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Record W4409468671 · doi:10.1016/j.isci.2025.112440

Inhibition of acid or neutral sphingomyelinases differentially impacts RNA and protein cargo sorting to extracellular vesicles

2025· article· en· W4409468671 on OpenAlexafffund
Juan-Carlos A Padilla, Seda Barutcu, Jonathan Boulais, Janaína Carla da Silva, Yiran Chen, Easin Uddin Syed, Eun‐Jeong Kwon, Éric Lécuyer

Bibliographic record

VenueiScience · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsMcGill UniversityUniversité de MontréalMontreal Clinical Research Institute
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchInstitut de Recherche Clinique De MontréalNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsExtracellular vesiclesCell biologyPlanarianExtracellularChemistrySortingRNAVesicleExtracellular vesicleMicrovesiclesBiochemistryBiologyRegeneration (biology)microRNAMembraneGene

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.281

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.264
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2025
Admission routes2
Has abstractyes

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