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Summary of Lipid Metabolism

 

Author: Larry H. Bernstein, MD, FCAP

 

Lipid Classification System

The LIPID MAPS Lipid Classification System is comprised of eight lipid categories, each with its own sublassification hierarchy.

http://www.lipidmaps.org/resources/tutorials/lipid_cns.html

Each LMSD record contains an image of the

  • molecular structure,
  • common and systematic names,
  • links to external databases,
  • Wikipedia pages (where available),
  • other annotations and links to structure viewing tools.

All lipids in the LIPID MAPS Structure Database (LMSD) have been classified using this system and have been assigned LIPID MAPS ID’s (LM_ID) which reflects their position in the classification hierarchy.

The LIPID MAPS Structure Database (LMSD) is a relational database encompassing structures and annotations of biologically relevant lipids. As of May 3, 2013, LMSD contains over 37,500 unique lipid structures, making it the largest public lipid-only database in the world. Structures of lipids in the database come from several sources:

  • LIPID MAPS Consortium’s core laboratories and partners;
  • lipids identified by LIPID MAPS experiments;
  • biologically relevant lipids manually curated from LIPID BANK, LIPIDAT, Lipid Library, Cyberlipids, ChEBI and other public sources;
  • novel lipids submitted to peer-reviewed journals;
  • computationally generated structures for appropriate classes.

All the lipid structures in LMSD adhere to the structure drawing rules proposed by the LIPID MAPS consortium. A number of structure viewing options are offered: gif image (default), Chemdraw (requires Chemdraw ActiveX/Plugin), MarvinView (Java applet) and JMol (Java applet).

(as of 10/8/14)

Number of lipids per category

Fatty acyls          5869

Glycerolipids       7541

Glycerophospholipids       8002

Sphingolipids      4338

Sterol lipids         2715

Prenol lipids        1259

Sacccharolipids  1293

Polyketides         6742

TOTAL  37,759 structures

References

Sud M, Fahy E, Cotter D, Brown A, Dennis EA, Glass CK, Merrill AH Jr, Murphy RC, Raetz CR, Russell DW, Subramaniam S. LMSD: LIPID MAPS structure database Nucleic Acids Research 35: p. D527-32. PMID:17098933 [doi:10.1093/nar/gkl838] PMID: 17098933

Fahy E, Sud M, Cotter D & Subramaniam S. LIPID MAPS online tools for lipid research Nucleic Acids Research (2007) 35: p. W606-12.PMID:17584797 [doi:10.1093/nar/gkm324] PMID: 17584797 

The Recognition of Essential Fatty Acids

Dietary fat has long been recognized as an important source of energy for mammals, but in the late 1920s, researchers demonstrated the dietary requirement for particular fatty acids, which came to be called essential fatty acids. It was not until the advent of intravenous feeding, however, that the importance of essential fatty acids was widely accepted: Clinical signs of essential fatty acid deficiency are generally observed only in patients on total parenteral nutrition who received mixtures devoid of essential fatty acids or in those with malabsorption syndromes.

These signs include dermatitis and changes in visual and neural function. Over the past 40 years, an increasing number of physiological functions, such as immunomodulation, have been attributed to the essential fatty acids and their metabolites, and this area of research remains quite active.1, 2

Fatty Acid Nomenclature

The fat found in foods consists largely of a heterogeneous mixture of triacylglycerols (triglycerides)–glycerol molecules that are each combined with three fatty acids. The fatty acids can be divided into two categories, based on chemical properties: saturated fatty acids, which are usually solid at room temperature, and unsaturated fatty acids, which are liquid at room temperature. The term “saturation” refers to a chemical structure in which each carbon atom in the fatty acyl chain is bound to (saturated with) four other atoms, these carbons are linked by single bonds, and no other atoms or molecules can attach; unsaturated fatty acids contain at least one pair of carbon atoms linked by a double bond, which allows the attachment of additional atoms to those carbons (resulting in saturation). Despite their differences in structure, all fats contain approximately the same amount of energy (37 kilojoules/gram, or 9 kilocalories/gram).

The class of unsaturated fatty acids can be further divided into monounsaturated and polyunsaturated fatty acids. Monounsaturated fatty acids (the primary constituents of olive and canola oils) contain only one double bond. Polyunsaturated fatty acids (PUFAs) (the primary constituents of corn, sunflower, flax seed and many other vegetable oils) contain more than one double bond. Fatty acids are often referred to using the number of carbon atoms in the acyl chain, followed by a colon, followed by the number of double bonds in the chain (e.g., 18:1 refers to the 18-carbon monounsaturated fatty acid, oleic acid; 18:3 refers to any 18-carbon PUFA with three double bonds).

PUFAs are further categorized on the basis of the location of their double bonds. An omega or n notation indicates the number of carbon atoms from the methyl end of the acyl chain to the first double bond. Thus, for example, in the omega-3 (n-3) family of PUFAs, the first double bond is 3 carbons from the methyl end of the molecule.  Finally, PUFAs can be categorized according to their chain length. The 18-carbon n-3 and n-6 short-chain PUFAs are precursors to the longer 20- and 22-carbon PUFAs, called long-chain PUFAs (LCPUFAs).

Fatty Acid Metabolism

Mammalian cells can introduce double bonds into all positions on the fatty acid chain except the n-3 and n-6 position. Thus, the short-chain alpha- linolenic acid (ALA, chemical abbreviation: 18:3n-3) and linoleic acid (LA, chemical abbreviation: 18:2n-6) are essential fatty acids.

No other fatty acids found in food are considered ‘essential’ for humans, because they can all be synthesized from the short chain fatty acids.

Following ingestion, ALA and LA can be converted in the liver to the long chain, more unsaturated n-3 and n-6 LCPUFAs by a complex set of synthetic pathways that share several enzymes (Figure 1). LC PUFAs retain the original sites of desaturation (including n-3 or n-6). The omega-6 fatty acid LA is converted to gamma-linolenic acid (GLA, 18:3n-6), an omega- 6 fatty acid that is a positional isomer of ALA. GLA, in turn, can be converted to the longerchain omega-6 fatty acid, arachidonic acid (AA, 20:4n-6). AA is the precursor for certain classes of an important family of hormone- like substances called the eicosanoids (see below).

The omega-3 fatty acid ALA (18:3n-3) can be converted to the long-chain omega-3 fatty acid, eicosapentaenoic acid (EPA; 20:5n-3). EPA can be elongated to docosapentaenoic acid (DPA 22:5n-3), which is further desaturated to docosahexaenoic acid (DHA; 22:6n-3). EPA and DHA are also precursors of several classes of eicosanoids and are known to play several other critical roles, some of which are discussed further below.

The conversion from parent fatty acids into the LC PUFAs – EPA, DHA, and AA – appears to occur slowly in humans. In addition, the regulation of conversion is not well understood, although it is known that ALA and LA compete for entry into the metabolic pathways.

Physiological Functions of EPA and AA

As stated earlier, fatty acids play a variety of physiological roles. The specific biological functions of a fatty acid are determined by the number and position of double bonds and the length of the acyl chain.

Both EPA (20:5n-3) and AA (20:4n-6) are precursors for the formation of a family of hormone- like agents called eicosanoids. Eicosanoids are rudimentary hormones or regulating – molecules that appear to occur in most forms of life. However, unlike endocrine hormones, which travel in the blood stream to exert their effects at distant sites, the eicosanoids are autocrine or paracrine factors, which exert their effects locally – in the cells that synthesize them or adjacent cells. Processes affected include the movement of calcium and other substances into and out of cells, relaxation and contraction of muscles, inhibition and promotion of clotting, regulation of secretions including digestive juices and hormones, and control of fertility, cell division, and growth.3

The eicosanoid family includes subgroups of substances known as prostaglandins, leukotrienes, and thromboxanes, among others. As shown in Figure 1.1, the long-chain omega-6 fatty acid, AA (20:4n-6), is the precursor of a group of eicosanoids that include series-2 prostaglandins and series-4 leukotrienes. The omega-3 fatty acid, EPA (20:5n-3), is the precursor to a group of eicosanoids that includes series-3 prostaglandins and series-5 leukotrienes. The AA-derived series-2 prostaglandins and series-4 leukotrienes are often synthesized in response to some emergency such as injury or stress, whereas the EPA-derived series-3 prostaglandins and series-5 leukotrienes appear to modulate the effects of the series-2 prostaglandins and series-4 leukotrienes (usually on the same target cells). More specifically, the series-3 prostaglandins are formed at a slower rate and work to attenuate the effects of excessive levels of series-2 prostaglandins. Thus, adequate production of the series-3 prostaglandins seems to protect against heart attack and stroke as well as certain inflammatory diseases like arthritis, lupus, and asthma.3.

EPA (22:6 n-3) also affects lipoprotein metabolism and decreases the production of substances – including cytokines, interleukin 1ß (IL-1ß), and tumor necrosis factor a (TNF-a) – that have pro-inflammatory effects (such as stimulation of collagenase synthesis and the expression of adhesion molecules necessary for leukocyte extravasation [movement from the circulatory system into tissues]).2 The mechanism responsible for the suppression of cytokine production by omega-3 LC PUFAs remains unknown, although suppression of omega-6-derived eicosanoid production by omega-3 fatty acids may be involved, because the omega-3 and omega-6 fatty acids compete for a common enzyme in the eicosanoid synthetic pathway, delta-6 desaturase.

DPA (22:5n-3) (the elongation product of EPA) and its metabolite DHA (22:6n-3) are frequently referred to as very long chain n-3 fatty acids (VLCFA). Along with AA, DHA is the major PUFA found in the brain and is thought to be important for brain development and function. Recent research has focused on this role and the effect of supplementing infant formula with DHA (since DHA is naturally present in breast milk but not in formula).

Overview of Lipid Catabolism:

http://www.elmhurst.edu/~chm/vchembook/622overview.html

The major aspects of lipid metabolism are involved with

  • Fatty Acid Oxidation to produce energy or
  • the synthesis of lipids which is called Lipogenesis.

The metabolism of lipids and carbohydrates are related by the conversion of lipids from carbohydrates. This can be seen in the diagram. Notice the link through actyl-CoA, the seminal discovery of Fritz Lipmann. The metabolism of both is upset by diabetes mellitus, which results in the release of ketones (2/3 betahydroxybutyric acid) into the circulation.

 

fatty acid metabolism

fatty acid metabolism

 

http://www.elmhurst.edu/~chm/vchembook/images/590metabolism.gif

The first step in lipid metabolism is the hydrolysis of the lipid in the cytoplasm to produce glycerol and fatty acids.

Since glycerol is a three carbon alcohol, it is metabolized quite readily into an intermediate in glycolysis, dihydroxyacetone phosphate. The last reaction is readily reversible if glycerol is needed for the synthesis of a lipid.

The hydroxyacetone, obtained from glycerol is metabolized into one of two possible compounds. Dihydroxyacetone may be converted into pyruvic acid, a 3-C intermediate at the last step of glycolysis to make energy.

In addition, the dihydroxyacetone may also be used in gluconeogenesis (usually dependent on conversion of gluconeogenic amino acids) to make glucose-6-phosphate for glucose to the blood or glycogen depending upon what is required at that time.

Fatty acids are oxidized to acetyl CoA in the mitochondria using the fatty acid spiral. The acetyl CoA is then ultimately converted into ATP, CO2, and H2O using the citric acid cycle and the electron transport chain.

There are two major types of fatty acids – ω-3 and ω-6.  There are also saturated and unsaturated with respect to the existence of double bonds, and monounsaturated and polyunsatured.  Polyunsaturated fatty acids (PUFAs) are important in long term health, and it will be seen that high cardiovascular risk is most associated with a low ratio of ω-3/ω-6, the denominator being from animal fat. Ω-3 fatty acids are readily available from fish, seaweed, and flax seed. More can be said of this later.

Fatty acids are synthesized from carbohydrates and occasionally from proteins. Actually, the carbohydrates and proteins have first been catabolized into acetyl CoA. Depending upon the energy requirements, the acetyl CoA enters the citric acid cycle or is used to synthesize fatty acids in a process known as LIPOGENESIS.

The relationships between lipid and carbohydrate metabolism are
summarized in Figure 2.

fatty acid spiral

 

Energy Production Fatty Acid Oxidation:

Visible” ATP:

In the fatty acid spiral, there is only one reaction which directly uses ATP and that is in the initiating step. So this is a loss of ATP and must be subtracted later.

A large amount of energy is released and restored as ATP during the oxidation of fatty acids. The ATP is formed from both the fatty acid spiral and the citric acid cycle. 

Connections to Electron Transport and ATP:

One turn of the fatty acid spiral produces ATP from the interaction of the coenzymes FAD (step 1) and NAD+ (step 3) with the electron transport chain. Total ATP per turn of the fatty acid spiral is:

Electron Transport Diagram – (e.t.c.)

Step 1 – FAD into e.t.c. = 2 ATP
Step 3 – NAD+ into e.t.c. = 3 ATP
Total ATP per turn of spiral = 5 ATP

In order to calculate total ATP from the fatty acid spiral, you must calculate the number of turns that the spiral makes. Remember that the number of turns is found by subtracting one from the number of acetyl CoA produced. See the graphic on the left bottom.

Example with Palmitic Acid = 16 carbons = 8 acetyl groups

Number of turns of fatty acid spiral = 8-1 = 7 turns

ATP from fatty acid spiral = 7 turns and 5 per turn = 35 ATP.

This would be a good time to remember that single ATP that was needed to get the fatty acid spiral started. Therefore subtract it now.

NET ATP from Fatty Acid Spiral = 35 – 1 = 34 ATP

SREBPs: activators of the complete program of cholesterol and fatty acid synthesis in the liver

Jay D. Horton1,2, Joseph L. Goldstein1 and Michael S. Brown1

1Department of Molecular Genetics, and
2Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA

J Clin Invest. 2002;109(9):1125–1131.
http://dx.doi.org:/10.1172/JCI15593
Lipid homeostasis in vertebrate cells is regulated by a family of membrane-bound transcription factors designated sterol regulatory element–binding proteins (SREBPs). SREBPs directly activate the expression of more than 30 genes dedicated to the synthesis and uptake of cholesterol, fatty acids, triglycerides, and phospholipids, as well as the NADPH cofactor required to synthesize these molecules (14). In the liver, three SREBPs regulate the production of lipids for export into the plasma as lipoproteins and into the bile as micelles. The complex, interdigitated roles of these three SREBPs have been dissected through the study of ten different lines of gene-manipulated mice. These studies form the subject of this review.

SREBPs: activation through proteolytic processing

SREBPs belong to the basic helix-loop-helix–leucine zipper (bHLH-Zip) family of transcription factors, but they differ from other bHLH-Zip proteins in that they are synthesized as inactive precursors bound to the endoplasmic reticulum (ER) (1, 5). Each SREBP precursor of about 1150 amino acids is organized into three domains: (a) an NH2-terminal domain of about 480 amino acids that contains the bHLH-Zip region for binding DNA; (b) two hydrophobic transmembrane–spanning segments interrupted by a short loop of about 30 amino acids that projects into the lumen of the ER; and (c) a COOH-terminal domain of about 590 amino acids that performs the essential regulatory function described below.

In order to reach the nucleus and act as a transcription factor, the NH2-terminal domain of each SREBP must be released from the membrane proteolytically (Figure1). Three proteins required for SREBP processing have been delineated in cultured cells, using the tools of somatic cell genetics (see ref. 5for review). One is an escort protein designated SREBP cleavage–activating protein (SCAP). The other two are proteases, designated Site-1 protease (S1P) and Site-2 protease (S2P). Newly synthesized SREBP is inserted into the membranes of the ER, where its COOH-terminal regulatory domain binds to the COOH-terminal domain of SCAP (Figure 1).

Figure 1

Model for the sterol-mediated proteolytic release of SREBPs from membranes JCI0215593.f1

Model for the sterol-mediated proteolytic release of SREBPs from membranes. SCAP is a sensor of sterols and an escort of SREBPs. When cells are depleted of sterols, SCAP transports SREBPs from the ER to the Golgi apparatus, where two proteases, Site-1 protease (S1P) and Site-2 protease (S2P), act sequentially to release the NH2-terminal bHLH-Zip domain from the membrane. The bHLH-Zip domain enters the nucleus and binds to a sterol response element (SRE) in the enhancer/promoter region of target genes, activating their transcription.

SCAP is both an escort for SREBPs and a sensor of sterols. When cells become depleted in cholesterol, SCAP escorts the SREBP from the ER to the Golgi apparatus, where the two proteases reside. In the Golgi apparatus, S1P, a membrane-bound serine protease, cleaves the SREBP in the luminal loop between its two membrane-spanning segments, dividing the SREBP molecule in half. (Fig 1)  The NH2-terminal bHLH-Zip domain is then released from the membrane via a second cleavage mediated by S2P, a membrane-bound zinc metalloproteinase. The NH2-terminal domain, designated nuclear SREBP (nSREBP), translocates to the nucleus, where it activates transcription by binding to nonpalindromic sterol response elements (SREs) in the promoter/enhancer regions of multiple target genes.

SREBPs: two genes, three proteins

The mammalian genome encodes three SREBP isoforms, designated SREBP-1a, SREBP-1c, and SREBP-2.

SREBP-1a is a potent activator of all SREBP-responsive genes, including those that mediate the synthesis of cholesterol, fatty acids, and triglycerides. High-level transcriptional activation is dependent on exon 1a, which encodes a longer acidic transactivation segment than does the first exon of SREBP-1c. The roles of SREBP-1c and SREBP-2 are more restricted than that of SREBP-1a. SREBP-1c preferentially enhances transcription of genes required for fatty acid synthesis but not cholesterol synthesis.

SREBP-1c and SREBP-2 activate three genes required to generate NADPH, which is consumed at multiple stages in these lipid biosynthetic pathways (8) (Figure 2).

major metabolic intermediates in the pathways for synthesis of cholesterol, fatty acids, and triglycerides

major metabolic intermediates in the pathways for synthesis of cholesterol, fatty acids, and triglycerides

Steroids

A major class of lipids, steroids, have a ring structure of three cyclohexanes and one
cyclopentane in a fused ring system as shown below. There are a variety of functional
groups that may be attached. The main feature, as in all lipids, is the large number of
carbon-hydrogens which make steroids non-polar.

Steroids include such well known compounds as cholesterol, sex hormones, birth
control pills, cortisone, and anabolic steroids.

 

sex hormones

sex hormones

cortisone

cortisone

Adrenocorticoid Hormones

The adrenocorticoid hormones are products of the adrenal glands.

The most important mineralcorticoid is aldosterone, which regulates the
reabsorption of sodium and chloride ions in the kidney tubules and increases
the loss of potassium ions.Aldosterone is secreted when blood sodium ion
levels are too low to cause the kidney to retain sodium ions. If sodium
levels are elevated, aldosterone is not secreted, so that some sodium
will be lost in the urine. Aldosterone also controls swelling in the tissues.

Cortisol, the most important glucocortinoid, has the function of increasing
glucose and glycogen concentrations in the body. These reactions are
completed in the liver by taking fatty acids from lipid storage cells and
amino acids from body proteins to make glucose and glycogen.

In addition, cortisol is elevated in the circulation with cytokine mediated
(IL1, IL1, TNFα) inflammatory reaction, called the systemic inflammatory
response syndrome. Its ketone derivative, cortisone, has the ability
to relieve inflammatory effects. Cortisone or similar synthetic derivatives
such as prednisolone are used to treat inflammatory diseases, rheumatoid
arthritis, and bronchial asthma. There are many side effects with the use
of cortisone drugs, such as bone resorption, so there use must be
monitored carefully.

Hormone Receptors

Steroid hormone receptors are found on the plasma membrane, in the cytosol and also in the nucleus of target cells. They are generally intracellular receptors (typically cytoplasmic) and initiate signal transduction for steroid hormones which lead to changes in gene expression over a time period of hours to days. The best studied steroid hormone receptors are members of the nuclear receptor subfamily 3 (NR3) that include receptors for estrogen (group NR3A)[1] and 3-ketosteroids (group NR3C).[2] In addition to nuclear receptors, several G protein-coupled receptors and ion channels act as cell surface receptors for certain steroid hormones.

 

Steroid Hormone Receptors and their Response Elements

Steroid hormone receptors are proteins that have a binding site for a particular steroid molecule. Their response elements are DNA sequences that are bound by the complex of the steroid bound to its Steroid receptor.

The response element is part of the promoter of a gene. Binding by the receptor activates or represses, as the case may be, the gene controlled by that promoter.

It is through this mechanism that steroid hormones turn genes on (or off).

steroid hormone receptor

steroid hormone receptor

http://users.rcn.com/jkimball.ma.ultranet/BiologyPages/S/Sigler.jpg

The glucocorticoid receptor, like all steroid hormone receptors, is a zinc-finger transcription factor; the zinc atoms are the four yellow spheres. Each is attached to four cysteines.

For a steroid hormone to regulate (turn on or off) gene transcription, its receptor must:

  1. bind to the hormone (cortisol in the case of the glucocorticoid receptor)
  2. bind to a second copy of itself to form a homodimer
  3. be in the nucleus, moving from the cytosol if necessary
  4. bind to its response element
  5. bind to other protein cofactors

Each of these functions depend upon a particular region of the protein (e.g., the zinc fingers for binding DNA).

Each of these functions depend upon a particular region of the protein (e.g., the zinc fingers for binding DNA). Mutations in any one region may upset the function of that region without necessarily interfering with other functions of the receptor.

Positive and Negative Response Elements

Some of the hundreds of glucocorticoid response elements in the human genome activate gene transcription when bound by the hormone/receptor complex. Others inhibit gene transcription when bound by the hormone/receptor complex.

Example: When the stress hormone cortisol — bound to its receptor — enters the nucleus of a liver cell, the complex binds to the positive response elements of the many genes needed for gluconeogenesis — the conversion of protein and fat into glucose resulting in a rise in the level of blood sugar.

the negative response element of the insulin receptor gene thus diminishing the ability of the cells to remove glucose from the blood. (This hyperglycemic effect is enhanced by the binding of the cortisol/receptor complex to a negative response element in the beta cells of the pancreas thus reducing the production of insulin.)

Note that every type of cell in the body contains the same response elements in its genome. What determines if a given cell responds to the arrival of a hormone depends on the presence of the hormone’s receptor in the cell.

The Nuclear Receptor Superfamily

Retinoids

Retinoids

http://users.rcn.com/jkimball.ma.ultranet/BiologyPages/R/Retinoids.png

 The zinc-finger proteins that serve as receptors for glucocorticoids and progesterone are members of a large family of similar proteins that serve as receptors for a variety of small, hydrophobic molecules. These include:

  1. other steroid hormones like
  2. the mineralocorticoid aldosterone
  3. estrogens
  4. the thyroid hormone, T3
  5. calcitriol, the active form of vitamin D
  6. retinoids: vitamin A (retinol) and its relatives
    1. retinal
    2. retinoic acid (tretinoin — also available as the drug Retin-A®); and its isomer
  7. isotretinoin (sold as Accutane® for the treatment of acne).
  8. bile acids
  9. fatty acids.

These bind members of the superfamily called peroxisome-proliferator-activated receptors (PPARs). They got their name from their initial discovery as the receptors for

  • drugs that increase the number and size of peroxisomes in cells.

In every case, the receptors consist of at least

  • three functional modules or domains.

From N-terminal to C-terminal, these are:

  1. a domain needed
  2. the zinc-finger domain needed for DNA binding (to the response element)
  3. the domain responsible for binding the particular hormone as well as the second unit of the dimer.
  4. for the receptor to activate the promoters of the genes being controlled

Schematic diagram of type II zinc finger proteins characteristic of the DNA-binding domain structure of members of the steroid hormone receptor superfamily. Zinc fingers are common features of many transcription factors, allowing proteins to bind to DNA. Each circle represents one amino acid. The CI zinc finger interacts specifically with five base pairs of DNA and determines the DNA sequence recognized by the particular steroid receptor. The three shaded amino acids indicated by the arrows in the knuckle of the CI zinc finger are in the “P box” that allows HRE sequence discrimination between the GR and ERα. The vertically striped aa within the knuckle of the CII zinc finger constitutes the “D box” that is important for dimerization and contacts with the DNA phosphate backbone. Adapted from Tsai M-J, O’Malley BW. Molecular mechanisms of action of steroid/thyroid receptor superfamily members. Annu Rev Biochem 1994;63:451-483; Gronemeyer H. Transcription activation by estrogen and progesterone receptors. Annu Rev Genet 1991;25:89-123.

type II zinc finger proteins

type II zinc finger proteins

Cytoskeleton and Cell Membrane Physiology

http://pharmaceuticalinnovation.com/10/28/2014/larryhbern/Cytoskeleton_
and_Cell_Membrane_Physiology

Definition and Function

The cytoskeleton is a series of intercellular proteins that help a cell with

  1. shape,
  2. support, and
  3. movement.

Cytoskeleton has three main structural components:

  1. microfilaments,
  2. intermediate filaments, and
  3. movement

The cytoskeleton mediates movement by

  • helping the cell move in its environment and
  • mediating the movement of the cell’s components.

Thereby it provides an important structural framework for the cell –

  • the framework for the movement of organelles, contiguous with the cell membrane, around the cytoplasm. By the activity of
  • the network of protein microfilaments, intermediate filaments, and microtubules.

The structural framework supports cell function as follows:

Cell shape. For cells without cell walls, the cytoskeleton determines the shape of the cell. This is one of the functions of the intermediate filaments.

Cell movement. The dynamic collection of microfilaments and microtubles can be continually in the process of assembly and disassembly, resulting in forces that move the cell. There can also be sliding motions of these structures. Audesirk and Audesirk give examples of white blood cells “crawling” and the migration and shape changes of cells during the development of multicellular organisms.

Organelle movement. Microtubules and microfilaments can help move organelles from place to place in the cell. In endocytosis a vesicle formed engulfs a particle abutting the cell. Microfilaments then attach to the vesicle and pull it into the cell. Much of the complex synthesis and distribution function of the endoplasmic reticulum and the Golgi complex makes use of transport vescicles,  associated with the cytoskeleton.

Cell division. During cell division, microtubules accomplish the movement of the chromosones to the daughter nucleus. Also, a ring of microfilaments helps divide two developing cells by constricting the central region between the cells (fission).

References:
Hickman, et al. Ch 4 Hickman, Cleveland P., Roberts, Larry S., and Larson, Allan, Integrated Principles of Zoology, 9th. Ed., Wm C. Brown, 1995.
Audesirk & Audesirk Ch 6 Audesirk, Teresa and Audesirk, Gerald, Biology, Life on Earth, 5th Ed., Prentice-Hall, 1999.
http://hyperphysics.phy-astr.gsu.edu/hbase/biology/bioref.html#c1
http://hyperphysics.phy-astr.gsu.edu/hbase/biology/cytoskel.html

 

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Oxidation and Synthesis of Fatty Acids

Author and Curator: Larry H. Bernstein, MD, FCAP 

 

Lipid Metabolism

http://www.elmhurst.edu/~chm/vchembook/622overview.html

Overview of Lipid Catabolism:

The major aspects of lipid metabolism are involved with

  • Fatty Acid Oxidation to produce energy or
  • the synthesis of lipids which is called Lipogenesis.

The metabolism of lipids and carbohydrates are related by the conversion of lipids from carbohydrates. This can be seen in the diagram. Notice the link through actyl-CoA, the seminal discovery of Fritz Lipmann. The metabolism of both is upset by diabetes mellitus, which results in the release of ketones (2/3 betahydroxybutyric acid) into the circulation.

metabolism of fats

metabolism of fats

http://www.elmhurst.edu/~chm/vchembook/images/590metabolism.gif

The first step in lipid metabolism is the hydrolysis of the lipid in the cytoplasm to produce glycerol and fatty acids.

Since glycerol is a three carbon alcohol, it is metabolized quite readily into an intermediate in glycolysis, dihydroxyacetone phosphate. The last reaction is readily reversible if glycerol is needed for the synthesis of a lipid.

The hydroxyacetone, obtained from glycerol is metabolized into one of two possible compounds. Dihydroxyacetone may be converted into pyruvic acid, a 3-C intermediate at the last step of glycolysis to make energy.

In addition, the dihydroxyacetone may also be used in gluconeogenesis (usually dependent on conversion of gluconeogenic amino acids) to make glucose-6-phosphate for glucose to the blood or glycogen depending upon what is required at that time.

Fatty acids are oxidized to acetyl CoA in the mitochondria using the fatty acid spiral. The acetyl CoA is then ultimately converted into ATP, CO2, and H2O using the citric acid cycle and the electron transport chain.

There are two major types of fatty acids – ω-3 and ω-6.  There are also saturated and unsaturated with respect to the existence of double bonds, and monounsaturated and polyunsatured.  Polyunsaturated fatty acids (PUFAs) are important in long term health, and it will be seen that high cardiovascular risk is most associated with a low ratio of ω-3/ω-6, the denominator being from animal fat. Ω-3 fatty acids are readily available from fish, seaweed, and flax seed. More can be said of this later.

Fatty acids are synthesized from carbohydrates and occasionally from proteins. Actually, the carbohydrates and proteins have first been catabolized into acetyl CoA. Depending upon the energy requirements, the acetyl CoA enters the citric acid cycle or is used to synthesize fatty acids in a process known as LIPOGENESIS.

The relationships between lipid and carbohydrate metabolism are
summarized in Figure 2.

fattyacidspiral

fattyacidspiral

http://www.elmhurst.edu/~chm/vchembook/images/620fattyacidspiral.gif

 Energy Production Fatty Acid Oxidation:

Visible” ATP:

In the fatty acid spiral, there is only one reaction which directly uses ATP and that is in the initiating step. So this is a loss of ATP and must be subtracted later.

A large amount of energy is released and restored as ATP during the oxidation of fatty acids. The ATP is formed from both the fatty acid spiral and the citric acid cycle.

