<i>In Vitro</i> Validation of a CRISPR-Mediated CFTR Correction Strategy for Preclinical Translation in Pigs
Bibliographic record
Abstract
Early efforts in cystic fibrosis (CF) gene therapy faced major challenges in delivery efficiency and sustained therapeutic gene expression. Recent advancements in engineered site-specific endonucleases such as clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 make permanent CF transmembrane conductance regulator ( CFTR ) gene correction possible. However, because of safety concerns of the CRISPR/Cas9 system and challenges in in vivo delivery to inflamed CF airway, CRISPR-based gene correction strategies need to be tested in proper animal models. In this study, we aimed at creating vectors for testing CFTR gene correction in pig models. We constructed helper-dependent adenoviral (HD-Ad) vectors to deliver CRISPR/Cas9 and a donor template (a 6 kb LacZ or 8.7 kb human CFTR expression cassette) into cultured pig cells. We demonstrated precise integration of each donor into the GGTA1 safe harbor through Cas9-induced homology directed repair with 3 kb homology arms. In addition, we showed that both LacZ and hCFTR were persistently expressed in transduced cells. Furthermore, we created a CFTR-deficient cell line for testing CFTR correction. We detected hCFTR mRNA and protein expression in cells transduced with the hCFTR vector. We also demonstrated CFTR function in the CF cells transduced with the HD-Ad delivering the CRISPR-Cas9 system and hCFTR donor at late cellular passages using the membrane potential sensitive dye-based assay (FLIPR ® ). Combined with our previous report on gene delivery to pig airway basal cells, these data provide the feasibility of testing CRISPR/Cas9-mediated permanent human CFTR correction through HD-Ad vector delivery in pigs.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".