Enteric neural pathways mediate the anti-inflammatory actions of glucagon-like peptide 2
Bibliographic record
Abstract
Glucagon-like peptide-2 (GLP-2) is an important regulator of nutritional absorptive capacity with anti-inflammatory actions. We hypothesized that GLP-2 reduces intestinal mucosal inflammation by activation of vasoactive intestinal polypeptide (VIP) neurons of the submucosal plexus. Ileitis or colitis was induced in rats by injection of trinitrobenzene sulfonic acid (TNBS), or colitis was induced by administration of dextran sodium sulfate (DSS) in drinking water. Subsets of animals received (1-33)-GLP-2 (50 mug/kg sc bid) either immediately or 2 days after the establishment of inflammation and were followed for 3-5 days. The involvement of VIP neurons was assessed by concomitant administration of GLP-2 and the VIP antagonist [Lys(1)-Pro(2,5)-Arg(3,4)-Tyr(6)]VIP and by immunohistochemical labeling of GLP-2-activated neurons. In all models, GLP-2 treatment, whether given immediately or delayed until inflammation was established, resulted in significant improvements in animal weights, mucosal inflammation indices (myeloperoxidase levels, histological mucosal scores), and reduced levels of inflammatory cytokines (IFN-gamma, TNF-alpha, IL-1beta) and inducible nitric oxide synthase, with increased levels of IL-10 in TNBS ileitis and DSS colitis. Reduced rates of crypt cell proliferation and of apoptosis within crypts in inflamed tissues were also noted with GLP-2 treatment. These effects were abolished with coadministration of GLP-2 and the VIP antagonist. GLP-2 was shown to activate neurons and to increase the number of cells expressing VIP in the submucosal plexus of the ileum. These findings suggest that GLP-2 acts as an anti-inflammatory agent through activation of enteric VIP neurons, independent of proliferative effects. They support further studies to examine the role of neural signaling in the regulation of intestinal inflammation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".