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Relationship: 1269

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

Altered expression of hepatic CAR-dependent genes leads to Increase, cell proliferation (hepatocytes)

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
Constitutive androstane receptor activation leading to hepatocellular adenomas and carcinomas in the mouse and the rat adjacent High Moderate Brendan Ferreri-Hanberry (send email) Open for citation & comment EAGMST Under Review

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
rat Rattus norvegicus High NCBI
mouse Mus musculus 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
Mixed 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
All life stages Moderate

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 altered expression of mouse and rat genes that are related to increased cell proliferation will, by definition, produce measurable changes in cell proliferation in hepatocytes (Elcombe et al., 2014; Yang and Wang, 2014). Hepatocytes have the ability to regenerate when properly stimulated, such as following partial hepatectomy. The CAR-responsive genes such as Gadd45b, Ki67 and the Cdc20 are necessary gene targets that are part of this synchronized response that results in progress out of the quiescent cell cycle stage (G0), resulting in DNA replication during S-phase (a measurable marker of cell proliferation), and eventual cell division.

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 CAR-mediated changes in expression of pro-proliferative genes such as Gadd45b, Ki67 and Cdc20 and the genomic pathways such as cell proliferation or cell cycle progression have been demonstrated to occur with multiple CAR activators (Currie et al., 2014; Deguchi et al., 2009; Geter et al., 2014; Oshida et al., 2015a; Ross et al., 2010; Tojima et al., 2012), and markers of cell proliferation such as BrdU labeling index or Ki67 labeling index have also been demonstrated to occur in mice and rats exposed to these same CAR activators. It is highly plausible that gene expression changes lead to increased cell proliferation signals in hepatocytes, as genes in the Gadd45 family are known to interact with cyclins, cyclin-dependent kinase inhibitors and p53 to alter progression through the cell cycle (Liebermann and Hoffman, 2008). Other CAR-mediated changes in gene expression such as those related to metabolism enzymes (e.g. CYP2B isoforms) lead to associative events in the liver such as increased CYP2B activity and/or protein, hepatocellular hypertrophy and increase liver weight.

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

One minor uncertainty in this relationship is related to the different time courses of the two linked key events. With most CAR activators, the increase in cell proliferation (e.g. by BrdU, Ki67 or PCNA labeling index) is an early event (days) that is transient when one measures the response across the whole liver. For example, phenobarbital produced an increased BrdU labeling index in B6C3F1 mice that was maximal at 7 days and gradually dissipated at days 14, 21 and 28; no difference was observed vs. control at day 90 (Kolaja et al., 1996a) Phenobarbital in F344 rats produced an increase in labelling index at day 7 that was only marginally affected at day 14 and returned to control levels from day 21 onward (Kolaja et al., 1996a). Currie (Currie et al., 2014) compared cell proliferation by BrdU and cell proliferation gene pathways by IPA. Phenobarbital and the direct CAR activator propiconazole given to male CD-1 mice produced significant changes in the cell cycle/cell proliferation IPA pathways at both 4 and 30 days of treatment, whereas BrdU labeling index was maximal at 1 or 2 days of treatment, with no difference from controls at 14 days and beyond. The work of Kolaja (1996a; 1996b) has demonstrated that the cell proliferation in the liver does persist for longer time intervals in mice, if one examines the response within altered foci in an initiation-promotion model, with 500 ppm dietary phenobarbital as the promoter. Therefore, a genomic response to CAR activators in rodent liver (as a whole) can be measured and shown to produce a sustained response of enhanced signaling for increased cell proliferation, but the downstream key event of increased cell proliferation (as measured via BrdU or Ki67 labeling) is often insufficiently sensitive to detect a sustained difference vs. controls in the whole liver. The time course of these related key events can be very dependent on the zones of the liver examined (periportal, midzonal, centrilobular; Kolaja et al., 1996a), and on the specific CAR activator and the species/strain of rodent that is tested (Elcombe et al., 2014; Huang et al., 2005; Kolaja et al., 1996a).

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 pathway leading from CAR-mediated gene expression changes to an increase in cell proliferation (BrdU, PCNA or Ki67 labeling index) has strong evidence indicating it is selectively observed in mice and rats, but not in other mammalian species (Deguchi et al., 2009; Foster, 2000; LeBaron et al., 2013; Parzefall et al., 1991; Plant et al., 1998). Recently a chimeric mouse model that had >90% replacement of mouse hepatocytes with human hepatocytes was used to examine the comparative in vivo effects of 3-5 dietary concentrations of phenobarbital in CD-1 mouse hepatocytes vs. the chimeric human hepatocytes (Yamada et al., 2014). In the liver cells of CD-1 mice after 1 week of treatment, increased mRNA levels of Cyp2b10, Cyp3a11, Ki67 and Gadd45b were observed, along with increased BrdU labeling index. In the human liver cells of chimeric mice, increased mRNA levels of CYP2B and CYP3A were observed, but there were no differences from control for Ki67 and GADD45B mRNA levels nor any increases in BrdU labeling index (Yamada et al., 2014). In summary, a large amount of experimental data including both in vitro and in vivo studies has demonstrated that the downstream key event of increased cell proliferation following treatment with CAR activators occurs selectively in mice and rats, and is correlated with CAR-mediated gene expression changes that reflect pathways of cell cycle control and cell proliferation.

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

Chen, T., Tompkins, L. M., Li, L., Li, H., Kim, G., Zheng, Y. and Wang, H. (2010), A single amino acid controls the functional switch of human constitutive androstane receptor (CAR) 1 to the xenobiotic-sensitive splicing variant CAR3. J Pharmacol Exp Ther 332, 106-15, 10.1124/jpet.109.159210.

Currie, R. A., Peffer, R. C., Goetz, A. K., Omiecinski, C. J. and Goodman, J. I. (2014), Phenobarbital and propiconazole toxicogenomic profiles in mice show major similarities consistent with the key role that constitutive androstane receptor (CAR) activation plays in their mode of action. Toxicology 321, 80-8, 10.1016/j.tox.2014.03.003.

Deguchi, Y., Yamada, T., Hirose, Y., Nagahori, H., Kushida, M., Sumida, K., Sukata, T., Tomigahara, Y., Nishioka, K., Uwagawa, S., Kawamura, S. and Okuno, Y. (2009), Mode of action analysis for the synthetic pyrethroid metofluthrin-induced rat liver tumors: evidence for hepatic CYP2B induction and hepatocyte proliferation. Toxicol Sci 108, 69-80, 10.1093/toxsci/kfp006.