Prime Editing Enables High-Efficiency Correction of the Ryr1 T4706M Mutation: A Promising Therapeutic Approach for RyR1-Related Myopathies
Bibliographic record
Abstract
Abstract Prime editing has emerged as a powerful genome-editing tool for precise correction of pathogenic mutations, offering a promising therapeutic approach for genetic myopathies. Here, we evaluate the correction efficiency of the T4706M mutation in the Ryr1 gene, which is implicated in severe skeletal muscle dysfunction. Using an optimized epegRNA design and RNA electroporation, we achieved a remarkable 80% editing efficiency in immortalized C2C12 myoblasts and 37% correction in primary myoblasts derived from the RYR1 TM/TM mouse model. Our results demonstrate that the PE6 prime editing strategy, combined with rationally designed epegRNAs, significantly enhances editing efficiency in unselected cell populations. These findings establish a critical ex vivo foundation for the development of in vivo Ryr1 gene therapy in preclinical mouse models. They also provide a validated editing design that can support delivery-focused applications in both academic and industry settings.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".