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Posts Tagged ‘isoenzymes’


A Reconstructed View of Personalized Medicine

Author: Larry H. Bernstein, MD, FCAP

 

There has always been Personalized Medicine if you consider the time a physician spends with a patient, which has dwindled. But the current recognition of personalized medicine refers to breakthrough advances in technological innovation in diagnostics and treatment that differentiates subclasses within diagnoses that are amenable to relapse eluding therapies.  There are just a few highlights to consider:

  1. We live in a world with other living beings that are adapting to a changing environmental stresses.
  2. Nutritional resources that have been available and made plentiful over generations are not abundant in some climates.
  3. Despite the huge impact that genomics has had on biological progress over the last century, there is a huge contribution not to be overlooked in epigenetics, metabolomics, and pathways analysis.

A Reconstructed View of Personalized Medicine

There has been much interest in ‘junk DNA’, non-coding areas of our DNA are far from being without function. DNA has two basic categories of nitrogenous bases: the purines (adenine [A] and guanine [G]), and the pyrimidines (cytosine [C], thymine [T], and  no uracil [U]),  while RNA contains only A, G, C, and U (no T).  The Watson-Crick proposal set the path of molecular biology for decades into the 21st century, culminating in the Human Genome Project.

There is no uncertainty about the importance of “Junk DNA”.  It is both an evolutionary remnant, and it has a role in cell regulation.  Further, the role of histones in their relationship the oligonucleotide sequences is not understood.  We now have a large output of research on noncoding RNA, including siRNA, miRNA, and others with roles other than transcription. This requires major revision of our model of cell regulatory processes.  The classic model is solely transcriptional.

  • DNA-> RNA-> Amino Acid in a protein.

Redrawn we have

  • DNA-> RNA-> DNA and
  • DNA->RNA-> protein-> DNA.

Neverthess, there were unrelated discoveries that took on huge importance.  For example, since the 1920s, the work of Warburg and Meyerhoff, followed by that of Krebs, Kaplan, Chance, and others built a solid foundation in the knowledge of enzymes, coenzymes, adenine and pyridine nucleotides, and metabolic pathways, not to mention the importance of Fe3+, Cu2+, Zn2+, and other metal cofactors.  Of huge importance was the work of Jacob, Monod and Changeux, and the effects of cooperativity in allosteric systems and of repulsion in tertiary structure of proteins related to hydrophobic and hydrophilic interactions, which involves the effect of one ligand on the binding or catalysis of another,  demonstrated by the end-product inhibition of the enzyme, L-threonine deaminase (Changeux 1961), L-isoleucine, which differs sterically from the reactant, L-threonine whereby the former could inhibit the enzyme without competing with the latter. The current view based on a variety of measurements (e.g., NMR, FRET, and single molecule studies) is a ‘‘dynamic’’ proposal by Cooper and Dryden (1984) that the distribution around the average structure changes in allostery affects the subsequent (binding) affinity at a distant site.

What else do we have to consider?  The measurement of free radicals has increased awareness of radical-induced impairment of the oxidative/antioxidative balance, essential for an understanding of disease progression.  Metal-mediated formation of free radicals causes various modifications to DNA bases, enhanced lipid peroxidation, and altered calcium and sulfhydryl homeostasis. Lipid peroxides, formed by the attack of radicals on polyunsaturated fatty acid residues of phospholipids, can further react with redox metals finally producing mutagenic and carcinogenic malondialdehyde, 4-hydroxynonenal and other exocyclic DNA adducts (etheno and/or propano adducts). The unifying factor in determining toxicity and carcinogenicity for all these metals is the generation of reactive oxygen and nitrogen species. Various studies have confirmed that metals activate signaling pathways and the carcinogenic effect of metals has been related to activation of mainly redox sensitive transcription factors, involving NF-kappaB, AP-1 and p53.

I have provided mechanisms explanatory for regulation of the cell that go beyond the classic model of metabolic pathways associated with the cytoplasm, mitochondria, endoplasmic reticulum, and lysosome, such as, the cell death pathways, expressed in apoptosis and repair.  Nevertheless, there is still a missing part of this discussion that considers the time and space interactions of the cell, cellular cytoskeleton and extracellular and intracellular substrate interactions in the immediate environment.

There is heterogeneity among cancer cells of expected identical type, which would be consistent with differences in phenotypic expression, aligned with epigenetics.  There is also heterogeneity in the immediate interstices between cancer cells.  Integration with genome-wide profiling data identified losses of specific genes on 4p14 and 5q13 that were enriched in grade 3 tumors with high microenvironmental diversity that also substratified patients into poor prognostic groups. In the case of breast cancer, there is interaction with estrogen , and we refer to an androgen-unresponsive prostate cancer.

Finally,  the interaction between enzyme and substrates may be conditionally unidirectional in defining the activity within the cell.  The activity of the cell is dynamically interacting and at high rates of activity.  In a study of the pyruvate kinase (PK) reaction the catalytic activity of the PK reaction was reversed to the thermodynamically unfavorable direction in a muscle preparation by a specific inhibitor. Experiments found that in there were differences in the active form of pyruvate kinase that were clearly related to the environmental condition of the assay – glycolitic or glyconeogenic. The conformational changes indicated by differential regulatory response were used to present a dynamic conformational model functioning at the active site of the enzyme. In the model, the interaction of the enzyme active site with its substrates is described concluding that induced increase in the vibrational energy levels of the active site decreases the energetic barrier for substrate induced changes at the site. Another example is the inhibition of H4 lactate dehydrogenase, but not the M4, by high concentrations of pyruvate. An investigation of the inhibition revealed that a covalent bond was formed between the nicotinamide ring of the NAD+ and the enol form of pyruvate.  The isoenzymes of isocitrate dehydrogenase, IDH1 and IDH2 mutations occur in gliomas and in acute myeloid leukemias with normal karyotype. IDH1 and IDH2 mutations are remarkably specific to codons that encode conserved functionally important arginines in the active site of each enzyme. In this case, there is steric hindrance by Asp279 where the isocitrate substrate normally forms hydrogen bonds with Ser94.

Personalized medicine has been largely viewed from a lens of genomics.  But genomics is only the reading frame.  The living activities of cell processes are dynamic and occur at rapid rates.  We have to keep in mind that personalized in reference to genotype is not complete without reconciliation of phenotype, which is the reference to expressed differences in outcomes.

 

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Therapeutic Implications for Targeted Therapy from the Resurgence of Warburg ‘Hypothesis’

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

(Note that each portion of the discussion is followed by a reference)

It is now a time to pause after almost a century of a biological scientific discoveries that have transformed the practice of medicine and impacted the lives of several generations of young minds determined to probe the limits of our knowledge.  In the century that we have entered into the scientific framework of medicine has brought together a difficult to grasp evolution of the emergence of human existence from wars, famine, droughts, storms, infectious diseases, and insect born pestilence with betterment of human lives, only unevenly divided among societal classes that have existed since time immemorial. In this short time span there have emerged several generations of physicians who have benefited from a far better medical education that their forebears could have known. In this expansive volume on cancer, we follow an incomplete and continuing challenge to understand cancer, a disease that has become associated with longer life spans in developed nations.

While there are significant improvements in the diagnosis and treatment of cancers, there is still a personal as well as locality factor in the occurrence of this group of diseases, which has been viewed incorrectly as a “dedifferentiation” of mature tissue types and the emergence of a cell phenotype that is dependent on glucose, reverts to a cancer “stem cell type” (loss of stemness), loses cell to cell adhesion, loses orderly maturation, and metastasizes to distant sites. At the same time, physician and nurses are stressed in the care of patients by balancing their daily lives and maintaining a perspective.

The conceptual challenge of cancer diagnosis and management has seemed insurmountable, but owes much to the post World War I activities of Otto Heinrich Warburg. It was Warburg who made the observation that cancer cells metabolize glucose by fermentation in much the way Pasteur 60 years earlier observed fermentation of yeast cells. This metabolic phenomenon occurs even in the presence of an oxygen supply, which would provide a huge deficit in ATP production compared with respiration. The cancer cell is “addicted to glucose” and produced lactic acid. Warburg was awarded the Nobel Prize in Medicine for this work in 1931.

In the last 15 years there has been a resurgence of work on the Warburg effect that sheds much new light on the process that was not previously possible, with significant therapeutic implications.  In the first place, the metabolic mechanism for the Warburg effect was incomplete even at the beginning of the 21st century.  This has been partly rectified with the enlightening elucidation of genome modifications, cellular metabolic regulation, and signaling pathways.

The following developments have become central to furthering our understanding of malignant transformation.

  1. There is usually an identifiable risk factor, such as, H. pylori, or of a chronic inflammatory state, as in the case of Barrett’s esophagus.
  2. There are certain changes in glucose metabolism that have been unquestionably been found in the evolution of this disease. The changes are associated with major changes in metabolic pathways, miRN signaling, and the metabolism geared to synthesis of cells with an impairment of the cell death cycle. In these changes, mitochondrial function is central to both the impaired respiration and the autophagy geared to the synthesis of cancer cells.

The emergence of this cell prototype is characterized by the following, again related to the Warburg effect:

  1. Cancer cells oxidize a decreased fraction of the pyruvate generated from glycolysis
  2. The mitochondrial pyruvate carrier (MPC), composed of the products of the MPC1 and MPC2 genes, modulates fractional pyruvate oxidation. MPC1 is deleted or underexpressed in multiple cancers and correlates with poor prognosis.
  3. Cancer cells tend to express a partially inhibited splice variant of pyruvate kinase (PK-M2), leading to decreased pyruvate production.
  4. The two proteins that mediate pyruvate conversion to lactate and its export, M-type lactate dehydrogenase and the monocarboxylate transporter MCT-4, are commonly upregulated in cancer cells leading to decreased pyruvate oxidation.
  5. The enzymatic step following mitochondrial entry is the conversion of pyruvate to acetyl-CoA by the pyruvate dehydrogenase (PDH) complex. Cancer cells frequently exhibit increased expression of the PDH kinase PDK1, which phosphorylates and inactivates PDH. This PDH regulatory mechanism is required for oncogene induced transformation and reversed in oncogene-induced senescence.
  6. The PDK inhibitor dichloroacetate has shown some clinical efficacy, which correlates with increased pyruvate oxidation. One of the simplest mechanisms to explain decreased mitochondrial pyruvate oxidation in cancer cells, a loss of mitochondrial pyruvate import, has been observed repeatedly over the past 40 years. This process has been impossible to study at a molecular level until recently, however, as the identities of the protein(s) that mediate mitochondrial pyruvate uptake were unknown.
  7. The mitochondrial pyruvate carrier (MPC) as a multimeric complex that is necessary for efficient mitochondrial pyruvate uptake. The MPC contains two distinct proteins, MPC1 and MPC2; the absence of either leads to a loss of mitochondrial pyruvate uptake and utilization in yeast, flies, and mammalian cells.

A Role for the Mitochondrial Pyruvate Carrier as a Repressor of the Warburg Effect and Colon Cancer Cell Growth

John C. Schell, Kristofor A. Olson, Lei Jiang, Amy J. Hawkins, et al.
Molecular Cell Nov 6, 2014; 56: 400–413.
http://dx.doi.org/10.1016/j.molcel.2014.09.026

In addition to the above, the following study has therapeutic importance:

Glycolysis has become a target of anticancer strategies. Glucose deprivation is sufficient to induce growth inhibition and cell death in cancer cells. The increased glucose transport in cancer cells has been attributed primarily to the upregulation of glucose transporter 1 (Glut1),  1 of the more than 10 glucose transporters that are responsible for basal glucose transport in almost all cell types. Glut1 has not been targeted until very recently due to the lack of potent and selective inhibitors.

First, Glut1 antibodies were shown to inhibit cancer cell growth. Other Glut1 inhibitors and glucose transport inhibitors, such as fasentin and phloretin, were also shown to be effective in reducing cancer cell growth. A group of inhibitors of glucose transporters has been recently identified with IC50 values lower than 20mmol/L for inhibiting cancer cell growth. However, no animal or detailed mechanism studies have been reported with these inhibitors.

Recently, a small molecule named STF-31 was identified that selectively targets the von Hippel-Lindau (VHL) deficient kidney cancer cells. STF-31 inhibits VHL deficient cancer cells by inhibiting Glut1. It was further shown that daily intraperitoneal injection of a soluble analogue of STF-31 effectively reduced the growth of tumors of VHL-deficient cancer cells grafted on nude mice. On the other hand, STF-31 appears to be an inhibitor with a narrow cell target spectrum.

These investigators recently reported the identification of a group of novel small compounds that inhibit basal glucose transport and reduce cancer cell growth by a glucose deprivation–like mechanism. These compounds target Glut1 and are efficacious in vivo as anticancer agents. A novel representative compound WZB117 not only inhibited cell growth in cancer cell lines but also inhibited cancer growth in a nude mouse model. Daily intraperitoneal injection of WZB117 resulted in a more than 70% reduction in the size of human lung cancer of A549 cell origin. Mechanism studies showed that WZB117 inhibited glucose transport in human red blood cells (RBC), which express Glut1 as their sole glucose transporter. Cancer cell treatment with WZB117 led to decreases in levels of Glut1 protein, intracellular ATP, and glycolytic enzymes. All these changes were followed by increase in ATP sensing enzyme AMP-activated protein kinase (AMPK) and declines in cyclin E2 as well as phosphorylated retinoblastoma, resulting in cell-cycle arrest, senescence, and necrosis. Addition of extracellular ATP rescued compound-treated cancer cells, suggesting that the reduction of intracellular ATP plays an important role in the anticancer mechanism of the molecule.

A Small-Molecule Inhibitor of Glucose Transporter 1 Downregulates Glycolysis, Induces Cell-Cycle Arrest, and Inhibits Cancer Cell Growth In Vitro and In Vivo

Yi Liu, Yanyan Cao, Weihe Zhang, Stephen Bergmeier, et al.
Mol Cancer Ther Aug 2012; 11(8): 1672–82
http://dx.doi.org://10.1158/1535-7163.MCT-12-0131

Alterations in cellular metabolism are among the most consistent hallmarks of cancer. These investigators have studied the relationship between increased aerobic lactate production and mitochondrial physiology in tumor cells. To diminish the ability of malignant cells to metabolize pyruvate to lactate, M-type lactate dehydrogenase levels were knocked down by means of LDH-A short hairpin RNAs. Reduction in LDH-A activity resulted in stimulation of mitochondrial respiration and decrease of mitochondrial membrane potential. It also compromised the ability of these tumor cells to proliferate under hypoxia. The tumorigenicity of the LDH-A-deficient cells was severely diminished, and this phenotype was reversed by complementation with the human ortholog LDH-A protein. These results demonstrate that LDH-A plays a key role in tumor maintenance.

The results are consistent with a functional connection between alterations in glucose metabolism and mitochondrial physiology in cancer. The data also reflect that the dependency of tumor cells on glucose metabolism is a liability for these cells under limited-oxygen conditions. Interfering with LDH-A activity as a means of blocking pyruvate to lactate conversion could be exploited therapeutically. Because individuals with complete deficiency of LDH-A do not show any symptoms under ordinary circumstances, the genetic data suggest that inhibition of LDH-A activity may represent a relatively nontoxic approach to interfere with tumor growth.

Attenuation of LDH-A expression uncovers a link between glycolysis, mitochondrial physiology, and tumor maintenance

Valeria R. Fantin Julie St-Pierre and Philip Leder
Cancer Cell Jun 2006; 9: 425–434.
http://dx.doi.org:/10.1016/j.ccr.2006.04.02

The widespread clinical use of positron-emission tomography (PET) for the detection of aerobic glycolysis in tumors and recent findings have rekindled interest in Warburg’s theory. Studies on the physiological changes in malignant conversion provided a metabolic signature for the different stages of tumorigenesis; during tumorigenesis, an increase in glucose uptake and lactate production have been detected. The fully transformed state is most dependent on aerobic glycolysis and least dependent on the mitochondrial machinery for ATP synthesis.

Tumors ferment glucose to lactate even in the presence of oxygen (aerobic glycolysis; Warburg effect). The pentose phosphate pathway (PPP) allows glucose conversion to ribose for nucleic acid synthesis and glucose degradation to lactate. The nonoxidative part of the PPP is controlled by transketolase enzyme reactions. We have detected upregulation of a mutated transketolase transcript (TKTL1) in human malignancies, whereas transketolase (TKT) and transketolase-like-2 (TKTL2) transcripts were not upregulated. Strong TKTL1 protein expression was correlated to invasive colon and urothelial tumors and to poor patients outcome. TKTL1 encodes a transketolase with unusual enzymatic properties, which are likely to be caused by the internal deletion of conserved residues. We propose that TKTL1 upregulation in tumors leads to enhanced, oxygen-independent glucose usage and a lactate based matrix degradation. As inhibition of transketolase enzyme reactions suppresses tumor growth and metastasis, TKTL1 could be the relevant target for novel anti-transketolase cancer therapies. We suggest an individualized cancer therapy based on the determination of metabolic changes in tumors that might enable the targeted inhibition of invasion and metastasis.

Other important links between cancer-causing genes and glucose metabolism have been already identified. Activation of the oncogenic kinase Akt has been shown to stimulate glucose uptake and metabolism in cancer cells and renders these cells susceptible to death in response to glucose withdrawal. Such tumor cells have been shown to be dependent on glucose because the ability to induce fatty acid oxidation in response to glucose deprivation is impaired by activated Akt. In addition, AMP-activated protein kinase (AMPK) has been identified as a link between glucose metabolism and the cell cycle, thereby implicating p53 as an essential component of metabolic cell-cycle control.

Expression of transketolase TKTL1 predicts colon and urothelial cancer patient survival: Warburg effect reinterpreted

S Langbein, M Zerilli, A zur Hausen, W Staiger, et al.
British Journal of Cancer (2006) 94, 578–585.
http://dx.doi.org:/10.1038/sj.bjc.6602962

The unique metabolic profile of cancer (aerobic glycolysis) might confer apoptosis resistance and be therapeutically targeted. Compared to normal cells, several human cancers have high mitochondrial membrane potential (DJm) and low expression of the K+ channel Kv1.5, both contributing toapoptosis resistance. Dichloroacetate (DCA) inhibits mitochondrial pyruvate dehydrogenase kinase (PDK), shifts metabolism from glycolysis to glucose oxidation, decreases DJm, increases mitochondrial H2O2, and activates Kv channels in all cancer, but not normal, cells; DCA upregulates Kv1.5 by an NFAT1-dependent mechanism. DCA induces apoptosis, decreases proliferation, and inhibits tumor growth, without apparent toxicity. Molecular inhibition of PDK2 by siRNA mimics DCA. The mitochondria-NFAT-Kv axis and PDK are important therapeutic targets in cancer; the orally available DCA is a promising selective anticancer agent.

Cancer progression and its resistance to treatment depend, at least in part, on suppression of apoptosis. Although mitochondria are recognized as regulators of apoptosis, their importance as targets for cancer therapy has not been adequately explored or clinically exploited. In 1930, Warburg suggested that mitochondrial dysfunction in cancer results in a characteristic metabolic phenotype, that is, aerobic glycolysis (Warburg, 1930). Positron emission tomography (PET) imaging has now confirmed that most malignant tumors have increased glucose uptake and metabolism. This bioenergetic feature is a good marker of cancer but has not been therapeutically pursued..

The small molecule DCA is a metabolic modulator that has been used in humans for decades in the treatment of lactic acidosis and inherited mitochondrial diseases. Without affecting normal cells, DCA reverses the metabolic electrical remodeling that we describe in several cancer lines (hyperpolarized mitochondria, activated NFAT1, downregulated Kv1.5), inducing apoptosis and decreasing tumor growth. DCA in the drinking water at clinically relevant doses for up to 3 months prevents and reverses tumor growth in vivo, without apparent toxicity and without affecting hemoglobin, transaminases, or creatinine levels. The ease of delivery, selectivity, and effectiveness  make DCA an attractive candidate for proapoptotic cancer therapy which can be rapidly translated into phase II–III clinical trials.

A Mitochondria-K+ Channel Axis Is Suppressed in Cancer and Its Normalization Promotes Apoptosis and Inhibits Cancer Growth

Sebastien Bonnet, Stephen L. Archer, Joan Allalunis-Turner, et al.

Cancer Cell Jan 2007; 11: 37–51.
http://dx.doi.org:/10.1016/j.ccr.2006.10.020

Tumor cells, just as other living cells, possess the potential for proliferation, differentiation, cell cycle arrest, and apoptosis. There is a specific metabolic phenotype associated with each of these conditions, characterized by the production of both energy and special substrates necessary for the cells to function in that particular state. Unlike that of normal living cells, the metabolic phenotype of tumor cells supports the proliferative state. Aim: To present the metabolic hypothesis that (1) cell transformation and tumor growth are associated with the activation of metabolic enzymes that increase glucose carbon utilization for nucleic acid synthesis, while enzymes of the lipid and amino acid synthesis pathways are activated in tumor growth inhibition, and (2) phosphorylation and allosteric and transcriptional regulation of intermediary metabolic enzymes and their substrate availability together mediate and sustain cell transformation from one condition to another. Conclusion: Evidence is presented that demonstrates opposite changes in metabolic phenotypes induced by TGF-β, a cell transforming agent, and tumor growth-inhibiting phytochemicals such as genistein and Avemar, or novel synthetic antileukemic drugs such as STI571 (Gleevec).  Intermediary metabolic enzymes that mediate the growth signaling pathways and promote malignant cell transformation may serve as high efficacy nongenetic novel targets for cancer therapies.

A Metabolic Hypothesis of Cell Growth and Death in Pancreatic Cancer

Laszlo G. Boros, Wai-Nang Paul Lee, and Vay Liang W. Go
Pancreas 2002; 24(1):26–33

Clear cell renal cell carcinoma (ccRCC) is the most common pathological subtype of kidney cancer. Here, we integrated an unbiased genome-wide RNA interference screen for ccRCC survival regulators with an analysis of recurrently overexpressed genes in ccRCC to identify new therapeutic targets in this disease. One of the most potent survival regulators, the monocarboxylate transporter MCT4 (SLC16A3), impaired ccRCC viability in all eight ccRCC lines tested and was the seventh most overexpressed gene in a meta-analysis of five ccRCC expression datasets.