 

Connections to Electron Transport and ATP:

One turn of the fatty acid spiral produces ATP from the interaction of the coenzymes FAD (step 1) and NAD+ (step 3) with the electron transport chain. Total ATP per turn of the fatty acid spiral is:

Electron Transport Diagram – (e.t.c.)

Step 1 – FAD into e.t.c. = 2 ATP
Step 3 – NAD+ into e.t.c. = 3 ATP
Total ATP per turn of spiral = 5 ATP

In order to calculate total ATP from the fatty acid spiral, you must calculate the number of turns that the spiral makes. Remember that the number of turns is found by subtracting one from the number of acetyl CoA produced. See the graphic on the left bottom.

Example with Palmitic Acid = 16 carbons = 8 acetyl groups

Number of turns of fatty acid spiral = 8-1 = 7 turns

ATP from fatty acid spiral = 7 turns and 5 per turn = 35 ATP.

This would be a good time to remember that single ATP that was needed to get the fatty acid spiral started. Therefore subtract it now.

NET ATP from Fatty Acid Spiral = 35 – 1 = 34 ATP

Review ATP Summary for Citric Acid Cycle:The acetyl CoA produced from the fatty acid spiral enters the citric acid cycle. When calculating ATP production, you have to show how many acetyl CoA are produced from a given fatty acid as this controls how many “turns” the citric acid cycle makes.Starting with acetyl CoA, how many ATP are made using the citric acid cycle? E.T.C = electron transport chain

 Step  ATP produced
7  1
Step 4 (NAD+ to E.T.C.) 3
Step 6 (NAD+ to E.T.C.)  3
Step10 (NAD+ to E.T.C.)  3
Step 8 (FAD to E.T.C.) 2
 NET 12 ATP
 ATP Summary for Palmitic Acid – Complete Metabolism:The phrase “complete metabolism” means do reactions until you end up with carbon dioxide and water. This also means to use fatty acid spiral, citric acid cycle, and electron transport as needed.Starting with palmitic acid (16 carbons) how many ATP are made using fatty acid spiral? This is a review of the above panel E.T.C = electron transport chain

 Step  ATP (used -) (produced +)
Step 1 (FAD to E.T.C.) +2
Step 4 (NAD+ to E.T.C.) +3
Total ATP  +5
 7 turns  7 x 5 = 35
initial step  -1
 NET  34 ATP

The fatty acid spiral ends with the production of 8 acetyl CoA from the 16 carbon palmitic acid.

Starting with one acetyl CoA, how many ATP are made using the citric acid cycle? Above panel gave the answer of 12 ATP per acetyl CoA.

E.T.C = electron transport chain

 Step  ATP produced
One acetyl CoA per turn C.A.C. +12 ATP
8 Acetyl CoA = 8 turns C.A.C. 8 x 12 = 96 ATP
Fatty Acid Spiral 34 ATP
GRAND TOTAL  130 ATP

Fyodor Lynen

Feodor Lynen was born in Munich on 6 April 1911, the son of Wilhelm Lynen, Professor of Mechanical Engineering at the Munich Technische Hochschule. He received his Doctorate in Chemistry from Munich University under Heinrich Wieland, who had won the Nobel Prize for Chemistry in 1927, in March 1937 with the work: «On the Toxic Substances in Amanita». in 1954 he became head of the Max-Planck-Institut für Zellchemie, newly created for him as a result of the initiative of Otto Warburg and Otto Hahn, then President of the Max-Planck-Gesellschaft zur Förderung der Wissenschaften.

Lynen’s work was devoted to the elucidation of the chemical details of metabolic processes in living cells, and of the mechanisms of metabolic regulation. The problems tackled by him, in conjunction with German and other workers, include the Pasteur effect, acetic acid degradation in yeast, the chemical structure of «activated acetic acid» of «activated isoprene», of «activated carboxylic acid», and of cytohaemin, degradation of fatty acids and formation of acetoacetic acid, degradation of tararic acid, biosynthesis of cysteine, of terpenes, of rubber, and of fatty acids.

In 1954 Lynen received the Neuberg Medal of the American Society of European Chemists and Pharmacists, in 1955 the Liebig Commemorative Medal of the Gesellschaft Deutscher Chemiker, in 1961 the Carus Medal of the Deutsche Akademie der Naturforscher «Leopoldina», and in 1963 the Otto Warburg Medal of the Gesellschaft für Physiologische Chemie. He was also a member of the U>S> National Academy of Sciences, and shared the Nobel Prize in Physiology and Medicine with Konrad Bloch in 1964, and was made President of the Gesellschaft Deutscher Chemiker (GDCh) in 1972.

This biography was written at the time of the award and first published in the book series Les Prix Nobel. It was later edited and republished in Nobel Lectures, and shortened by myself.

The Pathway from “Activated Acetic Acid” to the Terpenes and Fatty Acids

My first contact with dynamic biochemistry in 1937 occurred at an exceedingly propitious time. The remarkable investigations on the enzyme chain of respiration, on the oxygen-transferring haemin enzyme of respiration, the cytochromes, the yellow enzymes, and the pyridine proteins had thrown the first rays of light on the chemical processes underlying the mystery of biological catalysis, which had been recognised by your famous countryman Jöns Jakob Berzelius. Vitamin B2 , which is essential to the nourishment of man and of animals, had been recognised by Hugo Theorell in the form of the phosphate ester as the active group of an important class of enzymes, and the fermentation processes that are necessary for Pasteur’s “life without oxygen”

had been elucidated as the result of a sequence of reactions centered around “hydrogen shift” and “phosphate shift” with adenosine triphosphate as the phosphate-transferring coenzyme. However, 1,3-diphosphoglyceric acid, the key substance to an understanding of the chemical relation between oxidation and phosphorylation, still lay in the depths of the unknown. Never-

theless, Otto Warburg was on its trail in the course of his investigations on the fermentation enzymes, and he was able to present it to the world in 1939.

This was the period in which I carried out my first independent investigation, which was concerned with the metabolism of yeast cells after freezing in liquid air, and which brought me directly into contact with the mechanism of alcoholic fermentation. This work taught me a great deal, and yielded two important pieces of information.

  • The first was that in experiments with living cells, special attention must be given to the permeability properties of the cell membranes, and
  • the second was that the adenosine polyphosphate system plays a vital part in the cell,
    • not only in energy transfer, but
    • also in the regulation of the metabolic processes.

.

This investigation aroused by interest in problems of metabolic regulation, which led me to the investigation of the Pasteur effects, and has remained with me to the present day.

My subsequent concern with the problem of the acetic acid metabolism arose from my stay at Heinrich Wieland’s laboratory. Workers here had studied the oxidation of acetic acid by yeast cells, and had found that though most of the acetic acid undergoes complete oxidation, some remains in the form of succinic and citric acids.

The explanation of these observations was provided-by the Thunberg-Wieland process, according to which two molecules of acetic acid are dehydrogenated to succinic acid, which is converted back into acetic acid via oxaloacetic acid, pyruvic acid, and acetaldehyde, or combines at the oxaloacetic acid stage with a further molecule of acetic acid to form citric acid (Fig. 1). However, an experimental check on this view by a Wieland’s student Robert Sonderhoffs brought a surprise. The citric acid formed when trideuteroacetic acid was supplied to yeast cells contained the expected quantity of deuterium, but the succinic acid contained only half of the four deuterium atoms required by Wieland’s scheme.

This investigation aroused by interest in problems of metabolic regulation, which led me to the investigation of the Pasteur effects, and has remained with me to the present day. My subsequent concern with the problem of the acetic acid metabolism arose from my stay at Heinrich Wieland’s laboratory. Workers here had studied the oxidation of acetic acid by yeast cells, and had found that though most of the acetic acid undergoes complete oxidation, some remains in the form of succinic and citric acid

The answer provided by Martius was that citric acid  is in equilibrium with isocitric acid and is oxidised to cr-ketoglutaric acid, the conversion of which into succinic acid had already been discovered by Carl Neuberg (Fig. 1).

It was possible to assume with fair certainty from these results that the succinic acid produced by yeast from acetate is formed via citric acid. Sonderhoff’s experiments with deuterated acetic acid led to another important discovery.

In the analysis of the yeast cells themselves, it was found that while the carbohydrate fraction contained only insignificant quantities of deuterium, large quantities of heavy hydrogen were present in the fatty acids formed and in the sterol fraction. This showed that

  • fatty acids and sterols were formed directly from acetic acid, and not indirectly via the carbohydrates.

As a result of Sonderhoff’s early death, these important findings were not pursued further in the Munich laboratory.

  • This situation was elucidated only by Konrad Bloch’s isotope experiments, on which he reports.

My interest first turned entirely to the conversion of acetic acid into citric acid, which had been made the focus of the aerobic degradation of carbohydrates by the formulation of the citric acid cycle by Hans Adolf Krebs. Unlike Krebs, who regarded pyruvic acid as the condensation partner of acetic acid,

  • we were firmly convinced, on the basis of the experiments on yeast, that pyruvic acid is first oxidised to acetic acid, and only then does the condensation take place.

Further progress resulted from Wieland’s observation that yeast cells that had been “impoverished” in endogenous fuels by shaking under oxygen were able to oxidise added acetic acid only after a certain “induction period” (Fig. 2). This “induction period” could be shortened by addition of small quantities of a readily oxidisable substrate such as ethyl alcohol, though propyl and butyl alcohol were also effective. I explained this by assuming that acetic acid is converted, at the expense of the oxidation of the alcohol, into an “activated acetic acid”, and can only then condense with oxalacetic acid.

In retrospect, we find that I had come independently on the same group of problems as Fritz Lipmann, who had discovered that inorganic phosphate is indispensable to the oxidation of pyruvic acid by lactobacilli, and had detected acetylphosphate as an oxidation product. Since this anhydride of acetic acid and phosphoric acid could be assumed to be the “activated acetic acid”.

I learned of the advances that had been made in the meantime in the investigation of the problem of “activated acetic acid”. Fritz Lipmann has described the development at length in his Nobel Lecture’s, and I need not repeat it. The main advance was the recognition that the formation of “activated acetic acid” from acetate involved not only ATP as an energy source, but also the newly discovered coenzyme A, which contains the vitamin pantothenic acid, and that “activated acetic acid” was probably an acetylated coenzyme  A.

http://www.nobelprize.org/nobel_prizes/medicine/laureates/1964/lynen-bio.html

http://onlinelibrary.wiley.com/store/10.1002/anie.201106003/asset/image_m/mcontent.gif?v=1&s=1e6dc789dfa585fe48947e92cc5dfdcabd8e2677

Fyodor Lynen

Lynen’s most important research at the University of Munich focused on intermediary metabolism, cholesterol synthesis, and fatty acid biosynthesis. Metabolism involves all the chemical processes by which an organism converts matter and energy into forms that it can use. Metabolism supplies the matter—the molecular building blocks an organism needs for the growth of new tissues. These building blocks must either come from the breakdown of molecules of food, such as glucose (sugar) and fat, or be built up from simpler molecules within the organism.

Cholesterol is one of the fatty substances found in animal tissues. The human body produces cholesterol, but this substance also enters the body in food. Meats, egg yolks, and milk products, such as butter and cheese, contain cholesterol. Such organs as the brain and liver contain much cholesterol. Cholesterol is a type of lipid, one of the classes of chemical compounds essential to human health. It makes up an important part of the membranes of each cell in the body. The body also uses cholesterol to produce vitamin D and certain hormones.

All fats are composed of an alcohol called glycerol and substances called fatty acids. A fatty acid consists of a long chain of carbon atoms, to which hydrogen atoms are attached. There are three types of fatty acids: saturated, monounsaturated, and polyunsaturated.

Living cells manufacture complicated chemical compounds from simpler substances through a process called biosynthesis. For example, simple molecules called amino acids are put together to make proteins. The biosynthesis of both fatty acids and cholesterol begins with a chemically active form of acetate, a two-carbon molecule. Lynen discovered that the active form of acetate is a coenzyme, a heat-stabilized, water-soluble portion of an enzyme, called acetyl coenzyme A. Lynen and his colleagues demonstrated that the formation of cholesterol begins with the condensation of two molecules of acetyl coenzyme A to form acetoacetyl coenzyme A, a four-carbon molecule.

http://science.howstuffworks.com/dictionary/famous-scientists/biologists/feodor-lynen-info.htm

Fyodor Lynen

Fyodor Lynen

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Introduction to Metabolic Pathways

Author: Larry H. Bernstein, MD, FCAP

 

Humans, mammals, plants and animals, and eukaryotes and prokaryotes all share a common denominator in their manner of existence.  It makes no difference whether they inhabit the land, or the sea, or another living host. They exist by virtue of their metabolic adaptation by way of taking in nutrients as fuel, and converting the nutrients to waste in the expenditure of carrying out the functions of motility, breakdown and utilization of fuel, and replication of their functional mass.

There are essentially two major sources of fuel, mainly, carbohydrate and fat.  A third source, amino acids which requires protein breakdown, is utilized to a limited extent as needed from conversion of gluconeogenic amino acids for entry into the carbohydrate pathway. Amino acids follow specific metabolic pathways related to protein synthesis and cell renewal tied to genomic expression.

Carbohydrates are a major fuel utilized by way of either of two pathways.  They are a source of readily available fuel that is accessible either from breakdown of disaccharides or from hepatic glycogenolysis by way of the Cori cycle.  Fat derived energy is a high energy source that is metabolized by one carbon transfers using the oxidation of fatty acids in mitochondria. In the case of fats, the advantage of high energy is conferred by chain length.

Carbohydrate metabolism has either of two routes of utilization.  This introduces an innovation by way of the mitochondrion or its equivalent, for the process of respiration, or aerobic metabolism through the tricarboxylic acid, or Krebs cycle.  In the presence of low oxygen supply, carbohydrate is metabolized anaerobically, the six carbon glucose being split into two three carbon intermediates, which are finally converted from pyruvate to lactate.  In the presence of oxygen, the lactate is channeled back into respiration, or mitochondrial oxidation, referred to as oxidative phosphorylation. The actual mechanism of this process was of considerable debate for some years until it was resolved that the mechanism involve hydrogen transfers along the “electron transport chain” on the inner membrane of the mitochondrion, and it was tied to the formation of ATP from ADP linked to the so called “active acetate” in Acetyl-Coenzyme A, discovered by Fritz Lipmann (and Nathan O. Kaplan) at Massachusetts General Hospital.  Kaplan then joined with Sidney Colowick at the McCollum Pratt Institute at Johns Hopkins, where they shared tn the seminal discovery of the “pyridine nucleotide transhydrogenases” with Elizabeth Neufeld,  who later established her reputation in the mucopolysaccharidoses (MPS) with L-iduronidase and lysosomal storage disease.

This chapter covers primarily the metabolic pathways for glucose, anaerobic and by mitochondrial oxidation, the electron transport chain, fatty acid oxidation, galactose assimilation, and the hexose monophosphate shunt, essential for the generation of NADPH. The is to be more elaboration on lipids and coverage of transcription, involving amino acids and RNA in other chapters.

The subchapters are as follows:

1.1      Carbohydrate Metabolism

1.2      Studies of Respiration Lead to Acetyl CoA

1.3      Pentose Shunt, Electron Transfer, Galactose, more Lipids in brief

1.4      The Multi-step Transfer of Phosphate Bond and Hydrogen Exchange Energy

Complex I or NADH-Q oxidoreductase

Complex I or NADH-Q oxidoreductase

Fatty acid oxidation and ETC

Fatty acid oxidation and ETC

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Introduction to Metabolomics

Introduction to Metabolomics

Author: Larry H. Bernstein, MD, FCAP

 

This is the first volume of the Series D: e-Books on BioMedicine – Metabolomics, Immunology, Infectious Diseases.  It is written for comprehension at the third year medical student level, or as a reference for licensing board exams, but it is also written for the education of a first time bachalaureate degree reader in the biological sciences.  Hopefully, it can be read with great interest by the undergraduate student who is undecided in the choice of a career.

In the Preface, I failed to disclose that the term Metabolomics applies to plants, animals, bacteria, and both prokaryotes and eukaryotes.  The metabolome for each organism is unique, but from an evolutionary perspective has metabolic pathways in common, and expressed in concert with the environment that these living creatures exist. The metabolome of each has adaptive accommodation with suppression and activation of pathways that are functional and necessary in balance, for its existence.  Was it William Faulkner who said in his Nobel Prize acceptance that mankind shall not merely exist, but survive? That seems to be the overlying theme for all of life. If life cannot persist, a surviving “remnant” might continue. The history of life may well be etched into the genetic code, some of which is not expressed.

This work is apportioned into chapters in a sequence that is first directed at the major sources for the energy and the structure of life, in the carbohydrates, lipids, and fats, which are sourced from both plants and animals, and depending on their balance, results in an equilibrium, and a disequilibrium we refer to as disease.  There is also a need to consider the nonorganic essentials which are derived from the soil, from water, and from the energy of the sun and the air we breathe, or in the case of water-bound metabolomes, dissolved gases.

In addition to the basic essential nutrients and their metabolic utilization, they are under cellular metabolic regulation that is tied to signaling pathways.  In addition, the genetic expression of the organism is under regulatory control by the interaction of RNAs that interact with the chromatin genetic framework, with exosomes, and with protein modulators.This is referred to as epigenetics, but there are also drivers of metabolism that are shaped by the interactions between enzymes and substartes, and are related to the tertiary structure of a protein.  The framework for diseases in a separate chapter.  Pharmaceutical interventions that are designed to modulate specific metabolic targets are addressed as the pathways are unfolded. Neutraceuticals and plant based nutrition are covered in Chapter 8.

Chapter 1: Metabolic Pathways

Chapter 2. Lipid Metabolism

Chapter 3. Cell Signaling

Chapter 4. Protein Synthesis and Degradation

Chapter 5: Sub-cellular Structure

Chapter 6: Proteomics

Chapter 7: Metabolomics

Chapter 8. Impairments in Pathological States: Endocrine Disorders; Stress Hypermetabolism and Cancer

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Metformin, Thyroid-Pituitary Axis, Diabetes Mellitus, and Metabolism

Metformin, Thyroid-Pituitary Axis, Diabetes Mellitus, and Metabolism

Larry H, Bernstein, MD, FCAP, Author and Curator
and Aviva Lev-Ari, PhD, RN

http://pharmaceuticalintelligence.com/9/27/2014/Metformin,_thyroid-pituitary_ axis,_diabetes_mellitus,_and_metabolism

The following article is a review of the central relationship between the action of
metformin as a diabetic medication and its relationship to AMPK, the important and
essential regulator of glucose and lipid metabolism under normal activity, stress, with
its effects on skeletal muscle, the liver, the action of T3 and more.

We start with a case study and a publication in the J Can Med Assoc.  Then we shall look
into key literature on these metabolic relationships.

Part I.  Metformin , Diabetes Mellitus, and Thyroid Function

Hypothyroidism, Insulin resistance and Metformin
May 30, 2012   By Janie Bowthorpe
The following was written by a UK hypothyroid patient’s mother –
Sarah Wilson.

My daughter’s epilepsy is triggered by unstable blood sugars. And since taking
Metformin to control her blood sugar, she has significantly reduced the number of
seizures. I have been doing research and read numerous academic medical journals,
which got me thinking about natural thyroid hormone and Hypothyroidism. My hunch
was that when patients develop hypothyroid symptoms, they are actually becoming
insulin resistant (IR). There are many symptoms in common between women with
polycystic ovaries and hypothyroidism–the hair loss, the weight gain, etc.
(http://insulinhub.hubpages.com/hub/PCOS-and-Hypothyroidism).

A hypothyroid person’s body behaves as if it’s going into starvation mode and so, to
preserve resources and prolong life, the metabolism changes. If hypothyroid is prolonged
or pronounced, then perhaps, chemical preservation mode becomes permanent even
with the reintroduction of thyroid hormones. To get back to normal, they need
a “jump-start” reinitiate a higher rate of metabolism. The kick start is initiated through
AMPK, which is known as the “master metabolic regulating enzyme.”
(http://en.wikipedia.org/wiki/AMP-activated protein kinase).

Guess what? This is exactly what happens to Diabetes patients when Metformin is
introduced. http://en.wikipedia.org/wiki/Metformin
Suggested articles: http://www.springerlink.com/content/r81606gl3r603167/  and
http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2265.2011.04029.x/pdf

Note the following comments/partial statements:
“Hypothyroidism is characterized by decreased insulin responsiveness”;
“the pivotal regulatory role of T3 in major metabolic pathways”.

The community knows that T3/NTH (natural thyroid hormone [Armour]) makes
hypothyroid patients feel better – but the medical establishment is averse to T3/NTH
(treating subclinical hypoT (T3/T4 euthyroid) with natural dessicated thyroid (NDT).
The medical establishment might find an alternative view about impaired metabolism
more if shown real proof that the old NDT **was/is** having the right result –i.e., the
T3 is jump-starting the metabolism by re-activating
 AMPK.

If NDT also can be used for hypothyroidism without the surmised “dangers” of NTH,
then they should consider it. [The reality in the choice is actually recombinant TH
(Synthroid)]. Metformin is cheap, stable and has very few serious side effects. I use the
car engine metaphor, and refer to glucose as our petrol, AMPK as the spark plug and
both T3 and Metformin as the ignition switches. Sometimes if you have flat batteries in
the car, it doesn’t matter how much you turn the ignition switch or pump the petrol
pedal, all it does is flatten the battery and flood the engine.

Dr. Skinner in the UK has been treating “pre-hypothyroidism” the way that some
doctors treat “pre-diabetes”. Those hypothyroid patients who get treated early
might not have had their AMPK pathways altered and the T4-T3 conversion still works.
There seems to be no reason why thyroid hormone replacement therapy shouldn’t
logically be given to ward off a greater problem down the line.

It’s my belief that there is clear and abundant academic evidence that the AMPK/
Metformin research should branch out to also look at thyroid disease.

Point – direct T3 is kicking the closed -down metabolic process back into life,
just like Metformin does for insulin resistance.
http://www.hotthyroidology.com/editorial_79.html
There is serotonin resistance! http://www.ncbi.nlm.nih.gov/pubmed/17250776

Metformin Linked to Risk of Low Levels of Thyroid Hormone

CMAJ (Canadian Medical Association Journal) 09/22/2014

Metformin, the drug commonly for treating type 2 diabetes,

  • is linked to an increased risk of low thyroid-stimulating hormone
    (TSH) levels
  • in patients with underactive thyroids (hypothyroidism),

according to a study in CMAJ (Canadian Medical Association Journal).

Metformin is used to lower blood glucose levels

  • by reducing glucose production in the liver.

previous studies have raised concerns that

  • metformin may lower thyroid-stimulating hormone levels.

Study characteristics:

  1. Retrospective  long-term
  2. 74 300 patient who received metformin and sulfonylurea
  3. 25-year study period.
  4. 5689 had treated hypothyroidism
  5. 59 937 had normal thyroid function.

Metformin and low levels of thyroid-stimulating hormone in
patients with type 2 diabetes mellitus

Jean-Pascal Fournier,  Hui Yin, Oriana Hoi Yun Yu, Laurent Azoulay  +
Centre for Clinical Epidemiology (Fournier, Yin, Yu, Azoulay), Lady Davis Institute,
Jewish General Hospital; Department of Epidemiology, Biostatistics and Occupational
Health (Fournier), McGill University; Division of Endocrinology (Yu), Jewish General
Hospital; Department of Oncology (Azoulay), McGill University, Montréal, Que., Cananda

CMAJ Sep 22, 2014,   http://dx.doi.org:/10.1503/cmaj.140688

Background:

  • metformin may lower thyroid-stimulating hormone (TSH) levels.

Objective:

  • determine whether the use of metformin monotherapy, when compared with
    sulfonylurea monotherapy,
  • is associated with an increased risk of low TSH levels(< 0.4 mIU/L)
  • in patients with type 2 diabetes mellitus.

Methods:

  • Used the Clinical Practice Research Datalink,
  • identified patients who began receiving metformin or sulfonylurea monotherapy
    between Jan. 1, 1988, and Dec. 31, 2012.
  • 2 subcohorts of patients with treated hypothyroidism or euthyroidism,

followed them until Mar. 31, 2013.

  • Used Cox proportional hazards models to evaluate the association of low TSH
    levels with metformin monotherapy, compared with sulfonylurea monotherapy,
    in each subcohort.

Results:

  • 5689 patients with treated hypothyroidism and 59 937 euthyroid patients were
    included in the subcohorts.

For patients with treated hypothyroidism:

  1. 495 events of low TSH levels were observed (incidence rate 0.1197/person-years).
  2. 322 events of low TSH levels were observed (incidence rate 0.0045/person-years)
    in the euthyroid group.
  • metformin monotherapy was associated with a 55% increased risk of low TSH
    levels 
    in patients with treated hypothyroidism (incidence rate 0.0795/person-years
    vs.0.1252/ person-years, adjusted hazard ratio [HR] 1.55, 95% confidence
    interval [CI] 1.09– 1.20), compared with sulfonylurea monotherapy,
  • the highest risk in the 90–180 days after initiation (adjusted HR 2.30, 95% CI
    1.00–5.29).
  • No association was observed in euthyroid patients (adjusted HR 0.97, 95% CI 0.69–1.36).

Interpretation: The clinical consequences of this needs further investigation.

 

Crude and adjusted hazard ratios for suppressed thyroid-stimulating hormone
levels (< 0.1 mIU/L) associated with the use metformin monotherapy, compared
with sulfonylurea monotherapy, in patients with treated hypothyroidism or
euthyroidism and type 2 diabetes
Variable No. events
suppressed
TSH levels
Person-years of
exposure
Incidence rate,
per 1000 person-years (95% CI)
Crude
HR
Adjusted HR*(95% CI)
Patients with treated hypothyroidism, = 5689
Sulfonylure,
= 762
18 503 35.8
(21.2–56.6)
1.00 1.00
(reference)
Metformin,
= 4927
130 3 633 35.8
(29.9–42.5)
1.05 0.99
(0.57–1.72)
Euthyroid patients, = 59 937
Sulfonylurea,
= 7980
12 8 576 1.4
(0.7–2.4)
1.00 1.00
(reference)
Metformin,
= 51 957
75 63 047 1.2
(0.9–1.5)
0.85 1.03
(0.52–2.03)

 

Part II. Metabolic Underpinning 
(Source: Wikipedia, AMPK and thyroid)

5′ AMP-activated protein kinase or AMPK or 5′ adenosine monophosphate-activated protein kinase
is an enzyme that plays a role in cellular energy homeostasis.
It consists of three proteins (subunits) that

  1. together make a functional enzyme, conserved from yeast to humans.
  2. It is expressed in a number of tissues, including the liver, brain, and skeletal
    muscle.
  3. The net effect of AMPK activation is stimulation of
    1. hepatic fatty acid oxidation and ketogenesis,
    2. inhibition of cholesterol synthesis,
    3. lipogenesis, and triglyceride synthesis,
    4. inhibition of adipocyte lipolysis and lipogenesis,
    5. stimulation of skeletal muscle fatty acid oxidation and muscle
      glucose uptake, and
    6. modulation of insulin secretion by pancreatic beta-cells.

The heterotrimeric protein AMPK is formed by α, β, and γ subunits. Each of these three
subunits takes on a specific role in both the stability and activity of AMPK.

  • the γ subunit includes four particular Cystathionine beta synthase (CBS) domains
    giving AMPK its ability to sensitively detect shifts in the AMP:ATP ratio.
  • The four CBS domains create two binding sites for AMP commonly referred to as
    Bateman domains. Binding of one AMP to a Bateman domain cooperatively
    increases the binding affinity of the second AMP to the other Bateman domain.
  • As AMP binds both Bateman domains the γ subunit undergoes a conformational
    change which exposes the catalytic domain found on the α subunit.
  • It is in this catalytic domain where AMPK becomes activated when
    phosphorylation takes place at threonine-172by an upstream AMPK kinase
    (AMPKK). The α, β, and γ subunits can also be found in different isoforms.

AMPK acts as a metabolic master switch regulating several intracellular systems

  1. the cellular uptake of glucose,
  2. the β-oxidation of fatty acids and
  3. the biogenesis of glucose transporter 4 (GLUT4) and
  4. mitochondria

The energy-sensing capability of AMPK can be attributed to

  • its ability to detect and react to fluctuations in the AMP:ATP ratio that take
    place during rest and exercise (muscle stimulation).

During muscle stimulation,

  • AMP increases while ATP decreases, which changes AMPK into a good substrate
    for activation.
  • AMPK activity increases while the muscle cell experiences metabolic stress
    brought about by an extreme cellular demand for ATP.
  • Upon activation, AMPK increases cellular energy levels by
    • inhibiting anabolic energy consuming pathways (fatty acid synthesis,
      protein synthesis, etc.) and
    • stimulating energy producing, catabolic pathways (fatty acid oxidation,
      glucose transport, etc.).

A recent JBC paper on mice at Johns Hopkins has shown that when the activity of brain
AMPK was pharmacologically inhibited,

  • the mice ate less and lost weight.

When AMPK activity was pharmacologically raised (AICAR see below)

  • the mice ate more and gained weight.

Research in Britain has shown that the appetite-stimulating hormone ghrelin also
affects AMPK levels.

The antidiabetic drug metformin (Glucophage) acts by stimulating AMPK, leading to

  1. reduced glucose production in the liver and
  2. reduced insulin resistance in the muscle.

(Metformin usually causes weight loss and reduced appetite, not weight gain and
increased appetite, ..opposite of expected from the Johns Hopkins mouse study results.)

Triggering the activation of AMPK can be carried out provided two conditions are met.

First, the γ subunit of AMPK

  • must undergo a conformational change so as to
  • expose the active site(Thr-172) on the α subunit.

