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Relationship: 1690

Title

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

Oxidative Stress leads to Cell injury/death

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
Binding of electrophilic chemicals to SH(thiol)-group of proteins and /or to seleno-proteins involved in protection against oxidative stress during brain development leads to impairment of learning and memory non-adjacent High High Marie-Gabrielle Zurich (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 Do not cite EAGMST Under Review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
rat Rattus norvegicus High NCBI
mouse Mus musculus High NCBI
zebra fish Danio rerio High NCBI
salmonid fish salmonid fish 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
Male
Female

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

Oxidative stress (OS) as a concept in redox biology and medicine has been formulated in 1985 (Sies, 2015). OS is intimately linked to cellular energy balance and comes from the imbalance between the generation and detoxification of reactive oxygen and nitrogen species (ROS/RNS) or from a decay of the antioxidant protective ability. OS is characterized by the reduced capacity of endogenous systems to fight against the oxidative attack directed towards target biomolecules (Wang and Michaelis, 2010; Pisoschi and Pop, 2015).  Glutathione, the most important redox buffer in cells (antioxidant), cycles between reduced glutathione (GSH) and oxidized glutathione disulfide (GSSG), and serves as a vital sink for control of ROS levels in cells (Reynolds et al., 2007).  Several case-control studies have reported the link between lower concentrations of GSH, higher levels of GSSG and the development of diseases (Rossignol and Frye, 2014). OS can cause cellular damage and subsequent cell death because the ROS oxidize vital cellular components such as lipids, proteins, and nucleic acids (Gilgun-Sherki, Melamed and Offen, 2001; Wang and Michaelis, 2010).

The central nervous system is especially vulnerable to free radical damage since it has a high oxygen consumption rate, an abundant lipid content and reduced levels of antioxidant enzymes (Coyle and Puttfarcken, 1993; Markesbery, 1997). It has been show that the developing brain is particularly vulnerable to neurotoxicants and OS due to differentiation processes, changes in morphology, lack of physiological barriers and less intrinsic capacity to cope with cellular stress (Grandjean and Landrigan, 2014; Sandström et al., 2017). However, it has to be noted that neural stem cells distinguish themeselves from post-mitotic neural cells by their lower ROS levels and higher expression of the key antioxidant enzymes glutathione peroxidase. This increased "vigilance" of antioxidant mechanisms might represent an innate characteristic of NSCs, which not only defines their cell fate, but also helps them to encounter oxidative stress (Madhavan et al., 2006).

OS has been linked to brain aging, neurodegenerative diseases, and other related adverse conditions.  There is evidence that free radicals play a role in cerebral ischemia-reperfusion, head injury, Parkinson’s disease, amyotrophic lateral sclerosis, Down’s syndrome, and Alzheimer’s disease due to cellular damage (Markesbery, 1997; Gilgun-Sherki, Melamed and Offen, 2001; Wang and Michaelis, 2010). OS has also been linked to neurodevelopmental diseases and deficits like autism spectrum disorder and postnatal motor coordination deficits (Wells et al., 2009; Rossignol and Frye, 2014; Bhandari and Kuhad, 2015).

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

A noteworthy insight, early on, was the perception that oxidation-reduction (redox) reactions in living cells are utilized in fundamental processes of redox regulation, collectively termed ‘redox signaling’ and ‘redox control’ (Sies, 2015).

Free radical-induced damage in OS has been confirmed as a contributor to the pathogenesis and patho-physiology of many chronic diseases, such as Alzheimer, atherosclerosis, Parkinson, but also in traumatic brain injury, sepsis, stroke, myocardial infraction, inflammatory diseases, cataracts and cancer (Bar-Or et al., 2015; Pisoschi and Pop, 2015). It has been assessed that oxidative stress is correlated with over 100 diseases, either as source or outcome (Pisoschi and Pop, 2015).

Therefore, the fact that ROS over-production can kill neurons is well accepted (Brown and Bal-Price, 2003; Taetzsch and Block, 2013). This ROS over-production can occur in the neurons themselves or can also have a glial origin (Yuste et al., 2015).

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

Mercury-induced upregulation of GSH level and GR activity as an adaptive mechanism following lactational exposure to methylmercury (10 mg/L in drinking water) associated with motor deficit, suggesting neuronal impairment (Franco et al., 2006).

