mTOR-sensitive translation: Cleared fog reveals more trees
Bibliographic record
Abstract
Translation is fundamental for many biologic processes as it enables cells to rapidly respond to stimuli without requiring de novo mRNA synthesis. The mammalian/mechanistic target of rapamycin (mTOR) is a key regulator of translation. Although mTOR affects global protein synthesis, translation of a subset of mRNAs appears to be exceptionally sensitive to changes in mTOR activity. Recent efforts to catalog these mTOR-sensitive mRNAs resulted in conflicting results. Whereas ribosome-profiling almost exclusively identified 5'-terminal oligopyrimidine (TOP) mRNAs as mTOR-sensitive, polysome-profiling suggested that mTOR also regulates translation of non-TOP mRNAs. This inconsistency was explained by analytical and technical biases limiting the efficiency of ribosome-profiling in detecting mRNAs showing differential translation. Moreover, genome-wide characterization of 5'UTRs of non-TOP mTOR-sensitive mRNAs revealed 2 subsets of transcripts which differ in their requirement for translation initiation factors and biologic functions. We summarize these recent advances and their impact on the understanding of mTOR-sensitive translation.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".