Amphiphilic shuttle peptide delivers base editor ribonucleoprotein to correct the CFTR R553X mutation in well-differentiated airway epithelial cells
Bibliographic record
Abstract
Base editing could correct nonsense mutations that cause cystic fibrosis (CF), but clinical development is limited by the lack of delivery methods that efficiently breach the barriers presented by airway epithelia. Here, we present a novel amphiphilic shuttle peptide based on the previously reported S10 peptide that substantially improved base editor ribonucleoprotein (RNP) delivery. Studies of the S10 secondary structure revealed that the alpha-helix formed by the endosomal leakage domain (ELD), but not the cell penetrating peptide (CPP), was functionally important for delivery. By isolating and extending the ELD, we created a novel shuttle peptide, termed S237. While S237 achieved lower delivery of green fluorescent protein, it outperformed S10 at Cas9 RNP delivery to cultured human airway epithelial cells and to pig airway epithelia in vivo, possibly due to its lower net charge. In well-differentiated primary human airway epithelial cell cultures, S237 achieved a 4.6-fold increase in base editor RNP delivery, correcting up to 9.4% of the cystic fibrosis transmembrane conductance regulator (CFTR) R553X allele and restoring CFTR channel function close to non-CF levels. These findings deepen the understanding of peptide-mediated delivery and offer a translational approach for base editor RNP delivery for CF airway 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 | 0.000 | 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".