Sphingomyelin-enriched microdomains define the efficiency of native Ca2+-triggered membrane fusion
Bibliographic record
Abstract
Membrane microdomains or ;rafts' are suggested to act as regulators of the exocytotic process and also appear to be the sites of Ca(2+)-triggered membrane fusion. Microdomains are postulated to maintain the localization of ;efficiency' factors, including Ca(2+) sensors and other protein and lipid components. Separation of the fundamental ability to fuse from the efficiency of the process has suggested dependence of efficiency factors on microdomain organization. Cholesterol, a key component of membrane microdomains, contributes to both the efficiency and the fundamental ability to fuse. However, testing for a selective effect of native microdomains on the efficiency of fusion, without affecting membrane cholesterol density, has not been assessed. Hydrolysis of sphingomyelin disrupts native raft domains on secretory vesicles. Disruption of microdomains enriched in sphingomyelin-cholesterol by treatment with sphingomyelinase selectively and dose dependently inhibited the Ca(2+) sensitivity and late kinetics of secretory vesicle fusion. As a native microdomain constituent, sphingomyelin is associated with Ca(2+) sensing through its interaction with other raft-bound lipid and/or protein factors, thereby supporting the physiological Ca(2+) sensitivity of membrane fusion. Furthermore, the sphingomyelinase-driven generation of ceramide, contributing to the total membrane negative curvature, preserves the ability to fuse despite extensive cholesterol removal. Membrane microdomain integrity thus underlies the efficiency of fusion but not the fundamental ability of native vesicles to undergo Ca(2+)-triggered membrane merger. The results are consistent with a fundamental fusion machine of intrinsically low Ca(2+) sensitivity that, supported by accessory ;efficiency' components, facilitates Ca(2+)-triggered bilayer merger under physiological conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".