MCT4 silencing impaired secretion of lactate generated through glycolysis and induced cell cycle arrest and apoptosis. Silencing MCT4 resulted in intracellular acidosis, and reduction in intracellular ATP production together with partial reversion of the Warburg effect in ccRCC cell lines. Intra-tumoral heterogeneity in the intensity of MCT4 protein expression was observed in primary ccRCCs.

MCT4 protein expression analysis based on the highest intensity of expression in primary ccRCCs was associated with poorer relapse-free survival, whereas modal intensity correlated with Fuhrman nuclear grade. Consistent with the potential selection of subclones enriched for MCT4 expression during disease progression, MCT4 expression was greater at sites of metastatic disease. These data suggest that MCT4 may serve as a novel metabolic target to reverse the Warburg effect and limit disease progression in ccRCC.

Clear cell carcinoma (ccRCC) is the commonest subtype of renal cell carcinoma, accounting for 80% of cases. These tumors are highly resistant to cytotoxic chemotherapy and until recently, systemic treatment options for advanced ccRCC were limited to cytokine based therapies, such as interleukin-2 and interferon-α. Recently, anti-angiogenic drugs and mTOR inhibitors, all targeting the HIF–VEGF axis which is activated in up to 91% of ccRCCs through loss of the VHL tumor suppressor gene [1], have been shown to be effective in metastatic ccRCC [2–5]. Although these drugs increase overall survival to more than 2 years [6], resistance invariably occurs, making the identification of new molecular targets a major clinical need to improve outcomes in patients with metastatic ccRCC.

Genome-wide RNA interference analysis of renal carcinoma survival regulators identifies MCT4 as a Warburg effect metabolic target

Marco Gerlinger, Claudio R Santos, Bradley Spencer-Dene, et al.
J Pathol 2012; 227: 146–156
http://dx.doi.org:/10.1002/path.4006

Hypoxia-inducible factor 1 (HIF-1) plays a key role in the reprogramming of cancer metabolism by activating transcription of genes encoding glucose transporters and glycolytic enzymes, which take up glucose and convert it to lactate; pyruvate dehydrogenase kinase 1, which shunts pyruvate away from the mitochondria; and BNIP3, which triggers selective mitochondrial autophagy. The shift from oxidative to glycolytic metabolism allows maintenance of redox homeostasis and cell survival under conditions of prolonged hypoxia. Many metabolic abnormalities in cancer cells increase HIF-1 activity. As a result, a feed-forward mechanism can be activated that drives HIF-1 activation and may promote tumor progression.

Metastatic cancer is characterized by reprogramming of cellular metabolism leading to increased uptake of glucose for use as both an anabolic and a catabolic substrate. Increased glucose uptake is such a reliable feature that it is utilized clinically to detect metastases by positron emission tomography using 18F-fluorodeoxyglucose (FDG-PET) with a sensitivity of >90% [1]. As with all aspects of cancer biology, the details of metabolic reprogramming differ widely among individual tumors. However, the role of specific signaling pathways and transcription factors in this process is now understood in considerable detail. This review will focus on the involvement of hypoxia-inducible factor 1 (HIF-1) in both mediating metabolic reprogramming and responding to metabolic alterations. The placement of HIF-1 both upstream and downstream of cancer metabolism results in a feed-forward mechanism that may play a major role in the development of the invasive, metastatic, and lethal cancer phenotype.

O2 concentrations are significantly reduced in many human cancers compared with the surrounding normal tissue. The median PO2 in breast cancers is 10 mmHg, as compared with65 mmHg in normal breast tissue. Reduced O2 availability induces HIF-1, which regulates the transcription of hundreds of genes that encode proteins involved in every aspect of cancer biology, including: cell immortalization and stem cell maintenance; genetic instability; glucose and energy metabolism; vascularization; autocrine growth factor signaling; invasion and metastasis; immune evasion; and resistance to chemotherapy and radiation therapy.

HIF-1 is a transcription factor that consists of an O2 regulated HIF-1a and a constitutively expressed HIF-1b subunit. In well-oxygenated cells, HIF-1a is hydroxylated on proline residue 402 (Pro-402) and/or Pro-564 by prolyl hydroxylase domain protein 2 (PHD2), which uses O2 and a-ketoglutarate as substrates in a reaction that generates CO2 and succinate as byproducts. Prolylhydroxylated HIF-1a is bound by the von Hippel–Lindau tumor suppressor protein (VHL), which recruits an E3-ubiquitin ligase that targets HIF-1a for proteasomal degradation (Figure 1a). Asparagine 803 in the transactivation domain is hydroxylated in well-oxygenated cells by factor inhibiting HIF-1 (FIH-1), which blocks the binding of the coactivators p300 and CBP. Under hypoxic conditions, the prolyl and asparaginyl hydroxylation reactions are inhibited by substrate (O2) deprivation and/or the mitochondrial generation of reactive oxygen species (ROS), which may oxidize Fe(II) present in the catalytic center of the hydroxylases.

The finding that acute changes in PO2 increase mitochondrial ROS production suggests that cellular respiration is optimized at physiological PO2 to limit ROS generation and that any deviation in PO2 – up or down – results in increased ROS generation. If hypoxia persists, induction of HIF-1 leads to adaptive mechanisms to reduce ROS and re-establish homeostasis, as described below. Prolyl and asparaginyl hydroxylation provide a molecular mechanism by which changes in cellular oxygenation can be transduced to the nucleus as changes in HIF-1 activity.

HIF-1: upstream and downstream of cancer metabolism

Gregg L Semenza
Current Opinion in Genetics & Development 2010, 20:51–56

This review comes from a themed issue on Genetic and cellular mechanisms of oncogenesis Edited by Tony Hunter and Richard Marais

http://dx.doi.org:/10.1016/j.gde.2009.10.009

Hypoxia-inducible factor 1 (HIF-1) regulates the transcription of many genes involved in key aspects of cancer biology, including immortalization, maintenance of stem cell pools, cellular dedifferentiation, genetic instability, vascularization, metabolic reprogramming, autocrine growth factor signaling, invasion/metastasis, and treatment failure. In animal models, HIF-1 overexpression is associated with increased tumor growth, vascularization, and metastasis, whereas HIF-1 loss-of-function has the opposite effect, thus validating HIF-1 as a target. In further support of this conclusion, immunohistochemical detection of HIF-1a overexpression in biopsy sections is a prognostic factor in many cancers. A growing number of novel anticancer agents have been shown to inhibit HIF-1 through a  variety of molecular mechanisms. Determining which combination of drugs to administer to any given patient remains a major obstacle to improving cancer treatment outcomes.

Intratumoral hypoxia The majority of locally advanced solid tumors contain regions of reduced oxygen availability. Intratumoral hypoxia results when cells are located too far from a functional blood vessel for diffusion of adequate amounts of O2 as a result of rapid cancer cell proliferation and the formation of blood vessels that are structurally and functionally abnormal. In the most extreme case, O2 concentrations are below those required for survival, resulting in cell death and establishing a selection for cancer cells in which apoptotic pathways are inactivated, anti-apoptotic pathways are activated, or invasion/metastasis pathways that promote escape from the hypoxic microenvironment are activated. This hypoxic adaptation may arise by alterations in gene expression or by mutations in the genome or both and is associated with reduced patient survival.

Hypoxia-inducible factor 1 (HIF-1) The expression of hundreds of genes is altered in each cell exposed to hypoxia. Many of these genes are regulated by HIF-1. HIF-1 is a heterodimer formed by the association of an O2-regulated HIF1a subunit with a constitutively expressed HIF-1b subunit. The structurally and functionally related HIF-2a protein also dimerizes with HIF-1b and regulates an overlapping battery of target genes. Under nonhypoxic conditions, HIF-1a (as well as HIF-2a) is subject to O2-dependent prolyl hydroxylation and this modification is required for binding of the von Hippel–Lindau tumor suppressor protein (VHL), which also binds to Elongin C and thereby recruits a ubiquitin ligase complex that targets HIF-1a for ubiquitination and proteasomal degradation. Under hypoxic conditions, the rate of hydroxylation and ubiquitination declines, resulting in accumulation of HIF-1a. Immunohistochemical analysis of tumor biopsies has revealed high levels of HIF-1a in hypoxic but viable tumor cells surrounding areas of necrosis.

Genetic alterations in cancer cells increase HIF-1 activity In the majority of clear-cell renal carcinomas, VHL function is lost, resulting in constitutive activation of HIF-1. After re-introduction of functional VHL, renal carcinoma cell lines are no longer tumorigenic, but can be made tumorigenic by expression of HIF2a in which the prolyl residues that are subject to hydroxylation have been mutated. In addition to VHL loss-of-function, many other genetic alterations that inactivate tumor suppressors

Evaluation of HIF-1 inhibitors as anticancer agents

Gregg L. Semenza
Drug Discovery Today Oct 2007; 12(19/20).
http://dx.doi.org:/10.1016/j.drudis.2007.08.006

Hypoxia-inducible factor-1 (HIF-1), which is present at high levels in human tumors, plays crucial roles in tumor promotion by upregulating its target genes, which are involved in anaerobic energy metabolism, angiogenesis, cell survival, cell invasion, and drug resistance. Therefore, it is apparent that the inhibition of HIF-1 activity may be a strategy for treating cancer. Recently, many efforts to develop new HIF-1-targeting agents have been made by both academic and pharmaceutical industry laboratories. The future success of these efforts will be a new class of HIF-1-targeting anticancer agents, which would improve the prognoses of many cancer patients. This review focuses on the potential of HIF-1 as a target molecule for anticancer therapy, and on possible strategies to inhibit HIF-1 activity. In addition, we introduce YC-1 as a new anti-HIF-1, anticancer agent. Although YC-1 was originally developed as a potential therapeutic agent for thrombosis and hypertension, recent studies demonstrated that YC-1 suppressed HIF-1 activity and vascular endothelial growth factor expression in cancer cells. Moreover, it halted tumor growth in immunodeficient mice without serious toxicity during the treatment period. Thus, we propose that YC-1 is a good lead compound for the development of new anti-HIF-1, anticancer agents.

Although many anticancer regimens have been introduced to date, their survival benefits are negligible, which is the reason that a more innovative treatment is required. Basically, the identification of the specific molecular features of tumor promotion has allowed for rational drug discovery in cancer treatment, and drugs have been screened based upon the modulation of specific molecular targets in tumor cells. Target-based drugs should satisfy the following two conditions.

First, they must act by a described mechanism.

Second, they must reduce tumor growth in vivo, associated with this mechanism.

Many key factors have been found to be involved in the multiple steps of cell growth signal-transduction pathways. Targeting these factors offers a strategy for preventing tumor growth; for example, competitors or antibodies blocking ligand–receptor interaction, and receptor tyrosine kinase inhibitors, downstream pathway inhibitors (i.e., RAS farnesyl transferase inhibitors, mitogen-activated protein kinase and mTOR inhibitors), and cell-cycle arresters (i.e., cyclin-dependent kinase inhibitors) could all be used to inhibit tumor growth.

In addition to the intracellular events, tumor environmental factors should be considered to treat solid tumors. Of these, hypoxia is an important cancer-aggravating factor because it contributes to the progression of a more malignant phenotype, and to the acquisition of resistance to radiotherapy and chemotherapy. Thus, transcription factors that regulate these hypoxic events are good targets for anticancer therapy and in particular HIF-1 is one of most compelling targets. In this paper, we introduce the roles of HIF-1 in tumor promotion and provide a summary of new anticancer strategies designed to inhibit HIF-1 activity.

New anticancer strategies targeting HIF-1

Eun-Jin Yeo, Yang-Sook Chun, Jong-Wan Park
Biochemical Pharmacology 68 (2004) 1061–1069
http://dx.doi.org:/10.1016/j.bcp.2004.02.040

Classical work in tumor cell metabolism focused on bioenergetics, particularly enhanced glycolysis and suppressed oxidative phosphorylation (the ‘Warburg effect’). But the biosynthetic activities required to create daughter cells are equally important for tumor growth, and recent studies are now bringing these pathways into focus. In this review, we discuss how tumor cells achieve high rates of nucleotide and fatty acid synthesis, how oncogenes and tumor suppressors influence these activities, and how glutamine metabolism enables macromolecular synthesis in proliferating cells.

Otto Warburg’s demonstration that tumor cells rapidly use glucose and convert the majority of it to lactate is still the most fundamental and enduring observation in tumor metabolism. His work, which ushered in an era of study on tumor metabolism focused on the relationship between glycolysis and cellular bioenergetics, has been revisited and expanded by generations of tumor biologists. It is now accepted that a high rate of glucose metabolism, exploited clinically by 18FDGPET scanning, is a metabolic hallmark of rapidly dividing cells, correlates closely with transformation, and accounts for a significant percentage of ATP generated during cell proliferation. A ‘metabolic transformation’ is required for tumorigenesis. Research over the past few years has reinforced this idea, revealing the conservation of metabolic activities among diverse tumor types, and proving that oncogenic mutations can promote metabolic autonomy by driving nutrient uptake to levels that often exceed those required for cell growth and proliferation.

In order to engage in replicative division, a cell must duplicate its genome, proteins, and lipids and assemble the components into daughter cells; in short, it must become a factory for macromolecular biosynthesis. These activities require that cells take up extracellular nutrients like glucose and glutamine and allocate them into metabolic pathways that convert them into biosynthetic precursors (Figure 1). Tumor cells can achieve this phenotype through changes in the expression of enzymes that determine metabolic flux rates, including nutrient transporters and enzymes [8– 10]. Current studies in tumor metabolism are revealing novel mechanisms for metabolic control, establishing which enzyme isoforms facilitate the tumor metabolic phenotype, and suggesting new targets for cancer therapy.

The ongoing challenge in tumor cell metabolism is to understand how individual pathways fit together into the global metabolic phenotype of cell growth. Here we discuss two biosynthetic activities required by proliferating tumor cells: production of ribose-5 phosphate for nucleotide biosynthesis and production of fatty acids for lipid biosynthesis. Nucleotide and lipid biosynthesis share three important characteristics.

  • First, both use glucose as a carbon source.
  • Second, both consume TCA cycle intermediates, imposing the need for a mechanism to replenish the cycle.
  • Third, both require reductive power in the form of NADPH.

In this Essay, we discuss the possible drivers, advantages, and potential liabilities of the altered metabolism of cancer cells (Figure 1, not shown). Although our emphasis on the Warburg effect reflects the focus of the field, we would also like to encourage a broader approach to the study of cancer metabolism that takes into account the contributions of all interconnected small molecule pathways of the cell.

The Tumor Microenvironment Selects for Altered Metabolism One compelling idea to explain the Warburg effect is that the altered metabolism of cancer cells confers a selective advantage for survival and proliferation in the unique tumor microenvironment. As the early tumor expands, it outgrows the diffusion limits of its local blood supply, leading to hypoxia and stabilization of the hypoxia-inducible transcription factor, HIF. HIF initiates a transcriptional program that provides multiple solutions to hypoxic stress (reviewed in Kaelin and Ratcliffe, 2008). Because a decreased dependence on aerobic respiration becomes advantageous, cell metabolism is shifted toward glycolysis by the increased expression of glycolytic enzymes, glucose transporters, and inhibitors of mitochondrial metabolism. In addition, HIF stimulates angiogenesis (the formation of new blood vessels) by upregulating several factors, including most prominently vascular endothelial growth factor (VEGF).

Blood vessels recruited to the tumor microenvironment, however, are disorganized, may not deliver blood effectively, and therefore do not completely alleviate hypoxia (reviewed in Gatenby and Gillies, 2004). The oxygen levels within a tumor vary both spatially and temporally, and the resulting rounds of fluctuating oxygen levels potentially select for tumors that constitutively upregulate glycolysis. Interestingly, with the possible exception of tumors that have lost the von Hippel-Lindau protein (VHL), which normally mediates degradation of HIF, HIF is still coupled to oxygen levels, as evident from the heterogeneity of HIF expression within the tumor microenvironment. Therefore, the Warburg effect—that is, an uncoupling of glycolysis from oxygen levels—cannot be explained solely by upregulation of HIF. Other molecular mechanisms are likely to be important, such as the metabolic changes induced by oncogene activation and tumor suppressor loss.

Oncogene Activation Drives Changes in Metabolism Not only may the tumor microenvironment select for a deranged metabolism, but oncogene status can also drive metabolic changes. Since Warburg’s time, the biochemical study of cancer metabolism has been overshadowed by efforts to identify the mutations that contribute to cancer initiation and progression. Recent work, however, has demonstrated that the key components of the Warburg effect—

  • increased glucose consumption,
  • decreased oxidative phosphorylation, and
  • accompanying lactate production—
  • are also distinguishing features of oncogene activation.

The signaling molecule Ras, a powerful oncogene when mutated, promotes glycolysis (reviewed in Dang and Semenza, 1999; Ramanathan et al., 2005). Akt kinase, a well-characterized downstream effector of insulin signaling, reprises its role in glucose uptake and utilization in the cancer setting (reviewed in Manning and Cantley, 2007), whereas the Myc transcription factor upregulates the expression of various metabolic genes (reviewed in Gordan et al., 2007). The most parsimonious route to tumorigenesis may be activation of key oncogenic nodes that execute a proliferative program, of which metabolism may be one important arm. Moreover, regulation of metabolism is not exclusive to oncogenes.

Cancer Cell Metabolism: Warburg & Beyond

Hsu PP & Sabatini DM
Cell  Sep 5, 2008; 134, 703-705
http://dx.doi.org:/10.1016/j.cell.2008.08.021

Tumor cells respond to growth signals by the activation of protein kinases, altered gene expression and significant modifications in substrate flow and redistribution among biosynthetic pathways. This results in a proliferating phenotype with altered cellular function. These transformed cells exhibit unique anabolic characteristics, which includes increased and preferential utilization of glucose through the non-oxidative steps of the pentose cycle for nucleic acid synthesis but limited de novo fatty  acid   synthesis   and   TCA   cycle   glucose   oxidation. This  primarily nonoxidative anabolic profile reflects an undifferentiated highly proliferative aneuploid cell phenotype and serves as a reliable metabolic biomarker to determine cell proliferation rate and the level of cell transformation/differentiation in response to drug treatment.

Novel drugs effective in particular cancers exert their anti-proliferative effects by inducing significant reversions of a few specific non-oxidative anabolic pathways. Here we present evidence that cell transformation of various mechanisms is sustained by a unique disproportional substrate distribution between the two branches of the pentose cycle for nucleic acid synthesis, glycolysis and the TCA cycle for fatty acid synthesis and glucose oxidation. This can be demonstrated by the broad labeling and unique specificity of [1,2-13C2]glucose to trace a large number of metabolites in the metabolome. Stable isotope-based dynamic metabolic profiles (SIDMAP) serve the drug discovery process by providing a powerful new tool that integrates the metabolome into a functional genomics approach to developing new drugs. It can be used in screening kinases and their metabolic targets, which can therefore be more efficiently characterized, speeding up and improving drug testing, approval and labeling processes by saving trial and error type study costs in drug testing.

Metabolic Biomarker and Kinase Drug Target Discovery in Cancer Using Stable Isotope-Based Dynamic Metabolic Profiling (SIDMAP)

László G. Boros, Daniel J. Brackett and George G. Harrigan
Current Cancer Drug Targets, 2003, 3, 447-455 447

Pyruvate constitutes a critical branch point in cellular carbon metabolism. We have identified two proteins, Mpc1 and Mpc2, as essential for mitochondrial pyruvate transport in yeast, Drosophila, and humans. Mpc1 and Mpc2 associate to form an ~150 kilodalton complex in the inner mitochondrial membrane. Yeast and Drosophila mutants lacking MPC1 display impaired pyruvate metabolism, with an accumulation of upstream metabolites and a depletion of tricarboxylic acid cycle intermediates. Loss of yeast Mpc1 results in defective mitochondrial pyruvate uptake, while silencing of MPC1 or MPC2 in mammalian cells impairs pyruvate oxidation. A point mutation in MPC1 provides resistance to a known inhibitor of the mitochondrial pyruvate carrier. Human genetic studies of three families with children suffering from lactic acidosis and hyperpyruvatemia revealed a causal locus that mapped to MPC1, changing single amino acids that are conserved throughout eukaryotes. These data demonstrate that Mpc1 and Mpc2 form an essential part of the mitochondrial pyruvate carrier.

A Mitochondrial Pyruvate Carrier Required for Pyruvate Uptake in Yeast, Drosophila , and Humans

Daniel K. Bricker, Eric B. Taylor, John C. Schell, Thomas Orsak, et al.
Science Express 24 May 2012
http://dx.doi.org:/10.1126/science.1218099

Adenosine deaminase acting on RNA (ADAR) enzymes convert adenosine (A) to inosine (I) in double-stranded (ds) RNAs. Since Inosine is read as Guanosine, the biological consequence of ADAR enzyme activity is an A/G conversion within RNA molecules. A-to-I editing events can occur on both coding and non-coding RNAs, including microRNAs (miRNAs), which are small regulatory RNAs of ~20–23 nucleotides that regulate several cell processes by annealing to target mRNAs and inhibiting their translation. Both miRNA precursors and mature miRNAs undergo A-to-I RNA editing, affecting the miRNA maturation process and activity. ADARs can also edit 3′ UTR of mRNAs, further increasing the interplay between mRNA targets and miRNAs. In this review, we provide a general overview of the ADAR enzymes and their mechanisms of action as well as miRNA processing and function. We then review the more recent findings about the impact of ADAR-mediated activity on the miRNA pathway in terms of biogenesis, target recognition, and gene expression regulation.

Review ADAR Enzyme and miRNA Story: A Nucleotide that Can Make the Difference 

Sara Tomaselli, Barbara Bonamassa, Anna Alisi, Valerio Nobili, Franco Locatelli and Angela Gallo
Int. J. Mol. Sci. 19 Nov 2013; 14, 22796-22816 http://dx.doi.org:/10.3390/ijms141122796

The fermented wheat germ extract (FWGE) nutraceutical (Avemar™), manufactured under “good manufacturing practice” conditions and, fulfilling the self-affirmed “generally recognized as safe” status in the United States, has been approved as a “dietary food for special medical purposes for cancer patients” in Europe. In this paper, we report the adjuvant use of this nutraceutical in the treatment of high-risk skin melanoma patients. Methods: In a randomized, pilot, phase II clinical trial, the efficacy of dacarbazine (DTIC)-based adjuvant chemotherapy on survival parameters of melanoma patients was compared to that of the same treatment supplemented with a 1-year long administration of FWGE. Results: At the end of an additional 7-year-long follow-up period, log-rank analyses (Kaplan-Meier estimates) showed significant differences in both progression-free (PFS) and overall survival (OS) in favor of the FWGE group. Mean PFS: 55.8 months (FWGE group) versus 29.9 months (control group), p  0.0137. Mean OS: 66.2 months (FWGE group) versus 44.7 months (control group), p < 0.0298. Conclusions: The inclusion of Avemar into the adjuvant protocols of high-risk skin melanoma patients is highly recommended.

