at least in part, due to changes in cellular metabolism. Final Comments The emerging data now argue that metabolism has duel roles in immune cells to facilitate requirements for energy and biosynthesis and to directly regulate immune cell functions. There are likely to be numerous opportunities for novel therapeutic strategies that modulate this metabolic regulatory axis.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/29%3A_Integration_of_Mammalian_Metabolism_-_Capstone_Volume_II/29.09%3A_Immunometabolism-__Cellular_Metabolism_Turns_Immune_Regulator.txt

David F.WilsonFranz M.Matschinsky. Medical Hypotheses, Volume 140, July 2020, 109638 https://doi.org/10.1016/j.mehy.2020.109638. Under a Creative Commons license Abstract Throughout the world, ethanol is both an important commercial commodity and a source of major medical and social problems. Ethanol readily passes through biological membranes and distributes throughout the body. It is oxidized, first to acetaldehyde and then to acetate, and finally by the citric acid cycle in virtually all tissues. The oxidation of ethanol is irreversible and unregulated, making the rate dependent only on local concentration and enzyme activity. This unregulated input of reducing equivalents increases reduction of both cytoplasmic and intramitochondrial NAD and, through the latter, cellular energy state {[ATP]/([ADP][Pi])}. In brain, this increase in energy state stimulates dopaminergic neural activity signalling reward and a sense of well being, while suppressing glutamatergic neural activity signalling anxiety and unease. These positive responses to ethanol ingestion are important to social alcohol consumption. Importantly, decreased free [AMP] decreases AMP-dependent protein kinase (AMPK) activity, an important regulator of cellular energy metabolism. Oxidation of substrates used for energy metabolism in the absence of ethanol is down regulated to accommodate the input from ethanol. In liver, chronic ethanol metabolism results in fatty liver and general metabolic dysfunction. In brain, transport of other oxidizable metabolites through the blood-brain barrier and the enzymes for their oxidation are both down regulated. For exposures of short duration, ethanol induced regulatory changes are rapid and reversible, recovering completely when the concentrations of ethanol and acetate fall again. Longer periods of ethanol exposure and associated chronic suppression of AMPK activity activates regulatory mechanisms, including gene expression, that operate over longer time scales, both in onset and reversal. If chronic alcohol consumption is abruptly ended, metabolism is no longer able to respond rapidly enough to compensate. Glutamatergic neural activity adapts to chronic dysregulation of glutamate metabolism and suppression of glutamatergic neural activity by increasing excitatory and decreasing inhibitory amino acid receptors. A point is reached (ethanol dependence) where withdrawal of ethanol results in significant metabolic energy depletion in neurons and other brain cells as well as hyperexcitation of the glutamatergic system. The extent and regional specificity of energy depletion in the brain, combined with hyperactivity of the glutamatergic neuronal system, largely determines the severity of withdrawal symptoms. Overview: ethanol and human metabolism In nature, ethanol is rarely found in foodstuffs at levels where consumption can raise blood concentrations to the levels reached by humans during social drinking. When it does occur, such as when fruit ferments either on the tree or after falling to the ground, exposure to that food source is brief. As a result, the amount of ethanol consumed is a tiny fraction of that consumed by even modest social drinkers. Ethanol is a “rogue” nutrient for which there was no evolutionary pressure for its regulation. Ethanol dependence became a problem only after humans developed methods for preservation of food, one of which was fermentation. In order to provide a framework for understanding the importance of ethanol metabolism and its effect on metabolic homeostasis in humans, Table 1 summarizes some aspects of ethanol consumption important to understanding its impact. Dettling et al. [25] measured the rate of elimination of ethanol by humans following ingestion of 0.9 g/kg body weight over a period of 2 h, an amount that resulted in maximal blood alcohol levels near 0.08 g/dl (legal limit for driving in US). After consumption stopped, ethanol disappearance, measured by decrease in blood levels, occurred at a constant rate (zero order) of about 0.016 g/dl/h for men and 0.018 g/dl/h for women. Translated into kilocalories (cal), for an 80 kg man this is 73 cal/h while for 60 kg women it is 55 cal/h. A 70 kg (155 lb) person expends about 102 cal/h working on a computer, 120 cal/h sitting in meetings, 130 cal/h during desk work or sitting in class, 186 cal/h doing police work, bartending or waitressing (Table 2). Maintenance diets for average adults are 2000 cal/day for females (83 cal/h) and 2600 cal/day (108 cal/h) for males. As such, ethanol becomes responsible for more than 50% of each individual’s energy metabolism until it is eliminated. Elimination requires about 5hr after consumption stops (7h total) and throughout that time, ethanol oxidation is the largest carbon source for energy metabolism. Further increase in alcohol consumption, either acutely or chronically, extends the time before blood alcohol levels return to near zero but not the rate at which it is removed. For ethanol dependent individuals, blood alcohol levels remain significant throughout each day. This means ethanol contributes about 1700 cal/day (80 kg male) and 1300 cal/day (60 kg female), and many metabolic and nutrient deficiencies may develop [66], [69], [62], [63], [64]. Table 1. Ethanol metabolism and the human diet (social drinking). 0.9 g/kg (male) Blood max 0.084 g/dl Elimination 0.016 g/dl/h time 7 h#; 5 h* 0.84 g/kg (female) Blood max 0.082 g/dl Elimination 0.018 g/dl/h Time 6.6 h#; 4.6hr* 80 kg/male (176 lb) Total, 72 g (5drinks) 511 calories 73 calories/h 60 kg/female (132 lb) Total, 50 g (3.6 drinks) 355 calories 54 calories/h Measurements of the rate of elimination of ethanol from the body in human volunteers [25]. Ethanol ingestion followed a typical breakfast and consisted of consuming drinks of the subject’s choice. The number of drinks was calculated for 14 g of pure ethanol per drink. The amount consumed raised blood alcohol to near 0.08 mg/dl (17 mM, legal limit for driving in US) by the end of the 2 h period for consumption. After consumption stopped, ethanol disappeared from the blood at a nearly linear (zero order) rate of about 0.016 g/dl/h for men and 0.018 g/dl/h for women. The times required for blood alcohol to fall to near zero, h# and h*, were measured from beginning and end of consumption, respectively. Table 2. Metabolic energy utilization by a 70 kg (155 lb) person. Sitting (computer work) 102 calories/h Sitting in meetings 120 calories/h Desk work or sitting in class 130 calories/h Police work, bartending, waitressing 186 calories/h As noted in Table 1, for a 70 kg person ethanol contributes about 63 calories/h while the total hourly caloric expenditure is shown in the table. Assuming a drinker is awake and at activities similar to sitting in class both while and after drinking, they would be expected to expend about 130 calories/h. In that case, ethanol would provide about 50% of whole body energy metabolism until the blood alcohol fell below about 0.006 g/dl (below the KM for ADH1 of 1.4 mM). Oxidation of ethanol occurs through three enzymes, alcohol dehydrogenase (ADH), catalase, and P450 (CYP2E1), and they all produce acetaldehyde. In the present paper, focus is on the first two because they are responsible for oxidation of most of the consumed ethanol. Ethanol readily passes through most biological membranes, and the concentration in all water spaces, including the brain, approximates that in blood plasma. Thus, tissues and cells throughout the body are nearly equally exposed to ethanol and its rate of metabolism is determined by local enzyme content. The reactions of the three ethanol oxidizing systems and the KM of each for ethanol are: 1. Alcohol dehydrogenase; ADH1, KM = 1.4 mM [12]: (1)Ethanol + NAD+ → Acetaldehyde + NADH + H+ 2. Catalase; KM = 12 mM [103]: (2)Ethanol + H2O2 → Acetaldehyde + 2 H2O 3. Cytochrome P450 2E1 (CYP2E1); KM = 8–10 mM (12): (3)Ethanol + O2 + NADPH → NADP+ + acetaldehyde + acetate The equilibrium constant for the ADH reaction at pH 7.0 is approximately 10−4 [1], [6], [83]. In the cytoplasm, where the reaction occurs, the [NAD+]/[NADH] is greater than 100 and acetaldehyde concentrations remain less than 20 μM [83]. The [NAD+)/[NADH) is high enough that the ratio of the forward to reverse reactions of ADH is about 10 and the net reaction is strongly toward acetaldehyde (and NADH) formation. The reactions of catalase and CYP2E1 are irreversible under all physiological conditions. Acetaldehyde is chemically reactive and can react nonenzymatically with other cellular components to form products that are metabolically active and/or cytotoxic. The concentration of acetaldehyde is kept low through rapid removal by aldehyde dehydrogenase (ALDH) which is widely distributed in tissues [12], [127], [128]. ALDH oxidizes acetaldehyde to acetate, passing the reducing equivalents to NAD+:(4)Acetaldehyde + NAD+ → Acetate + NADH + H+ ALDH2 is localized in the mitochondrial matrix, has a high affinity for acetaldehyde (KM = 1.3 μM in brain cortex [40], and high specific activity. The equilibrium constant for this reaction at pH 7.0 is approximately 4 × 109, and it is irreversible under physiological conditions [1]. As a result of ALDH activity, systemic levels of acetaldehyde are kept low [40]. Acetaldehyde is a chemically reactive compound with significant toxicity and, for individuals who express an inactive form of ALDH2, ethanol consumption can have side effects ranging from unpleasant sensations to serious illness [12], [60]. Neither ethanol oxidation (ADH, catalase, CYP2E1) nor acetaldehyde oxidation (ALDH) is subject to significant regulation and increasing alcohol concentration reduces both cytoplasmic (by ADH) and mitochondrial (by ALDH2) NAD pools. Because ethanol oxidation is not regulated, local (individual cell) rates of oxidation are cell specific, determined by the activity and degree of saturation of the responsible enzyme(s) in each cell. Acetate, the major product of ethanol oxidation, has roles both in energy metabolism and in regulation of metabolism. Acetate is converted to acetylCoA by acetylCoA synthetase:(5)acetate + CoASH + ATP → acetylCoA + AMP + pyrophosphate In mitochondria, acetylCoA primarily enters the citric acid cycle and is oxidized, but at the cellular level is also used to acetylate proteins, altering the activity of individual enzymes and, through histone acetylation, gene expression [74]. The regulatory role of protein acetylation, although important, is outside the scope of this paper and will not be further discussed. In summary: Ethanol readily distributes throughout the body and can be a major fraction of caloric utilization. Oxidation of ethanol and its products, acetaldehyde and acetate, is irreversible and unregulated. This “pushes” energy metabolism and can increase reduction of both cytoplasmic and mitochondrial NAD, thereby increasing cellular energy state. The rate of ethanol oxidation, and its associated metabolic disturbance, is dependent on ethanol concentration and local (cellular) enzyme content. Ethanol oxidation in liver and its metabolic consequences Most of consumed alcohol is oxidized in liver by ADH1 to acetate [52], [67]. In the process, there are profound alterations in hepatic metabolism (Fig. 1, including: excessive reduction of cytoplasmic and mitochondrial NAD pools, inhibition of gluconeogenesis and fatty acid oxidation, increased [ATP]/([ADP]f[Pi]), and decrease in [AMP]f. The subscript f is used to emphasis that the concentrations of ADP and AMP relevant to metabolic regulation are free concentrations, not total amounts in the cell. Lundquist and coworkers [67] reported that, in healthy humans infused iv with ethanol at rates sufficient to result in blood concentrations near 26 mM (0.12 g/dL) oxidation of ethanol to acetate accounted for most of hepatic oxygen consumption. Although some of the acetate produced in liver was further oxidized through the citric acid cycle, most (>75%) was released into the blood. The rate of NADH production from ethanol oxidation to acetate is high enough that flux through the citric acid cycle (CAC), and therefore CO2 production by the liver, is substantially decreased [52]. The lactate/pyruvate ratio is increased, indicating reduction of cytosolic NAD, as is the β-hydroxybutyrate/acetoacetate ratio, indicating increased reduction of intramitochondrial NAD [29], [52], [67]. Increase in intramitochondrial NAD reduction with little or no change in oxygen concentration or the rate of oxygen consumption [97] is consistent with increase in cellular energy state {[ATP]/([ADP]f[Pi])} [115], [118]. In liver, increase in energy state suppresses the activity of pyruvate dehydrogenase (increased acetylCoA, [NADH]/[NAD+], and [ATP]/[ADP]f), activates pyruvate carboxylase (high acetylCoA), decreases [AMP]f, lowering AMP-dependent protein kinase (AMPK) activity [61], [98], [105], [124]. Decreased AMPK activity increases acetylCoA carboxylase activity [19], [30], [38], [39], [42], [124] and the concentration of malonylCoA [19]. MalonylCoA inhibits carnitine palmitoyl transferase 1 [30], [73] suppressing uptake and oxidation of long chain fatty acids by mitochondria. Fatty acid synthesis is activated and this, combined with excess fatty acids taken up by the liver, results in excess fatty acids being made into triglycerides. Interestingly, Galli and coworkers [32] reported that expression of ADH1 in HeLa cells was sufficient to result in ethanol induced fat accumulation, consistent with ethanol oxidation induced inhibition of AMPK being responsible for cellular lipid accumulation. Acetate, the primary product of ethanol oxidation by the liver, is a short (shortest) chain fatty acid and oxidation of short chain fatty acids is not subject to the regulation imposed on oxidation of long chain fatty acids. Acetate produced by the liver results in a large increase in acetate concentration in the blood, to about 1 mM [44], [78]. The rate at which acetate is taken up, and oxidized, by other tissues is dependent on concentration in the blood. In both humans and rats, if blood acetate concentrations are maintained near 1 mM or higher, this results in under consumption of long chain fatty acids and activation of fatty acid synthesis [19], [30], [62], [64], [95], [122]. Since ethanol and acetate are only two carbons long, their products cannot be used for net synthesis of glucose or for anaplerotic support of the citric acid cycle or net glucose synthesis. The lactate/pyruvate ratio is increased, in large part due to decrease in pyruvate [52], and this limits the activity of pyruvate carboxylase, suppressing gluconeogenesis [29], [52], [53]. Blood glucose concentrations decrease [53], but only slightly because the set point for glucose homeostasis in only slightly decreased [72] and glucose consumption by peripheral tissue and brain is decreased. AMP-dependent protein kinase and the metabolic responses to ethanol oxidation. Essentially all cells have adenylate kinase and the activity is sufficient to maintain the reaction:(1)ATP + AMP = 2 ADPnear equilibrium. The equilibrium constant is approximately 1.0, and [ATP] is maintained nearly constant, so [AMP]f decreases as the square of the decrease in free [ADP]f. This makes [AMP]f a very sensitive measure of the energy state [51], [115], [118]. As a result, energy metabolism has evolved with [AMP]f as a core regulatory parameter in setting metabolic homeostasis. It is notable that metabolic homeostasis in eukaryotes has an [AMP]f “set point” and cellular metabolism operates over a narrow range of [AMP]f [115], [118]. Central to maintaining metabolic homeostasis is AMP-dependent protein kinase (AMPK) [30], [38], [39], [42], [49], [118]. Although [AMP]f also contributes to the regulation of many other enzymes and regulatory pathways, the importance of AMPK is shown schematically in Fig. 2A. This includes modulation not only of complex cellular functions, such as protein synthesis, autophagy, mitophagy, and gene expression, but also of individual enzymes and regulatory proteins (Fig. 2B). Most discussions of metabolic regulation involving AMPK focus on the conditions where [AMP]f increases, as occurs in exercise, hypoxia, or inhibition of oxidative phosphorylation, and AMPK activity increases. Activation of AMPK is designed to increase ATP production by increasing catabolic ATP production and inhibiting anabolic ATP consumption. Ethanol ingestion, however, decreases [AMP]f and thereby AMPK activity, suppressing catabolic metabolism and enhancing anabolic metabolism. Indeed, treatment with 5-aminoimidazole-4-carboxamide-1-β-D-ribofuranoside (AICAR), which activates AMPK, has been reported to provide significant protection from alcohol induced fatty liver in rats [105]. Because oxidation of ethanol and acetate is cell specific and largely unregulated, the metabolic consequences differ among cell types and overall metabolic integration disrupted. Ethanol/acetate metabolism and its effects on muscle Although liver and brain are the primary tissues of concern in when considering the consequences of ethanol consumption, other tissues are also affected. This includes cardiac and skeletal muscle. Although neither has significant ADH activity, they have high acetylCoA synthetase and acetylCoA carboxylase and rely heavily on fatty acid oxidation for energy metabolism. This is particularly true when only moderately active (heart) or during rest and after endurance exercise (skeletal muscle) (Fig. 3). Chen et al. [16] reported that chronic ethanol feeding in rats suppresses expression of AMPK, myocyte enhancer factor 2, and glucose transporter 4 (Glut4) in myocardium. The effects of ethanol on muscle metabolism would be expected to be indirect, arising from elevated blood acetate. This is consistent with the report by Kiviluoma [48] that addition of acetate to the perfusate of isolated rat hearts increases incorporation of fatty acids into myocardial lipids. In addition, adding acetate at mM concentrations to the perfusate of isolated rat hearts increases reduction of intramitochondrial NAD and energy state relative to glucose perfusion [48], [99]. Similar observations have been reported for rat skeletal muscle in vivo by Bertocci et al. [11]. When lactate and acetate consumption were measured in resting skeletal muscle, acetate provided most of the acetylCoA (65%) oxidized by the citric acid cycle (CAC). In contracting muscle, the fraction provided by acetate decreased, but only to 43%. Thus, in heart under moderate work load and in resting skeletal muscle, acetate taken up from the blood can be oxidized at rates sufficient to maintain energy metabolism. Acetate displaces (down regulates) oxidation of the physiologically preferred substrate, long chain fatty acids. Putman et al. [82] measured metabolites and pyruvate dehydrogenase activity in muscle during acetate infusion in humans. The reported changes included increase in acetylCoA/CoASH and acetyl-carnitine, as well as decrease in active pyruvate dehydrogenase (PDHa). Uncontrolled production of acetylCoA from acetate in mitochondria and the resultant increase in acetylCoA/CoA, if sustained, would deplete intramitochondrial CoASH. CoASH is required for citric acid cycle. Synthesis of acetyl-carnitine by carnitine acetyl transferase allows export of excess active acetyl groups to the cytoplasm but at the expense of intramitochondrial carnitine. This process appears to be self limiting due to depletion of intramitochondrial CoASH for acetylCoA synthase combined with decreased oxalacetate for citrate synthase limits input of acetylCoA to the rate of oxidation through the citric acid cycle. Evidence supports the view that, following ethanol ingestion, elevated blood acetate becomes a major energy source for resting and moderately active muscle, consistent with associated suppression of AMPK activity [16], [48], [99]. Interestingly, tissues from alcohol dependent humans show dysfunctions in protein synthesis, mitochondrial content, and morphology, consistent with chronic suppression of AMPK activity (Fig. 2A). Ethanol metabolism in brain and its metabolic consequences: A. General considerations Brain is a complex organ with several types of specialized cells, each with its own metabolic requirements and supporting complement of enzymes and metabolite transporters. Ethanol produces major metabolic disturbances in brain metabolism (shown schematically in Fig. 4) including: decreased glucose uptake and metabolism, increased monocarboxylate uptake and metabolism, stimulation of dopaminergic neural activity, suppression of glutamatergic neural activity, and disruption of glutamate metabolism. Catalase, ADH, and P450 systems for oxidizing ethanol are all present in brain, and brain actively takes up acetate from the blood. Determining the role of ADH, however, is complicated by the presence of multiple different forms of alcohol dehydrogenase as well as heterogeneous distributions among cells and tissues [5], [12], [13], [47]. Initial reports by Raskin and Sokoloff [84] of low, but significant, rates of oxidation of ethanol by ADH in subfractions of brain tissue were largely discounted because of the low activity and concerns about the method of analysis [126], [127]. The enzyme properties reported, however, were consistent with the presence of small amounts of ADH1. Initial immunohistochemical data focused on ADH3, an alcohol dehydrogenase that is widely distributed in brain but has negligible activity for ethanol [27], [126], leading some to conclude that metabolism of ethanol by ADH in brain was not significant. Later measurements using antibodies grown to purified liver ADH indicated that while the total amount of enzyme are low, it is concentrated in individual neurons of cerebral cortex, hypothalamus, infundibular stalk of pituitary, and Purkinje cells of cerebellum. This distribution correlates with known sites of ethanol toxicity (13. 47). More recently, selective antibodies for ADH1 (KM = 1.4 mM) and ADH4 (KM = 37 mM) have been used [27]. Again the total amounts were low but concentrated in a small fraction of the brain cells. Martinez et al [70] examined tissue sections from several regions of adult rat brain by in situ hybridization to detect expression of genes encoding ADH1. ADH1 mRNA was found in granular and Purkinje cell layers of cerebellum, in pyramidal and granule cells of hippocampal formation, and in some types of cells in cerebral cortex. ADH4 expression was detected in Purkinje cells, pyramidal and granule cells of the hippocampal formation, and pyramidal cells of cerebral cortex. Substantial levels of both ADH1 and ADH4 mRNAs were detected cells of the CNS epithelial and vascular tissues: leptomeninges, choroidplexus, ependymocytes of ventricle walls, and endothelium. Thus, current evidence is consistent with oxidation of ethanol by ADH capable of significantly affecting brain function in subpopulations of cells in which ADH1 and/or ADH4 are expressed. Ethanol and acetate metabolism in brain: B. Rates and contributions to total energy production Ethanol, acetaldehyde, and acetate have been shown to be oxidized in brain and the combined rates are high enough to disrupt glucose and amino acid metabolism. Wang and coworkers [111] studied the rate of ethanol oxidation in rat brain cortex in vivo by 13C MRI. Using a combination of 2-13C labeled ethanol and 1,2-13C labeled acetate, they reported significant rates of incorporation of carbon from ethanol into glutamate and glutamine. The authors calculated ethanol oxidation accounted for 12% and 20% cortical oxidative metabolism in ethanol naïve and ethanol pre-treated rats. The measurements were specifically of ethanol oxidation and did not include oxidation of acetate taken up from the blood. Measurements of acetate metabolism in rat brain in vivo using 13C MRI [21], [81], [107] show it is readily transported through the blood-brain barrier and metabolized. As acetate concentration in blood was raised to 2–3 mM, the rate of metabolism by the brain saturated. At saturation, enrichment of glutamine and glutamate at the C4 position were consistent with most of the acetylCoA entering the citric acid cycle in astrocytes arising from acetate with the remaining coming from unlabeled glucose. The models used in the interpretation assumed, however, that acetate metabolism occurred almost entirely in astrocytes with negligible contribution by neurons. This may not be appropriate [87] particularly as the neurons contain substantial levels of acetylCoA synthase. As noted earlier, when ethanol is ingested, acetate is produced in the liver and released into the blood. Blood acetate levels rise rapidly to about 1 mM or more [44], [78], and ethanol oxidation in brain is augmented by oxidation of acetate taken up from the blood. When these are combined, ethanol becomes a major source of acetylCoA for production of ATP used for ATP production in many brain cells. Recent reports include evidence that in mice ethanol consumption leads to rapid increase in histone acetylation in the brain with the acetyl moiety arising from ethanol [74]. This acetylation is reported to alter gene expression in hippocampus and to affect cellular mechanisms related to learning. The level of acetylCoA and the acetylCoA/CoASH ratio are important regulators of protein acetylation and are coupled to cellular energy state and AMPK activity. Ethanol exposure Phase 1: Acute effects of ethanol ingestion on metabolism As noted above, alcohol ingestion results in rapid changes in the metabolism of brain and other tissues. Following ingestion, oxidation of ethanol takes precedence over other metabolites used for energy metabolism and the latter are down regulated to accommodate the input from ethanol. The effects are cell and tissue specific and depend on how rapidly ethanol is oxidized and/or acetate is taken up through the monocarboxylate transporter. Liver, due to its large size, immediate exposure to the ingested alcohol from the portal circulation, and high content of ADH1 dominates systemic ethanol disposal. Effects of acute ethanol ingestion on liver include rapid increase in metabolic energy state, production and export of acetate, suppression of long chain fatty acid oxidation and synthesis, increased β-hydroxybutyrate/acetoacetate ratios, and under production of glucose by gluconeogenesis. The response of other tissues to ethanol exposure is heterogeneous. In pancreas, increased energy state due to ethanol/acetate metabolism slightly lowers the glucose homeostasis set point [116], [117], [119]. In most other tissues, input of acetylCoA from ethanol and acetate largely replaces acetylCoA from long chain fatty acids and amino acids for energy metabolism [31], [107], [110]. The oxidation of ethanol and/or acetate induces an increase in energy state and associated decrease in [AMP]f and AMPK activity [19], [30], [31], [38], [39]. Increase in blood acetate in rats to 300 µM, for example, decreased AMPK phosphorylation in hypothalmus by about 50% [31]. Decreased AMPK activity suppresses GLUT1, GLUT3, and GLUT4, the dominant glucose transporters in brain [31], [37], [75], [119]. Handa et al. [37] isolated plasma membranes and low density microsomal fractions from rat brain 4 h after their treatment with ethanol (3 g/kg body weight) and reported immunoblot measured protein decreased by 17% and 71% for GLUT1 and 54% and 21% for GLUT3, respectively. In addition, immunofluorescence imaging showed decreased GLUT1 in choroid plexus and cortical microvessels. This is consistent with reports by Muneer and coworkers [75] that chronic ethanol treatment suppresses glucose transport and GLUT1 protein expression in microvessels of the blood-brain barrier of rats and by Frost et al. [31] that it decreased GLUT1 in the plasma membrane of brain cells in primary culture. Accompanying ethanol induced decrease in glucose transporters is increase in monocarboxylate transporter [65]. Pyruvate is needed not only for anaplerotic maintenance of citric acid cycle activity but also for de novo synthesis of glutamate in astrocytes through the activity of pyruvate carboxylase [125]. Increased uptake of lactate from the blood [65] would normally provide increased pyruvate through oxidation by cytoplasmic NAD+. For cells with ADH1 and/or ADH4, however, oxidation of ethanol in the cytoplasm results in increased reduction of the cytoplasmic NAD pool and an increase in the lactate/pyruvate ratio [52]. In these and other cells, acetaldehyde and acetate oxidation in the mitochondria also increases reduction of intramitochondrial NAD, suppressing aspartate-glutamate shuttle activity. Although intracellular lactate concentrations in brain increase during ethanol oxidation, this is due to elevated systemic lactate/pyruvate and pyruvate concentrations actually decrease. Intracellular pyruvate concentrations are normally less than the KM of pyruvate carboxylase, which is near 400 μM [92], making the rate of pyruvate carboxylation strongly dependent on intracellular pyruvate concentration [52]. Ethanol induced decrease in intracellular pyruvate suppresses de novo synthesis of oxalacetate, and thereby of glutamate, in astrocytes and this disrupts glutamate homeostasis in astrocytes and glutamatergic neurons. Ethanol exposure Phase 2: Metabolic consequences of alcohol ingestion in greater amounts and over intermediate periods of time (days, weeks) Ethanol dependence arises after significant periods of nearly continuous alcohol consumption, typically months to years. This is not an all or nothing phenomenon, however, and there is a transition period during which withdrawal of alcohol results in a progressively greater sense of unease, anxiety, and irritability that can be overcome by consuming alcohol. The shortest time of exposure that generates significant ethanol dependence may be binge drinking, in which substantial amounts of ethanol are consumed over periods of one or more days. A few hours after drinking stops, ethanol levels approach zero and there is a period that often includes severe headaches and other unpleasant after effects, referred to as a hangover. The colloquial antidote for a hangover is “hair of the dog that bit you”, i.e. drinking some of the same alcoholic beverage consumed during the binge in order to alleviate the hangover. This antidote acutely relieves many of the symptoms, consistent with hangovers arising in part through deficiency in oxidizable substrates for energy metabolism in brain. The extended time and extent of alcohol exposure during a binge allows participation of slower regulatory processes, including altered rates of protein turnover and gene expression. When the levels of alcohol and acetate fall too quickly for regulation to fully compensate by switching back to the usual (non-ethanol) energy sources, the deficiency can be overcome by providing some alcohol. This temporarily relieves the deficiency in energy metabolism, and thereby the hangover, as well as providing additional time for metabolic regulation to compensate for lack of ethanol/acetate. Ethanol exposure Phase 3: Ethanol dependence resulting from heavy alcohol consumption over periods of months and years Individuals with alcohol dependence consume a substantial fraction of their daily caloric intake as ethanol, typically near 50% of the total calories [5], [25]. This large intake, spread throughout the day and sustained for weeks to months, results significant (mM) levels of ethanol being in the blood most of the time. Diets of individuals with ethanol dependence are often deficient in essential nutrients, including vitamins, minerals, unsaturated fatty acids, and amino acids. In addition, ethanol is only 2 carbons long and can not be used for de novo synthesis of glucose, glutamate and other metabolites critical to long term metabolic homeostasis. In brain, chronic suppression of the oxidation of glucose and other (non-ethanol related) metabolites results in depletion of their associated transporters and enzymes. Conversely, the capacity for metabolism of ethanol and acetate increases [43], [65], [107]. If ethanol consumption ends abruptly, ethanol and acetate levels in the body fall to near zero within a few hours. The oxidizable metabolites used to support energy metabolism in non-ethanol dependent animals have been chronically down regulated and can no longer increase sufficiently to maintain the cellular energy state. As noted above, for individuals who are ethanol dependent, ethanol normally provides 50% or more of the daily caloric intake. Glucose consumption in brain is substantially decreased by ethanol consumption [107], [108], [110], [112]. Oxidation of one mole of glucose provides slightly more ATP (38 ATP/mole) than does oxidation of two moles of ethanol. Oxidation of alcohol to acetaldehyde by ADH and of acetaldehyde to acetate by ALDH each contributes one NADH for oxidative phosphorylation (6 ATP/ethanol) and oxidation of acetate through the citric acid cycle provides an additional 3 NADH and 1 FADH2 (total of 17 ATP/ethanol, 11 ATP/acetate). Even after correction for the ATP required for acetate activation (acetylCoA synthetase), oxidation of 2 mol of ethanol provides 30 mol of ATP, about 80% of that supplied by oxidation of 1 mol of glucose. Volkow et al. [108] measured brain glucose metabolism in twenty healthy control subjects using positron emission tomography (PET) and fluorodeoxyglucose after administration of placebo or either 0.25 g/kg, or 0.5 g/kg of ethanol over a 40 min period. Both doses of ethanol significantly decreased whole-brain glucose metabolism (10% and 23% respectively). Similar decreases have been reported by other authors, i.e. 26% for 0.75 g/kg [112], 25% for approximately 0.6 g/kg iv [91]. Volkow et al. [107] reported that the response to ethanol is regionally specific; whereas 0.25 g/kg predominantly reduced glucose metabolism in cortical regions, 0.5 g/kg reduced metabolism in cortical as well as subcortical regions (i.e. cerebellum, mesencephalon, basal ganglia and thalamus). As noted earlier, suppression of brain glucose consumption is in response to combination of oxidation of ethanol within the brain and of acetate taken up from the blood [107], [108], [110], [111]. Indeed, in heavy drinkers studied during sobriety, acetate metabolism in the occipital cortex is reported to be increased [43]. Persistent reliance on acetate for energy metabolism of alcoholics during early alcohol detoxification may explain why the decreased brain glucose consumption observed in alcoholics mostly disappears within the first 2 weeks of detoxification [109]. It can be inferred that for cells in brain for which ethanol (and/or acetate) oxidation is responsible for substantial fraction of their ATP production, sudden withdrawal of this important support of energy metabolism results in cellular malfunction and thereby abnormal brain activity. In ethanol dependent mice, withdrawal symptoms correlate with decrease in blood alcohol and acetate, symptoms maximizing as the concentrations approach zero [85]. Derr and coworkers [22], [23], [24] reported that, for ethanol dependent rats, providing acetate during withdrawal significantly alleviated, and providing a mixture of butyrate, lactate, and β-hydroxybutyrate fully suppressed [22],the tremulous portion of their withdrawal symptoms. It did not affect the handling induced convulsions. This is consistent with the tremulous, but not the convulsive, part of the withdrawal symptoms arising from a deficiency in energy metabolism needed to support neuronal activity in the brain. Neurological responses to ethanol consumption: a prolog Our paper focuses on metabolism of ethanol and the impact of ethanol consumption on metabolism as a whole. This is an integral and important part of understanding how alcohol influences human biochemistry, physiology, and social behavior. In brain, we focus on effects of ethanol on metabolism of neurons and astrocytes, where changes in energy state directly influences not only all aspects of neurotransmitter function (release, receptor sensitivity, transport, and metabolism) but also induces long term alterations in the underlying cellular machinery (through gene expression, etc). It is recognized, however, that connections between cellular metabolism and neuronal function, and from neural function to behavior, are very complex. Others have made great effort to understand how ethanol alters neuronal function; receptor affinities, ion channel conductance, synaptic connections etc, but many of the particulars remain poorly understood. There is general agreement that ethanol induces substantial changes in neural function but the relative contribution of metabolism and concentration remains under discussion. Due to the complexity of the neurological consequences of ethanol consumption we will address only those we consider to be primarily responses to ethanol and acetate metabolism. There is, for example, an extensive literature on the role of ethanol metabolism and of AMPK activity in modulation of neuronal function (see as examples, [3], [93], [94], [123]). We emphasize the role of energy metabolism and [AMP]f in order to illustrate not just their importance in the response to ethanol but also their central role in coordinating metabolic processes essential to biological existence as we know it. Neurological responses to ethanol consumption A: Early alterations in the dopaminergic system and potentiation of consumption Ethanol induces dopamine release and increased activity in the striatum as indicated by functional imaging [91]. Dopaminergic neurons that respond to nutrients, such as glucose, involve metabolism of that nutrient. Increase in nutrient concentration causes an increase in energy state and modulates ion channel (such as the KATP channel) conductance to initiate the dopaminergic signaling cascade. Ethanol, in contrast to other nutrients, has access to neurons behind the blood-brain barrier and this augments the usual nutrient reward circuitry. Dopaminergic projections go to the nucleus accumbens, which has a crucial role in the reward system of human brain. These dopaminergic effects have been attributed to inhibition of GABAergic interneurons of the ventral tegmental area (VTA) by alcohol. Ethanol-induced dopamine release in nucleus accumbens has been reported to be antagonized by administration of an opiate receptor antagonist [34], suggesting inhibition of GABAergic VTA interneurons are at least partly mediated by opioidergic afferents to these neurons. It is reasonable to suggest ethanol induced dopamine release is in response to increased energy state (nutrient sensing) in GABAergic and dopaminergic neurons. Considerable attention has been paid to acetaldehyde in relation to the dopaminergic response. Inhibition of catalase activity has been reported to prevent ethanol induced dopaminergic signaling in nucleus accumbens [46]. The authors attributed this effect to decrease in acetaldehyde concentration, but inhibiting catalase alters metabolism in many ways, of which acetaldehyde concentration is only one. Follow-up studies have reported that, in rats bred for ethanol preference, administration of a lentiviral vector coding for aldehyde dehydrogenase-2 (ALDH2) into the ventral tegmental area decreased long term ethanol consumption. This decrease was observed, however, only for ethanol naïve rats and not rats that had consumed ethanol on a 24-hour basis for 81 days [46]. Rivera-Meza et al. [86] reported that treating naïve UChB alcohol preferring rats with N-(1,3-benzodioxol-5-ylmethyl)-2,6-dichlorobenzamide, a drug that increased ALDH2 activity in brain by 3 fold, markedly lowered the amount of alcohol they consumed. The authors suggested that activation of ALDH2 in brain decreased acetaldehyde concentrations and this was responsible for inhibiting both acquisition and maintenance of chronic ethanol intake by alcohol-preferring rats. It has also been suggested that acetaldehyde produced from ethanol reacts with dopamine to form salsolinol and that salsolinol is an agonist for μ-opioid receptors, contributing to the reward response to alcohol [10]. There is, however, little evidence for a role for salsolinol in ethanol consumption and metabolism in humans [56], [57] and the contribution of acetaldehyde concentration remains to be established. In humans, expression of inactive aldehyde dehydrogenase 2 (ALDH2) is protective against alcohol use disorders [12], [60]. This decrease in ethanol consumption, however, has been attributed to the unpleasant side effects of systemic increased acetaldehyde. Lack of ALDH2 also suppresses oxidation of acetaldehyde in mitochondria, disrupting ethanol induced increase energy state, particularly in cells with ADH1 and the reward response to ethanol consumption. Studies in mice by Tabakoff et al. [101] indicated that ethanol tolerance and physical dependence involve different mechanisms. The authors used intraventricular injection of 6-OH dopamine to deplete dopamine and norepinephrine in the brain. Injected prior to chronic ethanol exposure, this prevented development of tolerance to the hypnotic and hypothermic effects of ethanol without significantly affecting development of physical dependence as measured by withdrawal symptoms (convulsions etc). Once tolerance was established, however, 6-OH dopamine injection did not alleviate that tolerance. It is reasonable to conclude that dopaminergic and noradrenergic systems contribute substantially to the positive physiological response to ethanol and to development of alcohol tolerance, but not to alcohol dependence or withdrawal symptoms. Neurological responses to ethanol consumption B: Early effects on glutamate and GABA metabolism Chronic ethanol intake in rodents, primates, and humans results in physical dependence and it is widely considered that dependence involves excitatory glutamate and inhibitory GABA neurons. Ethanol oxidation is particularly disruptive of glutamate and GABA metabolism and handling in brain. It is hypothesized that metabolism of astrocytes and neurons is coordinated through glutamate/glutamine cycling [90] and/or lactate transfer [9]. Our version of that metabolic coordination in the absence of ethanol is shown schematically in Fig. 5. Astrocytes have high glycolytic activity and glycogen storage as well as high pyruvate carboxylase activity whereas neurons are oxidative and have high pyruvate dehydrogenase activity. Pyruvate carboxylase in astrocytes is responsible for de novo synthesis of oxaloacetate (and thereby glutamate and glutamine) from pyruvate provided by glycolysis (anaplerosis). Pyruvate carboxylase provides α-ketoglutarate (α-KG) for synthesis of glutamate by aspartate/α-KG aminotransferase. Glutamate is then condensed with ammonia (glutamine synthetase) to form glutamine which is exported and taken up by neurons. In neurons, the glutamine is deaminated (glutaminase) and used as both a neurotransmitter and energy source. In order to be used for ATP production, glutamate is first deaminated by either glutamate/oxalacetate aminotransferase [41] or glutamate dehydrogenase to α-KG which enters the CAC. The CAC, however, does not carry out net oxidation of substrates with chain lengths of 4 or more carbons [50]. α-KG is oxidized to malate in the CAC and malate is exported to the cytoplasm where malic enzyme oxidatively decarboxylates malate to pyruvate. In neurons, pyruvate dehydrogenase is active and pyruvate from either malate or lactate is oxidized to acetylCoA and then to CO2 and water by CAC plus Ox-Phos. Glutamate released at the synapse during neural activity is taken up partly by synaptic glutamate transporters [26], [120] while glutamate that diffuses out of the synapse into the perisynaptic space is taken up by astrocytes. Glutamate consumed for energy metabolism in neurons is replaced by de novo synthesis from pyruvate in astrocytes. Metabolism of ethanol and/or its metabolites inhibits both glucose uptake and glycolytic production of pyruvate through increased energy state (decreased [AMP]f) and increased NADH. Ethanol induced increase in systemic lactate/pyruvate also suppresses intracellular pyruvate concentrations, despite an increase in intracellular lactate through increased monocarboxylate transport. Intracellular pyruvate concentrations are typically below the KM for pyruvate carboxylase (about 400 μM), and decrease would be expected to suppress astrocytic de novo glutamate synthesis. Extracellular glutamate levels in the synapse also decrease due to increase in energy state through its effect on the energy dependence of synaptosomal glutamate transport [26]. The net early effects of ethanol on glutamatergic neural activity include decreased glutamate release in response to the increased energy state in neurons and increased levels of inhibitory neurotransmitter GABA [33]. These contribute to the calming effects of ethanol through suppression of glutamatergic anxiety producing neural activity. Neurological responses to ethanol consumption: C. Long term alterations in glutamatergic neural system and alcohol withdrawal syndrome It has been proposed that neurophysiological and pathological effects of ethanol are mediated, to an important extent, by glutamatergic neurons [35], [36], [79], [96], [106]. Biochemical and electrophysiological studies have reported that chronic ethanol treatment increases the number of excitatory N-methyl-D-aspartate (NMDA) receptor-ionophore complexes in hippocampus, a brain area associated with ethanol withdrawal seizure activity [35], [36], [96]. Withdrawal symptoms vary, but in mice removal of ethanol from the diet induces withdrawal-associated tremors and handling-induced seizures [35], [36], [85]. The withdrawal symptoms are reported to correlate with decrease in blood alcohol and acetate levels, with the most severe seizure activity occurring when these concentrations fall to near zero. In mice, the NMDA receptor levels return to control in 24 h and the mice no longer have handling induced seizures. Treatment with NMDA during withdrawal exacerbated handling induced withdrawal seizures, while administration of MK-801, a noncompetitive NMDA receptor antagonist, decreased the occurrence and severity of seizures [35]. Treatment with MK-801 did not, in alcohol dependent mice, suppress the tremors which occur during withdrawal [35], [36]. Thus, following chronic ethanol treatment the glutamate receptors, particularly NMDA receptors, appear to mediate handling induced seizures, but not tremors, associated with ethanol withdrawal. Blocking NMDA receptors by memantine, a moderate affinity but clinically approved NMDA receptor antagonist, has been shown to reduce withdrawal induced-seizures and neurotoxicity in mice [100]. In clinical studies, memantine has been reported to attenuate cue-induced craving for alcohol and withdrawal symptoms associated with alcohol consumption [54], [55]. In rodents, exposure to alcohol has been reported to induce a significant increase in the expression and synaptic localization of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPA receptors) in brain regions with reward circuitry [15], [17]. AMPA receptors are ionotropic transmembrane receptors for glutamate that mediate fast synaptic transmission in the CNS. Infusion of an AMPA receptor inhibitor into the dorsomedial striatum exhibited promising results in reducing alcohol consumption in rats [113] and an AMPA/kainate receptor antagonist, topiramate, is reported to suppress withdrawal symptoms in humans [55]. Another drug, acamprosate, known to affect two neurotransmitter systems, GABA (as an agonist) and glutamate (as an NMDA receptor agonist and mGluR5 antagonist), has been reported to be effective in increasing complete abstinence rate as well as cumulative abstinence duration in several long-term placebo-controlled trials in alcohol-dependent patients [59], [80], [88]. In a large clinical trial involving 1383 patients in nine possible treatment groups, however, acamprosate neither alone nor with naltrexone or combined behavioral intervention showed statistically significant reduction in alcohol consumption over placebo [4]. Despite the inconsistencies in findings, overall advantageous pharmacological effects of acamprosate on alcohol consumption are such that it is approved for treatment of alcohol use disorders in Europe and USA. Understanding how glutamate signaling may contribute to addiction needs to account for multiple pools of intra- and extra-cellular glutamate. Glutamate concentrations within glutamatergic synapses are determined by balance of vesicular release and energy dependent glutamate reuptake whereas that in the perisynaptic space is determined by diffusion from the synapse and extrasynaptic release/uptake [28], [104]. Perisynaptic glutamate regulates neurotransmission by stimulating group II glutamate receptors (mGluRs) in the presynaptic area [7], [121]. These are presynaptic receptors capable of inhibiting vesicular release [8], [18], [89]. Thus, presynaptic receptors permit cross talk between the two pools and altered nonvesicular glutamate release may contribute to pathological glutamate signaling linked to addiction [110]. As indicated above, under normal physiological conditions, plasma acetate concentration is low but with ethanol ingestion it rapidly increases to about 1 mM. Acetate is readily taken up and metabolized by brain [20], [81], [114], largely by astrocytes. Calculated rates of utilization of acetate from the blood were reported to be 15–25% of total glucose consumption in non-stimulated tissue and to range from 28 to 115% of estimated rates of glucose oxidation in astrocytes [20], [114]. During intoxication, blood acetate concentrations are higher for alcoholics than for controls [78]. This would facilitate metabolism of acetate by glial cells, as shown for laboratory animals chronically exposed to alcohol [107], [111]. Madayag and coworkers [68] reported that repeated N-acetylcysteine administration alters plasticity-dependent effects of cocaine. The authors provided evidence that the ability of N-acetylcysteine to regulate drug-seeking behavior results from actions on cystine-glutamate exchanger (system xc) and is associated with suppression of the large release of glutamate that occurs in accumbens during drug seeking. In vitro measurements have been reported to show that cystine induced activation of cystine/glutamate exchange system xc results in release of glial glutamate and stimulation of mGlu2 receptors [45]. This inhibits neuronal glutamate release. In cocaine addiction, decrease in reinstated cocaine seeking elicited by activating xc with N-acetylcysteine is prevented by pretreatment with an mGlu2/3 antagonist [58]. Pharmacological treatment with N-acetylcysteine is reported to modulate responses to stress and depressive-like behaviors by increasing xc expression and to indirectly activate mGlu2 receptors [76]. The fact that pharmacological blockade of the xc system also blocked N-acetylcysteine effects on mGlu2 receptors further supports linkage of mGlu2 regulation in promoting resilience to stress. A consistent biomarker of mood-related behaviors associated with electrophysiological changes in the dentate gyrus is reduction in the mGlu2 receptors that regulate vesicular release of glutamate at synapses [77]. Ethanol metabolism and associated alcohol withdrawal syndrome: a synopsis Ethanol, through its role as a major but essentially unregulated metabolite for energy metabolism, causes a truly metabolic disease. An initial positive sense of well being is induced by “pushing” energy metabolism and increasing energy state to above its homeostatic set point. The positive sense of well being is part of the neurological signaling system that has evolved to guide adaptation to the environment. Positive reward feelings associated with adequate food, warmth etc. are enhanced and negative feelings associated with hunger, anxiety, etc are suppressed. This leads to a euphoric state with an enhanced sense of being well fed and unstressed. Short exposures are reversible and cause little long term injury, but as ethanol exposure continues there is progressive disruption of critical regulatory mechanisms that maintain metabolic homeostasis. The extent of disruption is dependent on individual tissues and their metabolic requirements with many other contributing parameters (including genetic differences, diet, etc). A key aspect of ethanol metabolism appears to be the decrease in [AMP]f and attendant decrease in AMPK activity. Both chronic decrease in AMPK activity and alcohol dependence are reported to cause decreased activity of oxidative phosphorylation, abnormally shaped mitochondria and other cellular morphological and enzymatic changes [14], [102]. In addition to the debilitating effects of prolonged ethanol consumption, it is necessary to deal with alcohol withdrawal syndrome. As described earlier, this is a complex response to stopping ethanol consumption and correlates with disappearance of ethanol and its metabolic products from the body. The symptoms can be severe and life threatening but do not arise from a single cause. Due to the multiplicity in causes, a single drug is unlikely to fully alleviate the withdrawal symptoms. Effective treatment is likely to require simultaneous treatment of multiple causes. Identifiable primary metabolic contributions to alcohol withdrawal symptoms and treatments that should substantially suppress the symptoms include: 1). Correct the energy deficiency in specific brain cells (neurons and astrocytes). Derr and coworkers [22] reported this can be addressed by providing a metabolite “cocktail” (butyrate, lactate, and β-hydroxybutyrate) for energy metabolism and anaplerotic support of the citric acid cycle. Derr and Derr [23] quantified the tremulous and rigidity symptoms of alcohol withdrawal in rats and concluded that feeding β-hydroxybutyrate suppressed the tremulous, but not the rigidity, symptoms. Other suitable nutrients include branched chain amino acids and branched short chain fatty acids, as these also rapidly cross the blood-brain barrier. Importantly, each can provide both acetylCoA for oxidative metabolism and pyruvate for anaplerotic synthesis of glutamate. Given the importance of carnitine in acetate metabolism, supplementing with carnitine and/or acetyl-L-carnitine may also be helpful [71]. 2A). Stabilize glutamate distribution and metabolism in the brain. Treatment with N-acetylcysteine has been reported to modulate responses to stress and depressive-like behaviors in several ways. These include increasing expression of glutamate-cystine antiporter (system xc), increasing the concentrations of cysteine and cystine, and indirectly by activating mGlu2 receptors [2], [76]. This helps to rebalance glutamate homeostasis and glutamatergic neuronal/astrocyte interactions. 2B). Pharmacological compensation for increased sensitivity of the glutamatergic neural system. As noted above, chronic ethanol consumption induced suppression of glutamatergic activity results in increase in excitatory (NMDA, AMPA) and decrease in inhibitory (mGluR) glutamate receptors. The contribution of enhanced glutamatergic sensitivity and neurotoxicity of excessive NMDA receptor activity to ethanol withdrawal symptoms can be suppressed by antagonists for excitatory glutamate receptors. Among the glutamate receptor antagonists currently clinically approved or in trials are acamprosate, memantine, and MK-801. Suppression of glutamatergic sensitivity through the critical withdrawal period has been reported to decrease the severity of symptoms [55]. Once energy and glutamate metabolism have normalized, receptor numbers should return to near normal. Alcohol dependence is not just a metabolic disease, and there are substantial genetic, psychological, and social contributions. Identifying and characterizing the metabolic consequences of ethanol ingestion and of alcohol dependence is important to understanding the disease and how to more effectively treat alcohol dependence. This is, however, just one piece of a complex medical and social problem. Conflict of interest statement Neither DFW nor FMM have any financial and personal relationships that could influence the work presented in this manuscript. Acknowledgement The authors are indebted to Dr. Joseph J. Higgins for insightful and memorable discussions concerning ethanol metabolism and its regulation.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/29%3A_Integration_of_Mammalian_Metabolism_-_Capstone_Volume_II/29.10%3A_Ethanol_metabolism-_The_good_the_bad_and_the_ugly.txt

This article is directly taken from: Agus A, Clément K, Sokol H. Gut microbiota-derived metabolites as central regulators in metabolic disorders. Gut 2021;70:1174-1182. https://gut.bmj.com/content/70/6/1174. Creative Commons Attribution Non Commercial (CC BY-NC 4.0) http://creativecommons.org/licenses/by-nc/4.0/. References can be found in the original article. Abstract Metabolic disorders represent a growing worldwide health challenge due to their dramatically increasing prevalence. The gut microbiota is a crucial actor that can interact with the host by the production of a diverse reservoir of metabolites, from exogenous dietary substrates or endogenous host compounds. Metabolic disorders are associated with alterations in the composition and function of the gut microbiota. Specific classes of microbiota-derived metabolites, notably bile acids, short-chain fatty acids, branched-chain amino acids, trimethylamine N-oxide, tryptophan and indole derivatives, have been implicated in the pathogenesis of metabolic disorders. This review aims to define the key classes of microbiota-derived metabolites that are altered in metabolic diseases and their role in pathogenesis. They represent potential biomarkers for early diagnosis and prognosis as well as promising targets for the development of novel therapeutic tools for metabolic disorders. Key messages • Metabolic disorders, a growing worldwide health challenge, are associated with alterations in the composition and function of the gut microbiota. • Microbial metabolites are key factors in host-microbiota cross-talk. • Specific classes of microbiota-derived metabolites, notably bile acids, short-chain fatty acids, branched-chain amino acids, trimethylamine N-oxide, tryptophan and indole derivatives, have been strongly implicated in the pathogenesis of metabolic disorders. • Gut microbiota-derived metabolites represent potential biomarkers for the early diagnosis and show promise for identifying targets for the development of novel therapeutic tools for metabolic disorders. Introduction The human intestine harbours a complex and diverse system of mutualistic microorganisms, consisting of bacteria, fungi, viruses, archaea and protozoa. This rich ecosystem contributes to a large number of physiological functions: fermentation of indigestible dietary components and vitamin synthesis, defenses against pathogens, host immune system maturation and maintenance of gut barrier function. Thus, this central regulator, sometimes qualified as the ‘second brain’, plays a significant role in maintaining host physiology and homeostasis. All the species interconnected in the gut produce an extremely diverse reservoir of metabolites from exogenous dietary components and/or endogenous compounds generated by microorganisms and the host. Notably, while food is generally examined for calories and macronutrients and micronutrients, microbial metabolism (and even human enzymes) recognises food molecules and transforms them into metabolites. These microbial metabolites are key actors in host-microbiota cross-talk. The beneficial or detrimental effect of specific microbiota-derived metabolites depends on the context and the host state, suggesting the primordial nature of the symbiotic microbiota in ensuring optimal health in humans. With the widespread westernisation of lifestyles, alteration of the gut microbiota composition and functions has become a worldwide phenomenon. Despite the difficulty to distinct a direct causal relationship and an association between dysbiosis and diseases, several lines of evidence demonstrate that the alteration of the gut microbiota is involved in the pathogenesis of multiple diseases affecting the GI tract, such as IBD or colorectal cancer, as well as many non-digestive systems. Metabolic disorders have been recognised to be massively impacted by gut microbiota. In the last two decades, increasing calorie intake and decreasing levels of physical activity have contributed to a progression in the prevalence of metabolic disorders. Metabolic disorders represent a group of disorders with the clustering of various inter-related pathological conditions combining obesity, non-alcoholic steatohepatitis (NASH), dyslipidaemia, glucose intolerance, insulin resistance, hypertension and diabetes that, when occurring together, strongly increase the incidence of cardiovascular diseases and mortality. Deciphering the mechanisms of host-intestinal microbiota interactions represents a major public health challenge in the development of new preventive or curative therapeutic strategies. In the present review, we will focus on the results from the most significant studies dealing with the role of microbiota-derived metabolites in metabolic disorders. Disrupted equilibrium of the gut microbiome-host interactions in metabolic disorders The gut microbiota plays a crucial role in maintaining the physiological functions of the host. A disruption of the fragile host-microbiota interaction equilibrium can play a role in the onset of several metabolic diseases. The gut microbiota can interact with the host by producing metabolites, which are small molecules (<1500 Da) representing intermediates or end-products of microbial metabolism. These metabolites can derive directly from bacteria or the transformation of dietary or host-derived substrates. Gut microbiota incrimination The implication of the gut microbiota in the regulation of host metabolic balance has been demonstrated in the last decade. Studies conducted both in animal models and humans revealed a significant role of the gut microbiota in the pathogenesis of metabolic disorders, strongly influenced by diet and lifestyle modifications. Evidence from animal experiments The gut microbiota modulates energy expenditure and homeostasis in several animal models, including germ-free mice (GF mice) and genetically induced mice with obesity (ob/ob mice). GF mice are protected against obesity in a Western diet setting. Independent of daily food intake, Bäckhed et al reported a 60% increase in body fat, hepatic triglycerides and insulin resistance in conventionalised adult GF mice compared with GF mice, notably due to better absorption of monosaccharides. Interestingly, the transfer of gut microbiota from ob/ob mice to GF mice results in a significant increase in body weight and fat mass compared with colonisation with a lean microbiota, showing a causal relationship. The gut microbiota composition is unique to each individual. Caecal microbiota transplantation, from two mice with different responses to high-fat diet (HFD), into GF mice leads to the transmission of the donor’s responder (RR) or non-responder (NR) phenotype. The gut microbiota of severely hyperglycaemic RR mice is enriched in Firmicutes, whereas NR is dominated by Bacteroidetes and Actinobacteria. Moreover, the transplantation of faecal microbiota from human twin pairs, discordant for obesity, into GF mice led to the acquisition of lean and obese phenotypes according to the donor. This phenotype transmission is strongly diet-dependent and notably favoured by a low-fat diet enriched in vegetables and fruits and thus enriched in fibre. The effect of the gut microbiota seems to occur even before birth, as the maternal gut microbiota, through short-chain fatty acid (SCFAs), triggers embryonic GPR41 and GPR43 and influences prenatal development of neural, enteroendocrine and pancreatic systems of the offspring to maintain postnatal energy homeostasis and eventually prevent metabolic disorder development. Overall, these animal studies demonstrate the tight interconnection between diet and the gut microbiome in the pathogenesis of metabolic disorders as well as in its vertical transmissibility. Evidence from human studies Alterations in the gut microbiome composition and functions are associated with various traits observed in metabolic disorders. Although there are some conflicting results, the obesity-associated gut microbiota has been characterised by a decline in Bacteroidetes and a compensatory expansion of the Firmicutes phylum and by a reduction in microbial diversity and richness. There is notably a negative correlation between the severity of metabolic markers and the richness of the gut microbiota. Individuals with low microbiota gene content present more adiposity, insulin resistance and dyslipidaemia than high bacterial richness populations. Even in severe obesity conditions, those with diminished gut microbiota richness have a more severe metabolic condition. In patients with diabetes, the higher proximity of the altered microbiota to epithelial cells could promote pro-inflammatory signals, contributing to the development of aggravated metabolic alterations. In humans, faecal microbiota transplantation (FMT) demonstrated some positive but moderate effects in patients with metabolic syndrome traits, proving the involvement of the gut microbiota in the pathogenesis and its potential therapeutic role.However, the efficiency of FMT in improving metabolic amelioration was dependent on the recipient gut microbiota profile, with low baseline richness promoting gut microbiota engraftment. Gut microbiota-derived metabolite implications in metabolic diseases The gut metabolome Metabolomics, which consists of the study of the small molecules present in any type of biological sample, has proven to be helpful in enriching the knowledge on microbiota-host interactions. Several hundred faecal or serum metabolites have been associated with clinical features associated with metabolic disorders. Moreover, a combination of metagenomics and metabolomics was used to elucidate the associations between gut microbiota imbalances and metabolic disturbances. This field is still in its infancy and, for some metabolites, it remains difficult to determine whether they are fully microbiota-derived or if other sources are involved, including diet or the host itself. Metagenome and metabolome studies led to the discovery of new associations between microbial-derived metabolites and metabolic syndrome, but additional arguments are needed to establish a potential causality link. Notably, the decreased abundance of Bacteroides thetaiotaomicron, a glutamate fermenting commensal, in subjects with obesity is inversely correlated with serum glutamate. Furthermore, positive correlations between insulin resistance and microbial functions are driven mainly by a few species, such as Prevotella copri and Bacteroides vulgatus, suggesting that they may directly impact host metabolism. Metabolomics studies in plasma, saliva or urine identified different biochemical classes of metabolites that may be altered in metabolic disorders in association with gut microbiota perturbations. Dysregulation of lipolysis, fatty acid oxidation and aminogenesis and ketogenesis, as well as changes in the levels of triglycerides, phospholipids and trimethylamine N-oxide (TMAO) are described in samples from humans with metabolic disorders, and more recently, imidazole propionate (IMP) was discovered as being involved in insulin resistance. Shotgun metagenomics data suggest that hepatic steatosis and metabolic alterations are associated with dysregulated aromatic and branched-chain amino acid (BCAA) metabolism. The dysregulation of SCFA and bile acid (BA) metabolism are also associated with metabolic diseases, including obesity, type 2 diabetes mellitus and non-alcoholic fatty liver diseases. Bile acids BAs are small molecules synthesised in hepatocytes from cholesterol. The primary BAs chenodeoxycholic acid (CDCA) and cholic acid (CA), conjugated to glycine or taurine, are essential for lipid/vitamin digestion and absorption. Ninety-five per cent of them are reabsorbed actively from the terminal ileum and are recycled in the liver (enterohepatic circulation). Primary BAs are also transformed into secondary BAs and deconjugated by gut microbiota. They can be either passively reabsorbed to reenter the circulating BA pool or excreted in the faeces as shown in Figure 1 below. Bile acid (BA) dysmetabolism in metabolic syndrome. BA metabolism is altered in patients with metabolic syndrome (MetS) and is associated with hepatic steatosis and glucose and lipid dysmetabolism. Dietary animal fat consumption promotes taurocholic acid (TCA) production, which favours the proliferation of sulfite-reducing bacteria, Bilophila wadsworthia, leading to an increase in intestinal permeability and inflammation (panel 1). Gut microbiota alterations induce an impairment in the ileal absorption of BAs, which occurs normally via the apical-sodium BA transporter (ASBT). This induces a decrease in the expression of nuclear Farnesoid-X receptor (FXR) and fibroblast growth factor 19 (FGF19) in intestinal epithelial cells and the abundance of colonic primary conjugated BAs (panel 2). Gut microbiota dysfunction leads to a decreased transformation of primary conjugated BAs to secondary BAs in the colon, leading to defective activation of Takeda-G-protein-receptor-5 (TGR5). The effect of TGR5 activation on the increase in glucagon-like peptide 1 (GLP-1) and white adipose tissue (WAT) browning was thus inhibited (panel 3). Gut microbiota alterations impair bile salt hydrolase (BSH) activity, leading to primary conjugated BA accumulation in the colon (panel 4). BMI, body mass index; HDL, high-density lipoprotein; LDL, low-density lipoprotein. Increased total circulating BA levels in individuals with obesity positively correlate with body mass index and serum triglycerides in patients with hyperlipidaemia. BAs regulate their synthesis through FGF19/FGF15, but they also have metabolic effects through their receptors Farnesoid-X receptor (FXR) and Takeda-G-protein-receptor-5 (TGR5). Activation of FXR and TGR5 (1) promotes glycogen synthesis and insulin sensitivity in the liver; (2) increases insulin secretion by the pancreas; (3) facilitates energy expenditure, especially in the liver, brown adipose tissue and muscles (browning); (4) favours thermogenesis, resulting in a decrease in body weight and (5) mediates satiety in the brain. BAs also impact lipid metabolism, especially by exerting profound effects on triacylglycerol. The perturbations of the intestinal microbiota composition in metabolic disorders strongly impact BA metabolism, especially characterised by a failure to metabolise primary BAs, thus leading to their accumulation. Indeed, an increase in primary CDCA levels induces a decrease in very low-density lipoprotein production and plasma triglyceride concentrations. Short-term antibiotic supplementation in mice induces a decrease in secondary BA-producing bacteria and a reduction in hepatic deoxycholic acid (DCA) and lithocholic acid concentrations as well as serum triglyceride levels, suggesting that secondary BAs can act as regulators to maintain metabolic host homeostasis. Moreover, this alteration in the primary to secondary BA pool in metabolic disorders might play a role in the observed low-grade intestinal inflammation, as conjugated primary BAs exhibit pro-inflammatory effects on intestinal epithelial cells. Conversely, secondary BAs have anti-inflammatory properties. In addition, Parséus et al showed that the promoting effect of the gut microbiome on obesity and hepatic steatosis is dependent on the FXR pathway.However, the FXR-dependent role of secondary BAs in the regulation of glucose and lipid metabolism is debated and might be context-dependent. The accumulation of hepatic lipids, triglycerides and cholesterol has been observed in FXR-deficient mice on a normal chow diet, while in HFD-fed mice or an obese background, FXR deficiency improves glucose homeostasis and decreases body weight, possibly a consequence of different basal gut microbiota. The effects of FXR in the pathogenesis of metabolic disorders are also likely to be different from one tissue to the other, as demonstrated by studies in conditional knockout mice. FXR induces the transcription of fibroblast growth factor 19 (FGF19) in intestinal epithelial cells, which reach the liver and inhibit BA synthesis in a feedback loop. Mice overexpressing FGF19 exhibit increased metabolic activity and energy expenditure by increasing brown adipose tissue and decreasing liver expression of acetyl coenzyme A carboxylase 2, thus leading to protection against HFD-induced metabolic injury. Gut microbiota perturbations induce impairment in the ileal absorption of BAs, which normally occurs via the apical-sodium bile acid transporter, resulting in decreased expression of FXR and FGF19 and an imbalance of BAs, notably characterised by an increase in colonic primary conjugated BAs. Transgenic mice overexpressing TGR5 exhibit improved glucose tolerance with increased secretion of glucagon-like peptide 1 (GLP-1) and insulin. This BA-TGR5 axis elicits beige remodelling in subcutaneous white adipose tissue and may contribute to improvement in whole-body energy homeostasis. The alteration of gut microbiota-dependent BA metabolism, through qualitative (primary vs secondary and conjugated vs deconjugated BAs) or quantitative modification of the BA pool, is likely to participate in the pathogenesis of metabolic disorders. Moreover, BAs have an important impact on intestinal epithelium function. Primary BAs, such as CA and CDCA, and some secondary deconjugated BAs, such as DCA, increase epithelial permeability through the phosphorylation of occludin in intestinal Caco-2 cells. Some correlations have been observed between BA levels and intestinal permeability in mouse models. The effect of the BA-microbiota dialogue is massively impacted by diet. High consumption of animal fat promotes taurocholic acid production, leading to a shift in microbiota composition with a bloom of sulfite-reducing microorganisms such as Bilophila wadsworthia and to increased susceptibility to colitis in IL-10−/− mice and more severe liver steatosis, barrier dysfunction and glucose metabolism alteration in HFD-fed mice. Moreover, bile salt hydrolase (BSH) activity, which is responsible for BA deconjugation in the normal gut microbiota, is impaired in metabolic disorders and likely plays a role in the accumulation of primary conjugated BAs in the colon of these patients. In mouse models, correcting BSH defects by the administration of BSH-overexpressing Escherichia coli improved lipid metabolism, homeostasis and circadian rhythm in the liver and GI tract, resulting in protection against metabolic disorders. Short-chain fatty acids SCFAs, such as butyrate, propionate and acetate, are end-products of microbial fermentation implicated in a multitude of physiological functions. SCFAs participate in the maintenance of intestinal mucosa integrity, improve glucose and lipid metabolism, control energy expenditure and regulate the immune system and inflammatory responses as shown in Figure 2 below. They act through different mechanisms, including specific G protein-coupled receptor family (GPCR) and epigenetic effects. Short-chain fatty acids (SCFAs), branched-chain amino acids (BCAAs) and Trimethylamine N-oxide (TMAO): relevant effects for metabolic syndrome on the host. Microbiota-derived metabolites mediate diverse effects on host metabolism. SCFAs (green frame): (i) increase satiety and browning of white adipose tissue (WAT); (ii) induce a decrease in lipogenesis and associated inflammation; (iii) increase the secretion of glucagon-like peptide 1 (GLP-1) and peptide YY (PYY) and (iv) participate in the maintenance of intestinal barrier integrity. BCAAs (yellow frame): (i) increase thermogenesis, protein synthesis and hepatocyte proliferation but (ii) are also associated with insulin resistance and visceral fat accumulation. TMAO (red frame): increases cardiovascular risks by inducing hyperlipidaemia, oxidative stress and pro-inflammatory cytokines. The amount of SCFA-producing bacteria and SCFAs is reduced in faecal samples of dysmetabolic mice and in humans with obesity and diabetes. In rodents with diabetes and obesity, supplementation with SCFAs improves the metabolic phenotype by increasing energy expenditure, glucose tolerance and homeostasis. Adding back fermentable fibres (inulin) to an HFD seems to be enough to protect against metabolic alterations. In humans, SCFA administration (inulin-propionate ester, acetate or propionate) stimulates the production of GLP-1 and PYY, leading to a reduction in weight gain. The protective effects of SCFAs on metabolic alterations might occur as early as in utero. In mice, high-fibre diet-induced propionate from the maternal microbiota crosses the placenta and confers resistance to obesity in offspring through the SCFA-GPCR axis. Branched-chain amino acids The most abundant BCAAs, valine, isoleucine and leucine, are essential amino acids synthesised by plants, fungi and bacteria, particularly by members of the gut microbiota. They play a critical role in maintaining homeostasis in mammals by regulating protein synthesis, glucose and lipid metabolism, insulin resistance, hepatocyte proliferation and immunity. BCAA catabolism is essential in brown adipose tissue (BAT) to control thermogenesis. It occurs in mitochondria via SLC25A44 transporters and contributes to an improvement in metabolic status. Moreover, supplementation of mice with a mixture of BCAAs promotes a healthy microbiota with an increase in Akkermansia and Bifidobacterium and a decrease in Enterobacteriaceae. However, the potential positive effects of BCAAs are controversial. Elevated systemic BCAA levels are associated with obesity and diabetes, probably a consequence of the 20% increased consumption of calories over the last 50 years. In genetically obese mice (ob/ob mice), BCAA accumulation induces insulin resistance. The gut microbiota is a modulator of BCAA levels, as it can both produce and use BCAAs. Prevotella copri and B. vulgatus are potent producers of BCAAs, and their amounts correlate positively with BCAA levels and insulin resistance. In parallel, a reduced abundance of bacteria able to take up BCAAs, such as Butyrivibrio crossotus and Eubacterium siraeum, occurs in patients with insulin resistance. Further studies are needed to more precisely elucidate the effects of BCAAs in the pathogenesis of metabolic disorders. Trimethylamine N-oxide The gut microbiota can metabolise choline and L-carnitine from dietary sources (eg, red meat, eggs and fish) to produce trimethylamine (TMA). This gut microbiota-derived TMA is then absorbed and reaches the liver where it is converted into TMAO through the enzymatic activity of hepatic flavin monooxygenases 3. In humans, the level of TMAO increases in patients with diabetes or at risk of diabetes and in obesity. Increasing evidence demonstrates that the gut microbiota-dependent metabolite TMAO is also associated with a higher risk of developing cardiovascular disease and kidney failure. In mice, dietary supplementation with TMAO, carnitine or choline alters the caecal microbial composition, leading to TMA/TMAO production that increases the atherosclerosis risk. This effect is dependent on the gut microbiota, as it is lost in antibiotic-treated mice. Moreover, transferring the gut microbiota of high-TMAO mice recapitulates atherosclerosis susceptibility in recipient low-TMAO mice. Importantly, the role of the gut microbiota in the production of TMAO from TMA has also been demonstrated in humans. Overall, in metabolic disorders, the altered microbiota associated with an increased intake of choline and L-carnitine from dietary sources leads to an increase in plasma levels of TMAO, which is directly involved in the pathogenesis of metabolic disease comorbidities and particularly cardiovascular disorders. However, detailed investigations are needed in populations from different countries to understand the interaction between food consumption patterns, TMAO production and cardiovascular risks. Tryptophan and indole-derivative metabolites Tryptophan is an essential aromatic amino acid acquired through common diet sources, including oats, poultry, fish, milk and cheese. In addition to its role in protein synthesis, tryptophan is a precursor for crucial metabolites. Dietary tryptophan can follow two main pathways in host cells, namely, the kynurenine and serotonin routes. The third pathway implicates gut microorganisms in the direct metabolism of tryptophan into several molecules, such as indole and its derivatives, with some of them acting as aryl hydrocarbon receptor (AhR) ligands, as shown in Figure 3 below. Tryptophan metabolism alterations in metabolic syndrome. Tryptophan dysmetabolism is associated with liver inflammation, steatosis and insulin resistance. In metabolic syndrome (MetS), the inflammatory state is associated with kynurenine (KYN) production through the activation of indoleamine 2,3-dioxygenase 1 (IDO1). This leads to an increase in kynurenine-derived metabolites, such as kynurenic acid (KYNA), xanthurenic acid (XA), 3-hydroxykynurenine (3-H-KYN), 3-hydroxyanthranilic acid (3-HAA) and quinolinic acid (QA). In parallel, the gut microbiota presents a defect in the production of aryl hydrocarbon receptor (AhR) ligands such as indole-3-propionic acid (IPA). The incretin hormone glucagon-like peptide 1 (GLP-1) secretion from intestinal enteroendocrine L cells and interleukin (IL)-22 production are decreased, altering gut permeability and promoting lipopolysaccharide (LPS) translocation. Serotonin (5-HT) biosynthesis from intestinal enterochromaffin cells is also reduced in the context of MetS due to a decrease in the production of microbiota-derived metabolites inducing the production of host 5-HT. We have identified in a previous study, in both preclinical and clinical settings, that metabolic disorders are characterised by a reduced capacity of the microbiota to metabolise tryptophan into AhR agonists. Defective activation of the AhR pathway leads to decreased production of GLP-1 and IL-22, which contribute to intestinal permeability and lipopolysaccharide (LPS) translocation, resulting in inflammation, insulin resistance and liver steatosis. In this context, treatment with AhR agonists or administration of Lactobacillus reuteri, which naturally produces AhR ligands, can reverse metabolic dysfunction. Similarly, indole prevents LPS-induced alterations of cholesterol metabolism and alleviates liver inflammation in mice. Moreover, exploring human jejunum samples from patients with severe obesity led to the observation that a low AhR tone correlated with a high inflammatory score. Interestingly, the use of the AhR ligand is able to prevent damage to barrier integrity and inflammation in Caco-2/TC7 cells. We and others also showed strong activation of the kynurenine pathway in metabolic diseases. Genetic or pharmacological approaches inhibiting the activity of indoleamine 2,3-dioxygenase (IDO), the rate-limiting enzyme of the kynurenine pathway, are protective against HFD-induced obesity and metabolic alterations. The mechanism is likely to be mediated by the microbiota and AhR. The increased amount of available tryptophan, due to the inactivation of IDO, can be converted by the microbiota in AhR agonists. Conversely, in obesity, the overactivation of IDO, associated with an increase in plasma levels of downstream metabolites such as kynurenic acid, xanthurenic acid, 3-hydroxykynurenine, 3-hydroxyanthranilic acid and quinolinic acid, decreases the tryptophan pool, which is less available for the production of AhR agonists by the microbiota. The third pathway of tryptophan metabolism, serotonin (5-HT), is also involved, as it affects feeding behaviour and satiety and is thus important for obesity development. The gut microbiota, and primarily indigenous spore-forming bacteria, represent an essential modulator of the intestinal production of 5-HT in enterochromaffin cells that represents >80% of the whole body 5-HT synthesis. These effects are notably mediated by SCFAs and BAs. Mice deficient for the production of peripheral serotonin are protected from HFD-induced obesity. Mechanistically, 5-HT inhibits brown adipose tissue thermogenesis, thus leading to fat accumulation. Human data support these results, as elevated plasma levels of 5-hydroxyindole-3-acetic acid, an end-product of serotonin metabolism, are increased in patients with metabolic disorders. Imidazole propionate Exploring the interaction between food intake, gut microbiota and derived metabolites might be of interest to discover metabolites impacting metabolic health. As such, it was recently shown that IMP, a metabolite produced by histidine utilisation of gut microbiota, was enhanced in type 2 diabetes and associated with insulin resistance. In the liver, IMP appeared to affect the insulin signalling pathway via mammalian target of rapamycin complex 1 (mTORC1). The examination of IMP in large human cohorts also links it with metabolic health and lifestyle. IMP was elevated in subjects with prediabetes and diabetes in the MetaCardis cohort and in subjects with low bacterial gene richness and Bacteroides 2 enterotype in this cohort. Associations between IMP levels and markers of low-grade inflammation were also identified. Importantly, relationships were observed between serum IMP levels and unhealthy diet measured by dietary quality scores emphasising the importance of nutrition in this context. Thus, this study confirms that in type 2 diabetes, the intestinal microbiota may is switched towards IMP production, which can impact host inflammation and metabolism. Therapeutic relevance The mechanistic links between gut microbiota-derived metabolites and metabolic disorders make these interactions a promising therapeutic target in these complex diseases. Lessons from faecal microbiota transplantation FMT is a drastic strategy to modify the gut microbiome. It is highly effective in the treatment of recurring Clostridioides difficile infections and has been evaluated in small trials in metabolic syndrome and obesity. The clinical efficacy of this strategy is so far mild, with mostly some positive effects on insulin sensitivity in subgroups of patients. However, these studies had several limitations, including small size and limited duration of intervention. Nevertheless, they provide relevant information to identify the critical molecules involved in biological effects. Following successful FMT, both the microbiota composition and metabolomics, such as BA and SCFA profiles, can be restored. In patients with obesity, FMT can induce engraftment of the butyrate-producing and bile-hydrolysing genus Faecalibacterium, leading to a restoration of the BA profile and microbiota BSH activity. FMT increases the relative abundance of SCFA-producing bacteria such as Roseburia intestinalis and the protective strain Akkermansia muciniphila, with a possible role in the improvement in insulin sensitivity through regulation of GLP-1. A. muciniphila supplementation alone improves metabolic parameters in overweight/obese insulin-resistant volunteers characterised by better insulin sensitivity and a reduction in plasma total cholesterol levels and fat mass. In mice, A. muciniphila promotes the production of SCFAs and the restoration of HFD-induced alterations in tryptophan metabolism. These data highlight the key family of microbiota-derived metabolites with potential therapeutic effects. Synthetic agonists of bile acid receptors Given their potential benefits in metabolic diseases, BAs and synthetic FXR and TGR5 agonists are currently under development in the metabolic field. Preclinical trials based on in vitro and in vivo studies identified potent synthetic FXR and TGR5 agonists, which are currently being investigated in phase II or III clinical trials. Due to the regulatory roles of FXR and TGR5 receptors on glucose and lipid metabolism, multiple specific agonists have been designed. Obeticholic acid (OCA), one of the best-characterised FXR agonists, protects the liver from damage in mice with a reduction in hepatic steatosis and inflammation and is currently being evaluated in a phase III trial in patients with NASH. The synthetic FXR agonist GW4064 improves hyperglycaemic and hyperlipidaemia in mice with diabetes and is able to correct BA dysmetabolism and alleviate liver toxicity in rodents with short bowel. The intestine-restricted FXR agonist fexaramine can also promote adipose tissue browning and GLP-1 secretion in wild type (WT) and leptin receptor-deficient diabetic mice. Finally, a TGR5 agonist ameliorated insulin resistance and glucose homeostasis in mice with diabetes by the cyclic AMP/protein kinase A pathway in skeletal muscles. Short-chain fatty acid and branched-chain amino acid treatment Dietary supplementation with fermentable fibres, such as inulin in HFD-fed mice or inulin-propionate ester in overweight humans, protects against metabolic disturbances by restoring the gut microbial composition and the action of the IL-22-mediated axis.Oral SCFA treatment in obese mice can modulate lipid synthesis and insulin receptors by upregulating peroxisome proliferator-activated receptor-γ. It also improves intestinal barrier functions with a lower serum LPS concentration. SCFAs exert their beneficial effects partly through specific G-protein-coupled receptors, and their activation by specific agonists is an attractive strategy in the treatment of MetS. GPR40/FFA1, GPR41/FFA3, GPR43/FFA2 and GPR120/FFA4 agonists induce protection against diet-induced obesity in mice through the improvement in insulin, GLP-1 and incretin secretion and anti-inflammatory effects. In addition, a link between dietary BCAAs and energy balance was noted in animals with obesity, and reducing the proportion of dietary BCAAs was associated with a restoration of metabolic health. Concluding remarks Gut microbiota-derived metabolites have a central role in the physiology and physiopathology of metabolic disorders. The microbial metabolites described above, specifically BAs, SCFAs, BCAAs, TMAO, tryptophan and indole derivatives, are implicated in the pathogenesis of these complex disorders and represent potential biomarkers for the early diagnosis and prognosis of these diseases. Moreover, microbiota-derived metabolites and their host receptors, possibly in combination with dietary intervention, represent promising targets for the development of novel therapeutic tools for metabolic disorders. Acknowledgments The authors would like to thank BioRender, for its revolutionised tool to create custom scientific figures (https://biorender.com/)

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/29%3A_Integration_of_Mammalian_Metabolism_-_Capstone_Volume_II/29.11%3A_Gut_microbiota-derived_metabolites_as_central_regulators_in_m.txt

The Start of Life This book began with the notion that understandings derived from the study of simple molecules can be applied to complex biological macromolecules and systems. We developed an understanding of the structural, thermodynamic, and kinetic properties of the "simplest" biomolecules, including single chain amphiphiles like fatty acids, and double chain ones like phospholipids, and how these properties could explain the propensity of these molecules to form complex lipid aggregates (micelles and bilayers). We extended these ideas to the process of protein folding and the assembly of biological complexity. Is there something intrinsic to the property of molecules such that their localization together in the right microenvironment could lead to a "living cell"? How did life originate? That is the topic of this last capstone chapter. Defining life is actually quite difficult. Here is a list of requirements that seem reasonable, but other have noted that this list would exclude the mule. Life can self-replicate, self-sustain, evolve, respond to environmental changes, and die. The earliest known fossils (stromatolites from cyanobacteria) are approximately 3.5 billion years old. We have just finished studying the complex interactions involved in cell signaling. How could they have evolved? Consider the central dogma of biology. In the present biological world, proteins (DNA polymerase, RNA polymerase, transcription factors) are necessary for DNA synthesis, replication and gene transcription. But you need DNA to encode the proteins. This "chicken vs egg" dilemma has been addressed when it was realized that RNA can both carry genetic information as well as enzymatic activity (even at the level of the ribosome used for protein synthesis). https://clockwise.software/blog/solv...d-egg-problem/ for new picture Abiotic Synthesis of Amino Acids and Peptides Much work has been done to determine if the building blocks for present biological molecules could have been synthesized early in Earth's history. Amino acids and fatty acids have been found in meteors suggesting the possibility. Earth's early atmosphere would have had little oxygen, so most components should have been reduced. It probably consisted of methane, ammonia, hydrogen and water similar to the atmospheres of other planets in our solar system. The composition of the early atmosphere is still contentious. In 1953 (the same year that Watson and Crick published the structure of double-stranded DNA), Stanley Miller showed that electric discharges (to simulate lightening) in a reducing atmosphere over a "simulated sea" produced many amino acids. Up to 11 different amino acids have been produced in this fashion along with purines and pyrimidines (these required concentrated reaction mixtures) necessary for nucleic acids. Adenine can be produced just through the reaction of hydrogen cyanide and ammonia in an aqueous solution. Other nucleic acid bases can be made with hydrogen cyanide, cyanogen (C2N2) and cyanoacetylene (HC3N). No complex polymers arise through these reactions. However, in 2004, Lehman, Orgel and Ghadiri were able to show that in the presence of carbon disulfide, a gas discharged from volcanoes, homo- and hetero-peptides were produced. Amphiphilic peptides can even catalyze their own formation from peptide fragments, if the fragments are activated. The fragments would bind to the larger "template" peptide through nonpolar actions of the side chains which are oriented along one face of the helical axes. If the fragments bind in a fashion in which the electrophilic end is adjacent to the nucleophilic end of the other peptide fragment, condensation of the two peptide fragments results. The larger template peptide acts as a template (effectively as an "enzyme") in orienting the two fragments for chemical reaction and effectively increasing their local concentration. The reaction of the bound fragments is essentially intramolecular. The reaction even proceeds with amplification of homochirality. Could the prebiotic amino acids have polymerized into a protein that could fold in a fashion similar to modern proteins? That question has recently been addressed by Longo et al (2013). They asked the question whether the amino acids found in Miller-type prebiotic synthesis mixture and in comets/meteors (Ala, Asp, Glu, Gly, Ile, Leu, Pro, Ser, Thr and Val), a restricted set (10) compared to the present 20 naturally-occurring amino acid, could form a polymer that could fold. Notice that this reduced ensemble of amino acids lacks aromatic and basic amino acids. These proteins would be acidic with a low pI and may have trouble, given the lack of nonpolar aromatic amino acids, in forming a buried hydrophobic core which stabilize proteins. Nevertheless Longo et al were able to synthesize a protein with a slightly expanded set of amino acids (12, including Asn and Gln, with 70% prebiotic amino acids). The structure of one of the proteins, PV2, is shown below. The protein was more stable in 2 M NaCl (compared to 0.1 M) in which it showed a cooperative thermal denaturation with a melting point near 650C using differential scanning calorimetry. The protein had properties similar to those from halophilic organisms that thrive in high salt. These properties include low pIs and high negative charge density, which allows cation-protein interactions in the high salt environment, and lower stability in low salt environments. Earlier oceans were saltier. Halophiles are an example of extremeophiles which are highly represented in archea. Although most halophiles are aerobic, some are anerobic. Perhaps life arose in high salt environments. Figure: Structure of PV2 protein comprised of a reduced alphabet of mainly prebiotic amino acids. Abiotic Synthesis of Sugars Sugars are required for present energy production but also as a part of the backbone (ribose, deoxyribose) of present genetic material. Many sugars can be synthesized in prebiotic conditions, using carbon based molecules with oxygen, such as glycoaldehyde and formaldehyde (both found in interstellar gases), as shown in the figure below. Glycoaldehyde was recently found in star forming regions of the Milky Way where planets are likely to form. The presence of borate, which stabilizes vicinal OHs on sugars, is required for the production of sugars instead of tars. RNA molecules containing sugars such as threose, aldopentopyraonses and hexopyranose can also form stable secondary structures like helices. (Remember, RNA probably preceded DNA as the genetic carrier of information given that is also has enzymatic activity). Is there something special about ribose that made it selected over other sugars for nucleic acids, especially since it is found in low abundancy in the products in synthesis reactions conducted under prebiotic conditions? One probable reason is it unusually high (compared to other sugars) permeability coefficient through vesicles made of phospholipids or single chain fatty acids, as shown below. In general, the greater the number of carbon atoms, the smaller the permeability. However, the table above clearly shows large differences in permeability for sugar isomers with the same number of C atoms, and the difference is not affected by the lipid composition. Ribose has markedly elevated permeability compared to other 5C sugars (as do erythritol and threitol among 4 C sugar alcohols). What is so unique about ribose? 20% of the sugar is in the furanose form. Rate constants for ring opening of furanoses are elevated, suggesting greater flexibility. The α-furanose anomer is amphiphilic in that one face is hydrophobic and the other hydrophilic. All of these may promote ribose permeability. Abiotic Synthesis of Nucleobases As mentioned above, nucleobases can be made under abiotic conditions with appropriate sources of carbon molecules with nitrogen. These include hydrogen cyanide, cyanoamide (NH2CN) (along with ammonia), hydrogen cyanide, cyanogen (C2N2) and cyanoacetylene (HC3N). Cyanoamide and cyanoacetylene can both react with water to form urea and cyanoacetaldehyde, respectively. The latter two can condense to form cytosine as shown below. Figure: Abiotic Synthesis of Cytosine A major problem that has plagued prebiotic researchers is how the product of the carbon-oxygen compound (sugar) links to the product of the carbon-nitrogen compound (nucleobase) to form the nucleoside. Recent research by Powner et al has offered an innovative solution. Instead of forming the sugar and base in separate reaction, and then linking them covalently, the combined molecule could be synthesized in a single set of linked reactions. Inorganic phosphate serves as both a general acid/base catalyst (HA/A- in the figure below) in these new reactions in the formation of an important intermediate, 2-amino-oxazole, and as a nucleophilic catalyst. Glyceraldehyde, 2-amino-oxazole, and inorganic phosphate can react to form a ribocytidine phosphate. Possible reaction mechanisms are shown below. Abiotic Synthesis of Genetic Polymers Abiological synthesis of polymer precursors is a long way from creating genetic polymers like RNA and DNA. These genetic polymers have one property that at first glance seems not conducive to a genetic molecule. Both are polyanions, which must be packed into a cell and folded onto itself to form the classic dsDNA helix and many different RNA structures. This problem is solved to some degree by the presence of counterions that help mask the charge on the negative backbone of the nucleic acids. The presence of phosphate in the phospodiester backbone linkage does confer an important advantage over other possible links (carboxylic acid esters, amides and anhydrides). The electrophilic phosphorous atom is hindered from nucleophilic attack by the negative O attached to the phosphorous. Also, the phosphorous is sp3 hybridized compared to the sp2 hybridization of the electrophilic carbon atom in anhydrides, esters, and amides, and hence is less accessible to nucleophilic attack. Most people now believe that RNA, which can act both as an enzyme and genetic template, preceded DNA as the genetic carrier. The evolution of DNA as the primary genetic carrier required an enzyme to convert ribose to deoxyribose. This would make the nucleic acid less likely to cleave at the phosphodiester bond with the replacement of a nucleophilic 2' OH with an H, and make the genetic molecule more stable. Other types of genetic carriers might have preceded the RNA world, especially if the monomer required could be more readily synthesized from abiological sources. One such alternative are threose nucleic acids (TNA). Synthetics ssTNA can base pair with either RNA, DNA, or itself to form duplexes. Other possible candidate include peptide nucleic acids (PNA). These can also form double stranded structures with DNA, RNA, or PNA single strands. They were initially designed to bind to dsDNA in the major grove forming a triple-stranded structure. Binding could alter DNA activity, possibly by inhibiting transcription, for example. The structure of a single-stranded PNA is shown. Note that the backbone, a polymer of N-(2-aminoethyl)glycine (AEG) which can be made in prebiotic soups, is not charged, making it easier to bind to dsDNA. AEG polymerizes at 100oC to form the backbone. In addition to changing the backbone, additional bases other than A, C, T, G, and U can be accommodated into dsDNA and ssRNA molecules (Brenner, 2004) In a recent extension, Pinheiro et al have shown that 6 different foreign backbone architectures can produce xeno-nucleic acids (XNAs) that can be replicated by engineered polymerases which make XNAs from a complementary DNA strand, and a polymerase that can make a complementary copy of DNA from an XNA. XNAs can also be evolved as aptamers to bind specific target molecules. The investigators replaced the deoxyribose and ribose backbone sugar with xenoanalogs (congeners) including 1,5-anhydrohexitol (HNAs), cyclohexene (CeNA), 2'-O,4'-C-methylene-b-D ribose (locked nucleic acids - LNA), L-arabinose (LNA), 2'-fluoro-L-arabinose (FANAs) and threose (TNAs) as shown in the figure below. Figure: Xeno-nucleic acid sugar congeners Polymers of these XNA can bind to complementary RNA and DNA and as such act as nuclease-resistant inhibitors of translation and transcription. Von Kiedrowski, in an experiment similar to the self-replication of peptides described above, has shown that a single stranded 14 mer DNA strand, when immobilized on a surface, can serve as a template for the binding of complementary 7 mers and their conversion to 14 mers. When released by base, this process can occur with exponential growth of the complementary 14 mers. (von Kiedrowski Nature, 396, Nov 1998). Ferris has shown that if the clay montmorillonite is added to an aqueous solution of diadensosine pyrophosphate, polymerization occurs to produce 10 mers which are 85% linked in a 5' to 3' direction. Polyanions as Carriers of Genetic Information There are other reasons why polyanions are useful genetic molecules, other than their resistance to nucleophilic attack. The biological form of DNA is a large double stranded polyanionic polymer, in contrast to RNA which is a single-stranded polyanion polymer and protein which are polymers with varying combination of anionic, cationic, and hydrophobic properties. Even with counterions, it would be difficult to fold DNA into complicated and compact 3D structures as occurs for proteins, given the large electrostatic repulsions among the charged phosphates. Rather it forms a elongated double stranded rod, not unlike the rod-like structure of proteins denatured with sodium dodecyl sulfate (used in SDS PAGE gels). The elonged rod-shaped structure of ds-DNA is critical for the molecule which is the main carrier of our genetic information since mutations in the bases (leading to a switch in base pairs) causes no change in the overall structure of dsDNA. This enables evolutionary changes in the genetic material to produce new functionalities. A single change an amino acid of a protein, however, can cause a large change in the structure of a whole protein, a feature unacceptable for a carrier of genetic information. RNA structure effectively lies between that of DNA and proteins. Since it has less charge density than dsDNA, it can actually form dsRNA helices, so it can carry genetic information, as well as form complex 3D shapes necessary for its activity as an ribozyme. Perhaps more importantly, steric interference prevents ribose in RNA from adopting the 2'endo conformation, and allows only the 3'endo form, precluding the occurrences of extended ds-B-RNA helices. The Lipid World Let's assume that abiological precursors would react to form polymer-like molecules that might be complex enough to fold to structures that would allow binding, catalysis, and rudimentary replication. All this would be worthless unless they could be sequestered in a small volume which would limit diffusion and increase their local concentration. What is required is a membrane structure. Amphiphilic molecules, like lipids, with which we started this book, would be prime candidates since they spontaneously assembly to form micelles and bilayers, as shown in the review diagram below. As mentioned in Chapter 1, alternative lipid phases are possible. Bilayers can also be formed from single chain amphiphiles, such as certain fatty acids, as illustrated in the equilibrium shown below. This occurs more readily at pH values close to the the pKa of the fatty acid, at which the fatty acids are not all deprotonated with full maximal negative charges. Single chain amphiphiles like fatty acids, which were more likely to formed in abiotic conditions, have been found in meteorites. Clay surfaces, which have been shown to facilitate the formation of nucleic acids polymers, can also promote the conversion of fatty acid micelles to bilayers (Szostak). One such clay surface, montmorillonite, whose structure is shown below, promotes bilayer formation. It has an empirical formula of Na0.2Ca0.1Al2Si4O10(OH)2(H2O)10. Chime Molecule Modeling: montmorillonite : montmorillonite The effect of montmorillonite on vesicle formation can be shown by simple measurements of turbidity with time. Microscopy of fluorophore-encapsulated vesicles also shows encapsulated montmorillonite. The fatty acids presumably absorb to the cation layer of the clay particles. Time studies using light scattering also indicate that the vesicles grow in the presence of fatty acid micelles. To differentiate between the formation of new vesicles and the increase in size of pre-existing vesicles (which couldn't be done by simple light scattering without separation of the vesicles), investigators used two different fluorescent molecules to label fatty acid vesicles. The two probes were selected such that if the two probes came in close contact, energy transfer from the excited state of one fluorophore to the other fluorophore could occur, an example of fluorescence resonance energy transfer (FRET). FRET is observed when emission of the second dye occurs after excitation of the first dye, at a wavelength outside of the excitation wavelength of the second dye. If unlabeled vesicles were added to either labeled vesicles, no changes in FRET were observed, suggesting that the dyes did not move between vesicles. If fatty acid micelles were added, a decrease in FRET was observed, suggesting that new fatty acids were transferred to the doubly-labeled vesicles, effectively diluting the dye concentrations in the bilayer and their relative proximity, both which would decrease FRET. Most of the new fatty acid was incorporated into pre-existing vesicles which grew. The vesicles could also divide if extruded through a small pore. Later we will see that the energy to grow the vesicles can derive in part from a transmembrane proton concentration collapse. Division of vesicles might be promoted by bilayer assymetries associated with addition of substances to the outer leaflet, causing membrane distortion. Protocells At some point, early genetic material must have been encapsulated in a membranous vesicles. Would new properties emerge from this mixture that might have a competitive (evolutionary) advantage over either component alone, and thus be a step on the way to the formation of a "living" cell? The answer appears to be yes. Chen et al. have incorporated RNA into fatty acid vesicles with interest effects. They asked the question as whether those vesicles could grow at the expense of vesicles without encapsulated RNA. RNA, with a high charge density and its associated counter ions would create osmotic stress on the vesicles membranes. To relieve that stress they could acquire fatty acids from other fatty acid vesicles (or fatty acid micelles), increasing their surface area, and concomitantly reducing tension in the membrane. Oleic acids vesicles were first placed under stress by encapsulating 1 M sucrose in the vesicle and then diluting it in hypotonic media. Water would enter and swell the vesicle (but without bursting and resealing, as evident from control experiments). Then they prepared stressed and unstressed oleic acid vesicles in the presence of two nonpolar flurophores, NBD-PE (excitation at 430 nm, emission at 530 nm) and Rh-DHPE (emission at 586 nm). These fluorophores were chosen for fluorescence resonance energy transfer measurements. If the membrane vesicles changed size, the FRET signal would change, based on the relative concentration and proximity of the dual fluorophores. If the separation between probe molecules increased, the FRET signal would decrease. Conversely, if the vesicle shrunk, the FRET signal would increase. The results showing the effect of adding unlabeled swollen vesicles to labeled normal vesicles, and labeled swollen vesicles to unlabeled normal are shown below. The surface area of normal labeled vesicles decreased by about 25% when unlabeled swollen vesicles were added, but not when unlabeled normal vesicles were added. Labeled swollen vesicles increased 25% in size only if mixed with unlabeled normal vesicles, not with unlabeled swollen vesicles. Hence swollen vesicles win the competition and "steal" lipid from normal vesicles. Now what about vesicles swollen with encapsulated RNA? RNA, with its associated charge and charged counter ions also placed an osmotic stress on the vesicles. FRET labels (the two fluorophores) were place in vesicles without RNA. Fatty acids were removed from isotonic labeled vesicles in the presence of unlabeled tRNA swollen vesicles (left panel below). Labeled vesicles swollen with glycerol took fatty acids from unswollen vesicles (without tRNA), but not from tRNA swollen vesicles, as both were swollen so no net drive to reduce swelling by lipid exchange was present. These results show the vesicles with encapsulated RNA have a competitive (evolutionary) advantage over normal vesicles. This data suggests that having a polyanion as the source of genetic material is actually advantageous to the protocell. In addition the move in modern membranes to phospholipids with esterified fatty acids (instead of free ones) may actually have stabilized membranes, given the movement of free fatty acids to different membranes. Energy Transduction in Protocells In addition to a genetic macromolecule and a semipermeable membrane, a source of energy to drive intracellular processes must be present. A common source of free energy used in many cells to drive unfavorable processes is a proton gradient, whose formation in modern cells can be coupled to energy input from oxidation, ATP cleavage, light, or the collapse of another gradient. Could a proton gradient be formed in protocells? It can, quite easily, when coupled to the growth of fatty acid vesicles. If a fatty acid vesicle is to grow, more fatty acid must be added to the outer leaflet. The protonated, uncharged form of the fatty acid would preferentially be added, since it would lead to less electrostatic repulsion between adjacent head groups. The protonated, uncharged form of the fatty acid would also be most likely to flip to the inner leaflet to minimize stress asymmetries in the leaflets. Once in the inner leaflet, it could deprotonate to form H+(aq) in the inside of the membrane, creating a transmembrane proton gradient and transmembrane potential. The energy released on growth of the membrane is partly captured in the formation of a proton gradient, as shown in the figure below. The proton gradient would soon inhibit its own formation since further movement of protons into the cell would be attenuated by the positive transmembrane potential unless metal ions inside moved outside. In addition, the gradient would collapse after growth stopped. The investigators made fatty acids vesicles in the presence of pH 8.5 buffers whose pH was adjusted with an alkali metal hydroxide. The external pH was reduced to 8.0, resulting in a 0.5 pH unit proton concentration gradient. (Changes in intravesicular pH were measured with pH-sensitive fluorophore, HPTS.) Inward movement of protons down a concentration gradient, as shown in the figure below, would occur with time, collapsing the imposed concentration gradient. With fatty acid vesicles, this artificial pH gradient collapsed quickly, suggesting the vesicle permeability to protons was high. The rate was too high for simple flip-flop diffusion. Inward movement of protons appeared to be facilitated by outward movement of the M+ ions. The rate of decay of the proton gradient was exponential, and the resulting first order rate constant was easily determined. A graph of the rate constant for pH gradient collapse vs unsolvated ionic radius of M+ decreased with increasing radius (i.e. kNa > kK > KRb > KCs, suggesting that the pH gradient would be more stable if large, impermeable or otherwise trapped cations were encapsulated. When vesicles were made with encapsulated Arg+, the imposed pH gradient did not collapse for hours. If oleic acid micelles were added to oleic acid vesicles with encapsulated Arg+, with no artificial pH gradient induced across the membrane, the vesicle grew with concomitant movement of protons into the vesicle, producing a pH gradient of 0.3 within seconds. These experiments show that membrane growth and energy storage could be coupled, and the right composition of encapsulated material could lead to a stable transmembrane pH gradient, a source of energy to drive biological processes. It even suggests that a charge polyanion would be beneficial as a genetic carrier. Hydrothermal Vents or Primoridal Soup The case for the origin of life in deep sea hydrothermal vents and not in a primordial "Campbell's" soup has been argued convincingly by Lane et al (2010). What's needed for life are reasonably complex molecules and an energy source to drive unfavorable reactions. It's the latter on which that Lane et al focus. In an early primordial world that was low in oxygen, exergonic oxidation reaction of organic molecules would provide little energy. This can be surmised from the low energy yield (compared to aerobic respiration) achieved in present day glycolytic (fermentative) pathways from all major domains of life, archaea, bacteria, and eukaryotes. Background: Based on rRNA sequences, a primordial cell evolved into two different types of cells, one that became bacteria, and another that split further into archaea (single cells, similar to bacteria) and eukaryotic cells (complex cells with internal organelles that eventually formed multicellular organisms). Bacterial and archaea are collected called prokaroytic cells. In addition, these pathways required the evolution of up to dozen different enzymes to produce their relatively meager energy yield which ultimately depends on the oxidation of an organic molecule by another organic molecule instead of by a powerful oxidant like dioxygen. An anisotropic arrangement of molecules in a concentrated soup could lead to transient chemical potential fluctuations but these would be inefficient and impermanent sources of energy. Effectively the primordial soup would be at equilibrium and hardly expected to provide the energy for synthesis of RNA enzymes and replicators. UV light leads to photo-damage and photolysis not replication of complex molecules. What is needed is a way to drive the synthesis of molecule with high chemical potential energy (like sulfur esters and phosphoanhydrides) compared to their lytic products. These could then provide an energy sources to drive ATP synthesis, for example. A detailed look a the bioworld shows that the earliest organisms used energy from the collapse of the proton gradient (chemisomotic principle elucidated by Peter Mitchell). All present autotrophs (organisms that can fix CO2 and form complex organic molecules) and many heterotrophs (use complex organic molecules of other organisms for fuel) use redox complexes in membranes coupled to membrane gradients. These complexes would take reduced molecules and pass electrons from them to oxidizing agents (electron acceptors), including O2, CO2, and Fe3+ to form H2O, CH4, and Fe2+. Fermentors also use ATPase membrane enzymes to transport nutrients. Yet genomic analysis of bacteria and archaea show that enzymes involved in fermentation differ significantly, suggesting that they evolved separately towards a convergent function. Structure in common include DNA, RNA, ribosomes and membrane ATPases, which Lane et al suggest were in a the Last Universal Common Cell (LUCA). All autotrophs produce their energy source by fixing CO2 using either H2 directly or indirectly using H2O and H2S. All of the are available in nonhydrothermal deep sea vents. Volcanic vents, however, are extremely hot (not optimal for organic molecule synthesis), very acidic, and lack hydrogen gas. A different type of nonvolcanic vent, an alkaline hydrothermal one, might produce more conducive as a site of the origin of life. In these vents, water chemically reacts with minerals in the crust (such as olivine) leading to their hydroxylation and subsequent fracture, with promotes more water entry into the crust. It has been reported that there is more water found as hydroxylated minerals in the crust, that there is liquid water in the oceans. These processes result in temperatures up to 200 degrees Celsius and release of hydrogen gas into a moderately alkaline vents into the sea water at temperatures more conducive (70 degrees C) to the origin of life. Figure: Alkaline Vent Fixing CO2 Of the five different pathways known to fix CO2, all require ATP except one. That one is present in both methanogen, which produce methane from CO2 and H2 and in the acetogens, which produce acetate (CH3CO2-) in the form of acetylCoA. The simpler reactions of forming acetic acid and methane are shown below: 2CO2 + 4H2 --> CH3CO2H + 2H2O. CO2 + 4H2 --> CH4 + 2H2O. The DG0 values for these reaction (calculated using DG0f for gas phase H2, CO2 and CH4 and liquid acetic acid and water are -75 and -131 kJ, respectively, at 250C, showing that they are thermodynamically favored. Making AcetylCoA, a "high" energy molecule compared to its hydrolysis products (as is ATP) from acetic acid and CoASH, a would require energy input. A proton gradient is the likely source. Some bacteria and Achaea cells (primordial or present) use the reductive acetylCoA pathway, also known as the Wood-Ljungdahl pathway, to form, in a noncyclic process, acetyl CoA from CO2 and at the same time makes ATP. This process is paid for by a proton gradient. This has been described by Shock as "a free lunch you get paid to eat". The energetics of the present acetyl CoA pathway based on the overall reaction below show an approximate DG0 value of -59 kJ/mol which can drive ATP synthesis. 2CO2 + 4H2 + CoASH --> CH3COSCoA + 3H2O. The concentration of carbon dioxide in the primordial ocean was 1000 times higher than now. Vents produced large amounts of methane and hydrogen gas. There was little oxygen and hence lots of Fe2+. The enzymes involved in this acetyl-CoA pathway of carbon fixation have FeS clusters. It has also been shown that bubbles (which are really membrane bound spaces) of FeS and NiS can be made in deep sea vents. These could not only encapsulate precursor molecules but also serve as catalysts. Vents also can catalyze the fixation of nitrogen (to ammonia) and laboratory studied show that FeS can catalyze the conversion of formate (found in vents) into pyrimidines and purines. The studies of present methanogens and vent chemistry suggest that the critical ingredients and conditions for development of the first biological cells probably occurred in the vents. To produce polymers, an energy source and monomers must exists. Concentration gradients found in simulations of vents produce million fold concentrated molecules. The transient heating and cooling of any double-stranded nucleic acids could lead to concentration amplification by a PCR like strand separation followed by reannealing. In addition, these vent regions possess a powerful, reoccurring energy source, a pH gradient, as the alkaline vented material entered the acidic oceans that exists with high CO2 concentrations, creating a gradient across an inorganic membrane. This is startlingly analogous to the pH gradient across membranes (acidic outside, alkaline inside) driven by the membrane complexes in the mitochondria and bacteria. Lane et al argue that the existence of membrane proton gradients as an energy source in all cells (eukaryotes, bacteria, and archaea) and in chloroplasts, mitochondria, corroborate their hypothesis. Bacteria and archaea share homologous ATPases and electron carriers (ferredoxins, quinones, and cytochromes). These similarities contrast to the differences in enzyme structures in fermentative pathways. Arguments that proton pumps evolved to pump proteins (and reduce pH gradients) can't explain their ubiquitous presence even in organisms not subjected to low pH. Hence the ubiquity of proton pumps supports the conjecture that they arose from the first protocells, possible comprised of inorganic walls and ultimately with amphiphilic molecules synthesized from precursors. Creationists would argue that it would be impossible to evolve a structure with the complexity of membrane ATPase (which serve to collapse a pH gradient as the power the synthesis of molecules with large negative DG0 of hydrolysis). Lane et al propose that the earliest cells evolved ATPase like molecules in alkaline vents where pH gradients analogous to those in cells today arose. They envision cell-like columns lined by FeS membrane like structure with alkaline conditions inside and acid conditions outside. Nonpolar or amphiphilic molecule would line the inside of the cells/columns. A ATPase-like system could then take advantage of the pH gradient which constantly replenishes itself. If structures as complicated as ribosomes evolved from a subsequent RNA world, surely ATPase-like molecules could also. Other chemistry might have evolved earlier to utilize the energy source provided by the pH gradient. If life originated in the vents, it would need an energy source to leave the vents. Presumably it would have evolved one to utilized pH gradient to replace the one it left in the alkaline vents. The substrate level phosphorylation of glycolysis that requires ATP input to make ATP would not provide the energy source needed. Cells that left would have had to produce their own proton gradient. Perhaps all the was needed initially was concerted conformational changes in proteins that upon exposure of a different pH changed their shape inducing pKa shifts in adjacent proton donors/acceptors leading to vectorial discharge of protons across a membrane. Perhaps the method described above in protocells was sufficient. Recent analyses by Poehlein et a show that CO2 reduction (fixation) can be coupled to the production of a sodium ion gradient, which could collapse to drive ATP synthesis. Analysis of the genome of a gram positive bacteria, Acetobacterium woodii, an acetogen, shows the it has an ancient pathway for production of acetyl-CoA that can, in an anabolic fashion form biomass or in a catabolic fashion be cleaved to acetate with the production of ATP. It does not require classic electron carriers like ubiquinone or cytochrome C linked to protein gradient formation to drive ATP synthesis. Rather it has only a ferredoxin:NAD+ oxioreductase which couples oxidation to the formation of a sodium ion gradient, which collapses through an sodium ion transporter/ATP synthase to drive ATP synthase. A plausible reaction scheme based on genomic analysis is shown below: Figure: Acetyl-CoA Synthase and Acetogenesis The Role of Fe/S Center Let's return to the chicken and egg dilemma one more time. What is needed for biological polymer formation are monomeric precursors, an energy source, and a way to compartmentalized them all. We discuss how monomeric precursors could form, but wouldn't it be far better if even the synthesis of precursors could be catalyzed? One source of catalysis mostly absent from the "bioorganic" abiotic chemistry in the above discussion is the transition metals. Transition metals can form complexes. Ligands containing lone pairs on O, N, and S atoms can donate them to transition metals ions, which can hold up to 18 electrons in s, p, and d orbitals. Hence as many as 9 lone pairs on ligand molecules (which are often multidentate) could be accommodated around the transition metal ion. Many present small molecule metabolites and their abiotic precursors (H2O, CO, CO2, NH3 and thiols) bind cations as mono- or polydentate donors of electrons. Hence transition metal ions would have a thermodynamic tendencies to be bound in complexes. Bound ligands that contain potentially ionizable hydrogens could become deprotonated and made better nucleophiles for reactions. Hence the transition state metal ion, acting with the complex, becomes a catalyst as it decreases the pKa of a bound ligand (such as water). In addition, since transition metals ions can have multiple charge and oxidation states, they can easily act as redox centers in the oxidation/reduction of bound ligands that were redox active. Given the relative anoxic conditions of the early oceans, Fe2+ would predominant. It could easily be oxidized to Fe3+ as it reduced a bound ligand. Highly charged transition states would withdraw electron density from bound ligands leading to their possible oxidation. Metals obviously still play a strong role in catalysis, both indirectly in promoting correct protein folding and directly in stabilizing charge in both the transition state and intermediates in chemical reaction pathways. FeS clusters are of significant importance. Their biosynthesis involves removal by an active site Cys in a desulfurase enzyme of a sulfur from a free amino acid Cys followed by its transfer to an Fe in a growing FeS cluster in a FeS scaffold protein, which then transfers the cluster to an acceptor protein where it acts as a cofactor. FeS clusters can adopt a variety of stoichiometries and shapes, as well as redox states for the participating Fe ions. The continuing importance of FeS clusters in all cells, their involvement in not only redox enzymes in which electron transfer is facilitated by delocalization of electrons over both Fe and S centers, but also in coupled electron/proton transport in mitochondrial electron transport, Fe storage (ferrodoxins), and in regulation of enzyme activity and gene expression, suggests that they were of primordial importance in the evolution of life. T hey are often found at substrate binding sites of FeS enzymes involved in both redox and nonredox catalysis. A ligand can bind to a particular Fe in the cluster, activating it for hydration or dehydrogenation reactions. Fe 4 of the FeS cluster in the TCA enzyme aconitase can have a coordination numbers of 4, 5, or 6 as it binds water, hydroxide or substrate. It acts to both decrease electron density in the transition state and to change the pKa of bound water as the enzyme catalyzes an isomerization of tricarboxylic acids (citric and isocitric acid) through an elimination/addition reaction with water. In another example it can bind S-adenosylmethionine through its amine and carboxylate groups, which activates the molecule for cleavage and radical formation. In some cases metals other than Fe (Ni for example) are incorporated into the cluster. FeS effects on transcription factors involves facilitation of optimal structure for DNA binding. FeS and FeNi centers in proteins are similar in structure tp FeS units in minerals like greigite and presumably to FeS structure formed when H2S and S2- react with Fe2+ (present in abundance in the early ocean) and other metals in vents Metal sulfides participate in reduction of both CO and CO2. For example the synthesis of CH3SH from CO2 and H2S is catalyzed by "inorganic" FeS. The Minimal Genome This question is being addressed by eliminating "unnecessary" gene from simple bacteria. Cells placed in a rich nutrient broth with essential lipids, vitamins, and amino acids would need fewer genes than those placed in a more nutrient-poor medium. Bacteria cells like Mycoplasma genetalium, that live within "nutrient rich" eukaryotic cell, have been genetically manipulated to delete unnecessary genes. Based on knockout studies, it may be possible for the cell to survive with only 300-350 genes. Bacillus subtilis has approximately 4100 genes. Estimates have been made that it could survive with as few as 271 genes. more to be added 30: Abiotic Origins of Life "Ugly giant bags of mostly water" - The crystal life form describing humans on Star Trek Next Generation, Home Soil Episode. The Start of Life This book began with the notion that understandings derived from the study of simple molecules can be applied to complex biological macromolecules and systems. We developed an understanding of the structural, thermodynamic, and kinetic properties of the "simplest" biomolecules, including single chain amphiphiles like fatty acids, and double chain ones like phospholipids, and how these properties could explain the propensity of these molecules to form complex lipid aggregates (micelles and bilayers). We extended these ideas to the process of protein folding and the assembly of biological complexity. Is there something intrinsic to the property of molecules such that their localization together in the right microenvironment could lead to a "living cell"? How did life originate? That is the topic of this last capstone chapter. Defining life is actually quite difficult. Here is a list of requirements that seem reasonable, but other have noted that this list would exclude the mule. Life can self-replicate, self-sustain, evolve, respond to environmental changes, and die. The earliest known fossils (stromatolites from cyanobacteria) are approximately 3.5 billion years old. We have just finished studying the complex interactions involved in cell signaling. How could they have evolved? Consider the central dogma of biology. In the present biological world, proteins (DNA polymerase, RNA polymerase, transcription factors) are necessary for DNA synthesis, replication and gene transcription. But you need DNA to encode the proteins. This "chicken vs egg" dilemma has been addressed when it was realized that RNA can both carry genetic information as well as enzymatic activity (even at the level of the ribosome used for protein synthesis). https://clockwise.software/blog/solv...d-egg-problem/ for new picture Abiotic Synthesis of Amino Acids and Peptides Much work has been done to determine if the building blocks for present biological molecules could have been synthesized early in Earth's history. Amino acids and fatty acids have been found in meteors suggesting the possibility. Earth's early atmosphere would have had little oxygen, so most components should have been reduced. It probably consisted of methane, ammonia, hydrogen and water similar to the atmospheres of other planets in our solar system. The composition of the early atmosphere is still contentious. In 1953 (the same year that Watson and Crick published the structure of double-stranded DNA), Stanley Miller showed that electric discharges (to simulate lightening) in a reducing atmosphere over a "simulated sea" produced many amino acids. Up to 11 different amino acids have been produced in this fashion along with purines and pyrimidines (these required concentrated reaction mixtures) necessary for nucleic acids. Adenine can be produced just through the reaction of hydrogen cyanide and ammonia in an aqueous solution. Other nucleic acid bases can be made with hydrogen cyanide, cyanogen (C2N2) and cyanoacetylene (HC3N). http://www.hencoup.com/Photo%20Stanley%20Miller.jpg No complex polymers arise through these reactions. However, in 2004, Lehman, Orgel and Ghadiri were able to show that in the presence of carbon disulfide, a gas discharged from volcanoes, homo- and hetero-peptides were produced. Amphiphilic peptides can even catalyze their own formation from peptide fragments, if the fragments are activated. The fragments would bind to the larger "template" peptide through nonpolar actions of the side chains which are oriented along one face of the helical axes. If the fragments bind in a fashion in which the electrophilic end is adjacent to the nucleophilic end of the other peptide fragment, condensation of the two peptide fragments results. The larger template peptide acts as a template (effectively as an "enzyme") in orienting the two fragments for chemical reaction and effectively increasing their local concentration. The reaction of the bound fragments is essentially intramolecular. The reaction even proceeds with amplification of homochirality. Could the prebiotic amino acids have polymerized into a protein that could fold in a fashion similar to modern proteins? That question has recently been addressed by Longo et al (2013). They asked the question whether the amino acids found in Miller-type prebiotic synthesis mixture and in comets/meteors (Ala, Asp, Glu, Gly, Ile, Leu, Pro, Ser, Thr and Val), a restricted set (10) compared to the present 20 naturally-occurring amino acid, could form a polymer that could fold. Notice that this reduced ensemble of amino acids lacks aromatic and basic amino acids. These proteins would be acidic with a low pI and may have trouble, given the lack of nonpolar aromatic amino acids, in forming a buried hydrophobic core which stabilize proteins. Nevertheless Longo et al were able to synthesize a protein with a slightly expanded set of amino acids (12, including Asn and Gln, with 70% prebiotic amino acids). The structure of one of the proteins, PV2, is shown below. The protein was more stable in 2 M NaCl (compared to 0.1 M) in which it showed a cooperative thermal denaturation with a melting point near 650C using differential scanning calorimetry. The protein had properties similar to those from halophilic organisms that thrive in high salt. These properties include low pIs and high negative charge density, which allows cation-protein interactions in the high salt environment, and lower stability in low salt environments. Earlier oceans were saltier. Halophiles are an example of extremeophiles which are highly represented in archea. Although most halophiles are aerobic, some are anerobic. Perhaps life arose in high salt environments. Figure: Structure of PV2 protein comprised of a reduced alphabet of mainly prebiotic amino acids. Abiotic Synthesis of Sugars Sugars are required for present energy production but also as a part of the backbone (ribose, deoxyribose) of present genetic material. Many sugars can be synthesized in prebiotic conditions, using carbon based molecules with oxygen, such as glycoaldehyde and formaldehyde (both found in interstellar gases), as shown in the figure below. Glycoaldehyde was recently found in star forming regions of the Milky Way where planets are likely to form. The presence of borate, which stabilizes vicinal OHs on sugars, is required for the production of sugars instead of tars. RNA molecules containing sugars such as threose, aldopentopyraonses and hexopyranose can also form stable secondary structures like helices. (Remember, RNA probably preceded DNA as the genetic carrier of information given that is also has enzymatic activity). Is there something special about ribose that made it selected over other sugars for nucleic acids, especially since it is found in low abundancy in the products in synthesis reactions conducted under prebiotic conditions? One probable reason is it unusually high (compared to other sugars) permeability coefficient through vesicles made of phospholipids or single chain fatty acids, as shown below. In general, the greater the number of carbon atoms, the smaller the permeability. However, the table above clearly shows large differences in permeability for sugar isomers with the same number of C atoms, and the difference is not affected by the lipid composition. Ribose has markedly elevated permeability compared to other 5C sugars (as do erythritol and threitol among 4 C sugar alcohols). What is so unique about ribose? 20% of the sugar is in the furanose form. Rate constants for ring opening of furanoses are elevated, suggesting greater flexibility. The α-furanose anomer is amphiphilic in that one face is hydrophobic and the other hydrophilic. All of these may promote ribose permeability. Abiotic Synthesis of Nucleobases As mentioned above, nucleobases can be made under abiotic conditions with appropriate sources of carbon molecules with nitrogen. These include hydrogen cyanide, cyanoamide (NH2CN) (along with ammonia), hydrogen cyanide, cyanogen (C2N2) and cyanoacetylene (HC3N). Cyanoamide and cyanoacetylene can both react with water to form urea and cyanoacetaldehyde, respectively. The latter two can condense to form cytosine as shown below. Figure: Abiotic Synthesis of Cytosine A major problem that has plagued prebiotic researchers is how the product of the carbon-oxygen compound (sugar) links to the product of the carbon-nitrogen compound (nucleobase) to form the nucleoside. Recent research by Powner et al has offered an innovative solution. Instead of forming the sugar and base in separate reaction, and then linking them covalently, the combined molecule could be synthesized in a single set of linked reactions. Inorganic phosphate serves as both a general acid/base catalyst (HA/A- in the figure below) in these new reactions in the formation of an important intermediate, 2-amino-oxazole, and as a nucleophilic catalyst. Glyceraldehyde, 2-amino-oxazole, and inorganic phosphate can react to form a ribocytidine phosphate. Possible reaction mechanisms are shown below. Abiotic Synthesis of Genetic Polymers Abiological synthesis of polymer precursors is a long way from creating genetic polymers like RNA and DNA. These genetic polymers have one property that at first glance seems not conducive to a genetic molecule. Both are polyanions, which must be packed into a cell and folded onto itself to form the classic dsDNA helix and many different RNA structures. This problem is solved to some degree by the presence of counterions that help mask the charge on the negative backbone of the nucleic acids. The presence of phosphate in the phospodiester backbone linkage does confer an important advantage over other possible links (carboxylic acid esters, amides and anhydrides). The electrophilic phosphorous atom is hindered from nucleophilic attack by the negative O attached to the phosphorous. Also, the phosphorous is sp3 hybridized compared to the sp2 hybridization of the electrophilic carbon atom in anhydrides, esters, and amides, and hence is less accessible to nucleophilic attack. Most people now believe that RNA, which can act both as an enzyme and genetic template, preceded DNA as the genetic carrier. The evolution of DNA as the primary genetic carrier required an enzyme to convert ribose to deoxyribose. This would make the nucleic acid less likely to cleave at the phosphodiester bond with the replacement of a nucleophilic 2' OH with an H, and make the genetic molecule more stable. Other types of genetic carriers might have preceded the RNA world, especially if the monomer required could be more readily synthesized from abiological sources. One such alternative are threose nucleic acids (TNA). Synthetics ssTNA can base pair with either RNA, DNA, or itself to form duplexes. Other possible candidate include peptide nucleic acids (PNA). These can also form double stranded structures with DNA, RNA, or PNA single strands. They were initially designed to bind to dsDNA in the major grove forming a triple-stranded structure. Binding could alter DNA activity, possibly by inhibiting transcription, for example. The structure of a single-stranded PNA is shown. Note that the backbone, a polymer of N-(2-aminoethyl)glycine (AEG) which can be made in prebiotic soups, is not charged, making it easier to bind to dsDNA. AEG polymerizes at 100oC to form the backbone. In addition to changing the backbone, additional bases other than A, C, T, G, and U can be accommodated into dsDNA and ssRNA molecules (Brenner, 2004) In a recent extension, Pinheiro et al have shown that 6 different foreign backbone architectures can produce xeno-nucleic acids (XNAs) that can be replicated by engineered polymerases which make XNAs from a complementary DNA strand, and a polymerase that can make a complementary copy of DNA from an XNA. XNAs can also be evolved as aptamers to bind specific target molecules. The investigators replaced the deoxyribose and ribose backbone sugar with xenoanalogs (congeners) including 1,5-anhydrohexitol (HNAs), cyclohexene (CeNA), 2'-O,4'-C-methylene-b-D ribose (locked nucleic acids - LNA), L-arabinose (LNA), 2'-fluoro-L-arabinose (FANAs) and threose (TNAs) as shown in the figure below. Figure: Xeno-nucleic acid sugar congeners Polymers of these XNA can bind to complementary RNA and DNA and as such act as nuclease-resistant inhibitors of translation and transcription. Von Kiedrowski, in an experiment similar to the self-replication of peptides described above, has shown that a single stranded 14 mer DNA strand, when immobilized on a surface, can serve as a template for the binding of complementary 7 mers and their conversion to 14 mers. When released by base, this process can occur with exponential growth of the complementary 14 mers. (von Kiedrowski Nature, 396, Nov 1998). Ferris has shown that if the clay montmorillonite is added to an aqueous solution of diadensosine pyrophosphate, polymerization occurs to produce 10 mers which are 85% linked in a 5' to 3' direction. Polyanions as Carriers of Genetic Information There are other reasons why polyanions are useful genetic molecules, other than their resistance to nucleophilic attack. The biological form of DNA is a large double stranded polyanionic polymer, in contrast to RNA which is a single-stranded polyanion polymer and protein which are polymers with varying combination of anionic, cationic, and hydrophobic properties. Even with counterions, it would be difficult to fold DNA into complicated and compact 3D structures as occurs for proteins, given the large electrostatic repulsions among the charged phosphates. Rather it forms a elongated double stranded rod, not unlike the rod-like structure of proteins denatured with sodium dodecyl sulfate (used in SDS PAGE gels). The elonged rod-shaped structure of ds-DNA is critical for the molecule which is the main carrier of our genetic information since mutations in the bases (leading to a switch in base pairs) causes no change in the overall structure of dsDNA. This enables evolutionary changes in the genetic material to produce new functionalities. A single change an amino acid of a protein, however, can cause a large change in the structure of a whole protein, a feature unacceptable for a carrier of genetic information. RNA structure effectively lies between that of DNA and proteins. Since it has less charge density than dsDNA, it can actually form dsRNA helices, so it can carry genetic information, as well as form complex 3D shapes necessary for its activity as an ribozyme. Perhaps more importantly, steric interference prevents ribose in RNA from adopting the 2'endo conformation, and allows only the 3'endo form, precluding the occurrences of extended ds-B-RNA helices. The Lipid World Let's assume that abiological precursors would react to form polymer-like molecules that might be complex enough to fold to structures that would allow binding, catalysis, and rudimentary replication. All this would be worthless unless they could be sequestered in a small volume which would limit diffusion and increase their local concentration. What is required is a membrane structure. Amphiphilic molecules, like lipids, with which we started this book, would be prime candidates since they spontaneously assembly to form micelles and bilayers, as shown in the review diagram below. As mentioned in Chapter 1, alternative lipid phases are possible. Bilayers can also be formed from single chain amphiphiles, such as certain fatty acids, as illustrated in the equilibrium shown below. This occurs more readily at pH values close to the the pKa of the fatty acid, at which the fatty acids are not all deprotonated with full maximal negative charges. Single chain amphiphiles like fatty acids, which were more likely to formed in abiotic conditions, have been found in meteorites. Clay surfaces, which have been shown to facilitate the formation of nucleic acids polymers, can also promote the conversion of fatty acid micelles to bilayers (Szostak). One such clay surface, montmorillonite, whose structure is shown below, promotes bilayer formation. It has an empirical formula of Na0.2Ca0.1Al2Si4O10(OH)2(H2O)10. Chime Molecule Modeling: montmorillonite : montmorillonite The effect of montmorillonite on vesicle formation can be shown by simple measurements of turbidity with time. Microscopy of fluorophore-encapsulated vesicles also shows encapsulated montmorillonite. The fatty acids presumably absorb to the cation layer of the clay particles. Time studies using light scattering also indicate that the vesicles grow in the presence of fatty acid micelles. To differentiate between the formation of new vesicles and the increase in size of pre-existing vesicles (which couldn't be done by simple light scattering without separation of the vesicles), investigators used two different fluorescent molecules to label fatty acid vesicles. The two probes were selected such that if the two probes came in close contact, energy transfer from the excited state of one fluorophore to the other fluorophore could occur, an example of fluorescence resonance energy transfer (FRET). FRET is observed when emission of the second dye occurs after excitation of the first dye, at a wavelength outside of the excitation wavelength of the second dye. If unlabeled vesicles were added to either labeled vesicles, no changes in FRET were observed, suggesting that the dyes did not move between vesicles. If fatty acid micelles were added, a decrease in FRET was observed, suggesting that new fatty acids were transferred to the doubly-labeled vesicles, effectively diluting the dye concentrations in the bilayer and their relative proximity, both which would decrease FRET. Most of the new fatty acid was incorporated into pre-existing vesicles which grew. The vesicles could also divide if extruded through a small pore. Later we will see that the energy to grow the vesicles can derive in part from a transmembrane proton concentration collapse. Division of vesicles might be promoted by bilayer assymetries associated with addition of substances to the outer leaflet, causing membrane distortion. Protocells At some point, early genetic material must have been encapsulated in a membranous vesicles. Would new properties emerge from this mixture that might have a competitive (evolutionary) advantage over either component alone, and thus be a step on the way to the formation of a "living" cell? The answer appears to be yes. Chen et al. have incorporated RNA into fatty acid vesicles with interest effects. They asked the question as whether those vesicles could grow at the expense of vesicles without encapsulated RNA. RNA, with a high charge density and its associated counter ions would create osmotic stress on the vesicles membranes. To relieve that stress they could acquire fatty acids from other fatty acid vesicles (or fatty acid micelles), increasing their surface area, and concomitantly reducing tension in the membrane. Oleic acids vesicles were first placed under stress by encapsulating 1 M sucrose in the vesicle and then diluting it in hypotonic media. Water would enter and swell the vesicle (but without bursting and resealing, as evident from control experiments). Then they prepared stressed and unstressed oleic acid vesicles in the presence of two nonpolar flurophores, NBD-PE (excitation at 430 nm, emission at 530 nm) and Rh-DHPE (emission at 586 nm). These fluorophores were chosen for fluorescence resonance energy transfer measurements. If the membrane vesicles changed size, the FRET signal would change, based on the relative concentration and proximity of the dual fluorophores. If the separation between probe molecules increased, the FRET signal would decrease. Conversely, if the vesicle shrunk, the FRET signal would increase. The results showing the effect of adding unlabeled swollen vesicles to labeled normal vesicles, and labeled swollen vesicles to unlabeled normal are shown below. The surface area of normal labeled vesicles decreased by about 25% when unlabeled swollen vesicles were added, but not when unlabeled normal vesicles were added. Labeled swollen vesicles increased 25% in size only if mixed with unlabeled normal vesicles, not with unlabeled swollen vesicles. Hence swollen vesicles win the competition and "steal" lipid from normal vesicles. Now what about vesicles swollen with encapsulated RNA? RNA, with its associated charge and charged counter ions also placed an osmotic stress on the vesicles. FRET labels (the two fluorophores) were place in vesicles without RNA. Fatty acids were removed from isotonic labeled vesicles in the presence of unlabeled tRNA swollen vesicles (left panel below). Labeled vesicles swollen with glycerol took fatty acids from unswollen vesicles (without tRNA), but not from tRNA swollen vesicles, as both were swollen so no net drive to reduce swelling by lipid exchange was present. These results show the vesicles with encapsulated RNA have a competitive (evolutionary) advantage over normal vesicles. This data suggests that having a polyanion as the source of genetic material is actually advantageous to the protocell. In addition the move in modern membranes to phospholipids with esterified fatty acids (instead of free ones) may actually have stabilized membranes, given the movement of free fatty acids to different membranes. Energy Transduction in Protocells In addition to a genetic macromolecule and a semipermeable membrane, a source of energy to drive intracellular processes must be present. A common source of free energy used in many cells to drive unfavorable processes is a proton gradient, whose formation in modern cells can be coupled to energy input from oxidation, ATP cleavage, light, or the collapse of another gradient. Could a proton gradient be formed in protocells? It can, quite easily, when coupled to the growth of fatty acid vesicles. If a fatty acid vesicle is to grow, more fatty acid must be added to the outer leaflet. The protonated, uncharged form of the fatty acid would preferentially be added, since it would lead to less electrostatic repulsion between adjacent head groups. The protonated, uncharged form of the fatty acid would also be most likely to flip to the inner leaflet to minimize stress asymmetries in the leaflets. Once in the inner leaflet, it could deprotonate to form H+(aq) in the inside of the membrane, creating a transmembrane proton gradient and transmembrane potential. The energy released on growth of the membrane is partly captured in the formation of a proton gradient, as shown in the figure below. The proton gradient would soon inhibit its own formation since further movement of protons into the cell would be attenuated by the positive transmembrane potential unless metal ions inside moved outside. In addition, the gradient would collapse after growth stopped. The investigators made fatty acids vesicles in the presence of pH 8.5 buffers whose pH was adjusted with an alkali metal hydroxide. The external pH was reduced to 8.0, resulting in a 0.5 pH unit proton concentration gradient. (Changes in intravesicular pH were measured with pH-sensitive fluorophore, HPTS.) Inward movement of protons down a concentration gradient, as shown in the figure below, would occur with time, collapsing the imposed concentration gradient. With fatty acid vesicles, this artificial pH gradient collapsed quickly, suggesting the vesicle permeability to protons was high. The rate was too high for simple flip-flop diffusion. Inward movement of protons appeared to be facilitated by outward movement of the M+ ions. The rate of decay of the proton gradient was exponential, and the resulting first order rate constant was easily determined. A graph of the rate constant for pH gradient collapse vs unsolvated ionic radius of M+ decreased with increasing radius (i.e. kNa > kK > KRb > KCs, suggesting that the pH gradient would be more stable if large, impermeable or otherwise trapped cations were encapsulated. When vesicles were made with encapsulated Arg+, the imposed pH gradient did not collapse for hours. If oleic acid micelles were added to oleic acid vesicles with encapsulated Arg+, with no artificial pH gradient induced across the membrane, the vesicle grew with concomitant movement of protons into the vesicle, producing a pH gradient of 0.3 within seconds. These experiments show that membrane growth and energy storage could be coupled, and the right composition of encapsulated material could lead to a stable transmembrane pH gradient, a source of energy to drive biological processes. It even suggests that a charge polyanion would be beneficial as a genetic carrier. Hydrothermal Vents or Primoridal Soup? The case for the origin of life in deep sea hydrothermal vents and not in a primordial "Campbell's" soup has been argued convincingly by Lane et al (2010). What's needed for life are reasonably complex molecules and an energy source to drive unfavorable reactions. It's the latter on which that Lane et al focus. In an early primordial world that was low in oxygen, exergonic oxidation reaction of organic molecules would provide little energy. This can be surmised from the low energy yield (compared to aerobic respiration) achieved in present day glycolytic (fermentative) pathways from all major domains of life, archaea, bacteria, and eukaryotes. Background: Based on rRNA sequences, a primordial cell evolved into two different types of cells, one that became bacteria, and another that split further into archaea (single cells, similar to bacteria) and eukaryotic cells (complex cells with internal organelles that eventually formed multicellular organisms). Bacterial and archaea are collected called prokaroytic cells. In addition, these pathways required the evolution of up to dozen different enzymes to produce their relatively meager energy yield which ultimately depends on the oxidation of an organic molecule by another organic molecule instead of by a powerful oxidant like dioxygen. An anisotropic arrangement of molecules in a concentrated soup could lead to transient chemical potential fluctuations but these would be inefficient and impermanent sources of energy. Effectively the primordial soup would be at equilibrium and hardly expected to provide the energy for synthesis of RNA enzymes and replicators. UV light leads to photo-damage and photolysis not replication of complex molecules. What is needed is a way to drive the synthesis of molecule with high chemical potential energy (like sulfur esters and phosphoanhydrides) compared to their lytic products. These could then provide an energy sources to drive ATP synthesis, for example. A detailed look a the bioworld shows that the earliest organisms used energy from the collapse of the proton gradient (chemisomotic principle elucidated by Peter Mitchell). All present autotrophs (organisms that can fix CO2 and form complex organic molecules) and many heterotrophs (use complex organic molecules of other organisms for fuel) use redox complexes in membranes coupled to membrane gradients. These complexes would take reduced molecules and pass electrons from them to oxidizing agents (electron acceptors), including O2, CO2, and Fe3+ to form H2O, CH4, and Fe2+. Fermentors also use ATPase membrane enzymes to transport nutrients. Yet genomic analysis of bacteria and archaea show that enzymes involved in fermentation differ significantly, suggesting that they evolved separately towards a convergent function. Structure in common include DNA, RNA, ribosomes and membrane ATPases, which Lane et al suggest were in a the Last Universal Common Cell (LUCA). All autotrophs produce their energy source by fixing CO2 using either H2 directly or indirectly using H2O and H2S. All of the are available in nonhydrothermal deep sea vents. Volcanic vents, however, are extremely hot (not optimal for organic molecule synthesis), very acidic, and lack hydrogen gas. A different type of nonvolcanic vent, an alkaline hydrothermal one, might produce more conducive as a site of the origin of life. In these vents, water chemically reacts with minerals in the crust (such as olivine) leading to their hydroxylation and subsequent fracture, with promotes more water entry into the crust. It has been reported that there is more water found as hydroxylated minerals in the crust, that there is liquid water in the oceans. These processes result in temperatures up to 200 degrees Celsius and release of hydrogen gas into a moderately alkaline vents into the sea water at temperatures more conducive (70 degrees C) to the origin of life. Figure: Alkaline Vent Fixing CO2 Of the five different pathways known to fix CO2, all require ATP except one. That one is present in both methanogens, which produce methane from CO2 and H2, and in the acetogens, which produce acetate (CH3CO2-) in the form of acetyl-CoA. The simpler reactions of forming acetic acid and methane are shown below: 2CO2 + 4H2 → CH3CO2H + 2H2O. CO2 + 4H2 → CH4 + 2H2O. The ΔG0 values for these reaction (calculated using ΔG0f for gas phase H2, CO2 and CH4 and liquid acetic acid and water are -75 and -131 kJ, respectively, at 250C, showing that they are thermodynamically favored. Making AcetylCoA, a "high" energy molecule compared to its hydrolysis products (as is ATP) from acetic acid and CoASH, requires an additional source of energy to drive the reaction. A proton gradient is the likely source. Some bacteria and Achaea cells (primordial or present) use the reductive acetyl-CoA pathway, also known as the Wood-Ljungdahl pathway, to form, in a noncyclic process, acetyl CoA from CO2 and at the same time makes ATP. This process is paid for by a proton gradient. This has been described by Shock as "a free lunch you get paid to eat". The energetics of the present acetyl CoA pathway based on the overall reaction below show an approximate ΔG0 value of -59 kJ/mol which can drive ATP synthesis. 2CO2 + 4H2 + CoASH → CH3COSCoA + 3H2O. The concentration of carbon dioxide in the primordial ocean was 1000 times higher than now. Vents produced large amounts of methane and hydrogen gas. There was little oxygen and hence lots of Fe2+. The enzymes involved in this acetyl-CoA pathway of carbon fixation have FeS clusters. It has also been shown that bubbles (which are really membrane-bound spaces) of FeS and NiS can be made in deep-sea vents. These could not only encapsulate precursor molecules but also serve as catalysts. Vents also can catalyze the fixation of nitrogen (to ammonia) and laboratory studies show that FeS can catalyze the conversion of formate (found in vents) into pyrimidines and purines. The studies of present methanogens and vent chemistry suggest that the critical ingredients and conditions for the development of the first biological cells probably occurred in the vents. To produce polymers, an energy source and monomers must exist. Concentration gradients found in simulations of vents produce million-fold concentrated molecules. The transient heating and cooling of any double-stranded nucleic acids could lead to concentration amplification by a PCR-like strand separation followed by reannealing. In addition, these vent regions possess a powerful, reoccurring energy source, a pH gradient, as the alkaline vented material entered the acidic oceans that exist with high CO2 concentrations, creating a gradient across an inorganic membrane. This is startlingly analogous to the pH gradient across membranes (acidic outside, alkaline inside) driven by the membrane complexes in the mitochondria and bacteria. Lane et al argue that the existence of membrane proton gradients as an energy source in all cells (eukaryotes, bacteria, and archaea) and in chloroplasts and mitochondria, corroborate their hypothesis. Bacteria and Archaea share homologous ATPases and electron carriers (ferredoxins, quinones, and cytochromes). These similarities contrast to the differences in enzyme structures in fermentative pathways. Arguments that proton pumps evolved to pump proteins (and reduce pH gradients) can't explain their ubiquitous presence even in organisms not subjected to low pH. Hence the ubiquity of proton pumps supports the conjecture that they arose from the first protocells, possibly comprised of inorganic walls and ultimately with amphiphilic molecules synthesized from precursors. Creationists would argue that it would be impossible to evolve a structure with the complexity of membrane ATPase (which collapse a pH gradient as the power the synthesis of molecules with large negative ΔG0 of hydrolysis). Lane et al propose that the earliest cells evolved ATPase-like molecules in alkaline vents where pH gradients analogous to those in cells today arose. They envision cell-like columns lined by FeS membrane-like structures with alkaline conditions inside and acid conditions outside. Nonpolar or amphiphilic molecules would line the inside of the cells/columns. An ATPase-like system could then take advantage of the pH gradient which constantly replenishes itself. If structures as complicated as ribosomes evolved from a subsequent RNA world, surely ATPase-like molecules could also. Other chemistry might have evolved earlier to utilize the energy source provided by the pH gradient. If life originated in the vents, it would need an energy source to leave the vents. Presumably it would have evolved one to utilized pH gradient to replace the one it left in the alkaline vents. The substrate level phosphorylation of glycolysis that requires ATP input to make ATP would not provide the energy source needed. Cells that left would have had to produce their own proton gradient. Perhaps all the was needed initially was concerted conformational changes in proteins that upon exposure of a different pH changed their shape inducing pKa shifts in adjacent proton donors/acceptors leading to vectorial discharge of protons across a membrane. Perhaps the method described above in protocells was sufficient. Recent analyses by Poehlein et a show that CO2 reduction (fixation) can be coupled to the production of a sodium ion gradient, which could collapse to drive ATP synthesis. Analysis of the genome of a gram positive bacteria, Acetobacterium woodii, an acetogen, shows the it has an ancient pathway for production of acetyl-CoA that can, in an anabolic fashion form biomass or in a catabolic fashion be cleaved to acetate with the production of ATP. It does not require classic electron carriers like ubiquinone or cytochrome C linked to protein gradient formation to drive ATP synthesis. Rather it has only a ferredoxin:NAD+ oxioreductase which couples oxidation to the formation of a sodium ion gradient, which collapses through an sodium ion transporter/ATP synthase to drive ATP synthase. A plausible reaction scheme based on genomic analysis is shown below: Figure: Acetyl-CoA Synthase and Acetogenesis The Role of Fe/S Centers Let's return to the chicken and egg dilemma one more time. What is needed for biological polymer formation are monomeric precursors, an energy source, and a way to compartmentalized them all. We discuss how monomeric precursors could form, but wouldn't it be far better if even the synthesis of precursors could be catalyzed? One source of catalysis mostly absent from the "bioorganic" abiotic chemistry in the above discussion is the transition metals. Transition metals can form complexes. Ligands containing lone pairs on O, N, and S atoms can donate them to transition metals ions, which can hold up to 18 electrons in s, p, and d orbitals. Hence as many as 9 lone pairs on ligand molecules (which are often multidentate) could be accommodated around the transition metal ion. Many present small molecule metabolites and their abiotic precursors (H2O, CO, CO2, NH3 and thiols) bind cations as mono- or polydentate donors of electrons. Hence transition metal ions would have a thermodynamic tendencies to be bound in complexes. Bound ligands that contain potentially ionizable hydrogens could become deprotonated and made better nucleophiles for reactions. Hence the transition state metal ion, acting with the complex, becomes a catalyst as it decreases the pKa of a bound ligand (such as water). In addition, since transition metals ions can have multiple charge and oxidation states, they can easily act as redox centers in the oxidation/reduction of bound ligands that were redox active. Given the relative anoxic conditions of the early oceans, Fe2+ would predominant. It could easily be oxidized to Fe3+ as it reduced a bound ligand. Highly charged transition states would withdraw electron density from bound ligands leading to their possible oxidation. Metals obviously still play a strong role in catalysis, both indirectly in promoting correct protein folding and directly in stabilizing charge in both the transition state and intermediates in chemical reaction pathways. FeS clusters are of significant importance. Their biosynthesis involves removal by an active site Cys in a desulfurase enzyme of a sulfur from a free amino acid Cys followed by its transfer to an Fe in a growing FeS cluster in a FeS scaffold protein, which then transfers the cluster to an acceptor protein where it acts as a cofactor. FeS clusters can adopt a variety of stoichiometries and shapes, as well as redox states for the participating Fe ions. The continuing importance of FeS clusters in all cells, their involvement in not only redox enzymes in which electron transfer is facilitated by delocalization of electrons over both Fe and S centers, but also in coupled electron/proton transport in mitochondrial electron transport, Fe storage (ferrodoxins), and in regulation of enzyme activity and gene expression, suggests that they were of primordial importance in the evolution of life. T hey are often found at substrate binding sites of FeS enzymes involved in both redox and nonredox catalysis. A ligand can bind to a particular Fe in the cluster, activating it for hydration or dehydrogenation reactions. Fe 4 of the FeS cluster in the TCA enzyme aconitase can have a coordination numbers of 4, 5, or 6 as it binds water, hydroxide or substrate. It acts to both decrease electron density in the transition state and to change the pKa of bound water as the enzyme catalyzes an isomerization of tricarboxylic acids (citric and isocitric acid) through an elimination/addition reaction with water. In another example it can bind S-adenosylmethionine through its amine and carboxylate groups, which activates the molecule for cleavage and radical formation. In some cases metals other than Fe (Ni for example) are incorporated into the cluster. FeS effects on transcription factors involves facilitation of optimal structure for DNA binding. FeS and FeNi centers in proteins are similar in structure tp FeS units in minerals like greigite and presumably to FeS structure formed when H2S and S2- react with Fe2+ (present in abundance in the early ocean) and other metals in vents Metal sulfides participate in reduction of both CO and CO2. For example the synthesis of CH3SH from CO2 and H2S is catalyzed by "inorganic" FeS. The Minimal Genome This question is being addressed by eliminating "unnecessary" gene from simple bacteria. Cells placed in a rich nutrient broth with essential lipids, vitamins, and amino acids would need fewer genes than those placed in a more nutrient-poor medium. Bacteria cells like Mycoplasma genetalium, that live within "nutrient rich" eukaryotic cell, have been genetically manipulated to delete unnecessary genes. Based on knockout studies, it may be possible for the cell to survive with only 300-350 genes. Bacillus subtilis has approximately 4100 genes. Estimates have been made that it could survive with as few as 271 genes. more to be added

