To the extent possible under law, AOP-Wiki has waived all copyright and related or neighboring rights to KER:1688

Relationship: 1688

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

Cell injury/death leads to Neuronal network function, Decreased

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 Do not cite EAGMST Under Review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
fathead minnow Pimephales promelas Moderate 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
Male
Female

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

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

Under physiological conditions, in the developing nervous system, apoptosis occurs during the process of synaptogenesis, where competition leads to the loss of excess neurons and to the connection of the appropriate neurons (Buss et al., 2006; Mennerick and Zorumski, 2000; Oppenheim, 1991). When a stressor increases the number of apoptotic cells this KE has a negative effect on synaptogenesis as the reduced number of neurons (besides the ones that have been already eliminated through the physiological process of apoptosis) provides limited dendritic fields for receiving synaptic inputs from incoming axons. At the same time the loss of neurons also means that there are less axons to establish synaptic contacts (Olney, 2014), leading to reduced synaptogenesis. The ability of a neuron to communicate is based on neural network formation that relies on functional synapse establishment (Colón-Ramos, 2009). The main roles of synapses are the regulation of intercellular communication in the nervous system, and the information flow within neural networks. The connectivity and functionality of neural networks depends on where and when synapses are formed. Therefore, the decreased synapse formation due to cell death during the process of synaptogenesis is critical and leads to decrease of neural network formation and function in the adult brain.

Synaptic transmission and plasticity require the integrity of the anatomical substrate. The connectivity of axons emanating from one set of cells to post-synaptic side of synapse on the dendrites of the receiving cells must be intact for effective communication between neurons. Changes in the placement of cells within the network due to delays in neuronal migration, the absence of a full formation of dendritic arbors and spine upon which synaptic contacts are made, and the lagging of transmission of electrical impulses due to insufficient myelination will individually and cumulatively impair synaptic function.

Therefore, chemicals inducing neuronal cell death by apoptosis or necrosis, or interfering with a particular system of neurotransmitters, will alter network structure and function.

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

Recently, Dekkers et al. 2013 have reviewed how under physiological conditions components of the apoptotic machinery in developing brain regulate synapse formation and neuronal connectivity. For example, caspase activation is known to be required for axon pruning during development to generate neuronal network (reviewed in Dekkers et al., 2013). Experimental work carried out in Drosophila melanogaster and in mammalian neurons shows that components of apoptotic machinery are involved in axonal degeneration that can consequently interfere with synapse formation (reviewed in Dekkers et al., 2013). Furthermore, Bax mutant mice studies indicate that the lack of this pro-apoptotic protein BAX leads to disruption of intrinsically photosensitive retinal ganglion cells spacing and dendritic stratification that affects synapse localization and function (Chen et al., 2013).

Neuronal network formation and function are established via the process of synaptogenesis. The developmental period of synaptogenesis is critical for the formation of the basic circuitry of the nervous system, although neurons are able to form new synapses throughout life (Rodier, 1995). The brain electrical activity dependence on synapse formation is critical for proper neuronal communication.

Alterations in synaptic connectivity lead to refinement of neuronal networks during development (Cline and Haas, 2008). Indeed, knockdown of PSD-95 arrests the functional and morphological development of glutamatergic synapses (Ehrlich et al., 2007).

Studies of the last 30 years demonstrated that astrocytes possess functional receptors for neurotransmitters and respond to their stimulation via release of gliotransmitters, including glutamate. These findings have led to a new concept of neuron–glia intercommunication where astrocytes play an unsuspected dynamic role by integrating neuronal inputs and modulating synaptic activity (Rossi and Volterra, 2009). According to the concept termed "tripartite synapse", the emerging view is that brain function actually arises from the coordinated activity of a network comprising both neurons and astrocytes. Furthermore, myelinating glial cells are well-known to insulate axons and to speed up action potential propagation. Be it motor skill learning or social behaviors in higher vertebrates, proper myelination is critical in shaping brain functions. Neurons rely on their myelinating partners not only for setting conduction speed, but also for regulating the ionic environment and fueling their energy demands with metabolites. Also, long-term axonal integrity and neuronal survival are maintained by oligodendrocytes and loss of this well-coordinated axon-glial interplay contributes to brain diseases (Saab and Nave, 2017). Therefore, reduction in glial cell number and/or reduction in myelination of axons, will very much impact the neural network function.

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

Ogawa et al. (2011) reported decreased apoptosis and an increase in the number of Gabaergic interneurons in the dentate gyrus of Sprague-Dawley pups either maternally exposed to acrylamide or directly injected with acrylamide.

Although it appears evident that a decrease in cell number, in dendritic arborization or in axonal growth, as well as synapse alterations may lead to decreased neuronal network formation and function, the exact mechanism remain to be elucidated.

