I001 Cas9 nickase-mediated contraction of CAG repeats in Huntington’s disease
Bibliographic record
Abstract
Huntington’s disease (HD) is a hereditary neurodegenerative disease characterized by debilitating cognitive and motor symptoms. It is caused by the expansion of a CAG repeat in the exon 1 of the huntingtin (HTT) gene, and it remains without an effective disease modifying treatment. Because the size of the repeat tract accounts for most of the variation in disease severity, preventing expansions or contracting them presents an attractive therapeutic avenue. Typical gene editing approaches aim to excise the mutant allele, or affect both alleles, both of which are error-prone processes which may have undesirable consequences. Additionally, allele-specific approaches based on single-nucleotide polymorphisms only currently benefit a minority of patients. To overcome these challenges, we employ a CRISPR-Cas9 nickase which targets the CAG repeat tract itself which leads to efficient contractions in HD patient-derived neurons and astrocytes (figure 1). Moreover, nickase system induce contraction in DMKP1 locus in DM1 patient-derived neurons. Using single-cell DNA sequencing, PCR-free whole genome sequencing, and targeted long-read sequencing of the HTT locus, we found no off-target mutations above background in neurons and astrocytes. Furthermore, we delivered the Cas9 nickase and sgRNA stereotactically to a mouse model of Huntington’s disease using adeno-associated viruses and found contractions accumulating in vivo in over half of the infected cells over a period of 5 months. Importantly, the nickase induced contractions in the expanded allele to non-pathological CAG repeat length. We also found that the Cas9 nickase was prone to silencing, further improving the safety of the approach. Our results provide the proof of concept for using the Cas9 nickase to contract the repeat tract safely in multiple cell types and diseases.
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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".