Adjuvant Fermented Wheat Germ Extract (Avemar™) Nutraceutical Improves Survival of High-Risk Skin Melanoma Patients: A Randomized, Pilot, Phase II Clinical Study with a 7-Year Follow-Up

LV Demidov, LV Manziuk, GY Kharkevitch, NA Pirogova, and EV Artamonova
Cancer Biotherapy & Radiopharmaceuticals 2008; 23(4)
http://dx.doi.org:/10.1089/cbr.2008.0486

Cancer cells possess unique metabolic signatures compared to normal cells, including shifts in aerobic glycolysis, glutaminolysis, and de novo biosynthesis of macromolecules. Targeting these changes with agents (drugs and dietary components) has been employed as strategies to reduce the complications associated with tumorigenesis. This paper highlights the ability of several food components to suppress tumor-specific metabolic pathways, including increased expression of glucose transporters, oncogenic tyrosine kinase, tumor-specific M2-type pyruvate kinase, and fatty acid synthase, and the detection of such effects using various metabonomic technologies, including liquid chromatography/mass spectrometry (LC/MS) and stable isotope-labeled MS. Stable isotope-mediated tracing technologies offer exciting opportunities for defining specific target(s) for food components. Exposures, especially during the early transition phase from normal to cancer, are critical for the translation of knowledge about food components into effective prevention strategies. Although appropriate dietary exposures needed to alter cellular metabolism remain inconsistent and/or ill-defined, validated metabonomic biomarkers for dietary components hold promise for establishing effective strategies for cancer prevention.

Bioactive Food Components and Cancer-Specific Metabonomic Profiles

Young S. Kim and John A. Milner
Journal of Biomedicine and Biotechnology 2011, Art ID 721213, 9 pages
http://dx.doi.org:/10.1155/2011/721213

This reviewer poses the following observation.  The importance of the pyridine nucleotide reduced/oxidized ratio has not been alluded to here, but the importance cannot be understated. It has relevance to the metabolic functions of anabolism and catabolism of the visceral organs.  The importance of this has ties to the pentose monophosphate pathway. The importance of the pyridine nucleotide transhydrogenase reaction remains largely unexplored.  In reference to the NAD-redox state, the observation was made by Nathan O. Kaplan that the organs may be viewed with respect to their primary functions in anabolic or high energy catabolic activities. Thus we find that the endocrine organs are largely tied to anabolic functioning, and to NADP, whereas cardiac and skeletal muscle are highly dependent on NAD. The consequence of this observed phenomenon appears to be related to a difference in the susceptibility to malignant transformation.  In the case of the gastrointestinal tract, the rate of turnover of the epithelium is very high. However, with the exception of the liver, there is no major activity other than cell turnover. In the case of the liver, there is a major commitment to synthesis of lipids, storage of fuel, and synthesis of proteins, which is largely anabolic, but there is also a major activity in detoxification, which is not.  In addition, the liver has a double circulation. As a result, a Zahn infarct is uncommon.  Now we might also consider the heart.  The heart is a muscle syncytium with a high need for oxygen.  Cutting of the oxygen supply makes the myocytes vulnerable to ischemic insult and abberant rhythm abnormalities.  In addition, the cardiomyocyte can take up lactic acid from the circulation for fuel, which is tied to the utilization of lactate from vigorous skeletal muscle activity.  The skeletal muscle is tied to glycolysis in normal function, which has a poor generation of ATP, so that the recycling of excess lactic acid is required by cardiac muscle and hepatocytes.  This has not been a part of the discussion, but this reviewer considers it important to remember in considering the organ-specific tendencies to malignant transformation.

Comment (Aurelian Udristioiu):

Otto Warburg observed that many cancers lose their capacity for mitochondrial respiration, limiting ATP production to anaerobic glycolytic pathways. The phenomenon is particularly prevalent in aggressive malignancies, most of which are also hypoxic [1].
Hypoxia induces a stochastic imbalance between the numbers of reduced mitochondrial species vs. available oxygen, resulting in increased reactive oxygen species (ROS) whose toxicity can lead to apoptotic cell death.
Mechanism involves inhibition of glycolytic ATP production via a Randle-like cycle while increased uncoupling renders cancers unable to produce compensatory ATP from respiration-.generation in the presence of intact tricarboxylic acid (TCA) enzyme.
One mitochondrial adaptation to increased ROS is over-expression of the uncoupling protein 2 (UCP2) that has been reported in multiple human cancer cell lines [2-3]. Increased UCP2 expression was also associated with reduced ATP production in malignant oxyphilic mouse leukemia and human lymphoma cell lines [4].
Hypoxia reduces the ability of cells to maintain their energy levels, because less ATP is obtained from glycolysis than from oxidative phosphorylation. Cells adapt to hypoxia by activating the expression of mutant genes in glycolysis.
-Severe hypoxia causes a high mutation rate, resulting in point mutations that may be explained by reduced DNA mismatch repairing activity.
The most direct induction of apoptosis caused by hypoxia is determined by the inhibition of the electron carrier chain from the inner membrane of the mitochondria. The lack of oxygen inhibits the transport of protons and thereby causes a decrease in membrane potential. Cell survival under conditions of mild hypoxia is mediated by phosphoinositide-3 kinase (PIK3) using severe hypoxia or anoxia, and then cells initiate a cascade of events that lead to apoptosis [5].
After DNA damage, a very important regulator of apoptosis is the p53 protein. This tumor suppressor gene has mutations in over 60% of human tumors and acts as a suppressor of cell division. The growth-suppressive effects of p53 are considered to be mediated through the transcriptional trans-activation activity of the protein. In addition to the maturational state of the clonal tumor, the prognosis of patients with CLL is dependent of genetic changes within the neoplastic cell population.

1.Warburg O. On the origin of cancer cells. Science 1956; 123 (3191):309-314
PubMed Abstract ; Publisher Full Text

2.Giardina TM, Steer JH, Lo SZ, Joyce DA. Uncoupling protein-2 accumulates rapidly in the inner mitochondrial membrane during mitochondrial reactive oxygen stress in macrophages. Biochim Biophys Acta 2008, 1777(2):118-129. PubMed Abstract | Publisher Full Text

3. Horimoto M, Resnick MB, Konkin TA, Routhier J, Wands JR, Baffy G. Expression of uncoupling protein-2 in human colon cancer. Clin Cancer Res 2004; 10 (18 Pt1):6203-6207. PubMed Abstract | Publisher Full Text

4. Randle PJ, England PJ, Denton RM. Control of the tricarboxylate cycle and it interactions with glycolysis during acetate utilization in rat heart. Biochem J 1970; 117(4):677-695. PubMed Abstract | PubMed Central Full Text

5. Gillies RJ, Robey I, Gatenby RA. Causes and consequences of increased glucose metabolism of cancers. J Nucl Med 2008; 49(Suppl 2):24S-42S. PubMed Abstract | Publisher Full Text

Shortened version of Comment –

Hypoxia induces a stochastic imbalance between the numbers of reduced mitochondrial species vs. available oxygen, resulting in increased reactive oxygen species (ROS) whose toxicity can lead to apoptotic cell death.
Mechanism involves inhibition of glycolytic ATP production via a Randle-like cycle while increased uncoupling renders cancers unable to produce compensatory ATP from respiration-.generation in the presence of intact tricarboxylic acid (TCA) enzyme.
One mitochondrial adaptation to increased ROS is over-expression of the uncoupling protein 2 (UCP2) that has been reported in multiple human cancer cell lines. Increased UCP2 expression was also associated with reduced ATP production in malignant oxyphilic mouse leukemia and human lymphoma cell lines.
Severe hypoxia causes a high mutation rate, resulting in point mutations that may be explained by reduced DNA mismatch repairing activity.

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

Isozymes

An example of an isozyme is glucokinase, a variant of hexokinase which is not
inhibited by glucose 6-phosphate.  Its different regulatory features and lower
affinity for glucose (compared to other hexokinases), allows it to serve different
functions in cells of specific organs, such as

  • control of insulinrelease by the beta cells of the pancreas, or
  • initiation ofglycogen synthesis by liver
  • Both of these processes must only occur when glucose is abundant,or
    problems occur.

Isozymes or Isoenzymes are proteins with different structure which catalyze
the same reaction. Frequently they are oligomers made with different
polypeptide chains, so they usually differ in regulatory mechanisms and in
kinetic characteristics.

From the physiological point of view, isozymes allow the existence of similar
enzymes with different characteristics, “customized” to specific tissue
requirements or metabolic conditions.

One example of the advantages of having isoenzymes for adjusting the
metabolism to different conditions and/ or in different organs is the following:

Glucokinase and Hexokinase are typical examples of isoenzymes. In fact,
there are four Hexokinases: I, II, III and IV. Hexokinase I is present in all
mammalian tissues, and Hexokinase IV, aka Glucokinase, is found mainly
in liver, pancreas  and brain.

Both enzymes catalyze the phosphorylation of Glucose:

Glucose + ATP —–à Glucose 6 (P) + ADP

Hexokinase I has a low Km and is inhibited by glucose 6 (P).  Glucokinase
is not inhibited by Glucose 6 (P) and his Km is high. These two facts
indicate that the activity of glucokinase depends on the availability
of substrate and not on the demand of the product.

Since Glucokinase is not inhibited by glucose 6 phosphate, in
conditions of high concentrations of glucose this enzyme
continues phosphorylating glucose, which can be used for
glycogen synthesis in liver. Additionally, since Glucokinase
has a high Km, its activity does not compromise the supply
of glucose to other organs; in other words, if Glucokinase
had a low Km, and since it is not inhibited by its product, it
would continue converting glucose to glucose 6 phosphate
in the liver,  making glucose unavailable for other organs
(remember that after meals, glucose arrives first to the liver
through the portal system).

The enzyme Lactate Dehydrogenase is made of two (H-
and M-)  sub units, combined in different Permutations
and 
Combinations  depending on the tissue in which it
is present as shown in table,

Type Composition Location
LDH1 HHHH Heart and Erythrocyte
LDH2 HHHM Heart and Erythrocyte
LDH3 HHMM Brain and Kidney
LDH4 HMMM Skeletal Muscle and Liver
LDH5 MMMM Skeletal Muscle and Liver
  • While isozymes may be almost identical in function
    (defined by Michaelis constant, KM)
  • they differ in amino acidsubstitutions that change the
    electric charge of the enzyme (such as replacing
    aspartic acid with glutamic acid)
  • The sum of zwitterion charges result in identifyjng
    difference inmigratiion toward the anode by gel
    electrophoresis
    , and this forms the basis for the use
    of isozymes as molecular markers.
  • To identify isozymes, a crude protein extract is made by
    grinding animal or plant tissue with an extraction buffer,
    and the components of extract are separated according
    to their charge by gel electrophoresis.
  • They were classically purified by ion-exchange column
    chromatography after first precipitation with ammonium
    sulfate, followed by dialysis.

The cytochrome P450 isozymes play important roles in
metabolism and steroidogenesis. The multiple forms of
phosphodiesterase also play major roles in various
biological processes.

These isoforms of the enzyme are unequally distributed
in the various cells of an organism.

Further the main isoenzymes may have closely grouped
“isoforms” having unclear significance.

There are many examples of isoenzymes in cell
metabolism that distinguish cells:

  • Adenylate kinase (AL in liver, and myokinase) – that
    are distinguished by reactivity with sulfhydryl reagents
  • Pyruvate kinase
  • AMPK, and Calmodulin kinase
  • Malate, isocitrate, alcohol, and aldehyde dehydrogenase
  • Nitric oxide synthase (i, e, and n)…

References[edit]

Hunter, R. L. and C.L. Markert. (1957) Histochemical
demonstration of enzymes separated by zone electrophoresis
in starch gels. Science 125: 1294-1295

Uzunov, P. and Weiss, B.(1972) “Separation of multiple
molecular forms of cyclic adenosine 3′,5′-monophosphate
phosphodiesterase in rat cerebellum by polyacrylamide
gel electrophoresis.”  Biochim. Biophys. Acta 284:220-226.

Uzunov, P., Shein, H.M. and Weiss, B.(1974) “Multiple
forms of cyclic 3′,5′-AMP phosphodiesterase
of rat cerebrum and cloned astrocytoma and
neuroblastoma cells.” Neuropharmacology 13:377-391.

Weiss, B., Fertel, R., Figlin, R. and Uzunov, P. (1974)
“Selective alteration of the activity of the multiple forms
of adenosine 3′,5′-monophosphate phosphodiesterase
of rat cerebrum.” Mol. Pharmacol.10:615-625.

Lactate dehydrogenase

In cells, the immediate energy sources involve glucose oxidation. In anaerobic metabolism, the donor of the phosphate group is adenosine triphosphate (ATP), and the reaction is catalyzed via the hexokinase or glucokinase: Glucose +ATP-Mg²+ = Glucose-6-phosphate (ΔGo = – 3.4 kcal/mol with hexokinase as the co-enzyme for the reaction.).
In the following step, the conversion of G-6-phosphate into F-1-6-bisphosphate is mediated by the enzyme phosphofructokinase with the co-factor ATP-Mg²+. This reaction has a large negative free energy difference and is irreversible under normal cellular conditions. In the second step of glycolysis, phosphoenolpyruvic acid in the presence of Mg²+ and K+ is transformed into pyruvic acid. In cancer cells or in the absence of oxygen, the transformation of pyruvic acid into lactic acid alters the process of glycolysis.
The energetic sum of anaerobic glycolysis is ΔGo = -34.64 kcal/mol. However a glucose molecule contains 686kcal/mol and, the energy difference (654.51 kcal) allows the potential for un-controlled reactions during carcinogenesis. The transfer of electrons from NADPH in each place of the conserved unit of energy transmits conformational exchanges in the mitochondrial ATPase. The reaction ADP³+ P²¯ + H²–à ATP + H2O is reversible. The terminal oxygen from ADP binds the P2¯ by forming an intermediate pentacovalent complex, resulting in the formation of ATP and H2O. This reaction requires Mg²+ and an ATP-synthetase, which is known as the H+-ATPase or the Fo-F1-ATPase complex. Intracellular calcium induces mitochondrial swelling and aging. [12].
The known marker of monitoring of treatment in cancer diseases, lactate dehydrogenase (LDH) is an enzyme that is localized to the cytosol of human cells and catalyzes the reversible reduction of pyruvate to lactate via using hydrogenated nicotinamide deaminase (NADH) as co-enzyme.
The causes of high LDH and high Mg levels in the serum include neoplastic states that promote the high production of intracellular LDH and the increased use of Mg²+ during molecular synthesis in processes pf carcinogenesis (Pyruvate acid>> LDH/NADH >>Lactate acid + NAD), [13].
LDH is released from tissues in patients with physiological or pathological conditions and is present in the serum as a tetramer that is composed of the two monomers LDH-A and LDH-B, which can be combined into 5 isoenzymes: LDH-1 (B4), LDH-2 (B3-A1), LDH-3 (B2-A2), LDH-4 (B1-A3) and LDH-5 (A4). The LDH-A gene is located on chromosome 11, whereas the LDH-B gene is located on chromosome 12. The monomers differ based on their sensitivity to allosteric modulators. They facilitate adaptive metabolism in various tissues. The LDH-4 isoform predominates in the myocardium, is inhibited by pyruvate and is guided by the anaerobic conversion to lactate.
Total LDH, which is derived from hemolytic processes, is used as a marker for monitoring the response to chemotherapy in patients with advanced neoplasm with or without metastasis. LDH levels in patients with malignant disease are increased as the result of high levels of the isoenzyme LDH-3 in patients with hematological malignant diseases and of the high level of the isoenzymes LDH-4 and LDH-5, which are increased in patients with other malignant diseases of tissues such as the liver, muscle, lungs, and conjunctive tissues. High concentrations of serum LDH damage the cell membrane [11, 31].

Relation between LDH and Mg as Factors of Interest in the Monitoring and Prognoses of Cancer

Aurelian Udristioiu, Emergency County Hospital Targu Jiu Romania, Clinical Laboratory Medical Analyses, E-mail: aurelianu2007@yahoo.com

Lactate Dehydrogenase (LDH) is ubiquitous in animals and
man, and  it occurs in different organs of the body, each
region having a unique conformation of the subunits, but
the significance was once disputed. Perhaps the experiments
of Jakob and Monod on the lac 1 operon put to rest any
notions that isoenzymes and their conformational forms are
something of no real significance.  This concept does not
necessarily apply in all cases of isoenzyme differences, by
which I mean that there may be a difference in reactivity at
the active site.

For that matter, Jakob and Monod discovered and elucidated
allosterism.

300px-Enzyme_Model  allosterism
In biochemistryallosteric regulation is the regulation of a
protein by binding an effector molecule at a site other than
the protein’s active site.

The site the effector binds to is termed the allosteric site.
Allosteric sites allow effectors to bind to the protein, often
resulting in a conformational change. Effectors that enhance
the protein’s activity are referred to as allosteric activators,
whereas  those that decrease the protein’s activity are called
allosteric inhibitors.

Allosteric regulations are a natural example of control loops,
such as feedback from downstream products or feedforward
 from upstream substrates. Long-range allostery is especially
important in cell signaling. Allosteric regulation
is also particularly important in the cell’s ability to adjust
enzyme activity.

The term allostery comes from the Greek allos (ἄλλος), “other,”
and stereos (στερεὀς), “solid (object).” This is in reference
to the fact that the regulatory site of an allosteric protein is
physically distinct from its active site.

Jacob and Monod model of transcriptional regulation of the lac operon by lac repressor

Jacob and Monod model of  lac repressor

Most allosteric effects can be explained by the concerted
MWC model put forth by Monod, Wyman, and Changeux[2]
or by the sequential model described by Koshland, Nemethy,
and Filmer.[3] Both postulate that enzyme subunits exist in
one of two conformations, tensed (T) or relaxed (R), and
that relaxed subunits bind substrate more readily than
those in the tense state. The two models differ most in
their assumptions about subunit interaction and the pre-
existence of both states.

Allosteric_Regulation Model

Allosteric_Regulation Model

  1.  Monod, J. Wyman, J.P. Changeux. (1965). On the nature of
    allosteric transitions:A plausible model. J. Mol. Biol.;12:88-118.
  2. E. Jr Koshland, G. Némethy, D. Filmer (1966). Comparison of
    experimental binding data and theoretical models in proteins
    containing subunits. Biochemistry. Jan;5(1):365-8

The sequential model (2) of allosteric regulation holds that subunits
are not connected in such a way  that a  conformational change in
one induces a similar change in the others. Thus, all enzyme
subunits do not necessitate the  same conformation. Moreover,
the sequential model dictates that molecules of substrate
bind via an
 induced fit  protocol. In general, when a subunit
randomly collides with a molecule of substrate, the active site,
in essence, forms a  glove around its substrate.

While such an induced fit converts a subunit from the tensed
state to relaxed state, it does not propagate the conformational
change to adjacent subunits. Instead, substrate-binding at
one subunit  only slightly  alters the structure of other
subunits so that their binding sites are more receptive to
substrate.
To summarize:

  • subunits need not exist in the same conformation
  • molecules of substrate bind via induced-fit protocol
  • conformational changes are not propagated to all
    subunits

The discovery of morpheeins has revealed a previously
unforeseen mechanism to target universally essential
enzymes for species-specific drug design and discovery.
A morpheein-based inhibitor would function by  binding
to and stabilizing  the inactive morpheein form of the
enzyme, thereby shifting the equilibrium to favor that form (3).

  1. K. Jaffe, S.H. Lawrence (2008). “Expanding the
    concepts in protein structure-function relationships
    and  enzyme kinetics: Teaching using morpheeins”
    .
    Biochemistry and Molecular Biology  Education36 (4)
    : 274–283. http://dx.doi.org:/10.1002/bmb.20211.
    PMC 2575429PMID 19578473

Important related points are:

Non-regulatory allostery

A non-regulatory allosteric site refers to any non-regulatory
component of an enzyme (or any protein), that is not  itself
an amino acid. For instance, many enzymes require sodium
binding to ensure proper function. However, the sodium
does not necessarily act as a regulatory subunit; the sodium
is always present and there are no known biological processes
to add/remove sodium to regulate enzyme activity. Non-
regulatory allostery could comprise any other  ions besides
sodium (calcium, magnesium, zinc), as well as other chemicals
and possibly vitamins.

Lactate and malate dehydrogenases

LDH is a key enzyme in glycolysis. Anaerobic glycolysis is the
conversion of pyruvate into lactate acid in the absence
of oxygen. This pathway is important to glycolysis in two main
ways. The first is that

  • if pyruvate were to build up glycoysis
  • the generation of ATP would slow.

The second is anaerobic respiration

  • allows for the regeneration of NAD+ from NADH.

NAD+ is required when glyceraldehyde-3-phosphate
dehydrogenase oxidizes glyceraldehyde-3-phosphate in
glycolysis, which generates NADH. Lactate dehydrogenase
is responsible for the anaerobic conversion of NADH to
NAD+. Click here to see the residues which form
inter
actions with pyruvate in the Lactate Dehydrogenase
from Cryptosporidium  parvum (2fm3). (Wikipedia)

Glycolysis ends with the synthesis of pyruvate.  But, to be
self-functioning, it must end with lactate.  Why?  Anaerobic
means “without oxygen”.  This is tantamount to saying
“without mitochondria”.

  1. The mitochondria are especially adept at oxidizing
    NADH to NAD+. NAD+ is needed to keep the glyceraldehyde-
    3-PO4 dehydrogenase reaction functioning.
  2. If glycolysis is to continue when no oxygen is present or in
    short supply (as in a working muscle), an alternative means
    of oxidizing NADH must occur.

Pyruvate has 2 metabolic fates:

  • it can either be converted into lactate or to acetyl-CoA .
    Note that in animals and plants the electrons in  NADH
    are transferred  to pyruvate which reduces the carbonyl
    carbon in the pyruvate molecule to an alcohol. The
    reaction is catalyzed by the enzyme lactate dehydrogenase.
    Lactate (or L-lactate to be more precise)  is thus  a
    “waste product”, since it has no metabolic fate other
    than to be converted back into pyruvate in a reverse of
    the  forward reaction.
  • More importantly, the NAD+ feeds back to the glyceraldehyde-
    3-PO4 dehydrogenase reaction, which  allows glycolysis
    to continue.  Were it not for lactate formation, glycolysis
    as a self-functioning pathway could not exist.

