Elucidating the Signaling Events Downstream of Aryl Hydrocarbon Receptor Activation in Zebrafish

AN ABSTRACT OF THE DISSERTATION OF

Prarthana Shankar for the degree of Doctor of Philosophy in Toxicology presented on February 19, 2021.

Title: Elucidating the Signaling Events Downstream of Aryl Hydrocarbon Receptor Activation in Zebrafish

Abstract approved:

______Robyn L. Tanguay

All animals have developed the critical ability to detect, respond to, and detoxify a large array of environmental chemicals and stressors that can cause adverse health effects.

Important examples of landmark contaminants around the world are polycyclic aromatic hydrocarbons (PAHs) and dioxins, both of which can act via the aryl hydrocarbon receptor

(AHR), a protein receptor and ligand activated transcription factor. The AHR is conserved across multiple phyla, is required for proper development, and mediates the adverse developmental effects of several PAHs across vertebrates, including humans. While a plethora of research has been conducted investigating PAH toxicity, significant challenges still persist around our lack of understanding of the diversity in toxicity mechanisms, especially due to our narrow focus on select PAHs and few biomarkers. Our knowledge of

AHR-regulated mechanisms of PAH toxicity is still in its infancy.

The zebrafish (Danio rerio) is an established model organism in toxicological and biomedical sciences that is well-suited for investigating AHR-regulated biological

processes, especially due to the presence of a functional ortholog (AHR2) of the mammalian AHR. To this end, the overall objective of my dissertation is to leverage the zebrafish model to characterize and classify PAHs, and further our understanding of the downstream signaling events upon AHR activation. To achieve this, I first compiled a comprehensive review (Chapter 2) spanning twenty years of AHR research in zebrafish.

The review demonstrates the magnitude of research that has been conducted navigating the complexity of AHR signaling as it relates to zebrafish exposure to PAHs and other xenobiotic AHR ligands, such as 2,3,7,8-Tetrachlorodibenzodioxin (TCDD). The chapter identifies significant knowledge gaps such as our lack of understanding of how different

PAHs differentially alter the AHR signaling cascade and AHR’s crosstalk with other signaling pathways. To answer these questions, I conducted two studies (Chapters 3 and 4) that leveraged large compendiums of RNA sequencing data collected from developing zebrafish exposed to a diverse array of chemicals. Chapter 3 compares 16 individual PAHs and couples transcriptomic and developmental toxicity data to both characterize and classify PAHs. Using the Context Likelihood of Relatedness algorithm, I identified two major groups of PAHs, one being more developmentally toxic, predominantly activating

AHR2, and leading to transcriptional profiles that had both similarities and differences.

One important finding was that the expression of cyp1a (an AHR-dependent phase I metabolic enzyme commonly used as a biomarker for AHR activation) was more indicative of transcriptional profiles and not developmental toxicity phenotypes, suggesting the need for additional biomarkers that can better predict toxicity outcomes. Chapter 3 leverages a

novel gene co-expression approach in zebrafish to compare and contrast PAHs and TCDD

(AHR2 Activators), along with a large array of diverse flame retardant chemicals (FRCs).

I found that the AHR2 Activators and FRCs localized to distinct regions of the network, with the FRCs associated with broad neurobehavioral and vascular developmental processes. On the contrary, the AHR2 Activators localized to one region of the network that was primarily associated with chemical metabolism processes. Guided by the network,

I identified several new members of the AHR2 signaling pathway that should be investigated in future research. Both Chapters 3 and 4 leveraged co-expression analyses to narrow down on several potentially important candidates for biomarkers of PAH exposure.

In Chapter 5, I investigate one such AHR-regulated gene, wfikkn1 (WAP, Follistatin/Kazal,

Immunoglobulin, Kunitz And Netrin Domain Containing 1). I found that the expression of wfikkn1 was AHR2-dependent in developing zebrafish exposed to TCDD. Using a combination of CRISPR-Cas9 to generate a mutant zebrafish line, and transcriptomic, proteomic, and high-throughput neurobehavioral assays, I discovered that this AHR2- regulated gene has potentially important roles in muscle developmental processes. Upon exposure to TCDD, wfikkn1 mutant zebrafish had a significantly altered transcriptome and larval neurobehavioral processes compared to wildtype zebrafish, suggestive of its additional role in AHR-regulated neurodevelopment. These data also highlight potential crosstalk between AHR and other signaling pathways such as Estrogen Receptor signaling via wfikkn1, which should be investigated in future studies.

Overall, within this dissertation, I leveraged the advantages of the zebrafish model

organism to characterize and classify PAHs by their transcriptomic and developmental

toxicity responses. I also investigated the functional role of a novel AHR-dependent gene

that might contribute to AHR-regulated toxicity responses. Increasing our knowledge of biological processes and mechanisms associated with AHR activation can help us better understand how PAHs cause toxicity, which will lead to more guided risk assessment measures and opportunities for susceptibility research.

Elucidating the Signaling Events Downstream of Aryl Hydrocarbon Receptor Activation in Zebrafish

by Prarthana Shankar

A DISSERTATION

submitted to

Oregon State University

in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

Presented February 19, 2021 Commencement June 2021

Doctor of Philosophy dissertation of Prarthana Shankar presented on February 19, 2021

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.

Prarthana Shankar, Author

ACKNOWLEDGEMENTS

Quoting Aristotle’s words, “The more you know, the more you realize you don’t know.”

This has been the essence of my incredible graduate school learning experience at Oregon

State University. As my time here comes to an end, I remind myself that this journey would

not have been possible without the support of many, many people. First and foremost, I

would like to extend my sincere gratitude to my graduate mentor, Dr. Robyn Tanguay, for

her patient guidance throughout my time here. In addition to the immense scientific knowledge I have gained from her, I have also learned to work systematically yet efficiently, while never being the roadblock for anyone. I would like to thank all previous and current members of my committee: Dr. Kim Anderson, Dr. Thomas Sharpton, Dr.

Susan Tilton, and Dr. Jaga Giebultowicz, and Dr. Kate Shilke for their critical feedback and guidance. I also extend my gratitude to the entire faculty and administrative staff in the

Department of Environmental and Molecular Toxicology, particularly Mary Mucia,

Cheyenne Pozar, and Joey Carson for their continued help and support.

All the members of the Sinnhuber Aquatic Research Laboratory have been a part of my

academic family for the last 4.5 years and I would not have succeeded without their help.

I extend my sincere thanks to Dr. Lisa Truong for being my life guru, for always being

available, for providing me with much-needed reality-checks, and for teaching me to make

(almost) perfect figures. I gratefully acknowledge Jane LaDu for being my favorite bench- mate and for patiently putting up with all my questions and requests over the last few years.

I will definitely miss our IHC conversations by the Keyence. Special thanks to Dr. Gloria

Garcia for taking me on as a mentee when I first started as a graduate student, and for

showing me the wonders of molecular biology bench work. I would also like to

acknowledge Carrie Barton for all her help with fish husbandry, Kimberly Hayward for

patiently dealing with my million complicated ZAAP requests, Dr. Michael Simonich for

his incredible support editing my manuscripts, and Connor Leong and Dante Perone for

assistance with the juvenile and adult behavior studies. I thank Dr. Subham Dasgupta for

all his help editing my dissertation and providing the much-needed jellybeans for all the

long nights. A huge shout out to Brian Head for putting up with all my loooong

experimental rants, for troubleshooting with me, and for being my forever dissertation

buddy. Special thanks to other Tanguay Lab graduate and post-doctoral trainees, Joeshen

Shen, Chenglian Bai, Lindsay Wilson, Yvonne Rericha, Dr. Mitra Geier, Dr. Mike

Garland, Dr. Courtney Roper, and Dr. Delia Shelton, for all the help, guidance, lab get- togethers, and fun hallway conversations.

Over the years, I have had the fortunate chance to collaborate with and be mentored by several incredibly knowledgeable and talented scientists. I would like to extend my gratitude to Dr. Ryan McClure for not only being the mastermind behind all of our network studies, but for also being the most easiest and approachable of collaborators to work with.

I also thank Dr. Katrina Waters for always asking me the hard but right questions at my posters, and for guiding me to better appreciate the bioinformatics world. I thank Dr. Lane

Tidwell and Dr. Lindsay Denluck for taking me on as a mentee during my initial years in graduate school. I have carried with me their words of wisdom throughout my time at OSU.

I thank Dr. Alix Robel for always being only one text away, and for constantly ensuring that I am believing in myself. I will always look forward to her “Good morning sunshine!” emails. Life-long mentors and friends can be hard to find, but I am lucky to have several in my life who are always looking out for me. I would like to thank Dr. Bill Hoese for believing in me from the day he met me and for always answering my questions with more questions. I extend my gratitude to Dr. Kristy Forsgren for bringing out the perfectionist in me; I can always hear your voice at the back of my head when making a poster or a

PowerPoint presentation. I would like to thank Dr. Kimo Morris for being the first person to show me the natural world through the eyes of a scientist, and for encouraging me to pursue graduate school. Last but not the least, I would like to thank my wonderful, adventure-loving friends Linh To, Vu Luu, Evelyn Ruelas, Austin Xu, and Joseph Gamez for being constant sources of inspiration and motivation, for always listening to my science-talk, and for traveling the world with me.

All the friends that I have made during my time at OSU have served as my Corvallis family, and I will always cherish memories of all the wonderful shenanigans we have run in the past few years. I have not only learned to work hard from them, but also to party hard and workout harder. First and foremost, I sincerely thank my awesome housemates, Thomas

Stokely, Sean Gilligan, Pedro Belavenutti, Kristen Finch, Talon Stokely, Inga

Banschikova, Andi Carnahan, and Allie Nash for all the mental support they provided over evening beers and elaborate roomie dinners. I appreciate my running buddies, Paris

Kalathas, Nicolas Aziere, and Analu and Carlos, for not only joining me on some crazy

long runs, but for also believing with me that limits rarely exist. I extend my heartfelt thanks to all my other Corvallis friends especially, Serhan Mermer, Hannah Rolston, Ludwig,

Hannah, and Koby Ring, Fan Wu, Matt Mueller, Lauren Crandon, Daniel Elson, Sophie

Marie, Paco Mauro, Javier and family, Arash, Raymmah Garcia, and Yvonne Chang for sharing my love of the outdoors and good food, for jiu jitsu-ing with me, and for always being down to hangout over drinks and tater tots. I have thoroughly enjoyed every one of our camping and backpacking trips, barbeques, potlucks, and spontaneous dessert get-

togethers. A huge shout out to my favorite dancing buddies, Ian Moran, Matt Slattery, and

Brianna Rivera for enjoying the wonders of the Peacock with me. That place will always

hold a special place in my heart.

Finally, I would like to thank my incredibly supportive family for always being patient

with me despite my sometimes rebellious nature. I would especially like to thank my

parents, Shankar Swaminathan and Kavitha Shankar, for teaching me to never doubt myself, to love and be accepting of everyone around me no matter what, and for constantly encouraging me to maintain a sustainable work-life balance. They were my first teachers and have always pushed me to be the best version of myself. I extend an immense thank you to my California parents, Sridhar and Shanthi Nathan for “kidnapping” me from India, for always believing in and trusting me, and for encouraging me to follow my dreams. My acknowledgements would be incomplete if I did not thank and appreciate my sister,

Archana Shankar, for ALWAYS being one text message away, and for teaching me to be a careful listener. I could not have made it so far without her listening to my daily mundane

rants. I also thank my cousin, Ajay Nathan, for teaching me the importance of being passionate and resilient no matter what I do, and of course, for always giggling with (and at) me. Last but not the least, I extend my heartfelt thanks to other members of my family including Visman Nathan, Ajax Manohar, Madhu and Conal McCardle, Adit Nathan, Amy

DeSalvo, Asha and Aryana, , Vasanthi, Michael, Narayan, and Sachin Okuma, and all my paatis and thathas for always accepting me and helping me follow my dreams. Their actions are a constant reminder of the importance of a nurturing, supportive, and cohesive family. Now that I am done with graduate school, I might focus on other things, ahem 

CONTRIBUTION OF AUTHORS

In all chapters, Dr. Robyn L. Tanguay contributed to intellectual formulation, writing, and

study design.

Chapter 2: Prepared by Prarthana Shankar and Subham Dasgupta, and Mark E. Hahn.

Robyn L. Tanguay assisted with intellectual formulation, writing, and editorial comments.

Chapter 3: Prepared by Prarthana Shankar with editorial comments provided by Ryan S.

McClure, Katrina M. Waters, Mitra C. Geier, Robyn L. Tanguay and Paritosh Pande. Mitra

C. Geier performed exposures, and sample collection and processing. Ryan S. McClure,

Lisa Truong, and Prarthana Shankar conducted data analysis and figure formulation.

Chapter 4: Prepared by Prarthana Shankar and Ryan S. McClure. Editorial comments provided by Katrina M. Waters and Robyn L. Tanguay. Ryan S. McClure conducted network analysis and figure formulation. Prarthana Shankar performed data interpretation and laboratory validation studies.

Chapter 5: Prepared by Prarthana Shankar. Editorial comments provided by Robyn L.

Tanguay and Jane K. La Du. Britton C. Goodale originally identified wfikkn1. Gloria R.

Garcia, Jane K. La Du, and Prarthana Shankar generated and validated the CRISPR-Cas9 mutant zebrafish line. Christopher M. Sullivan and Cheryl L. Dunham assisted with RNA

sequencing analyses. Stanislau Stanisheuski and Clauria S. Maier conducted mass spectrometry analysis for proteomics study. Preethi Thunga and David M. Reif performed

two-factor statistical analyses of behavior data.

TABLE OF CONTENTS

Page

CHAPTER 1 – GENERAL INTRODUCTION ...... 2

1.1. Discovery and Characteristics of the Aryl Hydrocarbon Receptors (AHRs) 2

1.2. AHR Functions ...... 3

1.3. Xenobiotic ligands of the AHR: Polycyclic Aromatic Hydrocarbons (PAHs) 5

1.4. Zebrafish as a Model Organism ...... 6

1.5. Dissertation objectives ...... 7

CHAPTER 2 – A REVIEW OF THE FUNCTIONAL ROLES OF THE ZEBRAFISH ARYL HYDROCARBON RECEPTORS ...... 11

2.1. Abstract ...... 12

2.2. Introduction ...... 13

2.2.1. Aryl Hydrocarbon Receptor (AHR) ...... 13

2.2.2. Evolution of the AHR in different species ...... 14

2.2.3. Zebrafish as a toxicological model organism ...... 16

2.2.4. The zebrafish AHRs ...... 16

2.3. Receptor characteristics ...... 19

2.3.1. Expression of AHRs in zebrafish ...... 19

2.3.2. Endogenous AHR roles in zebrafish ...... 21

TABLE OF CONTENTS (Continued)

Page

2.3.3. Inducible roles of the zebrafish AHRs ...... 25

2.3.4. Homology modeling of zebrafish AHRs ...... 27

2.4. Early stage toxicity ...... 28

2.4.1. AHR2 ...... 29

2.4.2. AHR1a ...... 41

2.4.3. AHR1b ...... 43

2.5. Interaction between AHR and other pathways ...... 45

2.5.1. AHR and oxidative stress ...... 45

2.5.2. AHR-Wnt crosstalk and tissue regeneration ...... 48

2.5.3. Estrogen receptor (ER) ...... 50

2.5.4. Pregnane X receptor (PXR) ...... 51

2.5.5. Fibroblast growth factor (FGF) ...... 51

2.6. Adult toxicity, epigenetics and multigenerational effects ...... 52

2.6.1. Adult toxicity...... 52

2.6.2. Epigenetics and multigenerational effects ...... 54

2.7. Conclusions and future directions ...... 56

TABLE OF CONTENTS (Continued)

Page

CHAPTER 3 - COUPLING GENOME-WIDE TRANSCRIPTOMICS AND DEVELOPMENTAL TOXICITY PROFILES IN ZEBRAFISH TO CHARACTERIZE POLYCYCLIC AROMATIC HYDROCARBON (PAH) HAZARD ...... 70

3.1. Abstract ...... 71

3.2. Introduction ...... 72

3.3. Methods ...... 76

3.3.1 Chemicals ...... 76

3.3.2. Zebrafish husbandry ...... 77

3.3.3. Exposures ...... 77

3.3.4. RNA sequencing (RNA-seq) ...... 78

3.3.5. Immunohistochemistry ...... 82

3.4. Results and Discussion ...... 84

3.4.1. Classification of 123 PAHs into eight bins and characterization of bins ...... 84

3.4.2. Selection of 16 representative PAHs for RNA sequencing from the eight PAH bins ...... 88

3.4.3. Number of differentially expressed genes (DEGs) generally correlates with PAH developmental toxicity profiles ...... 90

3.4.4. Cluster analysis of response to PAHs reveals two broad clusters ...... 92

3.4.5. Cyp1a expression is an inadequate biomarker for PAH developmental toxicity, but is associated with transcriptomic response ...... 96

3.4.6. Cluster B PAHs activated Ahr2 ...... 99

TABLE OF CONTENTS (Continued)

Page

3.4.7. Cluster B PAHs that activate Ahr2 uniquely enrich several pathways ...... 102

3.4.8. Cluster B PAHs had seventeen common DEGs ...... 104

3.5. Conclusions ...... 105

CHAPTER 4 – TRANSCRIPTIONAL CO-EXPRESSION NETWORK ANALYSIS IN ZEBRAFISH REVEALS FLAME RETARDANT AND ARYL HYDROCARBON RECEPTOR LIGAND SPECIFIC MODULES ...... 119

4.1. Abstract ...... 120

4.2. Introduction ...... 121

4.3. Materials and Methods ...... 125

4.3.1. Characterization of chemical datasets ...... 125

4.3.2. Chemicals ...... 125

4.3.3. Zebrafish husbandry ...... 126

4.3.4. Exposures for chemicals examined initially in this study ...... 126

4.3.5. RNA-sequencing sample preparation and sequencing ...... 127

4.3.6. Alignment and analysis of RNA-seq data ...... 128

4.3.7. Inferring Gene Co-Expression Networks ...... 129

4.3.8. TCDD exposure, RNA extraction, and quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) ...... 130

4.4. Results ...... 131

4.4.1. Characterization of Full Dataset ...... 131

TABLE OF CONTENTS (Continued)

Page

4.4.2. Global Analysis of Full Co-Expression Network ...... 132

4.4.3. Centrality Analysis ...... 134

4.4.4. Analysis of Module 13 ...... 136

4.4.5. Chemical Type Analysis in Networks ...... 138

4.5. Discussion ...... 139

4.6. Conclusions ...... 145

CHAPTER 5 – THE ARYL HYDROCARBON RECEPTOR 2-DEPENDENT WFIKKN1 GENE INFLUENCES ZEBRAFISH BEHAVIORAL PHENOTYPES AND DEVELOPMENTAL GENE AND PROTEIN EXPRESSION PROFILES ...... 157

5.1. Abstract ...... 158

5.2. Introduction ...... 159

5.3. Materials and Methods ...... 162

5.3.1. Fish husbandry ...... 162

5.3.2. Waterborne TCDD exposure ...... 162

5.3.3. Creation and generation of a wfikkn1 mutant zebrafish line ...... 163

5.3.4. RNA Sequencing Analyses ...... 167

5.3.5. Proteomic analysis ...... 172

5.3.6. Behavior analyses ...... 1754

TABLE OF CONTENTS (Continued)

Page

5.4. Results ...... 178

5.4.1. Wfikkn1 is highly induced in parallel with cyp1a expression ...... 178

5.4.2. Induction of wfikkn1 expression is AHR2-dependent ...... 179

5.4.3. Creation, generation, and validation of wfikkn1 mutant line ...... 180

5.4.4. Unbiased transcriptomics in TCDD-exposed wildtype and wfikkn1 mutant zebrafish ...... 182

5.4.5. Impact of lack of basal wfikkn1 expression on the 48-hpf zebrafish transcriptome ...... 183

5.4.6. Role of wfikkn1 in TCDD-induced gene expression alterations ...... 184

5.4.7. Impact of lack of wfikkn1 on basal protein expression in 48-hpf zebrafish ... 186

5.4.8. Wfikkn1 mutant zebrafish have altered neurobehaviors in response to TCDD that do not persist to adulthood ...... 187

5.5. Discussion ...... 189

5.5.1. Characterization of wfikkn1 expression in zebrafish ...... 189

5.5.2. Lack of basal wfikkn1 expression influences the zebrafish transcriptome, proteome, and behavior ...... 191

5.5.3. Role of wfikkn1 in TCDD-induced gene expression and behavior alterations ...... 194

5.6. Conclusions ...... 195

CHAPTER 6 – CONCLUSIONS AND FUTURE DIRECTIONS ...... 211

BIBLIOGRAPHY ...... 219

APPENDICES ...... 261

LIST OF FIGURES

Figure Page

2.1. Shared synteny between zebrafish and other vertebrate AHR genes...... 58

3.1. Heatmap of the morphological and behavioral responses, and Cyp1a protein localization patterns of 123 PAHs assessed using the embryonic zebrafish model...... 106

3.2. Heatmap of the morphological and behavioral endpoints, Cyp1a protein localization pattern, and DEGs associated with exposure to the 16 representative PAHs...... 108

3.3. Hierarchical clustering and network analysis for the 16 PAHs...... 109

3.4. Ahr2 dependency of Cluster B PAHs...... 111

3.5. Unique DEGs (FC >1.5, adjusted p-value <0.05) and pathways associated with each of the six cluster B PAHs...... 112

3.6. Top 10 elevated and reduced DEGs for each of the Cluster B PAHs: Retene, BbF, BjF, BkF, DB(a,h)P, and DB(a,i)P...... 113

4.1. Heatmap of 48-hpf zebrafish transcriptomic response to ten Flame Retardant Chemical (FRC), 22 Polycyclic Aromatic Hydrocarbon (PAH), and 1 TCDD treatment...... 146

4.2. Gene co-expression network of 48-hpf zebrafish transcriptomic response to chemicals...... 147

4.3. Network response to FRCs and AHR2 Activators...... 148

4.4. Network neighborhood of cyp1a...... 149

4.5. Heatmap of chemical response of genes in the first-degree network neighborhood of cyp1a...... 150

4.6. Validation of cyp1a network neighborhood genes identified from the network analysis...... 151

5.1. Comparison between cyp1a and wfikkn1 gene expression in developing zebrafish.197

5.2. AHR2-dependence of wfikkn1 developmental expression...... 199 LIST OF FIGURES (Continued)

Figure Page

5.3. Schematic diagrams of predicted Wfikkn1 protein in Wildtype zebrafish, and characterization of wfikkn1 mutant CRISPR-Cas9 line ...... 201

5.4. Overview of RNA sequencing data from 48 hpf wildtype (WT) and wfikkn1 mutants (mut) exposed to 0.1% DMSO or 1 ng/mL TCDD...... 202

5.5. Impact of the lack of wfikkn1 on the 48-hpf zebrafish transcriptome...... 203

5.6. Effect of the lack of wfikkn1 on the 48-hpf transcriptome when zebrafish are exposed to 1 ng/mL TCDD...... 204

5.7. Functional enrichment of genes differently differentially expressed in wildtype and wfikkn1 mutant zebrafish upon exposed to TCDD...... 205

5.8. Transcriptional Regulation of AHR-associated differently differentially expressed genes in wildtype and wfikkn1 mutant zebrafish exposed to TCDD...... 206

5.9. Whole animal proteomic comparison between wildtype and wfikkn1 mutant zebrafish at 48 hpf...... 208

5.10. Behavior Analyses of wfikkn1 mutant zebrafish exposed to 50 pg/mL TCDD. .... 209

LIST OF TABLES

Table Page

2.1. Zebrafish AHR genes and their respective translation products...... 60

2.2. Receptor characteristics (developmental baseline and TCDD-induced mRNA expression, endogenous ligands and roles, and binding partners) of AHR2, AHR1a, and AHR1b. See text for citations...... 61

2.3. Predicted binding of different ligands to the zebrafish AHRs...... 63

2.4. Developmental toxicity endpoints and CYP1A expression patterns mediated by AHR2 from morpholino knockdown studies...... 64

2.5. Developmental toxicity endpoints and CYP1A expression patterns mediated by AHR1a from morpholino knockdown studies...... 68

2.6. Developmental toxicity endpoints and CYP1A expression patterns mediated by AHR1b from morpholino knockdown studies...... 69

3.1. PAHs used in this study with associated registry and use parameters...... 114

3.2. Six PAHs that induced morphological effects with the concentrations tested to compute the EC80...... 116

3.3. Summary table of the eight bins the 123 PAHs were classified into with the general characteristics of each bin. The x represents the presence of morphology and behavior malformations, and Cyp1a protein expression...... 117

3.4. Cyp1a transcript levels and adjusted p-values (PADJ) associated with each of the 16 PAHs...... 118

4.1. Top GO term enrichment of genes in the 12 largest modules of the full gene co- expression network ...... 152

4.2. Top 20 genes with highest betweenness centrality in network ...... 153

4.3. Top 20 genes with highest degree centrality in network...... 154

LIST OF TABLES (Continued)

Table Page

4.4. Genes with the highest degrees from the modules associated with FRC exposure (Modules 2, 7, 10, and 22) ...... 155

4.5. Removal of AHR2 Activators or FRCs from the network, and comparison of co- expression values of each pathway listed relative to networks lacking random datasets...... 156

LIST OF APPENDICES

Page

Appendix A – Supplemental Data for Chapter 3 ...... 262

Appendix B – Supplemental Data for Chapter 4 ...... 283

Appendix C – Supplemental Data for Chapter 5 ...... 300

Appendix D - Developmental Toxicity in Zebrafish (Danio rerio) Exposed to Uranium: A Comparison with Lead, Cadmium, and Iron ...... 312

D.1. Abstract ...... 313

D.2. Introduction ...... 314

D.3. Materials and Methods ...... 318

D.4. Results and Discussion...... 323

D.5. Conclusions ...... 333

D.6. References ...... 334

LIST OF APPENDIX FIGURES

Figure Page

A.1. Concentration Uptake Ratio (CUR) for six PAHs compared to log KOW ...... 262

B.1. High Centrality Network Genes...... 283

C.1. Confirmation of wfikkn1 sequence in tropical 5D wildtype zebrafish...... 300

C.2. PCA Plot showing the four replicates of each treatment of the RNA-sequencing experiment...... 302

C.3. Reaction norm plots for the behavior assays conducted in wildtype and wfikkn1 mutant zebrafish exposed to 0.1% DMSO or 50 pg/mL TCDD...... 303

D.1. Uranium uptake by developing zebrafish...... 343

D.2. UA exposure did not affect embryonic photomotor response (EPR)...... 344

D.3. UA exposure altered larval photomotor response (LPR)...... 345

D.4. Comparison of developmental toxicity of UA with lead, cadmium, and iron...... 346

LIST OF APPENDIX TABLES

Table Page

A.1. List of 123 PAHs, the lowest effect level (LEL) exposure concentrations for each endpoint measured, and the developmental toxicity bins of each PAH...... 263

A.2. List of DEGs (fold-change > 1.5, adjusted p-value < 0.05) for 14 of the 16 PAHs. 3- NF and 1,5- DMN had zero DEGs...... 267

A.3. Top 200 genes by coefficient of variation (CV) across the 18 treatments (16 PAHs, 2 controls)...... 274

A.4. Genes that are common to 1, 2, 3, 4, 5, or 6 of the cluster B PAHs...... 279

B.1. Summary of all 48-hpf zebrafish datasets included in this study ...... 284

B.2. Functional enrichment of all genes in each module...... 286

B.3. Gene Specific Primers for RT-qPCR Analysis ...... 299

C.1. Primer and oligo sequences for creation and confirmation of wfikkn1 CRISPR-Cas9 mutant zebrafish line...... 304

C.2. The top 20 most increased and top 20 most decreased significant differentially expressed genes between the four treatment groups...... 305

C.3. Top 20 MetaCore processes, the FDR-adjusted p values, and the associated human genes of the significantly differentially expressed genes in mut_DMSO compared to WT_DMSO ...... 309

C.4. Top 20 MetaCore processes, the FDR-adjusted p values, and the associated human genes of the genes in Cluster 2 of the heatmap in Figure 5.6 ...... 311

D.1. Chemicals tested with sampling time point and purpose (Abbreviations: EM – Embryo media, hpf – hours post fertilization, DMSO – dimethyl sulfoxide)...... 347

DEDICATION

I dedicate this dissertation to my grandfather, Madras thatha, for always believing in the

importance of a good education and a happy family.

Elucidating the Signaling Events Downstream of Aryl Hydrocarbon Receptor Activation

in Zebrafish

CHAPTER 1 – GENERAL INTRODUCTION

All animals have developed the critical ability to detect, respond to, and detoxify a large

array of environmental chemicals and stressors that can cause adverse health effects.

Protein receptors and their complex molecular signaling pathways are of particular

importance in mediating the biological response to these chemical signals. Nuclear

receptors, such as the thyroid, retinoic acid, and vitamin D receptors (Forman et al. 1995),

are generally recognized for their ability to interact with endogenous ligands. Additionally,

multiple xenobiotic sensing receptors have been discovered including the constitutive androstane receptor (CAR), the pregnane X receptor (PXR), and the aryl hydrocarbon receptor (AHR) (Mackowiak et al. 2018).

1.1. Discovery and Characteristics of the Aryl Hydrocarbon Receptors (AHRs)

The AHR was first discovered in mice as a ligand-dependent transcription factor activated by its potent agonist, 2,3,7,8-tetrachlorodibenzodioxin (TCDD) (Poland et al. 1976). Since then, canonical AHR signaling has been identified in several phyla within the metazoans.

The presence of this well-conserved pathway in various animals has provided ample opportunity to study AHR evolution and elucidate its potential functions (Hahn 2002).

The AHR is classified as a transcription factor part of the basic helix-loop-helix-PER-

ARNT-SIM (bHLH-PAS) protein superfamily, and is present in the cytosol with its

chaperone proteins (Petrulis et al. 2002) in its inactivated form until bound by a ligand

(Nebert 2017). Upon ligand activation, the AHR translocates into the nucleus where it

dimerizes with its partner protein, the AHR nuclear translocator (ARNT), another bHLH-

PAS protein (Hoffman et al. 1991). The AHR-ARNT complex binds to various Aryl

Hydrocarbon Receptor Response Elements (AHREs or XREs or DREs) in the promoter regions of downstream target genes (Denison et al. 1989), leading to either the induction or repression of expression of several genes. For example, the expression of xenobiotic metabolizing enzymes such as CYP1A1, and AHR’s negative feedback inhibitor, the aryl hydrocarbon receptor repressor (AHRR), are highly induced upon xenobiotic AHR activation (Mimura et al. 1999; Watson et al. 1992). Finally, the AHR dissociates from the

AHRE and is transported from the nucleus to be targeted for proteasome-mediated degradation (Roberts et al. 1999; Song et al. 2002).

The AHR has been best characterized in mammals. Phylogenetic analysis of AHR and

AHRR revealed that AHR genes across species may generally be categorized into three groups: AHR/AHR1, AHR2, and AHRR. To date, AHR2 is only known to be present in birds and fishes (Hahn 2002; Yasui et al. 2007). While the AHRs from different fish species can have different physiochemical properties (Bank et al. 1992; Denison et al. 1986), many of them have been found to mediate the toxicological effects of TCDD (Korkalainen et al.

2000). Majority of research conducted so far has focused on how the AHR modulates the effects of TCDD in addition to various other exogenous xenobiotic ligands.

1.2. AHR Functions

The primary role of the AHR as a xenobiotic-sensing transcription factor is the regulation of expression of many downstream genes that have two broad toxicological functions: 1)

Adaptation: AHR activation enables the biotransformation of potentially detrimental xenobiotic ligands. The most notable genes involved in this function are the cytochrome

P450 family of enzymes, particularly CYP1A (Ma et al. 2007), which is unique in that it is

highly inducible by a variety of ligands at both the mRNA and protein levels (Whitlock

1999). Leveraging genetic mutation studies and whole animal chemical exposures, CYP1A

has been repeatedly implicated in reducing the detrimental effects of AHR ligand exposure

(Nebert et al. 2004; Uno et al. 2004). 2) Toxicity: AHR activation also mediates the toxic effects of the xenobiotics that activate the receptor. A classic example is the AHR-

dependent sox9b gene (in the model organism, zebrafish) whose decrease in expression

upon TCDD exposure has been shown to mediate craniofacial malformations induced by

the chemical (Xiong et al. 2008). In addition, contrary to the aforementioned animal

experiments, early in vitro studies revealed CYP1A’s role in generating toxic intermediates

of chemicals such as benzo[a]pyrene, which lead to both tissue- and ligand-dependent

genotoxic and mutagenic effects (Conney 1982; Ellard et al. 1991). While TCDD is also a

strong inducer of CYP1A, it is well known that the chemical is metabolically resistant to

the effects of CYP1A (Carney et al. 2004). Thus, a number of studies have investigated the

other downstream target genes of the AHRs to understand the toxicological molecular

players of the AHR signaling pathway (Franc et al. 2008; Tijet et al. 2006).

The toxicological studies thus far have laid the groundwork for elucidating AHR’s role in

toxicity. Interestingly, these studies have also revealed a third, evolutionarily conserved

function of the AHR—one involved in normal development, cell cycle control, and

inflammation processes, among many others (Barouki et al. 2007; Lindsey et al. 2012).

Additionally, AHR-deficient animals also reveal a critical endogenous role for this receptor

(Larigot et al. 2018). More recently, the AHR has been shown to interact with several endogenous ligands as well (Nguyen et al. 2008), further implicating AHR in mediating

normal physiological processes. This also contributes to the hypothesis that AHR-

dependent xenobiotic toxicity is at least partially due to impaired endogenous AHR

signaling.

1.3. Xenobiotic ligands of the AHR: Polycyclic Aromatic Hydrocarbons (PAHs)

In addition to TCDD, the AHR binds many other xenobiotics, including halogenated

aromatic hydrocarbons (HAHs), such as the polychlorinated biphenyls (PCBs) and

polybrominated diphenyl ethers (PBDEs), as well as polycyclic aromatic hydrocarbons

(PAHs). PAHs belong to a large, structurally diverse, and ubiquitous class of

environmental organic pollutants. PAHs are produced from natural sources, such as forest

fires and volcanic activity, and anthropogenic sources, such as diesel exhaust and tobacco

smoke. While the most significant routes of human exposure include ingestion of grilled

or smoked foods and smoking cigarettes, PAH exposure also occurs via inhalation of both

polluted and urban ambient air (Abdel-Shafy et al. 2016; Bansal et al. 2015). These environmental contaminants, comprised of two or more fused benzene rings, are well- known carcinogens, mutagens, and teratogens. Additionally, many PAHs are associated with other acute and chronic health effects including reproductive, respiratory, and gastrointestinal effects (Abdel-Shafy et al. 2016; Kim et al. 2013). Research has been conducted to determine and characterize the link between PAHs and their health effects via the AHR in humans and other vertebrate organisms (Billiard et al. 2006; Shimizu et al.

2000; Vondracek et al. 2017). However, the majority of health data has been collected for only the U.S. Environmental Protection Agency’s (EPA’s) priority 16 PAHs, with the remaining PAHs largely ignored. Additionally, bulk of research has focused on only the

carcinogenic and mutagenic potential of PAHs (Boffetta et al. 1997; Cavalieri et al. 1991;

Collins et al. 1998), with extensive knowledge gaps existing for the other AHR-mediated

health effects of PAHs. Risk assessment of PAHs also generally makes the incorrect

assumption that the structurally diverse PAHs are mechanistically similar (EPA 2010;

Schoeny et al. 1993); however, recent research has demonstrated varying mechanisms of

toxicity (Chlebowski et al. 2017; Honda et al. 2020). Thus, there exists an overall need to

better understand the bioactivity of more structurally varied yet environmentally relevant

PAH structures to make fully informed toxicity predictions and regulatory decisions.

1.4. Zebrafish as a Model Organism

The zebrafish (Danio rerio) is a well-established model organism that is utilized in the fields of molecular and environmental toxicology, developmental biology, neurobehavior, and other biomedical sciences (Bailey et al. 2013; Bambino et al. 2017; Bugel et al. 2014).

Zebrafish have high genetic similarity with humans; recent sequencing and annotation of the zebrafish genome revealed that around 70% of human protein-coding genes have at

least one zebrafish ortholog (Howe et al. 2013), allowing for studies to be easily

translatable between the two organisms. Zebrafish have high fecundity (producing ~200-

300 embryos per pair of adult fish per day) and rapid ex utero development. The majority

of organs, such as the heart, liver, and brain, are fully developed by 120 hours post

fertilization (hpf) (Kimmel et al. 1995). Thus, zebrafish studies typically allow for large

sample sizes, at a scale far greater than laboratory mammalian studies. Zebrafish embryos

are also transparent, enabling external analysis of organ development in live animals

exposed to xenobiotic chemicals. To this end, zebrafish serve as an invaluable tool for

large-scale and high-throughput chemical exposure studies with relatively easy sample collection paradigms. Zebrafish can also be conveniently genetically manipulated, favoring reverse genetics studies paired with comprehensive investigation of genetic and transcriptomic outcomes. Zebrafish are now routinely used to unravel complex molecular signaling pathways and have become an important platform for investigation of AHR signaling.

Developmental toxicity of several PAHs is differentially dependent on the zebrafish AHRs

(Incardona et al. 2006; Incardona et al. 2011). Zebrafish have three AHR orthologs,

AHR1a, AHR1b, and AHR2, with the latter being the most responsive to xenobiotic chemicals such as TCDD (Shankar et al. 2020). Similar to rodent models, AHR2 knockout zebrafish demonstrated the receptor’s requirement for TCDD toxicity (Garcia et al.

2018; Goodale et al. 2012). AHR2 is also required for normal reproductive, neurobehavioral, and skeletal development in zebrafish (Garcia et al. 2018). These studies emphasize the prominent role of the zebrafish AHR2 in mediating crucial developmental and toxicological processes. Further, they underscore the advantages of using zebrafish to further uncover the mechanisms involved in the AHR signaling pathway.

1.5. Dissertation objectives

The primary goal of my dissertation is to leverage the zebrafish model organism to characterize PAHs using their bioactivity and gene expression profiles, and to further our understanding of the downstream signaling events upon AHR activation. To achieve this,

I used high-throughput phenotypic screening in zebrafish, including neurobehavior assessments, -omics-based approaches such as transcriptomics and proteomics, as well as

genetic editing techniques such as CRISPR-Cas9, to answer key questions that will broaden our appreciation for how PAHs and TCDD cause developmental toxicity.

In Chapter 2, I compiled a comprehensive review on what is known about the AHRs in zebrafish. I first describe the evolution of the ahr genes in zebrafish, the developmental

and adult expression of the receptors, their endogenous and inducible roles, and their

predicted ligands from homology modeling. I then summarize what is known about the developmental, adult, and transgenerational toxicity of the broad spectrum of AHR ligands, and review the transcriptomic and epigenetic mechanisms, and the crosstalk between AHR and other molecular signaling pathways. This chapter reveals that future research must focus on determining the endogenous and toxicological roles of AHR1a and AHR1b, elucidating the roles of several genes that are in the AHR signaling pathway and how they interact with other signaling pathways, and acknowledging the diverse modes of toxicity of the various AHR ligands such as the PAHs.

In Chapter 3, I conducted RNA sequencing in 48 hpf-zebrafish exposed to 16 individual

PAHs to identify disruption of gene expression upon PAH exposure. The PAHs formed into two broad clusters when I grouped them using the Context Likelihood of Relatedness

(CLR) algorithm, with Cluster A being more similar to the controls, and Cluster B PAHs being more developmentally toxic. Cluster B PAHs also highly induced cyp1a gene expression and resulted in AHR2-dependent Cyp1a protein expression primarily in the skin

(epidermis). This chapter reveals that cyp1a transcript levels are associated with only transcriptomic response and not with zebrafish developmental toxicity. Additionally, amongst the PAHs that predominantly activated AHR2, I highlight both common and

unique gene expression changes, suggesting different molecular events downstream of

AHR2 between the PAHs. In addition to cyp1a being induced by all Cluster B PAHs, I identify the wfikkn1 gene as also being highly expressed, and the role of this novel AHR- regulated gene is investigated in Chapter 5 of this dissertation.

In Chapter 4, I leverage a gene co-expression network analysis to investigate how the chemical-exposed 48-hpf zebrafish transcriptome organizes itself into clusters. I compared a variety of flame retardant chemicals (FRCs) and AHR2 ligands (multiple PAHs and

TCDD) and found that, in general, the different chemical sub-classes localized to different regions of the network. Interestingly, the network revealed genes that were highly co- expressed with each other that were strongly associated with the chemicals within each sub-class, highlighting the advantage of using a gene co-expression network approach compared to traditional hierarchical clustering, which is more reliable for fewer chemical comparisons. I also paired the in silico modeling studies with laboratory validation experiments to demonstrate how the network analysis can be leveraged to identify genes that could belong to the same signaling pathway (in this case, the AHR2 pathway) that may be missed if we were only studying the gene expression effects of a single chemical.

In Chapter 5, I explore and characterize the potential functional role of an AHR2- dependent gene, wfikkn1 (WAP, Follistatin/Kazal, Immunoglobulin, Kunitz And Netrin

Domain Containing 1). Wfikkn1 was identified in Chapters 3 of this dissertation, as well as in several previous zebrafish transcriptomic studies as being highly induced upon TCDD and PAH exposure. Using a combination of CRISPR-Cas9-generated wfikkn1 mutant zebrafish, whole-animal transcriptomic and proteomic studies, and sensitive

neurobehavioral assays, I identify wfikkn1 as a prominent player in the AHR signaling pathway. While this gene does not appear to be directly involved in the overt toxicological impact of TCDD exposure in zebrafish, wfikkn1 significantly influences the developing zebrafish transcriptome. Additionally, this chapter reveals wfikkn1’s potential role in skeletal muscle and neurobehavioral development processes, as well as its interactions with other signaling pathways including Estrogen Receptor signaling.

Overall, this dissertation explores whole-animal zebrafish RNA sequencing as a platform to classify and characterize PAHs, and investigates the largely ignored downstream gene expression events upon xenobiotic AHR activation. Using comprehensive transcriptomic studies paired with sophisticated gene co-expression analyses, these studies deliberately shift focus from the often-prioritized cyp1a gene, and provide strong evidence for the need to elucidate the functions of the other key players within the AHR signaling cascade. This dissertation also systematically reveals the functional role of one such AHR signaling gene

(wfikkn1) leveraging zebrafish as the model organism. Overall, my dissertation significantly adds to AHR toxicology research and provides important preliminary data for future studies revolving around AHR molecular biology and mechanisms of PAH toxicity that will enable better risk assessment methodologies, remediation strategies, and opportunities for susceptibility research.

CHAPTER 2 – A REVIEW OF THE FUNCTIONAL ROLES OF THE ZEBRAFISH ARYL HYDROCARBON RECEPTORS

Prarthana Shankar‡1, Subham Dasgupta1, Mark E. Hahn2, Robert L. Tanguay1*

1 Department of Environmental and Molecular Toxicology, Oregon State University,

Corvallis, OR 97331, USA.

2 Biology Department, Woods Hole Oceanographic Institution, Woods Hole,

Massachusetts 02543, USA.

Reprinted from Toxicological Sciences under the Creative Commons License.

2.1. Abstract

Over the last 2 decades, the zebrafish (Danio rerio) has emerged as a stellar model for

unraveling molecular signaling events mediated by the aryl hydrocarbon receptor (AHR),

an important ligand-activated receptor found in all eumetazoan animals. Zebrafish have 3

AHRs—AHR1a, AHR1b, and AHR2, and studies have demonstrated the diversity of both

the endogenous and toxicological functions of the zebrafish AHRs. In this contemporary

review, we first highlight the evolution of the zebrafish ahr genes, and the characteristics

of the receptors including developmental and adult expression, their endogenous and

inducible roles, and the predicted ligands from homology modeling studies. We then

review the toxicity of a broad spectrum of AHR ligands across multiple life stages (early

stage, and adult), discuss their transcriptomic and epigenetic mechanisms of action, and

report on any known interactions between the AHRs and other signaling pathways.

Through this article, we summarize the promising research that furthers our understanding

of the complex AHR pathway through the extensive use of zebrafish as a model, coupled

with a large array of molecular techniques. As much of the research has focused on the

functions of AHR2 during development and the mechanism of TCDD (2,3,7,8-

tetrachlorodibenzo-p-dioxin) toxicity, we illustrate the need to address the considerable

knowledge gap in our understanding of both the mechanistic roles of AHR1a and AHR1b,

and the diverse modes of toxicity of the various AHR ligands.

2.2. Introduction

2.2.1. Aryl Hydrocarbon Receptor (AHR)

The Aryl Hydrocarbon Receptors (AHRs) are ligand-dependent transcription factors that

mediate a wide range of biological and toxicological effects in animals (Abel et al. 2010;

Barouki et al. 2007; Esser et al. 2009; Hankinson 1995; Nguyen et al. 2018; Safe et al.

2013). Although several endogenous ligands have been identified since the discovery of

the AHR in 1976 (Poland et al. 1976a), the focus has been on characterizing the toxicity of

numerous environmental chemicals including the halogenated aromatic hydrocarbons

(HAHs) and polycyclic aromatic hydrocarbons (PAHs), many of which cause toxicity via

the AHR signaling pathway (Denison et al. 2003; Nguyen et al. 2008). 2,3,7,8-

tetrachlorodibenzo-p-dioxin (TCDD), an HAH, is the most potent and thoroughly

investigated of the known AHR ligands and it elicits many species- and tissue-specific toxicological effects (Couture et al. 1990; Denison et al. 2003; Mandal 2005). By virtue of

its limited metabolism (Vinopal et al. 1973), TCDD is typically utilized as the prototypical

molecular probe to study the signaling events downstream of AHR activation (Poland et

al. 1976b), and forms the basis of investigation of many of the AHR-dependent

mechanisms reviewed in this paper.

Canonical signaling for the AHRs, which are part of the basic Helix-Loop-Helix Per-Arnt-

Sim (bHLH/PAS) family of proteins, involves the conversion into an active form that can dimerize with another bHLH/PAS protein, the AHR nuclear translocator (ARNT)

(Hoffman et al. 1991; Kewley et al. 2004). In their latent and unbound state, the AHRs are found in the cytoplasm and are stably associated with two molecules of the 90kDa

molecular chaperone heat shock protein 90 (Hsp90), p23, and AHR-interacting protein

(AIP/XAP2/Ara9) (Carver et al. 1997; Kazlauskas et al. 1999; Ma et al. 1997; Perdew

1988). Upon ligand binding, the AHR is activated and the AHR/Hsp90 complex translocates to the nucleus where Hsp90 is exchanged for the partner protein, ARNT

(Hoffman et al. 1991; Reyes et al. 1992; Swanson 2002). The AHR/ARNT heterodimer recognizes and regulates transcription of downstream genes such as the cytochrome P450 family of genes (CYPs) and the aryl hydrocarbon receptor repressor (AHRR) via aryl hydrocarbon response elements (AHREs; also known as DREs or XREs) in their promoter regions (Mimura et al. 1999; Watson et al. 1992). The CYP1s are amongst the most well- studied AHR gene targets and are involved in both the metabolic activation and detoxification of the various AHR ligands (Nebert et al. 2004). In addition to the CYPs and

AHRR, the AHR can also directly or indirectly regulate expression of a large battery of genes, the identities and functions of which are still being discovered (Abel et al. 2010;

Beischlag et al. 2008). While our review predominantly focuses on what we know about canonical AHR signaling in zebrafish, we acknowledge that the AHRs have several non- canonical functions as well (Jackson et al. 2015). Some examples include AHR as an E3 ubiquitin ligase in cytosol (Ohtake et al. 2007), its interaction with p300, pRb, and E2F

(Marlowe et al. 2004; Puga et al. 2000), and as a partner for KLF6 (Wright et al. 2017) and

RelB (Vogel et al. 2007).

2.2.2. Evolution of the AHR in different species

The AHR is an ancient protein found in all eumetazoan animals, indicating that it originated more than 600 million years ago (Hahn et al. 2017). A fundamental difference between

AHRs in invertebrates and vertebrates is that most of the vertebrate AHR proteins exhibit

high-affinity binding of halogenated and non-halogenated aromatic hydrocarbons, whereas all invertebrate AHRs examined to date lack that ability and appear to have roles primarily in developmental processes (Butler et al. 2001; Hahn 2002). During animal evolution, AHR genes have been duplicated, including in early vertebrate evolution (a tandem duplication and an expansion associated with two early vertebrate, whole-genome duplication events) and in specific vertebrate lineages, especially fish (Hahn et al. 2017). These duplications, coupled with some lineage-specific gene losses, result in the presence of between one and five AHR genes per species.

An important difference between AHR signaling in mammals and fishes is that most mammals—including most of those used as models in toxicology research—possess a single AHR gene, whereas most fishes have multiple AHRs. Fish typically possess four

AHR genes—two pairs of tandem AHR1-AHR2—although additional gene duplications and losses have led to some variation, including in zebrafish (Hahn et al. 2006). It is not clear why fish have retained multiple AHR genes, including the AHR2 paralogs that have been lost from most mammals, as well as the additional duplicates of AHR1 and AHR2 resulting from a fish-specific whole genome duplication event that occurred ~350 million years ago (Amores et al. 1998; Glasauer et al. 2014). The maintenance of multiple AHRs

in modern fish is notable considering that more than 80% of the gene duplicates formed

during the fish-specific whole genome duplication were subsequently lost. The prevailing

hypothesis for retention of gene duplicates is that they have become more specialized by

partitioning the multiple functions of their common ancestor (subfunctionalization;

(Amores et al. 1998; Force et al. 1999; Lynch et al. 2000)). However, they may also evolve

new functions (neofunctionalization). Therefore, fish models can serve as an ideal platform

to study the role of AHR in both physiology and toxicology.

2.2.3. Zebrafish as a toxicological model organism

Zebrafish is a well-established vertebrate model for studying embryonic development and

developmental toxicology, and has been used extensively to unravel AHR pathway

complexity (Garcia et al. 2016; Sipes et al. 2011; Teraoka et al. 2003a). Zebrafish embryos

are transparent, and they develop externally and rapidly, with primary organogenesis

complete around 48 hours post fertilization (hpf), and the heart, liver, and brain well

developed by 120 hpf (Kimmel et al. 1995). To this end, most early stage toxicity studies

are conducted with morphological, behavioral, and molecular evaluations occurring within

the first 120 hours of development (Nishimura et al. 2016). Zebrafish also possess high genetic relatedness to humans; 76% of human genes have a zebrafish ortholog, and 82% of human genes that cause disease are present in zebrafish, increasing the translational value of the zebrafish model (Howe et al. 2013). Furthermore, zebrafish share similar morphology, physiology, and xenobiotic metabolic pathways with mammals (Diekmann et al. 2013), possessing direct orthologs of the human CYP1 enzymes like cyp1a and cyp1b1, in addition to cyp1c1 and cyp1c2 that lack human orthologs (Goldstone et al.

2010).

2.2.4. The zebrafish AHRs

Zebrafish possess three AHR genes (ahr1a, ahr1b, and ahr2) that were named at the time of discovery according to their hypothesized evolutionary relationships to the AHR genes

in other fish. Thus, the ahr1 genes were thought to be most closely related to the ahr1 genes

of other fish and to the mammalian AHR (Andreasen et al. 2002a; Karchner et al. 2005);

the designation “a” and “b” reflected the initial conclusion that ahr1a and ahr1b were

paralogs formed during the fish-specific whole genome duplication, and was consistent with the standard zebrafish nomenclature for such paralogs (Karchner et al. 2005). More recent analysis taking into account new AHR sequences from a variety of species, combined with analysis of shared synteny between zebrafish and human chromosomes, suggested that zebrafish ahr1a is orthologous to the mammalian AHR, rather than a paralog of ahr1b resulting from the fish-specific whole genome duplication (Hahn et al. 2017).

(Orthology refers only to evolutionary relationships, and does not necessarily imply identical functions. Two genes are orthologous if they have descended from the same gene in the most recent common ancestor of the species in which they are found (Fitch 1970).)

Zebrafish ahr2 is orthologous to ahr2b genes of other fishes (Karchner et al. 2005; Tanguay et al. 1999).

Further insight into the relationships of zebrafish ahr genes to AHR genes in other vertebrates can be obtained by additional analyses of shared synteny, which can complement gene phylogenies to help reveal evolutionary relationships (Postlethwait

2007). A comparison of the shared synteny among AHR-containing chromosomes in zebrafish, human, mouse, and chicken using Genomicus (Muffato et al. 2010; Nguyen et al. 2018) is illustrative (Figure 2.1). The zebrafish ahr1a gene is located on chromosome

16 (Andreasen et al. 2002a; Barbazuk et al. 2000; Hahn et al. 2017; Le Beau et al. 1994).

Zebrafish chromosome 16 exhibits extensive shared synteny with human chromosome 7,

mouse chromosome 12, and chicken chromosome 2 – the locations of the canonical AHR

genes in each of these species (Figure 2.1A). This supports the earlier suggestion that ahr1a

is the ortholog of human AHR (Hahn et al. 2017). A reciprocal analysis of shared synteny

using the AHR on human chromosome 7 as the reference gene (Figure 2.1C) confirms the

relationship between AHR-containing human chromosome 7, mouse chromosome 12,

chicken chromosome 2, and zebrafish chromosome 16, and also reveals the loss of the

predicted paralog of ahr1a that would have been expected from the fish-specific whole

genome duplication (see chromosome 19). Zebrafish ahr2 and ahr1b are located on

chromosome 22 (Karchner et al. 2005; Tanguay et al. 1999; Wang et al. 1998a). This

chromosome exhibits extensive shared synteny with chicken chromosome 7, the location

of two additional chicken AHR genes, designated AHR2 and AHR1B (Lee et al. 2013;

Yasui et al. 2007) (Figure 2.1B). Both zebrafish chromosome 22 and chicken chromosome

7 exhibit shared synteny with human chromosome 2 and mouse chromosome 1, which lack the AHR2-AHR1 pair, confirming the loss of these genes in the mammalian lineages leading to human and mouse (Figure 2.1B).

See Table 2.1 for more details on the three zebrafish ahr genes and their respective translation products.

The presence of three AHRs in zebrafish is intriguing. Despite the plethora of research that has been conducted, we are only beginning to understand their functional roles in development and in adult tissues, and we do not yet have a clear picture of the extent to which each paralog is involved in endogenous vs. toxicological roles. In this paper, we survey current AHR zebrafish toxicology research, and identify specific knowledge gaps

and opportunities for future research. We begin by reviewing the receptor characteristics

(Section 1), followed by early stage toxicity (Section 2) and interaction of AHRs with other signaling pathways (Section 3), and conclude with adult toxicity and potential AHR- associated epigenetic effects (Section 4).

2.3. Receptor characteristics

General characteristics of the zebrafish AHRs, beginning with their baseline and chemically induced expression (Section 1.1) and endogenous roles in both developing and adult zebrafish (Section 1.2) are summarized in Table 2.2. We later elucidate the inducible roles of the three AHRs along with their known binding partners (Section 1.3), and conclude this section by reviewing homology modeling of the three receptors (Section 1.4).

2.3.1. Expression of AHRs in zebrafish

Transcriptomic, in situ hybridization, and immunohistochemical techniques have been used to understand the developmental, tissue-specific, and chemically induced expression of the AHRs. Ahr2 mRNA is expressed during normal zebrafish development in several regions including the head and the trunk (Andreasen et al. 2002b; Sugden et al. 2017); expression is detected as early as 5 hpf and does not change through 120 hpf (Andreasen et al. 2002b; Tanguay et al. 1999). Upon zebrafish embryonic exposure to TCDD, ahr2 expression increases and is detected in several locations across zebrafish development from

24 to 120 hpf (Andreasen et al. 2002b; Garcia et al. 2018a; Karchner et al. 2005; Tanguay et al. 1999). Other chemicals such as beta-naphthoflavone (BNF), a synthetic flavonoid commonly used as a surrogate model PAH (Poland et al. 1976b; Sugden et al. 2017), cardiosulfa, a sulfonamide drug (Ko et al. 2009), and the polychlorinated biphenyl, PCB-

126 (Kubota et al. 2014) induce ahr2 expression in developing zebrafish. On the other hand,

exposure to benzo[a]pyrene (BaP) and some other oxy-PAHs significantly reduce ahr2

expression suggesting the complexity of AHR regulation by different PAHs (Cunha et al.

2020). In adults, ahr2 mRNA is detected in the brain, heart, muscle, swim bladder, liver,

gill, skin, eye, kidney, fin both in unexposed and TCDD-exposed animals (Andreasen et al. 2002a). An antibody to AHR2 has been used to investigate AHR2 function in zebrafish cell culture (Wentworth et al. 2004) and in a heterologous cell system (Evans et al. 2008), but has not been used successfully in vivo.

Ahr1a mRNA is expressed during normal zebrafish development; expression is detected from 24 hpf, increases by 72 hpf, and stays relatively constant through 120 hpf (Andreasen et al. 2002a; Karchner et al. 2005). Ahr1a has more restricted expression patterns compared to ahr2 and is detected weakly in the liver at 52 hpf (Sugden et al. 2017), in a regenerating fin 3 days post amputation (Mathew et al. 2006), and in the adult brain (Webb et al. 2009), liver, heart, swim bladder, and kidney (Andreasen et al. 2002a). Upon embryonic exposure to TCDD, ahr1a expression significantly increases at 72 and 120 hpf (Andreasen et al.

2002a; Karchner et al. 2005), while BNF slightly induces expression of ahr1a in 48 hpf zebrafish (Sugden et al. 2017). There are no published antibodies experimentally shown to detect AHR1a protein expression in zebrafish.

Like ahr2 and ahr1a, ahr1b mRNA is expressed during normal development; expression is detected from 24 hpf and is increased at 48 and 72 hpf (Karchner et al. 2005). Ahr1b mRNA is highly expressed in the developing eye (Karchner et al. 2017; Sugden et al. 2017). Unlike ahr2 and ahr1a, ahr1b expression does not change after exposure to TCDD or BNF

(Karchner et al. 2005; Sugden et al. 2017; Ulin et al. 2019). However, BaP exposure

increased expression of ahr1b in 72 hpf zebrafish (Huang et al. 2012), while low level

pyrene exposure did not induce expression of any of the three ahr genes (Zhang et al. 2012).

A rabbit polyclonal antibody targeting the AHR1b protein (Ulin et al. 2019) detected

protein expression by western blot in 24-hpf zebrafish. Using this same antibody, Karchner

et al. performed immunohistochemical staining of 96-hpf larvae and showed that, like its

mRNA, the AHR1b protein is also expressed in the developing eye, including the retinal

inner and outer plexiform layers (Karchner et al. 2017). Overall, these studies show that

the spatiotemporal expression of the AHRs is dependent on the specific AHR receptor.

2.3.2. Endogenous AHR roles in zebrafish

To effectively study the endogenous and toxicological roles of the zebrafish AHRs, reverse

genetics tools including transient knockdown of translation using morpholino

oligonucleotides (Heasman 2002; Timme-Laragy et al. 2012b) and stable and heritable

genetic knockout lines have been generated. While both of these tools can greatly enable

the understanding of the functions of the zebrafish AHRs, we acknowledge their limitations

here. In addition to their specific targets, morpholinos can nonspecifically affect expression

of other targets so without rigorous controls it can be challenging to conclude whether an

observed morpholino phenotype is due to its specific or off-target effects (Kok et al. 2015;

Stainier et al. 2017). On the other hand, heritable mutations—especially those generating

premature termination codons and nonsense-mediated decay of the resulting mRNA—can

be subject to genetic compensation, where in response to the mutation, cells upregulate

related genes that rescue the mutant phenotype (El-Brolosy et al. 2019; Ma et al. 2019;

Rossi et al. 2015). Additionally, a mutation presumed to be loss-of-function might be rescued by altered mRNA processing, such as exon-skipping or alternative splicing, that produces a functional or partly functional protein (Anderson et al. 2017). This means that a heritable mutation that produces no phenotype could be a false negative result. It is important to take these drawbacks of both morpholinos and knockout lines into consideration while interpreting the results of the studies presented below.

Both splice-blocking and translation-blocking morpholinos have been designed for ahr1a

(Incardona et al. 2005; Seifinejad et al. 2019), ahr1b (Goodale et al. 2012; Ulin et al. 2019),

and ahr2 (Bugel et al. 2013; Prasch et al. 2003; Teraoka et al. 2003b). Morpholino

knockdown of ahr2 does not produce visible phenotypes (Dong et al. 2004; Prasch et al.

2003) likely due to the incomplete and transient receptor knockdown, prompting several

groups to generate stable AHR mutant lines. The first functional AHR2 knockout line was

established using Targeted Induced Local Lesions in Genomes (TILLING) (Goodale et al.

2012). Later, Transcription Activator-Like Effector Nucleotide (TALEN)-Mediated

Mutagenesis was used to generate AHR1a, AHR1b, and AHR2 mutants (Sugden et al.

2017). More recently, two CRISPR-Cas9 AHR2 homozygous mutant lines (Garcia et al.

2018a; Souder et al. 2019) and CRISPR-Cas9 AHR1a and AHR1b homozygous mutant

lines (Karchner et al. 2017; Souder et al. 2019) have been established. These mutant lines

are being intensively used to study both the endogenous and toxicological roles of the

AHRs; the endogenous roles are reviewed here while the toxicological roles are examined

in later sections.

Some studies have suggested that all three AHRs are dispensable specifically for

embryonic vascular patterning, and normal larval fin development and jaw growth (Souder

et al. 2019; Sugden et al. 2017). However, AHR2-null background zebrafish have fin and

craniofacial malformation as adults (Garcia et al. 2018a; Goodale et al. 2012; Souder et al.

2019), and both abnormal larval and adult behavior (Garcia et al. 2018a; Knecht et al.

2017b; Wu et al. 2019). AHR2-null zebrafish are also largely infertile and show decreased

survival and diminished reproductive health (Garcia et al. 2018a). Loss of AHR2 does not

affect basal developmental mRNA expression of cyp1a, ahr1b, ahrra, ahrrb, cyp1b1,

cyp1c1, cyp3a65, slincR, and sox9B, all known AHR-regulated genes (Garcia et al. 2018a;

Goodale et al. 2012; Prasch et al. 2003). On the other hand, AHR2 is important for

endogenous cyp1a expression specifically in the developing zebrafish eye but not in the

trunk or brain (Sugden et al. 2017). Only the lack of all three AHRs caused a complete loss

of cyp1a mRNA expression (but not cyp1b1 expression) throughout the developing

zebrafish (Sugden et al. 2017). These studies demonstrate the various possible roles of

AHR2 in maintaining normal morphology and development. AHR2 also binds endogenous

AHR ligands identified in other systems. Formylindolo[3,2-b]carbazole (FICZ), a

tryptophan oxidation product formed upon exposure to UV or visible radiation, binds both

AHR2 and AHR1b in vitro and induces expression of cyp1a and cyp1b1 in an AHR2-

dependent manner (Jonsson et al. 2009). Morpholino knockdown experiments illustrate that FICZ has increased and decreased toxicity in the absence of cyp1a and ahr2 respectively, suggesting that the biological effects of FICZ are AHR2-dependent and

regulated by its Cyp1a-mediated metabolism (Wincent et al. 2016). Zebrafish have also

been used to define the endogenous roles of 3α,5α-tetrahydrocorticosterone and 3α,5β-

tetrahydrocorticosterone (5α- and 5β-THB). These neuroactive steroids induce AHR2- dependent cyp1a, mbp, and sox10, the latter two of which are markers for myelinating cells

(Wu et al. 2019). 5α-THB exposure also alters zebrafish larval behavior in an AHR2- dependent manner suggesting the importance of THB-AHR2 signaling in normal nervous system development (Wu et al. 2019).

There are no identified endogenous roles for AHR1a in zebrafish. AHR1a does not seem to play a role in normal development, larval feeding, or endogenous Cyp1a protein expression evidenced from AHR1a mutant fish that appear normal(Sugden et al. 2017).

Further, neither AHR1a nor AHR1b morphants or mutants display overt phenotypes

(Garner et al. 2013; Goodale et al. 2012; Sugden et al. 2017). However, a recent study showed that morpholino knockdown of ahr1a led to loss of hypocretin/orexin expression and developmental deformities (Seifinejad et al. 2019). Additionally, another study identified crosstalk between AHR1b and Nrf signaling during zebrafish development (Ulin et al. 2019). Many have suggested that partial overlapping functional redundancy of AHR2 and AHR1b allows for compensatory activity when AHR2 is lost (Prasch et al. 2003;

Sugden et al. 2017); however, additional research is needed to clarify this. It is possible that the level of investigation to date has been insufficient to identify and confirm subtle development roles for these orthologs.

2.3.3. Inducible roles of the zebrafish AHRs

To understand the inducible roles of the zebrafish AHRs, a combination of in vitro binding studies, transactivation assays in COS-7 mammalian cells, and in vivo zebrafish developmental studies has been utilized.

AHR2 is a functional receptor whose signaling is modulated not only by its various ligands, but also by its binding partners and downstream genes. Zebrafish have two ARNT genes, arnt1 and arnt2, each present as three splice forms (ARNT1a, 1b, 1c, and ARNT2a, 2b,

2c)(Prasch et al. 2006; Tanguay et al. 2000; Wang et al. 1998b; Wang et al. 2000). AHR2 is capable of binding ARNT1b, ARNT1c, ARNT2b, and ARNT2c in vitro but only the complexes of AHR2 with ARNT1b, ARNT1c, or ARNT2b are able to promote transactivation by inducing AHRE-driven transcription with TCDD (Prasch et al. 2006;

Tanguay et al. 2000). It was later shown using morpholino studies that both AHR2 and some form of the ARNT1 protein, but not the ARNT2 protein, are required for generating toxic responses to TCDD in developing zebrafish (Antkiewicz et al. 2006; Prasch et al.

2004; Prasch et al. 2006). It is not yet known how the AHR2-ARNT complexes mediate responses induced by other ligands. Furthermore, the functions of the various splice variants of the ARNTs are yet to be elucidated. AHR signaling can also be subjected to down-regulation by proteasomal degradation of AHR2 (Wentworth et al. 2004) as well as by transcriptional repression of its target genes by the aryl hydrocarbon receptor repressor

(AHRR). Zebrafish have two distinct AHRRs, AHRRa (originally designated AHRR1) and

AHRRb (AHRR2), which are co-orthologs of the mammalian AHRR (Evans et al. 2005).

Both AHRRa and AHRRb are induced in an AHR2-dependent manner similar to cyp1a,

only by compounds that activate the AHR signaling pathway (Evans et al. 2005; Garcia et al. 2018a; Jenny et al. 2009; Timme-Laragy et al. 2007). AHRRa blocks AHR2 function by competing for binding to AHREs as well as by a transrepression mechanism that is independent of DNA binding (Evans et al. 2008). Knockdown of AHRRa, but not AHRRb, using a morpholino in the absence of TCDD exposure, phenocopied TCDD developmental toxicity and caused a large number of gene expression changes compared to wild type fish, while knockdown of either AHRRa or AHRRb enhanced TCDD-induced pericardial edema (Aluru et al. 2014; Jenny et al. 2009). These results suggest that while AHRRa is involved in regulating constitutive AHR signaling, both AHRRa and AHRRb play a role in modulating TCDD developmental toxicity. The ability of AHRRa knockdown to both phenocopy TCDD toxicity (in the absence of TCDD exposure) and enhance TCDD toxicity is consistent with a role for AHRRa in controlling constitutive AHR activity (in unexposed embryos), and a role for the AHR2-dependent induction of AHRRa after TCDD exposure to limit the AHR-dependent TCDD effects in a negative feedback loop. Further, zebrafish embryos in which AHRRb or both AHRRs (but not AHRRa alone) were knocked down had increased TCDD-induced expression of cyp1a, cyp1b1, and cyp1c1 at 72 hpf, suggesting that AHRRb may have a role in controlling TCDD-activated AHR signaling

(Jenny et al. 2009). To date, we do not know how AHRRa or AHRRb interact with AHR1a or AHR1b. Future work in single and double mutant lines for the AHRRs and AHRs will enhance our understanding of their interactions and functions in zebrafish.

The zebrafish AHR1a is a functional receptor in vivo (Goodale et al. 2012), but does not bind the canonical exogenous ligand, TCDD, in an in vitro heterologous cell system

(Andreasen et al. 2002a; Karchner et al. 2005); the receptor is able to bind ARNT2b, can recognize AHREs more weakly compared to AHR2, and lacks transactivation activity with all ARNT2 proteins in vitro (Andreasen et al. 2002a). AHR1b was identified as a fully functional zebrafish receptor when assessed in vitro and in a heterologous cell system

(Karchner et al. 2005). TCDD can bind AHR1b which interacts with ARNT2b, and promotes transactivation with efficacy comparable with that of AHR2 but with an 8-fold lower sensitivity (Karchner et al. 2005). It remains unknown to what extent AHR1a and

AHR1b are able to interact with the ARNT1 proteins.

2.3.4. Homology modeling of zebrafish AHRs

Several in silico-based modeling studies have investigated the structure and ligand binding properties of the three zebrafish AHRs (Bisson et al. 2009; Fraccalvieri et al. 2013; Zhang et al. 2018a; Zhang et al. 2018b). Using molecular dynamics simulations, it was determined that TCDD and many dioxin-like compounds interact with six amino acid residues in the

AHR2 ligand-binding domain (LBD) (Zhang et al. 2018b). The results supported, for the first time, the finding that polychlorinated diphenylsulfides (PCDPS) can bind and activate

AHR2 (Zhang et al. 2018b). Similarly, 2,2,’,4,4’5-penta-BDE (BDE-99) is able to bind to both the zebrafish AHR2 as well as the Pregnane X Receptor (PXR) (Zhang et al. 2018a).

While the ligand-binding pocket was more compact in the Bisson model (Bisson et al.

2009) compared to Fraccalvieri (Fraccalvieri et al. 2013), both models predicted that

TCDD binds to AHR2 and AHR1b, but not AHR1a. Using site-directed mutagenesis coupled with functional analyses, it was determined that AHR1a was not able to bind

TCDD because of differences in three amino acid residues in the ligand binding domain of

AHR1a compared to that of AHR2 (Fraccalvieri et al. 2013). The differences make the

AHR1a binding cavity much shorter than that of AHR2 with too limited space for TCDD

binding. The Bisson model (Bisson et al. 2009) has also been utilized to predict binding

with molecular docking of structurally different AHR ligands to the three zebrafish AHRs,

summarized in Table 2.3. The table reveals that in general, xenobiotic ligands bind to more

than one zebrafish AHR, making it likely that their overall toxicity is mediated by a

combination of the receptors.

Overall, the studies reviewed in this section demonstrate that the three zebrafish AHRs are

diverse in their local expression patterns, with only partial overlap in developmental and adult expression indicating cell-type-specific regulation. AHR2 and AHR1a are more widely expressed compared to AHR1b, and while AHR2 has been associated with normal developmental and physiological functions, such roles are not yet apparent for AHR1a and

AHR1b. Importantly, all three receptors bind a variety of ligands evidenced by empirical and homology modeling studies. Clearly, the changing levels of expression of all three receptors across development testify to their dynamic nature and allude to the complexity of accurately understanding the AHRs’ functional roles at different life stages.

2.4. Early stage toxicity

In this section, we discuss ligands that produce adverse developmental effects dependent

on the presence of each of the zebrafish AHRs. The majority of the research has focused

on AHR2 (Section 2.1) and environmental contaminants including PAHs, TCDD,

polychlorinated biphenyls (PCBs), and pharmaceuticals. It is noteworthy that several PAHs

also activate AHR1a (Section 2.2) and AHR1b (Section 2.3), and below we specifically

review the Cyp1a expression patterns dependent on the three AHRs.

2.4.1. AHR2

Despite much research focused on TCDD, several studies have explored a diversity of xenobiotics and suggest that in vivo toxicity may be mediated by more than one zebrafish

AHR (Goodale et al. 2012). Majority of the research conducted so far utilizes morpholino

knockdown (with regulation of cyp1a induction as confirmation for knockdown) to reveal

receptor-dependent toxicity effects; however, as groups are beginning to generate stable

genetic knockout lines, more AHR2 mutant studies are being conducted. While morpholino

knockdown can inform on which of the three receptors are important for mediating toxicity,

only mutant studies with complete knockouts can definitively demonstrate toxicologically

functional roles for the AHR paralogs. In this section, we focus on the xenobiotics whose

toxicity is mediated primarily by AHR2 to collate what we know about AHR2’s

functionality. We will begin by reviewing the early stage toxicity of PAHs (Section 2.1.1)

and other xenobiotics (Section 2.1.2), then we will summarize what we know about TCDD

(Section 2.1.3) early stage toxicity in zebrafish. The functional role of AHR2 upon

exposure to diverse ligands is summarized in Table 2.4.

Polycyclic Aromatic Hydrocarbons (PAHs)

Many PAHs cause dioxin-like AHR2-dependent phenotypic endpoints including pericardial and yolk sac edemas, bent axes, cardiotoxicity, and eye and jaw malformations.

Morpholino knockdown studies suggest that the following PAHs cause developmental toxicity primarily via AHR2: BaP (Cunha et al. 2020; Incardona et al. 2011), retene (Scott

et al. 2011), benz[a]anthracene (BAA) (Incardona et al. 2006), pyrene (Incardona et al.

2005), and 1,9-benz-10-anthrone (BEZO) and Benz(a)anthracene-7,12-dione (7,12-

B[a]AQ) (Goodale et al. 2015). Dozens of different PAH exposures have been associated with altered embryonic and larval behavior (Geier et al. 2018a); however only little is known about AHR2’s role in behavioral outcomes. Specifically, BaP-exposed wild type zebrafish exhibit a hyperactive swimming response in the 120-hpf larval photomotor response (LPR) assay while BaP-exposed AHR2 mutants do not display a significantly altered LPR This suggests that disruption of the AHR2 signaling pathway can lead to detrimental consequences to nervous system development and functioning.(Knecht et al.

2017b). Some AHR2 knockdown studies revealed that many PAHs, including phenanthrene, dibenzothiophene, and benzo[k]fluoranthene (BkF), produce adverse developmental outcomes independent of AHR2 (Incardona et al. 2005; Incardona et al.

