Design, Characterization, and Lead Selection of Therapeutic miRNAs Targeting Huntingtin for Development of Gene Therapy for Huntington's Disease
Bibliographic record
Abstract
Huntington's disease (HD) is a neurodegenerative disorder caused by accumulation of CAG expansions in the huntingtin (HTT) gene. Hence, decreasing the expression of mutated HTT (mtHTT) is the most upstream approach for treatment of HD. We have developed HTT gene-silencing approaches based on expression cassette-optimized artificial miRNAs (miHTTs). In the first approach, total silencing of wild-type and mtHTT was achieved by targeting exon 1. In the second approach, allele-specific silencing was induced by targeting the heterozygous single-nucleotide polymorphism (SNP) rs362331 in exon 50 or rs362307 in exon 67 linked to mtHTT. The miHTT expression cassette was optimized by embedding anti-HTT target sequences in ten pri-miRNA scaffolds and their HTT knockdown efficacy, allele selectivity, passenger strand activity, and processing patterns were analyzed in vitro. Furthermore, three scaffolds expressing miH12 targeting exon 1 were incorporated in an adeno-associated viral serotype 5 (AAV5) vector and their HTT knock-down efficiency and pre-miHTT processing were compared in the humanized transgenic Hu128/21 HD mouse model. Our data demonstrate strong allele-selective silencing of mtHTT by miSNP50 targeting rs362331 and total HTT silencing by miH12 both in vitro and in vivo. Ultimately, we show that HTT knock-down efficiency and guide strand processing can be enhanced by using different cellular pri-miRNA scaffolds.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".