Regulated secretion: SNARE density, vesicle fusion and calcium dependence
Bibliographic record
Abstract
SNAREs such as VAMP, SNAP-25 and syntaxin are essential for intracellular trafficking, but what are their exact molecular roles and how are their interactions with other proteins manifest? Capitalizing on the differential sensitivity of SNAREs to exogenous proteases, we quantified the selective removal of identified SNAREs from native secretory vesicles without loss of fusion competence. Using previously established fusion assays and a high sensitivity immunoblotting protocol, we analyzed the relationship between these SNARE proteins and Ca2+-triggered membrane fusion. Neither the extent of fusion nor the number of intermembrane fusion complexes per vesicle were correlated with the measured density of identified egg cortical vesicle (CV) SNAREs. Without syntaxin, CVs remained fusion competent. Surprisingly, for one (but not another) protease the Ca2+ dependence of fusion was correlated with CV SNARE density, suggesting a native protein complex that associates with SNAREs, the architecture of which ensures high Ca2+ sensitivity. As SNAREs may function during CV docking in vivo, and as further proteolysis after SNARE removal eventually ablates fusion, we hypothesize that the triggered steps of regulated fusion (Ca2+ sensitivity and the catalysis and execution of fusion) require additional proteins that function downstream of SNAREs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".