AOP ID and Title:

Short Title: PPARa Agonism Impairs Fish Reproduction

Authors

Ashley Kittelson, ORISE participant at US Environmental Protection Agency

John Hoang, ORISE participant at US Environmental Protection Agency

Robin Kutsi, ORISE participant at US Environmental Protection Agency

Jennifer H. Olker, US Environmental Protection Agency

Kathleen Jensen, US Environmental Protection Agency

David H. Miller, US Environmental Protection Agency

Status

Abstract

This adverse outcome pathway details the linkage from peroxisome proliferator-activated receptor alpha (PPARα) activation to the adverse effects of decreased viable offspring and decrease in population growth rate in fish. PPARα is a ligand-activated nuclear receptor that, after forming a heterodimer with retinoid X receptor (RXR), promotes transcription of many genes including those involved in fatty acid β-oxidation and cholesterol metabolism. Synthetic ligands have been designed as pharmaceuticals to target PPARα for treatment of human metabolic diseases. Exposure to these pharmaceuticals or other contaminants in environment can disrupt metabolic processes in fish, including the activation of PPARα. In fish, this can lead to decreased cholesterol which in turn causes a decrease in reproductive hormones, notably 11-ketotestosterone (11-KT). A decrease in reproductive hormones impairs the fish’s ability to reproduce. Described here is the pathway in which decreased 11-KT impairs inducement of spermatogenesis and sperm production which results in a reduced number of viable offspring. This can lead to impacts on population growth rate due to the decreased number of viable offspring resulting in a decline in recruitment and contribution of offspring to the next generation.  

Background

This AOP was developed to address one potential effect of per- and polyfluoroalkyl substances (PFAS) on fish. Through review of the human health and in vitro toxicity data on conserved pathways and molecular targets for PFAS disruption, activation of PPARα was identified as a potential target of several PFAS which could result in altered lipid metabolism. This AOP focused primarily on teleost fish using experimental data from prototypical stressors, along with knock-out and genetic mutation experiments, for evidence of causality and essentiality for existing and newly developed KEs and KERs. 

Summary of the AOP

Events

Molecular Initiating Events (MIE), Key Events (KE), Adverse Outcomes (AO)

Key Event Relationships

Stressors

Overall Assessment of the AOP

Domain of Applicability

Life Stage Applicability Taxonomic Applicability Sex Applicability

The empirical evidence suggests that this AOP is applicable to adult, reproductively mature, male teleost fish.

Life Stage

The life stage applicable to this AOP is adult, reproductively mature organisms.

Sex

The process of spermatogenesis occurs in reproductively mature males. Therefore, this AOP is only applicable to males.

Taxonomic

This AOP is considered most relevant for teleost fish. Most of the experimental evidence compiled for this AOP is from teleost fish, for which 11-KT is the dominant androgen. However, PPARs including PPARα are highly conserved across humans, rodents, and fish. An evaluation of protein sequence conservation via SeqAPASS (https://seqapass.epa.gov/seqapass/) predicted similarity in cross-species susceptibility to PPARα agonists among humans, zebrafish, medaka, and other fish species. Thus, PPARα agonism and downstream effects on cholesterol, hormone production (not limited to 11-KT), spermatogenesis (a highly conserved biological process), and production of offspring could have more broad taxonomic relevance.

Essentiality of the Key Events

Essentiality of most of key events in this AOP is supported with experimental exposures with prototypical stressors that demonstrate modification of a more upstream KE associated with a corresponding change in downstream KE(s). Several of the key events have further support for essentiality with knock-out and genetic mutations experiments as well as rescue studies. Key studies are listed below.

Although it is challenging to directly measure PPARα activation in fish in vivo studies, there are multiple studies that have shown that fish exposed to fibrates (and thus assumed activation of PPARα) have decreased cholesterol. This relationship has been demonstrated in a variety of fish species [fathead minnow (Runnalls et al., 2007), grass carp (Du et al., 2008; Guo et al., 2015), Nile tilapia (Ning et al., 2017), rainbow trout (Prindiville et al., 2011), medaka (Lee et al., 2019), zebrafish (AL-Habsi et al., 2016; Velasco-Santamaria et al., 2011; Fraz et al., 2018), turbot (Urbatzka et al., 2015)], with temporal and dose concordance in one study (Velasco-Santamaria et al., 2011).

The process of steroid hormone biosynthesis is well understood, and cholesterol is the precursor for all steroid hormones, including 11-KT. The relationship between decreased cholesterol and decreased 11-KT is well-established. There are several experimental exposure studies that showed decreased 11-KT associated with decreased cholesterol with dose and temporal concordance (Lee et al., 2019; Velasco-Santamaria et al., 2011). The essentiality of cholesterol for production of 11-KT is further supported by an ex vivo study which showed that exposure to gemfibrozil (a known PPARα agonist) resulted in decreased 11-KT production unless supplemented with 25OH-cholesterol (Fraz et al., 2018), demonstrating that decreased cholesterol availability was the cause of the decreased steroid synthesis.

11-KT is well documented as a critical androgen for proper male reproduction in teleost fish and has well-documented involvement in spermatogenesis and spermiation. The essentiality of 11-KT for spermatogenesis has been documented in zebrafish knock-out studies with rescue (Zhang et al., 2020) which showed that zebrafish with cyp11c1 knockout have reduced 11-KT levels, smaller genitalia, inability naturally mate, defective Leydig and Sertoli cells, and insufficient spermatogenesis. The treatment of100 nM 11-KA (which is converted to 11-KT in vivo) for 4 hours per day for 10 days corrected these effects, demonstrating that insufficient 11-KT levels was the cause of arrested spermatogenesis.

Successful oocyte fertilization and production of viable offspring is dependent on spermatogenesis and the production of sufficient quality and quantity of sperm. Essentiality is  strongly supported by gene modification studies, such knock-out studies targeting genes associated with spermatogenesis and meiotic division as well as exposure studies with known endocrine disruptors (e.g., DEHP, EE2). Multiple studies with zebrafish have shown that knockouts targeting genes associated with spermatogenesis (e.g., Tdrd12, AR) and meiotic division (e.g., E2f5, Mettl3, mlh1) resulted in interference with spermatogenesis (i.e., delayed or arrested progression, apoptosis, and decrease in sperm density, quality and/or  motility) and male zebrafish that were either infertile or exhibited decreased fertilization rates when mated with WT females (Dai et al., 2017; Leal et al, 2008; Tang et al., 2018; Xia et al., 2018; Xie et al., 2020).

By definition, there must be viable offspring to maintain a population. However, there are other vital rates that are essential here as well, such as survival to reproductive age.

Weight of Evidence Summary

The weight of evidence for each of the KERs within this AOP are ranked moderate to high. Each of the KERs is biologically plausible, with the highest ratings for the intermediate KERs (Decreased, cholesterol leading to Decreased, 11-KT and Decreased, 11-KT leading to Impaired, Spermatogenesis). The relationship between the MIE and the first key event is considered moderate for biological plausibility due to challenges in directly measuring the PPARα activation in in vivo studies. Whereas the links to the individual adverse outcome (Decreased, viable offspring) and the population level adverse outcome are considered moderate for biological plausibility due to the other factors that can influence each of these outcomes. There is substantial experimental evidence in fish to support this AOP, however, few studies measured multiple sequential key events and the final link to decreased population growth rate is based on biological plausibility and population modeling. Overall weight of evidence is moderate.

