DNA Repair in Huntington’s Disease and Spinocerebellar Ataxias: Somatic Instability and Alternative Hypotheses
Bibliographic record
Abstract
The use of genome wide association studies (GWAS) in Huntington's disease (HD) research, driven by unbiased human data analysis, has transformed the focus of new targets that could affect age at onset. While there is a significant depth of information on DNA damage repair, with many drugs and drug targets, most of this development has taken place in the context of cancer therapy. DNA damage repair in neurons does not rely on DNA replication correction mechanisms. However, there is a strong connection between DNA repair and neuronal metabolism, mediated by nucleotide salvaging and the poly ADP-ribose (PAR) response, and this connection has been implicated in other age-onset neurodegenerative diseases. Validation of leads including the mismatch repair protein MSH3, and interstrand cross-link repair protein FAN1, suggest the mechanism is driven by somatic CAG instability, which is supported by the protective effect of CAA substitutions in the CAG tract. We currently do not understand: how somatic instability is triggered; the state of DNA damage within expanding alleles in the brain; whether this damage induces mismatch repair and interstrand cross-link pathways; whether instability mediates toxicity, and how this relates to human ageing. We discuss DNA damage pathways uncovered by HD GWAS, known roles of other polyglutamine disease proteins in DNA damage repair, and a panel of hypotheses for pathogenic mechanisms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".