Major involvement of mTOR in the PPARγ-induced stimulation of adipose tissue lipid uptake and fat accretion
Bibliographic record
Abstract
Evidence points to a role of the mammalian target of rapamycin (mTOR) signaling pathway as a regulator of adiposity, yet its involvement as a mediator of the positive actions of peroxisome proliferator-activated receptor (PPAR)γ agonism on lipemia, fat accretion, lipid uptake, and its major determinant lipoprotein lipase (LPL) remains to be elucidated. Herein we evaluated the plasma lipid profile, triacylglycerol (TAG) secretion rates, and adipose tissue LPL-dependent lipid uptake, LPL expression/activity, and expression profile of other lipid metabolism genes in rats treated with the PPARγ agonist rosiglitazone (15 mg/kg/day) in combination or not with the mTOR inhibitor rapamycin (2 mg/kg/day) for 15 days. Rosiglitazone stimulated adipose tissue mTOR complex 1 and AMPK and induced TAG-derived lipid uptake (136%), LPL mRNA/activity (2- to 6-fold), and fat accretion in subcutaneous (but not visceral) white adipose tissue (WAT; 50%) and in brown adipose tissue (BAT; 266%). Chronic mTOR inhibition attenuated the upregulation of lipid uptake, LPL expression/activity, and fat accretion induced by PPARγ activation in both subcutaneous WAT and BAT, which resulted in hyperlipidemia. In contrast, rapamycin did not affect most of the other WAT lipogenic genes upregulated by rosiglitazone. Together these findings demonstrate that mTOR is a major regulator of adipose tissue LPL-mediated lipid uptake and a critical mediator of the hypolipidemic and lipogenic actions of PPARγ activation.
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) | 0.000 | 0.001 |
| 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) | 0.002 | 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".