To the extent possible under law, AOP-Wiki has waived all copyright and related or neighboring rights to KER:1910

Relationship: 1910


The title of the KER should clearly define the two KEs being considered and the sequential relationship between them (i.e., which is upstream and which is downstream). Consequently all KER titles take the form “upstream KE leads to downstream KE”.  More help

N/A, Inadequate DNA repair leads to Increase, DNA strand breaks

Upstream event
Upstream event in the Key Event Relationship. On the KER page, clicking on the Event name under Upstream Relationship will bring the user to that individual KE page. More help
Downstream event
Downstream event in the Key Event Relationship. On the KER page, clicking on the Event name under Upstream Relationship will bring the user to that individual KE page. More help

Key Event Relationship Overview

The utility of AOPs for regulatory application is defined, to a large extent, by the confidence and precision with which they facilitate extrapolation of data measured at low levels of biological organisation to predicted outcomes at higher levels of organisation and the extent to which they can link biological effect measurements to their specific causes. Within the AOP framework, the predictive relationships that facilitate extrapolation are represented by the KERs. Consequently, the overall WoE for an AOP is a reflection in part, of the level of confidence in the underlying series of KERs it encompasses. Therefore, describing the KERs in an AOP involves assembling and organising the types of information and evidence that defines the scientific basis for inferring the probable change in, or state of, a downstream KE from the known or measured state of an upstream KE. More help

AOPs Referencing Relationship

This table is automatically generated upon addition of a KER to an AOP. All of the AOPs that are linked to this KER will automatically be listed in this subsection. Clicking on the name of the AOP in the table will bring you to the individual page for that AOP. More help
AOP Name Adjacency Weight of Evidence Quantitative Understanding Point of Contact Author Status OECD Status
Oxidative DNA damage leading to chromosomal aberrations and mutations adjacent High Low Brendan Ferreri-Hanberry (send email) Open for comment. Do not cite EAGMST Under Review
Alkylation of DNA leading to reduced sperm count adjacent Brendan Ferreri-Hanberry (send email) Under development: Not open for comment. Do not cite

Taxonomic Applicability

Select one or more structured terms that help to define the biological applicability domain of the KER. In general, this will be dictated by the more restrictive of the two KEs being linked together by the KER. Authors can indicate the relevant taxa for this KER in this subsection. The process is similar to what is described for KEs (see pages 30-31 and 37-38 of User Handbook) More help
Term Scientific Term Evidence Link
human Homo sapiens NCBI
mouse Mus musculus NCBI
rat Rattus norvegicus NCBI

Sex Applicability

Authors can indicate the relevant sex for this KER in this subsection. The process is similar to what is described for KEs (see pages 31-32 of the User Handbook). More help
Sex Evidence

Life Stage Applicability

Authors can indicate the relevant life stage for this KER in this subsection. The process is similar to what is described for KEs (see pages 31-32 of User Handbook). More help
Term Evidence
All life stages

Key Event Relationship Description

Provide a brief, descriptive summation of the KER. While the title itself is fairly descriptive, this section can provide details that aren’t inherent in the description of the KEs themselves (see page 39 of the User Handbook). This description section can be viewed as providing the increased specificity in the nature of upstream perturbation (KEupstream) that leads to a particular downstream perturbation (KEdownstream), while allowing the KE descriptions to remain generalised so they can be linked to different AOPs. The description is also intended to provide a concise overview for readers who may want a brief summation, without needing to read through the detailed support for the relationship (covered below). Careful attention should be taken to avoid reference to other KEs that are not part of this KER, other KERs or other AOPs. This will ensure that the KER is modular and can be used by other AOPs. More help

Inadequate repair of DNA damage includes incorrect repair (i.e., incorrect base insertion), incomplete repair (i.e., accumulation of repair intermediates such as strand breaks, stalled replications forks, and/or abasic sites), and absent repair resulting in the retention of DNA damage.