In yeast a slightly different end of glycolysis becomes apparent.
Yeast do not synthesize lactate.  They do, however, oxidize
NADH back to NAD+ anaerobically.  How do they do this?  The
answer is they make ethanol.  In the reaction the pyruvate is
converted into acetaldehyde.  The reaction is catalyzed by a
lyase enzyme, pyruvate decarboxylase, which removes the
carboxyl group as a CO2.  Acetaldehyde is formed because
the electron pair that bonds the –COO group is not removed
by the decarboxylation.  A proton is plucked from the
environment giving the final product, acetaldehyde.
Acetaldehyde is now the substrate that will oxidize NADH to
NAD+ and in the process ethanol is formed.

There is another advantage to the pyruvate-lactate interchange.
The lactate formed by lactate  dehydrogenase  can  be
reconverted. This allows a cell to synthesize glucose from lactate.
Converting lactate to glucose is a major feature of gluconeogenesis,
an anabolic pathway that synthesizes glucose from smaller
precursors such as lactate. This is important because acetyl-CoA
cannot be converted back to pyruvate and hence cannot be a
source of carbons  for glucose biosynthesis.

ADP.  ADP is required in the 3-phosphoglycerate kinase reaction
and in the pyruvate kinase reaction.  It is formed from ATP in the
hexokinase reaction and the phosphofructokinase-I reaction.

NADH, ADP and PO4.   NADH oxidation is important in glycolysis.
NADH is converted into NAD+ in the mitochondria.  That
reaction is promoted by O2 ; NAD+ stays in the mitochondria.
Also in the mitochondria, ATP is formed by condensing ADP
with PO4.  Thus, O2 allows mitochondria to out-compete the
cytosol for ADP,  NADH and PO4, all limiting  substrates or
coenzymes.

In vertebrates, gluconeogenesis takes place mainly in the liver
and, to a lesser extent, in the cortex of kidneys. In many
animals, the process occurs during periods of fasting,
starvationlow-carbohydrate diets, or intense exercise.
The process is highly endergonic until it is coupled to the
hydrolysis of ATP or GTP, effectively making the process
exergonic. For example, the pathway leading from pyruvate
to glucose-6-phosphate requires 4 molecules of  ATP and
2 molecules of GTP to proceed spontaneously. Gluco-
neogenesis is a target of therapy for type II diabetes,
such as metformin, which inhibits glucose formation
and stimulates glucose uptake by cells.

Lactate is formed at the endstage of glycolysis with insufficient
oxygen is transported to the liver where it is converted into
pyruvate by the Cori cycle using the enzyme lactate
dehydrogenase
. In this reaction lactate loses two electrons
(becomes oxidized) and is converted to pyruvate. NAD+
gains two electrons (is reduced) and is converted to NADH.

Both lactate and NAD+ bind to the active site of the enzyme
lactate dehydrogenase and both lactate and NAD+ participate
in the catalysis reaction. In fact, catalysis could not occur
unless the coenzyme NAD+ bound to the active site.

lactat-pyr.LDH

lactat-pyr.LDH

http://academic.brooklyn.cuny.edu/biology/bio4fv/page/couple.gif

What is not shown:

  1. The liver LDH is composed of predominantly M-type subunits.
  2. The forward reaction is regulated in the H-type LDH, but not
    the M-type   enzyme by the formation of a ternary complex
    of LDH-ox. NAD-lactate
  3. The formation and breakup of the ternary complex is
    dependent on the pyruvate in the forward reaction in a
    concentration dependent manner.
  4. The M-type LDH doesn’t have this tight binding of the LDH –
    NAD+ – lactate  (see catalysis below)
  5. As lactate concentration builds in the circulation from heavy
    muscle production (M-type), or from circulatory insufficiency,
    the circulating lactic acid reaches the liver.
  6. The lactic acid is taken up by the liver, and the high
    concentration of lactic acid drives the backward reaction,
    unrestricted.

Pyruvate, the first designated substrate of the gluconeogenic
pathway, can then be used to generate glucose. Transamination
or deamination of amino acids facilitates entering of their
carbon skeleton into the cycle directly  (as pyruvate or
oxaloacetate), or indirectly via the citric acid cycle.  It is
known that odd-chain fatty acids can be  oxidized to yield
propionyl-CoA, a precursor for succinyl-CoA, which can
be converted to  pyruvate and  enter  into gluconeogenesis.

gluconeogenesis

gluconeogenesis

http://upload.wikimedia.org/wikipedia/commons/thumb/6/63/Amino_acid_catabolism.svg/300px-Amino_acid_catabolism.svg.png

Catalysis

Studies have shown that the reaction mechanism of LDH follows an ordered sequence.

mechanism of LDH reaction

mechanism of LDH reaction

In the forward reaction

  1. NADH must bind to the enzyme  Several residues are
    involved in the binding of NADH
    . Once the NADH is
    bound to the enzyme,
  2. pyruvatebinds (substrate oxamate is shown; the CH3
    group is replaced by NH2 to form oxamate). (see the
    direction of the arrow)
  3. binds to the enzyme between the nicotinamide ring
    and several LDH residues.-
  4. transfer of a hydride ion then happens quickly
  5. in either direction giving a mixture of the two ternary
    complexes,
  6. enzyme-NAD+-lactate and enzyme-NADH-pyruvate .
  7. finally L-lactate dissociates from the enzyme followed
    by NAD+[2].

What is not shown is:

  1. The dissocation of NAD+ and lactate from the H-type LDHs
    is  dependent on the pyruvate  in the forward reaction in a
    concentration dependent manner
  2. This results in inhibition of the reaction as it proceeds as
    a result of the abortive ternary complex that forms in about
    500 msec carried out in the Aminco-Morrow stop flow analyzer.
  3. The regulatory effect of the tighter binding of the LDH (H)-
    NAD+-lactate is not seen with the M-type LDH.
  4. The result of this is that the H-type LDH is regulated by the
    formation of oxidized coenzyme  bound with reduced substrate.

Genetics and Mutagenesis of Fish 1973, pp 243-276.
Developmental and Biochemical Genetics of Lactate
Dehydrogenase Isozymes in Fishes
.
G. S. WhittE. T. MillerJ. B. Shaklee
 http://link.springer.com/article/10.1007%2F978-3-642-
65700-9_23/lookinside/000.png

In the teleost there are only three of the isoenzymes.  LDH-1,
3, and 5 (H4, H2M2, M4).

 teleost

Lactic dehydrogenase isozymes in lens and cornea 
Larry BernsteinMichael KerriganHarry Maisel
Experimental Eye Research Oct 1966; 5, (4): Pp 309–314, IN23–IN28
http://dx.doi.org:/10.1016/S0014-4835(66)80041-6

Lactic dehydrogenase isozymes of bovine and rabbit lens and
cornea were analyzed by starch gel electrophoresis.
Although there was a progressive loss of enzyme activity in
the lenses of both species with increasing age, the loss of
isozymes was more clearly evident in the bovine lens. In
the adult bovine lens, 

  • lactic dehydrogenase isozyme Iwas predominant,
  • while in the adult rabbit lens, isozymes 3–5were mainly present.

The mobility of lens isozymes was identical to that of isozymes
in other tissues. Furthermore, the isozymes were not  localized
to any major specific lens crystallin.

Lactate Dehydrogenase Isozyme Patterns of Human
Platelets and Bovine Lens Fibers

Elliot S. Vesell
Science 24 Dec 1965; 150(3704): pp.1735-1737   
http://dx.doi.org:/10.1126/science

Since the platelets and lens fibers, like mature human erythrocytes,
lack a nucleus, the results strengthen the case for a

  • previously developed association between LDH-5 and the
    cell nucleus.

These three cell types are mainly anaerobic, and therefore

  • their isozyme patterns are incompatible with the theory
    that anaerobic `  tissues exhibit predominantly LDH-5
    and aerobic tissues mainly LDH-1.

Lactate dehydrogenase isozymes and their relationship
to lens cell differentiation 

James A. StewartJohn Papaconstantinou
Biochimica et Biophysica Acta (BBA) – General Subjects
26 May 1966; 121,(1): Pp 69–78
http://dx.doi.org:/10.1016/0304-4165(66)90349-7

Changes in the activity of lactate dehydrogenase (LDH) (l-lactate:
NAD+ oxidoreductase EC 1.1.1.27) isozymes are associated with
the growth and differentiation of bovine lens cells. Calf and adult
lens epithelial cells contain all 5 isozymes. The cathodal forms are
most active in the calf-epithelial cells; the anodal forms are most
active in the fiber cells
. This transition from cathodal to anodal
forms of lactate dehydrogenase in the epithelial cells is associated
with cellular aging.

During the differentiation of an epithelial cell to a fiber cell, in calf
and adult lenses there is an enhancement of 

  • the transition from cathodal forms to anodal forms. 

The regulation of lactate dehydrogenase subunit synthesis may
be associated, therefore, with

  • the replicative activity of these cells.

In cells having the greatest replicative activity (calf epithelial
cells) the cathodal isozymes are most active; in cells having a
decreased mitotic activity (adult epithelial cells) the anodal
isozymes are most active. The non-replicative

  • fiber cell of calf and adult shows a transition toward the
    anodal forms.

Although lens fiber cells have a low rate of oxidative metabolism
lactate dehydrogenase-I is the most active isozyme in these
cells. Kinetically,

  • lactate dehydrogenase-I factors other than, or in addition
    to, the regulation of carbohydrate metabolism
  • are involved in regulating the synthesis of lactate dehydrogenase subunits.

Abbreviations   LDH; lactate dehydrogenase

What is not examined to resolve the discrepancy (see the next item):

The Vessell paper was a challenge to the work in Nathan
Kaplan’s lab.  However, there is sufficient complexity revealed
in these works that there is no conceptual foundation.

  1. The analogy is to the loss of cell nuclei in crystallin lens
    fiber formation with the LDH-H type subunits (aerobic?)
  2. The findings are reproduced in several laboratories.
  3. In the lens, glucose is catabolized primarily to lactic
    acid, and is not appreciably combusted to CO2
    (J Kinoshita. Glucose metabolism of Lens)
  4. However, synthetic processes, including nuclear DNA and
    cell replication requires TPNH. This is produced by means
    of the Pentose Shunt.
  5. The most favorable conditions for the lens are achieved
    by incubating in a medium containing glucose in the
    presence of oxygen. Under these conditions of
    incubation (Kinoshita)
  • the lens remains completely transparent,
  • it maintains normal levels of high energy phosphate
    bonds and cations, and
  • it shows a high rate of arginine incorporationinto protein.

incubation in the absence of glucose, but in the presence of oxygen

  • a haze is found in the lens,
  • a drop in high energy phosphate level is observed, and
  • Changes in cation levels are apparent.
  • A 50 percent decrease in the incorporation of arginine
    into lens protein is also observed.

the most unfavorable condition for the lens is an anaerobic
incubation in a medium without glucose

Pirie2 observed that a-glycerophosphate is one of the end products
of lens metabolism. Its oxidation with DPN as the cofactor could
channel its electrons directly into the ETC to produce energy without
involving the Krebs cycle. a-Glycerophosphate is formed from intermediates of the glycolytic scheme by reduction of dihydroxy-
acetone phosphate, one of the triose phosphates produced in
glycolysis.

the dehydrogenase of the mitochondria catalyzes the transfer
of elections to form DPNH by the following reactions:

a-glycerophosphate + DPN+ ± dihydroxyacetone ……..

phosphate + DPNH.

The DPNH is channeled into the oxidative phosphorylation
mechanism to form ATP. The dihydroxyacetone phosphate
then diffuses out into the soluble cytoplasm, interacts with
the glycolytic intermediates by the reversal of the above reaction,

  • and the cyclic mechanism is begunover again.

That this type of electron transport system functions in the
lens was proposed by Pirie.
http://www.iovs.org/content/4/4/619.full.pdf

Lactate dehydrogenase activity and its isoenzymes in
concentric layers of adult bovine and calf lenses.
  
Sempol DOsinaga EZigman SKorc IKorc BSans ARadi R, et al.
Curr Eye Res. 1987 Apr;6(4):555-60.

The activity of lactate dehydrogenase (LDH) and its isoenzyme
pattern were studied in four concentric layers of adult
bovine and calf lenses. In both groups the specific activity of
the total LDH diminished progressively toward the internal
nuclear layer; the decrease was greater in the adult lenses.
The enzyme activities in the cortical layers of the calf lens
were lower than in the adult lens, but in the inner nuclear layers,
the opposite was found. All of the 5 LDH isoenzymes were found
in each layer. In both groups of animals the LDH1 isoenzyme
prevailed, followed by LDH2. No differences were found in the
percentage of each isoenzyme in the different lens layers.
The differences in the activitie(s) of LDH found may be due

  • to post-translational or post-synthetic modifications which
    may occur during the aging process.

Structural basis for altered activity of M- and H-isozyme
forms of human lactate dehydrogenase.

Read JA1, Winter VJEszes CMSessions RBBrady RL.
Author information  Proteins. 2001 May 1;43(2):175-85

Lactate dehydrogenase (LDH) interconverts pyruvate and
lactate with concomitant interconversion of NADH and NAD(+).
Although crystal structures of a variety of LDH have previously
been described, a notable absence has been any of the
three known human forms of this glycolytic enzyme. We have
now determined the crystal structures of two isoforms of
human LDH-the M form, predominantly found in muscle; and
the H form, found mainly in cardiac muscle. Both structures
have been crystallized as ternary complexes in the presence
of the NADH cofactor and oxamate, a substrate-like inhibitor.

Although each of these isoforms has different kinetic properties,
the domain structure, subunit association, and active-site regions
are indistinguishable between the two structures.

The pK(a) that governs the K(M) for pyruvate for the two isozymes
is found to differ by about 0.94 pH units, consistent with variation in
pK(a) of the active-site histidine.

The close similarity of these crystal structures suggests the distinctive
activity of these enzyme isoforms is likely to result

  • directly from variation of charged surface residues peripheral to the active site,
  • a hypothesis supported by electrostatic calculations based on each structure.

Proteins 2001;43:175-185.

Mechanistic aspects of biological redox reactions involving NADH.
Part 4. Possible mechanisms and corresponding intermediates for
the catalytic reaction in L-lactate dehydrogenase

J Molec Structure: THEOCHEM,25 Feb 1993; 279, Pp 99-125
Kathryn E. Norris, Jill E. Gready

The catalytic step in the conversion of pyruvate to L-lactate in the
enzyme L-lactate dehydrogenase involves the transfer of both a
proton and a hydride ion (A.R. Clarke, T. Atkinson and J.J. Holbrook,
TIBS, 14 (1989) 101.) However, it is not known whether the
reaction is concerted or, if a multistep process, the order in
which the transfers of the proton and the hydride ions take
place. Four possible non-concerted mechanisms can be
proposed, which differ in the order of the transfers of the
proton and hydride ion and the protonation state of the substrate
carboxylate group during the transfers. The energies and
optimized geometries of the corresponding intermediates,
protonated pyruvate, protonated pyruvic acid, deprotonated
L-lactate and deprotonated L-lactic acid, are computed using
the semiempirical AM 1 and ab initio SCF/3–21 G – methods.
These calculations are complementary to the study of
the substrates for the enzyme discussed in a previous paper
(K.E. Norris and J.E. Gready, J. Mol. Struct. (Theochem),
258 (1992) 109.) The structures and energetics of protonated
pyruvate and deprotonated L-lactate provide some
important insights into the requirements for enzymic reaction
and the characteristics of the transition state.

Pyruvate production by Enterococcus casseliflavus A-12
from gluconate in an alkaline medium

J Fermentation and Bioengineering, 1992; 73(4):287-291
H Yanase, N Mori, M Masuda, K Kita, M Shimao, N Kato

A newly isolated lactic acid bacterium, Enterococcus casseliflavus
A-12, produced pyruvic acid (16 g/l) during aerobic culture in
an alkaline medium containing sodium gluconate (50 g/l) as
the carbon source. The production was dependent on the pH
of the culture, the optimum initial pH being 10.0. With static
culture, the organism produced lactic acid (2.7 g/l) from both
gluconate and glucose. Pyruvate did not accumulate in growing
cultures on glucose, but resting cells obtained from a culture
on gluconate produced pyruvate from glucose as well as
gluconate. The enzyme profiles of the organism, which
grew on gluconate and glucose, suggested that gluconate
was metabolized via the Entner-Doudoroff and Embdem-
Meyerhof-Parnas pathways in aerobic culture, and that glucose
was oxidized mainly via the latter pathway under both aerobic
and anaerobic conditions. Gluconokinase, a key enzyme in
the aerobic metabolism of gluconate, was partially purified
from this strain and characterized.

A specific, highly active malate dehydrogenase by redesign
of a lactate dehydrogenase framework

HM WilksKW HartR FeeneyCR DunnH MuirheadWN Chiaet al.

Department of Biochemistry, University of Bristol, United Kingdom.
Science 16 Dec1988: 242(4885),  pp. 1541-1544
http://dx.doi.org:/10.1126/science.3201242

 Three variations to the structure of the nicotinamide adenine
dinucleotide (NAD)-dependent L-lactate dehydrogenase
from Bacillus stearothermophilus were made to try to
change the substrate specificity from lactate to malate:
Asp197—-Asn, Thr246—-Gly, and Gln102—-Arg).

Each modification shifts the specificity from lactate to malate, although

  • only the last (Gln102—-Arg) provides an effective and
    highly specific catalyst for the new substrate.

This synthetic enzyme has a ratio of catalytic rate (kcat) to
Michaelis constant (Km) for oxaloacetate of 4.2 x 10(6)M-1 s-1,

  • equal to that of native lactate dehydrogenase for its natural
    substrate, pyruvate, and a maximum velocity (250 s-1),
    which is double that reported for a natural malate from B.
    stearothermophilus.

Malate dehydrogenase: distribution, function and properties.

Musrati RA1, Kollárová MMernik NMikulásová D.
Author information
Gen Physiol Biophys. 1998 Sep;17; (3):193-210.

Malate dehydrogenase (MDH) (EC 1.1.1.37) catalyzes the
conversion of oxaloacetate and malate. This reaction is
important in cellular metabolism, and it is coupled with
easily detectable cofactor oxidation/reduction. It is a
rather ubiquitous enzyme, for which several isoforms
have been identified, differing in their subcellular
localization and their specificity for the cofactor NAD
or NADP. The nucleotide binding characteristics can
be altered by a single amino acid change. Multiple
amino acid sequence alignments of MDH show there is a

  • low degree of primary structural similarity, apart from
    several positions crucial for catalysis, cofactor binding
    and the subunit interface.
  • Despite the low amino acids sequence identity their
    3-dimensional structures are very similar.
  • MDH is a group of multimeric enzymes consisting of
    identical subunits usually organized as either dimer
    or tetramers with subunit molecular weights of 30-35 kDa.

Malate dehydrogenase, mitochondrial (MDH2)

UniProt Number: P40926
Alternate Names: Malate DH

Structure and Function:
Malate dehydrogenase (MDH2) is an enzyme in the citric
acid cycle that catalyzes the conversion of malate into
oxaloacetate (using NAD+) and vice versa (this is a
reversible reaction). Malate dehydrogenase is also
involved in gluconeogenesis, the synthesis of glucose
from smaller molecules.Pyruvate in the mitochondria is acted upon by pyruvate
carboxylase  to form oxaloacetate, a citric acid cycle
intermediate.In order to get the oxaloacetate out of the mitochondria,
malate dehydrogenase reduces it to malate, and it then
traverses the inner mitochondrial membrane.Once in the cytosol, the malate is oxidized back to
oxaloacetate by cytosolic malate dehydrogenase.

Finally, phosphoenol-pyruvate carboxy kinase (PEPCK)
converts oxaloacetate to phosphoenol pyruvate.

Malate Dehydrogenase (MDH)(PDB entry 2x0i) is most known
for its role in the metabolic pathway of the tricarboxylic acid cycle,
critical to cellular respiration; The enzyme has other metabolic roles in –

  •  glyoxylate bypass,
  • amino acid synthesis,
  • glucogenesis, and
  • oxidation/reduction balance .

An oxidoreductase, MDH has been extensively studied due to its
isozymes The enzyme exists in two places inside a cell:

  • the mitochondria and cytoplasm.
  • In the mitochondria, the enzyme catalyzes the reaction of
    malate to oxaloacetate;
  • in the cytoplasm, the enzyme catalyzes oxaloacetate to
    malate to allow transport.

The enzyme malate dehydrogenase is composed of either
a dimer or tetramer depending on the location of the enzyme
and the organism it is located in. During catalysis, the enzyme
subunits are

  • non-cooperative between active sites.

The mitochondrial MDH is complexly,

  • allosterically controlled by citrate, but no other known
    metabolic regulation mechanisms have been discovered.
  • the exact mechanism of regulation has yet to be discovered.

Kinetically, the pH of optimization is 7.6 for oxaloacetate
conversion and 9.6 for malate conversion. The reported
K(m) value for malate conversion is 215 uM and the V(max)
value is 87.8 uM/min.

Comment:

The mMDH and the cMDH both form ternary complex
of MDH-NAD+-OAA formed during the forward reaction,
like the LDH H-type isozyme LDH-NAD+-PYR (mot the M-type).
However, the binding of the Enz-coenzyme-substrate is not
as strong as for the H-type LDH.  .The regulatory role has
not been established.

References

  1. Minarik P, Tomaskova N, Kollarova M, Antalik M. Malate
    dehydrogenases–structure and function. Gen Physiol Biophys.
    2002 Sep;21(3):257-65. PMID:12537350
  2. Musrati RA, Kollarova M, Mernik N, Mikulasova D.
    Malate dehydrogenase: distribution, function and properties.
    Gen Physiol Biophys. 1998 Sep;17(3):193-210. PMID:9834842
  3. Boernke WE, Millard CS, Stevens PW, Kakar SN, Stevens FJ,
    Donnelly MI. Stringency of substrate specificity of
    Escherichia coli malate dehydrogenase. Arch Biochem
    Biophys. 1995 Sep 10;322(1):43-52. PMID:7574693
    doi:http://dx.doi.org/10.1006/abbi.1995.1434
  4. Goward CR, Nicholls DJ. Malate dehydrogenase: a model
    for structure, evolution, and catalysis. Protein Sci. 1994
    Oct;3(10):1883-8. PMID:7849603
    doi:http://dx.doi.org/10.1002/pro.5560031027

Kinetic determination of malate dehydrogenase isozymes.

