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Relationship: 973

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

dimerization, AHR/ARNT leads to reduced dimerization, ARNT/HIF1-alpha

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
Aryl hydrocarbon receptor activation leading to early life stage mortality, via reduced VEGF adjacent Moderate Low Amani Farhat (send email) Open for citation & comment WPHA/WNT Endorsed
AhR activation leading to preeclampsia adjacent Sabrina Tait (send email) Under development: Not open for comment. Do not cite Under Development

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
chicken Gallus gallus High NCBI
mouse Mus musculus High NCBI
Atlantic killifish Fundulus heteroclitus High NCBI
zebrafish Danio rerio High NCBI
Fundulus heteroclitus Fundulus heteroclitus High NCBI
human Homo sapiens High 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
Unspecific High

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
Embryo High
During development and at adulthood High

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

The aryl hydrocarbon receptor nuclear translocator (ARNT) is common dimerization partner for both the aryl hydrocarbon receptor (AHR) and hypoxia inducible factor alpha (HIF-1α). There is considerable cross talk between the two nuclear receptors, leading to the hypothesis that AHR activation leads to sustained AHR/ARNT dimerization and reduced HIF-1α/ARNT dimerization, assuming ARNT is not available in excess (Chan et al. 1999; Vorrink et al 2014b).

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

The ARNT serves as a dimerization partner for multiple transcription factors including the xenobiotic sensing AHR and HIF1α; therefore, it is plausible that sequestration of ARNT by one receptor would reduce the responsiveness of the other, assuming that ARNT is available in limited quantity (Vorrink et al. 2014b). Gel-shift and coimmunoprecipitation experiments have shown that the AHR and HIF1α compete for ARNT in vitro, with approximately equal dimerization efficiencies (Schmidt and Bradfield 1996).

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

Although crosstalk between AHR and HIF1α clearly exists, the nature of the relationship is still not clearly defined (Vorrink et al 2014). It has been suggested that HIF1α and AHR do not competitively regulate each other for hetero-dimerization with ARNT, as ARNT is constitutively and abundantly expressed in cells and does not deplete due to hypoxia or AHR activation (Chan et al. 1999; Pollenz et al. 1999). Nie et al. (2001) hypothesized that the degree of interaction among ARNT-dependent pathways depends on the abundance of ARNT in the cells. They observed crosstalk in Hepa 1 cells but not H4IIE cells, and attributed this to the ratio of AhR to ARNT of 0.3 (i.e. excess ARNT), compared to a ratio of 10 in Hepa 1 cells (Holmes and Pollenz, 1997)

Some studies have shown that the effect of hypoxia on AHR mediated pathways is stronger than effects of a AHR-mediated xenobiotic response on the HIF1α pathway (Gassmann et al. 1997; Gradin et al. 1996; Nie et al. 2001; Prasch et al. 2004); this has been attributed to the stronger binding affinity of HIF1α to ARNT relative to AHR (Gradin et al. 1996).

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

The cross-talk between AHR and HIF1α has been demonstrated in chicken embryos (Ivnitski-Steele et al. 2004) mice (Ichihara et al. 2007) Atlantic killifish and zebrafish (McElroy et al. 2012),  Mummichog (Kraemer et al. 2004) and a number of human cell lines (Chan et al. 1999; Seifert et al. 2008; Vorrink et al. 2014a, Vorrink et al. 2014b).

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

1. Chan, W. K., Yao, G., Gu, Y. Z., and Bradfield, C. A. (1999). Cross-talk between the aryl hydrocarbon receptor and hypoxia inducible factor signaling pathways. Demonstration of competition and compensation. J Biol. Chem. 274(17), 12115-12123.

2. Ichihara, S., Yamada, Y., Ichihara, G., Nakajima, T., Li, P., Kondo, T., Gonzalez, F. J., and Murohara, T. (2007). A role for the aryl hydrocarbon receptor in regulation of ischemia-induced angiogenesis. Arterioscler. Thromb. Vasc. Biol. 27(6), 1297-1304.

3. Ivnitski-Steele, I. D., Sanchez, A., and Walker, M. K. (2004). 2,3,7,8-tetrachlorodibenzo-p-dioxin reduces myocardial hypoxia and vascular endothelial growth factor expression during chick embryo development. Birth Defects Res. A Clin. Mol. Teratol. 70(2), 51-58.

4. Nie, M., Blankenship, A. L., and Giesy, J. P. (2001). Interactions between aryl hydrocarbon receptor (AhR) and hypoxia signaling pathways. Environ. Toxicol. Pharmacol. 10(1-2), 17-27.

5. Pollenz, R. S., Davarinos, N. A., and Shearer, T. P. (1999). Analysis of aryl hydrocarbon receptor-mediated signaling during physiological hypoxia reveals lack of competition for the aryl hydrocarbon nuclear translocator transcription factor. Mol. Pharmacol. 56(6), 1127-1137.

6. Schmidt, J. V., and Bradfield, C. A. (1996). Ah receptor signaling pathways. Annu. Rev. Cell Dev. Biol. 12, 55-89.

7. Seifert, A., Katschinski, D. M., Tonack, S., Fischer, B., and Navarrete, S. A. (2008). Significance of prolyl hydroxylase 2 in the interference of aryl hydrocarbon receptor and hypoxia-inducible factor-1 alpha signaling. Chem Res. Toxicol. 21(2), 341-348.

8. Vorrink, S. U., Severson, P. L., Kulak, M. V., Futscher, B. W., and Domann, F. E. (2014a). Hypoxia perturbs aryl hydrocarbon receptor signaling and CYP1A1 expression induced by PCB 126 in human skin and liver-derived cell lines. Toxicol. Appl. Pharmacol. 274(3), 408-416.

9. McElroy, A., Clark, C., Duffy, T., Cheng, B., Gondek, J., Fast, M., Cooper, K., and White, L. (2012). Interactions between hypoxia and sewage-derived contaminants on gene expression in fish embryos. Aquat. Toxicol. 108, 60-69.

10. L.D. Kraemer and P.M. Schulte, (2004) Prior PCB exposure suppresses hypoxia-induced up-regulation of glycolytic enzymes in Fundulus heteroclitus, Comp. Biochem. Physiol. C Toxicol. Pharmacol. 139: 23–29.

11. Vorrink, S.A. and Domann, F.E. (2014b) Regulatory crosstalk and interference between the xenobiotic and hypoxia sensing pathways at the AhR-ARNT-HIF1a signaling node. Chemico-Biological Interactions 218: 82–88