Regulation of food intake by astrocytes in the brainstem dorsal vagal complex
Bibliographic record
Abstract
A role for glial cells in brain circuits controlling feeding has begun to be identified with hypothalamic astrocyte signaling implicated in regulating energy homeostasis. The nucleus of the solitary tract (NTS), within the brainstem dorsal vagal complex (DVC), integrates vagal afferent information from the viscera and plays a role in regulating food intake. We hypothesized that astrocytes in this nucleus respond to, and influence, food intake. Mice fed high-fat chow for 12 hr during the dark phase showed NTS astrocyte activation, reflected in an increase in the number (65%) and morphological complexity of glial-fibrillary acidic protein (GFAP)-immunoreactive cells adjacent to the area postrema (AP), compared to control chow fed mice. To measure the impact of astrocyte activation on food intake, we delivered designer receptors exclusively activated by designer drugs (DREADDs) to DVC astrocytes (encompassing NTS, AP, and dorsal motor nucleus of the vagus) using an adeno-associated viral (AAV) vector (AAV-GFAP-hM3Dq_mCherry). Chemogenetic activation with clozapine-N-oxide (0.3 mg/kg) produced in greater morphological complexity in astrocytes and reduced dark-phase feeding by 84% at 4 hr postinjection compared with vehicle treatment. hM3Dq-activation of DVC astrocytes also reduced refeeding after an overnight fast (71% lower, 4 hr postinjection) when compared to AAV-GFAP-mCherry expressing control mice. DREADD-mediated astrocyte activation did not impact locomotion. hM3Dq activation of DVC astrocytes induced c-FOS in neighboring neuronal feeding circuits (including in the parabrachial nucleus). This indicates that NTS astrocytes respond to acute nutritional excess, are involved in the integration of peripheral satiety signals, and can reduce food intake when activated.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".