To the extent possible under law, AOP-Wiki has waived all copyright and related or neighboring rights to KER:164

Relationship: 164


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, Mutations

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
Alkylation of DNA in male pre-meiotic germ cells leading to heritable mutations adjacent High Moderate Carole Yauk (send email) Open for citation & comment TFHA/WNT Endorsed
Alkylation of DNA leading to cancer 2 adjacent High Moderate Carole Yauk (send email) Not under active development
Alkylation of DNA leading to cancer 1 non-adjacent High Moderate Carole Yauk (send email) Open for adoption
Oxidative DNA damage leading to chromosomal aberrations and mutations adjacent High Low Carole Yauk (send email) Open for comment. Do not cite EAGMST Under Review
Direct deposition of ionizing energy leading to lung cancer adjacent Moderate Moderate Vinita Chauhan (send email) Under development: Not open for comment. Do not cite EAGMST Under Review

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
mouse Mus musculus High NCBI
human Homo sapiens High NCBI
rat Rattus norvegicus High 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
Unspecific High

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 High

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

Insufficient repair results in the retention of damaged DNA that is then used as a template during DNA replication. During replication of damaged DNA, incorrect nucleotides may be inserted, and upon replication these become ‘fixed’ in the cell. Further replication propagates the mutation to additional cells.

For example, it is well established that replication of alkylated DNA can cause insertion of an incorrect base in the DNA duplex (i.e., mutation). Replication of non-repaired O4 thymine alkylation leads primarily to A:T→G:C transitions. Retained O6 guanine alkylation causes primarily G:C→A:T transitions.

For repairing DNA double strand breaks (DSBs), non-homologous end joining (NHEJ) is one of the repair mechanisms used in human somatic cells (Petrini et al., 1997; Mao et al., 2008). However, this mechanism is error-prone and may create mutations during the process of DNA repair (Little, 2000). NHEJ is considered error-prone because it does not use a homologous template to repair the DSB. The NHEJ mechanism involves many proteins that work together to bridge the DSB gap by overlapping single-strand termini that are usually less than 10 nucleotides long (Anderson, 1993; Getts & Stamato, 1994; Rathmell & Chu, 1994). Inherent in this process is the introduction of errors that may result in mutations such as insertions, deletions, inversions, or translocations.

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

If DNA repair is able to correctly and efficiently repair DNA lesions introduced by a genotoxic stressor, then no increase in mutation frequency will occur.

For example, for alkylated DNA, efficient removal by AGT will result in no increases in mutation frequency. However, above a certain dose AGT becomes saturated and is no longer able to efficiently remove the alkyl adducts. Replication of O-alkyl adducts leads to mutation. The evidence demonstrating that replication of unrepaired O-alkylated DNA causes mutations is extensive in somatic cells and has been reviewed (Basu and Essigmann 1990; Shrivastav et al. 2010); specific examples are given below.

It is important to note that not all DNA lesions will cause mutations. It is well documented that many are bypassed error-free. For example, N-alkyl adducts can quite readily be bypassed error-free with no increase in mutations (Philippin et al., 2014).

Inadequate repair of DSB

Collective data from tumors and tumor cell lines has emerged that suggests that DNA repair mechanisms may be error-prone (reviewed in Sishc et al., 2017) (Sishc & Davis, 2017).  NHEJ, the most common pathway used to repair DSBs, has been described as error-prone. The error-prone nature of NHEJ, however, is thought to be dependent on the structure of the DSB ends being repaired, and not necessarily dependent on the NHEJ mechanism itself (Bétermier et al., 2014). Usually when perfectly cohesive ends are formed as a result of a DSB event, ligase 4 (LIG4) will have limited end processing to perform, thereby keeping ligation errors to a minimum (Waters et al., 2014). When the ends are difficult to ligate, however, the resulting repair may not be completed properly; this often leads to point mutations and other chromosomal rearrangements. It has been shown that approximately 25 - 50% of DSBs are misrejoined after exposure to ionizing radiation (Löbrich et al., 1998; Kuhne et al., 2000; Lobrich et al., 2000). Defective repair mechanisms can increase sensitivity to agents that induce DSBs and lead eventually to genomic instability (reviewed in Sishc et al., (2017)).

