Direct Measure of DNA Damage in Huntington Disease Reveals Elevated Oxidative Genotoxic Stress and Dysfunctional DNA Repair
Bibliographic record
Abstract
Abstract DNA damage repair (DDR) pathway proteins are genetic modifiers of Huntington disease (HD) age of onset and severity. Deficient DDR in HD at the first steps of poly ADP-ribosylation makes DNA damage quantification by traditional downstream DNA damage response markers inaccurate. Repair Assisted Damage Detection (RADD) allows for an accurate direct assessment of oxidative DNA damage, a DDR pathway in which huntingtin protein directly participates. Using RADD, we show that human HD-derived cells have elevated oxidative DNA damage and assess the effect of relevant HD therapeutic targets in rescuing this phenotype. Using huntingtin protein level lowering, we define a dysfunctional role of mutant huntingtin in oxidative DDR. We show that ataxia-telangiectasia mutated (ATM) signaling is deficient and that ATM inhibition rescues elevated oxidative DNA damage in HD cells. In contrast, we show that N6-furfuryladenine (N6FFA) treatment, to increase huntingtin phosphorylation within the amino terminal N17 domain (p-N17), is not effective at restoring HD DDR but reveals dysfunctional N6FFA mediated DDR signaling. We propose a model in which elevated DNA damage arises from both aberrant mutant huntingtin involvement in oxidative DDR and the impairment of oxidative DDR pathways, such as ATM kinase activity, N6FFA processing, and poly ADP-ribose signaling in HD. This highlights the importance of using direct measures of DNA damage such as RADD, rather than measures of a DNA damage response pathway.
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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".