Spell Checking Nature: Development of a CRISPR- Mediated Gene Editing Approach for the Treatment of Pathogenic Duplications
Bibliographic record
Abstract
Duchenne muscular dystrophy (DMD) is a neuromuscular disorder that leads to progressive muscle deterioration, loss of ambulation, and respiratory complications. It is caused by genetic mutations that result in the absence of dystrophin protein expression needed for muscle function. Despite significant advances in our understanding of the pathogenesis of DMD, no curative treatment has been identified to date and the disorder has a life-limiting disease trajectory. Recently, we have pioneered an approach to successfully remove large duplications in patient cells. We first tested this approach in vitro by removing a multi-exon (18-30) duplication of 139 kb in the DMD gene using Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR-associated Nuclease (Cas9) with a single guide. To test our treatment approach in vivo, I first generated a mouse model harboring a multiexon duplication of 136.8 kb in Dmd using CRISPR/Cas9. This first multiexon duplication model of DMD specifically mimics a patient duplication of Exons 18-30. Molecular and functional characterization of this model reveals dystrophin deficiency with characteristic markers of dystrophic muscle. Furthermore, using our previously described CRISPR/Cas9 single guide strategy, we have for the first time treated a large genomic duplication in vivo and shown successful removal of the duplication fragment leading to restoration of full-length dystrophin in skeletal and cardiac muscles. Additionally, histopathological analysis shows that treated mice have less indications of dystrophy including fewer centrally localized nuclei as well as significantly improved muscle function. Our findings establish the far-reaching therapeutic utility of CRISPR/Cas9, which can be tailored to target numerous inherited disorders caused by duplications.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".