It is well-established that DNA excision repair pathways require DNA strand breakage for removing the damaged sites; for example, base excision repair (BER) of oxidative lesions involves removal of oxidized bases by glycosylases followed by cleavage of the DNA strand 5’ from the abasic site. If the repair process is disrupted at this point, repair intermediates including single strand breaks (SSB) may persist in the DNA. A SSB can turn into a double strand break (DSB) if it occurs sufficiently close to another SSB on the opposite strand. SSBs can be converted into DSBs when helicase unwinds the DNA strands during replication. Furthermore, SSBs and abasic sites can act as replication blocks causing the replication fork to stall and collapse, giving rise to DSBs (Minko et al., 2016; Whitaker et al., 2017).

The two most common DSB repair mechanisms are non-homologous end joining (NHEJ) and homologous recombination (HR). NHEJ is may favoured over HR and has also been shown to be 104 times more efficient than HR in repairing DSBs (Godwin et al., 1994; Benjamin and Little, 1992). There are two subtypes of NHEJ: canonical NHEJ (C‐NHEJ) or alternative non-homologous end joining (alt-NHEJ). During C-NHEJ, broken ends of DNA are simply ligated together. In alt‐NHEJ, one strand of the DNA on either side of the break is resected to repair the lesion (Betermeir et al., 2014). Although both repair mechanisms are error‐prone (Thurtle‐Schmidt and Lo, 2018), alt-NHEJ is considered more error-prone than C-NHEJ (Guirouil-Barbat et al., 2007; Simsek and Jasin, 2010). While NHEJ may prevent cell death due to the cytotoxicity of DSBs, it may lead to mutations and genomic instability downstream.  

Evidence Supporting this KER

Assembly and description of the scientific evidence supporting KERs in an AOP is an important step in the AOP development process that sets the stage for overall assessment of the AOP (see pages 49-56 of the User Handbook). To do this, biological plausibility, empirical support, and the current quantitative understanding of the KER are evaluated with regard to the predictive relationships/associations between defined pairs of KEs as a basis for considering WoE (page 55 of User Handbook). In addition, uncertainties and inconsistencies are considered. More help
Biological Plausibility
Define, in free text, the biological rationale for a connection between KEupstream and KEdownstream. What are the structural or functional relationships between the KEs? For example, there is a functional relationship between an enzyme’s activity and the product of a reaction it catalyses. Supporting references should be included. However, it is recognised that there may be cases where the biological relationship between two KEs is very well established, to the extent that it is widely accepted and consistently supported by so much literature that it is unnecessary and impractical to cite the relevant primary literature. Citation of review articles or other secondary sources, like text books, may be reasonable in such cases. The primary intent is to provide scientifically credible support for the structural and/or functional relationship between the pair of KEs if one is known. The description of biological plausibility can also incorporate additional mechanistic details that help inform the relationship between KEs, this is useful when it is not practical/pragmatic to represent these details as separate KEs due to the difficulty or relative infrequency with which it is likely to be measured (see page 40 of the User Handbook for further information).   More help

1. DNA strand breaks generated due to faulty attempted repair

Excision repair pathways require the induction of SSB as part of damage processing. Increases in DNA lesions may lead to the accumulation of intermediate SSB. Attempted excision repair of lesions on opposite strands can turn into DSBs if the two are in close proximity (Eccles et al., 2010). Generation of DSBs has been observed in both nucleotide excision repair (NER) and BER (Ma et al., 2009; Wakasugi et al., 2014).

Previous studies have demonstrated that an imbalance in one of the multiple steps of BER can lead to an accumulation of repair intermediates and failed repair. It is highly likely that a disproportionate increase in oxidative DNA lesions compared to the level of available BER glycosylases leads to an imbalance between lesions and the initiating step of BER (Brenerman et al., 2014). Accumulation of oxidative lesions, abasic sites, and SSBs generated from OGG1, NTH1, and APE1 activities would be observed as a result. Moreover, studies have reported accumulation of SSB due to OGG1- and NHT1-overexpression (Yang et al., 2004; Yoshikawa et al., 2015; Wang et al., 2018). BER repair intermediates have been observed to interfere with transcription as well (Kitsera et al., 2011). While overexpression may lead to imbalanced lyase activities that generate excessive SSB intermediates, deficiency of these enzymes is also known to cause an accumulation of oxidative lesions that could lead to strand breaks downstream. Hence, both the overexpression and deficiencies of repair enzymes can lead to strand breaks due to excessive activity or inadequate repair, respectively.

