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Relationship: 1690


A descriptive phrase which clearly defines the two KEs being considered and the sequential relationship between them (i.e., which is upstream, and which is downstream). More help

Oxidative Stress leads to Cell injury/death

Upstream event
The causing Key Event (KE) in a Key Event Relationship (KER). More help
Downstream event
The responding Key Event (KE) in a Key Event Relationship (KER). 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

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) Open for citation & comment WPHA/WNT Endorsed

Taxonomic Applicability

Latin or common names of a species or broader taxonomic grouping (e.g., class, order, family) 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.  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

An indication of the the relevant sex for this KER. More help
Sex Evidence

Life Stage Applicability

An indication of the the relevant life stage(s) for this KER.  More help
Term Evidence
All life stages High

Key Event Relationship Description

Provides a concise overview of the information given below as well as addressing details that aren’t inherent in the description of the KEs themselves. 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 Collection Strategy

Include a description of the approach for identification and assembly of the evidence base for the KER. For evidence identification, include, for example, a description of the sources and dates of information consulted including expert knowledge, databases searched and associated search terms/strings.  Include also a description of study screening criteria and methodology, study quality assessment considerations, the data extraction strategy and links to any repositories/databases of relevant references.Tabular summaries and links to relevant supporting documentation are encouraged, wherever possible. More help

Evidence Supporting this KER

Addresses the scientific evidence supporting KERs in an AOP setting the stage for overall assessment of the AOP. More help
Biological Plausibility
Addresses the biological rationale for a connection between KEupstream and KEdownstream.  This field 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.   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
Addresses inconsistencies or uncertainties in the relationship including the identification of experimental details that may explain apparent deviations from the expected patterns of concordance. 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).

Known modulating factors

This table captures specific information on the MF, its properties, how it affects the KER and respective references.1.) What is the modulating factor? Name the factor for which solid evidence exists that it influences this KER. Examples: age, sex, genotype, diet 2.) Details of this modulating factor. Specify which features of this MF are relevant for this KER. Examples: a specific age range or a specific biological age (defined by...); a specific gene mutation or variant, a specific nutrient (deficit or surplus); a sex-specific homone; a certain threshold value (e.g. serum levels of a chemical above...) 3.) Description of how this modulating factor affects this KER. Describe the provable modification of the KER (also quantitatively, if known). Examples: increase or decrease of the magnitude of effect (by a factor of...); change of the time-course of the effect (onset delay by...); alteration of the probability of the effect; increase or decrease of the sensitivity of the downstream effect (by a factor of...) 4.) Provision of supporting scientific evidence for an effect of this MF on this KER. Give a list of references.  More help
Response-response Relationship
Provides sources of data that define the response-response relationships between the KEs.  More help
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?). More help
Known Feedforward/Feedback loops influencing this KER
Define whether there are known positive or negative feedback mechanisms involved and what is understood about their time-course and homeostatic limits. More help

Domain of Applicability

A free-text section of the KER description that the developers can use to explain their rationale for the taxonomic, life stage, or sex applicability structured 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.


List of the literature that was cited for this KER description. 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.