Biological Plausibility

This AOP is considered highly plausible, based on the evaluation of available evidence for the mechanistic (structural or functional) relationships between upstream and downstream KEs that are consistent with established biological knowledge. There is a broad understanding of lipid metabolism pathways and supporting in vivo and in vitro experimental data on the role of PPARα in lipid metabolism. PPARα is conserved across vertebrates and has been documented in multiple fish species, therefore biological plausibility is considered moderate for activation of PPARα leading to decreased cholesterol. The next two KERs are considered highly plausible. The process of steroid hormone biosynthesis is well understood, and cholesterol is the precursor for all steroid hormones including testosterone and 11-ketotestosterone (Norris and Carr, 2020). Similarly, the 11-ketotestosterone is well documented as necessary for spermatogenesis and sperm production (Amer et al., 2001; Borg, 1994; Geraudie et al., 2010). Because there are multiple factors required to produce viable offspring, biological plausibility is considered moderate for the process of impaired spermatogenesis leading to decreased viable offspring. The link from the individual level adverse outcome (decreased viable offspring) to the population level adverse outcome (decrease in population growth rate) is also influenced by multiple factors.  

Empirical Support

There is substantial experimental evidence to support this AOP. Experimental results from a variety of fish studies with prototypical stressors demonstrate concordance and consistency throughout the AOP. However, there were few studies that measured multiple sequential KEs and limited concentration or dose-response data and temporal measurements across diversity of taxa. Due to these limitations, response-response relationships for a quantitative understanding of this AOP could not be evaluated. Concordance of empirical support across the AOP is summarized in Attachment A.

There are multiple studies in fish that demonstrate exposure to known PPARα agonists (considered prototypical stressors or model chemicals) resulted in decreased total cholesterol. These studies include experimental exposure of seven different fish species [fathead minnow (Runnalls et al., 2007), grass carp (Du et al., 2008; Guo et al., 2015), Nile tilapia (Ning et al., 2017), rainbow trout (Prindiville et al., 2011), medaka (Lee et al., 2019), zebrafish (AL-Habsi et al., 2016; Velasco-Santamaria et al., 2011; Fraz et al., 2018), turbot (Urbatzka et al., 2015)] to several different fibrates (clofibrate, clofibric acid, gemfibrozil, fenofibrate, WY-14643). Temporal and dose concordance was demonstrated in one study ( Velasco-Santamaría et al., 2011); however, there is insufficient empirical evidence for development of a quantitative relationship between the KEs.

While the following KER (decreased cholesterol leading to decreased 11-KT) has strong biological plausibility, there are relatively few fish experimental exposure studies that measured both cholesterol and 11-KT. Two exposure studies that measured both KEs showed dose and temporal concordance (Lee et al., 2019; Velasco-Santamaria et al., 2011), and the third study provided strong evidence essentiality of cholesterol for the production of 11-KT (Fraz et al., 2018). 

There is substantial empirical evidence showing spermatogenesis in numerous fish species is dependent on 11-KT, with several studies demonstrating temporal and dose concordance for this relationship. These studies include testing of both higher 11-KT (treatments with 11-KT or increased production) and decreased 11-KT. For example, increased 11-KT has been related to measures of successful spermatogenesis such as greater number of spermatids (Agulleiro et al., 2007; Selvaraj et al., 2013), more advanced testicular stages (Cavaco et al., 1998, 2001), and more differentiated and later type spermatogonia (Melo et al., 2015; Miura et al., 1991). Whereas, decreased 11-KT in fish has been associated with negative impacts or delays in spermatogenesis including decreased number of spermatocytes, spermatids, and/or spermatozoa (Agbohessi et al., 2015; Chen et al., 2017; de Waal et al., 2009; Liu et al., 2018; Pereira et al., 2015; Sales et al., 2020; Xia et al,. 2018). Melo et al. (2015) is one example of studies that demonstrated temporal concordance; in this study exposure to adrenosterone (ketoandrostenedione; which is converted to 11-KT in vivo) caused an increase in 11-KT levels at 7 and 14 d, with Type A differentiated spermatogonial numbers also increased 14 d after treatment.

There is substantial empirical evidence demonstrating that impaired spermatogenesis results in decreased oocyte fertilization and a reduction in viable offspring. Much of the cited literature is from fish exposed to prototypical stressors (endocrine disruptors), with several studies demonstrating dose and temporal concordance.  In addition to the gene modification studies previously described for essentiality, exposure studies with endocrine disruptors [e.g., di(2-ethylhexyl) phthalate (DEHP), 17α-ethinylestradiol (EE2), nonylphenol] provide evidence of concordance and consistency of this KER. These include studies with zebrafish, Nile tilapia, Japanese medaka, and marine medaka (Corradetti et al., 2013; Hill & Janz, 2003; Kang et al., 2002, Nash et al., 2004; Seki et al., 2002). Several studies provide evidence of dose-response concordance such as a concentration dependent effect on both spermatogenesis and fertilization rate of when male fish exposed to DEHP are mated with wild-type females (Ma et al., 2018; Uren-Webster et al., 2010; Ye et al., 2014). 

Direct empirical evidence on population size decreases associated with decreased viable offspring is very limited. There are no empirical data suitable for evaluating the dose-response, temporal, or incidence concordance between these two adverse outcomes. This relationship is based on biological plausibility and population modeling (e.g., Miller & Ankley, 2004; Miller et al., 2020).

Uncertainties, inconsistencies, and data gaps

• There were no notable inconsistencies in the literature that was reviewed for development of this AOP. However, there are several areas of uncertainty. These include:
• It is challenging to directly measure PPARα agonism in fish in vivo studies. Therefore, we relied on fish exposure studies with pharmaceuticals designed to activate PPARα in humans. However, there is uncertainty of whether all fibrates shown effective in humans are PPARα agonists in fish. This AOP was developed on the assumption that these pharmaceutical also activate PPARα in fish, which is supported by a cross-species comparison in vitro and susceptibility evaluation based on gene sequences support similarity in responses across vertebrates.
• 11-KT levels can be highly variable between fish species and have seasonal fluctuations within a species (with highest levels at spawning).
• For the relationship between 11-KT and spermatogenesis, a few studies documented a significant change in one without a significant change in the other, highlighting the complexity of this relationship.
• Both of the adverse outcomes (Decreased, viable offspring and Decrease, population growth rate) are influenced by multiple factors. The key events in this AOP are just one potential path to these outcomes. In addition, PPARα agonism could result in other toxicity pathways, such as decreased juvenile growth, which were not included in the development of this AOP.  
• Finally, few studies measured multiple sequential key events; thus evidence had to be compiled KER by KER to support this AOP.

 

Quantitative Consideration

At this time available data are insufficient to develop a quantitative AOP linking PPARα agonism with decreased viable offspring or decreased population growth rate.

Considerations for Potential Applications of the AOP (optional)

• The present AOP can inform a tiered testing approach for PPARα agonists (including some PFAS) based on in vitro screening results (e.g., Houck et al., 2021) and targeted in vivo testing (illustrated by Villeneuve et al., 2023).
• The present AOP can inform the development of microphysiological or computational systems models to evaluated probable effects on reproduction.
• The present AOP can aid in prediction of potential effects when PPAR agonists are measured in environmental samples and interpretation (along with selection of additional endpoints to measure) when PPAR activity is detected with effects-based environmental monitoring (e.g., Blackwell et al., 2019).

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Appendix 1

List of MIEs in this AOP

Event: 227: Activation, PPARα

Short Name: Activation, PPARα

Key Event Component

AOPs Including This Key Event

Stressors

Biological Context

Cell term

Organ term

Evidence for Perturbation by Stressor

Overview for Molecular Initiating Event

Fibrates are ligands of PPARα (Staels et al. 1998).

Phthalates

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).

Organotin

Tributyltin (TBT) activates all three heterodimers of PPAR with RXR, primarily through its interaction with RXR (le Maire et al. 2009)

Domain of Applicability

Taxonomic Applicability

PPARα has been identified in frog (Xenopus laevis), mouse, human, rat, fish, hamster and chicken (reviewed in (Wahli and Desvergne 1999)).

