AN ABSTRACT OF THE DISSERTATION OF

Derik Haggard for the degree of Doctor of Philosophy in Toxicology present on December 2, 2016. Title: Classifying Chemical Bioactivity by Coupling High-throughput Phenotypic Anchoring and Transcriptome Profiling in Zebrafish.

Abstract approved:

______Robert L. Tanguay

There are more than 87,000 chemicals in current use with little to no toxicity information available. Assessing such a large number of chemicals using traditional methods would take an unreasonable amount of time and money, and require the use a large number of animals. The incorporation of high- throughput in vivo model systems that can overcome limitations of in vitro screens while providing a rapid genome-wide view of chemical bioactivity are needed to help fully realize the vision of 21st century toxicity testing. In this dissertation, I employed transcriptomics using the high-throughput in vivo developmental zebrafish model to generate hypotheses regarding the mechanism of action of specific chemicals and to identify unique transcriptional signatures for a set of endocrine disrupting chemicals that can be used to classify the activity of known and unknown chemicals. We employed comparative transcriptomics in wild-type and ahr2-null zebrafish exposed to mono-substituted isopropylated triaryl phosphate (mITP), a component of the

Firemaster 550 flame retardant mixture, and demonstrated that the cardiotoxic effects of mITP are likely mediated through inhibition of retinoic acid receptor

signaling and not the aryl hydrocarbon receptor (AhR). Although mITP induced a transcriptional profile indicative of AhR activation in wild-type zebrafish, these signatures were not related to cardiotoxicity. I found that mITP exposure, independent of ahr2 status, decreased the expression of many Hox genes as well as enzymes responsible for retinoic acid metabolism, which are known to be regulated by retinoic acid receptors. Several of the dysregulated Hox genes are involved in cardiac cell lineage determination in early heart development, and in heart tube elongation and looping via cell recruitment in the second heart field. I also employed transcriptomics to investigate the mechanism of developmental toxicity of the antimicrobial agent, triclosan (TCS). Exposure to

TCS resulted in robust transcriptome changes with a large number of transcripts being significantly decreased. Downstream functional analyses showed that many of the transcripts significantly affected by TCS are involved in liver functioning and development, suggesting that TCS is hepatotoxic in embryonic zebrafish. I also compared our transcriptomic analysis with the comprehensive in vitro bioactivity profile of TCS from the Environmental Protection Agency’s

Toxicity Forecaster (ToxCast) program, and observed low concordance between these two screening strategies. Lastly, I conducted transcriptome profiling of 25 endocrine disrupting chemicals in order to identify discriminatory transcriptional signatures that would help classify chemicals with estrogen, androgen, or thyroid hormone activity. Clustered correlation analysis of the top

1000 significantly differentially expressed transcripts revealed four chemicals with highly similar and unique transcriptional profiles compared to the other 21

chemicals. These four chemicals included three known thyroid hormone receptor agonists, and one unknown chemical, which was later identified as a failed pharmaceutical with thyroid receptor agonist activity. I identified a panel of 27 transcripts as well as a unique pigmentation phenotype for these four chemicals which can be used in future chemical screens to detect other thyroid receptor agonist chemicals in zebrafish. Overall, the work presented here demonstrates the utility of phenotypically anchored whole genome transcriptomics in zebrafish to identify putative mechanisms of action for many chemicals. Furthermore, the information obtained from these types of analyses can be used to develop predictive models to classify the bioactivity of known and unknown chemicals, or can identify biomarkers that can be used in future high-throughput screens.

Copyrighted by Derik Haggard December 2, 2016 All Rights Reserved

Classifying Chemical Bioactivity by Coupling High-throughput Phenotypic Anchoring and Transcriptome Profiling in Zebrafish. by Derik Haggard

A DISSERTATION

submitted to

Oregon State University

in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

Presented December 2, 2016 Commencement June 2017

Doctor of Philosophy dissertation of Derik Haggard presented on December 2, 2016.

APPROVED:

Major Professor, representing Toxicology

Head of the Department of Environmental and Molecular Toxicology

Dean of the Graduate School

I understand that my dissertation will become part of the permanent collection of Oregon State University libraries. My signature below authorizes release of my dissertation to any reader upon request.

Derik Haggard, Author

ACKNOWLEDGEMENTS

There are many people that have aided, provided guidance, and given me the confidence to pursue a career in toxicology. First and foremost, I want to express my most sincere appreciation to Dr. Robert Tanguay for being my research advisor and mentor throughout my time at Oregon State University.

Not only did he allow me to join the amazing research environment his lab represents, but he has provided continuous support and opportunities to better myself as a scientist and as a person. Without his guidance and encouragement, I would not be who I am today, nor would I have discovered the challenge, excitement, and rewards the field of computational toxicology has to offer. I would also like to extend my deepest gratitude to my undergraduate mentor, Dr. Sara Heggland, who was kind enough to allow a lowly freshman the opportunity to experience a research lab, and had the patience to keep me there for four years. I would never have been inspired to continue on to graduate school, nor would I have found a love of researching endocrine disrupting chemicals without her.

Dr. Pamela Noyes also deserves particular thanks for her help and constructive discussions on the experimental design of my dissertation research described in Chapter 4. From surviving the challenging summer of 2014, to her assistance with screening and helping in the mornings of RNA collections, her help was paramount to the success of that project, and I am so very thankful.

There have been so many great friendships and thoughtful discussions with so many people over my 6 years here that acknowledging everyone would likely double the length of this dissertation. I want to thank all of the members of the Tanguay lab that I have worked with: Jane LaDu for training me when I first joined that lab; Siba Das for being my primary mentor during my rotation in the lab; Jill Franzosa, Britton Goodale, Galen Miller, Margaret Corvi, Kate Saili,

Kitae Kim, Andrea Knecht, Joe Fisher, Leah Wehmas, Yasmeen Nkrumah-Elie, and Sean Bugel for being the original members of the lab when I joined, who had profound effects on my initial research directions, and gave thoughtful advice and encouragement as I was finding my place in the lab; Cory Gerlach,

Amber Roegner, Mitra Geier, Anna Chlebowski, Mike Garland, Lindsay St.

Mary, Paroma Chatterjee, Courtney Roper, and Gloria Garcia for our conversations and assistance during my later stages in the lab; Chris Sullivan for help and support at the command line throughout my time here. I would also like to thank all of my committee members (both past, present, and temporary) who have helped guide my research and evaluate my ability to be a successful scientist: Nancy Kerkvliet, Michael Freitag, Siva Kolluri, David Hendrix, Kim

Anderson, and Anna Harding.

I would like to thank the staff at the Sinnhuber Aquatic Research

Laboratory for all of their help and support: Chapell Miller, Greg Gonnerman,

Eric Johnson, Michael Simonich, and Carrie Barton. Lisa Truong for all of her help with manuscript organization and career advice and encouragement. I would also like to thank Katrina Waters at the Pacific Northwest National

Laboratory, for guidance during the chemical selection process and microarray analysis advice for the research presented in Chapter 3 and 4. I would also like to thank the many people in the Department of Environmental and Molecular

Toxicology for their support, conversations, administrative assistance, and for allowing me the opportunity to even apply as a graduate student to this esteemed research program. I also thank the National Institutes of Health and the Environmental Protection Agency who provided the funding necessary to conduct my studies.

Throughout my time here, I have made a many great friendships from people affiliated and unaffiliated with the research department for which I am deeply grateful. To those who helped keep my sanity by climbing, exploring, going to Timbers games, and showing me the joys of surfing on the Oregon

Coast. I am so thankful to TJ Newton, for his love and support through the struggles, anxieties, and trials of being with a graduate student/mad scientist.

Lastly, I would like to thank my family. To my brother, Mason: Thank you for always being there and letting me enjoy our mutual love of fly fishing with you whenever I visited. To my father: Thank you for always encouraging my pursuits in higher education and for teaching me to be independent. To my mother: I would have never made it this far without your love and ability to listen and assuage my fears and anxieties throughout my education and for always encouraging me to never give up.

CONTRIBUTION OF AUTHORS

The research presented in this dissertation is a result of the collective efforts of past members of the Tanguay lab and collaborators at the Pacific Northwest

National Laboratory:

• In Chapter 2, Dr. Siba Das collected the RNA samples for the mITP

microarray analysis.

• In Chapters 3 and 4, Katrina Waters at the Pacific Northwest National

Laboratory provided assistance and advice during the chemical selection

process and microarray processing. Dr. Pamela Noyes aided in the

developmental toxicity assessments, as well as with embryo collection

and RNA isolation.

TABLE OF CONTENTS

Page

CHAPTER 1 - INTRODUCTION ...... 1

21st Century Toxicity Testing ...... 2

Lessons Learned and Data Gaps from Using HTS in Regulatory Toxicology ...... 5

The Zebrafish as an in vivo HTS Model System ...... 7

Transcriptomics in Zebrafish ...... 9

Summary and Study Objectives ...... 12

References ...... 15

CHAPTER 2 - COMPARATIVE TOXICOGENOMIC RESPONSES TO THE FLAME RETARDANT MITP IN DEVELOPING ZEBRAFISH .... 21

Abstract ...... 22

Introduction ...... 24

Materials and Methods ...... 27

Results and Discussion ...... 32

Conclusion ...... 41

Acknowledgements ...... 43

References ...... 43

CHAPTER 3 - PHENOTYPICALLY ANCHORED TRANSCRIPTOME PROFILING OF DEVELOPMENTAL EXPOSURE TO THE ANTIMICROBIAL AGENT, TRICLOSAN, REVEALS HEPATOTOXICITY IN EMBRYONIC ZEBRAFISH ...... 58

TABLE OF CONTENTS (CONTINUED)

Page

Abstract ...... 59

Introduction ...... 61

Materials and Methods ...... 66

Results ...... 75

Discussion ...... 81

Acknowledgements ...... 95

References ...... 96

CHAPTER 4 - CLASSIFYING CHEMICAL ENDOCRINE ACTIVITY USING TRANSCRIPTOME PROFILING IN ZEBRAFISH ...... 124

Abstract ...... 125

Introduction ...... 127

Materials and Methods ...... 131

Results and Discussion ...... 138

Conclusion ...... 149

Acknowledgements ...... 151

References ...... 151

CHAPTER 5 - DISCUSSION ...... 171

References ...... 177

CHAPTER 6 - FUTURE DIRECTIONS ...... 179

References ...... 183

BIBLIOGRAPHY ...... 184

TABLE OF CONTENTS (CONTINUED)

Page

APPENDICES ...... 203

Appendix 1 – Developmental Toxicity Profiles, EC80 Model Statistics, and Calculations for Chapter 4 ...... 204

LIST OF FIGURES

Figure Page

Figure 2- 1. Transcriptome profiling of 0.2 µM mITP exposure...... 49

Figure 2- 2. mITP-induced AhR-dependent and independent transcriptional network...... 50

Figure 2- 3. The transcriptional network of the predicted transcription factor,

NRF2...... 51

Figure 3- 1. Developmental toxicity (mortality and morbidity) profile and logistic regression analysis of TCS exposure...... 104

Figure 3- 2. TCS exposure induces robust transcriptional changes in embryonic zebrafish...... 105

Figure 3- 3. TCS-induced transcriptional changes are significantly enriched in the liver and brain...... 106

Figure 3- 4. Developmental time course study of the TCS EC80...... 107

Figure 3- 5. Quantitative RT-PCR validation of fifteen significantly differentially expressed genes identified in the microarray analysis at the EC20, EC50, and

EC80 of TCS...... 108

Figure 3- 6. TCS bioactivity profile across three ToxCast assay annotation levels...... 109

Figure S3- 1. Developmental toxicity profile of the repeated TCS EC80 experiment...... 111

LIST OF FIGURES (CONTINUED)

Figure Page

Figure S3- 2. Developmental toxicity profile of the EC80 of the new TCS chemical stock across embryonic development...... 113

Figure 4- 1. Chemical selection workflow based on the TEDX and USEPA EDC lists and our zebrafish developmental toxicity assessment of ToxCast Phase I and II chemicals. 159

Figure 4- 2. Distribution of EC80 values for all 25 chemicals...... 160

Figure 4- 3. Developmental toxicity of triiodothyronine, triac, tetrac, and CP-

634384...... 161

Figure 4- 4. Clustered correlation analysis of the top 1000 significantly expressed transcripts for the 25 EDCs...... 162

Figure 4- 5. Scatter matrix and significant transcript overlap between triiodothyronine, triac, tetrac, and CP-634384...... 163

Figure S4- 1. Developmental toxicity profile of all 25 EDCs...... 164

LIST OF TABLES

Table Page

Table S2- 1. Overlapping significant differentially expressed transcripts in WT and ahr2-null samples...... 52

Table S2- 2. Overlappin significant differentially expressed transcripts in WT and ahr2-null samples...... 54

Table S2- 3. Overlappin significant differentially expressed transcripts in WT and ahr2-null samples...... 57

Table 3- 1. Significantly enriched MetaCore process networks...... 114

Table 3- 2. Statistically enriched transcription factors...... 115

Table S3- 1. Primer sets for qRT-PCR validation...... 116

Table S3- 2. qRT-PCR data...... 117

Table S3- 3. "Biological_process_target" assay hit data...... 119

Table S3- 4. "Intended_target_family" assay hit data...... 120

Table S3- 5. Nuclear receptor assay hit data...... 121

Table 4- 1. 25 endocrine disrupting compounds used in this study...... 165

Table 4- 2. Overlapping significant transcripts across triiodothyronine, triac, tetrac, and CP-634384 (log2 fold change)...... 167

Table 4- 3. qRT-PCR validation of target transcripts in the unique transcriptional signature of triiodothyronine, triac, tetrac, and CP-634384. .. 168

Table S4- 1. Primer Sets for qRT-PCR Validation...... 170

Classifying Chemical Bioactivity by Coupling High-throughput Phenotypic Anchoring and Transcriptome Profiling in Zebrafish

1

CHAPTER 1 - INTRODUCTION

The field of toxicology has hit a crossroads regarding the management and hazard assessment of the large numbers of chemicals that are in current use. Within the United States, not only is there a backlog of tens of thousands of legacy chemicals with no toxicological information that were grandfathered in due to the passage of the Toxic Substances Control Act (TSCA) of 1976, but thousands more have been produced since then. In 1998, the newly formed

United States Environmental Protection Agency (USEPA) Endocrine Disrupting

Screening Program (EDSP) estimated the total universe of chemicals that belong in the EDSP program to be ~87,000, which included the ~76,000 chemical TSCA inventory (U.S. EPA, 2012b). The difficulty in screening such a large universe of chemicals is compounded by the fact that, within the United

States, the burden of testing the safety of chemicals as well as setting regulatory limits on exposures falls to the USEPA, with the process generally occurring after a chemical is already in use. With the recent 2016 amendment to TSCA, in the form of the Frank R. Lautenberg Chemical Safety for the 21st Century

Act, new chemicals must first be proven safe to the USEPA, which will decrease the introduction of new chemicals in the future (U.S. EPA, 2016c); however, the problem still remains of how to screen the large number of backlogged chemicals and identify those that pose a hazard to public health and the environment. 2

There is a need to develop new methods and models to systematically evaluate and determine the potential hazards of diverse chemical exposures in a rapid, cost-effective manner to prioritize the most hazardous chemicals for more rigorous screening, while also obtaining comprehensive mechanistic data to better define bioactivity. All efforts have utilized in vitro and in silico assays which, although very high-throughput, have systematic disadvantages such as a lack of xenobiotic metabolism, low cellular complexity, lack of dosimetry measurements, and are typically only used for acute toxicity assessments (Tice et al., 2013). Therefore, the use of a high-throughput in vivo model system is highly desirable as an alternative screening method. In this dissertation, I will describe the use of phenotypically anchored transcriptomic analysis in zebrafish to rapidly assess the developmental toxicity and bioactivity of chemicals in order to both identify putative mechanisms of toxicity (Haggard et al., 2016), and classify chemical action using transcriptional profiling. This research has contributed to the field of toxicology by demonstrating the viability of the zebrafish as a high-throughput in vivo chemical screening model as a means to identify the potential bioactivity of unknown chemicals and define toxicity biomarkers which will help drive the design and manufacturing of inherently safer chemicals.

21st Century Toxicity Testing Implementing high-throughput screening (HTS) approaches in regulatory toxicology came to the forefront with the publication of the National Research 3

Council’s seminal report entitled “Toxicity Testing in the 21st Century: A Vision and a Strategy” (NRC, 2007). Using classical whole-animal screening and toxicity evaluations for a single chemical would require the use of thousands of animals and cost millions of dollars; an approach that is not feasible to apply to tens of thousands of chemicals. Therefore, the document proposed a paradigm shift in chemical hazard evaluations to incorporate high-throughput and high- content assays in order to realistically meet all of the regulatory requirements set by congressional mandate. In response to this publication, regulatory agencies, including the USEPA, proposed new programs that utilize HTS methods to determine their viability to rapidly and accurately evaluate the toxicity of chemicals.

One of the first programs to be implemented in response to the NRC report was the USEPA Toxicity Forecaster (ToxCast) program in 2008. The primary goal of ToxCast is to develop methods that prioritize chemicals for further screening and risk characterization (Dix et al., 2007; Kavlock et al.,

2009). As such, it consists of in vitro screening assays to provide a comprehensive readout of chemical bioactivity across different biological scales, such as biochemical (e.g. receptor-mediated, or signal-transduction pathways assays, ion channels) or cellular (oxidative stress, apoptosis, cell cycle), providing bioactivity fingerprinting for many chemicals which could be used to examine associations between structure and activity as well as inferring the mechanism of action of unknown chemicals through direct comparison with 4 known toxicants (Dix et al., 2007). Beginning with the original proof-of-concept study, which consisted of ~300 pesticide active chemicals, the program is now in phase III screening and has processed 2000+ chemicals in over 700 in vitro assays (Richard et al., 2016).

The Toxicology in the 21st Century (Tox21) program was announced around the same time, utilizing the pooled resources, experience, and expertise between the USEPA, two National Institutes of Health institutes, and the Food and Drug Administration (Collins et al., 2008; Tice et al., 2013). Compared to the limited breadth of tested chemicals but a significant depth in toxicity pathway targets/assays in ToxCast, Tox21 has taken the opposite approach to hazard characterization; focusing on screening a significant number of chemicals

(>10,000) through a smaller number of assays (Tice et al., 2013; Huang et al.,

2016; Richard et al., 2016). Originally consisting of cytotoxicity assays using multiple different cell lines (Huang et al., 2008; Xia et al., 2008), Tox21 has transitioned to later phases in development with assays focused on characterizing the activity of chemicals with nuclear receptors and toxicity pathways, and will eventually incorporate more complex cellular model systems, and comprehensive gene expression analyses to better define chemical bioactivity (NCATS, 2015).

The primary readout for the HTS assays is the half-maximal activity concentration (AC50) where a given chemical causes a response in each assay compared to a basal response using a reference compound. Although a 5 simplistic approach to define chemical activity, the usefulness of this type of metric is fully apparent when examining AC50 profiles across a large assay space. This allows for the resolution of relationships between chemical classes or structures, as well as defining mechanistic activities across biological or toxicity pathways.

Lessons Learned and Data Gaps from Using HTS in Regulatory Toxicology The data generated from ToxCast have identified functional moieties associated with specific biochemical activities or interactions (a step towards better green chemistry) (Sipes et al., 2013), endocrine profiling of the ToxCast chemical space (Reif et al., 2010; Filer et al., 2014), as well as aiding in the identification of biological pathways that may be responsible for off-target effects in failed pharmaceuticals (Kleinstreuer et al., 2014). The AC50 values generated from ToxCast have also been used to extrapolate the measured assay data to human exposure levels (Rotroff et al., 2010; Wambaugh et al., 2013). These types of analyses have been informative in terms of relating in vitro response data to possible bioactivity in a realistic exposure situation, and have demonstrated that, although possible, the number of chemicals with modeled human exposures that overlap with in vitro bioactivity concentrations is rather small (Rotroff et al., 2010).

Tox21 initial proof-of-concept studies using HTS demonstrated the feasibility of screening thousands of environmental chemicals in a reproducible 6 manner with the ability to observe structure-response relationships with increasing assay number and complexity (Huang et al., 2008; Xia et al., 2008).

Since Tox21 began in 2008, the HTS capabilities have been used to screen

~8,300 unique chemicals across 30 different assays (Huang et al., 2016) including the activity in several hormone receptors (e.g. estrogen, androgen, vitamin D, and thyroid hormone receptors) (Huang et al., 2014b; Huang et al.,

2016), mitochondrial membrane potential disruption (Attene-Ramos et al.,

2015), and farnesoid X receptor activity (Hsu et al., 2014).

Overall, implementation of HTS programs have established a foundation for chemical prioritization strategies and provided the scientific community with novel computational tools and systems approaches that aid in delivering highly pertinent chemical toxicity data for regulatory decision making. However, these studies also highlighted significant data gaps and limitations in in vitro HTS assays that have yet to be fully controlled for with the current state of the science

(Tice et al., 2013). Autofluorescence of test chemicals or cellular substituents is a problem when performing in vitro assessments, particularly when using fluorescence-based reporter assays which are commonly used in HTS screens

(Huang et al., 2011). The comparability and correlation between different assays measuring the same biological endpoint is a known problem, particularly in assays measuring nuclear receptor activity (Huang et al., 2014b). It is also known that differential sensitivity between cell-lines of different species exist, which can confound bioactivity results (Xia et al., 2008). Cellular complexity, in 7 terms of interaction with other cell types or tissues, also becomes an issue in in vitro testing, as well as extrapolating measured exposure levels in vitro to in vivo. However, one of the biggest problems facing in vitro HTS strategies is the lack of xenobiotic metabolism that is inherent in in vivo systems, which have led to discussion on whether to include primary hepatocytes, alone or in co-culture, or use other methods to include metabolism for the many in vitro assay systems commonly used in these chemical screens (Tice et al., 2013). Additional models are needed which are able to overcome many of these data gaps that have high-throughput capacity and can incorporate endogenous xenobiotic metabolism to more accurately model chemical exposures in vivo.

The Zebrafish as an in vivo HTS Model System Zebrafish meet many of the requirements for an HTS system and provides the benefits of an in vivo developmental system. Their rapid, external, and transparent development allows for the tracking and visualization of chemical toxicity at target organ resolution. They are highly prolific, allowing for large sample sizes, which overcome some the challenges of using rodent models, all the while increasing the statistical power of each experiment to detect biologically meaningful effects (Truong et al., 2011). Due to their small size, they are adaptable for use in 96-well plates, and with the implementation of automated robotics for embryo handling and loading, they are amenable to high-throughput screening (Mandrell et al., 2012). Furthermore, zebrafish become metabolically competent around 72 hours post-fertilization (hpf), when 8 the liver has fully formed; overcoming the major limitation of xenobiotic metabolism in in vitro HTS models (Field et al., 2003; Cox and Goessling, 2015).

Humans and zebrafish share ~70% of genetic homology and ~82% of disease related human genes are present in the zebrafish (Howe et al., 2013).

The entire developmental process occurs in the span of five days, which involves participation of master regulators of cell differentiation, fate transformation, anterior-posterior patterning, cell migration, and organ system development; therefore, the full repertoire of the zebrafish genome will be expressed at some point during this time. From the single cell stage (0.2 hpf) to

50% epiboly (~5 hpf), roughly 82% of all genes are detected using RNA- sequencing (RNA-seq), with the remaining 18% attributed to non-early developmental biological processes such as visual/sensory perception and olfaction (Vesterlund et al., 2011). As a result, exogenous chemicals will have the potential to interact with nearly every molecular target, allowing for the screening of chemicals with diverse bioactivities.

The utility of zebrafish in a HTS capacity has been previously demonstrated by several independent laboratories. This has included comprehensive phenotypic developmental toxicity assessment of 1060 unique chemicals in the ToxCast phase I and II chemical libraries (Padilla et al., 2012;

Truong et al., 2014), flame retardants (Noyes et al., 2015), plant-derived phytoestrogens (Bugel et al., 2016), and polycyclic aromatic hydrocarbons

(PAHs) (Knecht et al., 2013). Aside from phenotypic assessments, HTS studies 9 in zebrafish have assayed embryonic and larval zebrafish behavior in chemical screens, including the ToxCast phase I and II chemicals (Reif et al., 2016), and a screen of ~14,000 potential psychoactive chemicals (Kokel et al., 2010; Kokel and Peterson, 2011). Similar to the in vitro HTS screens of ToxCast and Tox21, the application of computational techniques to the phenotypic and behavioral readouts from HTS in zebrafish have allowed for the identification of structure response relationships. However, relying on these readouts alone has been inefficient to fully capture the associations between chemicals with related mechanisms of action and to begin to classify unknown chemicals.

Transcriptomics in Zebrafish Described in one of the original articles relaying the USEPA’s rationale for the ToxCast program, HTS assays can be categorized as either target-based or phenotype-based, with both types providing useful information for classifying and defining the bioactivity of chemicals (Dix et al., 2007). These assays only measure one biological response and are generally restricted to a bimodal readout of increased or decreased activity compared to a known reference compound. This limits the amount of information that can be obtained in a single assay. Unlike in vitro HTS assays, the zebrafish model has the capacity to provide both phenotypic and target-based responses. When the zebrafish model is used for HTS, it provides phenotypic data regarding the specific toxic effects associated with chemical exposure; however, this provides limited insight on chemical specific mechanism of action. Furthermore, the 10 developmental phenotypes evaluated in the zebrafish are known to be highly correlated with one another, making structure-activity relationships based solely on these data difficult and not truly representative of the underlying biology driving the observed adverse phenotypes (Truong et al., 2014). Consequently, a targeted approach paired with the phenotypic readouts is needed to provide information to more accurately classify large numbers of chemicals with known and unknown activities, while also delivering enough information to form hypotheses regarding the specific mechanisms of action of individual chemicals.

The well annotated genome and high genetic tractability of zebrafish allow for the application of many target-based screening strategies to identify chemical bioactivity across development. There are a variety of transgenic zebrafish lines available to allow for examination of how chemicals may perturb specific pathways (Garcia et al., 2016). For example, reporter lines are available to detect hepatotoxicants (Zhang et al., 2014), thyroid gland disruption (Raldua and Babin, 2009), and the activity of many nuclear receptors (Tiefenbach et al.,

2010; Garcia et al., 2016). With the recent advances in genome editing technologies, such as the CRISPR-Cas9 system, the ability to produce stable transgenic reporters, as well as knock-out and knock-in lines, has vastly increased, allowing for further development of novel lines relevant to HTS applications as well as modeling various human diseases. Though these single target systems can provide meaningful insight into chemical activity, they are greatly limited in scope. As such, a more comprehensive approach is needed to 11 examine a large number of potential targets. A proposed method is to take a global approach and examine the transcriptional perturbations due to chemical exposure, using whole genome microarrays as well as RNA-seq.

