Lipid scrambling via TMEM16F mediates the formation and release of apoptotic vesicles
Bibliographic record
Abstract
Abstract The ubiquitous and highly conserved programmed cell death pathways that are essential for tissue development and homeostasis are accompanied by distinct morphological alterations. Apoptotic cells undergo fragmentation that is concomitant with the exposure of phosphatidylserine (PS) on the membrane surface. Large fragments, called apoptotic bodies, as well as much smaller and more numerous vesicles are released. While the molecular mechanisms underlying apoptotic body formation have been explored, much less is known about vesicle biogenesis. We used an inducible, active form of TMEM16F to determine the role of lipid scrambling in vesiculation, separately from other apoptotic signaling events. Plasmalemmal lipid scrambling sufficed to release apoptotic-like vesicles without causing changes in cytosolic calcium or the submembrane cytoskeleton. The scrambled bilayer showed pronounced segregation of exofacial lipids and redistribution of apparent cholesterol to the inner leaflet. The clustering of raft-associated components with bulky headgroups –typified by glycophosphatidylinositol-linked proteins– formed domains of outward (convex) curvature, while regions of accumulation of phosphatidylethanolamine (PE) generated inward (concave) curvature that facilitated the scission of vesicles. Thus, scrambling of plasma membrane lipids suffices to induce regions of acute membrane curvature and facilitates detachment of vesicles analogous to those released from the surface of apoptotic cells.
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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".