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Record W2008253949 · doi:10.1074/jbc.m413428200

Rb Enhances p160/SRC Coactivator-dependent Activity of Nuclear Receptors and Hormone Responsiveness

2005· article· en· W2008253949 on OpenAlexafffund
Éric Batsché, Julien Desroches, Steve Bilodeau, Yves Gauthier, Jacques Drouin

Bibliographic record

VenueJournal of Biological Chemistry · 2005
Typearticle
Languageen
FieldNeuroscience
TopicNuclear Receptors and Signaling
Canadian institutionsMontreal Clinical Research Institute
FundersLigue Contre le CancerCanadian Institutes of Health ResearchCancer Research Society
KeywordsCoactivatorNuclear receptorTranscription factorNuclear receptor coactivator 3Nuclear receptor coactivator 1Cancer researchCell biologyProto-oncogene tyrosine-protein kinase SrcNuclear receptor coactivator 2BiologyChemistrySignal transductionGeneGenetics

Abstract

fetched live from OpenAlex

The retinoblastoma tumor suppressor protein (Rb) is best known as a repressor of genes involved in cell cycle progression. Rb has also been implicated in activation of transcription, in particular by nuclear receptors (NRs) and by differentiation-related transcription factors, but the relevance of this activity is unclear. We show that Rb and the related proteins p107 and p130 enhance the activity of NRs related to NGFI-B (Nur factors) through direct interactions with NGFI-B and SRC-2. Although recruitment of SRC/p160 coactivators to the NGFI-B AF1 domain is independent of Rb, its presence enhances SRC-dependent transcription. Rb potentiation of SRC coactivators is exerted on a subset (Nur factors, hepatocyte nuclear factor-4 (HNF-4), SF-1, and ER) but not all NRs. The levels of Rb-related proteins modulate hormone responsiveness of the NGFI-B-dependent pituitary proopiomelanocortin gene and HNF-4-dependent transcription during enterocyte differentiation. Increased Rb expression upon cell differentiation may promote differentiated functions, at least in part, by potentiation of NR activity.

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)0.001
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 score0.008
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.033
GPT teacher head0.267
Teacher spread0.234 · 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

Citations43
Published2005
Admission routes2
Has abstractyes

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