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Relationship: 2565


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

Inhibition, ECT complexes of the respiratory chain leads to Increase, Oxidative Stress

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
Inhibition of mitochondrial electron transport chain (ETC) complexes leading to kidney toxicity adjacent Not Specified Not Specified Agnes Aggy (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 Do not cite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

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

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

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

Reactive oxygen species (ROS) are molecules such as hydrogen peroxide and superoxide, which are highly reactive and are able to oxidize many of the cellular components they interact with (Zhao et al., 2019). Mitochondrial electron transport chain inhibition results in the increased formation of ROS, lipid peroxidation, and protein peroxidation (Shaki et al., 2012; Huerta-García et al., 2014). GSH and other antioxidants are also oxidized by the excess formation of ROS, resulting in an imbalance in the antioxidant and ROS levels (Shaki et al., 2012). These processes are all components of oxidative stress (Shaki et al., 2012; García-Niño et al., 2013; Ma et al., 2017).

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

The biological plausibility for this KER is moderate, as some specific stressors showed the dependent change in both events, however there was an inconsistency with one article where KE1 preceded the MIE.

ROS formation occurs mainly in the mitochondria of a cell, specifically by the complexes of the electron transport chain (ETC) (Zhao et al., 2019; Yu et al., 2021; Shaki et al., 2012; Huerta-García et al., 2014). ROS formation is the result of the leaking of electrons from the ETC, which can then interact with oxygen molecules to form hydrogen peroxide and superoxide (Zhao et al., 2019). In particular the superoxide anion is created as a result of the reaction of oxygen with the iron-sulfur centers in complexes I and III (Kruidering et al., 1997). This is a normal function of the mitochondria when ROS formation is only produced at very low levels, as ROS molecules are involved in signalling pathways within the cell (Zhao et al., 2019). These molecules are then scavenged in the cell by antioxidants in order to maintain a balance of ROS levels in the cell (Kruidering et al., 1997, Zhao et al., 2019). However, complex inhibition in the ETC results in a disrupted electron flow and therefore leads to an increased incidence of electron leakage (Zhao et al., 2019). Complex I and III in particular are considered to be the most common sites of ROS formation within the mitochondria (Zhao et al., 2019, Kuridering et al., 1997, Shaki et al., 2012). Superoxide and hydrogen peroxide molecules then further the increase of oxidative stress by oxidizing lipid molecules and protein molecules while depleting antioxidant molecules (Shaki et al., 2012; Santos et al., 2007).

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
  1. One of the articles, the Shaki et al.’s 2012 article, did not show dose concordance for this KER when using uranium as a treatment, as oxidative stress was induced before mitochondrial electron transport chain inhibition occurred, at 50 and 100 μM respectively.
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

There are currently no articles detailing the response-response relationship between the inhibition of the mitochondrial ETC and an increase in oxidative stress. Further studies will need to be conducted in order to determine a response-response relationship.

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

There are currently not enough articles which investigate the time-scale over which inhibition of the mitochondrial ETC occurs and instigates oxidative stress and further research must therefore be conducted to identify the time-scale for this relationship.

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

There are no known modulating factors.

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

There is a known feedback loop which influences this key event relationship. Inhibition of the mitochondrial electron transport chain results in increased oxidative stress, which in turn further inhibits the mitochondrial electron transport chain (Guo et al., 2013). The molecular basis behind this is that the ROS molecules are damaging to the macromolecules, such as DNA, proteins, and lipids that they interact with in the mitochondria (Guo et al., 2013). Unrepaired damage to mitochondrial DNA, which is known to be more sensitive than nuclear DNA to ROS molecules due to proximity to the ETC, leads to defective complex I and III function and results in increased reduction of oxygen to it’s reactive forms (Guo et al., 2013; Gonzalez-Hunt et al., 2018). Similarly, damage to the mitochondrial DNA coding for other critical proteins for electron transport can lead to further generation of ROS molecules, all leading to a cycle of ROS molecule generation and organelle dysfunction which ultimately results in the induction of apoptosis (Guo et al., 2013). 

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 pertains to only eukaryotic organisms, as prokaryotic organisms do not have mitochondria (Lynch and Marinov, 2017).


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

Ferreira, G. K., Cardoso, E., Vuolo, F. S., Michels, M., Zanoni, E. T., Carvalho-Silva, M., . . .

Paula, M. M. S. (2015). Gold nanoparticles alter parameters of oxidative stress and energy metabolism in organs of adult rats. Biochem. Cell Biol., 93, 548-557. doi:10.1139/bcb-2015-0030

García-Niño, W. R., Tapia, E., Zazueta, C., Zatarain-Barrón, Z. L., Hernández-Pando, R., Vega-

García, C. C., & Pedraza-Chaverrí, J. (2013). Curcumin pretreatment prevents potassium dichromate-induced hepatotoxicity, oxidative stress, decreased respiratory complex I activity, and membrane permeability transition pore opening. Evidence-Based Complementary and Alternative Medicine, (424692), 1-19. doi:10.1155/2013/424692

Gonzalez-Hunt, C. P., Wadhawa, M., Sanders, L. H. (2018). DNA damage by oxidative stress:

Measurement strategies for two genomes. Current Opinion in Toxicology, 7, 87-94. ISSN 2468-2020. doi:10.1016/j.cotox.2017.11.001.