The conformational change of the γ subunit of AMPK can be accomplished

  • under increased concentrations of AMP.

Increased concentrations of AMP will

  • give rise to the conformational change on the γ subunit of AMPK
  • as two AMP bind the two Bateman domains located on that subunit.
  • It is this conformational change brought about by increased concentrations
    of  AMP that exposes the active site (Thr-172) on the α subunit.

This critical role of AMP is further substantiated in experiments that demonstrate

  • AMPK activation via an AMP analogue 5-amino-4-imidazolecarboxamide
    ribotide (ZMP) which is derived fromthe familiar
  • 5-amino-4-imidazolecarboxamide riboside (AICAR)

AMPK is a good substrate for activation via an upstream kinase complex, AMPKK
AMPKK is a complex of three proteins,

  1. STE-related adaptor (STRAD),
  2. mouse protein 25 (MO25), and
  3. LKB1 (a serine/threonine kinase).

The second condition that must be met is

  • the phosphorylation/activation of AMPK on its activating loop at
    Thr-172of the α subunit
  • brought about by an upstream kinase (AMPKK).

The complex formed between LKB1 (STK 11), mouse protein 25 (MO25), and the
pseudokinase STE-related adaptor protein (STRAD) has been identified as

  • the major upstream kinase responsible for phosphorylation of AMPK
    on its activating loop at Thr-172

Although AMPK must be phosphorylated by the LKB1/MO25/STRAD complex,

  • it can also be regulated by allosteric modulators which
  • directly increase general AMPK activity and
  • modify AMPK to make it a better substrate for AMPKK
  • and a worse substrate for phosphatases.

It has recently been found that 3-phosphoglycerate (glycolysis intermediate)

  • acts to further pronounce AMPK activation via AMPKK

Muscle contraction is the main method carried out by the body that can provide
the conditions mentioned above needed for AMPK activation

  • As muscles contract, ATP is hydrolyzed, forming ADP.
  • ADP then helps to replenish cellular ATP by donating a phosphate group to
    another ADP,

    • forming an ATP and an AMP.
  • As more AMP is produced during muscle contraction,
    • the AMP:ATP ratio dramatically increases,
  • leading to the allosteric activation of AMPK

For over a decade it has been known that calmodulin-dependent protein kinase
kinase-beta (CaMKKbeta) can phosphorylate and thereby activate AMPK,

  • but it was not the main AMPKK in liver.

CaMKK inhibitors had no effect on 5-aminoimidazole-4-carboxamide-1-beta-4-
ribofuranoside (AICAR) phosphorylation and activation of AMPK.

  • AICAR is taken into the celland converted to ZMP,
  • an AMP analogthat has been shown to activate AMPK.

Recent LKB1 knockout studies have shown that without LKB1,

  • electrical and AICAR stimulation of muscleresults in very little
    phosphorylation of AMPK and of ACC, providing evidence that
  • LKB1-STRAD-MO25 is the major AMPKK in muscle.

Two particular adipokines, adiponectin and leptin, have even been demonstrated
to regulate AMPK. A main functions of leptin in skeletal muscle is

  • the upregulation of fatty acid oxidation.

Leptin works by way of the AMPK signaling pathway, and adiponectin also
stimulates the oxidation of fatty acids via the AMPK pathway, and

  • Adiponectin also stimulates the uptake of glucose in skeletal muscle.

An increase in enzymes which specialize in glucose uptake in cells such as GLUT4
and hexokinase II are thought to be mediated in part by AMPK when it is activated.
Increases in AMPK activity are brought about by increases in the AMP:ATP ratio
during single bouts of exercise and long-term training.

One of the key pathways in AMPK’s regulation of fatty acid oxidation is the

  • phosphorylation and inactivation of acetyl-CoA carboxylase.
  1. Acetyl-CoA carboxylase (ACC) converts acetyl-CoA (ACA) to malonyl-CoA
    (MCA), an inhibitor of carnitine palmitoyltransferase 1 (CPT-1).
  2. CPT-1 transports fatty acids into the mitochondria for oxidation.
  3. Inactivation of ACC results in increased fatty acid transport and oxidation.
  4. the AMPK induced ACC inactivation  and reduced conversion to MCA
    may occur as a result of malonyl-CoA decarboxylase (MCD)
  5. MCD as an antagonist to ACC, decarboxylatesmalonyl-CoA to acetyl-CoA
    (reversal of ACC conversion of ACA to MCA)
  6. This resultsin decreased malonyl-CoA and increased CPT-1 and fatty acid oxidation.

AMPK also plays an important role in lipid metabolism in the liver. It has long been
known that hepatic ACC has been regulated in the liver.

  1. It phosphorylates and inactivates 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR)
  2. acetyl-CoA(ACA) is converted to mevalonic acid (MVA) by ACC
    with inhibition of CPT-1
  3. HMGR converts 3-hydroxy-3-methylglutaryl-CoA, which is made from MVA
  4. which then travels down several more metabolic steps to become cholesterol.

Insulin facilitates the uptake of glucose into cells via increased expression and
translocation of glucose transporter GLUT-4. In addition, glucose is phosphorylated
by hexokinase wheni iot enters the cell. The phosphorylated form keeps glucose from
leaving the cell,

  • The decreasedthe concentration of glucose molecules creates a gradient for more
    glucose to be transported into the cell.
AMPK and thyroid hormone regulate some similar processes. Knowing these similarities,
Winder and Hardie et al. designed an experiment to see if AMPK was influenced by thyroid
hormone. They found that all of the subunits of AMPK were increased in skeletal muscle,
especially in the soleus and red quadriceps, with thyroid hormone treatment. There was
also an increase in phospho-ACC, a marker of AMPK activity.
  •  Winder WW, Hardie DG (July 1999). “AMP-activated protein kinase,
    a metabolic master switch: possible roles in type 2 diabetes”. J. Physiol. 277
    (1 Pt 1): E1–10. PMID 10409121.
  • Winder WW, Hardie DG (February 1996). “Inactivation of acetyl-CoA
    carboxylase and activation of AMP-activated protein kinase in muscle
    during exercise”. J. Physiol. 270 (2 Pt 1): E299–304. PMID 8779952.
  • Hutber CA, Hardie DG, Winder WW (February 1997). “Electrical stimulation
    inactivates muscle acetyl-CoA carboxylase and increases AMP-activated
    protein kinase”. Am. J. Physiol. 272 (2 Pt 1): E262–6. PMID 9124333
  • Durante PE, Mustard KJ, Park SH, Winder WW, Hardie DG (July 2002).
    “Effects of endurance training on activity and expression of AMP-activated
    protein kinase isoforms in rat muscles”. Am. J. Physiol. Endocrinol.
    Metab. 283 (1): E178–86. doi:10.1152/ajpendo.00404.2001. PMID 12067859
  • Corton JM, Gillespie JG, Hardie DG (April 1994). “Role of the AMP-activated
    protein kinase in the cellular stress response”. Curr. Biol. 4 (4):
    315–24. doi:10.1016/S0960-9822(00)00070-1. PMID 7922340
  • Winder WW (September 2001). “Energy-sensing and signaling by
    AMP-activated protein kinase in skeletal muscle”. J. Appl. Physiol. 91 (3):
    1017–28. PMID 11509493
  • Suter M, Riek U, Tuerk R, Schlattner U, Wallimann T, Neumann D (October
    2006). “Dissecting the role of 5′-AMP for allosteric stimulation, activation,
    and deactivation of AMP-activated protein kinase”.  J. Biol. Chem.
    281 (43): 32207–6. doi:10.1074/jbc.M606357200. PMID 16943194

 

Part III. Pituitary-thyroid axis and diabetes mellitus
The Interface Between Thyroid and Diabetes Mellitus

Leonidas H. Duntas, Jacques Orgiazzi, Georg Brabant   Clin Endocrinol. 2011;75(1):1-9.
Interaction of Metformin and Thyroid Function

Metformin acts primarily by

  • suppressing hepatic gluconeogenesis via activation of AMPK
  • It has the opposite effects on hypothalamic AMPK,
    • inhibiting activity of the enzyme.
  • the metformin effects on hypothalamic AMPK activity will
    • counteractT3 effects at the hypothalamic level.
  • AMPK therefore represents a direct target for dual regulation
    • in the hypothalamic partitioning of energy homeostasis.
  • metformin crossesthe blood–brain barrier and
    • levels in the pituitary gland are substantially increased.
  • It convincinglysuppresses TSH

A recent study recruiting 66 patients with benign thyroid nodules furthermore
demonstrated that metformin significantly decreases nodule size in patients with
insulin resistance.[76] The effect of metformin, which was produced over a
6-month treatment period, parallelled a fall in TSH concentrations and achieved a
shrinkage amounting to 30% of the initial nodule size when metformin was
administered alone and up to 55% when it was added to ongoing LT4 treatment.

These studies reveal a

  • suppressive effect of metformin on TSH secretion patterns in
    hypothyroid patients, an effect that is apparently
  • independent of T4 treatment and does not alter the TH profile.
  • A rebound of TSH secretion occurs at about 3 months following metformin
    withdrawal.

It appears that recommendations for more frequent testing, on an annual to
biannual basis, seems justified in higher risk groups like patients over 50 or 55,
particularly with suggestive symptoms, raised antibody titres or dylipidaemia.
We thus would support the suggestion of an initial TSH and TPO antibody testing
which, as discussed, will help to predict the development of hypothyroidism in
patients with diabetes.

Hypothalamic AMPK and fatty acid metabolism mediate thyroid
regulation of energy 
balance
M López,  L Varela,  MJ Vázquez,  S Rodríguez-Cuenca, CR González, …, & Vidal-Puig
Nature Medicine  29 Aug 2010; 16: 1001–1008 http://dx.doi.org:/10.1038/nm.2207

Thyroid hormones have widespread cellular effects; however it is unclear whether
their effects on the central nervous system (CNS) contribute to global energy balance.
Here we demonstrate that either

  • whole-body hyperthyroidism or central administration of triiodothyronine
    (T3) decreases

    • the activity of hypothalamic AMP-activated protein kinase (AMPK),
    • increases sympathetic nervous system (SNS) activity and
    • upregulates thermogenic markers in brown adipose tissue (BAT).

Inhibition of the lipogenic pathway in the ventromedial nucleus of the hypothalamus
(VMH) prevents CNS-mediated activation of BAT by thyroid hormone and reverses
the weight loss associated with hyperthyroidism. Similarly, inhibition of thyroid
hormone receptors in the VMH reverses the weight loss associated with hyperthyroidism.

This regulatory mechanism depends on AMPK inactivation, as genetic inhibition of this
enzyme in the VMH of euthyroid rats induces feeding-independent weight loss and
increases expression of thermogenic markers in BAT. These effects are reversed by
pharmacological blockade of the SNS. Thus, thyroid hormone–induced modulation
of AMPK activity and lipid metabolism in the hypothalamus is a major regulator of
whole-body energy homeostasis.

Metabolic Basis for Thyroid Hormone Liver Preconditioning:
Upregulation of AMP-Activated Protein Kinase Signaling
  
LA Videla,1 V Fernández, P Cornejo, and R Vargas
1Molecular and Clinical Pharmacology Program, Institute of Biomedical Sciences,
Faculty of Medicine, University of Chile, 2Faculty of Medicine, Diego Portales University,
Santiago, Chile
Academic Editors: H. M. Abu-Soud and D. Benke
The Scientific World Journal 2012; 2012, ID 475675, 10 pp
http://dx.doi.org/10.1100/2012/475675

The liver is a major organ responsible for most functions of cellular metabolism and

  • a mediator between dietary and endogenous sources of energy for extrahepatic tissues.
  • In this context, adenosine-monophosphate- (AMP-) activated protein kinase (AMPK)
    constitutes an intrahepatic energy sensor
  • regulating physiological energy dynamics by limiting anabolism and stimulating
    catabolism, thus increasing ATP availability.
  • This is achieved by mechanisms involving direct allosteric activation and
    reversible phosphorylation of AMPK, in response to signals such as

    • energy status,
    • serum insulin/glucagon ratio,
    • nutritional stresses,
    • pharmacological and natural compounds, and
    • oxidative stress status.

Reactive oxygen species (ROS) lead to cellular AMPK activation and

  • downstream signaling under several experimental conditions.

Thyroid hormone (L-3,3′,5-triiodothyronine, T3) administration, a condition
that enhances liver ROS generation,

  • triggers the redox upregulation of cytoprotective proteins
    • affording preconditioning against ischemia-reperfusion (IR) liver injury.

Data discussed in this work suggest that T3-induced liver activation of AMPK

  • may be of importance in the promotion of metabolic processes
  • favouring energy supply for the induction and operation of preconditioning
    mechanisms.

These include

  1. antioxidant,
  2. antiapoptotic, and
  3. anti-inflammatory mechanisms,
  4. repair or resynthesis of altered biomolecules,
  5. induction of the homeostatic acute-phase response, and
  6. stimulation of liver cell proliferation,

which are required to cope with the damaging processes set in by IR.

The liver functions as a mediator between dietary and endogenous sources
of energy and extrahepatic organs that continuously require energy, mainly
the brain and erythrocytes, under cycling conditions between fed and fasted states.

In the fed state, where insulin action predominates, digestion-derived glucose is
converted to pyruvate via glycolysis, which is oxidized to produce energy, whereas
fatty acid oxidation is suppressed. Excess glucose can be either stored as hepatic
glycogen or channelled into de novo lipogenesis.

In the fasted state, considerable liver fuel metabolism changes occur due to decreased
serum insulin/glucagon ratio, with higher glucose production as a consequence of
stimulated glycogenolysis and gluconeogenesis (from alanine, lactate, and glycerol).

Major enhancement in fatty acid oxidation also occurs to provide energy for liver
processes and ketogenesis to supply metabolic fuels for extrahepatic tissues. For these
reasons, the liver is considered as the metabolic processing organ of the body, and
alterations in liver functioning affect whole-body metabolism and energy homeostasis.

In this context, adenosine-monophosphate- (AMP-) activated protein kinase (AMPK)
is the downstream component of a protein kinase cascade acting as an

  • intracellular energy sensor regulating physiological energy dynamics by
  • limiting anabolic pathways, to prevent excessive adenosine triphosphate (ATP)
    utilization, and
  • by stimulating catabolic processes, to increase ATP production.

Thus, the understanding of the mechanisms by which liver AMPK coordinates hepatic
energy metabolism represents a crucial point of convergence of regulatory signals
monitoring systemic and cellular energy status

Liver AMPK: Structure and Regulation

AMPK, a serine/threonine kinase, is a heterotrimeric complex comprising

  1. a catalytic subunit α and
  2. two regulatory subunits β and γ .

The α subunit has a threonine residue (Thr172) within the activation loop of the kinase
domain, with the C-terminal region being required for association with β and γ subunits.
The β subunit associates with α and γ by means of its C-terminal region , whereas

  • the γ subunit has four cystathionine β-synthase (CBS) motifs, which
  • bind AMP or ATP in a competitive manner.

75675.fig.001 (not shown)

Figure 1: Regulation of AMP-activated protein kinase (AMPK) by
(A) direct allosteric activation and
(B) reversible phosphorylation and downstream responses maintaining
intracellular energy balance.

Regulation of liver AMPK activity involves both direct allosteric activation and
reversible phosphorylation. AMPK is allosterically activated by AMP through

  • binding to the regulatory subunit-γ, which induces a conformational change in
    the kinase domain of subunit α that protects AMPK from dephosphorylation
    of Thr172, probably by protein phosphatase-2C.

Activation of AMPK requires phosphorylation of Thr172 in its α subunit, which can be
attained by either

(i) tumor suppressor LKB1 kinase following enhancement in the AMP/ATP ratio, a
kinase that plays a crucial role in AMPK-dependent control of liver glucose and
lipid metabolism;

(ii) Ca2+-calmodulin-dependent protein kinase kinase-β (CaMKKβ) that
phosphorylates AMPK in an AMP-independent, Ca2+-dependent manner;

(iii) transforming growth-factor-β-activated kinase-1 (TAK1), an important
kinase in hepatic Toll-like receptor 4 signaling in response to lipopolysaccharide.

Among these kinases, the relevance of CaMKKβ and TAK1 in liver AMPK activation
remains to be established in metabolic stress conditions. Both allosteric and
phosphorylation mechanisms are able to elicit

  • over 1000-fold increase in AMPK activity, thus allowing
  • the liver to respond to small changes in energy status in a highly sensitive fashion.

In addition to rapid AMPK regulation through allosterism and reversible phosphorylation

  • long-term effects of AMPK activation induce changes in hepatic gene expression.

This was demonstrated for

(i) the transcription factor carbohydrate-response element-binding protein (ChREBP),

  • whose Ser568 phosphorylation by activated AMPK
  • blocks its DNA binding capacity and glucose-induced gene transcription
  • under hyperlipidemic conditions;(ii) liver sterol regulatory element-binding
    protein-1c (SREBP-1c), whose mRNA and protein expression and those of
    its target gene for fatty acid synthase (FAS)
  • are reduced by metformin-induced AMPK activation,
  • decreasing lipogenesis and increasing fatty acid oxidation due to
    malonyl-CoA depletion;

(iii) transcriptional coactivator transducer of regulated CREB activity-2 (TORC2),
a crucial component of the hepatic gluconeogenic program, was reported
to be phosphorylated by activated AMPK.

This modification leads to subsequent cytoplasmatic sequestration of TORC2 and
inhibition of gluconeogenic gene expression, a mechanism underlying

  • the plasma glucose-lowering effects of adiponectin and metformin
  • through AMPK activation by upstream LKB1.

Activation of AMPK in the liver is a key regulatory mechanism controlling glucose
and lipid metabolism,

  1. inhibiting anabolic processes, and
  2. enhancing catabolic pathways in response to different signals, including
    1. energy status,
    2. serum insulin/glucagon ratio,
    3. nutritional stresses,
    4. pharmacological and natural compounds, and
    5. oxidative stress status

Reactive Oxygen Species (ROS) and AMPK Activation

The high energy demands required to cope with all the metabolic functions
of the liver are met by

  • fatty acid oxidation under conditions of both normal blood glucose levels and
    hypoglycemia, whereas
  • glucose oxidation is favoured in hyperglycemic states, with consequent
    generation of ROS.

Under normal conditions, ROS occur at relatively low levels due to their fast processing
by antioxidant mechanisms, whereas at acute or prolonged high ROS levels, severe
oxidation of biomolecules and dysregulation of signal transduction and gene expression
is achieved, with consequent cell death through necrotic and/or apoptotic-signaling
pathways.

Thyroid Hormone (L-3,3′,5-Triiodothyronine, T3), Metabolic Regulation,
and ROS Production

T3 is important for the normal function of most mammalian tissues, with major actions
on O2 consumption and metabolic rate, thus

  • determining enhancement in fuel consumption for oxidation processes
  • and ATP repletion.

T3 acts predominantly through nuclear receptors (TR) α and β, forming

  • functional complexes with retinoic X receptor that
  • bind to thyroid hormone response elements (TRE) to activate gene expression.

T3 calorigenesis is primarily due to the

  • induction of enzymes related to mitochondrial electron transport and ATP
    synthesis, catabolism, and
  • some anabolic processes via upregulation of genomic mechanisms.

The net result of T3 action is the enhancement in the rate of O2 consumption of target
tissues such as liver, which may be effected by secondary processes induced by T3

(i) energy expenditure due to higher active cation transport,

(ii) energy loss due to futile cycles coupled to increase in catabolic and anabolic pathways, and

(iii) O2 equivalents used in hepatic ROS generation both in hepatocytes and Kupffer cells

In addition, T3-induced higher rates of mitochondrial oxidative phosphorylation are
likely to induce higher levels of ATP, which are partially balanced by intrinsic uncoupling
afforded by induction of uncoupling proteins by T3. In agreement with this view, the
cytosolic ATP/ADP ratio is decreased in hyperthyroid tissues, due to simultaneous
stimulation of ATP synthesis and consumption.

Regulation of fatty acid oxidation is mainly attained by carnitine palmitoyltransferase Iα (CPT-Iα),

  • catalyzing the transport of fatty acids from cytosol to mitochondria for β-oxidation,
    and acyl-CoA oxidase (ACO),
  • catalyzing the first rate-limiting reaction of peroxisomal β-oxidation, enzymes that are
    induced by both T3 and peroxisome proliferator-activated receptor α (PPAR-α).

Furthermore, PPAR-α-mediated upregulation of CPT-Iα mRNA is enhanced by PPAR-γ
coactivator 1α (PGC-1α), which in turn

  • augments T3 induction of CPT-Iα expression.

Interestingly, PGC-1α is induced by

  1. T3,
  2. AMPK activation, and
  3. ROS,

thus establishing potential links between

  • T3 action, ROS generation, and AMPK activation

with the onset of mitochondrial biogenesis and fatty acid β-oxidation.

Liver ROS generation leads to activation of the transcription factors

  1. nuclear factor-κB (NF-κB),
  2. activating protein 1 (AP-1), and
  3. signal transducer and activator of transcription 3 (STAT3)

at the Kupffer cell level, with upregulation of cytokine expression (TNF-α, IL-1, IL-6),
which upon interaction with specific receptors in hepatocytes trigger the expression of

  1. cytoprotective proteins (Figure 3(A)).

These responses and the promotion of hepatocyte and Kupffer-cell proliferation
represent hormetic effects reestablishing

  1. redox homeostasis,
  2. promoting cell survival, and
  3. protecting the liver against ischemia-reperfusion injury.

T3 liver preconditioning also involves the activation of the

  1. Nrf2-Keap1 defense pathway
  • upregulating antioxidant proteins,
  • phase-2 detoxifying enzymes, and
  • multidrug resistance proteins, members of the ATP binding cassette (ABC)
    superfamily of transporters (Figure 3(B))

In agreement with T3-induced liver preconditioning, T3 or L-thyroxin afford
preconditioning against IR injury in the heart, in association with

  • activation of protein kinase C and
  • attenuation of p38 and
  • c-Jun-N-terminal kinase activation ,

and in the kidney, in association with

  • heme oxygenase-1 upregulation.

475675.fig.002

http://www.hindawi.com/journals/tswj/2012/floats/475675/thumbnails/475675.fig.002_th.jpg

Figure 2: Calorigenic response of thyroid hormone (T3) and its relationship with O2
consumption, reactive oxygen species (ROS) generation, and antioxidant depletion in the liver.
Abbreviations: CYP2E1, cytochrome P450 isoform 2E1; GSH, reduced glutathione; QO2, rate
of O2 consumption; SOD, superoxide dismutase.

475675.fig.003

genomic signaling in T3 calorigenesis and ROS production 475675.fig.003

genomic signaling in T3 calorigenesis and ROS production 475675.fig.003

http://www.hindawi.com/journals/tswj/2012/floats/475675/thumbnails/475675.fig.003_th.jpg

Figure 3: Genomic signaling mechanisms in T3 calorigenesis and liver reactive oxygen
species (ROS) production leading to
(A) upregulation of cytokine expression in Kupffer cells and hepatocyte activation of genes
conferring cytoprotection,
(B) Nrf2 activation controling expression of antioxidant and detoxication proteins, and
(C) activation of the AMPK cascade regulating metabolic functions.Abbreviations: AP-1, activating protein 1; ARE, antioxidant responsive element; CaMKKβ,
Ca2+-calmodulin-dependent kinase kinase-β; CBP, CREB binding protein; CRC, chromatin
remodelling complex; EH, epoxide hydrolase; HO-1, hemoxygenase-1; GC-Ligase,
glutamate cysteine ligase; GPx, glutathione peroxidase; G-S-T, glutathione-S-transferase;
HAT, histone acetyltransferase; HMT, histone arginine methyltransferase; IL1,
interleukin 1; iNOS, inducible nitric oxide synthase; LKB1, tumor suppressor LKB1 kinase;
MnSOD, manganese superoxide dismutase; MRPs, multidrug resistance proteins; NF-κB,
nuclear factor-κB; NQO1, NADPH-quinone oxidoreductase-1; NRF-1, nuclear respiratory
factor-1; Nrf2, nuclear receptor-E2-related factor 2; PCAF, p300/CBP-associated
factor; RXR, retinoic acid receptor; PGC-1, peroxisome proliferator-activated receptor-γ
coactivator-1; QO2, rate of O2 consumption; STAT3, signal transducer and activator
of transcription 3; TAK1, transforming-growth-factor-β-activated kinase-1; TNF-α, tumor
necrosis factor-α; TR, T 3 receptor; TRAP, T3-receptor-associated protein; TRE,  T3 responsive element; UCP, uncoupling proteins; (—), reported mechanisms;
(- - - -), proposed mechanisms.

 

T3 is a key metabolic regulator coordinating short-term and long-term energy needs,
with major actions on liver metabolism. These include promotion of

(i) gluconeogenesis and hepatic glucose production, and

(ii) fatty acid oxidation coupled to enhanced adipose tissue lipolysis, with

  • higher fatty acid flux to the liver and
  • consequent ROS production (Figure 2) and
  • redox upregulation of cytoprotective proteins

affording liver preconditioning (Figure 3).

Thyroid Hormone and AMPK Activation: Skeletal Muscle and Heart

In skeletal muscle, T3 increases the levels of numerous proteins involved in

  1. glucose uptake (GLUT4),
  2. glycolysis (enolase, pyruvate kinase, triose phosphate isomerase),
  3. fatty acid oxidation (carnitine palmitoyl transferase-1, mitochondrial thioesterase I),
    and uncoupling protein-3,

effects that are achieved through enhanced transcription of TRE-containing genes

Skeletal muscle AMPK activation is characterized by

(i) being a rapid and transient response,

(ii) upstream activation by Ca2+-induced mobilization and CaMKKβ activation,

(iii) upstream upregulation of LKB1 expression, which requires association with STRAD
and MO25 for optimal phosphorylation/activation of AMPK, and

(iv) stimulation of mitochondrial fatty acid β-oxidation.

T3-induced muscle AMPK activation was found to trigger two major downstream

signaling pathways, namely,

(i) peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α) mRNA
expression and phosphorylation, a transcriptional regulator for genes related to

  • mitochondrial biogenesis,
  • fatty acid oxidation, and
  • gluconeogenesis and

(ii) cyclic AMP response element binding protein (CREB) phosphorylation, which

  • in turn induces PGC-1α expression in liver tissue, thus
  • reinforcing mechanism (i).

These data indicate that AMPK phosphorylation of PGC-1α initiates many of the
important gene regulatory functions of AMPK in skeletal muscle.

In heart, hyperthyroidism increased glycolysis and sarcolemmal GLUT4 levels by the
combined effects of AMPK activation and insulin stimulation, with concomitant increase
in fatty acid oxidation proportional to enhanced cardiac mass and contractile function.

Thyroid Hormone, AMPK Activation, and Liver Preconditioning

Recent studies by our group revealed that administration of a single dose of 0.1 mg T3/kg
to rats activates liver AMPK (Figure 4; unpublished work).

  1. enhancement in phosphorylated AMPK/nonphosphorylated AMPK ratios in T3-
    treated rats over control values thatis significant in the time period of 1 to 48
    hours after hormone treatment
  2. Administration of a substantially higher dose (0.4 mg T3/kg) resulted in
    decreased liver AMPK activation at 4 h to return to control values at 6 h
    after treatment

Activation of liver AMPK by T3 may be of relevance in terms of

  • promotion of fatty acid oxidation for ATP supply,
  • supporting hepatoprotection against IR injury (Figure 3(C)).

This proposal is based on the high energy demands underlying effective liver
preconditioning for full operation of hepatic

  • antioxidant, antiapoptotic, and anti-inflammatory mechanisms,
  • oxidized biomolecules repair or resynthesis,
  • induction of the homeostatic acute-phase response, and
  • promotion of hepatocyte and Kupffer cell proliferation,

mechanisms that are needed to cope with the damaging processes set in by IR.
T3 liver preconditioning , in addition to that afforded by

  • n-3 long-chain polyunsaturated fatty acids given alone or
  • combined with T3 at lower dosages, or
  • by iron supplementation,

constitutes protective strategies against hepatic IR injury.

Studies on the molecular mechanisms underlying T3-induced liver AMPK
activation (Figure 4) are currently under assessment in our laboratory.

References

Fernández and L. A. Videla, “Kupffer cell-dependent signaling in thyroid hormone
calorigenesis: possible applications for liver preconditioning,” Current Signal
Transduction Therapy 2009; 4(2): 144–151.

Viollet, B. Guigas, J. Leclerc et al., “AMP-activated protein kinase in the regulation
of  hepatic energy metabolism: from physiology to therapeutic perspectives,” Acta
Physiologica 2009; 196(1): 81–98.

Carling, “The AMP-activated protein kinase cascade – A unifying system
for energy control,” Trends in Biochemical Sciences, 2004;. 29(1): 18–24.

E. Kemp, D. Stapleton, D. J. Campbell et al., “AMP-activated protein kinase,
super 
metabolic regulator,” Biochemical Society Transactions 2003; 31(1):
162–168
.

G. Hardie, “AMP-activated protein kinase-an energy sensor that
regulates all ;aspects of cell function,” Genes and Development,
2011; 25(18): 1895–1908.

Woods, P. C. F. Cheung, F. C. Smith et al., “Characterization of AMP-activated
protein kinase βandγ subunits Assembly of the heterotrimeric complex in vitro,”
Journal of Biological Chemistry 1996;271(17): 10282–10290.

Xiao, R. Heath, P. Saiu et al., “Structural basis for AMP binding to mammalian AMP-
activated protein kinase,” Nature 2007; 449(7161): 496–500.

more…

Impact of Metformin and compound C on NIS expression and iodine uptake in vitro and in vivo: a role for CRE in AMPK modulation of thyroid function.
Abdulrahman RM1, Boon MRSips HCGuigas BRensen PCSmit JWHovens GC.
Author information 
Thyroid. 2014 Jan;24(1):78-87.  Epub 2013 Sep 25.  PMID: 23819433
http://dx.doi.org:/10.1089/thy.2013.0041.