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/30%3A_Abiotic_Origins_of_Life/30.01%3A_Abiotic_Origins_of_Life.txt

Search Fundamentals of Biochemistry Learning Objectives • to demonstrate how climate has changed over geological time through the present • to explain mechanisms, using knowledge from biology, chemistry, and physics, for climate change • to show the central role of atmospheric CO2 as a causative agent of past and present climate change • to contrast the effects of anthropogenic burning of fossil fuels on climate change with causes of past climate changes • to address arguments made by climate change skeptics Introduction We've known for a very long time that burning fossil fuels and releasing CO2 into the atmosphere would warm our climate. Perhaps the first paper addressing this, Circumstances affecting the Heat of the Sun's Rays, was published in 1856, before the US Civil War, by a woman scientist, Eunice Foote. John Tyndall (of the Tyndall effect) published more comprehensively on greenhouse gases in 1859. Given the complexity of the biosphere's climate, it was not until the 1980s that climate models became sophisticated enough for scientists like James Hansen to become convinced and alarmed enough to discuss in Congressional hearings the role of anthropogenic (made by humans) CO2 released into the atmosphere as a cause of ever-worsening global warming. The knowledge of human-induced climate change has been politicized and subjected to an orchestrated campaign of misinformation and disinformation by fossil fuel companies and their political contributors. We have delayed global actions for so long that we must act immediately and aggressively to address climate change before we reach climatic conditions that are so austere for humans that parts of the world become uninhabitable. Homo sapiens evolved in a world dominated by repetitive glaciation and deglaciation. Hans Joachim Schellnhuber, an atmospheric physicist, climatologist, and founding director of the Potsdam Institute for Climate Impact Research, has stated that humans have so affected the world that we have eliminated the possibility of the next glaciation cycle. Many readers might not be familiar with the data and models supporting human-caused climate change and that climate scientists are almost unanimous in their support of the data and models. As in any field, however, you will find outliers who don't and whose ideas carry disproportionate weight among climate change skeptics. Hence, we provide the basic data to show the relationship between increasing atmospheric CO2 to global warming, both drought and flooding, ocean acidification, and loss of biodiversity.  We also provide supportive information that would allow users to address questions from those who question the reality of present human-induced climate change. We don't shy away from using basic physics as well since most students studying biochemistry at the level found in this book have also studied physics as well as biology. In subsequent sections, we will then address the biochemistry of climate change and its mitigation. Green House Effect Before the advent of the industrial revolution, the earth's climate was fairly constant since the last ice age, which peaked about 22,000 years ago (YA) and ended about 12,000 YA. There have been short (on a geological time scale) periods of cooling since the end of the last ice age. Humans evolved around 200,000 years ago with modern civilizations arising about 4000 BCE so it could be said that humans are ice-age peoples (a distinctly Northern Hemisphere perspective). Humans have had the benefits of a fairly stable climate since then. The sun's energy warms the earth. If the earth did not radiate back into space an equivalent amount of energy, it would slowly and continually warm. The earth reflects energy back in the form of light. In addition, as the earth is heated by the sun, the earth releases heat in the form of infrared light (as do all warm objects). Earth's temperatures are stable when the sum of the energy emitted by the earth equals the energy it receives from the sun.  This is illustrated in Figure $1$. Figure $1$ Our stable climate has been enabled by fairly constant levels of atmospheric CO2, a trace atmospheric gas, which has hovered around 280 parts per million (ppm) until the start of the industrial revolution in 1770. CO2 is a greenhouse gas, which as anyone who has run an IR spectra knows, absorbs in the infrared. CO2 in the atmosphere absorbs some of the infrared radiation released by the earth, allowing the earth to be warmer than in its absence. The CO2 effectively acts as an insulating blanket. In fact, without CO2 or other "greenhouse" gases, the earth would be completely covered by snow. Other greenhouse gases in the atmosphere include methane and nitrous oxide. The IR spectra of these gases are shown in Figure $2$. Students who have taken organic chemistry labs and obtained IR spectra of samples always blank the instrument to remove spectral signals from both CO2 and H2O. Figure $2$: IR spectra of some greenhouse gases. NIST (ex: https://webbook.nist.gov/cgi/cbook.c...ndex=1#IR-SPEC) Since the start of the industrial revolution, humans have been releasing into the atmosphere ever-increasing amounts of CO2 from the burning of fossil fuels and methane from agricultural practices and natural gas production.  CO2 levels, as of November 2022 have reached 415 ppm, while methane has increased to 1900 part per billion (ppb) or 1.9 ppm. Increasing methane in the atmosphere contributes about 20% of the global warming effect of the more concentrated CO2, given methane's intense IR absorbance spectra. It has a short half-life in the atmosphere (about 20 years) compared to that of CO2 (hundreds of years). Nitrous oxide (N2O) is also a powerful greenhouse gas, which also depletes ozone in the stratosphere. Our increased use of synthetic fertilizers and manure is the primary anthropogenic source of N2O. Its emission is exacerbated from poorly drained farmlands. Global Warming Potential The global warming potential (GWP) is used to calculate the total contribution of all emitted greenhouse gases. It is expressed in units of CO2 equivalents. It adds the contribution of other greenhouse gases like CH4 and nitrous oxide (N2O), each of which has unique IR absorption spectra (as shown in Figure 2 above) and atmospheric half-lives. The IPCC uses a 100-year time frame for the calculation of the GWP, which is often abbreviated as GWP100, and uses this formula: \mathrm{CO}_2 \text { equivalent } \mathrm{kg}=\mathrm{CO}_2 \mathrm{~kg}+\left(\mathrm{CH}_4 \mathrm{~kg} \times 28\right)+\left(\mathrm{N}_2 \mathrm{O} k g \times 265\right) • CO2 has GWP of 1 by definition since it is the reference. Its time frame in the atmosphere (100s to 1000 years) doesn't matter since it is the reference. • CH4 has a GWP of around 27-30 over 100 years. It reflects its higher IR absorbance but lower life-time (around 12 years). • N2O has a GWP of around 265-273 over a 100-year timescale. N2O has a life-time of around 109 years. Water is also a greenhouse gas as you can attest to on humid days and how its lack in the atmosphere in deserts leads to a large temperature drop at night. It's very different than other greenhouse gases. Its concentration varies enormously (from 40 ppm to 40000 or more) based on humidity and precipitation events, which remove it from the atmosphere. The amount of water in the atmosphere increases with increasing global temperatures, which gives rise to more intense precipitation events and also to warmer temperatures in a positive feedback loop. Its concentration in the atmosphere hence changes enormously on the time scale of hours and days, so its half-life in the atmosphere is short. In contrast, the half-life of CO2 in the atmosphere is measured in decades to centuries. Climate changes over the last million years The Skeptic's Corner: Climate Change Misinformation Climate has always changed. Our present period is no different, so there is no need for action. Indeed, the earth has been subject to cycles of glaciation and deglaciation for hundreds of thousands of years. Luckily, we are able to determine atmospheric levels of COdating back to hundreds of thousands of years ago by measuring entrapped CO2 in ice cores from Antarctica and Greenland. In addition, we've been able to infer the temperature over this time frame using proxies for temperature (tree rings, fossils, and more as described in section 31.2). Figure $3$ shows how atmospheric CO2 and temperature have varied over the last 800,000 years using ice core data. Several key features of the graph should be apparent: • Both atmospheric CO2 and temperature change (ΔT) are periodic. So yes, it is obviously true that "climate changes" as climate change skeptics argue • Both CO2 and ΔT change in synchrony. An obvious question might be what changes first. Does ΔT drive CO2 changes or vice versa? More on that in a bit. • The CO2 levels in more modern times (right hand side of the graph) have soared in ways not seen in the last 800,000 years! This change is caused by CO2 emissions from the burning of fossil fuels. In those 800K years, the earth has experience cycles of glaciation/deglaciation with recurring ice ages. Figure (4\) below shows a depiction of the last ice age which peaked 21,000 years ago (left). At that time, the ice cap over New York City was about 1 mile high (right) as CO2 was at 185 ppm! By 5000 BCE, the glacier had retreated to more modern levels, leaving ice over the Arctic ocean, and over Greenland. CO2 levels were then around 260 ppm.  A change of just 100 ppm in CO2 was sufficient to lead to the melting of the Northern Hemisphere glaciers. The image above is not "Northern Hemisphere-Centric" since the great glaciers were localized in the Northern Hemisphere in the ice ages. That's because glaciers grow over land and most of the land on the planet is in the Northern Hemisphere. (Our climate studies won't include the time when one continent - Pangea- existed.) The video below shows an animation of the Northern Hemisphere ice shield as it changed with time from 19,000 BCE to now to a projected future that assumes little action to change CO2 emissions.. Pay special attention to the graphs which show sea level changes as well. Best estimates by Tierney et al now show that during the last ice age, the average global temperature was 6 degrees Celsius (11 F) cooler than today, which in the 20th century is 14 C (57 F). The Arctic however was much colder (about 14 C or 25 F). The group also came up with an estimate of climate sensitivity, the increase in temperature with increasing CO2. That value is a rise of 3.4 C (6.1 F) for a doubling in CO2. In 1896, Arrhenius, recognizing that CO2 was a greenhouse gas, actually calculated that doubling atmospheric CO2 would cause a rise of 4-5 °C. No one can say we haven't known! What the science shows Climate, CO2 and temperature have always changed over geological time, but our present rise in anthropogenic CO2 in such a brief time is unprecedented and has led to CO2 levels that far exceed those during the warmer interglacial periods when Northern Hemisphere glaciers had retreated. The Ice Ages, CO2 and Temperature The Skeptic's Corner: Climate Change Misinformation It's not increasing CO2 that is causing any observed increases in temperature. CO2 is going up after temperature increases so we don't have to worry about CO2 levels. It's just a natural process and requires no action to reduce fossil fuel use. Why reduce it if it doesn't cause global warming? Data and models show that the global increase in temperature is driven mostly by increases in CO2 (and not increasing temperatures driving increasing CO2) as the predominant cause. That begs the question as to what starts the process of deglaciation. It turns out that cyclic increases in solar irradiance that increase temperatures, especially in the Northern Hemisphere, start deglaciation. A prime factor is the changes in the orbital dynamics of the earth with respect to the sun. As you know, the orientation of the earth's rotation axis remains generally fixed and pointed in the same orientation as the earth rotates around the sun. This fixed orientation leads to our annual spring, summer, fall, and winter cycles on earth. In the winter, the northern hemisphere is pointed away from the sun, leading to decreased solar irradiance per square meter in the Northern Hemisphere, causing winter there. When the earth is on the opposite side of the sun, the axis points in the same direction but tilts towards the sun, leading to summer in the northern hemisphere. However, the orbital dynamics of the earth do change in cyclic fashions over long periods of time. These long-term changes in the earth's orbital shape (eccentricity), tilt (obliquity), and wobble (precession) are called the Milankovitch cycle, and are illustrated in Figure $5$. These cycles cause small temperature increases that start deglaciation. Click on each image below to download and view very short videos illustrating these orbital changes. Change in eccentricity (orbital shape) - (100,000 yr cycle) Change in obliquity (tilt) (41,000 yr cycle) Axle precession (wobble) (26,000 yr cycle) Figure $5$: The Milankovitch cycle showing changes in the earth's orbital dynamics with respect to the sun. https://climate.nasa.gov/news/2948/m...arths-climate/ Based on these cycles, Milankovitch calculated that recurring ice ages should occur approximately every 41000 years. Ice ages did occur at this interval from about 3 million years ago (MYA) to 1 million years ago (MYA). About 800,000 YA they lengthened to about 100,000 years, which corresponds to the earth's eccentricity cycle. The increased duration of the cycle led to longer-lasting glaciers which moved further south in the Northern Hemisphere. One likely explanation for the increase in time between ice ages is that repeated glaciation/deglaciation eroded the bedrock in the Northern Hemisphere, converting it to regolith (rocks, soil, and dust). This allowed an increased velocity of movement of the glaciers to the south due to decreased frictional resistance, and thicker ice cap formation (more time to accrue ice), which required a longer time to melt. This also provided a positive feedback loop as the increased northern ice area would reflect more of the sun's energy back into space, cooling the planet. Punctuating these rhythmic orbital and ice age cycles are other events such as large volcanic eruptions, asteroid impacts, etc, that could produce minor to major changes in climate, and resulting mass extinctions. Figure $6$ shows how a combination of tilt angle, precession axis, and orbital shape at around 200 KYA (narrow rectangle across all the graphs) combined to lead to low glacial ice volume (bottom graph). Figure $6$: Milankovitch cycle contribution to ice volume over the past 1M years If orbital changes (or forcing) trigger deglaciation, what is the role of increasing levels of the greenhouse gas CO2, which clearly covary with temperature (see Fig 3)? Temperature increases derived from orbital and hence solar "forcing" seem to precede CO2 increases for just short periods of time (perhaps 100 - 200 years). After that, CO2 causes almost all of the global increase in temperatures during deglaciation, with CO2 and temperature going up together. A global increase of about 0.3 C due to the Milankovitch cycle leads to greater Northern Hemisphere irradiance. This causes localized and limited melting of the Northern ice shield, leading to increases in ocean temperatures in the northern oceans. These increases slow a major ocean current (the Atlantic Meridional Overturning Circulation - AMOC) which inhibited the burial and return of cold water in tropical and southern oceans. This in turn led to a warming in the south accompanied by the release of large amounts of CO2 stored in the oceans (see Carbon Cycle in 31.3). The release of this greenhouse gas was then responsible for most of the warming that lead to massive deglaciation. This "interhemispheric see-saw" transfer of heat from the north waters to the southern waters is key. For the far majority of the warming during glacial melting, CO2 and temperature change synchronously. Interpreting climate data is difficult. For example, it was found through measuring 15N/14N ratios that gases like N2 and by extension CO2 could rapidly diffuse through the compacting snow (firn, comprising the top 50-100 meters of the ice cap) until it became trapped in the solid ice beneath it. This would lead to the presence of "newer" CO2 in older ice samples, and the conclusion the temperature changes preceded changes in CO2. Corrections are made to the data to address the "apparent" time shift. The CO2 trapped in bubbles in the ice core samples from Antarctica reflects global CO2 levels given atmospheric circulation but the temperatures measured from the same core samples (see Chapter 31.2) represent local (Antarctic) temperatures. Ice core samples from Greenland and ocean sediment samples from around the world are used to measure temperature at different locations over time. All of these data are required to model climate. Combined they lead us to our present interpretation of the linkage of CO2 and temperature rise over time. What the science shows Increased solar irradiance on earth arising from cyclic changes in the Earth's orbit leads to short, small temperature increases in the North Hemisphere. These lead to the release of the greenhouse gas COfrom the oceans, which causes synchronous warming of the planet and subsequent deglaciation. So when skeptics say that temperature increases preceded CO2 increases, you can acknowledge they did but that the bulk of the warming is attributed to increasing COreleased from ocean stores which leads to synchronous temperature increases and deglaciation. Using the words of chemistry, small temperature increases from orbital forcing catalyzed the release of huge amounts of CO2 dissolved in the ocean. In Chapter 31.3 we will explore the carbon cycle in more detail and look at how it affects CO2 levels. Termination of the Ice Ages How did the ice ages terminate? Contributions from the orbital forcing derived from the Milankovitch cycle play a part. Another factor seems to be dust derived from regolith, itself made by glacier movement as we mentioned above. How can that hypothesis be tested? By using proxies for dust, namely iron and long-chain n-alkanes (derived from plant waxes) that have been deposited in sediments. First let's look at a graph of CO2 and temperature changes and superimpose those on iron and long-chain fatty acid levels, as shown in Figure $7$. A close examination of the two vertically aligned graphs from around 120 K to 130 KYA shows that the iron and n-alkane depositions are at a minimum at the same that CO2 and temperature are peaking! What explains this negative correlation? It depends on the intimate connection of the biosphere with the nonbiological world (an arbitrary distinction). Iron and n-alkanes are circulated and delivered in dust. The long-chain alkanes, highly abundant in waxes and enriched in odd carbon number chains, were presumably derived from leaf waxes which prevent water loss from plants, especially during higher temperatures. Dust deposits were first observed in geological time in the switch from the warmer Pliocene (5.3 to 2.6 MYA) to the Pleistocene (2.6 MYA to 11.7KYA, see Fig 8 below). During the warmer Pliocene, the difference in global and atmospheric temperatures was lower, and with this smaller temperature gradient, winds that could globally transfer dust would be diminished. Also, the warmer Plicoene (5.3 to 2.6 MYA) would have more rain, which would have removed dust from the global circulation. As temperatures cooled in the Pleistocene (2.6 MYA to 11.7KYA), cycles of glaciation would produce more dust-containing regolith (rocks, soil, and dust), which would be dispersed through stronger global winds from higher temperature gradients and and less rain. Dust contains carbon (for example long chain fatty acids) and perhaps more importantly iron, which is needed for oceanic phytoplankton growth. Without Fe, the uptake of CO2 by phytoplankton (primary production) would not occur, leading to increased CO2 in the atmosphere. Stronger regional atmospheric winds would lead to increased upwelling of nutrients as well as deep ocean CO2. The CO2 would enter the atmosphere more readily in the absence of dust deposition of iron. In summary: • High CO2 and high temperature (lower global temperature gradients, more rain) are associated with low dust, as measured with the proxies Fe and n-alkanes). Low dust leads to low deposition of Fe and n-alkanes in the ocean, which decreases phytoplankton primary production, the fixing of CO2 into biomass), leading to increased CO2 movement from the ocean to the atmosphere, increasing temperature. This is an example of a positive feedback loop (higher temperatures leading to higher temperatures. • Low CO2 and low temperature (higher global temperature gradients, stronger winds, less rain) are associated with high dust with Fe and n-alkanes deposition. This increases phytoplankton primary production and decreases CO2 movement from the ocean to the atmosphere, in a negative feedback loop. By the end of a glacial deposition cycle, dust, blown by stronger winds from higher temperature gradients, was increasingly deposited on the ice sheets. Along with leading to more heat absorption by the sheets, it would also decrease their reflectivity (albedo). Both effects would promote ice sheet melting. Also, a cooler planet during glacial maximum had less precipitation, which along with lower CO2, would lead to more plant and tree death, increasing soil erosion and desertification, both effects which would have increased dust production and its deposition on ice sheets. Then when CO2 rose to 280 ppm, plant life renewed itself, and dust levels dropped. Climate change from 66 million years ago to now Antarctic ice core data are now available for the past 2 M years. Ocean sediment data can be used to go back even further in time to 66 million years ago (MYA) just before the dinosaurs died after the massive asteroid impact forming the Chicxulub crater buried underneath the Yucatán Peninsula in Mexico. A brief review of geological eras, periods and epochs is shown below in Figure $8$ Figure $8$: Geological Era, Periods and Epochs CO2 levels and associated temperatures derived from ocean sediment cores going back to 66 MYA are shown in Figure $9$. Figure $9$ CO2 levels (red) and temperatures (blue) derived from ocean sediment cores going back to 66 MYA = 66,000 KYA . Data from Rae et al. Annual review of earth and planetary sciences, 49, 2021 Note again the parallel rise and fall of CO2 and temperature. Eventually, they fall further in the Pliocene (5.3 to 2.6 MYA) and Pleistocene (2.6 MYA to 11.7KYA) epochs with cyclic glacier/interglacial periods we've discussed above. It wasn't until the late Miocene (10 to 6 MYA) that Northern hemisphere glaciation started and both poles of the planet had glacial sheets. The time frame shown in Figure 9 encompasses the Cenozoic era (65 MYA when the dinosaurs died to about now). CO2 levels were much higher than today in the greenhouse Paleocene and Eocene eras but decreased to about 500 ppm in the Oligocene (34 MYA). An almost stepwise drop in CO2 and temperature occurred in the Eocene to Oligocene transition (EOT), about 33 MYA. Data shows the development of large ice sheets appearing on Antarctica at this time. Before the EOT (33 MYA), Antarctica was ice-free, as shown in the recreation in Figure $10$. Figure $10$: Reconstruction of the West Antarctic mid-Cretaceous temperate rainforest. Image credit: J. McKay / Alfred-Wegener-Institut / CC-BY 4.0. https://www.sci.news/othersciences/p...ing-09921.html Proxy data for temperatures show that the transition was most likely caused by a decrease in CO2 and some orbital forcing was probably involved. Present models still struggle to explain the EOT (33 MYA) transition, but it is clear that both CO2 and temperature decreased. Where did the CO2 go? Most assuredly into the oceans. To understand that, we have to understand a bit about the carbon cycle, which we will discuss more fully in the next chapter section. Let's briefly discuss the role of atmospheric CO2 and its interaction with the ocean. The main gases in the atmosphere, N2 and O2, are found in very low concentrations in the ocean since they are nonpolar and generally unreactive. CO2 is also a nonpolar trace gas, but in contrast, it can readily react with water to form HCO3- and CO3-2, which are found in great abundance in ocean reserves. Hence the ocean chemistry of CO2 determines in large part the levels of atmospheric CO2. The coupled reactions of CO2 are shown below. \mathrm{CO}_2(\mathrm{~g}, \mathrm{~atm}) \leftrightarrow \mathrm{CO}_2(\mathrm{aq}, \text { ocean) } \mathrm{CO}_2(\mathrm{aq} \text {, ocean })+\mathrm{H}_2 \mathrm{O}(\mathrm{I} \text {, ocean }) \leftrightarrow \mathrm{H}_3 \mathrm{O}^{+}(\mathrm{aq})+\mathrm{HCO}_3^{-}(\mathrm{aq}) \mathrm{H}_2 \mathrm{O}(\mathrm{I})+\mathrm{HCO}_3^{-}(\mathrm{aq}) \leftrightarrow \mathrm{H}_3 \mathrm{O}^{+}(\mathrm{aq})+\mathrm{CO}_3{ }^{2-}(\mathrm{aq} \text {, sparingly soluble }) This chemistry helps determine the pH of the ocean. Figure $11$ shows atmospheric levels of CO2 and ocean pH over the last 66 million years. Figure $11$: Atmospheric levels of CO2 and ocean pH over the last 66 million years Before the EOT at 34 MYA, atmospheric CO2 levels were higher and ocean pH levels lower (around 7.7). After the EOT (33 MYA), atmospheric CO2 is much lower and ocean pH is higher (more basic, 7.9 rising to 8.1). What happened to the CO2 is a bit unclear.  Atmospheric CO2 decreased by moving into the oceans but wouldn't that have lowered the pH based on the chemical equations presented above? It would have but it turns out that the ocean alkalinity is determined not just by H3O+ produced by the equations above, but by the dissolved inorganic carbon ions, HCO3- (aq) and CO32- (aq), which are conjugate bases. Increased HCO3- (aq) and SiO4-2 (aq) from weathering solid carbonates and silicates that entered the oceans would raise the pH of the oceans. A little review of introductory chemistry helps here. Let's take bicarbonate, the weak conjugate base of the weak acid carbonic acid. HCO3- can act as both an acid and base. Rx 1: Acts as an acid: HCO3- (aq) + H2O (l) ↔ H3O+(aq) + CO32- (aq) K_{a 2}=\frac{\left[\mathrm{H}_3 \mathrm{O}^{+}\right]\left[\mathrm{CO}_3^{2-}\right]}{\left[\mathrm{HCO}_3^{-}\right]}=4.7 \times 10^{-11} Rx 2: Acts as a base: HCO3- (aq) + H2O (l) ↔ H2CO3 (aq) + OH- (aq) K_{b 2}=\frac{\left[\mathrm{H}_2 \mathrm{CO}_3\right]\left[\mathrm{OH}^{-}\right]}{\left[\mathrm{HCO}_3^{-}\right]}=2.2 \times 10^{-8} The equilibrium constant for the reaction of HCO3- as a base is much larger so bicarbonate is a stronger base than acid. Whatever the mechanism of the CO2 drawdown, it led to decreasing temperatures in the EOT transition. Increased alkalinity of the ocean would also consume H3O+, increasing ocean pH. A summary of planetary temperatures across geological time is shown in Figure $12$. Figure $12$: Temperature of earth over 500 million years. https://commons.wikimedia.org/wiki/F...alaeotemps.png. (Excel available).  Creative Commons Attribution-Share Alike 3.0 Unported There are several key features to note. The last time CO2 was as high as today (415 ppm) was about 3 million years ago. Repetitive cycles of glaciation/deglaciation are obvious in the Pleistocene (2.6 MYA to 11.7KYA). In addition, at around 55 MYA, a spike in temperatures of about 50 F occurred over about a 100K year timeframe. This was accompanied by a dramatic spike in CO2 and a dramatic drop in ocean pH as measured by the loss of deep-sea CaCO3 (chalk). These latter changes are visually evident in geological deep-sea sediment records as shown in Figure $13$. This very short time frame is called the Paleocene/Eocene thermal maximum (PETM, 55.5 MYA), which shows very quick spikes (on the geological time scale) can and do occur. Approximately 1.5 petagrams (1015) of CO2 were released annually during the PETM. Now we are releasing about 25 petagram per year. Our present rate of warming is much greater than the rate of warming during the PETM (55.5 MYA). The best candidates for the source of CO2 release that caused the PETM are volcanoes, the oceans, and the permafrost. In addition, methane hydrates (a solid form of methane found in low- temperature high-pressure waters) might also be another factor. Figure $13$: Overview for the Paleocene–Eocene Thermal Maximum (PETM, 55.5 MYA) data from deep-sea records and the terrestrial Polecat Bench (PCB) drill core against age. Westerhold et al. Clim. Past, 14, 303–319, 2018. https://doi.org/10.5194/cp-14-303-2018. Creative Commons Attribution 3.0 License Sediment cores were taken at various sites (1262, 1267, 1266, 1265, 1263, and 690) that are aligned from left to right according to the water depth from deep to shallow.  Note at 55.93 million years ago, at the start of the PETM, there was a sharp transition from light brown/gray which is enriched in chalk, to dark brown enriched in clay. Ocean acidification dissolved the chalk. It took over 100,000 years to recover. Back to the Present Let's return to more recent human history and anthroprogenic forcing of our climate. Figure $14$ shows an interactive graph of atmospheric CO2 over more recent times. Zoom into the steep rise in CO2 starts which around 1760 with the industrial revolution. Figure $14$: Interactive graph of atmospheric CO2 vs time over the last 1000 years. Historical CO2 record from the Law Dome DE08, DE08-2, and DSS ice cores. Credits: D.M. Etheridge, L.P. Steele, R.L. Langenfelds, R.J. Francey and the Division of Atmospheric Research, CSIRO, Aspendale, Victoria, Australia. 2 Degrees Institute. Orbital mechanics cannot explain the warming of the planet in the brief (in geological terms) times since the industrial revolution. Neither can volcanic activity, changes in solar activity, changes in land use (for example deforestation that slows down photosynthesis and CO2 removal from the atmosphere), or even aerosols released on burning fossil fuels (which would actually decrease global temperature due to increased reflection of sunlight). Click on Figure $15$ to see an animated explanation of which factors best explains the global temperature increase since 1880. The results are clear: It's us! Figure $15$: Factors contributing to global warming based on NASA data and NASA's Goddard Institute for Space Studies (GISS) climate models. Unfortunately, other greenhouse gases have risen as well since 1975, as shown in Figure $16$. Figure $16$: Rise in greenhouse gases since 1975. https://www.co2.earth/annual-ghg-index-aggi Take special note of the zig-zag nature of the CO2 curve. The curve dips a bit in the summer when CO2 is actively removed by plants in the Northern hemisphere. Increasing methane now accounts for up to 20% of the warming observed. Figure $17$ shows as interactive graph a very worrisome rise of atmospheric methane with time. Figure $17$: Interactive graph of atmospheric CH4 vs time over the last 1000 years. 2 Degrees Institute. https://www.2degreesinstitute.org/ Present-day warming unequivocally is caused by humans burning of fossil fuels. Past Climate Anomalies The Skeptic's Corner: Climate Change Misinformation In recorded human history there have been other times of climate change, so we shouldn't worry about the present time! Look at the Little Ice Ages! Several dramatic but short-lived (in geological time) climate changes have punctuated recorded human history. Let's look at two, mostly to equip you to address climate skeptics. They also show the sensitivity of our climate to subtle changes. The Little Ice Ages Actual and proxy temperature records show a mild period in Europe from around 950-1100 followed by colder weather, especially from 1450 to 1850. The latter period is called the "Little Ice Ages" although there was no significant expansion of the North Hemisphere ice shield. It was especially cold worldwide in 1816 when much of the world experienced a "year without summer". The effect in 1816 has a clear cause, the explosion of the volcano Mount Tambora in Indonesia on April 10, 1815. But in addition to this identifiable influence in 1816, there was a cool period reported for the northern hemisphere from about 1800 to 1820 that started earlier than the Tambora eruption. Also, a low period of the sun's irradiance, called the Dalton Minimum, occurs from 1790-1860. Proxies for solar activity in the 1600s also show small solar irradiance drops, as we will discuss below. The dip in global average temperatures following the Medieval warm period, is shown in Figure $18$. Figure $18$: Dip in global average temperatures following the Medieval warm period, By RCraig09 - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/inde...curid=87832845 Modern climate changes have been captured in literature and art. One example is a painting showing "Ice Fairs" on the Thames in London, shown in Figure $19$. Figure $19$:https://commons.wikimedia.org/wiki/F...enell.jpgdfdfd Many factors probably contributed to the Little Ice Ages including a drop in solar irradiance. A newer explanation has also been proposed. Marine records show that the water near Greenland and the Nordic seas were warmer, caused by a strengthening of the Atlantic Meridional Overturning Circulation (AMOC). This would have caused the loss of Arctic ice in the late 1300s and 1400s, cooling the water and diluting its salinity, since ice when it crystallized with a tetrahedral hydrogen-bonded coordination of water, excludes salt. This would have collapsed the AMOC and its transfer of heat to the northern waters, leading to rapid and prolonged cooling. An analogous strengthening of the AMOC was observed between 1960 and 1980, which was attributed to a long-duration high-pressure system over Greenland. A similar event might have occurred to kick-start the Little Ice Ages. Tree rings show evidence of higher solar irradiance before the Little Ice Ages, which may be associated with the initial strengthening of the AMOC. The Little Ice Ages also affected China and may account in part for a crop failure in 1644, the year in which the Ming Dynasty fell. There was also an Arctic hurricane in 1588 that helped destroy the Spanish Armada. The Great Fire in London in 1666 was preceded by a very dry summer that followed an exceptionally cold winter. Food production were severely disrupted, which might have led to significant social change in Europe and elsewhere, much as the Plague in Europe shattered societal and cultural norms. The explosion of Mount Tambora, in present-day Indonesia, in 1816 greatly exacerbated the effects of cooling. The ash and SO2 aerosols block solar irradiance, Droughts, floods, cholera epidemics, famine, and migration from Europe to the US and from East to West arose in part from this event. One of the worst times to be alive: 536 Historians report that in 536 AD, parts of Europe, the Middle East, and Asia experienced 24 hours of darkness for up to 18 months.  Summer temperatures plummeted. Famines occurred for a few years after. It snowed in China in the summer. The worst effects were in the Northern Hemisphere but the effects were world-wide. It was probably the most pronounced cooling in the last 2000 years. To make matters worse, a pandemic erupted around 541 that spread from southern Asia to northern Europe. It had a huge effect on the Byzantine Empire and has been called the Justinian (bubonic) Plague after the Byzantine emperor. Crop failures, an expansion of trade, and an influx of rodents derived from the cold temperatures could have led to and also exacerbated the plague. This second and severe example of cooling was shorter-lived in a geological time frame. Temperatures fell in the summer about 1.5-2.50C. A "smoking gun" has been linked to this cooling, a volcanic explosion in Iceland. In addition, another eruption occurred in 540, which dropped the temperature another 1.5-2.50C, and in 547. The combined effects of climate change and the plague led to a significant economic fall in Europe. Signs of airborne lead in the ice in 640, arising from silver mining, suggest a recovery of economic growth. You should ask yourself how the modern world with cope with such an occurrence. What the science shows The Little Ice Ages and the climate changes preceding and after the Justinian plague had multiple causes, including volcanic eruptions, small changes in solar irradiance, and changes in the North Atlantic ocean currents and associated weather patterns. These short-term climate changes had disastrous effects on people's lives and the economic health of societies. Predicted future warming arising from CO2 emitted from fossil fuel use (and other greenhouse causes) would bring far worse immediate and potentially irreversible consequences. It is incumbent on us as people who know the causes of climate change to act with due diligence and speed to avert the worst climate futures. Solar Activity and Climate Change The Skeptic's Corner: Climate Change Misinformation It's not increasing CO2 that causes any observed increases in temperature. The sun's activity is changing. It always has and always will. There's nothing we can do about it. We have discussed how the orbital forcing of the climate kick-started each of the recurring ice ages in the Pleistocene. Some effects of the change in solar activity independent of the sun's orbit have been noted above. Specifically, we have shown that it cannot account for present warming. We present a series of graphs from the NOAA (National Oceanic and Atmospheric Administration) in the collective Figure $20$ below to show the actual change in solar activity over recent times. Comments are shown at the bottom of each graph. (Above) The maximal % spread from the lowest to the highest is very small. Such a small change shouldn't have such dramatic effects on climate unless it is sustained, as it was from around 1630-1700. Hence this decline in solar activity probably played some part in part of the Little Ice Ages. The regular rise and fall (spikes) are associated with the 11-year sunspot cycle activity. Note that the rise in average temperature since 1910 (shown in red) cannot be accounted for by change sin solar activity The above graph shows that the irradiance decreased by about 0.06% (although other values have been reported as high as 0.22%) during the Maunder Minimum, which occurred in the Little Ice Ages. The average decrease in terrestrial temperatures was 1.0-20C. The graph above shows yearly average temperatures in the Northern Hemisphere. The dark red line shows the average change. Note that the averages are clearly lower in the Little Ice Age with the lowest values and lowest spike temperatures close to and in the Maunder Minimum. (Above) The 11-year repeat of sunspot activity and resulting solar irradiance is clearly seen in the graphs. In 2020, a low in activity occurred, yet 2020 was the second warmest year on record since 1880. This graph does not show the effects of climate forcing due to orbitals changes. Rather it shows that solar activity has not changed significantly for the 10000 years prior to 0 CE. Figures $20$: Changing in solar activity in recent geologic time. This would be true if not for the massive amount of CO2, approximately 1.5 trillion tons, injected into the atmosphere since the industrial revolution from the use of fossil fuels. Not all of that is still in the atmosphere, of course, but enough to raise CO2 to levels not seen for 3 million years. Based on the relationship between CO2 and temperature across the ice ages, science can predict when conditions might exist to initiate and propagate the next ice age. The data arising from these models, illustrated in Figure $21$, show how much incoming solar radiation (insolation) must arrive at the earth (watts/m2) to trigger the next ice age. Figure $21$: Incoming solar radiation required to trigger the next ice age. As shown in the left side of the figure, if CO2 were 280 ppm (typical of peaks in past interglacial periods), it would take repetitive drops of insolation below the threshold of about 455 watts/m2 (red line) to start glaciation. As of November 2022, we are at 415 ppm and rising. If it rises to 450 ppm, as it assuredly will in the absence of carbon capture, it would require much less insolation, since the greenhouse effect of the higher CO2 would warm the atmosphere The right graph shows there is little chance of another ice age in the absence of large and sustained volcanic activity or asteroid impact that would lead to blocking of solar radiation. What science says Changes in solar irradiance (not changes in earth's orbital dynamics) cannot account for warming since the Industrial Revolution. They have contributed to short-term (on a geological time scale) cooling during the Little Ice Ages. Summary of Climate Change Causes and Effects Since 900 AC Figure $22$ shows a great summary of possible contributions to temperature change over the last 1000 years. Note again that present-day warming can only be attributed to greenhouse gases (GHG). One panel shows changes in land use. This has caused a temperature drop since 1800. That effect is caused by deforestation and other land cover changes, which leads to more reflection of incident solar radiation back into space. This effect is increased in the winter if the changed land is snow- covered. Deforestation would also decrease CO2 capture (photosynthesis) by plants, which would raise the temperature. That component has been added to the GHG panel. Figure $22$: Simulated northern hemisphere temperature changes, smoothed with an 11 year running mean, relative to the period AD 950–1250. Owens et al. J. Space Weather Space Clim. 2017, 7, https://doi.org/10.1051/swsc/2017034. Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0). The black line in the top panel shows the observed instrumental northern hemisphere temperature variations with their associated uncertainties (Morice et al., 2012), which match the simulations well. The bottom panel shows a simulation with no changes to the radiative forcings. This quantifies the magnitude of natural internal variability in the simulations in the absence of changes in forcings. Note periodic dip but short time frame dip in temperature due to volcanic activity. Clearly, warming since the Industrial Revolution is due to emissions from the use of fossil fuels. Climate Justice: The Emitters and the Affected The Skeptic's Corner: Climate Change Misinformation Why should we make changes to reduce fossil fuel emissions when China is the biggest emitter of CO2! We present a series of graphs in Figure $24$ below, taken from CO2 Emissions - Our World in Data to show which countries have emitted the most CO2 in the past and now. In a just world, those countries which have emitted the most should move swiftly and forcefully not only to decrease emissions but to aid other countries' transitions to clean fuels and to help them with climate change mitigation and adaptation. We don't wish to demonize the fossil fuel industry and those who work in it. The use of fossil fuels, which are high energy, high density, and cheap fuel (because of historically massive subsidies) has lifted millions if not billions out of poverty over time. We had no alternative to fossil fuels until recently. Most did not realize how significantly fossil fuel use would affect our present and future climate and the health of not only humans but the entire biosphere. (Yet there is evidence that.) We can't just stop the use of fossil fuels without inflicting great economic pain on those who can least afford it. In order to help those who are currently suffering and who will suffer most in the future, as well as to help ourselves, our children and out grandchildren, we must move away from the use of fossil fuels as soon as possible. Above: The dip in total world emissions in 2020 was due to the COVID pandemic. Unfortunately, the rise has resumed. Note that China is now the biggest net emitter but the US and EU emissiond are dropping. India is on the rise and if they follow a similar economic path as China, which they need to lift many out of poverty, it will come with a huge cost in CO2 emission unless they can jumpstart their conversion of clean fuels. The world needs to help. Above: Although China is the biggest net emitter, the US and Australia are the biggest emitters per person, although that is dropping The US still leads the world in the total amount of CO2 emitted since the industrial revolution. We also have the greatest GDP. Pakistan suffered tragical flooding, exacerbated by climate change, in 2022. Up to a 1/3 of the country was under water. In a just world, the biggest emitters would aid the rest of the world. Above: Inequality is clearly evident in this graph as the wealthiest people (high and upper-middle income) collectively contribute 86% of CO2 emissions Figure $24$: CO2 emissions by country and income since the industrial revolution. Creative Commons BY license What science says The United States has emitted the most COsince the beginning of the industrial revolution and the most per capita. China is not even close. Future Projections We know the science, and we know the consequences if we choose not to act or act in ways insufficient to meet the challenges of climate change. It is one of the most difficult challenges we have faced as a species. It requires sacrifice and united action for the common good. The benefits of our choice are mostly in the future and for future generations. The Intergovernmental Panel on Climate Change (IPCC), a body composed of leading climate scientists and experts, has defined several different Relative Concentration Pathways (RCPs) leading to different emissions and different climate futures. Where we end up depends on economic, social, and political choices. The IPCC initially designated four pathways, RCP 2.6, 4.5, 6, and 8.5, with higher numbers associated with higher temperatures and CO2 levels. Each assumes a starting value and estimated emissions (which depend on technology, politics, economics, etc). RCP 8.5 assumes extra radiative forces (heat energy/(m2s)) by 2100 equal to 8.5 J/(s m2) or 8.5 watts/m2. This worst-case scenario assumes business as usual with no interventions to reduce our emissions, a totally unlikely scenario given present actions (including the rapid rise of clean energy). The RCP 2.6 scenario assumes that the peak radiative forcing would be 3 watts/m2 which would decline through very strong governmental and economic actions to 2.5 by 2030-2040. Table $1$ below shows the four RCP scenarios with projected ending CO2 equivalents (which include other greenhouse gases) and temperature increases. RCP (W/m2) Timeframe CO2 atm equivalents (ppm) Temp increase (oC/oF) Description 8.5 in 2100 1370 4.9/8.8 rising 6.0 after 2100 850 3/5.4 stabilizing without overshoot 4.5 after 2100 650 2.4/4.3 stabilizing without overshoot 2.6 decline from 3 before 2100 490 1.5/2.7 peak and decline Translating the projected CO2 equivalents in the atmosphere into associated temperature increases requires a high-quality value for climate sensitivity (rise in temperate/rise in CO2). Figure $25$ shows the likely increase in temperatures for the four different scenarios. The scenarios in Figure 25 are labeled SSP#-## with the second number ## representing the RCP number. The IPCC 6th report issued in 2021 changed from using RCP scenarios to Shared Socioeconomic Pathways (SSPs) scenarios which are based on possible social and economic developments that would pose different challenges to reduce future temperature increases and hence different strategies for mitigation and adaptation. The SSP scenarios are consistent with the RCP scenarios but use a more enhanced socio-economic and political framework for their construction. The mitigation strategies are based on the RCP forcing levels. The SSP scenarios are described below. They start with SSP1, which leads to a world that has adapted well and moved away from fossil fuels, to SSP5, which assumes a continued and high reliance on fossil fuels. SSP1:  Sustainability – Taking the Green Road (Low challenges to mitigation and adaptation) The world shifts gradually, but pervasively, toward a more sustainable path, emphasizing more inclusive development that respects perceived environmental boundaries. Management of the global commons slowly improves, educational and health investments accelerate the demographic transition, and the emphasis on economic growth shifts toward a broader emphasis on human well-being. Driven by an increasing commitment to achieving development goals, inequality is reduced both across and within countries. Consumption is oriented toward low material growth and lower resource and energy intensity. SSP2Middle of the Road (Medium challenges to mitigation and adaptation) The world follows a path in which social, economic, and technological trends do not shift markedly from historical patterns. Development and income growth proceeds unevenly, with some countries making relatively good progress while others fall short of expectations. Global and national institutions work toward but make slow progress in achieving sustainable development goals. Environmental systems experience degradation, although there are some improvements and overall the intensity of resource and energy use declines. Global population growth is moderate and levels off in the second half of the century. Income inequality persists or improves only slowly and challenges to reducing vulnerability to societal and environmental changes remain. SSP3:  Regional Rivalry – A Rocky Road (High challenges to mitigation and adaptation) A resurgent nationalism, concerns about competitiveness and security, and regional conflicts push countries to increasingly focus on domestic or, at most, regional issues. Policies shift over time to become increasingly oriented toward national and regional security issues. Countries focus on achieving energy and food security goals within their own regions at the expense of broader-based development. Investments in education and technological development decline. Economic development is slow, consumption is material-intensive, and inequalities persist or worsen over time. Population growth is low in industrialized and high in developing countries. A low international priority for addressing environmental concerns leads to strong environmental degradation in some regions. SSP4:  Inequality – A Road Divided (Low challenges to mitigation, high challenges to adaptation) Highly unequal investments in human capital, combined with increasing disparities in economic opportunity and political power, lead to increasing inequalities and stratification both across and within countries. Over time, a gap widens between an internationally-connected society that contributes to knowledge- and capital-intensive sectors of the global economy, and a fragmented collection of lower-income, poorly educated societies that work in a labor-intensive, low-tech economy. Social cohesion degrades and conflict and unrest become increasingly common. Technology development is high in the high-tech economy and sectors. The globally connected energy sector diversifies, with investments in both carbon-intensive fuels like coal and unconventional oil, but also low-carbon energy sources. Environmental policies focus on local issues around middle and high income areas. SSP5:  Fossil-fueled Development – Taking the Highway (High challenges to mitigation, low challenges to adaptation) This world places increasing faith in competitive markets, innovation and participatory societies to produce rapid technological progress and development of human capital as the path to sustainable development. Global markets are increasingly integrated. There are also strong investments in health, education, and institutions to enhance human and social capital. At the same time, the push for economic and social development is coupled with the exploitation of abundant fossil fuel resources and the adoption of resource and energy-intensive lifestyles around the world. All these factors lead to rapid growth of the global economy, while global population peaks and declines in the 21st century. Local environmental problems like air pollution are successfully managed. There is faith in the ability to effectively manage social and ecological systems, including by geo-engineering if necessary. The projected increases in emitted CO2 (Gigatons/yr) and other greenhouse gases over the next 80 years for each SSP scenario are shown in Figure $26$.  The second number in the SSP label is the RCP scenario number based on radiative forcing listed in Table 1 above. Figure $26$: Projected increases in greenhouse gases under different SSP (RCP) scenarios. Masson-Delmotte et al. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I. to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001. Note the welcome decline in SO2 which causes acid rain as well as aerosols. This shows that under all SSP scenarios, we are moving to clean up our air (in this case reducing SO2 from burning sulfur-enriched coal or capturing SO2 before it enters the atmosphere). Paradoxically and unfortunately, decreasing aerosols leads to increasing temperatures due to lower reflectance of incident solar irradiation. Our final figure, Figure $27$, shows how each greenhouse gas and SO2 are projected to change in 2081-2100, compared to 1850-1900 levels, for each of the SSP scenarios. Figure $26$: Projected changes in greenhouse gas es and SO2 in 2081-2100 compared to 1850-1900 levels for different SSP scenarios. Masson-Delmotte et al, ibid. As all the data presented in this chapter shows, our climate fate will depend on the choices we make individually and collectively as societies. Key Points - Beta version from Chat.openai 1. Climate change is the long-term change in the average weather patterns on Earth. 2. The primary cause of climate change is the burning of fossil fuels, which releases large amounts of greenhouse gases, such as carbon dioxide (CO2), into the atmosphere. 3. Greenhouse gases trap heat in the atmosphere, causing the Earth's temperature to rise. This is known as the greenhouse effect. 4. The most significant contributor to climate change is CO2, which is released when fossil fuels are burned. Other significant contributors include methane, nitrous oxide, and fluorinated gases. 5. Climate change has a wide range of impacts on the Earth's systems, including rising sea levels, changes in precipitation patterns, increased frequency and intensity of extreme weather events, and disruptions to ecosystems. 6. The global temperature has already risen by 1 degree Celsius (1.8 degrees Fahrenheit) since the pre-industrial era, with most of the warming occurring in the last few decades. 7. The Intergovernmental Panel on Climate Change (IPCC) has stated that limiting global warming to 1.5 degrees Celsius (2.7 degrees Fahrenheit) above pre-industrial levels could significantly reduce the risks and impacts of climate change. 8. Reducing greenhouse gas emissions is essential in order to slow or stop climate change. This can be achieved through a combination of actions, such as increasing the use of renewable energy sources, improving energy efficiency, and reducing deforestation.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.01%3A__The_Basics_of_Climate_Change.txt

Search Fundamentals of Biochemistry In this chapter section, we will explore how we are able to reconstruct CO2 and temperature values across millions of years time. It is truly a remarkable, if not awe-inspiring achievement that shows the intellectual rigor and endurance scientists employ to obtain, interpret, and model data. The graphs of CO2 vs temperature across such a vast swath of time shown in the previous chapter section were obtained through analyses of isotopes of oxygen and carbon found in chemical species in ice and ocean floor sediments. The interpretation of the isotope data requires an understanding of the link between individual and linked chemical and biochemical reactions. So we have to take a deep dive into isotopes and their use. Why study isotope effects • Isotopes and their effects, critical in understanding both structure and activity in biochemistry, are key in climate science. • Both equilibrium and nonequilibrium reactions and processes apply to isotope partitioning into water and biomolecules, in ways similar to linked biochemical reactions and pathways. • The study and application of isotope effects can integrate and expand learning from previous science courses In more global terms • The strength and quality of our data and models that support explanations in biochemistry and climate analyzes must be explored. • Scientific and ethical rigor in pursuit of knowledge, explanations, and solutions as we pursue complex fields like biochemistry and climate change is essential if we are to place trust in our experts and faith in their findings. Absolute and proxy measurements for CO2 and temperature It's simple to see how CO2 levels over the last 800,000 years have been determined from ice cores since ancient air is actually trapped in bubbles in them. The bubbles can be liberated by melting/shattering and analyzed. However, how can we infer temperature changes or actual temperature from ice core samples and both CO2 and temperatures from ocean sediment cores? Scientists use "proxies" to determine temperature as modern thermometers and temperature scales were invented only recently (early- and mid-1700s by Fahrenheit and Celsius). These proxies include tree rings, growth bands in coral, pollens found in core samples, and calcite shells from marine organisms found in lake and ocean sediments. The organisms include certain types of algae, phytoplankton like dinoflagellates and diatoms, and foraminferas (single-celled protozoans with shells). From a more chemical perspective, the analyses of the isotopic compositions of ice, using the isotope ratios of 18O/16O in water, and of marine sediments, using the ratio of 18O/16O and 13C/12C in carbonate-containing shells, have proved critical in determining temperatures back millions of years ago. Even molecules like components of leaf waxes (as we discussed in Chapter 31.1) can be used. Isotope analyses of diatoms (with silicate shells) in lake sediments are also useful. The analysis of proxies is quite complicated since many factors contribute to the measurement derived from proxy use. Take tree rings as an example. The width of a given ring depends not only on temperature but precipitation. Calibrations for each proxy must be made using alternative data such as rings from a variety of other trees. Proxy data from ice cores go back a few million years, while data from marine sediments back as far as 100 million years ago. Isotope analysis in rocks formed from marine or land sediments can go back billions of years. Proxy data (tree rings) taken from recent times can be compared to actual temperatures measurement from the same time. The calibration relationships can then be used for samples from the past. Alternatively, proxy data can be taken across many different places and temperatures to develop calibration constants, an approach useful for pollen analysis. Organisms could also be cultured in different temperatures, nutrient, and CO2 conditions to calibrate past data. From a statistical sense, it is best to combine multiple proxies to develop past temperature records. Proxy data sets are available for over 10,000 sites around the world. Figure $\PageIndex{x}$ below gives a link to sites compiled by Carbon Brief where databases with information about each study is available. Figure $1$: Map of over 10,000 proxy data studies and sites. Robert McSweeney and Zeke Hausfather and Tom Prater. https://interactive.carbonbrief.org/...-distant-past/ Most people might care little about climate change millions of years ago. The value in understanding climate change so far in the past is, in part, to build confidence in the data, methods of analysis, and climate model to better understand the relationship between CO2 and temperature. Some, particularly the PAGES 2k Consortium, focus their attention on the last 2000 years of the Common Era. Figure $2$ shows global mean temperatures obtained from proxy data (yrs 0 - 2000) and direct observations (through the use of thermometer and satellite measurements) since around 1850. Figure $2$: Global mean surface temperature reconstruction (yellow line) and uncertainties (yellow range) for the years 0-2000 period from the PAGES 2k Consortium along with observations from Cowtan and Way from 1850-2017. Data available in the NOAA Paleoclimate Archive. Note the overlap of proxy measurements and direct observations of temperatures since around 1850. Ocean Microorganisms Students of biochemistry come from many backgrounds and all do not have a strong biology background. To help with that, and to develop a sense of wonder about the microorganisms that inhabit the oceans and play such a key part in the biosphere, let's look at a few relevant to this chapter. For those with a "chemistry-centric" background, these descriptions are probably new. Plankton The word plankton derives from a Greek word meaning drifter or wanderer. There are two main types. One is zooplankton, which are not plants, but rather microscopic animals and protozoans. They are heterotrophs that don't synthesize their own food. Most have calcite shells. The other is phytoplankton, which are autotrophic plants that use photosynthesis for food production. Hence they are carbon "capturers", which are key players in the carbon cycle in maintaining atmospheric CO2 and in producing O2. The phytoplankton broadly include algae (protists), cyanobacteria (also known as blue-green algae), and dinoflagellates which also fit into other groups. Here are some examples. Also remember that protists are eukaryotic organisms that are not animals, plants, or fungi. Table $1$ below lists types and examples of plankton. Type of plankton Examples Zooplankton (heterotrophs) benthic foraminiferans, which live mostly at sea bottoms and in sediment and capture carbon indirectly through the carbon cycle though the use of CO3- in their shells. planktonic foraminifera, which live near the surface but are found buried in ocean sediments after their death dinoflagellates that don't photosynthesize are small animals including tiny fish and crustaceans such as krill and jellyfish. radiolarians, single-cell protozoans that have calcium silicate shells Phytoplankton (autotrophs, primary producers) diatoms photosynthesizing dinoflagellates blue-green algae which are prokaryotic bacteria green algae, a photosynthetic eukaryotic protist. some foraminifera that live near the surface watera and can photosynthesize Zooplankton Existing shells in sea sediments from foraminifera have been critical in dating studies and determining CO2 and temperatures over millions of years. There are two major types which include benthic foraminifera (which live at the sea bottom and in sediment) and a smaller group of planktonic foraminifera which live near the surface. They are heterotrophs, but some, on ingestion of small autotrophic phytoplankton, can retain and sequester their chloroplasts, which can engage in photosynthesis for a period of time. Figures $\PageIndex{3-5}$ below show examples of zooplankton that have been important in climate studies. Figures $3$ above: Benthic foraminifera: Live mostly at sea bottoms and in sediment); capture carbon indirectly through the carbon cycle through the incorporation of CO3- into their shells. Living benthic foraminifera in the Bohai Sea, showing normal specimens and abnormal individuals (indicated by arrows). Figures $4$ above: Planktonic foraminifera Live near the surface but are found buried in ocean sediments after their death. (ah) Nano-CT scan of planktonic foraminifera specimens with color map of test thickness, warm colors indicating areas of relatively thicker shell; (a,b) Globigerinoides ruber (Tara), (c) Globigerina bulloides (Tara), (d) Neogloboquadrina dutertrei (Tara), (e) G. ruber (Challenger), (f) Trilobatus trilobus (Challenger), (g) N. acostaensis (Challenger), (h) N. dutertrei (Challenger); (ip) SEM images of selected planktonic foraminifera specimens; (i) T. trilobus (Tara), (j) G. ruber (Tara), (k) G. ruber (Challenger), (l) G. bulloides (Challenger), (m,n) G. ruber test cracked to reveal wall texture (Tara), (o,p) G. ruber test cracked to reveal wall texture (Challenger). Figures $5$ Above: Radiolaria .single-cell protists that secrete silica Figures $\PageIndex{3-5}$: Examples of zooplankton. Benthic foraminifera: https://commons.wikimedia.org/wiki/F...raminifera.p; Planktonic foraminifera: Creative Commons Attribution 4.0 International License. Fox, L., Stukins, S., Hill, T. et al. Quantifying the Effect of Anthropogenic Climate Change on Calcifying Plankton. Sci Rep 10, 1620 (2020). https://doi.org/10.1038/s41598-020-58501-w. http://creativecommons.org/licenses/by/4.0/.; Radilaria: https://commons.wikimedia.org/wiki/F...ria-sp2_hg.jpg Phytoplankton Phytoplankton are microscopic plants, and as such, engage in photosynthesis, capture CO2, and produce O2. Hence they are primary autotrophs. We will consider three types, diatoms, photosynthetic dinoflagellates and coccolithophores. Diatoms and photosynthetic dinoflagellates are the major ones and are prey for the zooplankton. They are described in Figures $\PageIndex{6-8}$ below. Figures $6$ Above: diatoms Single-celled eukaryotic algae surrounded by a silica shell (test). These can reach 1 mm in diameter and can form an assortment of shapes. Some can form multicellular chains. They engage in high efficiency photosynthesis and resulting carbohydrate synthesis. They are found in coastal and cold waters with lots of nutrients. Figures $7$ Above: photosynthetic dinoflagellates Algae with a single shell. They are smaller than diatoms. Most have two flagella for motion. They have a cellulose shell, which degrade on death. Hence, they don't have shells that enter the sediment. Some are nonphotosynthetic and are considered zooplankton. Figures $8$ Above: coccolithophores Coccolithus pelagicus; coccosphere. These are very small single cell algae, which form interlinked calcium carbonate circulate plates that cover the surface. Figures $\PageIndex{6-8}$: Some phytoplankton. Diatoms: https://commons.wikimedia.org/wiki/C...le:Diatom2.jpg; photosynthetic dinoflagellates: https://commons.wikimedia.org/wiki/F...lagellates.jpg; coccolithophores: https://commons.wikimedia.org/wiki/F..._pelagicus.jpg Along the coast in summer, nutrient-rich upwelling of water occurs which can lead to explosive growth of dinoflagellates, causing the water to become red-gold (often called a red tide). Some species in these blooms produce neurotoxins such as saxitoxin (inhibitor of sodium channels), which can produce paralytic shellfish poisoning if shellfish from the bloom area are eaten, and brevitoxin (stimulate voltage-gated sodium channels in nerve and muscle). Ice cores from Antarctica and Greenland can extend to over 3.4 km (2.1 miles) in depth and yield direct information on CO2 and indirect measurements of temperature. The oldest continuous ice core records extend to 130,000 years in Greenland, and 800,000 years in Antarctica. Data going back 2 million years is available using discontinuous cores. Concentrations of trapped CO2 as a function of time, and the temperature of each layer can be determined. Figure $9$ shows a section of an ice core from the West Antarctic Ice Sheet Divide (WAIS Divide). Figure $9$: The dark band in this ice core from the West Antarctic Ice Sheet Divide (WAIS Divide) is a layer of volcanic ash that settled on the ice sheet approximately 21,000 years ago. Credit: Heidi Roop, NSFhttps://icecores.org/about-ice-cores The remains of plankton shells described above are found in cores from sea sediments. Analyses of shells, especially from foraminifera, have provided climate data going back tens of millions of years ago. For both ice and ocean sediment cores, isotope analyses have been the key to obtaining CO2 and temperature data. Isotope Analyses In analyzing ice and ocean sediment cores, three things are needed: the age of the layer, a direct or indirect measure of the atmospheric CO2 at the time the layer was deposited, and an indirect measurement of the temperature at the time of deposition. As shown in the figure above, ice core samples have rings, similar to trees, that can be used to count backward in time. The rings get harder to distinguish the further back you go. Figure 3 above shows a visible dark band deposited by volcanoes 21,000 years ago. Ultimately, isotopic analyses of H2O and CO2 in ice samples and of carbonates in minerals and deposited microfossils in ocean samples are critical in determining past CO2 and temperature values. Most readers are familiar with 14C radioisotope dating and 13C-NMR. Metabolic pathways have been elucidated using 2H (deuterium), 3H (tritium), 13C, and 14C to label specific atoms in substrates and follow their flow into products. These same isotopes have been used in kinetic experiments to determine enzyme reaction mechanisms. We will explore isotopes in some detail in this section. Use of unstable radioactive isotopes The use of 14C radioisotope dating is limited in climate analyses given its short half live (t1/2 = 5730 years). In contrast to most isotopes made in stellar nucleosynthesis or by the radioactive decay of a precursor radioactive elements to an isotope of another element, 14C is made on a continual basis in the atmosphere when high energy neutrons (n) from solar radiation react with atmospheric nitrogen (N). The neutron kicks out a proton to form 14C as shown in the nuclear reaction below. $n+{ }_7^{14} \mathrm{~N} \rightarrow{ }_6^{14} \mathrm{C}+p$ 14C becomes oxidized to form 14CO2 which can then enter the carbon cycle and enter the organic carbon pool through uptake by photosynthetic organisms and organisms that consume them. It can also form inorganic bicarbonates and carbonates, which could enter into shells. All living things take in 14C until their death, after which 14C decays through the conversion of a neutron to a proton, a beta particle (electron) and an antineutrino, forming stable 14N. Hence the amount of 14C in dead organisms or their remains diminishes with a t1/2 = 5730 years, in a process not affected by temperature or pressure. 14C dating can be used in samples dating back about 55,000 years, a time span representing 9.6 half-lives. Only 0.13% of the original 14C would be left. Data by this method give the age of death of the organism. Carbon-14 dating depends on the assumption that its amount in the environment is constant, but the burning of fossil fuels and detonation of nuclear weapons has altered its amount (see box below). Changes in solar activity and resulting changes in high-energy neutrons also affect the amount of carbon 14. Also given the relatively short time frame used in 14C dating, differences in CO2 based on its sequestration and circulation in the oceans are also factors. 61% of the Northern Hemisphere is covered by oceans compared to 81% of the Southern Hemisphere. Books of calibration factors can be used to control for these effects. The calibrations are based on tree rings, lake and ocean sediments, corals and stalagmites data which allows dating back to 55,000 years ago. Nuclear Weapons, Fossil Fuels and 14C dating In the 1950s up to 1962, nuclear weapons were tested in the air, doubling the amount of 14C in the air. This spike has been taken up into organisms and into the ocean. Also since then, the amount of CO2 from the burning of fossil fuels has gone up dramatically. This source does not contain 14C as it derives from fossils that long ago decayed. The effects canceled each other in 2021. Since 2021 a lot more CO2 from fossil fuels has been added so the net effect is now lower levels of 14C equivalent to preindustrial time. It will continue to lessen until well after we stop using fossil fuels. By 2050 the levels might be equivalent to those in the Middle Ages. This, and the human-made stoppage of the next ice age glaciation cycle is yet another warning to us about our effects on the entire biosphere. The decay of other "unstable" radioactive isotopes is used for dating samples and determining their age of burial: • 39Ar , an extremely rare isotope (t1/2 =269 yr), has been used to date ice cores from the Tibetan Plateau over the last 1,300 years. • 40K (t1/2 =1.25 billion yr) decays to 40Ar (stable), so their ratios can be used to determine how much time has passed since magma solidification into rock, based on rates of diffusion of the resulting stable 40Ar . • The ratio of 26Al/10Be in buried samples is used in dating analyses. The two isotopes are rare and produced in a fixed ratio (6.75/1) when they are formed in surface quartz by solar radiation (much like the formation of 14C). When buried through geological processes, there is no further production of the isotope, but fortunately (for those who measure age of burial), they decay with different half lives (t1/2 = 717,000 yr for 26Al and t1/2 = 1.39 million yr 10Be) • The ratios of 21Ne/26Al and 21Ne/10Be can be used. 21Ne is a stable isotope and these ratios are independent of the 26Al/10Be rate. • Uranium isotopes are widely used in age measures on the long time scale. 238U (t1/2 =4.45 billion yr) is converted to 206Pb (stable) and 235U (t1/2 =704 million yr) to 207Pb (stable) by parallel decay routes which allow for multiple types of dating measurement. Use of stable isotopes Most of the data and graphs of CO2 and temperature vs time (years ago) presented in Chapter 31.1 were determined by using stable isotopes that do not decay. Much of the data is based on either the ratio of the stable isotope pairs of oxygen (18O/16O) or carbon (13C/12C) in buried ice or ocean sediment cores. These isotopes have also been used to infer the temperature or temperature change when targets were buried in ice core or ocean sediments. Temperatures at the time of deposition of water in the ice layers are often inferred from 18O/16O ratios in the ice layers. Ice core 18O/16O analyses The oceans are huge and generally homogenous reservoirs that can give clues to long-term changes in climate. Short-term climate change would have limited effects on the oceans. The 18O/16O ratios in Greenland and Antarctic ice cores have allowed dating and temperature reconstruction over geological time since the ratio is determined by the 18O/16O in the liquid oceans at the time of ice formation. The % natural abundance of 18O (0.205 %) and 16O (99.757 %) gives a ratio of the two isotopes of 0.0021, which is so small that the exact ratio is inconvenient for routine use. Rather, a comparison of the ratios in a target sample vs a universal reference, the δ18O value, is determined using a mass spectrometer. The δ18O value is calculated by the following equation: $\delta^{18} O=\left[\frac{\left(\frac{18}{16} O\right)_{\text {sample }}}{\left(\frac{18}{16} \mathrm{O}\right)_{\text {reference }}}-1\right] * 1000$ Similar δ values are determined for D/H ratios (δ2H) and for 13C/12C (δ13C) • The reference for δ18O calculations is the Standard Mean Ocean Water (SMOW or V-SNOW) • The δ2D reference value is also based on SMOW or V-SNOW • The standard for the analogous δ13C value is the Cretaceous Peedee Belemnite (an extinct order of squid-like cephalopods with an internal cone skeleton) sample from the Peedee belemnite (PDB) formation in South Carolina, USA. This standard is no longer available so an alternative, NBS 19, a carbonate material, is used in a new V-PDB (Vienna-PDB) scale. Table $4$ shows the ratio of the isotope abundancies in nature and in the references. Element ratio Ratio of natural abundance Reference ratio 18O/16O 0.205/99.757 = 0.00205 0.0020052 (SMOW or V-SMOW) 13C/12C 1.1/98.9 =0.0111 0.011238 (PDB or V-PDB) 2H/1H (D/H) 0.0156/99.9844 = 0.000156 0.00015576 Note that the ratios of the standards, and likewise of the samples, are small. Also note that in the equation for δ18O, the bracketed term is multiplied by 1000. If multiplied by 100, the value for δ18O would be a percentage. Instead, it's multiplied by 1000 to convert it to permill (per mil or %o) or parts per thousand (just like percent is parts per 100). Hence 1%o is 1 part per 1000 or 0.1%. Equations can be just collections of letters with little intuitive meaning, or they can be deconstructed by the user to make intuitive sense. To help understand this equation, which most readers have likely never encountered, given its importance in climate studies, let's look at 3 sets of conditions. If ... • 18O is enriched in the sample compared to the reference, then (18O/16O)sample/(18O/16O)reference is >1, so subtracting 1 from it makes the bracketed term +, along with δ18O; • 18O in the sample is equal to that in the reference, then (18O/16O)sample/(18O/16O)reference =1, so the bracketed term = 0, and δ18O = 0; • 18O in depleted in the sample compared to the reference, then (18O/16O)sample/(18O/16O)reference <1, so the bracketed term is -, along with δ18O. In summary, a sample with a higher 18O/16O ratio (enriched in heavier isotope) than the SMOW reference will have a positive (+) δ value. If the 18O/16O ratio of the substance is lower (depleted in heavier isotope) than the SMOW reference, the δ value will be negative (-). The δ values of SMOW (O and H isotopes) and PDB (C isotopes) are zero as they are compared to themselves. Now let's apply this to the analysis of ice and ocean δ18O values and see how delta values are used as a proxy for temperature or change in temperature at deposition. The ice shields come from snow which comes from water evaporated from the oceans. Water can have multiple isotopic compositions, but from the % abundancies, the most likely ones are H216O and H218O, which is heavier than the light form, H216O. H216O evaporates more readily from the mid-latitudes of the oceans, and when it reaches the poles, condenses to form snow and eventually ice enriched in H216O. Urey showed that the vapor pressure of H218O is about 1% less that that of H216O between 46.35°C and 11.25°C. In addition, the H218O that evaporates at lower-latitudes is more likely to condense and be removed in rain, leaving the southern oceans enriched in H218O. This effect is actually quite significant as water, in the form of ice, in Greenland and Antarctica has about 5% less H218​​​​​O than water at 200C from midlatitudes, making the δ18O for water a proxy for temperature but even better as measures of ice volume and from that sea levels. Now consider the Ice Ages, when oceans were enriched in H218O. On glacial melting of H216O-enriched ice, with the melting flowing into the oceans, the H218O would get diluted with H216O. At the same time, the salinity of the ocean would decrease since ice condenses without oceans salts. These differences in H218O/H216O values in ice core samples are converted to δ18O values, which can be positive or negative, as shown in Figure $10$. Figure $10$: Typical δ18O values (in permil). Andreas Schmittner. https://eng.libretexts.org/Bookshelv...A_Paleoclimate Surface ocean water has δ18O values of around zero. Due to fractionation during evaporation, less heavy isotopes make it into the air, which leads to negative delta values of around -10 ‰ for the evaporated water vapor. Condensation prefers the heavy isotopes, as described above. In this example, the first precipitation thus has a δ18O value of about -2 ‰,(more positive than the first vapor). The remaining water vapor will be further depleted in 18O relative to 16O and its δ18O value become morr negative (-20 ‰). Any subsequent precipitation event further depletes 18O. This process is known as Rayleigh distillation and leads to very low δ18O values of less than -30 ‰ for snow falling onto ice sheets. Thus, ice has very negative δ18O of between -30 and -55 ‰. Deep ocean values today are about +3 to +4 ‰. During the last glacial maximum, as more water was locked up in ice sheets, the remaining ocean water became heavier in δ18O by about 2 ‰. We know this, as we explain below, because foraminifera build their calcium carbonate (CaCO3) shells using the surrounding sea water. Thus they incorporate the oxygen isotopic composition of the water into their shells which are then preserved in the sediments and can be measured in the lab. Bralower and David Bice. https://www.e-education.psu.edu/earth103/node/5. Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License(link is external). In summary, in cold conditions, Greenland and Antarctic ice is enriched in 16O since H216O preferentially evaporates and then condenses and freezes into ice a the poles. In addition, as we will see below, deep sea Foraminifera shells contain more 18O in shells since water is enriched H218O in cold conditiond, since less evaporates. Hence δ18O becomes more positive. Ocean sediment core 18O/16O analyses In the last section, we looked at δ18O values in ice core layers and their use as proxies for land and ocean temperatures as more fundamentally as measures of ice volume., which affects sea levels as well as ocean salinity. It's amazing what we can surmise about past climate based on the fact that H216O evaporates more readily than H218O and that H218O that did evaporate condenses at low and mid-latitudes more readily than H216O. These two factors lead to the enrichment of H216O in polar ice and the enrichment of H218O in low and mid-latitude ocean water. Remember that evaporation and condensation are physical reactions involving a change in state. Yet ice cores go back only so far in geological time. To go back further, scientists analyze shells buried in ocean sediments. More specifically, they analyze the isotopic ratio of 18O/16O in calcium carbonate (calcite, CaCO3) in buried shells of organisms like Foraminifera. (Calcite is a stable anhydrous form of CaCO3, but under high pressure it can form different calcite phases.) Now we are dealing with a new atom, C, in the carbonates, so the interpretation of δ18O of buried carbonate depends on many factors compared to than δ18O values of solid and liquid water. It must include these factors • the chemical reactions of inorganic carbonate formation (precipitation) and dissolution (compared to the physical reactions of water evaporation and condensation) • the biological formation of CaCO3 in shells • an understanding of the carbon cycle • the temperature at which the CaCO3 was deposited • the salinity at deposition as the formation of CaCO3 from its ions as the overall ionic strength of the medium in which CaCO3 formed would influence the thermodynamics and kinetics of how the separate ions approach each to form the solid • equilibrium and kinetic controls of the precipitation reactions. To truly understand biochemistry, we must include both biological and chemical aspects. Biochemistry requires a synthesis of knowledge from many disciplines, including introductory chemistry (where precipitation reactions were likely covered for the first time) and analytic chemistry (which delves more deeply into precipitation reactions). Hence we don't apologize for bringing back your previous knowledge of precipitation reactions. At the same time, our description of the use of δ18O values in buried ocean sediments is very simplified. We need to consider two different reactions to understand δ18O values in calcite Foraminifera shells. The first describes how 18O gets into CO32- in the first place. The second describes seemingly simple reactions for the formation of CaCO3 from its ions. Enrichment of CaCO3 with 18O The incorporation or fractionation of 18O from H218O into calcite shells can be described most easily by the reaction below. Fractionation Reaction: (1/3) CaC16O3 + H218O → (1/3) CaC18O3 + H216O In this reaction, the source of 18O comes from the most abundant and likely sources, H218O. A simplified reaction mechanism is shown in Figure $11$. Figure $11$: Incorporation of 18O into carbonate from H218O More broadly, there would be an exchange of isotopes in the entire dissolved inorganic pool (DIC) = CO2(aq) + H2CO3 + HCO3 (bicarbonate) + CO32 (carbonate) with H2O. Formation of CaCO3 Two reactions in general describe calcite formation and its growth: $\mathrm{Ca}^{2+}(\mathrm{aq})+\mathrm{CO}_3^{2-}(\mathrm{aq}) \leftrightarrow \mathrm{CaCO}_3$ and $\mathrm{Ca}^{2+}(\mathrm{aq})+\mathrm{HCO}_3^{-}(\mathrm{aq}) \leftrightarrow \mathrm{CaCO}_3+\mathrm{H}^{+}$ These show the uptake of 18O into CaCO3 should also include a consideration of HCO3-. In a simple and environmentally-controlled in the lab, reaction 2 above can be considered in equilibrium and defined by a Ksp value (as you learned in introductory chemistry courses. In Chapter 4.12, we saw the relationships between Keq, ΔG0, ΔH0, DS0 and temperature. There is an inverse relationship between Keq and temperature. \begin{gathered} \Delta \mathrm{G}^{0}=\Delta \mathrm{H}^{0}-\mathrm{T} \Delta \mathrm{S}^{0}=-\mathrm{RTln} \mathrm{K}_{\mathrm{eq}} \ \ln \mathrm{K}_{\mathrm{eq}}=-\frac{\Delta \mathrm{H}^{0}-\mathrm{T} \Delta \mathrm{S}^{0}}{\mathrm{RT}} \ \ln \mathrm{K}_{\mathrm{eq}}=-\frac{\Delta \mathrm{H}^{0}}{\mathrm{RT}}+\frac{\Delta \mathrm{S}^{0}}{\mathrm{R}} \end{gathered} Assuming that the formation of CaCO3 is in equilibrium, then you would expect that CaCO3 would be enriched in 18O, and have a +δ18O value. Why? Urey showed that under equilibrium conditions, calcite is enriched in C18O32- probably because of lower vibrational energy of the heavier form of carbonate, which favors stabiity and formation of the solid. In addition, the incorporation of 18O is even more pronounced in climatically colder water, which as we have seen, has a +δ18O value. Hence in cold periods with large ice shields, δ18O values from shells of foraminifera living both in the illuminated upper ocean (planktic foraminifera, which engage in photosynthesis) and deep sea benthic foraminifera, are more positive. On ice shield melting, as the δ18O values of water become more negative, so do the values of δ18O values of the foraminifera. Benthic foraminifera give a global temperature estimate as deep waters are more homogeneous. Planktic foraminifera δ18O values are proxies for more local temperatures as they are in a more changing, less mixed environment, and are more affected by evaporation and precipitation. Yet the formation of CaCO3 in shells in many cases is not in equilibrium and is in part determined by the concentration of the reactants, the rate of diffusion of ions into and out of the growing calcite shell, which would also depend on salinity (affecting electrostatic attractions of the ions to the growing crystal), the pH (which affects the ratio of CO32 and HCO3) and biological effects (from the mechanism by which shells are formed which at some point may involve HCO3 transporters). It also depends on the of transfer of carbonate within the dissolved inorganic carbon pool (DIC). The reaction has been shown to be in equilibrium in some species of foraminifera but not in others. The term fractionation is often used in the isotope and climate literature. Using water as an example, it describes the ratio of heavy to light isotopes of O that partition into the liquid, solid, and gas phases of water. The fractionation factor determines δ18O value of water. Likewise, there is a fractionation process that determines the partitioning of 18O from water into CO32- and CaCO3 during the precipitation of calcite. The fractionation factor α shows how the ratio of the isotopes changes in either a physical (such as a phase transition) or chemical process. It is the factor by which the abundance ratio of two isotopes will change during a chemical reaction or a physical process. The formation of calcite from HCO3 in controlled studies shows that CaCO3 has different oxygen isotope concentrations depending on the initial concentrations of reactants. The size of a shell can also affect the δ18O of additionally deposited CaCO3. These ideas support the notion that the fractionation of isotopes in CaCO3 occurs through both equilibrium fractionation and kinetic fractionation. Several different theoretical "paleotemperature" equations have been developed to show how temperature T is related to δ18O in calcite during equilibrium conditions. One theoretical quadratic equation is shown below. \begin{aligned} T &=16.9-4.38\left(\delta_{\mathrm{c}}-A\right)+0.10\left(\delta_{\mathrm{c}}-A\right)^2 \ A &=\delta_{\mathrm{w}}-0.27 \% \end{aligned} where T is the temperature in oC, δc and δw are the δ18O of calcite and water, respectively. The 0.27% is just an adjustment factor to convert from the PDB to the VPDB reference values. The equation applies to sea water of normal salinity and freshwater. A more recent calibration equation for the formation of calcite from HCO3- (done with bubbling of the reaction mixture with N2 for a variety of temperatures) was derived by Kim and O'Neil in 1997. The equation was derived from carefully controlled laboratory studies that apply under equilibrium conditions and is shown below. The Kim and O’Neil equation shows the relationship between the fractionation factor alpha (α) of 180/160 between inorganically precipitated CaCO3 and H20 as a function of the temperature, and is shown below. $1000 \ln \alpha\left(\text { Calcite- } \mathrm{H}_2 \mathrm{O}\right)=18.03\left(10^3 T^{-1}\right)-32.42$ Alpha is the fractionation factor, and T is in Kelvin. Note: An update of this equation to conform to IUPAC conventions gives 103 ln α = 18.04 x 1000 / T - 32.18) The oxygen isotope fractionation factor alpha between two substances A and B is defined as $\alpha=\left({ }^{18} \mathrm{O} /{ }^{16} \mathrm{O}\right)_{\mathrm{A}} /\left({ }^{18} \mathrm{O} /{ }^{16} \mathrm{O}\right)_{\mathrm{B}}$ The left hand side of the equation (1000xlnα) is used for convenience and its relationship to δ18O values (which are expressed per %o), similar in a way to the use of pKa = -log[KA] instead of KA. Here is an alternative form of the Kim and O'Neil equation expressed in quadratic form. $T\left({ }^{\circ} \mathrm{C}\right)=16.1-4.64 \cdot\left(\delta^{18} \mathrm{O}_{\mathrm{f}}-\delta^{18} \mathrm{O}_{\mathrm{w}}\right)+0.09 \cdot\left(\delta^{18} \mathrm{O}_{\mathrm{f}}-\delta^{18} \mathrm{O}_{\mathrm{w}}\right)^2$ A controlled equilibrium study using the cultured foraminifera B. marginata of different sizes at different temperatures was used to develop an experimental equation to compare with the theoretical equations described above. Figure $12$ shows graphs of the empirically-determined equation (nonred lines) vs the theoretical Kim and O'Neil equation (red line). Figure $12$: Comparison of experimental calibration equation with the theoretical equation for equilibrium calcite of Kim and O’Neil (1997). Barras, Christine & Duplessy, J.-C & Geslin, Emmanuelle & Michel, Elisabeth & Jorissen, Frans. (2010). Calibration of δ 18O of cultured benthic foraminiferal calcite as a function of temperature. Biogeosciences. 7. 1349-1356. 10.5194/bg-7-1349-2010. CC Attribution 3.0 License The brown, blue and green lines represent the calibration equations of cultured B. marginata from < 150, 150–200 and 200–250 μm size fractions, respectively. The quadratic equation derived from Kim and O’Neil (1997) relationship is represented by the red line. A quick inspection of the empirical equation for different sizes of B. marginata shows the same relationships between T an dδ18O values as shown in Table $5$ below. Table $5$: Best fit linear plot of temperature T vs (δ18Of - δ18Ow) for foraminifera B. marginata vs size, where the subscript f is foraminifera and w is water. We take this opportunity to reshow the graph that reconstructs changes in planetary temperatures over the last 66+ million years (Figure $13$). The data between 66 MYA and 100,000 years ago (note the change in scale in the x-axis to allow fitting of a large time range in one graph) was obtained, in large part, from δ18O values from deep ocean sediments, while the data from around 100,000 YA to the advent of modern temperature recordings were obtained mostly from δ18O from ice core samples from Antarctica and Greenland. Of course, other temperature proxies, as described above, were important as well. Figure $13$: https://commons.wikimedia.org/wiki/F...alaeotemps.png. (Excel available). Creative Commons Attribution-Share Alike 3.0 Unported These detailed, but hopefully understandable explanations of the relationship of temperature with δ18O in foraminifera shells from ocean sediment cores were presented for reasons expressed in the beginning of Chapter 31.2: • Isotopes and their effects, critical in understanding both structure and activity in biochemistry, are key in climate science. • Both equilibrium and nonequilibrium reactions and processes apply to isotope partitioning into water and biomolecules, in ways similar to linked biochemical reactions and pathways. • The study and application of isotope effects can integrate and expand learning from previous science courses Astute readers will notice that we concentrated on δ18O values (in water and carbonates) and barely mentioned δ13C values for carbonate precipitations. We will discuss that in the next chapter section as we consider the carbon cycle. Key Points - Beta version from Chat.openai 1. Isotope analysis is a technique used to measure the isotopic composition of elements in order to understand the processes and interactions that have occurred in the past, present, and future. 2. Isotopes of carbon, oxygen, and hydrogen can be used to study the effects of climate change on different Earth systems, such as the atmosphere, oceans, and biosphere. 3. Carbon isotopes can be used to study the sources and sinks of CO2 in the atmosphere, and to understand the role of different types of vegetation in the carbon cycle. 4. Oxygen isotopes can be used to study the sources and sinks of water vapor in the atmosphere, and to understand the effects of climate change on precipitation patterns. 5. Hydrogen isotopes can be used to study the sources and sinks of water vapor in the atmosphere and to understand the effects of climate change on the water cycle. 6. Isotope analysis is an important tool for understanding the complex dynamics of the Earth's climate system and for developing effective strategies to mitigate the impacts of climate change. 7. Isotope analysis can provide important information about the Earth's climate and environment and can help scientists understand the causes and impacts of climate change.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.02%3A__Use_of_Isotope_Analysis_in_Measuring_Climate_Change.txt

Search Fundamentals of Biochemistry The Carbon Cycle In the last chapter section, we used oxygen isotopes in ice and ocean sediment cores going back millions of years ago to address the history and mechanisms of climate change.  We focused on 18O/16O ratios in H2O and in calcite shells (CaCO3 ), and their corresponding δ18O values in ice and sediment cores, to determine CO2 and temperature over climatic history.  Now it's time to talk about the other key atom, C, the ratio of 13C/12C and corresponding δ13C values, not only in CaCO3 but also in CO2 and the organic molecules it transforms into through photosynthesis and the heterotrophic organisms that consume them.  13C partitions not only into inorganic carbon but also into organic molecules throughout life.  Hence we need a more detailed understanding of the carbon cycle The carbon cycle is likely discussed in introductory chapters in biology textbooks, but probably never in chemistry texts. It is fundamental to an understanding of climate, its control and change, and human processes that alter it.  Figure $1$ shows a representation of the carbon cycle.  Calculated amounts of carbon found in the lithosphere (the solid part of the earth), the atmosphere (specifically the lower part, the troposphere), and the hydrosphere are shown. (The cryosphere, the frozen ice found in Greenland and Antarctica, is not shown). The biosphere includes part of each of these "spheres" that harbor life. Since life has been shown to exist 10 km down in the crust, we'll refer to the entire region in the diagrams as the biosphere.  Figure 1 presents carbon stores in petagrams (1015 g) or gigatons of carbon (GtC), as 1 petagram equals 1 billion metric tons (or approximately 1.1 billion US tons). Figure $1$: The carbon cycle.  https://commons.wikimedia.org/wiki/F...te_diagram.svg In addition to the total amount of carbon stored in each region, (GtC), the net changes in carbon per year as it moves into and out of reserves (GtC/yr) are shown in blue arrows with attached numbers. The exchanges of carbon in the cycle occur at different time scales.  Geologically "fast" exchange, on a time scale up to 1000s of years, occurs among the oceans, atmosphere, and land, while a slow exchange (over hundreds of thousands to millions of years) occurs in deep soils, deep ocean sediments, and rocks.  We will mostly consider exchanges among the atmosphere, land, and oceans. CO2 in the terrestrial biosphere is removed by photosynthesis and returned by respiration by autotrophs like plants, and heterotrophs like microbes that consume soil carbon and plant remains. CO2 in the atmosphere is also removed by ocean autotrophs like ocean phytoplankton and through partitioning into dissolved inorganic carbons (DIC) molecules like HCO3- and CO32- into the oceans. Before we probe some relevant reactions within it, let's look at the big picture and perhaps the most relevant to our climate crisis - the factors causing our increasing CO2 atm and global warming.  To do that, we must put numbers on the cycle to quantify it. Quantitating the carbon cycle In Chapter 31.1, we used parts per million (ppm) as a unit for expressing the amount of CO2 in the atmosphere. Table $1$ below shows how to translate the percentage (parts per 100) for each component gas in the atmosphere (with which you are familiar) into ppm. Gas  % (parts per 100) in atm part per million N2 78.09 780,900 O2 20.94 209,400 Ar 0.93 9300 CO2 0.0415 415 Table $1$:  Unit conversion - % to ppm Climate scientists use ppm instead of concentration (in molecules/m3) since they wish to know the relative percent or ppm increase with time, which does not depend on temperature and pressure.  In contrast, concentration does depend on T and P, as you will remember from the ideal gas law, PV=nRT or n/V=P/RT that you studied in introductory chemistry. It is important to use dimensional analyses to interconvert units as well.  Table $2$ below shows conversion factors to switch between GtC, Gt CO2, and ppm. Convert from to conversion factor GtC (Gigatons of carbon) ppm CO2 divide by 2.124 GtC (Gigatons of carbon) PgC (Petgrams of carbon) multiply by 1 Gt CO2 (Gigatons of carbon) GtC (Gigatons of C) divide by 3.664 = 44.01/12.01) GtC (Gigatons of carbon) MtC multiply by 10000 Table $2$: Unit conversion - GtC and Gt CO2 To be more technical, atmospheric CO2 concentrations are expressed in mol fraction of CO2 in the dry air atmosphere.  The ppm for CO2 is hence μmol CO2 per mole of dry air. We have to put numbers on the components of the carbon cycle to quantitatively analyze changes in its components, otherwise, we can't know what is presently happening nor will we be able to predict with some certainty the future.  Stoichiometry and reaction kinetics are probably the least liked parts of chemistry for many, but they are critical in understanding climate change  We have to apply them on a global scale.  Two key terms are important: Stocks or reserves: How much carbon (mass in Gigatons or petagrams) is stored in given locations in the biosphere. This allows us to understand what % of all carbon stocks are in the ocean, for example.  Stocks are usually reported as gigatons of carbon (GtC), not gigatons of carbon dioxide, since many stocks (like fossil fuels) consist of mostly C and H without oxygen.  As in stoichiometry calculations in introductory chemistry courses, GtC in the atmosphere can be converted to gigatons of CO2 by using dimensional analysis. Fluxes (rates):  How much carbon is transferred from one reserve to another per year (Gigatons/yr).  Climate scientists are simply applying the Law of Mass Conservation that you learned in introductory chemistry, to the entire biosphere. Figure $2$ shows the reserves/stock (GtC) for reserves and decadal (2012-2021) average fluxes (large and small arrows, GtC/yr) for individual or aggregated stocks. Figure $2$:  Schematic representation of the overall perturbation of the global carbon cycle caused by anthropogenic activities averaged globally for the decade 2012–2021. E represents emission and S "sink". Earth Syst. Sci. Data, 14, 4811–4900, 2022.  https://doi.org/10.5194/essd-14-4811-2022.  © Author(s) 2022. This work is distributed under the Creative Commons Attribution 4.0 License. The abbreviations used are: EFOS (emissions, fossil fuels), ELUC (emissions land use changes - mostly deforestation), SLAND (terrestrial CO2 sink), SOCEAN (ocean CO2 sink), GATM (Growth Rate CO2 atm), BIM (carbon budget imbalance).  Uncertainties are also shown except for the atmospheric CO2 growth rate which is known precisely and accurately through modern measurements.  (It's also the easiest to measure). Human (anthropogenic changes) occurs on top of the carbon cycle. The upward arrows indicate release into the atmosphere and the downward arrows the absorption in the oceans and land.  The thickness of the arrows gives a relative measure of the size of emission or absorption.  The thickest arrow and highest value (9.6 GtC/yr) is for the anthropogenic emission of carbon from our use of fossil fuels. Think about that!  Humans are presently the biggest contributor to the carbon cycle.  Before the industrial revolution, human contributions were minimal. It's worse than that! Figure x above shows that on average, 9.6 GtC/yr was released from fossil fuel use between 2012-2021. The actual flux of anthropogenic carbon release in 2021 was 9.9 GtC, equivalent to 36.4 Gt CO2. If you add the up arrows and subtract from that sum the down ones, you get +4.8 GtC/yr.  This represents the net average increase in GtC in atmosphere CO2 per year for 2012-2021. That is close to the accurately, and precisely known value of +5.2  GtC/yr increase from the CO2 we pour into the atmosphere through our use of fossil fuels.  Hence the figure above is a bit out of balance (about 0.3 GtC too low - the BIM carbon budget imbalance), but given the difficulty in calculating these values, it is remarkably close to "mass balance" as you learned in introductory chemistry classes.  In general, before the industrial revolution, the sum of the fluxes leading to the addition of CO2 into the atmosphere was equal to the sum of the fluxes that removed it.  That is, the system was in a steady state.  That is no longer the case. Figure $3$ shows a breakdown of the factors contributing to annual (left) and cumulative (right) fluxes of carbon (GtC/yr), a metric for CO2 flux, over time since 1850. Figure $3$: Combined components of the global carbon budget as a function of time for fossil CO2 emissions (EFOS, including a small sink from cement carbonation; grey) and emissions from land-use change (ELUC; brown), as well as their partitioning among the atmosphere (GATM; cyan), ocean (SOCEAN; blue), and land (SLAND; green).  Panel (a) shows annual estimates of each flux ( GtC yr−1, and panel (b) shows the cumulative flux (the sum of all prior annual fluxes) since the year 1850.  Again, the graph shows GtC not Gt CO2. . © Author(s), ibid You might ask why the atmospheric growth in CO2 (shown in green) is negative.  We'll answer that question below. Lastly, let's think about the total cumulative changes in GtC released and absorbed since 1850 (pre-US civil war and before the big release of CO2 in modern times).   Those data are shown in a bar graph in panel A of Figure $4$.  The bar graph in the right panel shows the mean decadal averages that are shown in Figure 2 above. Figure $4$: Total cumulative changes in GtC released and absorbed since 1850  (panel A) and mean decadal fluxes (panel B).  EFOS (emissions, fossil fuels), ELUC (emissions land use changes - mostly deforestation), SLAND (terrestrial CO2 sink), SOCEAN (ocean CO2 sink), GATM (Growth Rate CO2 atm), BIM (carbon budget imbalance). © Author(s), ibid The positive emission and negative absorption contributions are easy to see in the bar graph.  The blue bar represents the net emission of carbon from fossil fuels and fills the gap to complete  mass balance as we discussed above.  It also explains the negative blue region in Figure 3.  Just keep in mind that the blue net flux from fossil fuels is positive. The cumulative contributions from fossil fuel emissions required to close the gap and fulfill mass balance is  is +275 GtC, which when multiplied by the conversion factor (1ppm/2.124 GtC) translates into a 129.5 ppm increase in atmospheric CO2 over that time. This is very close to independent measurements of a rise of 129.3 ppm (14.7-284.7) over that time. The data from Figure $4$ has been entered in the first four columns of Table $3$ below. Source Subtype Stock reserves (GtC) J (Fluxes) GtC/yr (avg 2012/2021) + emission - absorption J=kapp[stock] Atmosphere  - 875 Buried Fossil Fuels Coal 560 +9.6 J=+9.6=k[905] k=0.0106 Oil 230 Gas 115 Terrestrial Permafrost 1,400 +1.2 (Land use Δ) -3.1 (Land uptake) Juse=+1.2=kuse[3550];  kuse=0.000338 Jup=-3.1=kup[875];  kup=0.00354 Soil 1,700 Vegetation 450 Oceans Coasts 10-45 -2.9 J=-2.9=k[875] k=0.00331 Ocean Surface Sediments 1,750 Organic carbon 700 Marine Biota 3 Dissolve Inorganic Carbon (DIC) 37,000 We can use this data to develop our own crude computational model predicting future CO2 emissions using Vcell, the program we used to produce time course (concentration vs time) graphs for both simple and coupled signal transduction reaction pathways. Vcell can be used to calculate fluxes (J) in reaction pathways, where J is the change in concentrations of a species with time, given the initial concentration or amount of a reactant, and the rate constants affecting its production or removal.  If we use the amount of carbon (GtC) in each reservoir in the biosphere and crust as a relative measure of "concentration" and the known fluxes (GtC/yr) for the transfer of carbon to and from the atmosphere as given in Table 3, we could calculate an "apparent rate constant for each flux using this equation: Jstock = kapp[stock] (where stock is given in GtC). \mathrm{J}_{\text {stock }}=\mathrm{k}_{\mathrm{app}}[\text { stock in } \mathrm{GtC}] These "apparent" rate constants are needed to run the Vcell simulation that can reproduce the actual fluxes shown in Figures 2 and 4).  The simulation can be run over time A simple four-term model based on Figures 2 and 4 is shown below.  Run the simulation and see how atmospheric CO2 changes with time.  This model is offered only to show how climate models are made and used, and also for fun.  The graphs are valid and sound based on the input parameters, but the outputs are based on many assumptions that vastly simplify the model. Global Carbon Budget 2022 Model: Initial Values: kf = 2, kr = 4, A t=0 = A0 = 10 Note: Y-axis values on the generated plots are scaled incorrectly in the Vcell plots. This will be fixed in a future update. However, the shapes of the curves are accurate. To get the correct Y-axis values, download the .csv file and scale all concentration values by normalizing them to the actual intended initial concentrations of the reactants.  An update will occur when the newest version of Vcell is released. The simulation shows CO atm levels peaking at about 982 GtC in 51 years (2073) from its average decadal (2011-2021) value of 875.  That is an increase of 107 GtC over now (50 ppm CO2 rise from the present 414 to 463 ppm).  Over a 50-year period, this gives an average annual rise of 2.14 GtC/yr or about 1 ppm CO2/yr.  A comparison of the predicted atmospheric CO2 (ppm) levels through 2100 for the IPCC SSP1-2.6 scenario (blue) and simple Vcell model (red) is shown in Figure $5$. Figure $5$:  Predicted atmospheric CO(ppm) for SSP1-2.6 scenario (blue) and simple Vcell model (red) SSP1-2.6 data - History: Meinshausen et al. GMD 2017 (https://doi.org/10.5194/gmd-10-2057-2017); Future: Meinshausen et al., GMD, 2020 (https://doi.org/10.5194/gmd-2019-222).  https://climateanalytics.org/media/g...-3571-2020.pdfhttps://gmd.copernicus.org/articles/13/3571/2020/ However imperfect the Vcell model is (incorrect assumptions, lack of complexity and feedback mechanisms, etc), the results shown above are remarkably close to the projected increases in carbon dioxide in ppm described in IPCC reports for the SSP1-2.6 socioeconomic pathways, shown in the right panel (dark blue line) of Figure $6$.  This pathway predicts a rise of approximately 1.80 C in average global temperatures. Figure $6$:   IPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Again, remember that the model is based on a ten-year average of CO2 emissions. Think of all the other assumptions in this model (other than the stock reserves and fluxes) that would give higher or lower values of future CO2 levels.  One major one is that flux values are all held constant to allow calculations of the apparent rate constants for Vcell use.  The model depletes much of the fossil fuel reserve. In addition, CO2 emissions in 2021 were actually 9.9 GtC/yr and going up! In addition, a change in one parameter can affect the others.  For example, the net uptake of atmospheric CO2 into the land and oceans has increased from 1960-2010, which makes sense given increased CO2 in the air forcing additional uptake (think LaChatelier's Principle). The oceans have taken up nearly 40% of the CO2 from fossil fuel use since the industrial revolution.  If the rate of uptake decreases (i.e. if we start to saturate the uptake into oceans), CO2 accumulation in the atmosphere would accelerate.  Data also suggest that if we successfully decrease CO2 in the atmosphere, the oceans would respond by decreasing uptake as well, which would slow the progress in reducing temperatures. An interesting example relating atmospheric and ocean CO2 occurred from 1990-2000 when it has been shown that the ocean acted as a weaker sink. This occurred because of a decreasing gradient (the Δ or"effective concentration differences") between atmospheric CO2 and ocean "CO2", which decreased the ability of the ocean to act as a sink for CO2.  You can decrease the Δ in two ways: • decreasing the rate of entry of CO2 into the atmosphere from fossil fuel use.  There indeed was a temporary slowdown in this decade. • paradoxically, by briefly making the ocean in a shorter term a better sink.  This happened in 1991 after the eruption of Mt. Pinatubo,  which led to decreased air and ocean temperatures. CO2 is a nonpolar gas, which has higher solubility in water at lower temperatures (think about soda).  This was a short-term and more minor effect than the decreased rate of fossil fuel emissions. More complex models with more terms for emissions and absorptions of CO2 can be made.  One is shown in Figure $7$. This model adds CO2 release from the soil through respiration by microorganisms, as well as from plant respiration (CHO to CO2atm).  Another term has been added for release by oceans. Figure $7$:  More complicated Vcell climate model. Fortunately, we don't have to rely on these simple models to predict future trends in temperature and CO2.  A complex dynamic model simulator that is in accord with many different climate models is available at your fingertips. Developed at MIT and Climate Interactive, and available for free from any web browser, the EN-ROADS program allows users to change sliders for key inputs and see future predicted temperature and CO2 levels.  In accordance with RCP and SSP IPCC pathways that tie future emissions to socio-economic policies (discussed in Chapter 31.1), the program allows users to change variables such as carbon pricing and incentives to move to clean energy in transportation, building and energy supplies sectors. Access the program directly from this page by clicking the Close icon in the program window in Figure $8$ below. Figure $8$: EN-ROADS global climate simulator Here is also an external link to the En-Roads global climate simulator (Developed by Climate Interactive, the MIT Sloan Sustainability Initiative, and Ventana Systems) Move the interactive sliders and see the effect on greenhouse gas emissions and global temperatures.  Here is a link to a one-page tutorial on its use. The Skeptic's Corner:  Climate Change Misinformation We should all be skeptical of models, especially ones that predict changes 80 or more years into the future.  We gain confidence in a model if it accurately fits data going back in time and into the future data as well.  We mentioned in Chapter 31.1 that oil company scientists knew of the likely climatic effects arising from fossil fuel emissions, but the company executives did not act on their models.  Their models were startlingly accurate as shown in Figure $9$ below, which shows their predictions for both CO2 levels and the associated increases in temperature caused by them. Figure $9$: Historically observed temperature change (red) and atmospheric carbon dioxide concentration (blue) over time, compared against global warming projections reported by ExxonMobil scientists.  Supran, G., Rahmstorf, S., and Oreskes, N. Assessing ExxonMobil's global warming projections. Science (2023).   https://www.science.org/doi/abs/10.1...cience.abk0063.  Reprinted with permission from AAAS.  Not for reuse. Panel (A) shows “Proprietary” 1982 Exxon-modeled projections. Panel (B) shows a summary of projections in seven internal company memos and five peer-reviewed publications between 1977 and 2003 (gray lines). Panel (C) shows a 1977 internally reported graph of the global warming “effect of CO2 on an interglacial scale.” (A) and (B) display averaged historical temperature observations, whereas the historical temperature record in (C) is a smoothed Earth system model simulation of the last 150,000 years. As these graphs clearly show, oil companies knew since the late 1970s, over 40 years ago, of the climatic effects of CO2 emissions.  They could even predict the temperatures since the last ice age.   In the 70s, solar and wind energy were much more expensive to produce and use than now, but if we had subsidized their development back then as we have done for decades for the fossil fuel industries, our climatic situation now would be much less precarious.  Figure $10$ below shows worldwide fossil fuel subsidies in US $billion and in % global GDP from 2015 to 2020 and projections after that. Figure $10$: Worldwide subsidizes in US$billion and in % global GDP.  Bar graphs are for US$biillons and the circles and triangles for % global GDP. IMF. ttps://www.imf.org/en/Publications/W...bsidies-466004 The subsidies are broken down into explicit subsidies (tax breaks or direct payments to help fossil fuel companies to fund their uncompensated costs) and implicit ones (undercharging for environmental costs of fossil fuel use that the oil companies don't pay). These latter "hidden" costs are passed down to countries, states, and individuals. In 2020, global subsidies were$5.9 trillion or 6.8% of the world's GDP.  The explicit subsidies given to fossil fuel companies, about 8% of the total, amounted to  \$472 billion just in 2020! "Company executives chose to publicly denigrate climate models, insist there was no scientific consensus on anthropogenic climate change, and claim the science was highly uncertain when their own scientists were telling them the opposite" (ref).  They also propagated the myth that the global climate was actually cooling.  This is a powerful and unsettling example of disinformation with enormous consequences. Now that we have seen the big picture, let's look at how carbon moves through various pools of carbon-containing molecules.  We have already discussed photosynthesis in great detail in Chapter 20, so we fill focus our attention more on dissolved inorganic carbons (DIC) including species such as HCO3- and CO32-.  Another view of the carbon cycle that includes weathering of rocks to produce silicates and bicarbonates, and the formation of shells in the ocean from HCO3-, CO32- and silicates, is shown in Figure $10$. Figure $10$: Let's focus on the oceans first. The reversible movement of CO2 from the atmosphere to the oceans, CO2 atm ↔ CO2 ocean, depends on the difference in the partial pressures of CO2 (ΔpCO2) in the atmosphere and surface waters.  The reaction is also driven to the right by the removal of CO2 (aq) as it forms carbonic acid (H2CO3), which then forms bicarbonate (HCO3) and carbonate (CO32). These coupled reactions chemically buffer ocean water, thus regulating ocean pCO2 and pH. pCO2 is not homogenous in ocean surface waters and varies with different conditions of current and temperature.  CO2 can be more readily released from upwellings of nutrient-rich and warm waters, especially in the tropics.  In cold Northern waters and also in the Southern Ocean, where water sinks, it is taken up from the atmosphere (again CO2 is more soluble in cold water). As we discussed in Chapter 31.1, the ocean chemistry of CO2 determines in large part the levels of atmospheric CO2.  The coupled reactions of CO2 in the oceans are shown below. \mathrm{CO}_2(\mathrm{~g}, \mathrm{~atm}) \leftrightarrow \mathrm{CO}_2(\mathrm{aq}, \text { ocean) } \mathrm{CO}_2(\mathrm{aq} \text {, ocean })+\mathrm{H}_2 \mathrm{O}(\mathrm{I} \text {, ocean }) \leftrightarrow \mathrm{H}_3 \mathrm{O}^{+}(\mathrm{aq})+\mathrm{HCO}_3^{-}(\mathrm{aq}) \mathrm{H}_2 \mathrm{O}(\mathrm{I})+\mathrm{HCO}_3^{-}(\mathrm{aq}) \leftrightarrow \mathrm{H}_3 \mathrm{O}^{+}(\mathrm{aq})+\mathrm{CO}_3{ }^{2-}(\mathrm{aq} \text {, sparingly soluble }) These reactions should be familiar to all chemistry students and were presented previously in Chapter 31.1 and in Chapter 2.  A significant contributor to ocean bicarbonate is weathering of rocks like limestone. and marble, which are both forms of CaCO3. The relevant reactions are shown below. \begin{aligned} &\mathrm{CaCO}_3(\mathrm{~s})+\mathrm{H}_2 \mathrm{O} \leftrightarrow \mathrm{Ca}^{2+}(\mathrm{aq})+\mathrm{CO}_3{ }^{2-}(\mathrm{aq}) \ &\mathrm{CO}_3{ }^{2-}(\mathrm{aq})+\mathrm{H}_2 \mathrm{O} \leftrightarrow \mathrm{HCO}_3{ }^{-}(\mathrm{aq})+\mathrm{OH}^{-}(\mathrm{aq}) \end{aligned} CO2 is nonpolar and not very soluble in water.  Either is CO32- in the presence of divalent cations like Ca2+. However HCO3- is and can be considered a "soluble" form of carbon.  This soluble form from terrestrial weatherings ends up in rivers and eventually enters the ocean.  It is also the form of carbonate that is transferred into cells by anion transporters for eventual shell formation. HCO3- is also a chief regulator of both blood and ocean pH.  Weathering is slow compared to anthropogenic emissions of CO2 from fossil fuel use, but it is nevertheless a key player in the carbon cycle and the regulation of ocean pH. The same weathering reactions on silicate rocks lead to the transfer of silicate ions into rivers and then into the ocean, where they can be taken up by diatoms in the formation of CaSiO4 shells. As the oceans take up more CO2, they become more acidic, which leads to the equivalent of "weathering" of shells of living organisms, leading to their potential death. Silicon is directly underneath carbon in the periodic table so the following simplified reaction is analogous to those we seen with CO2 and its inorganic ions. \mathrm{H}_4 \mathrm{SiO}_4=\mathrm{SiO}_2+2 \mathrm{H}_2 \mathrm{O} H4SiO4 is silicic acid. 13C/12C  ratios in ice core and ocean sediments We are now in the position to explore how isotopes of carbon can be used for more than radio- 14C dating, which is quite limited in climate studies. 13C, a stable isotope of carbon, however, is extremely useful because C-13C bond dynamics are influenced by it.  Reaction rates are affected by the presence of 13C when C-C bonds are made or cleaved.  This isotope effect leads to different 13C/12C ratios in reactants and products, and hence different δ13C values. Isotopes have a long history in the study of biochemical reactions.  The kcat and kcat/KM  values for enzyme-catalyzed reactions can be affected if the rate-limiting step involves cleavage or the creation of a C-13C, C-D (deuterium) or C-T (tritium) bond.  Substrates labeled with the isotopes have similar transition state energies for the formation/cleavage of a bond involving an isotope, but the ground state vibrational energy for the isotope-substituted atom are proportionately lower, as illustrated in Figure $11$. Figure $11$: Kinetic Isotope Effects. This primary kinetic isotope effect leads to higher activation energy for the formation/cleavage of a bond with the isotope.  For C-D and C-T bond cleavages that are rate-limiting, the rates are 7X and 16X slower than the cleavage of a C-H bond, respectively.  Cleavage or formation of bonds to heavy isotopes of carbon, oxygen, nitrogen, sulfur, and bromine have much smaller isotope effects (between 1.02 and 1.10). The difference in the magnitude of the kinetic isotope effect is directly related to the percentage change in mass. Large effects are seen when hydrogen is replaced with deuterium because the percentage mass change is very large (mass is being doubled). . Hence the kinetic isotope effect is at play in carbon fixation in photosynthesis, for example. This is evidenced by the observation that the 13C/12C ratios are lower in plants than in the atmosphere, showing that 12CO2 is preferentially "fixed" in the ribulose bisphosphate carboxylase/oxygenase reaction in plants and other photosynthetic organisms. Also, 12CO2, a lighter molecule, has a faster rate of diffusion through the stromata, regulated pores in leaves that facilitate the passage of CO2, O2 and H2O. In Chapter 31.2, we discussed the use of δ18O values in ice core and ocean core sediments for measuring past CO2 and temperatures. δ18O values for ice core water samples werer easier to interpret than δ18O values for CaCO3 sampls, since the deposition of ice is a simple physical process compared to the complexity of the deposition of CaCO3 in ocean sediments, which depends on chemical reactions and nonequilbrium processes (as described in Chapter 31). Climate scientists can determine and use δ13C values.  An analogous equation for it is shown below. \delta^{13} C=\left[\frac{\left(\frac{13}{12} CO\right)_{\text {sample }}}{\left(\frac{13}{12} \mathrm{C}\right)_{\text {reference }}}-1\right] * 1000 As for using δ18O in carbonate samples, using δ13C is more difficult as well.  The shells of ocean sediment foraminifera were made from dissolved inorganic carbon (DIC) in the ocean at the time so their δ13C values reflect that. However, shell formation is not a simple equilibrium process since biological shells are formed rapidly so kinetic effects in carbonate and hence isotope fractionation are important.  In addition, the biochemistry of shell formation is complicated. In the open ocean, planktic foraminifera are perhaps the most important marine organism that forms shells given that they produce and export into the ocean about 2.9 Gt CaCO3/yr.  Their shells form in a process involving many metastable calcite phases. It starts with a soft template that contains Mg2+ and Na+ ions which play a key role in crystallization.  Growth occurs by successive additions of "chambers" to the shell.  An F-actin mesh, which forms microtubular structures, leads to the formation of protective envelopes for chamber formation. The layered templates sequester and help control the mineralization of shells and separate bulk sea water for a more intracellular vs extracellular process for biomineralization.  Seawater containing minerals becomes vacuolized in a process which for some foraminfera excludes a competing cation, Mg2+.  In addition, both Ca2+ and HCO3- transporters are required.  This all combines to form an environment low in Mg2+ and supersaturated in Ca2+ and CO32for calcite formation.  The kinetic fractionation of 13C isotopes into shells is also different than for 18O isotopes since the "pool" of oxygen in the oceans is much greater than carbon.  Likewise, the δ13C is more location-dependent that the δ18O. Buried organic matter can also be studied. The δ13C value for buried organic matter depends on primary productivity on land and in the oceans.  As mentioned above, autotrophs preferentially take up 12CO2. Heterotrophs that eat them also become enriched in 12C.  Hence organisms have negative δ13C values, typically around −25‰, with the number depending on pathways of incorporation and metabolism.  Methane in hydrates in the ocean can be either biogenic, made by methanogens, for example, at low temperatures, or thermogenic, made during high-temperature reactions. Biogenic methane has a δ13C of around - 60‰, while thermogenic methane has a value of around −40‰.  Terrestrial plants have different δ13 values.  δ13C in C4 plants range from -16 to −10‰ while for C3 plants they range from −33 to −24‰. Changes in δ13C in ice cores and ocean sediments are used in climate studies.  Sometimes it's confusing to understand the cause and effect of the changes.  This following explanation for changes in the already negative values of  δ13C might offer help to those with a chemistry-centric view of biochemistry who struggle with mass balance outside of simple chemical equations. Under climatic conditions, when there is an abundance of terrestrial plants that lock in and sequester atmospheric 12CO2, the atmosphere becomes depleted in 12CO2  and correspondingly enriched in 13CO2. Hence primary production (fixing of carbon and anabolic metabolism) by photoplankton in the oceans, under robust growing conditions, would sequester more 13C, causing an increase in δ13C (i.e. more positive) values for buried organic and calcite sediments. During times of mass extinction, when terrestrial plant primary production drops precipitously, the  δ13C becomes more negative with the decrease in primary production and release of plant carbon, leaving more 12CO2  in the atmosphere.  This drop is called a negative δ13C excursion.  When life is robustly favored and carbon is fixed by autotrophs, and the organic carbon resulting from them is eventually buried in sedimentary rocks, the rise in δ13C is called a positive δ13C excursion. Examples of climatic events accompanied by changes in δ13C. Late Devonian period Fossil evidence from the late Devonian, when large terrestrial plants evolved and expanded, is characterized by increases in δ13C. Paleocene/Eocene Thermal Maximum We saw in Chapter 31.1 that around 55 MYA, sediment records indicate a spike in temperatures of about 50 F occurring over about a 100K year timeframe.  This was accompanied by a dramatic spike in CO2 and a dramatic drop in ocean pH as measured by the loss of deep sea CaCO3 (chalk). This very short time frame is called the Paleocene/Eocene thermal maximum (PETM), which shows very quick spikes (on the geological time scale) can and do occur.  Sediment records for this time indicate a large negative δ13C excursion, consistent with a loss of plants with their preferential uptake of 12CO2, leading to an accompanying increase in 12CO2 in the atmosphere. 1500-1650 CE We examined δ18O values during the Little Ice Ages in Chapter 31.2.  What about δ13C values?  CO2 and δ13C values from 1000 to 1900 are shown in Figure $12$. Figure $12$:  CO2 and δ13C values from 1000 to 1900. Koch et al. Quaternary Science Reviews, 207, 2019, 13-36.  https://doi.org/10.1016/j.quascirev.2018.12.004.  CC BY license (http://creativecommons.org/licenses/by/4.0/). Panel (A) shows the CO2 concentrations recorded in two Antarctic ice cores: Law Dome (grey, MacFarling Meure et al., 2006) and West Antarctic Ice Sheet (WAIS) Divide (blue, Ahn et al., 2012). Panel (B) shows the carbon isotopic ratios recorded in CO2 from the WAIS Divide ice core (black, Bauska et al., 2015) showing an increased terrestrial carbon uptake over the 16th century (B). The yellow box is the span of the major indigenous depopulation event (1520e1700 CE). Loess smoothed lines for visual aid. Koch et al have strong evidence to suggest that the cooling after 1510 (area in the yellow box in the above figure) was associated with a dip in CO2 caused by the reforestation of indigenous peoples' land in Meso and South American after epidemics of European disease killed upwards of 90% (around 55 million) of the indigenous peoples. The open and agricultural land reverted back to forests. The diseases included smallpox, measles, influenza, the bubonic plague, and eventually malaria, diphtheria, typhus and cholera. Domesticated farm animals brought from Europe to the Americans led to most of the disease.  Along with the death of so many people was a concomitant return of cleared and agricultural lands (about 56 million hectares or 212,000 mi2) to forest and plant growth.  This may have led to a 7-10 ppm drop in CO2 in the late 1500s and early 1600s, peaking in 1601 (middle of the yellow box). This decrease in temperature was associated with a small rise (small positive excursion) in the δ13C values, as 12CO2 was preferentially removed from the atmosphere. Global surface air temperatures decreased by around 0.15oC.  This "Great Dying" of Indigenous peoples shows the power of humankind to globally alter climate in calamitous ways, even before the use of fossil fuels.  The decrease in δ13C values before 1500 was unexplained. 1800-the present δ13C values can also be used to unequivocally prove that the increase in CO2 since the industrial revolution is from the burning of fossil fuels, which is of biogenic origin and hence have more negative δ13C values. Figure $13$ shows atmospheric CO2 levels in ppm plotted along with δ13C values.  There is a perfect correlation between the rise in atmospheric CO2 starting with the industrial revolution with the decrease in the δ13C values over the same time. Figure $13$: COconcentration (black circles) and the δ13C (brown circles) from 1000 to 2010.   Rubino et al.  Journal of Geophysical Research: Atmospheres.  https://doi.org/10.1002/jgrd.50668.  With permission (Copyright Clearance Center) Summary In the first three sections of Chapter 31 (31.1, 31.2, and this one), we have introduced the basics of climate change, and how climate scientists obtain, analyze and interpret climate data.  We emphasized the scientific rigor by which they do that and offered a detailed analysis of the use of isotopes to document past and present changes in climate, Finally, we offered models to predict and mitigate future climate changes. After reading this material, you should be enabled to discuss climate change with others from a sound and valid position.  More importantly for this book, you will have a better knowledge base and understanding for the rest of the chapter sections, which will address specific topics in "biochemistry and climate change". Key Points - Beta version from Chat.openai 1. The carbon cycle is the process by which carbon moves through the Earth's systems, including the atmosphere, oceans, and biosphere. 2. The carbon cycle is driven by the exchange of carbon between different reservoirs, such as the atmosphere, oceans, and living organisms. 3. The main processes involved in the carbon cycle include photosynthesis, respiration, and the formation and weathering of rocks. 4. Human activities, such as burning fossil fuels and deforestation, have significantly increased the amount of carbon dioxide (CO2) in the atmosphere, disrupting the natural balance of the carbon cycle. 5. The increase in atmospheric CO2 is the primary driver of climate change, as it causes the greenhouse effect, trapping heat in the atmosphere and warming the Earth's surface. 6. The ocean also plays a critical role in the carbon cycle, as it acts as a sink for CO2, absorbing about 25% of the CO2 emitted by human activities. 7. The acidification of the ocean caused by the uptake of CO2 is having a significant impact on marine ecosystems, altering the chemistry of seawater and making it more difficult for some organisms to build and maintain their shells and skeletons. 8. Understanding the carbon cycle and carbon chemistry is crucial for understanding the causes and impacts of climate change and for developing strategies to mitigate and adapt to its effects.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.03%3A__Climate_Change_-_The_Carbon_Cycle_and_Carbon_Chemistry.txt

