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Key Event Title
|Level of Biological Organization|
Key Event Components
|peroxisome proliferator activated receptor signaling pathway||peroxisome proliferator-activated receptor alpha||increased|
Key Event Overview
AOPs Including This Key Event
|AOP Name||Role of event in AOP||Point of Contact||Author Status||OECD Status|
|PPARα activation leading to impaired fertility||MolecularInitiatingEvent||Elise Grignard (send email)||Open for citation & comment||EAGMST Under Review|
|PPAR and reproductive toxicity||MolecularInitiatingEvent||Elise Grignard (send email)||Not under active development||Under Development|
|NRF2/FXR to steatosis||KeyEvent||Michelle Angrish (send email)||Under Development: Contributions and Comments Welcome|
|PPARalpha-dependent liver tumors in rodents||MolecularInitiatingEvent||Chris Corton (send email)||Under development: Not open for comment. Do not cite||Under Development|
|PPARa Agonism Impairs Fish Reproduction||MolecularInitiatingEvent||Ashley Kittelson (send email)||Under development: Not open for comment. Do not cite|
Key Event Description
Gene expression occurs in a coordinated fashion (Judson et al., 2012). The many observations of altered gene expression following binding of ligand to PPARα led to systematic investigations of the genomic signature that corresponds to PPARα activation (Tamura et al., 2006; Kupershmidt et al., 2010; Rosen et al., 2017; Rooney et al., 2018; Corton et al., 2020; Hill et al., 2020; Lewis et al., 2020). Specific gene with increased expression following PPARα activation include Cyp4a1, Cpt1B, and Lpl. More generally, the pathways activated include:
- Genes involved in Metabolism of lipids and lipoproteins
- Fatty acid metabolism
- Genes involved in Fatty acid, triacylglycerol, and ketone body metabolism
- PPAR signaling pathway
- Genes involved in Cell Cycle
The Peroxisome Proliferator Activated receptor α (PPARα) belongs to the Peroxisome Proliferator Activated receptors (PPARs; NR1C) steroid/thyroid/retinoid receptor superfamily of transcription factors.
PPARα is expressed in high levels in tissues that perform significant catabolism of fatty acids (FAs), such as brown adipose tissue, liver, heart, kidney, and intestine (Michalik et al. 2006). The receptor is present also in skeletal muscle, intestine, pancreas, lung, placenta and testes (Mukherjee et al. 1997), (Schultz et al. 1999).
General role in biology
PPARs are activated by fatty acids and their derivatives; they are sensors of dietary lipids and are involved in lipid and carbohydrate metabolism, immune response and peroxisome proliferation (Wahli and Desvergne 1999), (Evans, Barish, & Wang, 2004). PAPRα is a also a target of hypothalamic hormone signalling and was found to play a role in embryonic development (Yessoufou and Wahli 2010).
Fibrates, activators of PPARα, are commonly used to treat hypertriglyceridemia and other dyslipidemic states as they have been shown to decrease circulating lipid levels (Lefebvre et al. 2006).
How It Is Measured or Detected
Binding of ligands to PPARα is measured using binding assays in vitro and in silico, whereas the information about functional activation is derived from transactivation assays (e.g. transactivation assay with reporter gene) that demonstrate functional activation of a nuclear receptor by a specific compound. Binding of agonists within the ligand-binding site of PPARs causes a conformational change of nuclear receptor that promotes binding to transcriptional co-activators. Conversely, binding of antagonists results in a conformation that favours the binding of co-repressors (Yu and Reddy 2007), (Viswakarma et al. 2010). Transactivation assays are performed using transient or stably transfected cells with the PPARα expression plasmid and a reporter plasmid, respectively. There are also other methods that have been used to measure PPARα activity, such as the Electrophoretic Mobility Shift Assay (EMSA) or commercially available PPARα transcription factor assay kits, see Table 1. The transactivation (stable transfection) assay provides the most applicable OECD Level 2 assay (i.e. In vitro assays providing mechanistic data) aimed at identifying the initiating event leading to an adverse outcome (LeBlanc, Norris, and Kloas 2011). A recent study characterized the PPARα ligand binding domain for the purpose of next-generation metabolic disease drugs (Kamata et al. 2020).
