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Record W2039586210 · doi:10.4161/cc.27527

The retinoblastoma protein and PML collaborate to organize heterochromatin and silence E2F-responsive genes during senescence

2013· article· en· W2039586210 on OpenAlexafffund
Srikanth Talluri, Frederick A. Dick

Bibliographic record

VenueCell Cycle · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinoids in leukemia and cellular processes
Canadian institutionsChildren’s Health Research InstituteWestern University
FundersCanadian Institutes of Health Research
KeywordsE2FBiologyRetinoblastoma proteinSenescenceGene silencingCell biologyChromatinCell cycleHeterochromatinTranscription factorGeneGenetics

Abstract

fetched live from OpenAlex

Cellular senescence is characterized by silencing of genes involved in DNA replication and cell cycle progression. Stable repression is crucial for preventing inappropriate DNA synthesis and the maintenance of a prolonged senescent state. Many of these genes are targets for E2F transcription factors. The pRB pathway plays a major role in senescence by directly repressing E2Fs and also by regulating chromatin at the promoters of E2F target genes using its LXCXE cleft-dependent interactions. In this study, we sought to investigate the mechanisms by which pRB stably silences E2F target gene transcription during cellular senescence. We report that in mouse embryonic fibroblasts, endogenous promyelocytic leukemia protein (PML) associates with E2F target genes in a pRB LXCXE-dependent manner during HrasV12-induced senescence. Furthermore, using a PML-IV-induced senescence model, we show that the pRB LXCXE binding cleft is essential for PML association with gene promoters, silencing of E2F target genes, and stable cell cycle exit. Binding assays show that pRB can interact with PML specifically during senescence, suggesting that signaling events in senescence regulate assembly of PML and pRB to establish heterochromatin and create a permanent cell cycle arrest.

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 score0.004
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.001
GPT teacher head0.176
Teacher spread0.175 · 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

Citations17
Published2013
Admission routes2
Has abstractyes

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