Role of glial cell-line derived neurotropic factor family receptor α2 in the actions of the glucagon-like peptides on the murine intestine
Bibliographic record
Abstract
The intestinal glucagon-like peptides GLP-1 and GLP-2 inhibit intestinal motility, whereas GLP-2 also stimulates growth of the intestinal mucosa. However, the mechanisms of action of these peptides in the intestine remain poorly characterized. To determine the role of the enteric nervous system in the actions of GLP-1 and GLP-2 on the intestine, the glial cell line-derived neurotropic factor family receptor alpha(2) (GFRalpha2) knockout (KO) mouse was employed. The mice exhibited decreased cholinergic staining, as well as reduced mRNA transcripts for substance P-ergic excitatory motoneurons in the enteric nervous system (ENS) (P < 0.05). Examination of parameters of intestinal growth (including small and large intestinal weight and small intestinal villus height, crypt depth, and crypt cell proliferation) demonstrated no differences between wild-type and KO mice in either basal or GLP-2-stimulated mucosal growth. Nonetheless, KO mice exhibited reduced numbers of synaptophysin-positive enteroendocrine cells (P < 0.05), as well as a markedly impaired basal gastrointestinal (GI) transit rate (P < 0.05). Furthermore, acute administration of GLP-1 and GLP-2 significantly inhibited transit rates in wild-type mice (P < 0.05-0.01) but had no effect in GFRalpha2 KO mice. Despite these changes, expression of mRNA transcripts for the GLP receptors was not reduced in the ENS of KO animals, suggesting that GLP-1 and -2 modulate intestinal transit through enhancement of inhibitory input to cholinergic/substance P-ergic excitatory motoneurons. Together, these findings demonstrate a role for GFRalpha2-expressing enteric neurons in the downstream signaling of the glucagon-like peptides to inhibit GI motility, but not in intestinal growth.
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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) | 0.001 | 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) | 0.001 | 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".