MTOR modulation induces selective perturbations in histone methylation which influence the anti-proliferative effects of mTOR inhibitors
Bibliographic record
Abstract
Summary 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 regulator of cellular metabolism, and epigenetic modifications remains poorly understood. We thus explored the impact of modulating mTOR signaling on histone methylation, a well-known epigenetic modification. Our results showed that mTORC1 activation caused by abrogation of TSC2 increased H3K27me3 but not H3K4me3 or H3K9me3. This appeared to be mediated via the induction of EZH2 protein synthesis, downstream of 4EBPs. Surprisingly, mTOR inhibition also induced H3K27me3 independently of TSC2. This coincided with 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 ability of mTOR inhibitors to induce H3K27me3 influences their anti-proliferative effects. Highlights Paradoxically, both mTOR activation and inhibition induce H3K27me3. The effect of mTOR inhibitors on H3K27me3 are not secondary to cell cycle arrest. H3K27me3 triggered by mTOR suppression coincides with perturbations in EZH1/2 ratio. H3K27me3 impacts on the anti-proliferative effects of mTOR inhibitors.
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) | 0.003 | 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".