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Relationship: 1719

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

Increased pro-inflammatory mediators 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 adjacent Moderate 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 Do not cite EAGMST Under Review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

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
During brain development, adulthood and aging 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

Cells of the innate (microglia and astrocytes) and of the adaptive (infiltrating monocytes and lymphocytes) immune system of the brain have various ways to kill neighboring cells. This is in part due to evolutionary-conserved mechanisms evolved to kill virus-infected cells or tumor cells; in part it is a bystander phenomenon due to the release of mediators that should activate other cells and contribute to the killing of invading micro-organisms. An exaggerated or unbalanced activation of immune cells can thus lead to parenchymal (neuronal) cell death (Gehrmann et al., 1995). Mediators known to have such effects comprise components of the complement system and cytolkines/death receptor ligands triggering programmed cell death (Dong and Benveniste, 2001). Various secreted proteases (e.g. matrix metalloproteases), lipid mediators (e.g. ceramide or gangliosides) or reactive oxygen species can contribute to bystander death of neurons (Chao et al., 1995; Nakajima et al., 2002; Brown and Bal-Price, 2003; Kraft and Harry, 2011; Taetsch and Block, 2013). The equimolar production of superoxide and NO from glial cells can lead to high steady levels of peoxynitrite, which is a very potent cytotoxicant (Yuste et al., 2015). Already stressed neurons, with an impaired anti-oxidant defence system, are more sensitive to such mediators (Xu et al., 2015). Healthy cells continuously display anti "eat-me" signals, while damaged and stressed neurons/neurites display "eat-me" signals that may be recognized by microglia as signals to start phagocytosis (Neher et al., 2012). Reactive astrocytes are also able to release neurotoxic molecules (Mena and Garcia de Ybenes, 2008; Niranjan, 2014). However, astrocytes may also be protective due to their capacity to quench free radicals and secrete neurotrophic factors. The activation of astrocytes may reduce neurotrophic support to neurons (for review, Mena and Garcia de Ybenes, 2008).

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

In vitro co-culture experiments have demonstrated that reactive glial cells (microglia and astrocytes) can kill neurons (Chao et al., 1995; Brown and Bal-Price, 2003; Kraft and Harry, 2011; Taetzsch and Block, 2013) and that interventions with e.g. i-NOS inhibition can rescue the neurons (Yadav et al., 2012; Brzozowski et al., 2015). Drugs that block Toll like receptor pathways, which are expressed by glial cells have been proven to be protective by decreasing ROS and RNS production (Lucas et al., 2013).

Reactive microglia can remove synapses, a process known as synapse stripping (Banati et al., 1993; Kettenmann et al., 2013). Reactive astrocytes were also associated with neurite and synapse reduction (Calvo-Ochoa et al., 2014). Microglia can modulate synapse plasticity, an effect mediated by cytokines. During development, microglia can promote synaptogenesis or engulf synapses, a process known as synaptic pruning (for review, Jebelli et al., 2015). It is hypothesized that alterations in microglia functioning during synapse formation and maturation of the brain can have significant long-term effects on the final established neural circuits (for review, Harry and Kraft, 2012). The fact that astrocytes can receive and respond to the synaptic information produced by neuronal activity, owing to their expression of a wide range of neurotransmitter receptors, has given rise to the concept of tripartite synapse (for review, Perez-Alvarez and Araque, 2013; Bezzi and Volterra, 2001). Pro-inflammatory cytokines, such as TNF-a, IL-1b and IL-6, which are produced by reactive astrocytes, are on one side implicated in synapse formation and scaling, long-term potentiation and neurogenesis (for review, Bilbo and Schwartz, 2009) and on the other side can kill neurons (Chao et al., 1995; Kraft and Harry, 2011). Taken together, this suggests that neuron-glia interactions are tightly regulated and that an imbalance, such as increased or long-term release of these inflammatory mediators may lead to deleterious effects on neurons.

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

In 3D rat brain cell-cultures, co-administration of the pro-inflammatory cytokine IL-6 (10 ng/ml) together with non-cytotoxic concentrations of MeHgCl (3 x 10-7 M) for 10 days protected from the mercury-induced decreased in MAP2 immunostaining, suggesting a positive effect of IL-6, in accord with its descibed trophic activity (Eskes et al., 2002).

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

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

Allen, J.W., Shanker, G., Aschner, M., 2001. Methylmercury inhibits the in vitro uptake of the glutathione precursor, cystine, in astrocytes, but not in neurons. Brain Res. 894, 131-40.

Aschner, M., et al., 2007. Involvement of glutamate and reactive oxygen species in methylmercury neurotoxicity. Braz J Med Biol Res. 40, 285-91.

Banati, R.B., et al., 1993. Cytotoxicity of microglia. Glia. 7, 111-8.

Bezzi, P., Volterra, A., 2001. A neuron-glia signalling network in the active brain. Curr Opin Neurobiol. 11, 387-94.

Bilbo, S.D., Schwarz, J.M., 2009. Early-life programming of later-life brain and behavior: a critical role for the immune system. Front Behav Neurosci. 3, 14.

Brown GC, Bal-Price A. 2003. Inflammatory neurodegeneration mediated by nitric oxide, glutamate, and mitochondria. Mol Neurobiol 27(3): 325-355.

Brzozowski MJ, Jenner P, Rose S. 2015. Inhibition of i-NOS but not n-NOS protects rat primary cell cultures against MPP(+)-induced neuronal toxicity. J Neural Transm 122(6): 779-788.

