Stable isotope-labelling analysis of the impact of inhibition of the mammalian target of rapamycin on protein synthesis
Bibliographic record
Abstract
mTORC1 [mTOR (mammalian target of rapamycin) complex 1] regulates diverse cell functions. mTORC1 controls the phosphorylation of several proteins involved in mRNA translation and the translation of specific mRNAs, including those containing a 5'-TOP (5'-terminal oligopyrimidine). To date, most of the proteins encoded by known 5'-TOP mRNAs are proteins involved in mRNA translation, such as ribosomal proteins and elongation factors. Rapamycin inhibits some mTORC1 functions, whereas mTOR-KIs (mTOR kinase inhibitors) interfere with all of them. mTOR-KIs inhibit overall protein synthesis more strongly than rapamycin. To study the effects of rapamycin or mTOR-KIs on synthesis of specific proteins, we applied pSILAC [pulsed SILAC (stable isotope-labelling with amino acids in cell culture)]. Our results reveal, first, that mTOR-KIs and rapamycin differentially affect the synthesis of many proteins. Secondly, mTOR-KIs inhibit the synthesis of proteins encoded by 5'-TOP mRNAs much more strongly than rapamycin does, revealing that these mRNAs are controlled by rapamycin-insensitive outputs from mTOR. Thirdly, the synthesis of certain other proteins shows a similar pattern of inhibition. Some of them appear to be encoded by 'novel' 5'-TOP mRNAs; they include proteins which, like known 5'-TOP mRNA-encoded proteins, are involved in protein synthesis, whereas others are enzymes involved in intermediary or anabolic metabolism. These results indicate that mTOR signalling may promote diverse biosynthetic processes through the translational up-regulation of specific mRNAs. Lastly, a SILAC-based approach revealed that, although rapamycin and mTOR-KIs have little effect on general protein stability, they stabilize proteins encoded by 5'-TOP mRNAs.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".