L H Bernstein, M B Grisham

Journal of Molecular and Cellular Cardiology (Impact Factor: 5.15).
11/1978; 10(10):931-44. http://dx.doi.org/10.1016/0022-2828(78)90339-5

Source: PubMed

ABSTRACT These studies determine the levels of malate
dehydrogenase isoenzymes in cardiac muscle by a steady
state kinetic method which depends on the differential inhibition
of these isoenzyme forms by high concentrations of oxaloacetate.
This inhibition is similar to that exhibited by lactate dehydrogenase
in the presence of high concentrations of pyruvate. The results
obtained by this method are comparable in resolution to those
obtained by CM-Sephadex fractionation and by differential
centrifugation for the analyses of mitochondrial malate
dehydrogenase and cytoplasmic malate dehydrogenase in
tissues. The use of standard curves of percent inhibition of
malate dehydrogenase activity plotted against the ratio of
mitochondrial MDH activity to the total of mMDH and cMDH
activities [ malate dehydrogenase ratio] (percent m-type) is
introduced for studies of comparative mitochondrial
function in heart muscle of different species or in different
tissues of the same species.

Calmodulin and Protein Kinase C Increase Ca21-stimulated
Secretion by Modulating Membrane-attached Exocytic Machinery

YA Chen, V Duvvuri, H Schulmani, and RH Scheller
Hughes Medical Institute, Department of Molecular and Cellular
Physiology, and the iDepartment of Neurobiology, Stanford
University School of Medicine, Stanford, California 94305-5135
JBC Sep 10, 1999; 274( 37): 26469–26476

Using a reconstituted [3H]norepinephrine
release assay in permeabilized PC12 cells, we
found that essential proteins that support the triggering
stage of Ca21-stimulated exocytosis are enriched in an
EGTA extract of brain membranes. Fractionation of this
extract allowed purification of two factors that stimulate
secretion in the absence of any other cytosolic proteins.
These are calmodulin and protein kinase Ca
(PKCa). Their effects on secretion were confirmed using
commercial and recombinant proteins. Calmodulin enhances
secretion in the absence of ATP, whereas PKC
requires ATP to increase secretion, suggesting that
phosphorylation is involved in PKC- but not calmodulin
mediated stimulation. Both proteins modulate release
events that occur in the triggering stage of exocytosis.

Endothelial nitric oxide synthase (eNOS) variants in
cardiovascular disease: pharmacogenomic implications  

Indian J Med Res  May 2011;  133:  464-466

Commentary

Manjula Bhanoori

Department of Biochemistry, University College of Science,
Osmania University, Hyderabad 500 007, India

 

The maintenance of regular vascular tone substantially
depends on the bioavailability of endothelium-derived
nitric oxide (NO) synthesized by eNOS. The essential
role of NO, as the elusive endothelium-derived relaxing
factor (EDRF), was the topic of research that won the
1998 Nobel Prize in Physiology or Medicine. The eNOS
gene, as a candidate gene in the investigations on
hypertension genetics, has attracted the attention of
several researchers because of the established role
of NO in vascular homeostasis. The eNOS variants
located in the 7q35-q36 region have been investigated
for their association with CVD, particularly hypertension.
Three variants, viz., (i) G894T substitution in exon 7
resulting in a Glu to Asp substitution at codon 298 (rs1799983),
(ii) an insertion-deletion in intron 4 (4a/b) consisting of two
alleles (the a*-deletion which has four tandem 27-bp repeats
and the b*-insertion having five repeats), and (iii) a T786C
substitution in the promoter region (rs2070744), have been
extensively studied20-22. Individual SNPs often cause only
a modest change in the resulting gene expression or function.
It is, therefore, the concurrent presence of a number of SNPs
or haplotypes within a defined region of the chromosome that
determines susceptibility to disease development and progression,
particularly in case of polygenic diseases.

Shankarishan et al24 analysed for the first time the prevalence
of eNOS exon 7 Glu298Asp polymorphism in tea garden community
of North Eastern India, who are a high risk group for CVD. This study
also included indigenous Assamese population and found no
significant difference between the distribution patterns of eNOS
exon 7 Glu298Asp variants between the communities. They have
rightly mentioned that for developing public health policies and
programmes it is necessary to know the prevalence and distribution
of the candidate genes in the population, as well as trends in
different population groups. They have also observed that the
eNOS exon 7 homozygous GG wild genotype (75.8%) was
predominant in the study population followed by heterozygous
GT genotype (21.5%) and homozygous TT genotype (2.7%).
The frequency distribution of the homozygous GG, heterozygous
GT and homozygous mutant TT genotypes were comparable to
that of the north Indian and south Indian population.

Polymorphisms in the endothelial nitric oxide synthase gene have
been associated inconsistently with cardiovascular diseases.
Varying distribution of eNOS variants among ethnic groups may
explain inter-ethnic differences in nitric oxide mediated vasodilation
and response to drugs28. Different population studies showed
association of eNOS polymorphisms with variations in NO
formation and response to drugs. Cardiovascular drugs including
statins increase eNOS expression and upregulate NO formation
and this effect may be responsible for protective, pleiotropic
effects produced by statins31. With respect to hypertension,
studies have reported interactions between diuretics and
polymorphisms in eNOS gene. Particularly, the Glu298Asp
polymorphism made a statistically significant contribution to
predicting blood pressure response to diuretics.

Neuronal Nitric Oxide Synthase and Its Interaction
With Soluble Guanylate Cyclase Is a Key Factor for
the Vascular Dysfunction of Experimental Sepsis

GM. Nardi, K Scheschowitsch, D Ammar, SK de
Oliveira, TB. Arruda; J Assreuy

Vascular dysfunction plays a central role in sepsis, and it is
characterized by hypotension and hyporesponsiveness to
vasoconstrictors. Nitric oxide is regarded as a central element
of sepsis vascular dysfunction. The high amounts of nitric
oxide produced during sepsis are mainly derived from the
inducible isoform of nitric oxide synthase 2.
We have previously shown that nitric oxide synthase 2 levels
decrease in later stages of sepsis, whereas levels and activity
of soluble guanylate cyclase increase. Therefore, we studied
the putative role of other relevant nitric oxide sources, namely,

  • the neuronal (nitric oxide synthase 1) isoform, in sepsis
  • and its relationship with soluble guanylate cyclase.

We also studied the consequences of

  • nitric oxide synthase 1 blockade in the hyporesponsiveness
    to vasoconstrictors.

1) Both nitric oxide synthase 1 and soluble guanylate cyclase
are expressed in higher levels in vascular tissues during sepsis;

2) both proteins physically interact and nitric oxide synthase 1
blockade inhibits cyclic guanosine monophosphate production;

3) pharmacological blockade of nitric oxide synthase 1 using
7-nitroindazole or S-methyl-l-thiocitrulline reverts the hypo
responsiveness to phenylephrine and increases the vaso
constrictor effect of norepinephrine and phenylephrine.

Sepsis induces increased expression and physical association
of nitric oxide synthase 1/soluble guanylate cyclase and a higher
production of cyclic guanosine monophosphate that together
may help explain sepsis-induced vascular dysfunction.

In addition, selective inhibition of nitric oxide synthase 1
restores the responsiveness to vasoconstrictors.

Therefore, inhibition of nitric oxide synthase 1 (and possibly
soluble guanylate cyclase) may represent a valuable
alternative to restore the effectiveness of vasopressor
agents during late sepsis.  (Crit Care Med 2014; XX:00–00)

Nitric Oxide Synthase Inhibitors That Interact with Both Heme
Propionate and Tetrahydrobiopterin Show High Isoform Selectivity

S Kang, W Tang, H Li, G Chreifi, P Martásek, LJ. Roman,
TL. Poulos, and RB. Silverman

†Department of Chemistry, Department of Molecular Biosciences,
Chemistry of Life Processes Institute, Center for Molecular Innovation
and Drug Discovery, Northwestern University, Evanston, Illinois
‡Departments of Molecular Biology and Biochemistry, Pharmaceutical
Sciences, and Chemistry, University of California, Irvine, California,
Department of Biochemistry, University of Texas Health Science Center,
San Antonio, Texas

Overproduction of NO by nNOS is implicated in the pathogenesis of
diverse neuronal disorders. Since NO signaling is involved in
diverse physiological functions, selective inhibition of nNOS
over other isoforms is essential to minimize side effects. A series of
α-amino functionalized aminopyridine derivatives (3−8) were
designed to probe the structure−activity relationship between ligand,
heme propionate, and H4B. Compound 8R was identified as the
most potent and selective molecule of this study, exhibiting a Ki of
24 nM for nNOS, with 273-fold and 2822-fold selectivity against
iNOS and eNOS, respectively.Although crystal structures of 8R
complexed with nNOS and eNOS revealed a similar binding mode,
the selectivity stems from the distinct electrostatic environments in
two isoforms that result in much lower inhibitor binding free energy
in nNOS than in eNOS. These findings provide a basis for further
development of simple, but even more selective and potent, nNOS
inhibitors

  • Aurelian Udristioiu

    Aurelian

    Aurelian Udristioiu

    Lab Director at Emergency County Hospital Targu Jiu

    In cells, the immediate energy sources involve glucose oxidation. In anaerobic metabolism, the donor of the phosphate group is adenosine triphosphate (ATP), and the reaction is catalyzed via the hexokinase or glucokinase: Glucose +ATP-Mg²+ = Glucose-6-phosphate (ΔGo = – 3.4 kcal/mol with hexokinase as the co-enzyme for the reaction.).
    In the following step, the conversion of G-6-phosphate into F-1-6-bisphosphate is mediated by the enzyme phosphofructokinase with the co-factor ATP-Mg²+. This reaction has a large negative free energy difference and is irreversible under normal cellular conditions. In the second step of glycolysis, phosphoenolpyruvic acid in the presence of Mg²+ and K+ is transformed into pyruvic acid. In cancer cells or in the absence of oxygen, the transformation of pyruvic acid into lactic acid alters the process of glycolysis.
    The energetic sum of anaerobic glycolysis is ΔGo = -34.64 kcal/mol. However a glucose molecule contains 686kcal/mol and, the energy difference (654.51 kcal) allows the potential for un-controlled reactions during carcinogenesis. The transfer of electrons from NADPH in each place of the conserved unit of energy transmits conformational exchanges in the mitochondrial ATPase. The reaction ADP³+ P²¯ + H²–à ATP + H2O is reversible. The terminal oxygen from ADP binds the P2¯ by forming an intermediate pentacovalent complex, resulting in the formation of ATP and H2O. This reaction requires Mg²+ and an ATP-synthetase, which is known as the H+-ATPase or the Fo-F1-ATPase complex. Intracellular calcium induces mitochondrial swelling and aging. [12].
    The known marker of monitoring of treatment in cancer diseases, lactate dehydrogenase (LDH) is an enzyme that is localized to the cytosol of human cells and catalyzes the reversible reduction of pyruvate to lactate via using hydrogenated nicotinamide deaminase (NADH) as co-enzyme.
    The causes of high LDH and high Mg levels in the serum include neoplastic states that promote the high production of intracellular LDH and the increased use of Mg²+ during molecular synthesis in processes pf carcinogenesis (Pyruvate acid>> LDH/NADH >>Lactate acid + NAD), [13].
    LDH is released from tissues in patients with physiological or pathological conditions and is present in the serum as a tetramer that is composed of the two monomers LDH-A and LDH-B, which can be combined into 5 isoenzymes: LDH-1 (B4), LDH-2 (B3-A1), LDH-3 (B2-A2), LDH-4 (B1-A3) and LDH-5 (A4). The LDH-A gene is located on chromosome 11, whereas the LDH-B gene is located on chromosome 12. The monomers differ based on their sensitivity to allosteric modulators. They facilitate adaptive metabolism in various tissues. The LDH-4 isoform predominates in the myocardium, is inhibited by pyruvate and is guided by the anaerobic conversion to lactate.
    Total LDH, which is derived from hemolytic processes, is used as a marker for monitoring the response to chemotherapy in patients with advanced neoplasm with or without metastasis. LDH levels in patients with malignant disease are increased as the result of high levels of the isoenzyme LDH-3 in patients with hematological malignant diseases and of the high level of the isoenzymes LDH-4 and LDH-5, which are increased in patients with other malignant diseases of tissues such as the liver, muscle, lungs, and conjunctive tissues. High concentrations of serum LDH damage the cell membrane [11, 31].

    Relation between LDH and Mg as Factors of Interest in the Monitoring and Prognoses of Cancer

    Aurelian Udristioiu, Emergency County Hospital Targu Jiu Romania, Clinical Laboratory Medical Analyses, E-mail: aurelianu2007@yahoo.com

    Larry Bernstein likes this

  • Larry Bernstein

    Larry Bernstein

    CEO/CSO at Triplex Consulting

    The inhibition be pyruvate is related by a ternary complex formed by NAD+ formed in the catalytic forward reaction Pyruvate + NADH –> Lactate + NAD(+). The reaction can be followed in an Aminco-Morrow stop-flow analyzer and occurs in ~ 500 msec. The reaction does not occur with the muscle type LDH, and it is regulatory in function. I did not know about the role of intracellular Mg(2+) in the catalysis, as my own work was in Nate Kaplan’s lab in 1970-73.

    This difference in the behavior of the isoenzyme types was considered to be important then in elucidating functional roles, but it was challenged by Vessell earlier. The isoenzymes were first described by Clement Markert at Yale. I think, but don’t know, that the Mg++ would have a role in driving the forward reaction, but I can’t conceptualize how it might have any role in the difference between muscle and heart.

    I didn’t quite know why oncologists used it specifically. Cancer cells exhibit the reliance on the anaerobic (muscle) type enzyme, which is also typical of liver, but with respect to the adenylate kinases – the liver AK and muscle AK (myokinase) are different. That difference was discovered by Masahiro Chiga, and differences in the reaction with sulfhydryl reagents were identified by Percy Russell.

    Oddly enough, Vessell had a point. The RBC has the heart type predominance, not the M-type. He thought that it was related to the loss of nuclei from the reticulocyte. I did not buy that, and I had worked on the lens of the eye at the time.

  • Aurelian Udristioiu

    Aurelian

    Aurelian Udristioiu

    Lab Director at Emergency County Hospital Targu Jiu

    Very interesting scientific comments. Thanks. !

  • Aurelian Udristioiu

    Aurelian

    Aurelian Udristioiu

    Lab Director at Emergency County Hospital Targu Jiu

    The IDH1 and IDH2 genes are mutated in > 75% of different malignant diseases. Two distinct alterations are caused by tumor-derived mutations in IDH1 or IDH2,
    IDH1 and IDH2 mutations have been observed in myeloid malignancies, including de novo and secondary AML (15%–30%), and in pre-leukemic clone malignancies, including myelodysplastic syndrome and myeloproliferative neoplasm (85% of the chronic phase and 20% of transformed cases in acute leukemia.
    Aurelian Udristioiu, M.D
    City Targu Jiu, Romania
    AACC, NACB, Member, USA.

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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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Larry H Bernstein, MD, FCAP, Author and Curator

https://pharmaceuticalintelligence.com/2014/06/22/Proteomics – The Pathway to Understanding and Decision-making in Medicine

This dialogue is a series of discussions introducing several perspective on proteomics discovery, an emerging scientific enterprise in the -OMICS- family of disciplines that aim to clarify many of the challenges toward the understanding of disease and aiding in the diagnosis as well as guiding treatment decisions. Beyond that focus, it will contribute to personalized medical treatment in facilitating the identification of treatment targets for the pharmaceutical industry. Despite enormous advances in genomics research over the last two decades, there is a still a problem in reaching anticipated goals for introducing new targeted treatments that has seen repeated failures in stage III of clinical trials, and even when success has been achieved, it is temporal.  The other problem has been toxicity of agents widely used in chemotherapy.  Even though the genomic approach brings relieve to the issues of toxicity found in organic chemistry derivative blocking reactions, the specificity for the target cell without an effect on normal cells has been elusive.

This is not confined to cancer chemotherapy, but can also be seen in pain medication, and has been a growing problem in antimicrobial therapy.  The stumbling block has been inability to manage a multiplicity of reactions that also have to be modulated in a changing environment based on 3-dimension structure of proteins, pH changes, ionic balance, micro- and macrovascular circulation, and protein-protein and protein- membrane interactions. There is reason to consider that the present problems can be overcome through a much better modification of target cellular metabolism as we peel away the confounding and blinding factors with a multivariable control of these imbalances, like removing the skin of an onion.

This is the first of a series of articles, and for convenience we shall here  only emphasize the progress of application of proteomics to cardiovascular disease.

growth in funding proteomics 1990-2010

growth in funding proteomics 1990-2010

Part I.

Panomics: Decoding Biological Networks  (Clinical OMICs 2014; 5)

Technological advances such as high-throughput sequencing are transforming medicine from symptom-based diagnosis and treatment to personalized medicine as scientists employ novel rapid genomic methodologies to gain a broader comprehension of disease and disease progression. As next-generation sequencing becomes more rapid, researchers are turning toward large-scale pan-omics, the collective use of all omics such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics and lipoprotein proteomics, to better understand, identify, and treat complex disease.

Genomics has been a cornerstone in understanding disease, and the sequencing of the human genome has led to the identification of numerous disease biomarkers through genome-wide association studies (GWAS). It was the goal of these studies that these biomarkers would serve to predict individual disease risk, enable early detection of disease, help make treatment decisions, and identify new therapeutic targets. In reality, however, only a few have gone on to become established in clinical practice. For example in human GWAS studies for heart failure at least 35 biomarkers have been identified but only natriuretic peptides have moved into clinical practice, where they are limited primarily for use as a diagnostic tool.

Proteomics Advances Will Rival the Genetics Advances of the Last Ten Years

Seventy percent of the decisions made by physicians today are influenced by results of diagnostic tests, according to N. Leigh Anderson, founder of the Plasma Proteome Institute and CEO of SISCAPA Assay Technologies. Imagine the changes that will come about when future diagnostics tests are more accurate, more useful, more economical, and more accessible to healthcare practitioners. For Dr. Anderson, that’s the promise of proteomics, the study of the structure and function of proteins, the principal constituents of the protoplasm of all cells.

In explaining why proteomics is likely to have such a major impact, Dr. Anderson starts with a major difference between the genetic testing common today, and the proteomic testing that is fast coming on the scene. “Most genetic tests are aimed at measuring something that’s constant in a person over his or her entire lifetime. These tests provide information on the probability of something happening, and they can help us understand the basis of various diseases and their potential risks. What’s missing is, a genetic test is not going to tell you what’s happening to you right now.”

Mass Spec-Based Multiplexed Protein Biomarkers

Clinical proteomics applications rely on the translation of targeted protein quantitation technologies and methods to develop robust assays that can guide diagnostic, prognostic, and therapeutic decision-making. The development of a clinical proteomics-based test begins with the discovery of disease-relevant biomarkers, followed by validation of those biomarkers.

“In common practice, the discovery stage is performed on a MS-based platform for global unbiased sampling of the proteome, while biomarker qualification and clinical implementation generally involve the development of an antibody-based protocol, such as the commonly used enzyme linked ELISA assays,” state López et al. in Proteome Science (2012; 10: 35–45). “Although this process is potentially capable of delivering clinically important biomarkers, it is not the most efficient process as the latter is low-throughput, very costly, and time-consuming.”

Part II.  Proteomics for Clinical and Research Use: Combining Protein Chips, 2D Gels and Mass Spectrometry in 

The next Step: Exploring the Proteome: Translation and Beyond

N. Leigh Anderson, Ph.D., Chief Scientific Officer, Large Scale Proteomics Corporation

Three streams of technology will play major roles in quantitative (expression) proteomics over the coming decade. Two-dimensional electrophoresis and mass spectrometry represent well-established methods for, respectively, resolving and characterizing proteins, and both have now been automated to enable the high-throughput generation of data from large numbers of samples.

These methods can be powerfully applied to discover proteins of interest as diagnostics, small molecule therapeutic targets, and protein therapeutics. However, neither offers a simple, rapid, routine way to measure many proteins in common samples like blood or tissue homogenates.

Protein chips do offer this possibility, and thus complete the triumvirate of technologies that will deliver the benefits of proteomics to both research and clinical users. Integration of efforts in all three approaches are discussed, highlighting the application of the Human Protein Index® database as a source of protein leads.

leighAnderson

leighAnderson

N. Leigh Anderson, Ph D. is Chief Scientific Officer of the Proteomics subsidiary of Large Scale Biology Corporation (LSBC).
Dr. Anderson obtained his B.A. in Physics with honors from Yale and a Ph.D. in Molecular Biology from Cambridge University
(England) where he worked with M. F. Perutz as a Churchill Fellow at the MRC Laboratory of Molecular Biology. Subsequently
he co-founded the Molecular Anatomy Program at the Argonne National Laboratory (Chicago) where his work in the development
of 2D electrophoresis and molecular database technology earned him, among other distinctions, the American Association for
Clinical Chemistry’s Young Investigator Award for 1982, the 1983 Pittsburgh Analytical Chemistry Award, 2008 AACC Outstanding
Research Award, and 2013 National Science Medal..

In 1985 Dr. Anderson co-founded LSBC in order to pursue commercial development and large scale applications of 2-D electro-
phoretic protein mapping technology. This effort has resulted in a large-scale proteomics analytical facility supporting research
work for LSBC and its pharmaceutical industry partners. Dr. Anderson’s current primary interests are in the automation of proteomics
technologies, and the expansion of LSBC’s proteomics databases describing drug effects and disease processes in vivo and in vitro.
Large Scale Biology went public in August 2000.

