Turnover of AU‐rich‐containing mRNAs during stress: a matter of survival
Bibliographic record
Abstract
Abstract Cells undergo various adaptive measures in response to stress. Among these are specific changes in the posttranscriptional regulation of various genes. In particular, the turnover of mRNA is modified to either increase or decrease the abundance of certain target messages. Some of the best‐studied mRNAs that are affected by stress are those that contain adenine/uridine‐rich elements (AREs) in their 3′‐untranslated regions. ARE‐containing mRNAs are involved in many important cellular processes and are normally labile, but in response to stress they are differentially regulated through the concerted efforts of ARE‐binding proteins (AUBPs) such as HuR, AUF1, tristetraprolin, BRF1, and KSRP, along with microRNA‐mediated effects. Additionally, the fate of ARE‐containing mRNAs is modified by inducing their localization to stress granules or mRNA processing bodies. Coordination of these various mechanisms controls the turnover of ARE‐containing mRNAs, and thereby enables proper responses to cellular stress. In this review, we discuss how AUBPs regulate their target mRNAs in response to stress, along with the involvement of cytoplasmic granules in this process. WIREs RNA 2011 2 336–347 DOI: 10.1002/wrna.55 This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein–RNA Interactions: Functional Implications RNA Turnover and Surveillance > Turnover/Surveillance Mechanisms RNA Turnover and Surveillance > Regulation of RNA Stability
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".