Although adenosine monophosphate activated protein kinase (AMPK) plays a crucial role
in energy metabolism, a direct effect of AMPK modulation on thyroid function has only
recently been reported, and much of its function in the thyroid is currently unknown.

The aim of this study was

  1. to investigate the mechanism of AMPK modulation in iodide uptake.
  2. to investigate the potential of the AMPK inhibitor compound C as an enhancer of
    iodide uptake by thyrocytes.

Metformin reduced NIS promoter activity (0.6-fold of control), whereas compound C
stimulated its activity (3.4-fold) after 4 days. This largely coincides with

  • CRE activation (0.6- and 3.0-fold).

These experiments show that AMPK exerts its effects on iodide uptake, at least partly,
through the CRE element in the NIS promoter. Furthermore, we have used AMPK-alpha1
knockout mice to determine the long-term effects of AMPK inhibition without chemical compounds.
These mice have a less active thyroid, as shown by reduced colloid volume and reduced
responsiveness to thyrotropin.

NIS expression and iodine uptake in thyrocytes

  • can be modulated by metformin and compound C.

These compounds exert their effect by

  • modulation of AMPK, which, in turn, regulates
  • the activation of the CRE element in the NIS promoter.

Overall, this suggests that AMPK modulating compounds may be useful for the
enhancement of iodide uptake by thyrocytes, which could be useful for the
treatment of thyroid cancer patients with radioactive iodine.

AMPK: Master Metabolic Regulator

© 1996–2013 themedicalbiochemistrypage.org, LLC | info
@ themedicalbiochemistrypage.org

AMPK-activating drugs metformin or phenformin might provide protection against cancer 1741-7007-11-36-5

AMPK-activating drugs metformin or phenformin might provide protection against cancer 1741-7007-11-36-5

 

AMPK and AMPK-related kinase (ARK) family 1741-7007-11-36-4

AMPK and AMPK-related kinase (ARK) family 1741-7007-11-36-4

 

central role of AMPK in the regulation of metabolism

 

 

AMP-activated protein kinase (AMPK) was first discovered as an activity that

AMPK induces a cascade of events within cells in response to the ever changing energy
charge of the cell. The role of AMPK in regulating cellular energy charge places this
enzyme at a central control point in maintaining energy homeostasis.

More recent evidence has shown that AMPK activity can also be regulated by physiological stimuli, independent of the energy charge of the cell, including hormones and nutrients.

 

Once activated, AMPK-mediated phosphorylation events

These events are rapidly initiated and are referred to as

  • short-term regulatory processes.

The activation of AMPK also exerts

  • long-term effects at the level of both gene expression and protein synthesis.

Other important activities attributable to AMPK are

  1. regulation of insulin synthesis and
  2. secretion in pancreatic islet β-cells and
  3. modulation of hypothalamic functions involved in the regulation of satiety.

How these latter two functions impact obesity and diabetes will be discussed below.

Regulation of AMPK

In the presence of AMP the activity of AMPK is increased approximately 5-fold.
However, more importantly is the role of AMP in regulating the level of phosphorylation
of AMPK. An increased AMP to ATP ratio leads to a conformational change in the γ-subunit
leading to increased phosphorylation and decreased dephosphorylation of AMPK.

The phosphorylation of AMPK results in activation by at least 100-fold. AMPK is
phosphorylated by at least three different upstream AMPK kinases (AMPKKs).
Phosphorylation of AMPK occurs in the α subunit at threonine 172 (T172) which

  • lies in the activation loop.

One kinase activator of AMPK is

  • Ca2+-calmodulin-dependent kinase kinase β (CaMKKβ)
  • which phosphorylates and activates AMPK in response to increased calcium.

The distribution of CaMKKβ expression is primarily in the brain with detectable levels
also found in the testes, thymus, and T cells. As described for the Ca2+-mediated
regulation of glycogen metabolism,

  • increased release of intracellular stores of Ca2+ create a subsequent demand for
    ATP.

Activation of AMPK in response to Ca fluxes

  • provides a mechanism for cells to anticipate the increased demand for ATP.

Evidence has also demonstrated that the serine-threonine kinase, LKB1 (also called
serine-threonine kinase 11, STK11) which is encoded by the Peutz-Jeghers syndrome
tumor suppressor gene, is required for activation of AMPK in response to stress.

The active LKB1 kinase is actually a complex of three proteins:

  1. LKB1,
  2. Ste20-related adaptor (STRAD) and
  3. mouse protein 25 (MO25).

Thus, the enzyme complex is often referred to as LKB1-STRAD-MO25. Phosphorylation
of AMPK by LKB1 also occurs on T172. Unlike the limited distribution of CaMKKβ,

  • LKB1 is widely expressed, thus making it the primary AMPK-regulating kinase.

Loss of LKB1 activity in adult mouse liver leads to

  • near complete loss of AMPK activity and
  • is associated with hyperglycemia.

The hyperglycemia is, in part, due to an increase in the transcription of gluconeogenic
genes. Of particular significance is the increased expression of

  • the peroxisome proliferator-activated receptor-γ (PPAR-γ) coactivator 1α
    (PGC-1α), which drives gluconeogenesis.
  • Reduction in PGC-1α activity results in normalized blood glucose levels in
    LKB1-deficient mice.

The third AMPK phosphorylating kinase is transforming growth factor-β-activated
kinase 1 (TAK1). However, the normal physiological conditions under which TAK1
phosphorylates AMPK are currently unclear.

The effects of AMP are two-fold:

  1. a direct allosteric activation and making AMPK a poorer substrate for
    dephosphorylation.

Because AMP affects both
the rate of AMPK phoshorylation in the positive direction and
dephosphorylation in the negative direction,

the cascade is ultrasensitive. This means that

  1. a very small rise in AMP levels can induce a dramatic increase in the activity of
    AMPK.

The activity of adenylate kinase, catalyzing the reaction shown below, ensures that

  • AMPK is highly sensitive to small changes in the intracellular [ATP]/[ADP] ratio.

2 ADP ——> ATP + AMP

Negative allosteric regulation of AMPK also occurs and this effect is exerted by
phosphocreatine. As indicated above, the β subunits of AMPK have a glycogen-binding domain, GBD. In muscle, a high glycogen content

  • represses AMPK activity and
  • this is likely the result of interaction between the GBD and glycogen,
  • the GBD of AMPK allows association of the enzyme with the regulation of glycogen metabolism
  • by placing AMPK in close proximity to one of its substrates glycogen synthase.

AMPK has also been shown to be activated by receptors that are coupled to

  • phospholipase C-β (PLC-β) and by
  • hormones secreted by adipose tissue (termed adipokines) such as leptinand adiponectin (discussed below).

Targets of AMPK

The signaling cascades initiated by the activation of AMPK exert effects on

  • glucose and lipid metabolism,
  • gene expression and
  • protein synthesis.

These effects are most important for regulating metabolic events in the liver, skeletal
muscle, heart, adipose tissue, and pancreas.

Demonstration of the central role of AMPK in the regulation of metabolism in response
to events such as nutrient- or exercise-induced stress. Several of the known physiologic
targets for AMPK are included as well as several pathways whose flux is affected by
AMPK activation. Arrows indicate positive effects of AMPK, whereas, T-lines indicate
the resultant inhibitory effects of AMPK action.

The uptake, by skeletal muscle, accounts for >70% of the glucose removal from the
serum in humans. Therefore, it should be obvious that this event is extremely important
for overall glucose homeostasis, keeping in mind, of course, that glucose uptake by
cardiac muscle and adipocytes cannot be excluded from consideration. An important fact
related to skeletal muscle glucose uptake is that this process is markedly impaired in
individuals with type 2 diabetes.

The uptake of glucose increases dramatically in response to stress (such as ischemia) and
exercise and is stimulated by insulin-induced recruitment of glucose transporters
to the plasma membrane, primarily GLUT4. Insulin-independent recruitment of glucose
transporters also occurs in skeletal muscle in response to contraction (exercise).

The activation of AMPK plays an important, albeit not an exclusive, role in the induction of
GLUT4 recruitment to the plasma membrane. The ability of AMPK to stimulate
GLUT4 translocation to the plasma membrane in skeletal muscle is by a different mechanism
than that stimulated by insulin and insulin and AMPK effects are additive.

Under ischemic/hypoxic conditions in the heart the activation of AMPK leads to the
phosphorylation and activation of the kinase activity of phosphofructokinase-2, PFK-2
(6-phosphofructo-2-kinase). The product of the action of PFK-2 (fructose-2,6-bisphosphate,
F2,6BP) is one of the most potent regulators of the rate of flux through
glycolysis and gluconeogenesis.

In liver the PKA-mediated phosphorylation of PFK-2 results in conversion of the
enzyme from a kinase that generates F2,6BP to a phosphatase that removes the
2-phosphate thus reducing the levels of the potent allosteric activator of the glycolytic
enzyme 6-phosphfructo-1-kinase, PFK-1 and the potent allosteric inhibitor
of the gluconeogenic enzyme fructose-1,6-bisphosphatase (F1,-6BPase).

It is important to note that like many enzymes, there are multiple isoforms of PFK-2
(at least 4) and neither the liver or the skeletal muscle isoforms contain the AMPK
phosphorylation sites found in the cardiac and inducible (iPFK2) isoforms of PFK-2.

Inducible PFK-2 is expressed in the monocyte/macrophage lineage in response to pro-
inflammatory stimuli. The ability to activate the kinase activity by phosphorylation of
PFK-2 in cardiac tissue and macrophages in response to ischemic conditions allows these
cells to continue to have a source of ATP via anaerobic glycolysis. This phenomenon is
recognized as the Pasteur effect: an increased rate of glycolysis in response to hypoxia.

Of pathological significance is the fact that the inducible form of PFK-2 is commonly
expressed in many tumor cells and this may allow AMPK to play an important role in
protecting tumor cells from hypoxic stress. Indeed, techniques for depleting AMPK in
tumor cells have shown that these cells become sensitized to nutritional stress upon loss
of AMPK activity.

Whereas, stress and exercise are powerful inducers of AMPK activity in skeletal muscle,
additional regulators of its activity have been identified.

Insulin-sensitizing drugs of the thiazolidinedione family (activators of PPAR-γ, see
below) as well as the hypoglycemia drug metformin exert a portion of their effects
through regulation of the activity of AMPK.

As indicated above, the activity of the AMPK activating kinase, LKB1, is critical for
regulating gluconeogenic flux and consequent glucose homeostasis. The action of
metformin in reducing blood glucose levels

  • requires the activity of LKB1 in the liver for this function.

Also, several adipokines (hormones secreted by adipocytes) either stimulate or inhibit
AMPK activation:

  1. leptin and adiponectin have been shown to stimulate AMPK activation, whereas,
  2. resistininhibits AMPK activation.

Cardiac effects exerted by activation of AMPK also include

AMPK-mediated phosphorylation of eNOS leads to increased activity and consequent
NO production and provides a link between metabolic stresses and cardiac function.

In platelets, insulin action leads to an increase in eNOS activity that is

  • due to its phosphorylation by AMPK.

Activation of NO production in platelets leads to

  • a decrease in thrombin-induced aggregation, thereby,
  • limiting the pro-coagulant effects of platelet activation.

The response of platelets to insulin function clearly indicates why disruption in insulin
action is a major contributing factor in the development of the metabolic syndrome

Activation of AMPK leads to a reduction in the level of SREBP

  • a transcription factor &regulator of the expression of numerous
    lipogenic enzymes

Another transcription factor reduced in response to AMPK activation is

  • hepatocyte nuclear factor 4α, HNF4α
    • a member of the steroid/thyroid hormone superfamily.
    • HNF4α is known to regulate the expression of several liver and
      pancreatic β-cell genes such as GLUT2, L-PK and preproinsulin.
  • Of clinical significance is that mutations in HNF4α are responsible for
    • maturity-onset diabetes of the young, MODY-1.

Recent evidence indicates that the gene for the carbohydrate-response-element-
binding protein (ChREBP) is a target for AMPK-mediated transcriptional regulation
in the liver. ChREBP is rapidly being recognized as a master regulator of lipid
metabolism in liver, in particular in response to glucose uptake.

The target of the thiazolidinedione (TZD) class of drugs used to treat type 2 diabetes is
the peroxisome proliferator-activated receptor γPPARγ which

  • itself may be a target for the action of AMPK.

The transcription co-activator, p300, is phosphorylated by AMPK

  • which inhibits interaction of p300 with not only PPARγ but also
  • the retinoic acid receptor, retinoid X receptor, and
  • thyroid hormone receptor.

PPARγ is primarily expressed in adipose tissue and thus it was difficult to reconcile how
a drug that was apparently acting only in adipose tissue could lead to improved insulin
sensitivity of other tissues. The answer to this question came when it was discovered that the TZDs stimulated the expression and release of the adipocyte hormone (adipokine),
adiponectin. Adiponectin stimulates glucose uptake and fatty acid oxidation in skeletal
muscle. In addition, adiponectin stimulates fatty acid oxidation in liver while inhibiting
expression of gluconeogenic enzymes in this tissue.

These responses to adiponectin are exerted via activation of AMPK. Another
transcription factor target of AMPK is the forkhead protein, FKHR (now referred to as
FoxO1). FoxO1 is involved in the activation of glucose-6-phosphatase expression and,
therefore, loss of FoxO1 activity in response to AMPK activation will lead to reduced
hepatic output of glucose.

This concludes a very complicated perspective that ties together the thyroid hormone
activity, the hypophysis, diabetes mellitus, and AMPK tegulation of metabolism in the
liver, skeletal muscle, adipose tissue, and heart.  I also note at this time that there
nongenetic points to be made here:

  1. The tissue specificity of isoenzymes
  2. The modulatory role of AMP:ATP ratio in phosphorylation/dephosphorylation
    effects on metabolism tied to AMPK
  3. The tie in of stress or ROS with fast reactions to protect harm to tissues
  4. The relationship of cytokine activation and release to the above metabolic events
  5. The relationship of effective and commonly used diabetes medications to AMPK
    mediated processes
  6. The preceding presentation is notable for the importance of proteomic and
    metabolomic invetigations in elucidation common chronic and nongenetic diseases

 

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Compilation of References in Leaders in Pharmaceutical Intelligence about proteomics, metabolomics, signaling pathways, and cell regulation

Compilation of References in Leaders in Pharmaceutical Intelligence about
proteomics, metabolomics, signaling pathways, and cell regulation

Curator: Larry H. Bernstein, MD, FCAP

 

Proteomics

  1. The Human Proteome Map Completed
    Reporter and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/28/the-human-proteome-map-completed/
  1. Proteomics – The Pathway to Understanding and Decision-making in Medicine
    Author and Curator, Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/06/24/proteomics-the-pathway-to-understanding-and-decision-making-in-medicine/
  1. Advances in Separations Technology for the “OMICs” and Clarification of Therapeutic Targets
    Author and Curator, Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2012/10/22/advances-in-separations-technology-for-the-omics-and-clarification-of-therapeutic-targets/
  1. Expanding the Genetic Alphabet and Linking the Genome to the Metabolome
    Author and Curator, Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2012/09/24/expanding-the-genetic-alphabet-and-linking-the-genome-to-the-metabolome/
  1. Synthesizing Synthetic Biology: PLOS Collections
    Reporter: Aviva Lev-Ari
    http://pharmaceuticalintelligence.com/2012/08/17/synthesizing-synthetic-biology-plos-collections/

 

Metabolomics

  1. Extracellular evaluation of intracellular flux in yeast cells
    Larry H. Bernstein, MD, FCAP, Reviewer and Curator
    http://pharmaceuticalintelligence.com/2014/08/25/extracellular-evaluation-of-intracellular-flux-in-yeast-cells/ 
  2. Metabolomic analysis of two leukemia cell lines. I.
    Larry H. Bernstein, MD, FCAP, Reviewer and Curator
    http://pharmaceuticalintelligence.com/2014/08/23/metabolomic-analysis-of-two-leukemia-cell-lines-_i/ 
  3. Metabolomic analysis of two leukemia cell lines. II.
    Larry H. Bernstein, MD, FCAP, Reviewer and Curator
    http://pharmaceuticalintelligence.com/2014/08/24/metabolomic-analysis-of-two-leukemia-cell-lines-ii/ 
  4. Metabolomics, Metabonomics and Functional Nutrition: the next step in nutritional metabolism and biotherapeutics
    Reviewer and Curator, Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/22/metabolomics-metabonomics-and-functional-nutrition-the-next-step-in-nutritional-metabolism-and-biotherapeutics/ 
  5. Buffering of genetic modules involved in tricarboxylic acid cycle metabolism provides homeomeostatic regulation
    Larry H. Bernstein, MD, FCAP, Reviewer and curator
    http://pharmaceuticalintelligence.com/2014/08/27/buffering-of-genetic-modules-involved-in-tricarboxylic-acid-cycle-metabolism-provides-homeomeostatic-regulation/

 

Metabolic Pathways

  1. Pentose Shunt, Electron Transfer, Galactose, more Lipids in brief
    Reviewer and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/21/pentose-shunt-electron-transfer-galactose-more-lipids-in-brief/
  2. Mitochondria: More than just the “powerhouse of the cell”
    Reviewer and Curator: Ritu Saxena
    http://pharmaceuticalintelligence.com/2012/07/09/mitochondria-more-than-just-the-powerhouse-of-the-cell/
  3. Mitochondrial fission and fusion: potential therapeutic targets?
    Reviewer and Curator: Ritu saxena
    http://pharmaceuticalintelligence.com/2012/10/31/mitochondrial-fission-and-fusion-potential-therapeutic-target/ 
  4. Mitochondrial mutation analysis might be “1-step” away
    Reviewer and Curator: Ritu Saxena
    http://pharmaceuticalintelligence.com/2012/08/14/mitochondrial-mutation-analysis-might-be-1-step-away/
  5. Selected References to Signaling and Metabolic Pathways in PharmaceuticalIntelligence.com
    Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/14/selected-references-to-signaling-and-metabolic-pathways-in-leaders-in-pharmaceutical-intelligence/
  6. Metabolic drivers in aggressive brain tumors
    Prabodh Kandal, PhD
    http://pharmaceuticalintelligence.com/2012/11/11/metabolic-drivers-in-aggressive-brain-tumors/ 
  7. Metabolite Identification Combining Genetic and Metabolic Information: Genetic association links unknown metabolites to functionally related genes
    Author and Curator: Aviva Lev-Ari, PhD, RD
    http://pharmaceuticalintelligence.com/2012/10/22/metabolite-identification-combining-genetic-and-metabolic-information-genetic-association-links-unknown-metabolites-to-functionally-related-genes/
  8. Mitochondria: Origin from oxygen free environment, role in aerobic glycolysis, metabolic adaptation
    Author and curator:Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2012/09/26/mitochondria-origin-from-oxygen-free-environment-role-in-aerobic-glycolysis-metabolic-adaptation/
  9. Therapeutic Targets for Diabetes and Related Metabolic Disorders
    Reporter, Aviva Lev-Ari, PhD, RD
    http://pharmaceuticalintelligence.com/2012/08/20/therapeutic-targets-for-diabetes-and-related-metabolic-disorders/
  10. Buffering of genetic modules involved in tricarboxylic acid cycle metabolism provides homeomeostatic regulation
    Larry H. Bernstein, MD, FCAP, Reviewer and curator
    http://pharmaceuticalintelligence.com/2014/08/27/buffering-of-genetic-modules-involved-in-tricarboxylic-acid-cycle-metabolism-provides-homeomeostatic-regulation/
  11. The multi-step transfer of phosphate bond and hydrogen exchange energy
    Curator:Larry H. Bernstein, MD, FCAP,
    http://pharmaceuticalintelligence.com/2014/08/19/the-multi-step-transfer-of-phosphate-bond-and-hydrogen-exchange-energy/
  12. Studies of Respiration Lead to Acetyl CoA
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/18/studies-of-respiration-lead-to-acetyl-coa/
  13. Lipid Metabolism
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/15/lipid-metabolism/
  14. Carbohydrate Metabolism
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/13/carbohydrate-metabolism/
  15. Prologue to Cancer – e-book Volume One – Where are we in this journey?
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/04/13/prologue-to-cancer-ebook-4-where-are-we-in-this-journey/
  16. Introduction – The Evolution of Cancer Therapy and Cancer Research: How We Got Here?
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/04/04/introduction-the-evolution-of-cancer-therapy-and-cancer-research-how-we-got-here/
  17. Inhibition of the Cardiomyocyte-Specific Kinase TNNI3K
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2013/11/01/inhibition-of-the-cardiomyocyte-specific-kinase-tnni3k/
  18. The Binding of Oligonucleotides in DNA and 3-D Lattice Structures
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2013/05/15/the-binding-of-oligonucleotides-in-dna-and-3-d-lattice-structures/
  19. Mitochondrial Metabolism and Cardiac Function
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2013/04/14/mitochondrial-metabolism-and-cardiac-function/
  20. How Methionine Imbalance with Sulfur-Insufficiency Leads to Hyperhomocysteinemia
    Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2013/04/04/sulfur-deficiency-leads_to_hyperhomocysteinemia/
  21. AMPK Is a Negative Regulator of the Warburg Effect and Suppresses Tumor Growth In Vivo
    Author and Curator: SJ. Williams
    http://pharmaceuticalintelligence.com/2013/03/12/ampk-is-a-negative-regulator-of-the-warburg-effect-and-suppresses-tumor-growth-in-vivo/
  22. A Second Look at the Transthyretin Nutrition Inflammatory Conundrum
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2012/12/03/a-second-look-at-the-transthyretin-nutrition-inflammatory-conundrum/
  23. Overview of Posttranslational Modification (PTM)
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/07/29/overview-of-posttranslational-modification-ptm/
  24. Malnutrition in India, high newborn death rate and stunting of children age under five years
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/07/15/malnutrition-in-india-high-newborn-death-rate-and-stunting-of-children-age-under-five-years/
  25. Update on mitochondrial function, respiration, and associated disorders
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/07/08/update-on-mitochondrial-function-respiration-and-associated-disorders/
  26. Omega-3 fatty acids, depleting the source, and protein insufficiency in renal disease
    Larry H. Bernstein, MD, FCAP, Curator
    http://pharmaceuticalintelligence.com/2014/07/06/omega-3-fatty-acids-depleting-the-source-and-protein-insufficiency-in-renal-disease/ 
  27. Late Onset of Alzheimer’s Disease and One-carbon Metabolism
    Reporter and Curator: Dr. Sudipta Saha, Ph.D.
    http://pharmaceuticalintelligence.com/2013/05/06/alzheimers-disease-and-one-carbon-metabolism/
  28. Problems of vegetarianism
    Reporter and Curator: Dr. Sudipta Saha, Ph.D.
    http://pharmaceuticalintelligence.com/2013/04/22/problems-of-vegetarianism/

 

Signaling Pathways

  1. Introduction to e-Series A: Cardiovascular Diseases, Volume Four Part 2: Regenerative Medicine
    Larry H. Bernstein, MD, FCAP, writer, and Aviva Lev- Ari, PhD, RN  http://pharmaceuticalintelligence.com/2014/04/27/larryhbernintroduction_to_cardiovascular_diseases-translational_medicine-part_2/
  2. Epilogue: Envisioning New Insights in Cancer Translational Biology
    Series C: e-Books on Cancer & Oncology
    Author & Curator: Larry H. Bernstein, MD, FCAP, Series C Content Consultant
    http://pharmaceuticalintelligence.com/2014/03/29/epilogue-envisioning-new-insights/
  3. Ca2+-Stimulated Exocytosis:  The Role of Calmodulin and Protein Kinase C in Ca2+ Regulation of Hormone and Neurotransmitter  Writer and Curator: Larry H Bernstein, MD, FCAP and Curator and Content Editor: Aviva Lev-Ari, PhD, RN
    http://pharmaceuticalintelligence.com/2013/12/23/calmodulin-and-protein-kinase-c-drive-the-ca2-regulation-of-hormone-and-neurotransmitter-release-that-triggers-ca2-stimulated-exocy
  4. Cardiac Contractility & Myocardial Performance: Therapeutic Implications of Ryanopathy (Calcium Release-related Contractile Dysfunction) and Catecholamine Responses
    Author, and Content Consultant to e-SERIES A: Cardiovascular Diseases: Justin Pearlman, MD, PhD, FACC
    Author and Curator: Larry H Bernstein, MD, FCAP and Article Curator: Aviva Lev-Ari, PhD, RN
    http://pharmaceuticalintelligence.com/2013/08/28/cardiac-contractility-myocardium-performance-ventricular-arrhythmias-and-non-ischemic-heart-failure-therapeutic-implications-for-cardiomyocyte-ryanopathy-calcium-release-related-contractile/
  5. Role of Calcium, the Actin Skeleton, and Lipid Structures in Signaling and Cell Motility
    Author and Curator: Larry H Bernstein, MD, FCAP Author: Stephen Williams, PhD, and Curator: Aviva Lev-Ari, PhD, RN
    http://pharmaceuticalintelligence.com/2013/08/26/role-of-calcium-the-actin-skeleton-and-lipid-structures-in-signaling-and-cell-motility/
  6. Identification of Biomarkers that are Related to the Actin Cytoskeleton
    Larry H Bernstein, MD, FCAP, Author and Curator
    http://pharmaceuticalintelligence.com/2012/12/10/identification-of-biomarkers-that-are-related-to-the-actin-cytoskeleton/
  7. Advanced Topics in Sepsis and the Cardiovascular System at its End Stage
    Author and Curator: Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2013/08/18/advanced-topics-in-Sepsis-and-the-Cardiovascular-System-at-its-End-Stage/
  8. The Delicate Connection: IDO (Indolamine 2, 3 dehydrogenase) and Cancer Immunology
    Demet Sag, PhD, Author and Curator
    http://pharmaceuticalintelligence.com/2013/08/04/the-delicate-connection-ido-indolamine-2-3-dehydrogenase-and-immunology/
  9. IDO for Commitment of a Life Time: The Origins and Mechanisms of IDO, indolamine 2, 3-dioxygenase
    Demet Sag, PhD, Author and Curator
    http://pharmaceuticalintelligence.com/2013/08/04/ido-for-commitment-of-a-life-time-the-origins-and-mechanisms-of-ido-indolamine-2-3-dioxygenase/
  10. Confined Indolamine 2, 3 dioxygenase (IDO) Controls the Homeostasis of Immune Responses for Good and Bad
    Author and Curator: Demet Sag, PhD, CRA, GCP
    http://pharmaceuticalintelligence.com/2013/07/31/confined-indolamine-2-3-dehydrogenase-controls-the-hemostasis-of-immune-responses-for-good-and-bad/
  11. Signaling Pathway that Makes Young Neurons Connect was discovered @ Scripps Research Institute
    Reporter: Aviva Lev-Ari, PhD, RN
    http://pharmaceuticalintelligence.com/2013/06/26/signaling-pathway-that-makes-young-neurons-connect-was-discovered-scripps-research-institute/
  12. Naked Mole Rats Cancer-Free
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2013/06/20/naked-mole-rats-cancer-free/
  13. Amyloidosis with Cardiomyopathy
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2013/03/31/amyloidosis-with-cardiomyopathy/
  14. Liver endoplasmic reticulum stress and hepatosteatosis
    Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2013/03/10/liver-endoplasmic-reticulum-stress-and-hepatosteatosis/
  15. The Molecular Biology of Renal Disorders: Nitric Oxide – Part III
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2012/11/26/the-molecular-biology-of-renal-disorders/
  16. Nitric Oxide Function in Coagulation – Part II
    Curator and Author: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2012/11/26/nitric-oxide-function-in-coagulation/
  17. Nitric Oxide, Platelets, Endothelium and Hemostasis
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2012/11/08/nitric-oxide-platelets-endothelium-and-hemostasis/
  18. Interaction of Nitric Oxide and Prostacyclin in Vascular Endothelium
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2012/09/14/interaction-of-nitric-oxide-and-prostacyclin-in-vascular-endothelium/
  19. Nitric Oxide and Immune Responses: Part 1
    Curator and Author:  Aviral Vatsa PhD, MBBS
    http://pharmaceuticalintelligence.com/2012/10/18/nitric-oxide-and-immune-responses-part-1/
  20. Nitric Oxide and Immune Responses: Part 2
    Curator and Author:  Aviral Vatsa PhD, MBBS
    http://pharmaceuticalintelligence.com/2012/10/28/nitric-oxide-and-immune-responses-part-2/
  21. Nitric Oxide and iNOS have Key Roles in Kidney Diseases – Part II
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2012/11/26/nitric-oxide-and-inos-have-key-roles-in-kidney-diseases/
  22. New Insights on Nitric Oxide donors – Part IV
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2012/11/26/new-insights-on-no-donors/
  23. Crucial role of Nitric Oxide in Cancer
    Curator and Author: Ritu Saxena, Ph.D.
    http://pharmaceuticalintelligence.com/2012/10/16/crucial-role-of-nitric-oxide-in-cancer/
  24. Nitric Oxide has a ubiquitous role in the regulation of glycolysis -with a concomitant influence on mitochondrial function
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2012/09/16/nitric-oxide-has-a-ubiquitous-role-in-the-regulation-of-glycolysis-with-a-concomitant-influence-on-mitochondrial-function/
  25. Nitric Oxide and Immune Responses: Part 2
    Author and Curator: Aviral Vatsa, PhD, MBBS
    http://pharmaceuticalintelligence.com/2012/10/28/nitric-oxide-and-immune-responses-part-2/
  26. Mitochondrial Damage and Repair under Oxidative Stress
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2012/10/28/mitochondrial-damage-and-repair-under-oxidative-stress/
  27. Is the Warburg Effect the cause or the effect of cancer: A 21st Century View?
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2012/10/17/is-the-warburg-effect-the-cause-or-the-effect-of-cancer-a-21st-century-view/
  28. Targeting Mitochondrial-bound Hexokinase for Cancer Therapy
    Curator and Author: Ziv Raviv, PhD, RN 04/06/2013
    http://pharmaceuticalintelligence.com/2013/04/06/targeting-mitochondrial-bound-hexokinase-for-cancer-therapy/
  29. Ubiquinin-Proteosome pathway, autophagy, the mitochondrion, proteolysis and cell apoptosis
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2012/10/30/ubiquinin-proteosome-pathway-autophagy-the-mitochondrion-proteolysis-and-cell-apoptosis/
  30. Ubiquitin-Proteosome pathway, Autophagy, the Mitochondrion, Proteolysis and Cell Apoptosis: Part III
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2013/02/14/ubiquinin-proteosome-pathway-autophagy-the-mitochondrion-proteolysis-and-cell-apoptosis-reconsidered/
  31. Biochemistry of the Coagulation Cascade and Platelet Aggregation – Part I
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2012/11/26/biochemistry-of-the-coagulation-cascade-and-platelet-aggregation/

 

Genomics, Transcriptomics, and Epigenetics

  1. What is the meaning of so many RNAs?
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/06/what-is-the-meaning-of-so-many-rnas/
  2. RNA and the transcription the genetic code
    Larry H. Bernstein, MD, FCAP, Writer and Curator
    http://pharmaceuticalintelligence.com/2014/08/02/rna-and-the-transcription-of-the-genetic-code/
  3. A Primer on DNA and DNA Replication
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/07/29/a_primer_on_dna_and_dna_replication/
  4. Pathology Emergence in the 21st Century
    Author and Curator: Larry Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/03/pathology-emergence-in-the-21st-century/
  5. RNA and the transcription the genetic code
    Writer and Curator, Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/02/rna-and-the-transcription-of-the-genetic-code/
  6. Commentary on Biomarkers for Genetics and Genomics of Cardiovascular Disease: Views by Larry H Bernstein, MD, FCAP
    Author: Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/07/16/commentary-on-biomarkers-for-genetics-and-genomics-of-cardiovascular-disease-views-by-larry-h-bernstein-md-fcap/
  7. Observations on Finding the Genetic Links in Common Disease: Whole Genomic Sequencing Studies
    Author an Curator: Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2013/05/18/observations-on-finding-the-genetic-links/
  8. Silencing Cancers with Synthetic siRNAs
    Larry H. Bernstein, MD, FCAP, Reviewer and Curator
    http://pharmaceuticalintelligence.com/2013/12/09/silencing-cancers-with-synthetic-sirnas/
  9. Cardiometabolic Syndrome and the Genetics of Hypertension: The Neuroendocrine Transcriptome Control Points
    Reporter: Aviva Lev-Ari, PhD, RN
    http://pharmaceuticalintelligence.com/2013/12/12/cardiometabolic-syndrome-and-the-genetics-of-hypertension-the-neuroendocrine-transcriptome-control-points/
  10. Developments in the Genomics and Proteomics of Type 2 Diabetes Mellitus and Treatment Targets
    Larry H. Bernstein, MD, FCAP, Reviewer and Curator
    http://pharmaceuticalintelligence.com/2013/12/08/developments-in-the-genomics-and-proteomics-of-type-2-diabetes-mellitus-and-treatment-targets/
  11. CT Angiography & TrueVision™ Metabolomics (Genomic Phenotyping) for new Therapeutic Targets to Atherosclerosis
    Reporter: Aviva Lev-Ari, PhD, RN
    http://pharmaceuticalintelligence.com/2013/11/15/ct-angiography-truevision-metabolomics-genomic-phenotyping-for-new-therapeutic-targets-to-atherosclerosis/
  12. CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics & Computational Genomics
    Genomics Curator, Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/30/cracking-the-code-of-human-life-the-birth-of-bioinformatics-computational-genomics/
  13. Big Data in Genomic Medicine
    Author and Curator, Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2012/12/17/big-data-in-genomic-medicine/
  14.  From Genomics of Microorganisms to Translational Medicine
    Author and Curator: Demet Sag, PhD
    http://pharmaceuticalintelligence.com/2014/03/20/without-the-past-no-future-but-learn-and-move-genomics-of-microorganisms-to-translational-medicine/
  15.  Summary of Genomics and Medicine: Role in Cardiovascular Diseases
    Author and Curator, Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/01/06/summary-of-genomics-and-medicine-role-in-cardiovascular-diseases/

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Metabolomic analysis of two leukemia cell lines. II.

