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Relationship: 1686


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

Glutamate dyshomeostasis 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 High Moderate Marie-Gabrielle Zurich (send email) Under development: Not open for comment. Do not cite EAGMST Approved

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

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

Glutamate is the major excitatory neurotransmitter in the mammalian CNS, where it plays key roles in development, learning, memory and response to injury. However, glutamate at high concentrations at the synaptic cleft acts as a toxin, inducing neuronal injury and death (Meldrum, 2000; Ozawa et al., 1998). Glutamate-mediated neurotoxicity has been dubbed as “excitotoxicity”, referring to the consequence of the overactivation of the N-methyl D-aspartate (NMDA)–type glutamate receptors (cf AOP 48), leading to increased Na+ and Ca2+ influx into neurons (Choi, 1992; Pivovarova and Andrews, 2010). Increased intracellular Ca2+ levels are associated with the generation of oxidative stress and neurotoxicity (Lafon-Cazal et al., 1993). Accordingly, the control of extracellular levels of glutamate dictates its physiological/pathological actions and this equilibrium is maintained primarily by the action of several glutamate transporters (such as GLAST, GLT1, and EAAC1) located on astrocytic cell membranes, which remove the excitatory neurotransmitter from the synaptic cleft, keeping its extracellular concentrations below toxic levels (Anderson and Swanson, 2000; Maragakis and Rothstein, 2001; Szydlowska and Tymianski, 2010).

In addition to synaptic transmission, physiological stimulation of glutamate receptors can mediate trophic effects and promote neuronal plasticity. During development, NMDA receptors initiate a cascade of signal transduction events and gene expression changes primarily involving Ca2+-mediated signaling, induced by activation of either Ca2+- permeable receptor channels or voltage-sensitive Ca2+ channels. The consecutive activation of major protein kinase signaling pathways, such as Ras-MAPK/ERK and PI3-K-Akt, contributes to regulation of gene expression through the activation of key transcription factors, such as CREB, SRF, MEF-2, NF-kappaB. Metabotropic glutamate receptors can also engage these signaling pathways, in part by transactivating receptor tyrosine kinases. Indirect effects of glutamate receptor stimulation are due to the release of neurotrophic factors, such as brain derived neurotrophic factor through glutamate-induced release of trophic factors from glia. The trophic effect of glutamate receptor activation is developmental stage-dependent and may play an important role in determining the selective survival of neurons that made proper connections. During this sensitive developmental period, interference with glutamate receptor function may lead to widespread neuronal loss (Balazs, 2006).

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

Glutamate dyshomeostasis and in particular excess of glutamate in the synaptic cleft will lead to overactivation of ionotropic glutamate receptors and cause cell injury/death, as described in AOP 48. The excess of glutamate can result from decreased uptake in astrocytes (Aschner et al., 2000; Brookes and Kristt, 1989), or neurons (Moretto et al., 2005; Porciuncula et al., 2003). But also from the increased release (Reynolds and Racz, 1987). This neurotoxic cascade involves calcium overload and ROS production leading to oxidative stress (Ceccatelli et al., 2010; Lafon-Cazal, 1993; Meldrum, 2000; Ozawa, 1998). Chemicals binding to sulfhydryl (SH)-/seleno-proteins cause a direct oxidative stress by perturbing mitochondrial respiratory chain proteins and by decreasing anti-oxidant defense mechanism (see KER : MIE to KEdown oxidative stress) and an indirect oxidative stress via perturbation of glutamate homeostasis/excitotoxicity. Thus, there may be some redundancy in the empirical support between this KER and the KER linking KEup oxidative stress and KEdown cell injury/death.

Glutamate has been shown to regulate BDNF production (Tao et al., 2002). Accordingly, glutamate may also indirectly contribute to cell injury/death by inducing modifications in the brain levels of trophic factors, since it is known that changes in trophic support can lead to cell injury/death, as well as to perturbation in the physiological establishment of neuronal network (Zhao et al., 2017).

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

No uncertainty or inconsistency reported yet.

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
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

Support for the link between glutamate dyshomeostasis and cell injury /death can be found in rats, and mouse. However, as the neurotransmitter glutamate is already found in insects, it is plausible that this KER is valid throughout taxa (Harris et al., 2014).


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

Albrecht, J., Talbot, M., Kimelberg, H.K., Aschner, M. (1993) The role of sulfhydryl groups and calcium in the mercuric chloride-induced inhibition of glutamate uptake in rat primary astrocyte cultures. Brain Res 607, 249-254.

Anderson, C.M., Swanson, R.A. (2000) Astrocyte glutamate transport: review of properties, regulation, and physiological functions. Glia 32, 1-14.

Aschner, M., Yao, C.P., Allen, J.W., Tan, K.H. (2000) Methylmercury alters glutamate transport in astrocytes. Neurochem Int 37, 199-206.

Balazs, R. (2006) Trophic effect of glutamate. Curr Top Med Chem 6, 961-968.

Brookes, N., Kristt, D.A. (1989) Inhibition of amino acid transport and protein synthesis by HgCl2 and methylmercury in astrocytes: selectivity and reversibility. J Neurochem 53, 1228-1237.

