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Relationship: 368

Title

A descriptive phrase which clearly defines the two KEs being considered and the sequential relationship between them (i.e., which is upstream, and which is downstream). More help

N/A, Neurodegeneration leads to Neuronal network function, Decreased

Upstream event

The causing Key Event (KE) in a Key Event Relationship (KER). More help

N/A, Neurodegeneration

Downstream event

The responding Key Event (KE) in a Key Event Relationship (KER). More help

Neuronal network function, Decreased

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

Taxonomic Applicability

Latin or common names of a species or broader taxonomic grouping (e.g., class, order, family) 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. More help

Sex Applicability

An indication of the the relevant sex for this KER. More help

Life Stage Applicability

An indication of the the relevant life stage(s) for this KER. More help

Key Event Relationship Description

Provides a concise overview of the information given below as well as addressing details that aren’t inherent in the description of the KEs themselves. More help

Evidence Collection Strategy

Include a description of the approach for identification and assembly of the evidence base for the KER. For evidence identification, include, for example, a description of the sources and dates of information consulted including expert knowledge, databases searched and associated search terms/strings. Include also a description of study screening criteria and methodology, study quality assessment considerations, the data extraction strategy and links to any repositories/databases of relevant references.Tabular summaries and links to relevant supporting documentation are encouraged, wherever possible. More help

Evidence Supporting this KER

Addresses the scientific evidence supporting KERs in an AOP setting the stage for overall assessment of the AOP. More help

Biological Plausibility

Addresses the biological rationale for a connection between KEupstream and KEdownstream. This field 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. More help

Based on neuropathological findings and neuroimaging from patients suffering from neurodegeneration as well as from evidence derived by transgenic animal models of neurodegeneration, it has been suggested that neurodegeneration is relate to neural network dysfunction (Palop et al., 2007; Seeley et al., 2009). Neurodegeneration leads to impairment of retrograde axonal transport that prohibits the growth factor supply to long-range projection neurons, causing synapse loss, and post-synaptic dendrite retraction that lead to decrease of neuronal network (Seeley et al., 2009).

Empirical Evidence

Provides specific (citable) evidence that supports the idea of a change in the upstream KE (KEupstream) leading to, or being associated with, a subsequent change in the downstream KE (KEdownstream), assuming the perturbation of KEupstream is sufficient. More help

Include consideration of temporal concordance here

The effective concentration of DA causing decrease to 50% of control mean firing rate (MFR) values (EC50) in rat primary cultures (13-30 DIV) is 0.28 µM (Mack et al., 2014). Decrease of MFR has also been reported before by Hogberg et al. 2011, where mature cultures (28-35 DIV) have been exposed acutely to a wide range of concentrations of DA. The concentration of 0.5 μM DA significantly reduces MFR (77 %), the MBR (78 %) and the number of spikes per burst (71 %). Higher concentrations of DA (1 and 2 μM) also significantly decrease the MFR, whereas concentrations up to 0.1 μM of DA do not cause any effect on MFR (Hogberg et al., 2011). In primary rat cortical neurons (12-22 DIV), DA (50 µM) has been reported to reduce MFR by more than 90% (McConnell et al., 2012).

Ten-minute exposure of rat hippocampal CA1 region slices to 400 nM DA causes depression of fEPSP (Qiu et al., 2009). After 1 h washout, fEPSP gradually has been recovered. DA-potentiated slices have shown also less tetanus-induced LTP compared with control slices when tested with either original stimulus or reset stimulus (Qiu et al., 2009). In addition, prolonged application of 400 nM DA reversibly depresses CA1 fEPSP and impairs the subsequent development of tetanus LTP (Qiu et al., 2009).

Uncertainties and Inconsistencies

Addresses inconsistencies or uncertainties in the relationship including the identification of experimental details that may explain apparent deviations from the expected patterns of concordance. More help

Administration of high dose DA (4.4 mg/kg) to adult male Sprague-Dawley rats causes elevation of electrocorticogram (ECoG) beginning 30 min post injection, whereas at low dose (2.2 mg/kg) ECoG becomes elevated after 110 min (Binienda et al., 2011).

