Interaction of CFTR Modulators with Mammalian Membrane Mimetics: The Role of Cholesterol
Bibliographic record
Abstract
Lumacaftor and Ivacaftor are two FDA-approved medications currently used to treat cystic fibrosis (CF), a genetic disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR), a chloride ion channel located in epithelial cell membranes; however, the detailed mechanism(s) of their action remains to be elucidated. Both drugs, termed modulators, bind CFTR at a protein-lipid interface, yet Lumacaftor acts at the endoplasmic reticulum (ER), while Ivacaftor acts at the plasma membrane (PM). A major difference among biological membranes is their level of cholesterol (viz., the ER, 5% cholesterol; the Golgi apparatus, 12.5%; and the PM, 30%). Therefore, we investigated the ability of each molecule to interact with membranes of the corresponding cholesterol content to determine if lipid cholesterol content provides a physical basis for their observed localized activity. Using differential scanning calorimetry and a terbium-based liposome disruption assay, we show that both Lumacaftor (a corrector) and Ivacaftor (a potentiator) penetrate/diffuse through membranes containing high cholesterol concentrations, such as in Golgi and the PM. The results further suggest that (1) Lumacaftor resides within membranes containing 5% cholesterol, supporting the proposition that Lumacaftor acts as a corrector of the CFTR channel at the ER level where the nascent protein is in its initial folding stage; and (2) Ivacaftor is well-suited to penetrate the PM and reach its binding pocket on CFTR. Our findings provide evidence that membrane cholesterol levels significantly modulate CFTR corrector/potentiator activity and consequently may affect sensitivity to clinical therapeutics in CF patients.
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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".