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
• Peroxisome
• Genes involved in Cell Cycle

Biological state

The Peroxisome Proliferator Activated receptor α (PPARα) belongs to the Peroxisome Proliferator Activated receptors (PPARs; NR1C) steroid/thyroid/retinoid receptor superfamily of transcription factors.

Biological compartments

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).

.

Measurements of PPARα Activation

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

References

Bhattacharya, Nandini, Jannette M Dufour, My-Nuong Vo, Janice Okita, Richard Okita, and Kwan Hee Kim. 2005. “Differential Effects of Phthalates on the Testis and the Liver.” Biology of Reproduction 72 (3) (March): 745–54. doi:10.1095/biolreprod.104.031583.

Bility, Moses T, Jerry T Thompson, Richard H McKee, Raymond M David, John H Butala, John P Vanden Heuvel, and Jeffrey M Peters. 2004. “Activation of Mouse and Human Peroxisome Proliferator-Activated Receptors (PPARs) by Phthalate Monoesters.” Toxicological Sciences : An Official Journal of the Society of Toxicology 82 (1) (November): 170–82. doi:10.1093/toxsci/kfh253.

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

Hurst, Christopher H, and David J Waxman. 2003. “Activation of PPARalpha and PPARgamma by Environmental Phthalate Monoesters.” Toxicological Sciences : An Official Journal of the Society of Toxicology 74 (2) (August): 297–308. doi:10.1093/toxsci/kfg145.

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

Kaya, Taner, Scott C Mohr, David J Waxman, and Sandor Vajda. 2006. “Computational Screening of Phthalate Monoesters for Binding to PPARgamma.” Chemical Research in Toxicology 19 (8) (August): 999–1009. doi:10.1021/tx050301s.

Koppen, A., Houtman, R., Pijnenburg, D., Jeninga, E. H., Ruijtenbeek, R., & Kalkhoven, E. (2009). Nuclear receptor-coregulator interaction profiling identifies TRIP3 as a novel peroxisome proliferator-activated receptor gamma cofactor. Mol Cell Proteomics, 8(10), 2212-2226. https://doi.org/10.1074/mcp.M900209-MCP200

Kupershmidt, I., Su, Q. J., Grewal, A., Sundaresh, S., Halperin, I., Flynn, J., Shekar, M., Wang, H., Park, J., Cui, W., Wall, G. D., Wisotzkey, R., Alag, S., Akhtari, S., & Ronaghi, M. (2010). Ontology-based meta-analysis of global collections of high-throughput public data. PLoS One, 5(9). https://doi.org/10.1371/journal.pone.0013066

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).

Lefebvre, Philippe, Giulia Chinetti, Jean-Charles Fruchart, and Bart Staels. 2006. “Sorting out the Roles of PPAR Alpha in Energy Metabolism and Vascular Homeostasis.” The Journal of Clinical Investigation 116 (3) (March): 571–80. doi:10.1172/JCI27989.

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

Maloney, Erin K., and David J. Waxman. 1999. “Trans-Activation of PPARα and PPARγ by Structurally Diverse Environmental Chemicals.” Toxicology and Applied Pharmacology 161 (2): 209–218.

Michalik, Liliane, Johan Auwerx, Joel P Berger, V Krishna Chatterjee, Christopher K Glass, Frank J Gonzalez, Paul A Grimaldi, et al. 2006. “International Union of Pharmacology. LXI. Peroxisome Proliferator-Activated Receptors.” Pharmacological Reviews 58 (4) (December): 726–41. doi:10.1124/pr.58.4.5.

Mukherjee, R, L Jow, G E Croston, and J R Paterniti. 1997. “Identification, Characterization, and Tissue Distribution of Human Peroxisome Proliferator-Activated Receptor (PPAR) Isoforms PPARgamma2 versus PPARgamma1 and Activation with Retinoid X Receptor Agonists and Antagonists.” The Journal of Biological Chemistry 272 (12) (March 21): 8071–6.

Ong, K. M., Blackford, J. A., Kagan, B. L., Simons, S. S., & Chow, C. C. (2010). A theoretical framework for gene induction and experimental comparisons. Proc Natl Acad Sci U S A, 107(15), 7107-7112. https://doi.org/10.1073/pnas.0911095107

Rooney, J., Hill, T., Qin, C., Sistare, F. D., & Corton, J. C. (2018). Adverse outcome pathway-driven identification of rat liver tumorigens in short-term assays. Toxicol Appl Pharmacol, 356, 99-113. https://doi.org/10.1016/j.taap.2018.07.023

Schultz, R, W Yan, J Toppari, A Völkl, J A Gustafsson, and M Pelto-Huikko. 1999. “Expression of Peroxisome Proliferator-Activated Receptor Alpha Messenger Ribonucleic Acid and Protein in Human and Rat Testis.” Endocrinology 140 (7) (July): 2968–75. doi:10.1210/endo.140.7.6858.

Simon, T. W., Budinsky, R. A., & Rowlands, J. C. (2015). A model for aryl hydrocarbon receptor-activated gene expression shows potency and efficacy changes and predicts squelching due to competition for transcription co-activators. PLoS One, 10(6), e0127952. https://doi.org/10.1371/journal.pone.0127952.

Staels, B., J. Dallongeville, J. Auwerx, K. Schoonjans, E. Leitersdorf, and J.-C. Fruchart. 1998. “Mechanism of Action of Fibrates on Lipid and Lipoprotein Metabolism.” Circulation 98 (19) (November 10): 2088–2093. doi:10.1161/01.CIR.98.19.2088.

Svoboda, D. L., Saddler, T., & Auerbach, S. S. (2019). An Overview of National Toxicology Program’s Toxicogenomic Applications: DrugMatrix and ToxFX.  In Advances in Computational Toxicology (pp. 141-157). Springer. https://link.springer.com/chapter/10.1007/978-3-030-16443-0_8

ToxCastTM Data. “ToxCastTM Data.” US Environmental Protection Agency. http://www.epa.gov/ncct/toxcast/data.html

Vanden Heuvel, John P, Jerry T Thompson, Steven R Frame, and Peter J Gillies. 2006. “Differential Activation of Nuclear Receptors by Perfluorinated Fatty Acid Analogs and Natural Fatty Acids: A Comparison of Human, Mouse, and Rat Peroxisome Proliferator-Activated Receptor-Alpha, -Beta, and -Gamma, Liver X Receptor-Beta, and Retinoid X Rec.” Toxicological Sciences : An Official Journal of the Society of Toxicology 92 (2) (August): 476–89. doi:10.1093/toxsci/kfl014.

Venkata, Nagaraj Gopisetty, Jodie a Robinson, Peter J Cabot, Barbara Davis, Greg R Monteith, and Sarah J Roberts-Thomson. 2006. “Mono(2-Ethylhexyl)phthalate and Mono-N-Butyl Phthalate Activation of Peroxisome Proliferator Activated-Receptors Alpha and Gamma in Breast.” Toxicology Letters 163 (3) (June 1): 224–34. doi:10.1016/j.toxlet.2005.11.001.

Viswakarma, Navin, Yuzhi Jia, Liang Bai, Aurore Vluggens, Jayme Borensztajn, Jianming Xu, and Janardan K Reddy. 2010. “Coactivators in PPAR-Regulated Gene Expression.” PPAR Research 2010 (January). doi:10.1155/2010/250126.

Wahli, Walter, and B Desvergne. 1999. “Peroxisome Proliferator-Activated Receptors: Nuclear Control of Metabolism.” Endocrine Reviews 20 (5) (October): 649–88. Wu, Bin, Jie Gao, and Ming-wei Wang. 2005. “Development of a Complex Scintillation Proximity Assay for High-Throughput Screening of PPARgamma Modulators.” Acta Pharmacologica Sinica 26 (3) (March): 339–44. doi:10.1111/j.1745-7254.2005.00040.x.