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

Support for the link between cell injury/death and decreased neuronal network formation and function can be found in rat, mouse and minnow. (for references, see empirical evidences)

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

Buss, R.R., Sun, W., Oppenheim, R.W., 2006. Adaptive roles of programmed cell death during nervous system development. Annu Rev Neurosci 29, 1-35.

Chen, S.K., Chew, K.S., McNeill, D.S., Keeley, P.W., Ecker, J.L., Mao, B.Q., Pahlberg, J., Kim, B., Lee, S.C., Fox, M.A., Guido, W., Wong, K.Y., Sampath, A.P., Reese, B.E., Kuruvilla, R., Hattar, S., 2013. Apoptosis regulates ipRGC spacing necessary for rods and cones to drive circadian photoentrainment. Neuron 77, 503-515.

Cline, H., Haas, K., 2008. The regulation of dendritic arbor development and plasticity by glutamatergic synaptic input: a review of the synaptotrophic hypothesis. J Physiol 586, 1509-1517.

Colon-Ramos, D.A., 2009. Synapse formation in developing neural circuits. Curr Top Dev Biol 87, 53-79.

Dekkers, M.P., Nikoletopoulou, V., Barde, Y.A., 2013. Cell biology in neuroscience: Death of developing neurons: new insights and implications for connectivity. J Cell Biol 203, 385-393.

Ehrlich, I., Klein, M., Rumpel, S., Malinow, R., 2007. PSD-95 is required for activity-driven synapse stabilization. Proc Natl Acad Sci U S A 104, 4176-4181.

Falluel-Morel, A., Sokolowski, K., Sisti, H.M., Zhou, X., Shors, T.J., Dicicco-Bloom, E., 2007. Developmental mercury exposure elicits acute hippocampal cell death, reductions in neurogenesis, and severe learning deficits during puberty. J Neurochem 103, 1968-1981.

Ferraro, L., Tomasini, M.C., Tanganelli, S., Mazza, R., Coluccia, A., Carratu, M.R., Gaetani, S., Cuomo, V., Antonelli, T., 2009. Developmental exposure to methylmercury elicits early cell death in the cerebral cortex and long-term memory deficits in the rat. Int J Dev Neurosci 27, 165-174.

Jacob, S., Thangarajan, S., 2017. Effect of Gestational Intake of Fisetin (3,3',4',7-Tetrahydroxyflavone) on Developmental Methyl Mercury Neurotoxicity in F1 Generation Rats. Biol Trace Elem Res 177, 297-315.

Mennerick, S., Zorumski, C.F., 2000. Neural activity and survival in the developing nervous system. Mol Neurobiol 22, 41-54.

Nagashima, K., 1997. A review of experimental methylmercury toxicity in rats: neuropathology and evidence for apoptosis. Toxicol Pathol 25, 624-631.

Nagashima, K., Fujii, Y., Tsukamoto, T., Nukuzuma, S., Satoh, M., Fujita, M., Fujioka, Y., Akagi, H., 1996. Apoptotic process of cerebellar degeneration in experimental methylmercury intoxication of rats. Acta Neuropathol 91, 72-77.

Ogawa, B., Ohishi, T., Wang, L., Takahashi, M., Taniai, E., Hayashi, H., Mitsumori, K., Shibutani, M., 2011. Disruptive neuronal development by acrylamide in the hippocampal dentate hilus after developmental exposure in rats. Arch Toxicol 85, 987-994.

Olney, J.W., 2014. Focus on apoptosis to decipher how alcohol and many other drugs disrupt brain development. Front Pediatr 2, 81.

Oppenheim, R.W., 1991. Cell death during development of the nervous system. Annu Rev Neurosci 14, 453-501.

Perea, G., Navarrete, M., Araque, A., 2009. Tripartite synapses: astrocytes process and control synaptic information. Trends Neurosci 32, 421-431.

Rodier, P.M., 1995. Developing brain as a target of toxicity. Environ Health Perspect 103 Suppl 6, 73-76.

Rossi, D., Volterra, A., 2009. Astrocytic dysfunction: insights on the role in neurodegeneration. Brain Res Bull 80, 224-232.

Saab, A.S., Nave, K.A., 2017. Myelin dynamics: protecting and shaping neuronal functions. Curr Opin Neurobiol 47, 104-112.

Wang, Y.T., Lin, H.C., Zhao, W.Z., Huang, H.J., Lo, Y.L., Wang, H.T., Lin, A.M., 2017. Acrolein acts as a neurotoxin in the nigrostriatal dopaminergic system of rat: involvement of alpha-synuclein aggregation and programmed cell death. Sci Rep 7, 45741.