Allele-Specific Prevention of Nonsense-Mediated Decay in Cystic Fibrosis Using Homology-Independent Genome Editing
Bibliographic record
Abstract
Nonsense-mediated decay (NMD) is a major pathogenic mechanism underlying a diversity of genetic disorders. Nonsense variants tend to lead to more severe disease phenotypes and are often difficult targets for small molecule therapeutic development as a result of insufficient protein production. The treatment of cystic fibrosis (CF), an autosomal recessive disease caused by mutations in the CFTR gene, exemplifies the challenge of therapeutically addressing nonsense mutations in human disease. Therapeutic development in CF has led to multiple, highly successful protein modulatory interventions, yet no targeted therapies have been approved for nonsense mutations. Here, we have designed a CRISPR-Cas9-based strategy for the targeted prevention of NMD of CFTR transcripts containing the second most common nonsense variant listed in CFTR2, W1282X. By introducing a deletion of the downstream genic region following the premature stop codon, we demonstrate significantly increased protein expression of this mutant variant. Notably, in combination with protein modulators, genome editing significantly increases the potentiated channel activity of W1282X-CFTR in human bronchial epithelial cells. Furthermore, we show how the outlined approach can be modified to permit allele-specific editing. The described approach can be extended to other late-occurring nonsense mutations in the CFTR gene or applied as a generalized approach for gene-specific prevention of NMD in disorders where a truncated protein product retains full or partial functionality.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".