Perturbation of the Pore of the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) Inhibits Its ATPase Activity
Bibliographic record
Abstract
Mutations in the cystic fibrosis gene coding for the cystic fibrosis transmembrane conductance regulator (CFTR) lead to altered chloride (Cl(-)) flux in affected epithelial tissues. CFTR is a Cl(-) channel that is regulated by phosphorylation, nucleotide binding, and hydrolysis. However, the molecular basis for the functional regulation of wild type and mutant CFTR remains poorly understood. CFTR possesses two nucleotide binding domains, a phosphorylation-dependent regulatory domain, and two transmembrane domains that comprise the pore through which Cl(-) permeates. Mutations of residues lining the channel pore (e.g. R347D) are typically thought to cause disease by altering the interaction of Cl(-) with the pore. However, in the present study we show that the R347D mutation and diphenylamine-2-carboxylate (an open pore inhibitor) also inhibit CFTR ATPase activity, revealing a novel mechanism for cross-talk from the pore to the catalytic domains. In both cases, the reduction in ATPase correlates with a decrease in nucleotide turnover rather than affinity. Finally, we demonstrate that glutathione (GSH) inhibits CFTR ATPase and that this inhibition is altered in the CFTR-R347D variant. These findings suggest that cross-talk between the pore and nucleotide binding domains of CFTR may be important in the in vivo regulation of CFTR in health and disease.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".