Monoacylglycerol as a Metabolic Coupling Factor in Glucose-Stimulated Insulin Secretion
Bibliographic record
Abstract
Glycerolipid/free fatty acid cycle is an essential component of the lipid amplification pathway of glucose stimulated insulin secretion (GSIS) by β-cells. However, little is known about the lipid metabolic coupling factor(s) involved in this process. We now provide evidence that monoacylglycerol (MAG) acts as a metabolic coupling factor in GSIS. In rat islets and INS832/13 β-cells MAG levels increase in the presence of high glucose concentration. Inhibition of the major membrane-bound MAG hydrolase, ABHD6, in INS832/13 cells and islets with WWL70 leads to accumulation of MAG with concomitant increase in GSIS. The predominant MAG species elevated are the saturated long chain MAGs 1-stearoylglycerol (C18:0) and 1-palmitoylglycerol (C16:0), which strongly potentiate GSIS in vitro, unlike monounsaturated and polyunsaturated MAGs. Overexpression and RNAi-knockdown of ABHD6 in INS832/13 cells, resulted in decreased and increased GSIS, respectively. Administration of WWL70 (i.p.) in normal CD-1 mice enhance GSIS, and it greatly improves glucose tolerance by increasing insulin secretion in the low-dose streptozotocin type 2 diabetes mouse model. We also show that MAGs can bind the C1-domain of the exocytotic effector protein, Munc13-1 more efficiently than diacylglycerol, and as efficiently as the phorbol ester PMA, an established activator of Munc13-1. Collectively, the results provide strong evidence that the lipid amplification arm of GSIS in β-cells is mediated by MAGs that activate insulin exocytosis via Munc 13-1. ABHD6 is the major MAG hydrolase in the ß-cell and is a new target for the development of anti-diabetic drugs.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".