Transcriptomics in zebrafish has the capability to provide meaningful information to classify chemical activity, including the identification of biomarkers indicative of perturbations of a specific target pathway, comparisons of transcriptional profiles to ‘bin’ chemicals that elicit similar transcriptional responses, as well as in-depth information regarding specific mechanisms of action by combining systems biology methods and the molecular tools available in the zebrafish model. Transcriptome analyses in 48 hpf zebrafish have been established as a useful time point for mechanistic evaluation of many chemicals.

The 48 hpf time point is important because it models early upstream transcriptional responses that, in most cases, precede eventual toxicity; providing data near or concurrent with the ‘molecular initiating event’ level for chemical evaluation, which is applicable to the adverse outcome pathway approach being developed for ecotoxicology and risk assessments (Ankley et al., 2010; Volz et al., 2011). Furthermore, the emphasis on early detection reduces the chance of measuring secondary transcriptional effects that are not mechanistically related to a chemical’s activity, but may be associated with common signaling cascades associated with toxic outcomes, similar to the highly correlated endpoints measured in phenotypic screens. Transcriptional studies at this developmental stage have revealed differential mechanisms of 12 action of PAHs and their oxygenated derivatives (Goodale et al., 2013; Goodale et al., 2015), as well as endocrine disrupting chemicals (EDCs) such as bisphenol A (Saili et al., 2013), and examination of transcriptional signatures associated with excess thyroid hormone (Pelayo et al., 2012). Most transcriptional studies in zebrafish, so far, have focused on examining the transcriptional signatures of only a small set of test chemicals, likely due to cost.

However, as transcriptomics technology becomes increasingly affordable, it is becoming easier to fully take advantage of these technologies alongside the

HTS capabilities the model offers. To my knowledge, only one study performed transcriptional analyses on a set of estrogenic and anti-androgenic compounds, eight in total, and observed disruption in several biological pathways related to their known endocrine activity (Schiller et al., 2013). This allowed the authors to classify the estrogenic or anti-androgenic activities of non-reference chemicals using measured pathway perturbations and comparing those to the reference compounds. This study demonstrated the feasibility of using a transcriptome profiling approach in zebrafish to classify chemical activity and make predictions regarding the biological effects of unknown chemicals.

Summary and Study Objectives I hypothesized that pairing transcriptomics with a well-defined phenotypically anchored exposure concentration will provide the data necessary to form hypotheses regarding the mechanism of action of chemicals, and to identify chemical activity-specific biomarkers useful for broader 13 application in future high-throughput chemical screens. To this end, I performed two studies focused on identifying putative mechanisms of action of chemicals

(Chapter 2 and 3), and one study classifying chemicals by transcriptional profiling based on their ability to disrupt the estrogen, androgen, or thyroid endocrine systems (Chapter 4).

A potential weakness in toxicogenomics is the inability to identify reproducible and robust expression responses that are indicative of chemical action by using too low a concentration (Schiller et al., 2013); likely due to factors such as the variability in expression levels associated with pooled sample populations and the signal-to-noise issues inherent with using whole embryos. Therefore, a major objective for these studies was to select an appropriate exposure concentration to anchor our transcriptome data. I chose concentrations that elicited either 100% (EC100; Chapter 2) or 80% (EC80;

Chapter 3 and 4) adverse phenotypic effects by 120 hpf, which results in a large proportion of affected animals and increases the probability that observed expression changes are causally related to the eventual adverse phenotypic responses due to exposure.

In Chapter 2, I set out to explore the mechanism of action of the flame retardant, mITP, which was previously demonstrated to cause cardiotoxicity in an AhR-independent manner (McGee et al., 2013; Gerlach et al., 2014). I performed a comparative microarray study using wild-type and ahr2-null zebrafish and identified differential and common transcriptional signatures that 14 were related to the AhR-dependent effects of mITP, as well as a likely mechanism by which mITP exposure induces cardiotoxicity that is independent of AhR activation. Our experimental design also allowed us to investigate the transcriptional consequences of ahr2 knockout, which showed potential crosstalk between ahr2 and other transcription factors under basal conditions.

Similarly, in Chapter 3, I examined the putative mechanism of action of the common antimicrobial agent, triclosan (TCS). Exposure to the EC80 of TCS resulted in a robust transcriptional profile with a majority of the differentially expressed transcripts being decreased. Functional analysis of the transcriptional changes implicated a variety of biological processes associated with normal liver functioning, suggesting TCS is hepatotoxic in embryonic zebrafish. I also compared our transcriptional data to all of the ToxCast in vitro bioactivity data for TCS to examine the concordance between these two different approaches to characterize chemical bioactivity.

In Chapter 4, I sought to classify a set of 25 known or suspected endocrine disrupting chemicals (EDCs) across the estrogen, androgen, and thyroid hormone signaling pathways using phenotypically anchored transcriptome profiling using high-density oligo microarrays. These chemicals were selected based on the initial ToxCast phase I and II developmental toxicity screens performed by our laboratory (Truong et al., 2014). I took a transcript- centric approach to this analysis by examining the correlation of the top 1000 significantly differentially expressed transcripts across all chemicals exposures 15 in order to identify whether any discriminatory transcriptional signatures were present that would allow for classification of chemicals based on their activity.

The overall goal of HTS transcriptional studies is to generate enough data to develop sensitive and accurate models that are able to predict the bioactivity of unknown chemicals. The studies presented here demonstrate the utility of using phenotypically anchored transcriptomics in zebrafish as an in vivo

HTS model that can assess thousands of environmental chemicals that currently have little to no toxicity information and provide a global transcriptional profile of bioactivity. I show that this approach can provide relevant information to both identify putative mechanisms of action of environmental chemicals, and acquire enough relevant data to develop classification methods to group a diverse set of chemicals based on their global transcriptional similarities. Taken together, I illustrate the advantages of using an embryonic zebrafish transcriptomics-based screen and the potential for incorporation into existing

HTS programs, such as ToxCast, Tox21, and EDSP21, to provide a high- throughput and comprehensive in vivo assay that can generate valuable transcriptome-wide information to assist in the chemical screening and prioritization process.

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21

CHAPTER 2 - COMPARATIVE TOXICOGENOMIC RESPONSES TO THE FLAME RETARDANT mITP IN DEVELOPING ZEBRAFISH

Derik E. Haggard1, Siba R. Das2, and Robert L. Tanguay1

1Department of Environmental and Molecular Toxicology, Oregon State University, Corvallis, OR, USA 2Pacific Northwest Diabetes Research Institute, Seattle, WA, USA

Reprinted with permission from Haggard, D. E., Das, S. R., and Tanguay, R. L. (2016) Comparative Toxicogenomic Responses to the Flame Retardant mITP in Developing Zebrafish. Chemical Research in Toxicology. Copyright 2016 American Chemical Society. 22

Abstract Mono-substituted isopropylated triaryl phosphate (mITP) is a major component of Firemaster 550, an additive flame retardant mixture commonly used in polyurethane foams. Developmental toxicity studies in zebrafish established mITP as the most toxic component of FM 550, associated with pericardial edema and heart looping failure. Mechanistic studies showed that mITP is an aryl hydrocarbon receptor (AhR) ligand; however, the cardiotoxic effects of mITP were independent of the AhR. We performed comparative whole genome transcriptomics in wild-type and ahr2hu3335 zebrafish, which lack functional ahr2, to identify transcriptional signatures causally involved in the mechanism of mITP-induced cardiotoxicity. Regardless of ahr2 status, mITP exposure resulted in decreased expression of transcripts related to the synthesis of all-trans-retinoic acid and a host of Hox genes. Clustered gene ontology enrichment analysis showed unique enrichment in biological processes related to xenobiotic metabolism and response to external stimuli in wild-type samples. Transcript enrichments overlapping both genotypes involved the retinoid metabolic process and sensory/visual perception biological processes. Examination of the gene-gene interaction network of the differentially expressed transcripts in both genetic backgrounds demonstrated a strong AhR interaction network specific to wild-type samples, with overlapping genes regulated by retinoic acid receptors (RARs). A transcriptome analysis of control ahr2-null zebrafish identified potential cross-talk between AhR, Nrf2, and

Hif1α. Collectively, we confirmed that mITP is an AhR ligand and present 23 evidence in support of our hypothesis that mITP’s developmental cardiotoxic effects are mediated by inhibition at the RAR level.

24

Introduction Flame retardant chemicals are commonly applied to the polyurethane foams in furniture or plastics in electronics to decrease flammability and meet safety standards set out by California’s Technical Bulletin 117 . Firemaster 550

(FM 550) is one such flame retardant and is a replacement for the polybrominated diphenyl ether formulation, PentaBDE, which was phased out voluntarily in 2004 due to toxicity and bioaccumulation concerns (Tullo, 2003).

Like most flame retardants, FM 550 is not chemically bonded to the material it is applied to, which enables leaching over time into the environment. As a result, components of FM 550 are commonly found in household dust (Stapleton et al.,

2008; Stapleton et al., 2009; Dodson et al., 2012). Ingestion or inhalation of contaminated dust is likely the primary route of human exposure, and metabolites of FM 550 have been measured in the urine of adults and children, with children having higher exposure levels compared to adults, attributed to crawling and mouthing behaviors (Butt et al., 2014; Hoffman et al., 2014; Butt et al., 2016).

Little is known about the developmental and toxicological effects of FM

550 and its components. Adverse effects of FM550 have been reported in animal models including obesogenic responses, endocrine disruption-related effects, trans-placental and lactational transfer, neurobehavioral changes in larval and adolescent zebrafish, and DNA damage (Bearr et al., 2010; Patisaul et al., 2013; Bailey and Levin, 2015; Phillips et al., 2016). FM 550 is a mixture 25 of brominated and organophosphate-based chemicals whose main components include 2-ethylhexyl 2,3,4,5-tetrabromobenzoate (TBB), bis(2-ethylhexyl)

3,4,5,6-tetrabromophthalate (TBPH), triphenyl phosphate (TPP), and various isopropylated triaryl phosphates. The latter component is a mixture of mono-, di-, tri-, and tetra-ITPs with either ortho-, meta-, and para- substitution patterns. mITP, TBB, and TPP are considered the major components of FM 550, comprising 32%, 30%, and 17% of the bulk mixture, respectively (McGee et al.,

2013). Studies examining the individual components of FM 550 have primarily focused on the two brominated compounds, TBB and TBPH, likely due to their higher bioaccumulative potential compared to the more readily metabolized organophosphate components (Springer et al., 2012; Saunders et al., 2013;

Saunders et al., 2015). However, recent studies demonstrated that the organophosphate components were more developmentally toxic than the brominated components (McGee et al., 2013; Noyes et al., 2015). When the individual components of FM 550 were tested at equivalent nominal aqueous concentrations in embryonic zebrafish (1 µM), only TPP and mITP induced adverse phenotypic effects at 96 hours post-fertilization (hpf), with mITP inducing malformations at concentrations an order of magnitude lower than TPP

(McGee et al., 2013). In a comprehensive high-throughput screen of 44 halogenated and organophosphate flame retardants, mITP was the second most bioactive while up to 64 µM TBB and TBPH elicited no adverse developmental malformations (Noyes et al., 2015). Therefore, further studies 26 examining the mechanism of action of mITP-induced developmental toxicity are warranted.

Embryonic zebrafish exposed to mITP exhibited severe pericardial edema and blocked heart looping (a “tube heart” phenotype), similar to the malformations observed after exposure to TCDD (McGee et al., 2013).

Mechanistic studies showed that mITP exposure increased the expression of cyp1a mRNA and co-exposure to CH223191, a purported AhR-specific antagonist, blocked the heart malformations from mITP exposure. However, transient knockdown of ahr2 using anti-sense morpholinos (MOs) did not recapitulate the effects of CH223191, suggesting that mITP-induced heart malformations may not be mediated by ahr2. Gerlach et al. (Gerlach et al., 2014) showed that mITP is predicted to bind ahr2 and ahr1b, with comparable binding scores to CH223191, that mITP exposure induced a similar cyp1a expression pattern to that of TCDD, and that it was blocked in the absence of ahr2 and ahr1b. However, the mITP heart malformations were still present in the absence of all AhR isoforms, suggesting that the cardiotoxicity was AhR-independent.

To determine how developmental mITP exposure causes cardiotoxicity independent of zebrafish AhR signaling, we conducted a comparative transcriptomics study using wild-type (WT) and a functional ahr2 knockout zebrafish line (ahr2hu3335; referred to as ahr2-null). Using this approach, we observed transcriptional signatures indicative of ligand activation of the AhR in

WT zebrafish; a signature which was absent in ahr2-null zebrafish. Independent 27 of ahr2 status, mITP caused a strong repression in transcripts involved in the synthesis of all-trans-retinoic acid (RA) and a host of Hox genes which are downstream targets of RA receptors (RARs), and suggested mITP as a potential RAR inhibitor. We also examined the transcriptome of unexposed ahr2-null zebrafish, which highlighted possible nuclear receptor cross-talk between AhR, Nrf2, and Hif1α, which may be involved in the reproductive irregularities and malformations that are sometimes observed in ahr2-null zebrafish.

Materials and Methods

Chemicals

mITP (≥90% purity) was obtained through purification of FM 550

(Chemtura) as previously described (McGee et al., 2013). For all exposures, the maximum DMSO concentration used was 0.1%.

Zebrafish husbandry

Adult zebrafish were housed following the approved Institutional Animal

Care and Use Committee protocols at the Oregon State University Sinnhuber

Aquatic Research Laboratory (Corvallis, OR) and maintained on a 28°C recirculating water system with a 14:10 h light/dark cycle. All experiments were conducted with the WT 5D Tropical or ahr2-null (ahr2hu3335) strains. More details on the ahr2-null strain characterization can be found in Goodale et al.(Goodale 28 et al., 2012). Embryos from adult WT zebrafish were collected from group spawns as previously described (Reimers et al., 2006). Homozygous ahr2-null embryos were obtained through in-crosses of heterozygous adults (ahr2hu3335/+) with a 1:1 ratio of male to female. All WT or ahr2-null embryos were collected, staged, and maintained in sterile petri dishes under the same conditions prior to chemical exposure (Kimmel et al., 1995).

Chemical Exposure

For all experiments, exposures were conducted using a static, non- renewal procedure as described in Gerlach et al. (Gerlach et al., 2014). In brief,

WT or ahr2-null embryos were batch-exposed in groups of ten in glass vials to either vehicle control (0.1% DMSO) or 0.2 µM mITP from 6 - 48 hours post- fertilization (hpf). A total of three biological replicates were used for each treatment group. The concentration of mITP used was based on the lowest concentration to elicit visible pericardial edema, significant cyp1a expression in

WT, and blocking of cyp1a expression in ahr2-null zebrafish (Gerlach et al.,

2014).

RNA isolation

RNA was isolated from at least nine pooled 48 hpf embryos per treatment group, with three biological replicates for each treatment. Embryos were homogenized in 500 µl of RNAzol® RT (Molecular Research Center, Cincinnati,

OH) with 0.5 mm zirconium oxide beads using a bullet blender (Next Advance, 29

Averill Park, NY) at speed 8 for 3 minutes. Total RNA was extracted as previously described (Gerlach et al., 2014). RNA sample concentration and quality were measured using a SynergyMx microplate reader (BioTek,

Winooski, VT) with the Gen5 Take3 module and RNA integrity was measured with the Agilent Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA).

Microarray design and hybridization

Microarray preparation, hybridization, and scanning was performed by

Miltenyi Biotec Genomic Services (San Diego, CA) using Agilent Whole

Zebrafish Genome Oligo Microarrays V3 4x44k (Agilent Technologies). RNA samples were amplified and labeled using the Agilent Low Input Quick Amp

Labeling Kit (Agilent Technologies) using the manufacturer’s protocol.

Unexposed (vehicle control) and mITP-exposed WT samples were labeled with

Cy3 and Cy5, respectively. However, the opposite labeling strategy was used in ahr2-null samples, where unexposed (vehicle control) and mITP-exposed ahr2-null samples were labeled with Cy5 and Cy3, respectively. This represents an unconnected direct two-color microarray design, with no arrays linking the

WT and ahr2-null genotypes. Samples were hybridized using the Agilent Gene

Expression Hybridization Kit (Agilent Technologies) following the Agilent 60-mer oligo microarray protocol. Microarrays were scanned using the Agilent DNA microarray scanner (Agilent Technologies) and Agilent Feature Extraction

Software was used to read out and process the image files. Raw data were then provided to use for further processing and statistical analysis. All microarray 30 data are publically available and can be accessed at the Gene Expression

Omnibus repository (GSE86834).

Microarray data analysis

Quality control, outlier detection, data preprocessing, and statistical analysis was conducted using the Linear Models for Microarray and RNA-seq

Data (limma) package in R (Smyth, 2004; Ritchie et al., 2015; R Core Team,

2016). To make all of the statistical comparisons of interest, we implemented the separate-channel analysis for two-color microarrays method in limma utilizing a 2x2 factorial statistical model (Smyth and Altman, 2013). Data were background corrected, lowess normalized, and the distribution of the average probe intensities for all arrays were quantile normalized. To control for the technical pairing between channels that originated from the same two-channel array, the probe-wise correlation between all array pairs was estimated prior to statistical testing. Data preprocessing identified seven probes on the arrays with aberrant intensities, which were removed from the analysis. Normalized data were analyzed using a moderated t-test in conjunction with an empirical Bayes method to shrink probe-wise sample variances and a false-discovery rate (FDR) of 5% (Benjamini and Hochberg, 1995). Array probes with a FDR-adjusted P- value ≤ 0.05 and a fold-change of ≥ 1.5 were considered statistically significant.

Gene expression profiles were visualized as bi-hierarchically clustered heatmaps with custom R scripts using Euclidean distance metrics and the 31 complete linkage hierarchical clustering algorithm based on log2 fold change responses (R Core Team, 2016).

Pathway Analysis

To perform downstream functional analysis, we mapped all of the Agilent

Zebrafish V3 transcripts to orthologous human Ensembl gene identifiers using

Bioinformatics Resource Manager (Tilton et al., 2012). Of the 43603 unique transcripts on the Agilent Zebrafish V3 array, we successfully identified 30568

(70.1%) orthologous human genes with Ensembl gene identifiers. Gene ontology (GO) enrichment analysis, transcription factor prediction, and gene- gene interaction network building was performed using MetaCore GeneGo software. Enriched GO terms and predicted transcription factors with a FDR- corrected P-value ≤ 0.05 or ≤ 5e-05, respectively, were considered significant.

To compare the differential GO enrichment profiles as a result of mITP exposure in WT and ahr2-null samples, we utilized the EnrichmentMap application in

Cytoscape (Merico et al., 2010). EnrichmentMap organizes GO terms based on similarity of gene membership to generate a clustered network, overcoming the term redundancy problem inherent in GO analyses. Highly grouped clusters of enriched GO terms were manually annotated based on the dominant GO term for that cluster. To explore the gene-gene interactions due to mITP exposure and the effect of ahr2 knockout in unexposed control embryos, we used

MetaCore’s network building algorithms. These networks connect genes known to interact with one another based on prior information that is annotated in the 32 manually curated MetaCore knowledgebase. Genes were labeled using

MetaCore Network Objects identifiers, which are either known human gene symbols or names. For the genotype comparative gene-gene interaction network, we also included RAR (combined RAR, RAR, RAR) into the network building algorithm, even though mITP did not directly affect the expression of these receptors. This network was generated using the Shortest

Paths algorithm allowing for only a one-step path through all supplied genes. To examine the NRF2 transcriptional hub for the ahr2 knockout comparison, we used the Transcription Regulation algorithm in MetaCore focusing on the NRF2 transcription factor. All predicted biological networks were visualized using

Cytoscape.

Results and Discussion

Comparative transcriptional profiles of WT and ahr2-null zebrafish after exposure to mITP

Exposure to 0.2 µM mITP resulted in 239 differentially expressed transcripts in WT embryos compared to 58 in ahr2-null embryos (FDR adjusted

P-value ≤ 0.05, fold change ≥ 1.5). A comparison of the transcriptional responses showed that there were a total of 38 differentially expressed transcripts that were similar between the two genotypes (Table S1). To visualize the expression profiles between the two genotypes and identify clusters of similarly expressed transcripts, we generated a bi-hierarchically clustered 33 heatmap of the 259 unique statistically differentially expressed transcripts

(Figure 1A). We observed an overall distinct separation between the two genotypes, demonstrating strong discriminatory expression signatures due to mITP exposure. Closer examination of the clusters with the most highly increased transcripts showed that only two cyp1a transcripts are significantly expressed in both genotypes, but a suite of transcripts including cyp1c1, cyp1c2, cyp1b1, ahrra, ctsl.1, and wfikkn, are only differentially expressed in the

WT (Figure 1A, cluster 1). We also observed several other clusters with similarly decreased expression patterns between the two genotypes. This cluster included cyp26a1, dhrs3a, dhrs3b, rho, and a variety of Hox genes including hoxa5a, hoxb5a, hoxb5b, hoxb6b, hoxc1a, and hoxb8b (Figure 1A, cluster 2).

GO enrichment analysis using MetaCore identified 33 significant terms in WT and 11 in ahr2-null zebrafish (Table S2 and S3). Clustering the significant

GO terms resulted in four dominant clusters of related GO terms and included

“Response to External Stimulus”, “Response to Xenobiotic”, “Visual/Sensory

Perception”, and “Retinoid Metabolic Process” (Figure 1B). The “Response to

External Stimulus” and “Response to Xenobiotic” clusters consisted of GO terms that were only enriched in WT samples. The GO terms within these two clusters consisted of many known transcriptional targets of AhR including cyp1a, cyp1b1, ahrra, ugt1a6, ahr2, and mmp9, which likely represents an ahr2- dependent transcriptional response to mITP. In contrast, the “Visual/Sensory

Perception” and ‘Retinoid Metabolic Process” clusters contained overlapping 34

GO terms found in both WT and ahr2-null samples. Transcripts within the

“Visual/Sensory Perception” cluster consisted of rho, crygm2d4, dhsr3a, dhrs3b, hoxb8b, and hoxc1a. The transcripts in the “retinoid Metabolic Process” cluster included cyp26a1, dhrs3a, dhrs3b, rho, and cyp1a. Many of the transcripts in these two clusters are involved in RA signaling, which suggests its involvement in the ahr2-independent transcriptional activity of mITP.

Both the transcriptome profile and functional GO enrichment analysis highlighted processes involved in AhR signaling and related to RA metabolism.

We examined direct gene targets present within the two transcriptional profiles and how they compare with one another focusing on the AhR and RAR signaling pathways. mITP exposure in WT samples elicited a large interconnected network of genes that interact with the AhR, and which was absent in the ahr2- null samples (Figure 2). Although the number of direct AhR gene targets is small, there was a large number of differentially affected genes that may involve cross-talk between the AhR, MMP-9, and HMGA1. Examination of the genes regulated by RARs showed that both HOXA5 (orthologous to zebrafish hoxb5b and hoxa5a) and CYP26A1 are regulated by RARs in both genotypes. All other

RAR regulated genes were unique in the WT samples, including HOXD4 RBP1,

PGHD, CETP, ASB2, and Olfactomedin-4. However, the expression of HOXD4

(orthologous to zebrafish hoxc5a) and ASB2 (orthologous to zebrafish asb2b) were significantly differentially expressed in ahr2-null but did not pass our fold change cutoff. 35

Previously, Gerlach et al. (Gerlach et al., 2014) demonstrated that knockout of ahr2, ahr1a and ahr1b did not rescue mITP-induced cardiotoxicity.

The robust transcriptional changes and functional processes we observed in the

WT animals further demonstrated that mITP is an ahr2 and ahr1b ligand and modulates downstream expression of AhR target genes. The cluster of significantly increased transcripts unique to WT are commonly expressed after exposure to strong AhR ligands (Goodale et al., 2013; Goodale et al., 2015).

Moreover, the significant but diminished induction of cyp1a in the ahr2-null samples (log2 fold change ≈ 2.3 compared to 3.3 in WT), confirmed that induction of cyp1a by mITP involves cooperative regulation by ahr2 and ahr1b; however, the majority of AhR responsive genes induced by mITP appear to be regulated by ahr2 and not ahr1b. GO enrichment analysis showed that many of the differentially expressed transcripts in WT animals are involved in biological processes related to response to a xenobiotic or an external stimulus, indicative of AhR activation; a signature absent in the ahr2-null expression profile (Figure

1B). Examination of the gene-gene interaction network specific to WT showed that many of the transcriptional changes were regulated by AhR directly, or through other interactions involving AhR-induced MMP-9 and HMGA1 (Figure

2). AhR is a known inducer of MMP-9 expression, which plays a role in extracellular matrix remodeling. In a zebrafish fin regeneration model, activation of the AhR by TCDD was shown to increase the expression of MMP-9 and other matrix-remodeling genes which was associated with impairment of the 36 regenerative process (Andreasen et al., 2007). Interestingly, the expression of tissue inhibitor of metalloproteinase 2 (TIMP2), which inhibits MMP activity, was increased and may reflect a homeostatic response to AhR-induced MMP-9 expression from mITP exposure. HMGA1 is a small protein that regulates gene expression through chromatin reorganization and cooperation with other proteins at promoters and enhancer regions. Overexpression of HMGA1 is commonly observed in many forms of cancer, and has been shown to play a role in oncogenic transformation (Resar, 2010); however, the biological effects of decreased expression of HMGA1 and its interaction with the AhR are unknown. Overall, the unique transcriptional changes and downstream functional processes affected by mITP exposure in a WT background confirm that mITP is an activator of AhR in embryonic zebrafish, but these changes appeared to play no role in the cardiotoxic effects of mITP.

Potential role of RA in mITP-induced cardiotoxicity

Regardless of ahr2 status, we observed a robust signature of transcripts involved in RA metabolism and signaling in the mITP exposed animals. RA is a requirement for normal development, but excessive or insufficient RA is teratogenic (Niederreither and Dolle, 2008). The neckless zebrafish line, having no functional retinaldehyde dehydrogenase type 2 (aldh1a2; the rate-limiting enzyme in RA synthesis), present with pericardial edema, heart malformations, and loss of the pectoral fin (Begemann et al., 2001; Liu and Stainier, 2012). We noted that mITP exposure resulted in a decrease in cyp26a1 expression. The 37 cyp26a1 enzyme is responsible for the metabolic degradation of active RA and cooperates with aldh1a2 to define a precise posterior-to-anterior RA gradient in early development (White et al., 2007; Shimozono et al., 2013). dhrs3a and dhrs3b, which degrade all-trans-retinaldehyde into vitamin A and help control

RA levels in the developing brain and eye, were similarly decreased after exposure to mITP (Feng et al., 2010). Decreased expression of cyp26a1, dhrs3a, and dhrs3b, but no effect on aldh1a2, suggested that mITP did not impact RA levels in zebrafish, but might instead act by inhibiting RAR. For instance, we would expect fluctuations in RA to result in opposite changes in expression for the enzymes that degrade or synthesize RA, respectively

(Dobbs-McAuliffe et al., 2004). A recent study examining TPP observed similar pericardial edema and heart looping defects after exposure to mITP at higher concentrations and a similar decrease in cyp26a1 expression in developmentally exposed zebrafish (Isales et al., 2015). And in an in vitro chimeric RAR reporter assay, TPP inhibited the activity of human RARα, β, and

γ. Importantly, this study showed that co-exposure of TPP to non-toxic concentrations of the pan-RAR antagonist, BMS493, resulted in greater toxicity compared to TPP alone; therefore, suggestive of possible interaction between

TPP and BMS493 through RARs or RAR-regulated pathways. mITP is structurally similar to TPP, the only difference being the presence of the isopropyl group; therefore, it is possible that mITP and TPP similarly affect heart development in embryonic zebrafish through inhibition of RAR activity. It is 38 interesting to speculate whether co-exposure with RAR antagonists, such as

BMS493, would result in enhanced toxicity for mITP, similar to what was observed with TPP (Isales et al., 2015), and this is an exciting prospect for additional studies. It is also possible that decreased expression of cyp26a1, dhsr3a, and dhrs3b represents a homeostatic mechanism to limit the degradation of RA, leaving more available for signaling.