2011), despite inducing AHR2-dependent CYP1A protein expression (Incardona et al.

2005; Incardona et al. 2011; Shankar et al. 2019). It is possible that the developmental toxicity produced by these PAHs is mediated by other zebrafish AHRs, or that incomplete morpholino knockdown confounded these studies. One recent morpholino study however, showed that exposure to BkF and three other fluoranthenes produced caudal fin duplication that is AHR2-dependent(Garland et al. 2020). This suggests that AHR2 may mediate specific malformations such as the fin duplication, while chemical interaction with other receptors such as AHR1b may mediate other developmental toxicity endpoints. Future studies testing the toxicity of these PAHs in both AHR1b and AHR2-null backgrounds are crucial to verify these results.

Both cyp1a mRNA and protein expression are frequently used as indicators of AHR

activation by PAHs. In general, PAHs that elicit AHR2-dependent toxicity also induce

cyp1a mRNA expression (Goodale et al. 2015; Knecht et al. 2013). Studies have also

demonstrated that the obligate AHR isoforms for PAH toxicity can be inferred by

determining the larval Cyp1a protein expression pattern. For example, developmental

exposure to oxy-PAHs 7,12-B[a]AQ, BEZO, and BaP produce Cyp1a protein expression

in the vasculature that is partially or fully dependent on AHR2 (Goodale et al. 2015;

Incardona et al. 2011; Knecht et al. 2013). PAHs such as chrysene, retene, BAA, BkF, 1,6-

dinitropyrene, benzo[j]fluoranthene, dibenzo[a.h]pyrene, dibenzo[a,i]pyrene, and

benzo[b]fluoranthene (Table 2.4) induce Cyp1a protein in both the skin and the

vasculature, among other regions. Vascular Cyp1a expression in response to these latter

PAHs is only partially reduced upon AHR2 knockdown; however, Cyp1a expression in the

skin is lost, demonstrating its complete AHR2-dependence. Similarly, while the surrogate model PAH, BNF, induces cyp1a mRNA expression in the skin and vasculature, only the expression in the skin is completely lost in AHR2 mutants (Sugden et al. 2017). Because of this differential, we recently reported that induction of Cyp1a in the skin is a more robust and reliable biomarker for AHR2 activation in developing zebrafish (Shankar et al. 2019).

Studies have also identified nitro-PAHs such as 7-nitrobenz[a]anthracene and 3,7- dinitrobenzo[k]fluoranthene that do not cause visible developmental malformations at the tested concentrations but produce AHR2-dependent Cyp1a expression in a variety of organs (Chlebowski et al. 2017). Thus, these chemicals activate AHR2 without causing visible developmental toxicity, indicative of a potential adaptive response by Cyp1a. It is

also possible that the absence of developmental toxicity is due to the lack of sustained

activation of AHR2 by these chemicals, which has been hypothesized for other AHR

agonists such as retene (Billiard et al. 1999). Future work assessing toxicity with Cyp1a

inhibition and with daily renewal of the chemical exposure solution will help clarify this

hypothesis.

Several studies have investigated the specific functional role of Cyp1a induction in PAH

toxicity, and the apparent direct role for Cyp1a in PAH toxicity is chemical substrate-

dependent. Typically, studies utilize cyp1a morphants, or known Cyp1a competitive

inhibitors such as alpha-naphthoflavone (ANF) or fluoranthene. Retene (Scott et al. 2011)

and BAA (Incardona et al. 2006) cause AHR2-dependent, but Cyp1a-independent cardiovascular developmental toxicity in zebrafish. On the other hand, cyp1a knockdown delays the toxic effects of pyrene, but fails to entirely protect the developing zebrafish from toxicity (Incardona et al. 2005). Cyp1a morphants also display enhanced toxic responses to the strong AHR ligand BkF, suggesting a protective role for Cyp1a in BkF toxicity

(Incardona et al. 2011). Strong AHR agonists BkF (Garner et al. 2013; Van Tiem et al.

2011) and BaP (Garner et al. 2013; Jayasundara et al. 2015), and the weak AHR ligand phenanthrene (Brown et al. 2015) were more developmentally toxic when combined with

fluoranthene, suggesting that inhibition of Cyp1a-mediated metabolism can enhance toxicity of these PAHs. While the phenanthrene + fluoranthene toxicity was not AHR2- dependent (Brown et al. 2015), AHR2 knockdown offered a protective role against the cardiotoxicity induced by the BkF+ fluoranthene and BaP+ fluoranthene mixtures (Garner et al. 2013). Similarly, the AHR2-dependent toxicity of BNF synergistically increased in

combination with either ANF or a cyp1a morpholino, further demonstrating that Cyp1a can

play an important protective role against PAH toxicity (Billiard et al. 2006). It was later

shown that ANF did not act as an AHR antagonist, but rather a Cyp1a enzyme inhibitor,

potentially prolonging the time the AHR was being activated, enhancing developmental

toxicity (Timme-Laragy et al. 2007). Other than one study demonstrating that cyp1b1 did

not seem to play a role in PAH toxicity (Timme-Laragy et al. 2008), the roles of the other

zebrafish cyp genes in PAH toxicity are not well understood.

For PAHs whose toxicity is AHR2-dependent based on morpholino studies, there have been a number of corresponding whole-embryonic, genome-wide transcriptomic studies

(Fang et al. 2015; Goodale et al. 2015; Hawliczek et al. 2012; Shankar et al. 2019) albeit without comparing gene expression profiles in the presence and absence of AHR2. One study however identified several novel genes and potential mechanisms specifically in the developing zebrafish heart that could mediate cardiotoxicity via AHR2 upon exposure to

BaP, fluoranthene, and BaP+ fluoranthene (Jayasundara et al. 2015). It was concluded that

AHR2-dependent cardiotoxicity of BaP+ fluoranthene was mediated, at least in part, by perturbations to Ca2+ homoeostasis. Genome-wide transcriptomic studies for PAHs known to be primarily AHR2 agonists show that despite activating the same receptor, the transcriptomic changes downstream of AHR2 are ligand-dependent (Goodale et al. 2015;

Shankar et al. 2019) that either slight differences in how chemicals bind to the receptor, or the formation of metabolites that can activate their own receptors, or a combination of the two, are contributing to the ligand-dependent gene expression profiles. Future work investigating these hypotheses is needed to illuminate more specific interactions between

different ligands and the zebrafish AHRs. Some studies have utilized quantitative PCR to measure expression of specific genes including the cyps (mentioned above), and have identified genes that are regulated via AHR2 upon exposures to BAAQ (cyp1b1, wfikkn1, gstp2, igfbp1a) (Goodale et al. 2015), BEZO (gstp2, igfbp1a, arg2), and BkF (cyp1a, cyp1b, cyp1c, gstp2, gpx1, gclc) (Van Tiem et al. 2011). While several of these genes have been identified and well-studied (eg: gpx1, gstp2, gclc are involved in antioxidant responses), elucidation of functions of some genes such as wfikkn1 is ongoing.

Other xenobiotics: mixtures, pharmaceuticals, and halogenated hydrocarbons

AHR2 can at least partially mediate toxicity of cigarette smoke (Massarsky et al. 2016) and

PAH-containing soil extracts from a gasworks, a former wood preservation site, and a coke oven site (Wincent et al. 2015). A recent study found that the developmental cardiotoxicity effects of both the individual chemicals and the mixture of BaP and the oxy-PAH, 6H- benzo[cd]pyren-6-one were significantly reduced upon ahr2 knockdown (Cunha et al.

2020). Unlike these mixtures, weathered crude oil consisting of lower molecular weight

PAHs, elicits morphological deficits and cardiotoxicity in an AHR2-independent manner, highlighting the potential influence of the structure and size of PAHs (Incardona et al.

2005). One study investigated Cyp1a protein expression induced in 120-hpf zebrafish after exposure to an environmentally relevant PAH mixture. Upon ahr2 knockdown, Cyp1a vascular expression was eliminated, but there was production of Cyp1a protein in the liver attributed to the loss of AHR2 leading to the production of metabolites that had a higher affinity for AHR1a. Independently knocking down AHR1a or AHR1b did not alter Cyp1a protein expression; however, a triple morpholino knockdown of all three AHRs reduced

Cyp1a protein expression (Geier et al. 2018b). These results reiterate the need for considering the functional roles of all three zebrafish AHRs, especially when studying the mechanisms of toxicity of complex mixtures.

The zebrafish AHR2 mediates developmental toxicity of other small molecules and pharmaceuticals. Although all three AHRs can bind leflunomide, an anti-inflammatory drug (Bisson et al. 2009; Goodale et al. 2012), AHR2 mediates the bulk of its Cyp1a vascular expression at 120 hpf (Goodale et al. 2012; O'Donnell et al. 2010). Its metabolite

A771726 is not an AHR2 agonist (O'Donnell et al. 2010). The small molecule sulfonamide, cardiosulfa, also produces AHR2-dependent cardiotoxicity in developing zebrafish as seen in ahr2 morpholino knockdown studies (Ko et al. 2009; Ko et al. 2012). Similar to TCDD

(reviewed below), Cyp1a neither reduces nor exacerbates cardiosulfa toxicity (Ko et al.

2012). As noted above, the indole FICZ can cause developmental toxicity in an AHR2- dependent manner, especially when Cyp1a activity is inhibited or reduced by genetic knock-down (Jonsson et al. 2009; Wincent et al. 2016).

Other organic compounds such as phenanthroline (Ellis et al. 2016) and two halogenated carbazoles (Fang et al. 2016) are associated with PAH and TCDD-like developmental toxicity respectively, and the fungicide, paclobutrazol (Wang et al. 2015) causes digestive tract toxicity, all of which are AHR2-mediated. Some flame-retardant chemicals appear to buck the trend. For instance, ahr2 knockdown does not reduce the cardiotoxicity associated with exposure to mono-substituted isopropyl triaryl phosphate (mITP), a major component of Firemaster 550 commercial mixture (Gerlach et al. 2014; McGee et al. 2013). However, ahr2 knockdown prevents vascular Cyp1a protein expression in response to mITP,

suggesting that the mixture does activate AHR2 (Gerlach et al. 2014). An AHR antagonist

(CH223191) was able to block heart malformations induced by mITP but it was suggested

that CH223191 antagonizes another target in addition to AHR (Gerlach et al. 2014; McGee

et al. 2013).

PCB-126 (3,3’,4,4’,5-pentachlorobiphenyl) is one of the most potent AHR agonists (Kafafi

et al. 1993), and is associated with developmental toxicity in zebrafish (Grimes et al. 2008).

Ahr2 knockdown greatly reduces PCB-126-induced cardiac effects and mortality, but only provides minimal protection against the abnormal inflation of the swim bladder (Garner et al. 2013; Jonsson et al. 2007). A follow-up study showed that, at a lower PCB-126 exposure concentration of 5 nM, ahr2 gene knockdown prevented the swim bladder phenotype. This suggests that, again, incomplete ahr2 morpholino knockdown was probably operant and thus insufficient to block toxicity at the higher concentration (Jonsson et al. 2012). This pattern was similar to ahr2 knockdown that partly mitigated cardiotoxicity caused by a lower TCDD exposure concentration of 0.3 ppb, but not at higher concentrations of 0.5 and

1ppb (Dong et al. 2004). Ahr2 knockdown also significantly reduces cyp1a, cyp1b1, cyp1c1, and cyp1c2 mRNA expression, and Cyp1a protein activity produced by PCB-126 exposure (Garner et al. 2013; Jonsson et al. 2007; Jonsson et al. 2012). One recent study determined that PCB-126 exposure not only caused increased expression of few AHR target genes (ahrra, tiparp, and nfe2l2b), but also led to their mRNA being hypermethylated

(Aluru et al. 2020); future work to understand the specific role of this post-transcriptional modification is needed. Similar to TCDD, PCB-126 is not metabolized and accumulates in zebrafish, which leads to persistent expression of target genes (Garner et al. 2012; Meyer-

Alert et al. 2018). Waits & Nebert used a quantitative trait locus (QTL) approach to

investigate the genetic basis for zebrafish embryo susceptibility to PCB-126-induced developmental cardiotoxicity. Among the top-ranked QTLs was a region on chromosome

22 that includes ahr2 and ahr1b, implicating one or both of these receptors in having a role in PCB-126 toxicity (Waits et al. 2011).

TCDD

Developmental toxicity

TCDD is the most studied AHR2 ligand. In experiments using in vitro-translated AHR proteins or expression in heterologous cells, TCDD does not bind or activate AHR1a, but it binds AHR2 and AHR1b and activates them with comparable efficacies (Andreasen et al. 2002a; Karchner et al. 2005). When zebrafish are developmentally exposed to TCDD, they display reduced survival, and several phenotypes such as (but not limited to) cardiotoxicity, pericardial and yolk sac edemas, and craniofacial malformations (Henry et al. 1997). The various adverse developmental outcomes are reviewed in (Carney et al.

2006b). Knockdown and knockout studies have demonstrated the role of AHR2 in mediating TCDD-induced pericardial and yolk sac edemas, cardiovascular and craniofacial malformations, decrease in body length, and increased apoptosis, in addition to a significant increase in the mRNA levels of cyp1a, cyp1b1, cyp1c1, and cyp1c2 in zebrafish

(Carney et al. 2004; Dong et al. 2004; Garcia et al. 2018a; Goodale et al. 2012; Jonsson et al. 2007; Prasch et al. 2003; Souder et al. 2019; Teraoka et al. 2003b; Yin et al. 2008). One study also demonstrated that the constitutive activation of the AHR2 in zebrafish cardiac myocytes not only led to TCDD-like cardiotoxicity, but also other defects in craniofacial

development and failure to form swim bladders, suggesting the importance of the heart as

a target organ (Lanham et al. 2014). We note that ahr2 knockdown, however, was unable

to protect against TCDD-induced inhibition of swim bladder inflation and mortality; this

was attributed to the short half life of morpholinos after injection or a potential role of the

other zebrafish AHRs (Prasch et al. 2003). Future work clarifying these results in an

AHR2-null background zebrafish is necessary.

Mechanisms of TCDD developmental toxicity

The mechanisms of TCDD toxicity in humans and several vertebrate model organisms, including zebrafish, have been reviewed in (Carney et al. 2006b; King-Heiden et al. 2012;

Yoshioka et al. 2011; Yoshioka et al. 2019). TCDD-induced toxicity in zebrafish is associated with an array of transcriptomic changes, including modest changes to microRNA expression, in both developing zebrafish and in specific adult organs such as the heart (Alexeyenko et al. 2010; Carney et al. 2006a; Chen et al. 2008; Garcia et al.

2018b; Handley-Goldstone et al. 2005; Jenny et al. 2012). The gene expression changes

may be a function of a) large-scale toxicological phenotypes associated with TCDD, b) downstream effects of the AHR2/ARNT1 complex binding AHREs of various genes, or c) the interaction of the AHR with other pathways or transcription factors (Carney et al.

2006b). Here, we review what is known about the role of AHR2-regulated genes in TCDD-

induced developmental malformations in zebrafish.

Binding of TCDD to AHR2 induces expression of the cyp1 gene family. Upon exposure to

TCDD, cyp1a mRNA and protein are expressed early in development in a variety of organs

at the different development stages (Andreasen et al. 2002b; Kim et al. 2013; Yamazaki et

al. 2002; Zodrow et al. 2004). One study noted that the Cyp1a protein is first localized to the skin and the vasculature after which it transitions to the vasculature, kidney, and liver by 120 hpf (Andreasen et al. 2002b). When AHR2-null zebrafish are exposed to TCDD,

Cyp1a protein expression at 120 hpf is almost completely prevented (Goodale et al. 2012).

It was initially thought that the induction of cyp1a is required for TCDD developmental toxicity (Teraoka et al. 2003b); however, a later study demonstrated that, consistent with mammalian literature, TCDD produces developmental toxicity endpoints independent of cyp1a (Carney et al. 2004). TCDD also induces expression of cyp1b1; however, this does not appear to have a direct role in TCDD-induced pericardial edema and craniofacial malformations (Yin et al. 2008). Cyp1c1 and cyp1c2 likely play roles in TCDD-induced circulation failure in the mid-brain but the exact mechanism is unknown (Kubota et al.

2011). Unlike the other zebrafish cyp1s, cyp1d1 does not seem to be transcriptionally activated by TCDD or PCB126 (Goldstone et al. 2009).

Cyclooxygenase-2 (COX-2) enzymes, a family of heme-containing enzymes thought to be involved in acute inflammatory responses, have been studied in the context of the AHR signaling pathway in zebrafish and other model organisms. Zebrafish have two cox-2 genes, cox2a and cox2b (Ishikawa et al. 2007); both are induced in an AHR2-dependent manner upon exposure to PCB-126, but future work is needed to identify whether these genes are direct targets of AHR2 (Jonsson et al. 2012). To our knowledge, induction of these genes has not been demonstrated upon TCDD exposure. However, it is suggested that cox2a, in combination with the thromboxane receptor and thomboxane A synthase 1

(also known as cyp5a), is involved in local circulation failure in the dorsal midbrain of

developing zebrafish (Teraoka et al. 2009). Another study discovered the role of the cox2b-

thromboxane pathway in TCDD-induced AHR2-dependent “precadiac” edema, the

increased area of the small cavity between the heart and the body wall (Teraoka et al. 2014).

The study demonstrated that knockdown of cox2b, but not cox2a, prevented formation of

the precardiac edema in the TCDD-exposed zebrafish, and also showed the involvement

of the thromboxane pathway, concluding that thromboxane release by TCDD probably led

to the edema in the developing zebrafish. Additionally, other factors such as oxidative

stress are involved, as an antioxidant was able to inhibit both precardiac edema and

circulation failure caused by TCDD exposure (Dong et al. 2004).

The sry box containing 9b (sox9b) gene, a critical chondrogenic transcription factor, has

been linked to cardiotoxicity caused by TCDD (Hofsteen et al. 2013b). Upon exposure to

TCDD, sox9b expression is significantly reduced in an AHR2-dependent manner (Garcia

et al. 2018a; Xiong et al. 2008), and one study found that sox9b knockdown resulted in

TCDD-like heart malformations (Hofsteen et al. 2013b). Morpholino knockdown of sox9b

also caused a phenotype similar to TCDD-induced jaw malformation, and restoration of

sox9b with mRNA injection prevented craniofacial malformations suggesting sox9b’s role

in TCDD-induced craniofacial defects (Xiong et al. 2008). A sox9b promoter - eGFP

transgenic reporter fish (uw101Tg) (Plavicki et al. 2014) was produced and used to identify the TCDD-induced repression of sox9b in the developing zebrafish heart and brain (Garcia et al. 2017; Hofsteen et al. 2013b). A mechanism for linking activation of AHR2 and sox9b repression was recently suggested (Garcia et al. 2017; Garcia et al. 2018b). The sox9b long intergenic noncoding RNA (slincR) is significantly induced by TCDD in an AHR2-

dependent manner and appears to interact with the 5’ untranslated region of the sox9b gene

(Garcia et al. 2017; Garcia et al. 2018b). Exposure of slincR morphants to TCDD resulted

in altered jaw cartilage structure and reduced incidence of hemorrhaging, suggesting a

possible functional role of slincR in both TCDD-induced craniofacial malformations and

cardiotoxicity (Garcia et al. 2018b). The study also highlighted several PAHs that induce slincR expression at high levels without causing sox9b repression, indicating that slincR could not only have tissue-specific effects but could also regulate other genes beyond sox9b

(Garcia et al. 2018b).

A member of the forkhead box family of transcription factors, originally designated foxq1b

but now known as foxq1a, is highly induced by TCDD exposure at a rate faster than cyp1a

in developing zebrafish, in an AHR2-dependent manner (Planchart et al. 2010). In situ

hybridization experiments showed that the transcript is expressed in the jaw primordium

and is hypothesized to play a role in craniofacial abnormalities (Planchart et al. 2010).

TCDD also induces the paralogous gene foxq1b in zebrafish (Hahn et al. 2014). More work

is needed to identify the functional role of the foxq1 paralogs in the TCDD toxicity

pathway.

2.4.2. AHR1a

Initial in vitro studies concluded that AHR1a was non-functional because it did not bind

TCDD and was transcriptionally inactive when expressed in cells together with ARNT2b

(Andreasen et al. 2002a). These results are supported by in vivo studies in AHR1a mutant

fish, from which it was concluded that AHR1a was not required for TCDD-induced toxicity

and Cyp1a activity in zebrafish (Souder et al. 2019). BNF also does not activate AHR1a,

and it was suggested that the zebrafish ahr1a is a possible pseudogene (Karchner et al.

2005). However, more recent in vivo studies demonstrate that AHR1a is functional and can

be activated by chemicals including leflunomide (Goodale et al. 2012), the oxy-PAH xanthone (Knecht et al. 2013), several nitro-PAHs like 5-nitroacenaphthalene, 9- nitrophenanthrene, and 7-nitrobenzo[k]fluoranthene (Chlebowski et al. 2017), and the parent PAHs pyrene (Incardona et al. 2006) and chrysene (Incardona et al. 2005). Upon ahr1a knockdown and developmental exposure to each of these chemicals, either a reduction of toxicity(Chlebowski et al. 2017; Incardona et al. 2006) or a reduction of induced Cyp1a protein expression (Chlebowski et al. 2017; Goodale et al. 2012; Incardona et al. 2005; Knecht et al. 2013) was confirmed. Furthermore, AHR1a is the dominant receptor involved in regulating induction of larval hepatic Cyp1a; ahr1a knockdown reduces Cyp1a liver expression induced by pyrene (Incardona et al. 2006); leflunomide

(Goodale et al. 2012), xanthone (Knecht et al. 2013), and the nitro-PAHs, 5-

nitroacenaphthalene, 9-nitrophenanthrene, and 7-nitrobenzo[k]fluoranthene (Chlebowski

et al. 2017).

In contrast to the above-mentioned studies, Garner et al. found that morpholino knockdown

of ahr1a exacerbated the developmental toxicity caused by both PCB-126 and a mixture of

PAHs, BkF and fluoranthene (FL) (Garner et al. 2013). Although ahr1a knockdown did

not affect cyp1a, cyp1b1, and cyp1c1 gene expression, Cyp1a protein activity, measured

using the ethoxyresorufin-O-deethylase (EROD) assay, increased. From this study, the authors hypothesized that AHR1a likely mimics AHRR and consequently, the absence of

AHR1a results in excessive AHR2 signaling and enhances the cardiotoxicity measured by

pericardial edema (Garner et al. 2013). The study also highlights that AHR1a seems to

inhibit Cyp1a protein activity, as ahr1a knockdown led to increased activity. This was in

contrast to AHR2, which mediated an increase in Cyp1a activity (Garner et al. 2013).

Another study found that the prevalence of mITP-induced cardiotoxicity, but not its severity, increased when all three ahrs were knocked down compared to just ahr1b/ahr2 knockdown, suggesting that AHR1a may play a role in mITP-induced cardiotoxicity

(Gerlach et al. 2014). However, it is noteworthy that cyp1a transcript expression was not altered by ahr1a knockdown like it was by ahr1b/ahr2 knockdown, indicating that mITP likely does not activate AHR1a (Gerlach et al. 2014). Overall, AHR1a appears to have

relevance and ligand-specific functions that are currently enigmatic. Table 2.5 summarizes

the effects mediated by AHR1a in developing zebrafish.

2.4.3. AHR1b

Zebrafish morpholino studies reveal that AHR1b does not play a role in early life toxicity

caused by PCB-126, a PAH mixture of BkF and fluoranthene (Garner et al. 2013), or

TCDD (Souder et al. 2019). Although ahr1b knockdown did not prevent mITP-induced cardiotoxicity in zebrafish, there was a significant decrease in the prevalence of mITP- induced pericardial edema in an AHR2 mutant line injected with the ahr1b morpholino compared to control morpholino-injected AHR2 mutants. This suggests AHR1b’s possible role in mediating mITP-induced cardiotoxicity (Gerlach et al. 2014). Additionally, studies

suggest that AHR1b may be involved in not only developmental toxicity, but also adult

toxicity effects of chemicals like TCDD, some PAHs, and PCB-126 (Garner et al. 2013;

Goodale et al. 2012); however, a closer look with histopathology or immunohistochemistry

may be necessary to reveal possible subtle effects missed in gross morphology studies. It

was also suggested that AHR1b could be functionally redundant with AHR2, but so far, it

seems evident that AHR2 has a greater role in regulating the expression of xenobiotic- metabolizing enzymes and mediating toxicity compared to AHR1b (Garner et al. 2013).

While knockdown studies have not definitively shown a role for AHR1b in the

developmental toxicity of xenobiotics, AHR1b appears to be important for leflunomide-

induced Cyp1a protein expression in the vasculature, but not in the liver (Goodale et al.

2012). AHR1b may also play a role in Cyp1a protein expression induced by mITP in the

vasculature, heart, and liver (Gerlach et al. 2014), and by nitro-PAHs like 7-

nitrobenzo[k]fluoranthene in the vasculature, skin, and the neuromasts (Chlebowski et al.

2017). Ahr1b knockdown in mITP-exposed zebrafish also reduced cyp1a mRNA levels

(Gerlach et al. 2014). These studies demonstrate that AHR1b can be activated by various chemicals and they concur with earlier studies that showed AHR1b is a fully functional receptor (Karchner et al. 2005) with partially overlapping functions with AHR2. Unlike

AHR2 and AHR1a, AHR1b does not appear to mediate Cyp1a expression in any specific tissue. Table 2.6 summarizes the evidence for AHR1b’s role in developmental toxicity in zebrafish.

Overall, AHR2 is predominant in mediating the early stage toxicity of a large variety of ligands. AHR1a and AHR1b can also mediate developmental toxicity albeit to a lesser extent, and this supports the idea that the three AHRs have partitioned multiple AHR roles.

The three AHRs have distinct ligand profiles, and even when different chemicals activate

the same receptor, the downstream gene expression and developmental toxicity endpoints

can be considerably different, suggesting ligand-specific activation of the AHRs.

2.5. Interaction between AHR and other pathways

In addition to the direct AHR-mediated toxicity, the AHRs and AHR-responsive genes can

directly or indirectly interact with genes from several different signaling pathways while

modulating toxicological responses to a ligand. The developmental zebrafish model provides an ideal platform to study these interactions, since most signaling mechanisms are concurrently and dynamically at play during development. In this section, we focus on studies that have explored the crosstalk between AHR signaling and other pathways using embryonic zebrafish.

2.5.1. AHR and oxidative stress

Oxidative stress - the disruption of redox signaling and control (Jones 2006) - is a well- studied toxicological phenomenon that occurs in response to several classes of chemicals that produce reactive oxygen species (ROS) or disrupt thiol homeostasis (Di Giulio et al.

2008; Sies et al. 2017). AHR-mediated oxidative stress (Di Giulio et al. 2008) occurs through a variety of mechanisms, including stimulation of inflammatory responses and induction of pro-oxidant enzymes such as xanthine oxidase and CYP-dependent monooxygenases, which can release ROS or generate redox-cycling metabolites (Dalton et al. 2002; Reichard et al. 2006). Developmental exposures to AHR ligands such as PAHs

(Van Tiem et al. 2011), oxy-PAHs (Knecht et al. 2013), heterocyclic and nitro-PAHs

(Chlebowski et al. 2017), or PCB-126 (Liu et al. 2016) result in induction of redox-

responsive antioxidant genes such as glutathione peroxidase (gpx1), glutamate cysteine

ligase (gclc1), and superoxidase dismutase (sod1). In addition, PCB-126 also induces a significant increase in lipid peroxidation, a result of ROS-induced cellular damage (Liu et al. 2016). In fact, AHR activation and antioxidant responses act synchronously in response to toxicant exposures - this was evidenced by the mirroring of the activities of total SOD and Cyp enzymes in whole homogenates of fish exposed to the PAHs, phenanthrene and anthracene (Wang et al. 2018). Taken together, these studies suggest a robust antioxidant response as well as some levels of oxidative damage associated with AHR activation. A number of knockdown studies have also supported these outcomes. For example, ahr2 knockdown blocks the increased expression of gpx1, gclc1 and sod1 by the AHR agonist

BkF (Van Tiem et al. 2011). Ahr2 knockdown also prevents the induction of ROS and 8-

OHdG (8-hydroxy-2′ -deoxyguanosine, a marker of oxidative DNA damage) by the

chlorinated solvent trichloroethylene (TCE) (Jin et al. 2020), although wild type TCE-

exposed embryos do not show an induction of cyp1a1 or ahr2 transcripts. Nevertheless,

these studies confirm the specific role of AHR2 in mediating both oxidative damage and

antioxidant responses.

A major driver for AHR-induced antioxidant responses is the crosstalk between AHR and

their prime regulator, Nrf2 (Baird et al. 2020). This mechanism is particularly important for AHR2 ligands such as TCDD, which do not undergo substantial metabolism and hence, redox cycling (Dietrich 2016). Nrf2 (also called Nfe2l2) is a transcription factor that regulates the expression of a number of antioxidant enzymes such as NAD(P)H:quinone oxidoreductase (NQO1) as well as xenobiotic-metabolizing enzymes such as glutathione-

S-transferases (GSTs) (Dietrich 2016). In mammals, AHR regulates Nrf2 expression and

Nrf2 mediates the AHR-dependent induction of several xenobiotic-metabolizing enzymes by TCDD (Miao et al. 2005; Yeager et al. 2009). Zebrafish have two Nrf2 genes, nrf2a and nrf2b, both of which contain AHREs within their promoter regions (Timme-Laragy et al.

2012a). A number of chemicals such as TCDD (Hahn et al. 2014; Timme-Laragy et al.

2012a), PAHs (Knecht et al. 2013), and PCBs (Timme-Laragy et al. 2012a; Timme-Laragy et al. 2015) induce nrf2a or nrf2b mRNA expression at different life stages, in an AHR2- dependent manner (Timme-Laragy et al. 2012a). For example, embryonic exposures to the oxy-PAH 7,12-B[a]AQ results in increased expression of nrf2 and nqo1 in addition to genes associated with the glutathione redox cycle (gst, gpx, and sod families) (Knecht et al. 2013). One study showed that although exposures to PCB-126 in wild type embryos did not elicit any antioxidant responses, an nrf2a mutant displayed altered both basal expression and PCB-inducibility of certain ahr, nrf2 and gst family genes (Rousseau et al.

2015). Other nrf2-family genes and AHR forms may also be involved in this cross-talk; for example, AHR1b regulates the constitutive and TCDD-inducible expression of nrf2a as well as other members of the nrf gene family, nrf1a and nrf1b (Ulin et al. 2019). These studies suggest that antioxidant responses to TCDD, PCBs, and PAHs may be driven by a combination of oxidative stress and AHR-Nrf2 crosstalk. Indeed, it is well known that the glutathione and Nrf2 pathways are interdependent, and nrf2 knockdown can perturb the glutathione redox state (Sant et al. 2017). In contrast, TCDD, a strong inducer of nrf2 in both zebrafish (Hahn et al. 2014; Ulin et al. 2019), and in mammals (Miao et al. 2005;

Yeager et al. 2009), does not induce expression of antioxidant genes such as sod, gst or nqo1 (Alexeyenko et al. 2010; Hahn et al. 2014). Overall, these studies provide evidence

of the complexity of the role of AHR, the glutathione redox state, and the Nrf2 pathway in

trying to maintain oxidative homeostasis in response to xenobiotics.

2.5.2. AHR-Wnt crosstalk and tissue regeneration

While mammals, including humans, have a limited regenerative capacity restricted to some organs such as liver and skin, other vertebrates possess high regenerative capacity of the heart, liver, limbs, etc (Marques et al. 2019). The process of regeneration involves cellular migration, blastema formation, differentiation, and proliferation that are all regulated by multiple signaling pathways (Akimenko et al. 2003; Santamaria et al. 1991), and external

stressors can potentially inhibit regeneration (Mathew et al. 2009). Zebrafish, in particular,

has been widely used as a model for studying tissue regeneration following surgical

amputation of organs (Akimenko et al. 2003). Exposure to AHR ligands such as TCDD

and leflunomide (an anti-inflammatory drug) following fin amputation results in a failure

of adult and larval fin regeneration (Andreasen et al. 2007; Mathew et al. 2006; O'Donnell

et al. 2010; Zodrow et al. 2003). Morpholino knockdown of ahr2 and arnt1 results in both

unexposed and TCDD-exposed morphants showing normal fin regeneration, indicating

that the inhibition of regeneration by TCDD is AHR2 and ANRT1-dependent (Mathew et

al. 2006). Following TCDD exposures, adult regenerative tissues also show widespread

changes in the transcripts that regulate cellular differentiation, cartilage, collagen, cell

growth, tissue regeneration, and extracellular matrix - all important factors involved in

tissue regeneration (Andreasen et al. 2007). Specifically, TCDD exposure is associated

with both transcriptional activation of R-spondin 1, and a repression of sox9b (a

transcription factor regulated by AHR2, as discussed previously) in embryos (Mathew et

al. 2007). R-spondin 1 is a Wnt/β catenin signaling gene that contains an AHRE in its promoter region. Morphants resulting from partial suppression of both R-spondin 1 and

LRP6, a Wnt co-receptor, show normal caudal fin regeneration following TCDD exposure,

demonstrating that activation of these Wnt signaling genes is required for TCDD to inhibit

regeneration (Mathew et al. 2007). This result is also supported by the induction of a

number of other Wnt/β-catenin signaling genes by TCDD in regenerating tissues. In

conjunction with transcriptional activation of R-spondin 1, the expression of sox9b is repressed within regenerating fin tissues after TCDD exposure (Mathew et al. 2007).

Interestingly, although sox9b morphants show some levels of regeneration of caudal fins, the regenerative tissue still possesses defective structures, indicating that this process is not completely dependent on sox9b (Mathew et al. 2007). In humans, SOX9 is also a Wnt- target gene and is directly regulated by R-spondin 1 (Yano et al. 2005). Furthermore, SOX9 also inhibits expression of β catenin-associated genes and promotes degradation of β catenin (Yano et al. 2005). Therefore, it is likely that the inverse expression patterns between R-spondin1 and sox9b observed within regenerative fin tissues results from a crosstalk between the AHR and Wnt signaling mechanisms to regulate tissue regeneration.

In addition to the proposed AHR-Wnt crosstalk, other AHR-mediated mechanisms can govern tissue regeneration, depending on the tissue type. For example, one study showed that TCDD exposures of adult zebrafish with partially amputated hearts led to an inhibition of regeneration of myocardial tissues, but there was no impact on sox9b, R-spondin 1, or other Wnt signaling genes although, as seen with caudal fin amputation, expression of genes associated with tissue regeneration and extracellular matrix was altered (Hofsteen et

al. 2013a). The lack of change in transcript levels of sox9b and fin tissue regeneration, while largely governed by similar molecular factors, have some differences; for example:

while fin regeneration is co-regulated by Wnt signaling, myocardial regeneration is co-

regulated by TGFβ and NFκβ pathways (Sehring et al. 2016). Despite these differences, the studies show that only the chemical activation of AHR inhibits tissue regeneration.

2.5.3. Estrogen receptor (ER)

Both mammalian and zebrafish studies show clear evidence of crosstalk between AHR and

ER. In mammals, AHR interactions with estrogen signaling pathways have long been known to occur through a variety of mechanisms (Safe et al. 2003; Swedenborg et al. 2010), including the role of AHR as an E3 ubiquitin ligase controlling proteasomal degradation of ER (Ohtake et al. 2007; Ohtake et al. 2003; Wormke et al. 2003). It is not known whether fish AHRs can act in this way, but there is other evidence for AHR-ER crosstalk. In zebrafish, Cyp3c can be induced by both AHR and ER ligands, suggesting that there may be a crosstalk between these two receptor mechanisms in regulation of CYP3 (Shaya et al.

2019). The direct interaction between AHR and ER pathways has been shown in other studies where the transcriptional induction of ER-target cyp19b or vitellogenin by ER ligands 17α-ethynylestradiol and 17ß-estradiol, were reversed by TCDD (Bugel et al. 2013;

Cheshenko et al. 2007). In addition, this effect was partially blocked by ANF, an AHR antagonist (Cheshenko et al. 2007). Interestingly, a chemically induced pan-ER inhibition does not block TCDD-induced, AHR2-mediated cardiotoxicity, suggesting that AHR2 is not a constitutive partner of ER (Souder et al. 2019). However, these authors also conclude that an AHR-ER crosstalk may be tissue-dependent. This was supported by another study,

where 17β-estradiol increased ahr1a mRNA expression only in the 4-day old zebrafish

brain, but not in other organs (Hao et al. 2013). Likewise, an adult zebrafish study showed

that TCDD inhibited levels of the genes regulating the estrogen receptor as well as estrogen

synthesis and follicular development in the zebrafish ovary (King-Heiden et al. 2008),

highlighting the role of the AHR-ER crosstalk in the reproductive system (discussed later).

Therefore, interactions between AHR and ER in zebrafish is likely complicated and highly dependent on the specific target tissues.

2.5.4. Pregnane X receptor (PXR)

AHR displays some levels of crosstalk with the transcription factor PXR, which regulates a number of CYP2 and CYP3 enzymes and is involved in detoxification of an array of xenobiotics, primarily steroids. Embryonic zebrafish exposures to either pregnenolone (a

PXR agonist) or PCB-126 (an AHR agonist) result in the increased transcript levels of pxr, ahr2, cyp1a as well as a number of cyp2 and cyp3 genes (Kubota et al. 2015). Furthermore, knockdown of ahr2 reverses the PCB 126-induced transcriptional activation of cyp1a and pxr, as well as cyp2 and cyp3 genes. Taken together, these studies suggest an inevitable crosstalk between AHR and PXR in regulation of cyp2 and cyp3 genes.

2.5.5. Fibroblast growth factor (FGF)

Studies have explored the interaction between AHR and the FGF pathway in developmental processes. In mammals, the FGF pathway gene, fgf21, is a known AHR target gene (Cheng et al. 2014). In zebrafish, similar to AHR, the FGF pathway is known to independently regulate tissue regeneration. However, a study comparing fin regeneration after exposure to TCDD as well as an FGF pathway inhibitor, SU5042, showed that,

although both AHR activation and FGF inhibition lead to inhibition of fin regeneration, the

phenotypes were morphometrically different and there was no evidence of interaction

between the two pathways in the regenerative process (Mathew et al. 2006).

In summary, we note that much of the work concerning crosstalk with other signaling

pathways has focused on AHR2. Although it is evident that the AHR2 signaling pathway

interacts with several other transcription factors and signaling pathways, the interactions

are highly complex, and we have only begun to understand them in zebrafish.

2.6. Adult toxicity, epigenetics and multigenerational effects

Compared to the many studies assessing the role of AHRs in during development, only a

limited number have investigated the role of the zebrafish AHRs in both post- developmental physiology in juveniles and adults, and across generations. To our knowledge, AHR1a- or AHR1b-specific adult and multigenerational toxicity after exposure to xenobiotic chemicals has not been investigated. In this section, we first review the functional roles of AHR2 in TCDD-induced adult toxicity (Section 4.1) of the reproductive (Section 4.1.1) and musculoskeletal (Section 4.1.2) systems, and then describe what is known about the epigenetic effects (Section 4.2) of the ligands TCDD,

PCB-126, and BaP, whose toxicity endpoints are mediated primarily by AHR2.

2.6.1. Adult toxicity

Reproductive System

AHR plays a modest constitutive role in the reproductive system; a study with AHR

mutants showed altered follicular development in ovaries of the AHR2-null zebrafish compared to wild type zebrafish (Garcia et al. 2018a). However, more profound effects on

reproductive organs have been shown to be triggered by xenobiotic activation of AHR2 by

TCDD (King-Heiden et al. 2012) and these impacts are expected due to the crosstalk

between AHR and ER as described in the previous section. Indeed, dietary TCDD exposure

reduced mRNA levels of genes regulating the estrogen receptor and follicular development

in adult zebrafish ovaries, while inducing expression of cyp1a (King-Heiden et al. 2008).

Additionally, zebrafish exposed to TCDD at 3- and 7- weeks post fertilization displayed

reduced fecundity and reduced percentage of fertilized eggs (Baker et al. 2013). Paired spawning also showed that these impacts were independent of the sex of the fish. While female fish displayed abnormalities in ovarian structures, the testes of TCDD-exposed male fish displayed decreased spermatozoa with increase in spermatogonia, decreased germinal epithelial thickness, and altered responses in genes regulating testis development and steroidogenesis (Baker et al. 2016). Interestingly, TCDD-exposed fish also experienced a prevalent shift towards feminization, but a significant percentage of female fish possessed male gonads (Baker et al. 2013). From these studies, it is evident that both

AHR knockout (Garcia et al. 2018a) and its activation (other studies) result in reproductive deficiencies, suggesting that any disruption to the normal AHR signaling mechanism can have deleterious effects on reproductive physiology. These effects on the reproductive system may also be mediated through AHR-associated epigenetic mechanisms, discussed in Section 4.2. Taken together, these studies unequivocally highlight the significant role of

AHR in reproductive development, sex determination, and reproductive functions.

Musculoskeletal System

TCDD exposures have also been shown to affect musculoskeletal development in adults, with zebrafish exposed to TCDD at 3- and 7- weeks post fertilization displaying skeletal deficits during adulthood, including skeletal kinks, shortened jaw structures, and abnormal operculum and bone structures (Baker et al. 2013). Interestingly, AHR2-null fish also showed similar deficits in skeleton and fins, including defective fins, abnormal dentary, operculum and frontal structures, smaller orbital and supraorbital bones (Baker et al.

2014b; Garcia et al. 2018a; Goodale et al. 2012; Souder et al. 2019). These identical responses both to AHR2 deficiency and AHR2 activation mimic the equivocality of responses on reproductive development and highlight the need for more detailed studies on the role of AHR2 in musculoskeletal development. They also reiterate the sensitive nature of the AHR2 signaling pathway, where any alteration – either its deficiency or activation

– can have profound impacts on the musculoskeletal system.

2.6.2. Epigenetics and multigenerational effects

TCDD exposure results in several transgenerational effects (Baker et al. 2014a; Baker et al. 2014c; King-Heiden et al. 2005; Meyer et al. 2018) which have been reviewed recently

(Viluksela et al. 2019). It has been hypothesized that epigenetic mechanisms mediate these effects of TCDD, although specific epigenetic modifications have not been identified as having a causal role in the responses observed in F1 and F2 generations (Baker et al.

2014a). There is increasing evidence in other organisms showing how epigenetic modifications are related to AHR signaling and TCDD toxicity (Patrizi et al. 2018;

Viluksela et al. 2019). The expression of cyp1a1 and cyp1b1 in the F1 generation of a

TCDD-exposed zebrafish lineage was significantly higher than in control-lineage F1 animals, suggesting an AHR role (Olsvik et al. 2014). Within the genome region queried, there was no effect of TCDD on the methylation pattern of the ahr2 promoter in developing zebrafish (Aluru et al. 2015). However, the study found altered promoter methylation of

AHR target genes, ahrra and c-fos. TCDD exposure also altered dnmt expression in the F0 generation, and in vitro transactivation studies identified that three of the five tested dnmt promoters caused transactivation of luciferase reporter by AHR2/ARNT2 in the presence of TCDD (Aluru et al. 2015). A recent study that examined genome-wide changes in DNA methylation in adult testes of zebrafish exposed to TCDD found differential methylation of genes involved in reproductive and epigenetic processes (Akemann et al. 2018) and

some of these histological and transcriptomic effects persisted in subsequent F1 and F2

generations (Meyer et al. 2018), suggesting a methylation-dependent transfer of

biomarkers across generations. PCB-126 exposure of adult zebrafish caused extensive

alteration in genome-wide DNA methylation patterns in liver and brain, which were not

strongly correlated with altered gene expression, suggesting a complex relationship

between DNA methylation and gene regulation (Aluru et al. 2018). Taken together, these

studies suggest the potential role of methylation-dependent epigenetics in driving TCDD-

and PCB-126-induced reproductive outcomes.

BaP exposure leads to transgenerational effects including alterations to locomotor activity,

decreased heartbeat and mitochondrial function, reduced hatch rate, reduced egg

production and offspring survival, increased mortality and incidence of malformations up

to the F2 generation (Corrales et al. 2014a; Corrales et al. 2014b; Fang et al. 2013; Knecht

et al. 2017a). These effects were likely a result of global hypomethylation in conjunction

with alterations of expression of developmental and cancer-related genes in BaP-exposed

F0 zebrafish (Corrales et al. 2014a; Fang et al. 2013; Knecht et al. 2017a). Dnmt expression was generally reduced with BaP exposure, an effect that was further strengthened by AHR2 knockdown (Knecht et al. 2017a). This was unexpected and may have been due to a mechanism different from AHR2/ARNT acting via AHREs in the dnmt promoters.

Another study found that, following BaP exposure, dnmt1 and dnmt3a had increased mRNA expression in 96 hpf larvae and in adult brains; however, the role of the AHRs in mediating the altered expression was not investigated (Gao et al. 2017).

In summary, the functional role of AHR2 in mediating adult toxicity and epigenetic perturbation is in the early stages of investigation and more studies are being conducted across several labs to better understand these mechanisms.

2.7. Conclusions and future directions

The studies conducted so far have demonstrated the vast diversity of both the endogenous and toxicological functional roles of the zebrafish AHRs. Research in zebrafish (and other fish models) has enhanced our understanding of AHR biology in all its richness, including endogenous and toxicological AHR roles. This research has complemented studies in other animal models, and in particular has contributed to knowledge about AHR functions during vertebrate development. Additionally, research on the zebrafish AHRs has explored and

revealed the heterogeneity of ligands that are able to bind to each of the three receptors.

The majority of the research so far on all aspects of functionality has focused on AHR2;

future work concentrating on how AHR1a and AHR1b contribute to normal physiology

and toxicity of xenobiotics will not only inform us of their roles, but could also reveal

unknown functions of the AHR, many of which are conserved across vertebrates. Further,

understanding the downstream signaling events upon AHR activation has centered on

TCDD as a ligand. Exploration of the functions of AHR-regulated genes upon exposure to other chemicals will facilitate a gene biomarker approach to further characterize and classify xenobiotics that act via AHR. With the remarkable diversity of AHR ligands, the wide-ranging downstream AHR-regulated genes, and the crosstalk interactions with other signaling pathways, it is clear we must discern how the activation of the AHRs by its various ligands can differentially modulate signaling pathways that dictate biological outcomes.

Figure 2.1. Shared synteny between zebrafish and other vertebrate AHR genes. Shared synteny was analyzed using Genomicus (versions 93.0 and 100.01) (Muffato et al., 2010; Nguyen et al., 2018) with manual curation using Ensembl. The AlignView tool in Genomicus was used to visualize the syntenic relationships. Genomes for human (Homo sapiens), mouse (Mus musculus), and chicken (Gallus gallus) were used to illustrate syntenic relationships with the chromosomes containing 3 zebrafish ahrs. The panels show the shared synteny obtained when using (A) zebrafish ahr1a (chromosome 16), (B)

zebrafish ahr2-ahr1b (chromosome 22), and (C) human AHR (chromosome 7) as reference genes. The genes on either side of the reference gene are shown in the correct order and orientation. Orthologs (or in some cases paralogs) of the reference gene and its flanking genes are shown in the same color, below the reference chromosome, organized by species and chromosome. The position and order of genes below the reference chromosome do not necessarily reflect their position and order on the indicated chromosome.

Table 2.1. Zebrafish AHR genes and their respective translation products.

Characteristic AHR2 AHR1a AHR1b

Zebrafish chromosome/linkage group 22 16 22

Mammalian orthologs -- AHR --

Amino acid length 1027 aa 805 aa 954 aa

Predicted molecular mass of protein 113 kDa 90.4 kDa 104.8 kDa

Overall amino acid identity comparison with 51 % 52 % 67 % human AHR Amino acid identity comparison with human AHR 71 % 68 % 71 % ligand binding domain Overall amino acid identity comparison with 45 % 44 % 100 % AHR1b Conserved N-terminal halves identity comparison 66 % 63 % 100 % with AHR1b protein

Table 2.2. Receptor characteristics (developmental baseline and TCDD-induced mRNA expression, endogenous ligands and roles, and binding partners) of AHR2, AHR1a, and AHR1b. See text for citations.

Characteristic AHR2 AHR1a AHR1b Earliest detected 5 hpf 24 hpf 24 hpf expression (Andreasen et al. 2002b; Tanguay et al. (Andreasen et al. 2002a; (Karchner et al. 1999) Karchner et al. 2005) 2005) mRNA Several regions including Liver at 52 hpf Developing localization the head and trunk (Sugden et al. 2017), eye during (Andreasen et al. 2002b; Sugden et regenerating fin (Karchner et al. al. 2017) (Sugden et al. 2017) 2017; Sugden et al. development 2017) mRNA Brain, heart, muscle, Brain (Webb et al. Unknown localization in swim bladder, liver, gill, 2009), liver, heart, adults skin, eye, kidney, fin swim bladder, and (Andreasen et al. 2002a) kidney (Andreasen et al. 2002a) Effect of TCDD Increase in expression Increase in No change in exposure on (Andreasen et al. 2002b; Garcia et expression expression mRNA al. 2018a; Karchner et al. 2005; (Andreasen et al. 2002a; (Karchner et al. Tanguay et al. 1999) Karchner et al. 2005) 2005; Ulin et al. expression 2019) Endogenous Several at both Possible roles in Crosstalk roles embryonic/larval and hypocretin/orexin detected with adult life stages signaling NRF signaling (See Section 1.2.) (Seifinejad et al. 2019) (Ulin et al. 2019) Known FICZ (Jonsson et al. 2009; None identified FICZ endogenous Wincent et al. 2016), 3α,5α- (Jonsson et al. 2009) ligands tetrahydrocorticosterone and 3α,5β- tetrahydrocorticosterone (5α- and 5β-THB) (Wu et al. 2019) Endogenous Expression in the None None Cyp1a developing zebrafish eye expression but not in the trunk or regulation brain (Sugden et al. 2017) In vitro binding ARNT1b, 1c, 2b, 2c ARNT2b ARNT2b with ARNTs (Prasch et al. 2006; Tanguay et al. (Andreasen et al. 2002a) (Karchner et al. 2000) 2005) In vitro binding Yes No Yes with TCDD (Prasch et al. 2006; Tanguay et al. (Andreasen et al. 2002a; (Karchner et al. 2000) Karchner et al. 2005) 2005)

In vitro Yes Not applicable Yes but less transactivation (Prasch et al. 2006; Tanguay et al. sensitive than 2000) activity with AHR2 TCDD (Karchner et al. 2005) ARNT required ARNT1 Not applicable Unknown for in vivo (Antkiewicz et al. 2006; Prasch et al. 2004; Prasch et al. 2006) activation

Table 2.3. Predicted binding of different ligands to the zebrafish AHRs.

Ligand AHR2 AHR1a AHR1b References Anthracene Yes Not Not (Goodale et al. 2015) tested tested Anthrone derivative Yes Not Not (Goodale et al. 2015) SP600125 tested tested BAA Yes Not Not (Goodale et al. 2015) tested tested BaP Yes Not Not (Goodale et al. 2015) tested tested BEZO Yes Not Not (Goodale et al. 2015) tested tested CH223191 Yes Yes Weak (Gerlach et al. 2014) Leflunomide Yes Yes Yes (Bisson et al. 2009; Goodale et al. 2012; O'Donnell et al. 2010) ortho-mITP Yes No No (Gerlach et al. 2014) meta-mITP Yes Yes Weak (Gerlach et al. 2014) para-mITP Weak Yes No (Gerlach et al. 2014) TCDD Yes No Yes (Bisson et al. 2009) NPAHs, HAHs, amino Yes Yes Yes (Chlebowski et al. 2017) PAHs

Table 2.4. Developmental toxicity endpoints and CYP1A expression patterns mediated by AHR2 from morpholino knockdown studies.

Xenobiotic ligand Endpoints Results of AHR2 References mediated by knockdown ligand x AHR2 Polycyclic Aromatic Hydrocarbons (PAHs) Pyrene - Pericardial - Prevention of (Incardona et edema, cell death morphological al. 2005) in the neural tube, effects anemia - CYP1A protein expression - Reduced vasculature expression Chrysene - CYP1A protein - Prevention of (Incardona et expression epidermal expression al. 2005) - No change to vasculature expression Dibenzothiophene - CYP1A protein - Reduction of weak (Incardona et expression vascular expression al. 2005) - No change to cardiotoxicity Phenanthrene - CYP1A protein - Reduction of weak (Incardona et expression vascular expression al. 2005) - No change to cardiotoxicity Benz[a]anthracene - Pericardial - Reduction in (Incardona et (BAA) edema prevalence of al. 2006; pericardial edema Incardona et - Intracranial - Prevention al. 2011) hemorrhage - Reduction in - CYP1A protein ventricular expression myocardium and epidermis - No reduction in endocardial and other vascular endothelial CYP1A protein induction

Benzo[a]pyrene (BaP) - Pericardial - Partial protection (Cunha et al. edema - Loss of myocardial 2020; - CYP1A protein expression Incardona et expression - No reduction in al. 2011; endocardial Knecht et al. Larval expression 2017b) hyperactive - Decreased larval swimming swimming response response Benzo[k]fluoranthene - CYP1A protein - Markedly reduced (Incardona et (BkF) expression epidermal CYP1A al. 2011) expression - No reduction in endocardial expression or pericardial edema Retene - Pericardial - Prevalence and (Scott et al. edema severity of pericardial 2011) edema lower - CYP1A protein - Reduced epidermal expression expression - No change to vascular expression Benz(a)anthracene-7,12- - Morphology - Prevention (Goodale et dione (7,12-B[a]AQ) malformations: al. 2015; axis, trunk, brain, Knecht et al. yolk and 2013) pericardial edemas, circulation, eye - Prevention of and jaw vasculature malformations expression - CYP1A protein expression 1,9-benz-10-anthrone - Morphology - Prevention (Goodale et (BEZO) malformations: al. 2015) axis, trunk, brain, yolk and pericardial edemas, circulation, eye and jaw malformations

1,6-dinitropyrene, - CYP1A protein - Reduction in skin (Chlebowski expression and vasculature et al. 2017) benzo[j]fluoranthene, - CYP1A protein - Reduction in (Shankar et al. dibenzo[a.h]pyrene, expression vasculature and 2019) dibenzo[a,i]pyrene, and prevention of skin benzo[b]fluoranthene expression 6H-benzo[cd]pyren-6- - Cardiotoxicity - Prevention (Cunha et al. one 2020) Other chemicals: mixtures, pharmaceuticals, indoles, and halogenated aromatic hydrocarbons PAH-containing soil - Edema and - Decrease in edema (Wincent et extracts from a viability occurrence, and al. 2015) gasworks, a former wood increase in viability preservation site, and a coke oven Total particulate matter - Several - Reduction in (Massarsky et (cigarette smoke) morphological morphological al. 2016) endpoints, endpoints, increased viability, hatching viability and success hatching success Environmentally - CYP1A protein - Supermix 3: (Geier et al. relevant PAH mixtures expression decrease 2018b) (“Supermix 3” and invasculature “Supermix 10”) expression and increase in liver expression Supermix 10: persistence of both vasculature and liver expression BaP and 6H- - Cardiotoxicity - Prevention (Cunha et al. benzo[cd]pyren-6-one (pericardial 2020) edema and string heart formation) Cardiosulfa - Cardiotoxicity - Reduction (Ko et al. (sulfanomide drug) 2009; Ko and Shin 2012) Leflunomide - CYP1A protein Reduction (Goodale et expression al. 2012; O'Donnell et al. 2010)

3,3’,4,4’,5- - Cardiotoxicity - Reduction (Garner et al. pentachlorobiphenyl and mortality 2013; Jonsson (PCB-126) et al. 2007) Mono-substituted - CYP1A protein - Reduction (Gerlach et al. isopropyl triaryl expression 2014) phosphate (mITP) (a major component of Firemaster 550, a flame retardant mixture) Paclobutrazol - Digestive tract - Reduction (Wang et al. (fungicide) toxicity 2015) Formylindolo[3,2- - Mortality, - Prevention (Wincent et b]carbazole (FICZ) pericardial al. 2016) edema, circulatory failure Phenanthroline - PAH-like - Reduction (Ellis and toxicity Crawford 2016) 2,7-dibromocarbazole - TCDD-like - Reduction (Fang et al. and 2,3,6,7- developmental 2016) tetrachlorocarbazole toxicity 3α,5α- - Enhanced larval - Blocked (Wu et al. tetrahydrocorticosterone locomotor enhancement 2019) (5α-THB) activity - AHR2 knockdown did not block 5β- THB-induced enhanced larval locomotor activity 2,3,7,8- - Developmental - Prevention See text tetrachlorodibenzo-p- toxicity (multiple - No change to dioxin (TCDD) endpoints) inhibition of swim bladder inflation and mortality

Table 2.5. Developmental toxicity endpoints and CYP1A expression patterns mediated by AHR1a from morpholino knockdown studies.

Xenobiotic ligand Endpoints Results of References mediated by AHR1a ligand x AHR1a knockdown Chrysene - Vasculature - Reduction (Incardona et al. CYP1A - No change to 2005) expression epidermal CYP1A expression Pyrene - Liver - Reduction (Incardona et al. abnormalities, - No change to 2006) pericardial dorsal curvature edema, neural caused by pyrene tube cell death exposure Pyrene - Liver CYP1A - Reduction (Incardona et al. expression - No change to 2006) vascular CYP1A expression Leflunomide - Liver CYP1A - Reduction (Goodale et al. expression 2012) BkF + FL, - Pericardial - Greater (Garner et al. PCB-126 effusion pericardial 2013) effusion - CYP1A activity - Increased activity Xanthone - Several - Attenuated (Knecht et al. developmental malformations 2013) endpoints - Reduction - Liver CYP1A expression mITP - Pericardial - Increased (Gerlach et al. edema prevalence 2014) 5-nitroacenaphthalene - Pericardial and - Reduction (Chlebowski et yolk sac edemas al. 2017) 5-nitroacenaphthalene, 9- - Liver CYP1A - Reduction (Chlebowski et nitrophenanthrene, and 7- expression al. 2017) nitrobenzo[k]fluoranthene

Table 2.6. Developmental toxicity endpoints and CYP1A expression patterns mediated by AHR1b from morpholino knockdown studies.

Xenobiotic ligand Endpoints Results of References mediated by AHR1b ligand x knockdown AHR1b Leflunomide - Vasculature - Prevention (Goodale et al. CYP1A 2012) expression mITP - Pericardial - Reduction in (Gerlach et al. edema prevalence of 2014) pericardial edema 7- - Vasculature, - Slight reduction (Chlebowski et al. nitrobenzo[k]fluoranthene skin, neuromast 2017) CYP1A protein expression

CHAPTER 3 - COUPLING GENOME-WIDE TRANSCRIPTOMICS AND DEVELOPMENTAL TOXICITY PROFILES IN ZEBRAFISH TO CHARACTERIZE POLYCYCLIC AROMATIC HYDROCARBON (PAH) HAZARD

Prarthana Shankar‡1, Mitra C. Geier‡1, Lisa Truong1, Ryan S. McClure2, Paritosh Pande2,

Katrina M. Waters2, Robyn L. Tanguay1*

1 Department of Environmental and Molecular Toxicology, Oregon State University,

Corvallis, OR 97331, USA.

2 Biological Sciences Division, Pacific Northwest Laboratory, 902 Battelle Boulevard,

P.O. Box 999, Richland, WA 99352, USA.

‡ Authors contributed equally

Reprinted from International Journal of Molecular Sciences under the Creative Commons License.

3.1. Abstract

Polycyclic Aromatic Hydrocarbons (PAHs) are diverse environmental pollutants

associated with adverse human health effects. Many studies focus on the carcinogenic

effects of a limited number of PAHs and there is an increasing need to understand

mechanisms of developmental toxicity of more varied yet environmentally relevant PAHs.

A previous study characterized the developmental toxicity of 123 PAHs in zebrafish. Based on phenotypic responses ranging from complete inactivity to acute mortality, we classified these PAHs into eight bins, selected 16 representative PAHs, and exposed developing

zebrafish to the concentration of each PAH that induced 80% phenotypic effect. We

conducted RNA sequencing at 48 hours post fertilization to identify gene expression

changes as a result of PAH exposure. Using the Context Likelihood of Relatedness

algorithm, we inferred a network that links the PAHs based on coordinated gene responses

to PAH exposure. The 16 PAHs formed two major clusters: Cluster A was transcriptionally

more similar to the controls, while Cluster B consisted of PAHs that were generally more

developmentally toxic, significantly elevated cyp1a transcript levels, and induced Ahr2- dependent Cyp1a protein expression in the skin confirmed by gene-silencing studies. We

found that cyp1a expression was associated with transcriptomic response, but not with

PAH developmental toxicity. While all cluster B PAHs predominantly activated Ahr2, they

also each enriched unique pathways like ion transport signaling, which likely points to

differing molecular events between the PAHs downstream of Ahr2. Thus, using a systems

biology approach, we have begun to evaluate, classify, and define mechanisms of PAH

toxicity.

3.2. Introduction

Polycyclic aromatic hydrocarbons (PAHs) are a diverse class of hundreds of compounds,

many of which are ubiquitous and persistent in the environment (Abdel-Shafy et al. 2016;

Kim et al. 2013). PAHs are detected in a range of environmental matrices including air

(Liu et al. 2017), soil (Liu et al. 2018), water and sediment samples (Hong et al. 2016;

Kabziński et al. 2002; Sarria-Villa et al. 2016), and tissues of both animals and plants

(Desalme et al. 2013; Hong et al. 2016). They originate from both petrogenic sources

associated with crude and refined oil products, and pyrogenic sources associated with

incomplete combustion of organic materials (Jang et al. 2013; Xu et al. 2006). PAHs

enter the environment via both natural (e.g. forest fires and volcanic eruptions) and

anthropogenic (e.g. burning fossil fuels, oil refining, and coal tar seal coating) processes

(Abdel-Shafy et al. 2016; Nikolaou et al. 1984). Human exposure to PAHs typically

occurs through eating food containing PAHs, smoking cigarettes, and inhalation of urban

ambient air or wood smoke (Abdel-Shafy et al. 2016). Exposure to PAHs has been linked to adverse health effects (Armstrong et al. 2004; Mastrangelo et al. 1996; Perera et al.

2005), with the bulk of research focused on only carcinogenic and mutagenic potential

(Boffetta et al. 1997; Cavalieri et al. 1991; Collins et al. 1998; Norramit et al. 2005).

In 1976, the U.S. EPA placed 16 PAHs on the Priority Pollutant List for regulatory purposes based on the potential for human exposure and carcinogenicity, high frequency of occurrence at hazardous waste sites, and the extent of available toxicological information ((ATSDR) 1995; Andersson et al. 2015; Keith 2015; Samburova et al. 2017).

The cancer risk for only 27 PAHs including the 16 priority PAHs has been estimated using

EPA’s Relative Potency Factor (RPF) approach that benchmarks benzo[a]pyrene as the

standard to which potency of other PAHs is compared (EPA 2010). By benchmarking to a

single PAH and a single endpoint, the RPF approach makes potentially inaccurate

assumptions about dose additivity and similarity of mechanism of action among PAHs

(EPA 2010; Schoeny et al. 1993). Thus, there is a need to better understand the overall

bioactivity of more structurally varied yet environmentally relevant PAH structures to

make fully informed toxicity predictions and regulatory decisions.

In addition to known carcinogenic effects, PAHs are implicated in a variety of

developmental effects in humans. Epidemiological studies have identified correlations

between PAH exposure and decreased head circumference, weight, and length at birth

(Perera et al. 1998), as well as increased childhood obesity and asthma rates (Jung et al.

2014). Studies in humans also associate developmental PAH exposure with neurodevelopmental effects, which include increased rates of ADHD, decreased learning and memory capacity (Genkinger et al. 2015; Jedrychowski et al. 2015; Perera et al. 2006),

and cardiovascular defects and oxidative stress (Billiard et al. 2006; Billiard et al. 2008;

Burczynski et al. 1999; Burstyn et al. 2005; Jayasundara et al. 2015; Knecht et al. 2013;

Xu et al. 2010). While the potential for PAH exposure to cause adverse health effects is

clear, the approaches used to study these effects have been insufficient to identify the

specific mechanisms of developmental toxicity. Given the diversity of PAH structures,

including parent and substituted PAHs such as nitro-, oxy- and methyl-PAHs (Qiao et al.

2013), and the variety of adverse effects they appear to cause, investigators must use more

comprehensive approaches to understand the range of the hazard potentials and the unique

mechanisms by which PAHs cause developmental effects.

Zebrafish (Danio rerio) is a vertebrate model used extensively to unravel complex

molecular signaling pathways (Prasch et al. 2006; Tanguay et al. 1999; Yoshioka et al.

2011). Zebrafish biology is favorable for high-throughput chemical testing at a systems

biological level (Truong et al. 2011). Embryos develop rapidly and externally, are

transparent, and are amenable to molecular and genetic techniques (Tanguay et al. 1999;

Udvadia et al. 2003). The zebrafish genome is fully sequenced, which enables anchoring

of complex phenotypes to cellular and molecular events. Zebrafish also possess a

remarkably high genetic homology with humans; 76% of human genes have a zebrafish

ortholog, and 82% of human genes that cause disease are present in zebrafish, increasing

the translational value of the zebrafish model (Howe et al. 2013). In zebrafish embryos,

developmental exposure to PAHs is associated with reproducible, dose-responsive

morphological and behavioral abnormalities (Chlebowski et al. 2017; Geier et al. 2018a;

Incardona et al. 2004; Knecht et al. 2013; Knecht et al. 2017). The zebrafish model has

previously been leveraged to anchor complex exposure phenotypes with their underlying

transcriptome changes, enabling understanding of the mechanisms by which PAHs cause

developmental toxicity (Chlebowski et al. 2017). As a relatively new avenue of research,

these studies have only addressed a small fraction of PAH structural diversity (Brinkmann

et al. 2016; Goodale et al. 2015; Goodale et al. 2013). There is a considerable need to fill

the gap in our understanding of structure-mechanism relationships as they can both reveal therapeutic targets and provide a compelling means of predicting PAH mixture hazard.

Previously, we screened a large library of PAHs in a zebrafish exposure platform (Geier et

al. 2018a). The developmental toxicity profiles of 123 parent and substituted PAHs were

characterized in the zebrafish model using a combination of 22 morphological toxicity

endpoints, two behavioral assays, and Cyp1a protein expression patterns. The cyp1a gene

is one of several direct targets of the aryl hydrocarbon receptor (Ahr), is highly induced

upon exposure to many PAHs, and is used as a biomarker for the activation of the Ahr

(Nebert et al. 2004). Zebrafish have three Ahr isoforms: Ahr1a, Ahr1b, and Ahr2. Only

Ahr2 and Ahr1b can bind the potent Ahr ligand 2,3,7,8-tetrachlorodibenzodioxin (TCDD)

(Andreasen et al. 2002; Karchner et al. 2005), while Ahr1a has been shown to mediate the

toxicity of some PAHs (Goodale et al. 2012; Knecht et al. 2013). Differences in Cyp1a

expression patterns can suggest binding to the different isoforms (Chlebowski et al. 2017;

Knecht et al. 2013). PAHs bind to the Ahr isoforms with different ligand-dependent affinities, leading to highly variable developmental toxicity (Andreasen et al. 2002;

Chlebowski et al. 2017; Tanguay et al. 1999). Induction of cyp1a does not necessarily

mediate these developmental toxicity effects (Incardona et al. 2006). Recent evidence

indicates that some PAHs act independently of Ahr activation, further complicating the

molecular signaling that leads to toxicity (Incardona et al. 2005). Thus, there is a need to

identify the genes differentially expressed following PAH exposures to help discover PAH targets causally responsible for producing developmental toxicity. Transcriptional profiling gives unbiased insight into both conserved mechanisms and uniquely disrupted transcripts to make sense of how exposure to a given PAH can cause toxicity (Goodale et al. 2015; Goodale et al. 2013).

In this study, we sought to characterize, classify, and to begin to define mechanisms of toxicity for PAHs using a combination of developmental toxicity endpoints and genome- wide transcriptomics. In previously published work, the development toxicity profiles of

123 PAHs in zebrafish were found to range from acutely toxic to biologically inactive

(Geier et al. 2018a). Based on the phenotypic effects, we sorted the 123 PAHs into eight bins and selected 16 representative PAHs (one to four PAHs from each bin) to conduct whole-animal transcriptomics at a single concentration, at 48 hours post fertilization (hpf), prior to the onset of visible adverse phenotypes assessed at 120 hpf. To understand the similarities and differences between the transcriptional responses, we used the Context

Likelihood of Relatedness (CLR) algorithm that, based on the coordinated responses of genes to exposure, grouped the 16 PAHs into two broad clusters, one of which consisted of the more developmentally toxic PAHs. Overall, we demonstrate the feasibility of using high throughput screening data, transcriptomics, and gene silencing as a systems approach to identify differences in PAH toxicity mechanisms.

3.3. Methods

3.3.1 Chemicals

Analytical grade standards were obtained from AccuStandard (New Haven, CT, USA),

Chiron Chemicals (Hawthorn, Australia), and Santa Cruz Biotechnology (SCB) (Dallas,

TX, USA). The standards for the 16 PAHs were analytically verified by the Anderson Lab at Oregon State University before being dissolved in 100% DMSO to make the chemical stock solutions (Table 3.1).

3.3.2. Zebrafish husbandry

Zebrafish (Danio rerio) of the tropical 5D line were maintained at the Sinnhuber Aquatic

Research Laboratory (SARL), at Oregon State University (Corvallis, OR, USA) according

to Institutional Animal Care and Use Committee protocols (ACUP 5113). Adult fish were

raised in densities of ~500 fish/50-gallon tank at 28°C under a 14-hour:10-hour light:dark

cycle in recirculating filtered water supplemented with Instant Ocean salts. Adult fish were

fed GEMMA Micro 300 or 500 (Skretting, Inc., Fontaine Les Vervins, France) twice a day.

Larval and juvenile fish were fed GEMMA Micro 75 and 150 respectively thrice a day

(Barton et al. 2016). Spawning funnels were placed in tanks the night prior, and the

following morning, embryos were collected, staged, and maintained in an incubator at

28°C in embryo media (EM) (Kimmel et al. 1995). EM consisted of 15 mM NaCl, 0.5 mM

KCl, 1 mM MgSO4, 0.15 mM KH2PO4, 0.05 mM Na2HPO4, and 0.7 mM NaHCO3

(Westerfield 2000).

3.3.3. Exposures

The chorions of 4 hours post fertilization (hpf) zebrafish embryos were enzymatically

removed using a custom automated dechorionator (Mandrell et al. 2012). At 6 hpf, the embryos were placed into round bottom 96-well exposure plates (Falcon®, product number: 353227) with one embryo per well prefilled with 100 µL embryo medium (EM), using automated embryo placement systems (Mandrell et al. 2012). Chemical stocks in

100% dry dimethyl sulfoxide (DMSO) (Table 3.1) were dispensed using a Hewlett Packard

D300e chemical dispenser into the exposure plates (Truong et al. 2016). Final DMSO concentrations were normalized to 1% (volume/volume) and gently shaken by the chemical

dispenser during dispensing. The plates were sealed with Parafilm placed between the lid and plate to minimize evaporation, wrapped in foil to prevent exposure to light, and shaken overnight at 235 revolutions per minute (rpm) on an orbital shaker at 28°C to enhance uniform exposure (Truong et al. 2016). Zebrafish embryos during this period of development can adapt to the dark and develop normally (Kokel et al. 2013). Embryos were statically exposed in a 28°C incubator without shaking for the remaining duration of the exposure (until 48 hpf for RNA collection, 72 hpf for immunohistochemistry, and 120 hpf for morphological analysis).

3.3.4. RNA sequencing (RNA-seq)

Exposure Concentrations

The data for the morphological and behavioral effects, and Cyp1a localization patterns at

120 hpf of 123 PAHs is reported in (Geier et al. 2018a). Based on the effects ranging from acutely toxic to biologically inactive in the zebrafish model, we grouped the PAHs into eight bins and selected 16 representative PAHs for our transcriptomic study. RNA sequencing test concentrations were determined by individual chemical responses. The goal was to identify a concentration for each PAH that caused an 80% phenotypic effect

(EC80) by 120 hpf. An initial screening phase was conducted with two concentration lists depending on the stock concentration of either 10 or 1 mM based on the solubility of the

PAH in DMSO. Exposure concentrations for the 10 mM stock solutions were: 50, 35.6,

11.2, 5, and 1 µM. Exposure concentrations for the 1 mM stock solutions were 5, 3.56,

1.12, 0.5, and 0.1 µM. Ten of the 16 representative PAHs did not elicit morphological effects, and transcriptomics was conducted at the highest concentration tested in the initial

screening phase (50 or 5 µM). Six of the 16 PAHs induced morphological effects (see

Developmental Toxicity Assessments below). For these PAHs, a well-defined concentration response was established. The lower end of the concentration range was the highest concentration that did not cause any morphological effect and the upper end was the lowest concentration that resulted in nearly 100% mortality in the Developmental

Toxicity Assessments. We fit the mean percentage of affected zebrafish for any morphological endpoint using a Hill Model (specifically a four-parameter log-logistic function). All curves were fit with the drm function for fitting dose-response models from the drc package in R (R Core Team 2017). This function uses least squares estimation to fit the curves. The Hill model was applied to estimate a concentration that caused 80% effects (EC80). The computed EC80 was confirmed prior to conducting the RNA-seq. The concentrations tested for the six chemicals are listed in Table 3.2. The computed EC80 concentrations are listed in Table 3.1 (RNA-seq Conc.).

Developmental Toxicity (Morphology) Assessments

At 24 hpf, embryos were assessed for 4 developmental toxicity endpoints: 24 hpf mortality, developmental progression, spontaneous movement, and notochord distortion. At 120 hpf, embryos were assessed for 18 morphology endpoints: mortality, body axis, eye, snout, jaw, otic vesicle, notochord, heart (pericardial edema), brain, somite, pectoral fins, caudal fin, yolk sac, trunk, circulation, pigment, swim bladder, and tactile response. Responses were recorded as a binary presence or absence of an abnormal morphology for each endpoint.

Lowest effect levels (LELs) were calculated for each endpoint using a binomial test to estimate significance thresholds (p < 0.05) as previously described (Truong et al. 2016).