Known modulating factors

This table captures specific information on the MF, its properties, how it affects the KER and respective references.1.) What is the modulating factor? Name the factor for which solid evidence exists that it influences this KER. Examples: age, sex, genotype, diet 2.) Details of this modulating factor. Specify which features of this MF are relevant for this KER. Examples: a specific age range or a specific biological age (defined by...); a specific gene mutation or variant, a specific nutrient (deficit or surplus); a sex-specific homone; a certain threshold value (e.g. serum levels of a chemical above...) 3.) Description of how this modulating factor affects this KER. Describe the provable modification of the KER (also quantitatively, if known). Examples: increase or decrease of the magnitude of effect (by a factor of...); change of the time-course of the effect (onset delay by...); alteration of the probability of the effect; increase or decrease of the sensitivity of the downstream effect (by a factor of...) 4.) Provision of supporting scientific evidence for an effect of this MF on this KER. Give a list of references. More help

Quantitative Understanding of the Linkage

Captures information that helps to define how much change in the upstream KE, and/or for how long, is needed to elicit a detectable and defined change in the downstream KE. More help

Response-response Relationship

Provides sources of data that define the response-response relationships between the KEs. More help

Time-scale

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?). More help

Known Feedforward/Feedback loops influencing this KER

Define whether there are known positive or negative feedback mechanisms involved and what is understood about their time-course and homeostatic limits. More help

Domain of Applicability

A free-text section of the KER description that the developers can use to explain their rationale for the taxonomic, life stage, or sex applicability structured terms. More help

It has been shown at the neuromascular junction of D. melanogaster that quisqualate-type glutamate receptors are blocked by DA (1 mM) (Lee et al., 2009). However, in crayfish (Procambarus clarkia) the same concentration of DA has no effect in spike activity (Bierbower and Cooper, 2013).

References

List of the literature that was cited for this KER description. More help

Bierbower SM, Cooper RL. The mechanistic action of carbon dioxide on a neural circuit and NMJ communication. J Exp Zool A Ecol Genet Physiol. 2013. 319:340-54.

Binienda ZK, Beaudoin MA, Thorn BT, Ali SF. Analysis of electrical brain waves in neurotoxicology: γ-hydroxybutyrate. Curr Neuropharmacol. 2011. 9:236-9.

Hogberg HT, Sobanski T, Novellino A, Whelan M, Weiss DG, Bal-Price AK. Application of micro-electrode arrays (MEAs) as an emerging technology for developmental neurotoxicity: evaluation of domoic acid-induced effects in primary cultures of rat cortical neurons. Neurotoxicology 2011. 32:158-168.

Mack CM, Lin BJ, Turner JD, Johnstone AF, Burgoon LD, Shafer TJ. Burst and principal components analyses of MEA data for 16 chemicals describe at least three effects classes. Neurotoxicology 2014. 40:75-85.

McConnell ER, McClain MA, Ross J, Lefew WR, Shafer TJ. Evaluation of multi-well microelectrode arrays for neurotoxicity screening using a chemical training set. Neurotoxicology. 2012. 33:1048-1057.

Palop JJ, Chin J, Roberson ED, Wang J, Thwin MT, Bien-Ly N, Yoo J, Ho KO, Yu GQ, Kreitzer A, et al. Aberrant excitatory neuronal activity and compensatory remodeling of inhibitory hippocampal circuits in mouse models of Alzheimer's disease. Neuron. 2007. 55: 697-711.

Qiu S, Jebelli AK, Ashe JH, Currás-Collazo MC. Domoic acid induces a long-lasting enhancement of CA1 field responses and impairs tetanus-induced long-term potentiation in rat hippocampal slices. Toxicol Sci. 2009. 111:140-150.

Seeley WW, Crawford RK, Zhou J, Miller BL, Greicius MD. Neurodegenerative diseases target large-scale human brain networks. Neuron. 2009. 62:42-52.