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Relationship: 2073


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

Activation, PPARα leads to Decreased, cholesterol

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
PPARalpha Agonism Impairs Fish Reproduction adjacent High Not Specified Ashley Kittelson (send email) Open for citation & comment

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
teleost fish teleost fish High NCBI
Homo sapiens Homo sapiens High NCBI
mice Mus sp. High NCBI
mammals mammals Moderate 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
Male High
Female Moderate

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
Adults 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

PPARα is a nuclear receptor. With an agonist it promotes transcription of many genes, several of which are involved in cholesterol transport and metabolism (reviewed in Rakhshandehroo et al., 2010).

Hydrophobic lipid molecules (such as cholesterol, cholesteryl ester, and triglycerides) are transported in the aqueous plasma of organisms by forming lipoprotein complexes with apolipoproteins. There are different groups of lipoproteins which use different apolipoproteins and ratios of lipids: low-density (LDL), very low-density (VLDL), and high density (HDL).

Fibrates are a class of drug that agonize PPARα to lower LDL and VLDL while slightly increasing HDL in humans (Singh & Correa, 2020).

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

See below.

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

There are 4 proposed mechanisms through which PPARα agonists [fibrates] lower cholesterol in humans (Staels et al., 1998; Chruściel et al., 2015):

  1. Increasing lipoprotein lipase (LPL) and decreasing its inhibitor, APOC3. LPL catabolizes triglycerides in VLDL which lowers the amount VLDL.
  2. Formation of LDL with a higher affinity for the LDL receptor resulting in increased cellular uptake and breakdown of LDL.
  3. Reduced cholesterol ester transfer protein (CEPT) expression. CEPT transfers cholesteryl ester and triglycerides between HDL and VLDL
  4. Increased APOA1 and APOA2, the protein components of HDL, in the liver causing increased production of HDL.
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 humans taking fibrate medications show lowed LDL and VLDL but slightly increased HDL, this pattern is not seen in fish (Prindiville et al., 2011). The exact reason(s) why is not well understood. 

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

After a 7 day exposure to bezafibrate (BZF), male zebrafish exposed to 1.7 mg BZF/g food showed no significant decrease in plasma cholesterol (p>0.05). However, those exposed to 33 and 70 mg BZF/g food showed a 25 and 48% reduction, respectively, in plasma cholesterol (p=0.04 and p<0.001, respectively) (Velasco-Santamaría et al., 2011).

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

Lowered cholesterol in adult male zebrafish due to bezafibrate exposure can be seen after 7 days, but not after just 48 hours (Velasco-Santamaría et al., 2011). 

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

Modulating factors haven't been evaluated yet.

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

Feedback/feedforward loops haven't been evaluated yet.

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 understanding of the effects of PPARα agonists on cholesterol primarily comes from studies on mice and humans to develop pharmaceuticals. However, lowered cholesterol in response to a PPARα agonist occurs in other mammals including rats, dogs, and guinea pigs at low, non-toxic doses (Meyer et al., 1999).

There are several studies showing that in fish PPARα agonism decreases cholesterol via the same mechanisms as in humans: 

  1. LPL is conserved in zebrafish (NCBI). It is increased in several fish species exposed to PPARα agonists (Prindiville et al., 2011; Teles et al., 2016; Guo et al., 2015)
  2. LDL is decreased in several fish species exposed to PPARα agonists (see Table 1)
  3. CETP is conserved in zebrafish (NCBI)
  4. APOA1 is conserved in zebrafish (NCBI). However, results are mixed on the effects of PPARα agonists on APOA1 (Corcoran et al., 2015; Teles et al., 2016) and HDL (see table 1) . In mice APOA1 is not regulated by PPARα (Staels & Auwerx, 1998), so this may be the case in fish.


Male and female mice show different effects in several endpoints, including total cholesterol, in response to fibrate administration. This is likely due to estrogen partially and indirectly inhibiting PPARα (Yoon, 2010; Jeong & Yoon, 2012). In fish, males and females often show differing effects on cholesterol (Lee et al., 2019; Runnalls et al., 2007).


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

Al-Habsi, A.A., A. Massarsky, T.W. Moon (2016) “Exposure to gemfibrozil and atorvastatin affects cholesterol metabolism and steroid production in zebrafish (Danio rerio)”, Comparative Biochemistry and Physiology, Part B, Vol. 199, Elsevier, pp. 87-96.

