mTORC1-dependent increase in oxidative metabolism in POMC neurons regulates food intake and action of leptin
Bibliographic record
Abstract
Nutrient availability modulates reactive oxygen species (ROS) production in the hypothalamus. In turn, ROS regulate hypothalamic neuronal activity and feeding behavior. The mechanistic target of rapamycin complex 1 (mTORC1) pathway is an important cellular integrator of the action of nutrients and hormones. Here we tested the hypothesis that modulation of mTORC1 activity, particularly in Proopiomelanocortin (POMC)-expressing neurons, mediates the cellular and behavioral effects of ROS. C57BL/6J mice or controls and their knockout (KO) littermates deficient either for the mTORC1 downstream target 70-kDa ribosomal protein S6 kinase 1 (S6K1) or for the mTORC1 component Rptor specifically in POMC neurons (POMC-rptor-KO) were treated with an intracerebroventricular (icv) injection of the ROS hydrogen peroxide (H2O2) or the ROS scavenger honokiol, alone or, respectively, in combination with the mTORC1 inhibitor rapamycin or the mTORC1 activator leptin. Oxidant-related signal in POMC neurons was assessed using dihydroethidium (DHE) fluorescence. Icv administration of H2O2 decreased food intake, while co-administration of rapamycin, whole-body deletion of S6K1, or deletion of rptor in POMC neurons impeded the anorectic action of H2O2. H2O2 also increased oxidant levels in POMC neurons, an effect that hinged on functional mTORC1 in these neurons. Finally, scavenging ROS prevented the hypophagic action of leptin, which in turn required mTORC1 to increase oxidant levels in POMC neurons and to inhibit food intake. Our results demonstrate that ROS and leptin require mTORC1 pathway activity in POMC neurons to increase oxidant levels in POMC neurons and consequently decrease food intake.
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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) | 0.000 | 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) | 0.002 | 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".