Universal Prime Editing Therapeutic Strategy for RyR1-Related Myopathies: A Protective Mutation Rescues Leaky RyR1 Channel
Bibliographic record
Abstract
RyR1-related myopathies (RyR1-RMs) include a wide range of genetic disorders that result from mutations in the RYR1 gene. Pathogenic variants lead to defective intracellular calcium homeostasis and muscle dysfunction. Fixing intracellular calcium leaks by stabilizing the RyR1 calcium channel has been identified as a promising therapeutic target. Gene therapy via prime editing also holds great promise as it can cure diseases by correcting genetic mutations. However, as more than 700 variants have been identified in the RYR1 gene, a universal treatment would be a more suitable solution for patients. Our investigation into the RyR1-S2843A mutation has yielded promising results. Using a calcium leak assay, we determined that the S2843A mutation was protective when combined with pathogenic mutations and significantly reduced the Ca2+ leak of the RyR1 channel. Our study demonstrated that prime editing can efficiently introduce the protective S2843A mutation. In vitro experiments using the RNA electroporation of the prime editing components in human myoblasts achieved a 31% introduction of this mutation. This article lays the foundation for a new therapeutic approach for RyR1-RM, where a unique once-in-a-lifetime prime editing treatment could potentially be universally applied to all patients with a leaky RyR1 channel.
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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 | 0.001 | 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".