Chruściel, P. et al. (2015) “Statins and fibrates: Should they still be recommended?”, in Combination Therapy in Dyslipidemia, Springer, pp. 11-23. doi: 10.1007/978-3-319-20433-8_2

Corcoran, J. et al. (2015) “Effects of the lipid regulating drug clofibric acid on the PPARα-regulated gene transcript levels in common carp (Cyprinus carpio) at pharmacological and environmental exposure levels”, Aquatic Toxicology, Vol. 161, Elsevier, pp. 127-137.

Du, Z. et al. (2008) “Hypolipidaemic effects of fenofibrate and fasting in the herbivorous grass carp (Ctenopharyngodon Idella) fed a high-fat diet”, British Journal of Nutrition, Vol. 100, Cambridge University Press, pp. 1200-1212. doi:10.1017/S0007114508986840

Guo, X. et al. (2015) “Effects of lipid-lowering pharmaceutical clofibrate on lipid and lipoprotein metabolism of grass carp (Ctenopharyngodon idellal Val.) fed with the high non-protein energy diets”, Fish Physiology and Biochemistry, Vol. 41, Springer, pp. 331-343. doi: 10.1007/s10695-014-9986-8

Jeong, S. & M. Yoon (2012) “Inhibition of the actions of peroxisome proliferator-activated receptor α on obesity by estrogen”, Obesity, Vol. 15(6), Wiley, pp. 1430-1440.

Lee, G. et al. (2019) “Effects of gemfibrozil on sex hormones and reproduction related performances of Oryzias latipes following long-term (155 d) and short-term (21 d) exposure”, Ecotoxicology and Environmental Safety, Vol. 173, Elsevier, pp. 174-181.

Meyer, K. et al. (1999) “Species difference in induction of hepatic enzymes by BM17.0744, an activator of peroxisome proliferator-activated receptor alpha (PPARα)”, Molecular Toxicology, Vol. 73, Springer-Verlag, pp. 440-450.

Ning, L. et al. (2017) “Nutritional background changes the hypolipidemic effects of fenofibrate in Nile tilapia (Oreochromis niloticus)”, Scientific Reports, Vol. 7(41706), Nature.

Prindiville, J.S. et al. (2011) “The fibrate drug gemfibrozil disrupts lipoprotein metabolism in rainbow trout”, Toxicology and Applied Pharmacology, Vol. 251, Elsevier, pp. 201-238. doi:10.1016/j.taap.2010.12.013

Rakhshandehroo, M. et al. (2010) “Peroxisome Proliferator-Activated Receptor Alpha Target Genes”, PPAR Research, Vol. 2010, Hindawi,

Runnalls, T. J., D. N. Hala, J. S. Sumpter (2007) “Preliminary studies into the effects of the human pharmaceutical clofibric acid on sperm parameters in adult fathead minnow”, Aquatic Toxicology, Vol. 84, Elsevier, pp. 111-118. doi:10.1016/j.aquatox.2007.06.005

Singh, G. and R. Correa (2020) “Fibrate Medications”, in StatPearls. StatPearls Publishing.

Staels, B. & J. Auwerx (1998) “Regulation of apo A-I gene expression by fibrates”, Atherosclerosis, Vol. 137, Elsevier, pp. s19-23.

Staels, B. et al. (1998) “Mechanism of action of fibrates on lipid and lipoprotein metabolism”, Cardiovascular Drugs, Vol. 98(19), American Heart Association, pp. 2088-2093.

Teles, M. et al. (2016) “Evaluation of gemfibrozil effects on a marine fish (Sparus aurata) combining gene expression with conventional endocrine and biochemical endpoints”, Journal of Hazardous Materials, Vol. 318, Elsevier, pp. 600-607.

Urbatzka, R. et al. (2015) “Effects of the PPARα agonist WY-14,643 on plasma lipids, enzymatic activities and mRNA expression of lipid metabolism genes in a marine flatfish, Scophthalmus maximus”, Aquatic Toxicology, Vol. 164, Elsevier, pp. 155-162.

Velasco-Santamaría, Y.M. et al. (2011) “Bezafibrate, a lipid-lowering pharmaceutical, as a potential endocrine disruptor in male zebrafish (Danio rerio)”, Aquatic Toxicology, Vol. 105, Elsevier, pp. 107-118. doi:10.1016/j.aquatox.2011.05.018

Yoon, M (2010) “PPARα in Obesity: Sex Differences and Estrogen Involvement”, PPAR Research, Vol. 2010, Hindawi,