Larry H. Bernstein, MD, FCAP, Reviewer and Curator

Leaders in Pharmaceutical Intelligence

 

In Part I of metabolomics of two leukemia cell lines, we have established a major premise for the study, an insight into the use of an experimental model, and some insight into questions raised.

I here return to examine these before pursuing more detail in the study.

Q1. What strong metabolic pathways come into focus in this study?

Answer – The aerobic and anaerobic glycolytic pathways, with a difference measured in the extent of participation of mitochondrial oxidative phosphorylation.

Q2. Would we expect to also gain insight into the effect, on balance, played by a suppressed ubiquitin pathway?

Answer – lets look into this in Part II.

Q3. Would the synthesis of phospholipid and the maintenance of membrane structures requires availability of NADPH, which would be a reversal of the TCA cycle at the cost of delta G in catabolic energy, be consistent with increased dependence of anaerobic glycolysis  with unchecked replication?

Answer: Part II might show this, as the direction and the difference between the cell lines is consistent with a Warburg (Pasteur) effect.

Recall the observation that the model is based on experimental results from  lymphocytic leukemia cell lines in cell culture.  The internal metabolic state is inferred from measurement of external metabolites.

The classification of the lymphocytic leukemias in humans is based on T-cell and B-cell lineages, but actually uses cell differentiation (CD) markers on the cytoskeleton for recognition.  It is only a conjecture that if the cells line were highly anaplastic, they might not be sustainable in cell culture in perpetuity.
The analogue of these cells to what I would expect to see in humans is the SLL having the characteristic marking: CD5, see http://www.pathologyoutlines.com/topic/lymphomaSLL.html

Micro description
=======================================================

● Effacement of nodal architecture by pale staining pseudofollicles or proliferation centers with ill-defined borders, containing small round mature lymphocytes, prolymphocytes (larger than small lymphocytes, abundant basophilic cytoplasm, prominent nucleoli), paraimmunoblasts (larger cells with distinct nucleoli) and many smudge cells
● Pseudofollicular centers are highlighted by decreasing light through the condenser at low power; cells have pale cytoplasm but resemble soccer balls or smudge cells on peripheral smear (cytoplasm is bubbly in mantle cell lymphoma); may have plasmacytoid features
● May have marginal zone, perifollicular or interfollicular patterns, but these cases also have proliferation centers (Mod Pathol 2000;13:1161)
● Interfollicular pattern: large, reactive germinal centers; resembles follicular lymphoma but germinal centers are bcl2 negative and tumor cells resemble SLL by morphology and immunostains
(Am J Clin Path 2000;114:41)
● Paraimmunoblastic variant: diffuse proliferation of paraimmunoblasts (normally just in pseudoproliferation centers); rare, <30 reported cases; usually multiple lymphadenopathies and rapid disease progression; case report in 69 year old man (Hum Pathol 2002;33:1145); consider as mantile cell lymphoma if t(11;14)(q13;q32) is present; may also represent CD5+ diffuse large B cell lymphoma
Bone marrow: small focal aggregates of variable size with irregular, poorly circumscribed outlines; lymphocytes are well differentiated, small, round with minimal atypia; may have foci of transformation; rarely has granulomas (J Clin Pathol 2005;58:815)
● Marrow infiltrative patterns are also described as diffuse (unmutated IgH genes, ZAP-70+, more aggressive), nodular (associated with IgH hypermutation, ZAP-70 negative) or mixed (variable mutation of IgH, variable ZAP-70, Hum Pathol 2006;37:1153)

 

Positive stains
=======================================================

● CD5, CD19, CD20 (dim), CD23, surface Ig light chain, surface IgM (dim)
● Also CD43, CD79a, CD79b (dim in 20%, Arch Pathol Lab Med 2003;127:561), bcl2
● Variable CD11c, FMC7 (42%)
Negative stains
=======================================================

● CD10, cyclin D1
Molecular
=======================================================

● Trisomy 12 (30%, associated with atypical CLL and CD79b), deletion 13q14 (25-50%),
deletion of 11q23 (worse prognosis, 10-20%)

 

Results

We set up a pipeline that could be used to

  • infer intracellular metabolic states from semi-quantitative data
  • regarding metabolites exchanged between cells and their environment.

Our pipeline combined the following four steps:

  1. data acquisition,
  2. data analysis,
  3. metabolic modeling and
  4.  experimental validation of
  • the model predictions (Fig. 1A).

We demonstrated the pipeline and the predictive potential

  • to predict metabolic alternations in diseases such as cancer
  • based on two lymphoblastic leukemia cell lines.

The resulting Molt-4 and CCRF-CEM condition-specific cell line models were able

  • to explain metabolite uptake and secretion
  •  by predicting the distinct utilization of central metabolic pathways by the two cell lines.

Whereas the CCRF-CEM model

  • resembled more a glycolytic, commonly referred to as ‘Warburg’ phenotype,
  • our predictions suggested  a more respiratory phenotype for the Molt-4  model.

We found these predictions to be in agreement with measured gene expression differences

  • at key regulatory steps in the central metabolic pathways, and
  • they were also consistent with  data regarding the energy and redox states of the cells.

After a brief discussion of the data generation and analysis steps, the results

  • derived from model generation and analysis will be described in detail.

 

2.1 Pipeline for generation of condition-specific metabolic cell line models

2.1.1 Generation of experimental data

We monitored the growth and viability of lymphoblastic leukemia cell lines in
serum- free medium (File S2, Fig. S1). Multiple omics  data sets  were derived  from these cells.

Extracellular metabolomics (exo-metabolomic) data,

  • comprising measurements of the metabolites in the spent medium of the cell cultures
    (Paglia et al. 2012a),
  • were collected along with transcriptomic data, and
  • these data sets were used to construct the models.

 

2.1.4 Condition-specific models for CCRF-CEM and Molt-4 cells

To determine whether we had obtained two distinct models,

  • we evaluated the reactions, metabolites, and genes of the two models.

Both the Molt-4 and CCRF-CEM models contained approximately

  • half of the reactions and metabolites present in the global model (Fig. 1C).

They were very similar to each other in terms of their

  • reactions,
  • metabolites, and
  • genes (File S1, Table S5A–C).

The Molt– 4 model contained

  • seven reactions that were not present in the CCRF-CEM model
    (Co-A biosynthesis pathway and exchange reactions).

In contrast, the CCRF-CEM  contained

31 unique reactions

  • arginine and proline metabolism,
  • vitamin B6  metabolism,
  • fatty acid activation,
  • transport, and exchange reaction.
  • There  were 2 and 15 unique metabolites in the Molt-4 and CCRF-CEM models,  respectively
    (File S1, Table S5B).
    Approximately three quarters of the global  model  genesremained in the condition-specific cell line models  (Fig. 1C).

The Molt-4 model contained

  • 15 unique genes, and

the CCRF-CEM model had

  • 4 unique genes (File S1, Table S5C).

Both models lacked NADH dehydrogenase
(complex I of the electron transport chain—ETC),

  •  determined by  the  absence of expression of a mandatory subunit
    (NDUFB3, Entrez gene ID 4709).

The ETC was fueled by FADH2 originating from

  1. succinate dehydrogenase and
  2. from fatty acid oxidation, which
  • through flavoprotein electron transfer
  • could contribute to the same ubiquinone pool as
  • complex I and complex II (succinate dehydrogenase).

Despite their different in vitro growth rates
(which differed by 11 %, see File S2, Fig. S1) and

  • differences in exo-metabolomic data (Fig. 1B) and
  • transcriptomic data,
  • the internal networks were largely conserved
  • in the two condition-specific cell line models.

 

2.1.5 Condition-specific cell line models predict distinct metabolic strategies

Despite the overall similarity of the metabolic models,

  • differences in their cellular uptake and secretion patterns suggested
  • distinct metabolic states in the two cell lines
    (Fig. 1B and see “Materials and methods” section for more detail).

To interrogate the metabolic differences, we sampled the solution space

  • of each model  using an Artificial Centering Hit-and-Run (ACHR) sampler (Thiele et al. 2005).

For this  analysis, additional constraints were applied, emphasizing

  • the  quantitative differences in commonly uptaken and secreted metabolites.

The  maximum possible uptake and maximum possible secretion flux rates were

  • reduced according to the measured relative differences between the cell lines
    (Fig. 1D, see “Materials and methods” section).

We plotted the number of sample points containing a particular flux rate for each reaction. The resulting

  • binned histograms can be understood as representing the probability that
  • a particular reaction can have a certain flux value.

A comparison of the sample points obtained for the Molt-4 and CCRF-CEM models revealed

  • a  considerable shift in the distributions, suggesting
  • a higher utilization of  glycolysis by the CCRF-CEM model (File S2, Fig. S2).

This result  was further  supported by differences

  • in medians calculated from sampling points (File S1,  Table S6).

The shift persisted throughout all reactions of the pathway and

  • was  induced by the higher glucose uptake (35 %) from
  • the extracellular medium in CCRF-CEM cells.

The sampling median for glucose uptake was 34 % higher

  • in the  CCRF-CEM model than in Molt-4 model (File S2, Fig. S2).

The usage of the  TCA cycle was also distinct in the two condition-specific cell-line models (Fig. 2).

  • the models used succinate dehydrogenase differently (Figs. 23).

The Molt-4 model utilized an associated reaction to generate FADH2, whereas

  • in  the CCRF-CEM model, the histogram was shifted in the opposite direction,
  • toward  the generation of succinate.

Additionally, there was a higher efflux of  citrate toward

  • amino acid and lipid metabolism in the CCRF-CEM model (Fig. 2).

There was higher flux through anaplerotic and cataplerotic reactions

  • in the CCRF-CEM model than in the Molt-4 model (Fig. 2);
  • these reactions include the efflux  of citrate through

 

  1. ATP-citrate lyase,
  2. uptake of glutamine,
  3. generation of  glutamate from glutamine,
  4. transamination of pyruvate and
  5.  glutamate to alanine  and to 2-oxoglutarate,
  6. secretion of nitrogen, and
  7. secretion of alanine.

The Molt-4 model showed higher utilization of oxidative phosphorylation (Fig. 3),

  • supported by elevated median flux through ATP synthase (36 %) and other  enzymes,
  • which contributed to higher oxidative metabolism.

The sampling  analysis therefore revealed different usage of

  • central metabolic pathways by the condition-specific models.

 

Fig. 2

Differences in the use of the TCA cycle by the CCRF-CEM

Differences in the use of the TCA cycle by the CCRF-CEM

Differences in the use of the TCA cycle by the CCRF-CEM model (red) and the Molt-4 model (blue).
The table provides the median values of the sampling results. Negative values in histograms and Table

  • describe reversible  reactions with flux in the reverse direction.

There are multiple reversible  reactions for the transformation of

  1. isocitrate and α-ketoglutarate,
  2. malate and  fumarate, and
  3. succinyl-CoA and succinate.

These reactions are  unbounded,  and therefore histograms are not shown.
The details of participating cofactors  have been removed.

Atp ATP, cit citrate, adp ADP, pi phosphate, oaa oxaloacetate, accoa acetyl-CoAcoa coenzyme-A,
icit isocitrate, αkg α-ketoglutarate, succcoa succinyl-CoAsucc succinate, fumfumarate, mal malate,
oxa oxaloacetate,  pyr pyruvate, lac lactate, ala alanine, gln glutamine, ETC electron transport  chain.

 

Electronic supplementary material The online version of this article
http://dx.doi.org:/10.1007/s11306-014-0721-3 
contains supplementary material,  which  is available to authorized users.

  1.  K. Aurich _ G. Paglia _ O ´ . Rolfsson _ S. Hrafnsdo´ ttir _
  2. Magnu´sdo´ ttir _ B. Ø. Palsson _ R. M. T. Fleming _ I. Thiele. Center for Systems Biology,
    University of Iceland, Reykjavik, Iceland
  3.  K. Aurich _ R. M. T. Fleming _ I. Thiele (&). Luxembourg Centre for Systems Biomedicine,
    University of Luxembourg, Campus Belval, Esch-Sur-Alzette, Luxembourg
    e-mail: ines.thiele@uni.lu
  4. M. Stefaniak. School of Health Science, Faculty of Food Science and Nutrition,
    University of Iceland, Reykjavik, Iceland
  5. Ø. Palsson. Department of Bioengineering, University of California San Diego, La Jolla, CA, USA

http://link.springer.com/static-content/images/404/art%253A10.1007%252
Fs11306-014-0721-3/MediaObjects/11306_2014_721_Fig3_HTML.gif

 

Fig. 3

Fatty acid oxidation and ETC _Fig3

Fatty acid oxidation and ETC _Fig3

 

Sampling reveals different utilization of oxidative phosphorylation by the

  • generated models.

Different distributions are observed for the CCRF-CEM model (red) and the Molt-4 model (blue).

  • Molt-4 has higher  median  flux through ETC reactions II–IV.

The table provides the median values  of the sampling results. Negative values in the histograms and in the table describe

  • reversible reactions with flux in the reverse direction.

Both models lack Complex I of the ETC because of constraints

  • arising from the mapping of transcriptomic data.

Electron transfer flavoprotein and

  • electron transfer flavoprotein–ubiquinone oxidoreductase
  •  both also carry higher flux in the Molt-4 model

 

2.1.6 Experimental validation of energy and redox status of CCRF-CEM and Molt-4 cells

Cancer cells have to balance their needs

  •  for energy and biosynthetic precursors, and they have
  • to maintain redox homeostasis to proliferate (Cairns et al. 2011).

We conducted enzymatic assays of cell lysates to measure levels and/or ratios of

  • ATP,
  • NADPH + NADP,
  • NADH + NAD, and
  • glutathione.

These measurements were used to provide support for

  • the in silico predicted metabolic differences (Fig. 4).

Additionally, an Oxygen Radical Absorbance Capacity (ORAC) assay was used

  • to evaluate the cellular antioxidant status (Fig. 4B).

Total concentrations of NADH + NAD, GSH + GSSG, NADPH + NADP and ATP, were higher in Molt-4 cells  (Fig. 4A).

The higher ATP concentration in Molt-4 cells could either result from

  • high production rates, or intracellular  accumulation connected to high or
  • low reactions fluxes (Fig. 4A).

Our simplified view that oxidative Molt-4 produces less ATP and was contradicted by

  • the higher ATP concentrations measured (Fig. 4L).

Yet we want to emphasize that concentrations

  • cannot be compared to flux values,
  • since we are modeling at steady-state.

NADH/NAD+ ratios for both cell lines were shifted toward NADH (Fig. 4D, E), but

  • the shift toward NADH was more pronounced in CCRF-CEM (Fig. 4E),
  • which matched  our expectation based on the higher utilization of
  • glycolysis and 2-oxoglutarate  dehydrogenase in the CCRF-CEM model (Fig. 4L).

 

Fig. 4 (not shown)

A–K  Experimentally determined ATP, NADH + NAD, NADPH + NADP, and GSH + GSSG concentrations, and ROS detoxification in the CCRF-CEM and Molt-4 cells.

L Expectations for cellular energy and redox states. Expectations are based on predicted metabolic differences of the Molt-4 and CCRF-CEM models

2.1.7 Comparison of network utilization and alteration in gene expression

With the assumption that

  • differential expression of particular genes would cause reaction flux changes,

we determined how the differences in gene expression (between CCRF-CEM and Molt-4)

  • compared to the flux differences observed in the  models.

Specifically, we checked whether the reactions associated with genes upregulated
(significantly more expressed in CCRF-CEM cells compared to Molt-4  cells)

  • were indeed more utilized by the CCRF-CEM model,

and we  checked  whether downregulated genes

  • were associated with reactions more utilized by the Molt-4 model.

The set of downregulated genes was associated with 15 reactions, and

  • the set of 49 upregulated genes was associated with 113 reactions in the models.

Reactions were defined as differently utilized

  • if the difference in flux exceeded 10 % (considering only non-loop reactions).

Of the reactions associated with upregulated genes,

  • 72.57 % were more utilized by the CCRF-CEM model, and
  • 2.65 % were more utilized by the Molt-4 model (File S1, Table S7).

In contrast, all 15 reactions associated with the 12 downregulated genes

  • were more utilized in the CCRF-CEM model (File S1, Table S8).

After this initial analysis, we approached the question from a different angle, asking

  • whether the majority of the reactions associated with each individual gene
  • upregulated in CCRF-CEM were more utilized by the CCRF-CEM model.
  •  this was the case for 77.55 % of the upregulated genes.

The majority of reactions associated with two (16.67 %) downregulated genes

  • were more utilized by the Molt-4 model.

Taken together, our comparisons of the

  • direction of gene expression with the fluxes of the two cancer cell-line models
  • confirmed that reactions associated with upregulated genes in the CCRF-CEM
    cells were generally more utilized by the CCRF-CEM model.

2.1.8 Accumulation of DEGs and AS genes at key metabolic steps

After we confirmed that most reactions associated with upregulated genes

  • were more utilized by the CCRF-CEM model,

we checked the locations of DEGs within the network. In this analysis, we paid special attention to

  • the central metabolic pathways that we had found
  • to be distinctively utilized by the two models.

Several DEGs and AS events were associated with

  • glycolysis,
  • the ETC,
  • pyruvate metabolism, and
  • the PPP (Table 1).

 

Table 1

DEGs and AS events of central metabolic and cancer-related pathways

Full lists of DEGs and AS are provided in the supplementary material.

Upregulated significantly more expressed in CCRF-CEM compared to Molt-4 cells

PPP pentose phosphate pathway, OxPhos oxidative phosphorylation, Glycolysis/gluconglycolysis/gluconeogenesis, Pyruvate met. pyruvate metabolism

Moreover, in glycolysis, the DEGs and/or AS genes

  • were associated with all three rate-limiting steps, i.e., the steps mediated by
  1. hexokinase,
  2. pyruvate kinase, and
  3. phosphofructokinase.

Of these key enzymes,

  • hexokinase 1 (Entrez Gene ID: 3098) was alternatively spliced,
  • pyruvate kinase (PKM, Entrez gene ID: 5315) was significantly more
    expressed in the CCRF-CEM cells (Table 1),

in agreement with the higher in silico predicted flux.

However, in contrast to the observed

  • higher utilization of glycolysis in the CCRF-CEM model,
  • the gene associated with the rate-limiting glycolysis step, phosphofructokinase (Entrez Gene ID: 5213),
  • was significantly upregulated in Molt-4 cells relative to CCRF-CEM cells.

This higher expression was detected for only a single isozyme, however. Two of
the three genes associated with phosphofructokinase were also subject to
alternative splicing (Table 1). In addition to the key enzymes, fructose
bisphosphate aldolase (Entrez Gene ID: 230) was also significantly

  • upregulated in Molt-4 cells relative to CCRF-CEM cells,
  • in contrast to the predicted higher utilization of glycolysis in the CCRF-CEM model.

Additionally, glucose-6P-dehydrogenase (G6PD), which catalyzes

  • the first reaction and committed step of the PPP,
  • was an AS gene (Table 1).

A second AS gene associated with

  •  the PPP reaction of the deoxyribokinase
  • was RBKS (Entrez Gene ID: 64080).

This gene is also associated with ribokinase, but ribokinase was removed

  • because of the lack of ribose uptake or secretion.

Single AS genes were associated with different complexes of the ETC (Table 1).

Literature query revealed that at least 13 genes associated with alternative

  • splicing events were mentioned previously in connection with both alternative
    splicing and cancer (File S1, Table S14), and
  • 37 genes were associated with cancer, e.g., upregulated, downregulated at the
    level of mRNA or protein, or otherwise
  • connected to cancer metabolism and signaling.

One general observation was that there was a surprising

  • accumulation of metabolite transporters among the AS.

Overall, the high incidence of

  • differential gene expression events at metabolic control points
  • increases the plausibility of the in silico predictions.

 

2.1.9 Single gene deletion

Analyses of essential genes in metabolic models have been used

  • to predict candidate drug targets for cancer cells (Folger et al. 2011).

Here, we conducted an in silico gene deletion study for all model genes to identify

  • a unique set of knock-out (KO) genes
  • for each condition-specific cell line model.

The analysis yielded 63 shared lethal KO genes and

  • distinct sets of KO genes for the CCRF-CEM model (11 genes) and the Molt-4 model (3 genes).

For three of the unique CCRF-CEM KO genes,

  • the genes were only present in the CCRF-CEM model (File S1, Table S9).

 

The essential genes for both models were then

  • related to the cell-line-specific differences in metabolite uptake and secretion (Fig. 1B).

The CCRF-CEM model

  1. needed to generate putrescine from ornithine
    (ORNDC, Entrez Gene ID: 4953)
  2. to subsequently produce 5-methylthioadenosine for secretion (Fig. 1B).
  3. S-adenosylmethioninamine produced by adenosylmethionine decarboxylase
    (arginine and proline metabolism, associated with Entrez Gene ID: 262)
  • is a substrate required for generation of 5-methylthioadenosine.

Another example of a KO gene connected to an enforced exchange reaction was

  • glutamic-oxaloacetic transaminase 1 (GOT1, Entrez Gene ID: 2805).

Without GOT1, the CCRF-CEM model was forced to secrete

  • 4-hydroxyphenylpyruvate (Fig. 1B),
  • the second product of tyrosine transaminase,
  • which is produced only by that enzyme.

 

One KO gene in the Molt-4 model (Entrez Gene ID: 26227) was associated with

  • phosphoglycerate dehydrogenase (PGDH),
  • which catalyzes the conversion of 3-phospho-d-glycerate to 3-phosphohydroxypyruvate
  • while generating NADH from NAD+.

This KO gene is particularly interesting, given

  • the involvement of this reaction in a novel pathway for ATP generation in rapidly proliferating cells
    (Locasale et al. 2011; Vander Heiden 2011; Vazquez et al. 2011).

Reactions associated with unique KO genes were in many cases utilized more by the model, in which

  • the gene KO was lethal,
  • underlining the potential importance of these reactions for the models.

Thus, single gene deletion provided unique sets of lethal genes that could be

  • specifically targeted to kill these cells.

 

3 Discussion

In the current study, we explored the possibility of

  • semi-quantitatively integrating metabolomic data with
  • the human genome-scale reconstruction to facilitate analysis.

By constructing condition-specific cell line models

  • to provide a structured framework,
  • we derived insights that could not have been obtained from data analysis alone.

We derived condition-specific cell line models

  • for CCRF-CEM and
  • Molt-4 cells

that were able to explain the observed exo-metabolomic differences (Fig. 1B).

Despite the overall similarities between the models, the analysis revealed

  • distinct usage of central metabolic pathways (Figs. 234),
  • which we validated based on experimental data and
  • differential gene expression.

The additional data sufficiently supported

  • metabolic differences in the cell lines,
  • providing confidence in the generated models and the model-based predictions.

We used the validated models

  • to predict unique sets of lethal genes
  • to identify weak links in each model.

These weak links may represent potential drug targets.

Integrating omics data with the human genome-scale reconstruction

  • provides a structured framework (i.e., pathways)
  • that is based on careful consideration of the available biochemical literature
    (Thiele and Palsson2010).

This network context can simplify omics data analysis, and

  • it allows even non-biochemical experts
  • to gain fast and comprehensive insights
  • into the metabolic aspects of omics data sets.

Compared to transcriptomic data,

  • methods for the integration and analysis of metabolomic data
  • in the context of metabolic models are less well established,

although it is an active field of research (Li et al. 2013; Paglia et al. 2012b).
In contrast to other studies, our approach emphasizes

  • the representation of experimental conditions rather than
  • the reconstruction of a generic, cell-line-specific network,
  • which would require the combination of data sets from
  • many experimental conditions and extensive manual curation.

Rather, our way of model construction allowed us to efficiently

  • assess the metabolic characteristics of cells.

Despite the fact, that only a limited number of exchanged metabolites can be

  • measured by available metabolomics platforms and
  • at reasonable time-scale,

and that pathways of measured metabolites might still be unknown to date
(File S1, Tables S2–S3), our methods have the potential

  • to reveal metabolic characteristics of cells
  • which could be useful for biomedicine and personalized health.

The reasons why some cancers respond to certain treatments and not others
remain unclear, and choosing a treatment for a specific patient is often difficult
(Vander Heiden 2011). One potential application of our approach could be the
characterization of cancer phenotypes to explore how cancer cells or other cell
types

  • with particular metabolic characteristics respond to drugs.

The generation of our condition-specific cell line models involved

  • only limited manual curation,
  • making this approach a fast way to place metabolomic data
  • into a network context.

Model building mainly involves

  • the rigid reduction of metabolite exchanges
  • to match the observed metabolite exchange pattern
  • with as few additional metabolite exchanges as possible.

It should be noted that this reduction determines,

  • which pathways can be utilized by the model.

Our approach mostly conserved the internal network redundancy. However, a

  • more significant reduction may be achieved using different data.

Generally, a trade-off exists between the reduction of the internal network and

  • the increasing number of network gaps that need to be curated
  • by using additional omics data, such as transcriptomics and proteomics.

One way to prevent the emergence of network gaps would be

  • to use mapping algorithms that conserve network functionality,
    such as GIMME (Becker and Palsson 2008).

However, several additional methods exist for the integration of
transcriptomic data (Blazier and Papin 2012), and

  • which model-building method is best depends on the available data.

Interestingly, the lack of a significant contribution of our

  • gene expression data to the reduction of network size
  • suggests that the use of transcriptomic data is not necessary
  • to identify distinct metabolic strategies;
  • rather, the integration of exo-metabolomic data alone
    may provide sufficient insight.

