Bibliographic record
Abstract
PURPOSE OF REVIEW: The implication of the mammalian target of rapamycin (mTOR) complex 1 (mTORC1) in promoting protein synthesis has been well described. Over the past years, several studies revealed that mTORC1 also plays a crucial role in promoting lipid biosynthesis and that such connection could be linked to diseases including obesity, nonalcoholic fatty liver disease (NAFLD), and cancer. Here, we review the mechanisms by which mTORC1 regulates lipid synthesis by focusing on the key signaling events that trigger hepatic de-novo lipogenesis in response to nutrients and insulin. RECENT FINDINGS: mTORC1 promotes lipid synthesis by activating the transcription factor sterol regulatory element binding protein 1 (SREBP-1). Recent studies indicate that mTORC1 regulates SREBP-1 activation at multiple levels. Although mTORC1 was originally shown to be necessary and sufficient to activate SREBP-1 in vitro, new studies indicate that hyperactivation of mTORC1 is insufficient to trigger SREBP-1 activation and lipid biogenesis in vivo. These findings reveal that the molecular connection between mTORC1 and SREBP-1 is more complex than originally envisioned. SUMMARY: The discovery of a connection between mTORC1 and SREBP-1 opens a new chapter in our understanding of the molecular mechanisms regulating de-novo lipogenesis. A better comprehension of these mechanisms is key for the development of new tools to treat NAFLD and its complications.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".