Rapamycin differentially inhibits S6Ks and 4E-BP1 to mediate cell-type-specific repression of mRNA translation
Bibliographic record
Abstract
The mammalian translational initiation machinery is a tightly controlled system that is composed of eukaryotic initiation factors, and which controls the recruitment of ribosomes to mediate cap-dependent translation. Accordingly, the mTORC1 complex functionally controls this cap-dependent translation machinery through the phosphorylation of its downstream substrates 4E-BPs and S6Ks. It is generally accepted that rapamycin, a specific inhibitor of mTORC1, is a potent translational repressor. Here we report the unexpected discovery that rapamycin's ability to regulate cap-dependent translation varies significantly among cell types. We show that this effect is mechanistically caused by rapamycin's differential effect on 4E-BP1 versus S6Ks. While rapamycin potently inhibits S6K activity throughout the duration of treatment, 4E-BP1 recovers in phosphorylation within 6 h despite initial inhibition (1-3 h). This reemerged 4E-BP1 phosphorylation is rapamycin-resistant but still requires mTOR, Raptor, and mTORC1's activity. Therefore, these results explain how cap-dependent translation can be maintained in the presence of rapamycin. In addition, we have also defined the condition by which rapamycin can control cap-dependent translation in various cell types. Finally, we show that mTOR catalytic inhibitors are effective inhibitors of the rapamycin-resistant phenotype.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".