The most direct measure of this MIE is microarray profiling from large gene expression databases TG-GATEs and DrugMatrix coupled with t statistical analysis of whole genome expression profiles (Svoboda et al., 2019; Igarashi et al., 2015) From these data, A gene expression signature of 131 PPARα-dependent genes was built using microarray profiles from the livers of wild-type and PPARα-null mice. A quantitative measure of this expression signature is a measure of similarity/correlation between the PPARα signature and positive and negative test sets is provided by the Running Fisher test (Corton et al., 2020; Hill et al., 2020; Kupershmidt et al., 2010; Lewis et al., 2020; Rooney et al., 2018).
A gene expression signature of 131 PPARα-dependent genes was built using microarray profiles from the livers of wild-type and PPARα-null mice. A quantitative measure of this expression signature would be a measure of similarity/correlation between the PPARα signature and positive and negative test sets is provided by the Running Fisher test (Kupershmidt et al., 2010; Rooney et al., 2018; Corton et al., 2020).
For all substances, MIE activation does not rise monotonically over dose or time. These fluctuations are likely due to variations in cofactor availability or access to the site of transcription (Gaillard et al., 2006; Koppen et al., 2009; Kupershmidt et al., 2010; Ong et al., 2010; Chow et al., 2011; De Vos et al., 2011; Simon et al., 2015).
|Method/Test||Test Principle||Test Environment||Test Outcome||Assay Type/Domain|
molecular modelling; docking simulation
|Computational simulation of ligand binding||In silico||Prediction off binding interaction||Quantitative virtual screeings|
|Scintillation proximity binding assay||Direct binding of ligand||In vitro||Identifies compouds that bind to PPARα||Qualitative in vitro screening|
|PPARα reporter gene assay||Quantify changes in in PPARα activation via a sensitive surrogate||In vitro, Ex vivo||Measures changes in activity of genes linked to a PPARα receptor element||Quantitative in vitro screening|
|Electrophoretic Band Shift||determines if a protein or protein mixture will bind to a specific DNA or RNA sequence||In vitro||Measures cofactor binding by changes in gel mobility||Quantitative in vitro screening|
|Microarray profiling||Develop MIE-specific sets of gene expression biomarkers||In vivo||Classification of PPARα biomarker genes with statistical methods||Quantitative in vivo screening|
Domain of Applicability
PPARα has been identified in frog (Xenopus laevis), mouse, human, rat, fish, hamster and chicken (reviewed in (Wahli and Desvergne 1999)).
Evidence for Perturbation by Stressor
Overview for Molecular Initiating Event
Fibrates are ligands of PPARα (Staels et al. 1998).
MHEP (CAS 4376-20-9) directly binds in vitro to PPARα (Lapinskas et al. 2005) and activates this receptor in transactivation assays PPARα (Lapinskas et al. 2005), (Maloney and Waxman 1999), (Hurst and Waxman 2003), (Bility et al. 2004), (Lampen, Zimnik, and Nau 2003), (Venkata et al. 2006) ]. DEHP (CAS 117-81-7) has not been found to bind and activate PPARα (Lapinskas et al. 2005), (Maloney and Waxman 1999). However, the recent studies shown activation of PPARα (ToxCastTM Data).
Notably, PPARα are responsive to DEHP in vitro as they are translocated to the nucleus (in primary Sertoli cells) (Dufour et al. 2003), (Bhattacharya et al. 2005). Expression of PPARα [mRNA and protein] has been reported to be also modulated by phthtalates: (to be up-regulated in vivo upon DEHP treatment (Xu et al. 2010) and down-regulated by Diisobutyl phthalate (DiBP) (Boberg et al. 2008)).
Perfluorooctanoic Acid (PFOA) is known to activate PPARα (Vanden Heuvel et al. 2006).