RA deficiency during the blastula stage (4 hpf) resulted in an excess number of cardiomyocytes in zebrafish (Keegan et al., 2005). Once the heart tube has formed, RA is involved in establishing the boundaries of the second heart field (SHF), and RA deficiency was shown to posteriorly expand the SHF, which resulted in later disorganization of the heart tube (Ryckebusch et al.,

2008). Hox genes are involved in both cardiac fate transformations in the blastula and in the SHF, and studies have demonstrated that these genes are sensitive to RA levels. Expression of Hox genes are known to be regulated by

RA signaling, and even the expression of the posterior murine Hoxb genes

(Hoxb5-9) appear to be under regulation from long-range cis-regulatory RA response elements (Ahn et al., 2014). hoxb5b is a regulator of cardiac cell fate in zebrafish (Waxman et al., 2008) and can restrict the number of atrial cells in the developing heart field during the blastula stage. Mechanistic studies in rodents investigating the role of Hox genes in the SHF have shown that the

HoxA and HoxB members are expressed and play a role in heart development. 39

Deletion of murine HoxA and the HoxB cluster results in the failed looping of the developing heart (Soshnikova et al., 2013).

In the present study, developmental exposure to mITP in either the WT or ahr2-null backgrounds significantly decreased expression of five Hox genes

(hoxb5b, hoxb6b, hoxa5a, hoxc1a, and hoxb8b). The posterior hoxb genes were more sensitive to mITP bioactivity compared to the hoxa and hoxc genes.

Windowed exposure studies of mITP developmental toxicity showed that the heart defects only occur in zebrafish exposed to mITP from 6-48 hpf, which coincides with the time Hox genes are influencing heart development, either in determining the cardiac cell lineage or in the SHF (McGee et al., 2013). We surmised that inhibitory activity of mITP on RARs resulted in decreased Hox gene expression and subsequent heart malformations.

Possible cross-talk between AhR, Hif1α and Nrf2 in the transcriptome of ahr2-null zebrafish

The transcriptomic profiling of unexposed WT and ahr2-null zebrafish identified a total of 493 transcripts that were significantly differentially expressed in the absence of ahr2 (FDR adjusted P-value ≤ 0.05, fold change ≥ 1.5).

Surprisingly, the only upstream transcription factor prediction of the ahr2 transcriptome was NRF2. Examination of the NRF2 transcriptional hub showed a large regulatory network where NRF2 was primarily regulated by the AHR and

HIFIA (Figure 3). Alongside the expected decrease in ahr2 expression in ahr2- 40 null embryos we also observed a concomitant decrease in the known binding partner of inactive AhR, heat-shock protein 90 alpha. Pharmacologic inhibition of HSP90s has been shown to dampen the AhR response of the model polycyclic aromatic hydrocarbon, benzo[a]pyrene, including decreasing the induction of CYP1A and CYP1B (Hughes et al., 2008).

Nrf2 is a transcription factor and considered a master regulator of cellular response to a variety of stressors and can act, along with AhR, in the xenobiotic metabolism cascade to increase the expression of cytoprotective enzymes in response to reactive or oxidative metabolites formed by CYP activity

(Wakabayashi et al., 2010). There is likely bi-directional cross-talk between

NRF2 and AhR as evidenced by the xenobiotic response element in the NRF2 promotor (Miao et al., 2005), and functional antioxidant response elements in the AhR promoter (Shin et al., 2007; Hayes et al., 2009). Interestingly, our data show that 9 of the 12 genes (excluding AHR) that are predicted to be regulated by Nrf2 were significantly decreased in ahr2-null zebrafish (Figure 3). This suggests that basal ahr2 expression is involved in controlling proper Nrf2 expression and activity in zebrafish; helping to maintain the ability to respond to oxidative damage under normal conditions.

AhR and Hif1α are members of the class I bHLH/PAS protein family and both heterodimerize with the aryl hydrocarbon receptor nuclear translocator

(ARNT), a class II bHLH/PAS protein, as a means to regulate gene expression.

The availability of ARNT influences the activity of these two transcription factors 41 likely through nuclear receptor co-factor squelching. There is an antagonistic relationship between AhR and Hif1α, where induction of hypoxia attenuates the normal cellular response to AhR ligands, and, conversely, ligand activation of

AhR diminishes the Hif1α response under hypoxic conditions (Vorrink and

Domann, 2014). As we only measured the mRNA levels of Hif1α and not its activity, we can only assume that the increased expression of HIF1α mRNA is attributed, in part, to the increased availability of ARNT due to the loss of ahr2.

Whether this expression change translates to increased Hif1α activity or a higher sensitivity to hypoxia remains unclear.

Taken together, our comparative transcriptomics analysis in vehicle- exposed WT and ahr2-null embryonic zebrafish shows that ahr2 may function to maintain proper Nrf2 signaling in the absence of oxidative stress and that the endogenous expression of both Hif1α and AhR appear to be in competition for the pool of freely available ARNT. The roles of Nrf2 and Hif1α signaling in ahr2- null zebrafish require further study; however, it is possible that the disruption of these two signaling cascades due to loss of ahr2 manifests as the reproductive irregularities and embryonic and adult malformations that are observed in the ahr2-null zebrafish line (Goodale et al., 2012).

Conclusion mITP is a potent ligand for the AhR (McGee et al., 2013; Gerlach et al.,

2014). The transcriptional profiling reported here revealed a robust ahr2- 42 dependent transcriptional signature predominated by transcripts and functional biological processes related to xenobiotic metabolism and an organism-wide response to exogenous stimuli in WT embryos. mITP elicited expression changes in three enzymes involved in the synthesis and degradation of RA and mITP exposure resulted in a strong decrease in the expression of many Hox genes, which are regulated by RA and have known roles in heart development.

Based on these observations, we hypothesized that mITP is an inhibitor of

RARs, causing downstream transcriptional changes that ultimately result in the development of heart defects, and that ligand activation of AhR by mITP results in an adaptive rather than toxic AhR response. Inhibition of RAR signaling by mITP would lead to disruption of RA processing and metabolism, which has the potential to shift the boundaries of Hox gene expression in developing zebrafish and influence cardiac cell fate or recruitment of cells to the elongating heart tube, resulting in the observed cardiotoxicity (Waxman et al., 2008; Waxman and Yelon, 2009; Bertrand et al., 2011; Nolte et al., 2013; Laforest et al., 2014).

We must acknowledge that these all of these hypotheses require several additional studies for validation; however, the data presented here offer several possibilities for future studies that could easily be performed.. Confirmation that mITP is an RAR inhibitor is a priority next step. So too is confirmation that the hypothesized expression changes in Hox gene expression following mITP exposure are causally involved in cardiotoxicity. 43

Acknowledgements We are grateful to Dr. David Volz for kindly gifting us the stock of mITP used in our experiments, the staff at the Sinnhuber Aquatic Research

Laboratory for help and support with zebrafish husbandry, spawning, and embryo screening, and Cory V. Gerlach for assistance with mITP exposures, sample collections, and RNA isolation for the microarray study. We would also like to thank Dr. Lisa Truong, Michael Simonich, and Jane LaDu for assistance with manuscript preparation and editing.

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Figure 2- 1. Transcriptome profiling of 0.2 µM mITP exposure. A) Bi-hierarchically clustered heatmap of the mITP-induced significant transcriptional changes in WT and ahr2-null zebrafish (FDR adjusted P-value ≤ 0.05; fold change ≥ 1.5). Cluster 1 consists of a group of significantly increased transcripts that were primarily only observed in WT samples, and are indicative of an adaptive AhR response. Cluster 2 consists of a group of significantly decreased transcripts that were similarly affected by mITP in both WT and ahr2- null samples. B) Clustered GO enrichment analysis of mITP-induced transcriptional changes (FDR adjusted P-value ≤ 0.05). GO clusters with overlapping terms were manually annotated using the most common terms. Nodes represent specific GO terms and the edges represent overlap in gene members between GO terms, with the size of nodes and edges correlating to the number of genes within that GO term or overlap, respectively. The inner circle of each node represents WT samples and is colored red to indicate a significantly enriched GO term, whereas the outer circle represents ahr2-null samples and is colored blue to indicate a significantly enriched GO term. Non- significant GO terms for either genotype are colored grey.

50

Figure 2- 2. mITP-induced AhR-dependent and independent transcriptional network. Comparative gene-gene interaction network between WT (red ellipse) and ahr2- null (blue ellipse) samples using the Shortest Paths algorithm in MetaCore. Nodes represent genes and edges represent curated interactions. Red nodes indicate a significantly increased expression, blue nodes represent significantly decreased expression, and grey indicates no significant expression.

51

Figure 2- 3. The transcriptional network of the predicted transcription factor, NRF2. The gene-gene interaction network of the only predicted regulator of the ahr2- null transcriptome. Genes are represented as nodes and edges are curated interactions between connected genes. Nodes are colored based on the fold change expression for that gene where red, blue, and grey indicate increased, decreased, or no significant expression, respectively.

Table S2- 1. Overlapping significant differentially expressed transcripts in WT and ahr2-null samples. ProbeName GeneName zebrafish_ensemble_gene human_ensembl_gene WT_logFC WT_adj_pValue ahr2_null_logFC ahr2_null_adj_pValue A_15_P100578 cyp1a ENSDARG00000026039 ENSG00000140465 3.88 5.22E-04 2.97 7.57E-03 A_15_P659656 cyp1a ENSDARG00000026039 ENSG00000140465 3.12 1.53E-04 1.62 3.31E-02 A_15_P164531 mrpl20 ENSDARG00000090462 ENSG00000242485 0.79 6.27E-05 -0.71 4.47E-04 A_15_P738255 parn ENSDARG00000024702 ENSG00000140694 0.78 5.51E-05 -0.65 6.68E-04 A_15_P709476 AL926183 0.77 9.23E-05 -0.66 9.14E-04 A_15_P134611 LOC793066 ENSDARG00000079843 ENSG00000211599 0.74 1.67E-04 -0.61 1.55E-03 A_15_P176596 rgs5b ENSDARG00000017860 ENSG00000143248 0.73 2.84E-05 -0.61 3.83E-04 A_15_P774716 marveld2b ENSDARG00000061651 ENSG00000152939 0.72 5.05E-04 -0.61 3.59E-03 A_15_P763151 TC389889 0.72 1.56E-05 -0.61 2.43E-04 A_15_P397440 NP13323369 0.7 7.22E-04 -0.65 2.65E-03 A_15_P205886 zgc:153031 ENSDARG00000068876 ENSG00000178700 0.7 4.23E-05 -0.59 4.76E-04 A_15_P139518 acot9.2 ENSDARG00000058390 ENSG00000123130 0.68 1.87E-04 -0.62 1.06E-03 A_15_P745896 spag1b ENSDARG00000088340 ENSG00000025772 0.67 4.75E-04 -0.59 2.62E-03 A_15_P518032 exosc3 ENSDARG00000029187 ENSG00000107371 0.65 5.08E-05 -0.62 2.85E-04 A_15_P656636 wbp11 ENSDARG00000017046 ENSG00000084463 0.65 4.54E-04 -0.59 1.92E-03 A_15_P195076 zgc:77235 0.62 4.55E-05 -0.6 2.34E-04 A_15_P658711 zgc:73144 0.59 1.87E-04 -0.61 4.05E-04 A_15_P422030 gngt1 ENSDARG00000035798 ENSG00000127920 -0.64 2.69E-02 -0.76 2.76E-02 A_15_P113864 dhrs3b ENSDARG00000044803 ENSG00000162496 -0.68 3.31E-04 -0.78 3.52E-04 A_15_P757496 rho ENSDARG00000002193 ENSG00000163914 -0.74 3.10E-02 -1.09 9.55E-03 A_15_P170311 rho ENSDARG00000002193 ENSG00000163914 -0.79 5.94E-03 -1.38 3.06E-04 A_15_P169241 dhrs3a ENSDARG00000044982 ENSG00000162496 -0.86 4.25E-04 -0.61 1.02E-02 A_15_P166861 hoxb8b ENSDARG00000054025 ENSG00000120068 -0.87 1.18E-05 -0.6 7.26E-04 A_15_P728776 rho ENSDARG00000002193 ENSG00000163914 -0.88 1.92E-02 -2.3 1.08E-04 52

A_15_P747151 rho ENSDARG00000002193 ENSG00000163914 -0.89 3.09E-02 -2.26 2.43E-04 A_15_P111662 hoxb5a ENSDARG00000013057 ENSG00000106004 -1.01 2.91E-06 -0.78 1.08E-04 A_15_P213761 rho ENSDARG00000002193 ENSG00000163914 -1.01 1.69E-02 -2.07 4.05E-04 A_15_P737699 rho ENSDARG00000002193 ENSG00000163914 -1.03 1.80E-02 -2.1 4.47E-04 A_15_P130816 hoxa5a ENSDARG00000001784 ENSG00000106004 -1.04 1.46E-05 -0.64 1.69E-03 A_15_P630616 hoxb5a ENSDARG00000013057 ENSG00000106004 -1.24 1.80E-07 -0.91 1.59E-05 A_15_P630536 hoxb6b ENSDARG00000026513 ENSG00000108511 -1.24 3.49E-05 -0.84 1.69E-03 A_15_P657036 hoxb6b ENSDARG00000026513 ENSG00000108511 -1.27 6.62E-06 -1.2 5.76E-05 A_15_P626126 hoxc1a ENSDARG00000070337 ENSG00000128645 -1.32 2.32E-06 -0.65 1.55E-03 A_15_P130881 hoxb6b ENSDARG00000026513 ENSG00000108511 -1.34 6.62E-06 -1.22 6.67E-05 A_15_P131581 hoxb5b ENSDARG00000054030 ENSG00000106004 -1.39 6.08E-07 -1.14 1.59E-05 A_15_P162866 cyp26a1 ENSDARG00000033999 ENSG00000095596 -1.53 6.27E-05 -0.72 3.34E-02 A_15_P110118 cyp26a1 ENSDARG00000033999 ENSG00000095596 -1.6 3.82E-05 -0.89 7.34E-03 A_15_P632471 hoxb5b ENSDARG00000054030 ENSG00000106004 -1.87 1.18E-08 -1.41 9.60E-07

53

Table S2- 2. Overlappin significant differentially expressed transcripts in WT and ahr2-null samples. Processes Total In Term p-value FDR Total In Data Network Objects from Data response to 178 1.57E-05 4.20E-02 10 Aquaporin 4, CRP, AHR, MMP-9, Annexin I, CTGF, Par-4, CYP1B1, TNF-R1, estradiol SERT system process 1156 4.14E-05 4.20E-02 27 Aquaporin 4, CRP, L-FABP, AHR, CHASM, Gamma crystallin D, HOXD1, Annexin I, CD13, Angiotensinogen, CFTR, Par-4, HOXA5, PMCA2b, MYBPC2, HOXB8, ASCT1 (SLC1A4), CYP1B1, DHRS3, SDF-1, Myocilin, ACTA1, Rhodopsin, I-FABP, CART, SERT, Tricellulin retinoid 62 4.23E-05 4.20E-02 6 RBP1, CYP26A1, CYP1B1, CYP1A1, DHRS3, Rhodopsin metabolic process response to 248 5.26E-05 4.20E-02 11 Aquaporin 4, CRP, AHR, MMP-9, Annexin I, CTGF, CFTR, Par-4, CYP1B1, estrogen TNF-R1, SERT response to 520 9.11E-05 4.20E-02 16 eIF2S1, CRP, SAMHD1, L-FABP, Lactoferrin, MMP-9, Par-4, CYP1A1, other organism CLECSF9, TNF-R1, SDF-1, TBK1, ASB2, HMGI/Y, UGT1A6, IFITM3 response to 521 9.31E-05 4.20E-02 16 eIF2S1, CRP, SAMHD1, L-FABP, Lactoferrin, MMP-9, Par-4, CYP1A1, external biotic CLECSF9, TNF-R1, SDF-1, TBK1, ASB2, HMGI/Y, UGT1A6, IFITM3 stimulus multicellular 4944 9.64E-05 4.20E-02 72 RRP40, CYP24A1, RPS14, Aquaporin 4, CRP, VANGL2, Hox-B6, C16orf45, organismal L-FABP, CBX7, A1M, dUTPase (DUT), eEF2K, AHR, RAD23B, CHASM, process Gamma crystallin D, Beta crystallin S, HOXD1, Cathepsin L, Lactoferrin, SERP1, MMP-9, APOA5, Annexin I, CD13, MEOX1, CTGF, LBH, Angiotensinogen, CFTR, CRMP1, Par-4, HOXA5, PARN, PMCA2b, CYP26A1, MMP-8, TIMP2, MYBPC2, HOXB8, Cathepsin S, WFIKKN1, ASCT1 (SLC1A4), CYP1B1, CYP1A1, CLECSF9, RRM1, CETP, TNF-R1, DHRS3, SDF-1, TBK1, Myocilin, ACTA1, ITM2B, HOXD4, Rhodopsin, ZnT4, ERP5, ASB2, HMGI/Y, UGT1A6, MNK2(GPRK7), I-FABP, CART, IFITM3, FKHL1, SERT, Tricellulin, DSCR6, Factor D regulation of 1028 1.22E-04 4.20E-02 24 RRP40, eIF2S1, CRP, SAMHD1, Olfactomedin-4, L-FABP, A1M, AHR, immune system RBP1, Cathepsin L, Lactoferrin, MMP-9, Annexin I, Hemopexin, Par-4, process HOXA5, HOXB8, Cathepsin S, CLECSF9, SDF-1, TBK1, ASB2, CART, Factor D diterpenoid 75 1.24E-04 4.20E-02 6 RBP1, CYP26A1, CYP1B1, CYP1A1, DHRS3, Rhodopsin metabolic process defense 228 1.28E-04 4.20E-02 10 eIF2S1, CRP, SAMHD1, L-FABP, Lactoferrin, CLECSF9, TNF-R1, TBK1, response to ASB2, IFITM3 other organism response to 543 1.51E-04 4.20E-02 16 eIF2S1, CRP, SAMHD1, L-FABP, Lactoferrin, MMP-9, Par-4, CYP1A1, biotic stimulus CLECSF9, TNF-R1, SDF-1, TBK1, ASB2, HMGI/Y, UGT1A6, IFITM3 54

response to 1693 1.79E-04 4.20E-02 33 CYP24A1, eIF2S1, CRP, VANGL2, SAMHD1, L-FABP, FBXL3, RBP1, external Lactoferrin, MMP-9, Annexin I, Angiotensinogen, CRMP1, Par-4, PMCA2b, stimulus CYP1B1, CYP1A1, CLECSF9, TNF-R1, DHRS3, SDF-1, TBK1, ACTA1, Rhodopsin, ZnT4, ASB2, HMGI/Y, UGT1A6, CART, IFITM3, FKHL1, SERT, ADSSL1 terpenoid 81 1.90E-04 4.20E-02 6 RBP1, CYP26A1, CYP1B1, CYP1A1, DHRS3, Rhodopsin metabolic process response to 1059 1.93E-04 4.20E-02 24 PGHD, Aquaporin 4, CRP, eEF2K, AHR, Gamma crystallin D, Cathepsin L, hormone MMP-9, APOA5, Annexin I, CTGF, AATC, CFTR, Par-4, IBP1, TIMP2, Cathepsin S, G-protein gamma 11, CYP1B1, TNF-R1, SDF-1, ACTA1, UGT1A6, SERT response to 29 2.15E-04 4.20E-02 4 RBP1, MMP-9, CYP1A1, ZnT4 vitamin A xenobiotic 118 2.23E-04 4.20E-02 7 CYP24A1, AHR, AHRR, CYP26A1, CYP1B1, CYP1A1, UGT1A6 metabolic process regulation of 12 2.25E-04 4.20E-02 3 Olfactomedin-4, Par-4, SDF-1 cellular extravasation regulation of 12 2.25E-04 4.20E-02 3 Angiotensinogen, Par-4, CART transmission of nerve impulse cellular 121 2.60E-04 4.60E-02 7 CYP24A1, AHR, AHRR, CYP26A1, CYP1B1, CYP1A1, UGT1A6 response to xenobiotic stimulus response to 125 3.17E-04 4.73E-02 7 CYP24A1, AHR, AHRR, CYP26A1, CYP1B1, CYP1A1, UGT1A6 xenobiotic stimulus omega- 3 3.19E-04 4.73E-02 2 CYP1B1, CYP1A1 hydroxylase P450 pathway response to 1106 3.69E-04 4.73E-02 24 eIF2S1, Aquaporin 4, CRP, OSR1, L-FABP, eEF2K, FBXL3, RBP1, MMP-9, abiotic stimulus Annexin I, CTGF, Angiotensinogen, Par-4, PMCA2b, Cathepsin S, CYP1A1, RRM1, TNF-R1, DHRS3, SDF-1, ACTA1, Rhodopsin, SERT, ADSSL1 response to 531 3.86E-04 4.73E-02 15 PGHD, Aquaporin 4, CRP, AHR, MMP-9, Annexin I, CTGF, AATC, CFTR, steroid Par-4, CYP1B1, TNF-R1, ACTA1, UGT1A6, SERT hormone hormone 130 4.02E-04 4.73E-02 7 RBP1, CD13, Angiotensinogen, CYP26A1, CYP1B1, CYP1A1, DHRS3 metabolic process 55

positive 34 4.03E-04 4.73E-02 4 L-FABP, APOA5, Annexin I, Angiotensinogen regulation of fatty acid metabolic process single- 4577 4.19E-04 4.73E-02 66 RRP40, CYP24A1, RPS14, Aquaporin 4, CRP, VANGL2, Hox-B6, C16orf45, multicellular L-FABP, CBX7, dUTPase (DUT), eEF2K, AHR, RAD23B, CHASM, Gamma organism crystallin D, Beta crystallin S, HOXD1, Cathepsin L, Lactoferrin, SERP1, process MMP-9, APOA5, Annexin I, CD13, MEOX1, CTGF, LBH, Angiotensinogen, CFTR, CRMP1, Par-4, HOXA5, PMCA2b, CYP26A1, MMP-8, TIMP2, HOXB8, Cathepsin S, WFIKKN1, CYP1B1, CYP1A1, CLECSF9, RRM1, CETP, TNF-R1, DHRS3, SDF-1, TBK1, Myocilin, ACTA1, ITM2B, HOXD4, Rhodopsin, ZnT4, ERP5, ASB2, UGT1A6, MNK2(GPRK7), I-FABP, CART, IFITM3, FKHL1, SERT, DSCR6, Factor D negative 62 4.48E-04 4.73E-02 5 OSR1, MMP-9, COMMD1 (MURR1), Angiotensinogen, Par-4 regulation of ion transmembrane transport cellular 62 4.48E-04 4.73E-02 5 eEF2K, AHR, CFTR, CYP1B1, GPD1 response to cAMP positive 15 4.54E-04 4.73E-02 3 APOA5, Annexin I, Angiotensinogen regulation of fatty acid biosynthetic process macromolecula 15 4.54E-04 4.73E-02 3 APOA5, Angiotensinogen, CETP r complex remodeling plasma 15 4.54E-04 4.73E-02 3 APOA5, Angiotensinogen, CETP lipoprotein particle remodeling protein-lipid 15 4.54E-04 4.73E-02 3 APOA5, Angiotensinogen, CETP complex remodeling aging 318 4.64E-04 4.73E-02 11 eIF2S1, CRP, SERP1, MMP-9, CTGF, Angiotensinogen, IBP1, TIMP2, CYP1A1, HMGI/Y, FKHL1 56

Table S2- 3. Overlappin significant differentially expressed transcripts in WT and ahr2-null samples. Processes Total In Term p-value FDR Total In Data Network Objects from Data sensory perception 428 1.01E-05 1.08E-02 8 HOXD1, HOXB8, Rhodopsin, S-arrestin, ROM1, Tricellulin, DHRS3, Gamma crystallin D retinoid metabolic 62 1.92E-05 1.08E-02 4 CYP26A1, Rhodopsin, CYP1A1, DHRS3 process diterpenoid metabolic 75 4.08E-05 1.53E-02 4 CYP26A1, Rhodopsin, CYP1A1, DHRS3 process terpenoid metabolic 81 5.52E-05 1.56E-02 4 CYP26A1, Rhodopsin, CYP1A1, DHRS3 process visual perception 180 9.38E-05 1.90E-02 5 Rhodopsin, S-arrestin, ROM1, DHRS3, Gamma crystallin D sensory perception of 183 1.01E-04 1.90E-02 5 Rhodopsin, S-arrestin, ROM1, DHRS3, Gamma crystallin D light stimulus isoprenoid metabolic 102 1.36E-04 2.19E-02 4 CYP26A1, Rhodopsin, CYP1A1, DHRS3 process embryonic skeletal 119 2.46E-04 3.46E-02 4 HOXD1, HOXB8, Hox-B6, HOXA5 system development negative regulation of 12 4.30E-04 4.89E-02 2 CYP26A1, DHRS3 retinoic acid receptor signaling pathway neurological system 734 4.52E-04 4.89E-02 8 HOXD1, HOXB8, Rhodopsin, S-arrestin, ROM1, Tricellulin, DHRS3, process Gamma crystallin D system process 1156 4.78E-04 4.89E-02 10 HOXD1, HOXB8, Rhodopsin, S-arrestin, ROM1, PRKAR2A, Tricellulin, DHRS3, HOXA5, Gamma crystallin D 57

58

CHAPTER 3 - PHENOTYPICALLY ANCHORED TRANSCRIPTOME PROFILING OF DEVELOPMENTAL EXPOSURE TO THE ANTIMICROBIAL AGENT, TRICLOSAN, REVEALS HEPATOTOXICITY IN EMBRYONIC ZEBRAFISH

Derik E. Haggard1, Pamela D. Noyes1, 2, Katrina M. Waters3, and Robert L. Tanguay1*

1 Department of Environmental and Molecular Toxicology, Oregon State University, Corvallis, OR 2 Office of Science Coordination and Policy (OSCP), Office of Chemical Safety and Pollution Prevention, U.S. Environmental Protection Agency, Washington, DC 3 Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA

Reprinted with permission of Toxicology and Applied Pharmacology. All rights reserved.

