Munc18-1 domain-1 controls vesicle docking and secretion by interacting with syntaxin-1 and chaperoning it to the plasma membrane
Bibliographic record
Abstract
Munc18-1 plays pleiotropic roles in neurosecretion by acting as 1) a molecular chaperone of syntaxin-1, 2) a mediator of dense-core vesicle docking, and 3) a priming factor for soluble N-ethylmaleimide-sensitive factor attachment protein receptor-mediated membrane fusion. However, how these functions are executed and whether they are correlated remains unclear. Here we analyzed the role of the domain-1 cleft of Munc18-1 by measuring the abilities of various mutants (D34N, D34N/M38V, K46E, E59K, K46E/E59K, K63E, and E66A) to bind and chaperone syntaxin-1 and to restore the docking and secretion of dense-core vesicles in Munc18-1/-2 double-knockdown cells. We identified striking correlations between the abilities of these mutants to bind and chaperone syntaxin-1 with their ability to restore vesicle docking and secretion. These results suggest that the domain-1 cleft of Munc18-1 is essential for binding to syntaxin-1 and thereby critical for its chaperoning, docking, and secretory functions. Our results demonstrate that the effect of the alleged priming mutants (E59K, D34N/M38V) on exocytosis can largely be explained by their reduced syntaxin-1-chaperoning functions. Finally, our data suggest that the intracellular expression and distribution of syntaxin-1 determines the level of dense-core vesicle docking.
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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".