CRISPR-mediated Modelling and Treatment of Tandem Duplications in Rare Inherited Disorders
Bibliographic record
Abstract
Tandem duplication mutations are increasingly found to be the direct cause of many rare inheritable diseases, including Duchenne muscular dystrophy (DMD) and MeCP2 duplication syndrome (MDS). Unfortunately, animal models recapitulating such mutations are scarce, limiting our ability to study them and develop therapeutic options for the affected patients. In my thesis, I explored the potential of the CRISPR/Cas9 system to model and correct tandem duplication mutations at large. First, a novel DMD duplication mouse model (Dup18-30), harbouring a patient multi-exonic duplication in the Dmd gene, was generated and characterized. The Dup18-30 mouse model faithfully recapitulates DMD disease phenotypes, developing early-onset motor and cardiac complications. I then implemented an in vivo single-guide RNA CRISPR/Cas9 approach to correct the disease-causing tandem duplication. This approach precisely removed the duplication mutation, restored full-length dystrophin expression, and resulted in marked improvements in both histopathological and clinical phenotypes. In an effort to broaden the applicability of these strategies, I further translated them in the context of MDS, a rare neurodevelopmental disease. A CRISPR/Cas9 fusion proximity-based approach was developed to improve the efficiency of tandem duplication generation, and it was utilized to generate an Irak1-Mecp2 tandem duplication mouse model (Mecp2 Dup) that recapitulates an MDS patient mutation. The Mecp2 Dup model displays neurological phenotypes and an abnormal immune response in keeping with MDS disease manifestations. The Mecp2 Dup mouse line thus provides an innovative tool to investigate disease mechanisms and advance therapeutic development. Ultimately, the results derived from my PhD project highlight the tremendous potential of CRISPR/Cas9 in both modeling and treating tandem duplication mutations, opening new horizons in research for patients affected by duplication disorders.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".