Reactive oxygen species leading to growth inhibition via oxidative DNA damage and cell cycle disruption

You Song, Li Xie, Knut Erik Tollefsen

Norwegian Institute for Water Research (NIVA), Sognsveien 72, 0855, Oslo, Norway

This adverse outcome pathway (AOP 324) describes a linear genotoxic route by which increased reactive oxygen species (ROS) can lead to decreased organismal growth. In this AOP, increased ROS is treated operationally as the molecular initiating event because it represents the earliest common measurable redox perturbation shared by many stressors within the broader ROS-growth AOP network. Increased ROS leads to oxidative stress, which causes oxidative DNA damage. When oxidative DNA lesions exceed or impair repair capacity, inadequate DNA repair can occur, allowing DNA strand breaks to accumulate. Strand breaks activate cell cycle checkpoint responses and disrupt progression through the cell cycle. Sustained cell cycle disruption reduces cell proliferation, and reduced cell proliferation ultimately contributes to decreased growth. The AOP reuses and connects existing AOP-Wiki components, including key events (KEs) and key event relationships (KERs) from OECD/WPHA-WNT endorsed AOPs. In particular, the oxidative DNA damage module is derived from AOP 296, the oxidative stress and DNA damage context is supported by AOP 478, the cell-cycle disruption KE is shared with AOP 212, and the link from decreased cell proliferation to decreased growth is reused from AOP 263 (AOP-Wiki, 2026a, 2026b, 2026c, 2026d; OECD, 2022, 2023; Carrothers et al., 2025). The AOP is biologically plausible across aerobic eukaryotes because ROS metabolism, antioxidant defenses, DNA damage response pathways, DNA repair, cell cycle regulation, and growth processes are broadly conserved. Empirical support is derived from studies in algae, fish embryos and cell lines, mammalian cells, radiation models, and other systems exposed to oxidative or genotoxic stressors. The AOP is expected to be useful for mechanistic interpretation of oxidative stress-related toxicity, support of integrated approaches to testing and assessment (IATA), and prioritization of stressors that produce oxidative DNA damage and growth impairment.  

ROS are continuously formed during aerobic metabolism and are also generated in response to environmental stressors. At controlled levels, ROS participate in redox signaling, while excessive ROS can disturb redox homeostasis and initiate oxidative damage to cellular macromolecules (Schieber and Chandel, 2014; Sies et al., 2017). DNA is a major target of oxidative attack. Oxidative DNA lesions such as 8-oxo-2'-deoxyguanosine and other oxidized bases can arise endogenously or following toxic insult, and these lesions may contribute to mutation, strand break formation, and activation of DNA damage responses if they are not repaired correctly or efficiently (Cooke et al., 2003; OECD, 2023). This AOP was developed to represent the DNA damage-driven linear route within the broader ROS-growth AOP network. The route was selected because oxidative DNA damage is a well-established consequence of oxidative stress and because downstream events such as inadequate DNA repair, strand breaks, cell cycle disruption, and reduced cell proliferation provide a mechanistically coherent bridge between molecular damage and decreased organismal growth (Cuddihy and O'Connell, 2003; Conlon and Raff, 1999; OECD, 2022; OECD, 2023). The AOP was therefore designed to provide a focused, reviewable linear representation of one major mechanistic branch by which excessive ROS can impair growth.  