59

Abstract Triclosan (TCS) is an antimicrobial agent commonly found in a variety of personal care products and cosmetics. TCS readily enters the environment through wastewater and is detected in human plasma, urine, and breast milk due to its widespread use. Studies have implicated TCS as a disruptor of thyroid and estrogen signaling; therefore, research examining the developmental effects of TCS is warranted. In this study, we used embryonic zebrafish to investigate the developmental toxicity and potential mechanism of action of

TCS. Embryos were exposed to graded concentrations of TCS from 6-120 hours post-fertilization (hpf) and the concentration where 80% of the animals had mortality or morbidity at 120 hpf (EC80) was calculated. Transcriptomic profiling was conducted on embryos exposed to the EC80 (7.37 µM). We identified a total of 922 significant differentially expressed transcripts (FDR adjusted p-value ≤ 0.05; fold change ≥ 2). Pathway and gene ontology enrichment analyses identified biological networks and transcriptional hubs involving normal liver functioning, suggesting TCS may be hepatotoxic in zebrafish. Tissue-specific gene enrichment analysis further supported the role of the liver as a target organ for TCS toxicity. We also examined the in vitro bioactivity profile of TCS reported by the ToxCast screening program. TCS had a diverse bioactivity profile and was a hit in 217 of the 385 assay endpoints we identified. We observed similarities in gene expression and hepatic steatosis

60 assays; however, hit data for TCS were more concordant with the hypothesized

CAR/PXR activity of TCS from rodent and human in vitro studies.

Keywords: Triclosan, Zebrafish, Transcriptomics, Phenotypic anchoring,

Hepatotoxicity, ToxCast

Abbreviations: Triclosan, TCS; 80% effective concentration, EC80; 50% effective concentration, EC50; 20% effective concentration, EC20; hours post- fertilization, hpf

61

Introduction Triclosan (TCS) is a commonly used antimicrobial agent found in many personal care products including hand soaps, toothpastes, various cosmetics, textiles, and some plastics. TCS enters the environment through the general use and disposal of TCS-containing products, and is commonly found in wastewater effluent. Incomplete removal of TCS from wastewater treatment plants, and use of biosolids as soil amendments, results in the release of TCS into aquatic and terrestrial environments (Andrade et al., 2015; Barber et al.,

2015; Davis et al., 2015; Dhillon et al., 2015; Kerrigan et al., 2015). Due to its prevalence in many consumer products, a large proportion of humans are directly exposed to TCS. In urine samples collected by the National Health and

Nutrition Examination Survey in 2003-2004, TCS was detected in 74.6% of samples from adults and children 6 years or older with a range of 2.3-3790 µg/L, which was attributed to differing degrees of elective use of TCS-containing products (Calafat et al., 2008). Additionally, TCS has been detected in both adult and infant blood and breast milk, increasing concern for potential risk to human development (Allmyr et al., 2006; Calafat et al., 2008; Pycke et al., 2014;

Arbuckle et al., 2015; Azzouz et al., 2016; Han et al., 2016).

Efforts among regulatory agencies seek to gain a better understanding of potential hazards from chemicals that are prevalent in the environment or that lack safety information. Leading this effort is the US Environmental Protection

Agency National Center for Computational Toxicology’s Toxicity Forecaster

62 screening program (ToxCast) (Dix et al., 2007). ToxCast provides comprehensive high-throughput bioactivity data across a wide spectrum of toxicological and biological pathways and was developed to screen and prioritize thousands of chemicals; identifying those that pose the most hazard to human health and the environment for more testing. As part of the ToxCast program, a population-based in vitro-to-in vivo extrapolation model was developed to estimate oral equivalent doses (OED) for 35 environmental chemicals, including TCS (Rotroff et al., 2010). Only TCS and one other chemical from this screen had equivalent doses (average OED of three ToxCast assays: 0.0096 mg/kg/day) that overlapped with estimated chronic aggregate human exposure levels (0.13 mg/kg/day). Due to the concerns about human exposure, bioactivity, and potential unknown developmental effects, the use of

TCS in consumer products has come into question. The FDA is currently evaluating the safety and efficacy of TCS (Bergstrom, 2014; Kuehn, 2014), the state of Minnesota recently banned the sale of TCS-containing products beginning in 2017, and several companies (including Johnson & Johnson,

Colgate-Palmolive, and Avon) have or are in the process of removing TCS from their products.

Concerns have been raised about the potential for TCS to perturb endocrine functioning given the structural similarities of TCS to thyroid hormones and some estrogens. In terms of thyroid bioactivity, studies in amphibians have shown that exposure to TCS accelerates thyroid hormone-

63 dependent metamorphosis and alters expression of several amphibian thyroid hormone-dependent biomarker genes, including increases in proliferating cell nuclear antigen and decreases in thyroid receptor beta (Veldhoen et al., 2006;

Marlatt et al., 2013). In contrast, exposures to TCS caused a dose-dependent decrease in total serum thyroxine (T4) levels in rats at various life stages

(Crofton et al., 2007; Zorrilla et al., 2009; Paul et al., 2010a; Paul et al., 2010b;

Stoker et al., 2010; Paul et al., 2012; Axelstad et al., 2013). It has been hypothesized that TCS decreases serum T4 through increased hepatic catabolism mediated by activation of the constitutive androstane receptor (CAR) and/or pregnane X receptor (PXR) xenobiotic metabolism pathways (Crofton et al., 2007). Activation of these receptor pathways leads to upregulation in phase

I and II enzymes including several cytochrome P450s, sulfotransferases, and

UDP-glucuronosyltransferases (UGTs) which enhances the elimination of circulating T4 by the liver. Indeed, TCS activated the human PXR and differentially affected human CAR isoforms (Jacobs et al., 2005; Paul et al.,

2013). Furthermore, in vivo studies in rats exploring this hypothesis have shown transcriptional activation of Cyp2b and Cyp3a, and increased PROD and UGT enzymatic activity (Zorrilla et al., 2009; Paul et al., 2012). Studies that have targeted TCS estrogenic bioactivity have shown that it interacts with the ER.

Although there are conflicting in vitro studies showing that TCS has estrogenic and anti-estrogenic activity, the majority of in vivo studies have demonstrated that TCS is a potential ER agonist (Ishibashi et al., 2004; Ahn et al., 2008; Gee

64 et al., 2008; Stoker et al., 2010; Huang et al., 2014a; Yueh et al., 2015; Yueh and Tukey, 2016). TCS increased hepatic vitellogenin gene expression in adult male medaka (Ishibashi et al., 2004), as well as led to precocious puberty in female rats and enhanced the effects of ethinyl estradiol in a rat pubertal study and uterotrophic assay, respectively (Stoker et al., 2010; Louis et al., 2013).

TCS also increased estrogen response element luciferase activity in transfected rat pituitary GH3 cells (Jung et al., 2012). In the same study, three-day exposures to TCS significantly increased uterine weight, C3 expression, and uterine CaBP-9k expression in immature rats (Jung et al., 2012). Collectively, these data suggest that TCS disrupts endocrine activity, and more research is needed to fully elucidate the mechanisms of the observed endocrine effects.

The zebrafish is an ideal in vivo model for studying the developmental and reproductive toxicity of chemicals. It’s rapid, external development and transparency make it amenable to high-throughput chemical screens examining a wide range of biological endpoints (Padilla et al., 2012; Truong et al., 2014;

Noyes et al., 2015). Zebrafish share high genetic homogeneity to humans, with

71% of human genes having an ortholog (Howe et al., 2013), and many developmental and biological signaling pathways conserved between zebrafish and humans. The effects of TCS have previously been studied in both the developing and adult zebrafish (Tatarazako et al., 2004; Oliveira et al., 2009;

Padilla et al., 2012; Truong et al., 2014; Chen et al., 2015). Dietary exposure of adult zebrafish to TCS for 21 days resulted in hyperplasia of thyroid tissues, with

65 increased thyroid follicle size and number, and increased mRNA expression of the thyroid sodium-iodide symporter (NIS) and thyroid-stimulating hormone

(TSH) (Pinto et al., 2013). Developmental exposure to TCS caused changes in lipid droplet accumulation and decreased the expression of several transcripts involved in β-oxidation of fatty acids in larval zebrafish (Ho et al., 2016).

However, to our knowledge, no studies have taken a global, transcriptomic approach to examine the possible mechanism of the developmental toxicity of

TCS in zebrafish. Whole-genome transcriptomics in embryonic zebrafish represents an untargeted, unbiased method to investigate putative mechanisms of action for various compounds, as the full repertoire of the genome is expressed over the course of development. Transcriptomic studies of 48 hpf embryos have identified differential mechanisms and upstream transcriptional regulation after exposure to phenotypically anchored concentrations of polycyclic aromatic compounds and their oxygenated derivatives (Goodale et al., 2013; Goodale et al., 2015). Similarly, transcriptomic analysis of select estrogenic and anti-estrogenic compounds detected disruption of several distinct endocrine-related biological pathways, supporting the use of 48 hpf embryos in a mechanistic context to identify potential endocrine disrupting compounds (Schiller et al., 2013). For these reasons, we hypothesized that transcriptomics at 48 hpf would provide meaningful insight into the mechanism of TCS developmental toxicity in zebrafish and provide information regarding the endocrine disrupting potential of TCS.

66

Herein, we used a phenotypically anchored toxicogenomics approach to investigate the mechanism of toxicity after developmental exposure of embryonic zebrafish to TCS. Zebrafish were exposed from 6-120 hpf to TCS to identify the concentration at which 80% of exposed embryos have an adverse effect (EC80) in our model system, and whole-genome transcriptomics was conducted using high-density microarrays to examine the effects of TCS on the

48 hpf zebrafish transcriptome. TCS exposure induced robust transcriptional changes with a majority of transcripts being significantly decreased.

Downstream functional analysis of the transcriptional responses indicated disruption of many processes related to normal liver functioning, but no responses that would indicate disruption of thyroid or estrogen signaling at the early developmental life-stage tested.

Materials and Methods

Chemicals

Triclosan (CAS: 3380-34-5; 99.7%) was obtained from Sigma Aldrich

(catalog number PHR1338), and stock solutions were prepared in dimethyl sulfoxide (DMSO; Avantor Performance Materials, Center Valley, PA) at a concentration of 10 mM. For all experiments, the maximum DMSO percentage for all exposure concentrations, including control exposures, was 0.64%.

Zebrafish husbandry

67

Wild-type 5D zebrafish were used for all experiments. Zebrafish were maintained at the Sinnhuber Aquatic Research Laboratory on a 28°C recirculating water system with a 14:10 hour light/dark photoperiod. Embryos were collected in the morning from group spawns of adult zebrafish. In brief, large group tanks of adult zebrafish with a 1:1 male female ratio were set up one day prior to spawning. In the morning, embryos were collected from the tanks using an internal collection apparatus. Embryos were cleaned, age staged in spans of no more than one hour, and kept in petri dishes under the same conditions as adults prior to chemical exposure (Kimmel et al., 1995). Animal handling and use were conducted according to Institutional Animal Care and

Use Committee procedures at Oregon State University.

TCS exposure

At six hours post-fertilization, the chorions were removed and the embryos placed, one embryo per well, in 96-well microplates pre-filled with 100 µL embryo medium using our automated dechorionator and embryo placement systems (Mandrell et al., 2012). For all experiments, TCS stocks were dispensed to each well using the HP D300 Digital Dispenser and subsequently normalized to 0.64% DMSO (HP Development Company, L.P., Vancouver,

WA). For the initial developmental toxicity studies, zebrafish were exposed under static conditions to nominal concentrations of 1, 4, 6, 8 and 10 µM TCS in 0.64% DMSO or the 0.64% DMSO control for 6-120 hpf (n = 32 embryos per

68 exposure concentration). Mortality and morbidity were assessed at 24 and 120 hpf using a suite of 22 endpoints as previously described (Truong et al., 2011).

As adverse morphological effects in zebrafish provide little direct mechanistic information regarding a chemical’s toxicity and are often correlated with one another (Truong et al., 2014), we consolidated the mortality and morbidity data into an “effect” or “no effect” response across all exposure groups. As a result, each animal was assigned either a 1 or a 0 depending on whether it had any mortality/morbidity or had no adverse effect, respectively. Logistic regression analysis was performed (using the glm function for the binomial family with the logit model link function in R), and the EC80 was calculated based on the logistic curve using the dose.p function in R (R Core Team, 2016). For the microarray experiment, embryos were exposed to 0 or 7.37 µM TCS (the calculated EC80) from 6-48 hpf and RNA was isolated as described below. As the original chemical stock of TCS expired over the course of these studies after the microarray data had been collected, we repeated the EC80 experiment with a new chemical stock of TCS at 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 µM TCS in 0.64%

DMSO or the 0.64% DMSO control following the same exposure conditions and data analyses described above. For the EC80 time course study, embryos were exposed to 0 µM of 6.46 µM from 6-120 hpf. There were a total of 33 embryos in the 0 µM group and 143 embryos in the 6.46 µM group for this study. Mortality and morbidity were assessed at 24, 48, 72, 96, and 120 hpf. Images at each life

69 stage were taken using the Keyence BZ-X710 fluorescent microscope

(Keyence, Osaka, Japan).

Microarray processing

RNA was isolated from groups of 8 pooled embryos as described above with four biological replicates per treatment group. RNA integrity was determined using the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa

Clara, CA), with an allowable minimum RIN score of 7. Aliquots of 600 ng total

RNA for each sample were placed in RNAstable® tubes (Biomatrica, San

Diego, CA) and dried according to the manufacturer’s instructions for shipment to OakLabs GmBH (Hennigsdorf, Germany) for processing on ArrayXS

Zebrafish microarrays. Each array contains oligonucleotides for 48,370 coding and 8,075 non-coding sequences that are sourced from Ensembl ZV9 release

75. Upon arrival at the processing facility, samples were re-hydrated and the

Low Input Quick Amp WT Labeling Kit (Agilent Technologies) was used to generate cyanine 3-CT labeled cRNA according to the manufacturer’s protocols. Samples were hybridized to ArrayXS Zebrafish microarrays using the

Gene Expression Hybridization Kit (Agilent Technologies). Fluorescent signals on the microarrays were detected using the SureScan Microarray Scanner

(Agilent Technologies). After scanning, the image files were read and processed using Agilent Feature Extraction software (version 11) and raw data was provided for further in-house processing and analysis.

70

Pathway analysis

Since the ArrayXS Zebrafish platform uses Ensemble zebrafish transcript identifiers, we identified all orthologous human Ensembl gene identifiers using

Ensembl BioMart (http://www.ensembl.org/biomart/) based on the Zv9 (release

75) transcriptome and the Bioinformatics Resource Manager v 2.3 prior to pathway analysis (Tilton et al., 2012; Flicek et al., 2014). Of the 60,022 total unique probes, we were able to identify 41,143 orthologous human Ensembl gene identifiers (~69% mapping efficiency), 14,929 of which were unique. To better understand the functional consequences of TCS exposure, and to relate the observed transcriptional changes to human health and disease outcomes, we performed biological process network enrichment analysis and transcription factor prediction using MetaCore GeneGo software. Enrichment statistics are based on a hypergeometric distribution, where the P-value is the probability that transcripts in a differentially expressed gene set that map to a curated biological process, or are under regulation by a transcription factor, are overrepresented compared to the reference background set of genes, as we have done previously (Nikolsky et al., 2009; Waters et al., 2012). Only enriched biological process networks with a FDR-correct P-value ≤ 0.05 were considered significant. For the transcription factor analysis, predicted transcription factors with a P-value ≤ 5e-05 were considered significant.

Tissue-specific gene enrichment analysis

71

The orthologous human differentially expressed genes that were used in the MetaCore enrichment studies were converted to their associated gene symbol using Ensembl BioMart. After filtering for significant differential expression (adjusted P-value ≤ 0.05; fold change ≥ 2), the resulting 450 significant genes were used for tissue-specific gene enrichment analysis. We utilized the Tissue Specific Expression Analysis (TSEA) tool in order to perform tissue-specific gene enrichment analysis based on human tissue expression data (http://genetics.wustl.edu/jdlab/tsea/), which performs a Fisher’s exact test with Benjamini-Hochberg correction of the overlap between a user-supplied gene list and a reference gene list at different tissue specificity thresholds

(Dougherty et al., 2010; Xu et al., 2014; Wells et al., 2015). This tool was developed from data collected as part of the Genotype-Tissue Expression project, which includes RNA-seq data from 45 different tissues collected from

189 subjects. For 25 of these tissues, every gene identified was given tissue specificity P-values (pSI), based on a tissue-specific indexing method

(Dougherty et al., 2010; Xu et al., 2014). If a gene had a low pSI score for a specific tissue, its expression was highly enriched in that tissue compared to all other tissues. Enrichment statistics from the TSEA tool are visualized as sets of concentric hexagonal nodes, each representing a tissue type, and are hierarchically ordered as a dendrogram based on the similarity of tissue-specific genes across tissue types (described in Figure 2 of Wells, et al. (2015)). In brief, the size of the outermost hexagon of each node is proportional to the total

72 number of tissue-specific transcripts enriched in that particular tissue. Smaller hexagons within each tissue node represent a more stringent threshold of tissue-specific expression pSI values (0.05, 0.01, 0.001, and 0.001, respectively), and are indicative of increasing degrees of tissue-specific gene sets. The color of each hexagon is related to the FDR-corrected P-value from the Fisher’s exact tests of the overlap between the user-supplied differentially expressed gene list and the reference gene lists. Striped colors represent a low degree of tissue specificity in the reference gene set. A total of 450 TCS-induced differentially expressed genes were analyzed.

Quantitative real-time PCR

To provide further support that the robust gene expression response observed in the microarray study pertains to the mechanism of action of TCS, we performed quantitative RT-PCR (qRT-PCR) on fifteen significant differentially expressed genes from the microarray data at the EC20, EC50, and

EC80 of TCS based on the repeated EC80 developmental study. Zebrafish embryos were exposed to 0, 3.62, 5.04, or 6.46 µM TCS and RNA was isolated as described above at 48 hpf. Primers for the target genes are listed in

Supplementary Table 1. cDNA was generated from total RNA using the ABI

High-capacity cDNA Reverse Transcription kit (Thermo Fisher, Waltham, MA) following the manufacturer’s protocol. qRT-PCR was conducted in 12.5 µl reactions consisting of 6.25 µl ABI Power SYBR Green PCR Master Mix

73

(Thermo Fisher), 3.25 µ Hl2O, 0.5 µl of each primer, and 2 µl of cDNA (12.5 ng/µl) using the StepOnePlus Real-Time PCR system (Thermo Fisher). Cycling conditions were used according to manufacturer’s instructions. Fold-change measurements relative to control samples were calculated using the method described in Pfaffl (Pfaffl, 2001), using β-actin for normalization. Four biological replicates per treatment group were used for qRT-PCR studies. Data were statistically analyzed in R using a one-way ANOVA with Tukey post-hoc test or the Kruskal-Wallis test with Dunn’s post-hoc test for data that passed or failed normality testing, respectively. We qualitatively compared the microarray and the qRT-PCR data by taking the log2 fold-change value for probes that mapped to the same Ensembl zebrafish gene ID from the microarray and the average fold change value from the qRT-PCR study. qRT-PCR data and statistics are provided in Supplementary Table 2.

ToxCast in vitro bioactivity data analysis

TCS was part of the ToxCast phase I chemical library, and in vitro bioactivity data are publicly available for this chemical across a large number of screening assays. To our knowledge, data from this program represent the most comprehensive in vitro bioactivity assessment of TCS. As such, we were interested in examining the bioactivity profile of TCS across ToxCast to determine whether there were similarities in responses with our transcriptomic analysis in terms of gene expression signatures and predicted transcription

74 factor activities. All of the ToxCast bioactivity data for TCS was downloaded from the interactive Chemical Safety for Sustainability dashboard (iCSS; http://actor.epa.gov/dashboard/#chemical/3380-34-5; prod_dashboard_v2)

(USEPA, 2015). Only bioactivity data from assays that were tested at three or more concentrations were used. Data were filtered based on background control assays, assay endpoints in which the detected signal direction

(gain/loss) was not developed or validated for that specific assay. Since we are interested in hit percentages rather than calculated AC50 values, for assays in which there was a positive hit-call in only one statistical model, we retained the data for the positive hit-call only. In the case where data for an assay had no hit-call or was a hit for both models, we retained only the first instance of that assay. The ToxCast assays are organized by different annotation terms which contain specific information about each assay including manufacturer, cell type used, assay detection technology, biological process, and intended target family

(e.g. cytokine, nuclear receptor, etc.). The positive hit-call data for TCS were visualized based on three annotation levels defined by the ToxCast program: biological process target, intended target family, and nuclear receptors from the intended target family annotation level. Data were visualized using a radial pie chart using custom R code and the plotrix package (Lemon, 2006). We only report assay data that had a positive hit for each annotation level. However, all data are available in the supplement (Supplementary Table 3, 4, and 5).

75

Results

Evaluating the developmental toxicity of TCS and computing an EC80

Developmental exposure to 8 and 10 µM TCS resulted in a high prevalence of 24 hpf developmental delay (DP24) followed by 120 hpf mortality

(MORT; Figure 1A). Exposure to 1, 4, and 6 µM TCS was not associated with mortality but resulted in a variety of malformations including jaw, axial, caudal fin, pectoral fin, eye, jaw, snout, yolk sac edema, and pericardial edema defects with a total percent prevalence of 12.5%, 28.1%, and 40.6%, respectively, for any adverse effect (Figure 1A). We identified the concentration of TCS associated with an 80% incidence of morphological and mortality effects to be

7.37 µM, which was used in the gene expression studies described below

(Figure 1B; EC80 indicated by dashed lines). Since we are pooling larvae for

RNA collection before they display the full range of endpoints, it is critical to ensure that the selected TCS concentration used for transcriptome analysis results in a large majority of the larvae being affected three days later. This increases the probability that the gene expression changes measured will be causally related to later adverse responses. It was also important to use a high effect concentration in an attempt to maximize the transcriptional signal-to-noise ratio, which can be problematic when using fluorescence intensity-based microarray technologies. Selecting too low a concentration for the array study

(e.g. an EC10 or EC20) would mean that we would be measuring the expression

76 levels from mostly normal, unaffected fish. As a result, it would be more difficult to observe strong transcriptional signatures indicative of an effect as it would be masked by the expression profiles of embryos within that concentration group that are unaffected by the exposure.

Transcriptome profiling of TCS exposure

A total of 2646 transcripts were significantly differentially expressed after exposure to TCS compared to control (FDR-adjusted P-value ≤ 0.05). For downstream functional analysis, we focused only on transcripts with a fold change ≥ 2.0. This filtering resulted in 165 and 757 elevated or repressed transcripts, respectively. To visualize the expression profile, we generated a heatmap with bi-directional hierarchical clustering to see the treatment differences as well as clustering of similarly differentially expressed transcripts

(Figure 2). TCS exposure produced a strong transcriptional profile, highlighted by the separation of the TCS-treated and control replicates, with two primary transcript clusters that reflected either increased or decreased differential expression. Transcripts within the significantly increased cluster were associated with nucleosome assembly processes, whereas the cluster of decreased transcripts consisted of transcripts involved in oxygen transport, hypoxia, blood coagulation, platelet activation, hexose metabolism, glycolysis, nucleic acid biosynthesis or metabolism, and eye morphogenesis (Figure 2).

Pathway analysis of TCS

77

Of the 922 significantly differentially expressed transcripts with a fold change ≥ 2.0 from the microarrays, we were able to identify 476 unique human orthologous genes using a combination of Ensembl BioMart and the

Bioinformatics Resource Manager software. As shown in Table 1, we observed several significantly enriched processes involved in immune system development such as the Kallikrein-kinin and complement systems, and IL-6 signaling pathways. TCS exposure also affected processes involved in insulin signaling, blood coagulation, bile acid regulation of lipid metabolism and negative FXR-dependent regulation of bile acids concentration, visual perception, and synaptic vesicle exocytosis processes. To identify any potential regulators of the observed transcriptional changes induced by exposure to TCS, we also performed transcription factor prediction analysis. A total of 16 transcription factors were predicted to regulate many of the significant differentially expressed transcripts affected by developmental exposure to TCS

(p ≤ 5e-05; Table 2). Four hepatocyte nuclear factor (HNF) transcription factors were predicted to be involved in the transcriptional profile of TCS, all of which have been demonstrated to regulate a large number of liver-enriched transcripts in zebrafish (Cheng et al., 2006).

TCS transcripts are predicted to be enriched in the liver and brain

We performed tissue-specific gene set enrichment analysis to identify potential tissue-specific transcriptional responses, which may be indicative of

78

TCS target organ/tissue toxicity, using the publicly available TSEA tool. As shown in Figure 3, transcripts significantly differentially expressed by TCS exposure were shown to be highly enriched in the liver and brain. We also observed moderate transcriptional enrichment in muscle, heart, kidney, pituitary, and pancreas. However, there was no significant enrichment at more stringent specificity thresholds for these tissues. No significant enrichment in thyroid or reproductive tissues, such as the ovary or testis, was observed.

Developmental toxicity time course at the TCS EC80

As a result of the TCS stock used for the original developmental toxicity assessments and microarray study expiring, we repeated the initial EC80 developmental toxicity study using a new TCS chemical stock (Figure 4A). The

EC20, EC50, and EC80 for the new TCS stock was 3.62, 5.04, and 6.46 µM, respectively. Exposure to TCS resulted in a similar mortality and morbidity response compared to the initial study (Supplementary Figure 1). Exposure to the new EC80 resulted in a prevalence of any effect at 45%, 87%, 88%, 89%, and 90% at 24, 48, 72, 96, and 120 hpf, respectively. We observed a maximum mortality in 38% of the exposed embryos by 120 hpf, and morbidity in 52% by

120 hpf which was predominated by axial defects, pericardial edema, yolk-sac edema, and caudal fin malformations (Figure 4B and C; Supplementary Figure

2). qRT-PCR validation and gene expression concentration response of TCS

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We selected fifteen genes that were significantly affected by TCS from the microarray study using qRT-PCR for validation and to determine whether the observed gene signatures are present at lower exposure concentrations. As shown in Figure 5, exposure to the EC80 and EC50 of TCS resulted in significant expression changes similar to what was observed in the microarray study for five genes and included tfa, matn1, apoba, hp, and rorab. We also observed similar expression responses for rbp2b, fabp10a, mknk2b, atf5b, notch1a, and igfbp1a; however, these effects were only observed in the EC80 exposure group.

We did not observe any significant gene expression changes at the EC20 for

TCS.

ToxCast in vitro bioactivity profile of TCS

TCS was part of ToxCast phase I chemical library, and in vitro bioactivity data are publicly available for this chemical across a large number of screening assays. TCS had a positive hit-call in 217 out of 385 unique assay endpoints.