2. DNA strand breaks generated due to replication stress caused by accumulated DNA lesions

Retention of DNA lesions (i.e., damaged bases and SSB) can interfere with the progression of the replication fork. Thymidine glycol is an example of an oxidative DNA lesion that acts as a replication block (Dolinnaya et al., 2013). Persistent replication fork stalling and dissociation of replication machinery are known to cause the replication fork to collapse, which generates highly toxic DSBs (Zeman and Cimprich, 2014; Alexander and Orr-Weaver, 2016). Fork stalling also increases the risk of two replication forks colliding with each other, generating DSBs.

In addition, the replication fork can collide with SSBs generated during BER, hindering the completion of repair and giving rise to DSBs (Ensminger et al., 2014).

Uncertainties and Inconsistencies
In addition to outlining the evidence supporting a particular linkage, it is also important to identify inconsistencies or uncertainties in the relationship. Additionally, while there are expected patterns of concordance that support a causal linkage between the KEs in the pair, it is also helpful to identify experimental details that may explain apparent deviations from the expected patterns of concordance. Identification of uncertainties and inconsistencies contribute to evaluation of the overall WoE supporting the AOPs that contain a given KER and to the identification of research gaps that warrant investigation (seep pages 41-42 of the User Handbook).Given that AOPs are intended to support regulatory applications, AOP developers should focus on those inconsistencies or gaps that would have a direct bearing or impact on the confidence in the KER and its use as a basis for inference or extrapolation in a regulatory setting. Uncertainties that may be of academic interest but would have little impact on regulatory application don’t need to be described. In general, this section details evidence that may raise questions regarding the overall validity and predictive utility of the KER (including consideration of both biological plausibility and empirical support). It also contributes along with several other elements to the overall evaluation of the WoE for the KER (see Section 4 of the User Handbook).  More help
  • A variety of confounding factors and genetic characteristics (i.e., SNPs) may modulate which repair pathways are invoked and the degree to which they are inadequate. These have yet to be fully defined.
  • Both protective and damaging effects of OGG1 against strand breaks have been described in the literature. As demonstrated in the section above, the effect of OGG1-deficiency (BER-initiating enzyme) is observed to be different in different cell types; Wang et al. (2018) demonstrated strand breaks exacerbated by excessive OGG1 activity, while Wu et al. (2008) and Shah et al. (2018) demonstrated increased strand breaks due to lack of repair in mammalian cells in culture (Shah et al., 2018; Wu et al., 2008; Wang et al., 2018). Cell cycle and replication may influence the effect of DNA repair on exacerbating strand breaks. 
  • Dahle et al. (2008) exposed wild type and OGG1-overexpressing Chinese hamster ovary cells, AS52, to UVA. While OGG1-overexpression prevented the accumulation of Fpg-sensitive lesions (e.g., 8-oxo-dG and FaPyG) that were observed in wild type cells 4 hours after irradiation, there was no difference in the amount of strand breaks in the two cell types at 4h (Dahle et al., 2008)
  • A recent study suggests that the NHEJ may be more accurate than previously thought (reviewed in Betermier et al., 2014). The accuracy of NHEJ may be dependent on the structure of the termini. The termini processing rather than the NHEJ itself is thus argued to be error-prone process (Betemier et al., 2014).
Response-response Relationship
This subsection should be used to define sources of data that define the response-response relationships between the KEs. In particular, information regarding the general form of the relationship (e.g., linear, exponential, sigmoidal, threshold, etc.) should be captured if possible. If there are specific mathematical functions or computational models relevant to the KER in question that have been defined, those should also be cited and/or described where possible, along with information concerning the approximate range of certainty with which the state of the KEdownstream can be predicted based on the measured state of the KEupstream (i.e., can it be predicted within a factor of two, or within three orders of magnitude?). For example, a regression equation may reasonably describe the response-response relationship between the two KERs, but that relationship may have only been validated/tested in a single species under steady state exposure conditions. Those types of details would be useful to capture.  More help
This sub-section should be used to provide information regarding the approximate time-scale of the changes in KEdownstream relative to changes in KEupstream (i.e., do effects on KEdownstream lag those on KEupstream by seconds, minutes, hours, or days?). This can be useful information both in terms of modelling the KER, as well as for analyzing the critical or dominant paths through an AOP network (e.g., identification of an AO that could kill an organism in a matter of hours will generally be of higher priority than other potential AOs that take weeks or months to develop). Identification of time-scale can also aid the assessment of temporal concordance. For example, for a KER that operates on a time-scale of days, measurement of both KEs after just hours of exposure in a short-term experiment could lead to incorrect conclusions regarding dose-response or temporal concordance if the time-scale of the upstream to downstream transition was not considered. More help
Known modulating factors
This sub-section presents information regarding modulating factors/variables known to alter the shape of the response-response function that describes the quantitative relationship between the two KEs (for example, an iodine deficient diet causes a significant increase in the slope of the relationship; a particular genotype doubles the sensitivity of KEdownstream to changes in KEupstream). Information on these known modulating factors should be listed in this subsection, along with relevant information regarding the manner in which the modulating factor can be expected to alter the relationship (if known). Note, this section should focus on those modulating factors for which solid evidence supported by relevant data and literature is available. It should NOT list all possible/plausible modulating factors. In this regard, it is useful to bear in mind that many risk assessments conducted through conventional apical guideline testing-based approaches generally consider few if any modulating factors. More help
Known Feedforward/Feedback loops influencing this KER
This subsection should define whether there are known positive or negative feedback mechanisms involved and what is understood about their time-course and homeostatic limits? In some cases where feedback processes are measurable and causally linked to the outcome, they should be represented as KEs. However, in most cases these features are expected to predominantly influence the shape of the response-response, time-course, behaviours between selected KEs. For example, if a feedback loop acts as compensatory mechanism that aims to restore homeostasis following initial perturbation of a KE, the feedback loop will directly shape the response-response relationship between the KERs. Given interest in formally identifying these positive or negative feedback, it is recommended that a graphical annotation (page 44) indicating a positive or negative feedback loop is involved in a particular upstream to downstream KE transition (KER) be added to the graphical representation, and that details be provided in this subsection of the KER description (see pages 44-45 of the User Handbook).  More help

