Inhibition of acid or neutral sphingomyelinases differentially impacts RNA and protein cargo sorting to extracellular vesicles
Bibliographic record
Abstract
Extracellular vesicles (EVs) form through regulated biogenesis processes involving sphingomyelinases (SMases), enzymes that metabolize sphingomyelin to produce ceramide-a lipid influencing membrane rigidity and essential for EV generation. This study explores the impact of inhibiting neutral SMase (NSM) and acid SMase (ASM) on the sorting of EV protein and RNA cargoes in human MCF7 cells. Our results revealed that NSM inhibition reduces EV nanoparticles and diminishes RNA and protein cargoes, including endosomal, spliceosomal, and translation-related proteins. Conversely, ASM inhibition increased RNA-binding proteins within and enhanced the expression of ribonucleoprotein complex-associated RNA in released EVs, including several snRNAs and 7SL RNA. Intriguingly, ASM-inhibited EVs enhanced the migration and translational activity of recipient MCF10A cells. These findings suggest an important role for SMase-dependent vesiculation in governing RNA and protein trafficking to the extracellular space, unveiling potential implications for cellular communication and function.
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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 | 0.000 |
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".