Xu, Chuan, Ji-An Chen, Zhiqun Qiu, Qing Zhao, Jiaohua Luo, Lan Yang, Hui Zeng, et al. 2010. “Ovotoxicity and PPAR-Mediated Aromatase Downregulation in Female Sprague-Dawley Rats Following Combined Oral Exposure to Benzo[a]pyrene and Di-(2-Ethylhexyl) Phthalate.” Toxicology Letters 199 (3) (December 15): 323–32. doi:10.1016/j.toxlet.2010.09.015.

Yessoufou, a, and W Wahli. 2010. “Multifaceted Roles of Peroxisome Proliferator-Activated Receptors (PPARs) at the Cellular and Whole Organism Levels.” Swiss Medical Weekly 140 (September) (January): w13071. doi:10.4414/smw.2010.13071.

Yu, Songtao, and Janardan K Reddy. 2007. “Transcription Coactivators for Peroxisome Proliferator-Activated Receptors.” Biochimica et Biophysica Acta 1771 (8) (August): 936–51. doi:10.1016/j.bbalip.2007.01.008.

List of Key Events in the AOP

Event: 807: Decreased, cholesterol

Short Name: Decreased, cholesterol

Key Event Component

AOPs Including This Key Event

Stressors

Biological Context

Organ term

Evidence for Perturbation by Stressor

Gemfibrozil

Juvenile female rainbow trout have decreased cholesterol (including total, HDL, LDL, & VLDL) after exposure to gemfibrozil (Prindiville et al. 2011)

Male and female zebrafish fed gemfibrozil alone or in combination with atorvastatin have decreased cholesterol (Al-Habsi et al. 2016)

Bezafibrate

Adult male zebrafish fed bezafibrate have decreased cholesterol (Velasco-Santamaría et al. 2011)

Clofibrate

Feeding grass carp either a high-fat or high-carbohydrate diet causes increases in total cholesterol, HDL, and LDL. Clofibrate reduces the high cholesterol levels caused by these diets to levels similar to controls (Guo et al. 2015)

Fenofibrate

Feeding fenofibrate to grass carp on a high fat diet causes a decrease in cholesterol, LDL, body weight, and whole-body lipid content (Du et al. 2008)

Atorvastatin

Male and female zebrafish fed atorvastatin alone or in combination with gemfibrozil have decreased whole-body cholesterol (Al-Habsi et al., 2016)

Atorvastatin is a statin drug that lowers cholesterol by inhibiting HMG-CoA reductase. Other chemical that work by the same mechanism can be found at: https://comptox.epa.gov/dashboard/chemical_lists/STATINS

Simvastatin

Larval Zebrafish fed a high fat and high cholesterol diet show reduced liver cholesterol when given simvastatin (Dai et al., 2015)

Domain of Applicability

Taxonomic Applicability Life Stage Applicability Sex Applicability

Taxonomic Applicability: Cholesterol is synthesized in plants but acts as a precursor for different products than in animals (Sonawane et al. 2016). Within the animal kingdom most deuterostomes (including vertebrata, cyclostomata, cephalochordate, and echinodermata, but not chordata) possess the genes necessary for cholesterol biosynthesis. However, most protostomes (including arthropoda and nematomorpha) have lost these genes (Zhang et al., 2019). Thus far vertebrates are the primary consideration for this KE.

Lifestage Applicability: Cholesterol can be measured in organisms at all life stages. However, the size of young organisms may limit the ability to collect plasma for cholesterol analysis. Whole-body measurements or pooled samples may be more feasible.

Sex Applicability: Cholesterol measurements are applicable for all sexes

Key Event Description

Most cholesterol synthesis in vertebrates occurs within the endoplasmic reticulum of hepatic cells. First, acetyl-CoA is converted to HMG-CoA via HMG-CoA synthase. Next, HMG-CoA is converted to mevalonate via HMG-CoA reductase. Several other steps follow, but conversion of HMG-CoA to mevalonate is the rate-limiting step of cholesterol synthesis (Cerqueira et al. 2016; Risley 2002). Consequently, Statin drugs inhibit HMG-CoA reductase to reduce cholesterol (Pahan 2006).

Cholesterol synthesis may also occur to a limited extent in steroidogenic cells where it’s used to produce steroid hormones (Azhar et al., 2007)

Once cholesterol is produced in the liver, it’s transported in the plasma. Hydrophobic lipids like cholesterol, cholesteryl ester (a cholesterol molecule bound to a fatty acid), and triglycerides are transported via lipoprotein complexes. There are different groups of lipoproteins which use different proteins and ratios of lipids including high-density lipoprotein (HDL), low-density (LDL), and very low-density (VLDL).

Cholesterol metabolism KEGG Pathway  ko04979

 

How it is Measured or Detected

Commerical assay kits are available for measuring cholesterol using either colorimetric or fluorometric detection. Total cholesterol assay kits often include cholesteryl esters in the measurement (Cell Bio Labs, ThermoFisher). Additional kits are availalbe for measuring the cholesterol in the different lipoprotein complexes (Cell Bio Labs). 

Oil Red O staining can be used for organisms such as zebrafish larvae that are clear, however it stains triglycerides and lipids not just cholesterol (Zhou et al., 2015). 

Plasma cholesterol is a common clinical measurement in humans and the Abell-Kendall technique is the standard chemical determination method (Cox et al. 1990), although there are a wide variety of viable methods.

References

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. http://dx.doi.org/10.1016/j.cbpb.2015.11.009

Azhar, S., E. Reaven (2007) “Regulation of Leydig cell cholesterol metabolism”, in A.H. Payne, M.P. Hardy (eds.) The Leydig Cell in Health and Disease, Humana Press. https://doi.org/10.1007/978-1-59745-453-7

Cox RA, García-Palmieri MR. Cholesterol, Triglycerides, and Associated Lipoproteins. In: Walker HK, Hall WD, Hurst JW, editors. Clinical Methods: The History, Physical, and Laboratory Examinations. 3rd edition. Boston: Butterworths; 1990. Chapter 31. Available from: https://www.ncbi.nlm.nih.gov/books/NBK351/

Dai, W. et al. (2015) "High fat plus high cholesterol diet lead to hepatic steatosis in zebrafish larvae: a novel model for screening anti-hepatic steatosis drugs", Nutrition and Metabolism, Vol. 12(42), Springer Nature. DOI 10.1186/s12986-015-0036-z 

Du, Z.Y. et al. (2008) “Hypolipidaemic effect 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

Cerqueira, N. M., Oliveira, E. F., Gesto, D. S., Santos-Martins, D., Moreira, C., Moorthy, H. N., ... & Fernandes, P. A. (2016). Cholesterol biosynthesis: a mechanistic overview. Biochemistry, 55(39), 5483-5506.

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

Pahan, K. (2006). Lipid-lowering drugs. Cellular and molecular life sciences CMLS, 63(10), 1165-1178.

Risley, J. M. (2002). Cholesterol biosynthesis: Lanosterol to cholesterol. Journal of chemical education, 79(3), 377.