Search Fundamentals of Biochemistry Introduction The world has a great need for energy. We have invested vast sums of money in finding and using fossil fuels. Fossil fuels seem to be an ideal energy source since they are highly reduced, easily stored, energy-dense, and highly abundant. Yet we now know the immense cost of their use: pollution that shortens lives and climate change. We have dramatically increased our bioethanol production from corn and sugar cane to remove our reliance on fossil fuels for transportation. Ethanol is partially oxidized as it has one oxygen atom in the two-carbon molecule. Hence, the energy released per gram is about 63% (by mass) and 70% (by volume) compared to gasoline. The energy values for various fuels are shown in Table  $1$ below, where ΔHc° is the standard enthalpy of combustion. Table  $1$:  Energy values for a variety of fuelsData source:  https://www.engineeringtoolbox.com/s...nt-d_1987.html Name Formula State -ΔHc° kJ/mol -ΔHc° kJ/g or MJ/kg -ΔHc° kcal/kg Ammonia NH3 gas 383 22.48 5369 Butane C4H10 gas 2878 49.50 11823 Carbon (graphite) C cry 394 32.81 7836 Carbon monoxide CO gas 283 10.10 2413 Ethanol C2H6O liq 1367 29.67 7086 Hydrogen H2 gas 286 141.58 33817 Methane CH4 gas 891 55.51 13259 Methanol CH4O liq 726 22.65 5410 Naphthalene C10H8 cry 5157 40.23 9609 Octane C8H18 liq 5470 47.87 11434 Propane C3H8 gas 2220 50.33 12021 wood (red oak)       14.8 3540 coal (lignite)       15 3590 coal (anthracite)       27 4060 methyl stearate (biodiesel) (CH3(CH2)16(CO)CH3      40 9560 Nevertheless, ethanol is readily made and is a valuable biofuel. A glance at the table suggests that H2 would be the best possible fuel, given that it has the highest energy release per gram and contains no carbon. At present, it can't be produced at the scale needed, and it isn't easy to store and transport. The critical infrastructure for its widespread use is lacking. Yet these factors could be solved. We'll explore biohydrogen in a separate chapter section. In theory production of ethanol from plants at first glance is carbon neutral since each carbon in the ethanol is fixed from atmospheric CO2 during photosynthesis. Combustion of ethanol then returns the CO2 to the atmosphere in a net zero emission fashion, as shown in the reaction below. 6CO2 (g)  +  6H2O (l)    C6H12O6 (s)  + 6O2 (photosynthesis) C6H12O6 (s) → 2 CH3CH2OH (l)  +  2CO2 (g) (anaerobic ethanol biosynthesis) 2CH3CH2OH (l)  +  6O2   →  4CO2 (g)  +  6H2O (g) (combustion of ethanol) Six CO2s in, six out! It seems simple but it's not. We'll explain more later. First, let's explore how ethanol is synthesized for its two major uses, drinking and use as a biofuel. Ethanol Production Overview The scale of worldwide ethanol production is quite staggering. Let's first consider the production of ethanol by yeast for alcoholic beverages. About 100 billion US gallons/yr (BGY) of beer, 7 BGY of wine, and 6 BGY of spirits are produced yearly. Assuming beer, wine and spirits are about 5%, 12%, and 40% percent ethanol by volume, respectively, the volume of actual ethanol/year made by yeast in these alcoholic beverages is about 5 BGY (beer), 0.85 BGY (wine) and 2.4 BGY (spirits). This sums to about 8.3 billion gallons of ethanol produced by these microorganisms. Compare this to fuel ethanol production each year, shown in Figure $1$. Figure $1$: US Fuel Ethanol Production.  Data from U.S. Bioenergy Statistics Note that the y-axis is in units of 1000s gallons of ethanol. Peak US production was in 2018, when 16 billion gallons were produced, about 1/10 of the gasoline used yearly in the US. The year Renewable Fuel Standards (RFS) were introduced in the USA (2005) is also shown. This dip in 2020 is attributed to the COVID pandemic. Combined, the US and Brazil produce about 85% of fuel ethanol, as shown below in Figure $2$. Figure $2$:  Fuel ethanol production (billions of gallons or BG) around the world per year. https://afdc.energy.gov/data/10331 Almost all US ethanol is made from corn, while Brazil's primary source is sugar cane Since the significant ramp-up of fuel ethanol around 2005, the world now produces 3x the amount of ethanol to drive our outsized vehicles than microorganisms have for our drinking. These statistics show that the world can quickly respond when it meets our needs. An overview of ethanol biosynthesis Whether ethanol is made for the beverage or biofuel industries, yeast play the major role, as we explored in Chapter 14.2: Fates of Pyruvate under Anaerobic Conditions- Fermentation.  Yeast contains all the enzymes necessary to convert glucose (6C), made from various "feedstocks", to pyruvate (3C) through the glycolytic pathway.  This is followed by the conversion of pyruvate to ethanol using two key yeast enzymes.  First, pyruvate is decarboxylated to acetaldehyde by pyruvate decarboxylase, which uses TPP as a cofactor. Acetaldehyde is then reduced to ethanol by ethanol dehydrogenase, using NADH as a substrate, in a process that reforms NAD+, allowing glycolysis to continue.  These combined anaerobic reactions, known as fermentation, are shown in Figure $3$. Figure $3$: Summary of Ethanol Fermentation in Yeast Yeast are facultative (not obligate) anaerobes in that they can produce energy by glycolysis and ethanol fermentation in the absence of oxygen. Of course, in the presence of oxygen, the pyruvate made from glycolysis in yeast is preferentially converted to acetyl-CoA, which enters the citric acid cycle and oxidative phosphorylation pathways to maximize ATP production. Yeast is abundant, so all that is needed is a large source of glucose. An abundant source of glucose for bioethanol production are plants that contain starch (for example corn) or abundant simple sugars (for example sucrose in sugar cane). Starch, an α (1,4) polymer of glucose with α (1,6) branches, can easily be broken down in an industrial setting with amylases to form glucose.  A significant problem with this "first" generation source of glucose is that food crops (corn, and to a lesser degree sugar cane) are used for biofuel consumption instead of for food. "Second" generation sources of glucose are crop and wood waste products that contain cellulose, a β (1,4) polymer of glucose which is found with another carbohydrate polymer lignin.  A significant problem with the use of cellulose is the high chemical stability of the β (1,4) glycosidic bond. Fungi and bacteria are sources of β-glycosidases to liberate free glucose from cellulose.   "Third" generation sources of glucose use algae, which does not displace cropland for bioethanol production.  A "fourth" generation source of glucose are genetically engineered organisms, which might become future sources of bioethanol.  Figure $4$ summarizes the different generations of feedstock sources for bioethanol production. Figure $4$:  Generation feedstock sources for bioethanol production.   Tse, T.J.; Wiens, D.J.; Reaney, M.J.T.  Fermentation 20217, 268. https://doi.org/10.3390/fermentation7040268.  Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). We will discuss the use of first generation sources, corn and sugar cane, which are used to produce most of the world's bioethanol, in this chapter section, and the other two in subsequent sections. Corn Bioethanol Corn is a significant source of starch, an α (1,4) polymer of glucose with α (1,6) branches. Hence glucosidases are used to hydrolyze starch to glucose. First, the dry corn is ground in a mill, breaking the outer coat of the corn kernel and increasing access to the starch. Heated water is added to form a mash or slurry. Cooking at greater than 85o C helps hydrolyze some glycosidic bonds and lowers the viscosity of the slurry. In the process of liquification, the pH is adjusted to around 6.0. Different α-amylases (endoglycosidases) are added, which cleave the α (1,4) glycosidic bonds to produce shorter dextrins (containing branched glucose units not cleaved by alpha-amylases), and α (1,4) linked glucose oligosaccharides of lengths from 2 glucose units (called maltose) up to 7-8. β-amylase, an exoamylase, is also used, which successively cleaves maltose units, Glc α (1,4)Glc, from the nonreducing ends of the chains Alpha-amylases A mixed-rendered structure of the human pancreatic alpha-amylase is shown below in Figure $5$. Figure $5$: Surface representation of the active site of HPA (5TD4)  https://pdb101.rcsb.org/global-healt...ha-glucosidase.   CC-BY-4.0 license. Attribution: David S. Goodsell and the RCSB PDB. The surface view highlights the deep C-shaped groove into which the substrate, in this case, octaose, is bound. Consistent with substrate numbering for proteases, the starch substrate is numbered ..-2, -1, +1, +2, with cleavage occurring between the -1 and the +1 bound alpha-glucose residues. The protein has three domains (orange, blue, and pink). This particular structure had an active site mutant (Asp300Asn, D300N). The enzyme has bound calcium and chloride ions. Ca2+ maintains the necessary structure, while Cl-, bound in the C domain, is an allosteric activator. The octaose binding site is between the A and B domains.   Asp197, Glu233, and Asp300 are critical catalytic residues, with Asp 197 acting as a nucleophile to produce a glycosylated intermediate, which is hydrolyzed in the next step. Asp197 and Glu233 act as general acids/bases. We will explore in depth similar mechanisms for the action of beta-amylase (below) and cellulase (next chapter section). Figure $6$ shows an interactive iCn3D model of starch binding sites on the Human pancreatic alpha-amylase D300N variant complexed with an octaose substrate (5TD4) Figure $6$: Starch binding sites on the Human pancreatic alpha-amylase D300N variant complexed with an octaose substrate (5TD4). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...W29jf4yAc1JEq9 The domains in the enzyme are colored-coded, as in Figure 4. Key active site residues for binding and catalysis are shown as sticks and labeled. Beta amylase β-amylase (also called β-1,4-maltosidase) is a key enzyme in the saccharification process, in which starch and cellulose are broken down into monosaccharides. β-amylase is abundant in crops (wheat, barley, soybeans, etc.) and other higher plants, as well as bacilli and fungi. It is used in making beer and caramel (malt syrup). As an exo-glycosidase, it cleaves Glc α(1,4) Glc (maltose) units from the nonreducing end of starch. It is called β-amylase since the hydrolysis proceeds with the inversion of configuration at the reducing end of the freed maltose. It can't cleave at α-1,6 branches, so if used alone, this enzyme produces free maltose and large β-limit dextrins. When fruits ripen, the enzyme cleaves starch to produce sweet maltose. It is also used in seed germination. Malting Plants have to sprout, which requires energy and free sugars. Maltose is produced on activation of β-amylase during seed germination and sprouting. Although maltose is less sweet than sucrose and fructose, it is used in hard candies, given its tolerance to the heat needed in candy production. Malting of grains is accomplished by adding water to sprout them, leading to maltose and other sugars forming. This is followed by drying, with the malted grains used as sweeteners in the food industry.    Malted grains are used to produce beer, whisky, some baked goods, and drinks. Barley is the most commonly malted grain used in cereals. Huge amounts of amylases are needed for corn ethanol production, and they must withstand the conditions necessary for the industrial production of ethanol. Much effort has been devoted to finding and characterizing microbial β-amylases. We'll describe one, AmyBa, from B. aryabhattai.   Figure $7$ shows sequence similarities among various bacterial β-amylases. Figure $7$:  Sequence and structure analysis of AmyBa. .  Duan, X., Zhu, Q., Zhang, X. et al. Expression, biochemical and structural characterization of high-specific-activity β-amylase from Bacillus aryabhattai GEL-09 for application in starch hydrolysis. Microb Cell Fact 20, 182 (2021). https://doi.org/10.1186/s12934-021-01649-5.  Creative Commons Attribution 4.0 International License. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. Panel A shows multiple sequence alignments of β-amylases. The strictly conserved residues are displayed on a red background, and the highly conserved residues are shown on a yellow background. The secondary structure elements are shown for B. cereus β-amylase (PDB ID: 5BCA). The signal-peptide-cleavage site and two catalytic residues (E) are indicated by black triangles (black inverted triangles). Conservation of the flexible loop motif (HXCGGNVGD) is noted. β-amylase accession numbers are as follows: B. aryabhattai (WP_033580731.1), B. cereus (P36924.2), B. flexus (RIV10038.1), B. firmus (P96513.1), B. circulans (P06547.1), T. thermosulfurigenes (P19584.1). A comparison of the structures of B. aryabhattai β-amylase with soybean β-amylases is shown in Figure $8$. Figure $8$ B Three-dimensional molecular model of B. aryabhattai β-amylase (AmyBa). C Superimposition of AmyBa (Blue) and soybean β-amylases (PDB ID: 1Q6C) (gray) and D (PDB ID: 1Q6C) (gray). The C-terminal SBD in microbial β-amylases (box, purple) and the C-terminal loop in plants (box, red).  Duan, X, et al., ibid. The AmyBa has an additional starch binding domain at the carboxy terminus (Panel B) compared to soybean β-amylases (panel D). Since no structures of (AmyBa are publically available, we present Figure $9$, which shows an interactive iCn3D model of beta-amylase from Bacillus cereus var. mycoides in complex with maltose (1J0Z) Figure $9$: Beta-amylase from Bacillus cereus var. mycoides in complex with maltose (1J0Z). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...S2aWbL3z3jPnW8 Similar to alpha-amylase,  β-amylase has an N-terminal catalytic domain with a beta-barrel, a connecting second domain, and a third C-terminal domain which is primarily antiparallel β-sheets.   Two key catalytic side chains, Glu 172 and Glu 367, are found in the beta-barrel. In Chapter 20.6, we discussed starch synthesis (not its hydrolysis) in detail. We showed that the reaction, which uses a NDP sugar as a glycan donor, could proceed either with retention or inversion of the anomeric carbon of the donor NDP-sugar. This is illustrated for the reaction of a C1 α-NDP donor monosaccharide with a monosaccharide acceptor to produce the α(1,4) link with retention of configuration or the β(1,4) link with inversion as shown in Figure $10$ below. Figure $10$: Reaction of a donor NDP-monosaccharide and an acceptor monosaccharide with retention or inversion of configuration at the anomeric carbon of the donor The same stereochemical outcomes are possible for the hydrolysis of acetal bonds by glycosyl hydrolases. Alpha-amylases cleave the α (1,4) glycosidic bonds to produce shorter dextrins (containing branched glucose units not cleaved by alpha-amylases), and α (1,4) linked glucose oligosaccharides of lengths from 2 glucose units (maltose) up to 7-8. This reaction hence proceeds with the retention of configuration. In contrast, beta-amylases cleave starch to produce maltose with an inversion of configuration at the anomeric-reducing end of the maltose. We explore the chemistry of retention and inversion more in the next section on cellulase, which cleaves the β (1,4) acetal link in cellulose, but in general, reactions that proceed with inversion react in an SN2 response, similar to the nucleophilic attack on alkyl halides. For the glycosyl transferases that proceed with inversion, the attacking nucleophile on the acceptor is made more nucleophilic by general base catalysis by a deprotonated glutamic or aspartic acid. Figure $11$ shows the results of in silicon docking studies of a small glycan, maltotetraose, to AmyBa. Figure $11$: Molecular docking of AmyBa with maltotetraose. The overall structure and substrate binding pocket analysis of AmyBa are shown. AmyBa displayed very high amylase activity compared to other microbial β-amylases, and its enzymatic activity was much closer to sweet potato β-amylase. Molecular dynamic and docking programs can be used to calculate binding energies for substrates. The binding energy and enzymatic activities for bacteria and sweet potato β-amylase were highly correlated, suggesting that the extensive interactions of AmyBa and maltotetraose help drive catalysis by using the energy released on binding to lower activation energies for the reaction. Saccharification To enter glycolysis and fermentation, maltose must be converted to the monosaccharide glucose.   The conversion of a polysaccharide to its monomers is called saccharification. To complete the conversion of starch to glucose, another enzyme, glucoamylase (also called amyloglucosidase and γ-amylase), is added. It is an exoglucosidase that cleaves both α (1,4) in amylose, amylopectin and maltose and α (1,6) branches, to form free glucose.    It is a member of the glycoside hydrolase family 15 in fungi, glycoside hydrolase family 31 of human maltase-glucoamylase, and glycoside hydrolase family 97 of bacterial forms. Fermentation Glucose (C6H12O6) can now enter the glycolytic pathway and continue to ethanol after conversion of pyruvate to acetaldehyde by pyruvate decarboxylase and final conversion of acetaldehyde to ethanol by alcohol dehydrogenase: C6H12O6 (s) → 2 CH3CH2OH (l)  +  2CO2 (g) (anaerobic ethanol biosynthesis) The yeast Saccharomyces cerevisiae catalyzes this entire pathway. The final fermentation process yields a 12-15% ethanol solution, which is distilled to form a 95% ethanol/5% water azeotrope. The water is removed by adding zeolites (molecular sieves) which can adsorb water but not ethanol. Life Cycle Analysis of Bioethanol:  Is it better than fossil fuels? We reiterate the promise of bioethanol to address global warming and climate change by presenting again the chemical equations that suggest that its use as a fuel is carbon neutral: 6CO2 (g)  +  6H2O (l)    C6H12O6 (s)  + 6O2 (photosynthesis) C6H12O6 (s) → 2 CH3CH2OH (l)  +  2CO2 (g) (anaerobic ethanol biosynthesis) 2CH3CH2OH (l)  +  6O2   →  4CO2 (g)  +  6H2O (g) (combustion of ethanol) If only these three equations, this simple model for production and use of corn bioethanol, were the only factors influencing net CO2 emission on bioethanol burning, there would be no controversy about its use. Yet the actual CO2 emissions depend on many more hidden from view by these simple equations. What is needed is a life cycle analysis (LCA) that determines the environmental impact (in this case, net CO2 emissions) of corn ethanol through every phase of its existence, from cradle to grave, starting with the planting of corn to the combustion of bioethanol for transportation. All models must be tested. It's easiest to start with the simplest model. If the data fit the model, great, you're done. If not, new, more expansive models must be developed and tested. Those vociferously supporting bioethanol use often use the simple stoichiometry evident in the three equations to state that bioethanol is carbon neutral. Most, however, would want a detailed life cycle analysis (LCA) before jumping to an immediate conclusion. LCAs are very challenging, and data on a global scale is required. Some measurements at the worldwide scale have significant uncertainties (that don't include CO2 in the atmosphere, however) and are estimates, at best. A recent study looked at the impact of a specific event, the adoption of the US Renewable Fuel Standards (RFS) that regulate biofuels in the US (which produces about half of all the world's biofuels), on CO2 emission from the significant increase in corn plant and corn ethanol the followed the adoption of the standard. .  Using LCA based on a series of economic and environmental metrics, the model shows that bioethanol is not a panacea for CO2 emissions and may be more detrimental than fossil fuels use for vehicles. The study calculated the carbon intensity changes for corn ethanol that followed after the adoption of the standards. Scientists have used other events that led to immediate changes (9/11) and 1-2 year changes (Covid pandemic) on environmental parameters like CO2 emissions. Carbon intensity measures how much energy-related CO2 is emitted per dollar generated (GDP). Ideally, policies should be implemented that decrease carbon intensity. Green energy derived from both solar and wind is an example. A  similar metric is energy intensity, the total energy production per GDP, and both are consumption-based values. Figure $12$ shows carbon intensity per GDP per country over the last 30 years (data from Our World in Data). Figure $12$:  Consumption-based carbon intensity from 1990 to 2018. Our World in Data. Generally, the world is moving to more efficient energy production, but remember that our energy consumption is still dramatically increasing. The LCA model showed that the RFS led to these interrelated outcomes. It: • increased corn prices by 30% and the prices of other crops by 20% • increased US corn cultivation by 2.8 Mha (8.7%) and total cropland by 2.1 Mha (2.4%) in the years following policy enactment (2008 to 2016). (1 hectare is an area of a square with100 meters sides, equivalent to 10,000 m2 • increased annual nationwide fertilizer use by 3 to 8% • increased water quality degradation by 3 to 5% • increased emissions from domestic land use changes These all combined to lead to a carbon intensity of corn ethanol that was "no less than gasoline and likely at least 24% higher", according to the study. The changes in the metric are visually described in Figure $13$. Figure $13$:  Changes due to the RFS. (A) Corn planted area. (B) Cropland area. (C) Carbon emissions. (D) Nitrogen applications. (E) Nitrous oxide emissions. (F) Nitrate leaching. (G) Phosphorus applications. (H) Soil erosion. (I) Phosphorus runoff. Positive numbers indicate an increase due to the RFS. Field-level results were aggregated at the county level for enumeration and visualization.   Tyler J. Lark  et al. PNAS. 119, 2022 (https://doi.org/10.1073/pnas.2101084119) Creative Commons Attribution License 4.0 (CC BY). Land use changes include farming land that was retired or designated for conservation programs. Tilling additional land releases carbon stored in the soil. The increased farming significantly increased fertilizer production, which leads to N2O emissions.   In addition, more of the existing cropland was planted with corn. These finds contrast with a USDA study that shows that corn ethanol has a 39% lower corn ethanol intensity than gasoline which was stated to derive from carbon captures from the newly planted crops. However, that study did not account for emissions from land use changes. LCA can identify aspects of production that lead to the most negative consequences, which for the sake of this chapter is greenhouse gas emissions.  For example, the LCA for corn ethanol might improve if the CO2 released on its production during anaerobic ethanol biosynthesis could be captured. Outcomes would also change if renewable energy sources were used for stages of production that require fossil fuel use. This rigorous LCA did not address the moral question of using land that could be used to feed people to produce bioethanol for use in our ever-bigger vehicles.  In addition, opponents of solar energy installations suggest that solar installs would require so much land that it would remove land for agricultural purposes.  What is missing from their argument is the vast amount of land used now for corn ethanol.  Farmers planted 90 million acres of corn in 2022 in the US, a land area about 90% the size of the entire state of California.  44% of that corn went to biofuels, and only 12% went to human consumption.  In addition, approximately 44% percent was used to feed animals for human consumption, an inefficient and unsustainable use of crop land and resources. Production of sucrose and bioethanol from sugarcane Like corn, sugar cane, a tropical, perennial grass, is used (mainly in Brazil) to produce ethanol. Sugar cane is a C4 plant with a high ability to fix carbon. The fact that it is a perennial and does not need replanting each year makes it a more ideal feedstock than corn for bioethanol production. In 2020, sugar cane was by far the most-produced crop or livestock product in the world (1.87 billion metric tons), followed by corn (1.16 billion metric tons). The production by country for both corn and sugar cane is shown in Figure $14$. Figure $14$:  Corn and sugar cane production by country. Graphs from Our World in Data. https://ourworldindata.org/agricultural-production# That sugar cane production is so high compared to grain crops that provide nutrition (not just "sweet" calories) might come as a surprise, but it shouldn't, given our addiction to sweet foods. Sugar cane is often harvested manually in developing countries. It is then cut, milled, and mixed with water to extract the soluble sucrose (table sugar). The sugar cane components during extraction are shown below in Figure $15$. Figure $15$: Components of Sugar Cane (after Larissa Canilha et al.  2012; 2012: 989572. doi: 10.1155/2012/989572 Sucrose is a nonreducing disaccharide (O-α-D-glucopyranosyl-(1,2)-β-D-fructofuranoside). Its structure is shown in Figure $16$. Figure $16$: Structure of fructose Sucrose, Caramel and Molasses Sucrose decomposes at 186 °C (367 °F) instead of melting (a feared event for organic chemistry students who wish to record melting temperatures in the lab) to form caramel. Molasses is a very viscous liquid product from refining sugar cane or sugar beets. It is used as a sweetener with its own taste properties, and it's a component of brown sugar as well. On a sweetness scale, if sucrose is assigned a value of 100, fructose is 140, high fructose corn syrup is 120-160, and glucose is 70-80. For bioethanol production, sucrose is degraded by the enzyme invertase to form monomeric glucose and fructose. Invertases are activated on the milling and liquification of the sugar cane, so if sucrose is the desired commercial product, an additional clarification step (heat to 115°C and treat with lime and sulfuric acid) is necessary to prevent hydrolytic cleavage of sucrose. Bioethanol production from sugar cane sucrose Bioethanol can be made from either the fibrous lignocellulose remains of the sugar cane, called bagasse or from water-soluble sucrose. We will describe the production of cellulosic ethanol from field crop stalks, called stover, and leaves, straw, wood chips, and sawdust (all "waste" biomass), in Chapter 31.5. The same principles apply to bioethanol production from bagasse, the solid remains after the juice extraction from sugar cane.   (Bagasse is often burned to provide energy for sugar cane processing). In addition to sugar cane, sugar beets and sweet sorghum, a C4 plant similar to sugar cane, are used to produce bioethanol. As a C4 plant, sweet sorghum is very efficient at producing biomass through photosynthesis. It grows in temperate and tropical climates, has a short growing period, and is resistant to drought and cold. Its stalks have free sugars as well as lignocellulose stocks. This chapter will focus on bioethanol production from sugar cane sucrose. Again, this is accomplished using yeast (Saccharomyces cerevisiae), which has the enzyme invertase 2 (beta-fructofuranosidase 2 or Saccharase) needed to convert sucrose into sucrose fructose and glucose, which can enter glycolytic and fermentation pathways. Invertase, shown in 1842 to invert the stereochemistry of sugars, was first isolated from yeast in 1860. It has a secreted glycosylated homooctameric form and an intracellular form, all products of the same gene. It's a member of Family 32 of the glycoside hydrolases. The structure of the Saccharomyces invertase (SInv) octamer structure is shown in Figure $17$ below. Figure $17$: Structure of octameric SInv. M.Angela Sainz-Polo et al. JBC, 288, 9755-9766 (2013).  DOI:https://doi.org/10.1074/jbc.M112.446435Creative Commons Attribution (CC BY 4.0) Panel a shows a  view of the SInv octamer in ribbon (left) and solvent-accessible surface (right) representations, showing each subunit in a different color. Panel b shows that the octamer is rotated 90°, illustrating that it can be best described as a tetramer of two different kinds of dimers, AB/CD and EF/HG, which are compared by superimposing subunit F on subunit B in c Even though all eight subunits in the octamer are identical (58.5K, 512 aa), the quaternary structure of the 8-mer can best be viewed as a tetramer of dimers (i.e. 4 dimers pack to form two packed tetramers giving the octamer). The AB and CD  dimers pack in a "closed form" with a narrow active site pocket, allowing a glycan of 3-4 monomers. The EF and GH dimers pack in an "open form" with a wide active site pocket for longer glycans. Of course, our main interest here is in the binding of sucrose. Figure $18$ shows an interactive iCn3D model of Saccharomyces cerevisiae invertase (4EQV) Figure $18$: Saccharomyces cerevisiae invertase (4EQV). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...ysQW8YSrvPkus9 The color coding of the subunits is the same as shown in the right top image of Panel A, Figure 16 above. The GH32 enzymes, including invertase, have a catalytic domain consisting of a 5-bladed β-propeller, with each blade having four antiparallel beta-strands. The blades surround an active site enriched in carboxyl side chains. The "closed" form active site of the AB and CD dimers has at its base Phe 388 and Phe 296, which provide hydrophobic interactions. The "open" form active site of the EF and GH dimers also has a salt bridge between Asp 45 and Lys 385. These are shown in Figure $19$, along with a bound 1-kestose, which is a trisaccharide"sucrose analog" found in vegetables.  It consists of a β-D-fructofuranose connected to β-D-fructofuranosyl and α-D-glucopyranosyl residue at the 1- and 2-positions. Figure $19$:  Dimer interface at the active site. The octameric SInv active site interfaces are detailed, keeping the same color pattern as above with one subunit being shown in ribbon representation for clarity. Angela Sainz-Polo et al. JBC, 288, 9755-9766 (2013). DOI:https://doi.org/10.1074/jbc.M112.446435Creative Commons Attribution (CC BY 4.0) Panel A shows that the AB/CD dimers are tightly made by interactions among both their catalytic and β-sandwich domains. Hydrophobic interactions around found at the base of the catalytic pocket through Phe-388 and Phe-296. Panel b, by contrast, shows that the EF/GH dimers interact only through their β-sandwich domains. In addition, the catalytic pocket is also paved by a new salt bridge formed between Asp-45 and Lys-385 from the β-sandwich domain, which lines the cavity. A putative 1-kestose molecule is shown in a spherical representation. The hydrolysis of sucrose by invertase proceeds with the retention of configuration at the anomeric carbon. An active site Aspartate 22 acts as a nucleophile to form a glycosylated intermediate (fructose-Asp). This is followed by hydrolysis of the intermediate. An active site Glutamate 203 acts as a general acid/base. The fructose could also be transferred to another glycan in a transglycosylation reaction. The hydrophobic side chains Phe-388 and Phe-296 line the base of the active site pocket. Figure $20$ shows an interactive iCn3D model of the AB dimer of Saccharomyces cerevisiae invertase with key active site residues (4EQV) Figure $20$: AB dimer of Saccharomyces cerevisiae invertase with key active site residues (4EQV). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...hX9FbxLLip1f97 Figure $21$ shows an interactive iCn3D model of the EF dimer of Saccharomyces cerevisiae invertase with key active site residues (4EQV). It has an additional salt bridge between Asp-45 and Lys-385. Figure $21$: EF dimer of Saccharomyces cerevisiae invertase with key active site residues (4EQV). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...Dh33BtT5c8GXK6 Life Cycle Analysis of Sugar Cane Ethanol Does the production of bioethanol from sugar cane lead to lower net CO2 emissions than bioethanol produced from corn? The answer would depend on if sucrose (first generation) or lignocellulose (second generation) from bagasse is the feedstock. A recent LCA has been performed on the first-generation (feedstock is sucrose) production of bioethanol from sugar cane in Ecuador.  There is a lower cost of production from this sugar-based feedstock, which requires just grinding and the addition of yeast for fermentation. It does not require a saccharification step. Figure $22$ shows the various stages and processes used to perform LCA on the bioethanol production from sugar cane sucrose.  It's presented to show the complexity of such analyses, so look at the detail only if you are especially interested. Figure $22$: Anhydrous ethanol life cycle system boundaries and main product flows quantification for year 2018.  Arcentales-Bastidas, D.; Silva, C.; Ramirez, A.D. The Environmental Profile of Ethanol Derived from Sugarcane in Ecuador: A Life Cycle Assessment Including the Effect of Cogeneration of Electricity in a Sugar Industrial Complex. Energies 202215, 5421. https://doi.org/10.3390/en15155421.   Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/) The study analyzed four stages for analysis, agricultural, milling, distillation and electricity generation (presumably by burning the byproduct bagasse) for impacts. They defined two functional units: • 1 ton of sugarcane "at the farm gate” for the agricultural stage; • 1 L of ethanol "at the plant (factory) gate”. The key results are as follows: • The global warming potential (GWP) impact at the farm gate level was 53.6 kg of carbon dioxide equivalent (kg COequiv) per ton of sugarcane produced; This arose mostly from N2O (34%), a potent greenhouse gas released in the process, and diesel fuel used in agricultural machinery (24%). • The GWP for 1 L of ethanol produced at the plant gate was 0.60 kg COequiv, with the distillation phase contributing the most. Before proceeding further, let's explain the key term, global warming potential (GWP), which is widely used in LCA. It adds the contribution of other greenhouse gases like methane (CH4) and nitrous oxide (N2O), each of which has unique IR absorption spectra and atmospheric half-lives.  The IPCC uses a 100-year time frame for the calculation of the GWP, and uses this formula: CO2 Equivalent kg = CO2 kg + (CH4 kg x 28) + (N2O kg x 265) \mathrm{CO}_2 \text { equivalent } \mathrm{kg}=\mathrm{CO}_2 \mathrm{~kg}+\left(\mathrm{CH}_4 \mathrm{~kg} \times 28\right)+\left(\mathrm{N}_2 \mathrm{O} ~k g \times 265\right) • CO2 has  GWP of 1 by definition since it is the reference.  Its time frame in the atmosphere (100s  to 1000 years) doesn't matter since it is the reference. • CH4 has a GWP of around 27-30 over 100 years. It reflects its higher IR absorbance but lower lifetime (around 12 years). • N2O has a GWP of around 265-273 over a 100-year timescale. N2O has a lifetime of around 109 years. The equation can be amended by adding other greenhouse gases released in manufacturing and the use of refrigerants.  These include Freon-12 (Dichlorodifluoromethane) (CFC-12, with a lifetime of 100 years and a GWP100 of 10,200, and SF6 (used in the electricity industry to keep networks running safely and reliably) with a lifetime of 3200 years and GWP00 of 23,500! Nitrous Oxide - a potent greenhouse gas but not a laughing matter N2O (laughing gas) is an overlooked source of greenhouse gases, but it leads to about 7% of the warming effect of the greenhouse gases with long life-times and a high GWP100.  Agricultural practices lead to about 65% of its total emission.   It is a component of the soil and atmosphere nitrogen cycle.  In soil, its concentration depends on soil microbes that engage in nitrification and denitrification processes.  These in turn depend on the amount of fixed nitrogen, oxygen levels and metabolically available carbon sources.  The nitrification reaction, which occurs in aerated and moist soils, involves the oxidation of NH3↔NH4+ to NO2 and NO3-, with some N2O release. The major source of N2O occurs under anaerobic conditions.  These general reactions are shown below. • Nitrification (aerobic, oxidation):  N2 →  (NH3 ↔ NH4+) → NO2 → NO3- • Denitrification (anaerobic, reduction):  NO3 → NO2 → NO → N2O → N2 In anaerobic sites, NO is an electron receptor during microbial respiration.  N2O is produced when there is excess nitrogen available (past the needs of plants and microorganisms), so excess use of fertilizers and manure increases its production.  Nitrifying and denitrifying bacteria are most active in producing N2O in environments with abundant N relative to assimilatory demands by other microorganisms or plants (Firestone and Davidson, 1989), as is often the case following soil amendment of fertilizers, manure, or crop residues.  Physical processing of the soil (such as tillage) also affects N2O emissions by introducing crop residues in the soil, changing soil particle size and surface area, and by changing the porosity of the soil.  All of these affect soil substrate/product availability and their aqueous and gas diffusion rates. Let's use dimensional analysis from introductory chemistry to convert the GWP from the farm gate/agricultural stage (53.6 kg CO2 equiv/ton of sugar cane) to  kg CO2 equiv/1L of ethanol (EtOH) so we can add it the GWP from the pant gate, which is expressed in kg CO2 equiv/L ethanol produced.  The dimensional conversion is shown in Table  $2$ below. 53.6 kg CO2 equiv 1 ton SC 1 L Juice = 0.1 kg CO2 equiv 1 ton SC  800 L juice 0.7 L EtOH 1 L EtOH produced Table  $2$:  Conversion of 53.6 Kg CO2 equiv/ton of sugar cane from the farm gate (left hand column) to 0.1 Kg CO2 equiv/1L of ethanol (EtOH). Now add this to the reported 0.60 kg CO2 equiv/1 L of ethanol from the plant (factor) gate and you get a total of 0.7  kg CO2 equiv /1 L ethanol produced. Now use dimensional analysis from introductory chemistry to calculate how much CO2 is actually produced on the combustion of ethanol.  That value is calculated in Table  $3$ below. 1 L EtOH 1000 mL EtOH 0.789 g EtOH 1 mol EtOH 4 mol CO2 44 g CO2 1 kg CO2 = 1.5 kg CO2 1L EtOH 1 mL EtOH 46 g EtOH 2 mol EtOH 1 mol CO2 1000 g CO2 1 L EtOH Table  $3$:  Total Kg CO2 produced on combustion of 1 L of ethanol (EtOH) The promise of bioethanol is that for every 1 C atom used to create it, 1 C atom would be released.  We saw that the LCA analysis for corn ethanol in the US did not meet that expectation.  In the Ecuadorian analysis, it appears that it did, since 0.7 kg CO2 equivalents is required to produce 1 L of bioethanol from sugar cane, but 1.5 Kg CO2 is released on its burning The LCA analysis described above reflects just the global warming potential for the use of sugar cane sucrose for bioethanol production.  However, bioethanol production from sugar cane juice has other negative impacts as listed in Table  $4$ below. Table  $4$: Impact categories included in the LCA Impact Category Characterization Factor Reference Unit Climate change Climate change—GWP100 kg CO2eq. Freshwater eutrophication Freshwater eutrophication potential—FEP kg Peq. Marine eutrophication Marine eutrophication potential—MEP kg Neq. Abiotic depletion Metal depletion—MDP kg Feeq. Photo oxidant formation Photochemical oxidant formation potential—POFP kg NMVOCeq. Particulate matter emissions Particulate matter formation potential—PMFP kg PM10eq. Terrestrial acidification Terrestrial acidification potential—TAP100 kg SO2eq. Key Points - Beta version from Chat.openai 1. Biofuels are renewable energy sources derived from biomass, such as plant materials and waste. 2. Corn and sugar cane ethanol are two examples of biofuels that are produced by fermenting the sugars found in these crops. 3. Corn ethanol is typically produced by converting the starch in corn kernels into glucose, which is then fermented to produce ethanol. 4. Sugar cane ethanol is produced by crushing the cane to extract the juice, which is then fermented to produce ethanol. 5. Both corn and sugar cane ethanol are primarily used as a gasoline additive to increase octane and reduce emissions. 6. Corn ethanol is a controversial biofuel because of the high amount of energy used to produce it and the impact on food prices and the environment. 7. Sugar cane ethanol is considered to be a more sustainable biofuel because it is less energy-intensive to produce and can be produced on land not suitable for food crops. 8. However, large scale production of sugar cane ethanol has also been criticized for leading to deforestation, loss of biodiversity and displacement of local communities. 9. Cellulosic ethanol, made from non-food feedstocks like switchgrass or wood chips, is considered to be a more sustainable biofuel alternative to corn and sugar cane ethanol.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.04%3A__Biofuels_A_-_Corn_and_Sugar_Cane_Ethanol.txt

Search Fundamentals of Biochemistry Introduction In the last section, we explored how ethanol can be made from corn starch, an α(1,4) polymer of glucose with α(1,6) branches. Its production comes at a cost, however. Recent life cycle studies have shown that compared to fossil fuels, corn ethanol release as much but probably more CO2 than from fossil fuels. In addition, is it ethically justifiable to remove so much land from food production to produce bioethanol that, at present, is worse than fossil fuels from a climatic perspective? To address these issues, much work has been done to produce ethanol from cellulose, a β(1,4) polymer of glucose and the most abundant biomolecule in the world. Cellulose from trees, switch grasses, and "waste biomass" are prime sources of cellulose for the production of bioethanol. Waste biomass includes stover (field crop stalks and leaves), straw, wood chips and sawdust. From one ton of corn stover, about 113 gallons of ethanol can be made, close to the 124 gallons produced from corn. Nature breaks down cellulose routinely using cellulases, enzymes found in bacteria, fungi, protozoans, plants and some animals. Ruminants and even termites obtain cellulases from microbes living within their guts. The fungal-mediated decay of dead trees requires microbial cellulases but think how slow that process is. This stems partly from the very strong β(1,4) glycosidic link connecting glucose monomers in the polymer, which in the absence of a catalyst and at neutral pH, has an estimated half-life of 5 million years. Fossilized plants have been found to have intact cellulose and chitin, a β(1,4) polymer of N-acetylglucosamine. The β(1,4) glycosidic link in cellulose is orders of magnitude more stable than the phosphodiester bond of nucleic acids and the amide link of proteins. They are, however, readily cleaved by glycosidases such as cellulases, which can increase the kcat/KM over the uncatalyzed rate by up to 1017 fold, even in the absence of active site metal ions to facilitate hydrolysis (reference). Another reason for the slow decay of dead trees is the complex structure of the cell well, and in particular, the presence of a polymer called lignin, which stabilizes the cell wall and adds considerable barriers to the access of cellulose by added glycosidases. . A final reason for cellulose's extreme stability is the "quaternary" structure of the β(1,4)-linked cellulose strands, which consists of densely packed and intertwined strands of cellulose, which limits solvent (in this case water) accessibility necessary for hydrolysis. In addition,  some exposed surface planes of the packed cellulose strands are hydrophobic. This might seem startling, given the polar nature of the glucose subunits of the polymer. Let's review it here now since our goal is present climate change from a biochemical perspective! Some of this material has been presented in previous chapters,  but we will reuse it here so this chapter section can stand alone. A review:  The Plant Cell Wall (See Chapter 7.3 for more details.) There are about 35 different types of plant cells, and each may have a different cell wall depending on the local needs of a given cell. Cells synthesize thin cell wall that extends and stay thin as the cell grows. Figure \(1\) shows the primary cell wall of plants. The primary cell wall contains cellulose microfibrils (no surprise) and two other polymers, pectin and hemicellulose. The middle lamella consisting of pectins, is somewhat analogous to the extracellular matrix. After cell growth, the cell often synthesizes a secondary cell wall thicker than the first for extra rigidity. Since the enzymatic machinery for its synthesis is in the cytoplasm and the cell membrane, it is deposited between the cell membrane and the primary cell wall, as shown in the animated image in Figure \(2\). Figure \(3\) shows a structural representation of both the primary and secondary cell wall. The middle lamella, which contains pectins, lignins and some proteins, helps "glue together" the primary cell walls of surrounding plants. Primary Cell Wall: The main component of the primary plant wall is the homopolymer cellulose (40% -60% mass) in which the glucose monomers are linked β(1→4)-linked into strands that collect into microfibrils through hydrogen bond interactions. Two other groups of polymers, hemicellulose, and pectin, make up the plant cell wall. Hemicellulose can make up to 20-40% by the mass These polymers have β(1,4) backbones of glucose, mannose, or xylose (called xyloglucans, xylans, mannans, galactomannans, glucomannans, and galactoglucomanannans along with some β(1,3 and 1,4)-glucans. The most abundant hemicellulose in higher plants higher plants are the xyloglucans and have a cellulose backbone linked at O6 to α-D-xylose. Pectin consists of linked galacturonic acids forming homogalacturonans, rhamnogalacturonans, and rhamnogalacturonans II (RGII) [12] [13]. Homogalacturonans (α1→4) linked D-GalA make up more than 50% of the pectin. Figure \(4\) shows some of the structures.  The are generally branched, shorter than cellulose chains, and can often crystallize. Figure \(4\): Variant of the cell wall components of a plant. Costa and Plazanet. Advances in Biological Chemistry 06(03):70-105. DOI: 10.4236/abc.2016.63008License CC BY 4.0 Secondary Cell Wall The structure of the secondary cell wall depends on the function and environment of the cell. It contains cellulose fibers, hemicellulose, and a new polymer, lignin. It is abundant in xylem vessels and fiber cells of woody plants. It gives the plant extra stability and new functions, including the transport of fluids within the plant through channels. The proportion of cellulose in the secondary cell wall is higher than in the primary cell wall and is less hydrated than in the primary cell wall. Given the relative volume of the secondary and primary cell walls inferred from Fig 2, most of the tree-derived cellulose for bioethanol production comes from the secondary cell wall. Switch grasses, a perennial plant, are also valuable sources of cellulose (32–45% wt) and hemicellulose (21–31% wt) but also have significant amounts of lignin (12–28% wt). In summary, the secondary cell wall, formed after the cell stop growing, accounts for most of the carbohydrate biomass of plants. Glycosidases, mostly α- and β-amylases, are needed to convert corn-derived starch into glucose for fermentation and ethanol production. Likewise, cellulases are needed to degrade cellulose into glucose for cellulosic-ethanol production. However, it is a much more complex process since most of the cellulose is in the secondary cell wall. The lignin barrier in the walls protects cellulose from accessibility to cellulases, even after chemical and thermal pre-processing. In addition, xylans, which can make up 30% of the mass of the secondary cell wall, also inhibit cellulose degradation. A thermochemical process can convert cellulose to the synthetic gases CO and H2, which can be used as reactants to form ethanol. We'll discuss the biochemical process using pretreatment and enzymatic hydrolysis to make cellulosic ethanol. Lignin can be recovered and used to provide energy for the industrial-scale synthesis of cellulosic ethanol. Let's explore the barriers posed by lignin and how they can be surmounted to facilitate access to cellulose and the liberation of glucose for cellulosic ethanol production. Lignin Structure and reactivity Lignins, which can make up to 25% of the biomass weight of secondary walls, are made from phenylalanine derivatives but more directly from cinnamic acid. This derives from is made from phenylalanine which is hydroxylated and converted through other steps to hydroxycinnamic alcohols called monolignols, as shown in Figure \(5\). Three typical monomers, p-coumaryl, coniferyl, and sinapyl alcohols, can polymerize into lignins, with their units in the polymer (P) named hydroxyphenyl, guaiacyl and syringly, respectively. Lignols are activated phenolic compounds, which form phenoxide free radicals (catalyzed by enzymes called peroxidases), which can attack a second lignol to form covalent dimers. Reaction mechanisms for the dimerization of the MS sinapyl alcohol free radical are shown as an example in Figure \(6\). Now imagine this polymerization continuing through the formation of more phenolic free radicals and coupling at a myriad of sites to form a large covalent lignin polymer. Figure \(7\) shows one example of a larger lignin. Lignin strengthens the cell wall and further stabilizes the already unreactive cellulose fibers. Let's look at a specific example - using corn stover (CS) as a cellulose source - of how pretreatment of the biomass source with a chemical treatment followed by the addition of a bacterial strain Pandoraea sp. B-6 (B-6) isolated from long, narrow strips of bamboo (slips). Bamboo is a type of woody grass that grows rapidly. These bacteria produce two extracellular lignin-degrading enzymes, manganese peroxidase (MnP) and laccase (Lac). Laccase (Lac) is a multi-copper oxidase that uses O2 as an oxidizing agent in the degradation of the syringyl, guaiacyl and p-hydroxyphenyl monomers in lignins. MnP has similar properties. These and other enzymes can lead to the depolymerization of lignin and degradation of lignin-derived aromatic compounds The adddition of the B-6 bacteria (a source of MnP and Lac) to milled corn stover (CS) did not increase the rate of lignin degradation unless the corn stover was preincubated with a tetrahydrofuran–water (THF–H2O) with 0.5 wt% sulfuric acid and heated to 150 oC. This led to the erosion of the corn stover, allowing access to the bacterial enzyme. The untreated and pre-treated CS surface, along with a diagram showing access of Lac and MnP to the lignin, is shown in Figure \(8\). Figure \(8\): Untreated and pre-treated CS surface, and Lac and MnP interaction with lignins.  Zhuo, S., Yan, X., Liu, D. et al. Use of bacteria for improving the lignocellulose biorefinery process: importance of pre-erosion. Biotechnol Biofuels 11, 146 (2018). https://doi.org/10.1186/s13068-018-1...068-018-1146-4.  Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/) In addition to restricting access of cellulase to cellulose, cellulase can also nonspecifically adsorb to lignin and its pretreated forms since the lignin derivatives present a more hydrophobic surface that promotes cellulase interactions. Some plant laccases are involved in lignin biosynthesis, whereas in bacteria and fungi, they may be involved in lignin degradation Of course, fungi, which are prime degraders of dead biomass in forests, are also sources of enzymes for lignin degradation. For example, species of white rot fungi produce manganese peroxidase (MnP), lignin peroxidase (LiP), versatile peroxidase (VP), and laccase (Lac). They work through forming reactive lignin-derived aromatic free radicals (similar to those produced in lignin synthesis), leading to breaking ether bonds, aromatic ring cleavage and removal of methoxy groups from the substrate in a process called delignification. Pretreatment of the biomass increases yields higher amounts of available cellulose. Fungi, however, grow slowly, and the rate of delignification is still low. In addition, they also have hydrolytic enzymes that decrease the yield of cellulose. That is why bacterial sources like B6 are sought for delignification. As this is a biochemistry textbook, let's explore the structure and function of fungal laccase. The enzyme can bind a large variety of hydroxylated- and methyoxy-aromatic compounds as substrates, so its active site must be adaptable and likely dynamic. Structural analyses, in-silico docking experiments, and molecular dynamics simulations have been performed with the laccase (TvL) from the fungus Trametes versicolor. The enzyme has four copper ions in a T1 Cu site and a tri-nuclear Cu cluster (T2 Cu, T3α Cu and T3β Cu) at a T2/T3 site. As the mechanism involves free radical intermediates with O2 as an oxidant and substrate, 4 electrons are passed in single electron steps to the T1 Cu, then to the other three coppers, and finally to O2 to form two water molecules as products. The amino acid side chain ligands for the four copper ions are shown for white rot fungi laccase from Trametes Versicolor in Figure \(9\). Figure \(9\): T1 Cu (top left) and the trinuclear Cu cluster (T2, T3α and T3β) and their ligands for Trametes Versicolor laccase (TvL, pdb:  1GYC) Fungal laccases are extracellular proteins with about 550 amino acids arranged in three cupredoxin-like, beta-barrel domains. The T1 Cu is close to the surface and is found in domain 3, while the other copper ions are buried at the interface to domains 1 and 3. Figure \(10\) shows an interactive iCn3D model of Laccase from the Fungus Trametes Versicolor (1GYC) Figure \(10\): Laccase from the Fungus Trametes Versicolor (1GYC). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...cSYir86P9nxSW6 Domain 1 is green, domain 2 magenta and domain 3, which contains the single T1 Cu, orange. The protein is glycosylated, as shown in the blue glycan cube cartoons representing N-acetylglucosmine. Key substrate binding and catalytic side chains are shown in sticks and labeled; Asp 206 is a critical residue involved in substrate binding. The binding interactions of TvL with a wide variety of aromatic substrates are shown schematically in Figure \(11\). Figure \(11\):  Binding modes of representative compounds for TvL.  Mehra, R., Muschiol, J., Meyer, A.S. et al. A structural-chemical explanation of fungal laccase activity. Sci Rep 8, 17285 (2018). https://doi.org/10.1038/s41598-018-35633-8.  Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/ Most substrates interact with the highly conserved His-458 (blue color, ligand for the Ti Cu)and Asp-206 (orange color)  residues and form hydrogen bonds, salt bridges, or π-π stacking interactions with them. Asn-264 (blue) and Phe-265 (green) form important hydrogen bonding and π-π stacking interactions with substrates. The green color highlights nonpolar side chains. Ligands bind near the Ti Cu in domain 3 to initiate electron transfer. The active site of TvL must be dynamic to bind the various-sized ligands shown above. Molecular dynamics simulations, shown in Figure \(12\), support this. Figure \(12\):  Display of molecular dynamics simulations showing the loop regions of TvL (magenta colored) and another laccase, CuL (yellow colored), and high levels of fluctuations. Mehra, R., ibid Breaking down Cellulose We just explained how the lignin barrier could be degraded so that cellulase can access cellulose. As we described above, that also poses a difficult challenge given the stability of inaccessibility of the glucosidic bonds in cellulose.   The inaccessibility of "naked" cellulose fibers stems partly from the tight binding of cellulose strands into crystal lattices. Multiple crystal forms of cellulose, called polymorphs, can form. Plant cellulose has two predominant polymorphs, cellulose Iβ and Iα. Their structures are shown below in Figure \(13\). Figure \(13\): Natural and synthetic cellulose polymorphs. Christina M. Payne et al. Chem. Rev. 2015, 115, 3, 1308–1448 (2015), https://doi.org/10.1021/cr500351c. Open access through a Creative Commons public use license. They both form hydrogen bonds within a layer, with the main differences resulting from interlayer stacking. There are no hydrogen bonds between layers. You might find that surprising at first glance until you remember that all the OH groups in the lowest energy chair form of the glucose are equatorial, which allows intralayer hydrogen bonding. The interactions between layers predominantly arise from Van der Waals interactions, specifically induced dipole-induced dipole interactions. The hydrophobic planes, arising from axial H atoms projecting above and above each planar layer of the cellulose fibers, can be readily seen in Figure \(14\). Figure \(14\):  Hydrophobic planes arising from axial H atoms projecting above and above each planar layer of the cellulose fibers. Akira Isogai et al. Progress in Polymer Science, 86 (2018), https://doi.org/10.1016/j.progpolymsci.2018.07.007. Creative Commons license Now we can explore the structure of cellulases and how they bind to and cleaves cellulose. Cellulases, which cleave β(1,4) glycosidic bonds in cellulose, are members of a family of enzymes that go by many names, including glycosidases, or more recently, glycoside hydrolases (GH). The Carbohydrate Active Enzymes (CAZypedia) has over 128 glycoside hydrolase (GH) family web pages with enzymes that form hemiacetals on the cleavage of glycosidic bonds. The fungal cellulases that work on cellulose are found in GH families 5, 6, 7, 12 and 45. There are many types of secreted or cell-surface cellulases, including endoglucanases, exoglucanases (example is cellobiohydrolases (CBHs), and β -glucosidase (BG) ). We will focus on cellobiohydrolases (CBHs), the most studied one, which cleaves a 2-glucose unit (cellobiose) from either end of cellulose as it proceeds (processes) along the chain. Fungal and bacterial CBHs can work on crystalline cellulose as well. The resulting cellobiose is further cleaved by β-glucosidases. Ruminants and even termites obtain cellulases from microbes living within their guts.   The enzyme has a "tunnel" between two surface loops which interacts with and processively cleaves cellulose. As mentioned above, fungi are the major degraders of biomass, and are critical in the carbon cycle. Some fungi(brown-rot) use the Fenton reaction (Chapter 13.3) to produce the very reactive hydroxyl free radical (.OH) which causes biomass degradation. Filamentous fungi (like white and soft rot like T. reesei ) use enzymes.   The T. reesei cellulase called cellobiose hydrolase 1 is more recently named TrCel7A as it is in the GH 7A family. Cellulase mechanism. We have previously described the mechanisms of polysaccharide synthesis (Chapter 20.3), so we will discuss in less detail the mechanism of the very similar reaction of cellulose degradation by cellulase. Two general mechanisms are possible, one leading to the retention of configuration at the resulting hemiacetal end of the cellobiose and another that inverts the configuration. These mechanisms are shown in  Figure \(15\) for glucose in alpha-linkage at the anomeric carbon (not the beta-linkage found in cellulose). Figure \(15\): Two Primary Catalytic Mechanisms of GHs.  After Payne et al. Chem. Rev. 2015, 115, 3, 1308–1448. https://doi.org/10.1021/cr500351c. Scheme (A) shows the reaction that inverts the configuration. Water acts as a nucleophile in a SN2 type of reaction, with catalytic assistance by two proximal carboxylic acid side chains acting as general acids and bases. This results in an inversion of the stereochemistry at the anomeric carbon. Scheme (B) proceeds with the retention of configuration as two different nucleophilic attacks occur. In the first, an active site carboxylate forms a covalent acetal intermediate with the anomeric carbon. The carboxylate hence acts as a nucleophilic catalyst. Water, acting as a nucleophile, then attacks to form the hemiacetal with the expulsion of the carboxylate leaving group. As we discussed in Chapter 20.3 (section on glycosyl transferases), other variants of these mechanisms would include a SN1 reaction or one with an oxocarbenium-like transition state. The CBH1 (family 7) has a long tunnel for binding cellulose. The CBH1 (TrCel7A) cellulose catalytic site spans at least 9 glucose monomers (n-7, n-6,...,n-1,n+1, n+2) with cleavage typically of a cellobiose from the reducing end (between n-1 and n+1). The structure of the TrCel7A glycoside hydrolase (cellobiose hydrolase) with a small bound cellulose is shown in  Figure \(16\). Figure \(16\):  Crystal structure of the first GH7 CBH and EG.  Payne et al. Chem. Rev. 2015, 115, 3, 1308–1448. https://doi.org/10.1021/cr500351c Open access through Creative Commons public use license. The ligand from the TrCel7A Michaelis complex (PDB code 4C4C (441)) is shown in all panels. (A) CBH TrCel7A CD (PDB code 1CEL (172)) view from side, exhibiting the β sandwich structure that is characteristic of GH7 enzymes. TrCel7A was the first GH7 structure solved and is the best-characterized member of GH7. (B) TrCel7A view from bottom showing the more closed substrate binding "tunnel". (C) EG F. oxysporum Cel7B (PDB code 1OVW (174)) view from side. (D) FoCel7B view from the bottom showing the more open binding "groove". (E) TrCel7A Michaelis complex (PDB code 4C4C (441)) shows the standard numbering of the substrate binding sites (catalytic residues shown in green for reference). A cellulose chain enters from the −7 site. Hydrolysis occurs between the −1 and +1 sites. The +1/+2 sites are termed the "product sites". Active site carboxylates (E212, D214, and E217) are shown near the -1/+1 cleavage site in Figure \(17\). Glu 217 is covalently attached to the -1 glucose, supporting the retaining mechanism illustrated in Fig 15 above. Figure \(17\): Michaelis complex and glycosyl-enzyme intermediate of TrCel7A. Payne et al. ibid. Panel (A) shows the TrCel7A Michaelis complex (PDB code 4C4C (441)). Panel (B) shows a TrCel7A glycosyl-enzyme intermediate (PDB code 4C4D (441)) with a covalent bond between the nucleophile and the broken cellooligomer chain. There is an approximate 30° rotation of the E217 nucleophile during glycosylation. Figure \(18\) shows a more detailed view of the first step (glycosylation f Glu 217) for the Hypocrea jecorina GH Family 7 cellobiohydrolase Cel7A Figure \(18\): Figure 2. Glycosylation step for Hypocrea jecorina GH Family 7 cellobiohydrolase Cel7A.   Knott, Brandon C. et al. - J. Am. Chem. Soc.329 (2013)  https://doi.org/10.1021/ja410291u. Open access article published under an ACS AuthorChoice License Panel (a) shows a  snapshot of the reactant the conformation from a representative AS trajectory (with the substrate in green and catalytic residues in yellow) for the glycosylation step. The proton resides on the acid residue, Glu217. Panel (b) shows a representative snapshot of the transition state. The −1 glucopyranose ring now adopts a different conformation. Panel (c) shows the product of the glycosylation reaction. Panel (d) shows a schematic view of the overall glycosylation reaction with the collective variables identified by LM colored at the transition state. The best three-component RC identified by LM includes the forming/breaking bonds involving the anomeric carbon, the breaking bond between Glu217 and its proton, and the orientation of the nucleophile Glu212. Figure \(19\) shows the corresponding deglycosylation (of Glu 217) step. Figure \(19\): Figure 4. Deglycosylation step results. Knott, Brandon C. et al, ibid Panel (a) shows a snapshot of the reactant conformation from a representative AS trajectory (with the substrate in green and catalytic residues in yellow) for the deglycosylation step. The covalent glycosyl–enzyme bond is intact, and the cellobiose product is in primed GEI mode. Panel (b) shows a representative snapshot of the transition state. Note the distorted conformation of the −1 sugar, as the nucleophilic water molecule is ripped apart. Panel (c) shows a snapshot of the product in which the glycosyl-enzyme bond has been broken, and the catalytic residues have been regenerated. Panel (d) shows a schematic view of the overall deglycosylation reaction with the collective variables identified by LM colored at the transition state. The best three-component RC identified by LM includes the forming/breaking bonds involving the anomeric carbon, the forming/breaking bonds involving the transferring proton, and the orientation of the C3 hydroxyl of the +1 sugar. Binding of cellulase to cellulose fibers and lignin Many glycoside hydrolases contain distinct carbohydrate binding domains/modules (CBD/CBM) and catalytic domains (CD). For example, TrCel7A can be cleaved by the protease papain into a 56K domain with catalytic activity on small substrates but not large cellulose one and a smaller 10K (C terminal) domain that itself is glycosylated and which binds to the hydrophobic surface of cellulose crystals. Many GHs, in addition, have linkers connecting the catalytic domain (CD) and the carbohydrate module (CBM), which add different functions to the enzymes. The linkers vary in size and amino acid sequence. Linkers in fungi tend to be long and N- and O-glycosylated, affecting binding/catalysis. The linkers can also be intrinsically disordered, which adds dynamic complexity to their effects. The actual cellulose binding site on cellulase has been determined by solution NMR using a synthetic 36 amino acids protein fragment from the C-terminal domain of Trichoderma reesei Cel7A (the "carbohydrate binding module or CBM"). The amino acids involved in the binding of cellohexaose (6-mer) were determined by perturbation of the 2D NMR structure on binding cellohexaose. As we mentioned above, cellulase also binds lignin, decreasing their catalytic efficiency towards cellulase. Results of NMR binding studies of the TrCel7A carbohydrate binding module with cellohexaose and lignins from Japanese cedar (C-MWL) and Eucalyptus globulus (E-MWL) are shown in Figure \(20\). Figure \(20\): Comparison of interaction property between cellohexaose and MWLs.  Tokunaga, Y., Nagata, T., Suetomi, T. et al. NMR Analysis on Molecular Interaction of Lignin with Amino Acid Residues of Carbohydrate-Binding Module from Trichoderma reesei Cel7A. Sci Rep 9, 1977 (2019). https://doi.org/10.1038/s41598-018-38410-9.  Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. Panel (a) shows cellohexaose specifically bound to the flat plane surface and cleft.   The flat plane surface is defined by a triplet tyrosine (Y5, Y31, Y32) and H4, G6, Q7, I11, L28, N29, Q34, L36. Panel (b) shows both MWLs bound to multiple binding sites, some of which are included in the flat plane surface and cleft even in low concentrations of titrant. These non-specific binding sites are labeled green. Figure \(21\) shows an interactive iCn3D model of the C-terminal cellulose-binding module of cellobiohydrolase I from Trichoderma reesei (2CBH). Figure \(21\): C-terminal cellulose-binding module of cellobiohydrolase I from Trichoderma reesei (2CBH). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...Yva8BVMQCGdUZ7 This synthetic carbohydrate binding module (CBM) from the C-terminal domain of cellobiohydrolase I consists of 36 residues. NMR was used to determine the structure of the CBM.  2D NMR was used to determine the amino acids interacting with cellulose. The interacting side chains are shown as sticks underneath the molecular surface (gray). The side chains are colored according to hydrophobicity, with green followed by yellow being most hydrophobic. The numbering system refers to the 36 amino acid synthetic peptide, not the native protein. This model clearly shows this domain binds to the hydrophobic face of the cellulose microcrystal. The TrCel7A CBM has serine and threonine side chains that are glycosylated and affect binding. The interaction of the CBM with the nonpolar cellulose surface is shown in Figure \(22\). Figure \(22\): Glycosylated TrCel7A CBM on the hydrophobic surface of cellulose. Payne et al., ibid. Note that the aromatic groups of the triplet tyrosines, Y5, Y31, and Y32 (not labeled), are coplanar with the cellulose surface. As mentioned above, linkers that connect the C-terminal carbohydrate binding module (CBM) and the catalytic domain (CD) can be of different lengths and sequences and are also N- and O-glycosylated.  Figure \(23\) shows the interactions of the glycosylated linkers with the cellulose fibers. Figure \(23\): Molecular snapshots of TrCel7A and TrCel6A wherein the linker binds to the cellulose surface from microsecond-long MD simulations. Payne et al., ibid. These computational predictions of cellulose linkers enhancing binding of CBMs to the cellulose surface were corroborated experimentally via binding isotherm measurements. N-glycosylation and O-glycosylation are shown in blue and yellow. The glycans attached to the enzyme significantly enhance the binding of cellulase to the cellulose fibers. Payne et al. Chem. Rev. 2015, 115, 3, 1308–1448. https://doi.org/10.1021/cr500351c Open access through Creative Commons public use license A pictorial view of the hydrolytic cleavage of cellobiose from cellulose fibers is shown in Figure \(24\). Figure \(24\): Complete processive cycle of a GH7 CBH. TrCel7A is shown with its CD, linker, and CBM in gray "cartoon" representation. Payne et al., ibid. N-glycosylation and O-glycosylation are shown in blue and yellow, respectively. The cellulose surface is green, and the released cellobiose product magenta. Following the CBM and CD adsorption to the substrate and initial chain threading, TrCel7A processively cleaves cellobiose from a cellulose chain end. The "Processive Cycle" includes chain processivity, hydrolysis, and product expulsion (Figure 35). This processive cycle repeatedly occurs until the enzyme desorbs from the cellulose surface. Figure \(25\) shows an interactive iCn3D model of cellulose bound  to cellobiohydrolase I from Trichoderma reesei (7CEL) Figure \(25\): Cellulose bound to cellobiohydrolase I from Trichoderma reesei (7CEL). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...hfJFWg6hmJAxg7 Key Points - Beta version from Chat.openai 1. Cellulosic ethanol is a biofuel that is produced from the cellulose, hemicellulose, and lignin in plant material. 2. Unlike first-generation biofuels like corn and sugar cane ethanol, which are produced from sugars and starches, cellulosic ethanol can be produced from a wide range of plant material, including agricultural waste, wood chips, and switchgrass. 3. Cellulosic ethanol is considered a "second-generation" biofuel because it addresses many of the limitations of first-generation biofuels, including the competition with food crops for land and resources. 4. Cellulosic ethanol production involves a two-step process: first, the plant material is broken down into sugars through a process known as pretreatment, and then the sugars are fermented to produce ethanol. 5. The most common pretreatment methods include acid hydrolysis, ammonia fiber expansion, and steam explosion. 6. Cellulosic ethanol has a higher energy balance than first-generation biofuels and lower greenhouse gas emissions, making it a more sustainable option for biofuel production. 7. However, the technology for cellulosic ethanol production is still in the early stages of development, and the cost of production remains high. 8. Research is ongoing to improve the efficiency and cost-effectiveness of cellulosic ethanol production and to find ways to make it a viable alternative to fossil fuels.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.05%3A__Biofuels_B_-_Cellulosic_Ethanol.txt

Search Fundamentals of Biochemistry In the previous chapter sections, we discussed bioethanol production from plant starches (first generation) and lignocellulosic plants (second generation) from biomass waste such as stover and sugar cane. Each had its challenges. Now we will consider the third generation production of bioethanol from algae which has significant potential for minimizing damage to the environment. Below in Figure \(1\) is a summary of bioethanol production from each generation feedstock. Figure \(\PageIndex{x}\): Figure 1. General flowchart of bioethanol production, comparing the pre-fermentation processing of feedstocks for the first three generations of bioethanol production. The blue highlighted area provides an example of a value-added process that can enhance the value of bioethanol production. Tse, T.J.; Wiens, D.J.; Reaney, M.J.T. Production of Bioethanol—A Review of Factors Affecting Ethanol Yield. Fermentation 20217, 268. https://doi.org/10.3390/fermentation7040268.  Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/) There are so many types of algae that it can be daunting to read about them. Some are single cells, some can form filaments and colonies, and some are multicellular with different cell types. Some are prokaryotes; some are eukaryotes. The Algae Database suggests that they are best defined as "oxygenic photosynthesizers other than embryophyte land plants".   Naming and classification are generally based on botanical names ending as follows:  phyla = -phyta, classes = -phyceae, order = -ales, and family = -aceae.  The main groups are blue-green algae (cyanobacteria), rhodophytes, phaeophytes, chlorophytes, euglenophytes, charophytes, diatoms, dinoflagellates, and cryptophytes.  To make it more complicated, some animal species have been categorized as algae and have zoological name endings ( -zoa, -ea, -ida, -idae).  We apologize in advance for any errors and inconsistencies in the description of algae in the chapter section and would ask that you contact us with corrections.. Before we discuss biofuel production from algae, we will review the different algae types (much as we did for zooplankton and phytoplankton). Broadly, algae can be divided into microalgae (seen with a microscope) and macroalgae (seen with the eye). There are almost 170,00 species of algae listed in the Algaebase. Here is a summary of their properties. They can • fix carbon and produce food by photosynthesis. As such, they are primary producers. • be tiny (microalgae) or large and visible (macroalgae, also known as seaweed). Kelp, which can form large underwater forests, is a macroalgae. • be unicellular, form colonies or filaments, or larger multicellular structures • attach to objects or float freely. Some who study algae (phycologists) often consider any organism with chlorophyll but without the stems, roots, leaves, flowers, and vessels of plants to be algae. The naming and classification of algae are confusing, so let's start with a broad review of their classification. Biological classifications -  a review Life can be divided into domains, kingdoms, phyla, and additional subcategories. Carl Woese proposed three domains, Archaea, Bacteria, and Eukarya, in 1990, based on analyses of ribosomal RNA sequences. These domains are further classified into kingdoms - Archaebacteria, (Eu)bacteria, Protista, Fungi, Plantae, and Animalia. A seventh kingdom was added in 1981 by Thomas Cavalier-Smith, who divided Protista was two kingdoms, Protista (unicellular eukaryotes like some protozoa and some molds) and a new kingdom, Chromista (uni- or multicellular eukaryotes such as algae, diatoms, and some protozoans). Both Protista and Chromista have organisms with chlorophyll, and both also have heterotropic organisms. Newer classifications based on additional biochemical data may yet be proposed. Table \(1\) below shows a summary of the domains and kingdoms of life. Domains and Kingdoms Domain Bacteria Archaea Eukarya Kingdom (Eu)bacteria Archaebacteria Plantae Animale Fungi Protista Chromista Table \(1\): Domains and kingdoms of life. All life arose from the last universal common ancestor, LUCA, as shown below in Figure \(2\). Figure \(2\): Phylogenetic tree linking all major groups of living organisms to the LUCAhttps://upload.wikimedia.org/wikiped..._1990_LUCA.svg Their biological classification of algae (which illustrates how widely spread they are among different domains, kingdoms, and phyla, shown below in Figure \(3\) Figure \(3\):   Distribution of algae among groups in the Tree of Life as recognized by the ITIS and Species 2000 (and ife.org) in 2011. The deep classification of algae is the subject of great debate, and even the higher clades have been discussed and revised recently. Adapted from Verdelho Vieira, V.; Cadoret, J.-P.; Acien, F.G.; Benemann, J. Clarification of Most Relevant Concepts Related to the Microalgae Production Sector. Processes 202210, 175. https://doi.org/10.3390/pr10010175.  Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Microalgae Microalgae are single-cell organisms that can form filaments and colonies.  This group has one prokaryotic member, cyanobacteria, also known as blue-green algae. As a prokaryote, it does not have mitochondria or chloroplasts.   The rest of the microalgae are eukaryotic and include the phyla Chlorophyta, Rhodophyta, Glaucophyta, Cryptophyta, Euglenozoa, Cercozoa, Heterokontophyta, Haptophyta, and Miozoa (Myzozoa).  Another simpler organizational system divides microalgae into four categories: • cyanophyta (blue-green algae/cyanobacteria) • pyrrophyta (dinoflagellates and cryptomonads, and can be yellowish-green to golden-brown) • chrysophyta (diatoms, heterokonts and golden brown algae) • chlorophyta (microscopic green algae).  This term also applies to macroalgae (see below). Green algae are really diverse and are now broken up into two phyla, chlorophyta and charophyta with a combined 17 classes.  AlgaeBase dynamic species counts shows that there are about 4,500 species of Chlorophyta including those that live on land, in freshwater, and those that are considered macroalgae seaweeds.  There are about 2500 species of  Charophyta that are entirely freshwater. Within the microalgae are found green, brown, and red algae. (Unfortunately for students of algae, there are also green, brown, and red macroscopic algae as well). Microalgae are much more efficient at photosynthesis than land plants. The diatoms are an example of microalgae and are the precursor of the "fossil" fuel oil deposits. Spirogyra is a unicellular green alga that forms long filaments (colonies) up to 0.1 mm in length, so it loosk like a multicellular organism. One class of microalgae, green algae, arose when a microbe acquired a cyanobacterium, which allowed photosynthesis. Green algae eventually evolved into higher plants, and a similar process led to red algae. Brown algae and diatoms, dinoflagellates, and euglenoids, other types of algae, arose from the uptake of red or green algae into other eukaryotic host cells. In general, green microalgae, which absorb red wavelengths, are found on the surface. The red microalgae, which absorb green, and blue wavelengths, are at lower levels. The brown microalgae are generally found in between these water layers. Figure \(\PageIndex{x}\) below shows the light penetration spectrum in water. Light in the water The energy of light photons is given by E=hν=hc/λ, where ν if the frequency of the light and λ is the wavelength. As highly energetic x-rays penetrate matter, visible light can penetrate water. Water absorbs incident radiation, with the lower energy, higher wavelength photons of red light absorbed more readily in the top layers while the blue light penetrates farther into the water.   The depths of penetration of light in the open ocean (left) and coastal (right) waters are shown in Figure \(4\). Figure \(4\):  The light penetration spectrum in water as a function of color. https://oceanexplorer.noaa.gov/edu/m...fact-sheet.pdf When you are underwater, everything seems blue since red light is preferentially absorbed, leaving photons enriched in blue light reaching our eyes. Blue-enriched light reaches our eyes as it reflects off of objects. Clear water in the open ocean also appears blue since there are fewer particles like phytoplankton from which light scatters back to our eyes. Rayleigh (or elastic light scattering) depends on 1/λ6, so low wavelength light scatters most from particles. Sunsets and sunrises appear red when light passes through more of the atmosphere and the blue light is scattered from atmospheric particles before reaching our eyes. Coastal waters have more sediment, algae, and microscopic organisms like plankton that can scatter light.  These waters appear more green-blue, since red light is significantly absorbed by the microorganisms and blue light is more scattered. Microalgae exist as single cells or can form multicellular filaments and colonies. They proliferate in the presence of simple nutrients and can produce large amounts of polysaccharides for industrial bioethanol production or fatty acids/triacylglycerols for biodiesel production. They are, as mentioned above, the source of underground oil deposits. Both freshwater (like Chlorella and Haematococcus) and ocean microalgae (Dunaliella, Phaeodactylum, and Tetraselmis) can be used for the production of biofuels. The cyanobacteria Spirulina sp. is commonly used for commercial purposes. It and Synechococcus have large amounts of glycogen that could be used for bioethanol production. Their triacylglycerols are low, so they can't be used for biodiesel (which we will discuss in a future section of the chapter). Most are familiar with algae blooms in freshwater lakes (and even in saltwater environments). Cyanobacteria (blue-green algae) are the main culprit. Microcystin, a  potentially lethal toxin that targets serine/threonine protein phosphatases, is released from some algae blooms. Figure \(5\) shows a microalgae bloom in Lake Erie, a shallow freshwater lake polluted with agricultural runoff, making it an excellent site for cyanobacterial blooms. Figure \(5\): Microalgae bloom in Lake Erie, October 2011.  https://commons.wikimedia.org/wiki/F..._Lake_Erie.jpg Red tides are another type of toxic algae blooms that occurs in coastal water.  Along US coast they are caused by dinoflagellates and one diatom, both phytoplankton and types of microalgae.  Around the Gulf of Mexico, the main cause of red tides is the microalgae Karenia brevis.  They release large amounts of brevotoxin, a polycyclic ether that binds to and activates voltage-gated Na+ channels in nerve and muscle. Hence it is a potentially deadly neuortoxin.  The frequency of red tides is increasing around the world.  Two images of red tides are shown in Figure \(6\). Figure \(6\):  Red Tide.  License from Shutterstock Microalgae are sources of glycan polymers that can be used for bioethanol production; some are excellent candidates for biodiesel fuel, which is made from triacylglycerol reserves. Table \(2\) below shows a classification of microalgae and Cyanobacteria along with their characteristic pigments (that impart their distinctive colors) and their energy reserves that could be used for biofuel production. Phylum Class Pigments Reserve Habitat Cyanobacteria Cyanophyceae Chl a, β-carotene, flavacene, Echinenone isozea-, zea-, myxo-, oscillaxanthin APC, C-PC, C-PE Starch (granule) and glycogen Marine Freshwater Terrestrial Euglenophyta Euglenophyceae Some colorless Chl a, b, diadinoxanthin Paramylon Ergosterol Marine Freshwater Terrestrial Heterokontophyta/ Ochrophyta Xanthophyceae Eustigmatophyceae Chl a and c, β-carotene, heteroxanthin, diadinoxanthin (++) Oil Leucosin Ergosterol Marine Freshwater Terrestrial Miozoa Dinophyceae Chl a, c, β-carotene, diadinoxanthin, dinoxanthin, peridinins Starch Lipids Marine Freshwater Heterokontophyta/ Ochrophyta Chrysophycea Chl a, c, β-carotene, Fuco-, Diato-, diadinoxanthin Chrysolaminarin Fucosterol Porifasterol Marine Freshwater Haptophyta Coccolithophyceae Pavlovophyceae Rappephyceae Chl a, c, β-carotdnd, Fuco-, Diato-, diadinoxanthin Chrysolaminarin Fucosterol Porifasterol Marine Freshwater Bacillariophyta (Diatoms) Bacillariophyceae Chl a, c, β-carotdnd, Fuco-, Diato-, diadinoxanthin Chrysolaminarin Oil Marine Freshwater Terrestrial Cryptophyta Cryptophyceae Chl a, c, Biliproteins, α-carotene, Allo-, Croco-, Monado-xanthin Starch (granule) Oil Carbohydrates Marine Freshwater Table \(2\):  Classification of microalgae and Cyanobacteria.  Hachicha, R.; Elleuch, F.; Ben Hlima, H.; Dubessay, P.; de Baynast, H.; Delattre, C.; Pierre, G.; Hachicha, R.; Abdelkafi, S.; Michaud, P.; Fendri, I. Biomolecules from Microalgae and Cyanobacteria: Applications and Market Survey. Appl. Sci. 202212, 1924. https://doi.org/10.3390/app12041924. Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Cyanobacteria and red microalgae store glycogen and floridean starch (a hybrid between starch and glycogen) respectively, while green microalgae accumulate amylopectin-like polysaccharides. Macroalgae As mentioned above, macroalgae, known as seaweeds, do not have roots, stems, leaves, or flowers. Kelp in underwater forests appear to have roots, stems and leaves, but they are not plants. They have analogous structures such as holdfast, stipes, and blades which serve the functions of roots, stems and leaves found in plants. As do the microalgae, variants of macroalgae include red  (Rhodophyta), green (Chlorophyta, also a type of microalgae), and brown  (Phaeophyta) algae. It grows up to 30 times as quickly as land-based groups. In addition, it does not have lignin. They grow much faster than terrestrial plants and can form lots of biomass for commercial processing in much less area than land groups. They can be grown at low cost in sea farms without adding nutrients or pesticides. Hence they are ideal for both food and biofuel production. Brown and red algae have many present commercial uses. They have lots of carbohydrates for potential bioethanol production and triacylglycerols for biodiesel. Their carbohydrate composition includes mannitols and cell wall constituents which could also be used for fermentation.   In contrast to present petroleum sources, the biodiesel from macroalgae does not contain sulfur. Red macroalgae (some material below for red and green macroalgae from https://bio.libretexts.org/Bookshelv...nd_Green_Algae) The red algae are almost exclusively marine, and some are unicellular, but most are multicellular. They have true chloroplasts with two membranes (no remnant peptidoglycan) containing chlorophyll. Like the cyanobacteria, they use phycobilins as antenna pigments, phycoerythrin (which makes them red), and phycocyanin. Red pigment allows the red algae to photosynthesize at deeper depths than the green or brown algae, harnessing more of the blue light waves that penetrate deeper into the water column.   Unlike green algae and plants, red algae store carbohydrates as Floridean starch,  which has glucose in α(1,4) linkages and occasional α(1,6) linkages, similar to amylopectin. Agar, the base for culturing bacteria and other microorganisms, is extracted from a red alga.   Multicellular forms can be filamentous, leafy, sheet-like, coralloid, or even crust-like. Some examples are shown in Figure \(7\). Figure \(7\): These images show multicellular red algae, which can range from filamentous  (first image) to "leafy" (second image, left) to sheet-like (second image, right). The red color is due to an abundance of the red pigment phycoerythrin, which gives this group reddish chloroplasts. First image by Melissa Ha CC-BY-NC. Second image by Maria Morrow CC-BY-NC. Right image:  https://commons.wikimedia.org/wiki/F...ed_algae_3.jpg Green macroalgae These algae exhibit great diversity of form and function. Similar to red algae, green algae can be unicellular or multicellular. Many unicellular species form colonies and some green algae exist as large, multinucleate, single cells. Green algae primarily inhabit freshwater and damp soil and are a common component of plankton. They have chloroplasts and the photosynthetic pigments chlorophyll a and b, carotene, and xanthophylls. Examples include Chlamydomonas, Chlorella, Pediastrum, Netrium, Hydrodictyon, Acetabularia, Ulva, and Spirogyra. Lichens are a symbiotic combination of fungi and green algae. The nature of the evolutionary relationships between green algae is still debatable. As of 2019, genetic data supports splitting the green algae into two major lineages: chlorophytes and streptophytes. The green algae exhibit similar features to the land plants, particularly in chloroplast structure. They have chlorophyll a and b, have lost phycobilins but gained carotenoids, and store carbohydrates as starch inside plastids. Green algae are an important source of food for many aquatic animals.  Two types of green macroalgae are shown in Figure \(8\). Figure \(8\):Two types of green macroalgae left:  Figure 5.3.3.125.3.3.12: Trentepohlia is a genus of green algae found in terrestrial environments. It forms fluffy orange colonies on trees and is a photobiont in many lichens. One might not know they were looking at a green algae, due to the orange pigmentation. However, green algae have carotenoids. These terrestrial green algae produce an abundance of carotenoids, perhaps for protection from sun damage. Photo by Scott Loarie, CC0. Right: fresh water green algae. https://upload.wikimedia.org/wikiped...reen_Algae.jpg Green macroalgal blooms (called green tides) can also occur (just as blooms from the microalgae cyanobacteria). Green tides in the Yellow Sea (between China and Korea) are the largest known. A particularly large one is shown in Figure \(9\): Figure \(9\): Green Seaweed in the Yellow Sea , June 2021.  https://earthobservatory.nasa.gov/im...the-yellow-sea This particular green time was from nontoxic green macroalgae, Ulva prolifera. It is often called sea lettuce as it is edible. Blooms can affect the local ecosystem and lead to hypoxic zones as they decay. Brown macroalgae (also known as Kelp) (some material below for brown macroalgae from https://bio.libretexts.org/Bookshelv...3A_Brown_Algae) Macroscopic brown algae arose when a heterotrophic eukaryote merged with a unicellular photosynthetic eukaryotic red algae chloroplasts. The red alga degenerated into a chloroplast, this time with four membranes -- the engulfing membrane from the oomycete, the red alga's plasma membrane, and the two membranes of the original chloroplast within the red alga. The chloroplast has lost one of these membranes in many groups derived from secondary endosymbiosis. Figure \(10\) shows this secondary endosymbiosis event. Brown algae are brown due to the large amounts of carotenoids they produce, primarily one called fucoxanthin. These organisms are exclusively multicellular and can get so large that they require special conductive cells to transport photosynthates from their blades down to the rest of their tissues. These conductive cells are called trumpet hyphae and have sieve plates and resemble sieve tubes found in flowering plants. Much like Saprolegnia, the body of an alga is called a thallus because it is not differentiated into specialized tissues. The general morphology of a brown alga includes a holdfast, stipe, gas bladder(s)and blade(s). Figure \(111\) shows a diagram of kelp structure. Figure \(12\) shows a beautiful image of a kelp forest. Figure \(12\):  Kelp forest.  https://commons.wikimedia.org/wiki/F...1115735%29.jpg Figure \(13\) shows another type of a brown macroalgae. Figure \(12\): Underwater "roses" of a brown algae Padina pavonica, commonly known as the peacock's tail (Israel).  https://commons.wikimedia.org/wiki/F...l_(Israel).jpg Now we are in a position to discuss algae as a source of biofuels and nutritive foods in the next chapter section. Key Points - Beta version from Chat.openai 1. Algae are a diverse group of aquatic organisms that range from simple, single-celled organisms to complex, multicellular forms. 2. Algae are photosynthetic organisms and they play a critical role in the Earth's carbon cycle by converting carbon dioxide into oxygen. 3. Algae are found in a wide variety of environments, including freshwater, marine, and terrestrial ecosystems. 4. Algae have been used for a long time as a source of food, medicine and as a fertilizer. 5. Algae are also of great interest as a source of biofuel due to their high growth rates, ability to grow in non-arable land and their ability to produce large amounts of lipids (oils) that can be converted into biofuels. 6. Algae are a valuable resource for the production of biofuels, as they are able to produce large amounts of lipids and carbohydrates that can be converted into biofuels. 7. Algae-based biofuels have the potential to be more sustainable than biofuels produced from terrestrial crops, as they can be grown in non-arable land and do not compete with food crops for resources. 8. Algae can be grown in different systems, including open ponds, closed photobioreactors, and hybrid systems. 9. Algae can be grown using a variety of inputs, such as sunlight, CO2, and nutrients, and can produce a wide range of biofuels, including bioethanol, biodiesel, and biomethane. 10. Algae-based biofuels are still in the early stages of development, and research is ongoing to improve the efficiency and cost-effectiveness of algae cultivation and biofuel production. 11. Algae also have other potential uses, such as for the production of food and feed, as well as for bioremediation and carbon sequestration.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.06%3A__Algae_-_an_Introduction.txt

Search Fundamentals of Biochemistry Learning Objectives • Understand the role that algae play in the production of bioethanol. • Explore the process of converting algae into bioethanol. • Learn about the potential benefits and drawbacks of using algae for bioethanol production. • Evaluate the sustainability of algae-based bioethanol production. • Analyze the potential of algae as a renewable source of bioethanol and its potential impact on climate change. Now we are in a position to discuss algae as a source of biofuels and foods.  Algae can be used to produce bioethanol from the fermentation of algal polysaccharides (starch, cellulose, and other unique polysaccharides found in them).  In addition, triacylglycerols can be converted to biodiesel (which we will discuss in a different chapter).  Since we explored bioethanol production from starch and cellulose in previous sections, we'll focus mainly on the unique carbohydrates found in algae and how they can be converted into monomers for glycolytic fermentation by yeast to ethanol. Both microalgae and macroalgae contain starch reserves (in the cytoplasm and, in some cases, in the chloroplast) and cellulose (in cell walls).  They can be grown quickly in aquatic environments, and the key molecules are harvested and processed for use in yeast fermentation by yeast and also by bacteria such as Zymomonas mobilis to produce bioethanol.  This enzyme can use glucose, fructose, and sucrose as substrates. Starch from microalgae Green microalgae have been used as a source of both starch and cellulose.  Some species of microalgae have very high starch/glucose composition by mass.  Examples include Chlamydomonas reinhaedtil (60%),  Chlorococcum humicola (33%), and Chlorella vulgaris (50%).  Pretreatment of the microalgae biomass includes liquefaction using alpha-amylases followed by the addition of amyloglucosidase to produce glucose monomers (saccharification) or hydrolysis of glucosidic bonds using acid (sulfuric acid) or base (sodium hydroxide) pretreatment at elevated temperatures. As discussed previously, starch consists of amylose, an unbranched glucose polymer with α(1,4) glucosidic links, and amylopectin, which contains α(1,6) branches.  Algae starch (for example, in Chlorophyta, Cryptophyta, and Dinophyta) is found in the cytoplasm or chloroplast.  A particular type of starch, Floridean (the primary energy storage molecule in the red algae Rhodophyceae), is also found in the cytoplasm.  A generic structure of branched starches is shown below in Figure \(1\). Figure \(1\):  Branched starches  (https://en.wikipedia.org/wiki/Floridean_starch. Amylopectin has α(1,6) branches every 25-30 glucose units, while animal glycogen, another α(1,4) glucose polymer, has shorter branches every 8-12 glucose units.  Floridean starch has branch lengths between these repeat values but is closer to amylopectin.  β-amylases can cleave Floridean starch to mainly form glucose and maltose, while mild acid hydrolysis can lead to isomaltose formation. Other unique glycans are potential sources of glucose for bioethanol production.  These are described below. Starch-like molecules in microalgae Given algae diversity, it should be no surprise that some have alternative, starch-like glycans for their energy reserves that could also be used for bioethanol production. Laminarin (or Laminaran) Laminaran is a linear polymer of glucose with β(1,3) glycosidic links with β(1,6)-branches at a ratio of 3:1.  It is used for energy storage in brown algae.  It can be hydrolyzed by laminarinase, which cleaves β(1,3) glucosidic bonds.  It is a linear polysaccharide with a β(1→3):β(1→6) ratio of 3:1. Its structure is shown in  Figure \(2\) below. Figure \(2\): Structure of laminarin.  Left -  https://commons.wikimedia.org/wiki/F...ructure_V1.svg; Right -  https://biocyc.org/compound?orgid=ME...3602#tab=STRUC Chrysolaminarin This glycan is a linear polymer of glucose monomers linked through β(1,3) glycosidic bonds with some β(1,6) linkages.  It is found in Haptophyceae, Bacillariophyceae, and Chrysophyceae, which include diatoms.  Its generic structure is shown in Figure \(3\) below. Figure \(3\): Structure of Chrysolaminarin.  https://metacyc.org/compound?orgid=M...arin#tab=STRUC The ratio of branching is 11/1, as indicated in the figure.  Algae contain enzymes that can cleave the β(1,3) and β(1,4) links, which can be cleaved by acid hydrolysis. Paramylon Paramylon is a  linear polymer of glucose monomers linked through β(1,3) glycosidic bonds.  It is found in Euglenophyceae, Xanthophyceae, and Prymnesiophyta.  A β-1,3 glucanase from Euglena gracilis (Euglenozoa) cleaves the β(1,3) link.  Its structure is shown in Figure \(4\) below. Figure \(4\):  Paramylon.  https://biocyc.org/compound?orgid=ME...ylon#tab=STRUC Algae Cell Walls In a previous chapter, we discussed the use of lignocellulosic feedstocks from plant cell walls to produce bioethanol.   Both microalgae and macroalgae (red, green, and brown) have cellulose, a β(1,4) glucose polymer, in their cell walls.  It is found in Chlorophyta, Dinophyta, Phaeophyta, Prymnesiophyceae, Rhodophyceae, and Xanthophyceae.  There is little or much less lignin in algae and apparently none in macroalgae.  In addition, there is less hemicellulose.  These characteristics make algae excellent candidates for feedstocks for third-generation bioethanol production.  There are problems to overcome as well.  For example, the cell wall of Glaucocystis nostochinearum is almost 90% crystalline microfibrils, which adds to its physical and chemical stability.  In addition, there are other glycans in some algae's extracellular matrix/cell wall, which can make it difficult to extract and use cellulose.  Extracted cellulose can also be used as a feedstock for the chemical industry as well for the synthesis of bioplastics, a topic we will consider in another chapter sections. Algae cellulose synthesis is performed by membrane-bound cellulose synthase terminal complexes (TCs), with the geometry of the cellulose microfibrils determined by the geometry of the TCs, as shown in Figure \(5\).  The TCs are arranged in a hexagon with C6 symmetry, and they can form hexameric macrofibrils. Figure \(5\):  Organization and morphology of cellulose synthesizing terminal complexes (TCs) in different organisms.  Wahlström, N. et al. Cellulose 27, 3707–3725 (2020). https://doi.org/10.1007/s10570-020-03029-5.  Creative Commons Attribution 4.0 International License.  http://creativecommons.org/licenses/by/4.0/. Table \(1\)  below shows the different algae taxa's major polymers in the cell wall and extracellular matrix. Algal taxa Crystalline polysaccharides Hemicelluloses Matrix polysaccharides Chlorophyta (green algae) Cellulose Xyloglucans, xylans, mannans, glucuronan, (1 → 3)-β-glucan, (1 → 3),(1 → 4)-β-glucan Ulvans, pectins Charophyceae (green algae) Cellulose Xyloglucans, xylans, mannans, (1 → 3)-β-glucan, (1 → 3),(1 → 4)-β-glucan Pectins Phaeophyceae (brown algae) Cellulose Sulfated xylofucoglucan, sulfated xylofucoglucuronan, (1 → 3)-β-glucan Alginates, fucoidans Rhodophyta (red algae) Cellulose, (1 → 4)-β-mannan, (1 → 4)-β-xylan, (1 → 3)-β-xylan Xylans, mannans, glucomannans, sulfated (1 → 3),(1 → 4)-β-glucan, (1 → 3),(1 → 4)-β-xylan Agars, carrageenans, porphyran Dinophyta Cellulose Table \(1\): Major polymers found in the cell wall and extracellular matrix of different algae taxa.  Enio Zanchetta et al., Algal Research, 56 (2021). https://doi.org/10.1016/j.algal.2021.102288. The percent composition of cellulose, hemicellulose and lignin for bioethanol production is important.  Table \(2\) shows the % composition of these polymers based on total dry weight. (Note:  The values for Chlorophyta/Ulvophyceae are highlighted in yellow in both Tables 2 and 3 for convenience of comparision. Empty Cell Phylum/class Strain Cellulose [%] Hemicellulose [%] Lignin [%] Microalgae A mix of microalgae & cyanobacteria from the wastewater treatment plant 7.1 16.3 1.5 Chlorophyta/Trebouxiophyceae Chlorella vulgaris 10–47.5 n.d. n.d. Ochrophyta/Eustigmatophyceae Nannochloropsis gaditana 25 det. n.d. Macroalgae Chlorophyta/Ulvophyceae Cladophora glomerata 21.6 det. n.d. Chlorophyta/Ulvophyceae Ulva lactuca 12.4 6.0 16.6 n.d. 12.2 32.5 n.d. 9.8 1.5 Chlorophyta/Ulvophyceae Ulva prolifera 19.4 14.4 9.4 Chlorophyta/Ulvophyceae Ulva pertusa 6.7 16.8 n.d. Chlorophyta/Ulvophyceae Ulva sp. 40.7 7.1 7.9 Ochrophyta/Phaeophyceae Cystosphaera jacquinottii 4.6 6.1 19 Ochrophyta/Phaeophyceae Fucus vesiculosusLaminaria digitata 8 n.d. n.d. Rhodophyta/Florideophyceae Gelidium elegans 17.2 29.5 4.5 Tracheophyta/Monocots Posidonia oceanica 32.5 31.4–40.0 40.0 23.3 21.8–25.7 20.8 28.2 29.3–29.8 29.8 Tracheophyta/Monocots Posidonia australis 20.2 11.7 14.5 Table \(2\):  Cellulose, hemicellulose, and lignin content in total dry weight basis of algal feedstock.  n.d.: not determined, det.: detected (either directly or indirectly). Zanchetta et al, ibid. Table \(3\) below shows the % composition in the cell wall (instead of total biomass) for each of the three polymers. Empty Cell Phylum/class Strain Cellulose [%] Hemicellulose [%] Lignin [%] Microalgae Chlorophyta/Trebouxiophyceae Chlorella pyrenoidosa 15.4 31 n.d. Ochrophyta/Eustigmatophyceae Nannochloropsis gaditana 75 det. n.d. Charophyta/Zygnematophyceae Staurastrum sp. 72 4.0 1.2–5.6 Macroalgae Chlorophyta/Ulvophyceae Valonia ventricosa 75 det. abs. Chlorophyta/Ulvophyceae Cladophora rupestris 28.5 abs. n.d. Chlorophyta/Ulvophyceae Ulva lactuca 19 det. n.d. Chlorophyta/Ulvophyceae Chaetomorpha melagonium 41 det. n.d. Chlorophyta/Ulvophyceae Enteromorpha sp. 21 det. n.d. Ochrophyta/Phaeophyceae Fucus serratus 13.5 det. n.d. Ochrophyta/Phaeophyceae Laminaria digitata 20 det. n.d. Ochrophyta/Phaeophyceae Laminaria saccharina 18 det. n.d. Ochrophyta/Phaeophyceae Halidrys siliquosa 14 det. n.d. Ochrophyta/Phaeophyceae Himanthalia lorea 8 det. n.d. Rodophyta/Florideophyceae Ptilota plumosa 24 det. n.d. Rodophyta/Florideophyceae Rhodymenia palmata 7 det. n.d. Table \(3\):  Cellulose, hemicellulose, and lignin content in the cell wall of algal feedstock.  n.d.: not determined, det.: detected (either directly or indirectly), abs.: absent. Zanchetta et al, ibid. Note the complete absence of lignin in the cell walls of macroalgae, but the previous table does show some presence in some macroalgae as a whole. Other cell wall-associated glycans for bioethanol production. Macroalgae offer an abundant source of other glycans, three of which we describe below.  Each requires unique sets of enzymes to convert them to free glucose for fermentation and bioethanol production. Alginates These are among the most abundant biopolymers in the world and are the prime carbohydrate in brown seaweeds, where they can reach up to 40% of the dry mass.  It is found in the cell walls of macroalgae.  They are already used for food or other commercial uses.  For example, they are used as thickening agents in food and are also used in the textile industries. The alginate (high in brown algae) is a linear polymer of 1,4-β-D-mannuronic acid (M) and 1,4 α-L-guluronic acid (G) monomers, with stretches (blocks) of pure G, pure M, and mixed MG.  Representative structures are shown in Figure \(6\) below. Figure \(6\):  Left - https://commons.wikimedia.org/wiki/F...e_skeletal.svg; Right - https://biocyc.org/META/NEW-IMAGE?ty...bject=ALGINATE Agar/Agarose Agar is abundant in red seaweed.  Agar, used in labs, is a mixture of agarose and agaropectin.  It acts as a support for the cell wall and detaches with boiling.  Agarose is a linear polymer of a disaccharide repeating unit of D-galactose and 3,6-anhydro-L-galactopyranose linked by α(1,3) and β(1->4) glycosidic bonds.  It has many uses in the laboratory (agarose for chromatography and electrophoresis while agar for cell culture).   Its structure is shown in Figure \(7\) below. Figure \(7\): Agarose.  Left - https://commons.wikimedia.org/wiki/F...e_polymere.svg; Right - https://biocyc.org/compound?orgid=ME...oses#tab=STRUC Carrageenans These are linear sulfated glycans found in the cell walls of red seaweeds (Rhodophyta ).  They have widespread use in the food industry and are used for thickening.  They are similar to glycosaminoglycans (GAGs).  Their repeating motif is a disaccharide of an α(1,3)-linked D-galactose and a β(1,4)-linked D-galactose.  As with GAGs, their degree of sulfation can vary from 15-40%.  Different subtypes have a different number of sulfates in the repeating disaccharide unit.  For example, the κ form has one, iota (ι) has two, and λ has 3.  Representative structures are shown in Figure \(8\) below. Figure \(8\): Carrageenans.  https://upload.wikimedia.org/wikiped...enan_types.svg Variants include the carrageenoses, in which the α(1,3)-linked galactose has a 3,6-anhydro bridge.  The carrageenoses are often just called carrageenan.  Their structure is shown in Figure \(9\). Figure \(9\): https://biocyc.org/compound?orgid=ME...enan#tab=STRUC The 6-anhydro-D-galactose is not fermentable, so microorganisms that can contain enzymes that degrade carrageenan must also be used. Bioethanol production Now we can put all of this together and make bioethanol from algae.  Figure \(10\) presents an overview of the entire process. Figure \(10\): Overview of ethanol production from major algal carbohydrates.  Qusai Al Abdallah et al., Front. Energy Res., 04 November 2016.  https://doi.org/10.3389/fenrg.2016.00036.  Creative Commons Attribution License (CC BY). Panel (A) shows algae store simple sugars in the form of simple and complex food reserves and as structural polysaccharides. Panel (B) shows how food reserves and structural polysaccharides are degraded into their basic monosaccharides and uronic acids. Panel (C) shows the final fermentation into ethanol using microbial wild-type strains or their genetically engineered counterpartsDEHU, 4-deoxy-l-erythro-5-hexoseulose uronic acid. Figure \(11\) below shows a schematic diagram for converting the algae feedstocks to glucose using key glycan-cleaving enzymes.  Note that the colors and shapes are not those recommended in the SNFG standard.  Those standards show glucose and galactose as blue and yellow circles, respectively. Figure \(11\):  SCHEMATIC DIAGRAMS FOR THE ENZYMATIC HYDROLYSIS OF ALGAL POLYSACCHARIDES.  (A) Starch,  Floridean starch, and glycogen, (B) laminarin, chrysolaminarin, and paramylon, (C) cellulose, (D) agarose by β-agarases, (E) agarose by α-agarases, and (F) alginate.  DP, degree of polymerization; NAB, neoagarobiose; AB, agarobiose; DEHU, 4-deoxy-l-erythro-5-hexoseulose uronic acid; KDG, 2-keto-3-deoxy-gluconate; M, β-d-mannuronate; G, α-l-guluronaten. Macroalgae offer enormous advantages for producing food, bioethanol, and intermediates for conversion into bioplastics.  Ocean macroalgae don't require irrigation or pesticides, or fertilizers!  Figure \(12\) below shows rope and raft ocean cultivation of macroalgae. Figure \(12\): Macroalgae cultivation systems.  Godvin Sharmila V et al. Bioengineered, Volume 12, 2021.  https://doi.org/10.1080/21655979.2021.1996019.  Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) Figure \(13\) below summarizes how macroalgae (as well as microalgae) can be used to produce a variety of biofuels in addition to bioethanol.  The algae cell can be chemically processed to produce hydrogen, methane, and syngas for fuels or combusted (as in the use of wood and coal) to provide heat and electricity.  Alternatively, the post-harvest algae can be fractionated to produce carbohydrates for alcohol production, straight vegetable oils (SVO - pure triacylglycerols) for fuel, and fatty acid esters (biodiesel).  Of course, they can be used directly as food and for food products. Figure \(13\): Macroalgal biofuel refinery.  Godvin Sharmila V et al., ibid. Table \(4\) below details the species and methods presently used to produce biofuel from macroalgae. Type of Biofuel Species Type Pretreatment methods or Conversion techniques Pretreatment or conversion technique conditions Biofuel Yield or production potential Biodiesel Ulva fasciata Catalytic transesterification Molar ratio of methanol: oil – 9:1; Time – 6 hours; Temperature – 80-100°C 88% Chaetomorpha antennina Transesterification Chloroform-ethanol solvent- 1:20 (w/v) 2.1 mL/10 gbiomass Gracilaria corticata Transesterification Hexane-ether solvent – 1:20 (w/v) 2 mL/10 gbiomass Ulva intestinalis Transesterification - 32.3 mg/g dw Enteromorpha compressa Base transesterification Base – 1% NaOH, Methanol–oil ratio – 9:1, Temperature – 60°CTime – 70 min 90.6% Bioethanol Chaetomorpha linum Wet oxidation method Temperature – 200°C 44 g ethanol/100 g glucan Saccharina japonica Low acid pretreatment Acid – 0.06% (w/w) sulfuric acidTemperature – 170°CTime – 15 min 6.65 g/L Saccharinajaponica Thermal acid hydrolysis Acid – 40 mM H2SO4Temperature – 121°CTime – 60 min 7.7 g/L Laminaria digitata Oven drying Temperature – 70°C,Time −72 h 13.6 ± 0.2 μL/g DS Ulva linza Mild acid hydrolysis Acid condition – 3% H2SO4 12.01% Biohydrogen Laminaria japonica Microwave Temperature – 160°C,Time – 30 min 15.8 mL/g TS Laminaria japonica Ultrasonic Sonication frequency – 20 kHz 23.56 ± 4.5 mL/g Laminaria digitate Hydrothermal Temperature – 140°CTime – 20 min 44.0 ± 1.2 mL/g VS Chaetomorpha antennina Surfactant-aided microwave pretreatment Microwave power – 0.36 kW Ammonium dodecyl sulfate – 0.0035 74.5 mL/g COD Ulva reticulate Microwave-H2O2 alkali pretreatment Microwave power – 0.36 kWH2O2 dosage – 24 mg H2O2/g biomasspH – 10 87.5 mL H2/g COD Biomethane Palmaria palmata Anaerobic digestion Semi-continuous anaerobic digestion 320 mL CH4/g VS Chaetomorpha antennina Ozone disperser pretreatment Disperser g force – 1,613 g, Treatment time – 30 min, Ozone dosage – 0.00049 g O3/g TS 0.20 g COD/g COD Chaetomorpha antennina Thermo-chemo disperser Disperser g-force of 1613 g, Temperature – 80°C, NaOH – 1 N, pH – 11 215 mL/g VS Laminaria digitata Heat Temperature – 104°C Dried biomass – 97.66 m3 CH4/t fresh biomass – 67.24 m3 CH4/t Laminaria digitata Oven drying Temperature – 70°C,Time −72 h 235.4 ± 14.1 mL/gVS Bio-oil Saccharina japonica Fixed bed reactor pyrolysis Temperature – 450°C 47% conversion Ulva lactuca Microwave pyrolysis Temperature – 500°C 18.4 wt.% Porphyra tenera Packed tube reactor pyrolysis Temperature – 500°C 47.4 wt.% Laminaria japonica Packed tube reactor pyrolysis Temperature – 500°C 45.8 wt.% Undaria pinnatifida Packed tube reactor pyrolysis Temperature – 500°C 37.5 wt.% Table \(4\):  Biofuel production from macroalgae.  Godvin Sharmila V et al., ibid. We will discuss in another chapter the production of biodiesel from algae. Key Points - Beta version from Chat.openai 1. Algae can be used to produce bioethanol, a biofuel that is similar to traditional ethanol produced from corn or sugar cane. 2. Algae-based bioethanol production involves the cultivation of algae, followed by the extraction of sugars and the fermentation of these sugars to produce ethanol. 3. Algae have a high growth rate and high sugar content, making them a potential source of bioethanol. 4. The process of bioethanol production from algae is being commercialized, and research is ongoing to improve the efficiency and cost-effectiveness of the process. 5. Algae-based bioethanol production has several advantages over traditional bioethanol production methods, such as the ability to grow algae in non-arable land and the ability to produce bioethanol from CO2 and sunlight. 6. The algae-based bioethanol production process, is considered more sustainable and environmentally friendly than traditional bioethanol production methods, as it does not compete with food crops for resources and consumes CO2 during production.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.07%3A__Algae_-_Bioethanol_production.txt

Search Fundamentals of Biochemistry Diesel fuel was used in 1900 in an engine designed by Rudolf Diesel. The fuel was peanut oil. That might have worked fine in 1900, but a century later, the demands for diesel fuel are met not by peanut oil, a legume, but by oil.   Gasoline (petrol) consists of molecules containing 5-12 carbons compared to diesel at 12-20. Both are obtained through fractional distillation of oil (petroleum). Diesel has a higher boiling and melting point and releases more energy per liter (36.9 vs. 33.7 MJ) than gasoline.   Regular gasoline contains about 17% n-alkanes, 32% branched alkanes, 5% cycloalkanes, 2% alkenes (olefins), and 30% aromatics. High octane gas can contain around n- and branched alkanes, with the rest from alkenes. Diesel fuel contains about 75% saturated hydrocarbons and 25% aromatics, including alkylbenzenes and napthalenes. In a diesel engine, ignition occurs on compression of the fuel and air mixture and doesn't require a spark. They use glow plugs which provide heat but not spark. Biodiesel Oils (triacylglycerol) produced from biomass can be converted to diesel fuel. In the following sections, we will discuss the synthesis of gases and liquid fuels from nonpetroleum sources such as biomass through the creation of the synthetic gases H2 and CO (collectively called syngas) and their condensation into liquid fuels using the Fischer-Tropsch reaction. This section will limit our discussion to using fats to create biodiesel. Triacylglycerols for biodiesel production can come from plants, animals, algae, and even waste oils from the food industry. Because biodiesel fuel is composed of carbon-based molecules (often fatty acid esters) with high melting and boiling point ranges which hamper its utility in cold climates, it is often blended with regular diesel (for example, a 20% blend called B20). Still, it can be used at 100% (B100). Biodiesel enriched in unsaturated fatty acids has lower melting points and fewer problems in cold weather. As will the production of bioethanol from lignocellulosic food stocks, biodiesel production has evolved through multiple generations, as shown in Figure $1$ below. Figure $1$:  Palani Vignesh et al., Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles, 76 (2021) 6.  DOI: https://doi.org/10.2516/ogst/2020088. Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0) Table $1$ below shows the advantages and disadvantages of each generation of biodiesel. Biodiesel generation Advantages Disadvantages 1st generation biodiesel 1. Low emission of greenhouse gas. 2. Easy and low-cost technology for conversion. 1. Yield is inadequate to meet the demand. 2. Causes food shortages. 3. High land footprint. 2nd generation biodiesel 1. Using food waste as a feedstock. 2. Use of non-agricultural land to grow a limited amount of crops. 1. Costly pre-treatment. 2. Sophisticated technology is used to transform biomass into fuel. 3rd generation biodiesel 1. Simple to grow algae. 2. No competition for the use of food crops; wastewater, and seawater can be used. 1. More resource usage for algae cultivation. 2. Low lipid level or biomass accumulation in algae. 4th generation biodiesel 1. High biomass and production yield. 2. More capability to eliminate CO2. 1. The cost of the bio-reactor is higher. 2. At the early stage of research, a high investment is needed. Table $1$: Advantages and disadvantages of various biodiesel generations.  Palani Vignesh et al., ibid Triacylglycerol feedstocks for biodiesel are often converted to methyl or ethyl esters through an alcoholysis or transesterification reaction, as shown in Figure $2$ below. Figure $2$: Methanolysis/transestiferation of triacylglycerol to produce methyl-fatty acid ester for biofuels This reaction is simply a base-catalyzed cleavage of the ester bonds in the triacylglycerol. Alternatively, vegetable oil can be treated at high pressure and temperature in a hydrogenation reaction to produce a variant called "renewable diesel". Figure  $3$ below shows feedstocks and processing for first-generation biodiesel production. Feedstock: Processing Method 1st Gen Processing Waste  cooking oil:  esterification/transesterification Food crops:  extraction/transesterification Organic oils: hydrolysis, distillation Animal fat: hydrolysis, fermentation Bioethanol/butanol:  chemical synthesis Figure  $3$: Feedstocks and processing for first-generation biodiesel production.  Palani Vignesh et al., ibid Figure  $4$ below shows feedstocks and processing for second-generation biodiesel production Feedstock: Processing Method 2nd Gen Processing Cellulose:  advanced fermentation Hemicellulose:  hydrolysis Lignin:  gasification Tannins: biological synthesis Vegetable oil/animal fats:  hydrogenation Figure  $4$ below shows feedstocks and processing for second-generation biodiesel production.  Palani Vignesh et al., ibid Finally, Figure  $5$ below shows the processing steps for 3rd and 4th generation biodiesel from algae. Figure $5$: Processing steps for 3rd and 4th generation biodiesel from algae.  Palani Vignesh et al., ibid Let's consider 3rd and 4th generation biodiesel production using algae. First, algae can be used to produce many different products that can be used for biofuels and chemical feedstocks. These are reviewed in Figure $6$ below. Figure $6$: Macroalgal biofuel refinery;  Godvin Sharmila V et al.,  Bioengineered. 2021 Dec;12(2):9216-9238. doi: 10.1080/21655979.2021.1996019. PMID: 34709971; PMCID: PMC8809944. Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) To maximize biodiesel production from algae (3rd and 4th generation), steps in the anabolic pathways for fatty acid and triacylglycerol synthesis could be genetically modified. Below is an overview of triacylglycerol synthesis in microalgae in Figure $7$. Figure $7$: Schematic illustration of TAG synthesis in microalgae. NADPH; Nicotinamide adenine dinucleotide phosphate; ATP, Adenosine Triphosphate; DHAP, dihydroxyacetone phosphate; G3P/G3pDH, Glyceraldehyde 3-phosphate / G3P dehydrogenase; GPAT, Glycerol 3-phosphate acyltransferase; PA/LPA/LPAAT/PAP, Phosphatidic acid/Lyso-PA/LPA acyltransferase/PA phosphatase; DAG/DGAT, di-Acylglycerol/ DAG acyltransferase; FAT, Fatty acyl-ACP thioesterase; ACP, Acyl-carrier protein; ER, Endoplasmic reticulum; PC, Phosphatidylcholine; PDAT, Phospholipid: DGAT; ACCase, Acetyl-CoA carboxylase; FAS, Fatty acid synthase; KAS, 3-ketoacyl-ACP synthase; FAD, Flavin adenine dinucleotide.   Sharma PK, et al. Front. Mar. Sci. 5:382, 2018. doi: 10.3389/fmars.2018.00382.  Creative Commons Attribution License (CC BY) Each step in the combined pathways are sites for optimization, as shown in Figure $8$ below. Figure $8$: Schematic illustration of different genetic engineering strategies applied in microalgae for biodiesel application. WT, Wild type cells; TR, Transgenic cells; TF, Transcription factor; TCA, Tricarboxylic acid cycle; NADH, Nicotinamide adenine dinucleotide; FA, Fatty acid; LD, Lipid droplet. Sharma PK, et al., ibid Life cycle analyses for 3rd generation biodiesel production indicate they would lead to a net decrease in CO2 emissions, but most appear incomplete in their analyses. Synthetic Gas (Syngas) As an alternative to using fossil fuels as an energy stock to power our vehicles and as feedstock for chemical production, what if biomass could produce "gasoline-like" fuel for these purposes? We have already discussed the production of bioethanol from 1st (plant starch), 2nd (lignocellulose) and 3rd (algal) generation feedstocks. It is routinely added to gasoline to upward of 15%. It is also found in E85 (or flex fuel), a gasoline blend containing 50% to 80% ethanol. Instead of producing ethanol through the fermentation of glucose, wood could be incompletely burned to create "synthetic gases" (CO and H2), called syngas, which could be further burned in vehicles to power them or converted through chemical processes (Fisher-Tropsh reaction) to liquid organic fuels. Indeed, when fossil fuels were lacking, wood was used to create syngas power vehicles. Up to a million cars were powered by wood gas in Europe during World War II. A bus powered by wood gas (syngas) generated by a gasifier on a trailer is shown in Figure $9$ below. Figure $9$:  Bus power by wood gas c. 1943 in Leeds, England. By Ministry of Information Photo Division Photographer, Smith Norman? - http://media.iwm.org.uk/iwm/mediaLib.../large.jpgThis is photograph D 15675 from the collections of the Imperial War Museums., Public Domain, https://commons.wikimedia.org/w/inde...curid=24364067 The syngas emitted was cleaned up somewhat to remove tars and soot/ash particles by passing through charcoal before entering the vehicle through a tube. Tars with polycyclic aromatic hydrocarbons and methane could be lowered if wood or coals were first converted to char before use in a process called pyrolysis (heating to high temperatures in the relative absence of air). The gases, derived from the incomplete combustion of the wood, have CO and H2 in various proportions depending on the temperature of burning and the source (wood, coal). A general and very simplified reaction for the incomplete combustion reaction is: \text { Carbon feedstock }+\text { air } \rightarrow \mathrm{CO}+\mathrm{H}_2+\mathrm{CH}_4+\mathrm{CO}_2+\mathrm{H}_2 \mathrm{O}+\mathrm{N}_2 Of course, the reaction is not clean, and many organic side products are produced. The relative ratios of CO and H2 produced can be changed by the addition of water in a second reaction called the water gas shift (WGS) reaction (as water shifts the ratio of CO to H2): \mathrm{CO}+\mathrm{H}_2 \mathrm{O} \leftrightarrow \mathrm{CO}_2+\mathrm{H}_2 \quad(\Delta \mathrm{H}=-41.2 \mathrm{~kJ} / \mathrm{mol} .) If run in reverse (rWGS) and at high temperatures, the water shift reaction would be a way to capture carbon. The H2 could come from the electrolysis of water 2 \mathrm{H}_2 \mathrm{O}(\mathrm{I}) \rightarrow 2 \mathrm{H}_2(\mathrm{~g})+\mathrm{O}_2(\mathrm{~g}) \quad(\Delta \mathrm{H}=286 \mathrm{~kJ} / \mathrm{mol}) The electrocatalytic reduction of CO2 and H2O to produce syngas is shown in $10$. Figure $10$: Syngas Generation by electrocatalytic reduction of CO2 and H2O. (after Kang Cheng et al. Advances in Catalysis, 60 (2017). https://doi.org/10.1016/bs.acat.2017.09.003 The oxidation numbers of each element are shown in the diagram. The cathode acts as a catalyst for the reaction. The reaction requires a power source, so this process is greener if electricity derived from green energy sources (wind/solar) is used. Electrocatalytic Reduction of CO2 for small molecule fuel and chemical feedstocks Electrocatalytic CO2 reduction (ECR) offers the potential to capture CO2 before it is emitted into the air and convert it to small alkanes, alcohols, and acids for fuels (for example, methanol and ethanol) and chemical synthesis (for example, CO and formate). Again this would require a clean source of electricity to power these endergonic reactions.   Table $2$ below shows the standard reduction potentials for a variety of half-reactions that could be coupled to form the main ECR products. Table $2$: Lei Fan et al. Science Advances. 21 Feb 2020, Vol 6, DOI: 10.1126/sciadv.aay3111Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). The reactions are complex and require CO2 to adsorb onto the electrocatalytic surface of the cathode. Here is a possible reaction pathway for the conversion of CO2 to methane: \mathrm{CO}_2 \rightarrow * \mathrm{COOH} \rightarrow * \mathrm{CO} \rightarrow * \mathrm{CHO} \rightarrow * \mathrm{CH}_2 \mathrm{O} \rightarrow * \mathrm{CH}_3 \mathrm{O} \rightarrow \mathrm{CH}_4+* \mathrm{O} \rightarrow \mathrm{CH}_4+* \mathrm{OH} \rightarrow \mathrm{CH}_4+\mathrm{H}_2 \mathrm{O} The catalysts employed are heterogeneous (i.e., not solution phase) and are typically organometallic transition metal structures. Possible reaction pathways to produce small CO2 electrochemical reduction products are shown in Figure $11$ below. Figure $11$: Possible reaction pathways to produce small CO2 electrochemical reduction products. Lei Fan et al., ibid This technology is early in development and will require the development of more robust catalysts and cells before it becomes commercially viable. The synthesis of syngas (typically described as a mixture of CO, CO2, and H2) is widespread now, is used in various processes, and is used to make many products, including hydrocarbons for fuel and oxygen-containing derivatives, including methanol and ethanol. CO and H2 react in the Fischer-Tropsch reaction (described below) to produce alkanes and alkenes. \mathrm{CO}+2 \mathrm{H}_2 \rightarrow\left(\mathrm{CH}_2\right)+\mathrm{H}_2 \mathrm{O} \quad \Delta \mathrm{H}=-165 \mathrm{~kJ} / \mathrm{mol} where CH2 is a methylene repeat in longer-chain alkanes. The CO2 produced in syngas can be somewhat selectively removed by adsorption onto a CaO catalyst, as shown in Figure $12$ below. Figure $12$:  Adsorption configurations of CO2 on the surfaces of CaO-based catalysts at 650 °C: (a) CO2 adsorption on CaO (100) surface; (b) CO2 adsorption on 10 wt % Ni/CaO (100) surface. Green, red, gray, and purple balls represent Ca, O, C, and Ni atoms. Zhao, B. et al. Catalysts 20199, 757. https://doi.org/10.3390/catal9090757.  Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/) The adsorption energies of CO, CH4, and H2 are so small that they have little effect on CO2 adsorption. At present, the easiest way to make syngas is to react natural gas (methane) and steam under very high temperatures (up to 1000oC) over a Ni catalyst (a process called steam reforming). This results in a high H2/CO ratio of about 3. It can be done with liquified natural gas using Ni-ZrO2-CeO2-La2O3 catalyst. This process inherently would do little to decrease CO2 emissions. A gasification method converts coal, lignocellulosic biomass, and waste to syngas, with an H2/CO ratio of <1 for coal and about 0.6-1 for biomass. The electrocatalytic method described above has an H2/CO ratio of 0-2, depending on the nature of the cathodic catalyst. Syngas can also be made by the partial oxidation of methane. For the subsequent reactions (Fisher-Tropsch), the optimal H2/CO is about 2. In the gasification of lignocellulosic biomass, the water gas shift (WGS) is used to increase the H2/CO ratio. This requires lots of water and also produces CO2 as a product. The water shift reaction use catalysts such as Co–Mo–Al2O3, Fe2O3–Cr2O3, and Cu–ZnO–Al2O3) for coal gasification. Syngas can be used to make small molecule energy and chemical feedstocks, such as ethanol (as well as liquid alkanes and alkenes). Given the many different types of products, it is often essential to selectively make and purify products for commercial use. One method for ethanol production is shown in Figure $13$ below. Figure $13$: Conversion of syngas to ethanol proceeds through a tandem mechanism via methanol and acetic acid intermediates using a variety of sequentially positioned catalysts. Kang, J., He, S., Zhou, W. et al. Single-pass transformation of syngas into ethanol with high selectivity by triple tandem catalysis. Nat Commun 11, 827 (2020). https://doi.org/10.1038/s41467-020-14672-8.  Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. H-MOR is a zeolite, which is a microporous, crystalline structure made of aluminosilicate. In a steam reforming reaction, bioethanol could be converted to CO and H2 as well, as shown in the equation below. \mathrm{C}_2 \mathrm{H}_5 \mathrm{OH}+\mathrm{H}_2 \mathrm{O}(+\text { heat }) \rightarrow 2 \mathrm{CO}+4 \mathrm{H}_2 Even with the ability to capture CO2 from the synthesis of syngas, a central issue of concern is whether the production of syngas and syngas-derived fuels and their use is associated with lower net CO2 emissions. A life cycle analysis would be necessary to determine that. Fischer-Tropsch Synthesis (FTS) of Fuels Fischer was head of the Kaiser-Wilhelm Institute for Coal Research in Germany at the start of World War I.  Germany had abundant coal but needed oil for the war, so his efforts were redirected toward that end. Fischer and Tropsch developed the water shift reaction discussed above. They deployed new cobalt catalysts to produce oil which ultimately covered 25% of car fuel and 10% of the German military fuel needs in World War II. Large amounts were also made in South African during the Apartheid regime as well. The Fischer–Tropsch synthesis (FTS) is a polymerization-like reaction that is used to convert gas-to-liquids (GTL), coal-to-liquids (CTL), or biomass-to-liquids (BTL) fuels. It starts with syngas (H2 and CO) produced from the gasification of coal/biomass or steam reforming/partial oxidation of natural gas), with the ratios of H2/CO determined by the water-shift reaction. If coal or biomass is used, a cleanup of residual products with heteroatoms and metal ions is necessary. The clean syngas is then passed into a reactor containing the required catalyst to the FTS of fuels. These reaction systems are summarized in Figure $14$. Figure $14$: A simplified diagram of the Coal-to-Liquids (CTL), Gas-to-Liquids (GTL), and Biomass-to-Liquids (BTL) processes.  Shafer, W.D.; Gnanamani, M.K.; Graham, U.M.; Yang, J.; Masuku, C.M.; Jacobs, G.; Davis, B.H. Fischer–Tropsch: Product Selectivity–The Fingerprint of Synthetic Fuels. Catalysts 20199, 259. https://doi.org/10.3390/catal9030259.  Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). The FTS reaction is conducted at moderate temperatures and pressure to produce fuels (diesel and jet), lubricants, waxes, and chemical feedstocks. The main representative reactions are given by: N-alkane production: (2 \mathrm{n}+1) \mathrm{H}_2(\mathrm{~g})+\mathrm{nCO}(\mathrm{g}) \rightarrow \mathrm{C}_{\mathrm{n}} \mathrm{H}_{2 \mathrm{n}+2}+\mathrm{nH}_2 \mathrm{O} Alkene production: (2 \mathrm{n}) \mathrm{H}_2(\mathrm{~g})+\mathrm{nCO}(\mathrm{g}) \rightarrow \mathrm{C}_{\mathrm{n}} \mathrm{H}_{2 \mathrm{n}}+\mathrm{nH}_2 \mathrm{O} The products can have from one carbon (CH4) to over 70, depending on the catalyst, T, P, and H2/CO ratio. The FTS is a polymerization reaction, which starts with adsorption of the gas to the catalytic surface, followed by multiple cycles of free radical initiation, propagation, termination, desorption, and reabsorption. Since this book focuses on structure/function and reaction mechanisms, we would be remiss not to include at least a simplified mechanism for these complex reactions. Several mechanisms have been proposed. Since iron was/is used in the catalyst, and since iron can form iron carbides, Fisher proposed a carbide mechanism. A second enol mechanism has also been proposed. Both are shown in Figure $15$ below. Figure $15$:  Proposed mechanisms for the Fischer-Tropsch reaction. Left: A proposed FTS route based on the carbide mechanism; Right: A proposed FTS route based on the enol mechanism. M is the metal surface. In the carbide mechanism, CO adsorbs on the metal catalyst and dissociates in C and O atoms that cover the surface. These are hydrogenated to form H2O and CH2 (methylene), and H2 adds as the reaction proceeds, as shown. In the enol mechanism, CO adsorbs without dissociation into atoms. It reacts on the surface to surface-bound H atoms (that arise from the dissociation of adsorbed H2) to form hydroxymethylene (M-CHOH). This enol grows on condensation with adjacent hydroxymethylenes. The rate-determining step is the hydrogenation of adsorbed CO. Another proposed mechanism, CO insertion, is shown in Figure $16$ Figure $16$:  A proposed FTS route based on the CO insertion mechanism. CO in this model inserts into a bond from a hydrogen atom to a metal on the catalyst. The rate-limiting step here is the hydrogenation of CO to the CH2 methylene group. The assumed monomer for this mechanism is simply CO through its insertion into metal-carbon bonds. Generally, the FTS reaction catalyst has either cobalt or iron ions. The metal catalyst can also be doped with potassium and copper ions and bind silica and alumina. Iron is abundant and cheap and better promotes the water-gas-shift reaction, so it is best for FTS synthesis of fuels from coal and biomass since syngas derived from them have a lower H2/CO ratio. Bioaviation Fuel Now we can turn our attention to the production and analysis of bioaviation fuel, which is more like kerosene and diesel fuel than ethanol in its composition. As we saw for bioethanol productions, the feedstocks can be 1st, 2nd and 3rd generation, as shown in Table $3$ below. First-generation (1-G) Second-generation (2-G) Third-generation (3-G) Fourth-generation (4-G) • Oil-seed crops: camelina, oil palm, rapeseed, soybean, sunflower, salicornia • Sugar and starchy crops: corn, wheat, sugarcane, sugar beets • Oil-seed energy crops: jatropha, castor bean • Grass energy crops: switchgrass, miscanthus, Napier grass • Wood energy crops: poplar, willow, eucalyptus •  Agricultural and forestry residues: corn stover, sugarcane bagasse, wood harvesting/processing residues • Food and municipal waste: used cooking oil, animal fats, biogenic fraction of municipal solid waste • Algae: microalgae • Genetically modified organisms • Non-biological feedstocks: CO2, renewable electricity, water Table $3$: Feedstocks for bio-aviation fuel production. Doliente SS, Narayan A, Tapia JFD, Samsatli NJ, Zhao Y and Samsatli S (2020) Bio-aviation Fuel: A Comprehensive Review and Analysis of the Supply Chain Components. Front. Energy Res. 8:110. doi: 10.3389/fenrg.2020.00110. Creative Commons Attribution License (CC BY). A typical jet aviation fuel (Jet A-1) contains n- and branched-alkanes (often called paraffins) and some alkenes (often called olefins) with 8-16 carbon atoms, cycloalkanes, and aromatics. A comparison of the components of Jet-A1 with a typical bioaviation fuel, Bio-Jet, is shown in Figure $17$ below. Figure $17$: Molecular-class compositions of (a) Jet A-1 and (b) bio-jet identified by the relative signal area percentage analysis of GC–MS. After Cheon Hyeon Cho, Hee Sun Han, Chae Hoon Sohn, and Jeong Sik Han.  ACS Omega 2021 6 (40), 26646-26658.  DOI: 10.1021/acsomega.1c04002. Bioaviation fuel can be made from feedstocks containing triacylglycerol or, most readily using feedstocks shown in Table 2 above to create syngas for use in the Fischer-Tropsch reaction. We've already discussed those reactions above. Instead, let's focus on whether bioaviation fuel, better termed sustainable aviation fuels (SAF), is good for our climate. This presupposes that battery-powered planes and jets are not scalable to our current environmental needs. Life Cycle Analysis - Jet Fuel from Grasses As expected, the US is the largest user of aviation fuel and causes 25% of aviation CO2 emissions, as much as all the greenhouse gases emitted through fuel use in Spain. It is estimated that the US will need around 30 billion gallons/per (BGY) of jet fuel in 2040. A biojet fuel industry based on cellulose as a feedstock could theoretically produce that amounts. If the industry costs are estimated at $123 billion, then the cost of the jet fuel would be$4.30/gal. About 60% of the costs would arise from the conversion of biomass to sustainable aviation fuel (SAF), which we described above. Those costs include the building of the biorefineries. Their cost would be distributed over their lifetimes of the plants. The fuel would be derived from syngas from the Fisher-Tropsch reaction, a reasonably mature technology. The costs would be much lower (closer to $1/gal) if the infrastructure costs were not included. These total costs are comparable to the price paid for regular jet fuel (around$2.2/gal in 2021). Consumer costs would not go up 2-fold since fuels are only part of the cost paid by passengers (15-25%) Consumers' current price for fossil fuel is much less than its actual cost. The present price/gal does not include external costs associated with the use of fossil fuels. These include climate change effects on infrastructure, agriculture, industry, etc., and on human health (mostly from negative health consequences and diseases exacerbated by fossil pollution). We all ultimately pay for these hidden costs resulting from a failed market for pricing fossil fuels. On top of this, the fossil fuel industry has been massively subsidized for decades. Different prices have been placed on carbon emissions from fossil fuels to resolve this error in the market. The carbon price is based on the number of tons of CO2 equivalent emitted  ($US/ton CO2e). If a reasonable carbon price is added, biojet fuels made from lignocellulosic stocks through the Fisher-Tropsch reaction would be theoretically competitive with conventional jet fuels made from fossil fuels. The extra added cost for sustainable aviation fuel (SAF) compared to traditional aviation fuel at different prices placed on carbon placed on each are shown in Table $4$ below, assuming an average cost of$2.20/gal for traditional aviation fuel. Price on carbon ($US/t CO2e) SAF - Traditional Aviation Fuel ($/gal) 0 +$1.90/gal$50 +$0.60/gal$175 $0 If the cost of traditional jet fuel went to$3/gal, as it did in the US in March 2022, SAF and conventional jet fuel would cost the same if a price on carbon of $100/t CO2e were included for both. The actual "social" cost of carbon has been calculated (9/22) to be$175/ton CO2e. Other factors other than the cost of carbon should be included in these analyzes. These include the issue of sustainable land use to grow feedstocks for the SAF. A recent analysis shows that it would be possible to produce 30 billion gall/yr of cellulosic SAF by planting 23.2 Mha (Million hectares, about the size of the state of Wyoming) of marginal agricultural lands (about one-third of croplands and 2/3 noncrop lands) in the Midwest with miscanthus, with a net cost of $4.1/gal and assuming a carbon price of a$50/ t CO2e.  Miscanthus is a rapidly growing tall perennial grass with a high yield that grows in moderate climates. The life cycle analysis included interactions among atmospheric, land surface, ecosystem, and economic systems. Miscanthus gigantheus is shown below in Figure $18$. Figure $17$:Miscanthus gigantheus.  https://commons.wikimedia.org/wiki/F...us_Bestand.JPG.   Creative Commons Attribution-Share Alike 3.0 Unported Figure $19$ shows some data from the study. Four different scenarios are offered, each represented by bar graphs. Figure $19$: Land availability and conversion by existing use. Excel data and graph from https://dataverse.harvard.edu/datase...910/DVN/VBFLI2CC0 1.0 Public Domain. Four scenarios (left to right) were used to produce 30 MG/yr of SAF using a carbon price of \$50/t CO2e. • M25:  only 25% of the marginally useful land was used • M100-reg4:  marginal land bases with the lowest hydrological and climatic risks • M100:  all of the marginally included land was made available • Unrestricted Panel (a) shows that in each case, about 23.2 Mha of land was converted to growing miscanthus out of the available land. Pane (b) shows stacked bars showing the percentages of each type of land available and converted for each scenario. The last scenario shows that demand can be made with the lowest % conversion of the marginal lands now used for corn/soybeans and other crops. It would appear that the marginal croplands converted to SAF production would be the same lands diverted to bioethanol production. Nevertheless, it would appear that up to 76% of projected aviation fuel needs could be met by planting marginal cropland and noncrop land for cellulosic SAF production. The study found that using available lands in the Plains was not feasible. Key Points - Beta version from Chat.openai 1. Biodiesel, syngas and bioaviation fuel are alternative biofuels derived from biomass that can be used to reduce the dependence on fossil fuels. 2. Biodiesel is a liquid fuel that can be used in diesel engines, it is produced by chemically converting vegetable oils or animal fats into a fuel that can be used in place of diesel fuel. 3. Syngas (synthetic gas) is a mixture of hydrogen and carbon monoxide that can be produced from biomass through processes such as gasification. It can be used as a fuel for heat and power generation or further converted into chemicals and liquid fuels. 4. Bioaviation fuel is a form of biofuel that can be used in aviation, it is made from biomass such as algae or woody biomass, it can be blended with traditional jet fuel and can help reduce emissions from airplanes. 5. These biofuels have different advantages and limitations, and their production process is still in the research and development stage. 6. Biodiesel has the advantage of being able to be used in existing diesel engines with little or no modification, it reduces emissions and it is renewable. 7. Syngas has the advantage of being able to be converted into different chemicals and liquid fuels, it has a higher energy content than bioethanol and it can be used for power generation. 8. Bioaviation fuel has the advantage of reducing emissions from airplanes, it can reduce dependence on fossil fuels in the aviation industry and it can be renewable.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.08%3ABiodiesel_Syngas_and_Bioaviation_Fuel.txt

