The central role of DNA damage and repair in CAG repeat diseases
Bibliographic record
Abstract
Diseases such as Huntington's disease and certain spinocerebellar ataxias are caused by the expansion of genomic cytosine-adenine-guanine (CAG) trinucleotide repeats beyond a specific threshold. These diseases are all characterised by neurological symptoms and central neurodegeneration, but our understanding of how expanded repeats drive neuronal loss is incomplete. Recent human genetic evidence implicates DNA repair pathways, especially mismatch repair, in modifying the onset and progression of CAG repeat diseases. Repair pathways might operate directly on repeat sequences by licensing or inhibiting repeat expansion in neurons. Alternatively, or in addition, because many of the genes containing pathogenic CAG repeats encode proteins that themselves have roles in the DNA damage response, it is possible that repeat expansions impair specific DNA repair pathways. DNA damage could then accrue in neurons, leading to further expansion at repeat loci, thus setting up a vicious cycle of pathology. In this review, we consider DNA damage and repair pathways in postmitotic neurons in the context of disease-causing CAG repeats. Investigating and understanding these pathways, which are clearly relevant in promoting and ameliorating disease in humans, is a research priority, as they are known to modify disease and therefore constitute prevalidated drug targets.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".