Activation of mutagenic DNA repair pathways to withstand cellular or replication stress either from endogenous or exogenous sources can promote cellular viability, albeit at a cost of increased genome instability and mutagenesis (Fitzgerald et al., 2017). These salvage DNA repair pathways including, Break-induced Replication (BIR) and Microhomology-mediated Break-induced Replication (MMBIR). BIR repairs one-ended DSBs and has been extensively studied in yeast as well as in mammalian systems. BIR and MMBIR are linked with heightened levels of mutagenesis, chromosomal rearrangements and ensuing genome instability (Deem et al., 2011; Sakofsky et al., 2015; Saini et al., 2017; Kramara et al., 2018). In mammalian genomes BIR-like synthesis has been proposed to be involved in late stage Mitotic DNA Synthesis (MiDAS) that predominantly occurs at so-called Common Fragile Sites (CFSs) and maintains telomere length under s conditions of replication stress that serve to promote cell viability (Minocherhomji et al., 2015; Bhowmick et al., 2016; Dilley et al., 2016).       

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

Repair of alkylated DNA

There were no inconsistencies in the empirical data reviewed or in the literature relating to biological plausibility. Much of the support for this KER comes predominantly from data in somatic cells and in prokaryotic organisms. We note that all of the data in germ cells used in this KER are produced exclusively from ENU exposure. Data on other chemicals are required. We consider the overall weight of evidence of this KER to be strong because of the obvious biological plausibility of the KER, and documented temporal association and incidence concordance based on studies over-expressing and repressing DNA repair in somatic cells.

Repair of oxidative lesions

  • Thresholded concentration-response curve of mutation frequency was observed in AHH-1 human lymphoblastoid cells after treatment with pro-oxidants (H2O2 and  KBrO2) known to cause oxidative DNA damage (Seager et al., 2012), suggesting that cells are able to tolerate low levels of DNA damage using basal repair. However, increase in 8-oxo-dG lesions and up-regulation of DNA repair proteins were not observed under the same experimental condition.
  • Mutagenicity of oxidative DNA lesions other than 8-oxo-dG, such as FaPydG and thymidine glycol, has not been as extensively studied and there are mixed results regarding the mutagenic outcome of these lesions.


  • Mutation induction is stochastic, spontaneous, and dependent on the cell type as well as the individual’s capability to repair efficiently (NRC, 1990; Pouget & Mather, 2001).
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

Inadequate Repair of DSB

There is evidence of a response-response relationship between inadequate DNA repair and increased frequency of mutations. When exposed to a radiation stressor, there was a positive relationship between the radiation dose and the DSB misrepair rate, and between the mutation rate and the radiation dose (Mcmahon et al., 2016). Similarly, there was a negative correlation found between NER and the mutation densities at specific genomic regions in cancer patients. Specifically, inadequate NER resulted in more mutations in the promoter DHS and the TSS, but normal NER at DHS flanking regions resulted in fewer mutations (Perera et al., 2016).

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

Inadequate Repair of DSB

Two studies were used to provide data regarding the time scale of DNA repair and the appearance of mutations. In a study using plants, DNA damage was evident immediately following radiation with 30 Gy of radiation; 50% of repairs were complete by 51.7 minutes, 80% by 4 hours, and repair was completed by 24 hours post-irradiation. Although no mutational analysis was performed during the period of repair, irradiated plants were found to have increased mutations when they were examined 2 - 3 weeks later (Ptácek et al., 2001). Both DNA repair and mutation frequency were examined at the same time in a study comparing simple and complex ligation of linearized plasmids. In this study, repaired plasmids were first detected between 6 - 12 hours for simple ligation events and between 12 - 24 hours for more complex ligation events; this first period was when the most error-free rejoining occurred in both cases. After this initial period of repair until its completion at 48 hr, repair became increasingly more erroneous such that mutations were found in more than half of the repaired plasmids at 48 hr regardless of the type of required ligation (Smith et al., 2001).

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

Not identified.

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

Not identified.

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

The domain of applicability is multicellular eukaryotes (Lieber, 2008; Hartlerode & Scully, 2009), plants (Gorbunova, 1997; Puchta, 2005), certain strains of bacteria such as Mycobacteria, PseudomonasBacillus and Agrobacterium (Shuman & Glickman, 2007), and yeast (Wilson & Lieber, 1999).