RNA Isolation and Sequencing

Total RNA was isolated from pooled groups of eight zebrafish at 48 hpf with four replicates

per PAH treatment group using the Zymo Direct-zol RNA MiniPrep kit (Irvine, CA).

Briefly, embryos were homogenized in 500 µL RNAzol RT (Molecular Research Center,

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

Averill Park, NY) for 3 min at speed 8. Samples were stored at -80°C until RNA isolation.

The optional in-column DNase I digestion step was performed. Total RNA concentration

and quality was determined on a SynergyMix microplate reader using the Gen5 Take3

module (BioTek Instruments, Inc., Winooski, VT). RNA integrity was confirmed (RIN

score > 8) using an Agilent Bioanalyzer 2100. Samples were placed in a 96 well PCR plate

and submitted to Oregon State University’s Center for Genome Research and

Biocomputing (Corvallis, OR) for library preparation and sequencing. Since the samples

were collected on two separate days, there were two controls, one for each day. Each

treatment group (16 PAHs and two controls) contained four biological replicates totaling

72 samples. Samples were prepared using the Wafergen Robotic PolyA Enrichment

Library Prep and Wafergen Robotic Stranded RNA Library Prep Kits. Libraries were

multiplexed and randomized across 6 lanes and sequenced with 100bp single-end reads

using the Illumina HiSeq 3000. Raw sequence reads were quality checked prior to

processing.

Analysis of RNA-seq data

Fastq files resulting from sequencing were trimmed by quality using Trimmomatic (Bolger

et al. 2014) and characterized using FastQC

(http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Transcript-level count

estimates of sequence reads were accomplished using Salmon (Patro et al. 2017) against

Danio rerio Zv9 transcripts maintained by Ensembl (Zerbino et al. 2018). Experimental

metrics across samples, encompassing trimming and pseudoalignment rates, were

aggregated using MultiQC (Ewels et al. 2016). Transcript level counts were summed to the

gene level using tximport (Soneson et al. 2015). Raw counts for each sample were then

normalized with DESeq2 (Love et al. 2014). Multidimensional scaling was then used to

identify outliers, those samples that were not clustered with identical replicates, and

remove them before analysis continued. This resulted in a single replicate (out of four)

being removed for 5 PAHs (Anthracene, DB(a,h)P, 9-MA, 3-NF, and Acenapthene). At least three biological replicates were used for each PAH treatment, with most treatments having four biological replicates. Fold changes were calculated compared to control samples, and associated p-values (adjusted for multiple hypothesis testing using

Benjamini-Hochberg correction) were determined using DESeq2. Differentially expressed genes (DEGs) were defined as those showing a fold change compared to control of at least

1.5 with an adjusted p-value of less than 0.05. Pathway analysis was performed on the

DEGs using g:Profiler (Reimand et al. 2016).

Network analysis

Networks were inferred using the Context Likelihood of Relatedness (CLR) (Faith et al.

2007) function of the MINET package in R (Meyer et al. 2008). Gene expression levels for

each PAH treatment, or the controls, were averaged by mean and data was input into R

using the top 500 genes, ranked by CV. CLR computes the mutual information score

between all PAHs which is based on how similar the zebrafish gene expression profile

resulting from PAH treatment is. PAHs inducing a more similar transcriptional response

will have a higher mutual information score. CLR then converts these mutual information

scores for each PAH pair into Z-scores, a measure of how many standard deviations the

mutual information score for a particular PAH pair is above the average of all mutual

information scores. The final output of CLR is a matrix of these Z-scores. We then defined

a connector in our network as any Z-score higher than one. Any pair of PAHs whose mutual

information score was at least one standard deviation above the mean was linked in the

network. We also included information about the value of the Z-score by altering the thickness of the line indicating a connector, with thicker lines indicating a higher Z-score.

3.3.5. Immunohistochemistry

Morpholino injections

Embryos were injected at the single-cell stage with a previously published translation- blocking morpholino (MO) targeting Ahr2 (Ahr2-MO;

5′TGTACCGATACCCGCCGACATGGTT3′) and a standard nonsense negative control

(Co-MO; 5′CCTCTTACCTCAGTTACAATTTATA3′) obtained from GeneTools,

Philomath, OR (Hill et al. 2004; Prasch et al. 2003; Tseng et al. 2005). Approximately, 2

nL of a 1.2 mM solution of Ahr2-MO or 0.95 mM Co-MO was microinjected into the yolks

of tropical 5D zebrafish embryos.

Exposures and Immunohistochemistry (IHC)

Immunohistochemistry of cytochrome P450, family 1, subfamily A (Cyp1a) protein

localization was performed as previously described (Mathew et al. 2006). Based on gene

expression clustering data, we conducted IHC on the PAHs we hypothesized to activate

Ahr2: retene, BkF, BjF, DB(a,i)P, DB(a,i)P, and BbF. Briefly, embryos were injected with

either Ahr2-MO or Co-MO, then dechorionated, and exposed from 6 to 72 hpf to the

highest soluble concentration of each PAH that did not cause significant mortality. 1%

DMSO was the vehicle control. At 72 hpf, ten embryos per treatment group (and two

biological replicates) were euthanized with tricaine, and fixed overnight in 4% paraformaldehyde (PFA) at 4°C. Fixed embryos were permeabilized for 10 minutes on ice in 0.005% trypsin, rinsed with PBS + Tween 20 (PBST) and post-fixed in 4% PFA for 10 minutes. Embryos were blocked with 10% normal goat serum (NGS) in PBS + 0.5% Triton

X-100 (PBSTx) for 1 hour at room temperature, and incubated overnight in the primary antibody mouse α fish CYP1A monoclonal antibody (Biosense Laboratories, Bergen,

Norway) (1:500) in 1% NGS. An acetylated tubulin antibody (1:2000) in 1% NGS was used as a positive control for the process. Embryos were washed in PBST and incubated for 2 hours in secondary antibody (Fluor 594 goat anti-mouse IgG). Eight to ten embryos per treatment group per biological replicate were assessed by epi-fluorescence microscopy using a Keyence BZ-x700 microscope at 10x and 20x magnifications, and scored for the presence or absence of fluorescence in the vasculature, liver, skin, neuromasts, and the yolk sac. Samples were mounted on glass slides in 3% methylcellulose, and images were acquired using the Texas Red Filter (Emission wavelength: 604 – 644 nm, Excitation wavelength: 542 – 582 nm). Exposure time was set for the Co-MO samples of each PAH and the same exposure time was used for the Ahr2-MO samples as follows: retene – 1/25

s, BkF – 1/50 s, BjF – 1/35 s, DB(a,i)P – 1/20 s, DB(a,h)P – 1/30 s, BbF – 1/15 s. DMSO samples were analyzed for background signal at 1/15 s.

3.4. Results and Discussion

3.4.1. Classification of 123 PAHs into eight bins and characterization of bins

A previous study assembled and tested a 123 compound library of parent and substituted

PAHs for developmental toxicity in embryonic zebrafish (Geier et al. 2018a). The PAHs

were screened across a range of five concentrations between 0.1 and 50 µM for 22

morphological endpoints, two behavioral photomotor response assays (embryonic

photomotor response (EPR) at 24 hpf and larval photomotor response (LPR) at 120 hpf),

and five tissue localizations of Cyp1a (vasculature, liver, skin, neuromasts, and yolk). PAH

developmental toxicity ranged from completely inactive to acute mortality. For this study,

we used hierarchical clustering of all phenotypic endpoints from Geier et al. 2018 to

classify the 123 PAHs into eight bins of developmental toxicity (Figure 3.1,

Supplementary Table A.1) for selection of a subset of the PAHs for gene expression

analysis. Of the eight bins, Bin 1 had the most phenotypic effects, and Bin 8 PAHs were the least developmentally toxic to zebrafish. Bins 1 - 3 included PAHs that produced

morphological and behavioral effects, bins 4 - 7 contained PAHs with only behavioral

effects and no morphological effects, and Bin 8 consisted of PAHs that did not cause

morphological or behavioral effects (Table 3.3). PAHs in Bins 1 to 5 induced either no

Cyp1a protein expression or expression in up to 4 of the 5 locations identified during the

123 PAH screen (Geier et al. 2018a). PAHs in bins 6 - 8 did not induce Cyp1a protein

expression.

The 123 PAHs included nitrated, oxygenated, hydroxylated, methylated, heterocyclic, and

aminated substitutions (Geier et al. 2018a). Bin 1 (35 PAHs) was the largest and was most associated with high mortality, numerous morphological and behavioral defects (EPR and

LPR). This bin was dominated by hydroxylated (11), oxygenated (10), and nitrated (7)

PAHs. Bin 2 (14 PAHs) induced fewer morphological defects than Bin 1 and was most associated with LPR behavioral defects. Bin 3 (9 PAHs) PAHs were associated with comparatively fewer morphological defects and no behavioral defects. Bin 4 (17 PAHs)

was the second largest with equal representation of methylated (6) and parent (6) PAHs

and was most associated with causing LPR defects. Bin 5 (19 PAHs) was dominated by

parent (7) and nitrated (5) PAHs that caused no morphological effects, but had behavioral

defects (EPR and LPR). Bin 6 (12 PAHs) led to an aberrant LPR in both the lighted (VIS)

and dark (IR) phases of the assay, and Bin 7 (10 PAHs) caused an aberrant LPR only in

the lighted phase. PAHs in the smallest bin, Bin 8 (7 PAHs) produced no phenotypic effects

at the tested concentrations for any of the measured endpoints. The 7 PAHs in Bin 8 were

either parent (3), nitrated (2), heterocyclic (1) or methylated (1).

Examination of the bioactivity of substituted PAHs is a relatively recent expansion of the

field. In our 123 PAH screen, the most developmentally toxic bin (Bin 1) consisted mostly

of substituted PAHs, suggesting that they may be more toxic than parent PAHs. For

example, the parent PAH fluoranthene was placed into Bin 5, while its derivatives 3-

hydroxyfluoranthene, 3-nitrofluoranthene, and 2-nitrofluoranthene were placed into Bins

1, 2, and 3 respectively (Chlebowski et al. 2017). The reliance on benzo[a]pyrene as a

reference for toxicity estimates in regulatory decisions is also concerning, as it is far less

developmentally toxic than many substituted (and parent) PAHs in our screen (Geier et al.

2018a). Benzo[a]pyrene, a parent PAH, clustered into Bin 5 in our screen. Our results

demonstrate that substituted PAHs can be more bioactive than their parent compounds and

suggest the need to include more substituted PAH classes in hazard modeling and risk

assessment. Other recent studies uphold our finding that substituted PAHs are often

similarly or more bioactive than the respective parents (Brinkmann et al. 2014; Wang et al.

2011; Wincent et al. 2015). Thus, the current EPA priority PAH list may not only

underestimate hazard at contaminated sites, but can also inaccurately inform PAH mixture

models.

Only the PAHs within Bins 6 – 8 had identical developmental toxicity profiles; all PAHs in Bin 6 were associated with an aberrant LPR effect in both the dark and light phases, all

PAHs in Bin 7 were associated with an aberrant LPR effect in the light phase, and all PAHs in Bin 8 were biologically inactive in our assays. The PAHs within Bins 1- 5 had highly variable developmental toxiciy profiles, with no two PAHs resulting in identical profiles.

For example, 1-hydroxypyrene and xanthone were two PAHs in Bin 1 that localized Cyp1a expression at 120 hpf in the liver. Exposure to 1-hydroxypyrene caused 24 and 120 hpf mortality, yolk sac edema, and EPR and LPR behavior defects. The lowest effect level

(LEL) concentrations for EPR and LPR defects were ≤ 11.2 µM. On the other hand, xanthone negatively affected many more morphological endpoints, was less behaviorally bioactive, and the LEL was 50 µM for all affected endpoints. Despite these differences in phenotypic effects, the two PAHs were unequivocally placed in the same bin. Cyp1a protein localization was also variable within each bin. For example, in Bin 1, 3-

nitrobenzanthrone and 11-H-benzo[b]fluoren-11-one produced several morphological and behavioral malformations, and localized Cyp1a protein at 120 hpf in two locations.

However, pyrene-4,5-dione, perinaphthenone, and benzanthrone induced several morphological and behavioral malformations, but did not localize Cyp1a protein at 120 hpf

(Goodale et al. 2015). The differences in responses associated with PAHs within the same bin suggest potentially varied molecular signaling events which are not captured by just the phenotypic screening data presented in Figure 3.1. It is important to note that the developmental toxicity profile of each PAH was characterized based on nominal water concentration only, and acknowledge that different uptake rates between PAHs could affect the internal dose and thus the comparisons of outcomes between chemicals. Further, PAHs are known to sorb to polystyrene testing chambers used for these assays which would reduce the available concentration to which the zebrafish is exposed (Chlebowski et al.

2016; Schreiber et al. 2008). Different chemical properties like hydrophobicity contribute to different rates of adsorption to polystyrene (Wolska et al. 2005), confounding comparisons of uptake in the absence of careful mass balance accounting of chemical fate.

For example, a study found that the sorptive losses associated with PAHs with two or three rings was 10% or less compared to PAHs with four or more rings that had sorptive losses of 40 to 70% (Wolska et al. 2005). Another study demonstrated that there was a higher measured percent sorption to polystyrene at lower exposure concentrations (Chlebowski et al. 2016) . Thus, various factors likely contribute to the variety of developmental toxicity profiles, and thus the bins the PAHs are grouped into (Chlebowski et al. 2016). Future work

investigating these potential differentials may provide more accurate determination of potential health impacts for the PAHs presented here.

3.4.2. Selection of 16 representative PAHs for RNA sequencing from the eight PAH bins

We selected 16 PAHs in total (one to four representatives from each bin) with which to conduct genome-wide transcriptomic profiling at 48 hpf. To make comparisons across the

PAHs, zebrafish were exposed to either the EC80 concentration of each PAH, or the maximum concentration tested during the phenotypic screening (Geier et al. 2018a) if an

EC80 concentration was not attainable. For each of the eight bins defined by morphology or mortality effects (Bins 1, 2, 3), candidates were filtered for commercially available standards, an attainable EC80, and minimal 24 hpf mortality, then prioritized by known relative environmental abundance (Minick et al. 2017; Tidwell et al. 2017). For bins defined by behavioral endpoints or no observable developmental toxicity (Bins 4 - 8), candidates were filtered for the availability of larger volume standards and prioritized by known relative environmental abundance. Due to the high variability of the Cyp1a expression patterns within each bin, Cyp1a localization was a secondary filter after the morphological and behavioral endpoints were taken into consideration. After applying the criteria to the bins, one to four representatives from each bin were selected for RNA sequencing, resulting in a total of 16 PAHs representing eight general developmental toxicity profiles: Bin 1: 4h-cyclopenta[def]phenanthrene-4-one (4h-CPdefP) and Retene,

Bin 2: Benzo[k]fluoranthene (BkF), 3-nitrofluoranthene (3-NF), Benzo[j]fluoranthene

(BjF), and Carbazole, Bin 3: Dibenzo[a,i]pyrene (DB(a,i)P), Bin 4: Dibenzo[a,h]pyrene

(DB(a,h)P) and 9-methylanthracene (9-MA), Bin 5: Benzo[b]fluoranthene (BbF) and

Fluoranthene, Bin 6: 1,5-dimenthylnaphthalene (1,5-DMN), Acenaphthene, and 2-

methylnaphthalene (2-MN), Bin 7: Phenanthrene, and Bin 8: Anthracene (Table 3.1 in

methods).

Transcriptomics provides mechanistic information and identifies biomarkers that are both

common and unique to different chemicals. Only a few studies to date have investigated

transcriptomic responses to PAH exposure during development (Goodale et al. 2015;

Goodale et al. 2013). Consequently, we have very little mechanistic understanding of how

exposure to PAHs causes developmental toxicity. We previously measured a large battery

of phenotypic endpoints using our zebrafish high-throughput screening assay (Geier et al.

2018a), and while developmental toxicity profiles were highly variable, many of the

individual endpoints were common across the 123 PAHs. High correlation between

different endpoints, for example pericardial edema and bent body axis, also limits our

ability to infer mechanistic information from the phenotypic data alone (Truong et al.

2014). Querying the transcriptome offers a deeper understanding of how structurally

distinct PAHs cause different, but often overlapping developmental toxicity profiles. PAH

exposure was between 6 and 120 hpf, which is throughout most of zebrafish development.

Thus, any perturbations to the complex signaling occurring during that period would be

expected to alter some aspect of development (Truong et al. 2011). Here, we pursued the next logical dimension, comprehensive transcriptomic analysis using RNA sequencing to classify structure-bioactivity relationships and to identify signaling pathway similarities

and differences between 16 PAHs. Transcriptomics at 48 hpf in zebrafish is a desirable

time point because it is likely to be close to the molecular initiating event(s) of a chemical

exposure, and it precedes most observable toxicity, offering gene expression changes that

may be predictive (Goodale et al. 2015; Haggard et al. 2017). To our knowledge, with the

exception of retene, whole-animal developmental transcriptomic evaluations of the PAHs

in this study have not previously been investigated (Hawliczek et al. 2012).

3.4.3. Number of differentially expressed genes (DEGs) generally correlates with PAH

developmental toxicity profiles

Differentially expressed genes (DEGs) were defined as having a fold-change (FC)

compared to the control of at least 1.5 and adjusted p-value (PADJ) < 0.05. Based on these

criteria, 3-NF and 1,5-DMN had no associated DEGs (Figure 3.2). For each PAH, a full list of DEGs with their associated FC and PADJ are provided in Supplementary Table

A.2.

Six of the 16 PAHs produced modest transcriptional responses with less than or equal to

10 DEGs. The transcriptional responses mostly agreed with their developmental toxicity

profiles: five of these 6 PAHs were in the less developmentally toxic bins (Bins 6, 7, 8).

1,5-DMN, acenaphthene, 2-MN, and phenanthrene produced only LPR effects, while anthracene did not cause morphological or behavioral effects. These PAHs were also characterized by lack of Cyp1a protein expression. The only exception was 3-NF (Bin 2), which had no associated DEGs, but 3-NF exposure produced aberrant circulation. The

DEGs associated with acenaphthene, 2-MN, phenanthrene, and anthracene are potential biomarkers of their respective exposures. The remaining 10 PAHs that produced different combinations of morphological and behavioral effects, and Cyp1a protein expression

patterns, resulted in varying numbers of DEGs from 21 to 236 (Figure 3.2). DB(a,h)P from

Bin 4 which produced aberrant behavioral effects and Cyp1a localization in the

vasculature, liver, skin, and neuromasts, was associated with the most number of DEGs at

236. The two PAHs from the most developmental toxic Bin 1, 4h-CPdefP and retene, were

associated with 51 and 89 DEGs respectively. We note that Cyp1a protein expression

patterns did not always correlate well with DEGs, even for highly related structures. For example, 4h-CPdefP and retene were both associated with DEGs, but while 4h-CPdefP did not induce Cyp1a protein expression, retene produced vasculature Cyp1a protein expression at 120 hpf. Likewise, DB(a,h)P and DB(a,i)P exposures localized Cyp1a to the same 4 locations, but were associated with 236 and 44 DEGs respectively.

The PAHs in bins 6 – 8 which had little to no developmental toxicity also effected ≤ 10

DEGs. To determine if low uptake was the explanation for lack of phenotypic effects and low number of DEGs, we mined a previously published dataset that quantified the body burden of 48 hpf embryos for 6 of the 16 PAHs (Supplementary Figure A.1) (Geier et al.

2018b). The 6 PAHs were: retene (Bin 1), BbF and fluoranthene (Bin 5), acenaphthene and

2-MN (Bin 6), and phenanthrene (Bin 7). Briefly, the body burdens (nmol/embryo) of the

PAHs were measured at three concentrations, 5.39, 11.6, and 25 µM (Geier et al. 2018b).

Concentration uptake ratio (CUR) was defined as the ratio of nominal PAH concentration in the exposure medium to measured concentration in the embryo. For each PAH, the mean and standard deviation of nine CUR values for different nominal medium concentrations

(three concentrations) and replicates (three for each concentration value) were calculated and then log-transformed. For fluoranthene, acenaphthene, 2-MN, and phenanthrene, we

found that the CUR positively correlated (r2 = 0.9, p-value = 0.03) with the number of

DEGs (Supplementary Figure A.1). The four PAHs had the same nominal exposure concentration (50 µM), and log KOW < 5.5. The positive correlation diminished when retene and BbF, two PAHs with log KOW > 6 were included in the analysis. The uptake CURs

were within the range of the other four PAHs, but the number of DEGs associated with exposure to retene and BbF was 4-100 times higher than those associated with the other four PAHs. These findings demonstrate that number of DEGs do not broadly correlate with the CUR, at least when PAHs with log KOW > 5.5 are considered. More importantly, they

suggest that the minimal or lack of developmental toxicity for PAHs like fluoranthene,

acenaphthene, 2-MN and phenanthrene, may not be because of a lack of uptake into the

zebrafish. We note that we defined DEGs as those genes with at least a 1.5 fold change in

expression compared to the controls to identify key genes involved in bioactivity. It is

possible that certain genes are associated with developmental phenotypic effects but not

captured by our criteria. Therefore, although number of DEGs can give a very high-level

overview of transcriptomic response, it is only an approximation of the true PAH

transcriptomic profiles.

3.4.4. Cluster analysis of response to PAHs reveals two broad clusters

In order to identify PAHs with similar and unique transcriptomic responses, we performed

hierarchical clustering of PAHs using expression profiles for the 500 genes with the highest

coefficient of variation (CV) across the 18 conditions (16 PAH treatments and 2 controls)

(Figure 3.3A). For a list of the top 200 genes with the highest CV across the 18 treatments,

see Supplementary Table A.3. PAHs clustered into two broad groups. With the Context

Likelihood of Relatedness (CLR) algorithm, we inferred a network that links the PAHs

based on the coordinated transcription of genes as they respond to those PAHs. We again

used only the top 500 genes based on the CV to ensure a more relevant network as basal

level responses of genes with a low CV are not present to dilute the effect of the most

responsive genes. This analysis also clustered the 16 PAHs into the same two broad clusters

(Figure 3.3B).

The larger cluster (Cluster A) (Figures 3.3A and 3.3B) showed more varied Cyp1a

localization patterns and modest or no phenotypic response to PAH exposure. Many of

these PAHs, specifically 1,5-DMN, 2-MN, 3-NF, acenaphthene, anthracene, and phenanthrene also showed zero or very few DEGs. All of these PAHs except 3-NF, are from the less developmentally toxic Bins 6 - 8, and share connectors with the control samples (Figure 3.3B). This indicates the similarity of the transcriptomic responses of these PAHs to the control samples at 48 hpf. A strong overlap between phenotypic effects and transcriptomic changes was seen with some PAH pairs including acenaphthene and phenanthrene, and to a lesser degree, 2-MN and anthracene. The Cluster A PAHs that do not share connectors with either of the control samples are 4h-CPdefP (Bin 1), 9-MA (Bin

4), and fluoranthene (Bin 5). We note that these three PAHs had both varied developmental toxicity responses and no DEGs in common (Supplementary Table A.2). The DEGs that are unique to each of these PAHs are biomarkers of their respective exposures.

The smaller cluster (Cluster B) (Figures 3.3A and 3.3B) consisted of retene, BkF, BjF,

DB(a,i)P, DB(a,i)P, and BbF, all PAHs belonging to the more developmentally toxic bins

1 - 5. These six PAHs localized Cyp1a protein expression in the vasculature. All of them

except retene localized Cyp1a protein expression in the skin at 72 hpf and 120 hpf, unlike

the Cluster A PAHs which did not localize Cyp1a protein in the skin. There were other

similarities between Cluster B PAHs: BjF and BkF (Bin 2) both showed an unusual caudal

fin phenotype (Geier et al. 2018a), and are structurally similar five-ring PAHs with only one of the rings positioned differently (Achten et al. 2015). These PAHs are also adjacent to each other in our transcriptomic response dendrogram (Figure 3.3A), showing that transcriptomic and phenotypic responses of these PAHs are tightly correlated. The other two PAHs in Bin 2 (carbazole and 3-NF) are less structurally similar and did not cluster tightly with each other or with BkF and BjF, further suggesting different molecular signaling events. DB(a,h)P (Bin 4) and BbF (Bin 5) had the most similar gene expression changes depicted by their close proximity in the dendrogram (Figure 3.3A), and the

thickness of the connector between the two PAHs (Figure 3.3B). Both PAHs despite being in different developmental toxicity bins (Figure 3.1), produced aberrant behavioral phenotypes with no malformations.

Interestingly, there were PAH pairs with similar developmental toxicity profiles that binned together in our phenotypic screening heatmap (Figure 3.2) yet had less related transcriptomic responses (and vice versa). Retene and 4h-CPdefP are highly disparate in the dendrogram in Figure 3.3A and clustered separately in our network analysis (Figure

3.3B) but had similar developmental toxicity profiles (high mortality with morphological

and behavioral endpoints in common). The same was observed for the Bin 4 PAHs

DB(a,h)P and 9-MA which caused behavioral effects, but were far apart in the dendrogram

and the network (Figure 3.3A and 3.3B). Cyp1a expression patterns were also different

between these pairs of PAHs. Both the differences in Cyp1a expression pattern and the lack of clustering based on transcriptomics strongly suggest unique signaling mechanisms between these chemical pairs. The fact that PAHs, and many other classes of chemicals, can manifest the same developmental toxicity endpoints in a vertebrate model (Reimers et al. 2004; Shi et al. 2008) but have vastly different molecular signaling events underlying those endpoints, has often been regarded as a limitation of the zebrafish model, i.e., the responses are not specific enough. The utility of embryonic zebrafish is as a systems level biosensor of chemical insult. Development is the most sensitive period because it represents a highly coordinated and regulated framework of transcription and signaling events. Any perturbation is likely to manifest as abnormal morphology or behavior (Truong et al. 2011), therefore providing an efficient and sensitive hazard detection model. Our results highlight the importance of coupling developmental phenotypic effects with comprehensive transcriptomic data to understand the mechanisms by which chemicals cause toxicity.

Clustering of the PAHs in this way reveals how PAHs group together based on similarity in transcriptomic responses. As we conduct more RNA sequencing studies, we can examine the proximity of each new PAH to other PAHs in the network, and expand our classification of PAHs based on gene expression profiles. A potential limitation of our study may be that we chose only a single time point (48 hpf) for comparative RNA sequencing. If a PAH’s bioactivity was entirely dependent on its metabolites, its developmental window of bioactivity may not begin until after 24 hpf, which may explain the lack of developmental phenotypes at this time point in our phenotypic screen. If

metabolism of the PAH is most effective in the liver, the major site of metabolism for many

chemicals, then the chemicals may not be converted to their metabolites until 72 hpf when

the liver is fully developed (Ober et al. 2003). By sampling at 48 hpf, we may have preceded the relevant gene expression changes. Similarly, if the PAH was bioactive immediately after the beginning of the exposure, our 48 hpf transcriptomic data may no longer reflect the phenotype’s causative events. A previous microarray study of transcriptional responses found that three PAHs, benz(a)anthracene, pyrene, and dibenzothiophene, differentially regulated mRNA expression between the 24 and 48 hpf time points, highlighting the importance of a time-dependent transcriptional response study

(Goodale et al. 2013). Our single time point - single concentration approach emphasized breadth over depth in querying PAH transcriptome changes. It enabled us to examine all

16 PAHs in our study set, whereas only a small fraction of the structural diversity could have been investigated with multiple time points and concentrations. Future work conducting transcriptomic network analyses like the CLR program presented in this study, for multiple concentrations of a PAH at different time points of zebrafish development will be beneficial to gain a comprehensive understanding of its bioactivity.

3.4.5. Cyp1a expression is an inadequate biomarker for PAH developmental toxicity, but is associated with transcriptomic response

To assess Ahr activation, we investigated the correlation between the developmental toxicity profile and transcriptomic responses, and cyp1a transcript levels across the 16

PAHs. A widely used biomarker of Ahr activation is cyp1a because of its sensitive and robust induction upon exposure to many PAHs (Incardona et al. 2006; van der Oost et al.

2003). Cyp1a is also primarily involved in the metabolism of xenobiotics. In general, there

was no correlation between developmental toxicity bin number and cyp1a expression

(Table 3.4). For example, the most developmentally toxic PAHs were 4h-CPdefP and

retene (Bin 1), but cyp1a was significantly elevated only in the retene exposure. The low cyp1a log2FC value associated with 4h-CPdefP exposure, in combination with the lack of

Cyp1a protein localization at 120 hpf, suggests a non-Ahr dependent mechanism of action for 4h-CPdefP, an oxygenated PAH which may not require metabolic activation to manifest toxicity (Lundstedt et al. 2007). Amongst the 123 PAHs in Geier et al., 2018, there were

18 oxy-PAHs, three of which induced expression of Cyp1a protein in the vasculature, one in both the vasculature and the liver, one in both the liver and the yolk, and one in only the yolk. The remaining 12 did not induce Cyp1a expression at 120 hpf (Geier et al. 2018a).

We note that previous studies have shown that oxy-PAHs can elicit their biological effects through the Ahr (Knecht et al. 2013; Wincent et al. 2015), however our data suggest that

4h-CPdefP acted independently of Ahr.

The Cluster A PAHs, carbazole (Bin 2), 3-NF (Bin 2), 9-MA (Bin 4), and fluoranthene

(Bin 5) had lower cyp1a expression levels relative to the other PAHs in their respective bins. Despite the low induction of cyp1a, exposure to these PAHs resulted in phenotypic responses. The low cyp1a expression levels at 48 hpf suggest either a different isoform of the Ahr was activated, or that the 48 hpf time point is not when cyp1a is maximally induced for these PAHs (Knecht et al. 2013). Exposure to carbazole, 9-MA and fluoranthene induced Cyp1a protein expression in the liver and faint vasculature expression (data no shown). This suggests a possible dominant role for Ahr1a, which was not explored in this

study. Previous work demonstrated that Ahr1a mediates Cyp1a protein expression in the

liver induced by xanthone, leflunomide, and pyrene (Goodale et al. 2012; Incardona et al.

2006; Knecht et al. 2013). Cyp1a yolk expression induced by 3-NF and 24 PAHs in the

123 PAH screen is still unexplored in zebrafish (Geier et al. 2018a). However, the

expression of an isoform, cyp11a1, was previously detected in the yolk syncytial layer

(Goldstone et al. 2010; Hsu et al. 2002). Activation of different Ahr isoforms by the PAHs

could be the reason for both differential transcriptomic clustering of PAHs from the same

developmental toxicity bin, and for different cyp1a induction levels. The remaining PAHs

(Bins 6 - 8) were not associated with morphological malformations or Cyp1a protein

expression (Figures 3.1B, 3.2) and did not have significant cyp1a expression levels. These

PAHs were associated with either behavioral effects (2-MN, acenaphthene, 1,5-DMN,

phenanthrene) or had no observable effects (anthracene), with only anthracene having a

positive correlation between the absence of phenotypic effects and low cyp1a expression.

Unlike developmental toxicity bin number, we detected a correlation between PAH

transcriptomic response and cyp1a transcript levels (Table 3.4). No Cluster A PAH had

significant cyp1a transcript levels (cyp1a log2FC < 1, PADJ>0.05). Conversely, cyp1a was

significantly elevated in all Cluster B PAH exposures at 48 hpf (cyp1a log2FC > 1, PADJ

< 0.05). Our results suggest that the induction of cyp1a, in combination with the Cyp1a

protein expression patterns, is an early biomarker of general xenobiotic Ahr activation and downstream transcriptomic changes, but an inadequate biomarker of potential for morphological and behavioral effects of PAHs.

3.4.6. Cluster B PAHs activated Ahr2

To evaluate Ahr2 activation upon exposure to each of the six Cluster B PAHs, transient knockdown of Ahr2 via a translational blocking antisense morpholino was performed prior to exposure. Spatial Cyp1a protein expression in 72 hpf control and Ahr2 morphant samples exposed to each of the six PAHs was detected using immunohistochemistry (IHC).

Control morphant samples collected at 72 hpf expressed Cyp1a in the same locations as previously identified at 120 hpf (Figure 3.2): retene: vasculature; BjF and BkF: vasculature, skin, neuromasts; DB(a,i)P and DB(a,h): vasculature, skin, neuromasts, liver;

BbF: vasculature, skin (Figure 3.4). DMSO control fish had low background signal relative to the PAH treatments. In general, the absence of Ahr2 during development severely decreased Cyp1a protein expression associated with exposure to all 6 PAHs (Figure 3.4).

Specifically, expression in the skin induced by five of the six PAHs (all except retene) was completely Ahr2-dependent. When Ahr2 was knocked down, there was a partial reduction of vasculature expression associated with all six PAHs. Cyp1a expression in the liver as a result of exposure to DB(a,h)P and DB(a,i)P was not reduced by Ahr2 knockdown, which suggests an Ahr1a role in Cyp1a liver expression that is consistent with previous studies

(Goodale et al. 2012; Incardona et al. 2006; Knecht et al. 2013). The complete inhibition of Cyp1a expression in the neuromasts upon DB(a,h)P and DB(a,i)P exposure with Ahr2 knockdown indicates that Ahr2 is the primary Ahr isoform responsible for Cyp1a expression in the neuromasts, also consistent with previous work (Chlebowski et al. 2017).

One commonality among the Cluster B PAHs was the Cyp1a expression in the vasculature at 72 and 120 hpf. Previous studies have shown that induction of Cyp1a protein expression

100

in the vasculature is indicative of Ahr2-dependent toxicity (Chlebowski et al. 2017;

Goodale et al. 2015). All of the PAHs in Clusters A and B that induced any visible Cyp1a

protein expression induced Cyp1a in the vasculature. Only the PAHs in Cluster B induced

Cyp1a protein expression in the skin at 72 and 120 hpf, with the exception of retene, which

induced Cyp1a protein expression only in the vasculature at these time points. For all five

of these PAHs, Ahr2 knockdown led to a complete loss of Cyp1a protein expression in the

skin compared to only a partial reduction of the vasculature expression. Previously, of the

five PAHs, only exposure to BkF was associated with Cyp1a protein expression in the skin or epidermal cells (Incardona et al. 2011). We investigated Cyp1a protein expression upon exposure to retene at both 24 and 48 hpf. In addition to the expected vasculature expression,

Cyp1a expression was observed in the skin at both time points (data not shown). By 72 hpf, the skin expression shifted to the vasculature. These data suggest that Cyp1a protein expression in the skin may have broader temporal use as a biomarker of Ahr2-activation, reporting as early as 24 hpf, and Ahr2 knockdown leading to its complete loss. We limited our investigation to 72 hpf to detect Cyp1a protein localization in the liver, which is not fully developed and visible until 72 hpf (Ober et al. 2003).

A definitive test for Ahr2-dependent toxicity would assay morphological and behavioral effects in the complete absence of Ahr2. Because permanently knocking out Ahr2 causes background behavioral effects, in addition to low fecundity (Garcia et al. 2018), we relied on transient knockdown (Hill et al. 2004; Kubota et al. 2014; Prasch et al. 2003) with the caveat that time points were restricted to no later than 72 hpf. Of the six Cluster B PAHs, only retene, BjF, and BkF caused morphological malformations. A previous study

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identified that the cardiac effects of retene were Ahr2-dependent (Scott et al. 2011). The

toxicity of BkF is more complicated; a previous study showed that BkF associated

cardiotoxicity was not affected by Ahr2 knockdown (Incardona et al. 2011). We found that

the caudal fin malformation associated with both BkF and BjF failed to develop in the

absence of Ahr2 (Garland et al. 2020). The Ahr2-dependent caudal fin toxicity, in

combination with the almost complete loss of vasculature, skin, and neuromast Cyp1a

protein expression in the absence of Ahr2 suggests that BkF and BjF act primarily through

Ahr2. BbF, another five-ring PAH that did not cluster with BkF and BjF based on

developmental toxicity phenotypes (Figure 3.2), was part of transcriptional Cluster B, and

induced Ahr2-dependent Cyp1a expression in the skin. The differences in phenotypic

effects between BbF, and BkF and BjF despite Ahr2 activation suggest one or more of the

following: a potential role of the other Ahr orthologs in BbF’s bioactivity, differential PAH

stability in solution or uptake into the embryos, and thus dose differences in the embryos,

or metabolism of these parent PAHs to metabolites with varying bioactivity. With the exception of DB(a.h)P, the remaining PAHs only caused LPR effects at 120 hpf, precluding a transient knockdown test of Ahr ortholog dependency. Cyp1a protein expression at 72 hpf was instead used as a biomarker for Ahr2 activation. We recognize that the reduction of Cyp1a protein expression upon Ahr2 knockdown is not an indication of Ahr2-dependent toxicity for these PAHs, as Cyp1a expression does not seem to be indicative of morphological or behavioral malformations. Rather, it suggests that these PAHs primarily bind and activate Ahr2.

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3.4.7. Cluster B PAHs that activate Ahr2 uniquely enrich several pathways

After identifying the dominant role of Ahr2 in the bioactivity of Cluster B PAHs, we

examined the genes and pathways that were enriched to gain insight into the mechanisms

of their toxicity. We identified the DEGs uniquely altered by each of the 6 PAHs (Figure

3.5): Retene – 45, BjF – 10, BkF – 23, DB(a,i)P – 8, DB(a,h)P – 123, BbF – 11. A list of

the DEGs uniquely altered by each of the 6 PAHs is in Supplementary Table A.4. To

understand the functional consequences of exposure to each of these PAHs, we conducted

pathway analysis on each of the PAH’s DEGs using g:Profiler (Reimand et al. 2016). The

DEGs corresponded to a number of biological pathways for each PAH: retene – 27, BjF –

26, BkF – 25, DB(a,i)P – 14, DB(a,h)P – 31, BbF – 15.

All 6 Cluster B PAHs had DEG enrichment for pathways associated with cellular responses to xenobiotics, which contained the largest number of differentially expressed transcripts for retene, BkF, BjF, and DB(a,i)P. BbF and DB(a,h)P had similar DEG enrichment for cellular response to xenobiotics, but slightly more DEG enrichment for ion transmembrane transport pathways. Genes associated with response to xenobiotics common to all six PAHs were ugt1ab, cyp1a, and sult6b1. Ugt1ab is part of the family of glucuronosyl transferase enzymes involved in phase II metabolism of xenobiotics, and reported to be significantly induced when zebrafish are exposed to xenobiotic chemicals (Huang et al. 2016; Wang et al. 2013; Zhai et al. 2014). Sult6b1 (sulfotransferase family, cytosolic, 6b, member 1) belongs to the sulfotransferase family of enzymes, which are induced in zebrafish exposed to xenobiotic chemicals, and associated with phase II metabolism of xenobiotics in mice

(Bui et al. 2012; Takahashi et al. 2009). Our results are concordant with previous studies

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that associated PAH exposures with expression changes in phase I and II metabolism genes

(Chlebowski et al. 2017; Goodale et al. 2015; Knecht et al. 2013). Retene, BjF, and BkF

had DEG enrichment for oxidative stress and oculomotor nerve palsy pathways. DB(a,i)P

was associated with oxidative stress pathways, while DB(a,h)P had DEG enrichment for

pathways associated with biological regulation, cell communication, ion transport, cellular

signaling, and focal motor seizures.

A common mechanism of action that PAH and PAH-containing mixtures have been associated with is cellular oxidative stress response, which can cause damage to both DNA and proteins (Chlebowski et al. 2017; Goodale et al. 2015; Maikawa et al. 2016). Activation of the Ahr has also been linked to oxidative stress responses (Dalton et al. 2002). Our results suggest that oxidative stress response pathways may also contribute to the toxicity of retene, BkF, BjF, and DB(a,i)P. DB(a,h)P and BbF were associated with ion transport pathways which may play a causal role in their associated aberrant behavior phenotypes.

Previous studies in zebrafish have reported misregulation of ion transport pathways in response to exposures to particulate matter (PM) 2.5 and arsenic (Duan et al. 2017; Xu et al. 2013). One study found a correlation between differential expression of genes associated with calcium channels and altered behavioral development when zebrafish were exposed to the water-soluble fraction of crude oil or lead (Wang et al. 2018). Numerous chemicals have been shown to alter the behavior of larval zebrafish, however there is still limited understanding of the molecular mechanisms underlying altered behavioral responses (Legradi et al. 2015). The ligand-dependent DEGs and pathways associated with

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BbF and DB(a,h)P may further our mechanistic understanding of zebrafish behavior in

response to chemicals.

3.4.8. Cluster B PAHs had seventeen common DEGs

DEGs common to two or more of the Cluster B PAHs were identified. Seventeen DEGs

were common to all six Cluster B PAHs (Figure 3.5, Supplementary Table A.4), five of

which were among the top 10 most elevated DEGs for Cluster B (Figure 3.6A; black fill,

yellow text). Cyp1c1 had the highest log2FC for five of the six PAHs; for Db(a,i)P it was the third most differentially expressed gene. Cyp1c1 expression was highly increased by all six Cluster B PAHs and thus could be superior to cyp1a as biomarker of Ahr2 activation.

There is not a known toxicological role of wfikkn1 (WAP, follistatin/kazal, immunoglobin, kunitz and netrin domain containing 1), however its expression has been shown to significantly increase upon exposure to other PAHs and the flame retardant, mITP

(Goodale et al. 2015; Goodale et al. 2013; Haggard et al. 2017). Future studies will be conducted to characterize the toxicological role of wfikkn1. Sult6b1, as mentioned previously is part of a family of Phase II detoxification enzymes. Interestingly, among the top 10 most reduced DEGs, only the uncharacterized transcript si:dkey-65b12.6 was common to all six PAHs (Figure 3.6B), and Cyp2aa11 was common to five of the six

PAHs. Cyp2aa11 is part of the cyp2 gene family, the largest Cyp enzyme family in zebrafish (Saad et al. 2016) and, to our knowledge, its differential expression has not been associated with metabolism of any studied chemical.

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3.5. Conclusions

In this study, we used a combination of developmental toxicity phenotypic endpoints and genome-wide transcriptomics in 48 hpf zebrafish embryos to classify 16

PAHs and identify the downstream changes in mRNA levels. Based on transcriptomic responses, the 16 PAHs clustered into two general groups. One group consisted of PAHs that were transcriptionally similar to the control, while the other group included more developmentally toxic PAHs which not only had significantly elevated cyp1a gene expression levels, but also primarily activated Ahr2. Interestingly, despite activating the common receptor, these PAHs each had unique ligand-dependent downstream changes in gene expression that could be the cause of their unique developmental toxicity phenotypes.

The PAHs also induced Ahr2-dependent Cyp1a protein expression in the skin, which is an effective biomarker for Ahr2 activation. We show that cyp1a gene expression in combination with the Cyp1a protein expression patterns is an early reliable biomarker of xenobiotic Ahr activation and downstream transcriptomic changes, but an inadequate biomarker for morphological and behavioral effects from PAHs. We suggest considering other genes including cyp1c1 and wfikkn1, which may also serve as reliable biomarkers for prediction of Ahr2-dependent PAH developmental toxicity. In conclusion, we have begun to characterize and classify PAHs based on their transcriptomic and developmental toxicity phenotypic responses, which will help guide the hazard characterization of other PAHs in the future and shift the focus from EPA’s 16 priority PAHs to potentially more toxic PAHs.

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Figure 3.1. Heatmap of the morphological and behavioral responses, and Cyp1a protein localization patterns of 123 PAHs assessed using the embryonic zebrafish model. Figure illustrates the developmental toxicity (LEL) across four assays: (1) Morphology (22 endpoints) (2) Embryonic photomotor response (EPR) at 24 hpf (3) Larval photomotor response (LPR) at 120 hpf (4) Cyp1a protein expression in five tissue types. The color bar on the left side of the heatmap denotes the bins of the PAHs. The colors of the cells represent the LEL for each endpoint for each PAH. Gray denotes presence of Cyp1a expression in the specific tissue type. PAHs that were chosen from each bin for RNA

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sequencing analyses are denoted in blue and an asterisk. Abbreviations: Del = Delayed, Mvt = Movement, hpf = hours post fertilization.

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Figure 3.2. Heatmap of the morphological and behavioral endpoints, Cyp1a protein localization pattern, and DEGs associated with exposure to the 16 representative PAHs. The color bar on the left denotes the developmental toxicity bins of the PAHs selected from the 123 PAH screen. The names of the 16 PAHs are listed on the right of the heatmap. The colors in the heatmap represent the LEL concentration for each endpoint. Gray represents the presence of Cyp1a protein expression. The DEGs column represents the total number of statistically significant DEGs with FC >1.5 and PADJ <0.05. The number of DEGs with elevated and reduced expressions are also listed for each PAH. Abbreviations: Del = Delayed, Mvt = Movement, LEL = Lowest Effect Level, DEG = Differentially Expressed Gene, hpf = hours post fertilization, LPR = Larval photomotor response, EPR = Embryonic photomotor response.

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Figure 3.3. Hierarchical clustering and network analysis for the 16 PAHs. (A) Hierarchical clustering of the DEGs of the 16 PAHs compared to the control samples. Genes with elevated expression are in yellow and genes with reduced expression are in blue. Genes were ranked by the coefficient of variation and only the top 500 genes are included in the heatmap. (B) Network inferred using the Context Likelihood of Relatedness (CLR) program that links the 16 PAHs based on coordinated transcription of genes as they respond to each PAH. The nodes in the network are specific PAHs, with the colors representing the

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developmental toxicity bins they belong to. The two controls (in black) are included in the figure. The connectors are similarity of transcriptome response to those PAHs. The thicker the connector, the more similar the response of the PAHs. To produce a more relevant network, only the top 500 genes based on coefficient of variation (CV) was used. Abbreviations: Cont = Control.

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Figure 3.4. Ahr2 dependency of Cluster B PAHs. Cyp1a protein expression in 72-hpf zebrafish embryos injected with control morpholino (Co-MO) or Ahr2-morpholino (Ahr2- MO) and exposed to each of the six Cluster B PAHs (Retene, BkF, BjF, DB(a,i)P, DB(a,h)P, and BbF). 1% DMSO is the vehicle control. The color around each image represents the developmental toxicity bin the PAH belongs in. Cyp1a protein expression pattern is at the bottom right of each image. Scale bar = 300 µm.

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Figure 3.5. Unique DEGs (FC >1.5, adjusted p-value <0.05) and pathways associated with each of the six cluster B PAHs. Each cluster B PAH had DEGs associated with one to six of the other Cluster B PAHs. Each PAH also had several unique DEGs represented within the large colored clouds. The figure highlights the most significant pathways associated with each PAH.

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Figure 3.6. Top 10 elevated and reduced DEGs for each of the Cluster B PAHs: Retene, BbF, BjF, BkF, DB(a,h)P, and DB(a,i)P. The border colors of the cells with the PAH abbreviations represent the developmental toxicity bins they belong to. The background colors of the cells with transcript names represent the number of PAHs each differentially expressed transcript is common to amongst the 6 PAHs: Black = 6 PAHs, dark blue = 5, blue = 4, green = 3, purple = 2, gray = 1. (A) Top 10 elevated DEGs for each of the six Cluster B PAHs. The transcripts in yellow text are common to all six PAHs and are within the top 10 elevated DEGs. The transcripts in white text are common to all six PAHs but are not within the top 10 elevated DEGS for all six PAHs. (B) Top 10 reduced DEGs for each of the six Cluster B PAHs

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Table 3.1. PAHs used in this study with associated registry and use parameters.

Nominal RNA- IHC Purity Stock seq Conc PAH with abbreviation Structure CAS Supplier (%) Conc. Conc. . (mM) (µM) (µM) 4H-cyclopenta[def]phenanthren- 4-one 5737-13-3 Chiron 99.4 10 16.2 0.4 4h-CPdefP Retene 483-65-8 SCB 98 10 12.2 15.8

Benzo[k]fluoranthene 207-08-9 AccuStandard 100 10 1.9 8.9 BkF 3-nitrofluoranthene 892-21-7 AccuStandard 100 10 1.9 50 3-NF Benzo[j]fluoranthene 205-82-3 AccuStandard 98.1 10 14.9 50 BjF Carbazole 86-74-8 AccuStandard 99.7 10 50 35.6 Dibenzo[a,i]pyrene 189-55-9 AccuStandard 99.8 1 5 5 DB(a,i)P Dibenzo[a,h]pyrene 189-64-0 AccuStandard 99.9 1 10 10 DB(a,h)P 9-methylanthracene 779-02-2 AccuStandard 100 10 50 50 9-MA Benzo[b]fluoranthene 205-99-2 AccuStandard 99.2 10 50 50 BbF Fluoranthene 206-44-0 AccuStandard 97.2 10 50 50 1,5-dimethylnaphthalene 571-61-9 AccuStandard 100 10 50 50 1,5-DMN Acenaphthene 83-32-9 AccuStandard 100 10 50 50

2-methylnaphthalene 91-57-6 AccuStandard 100 10 50 50 2-MN

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65996-93- Phenanthrene AccuStandard 99.5 10 50 50 2 Anthracene 120-12-7 AccuStandard 99.6 10 50 50

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Table 3.2. Six PAHs that induced morphological effects with the concentrations tested to compute the EC80.

PAH Abbreviation Concentrations (µM) tested to compute the EC80 4h-CPdefP 8.5, 11.2, 13.9, 16.7, 19.4, 22.1, 24.8, 27.5, 30.2, 32.9, 35.6 Retene 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22 BkF 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5 3-NF 0.25, 1, 2.1, 3.3, 4.4, 5.5, 6.6, 7.8, 8.9, 10.1, 11.2 BjF 4.5, 6.9, 9.3, 11.7, 14.1, 16.5, 18.8, 21.2, 23.6, 26, 28.4 Carbazole 8.5, 12.7, 16.8, 21, 25.1, 29.3, 33.4, 37.6, 41.7, 45.9, 50

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Table 3.3. Summary table of the eight bins the 123 PAHs were classified into with the general characteristics of each bin. The x represents the presence of morphology and behavior malformations, and Cyp1a protein expression.

Endpoints Bin # Morphology Behavior Cyp1a 1 x x x 2 x x x 3 x x 4 x x 5 x x 6 x 7 x 8

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Table 3.4. Cyp1a transcript levels and adjusted p-values (PADJ) associated with each of the 16 PAHs.

cyp1a Bin # PAH PADJ log2FC Cluster A 1 4h-CPdefP 0.09 0.76 2 3-NF 0.24 0.82 Carbazole 0.15 0.62 4 9-MA 0.31 0.13 5 Fluoranthene 0.26 0.34 6 1,5-DMN 0.04 0.76 Acenaphthene 0.16 0.67 2-MN 0.02 0.99 7 Phenanthrene 0.11 0.86 8 Anthracene 0.33 0.99 Cluster B 1 Retene 2.06 3.05E-57 2 BkF 2.18 2.00E-64 BjF 2.08 1.44E-58 3 DB(a,i)P 1.37 1.84E-24 4 DB(a,h)P 1.22 1.09E-19 5 BbF 1.16 2.71E-17

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CHAPTER 4 – TRANSCRIPTIONAL CO-EXPRESSION NETWORK ANALYSIS IN ZEBRAFISH REVEALS FLAME RETARDANT AND ARYL HYDROCARBON RECEPTOR LIGAND SPECIFIC MODULES

Prarthana Shankar‡1, Ryan S. McClure‡2, Katrina M. Waters2, Robyn L. Tanguay1*

1 Department of Environmental and Molecular Toxicology, Oregon State University,

Corvallis, OR 97331, USA.

2 Biological Sciences Division, Pacific Northwest Laboratory, 902 Battelle Boulevard,

P.O. Box 999, Richland, WA 99352, USA.

‡ Authors contributed equally

Formatted for submission to Molecular Systems Biology

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4.1. Abstract

Zebrafish is a popular animal model used for high-throughput screening of chemical hazard

and investigation of transcriptomic mechanisms of toxicity. Our primary goal was to

identify important genes and biological pathways that Aryl Hydrocarbon Receptor 2

(AHR2) Activators (nine polycyclic aromatic hydrocarbons (PAHs) and TCDD), and ten

flame retardant chemicals (FRCs) alter in developing zebrafish. Taking advantage of a

large compendium of 48-hpf zebrafish RNA-sequencing data, we created a co-expression network that grouped genes by their similarity in transcriptional response. Genes responding to the FRCs and AHR2 Activators localized to distinct regions of the network, with FRCs inducing a broader response related to neurobehavior and ion signaling. AHR2

Activators centered in one module related to chemical stress responses, which contained genes known to respond to xenobiotic AHR2 activation, including cyp1a. We also discovered several highly co-expressed genes in this module, and we subsequently show that these genes are definitively within the AHR2 signaling pathway. Systematic removal of the two chemical classes from the network identified neurogenesis associated with the

FRCs, and regulation of vascular development associated with both classes. We also identified highly connected genes responding specifically to each class that are potential biomarkers of exposure. Overall, we created the first zebrafish chemical-specific gene co- expression network that revealed novel insight into how different chemicals alter the transcriptome relative to each other. Our network can be leveraged for future studies investigating chemical mechanisms of toxicity in developing zebrafish.

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4.2. Introduction

With advancements in technology and medical science, various types of chemicals

(xenobiotics, drugs, etc) are being applied to both the natural environment and human

body. However, the majority of these chemicals are yet to be evaluated for their potential

to cause adverse health effects. High-throughput (HTP) in vitro assays are popular methods used to estimate chemical toxicity and identify underlying molecular events (Tice et al.

2013). Despite their application to determine whether a chemical class may be toxic, we still lack adequate knowledge of the mechanisms of toxicity of many chemicals (Dix et al.

2007) preventing us from connecting assay measurements to phenotypes. Animal models can be a more translatable way of revealing chemical hazard potential to humans, with both metabolism and integrated tissue systems. However, high cost and low throughput are often barriers to testing across a large chemical space. These barriers have been serious deficiencies in advancing chemical risk assessment, and thus a HTP and accurate prediction of which chemicals may be toxic is an essential next step in the evaluation of chemical safety (Raies et al. 2016).

A popular HTP animal model, zebrafish (Danio rerio), by virtue of its rapid development

(Kimmel et al. 1995) and high physiological and genetic similarity to humans (Howe et al.

2013), is being leveraged to identify chemical hazards (Truong et al. 2020) and to determine the molecular signaling events that precede adverse phenotypic outcomes

(Goodale et al. 2015). One of the main -omics techniques that is applied to zebrafish is transcriptomics, which uses RNA sequencing analysis to provide an unbiased snapshot of gene expression changes associated with a particular chemical exposure (Williams et al.

2014). Numerous transcriptomic studies in both developing and adult zebrafish leave no

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doubt that diverse chemical exposures often result in diverse gene expression changes

(Kashyap et al. 2014; King-Heiden et al. 2008; Zheng et al. 2018). Such studies have led to the discovery of transcripts important for the manifestation of higher level toxicity effects (Garcia et al. 2017; Planchart et al. 2010) and to a large relational database of chemical-zebrafish transcriptome responses.

Much of this transcriptomic data collected for zebrafish have examined toxicants from a variety of different classes. Polycyclic aromatic hydrocarbons (PAHs), are a large class of ubiquitous environmental pollutants, and human exposure has been linked to cardiovascular disease, respiratory problems, carcinogenicity, and developmental deficits

(Abdel-Shafy et al. 2016; Kim et al. 2013a). The molecular initiating event of many PAHs is the aryl hydrocarbon receptor (AHR), and PAHs have been shown to bind all three orthologs of AHR in zebrafish, with AHR2 being the predominant receptor required for toxicity (Shankar et al. 2020). 2,3,7,8-Tetrachlorodibenzodioxin (TCDD) is a halogenated aromatic hydrocarbon and the best-characterized and most potent AHR2 ligand. It is widely used as a canonical xenobiotic ligand to study responses downstream of AHR activation

(Poland et al. 1976). Many studies have focused on the roles of the cytochrome P450 (CYP) metabolizing enzymes in PAH toxicity (Andreasen et al. 2002; Brown et al. 2015), while other molecular signaling events downstream of the AHR remain largely unknown. Flame- retardant chemicals (FRCs) are a diverse class of chemicals including polybrominated diphenyl ethers (PBDEs) and organophosphate flame retardants (OPFRs), commonly applied in an additive manner to manufactured materials such as furniture, clothing, and electronics, and often leach into surrounding environments and human bodies (Xiong et al.

2019; Yang et al. 2019). FRCs have been associated with neurodevelopmental effects

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(Dishaw et al. 2014), altered reproductive and thyroid function (Kim et al. 2014), and impacts on the immune and endocrine systems (Fowles et al. 1994). While many research groups have conducted transcriptomic studies in developing zebrafish exposed to PAHs and TCDD, only a limited number of FRC whole-genome expression studies have been published (Dasgupta et al. 2018; Dasgupta et al. 2017; Haggard et al. 2017; Mitchell et al.

2018).

The standard use of transcriptomic data, and the approach used in many of the studies above, is to compare gene expression levels between a control and a chemical treatment.

However, with a transcriptomic database of sufficient size (Marbach et al. 2012), it is possible to perform a meta-analysis of sequencing data and construct a network of genes based on co-expression values of each gene pair (Faith et al. 2007; Huynh-Thu et al. 2010).

Co-expressed genes show a similar pattern of either direct or inverse co-expression across

multiple conditions or biological replicates. Network analysis can be used to reveal

important chemical targets in a biological system (Ashtiani et al. 2018; Klein et al. 2012;

McDermott et al. 2009), and to indicate processes that are critical for or distinctive between responses to certain classes of chemicals (Larkin et al. 2013; Tilton et al. 2015). Network approaches also have a distinct advantage over standard control/condition comparisons in that data from a number of different studies, even those conducted under variable conditions, can be collected and integrated to produce a model formed from a compendium of many datasets. Network analysis can also highlight genes, pathways, and processes that may change their expression in a significant but subtle manner (less than the 2-fold cutoff normally applied to control/condition comparisons), expanding our ability to identify processes related to chemical class or phenotypic response. A whole transcriptome

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approach is thus far more efficient and informative to detect subtle but potentially

important shifts in gene expression patterns that describe interactions between processes impacted by chemical exposure (McDermott et al. 2011). Such patterns would be missed

by smaller scale targeted expression studies for specific biomarkers. Based on the large

amount of transcriptomic data collected for zebrafish exposed to a variety of different

chemicals, we are now in a position to apply these network approaches to zebrafish

response to chemicals.

While phenotypic outcomes of many chemicals have been studied in zebrafish (Geier et al.

2018; Truong et al. 2020), we still do not know many of the transcriptomic pathways and

processes that are induced by these chemicals early in response. Additionally, we do not

know to what degree different classes of chemicals may overlap in their transcriptomic

response compared to their potentially similar phenotypic responses. To fill this gap in

knowledge, the primary goal of our study was to utilize network analyses to identify both

common and distinctive biological pathways that polycyclic aromatic hydrocarbons

(PAHs), 2,3,7,8-Tetrachlorodibenzodioxin (TCDD), and several flame retardant chemicals

(FRCs) alter in developing zebrafish. Networks were inferred using a random forest

method (Huynh-Thu et al. 2010) applied to a compendium of both new and previously

published zebrafish transcriptomic data. We identified specific portions of this network

that represented tightly co-expressed genes enriched for certain pathways and responding

to certain chemical types. The network approach used here enabled discovery of novel

genes in the AHR2 signaling pathway as well as several pathways associated with the

FRCs. The inference of this gene co-expression network for zebrafish not only provides

novel insight into the transcriptional responses to chemical exposure, but can serve as a

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resource for other studies focused on transcriptomic coordination, predictive toxicology,

and identification of chemical-specific biomarkers and processes of interest in this model

organism.

4.3. Materials and Methods

4.3.1. Characterization of chemical datasets

In total, we collected RNA sequencing data from 48-hpf zebrafish exposed to 33 unique

chemical treatments (Supplementary Table B.1). For 19 of these treatments (18 PAHs, and TCDD), the data was collected from previously published previous studies (Garcia et al. 2018b; Goodale et al. 2015; Shankar et al. 2019), with the remaining 14 treatments (4

PAHs and 10 FRCs) initially analyzed in this study. Each treatment was examined with 3-

8 replicates for a total of 170 RNA sequencing samples included in the study. See

Supplementary Table B.1 for information on chemicals, exposure levels, references, and

GEO accession numbers for the dataset used in this study.

4.3.2. Chemicals

Detailed methods for published datasets can be found in our previous studies (Garcia et al.

2018b; Goodale et al. 2015; Shankar et al. 2019). Methods for chemicals initially analyzed in this study are described here. The PAHs (benzo[a]pyrene (B[a]P), 9,10 phenanthrenequinone (9,10-PQ), and dibenzo[a,l]pyrene (DBalP)) were dissolved to 10 mM in 100% DMSO and stored in a desiccator. The FRCs were generously provided at 20 mM in 100% DMSO by the US Environmental Protection Agency at >98% purity in a 96- well plate, and stored at -80 °C. TCDD was purchased from SUPELCO Solutions Within at 311 nM with 95.3% purity, and stored in the dark at room temperature. The chemical

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stocks were sonicated in a water bath sonicator between 10-30 minutes before each use

(FRCs for 10 min, PAHs for 15 min, and 30 min for TCDD).

4.3.3. Zebrafish husbandry

Tropical 5D wildtype zebrafish were housed at Oregon State University’s Sinnhuber

Aquatic Research Laboratory (SARL, Corvallis, OR) in densities of 1,000 fish per 100-

gallon tank according to the Institutional Animal Care and Use Committee protocols. Fish

were maintained at 28 °C on a 14:10 hour light/dark cycle in recirculating filtered water,

supplemented with Instant Ocean salts. Adult fish were fed GEMMA Micro 300 or 500

twice a day, and larval and juvenile fish were fed GEMMA Micro 75 and 150, respectively, three time a day (Barton et al. 2016). Spawning funnels were placed in the tanks at night

and the following morning embryos were collected and age staged (Kimmel et al. 1995;

Westerfield 2007). Embryos were maintained in embryo medium (EM) in an incubator at

28°C until further processing. EM consisted of 15 mM NaCl, 0.5 mM KCl, 1 mMMgSO4,

0.15 mMKH2PO4, 0.05 mM Na2HPO4, and 0.7 mMNaHCO3.

4.3.4. Exposures for chemicals examined initially in this study

PAHs (B[a]P, 9,10-PQ, and DBalP): Zebrafish embryos dechorionated using pronase at

4 hpf were batch-exposed to chemicals at 6 hpf in glass vials as described previously

(Goodale et al. 2015). Exposure concentrations were 1 and 10 µM for B[a]P, 1.2 µM for

9,10-PQ, and 10 µM for DBalP . The vehicle control was 1% DMSO, and there were 20 embryos per glass vial in 2 mL exposure solution. Vials were incubated at 28°C in the dark on a rocker until sample collection at 48 hpf.

FRCs: Embryos (n=48) were dechorionated at 4 hpf and exposed to each FRC from 6 to

48 hpf in 100 µL exposure solution in individual wells of 96-well polystyrene plates at

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28°C. Exposure concentration of each FRC was determined previously using a

concentration-response experiment where zebrafish were exposed to a broad concentration

range from 6 to 120 hpf of each FRC and evaluated for developmental toxicity as described

previously (Truong et al. 2020). The concentration that caused 80% of the embryos to be

adversely affected (EC80) with morphological malformations, behavior effects, or

mortality by 120 hpf was determined and utilized here for six of the ten FRCs in our dataset

(Supplementary Table B.1). For the remaining four FRCSs (TCPP, BDE-47, TBBPA-

DBPE, and TCEP) that did not cause a phenotypic effect, we exposed zebrafish embryos to 85 µM. The vehicle control was 0.64 % DMSO.

4.3.5. RNA-sequencing sample preparation and sequencing

Following exposure, 48-hpf whole embryos were homogenized using RNAzol (Molecular

Research Center, Inc.) and a bullet blender with 0.5 mM zirconium oxide beads (Next

Advance), as recommended by the Next Advance. The number of pooled 48-hpf zebrafish varied in each biological sample for each of the chemicals: PAHs = 20, and the FRCs = 8.

The RNA from the PAH exposures was isolated via phenol guanidine extraction. The RNA from the FRC exposures was purified using the Direct-zol MiniPrep kit (Zymo Research) and included the optional in-column DNase 1 digestion treatment for 15 minutes. RNA integrity was assessed (RIN score > 9) using an Agilent Bioanalyzer. Total RNA samples were sent to the Oregon State University Center for Genome Research and Biocomputing

Core facilities for library preparation and sequencing. This included mRNA enrichment by polyA selection. Libraries were prepared with the PrepX™ mRNA and Illumina sequencing workflow (Wafergen Biosystems). For the PAH exposures, 50 bp paired-end sequencing was conducted using an Illumina HiSeq 2000 sequencer. For the FRC

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exposures, 100 bp single-end sequencing was conducted using an Illumina HiSeq 3000

sequencer.

4.3.6. Alignment and analysis of RNA-seq data

Each fastq file was aligned to the Genome Reference Consortium Zebrafish Build 11

(GRCz11) (https://www.ncbi.nlm.nih.gov/assembly/GCF_000002035.6/) using the Star

Aligner (Dobin et al. 2013) with default settings. Resulting SAM files were then used to

count reads aligning to genes using HTSeq

(https://htseq.readthedocs.io/en/release_0.11.1/) (Anders et al. 2015) along with the gff file

for the GRCz11 genome, resulting in raw read counts for 39,701 zebrafish genes. Low

expression genes, defined as those with a count of ‘0’ in at least 43/170 (25%) of samples,

were removed from further analysis. The final raw dataset consisted of 21,854 genes across

170 samples.

Raw counts were normalized using Bioconductor’s DESeq2 (Love et al. 2014). DESeq2 was also used to calculate log2 fold changes (log2FC) comparing chemicals to their

respective DMSO controls. Among the 170 samples, 33 chemical vs. control comparisons

were made, and DESeq2 was also used to calculate the adjusted p-value for these

comparisons (correcting for multiple hypothesis testing). To further reduce the number of

genes used to infer a network, any gene that was not differentially expressed (defined as

an adjusted p-value > 0.05, no fold change cutoff) in at least 3 of the comparisons (10% of

the total) was removed from further analysis. This resulted in 10,346 genes across 170

samples from 33 conditions being included in subsequent networks. Log2FC values

(comparing chemicals to their respective DMSO controls) of these 10,346 genes were then used to infer the gene co-expression network.

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4.3.7. Inferring Gene Co-Expression Networks

GENIE3 (Huynh-Thu et al. 2010) was used to generate a matrix of co-expression

values for all gene pairs. We filtered low co-expression values to generate a network with

sufficient structure for topological analysis, as done previously (McClure et al. 2020;

McClure et al. 2018). Networks were viewed in Cytoscape (Shannon et al. 2003), which

was also used to calculate centrality values for all genes and determine network

neighborhoods for genes of interest. Modules within networks were determined using the

fastgreedy.community function within the igraph package in R (Csardi et al. 2006).

Functional enrichment of modules was done using g:Profiler (Raudvere et al. 2019). To

determine which functions and pathways may be related to certain classes of chemicals,

we selected two subsets of our data, chemicals in the FRC class or chemicals in the AHR2

Activator class (Supplementary Table B.1). For FRC analysis, we first inferred a GENIE3 co-expression matrix after removing FRC data (comprising 39 samples), and compared the resulting network to one inferred after randomly removing 39 samples of data. For each of these two networks (that lack FRC data or lack an identical amount of random data), we calculated the average co-expression value between all genes that belonged to each GO

Biological Process family identified using g:Profiler (Raudvere et al. 2019). Genes of a particular function that show lower co-expression in a network that is specifically lacking

FRC data compared to a network that has data randomly removed from it, suggests that this function is especially critical to the FRC response. A similar pair of networks and comparisons were made for the AHR2 Activators (comprising 46 samples).

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4.3.8. TCDD exposure, RNA extraction, and quantitative reverse transcriptase

polymerase chain reaction (qRT-PCR)

To confirm that the highly co-expressed genes associated with the AHR2 Activators in our

network are in the AHR signaling pathway, we performed a qRT-PCR experiment.

Wildtype and AHR2-null (Garcia et al. 2018a) zebrafish embryos were exposed to 0.1%

DMSO or 1 ng/mL TCDD at 6 hpf, as described previously (Garcia et al. 2018b), in a

manner similar to the TCDD RNA sequencing samples included in this study. The 1 ng/mL

TCDD concentration was selected as it results in 99-100% of 120-hpf zebrafish displaying

the expected TCDD-induced phenotypic malformations such as heart and cartilage

malformations (Henry et al. 1997). Briefly, exposures were conducted in 20-mL amber

glass vials with 1 embryo/100 µL exposure solution for 1 hour with gentle rocking. Vials

were also inverted every 15 minutes to ensure even exposure. After exposure, embryos

were rinsed three times with EM, transferred to 100-mm Petri dishes, and raised in EM at

28 °C to 48 hpf. RNA was extracted in a manner similar to the PAHs, as described above.

RNA quantification and quality assessment (O.D. 260/280 ratios) was conducted using a

BioTek® Synergy™ Mx microplate reader with the Gen5™ Take3™ module.

qRT-PCR was conducted in 10 µL reactions consisting of 5 μL SYBR® Green Master

Mix, 0.08 μL reverse transcriptase enzyme mix (Power SYBR® Green RNA-to-CT™ 1-

Step Kit; Applied Biosystems), 0.2 μL each of 10 μM forward and reverse primers, and

20 ng RNA per reaction. The gene specific primers (IDT) for qRT-PCR amplification are

listed in Supplementary Table B.3. The QuantStudio 5 Real-Time PCR System (Thermo

Fisher Scientific) was used and the cycling parameters were as follows: reverse transcription at 48°C for 30 min, denaturation and activation of SYBR® polymerase at

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95°C for 10 min, followed by 40 cycles of amplification (95°C for 15 s, 60°C for 1 min).

A melt curve analysis was conducted to assess for multiple products, and it was confirmed that all primers amplified a single product. Expression values were normalized to the β- actin control, and analyzed with the 2−ΔΔCT method as described previously (Livak et al.

2001). The data collected for each gene was tested for normality using the Shapiro-Wilk

normality test. To determine significant difference compared to control (p-value < 0.05),

either a two-way ANOVA with the post-hoc Tukey’s test or a Kruskal Wallis rank sum

test followed by a Dunnett’s test was conducted depending on if the data that passed or

failed the normality test, respectively,. Data analysis was conducted using RStudio (version

3.6.0), and data visualization was performed using GraphPad Prism version 8.0.0 for

Windows, GraphPad Software, San Diego, California, USA (www.graphpad.com).

4.4. Results

4.4.1. Characterization of Full Dataset

In this study, we sought to identify molecular signatures and biological pathways following

exposures to a diverse group of chemicals. This was done using a large array of

transcriptomic data combined with the first chemical-focused gene co-expression network inferred using zebrafish. Transcriptomic data included several previously published studies

(Garcia et al. 2018b; Goodale et al. 2015; Shankar et al. 2019) and new unpublished

datasets (see Supplementary Table B.1). To gain an overview of transcriptomic patterns

from the 33 unique chemical treatments, we first used Ward’s method of hierarchical clustering of the 10,346 genes that responded significantly to at least a subset of chemicals.

We observed that the treatments naturally clustered into six groups based on log2FC values

(Figure 4.1). Clusters 1 (orange) and 2 (red) represented all of the flame retardant

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chemicals (FRCs) examined and were also more similar to each other than to other clusters in the dataset, showing that the overall transcriptomic response to FRCs is distinct from

PAH and TCDD responses. In addition to differences in transcriptomic response based on chemical types, we also found that when we compared our clustering to a previous study investigating the morphology and behavior effects caused by FRC exposure, there were differences between transcriptomic response and developmental toxicity phenotype. For example, IPP exposure caused morphology and behavior malformations at 120 hpf (Truong et al. 2020) and yet, it clustered here with the relatively benign FRCs. While several PAHs in Cluster 3 (black) (9-MA, 3-NF, 2-MN, 1,5-DMN, Carbazole, and Anthracene) were previously determined to cause modest or no phenotypic responses in developing zebrafish, other chemicals in this cluster including 4h-CPdefP, 7,12-B[a]AQ, and TCDD are known to cause overt developmental toxicity (Geier et al. 2018; Goodale et al. 2015; Goodale et al. 2012). These observations demonstrated that transcriptional response and phenotypic outcome are often not strongly correlated. Clusters 4 and 5 consisted of the remaining

PAHs in this study. We noted that within all the PAHs and TCDD, the chemicals known to predominantly activate AHR2 (“AHR Activators”, see Supplementary Table B.1) did

not group together. The hierarchical clustering analysis presented here indicates that

chemical type is the strongest driver of overall transcriptomic response, rather than known

mechanism of action or magnitude of developmental toxicity within the chemical classes.

4.4.2. Global Analysis of Full Co-Expression Network

We next inferred a co-expression network using the random forest method, GENIE3

(Huynh-Thu et al. 2010), and grouped genes into one of 23 different modules ranging in

size from 14 to 425 genes. Figure 4.2 shows the location of the 12 largest modules

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calculated based on the number of genes they each contain. Table 4.1 provides module

information including number of genes in each module and colors for each module used in

Figure 4.2. We carried out functional enrichment on all modules using g:Profiler

(Raudvere et al. 2019) and found that several modules were significantly enriched (adjusted

p-value < 0.05) for one or more processes (Supplementary Table B.2). Table 4.1 shows

the pathway(s) that had the highest functional enrichment for each module, and

demonstrates the diversity in functional enrichment across all the modules, with some

examples of the most enriched functions highlighted in Figure 4.2. Many of the functions

are related to disruptions to normal development such as nervous system (Modules 1 and

5) and axon development (Module 10), in addition to processes associated with ion

transporter activities (Modules 2 and 6) and gene expression (Modules 4 and 14).