The bioactivity profile of TCS across the annotation terms

“biological_process_target”, “intended_target_family”, and nuclear receptor assays from the “intended_target_family” are shown in Figure 6 as radial pie diagrams where the length of the slice indicates the hit-call percentage and the angle of the slice indicates the total number of assays for a specific category term in the annotation level (e.g. cell cycle, receptor binding, protein stabilization). The “biological_process_target” annotation of ToxCast assays

80 can be considered analogous to GO process annotations and gives a good representation of overall bioactivity patterns of a chemical. At this annotation level, TCS was highly active in assays involved in the regulation of gene expression, cell-specific assays (e.g. cell death, cell cycle, cell proliferation), and was moderately active in the receptor binding and regulation of transcription factor activity assays (Figure 6A). It is important to note that the assays measuring the regulation of gene expression, receptor binding, and regulation of transcription factor activity have the greatest coverage across the ToxCast assay space as indicated by the size and angle of their corresponding slices in the figure (Supplementary Table 3). Many assays within these three categories target specific proteins, receptors, or enzymatic activities, and offer more mechanistic information regarding a chemical’s activity compared to others such as cell proliferation or cytotoxicity. Examination of the TCS activity profile across the intended target families shows that TCS was active in all protease inhibitor, cytokine, and cell adhesion molecule assays (Figure 6B; Supplementary Table

4). TCS had a 70% hit-call rate in the cytochrome P450 assays including

CYP19A1 inhibition, CYP2C9, CYP1A2, and CYP2D2. To leverage the most pertinent information about possible target pathways of TCS, we focused on assays that directly measure a chemical’s ability to act on nuclear receptors

(Figure 6C). TCS had a hit-call for 24 of the 80 nuclear receptor assays, which correspond to 13 of the 36 unique nuclear receptors covered in ToxCast

(Supplementary Table 5). Note that the assay coverage across the nuclear

81 receptors is dominated by estrogen receptor 1 and androgen receptor assays.

TCS was a hit in all assays for retinoic X receptor B, the peroxisome proliferator- activated receptor group, progesterone receptor, and PXR. TCS was a positive hit in 70% of the androgen receptor assays. Interestingly, TCS showed a low activity for the 13 assays targeting estrogen receptor 1. Together, these results show that TCS had a diverse bioactivity response for the overall ToxCast battery of assays and had specific interaction with several nuclear receptors and CYP enzymes that have been hypothesized to be involved in the hypothyroxinemic effects of TCS.

Discussion The present study used phenotypically anchored whole-genome transcriptomics to investigate the potential mechanism of action of the common antimicrobial agent, TCS. Developmental toxicity studies during the exposure window of 6-120 hpf exposure to TCS identified an EC80 of 7.37 µM in our model system. Exposure to the TCS EC80 resulted in a robust transcriptional response in 48 hpf zebrafish, predominated by significantly decreased transcripts.

Functional enrichment analysis using MetaCore identified several biological processes and transcription factors with roles in liver function and neural pathways. Furthermore, tissue-specific enrichment analysis showed that the liver and brain were potential target tissues for TCS-induced transcriptional changes. We also examined the ToxCast in vitro bioactivity profile of TCS and found a diverse activity profile across the suite of assays, including liver related

82 endpoints, and effects in select nuclear receptor assays which corroborate the hypothesized role of PXR in TCS-induced hypothyroxinemia in rat and human in vitro studies. Overall, these results highlight the potential role of TCS as a hepatotoxicant in embryonic zebrafish.

In our initial developmental toxicity assessment, we observed a relatively steep concentration-response curve from TCS exposure, where 8 µM and above caused a high prevalence of developmental delay at 24 hours followed by mortality by 5 days with minimal effects at lower concentrations (Figure 1A).

However, after a reevaluation of the developmental effects of TCS, using the new chemical stock with a higher resolution concentration curve, we observed an increase in morbidity at the mid-exposure concentrations at 120 hpf (Figure

4A; Supplementary Figure 2). Other developmental toxicity studies using zebrafish have observed similar morphological effects (Oliveira et al., 2009;

Chen et al., 2015). A zebrafish early-life stage assessment of TCS reported delayed hatching, pericardial edema, and axial defects which resulted in near- complete lethality by 120 hpf in embryos exposed to 1.73 µM TCS; however, exposure to 1.04 µM TCS resulted in no difference in morphology or mortality compared to vehicle control embryos (Oliveira et al., 2009).

Phenotypic anchoring is integral to toxicogenomic studies. A common practice is to use a concentration that has been anchored to a phenotypic outcome in a known percentage of exposed animals and selecting a time point

83 that precedes the outcome with the aim of detecting chemical-induced transcriptional changes as far upstream as possible. We selected a concentration that elicited eventual morbidity or mortality by 120 hpf, and a time point, 48 hpf, that has previously been demonstrated to provide useful mechanistic information from diverse chemical exposures (Goodale et al., 2013;

Schiller et al., 2013; Goodale et al., 2015). In this case, the use of an EC80 was intentionally selected because it guaranteed that a large proportion of exposed animals would have a phenotype at 120 hpf. As demonstrated in the time course study (Figure 4), the assumption that the time point used in our analysis preceded all phenotypic changes did not hold. As a result, the transcriptional changes we observed may reflect responses that are concurrent and possibly downstream of the early initiating events for TCS but nonetheless appear involved in the TCS mode of action. Furthermore, expression data representative of more downstream responses may provide additional insight regarding organ/tissue-specific toxic responses, which might not be detected at lower concentrations.

Based on our two developmental toxicity assessments, the EC80 of TCS was calculated to be in the range of 6.46 and 7.37 µM, and the EC50 to be ~5

µM. The EC50 value was similar to another study in which an AC50 value of 2.66

µM for TCS was calculated based on a six-day zebrafish high-throughput screen of the ToxCast phase I chemicals (Padilla et al., 2012). These differences

84 between studies can likely be attributed to zebrafish strain differences, chorionation status, chemical purity and delivery methods, and static non- renewal exposures in our study versus daily renewal exposures. And although a caveat of the study presented here is that we did observe morbidity at 48 hpf with the TCS EC80, it is unknown the extent to which these effects contributed to the transcriptional changes we observed. It is also important to note that these

EC values represent nominal aqueous concentrations and not body burden levels, as we did not directly measure TCS levels in our exposed animals. As such, the TCS exposure concentrations used at 48 hpf and throughout these studies do not equate to human doses and, therefore, provide no direct dose- response information between zebrafish and human exposures.

Exposure to TCS at the determined EC80 concentration elicited a robust transcriptomic response in 48 hpf zebrafish embryos, with a majority of the differentially expressed transcripts significantly decreased. Functional analysis of the two primary clusters shown in the heatmap using DAVID highlighted several biological processes that appear to be involved in liver function, namely blood coagulation, platelet activation, glycolysis, and oxygen transport (Figure

2). Functional enrichment analysis of the human orthologous genes of the significant differentially expressed transcripts using MetaCore further supported the hepatotoxic action of TCS through prediction of several biological processes that are involved in normal liver processes and potential toxicity (Table 1). The

MetaCore biological pathway “regulation of metabolism—bile acid regulation of

85 glucose and lipid metabolism via FXR”, which is directly related to the biological process network “bile acid regulation of lipid metabolism and negative FXR- dependent regulation of bile acids concentration”, was identified as the one concordant pathway in a species comparative toxicogenomics study of three hepatotoxins using embryonic zebrafish, in vivo and in vitro rodent models, and human primary hepatocytes (Driessen et al., 2015). Since a large proportion of detected transcripts that were significantly differentially expressed were decreased, it is likely that these biological processes are being negatively impacted by TCS, suggesting either disruption of normal liver development or a decrease in hepatic cells responsible for these processes due to toxicity. From our transcription factor analysis, we identified several transcription factors which are known to play essential roles in liver development and may be involved in the hepatotoxic mechanism of TCS in zebrafish (Table 2). Four HNF transcription factors were predicted to play a regulatory role in the TCS transcriptional profile. HNF transcription factors are liver-enriched transcription factors that regulate, and often co-regulate, the constitutive expression of many genes crucial for liver function (Cheng et al., 2006). For example, fabp10a, which was significantly decreased in the microarray and qRT-PCR studies at the TCS EC80, has hnf1α and hnf3β consensus binding sites in a regulatory region 435-bp upstream of the promoter in zebrafish (Her et al., 2003). HNF4α, the most significantly predicted transcription factor in this study, has also been shown to regulate HNF1α expression, and therefore, indirectly affects the

86 downstream activation of HNF1α gene targets (Jung and Kullak-Ublick, 2003).

Importantly, prospero homeobox protein 1, PROX1, was also predicted to have a functional role in TCS-mediated transcriptional changes. PROX1 plays an important role in early liver development and has been demonstrated to negatively regulate both HNF4α and RORα (Song et al., 2006; Takeda and

Jetten, 2013). This suggests that TCS exposure may be impacting the developing liver either through a potential interaction between TCS and PROX1 resulting in decreased transcriptional activation of many important target genes in the liver, mediated by repression of liver-enriched transcription factors, or by decreasing the number of hepatoblasts through an undefined toxic mechanism.

Additionally, using a freely available tissue-specific gene enrichment analysis tool, we found that the TCS-induced transcriptional changes in 48 hpf zebrafish embryos are specifically enriched in the liver and brain, providing further evidence of target organ toxicity (Figure 3).

As highlighted above, functional and tissue-specific analysis of the TCS- induced gene changes involve some integral process or regulatory mechanism involved in the normal function and development of the liver. The strong repressive transcriptional signal observed in the array study would suggest that

TCS exposure is causing liver developmental arrest or is directly toxic to developing hepatocytes. Indeed, many of the transcripts that were significantly decreased by TCS have been demonstrated to be reliable biomarkers of

87 hepatotoxicity in zebrafish, including tfa, fabp10a, cp, rbp2b, rbp2a, and apoba

(Zhang et al., 2014; Verstraelen et al., 2016). Similarly, the genes that were significantly increased due to TCS exposure have also been implicated in hepatotoxicity and liver disease/damage, including igfbp1a, notch1a, and atf5b.

Induction of igfbp1a is a potential biomarker of hepatotoxicity and alcohol- induced liver disease, and may reflect alterations in the growth hormone- insulin-like growth factors axis or general metabolic abnormalities as a result of impaired liver function (Donaghy et al., 1995; Li et al., 2013; Buness et al.,

2014). Notch signaling is involved in the development of several organ systems, including intrahepatic biliary duct formation in the liver of mammals, performing a similar function in zebrafish (Lorent et al., 2010). Notch genes play a role in the regenerative capacity of the liver and are increased in response to liver injury to induce proliferation or differentiation of the various cell types in the liver such as hepatocytes and biliary cells (Geisler and Strazzabosco, 2015). atf5b is a liver-enriched transcription factor, which was shown to induce CYP2B6 expression via cooperation with CAR after induction of endoplasmic reticulum stress in human hepatoma cells (Pascual et al., 2008). Importantly, the qRT-

PCR studies revealed that even at the EC50 level of exposure for five of these genes including tfa, hp, apoba, matn1, and rorab were similarly repressed, further supporting the hypothesis that TCS is negatively impacting liver function or development (Figure 5). tfa is an iron-binding serum protein synthesized in the liver and yolk-sac of zebrafish and is responsible for the transport of ferric

88 iron to developing erythrocytes. Transgenic zebrafish bearing a mutation in tfa develop severe anemia and have decreased iron levels in somites and the gut leading to eventual embryonic mortality, suggesting its requirement for hemoglobin production (Fraenkel et al., 2009). Similarly, hp is a globin binding protein synthesized in the liver involved in the recycling of free serum hemoglobin. Although increases in hp levels are commonly associated with liver inflammation, decreases in its expression are indicative of hemolysis or potential liver damage, such as cirrhosis (Kormoczi et al., 2006).

Apolipoproteins, including apoba, are lipid transporting proteins synthesized in the liver. Decreased levels of apoliproproteins are commonly associated with primary liver cancers, cirrhosis, and chronic liver disease (Shah and Desai,

2001; Irshad and Dubey, 2005). Matrilin 1, matn1, is a component of basement membranes and has been shown to aid in collagen secretion. The role of matn1 expression and liver function is unknown; however, transient knockdown of matn1 resulted in axial, eye, and craniofacial defects which may explain, in part, the malformations observed in our developmental studies (Figure 4C; (Neacsu et al., 2014). The retinoic acid-related orphan receptor, rorab, plays a role in the circadian regulation of lipid metabolism and is expressed in a variety of tissues including the liver, kidney, thymus, brain, and skeletal muscle (Solt et al., 2011).

Taken together, the observed decreases in these genes, and others, at the EC80 or EC50 of TCS demonstrate impaired liver functioning in developing zebrafish, in which prolonged disruption may result in deficiencies in iron homeostasis,

89 anemia due to decreased hemoglobin production or hemolysis, lipid transport defects, metabolic abnormalities, and decreases in the production of critical serum proteins that are produced by the liver. Furthermore, the increased expression of several target genes, including those highlighted in the qRT-PCR study, may represent a homeostatic response of the developing liver to TCS- induced toxicity in order to maintain proper liver development and repair mechanisms (e.g. notch1a signaling), metabolism, and excretion of TCS by activation of xenobiotic metabolizing enzymes. These data also provided evidence that the liver bud in 48 hpf zebrafish is beginning to express a variety of genes involved in mature liver function prior to the outgrowth phase of liver development, suggesting that many of these genes are required for development. We must acknowledge the possibility that the strong transcriptomic response elicited by TCS may be a result of systemic toxicity at the EC80 and not indicative of target organ toxicity involving the liver and, to a lesser extent, the brain. However, the 48 hpf transcriptomic analysis presented here cannot directly differentiate between these toxic mechanisms. We reason that the functional specificity of the transcriptional changes, along with the enriched tissue expression pattern elicited by TCS, indicate systemic toxicity is not occurring. Overall, the results of our phenotypically anchored toxicogenomics analysis show that the developmental toxicity of TCS in embryonic zebrafish appears to target the developing liver and may be acting

90 as a hepatotoxicant; however, further studies are needed to fully elucidate the mechanism by which this occurs.

Relatively few studies have focused on understanding the toxicity and endocrine effects of TCS in developing organisms, with a majority of those focusing on thyroid signaling in rats. As previously described, TCS influences thyroid-dependent metamorphosis in frogs (Veldhoen et al., 2006; Marlatt et al.,

2013). Studies in fish have primarily focused on characterizing the acute developmental toxicity of TCS (Ishibashi et al., 2004; Oliveira et al., 2009; Ho et al., 2016). The only mechanistic study in fish, to our knowledge, showed that five-day exposure to a no-effect concentration of TCS resulted in changes in lipid accumulation and decreased the expression of several genes involved in

β-oxidation of fatty acids in zebrafish (Ho et al., 2016). Interestingly, prolonged disruption of β-oxidation processes can result in microvesicular hepatocyte steatosis and eventual hepatic failure (Cullen, 2005). In maternally exposed fetal or neonatal rats, TCS decreased serum T4 levels that then recovered to normal levels over time suggesting toxicokinetic and toxicodynamic differences between fetuses and maturing offspring (Paul et al., 2010a; Paul et al., 2012).

However, maternally exposed offspring were still susceptible to decreases in serum T4 if directly exposed to TCS (Axelstad et al., 2013). Mechanistic studies in rats investigating the role of CAR/PXR activation after maternal exposure showed that, alongside decreased serum T4 levels, TCS increased PROD

91 activity in PND 4 animals, and although slight increases in the expression of

CAR/PXR-regulated cytochrome P450 or UGT genes were detected in the dams but not the offspring, upregulated hepatic catabolism may still be a contributing factor for TCS-induced hypothyroxinemia (Paul et al., 2012). In contrast to the possible role of CAR/PXR signaling in TCS-induced hypothyroxinemia as described above, our analysis of TCS-induced transcriptional changes in 48 hpf zebrafish did not identify transcriptional or downstream functional processes associated with either the thyroid or estrogen pathways as targets for TCS-induced developmental toxicity. Moreover, tissue- specific enrichment analysis showed no significant enrichment of transcripts in thyroid or reproductive tissues. We also did not observe any significant transcriptional changes in xenobiotic metabolizing enzymes that would implicate activation of the CAR/PXR signaling cascades. These included zebrafish orthologs for SULT1E1, SULT1B1, CYP2B6, CYP3A4, UGT1A1, or UGT1A6

(sult1st3, sult1st5, cyp2y3, cyp3a65, and ugt1ab respectively) nor did we observe any changes in the zebrafish PXR gene nr1i2. We should note that we did observe significant decreases in several cyp2aa family members including cyp2aa2, which was recently shown to be transcriptionally induced by the PXR agonists 1,4-bis [2-(3,5-dichloropyridyloxy)] benzene and phenobarbital in adult zebrafish livers (Kubota et al., 2013). However, we would expect cyp2aa2 to increase as a result of PXR activation in zebrafish, and it is likely that the observed decrease in expression was due to a decrease in the numbers of

92 differentiating hepatoblasts/hepatocytes as a result of TCS toxicity and was not mediated through PXR. Studies of TCS in human and rodent cell-based reporter have demonstrated differential activation of CAR and PXR, where TCS activated human but not rodent PXR, and elicited various agonistic and reverse agonistic activities for CAR (Paul et al., 2013). This suggests that species- specific effects may play a role in the affinity of TCS towards CAR/PXR and subsequent transcriptional activation of downstream xenobiotic metabolizing enzymes. Although, zebrafish lack a CAR gene, more research is needed to describe the effects of TCS on the zebrafish PXR (Kubota et al., 2013). The lack of endocrine responses we observed in this study does not necessarily mean that TCS is incapable of endocrine disruption in zebrafish. On the contrary, studies in adult zebrafish have demonstrated TCS-induced thyroid activity

(Pinto et al., 2013). However, the hepatotoxic transcriptome signature of TCS we show may be specific to the window of exposure used in our studies; a potential result of immature xenobiotic sensing mechanisms in the 48 hpf liver, as it is not metabolically competent until 72 hpf (Field et al., 2003). Therefore, the PXR-mediated activity of TCS would not be detected until later developmental stages, although in situ hybridization studies have shown that

PXR mRNA is present throughout early development (Bertrand et al., 2007). As such, further studies examining the transcriptomic profile of TCS across different susceptibility windows are warranted.

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The activity profile of TCS across the diverse battery of ToxCast screening assays can inform and potentially corroborate the findings we present here in our zebrafish transcriptomics analysis or those posed by other laboratories. After filtering the assay data (see Methods), we found that TCS had a relatively diverse bioactivity profile and was a hit in 217 of the 385 (56.4%) assays. Assays under the biological process “Regulation of gene expression” were largely affected by TCS, and interestingly, all of these assays reported decreased transcriptional responses similar to what was observed in our array data. TCS was also a hit in two of the three “Regulation of steatosis” assays which can indicate disruption of liver metabolism. TCS bioactivity at the

“intended_target_family” level highlighted unique protein classes that were affected by TCS. TCS decreased the expression of all of the cytokines within the ToxCast battery, including IL-6 which was a significantly enriched biological process network in our transcriptomic analysis. TCS was active in two of the seven FXR assays, and is likely not a direct receptor target for TCS. We also found that TCS was not a hit in the HNF4α, LXRα, LXRβ, or RORa which were predicted in our transcriptomic analysis. TCS also affected assays targeting several CYP genes regulated by CAR/PXR including human CYP2C9,

CYP2C8, CYP2C19, and rat Cyp1a2 (Tolson and Wang, 2010). Analysis of the assays targeting nuclear receptors showed that TCS was a hit in all three PXR assays and was a hit in 50% of the CAR assays, similar to that observed in previous studies (Jacobs et al., 2005; Paul et al., 2013). With respect to direct

94 endocrine nuclear receptor activity, TCS had the largest activity for assays targeting the androgen receptor compared to all other endocrine receptors.

Interestingly, of all the nuclear receptor assays with at least one hit-call, ESR1 was the least effected and was a hit in only 15% of ESR1-specific assays. These results show that the hit-call data for TCS across the ToxCast assays appeared to better support the hypothesized role of CAR/PXR signaling as demonstrated in human in vitro and rat in vivo studies compared to our observations and predicted functional responses of our in vivo transcriptomic analysis in zebrafish. This may reflect the species-dependent effects of CAR/PXR activation as previously demonstrated, or that TCS has differential mechanisms of action depending on the developmental stage resulting in presence/absence of putative molecular targets.

Collectively, these results suggest that TCS is hepatotoxic to embryonic zebrafish, and helps explain reports of acute toxicity of TCS in zebrafish and other aquatic species (Orvos et al., 2002; Ishibashi et al., 2004; Tatarazako et al., 2004; Oliveira et al., 2009; Padilla et al., 2012). We demonstrated that the endocrine-related role of TCS hypothesized in rodent and human in vitro studies appears to not be involved in TCS-induced toxicity in zebrafish at this specific developmental stage, or was masked by the robust effects of TCS on the developing liver. Compared to the bioactivity profile of TCS across the ToxCast program, several similarities with possible liver endpoints were identified; however, the assay responses better supported the role of CAR/PXR activation

95 by TCS in human in vitro systems. This may reflect biases in the assay coverage, as was demonstrated by the overrepresentation of estrogen and androgen receptor assays compared to other nuclear receptor signaling pathways. By leveraging the full repertoire of transcriptomic responses across embryonic development in the whole animal, we demonstrated that using a phenotypically anchored early developmental transcriptomics approach can inform and provide insight into putative mechanisms of chemical action.

Zebrafish can not only be used to rapidly identify phenotypic changes due to chemical exposure in a high-throughput manner (Truong et al., 2014; Noyes et al., 2015), it can also provide unbiased, global expression data to drive hypothesis generation about the molecular events that underlie chemical toxicity. Future studies will examine the transcriptomic response of TCS at lower concentrations and at different time points and windows of exposure to fully elucidate the role of the developing liver in TCS toxicity.

Acknowledgements We would like to acknowledge Carrie Barton and Greg Gonnerman,

Sinnhuber Aquatic Research Laboratory, for providing help and support with fish husbandry, spawning, and embryo screening. We would also like to thank

Dr. Lisa Truong and Dr. Michael Simonich for assistance with manuscript preparation and editing. This research was supported by NIH P30 ES000210,

T32 ES007060, P42 ES016465 and EPA #R835168. Pacific Northwest National

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Laboratory is a multi-program laboratory operated by Battelle for the U.S.

Department of Energy under Contract DE-AC05-76RL01830.

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Verstraelen, S., Peers, B., Maho, W., Hollanders, K., Remy, S., Berckmans, P., Covaci, A., Witters, H., 2016. Phenotypic and biomarker evaluation of zebrafish larvae as an alternative model to predict mammalian hepatotoxicity. J Appl Toxicol. Wells, A., Kopp, N., Xu, X., O'Brien, D.R., Yang, W., Nehorai, A., Adair-Kirk, T.L., Kopan, R., Dougherty, J.D., 2015. The anatomical distribution of genetic associations. Nucleic Acids Res 43, 10804-10820. Xu, X., Wells, A.B., O'Brien, D.R., Nehorai, A., Dougherty, J.D., 2014. Cell type-specific expression analysis to identify putative cellular mechanisms for neurogenetic disorders. J Neurosci 34, 1420-1431. Yueh, M.F., Taniguchi, K., Chen, S.J., Evans, R.M., Hammock, B.D., Karin, M., Tukey, R.H., 2015. The commonly used antimicrobial additive triclosan is a liver tumor promoter (vol 111, pg 17200, 2014). P Natl Acad Sci USA 112, E237-E237. Yueh, M.F., Tukey, R.H., 2016. Triclosan: A Widespread Environmental Toxicant with Many Biological Effects. Annu Rev Pharmacol Toxicol 56, 251-272. Zhang, X., Li, C., Gong, Z., 2014. Development of a convenient in vivo hepatotoxin assay using a transgenic zebrafish line with liver-specific DsRed expression. PLoS One 9, e91874. Zorrilla, L.M., Gibson, E.K., Jeffay, S.C., Crofton, K.M., Setzer, W.R., Cooper, R.L., Stoker, T.E., 2009. The effects of triclosan on puberty and thyroid hormones in male Wistar rats. Toxicol Sci 107, 56-64.

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Figure 3- 1. Developmental toxicity (mortality and morbidity) profile and logistic regression analysis of TCS exposure. (A) Concentration response profile for 0, 1, 4, 6, 8, and 10 µM TCS exposure across 11 phenotypic endpoints. MO24 and DP24 correspond to 24 hpf endpoints, and the remaining correspond to 120 hpf endpoints. (B) Logistic regression analysis of TCS developmental effects for any adverse phenotype, with logistic curve shown in red. Dashed lines indicate the EC80.

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Figure 3- 2. TCS exposure induces robust transcriptional changes in embryonic zebrafish. Heatmap visualization with bi-hierarchical clustering of significant differentially expressed transcripts due to developmental exposure to the EC80 of TCS (FDR corrected p-value ≤ 0.05, fold change ≥ 2.0). The two clusters are annotated by significant functional clusters of enriched GO terms as determined by DAVID.

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Figure 3- 3. TCS-induced transcriptional changes are significantly enriched in the liver and brain. Tissue-specific gene enrichment analysis of the significantly differentially expressed orthologous human genes was performed and visualized using the TSEA tool (http://genetics.wustl.edu/jdlab/tsea/). Tissue enrichment are visualized as hexagonal nodes aligned along a dendrogram indicating the similarity of tissue-enriched transcript lists across the 25 tissues.

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Figure 3- 4. Developmental time course study of the TCS EC80. (A) Repeated logistic regression analysis for any adverse effect from 0-10 µM of the new TCS stock. Dashed lines indicate the EC80. (B) Percent prevalence for mortality or morbidity at 0 or 6.46 µM TCS at 24, 48, 72, 96, and 120 hpf. (C) Images of zebrafish exposed to 0 (top panel) or 6.46 µM (lower panel) TCS across early development.

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Figure 3- 5. Quantitative RT-PCR validation of fifteen significantly differentially expressed genes identified in the microarray analysis at the EC20, EC50, and EC80 of TCS. log2 values are shown for comparison between the microarray and qRT-PCR data. A one-way ANOVA with Tukey’s post-hoc test or a Kruskal-Wallis with Dunn’s post-hoc test were used to test the significance of expression values between qRT-PCR treatment and controls (n=4; * p≤0.05; ** p ≤ 0.01; *** p ≤ 0.001).

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Figure 3- 6. TCS bioactivity profile across three ToxCast assay annotation levels. Hit-call data across the “biological_process_target” (A), “intended_target_family” (B), and the nuclear receptor subset of “intended_target_family” (C) assay annotation levels were visualized using radial pie diagrams. For reference, the height of a slice indicates the hit percent and the size/angle of the slice indicates the total number of assays within that slice.

