Sphingomyelin-enriched microdomains define the efficiency of native Ca2+-triggered membrane fusion
Why this work is in the frame
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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.
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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 | 0.001 | 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) | 0.000 | 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 it