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
Time-scale
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

Rat, Mouse: (Sarafian et al., 1994; Castoldi et al., 2000; Kaur et al., 2006; Franco et al., 2007; Lu et al., 2011; Polunas et al., 2011)

(Richetti et al., 2011) - Adult and healthy zebrafish of both sexes (12 animals and housed in 3 L) mercury chloride final concentration of 20 mg/L. Mercury chloride promoted a significant decrease in acetylcholinesterase activity and the antioxidant competence was also decreased.

(Berntssen, Aatland and Handy, 2003) - Atlantic salmon (Salmo salar L.) were supplemented with mercuric chloride (0, 10, or 100 mg Hg per kg) or methylmercury chloride (0, 5, or 10 mg Hg per kg) for 4 months.

Methylmercury chloride

  • accumulated significantly in the brain of fish fed 5 or 10 mg/kg
  • No mortality or growth reduction
  • - 2-fold increase in the antioxidant enzyme super oxide dismutase (SOD) in the brain
  • 10 mg/kg - 7-fold increase of lipid peroxidative products (thiobarbituric acid reactive substances, TBARS) and a subsequently 1.5-fold decrease in anti oxidant enzyme activity (SOD and glutathione peroxidase, GSH-Px). Fish also had pathological damage (vacoulation and necrosis), significantly reduced neural enzyme activity (5-fold reduced monoamine oxidase, MAO, activity), and reduced overall post-feeding activity behaviour.

Mercuric chloride

  • accumulated significantly in the brain only at 100 mg/kg
  • No mortality or growth reduction
  • 100 mg/kg -  significant reduced neural MAO activity and pathological changes (astrocyte proliferation) in the brain, however, neural SOD and GSH-Px enzyme activity, lipid peroxidative products (TBARS), and post feeding behaviour did not differ from controls.

References

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

Allam,  a et al. (2011) ‘Prenatal and perinatal acrylamide disrupts the development of cerebellum in rat: Biochemical and morphological studies.’, Toxicology and industrial health, 27, pp. 291–306. doi: 10.1177/0748233710386412.

Bar-Or, D. et al. (2015) ‘Oxidative stress in severe acute illness’, Redox Biology. Elsevier, 4, pp. 340–345. doi: 10.1016/j.redox.2015.01.006.

Berntssen, M. H. G., Aatland, A. and Handy, R. D. (2003) ‘Chronic dietary mercury exposure causes oxidative stress, brain lesions, and altered behaviour in Atlantic salmon (Salmo salar) parr’, Aquatic Toxicology, 65(1), pp. 55–72. doi: 10.1016/S0166-445X(03)00104-8.

Bhandari, R. and Kuhad, A. (2015) ‘Neuropsychopharmacotherapeutic efficacy of curcumin in experimental paradigm of autism spectrum disorders’, Life Sciences. Elsevier Inc., 141, pp. 156–169. doi: 10.1016/j.lfs.2015.09.012.

Brown, G.C. and Bal-Price, A. (2003) ‘Inflammatory neurodegeneration mediated by nitric oxide, glutamate, and mitochondria’, Molecular Biology, 27(3), pp. 325-355.

Castoldi, A. F. et al. (2000) ‘Early acute necrosis, delayed apoptosis and cytoskeletal breakdown in cultured cerebellar granule neurons exposed to methylmercury’, Journal of Neuroscience Research, 59(6), pp. 775–787. doi: 10.1002/(SICI)1097-4547(20000315)59:6<775::AID-JNR10>3.0.CO;2-T.

Coyle, J. and Puttfarcken, P. (1993) ‘Glutamate Toxicity’, Science, 262, pp. 689–95.

Franco, J. L. et al. (2006) ‘Cerebellar thiol status and motor deficit after lactational exposure to methylmercury’, Environmental Research, 102(1), pp. 22–28. doi: 10.1016/j.envres.2006.02.003.

Franco, J. L. et al. (2007) ‘Mercurial-induced hydrogen peroxide generation in mouse brain mitochondria: Protective effects of quercetin’, Chemical Research in Toxicology, 20(12), pp. 1919–1926. doi: 10.1021/tx7002323.

Gilgun-Sherki, Y., Melamed, E. and Offen, D. (2001) ‘Oxidative stress induced-neurodegenerative diseases: The need for antioxidants that penetrate the blood brain barrier’, Neuropharmacology, 40(8), pp. 959–975. doi: 10.1016/S0028-3908(01)00019-3.

Grandjean, P. and Landrigan, P. J. (2014) ‘Neurobehavioural effects of developmental toxicity’, The Lancet Neurology, 13(3), pp. 330–338. doi: 10.1016/S1474-4422(13)70278-3.