Sonawane, P.D. et al. (2016) “Plant cholesterol biosynthetic pathway overlaps with phytosterol metabolism”, Nature Plants, Vol. 3, Nature Publishing Group, https://doi.org/10.1038/nplants.2016.205

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

Zhang, T. et al. (2019) “Evolution of the cholesterol biosynthesis pathway in animals”, Molecular Biology and Evolution, Vol. 36(11), Oxford University Press, pp. 2548-2556. doi:10.1093/molbev/msz167

Zhou, J. et al. (2015) "Rapid analysis of hypolipidemic drugs in a live zebrafish assay", Journal of Pharmacological and Toxicological Methods, Vol. 72, Elsevier, pp. 47-52. http://dx.doi.org/10.1016/j.vascn.2014.12.002

Event: 1756: Decreased, plasma 11-ketotestosterone level

Short Name: Decreased, 11KT

Key Event Component

AOPs Including This Key Event

Stressors

Biological Context

Organ term

Evidence for Perturbation by Stressor

beta-Sitosterol

Beta-sitosterol causes a dose-depended reduction in 11KT in male goldfish (MacLatchy & Van Der Kraak 1995)

Bezafibrate

Bezafibrate reduces 11-KT in the plasma of adult male zebrafish (Velasco-Santamaría et al. 2011)

Gemfibrozil

Gemfibrozil reduced 11KT in the plasma of adult male medaka (Lee et al. 2019)

Gemfibrozil expsoure caused reduced 11KT in the testes, plasma, and whole-body samples of adult male zebrafish (Fraz et al., 2018)

Bis(2-ethylhexyl) phthalate

A review of androgen signaling in fish cites several studies showing DEHP decreased 11KT (Golshan et al., 2019)

Cypermethrin

Cypermethrin causes decreased 11KT in catfish (Singh & Singh, 2008)

Carbamazepine

Carbamazepine decreased 11KT in the testes, plasma, and whole-body samples of adult male zebrafish (Fraz et al., 2018)

Domain of Applicability

Taxonomic Applicability Life Stage Applicability Sex Applicability

Taxanomic Applicability: Most understand of 11KT comes from studies involving teleost fish as it is their dominant androgen. Some studies have measured 11KT in sharks of the order carcharhiniformes, but there is less research in this area (Manire et al., 1999; Garnier et al. 1999; Mills et al. 2010). Many mammals possess the genes necessary to produce 11KT (NCBI), but 11KT may not be as relevant when it’s not the dominant androgen.

Sex Applicability: Males and females use the same biological processes to produce steroids. However, sexual dimorphism in 11KT production varies between species. In humans, plasma levels of 11KT do not differ between sexes (Imamichi et al., 2016). In Zebrafish, gonad levels of 11KT are approximately two magnitudes higher in males than females (Wang & Orban, 2007). Of the 30 other fish species sampled by Lokman et al. (2002), 11KT levels are typically dramatically lower in females than in males, but a few species of the order Perciformes show no sexual dimorphism.

Life Stage Applicability: 11KT can be measured in fish larvae however individuals must be pooled for sufficient sample size (Hattori et al., 2009). Lokman et al. (2002) measured plasma levels of 11-KT in several species of juvenile and adult fish. 11KT levels tend to be higher in males although some fish species don’t show sexual dimorphism. Levels of 11KT in juveniles are similar to levels in females regardless of if the species shows sexual dimorphism in 11KT levels. In males, 11KT increases for spawning and decreases afterwards (Kindler et al., 1989; Páll et al., 2002). Because of it’s involvement in reproduction, 11KT levels may not be meaningful in juveniles.

Key Event Description

11-ketotestosterone (11KT; CAS 564-35-2 | DTXSID8036499) is an oxygenated steroidal androgen with a keto group at the C11 position (Pretorius et al. 2017). 

11-ketotestosterone is a dominant androgen in teleost fish (Borg 1994). It is synthesized from testosterone using the enzymes CYP11b1 and HSD11b (Yazawa et al., 2008; Swart et al., 2013). Zebrafish studies also show that cyp17a1 and cyp11c1 knockouts have dramatically reduced levels of 11KT (Shu et al., 2020; Zhang et al., 2020)

11KT is also produced by other vertebrates, although the site of its biosynthesis and physiological signficance in different taxa can vary widely. In humans, 11KT is primarily synthesized in the adrenal glands (Pretorius et al. 2017; Turcu et al. 2018). 

 

Although mutations in the mettl3 gene usually cause embryonic lethality, one particular mutation in non-lethal and causes significantly reduced 11KT levels in zebrafish (Xia et al., 2018)

How it is Measured or Detected

11KT production can be measured in an ex vivo steroidogenesis assay using the organism's gonad after it has been exposed to a compound.

The concentration of 11KT can be measured in a radioimmunoassay or enzyme-linked immunosorbent assay (ELISA). 

Several papers show that in fish, 11KT is correlated with testosterone levels (Spanò et al., 2004; Maclatchy & Vanderkraak, 1995; Lorenzi et al., 2008). 

References

Borg, B. (1994). Androgens in teleost fishes. Comparative Biochemistry and Physiology Part C: Pharmacology, Toxicology and Endocrinology, 109(3), 219-245.

Fraz, S. et al. (2018) “Gemfibrozil and carbamazepine decrease steroid production in zebrafish testes (Danio rerio)”, Aquatic Toxicology, Vol. 198, Elsevier, pp. 1-9. https://doi.org/10.1016/j.aquatox.2018.02.006 

Golshan, M. & S.M.H. Alvai (2019) “Androgen signaling in male fishes: Examples of anti-androgenic chemicals that cause reproductive disorders”, Theriogenology, Vol. 139, Elsevier, pp. 58-71. https://doi.org/10.1016/j.theriogenology.2019.07.020 

Hattori, R.S. et al. (2009) “Cortisol-induced masculinization: Does thermal stress affect gonadal fate in pejerrey, a teleost fish with temperature-dependent sex determination?”, PLoS ONE, Vol. 4(8), pp. 1-7. doi:10.1371/journal.pone.0006548

Imamichi, Y. et al. (2016) “11-Ketotestosterone is a major androgen produced in human gonads”, The Journal of Clinical Endocrinology & Metabolism, Vol. 101(10), Oxford Academic, pp. 3582-3591. https://doi.org/10.1210/jc.2016-2311

Kindler, P. M. et al. (1989) “Serum 11-ketotestosterone and testosterone concentrations associated with reproduction in male bluegill (Lepomis macrochirus: Centrarchidae)”, General and Comparative Endocrinology, Vol. 75(3), Elsevier, pp. 446-453. https://doi.org/10.1016/0016-6480(89)90180-9

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. https://doi.org/10.1016/j.ecoenv.2019.02.015

Lokman, P.M. et al. (2002) “11-Oxygenated androgens in female teleosts: prevalence, abundance, and life history implications”, General and Comparative Endocrinology, Vol. 129, Academic Press, pp. 1-12. doi: 10.1016/s0016-6480(02)00562-2

Lorenzi, V. et al. (2008) “Diurnal patterns and sex differences in cortisol, 11-ketotestosterone, testosterone, and 17β-estradiol in the bluebanded goby (Lythrypnus dalli)”, General and Comparative Endocrinology, Vol. 155(2)., Elsevier, pp. 438-446. https://doi.org/10.1016/j.ygcen.2007.07.010

MacLatchy, D.L. and G.J. Vanderkraak (1995) “The phytoestrogen β-sitosterol alters the reproductive endocrine status of goldfish”, Toxicology and Applied Pharmacology, Vol. 134(2), Elsevier, pp. 305-312. https://doi.org/10.1006/taap.1995.1196

Manire, C.A., L.E. Rasmussen & T.S. Gross (1999) “Serum steroid hormones including 11-ketotestosterone, 11-ketoandrostenedione, and dihydroprogesterone in juvenile and adult bonnethead sharks, Sphyrna tiburo”, Journal of Experimental Zoology, Vol. 284(5), Wiley-Blackwell, pp. 595-603. DOI: 10.1002/(sici)1097-010x(19991001)284:5<595::aid-jez15>3.0.co 

