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Record W4206034076 · doi:10.1242/jcs.259002

Lipid-driven CFTR clustering is impaired in cystic fibrosis and restored by corrector drugs

2022· article· en· W4206034076 on OpenAlexafffund
Asmahan Abu‐Arish, Elvis Pandžić, Yishan Luo, Yukiko Sato, Mark J. Turner, Paul W. Wiseman, John W. Hanrahan

Bibliographic record

VenueJournal of Cell Science · 2022
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsMcGill University Health CentreMcGill University
FundersCystic Fibrosis CanadaNatural Sciences and Engineering Research Council of CanadaMcGill UniversityCystic Fibrosis Foundation
KeywordsLipid raftBiologyCystic fibrosis transmembrane conductance regulatorCell biologyCystic fibrosisCeramideSphingolipidCell membraneCellBiochemistrySignal transductionGenetics

Abstract

fetched live from OpenAlex

Membrane proteins often cluster in nanoscale membrane domains (lipid rafts) that coalesce into ceramide-rich platforms during cell stress, however the clustering mechanisms remain uncertain. The cystic fibrosis transmembrane conductance regulator (CFTR), which is mutated in cystic fibrosis (CF), forms clusters that are cholesterol dependent and become incorporated into long-lived platforms during hormonal stimulation. We report here that clustering does not involve known tethering interactions of CFTR with PDZ domain proteins, filamin A or the actin cytoskeleton. It also does not require CFTR palmitoylation but is critically dependent on membrane lipid order and is induced by detergents that increase the phase separation of membrane lipids. Clustering and integration of CFTR into ceramide-rich platforms are abolished by the disease mutations F508del and S13F and rescued by the CFTR modulators elexacaftor plus tezacaftor. These results indicate CF therapeutics that correct mutant protein folding restore both trafficking and normal lipid interactions in the plasma membrane. This article has an associated First Person interview with the first author of the paper.

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)0.001
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 score0.587
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.285
Teacher spread0.274 · 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

Citations20
Published2022
Admission routes2
Has abstractyes

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