Identifying Critical Components of Native Ca<sup>2+</sup>‐triggered Membrane Fusion
Bibliographic record
Abstract
Ca(2+)-triggered membrane fusion is the defining step of exocytosis. Despite realization that the fusion machinery must include lipids and proteins working in concert, only of late has work in the field focused more equally on both these components. Here we use isolated sea urchin egg cortical vesicles (CV), a stage-specific preparation of Ca(2+)-sensitive release-ready vesicles that enables the tight coupling of molecular and functional analyses necessary to dissect molecular mechanisms. The stalk-pore hypothesis proposes that bilayer merger proceeds rapidly via transient, high-negative curvature, intermediate membrane structures. Consistent with this, cholesterol, a major component of the CV membrane, contributes to a critical local negative curvature that supports formation of lipidic fusion intermediates. Following cholesterol depletion, structurally dissimilar lipids having intrinsic negative curvature greater than or equal to cholesterol recover the ability of CV to fuse but do not recover fusion efficiency (Ca(2+) sensitivity and kinetics). Conversely, cholesterol- and sphingomyelin-enriched microdomains regulate the efficiency of the fusion mechanism, presumably by contributing spatial and functional organization of other critical lipids and proteins at the fusion site. Critical proteins are thought to participate in Ca(2+) sensing, initiating membrane deformations, and facilitating fusion pore expansion. Capitalizing on a novel effect of the thiol-reactive reagent iodoacetamide (IA), potentiation of the Ca(2+) sensitivity and kinetics, a fluorescently tagged IA has been used to enhance fusion efficiency and simultaneously label the proteins involved. Isolation of cholesterol-enriched CV membrane fractions, using density gradient centrifugation, is being used to narrow the list of protein candidates potentially critical to the mechanism of fast Ca(2+)-triggered membrane fusion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".