Base editing of trinucleotide repeats that cause Huntington’s disease and Friedreich’s ataxia reduces somatic repeat expansions in patient cells and in mice
Bibliographic record
Abstract
Trinucleotide repeat (TNR) diseases are neurological disorders caused by expanded genomic TNRs that become unstable in a length-dependent manner. The CAG•CTG sequence is found in approximately one-third of pathogenic TNR loci, including the HTT gene that causes Huntington's disease. Friedreich's ataxia, the most prevalent hereditary ataxia, results from GAA repeat expansion at the FXN gene. Here we used cytosine and adenine base editing to reduce the repetitiveness of TNRs in patient cells and in mice. Base editors introduced G•C>A•T and A•T>G•C interruptions at CAG and GAA repeats, mimicking stable, nonpathogenic alleles that naturally occur in people. AAV9 delivery of optimized base editors in Htt.Q111 Huntington's disease and YG8s Friedreich's ataxia mice resulted in efficient editing in transduced tissues, and significantly reduced repeat expansion in the central nervous system. These findings demonstrate that introducing interruptions in pathogenic TNRs can mitigate a key neurological feature of TNR diseases 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) | 0.001 | 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".