To visualize how the network groups genes that respond to particular classes of chemicals, we overlaid gene expression changes from response to either the FRCs or the AHR2

Activators onto the full co-expression network. Genes responding to the FRCs were spread

across several modules (Modules 2, 7, and 10, with some being in Modules 1 and 6, Figure

4.3A). Functional enrichment showed that Module 1 was strongly enriched for development processes including multicellular, neuronal, and anatomical structure development. Module 2 was enriched for transport mechanisms including metal ion, cation and calcium transporters among others, similar to Module 6, which was enriched for sodium channel complexes (Table 4.1). In addition, Module 7 was enriched for regulation of non-motile cilium assembly, while Module 10 was enriched for axon development. The large number of FRC-responsive genes making up these modules strongly suggests that response to the FRCs in our dataset centers around these pathways and processes. In

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contrast to the FRCs, genes responding to the AHR2 Activators were very tightly localized to one location in the network (Module 13, Figure 4.3B). Module 13 was specifically enriched for known chemical response pathways such as xenobiotic phase I and phase II metabolism, and oxidative stress, consistent with AHR functions (Dietrich 2016; Larigot et al. 2018). This module was also very tightly clustered (there were a number of edges linking genes within Module 13), indicating that the genes in this module are highly co- expressed relative to each other and are strongly regulated in response to AHR2 Activator exposure. Genes within Module 13 include known zebrafish AHR2-regulated genes upon

PAH exposure including, cyp1a, cyp1c1, ahr2, ahrra, ahrrb, and foxq1a (Shankar et al.

2019). Thus, our chemical subgroup-specific module analysis shows that gene responses to FRCs are more spread across the network and are associated with several different pathways and developmental processes, while AHR2 Activators induce a much more focused and tightly controlled response consisting specifically of pathways linked to xenobiotic exposure. The remaining modules did not contain large numbers of genes that responded to either the FRCs or the AHR2 Activators and were likely driven by the remaining chemicals in our dataset. Several were enriched for general housekeeping processes in 48-hpf zebrafish (Kimmel et al. 1995), including neuronal (Module 5) and eye

(Module 8) development (Table 4.1).

4.4.3. Centrality Analysis

While hierarchical clustering and module analysis can give broad overviews of pathway responses and modules associated with specific chemicals, networks also contain valuable gene-specific co-expression information. Centrality analysis of network genes based on betweenness or degree can reveal which genes are critical within the transcriptomic map

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of a biological system (McDermott et al. 2009). Genes of high degree are those with several connections to other genes and are termed “hubs”. Genes of high betweenness are those that occupy positions as links between larger clusters of genes, and are termed

“bottlenecks”. Both measures of centrality have been used to identify genes of importance within a network. Supplementary Figure B.1A shows the top 20 genes based on betweenness centrality. The gene with the highest betweenness in the network was apc,

coding for a regulator of the WNT signaling pathway (Haramis et al. 2006) (Table 4.2).

Bottleneck genes were also found throughout the network and in several different modules suggesting that such genes do not respond in a particularly strong way to any specific chemical, but are associated either with general chemical response or with zebrafish housekeeping processes.

Genes of high degree centrality showed different location patterns within the network compared to genes of high betweenness (Supplementary Figure B.1B). Table 4.3 shows that the high degree genes were primarily grouped into Module 13 (12 genes) which was enriched for AHR2 Activators, and Module 2 (7 genes), which was enriched for the FRCs.

There was one additional high degree gene found in Module 4. This degree centrality analysis shows that the most highly connected genes within our network are those specifically responding to AHR2 Activator or FRC exposure, in contrast to high betweenness genes which are more distributed throughout the network. The genes with the highest degree were cyp1a and sult6b, both previously shown to be highly induced by

PAHs (Shankar et al. 2019). While the cyps, gstp1, and ahrra are all involved in response to xenobiotic stimulus and metabolism, the functional roles of the other high degree genes are less clear. SlincR and foxq1a appear to have roles in TCDD-induced toxicity in

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zebrafish; slincR was recently identified as a long noncoding RNA that is involved in the

regulation of sox9b, one of the most highly depressed transcripts upon TCDD exposure

(Garcia et al. 2017), and foxq1a was induced by TCDD in the jaw primordium of

developing zebrafish (Planchart et al. 2010). It is unknown what the specific roles of

gng13b, wfikkn1, and mxd1 are as they relate to chemical exposure; however, identification

of these hub genes is suggestive of their potentially important roles in mediating toxicity

of some of the chemicals in our network. We also highlight three high degree Module 2

genes that might be involved in pathways associated with disruption of neurodevelopment

due to FRC exposure: srgap3, involved in neurodevelopmental processes as reviewed

previously (Bacon et al. 2013), tfe3a, an important transcription factor (Lister et al. 2011),

and cacna1da, associated with calcium ion transport in neuronal signaling (Duan et al.

2016; Liu et al. 2017). Other high degree genes associated with FRC exposure are

presented in Table 4.4, and the high centrality within their modules are suggestive of their

potentially important roles in FRC toxicity. We also identified two genes of high degree

that were completely uncharacterized, NA_732 (Module 13) and NA_146 (Module 2)

(Entrez GeneIDs: 108182865 and 100332468 respectively). Identification of these

uncharacterized genes in our analysis alongside genes that are strongly linked to AHR2

Activator or FRC exposure suggests that they should be investigated in future analyses.

4.4.4. Analysis of Module 13

Module 13 of the network is a critical AHR2 activation module associated with xenobiotic

metabolism pathways. Centered within Module 13 is cyp1a, a well-studied biomarker gene of AHR exposure that is involved in detoxification of xenobiotics. This gene has been previously shown to be highly induced by TCDD and several PAHs (Carney et al. 2004;

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Shankar et al. 2019), has a high degree centrality in our network, and also responds to a large number of treatments (20/33 chemical treatments induce cyp1a). We extracted a subnetwork of the second order network neighborhood for cyp1a (Figure 4.4A), which is defined as any gene connected directly to cyp1a through an edge, or any gene connected to a gene that is connected directly to cyp1a. Within this subnetwork, there were 55 genes

(including cyp1a), and 339 edges. Several of the high degree Module 13 genes (Table 4.3) were present in cyp1a’s first degree network neighborhood including sult6b1, cyp1c1, cyp1c2, gstp1, wfikkn1, foxq1a, ahrra, and slincR (Figure 4.4B). Additionally, clcn2c, fgf7, and tiparp were also part of this group. The second order network neighborhood of cyp1a consisted of ahr2, gstp1, and dhrs13la, and several other genes that have not been previously associated with PAH bioactivity in zebrafish. For genes in this group that have not yet been found to have a role in this response including edn1, plvapa, and thsb1b, and several uncharacterized genes, our analysis suggests that their roles as they relate to PAH exposure should be investigated in future studies.

We next examined the transcriptomic response of the first order network neighborhood genes of cyp1a to each chemical in our dataset. Figure 4.5 shows that while expression of cyp1a was increased with exposure to the AHR2 Activators, the expression of cyp1a’s network neighborhood genes showed some variability across all the chemicals, including the AHR2 Activators. Of note, even though cyp1a was induced by some of the other PAHs that were included in this study, the expression profiles of many of the first order network neighborhood genes were noticeably different from the AHR2 Activators (Figure 4.5).

Additionally, while most of the genes had increased expression with exposure to the AHR2

Activators, one exception was clcn2, which was decreased in its expression. However,

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among the other chemicals, the expression pattern of clcn2 was similar to other genes in

the cyp1a network neighborhood. Genes generally also showed a much stronger increase

in response to the AHR2 Activators compared to their decrease in response to other

chemicals. This was especially true for cyp1a, cyp1c1, and cyp1c2, showing that AHR2

Activators strongly induce increased expression of these genes.

A combination of the presence in cyp1a’s network neighborhood (Figure 4.4B) and the consistent induction of expression across all the AHR2 Activators (Figure 4.5) led us to a subset of genes (fgf7, mamdc2b, pkhd1l1, sult6b1, NA_632, NA_732, NA_145, and

NA_928) that we hypothesized would be closely associated with the AHR2 signaling pathway. To confirm the AHR2 dependence of these genes, we validated their expression upon TCDD exposure in both wildtype and AHR2-null zebrafish using qRT-PCR. We

confirmed significant induction of all genes by TCDD, compared to vehicle-treated 48-hpf wildtype zebrafish (Figure 4.6). Additionally, while cyp1a was significantly induced in

TCDD-treated AHR2-null zebrafish, albeit significantly lower than TCDD-treated wildtype zebrafish, expression of none of the other transcripts changed in TCDD-treated

AHR2-null zebrafish. These results confirm that our network analysis identifies multiple genes in the AHR2 signaling pathway.

4.4.5. Chemical Type Analysis in Networks

We next determined the relative contribution of certain chemical types towards the global functional pathways identified in our network by calculating how tightly network genes remain co-expressed when AHR2 Activator samples or FRC samples were removed from the dataset. This analysis reveals how genes of certain functional pathways are organized based on response to either the AHR2 Activators or the FRCs. When AHR2 Activator data

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was examined, we found several functions whose constituent genes were less tightly co- expressed (based on their average co-expression values) in networks lacking AHR2

Activator samples compared to networks containing them (Table 4.5). This included

negative regulation of vascular development, regeneration, and metabolic processes as well

as carboxylic and oxoacid metabolic processes, and actin and cytoskeletal processes. The

lower co-expression of genes within these functions when AHR2 Activators are removed

indicates that they are particularly important to the transcriptomic response of AHR2

Activators. We also found functions whose constituent genes were more tightly co-

expressed (suggesting lower importance to AHR2 Activators compared to all chemicals).

This included bone, skeletal and cartilage development, as well as eye development.

Functions showed even greater changes in co-expression when examining FRC data (Table

4.5). The functions showing the greatest decrease in co-expression in a network lacking

FRCs included regulation of vascular development and neurogenesis. Interestingly, many

of the functions that showed higher co-expression were also related to neuronal development. These included brain, forebrain, head, and central nervous system development. This observation suggests that while vascular development and neurogenesis generally are pathways responding strongly to FRC exposure, the specific genes and sub- roles within these broad pathways may respond differently to FRC exposure.

4.5. Discussion

The main goal of our study was to identify the potentially important genes and biologically

relevant pathways that were associated with two groups of chemicals, the AHR2 Activators

and the Flame Retardant Chemicals (FRCs), using a comparative network approach. We

took advantage of a large compendium of 48-hpf RNA sequencing data from zebrafish

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exposed to 33 unique chemical treatments to conduct the first meta-analysis of zebrafish

chemical transcriptomic data using gene co-expression networks. Using functional

pathway enrichment paired with the systematic removal of the individual chemical groups

from the network, we identified pathways associated with each of the chemical groups. The

network revealed that the AHR2 Activators were associated with specific xenobiotic

metabolism-related pathways while FRC exposure corresponded to broader, more general

pathways related to perturbation of normal development.

While the genes in our full co-expression network formed into 23 distinct modules, genes

responding to the FRCs or AHR2 Activators were found primarily in only three modules

and a single module, respectively. Furthermore, while there was some overlap in modules

associated with the two subsets of chemicals, most modules were associated with a certain

chemical class suggesting largely distinct and highly specific molecular signaling events

for these two chemical types. Our hierarchical clustering analysis reflected a similar pattern, where there was a strong separation between the FRCs and the rest of the chemicals in the study, but there was no separation between the AHR2 Activators and the remaining

PAHs. Additionally, developmental toxicity phenotypes identified in previous studies were not a driver of our clustering. While high throughput screening for developmental toxicity is the necessary first step for determining perturbation to development (Bugel et al. 2014),

the toxicology field is now moving towards leveraging these data for mechanistic studies

to reveal chemical modes of action. Thus, the network analysis here, showing that chemical

types induce very specific transcriptional responses, provides a platform to characterize

gene expression changes associated with a diverse group of chemicals.

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For our network analysis, we collected RNA sequencing datasets that had been previously published, and combined them with data gathered in this study. The resulting larger dataset has the advantage of examining gene expression changes associated with multiple chemicals at the same time point (48 hpf), and all at phenotypically anchored exposure concentrations. Such a dataset has never been analyzed before in zebrafish, allowing new conclusions to be gained here that describe transcriptomic response to chemicals. In addition to the strength of the dataset itself, we apply co-expression network analysis, an approach that is well designed for such a compendium of data. Network analyses can identify responses that lie outside the detection limit of more traditional pairwise comparisons of control and treatment conditions, and can also integrate data from a number of different studies and research groups (Faith et al. 2007; McClure et al. 2016). The variations in data collected across studies is an advantage for the network analysis we apply here as it represents additional biologically relevant variations in gene expression that can be used to infer edges between genes, leading to a more robust and accurate network. The combined strengths in this study (use of a comprehensive but coordinated dataset, and network analysis to move beyond traditional transcriptomic analysis) means that we are able to ask and answer questions that could not be queried in previous studies related to pathways responding to specific chemical treatments.

Despite the similar number of datasets included in the network analysis for each chemical subgroup, we found that FRC responses drove the majority of the network structure, while the AHR2 Activator gene expression changes were more restricted in the network. Both functional enrichment of FRC-responsive modules, as well as analysis of functions shifting their co-expression after removal of FRC datasets show that the FRCs influence functions

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related to developmental, neurological, and signaling and transport pathways. The analysis removing the FRCs from the network identified regulation of vascular development as being particularly important to this group of chemicals. Vascular developmental abnormalities and cardiac arrhythmia have been investigated in zebrafish exposed to only few FRCs (Lema et al. 2007; Mitchell et al. 2018; Xing et al. 2018); but our results highlight it as a critical organ system that should be more thoroughly investigated when studying FRC toxicity. Our conclusions also corroborate evidence from a number of previous studies demonstrating zebrafish FRC neurobehavioral toxicity upon exposure to multiple structurally diverse chemicals, including individual organophosphate FRCs

(Dishaw et al. 2014; Sun et al. 2016), DE-71 (a PBDE) (Chen et al. 2012), and TBBPA

(Chen et al. 2016). Additionally, all FRCs included in this network analysis were previously determined to induce both 24-hpf and 120-hpf behavior effects in zebrafish, albeit at varying degrees (Truong et al. 2020). Thus, our network analysis not only confirmed observations from previous studies, but also provides guidance for future studies investigating FRC developmental toxicity. We recognize that even though the FRCs as a group lit up distinct parts of the network compared to the rest of the chemicals in the study, the structurally distinct chemicals within the FRCs are likely to have some unique modes of toxicity that were not captured here. Our analysis contained only a limited number of

FRCs from each structural class (for example, there were two brominated phenols, TBBPA and TBBPA-DBPE), and future work should consider incorporating more FRC transcriptomic data, and using this increased FRC dataset to view how different types of

FRCs induce gene modules or functional pathways. Our understanding of the mechanisms by which the structurally diverse FRCs cause developmental toxicity is still in its infancy.

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This is likely because several FRCs have been shown to interact with multiple nuclear receptor signaling pathways to cause gene expression changes and developmental toxicity in zebrafish (Dasgupta et al. 2019; Liu et al. 2013). Thus, our discovery of some of the high centrality genes in our network such as kremen1, cacna1da, and tfe3a within Module 2

(highly associated with the FRCs), emphasizes their potentially important role in driving

FRC transcriptomic responses. Tfe3 is a transcription factor, and its high co-expression with genes associated with FRCs suggests that it might be involved in regulation of the genes within this module. The identification of the other high centrality genes present in the FRC modules (Table 4.4) should be investigated in future research to understand their roles in the pathways enriched by their modules.

Module 13, whose genes were enriched for xenobiotic metabolism by cytochrome P450, had a prominent role in our network analysis. Although Module 13 was a small module, it was very tightly co-expressed, and contained a large number of high degree genes. This is in line with our previous studies showing that high degree genes are those that are heavily involved in a small number of specific pathways (McClure et al. 2018). This is in contrast to high betweenness genes which are often associated with several pathways but not strongly with any one pathway, in agreement with their general position linking multiple larger groups of genes. Previous studies in centrality have found both degree and betweenness to be approximately equal in their identification of genes of importance

(McDermott et al. 2009). However, those studies looked at a broader range of conditions than we do here, where we infer a network very focused on chemical response. This may suggest that in more focused networks inferred from very similar datasets using degree as the primary centrality measure may be advantageous, though further studies are needed to

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determine if this is an effect of network structure generally or of the network presented

here in particular. However, it does suggest that for this specific network when identifying

genes that respond to certain chemicals of interest degree is a better metric of importance

than betweenness.

Module 13 was primarily made up of genes responding to the AHR2 Activators, which

consisted of TCDD and several PAHs, and at the center of this module was the cyp1a gene,

a widely use biomarker for PAH exposure in several organisms (Kim et al. 2013b; Payne

et al. 1987). We found several AHR signaling pathway genes such as ahrra and ahrrb

(Jenny et al. 2009), foxq1a (Planchart et al. 2010), and slincR (Garcia et al. 2017) within

Module 13 that were tightly co-expressed with cyp1a. More interestingly, our network

analysis also identified within Module 13 other annotated and non-annotated genes,

including novel long non-coding RNAs, that have not been previously associated with

AHR2. We hypothesized that they too were involved in the AHR signaling pathway. Our

in silico network analysis conclusions are corroborated through our RT-qPCR analysis

demonstrating that fgf7, mamdc2b, pkhd1l1, and sult6b1 expression is dependent on the

presence of AHR2. Importantly, several novel genes, NA_632, NA_732, NA_145, and

NA_928 (Entrez GeneIDs of 103910027, 108182865, 100332446 and 407643

respectively) were also associated with the AHR2 signaling pathway (Figure 4.6). NA_732 and NA_928 are long non-coding RNAs, while NA_632 and NA_145 are protein coding genes. Despite that fact that the functions of these genes are unknown, their presence in

Module 13 linked to cyp1a strongly suggest that they are important to AHR2 signaling.

Future functional studies investigating the roles of these novel AHR2-related genes will

help strengthen our mechanistic understanding of AHR2 Activator toxicity.

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4.6. Conclusions

Overall, the methods used in this study combined the strength of using an extensive

transcriptomic dataset with a gene co-expression analysis approach to measure how a

diverse group of chemicals altered the 48-hpf zebrafish transcriptome relative to each other.

We found that the FRCs and AHR2 Activators localized to distinct regions of the network

we created, highlighting very specific transcriptomic responses to each chemical group.

Additionally, FRCs induced a broad response related to neurobehavior, ion signaling, and

vascular development, while the AHR2 Activators centered in one module related to

chemical stress and metabolism-related responses. Guided by our network, we also

discovered novel genes associated with the AHR2 signaling pathway. Overall, while the

FRCs and AHR2 Activators have chemical-specific gene expression changes, we also

identified several candidate biomarker genes that future studies should focus on to gain a

better understanding of the toxicity of these two chemical groups. While the toxicology

field has thus far focused on understanding phenotypic responses associated with chemical

exposure, we are now transitioning to unraveling the mechanisms of chemical hazard,

which will enable more in-depth characterization of chemicals. This study provides

transcriptomic biomarkers that could be used in the future for exposure determination and

mixture component diagnosis as they relate to mode-of-action based risk assessment of

PAHs. Additionally, the transcriptomic network that we created can be used as a resource for future studies investigating mechanisms of toxicity in developing zebrafish.

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Figure 4.1. Heatmap of 48-hpf zebrafish transcriptomic response to ten Flame Retardant Chemical (FRC), 22 Polycyclic Aromatic Hydrocarbon (PAH), and 1 TCDD treatment. Average log2 fold change (log2FC) values for each chemical treatment (y-axis) are shown, with yellow indicating higher expression and blue indicating lower expression compared to each chemical’s DMSO control. Both genes and treatments are grouped via hierarchical clustering with clusters of treatments (Clusters 1-6) indicated by colors on the left.

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Module 2: Transport

Module 4: Regulation of transcription

Module 1: Neuronal Module 13: development Response to xenobiotics and drugs

Figure 4.2. Gene co-expression network of 48-hpf zebrafish transcriptomic response to chemicals. Small colored circles represent zebrafish genes (nodes), and lines (edges) connecting the genes represent instances of high co-expression. Nodes are colored by the module they belong to with processes highly enriched in four example modules indicated.

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A FRCs B AHR2 Activators

6 2

Figure 4.3. Network response to FRCs and AHR2 Activators. (A) Nodes are colored and sized by the number of FRCs they respond to (defined as an FDR p-value < 0.05 when comparing chemical to DMSO control, no fold-change cutoff). Larger green nodes respond to a more chemicals, smaller brown nodes respond to fewer chemicals. General location of modules highly associated with FRCs (Modules 1, 2, 6, 7, and 10) are indicated. (B) Same as (A) but showing response to AHR2 Activators. Location of Module 13, highly associated with the AHR2 Activators, is indicated.

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A B

Figure 4.4. Network neighborhood of cyp1a. (A) The full network is shown with each blue circle (node) representing a zebrafish gene (2141 nodes are shown which represent the main network), and each line representing an edge (4373 edges are shown which represent the main network). The dark blue node represents cyp1a. (B) A subset network showing the network neighborhood of cyp1a. The cyp1a gene is in dark blue, nodes in blue represent those in the first-degree network neighborhood (directly linked to cyp1a through an edge). Nodes in light blue represent those in the second-degree network neighborhood (linked to cyp1a through one additional gene within the first network neighborhood).

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Figure 4.5. Heatmap of chemical response of genes in the first-degree network neighborhood of cyp1a. Genes are shown on the right (y-axis) and chemicals below (x-axis). Both genes and chemicals are clustered by similarity of response. Yellow indicates higher expression of each gene in chemical treatment compared to respective DMSO control, blue indicates lower expression in chemical treatment compared to respective DMSO control.

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cyp1a fgf7 mamdc2b 10 * 8 4 DMSO DMSO DMSO TCDD 6 TCDD TCDD * 3 *

5 * 4

(ddCq) 2 (ddCq) (ddCq) 2 2 2 2 log log log 1 0 * 0

-2 0 Wild type AHR2-null Wild type AHR2-null Wild type AHR2-null

pkhd1l1 sult6b1 NA_632

3 4 5 DMSO DMSO DMSO 3 4 * TCDD * TCDD * TCDD 2 2 3 1

(ddCq) 2 2 (ddCq) (ddCq) 1 0 2 2

-1 log 1 log log 0 -2 0 -3 -1 -1 Wild type AHR2-null Wild type AHR2-null Wild type AHR2-null

NA_732 NA_145 NA_928 4 2 2.0 DMSO * DMSO * DMSO 3 * TCDD TCDD 1.5 TCDD 1 2 1.0 (ddCq) (ddCq) 1 0 (ddCq) 0.5 2 2 2 log log 0 log 0.0 -1 -1 -0.5

-2 -2 -1.0 Wild type AHR2-null Wild type AHR2-null Wild type AHR2-null

Figure 4.6. Validation of cyp1a network neighborhood genes identified from the network analysis. Comparative gene expression of cyp1a and selected cyp1a network neighborhood genes in 48-hpf Wildtype and AHR2 mutant zebrafish developmentally exposed to 0.1% DMSO or 1 ng/mL TCDD (n = 3-4 biological replicates). Beta-actin was used as the normalization control. Error bars indicate SD of the mean. * = p<0.05 compared to the Wild type vehicle control (DMSO). Statistical significance was determined using a Kruskal Wallis rank sum test followed by a Dunnett’s test for data that were not normal (NA_732), or a two-way ANOVA followed by a Tukey test for data that were normal (all other genes).

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Table 4.1. Top GO term enrichment of genes in the 12 largest modules of the full gene co-expression network

ModuleID # of Nodes Figure 2 Color Enriched Function(s) nervous system development anatomical structure development 1 184 multicellular organism development central nervous system development metal ion transmembrane transporter activity calcium channel complex 2 425 cation transmembrane transporter activity ion transmembrane transporter activity ATP synthesis coupled proton transport 3 93 ATP biosynthetic process The citric acid (TCA) cycle and respiratory electron transport DNA-binding transcription factor activity 4 297 DNA-binding transcription factor activity, RNA polymerase II-specific regulation of transcription, DNA-templated 5 108 nervous system development 6 102 sodium channel complex 7 135 regulation of non-motile cilium assembly 9 115 intracellular membrane-bounded organelle 10 61 axon development 12 109 c-Myc:Max; motif: GCCAYGYGSN response to xenobiotic stimulus metabolism of xenobiotics by cytochrome P450 13 93 response to chemical drug metabolism - other enzymes 14 48 Translation

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Table 4.2. Top 20 genes with highest betweenness centrality in network

Gene Function Betweenness Degree Indegree1 Outdegree2 ModuleID apc APC regulator of WNT signaling pathway 0.27773144 9 3 6 1 ubl3b ubiquitin-like 3b 0.24156245 7 2 5 1 mapk 7 mitogen-activated protein kinase 7 0.2232999 5 4 1 1 gata3 GATA binding protein 3 0.18845384 8 3 5 1 tfap2a transcription factor AP-2 alpha 0.07893617 11 4 7 1 k dm5ba lysine (K)-specific demethylase 5Ba 0.07449596 3 1 2 1 sf3b6 splicing factor 3b, subunit 6 0.0737653 3 2 1 1 pfk lb phosphofructokinase, liver b 0.07243628 5 1 4 1 k remen1 kringle containing transmembrane protein 1 0.22835862 14 4 10 2 cacna1da calcium channel, voltage-dependent, L type, alpha 1D subunit, a 0.10295178 30 5 25 2 tfe3a transcription factor binding to IGHM enhancer 3a 0.07136245 30 11 19 2 rusc1 RUN and SH3 domain containing 1 0.06976945 6 2 4 3 ppp2r3a protein phosphatase 2, regulatory subunit B'', alpha 0.25738053 4 2 2 4 bhlhe23 basic helix-loop-helix family, member e23 0.14505353 19 8 11 4 fosl2 fos-like antigen 2 0.10537935 18 7 11 4 sgsm1a small G protein signaling modulator 1a 0.07037308 11 5 6 7 ccdc43 coiled-coil domain containing 43 0.09757468 3 2 1 9 dimt1l DIM1 dimethyladenosine transferase 1-like (S. cerevisiae) 0.08821623 5 3 2 9 g3bp1 GTPase activating protein (SH3 domain) binding protein 1 0.07623317 7 4 3 11 prr12a proline rich 12a 0.09610248 7 7 0 12 1 Number of edges emanating to the gene 2 Number of edges emanating from the gene

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Table 4.3. Top 20 genes with highest degree centrality in network.

Gene Function Betweenness Degree Indegree1 Outdegree2 ModuleID cyp1a cytochrome P450, family 1, subfamily A 0.00235481 41 17 24 13 sult6b1 sulfotransferase family, cytosolic, 6b, member 1 0.01272304 41 12 29 13 cyp1c1 cytochrome P450, family 1, subfamily C, polypeptide 1 0.00466897 39 17 22 13 cyp1c2 cytochrome P450, family 1, subfamily C, polypeptide 2 0.00712917 37 19 18 13 gng13b guanine nucleotide binding protein (G protein), gamma 13b 0.03415074 37 12 25 4 gstp1 glutathione S-transferase pi 1 0.06511838 34 14 20 13 wfikkn1 WAP, follistatin/kazal, immunoglobulin, kunitz and netrin domain containing 1 0.00465263 34 16 18 13 ahrra aryl-hydrocarbon receptor repressor a 0.0040985 33 17 16 13 foxq1a forkhead box Q1a 0.00479027 33 13 20 13 mxd1 MAX dimerization protein 1 0.03254926 32 9 23 2 slincR None 8.00E-04 32 15 17 13 zgc.158689 None 0.01270125 31 13 18 2 cacna1da calcium channel, voltage-dependent, L type, alpha 1D subunit, a 0.10295178 30 5 25 2 osbpl2a oxysterol binding protein-like 2a 0.01197251 30 8 22 2 srgap3 SLIT-ROBO Rho GTPase activating protein 3 0.01114073 30 10 20 2 tfe3a transcription factor binding to IGHM enhancer 3a 0.07136245 30 11 19 2 cyb5a cytochrome b5 type A (microsomal) 0.00566261 28 15 13 13 pk hd1l1 polycystic kidney and hepatic disease 1 (autosomal recessive)-like 1 0.04172331 28 12 16 13 NA_732 None 7.84E-04 27 13 14 13 NA_146 None 0.01266327 26 5 21 2 1 Number of edges emanating to the gene 2 Number of edges emanating from the gene

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Table 4.4. Genes with the highest degrees from the modules associated with FRC exposure (Modules 2, 7, 10, and 22)

Module Degree Gene Name Known function in zebrafish from previous studies Reference 32 mxd1 Max Dimerization Protein 1 unknown 31 zgc.158689 unidentified unknown 30 osbpl2a oxysterol binding protein-like 2a unknown 30 srgap3 Slit-Robo GTPase activating protein 3 role in neurodevelopmental processes (Bacon et al. 2013) 2 Transcription factor-binding to IGHM Part of the MiT family coding for basic-helix-loop-helix/leucine 30 tfe3a (Lister et al. 2011) enhancer 3a zipper class transcription factors calcium channel-related gene whose expression was shown to calcium channel, voltage-dependent, L (Duan et al. 2016, Liu et al. 30 cacna1da be altered by triadimefon, a broad-spectrum fungicide, and silica type, alpha 1D subunit, a 2017) nanoparticle exposures to embryonic zebrafish 18 NA_1170 uncharacterized 12 purab Purine-rich element-binding protein Ab unknown 7 11 sgsm1a small G protein signaling modulator 1a associated with small G protein-mediated signal transduction (Yang et al. 2007) part of the Kindlin or Fermitin family of scaffold proteins 10 fermt2 important for signaling across membrane-spanning integrin Karaköse et al. 2010) fermitin family member 2 adhesion receptors APC regulator of WNT signaling in the family of genes coding for a regulator of the WNT signaling 10 apc2 (Haramis et al. 2006) cadherin, EGF pathwayLAG seven 2 pass G type pathway 10 10 celsr3 receptor 3 plays a role in the facial motor neuron migration in zebrafish (Wada et al. 2006) 9 adam22 ADAM metallopeptidase domain 22 unknown 9 NA_108 unidentified unknown

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Table 4.5. Removal of AHR2 Activators or FRCs from the network, and comparison of co-expression values of each pathway listed relative to networks lacking random datasets. The higher (yellow arrows) or lower (blue arrows) co-expression of genes within each of the listed functional enrichments in a network lacking AHR2 Activators (column 1) or FRCs (column 2) compared to a network lacking the same number of random chemicals as either the AHR2 Activators or FRCs, respectively.

AHR2 Activators FRCs Functions Positively Co-Expressed in Both AHR2 Activators and FRCs 1.013 1.031 developmental growth 1.067 1.078 positive regulation of nervous system development 1.072 1.079 positive regulation of neurogenesis 1.078 1.053 negative regulation of multicellular organismal process 1.089 1.030 positive regulation of cell differentiation 1.094 1.399 regulation of vasculature development 1.105 1.113 negative regulation of developmental process 1.357 1.246 negative regulation of vasculature development

AHR2 Activators FRCs Functions Positively Co-Expressed only in AHR2 Activators

1.028 0.685 head development 1.040 0.664 brain development 1.053 0.724 central nervous system development 1.066 0.608 forebrain development 1.073 0.733 eye morphogenesis 1.086 0.952 dorsal ventral pattern formation 1.087 0.822 regulation of transcription by RNA polymerase II 1.092 0.959 positive regulation of developmental process 1.095 0.806 actin filament based process 1.095 0.807 actin cytoskeleton organization 1.129 0.693 oxoacid metabolic process 1.130 0.691 carboxylic acid metabolic process 1.173 0.924 regeneration

AHR2 Activators FRCs Functions Positively Co-Expressed only in FRCs

0.939 1.097 mesenchymal cell differentiation 0.950 1.029 neuron projection guidance 0.963 1.038 axon guidance

AHR2 Activators FRCs Functions Negatively Co-Expressed in Both AHR2 Activators and FRCs

0.866 0.685 endochondral bone morphogenesis 0.899 0.672 cartilage development involved in endochondral bone morphogenesis 0.899 0.672 cartilage development bone morphogenesis 0.908 0.881 retina development in camera type eye 0.942 0.810 skeletal system development 0.955 0.931 response to chemical 0.974 0.847 embryonic organ morphogenesis 0.993 0.896 embryonic morphogenesis 0.997 0.983 camera type eye development

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CHAPTER 5 – THE ARYL HYDROCARBON RECEPTOR 2- DEPENDENT WFIKKN1 GENE INFLUENCES ZEBRAFISH BEHAVIORAL PHENOTYPES AND DEVELOPMENTAL GENE AND PROTEIN EXPRESSION PROFILES

Prarthana Shankar‡1, Gloria R. Garcia1,2, Jane K. LaDu1, Christopher M. Sullivan1, Cheryl

L. Dunham1, Britton Goodale1,3, Stanislau Stanisheuski4, Claudia S. Maier4, Preethi

Thunga5, David M. Reif5, Robyn L. Tanguay1*

1 Department of Environmental and Molecular Toxicology, Oregon State University,

Corvallis, OR 97331, USA .

2 Laboratory of Receptor Biology and Gene Expression, National Cancer Institute,

National Institutes of Health, Bethesda, Maryland 20892, USA.

3 Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth

College, Lebanon, New Hampshire 03766, USA.

4 Department of Chemistry, Oregon State University, Corvallis, OR 97331, USA.

5 Bioinformatics Research Center, Department of Biological Sciences, North Carolina

State University, Raleigh, NC 27695, USA.

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5.1. Abstract

The aryl hydrocarbon receptor (AHR) is required for vertebrate development and is also

activated by exogenous chemicals including polycyclic aromatic hydrocarbons (PAHs) and

TCDD. AHR activation is well-understood, but the roles of downstream molecular signaling events are largely unknown. From previous transcriptomic experiments in 48- hours post fertilization (hpf) zebrafish, we found wfikkn1 (WAP, kazal, immunoglobulin, kunitz and NTR domain-containing protein) was highly co-expressed with cyp1a when zebrafish were exposed to several PAHs and TCDD. Thus, we hypothesized wfikkn1’s critical role in AHR signaling. Wfikkn1 was expressed in developing zebrafish from 2.5 to

120 hpf, and in the absence of AHR2 (zebrafish ortholog of human AHR) and upon TCDD exposure, its expression was significantly lower than in wildtype zebrafish at 48 and 120 hpf, demonstrating AHR2-dependence. To functionally characterize wfikkn1, we made a

CRISPR-Cas9 mutant line with a 16bp deletion in wfikkn1’s exon that produced a predicted truncated protein. To identify genes and pathways associated with wfikkn1, wildtype and mutant zebrafish were exposed to DMSO or TCDD, and we conducted whole-animal RNA sequencing at 48 hpf. We identified over 700 genes differentially expressed (P<0.05, log2FC>1) between each pair of treatment combinations, suggesting both an important role

for wfikkn1 in altering the 48-hpf transcriptome, and TCDD-induced gene expression changes. Mass spectrometry-based proteomics of 48-hpf wildtype and mutant zebrafish revealed several significant differentially expressed proteins. Functional enrichment of transcriptomic and proteomic data, in combination with significant neurobehavioral disruptions demonstrated Wfikkn1’s potential role in skeletal muscle development processes, and in the presence of TCDD, a role in neurological pathways and potential

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crosstalk between AHR and Estrogen Receptor signaling. In conclusion, we have begun to

characterize a novel AHR-regulated gene, wfikkn1, that is likely a necessary functional

member of the AHR signaling cascade.

5.2. Introduction

The Aryl Hydrocarbon Receptors (AHRs) are ligand-dependent transcription factors of the

basic-helix-loop-helix (bHLH) superfamily of transcription factors (Kewley et al. 2004;

Schmidt et al. 1996). The AHRs are well-known for their ability to interact with several

xenobiotic chemicals, particularly the polycyclic aromatic hydrocarbons (PAHs) (Billiard

et al. 2006), polychlorinated biphenyls (PCBs) (Kafafi et al. 1993), and dioxins such as

2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) (Mimura et al. 2003). Both AHR knockout

mice and rats (Fernandez-Salguero et al. 1996; Harrill et al. 2016), as well as mice that

have mutations in either the nuclear localization or DNA binding domains of the AHR

(Bunger et al. 2008; Bunger et al. 2003) are resistant to TCDD-induced toxicity. In addition to AHR’s role in mediating xenobiotic effects, this receptor has been implicated in a number of important endogenous roles (Barouki et al. 2007). In rodents, the AHR is crucial during development, and plays a role in several physiological functions such as overall growth rate, blood pressure regulation, and immune, hepatic, and urinary systems (Larigot et al. 2018).

Upon activation by its ligands, the AHR, which resides in the cytoplasm with its chaperones, HSP90, AHR-interacting protein (AIP/XAP2/Ara9), and p23, translocates to the nucleus and forms a heterodimer with the aryl hydrocarbon nuclear translocator

(ARNT) (Hoffman et al. 1991). The AHR/ARNT heterodimer recognizes aryl hydrocarbon response elements (AHREs) in the promoter regions of downstream target genes such as

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the cytochrome P450 family of genes (CYPs) and the aryl hydrocarbon receptor repressor

(AHRR) (Fujii-Kuriyama et al. 2005; Mimura et al. 1999; Watson et al. 1992). While the

CYP1A and AHRR proteins are highly inducible by several AHR ligands, CYP1A is widely recognized as being critical for either metabolic activation or deactivation of chemicals (Ma et al. 2007), and AHRR has important roles in inflammation and tumorigenesis (Vogel et al. 2017). Evidence demonstrates that the AHR’s role as a master transcriptional regulator is critical during both development and upon exposure to xenobiotics. However, we understand the specific functions of only a limited number of genes within the AHR signaling cascade.

Zebrafish is an invaluable model in the fields of molecular toxicology, developmental biology, and neurobehavior (Bailey et al. 2013; Bambino et al. 2017; Bugel et al. 2014).

They have high fecundity and rapid ex-utero development, and are highly sensitive to chemical exposures which are ideal traits for high-throughput chemical toxicity screening in a whole animal (Hill et al. 2005). Zebrafish and mammals have highly conserved physiological and genetic similarities, with 76% of human genes having at least one zebrafish ortholog (Howe et al. 2013). Thus, zebrafish are routinely used to unravel complex molecular pathways, and have become an important platform for investigation of the AHR signaling pathway. Developmental toxicity of several PAHs is differentially dependent on zebrafish AHRs (Incardona et al. 2006; Incardona et al. 2011) of which there are three orthologs: AHR1a, AHR1b, and AHR2, with AHR2 being the most responsive to xenobiotic chemicals such as TCDD (Shankar et al. 2020). Similar to rodent models, AHR2 knock-out zebrafish demonstrated the receptor’s requirement for TCDD toxicity (Garcia et al. 2018a; Goodale et al. 2012). AHR2 is also required for normal reproductive,

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neurobehavioral, and skeletal development in zebrafish (Garcia et al. 2018a). These studies

emphasize the prominent role of AHR2 in mediating crucial developmental and

toxicological processes.

Zebrafish have been utilized to elucidate the transcriptional events that occur downstream

of AHR activation. For example, the AHR2-dependent gene, cox2b was discovered to play a role in the “pericardiac edema” caused by TCDD exposure in developing zebrafish

(Teraoka et al. 2014). Similarly, sox9b is one of the most downregulated transcripts upon

TCDD exposure, and has been associated with TCDD-induced cardiac toxicity and craniofacial malformations (Hofsteen et al. 2013; Xiong et al. 2008). Declining cost and widening availability of high throughput RNA sequencing has made it a popular discovery tool for transcriptomic changes from chemical exposure. Developing zebrafish exposed to

AHR2 activators exhibited several ligand-dependent transcriptional changes (Goodale et al. 2015; Shankar et al. 2019), and multiple transcriptional factors are predicted to regulate expression of genes associated with AHR2 activation (Garland et al. 2020; Goodale et al.

2015), emphasizing the intricate crosstalk between pathways following chemical exposure.

Our lab has published multiple transcriptomic studies in developmental zebrafish exposed to various chemicals. Several of these studies identified wfikkn1 (WAP, Follistatin/Kazal,

Immunoglobulin, Kunitz And Netrin Domain Containing 1) as highly induced in 48-hpf zebrafish exposed to individual PAHs and TCDD (Garcia et al. 2018b; Goodale et al. 2015;

Goodale et al. 2013; Shankar et al. 2019). Morpholino knockdown of AHR2 led to a significant decrease in wfikkn1 expression when zebrafish were developmentally exposed to the oxyPAH, benz(a)anthracene-7,12-dione (Goodale et al. 2015). High induction of

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wfikkn1 in developing zebrafish by structurally diverse PAHs and TCDD suggested role

for wfikkn1 in their toxicity, a hypothesis further supported by the downregulation of

wfikkn1 upon AHR2 knockdown. In the current study, we leveraged a combination of

CRISPR-Cas9-generated wfikkn1 mutant zebrafish, whole-animal transcriptomic and

proteomic analyses, and sensitive neurobehavioral assays to investigate the functional role

of wfikkn1 in both normal and TCDD-exposed zebrafish.

5.3. Materials and Methods

5.3.1. Fish husbandry

Tropical 5D zebrafish were housed at the Sinnhuber Aquatic Research Laboratory (SARL) at Oregon State University (Corvallis, OR, USA), and maintained according to Institutional

Animal Care and Use Committee protocols (ACUP 5143). Adult fish were raised in recirculating filtered water supplemented with Instant Ocean salts in densities of ~500

fish/50-gallon tank at 28 ᵒC under a 14/10 hour light/dark cycle. Adult zebrafish were fed

GEMMA Micro 300 or 500 (Skretting, Inc., Fontaine Les Vervins, France) twice a day and

larval and juvenile zebrafish were fed GEMMA Micro 75 and 150 respectively three times

a day (Barton et al. 2016). Spawning funnels were placed in the tanks the night prior, and

the following morning, embryos were collected, staged, and maintained in embryo medium

(EM) in a 28 ᵒC incubator (Westerfield 2007). EM consisted of 15 mM NaCl, 0.5 mM KCl,

1 mM MgSO4, 0.15 mM KH2PO4, 0.05 mM Na2HPO4, and 0.7 mM NaHCO3 (Mandrell et

al. 2012).

5.3.2. Waterborne TCDD exposure

Depending on the experiment, tropical 5D wildtype zebrafish, wfikkn1 mutant zebrafish

(described above), or AHR2 mutant zebrafish (ahr2osu1) (Garcia et al. 2018a) were exposed

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to TCDD. Shield-stage embryos (6 hpf) were exposed to 0.1 % dimethyl sulfoxide (DMSO)

(vehicle control) and TCDD at 50 pg/mL (juvenile and adult studies) or 1ng/mL (for all

other analyses) with gentle rocking for 1 hour. 250 mL (juvenile and adult studies) or 20

mL (all other analyses) glass vials with 10 embryos/mL were used for the exposures. The

50 pg/mL TCDD concentration was previously shown to have no significant effect on

mortality of zebrafish exposed at 4 hpf, with little impact on skeletal growth and deformity

(Baker et al. 2013). The 1 ng/mL TCDD concentration has been used in previous studies

as it results in 99-100% of 120-hpf zebrafish developing overt TCDD-induced malformations (Garcia et al. 2018b) (Henry et al. 1997). During the exposures, the vials were inverted every 15 minutes to ensure proper mixing and even exposure. After the 1 hour, embryos were rinsed three times with EM and raised in 100-mm Petri dishes at 28

ᵒC with up to 100 embryos in 50 mL of EM until collection at 48 hpf (RT-qPCR and RNA sequencing) or 120 hpf (juvenile and adult studies). From the exposures for juvenile and adult studies, a subset of the embryos (n = 96/treatment) were placed in round bottom 96-

well plates (Falcon®, product number: 353227) with one embryo per well pre-filled with

100 µL EM for assessment of Larval Photomotor Response (described below).

5.3.3. Creation and generation of a wfikkn1 mutant zebrafish line

Design, assembly, and preparation of sgRNA

Two single guide RNA (sgRNA) targets for the wfikkn1 gene were generated using the

http://www.crisprscan.org website (Moreno-Mateos et al. 2015). This program finds 23bp

target sequences, including the protospacer adjacent motif (PAM) site, with predicted high

activity scores, number of off targets, and 5’ end of sequence beginning with GG that is

required for efficient T7 transcription. The wfikkn1 gene has a single exon, and we selected

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two sgRNAs targeting regions as close as possible to the 5’ end of the gene to maximize

the potential for frameshift mutations resulting in an early premature stop codon.

To assemble the sgRNAs, we used the cloning-free method described previously

(Varshney et al. 2016) , with slight modifications. Briefly, gene specific oligo templates consisted of an 18bp T7 promoter sequence, a 20bp gene specific sequence (without the

PAM) and a 15bp overlap sequence to the constant tracrRNA (trans-activating crispr RNA)

which also is an 80bp sequence required for CAS9 recognition. Additionally, we used

universal forward and reverse primers matching the 5’ ends of the gene-specific and

constant oligos, respectively (Garcia et al. 2018a). sgRNA templates and primers for guide assembly were purchased from IDT (Coralville, IA), and their sequences are in

Supplementary Table C.1. Amplification of the template was performed using the KOD

Hot Start DNA Polymerase Kit (Millipore Sigma) according to the manufacturer’s

recommendation. 0.2 μM each of the gene-specific oligo, forward, and reverse universal

primers, and 0.008 μM of the constant oligo were added to the reaction and PCR was

performed under the following conditions: denaturation at 95 °C for 2 min, followed by 35

cycles of amplification (95 °C for 20 sec, 62 °C for 10 sec, 72 °C for 10 sec), and a final

extension at 72 °C for 5 min. 2ul of PCR template was used as input for in vitro

transcription of the sgRNAs with the HiScribe™ T7 Quick High Yield RNA Synthesis Kit

(New England Biolabs) according to manufacturer’s instructions. We included the optional

DNase treatment. The sgRNAs were precipitated using ethanol/sodium acetate (pH 5.2),

quality was checked on a 1.2% agarose gel, and RNA quantity was determined using a

BioTek SynergyMix microplate reader with the Gen5 Take3 module.

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Embryo injections

The Tropical 5D wildtype strain was used to create the wfikkn1 mutant line.

Microinjections of pooled sgRNAs of six different genes were conducted, with each gene

target located on a different chromosome to avoid large genomic deletion events (Shah et

al. 2016). Briefly, a multiplex injection mix of pooled guides and CAS9 protein was

calculated to deliver approximately 150-200pg of each sgRNA (diluted to 1000ng/µL) and

225-300pg CAS9 protein in a 1.5-2nl injection volume in to the yolk of a 0-1 cell embryo.

Purified Cas9 protein with a nuclear localization signal derived from SV40 Large T Ag

was purchased (Clonetech; Mountain View, CA).

Founder screening

Injected embryos were raised to maturity and screened for heritable mutations by pair

crossing potential founder zebrafish to wildtype 5D fish. Screening was performed using

fluorescent PCR and melt curve analysis (see below). DNA was extracted from eight

individual embryos per mating pair, and eight wildtype zebrafish embryos were used as

controls. Embryos (4–5 dpf) were plated in 96-well PCR plates with one embryo per well, euthanized on ice, and excess water removed from the wells. Embryos were then lysed by incubating in 20 μl of 50 mM NaOH, heated at 95°C for 10 minutes, and vortexed for 10-

15 seconds to dissociate remaining tissue. 4 μl of 1 M Tris-HCl (pH 8.0) neutralization

solution was added followed by addition of 100μl of ultrapure water to dilute out PCR

inhibitors, then spun down at 4680 rpm for 10 minutes to pellet debris.

RT-qPCR and melt curve analysis/sequencing

The 20 μl PCR reactions for melt curve analysis consisted of 10 μl 2X SYBR Green Master

Mix (Applied Biosystems; Foster City, CA), 0.4 μl each of 10 μM forward and reverse

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primers, and 2 μl of diluted DNA. The primers were designed to be at least 15 bp away

from the predicted Cas9 cut site (-3 bp from PAM site), and to produce 70 to 90 bp products. “High-melt primer” sequences are in Supplementary Table C.1. The reaction was performed using a StepOnePlus Real-Time PCR System (Applied Biosystems; Foster

City, CA) under the following conditions: denaturation and activation of SYBR polymerase at 95 °C for 5 min, followed by 40 cycles of amplification (95 °C for 15 sec,

58 °C for 1 min). Melt curve analysis was conducted with a ramp rate of +0.2 (95 °C for 2 min, 70 °C for 2 min, 95 °C for 2 min). Founders were identified by a shift in the melt-

curve peak in their offspring compared to the wildtype controls. To determine if the

identified founders produce frameshift mutations, we amplified a region 200–400bp up and

downstream of the sgRNA target site and sequenced the amplicon.. The 25 μl PCR reaction

had a 0.2 μM final concentration of forward and reverse primers (See Supplementary

Table C.1 for “sequencing primer” sequences), 2 μl of diluted DNA, and KOD Hot Start

DNA Polymerase (Millipore Sigma) according to the manufacturer’s instructions. PCR

conditions were as follows: denaturation at 95 °C for 2 min, 35 cycles of amplification

(95 °C for 20 sec, 62 °C for 10 sec, 72 °C for 10 sec), followed by an extension at 72 °C

for 5 min. The Core Facilities of the Center for Genome Research and Biocomputing at

Oregon State University performed Sanger sequencing using an ABI 3730 capillary

sequence machine. Sequencing results for mixed base reads in heterozygous offspring of

potential founders at the mutation site were interpreted manually and confirmed using

online tools for characterizing indel mutations

(https://tide.nki.nl/ and http://yosttools.genetics.utah.edu/PolyPeakParser/).

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5.3.4. RNA Sequencing Analyses

RNA extraction

For developmental expression of wfikkn1, total RNA was extracted from pooled groups of

15 wildtype embryos at 2.5, 4, 12, and 24 hpf, and 11 wildtype larvae at 48, 72, 96, and

120 hpf. For AHR2-dependence, total RNA was extracted from pooled groups of four 48

and 120-hpf wildtype and AHR2 mutant (ahrosu1) larvae exposed to 0.1% DMSO or 1

ng/mL TCDD as described above. The lower number of embryos was a result of impaired

fertility of the AHR2 mutant zebrafish described previously (Garcia et al. 2018a). RNA extraction was conducted using RNAzol® (Molecular Research Center, Inc.) and a bullet blender with 0.5 mm zirconium oxide beads (Next Advance, Averill Park, NY) for 3 minutes at speed 8, as recommended by the manufacturer. The RNA was purified using the

Direct-zol MiniPrep kit (Zymo Research, Irvine, CA) as recommended by the manufacturer. The optional in-column DNase 1 digestion step was performed for 15 minutes. RNA quality and quantity was determined using a BioTek® Synergy™ Mx

microplate reader (BioTek Instruments, Inc., Winooski, VT) with the Gen5™ Take3™

module.

mRNA quantification using qRT-PCR

10 µL one-step qRT-PCR reactions were set up consisting of 5 µL SYBR Green master

Mix and 0.08 µL reverse transcriptase enzyme mix (Power SYBR® Green RNA-to-CT™

1-Step Kit; Applied Biosystems, Foster City, CA), 0.2 µL each of 10 µM forward and

reverse primers, and 20 ng RNA per reaction. The QuantStudio 5 Real-Time PCR System

(Thermo Fisher Scientific, Waltham, MA) was used under the following cycling

conditions; reverse transcription at 48 ᵒC for 30 minutes, denaturation and activation of

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SYBR® polymerase at 95 ᵒC for 10 minutes, followed by 40 cycles of amplification (95

ᵒC for 15 seconds, 60 ᵒC for 1 minute). Expression values were normalized to β–actin and

analysed using the 2-ΔΔCT method (Livak et al. 2001). For the developmental expression of wfikkn1, the 2.5 hpf time point served as the calibrator. For all other experiments, the

DMSO treatment of wildtype zebrafish served as the calibrator.

Each treatment consisted of 2-4 replicates. Results were statistically analysed using R

(version 3.6.3) within RStudio (version 1.1.456), and graphed using the ggplot2 package

(Wickham 2016). The data were log2 transformed and assessed for normality and equal

variance using the Shapiro-Wilk test and Levene’s test, respectively. Statistical significance was determined using a one-way ANOVA with a posthoc Tukey (TCDD concentration response) or Dunnett’s test (developmental expression of cyp1a) for data that were normal, or a Kruskal-Wallis test followed by Dunn’s test (developmental expression of wfikkn1) for data that failed normality testing. For the two-factor experiments (AHR2- dependence), a two-way ANOVA with a post-hoc Tukey’s test was used.

Sample preparation and RNA sequencing

Wildtype and wfikkn1 mutant zebrafish embryos were exposed to 0.1% DMSO or 1 ng/mL

TCDD as described above. Total RNA was isolated from pooled groups of nine zebrafish

embryos at 48 hpf with 4 replicates per treatment group. RNA integrity was confirmed

(RIN score > 9) using a Bioanalyzer 2100 (Agilent, Santa Clara, CA). Samples were

submitted to Oregon State University’s Center for Genome Research and Biocomputing

(Corvallis, OR) for library preparation and sequencing. mRNA was polyA selected using

the Robotic PolyA Enrichment Library Prep and libraries were prepared with the Robotic

Stranded RNA Library Prep kit (WaferGen Biosystems, Fremont, CA). Single-end 100 bp

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sequencing was conducted with an Illumina HiSeq® 3000 sequencer. Raw sequence reads

were filtered based on Illumina quality scores and their quality was checked prior to

processing using FastQC (version 0.11.7). Reads were trimmed using Cutadapt version

1.8.1 to remove the adapter (AGATCGGAAGAGCA) with no other options (Martin 2011).

All sequencing data will be deposited in the NCBI Gene Expression Omnibus.

Sequence mapping, transcriptome assembly, and statistical analyses

The trimmed files were verified and aligned using HISAT2 (version 2.2.0) (Siren et al.

2014) with default options against the Zebrafish build 11 (Danio rerio GRCz11 release 97)

genome. The number of reads mapped to the reference genome was counted, sorted, and converted to binary alignment files (BAM files) using Samtools (version 1.10) (Li et al.

2009). Read counts per gene were estimated using HTSeq (version 0.12.4) (Anders et al.

2015). Normalization of counts and differential gene expression analysis were conducted using Bioconductor’s edgeR (Robinson et al. 2010), following a previously developed workflow (Chen et al. 2016) using RStudio. The custom R script was modified from a previously published study (Garcia et al. 2018b). Briefly, edgeR (version 3.22.5) was used to sum gene counts per sample, and multidimensional scaling was leveraged to identify outliers. One outlier each from the WT_DMSO and WT_TCDD treatments was removed, resulting in three replicates each for these two treatments, and four replicates each for the mut_DMSO and mut_TCDD treatments for a total of 14 samples. Genes were filtered to exclude those with low counts across all 14 libraries (20 to 27 million reads), only keeping genes that were expressed in a minimum of three samples with a minimum read count of

15 (Chen et al. 2016). This corresponded to a cut-off of 0.64296 for the average counts per million (CPM) reads per sample. Filtered genes were normalized using the trimmed mean

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of M values (TMM) method to eliminate composition biases between libraries. Principle

components analysis (PCA) was performed on RStudio and visualized using ggplot2.

Differential expression of WT vs. wfikkn1 mutants (mut) exposed to DMSO was calculated using edgeR which uses the negative binomial generalized linear model extended with quasi-likelihood estimates to fit the gene count data accounting for gene-specific variability, the Cox-Reid profile-adjusted likelihood method for estimating dispersions, and an empirical Bayes F-test (with the option, “Robust = TRUE”) for differential expression. All calculated p-values were adjusted for false discovery using the Benjamini-

Hochberg (BH) method. Genes with an adjusted p ≤ 0.5 were considered significantly

differentially expressed. We enlisted a log2Fold change (FC) cut-off of 1 for all genes and they are referred to as Differentially Expressed Genes (DEGs).

Pathway and interaction analyses

To evaluate the functional role of wfikkn1 on the 48-hpf zebrafish transcriptome, we identified the significant expression changes in the mut_DMSO samples compared to

WT_DMSO. We conducted biological process network enrichment using the MetaCore

GeneGo software (Clarivate Analytics), as previously described (Garcia et al. 2018b;

Haggard et al. 2016). To make the data compatible with MetaCore, the biomaRt package

(version 2.36.1) was used to assign the human orthologs of the zebrafish genes (Durinck et

al. 2005; Durinck et al. 2009) which were used for all downstream analyses. 74.99% of all

zebrafish genes were converted to their human orthologs. Processes with a false discovery

rate (FDR) adjusted P < 0.05 were considered significant. The Enrichment Ratio was

manually calculated as the intersection size divided by the expected number of terms in

each network. Interaction analysis was conducted using a MetaCore network building

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algorithm, as described previously (Haggard et al. 2016) with some modifications.

MetaCore connects genes that have been shown to interact with each other based on the

manually curated MetaCore knowledgebase. The network was generated using the Shortest

Paths algorithm allowing for two-step path through all supplied genes. We included AHR

and WFIKKN1 in the algorithm and the network was visualized using Cytoscape (Shannon

et al. 2003).

To understand how the lack of wfikkn1 alters TCDD-induced gene expression changes in

48-hpf zebrafish, the union of the DEGs between DMSO and TCDD in the wildtype and

wfikkn1 mutants was determined. The TMM normalized, regular log-transformed gene

values for each replicate of all four treatment groups was normalized to the mean of the

WT_DMSO (control) group. A heatmap with hierarchical clustering was created using the

R package gplots (version 3.1.1) (Warnes et al. 2016) to identify the changes in gene

expression between DMSO and TCDD in the two zebrafish genotypes. Biological process

network enrichment analyses were conducted on the human orthologs of the DEGs as

described above. To explore interactions of genes that were differentially expressed in opposite directions in wildtype zebrafish compared to the wfikkn1 mutants upon TCDD exposure, we used the MetaCore “Transcriptional Regulation” network building algorithm that allows for only one step between the genes in the input list and potential transcription factors (TFs). We also included AHR and WFIKKN1 into the network building algorithm since none of the zebrafish ahrs or wfikkn1 were expressed in opposite directions in the wildtype and mutant zebrafish. From the top 30 enriched TF networks identified by the algorithm, we identified the DEGs in our input gene list that were shown to directly interact

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with AHR, and extracted their predicted TFs. The network was visualized using Cytoscape

(Shannon et al. 2003).

5.3.5. Proteomic analysis

Sample collection and deyolking

At 48 hpf, wildtype and wfikkn1 mutant morphologically normal larvae (n=6) were

transferred to 1.5 mL Eppendorf tubes in EM, and euthanized by placing on ice for 15

minutes. EM was carefully removed and the individual zebrafish were deyolked with

mechanical disruption as described previously (Link et al. 2006) with slight modifications.

Briefly, 200 µL of ice-cold ½ Ginzburg Fish Ringer with Calcium and PMSF was added to each tube. Yolk sacs were disrupted by pipetting up and down with a 20 µL pipette tip until the yolk sac was disrupted. Tubes were vortexed briefly to dissolve the yolk and centrifuged at 1000 g for 30 seconds. After supernatant was removed and discarded, embryos were washed with HEPES buffer and PMSF (vortexed and centrifuged).

Supernatant was discarded and tubes were placed on ice till protein extraction.

Protein extraction and digestion

To extract embryos for proteomic analysis, solvent (100 µL 90:10 v/v methanol:water)

with protease inhibitors (cOmplete protease inhibitor cocktail with EDTA, Millipore

Sigma) was added to each tube. Embryos were homogenized with 0.5 mm zirconium oxide

beads using a counter-top bullet blender (Next Advance, Averill Park, NY) for 3 min at

speed 8, and placed on ice for 3 min. This was repeated for a total of three times. Solution

was transferred to new 1.5 mL tubes, and combined with 100 µL bead wash solution: 90:10

v/v methanol:water with ProteaseMax Trypsin Enhancer detergent (Promega). Tubes were

placed on ice. Protein was quantified using the BCA assay (ThermoFisher).

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To reduce the disulfate bonds of the proteins, 50 mM dithiothreitol (ThermoFisher) was added to samples and incubated at 56 °C for 1 hour. 100 mM iodoacetamide (MilliPore

Sigma) was used for alkylation of cysteine residues (1 hour RT incubation). Samples were digested overnight at 37°C using Trypsin Gold (Mass Spectrometry Grade, Promega).

After digestion, samples were spun down at 12000 g for 30 sec to collect condensate, and digestion was stopped with 0.5% trifluoroacetic acid (37°C incubation for 60 min). We cleaned the samples (6.16 µg protein/sample) using Oasis HLB cartridges (Waters) and eluted in acetonitrile solvent.

Mass-spectrometry and data analysis

A Waters nanoAcquityTM UPLC system (Waters, Milford, MA) was coupled online to an

Orbitrap Fusion Lumos mass spectrometer (Thermo Fisher Scientific). Peptides were loaded onto a trap 2G nanoAcquity UPLC Trap Column (100mm, 50mm, 5um) at a flow rate of 5 μl/min for 5 minutes. The results were obtained on a commercially available

Acquity UPLC Peptide BEH C18 column (100um, 100mm, 1.7um). Column temperature was maintained at 37 °C using the integrated column heater. Solvent A was 0.1% formic acid in LC-MS grade water and solvent B was 0.1% formic acid in LC-MS grade acetonitrile. The separation was performed at a flow rate of 0.5 μl/min, and using linear gradients of 3–10% B for 10 minutes, 10–30% B for 10 minutes, 30–70% B for 5 minutes,

70–95% B for 3 minutes, 95–3% B for 4 minutes, 95–3% B for 3 minutes. Total method length was 35 minutes. The outlet of the column was connected to Thermo Nanospray Flex ion source and +2300V were applied to the needle.

MS1 spectra were acquired at a resolution of 120,000 (at m/z 200) in the Orbitrap using a maximum IT of 50 ms and an automatic gain control (AGC) target value of 2E5. For MS2

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spectra, up to 10 peptide precursors were isolated for fragmentation (isolation width of 1.6

Th, maximum IT of 10 ms, AGC value of 2e4). Precursors were fragmented by HCD using

30% normalized collision energy (NCE) and analyzed in the Orbitrap at a resolution of

30,000. The dynamic exclusion duration of fragmented precursor ions was set to 30 s.

Raw files were processed by Thermo Proteome Discoverer 2.3. Precursor ion mass tolerance was set to 5 ppm, while fragment ion mass tolerance was 0.1 Da. The SequestHT search engine was used to search against the zebrafish proteome (downloaded January

2021, 46897 proteins) from Uniprot (Swissprot) database. b and y ions only were considered for peptide spectrum matching. Minimum peptide length was Peptide spectrum matches (PSMs) were grouped together into peptide groups, and peptide groups were grouped into corresponding proteins. Feature mapper and precursor quantification were used for label-free quantitation of the peptides. Protein abundances were calculated as a sum of abundances of unique peptides detected. P-values adjusted by Benjamini–Hochberg false discovery rate correction engine were calculated for evaluation of statistical significance of the differences between wildtype samples and wfikkn1 mutants.

Significant proteins were further filtered by considering only the ones that were expressed in at least three of the six biological replicates of either the wildtype or wfikkn1 mutant zebrafish and absent in all replicates of the other genotype. Proteins were converted to their corresponding gene IDs and human orthologs using the biomaRt package on R (47.8% conversion), and functional enrichment was carried out using MetaCore’s Process

Networks.

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5.3.6. Behavior analyses

Larval Photomotor Response (LPR)

The Viewpoint Zebrabox (Viewpoint Behaviour Technology) was used to evaluate photo- induced larval locomotor activity at 120 hpf in 96-well plates. Each plate consisted of 24 larvae/treatment and we had a total of 4 plates (n=96). Each plate was placed in a Zebrabox and the video tracking protocol on the Viewpoint Zebrabox software was used to track total larval movement over three alternating light/dark cycles. Each cycle consisted of three minutes in visible light (1000 lux) and three minutes in the dark (IR light). The integration time was set to 6 second bins and raw data files were processed using custom R scripts (R

Core Team, version 1.1.456) to average the total distance travelled for each integration.

Larvae that exhibited mortality or any morphological malformations at 120 hpf were excluded from the LPR analysis.

Juvenile assays

Wildtype and wfikkn1 mutant embryos were exposed to 0.1% DMSO or 50 pg/mL TCDD and raised to 120 hpf as described above. At 120 hpf, the larvae were placed on the recirculating water system and raised at a density of 1 larva/28 mL until juvenile behaviour studies. Once an assay was completed, the zebrafish were returned to their tanks.

28 days post fertilization (dpf) Mirror Response assay

The 28 dpf Mirror Response assay was conducted as described previously (Shen et al.

2020). Briefly, individual zebrafish (n=48) were transferred from their tanks to individual chambers of a custom 3D printed, 24-chamber plate with 2 mL laboratory fish water. Each chamber had a 2mm thick acrylic mirror on one side, with the other three sides made of white polyacetic acid (PLA). Each chamber was placed in a Zebrabox and the video

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tracking protocol on the Viewpoint Zebrabox software was used to track zebrafish location integrated into 30 second bins over the 10 min duration of the assay. A 3 mm wide arena adjacent and along the mirror was designated as the mirror zone. The average percent time spent in the mirror zone by the fish was calculated and data collected from 7.0 to 9.5 minutes (2.5 minutes) were statistically analysed.

28 dpf Shoaling assay

The 28 dpf Shoaling assay was conducted as described previously (Shen et al. 2020). Four zebrafish per treatment (n=12) were placed in 50 mL laboratory fish water in a custom 3D printed chamber. Each chamber was placed in a Zebrabox, and the video tracking protocol on the Viewpoint Zebrabox software was used to record the swimming behavior of the zebrafish in the light every 30 seconds for 7.5 minutes. Data were statistically analysed from 3.5 to 5 minutes (1.5 minutes). The nearest neighbour distance (NND) and inter- individual distance (IID) were calculated to investigate shoaling behavior or tightness of the shoaling group. NND is the average distance between the nearest zebrafish to each of the four zebrafish in the shoal, and IID is the average distance between each of the four zebrafish to the other three zebrafish in the shoal (Miller et al. 2012). Average swim speed was recorded to assess motor function.

Adult Assays

The adult behavior assays were conducted over a two-week period when zebrafish were approximately six months old. Zebrafish were raised in groups of 16 (eight males and eight females) in 2.8 L tanks until each assay was conducted, and returned to the tanks after the completion of the assay.

Shoaling assay

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Groups of four adult zebrafish (two males and two females) (n=16) were tested for their

shoaling behavior. The assay was conducted in 1.8L shoaling tanks as described previously

(Knecht et al. 2017a). Briefly, zebrafish were allowed to swim for 30 minutes and

movement was captured using Q-See cameras and Noldus (Leesburg, Virginia) Media

Recorder software. The Viewpoint Zebrabox software was used to analyse movement of

individual zebrafish. Similar to the juvenile shoaling assay (described above), NND, IID,

and speed were computed every minute.

Free Swim Assay

After 30 minutes of the shoaling assay, each individual zebrafish was separated into a new

1.8 L tank and recorded for an additional 30 minutes. Total swim distance was calculated

for each zebrafish, measured every minute.

Statistical analyses for behavior assays

The normality of all neurobehavior data was checked using normality q-q and residual

plots. Data from all assays were normal with the exception of the 28 dpf mirror assay.

However, the sample size (n≥42) was high enough to tolerate deviance from normality.

Prior to analysis, any zebrafish that showed no movement throughout the duration of any assay was removed from the analysis. For the larval assay (LPR), all three light/dark cycles were analysed. Statistical significance (p < 0.05) was quantified using a Type II ANOVA test to determine the presence of an interaction between the wfikkn1 mutation and TCDD exposure.For juvenile and adult zebrafish assays, the acclimation period for the control

(WT_DMSO) was determined using the Friedman test (P<0.05). Data were analysed using the Type II ANOVA test (p<0.05) to determine the effect of the mutation, the chemical

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exposure, and any interactions between the two. Custom R scripts were used for data

analyses of all assays.

5.4. Results

5.4.1. Wfikkn1 is highly induced in parallel with cyp1a expression

Wfikkn1 was first discovered as being highly induced in 48-hpf zebrafish exposed to the

PAHs, benz(a)anthracene (BAA), dibenzothiophene (DBT), and pyrene (PYR) (Goodale

et al. 2013). The same study showed that expression of the transcript had a similar temporal

pattern to the induction of cyp1a and, similar to cyp1a, also not significantly induced at 24 hpf. A closer look at a later RNA sequencing study conducted in 48-hpf zebrafish showed

that, similar to cyp1a, wfikkn1 expression was more highly elevated upon exposure to

benz(a)anthracene-7,12-dione (7,12-B[a]AQ) than 1,9-benz-10-anthrone at 48 hpf

(Goodale et al. 2015), further emphasizing a positive correlation between the expression of

the two genes. To this end, we compared multiple microarray and RNA sequencing datasets

from 48-hpf zebrafish exposed to a variety of chemicals (from Chapter 4) (Goodale et al.

2013), and found that wfikkn1 induction followed cyp1a induction, with both genes highly

induced by similar PAHs and TCDD (Figure 5.1A). This emphasized wfikkn1’s high co-

expression with cyp1a. Additionally, while cyp1a expression was generally higher than

wfikkn1, exceptions included dibenzothiophene and 4h-cyclopenta[def]phenanthren-4-one.

Of the datasets that we analysed for cyp1a and wfikkn1 expression at 48 hpf, only

benzo[a]pyrene (BaP) data came from two concentrations, 1 and 10 µM. Both genes had

higher BaP-associated expression at 10 µM. This suggested a concentration-dependent

expression profile for wfikkn1. To test this hypothesis, we exposed wildtype zebrafish to a

broad concentration range of TCDD (0, 0.0625, 0.125, 0.25, 0.5, and 1 ng/mL) and found

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that expression of wfikkn1 increased with concentration (Figure 5.1B). While wfikkn1

expression was consistently lower than cyp1a, the two transcripts increased in parallel.

Expression of cyp1a plateaued from 0.5 ng/mL TCDD, and expression of wfikkn1 plateaued from 0.25 ng/mL TCDD.

We investigated the developmental expression of the two genes in zebrafish from 2.5 hpf to 120 hpf and found that wfikkn1 expression increased up to 48 hpf and remained elevated to 120 hpf (Figure 5.1C). On the contrary, cyp1a expression did not show a consistent

increase over time, and only significantly increased at 120 hpf, which is consistent with

previous work (Andreasen et al. 2002).

5.4.2. Induction of wfikkn1 expression is AHR2-dependent

Since wfikkn1 expression increased upon exposure to those PAHs that also induced cyp1a,

and in a concentration-dependent manner with TCDD exposure, we hypothesized that

activation of AHR2 was necessary for induction of wfikkn1 expression. We tested this

hypothesis in AHR2 mutant and wildtype (WT) zebrafish exposed to 0.1% DMSO or 1

ng/mL TCDD and measured wfikkn1 expression at 48 hpf and 120 hpf. At both time points,

TCDD exposure significantly induced wfikkn1 expression in WT but not in AHR2 mutant

zebrafish (Figures 5.2A and 5.2B) demonstrating that wfikkn1 induction is AHR2-

dependent for xenobiotic ligands like TCDD. Of note, the background wfikkn1 expression

levels in AHR2 mutant fish at both 48 hpf and 120 hpf trended towards being lower than

WT background levels at each time point, suggestive of a partial control of endogenous

wfikkn1 expression by AHR in developing zebrafish.

Upon activation and translocation of the AHR, the AHR-ARNT heterodimer recognizes

and binds to aryl hydrocarbon response elements (AHREs, also known as DREs or XREs).

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The AHRE, 5’-GCGTG-3’, is a conserved sequence, and has been identified in upstream

promoter sequences of several downstream target genes of zebrafish and other species

(Chang et al. 2013; Karchner et al. 2002). To this end, we conducted a search for putative

transcription factor binding sites in the upstream region of the predicted wfikkn1 sequence

in zebrafish using MatInspector (Cartharius et al. 2005). MatInspector did not identify

putative AHRE sites in the upstream predicted promoter region of the gene, however, we

manually detected six potential AHREs in the 5kb bp upstream from the gene (Figure

5.2C). The top ten results from MatInspector had a Matrix similarity of 1.000, which refers to a 100% match for every nucleotide between the most conserved nucleotides of the

binding sequence, and the sequence detected upstream of the wfikkn1 gene locus (Figure

5.2C). These included three putative SMAD binding sites; two at ~500 bp and one at ~1000

bp from the transcription start site (TSS). There were multiple forkhead box domain factor

(FKHD) sequences, and a putative binding site for the TFG-β induced apoptosis proteins

(TAIP) at ~275 bp upstream from the wfikkn1 TSS.

5.4.3. Creation, generation, and validation of wfikkn1 mutant line

We first confirmed the presence of the wfikkn1 RNA sequence in our tropical 5D WT

zebrafish. The zebrafish wfikkn1 gene (ENSDARG00000101364) is located on

chromosome 1, and has a single exon and two predicted transcripts,

ENSDART00000167087.2 (transcript 1) and ENSDART00000193546.1 (transcript 2)

(Figure 5.2C). There was lack of consensus between the predicted transcripts on Ensembl

(Release 102, GRCz11)(Yates et al. 2020) and NCBI’s GenBank (Gene ID: 406570)

(Sayers et al. 2020). The NCBI mRNA sequence

(https://www.ncbi.nlm.nih.gov/nuccore/47550770) consisted of an additional 175 bp

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section upstream of the predicted sequence of transcript 2, which was distinct from the 27

bp addition in transcript 1. We chose the longest of the three predicted sequences (NCBI)

as the best chance of capturing the protein-coding region of the wfikkn1 transcript. See

Supplementary Figure C.1 for details on amplification methods. The amplified PCR

product from our tropical 5D WT zebrafish had >99% sequence similarity to the NCBI

predicted mRNA sequence (Supplementary Figure C.1). The deduced Wfikkn1 protein has 564 amino acids (Figure 5.3A) with a predicted molecular mass of 61.72 kDa. The predicted protein domains of the Wfikkn1 protein are highlighted: WAP-type “four-

disulphide core” (gray), Kazal serine protease inhibitors (blue), Ig-like domain (purple),

two Pancreatic trypsin inhibitor (Kunitz or BPTI) domains (light green), and the NTR

domain (dark green). We designed a custom-made zebrafish antibody against the Wfikkn1

protein (Boster Bio, San Francisco, CA) to target the carboxyl end consisting of one of the

Kazal domains and the NTR domain, highlighted in Figure 5.3A. However, we were

unable to detect the Wfikkn1 protein in vivo in developing zebrafish exposed to TCDD or

in adult zebrafish.

To begin to investigate the functional role of wfikkn1, we generated a zebrafish wfikkn1

mutant line (wfikkn1osu2/osu2) using the CRISPR/Cas9 system in wildtype 5D Tropical

zebrafish. Of the two sgRNAs we designed, we identified one founder female injected with

our second sgRNA. She had a 16 bp nucleotide deletion in the wfikkn1 gene (Figure 5.3B)

in her germ cells that was transferred to her offspring. The 16 bp deletion was predicted to

cause a frameshift mutation resulting in a premature stop codon (Figure 5.3C). The

expected truncated mutant protein should only contain the WAP and KAZAL domains

(Figure 5.3D).

