Cas9 Nickase-Mediated Contraction of CAG/CTG Repeats <i>in Vivo</i> is Accompanied by Improvements in Huntington’s Disease Pathology
Bibliographic record
Abstract
Abstract Expanded CAG/CTG repeats cause over 15 different diseases that all remain without a disease-modifying treatment. Because repeat length accounts for most of the variation in disease severity, contracting them presents an attractive therapeutic avenue. Here, we show that the CRISPR-Cas9 nickase targeted to CAG/CTG repeats leads to efficient contractions in Huntington’s disease patient-derived neurons and astrocytes, and in myotonic dystrophy type 1 patient-derived neurons. The approach is allele-selective and free of detectable off-target mutations. Striatal injection of the Cas9 nickase in a mouse model for Huntington’s disease using adeno-associated viral vectors led to contractions in over half the infected cells. Upon injection, we observed a reduction in the number of inclusion bodies, improved transcriptome, and ameliorated locomotion. The effects were greater than expected from the contractions induced and suggest that non-cell autonomous mechanisms may be involved. Our results provide the proof-of-concept that correction of CAG/CTG repeats can improve Huntington’s disease phenotypes in vivo . One sentence summary The Cas9 nickase contracts CAG/CTG repeats at multiple disease loci in patient-derived cells and improves molecular and behavioral phenotypes in HD.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".