A helper-dependent adenoviral vector rescues CFTR to wild-type functional levels in cystic fibrosis epithelial cells harbouring class I mutations
Bibliographic record
Abstract
Cystic fibrosis (CF) is a genetic disorder affecting multiple organs, including the pancreas, hepatobiliary system and reproductive organs; however, lung disease is responsible for the majority of morbidity and mortality. Management of CF involves CF transmembrane conductance regulator (CFTR) modulator agents including corrector drugs to augment cellular trafficking of mutant CFTR as well as potentiators that open defective CFTR channels. These therapies are poised to help most individuals with CF, with the notable exception of individuals with class I mutations where full-length CFTR protein is not produced. For these mutations, gene replacement has been suggested as a potential solution. In this work, we used a helper-dependent adenoviral vector (HD-CFTR) to express CFTR in nasal epithelial cell cultures derived from CF subjects with class I CFTR mutations. CFTR function was significantly restored in CF cells by HD-CFTR and reached healthy control functional levels as detected by Ussing chamber and membrane potential (FLIPR) assay. A dose–response relationship was observed between the amount of vector used and subsequent functional outcomes; small amounts of HD-CFTR were sufficient to correct CFTR function. At higher doses, HD-CFTR did not increase CFTR function in healthy control cells above baseline values. This latter observation allowed us to use this vector to benchmarkin vitroefficacy testing of CFTR-modulator drugs. In summary, we demonstrate the potential for HD-CFTR to informin vitrotesting and to restore CFTR function to healthy control levels in airway cells with class I or CFTR nonsense mutations.
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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".