Search Fundamentals of Biochemistry H2 as a fuel Hydrogen gas would be ideal if it could be produced at scale, easily transported and stored, or produced at local sites on demand.   The reaction for the "burning" of hydrogen shows that the only greenhouse gas emitted is H2O. 2 \mathrm{H}_2(\mathrm{~g})+\mathrm{O}_2(\mathrm{~g}) \rightarrow 2 \mathrm{H}_2 \mathrm{O}(\mathrm{g}) H2O comes and goes in our atmosphere in short timescales and does not continually build up, as does CO2 from burning fossil fuels. The standard heat of combustion (in kJ/g or kcal/kg) for H2 is far higher than any other fuel, as shown in Table  $1$ below, making it an ideal fuel. Name Formula State -ΔHc° kJ/mol -ΔHc° kJ/g or MJ/kg -ΔHc° kcal/kg Ammonia NH3 gas 383 22.48 5369 Butane C4H10 gas 2878 49.50 11823 Carbon (graphite) C cry 394 32.81 7836 Ethanol C2H6O liq 1367 29.67 7086 Hydrogen H2 gas 286 141.58 33817 Methane CH4 gas 891 55.51 13259 methyl stearate (biodiesel) (CH3(CH2)16(CO)CH3  liq 1764 40 9560 Naphthalene C10H8 cry 5157 40.23 9609 Octane C8H18 liq 5470 47.87 11434 Propane C3H8 gas 2220 50.33 12021 wood (red oak) - solid - 14.8 3540 coal (lignite) - solid - 15 3590 coal (anthracite) - solid   27 4060 Table  $1$:  Energy values for various fuels. Data source:  https://www.engineeringtoolbox.com/s...nt-d_1987.html We won't discuss large-scale H2 storage or transport, two fundamental engineering problems. Instead, we will focus on the production of "biohydrogen." Of course, the prefix "bio" can mean many things, including the production of H2 in syngas using cellulose as a feedstock, the electrolysis of water powered by solar/wind energy, and its production by hydrogenases, enzymes found in some microbes. The fuel industry uses different colors as descriptors of hydrogen based on how it is produced. They are shown in Table  $2$. Color Method of production Green electrolysis of H2O using solar/wind to generate electricity (expensive at present) Blue steam reforming of natural gas (CH4) with the other product, CO2 captured and stored (CCS) Grey steam reforming of natural gas (CH4) without CO2 capture and storage Black (coal/oil) gasification to form syngas Pink (purple/red) electrolysis powered by nuclear energy, which does not emit CO2; heat emitted produces steam for blue/gray H2 production Turquoise methane pyrolysis (heat in the absence of O2) to form H2 and C Yellow electrolysis using solar power without conversion to electricity as the power source. White underground H2 released through fracking Table  $2$:  Different "colors" of hydrogen based on production methods Of course, H2 in syngas can be produced from biomass, as described in Chapter 32.8, but it is unclear if a hydrogen color has been assigned to it. At present, the important feedstocks for H2 production around the world are natural gas (48%), oil (30%), coal (18%), and electrolysis (4%) - mostly all fossil fuels. Methods of Production H2 production is also classified based on the chemical processes used to produce it. These processes include 1. Biological (use of live bacteria and algae cells) 2. Thermochemical:  (gas and liquid fuel reforming, coal and biomass gasification), 3. Electrochemical (electrolytic):  (photothermal, photoelectrolytic, and photobiological) We will organize this chapter section using these three processes. We will start with Biological (1), followed by Electrochemical/Electrolytic (3), and end with Thermochemical (2). They are summarized in Figure $1$. Figure $1$:  The main pathways for H2 production based on biomass. M.G. Eloffy et al., Chemical Engineering Journal Advances, 12 (2022). https://doi.org/10.1016/j.ceja.2022.100410.  Creative Commons license Biomass can be used as the feedstock for all of these methods, so the resulting product can be called biohydrogen. Of course, nonbiological sources of feedstocks are the predominant ones used in thermochemical and electrochemical methods as well, as we discussed in the previous chapter section. 2H+ ↔  H2: An Overview We will mostly discuss the production of H2 as a society energy source. For industry use, it can be used in fuel cells to power spacecraft and cars, as shown in the reaction below. \begin{aligned} & \mathrm{O}_2+4 \mathrm{H}^{+}+4 \mathrm{e}^{-} \longrightarrow 2 \mathrm{H}_2 \mathrm{O} \ & \mathrm{H}_2 \longrightarrow 4 \mathrm{H}^{+}+4 \mathrm{e}^{-} \end{aligned} In the next chapter section, we will discuss in great detail the hydrogenases that produce and use H2 in microbes so that this chapter will treat them very generally. However, we need to review the topic. Use of H2 as a source of electrons for reduction reactions. Each hydrogen in H2 has an oxidation number of 0. Each hydrogen can be oxidized to H+ (oxidation number +1) with the 2 electrons passed on to a substrate/cofactor or a sequential series of substrates with higher and higher standard reduction potentials (better oxidizing agents), leading to the formation of reduced products. H2 + (substrate)OX  →  2H+  +  (product)RED This general reaction is analogous to the mitochondrial electron transport chain, in which electrons are passed from a source (NADH) to oxidized forms of acceptors. The general reaction below shows each redox pair in the electron transport chain. NADH/NAD+ →  FAD/FADH2 →  UQ/UQH2  →  Cyto COX/Cyto CRED  →   O2/H2 Some organisms have evolved to produce energy by the oxidation of H2. This reaction is analogous to those used by photosynthetic organisms to obtain energy through the oxidation of water. In photosystem II,  oxygen in H2O (oxidation number -2) gets oxidized by the oxygen-evolving complex to produce O2 (oxidation number 0). Some redox pairs, starting with H2O/O2, are shown below for photosystem II. H2O/O2 →  P680/P680* →  (Plastoquione)OX/(Plastoquione)RED The first reaction is endergonic and requires an energy source photons. Use of H+ as a sink for electrons for oxidation reactions that produce H2. H+ has an oxidation number of +1. Hence it can be reduced to H2 (oxidation number of 0) as it gains electrons from substrates/cofactors, which get oxidized. This general reaction is shown below. 2H+ + (substrate/cofactor)RED  →  H2  + (substrate/cofactor)OX Many microorganisms can produce H2 through variants of photosynthesis or through fermentation, both of which provide the two electrons needed. E. Coi has four hydrogenases (Hyd 1, 2, 3, and 4). It forms H2 through two reactions catalyzed by: • formate (HCO2-) dehydrogenase (FDH):  2HCO2⇌ 2CO2 + 2H+ + 2e- • hydrogenase (H2ase):  2H+ + 2e- → H2 The C in formate has an oxidation number of +2 and is oxidized to CO2, in which the C has an oxidation number of +4. Nothing is simple: H2 is an indirect greenhouse gas. H2 itself is not a greenhouse gas as it doesn't have any bond vibrations that produce transient dipoles and hence does not absorb in the infrared region of the spectrum. Yet by affecting atmospheric levels of methane, a very potent greenhouse gas, as well as levels of ozone, it can lead to warming. It's not emission from the combustion of H2 but rather the leakage into the atmosphere of transported and stored H2 gas that is problematic. Most of the H2 that finds its way into the atmosphere diffuses into the soil and is taken up by bacteria. The rest reacts with hydroxy radicals (.OH) in the atmosphere, as shown in the reaction below. H2  +  .OH →  H2O  +  H. (atomic hydrogen) The reaction of .OH with H2 decreases the hydroxy radical's availability to react with the very potent greenhouse gas methane, CH4. That reaction is shown below. CH4 + .OH →  .CH3 + H2O The methyl radical .CH3 reacts rapidly with oxygen to form the methylperoxy radical (CH3O2.). This eventually forms formaldehyde, a water-soluble molecule that is removed from the atmosphere on precipitation.   Hence the reaction of H2 with .OH increases the half-live of CH4 in the atmosphere. .OH is a key molecule in the troposphere and is considered a methane "sink"  that leads to the drawdown of methane.   We discussed the extreme reactivity of .OH in Chapter section's 12.3 and 12.4. It's so reactive that its half-life is in the order of seconds. It is also at very low concentrations of less than 1 part per trillion. .OH is produced from ozone, O3, by the following reactions: O3  + hν (UV) →  O2 + .O .O + H2O  →  2 .OH The first reaction is a photolysis, and experiments during a solar eclipse have shown the production of .OH in the atmosphere shuts down! Dr. Paul Crutzen, Nobel Prize winner in Chemistry, described .OH as the "detergent of the atmosphere" since it can react with and oxidize many deleterious trace gases in the troposphere, making them more water-soluble, leading to their elimination from the atmosphere. A main reaction of .OH is carbon monoxide (CO). It also reacts with volatile organic compounds (VOCs) and NOx (NO + NO2), which are precursors of tropospheric ozone, a health hazard. Even though dioxygen, which comprises 20% of the atmosphere, is also an excellent oxidizing agent, it is kinetically slow to react. Very few gases are not oxidized by .OH. One set includes the refrigerant gases chlorofluorocarbons, which without oxidation by .OH enter the stratosphere, where they react with stratospheric ozone and reduce its protective effect against dangerous UV light. It does react with hydrochlorofluorocarbons (HCFCs). Figure $2$ below summarizes the adverse climatic effects of the oxidation of H2 in the atmosphere. Figure $2$:  Effects of hydrogen oxidation on atmospheric greenhouse gas concentrations and warming. I. Ocko and Steven P. HamburgAtmos. Chem. Phys., 22, 9349–9368, 2022. https://doi.org/10.5194/acp-22-9349-2022.  Creative Commons Attribution 4.0 License. Note in the central panel that H. (atomic hydrogen) can start a free radical change reaction to produce tropospheric ozone, O3, a pollutant that is not only a greenhouse gas but which also causes serious health consequences. The message is this:  Care has to be taken to minimize methane and H2 leakage during their production and use as fuels. Biohydrogen from Microalgae We will focus most of our attention on the Biological (1) and Electrolytic (3) processes for producing biohydrogen from microalgae. The Biological processes (1) require hydrogenases for H2 production within cells. The Electrolytic (3) processes use microalgae as a feedstock to provide substrates that other microbes can ferment. These can be combined to increase production. Figure $3$ below summarizes the Biological (1) and Electrolytic (3)  metabolic processes that can be used for microalgae H2 production. Figure $3$: Metabolic pathways of biohydrogen production by micro-algal biomass. modified from Ahmed SF et al. Front. Energy Res. 9:753878. doi: 10.3389/fenrg.2021.753878.   Creative Commons Attribution License (CC BY). These are mainly classified into three categories: i) the photobiological process through which biohydrogen is produced via direct and indirect photolysis in the microalgae, ii) fermentation, and iii) the electrochemical process that comprises photoelectrochemical and electrolytic. BIOLOGICAL (1) - Biophotolysis (photosynthesis) This consists of two processes, Direct and Indirect Photolysis (photosynthesis). Both use light to drive the ultimate reduction of 2H+ to H2 using hydrogenase or nitrogenase. We will explore the details in the next chapter section. The biophotolysis process is divided into indirect (using electrons from substrates) and direct (using electrons from water). These processes are simplified in Figure $4$. Figure $4$: Schematic diagram for biological (biophotolysis) process.   M.G. Eloffy et al. Direct Biophotolysis (photosynthesis) In direct biophotolysis (photosynthesis), water molecules are oxidized in Photosystem II, which contains the Oxygen Evolving Complex (OEC). This endergonic process is driven by light. The electrons lost from water are passed through Cytochrome b6f and Photosystem I to ferredoxin then NADP+, which gets reduced to NADPH (as discussed in Chapter 20). These reactions are illustrated in Figure $5$. Figure $5$: Light reaction of photosynthesis and associated standard reduction potentials In direct photolysis, electrons are passed directly from reduced ferredoxin to 2H+ in a reaction catalyzed by a hydrogenase, as shown in Figure $6$ below. Figure $6$: Metabolic hydrogen production pathways used by Chlamydomonas reinhartii.FDX: ferredoxin; H2ase: hydrogenase; NPQR: NADPH−plastoquinone oxidoreductase; PFR: pyruvate:ferredoxin oxidoreductase; PSI: photosystem I; PSII: photosystem II. Touloupakis, E.; Faraloni, C.; Silva Benavides, A.M.; Torzillo, G. Recent Achievements in Microalgal Photobiological Hydrogen Production. Energies 202114, 7170. https://doi.org/10.3390/en14217170.  Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). The overall reaction is simplified in the equation below. 2 \mathrm{H}_2 \mathrm{O}+\text { Light } \rightarrow 2 \mathrm{H}_2+\mathrm{O}_2 One problem with direct photolysis is that O2 can damage hydrogenases. Again we will discuss the biochemistry of hydrogenases in great detail in the next chapter section. Indirect Biophotolysis This process bypasses the damaging effects of O2 on hydrogenase by being carried out in the absence of O2 using fermentation to provide electrons for the hydrogenase reduction of 2H+ to H2. Photosynthesis is required to make the carbohydrates necessary for fermentation. Glucose can then be oxidized anaerobically (in the dark to avoid O2 formation from photosynthesis) to form pyruvate through the glycolytic pathway. Pyruvate can then be oxidatively decarboxylated through the pyruvate:ferredoxin oxioreductase (PFR) as ferredoxin gets reduced. It then passes its electrons on through hydrogenase to produce H2. The pathway is illustrated in the top/right parts of the above figure and the reaction diagram in Figure $7$ below. Figure $7$: Model of fermentative pathways involved in dark anaerobic H2 production in C. reinhardtiiProteins are shown as ovals. Photosynthetic ferredoxin (PETF).   Jens Noth et al., Journal of Biological Chemistry, 288 (2013). https://doi.org/10.1074/jbc.M112.429985.  Creative Commons license. Glucose and some amino acids can be converted into pyruvate, a substrate for PFR1 in the single-cell algae C. reinhardtii. PFR1 converts pyruvate to acetyl-CoA and CO2 with the electrons used to reduce ferredoxin. The reduced FDX2 passes electrons through hydrogenase (HYDA1) to form H2 Another enzyme used to continue fermentation, pyruvate:formate lyase (PFL1), converts pyruvate to formate and acetyl-CoA, which can be metabolized further to acetate and ethanol. A shift to pyruvate oxidation to PFR1 occurs if PFL1 is mutated or long term anoxic conditions. The key enzyme, pyruvate:ferredoxin oxioreductase (PFR), uses thiamine pyrophosphate (TPP) as a cofactor for the oxidative decarboxylation of the α-keto acid pyruvate, as expected. Figure $8$ shows an interactive iCn3D model of the pyruvate ferredoxin oxidoreductase (PFOR) from Desulfocurvibacter africanus in anaerobic conditions (7PLM). Figure $8$: Pyruvate ferredoxin oxidoreductase (PFOR) from Desulfocurvibacter africanus in anaerobic conditions (7PLM). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...XRFMoVhaoRbW86 PFOR, abbreviated here, is a 267 kDa homodimer containing three [Fe4S4] clusters (spacefill) per monomer. Only one monomer is shown, and TPP is shown in sticks. Again indirect photolysis occurs in the absence of O2. Light illumination leads to only transient H2 synthesis. If sulfur is limited in the growth media of the algae, more sustained H2 production occurs, as the lack of sulfur reduces PSII activity. Hence H2 production can be maximized by depleting sulfur and minimizing O2 even in the presence of light. In the absence of O2, hydrogenase gene expression increases. Nutrient depletion also leads to the production of formate and acetyl-CoA through the enzyme pyruvate:formate lyase (PFL1). This is predominant in Chlamydomonas cells in the dark. The green microalgae C. reinhardtii makes most of its H(approximately 90%) using direct photolysis.   Commercially, the production of H2 in indirect photolysis is carried out in a separate sealed bioreactor to avoid  O2. Indirect photolysis is shown in the above figures. The reactions to this process are as follows : \begin{gathered} 12 \mathrm{H}_2 \mathrm{O}+6 \mathrm{CO}_2+\text { hν } \rightarrow \mathrm{C}_6 \mathrm{H}_{12} \mathrm{O}_6+6 \mathrm{O}_2 \ \mathrm{C}_6 \mathrm{H}_{12} \mathrm{O}_6+12 \mathrm{H}_2 \mathrm{O}+\text { hν } \rightarrow 12 \mathrm{H}_2+6 \mathrm{CO}_2 \end{gathered} BIOLOGICAL (1) - Fermentation We have just discussed fermentation processes within living microalgae cells. Now let's consider fermentation processes using nonliving biomass feedstocks supplied to microbes to produce H2. This offers a significant way to make biohydrogen. A schematic diagram for Biological (1) fermentation is shown below in Figure $9$: Figure $9$: Schematic diagram for biological (fermentation) process. M.G. Eloffy et al. Fermentation involves the decomposition of organic biomass to produce CO2 and H2.   The fermentation process can be separated into photofermentation (light fermentation) and dark fermentation. Photofermentation Some photosynthetic bacteria and microalgae use Photofermentation to produce H2 from organic acids like acetic, butyric, lactic, and succinic acids. Oxidation of the acids produces CO2 as well as H+s and e- for H2 production. Electrons are transferred through photosystem I and eventually, believe it or not, nitrogenase. It is a fermentation process as the process is anoxic. Some photosynthetic bacteria, like the purple nonsulfur bacteria, a facultative anoxygenic phototroph, and some microalgae, can produce H2 using a simplified system that has only one photosystem and uses the enzyme nitrogenase to produce H2. The photosystem can not generate an oxidizing agent strong enough to oxidize H2O, but under anaerobic conditions, they can oxidize organic acids and even H2S to provide electrons from H2 production. These reactions are shown below in Figure $10$. Figure $10$: Photofermentative hydrogen production in PNSB. Deo, D., Ozgur, E., Eroglu, I., Gunduz, U., & Yucel, M. (2012). Photofermentative Hydrogen Production in Outdoor Conditions. Hydrogen Energy - Challenges and Perspectives. doi: 10.5772/50390. Creative Commons Attribution 3.0 License We studied nitrogenase in Chapter x.xx. The net reaction for the fixation of nitrogen is shown below. \mathrm{N}_2+8 \mathrm{H}^{+}+8 \mathrm{e}^{-}+16 \mathrm{ATP} \rightarrow 2 \mathrm{NH}_3+\mathrm{H}_2+16 \mathrm{ADP}+16 \mathrm{Pi} In this reaction, N2 is reduced as the N atoms go from a 0 oxidation state to +3 in NH3. The needed electrons are made from organic acids and fed into the system and eventually go to ferredoxin, which transfers them to protons. The ratio of N2 to H2 produced is 1:1, at the expense of 16ATPs per H2 produced. The ATP produced by the collapse of the produced proton gradient through FoF1ATPase powers the reaction. In the absence of N2, the net reaction becomes 2 \mathrm{H}^{+}+2 \mathrm{e}^{-}+4 \mathrm{ATP} \rightarrow \mathrm{H}_2+4 \mathrm{ADP}+4 \mathrm{Pi} The electrons are still fed into nitrogenase, but in the absence of the substrate N2, they are used to reduce 2H+ to H2. Note that only 4 ATPs are required per each H2 produced, a significant energy gain. ATP produced during photosynthesis would be used for anabolic biosynthesis contributing to biomass, so extra ATP is needed to support H2 synthesis past that needed for growth. As anabolism is a reductive process (compared to oxidative catabolism), adequate sources of electrons for reduction are required. Multiple pathways need electrons, including CO2 fixation, N2 fixation (with associated H2 production, and organic acids like polyhydroxbutyrate.  The bacteria use photosynthesis and the Calvin cycle under photoautotrophic conditions to fix CO2. When external energy supplies from organic acids are present, the bacteria can become photoheterotropic. Under these conditions, the Calvin cycle is used to maintain redox balance. Dark Fermentation We studied this indirectly above section in our discussion of hydrogenases in microalgae. Hydrogenases are induced in dark conditions, and this pathway involved heterotrophic fermentation (anaerobic) in some bacteria and microalgae. Many microbial species are used. Industrial wastewater enriched in organic material can be used as a feedstock. Feedstock materials are hydrolyzed and subjected to fermentation, during which H2 can be produced. For example, pyruvate produced by glycolytic fermentation can be oxidatively decarboxylated to acetyl-CoA and CO2 by pyruvate:ferredoxin oxidoreductase with electrons passed on to ferredoxin and even through hydrogenase to form H2 (as we described above). Addition H2-produced steps after fermentation include acetogenesis and methanogenesis.    These processes are illustrated in Figure $11$ below. Figure $11$: The steps involved in anaerobic digestion [9]. Rosa, P. R. F., & Silva, E. L. (2017). Review of Continuous Fermentative Hydrogen-Producing Bioreactors from Complex Wastewater. Frontiers in Bioenergy and Biofuels. doi: 10.5772/65548. Creative Commons Attribution 3.0 License Examples of acidogenic (formation of short carboxylic/fatty acid), acetogenic (formation of acetic acid), and methanogenic (formation of methane) reactions that produce (and a few that consume) H2 are shown in Table $3$ below. Acidogenic reactions C6H12O6 + 2H2O → 2CH3COOH + 2CO2 + 4H2 C6H12O6 + 2H2O→ CH3CH2CH2COOH + 2CO2 + 2H2 Acetogenic reactions CO2+ 4H2→ CH3COOH+ 2 H2O CH3CHOHCOOH + H2O → CH3COOH + CO2 + 2H2 CH3CH2OH + H2O →CH3COOH + 2H2 CH3CH2COOH + 2 H2O → CH3COOH + CO2 + 3 H2 CH3(CH2)2COOH + 2 H2O → 2 CH3COOH + 2H2 Methanogenic reactions 4 H2 + CO2→ CH4 + 2 H2O CH3COOH → CH4 + CO2 2CH3(CH2)2COOH + 2H2O + CO2→ 4CH3COOH + CH4 Table $3$:  Example of acidogenic, acetogenic, and methanogenic reactions in dark fermentation. Adapted from Rosa, P. R. F., & Silva, E. L., ibid. We have described a few of the enzymes involved in acidogenic reactions above. Figure $12$ shows a summary of the steps in acidogenesis. Figure $12$: An overview of the metabolic pathways of acidogenesis.  Dzulkarnain, E.L.N., Audu, J.O., Wan Dagang, W.R.Z. et al. Microbiomes of biohydrogen production from dark fermentation of industrial wastes: current trends, advanced tools and future outlook. Bioresour. Bioprocess. 9, 16 (2022). https://doi.org/10.1186/s40643-022-00504-8.  http://creativecommons.org/licenses/by/4.0/. A more complex list and summary of dark fermentation reactions are shown in Figure $13$ below. Figure $13$: Key enzymes and dominant microbial taxa involved during anaerobic digestion of organic matter.  Dzulkarnain, E.L.N.et al. Ibid. ELECTROCHEMICAL/ELECTROLYTIC (3) Two primary electrochemical/electrolytic methods for H2 production are photoelectrochemical and electrolytic, as shown in Figure $14$ below. djfkjdk Figure $14$:  Schematic diagram for the electrochemical process. M.G. Eloffy et al. Electrolysis In a microbial electrolytic cell (MEC), microalgae/cyanobacteria use industrially- and metabolically-processed feedstocks to oxidize organic substrates (for example, acetic acid) to CO2. The released electrons move from the anode (where the oxidation occurs) to the cathode for H+ reduction to H2. An external voltage is applied to increase electron flow to the cathode to facilitate the process. This increases the production of H2 over and above that of just fermentation by microbes in the electrolytic cell. Cyanobacteria and a mix of green microalgae are used, as well as bacteria, which can use dark fermentation (i.e., combining the processes described above). Photoelectrochemical Microbial photoelectrochemical cells (PEC) use light-sensitive semiconductor electrodes for water electrolysis. A membrane separates the two electrodes so the protons can be reduced to form H2.  for the separated by a membrane, 2. THERMOCHEMICAL from Biomass We have already explored thermochemical methods to produce syngas  (H2 and CO) and further use in the Fishcer-Tropsch reaction to make small and large molecules for chemical feedstocks and fuels. We also discussed electrochemical methods to produce syngas and other small organic molecules like formate and ethanol from CO2. Figure $15$ shows a schematic diagram for thermochemical (gasification) processes to produce H2. Figure $15$:  Schematic diagram for thermochemical (aqueous phase reforming) process. M.G. Eloffy et al. Key Points - Beta version from Chat.openai 1. Biohydrogen is a form of biofuel that is produced from biomass through a process known as biological hydrogen production. 2. Biological hydrogen production involves the use of microorganisms, such as bacteria and algae, to convert organic matter into hydrogen gas. 3. Biohydrogen has the potential to be a clean, renewable, and sustainable source of energy, as it produces only water when burned and does not produce greenhouse gases. 4. Photosynthetic microorganisms such as algae and cyanobacteria are the most promising organisms for biohydrogen production, they can convert water and CO2 into hydrogen and oxygen through the process of photosynthesis. 5. Fermentative microorganisms such as bacteria and fungi can also be used to produce biohydrogen, they can convert organic materials such as sugars and starches into hydrogen through the process of fermentation. 6. Biohydrogen can be produced through different processes, including dark fermentation, light-driven fermentation, and photo-biological hydrogen production. 7. Dark fermentation is the process of using microorganisms to ferment organic matter in the absence of light to produce hydrogen gas. 8. Light-driven fermentation is the process of using microorganisms to ferment organic matter in the presence of light to produce hydrogen gas. 9. Photo-biological hydrogen production is the process of using algae or photosynthetic bacteria to produce hydrogen gas through photosynthesis. 10. Biohydrogen production is still in the early stages of development and research is ongoing to improve the efficiency and cost-effectiveness of the process. 11. Biohydrogen production from algae is considered more sustainable and environmentally friendly than traditional hydrogen production methods, which are often based on fossil fuels. 12. Biohydrogen has the potential to significantly reduce carbon emissions and to help decarbonize various sectors, including transportation and energy.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.09%3A__Biohydrogen_-_An_Introduction.txt

Search Fundamentals of Biochemistry Introduction In the last section, we described different ways to produce H2 and the colors ascribed to them based on the environmental impacts. Many look to the production and use of H2 to provide energy without releasing CO2. H2 can be used in fuel cells to power spacecraft and cars, as shown in the reaction below. \begin{aligned} & \mathrm{O}_2+4 \mathrm{H}^{+}+4 \mathrm{e}^{-} \longrightarrow 2 \mathrm{H}_2 \mathrm{O} \ & \mathrm{H}_2 \longrightarrow 4 \mathrm{H}^{+}+4 \mathrm{e}^{-} \end{aligned} Cars are already available that run on H2 considered a zero-emission fuel. These fully electric cars use fuel cells powered by the oxidation of H2 to produce electrical energy. Figure $1$:  https://afdc.energy.gov/vehicles/how...tric-cars-work Given the scale needed, most H2 is presently derived from the steam reformation of natural gas and the electrolysis of water. From a biochemical perspective, cells have evolved to make H2 and use H2 as an energy source. It's unlikely that direct microbial production of H2 would meet society's energy needs. The 2023 International Energy Agency (IEA) report, "Hydrogen patents for a clean energy future", doesn't mention direct microbial production. However, much can be learned by studying how hydrogenases (H2ases, Hyd), enzymes that make or use H2, work. (Don't confuse hydrogenases with dehydrogenases that directly use NAD+/NADH and FAD/FADH2) for redox reactions. Transition metal active site mimetics can be made as potential catalysts for more industrial-level production of H2. Although the reversible formation of H2 involves the most elemental particles in chemistry, H+ and e-, the biological reactions that produce and consume H2 are complex. Before we proceed, let's see how these reactions are similar to other biochemical reactions and pathways we have already discussed. Use of H2 as a source of electrons for reduction reactions. Each hydrogen in H2 has an oxidation number of 0. Each hydrogen can be oxidized to H+ (oxidation number +1) with the 2 electrons passed on to a substrate/cofactor or a sequential series of substrates with higher and higher standard reduction potentials (better oxidizing agents), leading to the formation of reduced products. H2 + (substrate)OX  →  2H+  +  (product)RED This general reaction is analogous to the mitochondrial electron transport chain, in which electrons are passed from a source (NADH) to oxidized forms of acceptors. The general reaction below shows each redox pair in the electron transport chain. NADH/NAD+ →  FAD/FADH2 →  UQ/UQH2  →  Cyto COX/Cyto CRED  →   O2/H2 Some organisms have evolved to produce energy by the oxidation of H2. This is analogous to photosynthetic organisms obtaining energy through the oxidation of water. In photosystem II,  oxygen in H2O (oxidation number -2) gets oxidized by the oxygen-evolving complex to produce O2 (oxidation number 0). Some redox pairs, starting with H2O/O2, are shown below for photosystem II. H2O/O2 →  P680/P680* →  (Plastoquione)OX/(Plastoquione)RED The first reaction is endergonic and requires as an energy source photons. Use of H+ as a sink for electrons for oxidation reactions that produce H2. H+ has an oxidation number of +1. Hence it can be reduced to H2 (oxidation number of 0) as it gains electrons from substrates/cofactors, which get oxidized. This general reaction is shown below. 2H+ + (substrate/cofactor)RED  →  H2  + (substrate/cofactor)OX Many microorganisms can produce H2 through variants of photosynthesis or through fermentation, both of which provide the two electrons needed. E. Coi has four hydrogenases (Hyd 1, 2, 3, and 4). It forms H2 through two reactions catalyzed by: • formate (HCO2-) dehydrogenase (FDH):  2HCO2⇌ 2CO2 + 2H+ + 2e- • hydrogenase (H2ase):  2H+ + 2e- → H2 The C in formate has an oxidation number of +2 and is oxidized to CO2, in which the C has an oxidation number of +4. The formate hydrogenlyase (FHL) complex contains both the formate dehydrogenase (FDH) and a hydrogenase (H2ase) and reversibly interconverts HCO2 and H2. The E. coli FHL-1 complex, which makes H2 using fermentation, is shown below in Figure $2$. The complex can be immobilized on a Macro-mesoporous inverse opal (IO) indium tin oxide (ITO) electrode (IO-IPO) or  ITO nanoparticles (NP), which can relay electrons. Figure $2$: Katarzyna P. Sokol et al. J. Am. Chem. Soc. 2019, 141, 44, 17498–17502.  https://doi.org/10.1021/jacs.9b09575.  CC-BY license Panel (a) shows the biological E. coli FHL-1 complex. FdhF, [Mo]-FDH; B/F/G, Fe–S cluster-containing proteins; HycE, [NiFe]-H2ase; HycD/C, membrane proteins. (17) Panel (b) shows a IO-ITO|FDH||IO-ITO|H2ase cell: IO-ITO|FDH wired to IO-ITO|H2ase electrode. Panel (c) shows a FDH–ITO–H2ase nanoparticle (NP) system with enzymes immobilized onto ITO NP in solution. Species size not drawn to scale. All you need to synthesize H2 are 2 protons and 2 electrons (potentially derived from photosynthesis). Let's take a deeper look at the hydrogenase that catalyzes H2 production. Hydrogenases (H2ases) Hydrogenases catalyze the reversible conversion of 2H+ →  H2.   A hydrogenase database, HydDB, a web tool for hydrogenase classification and analysis of sequence, shows their high diversity and metabolic roles. There are three classes of hydrogenases, the Ni-Fe (most abundant, primarily for H2 conversion to 2H+), the Fe-Fe (highest kcat for H2 production), and the single Fe hydrogenases, as shown in Table  $2$ below. We won't discuss the single Fe hydrogenases. CLASSES AND SUBCLASSES OF HYDROGENASES [NiFe] Group 1: Respiratory H2-uptake [NiFe]-hydrogenases 1a Periplasmic Electron input for sulfate, metal and organohalide respiration. [NiFeSe] variants. 1b Prototypical Electron input for sulfate, fumarate, metal and nitrate respiration. 1c Hyb-type Electron input for fumarate, nitrate and sulfate respiration. Physiologically reversible. 1d Oxygen-tolerant Electron input for aerobic respiration and oxygen-tolerant anaerobic respiration. 1e Isp-type Electron input primarily for sulfur respiration. Physiologically reversible. 1f Oxygen-protecting Unresolved role. May liberate electrons to reduce reactive oxygen species. 1g Crenarchaeota-type Electron input primarily for sulfur respiration. 1h Actinobacteria-type Electron input for aerobic respiration. Scavenges electrons from atmospheric H2. 1i Coriobacteria-type (putative) Undetermined role. May liberate electrons for anaerobic respiration. 1j Archaeoglobin-type Electron input for sulfate respirationπ. 1k Methanophenazine-reducing Electron input for methanogenic heterodisulfide respiration. [NiFe] Group 2: Alternative and sensory uptake [NiFe]-hydrogenases 2a Cyanobacteria-type Electron input for aerobic respiration. Recycles H2 produced by other cellular processes. 2b Histidine kinase-linked H2 sensing. Activates two-component system controlling hydrogenase expression. 2c Diguanylate cyclase-linked (putative) Undetermined role. May sense H2 and regulate processes through cyclic di-GMP production. 2d Aquificae-type Unresolved role. May generate reductant for carbon fixation or have a regulatory role. 2e Metallosphaera-type (putative) Undetermined role. May liberate electrons primarily for aerobic respiration. [NiFe] Group 3: Cofactor-coupled bidirectional [NiFe]-hydrogenases 3a F420-coupled Couples oxidation of H2 to the reduction of F420 during methanogenesis. Physiologically reversible. [NiFeSe] variants. 3b NADP-coupled Couples oxidation of NADPH to the evolution of H2. Physiologically reversible. May have sulfhydrogenase activity. 3c Heterodisulfide reductase-linked Bifurcates electrons from H2 to heterodisulfide and Fdox in methanogens. [NiFeSe] variants. 3d NAD-coupled Interconverts electrons between H2 and NAD depending on cellular redox state. [NiFe] Group 4: Respiratory H2-evolving [NiFe]-hydrogenases 4a Formate hydrogenlyase Couples formate oxidation to fermentative H2 evolution. May be H+-translocating. 4b Formate-respiring Respires formate or carbon monoxide using H+ as electron acceptor. Na+-translocating via Mrp. 4c Carbon monoxide-respiring Respires carbon monoxide using H+ as electron acceptor. H+-translocating. 4d Ferredoxin-coupled, Mrp-linked Couples Fdred oxidation to H+ reduction. Na+-translocating via Mrp complex. 4e Ferredoxin-coupled, Ech-type Couples Fdred oxidation to H+ reduction. Physiologically reversible via H+/Na+ translocation. 4f Formate-coupled (putative) Undetermined role. May couple formate oxidation to H2 evolution and H+ translocation. 4g Ferredoxin-coupled (putative) Undetermined role. May couple Fdred oxidation to proton reduction and H+/Na+ translocation. 4h Ferredoxin-coupled, Eha-type Couples Fdred oxidation to H+ reduction in anaplerotic processes. H+/Na+-translocating. 4i Ferredoxin-coupled, Ehb-type Couples Fdred oxidation to H+ reduction in anabolic processes. H+/Na+-translocating. [FeFe] Hydrogenases A1 Prototypical Couples ferredoxin oxidation to fermentative or photobiological H2 evolution. A2 Glutamate synthase-linked (putative) Undetermined role. May couple H2 oxidation to NAD reduction, generating reductant for glutamate synthase. A3 Bifurcating Reversibly bifurcates electrons from H2 to NAD and Fdox in anaerobic bacteria. A4 Formate dehydrogenase-linked Couples formate oxidation to H2 evolution. Some bifurcate electrons from H2 to ferredoxin and NADP. B Colonic-type (putative) Undetermined role. May couple Fdred oxidation to fermentative H2 evolution. C1 Histidine kinase-linked (putative) Undetermined role. May sense H2 and regulate processes via histidine kinases. C2 Chemotactic (putative) Undetermined role. May sense H2 and regulate processes via methyl-accepting chemotaxis proteins. C3 Phosphatase-linked (putative) Undetermined role. May sense H2 and regulate processes via serine/threonine phosphatases. [Fe] Hydrogenases All Methenyl-H4MPT dehydrogenase Reversibly couples H2 oxidation to 5,10-methenyltetrahydromethanopterin reduction. Dan Søndergaard et al., Scientific Reports volume 6, Article number: 34212 (2016). Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. The three main types have different main functions in general. The Ni-Fe, Fe-Fe, and Fe H2ases generally oxidize H2, produce H2 and promote H- (hydride) transfer, respectively, as shown in Figure $3$ below. Figure $3$:   The active site structures of [NiFe] H2ases that mainly catalyze Hoxidation reactions, [FeFe] H2ases that mainly catalyze H2 evolution reactions, and [Fe] H2ases that catalyze H− transfer to the substrate via heterolytic H2cleavage. X, possible H2 active sites; Y, methenyltetrahydromethanopterin; GMP, guanosine monophosphate. Seiji Ogo et al., Science Advances. (2020).  DOI: 10.1126/sciadv.aaz81.  Creative Commons Attribution-NonCommercial License 4.0 (CC BY-NC). Much effort has been devoted to making transition state analogs of the active site to act as catalysts for H2 production for fuel cells. Transition metal catalysts that mimic the structures and activities of the three hydrogenases have been made. Three specific ones are shown below in Figure $4$. Figure $4$: The differing reactivity of the three isomers. Y′, methylene blue [MB]+.Seiji Ogo et al., ibid. The ligand containing P and PH is bis(diphenylphosphino)ethane. First, we will explore the Ni-Fe hydrogenases. Ni-Fe H2ases (Hyd): We'll discuss two examples of Ni-Fe H2ases Group 1a periplasmic (membrane-bound) hydrogenases - MBH These are used in fuel cells and H2-producing devices since they can adhere to surfaces that can be useful heterogeneous (not in solution) catalysts. Some also are damaged by O2. The enzyme consists of a large subunit) found in the periplasm and small subunit, which anchors the protein in the plasma membrane of bacteria.  .  This enzyme oxidizes H2:  H2 → 2H++2e. The electrons enter the bacterial respiratory chain through quinones. The transmembrane part of the small subunit binds cytochrome b, which is involved in electron transfer with the quinones, as we saw in Complex II of mitochondrial electron transport. Some soil bacteria (like Ralstonia eutropha,) can use  H2 as their sole energy source. The orientation of a NiFe MBH within a bacterial cell is shown in Figure $5$ below. Figure $5$:  The orientation of a NiFe MBH within a bacterial cell. Lindsey A. Flanagan* and Alison Parkin 2016 Feb 15; 44(1): 315–328 (2016). doi: 10.1042/BST20150201  Creative Commons Attribution Licence 3.0. Panel (A) shows a cartoon depiction of how a NiFe MBH is located within the cytoplasmic membrane, with white boxes representing the redox active metal centers and blue, orange and purple blocks indicating the large, small and cytochrome subunits, respectively. Panel (B) shows how the E. coli hydrogenase-1 large (blue ribbon), small (orange ribbon) and cytochrome (purple ribbon) subunits can interact. Figure $6$ shows an interactive iCn3D model of the O2-Tolerant Membrane-Bound Hydrogenase 1 from Escherichia coli in Complex with Its Cognate Cytochrome b (4GD3). The same color coding is used for the subunits as in the above figures. Figure $6$: O2-Tolerant Membrane-Bound Hydrogenase 1 from Escherichia coli in Complex with Its Cognate Cytochrome b (4GD3). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...9p4CSQRDPM8nL7 The large and small chains of hydrogenase are shown in blue and orange, respectively, while cytochrome b is sown in magenta. Cofactors and key metals are shown in spacefill. F4S is the Fe4-S3 cluster, SF4 is the iron-sulfur cluster, HEM is hemoglobin, FCO is carbonmonoxide-(dicyano) Fe, and Ni is nickel. The biological (functional) unit consists of two heterodimers. Under low O2 levels, one dimer can reactivate the other if exposed to O2. The enzyme is found in the highest concentration during anaerobic fermentation. Remember that E. Coli is a facultative anaerobe and can shift its metabolic pathways to fit conditions. Perhaps its primary role is to reduce O2 to water and protect enzymes sensitive to it. The function of Cytochrome b may be mostly to anchor the dimeric H2ase in the membrane. A bifurcating Ni-Fe H2ase These enzymes are more complicated. They oxidize H2 and move the two electrons through a complicated path that bifurcates electron flow to different substrates/cofactors. They move an electron to a low-potential (i.e. not a great oxidizing agent), high-energy species, which gets reduced in an endergonic process. The other electron simultaneously moves to a high-potential (i.e., great oxidizing agent), low-energy species that also gets reduced in an exergonic process. The overall electron transfer is thermodynamically favorable. An example might prove helpful. NADH (E0' = -280 mV, higher potential) can reduce the protein ferredoxin (E0' = -500 mV, lower potential), which can then pass its electrons in other reactions, including the formation of H2, CH4, and NH3. ATP is not required. Bifurcating enzymes - We've seen one before! Four classes of bifurcating enzymes that use FAD/FADH2 or FMN/FMNH2 are known. They are optimal since they can participate in either 1 or 2 electron transfers. We will see an example of a Fe-Fe H2ase further below. In electron transport, we encountered an electron bifurcating complex in the Q-cycle of Complex III. Reduced ubiquinone (UQH2, or ubiquinol) is oxidized and the two lost electrons bifurcated to cytochrome C in a high potential pathway and to UQ to reform UQH2, as shown in Figure $7$ below. Figure $7$:  Electron bifurcation in Complex III When ubiquinone, UQH "Electron bifurcation was first described in the Q-cycle of the respiratory complex III where the two electrons originating from ubiquinol oxidation are bifurcated via a high-potential pathway to cytochrome c, and via a low-potential path to reduce ubiquinone to ubiquinol. One example of a bifurcating Ni-Fe H2ase is the NiFe-HydABCSL protein from the bacteria A. mobile. The general structure of the pentameric form of the functional decamer is shown in Figure $8$ below. Figure $8$: Structure of the A. mobile NiFe-HydABCSL pentamer. XIANG FENG et al. SCIENCE ADVANCES.  2022.   DOI: 10.1126/sciadv.abm7546.  Creative Commons Attribution License 4.0 (CC BY). https://creativecommons.org/licenses/by/4.0/ The five subunits are called HydA (Hyd = hydrogenase), HydB, HydC, HydL (large subunit), and HydS (small subunit). Pane (A) shows the domain structure of the five subunits. The NiFe-HydB NTD and CTDs are partially flexible, as indicated by dashed outlines. Panel (B) shows the subunit organization of the NiFe-Hyd complex and their associated cofactors. NiFe-HydABCSL hydrogenase can reversibly oxidize H2 with the two electrons reducing ferredoxin in an endergonic process and reducing NAD in an exergonic process. FMN is surrounded by an FeS cluster and appears to be the center of bifurcation.  The reaction is as follows: • The HydL oxidizes H2 with two electrons passing through the FeS centers in HydA to HybB. • The electrons are passed to FMN, where the bound NAD gets reduced. Figure $9$ jdkfjdkjfdkjff Figure $9$: Proposed mechanism of electron bifurcation/confurcation in A. mobile NiFe-HydABCSL. (A) Overall electron transfer pathway, highlighting the three branches of the electron transfer path. The mid-potential path is a black dashed line, the exergonic path is a blue dashed line, and the endergonic path is a red dashed line. (B) Conformational changes in the HydBC bifurcation core from the electron bifurcation state (BR state) to the electron transduction state (PB state). Figure $10$ shows an interactive iCn3D model of the electron bifurcating Ni-Fe hydrogenase complex HydABCSL in FMN/NAD(H) bound state 7T30 Figure $10$: Electron bifurcating Ni-Fe hydrogenase complex HydABCSL in FMN-NAD(H) bound state (7T30). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...G9nKGwqvuuh6G7 Figure $11$ shows an interactive iCn3D model of the cofactors in the electron bifurcating Ni-Fe hydrogenase complex HydABCSL in FMN/NAD(H) bound state 7T30 Figure $11$: Cofactors in the electron bifurcating Ni-Fe hydrogenase complex HydABCSL in FMN-NAD(H) bound state (7T30). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...hHuzaWUDYCMnAA. Zoom into the Ni-Fe center catalytic site. The ligands that form coordinate covalent bonds to the Fe are called FCC, or carbon monoxide-(dicyano)-Fe  Figure $12$ below. There are also bridging sulfurs between Fe and Ni. Figure $12$: Carbonmonoxo-dicyano-Fe and is shown in detail in Fe-Fe hydrogenases These  enzymes catalyze a variety of reactions as illustrated in Figure $13$ below. Figure $13$: [FeFe]-hydrogenases phylogeny and known functions.  Morra S. Front Microbiol. 2022 Mar 2;13:853626. doi: 10.3389/fmicb.2022.853626. PMID: 35308355; PMCID: PMC8924675.  Creative Commons Attribution License (CC BY) A phylogenetic tree shows the phylogeny of [FeFe]-hydrogenase sequences from public databases, as previously proposed. Enzymes that have been experimentally characterized are indicated on the tree to show their relative position. The proposed physiological function of each enzyme is also presented, where known. Hyd, hydrogenase subunit; FdhF, formate dehydrogenase subunit; Fdrex/ox, reduced/oxidized ferredoxin; NADH/NAD+, reduced/oxidized nicotinamide adenine dinucleotide.  They are found in prokaryotic and eukaryotic microorganisms, but not in Archaea. These are the most active for H2 production with a kcat around 10,000 s-1. They contain a Fe2S2 cluster with CO and CN ligands forming bonds to the iron with the iron ions bridged by a -SCH2-NH-CH2S- (aza-dithiolate). A cysteine links the Fe2S2 to a Fe4S4 cluster. These two are called the H-cluster (or [Fe]H. Within this class are a soluble, monomeric cytoplasmic form, a heterodimeric periplasmic form, and a soluble, monomeric chloroplastic form. This one has a ferredoxin, connecting it to the electron transport chain in photosynthesis. Some in this group using both NADH and ferredoxin are called bifurcating types, as they send two electrons from a donor in two different directions. More on this later. They contain multiple FeS clusters. The H-cluster consists of a Fe2S2  linked to a Fe4S4 cluster (cubane-like) by a cysteine. The Fe2S2 group has CO and CN ligands, and the two Fe ions of Fe2S2 unit are coordinated by an azadithiolato ligand, as shown below in Figure $14$. Figure $14$: Chemical structure of the H-cluster, which is the active site of the [FeFe] hydrogenase enzyme.  Rakesh C. Puthenkalathil et al., Phys. Chem. Chem. Phys., 2020, 22, 10447. https://pubs.rsc.org/fa/content/arti.../cp/c9cp06770a.  This article is licensed under a Creative Commons Attribution 3.0 Unported Licence The Fe in the [2Fe2S] cluster is linked to the cubane [4Fe-4S] and has six ligands, so it is saturated. The other Fe has an extra coordination site denoted by X, which can bind H+ or H2. The cluster is buried in a hydrophobic catalytic site which helps restrict O2 access. As we did for the Fe-Ni H2ases, we will study two examples of Fe-Fe H2ases. Fe-Fe hydrogenase (CpI) from Clostridium pasteurianum Figure $15$ shows an interactive iCn3D model of the H-Cluster (HC1) of Fe-Fe hydrogenase (CpI) from Clostridium pasteurianum (1FEH). Figure $15$: H-Cluster (HC1) of Fe-Fe hydrogenase (CpI) from Clostridium pasteurianum (1FEH). (Copyright; author via source). Click the image for a popup or use this external link:  https://structure.ncbi.nlm.nih.gov/i...TksW8FGn7crh39 In this model, the CN ligands are all displayed as CO. The sulfurs are shown in green. Hover over the atoms/ions to identify them.   (In iCn3D, choose, Select, Select on 3D, atom). The [4Fe-4S] subcluster forms coordinate covalent bonds with four cysteines (300, 355, 499, and 503) with one cysteine (503) forming a bridge to the [2Fe] cluster. The Fe ions in that cluster have an octahedral arrangement of ligands surrounding them. One of the ligands is water (no connecting C atom). Figure $16$ shows an interactive iCn3D model of the  Fe-Fe hydrogenase (CpI) from Clostridium pasteurianum (1FEH) Figure $16$:  Fe-Fe hydrogenase (CpI) from Clostridium pasteurianum (1FEH). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...QJsoZ8zbvkpDc7. As we mentioned above, the net reaction is: 2H+ + (substrate/cofactor)RED  →  H2  + (substrate/cofactor)OX Many microorganisms can produce H2 through variants of photosynthesis or through fermentation, both of which provide the two electrons needed. E. Coi has four different hydrogenases, (Hyd 1, 2, 3 and 4). It forms H2 through two reactions catalyzed by: • formate (HCO2-) dehydrogenase:  2HCO2⇌ 2CO2 + 2H+ + 2e- • hydrogenase 3:  2H+ + 2e- → H2 Figure $17$ below shows a reaction scheme for the production of H2 linked to photosystem I in the chloroplast of microalgae under anaerobic conditions.  It starts with absorption of a photon by P700, which in the excited state transfers an electron to a 4Fe4S cluster in ferredoxin, which then passes an electron to the HC-cluster and then onto H+. Figure $17$:  Schematic representation of electron flow from Photosystem I to an [FeFe]-hydrogenase via a ferredoxin redox mediator (Photosystem I). JuanAmaro-Gahete et  al.,Coordination Chemistry Reviews. 448, December 2021. https://doi.org/10.1016/j.ccr.2021.214172.  Creative Commons CC-BY A possible mechanism for the formation of H2 in the H clusteris shown below in Figure $18$ below. Figure $18$:  Proposed mechanistic cycle for hydrogen evolution in the H cluster by [FeFe]-hydrogenase adapted from Lubitz et al. JuanAmaro-Gahete et  al., ibid Start at the top left which shows the resting oxidized state. In the enzyme's most oxidized resting system (Hox), the [4Fe4S] cubane is in a 2 + oxidation state while the catalytic subcluster [2Fe] is a mixed-valence FeIFeII state. The first one-electron reduction results in the formation of the Hred state, where the [4Fe4S] subcluster is reduced to a 1 + oxidation state. Protonation of the N of aza-propane-1,3-dithiolate ligand (adt-N) triggers an intramolecular charge shift to form HredH+ in which the [4Fe4S] cubane is in the 2 + state and the [2Fe] subsite reduced to a homovalent FeIFeI state. Subsequent one-electron reduction of the subcluster [4Fe4S] gives rise to the “super-reduced” state HsredH+. In the next step of the catalytic cycle, an intermediate hydride state [Hhyd] is formed by an intramolecular proton shift from the adt-N to the distal iron Fd.  This process is coupled to an electron rearrangement in the [2Fe] subsite, leading to a formal FeIIFeII oxidation state. Addition of a second proton coupled to another charge shift from the reduced [4Fe4S] to the [2Fe] subsite either in one or two discrete steps gives rise to [HhydH+] that is characterized by a formal FeIFeII oxidation state. At this point, there is an equilibrium between the HhydH+ and Hox[H2] in which the hydride and the proton are combined into a hydrogen molecule at the distal iron of the system. The catalytic cycle is closed by H2 release, returning to the initial Hox configuration. A bifurcating [Fe-Fe] hydrogenase from Thermotoga maritima (HydABC) This enzyme, functionally a heterododecamer,  uses NADH as a source of electrons, which passes electrons to FMN, the bifurcation site, with an electron going to oxidized ferredoxin  (Fdox) and another to H+s for reduction to FDRED and H2. The enzyme consists of a dimer of a trimer of subunits HydA, HydB, and HydC, with dimer (HydABC)2 interacting with another (HydABC)2 to form a heterododecamer, with both halves acting independently. The two trimers (HydABC) in the dimer (HydABC)2 are connected by a [4Fe–4S] cluster. A flexible loop in the B and A chain has a "closed" and "open" bridge conformation with a nearby Zn2+ important in the loop conformation. Figure $19$ below shows the cryo-EM structure of the HydABC tetramer and the arrangement of the redox cofactors. Figure $19$: Cryo-EM structure of the HydABC tetramer and arrangement of the redox cofactors. Chris Furlan et al. (2022)  eLife 11:e79361.  https://doi.org/10.7554/eLife.79361.  Creative Commons Attribution License Panel (A) shows the unsharpened 2.3 Å map of Hyd(ABC)4 with D2 symmetry enforced, showing a tetramer of HydABC heterotrimers. All four copies of HydB and C are colored blue and green, respectively. The four HydA copies that make up the core of the complex are orange, yellow, pink, and red. The top and bottom halves of the complex are constituted by dimers of HydABC protomers (each HydABC unit is a protomer); the two protomers within the same dimer are strongly interacting, while a weaker interaction is present between the top and bottom dimers. Panel (B) shows the HydABC dimer highlighting the iron–sulfur clusters and flavin mononucleotide (FMN) constituting the electron transfer network. Panel (C) shows the arrangement of redox cofactors within the protein complex, showing two independent identical redox networks (dashed circles); each redox network is composed of iron–sulfur clusters belonging to a Hyd(ABC)2 unit consisting of two strongly interacting HydABC protomers. Panel (D) shows a schematic of the electron transfer network of one of the two identical Hyd(ABC)2 units showing edge-to-edge distances (in Å) between the various cofactors. Note that our structure is of apo-HydABC and contains only the [4Fe–4S]H subcluster of the H-cluster. The 2H+/H2 interconversion reaction in (B) illustrates the site at which this reaction occurs, but this will only occur in the fully assembled H-cluster, including [2Fe]H. Figure $20$ shows an interactive iCn3D model of the electron-bifurcating [FeFe] hydrogenase from Thermotoga maritima (HydABC) (7P5H), using the same colors as the figure above. Figure $20$: electron-bifurcating [FeFe] hydrogenase from Thermotoga maritima (HydABC) (7P5H). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...2hwCUnJ9BgwJi8. Only the dimer (HydABC)2 is shown. The A (gold), B (blue), and C (green) chains are colored as in the previous figure. The conformationally flexible loop at the C-terminal of a B chain in the closed state is shown in red. The gate also includes the C-terminal part of the A subunit near it. Figure $21$ shows the closed-bridge and open-bridge conformations of HydABC (the closed loop was shown in the model above). Figure $21$: closed-bridge and open-bridge conformations of HydABC from Thermotoga maritime. Panel (D) shows the HydB bridge domain in the open position and its fitted model. Panel (E) shows a Zn2+ hinge region and the two possible conformations of the HydB bridge domain, open (blue) and closed (light blue). The similarities in cofactor arrangement in the Thermotoga maritime Hyd A, B and C subunits compared to the Nqo1, Nqo2, and Nquo3 subunits in Complex I from Thermus thermophilus (discussed in Chapter 19.1) are shown in Figure $22$ below. Figure $22$: Comparison of the HydA, B and C subunits of the electron bifurcating [FeFe] hydrogenase from Thermotoga maritima with the Nqo3, 1 and 2 subunits from respiratory complex I from Thermus thermophilus. Panel (A) shows the subunits HydA (red), HydB (blues), and HydC (green) overlaid with, respectively, Nqo3, Nqo1, and Nqo2 (all yellow) of complex I from Tthermophilus (Gutiérrez-Fernández et al., 2020, PDB: 6ZIY). Panel (B) shows a comparison of the NADH-binding site of the Nqo1 subunit of complex I from T. thermophilus (light blue) with the flavin mononucleotide (FMN) site in HydB; the high similarity suggests NADH binds in the proximity of FMN in HydABC similar to complex I. Panel (C) shows an electron transfer network in HydABC compared to complex I from T. thermophilus with edge-to-edge distances indicated in bold. The red, blue, and green dotted lines indicate the cofactors present in the HydA (Nqo3), HydB (Nqo1), and HydC (Nqo2) subunits, respectively. Note that our structure is of the apo-HydABC and lacks the [2Fe]H subcluster of the H-cluster. The 2H+/H2 interconversion reaction in (C) illustrates the site at which this reaction occurs, but this will only happen in the fully assembled H-cluster, including [2Fe]H. Here is a link to a video showing the conformational change observed between the ‘Bridge closed forward’ (7P8N) and ‘Open bridge’ (7PN2) classes. In the video, the HydB C-terminal iron–sulfur cluster domain is colored blue, and the HydA C-terminal iron–sulfur cluster domain is colored orange. The zinc ion (gray sphere) and ligating residues (three cysteine ligands and one histidine) are also shown. The location of the HydA C-terminal domain when the bridge is open is unknown, so it is shown transparently in both states for reference. The geometric separation of catalytic sites and the bifurcation mechanism prevents these thermodynamically favored reactions from happening • H2 production from ferredoxin oxidation (in the absence of NADH oxidation) • NAD+ reduction by H2 (in the absence of ferredoxin reduction) • ferredoxin oxidation by NAD+ Oxygen Sensitivity of Fe-Fe H2ases We have alluded to the fact that Fe-Fe H2ases can be sensitive to O2.  A possible mechanism involves the interaction of one the Fe ions (Fed, the distal Fe) with oxygen, leading to the formation of damaging free radicals. As CO binds more strongly than O2 to the iron in hemoglobin, its interaction with the H-center can help protect the H2ases.  Sulfides can also afford protection.  These mechanisms are illustrated in Figure $23$: Figure $23$: Oxygen tolerance strategies in [FeFe]-hydrogenases. Morra S, ibid. Schematic representation of the H-cluster in the oxidized active state Hox (centre). In the absence of any exogenous protectant, numerous [FeFe]-hydrogenases undergo irreversible inactivation due to H-cluster damage with loss of Fe atoms (red pathway); carbon monoxide acts as a protective agent due to its ability to form Hox-CO, by binding reversibly to the H-cluster at the same site as O2 (purple pathway); in DdH, a similar mechanism occurs when sulphide binds to the H-cluster forming Hinact, via the Htrans intermediate (orange pathway); in CbA5H, a conformational change in the protein structure allows for a conserved cysteine to directly bind to the H-cluster, forming Hinact (green pathway). Fep, proximal iron atom; Fed, distal iron atom; Cys, cysteine residue. Key Points - Beta version from Chat.openai 1. Hydrogenases are enzymes that catalyze the reversible conversion of hydrogen gas (H2) to protons and electrons. 2. There are two main types of hydrogenases: [NiFe]-hydrogenases and [FeFe]-hydrogenases. 3. [NiFe]-hydrogenases are found in a variety of microorganisms and mostly used to oxidize H2. 4. [FeFe]-hydrogenases are mainly used to produce H2. 5. Hydrogenases play a crucial role in the metabolism of microorganisms, allowing them to produce or consume hydrogen gas as needed. 6. The activity of hydrogenases is regulated by various factors, including the availability of hydrogen and the presence of inhibitors such as oxygen. 7. Research is ongoing to improve the efficiency and cost-effectiveness of hydrogenase-based biohydrogen production and to understand the mechanisms of hydrogenase enzymes to develop more efficient and sustainable ways of producing hydrogen. 8. Genetic engineering techniques can be used to improve hydrogenase activity in microorganisms, and also to increase the tolerance of microorganisms to the toxic effects of H2.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.10%3A__Biohydrogen_-_Hydrogenases.txt

Search Fundamentals of Biochemistry Inspiration for the chapter comes from Biochemical Adaptation by Hochachka and Somero. Organisms adapt to their environment, with one of the main drivers being temperature. This has occurred over geological time (think of arctic camels 3.4 million years ago!) and space with temperature gradients in terrestrial and aquatic environments. This is evident in the different species that thrive at different mountain heights and ocean depths.  Species that can move have advantages in selecting an environment best suited to their thermal needs. Historically, homo sapiens have engaged in seasonal migration, and aquatic species in vertical migrations. Temperature effects are universal throughout life, and physiology and biochemistry adaptations are ubiquitous. Metabolically-active life can exist from around -15oC to about 121oC (thermal saline springs). Unless greenhouse gas emissions are significantly decreased from present levels, parts of the world will become increasingly uninhabitable due to high temperatures and sea level rise. Estimates for the number of climate refugees range up to 1 billion people by 2050. Two similar questions arise.  Can organisms adapt to increasing temperatures as the climate changes, and are organisms living close to their maximal survivable temperatures? Before we study the effects of temperature on chemical/biochemical reactions, let's review the basics of thermoregulation.  The following classification of organisms by types of thermoregulation is from BioLibre text. Types of Thermoregulation (Ectothermy vs. Endothermy) Thermoregulation in organisms runs along a spectrum from endothermy to ectothermy. Endotherms create most of their heat via metabolic processes, and are colloquially referred to as “warm-blooded.” Ectotherms use external sources of temperature to regulate their body temperatures. Ectotherms are colloquially referred to as “cold-blooded” even though their body temperatures often stay within the same temperature ranges as warm-blooded animals. Ectotherm An ectotherm, from the Greek (ektós) “outside” and (thermós) “hot,” is an organism in which internal physiological sources of heat are of relatively small or quite negligible importance in controlling body temperature. Since ectotherms rely on environmental heat sources, they can operate at economical metabolic rates. Ectotherms usually live in environments in which temperatures are constant, such as the tropics or ocean. Ectotherms have developed several behavioral thermoregulation mechanisms, such as basking in the sun to increase body temperature or seeking shade to decrease body temperature.  The cCommon frog is an ecotherm and regulates its body based on the temperature of the external environment Endotherms In contrast to ectotherms, endotherms regulate their own body temperature through internal metabolic processes and usually maintain a narrow range of internal temperatures. Heat is usually generated from the animal’s normal metabolism, but under conditions of excessive cold or low activity, an endotherm generate additional heat by shivering. Many endotherms have a larger number of mitochondria per cell than ectotherms. These mitochondria enables them to generate heat by increasing the rate at which they metabolize fats and sugars. However, endothermic animals must sustain their higher metabolism by eating more food more often. For example, a mouse (endotherm) must consume food every day to sustain high its metabolism, while a snake (ectotherm) may only eat once a month because its metabolism is much lower. Homeothermy vs. Poikilothermy Two other descriptors are also used.   A poikilotherm is an organism whose internal temperature varies considerably. It is the opposite of a homeotherm, an organism which maintains thermal homeostasis. Poikilotherm’s internal temperature usually varies with the ambient environmental temperature, and many terrestrial ectotherms are poikilothermic. Poikilothermic animals include many species of fish, amphibians, and reptiles, as well as birds and mammals that lower their metabolism and body temperature as part of hibernation or torpor. Some ectotherms can also be homeotherms. For example, some species of tropical fish inhabit coral reefs that have such stable ambient temperatures that their internal temperature remains constant.  Figure $1$ below shows the energy output vs temperature for a homeotherm (mouse) and poikilotherm (lizard). Another term is also employed, heterothermy, in which the temperature of a homeotherm can vary in different regions of the body (spatially) and also at different times (daily or seasonally as in hibernation).  The core body of a homeotherm is usually warmer than the extremities that allow cooling when needed.  In hibernation (or sustained torpor), both the body temperature and metabolic rates are decreased. Means of Heat Transfer Heat can be exchanged between an animal and its environment through four mechanisms: radiation, evaporation, convection, and conduction. Radiation is the emission of electromagnetic “heat” waves. Heat radiates from the sun and from dry skin the same manner. When a mammal sweats, evaporation removes heat from a surface with a liquid. Convection currents of air remove heat from the surface of dry skin as the air passes over it. Heat can be conducted from one surface to another during direct contact with the surfaces, such as an animal resting on a warm rock. Key Points • In response to varying body temperatures, processes such as enzyme production can be modified to acclimate to changes in temperature. • Endotherms regulate their own internal body temperature, regardless of fluctuating external temperatures, while ectotherms rely on the external environment to regulate their internal body temperature. • Homeotherms maintain their body temperature within a narrow range, while poikilotherms can tolerate a wide variation in internal body temperature, usually because of environmental variation. • Heat can be exchanged between the environment and animals via radiation, evaporation, convection, or conduction processes. Key Terms • ectotherm: An animal that relies on the external environment to regulate its internal body temperature. • endotherm: An animal that regulates its own internal body temperature through metabolic processes. • homeotherm: An animal that maintains a constant internal body temperature, usually within a narrow range of temperatures. • poikilotherm: An animal that varies its internal body temperature within a wide range of temperatures, usually as a result of variation in the environmental temperature. These terms are diagramed in Figure $2$ below. Figure $2$: Thermoregulatory Term.  Buffenstein et al., Biol. Rev. (2021), doi: 10.1111/brv.12791.  Creative Commons Attribution License We have discussed in previous chapter sections how temperature can affect macromolecules such as proteins (Chapter 4), nucleic acids (Chapter 9.1) as well as supramolecular assemblies such as membranes (Chapter 10.3).  Temperature effects on small molecules and ions (such as salts in the Hofmeister series and glycerol, Chapter 4.9) in the environment that regulate the function/activity of these larger molecules and assemblies are also important. Hence we'll review and discuss the effects of temperature on these key molecular species in the next chapter section.  First, we'll delve deeper into the general impact of temperature on chemical and biochemical reactions. Temperature Effects on the Rates of Chemical Reactions To understand temperature effects on metabolic processes, let's first review temperature effects on ordinary chemical and biochemical reactions.  You may remember the general rule that the rate of a chemical reaction approximately doubles when the temperature is increased 10o C (10 K).  How does that arise?  This is generally true in a specific temperature range, as we will see below. The rates of reactions, either endothermic or exothermic, depend on the activation energy (Ea).  The activation energy is required to move from a reactant to the transition state, which then can go on to form product. The activation energy can be obtained from the Arrhenius equation (that you learned in introductory chemistry), which shows how the rate of an individual chemical reaction depends on temperature. k=A e^{-E_a / R T} where k is the rate constant, Ea is the activation energy, Ea/RT is the average kinetic energy, and A is a constant (the "preexponential" factor). By taking the natural log (ln) of each side and rearranging the equation, you get a "linearized" equation that is easier for most. \ln k=\ln A-\frac{E_a}{R T} A plot of ln k vs 1/T has a slope = Ea/R, from which the activation energy can be calculated. An alternative form can be derived: \ln \frac{k_2}{k_1}=\frac{E_a}{R}\left(\frac{1}{T_1}-\frac{1}{T_2}\right) A derivation Here it is! Derivation From \ln k_1=\ln (A)-E_a / R T_1 solve for lnA \ln (A)=\ln \left(k_1\right)+E_a / R T_1 Substitute into the equation for ln(k2) gives \ln \left(k_2\right)=\ln \left(k_1\right)+E_a / R T_1-E_a / R T_2 Rearrange to get \ln \left(k_2\right)-\ln \left(k_1\right)=E_a / R T_1-E_a / R T_2 Simplify to get the final equation! \ln \left(\frac{k_2}{k_1}\right)=\frac{E_a}{R}\left(\frac{1}{T_1}-\frac{1}{T_2}\right) Solving for Ea gives E_a=\frac{R \ln \frac{k_2}{k_1}}{\frac{1}{T_1}-\frac{1}{T_2}} Let's use this equation to calculate an Ea that will give a doubling of the reaction rate (k2/k1 = 2) going from T1 = 295 K (21.90 C, 71.3F) to T2 = 305 K (21.90 C, 89.3F), a 10oC temperature rise \begin{aligned} E_a & =\frac{(8.314)(\ln 2)}{\frac{1}{295}-\frac{1}{305}} \ & =\frac{\left(8.314 \mathrm{~J} \mathrm{~mol}^{-1} \mathrm{~K}^{-1}\right)(0.693)}{0.00339 \mathrm{~K}^{-1}-0.00328 \mathrm{~K}^{-1}} \ & =\frac{5.76 \mathrm{Jmol}^{-1} \mathrm{~K}^{-1}}{\left(0.00011 \mathrm{~K}^{-1}\right)} \ & =52,400 \mathrm{Jmol}^{-1}=52.4 \mathrm{~kJ} \mathrm{~mol}^{-1} \end{aligned} Hence if a reaction has an activation energy Ea of about 54 kJ/mol, increasing the temperature from 295 to 305oC (i.e, by 10oC) doubles the reaction rate. Assuming that the activation energy is constant, the rate constants increase with temperature since a larger fraction of the molecules have the energy (> Ea) necessary to react.  This is illustrated in Figure $3$ below. Figure $3$: Plot of a Maxwell-Boltzmann distribution of speeds for different temperatures T=100K, T=1200K, T=5000K.  Points along the curve show (1) most likely speed, (2) average speed, and (3) thermal speed (velocity that a particle in a system would have if its kinetic energy were equal to the average energy of all the particles of the system).  https://commons.wikimedia.org/wiki/F...xis-labels.svg.   Creative Commons Attribution-Share Alike 4.0 International license. Let's look a the brown vertical line around 950 m/s.  If we take that as the activation energy, very few molecules in the blue distribution have the required kinetic energy > Eact. Ar progressively higher temperatures, great fractions (as measured by the area under the curve to the right of the dotted line at 950 m/s) have the required energy, hence the rates increase with temperature. When the temperature change is 10oC, the ratio of the rate constants (or rates), k2/k1 is often called Q10, the temperature coefficient (unitless).   Q10  is not a constant, since it depends on the two temperatures that differ by 100 C (10 K).  Hence the Q10  value for the 100 range from 273-283K is different than the Q10 value from 373-383K) .  Q10 for many reaction is around 2 (doubling of the reaction rate) - 3 (tripling the reaction rate) at physiological temperature .  Q10 =2 for a given Ea only at one set of temperatures that differ by 10oC.  The variation in Q10 values is illustrated in Table $1$ below for a reaction in which Ea = 44.5 kJ/mol. Q10 decreases from 2 as the temperatures T1 and T2=T1+10oC increase. T1 in K (oC)  T2 (K) (oC) k2/k1 (Q10) 273 (- 0.15 oC)  283 (9.85 oC)oC 2 373 (99.9 oC) 383 (110 oC) 1.45 473 (200 oC) 483 (210 oC) 1.26 Table $1$:  Q10 = k2/k1 values at different temperatures T1 and T2 that differ by 10oC. We will see how this is important in biological settings in a bit.  If Q10 = 1, the reaction is independent of temperature, and a Q10 <1 shows a reaction that is not functioning.  An example might be an enzyme-catalyzed reaction in which the threshold is reached at a higher temperature T2 = T1+10, at which the enzymes lose an active conformation and starts to unfold. The same equation and the Q10 parameters apply to enzyme-catalyzed reactions.  The activation energies (Ea) for four enzymes involved in the degradation of lignocellulose in the surface soil and subsoil are shown in Table $2$ below.  The enzymes include two hydrolases, β-glucosidase (BG) and cellobiohydrolase (CB), which cleave cellulose, and two oxidases, peroxidase (PER) and phenol oxidase (POX), which help degrade lignin. The overall average Ea for these enzymes is about 44.7 kJ/mol, similar to the example used in Table 1 above. Soil Type Ea (kJ/mol) BG CB PER POX Arctic surface 35.4 39.4 12.7 81.8 Subarctic surface 36.5 38.6 21.1 45.7 subsoil 52.2 41.5 22.4 39.4 Temperate 1 surface 40.9 38 64.9 102 subsoil 49.4 21.2 28 94.8 Temperate 2 surface 31 43.4 25.4 49.5 subsoil 40.9 39.9 19.8 47.5 Temperate 3 surface 51.5 53.6 28.8 73.2 subsoil 58.8 46.7 54.2 29 Tropical 1 surface 47.8 50.5 26.5 47.7 subsoil 56.6 47 47.1 27.1 Tropical 2 surface 39.3 42.5 58.3 82.5 subsoil 42.8 43.3 22.8 45.5 Avg 44.9 42.0 33.2 58.9 Table $2$: Activation Energies (Ea, kJ mol−1) for extracellular soil enzymes involved in the degradation of lignocellulose. Adapted from Steinweg JM et al. (2013) PLOS ONE 8(3): e59943. https://doi.org/10.1371/journal.pone.0059943.  Creative Commons CC0 public domain Q10 temperature coefficients are also used to describe biological processes like respiration, speed of neural signal propagation, metabolic rates, etc. Many biological processes are affected by temperature, especially for ectotherms that adjust temperatures to outside environments, including daily and seasonal temperature shifts.  Mammals and birds alter metabolic rates with temperature.  This is true for hibernating animals. The Q10 temperature coefficient can be considered to be the factor by which the reaction rates (k or R) increase (factor of 2, 3, 1.5, etc) for each 10-degree K or C temperature increase.  It is given by the following equation: Q_{10}=\left(\frac{k_2}{k_1}\right)^{10^{\circ} \mathrm{C} /\left(T_2-T_1\right)} It is also called the van't Hoff's temperature coefficient.  To help understand Q10, let's consider some examples. • If T2-T1=10o, then Q10 is simply k2/k1 for the specified temperature pairs separated by a 100 C range (T1 and T2=T1+10).  Remember that Q10 is not a constant but depends on the temperature pairs and that it goes down with increasing temperature. • If the temperature range is > 100 C, the the measured ratio k2/k1 is a factor > 1 x Q10 • If the temperature range is < 100 C, the the measured ratio k2/k1 is a fraction of Q10 This equation can be converted to k_2=k_1 Q_{10}^{\left(T_2-T_1\right) / 10^{\circ} \mathrm{C}} where the rate constant k2 is related to a "base" rate k1 at a base temperature of T1.  An interactive graph of the above equation is shown in Figure $4$ below. Figure $4$: Interactive graph of k2 (rate 2) vs. delta T at different base rates k1 Change the base rate constant, k1, at a base temperature of T1, and Q10 coefficient to see how they change k2. Note that if Q10 =1,  k2 at T1+10 = k1 at T1, the rate is independent of the temperature. For most biological systems, the Q10 value is ~ 2 to 3 under physiological relevant conditions.  The ratios of the rates (R2/R1) for different Q10 values are shown in Figure $5$ below. ​​​​​​ Figure $5$:  Idealized graphs showing the dependence on temperature of the rates of chemical reactions and various biological processes for several different Q10 temperature coefficients. The dots on the graph show how the rate change with a temperature difference of 10oC.   Wikipedia. https://en.wikipedia.org/wiki/Q10_(t...e_coefficient).  CC BY-SA 4.0 Again this hypothetical graph is meant to show the general meaning of Q10 values. The "Q" model has been used to fit complex reaction systems, not just individual reactions.  Figure $6$ below shows the daily mean soil respiration rate as a function of soil temperature. In these graphs, the x-axis is Temperature, not ΔT. Figure $6$: Relationships between daily mean soil respiration (Rs) and soil temperature (Ts).  Jia X et al.,  PLoS ONE 8(2): e57858. https://doi.org/10.1371/journal.pone.0057858.  Creative Commons Attribution License The soil temperature, Ts, was measured at a 10-cm depth. Open circles are from January to June; closed circles are from July to December. The solid lines use a Q10 model, in which the observed Rs vs Ts data are fit with an equation that optimizes the Q10 parameter  The dashed lines are fitted by a logistic model, which we used in Chapter 5.7 for fitting ELISAs data. Rs is significantly different between the first and second half of the year. The soil respiration rate, Rs, at 10 cm depth was strongly affected by temperature, with an annual Q10 value of 2.76.   Daily estimates of Q10 averaged 2.04 and decreased with increasing Ts. A study of seagrass showed the Q10 values are affected by plant tissue age and that Q10 varied significantly with the initial temperature and temperature ranges. The use of Q10 values from the Arrhenius equation is based on the assumption that the chemical/biochemical processes are exponential functions of temperature.  For complex processes like the decay of organic matter, it would be better to model the whole system by looking at the individual enzymes involved.  One problem with using Q10 values for very complex systems is the choice of the base temperature value for rate comparisons.  The anaerobic decomposition of organic matter is generally a linear function of temperature between 5°C and 30°C, which shows that a Q10 modeling system is not ideal.  A more complex systems biology approach using programs like Vcell and COPASI would be better and less likely to cause errors in predicted CH4 emissions from the decomposition process, for example. Getting Back to Proteins In Chapter 6.1 we explored the mechanisms used by enzymes to catalyze chemical reactions. These included general acid/base catalysis, metal ion (electrostatic) catalysis, covalent (nucleophilic) catalysis, and transition state stabilization. Some physical processes included intramolecular catalysis and strain/distortion.  The rate-limiting step in enzyme-catalyzed reactions can include actual bond breaking in the substrate, dissociation of product, and conformational change required to facilitate binding, catalysis, and dissociation.  A rate-limiting conformational change may occur not in the active site pocket but in nearby loops that modulate the accessibility of reactant to and dissociation of product from the active site.  These all may be influenced by temperature, with localized conformational flexibility especially important. An interesting example of localized conformational changes affecting enzyme activity is RNase A.  His 48, 18 Å from the enzyme active site, is involved in the rate-limiting enzymatic step involving product release. Figure $7$ shows an interactive iCn3D model of bovine pancreatic Ribonuclease A in complex with 3'-phosphothymidine (3'-5')-pyrophosphate adenosine 3'-phosphate (1U1B) Figure $7$: Bovine pancreatic Ribonuclease A in complex with 3'-phosphothymidine (3'-5')-pyrophosphate adenosine 3'-phosphate (1U1B). (Copyright; author via source).  Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...SnrGYXSVXcLCk6 The substrate is shown in spacefill.  The active site side chains and the distal His 48 are shown as sticks and labeled.  Two flexible loops, Loop 1 (magenta) near His 48, and Loop 4 (cyan) near the active site are highlighted.  On ligand binding, the loops move a few angstroms to make the active site more closed, inhibiting product release. Product release is associated with mobile regions including Loops 1 (20 Å from the active site) and 2.  Loop 4, near the active site, is involved in the specificity for purines 5' to the substrate cleavage site.  His 48 is conserved in pancreatic RNase A.  If mutated to alanine, the kcat decreases greater than 10X, indicating a change in the rate-determining conformational motion. The enzyme is still very active compared to the uncatalyzed reaction.  His 48 appears to regulate coupled motions in the protein that are rate-limiting. Figure $8$ below shows the subtle shift in the conformation of apo-RNase A (magenta, no ligand, 1FS3) to the substrate-bound form (cyan, ligand in sticks), 1U1B). Note the small motion in His 48 shown in sticks at the bottom of the image. Figure $8$:  Conformational changes apo-RNase A (magenta, no ligand, 1FS3) on conversion to the substrate-bound form (cyan, ligand in sticks, 1U1B). We will explore temperature effects on protein structure and function more in the next chapter section.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.11%3A__A_Warmer_World%3A_Temperature_Effects_On_Chemical_Reactions.txt

Search Fundamentals of Biochemistry Inspiration for the chapter comes from Biochemical Adaptation by Hochachka and Somero. Introduction In the previous chapter section, we discussed the generalized effects of temperature on chemical/biochemical reactions.  The rate for chemical reactions, including enzyme-catalyzed ones, typically increases 2-3 fold (Q10 values) with a 100 C temperature increase over an organism's typical temperature range.  Q10 values decrease at higher temperature pairs differing by 100 C.  At too high a temperature, a protein enzyme destabilizes, and Q10 values can fall to less than 1, a sign of potential trouble for an organism subjected to that higher temperature. As we saw in Chapter 32.11 and will here, two competing processes affect protein enzymes as temperatures increase.  They are increased rates for the catalyzed reaction and increased conformational dynamics, which leads to eventual denaturation at high enough temperatures.  Hence evolution would presumably select for increased protein stability for organisms adapted to higher temperatures. For cold-adapted organisms, the rates of catalysis are expected to decrease.  Hence evolution might lead to higher kcat values for cold-adapted organisms.  However, we saw in Chapter 32.11 that the rate-limiting step for enzyme-catalyzed reactions often involved localized conformational changes, which would be disfavored at colder temperatures.  Hence evolution would also select for enzymes that could maintain flexibility at low temperatures.  In Chapter 4.9, we discussed low-temperature protein denaturation.  Proteins can be destabilized at low temperatures.  In this section, we will study how enzymes can adapt to higher temperatures. We won't discuss how proteins adapt to cold since this topic is less relevant for human-caused climate change. We will follow the approach used throughout this book - that structure mediates function.  We will use a lot of enzyme kinetics since kinetic parameters can tell us much about how bound substrate goes to product at high substrate concentrations (kcat or VM) or low substrate concentrations (kcat/KM) at different temperatures and for different orthologs of enzymes from species that have adapted to grow at low (psychrophile), medium (mesophile) or high temperatures (thermophiles).  We'll next look at the structure of enzymes and which features allow them to adapt to their optimal temperature for growth.  Finally, we will look at entire pathways to discern clues as to how they adapt to increased temperatures. An Overview - Soil Enzymes Soil microbes play a key part in the carbon cycle as they can both store and release carbon.  Soil temperatures influence this balance between uptake and release of CO2 into the atmosphere. Soil Organic Carbon - SOC The soil is a sink for carbon and stores about 1500 gigatons [Gt] = 1.5 Pt = 1500 Pg), more than the atmosphere and vegetation combined. SOC derives ultimately from photosynthetic organisms.  When they die, their carbon is used by heterotrophs for energy and biosynthesis. Carbon can also be returned to the atmosphere by aerobic oxidation by microorganisms, but this requires O2, which diminishes rapidly with soil dept. Oxygen levels depend on soil porosity, relative amounts of sand and clay, and hydration.  Tilling of soil increases O2 exposure and hence oxidative respiration of SOC, increasing atmospheric CO2.  No-till farming hence can decrease CO2 release into the atmosphere.  Inorganic carbon from CO2 (HCO3, and CO3-2) bind with cations in the soil (mineralization) or is released into the atmosphere as CO2. Carbon input into the soil is mostly determined by photosynthesis, which correlates with root mass, and decay, while export is determined by soil microbial (bacteria, fungi, protists, animals) respiration.  Microorganisms play a key role in the balance of carbon input and release in the soil and hence are prime determinants of SOC. SOC is high in northern latitudes since colder temperatures promote lower respiration rates and accumulation of SOC over time.  SOC is low in the lush tropics (even given the high photosynthetic rates) because of a high microbial respiration rate at higher temperatures.  Deforestation of the lush Amazon Rain Forest will leave soil poor SOC with little to balance CO2 release from the decomposition of what's left by the abundant soil microorganisms. About 21 Gt of the 1500 Gt of SOC consists of microbial mass (12 Gt fungi, 7 Gt bacteria and 2 Gt from animals).  Fungi hence are key players in soil metabolism. They are involved in the slow decomposition of decaying organic matter and promote the growth of slow-growing organisms like trees.  In contrast, bacteria are fast metabolizers, and are found in abundance in grasslands.  Northern attitudes have a higher soil microbial mass than in the tropics, but they are less active, allowing great SOC stores. We often think of enzymes working in an aqueous environment in a test tube or a cell (which is very crowded with other molecules).   Figure \(1\) below represents the microenvironment of soil enzymes involved in the decomposition of SOC, like cellulose. Figure \(1\): Location of enzymes in soils and their importance for carbon and nutrient cycling.  Fanin, N. et al. (2022). Soil enzymes in response to climate warming: Mechanisms and feedbacks. Functional Ecology, 36, 1378– 1395. https://doi.org/10.1111/1365-2435.14027.  Permission from John Wiley and Sons and Copyright Clearance Center. Figure \(2\) below give a review and an overview of the effects of increasing temperature on soil enzymes. Figure \(2\): Effects of temperature at the enzyme scale.  Fanin, N. et al., ibid.  Permission from John Wiley and Sons and Copyright Clearance Center. Panel (a) shows the many steps involved in enzyme catalysis that can be affected by temperature changes.    Step 1 shows the binding of substrates.  The KM (units M) for the enzyme gives a "measure" of the strength of the interaction (but remember that KM = KD - the dissociation constant - only under rapid equilibrium conditions).  Step 2 reflects kcat, the "net" rate constant for converting bound substrate to product. Panel (b) shows how key constants change with increasing temperature.  The figure shows that kcat increases with temperature, consistent with the Arrhenius equation, as the temperature coefficient Q10 decreases (as discussed in Chapter 12.11).  (Remember that Q10 is the factor by which the reaction rate, k or R, changes for each 10-degree K or C temperature increase) )The enzyme's thermal inactivation rate, kinact, also increases with temperature, leading to the bell-shaped VM curve.  Km, a measure of the apparent KD of the substrate for the enzymes, increases, reflecting weaker binding.  The catalytic power, Epower = kcat /kinact, also decreases with increasing temperature as the slope of kinact is generally greater than that of kcat.  The values for the temperature axis would be different for microbes that grow best at cold temperatures (psychrophilic), moderate temperatures (mesophilic), and high temperatures (thermophilic). The graphs above represent temperature effects at the enzyme level. The gray rectangle represents the optimal growing conditions, which show that enzymes are poised near Vfor substrate conversion (assuming abundant substrate) but with low catalytic power. Increasing temperatures also have an effect at the microbial community level. These can affect SOC.  For example: • After an increase in the decomposition of SOC at higher temperatures, subsequent decreases in SOC can occur due to the depletion of available substrates (as enzymes are running at VM) and changes in carbon use among the microbial communities; • Additional decreases in SOC due to increased oxidation and shifts in the composition of the microbial community occur; • The levels and types of substrates for enzymes likely change; • Increased temperatures can lead to increases in microbial community mass, which requires more substrate, but in the long-term metabolic shifts might lead to a decrease of extracellular enzymes and microbial biomass; • Soil conditions also change with increasing temperatures, which affects biomass by changing substrate availability; • Increased temperature lead to short-term increases in CO2 emission due to higher microorganism metabolic rates explained by the Arrhenius equation, but additional effects caused by accompanying changes in the microbial community occur. Complex mathematical modeling (as we saw using Vcell with metabolic and signal transduction pathways) would be needed to understand the effects of warming on SOC stores and their return to the atmosphere as CO2 Enzyme properties with altitude - Mount Kilimanjaro It is possible that the loss of SOC with climate change may be mitigated to some extent as the soil microbial community thermally adapts to a lower respiration rate/microbial biomass. As Figure 1 shows, both extracellular and intracellular enzymes must be considered.  Extracellular enzymes break down polymers like cellulose into monomers, which are transported into the cell for intracellular respiration and the formation of CO2 by intracellular enzymes.  The extracellular (lytic) and intracellular (oxidative) enzymes might respond differently to higher temperatures.  Polymers that are hard to degrade have high activation energies, making soils with higher concentrations of these polymers more sensitive to climate warming (based on the Arrhenius equation). Changes in the microbial community might include shifts in the fungal/bacterial ratio, causing changes in degradation pathways and the rates of enzyme-catalyzed reactions.  At higher temperatures, such changes increase conformational flexibility, which could increase kcat but also decrease the apparent affinity of the enzyme for the substrate, as reflected in increased KM values. These compensatory effects might leave catalysis unaffected by increasing temperatures. Studies have been conducted on individual degradative enzymes in soil samples from Mount Kilimanjaro.  Enzyme kinetic analyses were done at two different temperatures differing by 10C  (10C and 20C), so Q10 values could be evaluated.  The soil samples were obtained from different heights on the mountain to allow for the comparison of the kinetic parameters of enzymes from microbes adapted to different heights.   All organisms in soil from different heights would experience 10 °C, while those at the highest altitudes (3000 m) would encounter 20C only in the summer. The microbes presumably would have slightly different optimal temperatures for growth, and would likely use different adaptive mechanisms at low and high altitudes.  Keep in mind in interpreting the results below that the Q10 value determines the sensitivity of a parameter (v0, KM, VM, etc.) to an increase in the temperature of 100 C. Extracellular enzyme activities in soil from one altitude The activity of three extracellular enzymes in soil samples were studied:  β-1,4-glucosidase (degrades cellulose), N-acetylglucosaminidase (degrades chitin from fungi and peptidoglycans from bacteria), and acid phosphatase.  The first two enzymes catalyze "recalcitrant" reactions with higher activation energies.   Michaelis-Menten plots for the three enzymes in soil samples taken at one height, 2010 meters (m), are shown in Figure \(3\) below.  In addition, a plot of glucose conversion to CO2, an intracellular process, which the authors termed "glucose" mineralization" (probably because they trapped CO2 using OH- to form HCO3-), is also shown. Figure \(3\):  Rates of reactions mediated by hydrolytic enzymes (a–c) and rates of glucose oxidation to CO2 (d) as dependent on substrate concentration at 10 and 20 °C for the site located at 2010 m a.s.l. Symbols – experimental data, lines – approximation by Michaelis–Menten kinetics.  The red lines indicate assays performed at 20C, while the green lines are for assays run at 10C. Blagodatskaya, Е. et al. Temperature sensitivity and enzymatic mechanisms of soil organic matter decomposition along an altitudinal gradient on Mount Kilimanjaro. Sci Rep 6, 22240 (2016). https://doi.org/10.1038/srep22240.  Creative Commons Attribution 4.0 International License.  http://creativecommons.org/licenses/by/4.0/ The mineralization rate was determined using trace amounts of 14C-labeled glucose, which if fully oxidized, is converted to 14CO2.  Given the conditions of the reactions, the added glucose did not cause microbial proliferation.  The authors used a fluorophore (4-methylumbelliferone or MUF)-labeled small substrate analogs for cellulose (MUF-β-D-glucopyranoside), chitin (MUF-N-acetyl-β-D-glucosaminide dehydrate) and for acid phosphatase (4-MUF-phosphate). Reactions were carried out in soil samples and valid initial velocities for the reaction were determined. The Km values for the glucose oxidation (mineralization) is not a valid KM value since CO2 would be produced from the combined actions of the enzymes in glycolysis, pyruvate dehydrogenase and the citric acid cycle.  It could be better called an "operational KM". For the three enzymes, the KM at 20 °C was 25–42% larger than the VM = kcatET at 20 °C, causing Q10KM > Q10VM.  These compensatory changes canceled any increases in enzyme activity at low substrate concentrations, but not at high ones when the enzyme was saturated.  Hence Q10 for the catabolic depolymerization reactions increased with substrate concentration.  Note, however, that the rate of intracellular glucose oxidation (mineralization) increased at all substrate concentrations going from 10 to 20 °C and the canceling effect was not detected, even at low substrate levels. The temperature response of monomer oxidation showed a strongly accelerated reaction rate instead of a canceling effect Extracellular Enzyme activities in soils from different altitudes Next, kinetic analyses were performed on soil samples from 2010 m (warm-adapted microorganisms) and 3020 m (cold-adapted) on the mountain. Plots of Q10 (not vas in Michaelis-Menten plots) vs. substrate for these studies are presented in Figure \(4\) below to show the temperature adaption capacities of the enzymes. Figure \(4\): The Q10 values for enzymatic activities (a–c) and glucose oxidation to CO2 (d) as dependent on substrate concentration at two altitudes. The blue and red rectangles show the concentration range at which no temperature effects occur (i.e < Scrit) with shading colors corresponding to different altitudes. The Q10 values derived from experimental data are shown as symbols. The model simulations based on experimentally obtained parameters of Michaelis–Menten kinetics are shown as curves (a–c). For glucose oxidation (d) at 3020 m elevation, a non-linear trend was very weakly expressed. Bars show standard deviations of the means (n = 3).  Blagodatskaya, Е. et al. ibid. The graphs show that Q10 for polymer degradation increased with increasing substrate concentration.  The authors defined a substrate concentration threshold (Scrit) below which KM and VM values canceled, so no increase in rate was seen with increasing temperature based.  The width of the rectangles is based on the best-fit dashed blue and red lines, not the data points.  The Scrit values were 35–42% larger for the 3020 m (blue rectangle) soil samples than at 2010 m (red rectangle), even though SOC was lower at that elevation.  Q10 values were always lower at S > Scrit at higher altitudes. This demonstrates that the enzymes responded less to higher temperatures at higher altitudes (blue dots and dashed lower curves), implying that the enzymes at higher latitudes demonstrated significant compensatory changes useful for microorganisms that experience a greater temperature range with larger shifts at these higher altitudes. Figure \(5\) below shows that the Q values generally decrease over a larger range of altitudes for both enzyme activity (panel A) and glucose oxidation (Panel B). Figure \(5\): The Q10total values for hydrolytic enzyme activity at saturating substrate concentrations (A) and the increase in Vmax and Km induced by a temperature increase from 10 to 20 °C for 14C-glucose oxidation (B) depending on altitude. Symbols – experimentally derived values for Q10total (B), Q10Vmax and Q10Km (A). Lines are the trend lines obtained by the best fitting of power (A) and linear functions (B) at P values < 0.05, bars show standard deviations of the means (n = 3).  Blagodatskaya, Е. et al. ibid. Panel B shows how Q10 values for KM apparent (green) and VM (red) for intracellular glucose oxidation/mineralization both decreased with increasing altitude.  Again consider these KM and VM values to be operationally defined and apply not to an individual enzyme but in less rigorous way to all the enzymes involved in the intracellular oxidation of glucose to CO2.  Investigators could determine these values only by fitting the kinetic data for CO2 vs [glucose] to the Michaelis-Menten equation.  At altitudes < 2435 m, all showed Q10 KM values > 1.9, showing that the apparent collective KM values were very sensitive to temperature increase.  This implies the "collective" set of enzymes responsible for intracellular glucose oxidation was more conformational flexible, and higher temperatures caused significant increases in apparent KM values.  However, at high altitudes, the Q10 values for the apparent KM were about 1, suggesting no temperature effects on the generic structure and apparent KM values for the enzymes.  The high-altitude enzymes were effectively temperature-stable with respect to KM values.  VM values were more sensitive to increasing temperatures at all altitudes, but little change was seen going from 2435 to 3020 m.  This again shows that the enzymes for microorganisms from high altitudes were more strongly adapted to temperature changes, especially at lower substrate concentrations. The next part can be a bit confusing.  The KM for a given enzyme increases with increasing temperature, as shown in Figure 2.  This suggests that the apparent affinity of the given enzyme for substrates decreases, which makes intuitive sense.  Figure \(6\) shows that the Q10 for KM (i.e. the sensitivity of KM to a temperature increase of 100 C) decreases with increasing elevation for each enzyme studied. Figure \(6\):  The values of Q10Km (a) and Q10Vmax (b) for hydrolytic reactions and for reactions of glucose oxidation at low and high altitudes. Bars show standard deviations of the means (n = 3). The figure shows that both Q10Km and Q10Vmax were lower at high altitudes. Hence the enzymes in organisms from higher altitudes did not respond as strongly to temperature changes. This again suggests that larger compensatory changes are found in enzymes at high latitudes, allowing them to better adapt to the greater temperature range they would experience. These data suggest that thermal adaptions in the intracellular enzyme are driven more by a large range of temperatures experienced by the organisms and not the mean temperature.  Compensatory and canceling changes in KM and VM at low substrate concentration led to a higher Scrit in cold-adapted organisms. Structural Mechanisms for enzyme temperature adaptation Structure determines function.  A detailed understanding of how proteins, and more directly enzymes, adapt to temperature changes must come from detailed structural analyses that can be correlated to functions such as enzyme catalytic activity.  Two approaches have been used to study the structural bases of enzyme temperature adaptation.  One involves structural analysis of a single enzyme in organisms adapted to different temperatures.  The other involves large computational analyses of databases of protein structure. We'll discuss both.  First, let's explore orthologs (in this case, a protein from the same gene in different species) of a single enzyme, ketosteroid isomerase (KSI), from mesophilic (grown in moderate temperatures) and thermophilic (grown in warm temperatures) bacteria. Figure \(7\) below shows structural and functional features that document KSIs temperature adaptation through changes in activity and stability.  Let's step through each of the panels in order. Figure \(7\):  Enzyme temperature adaptation through changes in activity and stability.  Pinney et al., Science, 371. (2021). DOI: 10.1126/science.aay2784 Reprinted with permission of the American Association for the Advancement of Science and Copyright Clearance Center. Panel (A) shows that as the optimal growth temperature for an organism increases, the optimal temperature for selected enzyme activity also increases. Each dot represents a different species with the enzymes broadly chosen across all enzyme commission classes (i.e. the dots do not represent just KSI).  There is a strong linear correlation. Starting with Panel (B), we look at KSIs.  Panel B shows the mechanism of isomerization of the steroid substrate, 5(10)-estren-3,17-dione [5(10)EST] by KSI.  KSI has one of the highest kcat values of enzymes.  The reaction changes the position of the C=C double bonds and proceeds through an enolate/oxyanion intermediate (EI) formed after the abstraction of a proton by Asp 40.  The transition state, which would have a developing negative charge on the O atom, is stabilized by proximal Tyr 16 and Asp 103 in a developing oxyanion hole. Panel (C) shows the KSI sequences from P. putida (a mesoKSI) and M. hassiacum (thermoKSI). The sequences are 33% identical, but some key resides (gray) are identical. Similar ones are shown in blue.  The thermophilic KSI (thermKSI) has Ser 103 instead of the often conserved Asp 103 in mesophilic organisms (mesoKSI).  D103 and S103 are shown in red. Now let's look at Panel (D), which shows the activity (v0/E0) vs substrate [5(10)EST], for the meso- and thermophilic enzymes.  At a nominal temperature (250 C, top left panel), the thermoKSI shows little activity.  At their optimal growth temperatures (300 C for the mesoKSI and 650 C for thermoKSI, bottom left), the thermoKSI has both a higher VM=kcatE0, and KM at 650 C.  The higher KM is consistent with the idea that Km values are usually higher at higher temperatures. The derived kcat and kcat/KM values are shown in the adjacent histogram.  Remember that kcat is a measure of how many bound substrate molecules are converted to product per sec (at saturating substrate concentrations).  The parameter kcat/KM is a measure of the effective biomolecular rate constant for product formation at low substrate concentrations ([S] <<KM). From an evolutionary perspective, early ancestral enzymes probably arose in warmer environments.  When the earth cooled, enzymes had to "solve" the problem easily apparent from the Arrhenius equation (the rate of reactions decreases with decreasing temperatures).  Hence evolution would lead to structural changes that would facilitate either higher kcat values, lower KM values, or both, at lower temperatures. We've seen in the examples above that KM decreases with decreasing temperatures, so evolutionary pressures would more likely lead to increased kcat values. This is consistent with localized changes in protein dynamics as modulators of kcat. The evolutionary pressure to maintain stability would be low since proteins become more stable at lower temperatures. Now the question arises, how thermally stable are the meso- and thermKSI?  Urea denaturation experiments were used to determine the ΔG0 H2OU for unfolding of the enzyme, as described in Chapter 4.12.  Panel (E) shows urea denaturation curves for mesoKSI (black) and thermoKSI (red) monitored by changes in internal tryptophan fluorescence (top) and stabilities extrapolated to 0 M urea (ΔGH2OU) (bottom). The denaturation curve for thermoKSI is significantly shifted to the right.   The calculated values for ΔG0 H2O unfolding are +18.5 kcal/mol (mesoKSI )and about +25.3 (thermoKSI), a +6.8 kcal/mol difference.  These values would be - 18.5 kcal/mol (mesoKSI) and about -25.3 (thermoKSI) for the reverse folding reaction (unfolded ↔ folded transition). Figure \(8\) shows an interactive iCn3D models  of the bacterial ketosteroid isomerase from the mesophilic bacteria Pseudomonas Putida (left) and from the thermophilic bacteria Mycobacterium hassiacum (right). Figure \(\PageIndex{8a}\):  Mesophilic Ketosteroid Isomerase D40N mutant (monomer) from Pseudomonas Putida (pKSI) bound to 3,4-dinitrophenol (6C17).  (Copyright; author via source).  Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...kH877KZKPxWsGA Figure \(\PageIndex{8b}\): Thermophilic Ketosteroid Isomerase D38N mutant (monomer) from Mycobacterium hassiacum (mhKSI) bound to 3,4-dinitrophenol (6P44).  (Copyright; author via source).  Click the image for a popup or use this external link:  https://structure.ncbi.nlm.nih.gov/i...YbGw5zr2dRpnK8 Both structures have a bound 3,4-dinitrophenolate, a stable oxyanion and transition state analog. The corresponding active site side chains are shown in color sticks.  Just one monomer of the dimer for each protein is shown for clarity.  The key active-site residues F86, V88, V101, and D103 in mesoKSI are replaced by W86, L88, I101, and S103 in thermoKSI (all very conservative except for D103S).  A water molecule forms a bridging hydrogen bond from S103 to the oxyanion in the phenolate. Figure \(9\) below shows the key change in Asp 103 (see the mechanism in Fig. 7B) in mesoKSI to Ser 103 + H2O in thermoKSI Figure \(9\): Change from Asp 103 in mesoKSI to Ser 103 + H2O in thermoKSI Asp 103 is initially protonated with a pKa is >9, much higher than in an aqueous solution (3.7).  The hydrogen bond from the mesoKSI D103 to the phenolate is stronger than the bridging one from Ser 103 in the thermoKSI.  This arises from the increased polarity of the OH on a carboxylic acid (Asp) compared to an alcohol (Ser). In addition, the hydrogen bond distance from the bound water to the phenolate is longer than from Asp 103. Hence the Asp 103 in the thermoKSI improves enzymatic stability.  In contrast, at lower temperatures, thermoKSI is less active, but at higher temperature it is more stable. The role of Ser 103 in stabilizing the folded state is shown in Figure \(10\) below Figure \(10\): Roles of Asp 103 and Ser 103 in the folding to unfolding transition of KSI When mesoKSI unfolds, Asp 103, which is protonated and not accessible to solvent in the native state, becomes solvent exposed on denaturation.  Its pka drops, which leads to its deprotonation.  This extra deprotonation step stabilizes the unfolded state (pulling the reaction to the right), making thermoKSI less thermally stable.  In contrast, Ser 103, on solvent exposure, does not deprotonate, so the unfolded state is not additionally stabilized.   Hence Ser 103 leads to greater stability of the folded thermoKSI.  The D103/S103 change is found in many KSI from many bacteria. Here are some additional findings: • The structures of mesoKSI and thermoKSI are highly similar even though they have only 33% sequence identity.  Figure \(11\) shows the conformational changes in the monomeric ketosteroid isomerase (KSI) going from the mesophilic enzyme (6C17, cyan) to the thermophilic one (6P44, magenta) enzymes Figure \(11\):  Conformational changes in the monomeric KSI going from the mesophilic enzyme (6C17, cyan) to the thermophilic one (6P44, magenta) enzyme • The F86W change in the thermoKSI stabilizes the conformation of S103 to maximize its stabilization of the oxyanion in the oxyanion hole and allows high-temperature activity. • Other KSI orthologs and mutants show higher enzyme activity if they have D103. • Changing key residues at 86, 88, and 101 in mesoKSI to those found in the thermoKSI additionally increased the stability of the mesoKSI • Analysis of 1140 KSIs showed that the fraction containing D103 decreases with increasing growth temperature, and the fraction containing S103 increases. It appears that the thermal adaption of KSI occurs mostly through one amino acid change (D103S). The change reduces kcat for the mesoKSI 10x at low temperatures but greatly increases stability at high temperatures. This switch and compensatory changes in activity and stability suggest that protonated Glu and Asp side chains involved in activity might be changed to other amino acids that confer more stability but reduce activity.  A conserved and protonated active site Glu is found in glycosidases from high-temperature orthologs.  Likewise, a protonated Asp side chain is found in thioredoxin. As a control, a change from a protonated Glu in triosphosphate isomerase distal to the active site to a Gln shows no effect on catalysis and was not found in thermophiles.   It should be noted that not all stabilizing mutations decrease activity since many examples are known that don't. 67 protonated Asp and Glu side chains were identified in the PDB, 14 of which were replaced in high-temperature orthologs. Conservation of chaining pairs of amino acids between mesophilic and thermophilic organisms. Are there other broadly found changes in amino acids (in addition to Asp/Glu) at each position in a mesophilic protein and its thermophilic ortholog?  Computational analysis in 2194 enzyme families in 5582 bacterial species (for a total of 17 million amino acid pairs) were performed to explore this question.  Half of all families had an amino acid at a given position which correlated with growth temperature, resulting in almost 160,000 key positions. The results of this study are broadly outlined in Figure \(12\) below. Figure \(12\): Examination of temperature-associated residues and their interactions. Pinney et al., ibid. Reprinted with permission of the American Association for the Advancement of Science and Copyright Clearance Center. Let's explain each part of this complex figure. Panel (A) - Preferences in the types of amino acids associated with high or low-temperature growth: Panel (A) shows the difference in frequency of association of given amino acids with high to low temperatures.  Some amino acids were associated with low-temperature growth. These include alanine (A), glutamine (Q), and aspartate (D), as in KSI).  Others were associated with high-temperature growth (E, I, Y and K).  F Panel (B)  - Identities and frequencies of site-specific residue changes across temperature growth conditions:  Now let's see how a specific acid changes in going from mesophilic (low T) to thermophilic (high T), which show key temperature-dependent amino acid changes. Panel (B) shows the frequency of observed site-specific changes in temperature-associated residues. The “+” indicates that the frequency of a change (such as Leu to Ile) is significant in comparison to the opposite change (Ile to Leu). Indeed the darkest square is from a Leu (at low temperatures) to Ile (at high temperatures change.  Other darker squares show these changes occur going from low to high temperature-adapted bacteria:  V → I, R → L, and D → E.  The specific one found for KSI, D → S, does not stand out, most likely because of the diverse types of enzymes included in the analysis. Panel (C) - Identities and frequencies of physically interacting temperature-associated residue pairs (in a single protein):   If one amino acid changes in going from a low to a high-temperature ortholog, it is likely that the original and changed amino acids physically contact different nearby amino acids in their respective protein.  Panel (C) shows the difference in frequency of association with high vs. low-temperature growth bacterial enzymes for "all possible physically interacting pairs of residues (made up of residues R1 and R2) that change concomitantly with the growth temperature".   The darker blue squares show interacting residue pairs found more often in low-growth temperature enzymes, while darker maroon squares show contacting pairs in high-growth temperature proteins.  The asterisk shows pairs are statistically significant. One of the darkest maroon squares (high-temperature enzymes) shows an Ile-Ile interaction pair.   The vertical column above Ile (high growth) in Panel B shows that changes to Ile in high-temperature enzymes occur frequently from 6 different amino acids found in low-temperature enzymes. Ile might be favored over Val to maximize buried hydrophobic surface area.  Also, compared to Leu, it has great conformation flexibility and could better pack empty spaces in protein interiors. A Lys to Glu (K to E) interaction is strongly associated with high-temperature enzymes, while an Arg to Asp (R to D) interaction is strongly associated with the low-temperature enzyme.  This implies that simply increasing the number of salt bridges (ion-ion interactions) does not make a protein more thermally adaptable.   Lys salt bridges would have greater conformational flexibility than those using Arg.  The same applies to Glu compared to Asp.  Lys also has the largest hydrophobic surface area, which could enhance hydrophobic packing.  Since Arg has hydrogen bond donors requiring more adjacent hydrogen bond acceptors, Arg use might depend more on adjacent amino acids and not just a binary pair. In summary, these results are more nuanced than previous explanations for high-temperature stability: • increasing branched chain residues like Ile, Leu, and Val in the packed hydrophobic core.  Indeed, as seen in Panel B,  the most frequent amino acid change observed are from Leu/Val in low-temperature growth orthologs to Ile in high-temperature growth ones.  These are all branched-chain amino acids. They occur 2-3x more frequently than the reverse, Ile in low-temperature growth to Leu/Val in high-temperature growth enzymes.  In fact, Panel C shows that Ile preferentially interacts with another Ile in high temperature adapted enzyme.  Hence stability is not just improved by substitution with any hydrophobic side chain. • increasing number of salt bridges  (ion-ion interactions) and hydrogen-bonding interaction charged side chains. There are more charge side chains and salt bridged in thermophilic proteins. However, the above data shows a clear preference for Lys in thermophilic proteins as changes from Arg to Lys are common in that group as shown in Panel B.  Likewise, Asp to Glu changes from low to high temperature-adapted proteins are 3 times more probably than the reverse.  In addition, Panel C shows that interactions between Lys and Glu are most strongly associated with high temperature-adapted proteins, while Arg and asp interactions are most often in low temperature-adapted proteins. Allosterism and Thermal Changes As we mentioned before, increasing temperature can cause local effects in a protein, instead of large global changes that obviously lead to denaturation at high temperatures. Changes occurring in the active site can clearly affect kcat, KM, and kcat/KM.  Allosteric effects also occur.  The protein imidazole glycerol phosphate synthase (HisFH) is a heterodimer with two active sites, so it is considered a bienzyme.  The H subunit is a glutaminase, which cleaves glutamine into glutamine acids and ammonia, which can diffuse through a channel to the active site of the F subunit, the cyclase.  The active form of the enzyme occurs only when both substrates are bound resulting on long-range allosteric activations. In particular, the oxyanion hole in the H subunit is formed in the activated form which allows the stabilization of the tetrahedral transition intermediate in the hydrolysis of glutamine. Figure \(13\) shows an interactive iCn3D models  of the heterodimeric imidazole glycerol phosphate synthase complex (7AC8) Figure \(13\):  (Copyright; author via source). Heterodimeric imidazole glycerol phosphate synthase complex (7AC8). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...S5MbiiA9bDvDQ8. The HisH (glutaminase) subunit is shown in orange with a molecular surface and the bound substrate (spacefill), glutamine.  The HisF subunit is shown in cyan with a bound substrate. An allosteric effector molecule can bind in the active site of His F and induce long-range conformational changes in this HisH active site which increases its activity 5000x.  The structures involved are shown in Figure \(14\) below. Figure \(14\):  Imidazole glycerol phosphate synthase (IGPS) from the thermophile Thermotoga maritima (T. maritime).  Maschietto, F., Morzan, U.N., Tofoleanu, F. et al. Turning up the heat mimics allosteric signaling in imidazole-glycerol phosphate synthase. Nat Commun 14, 2239 (2023). https://doi.org/10.1038/s41467-023-37956-1.  Creative Commons Attribution 4.0 International License.  http://creativecommons.org/licenses/by/4.0/. IThe HisF and HisH subunits are colored respectively in green-to-blue and red-to-yellow gradients, respectively, and separated by a dotted line which marks the interface between HisF and HisH. The labels (fα2, fα3, fβ2, loop1, hα1, Ω-loop) indicate secondary structure elements that are directly involved in the allosteric regulation. Molecular dynamics and NMR studies have shown that increases in the temperature lead to conformational changes resembling those that occur on binding of the allosteric effector at room temperature.  As the temperature of the apoenzyme (no substrates or effects bound) increases from 30 °C to 50 °C, the dynamics and structure increasingly resemble the state induced by the effector.  Increasing temperatures from climate change are likely to cause subtle conformational and dynamic changes in all proteins with some having negative consequences. Thermal determinants of yeast metabolism With this basic background, we can attempt to understand the thermal determinants for entire metabolic pathways. This has been attempted in the yeast Saccharomyces cerevisiae, a eukaryotic organism with optimal growth around ~30 °C, extremely limited growth at 40 °C, and no growth/death at 42 °C. Mathematical modeling all metabolic pathways in a cell is a daunting task. Accurate concentrations, rate constants, and dissociation constants are needed for all reactions.  Genome-scale metabolic models (GEM) use a multitude of constants that are experimentally or computationally determined.  There are usually significant uncertainties in the parameters used in the model.  Bayesian statistics has been used to decrease these uncertainties. In Bayesian statistics, parameters and models are updated with the known values and information.  It is similar to machine learning models, which uses data to train the model and refine it. A Bayesian model for S. cerevisiae was used as it is the most abundantly used organism in industry and has many GEMs.  The GEM used was the enzyme-constrained GEM (ecGEM).  It was then further developed into the enzyme and temperature-constrained GEM (etcGEM), which, in addition, incorporates the temperature dependence of both the concentration of the native enzyme (EN) and kcat for the enzyme. For each enzyme, the melting point (TM), the change in heat capacity (ΔCp) for the transition state, and the optimal temperature (Topt) were included.  We discussed both (TM) and the change in heat capacity (ΔCp) for proteins in Chapter 4.9.  In addition, another term for non-growth associated maintenance (NGAM) of the cells, which is also temperature-dependent, was included.  Examples of non-growth associated maintenance include maintaining membrane potential, turgor pressure, normal protein refolding and DNA repair. The Bayesian models reproduced the datasets well. Using the models, key enzymes that control the flux through metabolic pathways were determined at each temperature.  The most rate-limiting enzyme at superoptimal temperature in yeast was squalene epoxidase (ERG1), found in sterol metabolism pathways.   Replacing the S. cerevisiae enzyme with one from a thermotolerant yeast strain led to better growth than the wild-type cells. Figure \(15\) below the complexity and extent of the metabolic proteins and pathways of the enzyme-constrained GEM (ecGEM). Figure \(15\:  Metabolic proteins and pathways included in the enzyme-constrained GEM. Benjamín J Sánchez et al., Molecular Systems Biology (2017)13:935.  https://doi.org/10.15252/msb.20167411.  Creative Commons CC BY (add soon:  ecYeast7 model (both constrained and unconstrained; each as.mat,.sbml and.txt files): GitHub  (https://github.com/SysBioChalmers/GECKO/tree/v1.0/Models) Now, let's explore some of the results of the study.  Figure \(16\) below shows how the temperature dependencies of proteins were incorporated into the enzyme-constrained GEMs. Figure \(16\): Using Bayesian statistical learning to integrate temperature dependence in enzyme-constrained GEMs.  Li, G et al., Nat Commun 12, 190 (2021). https://doi.org/10.1038/s41467-020-20338-2.  Creative Commons Attribution 4.0 International License.  http://creativecommons.org/licenses/by/4.0/. Panel a shows the complexity of the metabolic network used to produce the ecYeast7.6, shown in greater detail in Fig. 13 above, and how EN and kcat depend on temperature. Panel b shows how a two-state denaturation model was used.  [E]N is the enzyme concentration in the native state; Topt is the optimal temperature at which the specific activity is maximized; Tm and T90 are temperatures at which there is a 50 and 90% probability that an enzyme is in the denatured state, respectively.  In Chapter 4.4 we indicated that for a reversible two-state transition, TM is the temperature at which half of the enzyme is native, and half denatured, giving a Keq for the N ↔ D equilibrium of 1. Panel c shows how kcat depends on temperature.  The insert show how the heat capacity change from E+S to the E-transition state.  We have previously seen that the ΔCP > 0 for protein unfolding, and this +ΔCP value is a signature of the hydrophobic and occurs when nonpolar groups become more solvent exposed.  We have shown previously that enzymes bind the transition state more tightly than the substrate.  This -ΔCP is more in line with the latter. Panel d shows the temperature dependence of enzyme's specific activity, r, as a function of temperature, which is determined by EN (panel B) and kcat (panel C). Now we can explore the outputs of the ecGEM run with the separate added effects of temperature on NGAM, kcat, and EN (denaturation). Finally, the combined etcGEM was run.  The combined etcGEM was able to produce the observed outcomes in yeast growth.  Given that the contributions of each of the three factors, NGAM, kcat, and protein denaturation, to whole-cell growth could be modeled. The outcomes from these models as a function of temperature are shown in Figure \(17\).  They support the notion that the growth rate of yeast is explained by temperature effects on its enzymes. Figure \(17\): Fig. 3: Yeast growth rate is explained by temperature effects on its enzymes.  Li, G et al., ibid This figure shows how the temperature dependence of different processes combines to affect the growth rate. EC is the prediction from the enzyme contained GEM, —predictions with the enzyme-constrained model;  ec+NGAM includes temperature effects on nongrowth associated maintenance; ec+kcat(T) incorporates the temperature effects on enzyme kcat values; ec+denaturation(T) incorporates the temperature effects on enzyme denaturation.  Finally, in the etc model, the enzyme and temperature-constrained model incorporates temperature effects on all three (NGAM, kcat and enzyme denaturation) into ec model. The solid lines indicate median values and shaded areas indicate regions between the 5th and 95th percentiles (n = 100). Here are some summarized results: • <29 °C, only temperature-dependent kcat affected the cell growth rate (green line under the orange etc line <29 °C; • at 29 °C <T < 35 °C, both kcat and NGAM (gray line) determined the growth rate; • at T > 35 °C, enzyme denaturation at major effects and by 40 °C was dominant, Figure \(\PageIndex{18d-e}\) shows using images how the temperature dependencies of the factors EN, kcat and r (specific activity) on yeast growth.  A phenomenal amount of data is displayed in these images. Figure \(\PageIndex{18d-e}\): Fig. 3: Yeast growth rate is explained by temperature effects on its enzymes.  Li, G et al., ibid Panel d shows the probability that a given enzyme is in the native state.  Think of the y-axis as increasing 1 pixel at a time from bottom to top, with each new added pixel representing a different enzyme for a total of 764 enzymes along the y-axis.  The x-axis shows with each pixel at a given y-axis  if the enzyme is native that temperature The interface between bright (native) and black (denatured) on the right side of the image shows that some enzymes (top) become unfolded at 400C, while some don't unfold until close to 600C Panel e shows normalized kcat values of 764 enzymes at different temperatures. The brightest white pixels show the highest kcat values. The image clearly shows the brightest vertical band at around 300C, the optimal growth temperature of yeast. Panel f  shows the normalized specific activities (r) of 764 enzymes at different temperatures. Again the highest specific activity is centered around  300C. Note in Fig. 15d that most enzymes also denature at temperatures < -10 0C, but cells were not viable under those conditions. The etcGEM was able to replicate a finding that above 37 °C, yeast cells switch from respiration to partial fermentation accompanied by a larger flux through glycolysis.  This occurred because of a decrease in specific activities of enzymes with increasing temperature, which constrains metabolism.  In addition, the total protein concentration reaches a limit and can't increase further, which could have increased enzyme activity.  Along with an increase in glycolysis, mitochondrial ATP production decreases.  Respiration produces more ATP per mole of glucose, but glycolysis/fermentation produces more ATP/protein mass so when protein concentration reaches a maximum, glycolysis is more efficient. Lastly, the etcGEM was used to find the enzymes whose flux changed most at superoptimal temperatures. (We introduced the flux control coefficient in Chapter 14.3). The results of this modeling are shown in Figure \(19\) below. Figure \(19\): Flux control coefficients at superoptimal model for yeast. One enzyme stands out in Panel a, ERG1, squalene epoxidase, an enzyme involved in sterol oxidation. If the wildt-type enzyme was replaced with a temperature-insensitive ERG1, the specific growth rate increased significantly (over 55%). What does this have to do with climate change? Life's Thermal Tolerance and Limits We've just explored the molecular and metabolic adaptations that allow organisms to thrive at higher temperatures. The question is how quickly present life can adapt to global warming caused by increasing greenhouse gases.  Life on the planet will adapt, but the diversity of life forms in the biosphere will change. Five mass extinction have occurred over the last 450 million years up until the present, as shown in Figure \(20\) below. Figure \(20\): Five mass extinction over the last 450 million years. Hannah Ritchie. Our World in Data.  https://ourworldindata.org/mass-extinctions.  Creative Commons BY license Many factors, often interrelated, can contribute to mass extinction.  These include volcanism, asteroid impact, climate change, ocean anoxia, and the release of methane from ocean hydrates and permafrost. All are correlated with increased temperatures. as shown in Figure \(21\) below.  It documents the correlation between temperature changes and the extinction rate for marine animals over the last 450 years. The gray bars highlight the extinction cycle. Figure \(21\): Temperature change and extinction rate over the past 450 million years.  Song, H., Kemp, D.B., Tian, L. et al. Nat Commun 12, 4694 (2021). https://doi.org/10.1038/s41467-021-25019-2.  Creative Commons Attribution 4.0 International License.  http://creativecommons.org/licenses/by/4.0/. Pane a shows the largest magnitude of temperature change (ΔT, absolute value) in each time interval (bin) Panel b shows the highest rate (R, absolute value) of temperature change in each time bin, defined at the million-year (Myr) scale. Panel c shows the generic extinction rates of marine animals calculated using gap-filler methods using data from the Paleobiology Database. The Big Five extinctions occurred in the end-Ordovician (OS), Frasnian-Famennian transition (FF), Permian-Triassic transition (PT), Triassic-Jurassic transition (TJ), and Cretaceous-Paleogene transition (KPg). Vertical bars show mean ± 1 x standard deviation (see Methods). O Ordovician, S Silurian, D Devonian, C Carboniferous, P Permian, T Triassic, J Jurassic, K Cretaceous, Pg Paleogene, N Neogene. Dark cyan, blue, and red dots represent ΔTR, and extinction rate, respectively. The authors suggest that a temperature change of >5.2 °C and a rate of >10 °C/Myr would lead to a new extinction rate comparable to the "Big Five".  A rise of 5.2 °C is in the upper range (but likely only with human inaction to prevent it) of IPCC projection. The present rate of temperature increase (almost 2 °C/200 years) is unprecedented in at least the last 3 million years.  So it appears that we are headed to a 6th mass extinction, caused by a combination of environmental degradation, pollution and climate change created by humans. Rothamn has suggested that we can tip over into an "official" mass extinction depending on the magnitude and the rate of change in the carbon cycle.  If long-scale changes occur too quickly and organisms can't adapt, extinction follows.  If the changes occur on a short time scale (as it happening now), the size of the change is a key factor.   His analysis suggests that a key factor is anthroprogenic-related increases in ocean CO2.  A threshold of 310 Pg (Gt), which we could reach by 2100, could officially trigger the sixth mass extinction.  However, many would argue that we are already in the 6th extinction cycle. For example, of about 30,000 terrestrial vertebrate species, about 1.7% (515 species) are on the verge of extinction (having <1000 individuals).   77 mammal and bird species have lost most of their populations. When it comes to how organisms will adapt to our present climate change, Hochahka and Somero ask 6 relevant questions: 1. What is the thermal optima and how much change leads to suboptimal or lethal conditions? 2. What mechanisms set upper limits? 3. How close do organisms live to the limits of their thermal tolerance? 4. To what extent can organisms acclimatize to temperature increase? 5. When are genetic changes necessary for survival? 6. Does the acquisition of heat tolerance reduce cold tolerance? More simply, we can ask if organisms can survive and thrive and at what cost.  Humans most assuredly can survive a warming world, but if temperatures become too high, they will not thrive and parts of the world we become inhabitable to them without great economic, social, political, and cultural costs.  Some organisms will become extinct.  Mass migrations of all species will occur as they seek more habitable environments. In an evolutionary sense, species occupy environments in which they can survive and adapt.  Our climate has been fairly constant since the end of the last ice age, about 12,000 years ago.  Climatic changes that make an environment suboptimal depend on how sensitive an organism is and how close it lives to its thermal limits.  We have already documented the thermal limits of individual enzymes as well as whole metabolic systems As you learned in introductory chemistry, multiple linked reactions can only go as fast as their slowest step.  An analogous insight is that a chain is only as strong as its weakest link.  It may be the membranes, not proteins, are the weakest link with respect to temperature adaptation as higher temperatures alter phases and subphases like rafts with lipid bilayers which in turn can disrupt the activity of membrane proteins.  Membrane functions (permeability, endo- and exocytosis, and maintenance of transmembrane potentials) are also very important. Temperature effects on neural communication in synapses might be key. An interesting example is a particular Antarctic fish, which after long exposures to 4 0C, dies if temperatures are raised to around 90C.  Synaptic transmission through the acetylcholine receptor is altered as the amount of acetylcholine in the synapse increases and the rate at which it is degraded by acetylcholinesterase decreases. The lethal temperature for an organism can depend on many factors, including the rate of temperature increase, the length of exposure,  previous long-term acclimatization, and for humans, humidity. Humans can't dissipate body heat without sweating if the external temperature is >370C (average body temperature).  If the humidity is high enough, sweating is ineffective in dissipating heat.  Since metabolism produces heat, the body temperature can increase past 370C even if the external temperature is lower.  A lethal limit for humans can occur as low as 290C (840C) at a relative humidity of 85%. Even now, about 30% of the human population is exposed to lethal thresholds at least 20 days/yr, and this will only grow as temperatures and humidity increase. Comparisons of similar species (congeneric) that live in intertidal regions (region above the water level at low tide and underwater at high tide) vs. subtidal regions (close to a shore but always submerged) show significant differences.  Organisms living in intertidal regions will experience a higher range of temperatures.  19 species of congeneric porcelain crabs exist that inhabit different latitudes and vertical depths in intertidal and subtidal regions. Intertidal crabs that experience a highly variable temperature region are exposed to temperatures much closer to their lethal temperature limit, showing the Tlethal - Tmax habitat get smaller. Heart function and neural activity at higher temperatures appear to be weak links in survival.  Heat shock protein function is also different in congeners of marine snails.  Lethal temperatures appear to be those at which protein synthesis dramatically decreases. Repair to damaged protein is decreased, which limits vertical migration to lower temperatures and can make organisms more susceptible to predation.  These effects can occur over a small increase in temperature (a few degrees Celsius).  It becomes more important to heed the warnings by the IPCC to limit global average temperature increases to 1.5 to the more likely 2.50C range. Laboratory experiments in directed evolution show that an organism's thermal tolerance can be increased, but there are limits. E. Coli can live at a variety of temperatures and experiments to evolve them at 37 0C (human gut temperature), at 42 0C (close to their lethal limit), and in an environment with fluctuating temperatures (between 32 0C - 42 0C) have been conducted. Over 1000s of generation, the rate of evolution was highest in the 42 0C group.  The group that evolved at fluctuating temperatures was better adapted to both higher and lower temperatures.  Under no circumstance did E. Coli evolve into a thermophilic species, which would require novel gene function and not just a small set of mutations that allow for more optimal values of kcat and KM for enzyme catalysis.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.12%3A__A_Warmer_World%3A_Temperature_Effects_On_Proteins.txt

