Accurate mutation detection and quantification are crucial for understanding mutagenesis and its potential health implications. Traditional in vivo mutagenicity assays, such as the transgenic rodent gene mutation assay, are limited by their focus on single reporter genes and inability to efficiently generate mutation spectra. Error-corrected sequencing (ECS) technologies like Duplex Sequencing (DS) offer significant advantages, including extremely low error rates and ability to measure mutation frequencies (MFs) across various tissues and model organisms. Before DS can be adopted for regulatory purposes, its performance characteristics, particularly the type 1 error rate must be rigorously established. We evaluated the type 1 error rate of DS through empirical analysis of vehicle control data and complementary simulation studies. Using 138 control mouse liver samples from 28 studies analyzed with the TwinStrand Mouse Mutagenesis Panel, we performed variance component analysis and found that experiment-level variability exceeds within-experiment sample variability. To evaluate the impact of between-study heterogeneity, we simulated overdispersed binomial data informed by the observed variance components. Removing the most variable studies reduced overdispersion and improved control of the type 1 error rate. Our findings demonstrate that DS maintains appropriate type 1 error rates (~0.05) when study heterogeneity is limited and at least four samples per group are used. Under greater overdispersion, sample sizes of five or six per group may be needed to achieve comparable control of the type 1 error rate. These results underscore the importance of combining empirical and simulation-based approaches to evaluate and optimize the statistical performance of emerging genomic technologies.

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.

A document by Elena Esina. Click on the document to view its contents.