Potent and Selective Antisense Oligonucleotides Targeting Single-Nucleotide Polymorphisms in the Huntington Disease Gene / Allele-Specific Silencing of Mutant Huntingtin
Bibliographic record
Abstract
Huntington disease (HD) is an autosomal dominant neurodegenerative disorder caused by CAG-expansion in the huntingtin gene (HTT) that results in a toxic gain of function in the mutant huntingtin protein (mHTT). Reducing the expression of mHTT is therefore an attractive therapy for HD. However, wild-type HTT protein is essential for development and has critical roles in maintaining neuronal health. Therapies for HD that reduce wild-type HTT may therefore generate unintended negative consequences. We have identified single-nucleotide polymorphism (SNP) targets in the human HD population for the disease-specific targeting of the HTT gene. Using primary cells from patients with HD and the transgenic YAC18 and BACHD mouse lines, we developed antisense oligonucleotide (ASO) molecules that potently and selectively silence mHTT at both exonic and intronic SNP sites. Modification of these ASOs with S-constrained-ethyl (cET) motifs significantly improves potency while maintaining allele selectively in vitro. The developed ASO is potent and selective for mHTT in vivo after delivery to the mouse brain. We demonstrate that potent and selective allele-specific knockdown of the mHTT protein can be achieved at therapeutically relevant SNP sites using ASOs in vitro and in vivo.
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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".