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Record W2589827948 · doi:10.1002/stem.2576

p107 Determines a Metabolic Checkpoint Required for Adipocyte Lineage Fates

2017· article· en· W2589827948 on OpenAlexafffund
Deanna P. Porras, Maryam Abbaszadeh, Debasmita Bhattacharya, Ninoschka C. D’Souza, Nareh Rinei Edjiu, Christopher G. R. Perry, Anthony Scimè

Bibliographic record

VenueStem Cells · 2017
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsYork UniversityUniversity Health Network
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyProgenitor cellCell biologyEmbryonic stem cellReprogrammingCell fate determinationProgenitorStem cellMitochondrionCellular differentiationGlycolysisCellGeneticsBiochemistryMetabolismTranscription factorGene

Abstract

fetched live from OpenAlex

We show that the transcriptional corepressor p107 orchestrates a metabolic checkpoint that determines adipocyte lineage fates for non-committed progenitors. p107 accomplishes this when stem cell commitment would normally occur in growth arrested cells. p107-deficient embryonic progenitors are characterized by a metabolic state resembling aerobic glycolysis that is necessary for their pro-thermogenic fate. Indeed, during growth arrest they have a reduced capacity for NADH partitioning between the cytoplasm and mitochondria. Intriguingly, this occurred despite an increase in the capacity for mitochondrial oxidation of non-glucose substrates. The significance of metabolic reprogramming is underscored by the disruption of glycolytic capacities in p107-depleted progenitors that reverted their fates from pro-thermogenic to white adipocytes. Moreover, the manipulation of glycolytic capacity on nonspecified embryonic and adult progenitors forced their beige fat commitment. These innovative findings introduce a new approach to increase pro-thermogenic adipocytes based on simply promoting aerobic glycolysis to manipulate nonspecified progenitor fate decisions. Stem Cells 2017;35:1378-1391.

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)0.000
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 score0.350
Threshold uncertainty score0.817

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.049
GPT teacher head0.316
Teacher spread0.267 · 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

Citations9
Published2017
Admission routes2
Has abstractyes

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