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For the generation of homozygous wfikkn1 mutant zebrafish, we first identified five F1

adult heterozygous zebrafish by sequencing. The fish were in-crossed to produce a 25%

homozygous population confirmed with sequencing the region around the 16 bp nucleotide

deletion. The established homozygous wfikkn1 mutant zebrafish line was screened for

possible gene mutations carried over from initial multiplex injections, of which none were

found.

5.4.4. Unbiased transcriptomics in TCDD-exposed wildtype and wfikkn1 mutant

zebrafish

To elucidate the functional role of wfikkn1 in influencing normal and TCDD-induced gene

expression changes, we conducted whole genome, whole embryo transcriptomic profiling

of 48-hpf wildtype (WT) and wfikkn1 mutant (mut) zebrafish exposed to 0.1% DMSO or

1 ng/mL TCDD. This led to four different treatment comparisons: mut_DMSO vs.

WT_DMSO, mut_TCDD vs. mut_DMSO, mut_TCDD vs. WT_TCDD, and

WT_TCDD vs. WT_DMSO. Principle components analysis indicated that the replicate

samples of each treatment clustered together and there was a strong separation between

wildtype and wfikkn1 mutant zebrafish along PC2 (y-axis, 30%) (Supplementary Figure

C.2). Separation between chemicals (DMSO and TCDD) occurred along the PC1 (x-axis,

50%). A total of 15,754 unique significant (BH-adjusted p < 0.05) genes were identified

across all treatments, and there were no significant genes that were common to all

treatments. In total, we identified greater than 2500 significant genes between each of the

treatment comparisons (Figure 5.4A). Due to the high number of significant genes, we

enlisted a log2FC = 1 cut-off (Figure 5.4A) for downstream analyses of differentially regulated transcripts. These genes are referred to as DEGs.

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While there were no common DEGs across all four treatment comparisons when DEGS

that were increased (Figure 5.4B) or decreased (Figure 5.4C) were considered separately,

we identified potentially important trends. In any two comparisons, at most 13.6% of

DEGS had increased expression (between mut_TCDD vs. WT_TCDD and mut_DMSO vs.

WT_DMSO), suggestive of the wfikkn1 mutation having a significant role on the 48-hpf zebrafish transcriptome. A high number of increased DEGs (159 or 12%) and decreased

DEGs (545 or 27.4%) were unique to the mut_TCDD vs. mut_DMSO comparison, suggestive of a unique transcriptomic response to TCDD in absence of wfikkn1. Thus, our overall observations were: 1. Lack of wfikkn1 caused significant expression alterations in

48-hpf zebrafish, and 2. TCDD exposure affected the 48-hpf zebrafish transcriptome of wfikkn1 mutants differently compared to WT zebrafish.

5.4.5. Impact of lack of basal wfikkn1 expression on the 48-hpf zebrafish transcriptome

We identified 1057 DEGs in mut_DMSO compared to WT_DMSO zebrafish, of which

49.1% (518) had increased expression, and 50.9% (539) were significantly decreased

(Figure 5.4A). Functional enrichment of all significant gene expression changes between

WT_DMSO and mut_DMSO led to seven significantly altered process networks in the wfikkn1 mutant zebrafish (Figure 5.5A). The top 20 process networks with their corresponding human gene IDs are in Supplementary Table C.3. We observed processes related to skeletal muscle development and muscle contraction as the most significantly enriched in the dataset. To explore how the absence of wfikkn1 could have a role in muscle- related processes, we generated a network on MetaCore (See Methods) investigating the interactions between the genes in the two most enriched pathways. We added both AHR

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and WFIKKN1 as additional inputs. MetaCore identified seven proteins that have been

predicted to interact with WFIKKN1 and at least one of the genes in the input dataset

(Figure 5.5B). SOX17, RUNX1, CTNNB1 (beta-catenin), and NANOG had the most number of connections with the genes involved in the muscle-related processes.

Importantly, the AHR also had interactions with CTNNB1 and NANOG indicating that these two TFs may be particularly important hubs mediating the normal function of wfikkn1 on skeletal muscle development and contraction processes.

5.4.6. Role of wfikkn1 in TCDD-induced gene expression alterations

To understand the functional role of wfikkn1 in TCDD-induced gene expression changes, we analyzed the 48-hpf transcriptome of wildtype and mutant zebrafish exposed to TCDD.

Wildtype zebrafish exposed to TCDD resulted in 732 DEGs (Figure 5.4A). The mut_TCDD compared to mut_DMSO zebrafish had 1208 DEGs. When comparing the gene expression changes in WT and wfikkn1 mutant zebrafish, each exposed to TCDD, 285

DEGs (19.1%) were unique to WT, 761 DEGs (54%) were unique to wfikkn1 mutants, and

447 DEGs (29.9%) were common to both zebrafish strains (Figure 5.6A). Our data demonstrate that most of the variability was driven by baseline differences in the

WT_DMSO and mut_DMSO samples (Supplementary Figure C.2 and Figure 5.4A).

Thus, we normalized each of the treatments to a common baseline, WT_DMSO (See

Methods) followed by unidirectional hierarchical clustering on all the DEGs significantly altered by TCDD (Figure 5.6A) in both the WT and mut samples. Based on the responses of the genes, we identified two major clusters (Figure 5.6B).

In general, genes in Cluster 1 consisted of genes that mostly responded in the same direction (increased) in the WT_TCDD and mut_TCDD samples relative to their individual

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normalized DMSO controls. Of note, multiple known AHR2-regulated genes such as

cyp1a, cyp1c1, cyp1c2, and ahrra, were found in this cluster, seen as a bright blue band

(Figure 5.6B). In addition, wfikkn1 was in this cluster of genes, and was significantly induced in both wildtype (log2FC = 4.81, FDR P=4.47e-6) and mutant (log2FC = 3.25,

FDR P=9.92 e-4) zebrafish exposed to TCDD, with mutants having nearly a 40% lower

log2FC. We found that while ahrrb was highly induced in TCDD-exposed WT zebrafish

(log2FC = 5.55, FDR P=1.28 e-3), its expression was reduced in the wfikkn1 mutants

(log2FC = 2.93, FDR P=1.47e-3) (Supplementary Tables C.2A-2D), which suggested a potential wfikkn1-ahrrb interaction.

Genes in Cluster 2 (1208) had opposite patterns of expression between WT and wfikkn1 mutants; increased in WT_TCDD and decreased in mut_TCDD compared to WT_DMSO and mut_DMSO, respectively. Dissimilar expression profiles of these genes were indicative of wfikkn1–dependent TCDD effects on the 48-hpf zebrafish transcriptome. We

conducted biological process network enrichment analysis of the human orthologs of the

genes in this cluster using the Metacore GeneGo software. Of the top 20 MetaCore process

network enrichments, there were eight significant processes (Figure 5.7) that were related

to developmental neurogenesis, cell adhesion, ion transport, and cytoskeleton structure.

The remaining pathways were associated with apoptosis, signal transduction, proteolysis,

development (keratinocyte differentiation and blood vessel morphogenesis), and

reproduction (progesterone signaling) (Figure 5.7).

We sought to explore how genes that are expressed in opposite directions in the wildtype

and wfikkn1 mutant zebrafish upon exposure to TCDD (Cluster 2) are associated with the

AHR signaling pathway. As a first step, we used the Transcriptional Regulation algorithm

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to identify the top 30 significantly enriched transcriptional factors that directly interact with

the genes in our dataset. In this analysis, we added both AHR and WFIKKN1 as additional

nodes, since neither were present in Cluster 2. In the second step, we filtered for the genes

that have direct interactions with the AHR, a well-known known transcription factor. This

analysis allowed us to build a network of enriched transcription factors (purple) that

potentially interact with the AHR signaling pathway via the genes altered by TCDD in

wfikkn1 mutant zebrafish (orange and black) (Figure 5.8).

We identified 13 unique genes that had direct interactions with AHR, with five (orange)

belonging to at least one of the significant enriched process networks. Of these genes,

NLGN1, DISC1, and DRD1 belonged to one of the nervous system process networks

(Supplementary Table C.4). KCHN7 was predicted to be involved in the potassium

transport network, and IGF1R has a role in signal transduction and reproduction

(progesterone signaling). ESRRA, ESRRG, FOXP3, BMAL1, and SOX2 were predicted

to interact with several genes in this AHR network, including the genes in the highly

enriched biological process networks. In addition, several TFs that interacted with the

AHR-associated genes were found in our dataset (yellow). For example, TFE3 was

predicted to regulate 5 of the 13 AHR genes. MetaCore also predicted that SOX17,

NANOG, TCF7L1 and RUNX1 regulate wfikkn1 in addition to regulating many of the

AHR-associated genes.

5.4.7. Impact of lack of wfikkn1 on basal protein expression in 48-hpf zebrafish

The predicted protein sequence of Wfikkn1 in zebrafish, just like its human ortholog

(Trexler et al. 2001), has multiple serine protease inhibitor domains. Thus, we hypothesized that the lack of the Wfikkn1 protein’s protease inhibitor activity might significantly alter

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the zebrafish proteome consequently leading to significant transcriptomic differences. To

evaluate our hypothesis, we conducted whole-animal mass spectrometry-based proteomics on whole-animal 48-hpf wildtype and wfikkn1 mutant individual zebrafish, and identified

481 significant peptides that differed significantly in abundance, and were associated with

326 unique protein isoforms. Principal Components Analysis (PCA) revealed a clear separation between wildtype and mutants (PC1 = 31.07%) when only the significant peptides were considered (Figure 5.9A). Further, there was only a 1.8% concordance between proteomic and transcriptomic profiles (Figure 5.9B). To have the most confidence

in our proteomic data, we further filtered the proteins for downstream analysis (see

Methods), and functional enrichment identified significant skeletal muscle and muscle contraction processes. The absence of wfikkn1 was associated with the absence of several

proteins in these categories, including MYLPF, TPM2, MYOM, RYR1, MYH6, and

MYH15. While TNNI2 was detected in mutants but not in Wildtype, TNNI2, MYBH, and

MYH6 were all identified as genes significantly altered in their mRNA expression between wildtype and zebrafish mutants.

5.4.8. Wfikkn1 mutant zebrafish have altered neurobehaviors in response to TCDD that do not persist to adulthood

To the best of our knowledge, the wfikkn1 CRISPR-Cas9 mutant line that we generated is the first of its kind in any species. To determine the functionality of the Wifikkn1 protein in our mutant line, we exposed wildtype and wfikkn1 mutant zebrafish to 0.1% DMSO or a concentration range of TCDD (Garcia et al. 2018b) at 6 hpf for 1 hour. We did not detect a shift in the concentration response curve in the wfikkn1 mutant zebrafish compared to the

wildtype controls, and mutants exposed to 1 ng/mL TCDD had the expected TCDD-

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induced toxicity phenotype at 120 hpf (data not shown). We also exposed the two zebrafish

lines to 0.1% DMSO or a low concentration of TCDD (50 pg/mL), and raised the fish up

until adulthood. We did not expect to see any morphological defects in wildtype zebrafish

at this 50 pg/mL TCDD exposure concentration (Baker et al. 2013), and in general, neither

zebrafish line had any visible malformations.

We evaluated the effect of lack of Wfikkn1 on zebrafish neurobehavior responses to TCDD

(Figure 5.10). The assays have not been used previously to measure the long-term effects of TCDD. We were interested in two main questions: 1. Does the lack of a functional

Wfikkn1 protein alter behavioral responses in the mutants compared to wildtype zebrafish?

2. Does the lack of Wfikkn1 alter how the zebrafish respond to TCDD exposure? We conducted five assays at multiple time points in larval, juvenile, and adult zebrafish.

While larval zebrafish assays showed a significant interactive effect between the lack of wfikkn1 and TCDD, this interaction was not detected in juvenile and adult zebrafish

(Figure 5.10E). Reaction norm plots for all assays are presented in Supplementary Figure

C.3. At 120 hpf, zebrafish were subjected to three alternating light-dark cycles in the Larval

Photomotor Response (LPR) assay. During all three cycles of the assay, wfikkn1 mutant zebrafish were significantly hypoactive compared to wildtype zebrafish (P < 0.01) (Figure

5.10A). There was a significant interaction between the mutation and TCDD exposure (P

= 0.0007) in cycle 2, and the trend towards an interaction persisted in cycle 3 (P = 0.1). At

28 dpf, the percent time spent in the “mirror zone” (see Methods) was calculated, and we found that the wfikkn1 mutant zebrafish spent significantly less time (P = 3.71e-05) in the mirror zone compared to wildtype zebrafish, irrespective of the chemical treatment (Figure

5.10B).

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Shoaling behavior was assessed at 28 dpf and as adults. At 28 dpf, there was a trend towards

an interaction effect (P = 0.0558) between the wfikkn1 mutation and TCDD exposure for

IID, suggesting that the effect of TCDD on shoaling behavior was altered in the absence of

Wfikkn1 (Figures 5.10C). This interaction effect did not persist into adulthood. However, a significant effect from lack of Wfikkn1 (P < 0.05) on shoaling behavior for all three measured endpoints was associated with both DMSO and TCDD treatments (Figure

5.10D). We also evaluated the free swim behavior of individual zebrafish as adults and

found no effects from lack of Wfikkn1 or from TCDD exposure on the total distance moved

by the zebrafish (data not shown). Behaviors of the wfikkn1 mutant zebrafish were also

generally more variable than wildtype zebrafish (data not shown).

5.5. Discussion

5.5.1. Characterization of wfikkn1 expression in zebrafish

The WFIKKN (WAP, kazal, immunoglobulin, kunitz and NTR domain-containing protein)

gene, located on human chromosome 16, was first discovered in human studies. The gene was found to be expressed in several organs in adults including pancreas, liver, and thymus, but not in the brain and ovary (Trexler et al. 2001). In fetal tissue, WFIKKN is highly expressed in the lung, skeletal muscle, and liver (Trexler et al. 2002). Two orthologs of this gene have been annotated in humans, rodents, and zebrafish, and, the WFIKKN2 ortholog is more easily detected in both mouse and human serum samples (Hill et al. 2003b). To date, while the protease inhibitory activity of these multi-domain proteins still remains unclear, their functions have been studied only in the context of their interactions with myostatin (growth and differentiation factor-8, GDF-8), and other members of the TGFβ family of proteins (Szlama et al. 2010). In zebrafish, wfikkn1 was first identified as being

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highly induced in 48-hpf zebrafish exposed to multiple polycyclic aromatic hydrocarbons

(PAHs) (Goodale et al. 2013). Here, for the first time, we definitively demonstrate

wfikkn1’s presence in the AHR signaling pathway, and investigate its potential functional

role in zebrafish. While we discovered wfikkn1 as being highly induced in several

developmental RNA sequencing datasets from zebrafish exposed to AHR ligands, its paralog, wfikkn2, is not induced by AHR activation (data not shown). The current study used a combination of CRISPR-Cas9-mediated gene editing, transcriptomics and proteomics, and multiple sensitive neurobehavioral assays to functionally characterize this novel AHR signaling gene.

The high correlation between cyp1a and wfikkn1 expression following exposure to several environmentally relevant PAHs as well as across multiple exposure concentrations of

TCDD strongly suggested that the two genes are likely regulated by AHR signaling. Other

AHR-regulated genes such as ahrra and ahrrb show similar concentration-dependent expression patterns (Evans et al. 2005). To validate our hypothesis, we measured wfikkn1 levels in absence of AHR2 and showed a significant reduction of expression compared to wildtype zebrafish. This was in agreement with a previous study from our lab that showed that wfikkn1 expression at 48 hpf was reduced in AHR2 morphants treated with benz(a)anthracene-7,12-dione (Goodale et al. 2015). Our study definitively placed wfikkn1

in the AHR2 signaling pathway. We also identified six core Aryl Hydrocarbon Response

Elements (AHREs) (5’-GCGTG-3’) between 1500 and 5000 bp upstream of the predicted

wfikkn1 transcriptional start site. One previous study in MCF-7 cells identified that while

the AHR/ARNT heterodimer’s peak density was within 1000 bp of the TSS, binding was

detected as far as 100kb from the transcriptional start site (TSS) (Lo et al. 2012) suggesting

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that wfikkn1 could be a potential direct target gene of AHR2. Future work investigating the

functionality of the identified AHREs with in vitro studies (Mimura et al. 1999) will help

determine if wfikkn1 is a direct downstream target of AHR2.

5.5.2. Lack of basal wfikkn1 expression influences the zebrafish transcriptome,

proteome, and behavior

Even though we detected wfikkn1 mRNA expression during development in wildtype

zebrafish, we were surprised by the large number of significant DEGs (1057) induced at

48 hpf by the lack of wfikkn1 because we did not see any obvious morphological effects

associated with the loss of wfikkn1. Mutant zebrafish embryos hatched at the normal timing

around 48 hpf, and there were no overt deficiencies in larval cartilage structures or adult

fin regeneration (data not shown), two phenotypic endpoints previously associated with

activation of the AHR2 signaling pathway (Burns et al. 2015; Zodrow et al. 2003). It is

possible that wfikkn1 plays a major role in an organ or tissue-specific manner that we were

unable to observe via our screening experiments.

We did, however identify abnormal behavioral responses in wfikkn1 mutant zebrafish at all

stages of development (Figure 5.10). Wfikkn1 mutant zebrafish were significantly hypoactive in both the light and dark phases of our LPR assay compared to wildtype

zebrafish, an endpoint seen previously in larval zebrafish exposed to several neurotoxicants

(Sun et al. 2016; Truong et al. 2016). LPR has also been used as a direct measurement for

visual function (Ahmad et al. 2012). It is possible that wfikkn1 mutants have impaired

visual development, since the juveniles also had significant behavior abnormalities in the

mirror assay. It is important to note that iterations of the mirror assay have traditionally

been used as a measure of aggression or social interaction (Shen et al. 2020; Way et al.

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2015). While it is too premature conclude the exact mechanism by which wfikkn1 alters

LPR behavior, and mirror response, future research including assays such as the optomotor

response (OMR) assay (Xie et al. 2019), that specifically interrogates vision, might further illuminate wfikkn1’s functional roles in zebrafish. Both xenobiotic AHR2 activation by

TCDD and other AHR2 agonists (Hill et al. 2003a; Knecht et al. 2017b), as well as AHR2

knockout (Garcia et al. 2018a) have all been associated with neurodevelopmental abnormalities in larval and adult zebrafish. These studies highlight the importance of maintaining normal AHR signaling for proper neurodevelopment and sensory system development. They do not identify specific gene expression alterations that cause the

observed phenotypes. It is plausible to expect that wfikkn1 is a player in some aspect of

AHR2 signaling required for normal zebrafish behavior.

We also observed a robust signature of transcripts involved in skeletal muscle

development, particularly muscle contraction, in our wfikkn1 mutants. While we did not

conduct in-depth analyses of muscle structure or function, a reasonable hypothesis is that

the associated hypoactive zebrafish behavior is due to impaired skeletal muscle

development (Babin et al. 2014). Results from our transcriptomic and behavior analyses were corroborated by our proteomic study that also significantly enriched muscle-related processes. Several of the proteins that were identified to be differentially abundant between wildtype and mutants such as TNNI2 (Ferrante et al. 2011), TPM2 (Dube et al. 2017), and

RYR (ryanodine receptor) (Hirata et al. 2007), are also important for skeletal muscle structure, development, and contraction in zebrafish. Previous research has shown that ortholog of wfikkn2 in rodents significantly inhibits myostatin activity, which is a negative regulator of skeletal muscle mass (Hill et al. 2003b). Thus, it is conceivable that Wfikkn1

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has a significant role in muscle developmental processes, evidenced by both our transcriptomic and proteomic data. It should however be noted that although our proteomics data suggested a response specific to muscle contraction, a research gap remains regarding this mechanism, specifically if Wfikkn1’s protease inhibitor domains are involved in biological activity. In addition, there was high variability for majority of the peptides identified and therefore, future work should consider experiments with more biological and technical replicates, or a comparison with pooled animals to yield more robust data. However, our efforts at conducting single-animal proteomics for the first time in developing zebrafish shows us with reasonable confidence that Wfikkn1 may be involved in skeletal muscle regulation.

Our network analysis focusing on the transcriptomic events involved in these pathways highlighted different players such as BMP2 and BMP4 that could mediate wfikkn1’s role in muscle development pathways. The two WFIKKN orthologs in humans were found to bind members of the TGFβ family of proteins, including GDF8 and GDF11 (myostatin) without altering their activity (Kondas et al. 2008; Szlama et al. 2010). It is possible that

WFIKKN1 is a reservoir for these growth factor proteins which can alter their gradients and consequently their downstream effects. SMAD3 mRNA expression was significantly altered in wfikkn1 mutants compared to wildtype zebrafish, and its role as a mediator of

TGFβ signaling (Nakao et al. 1997), especially via beta catenin (CTNNB1) (Jian et al.

2006; Zhang et al. 2010) might suggest that Wfikkn1 in zebrafish influences muscle development pathways via any of these important signaling mechanisms. Our identification of multiple SMAD binding sites in the zebrafish wfikkn1 gene promoter should be functionally investigated in future studies as a potential way by which

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WFIKKN1 interacts with SMAD3 and its associated signaling pathways. Additionally, we

observed that 4h-cyclopenta[def]phenanthrene-4-one and dibenzothiophene, two PAHs

that did not significantly induce cyp1a, but induced a ~2 log2FC increase of wfikkn1

mRNA. This was different from the remaining PAHs and TCDD that we investigated

(Figure 5.1A) suggesting that AHR2 might not be the only driver of wfikkn1 expression,

especially when AHR2 is not strongly activated by a xenobiotic chemical.

5.5.3. Role of wfikkn1 in TCDD-induced gene expression and behavior alterations

Both our larval and juvenile assays demonstrated that wfikkn1 plays a role in TCDD-

induced behavioral responses with interactions present in the 120 hpf LPR (P < 0.05) and

28 dpf shoaling (P < 0.1) assays. The interactive effect between TCDD and wfikkn1 completely disappears in adult zebrafish; it is likely that much of the TCDD from the early embryonic exposure was depurated in the zebrafish by the time the adult assays were conducted (Brambilla et al. 2007). Additionally, of the gene expression changes that were altered in opposite directions in the wildtype and wfikkn1 mutants upon TCDD exposure, the most significantly enriched pathways were associated with neurogenesis, cell adhesion, and ion transport, indicative that the lack of wfikkn1 targeted these functions. Interestingly,

NLGN1, DISC1, and DRD1- 3, genes associated with neurogenesis were predicted by

MetaCore to be regulated by AHR. To the best of our knowledge, there is no evidence that

NLGN1 and DISC1 are strongly induced by AHR activation, but DRD1 had nearly a 3x increase in its expression in a mammalian breast cancer cell culture experiment with TCDD exposure (DuSell et al. 2010). Our analysis suggests that the three genes may be involved in altering neurobehavioral outcomes by interacting with Wfikkn1 via the AHR signaling pathway. Additionally, all three genes were predicted to be associated with ESRRA

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(Estrogen-related receptor alpha), and there is some evidence from a previous study

suggesting AHR regulation of the ESRRA gene (Tijet et al. 2006). In zebrafish too, ER-

AHR signaling crosstalk has been demonstrated previously (Bugel et al. 2013; Shaya et al.

2019), with neurobehavioral signals, making it conceivable that wfikkn1 could be involved

in this potential ER-AHR interaction upon TCDD exposure during neurodevelopment.

Several aspects of our study point to Wfikkn1 as having an important role in the AHR2-

dependent neurodevelopmental outcomes, and the transcriptional regulation network

analysis provides some candidate genes and signaling pathways that can be investigated in

future studies to untangle the relationships between AHR2, Wfikkn1, and neurobehavior

in developing zebrafish.

5.6. Conclusions

Overall, we have begun to characterize a novel AHR-regulated gene, wfikkn1, in

developing zebrafish using a multi-omic approach paired with CRISPR-Cas9 and neurobehavioral assays in larval, juvenile, and adult zebrafish. We have multiple lines of evidence to believe that Wfikkn1 is involved in skeletal muscle and neurobehavioral processes in developing zebrafish. Upon xenobiotic AHR activation by TCDD, we identified Wfikkn1-dependent neurobehavioral effects induced by TCDD in larval zebrafish, and several significant transcriptomic alterations that point to Wfikkn1’s potential role in neurodevelopment and possible cross talk with signaling pathways such as Estrogen Receptor signaling. Several questions still remain, with the most important ones being, the mechanism by which Wfikkn1 is inducing its significant effects in developing zebrafish, and the extent to which xenobiotic AHR activation and the

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consequent significant induction of wfikkn1 expression can disrupt its normal functional roles.

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Figure 5.1. Comparison between cyp1a and wfikkn1 gene expression in developing zebrafish. (A) Comparison of cyp1a and wfikkn1 gene expression from previously published microarray (*) and RNA sequencing datasets collected from 48-hpf zebrafish. Wfikkn1 expression increases in parallel with cyp1a induction, with chemicals that strongly induce cyp1a, also strongly inducing wfikkn1

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expression. All microarray* (Benz(a)anthracene, Dibenzothiophene, and Pyrene) data are previously published {Goodale, 2013 #41408}. All RNA sequencing datasets (remaining chemicals) were re-analyzed with the same data analysis pipeline (Shankar et al. in progress). (B) qRT-PCR concentration response of cyp1a and wfikkn1 expression in 48-hpf Wildtype zebrafish embryos (n=3-4) exposed to 0.0625, 0.125, 0.25, 0.5, and 1 ng/mL TCDD. Expression of both genes increased in a concentration dependent manner. The control was 0.1% DMSO which is listed as 0 ng/mL TCDD. Expression values were determined using the 2-ΔΔCT method and the normalization control was β-actin. The data were log2 transformed, and data are represented as the mean +/-SEM (error bars). Normality was tested using a Shapiro test, and statistical significance for each gene was determined using a one-way ANOVA followed by a post-hoc Tukey test. Upper case (cyp1a) and lower case (wfikkn1) letters indicate expression values statistically different (p < 0.01) from each other. (C) qRT-PCR expression of cyp1a and wfikkn1 in developing Wildtype zebrafish (n=2-3) at multiple time points from 2.5 hpf to 120 hpf in the absence of xenobiotic AHR2 activation. Data analysis was similar to (B), and statistical significance was determined compared to 2.5 hpf for each gene using a one-way ANOVA followed by a post hoc Dunnett’s test (cyp1a) or the Kruskal-Wallis followed by Dunn’s test (wfikkn1) for data that passed or failed Shapiro’s normality test, respectively.

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Figure 5.2. AHR2-dependence of wfikkn1 developmental expression. Wfikkn1 expression in 48 hpf (A) and 120 hpf (B) Wildtype 5D (WT) and ahr2-null (ahr2osu1) whole embryos developmentally exposed to 0.1% DMSO or 1 ng/mL TCDD (n = 3-4 reps/treatment). Wfikkn1 expression was significantly increased upon exposure to TCDD only in WT zebrafish, and there was no significant increase in ahr2-null zebrafish at both time points. Expression values were analysed using the 2-ΔΔT method, and β-actin

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was used as the normalization control. Different letters indicate statistical difference (p < 0.001, Two-way ANOVA with a posthoc Tukey test). (C) Schematic diagram of the wfikkn1 gene locus (not to scale) on the reverse strand of Chromosome 1 of the zebrafish genome. Zebrafish have two predicted wfikkn1 transcripts (TSS = Transcription Start Site). The six identified aryl hydrocarbon response element (AHRE) consensus sequences in the 5kb region upstream of the TSS are highlighted in the top panel. The bottom panel shows the top 10 predicted transcription factor binding sites from Genomatix {Cartharius, 2005 #39416}. (NEUR = NeuroD, Beta2, HLH domain; BRAC = Brachyury gene, mesoderm developmental factor; NKXH = NKX homeodomain factors; FKHD = Fork head domain factors; TAIP = TFG-beta induced apoptosis proteins).

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Figure 5.3. Schematic diagrams of predicted Wfikkn1 protein in Wildtype zebrafish, and characterization of wfikkn1 mutant CRISPR-Cas9 line (A) Predicted amino acid sequence of Wfikkn1 with protein domains highlighted in different colors: Gray - WAP-type “four- disulphide core”, blue - Kazal serine protease inhibitors, purple – Immunoglobulin (IG)- like domain, light green - two Pancreatic trypsin inhibitor (Kunitz or BPTI) domains, and dark green – Netrin (NTR) domain. The target location of the custom zebrafish Wfikkn1 antibody is highlighted with an underline in the sequence. (B) The DNA sequence of the wfikkn1 exon in Wildtype (top) and wfikkn1 mutant (bottom) zebrafish. The PAM site is in italisized (CCG) and the guide RNA (sgRNA) sequence is in pink. (C) The translated mutant sequence results in a frameshift mutation and is predicted to result in a premature stop codon at amino acid residue 216. (D) The truncated protein in the mutant line only contains the WAP and KAZAL domains.

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Mut: TCDD: Mut: TCDD: TCDD vs. DMSO mut vs. WT TCDD vs. DMSO mut vs. WT

267 159 168 545 20.1% 12% 8.4% 27.4%

193 0 10 0 326 191 0 688 0 33 14.5% 0% 0.8% 0% 24.5% 9.6% 0% 34.6% 0% 1.7%

0 0 10 0 0% 0% 0.5% 0% 0 0 181 0% 48 325 0% 16 13.6% 2 1 3.6% 16.3% 4 0 0.8% 0.2% 0.1% 0.2% 0% 141 9 10.6% 0.5% D DMSO: mut vs. WT TCDD: mut vs. WT WT: TCDD vs. DMSO Mut: TCDD vs. DMSO

Gene Log2FC Gene Log2FC Gene Log2FC Gene Log2FC pimr134 5.20 casp6 5.18 cyp1a 7.32 cyp1a 8.21 phf5a 4.74 ifit8 4.53 cyp1c2 5.64 cxcl19 6.27 ifit8 4.38 znf1094 4.12 ahrrb 5.55 ahrra 6.14 casp6 4.29 phf5a 3.83 ahrra 5.30 cyp1c1 5.84 slc6a17 4.28 mhc1zka 3.48 cyp1c1 5.29 cyp1c2 5.38 olig4 -9.40 mhc1uba -9.35 znf1094 -2.73 nr6a1a -4.41 fsd1l -8.83 areg -7.62 crp2 -2.56 piezo2 -4.15 mhc1uba -8.63 olig4 -7.18 hpxa -1.81 slc6a17 -4.07 zp2l2 -7.52 tia1 -7.12 pdha1a -1.75 hmcn2 -4.01 areg ugt2a6 -6.97 cishb -1.68 rims3 -3.91 -7.30 Figure 5.4. Overview of RNA sequencing data from 48 hpf wildtype (WT) and wfikkn1 mutants (mut) exposed to 0.1% DMSO or 1 ng/mL TCDD. (A) Total number of significant genes differentially expressed across the four treatment comparisons along with a log2FC=1 cut-off (DEGs). Venn diagrams showing all DEGs (FDR-adjusted p < 0.05) with log2FC cut-off = 1 that had increased (B) or decreased (C) expression in each of the treatment comparisons. (D) The top five annotated DEGs with most increased and most decreased expressions between each of the treatment comparisons with their log2FC values.

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A B

Figure 5.5. Impact of the lack of wfikkn1 on the 48-hpf zebrafish transcriptome. (A) Top 10 process network enrichments of human orthologs of DEGs between WT_DMSO and mut_DMSO. Significant processes (FDR-adjusted p < 0.05) are bolded and italicized. The Intersection size is the number of observed genes from each cluster that belongs to the Process network, and Enrichment ratio is the ratio of the number of observed genes to the expected genes from each network. The top two processes are skeletal muscle development and muscle contraction. (B) MetaCore interactome of genes (gray) enriched in the top two biological process networks. AHR and WFIKKN1 were manually added to the network. Proteins predicted by MetaCore that interact with AHR or WFIKKN1 and the gray genes are depicted in other colors. Thicker colored lines show the direct interactions with WFIKKN1.

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Figure 5.6. Effect of the lack of wfikkn1 on the 48-hpf transcriptome when zebrafish are exposed to 1 ng/mL TCDD. (A) Venn diagram of DEGs in 1ng/mL TCDD-exposed WT or mut zebrafish compared to the respective DMSO controls. (B) Hierarchical clustering of the union of the genes in Figure 5.6A whose log normalized counts have been normalized to the control WT_DMSO. Blue indicates genes that have increased, and yellow indicates genes that have decreased expression compared to WT_DMSO. We identified two unique clusters, with Cluster 2 consisting of genes that had distinct gene expression changes in the WT zebrafish line compared to the wfikkn1 mutants upon exposure to TCDD.

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Figure 5.7. Functional enrichment of genes differently differentially expressed in wildtype and wfikkn1 mutant zebrafish upon exposed to TCDD. Top 20 process network enrichments of genes in Cluster 2. Significant processes (FDR-adjusted p < 0.05) bolded and italicized. The Intersection size is the number of observed genes from each cluster that belongs to the Process network, and Enrichment ratio is the ratio of the number of observed genes to the expected genes from each network.

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Figure 5.8. Transcriptional Regulation of AHR-associated differently differentially expressed genes in wildtype and wfikkn1 mutant zebrafish exposed to TCDD. Orange circles depict the genes from Cluster 2 (Figure 5.6B) and belong to at least one of the enriched

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biological process networks (Figure 5.7) that are predicted to have direct interactions with the AHR. Black circles are genes in Cluster 2 that do not belong to a biological process network, but have a direct interaction with the AHR. The transcription factors (TFs) (purple) either belong to the top 30 predicted enriched TFs for the dataset, or are found in the dataset (yellow). TF circle sizes refer to the number of predicted targets here with largest circles having 10 targets (ESRRA), and smallest circles having 1 target (ex: QK1 and FER). TFs shown as triangles interact with wfikkn1 in MetaCore’s manually curated database.

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Figure 5.9. Whole animal proteomic comparison between wildtype and wfikkn1 mutant zebrafish at 48 hpf. (A) Principle components analysis (PCA) plot of six biological replicates of the wildtype (wt) and wfikkn1 (mut) significant peptides (P < 0.05). (B) Comparison between genes of proteins predicted from significant peptides (P < 0.05) (proteomics) and significant differentially expressed genes (FDR P < 0.05) (transcriptomics). (C) Heatmap showing MetaCore Process networks (significant processes are bolded) on the Y-axis, and proteins from proteomic and transcriptomic (*) data sets on the X-axis. Blue boxes represent protein expressed in at least three biological replicates of wildtype or mutants and absent in all replicates of the other genotype.

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Figure 5.10. Behavior Analyses of wfikkn1 mutant zebrafish exposed to 50 pg/mL TCDD. (A) 120 hpf Larval Photomotor Response (LPR) showing wfikkn1 mutant zebrafish significantly hypoactive compared to wildtype (WT) zebrafish in light (quiescent) and dark

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(active) periods of the assay indicated by the gray-black bar at the bottom. Significant interaction between lack of wfikkn1 and TCD Exposure was detected in Cycle 2. (B) 28 dpf Mirror assay showing mutant zebrafish spending significantly less time in the “mirror zone” compared to WT zebrafish. 28 dpf juvenile (C) and adult (D) shoaling measured using the Nearest Neighbor Distance (NND), Inter-individual Distance (IID), and Speed of zebrafish within shoals of four zebrafish. NND was significantly different between mutants and WT in juvenile zebrafish, and all three endpoints were significant in adult zebrafish. (E) Summary table showing assay details, including P values and corresponding significance (Type II ANOVA test).

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CHAPTER 6 – CONCLUSIONS AND FUTURE DIRECTIONS

While several epidemiological, environmental, and mechanistic studies have been conducted to understand and characterize PAHs and their toxicity, research on these ubiquitous and diverse chemicals still endures three significant challenges:

1). Bulk of PAH research thus far has predominantly focused on their carcinogenic potential, yet, PAHs have been associated with several adverse human health effects including diminished neurodevelopment, oxidative stress, and cardiovascular toxicity.

Mechanisms of these effects have not been well addressed, leading to a major gap that requires further investigation into the other detrimental health outcomes associated with

PAH exposure.

2). Of the thousands of PAHs that have been detected, there has been a focus on understanding the effects of the EPA’s 16 priority PAHs. The toxicity of the remaining

PAHs is largely understudied, and we have a limited understanding of which specific PAHs cause adverse health effects, let alone their mechanisms of toxicity via the AHR signaling pathway.

3). PAHs are structurally diverse chemicals that occur in dynamic environmental mixtures, which make them especially difficult to study. Thus, it is crucial to build predictive approaches that leverage structure-activity relationships.

The primary focus of my dissertation was the utilization of genome-wide transcriptomics coupled with gene knockout and phenotypic characterization studies in the zebrafish model to better characterize and classify PAHs, and gain a better understanding of the AHR signaling pathway.

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Chapter 2 reviewed the usage of the zebrafish model for understanding the role of the Aryl

Hydrocarbon Receptors (AHRs)—a well-conserved receptor family across several

organisms—in driving toxicological effects of its known xenobiotic ligands, primarily

PAHs and TCDD. In addition to the toxicological roles identified during development, in

adulthood, and across generations, the AHRs are also involved in crosstalk with other

signaling pathways, and they mediate many of their effects via transcriptomic and

epigenetic mechanisms of action. This chapter revealed the large body of research that has been conducted to understand the specific roles of the three zebrafish AHR isoforms

(AHR1a, AHR1b, and AHR2). Chapter 2 also comprehensively reviewed what we know of the heterogeneity of PAHs as zebrafish AHR ligands, and determined that only few

members of the AHR signaling pathway (primarily, cyp1a) have been explored extensively

in zebrafish.

The literature review strongly demonstrated a disproportionate focus in the zebrafish

toxicology field on both AHR2 as a receptor and TCDD as a ligand. Relatively little is

understood about AHR1a and AHR1b, and future research should be conducted to discover

the functions of these two receptors in mediating toxicity of their ligands. Additionally,

some studies suggest an endogenous role for AHR1b (Ulin et al. 2019) which should be

further investigated. We also know very little about the canonical binding partner proteins

of the zebrafish AHRs (ARNTs), and almost nothing about AHR’s non-canonical binding

partners (Wright et al. 2017). Future research should conduct functional studies to understand how different splice variants of ARNT or the other potential AHR binding partners might influence the various toxicological outcomes associated with the AHR. The

CRISPR-Cas9 system is becoming a more commonly used tool in zebrafish studies.

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Continuing to leverage this technique for the creation of AHR signaling pathway-

associated double and triple mutant lines will greatly enhance our understanding of the

AHR’s toxicological and endogenous roles. Furthermore, these mutants may reveal unknown functions of the AHR, many of which are likely conserved across vertebrates.

While we now understand that different PAHs cause a variety of toxicity phenotypic profiles (Geier et al. 2018), our knowledge of their mechanisms of toxicity, and which

AHRs they are dependent on, is still in its infancy. This should be systematically investigated in future studies using the various mutant lines to further our understanding of the mechanisms of PAH toxicity. I think it is of critical importance to design studies elucidating the roles of the other genes in the AHR signaling cascade, to not only better understand AHR signaling, but to also unravel the molecular events (including potential crosstalk with other pathways) leading up to toxicity phenotypes. This will enable better risk assessment practices, possibilities for susceptibility research, and therapeutic interventions.

Chapters 3 and 4 of this dissertation undertook comparative approaches to analyze transcriptomic data for characterizing the gene expression changes associated with several

PAHs, TCDD, and flame retardant chemicals (FRCs). My analyses of large sets of chemicals with diverse uses and chemistry identified several patterns of altered gene expression along with their associated biological pathways. The main goal was the identification of both common and unique gene expression events occurring upon exposure to chemicals that can be used as biomarkers of exposure, along with utilizing transcriptomics to characterize and classify the chemicals.

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In Chapter 3, I compared RNA sequencing data collected from 48-hpf zebrafish exposed

to 16 individual PAHs (some overlap with EPA’s 16 priority PAHs) carefully selected from

a large 123-PAH library that was screened in developing zebrafish for phenotypic

endpoints. In addition to activating expression of common biomarkers such as cyp1a upon

exposure, the PAHs that predominantly activated AHR2 also induced expression of other

genes such as ahrra, and the novel AHR-regulated gene, wfikkn1. Interestingly, each of these PAHs also enriched unique pathways such as ion transport signaling, despite activating predominantly the same receptor. Additionally, the AHR2-dependent PAHs also induced AHR2-dependent Cyp1a protein expression in the skin of developing zebrafish.

An important finding in Chapter 3 was that cyp1a biomarker gene expression levels were more indicative of transcriptomic profiles, rather than developmental toxicity endpoints.

Overall, I characterized and classified PAHs based on their transcriptomic and developmental toxicity profiles, which will help guide hazard characterization of other

PAHs in the future, and shift the focus from EPA’s 16 priority PAHs to potentially more toxic chemicals.

The identification of unique gene expression changes despite the PAHs predominantly activating the same receptor is interesting; this finding could be either due to the parent

PAHs or their metabolites activating a combination of AHR2 and other receptors.

Alternatively, this could have been a result of differential binding of the PAHs to AHR2 leading to ligand-dependent differences in AHR2 structure and function (Denison et al.

2017). Future research should investigate chemical metabolism in zebrafish, the ability of metabolites to influence early and late gene expression changes, and their relationship to developmental toxicity outcomes. This can be done by conducting zebrafish uptake studies

215

over multiple developmental time points, and investigating the tissue levels and identities

of the various predicted metabolites. Time-series gene expression data will also help discern the gene expression changes that are directly a result of chemical exposure compared to those that are the consequence of disruption to normal development, which are more likely to occur later in development. This study conducted single concentration exposures at concentrations that were anchored to phenotypic endpoints. The next logical step is to collect concentration-response data for gene expression changes to visualize to

what extent concentration can alter the transcriptome. This will enable an easier transition

of the use of transcriptomics from the laboratory to real-life environmental risk assessment.

Our finding that there was a lack of concordance between developmental toxicity and

transcriptomic profiles is intriguing; characterization of specific gene expression changes

that contribute directly to toxicity should be the next step to elucidate the identity of

biomarkers indicative of chemical hazard. Additionally, future research might consider

transitioning from focusing on specific zebrafish developmental endpoints to transcriptomic signatures, as the endpoints may only be an indication of general bioactivity in zebrafish rather than disruption of very specific molecular pathways. Cyp1a protein expression in the skin was also identified as a possible indicator of AHR2 activation by

PAHs; investigation of the ability to use Cyp1a localization as a predictive tool for AHR2- dependence should be considered in future studies.

In Chapter 4, I built the first chemical-focused gene co-expression network of the zebrafish transcriptome using a variety of PAHs, TCDD, and several FRCs, leveraging the strengths of a large compendium of data and a novel data analysis method. The FRCs and

AHR2 Activators localized to distinct regions of the network, and the network revealed

216

potential biomarker genes (including novel long non-coding RNAs) for the chemicals

within each of the two groups. Using a functional laboratory study, I confirmed that the highly co-expressed genes hypothesized to be biologically related (Alexander-Dann et al.

2018) and associated with the AHR2 ligands were indeed present in the AHR2 signaling pathway of zebrafish. Systematic removal of the two chemical classes from the network also uncovered biological pathways that were associated specifically with the broad group of FRCs such as neurodevelopment, which is consistent with previous FRC studies

(Dishaw et al. 2014; Sun et al. 2016), in addition to cardiovascular development. Overall, the gene co-expression network revealed novel insight into how different chemicals alter the zebrafish transcriptome relative to each other, and identified several biomarkers of exposure for the two sub groups of chemicals.

The potential biomarkers that were identified in Chapter 4 should be candidates considered for future research investigating the mechanisms of both PAH and FRC toxicity.

Additionally, the next step would be to include several more FRCs of each class

(brominated, aryl phosphonates, etc) into the network that will enable the significant shift of modules within the network to reflect class-specific biological pathways and gene expression alterations. While the mechanisms of FRC toxicity is in its infancy, leveraging a network approach like the one presented in Chapter 3 will begin to group FRCs by their gene expression profiles and subsequent toxicity effects. Additionally, this network approach opens doors to investigate time series and concentration response experiments for individual chemicals. The robustness of a network is a function of how much data are available; thus, leveraging this analysis platform in future studies and incorporating newly acquired chemical datasets will allow for the elucidation of important biological pathways

217

associated with the chemicals in context of the chemically-altered transcriptomic network.

We will also be able to build a co-expression network database reflecting the impact of

chemical exposure on the zebrafish transcriptome. This will eventually enable the robust

identification of gene expression biomarkers that could be utilized for mixture component

diagnosis and exposure determination.

In Chapter 5, I characterized a novel gene (wfikkn1) of the AHR2 signaling pathway that

was identified in Chapter 3 of this dissertation. I used the CRISPR-Cas9 technique to

generate a wfikkn1 mutant zebrafish line and followed this with, whole-animal

transcriptomics, proteomics, as well as behavioral examinations at developmental,

juvenile, and adult stages, to uncover the functional role of wfikkn1 upon the xenobiotic

activation of AHR2. I found that while wfikkn1 does not appear to influence the expression

of direct target genes of AHR2 (like cyp1a) or drive the overt TCDD-induced developmental phenotypic effects, the lack of wfikkn1 significantly alters the basal as well as TCDD-altered transcriptome. Furthermore, Wfikkn1 likely plays a role in zebrafish

muscle development and chemically induced behavioral abnormalities, evidenced by the

functional pathways enriched from the gene expression and proteomics data, as well as

phenotypes identified in multiple behavioral assays. The chapter also provides evidence

for Wfikkn1’s potential crosstalk with multiple transcription factors such as NANOG,

SOX17, and ESRRA potentially via the AHR signaling pathway.

Future research must conduct functional studies to understand if wfikkn1 is a direct target

gene of AHR2, and if its protein domains possess protease inhibitor activity as seen in its

mammalian ortholog (Nagy et al. 2003). Protease inhibition is one hypothesis for how the

Wfikkn1 protein is altering several gene expression changes and interacting with other

218

signaling pathways. The data presented in this chapter suggests that although wfikkn1 has

a significant role in zebrafish, its functions might be localized to specific tissues or organs,

such as skeletal muscle, that were not apparent from my screening experiments. Future

research investigating where the wfikkn1 mRNA and protein are expressed within larval,

juvenile, and adult zebrafish might help clarify this. Additionally, while I conducted

majority of the experiments in developing zebrafish, future work must investigate potential

functions in adulthood since Wfikkn1 might have a more prominent role at this life stage

that we may have missed. All experiments in this Chapter utilized TCDD as the xenobiotic

activator. However, within Chapter 3, I also showed that wfikkn1 expression is significantly

impacted by several other PAHs; future studies must investigate the role of wfikkn1 upon

AHR activation by the PAHs, since they might reveal functions of wfikkn1 that are not

apparent from TCDD exposure.

In conclusion, this dissertation has utilized a combination of cutting-edge transcriptomic

analyses, sophisticated molecular techniques, and phenotypic screening in the zebrafish

model to expand our knowledge of PAH toxicity and the molecular mechanisms of action

via the AHR signaling pathway. With our advancements in research and scientific

technology, we are well positioned to conduct systematic high-throughput studies that will

help fill the knowledge gaps related to how PAHs and other chemicals cause adverse health

effects in humans and other organisms. Through my dissertation, I have provided both

important data and valuable tools that can be leveraged in future investigations of chemical

mechanisms of toxicity, guidance for risk management measures, and for various

therapeutic efforts, especially in highly contaminated sites.

219

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APPENDICES

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Appendix A – Supplemental Data for Chapter 3

Figure A.1. Concentration Uptake Ratio (CUR) for six PAHs compared to log KOW. Zebrafish embryos were exposed to three concentrations (5.39, 11.6, and 25 µM) of the six PAHs anthracene, acenaphthene, phenanthrene, fluoranthene, retene, and BbF, from 6 to 48 hpf. CUR was computed from the ratio of the concentration inside the embryo and to the nominal medium concentration. The color of the data point represents the developmental toxicity bin the PAH clustered into: 2-MN and acenaphthene: bin 6, phenanthrene: bin 7, fluoranthene and BbF: bin 5, and retene: bin 1. The dotted line is at log KOW =5.5. The number of DEGs associated with each PAH are in parentheses.

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Table A.1. List of 123 PAHs, the lowest effect level (LEL) exposure concentrations for each endpoint measured, and the developmental toxicity bins of each PAH.

LEL.any.except.MOR IHC.Vasculature IHC.Neuromasts LEL.any.effect X5d.DARK.LELX5d.LIGHT.LEL Transcriptomics ChemClass LEL.MO24 LEL.DP24 LEL.SM24 LEL.NC24LEL.MORT LEL.YSE_ LEL.AXIS LEL.EYE_LEL.SNOU LEL.JAW_ LEL.OTIC LEL.PE__ LEL.BRAI LEL.SOMI LEL.PFIN LEL.CFIN LEL.PIG_ LEL.CIRC LEL.TRUNLEL.SWIM LEL.NC__ LEL.TR__X24h.B.LELX24h.E.LELX24h.R.LEL IHC.Liver IHC.Skin IHC.Yolk PAH Bin T

4H- Hetero 1 11.2 11.2 35.6 11.2 5 5 5 5 5 5 5 11.2 11.2 5 11.2 11.2 11.2 11.2 11.2 1 cyclopenta[lmn]phenanthri cyclic Oxygen 2-ethylanthraquinone 1 11.2 11.2 35.6 11.2 11.2 11.2 11.2 11.2 11.2 11.2 11.2 11.2 11.2 11.2 11.2 11.2 11.2 11.2 Yes ated Oxygen Pyrene-4,5-dione 1 1.12 1.12 3.56 3.56 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 0.1 ated Oxygen Perinaphthenone 1 5 5 50 35.6 11.2 11.2 11.2 11.2 11.2 11.2 11.2 11.2 11.2 11.2 11.2 5 11.2 11.2 1 ated Oxygen Benzanthrone 1 5 5 11.2 11.2 5 5 5 11.2 5 11.2 11.2 5 11.2 11.2 11.2 5 5 ated 1,3-Dinitropyrene 1 Nitrate 0.1 0.1 1.12 0.5 0.5 0.1 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.1 0.1 0.1 Yes 1-Aminopyrene 1 Nitrate 5 5 35.6 5 11.2 5 11.2 5 5 5 5 11.2 11.2 11.2 5 11.2 5 5 1 Yes Yes Hydrox 3-hydroxyfluoranthene 1 5 5 11.2 5 5 5 5 5 5 5 5 5 5 5 5 5 ylated Hydrox 4-hydroxychrysene 1 5 5 5 5 5 5 5 5 5 5 5 5 5 ylated 3-Nitrobenzanthrone 1 Nitrate 0.5 0.5 1.12 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 1.12 0.5 0.5 Yes Yes 11-H-benzo[b]fluoren-11- Oxygen 1 11.2 11.2 11.2 11.2 11.2 11.2 11.2 11.2 11.2 35.6 11.2 11.2 11.2 35.6 Yes Yes one ated 1,6-Dinitropyrene 1 Nitrate 1.12 1.12 1.12 5 1.12 1.12 1.12 1.12 3.56 1.12 3.56 3.56 Yes Yes 3- Hydrox 1 11.2 11.2 35.6 11.2 11.2 11.2 11.2 11.2 11.2 11.2 11.2 11.2 11.2 1 Yes hydroxybenz[a]anthracene ylated Hydrox 10-hydroxybenzo[a]pyrene 1 11.2 35.6 11.2 11.2 11.2 11.2 11.2 11.2 11.2 11.2 11.2 1 Yes ylated Hetero Xanthone 1 35.6 35.6 50 50 50 50 50 50 50 50 50 50 50 50 50 1 5 Yes cyclic 4h- Oxygen 1 35.6 11.2 11.2 11.2 35.6 35.6 35.6 35.6 35.6 35.6 35.6 35.6 50 35.6 11.2 11.2 50 35.6 5 11.2 Yes cyclopenta[def]phenanthre ated Hydrox 3-hydroxyfluorene 1 35.6 35.6 50 35.6 35.6 35.6 35.6 35.6 35.6 35.6 35.6 35.6 35.6 35.6 35.6 35.6 35.6 35.6 35.6 11.2 1 Yes ylated 9-Nitrophenanthrene 1 Nitrate 5 5 50 5 5 5 5 5 5 5 1 Yes Methyl Retene 1 11.2 11.2 50 35.6 35.6 35.6 11.2 11.2 11.2 11.2 11.2 11.2 11.2 50 35.6 11.2 Yes Yes ated Hydrox 4-hydroxyphenanthrene 1 5 5 35.6 5 11.2 11.2 11.2 11.2 11.2 11.2 11.2 5 5 ylated Hetero 5,6-Benzoquinoline 1 35.6 35.6 50 35.6 35.6 35.6 35.6 35.6 35.6 35.6 35.6 35.6 35.6 35.6 35.6 35.6 35.6 35.6 11.2 5 cyclic Dibenzo[a,l]pyrene 1 Parent 11.2 11.2 35.6 35.6 50 5 35.6 50 50 11.2 35.6 1 Yes Hetero Acridine 1 35.6 35.6 50 35.6 35.6 35.6 35.6 35.6 35.6 35.6 35.6 50 35.6 50 50 5 Yes cyclic 5-Nitroacenaphthalene 1 Nitrate 35.6 11.2 35.6 35.6 35.6 35.6 35.6 35.6 35.6 35.6 50 50 35.6 1 Yes Oxygen 1,5-dihydroxynaphthalene 1 35.6 50 35.6 50 50 50 50 50 50 50 50 35.6 Yes ated Oxygen 1,4-anthraquinone 1 1.12 1.12 3.56 3.56 1.12 3.56 0.1 1.12 ated Oxygen 9,10-phenanthrenequinone 1 1.12 1.12 3.56 1.12 1.12 1.12 5 1.12 ated Aminat 9-aminophenanthrene 1 11.2 11.2 35.6 35.6 35.6 11.2 11.2 1 ed

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Oxygen 1,4-phenanthrenedione 1 1.12 1.12 0.5 0.5 0.5 0.1 Yes ated Hydrox 3-hydroxybenzo[e]pyrene 1 5 5 35.6 11.2 5 50 35.6 5 11.2 1 ylated Hydrox 9-hydroxyphenanthrene 1 5 5 11.2 5 11.2 11.2 5 1 ylated Hydrox 3-hydroxyphenanthrene 1 5 5 11.2 5 5 11.2 5 ylated 1,8-Dinitropyrene 1 Nitrate 3.56 3.56 5 5 5 5 3.56 0.5 1.12 Hydrox 1-hydroxypyrene 1 5 5 11.2 5 5 1 1 1 Yes ylated Hydrox 1-hydroxyphenanthrene 1 11.2 5 35.6 11.2 11.2 11.2 11.2 5 Yes ylated Benzo[k]fluoranthene 2 Parent 1 1 35.6 1 1 Yes Yes Yes Yes 3-nitrofluoranthene 2 Nitrate 5 5 5 Yes Yes Yes Benzo[j]fluoranthene 2 Parent 35.6 35.6 11.2 35.6 1 5 Yes Yes Yes Yes Dibenzo[b,k]fluoranthene 2 Parent 5 5 5 5 0.1 0.1 Yes Yes Yes Dibenz[a,h]anthracene 2 Parent 5 5 5 5 1.12 0.1 Yes Yes Yes Hydrox 1-hydroxynaphthalene 2 35.6 35.6 50 50 35.6 11.2 Yes Yes ylated Hydrox 2-hydroxynaphthalene 2 50 35.6 50 35.6 50 50 50 11.2 ylated Benzo[b]fluorene 2 Parent 50 50 35.6 50 1 Yes Pyrene 2 Parent 50 50 50 50 50 50 50 35.6 35.6 Yes 7-Nitrobenz[a]anthracene 2 Nitrate 50 50 50 50 5 11.2 Yes Hetero Carbazole 2 35.6 35.6 35.6 35.6 1 1 Yes Yes Yes cyclic Methyl 6-Methylchrysene 2 50 50 50 5 35.6 Yes ated Oxygen acenaphthenequinone 2 35.6 35.6 35.6 35.6 5 11.2 ated Oxygen 1,2-naphthoquinone 2 5 5 0.5 0.5 ated Dibenzo[a,h]pyrene 4 Parent 0.1 0.1 0.1 Yes Yes Yes Yes Yes 3,7- 4 Nitrate 0.5 Yes Yes 1-hydroxyindeno[1,2,3- Hydrox 4 11.2 Yes Yes c,d]pyrene ylated Coronene 4 Parent 0.1 Yes Hetero Xanthene 4 11.2 cyclic Methyl 1,8-Dimethylnaphthalene 4 5 ated Naphtho[1,2-b]fluoranthene 4 Parent 0.1 Methyl 1,2-Dimethylnaphthalene 4 1 ated Fluorene 4 Parent 1 Methyl 1-Methylnaphthalene 4 1 ated Chrysene 4 Parent 11.2 35.6 Yes Methyl 1,6-Dimethylnaphthalene 4 1 35.6 ated Hydrox 1,3-dihydroxynaphthalene 4 1 1 Yes ylated Methyl 9-methylanthracene 4 1 11.2 Yes Yes Yes ated Methyl 1,4-Dimethylnaphthalene 4 1 1 Yes ated

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Naphtho[2,3-e]pyrene 4 Parent 0.1 0.1 Yes Yes 1-Nitropyrene 4 Nitrate 5 1 Yes Triphenylene 6 Parent 5 11.2 Hetero Chromone 6 11.2 1 cyclic 3-Nitrodibenzofuran 6 Nitrate 1 5 Methyl 1,5-dimethylnaphthalene 6 1 5 Yes ated Acenaphthene 6 Parent 1 5 Yes Benzo[e]pyrene 6 Parent 0.1 0.1 Benzo[g,h,i]perylene 6 Parent 0.1 0.1 Hydrox 6H-benzo[cd]pyren-6-one 6 0.1 0.1 ylated Oxygen anthraquinone 6 1.12 0.5 ated Naphthalene 6 Parent 1 1 Methyl 2-methylnaphthalene 6 1 1 Yes ated Hydrox 1,6-dihydroxynaphthalene 6 1 1 ylated Oxygen 5,12-naphthacenequinone 3 11.2 35.6 11.2 35.6 Yes ated Dibenz[a,c]anthracene 3 Parent 50 50 50 Yes 7- 3 Nitrate 50 50 50 50 Yes Yes 2-Nitroanthracene 3 Nitrate 50 50 50 Yes Hetero Dibenzofuran 3 35.6 Yes cyclic Dibenzo[a,i]pyrene 3 Parent Yes Yes Yes Yes Yes Dibenzo[a,k]fluoranthene 3 Parent Yes 2-Nitrofluoranthene 3 Nitrate Yes Hetero 8-methylquinoline 3 Yes cyclic Naphtho[2,3-b]fluoranthene 8 Parent 2-Nitropyrene 8 Nitrate Hetero 2-Methylbenzofuran 8 cyclic 6-Nitrobenzo[a]pyrene 8 Nitrate Dibenzo[j,l]fluoranthene 8 Parent Anthracene 8 Parent Yes Methyl 2,6-Dimethylnaphthalene 8 ated Hydrox 2,3-dihydroxynaphthalene 5 35.6 5 11.2 Yes ylated 2-Nitrodibenzothiophene 5 Nitrate 1.12 0.1 0.1 Yes Methyl 3,6-Dimethylphenanthrene 5 35.6 5 11.2 Yes Yes ated Hetero Quinoline 5 1 cyclic

266

2-Nitrofluorene 5 Nitrate 0.5 0.1 Yes Hetero Thianaphthene 5 5 5 cyclic Oxygen 9-fluorenone 5 35.6 50 ated Benzo[b]fluoranthene 5 Parent 50 Yes Yes Yes Anthrathrene 5 Parent 50 Yes Hetero Indole 5 35.6 Yes cyclic Acenaphthylene 5 Parent 35.6 Naphtho[2,3-k]fluoranthene 5 Parent 0.1 Yes Yes Indeno[1,2,3-c,d]pyrene 5 Parent 1.12 Yes Yes 6-Nitrochrysene 5 Nitrate 1 Yes Fluoranthene 5 Parent 5 Yes Yes Yes 2,8- 5 Nitrate 0.1 Yes 3-Nitrophenanthrene 5 Nitrate 1 Yes Benzo[a]pyrene 5 Parent 5 Yes Yes Oxygen 9-anthracene carbonitrile 5 5 Yes Yes ated Methyl 2-Methylanthracene 7 11.2 ated Methyl 2,3-Dimethylanthracene 7 11.2 ated Oxygen 9-anthracene carboxylic acid 7 11.2 ated Naphtho[2,3-j]fluoranthene 7 Parent 3.56 1-Nitronaphthalene 7 Nitrate 1 Hydrox 2,7-dihydroxynaphthalene 7 1 ylated 2-Nitronaphthalene 7 Nitrate 1 Benzo[c]phenanthrene[1,4] Oxygen 7 1 dione ated Phenanthrene 7 Parent 1 Yes 9-Nitroanthracene 7 Nitrate 1

267

Table A.2. List of DEGs (fold-change > 1.5, adjusted p-value < 0.05) for 14 of the 16 PAHs. 3-NF and 1,5- DMN had zero DEGs.

4h-CPdefP Retene Ensembl ID Fold Change (log2) Adjusted p-value (PADJ) Ensembl ID Fold Change (log2) Adjusted p-value (PADJ) ENSDARG00000001721 0.801966816 1.98E-07 ENSDARG00000002196 0.585270267 1.36E-06 ENSDARG00000001910 -0.688845057 9.81E-16 ENSDARG00000002311 -0.851235292 4.03E-10 ENSDARG00000004358 -0.910125229 1.19E-08 ENSDARG00000002405 0.59116449 6.08E-09 ENSDARG00000004748 0.6453739 1.29E-07 ENSDARG00000003902 0.878256021 1.22E-08 ENSDARG00000004753 -0.674068455 8.12E-05 ENSDARG00000004748 0.790484595 2.57E-12 ENSDARG00000006467 -0.630680667 2.99E-08 ENSDARG00000005713 0.983435945 8.68E-23 ENSDARG00000007024 -0.669955404 7.89E-05 ENSDARG00000006220 1.309896167 3.01E-30 ENSDARG00000008263 -0.672131089 4.60E-07 ENSDARG00000007289 0.628465643 3.04E-06 ENSDARG00000011640 -0.815213299 9.31E-09 ENSDARG00000007950 0.665611977 4.35E-05 ENSDARG00000012126 -0.799329917 7.53E-08 ENSDARG00000010940 0.707075194 1.50E-05 ENSDARG00000014233 -0.609112101 0.000188317 ENSDARG00000014646 0.630036913 3.90E-10 ENSDARG00000019902 -0.70275612 1.05E-08 ENSDARG00000015144 0.632871918 2.49E-07 ENSDARG00000021787 0.610820892 1.86E-07 ENSDARG00000017034 0.770353779 5.00E-12 ENSDARG00000023082 -0.695692605 7.99E-06 ENSDARG00000017195 0.894248623 1.64E-26 ENSDARG00000027236 -0.615079341 0.00052838 ENSDARG00000019236 1.00642215 1.29E-27 ENSDARG00000033832 -0.633701903 7.23E-10 ENSDARG00000021149 0.927199412 5.54E-17 ENSDARG00000034007 -0.767543754 6.14E-09 ENSDARG00000021787 0.759831482 1.92E-12 ENSDARG00000036840 -0.612867683 1.22E-06 ENSDARG00000021833 0.757237473 4.61E-25 ENSDARG00000037588 -0.683886519 1.06E-07 ENSDARG00000027088 0.679041187 3.92E-06 ENSDARG00000037921 -0.85789138 2.21E-12 ENSDARG00000028012 0.64110725 0.000113003 ENSDARG00000040252 0.962374918 1.35E-09 ENSDARG00000030896 1.497881886 9.50E-38 ENSDARG00000040466 -0.689656785 7.99E-06 ENSDARG00000031952 -0.80285882 1.62E-11 ENSDARG00000041382 -0.595665046 0.000346779 ENSDARG00000032496 0.613227729 9.70E-07 ENSDARG00000041433 0.818279574 4.52E-07 ENSDARG00000033364 0.6931341 3.16E-12 ENSDARG00000042872 -0.727193386 8.82E-06 ENSDARG00000033544 -0.612848388 0.000167158 ENSDARG00000044441 -0.666398602 2.68E-06 ENSDARG00000035890 0.622876728 9.79E-10 ENSDARG00000052099 -0.709344933 7.59E-06 ENSDARG00000037432 0.594384574 6.19E-08 ENSDARG00000052575 -0.596434081 0.000928308 ENSDARG00000038729 0.90176214 2.02E-09 ENSDARG00000052700 -0.598009898 8.18E-07 ENSDARG00000043243 0.594915596 0.000649103 ENSDARG00000054797 -0.607525016 0.00038721 ENSDARG00000043442 0.719546115 4.97E-10 ENSDARG00000055190 -0.604370191 4.02E-10 ENSDARG00000043806 -0.626085307 6.03E-09 ENSDARG00000055253 0.820179946 4.60E-07 ENSDARG00000052215 0.629309946 3.22E-07 ENSDARG00000056036 -0.789060936 4.02E-10 ENSDARG00000052618 1.695331645 3.44E-36 ENSDARG00000062661 -0.693598548 1.70E-05 ENSDARG00000053005 0.801629504 4.41E-07 ENSDARG00000069615 -0.762302438 2.68E-06 ENSDARG00000055186 0.587767797 0.000210072 ENSDARG00000071341 0.759305147 4.80E-12 ENSDARG00000055253 0.967784157 1.79E-10 ENSDARG00000076055 -0.668576804 3.92E-05 ENSDARG00000055974 0.992981443 5.56E-16 ENSDARG00000078052 -0.677718397 1.17E-07 ENSDARG00000056638 0.712402095 1.61E-09 ENSDARG00000078440 -0.798844794 8.17E-08 ENSDARG00000057633 0.605145679 3.59E-06 ENSDARG00000078557 0.651784799 8.41E-05 ENSDARG00000058734 1.068893972 9.41E-19 ENSDARG00000092269 -0.812596241 5.31E-08 ENSDARG00000059387 0.783165708 2.18E-11 ENSDARG00000094559 0.636080026 0.000261201 ENSDARG00000059993 0.613370871 2.84E-12 ENSDARG00000096313 -0.74169894 8.18E-07 ENSDARG00000061481 1.196110403 6.93E-19 ENSDARG00000097613 -0.609045699 0.000255629 ENSDARG00000061634 1.198648432 1.01E-32 ENSDARG00000098392 -0.611785389 5.43E-06 ENSDARG00000061682 0.589799283 3.83E-09 ENSDARG00000099008 -0.618269482 0.000281748 ENSDARG00000061841 0.720321929 6.37E-24 ENSDARG00000102393 -0.710724732 4.53E-06 ENSDARG00000061896 0.877010552 4.28E-10 ENSDARG00000102430 -0.69146454 9.93E-06 ENSDARG00000063297 0.592129917 6.80E-05 ENSDARG00000103413 -0.669649633 2.02E-06 ENSDARG00000068551 0.593397816 0.000474609 ENSDARG00000104502 0.712022344 2.26E-05 ENSDARG00000068934 2.129019339 6.85E-82 ENSDARG00000104687 0.70765872 6.72E-06 ENSDARG00000071341 0.686410355 2.04E-10 ENSDARG00000071567 1.22357592 7.46E-29 ENSDARG00000073695 1.170427012 1.08E-15 ENSDARG00000073912 0.750154521 1.20E-06 ENSDARG00000074149 0.697986099 9.74E-07 ENSDARG00000076534 0.6179042 1.57E-05 ENSDARG00000076787 0.688205343 3.92E-06 ENSDARG00000076830 -0.642359017 1.00E-07 ENSDARG00000077549 0.641122395 3.66E-08

268

BkF B jF Ensembl ID Fold Change (log2) Adjusted p-value (PADJ) Ensembl ID Fold Change (log2) Adjusted p-value (PADJ ENSDARG00000002847 -0.596763175 0.00119113 ENSDARG00000002197 0.763198041 2.70E-08 ENSDARG00000003216 -0.623860587 1.26E-08 ENSDARG00000003902 0.666457983 0.000116491 ENSDARG00000003902 0.60996091 0.000731571 ENSDARG00000005713 0.998813818 1.26E-23 ENSDARG00000006220 0.864911372 1.27E-12 ENSDARG00000006220 1.169095499 8.09E-24 ENSDARG00000006868 0.917845771 7.61E-10 ENSDARG00000006978 0.610719035 0.000643342 ENSDARG00000014626 0.629817402 2.12E-11 ENSDARG00000014626 0.635433736 1.73E-11 ENSDARG00000015654 -0.675273467 2.59E-10 ENSDARG00000017034 0.809265455 2.24E-13 ENSDARG00000017739 0.595620772 0.000424 ENSDARG00000020086 0.589526397 1.49E-05 ENSDARG00000020086 0.67751781 9.62E-08 ENSDARG00000023082 0.59929725 0.000206142 ENSDARG00000021833 0.641550998 1.14E-17 ENSDARG00000024775 0.609972868 4.90E-06 ENSDARG00000027088 1.063022279 5.83E-16 ENSDARG00000027088 0.919459085 1.20E-11 ENSDARG00000030896 2.11145381 2.39E-77 ENSDARG00000028012 0.608429329 0.000465257 ENSDARG00000032571 0.585503573 0.000189135 ENSDARG00000030896 1.790447714 5.02E-55 ENSDARG00000036767 0.878418747 7.99E-13 ENSDARG00000038729 0.96367514 8.69E-11 ENSDARG00000036840 -0.812760198 1.35E-12 ENSDARG00000043243 0.781685261 5.27E-07 ENSDARG00000042824 0.654405083 1.04E-12 ENSDARG00000043442 0.648657413 6.26E-08 ENSDARG00000043243 0.871698297 4.73E-09 ENSDARG00000051925 0.612809842 0.000690483 ENSDARG00000043442 0.76062157 1.92E-11 ENSDARG00000052215 0.750740133 1.30E-10 ENSDARG00000045768 0.722865123 3.40E-09 ENSDARG00000055974 0.654087157 2.33E-06 ENSDARG00000052215 0.786701309 6.37E-12 ENSDARG00000060439 -0.707322557 2.01E-05 ENSDARG00000052618 1.396125838 4.34E-24 ENSDARG00000061481 1.150677907 2.37E-17 ENSDARG00000052705 -0.712488478 2.73E-11 ENSDARG00000061634 0.838046587 2.22E-15 ENSDARG00000055534 -0.613507972 0.000682899 ENSDARG00000061896 1.194328272 1.34E-19 ENSDARG00000060439 -1.017663003 5.07E-12 ENSDARG00000063661 0.670922163 1.61E-08 ENSDARG00000061481 0.744385591 7.87E-07 ENSDARG00000068934 2.127624159 1.01E-81 ENSDARG00000061896 1.232952335 4.51E-21 ENSDARG00000070331 -0.858347625 3.54E-13 ENSDARG00000062632 -0.850730436 8.95E-11 ENSDARG00000070578 -0.602965231 0.000412673 ENSDARG00000063661 0.630275965 1.43E-07 ENSDARG00000071173 -0.624810662 4.77E-06 ENSDARG00000068876 0.85588987 1.66E-10 ENSDARG00000071567 0.97207676 9.70E-18 ENSDARG00000068910 -0.743957306 3.93E-10 ENSDARG00000071626 0.710126269 2.83E-05 ENSDARG00000068934 2.673214547 4.02E-130 ENSDARG00000074656 0.661186267 0.000145153 ENSDARG00000070331 -1.039775763 1.10E-19 ENSDARG00000076830 -0.941810197 2.81E-17 ENSDARG00000070578 -0.589342875 0.000594497 ENSDARG00000077559 -0.594599918 4.60E-06 ENSDARG00000071173 -0.873336307 1.35E-12 ENSDARG00000078404 0.587763067 1.73E-06 ENSDARG00000071567 0.742361891 4.57E-10 ENSDARG00000078962 -0.616059298 0.000643342 ENSDARG00000071626 0.724895127 1.45E-05 ENSDARG00000079119 0.748121504 2.15E-08 ENSDARG00000073695 0.677270849 8.95E-05 ENSDARG00000079302 -0.698707794 1.85E-13 ENSDARG00000073912 0.587104382 0.000818616 ENSDARG00000086826 2.159899481 4.87E-121 ENSDARG00000074752 0.591453806 1.12E-09 ENSDARG00000089507 0.74659955 1.10E-08 ENSDARG00000076830 -0.989573218 3.71E-19 ENSDARG00000089697 1.169213698 9.27E-19 ENSDARG00000078567 0.822358638 9.32E-16 ENSDARG00000089936 0.731396707 8.73E-08 ENSDARG00000078962 -0.682233102 7.45E-05 ENSDARG00000090268 -0.631220295 2.74E-10 ENSDARG00000079119 0.926718981 1.89E-13 ENSDARG00000091116 2.01117887 2.26E-50 ENSDARG00000079302 -0.772461888 1.13E-16 ENSDARG00000091715 0.773665815 3.83E-07 ENSDARG00000079602 0.585596799 2.08E-05 ENSDARG00000091996 0.63009623 1.55E-05 ENSDARG00000086826 2.066739498 8.20E-111 ENSDARG00000093584 0.641833581 3.27E-12 ENSDARG00000087440 -0.665353361 0.000125026 ENSDARG00000097157 0.890236209 5.12E-09 ENSDARG00000088589 -0.628886684 0.000443837 ENSDARG00000097491 1.260291492 9.02E-61 ENSDARG00000089507 1.007224826 1.13E-16 ENSDARG00000098058 0.789126552 1.35E-07 ENSDARG00000089586 0.638807978 6.62E-06 ENSDARG00000098315 2.079723356 1.44E-58 ENSDARG00000089667 -0.604052464 0.000583144 ENSDARG00000098589 1.002459269 7.10E-20 ENSDARG00000089697 1.454149585 6.93E-30 ENSDARG00000098746 1.36994533 3.29E-28 ENSDARG00000089936 0.594962769 5.42E-05 ENSDARG00000099702 1.096533196 2.22E-15 ENSDARG00000090268 -0.786423371 1.74E-16 ENSDARG00000100052 0.63208914 4.03E-06 ENSDARG00000091116 2.372148573 1.88E-70 ENSDARG00000101195 2.511492217 1.34E-101 ENSDARG00000091715 0.738987204 1.55E-06 ENSDARG00000101364 1.963254149 5.75E-47 ENSDARG00000093048 0.650777336 0.000145081 ENSDARG00000101789 1.446146311 1.17E-56 ENSDARG00000094281 0.599530139 4.15E-05 ENSDARG00000102558 0.603907438 0.000233476 ENSDARG00000097157 1.236903753 3.31E-18 ENSDARG00000103199 1.197871058 1.44E-16