Part II. Plasma Proteomics: Lessons in Biomarkers and Diagnostics

Exposome Workshop
N Leigh Anderson
Washington 8 Dec 2011

QUESTIONS AND LESSONS:

CLINICAL DIAGNOSTICS AS A MODEL FOR EXPOSOME INDICATORS
TECHNOLOGY OPTIONS FOR MEASURING PROTEIN RESPONSES TO EXPOSURES
SCALE OF THE PROBLEM: EXPOSURE SIGNALS VS POPULATION NOISE

The Clinical Plasma Proteome
• Plasma and serum are the dominant non-invasive clinical sample types
– standard materials for in vitro diagnostics (IVD)
• Proteins measured in clinically-available tests in the US
– 109 proteins via FDA-cleared or approved tests
• Clinical test costs range from $9 (albumin) to $122 (Her2)
• 90% of those ever approved are still in use
– 96 additional proteins via laboratory-developed tests (not FDA
cleared or approved)
– Total 205 proteins (≅ products of 211genes, excluding Ig’s)
• Clinically applied proteins thus account for
– About 1% of the baseline human proteome (1 gene :1 protein)
– About 10% of the 2,000+ proteins observed in deep discovery
plasma proteome datasets

“New” Protein Diagnostics Are FDA-Cleared at a Rate of ~1.5/yr:
Insufficient to Meet Dx or Rx Development Needs

FDA clearance of protein diagnostics

FDA clearance of protein diagnostics

A  Major Technology Gulf Exists Between Discovery

Proteomics and Routine Diagnostic Platforms

Two Streams of Proteomics
A.  Problem Technology
Basic biology: maximum proteome coverage (including PTM’s, splices) to
provide unbiased discovery of mechanistic information
• Critical: Depth and breadth
• Not critical: Cost, throughput, quant precision

B.  Discovery proteomics
Specialized proteomics field,
large groups,
complex workflows and informatics

Part III.  Addressing the Clinical Proteome with Mass Spectrometric Assays

N. Leigh Anderson, PhD, SISCAPA Assay Technologies, Inc.

protein changes in biological mechanisms

protein changes in biological mechanisms

No Increase in FDA Cleared Protein Tests in 20 yr

“New” Protein Tests in Plasma Are FDA-Cleared at a Rate of ~1.5/yr:
Insufficient to Meet Dx or Rx Development Needs

See figure above

An Explanation: the Biomarker Pipeline is Blocked at the Verification Step

Immunoassay Weaknesses Impact Biomarker Verification

1) Specificity: what actually forms the immunoassay sandwich – or prevents its
formation – is not directly visualized

2) Cost: an assay developed to FDA approvable quality costs $2-5M per
protein

Major_Plasma_Proteins

Major_Plasma_Proteins

Immunoassay vs Hybrid MS-based assays

Immunoassay vs Hybrid MS-based assays

MASS SPECTROMETRY: MRM’s provide what is missing in..IMMUNOASSAYS:

– SPECIFICITY
– INTERNAL STANDARDIZATION
– MULTIPLEXING
– RAPID CONFIGURATION PROVIDED A PROTEIN CAN ACT LIKE A SMALL
MOLECULE

MRM of Proteotypic Tryptic Peptides Provides Highly Specific Assays for Proteins > 1ug/ml in Plasma

Peptide-Level MS Provides High Structural Specificity
Multiple Reaction Monitoring (MRM) Quantitation

ADDRESSING MRM LIMITATIONS VIA SPECIFIC ENRICHMENT OF ANALYTE  PEPTIDES: SISCAPA

– SENSITIVITY
– THROUGHPUT (LC-MS/MS CYCLE TIME)

SISCAPA combines best features of immuno and MS

SISCAPA combines best features of immuno and MS

SISCAPA Process Schematic Diagram
Stable Isotope-labeled Standards with Capture on Anti-Peptide Antibodies

An automated process for SISCAPA targeted protein quantitation utilizes high affinity capture antibodies that are immobilized on magnetic beads

An automated process for SISCAPA targeted protein quantitation utilizes high affinity capture antibodies that are immobilized on magnetic beads

Antibodies sequence specific peptide binding

Antibodies sequence specific peptide binding

SISCAP target enrichmant

SISCAP target enrichmant

Multiple reaction monitoring (MRM) quantitation

Multiple reaction monitoring (MRM) quantitation

protein-quantitation-via-signature-peptides.png

protein-quantitation-via-signature-peptides.png

First SISCAP Assay - thyroglobulin

First SISCAP Assay – thyroglobulin

personalized reference range within population range

Glycemic control in DM

Glycemic control in DM

Part IV. National Heart, Lung, and Blood Institute Clinical

Proteomics Working Group Report
Christopher B. Granger, MD; Jennifer E. Van Eyk, PhD; Stephen C. Mockrin, PhD;
N. Leigh Anderson, PhD; on behalf of the Working Group Members*
Circulation. 2004;109:1697-1703 doi: 10.1161/01.CIR.0000121563.47232.2A
http://circ.ahajournals.org/content/109/14/1697

Abstract—The National Heart, Lung, and Blood Institute (NHLBI) Clinical Proteomics Working Group
was charged with identifying opportunities and challenges in clinical proteomics and using these as a
basis for recommendations aimed at directly improving patient care. The group included representatives
of clinical and translational research, proteomic technologies, laboratory medicine, bioinformatics, and
2 of the NHLBI Proteomics Centers, which form part of a program focused on innovative technology development.

This report represents the results from a one-and-a-half-day meeting on May 8 and 9, 2003. For the purposes
of this report, clinical proteomics is defined as the systematic, comprehensive, large-scale identification of
protein patterns (“fingerprints”) of disease and the application of this knowledge to improve patient care
and public health through better assessment of disease susceptibility, prevention of disease, selection of
therapy for the individual, and monitoring of treatment response. (Circulation. 2004;109:1697-1703.)
Key Words: proteins diagnosis prognosis genetics plasma

Part V.  Overview: The Maturing of Proteomics in Cardiovascular Research

Jennifer E. Van Eyk
Circ Res. 2011;108:490-498  doi: 10.1161/CIRCRESAHA.110.226894
http://circres.ahajournals.org/content/108/4/490

Abstract: Proteomic technologies are used to study the complexity of proteins, their roles, and biological functions.
It is based on the premise that the diversity of proteins, comprising their isoforms, and posttranslational modifications
(PTMs) underlies biology.

Based on an annotated human cardiac protein database, 62% have at least one PTM (phosphorylation currently dominating),
whereas 25% have more than one type of modification.

The field of proteomics strives to observe and quantify this protein diversity. It represents a broad group of technologies
and methods arising from analytic protein biochemistry, analytic separation, mass spectrometry, and bioinformatics.
Since the 1990s, the application of proteomic analysis has been increasingly used in cardiovascular research.

prevalence-of-cardiovascular-diseases-in-adults-by-age-and-sex-u-s-2007-2010.

prevalence-of-cardiovascular-diseases-in-adults-by-age-and-sex-u-s-2007-2010.

Technology development and adaptation have been at the heart of this progress. Technology undergoes a maturation,

becoming routine and ultimately obsolete, being replaced by newer methods. Because of extensive methodological
improvements, many proteomic studies today observe 1000 to 5000 proteins.

Only 5 years ago, this was not feasible. Even so, there are still road blocks. Nowadays, there is a focus on obtaining
better characterization of protein isoforms and specific PTMs. Consequentl, new techniques for identification and
quantification of modified amino acid residues are required, as is the assessment of single-nucleotide polymorphisms
in addition to determination of the structural and functional consequences.

In this series, 4 articles provide concrete examples of how proteomics can be incorporated into cardiovascular
research and address specific biological questions. They also illustrate how novel discoveries can be made and
how proteomic technology has continued to evolve. (Circ Res. 2011;108:490-498.)
Key Words: proteomics technology protein isoform posttranslational modification polymorphism

Part VI.   The -omics era: Proteomics and lipidomics in vascular research

Athanasios Didangelos, Christin Stegemann, Manuel Mayr∗

King’s British Heart Foundation Centre, King’s College London, UK

Atherosclerosis 2012; 221: 12– 17     http://dx.doi.org/10.1016/j.atherosclerosis.2011.09.043

a b s t r a c t

A main limitation of the current approaches to atherosclerosis research is the focus on the investigation of individual
factors, which are presumed to be involved in the pathophysiology and whose biological functions are, at least in part, understood.

These molecules are investigated extensively while others are not studied at all. In comparison to our detailed
knowledge about the role of inflammation in atherosclerosis, little is known about extracellular matrix remodelling
and the retention of individual lipid species rather than lipid classes in early and advanced atherosclerotic lesions.

The recent development of mass spectrometry-based methods and advanced analytical tools are transforming
our ability to profile extracellular proteins and lipid species in animal models and clinical specimen with the goal
of illuminating pathological processes and discovering new biomarkers.

Fig. 1. ECM in atherosclerosis

Fig. 1. ECM in atherosclerosis. The bulk of the vascular ECM is synthesised by smooth muscle cells and composed primarily of collagens, proteoglycans and glycoproteins.During the early stages of atherosclerosis, LDL binds to the proteoglycans of the vessel wall, becomes modified, i.e. by oxidation (ox-LDL), and sustains a proinflammatory cascade that is proatherogenic

Lipidomics of atherosclerotic plaques

Lipidomics of atherosclerotic plaques

Fig. 2. Lipidomics of atherosclerotic plaques. Lipids were separated by ultra performance reverse phase
liquid chromatography on a Waters® ACQUITY UPLC® (HSS T3 Column, 100 mm × 2.1 mm i.d., 1.8 _m
particle size, 55 ◦C, flow rate 400 _L/min, Waters, Milford MA, USA) and analyzed on a quadrupole time-of-flight
mass spectrometer (Waters® SYNAPTTM HDMSTM system) in both positive (A) and negative ion mode (C).
In positive MS mode, lysophosphatidyl-cholines (lPCs) and lysophosphatidylethanolamines (lPEs) eluted first;
followed by phosphatidylcholines (PCs), sphingomyelin (SMs), phosphatidylethanol-amines (PEs) and cholesteryl
esters (CEs); diacylglycerols (DAGs) and triacylglycerols (TAGs) had the longest retention times. In negative MS mode,
fatty acids (FA) were followed by phosphatidyl-glycerols (PGs), phosphatidyl-inositols (PIs), phosphatidylserines (PS)
and PEs. The chromatographic peaks corresponding to the different classes were detected as retention time-mass to
charge ratio (m/z) pairs and their areas were recorded. Principal component analyses on 629 variables from triplicate
analysis (C1, 2, 3 = control 1, 2, 3; P1, 2, 3 = endarterectomy patient 1, 2, 3) demonstrated a clear separation of
atherosclerotic plaques and control radial arteries in positive (B) and negative (D) ion mode. The clustering of the
technical replicates and the central projection of the pooled sample within the scores plot confirm the reproducibility
of the analyses, and the Goodness of Fit test returned a chi-squared of 0.4 and a R-squared value of 0.6.

Challenges in mass spectrometry

Mass spectrometry is an evolving technology and the technological advances facilitate the detection and quantification
of scarce proteins. Nonetheless, the enrichment of specific subproteomes using differential solubilityor isolation of cellular
organelleswill remain important to increase coverage and, at least partially, overcome the inhomogeneity of diseased tissue,
one of the major factors affecting sample-to-sample variation.

Proteomics is also the method of choice for the identification of post-translational modifications, which play an essential
role in protein function, i.e. enzymatic activation, binding ability and formation of ECM structures. Again, efficient enrichment
is essential to increase the likelihood of identifying modified peptides in complex mixtures. Lipidomics faces similar challenges.
While the extraction of lipids is more selective, new enrichment methods are needed for scarce lipids as well as labile lipid
metabolites, that may have important bioactivity. Another pressing issue in lipidomics is data analysis, in particular the lack
of automated search engines that can analyze mass spectra obtained from instruments of different vendors. Efforts to
overcome this issue are currently underway.

Conclusions

Proteomics and lipidomics offer an unbiased platform for the investigation of ECM and lipids within atherosclerosis. In
combination, these innovative technologies will reveal key differences in proteolytic processes responsible for plaque rupture
and advance our understanding of ECM – lipoprotein interactions in atherosclerosis.

references

Virtualization in Proteomics: ‘Sakshat’ in India, at IIT Bombay(tginnovations.wordpress.com)

Proteome Portraits (the-scientist.com)

A Protease for ‘Middle-down’ Proteomics(pharmaceuticalintelligence.com)

Intrinsic Disorder in the Human Spliceosomal Proteome(ploscompbiol.org)

proteome

proteome

active site of eNOS (PDB_1P6L) and nNOS (PDB_1P6H).

active site of eNOS (PDB_1P6L) and nNOS (PDB_1P6H).

Table - metabolic  targets

Table – metabolic targets

HK-II Phosphorylation

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A Tribute to Johannes Everse

Author: Larry H Bernstein, MD, FCAP

 

Johannes Everse was a retired Tenured Professor at Texas Tech University Health Sciences Center in Lubbock, Texas, who dies on June 10, 2013.  He survived the Nazi invasion of Netherlands during World War II, and worked in the pharmaceutical industry after finishing a unique technical education the surpassed any that existed in United States that included an extensive knowledge of analytical instruments and expertise in organic chemical syntheses.  Given a unique opportunity, he applied for and obtained a position as a technician in the Laboratory of Nathan O Kaplan’s Laboratory at the time of Kaplan’s move from John’s Hopkins University to Brandeis University, where Kaplan with Sidney Colowick established the prestigious Methods in Enzymology series, and in a few short years built a worldclass Graduate Department of Biochemistry.  Kaplan was very sharp in selecting graduate students, postdoctoral students, and at administration, but his ability to recognioze potential talent was seen in his recruitment of Francis Stolzenbach and Johannes Everse.  He also gave considerable support to those who he had confidence in.  Consequently, Everse was able to take exams completing a BS degree, and eventually, the PhD degree at the University of California, San Diego, in the 1970s. When Prof. Kaplan was recruited to the UCSD campus by Martin Kamens, he was also installed in the National Academy of Sciences.

I worked with Jo Everse for several years as postdoctoral biochemist and resident-USPHS Fellow in  Pathology on the mechanism of the malate dehydrogenase (MDH) reaction and the regulatory function of the mitochondrial and cytoplasmic MDHs.   These were important formative years in my scientific training, and it was by no accident that I was sent to work in that laboratory by my previous mentor, a pathologist and biochemist who had worked on adenylate kinases, as I had been attracted to that problem as a medical student working on the ontogeny of the lactic dehydrogenases in the embryonic lens.  Jo Everse was responsible for synthesizing the pyridine nucleotide adducts that proved to be critical to understanding the pyridine nucleotide related dehydrogenase reactions.  Jo was undoubtedly a driving force in that laboratory.

It was at that time that my first daughter was born, and she had the opportunity to play with the Everse children, who as adults are both PhD biochemists.  I have been fortunate to live through a dynamic period in the history of scientific discovery, and most amazingly, at a time of decline in funding for science that has not been deterred since the Vietnam War.  You may consider it the cost of hegemony after the treaty that ended WWII and brought us the cold war.

Jo went on to a tenured faculty position at TTUHSS, and his retirement came shortly before his death at 80. While he stayed longer than his superiors wanted, his welcome was not so warm after he criticized the administration of the graduate program.  Unfortunately, he did not have the kind of backing that a colleague at Berkeley, Howard Schachman, Professor of the Graduate School Division of Biochemistry, Biophysics and Structural Biology, enjoyed.  It should not be a surprise how good health, power and money makes a difference in how it plays out.

Schachman was asked to retire in 2002 having a busy, well-funded study, that involved allostery and precisely – in the structure, function, assembly and interactions of biological macromolecules, with particular emphasis on the regulatory enzyme, aspartate transcarbamylase (ATCase).  The studies challenged earlier studies that designated the complex of ATCase with a bisubstrate ligand as the R state of the enzyme. but changes in the conformation were reinterpreted to be the result of the actual binding event rather than the allosteric transition whereby the enzyme is converted from an inactive, taut (T) state to the activated R conformation and they developed methods for understanding the formation of domains and the effect of deletions of helical regions on stability and the folding and assembly pathways.

Jo Everse came out of the depression in Europe (1931 birth), lived through WWII, and he managed to get a unique technical education that took him to Boston.  He became an excellent teacher.  He had a good marriage and father of two children.  He collected Packard automobiles and rebuilt them.  He also played the organ, and he made and maintained an organ for his home.  He lived a good life.

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Prologue to Cancer – e-book Volume One – Where are we in this journey?


Prologue to Cancer – e-book Volume One – Where are we in this journey?

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

Consulting Reviewer and Contributor:  Jose Eduardo de Salles Roselino, MD

 

LH Bernstein

LH Bernstein

Jose Eduardo de Salles Roselino

LES Roselino

 

 

This is a preface to the fourth in the ebook series of Leaders in Pharmaceutical Intelligence, a collaboration of experienced doctorate medical and pharmaceutical professionals.  The topic is of great current interest, and it entails a significant part of current medical expenditure by a group of neoplastic diseases that may develop at different periods in life, and have come to supercede infections or even eventuate in infectious disease as an end of life event.  The articles presented are a collection of the most up-to-date accounts of the state of a now rapidly emerging field of medical research that has benefitted enormously by progress in immunodiagnostics,  radiodiagnostics, imaging, predictive analytics, genomic and proteomic discovery subsequent to the completion of the Human Genome Project, advances in analytic methods in qPCR, gene sequencing, genome mapping, signaling pathways, exome identification, identification of therapeutic targets in inhibitors, activators, initiators in the progression of cell metabolism, carcinogenesis, cell movement, and metastatic potential.  This story is very complicated because we are engaged in trying to evoke from what we would like to be similar clinical events, dissimilar events in their expression and classification, whether they are within the same or different anatomic class.  Thus, we are faced with constructing an objective evidence-based understanding requiring integration of several disciplinary approaches to see a clear picture.  The failure to do so creates a high risk of failure in biopharmaceutical development.

The chapters that follow cover novel and important research and development in cancer related research, development, diagnostics and treatment, and in balance, present a substantial part of the tumor landscape, with some exceptions.  Will there ever be a unifying concept, as might be hoped for? I certainly can’t see any such prediction on the horizon.  Part of the problem is that disease classification is a human construct to guide us, and so are treatments that have existed and are reexamined for over 2,000 years.  In that time, we have changed, our afflictions have been modified, and our environment has changed with respect to the microorganisms within and around us, viruses, the soil, and radiation exposure, and the impacts of war and starvation, and access to food.  The outline has been given.  Organic and inorganic chemistry combined with physics has given us a new enterprise in biosynthetics that is and will change our world.  But let us keep in mind that this is a human construct, just as drug target development is such a construct, workable with limitations.

What Molecular Biology Gained from Physics

We need greater clarity and completeness in defining the carcinogenetic process.  It is the beginning, but not the end.  But we must first examine the evolution of the scientific structure that leads to our present understanding. This was preceded by the studies of anatomy, physiology, and embryology that had to occur as a first step, which was followed by the researches into bacteriology, fungi, sea urchins and the evolutionary creatures that could be studied having more primary development in scale.  They are still major objects of study, with the expectation that we can derive lessons about comparative mechanisms that have been passed on through the ages and have common features with man.  This became the serious intent of molecular biology, the discipline that turned to find an explanation for genetics, and to carry out controlled experiments modelled on the discipline that already had enormous success in physics, mathematics, and chemistry. In 1900, when Max Planck hypothesized that the frequency of light emitted by the black body depended on the frequency of the oscillator that emitted it, it had important ramifications for chemistry and biology (See Appendix II and Footnote 1, Planck equation, energy and oscillation).  The leading idea is to search below the large-scale observations of classical biology.

The central dogma of molecular biology where genetic material is transcribed into RNA and then translated into protein, provides a starting point, but the construct is undergoing revision in light of emerging novel roles for RNA and signaling pathways.   The term, coined by Warren Weaver (director of Natural Sciences for the Rockefeller Foundation), who observed an emergence of significant change given recent advances in fields such as X-ray crystallography. Molecular biology also plays important role in understanding formations, actions, regulations of various parts of cellswhich can be used efficiently for targeting new drugs, diagnosis of disease, physiology of the Cell. The Nobel Prize in Physiology or Medicine in 1969 was shared by Max Delbrück, Alfred D. Hershey, Salvador E. Luria, whose work with viral replication gave impetus to the field.  Delbruck was a physicist who trained in Copenhagen under Bohr, and specifically committed himself to a rigor in biology, as was in physics.

Dorothy Hodgkin  protein crystallography

Dorothy Hodgkin protein crystallography

Rosalind Franlin crystallographer double helix

Rosalind Franlin
crystallographer
double helix

 Max Delbruck         molecular biology

Max Delbruck        
molecular biology

Max Planck

Max Planck Quantum Physics

 

 

 

We then stepped back from classical (descriptive) physiology, with the endless complexity, to molecular biology.  This led us to the genetic code, with a double helix model.  It has recently been found insufficiently explanatory, with the recent construction of triplex and quadruplex models. They have a potential to account for unaccounted for building blocks, such as inosine, and we don’t know whether more than one model holds validity under different conditions .  The other major field of development has been simply unaccounted for in the study of proteomics, especially in protein-protein interactions, and in the energetics of protein conformation, first called to our attention by the work of Jacob, Monod, and Changeux (See Footnote 2).  Proteins are not just rigid structures stamped out by the monotonously simple DNA to RNA to protein concept.  Nothing is ever quite so simple. Just as there are epigenetic events, there are posttranslational events, and yet more.

JPChangeux-150x170

JP Changeux

 

 

 

 

 

 

 

 

The Emergence of Molecular Biology

I now return the discussion to the topic of medicine, the emergence of molecular biology and the need for convergence with biochemistry in the mid-20th century. Jose Eduardo de Salles Roselino recalls “I was previously allowed to make of the conformational energy as made by R Marcus in his Nobel lecture revised (J. of Electroanalytical  Chemistry 438:(1997) p251-259. (See Footnote 1) His description of the energetic coordinates of a landscape of a chemical reaction is only a two-dimensional cut of what in fact is a volcano crater (in three dimensions) (each one varies but the sum of the two is constant. Solvational+vibrational=100% in ordinate) nuclear coordinates in abcissa. In case we could represent it by research methods that allow us to discriminate in one by one degree of different pairs of energy, we would most likely have 360 other similar representations of the same phenomenon. The real representation would take into account all those 360 representations together. In case our methodology was not that fine, for instance it discriminates only differences of minimal 10 degrees in 360 possible, will have 36 partial representations of something that to be perfectly represented will require all 36 being taken together. Can you reconcile it with ATGC?  Yet, when complete genome sequences were presented they were described as though we will know everything about this living being. The most important problems in biology will be viewed by limited vision always and the awareness of this limited is something we should acknowledge and teach it. Therefore, our knowledge is made up of partial representations. If we had the entire genome data for the most intricate biological problems, they are still not amenable to this level of reductionism. But going from general views of signals andsymptoms we could get to the most detailed molecular view and in this case genome provides an anchor.”

