Muscarinic Receptors Control Postprandial Release of Glucagon-Like Peptide-1: In Vivo and in Vitro Studies in Rats
Bibliographic record
Abstract
Plasma levels of glucagon-like peptide-1 (GLP-1) rise rapidly after nutrient ingestion through an indirect mechanism triggered from the proximal intestine and involving the vagus nerve that stimulates the L cell in the distal gut. The role of muscarinic receptors in this pathway was thus investigated using the anesthetized rat and fetal rat intestinal cells (FRIC) in culture. GLP-1 secretion from the distal gut increased 5-fold after 3 ml corn oil were placed into the proximal duodenum (P < 0.001). Atropine (a nonspecific muscarinic receptor antagonist) completely inhibited fat-induced GLP-1 secretion in vivo (P < 0.01). Pirenzepine (an M1 muscarinic receptor antagonist) also inhibited fat-induced GLP-1 secretion in vivo, by 91 +/- 6% (P < 0.01). Gallamine (an M2 muscarinic receptor antagonist) and 4-diphenylacetoxy-N-methylpiperidine (an M3 muscarinic receptor antagonist) had no effect. Incubating FRIC cultures with bethanechol (a muscarinic receptor agonist) stimulated GLP-1 secretion to 200 +/- 22% of control (P < 0.01). Pirenzepine and gallamine significantly inhibited bethanechol-stimulated GLP-1 secretion, by 96 +/- 12% and 98 +/- 8%, respectively (P < 0.01). Unexpectedly, 4-diphenylacetoxy-N-methylpiperidine stimulated GLP-1 secretion by FRIC cells, to 324 +/- 52% of the control value (P < 0.01). Double immunofluorescent staining using GLP-1 and M1, M2, and M3 muscarinic receptor antibodies showed expression of the three subtypes of muscarinic receptors by the L cells in rat ileal sections and FRIC cultures. These results demonstrate the role of M1 muscarinic receptors expressed by L cells in the control of postprandial secretion of GLP-1. M2 muscarinic receptors also seem to play a role in controlling GLP-1 secretion by fetal, but not adult, L cells.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".