Mediobasal hypothalamic overexpression of DEPTOR protects against high-fat diet-induced obesity
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: The mechanistic target of rapamycin (mTOR) is a serine-threonine kinase that functions into distinct protein complexes (mTORC1 and mTORC2) that regulate energy homeostasis. DEP-domain containing mTOR-interacting protein (DEPTOR) is part of these complexes and is known to dampen mTORC1 function, consequently reducing mTORC1 negative feedbacks and promoting insulin signaling and Akt/PKB activation in several models. Recently, we observed that DEPTOR is expressed in several structures of the brain including the mediobasal hypothalamus (MBH), a region that regulates energy balance. Whether DEPTOR in the MBH plays a functional role in regulating energy balance and hypothalamic insulin signaling has never been tested. METHODS: We have generated a novel conditional transgenic mouse model based on the Cre-LoxP system allowing targeted overexpression of DEPTOR. Mice overexpressing DEPTOR in the MBH were subjected to a metabolic phenotyping and MBH insulin signaling was evaluated. RESULTS: We first report that systemic (brain and periphery) overexpression of DEPTOR prevents high-fat diet-induced obesity, improves glucose metabolism and protects against hepatic steatosis. These phenotypes were associated with a reduction in food intake and feed efficiency and an elevation in oxygen consumption. Strikingly, specific overexpression of DEPTOR in the MBH completely recapitulated these phenotypes. DEPTOR overexpression was associated with an increase in hypothalamic insulin signaling, as illustrated by elevated Akt/PKB activation. CONCLUSION: Altogether, these results support a role for MBH DEPTOR in the regulation of energy balance and metabolism.
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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) | 0.000 | 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 | 0.001 |
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".