Páll, M. K., I. Mayer and B. Borg (2002) “Androgen and behavior in the male three-spined stickleback, Gasterosteus aculeatus I. – Changes in 11-ketotestosterone levels during nesting cycle”, Hormones and Behavior, Vol. 41(4), Elsevier, pp. 377-383. https://doi.org/10.1006/hbeh.2002.1777

Pretorius, E, Arlt, W & Storbeck, K-H 2016, 'A new dawn for androgens: novel lessons from 11-oxygenated C19 steroids', Molecular and Cellular Endocrinology. https://doi.org/10.1016/j.mce.2016.08.014

Shu, T. et al. (2020) “Zebrafish cyp17a1 knockout reveals that androgen-mediated signaling is important for male brain sex differentiation”, General and Comparative Endocrinology, Vol. 295. doi:10.1016/j.ygcen.2020.113490 

Singh, P.B. & V. Singh (2008) “Cypermethrin induced histological changes in gonadotrophic cells, liver, gonads, plasma levels of estradiol-17beta and 11-ketotestosterone, and sperm motility in Heteropneustes fossilis (Bloch)”, Chemosphere, Vol. 72(3), Elsevier, pp. 422-431. DOI: 10.1016/j.chemosphere.2008.02.026 

Spanó, L. et al. (2004) “Effects of atrazine on sex steroid dynamics, plasma vitellogenin concentration and gonad development in adult goldfish (Carassius auratus)”, Aquatic Toxicology, Vol. 66(4), Elsevier, pp. 369-379. https://doi.org/10.1016/j.aquatox.2003.10.009

Swart, A.C. et al. (2013) “11β-hydroxyandrostenedione, the product of androstenedione metabolism in the adrenal, is metabolized in LNCaP cells by 5α-reductase yielding 11β-hydroxy-5α-androstanedione”, The Journal of Steroid Biochemistry and Molecular Biology, Vol 138, Elsevier, pp. 132-142. https://doi.org/10.1016/j.jsbmb.2013.04.010

Turcu AF, Nanba AT, Auchus RJ. The Rise, Fall, and Resurrection of 11-Oxygenated Androgens in Human Physiology and Disease. Horm Res Paediatr. 2018;89(5):284-291. doi: 10.1159/000486036. Epub 2018 May 9. PMID: 29742491; PMCID: PMC6031471.

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

Wang, X.G. and L. Orban (2007) “Anti-Müllerian hormone and 11β-hydroxylase show reciprocal expression to that of aromatase in the transforming gonad of zebrafish males”, Developmental Dynamics, Vol 236(5), Wiley-Liss, pp. 1329-1338. https://doi.org/10.1002/dvdy.21129

Xia, H. et al. (2018) “Mettl3 mutation disrupts gamete maturation and reduced fertility in zebrafish”, Genetics, Vol. 208(2), Genetics Society of America, pp. 729-743. DOI: 10.1534/genetics.117.300574 

Yazawa, T. (2008) “Cyp11b1 is induced in the murine gonad by luteinizing hormone/human chorionic gonadotropin and involved in the production of 11-ketotestosterone, a major fish androgen: Conservation and evolution of the androgen metabolic pathway”, Endocrinology, Vol. 149(4), Oxford Academy, pp. 1786-1792. https://doi.org/10.1210/en.2007-1015

Zheng, Q. et al. (2020) “Loss of cyp11c1 causes delayed spermatogenesis due to the absence of 11-ketotestosterone", Journal of Endocrinology, Vol. 244(3), Bioscientifica, pp. 487-499. https://doi.org/10.1530/JOE-19-0438 

Event: 1758: Impaired, Spermatogenesis

Short Name: Impaired, Spermatogenesis

Key Event Component

AOPs Including This Key Event

Stressors

Biological Context

Organ term

Evidence for Perturbation by Stressor

Flutamide

Flutamide impairs spermatogenesis in adult male zebrafish (Yin et al., 2017)

Male fathead minnows exposed to flutamide show spermatocyte degredation and necrosis in their testis (Jensen et al., 2004)

Vinclozolin

A review of androgen signaling in male fish cites several studies showing vinclozolin decreases sperm quality (Golshan et al., 2019)

Bis(2-ethylhexyl) phthalate

A review of androgen signaling in male fish cites several studies showing DEHP decreases sperm quality (Golshan et al., 2019)

Domain of Applicability

Taxonomic Applicability Life Stage Applicability Sex Applicability

Taxonomic Applicability: The relevance for invertebrates has not been evaluated. 

Life Stage Applicability: Only applicable for sexually mature adults

Sex Applicability: Only applicable to males

Key Event Description

Spermatogenesis is a multiphase process of cellular transformation that produces mature male gametes known as sperm for sexual reproduction (Xu et al., 2015). The process of spermatogenesis can be broken down into 3 phases: the mitotic proliferation of spermatogonia, meiosis, and post-meiotic differentiation(spermiogenesis) (Boulanger et al., 2015). Spermatogenesis can be impaired within these phases or due to external factors such as chemical exposures or the gonadal tissue environment. For example, zebrafish and fathead minnow exposed to flutamide, an antiandrogen, have shown signs of impaired spermatogenesis such as spermatocyte degradation(Jensen et al., 2004, Yin et al., 2017).

How it is Measured or Detected

Impairment of spermatogenesis can be measured and detected in a multitude of ways. One example of this is qualitative histological assessments (Jensen et al., 2004). Through histology, sperm morphology can be examined and quantified through the number and stage of the sperm. Sperm morphology, overall quantity, and quantity within each stage can be ways to detect impaired spermatogenesis(Uhrin et al., 2000, Xie et al., 2020). Additionally, sperm quality can also be another assessment of impaired spermatogenesis such as sperm motility, velocity, ATP content, and lipid peroxidation(Gage et al., 2004, Xia et al., 2018, Chen et al., 2015). Impaired spermatogenesis can also be seen by measuring sperm density(Chen et al., 2015).

References

Boulanger, G., Cibois, M., Viet, J., Fostier, A., Deschamps, S., Pastezeur, S., Massart, C., Gschloessl, B., Gautier-Courteille, C., & Paillard, L. (2015). Hypogonadism Associated with Cyp19a1 (Aromatase) Posttranscriptional Upregulation in Celf1 Knockout Mice. Molecular and cellular biology, 35(18), 3244–3253. https://doi.org/10.1128/MCB.00074-15

Chen, J., Xiao, Y., Gai, Z., Li, R., Zhu, Z., Bai, C., Tanguay, R. L., Xu, X., Huang, C., & Dong, Q. (2015). Reproductive toxicity of low level bisphenol A exposures in a two-generation zebrafish assay: Evidence of male-specific effects. Aquatic toxicology (Amsterdam, Netherlands), 169, 204–214. https://doi.org/10.1016/j.aquatox.2015.10.020

Golshan, M. & S.M.H. Alvai (2019) “Androgen signaling in male fishes: Examples of anti-androgenic chemicals that cause reproductive disorders”, Theriogenology, Vol. 139, Elsevier, pp. 58-71. https://doi.org/10.1016/j.theriogenology.2019.07.020 

Jensen, K.M. et al. (2004) “Characterization of responses to the antiandrogen flutamide in a short-term reproduction assay with the fathead minnow”, Aquatic Toxicology, Vol. 70(2), Elsevier, pp. 99-110. https://doi.org/10.1016/j.aquatox.2004.06.012 

Uhrin, P., Dewerchin, M., Hilpert, M., Chrenek, P., Schöfer, C., Zechmeister-Machhart, M., Krönke, G., Vales, A., Carmeliet, P., Binder, B. R., & Geiger, M. (2000). Disruption of the protein C inhibitor gene results in impaired spermatogenesis and male infertility. The Journal of clinical investigation, 106(12), 1531–1539. https://doi.org/10.1172/JCI10768