269

Carbazole DB(a,i)P Ensembl ID Fold Change (log2) Adjusted p-value (PADJ) Ensembl ID Fold Change (log2) Adjusted p-value (PADJ) ENSDARG00000001721 0.777424546 4.97E-07 ENSDARG00000003820 0.585054432 0.000461724 ENSDARG00000001760 -0.587248735 0.000971579 ENSDARG00000006220 0.737569541 1.53E-08 ENSDARG00000001993 -0.63472614 5.00E-05 ENSDARG00000015495 0.667882779 3.25E-08 ENSDARG00000004358 -0.860721717 8.52E-08 ENSDARG00000019949 -0.736066428 1.46E-07 ENSDARG00000007788 -0.606763382 3.86E-05 ENSDARG00000027088 0.776754012 7.04E-08 ENSDARG00000007823 0.845926106 8.52E-08 ENSDARG00000028396 1.018511766 3.77E-14 ENSDARG00000010658 -0.59977074 1.42E-08 ENSDARG00000029075 0.605167663 3.25E-08 ENSDARG00000011640 -0.769039744 6.20E-08 ENSDARG00000030896 0.728811129 7.04E-08 ENSDARG00000012126 -0.631020959 4.82E-05 ENSDARG00000034503 0.810870765 6.11E-09 ENSDARG00000014233 -0.738393796 1.36E-06 ENSDARG00000043243 0.60994846 0.000784459 ENSDARG00000016854 -0.646921465 8.15E-05 ENSDARG00000045768 1.200155906 1.56E-26 ENSDARG00000019532 -0.62877875 0.00033421 ENSDARG00000052618 1.103899329 1.33E-14 ENSDARG00000019902 -0.655727337 9.18E-08 ENSDARG00000056587 0.676900398 9.41E-05 ENSDARG00000024746 0.591289539 5.88E-05 ENSDARG00000056885 0.909469218 9.00E-09 ENSDARG00000026759 -0.666429549 2.75E-08 ENSDARG00000058606 -0.964846968 1.73E-23 ENSDARG00000027236 -0.666672793 0.000104987 ENSDARG00000058679 -0.70816225 9.66E-08 ENSDARG00000027495 -0.608507071 3.36E-07 ENSDARG00000059001 -0.639004444 1.92E-08 ENSDARG00000034007 -0.755763508 1.42E-08 ENSDARG00000061481 0.68993143 1.45E-05 ENSDARG00000036840 -0.608863002 1.41E-06 ENSDARG00000061896 1.253817462 2.10E-21 ENSDARG00000037921 -0.712904769 1.99E-08 ENSDARG00000068194 0.667812841 8.79E-06 ENSDARG00000038025 0.689075407 2.02E-06 ENSDARG00000068934 1.320935065 7.37E-30 ENSDARG00000040252 0.601780149 0.0007119 ENSDARG00000070331 -0.656402331 3.07E-07 ENSDARG00000040284 0.639573585 5.40E-05 ENSDARG00000071567 0.620364059 1.57E-06 ENSDARG00000041382 -0.601469016 0.000252202 ENSDARG00000074752 0.592606836 3.61E-09 ENSDARG00000041394 0.740341252 4.70E-07 ENSDARG00000076830 -0.668542851 4.58E-08 ENSDARG00000042816 0.684379468 1.16E-07 ENSDARG00000077236 0.849848365 5.32E-08 ENSDARG00000042872 -0.621740765 0.000273288 ENSDARG00000077559 -0.617829132 1.78E-06 ENSDARG00000043806 -0.617141792 3.70E-08 ENSDARG00000078567 1.178036932 1.16E-32 ENSDARG00000052700 -0.629675446 1.12E-07 ENSDARG00000079119 0.614988999 2.60E-05 ENSDARG00000053131 0.617269227 5.34E-05 ENSDARG00000086826 1.267482403 2.18E-39 ENSDARG00000055253 0.61265589 0.000541255 ENSDARG00000089697 1.104427766 2.87E-16 ENSDARG00000055294 0.60316853 1.52E-05 ENSDARG00000091116 1.463882318 1.16E-25 ENSDARG00000056036 -0.732885494 1.42E-08 ENSDARG00000093237 0.589738554 0.001616586 ENSDARG00000056084 -0.605754078 0.000645925 ENSDARG00000097491 0.75251869 3.23E-20 ENSDARG00000060893 -0.663130847 2.06E-06 ENSDARG00000098315 1.371369503 1.84E-24 ENSDARG00000061274 -0.864486759 2.01E-08 ENSDARG00000098746 0.832314806 2.20E-09 ENSDARG00000061836 -0.586150522 2.81E-05 ENSDARG00000099702 0.84408309 1.40E-08 ENSDARG00000062661 -0.753330736 1.74E-06 ENSDARG00000101195 1.355612442 6.10E-28 ENSDARG00000074656 0.665304251 9.86E-05 ENSDARG00000101364 1.191617783 4.39E-16 ENSDARG00000076055 -0.676223756 2.58E-05 ENSDARG00000102558 0.696755161 7.69E-06 ENSDARG00000078052 -0.681512318 8.52E-08 ENSDARG00000103199 0.934608655 2.36E-09 ENSDARG00000078440 -0.780870561 1.27E-07 ENSDARG00000103456 -0.808022186 1.90E-07 ENSDARG00000078527 -0.613617759 1.54E-05 ENSDARG00000105341 0.604499793 3.24E-06 ENSDARG00000079946 -1.01064544 5.10E-13 ENSDARG00000105863 0.834514154 1.08E-07 ENSDARG00000088140 -0.728418038 7.47E-09 ENSDARG00000091234 0.845597194 5.01E-08 ENSDARG00000092269 -0.840810193 1.99E-08 ENSDARG00000094559 0.620753749 0.000380394 ENSDARG00000096313 -0.686644221 7.03E-06 ENSDARG00000097613 -0.717631802 5.64E-06 ENSDARG00000099336 -0.645879249 1.02E-05 ENSDARG00000099351 0.737269027 8.37E-06 ENSDARG00000102393 -0.618701879 0.000106315 ENSDARG00000103413 -0.624498733 1.10E-05 ENSDARG00000104773 0.594299318 0.000885497

270

DB(a,h)P DB(a,h)P Ensembl ID Fold Change (log2) Adjusted p-value (PADJ) Ensembl ID Fold Change (log2) Adjusted p-value (PADJ) ENSDARG00000001968 -0.609324013 0.00019658 ENSDARG00000062550 -0.60738386 1.59E-05 ENSDARG00000002026 -0.614482397 0.000189848 ENSDARG00000062687 -0.733936969 6.26E-07 ENSDARG00000002231 -0.738290128 3.87E-06 ENSDARG00000062812 -0.636636223 1.23E-06 ENSDARG00000003544 -0.631083073 9.24E-05 ENSDARG00000062884 -0.6468258 3.25E-05 ENSDARG00000004931 -0.65076722 8.51E-05 ENSDARG00000062974 -0.662671239 6.08E-05 ENSDARG00000005783 -0.641089311 0.000108881 ENSDARG00000063157 -0.593840465 0.000272554 ENSDARG00000005786 -0.596562099 0.000154538 ENSDARG00000063299 -0.59335752 1.95E-06 ENSDARG00000005915 -0.622899179 5.98E-05 ENSDARG00000063361 -0.766505324 2.19E-06 ENSDARG00000006094 -0.670413296 2.87E-05 ENSDARG00000063594 -0.640913235 0.000105435 ENSDARG00000006220 0.74083865 5.59E-08 ENSDARG00000063706 -0.609049085 0.000132382 ENSDARG00000008372 -0.645463383 1.33E-08 ENSDARG00000068787 -0.67998775 3.55E-05 ENSDARG00000008398 -0.710641348 1.36E-05 ENSDARG00000068934 1.084211602 7.15E-18 ENSDARG00000009493 -0.703074443 1.74E-05 ENSDARG00000069958 -0.591756155 0.000216565 ENSDARG00000010565 -0.732372715 5.90E-06 ENSDARG00000070214 -0.613680462 0.000190716 ENSDARG00000012468 -0.599477584 4.35E-06 ENSDARG00000070227 -0.629172348 0.000156533 ENSDARG00000012586 -0.589217278 4.08E-05 ENSDARG00000070331 -0.815995104 2.46E-10 ENSDARG00000012699 -0.838429155 1.10E-07 ENSDARG00000070575 -0.66434245 2.06E-05 ENSDARG00000013005 -0.592841693 0.000493954 ENSDARG00000071524 -0.626047293 6.13E-07 ENSDARG00000013020 -0.688991922 2.23E-05 ENSDARG00000071567 0.653514085 7.92E-07 ENSDARG00000013072 -0.644714274 1.59E-05 ENSDARG00000073743 -0.629780818 0.000127979 ENSDARG00000013828 -0.769512819 1.32E-06 ENSDARG00000073857 -0.609588347 8.40E-05 ENSDARG00000014169 -0.609987355 9.16E-05 ENSDARG00000074245 -0.609023958 1.49E-07 ENSDARG00000014854 -0.763066361 2.12E-06 ENSDARG00000074677 -0.686762048 1.51E-07 ENSDARG00000016348 -0.762327556 2.56E-07 ENSDARG00000074723 -0.629125468 0.000173977 ENSDARG00000016439 -0.671676859 2.40E-05 ENSDARG00000074905 -0.683436821 2.22E-05 ENSDARG00000016490 -0.851902264 1.01E-08 ENSDARG00000075230 -0.801427974 6.18E-07 ENSDARG00000016667 -0.790662111 6.18E-07 ENSDARG00000075928 -0.734130754 1.41E-06 ENSDARG00000016788 -0.659131145 1.64E-05 ENSDARG00000076041 -0.619845128 0.000228483 ENSDARG00000017220 -0.749878059 1.32E-06 ENSDARG00000076213 -0.597272072 0.000459242 ENSDARG00000017365 -0.659531156 6.72E-05 ENSDARG00000076292 -0.776798112 3.81E-07 ENSDARG00000017785 -0.673003886 2.80E-05 ENSDARG00000076830 -0.890142914 1.65E-13 ENSDARG00000018393 -0.754786794 1.66E-06 ENSDARG00000076856 -0.687177367 2.06E-05 ENSDARG00000018716 -0.601870112 9.53E-05 ENSDARG00000076991 -0.679748512 3.25E-05 ENSDARG00000018773 -0.712320906 1.20E-05 ENSDARG00000077384 -0.621697572 5.34E-05 ENSDARG00000018896 -0.721438684 4.88E-06 ENSDARG00000077474 -0.746870299 2.05E-06 ENSDARG00000019442 -0.702182599 1.42E-05 ENSDARG00000077656 -0.638161467 1.59E-05 ENSDARG00000019963 -0.652927019 8.47E-05 ENSDARG00000077686 -0.623647079 8.55E-06 ENSDARG00000020057 -0.74544283 3.22E-06 ENSDARG00000077761 -0.636615246 8.47E-05 ENSDARG00000020845 -0.77540083 2.64E-07 ENSDARG00000077909 -0.601934478 0.000224538 ENSDARG00000021305 -0.848815654 9.12E-08 ENSDARG00000078011 -0.719789416 9.69E-06 ENSDARG00000022101 -0.586722453 0.000219055 ENSDARG00000078187 -0.686730674 3.00E-05 ENSDARG00000025206 -0.662250495 1.24E-05 ENSDARG00000078227 -0.810800841 1.31E-07 ENSDARG00000025299 -0.600803577 0.000328921 ENSDARG00000078327 -0.593870325 0.000200554 ENSDARG00000027082 -0.628182273 6.24E-05 ENSDARG00000078567 0.673149496 1.01E-08 ENSDARG00000027088 0.703131756 3.21E-06 ENSDARG00000078775 -0.633515698 0.000127979 ENSDARG00000027497 -0.763922352 2.26E-06 ENSDARG00000078979 -0.678581263 1.06E-05 ENSDARG00000027777 -0.594994786 0.000476833 ENSDARG00000079064 -0.82221343 2.55E-07 ENSDARG00000028857 -0.869492979 4.46E-08 ENSDARG00000079119 0.66158002 4.15E-06 ENSDARG00000030896 0.686651493 1.32E-06 ENSDARG00000079295 -0.649245305 1.78E-05 ENSDARG00000031095 -0.605807818 0.000339938 ENSDARG00000079491 -0.666508537 9.07E-06 ENSDARG00000032197 -0.627442742 8.11E-05 ENSDARG00000079741 -0.7071633 1.08E-05 ENSDARG00000032761 -0.680102525 3.52E-05 ENSDARG00000079742 -0.62034125 0.000175457 ENSDARG00000032990 -0.707359429 8.97E-06 ENSDARG00000079811 -0.613389842 9.55E-06 ENSDARG00000033498 -0.61064494 6.18E-07 ENSDARG00000079878 -0.848349057 8.24E-08 ENSDARG00000033840 -0.657785229 9.37E-06 ENSDARG00000086224 -0.667397813 3.46E-05 ENSDARG00000035133 -0.630951876 4.94E-06 ENSDARG00000086826 1.236986657 1.49E-33 ENSDARG00000035308 -0.655878745 6.23E-05 ENSDARG00000087822 -0.691818409 1.55E-05 ENSDARG00000036626 -0.665085801 5.50E-05 ENSDARG00000088136 -0.721453541 1.22E-07 ENSDARG00000036826 -0.734037605 3.33E-06 ENSDARG00000089507 0.615173955 1.59E-05

271

9-MA Fluoranthene Ensembl ID Fold Change (log2) Adjusted p-value (PADJ) Ensembl ID Fold Change (log2) Adjusted p-value (PADJ) ENSDARG00000001910 -0.632075375 3.09E-11 ENSDARG00000002311 -0.81661758 1.12E-08 ENSDARG00000001993 -0.78648393 3.20E-07 ENSDARG00000010658 -0.763580876 1.97E-15 ENSDARG00000004358 -0.851478338 1.33E-07 ENSDARG00000012499 0.611495954 6.30E-08 ENSDARG00000006467 -0.77800616 3.09E-11 ENSDARG00000019949 -0.730801372 4.07E-07 ENSDARG00000011640 -0.894017465 3.77E-10 ENSDARG00000028804 -0.658445833 0.000278178 ENSDARG00000012126 -0.64768151 6.17E-05 ENSDARG00000034503 0.592259263 0.000264847 ENSDARG00000014233 -0.585669734 0.000541752 ENSDARG00000038729 0.596071352 0.001651251 ENSDARG00000015732 -0.60260122 0.000162149 ENSDARG00000042816 0.61856933 6.79E-06 ENSDARG00000016854 -0.859356185 3.87E-08 ENSDARG00000045768 1.198790017 2.00E-26 ENSDARG00000019902 -0.714339532 3.07E-08 ENSDARG00000058606 -0.928957779 1.07E-21 ENSDARG00000023082 -0.605607609 0.000310659 ENSDARG00000058679 -0.649130944 3.16E-06 ENSDARG00000028804 -0.590427624 0.000934757 ENSDARG00000059001 -0.654176599 1.12E-08 ENSDARG00000034007 -0.786259718 7.37E-09 ENSDARG00000061274 -0.804473854 3.25E-07 ENSDARG00000037588 -0.841124501 1.00E-10 ENSDARG00000071341 0.97756485 5.03E-22 ENSDARG00000037921 -0.853079187 3.09E-11 ENSDARG00000078567 1.263934684 3.51E-38 ENSDARG00000041382 -0.657244114 7.90E-05 ENSDARG00000088137 0.585657756 2.11E-06 ENSDARG00000042872 -0.588451466 0.000923683 ENSDARG00000088140 -0.791845685 2.20E-11 ENSDARG00000043806 -0.819475202 1.81E-12 ENSDARG00000098761 -0.671698779 0.000198162 ENSDARG00000052700 -0.618414195 1.70E-06 ENSDARG00000102435 0.608520737 0.000980819 ENSDARG00000053364 -0.679505518 1.70E-06 ENSDARG00000104687 0.781366681 5.96E-07 ENSDARG00000054562 0.599415213 0.000568706 ENSDARG00000105341 0.781072638 1.48E-10 ENSDARG00000056036 -0.8133778 3.77E-10 ENSDARG00000056084 -0.641236566 0.000205328 ENSDARG00000058606 -0.699259773 2.91E-10 ENSDARG00000060893 -0.632185948 2.53E-05 ENSDARG00000062661 -0.724792144 9.65E-06 ENSDARG00000074656 0.620786042 0.000345325 ENSDARG00000077341 -0.67149166 8.32E-11 ENSDARG00000078440 -0.872068031 5.80E-09 ENSDARG00000078557 0.698857022 2.69E-05 ENSDARG00000079946 -0.613207518 0.000128552 ENSDARG00000089156 -0.615929626 0.00046005 ENSDARG00000092269 -0.833740846 3.87E-08 ENSDARG00000092759 -0.660255515 8.27E-05 ENSDARG00000096313 -0.709117788 7.47E-06 ENSDARG00000097613 -0.599302852 0.00047837 ENSDARG00000098761 -0.613559823 0.000495061 ENSDARG00000099299 -0.607841475 0.000551265 ENSDARG00000101364 0.734486383 1.40E-05 ENSDARG00000102113 0.663263418 1.19E-06 ENSDARG00000102393 -0.695821947 1.67E-05 ENSDARG00000104329 -0.603097689 0.000668252 ENSDARG00000107353 -0.597989077 5.98E-05

272

BbF BbF Ensembl ID Fold Change (log2) Adjusted p-value (PADJ) Ensembl ID Fold Change (log2) Adjusted p-value (PADJ) ENSDARG00000002026 -0.657653786 7.75E-05 ENSDARG00000061248 -0.685969535 7.76E-06 ENSDARG00000003544 -0.635397921 0.000211227 ENSDARG00000061481 1.191275554 1.78E-18 ENSDARG00000003902 0.683667137 6.47E-05 ENSDARG00000061634 0.627553942 4.36E-08 ENSDARG00000004789 -0.596977469 0.000660872 ENSDARG00000061635 -0.705270564 3.45E-05 ENSDARG00000004931 -0.654092161 0.000168606 ENSDARG00000061896 1.061142725 3.96E-15 ENSDARG00000005713 0.590941111 1.75E-07 ENSDARG00000061992 -0.678052366 2.76E-05 ENSDARG00000005915 -0.68066128 7.91E-06 ENSDARG00000062248 -0.62440086 0.000353792 ENSDARG00000006094 -0.679314514 2.81E-05 ENSDARG00000062974 -0.674403241 9.46E-05 ENSDARG00000006220 0.972090681 5.51E-16 ENSDARG00000063361 -0.689001338 5.75E-05 ENSDARG00000008398 -0.645248 0.000178678 ENSDARG00000063594 -0.657171014 0.000151813 ENSDARG00000010565 -0.715510647 2.48E-05 ENSDARG00000068934 1.175479242 1.11E-23 ENSDARG00000012499 0.679144197 1.81E-10 ENSDARG00000070214 -0.59662117 0.000646547 ENSDARG00000012586 -0.599298004 2.38E-05 ENSDARG00000071567 0.877412478 3.71E-14 ENSDARG00000012699 -0.6216307 0.000374291 ENSDARG00000073912 0.888449289 4.32E-09 ENSDARG00000014854 -0.700778769 2.79E-05 ENSDARG00000074245 -0.588470266 8.16E-08 ENSDARG00000016348 -0.615446982 6.25E-05 ENSDARG00000074723 -0.639368924 0.000228509 ENSDARG00000016490 -0.682575593 7.50E-06 ENSDARG00000074905 -0.604981669 0.000327156 ENSDARG00000016667 -0.650920613 0.000102112 ENSDARG00000075230 -0.640666939 0.000184856 ENSDARG00000017034 0.728281953 1.60E-10 ENSDARG00000075928 -0.608938442 0.000116406 ENSDARG00000017220 -0.643203113 6.39E-05 ENSDARG00000076041 -0.596764357 0.000646547 ENSDARG00000017365 -0.610573774 0.000497457 ENSDARG00000076292 -0.603478525 0.000179576 ENSDARG00000018773 -0.640692758 0.000181012 ENSDARG00000076830 -0.787058665 1.24E-11 ENSDARG00000019949 -0.724644139 2.58E-07 ENSDARG00000076856 -0.613942547 0.000270498 ENSDARG00000019963 -0.622664616 0.000356182 ENSDARG00000076991 -0.690496849 5.94E-05 ENSDARG00000020845 -0.588778801 0.000208149 ENSDARG00000078227 -0.602037987 0.000226325 ENSDARG00000021305 -0.664092819 0.00011921 ENSDARG00000078327 -0.622194103 0.000220972 ENSDARG00000025206 -0.682433853 2.30E-05 ENSDARG00000078567 1.047103071 4.46E-26 ENSDARG00000026988 -0.601890357 0.000400023 ENSDARG00000079064 -0.651453035 0.000139961 ENSDARG00000027088 0.610845345 8.59E-05 ENSDARG00000079491 -0.602560285 8.58E-05 ENSDARG00000027497 -0.633014503 0.000242707 ENSDARG00000079741 -0.592618875 0.000711442 ENSDARG00000028396 0.654870381 1.86E-05 ENSDARG00000079878 -0.602510047 0.000628411 ENSDARG00000028857 -0.633073209 0.000277158 ENSDARG00000086826 1.395004298 6.59E-49 ENSDARG00000030896 0.874958674 5.83E-12 ENSDARG00000089507 0.746373335 1.36E-08 ENSDARG00000033840 -0.597911912 8.04E-05 ENSDARG00000089697 1.021400449 5.69E-14 ENSDARG00000034503 0.709738445 7.16E-07 ENSDARG00000089936 0.650671841 5.80E-06 ENSDARG00000035127 -0.594719251 0.000660872 ENSDARG00000091116 0.905676582 3.35E-09 ENSDARG00000035133 -0.66686804 3.98E-07 ENSDARG00000091130 -0.726696878 1.37E-05 ENSDARG00000035308 -0.651943993 0.000116406 ENSDARG00000091715 0.629759157 0.000122673 ENSDARG00000036156 -0.599064013 0.000660872 ENSDARG00000097491 0.912882205 8.13E-31 ENSDARG00000036626 -0.651087259 0.000179576 ENSDARG00000098315 1.162397185 2.71E-17 ENSDARG00000036985 -0.587423013 0.00028935 ENSDARG00000098511 -0.76861372 2.80E-06 ENSDARG00000039901 -0.647107907 0.000183612 ENSDARG00000098605 -0.674647063 5.05E-07 ENSDARG00000042492 -0.636118551 0.000126668 ENSDARG00000098746 1.159594951 1.16E-19 ENSDARG00000043627 -0.613518471 0.000288486 ENSDARG00000098857 -0.657001096 8.84E-05 ENSDARG00000043646 -0.607306896 0.000435736 ENSDARG00000099024 0.630366072 0.000179576 ENSDARG00000045768 1.094744825 3.06E-22 ENSDARG00000099096 -0.619832303 0.000277158 ENSDARG00000052215 0.616506834 7.87E-07 ENSDARG00000099199 -0.592547736 0.000220972 ENSDARG00000052618 0.641182574 0.000116774 ENSDARG00000099364 -0.621411091 0.000379866 ENSDARG00000054003 -0.608801627 0.000330575 ENSDARG00000099437 -0.598663744 0.000660872 ENSDARG00000054771 -0.687226298 2.17E-05 ENSDARG00000099702 0.591263024 0.000305895 ENSDARG00000055154 -0.676981035 2.30E-05 ENSDARG00000100592 -0.694481364 4.28E-05 ENSDARG00000056885 0.767631083 3.70E-06 ENSDARG00000101195 2.38222324 1.02E-90 ENSDARG00000057107 -0.591611988 0.000660872 ENSDARG00000101364 1.208067692 1.03E-16 ENSDARG00000057903 -0.68217844 6.83E-05 ENSDARG00000101687 -0.654803538 0.000150703 ENSDARG00000058248 -0.651824278 0.000178678 ENSDARG00000101789 1.2627428 1.62E-42 ENSDARG00000058606 -1.059650234 6.64E-29 ENSDARG00000102064 -0.626653298 0.000229846 ENSDARG00000058679 -0.737611841 1.24E-08 ENSDARG00000102142 -0.612318917 0.000474083 ENSDARG00000058702 -0.602220824 0.000367215 ENSDARG00000102478 -0.624807299 0.000181012 ENSDARG00000058771 -0.661412257 0.000116774 ENSDARG00000102612 -0.5992177 0.000342459

273

Fluoranthene Acenaphthene Ensembl ID Fold Change (log2) Adjusted p-value (PADJ) Ensembl ID Fold Change (log2) Adjusted p-value (PADJ) ENSDARG00000002311 -0.81661758 1.12E-08 ENSDARG00000019949 -0.607796138 0.001249424 ENSDARG00000010658 -0.763580876 1.97E-15 ENSDARG00000045768 0.713511498 9.34E-07 ENSDARG00000012499 0.611495954 6.30E-08 ENSDARG00000058606 -0.64983389 7.73E-08 ENSDARG00000019949 -0.730801372 4.07E-07 ENSDARG00000061896 0.611273472 0.001851856 ENSDARG00000028804 -0.658445833 0.000278178 ENSDARG00000034503 0.592259263 0.000264847 ENSDARG00000038729 0.596071352 0.001651251 ENSDARG00000042816 0.61856933 6.79E-06 ENSDARG00000045768 1.198790017 2.00E-26 ENSDARG00000058606 -0.928957779 1.07E-21 ENSDARG00000058679 -0.649130944 3.16E-06 Anthracene ENSDARG00000059001 -0.654176599 1.12E-08 Ensembl ID Fold Change (log2) Adjusted p-value (PADJ) ENSDARG00000061274 -0.804473854 3.25E-07 ENSDARG00000092759 -0.597863646 0.042227174 ENSDARG00000071341 0.97756485 5.03E-22 ENSDARG00000078567 1.263934684 3.51E-38 ENSDARG00000088137 0.585657756 2.11E-06 ENSDARG00000088140 -0.791845685 2.20E-11 ENSDARG00000098761 -0.671698779 0.000198162 ENSDARG00000102435 0.608520737 0.000980819 ENSDARG00000104687 0.781366681 5.96E-07 ENSDARG00000105341 0.781072638 1.48E-10

2-MN Phenanthrene Ensembl ID Fold Change (log2) Adjusted p-value (PADJ) Ensembl ID Fold Change (log2) Adjusted p-value (PADJ) ENSDARG00000101628 -0.590897341 5.40E-06 ENSDARG00000012499 0.659104168 5.48E-09 ENSDARG00000030896 -0.599608093 0.000476744 ENSDARG00000036028 -0.599570964 7.78E-08 ENSDARG00000045768 1.117901198 1.29E-22 ENSDARG00000058606 -1.01032469 3.65E-25 ENSDARG00000059001 -0.653922723 2.43E-08 ENSDARG00000061896 0.619479419 0.000837885 ENSDARG00000078567 1.040608871 5.50E-25 ENSDARG00000088745 0.659863277 0.000837885 ENSDARG00000099024 0.61193939 0.002943549

274

Table A.3. Top 200 genes by coefficient of variation (CV) across the 18 treatments (16 PAHs, 2 controls).

4h- Carbazo DB(a,i) DB(a,h) Fluoran 1,5- Acenap Phenant Anthrac ENSEMBL ID GeneSymbol Product Retene BkF 3-NF BjF 9 -MA BbF 2 -MN CPdefP le P P thene DMN hthene hrene ene ENSDARG00000101195 CYP1C1 cytochrome P450, family 1, subfamily C, polypeptide 1 [Source:ZFIN;Acc:ZDB-GENE-050522-501] 0.283 4.147 3.104 0.375 2.511 0.506 1.356 1.397 0.450 2.382 0.493 0.232 0.152 -0.063 0.256 0.523

ENSDARG00000086826 SULT6B1 sulfotransferase family, cytosolic, 6b, member 1 [Source:ZFIN;Acc:ZDB-GENE-050417-228] 0.292 1.963 2.067 0.273 2.160 0.330 1.267 1.237 0.373 1.395 0.337 0.075 0.151 0.108 0.146 0.201 ENSDARG00000101789 CYP1C2 cytochrome P450, family 1, subfamily C, polypeptide 2 [Source:ZFIN;Acc:ZDB-GENE-050705-1] 0.267 3.165 1.948 0.343 1.446 0.014 0.528 0.440 0.303 1.263 0.380 0.214 0.008 0.162 0.205 0.270 WAP, follistatin/kazal, immunoglobulin, kunitz and netrin domain containing 1 ENSDARG00000101364 WFIKKN1 0.358 2.632 2.092 0.215 1.963 0.567 1.192 1.180 0.734 1.208 0.503 0.316 0.319 -0.033 0.191 0.449 [Source:ZFIN;Acc:ZDB-GENE-040426-2465] WU:FJ05G0 ENSDARG00000061481 wu:fj05g07 [Source:ZFIN;Acc:ZDB-GENE-030131-7307] 0.535 1.196 0.744 0.443 1.151 0.121 0.690 0.898 0.418 1.191 0.278 0.258 0.252 0.175 0.402 0.133 7 ENSDARG00000068934 CYP1B1 cytochrome P450, family 1, subfamily B, polypeptide 1 [Source:ZFIN;Acc:ZDB-GENE-030902-1] 0.327 2.129 2.673 -0.012 2.128 0.391 1.321 1.084 0.460 1.175 0.109 0.138 0.073 0.145 0.055 0.307 ENSDARG00000098315 CYP1A cytochrome P450, family 1, subfamily A [Source:ZFIN;Acc:ZDB-GENE-011219-1] 0.094 2.055 2.179 0.237 2.080 0.146 1.371 1.216 0.307 1.162 0.257 0.039 0.161 0.023 0.114 0.325 dehydrogenase/reductase (SDR family) member 13 like 1 [Source:ZFIN;Acc:ZDB-GENE-040426- ENSDARG00000098746 DHRS13L1 0.342 1.707 0.986 0.166 1.370 0.278 0.832 0.821 0.085 1.160 0.267 -0.050 0.193 -0.055 0.274 0.062 1907] ENSDARG00000104068 GSTP1 glutathione S-transferase pi 1 [Source:ZFIN;Acc:ZDB-GENE-020806-4] -0.069 1.962 0.988 0.279 1.817 0.157 0.541 0.592 0.371 1.100 0.131 0.113 0.080 0.095 0.049 0.192 ENSDARG00000045768 CRY1AA cryptochrome circadian clock 1aa [Source:ZFIN;Acc:ZDB-GENE-010426-2] 0.187 0.578 0.723 -0.044 0.424 -0.043 1.200 0.734 0.364 1.095 1.199 0.087 0.714 0.112 1.118 0.097 solute carrier organic anion transporter family, member 2A1 [Source:ZFIN;Acc:ZDB-GENE- ENSDARG00000061896 SLCO2A1 -0.017 0.877 1.233 0.027 1.194 -0.056 1.254 1.082 0.391 1.061 0.381 0.204 0.611 0.242 0.619 0.127 060606-3] LON peptidase N-terminal domain and ring finger 1, like [Source:ZFIN;Acc:ZDB-GENE-081104- ENSDARG00000078567 LONRF1L 0.156 0.799 0.822 0.243 0.455 0.095 1.178 0.673 0.169 1.047 1.264 0.075 0.485 0.150 1.041 0.179 397] ENSDARG00000089697 NFE2L2B nuclear factor, erythroid 2-like 2b [Source:ZFIN;Acc:ZDB-GENE-120320-3] 0.324 1.093 1.454 0.121 1.169 0.474 1.104 1.058 0.432 1.021 0.560 0.084 0.565 0.181 0.435 0.257 ENSDARG00000006220 UGT1A B UDP glucuronosyltransferase 1 family a, b [Source:ZFIN;Acc:ZDB-GENE-040426-2762] 0.398 1.310 0.865 0.148 1.169 0.505 0.738 0.741 0.383 0.972 0.284 0.040 0.229 0.170 0.093 0.303 ENSDARG00000097491 UGT1B1 UDP glucuronosyltransferase 1 family, polypeptide B1 [Source:ZFIN;Acc:ZDB-GENE-080227-10] 0.294 1.660 1.270 0.287 1.260 0.214 0.753 0.733 0.276 0.913 0.320 0.157 0.050 -0.083 0.172 0.113 polycystic kidney and hepatic disease 1 (autosomal recessive)-like 1 [Source:ZFIN;Acc:ZDB- ENSDARG00000091116 PKHD1L1 0.314 1.008 2.372 -0.013 2.011 -0.115 1.464 1.143 0.200 0.906 0.351 -0.057 -0.035 -0.083 0.163 0.177 GENE-060503-475] SI:CH211- ENSDARG00000073912 si:ch211-202h22.7 [Source:ZFIN;Acc:ZDB-GENE-090313-77] 0.360 0.750 0.587 0.172 0.578 0.130 0.490 0.358 0.183 0.888 0.075 0.035 0.077 0.179 0.027 0.146 202H22.7 ENSDARG00000071567 TSTD1 si:dkey-34l15.1 [Source:ZFIN;Acc:ZDB-GENE-081107-63] 0.087 1.224 0.742 0.185 0.972 0.211 0.620 0.654 0.343 0.877 0.074 0.104 0.242 0.101 0.103 0.188 ENSDARG00000030896 FOXQ1A forkhead box Q1a [Source:ZFIN;Acc:ZDB-GENE-070424-74] -0.252 1.498 2.111 0.218 1.790 -0.382 0.729 0.687 -0.180 0.875 -0.355 0.006 -0.450 0.113 -0.600 0.319 ENSDARG00000056885 PER1A period circadian clock 1a [Source:ZFIN;Acc:ZDB-GENE-011220-1] -0.047 0.283 0.485 -0.045 0.430 -0.085 0.909 0.446 0.040 0.768 0.299 0.090 0.459 0.229 0.570 0.049 ENSDARG00000089507 UGT1B5 UDP glucuronosyltransferase 1 family, polypeptide B5 [Source:ZFIN;Acc:ZDB-GENE-080227-14] 0.279 1.223 1.007 0.199 0.747 0.340 0.526 0.615 0.293 0.746 0.211 0.215 -0.208 -0.066 0.191 0.060 ENSDARG00000017034 SQRDL sulfide quinone reductase-like (yeast) [Source:ZFIN;Acc:ZDB-GENE-050417-436] -0.073 0.770 0.390 0.208 0.809 0.141 0.511 0.488 0.368 0.728 0.195 0.176 0.234 -0.051 0.228 0.180 ENSDARG00000034503 PER2 period circadian clock 2 [Source:ZFIN;Acc:ZDB-GENE-011220-2] 0.259 0.236 0.218 0.180 0.069 0.187 0.811 0.295 0.148 0.710 0.592 0.047 0.140 0.005 0.560 0.096 ENSDARG00000003902 CTSL.1 cathepsin L.1 [Source:ZFIN;Acc:ZDB-GENE-040718-61] 0.007 0.878 0.610 0.172 0.666 0.172 0.405 0.371 0.230 0.684 0.237 -0.042 0.156 0.221 0.155 0.145 ENSDARG00000012499 PER1B period circadian clock 1b [Source:ZFIN;Acc:ZDB-GENE-040419-1] 0.164 0.528 0.403 -0.470 0.211 -0.562 0.575 0.361 0.417 0.679 0.611 0.250 0.481 0.199 0.659 -0.020 ENSDARG00000028396 FKBP5 FK506 binding protein 5 [Source:ZFIN;Acc:ZDB-GENE-030616-630] -0.079 0.091 0.525 -0.083 0.375 -0.130 1.019 0.389 -0.056 0.655 -0.018 0.130 0.454 0.070 0.351 0.073 ENSDARG00000089936 SEPW2B selenoprotein W, 2b [Source:ZFIN;Acc:ZDB-GENE-030428-2] 0.246 1.083 0.595 0.043 0.731 0.135 0.530 0.498 0.218 0.651 0.120 0.083 0.134 -0.074 0.198 0.148 ENSDARG00000052618 AHRRB aryl-hydrocarbon receptor repressor b [Source:ZFIN;Acc:ZDB-GENE-051018-2] 0.071 1.695 1.396 0.140 1.282 0.040 1.104 0.703 0.204 0.641 0.269 0.061 0.246 0.077 0.015 0.146 SI:CH211- ENSDARG00000099024 si:ch211-188p14.5 [Source:ZFIN;Acc:ZDB-GENE-121214-312] 0.525 0.424 -0.160 0.465 0.335 0.205 0.303 0.616 0.328 0.630 0.393 0.257 0.484 0.185 0.612 0.509 188P14.5 SI:DKEY- ENSDARG00000091715 si:dkey-162h11.2 [Source:ZFIN;Acc:ZDB-GENE-121214-90] 0.255 0.594 0.739 0.018 0.774 0.097 0.398 0.429 0.203 0.630 0.266 0.032 0.083 0.206 0.349 0.096 162H11.2 apoptosis-inducing factor, mitochondrion-associated, 4 [Source:ZFIN;Acc:ZDB-GENE-070112- ENSDARG00000061634 AIFM4 -0.105 1.199 0.417 0.354 0.838 0.047 0.262 0.377 0.085 0.628 -0.128 0.089 -0.042 -0.017 -0.133 0.200 2282] ENSDARG00000052215 BTR02 bloodthirsty-related gene family, member 2 [Source:ZFIN;Acc:ZDB-GENE-060825-228] 0.212 0.629 0.787 -0.062 0.751 0.006 0.462 0.415 0.264 0.617 0.100 -0.012 0.085 0.300 0.112 0.259 ENSDARG00000027088 PTGDSB.1 prostaglandin D2 synthase b, tandem duplicate 1 [Source:ZFIN;Acc:ZDB-GENE-030131-8436] -0.018 0.679 1.063 0.323 0.919 -0.044 0.777 0.703 0.069 0.611 0.181 0.092 0.264 0.354 0.167 0.056 ENSDARG00000104292 CTGFB connective tissue growth factor b [Source:ZFIN;Acc:ZDB-GENE-070705-82] 0.285 1.194 0.833 0.205 0.632 -0.032 0.351 0.305 0.257 0.600 0.179 0.039 0.235 0.054 0.178 0.143 CABZ01103 ENSDARG00000099702 N/A 0.020 2.047 1.613 0.100 1.097 -0.040 0.844 0.624 0.131 0.591 0.173 -0.043 0.145 0.016 0.011 0.040 755.1 ENSDARG00000005713 ETHE1 ethylmalonic encephalopathy 1 [Source:ZFIN;Acc:ZDB-GENE-040426-2503] -0.164 0.983 0.471 0.166 0.999 0.013 0.229 0.329 0.139 0.591 -0.146 -0.017 -0.082 -0.016 -0.099 0.104 ENSDARG00000076554 CDKN1A cyclin-dependent kinase inhibitor 1A [Source:ZFIN;Acc:ZDB-GENE-070705-7] 0.309 0.086 0.130 0.029 0.135 0.228 0.051 0.087 0.122 0.567 0.040 0.026 0.283 0.401 0.097 0.086 ENSDARG00000089586 NCAM3 neural cell adhesion molecule 3 [Source:ZFIN;Acc:ZDB-GENE-131127-340] 0.244 1.142 0.639 0.092 0.531 0.201 0.345 0.459 0.223 0.558 0.309 0.056 0.278 0.182 -0.015 0.171 ENSDARG00000102887 ABHD4 abhydrolase domain containing 4 [Source:ZFIN;Acc:ZDB-GENE-050417-83] -0.131 0.579 0.306 0.059 0.577 0.200 0.347 0.350 0.307 0.547 0.274 0.078 0.113 -0.001 0.110 0.034

275

SI:CH211- ENSDARG00000079119 si:ch211-229d2.5 [Source:ZFIN;Acc:ZDB-GENE-121214-200] -0.110 0.523 0.927 0.016 0.748 0.042 0.615 0.662 -0.095 0.545 0.033 0.043 0.020 0.107 -0.143 0.082 229D2.5 ENSDARG00000043442 MAL zgc:158773 [Source:ZFIN;Acc:ZDB-GENE-070424-9] 0.269 0.720 0.761 0.168 0.649 0.164 0.460 0.473 0.202 0.545 0.321 0.121 0.115 0.145 0.211 0.124 ATP-binding cassette, sub-family B (MDR/TAP), member 5 [Source:ZFIN;Acc:ZDB-GENE- ENSDARG00000021787 ABCB5 0.611 0.760 0.213 0.320 0.438 0.289 0.139 0.327 0.434 0.544 0.247 0.205 0.132 0.042 0.335 0.196 030131-6414] ENSDARG00000017195 FOXF2A forkhead box F2a [Source:ZFIN;Acc:ZDB-GENE-110407-5] -0.017 0.894 0.520 0.061 0.387 0.228 0.317 0.325 0.134 0.533 0.123 0.289 0.038 0.155 0.083 0.178 ENSDARG00000074656 CTSS2.1 cathepsin S, ortholog2, tandem duplicate 1 [Source:ZFIN;Acc:ZDB-GENE-050522-559] 0.359 0.548 0.535 0.193 0.661 0.665 0.515 0.341 0.621 0.532 0.369 0.031 0.328 0.322 0.259 0.258 ENSDARG00000055974 TPMT.1 thiopurine S-methyltransferase, tandem duplicate 1 [Source:ZFIN;Acc:ZDB-GENE-050522-141] -0.024 0.993 0.261 0.159 0.654 0.084 0.375 0.172 0.114 0.522 0.165 -0.030 -0.123 0.024 -0.018 0.033 ENSDARG00000075014 SQSTM1 sequestosome 1 [Source:ZFIN;Acc:ZDB-GENE-040426-2204] 0.315 0.574 0.113 0.410 0.507 0.560 0.083 0.152 0.261 0.521 0.209 0.094 0.015 0.295 -0.010 0.042 ENSDARG00000074149 ITPR1B inositol 1,4,5-trisphosphate receptor, type 1b [Source:ZFIN;Acc:ZDB-GENE-070604-2] 0.416 0.698 0.456 0.310 0.475 0.327 0.318 0.497 0.280 0.520 0.425 0.250 0.367 0.277 0.409 0.235 ENSDARG00000104687 SLC16A9B solute carrier family 16, member 9b [Source:ZFIN;Acc:ZDB-GENE-040801-69] 0.708 0.211 0.219 -0.075 0.188 0.560 0.296 0.311 0.235 0.511 0.781 0.150 0.422 0.131 0.189 0.058 ENSDARG00000068194 KLF9 Kruppel-like factor 9 [Source:ZFIN;Acc:ZDB-GENE-060526-244] 0.023 0.192 0.270 0.060 0.307 -0.064 0.668 0.194 0.001 0.505 0.067 0.173 0.438 0.221 0.179 0.065 ENSDARG00000089706 ANPEP si:ch211-276a23.5 [Source:ZFIN;Acc:ZDB-GENE-141215-4] -0.009 0.547 0.358 0.076 0.373 0.106 0.287 0.372 0.019 0.498 0.132 -0.021 -0.065 0.036 -0.162 0.185 ENSDARG00000078683 RNF14 ring finger protein 14 [Source:HGNC Symbol;Acc:HGNC:10058] 0.499 0.058 -0.075 0.171 -0.128 0.435 -0.006 -0.026 0.260 0.494 0.041 0.122 -0.048 0.231 0.093 -0.033 ENSDARG00000077236 HSPB6 heat shock protein, alpha-crystallin-related, b6 [Source:ZFIN;Acc:ZDB-GENE-080214-7] 0.075 0.222 0.478 0.040 0.291 0.008 0.850 0.355 -0.141 0.492 -0.005 0.247 0.391 0.026 0.172 -0.063 SI:CH211- ENSDARG00000093048 si:ch211-158d24.4 [Source:ZFIN;Acc:ZDB-GENE-081104-141] 0.251 0.494 0.651 0.051 0.451 0.229 0.195 0.088 0.055 0.491 0.054 0.228 0.066 0.329 0.185 0.239 158D24.4 SI:CH211- ENSDARG00000097157 si:ch211-207n23.2 [Source:ZFIN;Acc:ZDB-GENE-131121-310] -0.080 0.384 1.237 -0.101 0.890 -0.011 0.500 0.343 0.183 0.491 0.045 0.063 0.048 0.022 0.041 0.096 207N23.2 apoptosis-inducing factor, mitochondrion-associated, 2 [Source:ZFIN;Acc:ZDB-GENE-050506- ENSDARG00000077549 AIFM2 -0.063 0.641 0.409 0.093 0.512 0.048 0.286 0.268 0.099 0.490 -0.035 0.061 0.065 -0.015 0.203 -0.081 76] solute carrier family 6 (neurotransmitter transporter), member 11a [Source:ZFIN;Acc:ZDB-GENE- ENSDARG00000074002 SLC6A11A 0.286 0.362 0.382 0.143 0.413 0.298 0.287 0.338 0.198 0.477 0.082 0.106 0.122 0.060 0.244 0.119 030131-3729] ENSDARG00000015793 CREB3L1 cAMP responsive element binding protein 3-like 1 [Source:ZFIN;Acc:ZDB-GENE-030219-181] 0.040 0.021 0.291 -0.041 0.254 0.059 0.477 0.483 0.176 0.474 0.273 0.147 0.291 0.172 0.466 0.173 SI:CH211- ENSDARG00000100190 si:ch211-188p14.4 [Source:ZFIN;Acc:ZDB-GENE-121214-355] 0.353 0.282 -0.286 0.105 0.202 0.105 0.146 0.431 0.061 0.466 0.263 0.152 0.102 0.119 0.435 0.274 188P14.4 ENSDARG00000060246 SLC16A6B solute carrier family 16, member 6b [Source:ZFIN;Acc:ZDB-GENE-110208-3] 0.411 0.051 0.221 -0.302 0.082 0.246 0.301 0.208 0.142 0.466 0.541 0.149 0.364 0.059 0.103 -0.083 ENSDARG00000055186 CCR9A chemokine (C-C motif) receptor 9a [Source:ZFIN;Acc:ZDB-GENE-060130-125] -0.088 0.588 0.184 -0.017 0.341 -0.035 0.262 0.198 -0.079 0.453 0.047 -0.095 0.177 -0.014 0.027 0.163 ENSDARG00000021149 CBR1L carbonyl reductase 1-like [Source:ZFIN;Acc:ZDB-GENE-030131-9642] 0.071 0.927 0.333 0.293 0.528 0.206 0.165 0.312 0.302 0.453 0.059 0.010 0.179 0.055 0.213 0.013 ENSDARG00000017739 AK5L adenylate kinase 5, like [Source:ZFIN;Acc:ZDB-GENE-050410-2] 0.023 0.462 0.596 0.296 0.439 0.293 0.441 0.511 0.262 0.450 0.157 0.270 0.289 0.294 0.245 0.240 ENSDARG00000033160 NR1D1 nuclear receptor subfamily 1, group d, member 1 [Source:ZFIN;Acc:ZDB-GENE-050105-1] 0.114 0.313 0.315 0.037 0.233 0.037 0.288 0.263 0.152 0.450 0.103 0.181 0.305 0.185 0.165 0.105 ENSDARG00000068876 ZGC:153031 zgc:153031 [Source:ZFIN;Acc:ZDB-GENE-060929-1190] 0.164 0.460 0.856 0.064 0.386 0.176 0.448 0.545 0.186 0.449 -0.093 0.030 -0.048 -0.038 0.012 -0.113 ENSDARG00000071626 PTGDSB.2 prostaglandin D2 synthase b, tandem duplicate 2 [Source:ZFIN;Acc:ZDB-GENE-030911-3] 0.005 0.398 0.725 0.086 0.710 0.052 0.584 0.454 0.108 0.446 0.153 -0.120 -0.035 0.095 -0.013 -0.043 SI:CABZ010 ENSDARG00000105590 si:cabz01007794.1 [Source:ZFIN;Acc:ZDB-GENE-160728-147] 0.338 0.349 0.337 0.112 0.535 0.091 0.337 0.390 0.183 0.446 0.245 0.087 0.140 0.025 0.293 0.117 07794.1 SI:CH211- ENSDARG00000098620 si:ch211-188p14.2 [Source:ZFIN;Acc:ZDB-GENE-121214-329] 0.324 0.290 -0.194 0.075 0.308 0.069 0.100 0.311 0.160 0.445 0.293 0.108 0.216 0.063 0.478 0.420 188P14.2 ENSDARG00000038729 S100Z S100 calcium binding protein Z [Source:ZFIN;Acc:ZDB-GENE-050522-69] -0.021 0.902 0.435 0.213 0.964 0.278 0.433 0.198 0.279 0.445 0.596 0.166 0.389 0.057 0.255 0.142 ENSDARG00000009123 SELE selectin E [Source:ZFIN;Acc:ZDB-GENE-041014-221] -0.018 0.237 0.414 0.013 0.325 0.262 0.145 0.285 0.011 0.441 0.182 -0.020 0.158 0.053 0.007 0.053 pleckstrin homology domain containing, family F (with FYVE domain) member 1 ENSDARG00000102435 PLEKHF1 0.444 0.040 0.041 0.254 0.097 0.476 0.322 0.317 0.430 0.438 0.609 0.213 0.087 0.135 0.560 0.080 [Source:ZFIN;Acc:ZDB-GENE-040426-1289] CABZ01020 ENSDARG00000090401 N/A 0.531 0.660 0.155 0.116 0.219 0.512 0.153 0.040 0.291 0.437 0.301 -0.004 0.194 0.208 0.201 0.119 840.1 ENSDARG00000098058 IM:7150988 im:7150988 [Source:ZFIN;Acc:ZDB-GENE-050309-169] 0.173 0.511 0.831 0.043 0.789 0.106 0.508 0.442 0.230 0.435 0.037 0.100 -0.130 -0.005 -0.014 -0.052 ENSDARG00000027529 HMOX1A heme oxygenase 1a [Source:ZFIN;Acc:ZDB-GENE-030131-3102] 0.152 0.383 0.219 -0.156 0.103 0.435 0.409 0.264 -0.001 0.434 0.063 0.107 -0.042 -0.106 -0.136 -0.241 ENSDARG00000098589 CYB5A cytochrome b5 type A (microsomal) [Source:ZFIN;Acc:ZDB-GENE-040426-2148] 0.083 0.862 1.554 -0.128 1.002 0.191 0.469 0.418 0.138 0.430 0.134 0.056 0.029 -0.015 0.044 0.000 ENSDARG00000045141 AQP8A.1 aquaporin 8a, tandem duplicate 1 [Source:ZFIN;Acc:ZDB-GENE-040912-106] 0.325 0.093 0.491 0.029 0.570 0.497 0.510 0.525 0.425 0.428 0.378 0.151 0.011 -0.078 0.124 0.165 ENSDARG00000095751 LEG1.2 liver-enriched gene 1, tandem duplicate 2 [Source:ZFIN;Acc:ZDB-GENE-040426-2281] -0.032 0.312 0.239 -0.091 0.496 -0.212 0.238 0.380 -0.043 0.427 0.207 0.180 0.324 0.105 0.193 0.041 ENSDARG00000021833 AHR2 aryl hydrocarbon receptor 2 [Source:ZFIN;Acc:ZDB-GENE-990714-16] -0.023 0.757 0.642 0.033 0.429 0.024 0.426 0.339 0.087 0.424 0.131 0.032 0.063 0.030 0.108 0.098 ENSDARG00000070353 HOXC13A homeobox C13a [Source:ZFIN;Acc:ZDB-GENE-000822-4] 0.167 0.521 0.562 0.150 0.546 0.255 0.353 0.287 0.273 0.423 0.100 0.167 0.256 0.212 0.261 0.076 ENSDARG00000091994 ZNF1046 zinc finger protein 1046 [Source:ZFIN;Acc:ZDB-GENE-030131-2092] 0.278 0.376 0.074 -0.039 0.252 0.004 0.071 0.133 -0.007 0.421 0.345 0.090 0.290 0.094 0.206 0.168 ENSDARG00000058734 PRDX1 peroxiredoxin 1 [Source:ZFIN;Acc:ZDB-GENE-050320-35] 0.276 1.069 0.457 0.201 0.233 0.212 0.056 0.205 0.440 0.419 -0.008 0.159 0.158 0.085 0.252 0.090 SI:CH211- ENSDARG00000093237 si:ch211-89o9.4 [Source:ZFIN;Acc:ZDB-GENE-090313-134] -0.002 0.441 0.377 -0.019 0.305 0.073 0.590 0.416 0.300 0.416 -0.008 0.169 0.188 0.116 0.465 0.000 89O9.4 ENSDARG00000055045 CASP3B caspase 3, apoptosis-related cysteine peptidase b [Source:ZFIN;Acc:ZDB-GENE-070607-1] 0.072 0.539 0.462 0.132 0.450 0.044 0.410 0.493 0.102 0.415 0.042 0.113 0.084 0.032 0.102 0.030 CABZ01052 ENSDARG00000100739 N/A 0.231 0.302 0.286 0.089 0.354 0.116 0.320 0.377 0.147 0.415 0.212 0.122 0.243 0.003 0.223 0.079 573.1 ENSDARG00000088137 A DGRG2A adhesion G protein-coupled receptor G2a [Source:ZFIN;Acc:ZDB-GENE-140106-206] 0.242 0.315 0.420 0.155 0.227 0.065 0.522 0.327 -0.027 0.410 0.586 0.041 0.340 0.083 0.422 0.139

276

SI:CH211- ENSDARG00000099538 si:ch211-188p14.3 [Source:ZFIN;Acc:ZDB-GENE-121214-334] 0.262 0.182 -0.349 -0.030 0.022 -0.032 0.069 0.173 -0.071 0.409 0.079 0.048 -0.049 -0.105 0.465 -0.022 188P14.3 ENSDARG00000098588 GCHFR GTP cyclohydrolase I feedback regulator [Source:ZFIN;Acc:ZDB-GENE-040426-1731] 0.134 0.948 0.513 0.180 0.325 0.282 0.289 0.300 0.084 0.408 0.217 0.178 0.294 0.000 0.235 0.080 ENSDARG00000104381 BX914218.1 N/A -0.104 0.494 0.237 -0.041 0.457 0.231 0.270 0.270 -0.053 0.404 0.110 0.107 0.008 -0.118 0.006 -0.022 ENSDARG00000019236 GSR glutathione reductase [Source:ZFIN;Acc:ZDB-GENE-050522-116] -0.055 1.006 0.175 0.189 0.556 0.043 -0.037 0.088 -0.013 0.404 -0.066 -0.013 -0.057 -0.097 -0.085 -0.037 SI:CH73- ENSDARG00000094281 si:ch73-196i15.3 [Source:ZFIN;Acc:ZDB-GENE-091116-4] -0.006 0.336 0.600 0.085 0.272 0.009 0.352 0.365 0.005 0.404 0.058 0.053 0.260 0.045 0.183 0.156 196I15.3 von Willebrand factor A domain containing 10, tandem duplicate 1 [Source:ZFIN;Acc:ZDB- ENSDARG00000077874 VWA10.1 -0.241 0.110 0.140 0.091 0.121 0.010 0.191 0.100 -0.051 0.402 0.018 0.077 0.202 -0.049 0.176 0.029 GENE-090313-291] ENSDARG00000042824 NFE2L2A nuclear factor, erythroid 2-like 2a [Source:ZFIN;Acc:ZDB-GENE-030723-2] -0.037 0.503 0.654 -0.080 0.392 0.037 0.424 0.342 0.192 0.400 0.047 0.027 0.024 -0.056 0.238 0.088 UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-acetylgalactosaminyltransferase 6 (GalNAc- ENSDARG00000014386 GA LNT6 0.338 0.433 0.313 -0.009 0.447 0.254 0.234 0.364 0.171 0.399 0.267 0.232 0.186 -0.036 0.347 0.015 T6) [Source:ZFIN;Acc:ZDB-GENE-040426-2272] ENSDARG00000053005 CD22 cd22 molecule [Source:ZFIN;Acc:ZDB-GENE-050522-521] 0.156 0.802 0.535 0.141 0.300 0.236 0.317 0.476 0.379 0.399 0.270 0.216 0.114 0.190 0.118 0.146 ENSDARG00000106724 CU655961.7 N/A 0.164 0.385 0.168 0.092 0.207 -0.021 0.324 0.287 0.203 0.398 0.233 0.236 0.244 0.103 0.365 0.165 ENSDARG00000104015 FGFR1BL fibroblast growth factor receptor 1b, like [Source:ZFIN;Acc:ZDB-GENE-091204-406] 0.343 0.281 0.199 0.522 0.381 0.247 0.434 0.377 0.225 0.398 0.410 0.480 0.234 0.258 0.390 0.255 SI:CH211- ENSDARG00000028096 si:ch211-95j8.2 [Source:ZFIN;Acc:ZDB-GENE-091204-28] 0.227 0.477 0.391 0.114 0.502 0.326 0.157 0.274 0.246 0.395 0.262 0.168 0.138 0.118 0.173 0.062 95J8.2 ENSDARG00000004748 ZGC:100868 zgc:100868 [Source:ZFIN;Acc:ZDB-GENE-040801-33] 0.645 0.790 0.270 0.125 0.440 0.335 0.178 0.271 0.274 0.395 0.520 0.020 0.047 0.137 0.123 -0.013 ENSDARG00000020794 NEUROD6B neuronal differentiation 6b [Source:ZFIN;Acc:ZDB-GENE-010608-2] -0.231 0.169 0.368 0.180 0.489 0.011 0.477 0.427 -0.177 0.394 0.093 0.272 0.315 0.177 0.135 0.340 ENSDARG00000040644 PXYLP1 2-phosphoxylose phosphatase 1 [Source:ZFIN;Acc:ZDB-GENE-040718-127] 0.027 0.244 0.203 0.211 0.335 0.173 0.377 0.333 0.183 0.393 0.341 -0.065 0.263 0.007 0.228 0.327 ENSDARG00000051925 CYB5R2 cytochrome b5 reductase 2 [Source:ZFIN;Acc:ZDB-GENE-060825-83] -0.235 0.566 0.092 0.050 0.613 -0.024 0.172 0.044 0.035 0.390 0.053 0.132 0.048 0.066 -0.163 0.036 SI:DKEY- ENSDARG00000105545 si:dkey-165a24.10 [Source:ZFIN;Acc:ZDB-GENE-160728-16] 0.269 0.250 0.187 0.104 0.175 0.172 0.161 0.317 0.328 0.390 0.267 0.179 0.289 0.175 0.256 0.094 165A24.10 SI:DKEY- ENSDARG00000105367 si:dkey-88n24.11 [Source:ZFIN;Acc:ZDB-GENE-160113-7] 0.321 0.321 0.039 -0.053 0.324 -0.066 -0.001 0.218 0.273 0.390 0.281 0.282 0.067 0.141 0.471 -0.074 88N24.11 ENSDARG00000063665 MAT2AL methionine adenosyltransferase II, alpha-like [Source:ZFIN;Acc:ZDB-GENE-060421-5255] 0.113 0.028 -0.149 0.168 -0.177 0.051 0.013 -0.053 -0.021 0.388 -0.027 0.043 0.108 0.044 0.044 -0.020 ENSDARG00000014646 AOC2 amine oxidase, copper containing 2 [Source:ZFIN;Acc:ZDB-GENE-050320-133] -0.137 0.630 0.468 -0.115 0.480 -0.131 0.302 0.218 -0.181 0.388 0.074 0.003 0.124 -0.072 0.063 -0.004 potassium inwardly-rectifying channel, subfamily J, member 1a, tandem duplicate 4 ENSDARG00000090635 KCNJ1A.4 0.042 -0.161 0.216 -0.033 0.183 -0.037 0.060 0.003 -0.145 0.386 0.059 -0.006 0.067 0.090 -0.232 0.167 [Source:ZFIN;Acc:ZDB-GENE-050420-186] ENSDARG00000044684 RBP4L retinol binding protein 4, like [Source:ZFIN;Acc:ZDB-GENE-030131-7591] 0.055 0.015 0.101 0.079 0.265 0.302 0.214 0.371 0.096 0.384 -0.051 0.148 0.062 0.203 0.022 0.127 SI:CH211- ENSDARG00000068947 si:ch211-264e16.1 [Source:ZFIN;Acc:ZDB-GENE-060503-226] 0.019 0.002 0.378 0.123 0.306 0.052 0.480 0.407 -0.003 0.383 0.001 -0.008 0.133 -0.067 0.076 0.026 264E16.1 ENSDARG00000063661 NUAK2 NUAK family, SNF1-like kinase, 2 [Source:ZFIN;Acc:ZDB-GENE-050208-563] 0.118 0.453 0.630 0.046 0.671 0.255 0.409 0.548 0.106 0.382 0.296 0.224 0.278 0.170 0.302 0.168 ENSDARG00000033364 M GST3B microsomal glutathione S-transferase 3b [Source:ZFIN;Acc:ZDB-GENE-061215-48] 0.068 0.693 0.279 0.135 0.393 0.100 0.213 0.235 0.113 0.381 -0.040 0.033 0.085 0.111 -0.018 0.016 SI:CH211- ENSDARG00000089919 si:ch211-161h7.8 [Source:ZFIN;Acc:ZDB-GENE-091204-302] -0.181 0.523 0.152 -0.020 0.224 0.063 0.299 0.284 0.095 0.380 -0.038 0.087 0.157 -0.007 0.067 -0.093 161H7.8 ENSDARG00000078404 CDH26.1 cadherin 26, tandem duplicate 1 [Source:ZFIN;Acc:ZDB-GENE-100922-193] 0.257 0.785 0.495 0.161 0.588 0.165 0.501 0.389 0.275 0.379 0.298 0.148 0.151 0.207 0.273 0.186 WU:FB18F0 ENSDARG00000097635 wu:fb18f06 [Source:ZFIN;Acc:ZDB-GENE-030131-261] -0.059 0.472 0.092 -0.035 0.504 0.053 0.185 0.245 -0.007 0.379 0.153 -0.030 0.097 0.061 -0.028 0.029 6 SI:CH1073- ENSDARG00000097973 si:ch1073-190k2.1 [Source:ZFIN;Acc:ZDB-GENE-131127-449] 0.219 0.356 0.367 0.008 0.278 -0.010 0.262 0.258 0.096 0.375 0.252 0.218 0.136 -0.100 0.365 -0.067 190K2.1 ENSDARG00000058332 KRT18 si:ch211-133j6.3 [Source:ZFIN;Acc:ZDB-GENE-090312-205] -0.055 0.221 0.368 0.075 0.242 -0.027 0.343 0.421 0.028 0.373 0.167 0.182 0.303 0.082 0.013 0.123 ENSDARG00000032571 IL20RA interleukin 20 receptor, alpha [Source:ZFIN;Acc:ZDB-GENE-040724-249] 0.050 0.260 0.586 0.189 0.544 0.144 0.365 0.498 0.068 0.373 -0.126 0.091 0.277 0.085 0.178 0.047 ENSDARG00000037555 ATOH8 atonal bHLH transcription factor 8 [Source:ZFIN;Acc:ZDB-GENE-061215-7] 0.232 0.337 0.287 0.243 0.300 0.417 0.193 0.312 0.295 0.371 0.105 0.243 -0.028 0.169 0.078 0.311 SI:CH211- ENSDARG00000096545 si:ch211-191a16.5 [Source:ZFIN;Acc:ZDB-GENE-121214-56] -0.002 0.778 0.098 0.116 0.540 0.111 0.315 0.134 0.034 0.369 0.124 0.006 0.127 -0.075 0.050 0.236 191A16.5 ENSDARG00000074393 SH3YL1 SH3 and SYLF domain containing 1 [Source:ZFIN;Acc:ZDB-GENE-040128-16] -0.036 0.361 0.196 0.089 0.452 -0.011 0.325 0.252 -0.087 0.368 0.136 -0.045 0.234 -0.063 0.242 0.000 ENSDARG00000071024 ZGC:171679 zgc:171679 [Source:ZFIN;Acc:ZDB-GENE-071004-95] -0.150 0.056 0.168 0.172 0.046 0.095 0.264 0.165 0.210 0.367 0.178 0.090 0.187 0.182 0.329 0.054 ATP-binding cassette, sub-family B (MDR/TAP), member 6a [Source:ZFIN;Acc:ZDB-GENE- ENSDARG00000063297 ABCB6A 0.033 0.592 0.205 0.275 0.406 0.040 0.152 0.278 0.111 0.367 0.220 -0.009 0.046 0.035 0.000 0.091 050517-9] ENSDARG00000015495 KLF3 Kruppel-like factor 3 (basic) [Source:ZFIN;Acc:ZDB-GENE-011116-1] -0.098 0.210 0.576 -0.252 0.281 -0.345 0.668 0.431 -0.136 0.366 -0.187 0.158 0.324 0.138 0.165 -0.038 membrane-spanning 4-domains, subfamily A, member 17A.9 [Source:ZFIN;Acc:ZDB-GENE- ENSDARG00000043802 MS4A17A.9 0.147 0.172 0.144 0.190 0.303 0.264 0.109 0.156 0.220 0.366 0.139 0.265 0.243 -0.047 0.220 0.229 050320-79] ENSDARG00000091131 CRY1BB cryptochrome circadian clock 1bb [Source:ZFIN;Acc:ZDB-GENE-010426-5] -0.016 -0.065 0.122 -0.031 0.030 -0.198 0.393 0.183 -0.194 0.366 0.385 -0.209 0.057 -0.013 0.399 -0.054 SI:CH73- ENSDARG00000093998 si:ch73-7i4.2 [Source:ZFIN;Acc:ZDB-GENE-060810-137] 0.284 -0.096 0.098 0.154 0.472 0.031 0.324 0.359 0.018 0.365 0.297 -0.007 -0.009 -0.064 0.441 0.051 7I4.2 SI:CH211- ENSDARG00000078138 si:ch211-202h22.8 [Source:ZFIN;Acc:ZDB-GENE-090313-78] -0.211 0.148 0.174 0.082 0.367 0.041 0.160 0.242 -0.017 0.363 -0.100 -0.032 -0.042 -0.033 0.118 0.036 202H22.8 ENSDARG00000069282 BBC3 BCL2 binding component 3 [Source:ZFIN;Acc:ZDB-GENE-070119-4] 0.270 0.282 0.264 0.298 0.339 0.358 0.080 0.061 0.171 0.362 0.057 0.064 0.107 0.326 -0.073 0.202 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 4b [Source:ZFIN;Acc:ZDB-GENE-031031- ENSDARG00000029075 PFKFB4B 0.108 -0.100 0.340 0.040 0.174 -0.046 0.605 0.275 -0.099 0.361 0.051 0.213 0.386 0.132 0.168 0.106 4] ENSDARG00000032496 PON1 paraoxonase 1 [Source:ZFIN;Acc:ZDB-GENE-040912-6] 0.086 0.613 0.296 0.129 0.352 0.318 0.071 0.162 0.280 0.360 -0.032 0.106 -0.068 0.073 0.039 -0.029 ENSDARG00000102849 FO681361.1 N/A -0.088 0.119 0.391 0.082 0.057 0.148 0.187 0.438 0.283 0.360 0.220 0.045 0.274 0.022 0.180 0.128

277

ENSDARG00000043243 PRKCHB protein kinase C, eta, b [Source:ZFIN;Acc:ZDB-GENE-041001-104] 0.369 0.595 0.872 0.357 0.782 0.083 0.610 0.611 0.296 0.360 0.217 0.219 0.426 0.244 0.410 0.291 ENSDARG00000089529 DESI1B desumoylating isopeptidase 1b [Source:ZFIN;Acc:ZDB-GENE-040426-936] -0.031 0.357 0.257 0.115 0.196 -0.105 0.047 0.193 0.010 0.358 -0.021 0.010 -0.050 -0.030 -0.093 -0.039 ENSDARG00000069261 METAP2A methionyl aminopeptidase 2a [Source:ZFIN;Acc:ZDB-GENE-061013-5] -0.148 0.154 0.097 0.020 0.428 -0.071 0.324 0.270 0.072 0.357 0.158 0.020 0.256 0.098 0.176 0.110 ENSDARG00000023082 KRT1-19D keratin, type 1, gene 19d [Source:ZFIN;Acc:ZDB-GENE-060316-1] -0.696 0.191 0.032 -0.048 0.599 -0.229 0.521 0.170 -0.606 0.357 -0.436 0.002 -0.030 0.102 -0.316 0.027 ENSDARG00000103019 GSTP2 glutathione S-transferase pi 2 [Source:ZFIN;Acc:ZDB-GENE-050601-1] -0.064 1.561 0.289 0.061 0.538 -0.072 0.310 0.218 0.012 0.354 0.128 0.082 0.244 0.019 0.146 0.054 potassium inwardly-rectifying channel, subfamily J, member 1a, tandem duplicate 5 ENSDARG00000089060 KCNJ1A.5 0.103 0.090 0.146 -0.032 0.286 0.054 0.037 0.043 -0.052 0.353 0.201 0.062 -0.019 0.149 -0.169 -0.143 [Source:ZFIN;Acc:ZDB-GENE-050420-120] ENSDARG00000104474 IL6R interleukin 6 receptor [Source:ZFIN;Acc:ZDB-GENE-080107-7] -0.104 0.593 0.544 -0.105 0.235 0.022 0.364 0.419 -0.015 0.352 0.063 -0.007 -0.057 -0.033 -0.073 0.062 ENSDARG00000041947 STYK1 serine/threonine/tyrosine kinase 1 [Source:ZFIN;Acc:ZDB-GENE-030131-3435] 0.206 0.481 0.318 0.213 0.517 0.248 0.165 0.338 0.266 0.352 0.258 0.084 0.068 0.016 0.174 0.077 SI:CH211- ENSDARG00000088408 si:ch211-171h4.7 [Source:ZFIN;Acc:ZDB-GENE-120215-25] -0.009 0.030 0.155 -0.043 0.158 0.027 0.025 0.156 0.119 0.352 0.075 0.127 0.046 0.277 0.026 0.171 171H4.7 SI:DKEY- ENSDARG00000101688 si:dkey-207l24.2 [Source:ZFIN;Acc:ZDB-GENE-131125-92] 0.144 0.356 0.237 0.174 0.264 0.028 0.321 0.282 0.182 0.351 0.123 0.005 0.325 0.071 0.238 0.098 207L24.2 SI:CH211- ENSDARG00000104919 si:ch211-153b23.3 [Source:ZFIN;Acc:ZDB-GENE-141216-408] 0.384 0.018 0.063 -0.038 0.213 0.502 0.083 0.085 0.149 0.350 0.271 0.061 0.276 0.197 0.032 0.205 153B23.3 UDP-Gal:betaGlcNAc beta 1,4- galactosyltransferase, polypeptide 1 [Source:ZFIN;Acc:ZDB- ENSDARG00000002634 B4GA LT1 0.047 0.322 0.224 -0.088 0.293 0.123 0.438 0.273 0.135 0.349 0.265 0.015 0.174 -0.093 0.407 0.017 GENE-050417-236] SI:CH211- ENSDARG00000086947 si:ch211-147m6.1 [Source:ZFIN;Acc:ZDB-GENE-131120-57] 0.507 0.499 0.160 -0.068 0.370 0.439 0.229 0.286 0.229 0.345 0.575 0.055 0.302 0.241 0.371 0.324 147M6.1 phosphoinositide-3-kinase, regulatory subunit 3a (gamma) [Source:ZFIN;Acc:ZDB-GENE-040426- ENSDARG00000103038 PIK3R3A -0.044 -0.072 0.305 -0.050 0.188 -0.094 0.494 0.207 -0.160 0.344 -0.104 0.018 0.432 0.128 0.202 -0.086 1978] ENSDARG00000101910 PCDH20 protocadherin 20 [Source:ZFIN;Acc:ZDB-GENE-091204-115] -0.010 0.153 0.351 -0.086 0.359 0.072 0.310 0.406 0.010 0.343 -0.070 0.014 0.081 -0.120 0.107 -0.002 ENSDARG00000028367 SULT2ST3 sulfotransferase family 2, cytosolic sulfotransferase 3 [Source:ZFIN;Acc:ZDB-GENE-061117-4] -0.037 0.532 0.538 0.255 0.187 0.095 0.189 0.298 0.093 0.341 0.324 0.229 -0.048 0.021 0.095 0.253

