A defective splicing machinery promotes senescence through MDM4 alternative splicing
Bibliographic record
Abstract
Defects in the splicing machinery are implicated in various diseases, including cancer. We observed a general reduction in the expression of spliceosome components and splicing regulators in human cell lines undergoing replicative, stress-induced, and telomere uncapping-induced senescence. Supporting the view that defective splicing contributes to senescence, splicing inhibitors herboxidiene, and pladienolide B induced senescence in normal and cancer cell lines. Furthermore, depleting individual spliceosome components also promoted senescence. All senescence types were associated with an alternative splicing transition from the MDM4-FL variant to MDM4-S. The MDM4 splicing shift was reproduced when splicing was inhibited, and spliceosome components were depleted. While decreasing the level of endogenous MDM4 promoted senescence and cell survival independently of the MDM4-S expression status, cell survival was also improved by increasing MDM4-S. Overall, our work establishes that splicing defects modulate the alternative splicing of MDM4 to promote senescence and cell survival.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".