Rational design of antisense oligonucleotides targeting single nucleotide polymorphisms for potent and allele selective suppression of mutant Huntingtin in the CNS
Bibliographic record
Abstract
Autosomal dominant diseases such as Huntington's disease (HD) are caused by a gain of function mutant protein and/or RNA. An ideal treatment for these diseases is to selectively suppress expression of the mutant allele while preserving expression of the wild-type variant. RNase H active antisense oligonucleotides (ASOs) or small interfering RNAs can achieve allele selective suppression of gene expression by targeting single nucleotide polymorphisms (SNPs) associated with the repeat expansion. ASOs have been previously shown to discriminate single nucleotide changes in targeted RNAs with ∼5-fold selectivity. Based on RNase H enzymology, we enhanced single nucleotide discrimination by positional incorporation of chemical modifications within the oligonucleotide to limit RNase H cleavage of the non-targeted transcript. The resulting oligonucleotides demonstrate >100-fold discrimination for a single nucleotide change at an SNP site in the disease causing huntingtin mRNA, in patient cells and in a completely humanized mouse model of HD. The modified ASOs were also well tolerated after injection into the central nervous system of wild-type animals, suggesting that their tolerability profile is suitable for advancement as potential allele-selective HD therapeutics. Our findings lay the foundation for efficient allele-selective downregulation of gene expression using ASOs-an outcome with broad application to HD and other dominant genetic disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".