Domain of Applicability

As for the KEs, there is also a free-text section of the KER description that the developer can use to explain his/her rationale for the structured terms selected with regard to taxonomic, life stage, or sex applicability, or provide a more generalizable or nuanced description of the applicability domain than may be feasible using standardized terms. More help

This KER applies to any cell type that has DNA repair capabilities.


List of the literature that was cited for this KER description using the appropriate format. Ideally, the list of references should conform, to the extent possible, with the OECD Style Guide (OECD, 2015). More help

Alexander, J., Orr-Weaver, T. (2016), Replication fork instability and the consequences of fork collisions from rereplication, Genes Dev, 30:2241-2252.

Brenerman, B., Illuzzi, J., Wilson III, D. (2014), Base excision repair capacity in informing healthspan, Carcinogenesis, 35:2643-2652.

Dahle, J., Brunborg, G., Svendsrud, D., Stokke, T., Kvam, E. (2008), Overexpression of human OGG1 in mammalian cells decreases ultraviolet A induced mutagenesis, Cancer Lett, 267:18-25.

Dolinnaya, N., Kubareva, E., Romanova, E., Trikin, R., Oretskaya, T. (2013), Thymidine glycol: the effect on DNA molecular structure and enzymatic processing, Biochimie, 95:134-147.

Eccles, L., Lomax, M., O’Neill, P. (2010), Hierarchy of lesion processing governs the repair, double-strand break formation and mutability of three-lesion clustered DNA damage, Nucleic Acids Res, 38:1123-1134.

