Lipid-driven CFTR clustering is impaired in cystic fibrosis and restored by corrector drugs
Bibliographic record
Abstract
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.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".