However, sampling of the cell line models constrained

  • according to the exo-metabolomic profiles only, or
  • increasing the cutoff for the generation of absent and present calls (p < 0.01),
  • did not yield the same insights as presented herein (File S1, Table S18).

Only recently Gene Inactivation Moderated by Metabolism, Metabolomics and
Expression (GIM(3)E) became available, which

  • enforces minimum turnover of detected metabolites
  • based on intracellular metabolomics data as well as
  • gene expression microarray data (Schmidt et al. 2013).

In contrast to this approach, we emphasized our analysis on the

  • relative differences in the exo-metabolomic data of two cell lines.

GIM(3)E constitutes another integration method when the analysis should be

  • emphasized on intracellular metabolomics data (Schmidt et al. 2013).

The metabolic differences predicted by the models are generally plausible.
Cancers are known to be heterogeneous (Cairns et al. 2011), and

  • the contribution of oxidative phosphorylation to cellular ATP production
    may vary (Zu and Guppy 2004).

Moreover, leukemia cell lines have been shown

  • to depend on glucose, glutamine, and fatty acids to varying extents
  • to support proliferation.

Such dependence may cause the cells to adapt their metabolism

  • to the environmental conditions (Suganuma et al. 2010).

In addition to identifying supporting data in the literature, we performed

  • several analyses to validate the models and model predictions.

Our expectations regarding the levels and ratios of metabolites

  • relevant to energy and redox state were largely met (Fig. 4L).

The more pronounced shift of the NADH/NAD+ ratio

  • toward NADH in the CCRF-CEM cells
  • was in agreement with the predicted Warburg phenotype (Fig. 4),
  • and the higher lactate secretion in the CCRF-CEM cells (File S2, Fig. S2)
  • implies an increase in NADH relative to NAD+
    (Chiarugi et al. 2012; Nikiforov et al. 2011), again
  • matching the known Warburg phenotype.

ROS production is enhanced in certain types of cancer (Droge 2002; Ha et al. 2000), and

  • the generation of ROS is thought to contribute to
  1. mutagenesis,
  2. tumor promotion, and
  3. tumor progression (Dreher and Junod1996; Ha et al. 2000).

However, decreased mitochondrial glucose oxidation and

  • a transition to aerobic glycolysis
  • protect cells against ROS damage during biosynthesis and cell division
    (Brand and Hermfisse1997).

The higher ROS detoxification capability in Molt-4 cells, in combination with

  • higher spermidine dismutase utilization by the Molt-4 model (Fig. 4),
  • provided a consistent picture of the predicted respiratory phenotype (Fig. 4L).

Control of NADPH maintains the redox potential through GSH and

  • protects against oxidative stress, yet
  • changes in the NADPH ratio in response to oxidative damage
  • are not well understood (Ogasawara et al.2009).

Under stress conditions, as assumed for Molt-4 cells,

  • the NADPH/NADP+ ratio is expected to decrease because of
  • the continuous reduction of GSSG (Fig. 4L), and
  • this was confirmed in the Molt-4 cells (Fig. 4).

The higher amounts of GSH found in Molt-4 cells in vitro may demonstrate

  • an additional need for ROS scavengers because of
  • a greater reliance on oxidative metabolism.

Cancer is related to metabolic reprogramming, which results from

  • alterations of gene expression and
  • the expression of specific isoforms or
  • splice forms to support proliferation
    (Cortes-Cros et al. 2013; Marin-Hernandez et al. 2009).

The gene expression differences detected between the two cell lines in this study
supported the existence of

  • metabolic differences in these cell lines, particularly because
  • key steps of the metabolic pathways central to cancer metabolism
  • seemed to be differentially regulated (Table 1).

The detailed analysis of the respective

  • differences on the pathway fluxes exceeds the scope of this study, which was to
  • demonstrate the potential of the integration of exo-metabolomic data into the network context.

We found discrepancies between differential gene regulation and

  • the flux differences between the two models as well as
  • the utilization AS gene-associated reaction.

This is not surprising, since analysis of the detailed system is required

  • to make any further assumptions on the impact that
  • the differential regulation or splicing might have on the reaction flux,
  • given that for many of the concerned enzymes isozymes exist, or
  • only one of multiple subunits of a protein complex was concerned.

Additionally, reaction fluxes are regulated by numerous post-translational factors, e.g.,

  • protein modification,
  • inhibition through proteins or metabolites,
  • alter reaction fluxes (Lenzen 2014),

which are out of the scope of constraint-based steady-state modeling.

Rather, the results of the presented  approach

  • demonstrate how the models can be used to generate
  • informed hypothesis that can guide experimental work.

The combination of our tailored metabolic models and

  • differential gene expression analysis seems well-suited
  • to determine the potential drivers
  • involved in metabolic differences between cells.

Such information could be valuable for drug discovery, especially when more

  • peripheral metabolic pathways are considered.

Statistical comparisons of gene expression data with sampling-derived flux data

  • could be useful in future studies (Mardinoglu et al. 2013).

A single-gene-deletion analysis revealed that PGDH was

  • a lethal KO gene for the Molt-4 model only.

Differences in PGDH protein levels

  • correspond to the amount of glycolytic carbon
  • diverted into glycine biosynthesis.

Rapidly proliferating cells may use an

  • alternative glycolytic pathway for ATP generation,
  • which may provide an advantage in the case of
  • extensive oxidative phosphorylation and proliferation
    (Locasale et al.2011; Vander Heiden 2011; Vazquez et al. 2011).

For breast cancer cell lines, variable dependency on

  • the expression of PGDH has already been demonstrated
    (Locasale et al. 2011).

This example of a unique KO gene demonstrates how

  • in silico gene deletion in metabolomics-driven models
  • can identify the metabolic pathways used by cancer cells.

This approach can provide valuable information for drug discovery.

In conclusion, our contextualization method produced

  • metabolic models that agreed in many ways with the validation data sets.

The analyses described in this study have great potential to reveal

  • the mechanisms of metabolic reprogramming,
  • not only in cancer cells but also in other cells affected by diseases, and
  • for drug discovery in general.

 

4.3 Analysis of the extracellular metabolome

Mass spectrometry analysis of the exo-metabolome was performed by
Metabolon®, Inc. (Durham, NC, USA) using a standardized analytical platform.
In total, 75 extracellular metabolites were detected in the initial data set for at
least 1 of the 2 cell lines (Paglia et al. 2012a). Of these metabolites, 15 were not
part of our global model and were discarded. Apart from being absent in our
global model, an independent search in HMDB (Wishart et al. 2013) revealed no
pathway information was available for most of these metabolites (File S1, Tables S2–S3).
It should be noted that metabolites e.g.,

  • N-acetylisoleucine,
  • N-acetyl-methionine or pseudouridine,

constitute protein and RNA degradation products, which were out of the scope
of the metabolic network.

Thiamin (Vitamin B1) was part of the minimal medium of essential compounds
supplied to both models.Riboflavin (Vitamin B2) and Trehalose were excluded
since these compounds cannot be produced by human cells. Erythrose and
fructose were also excluded. In contrast 46 metabolites that were part of the
global model. The data set included two different time points, which allowed us
to treat the increase/decrease of a metabolite signal between time points as

  • evidence for uptake or secretion when the change was greater than 5 %
    from what was observed in the control (File S1, Tables S2–S3).

We found 12 metabolites that were taken up by both cell lines and
10 metabolites that were commonly secreted by both cell lines over
the course of the experiment.

Molt-4 cells took up three metabolites not taken up by CCRF-CEM cells, and
secreted one metabolite not secreted by CCRF-CEM cells. Two of the three
uniquely uptaken metabolites were essential amino acids:

  1. valine and
  2. methionine.

It is unlikely that these metabolites were not taken up by the CCRF-CEM cells,
and the CCRF-CEM model was allowed to take up this metabolite. Therefore,
no quantitative constraints were applied for the sampling analysis either.
CCRF-CEM cells had

  • four unique uptaken
  • and seven unique secreted metabolites
    (exchange not detected in Molt-4 cells).

 

4.4 Network refinement based on exo-metabolic data

Despite its comprehensiveness, the human metabolic reconstruction is

  • not complete with respect to extracellular metabolite transporters
    (Sahoo et al. 2014; Thiele et al. 2013).

Accordingly, we identified metabolite transport systems

  • from the literature for metabolites that were already part of the global model,
  • but whose extracellular transport was not yet accounted for.

Diffusion reactions were included whenever a respective transporter could not be identified.

In total, 34 reactions [11 exchange reactions, 16 transport reactions and 7 demand reactions
(File S1, Table S11)] were added to Recon 2 (Thiele et al. 2013), and 2 additional reactions
were added to the global model (File S1, Table S10).

4.5 Expression profiling

Molt-4 and CCRF-CEM cells were grown in advanced RPMI 1640 and 2 mM
GlutaMax, and the cells were resuspended in medium containing DMSO
(0.67 %) at a concentration of 5 × 105 cells/mL. The cell suspension (2 mL)
was seeded in 12-well plates in triplicate. After 48 h of growth, the cells
were collected by centrifugation at 201×g for 5 min. Cell pellets were snap-frozen
in liquid N2 and kept frozen until RNA extraction and analysis by Aros
(Aarhus, Denmark).

4.6 Analysis of transcriptomic data

We used the Affymetrix GeneChip Human Exon 1.0 ST Array to measure whole
genome exon expression. We generated detection above background (DABG) calls
using ROOT (version 22) and the XPS package for R (version 11.1), with Robust
Multi-array Analysis summarization. Calls for data mapping were assigned based
on p < 0.05 as the cutoff probability to distinguish presence versus absence for
the 1,278 model genes (File S1, Table S12).

Differential gene expression and alternative splicing analyses were performed by
using AltAnalyse software (v2.02beta) with default options on the raw data files
(CEL files). The Homo sapiens Ensemble 65 database was used, probe set filtering
was kept as DABG p < 0.05, and non-log expression < 70 was used for
constitutive probe sets to determine gene expression levels. For the comparison,
CCRF-CEM was the experimental group and Molt-4 was the baseline group. The
set of DEGs between cell lines was identified based on a p < 0.05 FDR cutoff
(File S1, Table S13A–B). Alternative splicing analysis was performed on core probe sets
with a minimum alternative exon score of 2 and a maximum absolute gene
expression change of 3 because alternative splicing is a less critical factor among
highly DEGs (File S1, Table S14). Gene expression data, complete lists of DABG p-values,
DEGs and alternative splicing events have been deposited in the Gene
Expression Omnibus
 (GEO) database (Accession number: GSE53123).

 

4.7 Deriving cell-type-specific subnetworks

Transcriptomic data were mapped to the model in a manual fashion (COBRA
function: deleteModelGenes). Specifically, reactions dependent on gene products
that were called as “absent” were constrained to zero, such that fluxes through
these reactions were disabled. Submodels were extracted based on the set of
reactions carrying flux (network pruning) by running fastFVA
(Gudmundsson and Thiele 2010) after mapping the metabolomic and
transcriptomic data using the COBRA toolbox (Schellenberger et al. 2011).

 

…..

 

Electronic supplementary material

Below is the link to the electronic supplementary material.

File S1. Supplementary material 1 (XLSX 915 kb)

File S2. Supplementary material 2 (DOCX 448 kb)

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Metabolomic analysis of two leukemia cell lines. I.

Larry H. Bernstein, MD, FCAP, Reviewer and Curator

Leaders in Pharmaceutical Intelligence

 

I have just posted a review of metabolomics.  In the last few weeks, the Human Metabolome was published.  I am hopeful that my decision has taken the right path to prepare my readers adequately if they will have read the articles that preceded this.  I pondered how I would present this massive piece of work, a study using two leukemia cell lines and mapping the features and differences that drive the carcinogenesis pathways, and identify key metabolic signatures in these differentiated cell types and subtypes.  It is a culmination of a large collaborative effort that required cell culture, enzymatic assays, mass spectrometry, the full measure of which I need not present here, and a very superb validation of the model with a description of method limitations or conflicts.  This is a beautiful piece of work carried out by a small group by today’s standards.

I shall begin this by asking a few questions that will be addressed in the article, which I need to beak up into parts, to draw the readers in more effectively.

Q 1. What metabolic pathways do you expect to have the largest role in the study about to be presented?

Q2. What are the largest metabolic differences that one expects to see in compairing the two lymphoblastic cell lines?

Q3. What methods would be used to extract the information based on external metabolites, enzymes, substrates, etc., to create the model for the cell internal metabolome?

 

 

Abstract

Metabolic models can provide a mechanistic framework to analyze information-rich omics data sets, and are increasingly being used

  • to investigate metabolic alternations in human diseases.

An expression of the altered metabolic pathway utilization is

  • the selection of metabolites consumed and released by cells.

However, methods for the inference of intracellular metabolic states from extracellular measurements in the context of metabolic models

  • remain underdeveloped compared to methods for other omics data.

Herein, we describe a workflow for such an integrative analysis

  • extracting the information from extracellular metabolomics data.

We demonstrate, using the lymphoblastic leukemia cell lines Molt-4 and CCRF-CEM, how

  • our methods can reveal differences in cell metabolism.

Our models explain metabolite uptake and secretion by

  • predicting a more glycolytic phenotype for the CCRF-CEM model and
  • a more oxidative phenotype for the Molt-4 model, which
  • was supported by our experimental data.

Gene expression analysis revealed altered expression of gene products at

  • key regulatory steps in those central metabolic pathways,

and literature query emphasized

  • the role of these genes in cancer metabolism.

Moreover, in silico gene knock-outs identified

  • unique control points for each cell line model, e.g., phosphoglycerate dehydrogenase for the Molt-4 model.

Thus, our workflow is well suited to the characterization of cellular metabolic traits based on

  • extracellular metabolomic data, and
  • it allows the integration of multiple omics data sets into a cohesive picture based on a defined model context.

Keywords Constraint-based modeling _ Metabolomics _Multi-omics _ Metabolic network _ Transcriptomics

 

Reviewer Summary:

  1. A model is introduced to demonstrate a lymphocytic integrated data set using to cell lines.
  2. The method is required to integrate extracted data sets from extracellular metabolites to an intracellular picture of cellular metabolism for each cell line.
  3. The method predicts a more glycolytic or a more oxidative metabolic framework for one or the othe cell line.
  4. The genetic phenotypes differ with a unique control point for each cell line.
  5. The model presents an integration of omics data sets into a cohesive picture based on the model context.

Without having seen the full presentation –

  1. Is the method a snapshot of the neoplastic processes described?
  2. Does the model give insight into the cellular metabolism of an initial cell state for either one or both cell lines?
  3. Would one be able to predict a therapeutic strategy based on the model for either or both cell lines?

Before proceeding further into the study, I would conjecture that there is no way of knowing the initial state ( consistent with what is described by Ilya Prigogine for a self-organizing system) because the model is based on the study of cultured cells that had an unknown metabolic control profile in a host proliferating bone marrow that is likely B-cell origin.  So this is a snapshot of a stable state of two incubated cell lines.  Then the question that is raised is whether there is not only a genetic-phenotypic relationship between the cells in culture and the external metabolites produced, but also whether differences can be discerned between the  internal metabolic constructions that would fit into a family tree.

 

Introduction

Modern high-throughput techniques

  • have increased the pace of biological data generation.

Also referred to as the ‘‘omics avalanche’’, this wealth of data

  • provides great opportunities for metabolic discovery.

Omics data sets contain a snapshot of almost the entire repertoire of

  • mRNA, protein, or metabolites at a given time point or
  • under a particular set of experimental conditions.

Because of the high complexity of the data sets,

  • computational modeling is essential for their integrative analysis.

Currently, such data analysis

  • is a bottleneck in the research process and
  • methods are needed to facilitate the use of these data sets, e.g.,
  1. through meta-analysis of data available in public databases
    [e.g., the human protein atlas (Uhlen et al. 2010)
  2. or the gene expression omnibus (Barrett  et al.  2011)], and
  3. to increase the accessibility of valuable information
    for the biomedical research community.

Constraint-based modeling and analysis (COBRA) is

  • a computational approach that has been successfully used
  • to investigate and engineer microbial metabolism through
    the prediction of steady-states (Durot et al.2009).

The basis of COBRA is network reconstruction: networks are assembled

  1. in a bottom-up fashion based on genomic data and
  2. extensive organism-specific information from the literature.

Metabolic reconstructions

  1. capture information on the known biochemical transformations
    taking place in a target organism
  2. to generate a biochemical, genetic and genomic knowledge base
    (Reed et al. 2006).

Once assembled, a metabolic reconstruction

  • can be converted into a mathematical model
    (Thiele and Palsson 2010), and
  • model properties can be interrogated using a great variety of methods
    (Schellenberger et al. 2011).

The ability of COBRA models to represent

  • genotype–phenotype and environment–phenotype relationships
  • arises through the imposition of constraints,
  • which limit the system to a subset of possible network states
    (Lewis et al. 2012).

Currently, COBRA models exist for more than 100 organisms, including humans
(Duarte et al. 2007; Thiele et al. 2013).

Since the first human metabolic reconstruction was described
[Recon 1 (Duarte et al. 2007)],

  • biomedical applications of COBRA have increased
    (Bordbar and Palsson 2012).

One way to contextualize networks is to

  • define their system boundaries
  • according to the metabolic states of the system,
    e.g., disease or dietary regimes.

The consequences of the applied constraints

  • can then be assessed for the entire network
    (Sahoo and Thiele 2013).

Additionally, omics data sets have frequently been used

  • to generate cell-type or condition-specific metabolic models.

Models exist for specific cell types, such as

  • enterocytes (Sahoo and Thiele2013),
  • macrophages (Bordbar et al. 2010), and
  • adipocytes (Mardinoglu et al. 2013), and
  • even multi-cell assemblies that represent
    the interactions of brain cells (Lewis et al. 2010).

All of these cell type specific models,

  • except the enterocyte reconstruction
  • were generated based on omics data sets.

Cell-type-specific models have been used

  • to study diverse human disease conditions.

For example, an adipocyte model was generated using

  • transcriptomic,
  • proteomic, and
  • metabolomics data.

This model was subsequently used to investigate

  • metabolic alternations in adipocytes
  • that would allow for the stratification of obese patients
    (Mardinoglu et al. 2013).

One highly active field within the biomedical applications of COBRA is

  • cancer metabolism (Jerby and Ruppin, 2012).

Omics-driven large-scale models have been used

  • to predict drug targets (Folger et al. 2011; Jerby et al. 2012).

A cancer model was generated using

  • multiple gene expression data sets and
  • subsequently used to predict synthetic lethal gene pairs
  • as potential drug targets selective for the cancer model,
  • but non-toxic to the global model (Recon 1),
  • a consequence of the reduced redundancy in the
    cancer specific model (Folger et al. 2011).

In a follow up study, lethal synergy between

  • FH and enzymes of the heme metabolic pathway
    were experimentally validated and
  • resolved the mechanism by which FH deficient cells,
    e.g., in renal-cell cancer cells
  • survive a non-functional TCA cycle (Frezza et al. 2011).

Contextualized models, which contain only 

  • the subset of reactions active in 
  • a particular tissue (or cell-) type,
  • can be generated in different ways
    (Becker and Palsson, 2008; Jerby et al. 2010).

However, the existing algorithms mainly consider

  • gene expression and proteomic data to define the reaction sets
  • that comprise the contextualized metabolic models.

These subset of reactions are usually defined based on

  • the expression or absence of expression of the genes or proteins
    (present and absent calls), or
  • inferred from expression values or differential gene expression.

Comprehensive reviews of the methods are available
(Blazier and Papin, 2012; Hyduke et al. 2013).

Only the compilation of a large set of omics data sets

  • can result in a tissue (or cell-type) specific metabolic model, whereas

the representation of one particular experimental condition is achieved through

  • the integration of omics data set generated from one experiment only
    (condition-specific cell line model).

Recently, metabolomic data sets

  • have become more comprehensive and using these data sets allow
  • direct determination of the metabolic network components (the metabolites).

Additionally, metabolomics has proven to be

  1. stable,
  2. relatively inexpensive, and
  3. highly reproducible
    (Antonucci et al. 2012).

These factors make metabolomic data sets

  •  particularly valuable for interrogation of metabolic phenotypes. 

Thus, the integration of these data sets is now an active field of research
(Li et al. 2013; Mo et al. 2009; Paglia et al. 2012b; Schmidt et al. 2013).

Generally, metabolomic data can be incorporated into metabolic networks as

  1. qualitative,
  2. quantitative, and
  3. thermodynamic constraints
    (Fleming et al. 2009; Mo et al. 2009).

Mo et al. used metabolites detected in the spent medium
of yeast cells to determine

  • intracellular flux states through a sampling analysis (Mo et al. 2009),
  • which allowed unbiased interrogation of the possible network states
    (Schellenberger and Palsson 2009)
  • and prediction of internal pathway use.

Such analyses have also been used

  • to reveal the effects of enzymopathies on red blood cells (Price et al. 2004),
  • to study effects of diet on diabetes (Thiele et al. 2005) and
  • to define macrophage metabolic states (Bordbar et al. 2010).

This type of analysis is available as a function in the COBRA toolbox
(Schellenberger et al. 2011).

 

 

 

In this study, we established a workflow for the generation and analysis of

  • condition-specific metabolic cell line models that
  • can facilitate the interpretation of metabolomic data.

Our modeling yields meaningful predictions regarding

  • metabolic differences between two lymphoblastic leukemia cell lines
    (Fig. 1A).
Differences in the use of the TCA cycle by the CCRF-CEM

Differences in the use of the TCA cycle by the CCRF-CEM

 

 

 

http://link.springer.com/static-content/images/404/art%253A10.1007%252
Fs11306-014-0721-3/MediaObjects/11306_2014_721_Fig1_HTML.gif

Fig. 1

A  Combined experimental and computational pipeline to study human metabolism.
Experimental work and omics data analysis steps precede computational modeling. Model

  • predictions are validated based on targeted experimental data.

Metabolomic and transcriptomic data are used for

  • model refinement and submodel extraction.

Functional analysis methods are used to characterize

  • the metabolism of the cell-line models and compare it to additional experimental
    data.

The validated models are subsequently 

  • used for the prediction of drug targets.

B Uptake and secretion pattern of model.
All metabolite uptakes and secretions that were mapped during model
generation are shown.
Metabolite uptakes are depicted on the left, and

  • secreted metabolites are shown on the right.

A number of metabolite exchanges mapped to the model

  • were unique to one cell line.

Differences between cell lines were used to set

  • quantitative constraints for the sampling analysis.

C Statistics about the cell line-specific network generation.

 Quantitative constraints.
For the sampling analysis, an additional

  • set of constraints was imposed on the cell line specific models,
  • emphasizing the differences in metabolite uptake and secretion between cell lines.

Higher uptake of a metabolite was allowed in the model of the cell line

  • that consumed more of the metabolite in vitro, whereas
  • the supply was restricted for the model with lower in vitro uptake.

This was done by establishing the same ratio between the models bounds as detected in vitro.
X denotes the factor(slope ratio) that

  1. distinguishes the bounds, and
  2. which was individual for each metabolite.
  • (a) The uptake of a metabolite could be x times higher in CCRF-CEM cells,
    (b) the metabolite uptake could be x times higher in Molt-4,
    (c) metabolite secretion could be x times higher in CCRF-CEM, or
    (d) metabolite secretion could be x times higher in Molt-4 cells. LOD limit of detection.

The consequence of the adjustment was, in case of uptake, that  one model

  1. was constrained to a lower metabolite uptake (A, B), and the difference
  2. depended on the ratio detected in vitro.

In case of secretion,

  • one model had to secrete more of the metabolite, and again

the difference depended on

  • the experimental difference detected between the cell lines.

Q5. What is your expectation that this type of integrative approach could be used for facilitating medical data interpretations?

The most inventive approach was made years ago by using data constructions from the medical literature by a pioneer in the medical record development, but the technology was  not what it is today, and the cost of data input was high.  Nevertheless, the data acquisition would not be uniform across institutions, except for those that belong to a consolidated network with all of the data in the cloud, and the calculations would be carried out with a separate engine.  However, whether the uniform capture of the massive amount of data needed is not possible in the near foreseeable future.  There is no accurate way of assessing the system cost, and predicting the benefits.  In carrying this model forward there has to be a minimal amount of insufficient data.  The developments in the regulatory sphere have created a high barrier.

This concludes a first portion of this presentation.

 

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Metabolomics, Metabonomics and Functional Nutrition: the next step in nutritional metabolism and biotherapeutics

Metabolomics, Metabonomics and Functional Nutrition: the next step in nutritional metabolism and biotherapeutics

Reviewer and Curator: Larry H. Bernstein, MD, FCAP 

 

The human genome is estimated to encode over 30,000 genes, and to be responsible for generating more than 100,000 functionally distinct proteins. Understanding the interrelationships among

  1. genes,
  2. gene products, and
  3. dietary habits

is fundamental to identifying those who will benefit most from or be placed at risk by intervention strategies.

Unraveling the multitude of

  • nutrigenomic,
  • proteomic, and
  • metabolomic patterns

that arise from the ingestion of foods or their

  • bioactive food components

will not be simple but is likely to provide insights into a tailored approach to diet and health. The use of new and innovative technologies, such as

  • microarrays,
  • RNA interference, and
  • nanotechnologies,

will provide needed insights into molecular targets for specific bioactive food components and

  • how they harmonize to influence individual phenotypes(1).

Nutrigenetics asks the question how individual genetic disposition, manifesting as

  • single nucleotide polymorphisms,
  • copy-number polymorphisms and
  • epigenetic phenomena,

affects susceptibility to diet.

Nutrigenomics addresses the inverse relationship, that is how diet influences

  • gene transcription,
  • protein expression and
  • metabolism.

A major methodological challenge and first pre-requisite of nutrigenomics is integrating

  • genomics (gene analysis),
  • transcriptomics (gene expression analysis),
  • proteomics (protein expression analysis) and
  • metabonomics (metabolite profiling)

to define a “healthy” phenotype. The long-term deliverable of nutrigenomics is personalised nutrition (2).

Science is beginning to understand how genetic variation and epigenetic events

  • alter requirements for, and responses to, nutrients (nutrigenomics).

At the same time, methods for profiling almost all of the products of metabolism in a single sample of blood or urine are being developed (metabolomics). Relations between

  • diet and nutrigenomic and metabolomic profiles and
  • between those profiles and health

have become important components of research that could change clinical practice in nutrition.

Most nutrition studies assume that all persons have average dietary requirements, and the studies often

  • do not plan for a large subset of subjects who differ in requirements for a nutrient.

Large variances in responses that occur when such a population exists

  • can result in statistical analyses that argue for a null effect.

If nutrition studies could better identify responders and differentiate them from nonresponders on the basis of nutrigenomic or metabolomic profiles,

  • the sensitivity to detect differences between groups could be greatly increased, and
  • the resulting dietary recommendations could be appropriately targeted (3).

In recent years, nutrition research has moved from classical epidemiology and physiology to molecular biology and genetics. Following this trend,

  • Nutrigenomics has emerged as a novel and multidisciplinary research field in nutritional science that
  • aims to elucidate how diet can influence human health.

It is already well known that bioactive food compounds can interact with genes affecting

  • transcription factors,
  • protein expression and
  • metabolite production.

The study of these complex interactions requires the development of

  • advanced analytical approaches combined with bioinformatics.

Thus, to carry out these studies

  • Transcriptomics,
  • Proteomics and
  • Metabolomics

approaches are employed together with an adequate integration of the information that they provide(4).

Metabonomics is a diagnostic tool for metabolic classification of individuals with the asset of quantitative, non-invasive analysis of easily accessible human body fluids such as urine, blood and saliva. This feature also applies to some extent to Proteomics, with the constraint that

  • the latter discipline is more complex in terms of composition and dynamic range of the sample.

Apart from addressing the most complex “Ome”, Proteomics represents

  • the only platform that delivers not only markers for disposition and efficacy
  • but also targets of intervention.

Application of integrated Omic technologies will drive the understanding of

  • interrelated pathways in healthy and pathological conditions and
  • will help to define molecular ‘switchboards’,
  • necessary to develop disease related biomarkers.

This will contribute to the development of new preventive and therapeutic strategies for both pharmacological and nutritional interventions (5).

Human health is affected by many factors. Diet and inherited genes play an important role. Food constituents,

  • including secondary metabolites of fruits and vegetables, may
  • interact directly with DNA via methylation and changes in expression profiles (mRNA, proteins)
  • which results in metabolite content changes.

Many studies have shown that

  • food constituents may affect human health and
  • the exact knowledge of genotypes and food constituent interactions with
  • both genes and proteins may delay or prevent the onset of diseases.

Many high throughput methods have been employed to get some insight into the whole process and several examples of successful research, namely in the field of genomics and transcriptomics, exist. Studies on epigenetics and RNome significance have been launched. Proteomics and metabolomics need to encompass large numbers of experiments and linked data. Due to the nature of the proteins, as well as due to the properties of various metabolites, experimental approaches require the use of

  • comprehensive high throughput methods and a sufficiency of analysed tissue or body fluids (6).

New experimental tools that investigate gene function at the subcellular, cellular, organ, organismal, and ecosystem level need to be developed. New bioinformatics tools to analyze and extract meaning

  • from increasingly systems-based datasets will need to be developed.

These will require, in part, creation of entirely new tools. An important and revolutionary aspect of “The 2010 Project”  is that it implicitly endorses

  • the allocation of resources to attempts to assign function to genes that have no known function.

This represents a significant departure from the common practice of defining and justifying a scientific goal based on the biological phenomena. The rationale for endorsing this radical change is that

  • for the first time it is feasible to envision a whole-systems approach to gene and protein function.

This whole-systems approach promises to be orders of magnitude more efficient than the conventional approach (7).

