Glucagon-like peptide 2 induces vasoactive intestinal polypeptide expression in enteric neurons via phophatidylinositol 3-kinase-γ signaling
Bibliographic record
Abstract
Glucagon-like peptide 2 (GLP-2) is an enteroendocrine hormone trophic for intestinal mucosa; it has been shown to increase enteric neuronal expression of vasoactive intestinal polypeptide (VIP) in vivo. We hypothesized that GLP-2 would regulate VIP expression in enteric neurons via a phosphatidylinositol-3 kinase-γ (PI3Kγ) pathway. The mechanism of action of GLP-2 was investigated using primary cultures derived from the submucosal plexus (SMP) of the rat and mouse colon. GLP-2 (10(-8) M) stimulation for 24 h increased the proportion of enteric neurons expressing VIP (GLP-2: 40 ± 6% vs. control: 22 ± 5%). GLP-2 receptor expression was identified by immunohistochemistry on neurons (HuC/D+) and glial cells (GFAP+) but not on smooth muscle or fibroblasts in culture. Over 1-4 h, GLP-2 stimulation of SMP increased phosphorylated Akt/Akt ratios 6.1-fold, phosphorylated ERK/ERK 2.5-fold, and p70S6K 2.2-fold but did not affect intracellular cAMP. PI3Kγ gene deletion or pharmacological blockade of PI3Kγ, mammalian target of rapamycin (mTOR), and MEK/ERK pathways blocked the increase in VIP expression by GLP-2. GLP-2 increased the expression of growth factors and their receptors in SMP cells in culture [IGF-1r (3.2-fold increase), EGFr (5-fold), and ErbB-2-4r (6- to 7-fold)] and ligands [IGF-I (1.5-fold), amphiregulin (2.5-fold), epiregulin (3.2-fold), EGF (7.5-fold), heparin-bound EGF (2.0-fold), β-cellulin (50-fold increase), and neuregulins 2-4 (300-fold increase) (by qRT-PCR)]. We conclude that GLP-2 acts on enteric neurons and glial cells in culture via a PI3Kγ/Akt pathway, stimulating neuronal differentiation via mTOR and ERK pathways, and expression of receptors and ligands for the IGF-I and ErbB pathways.
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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) | 0.001 | 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".