Comparative Processing and Function of Human and Ferret Cystic Fibrosis Transmembrane Conductance Regulator
Bibliographic record
Abstract
The most common cystic fibrosis transmembrane conductance regulator (CFTR) gene mutation is ΔF508, and this causes cystic fibrosis (CF). New CF models in the pig and ferret have been generated that develop lung, pancreatic, liver, and intestinal pathologies that reflect disease in CF patients. Species-specific biology in the processing of CFTR has demonstrated that pig and mouse ΔF508-CFTR proteins are more effectively processed to the apical membrane of airway epithelia than human ΔF508-CFTR. The processing behavior of ferret WT- and ΔF508-CFTR proteins remains unknown, and such information is important to predicting the utility of a ΔF508-CFTR ferret. To this end, we sought to compare processing, membrane stability, and function of human and ferret WT- and ΔF508-CFTR proteins in a heterologous expression system using HT1080, HEK293T, BHK21, and Cos7 cells as well as human and ferret CF polarized airway epithelia. Analysis of the protein processing and stability by metabolic pulse-chase and surface On-Cell Western blots revealed that WT-fCFTR half-life and membrane stability were increased relative to WT-hCFTR. Furthermore, in BHK21, Cos7, and CuFi cells, human and ferret ΔF508-CFTR processing was negligible, whereas low levels of processing of ΔF508-fCFTR could be seen in HT1080 and HEK293T cells. Only the WT-fCFTR, but not ΔF508-fCFTR, produced functional cAMP-inducible chloride currents in both CF human and ferret airway epithelia. Further elucidation of the mechanism responsible for elevated fCFTR protein stability may lead to new therapeutic approaches to augment CFTR function. These findings also suggest that generation of a ferret CFTR(ΔF508/ΔF508) animal model may be useful.
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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 | 0.001 | 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".