Interventionally targeting somatic CAG expansions can be a rapid disease-modifying therapeutic avenue: Preclinical evidence
Bibliographic record
Abstract
Abstract Huntington disease (HD) is caused by inherited CAG expansions, which continue expanding somatically in affected brain regions to hasten disease onset and progression. Therapeutically diminishing somatic expansions is expected to be clinically beneficial. However, it is not known if interventionally modifying somatic CAG expansions will actually modify in vivo clinically-relevant phenotypes, what the therapeutic window is, or which phenotypes will be altered. Here we show that acute (6-week) delivery of the contraction-inducing slipped-CAG DNA ligand naphthyridine-azaquinolone to young (4-week-old) (CAG)120 HD mice, induces contractions throughout brain regions, improves motor function (locomotion, balance, coordination, muscle strength), molecular disease landmarks (mHTT aggregates, nuclear envelope morphology, nucleocytoplasmic mRNA transport, transcriptomic dysregulation, neuroinflammation), and neurodegeneration. Beneficial effects of modifying somatic expansions were also evident in muscle and blood, where blood CAG instability correlated with brain instability and blood serum had diminished levels of neurofilament light (a biomarker for neurodegeneration) — offering blood as having elements of target engagement and efficacy. These data support that targeting somatic repeat expansions can be a rapid disease-modifying therapeutic avenue for HD and possibly other repeat expansion diseases. Our findings support an etiologic pathway interconnected to somatic CAG expansions that will inform the design of clinical trials expecting clinical benefit by modulating somatic expansions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".