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Record W2253135181 · doi:10.1016/j.celrep.2015.12.086

The PGC-1α/ERRα Axis Represses One-Carbon Metabolism and Promotes Sensitivity to Anti-folate Therapy in Breast Cancer

2016· article· en· W2253135181 on OpenAlexafffund
Étienne Audet‐Walsh, David Papadopoli, Simon‐Pierre Gravel, Tracey Yee, Gaëlle Bridon, Maxime Caron, Guillaume Bourque, Vincent Giguère, Julie St‐Pierre

Bibliographic record

VenueCell Reports · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsMcGill Genome CentreMcGill University and Génome Québec Innovation CentreMcGill UniversityMcGill University Health Centre
FundersCanadian Institutes of Health ResearchMcGill University
KeywordsPsychological repressionCancer researchMetabolismAMPKCell biologyChemistryBreast cancerBiologyGene expressionCancerBiochemistryGeneEnzymeGenetics

Abstract

fetched live from OpenAlex

Reprogramming of cellular metabolism plays a central role in fueling malignant transformation, and AMPK and the PGC-1α/ERRα axis are key regulators of this process. The intersection of gene-expression and binding-event datasets for breast cancer cells shows that activation of AMPK significantly increases the expression of PGC-1α/ERRα and promotes the binding of ERRα to its cognate sites. Unexpectedly, the data also reveal that ERRα, in concert with PGC-1α, negatively regulates the expression of several one-carbon metabolism genes, resulting in substantial perturbations in purine biosynthesis. This PGC-1α/ERRα-mediated repression of one-carbon metabolism promotes the sensitivity of breast cancer cells and tumors to the anti-folate drug methotrexate. These data implicate the PGC-1α/ERRα axis as a core regulatory node of folate cycle metabolism and further suggest that activators of AMPK could be used to modulate this pathway in cancer.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.175
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.009
GPT teacher head0.236
Teacher spread0.227 · 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 teacher head, 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

Citations90
Published2016
Admission routes2
Has abstractyes

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