To the extent possible under law, AOP-Wiki has waived all copyright and related or neighboring rights to KER:683

Relationship: 683


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

Reduction, Neuronal synaptic inhibition leads to Generation, Amplified excitatory postsynaptic potential (EPSP)

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 to the picrotoxin site of ionotropic GABA receptors leading to epileptic seizures in adult brain adjacent High Moderate Cataia Ives (send email) Open for citation & comment WPHA/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
rat Rattus norvegicus High NCBI
human Homo sapiens 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

GABAA receptors mediate two distinct forms of inhibition, phasic and tonic. The first consists of fast inhibitory postsynaptic potentials (IPSPs), regulating point-to-point communication between neurons. The second consists of a persistent inhibitory conductance that plays a crucial role in regulating the membrane potential and network excitability (Farrant and Nusser 2005). In the case of phasic inhibition, synaptic GABAA receptors, facing presynaptic release sites, are activated by a brief exposure to a high concentration of GABA released by exocytosis of presynaptic vesicles. Once released, GABA diffuses throughout the neuropil before being taken up by selective plasma membrane transporters, which contribute to the clearance of the neurotransmitter (Cherubini and Conti 2001). In the case of tonic inhibition, extrasynaptic GABAA receptors, localized away from the synapses, are persistently exposed to low concentration of “ambient” GABA.

Fast inhibitory neurotransmission in the mammalian central nervous system (CNS) is mediated primarily by the neurotransmitters GABA and glycine. Glycine is predominantly used in the spinal cord and the brain stem, whereas GABA is more commonly used in the brain (Jentsch et al. 2002). As the dominant charge carrier through GABA-A receptors, chloride is directly implicated in the efficacy of fast neuronal synaptic inhibition (Prescott 2014). The binding of GABA to GABAA receptors opens intrinsic anion channels, which leads to a Cl influx that hyperpolarizes the neuron and thereby inhibits postsynaptic neuronal activity in the adult CNS (Jentsch et al. 2002). Neurons communicate through action potentials along their axons, and those action potentials are electrical events that depend on the movements of ions, particularly sodium and potassium, across the neuronal cell membrane (Jefferys 2010). Postsynaptic conductance changes and the potential changes that accompany them alter the probability that an action potential will be produced in the postsynaptic cell. Postsynaptic potentials (PSPs) are called excitatory (or EPSPs) if they increase the likelihood of a postsynaptic action potential occurring, and inhibitory (or IPSPs) if they decrease this likelihood (Purves et al. 2001). Given that most neurons receive inputs from both excitatory and inhibitory synapses, it is important to understand more precisely the mechanisms that determine whether a particular synapse excites or inhibits its postsynaptic partner. In order to generate large EPSPs underlying depolarization shift (cause of interictal spike discharge or epileptic seizure), the normal small EPSPs must be amplified (Dichter and Ayala 1987). Blocking of chloride channel by non-competitive blockers at the picrotoxin convulsant site on GABAA receptors reduces IPSPs or increases the probability of firing of the neuron, causing an enhancement of excitatory postsynaptic action potentials (EPSPs) (Dichter and Ayala 1987).

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

Seizure often involves the reorganizations occurring around the synapse that are extremely diverse and complex. What could be the functional consequences of synaptic reorganization? The hypotheses are admittedly very speculative, since we do not know the role of each parameter under physiological conditions. Very importantly, a drastic alteration of one parameter (e.g., the loss or reduction of GABAergic inhibition, or its transformation into excitation) may be without any functional impact (Bernard 2012). This is a key concept derived from the work of Prinz et al. (2004) performed in the stomatogastric system of the lobster, which has led to the concept that there are multiple solutions to a given biological problem. The stomatogastric system, which generates a rhythm vital for the animal, is composed of three nuclei connected to each other via different neurotransmitter systems. Knowing the types of channels expressed by the neurons in each nuclei and the type of connections, the researchers built a computer model in which each parameter (amplitude of the ionic current, strength of the connection) could take any biologically realistic value. They varied all the parameters, and selected the sets of parameters that produced the same rhythm recorded in vivo. They found that there are countless possible solutions, which produce the same behavior at the network level. Importantly, they also found that the system is “resistant” even if one type of channel is not expressed, or if a connection between two nuclei is missing. Further, the values taken by a given parameter (among the sets of solutions) match the biological variability (Schulz et al. 2007; Marder and Goaillard 2006). That is, the variability of a given parameter measured in a biological system (e.g. amplitude of GABAA receptor-mediated currents) may just reflect the different solutions that enable networks to function adequately. One might therefore consider that all the modifications occurring in epileptic networks may simply constitute the expression of another set of “solutions” to perform normal physiological function. Seizures are, after all, very infrequent events, which suggests that most of the time the system can cope with various parameters permutations without engaging in abnormal activity.

