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Relationship: 634


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

Decreased, Neuronal network function in adult brain leads to Impairment, Learning and memory

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 agonists to ionotropic glutamate receptors in adult brain causes excitotoxicity that mediates neuronal cell death, contributing to learning and memory impairment. adjacent Moderate Low Anna Price (send email) Open for citation & comment TFHA/WNT Endorsed

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
human Homo sapiens High NCBI
mouse Mus musculus High NCBI
rat Rattus norvegicus 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

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

It is well established in the existing literature that NMDA receptor–dependent synaptic potentiation (LTP) and depression (LTD) are two forms of activity directly linked to long-term changes in synaptic efficacy and plasticity, the fundamental processes underlying learning and memory. The best characterized form of LTP occurs in the CA3-CA1 region of the hippocampus, in which LTP is initiated by transient activation of NMDARs that leads to a persistent increase in synaptic transmission through AMPA receptors (Benke et al., 1998) that can be achieved either through increasing the number of AMPA receptors at the post-synaptic surface or by increasing the single channel conductance of the receptors expressed. It has been shown that LTP in the CA1 region of the hippocampus could be accounted for by these two mechanisms (Benke et al 1998). The degree of activity of NMDARs is determined in part by extracellular Mg(2+) and by the co-agonists for this receptor, glycine and D-serine. During strong stimulation, a relief of the voltage-dependent block of NMDARs by Mg(2+) provides a positive feedback for NMDAR Ca(2+) influx into postsynaptic CA1 spines. The induction of LTP at CA3-CA1 synapses requires further signal amplification of NMDAR activity. Src family kinases (SFKs) play a "core" role in the induction of LTP by enhancing the function and expression of NMDARs. At CA3-CA1 synapses, NMDARs are largely composed of NR1 (NMDA receptor subunit 1)-NR2A or NR1-NR2B containing subunits. Recent, but controversial, evidence has correlated NR1-NR2A receptors with the induction of LTP and NR1-NR2B receptors with LTD. However, LTP can be induced by activation of either subtype of NMDAR and the ratio of NR2A:NR2B receptors has been proposed as an alternative determinant of the direction of synaptic plasticity. Many transmitters and signal pathways can modify NMDAR function and expression and, for a given stimulus strength, they can potentially lead to a change in the balance between LTP and LTD (MacDonald et al., 2006).

Mammalian learning and memory is one of the outcomes of the functional expression of neurons connected into neural networks. Neuronal damage or cell death induced by chemical compounds disrupts integration and transmission of information through neural networks thereby setting the stage for subsequent impairment of learning and memory. Exposure to chemicals that will increase the risk of functional neuronal network damage lead to learning and memory impairment.

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

Long-term potentiation (LTP) is a long-lasting increase in synaptic efficacy after high-frequency stimulation of afferent fibers, and its discovery potentiated the idea that individual synapses possess the properties expected for learning and memory (reviewed in Lynch et al., 2014). Moreover, LTP is intimately related to the theta rhythm, an oscillation long-associated with learning. Learning-induced enhancement in neuronal excitability, a measurement of neural network function, has also been shown in hippocampal neurons following classical conditioning in several experimental approaches (reviewed in Saar and Barkai, 2003). On the other hand, memory requires the increase in magnitude of EPSCs to be developed quickly and to be persistent for a at least a few weeks without disturbing already potentiated contacts. Once again, a substantial body of evidence have demonstrated that tight connection between LTP and diverse instances of memory exist (reviewed in Lynch et al., 2014).

The recent studies suggest that NMDA receptor-dependent long-term depression of both LTD and LTP is usually accompanied by morphological changes in spines. LTD is characterized by long lasting dendritic spine shrinkage and reduced F-actin polymerization, in addition to reduced numbers of synaptic AMPA receptors. Moreover, the actin binding protein cofilin has been implicated in mediating such synaptic structural plasticity (Chen et al., 2007). If sustained, such LTD-changes in hippocampus or cortex, triggered by NMDARs overactivation could lead to synaptic dysfunction, contributing to learning and memory damage (Calabrese et al., 2014).

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

One of the most difficult issues for neuroscientists is to link neuronal network function to cognition, including learning and memory. It is still unclear exactly what modifications in neuronal circuits need to happen in order to alter motor behaviour as it is recorded in a learning and memory test (Mayford et al., 2012), meaning that there is no clear understanding about how these two KEs are connected.

It is unclear whether GLF affects only glutamatergic systems since other potential mechanisms underlying GLF neurotoxicity have not been widely investigated. Based on the existing data it is understood that exposure to GLF or NAcGLF could disrupt the neuronal network function through disruption of glutamatergic neurotransmission but further work is required to clarify molecular mechanisms that cause impairment of memory.