Guo, C., Sun, L., Chen, X., & Zhang, D. (2013). Oxidative stress, mitochondrial damage and

neurodegenerative diseases. Neural Regen Rex, 8(21), 2003-2014. doi:0.3969/j.issn.1673-5374.2013.21.009

Huerta-García, E., Perez-Arizti, J. A., Marquez-Ramirez, S. G., Delgado-Buenrostro, N. L.,

Chirino, Y. I., Iglesias, G. G., & Lopez-Marure, R. (2014). Titanium dioxide nanoparticles induce strong oxidative stress and mitochondrial damage in glial cells. Free Radical Biology and Medicine, 73, 84-94. doi:10.1016/j.freeradbiomed.2014.04.026

Kruidering, M., Van De Water, B., De Heer, E., Mulder, G. J., & Nagelkerke, J. F. (1997).

Cisplatin-induced nephrotoxicity in porcine proximal tubular cells: Mitochondrial dysfunction by inhibition of complexes I to IV of the respiratory chain. The Journal of Pharmacology and Experimental Therapeutics, 280(2), 638-649.

Li, X., Fang, P., Mai, J., Choi, E. T., Wang, H., & Yang, X. (2013). Targeting mitochondrial

reactive oxygen species as novel therapy for inflammatory diseases and cancers. Journal of Hematology and Oncology, 6(19), 1-19. doi:10.1186/1756-8722-6-19

Lynch, M., & Marinov, G. K. (2017). Membranes, energetics, and evolution across the

prokaryote-eukaryote divide. eLife6, e20437. 10.7554/eLife.20437

Ma, L., Liu, J., Dong, J., Xiao, Q., Zhao, J., & Jiang, F. (2017). Toxicity of Pb2+ on rat liver

mitochondria induced by oxidative stress and mitochondrial permeability transition. Toxicol.Res., 6, 822. doi:10.1039/c7tx00204a

Miyayama, T., Arai, Y., Suzuki, N., & Hirano, S. (2013). Mitochondrial electron transport is

inhibited by disappearance of metallothionein in human bronchial epithelial cells follwoing exposure to silver nitrate. Toxicology, 305, 20-29. doi:10.1016/j.tox.2013.01.004

Prakash, C., Soni, M., & Kumar, V. (2015). Biochemical and molecular alterations following

arsenic-induced oxidative stress and mitochondrial dysfunction in rat brain. Biol.Trace Elem.Res., 167, 121-129. doi:10.1007/s12011-015-0284-9

Santos, N. A. G., Catão, C. S., Martins, N. M., Curti, C., Bianchi, M. L. P., & Santos, A. C.

(2007). Cisplatin-induced nephrotoxicity is associated with oxidative stress, redox state unbalance, impairment of energetic metabolism and apoptosis in rat kidney mitochondria. Archives of Toxicology, 81(7), 495-504. doi:10.1007/s00204-006-0173-2

Shaki, F., Hosseini, M. J., Ghazi-Khansari, M., & Pourahmad, J. (2012). Toxicity of depleted

uranium on isolated rat kidney mitochondria. Biochimica Et Biophysica Acta - General Subjects, 1820(12), 1940-1950. doi:10.1016/j.bbagen.2012.08.015

Wang, Y., Fang, J., Leonard, S. S., & Krishna Rao, K. M. (2004). Cadmium inhibits the electron

transfer chain and induces reactive oxygen species. Free Radical Biology and Medicine, 36(11), 1434-1443. doi:10.1016/j.freeradbiomed.2004.03.010

Yu, L., Li, W., Chu, J., Chen, C., Li, X., Tang, W., . . . Xiong, Z. (2021). Uranium inhibits

mammalian mitochondrial cytochrome c oxidase and ATP synthase. Environmental Pollution, 271, 116377. doi:10.1016/j.envpol.2020.116377

Zhao, R., Jiang, S., Zhang, L., & Yu, Z. (2019). Mitochondrial electron transport chain, ROS

generation and uncoupling (review). International Journal of Molecular Medicine, 44(1), 3-15. doi:10.3892/ijmm.2019.4188

Zmijewski, J. W., Landar, A., Watanabe, N., Dickinson, D. A., Noguchi, N., Darley-Usmar, V.

M. (2005). Cell signalling by oxidized lipids and the role of reactive oxygen species in the endothelium. Biochem. Soc. Trans., 33(6),1385–1389. doi:10.1042/BST20051385