Xia, H., Zhong, C., Wu, X., Chen, J., Tao, B., Xia, X., Shi, M., Zhu, Z., Trudeau, V. L., & Hu, W. (2018). Mettl3 Mutation Disrupts Gamete Maturation and Reduces Fertility in Zebrafish. Genetics, 208(2), 729–743. https://doi.org/10.1534/genetics.117.300574

Xie, H., Kang, Y., Wang, S., Zheng, P., Chen, Z., Roy, S., & Zhao, C. (2020). E2f5 is a versatile transcriptional activator required for spermatogenesis and multiciliated cell differentiation in zebrafish. PLoS genetics, 16(3), e1008655. https://doi.org/10.1371/journal.pgen.1008655

Xu, K., Wen, M., Duan, W., Ren, L., Hu, F., Xiao, J., Wang, J., Tao, M., Zhang, C., Wang, J., Zhou, Y., Zhang, Y., Liu, Y., & Liu, S. (2015). Comparative analysis of testis transcriptomes from triploid and fertile diploid cyprinid fish. Biology of reproduction, 92(4), 95. https://doi.org/10.1095/biolreprod.114.125609

Yin, P. et al. (2017) “Diethylstilbestrol, flutamide and their combination impaired the spermatogenesis of male adult zebrafish through disrupting HPG axis, meiosis and apoptosis”, Aquatic Toxicology, Vol. 185, Elsevier, pp. 129-137. https://doi.org/10.1016/j.aquatox.2017.02.013

List of Adverse Outcomes in this AOP

Event: 2147: Decreased, Viable Offspring

Short Name: Decreased, Viable Offspring

Key Event Component

AOPs Including This Key Event

Biological Context

Domain of Applicability

Life Stage Applicability Sex Applicability

Taxonomic applicability: Decrease in viable offspring may have relevance for species with sexual reproduction, including fish, mammals, amphibians, reptiles, birds, and invertebrates.

Life stage applicability: Decrease in viable offspring is relevant for reproductively mature individuals.

Sex applicability: Decrease in viable offspring can be measured for both males and females.

Key Event Description

The production of viable offspring in sexual reproduction is through fertilization of oocytes that then develop into offspring. Producing viable offspring is dependent on multiple factors, including but not limited to, oocyte maturation and ovulation, spermatogenesis and sperm production, successful fertilization of oocytes, development including successful organogenesis, and adequate nutrition.

How it is Measured or Detected

Effects on the production of viable offspring is measured or detected through the ability (or inability) of reproductively mature organisms to produce offspring, number of offspring produced (per pair, individual, or population), and/or percent of fertilized, viable embryos.

Event: 360: Decrease, Population growth rate

Short Name: Decrease, Population growth rate

Key Event Component

AOPs Including This Key Event

Biological Context

Domain of Applicability

Taxonomic Applicability Life Stage Applicability Sex Applicability

Consideration of population size and changes in population size over time is potentially relevant to all living organisms.

Key Event Description

A population can be defined as a group of interbreeding organisms, all of the same species, occupying a specific space during a specific time (Vandermeer and Goldberg 2003, Gotelli 2008).  As the population is the biological level of organization that is often the focus of ecological risk assessments, population growth rate (and hence population size over time) is important to consider within the context of applied conservation practices.

If N is the size of the population and t is time, then the population growth rate (dN/dt) is proportional to the instantaneous rate of increase, r, which measures the per capita rate of population increase over a short time interval. Therefore, r, is a difference between the instantaneous birth rate (number of births per individual per unit of time; b) and the instantaneous death rate (number of deaths per individual per unit of time; d) [Equation 1]. Because  r is an instantaneous rate, its units can be changed via division.  For example, as there are 24 hours in a day, an r of 24 individuals/(individual x day) is equal to an r of 1 individual/(individual/hour) (Caswell 2001, Vandermeer and Goldberg 2003, Gotelli 2008, Murray and Sandercock 2020). 

Equation 1:  r = b - d

This key event refers to scenarios where r < 0 (instantaneous death rate exceeds instantaneous birth rate).

Examining r in the context of population growth rate:

● A population will decrease to extinction when the instantaneous death rate exceeds the instantaneous birth rate (r < 0).  

           ● The smaller the value of r below 1, the faster the population will decrease to zero.  

● A population will increase when resources are available and the instantaneous birth rate exceeds the instantaneous death rate (r > 0)

           ● The larger the value that r exceeds 1, the faster the population can increase over time      

● A population will neither increase or decrease when the population growth rate equals 0 (either due to N = 0, or if the per capita birth and death rates are exactly balanced).  For example, the per capita birth and death rates could become exactly balanced due to density dependence and/or to the effect of a stressor that reduces survival and/or reproduction (Caswell 2001, Vandermeer and Goldberg 2003, Gotelli 2008, Murray and Sandercock 2020).     

Effects incurred on a population from a chemical or non-chemical stressor could have an impact directly upon birth rate (reproduction) and/or death rate (survival), thereby causing a decline in population growth rate.  

● Example of direct effect on r:  Exposure to 17b-trenbolone reduced reproduction (i.e., reduced b) in the fathead minnow over 21 days at water concentrations ranging from 0.0015 to about 41 mg/L (Ankley et al. 2001; Miller and Ankley 2004).             

Alternatively, a stressor could indirectly impact survival and/or reproduction.  

● Example of indirect effect on r:  Exposure of non-sexually differentiated early life stage fathead minnow to the fungicide prochloraz has been shown to produce male-biased sex ratios based on gonad differentiation, and resulted in projected change in population growth rate (decrease in reproduction due to a decrease in females and thus recruitment) using a population model. (Holbech et al., 2012; Miller et al. 2022)

Density dependence can be an important consideration:

● The effect of density dependence depends upon the quantity of resources present within a landscape.  A change in available resources could increase or decrease the effect of density dependence and therefore cause a change in population growth rate via indirectly impacting survival and/or reproduction.  

● This concept could be thought of in terms of community level interactions whereby one species is not impacted but a competitor species is impacted by a chemical stressor resulting in a greater availability of resources for the unimpacted species.  In this scenario, the impacted species would experience a decline in population growth rate. The unimpacted species would experience an increase in population growth rate (due to a smaller density dependent effect upon population growth rate for that species).       

Closed versus open systems:

● The above discussion relates to closed systems (there is no movement of individuals between population sites) and thus a declining population growth rate cannot be augmented by immigration.  

● When individuals depart (emigrate out of a population) the loss will diminish population growth rate.  

Population growth rate applies to all organisms, both sexes, and all life stages.

 

How it is Measured or Detected

Population growth rate (instantaneous growth rate) can be measured by sampling a population over an interval of time (i.e. from time t = 0 to time t = 1).  The interval of time should be selected to correspond to the life history of the species of interest (i.e. will be different for rapidly growing versus slow growing populations). The population growth rate, r, can be determined by taking the difference (subtracting) between the initial population size, N_t=0 (population size at time t=0), and the population size at the end of the interval, N_t=1 (population size at time t = 1), and then subsequently dividing by the initial population size. 

Equation 2:  r = (N_t=1 - N_t=0) / N_t=0

The diversity of forms, sizes, and life histories among species has led to the development of a vast number of field techniques for estimation of population size and thus population growth over time (Bookhout 1994, McComb et al. 2021).  

● For stationary species an observational strategy may involve dividing a habitat into units. After setting up the units, samples are performed throughout the habitat at a select number of units (determined using a statistical sampling design) over a time interval (at time t = 0 and again at time t = 1), and the total number of organisms within each unit are counted. The numbers recorded are assumed to be representative for the habitat overall, and can be used to estimate the population growth rate within the entire habitat over the time interval.  