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Figure S3- 1. Developmental toxicity profile of the repeated TCS EC80 experiment. Embryos were exposed to 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 mM TCS from 6-120 hpf. Animals were evaluated across 22 individual endpoints and summarized for any mortality and morbidity score (”ANY”; n = 32 per treatment).

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Figure S3- 2. Developmental toxicity profile of the EC80 of the new TCS chemical stock across embryonic development. Embryos were exposed to 0, or 6.46 mM TCS from 6-120 hpf. Animals were evaluated at 24, 48, 72, 96, and 120 hpf across 81 endpoints (4 at 24 hpf, 18 at 48, 72, 96, and 120 hpf) and summarized for any mortality and morbidity score for each day (”ANY”). A total of 33 control embryos and 143 treated embryos were evaluated.

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Table 3- 1. Significantly enriched MetaCore process networks. Network Name FDR-adjusted P- Associated Human Gene Symbol value Kallikrein-kinin system 9.04E-09 A2M, SERPINC1, KNG1, C3, CPN2, F5, F7, F10, FGA, FGB, FGG, HABP2, SERPIND1, PLAU, SERPINF2, F2

Insulin signaling 1.87E-08 CPE, INS, FBP1, GYS2, PKLR, PFKM, PKM, PYGL, PYGM, SREBF1

Blood coagulation 2.79E-08 A2M, CD9, F10, F2, F5, F7, FGA, FGB, FGG, KNG1, MST1, PLAU, SERPINC1, SERPIND1, SERPINF2, TFPI

Complement system 3.16E-08 C3, C8A, C8B, C8G, C9, CRP, CFHR3, CFHR4, CFH, CFI

IL-6 signaling 1.7E-04 A2M, HBA1, HBA2, BAX, C3, CRP, CP, FGA, FGB, FGG, HABP2, INS, PIK3R3, PIK3R2, SERPINF2

Bile acid regulation of lipid 2.3E-04 ANGPTL3, APOB, APOE, FBP1, FABP6, INS, metabolism and negative FXR- PCK1, SREBF1 dependent regulation of bile acids concentration

Visual perception 1.05E-03 ABLIM1, ARR3, CYP1B1, CRYGD, OPN1LW, PDE6H, RAX, RDH11, RGR, RLBP1, RPE65, RCVRN, SIX3

Synaptic vesicle exocytosis 4.9E-02 AMPH, APBA1, DNM1, SEPT5, SLC1A3, SLC6A1, SNAP25, SNCB, STX1B, STXBP1, SV2B, SYT5, VAMP1

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Table 3- 2. Statistically enriched transcription factors. Transcription Factor P-value Z-score HNF4α 6.81E-14 9.452 USF1 9.01E-10 8.166 HNF3β 3.39E-09 7.514 HNF1α 9.27E-09 7.382 LHX2 2E-08 6.954 C/EBPα 2.53E-08 6.885 RORα 1.35E-07 7.64 PROX1 3.14E-07 7.929 SP1 3.71E-06 4.944 HNF3α 6.58E-06 5.557 SP3 6.91E-06 5.161 PPARγ 2.04E-05 5.036 LXRα 2.73E-05 5.614 COUP-TFI 3.76E-05 5.6 PPARα 4.89E-05 4.963 USF2 5E-05 5.149

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Table S3- 1. Primer sets for qRT-PCR validation. Gene Forward primer Reverse primer atf5b TTTGGTTTGACGTGATACTCC AAACTGAAACTCCATCGTGC igfbp1a TCTAACTTCCTTCAGGCTCG GTTACCCGTTTCAAAAGCTG fabp10a GAAAAACCGTCACCAACTCC CATTTCTCCAGCCTTGATCTC rorab TCAGGATGTACTTAATGATCACAG GTTATCACGCCATAATGAATGC tfa GTAAGGGCTCAGGTTTAACG AGCCTTCGCTAAAGAACTTG apoba TTGATTACGCTTAGCAGCTC GCTGGTACATTCATCAGTCC atf3 ACACCACGTTGTTAGTGAAG AACAAACTAGATGACGCCTG c3a CAGATTAAACCCCGAGTGTC CCATCTCCAATATACGTGGC gpx4a TCCAAATGAGGTAAAACCCC TTAGTTCCTGGTTCCTGACG hp TGGGAAGCACTTGGATAAAC ATTCACACTTGATGTTGGGG il17ra1 GTATATGTGTTTAGGAGAGCAAG TGGAAAAATGGCTGGATCTG matn1 AGATATTTGCCATCGGAGTG CACAGTCATGATCACCAGTC mknk2b TTGGGAAACACCTTAAGAGC TTAGGTAGGGATGGTGGAAG notch1a TCAATGTCAATTCCCGAACC CGGTCACTGAATCCTAACAC rbp2b GAGCTGAGCTTCACTATTGG CTTTTCGCTTGTACACTTGC B-actin AAGCAGGAGTACGATGAGTC TGGAGTCCTCAGATGCATTG

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Table S3- 2. qRT-PCR data. Treatment Target Average log2 FC Standard P-value (uM) Name Deviation (Treatment vs. Control) 0 apoba 0.0000 0.0459 1 6.46 apoba -0.7723 0.4995 0.0091 5.04 apoba -0.6462 0.1508 0.028 3.62 apoba -0.2186 0.1791 0.68 0 atf3 0.0000 0.2259 1 6.46 atf3 0.4737 0.1340 0.08 5.04 atf3 0.1331 0.3513 0.87 3.62 atf3 -0.1816 0.2335 0.73 0 atf5b 0.0000 0.2727 1 6.46 atf5b 2.2173 0.5114 <0.001 5.04 atf5b 0.5412 0.4550 0.23 3.62 atf5b 0.1114 0.1762 0.98 0 c3a 0.0000 0.1386 NS 6.46 c3a -0.1870 0.4014 NS 5.04 c3a -0.4832 0.1428 NS 3.62 c3a -0.2955 0.1998 NS 0 fabp10a 0.0000 0.3769 1 6.46 fabp10a -1.5717 1.2302 0.04 5.04 fabp10a 0.0821 0.4809 0.99 3.62 fabp10a 0.6715 0.4113 0.57 0 gpx4a 0.0000 0.0956 1 6.46 gpx4a -0.3613 0.2244 0.11 5.04 gpx4a -0.2530 0.3257 0.34 3.62 gpx4a 0.0938 0.0494 0.91 0 hp 0.0000 0.3473 1 6.46 hp -1.2626 0.6013 0.0095 5.04 hp -0.9560 0.3384 0.05 3.62 hp -0.1834 0.4838 0.94 0 igfbp1a 0.0000 0.1610 1 6.46 igfbp1a 0.9390 0.2676 < 0.001 5.04 igfbp1a 0.0985 0.2424 0.93 3.62 igfbp1a -0.3632 0.2305 0.17 0 il17ra1 0.0000 1.2917 NS 6.46 il17ra1 0.4463 0.0941 NS 5.04 il17ra1 -0.1339 0.7732 NS 3.62 il17ra1 0.1361 0.2462 NS

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0 matn1 0.0000 0.6007 1 6.46 matn1 -7.3190 1.4629 < 0.001 5.04 matn1 -4.0459 0.4920 < 0.001 3.62 matn1 -0.5483 0.2380 0.79 0 mknk2b 0.0000 0.1436 1 6.46 mknk2b 1.6639 0.4165 <0.001 5.04 mknk2b 0.3133 0.2029 0.42 3.62 mknk2b -0.5108 0.2811 0.097 0 notch1a 0.0000 0.1124 1 6.46 notch1a 0.7687 0.2504 <0.001 5.04 notch1a 0.2529 0.0917 0.21 3.62 notch1a -0.1266 0.1810 0.73 0 rbp2b 0.0000 0.2528 1 6.46 rbp2b -1.8194 0.7608 <0.001 5.04 rbp2b -0.5223 0.2353 0.38 3.62 rbp2b -0.1071 0.2931 0.99 0 rorab 0.0000 0.0964 1 6.46 rorab -1.7776 0.1872 <0.001 5.04 rorab -1.5295 0.2393 <0.001 3.62 rorab -0.2025 0.1674 0.42 0 tfa 0.0000 0.1920 1 6.46 tfa -1.4772 0.6128 <0.001 5.04 tfa -1.0590 0.2311 <0.001 3.62 tfa 0.0485 0.3348 1

NS: Data were not significant for global one-way ANOVA or Kruskal-Wallis test

Table S3- 3. "Biological_process_target" assay hit data. Annotation Name Total Assay Hit Mean Median Hit Percentage of Assays Annotation Name Assays Number AC50 AC50 Percentage Represents REGULATION_OF_CATALYTIC_ACTIVITY 62.00 15.00 2.26 0.00 24.19 0.16 PROTEIN_STABILIZATION 12.00 3.00 1.70 0.00 25.00 0.03 REGULATION_OF_TRANSCRIPTION_FACTOR_ 101.00 36.00 2.15 0.00 35.64 0.26 ACTIVITY RECEPTOR_BINDING 45.00 20.00 3.50 4.65 44.44 0.12 OXIDATIVE_PHOSPHORYLATION 6.00 4.00 2.87 4.14 66.67 0.02 REGULATION_OF_STEATOSIS 3.00 2.00 4.17 4.12 66.67 0.01 CELL_CYCLE 15.00 11.00 3.88 4.11 73.33 0.04 CELL_PROLIFERATION 26.00 20.00 4.46 4.44 76.92 0.07 NO_CATEGORY 15.00 12.00 4.30 4.13 80.00 0.04 MITO_DEPOLARIZATION 7.00 6.00 4.71 4.79 85.71 0.02 CELL_DEATH 16.00 15.00 4.50 4.93 93.75 0.04 REGULATION_OF_GENE_EXPRESSION 76.00 72.00 4.83 4.93 94.74 0.20 CYTOTOXICITY 1.00 1.00 5.03 5.03 100.00 0.00

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Table S3- 4. "Intended_target_family" assay hit data.

Annotation Name Total Assays Assay Hit Number Mean AC50 Median AC50 Hit Percentage Percentage of Assays Annotation Name Represents cell adhesion 16 16 4.9381895 4.91241534 1 0.041558442 molecules cell cycle 55 47 4.385940101 4.492791804 0.854545455 0.142857143 cell morphology 33 23 3.995036127 4.124785832 0.696969697 0.085714286 cyp 10 7 3.590803273 4.935748666 0.7 0.025974026 cytokine 41 41 4.948925741 4.945295356 1 0.106493506 dna binding 47 21 2.09550158 0 0.446808511 0.122077922 esterase 1 0 4.465177117 4.465177117 0 0.002597403 gpcr 23 11 3.623961853 4.524914346 0.47826087 0.05974026 growth factor 3 2 3.326356474 4.917096338 0.666666667 0.007792208 hydrolase 1 0 0 0 0 0.002597403 ion channel 3 0 0 0 0 0.007792208 kinase 20 3 1.352956296 0 0.15 0.051948052 lyase 1 0 0 0 0 0.002597403 misc protein 1 1 4.863357413 4.863357413 1 0.002597403 nuclear receptor 80 24 2.609338483 2.036946461 0.3 0.207792208 oxidoreductase 8 6 4.029641807 4.567358624 0.75 0.020779221 phosphatase 9 0 1.994768163 0 0 0.023376623 protease 15 8 4.461841954 4.878499087 0.533333333 0.038961039 protease inhibitor 2 2 4.911213274 4.911213274 1 0.005194805 steroid hormone 10 0 0 0 0 0.025974026 transporter 6 5 4.306649585 4.854080886 0.833333333 0.015584416 120

Table S3- 5. Nuclear receptor assay hit data.

Annotation Name Assay Name Total Assay Hit Mean Median Hit Percentage of Assays Assays Number AC50 AC50 Percentage Annotation Name Represents ESR1|ESR2 OT_ER_ERAERB_0480 1 0 0 0 0 0.0125 ESR2 OT_ER_ERBERB_0480 1 0 0 0 0 0.0125 ESRRA ATG_ERRA_TRANS 1 0 6.210048 6.2100489 0 0.0125 959 59 ESRRG ATG_ERRG_TRANS 1 0 0 0 0 0.0125 HNF4A ATG_HNF4A_TRANS 1 0 0 0 0 0.0125 NR1H2 ATG_LXRB_TRANS 1 0 0 0 0 0.0125 NR1H3 ATG_LXRA_TRANS 1 0 0 0 0 0.0125 NR1H3|NR1H2 ATG_DR4_LXR_CIS 1 0 0 0 0 0.0125 NR3C2 NVS_NR_RMR 1 0 0 0 0 0.0125 NR4A2 ATG_NURR1_TRANS 1 0 0 0 0 0.0125 PPARA "ATG_PPARA_TRANS", 2 0 3.041791 3.0417915 0 0.025 "NVS_NR_HPPARA" 531 31 PPARD "ATG_PPARD_TRANS", 3 0 2.170104 0 0 0.0375 "TOX21_PPARD_BLA_AGONIST_R 273 ATIO", "TOX21_PPARD_BLA_ANTAGONI ST_RATIO" RARA "ATG_RARA_TRANS", 2 0 0 0 0 0.025 "NVS_NR_HRARA_AGONIST" RARA|RARB|RAR ATG_DR5_CIS 1 0 0 0 0 0.0125 G RARB ATG_RARB_TRANS 1 0 6.124070 6.1240700 0 0.0125 009 09 RARG ATG_RARG_TRANS 1 0 6.354513 6.3545139 0 0.0125 932 32 RORA|RORB|RO ATG_RORE_CIS 1 0 0 0 0 0.0125 RC RORB ATG_RORB_TRANS 1 0 0 0 0 0.0125 RORC ATG_RORG_TRANS 1 0 0 0 0 0.0125

THRA ATG_THRA1_TRANS 1 0 0 0 0 0.0125 121

ESR1 "ACEA_T47D_80HR_POSITIVE", 13 2 2.285595 0 15.3846153 0.1625 "ATG_ERA_TRANS", 312 8 "ATG_ERE_CIS", "NVS_NR_BER", "NVS_NR_HER", "NVS_NR_MERA", "OT_ER_ERAERA_0480", "OT_ERA_EREGFP_0120", "OT_ERA_EREGFP_0480", "TOX21_ERA_BLA_AGONIST_RAT IO", "TOX21_ERA_BLA_ANTAGONIST_ RATIO", "TOX21_ERA_LUC_BG1_AGONIST ", "TOX21_ERA_LUC_BG1_ANTAGO NIST" NR3C1 "ATG_GR_TRANS", 5 1 2.155170 0 20 0.0625 "ATG_GRE_CIS", "NVS_NR_HGR", 198 "TOX21_GR_BLA_AGONIST_RATI O", "TOX21_GR_BLA_ANTAGONIST_ RATIO" NR1H4 "ATG_FXR_TRANS", 7 2 2.909338 4.3147754 28.5714285 0.0875 "ATG_IR1_CIS", 922 57 7 "NVS_NR_HFXR_AGONIST", "NVS_NR_HFXR_ANTAGONIST", "OT_FXR_FXRSRC1_0480", "TOX21_FXR_BLA_AGONIST_RAT IO", "TOX21_FXR_BLA_ANTAGONIST_ RATIO" PPARG "ATG_PPARG_TRANS", 6 2 3.518138 4.8213789 33.3333333 0.075 "NVS_NR_HPPARG", 44 23 3 "OT_PPARG_PPARGSRC1_0480", "OT_PPARG_PPARGSRC1_1440", "TOX21_PPARG_BLA_AGONIST_ RATIO", "TOX21_PPARG_BLA_ANTAGONI ST_RATIO" RXRA "ATG_RXRA_TRANS", 3 1 3.777396 4.9976061 33.3333333 0.0375 "OT_NURR1_NURR1RXRA_0480", 369 88 3 "OT_NURR1_NURR1RXRA_1440" 122

NR1I3 "ATG_CAR_TRANS", 2 1 2.663449 2.6634491 50 0.025 "ATG_PBREM_CIS" 114 14 THRB|THRA "TOX21_TR_LUC_GH3_AGONIST", 2 1 5.202725 5.2027257 50 0.025 "TOX21_TR_LUC_GH3_ANTAGON 753 53 IST" VDR "ATG_VDR_TRANS", 2 1 2.670821 2.6708216 50 0.025 "ATG_VDRE_CIS" 606 06 AR "ATG_AR_TRANS", 10 7 3.252214 4.5046126 70 0.125 "NVS_NR_CAR", "NVS_NR_HAR", 097 73 "NVS_NR_RAR", "OT_AR_ARELUC_AG_1440", "OT_AR_ARSRC1_0480", "TOX21_AR_BLA_AGONIST_RATI O", "TOX21_AR_BLA_ANTAGONIST_R ATIO", "TOX21_AR_LUC_MDAKB2_AGON IST", "TOX21_AR_LUC_MDAKB2_ANTA GONIST" NR1I2 "ATG_PXR_TRANS", 3 3 5.245215 5.3902333 100 0.0375 "ATG_PXRE_CIS", 932 35 "NVS_NR_HPXR" PGR NVS_NR_BPR 1 1 4.585271 4.5852718 100 0.0125 895 95 PPARA|PPARD|P ATG_PPRE_CIS 1 1 5.027123 5.0271233 100 0.0125 PARG 331 31 RXRB ATG_RXRB_TRANS 1 1 5.225390 5.2253906 100 0.0125 698 98

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CHAPTER 4 - CLASSIFYING CHEMICAL ENDOCRINE ACTIVITY USING TRANSCRIPTOME PROFILING IN ZEBRAFISH

Derik E. Haggard1, Pamela D. Noyes1, 2, Katrina M. Waters3, and Robert L. Tanguay1

1 Department of Environmental and Molecular Toxicology, Oregon State University, Corvallis, OR 2 Office of Science Coordination and Policy (OSCP), Office of Chemical Safety and Pollution Prevention, U.S. Environmental Protection Agency, Washington, DC 3 Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA

In preparation for submission to Reproductive Toxicology.

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Abstract There is a need to develop useful, high-throughput screening and prioritization methods to identify chemicals with adverse estrogen, androgen, or thyroid activity to protect human health and the environment and is of interest to the Endocrine Disruptor Screening Program. The current aim is to explore the utility of zebrafish as a testing paradigm to classify endocrine activity using phenotypically anchored transcriptome profiling. Transcriptome analysis was conducted on embryos exposed to 25 estrogen-, androgen-, or thyroid-active chemicals at a concentration that elicited adverse malformations or mortality at

120 hours post-fertilization in 80% of the animals exposed. Analysis of the top

1000 significant differentially expressed transcripts across all treatments identified a unique transcriptional and phenotypic profile for thyroid hormone receptor agonists, which can be used as a biomarker screen for potential thyroid hormone agonists.

Keywords

Transcriptomics; zebrafish; phenotypic anchoring; endocrine disruptors; high- throughput; ToxCast

Abbreviations

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Endocrine disrupting chemical, EDC; thyroid hormone receptor, TR;

United States Environmental Protection Agency, USEPA; Endocrine Disruptor

Screening Program, EDSP; hours post-fertilization, hpf

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Introduction It is well known that disruption of the endocrine systems can result in adverse developmental outcomes, including reproductive effects, teratogenicity, and neurobehavioral deficits (Gore et al., 2015). Due to these concerns, in 1996, US Congressional mandate with passage of the Food Quality

Protection Act, which amended the Federal, Food, Drug, and Cosmetic Act, and required EPA (USEPA) to develop a program to screen chemicals for their potential to affect estrogen signaling. In response, the USEPA formed the

Endocrine Disruptor Screening Program (EDSP) in 1998, which went beyond the mandated screening for estrogenic chemicals and included screening for androgenic and thryoidogenic chemicals (U.S. EPA, 2012a; U.S. EPA, 2016a).

The EDSP proposed a two tiered screening strategy to identify and prioritize potential endocrine active chemicals. The EDSP Tier 1 battery includes 11 assays, both in vivo and in vitro, to determine a chemical’s potential to interact with estrogen, androgen, or thyroid axis pathways. Based on weight-of- evidence evaluations of EDSP Tier 1 battery results and other scientifically relevant information, additional testing may be required under EDSP Tier 2, which are more rigorous and quantitative in terms of establishing dose-response relationships between the chemical and its endocrine effects (U.S. EPA, 2011b;

U.S. EPA, 2013; U.S. EPA, 2016b).

The number of chemicals that would need to be screened by the EDSP was estimated to be ~10,000 chemicals (U.S. EPA, 2012b). Such a large

128 chemical space presents challenges when relying on traditional in vivo assays for evaluation of potential endocrine effects of chemical; requiring a large amount of time, money, and animals to fully implement. This problem has been globally recognized, and many regulatory agencies have since adopted the need to develop new high-throughput, high-content, and next-generation technologies to apply to chemical hazard assessment (NTP, 2004; NRC, 2007).

This has resulted in the establishment of several research programs such as the USEPA Toxicity Forecaster (ToxCast) program and the inter-agency

Toxicology in the 21st Century (Tox21) collaboration (Dix et al., 2007; Collins et al., 2008; Kavlock et al., 2009; Judson et al., 2010). In a similar fashion, the

EDSP has adopted a similar new framework, EDSP in the 21st Century, to include high-throughput in vitro, in silico, and computational approaches to better screen and predict the endocrine activity of chemicals (U.S. EPA, 2011a).

Although these new strategies have been demonstrated to provide useful information regarding chemical activity, hazard potential, and endocrine effects, there are still weaknesses associated with only using in vitro and in silico approaches such as metabolic competency, in vitro assay complexity, dosimetry, extrapolation to a whole organism, and the relevance of exposure

(Reif et al., 2010; Rotroff et al., 2010; Padilla et al., 2012; Rotroff et al., 2013;

Wambaugh et al., 2013; Truong et al., 2014; Boyd et al., 2016; Richard et al.,

2016). Therefore, there is a need to develop in vivo systems with high- throughput capabilities to meet some of these challenges.

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One such model that meets many of the requirements for a high- throughput in vivo system is the zebrafish. There are many advantages to using embryonic zebrafish as a model organism for chemical screening including rapid, external development, transparency, and they are highly prolific.

Translationally, the zebrafish genome shares 71% homology with that of humans (Howe et al., 2013), and developmental and biological pathways are conserved. Throughout early development, nearly the entire repertoire of the genome is expressed (Vesterlund et al., 2011); therefore, any molecular target that a chemical has the potential to interact with will be present. This allows for the screening of large numbers of chemicals with diverse bioactivities. Zebrafish have already been used to examine the developmental and neurobehavioral toxicity of the chemicals in ToxCast phase I and II libraries (Padilla et al., 2012;

Truong et al., 2014; Reif et al., 2016). Similarly, other studies have screened flame retardants (Noyes et al., 2015), polycyclic aromatic hydrocarbons (PAHs)

(Knecht et al., 2013), phytoestrogens (Bugel et al., 2016), chemicals that disrupt cardiovascular function (Yozzo et al., 2013), as well as high-throughput neurobehavioral screening to identify chemicals with therapeutic potential as psychoactive drugs (Kokel et al., 2010).

Although developmental toxicity screens like those mentioned above provide a wealth of information regarding possible toxic effects of chemicals, adverse phenotypes alone are limiting, providing little to no mechanistic information regarding a chemical’s specific bioactivity and the high correlation

130 between phenotypes (Truong et al., 2014). For example, developmental exposure to prototypical estrogen, androgen, or thyroid-active chemicals cause overlapping phenotypes such as craniofacial abnormalities, pericardial and yolk sac edemas, axial defects, and delayed hatching. Therefore, for many chemicals, the observed adverse phenotypes in 120 hpf zebrafish cannot usually be mapped to a specific mode of action (MOA) (Kim et al., 2009; Saili et al., 2013; Jomaa et al., 2014; Rivero-Wendt et al., 2016). However, coupling high-throughput screening with transcriptomics can offer a two-tiered approach to categorize chemicals based on their developmental bioactivity profile and transcriptional changes likely associated with a chemical’s MOA. Within the context of the zebrafish model, transcriptomics has been used to identify putative MOAs and classify biological activities for a variety of chemicals including PAHs (Goodale et al., 2013; Goodale et al., 2015), antimicrobials

(Haggard et al., 2016), and endocrine disrupting compounds (Pelayo et al.,

2012; Saili et al., 2013; Schiller et al., 2013).

Herein, we explored the utility of using phenotypically-anchored transcriptomics in 48 hpf embryonic zebrafish to classify EDCs. We identified

25 endocrine-active chemicals from our previous developmental toxicity assessment of the ToxCast phase I and II chemicals (Truong et al., 2014). To understand the possible linkages between chemical-mediated changes in gene expression and adverse outcomes, we elected to use a concentration of each chemical that elicited an adverse phenotypic effects in 80% of the exposed

131 animals by 120 hpf (EC80) as sampling was from a pooled population. This concentration was independently identified for all 25 chemicals prior to transcriptomics analysis using high-density microarrays. Clustered correlation analysis of the top 1000 differentially regulated transcripts identified a cluster of four substances, 3,3’,5-triiodothyronine (T3), 3,3’,5-triiodothyroacetic acid

(triac), 3,3’,5,5’-tetraiodothyroacetic acid (tetrac), and the pharma compound,

CP-634384 , which had highly correlated transcriptional profiles that were unique compared to the remaining 21 chemicals. Analysis of this subset of possible thyroid-active chemicals revealed 27 overlapping transcripts, consisting of some known thyroid-regulated genes, which may serve as a diagnostic signature of exposure to thyroid hormone agonists in 48 hpf zebrafish. Furthermore, analysis of the developmental toxicity profiles of these four chemicals were highly similar and had a distinct phenotype in the gut, which may be indicative of overproduction of heme-containing proteins. Overall, we demonstrate the viability of using phenotypically anchored transcriptomics in zebrafish as a tool to screen and classify the endocrine disrupting potential of chemicals.

Materials and Methods

EDC Chemical Selection

Candidate EDC selection went through a tiered decision tree as shown in Figure 1. We first began with a universe of known and expected EDCs

132 that are available in The Endocrine Disruptor Exchange Database (TEDX; http://endocrinedisruption.org/endocrine-disruption/tedx-list-of-potential- endocrine-disruptors/overview; accessed Nov. 2013) and the first and draft second list of chemicals for tier 1 screening in the USEPA Endocrine Disruptor

Screening Program (U.S. EPA, 2016d; U.S. EPA, 2016e).This consisted of a universe of 1034 chemicals. We then queried these chemicals for those that were included in the ToxCast Phase I and II chemical library. This resulted in

289 unique chemicals. From this list, we mined our larval zebrafish developmental screening database (Truong et al., 2014) to identify chemicals which displayed high quality concentration response curves without developmental malformations at 24 hpf as we will be sampling at 48 hpf. This filtering protocol resulted in the selection of 25 chemicals for further evaluation;

Table 1 identifies the supplier, chemical stock purity, and concentration ranges tested. Test solutions were prepared in dimethyl sulfoxide (DMSO; Avantor

Performance Materials, Center Valley, PA). A majority of compounds were gifted by the USEPA and were the original chemicals used in the ToxCast program. All other compounds were obtained from Sigma Aldrich (St. Louis,

MO).

