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U13C-Glutamine and U13C-Glucose Flux Analysis (MFA SiHa B16F10)

ABSTRACT: Oxygenated cancer cells have a high metabolic plasticity as they can use glucose, glutamine and lactate as main substrates to support their bioenergetic and biosynthetic activities. Metabolic optimization requires integration. While glycolysis and glutaminolysis can cooperate to support cellular proliferation, oxidative lactate metabolism opposes glycolysis in oxidative cancer cells engaged in a symbiotic relation with their hypoxic/glycolytic neighbors. However, little is known concerning the relationship between oxidative lactate metabolism and glutamine metabolism. Using SiHa and HeLa human cancer cells, this study reports that intracellular lactate signaling promotes glutamine uptake and metabolism in oxidative cancer cells. It depends on the uptake of extracellular lactate by monocarboxylate transporter 1 (MCT1). Lactate first stabilizes hypoxia-inducible factor-2α (HIF-2α), and HIF-2α then transactivates c-Myc in a pathway that mimics a response to hypoxia. Consequently, lactate-induced c-Myc activation triggers the expression of glutamine transporter ASCT2 and of glutaminase 1 (GLS1), resulting in improved glutamine uptake and catabolism. Elucidation of this metabolic dependence could be of therapeutic interest. First, inhibitors of lactate uptake targeting MCT1 are currently entering clinical trials. They have the potential to indirectly repress glutaminolysis. Second, in oxidative cancer cells, resistance to glutaminolysis inhibition could arise from compensation by oxidative lactate metabolism and increased lactate. Research is published, core data not used in publication but project description is relevant: http://www.tandfonline.com/doi/full/10.1080/15384101.2015.1120930

ORGANISM(S): Human Homo Sapiens

TISSUE(S): Cultured Cells

SUBMITTER: Maureen Kachman

PROVIDER: ST000159 | MetabolomicsWorkbench | Tue Dec 02 00:00:00 GMT 2014

REPOSITORIES: MetabolomicsWorkbench

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Project description:Shlomi2011 - Warburg effect, metabolic model Using a genome-scale human metabolic network model accounting for stoichiometric and enzyme solvent capacity considerations, this model shows that the Warburg effect (a classical hallmark of cancer metabolism) is a direct consequence of the metabolic adaptation of cancer cells to increase biomass production rate. This model is described in the article: Genome-scale metabolic modeling elucidates the role of proliferative adaptation in causing the Warburg effect. Shlomi T, Benyamini T, Gottlieb E, Sharan R, Ruppin E PLoS Computational Biology. 2011; 7(3):e1002018 Abstract: The Warburg effect - a classical hallmark of cancer metabolism - is a counter-intuitive phenomenon in which rapidly proliferating cancer cells resort to inefficient ATP production via glycolysis leading to lactate secretion, instead of relying primarily on more efficient energy production through mitochondrial oxidative phosphorylation, as most normal cells do. The causes for the Warburg effect have remained a subject of considerable controversy since its discovery over 80 years ago, with several competing hypotheses. Here, utilizing a genome-scale human metabolic network model accounting for stoichiometric and enzyme solvent capacity considerations, we show that the Warburg effect is a direct consequence of the metabolic adaptation of cancer cells to increase biomass production rate. The analysis is shown to accurately capture a three phase metabolic behavior that is observed experimentally during oncogenic progression, as well as a prominent characteristic of cancer cells involving their preference for glutamine uptake over other amino acids. This model is hosted on BioModels Database and identified by: MODEL1105100000 . To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:Many cancers rely on glycolytic metabolism to fuel rapid proliferation. This has spurred interest in designing drugs that target tumor glycolysis such as AZD3965, a small molecule inhibitor of Monocarboxylate Transporter 1 (MCT1) currently undergoing Phase I evaluation for cancer treatment. Since MCT1 mediates proton-linked transport of monocarboxylates such as lactate and pyruvate across the plasma membrane (Halestrap and Meredith, 2004), AZD3965 is thought to block tumor growth through disruption of lactate transport and glycolysis. Here we show that MCT1 inhibition impairs proliferation of glycolytic breast cancer cells that express MCT4 via disruption of pyruvate rather than lactate export. We found that MCT1 expression is elevated in glycolytic breast tumors and cell lines as well as in malignant breast and lung tissues. High MCT1 expression predicts poor prognosis in breast and lung cancer patients. Stable knockdown and AZD3965-mediated inhibition of MCT1 promote oxidative metabolism. Acute inhibition of MCT1 reduces pyruvate export rate but does not consistently alter lactate transport or glycolytic flux in breast cancer cells that also express MCT4. Despite the lack of glycolysis impairment, MCT1 loss-of-function decreases breast cancer cell proliferation and blocks growth of mammary fat pad xenograft tumors. Our data suggest that MCT1 expression is elevated in glycolytic cancers to promote pyruvate export, which when inhibited enhances oxidative metabolism and reduces proliferation. This study presents an alternative molecular consequence of MCT1 inhibitors that further supports their use as anti-cancer therapeutics. Since MCT1 levels are elevated in glycolytic and malignant breast tumors, we hypothesized that MCT1 may contribute to the Warburg effect metabolic phenotype. To test this hypothesis, we generated whole genome microarray data from breast cancer cell lines either a) expressing a short hairpin (sh)RNA-mediated stable knockdown of MCT1; or b) treated for 24 hours with an MCT1 inhibitor (AZD3965). Scramble shRNA or DMSO were used as controls, and all conditions were analzed in triplicate. The cell lines used â HS578T, SUM149PT, and SUM159PT â are among the most glycolytic in a panel of 31 breast cancer cell lines.

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U13C-Glutamine and U13C-Glucose Flux Analysis (MFA SiHa B16F10)

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