The cell stress response: extreme times call for post‐transcriptional measures
Bibliographic record
Abstract
Following cell stress, a wide range of molecular pathways are initiated to orchestrate the stress response and enable adaptation to an environmental or intracellular perturbation. The post-transcriptional regulation strategies adopted during the stress response result in a substantial reorganization of gene expression, designed to prepare the cell for either acclimatization or programmed death, depending on the nature and intensity of the stress. Fundamental to the stress response is a rapid repression of global protein synthesis, commonly mediated by phosphorylation of translation initiation factor eIF2α. Recent structural and biochemical information have added unprecedented detail to our understanding of the molecular mechanisms underlying this regulation. During protein synthesis inhibition, the translation of stress-specific mRNAs is nonetheless enhanced, often through the interaction between RNA-binding proteins and specific RNA regulatory elements. Recent studies investigating the unfolded protein response (UPR) provide some important insights into how posttranscriptional events are spatially and temporally fine-tuned in order to elicit the most appropriate response and to coordinate the transition from an early, acute stage into the chronic state of adaptation. Importantly, cancer cells are known to hi-jack adaptive stress response pathways, particularly the UPR, to survive and proliferate in the unfavorable tumor environment. In this review, we consider the implications of recent research into stress-dependent post-transcriptional regulation and make the case for the exploration of the stress response as a strategy to identify novel targets in the development of cancer therapies. This article is categorized under: RNA in Disease and Development > RNA in Disease RNA Evolution and Genomics > RNA and Ribonucleoprotein Evolution Translation > Translation Mechanisms > Translation Regulation.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".