All organisms, from prokaryotes to eukaryotes, have DNA repair systems. Indeed, much of the empirical evidence on the fundamental principles described in this KER are derived from prokaryotic models. DNA adducts can occur in any cell type, and may or may not be repaired, leading to mutation. While there are differences among DNA repair systems across eukaryotic taxa, all species develop mutations following excessive burdens of DNA lesions like DNA adducts. Theoretically, any sexually reproducing organism (i.e., producing gametes) can also acquire DNA lesions that may or may not be repaired, leading to mutations in gametes.


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

Albertini, R.J. et al. (1997), "Radiation Quality Affects the Efficiency of Induction and the Molecular Spectrum of HPRT Mutations in Human T Cells", 148(5 Suppl):S76-86.

Amundson, S.A. & D.J. Chen (1996), "Ionizing Radiation-Induced Mutation of Human Cells With Different DNA Repair Capacities.", Adv. Space Res. 18(1-2):119-126.

Anderson, C.W. 1993, "DNA damage and the DNA-activated protein kinase.", Trends Biochem. Sci. 18(11):433–437. doi:10.1016/0968-0004(93)90144-C.

Arai, T., Kelly, V.P., Minowa, O., Noda, T., Nishimura, S. (2002), High accumulation of oxidative DNA damage, 8-hydroxyguanine, in Mmh/Ogg1 deficient mice by chronic oxidative stress, Carcinogenesis, 23:2005-2010.

Basu, A.K. and J.M. Essigmann (1990), "Site-specific alkylated oligodeoxynucleotides: Probes for mutagenesis, DNA repair and the structure effects of DNA damage", Mutation Research, 233: 189-201.

Beranek, D.T. (1990), "Distribution of methyl and ethyl adducts following alkylation with monofunctional alkylating agents", Mutation Research, 231(1): 11-30.

Bétermier, M., P. Bertrand & B.S. Lopez (2014), "Is Non-Homologous End-Joining Really an Inherently Error-Prone Process?", PLoS Genet. 10(1). doi:10.1371/journal.pgen.1004086.

Bhowmick, R., S. Minocherhomji & I.D. Hickson (2016), "RAD52 Facilitates Mitotic DNA Synthesis Following Replication Stress", Mol. Cell., 64(6):1117-1126.

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.

Deem, A. et al. (2011), "Break-Induced Replication Is Highly Inaccurate", PLoS Biol., 9(2):e1000594, doi: 10.1371/journal.pbio.1000594.

Dilley, R.L. et al. (2016), "Break-induced telomere synthesis underlies alternative telomere maintenance", Nature, 539:54-58.

Douglas, G.R., J. Jiao, J.D. Gingerich, J.A. Gossen and L.M. Soper (1995), "Temporal and molecular characteristics of mutations induced by ethylnitrosourea in germ cells isolated from seminiferous tubules and in spermatozoa of lacZ transgenic mice", Proc. Natl. Acad. Sci. USA, 92(16): 7485-7489.

Dubrova, Y.E. et al. (2002), "Elevated Minisatellite Mutation Rate in the Post-Chernobyl Families from Ukraine.", Am. J. Hum. Genet. 71(4): 801-809.

Ellison, K.S., E. Dogliotti, T.D. Connors, A.K. Basu and J.M. Essigmann (1989), "Site-specific mutagenesis by O6-alkyguanines located in the chromosomes of mammalian cells: Influence of the mammalian O6-alkylguanine-DNA alkyltransferase", Proc. Natl. Acad. Sci. USA, 86: 8620-8624.

Feldmann, E. et al. (2000), "DNA double-strand break repair in cell-free extracts from Ku80-deficient cells : implications for Ku serving as an alignment factor in non-homologous DNA end joining.", Nucleic Acids Res. 28(13):2585–2596.

Fitzgerald, D.M., P.J. Hastings, and S.M. Rosenberg (2017), "Stress-Induced Mutagenesis: Implications in Cancer and Drug Resistance", Ann. Rev. Cancer Biol., 1:119-140, doi: 10.1146/annurev-cancerbio-050216-121919.

Getts, R.C. & T.D. Stamato (1994), "Absence of a Ku-like DNA end binding activity in the xrs double-strand DNA repair-deficient mutant.", J. Biol. Chem. 269(23):15981–15984.

