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Record W2551428756 · doi:10.1016/j.celrep.2016.10.071

SRSF10 Connects DNA Damage to the Alternative Splicing of Transcripts Encoding Apoptosis, Cell-Cycle Control, and DNA Repair Factors

2016· article· en· W2551428756 on OpenAlexafffund
Lulzim Shkreta, Johanne Toutant, Mathieu Durand, James L. Manley, Benoı̂t Chabot

Bibliographic record

VenueCell Reports · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsUniversité de Sherbrooke
FundersNational Institute of General Medical SciencesCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsRNA splicingDNA damageAlternative splicingCell biologyDNA repairBiologyDNAMolecular biologyCell cycleRNAGeneticsMessenger RNAApoptosisGene

Abstract

fetched live from OpenAlex

RNA binding proteins and signaling components control the production of pro-death and pro-survival splice variants of Bcl-x. DNA damage promoted by oxaliplatin increases the level of pro-apoptotic Bcl-xS in an ATM/CHK2-dependent manner, but how this shift is enforced is not known. Here, we show that in normally growing cells, when the 5' splice site of Bcl-xS is largely repressed, SRSF10 partially relieves repression and interacts with repressor hnRNP K and stimulatory hnRNP F/H proteins. Oxaliplatin abrogates the interaction of SRSF10 with hnRNP F/H and decreases the association of SRSF10 and hnRNP K with the Bcl-x pre-mRNA. Dephosphorylation of SRSF10 is linked with these changes. A broader analysis reveals that DNA damage co-opts SRSF10 to control splicing decisions in transcripts encoding components involved in DNA repair, cell-cycle control, and apoptosis. DNA damage therefore alters the interactions between splicing regulators to elicit a splicing response that determines cell fate.

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 score0.003
Threshold uncertainty score0.412

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 head0.010
GPT teacher head0.242
Teacher spread0.231 · 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

Citations99
Published2016
Admission routes2
Has abstractyes

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