Vesicle cholesterol controls exocytotic fusion pore
Bibliographic record
Abstract
In some lysosomal storage diseases (LSD) cholesterol accumulates in vesicles. Whether increased vesicle cholesterol affects vesicle fusion with the plasmalemma, where the fusion pore, a channel between the vesicle lumen and the extracellular space, is formed, is unknown. Super-resolution microscopy revealed that after stimulation of exocytosis, pituitary lactotroph vesicles discharge cholesterol which transfers to the plasmalemma. Cholesterol depletion in lactotrophs and astrocytes, both exhibiting Ca2+-dependent exocytosis regulated by distinct Ca2+sources, evokes vesicle secretion. Although this treatment enhanced cytosolic levels of Ca2+ in lactotrophs but decreased it in astrocytes, this indicates that cholesterol may well directly define the fusion pore. In an attempt to explain this mechanism, a new model of cholesterol-dependent fusion pore regulation is proposed. High-resolution membrane capacitance measurements, used to monitor fusion pore conductance, a parameter related to fusion pore diameter, confirm that at resting conditions reducing cholesterol increases, while enrichment with cholesterol decreases the conductance of the fusion pore. In resting fibroblasts, lacking the Npc1 protein, a cellular model of LSD in which cholesterol accumulates in vesicles, the fusion pore conductance is smaller than in controls, showing that vesicle cholesterol controls fusion pore and is relevant for pathophysiology of LSD.
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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 | 0.000 |
| 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 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".