Roles of the mammalian target of rapamycin, mTOR, in controlling ribosome biogenesis and protein synthesis
Bibliographic record
Abstract
mTORC1 (mammalian target of rapamycin complex 1) is controlled by diverse signals (e.g. hormones, growth factors, nutrients and cellular energy status) and regulates a range of processes including anabolic metabolism, cell growth and cell division. We have studied the impact of inhibiting mTOR on protein synthesis in human cells. Partial inhibition of mTORC1 by rapamycin has only a limited impact on protein synthesis, but inhibiting mTOR kinase activity causes much greater inhibition of protein synthesis. Using a pulsed stable-isotope-labelling technique, we show that the rapamycin and mTOR (mammalian target of rapamycin) kinase inhibitors have differential effects on the synthesis of specific proteins. In particular, the synthesis of proteins encoded by mRNAs that have a 5'-terminal pyrimidine tract is strongly inhibited by mTOR kinase inhibitors. Many of these mRNAs encode ribosomal proteins. mTORC1 also promotes the synthesis of rRNA, although the mechanisms involved remain to be clarified. We found that mTORC1 also regulates the processing of the precursors of rRNA. mTORC1 thus co-ordinates several steps in ribosome biogenesis.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".