Atrophin-1 antisense oligonucleotide provides robust protection from pathology in a fully humanized DRPLA model
Bibliographic record
Abstract
Dentatorubral-pallidoluysian atrophy (DRPLA) is a fatal neurodegenerative disease arising from a CAG repeat expansion in the atrophin-1 (ATN1) gene. Because DRPLA, like many repeat expansion disorders (REDs), arises predominantly from toxic gain-of-function mechanisms, we hypothesized that ATN1 knockdown would have therapeutic potential. To test this, we established the first fully-humanized mouse model of a RED, in which one allele of mouse Atn1 is completely replaced by human ATN1, including 112 pure CAG repeats. This novel approach to exploring RED biology provides significant advantages, notably the ability to test sequence-specific therapeutics targeting human sequences, even in introns and untranslated regions of pre-mRNA. We found that our model, the Atn1Q112/+ mouse, recapitulates key features of human DRPLA, including behavioral alterations, reduced brain size and aggregate accumulation. We treated Atn1Q112/+ mice with antisense oligonucleotides (ASOs) targeting mouse Atn1 (to probe for loss of function concerns), human ATN1, or a combination. Treatment with human, but not mouse, ATN1-targeting ASOs provides remarkable protection from a range of disease-related behavioral phenotypes, including aggregation of mutant ATN1 (mATN1), and marked rescue of transcriptional dysregulation in the cerebellum. These results have helped motivate an ongoing human clinical study of ASOs targeting ATN1 for DRPLA.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".