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

Administration of DomA (9.0 mg DomA kg(-1) bw, i.p.) to Sparus aurata (seabream) caused neurological disturbances such as swimming in a circle, in a spiral, or upside down, that were reversed 24 hours after exposure (Nogueira et al., 2010). In rainbow trout (Oncorhynchus mykiss), DomA (0.75 mg/kg bw) administration caused increased aggressive behaviour 30 min after exposure compared to controls (Bakke et al., 2010).


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

Bakke MJ, Hustoft HK, Horsberg TE., Subclinical effects of saxitoxin and domoic acid on aggressive behaviour and monoaminergic turnover in rainbow trout (Oncorhynchus mykiss). Aquat Toxicol., 2010, 99: 1-9.

Baron AW, Rushton SP, Rens N, Morris CM, Blain PG, Judge SJ., Sex differences in effects of low level domoic acid exposure. Neurotoxicology, 2013, 34: 1-8.

Benke TA1, Lüthi A, Isaac JT, Collingridge GL., Modulation of AMPA receptor unitary conductance by synaptic activity. Nature, 1998, 25: 793-7.

Calabrese B, Saffin JM, Halpain S. Activity-dependent dendritic spine shrinkage and growth involve downregulation of cofilin via distinct mechanisms. PLoS One, 2014, 16;9(4):e94787.

Calas AG, Richard O, Même S, Beloeil JC, Doan BT, Gefflaut T, Même W, Crusio WE, Pichon J, Montécot C. Chronic exposure to glufosinate-ammonium induces spatial memory impairments, hippocampal MRI modifications and glutamine synthetase activation in mice. Neurotoxicology. 2008, 29(4): 740-7

Chen LY, Rex CS, Casale MS, Gall CM, Lynch G., Changes in synaptic morphology accompany actin signaling during LTP. J Neurosci., 2007, 27: 5363–5372.

Clayton EC, Peng YG, Means LW, Ramsdell JS. Working memory deficits induced by single but not repeated exposures to domoic acid. Toxicon. 1999, 37: 1025-1039.

D'Hooge R, De Deyn PP., Applications of the Morris water maze in the study of learning and memory. Brain Res Brain Res Rev., 2001, 36: 60-90.

Fuquay JM, Muha N, Pennington PL, Ramsdell JS., Domoic acid induced status epilepticus promotes aggressive behavior in rats. Physiol Behav., 2012, 105: 315-320.

Grant KS, Burbacher TM, Faustman EM, Gratttan L., Domoic acid: neurobehavioral consequences of exposure to a prevalent marine biotoxin. Neurotoxicol Teratol., 2010, 32: 132-141.

Grunwald T, Beck H, Lehnertz K, Blümcke I, Pezer N, Kurthen M, Fernández G, Van Roost D, Heinze HJ, Kutas M, Elger CE.. Evidence relating human verbal memory to hippocampal N-methyl-D-aspartate receptors. Proc Natl Acad Sci U S A., 1999, 96: 12085-12089.

Hiolski EM, Kendrick PS, Frame ER, Myers MS, Bammler TK, Beyer RP, Farin FM, Wilkerson HW, Smith DR, Marcinek DJ, Lefebvre KA., Chronic low-level domoic acid exposure alters gene transcription and impairs mitochondrial function in the CNS. Aquat Toxicol., 2014, 155: 151-9.

Koyama K, Andou Y, Saruki K, Matsuo H., Delayed and severe toxicities of a herbicide containing glufosinate and a surfactant. Vet Hum Toxicol., 1994, 36: 17–8.

Kuhlmann AC, Guilarte TR., The peripheral benzodiazepine receptor is a sensitive indicator of domoic acid neurotoxicity. Brain Res., 1997, 751: 281-288.

Lantz SR, Mack CM, Wallace K, Key EF, Shafer TJ, Casida JE., Glufosinate binds to N-methyl-D-aspartate receptors and increases neuronal network activity in vitro. Neurotoxicology, 2014, 45:38-47.

Lu J, Wu DM, Zheng YL, Hu B, Cheng W, Zhang ZF., Purple sweet potato color attenuates domoic acid-induced cognitive deficits by promoting estrogen receptor-α-mediated mitochondrial biogenesis signaling in mice. Free Radic Biol Med., 2012, 52: 646-59.

Lynch G, Cox CD, Gall CM., Pharmacological enhancement of memory or cognition in normal subjects. Front Syst Neurosci., 2014, 8: 90-103.

MacDonald JF1, Jackson MF., Beazely MAHippocampal long-term synaptic plasticity and signal amplification of NMDA receptors. Crit Rev Neurobiol., 2006, 18: 71-84.

