Dataset Information

Addiction to Coupling of the Warburg Effect with Glutamine Catabolism in Cancer Cells.

ABSTRACT: Metabolic reprogramming is critical to oncogenesis, but the emergence and function of this profound reorganization remain poorly understood. Here we find that cooperating oncogenic mutations drive large-scale metabolic reprogramming, which is both intrinsic to cancer cells and obligatory for the transition to malignancy. This involves synergistic regulation of several genes encoding metabolic enzymes, including the lactate dehydrogenases LDHA and LDHB and mitochondrial glutamic pyruvate transaminase 2 (GPT2). Notably, GPT2 engages activated glycolysis to drive the utilization of glutamine as a carbon source for TCA cycle anaplerosis in colon cancer cells. Our data indicate that the Warburg effect supports oncogenesis via GPT2-mediated coupling of pyruvate production to glutamine catabolism. Although critical to the cancer phenotype, GPT2 activity is dispensable in cells that are not fully transformed, thus pinpointing a metabolic vulnerability specifically associated with cancer cell progression to malignancy.

SUBMITTER: Smith B

PROVIDER: S-EPMC5108179 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Addiction to Coupling of the Warburg Effect with Glutamine Catabolism in Cancer Cells.

Smith Bradley B Schafer Xenia L XL Ambeskovic Aslihan A Spencer Cody M CM Land Hartmut H Munger Joshua J

Cell reports 20161001 3

Metabolic reprogramming is critical to oncogenesis, but the emergence and function of this profound reorganization remain poorly understood. Here we find that cooperating oncogenic mutations drive large-scale metabolic reprogramming, which is both intrinsic to cancer cells and obligatory for the transition to malignancy. This involves synergistic regulation of several genes encoding metabolic enzymes, including the lactate dehydrogenases LDHA and LDHB and mitochondrial glutamic pyruvate transami ...[more]

PMID: 27732857

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Project description:Cancer metabolism has received renewed interest as a potential target for cancer therapy. In this study, we use a multi-scale modeling approach to interrogate the implications of three metabolic scenarios of potential clinical relevance: the Warburg effect, the reverse Warburg effect and glutamine addiction. At the intracellular level, we construct a network of central metabolism and perform flux balance analysis (FBA) to estimate metabolic fluxes; at the cellular level, we exploit this metabolic network to calculate parameters for a coarse-grained description of cellular growth kinetics; and at the multicellular level, we incorporate these kinetic schemes into the cellular automata of an agent-based model (ABM), iDynoMiCS. This ABM evaluates the reaction-diffusion of the metabolites, cellular division and motion over a simulation domain. Our multi-scale simulations suggest that the Warburg effect provides a growth advantage to the tumor cells under resource limitation. However, we identify a non-monotonic dependence of growth rate on the strength of glycolytic pathway. On the other hand, the reverse Warburg scenario provides an initial growth advantage in tumors that originate deeper in the tissue. The metabolic profile of stromal cells considered in this scenario allows more oxygen to reach the tumor cells in the deeper tissue and thus promotes tumor growth at earlier stages. Lastly, we suggest that glutamine addiction does not confer a selective advantage to tumor growth with glutamine acting as a carbon source in the tricarboxylic acid (TCA) cycle, any advantage of glutamine uptake must come through other pathways not included in our model (e.g., as a nitrogen donor). Our analysis illustrates the importance of accounting explicitly for spatial and temporal evolution of tumor microenvironment in the interpretation of metabolic scenarios and hence provides a basis for further studies, including evaluation of specific therapeutic strategies that target metabolism.

Project description:The crucial transmission phase of tuberculosis (TB) relies on infectious sputum and yet cannot easily be modeled. We applied one-step RNA sequencing (RNA-Seq) to sputum from infectious TB patients to investigate the host and microbial environments underlying transmission of Mycobacterium tuberculosis. In such TB sputa, compared to non-TB controls, transcriptional upregulation of inflammatory responses, including an interferon-driven proinflammatory response and a metabolic shift toward glycolysis, was observed in the host. Among all bacterial sequences in the sputum, approximately 1.5% originated from M. tuberculosis, and its transcript abundance was lower in HIV-1-coinfected patients. Commensal bacterial abundance was reduced in the presence of M. tuberculosis infection. Direct alignment to the genomes of the predominant microbiota species also reveals differential adaptation, whereby firmicutes (e.g., streptococci) displayed a nonreplicating phenotype with reduced transcription of ribosomal proteins and reduced activities of ATP synthases, while Neisseria and Prevotella spp. were less affected. The transcriptome of sputum M. tuberculosis more closely resembled aerobic replication and shared similarity in carbon metabolism to in vitro and in vivo models with significant upregulation of genes associated with cholesterol metabolism and downstream propionate detoxification pathways. In addition, and counter to previous reports on intracellular M. tuberculosis infection in vitro, M. tuberculosis in sputum was zinc, but not iron, deprived, and the phoP loci were also significantly downregulated, suggesting that the pathogen is likely extracellular in location. IMPORTANCE Although a few studies have described the microbiome composition of TB sputa based on 16S ribosomal DNA, these studies did not compare to non-TB samples and the nature of the method does not allow any functional inference. This is the first study to apply such technology using clinical specimens and obtained functional transcriptional data on all three aspects simultaneously. We anticipate that an improved understanding on the biological interactions in the respiratory tract may also allow novel interventions, such as those involving microbiome manipulation or inhibitor targeting disease-specific metabolic pathways.

Dataset Information

Addiction to Coupling of the Warburg Effect with Glutamine Catabolism in Cancer Cells.

Publications

Addiction to Coupling of the Warburg Effect with Glutamine Catabolism in Cancer Cells.

Similar Datasets

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