Search Fundamentals of Biochemistry Introduction Climate change affects human health and of course the health of the biosphere to which we are inextricably linked.  Many, including the US CDC, describes the concept of One Health as "a collaborative, multisectoral, and transdisciplinary approach — working at the local, regional, national, and global levels — with the goal of achieving optimal health outcomes recognizing the interconnection between people, animals, plants, and their shared environment." We all know that pollution from the use of fossil fuels also has severe health consequences independent of effects mediated more directly by climate change.  A solution to both is to dramatically decrease the use of fossil fuels and mitigate pollution from their use.  People most likely do not understand the extent that climate change and fossil fuel use are linked to human health.  If they did, perhaps they would become advocates for climate change action.  This section will cover how climate change and fossil fuel use affect aspects of human health and diseases.  In this section, we will focus on heat-related illnesses and pulmonary/cardiovascular diseases. We'll tackle climate change, emerging diseases, and pandemics in the next chapter section. Fossil fuel use and climate change obviously affect other diseases as well, some by indirect means  For instance, increases in cancer deaths will occur due to lower availability of health care arising from extreme weather disasters that impact health facilities and peoples' access to them.  An increase in cancer deaths occurred during the Covid pandemic since people deferred preventive healthcare treatments as well as cancer surgeries during the pandemic.  Allergic illness will increase as growing seasons lengthen and species that cause allergic reactions shift to new growth regions. Heat-Related Illness Heat illnesses include heat cramps, heat exhaustion, heat syncope (fainting), and heat stroke, the latter of which can quickly become fatal.  Heat affects normal physiology and health.  It's estimated that about 1% of all cardiovascular deaths are linked to extreme temperatures.  We know that the number of warm days and the number of heat waves has increased with climate change. Figure $1$ below shows how heat waves have changed in the US in the decades from 960 to 2022 using data from 50 large metropolitan areas.  The 2020s is not even a third over but already shows increases in heat waves over the previous decade. Figure $1$: Climate Change Indicators: Heat Waves.  EPA.  https://www.epa.gov/climate-indicato...ors-heat-waves.  Data source: NOAA (2022) Many historical heat waves have occurred in recent times.  A heat wave in Europe in 2003 caused 15 000 heat-exposure-related deaths in France and 70,000 throughout Europe.  On June 28, 2019, France recorded a temperature of 45.9 °C (115 °F).  In 2022 China experienced a two-month heat wave and drought, with a record temperature of 45 °C (113 °F) set in Chongqing.  The Chicago Heat Wave of 1995, with a maximal temperature of 106 °F, caused by high temperatures and humidity, kill over 500 people. The worst might be the Russian Heat Wave of 2010 when temperatures were 5 °C (9 °F) higher than normal and reached 40 °C (104 °F). Around 55,000 people died. Figure $2$ shows temperature and excess mortality from the 2022 heat wave in Europe during which 60,000 people died from heat-related causes. Figure $2$: Weekly temperature and heat-related mortality numbers in Europe during the summer of 2022.  Ballester, J., Quijal-Zamorano, M., Méndez Turrubiates, R.F. et al. Heat-related mortality in Europe during the summer of 2022. Nat Med (2023). https://doi.org/10.1038/s41591-023-02419-z.  Creative Commons Attribution 4.0 International License.  http://creativecommons.org/licenses/by/4.0/ Panel a shows the weekly baseline (gray line) and observed (black line) temperature (°C) averaged over Europe. Temperature anomalies are defined as the difference between observed and baseline temperatures (gray shading). Baseline temperatures were computed as the mean annual cycle of observed temperatures in the reference period 1991–2020. Panels b and c show weekly heat-related mortality (weekly deaths) aggregated over Europe for the overall population (black), women (red) and men (blue) (b) and people aged 0–64 (blue), 65–79 (red) and 80+ (black) years (c), together with their 95% CIs (shadings). The numbers for women and men in b do not include the United Kingdom; values for the age groups in c do not include Germany, Ireland and the United Kingdom. Figure $3$ below shows the steady increase in deaths as the average summer temperature in Europe has increased. The outlier in 2022 is shown as a red dot. Figure $3$: The summer of 2022 within the context of rising temperatures in Europe.  Relationship between summer mean temperature (°C) and summer heat-related mortality (summer deaths) in the analyzed European countries. The straight line shows the linear fitting for the 2015–2022 period. Ballester et al., ibid. In exercise studies under controlled conditions, the heart rate increases and plateaus after a temperature increase, but with further temperatures increases, the heart rate increases with plateauing, which is a sign of cardiovascular strain. In humid condition, cardiovascular strain develops even on slow walking at 34 °C (93.2 °F).  Under dry condition, the strain developed at around 41 °C (106 °F).  The strain (as indicted by an increasing heart rate proceeds by about 20 minutes a rise in core temperature. With each temperature increase over pre-Industrial Revolution values, the annual probably of heat waves with apparent temperatures of 40 °C/104 °F (dangerous with a high incidence of heat cramps, heat exhaustion, and heat strokes) and 55 °C./131 °F (very dangerous with heat stoke very likely)  increases, as shown in Figure $4$ below. Figure $4$:  Annual probability of occurrence of heat waves with apparent temperature (with contributions from humidity) peaks greater than 40 °C and 55 °C. Russo, S., Sillmann, J. & Sterl, A. Humid heat waves at different warming levels. Sci Rep 7, 7477 (2017). https://doi.org/10.1038/s41598-017-07536-7.  Creative Commons Attribution 4.0 International License.  http://creativecommons.org/licenses/by/4.0/. Panels (ac) show the probability of occurrence of heat waves with AT peak  ≥ 40 (AT40C) calculated at each grid point for all model years with global mean temperature anomaly relative to 1861–1880 at 1.5, 2, and 4 degrees warming (see Fig. 2), respectively. Panels (df) are similar to panels (ac) but show the occurrence of heat waves with AT peak  ≥ 55 (AT55C). Heat Exposure and Kidney Disease Cumulative exposure to high effective temperatures caused by sublethal combinations of heat and humidity leads to chronic kidney disease.  This is happening with increasing frequency to workers in poor agricultural areas and others who work in such hot conditions in industry and outdoors. This type of kidney disease is not caused by diabetes, hypertension, and other known disease of the glomeruli.  Chronic kidney disease in such workers was noted in El Salvador and elsewhere in Central America.  The disease has a high mortality rate.  Just in El Salvador, the death from kidney disease is ten times higher than in the US.  Initially, the disease was called Mesoamerican nephropathy. Biochemical correlates of the diseases are yet unclear.  Serum creatinine levels are increased, which might affect renal perfusion and lead to kidney damage.  The effects might be generally cumulative or from repetitive episodes of exposure.  Sugarcane field workers who report nausea, vomiting, headaches, muscle weakness, back pain, and fevers have high levels of creatinine.  Kidney biopsies show inflammation and kidney fibrosis. Similar diseases in other parts of the world that seem to have the same presentation include Sri Lankan nephropathy and Uddanam (in the Indian state of Andhra Pradesh) nephropathy. Some have categorized it as CKDu or Chronic Kidney Disease of Unknown etiology/Uncertain cause or as chronic kidney disease of non-traditional origin (CKDnt). Figure $5$ below shows the distribution of kidney disease in the Western Hemisphere in 2019. Figure $5$:  Burden of Kidney Disease. 2019.  Men and Women.  Pan American Health Organization/WHO. Other factors such as increased exposure to herbicides, heavy metals, and microbial agents might also cause or contribute to the disease. However, the disease is most prevalent in the hotter regions of affected countries, as the incidence is lower when workers work at high altitudes.  Increased incidence of chronic kidney disease also appears to be occurring in workers in Florida and California. Biochemical Mechanism for Heat Stroke The actual biochemical mechanisms of heat stroke effects (circulatory failure, organ injury, uncontrolled clotting, death) are not fully understood.  Certainly, cell death plays a major role, but not through the classical apoptotic pathway which depends on the activation of caspases (see Chapter 28.14).  Rather, cell death occurs through necroptosis, a caspase-independent pathway. In necroptosis, an upstream protein kinase RIPK3 (receptor-interacting serine/threonine protein kinase 1) activates through phosphorylation the effector protein MLKL (mixed lineage kinase domain-like protein).  Phosphorylated-MLKL then translocates to the cell membrane where it leads to calcium influx and plasma membrane damage in the final "execution" phase of cell necrosis. The activation of RIPK3 and MLKL through other receptors, including the toll-like receptors (TLR3 and TLR4), and tumor necrosis factor receptor 1 (TNF-R1), is shown in Figure $6$ below. Figure $6$: Activation of RIPK3 by multiple stimuli. Morgan MJ, Kim YS. Roles of RIPK3 in necroptosis, cell signaling, and disease. Exp Mol Med. 2022 Oct;54(10):1695-1704. doi: 10.1038/s12276-022-00868-z. Epub 2022 Oct 12. PMID: 36224345; PMCID: PMC9636380.  Creative Commons Attribution 4.0 International License.  http://creativecommons.org/licenses/by/4.0/ RIPK3 can be activated via various receptors when bound by their respective ligands. These are TNF receptor 1 (TNF-R1), CD95, death receptors (DR4/5), Toll-like receptors (TLR3/4), and Z-DNA-binding protein-1 (ZBP1)/DAI. In the first three of these pathways (but not TLR3/4 or ZBP1), RIPK1 is required and binds to RIPK3 through its receptor-interacting protein homotypic interaction motif (RHIM). In the case of ZBP1, RIPK3 is recruited directly via the ZBP1 RHIM domain, while in the case of TLR3/4, RIPK3 is recruited indirectly via the RHIM domain of TRIF. Once activated, RIPK3 autophosphorylates and then phosphorylates and activates MLKL to induce a conformational change and translocation to the membrane, where membrane permeabilization follows. During this process, post-translational modifications positively and negatively regulate the necroptosis pathway. Two E3 ligases, Pellino-1 (PELI1) and carboxy terminus of HSC70-interacting protein (CHIP), may control the basal threshold of necroptosis. Another E3 ubiquitin ligase, TRIM21, is proposed to be a regulator of necroptotic cell death in response to TRAIL. PPM1B suppresses necroptosis by dephosphorylating RIPK3. The domain structure and phosphorylation sites on human RIPK3 are shown in Figure $7$ below. Figure $7$:  Meng, Y., Horne, C.R., Samson, A.L. et al. Human RIPK3 C-lobe phosphorylation is essential for necroptotic signaling. Cell Death Dis 13, 565 (2022). https://doi.org/10.1038/s41419-022-05009-y.  Creative Commons Attribution 4.0 International License.  http://creativecommons.org/licenses/by/4.0/ "Schematic of human RIPK3 domain architecture and the phosphorylation sites identified. Phosphorylation sites with proposed functions are shown at the top. pT224 and pS227 positively regulate necroptosis (green) by recruiting MLKL. pS164 and pT165 negatively regulate necroptosis by inhibiting RIPK3 kinase activity (red) [38]. Phosphorylation of T182 (grey) was proposed to promote RIPK3 kinase activity and to recruit PELI1 to mediate proteasomal degradation of RIPK3 [54]. Phosphorylation sites with unknown functions are shown on the bottom (white). Asterisks (*) denotes multiple serine/threonine on the same peptide, as such the exact site of phosphorylation could not be unambiguously identified." Figure x shows a complex, the necrosome, containing multiple activated RIPK3s along with RIPK1. Aggregation of RIPK3 occurs through the RHIM (RIP homotypic interaction motifs) domain through the formation of amyloid fibers. The necrosome then phosphorylates MLKL, which forms oligomers and traffics to the membrane. Figure x above also shows that an internal sensor protein for viral DNA can also activate RIPK3.  That protein is ZBP1, or Z-DNA-Binding Protein 1, which also binds Z-RNA.  Nuclear Z-RNA can derive from viruses like influenza A, leading to the activation of the same pathway.  Cytokine expression then produces a systemic inflammatory response. In addition to apoptosis and necroptosis, another type of programmed cell death caused by inflammation is called pyroptosis. Usually occurring in bacterial-infected macrophages, pyroptosis leads to the activation of intracellular inflammasomes, which then activate inflammatory cytokines through selective proteolysis by caspases. In pyroptosis, proteins called gasdermins are cleaved by caspases and their N-terminals self-associate in the cell membrane to form pores, from which the inflammatory cytokines IL-1β, and IL-18 are released. A final programmed cell death pathway for virally-infected cells is called PANoptosis, which uses the PANoptosome complex with downstream results not explained by the other three programmed cell death pathways (pyroptosis, apoptosis, and necroptosis)  ZBP-1 leads to the activation of RIPK3, caspase-8 (key in the apoptosis pathway) and the NLRP3 inflammasome. ZBP-1 seems to play a key role in heat stroke.  Its concentration increases with heat stress mediated by the heat shock transcription factor 1 (HSF1., which itself is induced by cellular stress.  HSF1 induces a heat shock response which causes increased transcription of chaperones and heat shock proteins (HSPs) such as ZBP-1.   Deletion/inactivation of ZBP-1, RIPK3, or MLKL and caspase 8 decreases heat stroke. The main role of ZBP-1 in cell death from heat stroke arises from the RIPK3/MLKL pathway and to less extent through cross-talk with the classical apoptosis pathway through caspase 8. How does ZBP-1 activate cell death during heat stroke without binding to and activating dsDNA or RNA derived from a viral infection?  Does ZBP-1 have an endogenous ligand other than viral Z-RNA or Z-DNA?  Let's first explore the domain structures of some key proteins in the RIPK3 activation pathway.  Figure 1 above shows three key proteins, RIPK1, TRIFF, and ZBP1 that interact with RIPK3.  Each of these proteins and RIPK3 have a RHIM domain for protein-protein interactions.   Figure $8$ shows the domain structure of our key protein, ZBP-1, the cytosolic Z-DNA/Z-RNA sensor. Figure $8$: Domain structure of ZBP-1 (http://www.ebi.ac.uk/interpro/protein/UniProt/Q9H171/ ) The green bars in the N-terminal part of the protein are the Z-DNA binding domain.  These are also called Zα domains.  These regions are the most ordered in the protein, as indicated by the blue in the AlphaFold confidence bar. Figure $9$ shows an interactive iCn3D models  of the AlphaFold-predicted model of human Z-DNA-Binding Protein 1 (ZBP1), (Q9H171) Figure $9$:  AlphaFold-predicted model of human Z-DNA-Binding Protein 1 (ZBP1), (Q9H171).  (Copyright; author via source).  Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...FMCRhJwHFVb1X7 The spacefill atoms labeled M1 represent the N-terminal methionine of the protein.  The two Z-DNA binding domains follow, are well-ordered, and are shown as blue cartoons.  Much of the protein can't be predicted as it is most likely intrinsically disordered.  Two fairly well-structured motifs, shown in magenta and cyan are the RHIM1 and RHIM2 protein interaction motifs, which can be shown self-associated through their amyloid-like structures.  These motifs allow ZBP1 to bind to other proteins with RHIM motifs and on to cell death through necrosis.  The C-terminal domain appears to be involved in signal transduction type I interferon-mediated by DNA. Figure $10$ shows an interactive iCn3D model of the second Z-DNA binding domain of human DAI (ZBP1) in complex with Z-DNA (3EYI) Figure $10$:  Second Z-DNA binding domain of human DAI (ZBP1) in complex with Z-DNA (3EYI).  (Copyright; author via source).  Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...SLBmo2xqV1WpS6 In the absence of viral DNA or RNA, the Zα domain can bind endogenous ligands.  Moreover, it appears that a deficiency in RIPK1 or of the RHIM in RIPK1 also triggers ZBP1 to induce necroptosis and inflammation. and that its Zα domain is required.  If nuclear export was stopped, ZBP1 activates nuclear RIPK3 and then necroptosis.  This suggests that nuclear ZBP1 interacts with endogenous nuclear Z-nucleic acids, probably Z-RNA from retroelements to activate RIPK3-dependent necroptosis and could lead to some forms of chronic inflammation. Here are a series of finding on RIPK3-dependent cell death on heat stress in mouse fibroblasts that show that Z-nucleic acid binding to ZBP1 is not required for heat stress effects: • Heat (43°C for 2 hr) induces phosphorylation of RIPK3 and MLKL within 2 hours, and cleavage of pro-caspases and GSDME in 6 hours but none occurred if RIPK3 was deleted. • Deletion of ZBP1 but not RIPK1, TRIF, affect heat induce death so so heat stress acts through ZBP1 and RIPK3. • In mice without ZBP1, the effects of heat stress (clotting, inflammation, organ injury, and death) were prevented. • Mutations in the RHIM domain, but not the Zα domains (made to prevent Z-nucleic acid binding) or in the C-terminal signaling region (to stop signaling) prevented death from heat stress .  Hence Z-nucleic acid binding is not required but may contribute to cell death from heat stress. • Heat stress caused the aggregation of a ZBP1-GFP (green fluorescent protein) fusion protein through the RHIM domains of ZBP-1 Hence ZBP1 is an innate pathogen sensor and also an initiator of heat-related death in the absence of pathogens. Heat Stroke-Induced Epigenetic Changes Short of death, heat stroke can also cause long-term health issues.  Increasing global temperatures are forcing people to work at more dangerous temperatures and at night to reduce heat exposure. Data suggests that people who have had a heat-related illness are more susceptible to additional heat exposure health consequences.   This has been noted in exertional heat illnesses. (such as in athletes).    Additional long-term effects on immune regulation have been observed. Epigenetics may play a role in long-term effects such as greater vulnerability to additional heat challenges.  Studies show that a single episode of exceptional heat stroke changes DNA methylation patterns in bone marrow-derived monocytes from mice.  The monocytes become immunosuppressed allowing for increased microbial disease and reduced heat shock responses.  The epigenetic changes are passed onto progeny monocytes which also shows compromised function.  The epigenetic changes persist for 30 days or more and we clearly noted in inflammatory cell signaling pathways.  This suggests a mechanism for the reduced tolerance to those with previous heat-related illnesses. Cardiovascular and Pulmonary Diseases Many factors can cause cancer and lead to mortality.  For example, mortalities from cancer can increase due to lower availability of health care arising from extreme weather disasters that impact health facilities and access to them.  Early detection of many cancers is key to survival.  Instead of discussing climate change links to cancer we will focus on pollution and in particular small particles. PM2.5 particulate pollution effects on cardiovascular and pulmonary health Pollution from the combustion of fossil fuels contributes to many chronic diseases.  Here we will focus on one type of pollutant, particulate matter which can be inhaled.  These particles can be liquids, solids, or combinations of both.  They are classified according to size with common categories including: • PM10:  diameters < 10 uM = 10,000 nm; • PM2.5: fine particles with diameters < 2.5 um = 2500 nm • PM0.1:  ultra-fine particles with diameters < 0.1 um = 100 nm  (also called nanoscopic particulate matter or NPM) Figure $11$ below shows the relative sizes of PM10 and PM2.5 particles compared to other biological structures. Figure $11$:  Relative sizes of PM particles compared to biological structures.  Sotirios Papathanasiou.  Particulate Matter (PM2.5) Mega Guide.  With Permission.  https://seetheair.org/2022/05/16/par...-5-mega-guide/ Figure $12$ below shows the relative sizes of PM0.1 particles compared to a PM2.5 particle. Figure $12$: Relative sizes of PM01 particles compared to a PM2.5 particle.Sotirios Papathanasiou, ibid. Composition of PM2.5 particles PM2.5 particles obtained by collection from polluted city air can actually be purchased from the National Institute of Standards and Technology (NIST, SRM1648a) and used for experimental studies on living cells.  It is typically added to water and a suspension produced through sonication.   PM2.5 particles are derived from human sources such as emissions from vehicles and industry, and both human-caused and natural processes such as the burning of biomass, and the release of dust from land.  They can also include salts from land and ocean sources. They arise from the burning of fossil fuels and wear and tear of products such as automobiles (including tires). PM2.5 particles contain mainly black carbon, polycyclic aromatic hydrocarbons (PAH), aryl hydrocarbon, volatile organic compounds (VOCs) as well as minerals, ions (sulfate, nitrate, ammonium), and general biological materials.   The metal composition includes Group 1A (K, Na, Fr), Group 2A (Ca, Mg), Group 3A (Al), transition metals (Al, As, Cr, Fr, Mn, Pb, Ti, Zn), and counter ions Br and Cl. They also contain silicon and silicates. Of course, particles in the air, including dust, also derive from non-anthroprogenic sources.  Atmospheric dust is also produced from land by winds and also by volcanic eruptions.  In homes, dust has an abundance of dead skin cells. along with pollens, hair, fur, and fibers from clothes and paper.   Humans have evolved with particulates in the air, but large increases in their abundance caused by human activities in many parts of the world pose serious health consequences. In addition, the reaction of pollutants in the atmosphere produces "secondary" pollutants.  One, the tropospheric gas ozone (O3) produced from hydrocarbons and nitrogen oxides, is a known health risk, and its levels are increased in cities on sunny, hot, and humid days.  Secondary organic carbon (SOC) is also generated from primary organic carbon, typically volatile organic compounds (VOC) through oxidative photochemical reactions. These VOCs (like m-xylene and 1,2,4-trimethylbenzene) can produce aerosols (larger particles, called secondary organic aerosols by reacting with each other to produce larger structures.  Terpenes containing isoprene units like α-pinene and limonene (a monoterpene found in large abundance in fruit peels) are reactants for the products of much larger structures.  (See Chapter 10.1 and Chapter 21.6 for a review of isoprene and terpenes). Figure $13$ below shows generalized pathways for the formation of particulate SOCs from smaller terpenes. Figure $13$:  Proposed mechanisms for the formation of C20H33N3O12, C20H32N4O14, and C30H48N4O16 in the simultaneous oxidation (MIX) experiment.  Takeuchi, M., Berkemeier, T., Eris, G. et al. Non-linear effects of secondary organic aerosol formation and properties in multi-precursor systems. Nat Commun 13, 7883 (2022). https://doi.org/10.1038/s41467-022-35546-1.   Creative Commons Attribution 4.0 International License.  http://creativecommons.org/licenses/by/4.0/. Gas-phase mechanism via cross-reactions of α-pinene and limonene peroxy radicals (RO2·), and particle-phase mechanism via hemiacetal formation involving α-pinene and limonene oxidation products. APN-RO2, APN(=O), and APN(-OH) represent α-pinene oxidation intermediate (i.e., RO2·), α-pinene oxidation products with carbonyls, and α-pinene oxidation products with hydroxyl functional groups, respectively. Figure $14$ below shows the morphology of PM2.5 particles. Figure $14$:  Morphology of PM2.5 particles. The scale bars are 20 μm for image (a) 2 μm for image (b) 1 μm for image (c) 40 μm for image (d).  Shi, Y., Ji, Y., Sun, H. et al. Nanoscale characterization of PM2.5 airborne pollutants reveals high adhesiveness and aggregation capability of soot particles.  Sci Rep 5, 11232 (2015). https://doi.org/10.1038/srep11232.  Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. Panel (a) shows a  large area image of as-collected PM2.5 on the filamentary filter. Panel (b) and (c) show SEM images of a particle with flat and rough top surface, respectively. Panel (d) shows a SEM image of the PM2.5 transferred on a Silicon substrate. Inset, zoom-in SEM image of an Iron-rich particle. Many of the PM2.5 particles have rough surfaces which can deform more easily interact with (i.e. stick to) other particles through noncovalent interactions to produce even larger particles. Figure $15$ below shows the elemental composition of rough, semi-rough, and flat PM2.5 particles. Figure $15$: EDAX Chemical composition histogram of the particles collected with SEM/EDAX classified by surface roughness; a larger surface roughness (and therefore, stickiness and deformation) are linked to a larger content of Carbon, while particles with a flat surface (low stickiness and viscosity) are richer in Oxygen and metals. Shi, Y. et al., ibid. Figure $16$ below shows electron micrographs of actual airborne particles. Most are PM2.5 particles with diameters < 2.5 uM = 2500 nm. Figure $16$: A collage of SEM images for airborne particulates. (A) General classification of airborne particulates; (B) particulates with seeds-coating composite morphology; (C) sulfate particulates with different morphologies.  Clara Yuan Li et al, Journal of Environmental Protection, Vol.7 No.10, 2016. https://www.scirp.org/journal/paperi...?paperid=71021 Creative Commons Attribution 4.0 International License. Given their composition and their structures, it doesn't take much thought to realize that the particles must cause significant health effects.  Would you want to breath in these particles routinely? Health Effects of PM2.5 Particles PM2.5 particles are associated with just about every type of illness, including cardiovascular and pulmonary diseases, including asthma, as well as cancer.  Since they are small, they can easily be inhaled and deposited in lung alveoli from where they can actually enter the bloodstream and be deposited in tissue.  Particles up to 240 nm (0.25 uM) can cross the placenta and black carbon particles have been found to cross the placenta.  PM2.5 particles can cause inflammation, DNA damage, organelle dysfunction and can also generate free radicals which are most likely involved in these toxic health effects. The Great London Fog of 1952 Aerosol particles can even have acute and lethal effects.  During  The Great London Smog in London in December 1952, around 12,000 people died in two weeks from its effects.  Figure $1$ people in the thick smog from that event.  The smog consisted of acidified water droplets arising from SO2 and NO2 released on burning coal that contains sulfur.  This gas can be oxidized to sulfate in gas-phase reactions probably through the .OH free radical or in aqueous phase reactions using O3, peroxides, and NO2 as reactants/catalysts. The reaction of SO2 with NO2 in aqueous droplets is shown below: \mathrm{SO}_2(\mathrm{~g})+2 \mathrm{NO}_2(\mathrm{~g})+2 \mathrm{H}_2 \mathrm{O}(\mathrm{aq}) \rightarrow 2 \mathrm{H}^{+}(\mathrm{aq})+\mathrm{SO}_4^{2-}(\mathrm{aq})+2 \mathrm{HONO}(\mathrm{g}) Given the stoichiometry of the reaction with NO2, the reaction proceeds significantly only in the presence of high NO2.  The reaction is favored under high relative humidity.  In large water drops in clouds, the droplets are not very acidic but as water evaporates from the drops, the sulfate concentration and acidity dramatically increase. However, as the acidity increases the rate of oxidation and the solubility decreases. Major cities in China, including Beijing and Xian, have experienced high levels of haze and particulate matter in the atmosphere until recently.  Yet these smogs were not lethal (very acidic) as in London due to the addition of NH3 in the droplets, which neutralizes the acidic particles.  Atmospheric ammonia is derived from large amounts of agricultural fertilizers that get aerosolized and also from vehicles, which produce NH3 in catalytic converters and from urea used in the catalytic reduction in diesel engines.   The relevant production of sulfates in the presence of NH3 is shown in the equation below. \begin{aligned} 2 \mathrm{NH}_3(\mathrm{~g})+\mathrm{SO}_2(\mathrm{~g})+2 \mathrm{NO}_2(\mathrm{~g}) & +2 \mathrm{H}_2 \mathrm{O}(\mathrm{aq}) \rightarrow 2 \mathrm{NH}_4^{+}(\mathrm{aq}) + \mathrm{SO}_4^{2-}(\mathrm{aq})+2 \mathrm{HONO}(\mathrm{g}) \end{aligned} Figure $17$ below shows a ghostly image of pedestrians in London during the Great Fog. Figure $17$: Ghost-like pedestrians making their way through the smog.  https://heritagecalling.com/2022/12/...f-london-1952/.  Public Domain The prevailing weather conditions (cold temperatures) increased emissions from coal use. A stalled high pressure system and resulting low winds caused the buildup of stagnant air with increasingly acidic PMs.  The appalling death toll led politicians to pass the Clean Air Act in 1954 which over many years led to huge improvements in air quality in London and dramatically reduced negative health effects and deaths. This was a prelude to the Clean Air Act in the US which dramatically improved air quality as well. High levels of PM2.5s still are prevalent in much of the world, although there have been dramatic decreases in the US.  The notable exceptions occur during forest fires that are exacerbated by climate change.  In the US, the Air Quaility Index (AQI) is used as an indicator of health risk. It measures the value of 5 pollutants, fine particles (PM2.5 and PM10), ground-level ozone, SO2, NO2 and CO.  The value of AQI at a given time is determined by which pollutant is highest.  In haze produced by smoke, the reported AQI represents PM2.5 particles.   AQI values < 50 or below represent good air quality, while an AQI value over 300 represents hazardous air quality. Western forest fires in Oregon in September 2020 lead to an AQI of 611 in Madras, Oregon.  Forest fires in Eastern Canada, along with slow-moving weather system, led to PM2.5 levels  over 800 (mg/m3)  in New York City on June 7, 2023.  No place is immune from PM2.5 particles from wild fires and human-caused pollution. • AirNow - gives present pollution data, primarily PM2.5 levels, based on US zip codes The following interactive graphs show the changes in PM2.5 particles over time (from Hannah Ritchie and Max Roser (2019), OurWorldInData.org/outdoor-air-pollution • CC BY.  Source: Brauer et al. (2017) via World Bank). Figure $18$ shows the share of the population exposed to PM2.5 levels higher than those suggested by the World Health Organization. Figure $18$: Share of the population exposed to PM2.5 levels higher than those suggested by the World Health Organization.OurWorldInData.org/outdoor-air-pollution • CC BY.  Source: Brauer et al. (2017) via World Bank Figure $19$ below shows the share of the population in the US and in India exposed to PM2.5 levels higher than those suggested by the World Health Organization. Figure $19\: PM2.5 levels in the US and India.Our World in Data. ibid Finally, Figure \(20$ below shows the death rate from PM2.5/100,000 people in 2017 in countries around the world. Figure $20$:  Death rate from PM2.5/100,000 people in 2017 in countries around the world. Mechanisms for PM2.5 Health Effects In Chapter 5.4, we discussed how solids such as silica, cholesterol crystals, uric acid crystals, and even aggregated proteins such as prions can be engulfed by monocytes/macrophages (much as they engulf bacteria as part of their immune function) in a process called phagocytosis. The particles are enveloped in plasma bilayer-derived membrane which buds off into the cell. This vesicle merges with a lysosome which gets damaged in the process. They then release ATP into the cytoplasm which acts as a damage signal to activate inflammation. PM2.5 particles can also be taken up by phagocytosis to produce intracelluar phagosomes.  They can also be taken up by pinocytosis, and caveolin and clathrin-mediated endocytosis to form endosomes.  These can fuse with lysosomes and mitochondria and induce damage.  The heavy metals from PM2.5 particles that are released into the cell also contribute to damage. Smaller PM2.5 particles of diameter less than 0.1 uM (100 nM), sometimes called nanoscale particulate matter (NPM), offer large surface areas to which proteins can adhere.  The adsorbed proteins form a "crown" called a protein corona.  The corona is actually larger than the NPM.  Proteins bounds to the particles include hemoglobin, albumin and fibrinogen.  The corona also mediates cellular interactions and participates in the mechanisms that lead to inflammation and cellular dysfunction.  As the extracellular matrix includes protein components such as collagen and fibrin, the interaction of PM2.5 with fibrin, has been studied as a model for how the particles might interact with cells. In particular, lung fibroblasts embedded in a 3D matrix of fibrin (i.e. a 3D organotypic culture) were exposed to NPM with protein coronas, and the effects of the NPM particles on cell proliferation, oxidative stress, etc. were monitored.  Figure $21\ below characterizes the interaction of the NPMs with the fibroblast in the 3D culture. Figure \(21$: Physicochemical characterization of airborne particulate matter. Li, Y., Wang, P., Hu, C. et al. Protein corona of airborne nanoscale PM2.5 induces aberrant proliferation of human lung fibroblasts based on a 3D organotypic culture. Sci Rep 8, 1939 (2018). https://doi.org/10.1038/s41598-018-20445-7.  Creative Commons Attribution 4.0 International License.  http://creativecommons.org/licenses/by/4.0/. Panel (A) shows SEM images of airborne PM2.5 (nanoscale PM2.5 is marked in red). Panel (B) shows a chemical element analysis of PM2.5 by EDX analysis. Panel (C) shows an atomic force microscopy (AFM) image of nanoscale PM2.5 (NPM) from air pollutant samples (scale 3.5 μm). Panel (D) AFM image of airborne NPM from air pollutant samples (scale 1 μm). Panel (E) shows an SEM image of airborne NPM from air pollutant samples. Panel (F) shows an SEM image of an NPM-protein corona. Panel (G) shows a schematic diagram of the biological interaction between NPM and protein. Panel (H) shows a FTIR spectra of NPM, serum, and NPM-protein corona. The NPM-protein corona particle leads to the proliferation of the 3D-cultured human lung fibroblast cells over and above stimulation of the cells with just NPMs or serum alone.  The bigger the size of the corona, the greater the proliferative effect. This is consistent with the extensive fibrosis of the lungs seen after chronic exposure to PM2.5 particles. Reactive oxygen species also increased in the presence of NPM-corona particles.  These data suggest that NPM-protein corona are important in PM2.5-induced lung fibrosis and pulmonary disease. Neural Effects of PMs PM particles, particularly the ultrafine PM0.1 particles (UFP), can enter the brain, and affect neural function. Exposure to PM2.5 over long periods of time is associated with increased incidence of dementia and Alzheimer's Disease (AD).  Four particular components of PMs (SO42, NH4+, black carbon, and organic matter) were most associated with a higher risk of dementia and AD.  In the US, the first admission to a hospital for Parkinson, AD, and other dementia is "significantly" associated with the average annual mean PM2.5 exposure. Even low levels of exposure pose risks.  Transgenic mice containing mutant forms of human presenilin 1, amyloid precursor protein, and the tau protein, were exposed to subchronic, "real-world" levels of PM2.5 through inhalation for 3 months.  Neuronal loss was observed in the cortex but no motor or cognitive impairment was noted.  Increased levels of phosphorylated forms of Tau were observed and free radical formation, as evidenced by levels of malondialdehyde (a marker of oxidative stress), was seen in the hippocampus and olfactory centers, consistent with inhalation of PM2.5 through the noise.  No abnormal amyloid plaques were observed in this short exposure time. PMs damage to neurons occur through the generation of reactive oxygen species increased inflammatory responses and organelle damage (all of which are interrelated).  Even in skin cells (keratinocytes), exposure to PM2.5 particles led to increased levels of ROS and malondialdehyde, decreased levels of superoxide dismutase, and increased DNA damage.  Inflammatory Caspase levels also increased. Cells have mechanisms to detect and eliminate aberrant species before they led to worse biological effects.  One process is autophagy which degrades misfolded protein and damaged organelles, processes important for normal neural function.  Aberrant autophagy is a key player in the pathogenesis of dementia. A second pathway is ferroptosis, a kind of apoptotic pathway, which kills cells that have accumulated large amounts of iron ions, which of course are free radicals themselves.  Through the Fenton reaction and others, iron ions can lead to the generation of damaging reactive oxygen species and oxidation of lipids, proteins, and nucleic acids (see Chapter 12.3). Several key proteins are involved in antioxidant defense and autophagy: • NRF2 (Nuclear factor erythroid 2-related factor 2):  This transcription factor binds to antioxidant response elements (ARE) in front of protective genes. • Keap1 (Kelch-like ECH-associated protein 1):  An adapter program that targets NRF2 for ubiquitination and as such is a sensor for oxidative stress. Under those conditions, electrophilic metabolites lead to post-translational modification of reactive Cys side chains which leads to the inactivation of ubiquitin ligase activity. This increases NRF2 and subsequent transcription of antioxidant genes. • SQSTM1 aka p62 (Sequestosome-1):   This protein bridges autophagosomes and polyubiquitinated proteins (cargo for degradation). Hence under low oxidative stress, NRF2 are low through its degradation mediated by KEAP1.  If SQSTM1 (p62), increases, and reduced autophagy, p62 biding to the NRF2 sites on KEAP1, leading to the release of NRF2, its transfer to the nucleus, and activation of gene transcription of protective genes.  These three proteins also protect cells from ferroptosis. In the long term, neural cell death occurs during dementia.  Hence apoptosis, necroptosis, and pyroptosis (from activation of the inflammatory response mediators capspase, Gasdermin, and key cytokines like IL-1β and IL-18) do "win out" to eventually kill cells. Figure $22$ below shows the domain structure of SQSTM1 (panel A) and the signaling process described above. Figure $22$:  Positive feedback-loop of Nrf2 activation by p62/SQSTM1.  Vomund S, Schäfer A, Parnham MJ, Brüne B, von Knethen A. Nrf2, the Master Regulator of Anti-Oxidative Responses. Int J Mol Sci. 2017 Dec 20;18(12):2772. doi: 10.3390/ijms18122772. PMID: 29261130; PMCID: PMC5751370.  Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Panel (A) shows the domain structure of p62/SQSTM1; Panel (B) shows p62/SQSTM1 as an important protein for selective autophagy, as binds to Keap1 and other long-lived proteins and forms polyubiquitinated protein aggregates. Furthermore, it binds to the autophagy marker LC3 within the autophagosome, thereby leading the aggregated proteins into the autophagosome. After fusion with a lysosome, proteins and organelles, such as mitochondria, are degraded within the autophagosome. By binding to Keap1, p62/SQSTM1 stabilizes Nrf2 and enhances its translocation into the nucleus, where Nrf2 activates its target genes (↑ = upregulation of Nrf2 target genes). One of these genes is p62/SQSTM1 PM2.5 particles intefer with the protective autophagy and ferroptosis pathways, leading to increased NRF2 activity and expression of antioxidant genes which are beneficial processes to rid cells of aberrant particles and kill damaged cells in normal conditions but not on exposure of neuronal cells to PM2.5 particles. Lysosomal membrane permeabilization (LMP) as well as mitochondrial and ER damage might be a likely mechanism to initiate ultimate neuronal death on long-term exposure to PM2.5 particles. PM2.5 particles inhibit lysosomal activity and increase their permeability and release to degradative enzymes into the cytoplasm.   Ultimately increased or decreased activation of Nrf2 lead to disease states. PMs and Cancer Long-term exposure can also cause lung cancers.  This is associated with a conversion of lung cells from normal epithelial to mesenchymal cells (called the EMT transition).  Phenotypically, mesenchymal cells can migrate, invade other tissues and cause enhanced production of the extracellular matrix, all hallmarks of tumor cells.  This EMT transition is associated with significant changes in cell signaling and the production of transcription factors.  These changes are documented in Figure $23$ below and its caption. Figure $23$:  Brief schema of the putative signaling transduction mechanisms underlying EMT.  Xu et al., Front. Physiol., 29 November 2019.  Sec. Renal Physiology and Pathophysiology.  Volume 10 - 2019 | https://doi.org/10.3389/fphys.2019.01404.   Creative Commons Attribution License (CC BY) Activation of the Wnt/β-catenin, PI3K/Akt, Ras/ERK, TGF-β/SMAD2/3, BMP/SMAD1/5/8, JAK/STAT3, Shh, and Notch pathways is highly correlated with EMT. After ligand-receptor binding, intracellular secondary messengers are activated and initiated downstream transduction, which generally induce the nuclear translocation of signaling-specific TFs and the transcriptional regulation of EMT-related genes, such as CDH1 and CDH2, EMT TFs, and mesenchymal markers, accompanied by a series of alterations on cellular physiological or pathological activities (e.g., dysjunction of adherin junctions, cytoskeleton remodeling, and increase of cellular motility). Arrows represent the molecular interactions in which downstream messengers are activated; T shape arrows represent inhibitive molecular interactions. EMT, epithelial-mesenchymal transition; PI3K, phosphoinositide 3-kinase; ERK, extracellular signal-regulated protein kinase; TGF-β, transforming growth factor β; JAK, Janus kinase; Shh, sonic hedgehog; TFs, transcription factors. PM2.5 particles and their deleterious contents (heavy metal ions, PAHs, etc), as well as ROS generated by them are associated with the changes seen in the EMT transition.  These include activation of transforming growth factor β (TGF-β)/SMADs, NF-κB, growth factor (GF)/extracellular signal-regulated protein kinase (ERK), GF/phosphatidylinositol 3-kinase (PI3K)/Akt, wingless/integrated (Wnt)/β-catenin, Notch, Hedgehog, high mobility group box B1 (HMGB1)-receptor for advanced glycation end-products (RAGE), and aryl hydrocarbon receptor (AHR) signaling cascades and to cytoskeleton rearrangement.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.13%3A_Biochemistry_Climate_Change_and_Human_Health.txt

Written by Henry Jakubowski Introduction Microorganisms can cause both chronic and acute diseases, both of which if left untreated can lead to death.  Infections with the Human Papillomavirus (HPV) can cause cancer of the cervix, vagina, vulva, penis, anus, and throat.  Modern vaccines against HPV can prevent over 90% of these cancers.  The bacteria H. pylori can, in some people, cause stomach illness (such as severe chronic gastritis and ulcer) which can lead to stomach cancer.  The Coxsackie virus, through binding to receptors on cardiac myocytes, can cause heart disease (acute myocarditis and cardiomyopathy), and ultimately death. Acute microbial diseases that occur immediately after infection can cause epidemics and pandemics (worldwide epidemics).  Everyone has experienced the COVID-19 pandemic caused by the SARS-CoV-2 coronavirus.  Johns Hopkins estimates as of 3/10/23 (end of their data collection), that there were about 677 million reported cases of Covid-19 and about 6.9 million deaths. The WHO estimates that just for the first two years of the pandemic (2020 and 2021), there were 14.83 million excess deaths globally, 2.74 times more than the number or reported death (5.42 million) from the virus.  Machine learning models suggest that there have been closer to 20 million excess death through the end of March 2023, as shown in Figure \(1\) below. Figure \(1\):  https://ourworldindata.org/excess-mortality-covid The data from Johns Hopkins suggest that the average mortality rate was about 1% (deaths/infections).  If there have been 20 million cases (based on excess deaths) out of a world population of 8 billion, the mortality rate was close to about 0.25% for the entire world. Another indicator of the severity of pandemics is a decrease in life expectancy.  Figure \(2\) below offers an interactive graph that shows the general rise in life expectancies since 1750 punctuated by steep drops. Figure \(2\): Life expectancies since 1750 Note the small drop in 2020 in the United State was caused by the Covid-19 pandemic with some contribution from opioid-associated deaths.  The graph is dominated by a stunning decline in 1918 due to the 1918 Flu Pandemic (also historically and inaccurately named the Spanish flu).  The large drop in life expectancy in Sweden in 1772-1773 was probably attributed to the Russian plague epidemic of 1770–1772, also known as the Plague of 1771.  Figure \(3\) below shows a history of pandemics back to the Antonine Plague of 165-180 CE.  In viewing the figure, remember that the numbers of deaths are estimates at best, especially for the historically early pandemics. Figure \(3\): Visualizing the History of Pandemics.  Attribution Visual Capitalist.  https://www.visualcapitalist.com/his...ics-deadliest/ The graphic misses another key plaque in world history, the Plaque of Athens, which hit the city from around 430 BCE - 427 BCE, during which up to 25% of the city's population died. Smallpox has emerged as a possible candidate for that outbreak. Figure \(4\): Figure \(4\):  Visualizing the History of Pandemics.  Attribution Visual Capitalist.  https://www.visualcapitalist.com/his...ics-deadliest/ The Black Death (also called the Bubonic Plague) was caused by the bacterium, Yersinia pestis. Humans usually get the bubonic plague after being bitten by a rodent flea that is carrying the plague bacterium or by handling an animal infected with the plague (notice the bold letter B to help you remember Black, Bubonic, Bacterium, Bite).  The Black Death/Bubonic Plagues derives its name from the fact that many had black tissue from gangrene.  Large buboes, and inflammatory swellings of lymphatic glands, especially in the groin or armpit, were common.  Another variant caused by Y. pestis is the Pneumonic Plague, caused by breathing particles containing Y. pestis into the lungs, which leads to death from pneumonia and its complications. That was more infectious since it could be spread from person to person.  Modern antibiotics are used to treat the plague, which still occurs. Measles:  A disease not shown in the figure is measles,  which probably has killed upwards of 200 million people throughout time.  It emerged from a viral infection, rinderpest, which infects cattle, deer, and buffalo. In 2021 there were 128,000 deaths out of 9 million cases worldwide, even though there is a highly effective vaccine.  Vaccinations have decreased since the Covid-19 pandemic.  Since it is one of the most infectious viruses known, and one contract leads to life-long immunity, a large population (250,00-500,000) is needed for it to self-sustain. The most recent analysis of historical sequences suggests that it emerge (jumped to humans) around the 6th century BCE, around the time when cities of high enough population formed to allow its emergence.  Measles is caused by an RNA virus, and since RNA is much more labile than DNA, few historical traces of the measles virus are available. The oldest one is from 1911 and it was from this and newer viruses that a phylogenetic RNA tree using a molecular clock model was constructed that led to the 6th century BCE time of emergence. Cities with a critical number of people for sustaining an emergence existed about 300 BCE in North Africa, India, China, Europe, and the Near East. A disease similar to measles was mentioned by Rhazes (Persia, 10th century CE).  Past pandemics of unclear etiology could be attributed to measles, but it's difficult to know for sure given the difficulty in differentiating measles from other diseases. Influenza: The genome of the influenza virus consists of 8 separate segments of ssRNA, much like the human genome resides on 23 different "segmented" chromosomes.  Because its genome consists of RNA, past traces of it that point to its origin is lacking.  The human influenza virus arose from swine (causing swine flu) and birds (avian flu).  Hippocrates wrote of a disease with similar symptoms in 412 BCE.  In 1357 an epidemic called “influenza di freddo,” or cold influence, swept Florence, Italy.  The influenza RNA genome and transcribed proteins are shown in Figure \(5\) below. Figure \(5\): Influenza RNA genes and their protein products.  Ahmed Mostafa, Elsayed M. Abdelwhab, Thomas C. Mettenleiter, and Stephan Pleschka - mdpi.com/1999-4915/10/9/497/htm, CC BY 4.0, https://commons.wikimedia.org/w/inde...curid=92987475 The hemagglutinin (H) membrane protein, responsible for viral binding to host cells, and neuraminidase (N), required for the exit of newly replicated viruses and hence viral propagation, are especially key in understanding past and future pandemics.  There are 18 different subtypes of H and 11 subtypes of N comprising 4 different types of viruses (A-D) with A and B being the most common.  The main types in circulation in 2022 were types A (H3N2) and B (H1N1). Since the genome consists of RNA replicated by a RNA polymerase, which does not have proofreading functions, mutations occur on viral replication.  This leads to slow changes in viral protein sequence and structure, called antigen drift, and hence to viruses less recognized by the host immune system.  This is why new influenza vaccines are formulated each year (through a process that requires growing the virus in eggs). Large-scale pandemics occur through antigen shifts.   This occurs when an animal such as a pig gets infected with an avian virus, a not unlikely occurrence given the co-farming of these animals in many places in the world.  Newly replicated pig viruses could then contain some avian viral segments, which when transmitted to humans could produce lethal disease since they have no immunological memory in the host to produce an immediate immune response.  Analyzes show that the horrific 1918 flu pandemic was caused by an avian influenza virus. An ancestral virus from the late 1880s is related to the horse (equine) H7N7 and equine H3N8 as well as to birds, humans, and swine viruses, and was the likely precursor of the 1918 flu virus.  This ancestral virus led to a global change in the avian influenza virus which contributed most of the RNA segments to the 1918 pandemic. Smaller pandemics in the last half of the 20th century were likely caused by quick replacements of H3N2 and H1N1 genes leading to evolutionary fitness and ease of transmission.  We should be on guard as there is an ongoing, worldwide highly virulent avian flu (H5N1) pandemic in wild birds and domestic poultry that has jumped to some animals, including humans who handle infected birds. The hemagglutinin protein, homotrimer (3 identical protein subunits), MW 220,000, is the most abundant protein on the viral surface. Only three have adapted to humans in the 20th century, giving pandemic strains H1 (1918), H2 (1957), and H3 (1968). Three recent avian variants (H5, H7, and H9) can jump directly to humans but have low human-to-human transmissibility. The viral hemagglutinin binds to glycoprotein receptors on human and other animal cells.  The receptor binding site on host cells contains a terminal sialic acid (Sia) covalently attached to a galactose. The sialic acid is usually connected through an α(2,3) or α(2,6) link to galactose on N-linked glycoproteins. The viral subtypes found in avian (and equine) influenza bind preferentially to host Sia α(2,3) Gal which predominates in the avian GI tract where viruses replicate. Human influenza binds preferentially to Sia α(2,6) Gal links on human cells.   The swine influenza HA binds to both Sia α(2,6) Gal and Sia α(2,3) Gal.  The structures of the Sia-Gal disaccharide are shown in Figure \(6\) below. Sia α(2,6) Gal (Human and swine) Sia α(2,3) Gal (Avian and Swine) (made with Sweet, with an OH, not AcNH on sialic acid on C5) (made with Sweet, with an OH, not AcNH on sialic acid on C5) Figure \(6\): Structures of Sia α(2,6) Gal (human) and Sia α(2,3) Gal Gal (avian/swine) The H5N1 avian virus is deadly but lacks human-to-human transmissibility. Why? One reason is that it appears to bind deep in the lungs and is not released easily on coughing or sneezing. It appears that cell surface glycoproteins deeper in the respiratory tract have Sia (α2,3) Gal linkages which account for this pathology. Small changes in the amino acids of the viral hemagglutinin (HA) could change the preference for binding between Sia α(2,6) Gal (predominant human form) and Sia α(2,3) Gal (predominate form in birds) on host cells, and could dramatically affect both human lethality and transmission.  Even though it was mostly of avian origin, the predominant 1918 hemagglutinin bound to the human Sia α(2,6) Gal. Figure \(7\) shows an interactive iCn3D model of a H1 1918 hemagglutinin with a human receptor (2WRG), in this case, just the Sia α(2,6) Gal disaccharide from a target N-linked glycoprotein. Figure \(7\):  H1 1918 hemagglutinin with the human receptor - the Sia α(2,6) Gal dissachharide (2WRG). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...svY14r7wmnM1r5 The HAs in each of the 20th-century influenza pandemics, 1918 (H1N1), 1957 (H2N2), and 1968 (H3N2), preferentially bound to the Sia α(2,6) Gal even though the 1918 viruses and presumably the other, arose from avian viruses with a Sia α(2,3) Gal preference. Figure \(8\) shows an interactive iCn3D model of α-2,6-linked sialyl-galactosyl ligand binding to the H1 1918 hemagglutinin (2WRG). Figure \(8\):  α-2,6-linked sialyl-galactosyl ligand binding to H1 1918 hemagglutinin (2WRG). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...yNJHQaQQebSuT9 A variant of the 1918 virus, A/South Carolina/1/18 (18H1), also circulated at the time.  It contained a single amino acid mutation, D225G. in the HA protein.  That variant switched the HA binding specificity on its target from Sia α(2,6) Gal to both Sia α(2,6) Gal and Sia α(2,3) Gal.  This change eliminated a salt bridge (ion-ion interaction) between K222 and D225 in the main variant (see the blue-dotted line in the above model). This in turn allowed another key residue, Q226, to bind to the host receptor. The viral HA in the 2009 human influenza pandemic had K222 and D225, giving it specificity for Sia α(2,6) Gal.  Late in that pandemic (as occurred in the 1918 pandemic), a mutated version, D225G, that produced more severe symptoms was isolated.  It had also gained dual specificity.  Another mutant D225E did not as the salt bridge was maintained and the binding to Sia α(2,6) Gal was actually strengthened.  Binding studies showed that the D225G mutants in the HA of both 18H1 and 09H1 viruses bound with higher affinity than the wild-type HAs which likely allowed binding to host glycoproteins deeper in the lung., The dissociation constant KD and the on rate, kon, and off rate, koff, for the 09H1 and 18H1 hemagglutinins and relevant mutants, were determined by surface plasmon resonance spectroscopy (see Chapter 5.2 for a review of SPR).  Table \(1\) below shows their values. Hemagluttinin Sia-Gal link KD (μM) kon (s-1) koff (M-1s-1) 09H1 α(2,6) 3.74 319 0.00119 09H1 α(2,3) nd nd nd 09H1 D225G α(2,6) 0.475 3650 0.00173 09H1 D225G α(2,3) 2.24 1460 0.00327 18H1 α(2,6) 13.7 125 0.0017 18H1 α(2,3) nd nd nd 18H1 D225G α(2,6) 8.35 531 0.00444 18H1 D225G α(2,3) 4.73 984 0.00466 Table \(1\):  Dissociation and are constants for the interaction of hemagglutinins (H) from the 2009 and 1918 pandemics with Sia-Gal ligands.  Adapted from Zhang et al., J Virol. 2013 May;87(10):5949-58. doi: 10.1128/JVI.00545-13. Epub 2013 Mar 20. PMID: 23514882; PMCID: PMC3648181. It's remarkable how one amino change that can lead from no to strong binding can alter the specificity of a protein for its ligand and human history as well. Slower-acting but very lethal microbial diseases have taken a vast number of lives Malaria: Each year there are an estimated 300-500 million cases that result in about 2.7 million death.  Most deaths are children under 5 in sub-Saharan Africa.  The disease is caused mainly by the female Anopheles mosquito which transmits Plasmodium falciparum and the less lethal Plasmodium vivax. In the 100 years of the 20th century between 150-300 million deaths have been attributed to malaria (2-5% of all deaths). It was brought to the new world from Africa by the slave trade of over 7 million Africans, and from Portugal and Spain (the main colonial powers where malaria was endemic.  The bacteria probably moved from gorillas to humans long ago in Africa.  No effective vaccine has yet been developed to prevent this disease. Proteins associated with the virus have been found in Egyptian samples from 3200 BCE and there were descriptions of the cyclic fevers associated with malaria in China in 270 BCE.  It was also described by Homer (750 BCE), Plato, and Hippocrates in ancient Greece.  It probably was first found in Rome around 0-100 ACE.  The virus persisted in Europe for 2000 years. Tuberculosis:  This disease is caused by the Mycobacterium tuberculosis bacteria and is spread through the breath. Estimates are that up to 1 billion people have died of TB over history. The BCG vaccine is somewhat effective against TB but not often administered given its low prevalence and the availability of antibiotics. Tuberculosis (TB) was called “phthisis” in ancient Greece, and “tabes” in ancient Rome.  The modern common ancestor of this bacteria arouse around 6000 years ago and is associated with disease in both the Old and New World..  Older strains were likely found in seals and sea lions.  Genetic analysis showed that the modern strain was found in Peru before the arrival of Europeans to the New World.  The disease in the Western Hemisphere probably derived from sea mammals which crossed the ocean. Vaccines against some of our worst infectious disease agents have saved millions of lives.  Here are some examples. Figure \(9\): Figure \(9\): :  https://ourworldindata.org/microbes-...ience-vaccines Mathematical models show that from 12/20 through 12/22, Covid vaccines prevented over 120 million infections, 18.5 million hospitalizations, and 3.2 million deaths just in the United States. In the first year of the pandemic (12/20-12/21), models show that 14.4 million deaths (and 19.4 million excess deaths) were prevented in the whole world. Epidemics that decimated Indigenous peoples in the New World Before Columbus came to the New World, there was no typhoid, flu, smallpox, or measles there.  These diseases were present in Eurasia where people lived in increasingly populated areas in close quarters with domesticated animals.  They would have developed some immunity over time. Their microbes likely derived from domestic animals before jumping species to humans, much as modern flu can be passed from swine to humans and less regularly but more lethally from birds to humans. Even with the buildup of some immunity, new pandemics were utterly devastating. Indigenous peoples in the new world were never exposed to these pathogens before the arrival of people from the Ols World.  They only utilized llamas for work and not generally for food and milk.  Deaths were staggering.  It's estimated that 90% of indigenous people died, a far higher proportion than seen even with the Black Plague in Europe.  Imagine the loss of culture and civilization that would accompany a decline in the population of central Mexico from 15 million to 1.5 million in the 100 years after 1519. Figure \(10\):  Sixteenth-century Aztec drawings of victims of smallpox. https://en.wikipedia.org/wiki/Native..._and_epidemics Social conditions after the initial collapse of the indigenous people in the Americas led to their continued decline, even though they would have gained some immunity.  An example is offered by Ostler who describes the health consequences of the Indian Removal Act of 1830, which led to the forced relocation of Native people east of the Mississippi River into "Indian Territory" (Oklahoma and Kansas). As an example, 16,000 Cherokee were expelled and forced to live in camps with few resources, where up to 2000 died of measles, malaria, dysentery, and whooping cough. 1500 more died as they moved west.  More died in Oklahoma, leading to a death toll of 25% of the original group. Cumulative death rates in the COVID-19 pandemic show that Indigenous peoples in the United State still have barriers to optimal health care. Figure \(11\): Cumulative Deaths and Age-Adjusted Rates per 100,000 in the United States. Infectious, Emerging and Pandemic Diseases - Links to Climate Change Our understanding of infectious diseases clearly shows that the great epidemics and pandemics of the world have arisen when microbial pathogens make the jump from animals to humans who have not experienced them before.  For example, HIV/AIDS arose when simian immunodeficiency viruses, to which non-human primates were adapted, jumped to humans in central Africa.  The best available data suggest that the SARS-CoV-2 virus jumped from bats to animals (raccoon dogs or other animals from Wuhan China live animal markets) and then to humans, although some data suggest the possibility of a lab leak. A Zoonotic disease (zoonosis, plural zoonoses) is a microbial infectious disease transmitted reversibly between animals and humans.  The major types of zoonoses are viral, bacterial, parasitic, mycotic/fungal,  rickettsial (obligate intracellular Gram-negative bacteria found in ticks, lice, fleas, mites, chiggers, and mammals), Chlamydial (bacteria that cause STDs), Protozoal or unconventional (such as prions).  The ones most prevalent in the US are influenza, Salmonellosis, West Nile virus, Plague, coronaviruses, rabies, Brucellosi, and Lyme disease.  Vector-borne diseases are caused by bacteria, viruses, and parasites transmitted through bites of vectors such as infected arthropods like mosquitoes, ticks, sandflies, and blackflies. The range of arthropod vectors expands with global warming as they are cold-blooded. Several anthropogenic (human-caused) factors, including climate change, increase the chances of such jumps.  These factors, many of which are interrelated include: • movement of humans into environments where contact with disease-carrying organisms would increase transmission • biodiversity loss which allows species and their microbes to move into new areas • land use change (deforestation, farming, etc) that allows the expansion of species and microbes into new areas • global warming, which encourages the movement of species and their microbes to new areas where human exposure is more likely • climate change-induced changes in plant life that allow altered distributions of animals and microbes • climate change-derived changes in precipitation patterns that affect the adaptation of species and their microbes. Humans affect all of these factors by causing climate change, and land use changes including the expansion of agriculture, urbanization, and the rapid global movement of people, commodities, and other animals.  Studies have shown that 58% of human infectious diseases have already worsened with climate change. Another study used databases of mammalian viruses and their host to see which ones might share viruses, an occurrence made much more likely when the species live in the same geographic area.  Machine learning was used to model how mammals might share viruses and change their living range in a warming world through 2070.  The study found over 4000 viruses could move among 3000 species, greatly enhancing the changes for the exchange of single and multiple viruses among species.  Bats (see below) are especially worrisome as they harbor many viruses capable of infecting humans. As bats move habitats due to climate warming, their chances of infecting new species that could then infect humans are greatly increased. 25 years of land use changes in Australia led to altered bat (flying foxes) behavior and to their more permanent presence in agricultural land.  This has resulted in viral"spillover" (transmission of a pathogen from a non-human vertebrate to a human) that is driven by periodic food shortages, especially in winters following El Nino weather patterns (characterized by less rain, warmer temperatures, and greater temperature extremes).  These changes in bat behavior led to the emergence of the Hendra virus, which infected domestic horses (an intermediate vector), and could pass the virus to humans. The virus does not cause disease in bats but leads to a high mortality rate in horses (75%) and humans (57% based on just four deaths). With climate and land use change, bats persistently spent winters in agricultural lands close to horses.  Spillovers occurred more frequently during low food conditions following an El Nino summer. The Black Death (Second Great Pandemic, 1347-1351), occurred during the Little Ice Age in Europe (1300-1850), which also led to a great famine from 1315-1322.   There is a link between the pandemic and climate change, but it's difficult to ascertain the strength of the association.  An association exists between periodic warm springs and wet summers in Central Asia (using tree-ring data) and outbreaks of the Plague in Europe about 15 years later. This suggests a continual re-importation of Yersinia pestis in Asian rodents into Europe and could explain how long the Plague lasted in Europe. The presence of the plague in gerbils in Kazakhstan would increase with warm spring and wet summer which increase gerbil and flee populations.  This Moran effect (time correlation of two populations of a species with change in environment) is well known in population ecology.  When gerbil populations collapse, the flea density of the remaining gerbils increases which also leads them to seek different hosts.  The spread across geographic distance would take time.  In the case of periodic import of flees to Europe, it has been proposed that the 4000 km from west central Asia to the Black Sea took 10-12 years (around 350km/y) . Pathogens from the North Most attention has been given to pathogens moving northward from the south as warmer temperatures allow them to thrive in traditionally colder climates.  There is growing parallel concern about pathogens moving south from the Arctic as it warms.  In fact, the high northern latitudes have experienced the greatest increase in temperature as the planet warms. The Arctic is predicted to be soon ice-free in the summers. One major concern is that thawing of the permafrost (comprising almost 1/4 of the northern hemisphere that is "permanently" frozen) will allow the release of CO2 (a metabolic product of microbes in the presence of oxygen) and CH4 (a metabolic product of Archeal microbes in an anoxic environment) from organic molecules previously found in frozen soils.  (A similar concern is the release of "frozen" methane clathrates from a warmer ocean). An emerging concern is the "activation" of microbes from the permafrost that have been sequestered and dormant for 500,000 years or more.  In fact, species like tardigrades, rotifers, and nematodes, that can go dormant and enter a state called cryptobiosis in harsh conditions such as freezing and dehydration, can reactivate.  Two species of nematodes (roundworms), dating back to 46,000 years ago (based on radioactive dating), the last years of the Pleistocence, were recovered and revived.  Periodic reoccurences of anthrax from the release of the Gram-positive bacterium Bacillus anthracis spores from permafrost thawed in summers have been reported. In 2016, when average temperatures were significantly elevated (see Figure \(12\) below), over 2000 reindeer died (close to a 90% mortality rate) and close to 100 people were hospitalized from an anthrax outbreak in Siberoia.  On June 20, 2020, a record temperature of 38 °C  (100 °F) in the Russian town of Verkhoyansk was recorded! Figure \(12\): Color map showing land surface temperature anomalies from -12 °C  (-21.6 °F) (darkest red) to + 12 °C  (+21.6 °F) (darkest red) during the week of July 20-27, 1916.  https://earthobservatory.nasa.gov/im...n-extreme-year We also have to worry about emerging viruses that are released from the thawing of the tundra.  Just as for the SARS-Covid-2 virus, we would have no immunity to these viruses.  Figure \(13\) below shows EM pictures of new infectious viruses isolated from seven different ancient Siberian permafrost samples. Figure \(13\): Morphological features guiding the preliminary identification of newly isolated viruses (negative staining, TEM).  Alempic, J.-M.; Lartigue, A.; Goncharov, A.E.; Grosse, G.; Strauss, J.; Tikhonov, A.N.; Fedorov, A.N.; Poirot, O.; Legendre, M.; Santini, S.; et al. An Update on Eukaryotic Viruses Revived from Ancient Permafrost. Viruses 202315, 564. https://doi.org/10.3390/v15020564.  Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Panel (A) shows large ovoid particle (1000 nm in length) of Pandoravirus yedoma (strain Y2) ( showing the apex ostiole (white arrowhead) and the thick tegument characteristic of the Pandoraviridae family. Panel (B) shows a mixture of Pandoravirus mammoth (strain Yana14) oblate particles and of Megavirus mammoth (strain Yana14) icosahedral particles exhibiting a “stargate” (white starfish-like structure crowning a vertex, white arrowhead) Panel (C) shows the elongated particle of Cedratvirus lena (strain DY0) (1500 nm in length) exhibits two apex cork-like structures (white arrowheads) Panel (D) shows the elongated particle of Pithovirus mammoth (1800 nm in length) exhibiting a single apex cork-like structure (white arrowhead). Panel (E) shows the large (770 nm in diameter) “hairy” icosahedral particle of Megavirus mammoth (strain Yana14), showing the “stargate” (white arrowhead) characteristic of the Megavirinae subfamily Panel (F) shows the smaller icosahedral particle (200 nm in diameter) of Pacmanvirus lupus (strain Tums2)  typical of asfarviruses/pacmanviruses. Fungal Diseases We have concentrated solely on bacteria and viral epidemics/pandemics.  We also have to consider fungal outbreaks that affect human health but also the foods that sustain us.  We have few medicines that treat fungal infections and no vaccines, so any outbreaks could be quite serious.  Here are some examples of changes in fungal pathogens that are likely exacerbated by climate change. Candida auris This was first found in 2009 in Japan as a cause of an ear infection.  It is now found around the world. Batrachochytrium dendrobatidis (Bd): This affects amphibians and has caused a high loss in amphibian diversity on all continents. Cryptococcus deuterogattii:, This was typically found in more tropical/subtropical climates but now is also found in western Canada and the Pacific Northwest.  It causes infections in people and animals. Puccinia striiformis:  This causes wheat rust which devastates crops and is now moving into warmer areas. Fusarium graminearum:  This causes diseases in wheat and other food crops, especially in warm and wet conditions. Coccidioides immitis:  This fungus, which grows in desert soil, can also spread through severe dust storms that cause fungal spores to be blown over wide regions. An example is the dispersal of Coccidioides immitis from Bakersfield, where it was endemic, to Sacramento County, where it wasn't, in 1977.  Another example is Apophysomyces trapeziformis, which caused disease in 2011 in Joplin, Missouri after a tornado. Bats, Viruses, and Climate Change We have seen that new infectious diseases arise from pathogen jumps to humans from other species.  The more distant the species, the more unlikely humans have encountered the disease and the more likely it could cause severe illness and pandemics. A clear example is the avian flu that led to the 1918 flu pandemic.  Yet we also have to worry about zoonoses from pathogen transfer from mammals, including rodents, bats, moles, shrews, monkeys, pigs, camels (a host of the deadly MERS virus), whales, cats, dogs, and seals (a likely source of the original TB virus). Bats are a key source of zoonotic disease, including Middle East respiratory syndrome (MERS), which had a death rate of around 35%.  Bats are the source of the Covid virus MERS-CoV which causes MERS.  The virus spreads to people from camels. Severe acute respiratory syndrome (SARS) is another coronaviral disease, caused by the called SARS-associated coronavirus (SARS-CoV) which emerged in China in 2003.  It had a death rate of around 12% but it was much higher in older people. Neither of these became lengthy full-blown pandemic, as with the SARS-CoV2 virus, the cause of the COVID-19 pandemic. In addition, viruses from bats include rabies, Ebola, and Marburg viruses, as well as the Nipah and Hendra viruses. Bats are more likely to be infected with zoonotic viruses than rodents. Why are bat viruses so key in our worst zoonotic diseases?  Two features are important.  The same viruses that are so virulent to humans do not kill bats.  A clue as to the special nature of bats is that they are the only flying mammal.  What might protect bats from their own viruses is that their core temperatures are quite elevated during flight, which requires a high metabolic rate.  These high temperatures likely prevent these viruses from harming bats but also make the viruses immune to the high-temperature fevers accompanying infection in humans. The average core temperature of flying bats derived from a variety of species was 39.6 0C or 103.3 0F.  Many pathogens replicate optimally at temperatures less than normal body temperature. Fevers in humans are regulated by the hypothalamus, mainly through prostaglandin E2 (PGE2).  This response is part of the innate immune response and is elicited by most pathogens.  PGE2 binds to the E-prostanoid-3 receptor (EP3), a G protein-coupled receptor in the hypothamlus, which determines the "set point" for body temperature.  Hypothalmic PGE2 is produced from the endocannabinoid 2-arachidonylglycerol by the action of monoacylglycerol lipase, at least in mice stimulated with a bacterial cell wall component (LPS), that stimulates fever production. Figure \(14\) shows an interactive iCn3D model of the human prostaglandin E receptor EP3 bound to prostaglandin E2 (6AK3). Figure \(14\): Human prostaglandin E receptor EP3 bound to prostaglandin E2 (6AK3).. (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...cCgTGEKu3KoGx9 The model shows a dimer of two, GPCRs each bound to 1 PGE2. Research suggests that bats have also evolved to have a lower inflammatory response, mediated by the inflammasome (discussed in detail in Chapter 5.4).  Here is a short review of the inflammasome modified from that chapter section.  It's needed to give readers a more biochemical explanation for immunosuppression in bat cells, a topic critical to understand the role of bats in present and future pandemics. The inflammasome, part of the innate immune system, is activated by a plethora of pathogens or damaged host molecules.  Our innate system immune cells (dendritic cells, macrophages, eosinophils, etc) have receptors that recognize common pathogen-associated molecular patterns (PAMPs) such as lipopolysaccharides (LPS) on the surface of bacteria, mannose on bacteria, and yeast, flagellin from bacterial flagella, dsRNA (from viruses) and nonmethylated CpG motifs in bacterial DNA. These antigens are recognized by pattern recognition receptors (PRRs) - specifically the Toll-like Receptors (TLRs) 1-10. These include plasma membrane TLRs (TL4 for LPS, TL5 for flagellin, TLR 1, 2 and 6 for membrane and wall components of fungi and bacteria) and intracellular endosomal TLRs (TLR3 for dsRNA, TLR 7 and 8 for ssRNA and TLR9 for dsDNA). Figure \(15\) shows the TLR family, their binding signals, and intracellular adapter proteins used to transmit signals into the cell. Inflammasomes are also activated by Damage-associated molecular patterns (DAMPs).  These are typically found on molecules released from the cell or intracellular compartments on cellular damage (hence the name DAMP). Many are nuclear or cytoplasmic proteins released from the cells. These would now find themselves in a more oxidizing environment which would further change their properties. Common DAMP proteins include heat shock proteins, histones and high mobility group proteins (both nuclear), and cytoskeletal proteins. Here are some other common non-protein DAMPS that can be released on cellular damage: ATP, uric acid, heparin sulfate, DNA, and cholesterol crystals. In the wrong location, these can be considered danger signals.  They are sometimes referred to as "sterile" signals. If TLRs recognize PAMPs, what recognizes DAMPs? They are recognized by another type of intracellular pattern recognition receptor (PRR) called NOD (Nucleotide-binding Oligomerization Domain (NOD)- Like Receptors or NLRs. NLRs also recognize PAMPs. The abbreviation NLR also comes from the Nucleotide-binding domain (NBD) and Leucine-Rich repeat (LRR)–containing proteins (NLR)s. This family of proteins participates in the formation of a large protein structure called the inflammasome. (Sorry about the multiple abbreviations and naming systems!) As both PAMPs and DAMPs pose dangers, it would make sense that once they recognize their cognate PRRs (TLRs and NLRs, respectively), pathways leading from the occupied receptors might converge in a common effector system for the release of inflammatory cytokines from immune cells. Given that uncontrolled immune effector release from cells in an inflammatory response might be dangerous, it would be sometimes helpful to require two signals to trigger cytokine release from the cell. Two such inflammatory cytokines are Interleukin 1-β (IL 1-β) and IL-18. Activation of TLRs by a PAMP leads to activation of a potent immune cell transcription factor, NF-kbeta, which leads to transcription of the gene for the precursor of the cytokine, pro-interleukin 1-β. Without a specific proteolytic cleavage, the active cytokine will not be released from the cell. The protease required for this cleavage is activated by a signal arising when a DAMP activates a NLR, which then through a sequence of interactions leads to the proteolytic activation of another inactive protease, procaspase 1, on the inflammasome. The activated inflammasome activates procaspase to produce the active protein caspase (a cysteine-aspartic protease). The convergence of the signals from the PAMP activation of a TLR and DAMP activation of a NLD at the inflammasome is shown in Figure \(16\). The active cytokine interleukin 1-β helps recruit innate immune cells to the site of infection. It also affects the activity of immune cells in the adaptive immune response (T and B cels). Active IL-18 leads to the increase of another cytokine, interferon-gamma and it also increases the activity of T cells that kill other cells. Figure \(17\) shows an interactive iCn3D model of the NLRP3 double-ring cage, 6-fold (12-mer) (7LFH) Figure \(17\): NLRP3 double-ring cage, 6-fold (12-mer) (7LFH). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/icn3d/share.html?DEbdkUoBtqRQ9bu59 The full-length mouse NLRP3 consists of 12- to 16-mer organized in a double-ring cage. It is held together by interactions between the leucine-rich repeats (LRR) domains. The pyrin domains are shielded by the structure, so they will not be activated without appropriate signals. The complex is also localized to the membrane. NLRP3 inflammasomes seem to be activated by cellular distress as well as cell exposure to pathogens. It is one of the main responders to a variety of microbial infections. In summary, two signals are again needed: Signal 1 The first signals are the bacterial and viral (influenza virus, poliovirus, enterovirus, rhinovirus, human respiratory syncytial virus, etc) PAMPs, which bind to TLRs and lead to the activation of the NFkb transcription factor. This activates not only the transcription of pro-interleukin 1-β and interleukin 18, but also the transcription of the NLRP3 sensor itself. Signal 2 Signal 2 is delivered by PAMPs and DAMPs indirectly to the sensor NLRP3.  This leads to the assembly of the inflammasome. These DAMPs appear to prime the activation of NLRP3 protein and subsequent formation of the active NLRP3 inflammasome. But what activates NLR3P3? After many studies, it became clear that the typical bacterial ligands that would activate TLRs and perhaps NLRs only prime NLRP3 for activation. They don't bind to it directly. Extracellular ATP is a major activator of NLRP3. Nanoparticles are known to release ATP as well. Most studies show that K+ efflux from the cell is an early signal. Back to Bats To survive the viruses they harbor, bats decrease their inflammatory response upstream at the level of PAMP and DAMP recognition, as well as downstream at the level of caspase-1 inhibition. In addition, additional cleavage sites in IL-1β cause its loss through proteolysis. These events decreased inflammasome signaling.  That multiple steps are inhibited suggests that they have been selected through evolutionary pressures. The activation of the bat NLRP3 inflammasome by a "sterile" agent (ATP), as well as 3 RNA viruses is significantly decreased in bat cells compared to responses to these signals in humans and mice cells. The viruses include: • H1N1 influenza A virus (a negative-sense single-stranded RNA virus known to activate the NLRP3 inflammasome); • the Melaka virus (a bat-borne zoonotic double-stranded RNA virus); • MERS-CoV (a positive-sense (+) ssRNA zoonotic virus). Even though the secretion of interleukin-1β is inhibited in bat cells, viral loads remained high in virally-infected bat cells. The altered NLRP3 activation in bat cells occurs in part through decreasing RNA splicing and an altered LRR domain in NLRP3. It should be clear that if bat population increases or if they move to new habitats, both events which could be promoted by climate change, humans are at greater risks for bat-derived pandemics.  Coronaviruses, with over 3000 species, make up about 1/3 of the bat's viral load.  The region comprising southern Yunan (in China, Myanmar and Laos have very high populations of bats and it is from these areas that new bat zoonoses are likely to derive.  Models of past climate (in the 1900s) and bat species richness compared to present climatic conditions, along with the knowledge of specific climate conditions necessary to support bat population and diversity, show that the greatest increase in bat species population in the 20th century occurred in SE Asia.  Less important sites included regions in Central Africa and some pockets of Central and South America.  The region in China, Myanmar, and Laos is likely the location for the origin of the SAR-CoV1 and SARS-CoV2 viruses.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.14%3A_Climate_Change_Infectious_Disease_and_Pandemics.txt

Under construction - 9/26/23 Written by Valerie Doze (change title to what you want) by clicking pencil icon when hovering over the title. Headings: Example On the menu bar, select Normal, Heading 2 You can use Heading 3 for subheadings but adding sub-subheadings gets to look cluttered.  For a sub-sub heading just use bold font for the title. Numbering figures: Example Figure \(\PageIndex{x}\) below shows the reaction of one key carboxylase. x is the figure number you fill in to make them sequential in the document. Figure \(\PageIndex{x}\): Carboxylase used in the CETCH pathways to fix CO2 iCn3D model:  Example (to replace) Figure \(\PageIndex{x}\) shows an interactive iCn3D model of ribulose 1,5-bisphosphate carboxylase/oxygenase from Synechococcus PCC6301 (1RBL).  (image is a png screen capture) I am sure you will have several to many iCn3D models.  I can make them or teach you how to do it. Short but real learning curve. I sometimes use Pymol for some images when I feel that iCn3D doesn't show well. Figure Types: • png, jpeg, animated gifs (I can make them for you).  Best file type is svg which can be made from PPTX images, ChemDraw, or exporting from other image file types using software like Adobe Illustrator or Affinity Designer • for svg files, must drag them from a folder on your computer to desired spot in the new section.  For other can drag them or use picture icon on menu bar to select and import them from your file. References: Incorporate as you like but essential for images et al directly used form a Creative Common CC  BY document, for example. 32.16: Fixing Carbon Fixation Search Fundamentals of Biochemistry Carbon Fixation Nature has produced many enzymes that can fix atmospheric CO2, and, of course, everyone knows that photosynthesis is the source of most fixed CO2 in the biosphere.  Plankton, cyanobacteria, algae, and plants are key in removing atmospheric CO2 through photosynthesis.  Yet we can't plant enough trees to reduce CO2 in the atmosphere in the next decade to avoid some of the worst consequences of anthropogenic climate change.  Old-growth trees (mostly gone or under significant stress) are best at removing CO2.  New trees would take decades of growth before their effect on carbon drawdown would be consequential.  We also need to fix carbon dioxide not only to decrease atmospheric CO2 but also to make more food! A lot of carbon (about 100 petagrams) is sequestered each year in net primary production (fixation of carbon into biomolecules). This is split almost equally between land and ocean organisms.  The key enzyme in this process is Rubisco in the C3 (Chapter 20.4) and C4/CAM (Chapter 20.5) pathways.  The enzyme is big containing many large subunits and an equivalent number of small ones.  A chaperonin is required for folding.  It is also a very slow enzyme with a kcat of around 2-10 CO2/sec. In addition, it can bind another substrate, O2, and engage in a competing reaction of photorespiration as described in Chapter 20.4. Figure \(1\) shows an interactive iCn3D model of ribulose 1,5-bisphosphate carboxylase/oxygenase from Synechococcus PCC6301 (1RBL) This structure is a hetero 16-mer of 8 small chains and 8 large chains. The small chains are in gray and the large ones in differing colors.  Each large subunit contains a bound reaction intermediate analog 2-carboxyarabinitol 1,5-bisphosphate. This chapter section will focus on improving and designing new ways to capture carbon dioxide as one way to reduce its concentration in the atmosphere.  Don't lose sight of the fact that the best way to deal with anthropogenic climate change is to stop putting CO2 in the atmosphere from burning fossil fuels. Naturally occurring pathways to fix carbon (Much of this immediate section derives from the following reference: Natural carbon fixation. Sulamita Santos et al.,  Natural carbon fixation and advances in synthetic engineering for redesigning and creating new fixation pathways, Journal of Advanced Research, Volume 47, 2023, Pages 75-92, ISSN 2090-1232, https://doi.org/10.1016/j.jare.  Creative Commons license There are six naturally occurring pathways that fix carbon. These are illustrated in Figure \(2\) below. Figure \(2\):  Natural carbon fixation. Sulamita Santos et al.,ibid. Panel (A) shows the CBB cycle which we discussed in great detail in Chapter 20.4.  Here are the enzymes: ribulose-1,5-bisphosphate carboxylase/oxygenase, 3-phosphoglycerate kinase, glyceraldehyde-3-phosphate dehydrogenase, ribulose-phosphate epimerase. Panel (B) shows the reverse (reductive)-TCA cycle which we discussed in Chapter 16.4 in the section on the α-ketoacid pathway - A primordial, prebiotic anabolic "TCA-like" pathway.  The enzymes include ATP-citrate lyase, malate dehydrogenase, succinyl-CoA synthetase, ferredoxin (Fd)-dependent-2-oxoglutarate synthase, isocitrate dehydrogenase, PEP carboxylase. Panel (C) shows the Wood–Ljungdahl (or reductive Acetyl-CoA) cycle which we discussed in Chapter 30.1.  At the top of the pathway are the acetogens Archaea and at the bottoms are the methanogens Archaea.  The enzymes include MPT-methylene tetrahydromethopterin, MFR-methanofuran, THF, tetrahydrofolate. Panel (D) shows the 3-hydroxypropionate (3HP) cycle.  The enzymes include acetyl-CoA carboxylase, propionyl-CoA carboxylase, methylmalonyl-CoA epimerase, succinyl-CoA:(S)-malate-CoA transferase, trifunctional (S)-malyl-CoA, -methylmalyl-CoA, mesaconyl-CoA transferase, mesaconyl-C4-CoA hydratase. Panel (E) shows the hydroxypropionate/4-hydroxybutyrate (HP/HB) and the dicarboxylate/4-hydroxybutyrate (DC/HB) cycles.  The enzymes include pyruvate synthase, PEP-carboxylase, malate dehydrogenase, fumarate hydratase/reductase, acetyl-CoA/propionyl-CoA carboxylase, 3-hydroxypropionate-CoA ligase/dehydratase, methylmalonyl-CoA mutase, succinyl-CoA reductase, 4-hydroxybutyrate-CoA ligase, crotonyl-CoA hydratase, acetoacetyl-CoA-ketothiolase. Table \(1\) below shows a comparative description of the natural and synthetic carbon fixation pathways. Pathway Organisms Energy Source Input Output Reductants Key Enzyme Calvin-Benson (N) Plants, Algae, Cyanobacteria, Aerobic Proteobacteria, Purple bacteria Light 3 CO2, 9 ATP,6 NAD(P)H Glyceraldehyde-3- phosphate NAD(P)H RuBisCO rTCA (N) * Green sulfur bacteria, Proteobacteria, Aquificae,  Nitrospirae Light and Sulfur 2 CO2, 2 ATP,4 NAD(P)H Pyruvate NAD(P)H & ferredoxin 2-Oxoglutarate synthase, Isocitrate dehydrogenase Wood–Ljungdahl (N) * Acetogenic, Methanogenic Archaea, Planctomycetes, Sulfate. Archaeoglobales, Hydrogen 2 CO2, 1 ATP, 4 NAD(P)H Acetyl-CoA Ferredoxin NAD-independent formate dehydrogenase, Acetyl-CoA synthase-CO dehydrogenase 3-HP (N) Chloroflexaceae Light 3 HCO , 5 ATP, 5 NAD(P)H Pyruvate NAD(P)H Acetyl-CoA carboxylase, Propionyl-CoA carboxylase HP/HB (N) Aerobic Sulfolobales Hydrogen/sulfur 2 HCO , 4 ATP, 4NAD(P)H Acetyl-CoA NAD(P)H Acetyl-CoA-Propionyl-CoA carboxylase DC/HB (N) * Anaerobic Thermoproteale,Desulfurococcales  Hydrogen/sulfur 1 CO2, 1 HCO , 3ATP, 4 NAD(P)H Acetyl-CoA NAD(PH &Ferredoxi Pyruvate synthase, PEP carboxylase RHP (CN) Methanospirillum  hungatei Hydrogen CO2, 3 ATP, 2 NAD(P)H Gluconeogenesis and glycolysis NAD(P)H RuBisCO Natural Reductive Glycine (CD) Candidatus  phosphitivorax, anaerolimiDesulfovibrio  desulfuricans Phosphite CO2, ATP, NAD(P), H Formate/ Pyruvate NAD(P)H &Ferredoxi CO2-reducing formate dehydrogenase(fdhAB) Reverse Otca (CD) Desulfurella  acetivorans Hydrogen CO2, ATP, NAD(P) H Acetyl-CoA Ferredoxin Citrate synthase CETCH (S) Theoretical 2 CO2, 2 ATP, 3 NAD(P)H Glyoxylate NAD(P)H CoA- dependent carboxylase Reductive Glycine (S) Demonstrated in E.  coli  as host CO2, NADH Pyruvate Ferredoxin Glycine cleavage system Synthetic malyl-CoA- glycerate (S) Demonstrated in E.  coli  and Synechococcus  elongatus  PCC7942 host CO2, 3 ATP, 3 NADH Acetyl-CoA NAD(P)H PEP-carboxylase, RuBisCO SACA Pathway (S) Demonstrated in E.  coli  as host CO2 Acetyl-CoA NAD-independent formate dehydrogenase Formolase pathway (S) Theoretical CO2, NADH, ATP Dihydroxyacetone- phosphate NADH NAD-independent formate dehydrogenase Sulamita Santos et al.,  Natural carbon fixation and advances in synthetic engineering for redesigning and creating new fixation pathways, Journal of Advanced Research, Volume 47, 2023, Pages 75-92, ISSN 2090-1232, https://doi.org/10.1016/j.jare.  Creative Commons license Plants and microorganisms that are photoautotrophic fix CO2 by the Calvin cycle using NADPH and ATP and produce O2.  Some anerobic photosynthetic bacteria don't produce O2.  In chemolithoautotrophic microorganisms, energy sources (i.e. electron donors like H2, H2S, sulfur, Fe2+, nitrite and NH3 found in bedrock - the lithosphere) other than NADPH, ATP and light can be used to drive CO2 uptake. For chemoorganotropic microbes, reduced organic molecules such as sugars and amino acids serve as electron (donor) sources. Genetic engineering and synthetic biology are now employed to improve preexisting enzymes and to create whole new pathways for carbon capture. As a simple example, people are trying to engineer carbonic anhydrase, used to convert CO2 to HCO3- for transport to the lung where it is converted back to CO2 and released.  It is a diffusion-controlled enzyme with a kcat/KM reported as high as 8 x 107 M-1s-1, so how can it be made better?  One way is to engineer thermal stability into the enzyme. A critical problem for the forward reaction is that the enzyme is readily reversible so bicarbonate, HCO3-, is a competitive inhibitor of the forward reaction. In addition, the enzyme can be engineered to be more stable at higher pH to allow product (HCO3-) removal by the addition of OH- in a process of mineralization, as shown in the equation below, HCO3- + OH- →  CO32- + H2O where the carbonate anion can precipitate in the presence of divalent cations like  Ca2+, Mg2+ and Fe2+.  Here is a link to a Literature-based Guided Assessment on thermoengineering of carbonic anhydrase. Now let's explore the use of new pathways created by synthetic biology to capture carbon.  Some of these pathways are engineered to produce reactants (feedstocks) for biofuels, which we discussed in-depth in previous Chapter 32 sections, and chemical synthesis.  We'll focus on three:  the CETCH pathway, the reductive glyoxylate and pyruvate synthesis (rGPS) cycle, and the malyl-CoA-glycerate (MCG) pathway. CETCH (Crotonyl-CoA-EThylmalonyl-CoA-4Hydorxybutyl-CoA) pathway To engineer a new pathway, Swander et al identified efficient carboxylases from known ones (acetyl-CoA carboxylase, Rubisco, propionyl-CoA carboxylase, PEP carboxykinase, 2-oxoglutarate carboxylase, and pyruvate carboxylase), all of which we have discussed in previous chapters.  They created new pathways, calculated free energy and ATP/NADPH requirements, and then optimized the pathways.  They chose CoASH–dependent carboxylases and enoyl-CoA carboxylases/reductases.  Figure \(3\) below shows the reaction of one key carboxylase. Figure \(3\): Carboxylase used in the CETCH pathways to fix CO2 The pathway was named CETCH (Crotonyl-CoA-EThylmalonyl-CoA-4Hydorxybutyl-CoA) which catalyzes this next reaction in cell lysates (in vitro): 2CO2 + 3NAD(P)H + 2ATP + FAD → glycolate + 3NAD(P) + 2ADP +2Pi + FADH2 The rate of COfixation by the CETCH pathways was similar to the rate of the Calvin cycle rates in cell lysates. In a more expansive approach, Gleizer used synthetic biology to change E. Coli from a heterotroph to an autotroph in which its biomass (carbon reservoir) came from CO2.  Formate (HCO2-) was used as a source of reducing power (electrons) as it was oxidized by an added formate dehydrogenase to produce NADH for the autotropic fixation of CO2 through the addition of Calvin cycle enzymes.  Using isotopically labeled 13CO2 to follow carbon flow, after 10 generations and evolution, the cells were completely autotrophic through fixation of CO2.  To accomplish this, they knocked out genes for phosphofructokinase (glycolysis) and glucose-6-phosphate-dehydrogenase (pentose-phosphate pathway) to impair these main metabolic pathways, and added carbonic anhydrase (to interconvert CO2 and HCO3-) as well as Rubisco and phosphoribulokinase.  As formate was ultimately converted to CO2, the net effect was not exactlycarbon neutral but could be if atmospheric CO2 was used to make formate (for a feedstock) by electrochemical reduction. Figure \(4\) below shows the next reactions in the synthetic E. Coli autotrophs. Figure \(4\):  Schematic Representation of the Engineered Synthetic Chemo-autotrophic E. coli.  Shmuel Gleizer et al., Conversion of Escherichia coli to Generate All Biomass Carbon from CO2, Cell, 179 (2019). https://doi.org/10.1016/j.cell.2019.11.009.   Creative Commons license. CO2 (green) is the only carbon source for all the generated biomass. The fixation of CO2 occurs via an autotrophic carbon assimilation cycle. Formate is oxidized by a recombinant formate dehydrogenase (FDH) to produce CO2 (brown) and NADH. NADH provides the reducing power to drive carbon fixation and serves as the substrate for ATP generation via oxidative phosphorylation (OXPHOS in black). The formate oxidation arrow is thicker than the CO2 fixation arrow, indicating a net CO2 emission even under autotrophic conditions. Figure \(5\) shows that almost 100% of carbon atoms after many generations are labeled with 13C (detected by mass spec analysis) derived from 13CO2. Figure \(5\): Isotopic Labeling Experiments Using 13C Show that All Biomass Components Are Generated from CO2 as the Sole Carbon Source.  Gleizer  et al., ibid. (A) Values are based on LC-MS analysis of stable amino acids and sugar-phosphates. The fractional contribution of 13CO2 to various protein-bound amino acids and sugar-phosphates of evolved cells grown on 13CO2 and naturally labeled formate showed almost full 13C labeling of the biosynthesized amino acids. The numbers reported are the 13C fraction of each metabolite, taking into account the effective 13CO2 fraction out of the total inorganic carbon (which decreases due to unlabeled formate oxidation to CO2). The numbers in parentheses are the uncorrected measured values of the 13C fraction of the metabolites. Synthetic reductive glyoxylate and pyruvate synthesis (rGPS) cycle and the malyl-CoA-glycerate (MCG) pathways These pathways were created to synthesize acetyl-CoA, pyruvate, and malate from CO2 in cell-free systems to free the system from cell growth and regulation requirements, and to make it insensitive to oxygen.  These molecules are also intermediates in the created cycle, which could operate continuously for hours at the same or greater rates of CO2 fixation compared to photosynthesis. The cycle is shown in Figure \(6\) below. Figure \(6\):  The rGPS–MCG cycle with acetyl-CoA as the end product. Luo, S., Lin, P.P., Nieh, LY. et al. A cell-free self-replenishing CO2-fixing system. Nat Catal 5, 154–162 (2022). https://doi.org/10.1038/s41929-022-00746-x .  Creative Commons Attribution 4.0 International License, http://creativecommons.org/licenses/by/4.0/ The rGPS cycle consists of the reductive glyoxylate sythesis (rGS) pathway (blue) and the reductive pyruvate synthesis (rPS) pathway (green). The malyl-CoA-glycerate (MCG) pathway (orange) consists of the rGS pathway and the glycerate pathway. The red arrow indicates the carboxylation reaction. Gcl, glyoxylate carboligase; Tsr, tartronate semialdehyde reductase; Gk, glycerate 2-kinase; Eno, enolase and 2PG, 2-phospho-d-glycerate Microbial electrosynthesis from CO2 We mentioned above that if the formate used in the CETCH pathway could be synthesized through electrochemistry from CO2, then the pathway would be truly carbon neutral. In fact, new microbial electrochemical methods are being designed to synthesize a variety of small molecules that could serve as feedstocks for chemical synthesis in industry.  The carbon in CO2 has an oxidation number of +4 while the carbon in formic acid has an oxidation number of +2.  Hence two electrons must be added by electrochemically to make the CETCH pathway truly carbon neutral.  Bigger electrochemical reductants of CO2 require more electrons.  Figure \(7\) below shows how key feedstocks could be made electrochemically from CO2 and how they are usually made in industry. Figure \(7\) below.  :  Overview of the Main Products Formed from Microbial Electrosynthesis (MES) From CO2, Along With the Main Industrial Methods to Manufacture These Products.  Jourdin et al., Trends in Biotechnology, April 2021, Vol. 39, No. 4 https://doi.org/10.1016/j.tibtech.2020.10.014.  Creative Commons Attribution (CC BY 4.0) These feedstocks could be made by reductive electrosynthesis using electrons from the oxidation of water though electrolysis.  Released electrons  (oxidation number of O in water is -2 and 0 in O2) move to a biocathode to reduce CO2, as illustrated in Figure \(8\) below. Figure \(8\):  Reactor configurations for MES-based CO2 conversion: (a) H-type, (b) single chamber, (c) dual chamber, (d) continuous stirred tank, and (e) schematic of electron transfer mechanism.  G. S. Lekshmi et al., Microbial electrosynthesis: carbonaceous electrode materials for CO2 conversion.  Mater. Horiz., 2023, 10, 292-312.  DOI: 10.1039/D2MH01178F.  Creative Commons Attribution-Non Commercial 3.0 Unported Licence The biocathode consists of a biofilm of cells printed onto a graphene, graphite, or carbon nanotube-laden support (all carbonaceous).

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.15%3A__Pandemic_Diseases_and_Drug_Discovery_-_Under_Construction.txt

Search Fundamentals of Biochemistry Nitrogen Fixation We spent most of Chapter 22.1 discussing the biochemistry of nitrogenase which fixes the stable molecule N2 to form NH3/NH4+.  It's a very complicated reaction conducted by symbiotic microbes (prokaryotes) that fix N2 for plants.  The world uses the Haber-Bosch process to produce over 100 million metric tons of nitrogen fertilizer that supports half of the world's population's food supply. Only about half of the ammonium added to soil is taken up by plants.  The rest is released into waterways or used by microbes, which can produce from it the potent greenhouse gas nitrous oxide (N2O).  It has a 300x greater effect than CO2 based on weight. The oxidation number of N in NH3  is -3 and in N2O it is +1 showing that the NH3 can be oxidatively metabolized for energy production by the microbes. Also, excess NH4+ goes into waterways and leads to eutrophication, the overproduction of algae and plankton, which depletes O2 from the waters and kills other organisms. N2O emissions have increased dramatically since 1850, as shown in the interactive graph from Our World in Data in Figure \(9\) below. Figure \(9\):  Our World in Data. https://ourworldindata.org/grapher/n...ions?tab=chart So what can we do to "fix" nitrogen fixation to reduce our reliance on the Haber process and its collateral climate effects?  Perhaps we could use mutagenesis to make nitrogenase a better and more efficient enzyme.  That would prove very difficult given the complexity of both the enzyme and the mechanism of N2 conversion to NH3 that requires many metal ion cofactors.  A better alternative would be to express nitrogenase in plants so they could synthesize their own nitrogen fertilizer! Genes of Nitrogen Fixation The catalytic nitrogenase enzyme complex is encoded by three genes: • nifH gene for the subunit Nitrogenase iron protein 1 (also called Nitrogenase component II, and Nitrogenase reductase).  It is a homodimer that binds one [4Fe-4S] cluster per dimer. • nifD gene for the subunit Nitrogenase molybdenum-iron protein alpha chain (also called Dinitrogenase and Nitrogenase component I).  It catalyzes the key enzymatic reactions along with its partner nifK as part of a heterodimer. It binds one [8Fe-7S] cluster per heterodimer with nifK and binds 1 [7Fe-Mo-9S-C-homocitryl] cluster per subunit. • nifK gene for the subunit Nitrogenase molybdenum-iron protein beta chain (also called Dinitrogenase and Nitrogenase component I).  With its partner nifD, it catalyzes the key enzymatic reactions as part of a heterodimer with nifD.  It binds one [8Fe-7S] cluster per heterodimer with nifD and binds 1 [7Fe-Mo-9S-C-homocitryl] cluster per subunit. The nitrogenase enzyme complex has regulatory proteins as well: • nifA - Nif-specific regulatory protein required for activation of most nif operons.  It senses N2.  If there is insufficient quantities of N2, the protein NtrC activates NifA expression which activates the rest of the genes. • nifB - FeMo cofactor biosynthesis protein NifB (also called FeMo-cofactor maturase NifB, Nitrogenase cofactor maturase NifB, and radical SAM assemblase NifB). • nifL - Nitrogen fixation regulatory protein required for the inhibition of NifA activity (i.e. nitrogenase formation) in response to oxygen and low level of fixed nitrogen. • nifE - Periplasmic [NiFe] hydrogenase small subunit. • NifM - a possible peptidyl prolyl cis‐trans isomerase (i.e. a protein chaperone) which helps in the folding of NifH. Figure \(10\) below shows a summary of these gene products. Figure \(10\): Minimum set of nif genes essential for nitrogen fixation with molybdenum-iron nitrogenase.  EMILY M. BENNETT et al., BIODESIGN RESEARCH.  10 Jan 2023, Vol 5, https://spj.science.org/doi/10.34133/bdr.0005 .  Creative Commons Attribution License 4.0 (CC BY 4.0).https://doi.org/10.34133/bdr.0005 The stoichiometry depicted has not been adjusted. NifB contains one catalytic cluster (shown in white) and 2 substrate [4Fe-4S] clusters that react to produce the NifB cofactor. NifEN matures the NifB cofactor producing the FeMo cofactor. The molybdenum-iron (MoFe) nitrogenase (NifHDK) contains the FeMo cofactor at its active site. Electron donors transfer single electrons to the [4Fe-4S] cluster at the interface of the NifH homodimer. Electrons are moved from the [4Fe-4S] cluster into the active site of nitrogenase using energy produced by ATP hydrolysis by NifH. A minimum of 8 electrons are used to reduce each molecule of N2. It would be especially important to express nitrogenase in the main cereal food crops (rice, corn, and wheat) which get their nitrogen from soil microbes (in contrast to legumes, which contain nitrogen-fixing bacteria in nodules in their roots). It's a daunting task given the complexity of the protein complex, its metal cofactors, their inhibition by O2, and the multiple genes required for nitrogenase regulation. Ideally, the relevant gene clusters could be moved into a chlorplast which is evolutionarily derived from bacteria so the gene regulation system might be more suitable. It also has low O2 levels at night.  However, O2 is produced in chloroplasts, which is problematic given the sensitivity of nitrogenase to O2. Using synthetic biology, Saccharomyces cerevisiae has been engineered to express the NifDK nitrogenase tetrameric protein in their mitochondria (after post-translational import).  Yeast is a model organism and tools have been developed for synthetic biology experiments using yeast, so much can be learned that could apply to other eukaryotic organisms like plants.  Mitochondria have high O2 consumption (as opposed to production as in the chloroplast) and the ability to synthesize bacterial-type iron–sulfur clusters.  The Nif gene clusters were engineered into the XV chromosome as shown in Figure \(11\) below. Figure \(11\): nif gene assembly in yeast. Buren et al., ACS Synth. Biol. 2017, 6, 6, 1043–1055. https://doi.org/10.1021/acssynbio.6b00371.  CC-BY license Panel (a) shows the assembly strategy for transcription units, subclusters, and full clusters inserted by homologous recombination in the genome of S. cerevisiae. Panel (d) shows a disgram of nif gene organization in DSN14, a strain of S. cerevisiae. Nitrogenase activity has also been functionally expressed in transgenic rice containing the NifH with a [4Fe-4S] cluster from Hydrogenobacter thermophilus  and NifM (a peptidyl prolyl cis‐trans isomerase from Azotobacter vinelandii) which helps NifH fold.  They were correctly targeted to the mitochondria which again minimizes O2 oxidative damage to the metal ion cofactors. The purified protein was able to transfer electrons to the MoFe protein (NfiDK dimer) but did so poorly.  It also assisted in the assembly of the FeMo cofactor.  However, the [4Fe-4S] cluster occupancy in the protein was poor. However purified protein was also able to reduce acetylene, HC=CH (an alternative substrate similar to N=N) after the addition of purified NifDK. Many steps have to be optimized to create a functional nitrogenase in plants like rice, corn, and wheat.  For example, mitochondrial-expressed NifD is readily cleaved by a mitochondria endoprotease. Some NifD subunits are more resistant to proteolysis and a single amino acid change (Y100Q) leads to enhanced stability on the protein.  AI will likely be extremely useful in maximizing the expression of nitrogen in crop plants. Synthetic Biology to Express Nitrogenase in Bacteria Bacteria can express nitrogenase that can fix atmospheric N2 but they won't work with the critical cereal crops unless the bacteria can interact with roots in the "rhizosphere", the layers of dirt intimately in contact with roots. This life in this area is often called the holobiont, which consists of the plant host and all species interacting with it in a symbiotic relationship.  The metabolism in the holobiont is complex.  For example, plant carbohydrates are used by other organisms in the holobiont. It's similar in a way to the gut biome, which consists of an ecosystem of human and microbial cells. Bacteria have now been engineered that express nitrogenase AND interact with corn roots to fix N2.  The cells are derivatives of γ-proteobacterium (KV137), found on corns roots and which can fix N2.  They have been engineered to turn nitrogenase genes on when N2 fixation is needed.  The engineered bacteria is added to liquid fertilizer, reducing the need for chemical fertilizer by 25 lb/acre) and at the same time increasing yields.  This bacterial-based fertilizer does not wash into waterways with its concomitant negative environmental effects.  Likewise, no N2O is produced on microbial metabolism of excess fertilizers, which decreases the release of this potent greenhouse gas. In 2021 it was used on 3 million acres of corn. One problem with the use of biological N2 microbes in agricultural settings is that high levels of chemical fertilizers can effectively inhibit microbial N2 fixation, which is regulated (as mentioned above) to shut down if nitrogen is bioavailable.  Still, bacteria that fix N2 can produce up to 10% of the nitrogen requirement.  Genetic engineering is needed to overcome this inhibition.  Such bacteria are diazotrophs as they can fix N2 and grow without exogenous sources of N2. Rhizobia are one example. which can fix N2 in the nodules of legumes. The diazotroph isolated from corn roots and mentioned above, Kv137, was gene-edited to produce a modified strain (Kv137-1036) that fixes N2 without inhibition by applied nitrogen fertilizers. The Kv137 strain has nifA and nifL genes and proteins which, as described above, regulate the expression of nitrogenase based on the need for nitrogen. These two genes are on one operon under the control of a single promoter.  nifL was replaced with another promoter which removes the down-regulation of nifA since no nifL was present.  This allowed the expression and activity of nitrogen even in the presence of exogenous fertilizer. Figure \(12\) below shows that Kv137-1036 strain (red dots) does colonize corn roots. Figure \(12\): Commercial efficacy of strain Kv137-1036 - Colonization of corn roots by microbes (red) after germination.  Wen et al., Enabling Biological Nitrogen Fixation for Cereal Crops in Fertilized Fields.  ACS Synth. Biol. 2021, 10, 12, 3264–3277. December 2021.  https://doi.org/10.1021/acssynbio.1c00049.  Attribution 4.0 International (CC BY 4.0)

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.17%3A__Fixing_Nitrogen_Fixation.txt

Search Fundamentals of Biochemistry Introduction Manufacturing of any kind is usually energy and environmentally damaging, and contributes to climate change through the release of CO2 and other pollutants.  A manufactured item has a lifetime after which it must be disposed of in a fashion that often involves little recycling.  A circular economy in which a used product is always recycled for further use if done well, would be highly beneficial for the environment. Synthetic biology as a field seeks to genetically alter and redesign organisms to produce traditional or novel products in more sustainable ways with less energy input and polluting output.   Although it is a nascent field, well know products are binding produced through its use.  We will explore several products made through synthetic biology as well as several in which novel cells themselves are the products. Products from cells Burgers by Impossible Foods Making plant-based foods that taste more like meat, if people would eat them, could have a large effect on greenhouse gas emissions and climate change. One example is the Impossible Burgers and other similar meats from Impossible Foods.  They have soy leghemoglobin, a monomeric heme-binding protein found in root nodules in legumes, to give the appearance and taste of blood in meat.  As a single-chain heme-binding protein, it has a high affinity of O2, similar to animal myoglobin.  The high affinity derives from very high on-rates for binding O2 (almost diffusion-controlled at around 2x108 s-1, and an off rate of around 20 s-1.  This high affinity keeps O2 bound which would otherwise inhibit nitrogenase and nitrogen fixation by root-associated microbes.  The heme is important for positive tastes when we eat red meat.   Plant-based burgers containing leghemoglobin require much less land and lead to far lower greenhouse gas emissions. Figure \(1\) shows an interactive iCn3D model of the alignment of sperm whale myoglobin (1MBO) and soy leghemoglobin (1BIN). Figure \(1\): Alignment of sperm whale myoglobin (1MBO, cyan) and soy leghemoglobin (1BIN, magenta). (Copyright; author via source). Click the image for a popup or use this external link:  https://structure.ncbi.nlm.nih.gov/i...4pUi1S6qFtGn4A The leghemoglobin in the Impossible burgers is produced in yeast so it can be scaled up easily.  To produce leghemoglobin in yeast, large amounts of heme are required, which is also produced in the engineered cells on the introduction of the appropriate genes.  The heme synthesis pathways (described in Chapter 22.3) for C4 (humans, animals, fungi, and purple non-sulfur phototrophic bacteria top) and C5 (archaea, plants, and other bacteria) for heme synthesis are shown in Figure \(2\) below.  The succinyl-CoA is derived from the citric acid cycle. Figure \(2\): Top - heme synthesis pathway for C4 (humans, animals, fungi, and purple non-sulfur phototrophic bacteria top).  Bottom - heme synthesis pathway for C5 (archaea, plants, and other bacteria).   Heme biosynthetic pathway. Wikimedia Commonsile: Heme-Synthesis-Chemical-Details-Mirror (top) and  Heme pathway in E. coli. Zhang, J., Kang, Z., Chen, J. et al. Optimization of the heme biosynthesis pathway for the production of 5-aminolevulinic acid in Escherichia coliSci Rep 5, 8584 (2015). https://doi.org/10.1038/srep08584. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/ (bottom). Note that both succinyl-CoA (C4 pathway) and α-ketoglutarate (C5 pathway) are derived from the citric acid cycle.  The key precursor 5-aminolevulinate, ALA needs to be elevated, through engineering of the C4 or C5 pathways with the C5 pathways generally producing more ALA in engineered E. Coli. Leghemoglobin from soy (species name Glycine max) can also be synthesized in the methylotrophic (uses methanol as a sole carbon source) yeast Pichia pastoris which is often used for recombinant protein expression. Three groups of enzymes are needed" • (group 1: porphobilinogen synthase (PBGS) • group 2: uroporphyrinogen III synthase (UROS), uroporphyrinogen III decarboxylase (UROD), coproporphyrinogen III oxidase (CPO) • group 3: Ala synthase (ALAS), protoporphyrinogen oxidase (PPO), and ferrochelatase (FECH) Transcription of these genes in P. pastoris can be controlled by the use of the methanol-induced alcohol oxidase (AOX1) promoter, which is often used to achieve high expression of recombinant proteins.  Hence when the cells also contain two copies of leghemoglobin along with the rest of the genes, high levels of the protein were made. A more detailed representation of the heme synthesis pathway is shown in Figure \(3\) below. Figure \(3\): The biosynthetic pathway of heme. Su, H.; Chen, X.; Chen, S.; Guo, M.; Liu, H. Applications of the Whole-Cell System in the Efficient Biosynthesis of Heme. Int. J. Mol. Sci. 202324, 8384. https://doi.org/10.3390/ijms24098384.  Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/) The two biosynthetic pathways of 5-aminolevulinic acid (bolded to indicate its importance) are shown in green (C4 pathway) and red (C5 pathway). The three downstream synthetic pathways of heme are marked with blue (CPD), indigo (SHD) and purple (PPD). Solid lines indicate single reactions, and dashed lines indicate more than two reactions. The names of genes encoding the individual enzymes are in italics and some reactions have alternative genes. The abbreviations of the corresponding enzymes are shown in the grey rectangle. See Table 1 for a list of names and abbreviations for heme synthesis enzymes Figure \(4\) below complements this figure and shows the synthetic biology strategies to enhance heme production. Figure \(4\):  Synthetic biology strategies to enhance heme production. Green, orange, and red color blocks indicate genes that need to be up-regulated, down-regulated, and knocked out, respectively.  Su, H. et al., ibid. Computational tools such as AI can help the design of new pathways and novel enzymes to enhance production. It is becoming easier to transfer large pathways into yeast as well. Other food products from microbes and sustainable plants Significant effort is being devoted to growing meat in cell culture in the lab.  This is a nascent field and has to overcome many problems, including consumer resistance to eating lab-created meat.  At present meat grown in tissue culture is very expensive.  Three key steps in growing meat are finding the best cells to grow, finding the nutrient conditions to maximize their growth, and adjusting conditions to make the lab meat taste like meat. Muscle stem cells have been used as they can multiply many times, but these have growth limits.  Alternatively, immortal cells, such as those derived from chicken fibroblasts, could be used. They can also be converted to fat cells.  Yet they could accrue mutations with possible, but unlikely health consequences. Animal cells grown in culture often use fetal cow serum for its rich composition of growth factors and nutrients.  However, it is very expensive and has ethical concerns as well since it's derived from animals.  Synthetic growth medium can be used but it is also expensive.  Whether lab-grown meat can overcome high costs and consumer resistance will determine its potential as a meat substitute. More simply, people can use more peas, soy, grains, and nuts in their diet (i.e. being a vegan or vegetarian is the best approach to reducing your carbon footprint).  Soy products have an extensive history of use as a source of protein but contain potential allergens (especially important in babies who use soy formulas) and isoflavones, which mimic human estrogen derivatives.  Pea-based protein infant formulas are an increasing-used substitute. Expressed recombinant proteins made in genetically modified bacteria and yeast are also becoming more popular.  Examples (other than leghemogloblin) include the production in the fungus Trichoderma reesei of β-lactoglobulin, a cow whey protein, for dairy and animal-free milk products.  The genetically-modified yeast Pichia pastoris has also been engineered to make milk casein proteins, egg-white proteins, muscle myoglobin, and human breast milk proteins. Enzymes used in the manufacture of cheese (derived from calves' stomachs) can be replaced by chymosin made in yeast. Production is linked to fermentation for many of these proteins.  Filamentous proteins that have a texture similar to chicken fiber can be made through fermentation in the filamentous fungi Fusarium venenatumin.  Macroalgae like seaweeds can provide high-protein food and have long been used in many cultures. Kelp farming can help not only provide protein but also capture carbon.  Finally, insects, long eaten in many cultures, could become more climate-friendly source of protein. If humans are in search of nonanimal sources of protein to fight climate change, why not produce and eat the most abundant protein in the biosphere, Rubisco?  New products derived from the duckweed plant (genus Lemna) are coming to market.  Figure \(5\): Figure \(5\): Duckweed (and a frog).  https://commons.wikimedia.org/wiki/F...7678481%29.jpg Duckweed is high in nutrients, fast-growing, and a great source of Rubisco.  It can be grown in aquaculture and does not require farmable land. It contains up to 50% protein.  After harvesting, the plants are filtered, milled, and dried, which are all very simple technologies.  Proteins, the most abundant being Rubisco, are then extracted. It can be used in baked goods and as meat and dairy substitutes.  It is equal to eggs and meat in supplying all the essential amino acids required by humans. Genetic Manufacturing of Industrial Feedstocks Let's look at one example in which synthetic biology and computational techniques are used to create products such as plexiglass from a biological source of acrylates.  Acrylates are esters of acrylic acid (typically made from propylene) synthesized by reacting it with alcohols like methanol.  Life cycle analyses show that almost 4000 kg CO2 are produced per metric ton of acrylic acid made.  To reduce the climate effect, biological feedstocks like glycerol and 3-hydroxypropanoic acid can be used, but large-scale supplies are needed.  Figure \(6\) shows an overview of acrylate production fossil and biological feedstocks. Figure \(6\):  Production pathway of acrylates using fossil fuel and renewable resources. Souza, L.R.d.; Whitfield, B.; Al-Tabbaa, A. Biobased Acrylate Shells for Microcapsules Used in Self-Healing of Cementitious Materials. Sustainability 202214, 13556. https://doi.org/10.3390/su142013556.  Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/) When the alcohol is methanol, the final product is methylacrylate (MA).  The structure of the cyclic acrylate monomer feedstock used to polymerize plexiglass (lucite) is methylene-butyrolactone, (MBL)whose structure is shown in Figure \(7\) below. Figure \(7\): Structure of methyl methacrylate and its lactone (a cyclic ester) Methyl methacrylate can undergo a free radical polymerization in the presence of an initiator (In.), as shown below in Figure \(8\). Figure \(8\): Mechanism of free radical polymerization of MMA This reaction can form large polymers like plexiglass.  The market for acrylic acid, the feedstock for plexiglass, is estimated to reach 12 million metric tons by 2030. MBL, the lactone of MMA, is made in tulips from pathways that are not completely elucidated. It can also be used as a feedstock for the polymerization of plexiglass.  Figure \(9\) shows the polymerization products from MMA and MBL. Figure \(9\): Structure of poly-MMA and poly-MBL Using synthetic biology and advanced computational methods, plexiglass can now be made from biological sources instead of fossil fuels.  To accomplish this, Azerda has designed synthetic pathways from millions of potential metabolic pathways (using a software package called Scylax™), and intelligently redesigned key enzymes to maximize their catalytic potential for the synthesis of MBL (using the software Archytas™). They used high-throughput DNA and protein analyses to maximize expression. Finally, they engineered expression strains and downstream purification processes to maximize the final output of MBL.  In summary, the key steps in the process were: • identifying a pathway from millions of reactions in databases of pathways that could produce MBL from simple sugars through a fermentation process; • engineering pathway enzymes to greatly increase catalytic efficiency and decrease inhibition; • producing test quantities of the products in cell strains; • scaling up production to levels needed for purification and reactions of the MBL • purifying sufficient amounts of MBL from large fermentation broths • making the desired product (plexiglass, for example) from the feedstock. Strains of bacteria, yeast, and filamentous fungi were modified to meet the above criteria. The ultimate substrate for the process was a lignocellulosic hydrolysate, so in the end the process converts trees to plexiglass (incredible to think about)! Of course, it is also amazing that CO2 from the air, water, and minerals/ions from the soil can become a tree! Starting with just a detectable level of product, the process was continually improved and scaled to eventually yield 5 g/L of broth, which is getting close to the 20 g/L required for commercial viability.  Figure \(10\) below shows plexiglass created from the lignocellulosic stock! Table \(10\): Plexiglass made from biosourced MBL. Table \(1\) below compares the key physical properties of the polymers from Arzeda's PMBL compared to literature values for fossil-fuel-based PMBL and for PMMA. Property Measure Lit PMBL Arzeda PMBL PMMA Thermal Glass transition pt Tg (oC) 194-195 195 105 Mechanical Elasticity (mPa) 1999/3439 5972 2855 Tensile strength (mPa) 36.7/62.7 72.7 70 Elongation at break 1.3%.6.5% 1.3% 2.5 Optical Light transmission NA >88% 92% Solvent resistance toluene, 30 days, 20oC NA Pass Fail Table \(1\): https://www.energy.gov/sites/default...-korkegian.pdf

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/32%3A_Biochemistry_and_Climate_Change/32.18%3A__Turning_Trees_into_Plexiglass_-_Synthetic_Biology_For_Production_of_Green_Products.txt

Exercise \(1\) What kinds of lipids are found in a eukaryotic cell membrane under normal conditions? Here is a Hint if you need one Answer Glycerophospholipids, sphingolipids, sterols and fatty acids. Authored by Arthur Sikora.  Last update:  6/5/2023 Date of origin Introduction Lipid rafts are formed and dissipated over time. They are regions where specific lipids are enriched, frequently resulting in protein aggregation as well New Heading [ADD TEXT] [ADD IMAGE] (saved to your computer and uploaded with picture icon from top menu bar or drag image file to location (required from svg image) *Use the following under your picture: Figure \(\PageIndex{x}\): [Add caption] *Center Picture and Caption together using top menu bar Lipids being removed and added to a membrane [ADD iCn3D Model] Figure \(\PageIndex{x}\) is an interactive iCn3D model of [INSERT DESCRIPTIVE TEXT - often a modified version of the title from the actual PDB page 4Q7Q Figure \(6\): [INSERT THE PDB TITLE as above (INSERT PDB CODE)]. Click the image for a popup or use this external link: [https://structure.ncbi.nlm.nih.gov/i...PGDeccDmTH3Nq8].  (Copyright; author via source).  iCn3D model made by Arthur Sikora MODEL No inhibition (left) and Uncompetitive Inhibition (right) Note that the Vcell reaction diagram is the same as for competitive and uncompetive inhibition.  It doesn't explicitly show that the mixed inhibitor binds to both free and substrate-bound enzymes.  Those interactions are addressed in the mathematical equations for mixed inhibition. Initial values No Inhibition Initial values With Uncompetitive Inhibitor I is fixed for each simulation (as it is not converted to a product) but can be changed in the simulation below. Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play  |  Select Slider to change which constants are displayed |  Select About  for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health.  Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington.  Funded by NIH/NIGMS (RO1-GM123032-04) sample modle My modle *your text and graph Figure \(\PageIndex{x}\): Add caption [ADD VCELL/SBML SIMULATION - REUSE] *your text before and after insert as needed New Heading Add what you want BioMolViz Framework BIOMOLVIZ Promoting Molecular Visualization Literacy The BMV framework is used with permission from BioMolViz.Org Copy the appropriate row when assigning a theme, goal, and objective to a designated iCn3D or other biomolecular visualization assessment Atomic Geometry (AG) Three‐atom and four‐atom (dihedral) angles, metal size and metal‐ligand geometries, steric clashes AG1. Students can describe the ideal geometry for a given atom within a molecule and deviations from the ideal geometry due to neighboring interactions. AG1.01 Students can identify atomic geometry/hybridization for a given atom. (Novice) AG1.02 Students can measure bond angles for a given atom. (Novice) AG1.03 Students can identify deviations from the ideal bond angles. (Amateur) AG1.04 Students can explain deviations from the ideal bond angles due to local effects. (Amateur, Expert) AG1.05 Students can predict the effect of deviations from ideal bond angles on the structure and function of a macromolecule. (Expert) AG1.06 Students can identify the geometric features of bonds (e.g., peptide bond, glycosidic, phosphoester). AG2. Students can compare and contrast different structural conformations with regard to energy, the addition of substituents, and the impact on the structure/function of a macromolecule. AG2.01 Students can describe different conformations that a structure can adopt using visualization tools. (Amateur) AG2.02 Students can describe different conformations of atoms about a bond using visualization tools. (Novice) AG2.03 Students can distinguish energetically favorable and unfavorable conformations that a structure can adopt. (Amateur) AG2.04 Students can predict the effect of a given substituent on the structure and function of a macromolecule (e.g., substituent on a carbohydrate/ligand, R groups/rotamers, phosphorylation, methylation of nucleic acids, post-translational modifications). (Expert) AG3. Students can describe dihedral/torsion angles in biomolecules. AG3.01 Students can identify a dihedral/torsion angle in a three-dimensional representation of a molecule. (Novice) AG3.02 Students can identify the planes between which a dihedral/torsion angle exists within a three-dimensional representation of a macromolecule. (Novice) AG3.03 Students can identify phi, psi, and omega torsion/dihedral angles in a three-dimensional representation of a protein. (Amateur) Alternate Renderings (AR) Rendering of a macromolecular structure such as a protein or nucleic acid structure in various ways from the simplest possible way (connections between alpha carbons) to illustration of secondary structure (ribbons) to surface rendering and space filling. AR1. Students can interpret or create molecular images that convey features such as secondary structure, CPK coloring, and active sites. AR1.01 Students can manipulate rendered structures to illustrate molecular properties. (Novice) AR1.02 REMOVED (integrated with SF2.02) AR1.03 Students can describe or label structural differences among multiple structures. (Amateur, Expert) AR1.04 Students can infer information from rendering a structure in different ways. (Novice, Amateur, Expert) AR1.05 Students can create renderings that distinguish secondary structural features. (Novice) AR1.06 Students can create an information rich rendering of a structure that depicts structural features found in the literature. (Amateur) AR1.07 Students can create an information rich rendering of a structure containing ligands, covalent modifications, and noncanonical amino acids or nucleotides. (Amateur, Expert) AR1.08 Students can use molecular visualization to tell a story about a macromolecular structure. (Expert) AR1.09 REMOVED (integrated with MI1.02) AR1.10 Students can convert textbook images of small molecules into 3D representations in a molecular visualization program. (Amateur) AR2. Students can choose the best rendering of a macromolecule to use in a given situation. AR2.01 Students can recognize that a cartoon rendering is a summary of the detail in a line rendering. (Novice, Amateur) AR2.02 Students can describe the atoms and their representations in different renderings (e.g., coloring, showing hydrogens/double bonds). (Novice) AR2.03 Students can identify or create a suitable rendering, or combination of renderings, for a specific purpose (e.g., a surface rendering overlaid with a cartoon to highlight the van der Waals surface alongside secondary structure, or active site sticks shown over a cartoon). (Novice, Amateur) AR2.04 Students can identify the limitations in various renderings of molecular structures. (Amateur) AR2.05 Students can understand the level of detail of different molecular representations. (Novice, Amateur, Expert) AR2.06 Students can transition comfortably between equivalent 2D and 3D renderings of biomolecules. (Novice, Amateur, Expert) AR2.07 Students can use and interpret color in the context of macromolecules to clarify and/or highlight features (e.g., coloring amino acids differently by property, different molecules uniquely in a complex, protein chains, secondary structure). (Novice) Construction and Annotation (CA) Ability to build macromolecular models, either physical or computerized, and, where possible, add commentary, either written or verbal, to tell a molecular story. CA1. Students can compose information‐rich renderings of macromolecule‐ligand interactions. CA1.01 Students can construct and annotate a model of a macromolecule bound to a ligand. (Amateur) CA1.02 Students can construct a model of a macromolecule bound to a ligand and identify the types of molecular interactions. (Amateur) CA1.03 Students can construct a model of a macromolecule bound to a ligand and assess the importance of molecular interactions. (Expert) CA1.04 Students can produce a model of a macromolecule based on a known structure of a related macromolecule. (Amateur, Expert) CA2. Students can compose a rendering to predict the cellular location of a protein (e.g., extracellular, membrane associated, or cytoplasmic) based on the properties and orientations of functional groups. CA2.01 Students can design a rendering that conveys properties such as polarity, charge, secondary structure, etc. to suggest the cellular location of a macromolecule. (Amateur) CA2.02 Students can create protein images with colored polar/nonpolar residues to determine whether they fold with a hydrophobic core. (Amateur) CA2.03 Students can create images to display polar/nonpolar residues and propose a role for the protein and/or how it interacts with its environment ‐ and that the predictions would be plausible based on the protein. (Amateur) CA2.04 Students can make accurate predictions of the location/function of the protein that incorporates additional protein features, such as transmembrane helices, apparent docking surfaces, etc. (Expert) Ligands and Modifications (LM) Metals and metal clusters, additions such as glycosylation, phosphorylation, lipid attachment, methylation etc. LM1. Students can identify ligands and modified building blocks (e.g., hydroxyproline, aminosaccharides, modified nucleobase) within a rendered structure. LM1.01 Students can use the annotation associated with a pdb file to identify and locate ligands and modified building blocks in a given biomolecule. (Amateur) LM1.02 Students can visually identify non‐protein chemical components in a given rendered structure. (Amateur) LM1.03 Students can distinguish between nucleic acid and ligands (e.g., metal ions) in a given nucleic acid superstructure. (Amateur) LM1.04 Students can explain how a ligand in a given rendered structure associates with the biomolecule (e.g., covalent interaction with residue X). (Amateur) LM1.05 Students can locate/identify ligands and modified building blocks in unannotated structures and describe their role. (Expert) LM2. Students can describe the impact of a ligand or modified building block on the structure/function of a macromolecule. LM2.01 Students can look at a given rendered structure and describe how the presence of a specific ligand or modified building block alters the structure of that biomolecule. (Amateur) LM2.02 Students can explain how the removal of a particular ligand or modified building block would alter the structure of a given biomolecule. (Expert) LM2.03 Students can use molecular visualization tools to predict how a specified ligand or modified building block contributes to the function of a given protein. (Amateur, Expert) LM2.04 Students can predict how a ligand or modified building block contributes to the function of a protein for which the structure has been newly solved. (Expert) Macromolecular Assemblies (MA) Polypeptides, oligosaccharides, and nucleic acid and lipid superstructures (e.g. protein–nucleic acid complexes, lipid membrane-associated proteins) MA1. Students can describe various macromolecular assemblies. MA1.01 Students can identify individual biomolecules in a macromolecular assembly. (Novice, Amateur, Expert) MA1.02 Students can describe functions of individual biomolecules within a macromolecular assembly. (Novice, Amateur, Expert) MA1.03 Students recognize the various lipid ultrastructures (e.g., micelles, bicelles, vesicles, and lipid bilayers) in a 3D structure. (Novice) MA2. Students can compose information‐rich renderings of macromolecular assemblies. MA2.01 Students can render a macromolecular assembly to highlight individual structures. (Amateur) MA2.02 Students can render a macromolecular assembly to illustrate structural features (e.g., binding interfaces, symmetry, tertiary structure, etc.). (Novice, Amateur, Expert) Macromolecular Building Blocks (MB) Recognition of native amino acids, nucleotides, sugars, and other biomonomer units/building blocks. Understanding of their physical and chemical properties, particularly regarding functional groups. MB1. Students can identify individual building blocks of biological polymers. MB1.01 Given a rendered structure of a biological polymer, students can identify the ends of a biological polymer. (Novice, Amateur, Expert) MB1.02 Given a rendered structure, students can divide the polymer into its individual building blocks. (Novice) MB1.03 Given a rendered structure, students can identify the individual building blocks. (Novice) MB2. Students can describe the contributions different individual building blocks make in determining the 3‐D shape of the polymer. MB2.01 Students can describe the physical/chemical properties of an individual building block/functional group in a rendered structure of a polymer. (Amateur) MB2.02 Students can describe the significance of the location of individual building blocks within the 3D structure of a polymer (protein, carbohydrate, or nucleic acid). (Novice, Amateur, Expert) MB2.03 Students can identify physical/chemical properties of individual building blocks/functional groups in different local environments. (Amateur) MB2.04 Using a visualized structure, students can identify stereochemistry (e.g., in carbohydrate, lipid, and protein structures). (Amateur) MB2.05 Students can modify/mutate a building block to change the 3D structure of a polymer (protein, carbohydrate, or nucleic acid). (Amateur, Expert) Molecular Dynamics (MD) Animated motion simulating conformational changes involved in ligand binding or catalysis, or other molecular motion/dynamics. MD1. Students can describe the impact of the dynamic motion of a biomolecule on its function. MD1.01 Students can recognize that biological molecules have different conformations. (Novice, Amateur) MD1.02 Students can correlate molecular movement with function. (Novice, Amateur, Expert) MD2. Students can predict limits to macromolecular movement. MD2.01 Students can locate potential regions of flexibility and inflexibility in the structure of a biomolecule. (Novice, Amateur) MD2.02 Students can recognize acceptable/unacceptable movement within a macromolecule by determining whether the movement is within allowable bond angles. (Expert) MD2.03 Students can recognize acceptable/unacceptable movement within a macromolecule by determining whether the movement results in steric hindrance. (Amateur) MD2.04 Students can recognize acceptable/unacceptable movement within a macromolecule by considering the atomic packing constraints. (Expert) Molecular Interactions (MI) Covalent and noncovalent bonding governing ligand binding and subunit‐subunit interactions. MI1. Students can predict the existence of an interaction using structural and environmental information (e.g. bond lengths, charges, pH, dielectric constant). MI1.01 Students can distinguish between covalent and noncovalent interactions. (Novice) MI1.02 Students can identify different noncovalent interactions (e.g., hydrogen bonds, ionic interactions, van der Waals contacts, induced dipole) given a 3D structure. (Amateur) MI1.03 Students can predict whether a functional group (region) would be a hydrogen bond donor or acceptor. (Amateur) MI1.04 Students can render the 3D structure of a biomolecule so as to demonstrate the ionic interactions and/or charge distribution of the different non‐covalent interactions. (Amateur) MI1.05 As it relates to a particular rendered structure, students can rank the relative strengths of covalent and noncovalent interactions. (Amateur) MI2. Students can evaluate the effect of the local environment on various molecular interactions. MI2.01 Students can identify regions of a biomolecule that are exposed to or shielded from solvent. (Novice) MI2.02 Students can identify other molecules in the local environment (e.g., solvent, salt ions, metals, detergents, other small molecules) that impact a molecular interaction of interest. (Novice) MI2.03 Students can predict the impact of other molecules in the local environment (e.g., solvent, salt ions, metals, detergents, other small molecules) on a molecular interaction of interest. (Amateur) MI2.04 Students can predict the pKa of an ionizable group based on the influence of its local three-dimensional environment. (Amateur) MI2.05 Students can propose a change to the local environment that would yield a desired change in a molecular interaction. (Expert) MI2.06 Using molecular visualization tools, students can determine which intermolecular force is most critical to stabilizing a given interaction. (Expert) Symmetry/ Asymmetry Recognition (SA) Recognition of symmetry elements within both single chain and multi-chain macromolecules. SA1. Students can identify symmetric or asymmetric features in rendered molecules. SA1.01 Students can identify symmetric features in a rendered molecule (shown in fixed orientation). (Novice) SA1.02 Students can rotate a single macromolecule, multi-chain macromolecules (e.g., homo- or heteromers), complexes of macromolecules, and supramolecular assemblies to identify axes of symmetry. (Amateur) SA1.03 Students can identify symmetric and asymmetric features in rendered molecules after coloring a given rendered molecule to reveal structural features (charge, hydrophobicity, etc.). (Amateur) SA2. Students can hypothesize the functional significance of symmetry or asymmetry in rendered molecules. SA2.01 Students can explain the functional significance of rotational axes of symmetry (or asymmetry) in a given rendered molecule. (Novice, Amateur, Expert) SA2.02 Students can predict functional significance of symmetry (or asymmetry) in a given rendered molecule. (Amateur, Expert) Structure‐Function Relationship (SF) Active/binding sites, microenvironments, nucleophiles, redox centers, etc. (please also see LM2.03) SF1. Students can evaluate biomolecular interaction sites using molecular visualization tools. SF1.01 Students can identify functionally relevant cofactors, ligands or substrates associated with a macromolecule and describe their role (e.g., an active site magnesium ion). (Amateur, Expert) SF1.02 Students recognize that the size and shape of the ligand must match the size and shape of the binding site. (Novice, Amateur) SF1.03 Students recognize that the polarity or electrostatic potential of a surface complements that of the ligand or substrate. (Novice, Amateur) SF1.04 Students recognize that the hydrophobicity of a surface complements that of the ligand or substrate. (Novice, Amateur) SF1.05 REMOVED (integrated with SF1.03) SF1.06 Students can use docking software to predict how the surface properties of a macromolecule guide and allow the binding of a ligand or substrate. (Amateur) SF2. Using molecular visualization, students can predict the function of biomolecules. SF2.01 Students can recognize structurally related molecules. (Novice) SF2.02 Students can superimpose structurally related molecules. (Novice, Amateur) SF2.03 Students can identify functionally relevant features of a macromolecule (e.g., an active site cysteine, a functional loop). (Amateur) SF2.04 Students can predict molecular function given a binding site. (Amateur, Expert) SF3. Using molecular visualization, students can predict the function of an altered macromolecule. SF3.01 Students can structurally alter a macromolecule. (Novice) SF3.02 Students can propose structural alterations to test interactions in a macromolecule. (Amateur) SF3.03 Students can predict the impact of a structural alteration on the function of a macromolecule. (Amateur, Expert) Structural Model Skepticism (SK) Recognition of the limitations of models to describe the structure of macromolecules. SK1. Students can critique the limitations of a structural model of a macromolecule. SK1.01 Students can explain that the pdb file is a model based on data and that, as a model, it has limitations. (Novice, Amateur) SK1.02 Students associate resolution with reliability of atom positions. (Amateur) SK1.03 Students can identify building blocks (for example, amino acid side chains) whose orientation in a biopolymer is uncertain. (Expert) SK1.04 Students can evaluate the flexibility/disorder of various regions of a macromolecular structure. (Novice, Amateur, Expert) SK1.05 Students can reconcile inconsistent numbering of individual building blocks among species and structure files. (Novice) SK1.06 Students can utilize a Ramachandran plot/steric clashes to interpret the validity of a structure. (Amateur, Expert) SK1.07 Students can describe the limitations of a macromolecule‐ligand docking simulation. (Amateur, Expert) SK2. Students can evaluate the quality of 3D models including features that are open to alternate interpretations based on molecular visualization and PDB flat files. SK2.01 Students can evaluate a crystal structure for crystal packing effects. (Novice, Amateur, Expert) SK2.02 Students can resolve differences between the asymmetric unit and the functional biological assembly. (Expert) SK2.03 Students can differentiate functional ligands (with biological/biochemistry role) from nonfunctional ligands (most solvents, salts, ions, and crystallization agents). (Novice, Amateur, Expert) SK3. Students can discuss the value of experimentally altering a biomolecule to facilitate structure determination. SK3.01 Students can identify non‐native structural features. (Amateur) SK3.02 Students can propose molecular modifications to facilitate structure determination. (Amateur, Expert) SK3.03 Students can propose a purpose for the introduction of non‐native structural features to facilitate structure determination. (Amateur, Expert) Topology and Connectivity (TC) Following the chain direction through the molecule, translating between 2D topology mapping and 3D rendering. TC1. Students can describe or illustrate the linkages between building blocks within a macromolecule. TC1.01 Students can trace the backbone of a macromolecule in three dimensions. (Novice, Amateur) TC1.02 Students can use appropriate terms to describe the linkages/bonds/interactions that join individual building blocks together in a macromolecule or macromolecular assembly. (Novice, Amateur) TC1.03 Given a virtual model of individual building blocks, students can predict the types of linkages/bonds/interactions that are possible or favorable. (Amateur) TC1.04 Given individual building blocks, students can appropriately connect them to create a biological polymer (e.g., drawing carbohydrate linkages, a small peptide). (Amateur, Expert) TC2. Students can describe the overall shape and common motifs within a 3D macromolecular structure. TC2.01 Using molecular visualization software, students can describe the three-dimensional structure of a macromolecule, including overall shape and common structural motifs. (Novice, Amateur, Expert) TC2.02 Students can identify common domains/motifs within a macromolecule. (Amateur, Expert) TC2.03 Students can identify connectivity features between domains or subunits in a macromolecular structure. (Amateur) TC2.04 Students can identify interactions between domains or subunits in a macromolecular structure. (Amateur, Expert) TC2.05 Students can describe how domains/motifs in a macromolecule work together to achieve a concerted function in the cell. (Amateur, Expert) TC2.06 Students can identify the levels of protein structure (e.g., parse a tertiary/quaternary structure into a series of secondary structures/motifs) and the ways in which they are connected from a three‐dimensional structure. (Novice, Amateur, Expert) TC3. Students can explain how any given biomolecular interaction site can be made by a variety of topologies. TC3.01 Students can recognize that the groups that comprise a functional site only require proper arrangement in three-dimensional space rather than a particular order or position in the linear sequence. (Amateur) TC3.02 Students can recognize similarities and differences in two similar ‐ but not identical ‐ three dimensional structures. (Amateur) TC3.03 Students can describe dissimilar portions of homologous proteins as arising from genetic insertions/deletions/rearrangements. (Amateur)

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/Chapter_33%3A__Your_Contribution_-_Sandbox/Arthur_Sikora_Lipid_rafts.txt

Authored by [YOUR NAME].  Last update:  [FILL IN DATE] Date of origin [ADD CONTENT] New Heading [ADD TEXT] [ADD IMAGE] (saved to your computer and uploaded with picture icon from top menu bar or drag image file to location (required from svg image) *Use the following under your picture: Figure \(\PageIndex{x}\): [Add caption] *Center Picture and Caption together using top menu bar [ADD iCn3D Model] [ADD MATHEMATIC GRAPH - REUSE] d graph Figure \(\PageIndex{x}\): Add caption [ADD VCELL/SBML SIMULATION - REUSE] *your text before and after insert as needed New Heading Add what you want Edmund - Structural Basis of Allostery The Kinase Model Authored by [YOUR NAME].  Last update:  [FILL IN DATE] Date of origin [ADD CONTENT] New Heading [ADD TEXT] [ADD IMAGE] (saved to your computer and uploaded with picture icon from top menu bar or drag image file to location (required from svg image) *Use the following under your picture: Figure \(\PageIndex{x}\): [Add caption] *Center Picture and Caption together using top menu bar [ADD iCn3D Model] Figure x is an interactive iCn3D model of [INSERT DESCRIPTIVE TEXT - often a modified version of the title from the actual PDB page (INSERT PDB CODE)] Figure 66: [INSERT THE PDB TITLE as above (INSERT PDB CODE)]. Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...KbCWx2etVfC2WA.  (Copyright; author via source).  iCn3D model made by [YOUR NAME] [ADD MATHEMATIC GRAPH - REUSE] *your text and graph Figure \(\PageIndex{x}\): Add caption [ADD VCELL/SBML SIMULATION - REUSE] *your text before and after insert as needed New Heading Exercise \(1\) What mutations to specific amino acid residues could be introduced to strengthen contacts between the upper and lower C-spine? Answer Mutations to create a greater extent of interaction or a stronger class of interaction are necessary to strengthen intramolecular contacts. Example 1: Mutation of a small hydrophobic amino acid to a bulky hydrophobic or aromatic amino acid can be used to bridge the gap. Example 2: Mutation to introduce a new hydrogen bond, salt bridge, or disulfide bond can be used to create a new link between these sections of the hydrophobic core. Emily Schmitt Sepiapterin Reductase-Beery Twins Story Exercise \(1\) Which mutation was contributed by the mother that affected the Sepiapterian reductase (SPR) in the Beery Twins situation? Answer a) early stop of the protein (Lys to termination) Lys251X b) Arg 150 Gly c) there was no mutation from the mother that caused the disease d) Lys 250 Glu Here is a hint if you need one Authored by [YOUR NAME].  Last update:  [FILL IN DATE] Date of origin New Heading [ADD TEXT] Figure 1: The pathway above shows where the drugs given to the twins exist in the pathway that involves Sepiapterin Reductase (SPR).  Note that the twins were prescribed 5-HTP and L-Dopa which are downstream of the "broken" SPR. In this way dopamine and serotonin can still be produced in the twins. Figure 2: iCN3D recreation of Alen et al., 2019 Figure 5- Crystal structure of inhibitor 3 bound to SPR (PDB code 6I6P) Figure \(\PageIndex{x}\) is an interactive iCn3D model of [INSERT DESCRIPTIVE TEXT - often a modified version of the title from the actual PDB page (INSERT PDB CODE)] INSERT PNG (just a screen snip) OF YOUR iCn3D MODEL Figure \(6\): [INSERT THE PDB TITLE as above (INSERT PDB CODE)]. Click the image for a popup or use this external link: [INSERT Lifelong short URL from File, Share Link in iCn3D)].  (Copyright; author via source).  iCn3D model made by [YOUR NAME] [ADD MATHEMATIC GRAPH - REU Page path to reuse: ``` ``` SE] *your text and graph Figure \(\PageIndex{x}\): Add caption [ADD VCELL/SBML SIMULATION - REUSE] *your text before and after insert as needed

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/Chapter_33%3A__Your_Contribution_-_Sandbox/Chetna_IMF_Test.txt