Tributyltin (TBT) activates all three heterodimers of PPAR with RXR, primarily through its interaction with RXR (le Maire et al. 2009)
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Chow, C. C., Ong, K. M., Dougherty, E. J., & Simons, S. S. (2011). Inferring mechanisms from dose-response curves. Methods Enzymol, 487, 465-483. https://doi.org/10.1016/B978-0-12-381270-4.00016-0
Corton, J. C., Hill, T., Sutherland, J. J., Stevens, J. L., & Rooney, J. (2020). A Set of Six Gene Expression Biomarkers Identify Rat Liver Tumorigens in Short-Term Assays. Toxicol Sci. https://doi.org/10.1093/toxsci/kfaa101
De Vos, D., Bruggeman, F. J., Westerhoff, H. V., & Bakker, B. M. (2011). How molecular competition influences fluxes in gene expression networks. PLoS One, 6(12), e28494. https://doi.org/10.1371/journal.pone.0028494
Dufour, Jannette M, My-Nuong Vo, Nandini Bhattacharya, Janice Okita, Richard Okita, and Kwan Hee Kim. 2003. “Peroxisome Proliferators Disrupt Retinoic Acid Receptor Alpha Signaling in the Testis.” Biology of Reproduction 68 (4) (April): 1215–24. doi:10.1095/biolreprod.102.010488.
Feige, Jérôme N, Laurent Gelman, Daniel Rossi, Vincent Zoete, Raphaël Métivier, Cicerone Tudor, Silvia I Anghel, et al. 2007. “The Endocrine Disruptor Monoethyl-Hexyl-Phthalate Is a Selective Peroxisome Proliferator-Activated Receptor Gamma Modulator That Promotes Adipogenesis.” The Journal of Biological Chemistry 282 (26) (June 29): 19152–66. doi:10.1074/jbc.M702724200.
Gaillard, S., Grasfeder, L. L., Haeffele, C. L., Lobenhofer, E. K., Chu, T.-M., Wolfinger, R., Kazmin, D., Koves, T. R., Muoio, D. M., Chang, C.-y., & McDonnell, D. P. (2006). Receptor-selective coactivators as tools to define the biology of specific receptor-coactivator pairs. Mol Cell, 24(5), 797-803. https://doi.org/10.1016/j.molcel.2006.10.012
Hill, T., Rooney, J., Abedini, J., El-Masri, H., Wood, C. E., & Corton, J. C. (2020). Gene Expression Thresholds Derived From Short-Term Exposures Identify Rat Liver Tumorigens. Toxicol Sci. https://doi.org/10.1093/toxsci/kfaa102
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Igarashi, Y., Nakatsu, N., Yamashita, T., Ono, A., Ohno, Y., Urushidani, T., & Yamada, H. (2015). Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res, 43(Database issue), D921-7. https://doi.org/10.1093/nar/gku955
Kamata S, Oyama T, Saito K, Honda A, Yamamoto Y, Suda K, Ishikawa R, Itoh T, Watanabe Y, Shibata T, Uchida K, Suematsu M, Ishii I. PPARα Ligand-Binding Domain Structures with Endogenous Fatty Acids and Fibrates. iScience. 2020;23(11):101727. 10.1016/j.isci.2020.101727
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Lampen, Alfonso, Susan Zimnik, and Heinz Nau. 2003. “Teratogenic Phthalate Esters and Metabolites Activate the Nuclear Receptors PPARs and Induce Differentiation of F9 Cells.” Toxicology and Applied Pharmacology 188 (1) (April): 14–23. doi:10.1016/S0041-008X(03)00014-0.
Lapinskas, Paula J., Sherri Brown, Lisa M. Leesnitzer, Steven Blanchard, Cyndi Swanson, Russell C. Cattley, and J. Christopher Corton. 2005. “Role of PPARα in Mediating the Effects of Phthalates and Metabolites in the Liver.” Toxicology 207 (1): 149–163.
Le Maire, Albane, Marina Grimaldi, Dominique Roecklin, Sonia Dagnino, Valérie Vivat-Hannah, Patrick Balaguer, and William Bourguet. 2009. “Activation of RXR-PPAR Heterodimers by Organotin Environmental Endocrine Disruptors.” EMBO Reports 10 (4) (April): 367–73. doi:10.1038/embor.2009.8.
LeBlanc, GA, DO Norris, and W Kloas. 2011. “Detailed Review Paper State of the Science on Novel In Vitro and In Vivo Screening and Testing Methods and Endpoints for Evaluating Endocrine Disruptors” (178).
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Lewis, R. W., Hill, T., & Corton, J. C. (2020). A set of six Gene expression biomarkers and their thresholds identify rat liver tumorigens in short-term assays. Toxicology, 443, 152547. https://doi.org/10.1016/j.tox.2020.152547
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