Nevertheless, important functional changes in epilepsy appear to stem from some of the synaptic modifications identified so far, including interictal activity (Bernard 2012). A shift to a depolarizing action in a minority of cells may be sufficient to favor the occurrence of interictal spikes (Cohen et al. 2002). Interictal-like activity appears very early after the initial insult in experimental models, and precedes (by days) and even predicts the appearance of the chronic phase of epilepsy defined by recurrent seizures (El-Hassar et al. 2007; Williams et al. 2009; White et al. 2010). Using a crude model of hippocampal circuitry, El-Hassar et al. (2007) have tried to determine the conditions sufficient for the genesis of interictal spikes. Many different solutions exist, which include decreased dendritic GABAergic inhibition and increased glutamatergic excitation (El-Hassar et al. 2007), in a range of values found experimentally (El-Hassar et al. 2007). This model does not explain why interictal activity is not permanent in vivo, but suggests clues regarding its underlying mechanisms. Since interictal activity is rarely encountered in non-epileptic individuals, it has been proposed that it is pathological. Studies performed in vitro suggest that interictal-like activity can produce long-term potentiation of synapses, thus contributing to the construction of hyperexcitable networks (Dzhala and Staley 2003). The presence of interictal-like activity during the earliest stages of epileptogenesis may not only constitute biomarkers for at-risk patients, but also one core mechanism of epileptogenesis (El-Hassar et al. 2007; Williams et al. 2007; White et al. 2010). One study performed in patients with temporal lobe epilepsy suggests that the size of the epileptogenic zone increases with the duration of epilepsy (Bartolomei et al. 2008). The brain regions outside the epileptogenic zone (i.e. the irritative zone) are often characterized by the presence of interictal spikes. Some of these regions become part of the epileptogenic zone as epilepsy evolves in time (Bartolomei et al. 2008). It is therefore tempting to propose that interictal spikes participate in the transformation of the irritative regions into epileptic ones.

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

A synapse involves three compartments: the presynaptic terminal, the postsynaptic site, and the glial cell processes surrounding them. Many features of the synapse can be modified. The number of synapses established by a given neuron on its targets can decrease (“pruning” or death of the presynaptic neuron) or increase (sprouting, neosynaptogenesis). The properties of the presynaptic terminal can be changed (release probability, neurotransmitter concentration in vesicles, control by presynaptic receptors). On the postsynaptic site, the number, subunit composition, and function (e.g., phosphorylation, anchoring) of the receptors can be changed. Finally, alterations at the glial cell level may affect the environment of the synapse and its function (neurotransmitter uptake, energy supply to neurons, etc.). See Bernard (2012) for a detailed review on the state of our current knowledge regarding the time-dependent reorganizations of GABAergic circuits at the synaptic level during epileptogenesis and a discussion on the possible functional consequences of these alterations (particularly on the fate of GABAergic circuits).

With respect to the cause of EPSP amplification, there exist at least four mechanisms other than the withdrawal or reduction of inhibition in the epileptic foci of normal CNS (Dichter and Ayala 1987): (i) frequency potentiation of EPSPs, (ii) changes in the space constant of the dendrites (or spines) of the postsynaptic neuron, (iii) activation of the NMDA receptor as the cell depolarizes as a result of a reduction in voltage-dependent block of the receptor by Mg2+, and (iv) potentiation by neuromodulators that are released during the ID (for example, norepinephrine, somatostatin, and acetylcholine). In addition to direct increases in excitatory synaptic efficacy, the depolarizing effects of EPSPs can be supplemented by several voltage-dependent intrinsic currents that exist in CNS neurons. These include slowly inactivating Na+ and Ca2+ currents and a large, transient Ca2+ current that is likely to be responsible for Ca2+-dependent action potentials.