ENSDARG00000053136 B2M beta-2-microglobulin [Source:ZFIN;Acc:ZDB-GENE-980526-88] 0.378 0.335 0.227 0.082 0.215 0.296 0.219 0.277 0.125 0.340 0.107 0.057 0.151 0.019 0.071 0.210 ENSDARG00000044002 CYP2X7 cytochrome P450, family 2, subfamily X, polypeptide 7 [Source:ZFIN;Acc:ZDB-GENE-110114-2] 0.004 0.050 0.312 0.123 0.116 0.338 0.200 0.179 0.201 0.340 0.184 -0.041 0.415 0.196 0.212 0.194 ENSDARG00000044691 PPP1R3B protein phosphatase 1, regulatory subunit 3B [Source:ZFIN;Acc:ZDB-GENE-030131-5496] 0.064 -0.143 0.254 0.098 0.145 0.139 0.313 0.168 0.120 0.340 0.068 -0.026 0.277 0.004 0.303 0.009 ENSDARG00000099902 IL17RC interleukin 17 receptor C [Source:ZFIN;Acc:ZDB-GENE-120111-3] -0.007 0.277 0.566 0.002 0.519 0.015 0.293 0.336 0.018 0.339 -0.087 0.076 -0.016 -0.014 0.033 -0.103 ENSDARG00000101816 COL5A3B collagen, type V, alpha 3b [Source:ZFIN;Acc:ZDB-GENE-110728-4] 0.116 0.416 0.320 0.117 0.442 0.197 0.363 0.387 0.043 0.338 0.047 0.068 0.092 -0.072 0.149 0.174 ENSDARG00000070597 PRELP proline/arginine-rich end leucine-rich repeat protein [Source:ZFIN;Acc:ZDB-GENE-080327-24] -0.228 -0.050 0.384 -0.061 0.234 -0.153 0.394 0.113 -0.133 0.335 0.000 0.079 0.260 0.153 -0.007 0.053 ENSDARG00000062788 IRG1L immunoresponsive gene 1, like [Source:ZFIN;Acc:ZDB-GENE-061103-301] 0.511 -0.040 0.266 -0.054 0.228 0.362 0.198 0.139 0.150 0.334 0.367 0.098 0.161 0.144 0.210 0.231 TMPRSS13 ENSDARG00000100969 transmembrane protease, serine 13a [Source:ZFIN;Acc:ZDB-GENE-090309-3] 0.021 0.480 0.214 0.090 0.387 0.024 0.200 0.124 -0.056 0.334 -0.032 -0.126 0.008 -0.113 -0.090 0.085 A ENSDARG00000054846 CU657980.1 N/A -0.005 0.018 0.158 0.203 0.282 0.227 0.251 0.285 -0.127 0.333 0.339 0.166 0.290 0.017 0.257 0.243 ENSDARG00000100213 CR388052.3 N/A 0.084 0.213 0.303 -0.008 0.190 -0.082 0.196 0.213 0.009 0.333 0.242 0.003 0.364 0.133 0.312 -0.214 SI:DKEY- ENSDARG00000077907 si:dkey-183p4.10 [Source:ZFIN;Acc:ZDB-GENE-081104-337] -0.034 0.235 0.178 0.106 0.278 0.059 0.149 0.255 0.220 0.331 0.038 0.259 0.167 0.281 0.023 0.119 183P4.10 SI:CH211- ENSDARG00000069998 si:ch211-145b13.6 [Source:ZFIN;Acc:ZDB-GENE-090313-36] 0.207 0.466 0.335 0.174 0.264 0.547 0.245 0.221 0.264 0.330 0.111 0.161 0.141 0.365 0.558 0.288 145B13.6 ENSDARG00000093584 ZGC:193505 zgc:193505 [Source:ZFIN;Acc:ZDB-GENE-030131-7103] -0.033 0.662 0.032 0.047 0.642 -0.079 0.388 0.533 -0.069 0.330 -0.006 0.065 0.173 0.008 0.021 0.055 ATP-binding cassette, sub-family A (ABC1), member 12 [Source:ZFIN;Acc:ZDB-GENE-030131- ENSDARG00000074749 ABCA12 0.205 0.361 0.352 0.259 0.369 0.073 0.293 0.422 0.128 0.330 0.268 0.118 0.157 -0.041 0.264 0.166 9790] ENSDARG00000100052 CCK cholecystokinin [Source:HGNC Symbol;Acc:HGNC:1569] 0.209 0.418 0.506 0.152 0.632 0.455 0.262 0.334 0.079 0.329 0.120 -0.122 0.014 -0.045 -0.008 0.058 SI:CH211- ENSDARG00000091996 si:ch211-117m20.5 [Source:ZFIN;Acc:ZDB-GENE-030131-12] 0.083 0.432 0.132 0.161 0.630 -0.050 0.508 0.359 0.318 0.327 0.221 0.140 0.048 0.056 -0.051 0.119 117M20.5 serpin peptidase inhibitor, clade F (alpha-2 antiplasmin, pigment epithelium derived factor), ENSDARG00000076448 SERPINF2A -0.233 -0.142 0.227 -0.028 -0.019 -0.210 0.200 0.068 -0.103 0.325 -0.060 -0.056 0.017 0.170 -0.208 -0.030 member 2a [Source:ZFIN;Acc:ZDB-GENE-060822-1] ENSDARG00000099960 ELOVL1A ELOVL fatty acid elongase 1a [Source:ZFIN;Acc:ZDB-GENE-041010-66] 0.222 0.100 0.221 -0.091 0.128 -0.339 0.386 0.189 -0.070 0.324 0.116 0.048 0.131 0.076 0.213 0.014 cysteine-rich secretory protein LCCL domain containing 2 [Source:ZFIN;Acc:ZDB-GENE-130131- ENSDARG00000088595 CRISPLD2 -0.053 0.559 0.513 0.027 0.435 -0.160 0.411 0.260 -0.051 0.323 0.049 0.024 0.191 0.037 0.057 -0.114 1] ENSDARG00000074752 HLFA hepatic leukemia factor a [Source:ZFIN;Acc:ZDB-GENE-061013-159] 0.074 0.361 0.591 0.103 0.389 -0.004 0.593 0.439 -0.059 0.322 0.070 0.130 0.092 0.180 0.175 0.151 ENSDARG00000097513 CT573383.1 N/A 0.375 0.467 0.145 0.458 0.288 0.355 0.386 0.394 0.433 0.322 0.467 0.304 0.467 0.177 0.434 0.180 SI:CH211- ENSDARG00000088915 si:ch211-241n15.3 [Source:ZFIN;Acc:ZDB-GENE-110913-169] 0.155 0.276 0.064 0.024 0.074 0.141 0.163 0.187 0.188 0.321 0.143 0.076 0.221 0.140 0.212 -0.017 241N15.3 CABZ01051 ENSDARG00000101012 N/A 0.037 0.456 0.795 0.059 0.545 0.015 0.210 0.207 0.201 0.321 0.096 0.019 0.089 0.029 0.053 0.153 600.1 ENSDARG00000010047 NEU3.2 sialidase 3 (membrane sialidase), tandem duplicate 2 [Source:ZFIN;Acc:ZDB-GENE-040801-262] 0.144 0.213 0.319 0.166 0.248 -0.024 0.265 0.193 -0.031 0.321 0.182 -0.013 0.171 0.052 0.228 0.036

278

CABZ01015 ENSDARG00000107600 N/A 0.255 0.207 0.445 0.274 0.214 0.208 0.457 0.210 0.335 0.320 0.453 0.319 0.346 0.426 0.355 0.302 530.2 ENSDARG00000032929 CRYBA1L1 crystallin, beta A1, like 1 [Source:ZFIN;Acc:ZDB-GENE-050417-249] -0.083 0.112 0.094 -0.006 0.312 0.150 0.220 0.291 0.188 0.319 0.074 0.150 0.153 -0.006 0.273 0.014 haloacid dehalogenase-like hydrolase domain containing 3 [Source:ZFIN;Acc:ZDB-GENE-040724- ENSDARG00000053934 HDHD3 0.137 0.330 0.375 0.321 0.279 0.070 0.244 0.254 0.112 0.317 0.190 0.223 0.224 0.327 0.086 0.118 118] ENSDARG00000057437 APODB apolipoprotein Db [Source:ZFIN;Acc:ZDB-GENE-051023-8] 0.253 0.239 0.195 -0.207 0.150 0.363 0.255 0.258 0.230 0.315 0.428 -0.081 0.044 -0.045 0.226 -0.022 ENSDARG00000059811 SMPD5 sphingomyelin phosphodiesterase 5 [Source:ZFIN;Acc:ZDB-GENE-061110-22] -0.057 0.462 0.137 0.044 0.344 -0.153 0.155 0.254 -0.072 0.315 0.193 0.057 0.201 -0.001 -0.024 0.112 ENSDARG00000010940 BX957278.1 N/A 0.130 0.707 0.108 -0.004 0.380 0.136 0.094 0.177 0.322 0.314 0.041 -0.068 0.005 0.000 0.040 0.065 ENSDARG00000103398 FABP1B.2 fatty acid binding protein 1b, liver, tandem duplicate 2 [Source:ZFIN;Acc:ZDB-GENE-100318-6] 0.032 0.732 0.121 0.024 0.124 0.262 0.233 0.203 0.246 0.311 0.353 0.060 0.333 0.169 0.136 0.207 ENSDARG00000026820 GC3 guanylyl cyclase 3 [Source:ZFIN;Acc:ZDB-GENE-011128-9] 0.098 0.299 0.211 0.141 0.213 0.126 0.129 0.315 0.269 0.311 0.137 0.247 0.302 0.174 0.141 0.071 SI:DKEY- ENSDARG00000098306 si:dkey-82i20.1 [Source:ZFIN;Acc:ZDB-GENE-081104-500] 0.094 0.524 0.302 0.209 0.394 0.239 0.298 0.314 0.134 0.311 0.098 0.079 0.284 0.055 0.219 0.162 82I20.1 SI:DKEY- ENSDARG00000091013 si:dkey-84h14.2 [Source:ZFIN;Acc:ZDB-GENE-110913-33] 0.217 0.397 0.108 0.124 0.075 0.026 0.166 0.150 0.098 0.310 0.082 -0.053 0.122 -0.103 0.100 -0.044 84H14.2 ENSDARG00000105241 BTR26 bloodthirsty-related gene family, member 26 [Source:ZFIN;Acc:ZDB-GENE-070705-378] 0.209 0.025 0.187 0.099 0.461 0.204 0.212 0.179 0.029 0.309 0.160 0.117 0.360 0.248 0.163 0.254 ENSDARG00000006202 ERBB3A erb-b2 receptor tyrosine kinase 3a [Source:ZFIN;Acc:ZDB-GENE-030916-3] -0.129 0.001 0.106 0.081 0.239 -0.058 0.226 0.261 -0.163 0.308 0.095 0.129 0.184 0.014 0.195 0.066 ENSDARG00000042189 TSPAN33B tetraspanin 33b [Source:ZFIN;Acc:ZDB-GENE-060503-607] -0.020 0.153 0.096 0.095 0.154 0.072 0.139 0.168 -0.147 0.308 0.000 0.051 0.058 0.081 0.216 0.142 SI:DKEY- ENSDARG00000087651 si:dkey-26g8.4 [Source:ZFIN;Acc:ZDB-GENE-121214-36] 0.184 0.221 0.060 0.153 0.192 0.379 -0.025 0.143 0.299 0.308 0.290 0.150 0.092 0.216 0.198 0.000 26G8.4 ENSDARG00000002197 PYGL phosphorylase, glycogen, liver [Source:ZFIN;Acc:ZDB-GENE-041205-1] 0.105 0.513 0.571 0.130 0.763 -0.047 0.197 0.417 0.097 0.307 0.330 -0.027 0.105 0.124 0.136 0.049 ENSDARG00000032631 LTB4R leukotriene B4 receptor [Source:ZFIN;Acc:ZDB-GENE-070705-164] 0.321 0.385 0.135 0.102 0.223 0.088 0.399 0.158 0.122 0.306 0.210 0.084 0.052 0.213 0.073 0.095 ENSDARG00000098103 TEFB thyrotrophic embryonic factor b [Source:ZFIN;Acc:ZDB-GENE-050522-224] -0.198 0.168 0.267 -0.081 0.122 -0.204 0.532 0.088 -0.068 0.306 0.067 0.089 0.334 0.103 0.304 0.104 ENSDARG00000068507 CRYBB1 crystallin, beta B1 [Source:ZFIN;Acc:ZDB-GENE-010813-1] -0.053 0.070 0.201 0.001 0.415 0.102 0.172 0.242 0.141 0.306 0.248 0.205 0.249 0.130 0.150 0.075 ENSDARG00000095369 ZGC:112966 zgc:112966 [Source:ZFIN;Acc:ZDB-GENE-050320-137] 0.044 0.191 0.092 0.191 0.039 0.097 0.168 0.002 0.159 0.306 -0.041 0.045 0.128 -0.045 0.375 0.085 SI:CH211- ENSDARG00000003381 si:ch211-266g18.6 [Source:ZFIN;Acc:ZDB-GENE-131121-599] 0.107 0.491 0.525 0.195 0.453 0.255 0.319 0.284 0.165 0.306 0.315 0.178 0.139 0.353 0.009 0.262 266G18.6 ENSDARG00000029848 ZGC:161973 zgc:161973 [Source:ZFIN;Acc:ZDB-GENE-070410-13] -0.005 0.081 0.112 0.001 -0.069 0.250 0.289 -0.057 0.049 0.305 -0.007 -0.032 0.111 -0.134 -0.011 0.088 ENSDARG00000036912 EDN1 endothelin 1 [Source:ZFIN;Acc:ZDB-GENE-000920-1] 0.052 0.274 0.233 0.025 0.395 0.058 0.119 0.082 0.373 0.305 -0.114 0.351 -0.209 0.404 -0.316 0.210 solute carrier organic anion transporter family, member 1D1 [Source:ZFIN;Acc:ZDB-GENE- ENSDARG00000104108 SLCO1D1 -0.096 0.314 0.561 -0.098 0.473 0.025 0.452 0.418 0.177 0.303 0.176 0.104 0.131 0.128 0.167 0.087 030131-5044] ENSDARG00000100471 CT573263.3 N/A 0.118 0.459 0.150 0.032 0.193 0.106 0.160 0.250 0.088 0.301 0.093 0.111 0.176 -0.018 0.357 -0.085 ENSDARG00000014626 DLX3B distal-less homeobox 3b [Source:ZFIN;Acc:ZDB-GENE-980526-280] 0.159 0.407 0.630 0.103 0.635 0.261 0.266 0.355 0.257 0.300 0.072 0.211 0.038 0.015 0.049 0.018 ENSDARG00000076667 CCNG1 cyclin G1 [Source:ZFIN;Acc:ZDB-GENE-020322-1] 0.142 -0.016 0.010 0.055 -0.106 0.176 -0.045 0.043 0.165 0.300 -0.135 -0.003 0.105 0.160 0.038 -0.085 ENSDARG00000029995 TNNI2B.2 troponin I type 2b (skeletal, fast), tandem duplicate 2 [Source:ZFIN;Acc:ZDB-GENE-040801-9] -0.240 0.241 0.147 -0.002 0.304 0.005 0.277 0.326 -0.023 0.299 -0.023 0.139 -0.103 0.018 0.019 -0.012 ENSDARG00000045733 CIAPIN1 cytokine induced apoptosis inhibitor 1 [Source:ZFIN;Acc:ZDB-GENE-040808-57] 0.133 0.415 0.106 0.172 0.529 0.228 0.145 0.185 0.360 0.298 0.083 0.122 0.061 -0.037 0.057 0.056 SI:DKEY- ENSDARG00000097480 si:dkey-263j23.1 [Source:ZFIN;Acc:ZDB-GENE-131120-109] 0.095 0.244 0.197 0.252 0.424 0.083 0.279 0.067 0.230 0.294 0.020 0.082 0.228 0.104 0.056 0.280 263J23.1 ENSDARG00000025757 TSPAN35 tetraspanin 35 [Source:ZFIN;Acc:ZDB-GENE-040426-1362] -0.043 0.318 0.497 -0.025 0.372 0.132 0.296 0.433 -0.011 0.294 -0.021 0.006 -0.023 -0.005 0.017 -0.092

279

Table A.4. Genes that are common to 1, 2, 3, 4, 5, or 6 of the cluster B PAHs.

Differentially Number of PAHs Differentially Number of PAHs Differentially Number of PAHs Differentially Number of PAHs PAH Expressed Gene the DEG PAH Expressed Gene the DEG PAH Expressed Gene the DEG PAH Expressed Gene the DEG (DEG) Symbol is common to (DEG) Symbol is common to (DEG) Symbol is common to (DEG) Symbol is common to

BbF QKIB 2 BbF PLXNB2B 1 BbF CAMK1DA 2 BbF ECE2B 2 BbF ADARB1B 2 BbF SH3GL2 2 BbF TET1 2 BbF ADAMTS7 2 BbF CTSL.1 4 BbF SI:DKEY-250D21.1 2 BbF HIVEP3A 2 BbF SLC15A4 1 BbF LRP1BA 1 BbF TBC1D9B 2 BbF WWC1 2 BbF CACNA1DA 2 BbF SLC8A3 2 BbF SLC6A8 2 BbF TNS2A 2 BbF GSTP1 5 BbF ETHE1 3 BbF CRY1AA 4 BbF SI:DKEY-65B12.6 6 BbF CDC42BPAA 2 BbF TCF3A 2 BbF BTR02 4 BbF FREM2A 2 BbF CTGFB 4 BbF IGF2R 2 BbF AHRRB 5 BbF DOLK 2 BbF GIPC1 2 BbF UGT1A B 6 BbF RARGB 2 BbF CSPG4 2 BbF SI:DKEY-223P19.1 1 BbF CACNA1C 2 BbF GHRA 2 BbF CEP170B 2 BbF CABZ01066921.1 2 BbF AQP4 2 BbF SLC8A4A 2 BbF LONRF1L 5 BjF PYGL 1 BbF PER1B 1 BbF PER1A 2 BbF PDE2A 2 BjF CTSL.1 4 BbF FMNL2A 2 BbF VA V3 1 BbF KCNA3A 2 BjF ETHE1 3 BbF BBX 2 BbF SI:CH211-266G18.10 2 BbF CABZ01075131.1 2 BjF UGT1A B 6 BbF REEP1 2 BbF SI:DKEYP-77H1.4 2 BbF TMEM151BB 2 BjF ZAK 1 BbF DIP2BB 2 BbF SIK1 3 BbF SULT6B1 6 BjF DLX3B 2 BbF RAB11FIP3 2 BbF ZGC:114104 2 BbF UGT1B5 5 BjF SQRDL 3 BbF GABBR1B 2 BbF VA X2 2 BbF NFE2L2B 6 BjF NUAK1A 2 BbF SQRDL 3 BbF NA V1B 2 BbF SEPW2B 4 BjF KRT1-19D 1 BbF OTUD7B 2 BbF PIM2 2 BbF PKHD1L1 6 BjF SI:CH211-129C21.1 1 BbF SLC23A2 2 BbF SH3PXD2AA 2 BbF KCNQ2B 2 BjF PTGDSB.1 6 BbF HIVEP2B 2 BbF SRGAP3 2 BbF SI:DKEY-162H11.2 4 BjF TPMT.2 2 BbF SERPINH1B 2 BbF RPS6KA5 2 BbF UGT1B1 6 BjF FOXQ1A 6 BbF SFXN1 2 BbF SCAI 2 BbF CYP1A 6 BjF S100Z 2 BbF TNS1B 2 BbF XYLT1 2 BbF NTRK2B 2 BjF PRKCHB 5 BbF SLC30A2 2 BbF WU:FJ05G07 6 BbF SLX1B 2 BjF MAL 3 BbF SYT2A 2 BbF AIFM4 3 BbF DHRS13L1 6 BjF CYB5R2 1 BbF TBC1D22B 1 BbF M YO5A A 2 BbF LIFRA 2 BjF BTR02 4 BbF PTGDSB.1 6 BbF SLCO2A1 6 BbF SI:CH211-188P14.5 2 BjF TPMT.1 2 BbF MAST1A 2 BbF DOT1L 2 BbF GABRB4 2 BjF CLCN2C 2 BbF FKBP5 2 BbF PTPN21 2 BbF ITCHB 2 BjF WU:FJ05G07 6 BbF SGSM1A 2 BbF ITGA 2.2 2 BbF BX511034.6 2 BjF AIFM4 3 BbF FOXQ1A 6 BbF SI:CH211-1E14.1 2 BbF SI:CH1073-268J14.1 2 BjF SLCO2A1 6 BbF FAT3A 2 BbF HIPK1A 2 BbF CABZ01103755.1 6 BjF NUAK2 2 BbF PER2 2 BbF CYP1B1 6 BbF SLC8A2B 2 BjF CYP1B1 6 BbF RXRAB 1 BbF SLC41A1 2 BbF CYP1C1 6 BjF MUC5.1 4 BbF PHO 2 BbF TSTD1 6 BbF WFIKKN1 6 BjF EDN2 2 BbF GRB10B 2 BbF SI:CH211-202H22.7 3 BbF CU929259.1 1 BjF SLC12A10.2 2 BbF FNBP1A 1 BbF SPEN 2 BbF CYP1C2 4 BjF TSTD1 6 BbF SEMA6BA 2 BbF MYO10L1 2 BbF KCNA2B 1 BjF PTGDSB.2 2

280

BjF CTSS2.1 1 BkF CA15A 1 BkF PONZR4 1 DbahP UGT1A B 6 BjF SI:DKEY-65B12.6 6 BkF AK5L 1 BkF PONZR3 1 DbahP SPRED2B 1 BjF CABZ01052290.1 2 BkF NUAK1A 2 BkF UGT1B5 5 DbahP CACNA1C 2 BjF CDH26.1 2 BkF AHR2 2 BkF NCAM3 2 DbahP MAP3K13 1 BjF DUOX2 2 BkF PTGDSB.1 6 BkF MFAP4 1 DbahP AQP4 2 BjF SI:CH211-229D2.5 4 BkF FOXQ1A 6 BkF NFE2L2B 6 DbahP AACS 1 BjF AND2 2 BkF IL20RA 1 BkF SEPW2B 4 DbahP FMNL2A 2 BjF SULT6B1 6 BkF SI:DKEY-204A24.10 1 BkF KRTT1C19E 2 DbahP BBX 2 BjF UGT1B5 5 BkF KRT15 1 BkF PKHD1L1 6 DbahP OPCML 1 BjF NFE2L2B 6 BkF NFE2L2A 1 BkF SI:DKEY-162H11.2 4 DbahP DTNBB 1 BjF SEPW2B 4 BkF PRKCHB 5 BkF SI:CH211-158D24.4 1 DbahP MMP15B 1 BjF KRTT1C19E 2 BkF MAL 3 BkF SI:CH73-196I15.3 1 DbahP SNX13 1 BjF PKHD1L1 6 BkF CRY1AA 4 BkF SI:CH211-207N23.2 2 DbahP CABZ01041610.1 1 BjF SI:DKEY-162H11.2 4 BkF BTR02 4 BkF UGT1B1 6 DbahP REEP1 2 BjF SI:CH211-117M20.5 1 BkF AHRRB 5 BkF IM:7150988 2 DbahP DIP2BB 2 BjF ZGC:193505 2 BkF PKP1B 1 BkF CYP1A 6 DbahP SLC7A1 1 BjF SI:CH211-207N23.2 2 BkF GRK7B 1 BkF CYB5A 3 DbahP RAB11FIP3 2 BjF UGT1B1 6 BkF CLCN2C 2 BkF DHRS13L1 6 DbahP GABBR1B 2 BjF IM:7150988 2 BkF WU:FJ05G07 6 BkF CABZ01103755.1 6 DbahP ZEB1A 1 BjF CYP1A 6 BkF SLCO2A1 6 BkF RHCGB 1 DbahP OTUD7B 2 BjF CYB5A 3 BkF DUOX 1 BkF CABZ01051600.1 1 DbahP SLC23A2 2 BjF DHRS13L1 6 BkF NUAK2 2 BkF CYP1C1 6 DbahP ZGC:158689 1 BjF CABZ01103755.1 6 BkF ZGC:153031 1 BkF WFIKKN1 6 DbahP KANK2 1 BjF CCK 1 BkF NOS1 1 BkF CYP1C2 4 DbahP DGKH 1 BjF CYP1C1 6 BkF CYP1B1 6 BkF PDE6H 3 DbahP HIVEP2B 2 BjF WFIKKN1 6 BkF MUC5.1 4 BkF SI:DKEY-247K7.2 4 DbahP MMP24 1 BjF CYP1C2 4 BkF EDN2 2 BkF CYP2AA11 5 DbahP AMOTL1 1 BjF PDE6H 3 BkF SLC12A10.2 2 BkF BHLHA9 1 DbahP SFXN1 2 BjF SI:DKEY-247K7.2 4 BkF TSTD1 6 BkF GSTP1 5 DbahP BMPR2B 1 BjF CYP2AA11 5 BkF PTGDSB.2 2 BkF CTGFB 4 DbahP TNS1B 2 BjF GSTP1 5 BkF MAMDC2B 2 BkF FO904970.1 3 DbahP SLC30A2 2 BjF CTGFB 4 BkF SI:CH211-202H22.7 3 DbahP DOCK5 1 DbahP OBSCNB 1 BjF WNT5A 1 BkF HLFA 2 DbahP QKIB 2 DbahP SYT2A 2 BjF SI:CH211-113D11.5 1 BkF SI:DKEY-65B12.6 6 DbahP PLPP2A 1 DbahP TSPAN9A 1 BkF FNDC1 1 BkF LONRF1L 5 DbahP ADARB1B 2 DbahP HMBOX1A 1 BkF ANXA2A 1 BkF DUOX2 2 DbahP SLC8A3 2 DbahP PTGDSB.1 6 BkF CTSL.1 4 BkF SI:CH211-229D2.5 4 DbahP NCANB 1 DbahP MAST1A 2 BkF UGT1A B 6 BkF AND2 2 DbahP PPM1F 1 DbahP TNFAIP3 1 BkF TRH 1 BkF CALHM3 1 DbahP TCF3A 2 DbahP SGSM1A 2 BkF DLX3B 2 BkF SULT6B1 6 DbahP IGF2R 2 DbahP FOXQ1A 6

281

DbahP VEPH1 1 DbahP SLC8A4A 2 DbahP CYP1B1 6 DbahP MCF2L2 1 DbahP KLF12B 1 DbahP SI:CH211-266G18.10 2 DbahP SH2B3 1 DbahP MICALL1A 1 DbahP PDE4D 1 DbahP SI:DKEYP-77H1.4 2 DbahP SLC41A1 2 DbahP TMEM151BB 2 DbahP PLXNB3 1 DbahP CELSR1B 1 DbahP NHSA 1 DbahP FAM155A 1 DbahP RORB 1 DbahP SOCS6A 1 DbahP MUC5.1 4 DbahP SULT6B1 6 DbahP FAT3A 2 DbahP SIK1 3 DbahP FAM131BB 1 DbahP AGAP3 1 DbahP PHO 2 DbahP VA X2 2 DbahP INSRB 1 DbahP PCDH2AA15 1 DbahP GRB10B 2 DbahP NA V1B 2 DbahP TSTD1 6 DbahP UGT1B5 5 DbahP SEMA6BA 2 DbahP SSTR2A 1 DbahP SLC25A37 1 DbahP RNF166 1 DbahP ANKRD52A 1 DbahP KCNQ3 1 DbahP KLF7A 1 DbahP ISM2A 1 DbahP ARF3B 1 DbahP KCNB1 1 DbahP SPEN 2 DbahP NFE2L2B 6 DbahP ZGC:113278 1 DbahP SH3PXD2AA 2 DbahP FREM3 1 DbahP PKHD1L1 6 DbahP AJAP1 1 DbahP SRGAP3 2 DbahP MYO10L1 2 DbahP KCNQ2B 2 DbahP SH3GL2 2 DbahP PLXNA2 1 DbahP CAMK1DA 2 DbahP MTCL1 1 DbahP BIN1A 1 DbahP RPS6KA5 2 DbahP TET1 2 DbahP STARD9 1 DbahP NUDT3B 1 DbahP ZGC:158659 1 DbahP HIVEP3A 2 DbahP SI:CH73-91K6.2 1 DbahP SI:DKEY-250D21.1 2 DbahP SCAI 2 DbahP WWC1 2 DbahP MVB12BA 1 DbahP HIPK2 1 DbahP FAM160A1A 1 DbahP DOCK4 1 DbahP AKAP12A 1 DbahP PRKCHB 5 DbahP XYLT1 2 DbahP TNS2A 2 DbahP A GO1 1 DbahP CHES1 1 DbahP LMLN 1 DbahP SI:DKEY-65B12.6 6 DbahP SETBP1 1 DbahP TBC1D9B 2 DbahP A GO2 1 DbahP FREM2A 2 DbahP SI:ZFOS-375H5.1 1 DbahP SLC6A8 2 DbahP KLF13 1 DbahP DOLK 2 DbahP UGT1B1 6 DbahP PTBP3 1 DbahP WU:FJ05G07 6 DbahP DNAH11 1 DbahP A TG2B 1 DbahP SI:DKEY-222B8.1 1 DbahP M YO5A A 2 DbahP PLA2R1 1 DbahP CYP1A 6 DbahP CRY1AA 4 DbahP CABZ01055347.1 1 DbahP ARFGEF3 1 DbahP SNTA1 1 DbahP CACNG6B 1 DbahP SIPA1L3 1 DbahP AAK1B 1 DbahP NTRK2B 2 DbahP B4GALNT4A 1 DbahP SLCO2A1 6 DbahP NLGN4B 1 DbahP SI:CH73-233F7.4 1 DbahP RBPJB 1 DbahP DOT1L 2 DbahP RCE1A 1 DbahP SLX1B 2 DbahP AHRRB 5 DbahP PTPN21 2 DbahP NA V1 1 DbahP DHRS13L1 6 DbahP GNA11B 1 DbahP RC3H1A 1 DbahP SLC12A5B 1 DbahP RGS2 2 DbahP CNTN3B 1 DbahP KCNH7 1 DbahP CSPG4 2 DbahP LIFRA 2 DbahP GA BRG2 1 DbahP GLSA 1 DbahP CEP170B 2 DbahP SI:CH211-188P14.5 2 DbahP APBB3 1 DbahP MAP3K2 1 DbahP LONRF1L 5 DbahP GABRB4 2 DbahP OSBPL2A 1 DbahP ITGA 2.2 2 DbahP RASA2 1 DbahP ITCHB 2 DbahP RARGB 2 DbahP MAPK8IP2 1 DbahP A RHGEF40 1 DbahP BX511034.6 2 DbahP SOBPA 1 DbahP PCLOA 1 DbahP PDE2A 2 DbahP SI:CH1073-268J14.1 2 DbahP SI:DKEY-166K12.1 1 DbahP SI:CH211-1E14.1 2 DbahP SI:CH211-229D2.5 4 DbahP CABZ01103755.1 6 DbahP GHRA 2 DbahP HIPK1A 2 DbahP CACNA1BB 1 DbahP FGD1 1 DbahP LDLRAD4B 1 DbahP PDE8B 1 DbahP KCNA3A 2 DbahP CABZ01080702.1 1 DbahP CENPF 1 DbahP SLC6A17 1 DbahP CABZ01075131.1 2 DbahP SLC8A2B 2

282

DbahP LRCH3 1 DbaiP PER1A 2 Retene BX957278.1 1 Retene CYP1B1 6 DbahP EXT1B 1 DbaiP SIK1 3 Retene AOC2 1 Retene ABCH1 1 DbahP MCU 1 DbaiP ZGC:114104 2 Retene SH2D4BB 1 Retene TSTD1 6 DbahP CYP1C1 6 DbaiP PIM2 2 Retene SQRDL 3 Retene MAMDC2B 2 DbahP WFIKKN1 6 DbaiP WU:FJ05G07 6 Retene FOXF2A 1 Retene SI:CH211-202H22.7 3 DbahP SCN2B 1 DbaiP SLCO2A1 6 Retene GSR 1 Retene ITPR1B 1 DbahP SI:CH73-233F7.5 1 DbaiP KLF9 1 Retene CBR1L 1 Retene SI:CH211-14A17.10 1 DbahP ECE2B 2 DbaiP CYP1B1 6 Retene ABCB5 1 Retene A NGPT4 1 DbahP CABZ01076250.1 1 DbaiP MUC5.1 4 Retene AHR2 2 Retene SI:DKEY-65B12.6 6 DbahP ADAMTS7 2 DbaiP TSTD1 6 Retene PTGDSB.1 6 Retene AIFM2 1 DbahP RSF1A 1 DbaiP HLFA 2 Retene TPMT.2 2 Retene CDH26.1 2 DbahP PCDH2AB3 1 DbaiP SI:DKEY-65B12.6 6 Retene FOXQ1A 6 Retene LONRF1L 5 DbahP CACNA1DA 2 DbaiP HSPB6 1 Retene MB 1 Retene SULT6B1 6 DbahP ITGA 4 1 DbaiP CABZ01052290.1 2 Retene PON1 1 Retene FO704810.1 1 DbahP EXT1A 1 DbaiP LONRF1L 5 Retene M GST3B 1 Retene UGT1B5 5 DbahP SI:DKEY-247K7.2 4 DbaiP SI:CH211-229D2.5 4 Retene ADAMTS15B 1 Retene NCAM3 2 DbahP CYP2AA11 5 DbaiP SULT6B1 6 Retene FUCA1.1 1 Retene NFE2L2B 6 DbahP EPHB3 1 DbaiP NFE2L2B 6 Retene HAUS7 1 Retene SEPW2B 4 DbahP PCDH2AB10 1 DbaiP PKHD1L1 6 Retene S100Z 2 Retene CABZ01020840.1 1 DbahP GSTP1 5 DbaiP SI:CH211-89O9.4 1 Retene PRKCHB 5 Retene PKHD1L1 6 DbahP CDC42BPAA 2 DbaiP UGT1B1 6 Retene MAL 3 Retene SI:DKEY-162H11.2 4 DbahP GIPC1 2 DbaiP CYP1A 6 Retene POSTNA 1 Retene ZGC:193505 2 DbahP SLC45A4 1 DbaiP DHRS13L1 6 Retene BTR02 4 Retene SI:DKEYP-1H4.6 1 DbahP RPH3AB 1 DbaiP CABZ01103755.1 6 Retene AHRRB 5 Retene SI:CH211-191A16.5 1 DbahP FO904970.1 3 DbaiP CYP1C1 6 Retene CD22 1 Retene UGT1B1 6 DbahP CABZ01066921.1 2 DbaiP WFIKKN1 6 Retene CCR9A 1 Retene CYP1A 6 DbahP ZNF821 1 DbaiP PDE6H 3 Retene SLC12A10.3 1 Retene GCHFR 1 DbaiP NR1D2A 1 DbaiP SI:DKEY-247K7.2 4 Retene TPMT.1 2 Retene CYB5A 3 DbaiP UGT1A B 6 DbaiP CYP2AA11 5 Retene PIR 1 Retene DHRS13L1 6 DbaiP KLF3 1 DbaiP SI:DKEY-9L20.3 1 Retene CXCR4A 1 Retene RGS2 2 DbaiP SERPINH1B 2 DbaiP FO904970.1 3 Retene PRDX1 1 Retene NA GA 1 DbaiP PTGDSB.1 6 Retene BACH1B 1 Retene FGF7 1 Retene CABZ01103755.1 6 DbaiP FKBP5 2 Retene FABP11B 1 Retene TRPM4A 1 Retene CYP1C1 6 DbaiP PFKFB4B 1 Retene SI:CH211-225B11.1 1 Retene WU:FJ05G07 6 Retene WFIKKN1 6 DbaiP FOXQ1A 6 Retene CTSL.1 4 Retene AIFM4 3 Retene CYP1C2 4 DbaiP PER2 2 Retene ZGC:100868 1 Retene BICC1A 1 Retene FABP1B.2 1 DbaiP PRKCHB 5 Retene ETHE1 3 Retene TIPARP 1 Retene CYP2AA11 5 DbaiP CRY1AA 4 Retene UGT1A B 6 Retene SLCO2A1 6 Retene GSTP1 5 DbaiP AHRRB 5 Retene METRNL 1 Retene ABCB6A 1 Retene CTGFB 4 DbaiP CYP2R1 1 Retene ITGA11B 1 Retene ELOVL8A 1 Retene IL6R 1

283

Appendix B – Supplemental Data for Chapter 4

A B

Figure B.1. High Centrality Network Genes. (A) Genes of high betweenness (top 20 ranked by betweenness) are indicated as larger green nodes in the network. (B) Genes of high degree (top 20 ranked by degree) are indicated as larger red nodes in the network.

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Table B.1. Summary of all 48-hpf zebrafish datasets included in this study

Treatment Short Exposure Reference - GEO accession AHR2 Treatment Treatment Replicates FRC Name Concentration numbers Activator Number DMSO Control for Retene, Benzo[k]fluoranthene, Benzo[j]fluoranthene, Dibenzo[a,h]pyrene, DMSO_A 1% 3 No No Dibenzo[a,i]pyrene, Benzo[b]fluoranthene, 1 Fluoranthene, Phenanthrene, Acenapthene 8 Fluoranthene Fluoranthene 50 uM 4 Shankar et al. 2019 - GSE156656 No No 9 Phenanthrene Phenanthrene 50 uM 4 Shankar et al. 2019 - GSE156656 No No 10 Acenapthene Acenaphthene 50 uM 4 Shankar et al. 2019 - GSE156656 No No DMSO Control for 4H-cyclopenta[def]phenanthren-4- one, Carbazole, 3-nitrofluoranthene, 1,5- DMSO_B 1% 4 No No dimethylnaphthalene, 9-methylanthracene, 2- 11 methylnaphthalene, Anthracene 12 4H-cyclopenta[def]phenanthren-4-one 4h-CPdefP 16.2 uM 4 Shankar et al. 2019 - GSE156656 No No 13 Carbozole Carbazole 50 uM 4 Shankar et al. 2019 - GSE156656 No No 14 3-nitrofluoranthene 3-NF 1.9 uM 4 Shankar et al. 2019 - GSE156656 No No 15 1,5-dimethylnaphthalene 1,5-DMN 50 uM 4 Shankar et al. 2019 - GSE156656 No No 16 9-methylanthracene 9-MA 50 uM 4 Shankar et al. 2019 - GSE156656 No No 17 2-methylnaphthalene 2-MN 50 uM 4 Shankar et al. 2019 - GSE156656 No No 18 Anthracene Anthracene 50 uM 4 Shankar et al. 2019 - GSE156656 No No

DMSO Control for 3,3',5,5'-tetrabromobisphenol A, Bis(2-ethylhexyl) tetrabromophthalate, Tris(2- chloroisopropyl)phosphate, 2,2,4,4-tetra-bromodiphenyl ether, Tris(2,3-dibromopropyl) phosphate, Triisobutyl DMSO_FRC 0.64% 4 No No phosphate, Triisopropylated phenyl phosphate, Triphenyl phosphate, TetrabromobisphenolA-2-3- dibromopropyl ether, Tris(2-chloroethyl) phosphate 19 20 3,3',5,5'-tetrabromobisphenol A TBBPA 4 uM 4 unpublished Yes No 21 Bis(2-ethylhexyl) tetrabromophthalate TBPH 72 uM 4 unpublished Yes No 22 Tris(2-chloroisopropyl)phosphate TCPP 85 uM 3 unpublished Yes No 23 2,2,4,4-tetra-bromodiphenyl ether BDE-47 85 uM 4 unpublished Yes No 24 Tris(2,3-dibromopropyl) phosphate TDBPP 3 uM 4 unpublished Yes No 25 Triisobutyl phosphate TiBP 158 uM 4 unpublished Yes No

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26 Triisopropylated phenyl phosphate IPP 19.8 uM 4 unpublished Yes No 27 Triphenyl phosphate TPP 8 uM 4 unpublished Yes No 28 Tetrabromobisphenol A-2-3-dibromopropyl ether TBBPA-DBPE 4 uM 4 unpublished Yes No 29 Tris(2-chloroethyl) phosphate TCEP 85 uM 4 unpublished Yes No DMSO Control for Benzo(a)pyrene, DMSO_PE50_AK 1% 7 No No 30 Dibenzo(a,l)pyrene, Benzo(pyrene) 32 Dibenzo(a,l) pyrene DBalP 10 uM 5 unpublished No No DMSO Control for Benzanthrone, Benz(a)anthracene- DMSO_PE50_BG 1% 8 No No 7,12-dione, Phenanthrene-quinone 34 35 Benzanthrone BEZO 10 uM 4 Goodale et al. 2015 - GSE68666 No No 36 Benz(a)anthracene-7,12-dione 7,12-B[a]AQ 10 uM 6 Goodale et al. 2015 - GSE68666 No Yes 37 9,10-Phenanthrene-quinone 9,10-PQ 1.2 uM 6 unpublished No No 38 DMSO Control for TCDD DMSO_TCDD 0.10% 4 No No 39 TCDD TCDD 1 ng/mL 4 Garcia et al. 2018 - GSE106131 No Yes 2 Retene Retene 12.2 uM 4 Shankar et al. 2019 - GSE156656 No Yes 3 Benzo[j]fluoranthene BjF 14.9 uM 4 Shankar et al. 2019 - GSE156656 No Yes 4 Benzo[k]fluoranthene BkF 1.9 uM 4 Shankar et al. 2019 - GSE156656 No Yes 5 Dibenzo[a,h]pyrene DBahP 10 uM 4 Shankar et al. 2019 - GSE156656 No Yes 6 Dibenzo[a,i]pyrene DBaiP 5 uM 4 Shankar et al. 2019 - GSE156656 No Yes 7 Benzo[b]fluoranthene BbF 50 uM 3 Shankar et al. 2019 - GSE156656 No Yes 31 Benzo(a)pyrene 10 uM BaP_10 10 uM 8 unpublished No Yes 33 Benzo(a)pyrene 1 uM BaP_1 1 uM 6 unpublished No Yes

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Table B.2. Functional enrichment of all genes in each module.

Intersection Module source term_name adjusted_p_value te rm_size que ry_size intersection_size Ratio 1 GO:MF DNA binding 0.00036 2461 149 36 0.24161 1 GO:BP nervous system development 0.00041 1727 146 31 0.21233 1 GO:BP developmental process 0.00047 4910 146 60 0.41096 1 GO:BP anatomical structure development 0.00057 4693 146 58 0.39726 1 GO:CC nucleus 0.00063 5251 146 61 0.41781 1 GO:BP multicellular organism development 0.00250 4291 146 53 0.36301 1 KEGG Gap junction 0.00378 94 43 6 0.13953 1 GO:BP system development 0.00384 3759 146 48 0.32877 1 GO:BP central nervous system development 0.00422 686 146 17 0.11644 1 GO:BP covalent chromatin modification 0.00458 288 146 11 0.07534 1 GO:BP regulation of cellular biosynthetic process 0.01107 2545 146 36 0.24658 1 REAC Neurotransmitter release cycle 0.01124 47 63 5 0.07937 1 GO:BP regulation of cellular macromolecule biosynthetic process 0.01265 2453 146 35 0.23973 1 GO:BP regulation of transcription, DNA-templated 0.01270 2242 146 33 0.22603 1 GO:BP regulation of biosynthetic process 0.01317 2565 146 36 0.24658

1 GO:CC intracellular 0.01330 11883 146 104 0.71233 1 GO:BP embryo development 0.01452 1179 146 22 0.15068 1 GO:BP anatomical structure formation involved in morphogenesis 0.01628 842 146 18 0.12329 1 GO:CC intracellular organelle 0.01678 9519 146 88 0.60274 1 GO:BP regulation of gene expression 0.01829 2823 146 38 0.26027 1 GO:BP regulation of nucleic acid-templated transcription 0.01866 2284 146 33 0.22603 1 GO:BP regulation of RNA biosynthetic process 0.01883 2285 146 33 0.22603 1 GO:BP transcription, DNA-templated 0.01896 2392 146 34 0.23288 1 GO:BP regulation of macromolecule biosynthetic process 0.01973 2504 146 35 0.23973 1 GO:CC organelle 0.01996 9850 146 90 0.61644

MIRN 1 dre-miR-144-3p 0.02019 4 3 2 0.66667 A 1 HP Interictal epileptiform activity 0.02022 244 46 11 0.23913 1 HP Interictal EEG abnormality 0.02101 245 46 11 0.23913

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1 GO:BP histone modification 0.02342 281 146 10 0.06849 1 REAC Deactivation of the beta-catenin transactivating complex 0.02431 29 63 4 0.06349 1 GO:BP nucleic acid-templated transcription 0.02681 2432 146 34 0.23288 1 KEGG Pentose phosphate pathway 0.02701 22 43 3 0.06977 1 GO:BP RNA biosynthetic process 0.02822 2438 146 34 0.23288 1 GO:BP protein dealkylation 0.02898 26 146 4 0.02740 1 GO:BP protein demethylation 0.02898 26 146 4 0.02740 1 GO:BP regulation of nucleobase-containing metabolic process 0.03133 2559 146 35 0.23973 1 GO:BP multicellular organismal process 0.03480 5316 146 58 0.39726 1 GO:BP regulation of RNA metabolic process 0.03724 2471 146 34 0.23288 1 GO:BP chromatin organization 0.03795 500 146 13 0.08904 1 KEGG Glycolysis / Gluconeogenesis 0.04278 57 43 4 0.09302 1 GO:BP anatomical structure morphogenesis 0.04470 2277 146 32 0.21918 1 GO:CC membrane-bounded organelle 0.04706 8459 146 79 0.54110 1 GO:BP embryonic organ development 0.04728 586 146 14 0.09589 2 GO:CC receptor complex 0.00017 328 262 17 0.06489 2 GO:MF metal ion transmembrane transporter activity 0.00076 533 287 21 0.07317 2 GO:CC calcium channel complex 0.00185 59 262 7 0.02672 2 GO:MF gated channel activity 0.00206 478 287 19 0.06620 2 GO:CC plasma membrane protein complex 0.00253 533 262 20 0.07634 2 GO:MF cation transmembrane transporter activity 0.00293 827 287 26 0.09059 2 GO:MF transmembrane transporter activity 0.00312 1429 287 37 0.12892 2 GO:MF protein binding 0.00318 8525 287 138 0.48084 2 GO:MF ion transmembrane transporter activity 0.00522 1123 287 31 0.10801 2 GO:CC ion channel complex 0.00761 270 262 13 0.04962 2 GO:CC cation channel complex 0.01026 202 262 11 0.04198 2 GO:CC transmembrane transporter complex 0.01149 281 262 13 0.04962 2 GO:CC transporter complex 0.01376 286 262 13 0.04962 2 GO:CC insulin receptor complex 0.01494 7 262 3 0.01145 2 GO:MF transporter activity 0.01611 1602 287 38 0.13240 2 GO:CC plasma membrane 0.01695 3607 262 69 0.26336 2 GO:MF voltage-gated ion channel activity 0.01804 249 287 12 0.04181 2 GO:MF insulin-activated receptor activity 0.02019 7 287 3 0.01045 2 GO:MF insulin receptor substrate binding 0.02019 7 287 3 0.01045 Factor: Pax-5; motif: 2 TF 0.02140 11826 301 174 0.57807 RRMSWGANWYCTNRAGCGKRACSRYNSM

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2 TF Factor: Pax-5; motif: RRMSWGANWYCTNRAGCGKRACS0.02140 11826 301 174 0.57807 2 GO:CC cell periphery 0.02241 3708 262 70 0.26718 2 GO:MF voltage-gated channel activity 0.02268 255 287 12 0.04181 2 KEGG MAPK signaling pathway 0.02749 325 95 15 0.15789 2 GO:MF GTPase binding 0.02769 481 287 17 0.05923 2 GO:BP positive regulation of protein kinase B signaling 0.02936 14 279 4 0.01434 2 GO:MF inorganic cation transmembrane transporter activity 0.02993 787 287 23 0.08014 2 GO:MF inorganic molecular entity transmembrane transporter activity 0.03097 1063 287 28 0.09756 2 GO:MF ion channel activity 0.03396 587 287 19 0.06620 2 GO:CC cellular anatomical entity 0.03424 19719 262 256 0.97710 2 GO:MF calcium ion transmembrane transporter activity 0.03695 154 287 9 0.03136 2 GO:CC membrane 0.03845 9669 262 148 0.56489 2 GO:MF transmembrane receptor protein kinase activity 0.04029 122 287 8 0.02787 2 GO:BP regulation of biological process 0.04483 10174 279 164 0.58781 2 KEGG Calcium signaling pathway 0.04723 238 95 12 0.12632 2 MIRNA dre-miR-140-5p 0.04969 1 1 1 1.00000 3 GO:BP ATP synthesis coupled proton transport 0.00001 23 71 5 0.07042 3 GO:BP energy coupled proton transport, down electrochemical gradien0.00001 23 71 5 0.07042 3 GO:MF proton-transporting ATP synthase activity, rotational mechanism0.00002 13 73 4 0.05479 3 GO:BP ATP biosynthetic process 0.00002 26 71 5 0.07042 3 GO:MF proton channel activity 0.00003 14 73 4 0.05479 3 REAC Mitochondrial biogenesis 0.00004 24 41 5 0.12195 3 REAC The citric acid (TCA) cycle and respiratory electron transport 0.00010 126 41 8 0.19512 3 GO:CC proton-transporting ATP synthase complex 0.00010 20 74 4 0.05405 3 KEGG Citrate cycle (TCA cycle) 0.00010 26 47 5 0.10638

3 GO:MF phosphotransferase activity, phosphate group as acceptor 0.00014 49 73 5 0.06849

3 GO:BP purine ribonucleoside triphosphate biosynthetic process 0.00020 39 71 5 0.07042 3 GO:BP purine nucleoside triphosphate biosynthetic process 0.00020 39 71 5 0.07042 3 GO:BP purine ribonucleoside triphosphate metabolic process 0.00034 43 71 5 0.07042

3 GO:CC cytoplasm 0.00036 7268 74 45 0.60811 3 REAC Formation of ATP by chemiosmotic coupling 0.00051 17 41 4 0.09756 3 REAC Cristae formation 0.00051 17 41 4 0.09756 3 GO:BP purine nucleoside triphosphate metabolic process 0.00059 48 71 5 0.07042 3 GO:BP ATP metabolic process 0.00068 149 71 7 0.09859 3 GO:BP ribonucleoside triphosphate biosynthetic process 0.00080 51 71 5 0.07042

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3 GO:BP purine-containing compound metabolic process 0.00089 310 71 9 0.12676 3 KEGG Carbon metabolism 0.00091 99 47 7 0.14894 3 GO:BP nucleoside triphosphate biosynthetic process 0.00118 55 71 5 0.07042 3 GO:BP ribonucleoside triphosphate metabolic process 0.00118 55 71 5 0.07042 3 GO:BP purine-containing compound biosynthetic process 0.00216 177 71 7 0.09859 3 GO:MF catalytic activity 0.00223 7959 73 43 0.58904 3 GO:BP purine ribonucleotide metabolic process 0.00229 257 71 8 0.11268 3 GO:CC mitochondrial protein complex 0.00345 242 74 7 0.09459 3 GO:BP ribonucleotide metabolic process 0.00399 277 71 8 0.11268 3 GO:BP nucleoside triphosphate metabolic process 0.00424 71 71 5 0.07042 3 GO:BP purine nucleotide metabolic process 0.00467 283 71 8 0.11268 3 GO:BP ribose phosphate metabolic process 0.00492 285 71 8 0.11268 3 GO:CC mitochondrial proton-transporting ATP synthase complex 0.00619 19 74 3 0.04054 3 GO:MF nucleotide binding 0.00679 3464 73 25 0.34247 3 GO:MF nucleoside phosphate binding 0.00679 3464 73 25 0.34247 3 GO:CC proton-transporting two-sector ATPase complex 0.00871 59 74 4 0.05405 3 GO:BP purine ribonucleotide biosynthetic process 0.01069 148 71 6 0.08451 3 GO:CC intracellular 0.01753 11883 74 57 0.77027 3 KEGG Oxidative phosphorylation 0.01889 114 47 6 0.12766 proton-transporting ATP synthase complex, catalytic core 3 GO:CC 0.01960 5 74 2 0.02703 F(1) 3 GO:BP ribonucleotide biosynthetic process 0.01986 165 71 6 0.08451 3 GO:MF anion binding 0.01993 3929 73 26 0.35616 3 GO:CC inner mitochondrial membrane protein complex 0.02032 139 74 5 0.06757 3 GO:BP small molecule metabolic process 0.02042 1308 71 15 0.21127 3 GO:BP purine nucleotide biosynthetic process 0.02275 169 71 6 0.08451 3 GO:BP ribose phosphate biosynthetic process 0.02352 170 71 6 0.08451 3 GO:BP nucleobase-containing small molecule metabolic process 0.02554 469 71 9 0.12676 3 GO:MF small molecule binding 0.03197 3794 73 25 0.34247 3 GO:MF ATPase activity 0.03214 467 73 8 0.10959 3 KEGG Valine, leucine and isoleucine degradation 0.03549 49 47 4 0.08511 3 REAC Pyruvate metabolism and Citric Acid (TCA) cycle 0.03959 49 41 4 0.09756 3 GO:MF ATP binding 0.04307 2477 73 19 0.26027 3 GO:BP nucleotide metabolic process 0.04740 390 71 8 0.11268 3 GO:BP generation of precursor metabolites and energy 0.04801 285 71 7 0.09859 4 GO:MF DNA-binding transcription factor activity 0.00000 1177 207 36 0.17391 DNA-binding transcription factor activity, RNA polymerase II- 4 GO:MF 0.00000 637 207 26 0.12560 specific 4 GO:MF transcription regulator activity 0.00000 1459 207 37 0.17874 4 GO:BP regulation of RNA biosynthetic process 0.00002 2285 202 49 0.24257 4 GO:BP regulation of transcription, DNA-templated 0.00003 2242 202 48 0.23762 4 GO:BP regulation of cellular metabolic process 0.00003 3837 202 68 0.33663 4 GO:BP regulation of nucleic acid-templated transcription 0.00005 2284 202 48 0.23762 4 GO:BP regulation of primary metabolic process 0.00005 3719 202 66 0.32673 4 GO:BP regulation of nitrogen compound metabolic process 0.00006 3647 202 65 0.32178 4 GO:BP regulation of transcription by RNA polymerase II 0.00007 1325 202 34 0.16832 4 GO:BP regulation of cellular biosynthetic process 0.00008 2545 202 51 0.25248

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4 GO:BP regulation of RNA metabolic process 0.00008 2471 202 50 0.24752 4 GO:BP regulation of biosynthetic process 0.00010 2565 202 51 0.25248 4 GO:BP regulation of macromolecule biosynthetic process 0.00012 2504 202 50 0.24752 4 GO:BP RNA biosynthetic process 0.00014 2438 202 49 0.24257 4 GO:BP regulation of cellular macromolecule biosynthetic process 0.00017 2453 202 49 0.24257 4 GO:BP transcription by RNA polymerase II 0.00019 1378 202 34 0.16832 4 GO:BP transcription, DNA-templated 0.00021 2392 202 48 0.23762 regulation of nucleobase-containing compound metabolic 4 GO:BP 0.00024 2559 202 50 0.24752 process 4 GO:BP nucleic acid-templated transcription 0.00034 2432 202 48 0.23762 4 GO:BP regulation of metabolic process 0.00040 4178 202 69 0.34158 4 GO:BP nucleobase-containing compound biosynthetic process 0.00148 2798 202 51 0.25248 4 GO:BP regulation of macromolecule metabolic process 0.00202 3921 202 64 0.31683 4 GO:BP aromatic compound biosynthetic process 0.00323 2873 202 51 0.25248 4 GO:BP heterocycle biosynthetic process 0.00357 2883 202 51 0.25248 4 GO:BP regulation of gene expression 0.00445 2823 202 50 0.24752 4 GO:BP neurogenesis 0.00562 1114 202 27 0.13366 4 GO:BP organic cyclic compound biosynthetic process 0.00831 2969 202 51 0.25248 4 GO:BP generation of neurons 0.01247 1029 202 25 0.12376 4 KEGG Biosynthesis of amino acids 0.02033 68 80 6 0.07500 4 GO:BP cell differentiation 0.02404 2422 202 43 0.21287 4 GO:BP response to chemical 0.02589 2189 202 40 0.19802 4 GO:BP cellular nitrogen compound biosynthetic process 0.02644 3438 202 55 0.27228 4 GO:BP response to external stimulus 0.02725 1147 202 26 0.12871 4 GO:BP anatomical structure development 0.02763 4693 202 69 0.34158 4 GO:BP cellular developmental process 0.02903 2441 202 43 0.21287 4 GO:BP neuron differentiation 0.02937 948 202 23 0.11386 4 GO:BP multicellular organism development 0.04093 4291 202 64 0.31683 4 GO:BP system development 0.04246 3759 202 58 0.28713 4 REAC Signaling by Non-Receptor Tyrosine Kinases 0.04623 49 79 5 0.06329 4 REAC Signaling by PTK6 0.04623 49 79 5 0.06329 4 HP Increased circulating IgE level 0.04756 40 76 6 0.07895 4 HP Abnormal circulating IgE level 0.04756 40 76 6 0.07895 5 GO:BP nervous system development 0.00010 1727 71 21 0.29577 5 GO:CC neuronal cell body 0.00092 71 75 5 0.06667 5 GO:CC cell body 0.00129 76 75 5 0.06667

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5 GO:BP regulation of nervous system development 0.00255 352 71 9 0.12676 5 GO:BP regulation of neurogenesis 0.00912 310 71 8 0.11268 5 GO:CC cation channel complex 0.01418 202 75 6 0.08000 5 GO:BP regulation of cell development 0.01670 337 71 8 0.11268 5 GO:BP generation of neurons 0.03002 1029 71 13 0.18310 5 GO:MF cation channel activity 0.03148 461 74 8 0.10811 5 GO:MF gated channel activity 0.04042 478 74 8 0.10811 6 GO:CC voltage-gated sodium channel complex 0.00846 23 66 3 0.04545 6 GO:CC sodium channel complex 0.02323 32 66 3 0.04545 7 GO:BP regulation of non-motile cilium assembly 0.01641 2 84 2 0.02381 8 GO:MF structural constituent of eye lens 0.00000 81 31 11 0.35484 8 GO:BP lens development in camera-type eye 0.00000 111 31 11 0.35484 8 GO:MF structural molecule activity 0.00000 613 31 15 0.48387 8 GO:BP visual perception 0.00000 198 31 11 0.35484 8 GO:BP sensory perception of light stimulus 0.00000 206 31 11 0.35484 8 GO:BP camera-type eye development 0.00000 420 31 11 0.35484 8 GO:BP sensory perception 0.00000 446 31 11 0.35484 8 GO:BP eye development 0.00000 498 31 11 0.35484 8 GO:BP visual system development 0.00000 498 31 11 0.35484 8 GO:BP sensory system development 0.00000 604 31 11 0.35484 8 GO:BP nervous system process 0.00000 629 31 11 0.35484 8 GO:BP sensory organ development 0.00000 677 31 11 0.35484 8 GO:BP system process 0.00001 1017 31 12 0.38710 8 GO:BP animal organ development 0.00085 2669 31 15 0.48387 8 GO:BP alpha-amino acid metabolic process 0.00475 179 31 5 0.16129 8 GO:BP system development 0.01246 3759 31 16 0.51613 8 GO:BP multicellular organism development 0.01445 4291 31 17 0.54839 8 TF Factor: PEA3; motif: ACWTCCK 0.02638 9315 34 23 0.67647 8 TF Factor: PEA3; motif: ACWTCCK; match class: 0 0.02638 9315 34 23 0.67647 8 GO:BP cellular amino acid metabolic process 0.03099 264 31 5 0.16129 8 GO:BP anatomical structure development 0.04921 4693 31 17 0.54839 9 GO:CC membrane-bounded organelle 0.00187 8459 78 50 0.64103 9 REAC Gene expression (Transcription) 0.00348 750 34 13 0.38235 9 GO:CC intracellular membrane-bounded organelle 0.00513 7936 78 47 0.60256 9 GO:CC intracellular organelle 0.01213 9519 78 52 0.66667 9 GO:CC membrane-enclosed lumen 0.01481 1331 78 15 0.19231 9 GO:CC organelle lumen 0.01481 1331 78 15 0.19231 9 GO:CC intracellular organelle lumen 0.01481 1331 78 15 0.19231 9 REAC RNA Polymerase II Transcription 0.02720 656 34 11 0.32353 9 GO:CC intracellular 0.03578 11883 78 59 0.75641

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9 GO:CC organelle 0.03583 9850 78 52 0.66667 9 REAC Generic Transcription Pathway 0.03698 561 34 10 0.29412 9 REAC RMTs methylate histone arginines 0.04706 25 34 3 0.08824 10 GO:BP neuron projection guidance 0.00041 289 36 7 0.19444 10 GO:BP cell surface receptor signaling pathway 0.00090 1904 36 14 0.38889 10 GO:BP semaphorin-plexin signaling pathway 0.00138 50 36 4 0.11111 10 GO:MF semaphorin receptor activity 0.00165 20 41 3 0.07317 10 HP Epileptic encephalopathy 0.00384 117 11 5 0.45455 10 GO:CC integral component of membrane 0.00610 7847 43 29 0.67442 10 GO:BP neuron projection development 0.00628 620 36 8 0.22222 10 GO:BP axonogenesis 0.00640 437 36 7 0.19444 10 GO:CC intrinsic component of membrane 0.00660 7876 43 29 0.67442 10 GO:BP axon guidance 0.00677 285 36 6 0.16667 10 GO:BP axon development 0.00973 466 36 7 0.19444 10 GO:BP cell morphogenesis involved in neuron differentiation 0.01014 469 36 7 0.19444 10 GO:BP chemotaxis 0.01227 483 36 7 0.19444 10 GO:CC membrane 0.01430 9669 43 32 0.74419 10 GO:BP plasma membrane bounded cell projection morphogenesis 0.01477 497 36 7 0.19444 10 GO:BP cell projection morphogenesis 0.01477 497 36 7 0.19444 10 GO:BP neuron projection morphogenesis 0.01477 497 36 7 0.19444 10 GO:BP taxis 0.01595 503 36 7 0.19444 10 GO:BP cell part morphogenesis 0.01637 505 36 7 0.19444 10 GO:BP neuron differentiation 0.01793 948 36 9 0.25000 10 GO:BP cell morphogenesis involved in differentiation 0.02965 554 36 7 0.19444 10 GO:BP neuron development 0.03038 771 36 8 0.22222 10 GO:BP generation of neurons 0.03426 1029 36 9 0.25000 10 GO:BP regulation of neuron differentiation 0.03750 231 36 5 0.13889 10 GO:BP locomotion 0.04754 1073 36 9 0.25000 10 GO:CC integral component of plasma membrane 0.04973 1386 43 10 0.23256 12 TF Factor: c-Myc:Max; motif: GCCAYGYGSN 0.00242 3851 78 27 0.34615 12 TF Factor: c-Myc:Max; motif: GCCAYGYGSN; match class: 0 0.00242 3851 78 27 0.34615 12 GO:BP bulbus arteriosus development 0.04947 4 63 2 0.03175 13 GO:BP response to xenobiotic stimulus 0.00000 101 53 12 0.22642 13 GO:BP cellular response to xenobiotic stimulus 0.00000 92 53 11 0.20755 13 GO:BP cellular response to chemical stimulus 0.00000 1479 53 20 0.37736 13 KEGG Metabolism of xenobiotics by cytochrome P450 0.00000 51 29 7 0.24138 13 GO:BP response to chemical 0.00000 2189 53 23 0.43396 13 KEGG Drug metabolism - other enzymes 0.00000 72 29 7 0.24138 13 KEGG Drug metabolism - cytochrome P450 0.00000 45 29 6 0.20690 13 GO:CC inclusion body 0.00003 5 59 3 0.05085

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13 REAC Biological oxidations 0.00010 156 24 7 0.29167 13 GO:BP cellular response to oxidative stress 0.00014 49 53 5 0.09434 13 GO:MF oxidoreductase activity 0.00018 975 59 13 0.22034 13 GO:BP cellular response to chemical stress 0.00021 53 53 5 0.09434 13 GO:BP response to toxic substance 0.00046 117 53 6 0.11321 13 GO:MF glutathione transferase activity 0.00084 39 59 4 0.06780 oxidoreductase activity, acting on single donors with 13 GO:MF incorporation of molecular oxygen, incorporation of two atoms 0.00165 46 59 4 0.06780 of oxygen oxidoreductase activity, acting on single donors with 13 GO:MF 0.00180 47 59 4 0.06780 incorporation of molecular oxygen 13 GO:MF thiopurine S-methyltransferase activity 0.00196 2 59 2 0.03390 13 GO:MF coumarin 7-hydroxylase activity 0.00196 2 59 2 0.03390 13 GO:MF benzo(a)pyrene 7,8-dioxygenase activity 0.00196 2 59 2 0.03390 13 GO:MF benzo(a)pyrene 9,10-dioxygenase activity 0.00196 2 59 2 0.03390 13 GO:MF heme binding 0.00362 204 59 6 0.10169 13 GO:MF tetrapyrrole binding 0.00393 207 59 6 0.10169 13 GO:BP response to oxidative stress 0.00397 95 53 5 0.09434 13 KEGG Glutathione metabolism 0.00557 52 29 4 0.13793 13 GO:MF testosterone 6-beta-hydroxylase activity 0.00586 3 59 2 0.03390 13 GO:BP glutathione metabolic process 0.00823 51 53 4 0.07547 transferase activity, transferring alkyl or aryl (other than 13 GO:MF 0.01046 73 59 4 0.06780 methyl) groups 13 GO:BP oxidation-reduction process 0.01136 1096 53 12 0.22642 13 REAC Phase I - Functionalization of compounds 0.01923 73 24 4 0.16667 13 GO:MF antioxidant activity 0.01993 86 59 4 0.06780 13 GO:MF flavin adenine dinucleotide binding 0.02594 92 59 4 0.06780 13 REAC Phase II - Conjugation of compounds 0.02889 81 24 4 0.16667 13 GO:MF glutathione disulfide oxidoreductase activity 0.02914 6 59 2 0.03390 13 GO:MF peptide disulfide oxidoreductase activity 0.02914 6 59 2 0.03390 13 GO:MF dioxygenase activity 0.02939 95 59 4 0.06780 13 REAC Metal sequestration by antimicrobial proteins 0.04978 7 24 2 0.08333 14 GO:CC ribonucleoprotein complex 0.00175 566 31 7 0.22581 14 GO:MF translation regulator activity, nucleic acid binding 0.01156 133 35 4 0.11429 14 GO:BP translation 0.01311 519 34 7 0.20588 14 GO:BP peptide biosynthetic process 0.01429 526 34 7 0.20588 14 KEGG Salmonella infection 0.01655 224 17 5 0.29412 14 GO:MF translation regulator activity 0.02152 156 35 4 0.11429 14 GO:CC melanosome 0.02408 14 31 2 0.06452 14 GO:CC pigment granule 0.02408 14 31 2 0.06452 14 GO:BP amide biosynthetic process 0.03658 609 34 7 0.20588 14 GO:MF RNA binding 0.03708 1081 35 8 0.22857 14 GO:BP peptide metabolic process 0.04313 625 34 7 0.20588 14 GO:CC eukaryotic 43S preinitiation complex 0.04504 19 31 2 0.06452

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14 GO:CC eukaryotic 48S preinitiation complex 0.04504 19 31 2 0.06452 14 GO:CC eukaryotic translation initiation factor 3 complex 0.05000 20 31 2 0.06452 15 KEGG DNA replication 0.00000 30 19 7 0.36842 15 GO:BP DNA replication 0.00000 142 32 9 0.28125 15 GO:BP nuclear DNA replication 0.00000 24 32 6 0.18750 15 GO:BP cell cycle DNA replication 0.00000 26 32 6 0.18750 15 GO:BP DNA-dependent DNA replication 0.00000 100 32 8 0.25000 15 GO:BP DNA replication initiation 0.00000 29 32 6 0.18750 15 REAC Cell Cycle 0.00000 467 22 13 0.59091 15 GO:BP DNA metabolic process 0.00000 585 32 12 0.37500 15 GO:MF DNA replication origin binding 0.00000 23 32 5 0.15625 15 REAC Cell Cycle, Mitotic 0.00000 415 22 12 0.54545 15 GO:CC MCM complex 0.00000 13 34 4 0.11765 15 REAC S Phase 0.00000 145 22 8 0.36364 15 GO:BP DNA repair 0.00000 363 32 9 0.28125 pre-replicative complex assembly involved in nuclear cell 15 GO:BP 0.00000 12 32 4 0.12500 cycle DNA replication pre-replicative complex assembly involved in cell cycle DNA 15 GO:BP 0.00000 12 32 4 0.12500 replication 15 GO:BP pre-replicative complex assembly 0.00000 12 32 4 0.12500 15 GO:MF catalytic activity, acting on DNA 0.00000 201 32 7 0.21875 15 GO:BP double-strand break repair via break-induced replication 0.00001 15 32 4 0.12500 15 GO:MF single-stranded DNA binding 0.00001 63 32 5 0.15625 15 REAC Activation of the pre-replicative complex 0.00001 32 22 5 0.22727 15 REAC Synthesis of DNA 0.00001 117 22 7 0.31818 15 GO:BP mitotic cell cycle 0.00001 776 32 11 0.34375 15 REAC DNA Replication 0.00001 124 22 7 0.31818 15 GO:BP mitotic DNA replication 0.00001 18 32 4 0.12500 15 GO:BP cellular response to DNA damage stimulus 0.00002 479 32 9 0.28125 15 GO:BP cell cycle 0.00005 1174 32 12 0.37500 15 GO:BP mitotic DNA replication initiation 0.00006 6 32 3 0.09375 15 GO:BP nuclear cell cycle DNA replication initiation 0.00006 6 32 3 0.09375 15 GO:BP cell cycle DNA replication initiation 0.00006 6 32 3 0.09375 15 GO:MF DNA-dependent ATPase activity 0.00009 110 32 5 0.15625 15 GO:CC nucleus 0.00012 5251 34 22 0.64706 15 REAC G1/S Transition 0.00014 112 22 6 0.27273 15 GO:BP protein-DNA complex subunit organization 0.00017 172 32 6 0.18750 15 GO:MF chromatin binding 0.00030 263 32 6 0.18750 15 REAC Mitotic G1 phase and G1/S transition 0.00036 131 22 6 0.27273 15 GO:MF DNA helicase activity 0.00054 69 32 4 0.12500 15 GO:MF 3'-5' DNA helicase activity 0.00061 20 32 3 0.09375 15 REAC DNA Replication Pre-Initiation 0.00083 84 22 5 0.22727

295

15 KEGG Cell cycle 0.00143 118 19 5 0.26316 15 REAC Removal of the Flap Intermediate 0.00175 14 22 3 0.13636 15 GO:BP double-strand break repair 0.00178 140 32 5 0.15625 15 GO:MF DNA binding 0.00185 2461 32 13 0.40625 15 GO:BP protein-DNA complex assembly 0.00211 145 32 5 0.15625 15 REAC Processive synthesis on the lagging strand 0.00218 15 22 3 0.13636 15 GO:BP DNA duplex unwinding 0.00407 74 32 4 0.12500 15 GO:BP DNA geometric change 0.00429 75 32 4 0.12500 15 GO:BP DNA strand elongation involved in DNA replication 0.00430 22 32 3 0.09375 15 GO:BP cellular response to stress 0.00451 913 32 9 0.28125 15 GO:BP recombinational repair 0.00477 77 32 4 0.12500 15 GO:BP double-strand break repair via homologous recombination 0.00477 77 32 4 0.12500 15 REAC Lagging Strand Synthesis 0.00542 20 22 3 0.13636 15 KEGG Base excision repair 0.00567 32 19 3 0.15789 15 GO:BP DNA strand elongation 0.00640 25 32 3 0.09375 15 GO:CC intracellular 0.00672 11883 34 30 0.88235 15 GO:BP nucleic acid metabolic process 0.00747 3914 32 17 0.53125 15 GO:MF ATPase activity 0.00790 467 32 6 0.18750 15 REAC Chromosome Maintenance 0.00811 66 22 4 0.18182 15 REAC DNA strand elongation 0.00837 23 22 3 0.13636 15 REAC Assembly of the pre-replicative complex 0.00861 67 22 4 0.18182 15 GO:CC organelle 0.01034 9850 34 27 0.79412 15 GO:MF helicase activity 0.01049 146 32 4 0.12500 15 GO:MF nucleoside-triphosphatase activity 0.01076 1014 32 8 0.25000 Resolution of AP sites via the multiple-nucleotide patch 15 REAC 0.01082 25 22 3 0.13636 replacement pathway 15 REAC Telomere C-strand (Lagging Strand) Synthesis 0.01082 25 22 3 0.13636 15 GO:BP DNA conformation change 0.01116 204 32 5 0.15625 15 REAC Extension of Telomeres 0.01221 26 22 3 0.13636 15 REAC Orc1 removal from chromatin 0.01275 74 22 4 0.18182 15 GO:MF pyrophosphatase activity 0.01553 1068 32 8 0.25000 hydrolase activity, acting on acid anhydrides, in phosphorus- 15 GO:MF 0.01574 1070 32 8 0.25000 containing anhydrides 15 GO:MF hydrolase activity, acting on acid anhydrides 0.01627 1075 32 8 0.25000 15 GO:BP cell cycle process 0.01646 576 32 7 0.21875 15 REAC DNA Repair 0.01698 257 22 6 0.27273 15 GO:CC intracellular organelle 0.02169 9519 34 26 0.76471 15 GO:BP retina development in camera-type eye 0.02299 237 32 5 0.15625 15 REAC Switching of origins to a post-replicative state 0.02627 89 22 4 0.18182 15 REAC Resolution of Abasic Sites (AP sites) 0.02767 34 22 3 0.13636 15 REAC Activation of ATR in response to replication stress 0.02767 34 22 3 0.13636 15 GO:BP nucleobase-containing compound metabolic process 0.03404 4369 32 17 0.53125

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15 GO:CC membrane-bounded organelle 0.03466 8459 34 24 0.70588 15 REAC HDR through MMEJ (alt-NHEJ) 0.03749 7 22 2 0.09091 15 REAC POLB-Dependent Long Patch Base Excision Repair 0.03749 7 22 2 0.09091 15 GO:CC nuclear lumen 0.03810 1105 34 8 0.23529 15 GO:CC intracellular membrane-bounded organelle 0.04162 7936 34 23 0.67647 15 GO:BP leading strand elongation 0.04308 7 32 2 0.06250 15 REAC Nucleotide Excision Repair 0.04456 102 22 4 0.18182 15 GO:BP heterocycle metabolic process 0.04967 4493 32 17 0.53125 16 GO:MF extracellular matrix structural constituent 0.00000 86 36 6 0.16667 16 GO:CC extracellular matrix 0.00003 313 31 7 0.22581 16 GO:BP skeletal system development 0.00300 405 35 7 0.20000 16 HP Soft skin 0.00422 36 15 4 0.26667 16 GO:CC collagen-containing extracellular matrix 0.00448 128 31 4 0.12903 16 GO:BP extracellular structure organization 0.00521 160 35 5 0.14286 16 GO:BP extracellular matrix organization 0.00521 160 35 5 0.14286 16 HP Neonatal short-limb short stature 0.00632 12 15 3 0.20000 16 GO:CC fibrillar collagen trimer 0.00957 9 31 2 0.06452 16 GO:CC banded collagen fibril 0.00957 9 31 2 0.06452 16 GO:CC extracellular space 0.01175 1041 31 8 0.25806 16 REAC ECM proteoglycans 0.01343 42 14 3 0.21429 16 HP Abnormality of the spinal cord 0.01554 289 15 7 0.46667 16 GO:CC complex of collagen trimers 0.01749 12 31 2 0.06452 16 GO:CC collagen trimer 0.02108 73 31 3 0.09677 16 HP Joint subluxation 0.02744 19 15 3 0.20000 16 HP Absent ossification of calvaria 0.03095 3 15 2 0.13333 16 HP Biconcave flattened vertebrae 0.03095 3 15 2 0.13333 16 HP Femoral bowing present at birth, straightening with time 0.03095 3 15 2 0.13333 16 HP Severe generalized osteoporosis 0.03095 3 15 2 0.13333 16 HP Generalized osteoporosis 0.03751 21 15 3 0.20000 16 KEGG ECM-receptor interaction 0.04106 81 15 3 0.20000 16 HP Atrophic scars 0.04335 22 15 3 0.20000 Factor: SMAD4; motif: GKSRKKCAGMCANCY; match 17 TF 0.01058 1055 28 7 0.25000 class: 1 17 TF Factor: c-Myc:Max; motif: GCCAYGYGSN; match class: 1 0.02335 532 28 5 0.17857 17 GO:CC eukaryotic 43S preinitiation complex 0.03060 19 26 2 0.07692 17 GO:CC eukaryotic 48S preinitiation complex 0.03060 19 26 2 0.07692

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17 GO:CC eukaryotic translation initiation factor 3 complex 0.03397 20 26 2 0.07692 17 GO:CC translation preinitiation complex 0.03752 21 26 2 0.07692 17 GO:BP dedifferentiation 0.03839 8 27 2 0.07407 17 GO:BP cell dedifferentiation 0.03839 8 27 2 0.07407 19 HP Membranoproliferative glomerulonephritis 0.00000 12 5 4 0.80000 19 HP Decreased serum complement C3 0.00000 15 5 4 0.80000 19 HP Anuria 0.00000 16 5 4 0.80000 19 HP Abnormal blood urea nitrogen concentration 0.00000 16 5 4 0.80000 19 HP Microangiopathic hemolytic anemia 0.00000 16 5 4 0.80000 19 HP Hemolytic-uremic syndrome 0.00000 16 5 4 0.80000 19 HP Increased blood urea nitrogen 0.00000 16 5 4 0.80000 19 HP Decreased urine output 0.00000 24 5 4 0.80000 19 HP Complement deficiency 0.00001 30 5 4 0.80000 19 HP Acute kidney injury 0.00001 31 5 4 0.80000 19 HP Glomerulonephritis 0.00001 32 5 4 0.80000 19 KEGG Phagosome 0.00001 138 4 4 1.00000 19 HP Systemic lupus erythematosus 0.00001 33 5 4 0.80000 19 HP Abnormality of complement system 0.00001 34 5 4 0.80000 19 KEGG Herpes simplex virus 1 infection 0.00001 150 4 4 1.00000 19 HP Elevated serum creatinine 0.00001 36 5 4 0.80000 19 HP Abnormal circulating creatinine level 0.00002 39 5 4 0.80000 19 GO:MF endopeptidase inhibitor activity 0.00002 156 8 4 0.50000 19 GO:BP complement activation 0.00002 79 10 4 0.40000 19 GO:MF endopeptidase regulator activity 0.00002 162 8 4 0.50000 19 GO:MF peptidase inhibitor activity 0.00002 169 8 4 0.50000 19 GO:MF peptidase regulator activity 0.00003 180 8 4 0.50000 19 HP Nephritis 0.00006 53 5 4 0.80000 19 GO:BP humoral immune response 0.00007 107 10 4 0.40000 19 GO:MF enzyme inhibitor activity 0.00013 255 8 4 0.50000 19 HP Abnormal urine output 0.00016 67 5 4 0.80000 19 HP Abnormality of renal excretion 0.00018 69 5 4 0.80000 19 GO:BP negative regulation of endopeptidase activity 0.00036 162 10 4 0.40000 19 GO:BP negative regulation of peptidase activity 0.00050 175 10 4 0.40000 19 GO:BP negative regulation of proteolysis 0.00055 180 10 4 0.40000 19 GO:BP activation of immune response 0.00057 181 10 4 0.40000 19 KEGG Neuroactive ligand-receptor interaction 0.00063 401 4 4 1.00000 19 GO:BP immune effector process 0.00067 189 10 4 0.40000 19 GO:BP positive regulation of immune response 0.00080 197 10 4 0.40000 19 GO:BP regulation of endopeptidase activity 0.00109 213 10 4 0.40000 19 GO:BP negative regulation of hydrolase activity 0.00140 227 10 4 0.40000 19 HP Azotemia 0.00144 115 5 4 0.80000 19 GO:BP regulation of peptidase activity 0.00152 232 10 4 0.40000 19 GO:BP regulation of immune response 0.00152 232 10 4 0.40000 19 HP Abnormal circulating nitrogen compound concentration 0.00166 119 5 4 0.80000 19 GO:CC extracellular space 0.00180 1041 8 5 0.62500

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19 GO:BP positive regulation of immune system process 0.00208 251 10 4 0.40000 19 HP Nephrotic syndrome 0.00222 128 5 4 0.80000 19 HP Anemia due to reduced life span of red cells 0.00357 144 5 4 0.80000 19 HP Hemolytic anemia 0.00357 144 5 4 0.80000 19 HP Recurrent bacterial infections 0.00377 146 5 4 0.80000 19 GO:BP regulation of proteolysis 0.00395 295 10 4 0.40000 19 HP Abnormal urine cytology 0.00398 148 5 4 0.80000 19 HP Hematuria 0.00398 148 5 4 0.80000 19 HP Abnormality of renal glomerulus morphology 0.00631 166 5 4 0.80000 19 HP Abnormal renal corpuscle morphology 0.00631 166 5 4 0.80000 19 GO:BP negative regulation of catalytic activity 0.00668 337 10 4 0.40000 19 HP Autoimmunity 0.00762 174 5 4 0.80000 19 HP Proteinuria 0.00853 179 5 4 0.80000 19 GO:BP negative regulation of cellular protein metabolic process 0.00945 368 10 4 0.40000 19 GO:BP negative regulation of protein metabolic process 0.00986 372 10 4 0.40000 19 GO:BP negative regulation of molecular function 0.01040 377 10 4 0.40000 19 HP Abnormal urine protein level 0.01061 189 5 4 0.80000 19 GO:MF enzyme regulator activity 0.01094 787 8 4 0.50000 19 HP Abnormal renal cortex morphology 0.01129 192 5 4 0.80000 19 GO:BP regulation of immune system process 0.01224 393 10 4 0.40000 19 GO:CC extracellular region 0.01510 1615 8 5 0.62500 19 HP Abnormal nephron morphology 0.01678 212 5 4 0.80000 19 HP Abnormality of humoral immunity 0.02706 239 5 4 0.80000 19 REAC Keratinization 0.03311 36 5 2 0.40000 19 REAC Formation of the cornified envelope 0.03311 36 5 2 0.40000 20 GO:BP response to hypoxia 0.00594 68 13 3 0.23077 20 GO:BP response to decreased oxygen levels 0.00594 68 13 3 0.23077 20 GO:BP response to oxygen levels 0.00649 70 13 3 0.23077 20 GO:CC signal recognition particle receptor complex 0.04991 1 10 1 0.10000 22 KEGG mTOR signaling pathway 0.00801 143 2 2 1.00000 23 GO:CC extracellular matrix 0.00000 313 8 6 0.75000 23 GO:CC extracellular region 0.00060 1615 8 6 0.75000 23 GO:MF extracellular matrix structural constituent 0.00095 86 11 3 0.27273 23 KEGG ECM-receptor interaction 0.00256 81 2 2 1.00000 23 KEGG Focal adhesion 0.01782 213 2 2 1.00000 23 HP Ulnar deviation of the hand or of fingers of the hand 0.02078 52 6 3 0.50000 23 GO:BP extracellular matrix organization 0.02728 160 10 3 0.30000 23 GO:BP extracellular structure organization 0.02728 160 10 3 0.30000 23 GO:CC extracellular space 0.04469 1041 8 4 0.50000 23 REAC ECM proteoglycans 0.04517 42 5 2 0.40000

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Table B.3. Gene Specific Primers for RT-qPCR Analysis

Gene Forward primer (5'->3') Reverse primer (5'->3') cyp1a TGCCGATTTCATCCCTTTCC AGAGCCGTGCTGATAGTGTC fgf7 GACACAAGATCCAAACAACTGCT GACGCTCTCTTTCCTCGTCTT mamdc2b GCGGTCTCCGAGGATGAAAG CACAAAACAAGAATCCCTCCCA pkhd1l1 CACAAGGCCATCATCACCCT GAGGCGAGTCCACGTCATAG sult6b1 GTGGGTTTAACTGGATGGTG GAGACCACTGTGTCTTTCG NA_632 CGAGATGTCTGTGCTCGTGA GTTGTCACTGGAGTGGCAGTA NA_732 ACCCATAATGCTCTGCTCCAC TGCTGTCTTGAGTTTCACACC NA_145 TCTGCCATTGACGAAGCTGT GCACCCCAAGAATCTCCACAA NA_928 ATCAGGCCAGGAGAGAAGGT AAGAGCCAGCAAAACGTAGG

300

Appendix C – Supplemental Data for Chapter 5

Methods: Zebrafish were exposed to 5 ng/mL TCDD and total RNA was extracted at 48 hpf similar to the 1 ng/mL exposure methods described in the Chapter. We conducted poly(A) enrichment using the NEBNext Poly(A) mRNA Magnetic Isolation Module following manufacturer’s instructions. cDNA was synthesized using the Maxima H Minus First strand cDNA synthesis kit (ThermoFisher). This was followed by PCR (KOD Hot Start polymerase) using forward and reverse primers (IDT) indicated in blue in figure A below. Sanger sequencing was conducted, and sequencing results were aligned with the NCBI wfikkn1 sequence using ClustalOmega (A).The PCR amplicon was cloned into a pET151/D-TOPO vector with a polyhistidine tag (ThermoFisher), following manufacturer’s instructions and restriction digest and Sanger sequencing was used to confirm the directionality and sequence of the wfikkn1 insert. We expressed the Wfikkn1 protein in E.coli cells (BL21 Star One Shot cells) using IPTG induction, and detected protein expression on a western blot using a His antibody at multiple time points after induction (B), in addition to weak detection by the custom-made Wfikkn1 antibody (data not shown).