AOP 324 was developed using the principles described in OECD AOP guidance, including modular description of KEs and KERs, reuse of existing AOP-Wiki content where appropriate, evidence evaluation using biological plausibility, empirical support, essentiality, and quantitative understanding, and clear description of the biological domain of applicability (OECD, 2018, 2021). The intent was not to create a fully de novo set of biological objects, but to assemble a linear AOP from established and reusable AOP-Wiki components wherever possible. This modular strategy is important because the AOP is part of a broader ROS-growth AOP network and because its individual KEs and KERs are expected to be reused in other oxidative stress, genotoxicity, and growth impairment AOPs. Existing AOP-Wiki content and OECD-endorsed AOPs were reviewed at an early stage to identify KEs and KERs that could be reused directly, adapted, or used as supporting context. AOP 296 was the primary source for the oxidative DNA damage module. It is an endorsed AOP describing oxidative DNA damage leading to chromosomal aberrations and mutations and includes KE 1634 (Increase, Oxidative DNA damage), KE 155 (Inadequate DNA repair), KE 1635 (Increase, DNA strand breaks), and relationships involving oxidative DNA damage, inadequate DNA repair, and strand break formation (AOP-Wiki, 2026b; OECD, 2023). AOP 478, an endorsed AOP on deposition of energy leading to cataracts, provided additional support for the upstream oxidative stress context, including KE 1392 (Oxidative stress), KE 1634, KE 155, KE 1635, and the relationship from oxidative stress to oxidative DNA damage in a radiation-relevant setting (AOP-Wiki, 2026a; Carrothers et al., 2025). AOP 212 was reviewed because it contains KE 1505 (Cell cycle, disrupted) and evidence that disruption of cell-cycle regulation can lead to downstream changes in cell fate (AOP-Wiki, 2026c). AOP 263 was reviewed because it is an endorsed AOP linking decreased cell proliferation to decreased growth, and AOP 324 reuses the downstream KE 1821 (Decrease, Cell proliferation), AO 1521 (Decrease, Growth), and KER 2205 (Decrease, Cell proliferation leads to Decrease, Growth) from that AOP (AOP-Wiki, 2026d; OECD, 2022; Song and Villeneuve, 2021). The resulting AOP 324 therefore represents an upstream extension and re-routing of established AOP-Wiki knowledge. Compared with AOP 296, AOP 324 extends upstream from oxidative DNA damage to increased ROS and oxidative stress and extends downstream from DNA damage response biology to decreased cell proliferation and growth. Compared with AOP 263, it reuses the final growth-relevant segment but provides a different upstream causal route, namely oxidative DNA damage and cell-cycle disruption rather than mitochondrial uncoupling and ATP depletion. Compared with AOP 478, it reuses the oxidative stress-DNA damage portion of the radiation AOP but directs that conserved genotoxic sequence toward growth inhibition rather than cataract formation. Compared with AOP 212, it reuses cell-cycle disruption as a modular KE but places it in the context of DNA strand break-mediated checkpoint activation rather than histone deacetylase inhibition. The literature review and evidence assembly process followed an AI-human hybrid workflow. The first phase consisted of AOP-helpFinder searching and preliminary analysis. Search terms were developed for each event in the pathway, including KE names, common synonyms, endpoint terms, assay terms, taxonomic descriptors, and relevant species names. These terms were used in AOP-helpFinder to search PubMed for co-occurrence patterns between key events and related mechanistic concepts, following published approaches for literature mining in support of AOP development (Carvaillo et al., 2019; Jornod et al., 2022). The exported results included PMIDs, titles, abstracts, and matched key-event terms. These outputs were subjected to overlap analysis to remove redundant records and to filter the initial literature pool, including elimination of clearly irrelevant records and separation of taxa-related evidence where needed. The second phase consisted of large-language-model (LLM)-assisted screening. Titles and abstracts from AOP-helpFinder, together with records identified from targeted manual searches, were pre-screened using an LLM as an auxiliary prioritization tool. The purpose of this step was not to replace expert judgment, but to increase efficiency and consistency during early evidence triage. The LLM was used to extract study metadata, including stressor, species, biological system, dose or concentration, and exposure duration; identify the evidence type represented in each study, such as biological plausibility, empirical support, or essentiality; and flag weight-of-evidence indicators including dose-response concordance, temporal concordance, incidence concordance, and intervention or rescue evidence. Studies were provisionally classified as high relevance, medium relevance, or low relevance. High-relevance studies were retrieved for full-text review, whereas medium-relevance studies were retained as potential supporting evidence. Low-relevance studies were documented as low priority and excluded from detailed curation. For studies moved forward to full-text review, a second LLM-assisted pass was used to organize relevant information from the full paper. All LLM-generated outputs were checked directly against the original article text by human reviewers. The LLM was therefore used only as a screening and structuring aid, not as an independent evaluator of the evidence. The final phase consisted of manual expert curation and weight-of-evidence evaluation. Domain experts verified the relevance and interpretation of the literature selected in the earlier stages, resolved ambiguous cases, and extracted information to populate KER evidence tables. These tables captured the biological system studied, stressor, method, endpoint, result, concordance pattern, weight-of-evidence category, and bibliographic source. Expert review was then used to evaluate the evidence for each KER in terms of biological plausibility, empirical support, essentiality, quantitative understanding, and evidence gaps. Targeted manual searches were also used to fill specific gaps for ROS biology, oxidative DNA damage, DNA repair, DNA strand breaks, DNA damage response, cell cycle disruption, cell proliferation, and growth inhibition. Studies were prioritized when they measured two or more KEs in the same biological system, reported exposure time and dose or concentration, or provided evidence relevant to dose-response, temporal, or incidence concordance. Mechanistic reviews and OECD reports were used primarily to support biological plausibility, whereas primary experimental studies were prioritized for empirical support wherever possible (Cooke et al., 2003; Cuddihy and O'Connell, 2003; Qian et al., 2009; Zachleder et al., 2011; Quevedo et al., 2021; OECD, 2023).

AOP 324 can support mechanistic interpretation of toxicity data where ROS generation, oxidative stress, DNA damage, cell cycle disruption, or reduced proliferation are observed together with growth effects. The AOP may be useful for hazard identification, prioritization of stressors that generate oxidative DNA damage, and organization of evidence in IATA or defined approaches. It can also support chemical grouping or read-across where chemicals share evidence of ROS-mediated DNA damage and downstream checkpoint or proliferation effects. The AOP also highlights assay opportunities and gaps. Several KEs can be measured using established or standardized methods, including DNA strand breaks using OECD TG 489 and growth using OECD growth-related test guidelines such as TG 201 and TG 210 (OECD, 2011, 2013, 2014). Other KEs, such as ROS increase, oxidative DNA damage, inadequate DNA repair, and cell cycle disruption, are supported by mechanistic assays but may require careful interpretation of assay specificity and biological context. High-throughput or new approach methodology assays measuring oxidative stress responses, DNA damage signaling, cell cycle arrest, and proliferation may be mapped to the AOP to support screening and prioritization. For regulatory application, the AOP is currently most suitable for qualitative or semi-quantitative use, such as supporting biological plausibility, organizing mechanistic evidence, and identifying data gaps. More quantitative applications, such as prediction of decreased growth from upstream ROS or oxidative DNA damage measurements, will require further development of response-response relationships, better characterization of modulating factors, and additional studies measuring multiple KEs in the same biological system over time.  

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