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      A metabolic core model elucidates how enhanced utilization of glucose and glutamine, with enhanced glutamine-dependent lactate production, promotes cancer cell growth: The WarburQ effect

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          Abstract

          Cancer cells share several metabolic traits, including aerobic production of lactate from glucose (Warburg effect), extensive glutamine utilization and impaired mitochondrial electron flow. It is still unclear how these metabolic rearrangements, which may involve different molecular events in different cells, contribute to a selective advantage for cancer cell proliferation. To ascertain which metabolic pathways are used to convert glucose and glutamine to balanced energy and biomass production, we performed systematic constraint-based simulations of a model of human central metabolism. Sampling of the feasible flux space allowed us to obtain a large number of randomly mutated cells simulated at different glutamine and glucose uptake rates. We observed that, in the limited subset of proliferating cells, most displayed fermentation of glucose to lactate in the presence of oxygen. At high utilization rates of glutamine, oxidative utilization of glucose was decreased, while the production of lactate from glutamine was enhanced. This emergent phenotype was observed only when the available carbon exceeded the amount that could be fully oxidized by the available oxygen. Under the latter conditions, standard Flux Balance Analysis indicated that: this metabolic pattern is optimal to maximize biomass and ATP production; it requires the activity of a branched TCA cycle, in which glutamine-dependent reductive carboxylation cooperates to the production of lipids and proteins; it is sustained by a variety of redox-controlled metabolic reactions. In a K-ras transformed cell line we experimentally assessed glutamine-induced metabolic changes. We validated computational results through an extension of Flux Balance Analysis that allows prediction of metabolite variations. Taken together these findings offer new understanding of the logic of the metabolic reprogramming that underlies cancer cell growth.

          Author summary

          Hallmarks describing common key events in initiation, maintenance and progression of cancer have been identified. One hallmark deals with rewiring of metabolic reactions required to sustain enhanced cell proliferation. The availability of molecular, mechanistic models of cancer hallmarks will mightily improve optimized personal treatment and new drug discovery. Metabolism is the only hallmark for which it is currently possible to derive large scale mathematical models, which have predictive ability. In this paper, we exploit a constraint-based model of the core metabolism required for biomass conversion of the most relevant nutrients—glucose and glutamine—to clarify the logic of control of cancer metabolism. We newly report that, when available oxygen is not sufficient to fully oxidize available glucose and glutamine carbons–a situation compatible with that observed under normal oxygen conditions in human and in cancer cells growing in vitro—utilization of glutamine by reductive carboxylation and conversion of glucose and glutamine to lactate confer advantage for biomass production. Redox homeostasis can be maintained through the use of different alternative pathways. In conclusion, this paper offers a logic interpretation to the link between metabolic rewiring and enhanced proliferation, which may offer new approaches to targeted drug discovery and utilization.

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          LDHA-Associated Lactic Acid Production Blunts Tumor Immunosurveillance by T and NK Cells.

          Elevated lactate dehydrogenase A (LDHA) expression is associated with poor outcome in tumor patients. Here we show that LDHA-associated lactic acid accumulation in melanomas inhibits tumor surveillance by T and NK cells. In immunocompetent C57BL/6 mice, tumors with reduced lactic acid production (Ldha(low)) developed significantly slower than control tumors and showed increased infiltration with IFN-γ-producing T and NK cells. However, in Rag2(-/-)γc(-/-) mice, lacking lymphocytes and NK cells, and in Ifng(-/-) mice, Ldha(low) and control cells formed tumors at similar rates. Pathophysiological concentrations of lactic acid prevented upregulation of nuclear factor of activated T cells (NFAT) in T and NK cells, resulting in diminished IFN-γ production. Database analyses revealed negative correlations between LDHA expression and T cell activation markers in human melanoma patients. Our results demonstrate that lactic acid is a potent inhibitor of function and survival of T and NK cells leading to tumor immune escape.
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            Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0.

            Over the past decade, a growing community of researchers has emerged around the use of constraint-based reconstruction and analysis (COBRA) methods to simulate, analyze and predict a variety of metabolic phenotypes using genome-scale models. The COBRA Toolbox, a MATLAB package for implementing COBRA methods, was presented earlier. Here we present a substantial update of this in silico toolbox. Version 2.0 of the COBRA Toolbox expands the scope of computations by including in silico analysis methods developed since its original release. New functions include (i) network gap filling, (ii) (13)C analysis, (iii) metabolic engineering, (iv) omics-guided analysis and (v) visualization. As with the first version, the COBRA Toolbox reads and writes systems biology markup language-formatted models. In version 2.0, we improved performance, usability and the level of documentation. A suite of test scripts can now be used to learn the core functionality of the toolbox and validate results. This toolbox lowers the barrier of entry to use powerful COBRA methods.
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              The effects of alternate optimal solutions in constraint-based genome-scale metabolic models.