Exercise $1$ Degradation of amino acids yields compounds that are common intermediates in the major   metabolic pathways. Explain the distinction between glucogenic and ketogenic amino acids in terms of their metabolic fates. Answer Glucogenic amino acids are those which can be catabolized into pyruvate, oxaloacetate, a-ketoglutarate , fumarate, or succinyl-CoA, and thus can serve as glucose precursors. Ketogenic amino acids are catabolized to Acetyl-CoA or acetoacetate, and thus can serve as precursors for fatty acids or ketone bodies. Here is a hint if you need it. Authored by Helena Prieto.  Last update:  06.05.23 Date of origin 06.05.23 [ADD CONTENT] New Heading [ADD TEXT] [ADD IMAGE] (saved to your computer and uploaded with picture icon from top menu bar or drag image file to location (required from svg image) *Use the following under your picture: Figure $\PageIndex{x}$: [Add caption] *Center Picture and Caption together using top menu bar [ADD iCn3D Model] [ADD MATHEMATIC GRAPH - REUSE] *your text and graph Reversible Competitive inhibition occurs when substrate (S) and inhibitor (I) both bind to the same site on the enzyme. In effect, they compete for the active site and bind in a mutually exclusive fashion. This is illustrated in the chemical equations and molecular cartoons shown in Figure $1$. v_0=\frac{V_M S}{K_M\left(1+\frac{I}{K is}\right)+S} There is another type of inhibition that would give the same kinetic data. If S and I bound to different sites, and S bound to E and produced a conformational change in E such that I could not bind (and vice versa), then the binding of S and I would be mutually exclusive. This is called allosteric competitive inhibition. Inhibition studies are usually done at several fixed and non-saturating concentrations of I and varying S concentrations. The key kinetic parameters to understand are VM and KM. Let us assume for ease of equation derivation that I binds reversibly, and with rapid equilibrium to E, with a dissociation constant KIS. The "s" in the subscript "is" indicates that the slope of the 1/v vs 1/S Lineweaver-Burk plot changes while the y-intercept stays constant. KIS is also named KIC where the subscript "c" stands for competitive inhibition constant. A look at the top mechanism shows that even in the presence of I, as S increases to infinity, all E is converted to ES. That is, there is no free E to which I could bind. Now, remember that VM= kcatE0. Under these conditions, ES = E0; hence v = VM. VM is not changed. However, the apparent KM, KMapp, will change. We can use LaChatelier's principle to understand this. If I binds to E alone and not ES, it will shift the equilibrium of E + S → ES to the left. This would increase the KMapp (i.e. it would appear that the affinity of E and S has decreased.). The double reciprocal plot (Lineweaver-Burk plot) offers a great way to visualize the inhibition as shown in Figure $2$. In the presence of I, VM does not change, but KM appears to increase. Therefore, 1/KM, the x-intercept on the plot will get smaller, and closer to 0. Therefore the plots will consist of a series of lines, with the same y-intercept (1/VM), and the x-intercepts (-1/KM) closer and closer to 0 as I increases. These intersecting plots are the hallmark of competitive inhibition. Here is an interactive graph showing v0 vs [S] for competitive inhibition with Vm and Km both set to 100. Change the sliders for [I] and Kis and see the effect on the graph. Here is the interactive graphs showing 1/v0 vs 1/[S] for competitive inhibition, with Vm and Km both set to 10. Note that in the first three inhibition models discussed in this section, the Lineweaver-Burk plots are linear in the presence and absence of an inhibitor. This suggests that plots of v vs S in each case would be hyperbolic and conform to the usual form of the Michaelis Menton equation, each with potentially different apparent VM and KM values. An equation for v0 in the presence of a competitive inhibitor is shown in the above figure. The only change compared to the equation for the initial velocity in the absence of the inhibitor is that the KM term is multiplied by the factor 1+I/Kis. Hence KMapp = KM(1+I/Kis). This shows that the apparent KM does increase as we predicted. KIS is the inhibitor dissociation constant in which the inhibitor affects the slope of the double reciprocal plot. If the data were plotted as v0 vs log S, the plots would be sigmoidal, as we saw for plots of ML vs log L in Chapter 5B. In the case of a competitive inhibitor, the plot of v0 vs log S in the presence of different fixed concentrations of inhibitor would consist of a series of sigmoidal curves, each with the same VM, but with different apparent KM values (where KMapp = KM(1+I/Kis), progressively shifted to the right. Enzyme kinetic data is rarely plotted this way. These plots are mostly used for simple binding data for the M + L ↔ ML equilibrium, in the presence of different inhibitor concentrations. Reconsider our discussion of the simple binding equilibrium, M + L ↔ ML. For fractional saturation Y vs a log L graphs, we considered three examples: 1. L = 0.01 KD (i.e. L << KD), which implies that KD = 100L. Then Y = L/[KD+L] = L/[100L + L] ≈1/100. This implies that irrespective of the actual [L], if L = 0.01 KD, then Y ≈0.01. 2. L = 100 KD (i.e. L >> KD), which implies that KD = L/100. Then Y = L/[KD+L] = L/[(L/100) + L] = 100L/101L ≈ 1. This implies that irrespective of the actual [L], if L = 100 Kd, then Y ≈1. 3. L = KD, then Y = 0.5 These scenarios show that if L varies over 4 orders of magnitude (0.01KD < KD < 100KD), or, in log terms, from -2 + log KD < log KD< 2 + log Kd), irrespective of the magnitude of the KD, that Y varies from approximately 0 - 1. In other words, Y varies from 0-1 when L varies from log KD by +2. Hence, plots of Y vs log L for a series of binding reactions of increasingly higher KD (lower affinity) would reveal a series of identical sigmoidal curves shifted progressively to the right, as shown below in Figure $3$. The same would be true of v0 vs S in the presence of different concentrations of a competitive inhibitor, for initial flux, Jo vs ligand outside, in the presence of a competitive inhibitor, or ML vs L (or Y vs L) in the presence of a competitive inhibitor. In many ways plots of v0 vs lnS are easier to visually interpret than plots of v0 vs S . As noted for simple binding plots, textbook illustrations of hyperbolas are often misdrawn, showing curves that level off too quickly as a function of [S] as compared to plots of v0 vs lnS, in which it is easy to see if saturation has been achieved. In addition, as the curves above show, multiple complete plots of v0 vs lnS at varying fixed inhibitor concentrations or for variant enzyme forms (different isoforms, site-specific mutants) over a broad range of lnS can be made which facilitates comparisons of the experimental kinetics under these different conditions. This is especially true if Km values differ widely. Now that you are more familiar with binding and enzyme kinetics curves, in the presence and absence of inhibitors, you should be able to apply the above analysis to inhibition curves where the binding or the initial velocity is plotted at varying competitive inhibitor concentrations at different fixed nonsaturating concentrations of ligand or substrate. Consider the activity of an enzyme. Let's say that at some reasonable concentration of substrate (not infinite), the enzyme is approximately 100% active. If a competitive inhibitor is added, the activity of the enzyme decreases until at saturating (infinite) I, no activity would remain. Graphs showing this are shown below in Figure $4$. Progress Curves for Competitive Inhibition In the previous section, we explored how important progress curve (Product vs time) analyses are in understanding both uncatalyzed and enzyme-catalyzed reactions. We are aware of no textbooks which cover progress curves for enzyme inhibition. Yet progress curves are what most investigators record and analyze to determine initial rates v0 and to calculate VM, KM and inhibition constants, as described above. We will use Vcell to produce progress curves for reversibly inhibited enzyme-catalyzed reactions. MODEL Competitive Inhibition with constant [I]: No inhibition (left) and competitive inhibition (right) Initial conditions for no inhibition Initial conditions for competitive inhibition I is fixed for each simulation (as it is not converted to a product) but can be changed in the simulation below. Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play  |  Select Slider to change which constants are displayed.  For this model, select Vm, Km, Ki and I |  Select About  for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health.  Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington.  Funded by NIH/NIGMS (RO1-GM123032-04) The graphs from your initial run show the concentrations of S, P and I as a function of time for just the initial conditions shown above. In typical initial rate laboratory analyzes, of competitive inhibition, at least three sets of reactions are run with the same varying substrate concentrations and different fixed concentrations of inhibitor. In the analyses above, [I] is fixed at 5 uM. Conduct a series of run at different values of I. Vary the KI, the dissociation constant for the EI complex, as follows: • I << KI, the dissociation constant for the EI complex • I >> KI, the dissociation constant for the EI complex. Then download the data and determine the initial rate for each of the initial conditions. Figure $5$ shows an interactive iCn3D model of human low molecular weight phosphotyrosyl phosphatase bound to a competitive inhibitor (5PNT) Figure $5$: Human low molecular weight phosphotyrosyl phosphatase bound to a competitive inhibitor (5PNT). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...XsEacG2tixDDi9 The competitive inhibitor, the deprotonated form of 2-(N-morpholino)-ethanesulfonic acid (MES), is actually the conjugate base of the weak acid (pKa = 6.15) of a commonly used component of a buffered solution.  It is shown in color sticks with the negatively charged sulfonate sitting at the bottom of the active site pocket.  The amino acids comprising the active site binding pocket are shown as color sticks underneath the transparent colored surface of the binding pocket.  The normal substrates for the enzyme are proteins phosphorylated on tyrosine side chains so the sulfonate is a mimic of the negatively charged phosphate group of the phosphoprotein target. Two specials cases of competition inhibition Product Inhibition Let's look at an enzyme that converts reactant S to product P. Since P arises from S, they may have structural similarities. For example, what if GTP was the reactant and GDP was a product? If so, then P might also bind in the active site and inhibit the conversion of S to P. This is called product inhibition. It probably occurs in most enzymes, and when it does occur it will start bending downward the beginning part of the progress curve for P formation. If the product binds very tightly, it might cause a significant underestimation of the initial velocity (v0) or flux (J0) of the enzyme. Let's use Vcell to explore product inhibition. The model will explore two reactions: • E + R ↔ ER → E + Q (no product inhibition) • E + S ↔ ES → E + P (with product inhibition) Note that the chemical equation above does not explicitly show the product P binding the enzyme to form an EP complex. An actual reaction diagram showing the inhibition of an enzyme by an inhibitor I and by the product P is shown in Figure $6$ below. Figure $6$: reaction diagram showing inhibition of an enzyme by an inhibitor I and by the product P Vcell uses much simpler diagrams since it is most often used for modeling whole pathways or even entire cells. In the simpler Vcell reaction diagrams, the inhibitor is typically not shown since the inhibition is built into the equation for the enzyme, represented by the node or yellow square in the figure above. Let's now explore product inhibition in Vcell. R and Q are the reactant and product, respectively, in the reaction without product inhibition. S and P are used for the reaction with product P inhibition. MODEL Irreversible MM Kinetics - Without (left rx 1) and With (right, rx 2) Product Inhibition Initial Conditions:  No product inhibition Initial Conditions: With product inhibition Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play  |  Select Slider to change which constants are displayed |  Select About  for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health.  Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington.  Funded by NIH/NIGMS (RO1-GM123032-04) Inhibition by a competing substrate - the specificity constant In the previous chapter, the specificity constant was defined as kcat/KM which we also described as the second-order rate constant associated with the bimolecular reaction of E and S when S << KM. It also describes how good an enzyme is in differentiating between different substrates. If an enzyme encounters two different substrates, one can be considered to be a competitive inhibitor of the other. The following equation gives the ratio of initial velocities for two competing substrates at the same concentration is equal to the ratio of their kcat/KM values. \frac{\mathrm{v}_{\mathrm{A}}}{\mathrm{v}_{\mathrm{B}}}=\frac{\frac{\mathrm{k}_{\mathrm{catA}}}{\mathrm{K}_{\mathrm{A}}} \mathrm{A}}{\frac{\mathrm{k}_{\mathrm{cat} \mathrm{B}}}{\mathrm{K}_{\mathrm{B}}} \mathrm{B}} A derivation of the specificity constant for an enzyme with competing substrates Here it is! Derivation \mathrm{v}_{\mathrm{A}}=\frac{\mathrm{V}_{\mathrm{A}} \mathrm{A}}{\mathrm{K}_{\mathrm{A}}\left(1+\frac{\mathrm{B}}{\mathrm{K}_{\mathrm{B}}}\right)+\mathrm{A}} \quad \mathrm{v}_{\mathrm{B}}=\frac{\mathrm{V}_{\mathrm{B}} \mathrm{B}}{\mathrm{K}_{\mathrm{B}}\left(1+\frac{\mathrm{A}}{\mathrm{K}_{\mathrm{A}}}\right)+\mathrm{B}} \frac{\mathrm{v}_{\mathrm{A}}}{\mathrm{v}_{\mathrm{B}}}=\frac{\frac{\mathrm{V}_{\mathrm{A}} \mathrm{A}}{\mathrm{K}_{\mathrm{A}}\left(1+\frac{\mathrm{B}}{\mathrm{K}_{\mathrm{B}}}\right)+\mathrm{A}}}{\frac{\mathrm{V}_{\mathrm{B}} \mathrm{B}}{\mathrm{K}_{\mathrm{B}}\left(1+\frac{\mathrm{A}}{\mathrm{K}_{\mathrm{A}}}\right)+\mathrm{B}}}=\frac{\frac{\mathrm{V}_{\mathrm{A}} \mathrm{A}}{\mathrm{K}_{\mathrm{A}}+\frac{\mathrm{K}_{\mathrm{A}} \mathrm{B}}{\mathrm{K}_{\mathrm{B}}}+\mathrm{A}}}{\frac{\mathrm{V}_{\mathrm{B}} \mathrm{B}}{\mathrm{K}_{\mathrm{B}}+\frac{\mathrm{K}_{\mathrm{B}} \mathrm{A}}{\mathrm{K}_{\mathrm{A}}}+\mathrm{B}}} Now in the above equation: multiple the top half of the right-hand expression by \frac{\frac{1}{K_A}}{\frac{1}{K_A}} multiple the bottom half of the right-hand expression by \frac{\frac{1}{K_B}}{\frac{1}{K_B}} replace VA with kcatAE0 and VB with kcatBE0 This gives the following expression for vA/vB: \frac{\mathrm{v}_{\mathrm{A}}}{\mathrm{v}_{\mathrm{B}}}=\frac{\frac{\mathrm{k}_{\mathrm{catA}}}{\mathrm{K}_{\mathrm{A}}} \mathrm{A}}{\frac{\mathrm{k}_{\mathrm{cat} \mathrm{B}}}{\mathrm{K}_{\mathrm{B}}} \mathrm{B}} Figure $\PageIndex{x}$: Add caption [ADD VCELL/SBML SIMULATION - REUSE] *your text before and after insert as needed New Heading Figure $\PageIndex{x}$ is an interactive iCn3D model of [2B4Z cytochrome c] INSERT PNG () OF YOUR iCn3D MODEL Figure $6$: [2B4Z]. Click the image for a popup or use this external link: .  (Copyright; author via source).  iCn3D model made by [Helena Prieto]

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/Chapter_33%3A__Your_Contribution_-_Sandbox/Helena-Test.txt

Example \(1\) Which elements can form hydrogen bonds? Solution fluorine, oxygen, or nitrogen Exercise \(1\) Can phosphorus form hydrogen bonds? Here is a hint if you need one! Answer No. Phosphorus and hydrogen have almost equal values of electronegativity. Box Question with floating Hint 1. On your Chapter 33 page, click Edit. 2. From the menu bar that appears, select Elements, Templates 3.  From the dropdown menu select Box: Exercise.  The following box will appear Example \(1\) Add example text here. Which elements can form hydrogen bonds? Solution Add example text here. fluorine, oxygen, or nitrogen \(1\) Add exercises text here. Can phosphorus form hydrogen bonds? Answer No.  Phosphorus and hydrogen have equal values of electronegativity. 4.  Edit the Box Exercise as shown below by clicking in the text areas and replacing the text with your own.  You can add an image by scrolling to the bottom of this page and selecting Attach a file A question \(1\) Which does NOT describe sodium dodecyl sulfate (SDS).  SDS .... 1. readily forms bilayers 2. readily form micelles 3. is a single-chain amphiphile 4. has (a) unsaturated acyl chain(s) Here is a hint if you need one! Answer It does not readily form a bilayer since it is a single-chain amphiphile.  Instead, it forms micelles. 5. The hint in this example is a file that has been uploaded (see the list of attached files below).  Right-click on the SDS.png file below and choose Copy the link address.  Then double-click the word "hint" in the above box to highlight it, choose the link icon from the top menu bar, and paste the link into the Link To box after replacing the default link in the box. Under link options, select Open in Contextual Help Overlay to get the hint to float above the page. Insert an Image into FOB Inserting an image into FOB 1. Do a Google search for an image with Creative Commons permission for reuse.  The optimal permission category is CC BY. 2. Make a copy and save it to your computer. 3. Open your Chapter 33.x Libretext file and select Edit in the top menu bar 4. Navigate to ADD IMAGE, remove these words but leave the cursor there 5. From the Editing menu bar, select Elements, Templates, and from the drop-down Template:FigureCenterCenter. 6. Click on the Copy/Paste Placeholder image and delete it. 7. Recenter the cursor centered above the automatic figure legend,  click the Image icon from the menu bar. 8. Select the Attach Files tab, followed by Choose File, and navigate to the image you wish to upload. 9.  Select Save Image, and the image will appear centered above the caption. 10.  Change the placeholder text in the caption to one of your choosing. Inserting a mathematical simulation (SBML) into a FOB 1.  Navigate to this page in a new window 2.  Select and view the graph of interest 3.  Navigate to your Chapter 33 section and select Edit from the top menu bar 4.  Move the cursor to the location you wish to insert the graph 5.  From the top menu bar select Elements, Content Reuse 6.  Navigate through the folder tree to get to the page with the interactive mathematical models: Home Learning Objects Visualization and Simulations Progress Curve Analysis SBML Computational Models 7.  Click the file name for the model of interest to insert. 8. Save the page.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/Chapter_33%3A__Your_Contribution_-_Sandbox/Insert_a_question_with_hidden_answer_and_floating_hint.txt

(If you need a refresher, here is a link to an iCn3D workshop tutorial presented by BioMolViz at the BMB 2023 in Seattle, WA on March 25, 2023.) A. Creating an iCn3D model in FOB 1.  Open iCn3D, input a PDB code, and do the following: • Color, Secondary, Sheets in Yellow • Style, Background, Transparent • File, Share Link • Copy the Original URL with commands 2.  Follow these instructions but use your code (open a new Word document for your code). Here is a sample code for 1XWW: https://www.ncbi.nlm.nih.gov/Structu...0&command=load mmdb 1xww | parameters &mmdbid=1xww&bu=1; set thickness | linerad 0.1 | coilrad 0.3 | stickrad 0.4 | crosslinkrad 0.4 | tracerad 0.1 | ribbonthick 0.2 | proteinwidth 1.3 | nucleotidewidth 0.8 | ballscale 0.3; set background transparent; color secondary structure yellow|||{"factor":"1.000","mouseChange":{"x":"0.000","y":"0.000"},"quaternion":{"_x":"-0.006542","_y":"0.8578","_z":"-0.04771","_w":"0.5117"}} 2.  Copy all of the code BEFORE the ||| and paste it in a  Word document. Then add the 3 small red codes in the example below, and add them in the corresponding positions in your sample code. It will be helpful to color the newly added code red to make sure it is correct. It will look like this but your code will replace the back code below. template('EmbediCn3D/iCn3D',{config: '{"fullURL": "https://www.ncbi.nlm.nih.gov/Structu...30430&v=3.25.0&closepopup=1&command=load mmdb 1xww | parameters &mmdbid=1xww&bu=1; set thickness | linerad 0.1 | coilrad 0.3 | stickrad 0.4 | crosslinkrad 0.4 | tracerad 0.1 | ribbonthick 0.2 | proteinwidth 1.3 | nucleotidewidth 0.8 | ballscale 0.3; set background transparent; color secondary structure yellow"}'}) 3.  Now go to this page in a new window to create your own iCn3D page for FOB 4.  Open the file Template for Your Own Separate iCn3D Page 5.  You must make a copy of this file. From the top menu bar click Options, Copy, and give the file a name derived from the PDB web page. For example Human Low Molecular Weight Protein Phosphatase (1XWW).  Don't overnight the original file with your new name. 6. Then from the top menu bar for your new file select Edit.  Click the + button (green circle) on the DekiScript box. 7.  Paste your entire modified URL code from your Word document (with the 3 inserted red codes) into the DekiScript box. 8. Choose the Save just above the DekiScript box and you should see a small version of the molecule in a workable iCn3D window. B.  Inserting your iCn3D model into your Chapter 33 Section 1.  Open your Chapter 33 section and click Edit 2.  Place your cursor on a new line where you would like to place the model to appear. 3. Go to this page in a new window, select Edit in the menu bar, and copy the material between the two horizontal lines into the desired location in your Chapter 33 section. 4.  Take a screen snip showing the molecule in the iCn3D modeling window.  Save it to your computer. Then insert the image in you Chapter 33 section where the text reads: INSERT PNG (just a screen snip) OF YOUR iCn3D MODEL.  Use the top menu bar picture icon to select the file or drag the file from your computer into your section page. 5.  Complete the captions for the image and make sure to include the PDB ID in your text as well as the iCn3D short link. 6.  To make a hovering iCn3D model with a full menu within your chapter section (and not just from the external iCn3D short link), right-click the image you inserted in Step 4, and select Create Link (see image below). 7. Delete the link shown (red arrow) and replace it with the URL from the LibreText iCn3D file you just made in Part A above.  Make sure that you choose Open in contextual help overlay. 8.  Save the link. Inserting an interactive mathematic graph into FOB 1.  Navigate to this page in a new window 2.  Select and view the graph of interest 3.  Navigate to your Chapter 33 section and select Edit from the top menu bar 4.  Move the cursor to the location you wish to insert the graph 5.  From the top menu bar select Elements, Content Reuse 6.  Navigate through the folder tree to get to the page with the interactive mathematical models: Home Learning Objects Visualization and Simulations Interactive Figures Interactive Biochemistry Graphs 7.  Click the file name for the model of interest to insert. 8. Save the page. KP Procko Test Authored by KP.  Last update:  5/4/23 Date of origin [add content] New Heading [ADD TEXT] your text your text and image [ADD iCn3D Model] Figure \(\PageIndex{x}\) is an interactive iCn3D model of mammalian respirasome (5GPN) Figure \(6\): [INSERT THE PDB TITLE as above (INSERT PDB CODE)]. Click the image for a popup or use this external link: [INSERT Lifelong short URL from File, Share Link in iCn3D)].  (Copyright; author via source).  iCn3D model made by [YOUR NAME] your text and model [ADD MATHEMATIC GRAPH - REUSE] your text and graph Exercise \(1\) What is the pKa of acetic acid? Answer 4.7 [ADD VCELL SIMULATION - REUSE] MODEL No inhibition (left) and Uncompetitive Inhibition (right) Note that the Vcell reaction diagram is the same as for competitive and uncompetive inhibition.  It doesn't explicitly show that the mixed inhibitor binds to both free and substrate-bound enzymes.  Those interactions are addressed in the mathematical equations for mixed inhibition. Initial values No Inhibition Initial values With Uncompetitive Inhibitor I is fixed for each simulation (as it is not converted to a product) but can be changed in the simulation below. Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play  |  Select Slider to change which constants are displayed |  Select About  for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health.  Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington.  Funded by NIH/NIGMS (RO1-GM123032-04) your tex New Heading Add what you want

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/Chapter_33%3A__Your_Contribution_-_Sandbox/Inserting_an_iCn3D_model_into_a_FOB_Chapter_section.txt

A.  Introduction Remixes are texts created from existing OER content.  Constructing Remixes on the LibreTexts platform is facilitated by the OER Remixer tool. The title of the Remix often starts with the campus acronym (e.g., the Chemistry 110A remix at the University of California is "UCD: Chem 110A Introductory Quantum Mechanics") If using the OER Remixer to build a large textbook, it is advisable to construct a Remixing Map in a spreadsheet including to organize the sections you'll include.  This content will be an effective Table of Contents. For this exercise, we’ll make a remix with just 3 chapter sections from 3 different chapters so no spreadsheet is needed. We’ll use these chapter sections: • Vol 1 – Chapter 6.5: Enzymatic Reaction Mechanisms (written by Henry and KP) • Vol 2 – Chapter 14.5: Metabolism and Signaling: The Steady State, Adaptation and Homeostasis • Vol 4 – Chapter 32.12: A Warmer World – Temperature Effects on Proteins (Note: you could add content from any LibreText book as well.) Instructions 1.  Go to the main FOB page 2.  An author with permission can see a blue vertical icon bar on the left side of the page. 3.  Select Tools and from the right side menu choose  OER Remixer. 4.  Complete the step indicated in the figure below • Step 1: Enter the name you want for your custom book (typically your name and the name of the class) in the box labeled "LibreText name" (Step 1 in Figure 7.3.57.3.5).  Ex:  Henry Jakubowski_TrialRemixFOB_1 • For Step 2 and Step 3: Accept the default 5.  In the left "Library Panel": Click the + by the bookshelves and continue to see Vol I, II, and IV (see image right). Keep expanding the list until you see Chapter 6 (Vol 1), then Chapter 14 (Vol 2), and finally Chapter 32 (Vol 4) 6. Insert the entire Chapter 6 by selecting it and dragging and dropping it to the right panel.  Position it by moving it to the correct position (you will see a little blue line indicating the position) 7. Repeat and move the entire Chapt 14 and Chapter 32. 8.  Now expand the moved Chapters in the panel.  Select the section you do NOT want and click the delete icon (recycle bin) in the menu bar in the right panel.  Continue deleting until each Chapter has just the desired section. 9.  Select the Publish button (see image below) 10.  In the next window select Publish again and wait until the remix is complete.  Click the “Your new LibreText is available here to see your custom book (which is your Sandbox). 11. Your sandbox can be seen by clicking in your name on any LibreText page when you have logged in as shown below B.  Making a PDF You can easily make a PDF of a chapter.  For example, navigate to Chapter 14 and select PDF then Chapter and follow prompts. Make your own Chapter 33.x Section Make Your Own Chapter 33.x Section 1. Navigate to this page in a new window 2. Open the file named Template to Copy 3. On the top menu bar select Options, Copy 4.       Add a page title with this format:  FirstName LastName Short Descriptive Title and then chose Copy Page. 5.  Editing a webpage is generally intuitive. Once you click the Edit button on the top left-hand side of the page, editing icons will appear, similar to a word processing program. The icons on the top menu bar next to the indent icon allow you to: create a link, add a picture directly, add a table, and search (magnifying glass). Pallavi- Test Authored by [YOUR NAME].  Last update:  [FILL IN DATE] Date of origin [ADD CONTENT] New Heading [ADD TEXT] [ADD IMAGE] (saved to your computer and uploaded with picture icon from top menu bar or drag image file to location (required from svg image) * Figure \(\PageIndex{x}\): [Add caption] *Center Picture and Caption together using top menu ba Figure \(\PageIndex{x}\) is an interactive iCn3D model of [GLN3] INSERT PNG (just a screen snip) OF YOUR iCn3D MODEL Figure \(6\): [INSERT THE PDB TITLE as above (INSERT PDB CODE)]. Click the image for a popup or use this external link: [INSERT Lifelong short URL from File, Share Link in iCn3D)].  (Copyright; author via source).  iCn3D model made by [YOUR NAME] [ADD MATHEMATIC GRAPH - REUSE] *your text and graph Figure \(\PageIndex{x}\): Add caption [ADD VCELL/SBML SIMULATION - REUSE] MODEL Initial Parameters Reaction Parameter Value r0 kf0 kr0 r1 kf1 kf1 r2 kf21 kf21 kf22 kf22 r3 VM KM Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play  |  Select Slider to change which constants are displayed |  Select About  for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health.  Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington.  Funded by NIH/NIGMS (RO1-GM123032-04) New Heading Add what you want

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/Chapter_33%3A__Your_Contribution_-_Sandbox/Make_Your_Own_Customized_FOB%3A__Assemble_and_Remix_a_Custom_Book__%28Short_Version%29.txt

Authored by [YOUR NAME].  Last update:  [FILL IN DATE] Date of origin Introduction Perilipin is a protein associated with lipid droplets that plays a key role in the regulated breakdown of triacylglycerols. New Heading [ADD TEXT] your text [ADD IMAGE] (saved to your computer and uploaded with picture icon from top menu bar or drag image file to location (required from svg image) *Use the following under your picture: Figure \(\PageIndex{x}\): [Add caption] *Center Picture and Caption together using top menu bar Figure 𝑥� is an interactive iCn3D model of Low Molecular Weight Phosphotyrosyl Phosphatase, 1xww Figure 66: [INSERT THE PDB TITLE as above (INSERT PDB CODE)]. Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...7&t=1XWW(MMDB) in iCn3D.  (Copyright; author via source).  iCn3D model made by Pam Mertz [ADD MATHEMATIC GRAPH - REUSE] Figure \(\PageIndex{x}\): Add caption MODEL AerobicGlycolysis Quantitative determinants of aerobic glycolysis identify flux through the enzyme GAPDH as a limiting step. .Shestov AA, Liu X, Ser Z, Cluntun AA, Hung YP, Huang L, Kim D, Le A, Yellen G, Albeck JG, Locasale JW.  eLife , 7/ 2014 , Volume 3 , PubMed ID: 25009227.  Biomodel MODEL1504010000 Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play  |  Select Slider to change which constants are displayed |  Select About  for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health.  Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington.  Funded by NIH/NIGMS (RO1-GM123032-04) *your text before and after insert as needed New Heading Add what you want Rebecca Roberts Chapter 33 testing Authored by Rebecca Roberts.  Last update:  6/5/2023 Date of origin Introduction Sometimes proteins need to bind to something specifically, but sometimes a protein needs to recognize a variety of ligands. How does this happen? New Heading [ADD TEXT] *Use the following under your picture: Figure \(\PageIndex{x}\): Major Histocompatability Complex Class II (MHC-II). The figure *Center Picture and Caption together using top menu bar Figure 3 is an interactive iCn3D model of 3DS8, a protein of unknown function Figure 66: [INSERT THE PDB TITLE as above (INSERT PDB CODE)]. Click the image for a popup or use this external link: [INSERT Lifelong short URL from File, Share Link in iCn3D)].  (Copyright; author via source).  iCn3D model made by [YOUR NAME] [ADD MATHEMATIC GRAPH - REUSE] *your text and graph Figure \(\PageIndex{x}\): This is an interactive model for Michaelis-Menten kinetics [ADD VCELL/SBML SIMULATION - REUSE] MODEL Reversible reaction E + S ↔ ES  ↔  EP  ↔  E + P Initial values Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play  |  Select Slider to change which constants are displayed |  Select About  for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health.  Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington.  Funded by NIH/NIGMS (RO1-GM123032-04) *your text before and after insert as needed New Heading Add what you want

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/Chapter_33%3A__Your_Contribution_-_Sandbox/Pam_Mertz_Chapter_33_Test.txt

Authored by [YOUR NAME].  Last update:  [FILL IN DATE] Date of origin [add content] New Heading [ADD TEXT] your text [ADD IMAGE] (saved to your computer and uploaded with picture icon from top menu bar or drag image file to location (required from svg image) *Use the following under your picture: Figure \(\PageIndex{x}\): [Add caption] *Center Picture and Caption together using top menu bar [ADD iCn3D Model] [ADD MATHEMATIC GRAPH - REUSE] Figure \(\PageIndex{x}\): Add caption MODEL Initial Parameters Reaction Parameter Value r0 kf0 kr0 r1 kf1 kf1 r2 kf21 kf21 kf22 kf22 r3 VM KM Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play  |  Select Slider to change which constants are displayed |  Select About  for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health.  Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington.  Funded by NIH/NIGMS (RO1-GM123032-04) [ADD VCELL/SBML SIMULATION - REUSE] *your text before and after insert as needed Figure x� is an interactive iCn3D model of Low molecular weight 1XWW IL Figure 66: Low molecular weight 1XWW. Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...ssdBPpCB3pWSV7  (Copyright; author via source).  iCn3D model made by [YOUR NAME] New Heading Question 1 \(1\) Which does NOT describe sodium dodecyl sulfate (SDS).  SDS .... 1. readily forms bilayers 2. readily form micelles 3. is a single-chain amphiphile 4. has (a) unsaturated acyl chain(s) Here is a hint if you need one! Answer 1. readily forms bilayers. Samantha Wilner Ch 33 Test Authored by Samantha Wilner.  Last update:  June 5, 2023 Date of origin [ADD CONTENT] New Heading [ADD TEXT] [ADD IMAGE] (saved to your computer and uploaded with picture icon from top menu bar or drag image file to location (required from svg image) *Use the following under your picture: Figure \(\PageIndex{x}\): [Add caption] *Center Picture and Caption together using top menu bar Figure \(1\) is an interactive iCn3D model of a hydrolase (3H04) Figure \(1\): 3H04. Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...Eng4pm1AcyxZx8. (Copyright; author via source). iCn3D model made by Samantha Wilner Figure \(2\): First and Second Order Reactions MODEL AerobicGlycolysis Quantitative determinants of aerobic glycolysis identify flux through the enzyme GAPDH as a limiting step. .Shestov AA, Liu X, Ser Z, Cluntun AA, Hung YP, Huang L, Kim D, Le A, Yellen G, Albeck JG, Locasale JW.  eLife , 7/ 2014 , Volume 3 , PubMed ID: 25009227.  Biomodel MODEL1504010000 Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play  |  Select Slider to change which constants are displayed |  Select About  for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health.  Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington.  Funded by NIH/NIGMS (RO1-GM123032-04) Figure \(3\): Aerobic Glycolysis New Heading Add what you want Subhasish-TEST2 Authored by [YOUR NAME].  Last update:  [FILL IN DATE] Date of origin [ADD CONTENT] New Heading [ADD TEXT] [ADD IMAGE] (saved to your computer and uploaded with picture icon from top menu bar or drag image file to location (required from svg image) *Use the following under your picture: Figure \(\PageIndex{x}\): [Add caption] *Center Picture and Caption together using top menu bar [ADD iCn3D Model] [ADD MATHEMATIC GRAPH - REUSE] Figure \(\PageIndex{x}\): Add caption [ADD VCELL/SBML SIMULATION - REUSE] *your text before and after insert as needed New Heading Add what you want Subhasish-test Goal After completing this how-to you will have ... Begin by ... Second Step Then continue with ... What's Next This is what was achieved and what was omitted in this how-to.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/Unit_IV_-_Special_Topics/Chapter_33%3A__Your_Contribution_-_Sandbox/Rico_Acevedo_Testy.txt

In the early 1900's many people thought that protein must be the genetic material responsible for inherited characteristics. One of the reasons behind this belief was the knowledge that proteins were quite complex molecules and therefore, they must be specified by molecules of equal or greater complexity (i.e. other proteins). DNA was known to be a relatively simple molecule, in comparison to proteins, and therefore it was hard to understand how a complex molecule (a protein) could be determined by a simpler molecule (DNA). What were the key experiments which identified DNA as the primary genetic material? 1928 F. Griffith Background: Diplococcus pneumoniae, or pneumococcus, is a nasty little bacteria which, when injected into mice, will cause pneumonia and death in the mouse. The bacteria contains a capsular polysaccharide on its surface which protects the bacteria from host defenses. Occasionally, variants (mutants) of the bacteria arise which have a defect in the production of the capsular polysaccharide. The mutants have two characteristics: 1) They are avirulent, meaning that without proper capsular polysaccharide they are unable to mount an infection in the host (they are destroyed by the host defenses), and 2) Due to the lack of capsular polysaccharide the surface of the mutant bacteria appears rough under the microscope and can be distinguished from the wild type bacteria (whose surface appears smooth). Figure 1.1.1: Wild type vs. Mutant type pneumococcus The virulent smooth wild type pneumococcus can be heat treated and rendered avirulent (still appears smooth under the microscope however). Finally, there are several different subtypes of pneumococcus capsular polysaccharide (subtypes I, II and III). These subtypes are readily distinguishable from one another, and each can give rise to mutants lacking capsular polysaccharide (i.e. the avirulent rough type). The experiments: Controls: • w.t. (smooth) + mouse = dead mouse • mutant (rough) + mouse = live mouse • heat treated w.t. (smooth) + mouse = live mouse Combinations: • heat treated w.t. (smooth) + mutant (rough) + mouse = dead mouse In this case when the bacteria were recovered from the cold lifeless mouse they were smooth virulent pneumococcus (i.e. indistinguishable from wild type). A closer look at what is going on, by keeping using, and keeping track of, different subtypes • heat treated w.t. (smooth) type I + mutant(rough) type II + mouse = dead mouse In this case when the bacteria were isolated from the cold lifeless mouse they were smooth virulent type I pneumococcus. The overall conclusions from these experiments was that there was a "transforming agent" in the the heat treated type I bacteria which transfomed the live mutant (rough) type II bacteria to be able to produce type I capsule polysaccharide. Question Was the "transforming agent" protein or DNA, or what? 1944 O.T. Avery Background: The experiment of Griffith could not be taken further until methods were developed to separate and purify DNA and protein cellular components. Avery utilized methods to extract relatively pure DNA from pneumococcus to determine whether it was the "transforming agent" observed in Griffith's experiments. The experiment: • w.t. (smooth) type I -> extract the DNA component • mutant (rough) type II + type I DNA + mouse = dead mouse Isolation of bacteria from the dead mouse showed that they were type I w.t. (smooth) bacteria A more sophisticated experiment: Purified type I DNA was divided into two aliquots. One aliquot was treated with DNAse - an enzyme which non-specifically degrades DNA. The other aliquot was treated with Trypsin - a protease which (relatively) non-specfically degrades proteins. • Type I DNA + DNAse + mutant (rough) type II + mouse = live mouse • Type I DNA + Trypsin + mutant (rough) type II + mouse = dead mouse Conclusion: The work of Avery provided strong evidence that the "transforming agent" was in fact DNA (and not protein). However, not everyone was convinced. Some people felt that a residual amount of protein might remain in the purified DNA, even after Trypsin treatment, and could be the "transforming agent". 1952 A.D. Hershey and M. Chase Background: T2 is a virus which attacks the bacteria E. coli. The virus, or phage, looks like a tiny lunar landing module: Figure 1.1.2: T2 phage The viral particles adsorb to the surface of the E. coli cells. It was known that some material then leaves the phage and enters the cell. The "empty" phage particles on the surface cells can be physically removed by putting the cells into a blender and whipping them up. In any case, some 20 minutes after the phage adsorb to the surface of the bacteria the bacteria bursts open (lysis) and releases a multitude of progeny virus. If the media in which the bacteria grew (and were infected) included 32P labeled ATP, progeny phage could be recovered with this isotope incorporated into its DNA (normal proteins contain only hydrogen, nitrogen, carbon, oxygen, and sulfur atoms). Likewise, if the media contained 35S labeled methionine the resulting progeny phage could be recovered with this isotope present only in its protein components (normal DNA contains only hydrogen, nitrogen, carbon, oxygen, and phosphorous atoms). The experiment: Phage were grown in the presence of either 32P or 35S isotopic labels. 1) E. coli were infected with 35S labeled phage. After infection, but prior to cell lysis, the bacteria were whipped up in a blender and the phage particles were separated from the bacterial cells. The isolated bacterial cells were cultured further until lysis occurred. The released progeny phage were isolated. Where the 35S label went: • Adsorbed phage shells 85% • Infected cells (prior to lysis) 15% • Lysed cell debris 15% • Progeny phage <1% 2) E. coli were infected with 32P labeled phage. The same steps as in 1) above were performed. Where the 32P label went: • Adsorbed phage shells 30% • Infected cells (prior to lysis) 70% • Lysed cell debris 40% • Progeny phage 30% Conclusion: The material which was being transferred from the phage to the bacteria during infection appeared to be mainly DNA. Although the results were not entirely unambiguous they provided additional support for the view that DNA was the "stuff" of genetic inheritance.

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/1%3A_DNA/1.1%3A_DNA_as_Genetic_Material.txt

The Double Helix DNA (deoxyribonucleic acid) and RNA (ribonucleic acid) are composed of two different classes of nitrogen-containing bases: the purines and pyrimidines. The most commonly occurring purines in DNA are adenine and guanine: Figure 1.2.1: Purines The most commonly occurring pyrimidines in DNA are cytosine and thymine: Figure 1.2.2: Pyramidines RNA contains the same bases as DNA with the exception of thymine. Instead, RNA contains the pyrimidine uracil: Figure 1.2.3: Thymine vs. Uracil Adenine, guanine, cytosine, thymine and uracil are usually abreviated using the single letter codes A, G, C, T and U, respectively. Purines and pyrimidines can form chemical linkages with pentose (5-carbon) sugars. The carbon atoms on the sugars are designated 1', 2', 3', 4' and 5'. It is the 1' carbon of the sugar that becomes bonded to the nitrogen atom at position N1 of a pyrimidine or N9 of a purine. DNA precursors contain the pentose deoxyribose. RNA precursors contain the pentose ribose (which contains an additional OH group at the 2' position): Figure 1.2.4: Nucleosides Before a nucleoside can become part of a DNA or RNA molecule it must become complexed with a phosphate group to form a nucleotide (either a deoxyribonucleotide or ribonucleotide). Nucleotides can posess 1, 2 or 3 phosphate groups, e.g. the nucleotides adenosine monophosphate (AMP), adenoside diphosphate (ADP) and adenosine triphosphate (ATP). The phosphate groups are attached to the 5' carbon of the ribose sugar moiety. Beginning with the phosphate group attached to the 5' ribose carbon, they are labeled a, b and g phosphate. It is the tri-phosphate nucleotide which is incorporated into DNA or RNA. Figure 1.2.5: Nucleotide DNA and RNA are simply long polymers of nucleotides called polynucleotides. Only the a phosphate is included in the polymer. It becomes chemically bonded to the 3' carbon of the sugar moiety of another nucleotide: Figure 1.2.6: Polynucleotide In other words, the polynucleotide is connected by a series of 5' to 3' phosphate linkages. Note the sequence of the bases in the above diagram. Polynucleotide sequences are referenced in the 5' to 3' direction. Typically, polynucleotides will contain a 5' phosphate and 3' hydroxyl terminal groups. The common representation of polynucleotides is as an arrow with the 5' end at the left and the 3' end at the right. Summary of terms: Base Nucleoside Nucleotide RNA (monophosphate) DNA (monophosphate) Code Adenine Adenosine (Adenylic acid) AMP dAMP A Guanine Guanosine (Guanylic acid) GMP dGMP G Cytosine Cytidine (Cytidylic acid) CMP dCMP C Thymine Thymidine (Thymidylic acid) dTMP T Uracil Uridine (Uridylic acid) UMP U What is the structure of DNA? How is the structure related to function? 1950's The primary chemical structure of polynucleotides was known (i.e. the 3'-5' phosphate linkage). 1951 E. Chargaff The experiment: Take DNA from a variety of species and hydrolyze it to yield individual pyrimidines and purines. Determine the relative concentrations of the A, T, C and G bases. Result: Although different species had uniquely different ratios of pyrimidines or purines, the relative concentrations of adenine always equaled that of thymine, and guanine equaled cytosine. Chargaff's Law: A=T, G=C 1950's R.E. Franklin X-ray diffraction studies of DNA fibers demonstrated that DNA adopted a highly ordered helical structure. Franklin concluded that two or more chains must coil around each other to form a helix. Some basic dimensions of the helix were calculated from the x-ray diffraction data. 1953 L. Pauling and R.B. Corey Propose a three chain helical structure for DNA with the phosphate backbone in the center and the bases on the outside. 1953 J.D. Watson and F.H.C. Crick Identified a hydrogen bonding arrangement between models of thymine and adenine bases, and between cytosine and guanine bases which fullfilled Chargaff's rule: Figure 1.2.7: Chargaff's Rule Bonding Note that the "TA" pair can overlay the "GC" pair with the bonds to the sugar groups in similar juxtaposition. In the "double helix" model of Watson and Crick the polynucleotide chains interact to form a double helix with the chains running in opposite directions. The bases are directed towards the center (and stack on top of one another) and the sugar backbones face the outside of the helix. The Watson and Crick model had the following physical dimensions: • 34 Å per helical repeat • 10 base pairs per repeat (i.e. per turn of the helix) • 3.4 Å inter-base stacking distance • 20 Å diameter for the helical width Physical characteristics of the model matched those determined by Rosalind Franklin's x-ray diffraction studies. Consequenses of the model for genetic information: The Watson and Crick paper was an exercise in brevity (1 page only in Nature). The structure was so rich with implication that quite a bit could be written. The authors, however, chose only to say "It has not escaped our notice that the specific pairing we have postulated immediately suggests a possible copying mechanism for the genetic material". 1. If G always paired with C, and T always paired with A, then either strand could be regenerated from the complementary information in the other strand. 2. The basis of the complementarity was hydrogen bonding, i.e. non-covalent interactions which could be easily broken and re-formed. 3. The information which DNA carried was within the unique base sequence of the DNA. 4. From the general interior location of the bases, it would appear that the double helix would have to dissociate in order to access the information. 5. The non-equitorial location of the sugar moieties (see above) suggested that the DNA helix would have a major groove and a minor groove. General notation of double stranded DNA:

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/1%3A_DNA/1.2%3A_Structure_of_DNA_and_RNA.txt

The restriction/modification system in bacteria is a small-scale immune system for protection from infection by foreign DNA. W. Arber and S. Linn (1969) Plating efficiencies of bacteriophage lambda (l phage) grown on E. coli strains C, K-12 and B, when plated on these bacteria: E. coli strain on which parental phage had been grown E. coli strain for plating phage C K-12 B C 1 <10-4 <10-4 K-12 1 1 <10-4 B 1 <10-4 1 • The DNA of phage which had been grown on strains K-12 and B were found to have chemically modified bases which were methylated. • Additional studies with other strains indicate that different strains had specific methylated bases. • Typical sites of methylation include the N6 position of adenine, the N4 position of cytosine, or the C5 position of cytosine. Figure 1.3.1: Methylation • In addition, only a fractional percentage of bases were methylated (i.e. not every adenine was methylated, for example) and these occurred at very specific sites in the DNA. • A characteristic feature of the sites of methylation, was that they involved palindromic DNA sequences. Figure 1.3.2: EcoR1 methylase specificity. Rubin and Modrich, 1977 • In addition to possessing a particular methylase, individual bacterial strains also contained accompanying specific endonuclease activities. • The endonucleases cleaved at or near the methylation recognition site. Figure 1.3.3: Cleavage at methylation sites • These specific nucleases, however, would not cleave at these specific palindromic sequences if the DNA was methylated. Thus, this combination of a specific methylase and endonuclease functioned as a type of immune system for individual bacterial strains, protecting them from infection by foreign DNA (e.g. viruses). • In the bacterial strain EcoR1, the sequence GAATTC will be methylated at the internal adenine base (by the EcoR1 methylase). • The EcoR1 endonuclease within the same bacteria will not cleave the methylated DNA. • Foreign viral DNA, which is not methylated at the sequence "GAATTC" will therefore be recognized as "foreign" DNA and will be cleaved by the EcoR1 endonuclease. • Cleavage of the viral DNA renders it non-functional. Such endonucleases are referred to as "restriction endonucleases" because they restrict the DNA within the cell to being "self". The combination of restriction endonuclease and methylase is termed the "restriction-modification" system. Of course, this type of protective system is beaten if the attacking phage was previously grown on the same strain as that which it is infecting. In this case the phage will have its DNA already methylated at the appropriate sequence, and will be recognized as "self" (see the table above). E. coli strain 'C' (above) is strain which has no known restriction-modification system. We will discuss DNA replication later, but it should be mentioned that: • replicating host DNA will initially have one strand (parental) methylated and the other (nascent strand) non-methylated. • This is recognized as "self" and is not cleaved by the restriction endonuclease. • It is subsequently methylated by the host methylase. Structural and biochemical studies have indicated that for the common R/M systems (so called type II), the methylase recognizes and methylates one strand of the DNA duplex, whereas the restriction endonuclease recognizes both strands of the DNA (i.e. both strands must be non-methylated for recognition). It is able to do this because it is a homo-dimer protein. Restriction endonucleases Since different bacterial strains and species have potentially different R/M systems, their characterization has made available over 200 endonucleases with different sequence specific cleavage sites. • They are one of the primary tools in modern molecular biology for the manipulation and identification of DNA sequences. • Restriction endonucleases are commonly named after the bacterium from which it was isolated. Examples of different restriction enzymes Name Source Recognition Sequence Comments Alu I Arthrobacter luteus ``` | 5'… A G C T … 3' 3'… T C G A … 5' | ``` "Four cutter". Leaves blunt ends to the DNA. Bfa I Bacteroides fragilis ``` | 5'… C T A G … 3' 3'… G A T C … 5' | ``` "Four cutter". Leaves 5' overhang. Nci I Neisseria cinerea ``` | C 5'… C C G G G … 3' 3'… G G C C C … 5' G | ``` "Five cutter". Middle base can be either cytosine or guanine. Leaves 5' overhang. Different recognition sites may have non-complementary sequences. Eco R1 Escherichia coli ``` | 5'… G A A T T C … 3' 3'… C T T A A G … 5' | ``` "Six cutter". Leaves 5' overhang. Behaves like a "four cutter" ('star' activity) in high salt buffer. \$44 for 10,000 units. Hae II Haemophilus aegyptius ``` | 5'… Pu G C G C Py … 3' 3'… Py C G C G Pu … 5' | ``` "Six cutter". Pu is any purine, Py is any pyrimidine. Leaves 3' overhang. EcoO109I Escherichia coli ``` | 5'… Pu G G N C C Py … 3' 3'… Py C C N G G Pu … 5' | ``` "Seven cutter". Pu is any purine, Py is any pyrimidine, N is any base. Leaves 5' overhang. Different recognition sites may have non-complementary sequences. Bgl I Bacillus globigii ``` | 5'… GCCN NNNNGGC … 3' 3'… CGGNNNN NCCG … 5' | ``` "Six cutter with interrupted palindrome". Leaves 5' overhang. Different recognition sites may have non-complementary sequences. Bsa HI Bacillus stearothermophilus ``` | 5'… G Pu C G Py C … 3' 3'… C Py G C Pu G … 5' | ``` "Six cutter". Different recognition sites will be complementary. Aat II Acetobacter aceti ``` | 5'… G A C G T C … 3' 3'… C T G C A G … 5' | ``` "Six cutter" with 3' overhang. Same recognition sequence as Bsa HI, but different cleavage position. Bpm I Bacillus pumilus ``` | 5'… C T G G A G N16 … 3' 3'… G A C C T C N14 … 5' | ``` Non-palindrome, distal cleavage. Leaves 3' overhang. \$50 for 50 units. Not I Nocardia otitidiscaviarum ``` | 5'… G C G G C C G C … 3' 3'… C G C C G G C G … 5' | ``` "Eight cutter". Leaves 5' overhang. Bsm I Bacillus stearothermophilus ``` | 5'… G A A T G C N … 3' 3'… C T T A C G N … 5' | ``` "weird". Leaves 3' overhang. • The utility of restriction endonucleases lies in their specificity and the frequency with which their recognition sites occur within any given DNA sample. • If there is a 25% probability for a specific base at any given site, then the frequency with which different restriction endonuclease sites will occur can be easily calculated (0.25n): Nucleotide Specificity Example Frequency of Occurrence Four Alu I 256 (0.25 Kb) Five Nci I 1024 (1.0 Kb) Six EcoR I 4096 (4.1 Kb) Seven EcoO109I 16384 (16.4 Kb) Eight Not I 65536 (65.5 Kb) Thus, on average, any given DNA will contain an Alu I site every 0.25 kilobases, whereas a Not I site occurs once about every 65.5 kilobases. • Not I is therefore a very useful enzyme for isolating large regions of DNA, typically in research involving genomic DNA manipulations. • Alu I would be expected to digest a DNA sample into lots of little pieces. The assortment of DNA fragments would represent a specific "fingerprint" of the particular DNA being digested. Different DNA would not yield the same collection of fragment sizes. Thus, DNA from different sources can be either matched or distinguished based on the assembly of fragments after restriction endonuclease treatment. These are termed "Restriction Fragment Length Polymorphisms", or RFLP's. This simple analysis is used in various aspects of molecular biology as well as a law enforcement and genealogy. For example, genetic variations which distingish individuals also may result in fewer or additional restriction endonuclease recognition sites. Restriction endonucleases are supplied in various concentrations with activities that are based upon cleavage rates of "standard" DNA samples. • One unit of activity is typically defined as the amount of enzyme required to digest (or "restrict") one microgram of reference DNA in one hour at 37 °C. • The reference DNA may actually have one or more recognition sites for the nuclease in question. DNA's used as "standard" samples may include phage l DNA, or the plasmid pBR322. • The endonuclease hydrolysis is a spontaneous reaction and does not, for example, require addition of ATP. Reaction buffers for restriction endonucleases usually contain a buffer component (typically 10 mM TRIS buffer around pH 8.0), magnesium salt (often 10 mM MgCl2), a reducing agent (usually 1mM dithiothreitol, or DTT), a protective carrier protein (typically 100 ug/ml bovine serum albumin, or BSA), and salt (sodium chloride). • The biggest determinant of enzyme activity is typically the ionic concentration (NaCl content) of the buffer. Although there are hundreds of different restriction endonucleases, the majority of them can exhibit between 30-100% activity using a simple system of three buffers, containing either low (20 mM), medium (100 mM) or high (250 mM) salt (NaCl) concentrations in the above described buffer. Enzyme digests are typically performed for 1-2 hours at 37 °C. However, quantitative digestion can sometimes only be achieved after extended incubation (i.e. overnight).

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/1%3A_DNA/1.3%3A_Bacterial_Restriction_Modification_system.txt

Methylases Just as the study of the bacterial restriction-modification system has provided a variety of specific endonucleases, there are also available a variety of specific DNA methylases. • The recognition sequences of the methylases are the same as the associated endonucleases (e.g. EcoR1 methylase recognizes and methylates at the sequence "GAATTC"). • All methylases transfer the methyl group from S-adenosylmethionine (SAM) to a specific base in the recognition sequence, and SAM is a required component in the methylation reaction. • Methylation of DNA usually has the effect of protecting the DNA from the related restriction endonuclease. However, there are methylases with minimal specificity. For example, Sss I methylase will methylate cytosine residues in the sequence 5' … CG … 3'. In this case, the methylated DNA will be protected from a wide variety of restriction endonucleases. • Some restriction endonucleases will only cut DNA at their recognition sites if the DNA is methylated (e.g. Dpn I). • Still other restriction endonucleases will cut both methylated and non-methylated DNA at their recognition sequences (e.g. BamH I). dam and dcm Methylation • The methylase encoded by the dam gene (dam methylase) transfers a methyl group from SAM to the N6 position of the adenine base in the sequence 5' … GATC … 3'. • The methylase encoded by the dcm gene (dcm methylase) methylates the internal cytosine base, at the C5 position, in the sequences 5' … CCAGG … 3' and 5' … CCTGG … 3'. • Almost all strains of E. coli commonly used in cloning have a dam+dcm+ genotype. The point here is not that we particularly want our DNA to be methylated, but that to make a dam-dcm- host someone has to mutate the bacteria and isolate the correct mutant. That apparently has not been done for a lot of bacterial strains. Probably because the dam and dcm methylation affects only a small subset of potential restriction endonucleases DNA isolated from dam+dcm+ strains will not actually be cut by a modest subset of available restriction endonucleases: Recognition sequence Restriction enzyme GATC GmeATC TGATCA Bcl I + - GATC Mbo I + - ATCGAT Cla I + - TCTAGA Xba I + - TCGA Taq I + - GAAGA Mbo II + - GGTGA Hph I + - DNA may have to be prepared from E. coli strains which are dam-dcm- in order to be cut by these enzymes. DNA Polymerases A wide variety of polymerases have been characterized and are commercially available. All DNA polymerases share two general characteristics: 1. They add nucleotides to the 3'-OH end of a primer 2. The order of the nucleotides in the nascent polynucleotide is template directed Figure 1.4.1: DNA Replication In addition to the 5'->3' polymerase activity, polymerases can contain exonuclease activity. This exonuclease activity can proceed either in the 5'->3'direction, or in the 3'->5' direction. • Exonuclease activity in the 3'->5' direction allows the polymerase to correct a mistake if it incorporates an incorrect nucleotide (so called "error correction activity"). It can also slowly degrade the 3' end of the primer. • Exonuclease activity in the 5'->3' direction will allow it to degrade any other hybridized primer it may encounter. Without 5'->3' exonuclease activity, obstructing primers may or may not be physically deplaced, depending on the polymerase being used. Different polymerases have differing error rates of misincorporation, and different rates of polymerization. E. coli DNA polymerase I E. coli DNA polymerase I - Klenow Fragment T4 DNA polymerase T7 DNA polymerase Taq DNA polymerase M-MuLV Reverse Transcriptase 5'->3' exonuclease activity * * 3'->5' exonuclease activity * * * * Error Rate (x10-6) 9 40 <1 15 285 Strand Displacement * Heat Inactivation * * * * Uses of polymerases The various activities of the different polymerases lend them to a variety of applications. For example, restriction endonucleases can yield fragments of DNA with either 3' or 5' nucleotide "overhangs". • In the case of 5' overhangs, the 5'->3' polymerase activity can fill these in to make blunt ends. • In the case of 3' overhangs, the 3'->5' exonuclease activity present in some polymerases (especially T4 DNA polymerase) can "chew back" these ends to also make blunt-ended DNA fragments. Figure 1.4.2: Polymerase activity "Nick-translation" This method is used to obtain highly radiolabeled single strand DNA fragments, which makes use of 5'->3' exonuclease activity present in some polymerases (E. coli DNA polymerase I, for example). • In this method a DNA duplex of interest is "nicked" (i.e. one of the strands is cut; see DNAse I). • Then DNA pol I is added along with radiolabeled nucleotides. The 5'->3' exonuclease activity chews away the 5' end at the "nick" site and the polymerase activity incorporates the radiolabeled nucleotides. The resulting polynucleotide will be highly radiolabeled and will hybridize to the DNA sequence of interest. Figure 1.4.3: Nick-translation • Thermostable polymerases have the ability to remain functional at temperature ranges where the DNA duplex will actually "melt" and become separated. This has allowed the development of the "Polymerase Chain Reaction" technique (PCR), which has had a profound impact on modern Biotechnology. We will discuss this method at a later date. • The incorporation of dideoxy bases (i.e. no hydroxyl groups on either the 2' or 3' carbon of the ribose sugar) leads to termination of the polymerase reaction. This will be discussed in greater detail later. However, this chain termination by incorporation of dideoxynucleotides is the basis of the Sanger method of DNA sequencing, as well as therapies to try to inhibit viral replication. Nucleases Nuclease BAL-31 • This is an exonuclease (starts at the termini and works inward) which will degrade both 3' and 5' termini of double-stranded DNA. It will not make internal cleavages ("nicks"), however, it will degrade the ends of DNA at existing internal "nicks" (which create both 3' and 5' termini). • The degradation of termini is not coordinated, meaning that the product is not 100% blunt-ended (even though the original duplex may have been blunt ended). • Such "ragged" ends can be made blunt by filling in and chewing back by a suitable polymerase (e.g. T4 DNA polymerase). The unit definition of 1 unit is the amount of enzyme required to remove 200 base pairs from each end of duplex DNA in 10 minutes at 30 °C. Figure 1.4.4: Nuclease BAL-31 activity Exonuclease III • Catalyzes the stepwise removal of nucleotides from the 3' hydroxyl termini of duplex DNA. • The enzyme will attack the 3' hydroxyl at duplex DNA with blunt ends, with 5' overhangs, or with internal "nicks". • Since duplex DNA is required, the enzyme will not digest the 3' end of duplex DNA where the termini are 3' overhangs. Figure 1.4.5: Exonuclease III Activity Mung Bean Nuclease (isolated from mung bean sprouts) • A single strand specific DNA and RNA endonuclease which will degrade single strand extensions from the ends of DNA and RNA leaving blunt ends. • The single strand extensions can be either 5' or 3' extensions - both are removed and a blunt duplex is left. Figure 1.4.6: Mung Bean Nuclease activity Deoxyribonuclease I (DNAse I) from Bovine pancrease • This enzyme hydrolyzes duplex or single DNA strands preferentially at the phosphodiester bonds 5' to pyrimidine nucleotides • In the presence of Mg2+ ion, DNAse I attacks each strand independently and produces nicks in a random fashion (useful for nick-translation) • In the presence of Mn2+ ion DNAse I cleaves both strands of DNA at approximately the same position (but leaving "ragged" ends) Ligases • Ligases catalyze the formation of a phosphodiester bond between juxtaposed 5' phosphate and 3' hydroxyl termini of nucleotides (potentially RNA or DNA depending on the ligase). • In a sense, they are the opposite of restriction endonucleases, but they do not appear to be influenced by the local sequence, per se. • Ligases require either rATP or NAD+ as a cofactor, and this contrasts with restriction endonucleases. The following are different types of ligases and their characteristics. T4 DNA ligase • Isolated from bacteriophage T4. • Will ligate the ends of duplex DNA or RNA. • This enzyme will join blunt-end termini as well as ends with cohesive (complementary) overhanging ends (either 3' or 5' complementary overhangs). • This enzyme will also repair single stranded nicks in duplex DNA, RNA or DNA/RNA duplexes. Requires ATP as a cofactor. Taq DNA ligase • This ligase will catalyze a phosphodiester bond between two adjacent oligonucleotides which are hybridized to a complementary DNA strand: Figure 1.4.7: Taq DNA ligase activity • The ligation is efficient only if the oligonucleotides hybridize perfectly with the template strand. • The enzyme is active at relatively high temperatures (45 - 65 °C). Requires NAD+ as a cofactor. T4 RNA ligase • Will catalyze formation of a phosphodiester bond between RNA/RNA oligonucleotides, RNA/DNA oligonucleotides, or DNA/DNA oligonucleotides. • Requires ATP as a cofactor. • This enzyme does not require a template strand. T4 RNA ligase can be used for a variety of purposes including constructing RNA/DNA hybrid molecules. Figure 1.4.8: T4 RNA ligase activity DNA ligase (E. coli) • Will catalyze a phosphodiester bond between duplex DNA containing cohesive ends. • It will not efficiently ligate blund ended fragments. • Requires NAD+ as a cofactor. Figure 1.4.9: DNA ligase (E. Coli) activity T4 polynucleotide kinase • Catalyzes the transfer and exchange of a phosphate group from the g position of rATP (adenine ribose triphosphate nucleotide) to the 5' hydroxyl terminus of double stranded and single stranded DNA or RNA, and nucleoside 3' monophosphates. • The enzyme will also remove 3' phosphoryl groups. • Oligonucleotides which are obtained from automated synthesizers lack a 5' phosphate group, and thus, cannot be ligated to other polynucleotides. T4 polynucleotide kinase can be used to phosphorylate the 5' end of such polynucleotides: Figure 1.4.10: T4 polynucleotide kinase activity Calf intestinal phosphatase (CIP) • Catalyzes the removal of 5' phosphate groups from RNA, DNA and ribo- and deoxyribo- nucleoside triphosphates (e.g. ATP, rATP). • CIP treated duplex DNA cannot self ligate. • Hemi-phosphorylated duplexes will be ligated on one strand (the phosphorylated strand) and remain "nicked" on the other. Figure 1.4.11: CIP activity

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/1%3A_DNA/1.4%3A_DNA_Modifying_Enzymes.txt

1953 • The DNA model of Watson and Crick suggested how genetic information might be replicated: either strand of the duplex can be used as a template to replicate the sequence information. • But, was the replication conservative (i.e. the original parental strands remain together after replication) or semi-conservative(one parental strand pairs with one newly synthesized strand)? The answer for prokaryotic organisms (i.e. lack a true membrane bound nucleus and cellular organelles; e.g. bacteria) came from the 1958 experiment of Meselson and Stahl. 1958 The nitrogen in ammonium salts in culture broth is incorporated into DNA bases. The most common isotope of nitrogen is 14N. However, 15N ammonium salts (a heavier isotope) can also be obtained. • DNA from E. coli cells grown with 15N ammonium salts will have a higher density than DNA grown in "normal" (14N) ammonium salts. • Such DNA will migrate differently on cesium chloride (CsCl2) equilibrium density gradient centrifugation. • The more dense DNA will migrate as a lower band (on this type of centrifugation the characteristic migration position is a function of density, and is independent of DNA length). Figure 1.5.1: Density of 15N and 14N DNA Meselson and Stahl reasoned that if they grew E. coli in 15N salts then switched media to 14N salts for additional rounds of replication, the mode of replication could be deduced from the density of the DNA. Figure 1.5.2: Meselson and Stahl experiment • After switching to the 14N media and allowing the cells to go through a round of replication a single band of intermediate density was observed (i.e. between 14N and 15N control DNA samples). • After a second round of replication in 14N media two bands were present in approximately equimolar amounts; one was intermediate in density and the other migrated as purely 14N labeled DNA. The results were consistent with a semi-conservative mode of replication for DNA. Evidence of semi-conservative replication of DNA has since been obtained with both plant and animal DNA. DNA Replication in E. coli E. coli DNA polymerase characteristics: • Polymerase will only elongate an existing polynucleotide. It cannot initiate polynucleotide formation: Figure 1.5.3: DNA Polymerase activity • Polymerase will catalyze polymerization of nucleotides only in one direction (5'­>3') via a phosphodiester bond between a 3' hydroxyl and 5' phosphate group. • DNA polymerase is unable to unwind duplex DNA to separate the two strands which need to be copied E. coli genome is circular duplex DNA of approximately 4 x 106 base pairs (i.e. 4 Mb) • The genome has a single origin of replication. • DNA duplication in E. coli begins at a specific site in the DNA called "oriC". OriC is a region of DNA approximately 240 nucleotides long. • It contains repetitive 9-base pair and 13-base pair sequences (known as the '9-mer' and '13-mer' regions). • These sequences are AT rich regions, which melt at lower temperatures than DNA containing GC pairs. • These regions are postulated to help melt the DNA duplex in the oriC region for initiation of DNA replication. Figure 1.5.4: oriC region The dnaA gene product: (dnaA protein) • Strains of E. coli with mutations in the dnaA gene were able to grow at 30 °C, but not at 39-42 °C. • However, if DNA synthesis was begun at 30 °C, and then the temperature was shifted to 42 °C, DNA synthesis continued until the genome was replicated (and the cell divided), but no new initiationof DNA synthesis was possible. Conclusion: Somehow the product of the dnaA gene (i.e. the dnaA protein) is required for initiation of DNA synthesis. Studies of purified dnaA protein: • dnaA protein binds to the '9-mer' region in oriC and forming a multimeric complex with 10-20 protein subunits (i.e. at a single oriC region there will be bound 10-20 dnaA protein molecules). • Binding requires ATP. • Further addition of ATP was observed to result in a melting and opening up of the DNA duplex in the oriC region. This was determined by addition of S1 nuclease (like mung bean, but will also cut DNA at the site of an internal nick), which resulted in cleavage of DNA at the site of oriC. The dnaB gene product: (dnaB protein) The protein encoded by the dnaB gene appears to be essential for DNA replication. The dnaB protein has been identified as ahelicase. A helicase moves along a DNA strand opening up the duplex to melt and separate the DNA strands. • dnaB protein binds to the single stranded DNA in the general region of the oriC DNA segment. • Binding requires ATP as well as the dnaC gene product (the dnaC protein). • After helicase/dnaC binds to the DNA, the dnaC protein is released. • Two helicases bind at the oriC region, one helicase on each strand of the DNA. This stage represents the prepriming complex: Figure 1.5.5: Prepriming complex Separated strands in the oriC region are prevented from reannealing by the binding of single-stranded binding protein (ssb protein). The dnaG gene protein: The dnaG gene protein is called primase. • Primase catalyzes synthesis of short RNA molecules that function as primers for DNA synthesis by E. coli DNA polymerase III(pol III). • Primase binds to dnaB protein at oriC and forms a primosome. • The primase within the primosome complex provides RNA primers for synthesis of both strands of duplex DNA. • Primase lays down tracks of pppAC(N)7-10 (RNA). Figure 1.5.6: Primase activity • After synthesis of the 9-12 mer RNA primer, DNA Pol III holoenzyme enters the replication fork and is able to utilize the RNA as a primer for DNA synthesis. • As the replication fork opens up, the leading strand synthesis can continue, but a gap develops in the lagging strand: Figure 1.5.7: Lagging Strand DNA Pol III is a large multicomplex enzyme (holoenzyme) which is somewhat dimeric in nature (there are two polymerase active sites). The two active polymerase sites in Pol III could actually function to synthesize both nacent strands at the fork. However, the synthesis of the lagging template strand would be in the opposited direction to the movement of the Pol III complex: Figure 1.5.8: Pol III movement Primase can bind to the Pol III complex, but the arrangement of the DNA strand as it passes through the Pol III/primase complex is quite unique. It forms a loop structure such that primase and the Pol III active site can accomplish discontinuous synthesis of the lagging template strand even though the general direction of the Pol III complex is opposite to the require direction of DNA synthesis: Figure 1.5.9: Loop for Pol III activity After primase makes another primer on the lagging template, the adjacent Pol III active site can extend the primer (incorporating dNTP's) by utilizing the same loop structure and feeding the template through in the direction shown. Figure 1.5.10: Synthesis of lagging strand The lagging strand loop cannot be fed through the Pol III complex forever, and after a nascent DNA strand is synthesized the loop is released and a new one is formed using the opened template DNA further up the fork: Figure 1.5.11: Continuation of lagging strand synthesis As synthesis continues: • there will be a single continuous DNA strand on the leading strand • there will be a series of short fragments on the lagging strand, containing both RNA and DNA, called Okazaki fragments: Figure 1.5.12: Okazaki Fragments How are these RNA/DNA fragments converted into one long continuous DNA strand? The RNA could be removed by a polymerase which has 5'->3' exonuclease activity, however, Pol III lacks this activity. • DNA Pol I does have 5'->3' exonuclease activity • it can extend the DNA synthesis via nick-translation. • The nick-translation activity restults in degradation of the RNA primers. • The end result is a series of "nicks" in the lagging strand, now 100% DNA: Figure 1.5.13: Nicks in lagging strand • DNA Pol I leaves and DNA ligase then joins these discontinuous DNA fragments to form a continuous DNA duplex on the lagging strand. Summary of steps in E. coli DNA Synthesis 1. dnaA protein melts duplex in oriC region. 2. dnaB (helicase), along with dnaC and ATP binds to replication fork (dnaC protein exits).1 (Pre-priming complex) 3. Single strand binding protein (ssb protein) binds to separated strands of DNA and prevents reannealing. 4. Primase complexes with helicase, creates RNA primers (pppAC(N)7-10) on the strands of the open duplex2 (Primase+helicase constitute the Primosome). 5. After making the RNA primers, DNA pol III holoenzyme comes in and extends the RNA primer (laying down dNTP's) on the leading strand. 6. As the replication fork opens up (via helicase + ATP action) leading strand synthesis is an uninterrupted process, the lagging strand experiences a gap. 7. The gap region of the lagging strand can wind around one active site unit of the Pol III complex, and bound Primase initiates an RNA primer in the gap region3. 8. On the lagging strand, Pol III extends the RNA primer with dNTP's as the lagging template strand is looped through the Pol III complex 9. After synthesis of a nascent fragment the lagging strand loop is released and the single strand region further up near the replication fork is subsequently looped through the Pol III complex. 10. Steps 7-9 are repeated. 11. Meanwhile, Pol I removes the RNA primer regions of the Okazaki fragments via 5' to 3' exonuclease activity ( nick translation 12. Pol I exits and ligase joints the DNA fragments (on lagging strand). Notes From Above 1. Polymerases are unable to open up duplex DNA, thus the requirement for helicase 2. Polymerases cannot replicate a DNA template in the absence of a primer (either DNA or RNA). 3. Polymerases extend a polynucleotide in the 5' to 3' direction only. Gaps at the 5' end must be filled by "upstream" discontinuous synthesis. Properties of E. coli polymerases (Pol I, II and III) DNA Pol I DNA Pol II DNA Pol III 5'->3' Polymerase activity * * * 3'->5' Exonuclease activity(proof reading) * * * 5'->3' Exonuclease activity(nick translation) * Synthesis from: Duplex DNA Primed single strand * Primed single strand plus ssb protein * * Chain elongation rate(in vitro) bp/min 600 ? 30,000 Molecules/cell 400 ? 10-20 Mutation Lethal? * * Pol Functions: • Pol I: gap filling during DNA synthesis and repair, removal of RNA primers • Pol II: involved in DNA synthesis of damaged templates • Pol III: functional polymerase at the replication fork Pol III Structure and function • A "holoenzyme" complex of 10 different polypeptides • resultant molecular weight is greater than 600 KDa (i.e. it is a large complex). • It is structurally an asymmetric dimer - it contains two copies of most of the polypeptides which comprise it, including two catalytic sites for nucleotide addition (i.e. polymerization). The various protein subunits have a variety of functions: 1. Subunits for polymerase activity: a, e, subunits 2. Subunits to dimerize the core polymerase (t) 3. Subunits to increase processivity (i.e. to increase the ability to synthesize long stretches w/o releasing from the DNA template): b subunits 4. Subunits to bind b to DNA-primer substrate: (g, d, d', c, ) Termination of DNA replication • Specific termination sites of DNA replication exist in E. coli. • Termination involves the binding of the tus gene product (tus protein). • This protein may act to prevent helicase from unwinding DNA (will therefore halt pol III and pol I action). • DNA replication produces two interlocking rings which must be separated. • This is accomplished via the enzyme topoisomerase. ColE1 Plasmid E. coli can contain a small extrachromosomal element called the ColE1 plasmid. This plasmid has the following general features: • 6.4 Kb circular duplex DNA • Autonomously replicating • 10-15 copies per cell (i.e. per E. coli chromosome) Although it is autonomously replicating, it does not contain an oriC type of sequence for initiation of replication, and does not undergo the same steps in replication. • The plasmid produces (among other things) two RNA oligonucleotides (RNA I and RNA II) • RNA II has complementarity to the ColE1 origin of replication, which contains an AT rich sequence • The bound RNA II molecule can serve as a primer for polIII • Since RNA II binds on one strand only, the replication of the ColE1 plasmid is unidirectional • RNA I has complementarity with RNAII, and such a duplex RNA cannot serve as a replication primer, thus control of the plasmid copy number is achieved by the interaction between RNA I and RNA II.

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/1%3A_DNA/1.5%3A_DNA_Replication_-_Introduction_to_Prokaryotic_replication.txt

Unwinding of the helix during DNA replication (by the action of helicase) results in supercoiling of the DNA ahead of the replication fork. • This supercoiling increases with the progression of the replication fork. • If the supercoiling is not relieved, it will physically prevent the movement of helicase. The topology of DNA can be described by three parameters: 1. Linking Number (L) An integer value. "Positive" is referenced as right-handed. 2. Twist (T) A real number (the "apparent" linkage number) 3. Writhe (W) A real number ("supercoils" in the DNA structure) Consider closed circular DNA: • Linking number is an integer value. • It refers to the number of times the two strands of the duplex make a complete 360 degree turn. For circularly closed DNA, like the E. coli genome, the linking number can only be changed if we do the following: 1. physically break the duplex 2. introduce (or remove) a 360 degree turn 3. ligate (covalently close) the break. Figure 1.6.1: Turns to change the linking number Rubber tubing "helix" experiment Cut two lengths of 1/8" rubber tubing, each about 20" long. Insert a smaller piece of tubing, or piece of pipette tip in the ends to allow the ends to be connected. These two pieces of rubber tubing represent each strand of a DNA duplex. DNA can be ligated, or joined, when we have 5' (phosphate) and 3' (hydroxyl) ends. So we need a way to keep track of which end is which for each piece of tubing. 1. You can either write "5" and "3" on opposite ends of each piece and align them in opposite directions, or 2. On one piece of tubing, mark both ends with a sharpie. Only similarly colored ends can be ligated (see diagram above) This will allow you to maintain correct strand "orientation" when you "ligate" the strands of the duplex. • Introduce a Linking number = +2 (two 360° right handed twists into the duplex) • then "ligate" the ends (make sure you maintain strand orientation, i.e. you connect the appropriate two strands). Confirm that the correct linking number has been introduced by "melting" the duplex on one side and forcing all turns into a small region of the duplex (easy to count this way). • Confirm that looking down the helix at the turns that they are "right handed" (does not matter which way you look down the helix). Note that the "duplex" when held between thumb and forefinger and allowed to hang, prefers a "supercoiled" topology, as opposed to "relaxed" [Note: this is usually seen with very skinny tubing, larger diameter tubing may not readily adopt this topology]. • Confirm that the "supercoils" are actually left handed as you look down the supercoils (regardless of direction down the supercoils). • The "supercoil" is most likely a full 360°, rather than 180°. In any case, hold the ends of the duplex so that a left handed 360° "supercoil" is present. • Now, count how many times the strands of the "duplex" cross each other. In this conformation, the strands of the "duplex" will not actually cross each other (Note: you may have one strand crossing and then later uncrossing, for a net result of no crossing). Thus, in response to the introduction of +2 Linking number, the "duplex" can adopt +2 (180°) "supercoils", such that the resulting apparent linkage number (i.e. "twist" value) of the two strands is zero Note A supercoil is considered positive, if it is "left handed"). • Remove one "positive" supercoil by unwinding by 180°. • Now hold the ends of the "duplex" and count the apparent linkage number (i.e. "twists"). There will be a single right-handed "twist". • Thus, a single 180° "positive" supercoil has the effect of removing a single "positive" twist (i.e. reduces the apparent linkage number by 1). The Writhe number refers to the number of supercoils present. • Although it may seem that the consequence of introducing supercoiling (Writhe) is changing the Linking number, it is not. • The consequence of Writhe is that the Twist (apparent linkage number) is altered (increased or decreased). DNA has a preferred "Twist" value (preferred apparent linkage number) for a specified length of DNA: • Watson and Crick's model of the DNA duplex had 10 basepairs per turn. • Under physiological conditions of salt (0.15 M NaCl) and temperature, DNA prefers to adopt about 10.6 bp/turn. • Writhe is introduced in the DNA to achieve this value for the "Twist" (apparent linkage number) For a given (fixed) Linkage number over a given length of DNA, the DNA can adopt either positive or negative supercoils to achieve a "twist" (apparent linkage number) such that there will be 10.6 basepairs/turn. Linkage number does not change with supercoiling (it can only change by breaking the duplex) • Writhe has the effect of changing the apparent Linkage number. • One supercoil is defined as being able to change the apparent linkage number by +/- 1. The twist value (apparent linkage number) for a given length of DNA is related to the number of base pairs per turn that the DNA wants to adopt: Linkage Number = (size of DNA in base pairs)/(basepairs/turn) + Writhe or Linkage Number = #of Twists + Writhe this is usually abbreviated as Linkage = Twist + Writhe L = T + W For example, if we have a circularly closed DNA molecule with a length of 5300 base pairs, and a preferred conformation of 10.6 basepairs per turn, can it achieve this conformation without having to introduce any supercoiling (i.e. writhe)? Apparent linkage number (Twist) = (5300 base pairs) / (10.6 base pairs/turn) Apparent linkage number (Twist) = 500 In other words, in order to achieve the desired conformation of 10.6 bp/turn in the helix, exactly 500 turns are required over the length of 5300 base pairs. Linkage number = 500 + Writhe We can have integral values for the linkage number, and we can certainly introduce 500, which would require no Writhe at all: 500 = 500 + 0 What is the DNA molecule was 5200 base pairs? Apparent linkage number (Twist) = (5200 base pairs) / (10.6 base pairs/turn) Apparent linkage number (Twist) = 490.6 We can introduce either 490 or 491 as a linkage number, but not 490.6. What happens if there is a linkage number of 490 in the DNA molecule? Linkage number = Twist + Writhe 490 = 490.6 + Writhe Writhe = -0.6 In this case, the DNA adopts a negative 0.6 supercoil (about 108° of a right-handed supercoil) which will increase the apparent linkage number from 490 to 490.6 (and achieve 10.6 basepairs per turn in the duplex). How many basepairs per turn would there be in the DNA if the DNA was not able to adopt any supercoil structure for this length of DNA with a linkage number of 490? Linkage number = Twist + Writhe 490 = Twist + 0 Twist = 490 turns Twist = (5200 base pairs) / (bp/turn) = 490 turns bp/turn = 5200 base pairs/490 turns bp/turn = 10.61 There are slightly more than 10.6 basepairs per turn in the DNA A small circularly closed genome The Simian Virus 40 (SV40) genome is a circular, closed, double stranded DNA genome. For the purposes of this discussion, it has 5300 bases. We expect that under physiological conditions the DNA will exhibit 10.6 base pairs per turn (i.e. one Twist = 10.6 bp/turn). In this case, with no Writhe, the Linking number would be: Linking number = 5300 bp/(10.6 bp/turn) + 0 Linking number = 500 turns i.e. we would expect 500 360° turns of the DNA strands over the length of the circular genome. • This form (with 10.6 base pairs per turn) with no Writhe represents the "standard", or undistorted, DNA helix. • This is also known as the "relaxed" form of DNA, and the duplex could physically be laid out flat on a surface because it needs no Writhe to achieve the preferred value of 10.6 basepairs per twist: Figure 1.6.2: Standard DNA helix However, when the replication of SV40 is initially completed it is observed that there remains an open duplex region in the DNA: Figure 1.6.3: DNA with open region The result is that there are about 475 turns of the helix within the duplex DNA (i.e. the Linking number = 475). • The DNA is said to be underwound. • An open area is energetically unfavorable. • The covalently closed molecule cannot adjust for this by increasing the Linking number. That is, it cannot spontaneously break one or both strands of the duplex, introduce another 25 turns into the duplex (increase the Linking number by 25) and re-ligate the duplex. The DNA has three choices: 1. It can adjust the number of basepairs per turn throughout the molecule from a desired 10.6 bp/turn to 11.2 bp/turn (i.e. 5300 bp/475 turns). (NOTE: an increase in the number of basepairs per turn will decrease the twist value; underwound DNA has a greater number of basepairs per turn). 2. The DNA can coil up into a "supercoil" topology and maintain the desired twist value (10.6) with the given linking number (475 in this case). 3. The duplex can exist with a twist of 10.6 bp/turn for most of the structure, and then have a region with zero twist (not necessarily a melted duplex). This is quite unfavorable due to the geometry required of bond angles. Thus for the 5300 bp SV40 genome, with a Linking number of 475, to maintain a value of 10.6 bp/twist, a total of 25 negative supercoils (Writhe=­25) are needed: 475 = (5300/10.6) + Writhe -25 = Writhe • That is, 25 negative supercoils (twenty five 180° turns of the DNA duplex, right handed as you look down the supercoiling). Topoisomerases The enzymes that control DNA topology are critical to DNA replication and transcription. • As the replication fork opens up, the region of the duplex in front of the fork becomes overwound - i.e. it has fewer basepairs per turn. • The linking number has not changed, but the length of DNA which contains all the turns is effectively shorter. • To maintain 10.6 bp/turn in that region, the DNA will adopt positive supercoils. For example, during the early stages of SV40 replication, the duplex around the origin of replication may initially melt (open up) a region of 750 bases. Since the Linkage number (500) is unchanged, it is effectively distributed over only: 5300 - 750 = 4550 bases. Assuming no supercoiling has been introduced: 500 = 4550 basepairs / (X basepairs/twist) + 0 = 9.1 basepairs/twist Thus, if no supercoiling is introduced, the DNA must adopt a conformation of 9.01 base pairs/twist of the helix within the region ahead of the replication fork. • This is energetically unfavorable, and one option for the DNA is to adopt a supercoiled configuration to achieve 10.6 bp/twist: 500 = (4550/10.6) + Writhe 70.8 = Writhe • Thus, movement of the growing fork causes the DNA to adopt positive supercoils. • In this case the DNA has adopted 70.8 left handed supercoils (180° each). • Twist (=basepairs * [twist/basepair]) and Writhe are both real numbers. Type I Topoisomerase • Type I topoisomerases cut one strand of the DNA (i.e. it "nicks" the DNA duplex). • The 5' phosphate of the nicked strand is covalently attached to a tyrosine in the protein. • The 3' end of the nick then passes once through the duplex. • The nick is then resealed, and the linkage number is change by a value of +1. • This can therefore result in the removal of a single negative supercoil. In E. coli, type I topoisomerase can only relieve negatively supercoiled DNA (negative supercoiling is the end result of newly replicated DNA genome). In eukaryotes, type I topoisomerase can also relieve positively supercoiled DNA. The net result of E. coli topoI can be diagrammed as follows: Figure 1.6.4: E. Coli Topoisomerase I Type II Topoisomerases • Type II topoisomerases actually cleave the duplex DNA in changing the linkage number. • Type II topoisomerases can convert a single positive supercoil into a negative supercoil. • Thus the linkage number is reduced by two (-2) in a single step. • Type II topoisomerases are involved in both decatenation of daughter chromosomes, and relieving the positive supercoiling ahead of the replication fork. • E. coli DNA gyrase is an example of a type II topoisomerase. Figure 1.6.5: Type II Isomerase activity

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/1%3A_DNA/1.6%3A_DNA_Supercoiling_and_Topoisomerases.txt

Bacteria evolved some 600 million years ago, and were probably responsible for the production of the earth's atmosphere (cyanobacteria). Bacteria were discovered in the 17th century after the development of the microscope. • Single cell organism • Widely dispersed in the environment • Invisible to naked eye, but discernible by their actions - milk sours, wounds become septic, meat putrefies, etc. • Prokaryotic type cells (other organisms are eukaryotic type cells) Major differences between prokaryotic and eukaryotic cells Prokaryotic cells Eukaryotic cells No nuclear membrane: chromosome(s) in direct contact with cytoplasm Chromosomes are enclosed in a double layered nuclear membrane Simple chromosome structure Complex chromosome structure; DNA associated with histone proteins Cell division does not involve meiosis Cell division involves mitosis and meiosis If present, cell walls contain peptidoglycan, no cellulose or chitin If present, cell walls contain cellulose or chitin, never peptidoglycan No mitochondria or chloroplasts Mitochondria usually present, chloroplasts in photosynthetic cells Cells contain ribosomes of only one size Cells contains two types of ribosomes, one in cytoplasm, and smaller type in mitochondria Flagella, if present, have a simple structure Flagella, if present, have complex structure Note: bacteria are microorganisms, but not all microorganisms are bacteria. Algae, fungi, lichens, protozoa, viruses and subviral agents are all microorganisms (with most of these being eukaryotic type cells Bacterial activities Pathogenic (disease causing) bacteria: • Cholera (vomiting, profuse diarrhea) • Botulism (muscle paralysis) • Tetanus (uncontrollable contractions of skeletal muscle) • Staphylococcal food poisoning (vomiting, diarrhea) • Shiga toxin (verotoxin) (classic dysentery) • Typhoid (septicemia: bacteria in blood, destruction of host tissue) • Oroya fever (Bartonella bacilliformis - destroys red blood cells) • Endotoxic shock (lipopolysaccharide cell wall component causes release of host inflammatory agents leading to shock and death) • Reactive arthritis (response in some people to Salmonella infection) Most bacteria do no harm to humans, and can be quite useful: • Antibiotic production • Enzyme additives for detergents • Insecticides • Production of biodegradable plastics • "Biomining" - leaching of metals from low grade ores • Uses in food industry • Butter • Cheese • Yogurt • Vinegar • Cocoa • Coffee • Soil fertility Classifying and Naming of bacteria Differences between bacteria can include • shape • size • structure • chemical activities • required nutrients • form of energy required • required environment • reaction to certain dyes Family, genus, species, strain Bacteria in the same Family, in general would have: • similar structure • use the same form of energy • react similarly to certain dyes Bacteria in the same Family may be divided into different Genera based on differences in • chemical activities • nutrient requirements • conditions for growth • shape and size (to some extent) Strains of bacteria are bacteria of the same species, but with some subtle difference (maybe a single mutational difference) Latin binomial name • Name of the genus, capitalized. • Followed by name of species, lower case • Italicized. Sometimes the genus name is abbreviated to a single letter (with a period) Escherichia coli E. coli • Strain name follows, usually in parentheses. In the vernacular, the strain name is commonly used to identify the bacteria E. coli (JM101) E. coli (CJ236) "Can I borrow some JM101?" Some Characteristics of Bacteria Shape • Rounded or spherical cells - cocci (singular: coccus) • Elongated or rod-shaped cells - bacilli (singular: bacillus) • Rigid spirals - spirilla (singular: spirillum) • Flexible spirals - spirochetes (singular: spirochete) Note There is a genus of bacteria called Bacillus. Some bacillus shaped bacteria belong to the genus Bacillus, some do not. Size • Bacteria are usually measured in micrometers (1x10-6 m) • The smallest bacteria are about 0.2 micrometers (Chlamydia) • The largest bacteria are about 600 micrometers (Epulopiscium fishelsoni. - inhabits the gut of a fish) • "Average" bacteria are 1-10 micrometers (note: limit of resolution of the light microscope is about 0.2 micrometers) A "generalized" bacterium: Figure 2.1.1: General bacterium diagram • The cell's DNA is extensively folded to form a body called the nucleoid • The cytoplasm fills the interior of the cells, and bathes the nucleoid • Storage granules contain a reserve of nutrients - typically polymeric forms of b-hydroxybutyrate and phosphate. Poly-b-hydroxybutyrate is the basis of a biodegradable plastic (Biopol) Figure 2.1.2: Poly-b-hydroxybutyrate • The nucleoid, ribosomes, cytoplasm and storage granules are bounded by a membranous sac, the cytoplasmic membrane (cell membrane, or plasma membrane) • The outermost layer is a tough cell wall. Together, the plasma membrane and cell wall are called the cell envelope • The region between the plasma membrane and the cell wall is called the periplasmic space • The flagellum is used for motility Cytoplasmic membrane • lipid bilayer, 7-8 nm thick, with protein molecules partly or completely embedded • The inner and outer layers are hydrophilic, while the interior of the bilayer is hydrophobic • In E. coli the main lipid is phosphatidylethanolamine; minor lipid components include phosphatidylglycerol and diphosphatidylglycerol Figure 2.1.3: Phospholipid bilayer The cytoplasmic membrane proteins include: • enzymes involved in the synthesis of the cell wall peptidoglycan • transport proteins (translocated ions and molecules across the cytoplasmic membrane • proteins of energy converting systems (ATPases and electron transport chains) • "sensory" proteins, which detect changes in cell's external environment The cytoplasmic membrane is not freely permeable to most molecules • some small uncharged molecules (O2, CO2, NH3, H2O) can freely pass through • charged ions typically cannot pass across the membrane, and must be transported (with the expenditure of energy) If the cell wall is removed, what remains of the cell is called the protoplast • can survive (in a test tube) and carry out most normal cell processes • quite sensitive to osmotic shock - if placed in pure water it will swell (as water enters the cell to balance the osmotic force) and rupture (osmotic lysis) • In an intact cell the cell wall prevents the protoplast from swelling and undergoing osmotic lysis • The cell wall also determines the shape of the bacteria - all protplasts are spherical, regardless of the shape of the intact bacteria The Cell Wall Among the Eubacteria (Kingdom of all bacteria excluding the archebacteria, which are typically halophiles and thermophiles) there are only two major types of cell wall • They can be identified by their reaction to certain dyes (characterized by Christian Gram in 1880's): Figure 2.1.4: Gram positive and Gram negative bacteria Gram positive type cell wall • relatively thick (30-100 nm) • 40-80% of the wall is made of a tough complex polymer called peptidoglycan (linear heteropolysaccharide chains cross-linked by short peptides) Figure 2.1.5: Gram positive cell wall • The cell wall of a gram-positive cell is a multi-layed network which appears to be continually growing by the addition of new peptidoglycan at the inner face, with concommitant loss at the outer surface Gram-negative type cell wall (e.g. E. coli) • thinner than gram-positive type cell wall (only 20-30 nm thick) • has distinctly layered appearance • inner region consists of a monolayer of peptidoglycan • outer layer of cell wall is essentially a protein containing lipid bilayer • inward facing lipids are phospholipids • outward facing lipids are macromolecules called lipopolysaccharides Figure 2.1.6: Gram negative cell wall • half the mass of the outer membrane consists of proteins • the Braun protein, which is covalently linked to the peptidoglycan layer • transport proteins • porins - molecules which span the outer membrane to create a 'pore' through the membrane. These pores allow certain molecules and ions to pass through the outer membrane (e.g. ompC, ompF proteins) • adjacent outer lipopolysaccharides are held together by electrostatic interactions with divalent metal ions (Ca2+, Mg2+) • the addition of chelating agents (e.g. EDTA) can disrupt these interactions and weaken the outer membrane • lysozyme (produced by phage lambda, for example) can cleave the saccharide links in the inner peptidoglycan layer How to lyse a gram-negative bacteria (e.g. E. coli): 1. Add a chelating agent of divalent metals (e.g. EDTA) to disrupt outer membrane lipopolysaccharides 2. Add lysozyme to break up peptidoglycan layer 3. cell wall is now structurally weakened and cannot protect the protoplast from osmotic shock 4. osmotically shock the cell to disrupt protoplast and release cytoplasmic contents (i.e. high osmotic shock using sucrose solution; low osmotic shock using pure water), 5. or use mechanical shear/cavitation (French Press, Menton Gaulin press) Human microflora: some of the bacteria commonly associated with the human body Location Genus Colon Bacteroides, Clostridium, Escherichia, Proteus Ear Corynebacterium, Mycobacterium, Staphylococcus Mouth Actinomyces, Bacteriodes, Streptococcus Nasal passages Corynebacterium, Staphylococcus Nasopharynx Streptococcus, Haemophilus (e.g. H. influenzae) Skin Propionibacterium, Staphylococcus, Others (personal hygiene, environment) Urethra Acinetobacter, Escherichia, Staphylococcus Vagina (adult, pre-menopausal) Acinetobacter, Corynebacterium, Lactobacillus, Staphylococcus

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/2%3A_Bacteria/2.1%3A_The_Bacteria_-_An_Introduction.txt

Flagella and motility • monotrichous flagella - the bacterial cell has a single flagella • peritrichous flagella - the bacterial cell has several flagella which are located at various sites on the cell surface (e.g. E. coli) Motility is due to the presence of one or more flagella. • in peritrichous flagellate bacteria the flagella rotate independently of one another • 95% of the time the flagella rotate counterclockwise • 5% of the time the flagella switch directions and rotate clockwise • When the flagella are all rotating counterclockwise, the flagella are bundled together and the bacteria travels in a straight line (i.e. it swims) • When one flagella switches direction the bundle disassociates, and the bacteria tumbles • alternative swimming and tumbling results in a three-dimensional random walk • chemoattractants and repellents can interaction with receptor proteins in the cell envelope, which in turn influence the rate of tumbling when the cell is moving in a given direction Growth and Reproduction Essential requirements for growth include: • supply of suitable nutrients • source of energy • water • appropriate temperature • appropriate pH • appropriate levels (or absence) of oxygen Nutrients Cells need a source of: • carbon • nitrogen • phosphorous • sulfur • other trace materials Although a given bacteria typically uses a limited range of compounds, bacteria as a group can utilize a wide range of compounds as nutrients: • sugars and carbohydrates • amino acids • sterols • alcohols • hydrocarbons • methane • inorganic salts • carbon dioxide Energy Energy is needed for • essential chemical reactions • uptake of nutrients • flagellar motility Phototrophic vs chemotrophic bacteria • phototrophic - energy derived from light source • chemotrophic - energy is obtained by processing chemicals from the environment Water • 80% of the mass of typical bacteria is water • Water is needed for growth and reproduction • Dessication (extreme lack of water) is tolerated to different degrees by different bacteria Temperature • Growth proceeds most rapidly at the optimum growth temperature for a particular bacteria (and decreases as temperature is raised or lowered from this optimum) • For any bacteria, there is a minimum and maximum temperature beyond which growth is not supported Thermophilic bacteria • optimum growth temperature is >45°C • occur in compost piles, hot springs and ocean floor hydrothermal vents • Pyrodictium have an optimum growth temperature of 105°C Mesophilic bacteria • optimum growth temperatures between 15 and 45°C • live in a wide range of habitats • since the human body is 37-42°C, human pathogenic bacteria are mesophiles Psychrophilic bacteria • optimum at 15°C or below • minimum temperature of 0°C, or less • maximum temperature of 20°C • occur in polar seas pH • most bacteria grow optimally near neutral pH (7.0) • acidophiles have an optimum pH with is more acidic (Thermoplasma acidophilum, found in hot springs, prefers pH 0.8-3, and will not grow at neutral pH) • alkalophiles have an optimum at higher (alkaline) pH ranges (Exiguobacterium aurantiacum, found in natural alkaline lakes, prefers pH 8.5-9.5) Oxygen • bacteria which must have oxygen for growth are termed obligate aerobes • bacteria which can grow only in the absence of oxygen are termed obligate anaerobes (e.g. environment which are isolated from the atmosphere; e.g. river mud, and within the intestines, for example) • bacteria which normally grow in the presence of oxygen, but which can manage to grow in its absence, are termed faculative anaerobes • conversely, bacteria which normally grow anaerobically, but which can manage to grow in the presence of oxygen, are termed faculative aerobes Inorganic ions All bacteria need low concentrations of certain inorganic ions in order to functions, e.g. • iron for cytochromes (energy metabolism), and certain enzymes • magnesium for cell wall stability • manganese and nickel in metabolic enzymes • high concentrations usually inhibit growth (e.g. salt has been used in the preservation of pork, beef and cod) • some bacteria, halophiles, grow only in the presence of high concentrations of certain salts (e.g. sodium chloride). Halobacteriaceae grow only in the presence of 3-4 M NaCl. This amount of salt is needed to maintain the structure of the cell wall and internal molecular assemblies (e.g. ribosomes). Growth in a single cell (e.g. Escherichia coli -a gram negative bacillus) The cycle of events in which a cell grows, and divides into two daughter cells, is called the cell cycle. "Slow Growth" Figure 2.2.1: Replication for slow growth • Replication begins at a specific place on the chromosome - the origin or "ori" region. • During "slow growth" each new daughter cell contains exactly one chromosome, because a new round of chromosomal replication does not begin until after completion of cell division The cell division cycle can be thought of as a linear sequence of three periods: I, C and D • C is the period during which chromosomal replication occurs • D is the period in which the septum forms, and cell division occurs at the end of the D period • I is the period between each successive initiation of chromosomal replication For the above type of slow growth, the relationship between these three periods is as follows: Figure 2.2.2: Periods of cell cycle for slow growth • I is also known as the doubling time of the bacterial growth "Rapid Growth" Figure 2.2.3: Replication for rapid growth • A new round of chromosome replication begins before cell division occurs Figure 2.2.4: Cell cycle for rapid growth • Each daughter cell has the equivalent of about 1 ½ chromosomes • In rapidly growing E. coli cells the C period is about 42 minutes and D is about 25 minutes. • The maximum doubling time for E. coli is about 20 minutes • "medium": any solid or liquid specially prepared for bacterial growth • "culture": a liquid or solid medium containing bacteria which have grown (or are growing) in or on that medium • "incubation": the process of maintaining a particular temperature (and/or other desirable conditions) for bacterial growth • "innoculation": the initial process of adding the cells to the medium Growth on a solid medium • Liquid solution of nutrients plus 1% agar (the preferred form of Jello in Asia. A polysaccharide extracted from seaweed; as opposed to Western Jello which is a protein extracted from the hooves of large farm animals) forms solid media Typical Solid Media Recipe (1 liter). The media here is commonly called "Luria Broth", or "LB". It is named after one of the scientists who developed it (not "Dr. Broth"). • Yeast extract 5 g (nucleic acids, cofactors, inorganic salts, carbohydrates) • Tryptic digest of casein (milk protein) 10g (peptides and amino acids) • NaCl 10g (note: final concentration is thus 0.17 M, or close to physiological) • Agar 10g • Water (bring volume up to 1.0 liter) • Autoclave, pour into petri dish, let cool An individual bacterial cell will divide and eventually become a visible mass of cells known as a colony If instead of a single cell, the solid media is initially populated with a large number of cells, confluent growth or a lawn of bacteria will be visible Figure 2.2.5: Growth on solid medium "Streaking" solid media plates • bacteria can be introduced onto solid media by a sterile transfer tool, such as a wire loop (nichrome wire), or autoclaved toothpick, which has been dipped into a bacterial culture • such a transfer contains thousands of individual bacterial cells • it is desirable to grow colonies from individual cells rather than from a large population This is done to avoid the takeover of the strain by potential wild-type revertants • "streaking" is a method to isolate individual cells for growth on solid media from an inoculation which originally contains thousands of cells Figure 2.2.6: Streaking Growth in liquid medium • No agar • Use Erlenmeyer flask, or fermenter • If necessary, aerate, agitate • progeny will be dispersed throughout medium (diffusion or locomotion) • as the cell density increases, the media becomes turbid • number of cells plotted versus time will yield a growth curve Figure 2.2.7: Growth curve in liquid medium • "lag phase" after inoculation cells are becoming acclimated to the new environment (temp, nutrients, etc.) • "log phase" cells have adapted and are dividing at a constant rate (i.e. the maximum for the species under the given conditions of temp, pH, nutrients, oxygen, etc.) • "stationary phase" cell growth ceases as nutrients are exhausted and/or waste products build up in the media • "death phase" number of viable (living cells) in the stationary phase culture decreases (usually due to toxicity of waste products) Cell density can be conveniently monitored using the absorbance of visible light (usually at 600 nm) Figure 2.2.8: Cell density curve Different media will result in different growth rates and different stationary phase densities • Rich media will have short (<1.0 hour) doubling times and will result in higher cell densities at stationary phase • Minimal media will exhibit slow growth (doubling times ~1.0 hour at 37°C) and low final densities • Efficient agitation and aeration can increase final cell densities (fermenters may achieve higher densities than shaker flasks). Minimal LB Terrific Phosphate salts 16g - 12g Ammonium salts 1g - - Magnesium salts 0.1g - - Glucose/glycerol 4g - 4g Sodium Chloride 0.5g 10g - Enzymatic digest of casein (milk protein) - 10g 12g Yeast extract - 5g 24g Approx doubling time (min) 60 45 30 Stationary phase density (A600) 3 7 15 Let's take a look at some raw data from a culture of E. coli growing in a fermenter. The absorbance at 600nm was recorded at various time intervals: Time (minutes) A600 0 0.09 18 0.102 78 0.124 142 0.253 205 0.487 255 1.02 322 1.98 378 3.95 446 5.88 504 6.76 564 7.2 ` The growth curve looks like this: Figure 2.2.9: Sample growth curve In order to calculate the doubling time we need to know the region of the growth curve for which the growth is logarithmic. We can evaluate this by plotting the absorbance data on a logarithmic (log10) scale. In this case, our data will look like this: Time (minutes) A600 log10 A600 0 0.09 -1.05 18 0.102 -0.99 78 0.124 -0.91 142 0.253 -0.60 205 0.487 -0.31 255 1.02 0.01 322 1.98 0.30 378 3.95 0.60 446 5.88 0.77 504 6.76 0.83 564 7.2 0.86 Figure 2.2.10: Sample logarithmic growth curve In this case, the data appears to have an initial lag period followed by logarithmic growth until about 400 minutes, then the rate slows. In other words, the log10 curve appears linear over the time period 100 - 400 minutes, or so. We can conveniently determine the doubling time by replotting the data over this period and converting the A600 into log2 values. Note logN(X) = log10(X)/log10(N) Time (minutes) A600 log10 A600 log2 A600 0 0.09 -1.05 18 0.102 -0.99 78 0.124 -0.91 -3.01 142 0.253 -0.60 -1.98 205 0.487 -0.31 -1.04 255 1.02 0.01 0.029 322 1.98 0.30 0.99 378 3.95 0.60 1.98 446 5.88 0.77 504 6.76 0.83 564 7.2 0.86 Figure 2.2.11: Sample log2 growth curve If we plot a straight line through these points the slope will give us the rate of change of the log2 A600 values as a function of time: Figure 2.2.12: Linear fit for sample log2 growth curve • Thus, for this culture, the growth (slope) can be described as a rate of 0.0167 log2A600/min. • The inverse of the slope will therefore tell us how many minutes it takes for the culture to increase its density by 1 log2A600 absorbance units. • Since it is log2, a change of 1 absorbance units means the absorbance has doubled. Therefore, the inverse of the slope for the log2 plot gives us the time it takes for the culture absorbance to double (i.e. the doubling time) For this experiment, the doubling time is 1/0.0167 or 59.9 minutes. This suggests that the media is probably not very rich (maybe like minimal media), however, the final absorbance is higher than that expected for minimal media. So, possibly the media LB, but the growth was done at a lower temperature (i.e. leading to a slower growth rate). Important parameters to determine: • A600 at stationary phase • A600 at ½ stationary phase (usually the time point chosen to stimulate bacteria to produce recombinant proteins) • Doubling time (plot log2 of A600 over logarithmic range of growth, take inverse slope)

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/2%3A_Bacteria/2.2%3A_Bacterial_Growth_and_Reproduction.txt

• Including an origin of replication (i.e. the E. coli oriC region) into a circular DNA molecule is a mechanism to have an extrachromosomal element in the prokaryotic cell. Figure 2.3.1: oriC Extrachromosomal element • Such an extrachromosomal element is called a plasmid, or vector • The plasmid uses the host cell machinery (i.e. polymerases, helicases, dNTP's etc.) to direct replication. Figure 2.3.2: Vector/Plasmid • However, since the added work of replicating the extrachromosomal element is a load on a cell, it will be out-competed by other cells which do not contain the plasmid. • Since in prokaryotic cells the segregation of plasmids is a random event, daughter cells can arise which do not contain the plasmid and these grow faster (out-compete) the parent cell. • In other words, in the absence of other pressures, after a period of time the population of cells in a culture will be those which have "lost" the plasmid. Figure 2.3.3: Loss of plasmid • In organisms with more than one chromosome (eukaryotes) there are a variety of mechanisms to ensure that proper segregation of chromosomes occurs, i.e. to make sure that daughter cells contain equal numbers of all the chromosomes. • One basic mechanism is that each chromosome contains essential genes, and if these are lost, the cell cannot survive. Drug resistance • By far the most common approach to the maintenance of plasmids is through the incorporation of drug resistance genes. • These are also known as selectable markers, i.e. we can select for their presence by including antibiotics in the growth media. Ampicillin • Ampicillin binds to and inhibits a number of enzymes in the bacterial membrane that are involved in the synthesis of the gram negative cell wall. • Therefore, proper cell replication cannot occur in the presence of ampicillin. • The ampicillin resistance gene (ampr) codes for an enzyme (b-lactamase) that is secreted into the periplasmic space of the bacterium where it catalyzes hydrolysis of the b-lactam ring of the ampicillin. • Thus, the gene product of the ampr gene destroys the antibiotic. • Over time the ampicillin in a culture medium or petri plate may be substantially destroyed by b-lactamase. • When this occurs, cell populations can arise which have "lost" the plasmid. Figure 2.3.4: Ampicillin resistance Tetracycline • Tetracycline binds to a protein of the 30S subunit of the ribosome and inhibits ribosomal translocation along the messenger RNA which codes for the protein (i.e. the drug interferes with normal translation or production of proteins). • The tetracycline resistance gene (tetr) encodes a 399 amino acid outer membrane associated protein of gram negative cells that prevents the antibiotic from entering the cell. • Thus, this drug resistance gene does not destroy the antibiotic. Pressure will be maintained throughout the cell culture process to keep the plasmid containing the drug resistant gene. Chloramphenicol • Chloramphenicol binds to the ribosomal 50S subunit and inhibits protein synthesis. • The chloramphenicol resistance gene (Cmr) codes for a protein known at the cat protein. • The cat protein is a tetrameric cytosolic protein that, in the presence of acetyl coenzyme A, catalyzes the formation of hydroxyl acetoxy derivatives of chloramphenicol that are unable to bind to the ribosome. • Like with ampicillin, the Cmr gene product destroys the antibiotic. • Additionally, the expression of cat protein is influenced (down regulated) by the presence of glucose in the media. Kanamycin and neomycin • Bind to ribosomal components and inhibits protein synthesis. • The Kanr gene codes for a protein which is secreted into the periplasmic space and interferes with the transport of these antibiotics into the cell. • Like tetracycline resistance, the Kanr gene does not destroy the antibiotic. Colicin E1 • This is a member of a general class of substances known as bacteriocins. • Colicin E1 causes lethal membrane changes in bacteria. • The drug resistance gene (cea) codes for a protein that interferes with the action of colicin in an unknown manner. Prokaryotic plasmids • In addition to making a plasmid by using the E. coli OriC region, there is a naturally occuring E. coli plasmid called the ColE1 plasmid • The ColE1 origin of replication is uni-directional (unlike oriC) • The replication from the ColE1 ori region does not require the associated proteins (e.g. dnaA protein) like oriC, (but it does require specific RNA molecules). • Depending on the exact region of the ColE1 origin which is inserted into a circular dna molecule, the extrachromosomal element will be maintained with either a "low" or "high" copy number • The rop gene near the ColE1 origin is involved in the regulation of replication. • If the ColE1 ori region includes this gene then the plasmid is maintained with an average copy number of 10-30 plasmids/cell. This is considered low copy number. • If the ColE1 ori region does not have the rop gene, then the resulting plasmid is maintained with an average copy number of 100-200 plasmids/cell. This is considered a high copy number type of plasmid. • If the plasmid contains a gene coding for a protein (like drug resistance genes) copy number can influence the amount of such protein in the cell. pBR322 (4.36 Kb) • One of the original cloning plasmids. • Constructed by ligating together: • the tetracycline resistance gene from plasmid pSC101 • ColE1 and rop region from the ColE1 plasmid • the ampicillin resistance gene from the transposon Tn3 • Contains both ampicillin and tetracycline resistance genes (markers). • Contains unique restriction sites inside and outside of these markers. • Contains rop region near CoE1 ori , therefore, it has a low copy number (10-30) • Numbering begins at the unique EcoR I restriction site (GAATTC). The first 'T' in this sequence is base number "1". Figure 2.3.5: pBR322 The plasmid diagram: • In the center is the plasmid name (usually starts with a lower case 'p') and the size in basepairs • the inner ring provides ticks in 1 Kilobase (Kb) intervals to give an idea of the general location of parts of the plasmid • the arrows indicate genes, markers, ori or replication, promoters, polylinkers, transcription terminators, and other important or functional items • the outer ring usually indicates the location of unique, or limited number (usually <3), restriction endonuclease sites. Restriction enzymes which have more than three sites will not be indicated. Be aware that enzymes which do not cut at all will also not be listed! PUC18/19 (2.69 Kb) • Lacks the rop gene near the ColE1 ori region. Thus, this plasmid tends to accumulate in high copy number (100-200). • This vector contains only the ampicillin resistance marker. • This vector contains a polylinker region • A synthetic DNA sequence which contains a clustering of unique restriction enzyme recognition sites • Allows DNA fragments, generated by a variety of restriction endonuclease cleavages, to be inserted into the plasmid • pUC18 has the polylinker in one orientation • pUC19 has the same polylinker, but in the opposite orientation • Thus, fragments with unique restriction sites on each end can be inserted in a specific orientation • The Pst I site in the ampr gene was mutated to remove it. The EcoR I site at position (1) was mutated to remove it. This was done to make the restriction sites in the polylinker region unique • This vector also contains a transcription promoter region from the lac operon, which allows foreign genes to be inserted and transcribed/translated. • The polylinker region is just downstream (3') to the lac promoter • Inserted genes can be transcribed from this promoter Figure 2.3.6: pUC18/19

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/2%3A_Bacteria/2.3%3A_Extrachromosomal_elements_plasmids_selectable_markers.txt

The "Central Dogma" We have seen how DNA, with the aid of specific polymerases and accessory proteins, is able to replicate. We have also seen how we can use this information to create autonomously replicating extra-chromosomal elements (i.e. plasmids). However, the real utility of such systems arises when we use them to create proteins of interest. To get to proteins we have to go through RNA first. Figure 2.4.1: Central dogma Structural features of RNA: 1. Similar to DNA except it contains a 2' hydoxyl group (makes phosphodiester bond more labile than DNA). 2. Thymine in DNA is replaced by Uracil in RNA Figure 2.4.2: Thymine vs. Uracil 3. RNA's can adopt regular three-dimensional structures which allow them to function in the process of genetic expression (i.e. the production of proteins). • This ability to adopt defined three dimensional structures which impart functionality places RNA in a unique class - somewhat akin to proteins, and different from DNA. • For example certain RNA molecules, when folded, exhibit catalytic capacities (e.g. the cleavage of RNA molecules). • The majority of RNA in cells is found in complexes with proteins. The most common example is ribosomes (involved in protein synthesis). Transcription: the copying of DNA by an RNA polymerase to make RNA. RNA polymerase: • Can initiate a new nucleic acid strand given a template. • DNA polymerases cannot; they require a primer (or more typically, an RNA polymerase to provide the primer). Protein Synthesis • Three kinds of RNA molecules perform different functions in the protein synthesizing apparatus: 1. Messenger RNA (mRNA) encodes the genetic information copied from DNA in the form of a sequence of bases that specifices a sequence of amino acids 2. Transfer RNA (tRNA) is part of the structural machinery which deciphers the mRNA code. They carry specific amino acids which are transfered to a nacent polypeptide according to the instructions contained within the mRNA. 3. Ribosomal RNA (rRNA) forms a complex with specific proteins to form the ribosome which is the key translational component • the ribosome complexes with mRNA and directs appropriate tRNA's and the synthesis of the polypeptide bond. Translation: The process by which the information contained within a mRNA is used to direct the synthesis of the corresponding polypeptide. The Genetic Code How is the information for a polypeptide sequence stored within an mRNA molecule? There are twenty different common amino acids, but only four different bases in RNA (A, C, G, and U). Base Arrangement Possible Combinations 1 41=4 2 42=16 3 43=64 4 44=256 A triplet arrangement would seem to be the minimum possible combination necessary to code for the 20 different amino acids. Although, there are obviously going to be a lot of codons "left over". Most amino acids are coded for by more than a single unique triplet, and therefore the genetic code is said to be degenerate. Experiments which led to the solution of the genetic code: Nirenberg and Matthei (1961): Nirenberg and Matthei worked with bacterial extracts which contained everything needed for translation, with the exception of mRNA. To this they added either poly A, poly U or poly C RNA. The proteins produced by the translation of these RNA's was determined (poly G did not work, probably due to conformational problems): Poly U Poly A Poly C Phe Lys Pro Thus, the triplet UUU = Phe, AAA = Lys, and CCC = Pro. Korana (1963): In a cell free extract system, Korana added mRNA with repeating nucleotide sequences. The sequence ...ACACACAC... resulted in a polypeptide with alternating threonine and histidine residues. But, was threonine coded by ACA, and histidine by CAC? Or vise versa? To determine the answer to this, the mRNA sequence ...AACAACAACAAC... was tried. There were three different possible reading frames for the translation of this mRNA: • AAC AAC AAC • ACA ACA ACA • CAA CAA CAA But CAC was not a possible triplet. This sequence was found to code for three different polypeptide chains: poly Asn, poly Thr, and poly Gln. Since no histidine was found, histidine was therefore coded for by the triplet CAC. Nirenberg and Leder (1964): Nirenberg and Leder used a filter which would allow RNA triplets and charged tRNA's to pass through, but would prevent passage of larger ribosomes. Specific triplet RNA sequences would bind to ribosomes and cause the binding of the associated charged tRNA molecules (coded for by the specific triplet). In a given experiment, if a unique charged tRNA were radiolabeled (on the amino acid), then it could be determined whether that particular charged tRNA was associated for by the unique triplet. In this way, all 61 codons for amino acids were determined. Figure 2.4.3: Nirenberg and Leder experiment The genetic code 5' End (Start) Second Position 3' End U C A G U Phe 0.24 Ser 0.34 Tyr 0.25 Cys 0.49 U Phe 0.76 Ser 0.37 Tyr 0.75 Cys 0.51 C Leu 0.02 Ser 0.02 Stop Stop A Leu 0.03 Ser 0.04 Stop Trp 1.00 G C Leu 0.04 Pro 0.08 His 0.17 Arg 0.74 U Leu 0.07 Pro 0.00 His 0.83 Arg 0.25 C Leu 0.00 Pro 0.15 Gln 0.14 Arg 0.01 A Leu 0.83 Pro 0.77 Gln 0.86 Arg 0.00 G A Ile 0.17 Thr 0.35 Asn 0.06 Ser 0.03 U Ile 0.83 Thr 0.55 Asn 0.94 Ser 0.20 C Ile 0.00 Thr 0.04 Lys 0.74 Arg 0.00 A Met (start) 1.00 Thr 0.07 Lys 0.26 Arg 0.00 G G Val 0.51 Ala 0.35 Asp 0.33 Gly 0.59 U Val 0.07 Ala 0.10 Asp 0.67 Gly 0.38 C Val 0.26 Ala 0.28 Glu 0.78 Gly 0.00 A Val 0.16 Ala 0.26 Glu 0.22 Gly 0.02 G Note E. coli codon preferences are indicated. All proteins in prokaryotes and eukaryotes begin translation with the initiator codon AUG (methionine). The three codons, UAA, UGA and UAG are termination codons (don't code for any amino acids but signal the end of the protein chain). Note the apparent relative importance of the middle base in the codon triplet Middle base in codon triplet U C A G Phe Leu Ile Met Val Ser Pro Thr Ala Tyr His Gln Asn Lys Asp Glu Stop Cys Trp Arg Ser Gly Stop Hydrophobic Small/Polar Charged/Polar Polar Can common protein architectures be patterned by a simple quaternary pattern of residues? The twenty common amino acids and their three-letter and single-letter acronyms: Amino Acid Three letter acronym One letter acronym Alanine Ala A Cysteine Cys C Aspartic Acid Asp D Glutamic Acid Glu E Phenylalanine Phe F Glycine Gly G Histidine His H Isoleucine Ile I Lysine Lys K Leucine Leu L Methionine Met M Asparagine Asn N Proline Pro P Glutamine Gln Q Arginine Arg R Serine Ser S Threonine Thr T Valine Val V Tryptophan Trp W Tyrosine Tyr Y

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/2%3A_Bacteria/2.4%3A_Central_Dogma_and_the_Genetic_Code.txt

Genes The entire nucleic acid sequence that is necessary for the synthesis of a functional polypeptide or RNA molecule. Thus, a gene contains additional sequence information beyond that which codes for the amino acids in a protein or the nucleotides in an RNA molecule. The gene also contains the DNA necessary to get a particular transcript made. Transcription control regions can be remote to the coding region (on the order of Kb's or 10's of Kb's away). Most prokaryotic genes lack introns (intervening DNA sequence). In prokaryotes, genes which encode proteins with relationships in a metabolic pathway form Operons - which produce polycistronic mRNA's. Definitions: Operon and Promoter • An operon is in bacterial DNA, a cluster of contiguous genes transcribed from one promoter that gives rise to a polycistronic mRNA. • A Promoter is a DNA sequence to which RNA polymerase binds prior to initiation of transcription - usually found just upstream of the transcription start site of a gene e.g. Trp Operon - involved in the biosynthesis of the amino acid tryptophan: Figure 2.5.1: Chemical pathway of trp operon Figure 2.5.2: Trp operon in DNA/RNA A consequence of the arrangement of bacteria genes into operons is that the level of mRNA for each of the genes in the operon is exactly the same. Ribosomes transcribe from the start of each gene, not only from the first gene. Another consequence of the arrangement of bacteria genes into operons is that an upstream mutation (i.e. possibly inhibiting transcription) can prevent "downstream" genes from being transcribed and expressed. Most eukaryotic transcription units produce monocistronic mRNA's, (i.e. they encode only one protein). There is a fundamental difference in the translation processes of prokaryotes and eukaryotes: 1. In prokaryotes ribosomes can bind at specific recognition sequences anywhere within the mRNA (called ribosome binding sites, or "Shine-Dalgarno" sites). 2. In eukaryotes, ribosomes bind via the interaction with specifically modified 5' region (so called 5' cap site) of mRNA molecules. 3. Most eukaryotic mRNA's are therefore monocistronic. • Mutations in simple eukaryotic transcription units affect only one protein. Complex Eukaryotic Transcription Units The primary RNA transcript encoded by complex transcription units can be spliced in more than one way. Because of the different processing possibilities, the exons (coding regions) in a single complex transcription unit can be linked in alternative ways, to yield different mRNAs and different proteins. Figure 2.5.3: Complex transcription units Transcriptional regulation Successful survival requires adaptability and economy: 1. The ability to switch from metabolizing one substrate to another as environmental resources change 2. It would be an energetic waste to produce enzymes for a metabolic pathway which is not needed. Induction versus Repression of Enzyme Synthesis In E. coli certain enzymes are produced only when the cells are grown on certain substrates. This effect is called enzyme induction. For example, when cells are grown in the absence of a type of sugar known as a b ­galactoside (e.g. lactose) the cells contain very few molecules (~5 per cell) of the enzyme b-galactosidase (which cleaves lactose into glucose and galactose). • There is no need for this enzyme in the absence of lactose. • If lactose is added to E. coli, in a very short amount of time there are approximately 5000 molecules of b-galactosidase per cell (approximately ~1,000 fold induction). • If lactose is removed from the media synthesis of b-galactosidase stops. A similar but opposite situation occurs in regard to the synthesis of tryptophan (the biosynthetic enzymes are contained in the trp operon). In this case production of the enzymes for tryptophan biosynthesis are rapidly shut down if tryptophan is present, in a process called repression. Repression is a transcriptional regulatory mechanism for commonly required gene products. Induction is a transcriptional regulatory mechanism for gene products which may be required under unusual or infrequent situations.

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/2%3A_Bacteria/2.5%3A_Gene_and_Operon.txt

The E. coli lac operon Figure 2.6.1: lac Operon • lac Z codes for b-galactosidase, which is an enzyme that cleaves b-galactosides (e.g. lactose). • lac Y codes for permease, which is involved in the transport of b-galactosides into the cell. • lac A codes for b-galactoside transacetylase, which acetylates b-galactosides. • A mutation in either lac Z or lac Y can lead to a lac- genotype, i.e. cells which cannot utilize b-galactosides as a nutrient. • A lac A- mutant, lacking transacetylase activity, can still utilize b-galactosides (it is still lac+ genotype). Its role in the metabolism of b­galactosides is not clear. Promoter a region of DNA involved in binding of RNA polymerase to initiate transcription. Terminator a sequence of DNA that causes RNA polymerase to terminate transcription. • The cluster of three genes, lac ZYA, is transcribed into a single mRNA (polycistronic message) from a promoter just upstream from the lac Z gene. • In the absence of an inducer the gene cluster is not transcribed. • When an inducer is added (e.g. lactose, or the non-hydrolyzable analog isopropyl thiogalactoside - IPTG) transcription starts at a single promoter (lac P) and proceeds through the lac ZYA genes to a terminator sequence located downstream of the lac A gene. Note The lac ZYA mRNA has a half life of ~3 minutes, which allows induction to be reversed relatively rapidly (i.e. cells stop producing enzymes rapidly after induction stops). Exercise 2.6.1 What molecule does the inducer (lactose) interact with to affect transcriptional regulation (i.e. induction of the lac operon)? Answer It is not b-galactosidase, permase or transacetylase, rather it is a separate protein called a repressor protein. • The lac genes are controlled by a mechanism called negative regulation. • This means that they are transcribed unless they are turned off by the regulator protein. • A mutation that inactivates the regulator protein causes the lacZYA genes to be continually expressed. Since the function of the regulator protein is to prevent expression, it is called a repressor protein. There are two types of genes in the lac operon: 1. Structural genes - they code for enzymes required for some biochemical pathway (e.g. lac Z, Y and A). 2. Regulator genes - they code for proteins involved in regulation of structural genes. Mutations in structural genes typically affect the function of only that structural gene. Mutations in regulator genes can affect the expression of all structural genes in an operon. lac I is the regulator gene of the lac operon. • This gene is located just upstream of the promoter region for the lac structural genes. • The lac I gene has its own promoter (constitutive) and terminator. • It makes a monocistronic message, and codes for one protein - the lac repressor protein. The crucial feature of the lac "control circuit" resides in the dual feature of the lac I repressor protein: 1. It can prevent transcription 2. It can recognize and bind the small molecule inducer (lactose or IPTG) Prevention of transcription by the lac repressor • lac repressor (active as a tetrameric protein) binds to a sequence of DNA called the operator (lac O region). • The operator region lies between the lac promoter region (site of RNA polymerase binding and transcription initiation) and the lac Z gene. • The first 26 base pairs of the lac Z gene comprise the operator region. When the repressor binds to the operator region, its presence prevents RNA polymerase from initiating transcription at the promoter. • It is not that the repressor protein "blocks" the movement of RNA polymerase through the lac Z gene. • Repressor binding and RNA polymerase binding (to the promoter) are mutually exclusive at the lac promoter/operator (lac PO) region. How does the repressor/operator interaction change in the presence of the inducer molecule? • The inducer can bind to the repressor to form a repressor/inducer complex that no longer associates with the operator. • The key feature of this interaction is that the repressor protein has two binding sites, one for the inducer and one for the operator. • When the inducer binds at its site, it changes the conformation of the repressor protein such that the operator binding site has a much reduced affinity for the DNA operator region. • This type of control is called allosteric control. • The result is that when the inducer is added the repressor is converted to a form which releases from the operator. Figure 2.6.2: Inducer Positive control of the lac operon is exerted by cAMP-CAP complex • E. coli prefers glucose over other carbon sources. When glucose enters an E. coli cell it is utilized directly without induction of any new enzymes. • When E. coli is grown on glucose, if another sugar (e.g. lactose) is added the induction of enzymes to utilize the other sugar does not occur until the glucose is used up. • When E. coli is starved for glucose it synthesizes an unusual nucleotide: cyclic 3'5' adenosine monophosphate (cyclic AMP, or cAMP): Figure 2.6.3: cAMP 1. In bacteria an increase in the cAMP level seems to be an "alert" signal indicating a low glucose level: Figure 2.6.4: Interaction of cAMP level and lac operon Dibutyryl cAMP • an analogue of cAMP which can pass through the E. coli membrane and into the cell. • If this is added to media containing glucose and lactose it will result in the induction of the lac operon. • Thus, it mimics the chemical message which tricks the E. coli to respond as though glucose levels were low. • Mutants of E. coli have been isolated which cannot be induced to metabolize any sugar other than glucose. There were two general categories of mutants: 1. Class I. Defective in the enzyme adenylate cyclase. These mutants are unable to make cAMP even when the glucose conentration is low. 2. Class II. Lacks a particular protein known as cAMP receptor protein (CRP) or, also known as catabolite receptor protein (CRP). • Maximum transcription from the lac operon requires the presence of a cAMP/CRP complex. • cAMP/CRP complex binds to a specific sequence in the lac control region called the "CAP" site. • The CAP site is just upstream from the RNA polymerase binding site. • Mutations in the CAP site that prevent cAMP-CRP binding also prevent high levels of expression of the lac operon. • Thus, bound cAMP/CRP complex activates transcription (positive control), whereas bound lac repressor inhibits transcription (negative control). • cAMP/CRP complex has affinity for DNA, and RNA pol. • Enhances complex formation of RNA pol with the DNA promoter region. Induction of the lac operon with lactose analogues • The lac operon can be induced with lactose • b-galactosidase (lacZ gene product) metabolizes the lactose • When levels of lactose are reduced, the lac operon is again repressed by the lac repressor (lacI gene product) • Non-metabolized lactose analogues can continually induce (i.e. de-repress) the lac operon • isopropyl b-thiogalactoside, or IPTG, is a non-metabolized lactose analogue DNA "footprinting" experiments • If a protein binds to a region of DNA, it can protect that region of DNA from digestion by dnase (DNAse I: an endonuclease at sites adjacent to pyrimidine nucleotides). • A fragment of DNA can be labeled at the 5' ends with 32P and then the label can be preferentially removed from one end (i.e. the 3' end of a gene) by an appropriate restriction endonuclease. • If this DNA fragment, with a label at one specific end, forms a complex with a DNA binding protein the protein will protect the region of DNA that it binds to from DNAse I digestion. • The digestion is done so as to be incomplete, for the purposes of this discussion, imagine that each DNA molecule is cleaved only once. Furthermore, the site of cleavage is randomly chosen from the available sites. • Fragments of the DNA, separated and analyzed by size (using gel electrophoresis) after digestion will indicate the protected region: Figure 2.6.5: DNA footprinting Results of footprinting experiments lac DNA incubated with either cAMP/CAP protein, or RNA polymerase, or lac I repressor protein: Figure 2.6.6: lac repressor with cAMP RNA polymerase interacts with specific promoter sequences and produces a "footprint" over a region of ~70 base pairs. • This protection was observed to be more obvious on one strand than the other (i.e. if the other strand was labeled the results did not show as much protection). • This region of DNAse protection included sites in the DNA from which mutagenesis experiments produced either "up" regulation or "down" regulation of promoter strength. • These mutagenic "hot" spots affecting promoter strength were located at positions either -10 or -30 upstream from the transcription start site (position +1 in the above diagram): Figure 2.6.7: Promoter Strength Mutations • Promoters can be classified according to their "strength". • This refers to the relative frequency of transcription initiation (transcriptional initiation events per minute), and is related to the affinity of RNA polymerase for the promoter region. • Many promoters in E. coli have been characterized and a "consensus" promoter sequence has been identified: Figure 2.6.8: Consensus Promoter Strength Note The lac promoter is a relatively weak promoter RNA Polymerase • E. coli RNA polymerase is a holoenzyme comprised of subunits b', b, a (dimer) and s70. • The s70 subunit is the subunit which binds to the promoter region, but is unable to initiate RNA synthesis. • After the s70 subunit subunit binds, the other subunits bind forming a function RNA polymerase. • After approximately 10 base pairs have been transcribed the s70 subunit leaves and the core polymerase continues on. The lac Operator Region • The lac operator region is comprised of an (imperfect) inverted repeat region. • Not surprisingly active repressor molecules are composed of a homodimer. • In the homodimer structure there are a pair of a-helix regions which insert into adjacent major grooves of the DNA. • The separation is approximately 34 angstroms apart.

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/2%3A_Bacteria/2.6%3A_The_lac_Operon_CAP_site_DNA_footprinting.txt

Transcription termination in prokaryotes • Because protein coding regions are closely spaced in the genomes of microorganisms, independent control of neighboring genes is possible only if a transcription termination site lies between them (i.e. downstream genes must be independently regulated). • There are two general mechanisms of transcription termination: 1. one requires the presence of a transcription termination protein called rho, 2. the other requires no associated proteins. Rho-independent termination • Rho-independent transcription termination sequences have two general characteristic features: 1. A 3' stretch of T residues 2. A 'GC' rich interrupted palindrome just upstream of the 3' poly T region. Figure 2.7.1: Rho-independent transcription termination site The important features of Rho-independent transcription termination are as follows: 1. The inverted repeat region can self-hybridize to form a "stem-loop" structure: Figure 2.7.2: Stem-loop structure 2. The GC rich stem loop structure interacts with RNA polymerase to cause it to pause. 3. The short UUU region, which is base pairing with AAA sequence on the anti-sense DNA strand, has low thermal stability and melts - releasing the nascent RNA transcript. Attenuation at the trp operon and premature mRNA chain termination • The trp operon is regulated by the trp repressor protein. • Trp repressor protein binds to tryptophan and undergoes a conformational change which allows it to bind to the trp operator region (downstream of the promoter). • This prevents transcription of the trp operon in the presence of tryptophan (i.e. tryptophan represses expression). • However the repression is somewhat incomplete and there is a second mechanism which contributes to transcription repression. • When tryptophan is present, and the operon is repressed, the few transcripts which are made are actually quite short and do not encompass the entire polycistronic message of the trp operon. • The short mRNA that is made comprises only a region called the leader sequence: Figure 2.7.3: Leader sequence • The leader region contains an attenuator sequence - a site where a choice is made between elongation of the growing trp transcript or (premature) termination. • Attenuation depends on the interplay between ribosome binding and translation of the nascent mRNA transcript and the formation of a particular stem-loop structure in the mRNA leader sequence. • Formation of this structure depends on the rate of ribosomal translation of the leader sequence (the leader sequence contains an AUG start codon). • Efficient translation of the leader sequence depends upon the concentration of charged tRNA's for the appropriate amino acids coded for by the mRNA leader sequence. • The leader sequence has the following characteristics: 1. It contains a ribosome binding site and an AUG start codon necessary for the initiation of translation. 2. The corresponding amino acid sequence coded for by the leader sequence contains several tryptophan residues. 3. It contains several inverted repeat sequences such that the mRNA can adopt different alternative structures. 4. One of these structures represents a rho-independent termination site. Figure 2.7.4: High vs. Low tryptophan Under conditions of low tryptophan • A ribosome translating the nascent trp polycistronic mRNA stalls at the codons for tryptophan in the leader mRNA sequence. • This prevents segments 1 and 2 of the nascent mRNA from forming a potential stem-loop structure, and frees up segment 2 to form a stem loop structure with segment 3. • In this case, nascent transcription can proceed through, transcribing the entire trp operon. Under conditions of high tryptophan • A ribosome translating the nascent trp polycistronic mRNA does not stall at segment 1 (the region with codons coding for tryptophan). • In this case the stem-loop structure between 1 and 2 is not prevented from forming, and thus the stem-loop structure 3-4 can form. • This stem loop structure is essentially a rho-independent transcription termination structure. • The RNA polymerase transcribing the nascent message will terminate. Rho-dependent transcription termination • About half the termination sites in E. coli require an accessory protein called the rho factor. • Many rho-dependent sites have been characterized from E. coli and no obvious sequence similarity is present. • Rho associates with the nascent RNA transcript and this interaction activates an ATPase activity which appears to allow rho to translocate along the mRNA in the 3' direction. • It may be that if the RNA polymerase pauses, rho can catch up and cause termination (i.e. bump RNA polymerase off the DNA template?). • Pausing of RNA polymerase is thought to be an important mechanism of rho-dependent transcription termination.

