Alternative polyadenylation and the stress response
Bibliographic record
Abstract
The cellular stress response is a universal mechanism necessary for the survival of all organisms. This multifaceted process is primarily driven by regulation of gene expression to produce an intracellular environment suitable for promoting cell survival and recovery. Posttranscriptional regulatory events are considered as critical mechanisms that modulate core characteristics of mRNA transcripts to promote cell adaptation to various assaults. While the impact of processes such as mRNA splicing, turnover, localization, and translation on the cellular stress response has been extensively studied, recent observations highlight the role of alternative polyadenylation (APA) in response to challenges such as oxidative stress, heat shock, and starvation. The role of APA is comprehensive with far reaching effects on mRNA stability, mRNA localization, and protein coding sequences. Nonetheless, APA remains a relatively unappreciated mode of gene regulation despite its role in regulating key mediators of the stress response. The goal of this review is to provide an overview of the recent advances in our understanding of the various ways by which APA affects cell adaptation to its environment and discuss how a defect in APA could have deleterious consequences on cell survival. This article is categorized under: RNA in Disease and Development > RNA in Disease RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems RNA Processing > 3' End Processing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".