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Relationship: 396

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

reduction in ovarian granulosa cells, Aromatase (Cyp19a1) leads to Reduction, 17beta-estradiol synthesis by ovarian granulosa cells

Upstream event

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

reduction in ovarian granulosa cells, Aromatase (Cyp19a1)

Downstream event

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

Reduction, 17beta-estradiol synthesis by ovarian granulosa cells

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

AOP Name	Adjacency	Weight of Evidence	Quantitative Understanding	Point of Contact	Author Status	OECD Status
Aromatase (Cyp19a1) reduction leading to impaired fertility in adult female	adjacent	Moderate		Allie Always (send email)	Open for citation & comment	EAGMST Under Review

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

Aromatase is the cytochrome P450 enzyme complex responsible for the conversion of androgens to estrogens during steroidogenesis [reviewed by (Simpson et al., 1994)], which is a key reaction in the sex differentiation in vertebrates. Reduction in level of aromatase or in the catalytic activity of the aromatase itself will reduce the levels of estrogens in tissues and dramatically disrupt estrogen (E2) hormone action.

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 Map 2.0

ID	Experimental Design	Species	Upstream Observation	Downstream Observation	Citation (first author, year)	Notes

Evidence Map

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

Title	First Author	Biological Plausibility	Dose Concordance	Temporal Concordance	Incidence Concordance

Biological Plausibility

Dose Concordance Evidence

Temporal Concordance Evidence

Incidence Concordance Evidence

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

Upstream events An upstream event has been postulated to involve PPARγ activation, however the studies confirming its role in the reduction of aromatase levels are incomplete. The mechanisms involving Peroxisome Proliferator Activated receptor γ activation leading to aromatase (Cyp19a1) reduction relating to the pathway are described in greater detail in the page Peroxisome Proliferator Activated receptor γ activation indirectly leads to aromatase (Cyp19a1) reduction .

Availability or reduced aromatase levels

Studies by Davis et al showed that MEHP impacts on availability (degradation) of aromatase as the decrease in E2 production is evident after the treatment with transcription and translation blockers (actinomycin D or cycloheximide). MEHP was further decreased E2 production independently of the presence of inhibitors pointing out at mechanisms of degradation rather than aromatase synthesis (Davis et al., 1994). MEHP can indirectly impact on aromatase rates by decreasing necessary cofactors (availability) or activation of aromatase inhibitors. Treinin et al showed in vitro dose dependent inhibition of progesterone production by MEHP in granulosa cells and reduced FSH-stimulated cAMP accumulation in granulosa cells implicating a direct or indirect effect of MEHP on FSH receptor (Treinen, Dodson, & Heindel, 1990). Similar effects of cAMP accumulation were seen in Sertoli cells (Lloyd & Foster, 1988), (Heindel & Chapin, 1989), (Heindel & Powell, 1992). Since granulosa and Sertoli cells share several structural and functional characteristics this mechanism is plausible. Study by Ma et al showed that inhaled DEHP (5 and 25 mg/m3) increased levels of LH and E2 in serum of prepubertal rats, and it increased ovarian Cyp19a1 expression (Ma et al., 2006), which is in disagreement with the key event relationship. This difference might be due to measurements of hormones during different phases of the estrous cycle, alterations in the experimental approaches used (in vivo versus in vitro) as well as exposure routes and doses given.

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

Is it known how much change in the first event is needed to impact the second? Are there known modulators of the response-response relationships? Are there models or extrapolation approaches that help describe those relationships?

Several mechanistically-based models of ovarian steroidogenesis have been developed (Breen et al. 2013; Breen et al. 2007; Shoemaker et al. 2010; Quignot and Bois 2013). These may be adaptable to predict in vitro E2 production and/or plasma E2 concentrations from in vitro or in vivo measurements of changes of aromatase expression/availability.

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

See table 1.