Kaur, P., Aschner, M. and Syversen, T. (2006) ‘Glutathione modulation influences methyl mercury induced neurotoxicity in primary cell cultures of neurons and astrocytes’, NeuroToxicology, 27(4), pp. 492–500. doi: 10.1016/j.neuro.2006.01.010.

Lakshmi, D. et al. (2012) ‘Ameliorating effect of fish oil on acrylamide induced oxidative stress and neuronal apoptosis in cerebral cortex’, Neurochemical Research, 37(9), pp. 1859–1867. doi: 10.1007/s11064-012-0794-1.

Lu, T. H. et al. (2011) ‘Involvement of oxidative stress-mediated ERK1/2 and p38 activation regulated mitochondria-dependent apoptotic signals in methylmercury-induced neuronal cell injury’, Toxicology Letters. Elsevier Ireland Ltd, 204(1), pp. 71–80. doi: 10.1016/j.toxlet.2011.04.013.

Madhavan, L. et al. (2006) ‘Increased "vigilance" of antioxidant mechanisms in neural stem cells potentiates their capability to resist oxidative stress’, Stem Cells 24(2) pp. 2110-2119.

Markesbery, W. R. (1997) ‘Oxidative stress hypothesis in Alzheimer’s disease’, Free Radical Biology and Medicine, 23(1), pp. 134–147. doi: 10.1016/S0891-5849(96)00629-6.

Pisoschi, A. M. and Pop, A. (2015) ‘The role of antioxidants in the chemistry of oxidative stress: A review’, European Journal of Medicinal Chemistry. Elsevier Masson SAS, 97, pp. 55–74. doi: 10.1016/j.ejmech.2015.04.040.

Polunas, M. et al. (2011) ‘Role of oxidative stress and the mitochondrial permeability transition in methylmercury cytotoxicity’, NeuroToxicology. Elsevier B.V., 32(5), pp. 526–534. doi: 10.1016/j.neuro.2011.07.006.

Reynolds, A. et al. (2007) ‘Oxidative Stress and the Pathogenesis of Neurodegenerative Disorders’, International Review of Neurobiology, 82(7), pp. 297–325. doi: 10.1016/S0074-7742(07)82016-2.

Richetti, S. K. et al. (2011) ‘Acetylcholinesterase activity and antioxidant capacity of zebrafish brain is altered by heavy metal exposure’, NeuroToxicology. Elsevier B.V., 32(1), pp. 116–122. doi: 10.1016/j.neuro.2010.11.001.

Rossignol, D. A. and Frye, R. E. (2014) ‘Evidence linking oxidative stress, mitochondrial dysfunction, and inflammation in the brain of individuals with autism’, Frontiers in Physiology, 5 APR(April), pp. 1–15. doi: 10.3389/fphys.2014.00150.

Sandström, J. et al. (2016) ‘Toxicology in Vitro Development and characterization of a human embryonic stem cell-derived 3D neural tissue model for neurotoxicity testing’, Tiv, pp. 1–12. doi: 10.1016/j.tiv.2016.10.001.

Sandström, J. et al. (2017) ‘Potential mechanisms of development-dependent adverse effects of the herbicide paraquat in 3D rat brain cell cultures’, NeuroToxicology, 60, pp. 116–124. doi: 10.1016/j.neuro.2017.04.010.

Sarafian, T. A. et al. (1994) ‘Bcl-2 Expression Decreases Methyle Mercury-Induced Free-Radical Generation and Cell Killing in a Neural Cell Line’, Toxicol. Lett., 74(2), pp. 149–155.

Sies, H. (2015) ‘Oxidative stress: A concept in redox biology and medicine’, Redox Biology. Elsevier, 4, pp. 180–183. doi: 10.1016/j.redox.2015.01.002.

Taetzsch, T. and Block, M.L. (2013) ‘Pesticides, microglial NOX2, and Parkinson's disease’, J Biochem Molecular Toxicology, 27(2), pp. 137-149.

Wang, X. and Michaelis, E. K. (2010) ‘Selective neuronal vulnerability to oxidative stress in the brain’, Frontiers in Aging Neuroscience, 2(MAR), pp. 1–13. doi: 10.3389/fnagi.2010.00012.

Wells, P. G. et al. (2009) ‘Oxidative stress in developmental origins of disease: Teratogenesis, neurodevelopmental deficits, and cancer’, Toxicological Sciences, 108(1), pp. 4–18. doi: 10.1093/toxsci/kfn263.

Yuste, J.E., et al., 2015. Implications of glial nitric oxide in neurodegenerative diseases. Front Cell Neurosci. 9, 322.