Gocke, E. and L. Muller (2009), "In vivo studies in the mouse to define a threhold for the genotoxicity of EMS and ENU", Mutat. Res., 678, 101-107.

Gorbunova, V. (1997), "Non-homologous DNA end joining in plant cells is associated with deletions and filler DNA insertions.", Nucleic Acids Res. 25(22):4650–4657. doi:10.1093/nar/25.22.4650.

Hartlerode, A.J. & R. Scully (2009), "Mechanisms of double-strand break in somatic mammalian cells.", Biochem J. 423(2):157–168. doi:10.1042/BJ20090942.Mechanisms.

Kaina, B., M. Christmann, S. Naumann and W.P. Roos (2007), "MGMT: Key node in the battle against genotoxicity, carcinogenicity and apoptosis induced by alkylating agents", DNA Repair, 6: 1079–1099.

Klungland, A., Rosewell, I., Hollenbach, S., Larsen, E., Daly, G., Epe, B., Seeberg, E., Lindahl, T., Barnes, D. (1999), Accumulation of premutagenic DNA lesions in mice defective in removal of oxidative base damage, Proc Natl Acad Sci USA, 96:13300-13305.

Kramara, J., B. Osia & A. Malkova (2018), "Break-Induced Replication: The Where, The Why, and The How", Trends Genet. 34(7):518-531, doi: 10.1016/j.tig.2018.04.002.

Kuhne, M., K. Rothkamm & M. Lobrich (2000), "No dose-dependence of DNA double-strand break misrejoining following a -particle irradiation.", Int. J. Radiat. Biol. 76(7):891-900

Lieber, M.R. (2008), "The mechanism of human nonhomologous DNA End joining.", J Biol Chem. 283(1):1–5. doi:10.1074/jbc.R700039200.

Little, J.B. (2000), "Radiation carcinogenesis.", Carcinogenesis 21(3):397-404 doi:10.1093/carcin/21.3.397.

Lobrich, M. et al. (2000), "Joining of Correct and Incorrect DNA Double-Strand Break Ends in Normal Human and Ataxia Telangiectasia Fibroblasts.", 68(July 1999):59–68. doi:DOI: 10.1002/(SICI)1098-2264(200001)27:1<59::AID-GCC8>3.0.CO;2-9.

Mao Z, Bozzella M, Seluanov A, Gorbunova V. 2008. DNA repair by nonhomologous end joining and homologous recombination during cell cycle in human cells. Cell Cycle. 7(18):2902–2906. doi:10.4161/cc.7.18.6679.

Matuo Y, Izumi Y, Furusawa Y, Shimizu K. 2018. Mutat Res Fund Mol Mech Mutagen Biological e ff ects of carbon ion beams with various LETs on budding yeast Saccharomyces cerevisiae. Mutat Res Fund Mol Mech Mutagen. 810(November 2017):45–51. doi:10.1016/j.mrfmmm.2017.10.003.

Mcmahon SJ, Schuemann J, Paganetti H, Prise KM. 2016. Mechanistic Modelling of DNA Repair and Cellular Survival Following Radiation-Induced DNA Damage. Nat Publ Gr.(April):1–14. doi:10.1038/srep33290.

Minocherhomji, S. et al. (2015), "Replication stress activates DNA repair synthesis in mitosis", Nature, 528(7581):286-290.

Minowa, O., Arai, T., Hirano, M., Monden, Y., Nakai, S., Fukuda, M., Itoh, M., Takano, H., Hippou, Y., Aburatani, H., Masumura, K., Nohmi, T., Nishimura, S., Noda, T. (2000), Mmh/Ogg1 gene inactivation results in accumulation of 8-hydroxyguanine in mice, Proc Natl Acad Sci USA, 97:4156-4161.

Muller, L., E. Gocke, T. Lave and T. Pfister (2009), "Ethyl methanesulfonate toxicity in Viracept – A comprehensive human risk assessment based on threshold data for genotoxicity", Toxicology Letters, 190: 317-329.

Nagashima, H. et al. (2018), "Induction of somatic mutations by low-dose X-rays : the challenge in recognizing radiation-induced events.", J. Radiat. Res., Na 59(October 2017):11–17. doi:10.1093/jrr/rrx053.