Ensminger, M., Iloff, L., Ebel, C., Nikolova, T., Kaina, B., Lobrich, M. (2014), DNA breaks and chromosomal aberrations arise when replication meets base excision repair, J Cell Biol, 206:29.

Kitsera, N., Stathis, D., Luhnsdorf, B., Muller, H., Carell, T., Epe, B., Khobta, A. (2011), 8-Oxo-7,8-dihydroguanine in DNA does not constitute a barrier to transcription, but is converted into transcription-blocking damage by OGG1, Nucleic Acids Res, 38:5926-5934.

Ma, W., Panduri, V., Sterling, J., Van Houten, B., Gordenin, D., Resnick, M. (2009), The Transition of Closely Opposed Lesions to Double-Strand Breaks during Long-Patch Base Excision Repair Is Prevented by the Coordinated Action of DNA Polymerase  and Rad27/Fen1  , Mol Cell Biol, 29:1212-1221.

Minko, I., Jacobs, A., de Leon, A., Gruppi, F., Donley, N., Harris, T., Rizzo, C., McCullough, A., Lloyd, R.S. (2016), Catalysts of DNA Strand Cleavage at Apurinic/Apyrimidinic Sites, Sci Rep, 6.

Peterson-Roth, E., Reynolds, M., Quievryn, G., Zhitkovich, A. (2005), Mismatch Repair Proteins Are Activators of Toxic Responses to Chromium-DNA Damage, Mol Cell Biol, 25:3596-3607.

Rothkamm, K., Lobrich, M. (2003), Evidence for a lack of DNA double-strand break repair in human cells exposed to very low x-ray doses, Proc Natl Acad Sci USA, 100:5057-5062.

Rydberg, B., Cooper, B., Cooper, P., Holley, W., Chatterjee, A. (2005), Dose-Dependent Misrejoining of Radiation-Induced DNA Double-Strand Breaks in Human Fibroblasts: Experimental and Theoretical Study for High- and Low-LET Radiation, Radiat Res, 163:526-534.

Shah, A., Gray, K., Figg, N., Finigan, A., Starks, L., Bennett, M. (2018), . Defective Base Excision Repair of Oxidative DNA Damage in Vascular Smooth Muscle Cells Promotes Atherosclerosis, Circulation, 138:1446-1462.

Wakasugi, M., Sasaki, T., Matsumoto, M., Nagaoka, M., Inoue, K., Inobe, M., Horibata, K., Tanaka, K., Matsunaga, T. (2014), Nucleotide Excision Repair-dependent DNA Double-strand Break Formation and ATM Signaling Activation in Mammalian Quiescent Cells, J Biol Chem, 289:28730-28737.

Wang, R., Li, C., Qiao, P., Xue, Y., Zheng, X., Chen, H., Zeng, X., Liu, W., Boldogh, I., Ba, X. (2018), OGG1-initiated base excision repair exacerbates oxidative stress-induced parthanatos, Cell Death and Disease, 9:628.

Whitaker, A., Schaich, M., Smith, M.S., Flynn, T., Freudenthal, B. (2017), Base excision repair of oxidative DNA damage: from mechanism to disease, Front Biosci, 22:1493-1522.

Wu, M., Zhang, Z., Che, W. (2008), Suppression of a DNA base excision repair gene, hOGG1, increases bleomycin sensitivity of human lung cancer cell line, Toxicol App Pharmacol, 228:395-402.

Yang, N., Galick, H., Wallace, S. (2004), Attempted base excision repair of ionizing radiation damage in human lymphoblastoid cells produces lethal and mutagenic double strand breaks, DNA Repair, 3:1323-1334.

Yoshikawa, Y., Yamasaki, A., Takatori., K., Suzuki, M., Kobayashi, J., Takao, M., Zhang-Akiyama, Q. (2015), Excess processing of oxidative damaged bases causes hypersensitivity to oxidative stress and low dose rate irradiation, Free Radic Res, 49:1239-1248.

Zeman, M., Cimprich, K. (2014), Causes and Consequences of Replication Stress, Nat Cell Biol, 12:2-9.