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Proteomic analysis reveals Warburg effect and anomalous metabolism of glutamine in pancreatic cancer cells.

ABSTRACT: In this present work, we characterized the proteomes of pancreatic ductal adenocarcinoma (PDAC) cell line PANC-1 and normal pancreatic duct cells by mass spectrometry using LTQ-Orbitrap and identified more than 1700 proteins from each sample. On the basis of the spectra count label-free quantification approach, we identified a large number of differentially expressed metabolic enzymes and proteins involved in cytoskeleton, cell adhesion, transport, transcription, translation, and cell proliferation as well. The data demonstrated that metabolic pathways were altered in PANC-1, consistent with the Warburg effect. In addition, the comparative MS analysis unveiled anomalous metabolism of glutamine, suggesting that glutamine was largely consumed as a nitrogen donor in nucleotide and amino acid biosynthesis in PANC-1. Our analysis provides a potentially comprehensive picture of metabolism in PANC-1, which may serve as the basis of new diagnostics and treatment of PDAC.

SUBMITTER: Zhou W

PROVIDER: S-EPMC5564318 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature

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Proteomic analysis reveals Warburg effect and anomalous metabolism of glutamine in pancreatic cancer cells.

Zhou Weidong W Capello Michela M Fredolini Claudia C Racanicchi Leda L Piemonti Lorenzo L Liotta Lance A LA Novelli Francesco F Petricoin Emanuel F EF

Journal of proteome research 20111117 2

In this present work, we characterized the proteomes of pancreatic ductal adenocarcinoma (PDAC) cell line PANC-1 and normal pancreatic duct cells by mass spectrometry using LTQ-Orbitrap and identified more than 1700 proteins from each sample. On the basis of the spectra count label-free quantification approach, we identified a large number of differentially expressed metabolic enzymes and proteins involved in cytoskeleton, cell adhesion, transport, transcription, translation, and cell proliferat ...[more]

PMID: 22050456

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Project description:BackgroundAlterations on glucose consumption and biosynthetic activity of amino acids, lipids and nucleotides are metabolic changes for sustaining cell proliferation in cancer cells. Irrevocable evidence of this fact is the Warburg effect which establishes that cancer cells prefers glycolysis over oxidative phosphorylation to generate ATP. Regulatory action over metabolic enzymes has opened a new window for designing more effective anti-cancer treatments. This enterprise is not trivial and the development of computational models that contribute to identifying potential enzymes for breaking the robustness of cancer cells is a priority.Methodology/principal findingsThis work presents a constraint-base modeling of the most experimentally studied metabolic pathways supporting cancer cells: glycolysis, TCA cycle, pentose phosphate, glutaminolysis and oxidative phosphorylation. To evaluate its predictive capacities, a growth kinetics study for Hela cell lines was accomplished and qualitatively compared with in silico predictions. Furthermore, based on pure computational criteria, we concluded that a set of enzymes (such as lactate dehydrogenase and pyruvate dehydrogenase) perform a pivotal role in cancer cell growth, findings supported by an experimental counterpart.Conclusions/significanceAlterations on metabolic activity are crucial to initiate and sustain cancer phenotype. In this work, we analyzed the phenotype capacities emerged from a constructed metabolic network conformed by the most experimentally studied pathways sustaining cancer cell growth. Remarkably, in silico model was able to resemble the physiological conditions in cancer cells and successfully identified some enzymes currently studied by its therapeutic effect. Overall, we supplied evidence that constraint-based modeling constitutes a promising computational platform to: 1) integrate high throughput technology and establish a crosstalk between experimental validation and in silico prediction in cancer cell phenotype; 2) explore the fundamental metabolic mechanism that confers robustness in cancer; and 3) suggest new metabolic targets for anticancer treatments. All these issues being central to explore cancer cell metabolism from a systems biology perspective.

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.

Dataset Information

Proteomic analysis reveals Warburg effect and anomalous metabolism of glutamine in pancreatic cancer cells.

Publications

Proteomic analysis reveals Warburg effect and anomalous metabolism of glutamine in pancreatic cancer cells.

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