Regulation of mTORC1 and its impact on gene expression at a glance
Bibliographic record
Abstract
The mechanistic (or mammalian) target of rapamycin (mTOR) is a kinase that regulates key cellular functions linked to the promotion of cell growth and metabolism. This kinase, which is part of two protein complexes termed mTOR complex 1 (mTORC1) and 2 (mTORC2), has a fundamental role in coordinating anabolic and catabolic processes in response to growth factors and nutrients. Of the two mTOR complexes, mTORC1 is by far the best characterized. When active, mTORC1 triggers cell growth and proliferation by promoting protein synthesis, lipid biogenesis, and metabolism, and by reducing autophagy. The fact that mTORC1 deregulation is associated with several human diseases, such as type 2 diabetes, cancer, obesity and neurodegeneration, highlights its importance in the maintenance of cellular homeostasis. Over the last years, several groups observed that mTORC1 inhibition, in addition to reducing protein synthesis, deeply affects gene transcription. Here, we review the connections between mTORC1 and gene transcription by focusing on its impact in regulating the activation of specific transcription factors including including STAT3, SREBPs, PPARγ, PPARα, HIF1α, YY1–PGC1α and TFEB. We also discuss the importance of these transcription factors in mediating the effects of mTORC1 on various cellular processes in physiological and pathological contexts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".