Corrector-mediated rescue of misprocessed CFTR mutants can be reduced by the P-glycoprotein drug pump
Bibliographic record
Abstract
The most common cause of cystic fibrosis is deletion of Phe508 in the first nucleotide-binding domain (NBD) of the CFTR chloride channel, which inhibits protein folding. ΔF508 CFTR can be rescued by indirect approaches such as low temperature but the protein is unstable. Here, we tested our predictions that (1) other CFTR mutants such V232D and H1085R were more stable at the cell surface than ΔF508 CFTR after low temperature rescue and (2) the advantages of rescue with specific correctors (pharmacological chaperones) are that they may stabilize ΔF508 CFTR and increase the effectiveness of the correctors by bypassing drug pumps such as P-glycoprotein (P-gp) (increased bioavailability). It was found that the stability of mutants V232D and H1085R after low-temperature (30°C) rescue was about 10-fold higher than ΔF508 CFTR. We show that the corrector, 4,5,7-trimethyl-N-phenylquinolin-2-amine (5a), could stabilize ΔF508 CFTR at the cell surface. Unlike most correctors, corrector 5a showed specificity for CFTR as it did not rescue the G268V P-gp processing mutant nor stimulate the ATPase activity of wild-type P-gp. By contrast, corrector KM11060 was a P-gp substrate as it stimulated P-gp ATPase activity and rescued the G268V mutant. Expression of wild-type P-gp reduced the effectiveness of CFTR rescue by corrector KM11060 by about 5-fold. The results underlie the importance of selecting correctors that are specific for CFTR because their efficiency can be reduced by drug pumps such as P-gp.
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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 | 0.001 |
| 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".