NRC (1990), "Health Effects of Exposure to Low Levels of Ionizing Radiation", (BEIR V).

O'Brien, J.M., A. Williams, J. Gingerich, G.R. Douglas, F. Marchetti and C.L. Yauk CL. (2013), "No evidence for transgenerational genomic instability in the F1 or F2 descendants of Muta™Mouse males exposed to N-ethyl-N-nitrosourea", Mutat. Res., 741-742:11-7

O’Brien, J.M., M. Walker, A. Sivathayalan, G.R. Douglas, C.L. Yauk and F. Marchetti (2015), "Sublinear response in lacZ mutant frequency of Muta™ Mouse spermatogonial stem cells after low dose subchronic exposure to N-ethyl-N-nitrosourea", Environ. Mol. Mutagen., 56(4): 347-55.

Pegg, A.E., (2011), "Multifaceted roles of alkyltransferase and related proteins in DNA repair, DNA damage, resistance to chemotherapy, and research tools", Chem. Res. Toxicol., 24(5): 618-639.

Perera, D. et al. (2016), "Differential DNA repair underlies mutation hotspots at active promoters in cancer genomes.", Nature 532, 259-263.

Petrini, J.H.J., D.A. Bressan & M.S. Yao (1997), "The RAD52 epistasis group in mammalian double strand break repair.", Semin Immunol. 9(3):181–188. doi:10.1006/smim.1997.0067

Philippin, G., J. Cadet, D. Gasparutto, G. Mazon, R.P. Fuchs (2014), "Ethylene oxide and propylene oxide derived N7-alkylguanine adducts are bypassed accurately in vivo", DNA Repair (Amst), 22:133-6.

Pouget, J.P. & S.J. Mather (2001), "General aspects of the cellular response to low- and high-LET radiation.", Eur. J. Nucl. Med. 28(4):541–561. doi:10.1007/s002590100484

Ptácek, O. et al. (2001), "Induction and repair of DNA damage as measured by the Comet assay and the yield of somatic mutations in gamma-irradiated tobacco seedlings.", Mutat Res. 491(1-2):17–23

Puchta, H. (2005), "The repair of double-strand breaks in plants: Mechanisms and consequences for genome evolution.", J. Exp. Bot. 56(409):1–14. doi:10.1093/jxb/eri025

Pzoniak, A., L. Muller, M. Salgo, J.K. Jone, P. Larson and D. Tweats (2009), "Elevated ethyl methansulfonate in nelfinavir mesylate (Viracept, Roche): overview", Aids Research and Therapy, 6: 18.

Rathmell, W.K. & G. Chu (1994), "Involvement of the Ku autoantigen in the cellular response to DNA double-strand breaks.", Proc. Natl. Acad. Sci. 91(16):7623–7627. doi:10.1073/pnas.91.16.7623

Rodriguez, G.P., Song, J.B., Crouse, G.F. (2013), In Vivo Bypass of 8-oxodG, PLoS Genetics, 9:e1003682.

Sage, E. & N. Shikazono (2017), "Free Radical Biology and Medicine Radiation-induced clustered DNA lesions : Repair and mutagenesis ☆.", Free Radic. Biol. Med. 107(December 2016):125–135. doi:10.1016/j.freeradbiomed.2016.12.008

Saini, N. et al. (2017), "Migrating bubble during break-induced replication drives conservative DNA synthesis", Nature, 502:389-392.

Sakofsky, C.J. et al. (2015), "Translesion Polymerases Drive Microhomology-Mediated Break-Induced Replication Leading to Complex Chromosomal Rearrangements", Mol. Cell, 60:860-872.

Sassa, A., Kamoshita, N., Kanemaru, Y., Honma, M., Yasui, M. (2015), Xeroderma Pigmentosum Group A Suppresses Mutagenesis Caused by Clustered Oxidative DNA Adducts in the Human Genome, PLoS One, 10:e0142218.

Seager, A., Shah, U., Mikhail, J., Nelson, B., Marquis, B., Doak, S., Johnson, G., Griffiths, S., Carmichael, P., Scott, S., Scott, A., Jenkins, G. (2012), Pro-oxidant Induced DNA Damage in Human Lymphoblastoid Cells: Homeostatic Mechanisms of Genotoxic Tolerance, Toxicol Sci, 128:387-397.

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