Acute Inhibition of Intestinal Neprilysin Enhances Insulin Secretion via GLP-1 Receptor Signaling in Male Mice
Bibliographic record
Abstract
The peptidase neprilysin modulates glucose homeostasis by cleaving and inactivating insulinotropic peptides, including some produced in the intestine such as glucagon-like peptide-1 (GLP-1). Under diabetic conditions, systemic or islet-selective inhibition of neprilysin enhances beta-cell function through GLP-1 receptor (GLP-1R) signaling. While neprilysin is expressed in intestine, its local contribution to modulation of beta-cell function remains unknown. We sought to determine whether acute selective pharmacological inhibition of intestinal neprilysin enhanced glucose-stimulated insulin secretion under physiological conditions, and whether this effect was mediated through GLP-1R. Lean chow-fed Glp1r+/+ and Glp1r-/- mice received a single oral low dose of the neprilysin inhibitor thiorphan or vehicle. To confirm selective intestinal neprilysin inhibition, neprilysin activity in plasma and intestine (ileum and colon) was assessed 40 minutes after thiorphan or vehicle administration. In a separate cohort of mice, an oral glucose tolerance test was performed 30 minutes after thiorphan or vehicle administration to assess glucose-stimulated insulin secretion. Systemic active GLP-1 levels were measured in plasma collected 10 minutes after glucose administration. In both Glp1r+/+ and Glp1r-/- mice, thiorphan inhibited neprilysin activity in ileum and colon without altering plasma neprilysin activity or active GLP-1 levels. Further, thiorphan significantly increased insulin secretion in Glp1r+/+ mice, whereas it did not change insulin secretion in Glp1r-/- mice. In conclusion, under physiological conditions, acute pharmacological inhibition of intestinal neprilysin increases glucose-stimulated insulin secretion in a GLP-1R-dependent manner. Since intestinal neprilysin modulates beta-cell function, strategies to inhibit its activity specifically in the intestine may improve beta-cell dysfunction in type 2 diabetes.
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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 | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".