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Relationship: 66

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

Up Regulation, CD36 leads to Increase, FA Influx

Upstream event

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

Up Regulation, CD36

Downstream event

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

Increase, FA Influx

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
LXR activation leading to hepatic steatosis	adjacent	Not Specified		Agnes Aggy (send email)	Not under active development
Pregnane X Receptor (PXR) activation leads to liver steatosis	adjacent	High	Not Specified	Evgeniia Kazymova (send email)	Under development: Not open for comment. Do not cite

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

Term	Scientific Term	Evidence	Link
human	Homo sapiens	Moderate	NCBI
Mus musculus	Mus musculus	Moderate	NCBI

Sex Applicability

An indication of the the relevant sex for this KER. More help

Sex	Evidence
Unspecific	Moderate

Life Stage Applicability

An indication of the the relevant life stage(s) for this KER. More help

Term	Evidence
Adult	High
Juvenile	Moderate

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

CD36 gene expression has been shown to be a key regulator of fatty acid influx, primarily in mammal studies. CD36 is a transmembrane protein, and increased CD36 gene expression can result in increased fatty acid influx. Chemical stressors or high fat diets can help trigger fatty acid influx.

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

This KER was identified as part of an Environmental Protection Agency effort to represent putative AOPs from peer-reviewed literature which were heretofore unrepresented in the AOP-Wiki. Support for this KER is referenced in publications cited in the originating work of Landesmann et al. (2012) and Negi et al. (2021).

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

The biological plausibility linking increased CD36 expression to increased fatty acid uptake is moderate. CD36 is a transmembrane protein, and upregulation of CD36 has been linked to increased fatty acid uptake, primarily in mammalian systems.

Empirical Evidence

Provides specific (citable) evidence that supports the idea of a change in the upstream KE (KEupstream) leading to, or being associated with, a subsequent change in the downstream KE (KEdownstream), assuming the perturbation of KEupstream is sufficient. More help

Since the link between upregulation of CD36 and increased fatty acid influx has been established, empirical studies often measure increased CD36 gene expression and increased lipid content in cells and infer that the mechanism was increased fatty acid influx (Moya et al. 2010).

Species	Duration	Dose	Upregulated CD36?	Increase FA influx?	Summary	Citation
Lab mice (Mus musculus)	5 weeks	Wild-type versus transgenic-human PXR mice.	yes	yes	Transgenic-human PXR mice showed increased expression of CD36 genes in livers and increased lipid accumulation versus wild-type mice. FA influx was inferred as there was no increase in gene expression of SREBP, which would be expected to be upregulated if de novo fatty acid synthesis was the mechanism for increased triglycerides.	Zhou et al. (2006)
Human (Homo sapiens)		Children and adolescents exhibiting steatosis versus children and adolescents without steatosis	Yes	Yes	CD36, FABPpm, SLC27A2, SLC27A5 gene expression were upregulated and CD36 and CPT-1 protein expression was upregulated in subjects exhibiting steatosis linking increased triglyceride levels to fatty acid influx; FASN, SCD1, and acyl-COA gene expression were also upregulated in subjects exhibiting steatosis linking increased triglyceride levels to de novo fatty acid synthesis; both pathways appear to be responsible for increased triglycerides.	Zhu et al. (2011)
Mouse (Mus musculus)	5 weeks	High fat versus low fat diet, transgenic mice	Yes	Yes	Mouse fed high fat diet had higher CD36 expression and triglyceride accumulation than mice fed low fat diet; transgenic mice and hepatocytes with CD36 gene had higher fatty acid influx than null mice and hepatocytes measured by the fluorescent fatty acid analog BODIPY.	Koonen et al. (2007)
Lab mice (Mus musculus)	24 hours	20 um efavirenz in vitro	Yes	Yes	Increased CD36 gene expression vs control in hepatocytes exposed to 20 um efavirenz and correlated higher fatty acid transport as measured by palmitic acid uptake.	Gwag et al. (2009)

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

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

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

Life Stage: Older individuals are more likely to manifest this adverse outcome pathway (adults > juveniles) due to accumulation of triglycerides.

Sex: Applies to both males and females.

Taxonomic: Appears to be present broadly in vertebrates, with most representative studies in mammals (humans, lab mice, lab rats).

References

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

Gwag, T., Meng, Z., Sui, Y., Helsley, R.N., Park, S.-H., Wang, S., Greenberg, R.N., and Zhou, C. 2019. Non-nucleoside reverse transcriptase inhibitor efavirenz activates PXR to induce hypercholesterolemia and hepatic steatosis Journal of Hepatology 70: 930–940.

Koonen, D.P.Y., Jacobs, R.L., Febbraio, M. Young, M.E., Soltys, C.-L.M., Ong, H., Vance, D.E., and Dyck, J.R.B. 2007. Increased hepatic CD36 expression contributes to dyslipidemia associated with diet-induced obesity. Diabetes 56: 2863-2871.

Landesmann, B., Goumenou, M., Munn, S., and Whelan, M. 2012. Description of Prototype Modes-of-Action Related to Repeated Dose Toxicity. European Commission Report EUR 25631, 49 pages. https://op.europa.eu/en/publication-detail/-/publication/d2b09726-8267-42de-8093-8c8981201d65/language-en

Moya, M., Gomez-Lechon, M.J., Castell, J.V., and Jover, R. 2010. Enhanced steatosis by nuclear receptor ligands: A study in cultured human hepatocytes and hepatoma cells with a characterized nuclear receptor expression profile. 184: 376–387.

Negi, C.K., Bajard, L., Kohoutek, J., and Blaha, L. 2021. An adverse outcome pathway based in vitro characterization of novel flame retardants-induced hepatic steatosis. Environmental Pollution 289: 117855.

Zhu, L., Baker, S.S., Liu, W., Tao, M.-H., Patel, R., Nowak, N.J., and Baker, R.D. 2011. Lipid in the livers of adolescents with nonalcoholic steatohepatitis: combined effects of pathways on steatosis. Metabolism Clinical and Experimental 60: 1001-1011.

Zhou, J., Zhai, Y., Mu, Y., Gong, H., Uppal, H., Toma, D., Ren, S., Evans, R.M., and Xie, W. 2006. A Novel Pregnane X Receptor-mediated and Sterol Regulatory Element-binding Protein-independent lipogenic pathway. The Journal of Biological Chemistry 281(21): 15013–15020.