● For species that are mobile throughout a large range, a strategy such as using a mark-recapture method may be employed (i.e. tags, bands, transmitters) to determine a count over a time interval (at time = 0 and again at time =1).   

Population growth rate can also be estimated using mathematical model constructs (for example, ranging from simple differential equations to complex age or stage structured matrix projection models and individual based modeling approaches), and may assume a linear or nonlinear population increase over time (Caswell 2001, Vandermeer and Goldberg 2003, Gotelli 2008, Murray and Sandercock 2020). The AOP framework can be used to support the translation of pathway-specific mechanistic data into responses relevant to population models and output from the population models, such as changing (declining) population growth rate, can be used to assess and manage risks of chemicals (Kramer et al. 2011). As such, this translational capability can increase the capacity and efficiency of safety assessments both for single chemicals and chemical mixtures (Kramer et al. 2011).  

Some examples of modeling constructs used to investigate population growth rate:

● A modeling construct could be based upon laboratory toxicity tests to determine effect(s) that are then linked to the population model and used to estimate decline in population growth rate.  Miller et al. (2007) used concentration–response data from short term reproductive assays with fathead minnow (Pimephales promelas) exposed to endocrine disrupting chemicals in combination with a population model to examine projected alterations in population growth rate.  

● A model construct could be based upon a combination of effects-based monitoring at field sites (informed by an AOP) and a population model.  Miller et al. (2015) applied a population model informed by an AOP to project declines in population growth rate for white suckers (Catostomus commersoni) using observed changes in sex steroid synthesis in fish exposed to a complex pulp and paper mill effluent in Jackfish Bay, Ontario, Canada. Furthermore, a model construct could be comprised of a series of quantitative models using KERs that culminates in the estimation of change (decline) in population growth rate.  

● A quantitative adverse outcome pathway (qAOP) has been defined as a mathematical construct that models the dose–response or response–response relationships of all KERs described in an AOP (Conolly et al. 2017, Perkins et al. 2019). Conolly et al. (2017) developed a qAOP using data generated with the aromatase inhibitor fadrozole as a stressor and then used it to predict potential population‐level impacts (including decline in population growth rate). The qAOP modeled aromatase inhibition (the molecular initiating event) leading to reproductive dysfunction in fathead minnow (Pimephales promelas) using 3 computational models: a hypothalamus–pituitary–gonadal axis model (based on ordinary differential equations) of aromatase inhibition leading to decreased vitellogenin production (Cheng et al. 2016), a stochastic model of oocyte growth dynamics relating vitellogenin levels to clutch size and spawning intervals (Watanabe et al. 2016), and a population model (Miller et al. 2007).

● Dynamic energy budget (DEB) models offer a methodology that reverse engineers stressor effects on growth, reproduction, and/or survival into modular characterizations related to the acquisition and processing of energy resources (Nisbet et al. 2000, Nisbet et al. 2011).  Murphy et al. (2018) developed a conceptual model to link DEB and AOP models by interpreting AOP key events as measures of damage-inducing processes affecting DEB variables and rates.

● Endogenous Lifecycle Models (ELMs), capture the endogenous lifecycle processes of growth, development, survival, and reproduction and integrate these to estimate and predict expected fitness (Etterson and Ankley, 2021).  AOPs can be used to inform ELMs of effects of chemical stressors on the vital rates that determine fitness, and to decide what hierarchical models of endogenous systems should be included within an ELM (Etterson and Ankley, 2021).

 

Regulatory Significance of the AO

Maintenance of sustainable fish and wildlife populations (i.e., adequate to ensure long-term delivery of valued ecosystem services) is a widely accepted regulatory goal upon which risk assessments and risk management decisions are based.

References

• Ankley GT, Jensen KM, Makynen EA, Kahl MD, Korte JJ, Hornung MW, Henry TR, Denny JS, Leino RL, Wilson VS, Cardon MD, Hartig PC, Gray LE. 2003. Effects of the androgenic growth promoter 17b-trenbolone on fecundity and reproductive endocrinology of the fathead minnow. Environ. Toxicol. Chem. 22: 1350–1360.
• Bookhout TA. 1994. Research and management techniques for wildlife and habitats. The Wildlife Society, Bethesda, Maryland. 740 pp.
• Caswell H. 2001. Matrix Population Models. Sinauer Associates, Inc., Sunderland, MA, USA
• Cheng WY, Zhang Q, Schroeder A, Villeneuve DL, Ankley GT, Conolly R.  2016.  Computational modeling of plasma vitellogenin alterations in response to aromatase inhibition in fathead minnows. Toxicol Sci 154: 78–89.
• Conolly RB, Ankley GT, Cheng W-Y, Mayo ML, Miller DH, Perkins EJ, Villeneuve DL, Watanabe KH. 2017. Quantitative adverse outcome pathways and their application to predictive toxicology. Environ. Sci. Technol. 51:  4661-4672.
• Etterson MA, Ankley GT.  2021.  Endogenous Lifecycle Models for Chemical Risk Assessment. Environ. Sci. Technol. 55:  15596-15608. 
• Gotelli NJ, 2008. A Primer of Ecology. Sinauer Associates, Inc., Sunderland, MA, USA.
• Holbech H, Kinnberg KL, Brande-Lavridsen N, Bjerregaard P, Petersen GI, Norrgren L, Orn S, Braunbeck T, Baumann L, Bomke C, Dorgerloh M, Bruns E, Ruehl-Fehlert C, Green JW, Springer TA, Gourmelon A. 2012 Comparison of zebrafish (Danio rerio) and fathead minnow (Pimephales promelas) as test species in the Fish Sexual Development Test (FSDT). Comp. Biochem. Physiol. C Toxicol. Pharmacol. 155:  407–415.
• Kramer VJ, Etterson MA, Hecker M, Murphy CA, Roesijadi G, Spade DJ, Stromberg JA, Wang M, Ankley GT.  2011.  Adverse outcome pathways and risk assessment: Bridging to population level effects.  Environ. Toxicol. Chem. 30, 64-76.
• McComb B, Zuckerberg B, Vesely D, Jordan C.  2021.  Monitoring Animal Populations and their Habitats: A Practitioner's Guide.  Pressbooks, Oregon State University, Corvallis, OR Version 1.13, 296 pp. 
• Miller DH, Villeneuve DL, Santana Rodriguez KJ, Ankley GT. 2022.  A multidimensional matrix model for predicting the effect of male biased sex ratios on fish populations. Environmental Toxicology and Chemistry 41(4): 1066-1077.
• Miller DH, Tietge JE, McMaster ME, Munkittrick KR, Xia X, Griesmer DA, Ankley GT. 2015. Linking mechanistic toxicology to population models in forecasting recovery from chemical stress: A case study from Jackfish Bay, Ontario, Canada. Environmental Toxicology and Chemistry 34(7):  1623-1633.
• Miller DH, Jensen KM, Villeneuve DE, Kahl MD, Makynen EA, Durhan EJ, Ankley GT. 2007. Linkage of biochemical responses to population-level effects: A case study with vitellogenin in the fathead minnow (Pimephales promelas). Environ Toxicol Chem 26:  521–527.
• Miller DH, Ankley GT. 2004. Modeling impacts on populations: Fathead minnow (Pimephales promelas) exposure to the endocrine disruptor 17b-trenbolone as a case study. Ecotox Environ Saf 59: 1–9.
• Murphy CA, Nisbet RM, Antczak P, Garcia-Reyero N, Gergs A, Lika K, Mathews T, Muller EB, Nacci D, Peace A, Remien CH, Schultz IR, Stevenson LM, Watanabe KH.  2018.  Incorporating suborganismal processes into dynamic energy budget models for ecological risk assessment.  Integrated Environmental Assessment and Management 14(5):  615–624.
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Appendix 2

List of Key Event Relationships in the AOP