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Record W2107419050 · doi:10.1242/bio.20123277

Capicua regulates proliferation and survival of RB-deficient cells in <i>Drosophila</i>

2012· article· en· W2107419050 on OpenAlexafffund
Kate Krivy, Mary-Rose Bradley-Gill, Nam-Sung Moon

Bibliographic record

VenueBiology Open · 2012
Typearticle
Languageen
FieldMedicine
TopicCancer-related Molecular Pathways
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsBiologyMutantCell cycleCell biologyCell growthTranscription factorCell cycle checkpointSuppressorE2FApoptosisCancer researchGeneGenetics

Abstract

fetched live from OpenAlex

Mutations in rbf1, the Drosophila homologue of the RB tumour suppressor gene, generate defects in cell cycle control, cell death, and differentiation during development. Previous studies have established that EGFR/Ras activity is an important determinant of proliferation and survival in rbf1 mutant cells. Here, we report that Capicua (Cic), an HMG box transcription factor whose activity is regulated by the EGFR/Ras pathway, regulates both proliferation and survival of RB-deficient cells in Drosophila. We demonstrate that cic mutations allow rbf1 mutant cells to bypass developmentally controlled cell cycle arrest and apoptotic pressure. The cooperative effect between Cic and RBF1 in promoting G1 arrest is mediated, at least in part, by limiting Cyclin E expression. Surprisingly, we also found evidence to suggest that cic mutant cells have decreased levels of reactive oxygen species (ROS), and that the survival of rbf1 mutant cells is affected by changes in ROS levels. Collectively, our results elucidate the importance of the crosstalk between EGFR/Ras and RBF1 in coordinating cell cycle progression and survival.

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 score0.003

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)0.0010.000

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 head0.026
GPT teacher head0.289
Teacher spread0.263 · 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

Citations8
Published2012
Admission routes2
Has abstractyes

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