New Roles of Syntaxin-1A in Insulin Granule Exocytosis and Replenishment
Bibliographic record
Abstract
In type-2 diabetes (T2D), severely reduced islet syntaxin-1A (Syn-1A) levels contribute to insulin secretory deficiency. We generated β-cell-specific Syn-1A-KO (Syn-1A-βKO) mice to mimic β-cell Syn-1A deficiency in T2D. Glucose tolerance tests showed that Syn-1A-βKO mice exhibited blood glucose elevation corresponding to reduced blood insulin levels. Perifusion of Syn-1A-βKO islets showed impaired first- and second-phase glucose-stimulated insulin secretion (GSIS) resulting from reduction in readily releasable pool and granule pool refilling. To unequivocally determine the β-cell exocytotic defects caused by Syn-1A deletion, EM and total internal reflection fluorescence microscopy showed that Syn-1A-KO β-cells had a severe reduction in the number of secretory granules (SGs) docked onto the plasma membrane (PM) at rest and reduced SG recruitment to the PM after glucose stimulation, the latter indicating defects in replenishment of releasable pools required to sustain second-phase GSIS. Whereas reduced predocked SG fusion accounted for reduced first-phase GSIS, selective reduction of exocytosis of short-dock (but not no-dock) newcomer SGs accounted for the reduced second-phase GSIS. These Syn-1A actions on newcomer SGs were partly mediated by Syn-1A interactions with newcomer SG VAMP8.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".