Glucagon-Like Peptide-2 Directly Regulates Hypothalamic Neurons Expressing Neuropeptides Linked to Appetite Control in Vivo and in Vitro
Bibliographic record
Abstract
Glucagon-like peptide-2 (GLP-2), a proglucagon-derived peptide, has been postulated to affect appetite at the level of the hypothalamus. To gain better insight into this process, a degradation-resistant GLP-2 analog, human (Gly(2))GLP-2(1-33) [h(Gly(2))GLP-2] was intracerebroventricularly injected into mice to examine its action on food and water intake and also activation of hypothalamic anorexigenic α-melanocyte-stimulating hormone/proopiomelanocortin, neurotensin, and orexigenic neuropeptide Y, and ghrelin neurons. Central h(Gly(2))GLP-2 administration significantly suppressed food and water intake with acute weight loss at 2 h. Further, central h(Gly(2))GLP-2 robustly induced c-Fos activation in the hypothalamic arcuate, dorsomedial, ventromedial, paraventricular, and the lateral hypothalamic nuclei. We found differential colocalization of neuropeptides with c-Fos in specific regions of the hypothalamus. To assess whether hypothalamic neuropeptides are directly regulated by GLP-2 in vitro, we used an adult-derived clonal, immortalized hypothalamic cell line, mHypoA-2/30, that endogenously expresses functional GLP-2 receptors (GLP-2R) and two of the feeding-related neuropeptides linked to GLP-2R activation in vivo: neurotensin and ghrelin. Treatment with h(Gly(2))GLP-2 stimulated c-Fos expression and phosphorylation of cAMP response element-binding protein/activating transcription factor-1. In addition, treatment with h(Gly(2))GLP-2 significantly increased neurotensin and ghrelin mRNA transcript levels by 50 and 95%, respectively, at 24 h after treatment in protein kinase A-dependent manner. Taken together, these findings implicate the protein kinase A pathway as the means by which GLP-2 can up-regulate hypothalamic neuropeptide mRNA levels and provide evidence for a link between central GLP-2R activation and specific hypothalamic neuropeptides involved in appetite regulation.
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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) | 0.001 | 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".