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Relationship: 1069

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

N/A, Neurodegeneration 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
Chronic binding of antagonist to N-methyl-D-aspartate receptors (NMDARs) during brain development leads to neurodegeneration with impairment in learning and memory in aging adjacent High Florianne Tschudi-Monnet (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

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

Animal models of neurodegenerative diseases, in particular Alzheimer's disease, contributed to the elucidation of the link between amyloid protein and tau hyperphosphorylation and cognitive deficits. Bilateral injections of amyloid-b peptide in the frontal cortex of rats leads to progressive decline in memory and neurodegeneration in hippocampus (for review see Eslamizade et al., 2016). Recent findings have shown that soluble forms of Ab rather than insoluble forms (fibrils and plaques) are associated with memory impairment in early stages of Alzheimer's disease (for review see Salgado-Puga and Pena-Ortega, 2015). Several lines of evidence suggest that the small oligomeric forms of Ab and tau may act synergistically to promote synaptic dysfunction in Alzheimer's disease (for review see Guerrerro-Minoz et al., 2015). Some reports proposed the concept of imbalance between production and clearance of Ab42 and related Ab peptides, as an initiating factor inducing hyperphosphorylation of tau and leading to neuritic dystrophy and synaptic dysfunction (for review see Selkoe and Hardy, 2016). Recent trials of three different antibodies against amyloid peptides have suggested a slowing of cognitive decline in post hoc analyses of mild Alzheimer subjects (for review see Selkoe and Hardy, 2016). Therefore cognitive deficits may be related to the level and extent of classical Alzheimer pathology landmarks, but it is also influenced by neurodegeneration (for review see Braskie and Thompson, 2013). Indeed decreased hippocampal volume due to widespread neurodegeneration and visualized by neuroimaging appears to be a significant predictor of memory decline  (for review see Braskie and Thompson, 2016).

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

It is well accepted that impairment of cell function or cell loss in hippocampus will interfere with memory processes, since the hippocampus plays a key role in memory (Barker and Warburton, 2011). In Alzheimer's disease, hippocampus and entorhinal cortex are affected early in the disease process and cognitive deficit is correlated with brain atrophy (for review Braskie and Thompson, 2013).

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

There are some inconsistencies regarding the time of exposure. Some papers clearly show that early Pb exposure increases amyloid and tau pathology and cognitive decline in aging. But few studies have addressed this complex question by using an ad hoc experimental design. Other studies have descibed the effects of lifetime or long-term exposure on cognitive functions but without a precise desciption of exposure onset and duration.

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

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

Bakulski KM, Park SK, Weisskopf MG, Tucker KL, Sparrow D, Spiro A, 3rd, et al. 2014. Lead exposure, B vitamins, and plasma homocysteine in men 55 years of age and older: the VA normative aging study. Environ Health Perspect 122(10): 1066-1074.

Barker GR, Warburton EC. 2011. When is the hippocampus involved in recognition memory? J Neurosci 31(29): 10721-10731.

Bihaqi SW, Bahmani A, Subaiea GM, Zawia NH. 2014. Infantile exposure to lead and late-age cognitive decline: relevance to AD. Alzheimer's & dementia : the journal of the Alzheimer's Association 10(2): 187-195.

Braskie MN, Thompson PM. 2013. Understanding cognitive deficits in Alzheimer's disease based on neuroimaging findings. Trends in cognitive sciences 17(10): 510-516.

Eslamizade MJ, Madjd Z, Rasoolijazi H, Saffarzadeh F, Pirhajati V, Aligholi H, et al. 2016. Impaired Memory and Evidence of Histopathology in CA1 Pyramidal Neurons through Injection of Abeta1-42 Peptides into the Frontal Cortices of Rat. Basic and clinical neuroscience 7(1): 31-41.

Gu H, Wei X, Monnot AD, Fontanilla CV, Behl M, Farlow MR, et al. 2011. Lead exposure increases levels of beta-amyloid in the brain and CSF and inhibits LRP1 expression in APP transgenic mice. Neurosci Lett 490(1): 16-20.

Gu H, Robison G, Hong L, Barrea R, Wei X, Farlow MR, et al. 2012. Increased beta-amyloid deposition in Tg-SWDI transgenic mouse brain following in vivo lead exposure. Toxicol Lett 213(2): 211-219.

Guerrero-Munoz MJ, Gerson J, Castillo-Carranza DL. 2015. Tau Oligomers: The Toxic Player at Synapses in Alzheimer's Disease. Frontiers in cellular neuroscience 9: 464.

Liu MC, Liu XQ, Wang W, Shen XF, Che HL, Guo YY, et al. 2012. Involvement of microglia activation in the lead induced long-term potentiation impairment. PLoS One 7(8): e43924.

Park JH, Lee DW, Park KS, Joung H. 2014. Serum trace metal levels in Alzheimer's disease and normal control groups. American journal of Alzheimer's disease and other dementias 29(1): 76-83.

Salgado-Puga K, Pena-Ortega F. 2015. Cellular and network mechanisms underlying memory impairment induced by amyloid beta protein. Protein and peptide letters 22(4): 303-321.

Schneider JS, Anderson DW, Talsania K, Mettil W, Vadigepalli R. 2012. Effects of developmental lead exposure on the hippocampal transcriptome: influences of sex, developmental period, and lead exposure level. Toxicol Sci 129(1): 108-125.

Selkoe DJ, Hardy J. 2016. The amyloid hypothesis of Alzheimer's disease at 25 years. EMBO molecular medicine 8(6): 595-608.