Zebrafish

All zebrafish handling and use were conducted according to the Oregon

State University Institutional Animal Care and Use Committee procedures. Wild-

133 type Tropical 5D zebrafish were maintained at the Sinnhuber Aquatic Research

Laboratory (Corvallis, OR) on a 28°C recirculating water system with a 14:10 hour light/dark photoperiod. Embryo collection was performed each morning from group spawns of adult zebrafish. In brief, group tanks of adults with a 1:1 male female ratio were set up the day prior to spawning. Embryos were collected from the tanks using an internal collection apparatus each morning, cleaned, developmentally staged in spans of no more than one hour, and kept in sterile petri dishes under the same conditions as adult zebrafish prior to exposures (Kimmel et al., 1995).

Chemical exposures

At 6 hpf, the chorions were removed enzymatically and the embryos were placed, in 96-well microplates pre-filled with 100 L embryo medium as previously described (Mandrell et al., 2012). For the EC80 range finding studies, zebrafish were exposed under static conditions to nominal graded concentrations of each chemical from 6-120 hpf (n = 32 embryos per exposure concentration; Table 1). Zebrafish were assessed for developmental toxicity across 22 endpoints at 24 and 120 hpf as previously described (Truong et al.,

2011). We conducted range finding studies based on the initial concentration response curves performed in the embryonic zebrafish high-throughput screen

(HTS) of the ToxCast chemicals (Truong et al., 2014), selecting concentrations that were expected to elicit no response and up to 100% adverse effects, to

134 allow for appropriate resolution for curve fitting. To calculate the EC80 for each chemical, the developmental toxicity data was consolidated to “no effect” and

“effect” scores across all the endpoints. Each animal was assigned a 0 or a 1 if there were no adverse malformations or if the animal had any mortality or malformation, respectively. We performed logistic regression analysis on the binomial developmental toxicity data for each chemical using custom R scripts, and the EC50, EC60, EC70, and EC80 were calculated for each regression curve using the dose.p function (Team, 2013). For the microarray studies, embryos were exposed to the EC80 of each chemical from 6-48 hpf and RNA was isolated as described below, with four biological replicates per chemical, in four separate grouped batches. Each group RNA isolation batch had its own vehicle (DMSO) control group to account for batch variability. Batch 1 consisted of vehicle control, 17-E2, tetrac, abamectin, BPA, propiconazole, triac, ziram, haloperidol, and kepone. Batch 2 consisted of vehicle control, triiodothyronine,

17-EE2, genistein, PFOS, 17-MT, and CP-634384. Batch 3 consisted of vehicle control, endrin, dimethipin, propylparaben, vinclozolin, BPAF, and butylparaben. Batch 4 consisted of vehicle control, diisobutyl phthalate, 5-

DHT, raloxifene hydrochloride, and pentachlorophenol. Representative images of 120 hpf larval zebrafish exposed to 0, 0.00016, 0.0008, 0.004, 0.02, and 0.1

M triac were taken using the Keyence BZ-X710 fluorescent microscope

(Keyence, Osaka, Japan). For the qRT-PCR studies, embryos were exposed to vehicle control, the EC50, or EC80 of triiodothyronine, triac, tetrac, or CP-

135

634384 from 6-48 hpf prior to RNA isolation, cDNA synthesis, and qRT-PCR analysis as described below.

RNA isolation

The Zymo Quick-RNA MiniPrep kit (Irvine, CA; Cat No. R1055) or the

Zymo Direct-zol RNA MiniPrep kit (Irvine, CA; Cat No. R2052) were used to isolate RNA for the microarray or qRT-PCR validation studies, respectively.

Each replicate consisted of total mRNA collected from pools of eight 48hpf embryos. In brief, embryos were homogenized using a bullet blender (Next

Advance, Averill Park, NY) for 3 minutes at speed 8 in either 300 µl lysis buffer for microarray samples or 500 µl RNAzol RT (Molecular Research Center,

Cincinnati, OH) for the qRT-PCR samples with 0.5 mm zirconium oxide beads.

RNA was extracted according to the manufacturer’s protocols. For the microarray samples, the optional DNase I digestion step was performed. The quality and concentration for each RNA sample was determined using the Gen5

Take3 and SynergyMX spectrophotometer (BioTek Instruments, Inc., Winooski,

VT).

Microarray processing

Microarrays were hybridized and processed by OakLabs GmBH

(Hennigsdorf, Germany) as described previously (Haggard et al., 2016). In brief,

RNA was isolated from pooled embryos as described above with four biological

136 replicates per treatment group. RNA integrity was assessed on the Agilent 2100

Bioanalyzer (Agilent Technologies, Santa Clara, CA), allowing for a minimum

RIN score of 7. 600 ng total RNA for each sample were placed in RNAstable® tubes (Biomatrica, San Diego, CA) and dried according to the manufacturer’s instructions prior to shipment to the processing facility. ArrayXS Zebrafish microarrays were used, which contain oligonucleotides for 48,370 coding and

8,075 non-coding sequences developed using the Ensembl ZV9 release 75 assembly. RNA samples were re-hydrated and the Low Input Quick Amp WT

Labeling Kit (Agilent Technologies) was used to generate cyanine 3-CT labeled cRNA according to the manufacturer’s protocols. Samples were hybridized to

ArrayXS Zebrafish microarrays using the Gene Expression Hybridization Kit

(Agilent Technologies) following a randomization procedure by OakLabs

GmBH, and scanned using the SureScan Microarray Scanner (Agilent

Technologies). Image files were processed with Agilent Feature Extraction software (version 11) and raw intensity data were provided for further processing and analysis.

Microarray analysis

The Linear Models for Microarray and RNA-seq Data (limma)

R/Bioconductor package was used for microarray data filtering, background correction, normalization, and statistical analyses (Smyth, 2004; Ritchie et al.,

2015). Arrays were first background corrected and quantile normalized (Silver

137 et al., 2009). We filtered all control probes and any probe that was less than

10% brighter than the negative control probes on at least four arrays. Data preprocessing and QA/QC visualization identified 9 arrays that had a similar aberrant intensity profile across many probes. As a results, these arrays were removed from analysis. This included biological replicates in two vehicle control batches, Abamectin, Haloperidol, Kepone, two Triiodothyronine replicates, CP-

634384, and Raloxifene hydrochloride. To account for potential batch effects, we implemented the ComBat algorithm (Johnson et al., 2007) to control for scan date variability as well as including the RNA isolation batches in the limma statistical model. Raw and normalized microarray data were uploaded to the

Gene Expression Omnibus and are publicly available (GSE89780). Differential expression was performed by fitting the data to a paired linear model in limma considering treatment and the grouped RNA batch effects, and calculating moderated t-statistics for each transcript using the empirical Bayes method of borrow information between genes. P-values were corrected at a 5% false discovery rate (FDR) using the Benjamini-Hochberg method. Transcripts with

FDR adjusted P-value ≤ 0.05 and a fold-change ≥ 1.5 were considered significant. To examine the similarity of transcriptional profiles across all of the treatment groups, we performed clustered correlation analysis using custom R scripts. The correlation matrix consisted of a dissimilarity matrix (1-Pearson correlation coefficient) of the probe-wise fold-changes for the top 1000 significantly differentially expressed transcripts, ranked by P-value, across all

138 treatment groups and was clustered using the agglomerative complete-linkage hierarchical clustering algorithm.

Quantitative real-time PCR

Primers for the target genes are listed in Table S1. A total of three biological replicates per treatment group, independent of the original microarray study, were used for the validation study. cDNA was generated from total RNA using the ABI High-capacity cDNA Reverse Transcription kit (Thermo Fisher,

Waltham, MA) according to the manufacturer’s protocol. We performed 12.5 µl qRT-PCR reactions using 6.25 µl ABI Power SYBR Green PCR Master Mix

(Thermo Fisher), 3.25 µl H2O, 0.5 µl of forward or reverse primer, and 2 µl of cDNA (12.5 ng/µl) using the StepOnePlus Real-Time PCR system (Thermo

Fisher). Manufacturer’s recommended cycling conditions were used. β-actin normalized fold-change measurements were calculated according to the procedure described by Pfaffl (Pfaffl, 2001). Data were analyzed in R using a one-way ANOVA with Tukey post-hoc test or the Kruskal-Wallis test with Dunn’s post-hoc test for data that passed or failed normality testing, respectively.

Results and Discussion

EC80 Determination of 25 EDCs

We conducted range finding studies based on the initial concentration response curves performed in the embryonic zebrafish high-throughput screen

139 of the ToxCast chemicals (Truong et al., 2014), selecting concentrations that were expected to elicit no response and up to 100% adverse effects, to allow for appropriate resolution for curve fitting. We calculated EC80 values for 23 of the 25 chemicals using this approach and observed a wide range in EC80 concentrations (Figure 2). We were unable to calculate an EC80 for Vinclozolin and Raloxifene hydrochloride, due to either no significant adverse effects at the highest tested concentration (100 uM) or solubility issues, respectively. As a result, these two chemicals were assigned an arbitrary EC value of 50 µM for transcriptome analysis. Detailed developmental toxicity profiles across the

22 endpoints evaluated, the logistic regression plot, model output, and EC calculations for each chemical can be found in Appendix 1. Triiodothyronine

(the endogenous active thyroid hormone), triac, and tetrac (thyroid hormone analogues for triiodothyronine and thyroxine, respectively), were the most potent chemicals tested with an EC80 range of 6.3-10.9 nM, suggesting that embryonic zebrafish are highly sensitive to these types of chemicals. In contrast, model estrogens and androgens were much less toxic, having EC80 values several orders of magnitude larger, ranging between 9.46-38.71 µM.

For many of the chemicals tested, we observed adverse phenotypic profiles and calculated EC values similar to those reported by others (Kim et al., 2009;

Saili et al., 2012; Rivero-Wendt et al., 2016). It should be noted that we did observe a small number of chemicals where exposure to the EC80 elicited

140 some adverse developmental progression effects at 24 and 48 hpf including

17β-E2, BPAF, diisobutyl phthalate, genistein, pentachlorophenol, and ziram.

In general, we observed a diversity of phenotypic responses across all of the chemicals tested, including common endpoints affected by many chemicals including axial defects, pectoral and caudal fin malformations, craniofacial abnormalities, and yolk sac and pericardial edemas. Clustered phenotypic profiling using the lowest effect level approach described previously (Truong et al., 2014) showed many chemicals having highly similar toxicity profiles across all the endpoints, regardless of supposed activity with estrogen, androgen, or thyroid hormone signaling (Figure S1). This suggests that phenotypic screening alone does not provide enough information to accurately classify chemicals based on their endocrine activity or mechanisms of action. However, we did observe some unique phenotypic responses for several compounds. Out of all 25 chemicals, ziram, a thiocarbamate pesticide, was the only chemical that elicited notochord malformations at 24 and 120 hpf, a phenotype that has already been well defined (Haendel et al., 2004; Teraoka et al., 2006; Tilton et al., 2006). The other chemicals which caused a unique phenotype after exposure were triiodothyronine, triac, tetrac, and CP-634384.

As described above, triiodothyronine, triac, and tetrac are known thyroid hormone agonists. CP-634384, to our knowledge, represents one of the failed pharmaceutical chemicals that were included in the ToxCast phase II library,

141 and we had no prior information regarding the bioactivity of this chemical.

Examination of the developmental toxicity profiles for these four compounds revealed very high similarity (Figure 3A). However, we did observe two phenotypes that were highly unique to only these compounds. Namely, the loss of pigmentation and the formation of the blue-green pigment in the liver at higher exposure concentrations, as shown in representative images of embryos exposed to triac (Figure 3B). A recent developmental toxicity screen of triiodothyronine as well as thyroid antagonists observed similar pigmentation in the liver of only triiodothyronine exposed zebrafish, suggesting that this effect is specific to thyroid receptor agonists (Jomaa et al., 2014). The authors concluded that the blue-green pigmentation in the liver was likely accumulation of biliverdin, a byproduct of heme catabolism. We also observed a notable decrease in the density of melanophores after exposure to these four chemicals (Figure 3B). This phenotype has been observed in other studies examining hyperthyroidism in adult and larval zebrafish, suggesting thyroid hormones repress the development and proliferation of melanophores

(Jomaa et al., 2014; McMenamin et al., 2014).

As the focus of this study is to demonstrate the utility of using phenotypically anchored transcriptomics to identify transcriptional signatures indicative of disruption of specific endocrine systems, the selection of the concentration and developmental time point to use was highly important. A problem with some transcriptomics studies is the ability to detect reproducible

142 and strong expression signatures that are indicative of chemical action. We surmised that the use of a high concentration is desirable as it should result in strong transcriptional changes that can be detected above the background expression noise during the expansive developmental process. It has been demonstrated that the use of lower concentrations (EC10 and EC20 values) to profile estrogenic and anti-androgenic chemicals provides unreliable transcriptional responses that can only be contextualized by using downstream functional analyses; not providing useful signatures that can act as biomarkers for application in larger chemical screens (Schiller et al., 2013). The time point for these types of studies is also important, and 48 hpf has already been demonstrated to be useful for mechanistic studies (Goodale et al., 2013;

Goodale et al., 2015; Haggard et al., 2016). This early time point also limits the possibility of detecting secondary effects that are further downstream of the original insult or are unrelated to the mode of action.

Transcriptome profiling of EDCs shows specific transcriptional signature for thyroid hormone active compounds

The redundant phenotypic effects we observed for many of the 25 tested chemicals, which have diverse endocrine activities and mechanisms of action, suggests that phenotypic profiling alone is unable to group chemicals

(Figure S1). Therefore, we took a transcriptome-wide approach to identify discriminatory transcriptional signatures that would separate chemicals based

143 on their known or suspected endocrine pathway. As highly correlated transcriptome profiles should be indicative of similar modes of action, we performed a clustered correlation analysis of the top 1000 significant differentially expressed transcripts across all tested chemicals (Figure 4). As shown, we observed a distinct separation with triiodothyronine, triac, tetrac, and CP-634384 from the other compounds, with little to no correlation with all other chemicals. We also observed groups of chemicals with highly correlated profiles such as Vinclozolin and BPAF, Propylparaben and Butylparaben, and a group of three chemicals including Genistein, 17-MT, and 17α-EE2.

Exposure to the EC80 of triiodothyronine, triac, tetrac, and CP-634384 resulted in 55, 148, 407, and 156 significant differentially expressed transcripts, respectively, for a total of 614 dysregulated transcripts across all four chemicals. As shown in Figure 5, we observed positive correlations for all pairwise comparisons of these four chemicals. We observed the greatest transcriptional overlap between triac and CP-634384 with a total of 52 transcripts. There were a total of 27 transcripts that were significantly differentially expressed across all four treatments (Table 2), including several for the same gene target (akr, b2m, hbz, opn1lw2, and si:ch1073-459j12.1) representing a total of 22 unique gene targets. Interestingly, on average, we only observed 2-3 out of these 27 transcripts (<10%) significantly dysregulated

144 in the other 21 chemicals, suggesting that these 27 transcripts represent a signature unique to triiodothyronine, triac, tetrac, and CP-634384.

To demonstrate that the unique transcriptional signature could be used as a possible biomarker for exposure to thyroid hormone active compounds, we selected eight of the 27 significant overlapping transcripts for qRT-PCR validation at both the calculated EC50 and EC80 exposure levels. As shown in

Table 3, all of the transcripts except for gch2 validated in one or more of these chemicals. We observed significant expression changes at both the EC80 and

EC50 exposure levels for dio3b, nfil3-6, sb:cb827, and si:ch211-103n10.5. plp1b was similarly significantly elevated for all tested chemicals at both the

EC50 and EC80 except for the EC50 of triiodothyronine (FDR adjusted P-value =

0.23). Interestingly, we observed a rather diminished increase in expression for si:ch211-103n10.5 between the microarray and qRT-PCR studies at the

EC80, with an average log2 fold change of 4.09 ± 0.35 and 1.72 ± 0.36, respectively. This variation in expression responses may be reflective of methodological differences between the two platforms for this particular transcript. We also observed mixed significant responses for both hbz and zgc:92880, which were not affected after exposure to triac, suggesting these transcripts may not be fully reliable as biomarkers at 48 hpf.

It is well known that thyroid hormones are responsible for regulating the spatiotemporal expression of many genes during development, particularly in

145 the developing brain (Bernal, 2000). This suggests that a major role of TRs during early development is the repression of gene expression until the normal endogenous thyroid hormone signaling cues are present. This logic also follows with the known roles of thyroid hormone in the metamorphosis of anurans as well as the changes that occur between developmental transitions

(i.e. embryo, larvae, juvenile, adult) of teleost fishes (Darras et al., 2015).

Therefore, increased levels of TR agonists will likely result in the aberrant expression of genes normally repressed until endogenous thyroid hormone is present. To support this, we observed significant increases in the expression of 23 of the 27 overlapping transcripts between these four chemicals, which suggests, under basal conditions, these transcripts are normally repressed by unliganded TRs (Table 2). Notable transcripts included dio3b, plp1b, hbz, zgc:92880, si:ch211-103n10.5, and b2m. Human orthology mapping of si:ch211-103n10.5 and zgc:92880 identified these two transcripts as

HIST1H1C and HBG1. dio3a/b are responsible for performing inner-ring deiodination of active thyroxine and triiodothyronine to the inactive 3,3’,5’- triiodothyronine (reverse-triiodothyronine) and 3,3’-diiodothyronine, respectively, and is involved in the regulation of intracellular thyroid hormone activity (Guo et al., 2014; Darras et al., 2015). Thyroid hormone disruption during brain development results in changes in myelination throughout the brain, where hypothyroidism decreases myelination and hyperthyroidism increases myelination (Bernal, 2000). plp1b is a structural protein that is the

146 predominant component of myelin and is commonly shown to be decreased in the hypothyroid brain (Bernal, 2000; Ferreira et al., 2007). Thyroid hormones have also been shown to play a role in the switching of embryonic to adult hemoglobin, and the observed changes in expression for both hbz and zgc:92880 may be involved in that process (Flavin et al., 1982; Pelayo et al.,

2012). It is interesting to speculate whether the large increases in the expression of hemoglobin genes contribute to the unique blue-green pigment in the liver, which was observed in 120 hpf animals during our phenotypic analysis (Figure 3B). si:ch211-103n10.5, an orthologue of human HIST1H1C, was the most significantly increased transcript observed for all four chemicals.

Human HIST1H1C codes for Histone H1.2, a histone complex protein that is able to bind to linker DNA between nucleosomes to form chromatin fibers, and is involved in the compaction of nucleosomes (Kim et al., 2015). Histone proteins are normally regulated in a DNA replication-dependent manner and to our knowledge only one study has observed a similar effect of thyroid hormones increase the abundance of histone proteins in vitro (Zambrano et al., 2015). In this particular study, the effects of triiodothyronine on histone proteins was suggested to be due to increased translational efficiency of histones rather than increased transcription. However, this was never confirmed for histone H1, which may have an independent mechanism or may be regulated differently by thyroid hormone in zebrafish. The extent to the significant induction of si:ch211-103n10.5 contributes to the observed

147 expression changes or downstream effects are unknown. We can speculate that increased expression of si:ch211-103n10.5 may be a homeostatic response to the loss of TR repressive activity in the presence of these four chemicals, or may cause aberrant repression of genes through chromatin remodeling (Kim et al., 2015). b2m, a protein found on the surface of lymphocytes, is a component of class I histocompatibility antigens and has been shown to be elevated in a hyperthyroid state, such as in patients with

Graves’ disease (Lervang et al., 1989; Roiter et al., 1991; Escobar-Morreale et al., 1996). The exact mechanism by which excessive thyroid hormone levels increase b2m levels is unknown.

Thyroid hormones are involved in a wide range of processes during vertebrate development such as neurodevelopment, pigmentation, morphogenesis, and growth; a classic example being thyroid-dependent metamorphosis in anurans (Morvan-Dubois et al., 2008). Furthermore, studies in both rodent and humans have shown that hypothyroidism during pregnancy negatively affects neurobehavior and cognition in offspring (Ghassabian et al.,

2014; O'Hare et al., 2015; Korevaar et al., 2016; Min et al., 2016). The thyroid axis is a complex system involving the synthesis of thyroid hormones, transport through the circulatory system via thyroid hormone binding proteins, feedback regulation by the hypothalamus and pituitary, and catabolism via hepatic enzyme or deiodinases, all of which can be potential targets of thyroid hormone disrupting chemicals (Crofton, 2008; Miller et al., 2009; Jugan et al.,

148

2010). Thyroid development and function in zebrafish occurs on a much faster time scale compared to rodents and humans, with the thyroid follicles forming and proliferating from 36-72 hpf, and endogenous thyroxine being synthesized by 72 hpf (Porazzi et al., 2009). As our transcriptomic analysis was performed prior to the onset of endogenous thyroxine production by mature thyroid follicles, any observed transcriptional effects likely involve either dysregulation in the development of the thyroid follicles, thereby influencing the production of endogenous hormone later in development and subsequent effects on the hypothalamus-pituitary-thyroid axis, or through direct activity with TRs. In our analysis, we did not observe any transcriptional changes in key transcription factors involved in thyroid development, such as hhex, nkx2.1a, and pax2a, or those involved in the development of the thyroid primordium, such as bon, gata5, or sox32 (Porazzi et al., 2009). Therefore, it is likely that exposure to these four chemicals does not influence the development of thyroid follicles, and likely elicited the observed transcriptional effects and later adverse morphological effects through direct activity at the TR level.

The lack of strong transcriptional profiles for the estrogenic and androgenic chemicals in this study is likely due to the single concentration and time point used for our analysis. Having a time course of transcriptional changes following chemical exposures would likely increase the sensitivity and reliability of any biomarkers of exposure as well as provide additional information regarding any signaling cascades associated with the adverse phenotypic

149 responses observed for all the chemicals. However, this would also likely need to be performed at lower concentrations to limit secondary effects associated with the progression of toxicity. Similarly, since we were interested on identifying discriminatory transcriptional signatures indicative of particular endocrine effects, we have focused our analysis at the transcript level and did not perform any downstream functional analyses. Many transcriptome analyses have observed a lack of correlation between related chemicals (Schiller et al., 2013) or species comparisons of the same chemicals (Driessen et al., 2015) at the transcript level, only to observe significant overlap in functional relationships between exposures at the biological pathway or gene ontology level. We can only speculate that we will observe similar functional relationships between the remaining 21 chemicals that did not have strong discriminatory transcriptional profiles.

Conclusion Developing new models with the potential to rapidly screen chemicals for endocrine activity is paramount to meet the statutory requirements established by Congress for the EDSP. Furthermore, additional work is needed to define in vivo models for these types of screens due to their advantages over in vitro and in silico assays. Here, we utilized phenotypically anchored transcriptomics with a diverse set of 25 EDCs to identify unique transcriptional signatures that can classify chemicals based on their interaction with either estrogen, androgen, or thyroid endocrine systems. Although the transcriptome analysis presented here

150 only consisted of one-time point, and therefore represents only a snapshot of the transcriptome changes due to chemical exposure, it is able to model chemical-induced expression changes across all cell and tissue types present in the 48 hpf zebrafish. Using this approach, we demonstrated that chemicals that act as TR agonists induce a unique transcriptional profile that is indicative of disruption of TR signaling, mediated by loss of the basal repressive signaling of unliganded TRs. It is important to note, other than the apparent potency of

CP-634384 from the initial screen of the ToxCast chemicals (Truong et al.,

2014) and in the assessment presented here, we had no prior information that this chemical had any interaction with the thyroid hormone system. The identification and subsequent validation of many of the transcriptional changes observed between all four chemicals, alongside their involvement in thyroid hormone signaling, suggests that these transcripts may serve as a suite of biomarkers of TR agonists in 48 hpf zebrafish. Moreover, the unique 120 hpf phenotypes, loss of pigmentation and biliverdin accumulation near the liver, offer a second screening approach to identify chemicals that are strong agonists of TRs (Figure 5B). Further study is needed to determine whether these transcripts continue to be disrupted or if any additional signatures become apparent later during development due to exposure to TR agonists. Taken together, phenotypically anchored whole genome transcriptomics in zebrafish offers a unique in vivo platform to rapidly screen potential EDCs and begin classifying them based on their global expression profiles, and could be used

151 meet some of the EDC screening requirements for the USEPA EDSP and other regulatory agencies.

Acknowledgements We would like to thank Greg Gonnerman, Carrie Barton, and the staff at the Sinnhuber Aquatic Research Laboratory for help and support with fish husbandry, spawning, and embryo screening. We would also like to acknowledge Dr. Amber Roegner for assistance during RNA collections, and

Dr. Lisa Truong and Dr. Michael Simonich for manuscript editing and preparation. This research was supported by NIH P30 ES000210, T32

ES007060, P42 ES016465 and EPA #R835168. Pacific Northwest National

Laboratory is a multi-program laboratory operated by Battelle for the U.S.

Department of Energy under Contract DE-AC05-76RL01830.

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Figure 4- 1. Chemical selection workflow based on the TEDX and USEPA EDC lists and our zebrafish developmental toxicity assessment of ToxCast Phase I and II chemicals. ** denotes the initial list and second list of chemicals for tier 1 screening in the USEAP EDSP.

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Figure 4- 2. Distribution of EC80 values for all 25 chemicals. EC80 concentrations for all 26 compounds ordered by potency. Vinclozolin and raloxifene hydrochloride had low overt toxicity, and the EC value was arbitrarily set to 50 µM.

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Figure 4- 3. Developmental toxicity of triiodothyronine, triac, tetrac, and CP-634384. A) Phenotypic profile for triiodothyronine, triac, tetrac, and CP-634384 across the suite of 22 phenotypic endpoints evaluated at 24 and 120 hpf. B) Representative bright field images of 120 hpf embryos exposed to 0, 0.16, 0.8, 4, 20, or 100 nM triac. White arrows denote possible biliverdin accumulation in the liver of exposed embryos.

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Figure 4- 4. Clustered correlation analysis of the top 1000 significantly expressed transcripts for the 25 EDCs. A dissimilarity matrix for the top 1000 significant transcripts (FDR adjusted P- value ≤ 0.05; fold change ≥ 1.5) for all pairwise comparisons was generated and hierarchically clustered using the complete linkage algorithm.

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Figure 4- 5. Scatter matrix and significant transcript overlap between triiodothyronine, triac, tetrac, and CP-634384. Transcriptional profiles of the thyroid chemical group using pairwise scatter matrices of the 614 unique significant differentially expressed transcripts are shown in the bottom half of the plot. Dashed lines indicate the regression line for each comparison and the Pearson correlation coefficient is provided in the bottom right of each plot. Venn diagrams representing the pairwise overlap of significant transcripts for each treatment pair are shown in the top half of the plot.

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Figure S4- 1. Developmental toxicity profile of all 25 EDCs. Lowest effect level (LEL) concentrations for all chemicals were calculated as described in Truong et al. (2014) for all 22 phenotypic endpoints and for ANY effect. LELs were log transformed (-log10(LEL/10^6)) into an activity score. Darker colors correspond to high activity at a low concentration. Chemicals were hierarchically clustered based on their similar toxicity profile across all endpoints using the complete linkage algorithm.

