MTOR modulation induces selective perturbations in histone methylation which influence the anti-proliferative effects of mTOR inhibitors
Bibliographic record
Abstract
Emerging data suggest a significant cross-talk between metabolic and epigenetic programs. However, the relationship between the mechanistic target of rapamycin (mTOR), which is a pivotal metabolic regulator, and epigenetic modifications remains poorly understood. Our results show that mTORC1 activation caused by the abrogation of its negative regulator tuberous sclerosis complex 2 (TSC2) coincides with increased levels of the histone modification H3K27me3 but not H3K4me3 or H3K9me3. This selective H3K27me3 induction was mediated via 4E-BP-dependent increase in EZH2 protein levels. Surprisingly, mTOR inhibition also selectively induced H3K27me3. This was independent of TSC2, and was paralleled by reduced EZH2 and increased EZH1 protein levels. Notably, the ability of mTOR inhibitors to induce H3K27me3 levels was positively correlated with their anti-proliferative effects. Collectively, our findings demonstrate that both activation and inhibition of mTOR selectively increase H3K27me3 by distinct mechanisms, whereby the induction of H3K27me3 may potentiate the anti-proliferative effects of mTOR inhibitors.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".