The above synaptic modifications and diverse causes of EPSP amplification constitute the uncertainty factors for the relationship between reduced IPSPs and amplification of EPSPs at the affected GABAergic neurons.

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

El-Hassar et al. (2007) and Li et al. (1999) provided evidence for rat whereas Bartolomei et al. (2008) reported evidence for human.


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

Bartolomei F, Chauvel P, Wendling F. 2008. Epileptogenicity of brain structures in human temporal lobe epilepsy: a quantified study from intracerebral EEG. Brain. 131:1818–30.

Bernard C. 2012. Alterations in synaptic function in epilepsy. In: Noebels JL, Avoli M, Rogawski MA, et al. (Eds), Jasper's Basic Mechanisms of the Epilepsies [Internet]. 4th edition. Bethesda (MD): National Center for Biotechnology Information (US); Website:

Cherubini E, Conti F. 2001. Generating diversity at GABAergic synapses. Trends Neurosci. 24:155–162

Cohen I, Navarro V, Clemenceau S, Baulac M, Miles R. 2002. On the origin of interictal activity in human temporal lobe epilepsy in vitro. Science. 298:1418–21.

Cossart R, et al. 2001. Dendritic but not somatic GABAergic inhibition is decreased in experimental epilepsy. Nat Neurosci. 4:52–62.

Dichter MA, Ayala GF. 1987. Cellular mechanisms of epilepsy: A status report. Science 237:157-64.

Dzhala VI, Staley KJ. 2003. Transition from interictal to ictal activity in limbic networks in vitro. J Neurosci. 23:7873–80.

El-Hassar L, et al. 2007. Cell domain-dependent changes in the glutamatergic and GABAergic drives during epileptogenesis in the rat CA1 region. J Physiol. 578:193–211.

Farrant M, Nusser Z. 2005. Variations on an inhibitory theme: phasic and tonic activation of GABA(A) receptors. Nat Rev Neurosci. 6:215–29.

Jefferys JGR. 2010. Advances in understanding basic mechanisms of epilepsy and seizures. Seizure 19:638–46.

Jentsch TJ, Stein V, Weinreich F, Zdebik AA. 2002 Molecular Structure and Physiological Function of Chloride Channels. Physiol Rev. 82(2):503-68.

Li Y, Evans MS, Faingold CL. 1999. Synaptic response patterns of neurons in the cortex of rat inferior colliculus. Hear Res. 137(1-2):15-28.

Marder E, Goaillard JM. 2006. Variability, compensation and homeostasis in neuron and network function. Nat Rev Neurosci. 7:563–74.

Prescott SA. 2014. Chloride channels. In: Jaeger D and Jung R (Eds.), Encyclopedia of Computational Neuroscience, Springer, New York, 2014. pp.1-4.

Prinz AA, Bucher D, Marder E. 2004. Similar network activity from disparate circuit parameters. Nat Neurosci. 7:1345–52.

Purves D, Augustine GJ, Fitzpatrick D, et al. (Eds). 2001. Chapter 7. Neurotransmitter Receptors and Their Effects. In: Neuroscience (2nd edition). Sunderland (MA): Sinauer Associates. Website:

Schulz DJ, Goaillard JM, Marder EE. 2007. Quantitative expression profiling of identified neurons reveals cell-specific constraints on highly variable levels of gene expression. Proc Natl Acad Sci USA. 104:13187–91.

White A, et al. 2010. EEG spike activity precedes epilepsy after kainate-induced status epilepticus. Epilepsia. 51:371–83.

Williams PA, et al. 2009. Development of spontaneous recurrent seizures after kainate-induced status epilepticus. J Neurosci. 29:2103–12.