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Record W2884803592 · doi:10.1016/j.celrep.2018.06.104

C24 Sphingolipids Govern the Transbilayer Asymmetry of Cholesterol and Lateral Organization of Model and Live-Cell Plasma Membranes

2018· article· en· W2884803592 on OpenAlexafffund
Kevin C. Courtney, Weria Pezeshkian, Riya Raghupathy, C. Zhang, Angie L. Darbyson, John H. Ipsen, David A. Ford, Himanshu Khandelia, John F. Presley, Xiaohui Zha

Bibliographic record

VenueCell Reports · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsMcGill UniversityOttawa HospitalUniversity of Ottawa
FundersNovo Nordisk FondenNatural Sciences and Engineering Research Council of CanadaLundbeckfondenVillum Fonden
KeywordsSphingolipidSphingomyelinLeaflet (botany)MembraneVesicleCell biologyCholesterolCaveolaeCell membraneCeramideCytoplasmBiologyBiophysicsChemistryBiochemistry

Abstract

fetched live from OpenAlex

Mammalian sphingolipids, primarily with C24 or C16 acyl chains, reside in the outer leaflet of the plasma membrane. Curiously, little is known how C24 sphingolipids impact cholesterol and membrane microdomains. Here, we present evidence that C24 sphingomyelin, when placed in the outer leaflet, suppresses microdomains in giant unilamellar vesicles and also suppresses submicron domains in the plasma membrane of HeLa cells. Free energy calculations suggested that cholesterol has a preference for the inner leaflet if C24 sphingomyelin is in the outer leaflet. We indeed observe that cholesterol enriches in the inner leaflet (80%) if C24 sphingomyelin is in the outer leaflet. Similarly, cholesterol primarily resides in the cytoplasmic leaflet (80%) in the plasma membrane of human erythrocytes where C24 sphingolipids are naturally abundant in the outer leaflet. We conclude that C24 sphingomyelin uniquely interacts with cholesterol and regulates the lateral organization in asymmetric membranes, potentially by generating cholesterol asymmetry.

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 score0.014
Threshold uncertainty score0.326

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 head0.006
GPT teacher head0.212
Teacher spread0.206 · 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

Citations74
Published2018
Admission routes2
Has abstractyes

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