Treatment of Huntington’s disease with a pan-HTT-targeting CRISPR nuclease
Bibliographic record
Abstract
Huntington's disease (HD) is an inherited neurodegenerative disorder caused by an expansion of a CAG trinucleotide repeat in the huntingtin (HTT) gene, which leads to a mutant protein that destroys neurons in the brain. Despite intense effort, there remains no approved disease-modifying therapy for HD. Here we develop a pan-HTT-targeting CRISPR-Cas9 system that, when delivered to the striatum of R6/2 and YAC128 mice by AAV5, lowered mutant HTT mRNA and protein by 55-80% via its induction of frameshift-inducing indel mutations in HTT exon 1. Cas9 targeting improved motor coordination and locomotor activity, decreased anxiety-like deficits, reduced clasping and weight loss, limited striatal atrophy, and decreased the formation of intranuclear inclusions immunoreactive for the mutant HTT protein. In Hu21/21 mice, which carry the wild-type human HTT gene in lieu of the mouse ortholog, Cas9 lowered the HTT protein by 44% but induced no measurable behavioral deficits and had no adverse effect on neuronal viability, though its targeting was associated with neuroinflammation. Altogether, our results demonstrate the ability for a newly developed pan-HTT-targeting Cas9 system to affect HD-related phenotypes across models and provides insights into its tolerability.
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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) | 0.001 | 0.000 |
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".