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/2%3A_Bacteria/2.7%3A_Transcriptional_Regulation%3A_Transcription_termination_the_trp_operon.txt

How is the code contained in mRNA translated into a protein? Structure and function of transfer RNA's • tRNA's have two functions: 1. To chemically link to a particular amino acid (covalent) 2. To recognize a specific codon in mRNA (non-covalent) so that its attached amino acid can be added to a growing peptide chain Amino-acyl tRNA synthetases • Function is to "charge" tRNA molecules; i.e. to chemically link a specific amino acid to its associated tRNA molecule. Amino Acids Amino-acyl tRNA synthetases tRNA's Codons 20 20 30-40 (prokaryotes) 50 (eukaryotes) 61 (3 stop codons) Conclusions: 1. There is one amino-acyl tRNA synthetase per amino acid (they are quite specific). 2. There is potentially more than one tRNA per amino acid. Therefore, amino-acyl tRNA synthetases must be able to recognize more than one tRNA. 1. There is potentially more than one codon per tRNA. Therefore each tRNA must be able to recognize more than one codon (there is not a unique tRNA for each codon). Structure of tRNA's • 70-80 nucleotides long • Form a series of stem/loop secondary structures • tRNA's are synthesized with the standard bases AGCU. However, after synthesis several bases may be modified: 1. Uridylate may be methylated to produce Thymidylate 2. Uridylate may be rearranged to produce pseudouridylate (i.e. ribose attached to Carbon 5 instead of Nitrogen 1). 3. Guanidylate may be methylated at different positions. • The amino acid is attached at the 3' end of the tRNA to either the 2' hydroxyl or the 3' hydroxyl. 1. Class I amino-acyl tRNA synthetases attach their associated amino acids to the tRNA 2' hydroxyl (NOTE: typically the hydrophobic amino acids) 2. Class II amino-acyl tRNA synthetases attach their associated amino acids to the tRNA 3' hydroxyl (NOTE: typically hydrophilic amino acids) Figure 2.8.1: tRNA • If perfect Watson-Crick base pairing were required at the codon/anti-codon triplet then 61 different tRNA's would be required. • We know this is not the case, therefore a single tRNA anti-codon must be able to recognized several different mRNA codon triplets. • This greater recognition of tRNA is possible due to "wobble" basepair interactions at the third base in the codon/first base in the anti-codon: Figure 2.8.2: Codon wobble Possible "wobble" codon base pairing (in addition to Watson-Crick): 1. U - G 2. I - C 3. I - A 4. I - U • Where U, G, A and C can be in either the codon (mRNA) or anti-codon (tRNA) • I (inosine) can be found in the anti-codon. For example, the codons UUU and UUC are both recognized by the tRNA which has GAA in the anti-codon position (making either G - C, or G - U base pairings). Recognition of amino acids by amino-acyl tRNA synthetases • Appears to involve not only the anti-codon triplet but significant other contacts as well (mostly involving the acceptor stem region). Ribosomes • The mRNA with its encoded information and the individual tRNAs loaded with their amino acids are brought together by a mutual affinity for an RNA-protein complex called the Ribosome. • The rate of protein synthesis by a ribosome is approximately 3-5 amino acids/minute. • For example, a large protein (e.g. Titin, 30,000 amino acids) takes 2-3 hours to make. • Ribosomes are composed of individual ribosomal RNA (rRNA) molecules and more than 50 accessory proteins, with a general prokaryotic organization of a small subunit (30S) and a large subunit (50S). Translation of mRNA to proteins • Protein synthesis is usually considered in three steps: 1. Initiation 2. Elongation 3. Termination AUG is the initiation signal in mRNA • The first event of the initiation stage is the attachment of a free molecule of methionine (Met) to the end of a tRNAMet by a specific aminoacyl-tRNA synthetase. • There are at least two types of tRNAMet: 1. tRNA i Met: can initiate protein synthesis (at AUG met codon) 2. tRNA Met: can incorporate Met residues during on-going protein synthesis (at AUG met codon) • Methionine tRNA synthetase attaches Methionine to both tRNA molecules. • Only methionyl-tRNA i Met can bind to the small ribosomal subunit to begin the process of protein synthesis. • In bacteria, the amino group of the methionine in methionyl­tRNAiMet is formylated. • The Met-tRNA i Met, along with a protein-GTP complex and the small (30S) ribosomal subunit bind to the mRNA at a specific site, near the AUG initiation codon. Initiation of protein synthesis • In most prokaryotes an RNA component (16S rRNA) in the small rRNA subunit (30S) recognizes and hybridizes to a specific sequence on the mRNA called the Shine-Dalgarno sequence: mRNA 5' -UAAGGAGG -(5-10 nucleotides)-AUG 3' 16S rRNA OH-AUUCCUCC -(~1400 nucleotides)-5' • The Shine-Dalgarno sequence is thus a ribosome binding site which is necessary for the intiation of translation. • Note that the ribosome does not bind at the AUG start codon, but 5-10 nucleotides upstream. • The Shine-Dalgarno sequence can be located anywhere within an mRNA. • A series of initiation factors, Met-tRNAiMet , mRNA and the 30S (i.e. 16S component) ribosomal subunit are necessary for formation of the 30S initiation complex. • The large (50S rRNA) rRNA binds along with release of initiation factors 1 and 2, and hydrolysis of GTP, to form the 70S inititation complex: Figure 2.8.3: Initiation complexes Elongation 1. In the first part of the elongation step of translation, the ribosome moves along the mRNA to position the fMet residue to the P site (peptidyl site) in the 50S subunit. • This allows the second codon of the mRNA to be positioned in the A site (amino acyl tRNA site). 2. The appropriate charged tRNA (with amino acid) specified by the second codon is positioned in the A site of the 50S subunit. 3. Next peptide bond formation is synthesized and the tRNA in the A site (which is covalently attached to the nascent polypeptide) is translocated to the P site. • This process requires GTP and the G elongation factor protein (prokaryotes). 4. The process is repeated. Termination 1. When a stop codon is reached the polypeptide is hydrolyzed away from the last tRNA. • The peptide is released and the ribosome typically dissociates. • This process requires GTP and three different termination factors (TF's; only one required in Eukaryotes)

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/2%3A_Bacteria/2.8%3A_mRNA_Translation.txt

Gel electrophoresis is used to characterize one of the most basic properties - molecular mass - of both polynucleotides and polypeptides. Gel electrophoresis can also be used to determine: (1) the purity of these samples, (2) heterogeneity/extent of degradation, and (3) subunit composition. DNA The most common gel electrophoresis materials for DNA molecules is agarose and acrylamide. DNA agarose gels The electrophoretic migration rate of DNA through agarose gels is dependent upon four main parameters: 1. The molecular size of the DNA. Molecules of linear duplex DNA travel through agarose gels at a rate which is inversely proportional to the log of their molecular weight. \[M_r \propto 1/log (Mw)\] Example: Compare molecular mass vs. expected migration rate: Molecular Mass (Da) log (Molec. Mass) 1/log (Molec. Mass) i.e. relative Mr 100,000 5.0 0.20 50,000 4.7 0.21 10,000 4.0 0.25 5,000 3.7 0.27 1,000 3.0 0.33 Figure 3.1.1: Relative migration rate with molecular mass 2. The agarose concentration. There is an inverse linear relationship between the logarithm of the electrophoretic mobility and gel concentration. \[\log (M_r) \propto 1/[gel]\] Gel % 1/Gel % inv log(1/Gel %) (i.e. relative Mr) 2.0 0.50 3.2 1.5 0.67 4.6 1.0 1.00 10.0 0.5 2.00 100.0 Figure 3.1.2: Relative migration rate with gel concentration 3. The conformation of the DNA. • closed circular DNA (form-I) - typically supercoiled • nicked circular (form-II) • linear DNA (form-III) These different forms of the same DNA migrate at different rates through an agarose gel. Almost always the linear form (form-III) migrates at the slowest rate of the three forms and supercoiled DNA (form-I) usually migrates the fastest. 4. The applied voltage. • Typical value for running an agarose gel is 5 volts per cm (length of gel). Agarose (%) Range of separation of linear DNA (in kilobases) 0.3 60 - 5 0.6 20 - 1 0.7 10 - 0.8 0.9 7 - 0.5 1.2 6 - 0.4 1.5 4 - 0.2 2.0 3 - 0.1 • Agarose gels are usually poured and run horizontally Finally, the DNA being an acidic molecule, migrates towards the positively charged electrode (cathode). Figure 3.1.3: Gel electrophoresis setup DNA acrylamide Gels Acrylamide gels are useful for separation of small DNA fragments typically oligonucleotides <100 base pairs. These gels are usually of a low acrylamide concentration (<=6%) and contain the non-ionic denaturing agent Urea (6M). The denaturing agent prevents secondary structure formation in oligonucleotides and allows a relatively accurate determination of molecular mass. Gel Electrophoresis for Proteins Gel electrophoresis of proteins almost exclusively utilizes polyacrylamide. The acrylamide solution usually contains two components: acrylamide and bis acrylamide. A typical value for the acrylamide:bis ratio is 19:1. The bis acrylamide is essentially a cross-linking component of the acrylamide polymer. The total acrylamide concentration in the gel affects the migration of proteins through the matrix (as with the concentration of agarose). Protein gels are usually performed under denaturing conditions in the presence of the detergent sodium dodecyl sulfate (SDS). The proteins are denatured by heat in the presence of SDS. The SDS binds, via hydrophobic interactions, to the proteins in an amount approximately proportional to the size of the protein. Due to the charged nature of the SDS molecule the proteins thus have a somewhat constant charge to mass ratio and migrate through the gel at a rate proportional to their molecular mass, The proteins migrate towards the anode. Acrylamide (%) Range of separation of Polypeptides (in kilodaltons) 8 200 - 25 10 100 - 15 12.5 70 - 10 15 60 - 6 20 40 - 4 Since the SDS treatment will dissociate non-covalent protein complexes, they may thus exhibit a much lower than expected molecular mass on SDS polyacrylamide gel electrophoresis (SDS PAGE). Protein PAGE gels are usually polymerized between two glass plates and run in the vertical direction. Figure 3.1.4: Effect of SDS treatment PAGE may also be run in the presence of reducing agents, such as b-mercaptoethanol (BME). BME is a reducing agent which will reduce any disulfide bonds (e.g. as exists between some pairs of cysteine residues in a protein). This helps to remove residual secondary structure in the SDS treated protein, but it may also allow the separation of polypeptide fragments from each other (i.e. their covalent interaction was entirely made up of one or more disulfide bonds). Thus, an apparently single protein may exhibit a set of small fragments under reducing PAGE conditions. Stains for DNA Ethidium The most convenient method to visualize DNA in gel electrophoresis is staining with the fluorescent dye ethidium bromide. This compound contains a planar group that intercalates between the stacked bases of DNA. The orientation and proximity of ethidium with the stacked bases causes the dye to display an increased florescence compared to free dye (in solution). U.V. radiation at 254 nm is absorbed by the DNA and transmitted to the bound dye and the energy is re-emitted at 590 nm in the red-orange region of the spectrum. Figure 3.1.5: Ethidium Ethidium bromide is usually prepared as a stock solution of 10 mg/ml in water, stored at room temp and protected from light. The dye is usually incorporated into the gel and running buffer, or conversely, the gel is stained after running by soaking in a solution of ethidium bromide (0.5 ug/ml for 30 min). The stain is visualized by irradiating with a UV light source (i.e. using a transiluminator) and photgraphing with polaroid film. The usual sensitivity of detection is better than 0.1 ug of DNA. Because ethidium is a DNA intercalating agent, it is a powerful mutagen. Incorporation of ethidium in the DNA of living organisms (i.e. you and I) can cause (unwanted) mutations. Note: • The intercalation of ethidium bromide causes a reduction in the number of effective basepairs per twist. • Thus, the DNA wants to adopt a conformation with fewer base pairs per twist. • The number of twists will increase for the given length of DNA • Writhe will decrease if the Linkage number remains unchanged • The net result is that addition of ethidium bromide will introduce negative supercoils into the DNA • Addition of ethidium bromide to a DNA sample prior to electrophoresis can cause positively supercoiled DNA to migrate as relaxed (nicked, or form II) DNA • After replication DNA is typically underwound (negative supercoils), thus it will become more underwound with EtBr treatment (migration rate may actually increase due to more compact state) Flourescence shadowing: • DNA fragments resolved on polyacrylamide gels can also be visualized by the method of UV shadowing. • In this method the gel is placed on top of a fluorescent material, usually a flourescent TLC silica plate. • The gel is then illuminated by a UV light source. • DNA bands in the gel will block transmittance of the UV light to the substrate. • This will result in a dark area (i.e. non-fluorescing) area on the substrate. • This method is usually used in gel purification of oligonucleotides (i.e. the dark bands can be cut out with a razor blade and the oligonucleotide extracted) Stains for Proteins Coomassie brilliant blue • Coomassie blue is a triphenylmethane textile dye which is able to stain proteins. • After a polyacrylamide gel is run it is usually "fixed" by placing in a 50% methanol/10 acetic acid solution for 30 minutes (to precipitate the proteins and prevent diffusion out of the gel). • The fixed gel is then soaked in a methanol/acetic acid solution containing 2.5 gm/liter of Coosmassie blue. • Destaining of the background gel is accomplished by soaking in changes of 10% methanol/7 % acetic acid. This method can typically detect protein samples of 0.1 ug or greater. Silver stain • Silver staining is a method which utilizes a silver nitrate solution to stain proteins in an acrylamide gel. • The method is similar in nature to the use of silver in photographic plates. • The sensitivity is approximately two orders of magnitude more sensitive than coomassie staining (i.e. one can detect approximately 1ng of protein). • Due to its high sensitivity this method is usually used to determine the presence of trace contaminants in protein samples. Molecular Weight Standards • Both DNA and protein gel electrophoresis utilize molecular weight standards to calibrate the size(s) of samples being analyzed • DNA molecular weight standards will consist of a mixture of DNA fragments of known sizes (molecular mass) • One convenient DNA molecular weight standard is constructed by partial ligation of a 100 base pair fragment of duplex DNA • Partial ligation will result in formation of dimers (200 bp), trimers (300 bp) and so on, as well as some amount of the original (100 bp) fragment • This produces a DNA "ladder" after gel electrophoresis • Another DNA fragment standard might be a known DNA sequence, such as the plasmid pBR322, which has been digested with a four-cutter restriction endonuclease (e.g. Alu I). • This produces a variety of fragments with different sizes • It is easily reproduced (just cut more pBR322) • Protein molecular weight markers usually consist of a mixture of a half dozen or so pure proteins with known molecular masses Protein Molecular Mass (Da) Phosphorylase B 94,000 Bovine Serum Albumin 67,000 Ovalbumin 43,000 Carbonic Anhydrase 30,000 Soybean Trypsin Inhibitor 20,100 a-Lactalbumin 14,400 • In both DNA and protein gels, molecular weight markers are run in a lane at one end of the gel Figure 3.1.6: DNA size standards

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/3._Biotechnology_1/3.1%3A_Gel_Electrophoresis.txt

DNA sequencing is most often accomplished using a procedure referred to by one of the following names: 1. Sanger sequencing 2. Di-deoxy sequencing 3. Chain termination sequencing • Each of these refers to the same method: • the use of di-deoxy base incorporation in a polymerization reaction • leads to termination of primer extension (a method pioneered by Fred Sanger, now retired and happily puttering about in his garden). The basic method involves: 1. annealing a primer 5' to a region of DNA we would like to sequence. 2. The primer is extended in the traditional manner (i.e. with DNA polymerase and the four dNTP's). 3. However, a small concentration of di-deoxy bases are included in the reaction mix. • Usually this is accomplished by having four separate reactions, into which one of the ddNTP's is added. • Thus, one tube would contain primer, template, DNA polymerase, the four dNTP's and ddATP, another tube would have the same thing but with ddTTP instead of ddATP, and so on for ddCTP and ddGTP. 4. During the reaction, the normal dNTP's are incorporated into the growing chain. • However, occasionally the DNA pol will incorporate a ddbase into the growing primer. • When this happens, the primer cannot be extended any further (because the 3' dd base does not have an available 3' hydroxyl group). • The resulting DNA fragment begins at the 5' end of the sequencing primer and ends at the site of dd base incorporation. Thus, in the reaction mixture containing the dd base ddATP, there will result an ensemble of fragements of varying lengths, each ending in with the ddA base (i.e. at all positions in the template where there was a comlementary 'T' base). The mixture containing ddCTP will have a different mix of fragements - they will contain ddC at the 3' ends (at positions in the template where 'G' bases were located). Figure 3.2.1: DNA fragments • If the fragments from the 'A' reaction mix are run on a urea/acrylamide gel (typically 6%) the fragments will separate according to size. • Likewise for the 'C', 'G' and 'T' reaction mix fragments. • If the four different reaction mixtures are run next to each other the fragment sizes can be directly compared to one another. • Note that the shortest expected fragment is the primer itself and the longer the fragment, the further from the primer the extension reaction went before termination. • Consider a case where the template has a stretch of six 'A' bases in a row. • In the 'T' reaction mix we will subsequently get six fragments; each ending in 'T' and differing by one base length. • None of the other reaction mixes will contain fragments between these lengths (they will either be longer or shorter) because none of the other reaction mixes will terminate within this region. • Thus, if we run the four reaction mixes side by side and look at the fragment patterns we would see the following: Figure 3.2.2: Example fragment patterns with 6 A's Now consider a template that contains the sequence 3' GATC 5' (note the orientation). • When the primer is extended in the different reaction mixes it can truncate first at the G (incorporating a dd 'C'), then at the A (incorporating a dd 'T') and so on. • The fragments run on a gel would thus look like: Figure 3.2.3: Example fragment patterns with GATC • Thus, following the ladder of fragments on the sequencing gel allows you to "read" the sequence of the template. • Note however, that in regard to the template when we read from the bottom of the gel to the top we are reading in the 3' to 5' direction and reading the complementary bases to the actual sequence. Visualization of fragments • If radiolabeled dATP is spiked into the mixtures it will be incorporated like a "normal" dATP base. • However, the resulting DNA fragment will be radiolabled. • Thus the acrylamide gel can be exposed to x-ray film and the location of the fragments determined. • Recently, automated sequencers have made use of specific dyes which are tagged to the dideoxy bases. • These dyes can be "read" by a laser and thus the specific terminating dd base for a particular DNA fragment can be identified. • Thus, the fragments can read as they elute, rather than stopping the gel and exposing it. • Furthermore, since each dd base can be uniquely identified, all four reactions (ddA, ddC, ddG and ddT chain termination) can be done in a single tube and run in a single lane on a sequencing gel • Automated sequencers can thus read further than with manual methods. • Since a single lane is used per sample (as compared to four lanes with the radiolabeled method) many more samples can be analyzed and the throughput is greater • The acrylamide gels used for sequence analysis are typically 50 cm to 100 cm long. • In manual sequencing the four reaction mixes are loaded and the gel is run for approximately 2 hours then the samples are reloaded on another part of the gel and the gel run is continued. A third set of samples may be loaded after another 2 hours. • The gel is stopped after the dye front of last sample loaded has just reached the bottom of the gel. • Thus, the short fragments can be visualized in the last load, medium fragements in the second load and the long fragments will be visualized in the first set of reaction mixtures loaded. • Manual sequencing can resolve on the order of 400 bases of continuous sequence. Automated sequencers can routinely provide twice this amount of information. • Automated sequencers use the same types of glass plates • The continuous running of the gel (and dye identification) means that typically 400-700 bases or more can be read • Automatic software will interpret the dye signals into a sequence • nuances of the sequencing chemistry and expert knowledge can be programmed into the sequence analysis software (e.g. the software can compensate for the "smile" of the gel)

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/3._Biotechnology_1/3.2%3A_DNA_Sequence_Analysis.txt

PCR is an in vitro technique for the amplification of a region of DNA which lies between two regions of known sequence. PCR amplification is achieved by using oligonucleotide primers. These are typically short, single stranded oligonucleotides which are complementary to the outer regions of known sequence. Figure 3.3.1: Complementary oligonucleotide primers The oligonucleotides serve as primers for DNA polymerase and the denatured strands of the large DNA fragment serves as the template. This results in the synthesis of new DNA strands which are complementary to the parent template strands. These new strands have defined 5' ends (the 5' ends of the oligonucleotide primers), whereas the 3' ends are potentially ambiguous in length. Figure 3.3.2: Synthesis of new DNA strands The oligonucleotide directed synthesis of daughter DNA strands can be repeated if the new duplex is denatured (by heating) and additional primers are allowed to anneal (by cooling to an appropriate temperature). The steps of: 1. Template denaturation 2. Primer annealing 3. Primer extension comprise a single "cycle" in the PCR amplification methodology. After each cycle the newly synthesized DNA strands can serve as templates in the next cycle. Figure 3.3.3: Product of elongation reaction as template Note that half of the newly synthesized strands from the second round of replication have 5' and 3' termini which are defined by the annealing location of the primer oligonucleotides. Summary of products at the end of each PCR cycle: Figure 3.3.4: PCR products The total of each of the different types of oligonucleotide fragments for each cycle can be summarized as follows: Figure 3.3.5: Types of fragments for each cycle • The amplification of the fragments follows the following pattern: • There is always 1x copy of each of the original templates - the PCR as outlined never reproduces the full length template • There will be 'n'x copies of each fragment with an indeterminate length (where 'n' is the number of cycles). The fragments of indeterminant length have one end defined by a PCR primer and the other end is of indeterminant length • There will be (2n - (n + 1)) x copies of each fragment of defined length (i.e. each end defined by the two PCR primers). • For example, after 6 cycles we will have • 1 x copy of each original template • 6 x copies of each fragment of indeterminant length • (64 - (6 + 1)) = 57 x copies of each fragment of defined length • In the above case, the desired PCR product will be a duplex of the defined length fragment. The question is: how many will be produced? • The original templates will not necessarily anneal with one another • Likewise, the templates of indeterminant length will not necessarily anneal with one another • Furthermore, as the number of cycles proceeds, the defined length fragments far outnumber the others • Therefore, the original template, and fragments of indeterminant length are most likely going to hybridize with defined length fragments • Our expected amplification would therefore be (using the 6 cycle PCR example) 57 - (6 + 1) = 50 x copies of each defined length fragments (i.e. 50 x duplexes of defined length) • The expected amplification of the desired defined length product with respect to the original template concentration ($x$) can thus be represented by the formula: $[(2^n - (n + 1)) - (n + 1)] x$ or $(2^n - 2(n + 1))$ (this is often abbreviated to a simple rule of thumb for the amplification: (2n - 2n) x) The interpretation of this formula is that • For a given number of cycles 'n' we make '2n x' total possible duplexes • For a given number of cycles there will be '2(n+1) (or 2n in our approximation) x' duplexes which are formed from either the original template, or a fragment of indeterminate length, along with a fragment of defined length (and represent an undesired product) • Thus, the total concentration of desired product (duplexes with a length defined by the PCR primers) will be (2n - 2(n+1)) x (where x is the concentration of the original duplex) • The theoretical amplification value is never achieved in practice. Several factors prevent this from occurring, including: 1. Competition of complementary daughter strands with primers for reannealing (i.e. two daughter strands reannealing results in no amplification). 2. Loss of enzyme activity due to thermal denaturation, especially in the later cycles 3. Even without thermal denaturation, the amount of enzyme becomes limiting due to molar target excess in later cycles (i.e. after 25 - 30 cycles too many primers need extending) 4. Possible second site primer annealing and non-productive priming Note PCR was invented in 1985 by Kary Mullis, working for Cetus corporation somewhere near Berkeley, California. The original method of PCR used the Klenow fragment of E. coli DNA polymerase I. This enzyme, however, denatures at temperatures lower than that required to denature most template duplexes. Thus, after each cycle, fresh enzyme had to be added to the reaction. This was quite tedious. In addition to this problem with the enzyme, the samples had to be moved from one temperature bath to another to allow the individual steps of denaturation, annealing and polymerization, which all required different temperatures. This was pretty tedious too. Thus, two main advances allowed the process to be automated, these advances were: 1. The use of thermostable DNA polymerases, which resisted denaturation (inactivation) at high temperatures. Thus, an initial aliquot of polymerase could last throughout the numerous cycles of the protocol. The first thermostable DNA polymerase to be used was isolated from the bacterium Thermus aquaticus. It was isolated from a hot spring in Yellowstone National Park where it lived happily (i.e. it replicated its DNA) at temperatures in excess of 85 °C 2. The development of temperature baths which could shift their temperatures up and down rapidly and in an automated programmed manner. These are known as thermal cyclers or PCR machines. These two developments let to the automation of PCR. The PCR process is covered by patents owned by Hoffmann-La Roche Inc. (a faceless conglomerate you can trust). Thermal cycling parameters • The thermal cycling parameters are critical to a successful PCR experiment. The important steps in each cycles of PCR include: 1. denaturation of template 2. annealing of primers 3. extension of the primers A representative temperature profile for each cycle might look like the following: Figure 3.3.6: Temperature for PCR cycle Template denaturation • The initial denaturation of template is accomplished at 95-100 °C. • Supercoiled plasmids are tougher to melt and may require boiling for several minutes, or may be initially denatured by using base (NaOH, followed by pH neutralization). • Denaturation during the PCR experiment (i.e. second cycle onward) is usually accomplished at temperatures of 92-95 °C (usually empirically determined). Primer annealing temperature • Primer annealing temperature is an important parameter in the success of the PCR experiment. • The annealing temperature is characteristic for each oligonucleotide: • it is a function of the length and base composition of the primer as well as the ionic strength of the reaction buffer. • Estimates of the annealing temperature can be calculated in several different ways. • These calculated annealing temperatures are a starting point for the PCR experiment, but ideal annealing temperatures are determined empirically. Primer extension • Primer extension is usually performed at 72 °C, or the optimum temperature of the DNA polymerase. • The length of time of the primer extension steps can be increased if the region of DNA to be amplified is long, however, for the majority of PCR experiments an extension time of 2 minutes is sufficient to get complete extension. Number of cycles • The number of cycles is usually between 25 and 35. • More cycles mean a greater yield of product. • However, with increasing number of cycles the greater the probability of generating various artifacts (e.g. mispriming products). • It is unusual to find procedures which have more than 40 cycles. Choice of Polymerases for PCR • One of the important advances which allowed development of PCR was the availability of thermostable polymerases. • This allowed initially added enzyme to survive temperature cycles approaching 100 °C. Thermostable DNA polymerases and their sources DNA Polymerase Natural or recombinant Source Taq Natural Thermus aquaticus Amplitaq® Recombinant T. aquaticus Amplitaq (Stoffel fragment)® Recombinant T. aquaticus Hot Tub™ Natural Thermus flavis Pyrostase™ Natural T. flavis Vent™ Recombinant Thermococcus litoralis Deep Vent™ Recombinant Pyrococcus GB-D Tth Recombinant Thermus thermophilus Pfu Natural Pyrococcus furiosus ULTma™ Recombinant Thermotoga maritima • Properties of DNA polymerases used in PCR Taq/Amplitaq® Stoffel fragment Vent™ Deep Vent™ Pfu Tth ULTma™ 95 °C half-life 40 min 80 min 400 min 1380 min >120 min 20 min >50 min 5'3' exo + + 3'5' exo + + + + Extension rate (nt/sec) 75 >50 >80 ? 60 >33 ? RT activity Weak Weak ? ? ? Yes ? Resulting ends 3' A 3' A >95% blunt >95% blunt blunt 3' A blunt Strand displacement + + M.W. (kDa) 94 61 ? ? 92 94 70 Buffers and MgCl2 in PCR reactions A typical reaction buffer for PCR would something like: • 10 mM Tris, pH 8.3 • 50 mM KCl • 1.5 mM MgCl2 • 0.01% gelatin • The MgCl2 concentration in the final reaction mixture is usually between 0.5 to 5.0 mM, and the optimum concentration is determined empirically (typically between 1.0 - 1.5 mM). Mg2+ ions: • form a soluble complex with dNTP's which is essential for dNTP incorporation • stimulate polymerase activity • increase the Tm (melting temperature) of primer/template interaction (i.e. it serves to stabilize the duplex interaction Generally, • low Mg2+ leads to low yields (or no yield) and • high Mg2+ leads to accumulation of nonspecific products (mispriming). Primers Primer design • Generally, primers used are 20 - 30 mer in length. This provides for practical annealing temperatures (of the high temperature regimen where the thermostable polymerase is most active). • Primers should avoid stretches of polybase sequences (e.g. poly dG) or repeating motifs - these can hybridize with inappropriate register on the template. • Inverted repeat sequences should be avoided so as to prevent formation of secondary structure in the primer, which would prevent hybridization to template • Sequences complementary to other primers used in the PCR should be avoid so as to prevent hybridization between primers (particularly important for the 3' end of the primer) • If possible the 3' end of the primer should be rich in G, C bases to enhance annealing of the end which will be extended • The distance between primers should be less than 10 Kb in length. Typically, substantial reduction in yield is observed when the primers extend from each other beyond ~3 Kb. Melting temperature (Tm) of primers • The Tm of primer hybridization can be calculated using various formulas. The most commonly used formula is: $Tm = [(number of A+T residues) \times 2 °C] + [(number of G+C residues) x 4 °C]$ • This formula was determined originally from oligonucleotide hybridization assays, which were performed in 1 M NaCl, and appears to be accurate in lower salt conditions only for primers less than about 20 nucleotides in length. • The common wisdom is that the Tm is more like 3-5 °C lower than the value calculated from this formula. $Tms = 81.5 + 16.6(\log_{10}[J+]) + 0.41(\%G+C) - (600/l)$ • Where [J+] = the molar concentration of monovalent cations (e.g. Na+ from NaCl), and l = the length of oligonucleotide. (%G+C) is the actual percentage value and not a fractional representation (i.e. the value to insert for a primer which had 90 % G+C content would be "90" and not "0.90"). • This formula is reportedly useful for primers of 14 to 70 bases in length. $Tmp = 22 + 1.46([2 \times (G+C)] + (A+T))$ • This formula is reportedly useful for primers of 20-35 bases in length. • The calculated annealing temperature is only a reference temperature from which to initiate experiments. • The actual annealing temperature may be 3-12 °C higher than the calculated Tm. • The actual annealing temperature condition should be determined empirically. • The highest annealing temperature which gives the best PCR product should be used. • Examples of Tm , Tms and Tmp calculations (0.05 M K+) G A T C Tm Tms Tmp 15 mer 3 5 2 5 46 42 56 20 mer 6 5 4 5 62 52 67 30 mer 8 6 8 8 92 62 89 Calculating primer concentrations • The molar concentration of a primer can be calculated based on the absorbance of the primer at 260 nm (A260) and the molar extinction coefficient for the primer at this wavelength. • The molar extinction coefficient for the primer can be calculated by knowing the sequence of the primer and then summing the molar extinction values for the individual bases which comprise the primer. • The individual bases have the following molar extinction coefficients at 260 nm: • A 1.0 molar solution of dT has a value of 8,400 absorbance units at 260 nm. • A 1.0 molar solution of dA has a value of 15,200 absorbance units at 260 nm. • A 1.0 molar solution of dG has a value of 12,010 absorbance units at 260 nm. • A 1.0 molar solution of dC has a value of 7050 absorbance units at 260 nm. • For example, the primer 5' TAGC 3' would have a molar extinction coefficient of 42,660 at 260 nm. Likewise, a 10 micromolar solution of this primer would give an absorbence of 0.427 at 260 nm. Cloning PCR Products Introduction of restriction sites • It is possible to introduce restriction site sequences into PCR products by having these sequences incorporated into the 5' end of the PCR primer(s). Figure 3.3.7: Restriction site sequence introduction • The short restriction site sequence on the 5' end of the PCR primer will not hybridize, but as long as the 3' hybridizing region is long enough (i.e. its Tm is high enough; ~20 mer) the primer will specifically bind to the appropriate site. • The PCR product will thus have an additional DNA squence at the 5' end which will contain the endonuclease restriction site. • A similar or different restriction site sequence can be added via the other PCR primer. • If the other primer has a different restriction sequence then the PCR fragment can be inserted in a directional dependent manner in a host plasmid. The potential problems with this method include: • There is no easy way to prevent internal sites containing similar restriction sequences from being cut when the end of the PCR product are cut • Restriction sequences are inverse repeat sequences, thus the potential exists for primer dimer association and resultant non-productive annealing Generation of half sites • This method is similar to the method of introducing restriction sites, described above. • The primary difference is that instead of the primer containing the entire restriction site sequence (say the six nucleotides of a six cutter) it will contain only the last three (and the other PCR primer will contain the complementary sequence for the first three). Figure 3.3.8: Half sites The advantages of this method are: • Typically internal restriction sites cleave with much greater efficiency (i.e. some sites if located at the ends of linear DNA never cut well at all) • There is no need to gel purify linker fragments after digestion • The DNA can be methylated (the half sites will not be). After concatenation the linkers will be cut but internal restriction sites will not • A disadvantage is that the same restriction site is incorporated into both ends so the PCR fragment cannot be ligated into a host vector in an orientation dependent manner. • Also, in this method 3' A overhang cannot be tolerated. Blunt end ligation • As stated earlier some thermostable DNA polymerases add a single dA residue onto the 3' end of the PCR product. • There are three choices to be made when attempting to subclone without the use of added restriction sites within the primers: • Use a DNA polymerase which leaves the 3' strand blunt (e.g. Vent) and do a blunt end ligation (i.e. host vector was opened up with blunt cutting restriction endonuclease) • "Fix" the 3' A overhang by chewing back with Pol I, dNTP's. • Use the 3' A overhang to anneal and ligate to a "T" vector - a vector which has a single dT overhang on its 3' ends. • "T" vectors can be made by opening up a vector, via a blunt cutting restriction endonuclease and ligating in a specific linker. • The linker contains a restriction sequence for a restriction endonuclease which recognizes an interrupted palindrome and cuts in the internal region with a single 3' overhang. • The linker will contain two copies of the restriction recognition sequence, the first with a sequence in the interrupted palindrome which leaves a 3' T overhang and the second with a sequence in the interrupted palindrome which leaves another 3' T overhang • This may sound confusing, so here's an example of how to make a T vector: Ahd I restriction endonuclease: | G A C N N N N N G T C C T G N N N N N C A G | Ahd I will cut this sequence to produce: G A C N N N N N G T C C T G N N N N N C A G We could design an oligonucleotide with two Ahd I restriction sequences, with slightly different sequences in the interrupted region of the palindrome, to give: | | G A C N N T N N G T C G A C N N A N N G T C C T G N N A N N C A G C T G N N T N N C A G | | If this this were inserted into a vector, and the vector then was cut with Ahd I, it would have the following sequence at the ends of the linearized vector: -G A C N N T N N G T C- -C T G N N T N N C A G- In other words, a 3' 'T' overhang at both ends of the vector. A PCR product, with 3' A overhangs could thus be inserted into such a 'T' vector Adding promoters, ribosome binding sites, start codons, and stop codons • The ability to add unique sequences to the 5' ends of PCR primers allows for short control elements to be directly incorporated. • These can include a start codon or stop codon (3 bases), a promoter (~30 nucleotide region) or a ribosome binding site (~8 bases). Figure 3.3.9: Inserting promoter/ribosome/binding site/start codon/stop codon PCR Mutagenesis Gene fusion • This method is useful for joining overlapping regions of a large gene, or for the construction of chimeric genes. Figure 3.3.10: Gene Fusion Creation of deletions within a gene • A very similar methodology can be used within a single gene for the production of a mutant gene containing a specific deletion: Figure 3.3.11: Deletions • If the gene is contained within circular DNA (i.e. a plasmid) deletions can be constructed in a single PCR reaction with a single set of primers (this type of methodology is also known as "inverse" PCR). Figure 3.3.12: Inverse PCR Generation of point mutation(s) - i.e. base substitution mutations • The generation of base substitutions can proceed along a similar route as with the deletion mutations. • However, in this case the primers are mutagenic - there will be a mismatch, or mismatches, between the primer and target sequences. • The mutagenic oligo will have a lower than expected Tm due to this mismatch(es). Figure 3.3.13: Point mutation Introduction of base substitutions via asymmetric PCR: Figure 3.3.14: Substitution with asymmetric PCR Insertion mutagenesis • Short insertions (~1-6 basepairs) can be incorporated directly into a PCR primer, either internally, or at the 5' end. • If the template DNA is linear and the desired site of insertion is not at the end of the template, then the entire gene (plus insertion) can be produced using asymmetric PCR or overlapping PCR (i.e. shown above). Figure 3.3.15: Insertion mutagenesis • Large insertions can be accomplished by using a template (the desired insertion) for PCR with the primers having 5' sequences which are complementary to the region of insertion in the desired gene: Figure 3.3.16: Large insertion "Random" mutagenesis with PCR • The PCR protocol can be modified so as to introduce mutations at random positions in the target DNA. • The principle behind the mutagenesis is misincorporation of bases at "random" positions. • Misincorporation by Taq polymerase, for example, can be achieved by adding Mn2+ to the reaction buffer, and decreasing the concentration of one of the four dNTP's. • At the sites in the template where the reduced base should be incorporated, there will be an increased probability of misincorporation. • Thus, the choice of base with diminished concentration determines the sites in the template which will potentially be mutated. • The misincorporated base is more or less random. • The ideal Mn2+ concentration to add varies between 0.1 to 0.5 mM and is determined empirically. The relative concentrations of bases is 1 mM for each base, except the reduced base, which is typically present at a 1:5 or 1:10 ratio (i.e. 0.2 to 0.1 mM).

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/3._Biotechnology_1/3.3%3A_Polymerase_Chain_Reaction_%28PCR%29_and_Cloning_of_PCR_Products.txt

The first vector we will consider is the pUC family of vectors Figure 3.4.1: pUC vector Although it is not typically used for the expression of recombinant proteins, it has all the necessary elements of an expression vector: 1. An origin of replication. The pUC family of vectors are high copy vectors. They have a ColE1 origin of replication, but a deletion of the rop replication regulatory region. 2. A drug resistance marker. The pUC vectors contain a gene for ampicillin resistance (b-lactamase). 3. An inducible promoter. This family of vectors contains the lac promoter (Plac) along with the associated lac Operator region (Olac) These vectors contain a couple of other elements which add utility to the vector: 1. The lacI gene. This gene codes for the lac repressor protein. Being a high copy plasmid the host lacrepressor levels may not be sufficient to efficiently repress the lac operator on the plasmids. The plasmid produces lac repressor to augment the host levels. 2. The lacZ' gene. This gene product is transcribed from the lac promoter and produces an amino-terminal fragment of the b-galactosidase protein. 3. A polylinker (multiple cloning site). This short stretch of DNA is located just downstream from the lacpromoter and after the first few codons of the lacZ' gene. It is a short stretch of nucleotides which contains a variety of restriction endonuclease sites. a-complementation The lac promoter of the pUC plasmid can be induced using the lactose analogue isopropylthiol-b-D-galactoside. ("IPTG"). • This will result in the expression of the lacZ' gene. • This gene codes for an amino-terminal fragment of the b-galactosidase protein • By itself, this fragment is non-functional (it will not hydrolyze lactose or other b-galactosides) However, b-galactosidase is an interesting protein: • If we express the carboxy-terminal fragement of the protein it is also non-functional • But, if we combine both peptide fragments they can combine to yield a functional b-galactosidase protein • This is termed a-complementation The lacZDM15 mutation in a bacteria means that its genome has suffered a deletion of the amino-terminal region of the lacZ gene. • Such a bacteria has a non-functional b-galactosidase protein • If this type of bacteria contains an extra-chromosomal element which expresses the lacZ' peptide, it can complement the lacZDM15 protein to produce functional b-galactosidase protein (a-complementation) The galactoside, 5-bromo-4-chloro-3-indoyl-b-D-galactoside ("X-gal"), can be hydrolyzed by b-galactosidase and produces a dark blue color. • lacZDM15 bacteria in the presence of lactose (or IPTG) and X-gal will appear a normal white-yellow color. • The same bacteria which harbor the above pUC plasmid will be dark blue in color when IPTG and X-gal are present in the media (due to a-complementation and a functional b-galactosidase protein) The pUC polylinker region The pUC polylinker region is a short stretch of DNA which is actually inserted within the lacZ' gene, just downstream from the start codon: Figure 3.4.2: pUC Polylinker region Note In the above diagram the codon numbers for the wild-type b-galactosidase are given in magenta (in this case the initiator methionine is not present on purified protein and the first codon is considered to be the ACC (threonine) residue). The codons from the polyliner (which interrupt the b-galactosidase gene) are given in black. • The different available plink regions are all a multiple of 3 nucleotides in length, therefore the lacZ' gene remains in-frame. • The resulting lacZ'/plink peptide can still function in a-complementation. What would happen to the lacZ' peptide if we inserted a DNA fragment into the plink region? • The insertion of a random piece of DNA into the plink region will result in three possible different reading frames after the insert (depending on the number of nucleotides in the inserted fragment) • There is a 33% (one in three) possibility that the downstream lacZ' gene will be translated in the correct reading frame • There is also the possibility that even if the reading frame is correct that the insertion of a large peptide at the start of the lacZ' protein will prevent a-complementation. • Therefore, most of the time, a DNA fragment inserted into the plink region will abolish b-galactosidase function • DNA inserts in the plink region can therefore be identified by growing the host bacteria on media containing IPTG and X-gal and looking for white-yellow colonies (with the caveat that some blue colonies may in fact contain an insert) DNA coding for a gene of interest can be inserted in the plink region in-frame with the lacZ' reading frame. • The protein coded for by this gene can be expressed by induction with IPTG (i.e. using the Plac) • The expressed protein will have as its amino-terminal sequence the first few amino acids of the b-galactosidase gene (in the absence of any other manipulations/mutagenesis). This is also known as a (short) fusion protein. • Although the lac promoter is considered to be relatively weak, the high copy number plasmid may result in a useful level of expression of the protein of interest The pET vector system (Novagen, Inc.) The pET vector looks like this: Figure 3.4.3: pET Vector It has the following important elements: • Ampicillin resistance marker • ColE1 origin of replication • f1 origin of replication (allows single stranded vector to be produced when co-infected with M13 helper phage) • lacI gene (lac repressor protein) • T7 transcription promoter (specfic for phage T7 RNA polymerase) • lac operator region 3' to the T7 promoter • multiple cloning site (polylinker region) downstream of the T7 promoter The pET vector is a little different from the pUC vector: pUC uses the lac promoter and pET uses a promoter from phage T7 • The phage T7 promoter is stronger than the lac promoter • Phage T7 RNA polymerase will specifically recognize the T7 promoter region and will not efficiently transcribe from other promoters • The T7 promoter will not be efficiently transcribed by E. coli RNA polymerase Where will the phage T7 RNA polymerase come from? The pET system involves not only an expression vector, but also a genetically engineered host bacteria. The host bacteria for the pET vector is typically E. coli strain BL(DE3) • This strain has integrated into its chromosome the gene for T7 RNA polymerase • The T7 RNA polymerase in the host genome is constructed such that it is under the control of a lac promoter and operator • Thus, induction by the lactose analogue, IPTG, causes the host to produce T7 RNA polymerase • The E. coli host genome also carries the lacI (repressor) gene Figure 3.4.4: E.Coli BL(DE3) chromosome Thus, induction by IPTG results in: • Derepression of T7 RNA Pol gene on host chromosome with subsequent production of this polymerase • Derepression of target gene under lac O regulation • Transcription of target gene by T7 RNA Pol Thus, the system couples a strong promoter with tight regulation (i.e. extremely low level of expression in the repressed state) The pET vector itself is available with several different polylinker sequences. They contain the same restriction sites, but differ in the reading frame leading into the pLink region: Figure 3.4.5: Polylinker sequences • The ggatcc site (BamH I restriction endonuclease site) is the first restriction site in the polylinker. • The polylinker region is thus available in the three different possible reading frames • A gene of interest can thus be cloned to be in-frame with the transcribed region downstream from the T7 promoter. Again, like the pUC vector the expressed protein will be a fusion protein (with 12-13 amino acids at the amino-terminal end of the polypeptide) The pTrcHis vector system (Invitrogen, Inc.) The pTrcHis vector looks like this: Figure 3.4.6: pTrcHis Vector The important elements are: • Ampicillin resistance marker • ColE1 origin of replication • lacIq gene. This is a lac repressor mutation which is upregulated (produces more lac repressor than normal) • Trc promoter. This is a hybrid of the lac and trp promoters which is a stronger promoter in comparison to the lac promoter • The lac operator region downstream of Ptrc. This allows regulation by IPTG. • An initial stretch of 6 histidine residues in the amino terminal region of the translated protein, also known as a "His tag" region. • An enterokinase (EK) cleavage recognition sequence (asp asp asp asp lys , with cleavage after the lys residue) • A polylinker region downstream from the EK site Purpose of the His tag • Histidine residues can coordinate to form a metal (Ni2+) binding site in a protein. • The stretch of six His residues forms such a binding site. • Proteins with a His tag have affinity for metal chelating resins, and this characteristic can be used to selectively purify such proteins. Purpose of the EK cleavage site • Although the His tag can allow rapid and selective purifiction of a cloned protein, the presence of these his residues may prevent normal function of the protein • The His tag can be cleaved away from the protein by introducing a specific recognition sequence for an endopeptidase • The sequence "asp asp asp asp lys" is recognized and cleaved by enterokinase. This sequence is not common and it is doubtful that the protein of interest contains another such sequence Figure 3.4.7: Enterokinase cleavage • His tags/EK sites are typically introduced at the amino terminus of proteins, but can be introduced at the carboxyl terminus as well Expression hosts One of the important characteristics of expression hosts (bacteria) is that they allow the expressed protein to accumulate. • Some expressed proteins may not be folded correctly. Although they may be refolded later to yield functional protein, misfolded proteins may be rapidly proteolyzed in the host bacterium • Host proteases may selectively cleave precursor forms of expressed proteins to produce mature active forms, which may not be desirable. • OmpT and lon genes code for proteases which can degrade expressed proteins. • Two common mutations in E. coli to eliminate the action of host proteases includes the OmpT- and lon- strains. • E. coli strain BL(DE3) is both OmpT- and lon-

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/3._Biotechnology_1/3.4%3A_Prokaryotic_Expression_Vectors.txt

Historically, some important disease states were identified as being caused by the lack of an important protein, or the presence of a dysfunctional mutated form of a protein. • For example, diabetes, types of dwarfism and hemophilia were found to be due to deficiencies in insulin, growth hormoneand clotting factor VIII, respectively. These diseases could be treated by injecting supplemental doses of purified, or partially purified, preparations of these proteins. • These proteins were isolated from natural materials, e.g. pig (insulin), human cadaver pituitaries (human growth hormone) or blood fractions pooled from normal donors (factor VIII). • In most cases, even if the protein was found in relatively abundant supply, the cost of production was substantial. More often than not, interesting bioactive properties were associated with proteins which could be isolated only in minute quantitites(e.g. the blood clot dissolving protein tissue plasminogen activator). Also, non-human proteins typically elicited an immune response when injected into humans, thus the human form of a protein was the only useful form. • If the protein were not readily available from blood, or urine, it would prove impractical to obtain adequate starting materialfor production. • Unfortunately, if the material were derived from human sources, the possibility existed for the spread of human disease (e.g. hepatitis and the AIDS virus). If the genetic information for these proteins could be isolated, and then transcribed and translated in an easily scaleable biological system, potentially large amounts of protein could be obtained - and hopefully, relatively cheaply. With the development of "molecular biology", i.e. • the structure of DNA, • the elucidation of the genetic code, • the identification of transcriptional promoters and ribosome binding sites, • the isolation of restriction endonuclases, • the identification of the origin of DNA replication • the development of plasmids with selectable markers, and • the culturing of E. coli, the possibility existed in the mid 1970's to put it all together and produce relatively large amounts of any human protein for therapeutic use. How would you go about the process of producing large amounts of some important human protein? (i.e. protein purification) The starting point is typically an assay for a functionality of interest. For example, we may have a hemophiliac whose blood does not clot. However, we find that if we take a sample of his blood and add to it a small amount of blood from a "normal" individual, the hemophiliac's blood will now clot. This will be the basis for our assay. Using this assay, we will fractionate normal blood using various means - chemical precipitation (with ethanol, or ammonium sulfate), and then various liquid chromatography steps, etc. • Along the way we will follow where our clotting activity is going. • Hopefully, at some point we will be unable to fractionate it further and will have a pure protein. Once we have a pure protein we can begin to characterize it with regard to its amino acid sequence. From there we can ultimately get the gene for the protein and express it. Figure 3.5.1: Protein production N-terminal peptide sequence analysis Polypeptides can be sequenced from their amino-terminus by automated procedures based upon the Edman degradation reaction: Figure 3.5.2: Edman degradation • Note that with Edman chemistry only the N-terminal residue is attacted and removed, the rest of the polypeptide remains intact after the reaction. • The new amino terminal group (previously the second amino acid in the polypeptide chain) is now available for another round of reactions. Thus, the method can be automated. • The amino acid side chain of the phenylthiohydantoin derivative can be identified using liquid chromatography. Modern amino acid sequencers can probably sequence on the order of two to three dozen cycles (amino acids) of a polypeptide. • Note that the reaction requires a free amino group on the N-terminal of the protein. If the amino-terminal residue is methylated or formylated then the reaction will not proceed (and the polypeptide is said to have a "blocked" N-terminal). C-terminal peptide sequence analysis C-terminal peptide sequence analysis is not as well developed as amino terminal analysis. • The method usually makes use of non-specific carboxypeptidases. • Carboxypeptidases will sequentially hydrolyze polypeptides from the carboxy-terminus end. The released amino acid can be identified using liquid chromatographic methods, and the remaining polypeptide is available for further reactions. • Various carboxypeptidases are available, usually they are not entirely non-specific (i.e. they have certain preferences): Name Source Specificity Carboxypeptidase A Bovine Pancreas Aromatics, aliphatics (hydrophobics) Carboxypeptidase B Pig Pancreas Arginine, Lysine, Ornithine Carboxypeptidase P Penicillium Generally non-specific Carboxypeptidase Y Yeast Aromatic, aliphatics Sometimes the choice of which carboxypeptidase to use is based upon the expected sequence information. In these types of experiments: 1. samples are taken at different time points during the digestion 2. free amino acids are separated from polypeptides 3. the released amino acids are identified via amino acid analysis (liquid chromatography). C-terminal analysis is usually only accurately for identification of the last half-dozen residues or so in a polypeptide. Peptide Mapping One of the obvious problems with protein sequencing is that even if the N-terminal is not "blocked" only limited sequence infomation can be obtained from an intact polypeptide (i.e. only about two dozen from the N-terminal and half a dozen from the C-terminal). How can sequence information for the entire polypeptide be obtained? One method is that of peptide mapping. Peptide mapping makes use of proteolytic cleavages of the polypeptide to produce smaller polypeptides. These smaller polypeptides can then be isoloted from one another and subject to sequence analysis. How do we order the different sequences which we obtain? One of the easiest ways is to repeat the experiment, but with a protease with a different specificity, and in this way obtain overlapping sequence information. Name Source Specificity Chymotrypsin Bovine Pancreas Cleavage after Tyr, Phe and Trp; some cleavage after Leu, Met and Ala Bromelain Pineapple Cleavage after Lys, Ala and Tyr Trypsin Bovine Pancreas Cleavage after Arg, less after Lys V8 protease Staphylococcus aureus Cleavage after Glu, less after Asp Figure 3.5.3: Overlapping cleavage products Overlapping sequence information can allow you to align the peptides in the correct order and determine the sequence of the original large polypeptide (i.e. protein). One problem which can arise deals with Cysteine residues and the nature of any covalent disulfide bridges in the protein. • Any "peptide" mobilities (on either liquid chromatographic or PAGE analyses) which split into two smaller peptides after treatment with a reducing agent (such as b-ME) indicate the presence of a cysteine mediated disulfide bond. • Upon sequencing these peptides should each contain a cysteine residue. If each peptide has only one cysteine then the disulfide bond assignment is unambiguous. Figure 3.5.4: Cysteine residues in cleavage products Corresponding genetic information Once we have partial, or complete, peptide sequence information we can begin to identify and isolate the corresponding genetic information. This is the main goal. Once we have the corresponding genetic information it may be possible to produce relatively large amounts of the desired polypeptide. Back translation Since we know the genetic code, we can back translate any polypeptide sequence into a corresponding genetic sequence. • Thus, from the amino acid sequence we could synthesize an artificial gene which would code for the protein of interest. • Since many amino acids are coded for by more than one codon, there is potential ambiguity with regard to the original exact genetic sequence. Amino Acid Number of Codons Met, Trp 1 Phe, Tyr, His, Gln, Asn, Lys, Asp, Glu, Cys 2 Ile 3 Val, Pro, Thr, Ala, Gly 4 Leu, Arg, Ser 6 However, making sure we back translate in such a way as to faithfully duplicate the original genetic sequence may not be critical - a correct protein sequence is the overall goal. In fact, if we are attempting to express the protein in another organism (say expressing a mammalian gene in a bacterial system) we may actually prefer to choose a codon bias appropriate for the expression host organism. Synthetic genes for small proteins are a reasonable way to proceed; this is one way in which human insulin has been expressed in bacterial systems. • However, automated synthesis of DNA oligonucleotides is practical for polymer lengths of approximately 60-90 bases or less (about 20-30 amino acids). • Furthermore, the method of construction of synthetic genes typically calls for overlapping complementary oligonucleotides (to be ligated into a single duplex DNA gene "cassette"). Thus, many oligonucleotides are required for even a single small synthetic gene. Figure 3.5.5: Synthetic gene construction One way to improve upon the above method of synthetic gene construction is with a direct PCR approach. This method does not utilize ligase, or even oligonucleotides that butt together. Instead, with this method many (~100) different overlapping oligonucleotides are simultaneously used in a PCR reaction. Their sequence complementarity can be represented as follows: The entire set of oligonucleotides may not line up to give the entire gene, but that is alright. We will do multiple rounds of PCR with the idea that some complementary oligo's will anneal and be extended and will lead, bit by bit, to construction of a contiguous synthetic gene: On the next PCR cycle, some of these extended fragments will anneal with others: These will be extended via the PCR and can go on to anneal with other larger PCR fragments. Eventually, the entire gene will be constructed. However, since the efficiency of construction of the full-length gene is probably not going to be very good, we need to conduct a subsequent PCR experiment to amplify the full-length gene (using outer primers). The principle features of this method are summarized as follows: • Many (as many as 1-2 hundred) overlapping oligo's are combined in a single PCR reaction • The oligos are designed to be as long as possible (~100mers) with limited overlap (~20 bases) • The full-length gene is constructed in an initial (low yield) PCR experiment • This full length gene is amplified with a subsequent typical PCR experiment using outer primers.

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/3._Biotechnology_1/3.5%3A_Protein_Sequencing_Peptide_Mapping_Synthetic_Genes.txt

Isolation of corresponding genetic information Instead of synthesizing a desired gene, can we used the amino acid information to directly isolate the corresponding genetic information? • There are two general sources of genetic information: 1. Genomic DNA 2. mRNA If we are considering genomic DNA from eukaryotes, then there are a couple of things to consider: 1. The coding region for a gene of interest may be interrupted by one or more intron regions, and thus the complete coding region could be quite long. 2. To a first approximation, it does not matter which tissue we use to isolate the genomic information, i.e. the genomic content is the same in all tissues. If we are considering mRNA from eukaryotes, we may realize the following advantages: 1. Introns will be spliced out and the mRNA will contain a contiguous coding region. 2. Tissue specific expression of the protein of interest may allow us to isolate appropriate mRNA at enhanced levels, i.e. in tissues where the protein is expressed the mRNA levels are considerably higher than the corresponding genomic levels (there are many more molecules of mRNA than copies of the gene). Libraries A "library" is a convenient storage mechanism of genetic information. • They are typically either "genomic" or "cDNA" (i.e. mRNA in DNA form) genetic information. • Deduced genetic sequences from corresponding polypeptide information can be used to identify specific genetic information within a library. cDNA library construction "Reverse transcription" is a mechanism whereby genetic information contained in mRNA is converted back into a double stranded DNA form. The enzyme responsible for this is an RNA dependent DNA polymerase called reverse transcriptase. • Reverse transcriptases have traditionally been isolated from viruses whose genome is actually in an RNA form and must be converted to duplex DNA. • These viruses typically carry a functional reverse transcriptase along with their mRNA genetic component when they infect cells. • One of the most common commercially available reverse transcriptases is Moloney murine leukemia virus (MMLV). • This RNA dependent DNA polymerase (as will all polymerases) add nucleotides to a nacent polynucleotide in the 5' to 3' direction using RNA as the template. It does not contain any 3'->5' exonuclease (proofreading) activity. MMLV will use mRNA as a template, but requires a primer (it can extend a DNA primer but cannot synthesize one). • One of the really neat things about eukaryotic mRNA's is the presence of the 3' poly A tracks. Therefore we can use poly dT as a single primer for a variety of different eukaryotic mRNA's. Figure 3.6.1: First strand synthesis Note that we have produced complementary DNA (or cDNA) to the original mRNA strand. If we can introduce "nicks" into the RNA half of this DNA/RNA duplex then the situation would be very similar to that observed in "lagging strand" synthesis of prokaryotic genomic DNA. • Nicks in the RNA half of the molecule can be introduced via the action of the enzyme RNAse H. • This enzyme exhibits endonucleolytic cleavage of the RNA moiety of RNA/DNA hybrids, as well as 5'->3' and 3'->5' exoribonuclease activity. • In other words, it will nick the RNA and then proceed to digest back in both directions: Figure 3.6.2: Nicks in mRNA • These RNA fragments can now serve as primers for DNA synthesis by E. coli Pol I. This enzyme will also translate the "nicks" to effectively remove the RNA primers: Figure 3.6.3: DNA synthesis Note that we will potentially have either a residual 5' RNA cap region, or a gap at the 5' end of the original mRNA strand. Insertion of cDNA into plasmid. To complete our construction of a useful cDNA library we need a way to maintain and propagate our cDNA. • We can accomplish this by inserting the cDNA into an appropriate plasmid. • There are two classical ways of accomplishing this feat: 1. Homopolymeric tailing 2. Linker addition Homopolymeric tailing Terminal transferase is an unusual DNA polymerase found only in a type of eukaryotic cell called a prelymphocyte. • In the presence of a divalent cation the enzyme catalyzes the addition of dNTP's to the 3'-hydroxyl termini of DNA. • When the nucleotide to be added is a purine, Mg2+ is the cation used. • When the nucleotide to be added is a pyrimidine, Co2+ is used. • Depending on the reaction conditions, anywhere from three to several thousand bases will be added. Figure 3.6.4: Terminal transferase activity How can we insert this into a plasmid? • If we cut our plasmid and also treat it with terminal transferase, except now we add the complementary base to the one we added to our cDNA, we can anneal and ligate the cDNA into the plasmid. Figure 3.6.5: Ligating cDNA into the plasmid • The utility of inserting the C-tailed cDNA insert into a G-tailed Pst I site in the vector is as follows: 1. The Pst I recognition sequence and cleavage site is 5' C T G C A G 3' 3' G A C G T C 5' 2. Cleavage of this site by Pst I, followed by G-tailing will produce 5' C T G C A (G)n G 3' 3' G (G)n A C G T C 5' Thus, the Pst I recognition cleavage site is regenerated and the C-tailed cDNA insert can be excised again from the library vector by cleaving with Pst I. Linkers An alternate method to insert cDNA fragments into a library vector is through the addition of "linkers". • Linkers are short oligonucleotides (~18 to 24 mers) which are typically palindromic and contain a single or repeated restriction endonuclease recognition sequence. • The palindromic nature allows the linker oligonucleotide to self-hybridize to form a blunt ended duplex. • If the ends of the cDNA fragments are blunt, then the linker can be ligated to both ends to introduce useful terminal restriction sites. The steps in linker addition are as follows: 1. Treatment of cDNA with S1 nuclease (to remove possible 5' cap mRNA fragment remaining in cDNA duplex 2. Convert potential "ragged" ends to blunt by treatment with Pol I (will fill in 5' overhangs and chew back 3' overhangs) 3. Methylate cDNA at potential internal Eco RI sites by treatment with Eco RI methylase (plus S-adenosyl methionine) 4. Ligate linkers to blunt, methylated cDNA using T4 DNA ligase 5. Cut linkers with Eco RI restriction endonuclease 6. Remove linker fragments from cDNA fragments by agarose gel electrophoresis 7. Ligate cDNA to vector DNA fragment (opened up by Eco RI restriction endonuclease Period of time between first man-powered flight and landing on the moon (1902-1969): 67 years Period of time between discovery of structure of DNA and determination of the sequence of the entire human genome (1953-2010?) 57 years (?) Editorial Note This textbook was published in 1998. The Human Genome Project was completed in 2003. Genomic DNA libraries Size of some genomes and chromosomes: Comparative Sequence Sizes (Bases) (yeast chromosome 3) 350 Thousand Escherichia coli (bacterium) genome 4.6 Million Largest yeast chromosome now mapped 5.8 Million Entire yeast genome (completed 5/96) 15 Million Smallest human chromosome (Y) 50 Million Largest human chromosome (1) 250 Million Entire human genome 3 Billion • The human genome contains approximately 50,000 unique genes within 3-4 billion base pairs of DNA, scattered about in 23 pairs of chromosomes. Fragmentation of genomic DNA for library construction Restriction endonuclease digestion • A six-cutter (e.g. Eco RI) will cut on average every 4.1 Kb. Complete digestion of human DNA with this type of enzyme will result in approximately 1 x 106 unique fragments. • What is the probability of finding a clone within a given library? The exact probability of having any given DNA sequence in the library can be calculated from the equation N = ln(1 -P)/ln(1 - f) P is the desired probability f is the fractional proportion of the genome in a single recombinant N is the necessary number of recombinants For example, how large a library (i.e. how many clones) would you need in order to have a 99% probability of finding a desired sequence represented in a library created by digestion with a 6-cutter? N = ln(1 - 0.99)/ln(1 - (4096/3x109)) N = 3.37 x 106 clones Thus, from this type of analysis we can see that we need a technology which will allow us to achieve the following: 1. Stable insertion of relatively large DNA fragments into our cloning vector 2. High efficiency of insertion and the ability to handle large numbers of clones • For example, when plating E. coli colonies on a 3" petri plate, the maximum practical density to allow isolation of individual colonies is about 100-200 colonies per plate. • If we were to try to plate our library of 3.37 x 106 in such a way would need about 22,500 plates. • Not only that, but such large DNA fragments are not well tolerated in typical E. coli cloning vectors such as pBR322. Bacteriophage lambda vectors are commonly used for construction of genomic libraries Bacteriophage l is an E. coli phage with a type of icosahedral phage particle which contains the viral genome: Figure 3.6.6: Bacteriophase l • During replication, the phage DNA is produced in a concatameric form, which is cleaved by appropriate endonucleases to allow packaging of a single genome within the phage capsid. • It was found that internal regions of the phage genome, which were not essential to phage replication, could be removed and replaced with DNA of interest. • This hybrid DNA could be efficiently packaged, and form an infective phage. Figure 3.6.7: Creation of ineffective phage The advantages of this type of system vs plasmids like pBR322 are: 1. The phage genome is able to package efficiently with DNA inserts as large as 20 Kb. 2. Furthermore, the packaged phage are highly infectious and infect E. coli at a much higher efficiency than plasmid transformation methods. Incomplete Digestion of Genomic DNA will allow identification of sequence overlaps Complete digestion with an endonuclease will result in a library containing no overlapping fragments: However, incomplete digestion will result in a library containing overlapping fragments: • Thus, the sequence information obtained from one clone will allow the isolation of clones containing neighboring (overlapping) sequence information. • This can allow large contiguous stretches of sequence information to be obtained ("Chromosome Walking"). Probing libraries Once a library (cDNA or genomic) has been constructed we want to be able to identify clones which contain DNA of interest. • For example, from protein sequence information we can deduce possible stretches of the corresponding DNA sequence (there will however be ambiguity due to the degeneracy of codons). • If we can synthesize an oligonucleotide complementary to our DNA sequence of interest we can use it to specifically hybridize to the appropriate clone in our libraray (i.e. to probe our library). In standard methodologies the oligonucleotide is phosphorylated at the 5' end with radiolabeled g32P-ATP and T4 polynucleotide kinase. • The probe is then incubated with individual phage plaques which have been fixed onto nitrocellulose and their DNA denatured by treatment with base. • If the plaque contains complementary DNA to to probe sequence, the probe will hybridize. • If the nitrocellulose (containing many individual plaques) is exposed to x-ray film, only those plaques with hybridized probe will show up (as a dark spot): Figure 3.6.8: Radiolabeled plaque Note that its important to keep track of the orientation of the nitrocellulose in relationship to the x-ray film (usually radioactive ink is used to identify the nitrocellulose orientation). False positives If we are designing DNA probes from protein sequence information we will have possible ambiguity in our deduced DNA sequence used for the design of the probe. • Usually 14-24mer oligonucleotides are used as probes, a 14-24mer probe means we need a stretch of 5-8 amino acids in the polypeptide. • Given the choice, the best amino acid sequences to look for in a polypeptide are those with low codon degeneracy (see above). • Thus, we would look for a short stretch of polypeptide sequence hopefully containing Met or Trp, and with the remaining amino acids comprising either Phe, Tyr, His, Gln, Asn , Lys, Asp, Glu or Cys. • Regions including Leu, Arg or Ser are to be avoided (6 codons each). During oligonucleotide synthesis multiple bases will be incorporated at ambiguous positions. • Thus our probe will actually be a mixture of oligonucleotides. • The higher the degeneracy, the greater the posibility of "false positives", i.e. clones which hybridize but are unrelated to the actual sequence we want. • Positive clones are sequenced and the deduced amino acid sequence is compared to our polypeptide sequence information to identify correct clones. Antibodies (Immunoglobulins) If the particular vector, or phage, used to construct a cDNA library contains a promoter region upstream of the insertion site we may be able to screen for desired clones by looking for expression of the protein of interest. • In this case, we need an assay which is both sensitive (we will not be producing a lot of protein) and specific (we want to minimize any false positives). • One of the best assays, which is both sensitive and specific, makes use of antibodies. Antigen, antibody, epitope One of the defense mechanisms of vertebrates is the ability to distinguish between self and non-self molecules. • Thus, if a foreign molecule (either from another species or sometimes from another individual within a species) invades a vertebrate organism, the immune system functions to learn to identify that molecule. • In future invasions by the same molecule, the organism mounts a defense against it by producing specific antibodies which recognize and bind to the foreign antigen. • When antibodies bind to antigen certain white blood cells (macrophages and monocytes) recognize the invading body as foreign and respond by destroying it. Antibodies are 'Y' shaped molecules which contain two identical heavy chains, and two identical light chains. • The stem of the 'Y' comprises the Fc (constant) domain, and the 'arms' of the 'Y' comprise the Fab (variable) domains. • Antigens bind to the complementarity-determining regions (CDR's) located at the ends of the Fab domains. Figure 3.6.9: Antibody structure Antibodies are synthesized by B lymphocytes. Each B lymphocyte is capable of producing a single type of antibody directed against a specific structural determinant, or epitope, on an antigen. • Thus, an immune response to a protein antigen may result in a population of B lymphocytes each producing antibodies which recognize a different structural determinant of the foreign protein. • An epitope may be a contiguous region of 5 or 6 amino acids in the foreign polypeptide, or the epitope may comprise a half dozen or so amino acids brought in juxtaposition in the native protein, yet widely spaced in the polypeptide sequence. • Thus, some antibodies will recognize native and denatured forms of a foreign protein equally well, while other antibodies may only recognize one or the other. If the protein of interest has been purified it can be used to induce an immune response in a host animal. • Typical host animals include mouse, chicken, rabbit, goat, sheep, horse and occasionally, human. • After an initial immunization, followed by one or more booster shots, the B lymphocytes of the host animal may produce antibodies directed against the antigen. • The antibodies can be be purified from blood samples withdrawn from the animal. Such preparations of antibodies are said to be polyclonal. • This refers to the fact that the antibodies present are from a collection of different B lymphocytes and thus will recognize a variety of different epitopes on the antigen protein. • The ability to isolate antibodies from blood samples means that the host animal does not need to be destroyed. • Of course, the size of the animal determines how much antibodies one can obtain. For example, a rabbit can provide 5 mls of blood every two weeks, a mouse provides significantly less, while a horse can provide quite a bit more. An antibodiy isolated from a single B lymphocyte cell population is termed monoclonal. • It recognizes a single epitope on the antigenic protein. • Antibody producing B lymphocytes can be isolated from the spleen or from lymph nodes. However, they have a finite life spanin culture, i.e. they will undergo a certain number of cell divisions and then die. • These cells can, however, be fused with immortal (cancerous myeloma) lymphocytes to produce a hybridoma cell. • Such a cell is immortal like the myeloma, and produces a specific antibody from the B lymphocyte. The ability to grow indefinitely in culture allows the isolation of useful amounts of specific monoclonal antibodies. Sometimes immunizing with the protein of interest is problematic: appropriate amounts of purified material cannot be produced, or the protein is itself toxic at the dosage level necessary to produce an immune response. • If partial sequence information is known, then large amounts of polypeptides representing short fragments of the protein, can be synthesized and used to immunize the animal. • Often these polypeptides are covalently attached to a carrier protein (typically serum albumin) to enhance the antigenic response. • Antibodies produced against such peptides will recognize only epitopes within the polypeptide. Thus, even polyclonal antibodies would be quite limited in their epitope recognition. As with radiolabeled oligonucleotides, antibodies can be used to identify library clones which contain a cDNA of interest. This method would of course rely upon a host vector or phage which contains a promoter upstream from the site of insertion of the genomic DNA. • Antibodies can be used to screen viral plaques or plasmid clonies which have been bound to nitrocellulose. • Bound antibodies can be identified using radiolabeled protein A (which binds to immunoglobulins) or via a second antibody (which, like protein A, can recognize general immunoglobulins) which has a dye or dye releasing enzyme covalently attached.

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/3._Biotechnology_1/3.6%3A_cDNA_and_Genomic_Libraries.txt

• 4.1: Protein Purification A successful protein purification procedure can be nothing short of amazing. Whether you are starting off with a recombinant protein which is produced in E. coli, or trying to isolate a protein from some mammalian tissue, you are typically starting with gram quantities of a complex mixture of protein, nucleic acids, polysaccharide, etc. from which you may have to extract milligram (or microgram!) quantities of desired protein at high purity, and hopefully with high yield. • 4.2: M13 Phage The bacteriophage known as "M13" forms the basis of cloning systems designed to easily introduce mutations into genes inserted into the phage genome. It also has been used in various "phage display" methodologies and "combinatorial" DNA and peptide libraries. • 4.3: M13 Phage Display Libraries • 4.4: SELEX (Selective Evolution of Ligands by Exponential Enrichment) Systematic evolution of ligands by exponential enrichment (SELEX) is a combinatorial chemistry technique in molecular biology for producing oligonucleotides of either single-stranded DNA or RNA that specifically bind to a target ligand or ligands. • 4.5: Protein-Protein Recognition Probed Using a Yeast Transcriptional Activator System • 4.6: Molecular Imprinting A major emphasis in biotechnology is the development of synthetic recognition molecules and systems which are specific for a ligand of interest. One branch of molecular design involves the synthesis of molecules with specific stereochemical structure designed to recognize and interact with a particular ligand. In contrast, molecular imprinting is a method of constructing a molecule, with specific recognition properties, by having the ligand itself direct the assembly of the desired structure 4. Biotechnology 2 Assays, Specific Activity, Initial Fractionation A successful protein purification procedure can be nothing short of amazing. Whether you are starting off with a recombinant protein which is produced in E. coli, or trying to isolate a protein from some mammalian tissue, you are typically starting with gram quantities of a complex mixture of protein, nucleic acids, polysaccharide, etc. from which you may have to extract milligram (or microgram!) quantities of desired protein at high purity, and hopefully with high yield. The first step in any purification is the development of a specific assay for the protein of interest. The specific assay can be based upon some unique characteristic of the protein of interest • Enzymatic activity • Immunological activity • Physical characteristics (e.g. molecular mass, spectroscopic properties, etc.) • Biological activity • Ideally, an assay should be • Specific (you don't want a false positive) • rapid (you don't want to wait a week for the results) • sensitive (you don't want to consume all your sample in order to assay it) • quantitative (you need an accurate way to measure the quantity of your protein at each step in the purification) Western Blotting Antibodies can be used in a method called Western blotting, which is useful for determining levels of protein expression and for assaying proteins during purification. This method usually involves the following steps: 1. A protein sample is subjected to polyacrylamide gel electrophoresis. 2. After this the gel is placed over a sheet of nitrocellulose and the protein in the gel is electrophoretically transfered to the nitrocellulose. 3. The nitrocellulose is then soaked in gelatin to "block" its ability to non-specifically bind proteins. 4. The nitrocellulose is then incubated with the specific antibody for the protein of interest. 5. The nitrocellulose is then incubated with a second antibody which is specific for the first antibody. For example, if the first antibody was raised in rabbits, the second antibody might be termed "goat anti-rabbit immunoglobulin". What this means is that rabbit immunoglobulins were used to elicit an antibody response in goats. The goat antibodies (polyclonal) will include those which recognize the conserved region in the rabbit antibodies. Since the Fc region is conserved, it will bind to any and all rabbit antibodies, including those on the nitrocellulose paper. 6. The second antibody will typically have a covalently attached enzyme which, when provided with a chromogenic substrate, will cause a color reaction. 7. Thus the molecular weight and amount of the desired protein can be characterized from a complex mixture (e.g. crude cell extract) of other proteins. In a variation of the above, the protein sample may be blotted directly on a nitrocellulose paper (called a dot blot) without first running a gel. This may be desirable if, for example, the antibody is monoclonal and recognizes an epitope which is dependent upon native structure (which would be destroyed upon running an SDS PAGE). In addition to their varied uses, antibodies can also be used to purify proteins. • If relatively large amounts of an antibody can be obtained, they can be covalently attached to a chromatography resin (e.g. sephadex beads). • If a crude cell extract is run over such a column, only the protein of interest should bind, and everything else will flow through. • The bound protein can then be eluted. This is typically achieved by moderately low pH conditions (using acetic acid). As long as the protein of interest is not irreversibly denatured by such conditions, the method will work quite well. • One potential pitfall involves that of monoclonal antibodies being utilized to purify mutant proteins. The regions of the protein comprising the epitope cannot be modified without destroying the ability of the antibody to bind. Thus, the use of monoclonal antibodies in a purification scheme may preclude its use in purifying certain mutants. Protein purification can be thought of as a series of fractionation steps designed so that: • The protein of interest is found almost exclusively in one fraction (and with good yield) • A significant amount of the contaminants can be found in a different fraction During purification you will need to monitor several parameters, including: 1. Total sample volume 2. Total sample protein (can be estimated by A280; 1.4 ~ 1.0 mg/ml) 3. Units of activity of desired protein (based on specific assay) This basic information will allow you to keep track of the following information during each step of purification: 1. % yield for each purification step 2. Specific activity of the desired protein (units/mg total protein) 3. Purification enhancement of each step (e.g. "3.5x purification) In designing a purification scheme you typically have to balance purification with yield. • For example, it may be relatively straightforward to obtain 90% pure material with good yield. • However, it may be difficult to improve that purity an additional few percentile with good yield. • The planned application of the purified protein determines the target purity. • If the protein is to be used to determine amino acid sequence information, maybe 90% is acceptable. However, if the material is to be used in clinical trials, 99.99+% may be the target purity. Initial steps in purification Figure 4.1.1: Purification steps • It is extremely helpful to have some information not only on the general physical and chemical characteristics of the protein you are trying to purify, but also on the contaminating components. • For example, many E. coli proteins are generally low molecular weight (<50,000 Da) and somewhat acidic in isoelectric point Usually the initial steps in purification make use of general physical and/or chemical differences between soluble proteins and other cell components. • For example, soluble proteins can be separated from general cellular debris, and intact cells, by centrifugation. • Thus, cells are physically disrupted (via homogenization or a cell press) to allow release of cell contents. This is then followed by centrifugation to separate generally soluble components from those which are insoluble. • It is at this point that data collection begins in order to monitor the purification. Nucleic acids can sometimes be readily removed from the sample by the addition of large cationic compounds such as polyethylene imine, or streptomycin sulfate. • The nucleic acids bind to these compounds via electrostatic interactions and the complex precipitates and can be removed via centrifugation. • The same general result can be obtained by mixing in ion exchange resins which are anion exchangers (i.e. the resins contain cationic groups) and then filtering or centrifuging to remove. As with either method, it should be confirmed that the desired protein is not bound as well. Crude fractionations of proteins can be achieved by adding various quantitites of precipitants such as ammonium sulfate, or polyethylene glycol (PEG). • For this type of purification step an initial experiment is performed to monitor the fraction of overall protein, as well as desired protein, remaining in solution (and pellet) as a function of precipitant concentration. Ammonium Sulfate (% saturated) 0 10 20 30 40 50 60 70 80 90 Sample A280 1000 900 600 200 100 75 50 40 25 20 Activity assay(units) 200 200 200 190 170 100 30 5 0 0 Figure 4.1.2: Protein activity as a function of precipitant concentration • In this particular example we are in luck: at around 30% ammonium sulfate we can precipitate about 80% of the total protein concentration in our sample, yet our activity assay for our desired protein indicates that about 95% of our desired protein is still soluble. • At 80% ammonium sulfate all of our desired protein has precipitated. Thus, from these results we would do the following: 1. Add ammonium sulfate to our sample to a concentration of 30% saturation 2. Centrifuge and discard the pellet 3. Add ammonium sulfate to 80% saturation 4. Centrifuge and keep the pellet. Resuspend the pellet in buffer to solubilize the protein. • We would expect about a 5-fold purification with about 95% yield. Column Chromatography - Ion exchange; Dialysis and Concentration Column chromatography After initial fractionation steps the typical procedure is to move to column chromatography. • In column chromatography we have a glass tube (column) which is filled with a material ("resin") which has certain physical/chemical characteristics. • These characteristics allow it to interact in various ways with different proteins. • Some common types of chromatographic resins include: 1. Ion exchange 2. Hydrophobic 3. Gel filtration 4. Affinity Ion exchange Ion exchange resins contain charged groups. • These may be acidic in nature (in which case the resin is a cation exchanger) • or basic (in which case it is an anion exchanger). • Cation and anion exchangers may be broken down further into weak and strong exchangers (reflecting binding affinity). Type of exchanger Functional group Common name Weak cation exchanger carboxymethyl CM cellulose/sephadex Strong cation exchanger sulfopropyl SP sephadex Weak anion exchanger diethylaminoethyl DE cellulose/sephadex Strong anion exchanger quaternary amine QAE sephadex • Usually, samples are loaded under low ionic strength conditions and bound material is eluted using either a step or gradient elution of buffer with higher ionic strength. • Generally speaking, a protein will bind to a cation exchange resin if the buffer pH is lower than the isoelectric point (pI) of the protein, and will bind to an anion exchange resin if the pH is higher than the pI. Figure 4.1.3: Protein binding to resins • Knowledge of the pI of the protein is therefore helpful in designing a purification protocol using ion exchange resins (however, you can always simply try different resins to see which works best). Generally speaking, ion exchange columns are short and fat in dimensions. Elution of proteins from ion exchange resins • Proteins bound to ion exchange resins are bound via non-covalent ionic (salt-bridge) interactions. We can compete for these ionic binding sites on the resin with other ionic groups, namely, salts • There are two general types of methods when eluting with a salt solution: 1. Gradient elution and 2. Step elution • A gradient elution refers to a smooth transition of salt concentration (from low to high) in the elution buffer. Weakly binding proteins elute first, and stronger binding proteins elute last (i.e. they require higher salt concentrations in the buffer to compete them off the column) • A gradient salt concentration can be made using a gradient maker. In its simplest form, this consists of two containers (must be the same shape) connected by a siphon (or tube at the bottom). One container contains the low salt buffer, and the other contains high salt buffer. The buffer is withdrawn from the low salt container: Figure 4.1.4: Gradient maker • This will produce a linear gradient from low to high salt concentrations over the total volume of the gradient Figure 4.1.5: Salt concentration and volume • If we know the concentration range of salt over which a protein of interest will elute we can simply elute with a buffer containing that concentration of salt. This is known as a step elution. • Step elutions are generally faster to run, and elute the protein in a smaller overall volume than with gradient elutions. They generally work best when contaminants elute at a significantly different salt concentration than the protein of interest Figure 4.1.6: Step elution Note that after ion exchange chromatography the protein of interest will be in a buffer with a potentially high salt concentration. This must be taken into account before proceeding with the next step in the purification scheme Dialysis • After an ammonium sulfate precipitation step, or an ion exchange chromatography step, the protein of interest may be in a high salt buffer. This may be undesirable for several reasons. How do we get rid of salt in our sample? • One of the most common methods is that of dialysis • The method of dialysis makes use of semi-permeable membranes. In the simplest example, this membrane is manufactured in the form of tubing (looking much like a sausage casing) • The main feature of this membrane is that it is porous. However, the pore size is such that while small salt ions can freely pass through the membrane, larger protein molecules cannot (i.e. they are retained). Thus, dialysis membranes are characterized by the molecular mass of the smallest typical globular protein which it will retain. • This is commonly referred to as the cutoff of the tubing (e.g. Spectrapore #6 dialysis tubing has a cutoff of 1,000 Daltons, meaning that a 1,000 Dalton protein will be retained by the tubing but that smaller molecular mass solutes will pass through the tubing) • Dialysis proceeds by placing a high salt sample in dialysis tubing (i.e. the dialysis "bag") and putting it into the desired low salt buffer: Figure 4.1.7: Dialysis • Over time the concentration of low molecular mass solutes within the bag, and in the low salt buffer, will come achieve equilibrium. In practical terms (for the above case) salt molecules will diffuse out of the bag into the low salt buffer: Figure 4.1.8: Salt diffusion • At equilibrium the salt concentration of the sample can be calculated as follows: $\frac {(sample \: volume) \times (sample \: salt \: concentration) + (buffer \: volume) \times (buffer \: salt \: concentration)}{total \:volume} = final \:salt \: concentration$ Note Often the buffer salt concentration is 0 M • The buffer volume for the dialysis is a function of the required final concentration of salt in the sample Example 4.1.1: Dialysis example We have a 10ml protein sample from an ion exchange column elution pool which contains 1.0M NaCl. For our next step in the purification we can have no more than 1mM NaCl in the sample. Therefore, the required buffer volume would be (total vol - sample vol) = 9.990 L (or ~ 10 L) • Thus, if we dialyzed 10mls of sample (with 1.0M NaCl conc) in 10 L of water after equilibrium the NaCl concentration in the sample would be 1.0 mM. • Note that in the above example this would commonly be referred to as a "1:1,000" dialysis. • Suppose that we don't want to make up 10 L of buffer? We can actually achieve the same results with two sequential "1:32" dialyses (i.e. the square root of the 1:1,000 dialysis - in other words, two sequential 1:32 dialyses is equivalent to a single 1:1,000 dialysis): First dialysis versus 310 ml of buffer: sample NaCl conc will be (10*1.0)/(320) = 31 mM Second dialysis versus 310 ml of buffer: sample NaCl conc will be (10*0.031)/(320) = 0.97 mM Thus, instead of making 10 L of buffer, we could make only 620 ml and achieve the same results with two dialysis steps • In this case, removing the salt would take twice as long, i.e. we need to perform two dialysis steps. How long does dialysis take? A useful rule of thumb is that for most types of dialysis tubing the dialysis is 80% compete after four hours • One consequence of dialysis to watch out for is that while salt ions are moving out of the bag, water molecules are moving into the bag. Thus the volume of sample may actually increase (the bag will swell) and, therefore, the protein concentration will decrease • In the extreme case, the bag may actually swell to the point of rupture. Therefore, it is a good idea not to fill the bag completely, but leave a void to allow for potential swelling. Concentration • What if our protein sample is too dilute for our needs? How can we concentrate our samples? • One common method is, again, to use a semi-permeable membrane for this purpose. • A very simple method is to place our sample in a dialysis bag and coat it with a high molecular weight solute which can readily be dissolved by the buffer. • For example, polyethylene glycols and polyvinyl pyrolidones can have very large molecular masses (i.e. 20,000 Da). These compounds are also readily dissolved in water. If our sample in a dialysis bag is coated with dry forms of the above polymers, water will leave the dialysis bag (it can go through the pores) and hydrate the polymers. The result is a decrease in volume of buffer in the dialysis bag (the protein will be concentrated). • In another variation, the semi-permeable membrane is manufactured into a flat disk and placed at the bottom of a container which holds our sample. In one method the container is pressurized and forces buffer out of the container (protein is retained and is concentrated). In another method, the vessel is centrifuged and the centripetal force achieves the same goal as pressure in the previous example. For both dialysis and concentration, it is essential that the membrane does not interact with the protein (i.e. has no affinity for, and will not bind, the protein) • With the pressure type concentrators, dialysis and concentration can be achieved in tandem. For example, the sample can be concentrated and then buffer added to the sample. The sample is then concentrated again. Every time buffer is added the salt concentration is reduced. After repeated cycles of this, the salt concentration is at the desired level and the sample is concentrated to the desired final volume. Gel Filtration, Affinity and Hydrophobic resins; Preparation of Resin, Plumbing Gel filtration Gel filtration does not rely on any chemical interaction with the protein, rather it is based on a physical property of the protein - that being the effective molecular radius (which relates to mass for most typical globular proteins). • Gel filtration resin can be thought of as beads which contain pores of a defined size range. • Large proteins which cannot enter these pores pass around the outside of the beads. • Smaller proteins which can enter the pores of the beads have a longer, tortuous path before they exit the bead. • Thus, a sample of proteins passing through a gel filtration column will separate based on molecular size: the big ones will elute first and the smallest ones will elute last (and "middle" sized proteins will elute in the middle). Figure 4.1.9: Gel filtration • If your protein is unusually "small" or "large" in comparison to contaminating proteins then gel filtration may work quite well. Where will a protein elute in a gel filtration experiment? • There are two extremes in the separation profile of a gel filtration column. • There is a critical molecular mass (large mass) which will be completely excluded from the gel filtration beads. All solutes in the sample which are equal to, or larger, than this critical size will behave identically: they will all eluted in the excluded volume of the column • There is a critical molecular mass (small mass) which will be completely included within the pores of the gel filtration beads. All solutes in the sample which are equal to, or smaller, than this critical size will behave identically: they will all eluted in the included volume of the column • Solutes between these two ranges of molecular mass will elute between the excluded and included volumes Figure 4.1.10: Excluded vs. included volume As a general rule of thumb, the excluded volume (Vo) is approximately equal to one third of the column volume, the included volume is approximately equal to two thirds of the column volume • In gel filtration the resolution is a function of column length (the longer the better) • However, one drawback is related to the maximum sample volume which can be loaded. The larger the volume of sample loaded, the more the overlap between separated peaks. Generally speaking, the sample size one can load is limited to about 3-5% of the total column volume. • Thus, gel filtration is best saved for the end stages of a purification ,when the sample can be readily concentrated to a small volume. • Gel filtration can also be used to remove salts from the sample, due to its ability to separate "small" from "large" components. • Finally, gel filtration can be among the most "gentle" purification methods due to the lack of chemical interaction with the resin. Affinity chromatography Affinity chromatography is a general term which applies to a wide range of chromatographic media. It can be basically thought of as some inert resin to which has been attached some compound which has a specific affinity for your protein of interest. • Thus, a specific antibody attached to an inert resin would be a type of affinity chromatography. • Other examples might include: a protease inhibitor attached to some matrix, designed to bind a specific protease • a cofactor bound to some matrix, designed to bind to a particular enzyme • a metal ion bound to a matrix, designed to chelate a protein with a metal binding site, and so on. In each case, the type of resins used and the method of attachment may vary, as will the method of elution. One generalization regarding method of elution is that the bound ligand can be competed off of the column's functional group by including in the elution buffer a high concentration of the free functional group. For example, if the functional group of the column is a cofactor, then the bound protein can be competed off the column by passing a buffer containing a high concentration of cofactor (or cofactor analog) through the column. Other methods of elution include changing the buffer conditions such that the protein is no longer in the native state (since it is the native state which confers the structure required for the specific binding interaction). This can be achieved by changing pH or by adding denaturing agents such as urea or guanidine. With affinity chromatography, typically the purification achieved in a single step can be dramatic - on the order of several thousand fold. Single step purifications with specific affinity columns are not unheard - in fact it is an ideal goal of purification - a matrix which recognizes only the protein of interest and none other. Hydrophobic resins Hydrophobic resins contain a non-polar functional group, such as an alkane or aromatic group. • Many proteins are able to sequester such groups on their surface and this exclusion from solvent provides the basis of the binding energy (i.e. the "hydrophobic effect"). • This interaction is enhanced by increasing ionic strength, such that proteins may bind under high salt conditions and elute under low salt conditions. • As such these columns may be used to not only provide purification, but to desalt samples (for example after an initial ammonium sulfate precipitation). • It is usually not possible to predict in advance which particular resin will bind a given protein, this is usually determined empirically. However, the longer the alkane, or the larger the aromatic compound, the stronger the binding typically will be. Due to the nature of hydrophobic interactions and ionic strength, hydrophobic chromatography and ion exchange chromatography can be conveniently used sequentially. For example, after ion exchange the protein is in high salt conditions, thus it can be loaded directly onto a hydrophobic column. Conversely, a hydrophobic column is eluted in low salt, which is a requirement for binding to an ion exchange resin. A distinction should be noted between hydrophobic interaction chromatorgraphy and reverse phase chromatography • Hydrophobic interaction chromatography is performed in aqueous solvent conditions and changes in ionic strength are used to elute the column. The protein typically binds in the native state via hydrophobic groups located on the surface of the protein. The native state is retained during the elution conditions • Reverse phase chromatography utilizes a hydrophobic solvent (typically acetonitrile) and the binding of a ligand is a function of the phase partition between the hydrophobic nature of the solvent and column functional group. Proteins are typically denatured in such solvents and bind due to the hydrophobic nature of the entire polypeptide sequence. Since the majority of hydrophobic groups are located in the core of globular proteins, the binding is related to the denaturation of the protein and the accessibility of these groups to the column functional groups. Proteins can be purified using reverse phase chromatography, but usually must be refolded in some way to regain functionality (i.e. the native state) Preparation of resins The steps in preparing a chromatographic resin typically involve: 1. Hydration of resin 2. Decanting fines 3. Equilibrating the resin and preparing a slurry 4. Degassing the slurry • Resins come either dry or preswollen. If they are dry they need to be hydrated. This is usually accomplished by mixing the dry resin with buffer and letting it hydrate slowly overnight (or faster at higher temperatures • After the resin has hydrated and settled, very fine particles will settle at the top. These "fines" slow the flow rate of the packed resin. The settled resin is therefore carefully decanted to discard these fines. • The resin is then equilibrated in the buffer to be used for the analysis. Equilibration usually involves pH'ing the resin, or buffer exchanges. Never use a stir bar when pH'ing the resin (it can mechanically shear the resin and produce fines), rather stir the resin slurry with a stir rod. • After the equilibrated resin has settled, an equal volume of buffer is added to produce a 50% slurry of resin. This is usually "thin" enough to allow air bubbles to escape when packing the column. • Finally, the slurry is degassed prior to packing the column. This will help minimize the formation of air bubbles. Packing the column Low pressure columns are typically packed using gravity. • Add a small amount of buffer to the bottom of the column. • Place a packing reservoir on the top of the column. Since we will be using a 50% slurry we will have a volume which is 2x the column volume and its best to pour the resin in all at one time. Thus, the packing reservoir should have a volume equal to, or greater, than the column volume. • Carefully pour the resin slurry into the packing reservoir/column, avoiding the introduction of air bubbles as much as possible • Let the column sit for about 5 minutes to allow large air bubbles to escape • Open the column valve at the bottom and allow the column to pack under gravity • Note the top of the resin bed. It will move down as the column packs. When the column is packed the top of the resin bed will no longer move down. Plumbing Chromatography systems may be run using only gravity and a beaker to collect the appropriate fraction. Most common systems, however, will include the following: • A pump. Usually a peristaltic pump with variable flow rate and a communications port for a controller. The pump is usually set up to push buffer through the column, rather than sucking buffer out of the column (which can cause a low pressure condition with production of air bubbles) • A detector. This is typically a UV (A280) detector. Most detectors are of the two-cell type - meaning that you can have plain buffer as a blank in the detector while analyzing your column fractions. The detector sends the absorbance information to a chart recorder to be displayed (printed) • A fraction collector. This allows you to collect fractions either by number of drops (~30 per ml) or by time. In conjunction with a controllable pump, time collection translates to volume. The fraction collector will typically have an communications port to output a signal when it changes fractions and to receive commands from the detector/controller on some sophisticated systems. • A chart recorder. This will print a continuous trace of the detector output and the fraction collector event marker (signalling when a fraction changes). Fractions can also be read individually on a UV spectrophotometer if a chart recorder is unavailable. Figure 4.1.11: Chromatography setup • If a gravity system is used, a safety loop should be installed to prevent the column from drying up if the buffer is used up when the column is unattended Figure 4.1.12: Safety loop Note that the bottom of the safety loop is lower than the outlet to the fraction collector. Running the Experiment, Resolving Peaks The following represents an example of a low pressure liquid chromatography (ion exchange resin) experiment. Sample: • Volume = 90 mls • A280 = 1.8 • Total A280 = 162 Column: • DE-52 (diethylaminoethyl cellulose; anion exchange) • Size = 1.0 x 12.7 cm • Volume = = 40 mL Fraction Collector: • 10 mls / fraction (~300 drops/fraction) The chromatogram for this experiment looked like this: Figure 4.1.13: Chromatogram The following events took place during this chromatography run: 1. Note the tick marks on the chromatogram. • The "event" marker from the fraction collector notifies the chart recorder when a tube change takes place. • The experiment begins with the tick next to the '0' on the x-axis ("tick 0"); this indicates the start of fraction (tube) number 1. • The next tick mark ("tick 1") indicates the end of fraction number 1, and the start of fraction number 2. • Thus, fractions span the gap between the tick marks. 2. The sample loading is begun at tick 0. 3. Sometime during fraction 5 we begin to notice the absorbence of the column effluent increasing • It has taken about (5 fractions x 10mls per fraction) or 50 mls from the start of loading until the detector notes any absorbance. • This compares well with the fact that the column volume is about 40 mls and there is some volume associated with the tubing going in and out of the column. • Thus, this 'delay' from sample load to sample detection is the dead volume of the system 4. Obviously, some material is not binding to the resin during the loading step. This is the flow-through. Is this some component of the sample which does not have affinity for the resin, or, does it represent that we have exceeded the capacity of the resin? • If we have exceeded the capacity of the resin, then the flow-through will have an A280 similar to the sample being loaded • Also, prior to exceeding the capacity, the flow-through will have some characteristic A280 which will then transition to another A280 (that of the loaded sample), resulting in a double-plateau chromatogram. • In the above experiment the flow-through plateaus around A280=0.5 or about 25% of the absorbance of the load. This would seem to indicate that a component, or component(s), representing one quarter of our sample, does not have affinity for the resin in the column 5. Around fraction 9 we begin to wash the column • This makes sense because 9 fractions x 10mls per fraction = 90 mls have loaded and this is equivalent to our original sample volume (i.e. all the sample has loaded) • The column is typically washed using the same buffer conditions in the protein sample 6. Around fraction 14 we note the A280 begins to decrease • This makes sense given that we determined the dead volume of the system to be approximately 50 mls or 5 fractions. Thus, a wash which was begun at fraction 9 is observed to decrease the absorbance around fraction 14 • We continue washing the column until the A280 approaches 0 (baseline). In other words, all of the non-binding material in the sample has been washed away 7. After the A280 comes back down to baseline we begin our elution protocol. In this particular experiment we will use a linear gradient of increasing salt (NaCl) concentration (in wash buffer) to compete off the material bound to the ion exchange resin. 8. Our elution has produced two peaks: a small peak centered around fraction 42 and a larger peak centered around fraction 50 • We will have to assay each peak (and the flow through) to find out where our protein of interest has gone • The two elution peaks are fairly well resolved. We could combine fractions 40-44 and call that "peak 1", and combine fractions 46-55 and call that "peak 2". 9. Is there any material left on the column? The integrated areas (i.e. summing the A280's of each fraction in a pool) of the flow-through, peak 1 and peak 2 are as follows • Flow through: 4 • Peak 1: 2 • Peak 2: 10 • This gives a total integrated area of 16. Each fraction is 10 mls, so this gives a total A280 = 16 x 10 = 160 which is quite close to the total A280 of our loaded sample. • In other words, it looks like our chromatogram is accounting for all the components in our original sample. 10. If our protein of interest was actually peak 1 (and if our yield was 100%), then this column has provided an eight fold purification ( 2 x 10 / 162). Resolving peaks • Contaminating peaks will not necessarily be completely separated from the peak which contains our protein of interest • In the following picture there are two components being resolved, and they are present in equimolar amounts (thus, the starting purity is 50%). The yield and purity are listed for the situation where we were to pool each peak by splitting at the midpoint between them (in this particular example the yield and purity are identical in each case) Figure 4.1.14: Contaminating peaks • This gives you some idea of the amount of cross-contamination in each peak as a function of their separation from one another. • Software to fit gaussians to a chromatogram can provide this type of information Pooling for purity verses yield Do we try to pool to maximize yield or to maximize purity? Usually, you will probably be pooling fractions in such a way as to maximize the recovery of your protein of interest. However, you always have the option of pooling to increase purity, and if you have lots of protein to work with this may allow you to achieve the desired purity with fewer steps. Here's an example of how it's done: Figure 4.1.15: Yield vs. purity • These are all the same chromatogram, however, we can pool them differently to get better purity (at the expense of yield • The blue peak is the peak of interest and it is not resolved from a contaminating peak (in red). • The vertical line represents the left-most fraction we use to pool the peak (we pool all fractions to the right of the vertical line to get our protein of interest) • In the last panel we see that we can achieve about 98.8% purity if we are willing to part with half our protein! Monitoring the purification How do you know when you are finished purifying a protein? There are several criteria. One criteria is that we cannot improve upon the specific activity of our sample. This value refers to the functional activity of our sample in relationship to the total protein concentration of the sample. • In the initial stages of purification this value will be low (not much activity in relationship to the total amount of protein). • This value will increase after each purification step as we remove other proteins from the sample. • At some point the specific activity will plateau, and by definition, if it is pure we cannot increase the specific activity. • There may be a published value for the specific activity which we can compare ours to. Also, each step of the purification should be monitored by gel electrophoresis. • In the initial stages of purification we will probably see a variety of bands, of various molecular weights, on our gel. • After the different purification steps, we should see the disappearance of certain bands concomitant with the increasing concentration of a certain band (or bands) representing our protein. • If we have successfully purified our protein (and if it is a single polypeptide) we should arrive at a constant specific activity and a single band on a gel. • Analytical methods like HPLC or densitometer scanning of a stained gel can give us a quantitative idea of the purity of our final sample. The following chart represents the typical data one would monitor during a purification: Step Total protein (mg) Total activity (units) Specific activity (units/mg) Purification % Yield Crude cell lysate 5500 6600 1.2 30-70% Ammonium sulfate cut 1020 5910 5.8 4.8 89.5 DEAE Sephadex pool 187 5070 27.1 4.7 85.8 CM Sephadex pool 102 4420 43.3 1.6 87.2 Phenyl Sepharose pool 56 3930 70.2 1.6 88.9 Gel Filtration pool 32 2970 92.8 1.3 75.6 Affinity resin type #1 pool 5.8 2520 434.5 4.7 84.8 Affinity resin type #2 pool 5.3 2390 450.9 1.0 94.8 Total purification 376 Total yield (%) 36

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/4._Biotechnology_2/4.1%3A_Protein_Purification.txt

The bacteriophage known as "M13" forms the basis of cloning systems designed to easily introduce mutations into genes inserted into the phage genome. It also has been used in various "phage display" methodologies and "combinatorial" DNA and peptide libraries. M13 infection and replication M13 is a filamentous bacteriophage which infects E. coli host. The M13 genome has the following characteristics: • Circular single-stranded DNA • 6400 base pairs long • The genome codes for a total of 10 genes (named using Roman numerals I through X) Figure 4.2.1: M13 genome • Gene VIII codes for the major structural protein of the bacteriophage particles • Gene III codes for the minor coat protein Figure 4.2.2: Gene III and gene VIII • The gene VIII protein forms a tubular array of approx. 2,700 identical subunits surrounding the viral genome • Approximately five to eight copies of the gene III protein are located at the ends of the filamentous phage (i.e. genome plus gene VIII assembly) • Allows binding to bacterial "sex" pilus • Pilus is a bacterial surface structure of E. coli which harbor the "F factor" extrachromosomal element Infection • Single strand genome (designated '+' strand) attached to pilus enters host cell • Major coat protein (gene VIII) stripped off • Minor coat protein (gene III) remains attached • Host components convert single strand (+) genome to double stranded circular DNA (called the replicative or "RF" form) • Transcription begins • Series of promoters • Provides a gradient of transcription such that gene nearest the two transcription terminators are transcribed the most • Two terminators • One at the end of gene VIII • One at the end of gene IV • Transcription of all 10 genes proceeds in same direction Amplification of viral genome • Gene II protein introduces 'nick' in (+) strand • Pol I extends the (+) strand using strand displacement (and the '-' strand as template) • After one trip around the genome the gene II protein nicks again to release a completed (linear) '+' genome • Linear (+) genome is circularized • During first 15-20 minutes of DNA replication the progeny (+) strands are converted to double stranded (RF) form • These serve as additional templates for further transcription • Gene V protein builds up • This is a single stranded DNA binding protein • Prevents conversion of single (+) strand to the RF form • Now get a buildup of circular single stranded (+) DNA (M13 genome) Figure 4.2.3: Amplification of genome Phage packaging • Major coat protein (Gene VIII) present in E. coli membrane • M13 (+) genome, covered in ss binding protein - Gene V protein, move to cell membrane • Gene V protein stripped off and the major coat protein (Gene VIII) covers phage DNA as it is extruded out • Packaging process is therefore not linked to any size constraint of the M13 genome • Length of the filamentous phage is determined by size of the DNA in the genome • Inserts of up 42 Kb have been introduced into M13 genome and packaged (7x genome size) • ~8 copies of the Gene III protein are attached at the end of the extruded genome Development of M13 into a cloning vector M13 was developed into a useful cloning vector by inserting the following elements into the genome: • a gene for the lac repressor (lac I) protein to allow regulation of the lac promoter • the operator-proximal region of the lac Z gene (to allow for a-complementation in a host with operator-proximal deletion of the lac Z gene). • a lac promoter upstream of the lac Z gene • a polylinker (multiple cloning site) region inserted several codons into the lac Z gene Figure 4.2.4: Insertions into genome • The vectors were named according to the specific polyliner region they contained • The vectors were typically constructed in pairs, with the polylinker regions in opposite orientations Figure 4.2.5: Polylinker regions Cloning into M13mp vectors • The RF (double stranded) form of the M13 phage can be isolated and treated just like any other plasmid • The polylinker region can be "opened" using restriction endonucleases appropriate for accepting the fragment of interest • The fragment is ligated into the plink region • The availability of inverse oriented plink's (e.g. mp18, mp19) means that inserted fragments with non-complementary ends can be inserted in either orientation Single stranded forms of the phage The ability to isolated a single stranded form of the phage has advantages in both sequencing and mutagenesis. • Single stranded DNA template can be read further than double stranded template An efficient mutagenesis method (the "Kunkel" method) was developed using the single stranded form of the phage. • The M13mp vector with insert is first grown in a mutant E. coli host (e.g. CJ236)which would occasionally incorporate uracil into the DNA instead of thymidine. • E. coli normally synthesizes an enzyme (uracil-N-glycosidase) that removes uracil residues in DNA. However, in ung- strains, the uracil is not removed • The level of uracil mis-incorporation into DNA is enhanced in strains which have a deficiency in dUTPase. This enzyme converts dUTP to dUDP, and therefore, in dut- strains the levels of dUTP are elevated and enhance misincorporation of dUTP into host DNA • The E. coli strain CJ236 has a genotype which includes dut-/ung- features • A mutagenic primer would be annealed to this single strand template (e.g. to produce a point mutation) • The primer is extended using the four dNTP's, and is ligated to produce duplex DNA • The duplex DNA is inserted into a different host E. coli (e.g. JM101) which recognizes and excises (degrades) uracil containing DNA (i.e. a strain which is ung+) • The parent (wild type) strand is preferentially degraded and the mutagenic strand is replicated. • The phage progeny typically have a high incidence (80-90%) of the desired mutation. Figure 4.2.6: Kunkel method

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/4._Biotechnology_2/4.2%3A_M13_Phage.txt

Microbial systems developed for experimental molecular evolution A protein or peptide is introduced at the amino-terminus of the major coat protein (Gene VIII) via PCR-based mutagenesis Figure 4.3.1: Introduction of protein at gene VIII The linear PCR product is amplified to regenerate an intact circular viral genome whose gene VIII protein now has an amino terminal extension. The mutagenized M13 is introduced into E. coli cells. It is naked DNA (not packaged with viral coat proteins) so it is introduced into E. coli like a plasmid (i.e. via electroporation or using CaCl2 competent E. coli cells). • The extruded viral progeny will: • Be somewhat longer as the genome is larger but should be packaged o.k. • Express the mutant gene VIII (major coat) protein as part of the filamentous phage assembly Figure 4.3.2: Mutated M13 progeny Selection The amino-terminal peptide may have affinity for a specific ligand, possibly an antibody. We can use this binding affinity to purify the mutant phage. We can can construct a chromatography column where the chromatography matrix has attached to it the ligand of interest. If we pass our phage through this column, the mutant phage containing the mutant gene VIII protein will bind to, and be immobilized, on the column Figure 4.3.3: Immobilized ligand • Any wild-type phage will not bind, and will pass through the column • The bound phage can be eluted and used directly to reinfect E coli to propagate viruses with the mutant gene VIII protein The mutant gene VIII protein which has been purified carries along with it the DNA which codes for itself. In other words, we have specifically selected for the DNA which codes for the gene VIII of interest (i.e. the mutant) Changing binding specificity Suppose we have a slightly different ligand attached to the matrix… Our phage with mutant gene VIII protein will not bind very well to this different ligand. If we introduce the appropriate amino acid changes into our mutant gene VIII protein, we may be able to improve the binding affinity (specificity) and get the phage to bind to the new ligand We do not know what specific amino acid changes to make, so we will introduce random changes into the amino terminal extension of the gene VIII protein Figure 4.3.4: Random changes in amino-terminal extension • After PCR and ligation, this will produce a library of phage genomes, each with a potentially different sequence for the amino terminal extension of the gene VIII protein • Generally speaking, when these are introduced into E. coli the efficiency is typically so low that, at most, each successful transformation results in only one phage genome per E. coli • Progeny phage from our library will, therefore, have only one type of gene VIII protein per phage (a single phage will have the gene VIII protein which it's DNA codes for). • If we pass the collection of progeny phage from our library over our ligand column, we will selectively bind those phage whose mutant gene VIII protein has affinity for the ligand Figure 4.3.5: Specific binding • If we isolate the bound phage we can re-infect E.coli, and isolate the progeny phage This will result in a second library which will be enriched for mutant gene VIII sequences with the desired ligand binding specificity • In this first selection we may have many other phage which might be weak, or generally non-selective, ligand binders. So, the process is repeated until we have a library of specific high-affinity mutants (this is sometimes called "Bio-mining" or "Bio-panning") • We can then sequence the DNA and find out what sequence(s) in the gene VIII mutant provide high binding specificity to our new ligand Practical limitations • Over what length of DNA/protein can we randomize? Example: a random stretch of 10 amino acids • 2010 possible different unique polypeptide sequences. Each amino acid is 110 g/mol or 1.8 x 10-22g per amino acid. This would mean that a complete random library of 10mer peptides would have a mass of 1.8 x 10-9 g. Example: a random stretch of 20 amino acids • 2020 possible different unique polypeptide sequences. Each amino acid is 110 g/mol or 1.8 x 10-22g per amino acid. This would mean that a complete random library of 20mer peptides would have a mass of 18.9 Kg! This does not even consider the significantly larger mass of the virus particle of which it would be a part. This type of calculation demonstrates that generating random DNA sequence libraries is limited to very short stretches of about half a dozen or so codon positions. Random Mutagenesis We could randomly introduce all four bases at each position (NNN) while trying to synthesize a mutagenic random codon, but what would this give us? Leu, Arg, Ser (6 codons/64) 9.4% Val, Pro, Thr, Ala, Gly (4 codons/64) 6.3% Ile (3 codons/64) 4.7% Phe, Tyr, His, Gln, Asn, Glu, Asp, Cys (2 codons/64) 3.1% Met, Trp (1 codon/64) 1.6% Stop (3 codons/64) 4.7% • Thus, not all amino acids are present in equal amounts, and we will have 4.7% stop codons • We can improve this by using all four bases at the first two positions, and an equal mix of G and C bases at the third (wobble) position. This would result in 32 possible codons and the following distribution of amino acids and codons: Phe, Tyr, Cys, Trp, His, Gln, Ile, Asn, Met, Lys, Asp, Glu, Stop (1 codon/32) 3.1% Gly, Val, Ala, Pro, Thr (2 codons/32) 6.3% Leu, Arg, Ser (3 codons/32) 9.4% Another general problem with the above method is that of non-specific binding • This is due in part to the large number of mutant gene VIII proteins (~2500) on the virus surface. • Ligand binding may be due to the interaction of two or more gene VIII proteins contributing to binding the ligand (i.e. "polyvalent" binding) • In this case, the isolated mutant peptide may show weak or no binding affinity at all How can we minimize the number of mutant proteins per phage particle? Approach #1 - Mutate gene III protein instead of gene VIII • While there are ~2,500 copies of the gene VIII (major coat protein) per phage, there are 5 copies of the gene III (minor coat protein) per phage Figure 4.3.6: Mutating gene III • We can introduce the mutagenic sequence to the amino-terminal of the gene III protein and thereby limit the number of copies on the phage particle • this may still result in polyvalent binding Approach #2 - Supplement with wild-type gene III protein during phage assembly • If the M13 phage contains a second copy of gene III which we do not mutate then when the phage assembles (as it extrudes through the outer membrane) the five gene III proteins it picks up will be a mixture of mutant and wild type (in roughly equal amounts). In this case, the progeny phage will have, on average, 2-3 copies of the mutant gene III protein • If we can increase the expression of the wild type gene III protein, to the point where it is present in four-fold excess over the mutant, then the progeny phage will, on average, contain 1 copy of the mutant gene III protein (and ligand binding will, by definition, be mono-valent) How can we do this? • We can put a strong promoter in front of the wild type gene III • We could place the wild type gene III near one of the terminators (after gene IV, for example). This position would result in higher expression of the wild type gene due to the cumulative effects of the upstream promoters Variation Expression of mutagenic protein as a fusion of an outer membrane protein of E.coli itself • We can attach our mutagenic sequence to a gene for an outer membrane protein (e.g. OmpT, LamB) • The surface of the E. coli cell itself displays the mutagenic peptide • Outer membrane proteins may be present at a level of 10,000 molecules or more • Our ligand column will selectively bind specifically binding E. coli cells. These can be eluted and cultured and the process repeated to select for the mutant sequence with binding affinity Expression of mutagenic protein as a fusion with a DNA binding protein • We can attach our mutagenic sequence to a gene for a protein which binds DNA (e.g. lac repressor) • The mutagenic lac repressor will be encoded by a plasmid which itself contains two lac operator sequences • The mutant repressor molecule expressed by the plasmid will bind strongly to the lac operator regions on the very same plasmid • The cells are lysed (the repressor remains bound to its associated plasmid DNA) and the ligand binding step is performed to enrich for ligand binding sequences • The associated plasmid DNA is isolated and transformed back into E. coli for amplification and another round of selection Figure 4.3.7: Fusing mutagenic protein and binding protein

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/4._Biotechnology_2/4.3%3A_M13_Phage_Display_Libraries.txt

Tuerk and Gold, Science (1990) 249:505-510 Phage T4 genome encodes a DNA polymerase (T4 DNA pol) The mRNA for T4 DNA pol contains a sequence which has binding affinity for T4 DNA pol. This sequence overlaps the Shine/Dalgarno sequence and binding of T4 DNA pol prevents the mRNA from being transcribed (and producing more T4 DNA pol). Thus, the expression of T4 DNA pol is self-regulating (autogenous regulation). The structure of the mRNA in this region forms a stem-loop structure Figure 4.4.1: T4 DNA polymerase mRNA The experimental design: In simple terms, generate a library of mRNA molecules with a random nucleotide sequence in the loop region. This library will have a size of 48 or 65,536 uniquely different sequences (if only the loop region is randomized). Identify those sequences with high affinity for T4 DNA pol and determine their sequence. Template Construction A "synthetic gene" duplex DNA template is constructed from five primers: Figure 4.4.2: Template construction • The synthetic gene is 110 nucleotides in length. It is composed of oligonucleotides #3, #4 and #5. Oligonucleotides #1 and #2 can be thought of as "bridging" oligos which allow #3, #4 and #5 to be ligated • Oligonucleotide #4 contains the 5' end of the T4 DNA Pol gene corresponding to the mRNA Shine/Dalgarno sequence and the T4 DNA pol binding step/loop. It is synthesized to contain random bases at the 8 base long loop structure of the T4 DNA pol binding domain. • Oligonucleotide #1 contains the sequence for T7 RNA polymerase promoter region. Transcription can be driven from this promoter by addition of T7 RNA polymerase and ribonucleotides. in vitro transcription • The 110 nucleotide gene (double-stranded DNA form) can be transcribed in vitro by T7 RNA pol • Transcription produces a 92 nucleotide long RNA molecule. There will be a library of potentially 65,536 unique RNA molecules Figure 4.4.3: In vitro transcription Selection by binding to T4 DNA pol Those RNA transcripts with a sequence which confers binding specificity for T4 DNA pol are selected for by incubating the RNA library with T4 DNA pol which has been immobilized on nitrocellulose filters. The filters are washed to remove non-specifically bound RNA molecules. Tight (specific) binding RNA molecules are eluted from the T4 DNA pol protein and are collected Figure 4.4.4: Specific binding cDNA construction The eluted RNA molecules are converted to single strand cDNA using oligonucleotide #5 as a primer, and adding Reverse transcriptase and dNTP's. Duplex DNA is produced from the single strand cDNA by PCR using oligonucleotides #1 and #5. Figure 4.4.5: cDNA Construction In vitro transcription • In vitro transcription produces a (smaller) library of RNA molecules enriched for sequences with binding affinity for T4 DNA pol • the process of selection and enrichment is continued until a collection of high affinity binding sequences has been produced Experimental results 1. Library construction • The original "synthetic gene" library was sequenced en mass • A "ladder" of bases at the random position indicated that the library started out being more or less a completely random collection of sequences in this region (i.e. there did not appear to be any sequence bias in the collection of molecules) 2. Samples from the sequential rounds of selection and amplification were saved for sequencing analysis • The results indicated that as the selection and amplification process continued certain bases were observed at specific positions in the 8 base mutagenized region Figure 4.4.6: Mutant sequence The wild type sequence could be found within this heterogenous mixture of tight binding sequences. 20 clones from this final library were sequenced. Figure 4.4.7: Mix of tight binding sequences • There were essentially only two different sequences present: the known wild type sequence and another different sequence (which varied at four positions) • Both had similar tight binding characteristics (the minor sequences identified exhibited weaker binding) • This alternative sequence may have a different stem-loop structure than the wild-type Figure 4.4.8: Change in stem loop structure Conclusions 1. The SELEX method successfully identified the wild type sequence and an alternative (previously unknown) sequence with high affinity T4 DNA pol binding properties 2. The results suggest that the sequence of the wild type translational operator is pretty much optimized for binding (unable to improve further?) 3. The SELEX method should simplify the study of interactions between • transcriptional activators and repressors and transcriptional complexes at promoter sites • replication accessory proteins and DNA pols at origins of replication • ribosomes and translational repressors at ribosome binding sites 4. Results may be relevant to DNA/protein binding interactions in certain cases (i.e. if interactions do not involve 2' hydroxyl group and if a structure which DNA can adopt is involved) 5. Instead of beginning with all possible sequences, start with a smaller library and combine replication with error prone polymerases to generate additional sequences

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/4._Biotechnology_2/4.4%3A_SELEX_%28Selective_Evolution_of_Ligands_by_Exponential_Enrichment%29.txt

Fields, S. and Ok-kyu, S. Nature (1989) 340:245-246 Background The yeast genome contains Upstream Activator Sequences (UAS's) UAS are located 5' to the coding region of a gene and are regulatory regions - they bind transcriptional activator proteins. These transcriptional activator proteins, when bound to UAS, stimulate transcription (i.e. they may contain binding sites for RNA polymerase Yeast gene, protein nomenclature Wild type genes are designated with capital letters in italics (e.g. GAL4). Mutant alleles of the genes are indicated in lowercase italic (e.g. gal4) and encoded proteins are indicated by roman type, with the first letter capitalized (e.g. Gal4) Growth of yeast in galactose containing media Incubation of wild type yeast in galactose results in >1,000 fold increase in the mRNA level for enzymes involved in galactose metabolism. This increase in mRNA levels is not observed in gal4 mutants. The Gal4 protein is not one of the enzymes involved in galactose metabolism, rather, it a appears to be a transcriptional activator protein which specifically activates the transcription of genes involved in the galactose metabolic pathway. Gal4 protein binds to a specific 17 nucleotide long region of DNA located in the 5' region of genes involved in galactose metabolism. These regions are termed UASGAL Binding of Gal4 to UASGAL increases transcription from the nearby promoter Gal4 Protein • Single polypeptide chain which contains two domains: Figure 4.5.1: Gal4 protein domains These domains are functionally separable in the polypeptide chain (Figure 4.5.2). The amino-terminal (1-74) domain by itself can bind to UASGAL sequences but cannot activate transcription. The carboxyl-terminal (738-881) domain contains the activating region but cannot activate transcription because it fails to localize to the UASGAL region (i.e. near the promoter region) Figure 4.5.2: Gal4 polypeptide chain Experimental Design Imagine we have two proteins (X and Y), which normally form a stable complex (XY) Figure 4.5.3: Complex If we make a Gal4(1-74)- X fusion protein and a Y -Gal4(738-881) fusion protein we can potentially form a functional Gal4 transcription activator (i.e. a Gal4 activator region in complex with a UASGAL region Figure 4.5.4: Formation of Gal4 transcription activator This fusion protein complex can act as a functional transcriptional activator for the galactose metabolic enzymes Figure 4.5.5: Protein complex acting as transcription activator To test this hypothesis, the authors used the following system • The protein complex involved the two proteins SNF1 and SNF4. The SNF1 protein is a serine-threonine specific protein kinase, and the SNF4 protein is known to be physically associated with SNF1 and is essential for function • 5 different gene constructs involving Gal4 and SNF1 and SNF4 were made: Figure 4.5.6: Gene constructs with Gal4, SNF1, SNF4 • These gene constructs were inserted into two different yeast plasmids: • All constructs containing amino terminal fusions of GAL4 were put into plasmids containing the HIS3 gene (coding for a biosynthetic enzyme necessary for the production of the amino acid histidine). • All constructs containing carboxy terminal fusions of GAL4 were put into plasmids containing the LEU2 gene (coding for a biosynthetic enzyme necessary for the production of the amino acid leucine) • These plasmids were inserted into a host yeast (GGY1::171)which had the GAL4 gene deleted, the LACZ gene deleted, the HIS3 and LEU2 genes mutated to be non-productive and which also contained a GAL1-LACZ gene fusion (i.e. a LacZ protein driven from the GAL1 promoter which has a UASGAL. This host is thus DGAL4, DLACZ, his3, leu2, gal1-lacZ. • Thus, selective pressure for the amino terminal GAL4 fusions can be maintained by eliminating histidine in the growth media (yeast relies on the HIS3 gene on the plasmid to make histidine) • Likewise, selective pressure for the carboxy terminal GAL4 fusions can be maintained by eliminating leucine in the growth media (yeast relies on the LEU2 gene on the plasmid to make leucine) • The host can harbor both kinds of plasmids (in which case the media would lack both histidine and leucine) • The expression of the fusion constructs on the plasmid is actually regulated by galactose. Adding galactose results in expression of the fusion constructs. However, since the host is DGAL4 all the other galactose metabolic genes (e.g. GAL1) will not be up-regulated in response to added galactose. The presence of a functional Gal4 protein in the GGY1::171 yeast host will result in the following: • Expression of the LacZ protein (contained in the host GAL1-LACZ gene fusion) will occur. Thus, in the presence of XGAL substrate the yeast colony will turn blue • All the host genes which contain an UASGAL will be up-regulated. This will include all the wild-type genes involved in galactose metabolism (e.g. GAL1) Experimental Results Plasmid LacZ activity 1. None <1 2. GAL4(1-881) 4,000 3. GAL4(1-147) <1 4. GAL4(1-147)-SNF1 <1 5. SNF4 <1 6. SNF4- GAL4(768-881) <1 7. GAL4(1-147)-SNF1; SNF4-GAL4(768-881) 180 8. GAL4(1-147)-SNF1; SNF4 7 9. GAL4(1-147); SNF4-GAL4(768-881) <1 • Functional Gal4 protein resulted in expression of the host GAL1-LACZ gene • Co-expression of plasmids containing GAL4(1-147)-SNF1; SNF4-GAL4(768-881) fusion proteins resulted in expression of the host GAL1-LACZ gene, indicating the presence of functional Gal4 protein (i.e. complex formation of the Gal4 fusion proteins) • The level of LacZ protein was less, however, indicating that the SFN1- and SFN4- Gal4 fusion protein complex was a less efficient as a transcription activator than wild type Gal4 protein • There was some low level LacZ protein activity with the GAL4(1-147)-SNF1; SNF4 protein complex. This suggests that the SNF4 may have some minor affinity for RNA polymerase Conclusions This method may prove useful for identifying proteins which can form stable complexes (i.e. identifying specific protein:protein interactions). For example, if we want to know which proteins bind to a certain receptor we can make a GAL4(1-147)-receptor fusion protein and screen a cDNA library for potential binding proteins by constructing cDNA-GAL4(768-881) fusion constructs. Potential binding proteins from such a library can be screened by looking for blue colonies with XGAL substrate, or selected for by growing the yeast in media with galactose as the carbon source.

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/4._Biotechnology_2/4.5%3A_Protein-Protein_Recognition_Probed_Using_a_Yeast_Transcriptional_Activator_System.txt

Mosbach, K. TIBS (1994) 19: 9-14 Molecular recognition is the foundation of many biological processes. A major emphasis in biotechnology is the development of synthetic recognition molecules and systems which are specific for a ligand of interest. One branch of molecular design involves the synthesis of molecules with specific stereochemical structure designed to recognize and interact with a particular ligand. In contrast, molecular imprinting is a method of constructing a molecule, with specific recognition properties, by having the ligand itself direct the assembly of the desired structure Technical aspects The general approach of the technique is to combine the desired "imprint" molecule with a solution of small monomeric molecules which has several desired features. They have a variety of functional groups (e.g. hydrogen bond donors, acceptors, possibly acidic or basic groups, or aliphatic or aromatic groups). One or more of these groups can specifically interact with the imprint molecule (e.g. they can form specific hydrogen bonds). The monomeric molecules can be made to polymerize to form a rigid, or semi-rigid polymer. The imprint molecule is soluble in the monomer solution. The monomers, either in solution or during the polymerization process, do not form covalent bonds with the imprint molecule (the interaction is strictly non-covalent) After polymerization of the monomers (in the presence of the imprint molecule) the general features of the polymer are as follows: 1. The polymer is porous enough to allow the imprint molecule to diffuse out (and likewise to diffuse in) 2. The polymer is rigid enough that the organization of monomeric molecules in response to the imprint molecule is retained. 3. Therefore, the stereochemistry (i.e. size, shape and orientation of appropriate functional groups) of the pocket where the imprint molecules were located is retained. These sites in the polymer constitute an "induced molecular memory" capable of selectively recognizing the imprint molecule The typical polymer production technique has been to produce a bulk imprinted polymer and then grind it up to produce small particles (25 mm). Such small beads are appropriate for liquid chromatography methods (i.e. can be used as chromatographic resins and packed in a column). "Suspension" polymerization can yield beads directly from the polymerization process Most of the reported molecular imprinted polymers reported have been made from monomers of acrylates, styrenes or silicates, and the imprinting step performed in organic solvents. Imprinting in organic solvents (e.g. low dielectric constant solvents) enhances hydrogen bonding (i.e. partial electrostatic) interactions. However, it can limit imprint molecules to those that are soluble in such solvents. Example \(1\) Imprinting of the amino acid L-phenylalanine with methacrylic acid monomers and the enantioselective binding of the imprint molecule. • Step I. "Imprinting" Figure 4.6.1: Imprinting • Step II. Use of the imprinted polymer as a chromatographic resin to preferentially achieve the enantiomeric selection of L-phenylalanine from a racemic mixture of L- and D-phenylalanine Figure 4.6.2: Enantiomeric selection Applications Three main areas of application of MIP's include chromatographic matrices for difficult (e.g. enantiomeric) separations, catalytically active polymers or enzyme mimics, and Bio-sensor devices. Separations There are about 500 drugs on the market which are optically active, about 90% of these are administered as racemic mixtures. The FDA now requires that for optically active drugs both enantiomers must be treated as separate substances in pharmacokinetic and toxicology profiling. There is a great need for preparative methods of enantiomer separations Other separations MIP's made against particular alkaloids or opiates can discriminate between the imprinted compounds and closely related compounds Figure 4.6.3: Closely related compounds that can be separated with MIP's Imprinted polymers as enzyme mimics If imprints are prepared against transition state analogs, the MIP will stabilize the transition state and result in enhanced catalytic rates. Increased catalytic rates (turnover) in comparison to substrate turnover rates in solution have been observed with polymers imprinted with transition state analogs. However, they are generally poor catalysts. This is most likely due to the fact that specific stereochemical placement of catalytic groups was not attempted. Thus, such MIP's represent an appropriate framework within which to place catalytic groups to achieve efficient catalysis Figure 4.6.4: Imprints as enhancer for catalytic rates Imprinted polymers as substrate selective sensors "biosensors" are devices which integrate specific receptor molecules with signal transduction devices. The transducer may convert the binding energy of the MIP/analyte into changes in protons, light absorbance, heat, etc. This physical/chemical signal is ampified and output to a measuring device. In principle, the analyte can be any molecule which can be specifically imprinted. For example, an environmental toxin. Figure 4.6.5: Imprinted polymer as biosensor Conclusions Although most work with MIP have involved small molecules, in principle it should be possible to extend MIP utility to include biomolecule recognition (i.e. proteins, nucleic acids). Such MIP should exhibit similar specificities. If MIP can mimic biomolecule binding sites, they may prove useful alternatives for the screening of new inhibitors, or antagonists. One potential limitation is that of "mutagenesis". Can a polymer which has already been imprinted be "mutagenized" (chemically modified?) to produce novel/enhanced recognition characteristics? Another limitation is that the exact stereochemical structure of the imprint is not known. Therefore, the structural information of a useful imprint is not available.

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/4._Biotechnology_2/4.6%3A_Molecular_Imprinting.txt

There are two main concerns in a biochemical laboratory environment, they are: • Safety • Accurate record keeping Safety A biochemical laboratory has safety concerns that include not only issues of chemical safety, but also issues of biohazard safety. In both cases, your safety depends upon a knowledge of the materials and procedures you are working with, as well as appropriate protective dress. Safety issues will be covered in detail in the syllabus, and in the lab by the lab director, however, a few key points will be covered here: 1. Read the relevant chapter of the lab manual BEFORE arriving for the lab. To reinforce this practice, the labs will start with a short quiz related to the lab for that day. The quiz will start PROMPTLY at the beginning of class, will last approximately 10 minutes, and there will be NO MAKEUP QUIZZES for late arrivals. The beginning of lab time is used to organize the lab, and includes relevant safety information. Therefore, it is essential for all concerned that students show up promptly for the labs. 2. Dress appropriately. No open toed shoes (i.e. sandals), shorts, or bare midriffs are permitted in a laboratory setting (OSHA guidelines). YOU WILL BE ASKED TO LEAVE IF YOU ARE NOT APPROPRIATELY DRESSED. Lab coat and goggle guidelines will be addressed by the lab manager. 3. Lab attitude and behavior. Both safety and a successful learning experience are keenly associated with behavior and attitude. A cavalier attitude will result in accidents and experimental failures. Thinking about what you are doing, and asking questions, will result in success and a long life. One of the biggest problems in a laboratory setting is equipment damage due to a lack of knowledge about how to use such equipment. DO NOT USE ANY EQUIPMENT WITHOUT FIRST BEING INSTRUCTED ON HOW TO USE IT. Due to the expense of scientific equipment, labs can only function through the use of shared equipment, thus, everyone using such equipment needs to take care of it. Accurate Record Keeping There are typically two levels of record keeping for scientists involved in biochemical research: 1. The laboratory notebook 2. The research report It is important to understand the purpose of each type of record keeping, and to adhere to the practices associated with each one. The laboratory notebook The lab notebook can be thought of as your "working record" or "experimental log". It is a sort of formalized diary, and has a specific purpose and associated formalism with regard to how to use and maintain it. • For purposes of invention, discovery and patents, it is the primary record used to establish ownership of an invention and dates of discovery. To be useful for this legal process, certain guidelines must be followed. • For the purpose of the scientific process it provides the necessary detail so that experiments can be repeated and the results replicated and confirmed • For the purpose of reporting results, the lab notebook is the repository of all primary data and results To meet the above needs, the lab notebook is: · bound (no loose leaf) and · pages are numbered sequentially. · Futhermore, entries in the lab notebook are always made in ink (black or blue), never pencil. If something is written that is incorrect, it can be crossed out. In this way, an examiner of the notebook can determine whether the lab notebook is intact or whether pages or information have been removed. Entries into the notebook for a given experiment always begin with: · Writing the date · Stating the purpose of the experiment The remainder of the notebook section that covers the experiment will include any and all relevant information associated with the experiment, including: • A description of the materials, equipment and reagents used • A description of the methods used • Calculations used (a truly essential aspect of trying to repeat the experiment and identifying errors) • Tabulation of results collected • Conclusions, hypotheses, notes, etc. The key point is that you include enough information so that someone else can use your notebook to replicate the experiment and obtain the same results you did. In this regard, the notebook does not have to have perfect penmanship, but it must be legible and understandable by others. Diagrams are often essential. In industrial (and some academic) settings, it is a requirement that another person in the lab sign off on your notebook that they have read, and can understand, your notebook entry for a particular experiment (this is associated with the legal function of the notebook). The notebook must not contain any loose bits of paper. If an experiment includes a photograph or some type of printout from an instrument, this must be firmly affixed to the notebook (i.e. glued & stapled). Any loose paper can be lost, and with it, some key information. The best approach, if you have a data printout from an instrument, is to copy it by hand into your notebook. Sometimes this is not practical, so the data must be glued & stapled. It is also not a good idea to have information beneath such added items. In some industrial settings, the lab notebooks are Xeroxed for archival purposes. In this case, Xeroxing a page will miss information if it is written underneath a photo, etc. The first few pages of the lab notebook should be reserved for a table of contents. This is associated with the function of being able to replicate an experiment. Part of this requirement is the ability to locate a particular experiment in a given notebook. Researchers can generate dozens of notebooks involving hundreds of experiments, and a table of contents is essential. Furthermore, a researcher will often have two or more experiments on-going at one time. It is common for a notebook entry to start on one page and then reference a later page for continuation of the experiment (sort of like some magazine or newspaper articles). If appropriate, notebook pages should indicate both the referring page number, and the continuation page number. The research report The lab notebook is your working journal, and is not intended as a formal research report. The formal research report has specific sections and organization, and is a final, polished document relating to your research. These sections include the following (in specific order): • Title • Introduction • Materials and Methods • Results • Discussion • References In many cases, such reports also have an abstract (a short, ~250 word report that precedes the Introduction and details the essential aspects and results). However, this will not be required in your own reports. Each section of the report has a specific function: Title • Typically a single sentence that explains to the reader what the experiment is all about Introduction • One or more paragraphs that explain the purpose of the experiment, and provide any relevant background information so that the reader is prepared to follow the narrative of the report. In published research reports the introduction also lays out a logical argument as to the importance of the experiment Materials and Methods • A detailed "recipe", describing instrumentation, materials and supplies, and procedures used in performing the experiment. The "M&M" section is detailed enough so that the experiment can be repeated and the results confirmed by others. An essential aspect of the scientific method is that results are meaningless unless they can be confirmed by others. Results • Statements of fact regarding the data collected, with associated errors or deviations (e.g. all relevant values and/or numbers relating to your data). This is often represented using tables, and sometimes, figures. Discussion • An interpretation of the results, and a discussion of its meaning, including a discussion of error (where relevant). In published research reports, the discussion will also put the work in the context of other published work in the same field. References • If you used any texts, primary literature, or web sites in preparing your experiment or report, these should be referenced. There are a variety of formats, but the reference generally includes author, title, volume, pages and year (also publisher, if it is a text). For web sites, author and URL. A key aspect is to avoid plagiarism. An addition to having the above sections, all research reports have a required format. The format of a report includes details of: • Type of font, line spacing, page breaks, margins, etc. • Page length and/or word number requirements • Limitations on number of tables, figures, use of color, etc. • Title page format • Formatting of references This course is no different, and there is a required format for research reports, and these can be found in the syllabus. Common mistakes in writing the research report Although it may seem straightforward, writing an effective research report can take years of practice. There a several aspects that can prove difficult. Here are a few things to consider: • Do not put Results into the Materials and Methods section. The M&M section may have equations, but will not have any data listed. • Do not put comments associated with the Discussion section into the Results section. Sometimes it is difficult to know what goes where, but here is a general guideline: information in the Results section can be thought of as statements of fact (i.e. your data), whereas, interpretation and hypotheses are part of the Discussion section. Someone might argue with your ideas in the Discussion section, but there should be no argument about information in the Results section. • Error associated with your data is presented in the Results section as well. Do not get hung up on semantics. A "discussion of error" goes into the Results section. • Do not duplicate statements or paragraphs in different sections. If you have said something in the Introduction, do not repeat it in the Results or Discussion section. • Do not duplicate data. If you present data in a table, do not also present it as a figure. However, having said this, it is important to recognize that, even with scientists, people can often grasp meaning easier with a figure as opposed to looking at numbers in a table. Often, selected data is extracted and compared using an appropriate figure. • YOU MUST FOLLOW THE FORMATTING GUIDELINES FOR THE RESEARCH REPORT. In the "real world" failure to follow such guidelines will result in immediate rejection of the report. An example of a research report is given here.

