Cap-independent regulation of gene expression in apoptosis
Bibliographic record
Abstract
Expression of the proteome is tightly regulated at the level of protein synthesis. Translational control is a critical homeostatic mechanism that allows the cell to rapidly change its phenotype in the face of an intra- and extra-cellular environment in constant flux. It is becoming increasingly clear that when it comes to protein translation during cell stress, all mRNAs are not treated equally. The translation of the majority of mRNAs is compromised during cell stresses that induce programmed cell death such as hypoxia, or DNA damage. However, cellular messages harbouring Internal Ribosome Entry Site elements (IRES) within their 5' untranslated regions are insensitive to stress-induced repression of global translation. Instead, these IRES-containing mRNAs use a poorly understood alternative mechanism of translation that allows continued expression of proteins that are required for the cell to recover from a transient stress or to proceed down the path toward apoptotic death. This review will highlight recent literature that suggests why global translation rates are impaired during stress and apoptosis and how these conditions mediate a switch in the mechanism by which pertinent proteins are synthesized. In addition, recent advances towards our understanding of the physiological role and mechanism of IRES-mediated translation in the context of cell stress-induced apoptosis and human disease will be examined.
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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 | 0.001 |
| 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".