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Relationship: 759


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

Neuroinflammation leads to Synaptogenesis, Decreased

Upstream event
The causing Key Event (KE) in a Key Event Relationship (KER). More help
Downstream event
The responding Key Event (KE) in a Key Event Relationship (KER). 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

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

Note: Due to the complexity of the role of brain immune cells (microglia and astrocytes) in synapse formation, maintenance and elimination, this KER is not described in details: The general concepts are presented and referenced by recent reviews.

The brain immune system plays critical roles both in normal homeostatic processes, as well as in pathology. Evidence from both animal and human studies implicates the immune system in a number of disorders with suspected developmental origin, giving rise to the concept of early-life programming of later life disorders (Bilbo and Schwarz, 2009). Although the function of glial cells, microglia and astrocytes, in synapse formation, elimination and efficacy is widely accepted, the understanding of all molecular and cellular mechanisms underlying these events is still not complete (for review, Diniz et al., 2014). Microglia can modulate synapse plasticity, an effect mediated by cytokines. During development, microglia can promote synaptogenesis or engulf synapses, a process known as synaptic pruning (for review, Jebelli et al., 2015). It is hypothesized that alterations in microglia functioning during synapse formation and maturation of the brain can have significant long-term effects on the final established neural circuit (for review, Harry and Kraft, 2012). The fact that astrocytes can receive and respond to the synaptic information produced by neuronal activity, owing to their expression of a wide range of neurotransmitter receptors, has given rise to the concept of tripartite synapse (for review, Perez-Alvarez and Araque, 2013; Bezzi and Volterra, 2001). Cytokines such as TNF-a, IL-1b, and IL-6 are produced by microglia and astrocytes and are implicated in synapse formation and scaling, long-term potentiation and neurogenesis (for review, Bilbo and Schwarz, 2009). Reactive glia can remove synapses, a process known as synapse stripping (Banati et al., 1993; Kettenmann et al., 2013). Similarly, astrocyte reactivity was associated with neurite and synapse reduction (Calvo-Ochoa et al., 2014). In neurodegenerative diseases, neuroinflammation might contribute to synapse loss though abnormal production of pro-inflammatory cytokines, chemokines, the complement system, as well as reactive oxygen and nitrogen (for review, Agosthino et al., 2010).

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
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

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


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

Agostinho P, Cunha RA, Oliveira C. 2010. Neuroinflammation, oxidative stress and the pathogenesis of Alzheimer's disease. Current pharmaceutical design 16(25): 2766-2778.

Banati RB, Gehrmann J, Schubert P, Kreutzberg GW. 1993. Cytotoxicity of microglia. Glia 7: 111-118.

Bezzi P, Volterra A. 2001. A neuron-glia signalling network in the active brain. Curr Opin Neurobiol 11(3): 387-394.

Bilbo SD, Schwarz JM. 2009. Early-life programming of later-life brain and behavior: a critical role for the immune system. Frontiers in behavioral neuroscience 3: 14.

Calvo-Ochoa E, Hernandez-Ortega K, Ferrera P, Morimoto S, Arias C. 2014. Short-term high-fat-and-fructose feeding produces insulin signaling alterations accompanied by neurite and synaptic reduction and astroglial activation in the rat hippocampus. J Cereb Blood Flow Metab 34(6): 1001-1008.

Diniz LP, Matias IC, Garcia MN, Gomes FC. 2014. Astrocytic control of neural circuit formation: highlights on TGF-beta signaling. Neurochem Int 78: 18-27.

Harry GJ, Kraft AD. 2012. Microglia in the developing brain: a potential target with lifetime effects. Neurotoxicology 33(2): 191-206.

Jebelli J, Su W, Hopkins S, Pocock J, Garden GA. 2015. Glia: guardians, gluttons, or guides for the maintenance of neuronal connectivity? Ann N Y Acad Sci.

Kettenmann H, Kirchhoff F, Verkhratsky A. Microglia: new roles for the synaptic stripper. Neuron. 2013 Jan 9;77(1):10-8.

Perez-Alvarez A, Araque A. 2013. Astrocyte-neuron interaction at tripartite synapses. Curr Drug Targets 14(11): 1220-1224.