Glucagon-Like Peptide 1 Regulates Sequential and Compound Exocytosis in Pancreatic Islet β-Cells
Bibliographic record
Abstract
Glucagon-like peptide 1 (GLP-1) has been postulated to potentiate insulin secretion by cAMP-mediated enhancement of mobilization and priming of secretory granules, but the precise exocytic events are unknown. We used epi-fluorescent microscopy of the fluorescent dye FM1-43, which incorporates into the plasma membrane and the exocytosing secretory granules (appearing as plasma membrane hotspots). KCl evoked exocytosis of 1.8 +/- 0.5 hotspots/rat beta-cell at the cell periphery, 82% of which are single transient increases of low amplitudes (151 +/- 7%), suggesting single secretory granule exocytosis; and the remaining 18% are stepwise increases in plasma membrane hotspots with higher amplitudes (170 +/- 9%), suggesting sequential secretory granule to secretory granule exocytic fusions. Addition of GLP-1 increased the hotspots to 6.0 +/- 0.7/beta-cell and exhibited a larger number of stepwise (41%) than transient (10%) increases with higher amplitudes of 259 +/- 19 and 278 +/- 23%, respectively. More interestingly, GLP-1 also evoked a robust and sustained pattern (49%) with even higher amplitudes of 354 +/- 18%, which are likely accelerated sequential secretory granule-secretory granule fusions. Electron microscopy studies collaborated with these imaging results, showing that GLP-1 increased the number of docked secretory granules at the plasma membrane and also increased the number of events showing direct contact of oncoming secretory granules with secretory granules undergoing exocytosis. We conclude that the potentiation of insulin secretion by GLP-1 is contributed by the mobilization of more insulin secretory granules to dock at the plasma membrane and the acceleration of sequential secretory granule-secretory granule fusions.
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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 | 0.000 |
| 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".