The Institute of Medicine recently convened a workshop to review the state of the various domains of nutritional genomics research and policy and to provide guidance for further development and translation of this knowledge into nutrition practice and policy (8). Nutritional genomics holds the promise to revolutionize both clinical and public health nutrition practice and facilitate the establishment of

(a) genome-informed nutrient and food-based dietary guidelines for disease prevention and healthful aging,

(b) individualized medical nutrition therapy for disease management, and

(c) better targeted public health nutrition interventions (including micronutrient fortification and supplementation) that

  • maximize benefit and minimize adverse outcomes within genetically diverse human populations.

As the field of nutritional genomics matures, which will include filling fundamental gaps in

  • knowledge of nutrient-genome interactions in health and disease and
  • demonstrating the potential benefits of customizing nutrition prescriptions based on genetics,
  • registered dietitians will be faced with the opportunity of making genetically driven dietary recommendations aimed at improving human health.

The new era of nutrition research translates empirical knowledge to evidence-based molecular science (9). Modern nutrition research focuses on

  • promoting health,
  • preventing or delaying the onset of disease,
  • optimizing performance, and
  • assessing risk.

Personalized nutrition is a conceptual analogue to personalized medicine and means adapting food to individual needs. Nutrigenomics and nutrigenetics

  • build the science foundation for understanding human variability in
  • preferences, requirements, and responses to diet and
  • may become the future tools for consumer assessment

motivated by personalized nutritional counseling for health maintenance and disease prevention.

The primary aim of ―omic‖ technologies is

  • the non-targeted identification of all gene products (transcripts, proteins, and metabolites) present in a specific biological sample.

By their nature, these technologies reveal unexpected properties of biological systems.

A second and more challenging aspect of ―omic‖ technologies is

  • the refined analysis of quantitative dynamics in biological systems (10).

For metabolomics, gas and liquid chromatography coupled to mass spectrometry are well suited for coping with

  • high sample numbers in reliable measurement times with respect to
  • both technical accuracy and the identification and quantitation of small-molecular-weight metabolites.

This potential is a prerequisite for the analysis of dynamic systems. Thus, metabolomics is a key technology for systems biology.

In modern nutrition research, mass spectrometry has developed into a tool

  • to assess health, sensory as well as quality and safety aspects of food.

In this review, we focus on health-related benefits of food components and, accordingly,

  • on biomarkers of exposure (bioavailability) and bioefficacy.

Current nutrition research focuses on unraveling the link between

  • dietary patterns,
  • individual foods or
  • food constituents and

the physiological effects at cellular, tissue and whole body level

  • after acute and chronic uptake.

The bioavailability of bioactive food constituents as well as dose-effect correlations are key information to understand

  • the impact of food on defined health outcomes.

Both strongly depend on appropriate analytical tools

  • to identify and quantify minute amounts of individual compounds in highly complex matrices–food or biological fluids–and
  • to monitor molecular changes in the body in a highly specific and sensitive manner.

Based on these requirements,

  • mass spectrometry has become the analytical method of choice
  • with broad applications throughout all areas of nutrition research (11).

Recent advances in high data-density analytical techniques offer unrivaled promise for improved medical diagnostics in the coming decade. Genomics, proteomics and metabonomics (as well as a whole slew of less well known ―omics‖ technologies) provide a detailed descriptor of each individual. Relating the large quantity of data on many different individuals to their current (and possibly even future) phenotype is a task not well suited to classical multivariate statistics. The datasets generated by ―omics‖ techniques very often violate the requirements for multiple regression. However, another statistical approach exists, which is already well established in areas such as medicinal chemistry and process control, but which is new to medical diagnostics, that can overcome these problems. This approach, called megavariate analysis (MVA),

  • has the potential to revolutionise medical diagnostics in a broad range of diseases.

It opens up the possibility of expert systems that can diagnose the presence of many different diseases simultaneously, and

  • even make exacting predictions about the future diseases an individual is likely to suffer from (12).

Cardiovascular diseases

Cardiovascular diseases are the leading cause of morbidity and mortality in Western countries. Although coronary thrombosis is the final event in acute coronary syndromes,

  • there is increasing evidence that inflammation also plays a role in development of atherosclerosis and its clinical manifestations, such as
  • myocardial infarction, stroke, and peripheral vascular disease.

The beneficial cardiovascular health effects of

  • diets rich in fruits and vegetables are in part mediated by their flavanol content.

This concept is supported by findings from small-scale intervention studies with surrogate endpoints including

  1. endothelium-dependent vasodilation,
  2. blood pressure,
  3. platelet function, and
  4. glucose tolerance.

Mechanistically, short term effects on endothelium-dependent vasodilation

  • following the consumption of flavanol-rich foods, as well as purified flavanols,
  • have been linked to an increased nitric oxide bioactivity.

The critical biological target(s) for flavanols have yet to be identified (13), but we are beginning to see over the horizon.

Nutritional sciences

Nutrition sciences apply

  1. transcriptomics,
  2. proteomics and
  3. metabolomics

to molecularly assess nutritional adaptations.

Transcriptomics can generate a

  • holistic overview on molecular changes to dietary interventions.

Proteomics is most challenging because of the higher complexity of proteomes as compared to transcriptomes and metabolomes. However, it delivers

  • not only markers but also
  • targets of intervention, such as
  • enzymes or transporters, and
  • it is the platform of choice for discovering bioactive food proteins and peptides.

Metabolomics is a tool for metabolic characterization of individuals and

  • can deliver metabolic endpoints possibly related to health or disease.

Omics in nutrition should be deployed in an integrated fashion to elucidate biomarkers

  • for defining an individual’s susceptibility to diet in nutritional interventions and
  • for assessing food ingredient efficacy (14).

The more elaborate tools offered by metabolomics opened the door to exploring an active role played by adipose tissue that is affected by diet, race, sex, and probably age and activity. When the multifactorial is brought into play, and the effect of changes in diet and activities studied we leave the study of metabolomics and enter the world of ―metabonomics‖. Adiponectin and adipokines arrive (15-22). We shall discuss ―adiposity‖ later.

Potential Applications of Metabolomics

Either individually or grouped as a profile, metabolites are detected by either

  • nuclear magnetic resonance spectroscopy or mass spectrometry.

There is potential for a multitude of uses of metabolome research, including

  1. the early detection and diagnosis of cancer and as
  2. both a predictive and pharmacodynamic marker of drug effect.

However, the knowledge regarding metabolomics, its technical challenges, and clinical applications is unappreciated

  • even though when used as a translational research tool,
  • it can provide a link between the laboratory and clinic.

Precise numbers of human metabolites is unknown, with estimates ranging from the thousands to tens of thousands. Metabolomics is a term that encompasses several types of analyses, including

(a) metabolic fingerprinting, which measures a subset of the whole profile with little differentiation or quantitation of metabolites;

(b) metabolic profiling, the quantitative study of a group of metabolites, known or unknown, within or associated with a particular metabolic pathway; and

(c) target isotope-based analysis, which focuses on a particular segment of the metabolome by analyzing

  • only a few selected metabolites that comprise a specific biochemical pathway.

 

Dynamic Construct of the –Omics

Dynamic Construct of the –Omics

 

Dynamic Construct of the –Omics

 

 

Iron metabolism – Anemia

Hepcidin is a key hormone governing mammalian iron homeostasis and may be directly or indirectly involved in the development of most iron deficiency/overload and inflammation-induced anemia. The anemia of chronic disease (ACD) is characterized by macrophage iron retention induced by cytokines and hepcidin regulation. Hepcidin controls cellular iron efflux on binding to the iron export protein ferroportin. While patients present with both ACD and iron deficiency anemia (ACD/IDA), the latter results from chronic blood loss. Iron retention during inflammation occurs in macrophages and the spleen, but not in the liver. In ACD, serum hepcidin concentrations are elevated, which is related to reduced duodenal and macrophage expression of ferroportin. Individuals with ACD/IDA have significantly lower hepcidin levels than ACD subjects. ACD/IDA patients, in contrast to ACD subjects, were able to absorb dietary iron from the gut and to mobilize iron from macrophages. Hepcidin elevation may affect iron transport in ACD and ACD/IDA and it is more responsive to iron demand with IDA than to inflammation. Hepcidin determination may aid in selecting appropriate therapy for these patients (23).

There is correlation between serum hepcidin, iron and inflammatory indicators associated with anemia of chronic disease (ACD), ACD, ACD concomitant iron-deficiency anemia (ACD/IDA), pure IDA and acute inflammation (AcI) patients. Hepcidin levels in anemia types were statistically different, from high to low: ACD, AcI > ACD/IDA > the control > IDA. Serum ferritin levels were significantly increased in ACD and AcI patients but were decreased significantly in ACD/IDA and IDA. Elevated serum EPO concentrations were found in ACD, ACD/IDA and IDA patients but not in AcI patients and the controls. A positive correlation exists between hepcidin and IL-6 levels only in ACD/IDA, AcI and the control groups. A positive correlation between hepcidin and ferritin was marked in the control group, while a negative correlation between hepcidin and ferritin was noted in IDA. The significant negative correlation between hepcidin expression and reticulocyte count was marked in both ACD/IDA and IDA groups. If the hepcidin role in pathogenesis of ACD, ACD/IDA and IDA, it could be a potential marker for detection and differentiation of these anemias (24).

Cancer

Because cancer cells are known to possess a highly unique metabolic phenotype, development of specific biomarkers in oncology is possible and might be used in identifying fingerprints, profiles, or signatures to detect the presence of cancer, determine prognosis, and/or assess the pharmacodynamic effects of therapy (25).

HDM2, a negative regulator of the tumor suppressor p53, is over-expressed in many cancers that retain wild-type p53. Consequently, the effectiveness of chemotherapies that induce p53 might be limited, and inhibitors of the HDM2–p53 interaction are being sought as tumor-selective drugs. A binding site within HDM2 has been dentified which can be blocked with peptides inducing p53 transcriptional activity. A recent report demonstrates the principle using drug-like small molecules that target HDM2 (26).

Obesity, CRP, interleukins, and chronic inflammatory disease

Elevated CRP levels and clinically raised CRP levels were present in 27.6% and 6.7% of the population, respectively. Both overweight (body mass index [BMI], 25-29.9 kg/m2) and obese (BMI, 30 kg/m2) persons were more likely to have elevated CRP levels than their normal-weight counterparts (BMI, <25 kg/m2). After adjusting for potential confounders, the odds ratio (OR) for elevated CRP was 2.13 for obese men and 6.21 for obese women. In addition, BMI was associated with clinically raised CRP levels in women, with an OR of 4.76 (95% CI, 3.42-6.61) for obese women. Waist-to-hip ratio was positively associated with both elevated and clinically raised CRP levels, independent of BMI. Restricting the analyses to young adults (aged 17-39 years) and excluding smokers, persons with inflammatory disease, cardiovascular disease, or diabetes mellitus and estrogen users did not change the main findings (27).

A study of C-reactive protein and interleukin-6 with measures of obesity and of chronic infection as their putative determinants related levels of C-reactive protein and interleukin-6 to markers of the insulin resistance syndrome and of endothelial dysfunction. Levels of C-reactive protein were significantly related to those of interleukin-6 (r=0.37, P<0.0005) and tumor necrosis factor-a (r=0.46, P<0.0001), and concentrations of C-reactive protein were related to insulin resistance as calculated from the homoeostasis model and to markers of endothelial dysfunction (plasma levels of von Willebrand factor, tissue plasminogen activator, and cellular fibronectin). A mean standard deviation score of levels of acute phase markers correlated closely with a similar score of insulin resistance syndrome variables (r=0.59, P<0.00005) and the data suggested that adipose tissue is an important determinant of a low level, chronic inflammatory state as reflected by levels of interleukin-6, tumor necrosis factor-a, and C-reactive protein (28).

A number of other studies have indicated the inflammatory ties of visceral obesity to adipose tissue metabolic profiles, suggesting a role in ―metabolic syndrome‖. There is now a concept of altered liver metabolism in ―non-alcoholic‖ fatty liver disease (NAFLD) progressing from steatosis to steatohepatitis (NASH) (31,32).

These unifying concepts were incomprehensible 50 years ago. It was only known that insulin is anabolic and that insulin deficiency (or resistance) would have consequences in the point of entry into the citric acid cycle, which generates 16 ATPs. In fat catabolism, triglycerides are hydrolyzed to break them into fatty acids and glycerol. In the liver the glycerol can be converted into glucose via dihydroxyacetone phosphate and glyceraldehyde-3-phosphate by way of gluconeogenesis. In the case of this cycle there is a tie in with both catabolism and anabolism.

 

TCA_reactions

TCA_reactions

 http://www.newworldencyclopedia.org/entry/Image:TCA_reactions.gif

 

For bypass of the Pyruvate Kinase reaction of Glycolysis, cleavage of 2 ~P bonds is required. The free energy change associated with cleavage of one ~P bond of ATP is insufficient to drive synthesis of phosphoenolpyruvate (PEP), since PEP has a higher negative G of phosphate hydrolysis than ATP.

The two enzymes that catalyze the reactions for bypass of the Pyruvate Kinase reaction are the following:

(a) Pyruvate Carboxylase (Gluconeogenesis) catalyzes:

pyruvate + HCO3 + ATP — oxaloacetate + ADP + Pi

(b) PEP Carboxykinase (Gluconeogenesis) catalyzes:

oxaloacetate + GTP — phosphoenolpyruvate + GDP + CO2

The concept of anomalies in the pathways with respect to diabetes was sketchy then, and there was much to be filled in. This has been substantially done, and is by no means complete. However, one can see how this comes into play with diabetic ketoacidosis accompanied by gluconeogenesis and in severe injury or sepsis with peripheral proteolysis to provide gluconeogenic precursors. The reprioritization of liver synthetic processes is also brought into play with the conundrum of protein-energy malnutrition.

The picture began to be filled in with the improvements in technology that emerged at the end of the 1980s with the ability to profile tissue and body fluids by NMR and by MS. There was already a good inkling of a relationship of type 2 diabetes to major indicators of CVD (29,30). And a long suspected relationship between obesity and type 2 diabetes was evident. But how did it tie together?

End Stage Renal Disease and Cardiovascular Risk

Mortality is markedly elevated in patients with end-stage renal disease. The leading cause of death is cardiovascular disease.

As renal function declines,

  • the prevalence of both malnutrition and cardiovascular disease increase.

Malnutrition and vascular disease correlate with the levels of

  • markers of inflammation in patients treated with dialysis and in those not yet on dialysis.

The causes of inflammation are likely to be multifactorial. CRP levels are associated with cardio-vascular risk in the general population.

The changes in endothelial cell function,

  • in plasma proteins, and
  • in lpiids in inflammation

are likely to be atherogenic.

That cardiovascular risk is inversely correlated with serum cholesterol in dialysis patients, suggests that

  • hyperlipidemia plays a minor role in the incidence of cardiovascular disease.

Hypoalbuminemia, ascribed to malnutrition, has been one of the most powerful risk factors that predict all-cause and cardiovascular mortality in dialysis patients. The presence of inflammation, as evidenced by increased levels of specific cytokines (interleukin-6 and tumor necrosis factor a) or acute-phase proteins (C-reactive protein and serum amyloid A), however, has been found to be associated with vascular disease in the general population as well as in dialysis patients. Patients have

  • loss of muscle mass and changes in plasma composition—decreases in serum albumin, prealbumin, and transferrin levels, also associated with malnutrition.

Inflammation alters

  • lipoprotein structure and function as well as
  • endothelial structure and function

to favor atherogenesis and increases

  • the concentration of atherogenic proteins in serum.

In addition, proinflammatory compounds, such as

  • advanced glycation end products, accumulate in renal failure, and
  • defense mechanisms against oxidative injury are reduced,

contributing to inflammation and to its effect on the vascular endothelium (33,34).

Endogenous copper can play an important role in postischemic reperfusion injury, a condition associated with endothelial cell activation and increased interleukin 8 (IL-8) production. Excessive endothelial IL-8 secreted during trauma, major surgery, and sepsis may contribute to the development of systemic inflammatory response syndrome (SIRS), adult respiratory distress syndrome (ARDS), and multiple organ failure (MOF). No previous reports have indicated that copper has a direct role in stimulating human endothelial IL-8 secretion. Copper did not stimulate secretion of other cytokines. Cu(II) appeared to be the primary copper ion responsible for the observed increase in IL-8 because a specific high-affinity Cu(II)-binding peptide, d-Asp-d-Ala-d-Hisd-Lys (d-DAHK), completely abolished this effect in a dose-dependent manner. These results suggest that Cu(II) may induce endothelial IL-8 by a mechanism independent of known Cu(I) generation of reactive oxygen species (35).

Blood coagulation plays a key role among numerous mediating systems that are activated in inflammation. Receptors of the PAR family serve as sensors of serine proteinases of the blood clotting system in the target cells involved in inflammation. Activation of PAR_1 by thrombin and of PAR_2 by factor Xa leads to a rapid expression and exposure on the membrane of endothelial cells of both adhesive proteins that mediate an acute inflammatory reaction and of the tissue factor that initiates the blood coagulation cascade. Other receptors that can modulate responses of the cells activated by proteinases through PAR receptors are also involved in the association of coagulation and inflammation together with the receptors of the PAR family. The presence of PAR receptors on mast cells is responsible for their reactivity to thrombin and factor Xa , essential to the inflammation and blood clotting processes (36).

The understanding of regulation of the inflammatory process in chronic inflammatory diseases is advancing.

Evidence consistently indicates that T-cells play a key role in initiating and perpetuating inflammation, not only via the production of soluble mediators but also via cell/cell contact interactions with a variety of cell types through membrane receptors and their ligands. Signalling through CD40 and CD40 ligand is a versatile pathway that is potently involved in all these processes. Many inflammatory genes relevant to atherosclerosis are influenced by the transcriptional regulator nuclear factor κ B (NFκB). In these events T-cells become activated by dendritic cells or inflammatory cytokines, and these T-cells activate, in turn, monocytes / macrophages, endothelial cells, smooth muscle cells and fibroblasts to produce pro-inflammatory cytokines, chemokines, the coagulation cascade in vivo, and finally matrix metalloproteinases, responsible for tissue destruction. Moreover, CD40 ligand at inflammatory sites stimulates fibroblasts and tissue monocyte/macrophage production of VEGF, leading to angiogenesis, which promotes and maintains the chronic inflammatory process.

NFκB plays a pivotal role in co-ordinating the expression of genes involved in the immune and inflammatory response, evoking tumor necrosis factor α (TNFα), chemokines such as monocyte chemoattractant protein-1 (MCP-1) and interleukin (IL)-8, matrix metalloproteinase enzymes (MMP), and genes involved in cell survival. A complex array of mechanisms, including T cell activation, leukocyte extravasation, tissue factor expression, MMP expression and activation, as well induction of cytokines and chemokines, implicated in atherosclerosis, are regulated by NFκB.

Expression of NFκB in the atherosclerotic milieu may have a number of potentially harmful consequences. IL-1 activates NFκB upregulating expression of MMP-1, -3, and -9. Oxidized LDL increases macrophage MMP-9, associated with increased nuclear binding of NFκB and AP-1. Expression of tissue factor, initiating the coagulation cascade, is regulated by NFκB. In atherosclerotic plaque cells, tissue factor antigen and activity were inhibited following over-expression of IκBα and dominant-negative IKK-2, but not by dominant negative IKK-1 or NIK. Tis supports the concept that activation of the ―canonical‖ pathway upregulates pro-thrombotic mediators involved in disease. Many of the cytokines and chemokines which have been detected in human atherosclerotic plaques are also regulated by NFκB. Over-expression of IκBα inhibits release of TNFα, IL-1, IL-6, and IL-8 in macrophages stimulated with LPS and CD40 ligand (CD40L). This report describes how NFκB activation upregulates major pro-inflammatory and pro-thrombotic mediators of atherosclerosis (37-41).

This review is both focused and comprehensive. The details of evolving methods are avoided in order to build the argument that a very rapid expansion of discovery has been evolving depicting disease, disease mechanisms, disease associations, metabolic biomarkers, study of effects of diet and diet modification, and opportunities for targeted drug development. The extent of future success will depend on the duration and strength of the developed interventions, and possibly the avoidance of dead end interventions that are unexpectedly bypassed. I anticipate the prospects for the interplay between genomics, metabolomics, metabonomics, and personalized medicine may be realized for several of the most common conditions worldwide within a few decades (42-44).

References

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The multi-step transfer of phosphate bond and hydrogen exchange energy

Curator: Larry H. Bernstein, MD, FCAP, Leaders in Pharmaceutical Intelligence

In this subtext of the series we expand on a tie between respiration and glycolysis, and the functioning of the mitochondrion to discover the key role played by oxidative phosphorylation, “acetyl coenzyme A, and electron transport.  This was crucial to understanding cellular energetics, which explains the high energy of fatty acid catabolism from stored adipose tissue, and the criticality of the multi-step sequence of reactions in energy transfer.

This portion considerably provides a response to the TWO points made by Jose EDS Rosallis:

  1. Just at the beginning, when phosphorylation of proteins is presented, I assume you must mention that some proteins are activated by phosphorylation. This is fundamental in order to present self –organization reflex upon fast regulatory mechanisms. This poiny needs further clarification, but he makes important observations here.
  • Even from an historical point of view. The first observation arrived from a sample due to be studied on the following day of glycogen synthetase. It was unintended left overnight out of the refrigerator. The result was it had changed from active form of the previous day to a non-active form.

The story could have being finished here, if the researcher did not decide to spent this day increasing substrate levels (it could be a simple case of denaturation of proteins that changes its conformation despite the same order of amino acids). He kept on trying and found restoration of maximal activity.

  • This assay was repeated with glycogen phosphorylase and the result was the opposite it increases its activity.

This led to the discovery of cAMP activated protein kinase and the assembly of a very complex system in the glycogen granule that is not a simple carbohydrate polymer. Instead

  • it has several proteins assembled and preserves the capacity to receive from a single event (rise in cAMP) two opposing signals with maximal efficiency,
  • stops glycogen synthesis, as long as levels of glucose 6 phosphate are low and
  • increases glycogen phosphorylation as long as AMP levels are high).

I did everything I was able to do by the end of 1970 in order to repeat these assays with

  • PK I, PKII and PKIII of M. Rouxii and Sutherland route to cAMP failed in this case.

I ask Leloir to suggest to my chief (SP) the idea of AA, AB, BB subunits as was observed in lactic dehydrogenase (tetramer)
(Nathan O. Kaplan discovery) indicating this as his idea. The reason was my “chief” (SP) more than once,  said to me: “Leave these great ideas for the Houssay, Leloir etc…We must do our career with small things. ” However, as she also had a faulty ability for recollection she also used to arrive some time later, with the very same idea but in that case, as her idea.

[This reminds me of when I was studying the emergence of lactic dehysrogenase isoenzyme patterns in the developing eye lens of cattle, I raised reservations about Elliott Vessells challenge to Nathan Kaplan, but that not being my primary problem, my brilliant mentor (H.M.), a very young full professor of anatomy said – leave that to NOK.}

Leloir, said to me: I will not offer your interpretation to her as mine. I think it is not phosphorylation, however I think it is

  • glycosylation that explains the changes in the isoenzymes with the same molecular weight preserved.

This dialogue explains why during the Schroedinger’s “What is life?” reading with him he asked me if from biochemist in exile, to biochemist I expressed all of my thoughts to him. Since I had considered that Schrödinger did not confront Darlington & Haldane for being in exile. This may explain why Leloir could have answered a bad telephone call from P. Boyer, Editor of The Enzymes in a way that suggests the the pattern could be of covalent changes over a protein. Our FEBS and Eur J. Biochemistry papers on pyruvate kinase of M. Rouxii is wrongly quoted in this way on his review about pyruvate kinase of
that year(1971).

  1. show in detail with different colors what carbons belongs to CoA a huge molecule, in comparison with the single two carbons of acetate that will produce the enormous jump in energy yield in comparison with anaerobic glycolysis. The idea is how much must have being spent in DNA sequences to build that molecule in order to use only two atoms of carbon. Very limited aspects of biology could be explained in this way. In case we follow an alternative way of thinking, it becomes clearer that proteins were made more stable by interaction with other molecules (great and small). Afterwards, it rather easy to understand how the stability of protein-RNA complexes where transmitted to RNA (vibrational +solvational reactivity stability pair of conformational energy). Latter, millions of years, or as soon as, the information of interaction leading to activity and regulation could be found in RNA, proteins like reverse transcriptase move this information to a more stable form (DNA). In this way it is easier to understand the use of CoA to make two carbon molecules more reactive.

The outline of what I am presenting in series is as follows:

  1. Signaling and Signaling Pathways
    http://pharmaceuticalintelligence.com/2014/08/12/signaling-and-signaling-pathways/
  1. Signaling transduction tutorial.
    http://pharmaceuticalintelligence.com/2014/08/12/signaling-transduction-tutorial/
  1. Carbohydrate metabolism
    http://pharmaceuticalintelligence.com/2014/08/13/carbohydrate-metabolism/

3.1  Selected References to Signaling and Metabolic Pathways in Leaders in Pharmaceutical Intelligence

http://pharmaceuticalintelligence.com/2014/08/14/selected-references-to-signaling-and-metabolic-pathways-in-leaders-in-pharmaceutical-intelligence/

  1. Lipid metabolism

4.1  Studies of respiration lead to Acetyl CoA

http://pharmaceuticalintelligence.com/2014/08/18/studies-of-respiration-lead-to-acetyl-coa/

4.2 The multi-step transfer of phosphate bond and hydrogen exchange energy

  1. Protein synthesis and degradation
  2. Subcellular structure
  3. Impairments in pathological states: endocrine disorders; stress hypermetabolism; cancer.

Oxidation-Reduction Reactions

Rachel Casiday, Carolyn Herman, and Regina Frey
Department of Chemistry, Washington University
St. Louis, MO 63130

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/cytochromes.html

 

OX-Phos steps

OX-Phos steps

http://s1.hubimg.com/u/6583902_f496.jpg

 

Key Concepts:

  • ATP as Free-Energy Currency in the Body
  • Coupled Reactions
    • Standard Free-Energy Change for Coupled Reactions
    • ATP Dephosphorylation Coupled to Nonspontaneous Reactions
    • Coupled Reactions to Generate ATP
  • Structure and Function of the Mitochondria
  • Oxidation-Reduction Reactions in the Electron-Transport Chain
    • Electron-Carrier Proteins (NOTE: This section includes a separate link and an animation.)
    • Relationship Between Reduction Potentials and Free Energy
  • Proton Gradient as Means of Coupling Oxidative and Phosphorylation Components of Oxidative Phosphorylation
  • ATP Synthetase Uses Energy From Proton Gradient to Generate ATP

Every day, we build bones, move muscles, eat food, think, and perform many other activities with our bodies. All of these activities are based upon chemical reactions. However, most of these reactions are not spontaneous (i.e., they are accompanied by a positive change in free energy, DG>0) and do not occur without some other source of free energy. Hence, the body needs some sort of “free-energy currency,” (Figure 1) a molecule that can store and release free energy when it is needed to power a given biochemical reaction.

The four questions:

  1. How does the body “spend” free-energy currency to make a nonspontaneous reaction spontaneous? The answer, which is based on thermodynamics, is to use coupled reactions.
  2. How is food used to produce the reducing agents (NADH and FADH2) that can regenerate the free-energy currency? The answer, from biology, is found in glycolysis and the citric-acid cycle.
  3. How are the reducing agents (NADH and FADH2) able to generate the free-energy currency molecule (ATP)? Once again, coupled reactions are key.
  4. What mechanism does the body use to couple the reducing agent reactions and the generation of ATP? ATP is synthesized primarily by a two-step process consisting of an electron-transport chain and a proton gradient.  This process is based on electrochemistry and equilibrium, as well as thermodynamics.

The free-energy change (DG) for the net reaction is given by the sum of the free-energy changes for the individual reactions.  The phospholipids that form cell membranes are formed from glycerol with a phosphate group and two fatty-acid chains attached.This step actually consists of two reactions:

(1) the phosphorylation of glycerol, and

(2) the dephosphorylation of ATP (the free-energy-currency molecule). The reactions may be added as shown in Equations 2-4, below:

      Glycerol + HPO42- –>  (Glycerol-3-Phosphate)2- + H2O DGo= +9.2 kJ
(nonspontaneous)
(2)
+      ATP4- + H2O –>       ADP3- + HPO42- + H+ DGo30.5 kJ
(spontaneous)
(3)
     Glycerol + ATP4- –> (Glycerol-3-Phosphate)2- +ADP3- + H+ DGo21.3 kJ
(spontaneous)
(4)
   

ATP is the most important “free-energy-currency” molecule in living organisms (see Figure 2, below). Adenosine triphosphate (ATP) is a useful free-energy currency because the dephosphorylation reaction is very spontaneous; i.e., it releases a large amount of free energy (30.5 kJ/mol). Thus, the dephosphorylation reaction of ATP to ADP and inorganic phosphate (Equation 3) is often coupled with nonspontaneous reactions (e.g., Equation 2) to drive them forward. The body’s use of ATP as a free-energy currency is a very effective strategy to cause vital nonspontaneous reactions to occur.

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/ATP.jpg

structure of ATP

structure of ATP

This is the two-dimensional (ChemDraw) structure of ATP, adenosine triphosphate. The removal of one phosphate group (green) from ATP requires the breaking of a bond (blue) and results in a large release of free energy. Removal of this phosphate group (green) results in ADP, adenosine diphosphate.

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/ATP.jpg

flowchart of food energy

flowchart of food energy

This flowchart shows that the energy used by the body for its many activities ultimately comes from the chemical energy in our food. The chemical energy in our food is converted to reducing agents (NADH and FADH2). These reducing agents are then used to make ATP. ATP stores chemical energy, so that it is available to the body in a readily accessible form.

