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Record W2506835611 · doi:10.1158/1538-7445.am2016-1031

Abstract 1031: Fumarate hydratase deficiency redirects glucose metabolism of hypoxic cancer cells into the pentose phosphate pathway

2016· article· en· W2506835611 on OpenAlexaff
Luana Schito, Sergio Rey, Judy Pawling, James W. Dennis, Bradly G. Wouters, Marianne Koritzinsky

Bibliographic record

VenueCancer Research · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsBiologyTransfectionCancer researchGlycolysisPentose phosphate pathwayMolecular biologyHypoxia-inducible factorsChemistryBiochemistryMetabolismGene

Abstract

fetched live from OpenAlex

Abstract Hypoxia is a common feature of all solid cancers and strongly correlated with poor prognosis. As an adaptive response to hypoxia, cancer cells reprogram their metabolism by increasing glycolysis and reductive carboxylation at the expense of mitochondrial respiration, a phenomenon orchestrated by the transcription factor hypoxia inducible factor (HIF) -1. Mutations in the gene encoding for the mitochondrial enzyme fumarate hydratase (FH), found in the hereditary leiomyomatosis and renal cell carcinoma (HLRCC) syndrome, lead to a similar phenotype despite the presence of O2, a phenomenon due to normoxic stabilization of HIF-1 (pseudohypoxia). Here, we report for the first time that FH loss-of-function (LOF) redirects glucose metabolism into the pentose phosphate pathway (PPP) in non-RCC cells subjected to severe hypoxia (O2< .02%). We show that this metabolic shift favors the buildup of biosynthetic precursors supporting hypoxic cell growth and proliferation. HCT-116 (colon), HeLa (cervix) and H460 (lung) adenocarcinoma cells were transfected with lentiviral vectors encoding for shRNAs targeting FH. Immunoblot analysis showed that FH LOF did not induce pseudohypoxia in these cells. In contrast, HLRCC-derived UOK262 cells showed accumulation of HIF-1 under normoxia which was reversed upon FH re-introduction. A comprehensive analysis utilizing a RT-qPCR array to profile the mRNA expression of 84 HIF-1 target genes, further confirmed that FH LOF did not result in a pseudohypoxic phenotype in HCT-116 cells. An unbiased analysis of 250 metabolites detected by liquid chromatography-tandem mass spectrometry followed by quantitative enrichment analysis, identified glycolysis and the PPP among the most enriched metabolic pathways in hypoxic FH knockdown cells (P< 5×10-8). Since the PPP provides precursors for synthesis of nucleic acids, we analyzed the effect of FH LOF on cell cycle progression and found an inhibition of hypoxia-induced cell cycle arrest in HCT-116 and HeLa cells. Our study reveals novel insights into the effect of FH loss-of-function in cancer cells and indicates a stark contrast between the pseudohypoxic phenotype described in kidney cancer cell lines obtained from HLRCC patients (i.e, UOK-262) and a HIF- independent mechanism of metabolic rerouting in colon, lung and cervix cancer cell lines. Our data show that FH LOF promotes an anabolic phenotype in hypoxic cancer cells that could be exploited to enhance the therapeutic response targeting this resistant subset of cancer cells. Citation Format: Luana Schito, Sergio Rey, Judy Pawling, James W. Dennis, Bradly G. Wouters, Marianne Koritzinsky. Fumarate hydratase deficiency redirects glucose metabolism of hypoxic cancer cells into the pentose phosphate pathway. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1031.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.332
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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