Activation of Arcuate nucleus Glucagon-like Peptide-1 receptor-expressing neurons suppresses food intake
Bibliographic record
Abstract
Abstract BackgroundCentral nervous system (CNS) control of metabolism plays a pivotal role in maintaining energy balance. In the brain, Glucagon-like peptide 1 (GLP-1), encoded by the proglucagon ‘Gcg’ gene, produced in a distinct population of neurons in the nucleus tractus solitarius (NTS), has been shown to regulate feeding behavior leading to the suppression of appetite. However, neuronal networks that mediate endogenous GLP-1 action in the CNS on feeding and energy balance are not well understood. This is mainly due to the presence of diverse neuronal subtypes and complex central neuronal connectivity. Results We systematically analyzed the distribution of GLP-1R-expressing neurons and axonal projections of NTSGcg proglucagon expressing neurons in the mouse brain. GLP-1R neurons were found to be broadly distributed in the brain and specific forebrain regions, particularly the hypothalamus, including the arcuate nucleus of the hypothalamus (ARC), received dense NTSGcg neuronal projections. For this reason, the impact of GLP-1 signaling in the ARC, a brain region known to regulate energy homeostasis and feeding behavior was examined. Application of GLP-1R specific agonist Exendin-4 (Exn-4) enhanced the ARC pro-opiomelanocortin (POMC) neuronal population’s action potential firing frequency and miniature excitatory postsynaptic spontaneous currents amplitude. Using a chemogenetic approach to activate the ARC GLP-1R neurons by using Cre-dependent hM3Dq AAV in the GLP-1R-ires-Cre mice, we established that acute activation of the ARC GLP-1R neurons significantly suppressed food intake but did not affect glucose homeostasis. ConclusionsThese results highlight the importance of central GLP-1 signaling and in particular neurons within the ARC that express GLP-1R that upon activation, regulate feeding behavior.
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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".