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Record W2072454789 · doi:10.1158/1535-7163.mct-08-0192

Anticancer drugs affect the alternative splicing of <i>Bcl-x</i> and other human apoptotic genes

2008· article· en· W2072454789 on OpenAlexaff
Lulzim Shkreta, Ulrike Froehlich, Éric R. Paquet, Johanne Toutant, Sherif Abou Elela, Benoı̂t Chabot

Bibliographic record

VenueMolecular Cancer Therapeutics · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell death mechanisms and regulation
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsAlternative splicingRNA splicingBiologyHeLaCell cultureGeneCancer cellApoptosisCell biologyCancer researchCancerGene isoformGenetics

Abstract

fetched live from OpenAlex

Inducing an apoptotic response is the goal of most current chemotherapeutic interventions against cancer. However, little is known about the effect of chemotherapeutic agents on the alternative splicing of apoptotic genes. Here, we have tested 20 of the mainstream anticancer drugs for their ability to influence the production of Bcl-x splice isoforms. We find that many drugs shift splicing toward the proapoptotic Bcl-x(S) splice variant in 293 cells. The drugs modulate splicing decisions most likely through signaling events because the splicing switch is not compromised by inhibiting de novo protein synthesis or the activity of caspases. Several drugs also shift Bcl-x splicing in cancer cell lines (MCF-7, HeLa, PC-3, PA-1, and SKOV-3), but the set of active drugs varies between cell lines. We also examined the effect of anticancer agents on the alternative splicing of 95 other human apoptotic genes in different cell lines. Almost every drug can alter a subset of alternative splicing events in each cell line. Although drugs of the same class often influence the alternative splicing of the same units in individual cell lines, these units differ considerably between cell lines, indicating cell line-specific differences in the pathways that control splicing.

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 score0.015
Threshold uncertainty score0.455

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 head0.023
GPT teacher head0.288
Teacher spread0.266 · 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

Citations64
Published2008
Admission routes1
Has abstractyes

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