Recent new insights into the role of SNARE and associated proteins in insulin granule exocytosis
Bibliographic record
Abstract
Initial work on the exocytotic machinery of predocked insulin secretory granules (SGs) in pancreatic β‐cells mimicked the SNARE hypothesis work in neurons, which includes SM/SNARE complex and associated priming proteins, fusion clamps and Ca 2+ sensors. However, β‐cell SGs, unlike neuronal synaptic vesicles, exhibit a biphasic secretory response that requires additional distinct features in exocytosis including newcomer SGs that undergo minimal docking time at the plasma membrane (PM) before fusion and multi‐SG (compound) fusion. These exocytotic events are mediated by Munc18/SNARE complexes distinct from that which mediates predocked SG fusion. We review some recent insights in SNARE complex assembly and the promiscuity in SM/SNARE complex formation, whereby both contribute to conferring different insulin SG fusion kinetics. Some SNARE and associated proteins play non‐fusion roles, including tethering SGs to Ca 2+ channels, SG recruitment from cell interior to PM, and inhibitory SNAREs that block the action of profusion SNAREs. We discuss new insights into how sub‐PM cytoskeletal mesh gates SG access to the PM and the targeting of SG exocytosis to PM domains in functionally polarized β‐cells within intact islets. These recent developments have major implications on devising clever SNARE replacement therapies that could restore the deficient insulin secretion in diabetic islet β‐cells.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".