Turnover of AU‐rich‐containing mRNAs during stress: a matter of survival
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it