“Warburg Effect” describes the preference of glycolysis and lactic acid fermentation rather than oxidative phosphorylation for energy production in cancer cells. Mitochondrial metabolism is an important and necessary component in the functioning and maintenance of the cell, and accumulating evidence suggests that dysfunction of mitochondrial metabolism plays a role in cancer. Progress has demonstrated the mechanisms of the mitochondrial metabolism-to-glycolysis switch in cancer development and how to target this metabolic switch.

 

 

Glycolysis

glycolysis

 

Otto Heinrich Warburg (1883-  )

Otto Warburg

435px-Louis_Pasteur,_foto_av_Félix_Nadar_Crisco_edit

Louis Pasteur

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The expression “Pasteur effect” was coined by Warburg when inspired by Pasteur’s findings in yeast cells, when he investigated this metabolic observation (Pasteur effect) in cancer cells. In yeast cells, Pasteur had found that the velocity of sugar used was greatly reduced in presence of oxygen. Not to be confused, in the “Crabtree effect”, the velocity of sugar metabolism was greatly increased, a reversal, when yeast cells were transferred from the aerobic to an anaerobic condition. Thus, the velocity of sugar metabolism of yeast cells was shown to be under metabolic regulatory control in response to change in environmental oxygen conditions in growth. Warburg had to verify whether cancer cells and tissue related normal mammalian cells also have a similar control mechanism. He found that this control was also found in normal cells studied, but was absent in cancer cells. Strikingly, cancer cells continue to have higher anaerobic gycolysis despite the presence of oxygen in their culture media (See Footnote 3).

Taking this a step further, food is digested and supplied to cells In vertebrates mainly in the form of glucose, which is metabolized producing Adenosine Triphosphate (ATP) by two pathways. Glycolysis, occurs via anaerobic metabolism in the cytoplasm, and is of major significance for making ATP quickly, but in a minuscule amount (2 molecules).  In the presence of oxygen, the breakdown process continues in the mitochondria via the Krebs’s cycle coupled with oxidative phosphorylation, which is more efficient for ATP production (36 molecules). Cancer cells seem to depend on glycolysis. In the 1920s, Otto Warburg first proposed that cancer cells show increased levels of glucose consumption and lactate fermentation even in the presence of ample oxygen (known as “Warburg Effect”). Based on this theory, oxidative phosphorylation switches to glycolysis which promotes the proliferation of cancer cells. Many studies have demonstrated glycolysis as the main metabolic pathway in cancer cells.

Albert Szent Gyogy (Warburg’s student) and Otto Meyerhof both studied striated skeletal muscle metabolism invertebrates, and they found those changes observed in yeast by Pasteur. The description of the anaerobic pathway was largely credited to Emden and Meyerhof. Whenever there is increase in muscle work, energy need is above what can be provided by blood supply, the cell metabolism changes from aerobic (where  Acetyl CoA  provides the chemical energy for aerobic production of ATP) to anaerobic metabolism of glucose. In this condition, glucose is obtained directly from its muscle glycogen stores (not from hepatic glycogenolysis).  This is the sole source of chemical energy that is independent of oxygen supplied to the cell. It is a physiological change on muscle metabolism that favors autonomy. It does not depend upon the blood oxygen for aerobic metabolim or blood sources of carbon metabolites borne out from adipose tissue (free fatty acids) or muscle proteins (branched chain amino acids), or vascular delivery of glucose. On that condition, the muscle can perform contraction by its internal source of ATP and uses conversion of pyruvate to lactate in order to regenerate much-needed NAD (by hydride transfer from pyruvate) as a replacement for this mitochondrial function. This regulatory change, keeps glycolysis going at fast rate in order to meet ATP needs of the cell under low yield condition (only two or three ATP for each glucose converted into two lactate molecules). Therefore, it cannot last for long periods of time. This regulatory metabolic change is made in seconds, minutes and therefore happens with the proteins that are already presented in the cell. It does not requires the effect of transcription factors and/or changes in gene expression (See Footnote 1, 2).

In other types mammalian cells, like those from the lens of the eye (86% gycolysis + pentose shunt),  and red blood cells (RBC)[both lacking mitochondria], and also in the deep medullary layer of the kidneys, for lack of mitochondria in the first two cases and normally reduced blood perfusion in the third – A condition required for the counter current mechanism and our ability to concentrate urine also have, permanent higher anaerobic metabolism. In the case of RBC, it includes the ability to produce in a shunt of glycolytic pathway 2,3 diphospho- glycerate that is required to place the hemogloblin macromolecule in an unstable equilibrium between its two forms (R and T – Here presented as simplified accordingly to the model of Monod, Wyman and Changeux. The final model would be even much complex (see for instance, H-W and K review Nature 2007 vol 450: p 964-972 )

Any tissue under a condition of ischemia that is required for some medical procedures (open heart surgery, organ transplants, etc) displays this fast regulatory mechanism (See Footnote 1, 2). A display of these regulatory metabolic changes can be seen in: Cardioplegia: the protection of the myocardium during open heart surgery: a review. D. J. Hearse J. Physiol., Paris, 1980, 76, 751-756 (Fig 1).  The following points are made:

1-       It is a fast regulatory response. Therefore, no genetic mechanism can be taken into account.

2-       It moves from a reversible to an irreversible condition, while the cells are still alive. Death can be seen at the bottom end of the arrow. Therefore, it cannot be reconciled with some of the molecular biology assumptions:

A-       The gene and genes reside inside the heart muscle cells but, in order to preserve intact, the source of coded genetic information that the cell reads and transcribes, DNA must be kept to a minimal of chemical reactivity.

B-       In case sequence determines conformation, activity and function , elevated potassium blood levels could not cause cardiac arrest.

In comparison with those conditions here presented, cancer cells keep the two metabolic options for glucose metabolism at the same time. These cells can use glucose that our body provides to them or adopt temporarily, an independent metabolic form without the usual normal requirement of oxygen (one or another form for ATP generation).  ATP generation is here, an over-simplification of the metabolic status since the carbon flow for building blocks must also be considered and in this case oxidative metabolism of glucose in cancer cells may be viewed as a rich source of organic molecules or building blocks that dividing cells always need.

JES Roselino has conjectured that “most of the Krebs cycle reaction works as ideal reversible thermodynamic systems that can supply any organic molecule that by its absence could prevent cell duplication.” In the vision of Warburg, cancer cells have a defect in Pasteur-effect metabolic control. In case it was functioning normally, it will indicate which metabolic form of glucose metabolism is adequate for each condition. What more? Cancer cells lack differentiated cell function. Any role for transcription factors must be considered as the role of factors that led to the stable phenotypic change of cancer cells. The failure of Pasteur effect must be searched for among the fast regulatory mechanisms that aren’t dependent on gene expression (See Footnote 3).

Extending the thoughts of JES Roselino (Hepatology 1992;16: 1055-1060), reduced blood flow caused by increased hydrostatic pressure in extrahepatic cholestasis decreases mitochondrial function (quoted in Hepatology) and as part of Pasteur effect normal response, increased glycolysis in partial and/or functional anaerobiosis and therefore blocks the gluconeogenic activity of hepatocytes that requires inhibited glycolysis. In this case, a clear energetic link can be perceived between the reduced energetic supply and the ability to perform differentiated hepatic function (gluconeogenesis). In cancer cells, the action of transcription factors that can be viewed as different ensembles of kaleidoscopic pieces (with changing activities as cell conditions change) are clearly linked to the new stable phenotype. In relation to extrahepatic cholestasis mentioned above it must be reckoned that in case a persistent chronic condition is studied a secondary cirrhosis is installed as an example of persistent stable condition, difficult to be reversed and without the requirement for a genetic mutation. (See Footnote 4).

 The Rejection of Complexity

Most of our reasoning about genes was derived from scientific work in microorganisms. These works have provided great advances in biochemistry.

250px-DNA_labeled  DNA diagram showing base pairing

double helix

 

hgp_hubris_220x288_72  genome cartoon

Dna triplex pic

Triple helix

 

formation of a triplex DNA structure

formation of triple helix

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1-      The “Gelehrter idea”: No matter what you are doing you will always be better off, in case you have a gene (In chapter 7 Principles of Medical Genetics Gelehrter and Collins Williams & Wilkins 1990).

2-      The idea that everything could be found following one gene one enzyme relationship that works fine for our understanding of the metabolism, in all biological problems.

3-      The idea that everything that explains biochemistry in microorganisms explains also for every living being (J Nirenberg).

4-      The idea that biochemistry may not require that time should be also taken into account. Time must be considered only for genetic and biological evolution studies (S Luria. In Life- The unfinished experiment 1977 C Scribner´s sons NY).

5-      Finally, the idea that everything in biology, could be found in the genome. Since all information in biology goes from DNA through RNA to proteins. Alternatively, are in the DNA, in case the strict line that includes RNA is not included.

This last point can be accepted in case it is considered that ALL GENETIC information is in our DNA. Genetics as part of life and not as its total expression.

For example, when our body is informed that the ambient temperature is too low or alternatively is too high, our body is receiving an information that arrives from our environment. This external information will affect our proteins and eventually, in case of longer periods in a new condition will cause adaptive response that may include conformational changes in transcription factors (proteins) that will also, produce new readings on the DNA. However, it is an information that moves from outside, to proteins and not from DNA to proteins. The last pathway, when transcription factors change its conformation and change DNA reading will follow the dogmatic view as an adaptive response (See Footnotes 1-3).

However, in case, time is taken into account, the first reactions against cold or warmer temperatures will be the ones that happen through change in protein conformation, activities and function before any change in gene expression can be noticed at protein level. These fast changes, in seconds, minutes cannot be explained by changes in gene expression and are strongly linked to what is needed for the maintenance of life.

“It is possible”, says Roselino, “desirable, to explain all these fast biochemical responses to changes in a living being condition as the sound foundation of medical practices without a single mention to DNA. In case a failure in any mechanism necessary to life is found to be genetic in its origin, the genome in context with with this huge set of transcription factors must be taken into account. This is the biochemical line of reasoning that I have learned with Houssay and Leloir. It would be an honor to see it restored in modern terms.”

More on the Mechanism of Metabolic Control

It was important that genomics would play such a large role in medical research for the last 70 years. There is also good reason to rethink the objections of the Nobelists James Watson and Randy Schekman in the past year, whatever discomfort it brings.  Molecular biology has become a tautology, and as a result deranged scientific rigor inside biology.

Crick & Watson with their DNA model, 1953

Eatson and Crick

Randy-Schekman Berkeley

Randy-Schekman Berkeley

 

 

According to JES Roselino, “consider that glycolysis is oscillatory thanks to the kinetic behavior of Phosphofructokinase. Further, by its effect upon Pyruvate kinase through Fructose 1,6 diphosphate oscillatory levels, the inhibition of gluconeogenesis is also oscillatory. When the carbon flow through glycolysis is led to a maximal level gluconeogenesis will be almost completely blocked. The reversal of the Pyruvate kinase step in liver requires two enzymes (Pyruvate carboxylase (maintenance of oxaloacetic levels) + phosphoenolpyruvate carboxykinase (E.C. 4.1.1.32)) and energy requiring reactions that most likely could not as an ensemble, have a fast enough response against pyruvate kinase short period of inhibition during high frequency oscillatory periods of glycolytic flow. Only when glycolysis oscillates at low frequency the opposite reaction could enable gluconeogenic carbon flow.”

In case it can be shown in a rather convincing way, the same reasoning could be applied to understand how simple replicative signals inducing Go to G1 transition in cells, could easily overcome more complex signals required for cell differentiation and differentiated function.

Perhaps the problem of overextension of the equivalence of the DNA and what happens to the organism is also related to the initial reliance on a single cell model to relieve the complexity (which isn’t fully the case).

For instance, consider this fragment:
“Until only recently it was assumed that all proteins take on a clearly defined three-dimensional structure – i.e. they fold in order to be able to assume these functions.”
Cold Spring Harbour Symp. Quant. Biol. 1973  p 187-193 J.C Seidel and J Gergely – Investigation of conformational changes in Spin-Labeled Myosin Model for muscle contraction:
Huxley, A. F. 1971 Proc. Roy. Soc (London) (B) 178:1
Huxley, A.F and R. M. Simmons,1971. Nature 233:633
J.C Haselgrove X ray Evidence for a conformational Change in the Actin-containing filaments…Cold Spring Harbour Symp Quant Biol.1972 v 37: p 341-352

Only a very small sample indicating otherwise. Proteins were held as interacting macromolecules, changing their conformation in regulatory response to changes in the microenvironment (See Footnote 2). DNA was the opposite, non-interacting macromolecules to be as stable as a library must be.

The dogma held that the property of proteins could be read in DNA alone. Consequenly, the few examples quoted above, must be ignored and all people must believe that DNA alone, without environmental factors roles, controls protein amino acid sequence (OK), conformation (not true), activity (not true) and function (not true).

It appeared naively to be correct from the dogma to conclude from interpreting your genome: You have a 50% increased risk of developing the following disease (deterministic statement).  The correct form must be: You belong to a population that has a 50% increase in the risk of….followed by –  what you must do to avoid increase in your personal risk and the care you should take in case you want to have longer healthy life.  Thus, genetics and non-genetic diseases were treated as the same and medical foundations were reinforced by magical considerations (dogmas) in a very profitable way for those involved besides the patient.

 Footnotes:

  1. There is a link of electricity with ions in biology and the oscillatory behavior of some electrical discharges.  In addition, the oscillatory form of electrical discharged may have allowed Planck to relate high energy content with higher frequencies and conversely, low energy content in low frequency oscillatory events.  One may think of high density as an indication of great amount of matter inside a volume in space.  This helps the understanding of Planck’s idea as a high-density-energy in time for a high frequency phenomenon.
  1. Take into account a protein that may have its conformation restricted by an S-S bridge. This protein also, may move to another more flexible conformation in case it is in HS HS condition when the S-S bridge is broken. Consider also that, it takes some time for a protein to move from one conformation for instance, the restricted conformation (S-S) to other conformations. Also, it takes a few seconds or minutes to return to the S-S conformation (This is the Daniel Koshland´s concept of induced fit and relaxation time used by him in order to explain allosteric behavior of monomeric proteins- Monod, Wyman and Changeux requires tetramer or at least, dimer proteins).
  1. In case you have glycolysis oscillating in a frequency much higher than the relaxation time you could lead to the prevalence of high NADH effect leading to high HS /HS condition and at low glycolytic frequency, you could have predominance of S-S condition affecting protein conformation. In case you have predominance of NAD effect upon protein S-S you would get the opposite results.  The enormous effort to display the effect of citrate and over Phosphofructokinase conformation was made by others. Take into account that ATP action as an inhibitor in this case, is a rather unusual one. It is a substrate of the reaction, and together with its action as activator  F1,6 P (or its equivalent F2,6 P) is also unusual. However, it explains oscillatory behaviour of glycolysis. (Goldhammer , A.R, and Paradies: PFK structure and function, Curr. Top Cell Reg 1979; 15:109-141).
  1. The results presented in our Hepatology work must be viewed in the following way: In case the hepatic (oxygenated) blood flow is preserved, the bile secretory cells of liver receive well-oxygenated blood flow (the arterial branches bath secretory cells while the branches originated from portal vein irrigate the hepatocytes.  During extra hepatic cholestasis the low pressure, portal blood flow is reduced and the hepatocytes do not receive enough oxygen required to produce ATP that gluconeogenesis demands. Hepatic artery do not replace this flow since, its branches only join portal blood fluxes after the previous artery pressure  is reduced to a low pressure venous blood – at the point where the formation of hepatic vein is. Otherwise, the flow in the portal vein would be reversed or, from liver to the intestine. It is of no help to take into account possible valves for this reasoning since minimal arterial pressure is well above maximal venous pressure and this difference would keep this valve in permanent close condition. In low portal blood flow condition, the hepatocyte increases pyruvate kinase activity and with increased pyruvate kinase activity Gluconeogenesis is forbidden (See Walsh & Cooper revision quoted in the Hepatology as ref 23). For the hemodynamic considerations, role of artery and veins in hepatic portal system see references 44 and 45 Rappaport and Schneiderman and Rappapaport.

 

 Appendix I.

metabolic pathways

metabolic pathways

Signals Upstream and Targets Downstream of Lin28 in the Lin28 Pathway

Signals Upstream and Targets Downstream of Lin28 in the Lin28 Pathway

 

 

 

 

 

 

 

 

1.  Functional Proteomics Adds to Our Understanding

Ben Schuler’s research group from the Institute of Biochemistry of the University of Zurich has now established that an increase in temperature leads to folded proteins collapsing and becoming smaller. Other environmental factors can trigger the same effect. The crowded environments inside cells lead to the proteins shrinking. As these proteins interact with other molecules in the body and bring other proteins together, understanding of these processes is essential “as they play a major role in many processes in our body, for instance in the onset of cancer”, comments study coordinator Ben Schuler.

Measurements using the “molecular ruler”

“The fact that unfolded proteins shrink at higher temperatures is an indication that cell water does indeed play an important role as to the spatial organisation eventually adopted by the molecules”, comments Schuler with regard to the impact of temperature on protein structure. For their studies the biophysicists use what is known as single-molecule spectroscopy. Small colour probes in the protein enable the observation of changes with an accuracy of more than one millionth of a millimetre. With this “molecular yardstick” it is possible to measure how molecular forces impact protein structure.

With computer simulations the researchers have mimicked the behaviour of disordered proteins. They want to use them in future for more accurate predictions of their properties and functions.

Correcting test tube results

That’s why it’s important, according to Schuler, to monitor the proteins not only in the test tube but also in the organism. “This takes into account the fact that it is very crowded on the molecular level in our body as enormous numbers of biomolecules are crammed into a very small space in our cells”, says Schuler. The biochemists have mimicked this “molecular crowding” and observed that in this environment disordered proteins shrink, too.

Given these results many experiments may have to be revisited as the spatial organisation of the molecules in the organism could differ considerably from that in the test tube according to the biochemist from the University of Zurich. “We have, therefore, developed a theoretical analytical method to predict the effects of molecular crowding.” In a next step the researchers plan to apply these findings to measurements taken directly in living cells.

Explore further: Designer proteins provide new information about the body’s signal processesMore information: Andrea Soranno, Iwo Koenig, Madeleine B. Borgia, Hagen Hofmann, Franziska Zosel, Daniel Nettels, and Benjamin Schuler. Single-molecule spectroscopy reveals polymer effects of disordered proteins in crowded environments. PNAS, March 2014. DOI: 10.1073/pnas.1322611111

 

Effects of Hypoxia on Metabolic Flux

  1. Glucose-6-phosphate dehydrogenase regulation in the hepatopancreas of the anoxia-tolerantmarinemollusc, Littorina littorea

JL Lama , RAV Bell and KB Storey

Glucose-6-phosphate dehydrogenase (G6PDH) gates flux through the pentose phosphate pathway and is key to cellular antioxidant defense due to its role in producing NADPH. Good antioxidant defenses are crucial for anoxia-tolerant organisms that experience wide variations in oxygen availability. The marine mollusc, Littorina littorea, is an intertidal snail that experiences daily bouts of anoxia/hypoxia with the tide cycle and shows multiple metabolic and enzymatic adaptations that support anaerobiosis. This study investigated the kinetic, physical and regulatory properties of G6PDH from hepatopancreas of L. littorea to determine if the enzyme is differentially regulated in response to anoxia, thereby providing altered pentose phosphate pathway functionality under oxygen stress conditions.

Several kinetic properties of G6PDH differed significantly between aerobic and 24 h anoxic conditions; compared with the aerobic state, anoxic G6PDH (assayed at pH 8) showed a 38% decrease in K G6P and enhanced inhibition by urea, whereas in pH 6 assays Km NADP and maximal activity changed significantly.

All these data indicated that the aerobic and anoxic forms of G6PDH were the high and low phosphate forms, respectively, and that phosphorylation state was modulated in response to selected endogenous protein kinases (PKA or PKG) and protein phosphatases (PP1 or PP2C). Anoxia-induced changes in the phosphorylation state of G6PDH may facilitate sustained or increased production of NADPH to enhance antioxidant defense during long term anaerobiosis and/or during the transition back to aerobic conditions when the reintroduction of oxygen causes a rapid increase in oxidative stress.

Lama et al.  Peer J 2013.   http://dx.doi.org/10.7717/peerj.21

 

  1. Structural Basis for Isoform-Selective Inhibition in Nitric Oxide Synthase

    TL. Poulos and H Li

In the cardiovascular system, the important signaling molecule nitric oxide synthase (NOS) converts L-arginine into L-citrulline and releases nitric oxide (NO). NO produced by endothelial NOS (eNOS) relaxes smooth muscle which controls vascular tone and blood pressure. Neuronal NOS (nNOS) produces NO in the brain, where it influences a variety of neural functions such as neural transmitter release. NO can also support the immune system, serving as a cytotoxic agent during infections. Even with all of these important functions, NO is a free radical and, when overproduced, it can cause tissue damage. This mechanism can operate in many neurodegenerative diseases, and as a result the development of drugs targeting nNOS is a desirable therapeutic goal.

However, the active sites of all three human isoforms are very similar, and designing inhibitors specific for nNOS is a challenging problem. It is critically important, for example, not to inhibit eNOS owing to its central role in controlling blood pressure. In this Account, we summarize our efforts in collaboration with Rick Silverman at Northwestern University to develop drug candidates that specifically target NOS using crystallography, computational chemistry, and organic synthesis. As a result, we have developed aminopyridine compounds that are 3800-fold more selective for nNOS than eNOS, some of which show excellent neuroprotective effects in animal models. Our group has solved approximately 130 NOS-inhibitor crystal structures which have provided the structural basis for our design efforts. Initial crystal structures of nNOS and eNOS bound to selective dipeptide inhibitors showed that a single amino acid difference (Asp in nNOS and Asn in eNOS) results in much tighter binding to nNOS. The NOS active site is open and rigid, which produces few large structural changes when inhibitors bind. However, we have found that relatively small changes in the active site and inhibitor chirality can account for large differences in isoform-selectivity. For example, we expected that the aminopyridine group on our inhibitors would form a hydrogen bond with a conserved Glu inside the NOS active site. Instead, in one group of inhibitors, the aminopyridine group extends outside of the active site where it interacts with a heme propionate. For this orientation to occur, a conserved Tyr side chain must swing out of the way. This unanticipated observation taught us about the importance of inhibitor chirality and active site dynamics. We also successfully used computational methods to gain insights into the contribution of the state of protonation of the inhibitors to their selectivity. Employing the lessons learned from the aminopyridine inhibitors, the Silverman lab designed and synthesized symmetric double-headed inhibitors with an aminopyridine at each end, taking advantage of their ability to make contacts both inside and outside of the active site. Crystal structures provided yet another unexpected surprise. Two of the double-headed inhibitor molecules bound to each enzyme subunit, and one molecule participated in the generation of a novel Zn site that required some side chains to adopt alternate conformations. Therefore, in addition to achieving our specific goal, the development of nNOS selective compounds, we have learned how subtle differences in and structure can control proteinligand interactions and often in unexpected ways.

