Inhibiting endocannabinoid biosynthesis: a novel approach to the treatment of constipation
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Endocannabinoids are a family of lipid mediators involved in the regulation of gastrointestinal (GI) motility. The expression, localization and function of their biosynthetic enzymes in the GI tract are not well understood. Here, we examined the expression, localization and function of the enzyme diacylglycerol lipase-α (DAGLα), which is involved in biosynthesis of the endocannabinoid 2-arachidonoylglycerol (2-AG). EXPERIMENTAL APPROACH: Cannabinoid CB1 receptor-deficient, wild-type control and C3H/HeJ mice, a genetically constipated strain, were used. The distribution of DAGLα in the enteric nervous system was examined by immunohistochemistry. Effects of the DAGL inhibitors, orlistat and OMDM-188 on pharmacologically induced GI hypomotility were assessed by measuring intestinal contractility in vitro and whole gut transit or faecal output in vivo. Endocannabinoid levels were measured by mass spectrometry. KEY RESULTS: DAGLα was expressed throughout the GI tract. In the intestine, unlike DAGLβ, DAGLα immunoreactivity was prominently expressed in the enteric nervous system. In the myenteric plexus, it was colocalized with the vesicular acetylcholine transporter in cholinergic nerves. In normal mice, inhibiting DAGL reversed both pharmacologically reduced intestinal contractility and pharmacologically prolonged whole gut transit. Moreover, inhibiting DAGL normalized faecal output in constipated C3H/HeJ mice. In colons incubated with scopolamine, 2-AG was elevated while inhibiting DAGL normalized 2-AG levels. CONCLUSIONS AND IMPLICATIONS: DAGLα was expressed in the enteric nervous system of mice and its inhibition reversed slowed GI motility, intestinal contractility and constipation through 2-AG and CB1 receptor-mediated mechanisms. Our data suggest that DAGLα inhibitors may be promising candidates for the treatment of constipation.
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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".