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/flowchart1.jpg

Glycolysis   Glucose + 2 HPO42- + 2 ADP3- + 2 NAD+ –>
2 Pyruvate + 2 ATP4- + 2 NADH + 2 H+ + 2 H2O
(5)
Intermediate Step   2(Pyruvate + Coenzyme A + NAD+ –>
Acetyl CoA + CO2 + NADH)
(6)
Citric-Acid Cycle 2(Acetyl CoA + 3 NAD++ FAD + GDP3-
+ HPO42- + 2H2O –> 2 CO2 + 3 NADH + FADH2
+ GTP4- + 2H+ + Coenzyme A)
(7)

The structures of the important molecules in Equations 5-7 are shown in Table 1, below.

How is Food Used to Make the Reducing Agents Needed for the Production of ATP?

To make ATP, energy must be absorbed. This energy is supplied by the food we eat, and then used to synthsize two reducing agents, NADH and FADH2 that are needed to produce ATP. One of the principal energy-yielding nutrients in our diet is glucose (see structure in Table 1 in the blue box below), a simple six-carbon sugar that can be broken down by the body. When the chemical bonds in glucose are broken, free energy is released. The complete breakdown of glucose into CO2 occurs in two processes: glycolysis and the citric-acid cycle. The reactions for these two processes are shown in the blue box below.

pyruvate

pyruvate

  Pyruvate

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/pyruvate.jpg

acetylCoA

acetylCoA

Acetyl CoA

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/acetylCoA.jpg

NADH

NADH

NADH

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/acetylCoA.jpg

 

FADH2

FADH2

FADH2

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/FADH2.jpg

two-dimensional representations of several important molecules in Equations 5-7.

As seen in Equations 5-7 in the blue box, glycolysis and the citric-acid cycle produce a net total of only four ATP or GTP molecules (GTP is an energy-currency molecule similar to ATP) per glucose molecule. This yield isfar below the amount needed by the body for normal functioning, and in fact is far below the actual ATP yield for glucose in aerobic organisms (organisms that use molecular oxygen). For each glucose molecule the body processes, the body actually gains approximately 30 ATP molecules! (See Figure 4, below.)  So, how does the body generate ATP?

The process that accounts for the high ATP yield is known as oxidative phosphorylation. A quick examination of Equations 5-7 shows that glycolysis and the citric-acid cycle generate other products besides ATP and GTP, namely NADH and FADH2 (blue). These products are molecules that are oxidized (i.e., give up electrons) spontaneously. The body uses these reducing agents (NADH and FADH2) in an oxidation-reduction reaction .  As you will see later in this tutorial, it is the free energy from these redox reactions that is used to drive the production of ATP.

flowchart - making of ATP

flowchart – making of ATP

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/flowchart2.jpg

This flowchart shows the major steps involved in breaking down glucose from the diet and converting its chemical energy to the chemical energy in the phosphate bonds of ATP, in aerobic (oxygen-using) organisms. Note: In this flowchart, red denotes a source of carbon atoms (originally from glucose),green denotes energy-currency molecules, and blue denotes the reducing agents that can be oxidized spontaneously.

In the discussion above, we see that glucose by itself generates only a tiny amount of ATP. However, during the breakdown of glucose, a large amount of NADH and FADHis produced; it is these reducing agents that dramatically increase the amount of ATP produced. How does this work?

How are the reducing agents (NADH and FADH2) able to generate the free-energy currency molecule (ATP)?

As discussed in an earlier section about coupling reactions, ATP is used as free-energy currency by coupling its (spontaneous) dephosphorylation (Equation 3) with a (nonspontaneous) biochemical reaction to give a net release of free energy (i.e., a net spontaneous reaction). Coupled reactions are also used to generate ATP by phosphorylating ADP. The nonspontaneous reaction of joining ADP to inorganic phosphate to make ATP (Equation 8, below, and Figure 2, above) is coupled to the oxidation reaction of NADH or FADH(Equation 9, below). (Recall, NADH and FADH2 are produced in glycolysis and the citric-acid cycle as described in the blue box). For simplicity, we shall henceforth discuss only the oxidation of NADH; FADH2 follows a very similar oxidation pathway.

The oxidation reaction for NADH has a larger, but negative, DG than the positive DG required for the formation of ATP from ADP and phosphate. This set of coupled reactions is so important that it has been given a special name: oxidative phosphorylation. This name emphasizes the fact that an oxidation (of NADH) reaction (Equation 9 and Figure 5, below) is being coupled to a phosphorylation (of ADP) reaction (Equation 8, below, and Figure 2, above). In addition, we must consider the reduction reaction (gaining of electrons) that accompanies the oxidation of NADH. (Oxidation reactions are always accompanied by reduction reactions, because an electron given up by one group must be accepted by another group.) In this case, molecular oxygen (O2) is the electron acceptor, and the oxygen is reduced to water (Equation 10, below) .

The individual reactions of interest for oxidative phosphorylation are:

Phosphorylation

ADP3- + HPO42- + H+ –>
ATP4- + H2O

DGo= +30.5 kJ
(nonspontaneous)
(8)
oxidation

NADH –> NAD+ + H+ +  2e

DGo158.2 Kj
(spontaneous)
(9)
reduction

1/2 O2 + 2H+ + 2e –> H2O

DGo61.9 kJ
(spontaneous)

                                                                       (10)                                    

The net reaction is obtained by summing the coupled reactions, as shown in Equation 11, below.

ADP3- + HPO42- + NADH + 1/2 O2 + 2H+ –>
ATP4- + NAD+ + 2 H2O
DGo= -189.6 kJ
(spontaneous)
(11)

The molecular changes that occur upon oxidation of NADH are shown:

NAD+_NADH

NAD+_NADH

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/NAD+_NADH.jpg

This is a two-dimensional (ChemDraw) representation showing the change that occurs when NADH is oxidized to NAD+. “R” represents the part of the structure that is shown in black in the drawing of NADH in Table 1, and does not change during the oxidation half-reaction. The molecular changes that occur upon oxidation are shown in red.

In this tutorial, we have seen that nonspontaneous reactions in the body occur by coupling them with a very spontaneous reaction (usually the ATP reaction shown in Equation 3). We have just seen that ATP is produced by coupling the phosphorylation reaction with NADH oxidation (a very spontaneous reaction). But we have not yet answered the question: by what mechanism are these reactions coupled?

Coupling Reactions in Biological Systems

Every day your body carries out many nonspontaneous reactions. As discussed earlier, if a nonspontaneous reaction is coupled to a spontaneous reaction, as long as the sum of the free energies for the two reactions is negative, the coupled reactions will occur spontaneously. How is this coupling achieved in the body? Living systems couple reactions in several ways, but the most common method of coupling reactions is to carry out both reactions on the same enzyme. Consider again the phosphorylation of glycerol (Equations 2-4). Glycerol is phosphorylated by the enzyme glycerol kinase, which is found in your liver. The product of glycerol phosporylation, glycerol-3-phosphate (Equation 2), is used in the synthesis of phospholipids.

Glycerol kinase is a large protein comprised of about 500 amino acids. X-ray crystallography of the protein shows us that there is a deep groove or cleft in the protein where glycerol and ATP attach (see Figure 6, below). Because the enzyme holds the ATP and the glycerol in place, the phosphate can be transferred directly from the ATP to glycerol. Instead of two separate reactions where ATP loses a phosphate (Equation 3) and glycerol picks up a phosphate (Equation 2), the enzyme allows the phosphate to move directly from ATP to glycerol (Equation 4).

The coupling in oxidative phosphorylation uses a more complicated (and amazing!) mechanism, but the end result is the same: the reactions are linked together, the net free energy for the linked reactions is negative, and, therefore, the linked reactions are spontaneous.

glyckin

glyckin

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/glyckin.jpg

This is a schematic representation of ATP and glycerol bound (attached) to glycerol kinase. The enzyme glycerol kinase is a dimer (consists of two identical subuits). There is a deep cleft between the subunits where ATP and glycerol bind. Since the ATP and phosphate are physically so close together when they are bound to the enzyme, the phosphate can be transferred directly from ATP to glycerol. Hence, the processes of ATP losing a phosphate (spontaneous) and glycerol gaining a phosphate (nonspontaneous) are linked together as one spontaneous process

Questions on ATP: The Body’s Free-Energy Currency (How Free-Energy Currency Works)

  • Biological systems involve many molecules containing phosphate groups, such as ATP. Although ATP is the most commonly used free-energy currency, any of these phosphorylated molecules could, in theory, be used as free-energy currency. The standard free-energy change (DGo) for the dephosphorylation (removal of a phosphate group) of several biological compounds is given below:
Acetyl phosphate DGo = -47.3 kJ/mol
Adenosine triphosphate (ATP) DGo = -30.5 kJ/mol
Glucose-6-phosphate DGo = -13.8 kJ/mol
Phosphoenolpyruvate (PEP) DGo = -61.9 kJ/mol
Phosphocreatine DGo = -43.1 kJ/mol

Neglecting any differences in difficulty synthesizing or accessing these molecules by biological systems, rank the molecules in order of their efficiency as a free-energy currency (i.e., the amount of nonspontaneous reactions enabled per phosphate removed from a molecule of free-energy currency) from the most efficient to the least efficient.

  • What, if any, changes are there in the shape of the ring as NADH is oxidized to NAD+(see Figure 5)? (Hint: Consider which atoms lie in the same plane in each structure.)

Mechanism of Coupling the Oxidative-Phosphorylation Reactions

In order to couple the redox and phosphorylation reactions needed for ATP synthesis in the body, there must be some mechanism linking the reactions together. In cells, this is accomplished through an elegant proton-pumping system that occurs inside special double-membrane-bound organelles (specialized cellular components) known as mitochondria. A number of proteins are required to maintain this proton-pumping system and catalyze the oxidative and phosphorylation reactions.

Synthesis of ATP (Equation 8) is coupled with the oxidation of NADH (Equation 9) and the reduction of O2 (Equation 10). There are three key steps in this process:

  1. Electrons are transferred from NADH, through a series of electron carriers, to O2. The electron carriers are proteins embedded in the inner mitochondrial membrane. (More detail about the structure of the mitochondria is presented in the next section.) (See Figure 7a.)
  2. Transfer of electrons by these carriers generates a proton (H+) gradient across the inner mitochondrial membrane. (See Figure 7b.)
  3. When Hspontaneously diffuses back across the inner mitochondrial membrane, ATP is synthesized. The large positive free energy of ATP synthesis is overcome by the even larger negative free energy associated with proton flow down the concentration gradient. (See Figure 7c.)

These steps are outlined below.

  1. Electron Transport (Oxidation-Reduction Reactions) Through a Series of Proteins in the Inner Membrane of the Mitochondria
e_transfer

e_transfer

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/e_transfer.jpg

Generation of H+(Proton) Concentration Gradient Across the Inner Mitochondrial Membrane During the Electron-Transport Process (via a Proton Pump)

. Generation of H+ (Proton) Concentration Gradient Across the Inner Mitochondrial Membrane

. Generation of H+ (Proton) Concentration Gradient Across the Inner Mitochondrial Membrane

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/gradient.jpg

Synthesis of ATP Using Free Energy Released From Spontaneous Diffusion of H+Back to the Matrix Inside the Inner Mitochondrial Membrane

. Synthesis of ATP Using Free Energy Released From Spontaneous Diffusion of H+

. Synthesis of ATP Using Free Energy Released From Spontaneous Diffusion of H+

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/ATP_produced.jpg

The three major steps in oxidative phosphorylation are

(a) oxidation-reduction reactions involving electron transfers between specialized proteins embedded in the inner mitochondrial membrane; 

(b) the generation of a proton (H+) gradient across the inner mitochondrial membrane (which occurs simultaneously with step (a)); and 

(c) the synthesis of ATP using energy from the spontaneous diffusion of electrons down the proton gradient generated in step (b).

Note: Steps (a) and (b) show cytochrome oxidase, the final electron-carrier protein in the electron-transport chain described above. When this protein accepts an electron (green) from another protein in the electron-transport chain, an Fe(III) ion in the center of a heme group (purple) embedded in the protein is reduced to Fe(II). The coordinates for the protein were determined using x-ray crystallography, and the image was rendered using SwissPDB Viewer and POV-Ray (see References).

Cells use a proton-pumping system made up of proteins inside the mitochondria to generate ATP. Before we examine the details of ATP synthesis, we shall step back and look at the big picture by exploring the structure and function of the mitochondria, where oxidative phosphorylation occurs.

Structure and Function of the Mitochondria

mitochondria

mitochondria

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/mitochondria.jpg

This is a schematic diagram showing the membranes of the mitochondrion. The purple shapes on the inner membrane represent proteins, which are described in the section below. An enlargement of the boxed portion of the inner membrane in this figure is shown in Figure.

The mitochondrial membranes are crucial for this organelle’s role in oxidative phosphorylation. As shown in Figure 8, mitochondria have two membranes, an inner and an outer membrane. The outer membrane ispermeable to most small molecules and ions, because it contains large protein channels called porins. The inner membrane is impermeable to most ions and polar molecules. The inner membrane is the site of oxidative phosphorylation. Although the membrane is mostly impermeable, it contains special H+ (proton) channels and pumps that enable the coupling of the redox reaction involving NADH and O2 (Equations 9-10) to the phosphorylation reaction of ADP (Equation 8), as described below (“Oxidation-Reduction Reactions and Proton Pumping in Oxidative Phosphorylation”). (Recall the discussion of protein channels in the “Maintaining the Body’s Chemistry: Dialysis in the Kidneys” Tutorial .)

As shown in Figure 8, inside the inner membrane is a space known as the matrix; the space between the two membranes is known as the intermembrane space. The matrix side of the inner membrane has a negative electrical charge relative to the intermembrane space due to an H+ gradient set up by the redox reaction (Equations 9 and 10). This charge difference is used to provide free energy (G) for the phosphorylation reaction (Equation 8).

Oxidation-Reduction Reactions and Proton Pumping in Oxidative Phosphorylation

Phosphorylation of ADP (Equation 8) is coupled to the oxidation-reduction reaction of NADH and O2 (Equations 9 and 10). Electrons are not transferred directly from NADH to O2, but rather pass through a series of intermediate electron carriers in the inner membrane of the mitochondrion. Why? This allows something very important to occur: the pumping of protons across the inner membrane of the mitochondrion. As we shall see, it is this proton pumping that is ultimately responsible for coupling the oxidation-reduction reaction to ATP synthesis.

Two major types of mitochondrial proteins (see Figure 9, below) are required for oxidative phosphorylation to occur. Both classes of proteins are located in the inner mitochondrial membrane.

  1. The electron carriers (NADH-Q reductase, ubiquinone (Q), cytochrome reductase, cytochrome c, and cytochrome oxidase shown in shades of purple in Figure 9 below) transport electrons in a stepwise fashion from NADH to O2.  Three of these carriers (NADH-Q reductase, cytochrome reductase, and cytochrome oxidase) are also proton pumps, and simultaneously pump H+ ions (protons) from the matrix to the intermembrane space. (Proton movement from one side of the membrane to the other is shown as blue arrows in Figure 9, below.) The protons that are pumped across the membrane complete the redox reaction (Equations 9 and 10). The creation of a proton gradient across the membrane is one way of storing free energy.
  2. ATP synthetase (shown in red in Figure 9 below) allows H+ ions to diffuse back into the matrix and uses the free energy released to synthesize ATP from ADP and HPO42-. The ATP synthetase is essential for the phosphorylation to occur (Equation 8). (Proton movement from one side of the membrane to the other is shown as blue arrows in Figure 9, below.)

The electron carriers can be divided into three protein complexes (NADH-Q reductase (1), cytochrome reductase (3), and cytochrome oxidase (5)) that pump protons from the matrix to the intermembrane space, and two mobile carriers (ubiquinone (2) and cytochrome c (4)) that transfer electrons between the three proton-pumping complexes. (Gold numbers refer to the labels on each protein in Figure 9, below.) Because electrons move from one carrier to another until they are finally transferred to O2, the electron carriers (shown in Figure 9,below) are said to form an electron-transport chain.

Figure  below, is a schematic representation of the proteins involved in oxidative phosphorylation. To see an animation of oxidative phosphorylation, click on “View the Movie.”

Proteins of inner space

Proteins of inner space

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/Proteins.jpg

This is a schematic diagram illustrating the transfer of electrons from NADH, through the electron carriers in the electron transport chain, to molecular oxygen. Please click on the pink button below to view a QuickTime animation of the functions of the proteins embedded in the inner mitochondrial membrane that are necessary for oxidative phosphorylation. Click the blue button below to download QuickTime 4.0 to view the movie.

NADH-Q reductase (1), cytochrome reductase (3) , and cytochrome oxidase (5) are electron carriers as well as proton pumps, using the energy gained from each electron-transfer step to move protons (H+) against a concentration gradient, from the matrix to the intermembrane space.Ubiquinone (Q) (2) and cytochrome c (Cyt C) (4) are mobile electron carriers. (Ubiquinone is not actually a protein.) All of the electron carriers are shown in purple, with lighter shades representing increasingly higher reduction potentials. Together, these electron carriers form a “chain” to transport electrons from NADH to O2. The path of the electrons is shown with the green dotted line.

ATP synthetase (red) has two components: a proton channel (allowing diffusion of protons down a concentration gradient, from the intermembrane space to the matrix), and a catalytic component to catalyze the formation of ATP.

For a more complete description of each step in oxidative phosphorylation (indicated by the gold numbers), click here.

view movie

view movie

http://www.apple.com/quicktime/

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/movie.jpg

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/QuickTime.jpg

Click here for a brief description of each of the electron carriers in the electron-transport chain. It is important to note that, although NADH donates two electrons and O2 ultimately accepts four electrons, each of the carriers can only transfer one electron at a time. Hence, there are several points along the chain where electrons can be collected and dispersed. For the sake of simplicity, these points are not described in this tutorial.

In the section above, we see that the oxidation-reduction process is a series of electron transfers that occurs spontaneously and produces a proton gradient. Why are the electron tranfers from one electron carrier to the next spontaneous?

What causes electrons to be transferred down the electron-transport chain?

As seen in Table 2, below, and Figure 7a, in these carriers, the species being oxidized or reduced is Fe, which is found either in a iron-sulfur (Fe-S) group or in a heme group. (Recall the heme group from the Chem 151 tutorial “Hemoglobin and the Heme Group: Metal Complexes in the Blood“.) The iron in these groups is alternately oxidized and reduced between Fe(II) (reduced) or Fe(III) (oxidized) states.

Table 2 shows that the electrons are transferred through the electron-transport chain because of the difference in the reduction potential of the electron carriers. As explained in the green box below, the higher the electrical potential (e) of a reduction half reaction is, the greater the tendency is for the species to accept an electron. Hence, in the electron-transport chain, electrons are transferred spontaneously from carriers whose reduction results in a small electrical potential change to carriers whose reduction results in an increasingly larger electrical potential change.

Reduction Potentials and Relationship to Free Energy

An oxidation-reduction reaction consists of an oxidation half reaction and a reduction half reaction. Every half reaction has an electrical potential (e). By convention, all half reactions are written as reductions, and the electrical potential for an oxidation half-reaction is equal in magnitude, but opposite in sign, to the electrical potential for the corresponding reduction (i.e., the opposite reaction). The electrical potential for an oxidation-reduction reaction is calculated by

erxn = eoxidation + ereduction (12)

For example, for the overall reaction of the oxidation of NADH paired with the reduction of O2, the potential can be calculated as shown below.

Reduction Potentials ereduction
NAD+ + 2H+ + 2e –> NADH + H+ -0.32 V
(1/2) O2 + 2H+ + 2e –> H2O +0.82 V

The overall reaction is

NADH + H–> NAD+ + 2H+ + 2e eoxidation = 0.32 V
(1/2) O2 + 2H+ + 2e –> H2O ereduction = 0.82 V
net: NADH + (1/2)O2 + H+ –>
H2O + NAD+
erxn = 1.14 V

The electrical potential (erxn) is related to the free energy (DG) by the following equation:

DG= -nFerxn (13)

where n is the number of electrons transferred (in moles, from the balanced equation), and F is the Faraday constant (96,485 Coulombs/mole). (Using this equation, DG is given in Joules; one Joule = 1 Volt x 1 Coulomb.)

Hence the overall reaction for the oxidation of NADH paired with the reduction of O2 has a negative change in free energy (DG =-220 kJ); i.e., it is spontaneous. Thus, the higher the electrical potential of a reduction half reaction, the greater the tendency for the species to accept an electron.

Just as in the box above, the electrical potential for the overall reaction (electron transfer) between two electron carriers is the sum of the potentials for the two half reactions. As long as the potential for the overall reaction is positive the reaction is spontaneous. Hence, from Table 2 below, we see that cytochrome c1 (part of the cytochrome reductase complex, #3 in Figure 9) can spontaneously transfer an electron to cytochrome c (#4 in Figure 9). The net reaction is given by Equation 16, below.

reduced cytochrome c–> oxidized cytochrome c+ e eoxidation = – .220 V (14)
oxidized cytochrome c + e –> reduced cytochrome c ereduction = .250 V (15)
NET: reduced cyt c1 + oxidized cyt c –>
oxidized cyt c+ reduced cyt c
erxn = 0.030 V (16) Spontaneous

We can also see from Table 2 that cytochrome c1 cannot spontaneously transfer an electron to cytochrome b (Equation 19):

reduced cyt c–> oxidized cyt c+ e eoxidation = – .220 V (17)
oxidized cyt b + e –> reduced cyt b ereduction = – 0.34 V (18)
NET: reduced cyt c1 + oxidized cyt c –>
oxidized cyt c+ reduced cyt c
erxn = – 0.56 V (19) NOT Spontaneous

Table 2 lists the reduction potentials for each of the cytochrome proteins (i.e., the last three steps in the electron-transport chain before the electrons are accepted by O2) involved in the electron-transport chain. Note that each electron transfer is to a cytochrome with a higher reduction potential than the previous cytochrome. As described in the box above and seen in Equations 14-19, an increase in potential leads to a decrease in DG (Equation 13), and thus the transfer of electrons through the chain is spontaneous.

Complex Name Half Reaction Reduction Potential
Cytochrome reductase

(also known as cytochrome b-c1 complex)

(3 in Figure 9)

Cytochrome b (Fe(III) center)
+ e –>
Cytochrome b (Fe(II) center)
-0.34 V
(at pH 7, T=30oC)
Cytochrome c1 (Fe(III) center)
+ e– –>
Cytochrome c1 (Fe(II) center)
+0.220 V
(at pH 7, T=30oC)
Cytochrome c

(4 in Figure 9)

Cytochrome c (Fe(III) center)
+ e– –>
Cytochrome c (Fe(II) center)
+0.250 V
(at pH 7, T=30oC)
Cytochrome oxidase

(5 in Figure 9)

Cytochrome oxidase
( Fe(III) center) + e– –>
Cytochrome oxidase
(Fe(II) center)
+0.285 V
(at pH 7.4, T=25oC)
Table 2

To view the cytochrome molecules interactively using RASMOL, please click on the name of the complex to download the pdb file.

Hence, the electron-transport chain (which works because of the difference in reduction potentials) leads to a large concentration gradient for H+. As we shall see below, this huge concentration gradient leads to the production of ATP.

Questions on Electron Carriers: Steps in the Electron-Transport Chain; Reduction Potentials and Relationship to Free Energy

  • Briefly, explain why electrons travel from NADH-Q reductase, to ubiquinone (Q), to cytochrome reductase, rather than in the opposite direction.
  • One result of the transfer of electrons from NADH-Q reductase down the electron transport chain is that the concentration of protons (H+ ions) in the intermembrane space is increased.  Could cells move protons (H+ ions) from the matrix to the intermembrane space without transporting electrons?  Why or why not?

 ATP Synthetase: Production of ATP

We have seen that the electron-transport chain generates a large proton gradient across the inner mitochondrial membrane. But recall that the ultimate goal of oxidative phosphorylation is to generate ATP to supply readily-available free energy for the body. How does this occur? In addition to the electron-carrier proteins embedded in the inner mitochondrial membrane, a special protein called ATP synthetase (Figure 9, the red-colored protein) is also embedded in this membrane. ATP synthetase uses the proton gradient created by the electron-transport chain to drive the phosphorylation reaction that generates ATP (Figure 7c).

ATP synthetase is a protein consisting of two important segments: a transmembrane proton channel, and a catalytic component located inside the matrix. The proton-channel segment allows H+ ions to diffuse from the intermembrane space, where the concentration is high, to the matrix, where the concentration is low. Recall from the Kidney Dialysis tutorial that particles spontaneously diffuse from areas of high concentration to areas of low concentration. Thus, since the diffusion of protons through the channel component of ATP synthetase is spontaneous, this process is accompanied by a negative change in free energy (i.e., free energy is released). The catalytic component of ATP synthetase has a site where ADP can enter. Then, using the free energy released by the spontaneous diffusion of protons through the channel segment, a bond is formed between the ADP and a free phosphate group, creating an ATP molecule. The ATP is then released from the reaction site, and a new ADP molecule can enter in order to be phosphorylated.

Questions on ATP Synthetase: Production of ATP

  • A scientist has created a phospholipid-bilayer membrane containing ATP-synthetase proteins. Instead of a proton gradient, this scientist has created a large Cs+ gradient (many Cs+ ions on the side of the membrane without the catalytic unit, and few Cs+ ions on the side of the membrane with the catalytic unit). Would you expect the ATP-synthetase proteins in this membrane to be able to generate ATP, given an abundant supply of ADP and phosphate? Briefly, explain your answer. (HINT: Draw on your knowledge of the structure of protein channels to predict what effect replacing H+ ions with Cs+ ions would have.)
  • Certain toxins allow H+ ions to move freely across the inner mitochondrial membrane (i.e., without needing to pass through the channel in ATP synthetase). What effect do you expect these toxins to have on the production of ATP? Briefly, explain your answer.

Summary

In this tutorial, we have learned that the ability of the body to perform daily activities is dependent on thermodynamic, equilibrium, and electrochemical concepts.   These activities, which are typically based on nonspontaneous chemical reactions, are performed by using free-energy currency. The common free-energy currency is ATP, which is a molecule that easily dephosphorylates (loses a phosphate group) and releases a large amount of free energy. In the body, the nonspontaneous reactions are coupled to this very spontaneous dephosphorylation reaction, thereby making the overall reaction spontaneous (DG < 0). As the coupled reactions occur (i.e., as the body performs daily activities), ATP is consumed and the body regenerates ATP by using energy from the food we eat (Figure 3). As seen in Figure 4, the breakdown of glucose (glycolysis) obtained from the food we eat cannot by itself generate the large amount of ATP that is needed for metabolic energy by the body. However, glycolysis and the subsequent step, the citric-acid cycle, produce two easily oxidized molecules: NADH and FADH2. These redox molecules are used in an oxidative-phosphorylation process to produce the majority of the ATP that the body uses. This oxidative-phosphorylation process consists of two steps: the oxidation of NADH (or FADH2) and the phosphorylation reaction which regenerates ATP. Oxidative phosphorylation occurs in the mitochondria, and the two reactions (oxidation of NADH or FADHand phosphorylation to generate ATP) are coupled by a proton gradient across the inner membrane of the mitochondria (Figure 9). As seen in Figures 7 and 9, the oxidation of NADH occurs by electron transport through a series of protein complexes located in the inner membrane of the mitochondria. This electron transport is very spontaneous and creates the proton gradient that is necessary to then drive the phosphorylation reaction that generates the ATP. Hence, oxidative-phosphorylation demonstrates that free energy can be easily transferred by proton gradients. Oxidative-phosphorylation is the primary means of generating free-energy currency for aerobic organisms, and as such is one of the most important subjects in the study of bioenergetics (the study of energy and its chemical changes in the biological world).

Additional Link:

  • This fun description of oxidative phosphorylation by Dr. E.J.Oakeley contains step-by-step animated illustrations of the redox reactions involved, as well as a quiz to test your understanding of the material.

References:

Alberts, B. et al. In Molecular Biology of the Cell, 3rd ed., Garland Publishing, Inc.: New York, 1994, pp. 653-684.

Becker, W.M. and Deamer, D.W. In The World of the Cell, 2nd ed., The Benjamin/Cummings Publishing Co., Inc.: Redwood City, CA, 1991, pp. 291-307.

Fasman, G.D. In Handbook of Biochemistry and Molecular Biology, 3rd ed., CRC Press, Inc.: Cleveland, OH, 1976, Vol. I (Physical and Chemical Data), pp. 132-137.

Guex, N. and Peitsch, M.C. Electrophoresis, 1997, 18, 2714-2723. (SwissPDB Viewer) URL: http://www.expasy.ch/spdbv/mainpage.htm.

Moa, C., Ozer, Z., Zhou, M. and Uckun, F. X-Ray Structure of Glycerol Kinase Complexed with an ATP Analog Implies a Novel Mechanism for the ATP-Dependent Gylcerol Phosphorylation by Glycerol Kinase.Biochemical and Biophysical Reaearch Communications. 1999, 259, 640-644.

Persistence of Vision Ray Tracer (POV-Ray). URL: http://www.povray.org.

Stryer, L. In Biochemistry, 4th. ed., W.H. Freeman and Co.: New York, 1995, pp. 490, 509, 513, 529-557.

Zubay, G. Biochemistry, 3rd. ed., Wm. C. Brown Publishers: Dubuque, IA, 1983, p. 42.

Acknowledgements:

The authors thank Dewey Holten (Washington University in St. Louis) for many helpful suggestions in the writing of this tutorial.

The development of this tutorial was supported by a grant from the Howard Hughes Medical Institute, through the Undergraduate Biological Sciences Education program, Grant HHMI# 71199-502008 to Washington University.

Copyright 1999, Washington University, All Rights Reserved.

 

 

 

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