The emerging role of pre-messenger RNA splicing in stress responses: Sending alternative messages and silent messengers
Bibliographic record
Abstract
Alternative splicing (AS) of pre-messenger RNAs is a major process contributing to both transcriptome and proteome diversity in various physiological and pathological situations. There is also accumulating evidence that various stresses impact on AS. In particular, recent analyses of the transcriptome reveal large numbers of AS events that are regulated by genotoxic stress inducers like radiations and chemotherapeutic agents. Many AS events have the potential to affect the relative production of protein isoforms with different activities, as shown in the case of several genes involved in apoptosis. There is also increasing evidence that stresses induce "non-productive" splice variants, leading to a decrease in gene expression levels or preventing increases in protein levels despite transcriptional stimulation. This is typically achieved by the production of splice variants that are subject to nonsense-mediated decay. In addition, recent studies suggest that pre-mRNA splicing efficiency or fidelity may be altered by stresses. For example, various genotoxic agents induce multiple exon skipping in MDM2 transcripts, thereby preventing the production of the main p53-ubiquitin ligase and favoring p53 activity in response to genotoxic agents. In terms of mechanisms, stresses can impact on pre-mRNA splicing by inducing post-translational modifications and subcellular redistribution of splicing factors, or by targeting the communication between the splicing and transcription machineries. Altogether, these data suggest that splicing regulatory networks play a key role in the cellular responses triggered by stresses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".