Mao Y-C, Hung D-Z, Wu M-L, Tsai W-J, Wang L-M, Ger J, et al. Acute human glufosinatecontaining herbicide poisoning. Clin Toxicol., 2012, 5: 1–7.

Mao Y-C, Wang J-D, Hung D-Z, Deng J-F, Yang C-C., Hyperammonemia following glufosinate-containing herbicide poisoning: a potential marker of severe neurotoxicity. Clin Toxicol (Phila), 2011a, 49: 48–52.

Mao Y-C, Yang C-C. Response to ‘‘Hyperammonemia following glufosinate-containing herbicide poisoning: A potential marker of severe neurotoxicity’’ by Yan-Chido Mao et al., Clin Toxicol (Phila) 2011b; 49: 48–52. Clin Toxicol 2011;49(July (6)): 513.

Mayford M, Siegelbaum SA, Kandel ER., Synapses and memory storage. Cold Spring Harb Perspect Biol., 2012:4(6). pii: a005751.

Meme S, Calas A-G, Monte´ cot C, Richard O, Gautier H, Gefflaut T, et al., MRI characteri- zation of structural mouse brain changes in response to chronic exposure to the glufosinate ammonium herbicide. Toxicol Sci 2009, 111: 321–30.

Matsumura N, Takeuchi C, Hishikawa K, Fujii T, Nakaki T., Glufosinate ammonium induces convulsion through N-methyl-D-aspartate receptors in mice. Neurosci Lett., 2001, 304(1-2): 123-5.

Morris RG, Anderson E, Lynch GS, Baudry M.,Selective impairment of learning and blockade of long-term potentiation by an N-methyl-Daspartate receptor antagonist, AP5. Nature, 1986, 319: 774-776.

Muha N, Ramsdell JS., Domoic acid induced seizures progress to a chronic state of epilepsy in rats. Toxicon., 2011, 57: 168-171.

Nakajima S, Potvin JL., Neural and behavioural effects of domoic acid, an amnesic shellfish toxin, in the rat. Can J Psychol., 1992, 46: 569-581.

Nogueira I, Lobo-da-Cunha A, Afonso A, Rivera S, Azevedo J, Monteiro R, Cervantes R, Gago-Martinez A, Vasconcelos V., Toxic effects of domoic acid in the seabream Sparus aurata. Mar Drugs, 2010, 8: 2721-232.

Ohtake T, Yasuda H, Takahashi H, Goto T, Suzuki K, Yonemura K, et al., Decreased plasma and cerebrospinal fluid glutamine concentrations in a patient with bialaphos poisoning. Hum Exp Toxicol., 2001, 20: 429–34.

Park JS1, Kwak SJ, Gil HW, Kim SY, Hong SY., Glufosinate herbicide intoxication causing unconsciousness, convulsion, and 6th cranial nerve palsy. J Korean Med Sci., 2013, 28:1687-9.

Park HY, Lee PH, Shin DH, Kim GW., Anterograde amnesia with hippocampal lesions following glufosinate intoxication. Neurology, 2006, 67:914–5.

Petrie BF, Pinsky C, Standish NM, Bose R, Glavin GB., Parenteral domoic acid impairs spatial learning in mice. Pharmacol Biochem Beh., 1992, 41: 211-214.

Pulido OM., Domoic acid toxicologic pathway: a review. Mar Drugs, 2008, 6:180-219.

Saar D, Barkai E. (2003) Long-term modifications in intrinsic neuronal properties and rule learning in rats. Eur J Neurosci. 17: 2727-2734.

Sobotka TJ, Brown R, Quander DY, Jackson R, Smith M, Long SA, Barton CN, Rountree RL, Hall S, Eilers P, Johannessen JN, Scallet AC., Domoic acid: neurobehavioral and neurohistological effects of low-dose exposure in adult rats. Neurotoxicol Teratol., 1996, 18: 659-670.

Tryphonas L, Truelove J, Nera E, Iverson F., Acute neurotoxicity of domoic acid in the rat. Toxicol Pathol., 1990, 18: 1-9.

Watanabe T, Sano T., Neurological effects of glufosinate poisoning with a brief review. Hum Exp Toxicol., 1998, 17(1): 35-9.

Wu DM, Lu J, Zheng YL, Zhang YQ, Hu B, Cheng W, Zhang ZF, Li MQ., Small interfering RNA-mediated knockdown of protein kinase C zeta attenuates domoic acid-induced cognitive deficits in mice. Toxicol Sci., 2012, 128: 209-222.

Wu DM, Lu J, Zhang YQ, Zheng YL, Hu B, Cheng W, Zhang ZF, Li MQ., Ursolic acid improves domoic acid-induced cognitive deficits in mice. Toxicol Appl Pharmacol., 2013, 271: 27-36.

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