              Genome-scale constraint-based models of several organisms have now been constructed and are being used for model driven research. A key issue that may arise in the use of such models is the existence of alternate optimal solutions wherein the same maximal objective (e.g., growth rate) can be achieved through different flux distributions. Herein, we investigate the effects that alternate optimal solutions may have on the predicted range of flux values calculated using currently practiced linear (LP) and quadratic programming (QP) methods. An efficient LP-based strategy is described to calculate the range of flux variability that can be present in order to achieve optimal as well as suboptimal objective states. Sample results are provided for growth predictions of E. coli using glucose, acetate, and lactate as carbon substrates. These results demonstrate the extent of flux variability to be highly dependent on environmental conditions and network composition. In addition we examined the impact of alternate optima for growth under gene knockout conditions as calculated using QP-based methods. It was observed that calculations using QP-based methods can show significant variation in growth rate if the flux variability among alternate optima is high. The underlying biological significance and general source of such flux variability is further investigated through the identification of redundancies in the network (equivalent reaction sets) that lead to alternate solutions. Collectively, these results illustrate the variability inherent in metabolic flux distributions and the possible implications of this heterogeneity for constraint-based modeling approaches. These methods also provide an efficient and robust method to calculate the range of flux distributions that can be derived from quantitative fermentation data.
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                Author and article information

                Contributors
                Role: InvestigationRole: MethodologyRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: SoftwareRole: Visualization
                Role: Investigation
                Role: Investigation
                Role: SoftwareRole: Visualization
                Role: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: Software
                Role: ConceptualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                28 September 2017
                September 2017
                : 13
                : 9
                : e1005758
                Affiliations
                [1 ] SYSBIO Centre of Systems Biology, Milano, Italy
                [2 ] Dept of Informatics, Systems and Communication, University Milano-Bicocca, Milano, Italy
                [3 ] Institute of Molecular Bioimaging and Physiology, CNR, Segrate, Milan, Italy
                [4 ] Dept of Biotechnology and Biosciences, University Milano-Bicocca, Milano, Italy
                [5 ] Dept of Statistics and Quantitative Methods, University Milano-Bicocca, Milano, Italy
                [6 ] Dept of Molecular Cell Physiology, Faculty of Earth and Life Sciences, VU University, Amsterdam, The Netherlands
                [7 ] Manchester Centre for Integrative Systems Biology, School of Chemical Engineering and Analytical Science, University of Manchester, Manchester, United Kingdom
                [8 ] Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands
                University of Michigan, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-3090-4823
                http://orcid.org/0000-0003-3520-4022
                Article
                PCOMPBIOL-D-17-00566
                10.1371/journal.pcbi.1005758
                5634631
                28957320
                1bd8afb7-02fa-4040-88da-65f7058479d9
                © 2017 Damiani et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 7 April 2017
                : 1 September 2017
                Page count
                Figures: 6, Tables: 0, Pages: 29
                Funding
                Funded by: MIUR
                Award Recipient :
                This work is supported with FOE funds to SYSBIO Italian Centre of Systems Biology, from the Italian Ministry of Education, Universities and Research (MIUR, http://www.istruzione.it/) - within the Italian Roadmap for ESFRI Research Infrastructures. HVW received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under Grant Agreement No. 311815 (SYNPOL project), the Netherlands Organization for Scientific Research (NWO) in the integrated program of WOTRO (W01.65.324.00/project 4) Science for Global Development as well as by various systems biology grants, including Synpol: EU-FP7 (KBBE.2012.3.4-02 #311815), Corbel: EU-H2020 (NFRADEV-4-2014-2015 #654248), Epipredict: EU-H2020 MSCA-ITN-2014-ETN: Marie Skłodowska-Curie Innovative Training Networks (ITN-ETN) #642691, BBSRC China: BB/J020060/1. LA and MV received funding from Epipredict: (ITN-ETN) #642691, and LA from FLAG-ERA grant ITFoC. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Physical Sciences
                Chemistry
                Chemical Compounds
                Organic Compounds
                Amino Acids
                Acidic Amino Acids
                Glutamine
                Physical Sciences
                Chemistry
                Organic Chemistry
                Organic Compounds
                Amino Acids
                Acidic Amino Acids
                Glutamine
                Biology and Life Sciences
                Biochemistry
                Proteins
                Amino Acids
                Acidic Amino Acids
                Glutamine
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Carbohydrate Metabolism
                Glucose Metabolism
                Physical Sciences
                Chemistry
                Chemical Compounds
                Organic Compounds
                Carbohydrates
                Monosaccharides
                Glucose
                Physical Sciences
                Chemistry
                Organic Chemistry
                Organic Compounds
                Carbohydrates
                Monosaccharides
                Glucose
                Physical Sciences
                Chemistry
                Chemical Elements
                Oxygen
                Physical Sciences
                Chemistry
                Chemical Compounds
                Acids
                Ketones
                Pyruvate
                Medicine and Health Sciences
                Pharmacology
                Pharmacokinetics
                Drug Metabolism
                Biology and Life Sciences
                Biochemistry
                Enzymology
                Enzymes
                Oxidoreductases
                Dehydrogenases
                Biology and Life Sciences
                Biochemistry
                Proteins
                Enzymes
                Oxidoreductases
                Dehydrogenases
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Metabolic Processes
                Citric Acid Cycle
                Custom metadata
                vor-update-to-uncorrected-proof
                2017-10-10
                All data are within the manuscript and supporting information.

                Quantitative & Systems biology
                Quantitative & Systems biology

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