CRISPR-induced deletion with SaCas9 restores dystrophin expression in dystrophic models <i>in vitro</i> and <i>in vivo</i>
Bibliographic record
Abstract
Abstract Duchenne Muscular Dystrophy (DMD), a severe hereditary disease, affecting 1 boy out of 3500, mainly results from the deletion of one or more exons leading to a reading frame shift of the DMD gene that abrogates dystrophin protein synthesis. We used the Cas9 of Staphylococcus aureus (SaCas9) to edit the human DMD gene. Pairs of sgRNAs were meticulously chosen to induce a genomic deletion to not only restore the reading frame but also produced a dystrophin protein with normally phased spectrin-like repeats. The formation of a dystrophin protein with spectrin-like repeats normally phased is not usually obtained by skipping or by deletion of complete exons. This can however be obtained in rare instances where the exon/intron borders of the beginning and the end of the complete deletion (patient deletion plus CRISPR-induced deletion are at similar positions in the spectrin-like repeat. We used pairs of sgRNAs, targeting exons 47 and 58 and a normal reading frame was restored in 67 to 86% of the resulting hybrid exons in myoblasts derived from muscle biopsies of 4 DMD patients with different exon deletions. The restoration of the DMD reading frame and restoration of the dystrophin expression was also obtained in vivo in the heart of the del52hDMD/mđx. Our results provide a proof-of-principle that SaCas9 could be used to edit the human DMD gene and could be considered for the further development of a therapy for DMD.
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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) | 0.002 | 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".