An AOP-informed testing strategy to identify chemicals causing genotoxicity through oxidative DNA damage: case study on 4-nitroquinoline 1-oxide
Bibliographic record
Abstract
Adverse outcome pathways (AOPs) provide a framework to organize and weigh evidence linking molecular interactions of toxicants in cells to outcomes of regulatory concern. Applying this framework facilitates the interpretation of data produced using new test methods. We used AOP #296 which describes how oxidative DNA damage leads to mutations and chromosomal aberrations to develop an integrated testing strategy to evaluate whether a chemical operates through this pathway. We exposed human TK6 cells to increasing concentrations of 4-nitroquinoline 1-oxide (4NQO), a tobacco mimetic that causes oxidative DNA damage, in a time-series design. We measured oxidative DNA damage and strand breaks using the high-throughput CometChip assay with and without formamidopyrimidine DNA glycosylase (Fpg), alongside analyses of micronucleus (MN) frequency by flow cytometry, and mutations by error-corrected next-generation sequencing (Duplex Sequencing). Our analysis shows how these methods can be combined to quantify 4NQO-induced, concentration- and time-dependent increases in: (a) oxidative DNA damage (occurred early and at lower concentrations than single strand breaks); (b) strand breaks (remained elevated to 6 hours post-exposures); (c) MN frequency (at 24 hours); (d) mutation frequency (at 48 hours); and, (e) C>A transversions consistent with expected substitutions induced by oxidative DNA lesions. The time-series shows the repair of oxidative DNA damage with persistent strand breaks remaining at 6 hours. Overall, we provide an example of an AOP-informed testing strategy and contribute to quantitative understanding of AOP #296. We also demonstrate the high value of Duplex Sequencing for elucidating the mechanisms associated with exposure to oxidative stress inducers.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".