 

300px-Nitric_Oxide_Synthase

Nitric oxide synthase

arginine-NO-citulline cycle

arginine-NO-citulline cycle

active site of eNOS (PDB_1P6L) and nNOS (PDB_1P6H).

active site of eNOS (PDB_1P6L) and nNOS (PDB_1P6H).

 

 

NO - muscle, vasculature, mitochondria

NO – muscle, vasculature, mitochondria

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure:  (A) Structure of one of the early dipeptide lead compounds, 1, that exhibits excellentisoform selectivity. (B, C) show the crystal structures of the dipeptide inhibitor 1 in the active site of eNOS (PDB: 1P6L) and nNOS (PDB: 1P6H). In nNOS, the inhibitor “curls” which enables the inhibitor R-amino group to interact with both Glu592 and Asp597. In eNOS, Asn368 is the homologue to nNOS Asp597.

Accounts in Chem Res 2013; 46(2): 390-98.

  1. Jamming a Protein Signal

Interfering with a single cancer-promoting protein and its receptor can open this resistance mechanism by initiating autophagy of the affected cells,  according to researchers at The University of Texas MD Anderson Cancer Center  in the journal Cell Reports.  According to Dr. Anil Sood and Yunfei Wen, lead and first authors, blocking  prolactin, a potent growth factor for ovarian cancer, sets off downstream events that result in cell by autophagy, the process  recycles damaged organelles and proteins for new use by the cell through the phagolysozome. This in turn, provides a clinical rationale for blocking prolactin and its receptor to initiate sustained autophagy as an alternative strategy for treating cancers.

Steep reductions in tumor weight

Prolactin (PRL) is a hormone previously implicated in ovarian, endometrial and other cancer development andprogression. When PRL binds to its cell membrane receptor, PRLR, activation of cancer-promoting cell signaling pathways follows.  A variant of normal prolactin called G129R blocks the reaction between prolactin and its receptor. Sood and colleagues treated mice that had two different lines of human ovarian cancer, both expressing the prolactin receptor, with G129R. Tumor weights fell by 50 percent for mice with either type of ovarian cancer after 28 days of treatment with G129R, and adding the taxane-based chemotherapy agent paclitaxel cut tumor weight by 90 percent. They surmise that higher doses of G129R may result in even greater therapeutic benefit.

 

3D experiments show death by autophagy

 

[video width=”1280″ height=”720″ mp4=”https://pharmaceuticalintelligence.files.wordpress.com/2014/04/1741-7007-11-65-s1-macromolecular-juggling-by-ubiquitylation-enzymes1.mp4″][/video]

 

Next the team used the prolactin-mimicking peptide to treat cultures of cancer spheroids which sharply reduced their numbers, and blocked the activation of JAK2 and STAT signaling pathways.

Protein analysis of the treated spheroids showed increased presence of autophagy factors and genomic analysis revealed increased expression of a number of genes involved in autophagy progression and cell death.  Then a series of experiments using fluorescence and electron microscopy showed that the cytosol of treated cells had large numbers of cavities caused by autophagy.

The team also connected the G129R-induced autophagy to the activity of PEA-15, a known cancer inhibitor. Analysis of tumor samples from 32 ovarian cancer patients showed that tumors express higher levels of the prolactin receptor and lower levels of phosphorylated PEA-15 than normal ovarian tissue. However, patients with low levels of the prolactin receptor and higher PEA-15 had longer overall survival than those with high PRLR and low PEA-15.

Source: MD Anderson Cancer Center

 

  1. Chemists’ Work with Small Peptide Chains of Enzymes

Korendovych and his team designed seven simple peptides, each containing seven amino acids. They then allowed the molecules of each peptide to self-assemble, or spontaneously clump together, to form amyloids. (Zinc, a metal with catalytic properties, was introduced to speed up the reaction.) What they found was that four of the seven peptides catalyzed the hydrolysis of molecules known as esters, compounds that react with water to produce water and acids—a feat not uncommon among certain enzymes.

“It was the first time that a peptide this small self-assembled to produce an enzyme-like catalyst,” says Korendovych. “Each enzyme has to be an exact fit for its respective substrate,” he says, referring to the molecule with which an enzyme reacts. “Even after millions of years, nature is still testing all the possible combinations of enzymes to determine which ones can catalyze metabolic reactions. Our results make an argument for the design of self-assembling nanostructured catalysts.”

Source: Syracuse University

Here are three articles emphasizing the value of combinatorial analysis, which can be formed from genomic, clinical, and proteomic data sets.

 

  1. Comparative analysis of differential network modularity in tissue specific normal and cancer protein interaction networks

    F Islam , M Hoque , RS Banik , S Roy , SS Sumi, et al.

As most biological networks show modular properties, the analysis of differential modularity between normal and cancer protein interaction networks can be a good way to understand cancer more significantly. Two aspects of biological network modularity e.g. detection of molecular complexes (potential modules or clusters) and identification of crucial nodes forming the overlapping modules have been considered in this regard.

The computational analysis of previously published protein interaction networks (PINs) has been conducted to identify the molecular complexes and crucial nodes of the networks. Protein molecules involved in ten major cancer signal transduction pathways were used to construct the networks based on expression data of five tissues e.g. bone, breast, colon, kidney and liver in both normal and cancer conditions.

Cancer PINs show higher level of clustering (formation of molecular complexes) than the normal ones. In contrast, lower level modular overlapping is found in cancer PINs than the normal ones. Thus a proposition can be made regarding the formation of some giant nodes in the cancer networks with very high degree and resulting in reduced overlapping among the network modules though the predicted molecular complex numbers are higher in cancer conditions.

Islam et al. Journal of Clinical Bioinformatics 2013, 3:19-32

  1. A new 12-gene diagnostic biomarker signature of melanoma revealed by integrated microarray analysis

    Wanting Liu , Yonghong Peng and Desmond J. Tobin
    PeerJ 1:e49;        http://dx.doi.org/10.7717/peerj.49

Here we present an integrated microarray analysis framework, based on a genome-wide relative significance (GWRS) and genome-wide global significance (GWGS) model. When applied to five microarray datasets on melanoma published between 2000 and 2011, this method revealed a new signature of 200 genes. When these were linked to so-called ‘melanoma driver’ genes involved in MAPK, Ca2+, and WNT signaling pathways we were able to produce a new 12-gene diagnostic biomarker signature for melanoma (i.e., EGFR, FGFR2, FGFR3, IL8, PTPRF, TNC, CXCL13, COL11A1, CHP2, SHC4, PPP2R2C, andWNT4).We have begun to experimentally validate a subset of these genes involved inMAPK signaling at the protein level, including CXCL13, COL11A1, PTPRF and SHC4 and found these to be overexpressed inmetastatic and primarymelanoma cells in vitro and in situ compared to melanocytes cultured from healthy skin epidermis and normal healthy human skin.

 

catalytic amyloid forming particle

catalytic amyloid forming particle

 

 

 

 

 

 

 

        8.    PanelomiX: A threshold-based algorithm to create panels of biomarkers

X Robin , N Turck , A Hainard , N Tiberti, et al.
               Translational Proteomics 2013.    http://dx.doi.org/10.1016/j.trprot.2013.04.003

The PanelomiX toolbox combines biomarkers and evaluates the performance of panels to classify patients better than singlemarkers or other classifiers. The ICBTalgorithm proved to be an efficient classifier, the results of which can easily be interpreted.

Here are two current examples of the immense role played by signaling pathways in carcinogenic mechanisms and in treatment targeting, which is also confounded by acquired resistance.

 

  1. Triple-Negative Breast Cancer

  1. epidermal growth factor receptor (EGFR or ErbB1) and
  2. high activity of the phosphatidylinositol 3-kinase (PI3K)–Akt pathway

are both targeted in triple-negative breast cancer (TNBC).

  • activation of another EGFR family member [human epidermal growth factor receptor 3 (HER3) (or ErbB3)] may limit the antitumor effects of these drugs.

This study found that TNBC cell lines cultured with the EGFR or HER3 ligand EGF or heregulin, respectively, and treated with either an Akt inhibitor (GDC-0068) or a PI3K inhibitor (GDC-0941) had increased abundance and phosphorylation of HER3.

The phosphorylation of HER3 and EGFR in response to these treatments

  1. was reduced by the addition of a dual EGFR and HER3 inhibitor (MEHD7945A).
  2. MEHD7945A also decreased the phosphorylation (and activation) of EGFR and HER3 and
  3. the phosphorylation of downstream targets that occurred in response to the combination of EGFR ligands and PI3K-Akt pathway inhibitors.

In culture, inhibition of the PI3K-Akt pathway combined with either MEHD7945A or knockdown of HER3

  1. decreased cell proliferation compared with inhibition of the PI3K-Akt pathway alone.
  2. Combining either GDC-0068 or GDC-0941 with MEHD7945A inhibited the growth of xenografts derived from TNBC cell lines or from TNBC patient tumors, and
  3. this combination treatment was also more effective than combining either GDC-0068 or GDC-0941 with cetuximab, an EGFR-targeted antibody.
  4. After therapy with EGFR-targeted antibodies, some patients had residual tumors with increased HER3 abundance and EGFR/HER3 dimerization (an activating interaction).

Thus, we propose that concomitant blockade of EGFR, HER3, and the PI3K-Akt pathway in TNBC should be investigated in the clinical setting.

Reference: Antagonism of EGFR and HER3 Enhances the Response to Inhibitors of the PI3K-Akt Pathway in Triple-Negative Breast Cancer. JJ Tao, P Castel, N Radosevic-Robin, M Elkabets, et al.  Sci. Signal., 25 March 2014;
7(318), p. ra29   http://dx.doi.org/10.1126/scisignal.2005125

 

                  10.   Metastasis in RAS Mutant or Inhibitor-Resistant Melanoma Cells

The protein kinase BRAF is mutated in about 40% of melanomas, and BRAF inhibitors improve progression-free and overall survival in these patients. However, after a relatively short period of disease control, most patients develop resistance because of reactivation of the RAF–ERK (extracellular signal–regulated kinase) pathway, mediated in many cases by mutations in RAS. We found that BRAF inhibition induces invasion and metastasis in RAS mutant melanoma cells through a mechanism mediated by the reactivation of the MEK (mitogen-activated protein kinase kinase)–ERK pathway.

Reference: BRAF Inhibitors Induce Metastasis in RAS Mutant or Inhibitor-Resistant Melanoma Cells by Reactivating MEK and ERK Signaling. B Sanchez-Laorden, A Viros, MR Girotti, M Pedersen, G Saturno, et al., Sci. Signal., 25 March 2014;  7(318), p. ra30  http://dx.doi.org/10.1126/scisignal.2004815

Appendix II.

The world of physics in the twentieth century saw the end of determinism established by Newton. This is characterized by discrete laws that describe natural observations. These are in gravity and in eletricity. In an early phase of investigation, an era of galvanic or voltaic electricity represented a revolutionary break from the historical focus on frictional electricity. Alessandro Voltadiscovered that chemical reactions could be used to create positively charged anodes and negatively charged cathodes.  In 1790, Prof. Luigi Alyisio Galvani of Bologna, while conducting experiments on “animal electricity“, noticed the twitching of a frog’s legs in the presence of an electric machine. He observed that a frog’s muscle, suspended on an iron balustrade by a copper hook passing through its dorsal column, underwent lively convulsions without any extraneous cause, the electric machine being at this time absent.  Volta communicated a description of his pile to the Royal Society of London and shortly thereafter Nicholson and Cavendish (1780) produced the decomposition of water by means of the electric current, using Volta’s pile as the source of electromotive force.

Siméon Denis Poisson attacked the difficult problem of induced magnetization, and his results provided  a first approximation. His innovation required the application of mathematics to physics.  His memoirs on the theory of electricity and magnetism created a new branch of mathematical physics.  The discovery of electromagnetic induction was made almost simultaneously and independently by Michael Faraday and Joseph Henry. Michael Faraday, the successor of Humphry Davy, began his epoch-making research relating to electric and electromagnetic induction in 1831. In his investigations of the peculiar manner in which iron filings arrange themselves on a cardboard or glass in proximity to the poles of a magnet, Faraday conceived the idea of magnetic “lines of force” extending from pole to pole of the magnet and along which the filings tend to place themselves. On the discovery being made that magnetic effects accompany the passage of an electric current in a wire, it was also assumed that similar magnetic lines of force whirled around the wire. He also posited that iron, nickel, cobalt, manganese, chromium, etc., are paramagnetic (attracted by magnetism), whilst other substances, such as bismuth, phosphorus, antimony, zinc, etc., are repelled by magnetism or are diamagnetic.

Around the mid-19th century, Fleeming Jenkin‘s work on ‘ Electricity and Magnetism ‘ and Clerk Maxwell’s ‘ Treatise on Electricity and Magnetism ‘ were published. About 1850 Kirchhoff published his laws relating to branched or divided circuits. He also showed mathematically that according to the then prevailing electrodynamic theory, electricity would be propagated along a perfectly conducting wire with the velocity of light. Herman Helmholtz investigated the effects of induction on the strength of a current and deduced mathematical equations, which experiment confirmed. In 1853 Sir William Thomson (later Lord Kelvin) predicted as a result of mathematical calculations the oscillatory nature of the electric discharge of a condenser circuit.  Joseph Henry, in 1842 discerned  the oscillatory nature of the Leyden jardischarge.

In 1864 James Clerk Maxwell announced his electromagnetic theory of light, which was perhaps the greatest single step in the world’s knowledge of electricity. Maxwell had studied and commented on the field of electricity and magnetism as early as 1855/6 when On Faraday’s lines of force was read to the Cambridge Philosophical Society. The paper presented a simplified model of Faraday’s work, and how the two phenomena were related. He reduced all of the current knowledge into a linked set of differential equations with 20 equations in 20 variables. This work was later published as On Physical Lines of Force in1861. In order to determine the force which is acting on any part of the machine we must find its momentum, and then calculate the rate at which this momentum is being changed. This rate of change will give us the force. The method of calculation which it is necessary to employ was first given by Lagrange, and afterwards developed, with some modifications, by Hamilton’s equations. Now Maxwell logically showed how these methods of calculation could be applied to the electro-magnetic field. The energy of a dynamical systemis partly kinetic, partly potential. Maxwell supposes that the magnetic energy of the field is kinetic energy, the electric energy potential.  Around 1862, while lecturing at King’s College, Maxwell calculated that the speed of propagation of an electromagnetic field is approximately that of the speed of light.   Maxwell’s electromagnetic theory of light obviously involved the existence of electric waves in free space, and his followers set themselves the task of experimentally demonstrating the truth of the theory. By 1871, he presented the Remarks on the mathematical classification of physical quantities.

A Wave-Particle Dilemma at the Century End

In 1896 J.J. Thomson performed experiments indicating that cathode rays really were particles, found an accurate value for their charge-to-mass ratio e/m, and found that e/m was independent of cathode material. He made good estimates of both the charge e and the mass m, finding that cathode ray particles, which he called “corpuscles”, had perhaps one thousandth of the mass of the least massive ion known (hydrogen). He further showed that the negatively charged particles produced by radioactive materials, by heated materials, and by illuminated materials, were universal.  In the late 19th century, the Michelson–Morley experiment was performed by Albert Michelson and Edward Morley at what is now Case Western Reserve University. It is generally considered to be the evidence against the theory of a luminiferous aether. The experiment has also been referred to as “the kicking-off point for the theoretical aspects of the Second Scientific Revolution.” Primarily for this work, Albert Michelson was awarded theNobel Prize in 1907.

Wave–particle duality is a theory that proposes that all matter exhibits the properties of not only particles, which have mass, but also waves, which transfer energy. A central concept of quantum mechanics, this duality addresses the inability of classical concepts like “particle” and “wave” to fully describe the behavior of quantum-scale objects. Standard interpretations of quantum mechanics explain this paradox as a fundamental property of the universe, while alternative interpretations explain the duality as an emergent, second-order consequence of various limitations of the observer. This treatment focuses on explaining the behavior from the perspective of the widely used Copenhagen interpretation, in which wave–particle duality serves as one aspect of the concept of complementarity, that one can view phenomena in one way or in another, but not both simultaneously.  Through the work of Max PlanckAlbert EinsteinLouis de BroglieArthur Compton, Niels Bohr, and many others, current scientific theory holds that all particles also have a wave nature (and vice versa).

Beginning in 1670 and progressing over three decades, Isaac Newton argued that the perfectly straight lines of reflection demonstrated light’s particle nature, but Newton’s contemporaries Robert Hooke and Christiaan Huygens—and later Augustin-Jean Fresnel—mathematically refined the wave viewpoint, showing that if light traveled at different speeds in different, refraction could be easily explained. The resulting Huygens–Fresnel principle was supported by Thomas Young‘s discovery of double-slit interference, the beginning of the end for the particle light camp.  The final blow against corpuscular theory came when James Clerk Maxwell discovered that he could combine four simple equations, along with a slight modification to describe self-propagating waves of oscillating electric and magnetic fields. When the propagation speed of these electromagnetic waves was calculated, the speed of light fell out. While the 19th century had seen the success of the wave theory at describing light, it had also witnessed the rise of the atomic theory at describing matter.

Matter and Light

In 1789, Antoine Lavoisier secured chemistry by introducing rigor and precision into his laboratory techniques. By discovering diatomic gases, Avogadro completed the basic atomic theory, allowing the correct molecular formulae of most known compounds—as well as the correct weights of atoms—to be deduced and categorized in a consistent manner. The final stroke in classical atomic theory came when Dimitri Mendeleev saw an order in recurring chemical properties, and created a table presenting the elements in unprecedented order and symmetry.   Chemistry was now an atomic science.

Black-body radiation, the emission of electromagnetic energy due to an object’s heat, could not be explained from classical arguments alone. The equipartition theorem of classical mechanics, the basis of all classical thermodynamic theories, stated that an object’s energy is partitioned equally among the object’s vibrational modes. This worked well when describing thermal objects, whose vibrational modes were defined as the speeds of their constituent atoms, and the speed distribution derived from egalitarian partitioning of these vibrational modes closely matched experimental results. Speeds much higher than the average speed were suppressed by the fact that kinetic energy is quadratic—doubling the speed requires four times the energy—thus the number of atoms occupying high energy modes (high speeds) quickly drops off. Since light was known to be waves of electromagnetism, physicists hoped to describe this emission via classical laws. This became known as the black body problem. The Rayleigh–Jeans law which, while correctly predicting the intensity of long wavelength emissions, predicted infinite total energy as the intensity diverges to infinity for short wavelengths.

The solution arrived in 1900 when Max Planck hypothesized that the frequency of light emitted by the black body depended on the frequency of the oscillator that emitted it, and the energy of these oscillators increased linearly with frequency (according to his constant h, where E = hν). By demanding that high-frequency light must be emitted by an oscillator of equal frequency, and further requiring that this oscillator occupy higher energy than one of a lesser frequency, Planck avoided any catastrophe; giving an equal partition to high-frequency oscillators produced successively fewer oscillators and less emitted light. And as in the Maxwell–Boltzmann distribution, the low-frequency, low-energy oscillators were suppressed by the onslaught of thermal jiggling from higher energy oscillators, which necessarily increased their energy and frequency. Planck had intentionally created an atomic theory of the black body, but had unintentionally generated an atomic theory of light, where the black body never generates quanta of light at a given frequency with energy less than .

In 1905 Albert Einstein took Planck’s black body model in itself and saw a wonderful solution to another outstanding problem of the day: the photoelectric effect, the phenomenon where electrons are emitted from atoms when they absorb energy from light.   Only by increasing the frequency of the light, and thus increasing the energy of the photons, can one eject electrons with higher energy. Thus, using Planck’s constant h to determine the energy of the photons based upon their frequency, the energy of ejected electrons should also increase linearly with frequency; the gradient of the line being Planck’s constant. These results were not confirmed until 1915, when Robert Andrews Millikan, produced experimental results in perfect accord with Einstein’s predictions. While  the energy of ejected electrons reflected Planck’s constant, the existence of photons was not explicitly proven until the discovery of the photon antibunching effect  When Einstein received his Nobel Prizein 1921, it was  for the photoelectric effect, the suggestion of quantized light. Einstein’s “light quanta” represented the quintessential example of wave–particle duality. Electromagnetic radiation propagates following  linear wave equations, but can only be emitted or absorbed as discrete elements, thus acting as a wave and a particle simultaneously.

Radioactivity Changes the Scientific Landscape

The turn of the century also features radioactivity, which later came to the forefront of the activities of World War II, the Manhattan Project, the discovery of the chain reaction, and later – Hiroshima and Nagasaki.

Marie Curie

Marie Curie

 

 

 

Marie Skłodowska-Curie was a Polish and naturalized-French physicist and chemist who conducted pioneering research on radioactivity. She was the first woman to win a Nobel Prize, the only woman to win in two fields, and the only person to win in multiple sciences. She was also the first woman to become a professor at the University of Paris, and in 1995 became the first woman to be entombed on her own merits in the Panthéon in Paris. She shared the 1903 Nobel Prize in Physics with her husband Pierre Curie and with physicist Henri Becquerel. She won the 1911 Nobel Prize in Chemistry.  Her achievements included a theory of radioactivity (a term that she coined, techniques for isolating radioactive isotopes, and the discovery of polonium and radium. She named the first chemical element that she discovered – polonium, which she first isolated in 1898 – after her native country. Under her direction, the world’s first studies were conducted into the treatment of neoplasms using radioactive isotopes. She founded the Curie Institutes in Paris and in Warsaw, which remain major centres of medical research today. During World War I, she established the first military field radiological centres.  Curie died in 1934 due to aplastic anemia brought on by exposure to radiation – mainly, it seems, during her World War I service in mobile X-ray units created by her.

 

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