Ceccatelli, S., Dare, E., Moors, M. (2010) Methylmercury-induced neurotoxicity and apoptosis. Chem Biol Interact 188, 301-308.

Choi, D.W. (1992) Excitotoxic cell death. J Neurobiol 23, 1261-1276.

Feng, S., Xu, Z., Liu, W., Li, Y., Deng, Y., Xu, B. (2014) Preventive effects of dextromethorphan on methylmercury-induced glutamate dyshomeostasis and oxidative damage in rat cerebral cortex. Biol Trace Elem Res 159, 332-345.

Fonfria, E., Vilaro, M.T., Babot, Z., Rodriguez-Farre, E., Sunol, C. (2005) Mercury compounds disrupt neuronal glutamate transport in cultured mouse cerebellar granule cells. J Neurosci Res 79, 545-553.

Harris, K.D., Weiss, M., Zahavi, A. (2014) Why are neurotransmitters neurotoxic? An evolutionary perspective. F1000Res 3, 179.

Juarez, B.I., Martinez, M.L., Montante, M., Dufour, L., Garcia, E., Jimenez-Capdeville, M.E. (2002) Methylmercury increases glutamate extracellular levels in frontal cortex of awake rats. Neurotoxicol Teratol 24, 767-771.

Lafon-Cazal, M., Pietri, S., Culcasi, M., Bockaert, J. (1993) NMDA-dependent superoxide production and neurotoxicity. Nature 364, 535-537.

Liu, W., Xu, Z., Deng, Y., Xu, B., Wei, Y., Yang, T. (2013) Protective effects of memantine against methylmercury-induced glutamate dyshomeostasis and oxidative stress in rat cerebral cortex. Neurotox Res 24, 320-337.

LoPachin, R.M., Schwarcz, A.I., Gaughan, C.L., Mansukhani, S., Das, S. (2004) In vivo and in vitro effects of acrylamide on synaptosomal neurotransmitter uptake and release. Neurotoxicology 25, 349-363.

Maragakis, N.J., Rothstein, J.D. (2001) Glutamate transporters in neurologic disease. Arch Neurol 58, 365-370.

Meldrum, B.S. (2000) Glutamate as a neurotransmitter in the brain: review of physiology and pathology. J Nutr 130, 1007S-1015S.

Moretto, M.B., Funchal, C., Santos, A.Q., Gottfried, C., Boff, B., Zeni, G., Pureur, R.P., Souza, D.O., Wofchuk, S., Rocha, J.B. (2005) Ebselen protects glutamate uptake inhibition caused by methyl mercury but does not by Hg2+. Toxicology 214, 57-66.

Morken, T.S., Sonnewald, U., Aschner, M., Syversen, T. (2005) Effects of methylmercury on primary brain cells in mono- and co-culture. Toxicol Sci 87, 169-175.

Ozawa, S., Kamiya, H., Tsuzuki, K. (1998) Glutamate receptors in the mammalian central nervous system. Prog Neurobiol 54, 581-618.

Pivovarova, N.B., Andrews, S.B. (2010) Calcium-dependent mitochondrial function and dysfunction in neurons. FEBS J 277, 3622-3636.

Porciuncula, L.O., Rocha, J.B., Tavares, R.G., Ghisleni, G., Reis, M., Souza, D.O. (2003) Methylmercury inhibits glutamate uptake by synaptic vesicles from rat brain. Neuroreport 14, 577-580.

Reynolds, J.N., Racz, W.J. (1987) Effects of methylmercury on the spontaneous and potassium-evoked release of endogenous amino acids from mouse cerebellar slices. Can J Physiol Pharmacol 65, 791-798.

Szydlowska, K., Tymianski, M. (2010) Calcium, ischemia and excitotoxicity. Cell Calcium 47, 122-129.

Tao, X., West, A.E., Chen, W.G., Corfas, G., Greenberg, M.E. (2002) A calcium-responsive transcription factor, CaRF, that regulates neuronal activity-dependent expression of BDNF. Neuron 33, 383-395.

Tian, S.M., Ma, Y.X., Shi, J., Lou, T.Y., Liu, S.S., Li, G.Y. (2015) Acrylamide neurotoxicity on the cerebrum of weaning rats. Neural Regen Res 10, 938-943.

Xu, B., Xu, Z.F., Deng, Y., Liu, W., Yang, H.B., Wei, Y.G. (2012) Protective effects of MK-801 on methylmercury-induced neuronal injury in rat cerebral cortex: involvement of oxidative stress and glutamate metabolism dysfunction. Toxicology 300, 112-120.

Yin, Z., Milatovic, D., Aschner, J.L., Syversen, T., Rocha, J.B., Souza, D.O., Sidoryk, M., Albrecht, J., Aschner, M. (2007) Methylmercury induces oxidative injury, alterations in permeability and glutamine transport in cultured astrocytes. Brain Res 1131, 1-10.

Zhao, H., Alam, A., San, C.Y., Eguchi, S., Chen, Q., Lian, Q., Ma, D. (2017) Molecular mechanisms of brain-derived neurotrophic factor in neuro-protection: Recent developments. Brain Res 1665, 1-21.