Munc18a Does Not Alter Fusion Rates Mediated by Neuronal SNAREs, Synaptotagmin, and Complexin
Bibliographic record
Abstract
Sec1/Munc18 (SM) proteins are essential for membrane trafficking, but their molecular mechanism remains unclear. Using a single vesicle-vesicle content-mixing assay with reconstituted neuronal SNAREs, synaptotagmin-1, and complexin-1, we show that the neuronal SM protein Munc18a/nSec1 has no effect on the intrinsic kinetics of both spontaneous fusion and Ca(2+)-triggered fusion between vesicles that mimic synaptic vesicles and the plasma membrane. However, wild type Munc18a reduced vesicle association ∼50% when the vesicles bearing the t-SNAREs syntaxin-1A and SNAP-25 were preincubated with Munc18 for 30 min. Single molecule experiments with labeled SNAP-25 indicate that the reduction of vesicle association is a consequence of sequestration of syntaxin-1A by Munc18a and subsequent release of SNAP-25 (i.e. Munc18a captures syntaxin-1A via its high affinity interaction). Moreover, a phosphorylation mimic mutant of Munc18a with reduced affinity to syntaxin-1A results in less reduction of vesicle association. In summary, Munc18a does not directly affect fusion, although it has an effect on the t-SNARE complex, depending on the presence of other factors and experimental conditions. Our results suggest that Munc18a primarily acts at the prefusion stage.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".