Limitations of Dual-Single Guide RNA CRISPR Strategies for the Treatment of Central Nervous System Genetic Disorders
Bibliographic record
Abstract
Huntington's disease (HD) is a fatal neurodegenerative disorder caused by a toxic gain-of-function CAG expansion in the first exon of the huntingtin (HTT) gene. The monogenic nature of HD makes mutant HTT (mHTT) inactivation a promising therapeutic strategy. Single nucleotide polymorphisms frequently associated with CAG expansion have been explored to selectively inactivate mHTT allele using the CRISPR/Cas9 system. One of such allele-selective approaches consists of excising a region flanking the first exon of mHTT by inducing simultaneous double-strand breaks at upstream and downstream positions of the mHTT exon 1. The removal of the first exon of mHTT deletes the CAG expansion and important transcription regulatory sites, leading to mHTT inactivation. However, the frequency of deletion events is yet to be quantified either in vitro or in vivo. Here, we developed accurate quantitative digital polymerase chain reaction-based assays to assess HTT exon 1 deletion in vitro and in fully humanized HU97/18 mice. Our results demonstrate that dual-single guide RNA (sgRNA) strategies are efficient and that 67% of HTT editing events are leading to exon 1 deletion in HEK293T cells. In contrast, these sgRNA actively cleaved HTT in HU97/18 mice, but most editing events do not lead to exon 1 deletion (10% exon 1 deletion). We also showed that the in vivo editing pattern is not affected by CAG expansion but may potentially be due to the presence of multiple copies of wildtype (wt)/mHTT genes HU97/18 mice as well as the slow kinetics of AAV-mediated CRISPR/Cas9 delivery.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".