Table 4- 1. 25 endocrine disrupting compounds used in this study. Abbreviation Chemical name [CAS No.] Supplier Purity Concentration range (µM) 17α-EE2 17α-ethinylestradiol [57-63-6] USEPA 99% 0, 4, 8, 16, 32, 64 17β-E2 17β-estradiol [50-28-2] Sigma 100% 0, 4, 8, 12, 16, 24 Aldrich 17-MT 17-methyltestosterone [58-18-4] USEPA 98% 0, 15, 30, 40, 50, 60 Tetrac 3,3’,5,5’-tetraiodothyroacetic acid [67-30-1] Sigma 100% 0, 0.0016, 0.008, 0.04, Aldrich 0.2, 1 Triiodothyronine 3,3’,5-triiodothyronine [6893-02-3] USEPA 99% 0, 0.00016, 0.0008, 0.004, 0.02, 0.1 5α-DHT 5α-dihydrotestosterone [521-18-6] USEPA 98% 0, 12.5, 25, 50, 75, 100 Abamectin Abamectin [71751-41-2] Sigma 98.9% 0, 0.25, 1, 4, 16, 64 Aldrich BPA Bisphenol A [80-05-7] Sigma 100% 0, 15, 30, 40, 50, 60 Aldrich BPAF Bisphenol AF [1478-61-1] USEPA 99.1% 0, 5, 10, 15, 25, 35 Butylparaben Butylparaben [94-26-8] USEPA 99% 0, 0.25, 1, 4, 16, 64 CP-634384 ({4-[(7-hydroxy-2,3-dihydro-1H-inden-4-yl)oxy]-3,5- USEPA ≥90% 0, 0.0016, 0.008, 0.04, dimethylphenyl}amino)(oxo)acetic acid [290352-28-2] 0.2, 1 Diisobutyl phthalate Diisobutyl phthalate [84-69-5] Sigma 99% 0, 2.5, 5, 10, 12.5, 15 Aldrich Dimethipin Dimethipin [55290-64-7] USEPA 99.5% 0, 12.5, 25, 50, 75, 100 Endrin Endrin [72-20-8] Sigma 99.3% 0, 0.125, 0.25, 0.5, 2, 4 Aldrich Genistein Genistein [446-72-0] USEPA 98% 0, 5, 10, 15, 25, 30 Haloperidol Haloperidol [52-86-8] USEPA 99.4% 0, 0.016, 0.08, 0.4, 2, 10 Kepone Kepone [143-50-0] USEPA 99.9% 0, 0.016, 0.08, 0.4, 2, 10 165

Pentachlorophenol Pentachlorophenol [87-86-5] USEPA ≥90% 0, 0.25, 0.5, 1, 1.5, 2 PFOS Perfluorooctane sulfonic acid [1763-23-1] USEPA 100% 0, 0.5, 2, 4, 8, 10 Propiconazole Propiconazole [60207-90-1] Sigma 99.2% 0, 15, 30, 40, 50, 60 Aldrich Propylparaben Propylparaben [94-13-3] Sigma 100% 0, 5, 10, 15, 25, 35 Aldrich Raloxifene hydrochloride Raloxifene hydrochloride [82640-04-8] USEPA 98% 0, 15, 30, 40, 50, 60 Triac 3,3’,5-triiodothyroacetic acid [51-24-1] Sigma 99% 0, 0.00016, 0.0008, Aldrich 0.004, 0.02, 0.1 Vinclozolin Vinclozolin [50471-44-8] Sigma 99.6% 0, 6.25, 12.5, 25, 50, Aldrich 100 Ziram Ziram [137-30-4] USEPA 99.2% 0, 0.0016, 0.008, 0.04, 0.2, 1

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Table 4- 2. Overlapping significant transcripts across triiodothyronine, triac, tetrac, and CP-634384 (log2 fold change). Transcript ID Gene Symbol Triiodothyronine Triac Tetrac CP-634384 ENSDART00000027550 ak4 0.82 0.72 0.59 1.01 ENSDART00000145715 ak4 0.71 0.73 0.67 0.90 ENSDART00000134370 ak4 0.67 0.72 0.96 0.81 ENSDART00000150427 b2m 0.68 0.77 0.61 0.86 ENSDART00000075127 b2m 0.93 0.91 0.64 1.17 ENSDART00000076816 cox6a2 0.73 1.32 0.75 0.98 ENSDART00000122835 dio3a 0.77 1.02 1.27 1.49 ENSDART00000131982 dio3b 0.95 1.65 1.60 1.30 ENSDART00000132277 opn1lw2 (processed) 1.03 1.79 1.52 1.59 ENSDART00000140547 cox6a2 (retained intron) 0.62 1.20 0.68 0.72 ENSDART00000144302 opn1lw2 (retained intron) 0.74 1.19 0.75 1.17 ENSDART00000150325 b2m (retained intron) 0.71 0.81 0.60 1.02 ENSDART00000049895 epd 0.80 1.08 0.59 0.83 ENSDART00000012265 gch2 -1.02 -2.07 -2.02 -2.02 GENSCAN00000005542 GENSCAN00000005542 -0.76 -1.00 -1.02 -0.82 ENSDART00000149920 hbz 2.52 5.13 2.86 2.87 ENSDART00000066383 hbz 2.68 4.92 2.86 2.82 ENSDART00000127604 nfil3-6 1.44 1.48 1.96 1.57 ENSDART00000065940 opn1lw2 1.06 1.70 1.44 1.58 ENSDART00000126540 plp1b 0.62 0.83 0.63 0.90 ENSDART00000129033 sb:cb827 0.97 1.21 1.04 0.96 ENSDART00000025875 si:ch1073-459j12.1 0.86 1.02 0.64 1.08 ENSDART00000132115 si:ch1073-459j12.1 0.88 0.91 0.59 1.03 ENSDART00000077635 si:ch211-103n10.5 4.34 4.38 3.62 4.02 ENSDART00000147009 si:dkey-251i10.2 -0.79 -1.32 -1.27 -1.16 ENSDART00000109132 tmem130 -0.73 -1.33 -1.50 -1.47 ENSDART00000053077 zgc:92880 1.95 5.13 2.33 1.85

Table 4- 3. qRT-PCR validation of target transcripts in the unique transcriptional signature of triiodothyronine, triac, tetrac, and CP-634384. Gene Triiodothyronine Triac Tetrac CP-634384

Microarray EC50 EC80 Microarray EC50 EC80 Microarray EC50 EC80 Microarray EC50 EC80

dio3a 0.77 1.96 ± 2.04 ± 1.02 1.19 ± 1.42 ± 1.27 1.85 ± 2.04 ± 1.49 1.63 ± 1.75 ±

0.16 0.55 0.13 0.24 0.10 0.69 0.1 0.32

gch2 -1.02 -1.02 ± -0.62 ± -2.07 -0.37 ± -0.88 ± -2.02 -0.04 ± -1.24 ± -2.02 -0.38 ± -0.35 ±

0.29 0.56 0.33 0.47 0.09 1.75 0.22 0.47

hbz 2.68 0.89 ± 1.06 ± 4.92 0.11 ± -0.12 ± 2.86 0.77 ± 1.20 ± 2.82 0.63 ± 0.91 ±

0.09 0.63 0.20 0.29 0.26 0.44 0.02 0.16

nfil3-6 1.44 0.59 ± 0.77 ± 1.48 0.89 ± 1.01 ± 1.96 1.48 ± 1.71 ± 1.57 1.24 ± 1.36 ±

0.05 0.36 0.18 0.29 0.13 0.34 0.12 0.06

plp1b 0.62 0.92 ± 2.58 ± 0.83 2.07 ± 1.51 ± 0.63 2.18 ± 2.09 ± 0.90 2.03 ± 2.24 ±

0.68 0.67 0.14 0.33 0.28 0.58 0.36 0.97

sb:cb827 0.97 1.48 ± 1.53 ± 1.21 1.47 ± 1.58 ± 1.04 1.78 ± 1.83 ± 0.96 1.27 ± 1.86 ±

0.45 0.42 0.23 0.27 0.21 0.21 0.33 0.08 168

si:ch211- 4.34 1.33 ± 1.69 ± 4.38 1.58 ± 1.73 ± 3.62 1.65 ± 2.18 ± 4.02 1.29 ± 1.55 ±

103n10.5 0.10 0.49 0.27 0.59 0.09 0.47 0.01 0.15

zgc:92880 1.95 0.84 ± 1.11 ± 5.13 -0.25 ± -0.13 ± 2.33 0.98 ± 1.30 ± 1.85 0.27 ± 0.83 ±

0.43 0.26 0.41 0.13 0.30 0.45 0.22 0.25

Note: Shading denotes statistically significant log2 fold changes in expression compared to vehicle control (FDR adjusted P-value ≤ 0.05).

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Table S4- 1. Primer Sets for qRT-PCR Validation. Gene Symbol Forward Reverse dio3b ACCGCTGATCCTCAACTTCG TTACGATGTACAGCCGCTCG gch2 ACGTATTTTTGTTAAATGCTCTG ACTGCATAAAAATGATGCCAC si:ch211-103n10.5 GATCGTGTCTTCCTCAAAGG TTTCGAGCCGATCTTGAATG hbz GTGTCCGCTCAACCACAAAC CAATTTGCTCTCCCTTTGGG nfil3-6 TGGTGTTAGAAAACCGTGTC CGTGGAAGGTAGTAATGCTG sb:cb827 ATTTAGTCCCCTCTGTGTGTTG TAAAGGGAGTGAAGTAACAGC zgc:92880 TCCAAGTGTGAATTAGATACGC AATGCTGAGCAAAGATAGGC plp1b GTGGACACTGGAGTTTATCTG CAGTCATAGCAACCTAGAGC actb1 AAGCAGGAGTACGATGAGTC TGGAGTCCTCAGATGCATTG

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CHAPTER 5 - DISCUSSION

Modern regulatory toxicology is faced with the daunting task of finding ways to characterize the potential hazards tens of thousands of chemicals pose to human health and the environment. It has been well established that comprehensive hazard assessment of even one chemical using the traditional in vivo approaches requires millions of dollars, years to complete, and thousands of animals in order to come to some form of regulatory decision (U.S.

EPA, 2011a). With the benefits 21st century toxicity testing present to regulatory toxicology (NRC, 2007), many agencies have responded by establishing in vitro

HTS programs to rapidly characterize the activity of thousands of environmental chemicals with the eventual goal to reduce the cost, time, and animal burden needed to meet the statutory demands set by Congress. Implementation of these programs, including ToxCast, Tox21 and EDSP21, has provided a wealth of publically available chemical bioactivity data for thousands of chemicals.

These programs have resulted in the development of several computational tools that are beginning to lay the groundwork for predictive modeling and classification schemes in regulatory toxicology. Moreover, the breadth of biological activity and pathway space these programs cover provide a comprehensive look at what data is most relevant during chemical screens to make prioritization decisions. However, the extensive use of in vitro assays in these programs have highlighted several failings and inefficiencies that cannot be overcome with current technologies; namely a lack of endogenous xenobiotic

172 metabolism and low biological complexity. Therefore, alternative model systems that are amenable to high-throughput screens and can provide comprehensive bioactivity information are needed. The objectives of the research presented here were to demonstrate the feasibility of using the phenotypically anchored transcriptomics in zebrafish to both define potential mechanisms of action for specific chemicals, and show that the data obtained using this approach can allow for the classification of chemicals using transcriptome profiling.

A single transcriptomics experiment generates a large amount of data representing a snapshot in time of the global expression patterns occurring throughout the animal. These studies can identify biomarkers that represent activation of specific signaling pathways, and can also provide information to determine specific modes of chemical action. Chapter 2 used comparative transcriptomics in wild-type and a knockout zebrafish line to generate hypotheses regarding the cardiotoxic effects of the flame retardant, mITP. It was well established that mITP is an AhR ligand, but the cardiotoxic effects are AhR- independent (McGee et al., 2013; Gerlach et al., 2014). By utilizing both wild- type and ahr2-null zebrafish lines, we were able to identify similar and unique transcriptional signatures that allowed us to determine the ahr2-independent transcriptional effects of mITP. We observed canonical AhR signaling that is common with exposure to many AhR ligands, such as the induction cyp1a and other phase I metabolizing genes; however, mITP is unique in that the activation of AhR is not associated with adverse phenotypic effects in zebrafish. Further

173 studies comparing the observed innocuous AhR signature to those that cause adverse toxicity, for which there are several datasets available (Goodale et al.,

2013; Goodale et al., 2015), may highlight biological pathways directly responsible for toxic activity and those associated only with the adaptive AhR response. The similarities in expression responses between the two genotypes showed that retinoic acid signaling is involved in the cardiotoxic action of mITP.

The robust expression changes in several Hox genes, which play fundamental roles in cardiac lineage cell fate and cell recruitment via their roles in the second heart field (SHF), as well as changes in the expression of enzymes responsible for the metabolisms of RA (e.g. cyp26a1, dhsr3a/b), would suggest that mITP is antagonizing retinoic acid receptors to disrupt heart formation in the developing zebrafish. Furthermore, significant enrichment in retinoic acid- relevant pathways were seen using gene ontology enrichment analysis.

Additionally, this dataset allowed us to examine the 48 hpf transcriptome in the absence of functional ahr2, which revealed possible crosstalk with other transcription factors. This suggests possible endogenous activity of ahr2 under normal conditions to maintain oxidative stress signaling via cooperation with

Nrf2 and influence Hif1a activity through nuclear receptor co-factor squelching.

Transcriptomics studies can also provide information to determine the concordance between hypothesized mechanisms of action from other model systems, such as rodents or human in vitro studies. Similarly, due to the breadth of expression responses that can be detected, functional analysis of the

174 transcriptional data can also be used to compare responses from publically available chemical bioactivity databases like those generated from the ToxCast program. Several in vitro and in vivo studies have linked TCS exposure to hypothyroidism, hypothesizing that TCS is acting through CAR/PXR signaling to increase hepatic degradation of circulating thyroxine levels (Paul et al., 2012;

Paul et al., 2013). To determine whether similar effects were occurring in zebrafish, in Chapter 3, we performed phenotypically anchored transcriptomics using high-density microarrays. Exposure to the EC80 of TCS elicited a robust expression profile, predominated by significantly decreased transcripts. Several of the transcripts significantly affected by TCS are known biomarkers of hepatotoxicity, such as fabp10a, tfa, hp, apoba, and matn1. Biological process enrichment analysis of the TCS transcriptional profile showed many of these transcripts are involved in processes associated with liver functioning and metabolism. Interestingly, we did not observe any significant expression signature for processes related to thyroid hormone signaling or CAR/PXR signaling, suggesting a species- or developmental stage-specific effect in zebrafish compared to what has been demonstrated in rodent studies. Analysis of the comprehensive bioactivity data ToxCast hit data showed that TCS was highly bioactive across many of the assays (~56% of the unique assay endpoints). However, when compared to our transcriptomic data, ToxCast assay activity was more concordant with the hypothesized role of TCS in

CAR/PXR-induced hypothyroxinemia. Overall, although this study showed that

175 the transcriptome profile of TCS exposure in zebrafish had low concordance with other model systems and the ToxCast data, we were able to infer that TCS is likely hepatotoxic in zebrafish. Furthermore, the transcriptome profile of TCS exposure could be used as a reference when performing additional transcriptome studies, and the set of possible biomarkers of hepatotoxicity we observed can be used to identify hepatotoxic chemicals in zebrafish.

Although pathway level analyses of transcriptomic data from low effect concentration exposures have revealed endocrine disruption in 48 hpf zebrafish

(Schiller et al., 2013), these analyses relied on identifying connections between chemicals that were only related because some of the transcriptional signatures were part of a much larger biological process or network, and were not consistently observed across chemicals that should have similar endocrine activity. These types of approaches are not overly diagnostic or useful for application to chemical prioritization schemes or screening programs.

Therefore, in Chapter 4, we sought to take a transcript-centric approach to identify expression signatures that would allow us to discriminate between chemicals with different endocrine activities by basing our studies on phenotypically anchored high effect concentrations. We first selected a test set of 25 EDCs based on our in vivo developmental toxicity database from the initial screen of ToxCast phase I and II chemicals (Truong et al., 2014) and modeled our choices based on the USEPA EDSP, which is mandated to screen chemicals for adverse estrogen, androgen, or thyroid hormone activity. Similar

176 to what was performed in Chapter 3, we identified an EC80 for all of the chemicals to phenotypically anchor our transcriptome data to fully characterized phenotypic effects in 120 hpf animals. Since our chemical list included known reference chemicals for the three target pathways and several unknown chemicals, we surmised that the most significant differentially expressed transcripts across all 25 chemicals should provide enough information to both group chemicals based on their activity and may provide diagnostic biomarkers useful for screening unknown chemicals. Clustered correlation analysis showed that four chemicals (3,3’,5-triiodothyronine, 3,3’,5,5’-tetraiodothyroacetic acid,

3,3’,5-triiodothyroacetic acid, and an unknown pharmaceutical compound, CP-

634384) had a highly correlated transcriptional signature that was unique to only these chemicals, with many of the transcripts having known roles in thyroid hormone signaling or in thyroid diseases. This unique transcriptional signature has the potential to act as a biomarker panel to rapidly identify chemicals that are thyroid hormone receptor agonists. Interestingly, these four chemicals also had a highly similar and unique phenotypic profile compared to all other chemicals tested, with pigmentation and the accumulation of what appears to be the heme catabolic byproduct, biliverdin, in the liver of exposed animals.

Therefore, our approach identified a two-tiered screen, consisting of both gene expression and phenotypic endpoints, to identify potential TR agonists.

However, further testing with other known TR agonists must be conducted to better define the specificity and sensitivity of these two metrics.

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Taken together, the work presented in this dissertation demonstrates the usefulness and applicability of combining both the HTS capacity of the zebrafish model and whole genome transcriptomics to quickly assess the mechanism of action of diverse chemicals and begin to define predictive transcriptional signatures for specific chemical activity. Furthermore, we demonstrate that incorporating phenotypic anchoring into these types of studies allows researchers to overcome the variability and uncertainties commonly associated with transcriptome analyses using pooled whole embryos, and can give confidence that measured transcriptional changes are causally involved with eventual adverse outcomes. This transcriptome approach provides enough in- depth toxicogenomic information to generate specific hypotheses that are useful in informing targeted testing strategies for chemicals that would require additional characterization. At the same time, these analyses will drive the development of refined in vivo predictive models and identify activity-specific biomarker panels that can be used during initial toxicity screening to better classify chemicals with unknown activities.

References Gerlach, C.V., Das, S.R., Volz, D.C., Bisson, W.H., Kolluri, S.K., Tanguay, R.L., 2014. Mono-substituted isopropylated triaryl phosphate, a major component of Firemaster 550, is an AHR agonist that exhibits AHR- independent cardiotoxicity in zebrafish. Aquat Toxicol 154, 71-79. Goodale, B.C., La Du, J., Tilton, S.C., Sullivan, C.M., Bisson, W.H., Waters, K.M., Tanguay, R.L., 2015. Ligand-Specific Transcriptional Mechanisms Underlie Aryl Hydrocarbon Receptor-Mediated Developmental Toxicity of Oxygenated PAHs. Toxicol Sci 147, 397-411.

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Goodale, B.C., Tilton, S.C., Corvi, M.M., Wilson, G.R., Janszen, D.B., Anderson, K.A., Waters, K.M., Tanguay, R.L., 2013. Structurally distinct polycyclic aromatic hydrocarbons induce differential transcriptional responses in developing zebrafish. Toxicol Appl Pharmacol 272, 656- 670. McGee, S.P., Konstantinov, A., Stapleton, H.M., Volz, D.C., 2013. Aryl phosphate esters within a major PentaBDE replacement product induce cardiotoxicity in developing zebrafish embryos: potential role of the aryl hydrocarbon receptor. Toxicol Sci 133, 144-156. NRC, 2007. Toxicity Testing in the 21st Century: A Vision and a Strategy. The National Academies Press, Washington, DC. Paul, K.B., Hedge, J.M., Bansal, R., Zoeller, R.T., Peter, R., DeVito, M.J., Crofton, K.M., 2012. Developmental triclosan exposure decreases maternal, fetal, and early neonatal thyroxine: a dynamic and kinetic evaluation of a putative mode-of-action. Toxicology 300, 31-45. Paul, K.B., Thompson, J.T., Simmons, S.O., Vanden Heuvel, J.P., Crofton, K.M., 2013. Evidence for triclosan-induced activation of human and rodent xenobiotic nuclear receptors. Toxicol In Vitro 27, 2049-2060. Schiller, V., Wichmann, A., Kriehuber, R., Schafers, C., Fischer, R., Fenske, M., 2013. Transcriptome alterations in zebrafish embryos after exposure to environmental estrogens and anti-androgens can reveal endocrine disruption. Reprod Toxicol 42, 210-223. Truong, L., Reif, D.M., St Mary, L., Geier, M.C., Truong, H.D., *Tanguay, R.L., 2014. Multidimensional in vivo hazard assessment using zebrafish. Toxicol Sci 137, 212-233. U.S. EPA, 2011a. Endocrine Disruptor Screening Program for the 21st Century: (EDSP21 Work Plan) The Incorporation of In Silico Models and In Vitro High Throughput Assays in the Endocrine Disruptor Screening Program (EDSP) for Prioritization and Screening, pp.

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CHAPTER 6 - FUTURE DIRECTIONS

Throughout this dissertation, phenotypically anchored whole genome transcriptomics in the HTS in vivo zebrafish model was implemented to understand the mechanisms of action of several chemicals as well as determine the utility of using this approach as a way to classify the activity of known and unknown EDCs using transcriptome profiling. The studies presented here have generated several specific hypotheses regarding chemical activity and has also provided a large, rich transcriptomics dataset for the 27 chemicals we studied to encourage future research.

From the comparative transcriptome studies of mITP, we concluded that mITP is a probable RAR inhibitor. We surmise that RAR inhibition by mITP alters the expression of several Hox genes and RA metabolizing enzymes, derailing their normal roles in anterior-posterior patterning and heart development (via cell fate transformation or SHF recruitment), which results in heart looping failure and subsequent cardiotoxicity in later developmental stages. Co- treatment studies using pan- or selective-RAR agonists may further support the inhibitory role of mITP on RARs, through a competitive binding mechanism.

Examination of the consequences of mITP exposure on the expression domains of the various Hox genes identified in this study, particularly expression of hoxb5b, as well as cyp26a1 across several developmental stages are also warranted. Furthermore, studies examining the effects of mITP on the

180 development of the SHF and Hox disruption on heart tube elongation and looping would further support our hypothesis presented in this chapter.

Chapter 3 showed that exposure to TCS causes a hepatotoxic transcriptional signature in 48 hpf zebrafish. Further characterization of the effects of TCS on liver development are warranted. Specifically, several reporter lines are available using the liver-specific promoters, such as fabp10a, which can allow the ability to track liver formation and development over time. This would be beneficial since our transcriptome analysis was performed prior to liver expansion and metabolic competence at 72 hpf. Furthermore, liver histology may be necessary to determine the specific mechanism of liver toxicity due to

TCS exposure, which may involve toxicity to hepatocytes, biliary cells, or other cell types in the mature liver. Several of the transcripts significantly dysregulated by TCS have known roles in liver functioning and many are established indicators of hepatotoxicity. Validation of these transcripts with other known hepatotoxicants, such as acetaminophen, has the potential to provide a specific panel of transcriptional biomarkers that can be used in future chemical screens to more rapidly identify potential hepatotoxicants.

Since we took a transcript-centric approach in the EDC study presented in Chapter 4, there are many exciting possibilities for future research for these chemicals. Validation of the set of 27 unique transcripts in the four TR agonists using other known TR agonists and antagonists is a major priority for future

181 work, since it has the potential to act as a rapid biomarker panel to identify possible TR active chemicals. Including additional time points and exposure levels for the 25 chemicals would allow for better prediction of endocrine activity in zebrafish, as well as a chance to generate hypotheses regarding the mechanism of action for all chemicals tested. It may be that 48 hpf is only relevant for TR agonists, and, therefore, other time points could provide similar discriminatory signatures for the remaining chemicals we studied. This may explain the lack of correlation in transcriptional responses between estrogenic and androgenic chemicals. However, pathway and gene ontology enrichment analyses of the current data may provide more insight into relationships between chemicals and identify biological themes associated with disruption of estrogen, androgen, and thyroid hormone pathways that could allow for better classification of these chemicals compared to the transcript-centric approach we employed.

A majority of the 25 EDCs selected for Chapter 4 have known interactions with estrogen, androgen, or thyroid hormone signaling, providing an a priori endocrine classification structure for these chemicals. As such, we have the potential to develop predictive models using all of the possible data streams available from our dataset (transcript expression, gene ontology, pathway enrichment, process enrichment, unique phenotypes, etc.) to determine the a priori classification accuracy for these chemicals. This would allow us to better understand how accurate the use of single time point transcriptomics is at

182 predicting endocrine activity in zebrafish. This model could also be improved upon with the addition of other time points, concentrations, and chemicals, which could result in the development of a comprehensive transcriptomics- based predictive model of estrogen, androgen, and thyroid hormone disruption in zebrafish.

The incorporation of transcriptome studies into 21st century toxicity testing is already underway with the Tox21 program, now in Phase III, integrating transcriptome assays that examine approximately 2750 genes, which comprise a unique gene set to evaluate chemical effects across many relevant biological and toxicity pathways, including additional assays spanning different species in the future (NTP, 2016). These assays should help overcome some of the limitations and biological diversity problems associated with the bimodal assays currently used; offering much higher sensitivity and specificity in defining the pathways that are most affected by specific chemicals. However, the continued use of cell lines for some of these assays will present the same drawbacks current HTS in vitro assays face. Although, inclusion of zebrafish into this testing strategy appears to be inevitable, at least for Tox21, this dissertation outlines an in vivo transcriptome methodology that provides comprehensive gene expression data to help define modes of action and develop classification models for environmental chemicals. We’ve also shown that this approach can identify potential target organ toxicity that would likely not be detected using in vitro transcriptomics. Moreover, leveraging the available

183 transgenic lines and possibilities with technologies like CRISPR-Cas9 can allow for more specific investigation into chemical activity in the presence or absence of important target genes. Overall, the need to better handle the overwhelming number of chemicals that require screening is of utmost importance to protect public health and the environment. Incorporation of the transcriptome strategy presented in this dissertation should provide a blueprint for implementing a much needed high-throughput in vivo model into current HTS programs. This will enable better chemical hazard evaluations, and may eventually help drive innovations in green chemistry approaches to ensure a cleaner environment for the future.

References NTP, 2016. Tox21 Research Phases, pp.

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Appendix 1 – Developmental Toxicity Profiles, EC80 Model Statistics, and Calculations for Chapter 4

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