The DNA Damage Response Pathway Regulates the Alternative Splicing of the Apoptotic Mediator Bcl-x
Bibliographic record
Abstract
Alternative splicing often produces effectors with opposite functions in apoptosis. Splicing decisions must therefore be tightly connected to stresses, stimuli, and pathways that control cell survival and cell growth. We have shown previously that PKC signaling prevents the production of proapoptotic Bcl-x(S) to favor the accumulation of the larger antiapoptotic Bcl-x(L) splice variant in 293 cells. Here we show that the genotoxic stress induced by oxaliplatin elicits an ATM-, CHK2-, and p53-dependent splicing switch that favors the production of the proapoptotic Bcl-x(S) variant. This DNA damage-induced splicing shift requires the activity of protein-tyrosine phosphatases. Interestingly, the ATM/CHK2/p53/tyrosine phosphatases pathway activated by oxaliplatin regulates Bcl-x splicing through the same regulatory sequence element (SB1) that receives signals from the PKC pathway. Convergence of the PKC and DNA damage signaling routes may control the abundance of a key splicing repressor because SB1-mediated repression is lost when protein synthesis is impaired but is rescued by blocking proteasome-mediated protein degradation. The SB1 splicing regulatory module therefore receives antagonistic signals from the PKC and the p53-dependent DNA damage response pathways to control the balance of pro- and antiapoptotic Bcl-x splice variants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".