Calvo-Ochoa, E., et al., 2014. Short-term high-fat-and-fructose feeding produces insulin signaling alterations accompanied by neurite and synaptic reduction and astroglial activation in the rat hippocampus. J Cereb Blood Flow Metab. 34, 1001-8.

Chao CC, Hu S, Peterson PK. 1995. Glia, cytokines, and neurotoxicity. CritRevNeurobiol 9: 189-205.

Dong Y, Benveniste EN. 2001. Immune Function of Astrocytes. Glia 36: 180-190.

Eskes C, Honegger P, Juillerat-Jeanneret L, Monnet-Tschudi F. 2002. Microglial reaction induced by noncytotoxic methylmercury treatment leads to neuroprotection via interactions with astrocytes and IL-6 release. Glia 37(1): 43-52.

Gehrmann J, Banati RB, Wiessnert C, Hossmann KA, Kreutzberg GW. 1995. Reactive microglia in cerebral ischaemia: An early mediator of tissue damage? NeuropatholApplNeurobiol 21: 277-289.

Harry, G.J., Kraft, A.D., 2012. Microglia in the developing brain: a potential target with lifetime effects. Neurotoxicology. 33, 191-206.

Hertz, L., Zielke, H.R., 2004. Astrocytic control of glutamatergic activity: astrocytes as stars of the show. Trends Neurosci. 27, 735-43.

Jebelli, J., et al., 2015. Glia: guardians, gluttons, or guides for the maintenance of neuronal connectivity? Ann N Y Acad Sci. 1351, 1-10.

Kettenmann, H., Kirchhoff, F., Verkhratsky, A., 2013. Microglia: new roles for the synaptic stripper. Neuron. 77, 10-8.

Kraft AD, Harry GJ. 2011. Features of microglia and neuroinflammation relevant to environmental exposure and neurotoxicity. International journal of environmental research and public health 8(7): 2980-3018.

Lucas, K., Maes, M., 2013. Role of the Toll Like receptor (TLR) radical cycle in chronic inflammation: possible treatments targeting the TLR4 pathway. Mol Neurobiol. 48, 190-204.

Matharasala, G., Samala, G., Perumal, Y., 2017. MG17, a novel triazole derivative abrogated neuroinflammation and related neurodegenerative symptoms in rodents. Curr Mol Pharmacol.

Mena MA, Garcia de Yebenes J. 2008. Glial cells as players in parkinsonism: the "good," the "bad," and the "mysterious" glia. Neuroscientist 14(6): 544-560.

Nakajima K, Tohyama Y, Kohsaka S, Kurihara T. 2002. Ceramide activates microglia to enhance the production/secretion of brain-derived neurotrophic factor (BDNF) without induction of deleterious factors in vitro. J Neurochem 80: 697-705.

Niranjan R. 2014. The role of inflammatory and oxidative stress mechanisms in the pathogenesis of Parkinson's disease: focus on astrocytes. Mol Neurobiol 49(1): 28-38.

Neher JJ, Neniskyte U, Brown GC. 2012. Primary phagocytosis of neurons by inflamed microglia: potential roles in neurodegeneration. Frontiers in pharmacology 3: 27.

Perez-Alvarez, A., Araque, A., 2013. Astrocyte-neuron interaction at tripartite synapses. Curr Drug Targets. 14, 1220-4.

Roda, E., et al., 2008. Cerebellum cholinergic muscarinic receptor (subtype-2 and -3) and cytoarchitecture after developmental exposure to methylmercury: an immunohistochemical study in rat. J Chem Neuroanat. 35, 285-94.

Sakamoto, M., et al., 2008. Possible involvement of cathepsin B released by microglia in methylmercury-induced cerebellar pathological changes in the adult rat. Neurosci Lett. 442, 292-6.

Shanker, G., Syversen, T., Aschner, M., 2003. Astrocyte-mediated methylmercury neurotoxicity. Biol Trace Elem Res. 95, 1-10.

Santhanasabapathy, R., et al., 2015. Farnesol quells oxidative stress, reactive gliosis and inflammation during acrylamide-induced neurotoxicity: Behavioral and biochemical evidence. Neuroscience. 308, 212-27.

Sidoryk-Wegrzynowicz, M., et al., 2011. Role of astrocytes in brain function and disease. Toxicol Pathol. 39, 115-23.

Taetzsch T, Block ML. 2013. Pesticides, microglial NOX2, and Parkinson's disease. J Biochem Mol Toxicol 27(2): 137-149.

Ximenes-da-Silva, A., 2016. Metal Ion Toxins and Brain Aquaporin-4 Expression: An Overview. Front Neurosci. 10, 233.

Xu, S., et al., 2015. Wogonin prevents rat dorsal root ganglion neurons death via inhibiting tunicamycin-induced ER stress in vitro. Cell Mol Neurobiol. 35, 389-398.

Yadav, S., et al., 2012. Role of secondary mediators in caffeine-mediated neuroprotection in maneb- and paraquat-induced Parkinson's disease phenotype in the mouse. Neurochem Res. 37, 875-84.

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

Zhang, Y., Bolivar, V.J., Lawrence, D.A., 2013. Maternal exposure to mercury chloride during pregnancy and lactation affects the immunity and social behavior of offspring. Toxicol Sci. 133, 101-11.