Syntaxin 2 Acts as Inhibitory SNARE for Insulin Granule Exocytosis
Bibliographic record
Abstract
Of the four syntaxins specialized for exocytosis, syntaxin (Syn)-2 is the least understood. In this study, we used Syn-2/epimorphin knockout mice to examine the role of Syn-2 in insulin secretory granule (SG) exocytosis. Unexpectedly, Syn-2 knockout mice exhibited paradoxical superior glucose homeostasis resulting from an enhanced insulin secretion. This was confirmed in vitro by pancreatic islet perifusion showing an amplified biphasic glucose-stimulated insulin secretion arising from an increase in size of the readily releasable pool of insulin SGs and enhanced SG pool refilling. The increase in insulin exocytosis was attributed mainly to an enhanced recruitment of the larger pool of newcomer SGs that undergoes no residence time on plasma membrane before fusion and, to a lesser extent, also the predocked SGs. Consistently, Syn-2 depletion resulted in a stimulation-induced increase in abundance of exocytotic complexes we previously demonstrated as mediating the fusion of newcomer SGs (Syn-3/VAMP8/SNAP25/Munc18b) and predocked SGs (Syn-1A/VAMP2/SNAP25/Muncn18a). This work is the first to show in mammals that Syn-2 could function as an inhibitory SNARE protein that, when relieved, could promote exocytosis in pancreatic islet β-cells. Thus, Syn-2 may serve as a potential target to treat 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.000 | 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".