Sphingolipids regulate the tethering stage of vacuole fusion by affecting membrane fluidity
Bibliographic record
Abstract
Abstract Sphingolipids are essential in membrane trafficking and cellular homeostasis. Here, we show that sphingolipids containing very long-chain fatty acids (VLCFAs) promote robust homotypic vacuolar fusion in Saccharomyces cerevisiae . The elongase Elo3 adds two carbons to 24-carbon (C24) acyl chains to make C26 VLCFAs that are incorporated into sphingolipids. Vacuoles isolated from elo3 Δ cells had increased fluidity relative to the wild-type and were attenuated for fusion. Upon further testing we found that vesicle the tethering stage was affected as elo3 Δ vacuole clusters contained fewer vesicles versus the WT. Vacuole tethering requires the interactions of late endosomal Rab GTPase Ypt7 and the HOPS tethering complex. Pulldown assays using GST-Ypt7 showed that HOPS from elo3 Δ vacuole extracts failed to bind Ypt7 while HOPS from WT extracts interacted with GST-Ypt7. Furthermore GFP-Ypt7 failed to localize at vertex microdomains of elo3 Δ vacuoles relative to the WT, whereas HOPS and regulatory lipids did accumulate at vertices. Finally, we found that elo3 Δ vacuoles had reduced V-ATPase. Together these data show that C26-VLCFA containing sphingolipids are important for Ytp7 function and vacuole homeostasis.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".