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/5._Lab_Notes_Part_1/5.1%3A_Basic_practices_and_techniques_Laboratory_notebooks_Presentation_of_data_Sections_of_a_scientific_report.txt

The human eye responds to electromagnetic radiation within a range of wavelengths between 400-750 nm (i.e. the "visible spectrum"). Samples of light that contain a continuous spectrum of all wavelengths between 400-750nm will be perceived by the brain as "white light" (e.g. the sun). Light comprising a specific wavelength within this range is perceived by the brain as being "colored": • ~400nm = "violet" • ~450nm = "blue" • ~500nm = "green" • ~550nm = "yellow" • ~600nm = "orange" • ~700nm = "red" Often objects appear colored because of their absorption of light within selective regions of the visible spectrum. The light from such objects that strikes our eyes (whose color we perceive) is composed of those wavelengths that the object DID NOT absorb. For example, plant leaves contain two photosynthetic pigments: chlorophyll A and chlorophyll B. The chlorophyll A molecule has the ability to absorb light with a wavelength in the range of 430 and 660 nm; the chlorophyll B molecule absorbs light with a wavelength in the range of 450 and 640 nm. Thus, these two pigments in leaves absorb violet/blue and orange/red wavelengths of light (the energy that these photons represent is transferred to the chlorophyll molecules). Light with wavelengths in the range of 500-600nm is not absorbed by either molecule. Thus, after sunlight interacts with leaves the wavelengths that remain (and that our eye can perceive) are green-yellow. Therefore, plants are "green" because they don't absorb green light. • What would an object look like if it contained a pigment that absorbed all wavelengths from 400-750nm? It would be "black" • This is the same situation as being in a closed room with the lights turned out. Without light there is no color. If we had a sample of leaf juice containing chlorophyll A and B we could quantitate the concentration of chlorophyll by the amount of violet/blue and orange/red light it absorbed. We COULD NOT quantitate it by the amount of green light transmitted (it would be essentially the same amount regardless of the amount of chlorophyll: Figure 5.1.1: Chlorophyll light transmission Even though the sample is "greener" with higher concentrations of chlorophyll, this is not due to an increase in the intensity of the green wavelength of light; it is due to a reduction in the violet/blue and orange/red wavelengths of light. It is this reduction that we can use to quantitate the concentration of chlorophyll. Although the sample appears green we cannot quantitate it by monitoring the green wavelength of light. Light, Energy and Molecular Structure Light is strange stuff, it is a radiative form of energy transfer, and in order to understand its properties we have to consider it to have both wave and particle properties. The different wavelengths of light differ in the energy they carry; the energy is directly proportional to the frequency of the light (the higher the frequency, the higher the energy): E α n The energy unit is the erg (1Joule = 107ergs) and the frequency unit is Hertz (i.e. cycles per second, or just units of sec-1). The constant of proportionality is Planck's constant, h, with a value and units of 6.6 x 10-27 erg sec E = hn erg = (erg sec)(sec-1) The frequency (n) of light is inversely proportional to the wavelength (l): n α 1/λ Frequency has units of sec-1 and wavelength has units of meters. The constant of proportionality is c (the speed of light) with a value and units of 3 x 108m/sec: n = c(1/λ) = c/λ This relationship is true in a vacuum; however, "optically dense" materials can slow the speed of light requiring the following correction to the above equation: n = c/nl where n is the "refractive index" of the material and has a value > 1 Therefore, the energy of light is inversely proportional to the wavelength: E = (hc)/(nl) The "photon" is a corpuscular (particle) description of light that is the carrier of the "quanta" of energy defined in the above equation. When light is absorbed by a molecule it transfers its quanta of energy, and the photon ceases to exist. Where did the energy go? • The atomic bonds, chemical structure and electrons of a given molecule have specific excited states and vibrational modes. • These excited states and vibrational modes have defined energy levels above the ground state. • The molecule can change from the ground state to an excited state (or vibrational mode) upon the absorption of a quanta of energy exactly equal to the difference between the ground and excited states. • Thus, molecules can absorb the energy associated with specific wavelengths of light, and the light is consumed in the process. Figure 5.1.2: Scale of light The atomic bonds, chemical and electronic structure of molecules are unique properties and differ from one type of molecule to another. • Thus, the energetic differences between the ground and excited states differs from one type of molecule to another. • Therefore, the ability to interact and absorb light, and the specific wavelengths of light absorbed differ from one type of molecule to another The characteristic absorption pattern of the different wavelengths of light is unique for each type of molecule and is a type of molecular "fingerprint" that can be used to identify and quantitate molecules In the example of chlorophyll given above the intensity of light passing through the sample is termed the "light transmittance" of the sample: Figure 5.1.3: Light transmittance of chlorophyll • Transmittance is a dimensionless number that varies from 1 (full transmittance) to 0 (no transmittance - complete absorption) It is important to note the distinction between transmittance and absorption: they are inversely proportional • A sample with high absorbance has a low transmission of light Figure 5.1.4: Chlorophyll absorption How are transmittance (T) and absorption (A) quantified? • The intensity of light shining on a sample is termed the incident light, I0 • The intensity of light measured after passing through a sample is the transmitted light, I • Transmittance, T, is defined as the ratio I/I0 and will vary between 0 and 1: Figure 5.1.5: Transmittance How is the transmittance affected by the sample concentration and physical size (i.e. path length)? • If the transmittance is reduced due to the absorption by the sample, then the higher the concentration (c), the lower the transmittance. In other words, the transmittance is inversely proportional to the concentration: (Beer's law) T α 1/c • Similarly, the thicker the sample (i.e. the greater the path length of the light through the sample), the lower the transmittance. Thus, the transmittance is also inversely proportional to the path length (l): (Lambert's law) T α 1/l Thus, we expect the equation relating T to c and l to take the general form of: T α 1/cl Transmittance turns out to decrease exponentially with increases in the concentration and path length, thus the equation has the form: logT α 1/cl -logT α cl The constant of proportionality is the extinction coefficient e: -logT = εcl Since log values are dimensionless, the units of e would appear to be inverse concentration and inverse distance (e.g. M-1cm-1) The -logT term on the left can be written as log(1/T) and identifies the inverse relationship between the transmittance T and the ecl term. Since absorbance and transmittance are inversely related, the ecl term would seem to be a convenient definition for the absorbance (A): log(1/T) = ε cl = A (the Beer-Lambert Law) Values for ε and l • Path length l is usually in units of cm • Molar extinction coefficient ε has units of M-1 cm-1 and is a constant of proportionality that relates the absorption of molar solutions • Mass extinction coefficient ε 1% refers to the absorbance of a 1% by mass solution. Typically this refers to an aqueous solution that we can take to have a density of 1000g/L. A 1% by mass aqueous solution would therefore refer to the dissolution of 10g/L, or a 10mg/ml solution of the molecule of interest. • Since the absorbance of a molecule is a function of the wavelength (i.e. the absorption is not equal for every wavelength) the extinction coefficient must also reference a wavelength. This is typically done using a subscript: ε 1%280nm = 14.5 g-1 L cm-1 · In this case a 10mg/ml solution of the molecule will have an absorbance reading of 14.5 (dimensionless units) at l = 280nm (the absorption at other wavelengths may not be known). The units of concentration are g/L, thus e will have dimensions of g-1 L cm-1. The relationship between changes in A, T and c · The direct relationship between A and c means that there is a linear relationship between absorbance and concentration. If you double the concentration the absorbance will double, etc. · The inverse log relationship between transmittance and absorbance can be stated as: T = 1/10A • A doubling of the concentration will therefore result in a 10-fold reduction in the transmittance. Instrumentation for absorbance measurements (spectrophotometers) actually measure transmittance, and naturally become less accurate at low values of transmittance. Thus, the higher the absorbance reading the less accurate it is. Most such instrumentation is inaccurate at absorbance readings > 1.5 (this works out to be at transmittance < 3% of full transmittance) Design of Spectrophotometers Spectrophotometers are precision instruments, however, conceptually they involve a relatively few number of parts. A simple design would look something like this: Figure 5.1.6: Spectrophotometer • A tungsten lamp is used to produce wavelengths of light spanning the visible range, while a deuterium lamp is used to produce light spanning the ultraviolet range. • The movable prism or diffraction grating is adjusted to direct the wavelength of interest towards the sample • This is a "single beam" spectrophotometer and the reference and sample data are collected separately (the reference sample is used to determine the maximum transmission value (effectively I0) A modification of this design, a "dual beam" spectrophotometer, can permit simultaneous measurement of I and I0: Figure 5.1.7: Dual beam spectrometer Both the single and dual beam detectors allow the investigator to monitor the absorbance properties for a single wavelength of light (the wavelength selected by the prism/slit settings. A modification that incorporates an array of detectors can permit simultaneous measurement of a spectrum of wavelengths: Figure 5.1.8: Simultaneous measurement of a spectrum Absorbance spectra of biological molecules Proteins Proteins do not absorb in the visible wavelength unless they have a prosthetic group (e.g. Fe2+) or an unnatural amino acid. However, the amino acids tryptophan, tyrosine and cysteine absorb light in the UV wavelength: Figure 5.1.9: Tryptophan absorption • Tryptophan has a peak of absorption at 280nm in the UV range • This is a useful wavelength to quantitate the absorption of tryptophan • Since the absorption is proportional to concentration, this is a useful way to quantitates protein concentration (for proteins containing Trp) Nucleic acids The aromatic rings in the bases of nucleic acids also absorb in the UV range: Figure 5.1.10: Nucleic acid absorption • Each DNA and RNA base has a slightly different absorption spectrum • 260 or 280nm is a typically useful wavelength to monitor concentration of nucleic acids Note that samples of nucleic acids and proteins can both absorb at 280nm, therefore, samples of biological molecules should be pure in order to quantitate using UV absorption spectroscopy.

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/5._Lab_Notes_Part_1/5.2%3A_Spectrophotometry.txt

Review: the Beer-Lambert Law log(1/T) = εcl = A T = 1/10A Values for ε and l • Path length l is usually in units of cm. (note: most spectrophotometers are designed to accept 1cm wide cuvettes) • Molar extinction coefficient ε has units of M-1 cm-1 and is a constant of proportionality that relates the absorption of molar solutions • Mass extinction coefficient ε 1% refers to the absorbance of a 1% by mass solution. Typically this refers to an aqueous solution that we can take to have a density of 1000g/L. A 1% by mass aqueous solution would therefore refer to the dissolution of 10g/L, or a 10mg/ml solution of the molecule of interest. • Since the absorbance of a molecule is a function of the wavelength (i.e. the absorption is not equal for every wavelength) the extinction coefficient must also reference a wavelength. This is typically done using a subscript: ε 1%280nm = 14.5 g-1 L cm-1 · In this case a 10mg/ml solution of the molecule will have an absorbance reading of 14.5 (dimensionless units) at l = 280nm (the absorption at other wavelengths may not be known). The units of concentration are g/L, thus e will have dimensions of g-1 L cm-1. Why is it important to be able to quantitate protein concentration in a sample? An important application of "Biotechnology" is the production of proteins as commercial products. Such products might have pharmaceutical applications (e.g. insulin, human growth hormone, tissue plasminogen activator, erythropoietin, blood clotting factor VIII.), industrial applications (e.g. subtilisin (an enzyme in detergents), 2,5-diketo-D-gluconate reductase (an enzyme in vitamin C production), as materials (e.g. silk protein in textiles, barnacle adhesion protein as a glue). In these cases, there are various aspects of successful production that require quantitation: • How much of the protein can be produced (i.e. what is the efficiency of production)? • How pure is the protein that is produced (industrial applications may require 90% pure, pharmaceutical applications may require 99.999% pure) Such proteins may be isolated from natural sources (e.g. blood clotting factor VIII may be extracted from human blood), or they may be produced recombinantly (e.g. E. coli bacterial cells can be genetically engineered to produce human growth hormone). In both cases, it may be necessary to purify the protein using a series of fractionation steps. We will go into more detail about such fractionation steps in a later lecture, but the general idea is that a heterogeneous mixture of molecules can be fractionated based upon some physical property of the molecules. The following are properties that can be used to fractionate a heterogeneous mixture of biomolecules: • Molecular mass (i.e. "big" molecules can be separated from "small" molecules) • pKa (i.e. "acidic" molecules can be separated from "basic" molecules) • Hydrophobicity (i.e. non-polar molecules can be separated from polar molecules) For such fractionation steps involving proteins, we need to keep track of how much of the contaminating proteins went into one fraction and how much of our desired protein went into the other fraction. Although the details are somewhat more complicated than this simple description, it is important to be able to quantitate protein concentration to be able to effectively purify a protein of interest. Once a protein is pure, it may be of considerable economic interest to be able to quantify the yield (and, therefore, be able to determine how much it cost to produce a given mass of protein). For example, the only source for human growth hormone (to treat small stature) used to be to extract it from human pituitary glands harvested from the brains of cadavers. Suffice it to say, this made the protein extremely expensive. Furthermore, the isolation from human tissues meant that the sample could also be potentially contaminated with human pathogens (hepatitis, CJD, AIDS, etc.). With the advent of genetic engineering, the production of human growth hormone by bacterial cells (i.e. E. coli) meant that relative large quantities could be produced far cheaper (and with no threat of human pathogens). Why not just weigh the protein? • Most samples are typically quantities of milligrams or even micrograms, not grams, and thus, it is difficult to transfer and measure such small amounts • Water is present in proteins, and it is extremely difficult to remove all the water (some water molecules hydrogen bond extremely tightly to proteins). Thus, the mass measurement would include some waters, and would increase the apparent mass of the protein Absorbance spectra of biological molecules Proteins Proteins do not absorb in the visible wavelength unless they have a prosthetic group (e.g. Fe2+) or an unnatural amino acid. However, the amino acids tryptophan, tyrosine and cysteine absorb light in the UV wavelength: Figure 5.3.1: Tryptophan absorption • Tryptophan has a peak of absorption at 280nm in the UV range • This is a useful wavelength to quantitate the absorption of tryptophan • Since the absorption is proportional to concentration, this is a useful way to quantitates protein concentration (for proteins containing Trp) Nucleic acids The aromatic rings in the bases of nucleic acids also absorb in the UV range: Figure 5.3.2: dAMP Absorption • Each DNA and RNA base has a slightly different absorption spectrum • 260 or 280nm is a typically useful wavelength to monitor concentration of nucleic acids Note that samples of nucleic acids and proteins can both absorb at 280nm, therefore, samples of biological molecules should be pure in order to quantitate using UV absorption spectroscopy (any contaminating nucleic acids in a protein sample will increase the apparent absorbance, likewise for contaminating proteins in a nucleic acid sample). Important aspects of quantification of proteins using UV absorbance • If a protein contains Trp, Tyr or Cys residues it will absorb in the UV. If it does not contain these amino acids, it will not absorb UV light, and we cannot quantify it using this method • Multiple Trp, Tyr or Cys residues will contribute to the Extinction coefficient for the protein. Thus, we need to know how many of these residues are present in the protein to know the correct extinction coefficient • Nucleic acids (DNA, RNA) contaminant will also absorb UV light, as will other proteins with Trp, Tyr and Cys residues. Thus, the sample must be PURE to use UV absorption to quantify a protein Molar extinction coefficients of Trp, Tyr and Cys amino acids: Amino Acid E280nm (M-1 cm-1) Trp 5690 Tyr 1280 Cys 120 Example 5.3.1: Bovine insulin Bovine insulin contains 4 Tyr residues, 6 Cys residues and 0 Trp residues. We can determine the expected molar extinction coefficient at 280nm, E280nm, by the following calculation: E280nm = (0)(5690) + (4)(1280) + (6)(120) E280nm = 5840 M-1 cm-1 Thus, a 1.0M solution of pure bovine insulin would give an absorbance of 5,840 at 280nm (obviously, it would have to be diluted considerably to be read accurately). A useful expression relating the parameters of E, concentration (C) and A are derived from the Beer-Lambert law (assuming 1cm path length): A/E = C For example, if a sample of bovine insulin was observed to give an absorbance at 280nm of 0.745 we could calculate the concentration to be: 0.745/5840 M-1 cm-1 = C C = 1.28 x 10-4M (note: cm-1 drops out with 1cm pathlength) It should be noted that a deuterium lamp is required for UV spectrophotometry, as well as quartz cuvettes (since glass absorbs UV light) Colorimetric (chromogenic) methods of protein concentration determination Chromo means color and genesis mean creation, so chromogenic means the "creation of color". Color means color (duh…) and metric means to measure, so colorimetric is to "measure color". Both terms refer to the same sort of thing in the present case - we can modify the protein sample with appropriate reagents so as to produce a color reaction (in visible spectrum) and measure protein concentration using a VIS spectrophotometer. Advantages are: • Cheap lamp! (tungsten light bulb versus deuterium for UV) • Cheap cuvette! (cheap glass or plastic versus quartz) • Not contaminating absorbance from proteins or nucleic acids! (no absorption in VIS spectrum) We will consider three methods: The Biuret, Lowry and Bradford methods of colorimetric determination of proteins. Biuret Under high pH (alkaline) conditions the copper II ion (Cu2+) is believed to form a complex with peptide nitrogens of proteins: Fiugre 5.3.3: Cu2+ complex This complex absorbs light at 550nm and has the following useful properties: • It is dependent upon at least a dipeptide structure (see above), thus, contaminating amino acids will not contribute to the 550nm absorption • The binding depends upon the peptide backbone nitrogen and not the side chain functional group. Thus, the binding is independent of sequence • Nucleic acids do not interfere since they don't share the peptide backbone structure • However, ammonia and certain amines can interfere. Thus, ammonium sulfate salts, and the common biological buffer TRIS (tris hydroxymethyl amino ethane) will provide a false positive and cannot be present in the sample • Also, the absorbtion is relatively weak, thus, the method is somewhat insensitive and requires a relatively high concentration of protein Lowry The Lowry method is a modification of the Biuret method. After treatment with Copper II, the protein is treated with phosphomolybdotungstate mixed acids (acidic compounds of molybdenum and tungsten ions). These acids are known as the Folin-Ciocalteu (or just Folin) reagent. The Folin reagent is added under alkaline conditions, and the Folin reagent is subsequently reduced by the the Copper ions as well as Tyr, Trp and polar amino acid side chains. The product of this reaction is heteropolymolybdenum blue, which absorbs strongly at 750nm. This assay has the following properties: • More sensitive than the Biuret assay (can detect lower concentrations of protein) • Somewhat dependent upon amino acid composition (i.e. relative concentrations of Tyr, Trp and polar amino acids) • Absobtion reaction is linearly dependent upon protein concentration, but only at low concentrations of protein (i.e. the standard curve and assay must be performed at a low concentration regime). • Sensitive to contaminants as with the Biuret method, as well as others related to the Folin reagent and redox reactions. • More critical to timing and precision of person doing the assay Bradford A dye known as Coomassie Brilliant Blue was developed by the textile industry. It was noticed to stain skin as well as the textiles. Thus, this dye (which normally absorbs at 465nm) was known to bind to proteins and to absorb strongly at 595nm. The dye forms a wide variety of strong, but non-covalent, interactions including hydrogen bonding donor and acceptor interactions as well as hydrophobic (non-polar) interactions. Figure 5.3.4: Coomassie Brilliant Blue binding The method is quite simple: a single step in which the dye is added to the protein solution under acidic conditions, and then the absorbance is read at 595nm. However, the method has the following properties: • The response is generally independent of the amino acid composition • The assay is sensitive, but somewhat non-linear. Thus, a standard curve must always be performed (using known concentrations of pure protein) • The dye stains pretty much everything, including cuvettes, floors, countertops. It can be a real mess if spilled (I know this from personal experience). And since it is a textile dye, if you get it on your clothes, you will need to learn to like blue polka dots.

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/5._Lab_Notes_Part_1/5.3%3A_Quantification_of_Protein_Concentration.txt

"Chroma" refers to color and "graphy" refers to writing. Chromatography involves the physical separation of a mixture of compounds, where historically the identification of the individual compounds is by their unique color. Chromatography can be used as a purification method (preparative scale) and also for the identification of compounds based on their chromatographic behavior (analytical scale). There are many types of chromatography, but all involve dissolving the compounds to be separated in a liquid solution (the "mobile" phase) and then passing this solution across (or through) some solid support (or "matrix") known as the "stationary" phase. The stationary phase is typically a slurry of solid beads of silica or polysaccharide compounds, or a solid surface of these compounds. As the mobile phase travels through or across the solid phase the dissolved solutes interact to varying degrees with the stationary phase (i.e non-covalent attractive forces). The stronger the attraction, the more the molecules are retarded in comparison to the mobile phase. If there is no attraction for the solid support, then the solute molecules move with the mobile phase. The different rates of movement for the different solutes results in their separation from each other. An example you can do at home involves a coffee filter (stationary phase) and water (mobile phase) in the separation and analysis of the dye in a water soluble marker pen (the mixture of compounds). If you draw a line with a purple marker on a strip of the filter paper, and place the bottom in a dish of water, the water will wick up the paper, and the differential affinity of the dye inks for the solid support (cellulose) results in differential migration. In the example below, the red ink has some affinity for the paper, but the blue ink does not (and migrates with the front edge of the mobile phase). Figure 5.4.1: Chromatography of purple pen Chromatography in biochemistry typically utilizes not paper, but beads of polysaccharide (often chemically derivatized) packed into a column, as the solid support. The solid support is often called the chromatography "resin". Some common types of chromatographic resins include: • Ion exchange • Affinity • Hydrophobic • Gel filtration Often, the solutes to be separated are proteins, and so the discussion will focus upon protein chromatography (but the principles are the same for any solute) Ion exchange Ion exchange resins contain charged groups. • These may be acidic in nature (in which case the resin is a cation exchanger) • or basic (in which case it is an anion exchanger). • Cation and anion exchangers may be broken down further into weak and strong exchangers (reflecting binding affinity). Type of exchanger Functional group Common name Weak cation exchanger carboxymethyl (-) CM cellulose/sephadex Strong cation exchanger sulfopropyl (-) SP sephadex Weak anion exchanger diethylaminoethyl (+) DE cellulose/sephadex Strong anion exchanger quaternary amine (+) QAE sephadex Usually, samples are loaded under low ionic strength conditions (which promotes electrostatic interactions) and bound material is eluted using either a step or gradient elution of buffer with higher ionic strength. • Generally speaking, a protein will bind to a cation exchange resin if the buffer pH is lower than the isoelectric point (pI) of the protein, and will bind to an anion exchange resin if the pH is higher than the pI. • Knowledge of the pI of the protein is therefore helpful in designing a purification protocol using ion exchange resins (however, you can always simply try different resins to see which works best). Elution of proteins from ion exchange resins Proteins bound to ion exchange resins are bound via non-covalent ionic (salt-bridge) interactions. We can compete for these ionic binding sites on the resin with other ionic groups, namely, salts • There are two general types of methods when eluting with a salt solution: 1. Gradient elution and 2. Step elution • A gradient elution refers to a smooth transition of salt concentration (from low to high) in the elution buffer. Weakly binding proteins elute first, and stronger binding proteins elute last (i.e. they require higher salt concentrations in the buffer to compete them off the column) • A gradient salt concentration can be made using a gradient maker. In its simplest form, this consists of two containers (must be the same shape) connected by a siphon (or tube at the bottom). One container contains the low salt buffer, and the other contains high salt buffer. The buffer is withdrawn from the low salt container: Figure 5.4.2: Gradient maker • This will produce a linear gradient from low to high salt concentrations over the total volume of the gradient • If we know the concentration range of salt over which a protein of interest will elute we can simply elute with a buffer containing that concentration of salt. This is known as a step elution. • Step elutions are generally faster to run, and elute the protein in a smaller overall volume than with gradient elutions. They generally work best when contaminants elute at a significantly different salt concentration than the protein of interest Note that after ion exchange chromatography the protein of interest will be in a buffer with a potentially high salt concentration. This must be taken into account before proceeding with the next step in the purification scheme Affinity chromatography Affinity chromatography is a general term which applies to a wide range of chromatographic media. It can be basically thought of as some inert resin to which has been attached some compound which has a specific affinity for your protein of interest. • Thus, a specific antibody attached to an inert resin would be a type of affinity chromatography. • Other examples might include: a protease inhibitor attached to some matrix, designed to bind a specific protease • a cofactor bound to some matrix, designed to bind to a particular enzyme • a metal ion bound to a matrix, designed to chelate a protein with a metal binding site, and so on. In each case, the type of resins used and the method of attachment may vary, as will the method of elution. One generalization regarding method of elution is that the bound ligand can be competed off of the column's functional group by including in the elution buffer a high concentration of the free functional group. For example, if the functional group of the column is a cofactor, then the bound protein can be competed off the column by passing a buffer containing a high concentration of cofactor (or cofactor analog) through the column. Other methods of elution include changing the buffer conditions such that the protein is no longer in the native state (since it is the native state which confers the structure required for the specific binding interaction). This can be achieved by changing pH or by adding denaturing agents such as urea or guanidine. With affinity chromatography, typically the purification achieved in a single step can be dramatic - on the order of several thousand fold. Single step purifications with specific affinity columns are not unheard - in fact it is an ideal goal of purification - a matrix which recognizes only the protein of interest and none other. Hydrophobic resins Hydrophobic resins contain a non-polar functional group, such as an alkane or aromatic group. • Many proteins are able to sequester such groups on their surface and this exclusion from solvent provides the basis of the binding energy (i.e. the "hydrophobic effect"). • This interaction is enhanced by increasing ionic strength, such that proteins may bind under high salt conditions and elute under low salt conditions. • As such these columns may be used to not only provide purification, but to desalt samples (for example after an initial ammonium sulfate precipitation). • It is usually not possible to predict in advance which particular resin will bind a given protein, this is usually determined empirically. However, the longer the alkane, or the larger the aromatic compound, the stronger the binding typically will be. Due to the nature of hydrophobic interactions and ionic strength, hydrophobic chromatography and ion exchange chromatography can be conveniently used sequentially. For example, after ion exchange the protein is in high salt conditions, thus it can be loaded directly onto a hydrophobic column. Conversely, a hydrophobic column is eluted in low salt, which is a requirement for binding to an ion exchange resin. A distinction should be noted between hydrophobic interaction chromatography and reverse phase chromatography • Hydrophobic interaction chromatography is performed in aqueous solvent conditions and changes in ionic strength are used to elute the column. The protein typically binds in the native state via hydrophobic groups located on the surface of the protein. The native state is retained during the elution conditions • Reverse phase chromatography utilizes a hydrophobic solvent (typically acetonitrile) and the binding of a ligand is a function of the phase partition between the hydrophobic nature of the solvent and column functional group. Proteins are typically denatured in such solvents and bind due to the hydrophobic nature of the entire polypeptide sequence. Since the majority of hydrophobic groups are located in the core of globular proteins, the binding is related to the denaturation of the protein and the accessibility of these groups to the column functional groups. Proteins can be purified using reverse phase chromatography, but usually must be refolded in some way to regain functionality (i.e. the native state) Gel filtration Gel filtration does not rely on any chemical interaction with the protein, rather it is based on a physical property of the protein - that being the effective molecular radius (which relates to mass for most typical globular proteins). • Gel filtration resin can be thought of as beads which contain pores of a defined size range. • Large proteins which cannot enter these pores pass around the outside of the beads. Therefore, the volume of the column appears smaller to a large molecule. • Smaller proteins which can enter the pores of the beads have a larger volume that they can explore, thus the volume of the column appear larger to a small molecule. • Both large and small molecules experience the same flow rate of mobile phase (i.e. L/min).Thus, a sample of proteins passing through a gel filtration column will separate based on molecular size: the big ones will elute first and the smallest ones will elute last (and "middle" sized proteins will elute in the middle). Figure 5.4.3: Gel filtration • If your protein is unusually "small" or "large" in comparison to contaminating proteins then gel filtration may work quite well. Where will a protein elute in a gel filtration experiment? • There are two extremes in the separation profile of a gel filtration column. • There is a critical molecular mass (large mass) which will be completely excluded from the gel filtration beads. All solutes in the sample which are equal to, or larger, than this critical size will behave identically: they will all eluted in the excluded volume of the column • There is a critical molecular mass (small mass) which will be completely included within the pores of the gel filtration beads. All solutes in the sample which are equal to, or smaller, than this critical size will behave identically: they will all eluted in the included volume of the column • Solutes between these two ranges of molecular mass will elute between the excluded and included volumes Figure 5.4.4: Protein elution in gel filtration As a general rule of thumb, the excluded volume (Vo) is approximately equal to one third of the column volume, the included volume is approximately equal to two thirds of the column volume (the "missing" third is taken up by the volume of the resin material). • In gel filtration the resolution is a function of column length (the longer the better) • However, one drawback is related to the maximum sample volume which can be loaded. The larger the volume of sample loaded, the more the overlap between separated peaks. Generally speaking, the sample size one can load is limited to about 3-5% of the total column volume. • Thus, gel filtration is best saved for the end stages of a purification ,when the sample can be readily concentrated to a small volume. • Gel filtration can also be used to remove salts from the sample, due to its ability to separate "small" from "large" components. • Finally, gel filtration can be among the most "gentle" purification methods due to the lack of chemical interaction with the resin.

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/5._Lab_Notes_Part_1/5.4%3A_Chromatography.txt

Gel electrophoresis is used to characterize one of the most basic properties - molecular mass - of both polynucleotides and polypeptides. Here we will focus exclusively on gel electrophoresis of proteins Gel electrophoresis can be used to determine: • the purity of a protein sample • heterogeneity and extent of degradation of a protein sample • subunit composition of a protein sample How does it work? The underlying principle of electrophoresis is the migration property of charged species within an electric field. Thus, it is the simple behavior of opposite charges attracting. An electric field is established across the electrodes of a power supply, and charged ions move in this electric field. Note that in such an apparatus, the word "ANODE" refers to the positively charged electrode (the ANODE attracts ANIONS) and the word "CATHODE" refers to the negatively charged electrode (the CATHODE attracts CATIONS). The cathode and anode terms are consistent with their redox reaction definitions in that reduction is subsequently occurring at the cathode and oxidation is occurring at the anode: Figure 5.5.1: Anode and cathode Warning: read the following note only if you are curious about batteries and labeling of electrodes, otherwise, don't bother (it may only be confusing). Note Redox chemistry within the external power supply is driving the redox reaction at the electrodes. Note that the electrons from the anode are going to the "+" terminal of the battery. Reduction at this electrode of the battery must be occurring and is driving the oxidation at the anode of the external electrodes. If this terminal of the battery is being reduced, then it must be the cathode in the redox reaction within the battery. Thus, batteries have their cathode labeled as "+" and anode labeled as "-". Took me hours to figure this out. Proteins are comprised of the 20 common amino acids, which include both negatively charged (i.e. acidic) side chains (e.g. aspartic acid, glutamic acid) and positively charged (i.e. basic) side chains (e.g. histidine, lysine and arginine). Thus, proteins can be charged, and will migrate in an electric field. At this point there are a couple of things to consider: 1) Any such separation is a non-equilibrium process. By this, we mean that if we let the process continue on until some equilibrium condition is met, all the anions will be on one electrode and all the cations will be on the other. It would be better to halt the separation process at some intermediate time point to permit achieve separation: Figure 5.5.2: Separation process 2) The other problem is that once the electric field is switched off, diffusion will cause the separated ions to move around (i.e. we will lose the separation we have tried to achieve). To solve this problem, the separation is not performed in solution, but within a matrix (i.e. a molecular mesh or network). The matrix provides a frictional component that resists diffusion. Furthermore, the friction of the matrix is an important factor in the rate of migration of the ions. 3) Note also that separation is achieved by initial application of the sample within a narrow zone (band). If the sample is initially dispersed, although the ions will move, they won't be neatly separated. Factors that influence the rate of electrophoresis migration (Rf) Three factors affect the rate of migration: · Strength of the electric field, E (directly proportional to migration rate) · Charge on the ionic species, q (directly proportional to migration rate) · Frictional coefficient of the support matrix, f (inversely proportional to migration rate) Rf α qE/f These factors can be varied in the following way: · The strength of the field is a function of the voltage of the power supply. Thus, we can vary the voltage directly. In a related issue, the voltage is proportional to the resistance across the electrodes. Current comes into play here also, but in short, it is difficult to achieve high voltage across the electrodes if the resistance is low. The resistance of a solution is inversely proportional to the ionic strength (i.e. concentration of ions). Thus, with high salt concentrations, the resistance is low, it is difficult to achieve high voltage, and the migration rate will decrease. · The charge on an ionic molecule is a function of the pI of the molecule and the pH of the solution If pI > pH the molecule is cationic (migrates towards cathode) If pI < pH the molecule is anionic (migrates towards anode) If pI = pH the molecule is neutral (no migration in electric field) We can therefore alter the migration rate (and possibly the direction of migration) by altering the pH of the solution · The frictional coefficient of the matrix can potentially be altered. The matrix is typically a polymer network (see below), referred to as a gel, and the frictional coefficient can be increased by increasing the polymer concentration The gel matrix for gel electrophoresis of proteins Gel electrophoresis of proteins almost exclusively utilizes polyacrylamide. This is a polymer comprised of two covalently-linked components: • acrylamide • bis acrylamide. The bis acrylamide is essentially a cross-linking component of the acrylamide polymer. A typical value for the acrylamide:bisratio is 19:1 and the total acrylamide concentration in the gel affects the migration of proteins through the matrix (i.e. determines the frictional coefficient). • High molecular mass proteins are separated using low frictional coefficient (i.e. low concentrations) of polyacrylamide. • Low molecular mass proteins are separated using high frictional coefficient (i.e. high concentrations) of polyacrylamide. • Although we now have everything in place to perform a gel electrophoresis experiment to separate proteins, there is another consideration that often is undesireable (although sometimes useful). This situation is the fact that different proteins have different charges for a given value of pH. Thus, proteins will migrate as a function of both their mass (big ones move slowly) and their overall net charge. • If there were some way to cause each protein to have an identical charge to mass ratio, we could separate a mixture of proteins based only upon mass effects The role of sodium dodecyl sulfate detergent in polyacrylamide gel electrophoresis Sodium dodecyl sulfate (SDS; also known as "laurel sulfate") is an ionic detergent with the following structure: Figure 5.5.3: Sodium laurel sulfate structure • The SDS binds, via hydrophobic interactions, to the proteins in a stoichiometry approximately proportional to the size of the protein (i.e. a small protein will bind a few molecules, and a large protein will bind a lot of molecules of SDS) • Due to the charged nature of the SDS molecule the proteins thus will have an approximate constant charge to mass ratiodue to the charge provided by the SDS, and will migrate through the gel at a rate proportional to their molecular mass • The proteins migrate towards the anode since the charge on the SDS is negative under all pH conditions except highly acidic. Protein gels are usually performed under denaturing conditions, meaning that the sample preparation involves heating the protein in the presence of SDS to fully unfold the protein and permit binding of SDS throughout the length of the polypeptide. Once SDS has been bound, the characteristic pI values of the proteins is no longer relevant; the protein takes on a negative charge, and each protein has essentially the same charge to mass ratio. Migration rate, protein mass, and the % acrylamide in the gel The greater the percent acrylamide in the gel support, the greater the frictional coefficient, and the slower the migration rate. If the proteins to be separated are of a high molecular mass, and if the % gel is high, the proteins may not even enter the gel (due to overwhelming friction). Thus, it is essential to match the % gel to the mass of the proteins being separated. The following table provides a general guideline: Acrylamide Range of separation of Polypeptides (length in amino acids) 8% 25-200 kDa (225-1800 a.a.) 10% 15-100 kDa (135-900 a.a.) 12.5% 10-70 kDa (90-630 a.a.) 15% 6-60 kDa (55-550 a.a.) 20% 4-40 kDa (36-360) In setting up the SDS PAGE (sodium dodecyl sulfate polyacrylamide gel electrophoresis) experiment we need to know when to stop the experiment (since it is not an equilibrium process). This is somewhat difficult to determine since proteins (even with SDS bound) do not absorb in the visible spectrum (i.e. we cannot simply look at the gel to determine when the proteins have been separated). Therefore, it is common to include in the protein sample a small anionic dye molecule (e.g. bromophenol blue): • The size of the dye molecule is chosen to be very small so that there is essentially no friction coefficient with the gel • The dye is chosen to be anionic so that it will migrate in the same direction as the protein/SDS complexes (i.e. towards the anode) • Since the dye is anionic, and small, it will migrate the fastest of any component in the mixture to be separated • The dye molecule is also chosen so as to absorb in the visible spectrum (and, hence, be visually detectable while the gel is running) • The protein/SDS/dye mixture is loaded on the top of the gel (i.e. cathode side) and when the dye molecule (the "dye front") reaches the bottom of the gel, the power is turned off and the experiment halted Visualization of the separated proteins Although the gel support provides some friction to molecular motions, as soon as the power is turned off the separated protein bands will begin to diffuse (they are freely soluble in aqueous solution). To prevent this, the gel is treated with an acetic acid and methanol solution which causes almost all proteins to precipitate (become insoluble). This is called "fixing" the gel. Now the separated proteins will not diffuse. The fixed proteins are, however, still invisible and must be visualized by staining. A common protein stain is Coomassie Brilliant Blue R-250 (related to the dye used previously in the Bradford assay). The fixed gel is incubated in a solution of "Coomassiestain" and then the stain is washed out of the gel by incubation in a weak solution of acetic acid and methanol. The stain will not bind to the acrylamide, and will wash out (leaving a clear gel). However, it remains strongly bound to the proteins in the gel, and these take on a deep blue color. Determination of molecular mass With SDS treatment, the proteins will migrate as a function of their molecular mass. The approximate molecular mass of the separated proteins is therefore a function of their migration distance. If a series of proteins with different and known molecular masses is analyzed on an identical gel, under identical conditions, then a standard curve can be established that can be used to determine the molecular mass of an unknown protein. A typical "molecular weight standard" includes the following mixture of proteins: Protein Molecular Mass (kDa) Phosphorylase B 94 Bovine Serum Albumin 67 Ovalbumin 43 Carbonic Anhydrase 30 Soybean Trypsin Inhibitor 20.1 a-Lactalbumin 14.4 The molecular mass is quantified as follows: • Measure (in cm) the migration distance of the dye front • Measure (in cm) the migration distance of the protein (will always be less than the dye front) • Divide the protein migration distance by the dye front distant to get the relative mobility value (always < 1) • Plot the relative mobility value (along x-axis) versus the log of the molecular mass (along y-axis) • The relationship between the relative mobility and the log of the molecular mass should be a linear function - thus, providing a standard curve against which the molecular mass of an unknown protein can be determined from its relative mobility Here is an example of a molecular mass standard, and two unknown samples, analyzed on a 10% SDS PAGE stained with Coomassie: Figure 5.5.4: Example gel electrophoresis The calculation of migration distances, relative mobility and relationship to Log of the molecular mass yields the following: Figure 5.5.5: Relative mobility and molecular mass • Unknown #1 has a value of 1.43 for the log of the molecular mass, or a mass of ~26.9 kDa • Unknown #2 has a value of 1.33 for the log of the molecular mass, or a mass of ~21.4 kDa • A visual check of the unknowns versus the standards in the above gel indicate the calculations appear to be correct There are other variations of SDS PAGE including: • Discontinuous gels (i.e. a composite of one gel on top of another) to achieve "focusing" of protein bands (i.e. sharper bands, yielding greater sensitivity and resolution) • Gradient gels (instead of pouring a single % polyacrylamide gel, pour a gradient with a high concentration at the bottom, and a lower concentration towards the top. This permits resolution of a wider range of molecular mass samples) • Silver staining (a more sensitive type of stain in comparison to Coomassie - allowing detection of lower concentrations of proteins) • Transfer of resolved protein bands to a secondary support (e.g. nitrocellulose) for probing with other reagents (i.e. antibodies) • PAGE (no SDS) of native proteins (to allow detection of non-covalent complexes of two or more proteins - since SDS will disrupt such complexes) • SDS PAGE with the addition of reducing agents (e.g. b-mercaptoethanol, dithiothreitol, etc.). These reagents will reduce disulfide bonds and separate polypeptide chains that are connected by such bonds However, the above information covers the basics.

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/5._Lab_Notes_Part_1/5.5%3A_Gel_Electrophoresis_of_Proteins.txt

A successful protein purification procedure can be nothing short of amazing. Whether you are starting off with a recombinant protein which is produced in E. coli, or trying to isolate a protein from some animal or plant tissue, you are typically starting with a complex mixture of proteins, nucleic acids, polysaccharides, lipids, etc. from which you may have to extract milligram (or microgram!) quantities of the desired protein, often with high purity, and hopefully with high yield. Protein purification can be thought of as a series of fractionation steps designed so that: • The protein of interest is found almost exclusively in one fraction (and with good yield) • A significant amount of the contaminants can be found in a different fraction Figure 5.6.1: Fractionation The first step in any purification is the development of an assay for the protein of interest The assay can be based upon some unique characteristic of the protein of interest, possibly involving: • Enzymatic activity • Immunological activity • Physical characteristics (e.g. molecular mass, spectroscopic properties, etc.) • Biological activity • Ideally, an assay should be • specific (you don't want a false positive or false negative) • rapid (you don't want to wait a week for the results) • sensitive (you don't want to consume all your sample in order to assay it) • quantitative (you need an accurate way to measure the quantity of your protein at each step in the purification) During purification you will need to keep track of several parameters, including: 1. The total volume of your sample 2. The total amount of protein in your sample · can be estimated by using E1%280nm =14.5 · In other words, measure absorbance at 280nm, divide by 1.4, and you will have the approximate protein concentration in mg/ml 1. The total amount of the protein of interest (the one you are trying to purify). This information will be determined from your quantitative assay This basic information will allow you to keep track of the following parameters during each step of purification: 1. % yield for each purification step 2. Specific activity of the desired protein ("units of activity" of desired protein/mg total protein) 3. Purification enhancement of each step (e.g. "3.5x purification) In designing a purification scheme you typically have to balance purification with yield. • For example, it may be relatively straightforward to obtain 90% pure material with good yield. • However, it may be difficult to improve that purity by an additional few percentile and still maintain a good yield. • The planned application of the purified protein determines the target purity. • If the protein is to be used to determine amino acid sequence information, maybe 90% is acceptable. However, if the material is to be used in clinical trials, 99.999+% may be the target purity. Initial steps in purification • Typically (but not always) the protein of interest is contained within the cytoplasm of a cell (sometimes, however, it might be secreted by the cell into the extracellular environment). Thus, cells must be broken open to release the cytoplasm, a process known as cell lysis. • It is extremely helpful to have some information not only on the general physical and chemical characteristics of the protein you are trying to purify, but also on the contaminating components. • For example, many E. coli proteins are generally low molecular weight (<50,000 Da) and somewhat acidic in isoelectric point A typical general outline of a protein purification procedure might look like this: Figure 5.6.2: Protein purification steps Usually the initial steps in purification make use of general physical and/or chemical differences between soluble proteins and other cell components. • For example, soluble proteins can be separated from general cellular debris, and intact cells, by centrifugation. • Thus, cells are physically disrupted (via homogenization or a cell press) to allow release of cell contents. This is then followed by centrifugation to separate generally soluble components from those which are insoluble. • It is at this point that data collection begins in order to monitor the purification. Nucleic acids can sometimes be readily removed from the sample by the addition of large cationic compounds such as polyethylene imine, or streptomycin sulfate. • The nucleic acids bind to these compounds via electrostatic interactions and the complex precipitates and can be removed via centrifugation. • The same general result can be obtained by mixing in ion exchange resins which are anion exchangers (i.e. the resins contain cationic groups) and then filtering or centrifuging to remove. As with either method, it should be confirmed that the desired protein is not bound as well. Crude fractionations of proteins can be achieved by adding various quantities of precipitants such as ammonium sulfate, orpolyethylene glycol (PEG). • For this type of purification step an initial experiment is performed to monitor the fraction of overall protein, as well as desired protein, remaining in solution (and pellet) as a function of precipitant concentration. Ammonium Sulfate (% saturated) 0 10 20 30 40 50 60 70 80 90 Sample A280 (in solution) 1000 900 600 200 100 75 50 40 25 20 Activity assay(units) (in solution) 200 200 200 190 170 100 30 5 0 0 Figure 5.6.3: Protein concentration and precipitant concentration How can we use this information to utilize ammonium sulfate precipitation as a useful purification step in our overall purification procedure? Let's begin by calculating the specific activity (a measure of purity) of the starting sample: Specific activity = total units of desired protein / mg of total protein To calculate these parameters we need to know the total sample volume (in addition to the information in the above table). In this case, the total sample volume is 1 L. Knowing this, and the absorbance, we can determine the total protein in the sample: Sample A280/1.4 » mg/ml of sample 1000/1.4 = 714 mg/ml 714 mg/ml * 1.0L(1000ml/L) = 714,000 mg total protein The protein of interest was assayed at 200 units present in the starting sample. Therefore, the specific activity of the starting sample is: Specific activity = 200 units / 714,000 mg = 2.80 x 10-4 units/mg Note Note: the "units" of activity depend upon the protein of interest. It could be a measure of enzymatic activity - i.e. 1.0 unit of enzymatic activity results in 1mmol of substrate being consumed in 1 min time. We can now expand upon the above table to include the specific activity of the sample Ammonium Sulfate (% saturated) 0 10 20 30 40 50 60 70 80 90 Sample A280 (in solution) 1000 900 600 200 100 75 50 40 25 20 Total mg protein (in solution) 714,000 643,000 429,000 143,000 71,400 53,600 35,700 28,600 17,900 14,300 Activity assay(units) (in solution) 200 200 200 190 170 100 30 5 0 0 Specific activity (units/mg) 2.80x10-4 3.11x10-4 4.66x10-4 13.3x10-4 23.9x10-4 18.7x10-4 8.40x10-4 1.75x10-4 0 0 We can plot the specific activity of the sample remaining in solution for the different concentrations of added ammonium sulfate: Figure 5.6.4: Specific activity and precipitant concentration • The highest specific activity is observed in solution when ammonium sulfate is added to 40% saturation • What is the increase in purity for the 40% solution sample? Purification = final specific activity / initial specific activity Purification = (23.9x10-4 units/mg) / (2.80x10-4 units/mg) = 8.5-fold purification However, purification is not the entire story. We also need to consider yield: Yield (%) = (final total units / initial total units) x 100 And we can add this information our purification statistics: Ammonium Sulfate (% saturated) 0 10 20 30 40 50 60 70 80 90 Sample A280 (in solution) 1000 900 600 200 100 75 50 40 25 20 Total mg protein (in solution) 714,000 643,000 429,000 143,000 71,400 53,600 35,700 28,600 17,900 14,300 Activity assay(units) (in solution) 200 200 200 190 170 100 30 5 0 0 Specific activity (units/mg) 2.80x10-4 3.11x10-4 4.66x10-4 13.3x10-4 23.9x10-4 18.7x10-4 8.40x10-4 1.75x10-4 0 0 Purification N/A 1.1 1.7 4.8 8.5 6.7 3.0 0.6 - - Yield (%) 100 100 100 95 85 50 15 2.5 0 0 Figure 5.6.5: Percent yield and precipitant concentration Information on the yield shows that our protein of interest begins to precipitate around 30% ammonium sulfate. Thus, while the addition of ammonium sulfate to 40% provides the greatest purification, it is associated with a 15% loss of our protein of interest. Another option is to added ammonium sulfate to 30%. In this case, we would realize a purification of 4.8-fold, and suffer losses of only 5%. At each step of a purification scheme we are typically faced with a decision regarding a tradeoff between purity and yield • If the starting material is cheap, plentiful, or easy to obtain, we may choose our fractionation steps so as to maximize purity at the expense of yield • If the starting material is expensive, limited, or difficult to get, we may choose to maximize yield at the expense of purity • Again, these decisions are also determined by the intended purpose of the final material, and therefore, the final purity desired. Thus, in the above experiment, we would choose to add ammonium sulfate to a concentration between 20-30% if we wanted to maximize yield, or perhaps 40% (or slightly higher) if we wanted to maximize the purification. Practical matters with ammonium sulfate fractionation steps We would probably like to remove the ammonium sulfate from our sample (perhaps the next purification procedure cannot tolerate high ionic strength buffers). Notice that from the data in the above table it looks like all our protein of interest is precipitated if ammonium sulfate is added to a concentration of 80%. Thus, this we can use this precipitation property to remove our protein from most of the ammonium sulfate: 1. Add ammonium sulfate to our sample to a concentration of 20-40% saturation (the exact percentage being determined by whether we wish to maximize purity or yield) 2. Centrifuge and discard the pellet 3. Add ammonium sulfate to 80% saturation to the retained solution 4. Centrifuge and keep the pellet. Resuspend the pellet in buffer to solubilize the protein.

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/5._Lab_Notes_Part_1/5.6%3A_Introduction_to_Protein_Purification.txt

Genetic transformation is the process by which an organism acquires and expresses a new gene. Genetic engineering is the directed transfer of a gene, or piece of DNA, into a cell (typically a bacteria). Typically the intent is to force the cell to express (produce) the protein that the newly introduced piece of DNA codes for (known as heterologous expression). The organism commonly used for genetic transformation and heterologous expression of human genes/proteins is the single celled bacteria known as Escherichia coli (E. coli). This organism has several traits of importance in the laboratory: • Single cell organism • Doubling time is 20 minutes (in rich media) to 1 hour (minimal media) • Naturally lives in human gut (part of normal intestinal flora). Thus, normal growth temperature is 37°C (human body temperature) • E. coli has a single chromosome that is a circular DNA molecule • E. coli is gram negative - has a inner cytosolic membrane, periplasmic space, and outer cell membrane Figure 6.1.1: E. Coli genome Foreign DNA can be introduced into the E. coli as a second "chromosome", or as another circular DNA molecule that can replicate autonomously due to having its own origin of replication. Such "autonomously replicating elements" are referred to as "plasmids" or "vectors". Figure 6.1.2: Plasmid in E. Coli In order to "stably retain" the plasmid, there needs to be some type of metabolic reason for the E. coli to keep the plasmid around. If the plasmid contains a gene that codes for a protein that protects against antibiotics, then, only cells that have the plasmid will survive in the presence of that antibiotic. Drug resistance can therefore form the basis of a "selectable marker" for the presence of the plasmid in a sample of E. coli. Ampicillin is an antibiotic for gram negative cells such as E. coli (it is part of the penicilln family of antibiotics): • Production of the outer cell membrane/proteoglycan structure is inhibited In the presence of ampicillin (a lethal event for the bacteria) • Ampicillin, as a molecule, contains a b-lactam ring structure. This ring can be cleaved (and the ampicillin destroyed) by the enzyme b-lactamase • b-lactamase enzyme is the product of the bla gene (genes are typically lower case and italicized) • If a plasmid contains the bla gene, it will confer resistance to ampicillin to the host E. coli • Such E. coli grown in the presence of ampicillin will be selected for. Under these conditions, any wild type E. coli in the sample will be selected against) • Thus, ampicillin resistance is a selectable marker for the plasmid Figure 6.1.3: Ampicillin resistance plasmid Other genes (that express other proteins) can now be introduced into the plasmid, and the host E. coli forced to express the protein of interest pGLO Plasmids are typically abbreviated with an acronym that begins with the lower case "p", and the name can provide some information regarding the person that designed the plasmid, or the contents of the plasmid. The pGLO plasmid contains an origin or replication, a selectable marker, and the gene for Green Fluorescent Protein (GFP). The plasmid also contains a gene for the arabinose C protein, which is a protein that regulates expression from the arabinose BAD promoter (PBAD). Promoters are usually indicated with an acronym that begins with an upper case "P". Figure 6.1.4: pGLO Plasmid. The ori is the origin of repliation for the pGLO plasmid, bla is the gene that codes for b-lactamase, and is the selectable drug-resistant marker for the plasmid, GFP is the GFP gene and araC is the gene coding for the arabinose C protein A "promoter" is a region of DNA that signals RNA polymerase to initiate transcription (for production of mRNA). Promoters are typically located at the start (5' end) of a gene that codes for a protein (since mRNA production proceeds 5'->3'). The bla gene includes a promoter at the 5' end of the gene. This is a weak constitutive promoter (always "on" at a low-level). It will instruct RNA polymerase to continually make a low-level of mRNA for this gene. The mRNA will be translated to produce low-levels of the b-lactamase protein. The GFP is transcribed by the arabinose PBAD promoter. This is a strong promoter and will cause RNA polymerase to make a large number of copies of mRNA from this gene (and therefore, a lot of GFP protein). However, the arabinose PBADpromoter is regulated by the protein coded for by the araC gene (which has its own promoter, much like the bla gene): • In the absence of the sugar arabinose, the araC protein binds to the PBAD promoter and prevents transcription (of GFP) • In the presence of arabinose in solution, the araC protein binds the arabinose, and this results in a conformational change to the araC protein, the result of which is that it now instructs RNA polymerase to make many copies of the GFP mRNA (and thus, a lot of GFP protein) How is the pGLO plasmid introduced into the E. coli cell? The general process by which foreign DNA is introduced into a cell is called transformation. There are several ways to transform DNA into an E. coli cell, but the most common way is by making the cells competent. "Competent" cells have the ability to take up DNA molecules from the environment. Normal E. coli are not competent, however, if they are treated with a solution of calcium chloride their cell membranes become competent. Actually, only a small fraction of the cells treated with CaCl2 are able to take up foreign DNA, however, since the number of cells in a sample is large, the low efficiency of transformation is not much of a problem. The selectable markers ensure that only the cells that did take up the foreign DNA (i.e. plasmid) will survive and grow. Figure 6.1.5: Transformation of E. Coli Expression of GFP Addition of arabinose sugar to the growth media will cause RNA polymerase to start transcribing the GFP gene (i.e. making mRNA molecules). The cellular machinery (e.g. ribosomes) will translate this mRNA into corresponding GFP protein. • GFP will fluoresce green under UV light • The presence of green light from E. coli cells indicates that the transformation (and drug resistance selection) has been successful • GFP is a gene from a jelly fish and is the reason that some jelly fish glow green. It is not a normal gene for E. coli, however, if introduced into E. coli, it will make the GFP protein (and fluoresce green)

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/6._Lab_Notes_Part_2/6.1%3A_Genetic_Transformation_%28using_bacteria_and_the_pGLO_plasmid%29.txt

Enzymes are protein catalysts, they influence the kinetics but not the thermodynamics of a reaction • Increase the rate of a chemical reaction • Do not alter the equilibrium Figure 6.2.1: Catalyst activity • They increase the rate by stabilizing the transition state (i.e. lowering the energy barrier to forming the transition state (they do not affect the energetics of either the reactant(s) or product(s) Michaelis-Menten derivation for simple steady-state kinetics The Michaelis-Menten equation is a mathematical model that is used to analyze simple kinetic data. The model has certain assumptions, and as long as these assumptions are correct, it will accurately model your experimental data. The derivation of the model will highlight these assumptions. In an enzyme catalyzed reaction the substrate initially forms a reversible complex with the enzyme (i.e. the enzyme and substrate have to interact for the enzyme to be able to perform its catalytic function). The standard expression to show this is the following: ASSUMPTION #1: • There is no product present at the start of the kinetic analysis • Therefore, as long as we monitor initial reaction rates we can ignore the reverse reaction of E+P going to ES ASSUMPTION #2: • During the reaction an equilibrium condition is established for the binding and dissociation of the Enzyme and Substrate (Briggs-Haldane assumption) • Thus, the rate of formation of the ES complex is equal to the rate of dissociation plus breakdown ASSUMPTION #3: • [E] << [S] • The enzyme is a catalyst, it is not destroyed and can be recycled, thus, only small amounts are required • The amount of S bound to E at any given moment is small compared to the amount of free S • It follows that [ES] << [S] and therefore [S] is constant during the course of the analysis (NOTE: this assumption requires that the reaction is monitored for a short period, so that not much S is consumed and [S] does not effectively change - see next assumption) ASSUMPTION #4: • Only the initial velocity of the reaction is measured • [P] = 0 (reverse E + P reaction can be ignored) • [S] » [S]initial ASSUMPTION #5: • The enzyme is either present as free enzyme or as the ES complex • [E]total = [E] + [ES] Michaelis-Menten derivation using above assumptions: Rate of ES formation = k1[E][S] + k-2[E][P] Assumption #1 says we can ignore the k-2 reaction, therefore: Rate of ES formation = k1[E][S] Assumption #5 says [E] = [E]total - [ES], therefore: Rate of ES formation = k1([E]total - [ES])[S] The rate of ES breakdown is a combination of the dissociation and the conversion to product: Rate of ES breakdown = k-1[ES] + k2[ES] Rate of ES breakdown = (k-1 + k2)[ES] Assumption #2 says the rate of ES formation equals the rate of breakdown: k1([E]total - [ES])[S] = (k-1 + k2)[ES] Rearrange to define in terms of rate constants: ([E]total - [ES])[S] / [ES] = (k-1 + k2) / k1 ([E]total [S] / [ES]) - [S] = (k-1 + k2) / k1 Define a new constant, Km = (k-1 + k2) / k1 ([E]total [S] / [ES]) - [S] = Km Solve for the [ES] term (for reasons that will be given in the next step): [ES] = [E]total [S] / (Km + [S]) The actual reaction velocity measured at any given moment is given by: V = k2[ES] Multiple both sides of the above equation by k2: k2[ES] = k2[E]total [S] / (Km + [S]) thus V = k2[E]total [S] / (Km + [S]) The maximum possible velocity (Vmax) occurs when all the enzyme molecules are bound with substrate [ES] = [E]total, thus: Vmax = k2[E]total Substituting this into the prior expression gives: V = Vmax [S] / (Km + [S]) This is the mathematical expression that is used to model your experimental kinetic data It is known as the Michaelis-Menten equation Experimental approach The general approach is to add a known concentration of substrate to the enzyme and to determine the initial reaction rate for that concentration of substrate • Reaction rates are typically given as moles (or micromole) of product produced per unit of time (sec or min) per mole (or micromole) of enzyme • The experiment is repeated for a wide range of substrate concentrations • A table of [S] versus V datapoints are collected • These datapoints are plotted (V versus S) and should fit a curve that agrees with the Michaelis-Menten equation The Vmax and Km terms are intrinsic properties of the particular enzyme/substrate combination that you are studying • They will be determined from the features of the V versus S plot Vmax There are a limited number of enzyme molecules and they can only perform a single reaction at a time. Thus, at high [S] the enzymes can be saturated • Under saturating conditions the reaction is going as fast as it can, and additional increases in [S] do not increase the reaction rate. • The maximum observable rate is Vmax and the data will asymptote to this value at high [S] • At low [S] the reaction rate is generally linearly proportional to the [S] (i.e. at low [S] if you double [S] the V will double) Km Km = (k-1 + k2) / k1 = (rate of breakdown of ES)/(rate of formation of ES) • Km is similar, but not exactly equal to, a dissociation constant (Kd) for the ES complex • If k-1 >> k2, then Km » Kd • Due to this similarity to the expression for Kd, a low value of Km is often interpreted as a high affinity of the enzyme for the substrate, and a large value for Km is often interpreted as a weak affinity of the enzyme for the substrate • Km has units of molar concentration (just like the units for [S]) There is a mathematical treatment that allows for the determination of Km from the experimental V versus [S] data • Consider the situation when the [S] being evaluated results in a value of V that is exactly 1/2 of the maximum reaction velocity: V = Vmax [S] / (Km + [S]) 1/2Vmax = Vmax [S] / (Km + [S]) 1/2 = [S] / (Km + [S]) Km + [S] = 2[S] Km = [S] Thus, Km equals the substrate concentration that results in exactly one half the maximum possible reaction velocity Figure 6.2.2: Km Lineweaver-Burke (the "double reciprocal" plot) • The Michaelis-Menten equation can be rearranged by taking the reciprocal, to yield: • If X = 1/[S] and Y=1/V then this is a linear equation with a slope of Km/Vmax and a Y intercept of 1/Vmax Figure 6.2.3: 1/S and1/V • Since the plot of 1/[S] versus 1/v data should be a straight line, it is easier to fit a linear function to the data in this form, and Vmax and Km can be readily determined from the plot Reversible Inhibition There are two major categories of reversible inhibitors: competitive reversible inhibitors, and noncompetitive reversible inhibitors: Competitive inhibitors The inhibitor (I) competes with the substrate (S) for the enzyme active site (also known as the S-binding site). Binding of either of these molecules in the active site is a mutually exclusive event • The substrate and inhibitor share a high degree of structural similarity. However, the inhibitor cannot proceed through the reaction to produce product. • Increasing the concentration of substrate will outcompete the inhibitor for binding to the enzyme active site • A competitive reversible inhibitor can be identified by its characteristic effects upon kinetic data The expression for the Michaelis-Menten expression in the presence of a reversible competitive inhibitor is: V = Vmax [S] / (Km(1+[I]/Ki) + [S]) Where Ki is the actual EI complex dissociation constant The effects of the reversible competitive inhibitor on the kinetics are as follows: • If no inhibitor is present (i.e. if [I] = 0) then the equations are the same • As inhibitor is added, the effect is to modify the apparent value of Km. In particular, the apparent Km will be increased by a value equal to (1 + [I]/KI). If Km is increased, the reaction velocity v will decrease. • Note that as [S] gets very large the value of the denominator is essentially equal to [S] and v @ vmax. Thus, the reaction velocity can be driven to vmax with a high enough substrate concentration The diagnostic criteria for reversible competitive inhibition is that while the apparent Km is affected by addition of the inhibitor, the value of vmax does not change Figure 6.2.4: Effect of reversible competitive inhibitor How is the Lineweaver-Burke double reciprocal plot affected by the presence of a reversible competitive inhibitor? Figure 6.2.5: Double reciprocal plot with reversible competitive inhibitor Noncompetitive Inhibitors Noncompetitive inhibitors react with both E and ES (this is because the noncompetitive inhibitor does not bind at the same site in the enzyme as the substrate) • Inhibition cannot be overcome by increasing the concentration of S • The effect on kinetics is as if the enzyme were less active (vmax is reduced), but that the affinity for substrate is unaffected (Km remains the same) since the substrate binding site is not occupied by the noncompetitive inhibitor. Figure 6.2.6: Effect of reversible noncompetitive inhibitor Figure 6.2.7: Double reciprocal plot with noncompetitive inhibitor

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/6._Lab_Notes_Part_2/6.2%3A_Enzyme_kinetics.txt

Molecular recognition is a key aspect of virtually all biochemistry, and involves: • DNA replication • Gene regulation • Transcription of DNA into mRNA • Translation of mRNA into proteins • Enzyme function • Molecular assembly • Signal transduction • Cell-cell communication • Immunology Intermolecular interactions, in the simplest example, involves two molecules recognizing and binding to each other: • The ligand • The receptor These are broad terms, but the general properties of receptor and ligand are as follows: • The ligand is typically, a small molecule, and it diffuses throughout the environment until it binds to a specific receptor • The receptor is typically a large, relatively stationary molecule that contains a specific binding site for the ligand. Upon binding ligand, the structural and/or chemical properties of the receptor are typically altered The interactions between receptor and ligand are typically non-covalent • There is a general molecular complementarity between the receptor and ligand • Often, but not always, there is an entropic cost associated with binding of the ligand to receptor, but this is more than offset by a favorable enthalpy (i.e. the molecular complimentarity) • The binding energy is typically utilized to cause a structural change associated with a molecular communication event There are two general methods to study receptor/ligand interactions: 1. Equilibrium thermodynamics, and 2. Association and dissociation kinetics Equilibrium ligand/receptor binding analysis The two possible states of a ligand/receptor interaction, and the rate constants associated with their formation, are given as: The equilibrium association (formation) constant, Kf, is given as: • [RL] is the concentration of the complex, [R] is the concentration of free R at equilibrium, and [L] is the concentration of free Lat equilibrium • Kf has units of M-1 • A large value for Kf means that the equilibrium favors the complex - there is a high affinity of the receptor for the ligand • A small value for Kf means that the equilibrium favors the separation of the receptor and ligand, and the receptor has a low affinity for the ligand Just to make life difficult, the typical frame of reference when describing receptor affinity for ligand is to reference the dissociation constant, Kd: • Kd has units of M • A small value for Kd means that the equilibrium favors the complex - there is a high affinity of the receptor for the ligand • A large value for Kd means that the equilibrium favors the separation of the receptor and ligand, and the receptor has a low affinity for the ligand At equilibrium, the rate of formation of the RL complex is equal to the rate of dissociation: The left-hand expression is the definition of the dissociation constant, Kd, therefore: The free energy expression and Kf and Kd From the original expression of the complex formation: The standard state free energy change, ΔG0, for the process (i.e. starting with 1M everything) would be given as: Since Kd is the inverse of Kf: In this case, a negative value for DG0 (indicating spontaneity) will occur when Kd < 1.0. This will happen when there is high affinity and the equilibrium favors the complex formation. Practical relationships between [R], [L], Kd and [RL] For the same concentration of added ligand and receptor, more complex formation will occur with a smaller value of Kd Example 6.3.1 Receptor and ligand are added to a total concentration of 1.0 x 10-4M. The Kd for the RL complex is 1.0 x 10-4M. What is the concentration of [R], [L] and [RL] at equilibrium? The added ligand, L, will be partitioned between free and bound forms: Ltotal = L + RL = 1.0 x 10-4M L = 1.0 x 10-4M - RL Similarly, the added receptor, R, will be partitioned between free and bound forms: Rtotal = R + RL = 1.0 x 10-4M R = 1.0 x 10-4M - RL Substituting these values of L and R into the expression for Kd yields: This is a quadratic with values a = 1, b = -3.0 x 10-4 and c = 1.0 x 10-8. This yields two possible values for RL: RL = 2.62 X 10-4M or RL = 3.82 X 10-5 Since the maximum possible value for [RL] is 1.0 x 10-4 (i.e. given the starting concentrations of R and L, this is the most amount of RL that can be formed), the first result is not possible and [RL] = 3.82 X 10-5M Thus, 38.2% of the added receptor is in the complex formation (i.e. 3.82 X 10-5M/1.0 x 10-4)x100% Example 6.3.2 Same amount of added R and L, but this time the Kd for the RL complex is 1.0 x 10-6M. What is the concentration of [R], [L] and [RL] at equilibrium? his is a quadratic with values a = 1, b = -2.01 x 10-4 and c = 1.0 x 10-8. This yields two possible values for RL: RL = 1.11 X 10-4M or RL = 9.05 X 10-5 Since the maximum possible value for [RL] is 1.0 x 10-4 (i.e. given the starting concentrations of R and L, this is the most amount of RL that can be formed), the first result is not possible and [RL] = 9.05 X 10-5M Thus, 90.5% of the added receptor is in the complex formation (i.e. 9.05 X 10-5M/1.0 x 10-4)x100% For the same concentration of added ligand and receptor, more complex formation will occur with a smaller value of Kd Determination of dissociation (Kd) or binding (Kf) constants The relationship between Kd, [R], [L] and [RL] is given by: • This is an equation with four variables, if you know three of them, you can solve for the fourth • In principle, you can solve for Kd if you know the concentration of free receptor [R], free ligand [L] and receptor/ligand complex [RL] at equilibrium To be able to solve for Kd, you need an assay that will provide the molar concentrations for each component. However, this is typically difficult to achieve. It is much more common to have an assay that will provide you with information regarding the fraction of added receptor (or ligand) that is present in the free or complex form. • If [Rt] is the total concentration of receptor in the sample, we know that it will be divided into two populations - bound and free: • The fraction of bound receptor sites is given by the ratio: • The fraction of bound receptors is known by the variable a, and a has a value between 0 and 1 • Substituting the value for [Rt] in terms of [R] and [RL] given above yields: • The relationship between [RL] and Kd is given by the expression for Kd: • This expression for [RL] can be substituted into the expression for a to yield: • Simplify by first dividing through by [R]: • Then multiply by Kd: This equation has three variables: 1. The concentration of free ligand at equilibrium [L] 2. The dissociation constant, Kd, for the RL complex 3. The fraction of receptor that is in the bound form at equilibrium, a If you know two of these variables, you can solve for the third (i.e. if the experimental assay quantifies [L] and a, you can solve for Kd) The above equation can be manipulated further by substitution of the value of a in terms of [RL] and [Rt]: This equation has four variables: 1. The concentration of free ligand at equilibrium [L] 2. The dissociation constant, Kd, for the RL complex 3. The concentration of receptor that is in the bound form at eqilibrium [RL] 4. The total concentration of receptor in the sample Usually, the ligand-binding experiment is setup so that [Rt] is held constant and [RL] is monitored as a function of [L] This equation looks a lot like the Michaelis-Menten equation: Plotting V versus [S] in the M-M equation yields a rectangular hyperbola. Thus, plotting [RL] versus [L] will likewise yield a rectangular hyperbola • The asymptote of the curve will yield the value of [Rt] • The free ligand concentration [L] that results in one-half the maximum value of [RL] is equal to the Kd value Yet another modification is to realize that if a equals the fraction of receptor bound in a complex (with a value 0 ≤ α ≤ 1.0), then (1 - α) is the fraction of free receptor (i.e. fraction of unbound receptor) This value can be substituted into the three variable expressions above: This equation has four variables: 1. The concentration of free ligand at equilibrium [L] 2. The dissociation constant, Kd, for the RL complex 3. The concentration of receptor that is in the free form at eqilibrium [R] 4. The total concentration of receptor in the sample [Rt] Usually, the ligand-binding experiment is setup so that [Rt] is held constant and [R] is monitored as a function of [L] (Note:Although this looks like a M-M equation, Kd is a constant. ) The above derivations are called "binding isotherms" in reference to their ability to solve for Kd under equilibrium conditions (and the original equilibrium thermodynamic equation utilized constant temperature) Plotting data for various values of [L], [Rt], and either [RL] or [R], and fitting to the appropriate function above, allows a more accurate determination of Kd (although a single experiment will provide an answer). Due to the similarity of the [RL] versus [L] plot with the M-M plot, it is often the more desirable experimental setup. In particular, a double-reciprocal plot can be used (much like the double-reciprocal M-M plot) to more accurately determine Kd • If you plot 1/[RL] versus 1/[L] you will get a straight line whose slop is equal to Kd/[Rt] • Y intercept will be equal to 1/[Rt]. Since this value is fixed and is known, the value of the y intercept can serve as a check to make sure the experiment fits the model An equilibrium dialysis experiment A semi-permeable membrane can be used in a dialysis-based equilibrium experiment to determined Kd for a ligand-receptor pair. • The membrane should be freely permeable as far as the ligand is concerned (usually it is a small molecule), but present a barrier to the receptor • The experiment is established with a known concentration of receptor on one side, and a known concentration of ligand on the other • The system is allowed to come to equilibrium • The experiment requires an assay for the ligand Figure 6.3.1: Semipermeable barrier After equilibrium is reached the ligand concentration on both sides of the semi-permeable membrane is assayed: Figure 6.3.2: Equilibrium concentration • Since the ligand is unaffected by the membrane te ligand concentration on the side with no receptor is equal to the free ligand concentration [L] everywhere in the sample (i.e. on both sides of the membrane) • The ligand concentration will be equal to the free ligand concentration [L] plus the ligand present in the receptor complex [RL] (in other words, there should be a higher concentration of ligand on the side with the receptor if the receptor has affinity for the ligand) • The concentration of the receptor/ligand complex [RL] can be determined from the difference between these two values • The total receptor concentration is a known value (experimenter determined at the start of the experiment, and does not change) • Either binding isotherm can be used to solve for Kd (since [R] can also be determined)

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/6._Lab_Notes_Part_2/6.3%3A_Ligand_binding.txt

The restriction/modification system in bacteria is a small-scale immune system for protection from infection by foreign DNA. In the late 1960's it was discovered that E. coli contains enzymes that will methylate specific nucleotide bases in DNA · Different strains of E. coli contained different types of these methylases • Typical sites of methylation include the N6 position of adenine, the N4 position of cytosine, or the C5 position of cytosine. Figure 6.4.1: Methylation sites • In addition, only a fractional percentage of bases were methylated (i.e. not every adenine was methylated, for example) and these occurred at very specific sites in the DNA. • A characteristic feature of the sites of methylation, was that they involved palindromic DNA sequences. • Here is an example from a particular E. coli strain R1: Figure 6.4.2: Palindromic DNA (EcoR1 methylase specificity. Rubin and Modrich, 1977) • In addition to possessing a particular methylase, individual bacterial strains also contained accompanying specific endonuclease activities. • The endonucleases cleaved at or near the methylation recognition site. Figure 6.4.3: Cleavage near methylation site • These specific nucleases, however, would not cleave at these specific palindromic sequences if the DNA was methylated. Thus, this combination of a specific methylase and associated endonuclease functioned as a type of immune system for individual bacterial strains, protecting them from infection by foreign DNA (e.g. viruses). • In the bacterial strain EcoR1, the sequence GAATTC will be methylated at the internal adenine base (by the EcoR1 methylase). • The EcoR1 endonuclease within the same bacteria will not cleave the methylated DNA. • Foreign viral DNA, which is not methylated at the sequence "GAATTC" will therefore be recognized as "foreign" DNA and willbe cleaved by the EcoR1 endonuclease. • Cleavage of the viral DNA renders it non-functional. Such endonucleases are referred to as "restriction endonucleases" because they restrict the DNA within the cell to being "self". The combination of restriction endonuclease and methylase is termed the "restriction-modification" system. Since different bacterial strains and species have potentially different R/M systems, their characterization has made available hundreds of endonucleases with different sequence specific cleavage sites. • They are one of the primary tools in modern molecular biology for the manipulation and identification of DNA sequences. • Restriction endonucleases are commonly named after the bacterium from which it was isolated. Name Source Recognition Sequence Comments Alu I Arthrobacter luteus ` |` `5'… A G C T … 3'` `3'… T C G A … 5'` ` |` "Four cutter". Leaves blunt ends to the DNA. Bfa I Bacteroides fragilis ` |` `5'… C T A G … 3'` `3'… G A T C … 5'` ` |` "Four cutter". Leaves 5' overhang. Nci I Neisseria cinerea ` |` ` C` `5'… C C G G G … 3'` `3'… G G C C C … 5'` ` G` ` |` "Five cutter". Middle base can be either cytosine or guanine. Leaves 5' overhang. Different recognition sites may have non-complementary sequences. Eco R1 Escherichia coli ` |` `5'… G A A T T C … 3'` `3'… C T T A A G … 5'` ` |` "Six cutter". Leaves 5' overhang. Behaves like a "four cutter" ('star' activity) in high salt buffer. \$44 for 10,000 units. Hae II Haemophilusaegyptius ` |` `5'… Pu G C G C Py … 3'` `3'… Py C G C G Pu … 5'` ` |` "Six cutter". Pu is any purine, Py is any pyrimidine. Leaves 3' overhang. EcoO109I Escherichia coli ` |` `5'… Pu G G N C C Py … 3'` `3'… Py C C N G G Pu … 5'` ` |` "Seven cutter". Pu is any purine, Py is any pyrimidine, N is any base. Leaves 5' overhang. Different recognition sites may have non-complementary sequences. Bgl I Bacillus globigii ` |` `5'… GCCN NNNNGGC … 3'` `3'… CGGNNNN NCCG … 5'` ` |` "Six cutter with interrupted palindrome". Leaves 5' overhang. Different recognition sites may have non-complementary sequences. Bsa HI Bacillusstearothermophilus ` |` `5'… G Pu C G Py C … 3'` `3'… C Py G C Pu G … 5'` ` |` "Six cutter". Different recognition sites will be complementary. Aat II Acetobacter aceti ` |` `5'… G A C G T C … 3'` `3'… C T G C A G … 5'` ` |` "Six cutter" with 3' overhang. Same recognition sequence as Bsa HI, but different cleavage position. Bpm I Bacillus pumilus ` |` `5'… C T G G A G N16 … 3'` `3'… G A C C T C N14 … 5'` ` |` Non-palindrome, distal cleavage. Leaves 3' overhang. \$50 for 50 units. Not I Nocardiaotitidiscaviarum ` |` `5'… G C G G C C G C … 3'` `3'… C G C C G G C G … 5'` ` |` "Eight cutter". Leaves 5' overhang. Bsm I Bacillusstearothermophilus ` |` `5'… G A A T G C N … 3'` `3'… C T T A C G N … 5'` ` |` "Weird". Leaves 3' overhang. • The utility of restriction endonucleases lies in their specificity and the frequency with which their recognition sites occur within any given DNA sample. • If there is a 25% probability for a specific base at any given site, then the frequency with which different restriction endonuclease sites will occur can be easily calculated (0.25n): Specificity Example Frequency of Occurrence Four base sequence Alu I 1 Alu site in every 256 bases (0.25 Kb) Five base sequence Nci I 1 Nci I site in every 1024 bases (1.0 Kb) Six base sequence EcoR I 1 EcoR1 site in every 4,096 bases (4.1 Kb) Seven base sequence EcoO109I 1 EcoO109I site in every 16,384 bases (16.4 Kb) Eight base sequence Not I 1 Not I site in every 65,536 bases (65.5 Kb) Thus, on average, any given DNA will contain an Alu I site every 0.25 kilobases, whereas a Not I site occurs once about every 65.5 kilobases. • Not I is therefore a very useful enzyme for isolating large regions of DNA, typically in research involving genomic DNA manipulations. • Alu I would be expected to digest a DNA sample into lots of little pieces. The assortment of DNA fragments would represent a specific "fingerprint" of the particular DNA being digested. Different DNA would not yield the same collection of fragment sizes. Thus, DNA from different sources can be either matched or distinguished based on the assembly of fragments after restriction endonuclease treatment. These are termed "Restriction Fragment Length Polymorphisms", or RFLP's. This simple analysis is used in various aspects of molecular biology as well as a law enforcement and genealogy. For example, genetic variations that distinguish individuals also may result in fewer or additional restriction endonuclease recognition sites. Gel Electrophoresis of DNA The most common gel electrophoresis solid support matrix for DNA molecules is • agarose and • acrylamide. DNA agarose gels The electrophoretic migration rate of DNA through agarose gels is dependent upon four main parameters: 1. The molecular size of the DNA. Molecules of linear duplex DNA travel through agarose gels at a rate which is inversely proportional to the log of their molecular weight. Mr α 1/log (Mw) Example: Compare molecular mass vs. expected migration rate: Molecular Mass (Da) log (Molec. Mass) 1/log (Molec. Mass) i.e. relative Mr 100,000 5.0 0.20 50,000 4.7 0.21 10,000 4.0 0.25 5,000 3.7 0.27 1,000 3.0 0.33 Figure 6.4.4: Molecular mass and migration rate 2. The agarose concentration. There is an inverse linear relationship between the logarithm of the electrophoretic mobility and gel concentration. Agarose (%) Range of separation of linear DNA (in kilobases) 0.3 60 - 5 0.6 20 - 1 0.7 10 - 0.8 0.9 7 - 0.5 1.2 6 - 0.4 1.5 4 - 0.2 2.0 3 - 0.1 3. The conformation of the DNA. • closed circular DNA (form-I) - typically supercoiled (compact) • nicked circular (form-II) - nick relaxes any supercoiling • linear DNA (form-III) These different forms of the same DNA migrate at different rates through an agarose gel. • Almost always the linear form (form-III) migrates at the slowest rate of the three forms • Supercoiled DNA (form-I) usually migrates the fastest Figure 6.4.5: Forms of DNA 4. The applied voltage. Other details: • Typical value for running an agarose gel is 5 volts per cm (length of gel). • Agarose gels are usually poured and run horizontally • Finally, the DNA being an acidic molecule, migrates towards the positively charged electrode (cathode). DNA naturally has a constant charge to mass ratio, so no detergents need to be added (as with proteins) Figure 6.4.6: Gel electrophoresis DNA acrylamide gels • Acrylamide gels are useful for separation of small DNA fragments • typically oligonucleotides <100 base pairs. • These gels are usually of a low acrylamide concentration (<=6%) and contain the non-ionic denaturing agent Urea (6M). • The denaturing agent prevents secondary structure formation in oligonucleotides and allows a relatively accurate determination of molecular mass. Staining of DNA • The most convenient method to visualize DNA in gel electrophoresis is staining with the fluorescent dye ethidium bromide. Figure 6.4.7: Ethidium bromide • This compound contains a planar group that intercalates between the stacked bases of DNA. • The orientation and proximity of ethidium with the stacked bases causes the dye to display an increased flourescencecompared to free dye (in solution). • U.V. radiation at 254 nm is absorbed by the DNA and transmitted to the bound dye. • The energy is re-emitted at 590 nm in the red-orange region of the spectrum. • Ethidium bromide is usually prepared as a stock solution of 10 mg/ml in water, stored at room temp and protected from light. • The dye is usually incorporated into the gel and running buffer, or conversely, the gel is stained after running by soaking in a solution of ethidium bromide (0.5 ug/ml for 30 min). • The stain is visualized by irradiating with a UV light source (i.e. using a transiluminator) and photgraphing with polaroid film. • The usual sensitivity of detection is better than 0.1 ug of DNA. Because ethidium is a DNA intercalating agent, it is a powerful mutagen. Incorporation of ethidium in the DNA of living organisms (i.e. you and I) can cause (unwanted) mutations. Combining restriction endonuclease digestion with gel electrophoresis of DNA: Restriction mapping A given sequence of DNA (e.g. a gene) will have a specific sequence, and therefore, specific restriction endonuclease sites • The number and location of such sites is a unique and predictable property for a given DNA molecule • The fragmentation pattern (i.e. number and size of fragments after restriction endonuclease digestion) can be characterized by gel electrophoresis as a type of "DNA fingerprint" • Any changes in the DNA sequence of a gene can result in the elimination of particular restriction sites, and conversely, create new ones: Figure 6.4.8: Change in restriction sites • The gel electrophoresis fragment pattern for the human and dog albumin gene will be characteristically different from each other • A sample of blood can potentially be identified as either human or dog by observing the restriction fragment length polymorphism • Genetic differences between individuals can also be identified using restriction fragment length polymorphism analysis Figure 6.4.9: Using RFLP to see genetic differences

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/6._Lab_Notes_Part_2/6.4%3A_Restriction_Mapping.txt

Review DNA replication is accomplished by the enzyme DNA polymerase which has the following characteristics: 1. DNA polymerase catalyzes extension of an oligonucleotide in the 5'→3' direction. It cannot go in the opposite direction. 2. A DNA template (i.e. single strand oligonucleotide) is required. This is the information that is replicated (via Watson-Crick base pairing). If you want to replicate the "sense" strand of a DNA duplex, then the template is the anti-sense strand. Since DNA is a duplex, held together by non-covalent forces, we can separate the strands of the duplex (and obtain an appropriate template) by heat (or alkali) denaturation. 3. DNA polymerase can only extend an existing oligonucleotide, it cannot initiate replication. Thus, in addition to a template, DNA polymerase requires a primer oligonucleotide. DNA polymerase extends the primer in the 5'3' direction. 4. Nucleotides incorporated into the nascent oligonucleotide must be nucleoside triphosphates (i.e. dNTP's). The energy released in the hydrolysis of the phosphate anhydride bonds is used in the formation of covalent linkages (incorporation of the a-phosphate into the phosphodiester backbone of the growing oligonucleotide) 5. Although DNA polymerases catalyze extension in the 5'3' direction, some have the ability to "proof-read". "Proof reading" is the ability to recognize a misincorporation of a base, and the ability to backup and excise the incorrect base, and then continue on. Figure 6.5.1: DNA synthesis Polymerase Chain Reaction (PCR) • PCR is an in vitro technique for the amplification of a region of DNA which lies between two regions of known sequence. • PCR amplification is achieved by using oligonucleotide primers. These are typically short, single stranded oligonucleotideswhich are complementary to the outer regions of known sequence. Figure 6.5.2: PCR amplification The oligonucleotides serve as primers for DNA polymerase and each of the denatured strands of the parental DNA duplex serves as the template. This results in the synthesis of new DNA strands which are complementary to the parent template strands. The steps of: 1. Template denaturation 2. Primer annealing 3. Primer extension comprise a single "cycle" in the PCR amplification methodology. • After each cycle the newly synthesized DNA strands can serve as templates in the next cycle (the PCR primers are typically added in substantial molar excess to the template DNA) Summary of products at the end of each PCR cycle: Figure 6.5.3: PCR products The desired PCR product will be a duplex of the defined length fragment. The question is: how many will be produced? · The expected amplification of the desired defined length product with respect to the original template concentration 'x' can thus be represented by the formula: [(2n - (n + 1)) - (n + 1)] x or (2n - 2(n + 1)) (this is often abbreviated to a simple rule of thumb for the amplification: (2n - 2n) x) · The interpretation of this formula is that • For a given number of cycles 'n' we make '2n x' total possible duplexes • For a given number of cycles there will be '2(n+1) (or 2n in our approximation) x' duplexes which are formed from either the original template, or a fragment of indeterminate length, along with a fragment of defined length (and represent an undesired product) • Thus, the total concentration of desired product (duplexes with a length defined by the PCR primers) will be (2n - 2(n+1)) x (where x is the concentration of the original duplex) The theoretical amplification value is never achieved in practice. Several factors prevent this from occuring, including: 1. Competition of complementary daughter strands with primers for reannealing (i.e. two daughter strands reannealing results in no amplification). 2. Loss of enzyme activity due to thermal denaturation, especially in the later cycles 3. Even without thermal denaturation, the amount of enzyme becomes limiting due to molar target excess in later cycles (i.e. after 25 - 30 cycles too many primers need extending) 4. Possible second site primer annealing and non-productive priming Thermal cycling parameters The thermal cycling parameters are critical to a successful PCR experiment. The important steps in each cycles of PCR include: 1. denaturation of template (typically performed at highest temp - 100°C) 2. annealing of primers (temperature is chosen based upon melting temperature of primer) 3. extension of the primers (performed at optimum for the polymerase being used) A representative temperature profile for each cycle might look like the following: Figure 6.5.4: Thermal cycling Buffers and MgCl2 in PCR reactions A typical reaction buffer for PCR would something like: • 10 mM Tris, pH 8.3 • 50 mM KCl • 1.5 mM MgCl2 • 0.01% gelatin • The MgCl2 concentration in the final reaction mixture is usually between 0.5 to 5.0 mM, and the optimum concentration is determined empirically (typically between 1.0 - 1.5 mM). Mg2+ ions: • form a soluble complex with dNTP's which is essential for dNTP incorporation • stimulate polymerase activity • increase the Tm (melting temperature) of primer/template interaction (i.e. it serves to stabilize the duplex interaction Generally, • low Mg2+ leads to low yields (or no yield) and • high Mg2+ leads to accumulation of nonspecific products (mispriming). Choice of Polymerases for PCR • One of the important advances which allowed development of PCR was the availability of thermostable polymerases. • This allowed initially added enzyme to survive temperature cycles approaching 100 °C. • Properties of DNA polymerases used in PCR Taq/Amplitaq® Vent™ Deep Vent™ Pfu Tth ULTma 95 °C half-life 40 min 400 min 1380 min >120 min 20 min >50 min Extension rate (nt/sec) 75 >80 ? 60 >33 ? Resulting ends 3' A >95% blunt >95% blunt blunt 3' A blunt Stranddisplacement + + 5'®3' exo + + 3'®5' exo + + + + Primers Primer design • Generally, primers used are 20 - 30 mer in length. This provides for practical annealing temperatures (of the high temperature regimen where the thermostable polymerase is most active). • Primers should avoid stretches of polybase sequences (e.g. poly dG) or repeating motifs - these can hybridize with inappropriate register on the template. • Inverted repeat sequences should be avoided so as to prevent formation of secondary structure in the primer, which would prevent hybridization to template • Sequences complementary to other primers used in the PCR should be avoid so as to prevent hybridization between primers (particularly important for the 3' end of the primer) • If possible the 3' end of the primer should be rich in G, C bases to enhance annealing of the end which will be extended • The distance between primers should be less than 10 Kb in length. Typically, substantial reduction in yield is observed when the primers extend from each other beyond ~3 Kb. Melting temperature (Tm) of primers • The Tm of primer hybridization can be calculated using various formulas. The most commonly used formula is: (1) Tm = [(number of A+T residues) x 2 °C] + [(number of G+C residues) x 4 °C] • Examples of Tm calculations G A T C Tm 15 mer 3 5 2 5 46 20 mer 6 5 4 5 62 30 mer 8 6 8 8 92 If the annealing temperature is too high, the primers will not anneal. If it is too low, mispriming can occur at sites of similar DNA sequence to the intended primer binding site Analysis of the PCR experiment • 25-30 cycles are common • Major product should be a duplex DNA molecule whose 5' and 3' ends are defined by the two PCR primers • Other products can, however, be produced. These will often represent products of secondary priming sites (mispriming) or misincorporation errors of the polymerase • Thus, PCR product can be heterogeneous and must be separated and analyzed • Agarose gel electrophoresis, in combination with ethidium bromide staining, is the most common method to separate and analyze PCR products

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/6._Lab_Notes_Part_2/6.5%3A_Polymerase_Chain_Reaction_%28PCR%29.txt

There are a variety of resources on the web related to biochemical research. These resources typically include general categories of: • Databases of information • Software for analysis of data • Software for graphing and representing data • Published reports and reviews (literature) Databases of information There are numerous repositories of information related to biological molecules and biochemistry. A few of these (along with some sample links) include: • Databases of nucleic acid sequences, including commonly used plasmids. The Entrez Nucleotide Database New England Biolabs ™ Database of vectors and plasmids • Protein sequence databases Protein sequence database (Protein Information Resource) Swiss-Prot knowledge database • Protein structure database The Protein Databank (PDB) • Nucleic acid structure database The Nucleic Acid Database • Protein stability and thermodynamic database Protherm Database • Protein 2-D gel electrophoresis databases (proteomics) EXPASY 2-D PAGE links Software for analysis of data Software can either be run on a local machine (i.e. your PC on your desktop), or on a server over the web. Sometimes, it is a combination of the two (i.e a Java™ applet that is served on a host machine, but requires Java software installed on your PC. In general terms, if your data is expected to follow a particular mathematical model (e.g. Michaelis-Menten kinetics) then you needgeneral-purpose curve-fitting software. Links to non-linear least-square fit software include: Datafit™ from Oakdale Engineering LSM Least Squares Method curve fitting Programs such as these allow you to simple write an equation, input your experimental values, and then the parameters of the equation (i.e. model) will be refined to give the best fit to the data. Thus, a single program can be used for a variety of applications (i.e. any situation where an equation is available that describes the behavior of the data) Special purpose programs are available for specific applications. Software for graphing and presenting data In distinction to software for analysis (although sometimes combined in the same package) is software for graphing and representing data. The subtle point here is that after the data has been analyzed, you may need to prepare a report to communicate the results. In this case, there are other issues of data presentation - i.e. colors, line thicknesses, arrow styles, axis-breaks, fonts, legends, etc. that may need to be fussed with. Basic fitting software often does not include extensive presentation tools, and similarly, presentation software often does not have robust and general purpose fitting tools. Examples of graphing and presentation software for scientific data are: Origin™ from MicroCal™ KaleidaGraph from Synergy Software™ SigmaPlot™ from SPSS, Inc. In addition to graphic representation of experimental data, some forms of data require three-dimensional representations. For example, biomolecular structures (e.g. RNA, DNA and protein molecules) are stored in databases (e.g. PDB) as a series of atomic coordinates, however, the data only really makes sense if you can convert such data into a representation of a three-dimensional molecule. Furthermore, understanding the structural information is possible by manipulation of such representations (e.g. rotations, translations, zooming in/out, etc.). Software for such manipulations includes: Swiss PDB viewer DS Viewer™ from Accelrys Pymol O Furthermore, publication of figures of molecular structures often necessitate artistic embellishments - such as raytracing, shading, etc. The above programs can often output their figures in POV format, which allows such modifications using the following: POV-RAY™ Literature databases Scientific reports are published in various journals, meeting proceedings, and various books. Search engines are available to search electronic repositories of these materials. PubMed, a service of the National Library of Medicine, includes over 14 million citations for biomedical articles back to the 1950's. These citations are from MEDLINE and additional life science journals. PubMedincludes links to many sites providing full text articles and other related resources. Here is a link to PubMed™ Additionally, it is also useful to be able to easily download, maintain, and format such references in papers or manuscripts that you have to prepare. Bibliography software allows you to do this quite easily. Examples of such software include ProCite, Reference Manager and Endnote (all from ISI ResearchSoft: Endnote™ from ISI ResearchSoft™ Understanding DNA sequence information files When requesting DNA sequence information there is a lot of additional information that is included in the file - in addition to the actual sequence. Here is an example of part of the file for the sequence of the plasmid pUC19: ```Plasmid pUC19 ``` ```Update 6/5/02 ``` `Features:` ` 469- 146 lacZ alpha CDS (start 469, complementary strand)` ` 519- 514 Plac promoter -10 sequence (TATGTT)` ` 543- 538 Plac promoter -35 sequence (TTTACA)` ` 575- 563 CAP protein binding site` ` 396- 452 multiple cloning site (EcoRI-HindIII)` ` 1455- 867 origin of replication (counterclockwise)` ` (RNAII -35 to RNA/DNA switch point):` ` 1273-1278 RNAI transcript promoter -35 sequence (TTGAAG)` ` 1295-1300 RNAI transcript promoter -10 sequence (GCTACA)` ` 1309-1416 RNAI transcript` ` 1419- 867 RNAII transcript (complementary strand)` ` 1434-1429 RNAII transcript promoter -10 sequence (CGTAAT)` ` 1455-1450 RNAII transcript promoter -35 sequence (TTGAGA)` ` 2486-1626 beta-lactamase (bla; amp-r) CDS` ` (start 2486, complementary strand)` ` 2486-2418 beta-lactamase signal peptide CDS` ` (start 2486, complementary strand)` ` 2521 bla RNA transcript start (complementary strand)` ` 2535-2530 bla promoter -10 sequence (GAGACA)` ``` 2556-2551 bla promoter -35 sequence (TTCAAA) ``` ``` puc19.seq Length: 2686 June 5, 2002 13:46 Type: N Check: 4141 .. ``` ``` 1 TCGCGCGTTT CGGTGATGAC GGTGAAAACC TCTGACACAT GCAGCTCCCG ``` ``` 51 GAGACGGTCA CAGCTTGTCT GTAAGCGGAT GCCGGGAGCA GACAAGCCCG ``` ``` 101 TCAGGGCGCG TCAGCGGGTG TTGGCGGGTG TCGGGGCTGG CTTAACTATG ``` ` 151 CGGCATCAGA GCAGATTGTA CTGAGAGTGC ACCATATGCG GTGTGAAATA ` The actual DNA sequence starts towards the bottom of this file (it is truncated because it goes on for about 3000 bases). The start of the file is called the "header" and provides notes about features of the DNA sequence. For example, nucleotide bases 867 - 1455 comprise an origin of replication. It is listed as 1455 - 867 to communicate that the orientation of this "ori" is actually counterclockwise around the plasmid (plasmids are circular). There also is a gene for b-lactamase (bla; which provides for ampicillin resistance, or ampR selectable marker) that runs from basepair 1626 to 2486. It is also counterclockwise. Furthermore, the promoter for transcribing this gene is located from basepairs 2551 - 2521 (i.e. just "upstream" of the bla gene). The vector looks like this: Figure 6.6.1: pUC Vector The "start" of the sequence is basepair number 1, and is oriented at 12:00 (straight up). The sequence then runs clockwise around the plasmid. The plasmid diagram will use arrows to indicate the locations of the various features of the DNA, and the basepair numbers associated with the start and end locations of such features is also typically given on such figures.

textbooks/bio/Biochemistry/Supplemental_Modules_(Biochemistry)/6._Lab_Notes_Part_2/6.6%3A__Use_of_PC_and_internet_for_biochemical_research.txt

• 1.1: Introduction Scientists use a methodology for systematically investigating natural phenomena. This method uses existing information or observations to acquire new information or validate previous knowledge. These knowledge types come from empirical (experiential) or measured information. Empirical and measured data (or knowledge) are referred to as observations. While empirical data comes from experiences, science has developed into a mode of inquiry using experimentation. • 1.2: Microscopy In 1665, Robert Hooke published Micrographia, a book that illustrated highly magnified items that included insects and plants. This book spurred on interest in the sciences to examine the microscopic world using lenses but is also notable for Hooke’s observations of cork where he used the word “cell” in a biological sense for the first time. • 1.3: Units of Measure The metric system is an internationally agreed-upon measurement system based on decimals or powers of 10. Scientists use a refined version called the International System of Units (abbreviated SI). In biology, you will often find a need to describe measurements of length, volume, mass, time, temperature or amount of substance. • 1.4: Quantitative Skills Experimental science looks at cause and effect types of relationships. Controlled experiments vary one of the factors or traits to observe the effect on another factor or trait. These factors are called variables. A dependent variable is something that is observed and expected to change as a result of modifying another factor in the experiment. That is to say, the outcome depends on another factor. • 1.5: Reporting On Science This page contains detailed information on how to write your lab report. 01: Biology Basics The Scientific Method Scientists use a methodology for systematically investigating natural phenomena. This method uses existing information or observations to acquire new information or validate previous knowledge. These knowledge types come from empirical (experiential) or measured information. Empirical and measured data (or knowledge) are referred to as observations. While empirical data comes from experiences, science has developed into a mode of inquiry using experimentation. Experimental science uses the pre-existing base of knowledge to ask a testable question called a hypothesis. As a youngster, we’re incorrectly taught that a hypothesis is an educated guess. Formulating previous observations and measurements into a cohesive line of inquiry requires no guessing. People often have “theories” on something, when they actually have hypotheses based on their observations and assumptions. Experimental Science Hypothesis testing is the means by which experimental science is conducted. Experimental science is designed to enhance the understanding of a problem and removing biases from the interpretation. The goal of hypothesis testing is to try every way possible to disqualify the validity of the hypothesis. By doing so, the experimenter removes any biases in the experimental design. If the experimenter is unable to invalidate the hypothesis, the hypothesis becomes more valid and better able to act as a predictor of phenomena. Experiments utilize controls. In a controlled experiment, there is a positive and negative control. These controls act as references in the experiment. A positive control is an experimental condition where the expected outcome that is tested will be produced. This control is necessary to assess the validity of a test or treatment. There can be multiple instances used as a positive control to examine the sensitivity of the experiment. A negative control is an experimental condition where the expected outcome is known not to occur. This type of control sometimes comes in the form of a sham or mock treatment such as giving someone a sugar pill (a placebo). Through the use of experimental science and hypothesis testing, an increased refinement of existing knowledge can aid in designing new hypotheses. Hypothesis testing is re-iterative. That is to say, we use new knowledge to continue to enhance our understanding of the universe. The scientific method is a reiterative process based on testing and revising knowledge. (CC-BY-NC-SA Jeremy Seto) Theories A scientific theory comes from repeated substantiation of multiple tested hypotheses. That is to say, confirmed hypotheses, observations, and experiments permit scientists to formulate a cohesive idea that integrates multiple substantiated pieces of evidence. As with hypotheses, theories are designed to be predictive and falsifiable. In the common language, we often hear the word theory to mean a conjecture, and as already discussed, conjectures based on evidence can be formulated into testable hypotheses. When a theory is accepted by a predominant population of the specialists, it is referred to as a scientific principle. An example of a scientific principle is the theory of evolution by natural selection. Numerous tested hypotheses have been confirmed that lead to the understanding of natural selection as a method of evolution. This theory allows scientists to understand the underlying relatedness of all living things on the planet. Additionally, it unifies the disparate fields of Biology that can utilize the theory in a predictive manner. It is therefore also referred to as a unifying principle of Biology. Classification of Life All living things on Earth share a relationship. The rules that govern life processes can be generalized across all organisms (living things), as well as non-living biological entities (viruses). The relatedness of organisms is often visualized as a phylogenetic tree. This tree is a hierarchical classification system that groups organisms together based on common features and used these similarities to name them. This is referred to as taxonomy with the broadest category is called a domain. The phylogenetic tree of life describing the inter-relatedness of all living things on Earth. Three domains exist, Archaea, Bacteria, and Eukarya. Archaea and Bacteria are also grouped together as Prokaryotes (pro– before; karya– nucleus). Eukarya (eu– true; karya– nucleus), or eukaryotes, is a group of organisms that have nuclei. The second most inclusive or broad category is Kingdom. Humans are in the Kingdom of animals. The third most inclusive or broad category is Phylum. Humans are in the phylum called Chordata. Each level of organization can be further subdivided and you may be more familiar with the subphylum called Vertebrata. Within this division, humans fall in the class of mammals. Amongst the mammals, humans are in the order of primates. Humans are categorized into a narrower group of organisms in the family of great apes or hominids. Within this family, humans fall into the genus of Homo. Biologists use a method of identifying specific organisms called binomial nomenclature. Binomial nomenclature uses the most specific groupings of taxonomy (genus and species) as a two-part name. While humans are of the species sapiens, the species name of humans using binomial nomenclature is Homo sapiens. Taxonomic ranking of the red fox illustrating the inclusiveness of Domain and the exclusiveness of the Species level of categorization. Credit: Annina Breen (CC-BY-SA 4.0)

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The Light Microscope Hooke’s Cell In 1665, Robert Hooke published Micrographia, a book that illustrated highly magnified items that included insects and plants. This book spurred on interest in the sciences to examine the microscopic world using lenses but is also notable for Hooke’s observations of cork where he used the word “cell” in a biological sense for the first time. The Father of Microbiology: van Leeuwenhoek The Dutch tradesman Antonie van Leeuwenhoek used high power magnifying lenses to examine the parts of insects and to examine the quality of fabric in his drapery business. He began to experiment with pulling glass to generate lenses and developed a simple microscope to observe samples. Using a simple single lens with a specimen mounted on a point, he was able to identify the first microscopic “animalcules” (little animals) that will be later known as protozoa (original animals). Though van Leeuwenhoek’s apparatus was simple, the magnifying power of his lenses and his curiosity enabled him to perform great scientific observations on the microscopic world. He was ridiculed for fabricating his observations of protists at first. Ever the scientist, van Leeuwenhoek examined samples of his own diarrhea to discover Giardia intestinalis. While he did not make the connection of the causative nature of this microorganism, he described the details of the way this organism could propel itself through the medium in great detail. Modern Compound Microscope Unlike van Leeuwenhoek’s single-lens microscope, we now combine the magnifying power of multiple lenses in what is called a compound microscope. 1. Ocular lens or eyepiece 2. Nose Piece/ Lens Carousel 3. Objective lens 4. Course Focus Knob 5. Fine Focus Knob 6. Stage 7. Lamp 8. Condenser 9. Stage control Magnification Magnification is the process of enlarging the appearance of an object. We calculate the magnification of an object by indicating the fold change in size. So if something appears to be double the size of the real item, then it is obviously magnified 2X. Because there is a magnification by the eye-piece (ocular lens), as well as the objective lenses, our final magnification of an item is the product of those two lenses. The lowest magnification objective lens (usually 4X or 5X) is referred to as a scanning lens. There is also usually a low power lens at 10X and a higher magnification lens at 40X. There may be a higher magnification lens at 100X but these usually require oil to function properly and are often reserved for microbiology labs. • What is the power of the ocular lens? • We can calculate that as: Magnificationtotal = Magnificationobjective X Magnificationocular • With this in mind, fill in the following table: Field of View (FOV) In a microscope, we ordinarily observe things within a circular space (or field) as defined by the lenses. We refer to this observable area as the field of view (FOV). Understanding the size of the FOV is important because actual sizes of the object can be calculated using the Magnification of the lenses. FOV can be described as the area of a circle: What are the effects of magnification on FOV? In image 1, we can see a model of DNA on a table with a water bottle and a large area of the room. Image 2 displays less of the room in the background but the DNA model is larger in appearance because the magnification is greater. In image 3, we no longer see evidence of a door and the DNA model is much larger than before. In image 4, we no longer see the table the model and water bottle rest upon. While the last image is largest, we see less of the surrounding objects. We have higher magnification at the cost of the field of view. FOV is inversely related to the magnification level. Field of View Calculation 1. Examine a ruler under scanning magnification • Measure the diameter in mm. • Diameter= _________________ • Radius= ____________________ • Calculate the field of view at this magnification= __________________ 2. Examine a ruler under low magnification (10x) • Measure the diameter in mm. • Diameter= _________________ • Radius= ____________________ • Calculate the field of view at this magnification= ____________________ 3. What is the relationship in the between the magnification and field of view?_______________________________________________________________________________ 4. What is the proportion of change in the field of view when doubling the magnification?______________________________________________________________________ The Letter “e” 1. Orient a slide with the Letter “e” so that it is read as an “e” without magnification 2. Draw the “e” at scanning, low and high magnification Depth of Field 1. Examine the slide of colored threads under scanning power so the cross-point of the threads is at the center of the field. 2. Raise the magnification to the low power objective • What do we notice about the threads and the focus? • How can we explain this observation with respect to the threads? • Close the diaphragm so allow a pinpoint of light through the slide. What effect does this have on the image? We notice that when we observe 3 overlapping threads of different color under a microscope, we can focus on one thread at a time. Similarly, when we zoom in a great deal on the DNA model below, we notice that the print on the water bottle is not sharp. Highest Magnification with shallow depth of field. Notice how the label on the water bottle is blurry while the lettering on the DNA model is sharp. We know that the water bottle is behind the DNA molecule. Under the microscope, the threads of differing color are also stacked on top of each other. We recognize that they are on different planes because they are three dimensional. Each thread has depth and does not occupy the same exact space. If we focus on the print of the water bottle on the image above, we would no longer see the lettering on the DNA molecule sharply. We refer to this concept as Depth of Field (DOF). Under the microscope, at low magnification, we can make out fewer finer details. However, most items appear on the same plane in this case and or comparably sharp. But as we increase the magnification and see finer details, the distances between the various planes in view become more apparent. We can see a similar phenomenon at low magnification of the DNA model. At the low magnification, we may not be able to read the print on the water bottle, but the bottle and DNA molecule are of a similar distance from our view that the small difference in apparent depth is not as noticeable. We can still draw on other visual cues to know that the bottle is behind the model, but the sharpness of both items are equivalent. Examining Cells 1. Choose a prepared slide of a Protist (Euglena, Amoeba, Paramecium) 2. Prepare a wet mount of a drop of pond water and place a coverslip over the drop. 3. Swab the inside of your cheek 1. Roll the swab across a slide 2. Drop some methylene blue onto the slide 3. Place a coverslip over the drop 4. Visualize and draw your cheek cells 4. Document your observations by drawing the cells and by using your phone to snap an image. Real Biological Examples We can see the concepts of FOV and DOF in the following pictures. In an even more extreme close-up (higher magnification), we would have difficulty focusing on both the eyes and beak since there is depth and distance between those features. How Do We Use Microscopes In our lab, we look at some pond water. What do we see? Why is this significant? How does the microscope help us study these items? What is the utility of the concepts of magnification, FOV, and DOF when we use microscopes to study biological samples?

textbooks/bio/Biotechnology/Bio-OER_(CUNY)/01%3A_Biology_Basics/1.02%3A_Microscopy.txt

The Metric System The metric system is an internationally agreed-upon measurement system based on decimals or powers of 10. Scientists use a refined version called the International System of Units (abbreviated SI). In biology, you will often find a need to describe measurements of length, volume, mass, time, temperature or amount of substance. Metric Units • length: meter (m) • volume: liter (L) • mass: gram (g) • time: second (s) • temperature: Celsius (°C) • Kelvin (K) is a unit of thermodynamic temperature and is the SI unit. The Kelvin scale in the same as the Celsius or centigrade scale but offset by 273.16 • Biology uses Celsius predominantly because of the range in which organisms live. • amount of substance: mole (mol) • A mole is a number representing 6.022×1023 of something • Just as a pair of shoes equals 2 shoes, a mole of shoes is 6.022×1023 shoes • Just as a dozen eggs equals 12 eggs, a mole of eggs is 6.022×1023 eggs Strategy for Conversions 1. What unit is being asked for? • 500ml = ____L → liters 2. What unit are you starting from? • 500ml = ____L → milliliters 3. Which unit is larger? By how much is that unit larger? • Liters are the larger unit. Liters are 1,000X (103) greater than milliliters. 4. Which direction are we moving? • Since we are moving to a larger unit, our value will be smaller. In this case, the value is smaller by 1,000X • In other words, the value is 1/1000 or 0.001 the value. • So what is the answer? Factoring Out Using the idea of factors of ten, you can assess the difference between the two units and cancel out the original unit algebraically to reach the desired final unit. • 500ml=_____L • or, • which states 1000 milliliter in every 1 liter • pay attention to the units and how we’ve canceled out the ml in the numerator of 500ml and in the denominator in the conversion of 1L in 1000ml Accuracy and Precision Accuracy refers to how closely a measured value agrees with the correct or target value. Precision refers to how closely individual measurements agree with each other and reflects the repeatability in those measurements. This illustrates accuracy. Measurements are on target. This illustrates precision. Measurements are very close to each other and repeatable. This illustrates Accuracy AND Precision. Each measurement is on target and also highly repeatable. Instruments have a finite amount of accuracy and it is important to report measurements within that level of accuracy. Significant figures, report the number of digits that are known to some degree of confidence with the measuring device. With the increased sensitivity of the equipment, the number of significant figures increases. Pipetting Basics Types of Micropipettors Pipettors are made by many different manufacturers and thus all do not look the same. Learning to correctly use one type of pipettor will provide you the knowledge to use others as they share the same method of distributing small volumes. This lab will illustrate the Rainin Pipetman® micropipettors. The top of the plunger shows the pipettor size for the Pipetman models. Each pipettor has its own volume range and it is CRITICAL to use a pipettor only in its proper volume range. The “P-number” represents the maximum volume in μL that the pipettor can measure. Pipettors are more accurate in the upper part of their range. 20 μL should be measured with a P20 rather than with a P200. The four pipettor sizes (P10, P20, P200, P1000) used in our lab will measure from 1 μL – 1000 μL as shown below. Correctly Adjusting the Pipettors Gilson Pipetman pipetting ranges chart. Note that the P200 officially has a range from 50-200μl Tutorial on Proper Usage Pipetting sequence. Ensure the plunger is depressed outside of the liquid to displace air and avoid blowing bubbles into solution. Carefully draw the plunger up slowly and follow the liquid to avoid drawing air. Depress the plunger in destination tube to the first stop. Depress to the second stop if fluid remains in the tip to expel full volume. https://www.youtube.com/watch?v=uEy_NGDfo_8 Rules For Use of the Micropipettors These are precision instruments which can easily be damaged. Treat them with respect and care. They are essential for your success in this course and shared amongst numerous students. 1. Never measure higher or lower than the range of the pipettor allows. • An exception to this rule is the P200 labeled 50-200 μl. • While we have P100 pipettors for this range, they appear too similar to P20 that they are often confused. • Originally, P200 were labeled 20-200 μl and we know that the lower range is less precise on these. 2. Never turn the volume adjuster above or below this range indicated on the pipettor or you risk breaking the instrument. 3. Never allow liquid to get into the micropipettor. • This causes contamination. • This weakens the seal on the o-rings and can damage them. 4. Never use the micropipettor without a tip. 5. Never invert or lay down the micropipettor with liquid in the tip. • Liquids will roll into the piston this way. 6. Never let the plunger snap back when filling or ejecting liquid. 7. Never immerse the barrel in fluid. • This causes contamination. 8. Never set the micropipettor on the edge of the bench; this may result in the micropipettor falling or being knocked onto the floor. Exercise: Pipetting Practice 1. Prepare seven dye mixtures as illustrated in the table below. 1. One student mix samples in column A and a second mixes in column C. 2. Column B is left empty and used if one student makes a mistake. 2. Each dye mixture prepared in the first well to reach a total volume of 45 μl. 3. Pipet 10 μl in triplicate from each well of the mixing plate into the center of the appropriate circles on the target card.

textbooks/bio/Biotechnology/Bio-OER_(CUNY)/01%3A_Biology_Basics/1.03%3A_Units_of_Measure.txt

Variables Experimental science looks at cause and effect types of relationships. Controlled experiments vary one of the factors or traits to observe the effect on another factor or trait. These factors are called variables. A dependent variable is something that is observed and expected to change as a result of modifying another factor in the experiment. That is to say, the outcome depends on another factor. Another name for dependent variable is responding variable. The independent variable is the factor or condition that is changing or being changed by the experimenter. Sometimes waiting is the condition that is changing, making the independent variable: time. Since we change the independent variable, it is also called the manipulated variable. Graphing a Line A graph displaying 2 lines and their equations A line can be described mathematically by the equation: This is referred to as the slope-intercept form. The 2 variables y and x refer to coordinates along each axis. The term m refers to the change in the y-values over the change in the x-values. This is referred to as the slope of the line. The term b, the y-intercept, is the y-value where the line crosses the y-axis. OR This is how the slope of a line (m) is determined. The slope of the line indicates the relationship between the two variables, x and y. The equation of the line indicates to us that “y” occurs as a function of changes to “x”. Sometimes this is represented by the equation . Since “y” depends on “x”, “y is the dependent variable and “x” is the independent variable. As “x” changes, how does “y” change in response? This is what the slope reveals to us. For more review, visit the following link. Slope Activity Click here to run the simulation. Data Plotting Activity 1. Use the following data from the USDA to plot a scatterplot and generate a trend-line. 2. Use the info from the table below or download (a text file that can be opened in notepad/textedit) 3. Follow a tutorial on how to graph a scatterplot with line of best fit (trend-line) 4. Or use this tutorial in plot.ly • Show the equation of the trend-line on the graph • Ensure that “lbs. Mozzarella” is the y-axis • Ensure that “Year” is the x-axis • Remember not to use a line graph Year lbs. Mozzarella 2000 9.33 2001 9.70 2002 9.66 2003 9.65 2004 9.94 2005 10.19 2006 10.52 2007 11.02 2008 10.57 2009 10.63 What Do We Learn? 1. What does this scatterplot tell us about the relationship between consumption of mozzarella in relationship to years? 2. How would this graph influence the way you invest in a mozzarella cheese company? Can you predict anything about the future of cheese consumption? 3. What does the slope of this line indicate to you? 1. Use mathematics to illustrate this point. 2. The slope has a unit related to “lbs.” and “year”, what is this unit? Creating a Line of Best Fit Not all points collected will fall on a straight line. A Line of Best Fit or a Trendline approximates the average of those points through a mathematical process called the least-squares method. While one could “eyeball” this line, the least-squares method uses the data to minimize the distance from all those points to the line to have an averaging effect. 1. Create a column of data for “x” values, “y” values, x2 and xy 2. At the bottom of these columns, sum the data. Σx, Σy, Σx2, Σxy 3. Calculate the slope from these values • (where N=number of entries in a column) 4. Calculate the y-intercept from these values 5. Which provides use the function Click here to run the simulation. 1.05: Reporting On Science Lab Reports Title A description of the main idea or question of the lab. This can also highlight a key finding or question. Abstract A brief summary of the main question, methods, and findings. This is usually the last thing written but the first thing presented in order to grab the attention of the reader. A rough breakdown of an abstract would contain about: • 3 sentences worth of introduction with the key question • 2 sentences of major methodology • 3-6 sentences of the major results and conclusions drawn from them Lengths will vary, but using this framework, you will not deviate too far from having a reader lose interest. Introduction Introduce the background that is relevant for forming the hypotheses being tested. What were the previous observations or prior knowledge used to come to these ideas? State the actual hypotheses to be tested and how it will further the understanding of the issue. Materials and Methods This section is a little like a cooking recipe. The main steps taken should be summarized as standard prose in a manner that anyone could follow and repeat. This is written in the past tense and 3rd person. Do not write in the first person as *you* have nothing to do with the experiments. Explain “What was done with which reagents?” Results This section is descriptive of what was observed. Figures and tables serve as a summary of the results to illustrate the data. They also serve as guides to outline the text of the section. Slowly describe each figure or table. Expand these points into sentences and paragraphs. Present the data as fully as possible, including stuff that at the moment does not quite make sense. This is written in the past tense and 3rd person. Conclusions are not provided in this section as they are made from analyzing the information and synthesizing the results. Discussions Discussions are the conclusions made by analyzing the results. At this time, you will be able to re-emphasize the original hypotheses made in the introduction. Indicate whether or not the hypotheses were demonstrated sufficiently. If this is not the case, offer alternatives and interpretations. Can you improve or modify your hypotheses? Explain how multiple lines of evidence corroborate each other and help to further the understanding of the problem.

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• 2.1: Introduction This page contains the link to a self-guided activity for reviewing basic chemistry before you continue to the Organic Chemistry section. • 2.2: Water H2O is a polar covalent molecule. The Bonds between the H atoms and the O atom arise from sharing electrons. These shared electrons form to satisfy the octet rule. However, oxygen is a “selfish” sharer. This electronegative aspect of oxygen means that the electrons of the H2O molecule preferentially associate near the oxygen atom, creating partial charges. We indicate this by placing a δ–near the O and δ+‘s near the H atoms. These partial charges make the H2O polar. • 2.3: Biologically Important Macromolecules Living things are composed of organic molecules primarily made up of the elements carbon and hydrogen. Molecules of hydrogen and carbon (referred to as hydrocarbons) have the property of being nonpolar. Yet 70- 90% of cells are composed of water (a polar compound). Polar substances mix with other polar substances. Likewise, non-polar substances interact with other non-polar compounds. Polar and non-polar compounds are immiscible (unable to mix). • 2.4: Chromatography Chromatography is a collective term for a set of analytical techniques used to separate mixtures. Chroma means color and graph means to write or draw. Paper chromatography is an analytical technique used to separate mixtures of chemicals (sometimes colored pigments) using a partitioning method. The paper in this method is called the stationary phase because it does not move and serves as a substrate or surface for the separation. • 2.5: pH We can call any compound that adds H+ ions (a free proton) into solution an acid. Along with this, we would expect that any compound that would decrease the concentration of free H+ of a solution as a base. pH is the power of H+ of a solution. We define this power as a molar concentration of H+ in solution. This concentration invariably ends up being a relatively small number (though great in absolute numbers) and is expressed as a decimal number. • 2.6: pH (Activity) This page contains a table which asks students to predict the nature and the pH of various solutions before measuring the actual pH and validating their predictions. • 2.7: Carbohydrates Carbohydrates serve 2 major functions: energy and structure. As energy, they can be simple for fast utilization or complex for storage. Simple sugars are monomers called monosaccharides. These are readily taken into cells and used immediately for energy. The most important monosaccharide is glucose (C6H12O6), since it is the preferred energy source for cells. • 2.8: Lipids Lipids are the class of macromolecules that mostly serve as long-term energy storage. Additionally, they serve as signaling molecules, water sealant, structure, and insulation. Lipids are insoluble in polar solvents such as water and are soluble in nonpolar solvents such as ether and acetone. • 2.9: Proteins Proteins provide much of the structural and functional capacity of cells. Proteins are composed of monomers called amino acids. Amino Acids are hydrocarbons that have an amino group (-NH2) and an acidic carboxyl group (-COOH). The R group represents a hydrocarbon chain with a modification that alters the properties of the amino acid. 20 universal amino acids are used to construct proteins. • 2.10: Nucleic Acids DNA and RNA are nucleic acids and make up the genetic instructions of an organism. Their monomers are called nucleotides, which are made up of individual subunits. Nucleotides consist of a 5-Carbon sugar (a pentose), a charged phosphate and a nitrogenous base (Adenine, Guanine, Thymine, Cytosine or Uracil). Each carbon of the pentose has a position designation from 1 through 5. One major difference between DNA and RNA is that DNA contains deoxyribose and RNA contains ribose. • 2.11: Biological Molecules (Concept) This page contains a Macromolecules Concept Map as well as a table which summarizes how each macromolecule can be detected. 02: Chemistry Chemistry Self-Guided activity: http://phet.colorado.edu/files/activities/3807/BAA-Student-Handout.doc Follow the self-guided activity for a review of basic chemistry before continuing to the organic chemistry section.

textbooks/bio/Biotechnology/Bio-OER_(CUNY)/02%3A_Chemistry/2.01%3A_Introduction.txt

Polar Covalent Bonds H2O is a polar covalent molecule. The Bonds between the H atoms and the O atom arise from sharing electrons. These shared electrons form to satisfy the octet rule. However, oxygen is a “selfish” sharer. This electronegative aspect of oxygen means that the electrons of the H2O molecule preferentially associate near the oxygen atom, creating partial charges. We indicate this by placing a δnear the O and δ+‘s near the H atoms. These partial charges make the H2O polar. The electron cloud around a water molecule lingers around the oxygen molecule to render it partially negative. Red illustrates the partial negative end of the molecule while blue indicates the partial positive. Because of this polarity, H2O molecules arrange in a highly structured way. Use the following simulation to explore polarity of molecules. These weak associations that arise from the polar: polar attractions are referred to as Hydrogen Bonds (H-bonds). While independently weak, the summation of all the H-bonds is very strong. These associations give rise to the special properties of water: surface tension, cohesion, adhesion, and high specific heat capacity. Polar materials mix with polar materials. Things that can dissolve in water are also polar and referred to as being hydrophilic (hydro = water, philic = liking). Non-polar substances do not interact or mix with polar solvents and are referred to as hydrophobic (hydro = water, phobic = hating). Since carbon and hydrogen share electrons equally, organic compounds are non-polar. Oil is a hydrocarbon that does not mix well with water or vinegar. Vinegar, however, is a polar compound that interacts with water. Detergents are called amphiphilic (amphi = both; philic = liking) because they have portions that are non-polar and portions that are very polar. Detergents can, therefore, dissolve in hydrocarbons and water. Water alone cannot effectively remove oil from your skin but a detergent can dissolve the oil and carry it away in water. The common detergent in soap – Sodium Lauryl Sulfate Surface Tension Surface tension presents as an invisible film that encompasses the surface of water. The attractive forces arising from the intermolecular cohesion holds the surface of water together. This paperclip would sink if it broke through the surface of the water. This water strider is not on top of the water because it is light. It has not broken through the surface of the water and is, therefore, on top of the water. Solutions Water is an excellent solvent of other polar compounds. Table salt (NaCl) ionizes readily in water. The δ O associate around Na+ while the δ+ H associate with the Cl. If NaCl is dissolved in H2O, what do you think happens to the intermolecular interactions between water molecules? What do you think would happen to the H-bonds? Would you expect there to be a difference in the surface tension? How do you think this explains the difference of boiling or freezing? Running on Water Basiliscus plumifrons (green basilisk) Basiliscus basiliscus (common basilisk) Lizards of the genus Basiliscus have the nickname “Jesus Christ lizard” for their very special adaptation regarding water. In the face of danger, these lizards run on their hind legs across the water to escape predation. Their hind legs have long toes that help in increasing surface area to distribute their weight so they can propel themselves on the surface of the water. They do not sink because the surface tension of the water is not broken by the large surface covered by their feet. After about 4.5m, they lose sufficient momentum to propel themselves on the water surface and break through. H-bonds enable this adaptation. Video from National Geographic What If? The namesake of these lizards walked on water but was also able to turn water into wine. Wine is a solution of ethanol (11%) in water. Ethanol has a polar end and a non-polar end. • How successful would the basilisk run on wine? • Why? • How could you test this without necessarily using a lizard?

textbooks/bio/Biotechnology/Bio-OER_(CUNY)/02%3A_Chemistry/2.02%3A_Water.txt

Understanding Chemistry in 3D 1. Click on the image above to launch the simulation. 2. Choose “Real Molecules”. 3. Click on “Show Bond Angles”. • You may also deselect “Show Lone Pairs”. 4. Drag the atoms to understand the 3-D shape. 5. Choose CH4 from the drop-down menu of molecules. • CH4 is methane, the simplest organic molecule. • Notice the restraints on the physical space occupied by the atoms as you drag the atoms around. • Remember these bond angles and constraints when we explore organic chemistry. • Toggling between “Model” and “Real” will illustrate if the position of the atoms are influenced by other factors we don’t see. 6. Choose NH4 from the drop-down menu. • NH4 is ammonia, a polar solvent. • NH4 is NOT an organic molecule. • Toggle the lone pairs to see how these electrons play a role in the positioning of atoms. 7. Choose CO2 from the drop-down. • Explore this non-organic molecule to visualize the effects of double bonds on geometry. Organic Chemistry Living things are composed of organic molecules primarily made up of the elements carbon and hydrogen. Molecules of hydrogen and carbon (referred to as hydrocarbons) have the property of being nonpolar. Yet 70- 90% of cells are composed of water (a polar compound). Polar substances mix with other polar substances. Likewise, non-polar substances interact with other non-polar compounds. Polar and non-polar compounds are immiscible (unable to mix). So How do Cells Keep From Falling Apart in a Liquid Environment? Functional groups are clusters of atoms in a group that impart a new “function” to the compound they are attached to. Hydrocarbons in cells have functional groups attached to them that permit them to interact with the water environment of the cell. These functional groups also define the type of molecule it is based on the characteristics of those groups. Functional Groups. Functional groups commonly found in organic compounds. R is a placeholder chemical that can be anything (like a hydrocarbon chain). The functional groups in this table are those that add polar or charged properties to the hydrocarbon chains. Bi-directional arrows indicate that those functional groups dynamically ionize and reach an equilibrium in solution. How are Macromolecules Assembled? The common organic compounds of living organisms are carbohydrates, proteins, lipids, and nucleic acids. Each of these are macromolecules or polymers made of smaller subunits called monomers. The bonds between these subunits are formed by a process called dehydration synthesis. This process requires energy; a molecule of water is removed (dehydration) and a covalent bond is formed between the subunits. Because a new water molecule is formed, this is also referred to as condensation. The opposite where water and energy are used to break apart polymers into simpler monomers is called hydrolysis (hydro– water, lysis– to break or split). Polymer formation. Dehydration reactions join monomers. Hydrolysis splits polymers. Where Do We Find Macromolecules? A nutrition label illustrates the breakdown of chemical components of Macaroni and Cheese. This is not limited to the macromolecules discussed here. Items like Iron and Sodium are ions that are important for the function of the cell. Vocabulary • Carbohydrates • Reducing sugar • Polymer • Monomer • Monosaccharide • Disaccharide • Polysaccharide • Dehydration synthesis • Hydrolysis • Reduction • Aldehyde • Ketone • Lipid 2.04: Chromatography Credit: Theresa Knott [CC BY-SA 3.0 or GFDL] Chromatography is a collective term for a set of analytical techniques used to separate mixtures. Chroma means color and graph means to write or draw. Paper chromatography is an analytical technique used to separate mixtures of chemicals (sometimes colored pigments) using a partitioning method. The paper in this method is called the stationary phase because it does not move and serves as a substrate or surface for the separation. Analytes (substances being analyzed) are separated from each other based on a differential affinity to a solvent. The solvent dissolves and carries the analytes along the matrix of the stationary phase. Since the solvent moves through a wicking action, it is called the mobile phase. The distance that the analyte migrates along the paper related to the total distance that the solvent or mobile phase moves is called the Retention Factor or RF. Are The Food Colorings Used in Colored Candy the Same as the FD&C Approved Chemicals? How many colored spots do you expect to see for each reference standard? 1. Obtain a 25 cm square piece of chromatography paper that will fit into the beaker that will serve as the chromatography chamber. 2. Draw a pencil line across the lower end of the chromatography paper about 2 cm from the bottom. 3. Draw additional vertical tick marks along this line every 2 cm. 4. Place colored candy in a flask with 2 ml ethanol until the color dissolves into the solution. 5. Using an applicator, create a very small spot on a tick mark and allow to dry. 6. Repeat application on the spot to make a very small and dark spot. 7. Continue to spot reference standards along other tick marks. These reference standards are food coloring. 8. Place approximately 1 cm of mobile phase solution (a very polar saltwater solution) into the beaker. 9. Roll the filter paper into a cylinder and fix with staples. 10. Place cylinder into the beaker and cover for 20 minutes or until the mobile phase reaches 2 cm from the top of the paper. 11. Mark the final distance of the mobile phase and dry the filter. 12. Measure the distance of each spot from the starting point. 1. Each spot is a separate analyte. 2. Some spots separate into multiple analytes. 3. Measure EACH one. 13. Measure the distance from the starting point to the final point that the solvent reached. 14. Calculate Rf values and tabulate results. Reflect 1. Compare and average the RF values of each analyte across the entire class. 2. Did you predict the number of spots that would appear from each analyte (reference or candy)? 3. Assuming all the dye molecules are of the same mass, what influenced the migration patterns of each spot? 4. Were the colors used in the candy the same as the references? 5. What does it mean if the candy color didn’t match anything from the food colors from the cake decorating set used as references?

textbooks/bio/Biotechnology/Bio-OER_(CUNY)/02%3A_Chemistry/2.03%3A_Biologically_Important_Macromolecules.txt