Efficient Restoration of the Dystrophin Gene Reading Frame and Protein Structure in DMD Myoblasts Using the CinDel Method
Bibliographic record
Abstract
The CRISPR/Cas9 system is a great revolution in biology. This technology allows the modification of genes in vitro and in vivo in a wide variety of living organisms. In most Duchenne muscular dystrophy (DMD) patients, expression of dystrophin (DYS) protein is disrupted because exon deletions result in a frame shift. We present here the CRISPR-induced deletion (CinDel), a new promising genome-editing technology to correct the DMD gene. This strategy is based on the use of two gRNAs targeting specifically exons that precede and follow the patient deletion in the DMD gene. This pair of gRNAs induced a precise large additional deletion leading to fusion of the targeted exons. Using an adequate pair of gRNAs, the deletion of parts of these exons and the intron separating them restored the DMD reading frame in 62% of the hybrid exons in vitro in DMD myoblasts and in vivo in electroporated hDMD/mdx mice. Moreover, adequate pairs of gRNAs also restored the normal spectrin-like repeat of the dystrophin rod domain; such restoration is not obtained by exon skipping or deletion of complete exons. The expression of an internally deleted DYS protein was detected following the formation of myotubes by the unselected, treated DMD myoblasts. Given that CinDel induces permanent reparation of the DMD gene, this treatment would not have to be repeated as it is the case for exon skipping induced by oligonucleotides.
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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) | 0.001 | 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 | 0.001 |
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".