Figure C.1. Confirmation of wfikkn1 sequence in tropical 5D wildtype zebrafish. (A) aligned with the NCBI reference sequence using Clustal Omega (cite) reveals >99% similarity between the two sequences. The forward and reverse primers are highlighted with blue and putative SNPs relative to the reference sequence are highlighted in yellow.

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The regions in gray closest to the forward and reverse primers were not confirmed by Sanger Sequencing. (B) Western blot showing IPTG induction and detection of protein expression using a His antibody.

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Outliers removed

Figure C.2. PCA Plot showing the four replicates of each treatment of the RNA- sequencing experiment. WD2 and WT2 were removed from all analysis since they were identified as outliers.

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Figure C.3. Reaction norm plots for the behavior assays conducted in wildtype and wfikkn1 mutant zebrafish exposed to 0.1% DMSO or 50 pg/mL TCDD.

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Table C.1. Primer and oligo sequences for creation and confirmation of wfikkn1 CRISPR- Cas9 mutant zebrafish line.

CRISPR-Cas9 sequences/primers (wfikkn1)

Target site sequence (with PAM) 5' - GGTGGAGGCGTGGGGTCCTGCGG -3' Gene-specific oligo - T7 Promoter 5' - + sgRNA + universal primer taatacgactcactataGGTGGAGGCGTGGGGT sequence: CCTGgttttagagctagaa - 3' 5’- AAAAGCACCGACTCGGTGCCACTTTTTCAA Constant oligo GTTGATAACGGACTAGCCTTATTTTAACTT GCTATTTCTAGCTCTAAAAC -3’

Universal primers Forward 5' - GCGTAATACGACTCACTATA - 3' Reverse 5' - AAAGCACCGACTCGGTGCCAC - 3'

Sequencing primers Forward 5' - CAGCCGGTGTGTAAGTGTCA - 3' Reverse 5' - ACATCGCAGTGGAAGCTGACG - 3'

High-melt primers Forward 5’ – CTGTCCAGCCCAAACTCC – 3’ Reverse 5’ - TCTGGGTGTCTGCGTCTGTC – 3’

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Table C.2. The top 20 most increased and top 20 most decreased significant differentially expressed genes between the four treatment groups. (A) mut_DMSO vs. WT_DMSO (B) WT_TCDD vs. WT_DMSO (C) mut_TCDD vs. mut_DMSO (D) mut_TCDD vs WT_TCDD

(A) Mut_DMSO vs. WT_DMSO

genes Symbol Biotype Hsap_ID Hsap_symbol logFC logCPM F PValue FDR ENSDARG00000079145 CABZ01077217.1 protein_coding -9.52471 2.197863 525.5454 1.26E-14 6.05E-11 ENSDARG00000102593 si:ch211-214b16.2 protein_coding ENSG00000281613 AL365214.3 -9.50029 0.659757 223.5811 5.35E-09 2.05E-06 ENSDARG00000052610 olig4 protein_coding -9.40043 0.405505 169.1688 1.78E-09 8.99E-07 ENSDARG00000088906 CABZ01059403.1 protein_coding ENSG00000213203 GIMAP1 -9.1117 0.315595 201.1484 5.44E-10 4.01E-07 ENSDARG00000059432 fsd1l protein_coding ENSG00000106701 FSD1L -8.82895 0.422493 166.8371 1.96E-09 9.38E-07 ENSDARG00000071590 si:ch211-236g6.1 protein_coding -8.81607 0.027133 70.59043 8.64E-07 8.23E-05 ENSDARG00000091847 si:ch211-181d7.1 protein_coding -8.80748 1.206679 22.68208 0.000534 0.007744 ENSDARG00000075963 mhc1uba protein_coding -8.63159 1.113934 137.3575 2.65E-08 6.34E-06 ENSDARG00000108525 CR749167.2 protein_coding -8.46443 -0.23397 116.4595 1.92E-07 2.75E-05 ENSDARG00000090847 si:ch211-209l18.4 protein_coding -8.33701 4.371766 469.9063 2.52E-12 5.37E-09 ENSDARG00000101688 si:dkey-207l24.2 protein_coding -8.18922 -0.79125 58.55592 1.66E-06 0.000134 ENSDARG00000096541 BX901942.1 antisense -7.8011 -0.62243 78.3006 2.8E-07 3.61E-05 ENSDARG00000004898 zp2l2 protein_coding ENSG00000205832 C16orf96 -7.51953 -0.79906 41.34319 1.24E-05 0.000582 ENSDARG00000036809 si:ch211-106j24.1 protein_coding ENSG00000166825 ANPEP -7.38861 1.614592 231.2637 1.38E-11 2.03E-08 ENSDARG00000068621 si:ch211-181d7.3 protein_coding -7.33306 -0.01512 38.21472 2.04E-05 0.000802 ENSDARG00000076853 areg protein_coding -7.30197 0.709602 213.7548 2.48E-11 2.8E-08 ENSDARG00000102003 CU695215.2 protein_coding -7.21453 -1.40437 64.14984 9.59E-07 8.81E-05 ENSDARG00000091514 CT737190.1 protein_coding -6.97679 -1.4632 50.40394 1.41E-05 0.000635 ENSDARG00000097845 si:ch211-105j21.9 protein_coding -6.92221 -1.36045 39.229 1.65E-05 0.000698 ENSDARG00000104258 FO681314.1 protein_coding ENSG00000213203 GIMAP1 -6.88702 -0.37804 94.25322 8.64E-08 1.5E-05 ENSDARG00000100571 BX927122.1 lincRNA 7.878855 -0.57959 71.02975 3.18E-07 3.93E-05 ENSDARG00000093224 CR456628.1 processed_transcript 7.590249 3.287531 265.9537 2.74E-11 2.91E-08 ENSDARG00000096412 si:ch211-15j1.7 protein_coding 7.483171 -0.33105 62.00215 1.87E-06 0.000147 ENSDARG00000094346 si:ch211-114l13.13 protein_coding 7.472878 -0.9556 61.07398 2.04E-06 0.000152 ENSDARG00000002097 si:dkeyp-52c3.7 protein_coding ENSG00000213203 GIMAP1 7.450364 -0.77572 55.79702 3.39E-06 0.000225 ENSDARG00000094297 si:dkey-222h21.2 protein_coding 6.523534 0.45839 126.5825 1.68E-09 8.92E-07 ENSDARG00000094497 BX548075.3 processed_transcript 6.406522 1.075727 140.9008 7.2E-10 4.76E-07 ENSDARG00000092604 si:ch211-15j1.4 protein_coding 6.353768 0.021612 104.3251 7.48E-09 2.61E-06 ENSDARG00000101964 zgc:171592 protein_coding ENSG00000141086 CTRL 6.084273 -0.09706 82.97546 4.18E-08 9.21E-06 ENSDARG00000094146 pimr134 protein_coding 5.203336 -0.01245 59.34584 4.61E-07 5.32E-05 ENSDARG00000045155 phf5a protein_coding ENSG00000100410 PHF5A 4.74148 2.558424 145.0376 1.53E-09 8.38E-07 ENSDARG00000042492 si:dkey-250d21.1 protein_coding 4.646493 2.940096 10.45407 0.007808 0.047662 ENSDARG00000100690 si:ch211-256e16.11 protein_coding 4.468509 1.6018 181.62 9.39E-11 9E-08 ENSDARG00000057173 ifit8 protein_coding ENSG00000152778 IFIT5 4.383269 -0.2873 72.99103 1.07E-07 1.78E-05 ENSDARG00000093405 casp6 protein_coding ENSG00000138794 CASP6 4.29803 1.892049 156.2224 3.16E-10 2.47E-07 ENSDARG00000068787 slc6a17 protein_coding ENSG00000197106 SLC6A17 4.27598 -0.99927 20.1781 0.000302 0.005268 ENSDARG00000103497 si:dkey-246j6.3 protein_coding 4.112872 -0.85942 25.60378 8.53E-05 0.002225 ENSDARG00000091607 arhgef28b protein_coding ENSG00000263264 AC119396.1 3.915243 2.061361 155.8786 3.22E-10 2.47E-07 ENSDARG00000107086 PIEZO2 (1 of many) protein_coding ENSG00000154864 PIEZO2 3.75603 1.118325 11.37342 0.005629 0.038822 ENSDARG00000100865 si:dkey-269o24.1 protein_coding 3.753041 1.981669 135.5733 9.77E-10 5.85E-07

306

(B) WT_TCDD vs. WT_DMSO genes Symbol Biotype Hsap_ID Hsap_symbol logFC logCPM F PValue FDR ENSDARG00000098315 cyp1a protein_coding ENSG00000140505 CYP1A2 7.322724 11.03493 3025.446 2.9E-21 5.55E-17 ENSDARG00000042492 si:dkey-250d21.1 protein_coding 5.812973 2.940096 15.83243 0.002094 0.023211 ENSDARG00000101789 cyp1c2 protein_coding 5.642443 6.688196 1396.548 2.59E-18 1.65E-14 ENSDARG00000052618 ahrrb protein_coding ENSG00000286169 AHRR 5.552714 -1.03836 39.13953 7.17E-06 0.001272 ENSDARG00000099702 ahrra protein_coding ENSG00000286169 AHRR 5.301202 3.362344 545.8873 9.11E-15 4.36E-11 ENSDARG00000101195 cyp1c1 protein_coding 5.293716 8.484353 1442.258 1.95E-18 1.65E-14 ENSDARG00000102776 cxcl19 protein_coding 5.023323 -0.3275 58.6388 5.01E-07 0.000246 ENSDARG00000101364 wfikkn1 protein_coding ENSG00000249139 EPPIN-WFDC6 4.804906 2.043501 130.7535 3.97E-09 4.47E-06 ENSDARG00000107086 PIEZO2 (1 of many) protein_coding ENSG00000154864 PIEZO2 4.713978 1.118325 17.64729 0.001261 0.017528 ENSDARG00000025299 tspan9a protein_coding ENSG00000011105 TSPAN9 4.156359 -0.99546 19.76071 0.000323 0.009036 ENSDARG00000097714 FP017215.1 lincRNA 4.108823 2.612801 205.5643 3.42E-11 7.28E-08 ENSDARG00000031095 veph1 protein_coding ENSG00000197415 VEPH1 3.919336 -1.15162 26.01268 7.82E-05 0.004433 ENSDARG00000059387 fgf7 protein_coding ENSG00000140285 FGF7 3.910837 1.526985 199.3661 4.4E-11 8.42E-08 ENSDARG00000090727 celf5b protein_coding ENSG00000161082 CELF5 3.641763 -0.18489 11.5594 0.004489 0.036424 ENSDARG00000101019 ext1b protein_coding ENSG00000182197 EXT1 3.603327 2.727969 21.57128 0.000649 0.012821 ENSDARG00000104727 ino80da protein_coding ENSG00000114933 INO80D 3.5416 0.434201 24.92443 0.000148 0.006169 ENSDARG00000093303 ifitm1 protein_coding 3.527896 -0.71459 18.94284 0.000396 0.010014 ENSDARG00000079741 CSRNP3 protein_coding ENSG00000178662 CSRNP3 3.517704 1.778587 20.18179 0.000728 0.013259 ENSDARG00000068787 slc6a17 protein_coding ENSG00000197106 SLC6A17 3.490152 -0.99927 11.16603 0.003756 0.032747 ENSDARG00000107053 CR848788.2 protein_coding 3.466744 -0.3598 20.23183 0.000348 0.00948 ENSDARG00000102370 znf1094 protein_coding -2.72506 1.017778 18.12792 0.000504 0.011352 ENSDARG00000056462 crp2 protein_coding -2.5594 -0.77993 13.86078 0.001591 0.020044 ENSDARG00000104012 BX323041.1 lincRNA -2.24132 -1.24059 24.32574 0.000112 0.005285 ENSDARG00000115830 BX465228.2 processed_transcript -2.04326 1.233715 14.8915 0.001177 0.016972 ENSDARG00000109579 CR925744.1 protein_coding -2.04286 -0.54852 10.83395 0.004121 0.034592 ENSDARG00000012609 hpxa protein_coding ENSG00000167346 MMP26 -1.80648 1.650586 13.23958 0.002327 0.0247 ENSDARG00000114601 CU914776.2 protein_coding ENSG00000237541 HLA-DQA2 -1.80241 -0.72957 10.64959 0.004383 0.035912 ENSDARG00000089831 si:dkey-207m2.4 protein_coding -1.75934 0.335462 16.39362 0.000774 0.013604 ENSDARG00000012387 pdha1a protein_coding ENSG00000131828 PDHA1 -1.74761 3.082119 36.91578 1.68E-05 0.002023 ENSDARG00000099573 CR759927.3 TEC -1.7356 1.655092 14.44011 0.001714 0.02083 ENSDARG00000098534 RF00067 snoRNA -1.68234 -0.96138 9.269939 0.007062 0.048162 ENSDARG00000016773 cishb protein_coding ENSG00000114737 CISH -1.67487 -0.80475 10.47719 0.004645 0.037214 ENSDARG00000039213 prpf38a protein_coding ENSG00000134748 PRPF38A -1.65659 1.127886 16.15695 0.000866 0.0145 ENSDARG00000095509 CR759885.1 processed_transcript -1.62248 -0.81803 9.449874 0.006626 0.04622 ENSDARG00000101123 CABZ01009512.1 protein_coding -1.60266 -0.43379 10.46379 0.004666 0.037307 ENSDARG00000100986 INHBC protein_coding ENSG00000175189 INHBC -1.60113 0.743846 16.87078 0.00068 0.012984 ENSDARG00000097536 CR848844.1 lincRNA -1.54403 -0.34689 13.80451 0.001618 0.020254 ENSDARG00000105320 AL590151.2 antisense -1.53721 -0.8429 9.559173 0.006375 0.045114 ENSDARG00000081044 dre-mir-125a-2 miRNA ENSG00000208008 MIR125A -1.53344 1.912641 16.74471 0.000803 0.013851 ENSDARG00000092784 znf1109 protein_coding -1.51784 -0.57608 10.2657 0.004991 0.038851

307

(C) mut_TCDD vs. mut_DMSO genes Symbol Biotype Hsap_ID Hsap_symbol logFC logCPM F PValue FDR ENSDARG00000098315 cyp1a protein_coding ENSG00000140505 CYP1A2 8.207209 11.03493 4701.381 5.92E-23 1.13E-18 ENSDARG00000102776 cxcl19 protein_coding 6.265096 -0.3275 79.58455 5.68E-08 9.81E-06 ENSDARG00000099702 ahrra protein_coding ENSG00000286169 AHRR 6.137692 3.362344 872.106 1.58E-16 7.57E-13 ENSDARG00000101195 cyp1c1 protein_coding 5.823845 8.484353 2180.064 5.19E-20 4.97E-16 ENSDARG00000101789 cyp1c2 protein_coding 5.375008 6.688196 1788.286 2.96E-19 1.89E-15 ENSDARG00000097714 FP017215.1 lincRNA 4.665449 2.612801 313.8799 1.01E-12 2.16E-09 ENSDARG00000059387 fgf7 protein_coding ENSG00000140285 FGF7 3.850427 1.526985 279.988 2.65E-12 4.12E-09 ENSDARG00000097726 si:ch211-149e23.4 protein_coding 3.326084 -0.4341 28.08465 5.12E-05 0.000639 ENSDARG00000030896 foxq1a protein_coding ENSG00000164379 FOXQ1 3.325116 4.730813 586.5396 4.91E-15 1.57E-11 ENSDARG00000101364 wfikkn1 protein_coding ENSG00000249139 EPPIN-WFDC6 3.247484 2.043501 26.4438 9.72E-05 0.000992 ENSDARG00000097157 si:ch211-207n23.2 protein_coding 3.102725 0.30165 67.06033 1.96E-07 2.08E-05 ENSDARG00000097491 ugt1b1 protein_coding 3.080717 6.249518 779.113 4.21E-16 1.61E-12 ENSDARG00000086826 sult6b1 protein_coding ENSG00000138068 SULT6B1 3.064278 5.764922 341.4929 5.39E-12 7.38E-09 ENSDARG00000003902 ctsl.1 protein_coding ENSG00000136943 CTSV 3.033776 1.665587 113.9493 3.8E-09 1.8E-06 ENSDARG00000003308 emc2 protein_coding ENSG00000104412 EMC2 2.984553 0.074374 8.927137 0.01066 0.031892 ENSDARG00000052618 ahrrb protein_coding ENSG00000286169 AHRR 2.926085 -1.03836 22.46999 0.00017 0.001469 ENSDARG00000012395 mmp13a protein_coding ENSG00000196611 MMP1 2.857329 1.595919 137.0166 8.99E-10 6.89E-07 ENSDARG00000054055 ostn protein_coding ENSG00000188729 OSTN 2.854943 1.209358 123.1895 2.07E-09 1.13E-06 ENSDARG00000073695 mamdc2b protein_coding ENSG00000177943 MAMDC4 2.7824 3.326289 139.087 1.74E-09 1.07E-06 ENSDARG00000074656 ctss2.1 protein_coding ENSG00000163131 CTSS 2.568243 1.166941 51.56898 1.2E-06 5.71E-05 ENSDARG00000042492 si:dkey-250d21.1 protein_coding -6.47489 2.940096 17.51766 0.001469 0.006891 ENSDARG00000092976 si:ch211-127i16.2 protein_coding ENSG00000069535 MAOB -4.48384 0.459047 9.510775 0.007864 0.025281 ENSDARG00000101508 nr6a1a protein_coding ENSG00000148200 NR6A1 -4.41233 0.767899 46.95661 4.85E-06 0.000137 ENSDARG00000107086 PIEZO2 (1 of many) protein_coding ENSG00000154864 PIEZO2 -4.14574 1.118325 16.22436 0.001713 0.00774 ENSDARG00000068787 slc6a17 protein_coding ENSG00000197106 SLC6A17 -4.07259 -0.99927 24.89842 0.000104 0.001039 ENSDARG00000086060 hmcn2 protein_coding ENSG00000143341 HMCN1 -4.00794 1.321035 32.64523 6.63E-05 0.000761 ENSDARG00000062305 rims3 protein_coding ENSG00000117016 RIMS3 -3.90921 -1.00109 23.82879 0.000125 0.001183 ENSDARG00000117673 LO017858.1 lincRNA -3.87801 -0.15592 18.01282 0.000649 0.003733 ENSDARG00000098903 tfe3a protein_coding ENSG00000068323 TFE3 -3.81428 2.232322 24.80387 0.000357 0.002452 ENSDARG00000098857 lifra protein_coding ENSG00000147168 IL2RG -3.67059 3.397045 18.73631 0.001152 0.005748 ENSDARG00000021909 VSTM2A protein_coding ENSG00000170419 VSTM2A -3.58922 -0.15665 15.65817 0.001334 0.006394 ENSDARG00000075754 mri1 protein_coding ENSG00000037757 MRI1 -3.58177 3.794861 37.26233 6.63E-05 0.000761 ENSDARG00000100203 CABZ01056637.1 protein_coding ENSG00000198198 SZT2 -3.55763 -1.30757 10.59817 0.004945 0.017603 ENSDARG00000117566 BX005005.2 lincRNA -3.54789 0.214164 26.0684 9.67E-05 0.000988 ENSDARG00000042259 tgfbr1b protein_coding ENSG00000106799 TGFBR1 -3.40916 4.013739 24.60589 0.000408 0.002678 ENSDARG00000102200 man2a1 protein_coding ENSG00000112893 MAN2A1 -3.40371 1.551916 27.98161 0.000135 0.001249 ENSDARG00000061685 hcn4 protein_coding ENSG00000138622 HCN4 -3.40197 -1.55236 8.429829 0.00964 0.029573 ENSDARG00000099199 itchb protein_coding ENSG00000078747 ITCH -3.34672 3.132354 30.80896 0.000149 0.001348 ENSDARG00000109262 CU928131.1 protein_coding ENSG00000138347 MYPN -3.32878 1.005172 21.53629 0.000408 0.002679 ENSDARG00000069608 palm2 protein_coding -3.30012 2.447059 18.98184 0.001052 0.005365

308

(C) mut_TCDD vs. WT_TCDD genes Symbol Biotype Hsap_ID Hsap_symbol logFC logCPM F PValue FDR ENSDARG00000096412 si:ch211-15j1.7 protein_coding 8.133245 -0.33105 83.66981 3.25E-07 3.31E-05 ENSDARG00000096247 CU633478.1 antisense 7.288967 -0.78453 53.40005 1.94E-06 0.000103 ENSDARG00000002097 si:dkeyp-52c3.7 protein_coding ENSG00000213203 GIMAP1 7.203158 -0.77572 50.56215 5.85E-06 0.000207 ENSDARG00000093224 CR456628.1 processed_transcript 6.677191 3.287531 236.9989 6.45E-11 7.27E-08 ENSDARG00000094346 si:ch211-114l13.13 protein_coding 6.674887 -0.9556 42.26574 1.54E-05 0.000368 ENSDARG00000092604 si:ch211-15j1.4 protein_coding 6.292117 0.021612 101.9847 8.9E-09 2.71E-06 ENSDARG00000094297 si:dkey-222h21.2 protein_coding 6.230448 0.45839 147.4817 5.01E-10 3.43E-07 ENSDARG00000094497 BX548075.3 processed_transcript 5.81195 1.075727 110.1095 4.95E-09 1.9E-06 ENSDARG00000101964 zgc:171592 protein_coding ENSG00000141086 CTRL 5.495811 -0.09706 84.6665 3.6E-08 7.93E-06 ENSDARG00000093405 casp6 protein_coding ENSG00000138794 CASP6 5.183147 1.892049 171.9351 1.46E-10 1.48E-07 ENSDARG00000115830 BX465228.2 processed_transcript 5.18146 1.233715 164.5072 2.09E-10 2E-07 ENSDARG00000057173 ifit8 protein_coding ENSG00000152778 IFIT5 4.524808 -0.2873 68.83707 1.62E-07 2.12E-05 ENSDARG00000102370 znf1094 protein_coding 4.117417 1.017778 54.099 9.58E-07 6.77E-05 ENSDARG00000096825 CU914157.1 lincRNA 4.106954 -0.69302 31.83916 2.5E-05 0.000506 ENSDARG00000045155 phf5a protein_coding ENSG00000100410 PHF5A 3.835112 2.558424 116.5231 7.58E-09 2.54E-06 ENSDARG00000097381 CR382363.1 processed_transcript 3.835017 -1.28413 12.2271 0.002623 0.013999 ENSDARG00000092077 si:dkey-147f3.4 protein_coding 3.797521 -0.22203 23.43178 0.000144 0.001616 ENSDARG00000071543 si:dkey-42i9.7 protein_coding 3.62813 0.180982 9.902667 0.00751 0.031044 ENSDARG00000095773 zgc:194906 protein_coding 3.60315 0.589569 70.6009 1.36E-07 1.84E-05 ENSDARG00000083424 RF01234 snoRNA ENSG00000238961 SNORA47 3.590868 0.769503 75.67365 8.21E-08 1.37E-05 ENSDARG00000093111 si:ch211-209l18.2 protein_coding -10.1848 1.259615 64.22582 1.11E-05 0.000309 ENSDARG00000090847 si:ch211-209l18.4 protein_coding -9.54229 4.371766 462.3666 2.82E-12 6.01E-09 ENSDARG00000075963 mhc1uba protein_coding -9.35204 1.113934 107.3234 1.15E-07 1.7E-05 ENSDARG00000102593 si:ch211-214b16.2 protein_coding ENSG00000281613 AL365214.3 -9.22374 0.659757 204.6399 8.78E-09 2.71E-06 ENSDARG00000092976 si:ch211-127i16.2 protein_coding ENSG00000069535 MAOB -8.96674 0.459047 99.06238 7.69E-08 1.36E-05 ENSDARG00000079145 CABZ01077217.1 protein_coding -8.48 2.197863 467.6302 3.44E-14 2.19E-10 ENSDARG00000108525 CR749167.2 protein_coding -8.36403 -0.23397 113.069 2.25E-07 2.6E-05 ENSDARG00000092364 si:ch211-218c6.8 protein_coding -8.31605 0.878197 160.2475 2.58E-10 2.19E-07 ENSDARG00000104258 FO681314.1 protein_coding ENSG00000213203 GIMAP1 -7.98111 -0.37804 84.61457 1.72E-07 2.21E-05 ENSDARG00000023868 si:dkey-172h23.2 protein_coding -7.71057 1.699682 226.7324 1.94E-11 3.1E-08 ENSDARG00000091847 si:ch211-181d7.1 protein_coding -7.6871 1.206679 15.13485 0.002362 0.012996 ENSDARG00000042492 si:dkey-250d21.1 protein_coding -7.64137 2.940096 24.72587 0.0004 0.003436 ENSDARG00000076853 areg protein_coding -7.625 0.709602 190.8905 6.27E-11 7.27E-08 ENSDARG00000096579 si:dkey-9c18.3 protein_coding -7.52512 -0.577 17.91209 0.00141 0.008803 ENSDARG00000105317 si:ch1073-365p7.2 protein_coding ENSG00000213203 GIMAP1 -7.49995 -1.28514 42.1685 3.3E-05 0.0006 ENSDARG00000099538 si:ch211-188p14.3 protein_coding -7.49895 1.217636 98.25888 4.19E-08 8.46E-06 ENSDARG00000088906 CABZ01059403.1 protein_coding ENSG00000213203 GIMAP1 -7.32031 0.315595 174.526 1.44E-09 7.67E-07 ENSDARG00000052610 olig4 protein_coding -7.17796 0.405505 124.8809 1.37E-08 3.76E-06 ENSDARG00000052536 tia1 protein_coding -7.12429 1.752045 266.5931 3.98E-12 7.63E-09 ENSDARG00000039501 ugt2a6 protein_coding -6.96925 -0.26663 120.7363 2.42E-09 1.13E-06

309

Table C.3. Top 20 MetaCore processes, the FDR-adjusted p values, and the associated human genes of the significantly differentially expressed genes in mut_DMSO compared to WT_DMSO.

FDR p BiologicaA2:Q21 Associated human gene IDs value Development_Skeletal PDGF-A, HB-EGF, Desmin, Beta TnTF, Myosin II, SKIP, MYOM1, FHL3, PAX3, ACTA1, Tropomyosin-1, MYL4, Sirtuin1, Calsequestrin 1, MEF2, MAP-1B, MIBP, 2.38E-06 muscle development SMAD3, Troponin I, fast skeletal muscle, MELC, Histone deacetylase class II, Troponin T, skeletal, ER81, MyHC, Actin, p38 MAPK, Nebulin, Calsequestrin, Muscle contraction 2.68E-04 MLCP (reg), Annexin VI, Phospholamban, Desmin, Beta TnTF, Myosin II, Galpha(i)-specific peptide GPCRs, MYOM1, ACTA1, Tropomyosin-1, MYL4, Galpha(q)- Cardiac SMYD1, Phospholamban, Tuberin, PAX3, c-Raf-1, SOS, NF-kB, SMAD3, Calcipressin 1, SOS1, PTEN, Troponin T, cardiac, CITED2, Tafazzin, PKC-delta, development_Role of 7.79E-03 Myoglobin, MyHC, ASK1 (MAP3K5), H-Ras, NF-AT2(NFATC1), SOX9, TBX3, Caspase-3, GYS1, gp91-phox, GLI-2, MEF2C, alpha-MHC NADPH oxidase and ROS Cytoskeleton_Actin MYH9, Annexin VI, Beta TnTF, Myosin II, Non-muscle myosin IIA, ACTA1, Tropomyosin-1, LIMK2, Pacsin, ARP2, Troponin I, fast skeletal muscle, ARP3, 1.36E-02 filaments Piccolo, ARPC5, Troponin T, cardiac, MELC, WASF3 (WAVE3), CapG, ERM proteins, Troponin T, skeletal, MyHC, Migfilin, Actin, Annexin II, N-WASP, DAL1, Cell adhesion_Integrin- CD151, Tubulin beta, ITGB5, Vitronectin, Alpha-parvin, PI3K cat class IA, c-Raf-1, CD82, ITGA6, Galectin-8, SOS, Beta-parvin, LIMK2, ITGA10, Lpd, Caveolin-2, mediated cell-matrix 2.50E-02 MELC, PI3K cat class IA (p110-beta), Fibronectin, Tenascin-C, ERM proteins, PIP5KI, Integrin, MyHC, Migfilin, PLC-gamma 1, Actin, PLC-gamma 2, N-WASP, adhesion NAG-2, H-Ras, ILK, MRLC, PLC-gamma, Paxillin, ITGA11, WIRE Transport_Iron transport 2.79E-02 TfR1, ADO, NRAMP2, KDM2B, HIF-prolyl hydroxylase, EGLN3, Ferritin mitochondrial, IRP, SFXN2, Inositol oxygenase, ALOX12, IRP1, HGD, TSAP6, PLOD3, Immune NF-kB p50/p50, PSMB5, p38alpha (MAPK14), PSMD11, Pyk2(FAK2), Sec15B, PSMD1, Vitronectin, HLAC, PI3K cat class IA, iC3b, NF-kB, CD74, TRAM1, SEC61 response_Phagosome in 4.72E-02 alpha, C3dg, NF-kB1 (p105), NF-kB1 (p50), HSP90, HSP70, RalA, PI3K cat class IA (p110-beta), Fibronectin, ERM proteins, PA200, PIP5KI, C3, Cathepsin S, PLC- antigen presentation gamma 1, Actin, PLC-gamma 2, N-WASP, p38 MAPK, PSMA6, HLAB, PLC-gamma, Paxillin, IP3 receptor, SEC61 beta Cardiac SMYD1, HB-EGF, Epo receptor, Tuberin, NF-AT3(NFATC4), c-Raf-1, SOS, MYBPC3, FRS2, Calcipressin 1, SOS1, Troponin T, cardiac, Tafazzin, PKC-delta, MyHC, development_FGF_ErbB 5.95E-02 Epo, ADAM19, H-Ras, TBX3, EDNRA, GLI-2, MEF2C, alpha-MHC signaling Cardiac development_Wnt_beta- Semaphorin 3C, IP3R1, Phospholamban, NF-AT3(NFATC4), PAX3, Calsequestrin 2 (cardiac muscle), c-Raf-1, MEF2, MYBPC3, DVL-3, Calcipressin 1, PTEN, 8.46E-02 catenin, Notch, VEGF, IP3 Troponin T, cardiac, Thrombospondin 1, Myoglobin, MyHC, Dsh, WNT, NF-AT2(NFATC1), TBX3, PLC-gamma, GYS1, IP3 receptor, GLI-2, MEF2C, alpha-MHC and integrin signaling Immune NF-kB p50/p50, MFGE8, p38alpha (MAPK14), Pyk2(FAK2), Myosin II, Vitronectin, PI3K cat class IA, APOE, PRK2, MARCKS, iC3b, NF-kB, LIMK2, C3dg, NF-kB1 9.03E-02 response_Phagocytosis (p105), NF-kB1 (p50), MELC, PI3K cat class IA (p110-beta), Fibronectin, ERM proteins, PIP5KI, MyHC, C3, PLC-gamma 1, Actin, PLC-gamma 2, N-WASP, p38 Inflammation_Kallikrein- NF-kB p50/p50, G-protein beta, p38alpha (MAPK14), PI3K cat class IA, Coagulation factor X, c-Raf-1, Carboxypeptidase N (cat), SOS, Coagulation factor XIII, 9.92E-02 kinin system NF-kB, Neprilysin, NF-kB1 (p50), Tissue factor, PLA2, C1 inhibitor, PI3K cat class IA (p110-beta), PKC-delta, C3a, Coagulation factor VII, C3, PLC-gamma 1, Inflammation_Compleme 9.92E-02 ITGAX, C1r, C8gamma, C4a, C8beta, iC3b, C3dg, C1 inhibitor, C3a, C3, C3b, C4, MASP2, C1qRp, C4b nt system Reproduction_Feeding Galpha(q)-specific Class A Orphan/other GPCRs, Tuberin, Galpha(i)-specific peptide GPCRs, PKA-reg (cAMP-dependent), PI3K cat class IA, c-Raf-1, Galpha(q)- and Neurohormone 1.49E-01 specific peptide GPCRs, SOS, GLUT2, NF-kB, DSPG3, PPAP2, SOS1, B4GT1, IBP1, HSP70, IDE, Integrin, Xanthine oxidase, PLC-gamma 1, TC21, PLC-gamma 2, signaling Epimorphin, G-protein alpha-i family, GPR10, H-Ras, SOX9, PLC-gamma, Stathmin, G-protein alpha-o, Paxillin, CDK2 I fl ti IL 2 NF kB 50/ 50 PI3K t l IA R f 1 SOS NF kB NF kB1 ( 50) PTEN PI3K t l IA ( 110 b t ) N i PLC 1 PLC 2 38

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y Reproduction_Feeding Galpha(q)-specific Class A Orphan/other GPCRs, Tuberin, Galpha(i)-specific peptide GPCRs, PKA-reg (cAMP-dependent), PI3K cat class IA, c-Raf-1, Galpha(q)- and Neurohormone 1.49E-01 specific peptide GPCRs, SOS, GLUT2, NF-kB, DSPG3, PPAP2, SOS1, B4GT1, IBP1, HSP70, IDE, Integrin, Xanthine oxidase, PLC-gamma 1, TC21, PLC-gamma 2, signaling Epimorphin, G-protein alpha-i family, GPR10, H-Ras, SOX9, PLC-gamma, Stathmin, G-protein alpha-o, Paxillin, CDK2 Inflammation_IL-2 NF-kB p50/p50, PI3K cat class IA, c-Raf-1, SOS, NF-kB, NF-kB1 (p50), PTEN, PI3K cat class IA (p110-beta), Neurogranin, PLC-gamma 1, PLC-gamma 2, p38 1.76E-01 signaling MAPK, H-Ras, NF-AT2(NFATC1), NF-AT, PLC-gamma, IP3 receptor, CDK2 Cytoskeleton_Regulation Tubulin beta, Desmin, Myosin II, ACTA1, Galpha(i)-specific amine GPCRs, PARD6A, ARHGEF2, PKC, ARP3, PTEN, ARPC5, MELC, ERM proteins, Vimentin, of cytoskeleton 1.76E-01 MyHC, PARD6, Actin, ECT2, N-WASP, G-protein alpha-i family, DAL1, Nebulin, SDF-1, MRLC, G-protein alpha-o, Beta-sarcoglycan, Paxillin, Actin muscle rearrangement Development_EMT_Regul PDGF-A, p38alpha (MAPK14), N-cadherin, Desmin, Tuberin, Tropomyosin-1, PI3K cat class IA, c-Raf-1, PARD6A, SOS, SMAD3, LIMK2, FRS2, PTEN, DAB2, ation of epithelial-to- 1.76E-01 Fibronectin, Vimentin, FARP2, Claudin-1, Dsh, WNT, Actin, p38 MAPK, IGF-2, G-protein alpha-i family, H-Ras, NF-AT2(NFATC1), SOX9, ILK, EDNRA, LOXL2, mesenchymal transition SIP1 (ZFHX1B), CDK2 Signal p38alpha (MAPK14), Pyk2(FAK2), Galpha(i)-specific peptide GPCRs, PKA-reg (cAMP-dependent), c-Raf-1, Galpha(q)-specific peptide GPCRs, SOS, FRS2, Transduction_Cholecystok 1.83E-01 CCKAR, PAK4, PLC-gamma 1, TC21, PLC-gamma 2, p38 MAPK, G-protein alpha-i family, H-Ras, PLC-gamma, IP3 receptor inin signaling Development_Melanocyt e development and 2.21E-01 PAX3, PKA-reg (cAMP-dependent), PI3K cat class IA, SOS, TYRP2, Galpha(s)-specific peptide GPCRs, Dsh, H-Ras, TYRO, TYRP1 pigmentation Cell adhesion_Integrin Vitronectin, PI3K cat class IA, c-Raf-1, SOS, SOS1, PTEN, PI3K cat class IA (p110-beta), Fibronectin, Integrin, PLC-gamma 1, Actin, PLC-gamma 2, G-protein 2.21E-01 priming alpha-i family, H-Ras, SDF-1, PLC-gamma, Paxillin, IP3 receptor Reproduction_Spermatog PDGF-A, HB-EGF, MFGE8, p38alpha (MAPK14), HSPA2, HMGI/Y, SPATA2, PKA-reg (cAMP-dependent), Calmegin, c-Raf-1, NCOA4 (ARA70), RAD23B, CREM enesis, motility and 2.21E-01 (activators), PKC, DNMT3L, NF-kB1 (p50), SOS1, Gamma-glutamyltranspeptidase, BRD2, HSP70, Oct-3/4, PKC-delta, Histone H2, Keratin 19, H-Ras, SRY, SOX9, copulation TBX3, BRD7, OAZ3, OCA2, SPAG9, Histone H1

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Table C.4. Top 20 MetaCore processes, the FDR-adjusted p values, and the associated human genes of the genes in Cluster 2 of the heatmap in Figure 5.6.

FDR p Biological Process Associated human gene IDs value Cell adhesion_Attractive and Plexin A2, UNC5B, Dcc, GIPC, MICAL, Ephrin-A5, Ephrin-A receptor 3, Plexin B3, Netrin-2, Semaphorin 3A, Plexin A1, PI3K cat class IA (p110-alpha), 6.51E-05 repulsive receptors SRGAP1, CRMP2, Ephrin-B receptor 3, UNC5C, SLIT1, Ephrin-B receptor 2, Collagen XII, Fer, ErbB2, Paxillin, PI3K reg class IA (p85-beta), ICAM3 Development_Neurogenesis_Axonal Plexin A2, UNC5B, TRIO, Dcc, GIPC, TrkB, CACNA1C, MICAL, DISC1, Ephrin-A5, Ephrin-A receptor 3, Transgelin-3, Plexin B3, Fe65, Netrin-2, Semaphorin 6.51E-05 guidance 3A, Plexin A1, ADAM23, ADAM17, SRGAP1, CRMP2, Ephrin-B receptor 3, PARD3, UNC5C, SLIT1, Ephrin-B receptor 2, Semaphorin 4B, DAB1, NT-3 Development_Neurogenesis_Synapto N-type Ca(II) channel alpha1B, TrkB, Neuroligin 1, RIMS4, Synaptotagmin II, KIS, nNOS, PSD-95, Syntrophin A, DLGAP1 (GKAP), Ephrin-B receptor 3, 1.73E-04 genesis TSLC1, ErbB4, Semaphorin 4G, Synaptopodin, Ephrin-B receptor 2, RIMS3, DAB1, ErbB2, ERC2/CAST, Endophilin A1, NT-3, Bassoon, LARGE, KCNQ2 ITGA4, Delta-sarcoglycan, ADAM12, ITGA1, ITGA9, MMP-15, MMP-16, MMP-24, ADAM23, ADAM17, SPOCK, ADAM-TS3, COL4A1, BCAM, Neurocan, Cell adhesion_Cell-matrix interactions 2.24E-03 Collagen XII, Aggrecan, CSPG4 (NG2), Tenascin-R, ADAM15, Perlecan Transport_Potassium transport 5.57E-03 Kv1.2, Kv8.1, Kir1.2, Kir3.4, TRPM3, TRPM2, HCN4, SLC24A4, Kv1.1, Kv1.4, KCNQ3, Kv2.2, KCNH7, KCNAB2, SLC24A2, Mucolipin-1, KCNQ4, KCNQ2 CACNA1I, N-type Ca(II) channel alpha1B, CACNA1C, CACNA1E, TRPM3, TRPM2, VDR, NCX2, NCX3, PSD-95, SLC24A4, CCG4, CACNA1H, Polycystin 2, Transport_Calcium transport 5.70E-03 TRPC3, SLC24A2, CACNA1D, Mucolipin-1, CACNB4, Ryanodine receptor 3 Transport_Sodium transport 6.54E-03 SMVT, SLC4A10, TRPM3, TRPM2, NCX2, GAT2, HCN4, NCX3, SLC24A4, SVCT2, SCN2B, SLC9A1, TRPC3, SLC24A2, SN1, SLC6A17, SCN4B, Mucolipin-1 Cytoskeleton_Cytoplasmic 2.60E-02 TAO2, MLK3(MAP3K11), MID1, PSD-95, KIF1A, DLGAP1 (GKAP), MLK2(MAP3K10), MARK1, KIF5B, Myosin Va, KIF1C, MAP6, MAST1, MAP4 Apoptosis_Apoptosis stimulation by 7.43E-02 ALK-4, TrkB, MKK7 (MAP2K7), ErbB4(ICD), ErbB4(CTF), JNK1(MAPK8), ADAM17, ErbB4, Alpha-1A adrenergic receptor, Bcl-2, NT-3, TGF-beta receptor Neurophysiological GLSK, N-type Ca(II) channel alpha1B, GABA-A receptor gamma-2 subunit, MUNC13-1, GABA-A receptor alpha-1 subunit, Kir3.4, Dynamin-1, GAT2, 8.42E-02 process_GABAergic neurotransmission GBR1, GBR2 Proteolysis_Connective tissue 1.20E-01 ADAM12, MMP-15, MMP-16, MMP-24, ADAM23, ADAM17, SPOCK, ADAM-TS3, Aggrecan, ADAM15 Development_Keratinocyte 1.20E-01 NOTCH1 (NICD), RBP-J kappa (CBF1), MAD, MKK7 (MAP2K7), VDR, JNK1(MAPK8), TGF-beta receptor type I Signal Transduction_TGF-beta, GDF SF1, ALK-4, NOTCH4 (ICD4), IGF-1 receptor, HIPK2, NOTCH4, JNK1(MAPK8), TGF-beta receptor type III (betaglycan), TGF-beta receptor type I, BMP 1.20E-01 and Activin signaling receptor 2, ActRIIB, NOTCH1 precursor, PLC-beta2, CDK6 Development_Blood vessel PDE3A, NOTCH1 (NICD), RBP-J kappa (CBF1), FKHR, NOTCH1 (NEXT), PCAF, NOTCH4, CRHR1, JNK1(MAPK8), ADAM17, ErbB4, Alpha-1A adrenergic 1.20E-01 morphogenesis receptor, ErbB2, NOTCH1 receptor, NOTCH1 precursor, Adenosine A2b receptor Neurophysiological CACNA1C, GABA-A receptor gamma-2 subunit, Dopamine D1A receptor, GABA-A receptor alpha-1 subunit, HCN4, nNOS, PSD-95, Kv1.1, KCNQ3, GBR2, 1.26E-01 process_Transmission of nerve SCN2B, CCG4, PRKAR1A, SCN4B, KCNQ2 Transport_Synaptic vesicle exocytosis 1.77E-01 SV2C, SNIP, MUNC13-1, RIMS4, Synaptotagmin II, Rabphilin-3A, Dynamin-1, GAT2, Rab-27B, ELKS, RIMS3, ERC2/CAST, Bassoon Development_Hedgehog signaling 1.91E-01 PTHR1, NOTCH1 (NICD), SF1, RBP-J kappa (CBF1), Beta-arrestin1, JIP-2, NOTCH1 (NEXT), TCF8, PAX3, p73, ADAM17, PAX5, MLK2(MAP3K10), Itch, DAB1, Cell adhesion_Synaptic contact 2.09E-01 GABA-A receptor gamma-2 subunit, Neuroligin 1, Synaptotagmin II, GABA-A receptor alpha-1 subunit, Rabphilin-3A, Ephrin-A5, Neurotrimin, nNOS, PDE3A, GABA-A receptor gamma-2 subunit, HSP40, IGF-1 receptor, PCAF, GABA-A receptor alpha-1 subunit, Dynamin-1, PI3K cat class IA (p110-alpha), Reproduction_Progesterone signaling 2.09E-01 NCOA1 (SRC1), TRRAP, KIBRA, PI3K reg class IA (p85-beta), NOTCH1 precursor, Oct-2, Oct-1 Development_Neurogenesis in NOTCH1 (NICD), SF1, RBP-J kappa (CBF1), Dopamine D1A receptor, NOTCH1 (NEXT), NOTCH4, PAX3, GFRalpha2, ADAM17, PAX5, HDAC4, 2.09E-01 general p90RSK3(RPS6KA2), SIAT8B, NOTCH1 receptor, NOTCH1 precursor

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Appendix D - Developmental Toxicity in Zebrafish (Danio rerio) Exposed to

Uranium: A Comparison with Lead, Cadmium, and Iron

Prarthana Shankar‡1, Erica J. Dashner-Titus‡2, Lisa Truong1, Kimberly Hayward1, Laurie

G. Hudson2, Robyn L. Tanguay1*

Affiliations:

1 Department of Environmental and Molecular Toxicology, Sinnhuber Aquatic Research

Laboratory, Oregon State University, Corvallis, OR 97331, USA.

2 Department of Pharmaceutical Sciences, College of Pharmacy, University of New

Mexico, Albuquerque, NM 87131, USA.

‡ Authors contributed equally to this manuscript.

Reprinted from Environmental Pollution under the Creative Commons License.

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D.1. Abstract

Populations of plants and animals, including humans, living in close proximity to

abandoned uranium mine sites are vulnerable to uranium exposure through drainage into

nearby waterways, soil accumulation, and blowing dust from surface soils. Little is known about how the environmental impact of uranium exposure alters the health of human populations in proximity to mine sites, so we used developmental zebrafish (Danio rerio) to investigate uranium toxicity. Fish are a sensitive target for modeling uranium toxicity, and previous studies report altered reproductive capacity, enhanced DNA damage, and gene expression changes in fish exposed to uranium. In our study, dechorionated zebrafish

embryos were exposed to a concentration range of uranyl acetate (UA) from 0 to 3,000

µg/L for body burden measurements and developmental toxicity assessments. Uranium

was taken up in a concentration-dependent manner by 48 and 120 hour post fertilization

(hpf)-zebrafish without evidence of bioaccumulation. Exposure to UA was not associated

with teratogenic outcomes or 24 hpf behavioral effects, but larvae at 120 hpf exhibited a

significant hypoactive photomotor response associated with exposure to 3 µg/L UA which

suggested potential neurotoxicity. To our knowledge, this is the first time that uranium has

been associated with behavioral effects in an aquatic organism. These results were

compared to potential metal co-contaminants using the same exposure paradigm. Similar

to uranium exposure, lead, cadmium, and iron significantly altered neurobehavioral

outcomes in 120-hpf zebrafish without inducing significant teratogenicity. Our study

informs concerns about the potential impacts of developmental exposure to uranium on

childhood neurobehavioral outcomes. This work also sets the stage for future,

environmentally relevant metal mixture studies.

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D.2. Introduction

Abandoned uranium mines are scattered throughout the world, located on every continent

excluding Antarctica, including thousands of sites in the southwestern region of the United

States, mainly on the Colorado Plateau (Moore-Nall 2015). Drainage from these mine sites

can lead to uranium entering nearby waterways (Blake et al. 2017), contaminating

groundwater and local soils (Abdelouas 2006), and being found in blowing dust from the

mine surface soils (Zychowski et al. 2018). Despite governments setting strict limits on

uranium in drinking water and food sources, many individuals living in rural areas, such

as the southwestern region of the United States, rely on private, unregulated wells for

drinking water, livestock water, and water to irrigate crops. In total, 12.5 % of the

unregulated water sources sampled in the eastern region of the Navajo Nation were found

to have uranium levels exceeding the United States Environmental Protection Agency

(U.S. EPA) maximum contaminant level of 30 µg/L (Hoover et al. 2018). Groundwater

sampled near abandoned uranium mines in the western region of the Navajo Nation range

from 0.04 to 490 µg/L uranium (Credo et al. 2019), while surface waters from the Rio

Paguate adjacent to the Jackpile uranium mine in Laguna Pueblo measured uranium levels

up to 772 µg/L (Blake et al. 2017). The potential routes of exposure leave plants, animals, and humans in proximity at risk of exposure to elevated levels of the toxic metal (Waseem et al. 2015). 238U has been shown to accumulate in vegetables grown near abandoned mines sites, coinciding with elevated uranium in soils (Carvalho et al. 2014).

Bioaccumulation of uranium in the food chain may leave higher consumers, particularly humans, more susceptible to the negative impacts of uranium exposure.

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Little is known about the developmental health effects of uranium exposure. A 1981 study showed an association between maternal proximity to uranium mines, tailings or waste sites, and adverse birth outcomes in offspring (Shields et al. 1992). Another study determined that compared to adults, children were more sensitive to uranium exposure from contaminated well water (Magdo et al. 2007). Uranium exposure is associated with several neurobehavioral disorders, but majority of the studies conducted so far have been in adult rodent models (Dinocourt et al. 2015). Some studies have demonstrated that different species of fish exposed to contaminated waters from nearby mine sites exhibit altered hatching times (Pyle et al. 2002), increased oxidative stress (Bessa et al. 2016), altered metabolic activity (Bessa et al. 2016), increased micronucleus frequencies in erythrocytes (Annamalai et al. 2017), and decreased acetylcholinesterase, an important enzyme that regulates the neurotransmitter acetylcholine (Gagnaire et al. 2015). However, uranium-associated neurobehavioral data, specifically during development, is lacking.

Zebrafish (Danio rerio) is a well-established vertebrate model and a valuable tool for understanding developmental toxicity from xenobiotic exposures (Garcia et al. 2016;

Nishimura et al. 2015; Teraoka et al. 2003). Zebrafish embryos are optically transparent; they develop rapidly and externally and thus support the rapid assessment of toxic effects on organ development and neurobehavior (Bugel et al. 2016; Geier et al. 2018a). The zebrafish genome is highly annotated and shares high genetic homology with humans; 76% of human protein-coding genes have a zebrafish counterpart, and 82% of human genes that cause disease are present in zebrafish, increasing the translational value of the zebrafish model (Howe et al. 2013). In addition, zebrafish brain anatomy is similar to mammals making zebrafish a popular model for central nervous system research as well as to

316

understand various brain disorders, and the neurobehavioral impact of chemical exposure

(Bailey et al. 2013; Stewart et al. 2014).

Several studies have investigated the effects of chronic uranium exposure in adult zebrafish. Uranium caused genotoxicity (Lerebours et al. 2013; Simon et al. 2018), modified reactive oxygen species production and phenoloxidase-like activity (Gagnaire et al. 2013), altered zebrafish metabolism (Augustine et al. 2015), negatively impacted the olfactory and lateral line systems (Faucher et al. 2012), and the reproductive system

(Armant et al. 2017; Bourrachot et al. 2014; Simon et al. 2018; Simon et al. 2011). Other studies identified significant gene expression alterations and skeletal muscle malformations in the progeny of adult zebrafish exposed to 20 µg/L depleted uranium, suggesting that developmental zebrafish are extremely sensitive to uranium exposure

(Armant et al. 2017; Gombeau et al. 2017). Waterborne exposure to 250 µg/L depleted uranium delayed hatching of zebrafish embryos, reduced prolarval body length, and increased mortality (Bourrachot et al. 2008). It was noted that uranium localized primarily in the chorion, which hindered uranium uptake at lower exposure concentrations and may have blocked more severe developmental effects (Bourrachot et al. 2008).

We compared the uranium toxicity data collected in this study with the zebrafish developmental toxicity of other metals: cadmium, iron, and lead using the same exposure paradigm. Similar to uranium, these metals are measured at detectable levels in unregulated water sources near abandoned uranium mine and mill sites with varying percentages above

U.S. national drinking water standards which are currently set at 5 μg/L for cadmium, 300

μg/L for iron, and 15 μg/L for lead (U.S. EPA 2020a , U.S. EPA 2020b; Credo et al. 2019;

Hoover et al. 2018). The metals enter the water primarily via the oxidation of metal sulfide

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minerals that are present in both uranium ore and in waste materials, and thus are likely to

occur as co-contaminants with uranium (Council 2011). Early-life exposures to lead and

cadmium have previously been associated with neurological effects in children (Hou et al.

2013; Kippler et al. 2012). Additionally, animal models have been utilized to identify the

sub-lethal effects of iron. Neonatal oral iron exposure causes long-term neurobehavioral

effects in mice (Fredriksson et al. 1999). These studies demonstrate that exposure to environmentally relevant concentrations of lead, cadmium, and iron is capable of altering neurocognitive functioning. Despite these metals being detected together in the environment, to the best of our knowledge, their developmental toxicity has not been compared in the same experimental platform.

The present study investigated the effects of exposure of dechorionated zebrafish embryos to uranyl acetate starting at 6 hours post fertilization (hpf). Inductively-coupled plasma mass spectrometry (ICP-MS) revealed the embryo uranium uptake was concentration- dependent at 48 hpf and 120 hpf. We did not detect significant teratogenicity associated with exposure to uranium, but we detected significant behavioral effects in larval zebrafish.

Our results will inform research, mitigation, and regulatory concerns about the potential impacts of prenatal, perinatal, and postnatal exposure to uranium on childhood developmental and neurobehavioral outcomes. In addition, the individual developmental toxicity effects of lead, cadmium, and iron were investigated in this study. Embryonic exposures to all three metals altered normal neurodevelopmental outcomes despite having no effect on teratogenicity. By comparing the toxicity of the four metals in the same model organism with the same exposure paradigm, we are providing a foundation for future research to investigate potential, environmentally relevant metal mixture effects.

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D.3. Materials and Methods

Zebrafish husbandry

Tropical wild type 5D zebrafish (Danio rerio) were housed at the Sinnhuber Aquatic

Research Laboratory (SARL) at Oregon State University (Corvallis, OR, USA). Larval,

juvenile, and adult fish were fed GEMMA Micro 75, 150, and 500 (Skretting, Inc.,

Fontaine Les Vervins, France) respectively. Larval and juvenile fish were fed thrice daily

while adult fish were fed twice daily (Barton et al. 2016). Adult fish were maintained on a

28°C recirculating water system with Instant Ocean salts in 50-gallon tanks at densities of

10 fish/gallon of water under a 14-hour:10-hour light:dark cycle. Fish were set up for group spawns the night before with spawning funnels placed in the tanks. After collection and cleaning in the morning, fertilized embryos were placed in a 28 °C incubator in Petri dishes

with embryo media (EM) (Kimmel et al. 1995). EM comprised 15 mM NaCl, 0.5 mM KCl,

1 mM MgSO4, 0.15 mM KH2PO4, 0.05 mM Na2HPO4, and 0.7 mM

NaHCO3.(Westerfield 2000). All zebrafish handling and use were conducted according to

Institutional Animal Care and Use Committee protocols (ACUP 5113, date: 11th October,

2018).

Metal Exposures

The chorions of 4-hours post fertilization (hpf) zebrafish embryos were enzymatically

removed with pronase (Sigma-Aldrich, catalog # 81750) using a custom automated

dechorionator to eliminate a potential barrier for exposure to the metal (Mandrell et al.

2012). Additionally, metals such as iron have been shown to cause hardening of the

zebrafish chorion which prevented normal hatching (Hassan et al. 2020); this could lead to

potential effects on the developmental endpoints tested in this study. For the uranyl acetate

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(UA) exposures, dechorionated zebrafish (n = 32-68 per exposure concentration) at 6 hpf

were placed one per well in round-bottom 96-well polystyrene plates (BD Falcon) prefilled with 100 µL exposure solution. The large sample size range is a result of experiments conducted on two different days; n = 68 is for the concentrations that overlapped between the two days, and for the control. UA was purchased from Electron Microscopy Sciences

(USA) with a 99.9% U238 / 0.1% U235 composition and handled according to the regulations set forth by the Radiation Safety office at Oregon State University. In an aerobic environment, such as some soils and water, uranium compounds exist with a +VI valence (Garnier–Laplace et al. 2001; Waseem et al. 2015). UA was chosen because the uranyl ion has a +VI valence in aqueous medium, mimicking an environmental exposure.

For exposures to lead (II) acetate trihydrate (Pb(CH3CO2)2 · 3H2O, Sigma), cadmium chloride (CdCl2, Sigma), and iron (III) chloride (FeCl3, Sigma), we used an automated embryo placement system (Mandrell et al. 2012). Embryos were placed one per well (n =

36 per exposure concentration) in round bottom 96-well plates prefilled with 100 µL EM.

A Hewlett Packard D300e chemical dispenser was used to dispense 100% dimethyl sulfoxide (DMSO) chemical stock solutions and final DMSO concentrations were normalized to 1% (V/V). The exposure concentrations of the metals tested in this study are listed in Table D.1. After exposure, the plates were sealed with Parafilm to minimize evaporation of the exposure solutions, and shaken overnight at 28 °C in the dark on an orbital shaker (235 rpm) to enhance solution uniformity (Truong et al. 2016). Embryos were statically exposed in a 28 °C incubator for the remaining duration of the exposure

(until 48 hpf for body burden assessments for UA, or until 120 hpf for body burden assessments for UA, and teratogenicity and behavior assessments for all metals). The pH

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of the UA exposure solutions were measured using the API pH Test Kit over the course of five days, and we confirmed that the UA did not alter pH of the exposure solution. pH was measured to be ~7.6 in both control and UA solutions (data not shown).

Body burden of UA with ICP-MS

After UA exposure (0, 30, 300, 3,000 µg/L), embryos were collected for body burden at

48 and 120 hpf. The zebrafish were euthanized on ice for 30 minutes, which was confirmed by observing the lack of heartbeat under the microscope. At each time point, three replicates of 30 zebrafish each per concentration were collected in 15 mL plastic tubes.

Embryos were washed five times with 1 mL ice-cold Chelex-treated water (Millipore

Sigma, MA, USA). Each wash consisted of removal of liquid from the tube, addition of 1 mL ice-cold Chelex-treated water, and swirling of fish. After the last wash, all liquid was removed from the tubes before storage at -80 °C until further processing. Pooled embryos were digested in 0.5 mL full strength OmniTrace Nitric Acid (Millipore Sigma, MA, USA), overnight at room temperature and diluted with 4.5 mL Chelex-treated MilliQ water.

Samples were diluted further with Chelex-treated MilliQ water for an overall dilution of

1:40. Samples were stored at -20 °C. ICP-MS analysis for uranium was performed by the

Arizona Laboratory for Emerging Contaminants (ALEC) at the University of Arizona,

Tucson, AZ. Samples were run on an Agilent 8900 ICP-QQQ following an analytical

QA/QC protocol that was adapted from U.S. EPA Method 200.8 for ICP-MS analysis.

Calibration standards for uranium were prepared from multi-element stock solutions resulting in a curve including at least seven points with a correlation coefficient of 0.9998.

The detection limit for uranium was 0.7445 ng/L.

Behavior and Teratogenicity Assessments

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Behavior Assessments

Embryonic Photomotor Response (EPR)

A custom-built photomotor response assay tool (PRAT) was utilized to measure the

Embryonic Photomotor Response (EPR) (Noyes et al. 2015). After exposure to each metal, the zebrafish were kept in the dark until the evaluation of the EPR test at approximately 24 hpf. EPR consisted of 30 s of darkness (IR light, Background Interval); first 1 s pulse of intense white light; 9 s of darkness (Excitation Interval); second 1 s pulse of intense light;

10 s of darkness (Refractory Interval). To quantify total movement for each embryo across the period of the assay, pixel changes between video frames were recorded. Embryos were examined immediately after the EPR assay, and embryos with mortality were excluded from the EPR analysis (Truong et al. 2016). Statistical significance was assessed separately for each interval using the non-parametric Kolmogorov-Smirnov (KS) test (Bonferroni- corrected p value threshold = 0.01) against the vehicle control animals with the additional criterion that the mean area under the curve (AUC, Excitatory interval) estimate for each treatment group had to be at least 50% different in either direction from the control group

AUC (Reif et al. 2016). Raw data collected from the EPR assay can be found in Table

DS1.

Larval Photomotor Response (LPR)

To determine if developmental exposure to UA, lead, cadmium, and iron altered larvae photomotor swimming behavior, the Larval Photomotor Response (LPR) was evaluated at

120 hpf using the ZebraBox system (ViewPoint Life Sciences, Lyon, France) (Knecht et al. 2017; Truong et al. 2012). The assay consisted of a total of three light-dark cycles, each cycle consisting of three minutes in visible light, and three minutes in the dark (IR light).

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Only the first light/dark cycle was included in the statistical analysis as it typically presents the greatest dynamic range. Larvae that exhibited any teratogenic effects at 120 hpf

(described below in “Teratogenicity Assessments”) were excluded from the LPR analysis.

Statistical significance was quantified using a KS test (p<0.05) on pairwise comparisons of mean treatment area under the curve (AUC) relative to the control group AUC, with the additional criterion that the relative AUC ratio calculated as (Treatment-Control/Control)

(Relative Ratio) (Zhang et al. 2017) had to be at least 10% (-10% for hypoactivity and 10% for hyperactivity). To allow for acclimatization, we analyzed only the third light-dark cycle to determine significant effects. Raw data collected from the LPR assay for uranium, lead, cadmium, and iron can be found in Tables DS2, DS3, DS4 and DS5, respectively.

Teratogenicity Assessments

At 24 hpf, zebrafish embryos were systematically screened for mortality, developmental progression, notochord formation, and spontaneous motion. Upon completion of the LPR assay at 120 hpf, larvae were euthanized with tricaine methanesulfonate (MS-222) and visually assessed for 18 developmental endpoints: mortality, yolk sac edema, pericardial edema, body axis, trunk length, caudal fin, pectoral fin, pigmentation, somite, eye, snout, jaw, otolith, brain, notochord and circulatory malformations, swim bladder presence and inflation, and touch response. A binary presence or absence response was recorded for each zebrafish for each teratogenicity endpoint, with “presence” indicating any deviation from normal development for each endpoint. The data were entered into a laboratory information management system (LIMS), and statistically analyzed as described previously (Truong et al. 2014). Briefly, a lowest effect level (LEL) was computed for each endpoint measured

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as the concentration at which the incidence of the endpoint exceeded a significance threshold over the background rate, estimated using a Fisher’s exact (binomial) test.

D.4. Results and Discussion

Uranium uptake

To quantify uranium uptake, we performed ICP-MS analysis at 48 and 120 hpf on dechorionated embryos exposed to 0, 30, 300, and 3,000 µg/L UA from 6 hpf. Figure D.1 reveals a concentration-dependent accumulation of uranium in the exposed zebrafish. We note that the uptake concentration from exposure to 300 µg/L UA was <0.05 µg uranium/g wet weight at both 48 and 120 hpf in our chorion-absent study. This was lower than the uptake concentration in a chorion-intact study (Bourrachot et al. 2008) where >2 µg uranium/g dry weight in embryos, and >10 mg uranium/g dry weight in the chorion, was detected at 48 hpf after exposure to 250 µg/L uranyl nitrate (UN) (Bourrachot et al. 2008).

The study showed that uranium primarily adsorbed to the chorion independent of uranium’s isotopic nature (Bourrachot et al. 2008). The chorion, with an approximate pore size of 0.6 – 0.7 µm, is an acellular envelope surrounding embryos until they hatch and acts as a chemical barrier irrespective of chemical size or hydrophilicity (Bonsignorio et al. 1996; Braunbeck et al. 2005; Lee et al. 2007; Ozoh 1980). The presence of the chorion could generate false-negative results in such studies (Henn et al. 2011) and thus we sought to quantify zebrafish uranium uptake in the absence of the chorion.

Our results demonstrate the total body burden of UA was lower relative to previous zebrafish studies with UN (Bourrachot et al. 2014; Bourrachot et al. 2008). While chemical differences between UA and UN could be contributing to uptake discrepancy, the difference in the pH of the exposure solutions between our study and previous studies could

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also be playing a role. While the exposure solution in our study was maintained at pH =

7.6 throughout the duration of the experiment, the pH of the exposure solutions in

Bourracho et al. was lowered to 6.5 ± 0.2 to maximize the bioavailability of uranium

(Bourrachot et al. 2014; Bourrachot et al. 2008). It has been shown that the aqueous speciation of uranium drastically changes within the pH range of 4 to 8, which can significantly alter uranium uptake into organisms (Fournier et al. 2004; Simon et al. 2014).

In the current study, we maintained the pH of the exposure solution at 7.6 for two reasons:

1). To avoid the potential confounding effects of lowered pH to developing zebrafish.

While zebrafish are well-known for being an acid-tolerant species (Kwong et al. 2014), one study demonstrated that development is altered, and hatching and survival rates of embryonic zebrafish are significantly decreased at pH ≤ 5 and ≥ 10 (Andrade et al. 2017).

Additionally, the impact of pH on the sensitive neurobehavior endpoints evaluated in this study is unknown, and thus, we maintained pH of the exposure solution at control levels.

2). To maintain the environmental relevance of our study. The pH of groundwater from a uranium-contaminated well on the Navajo Nation was found to be 7.36 (Austin et al. 2012).

Additionally, contaminated surface waters from the Rio Paguate adjacent to the Jackpile

Uranium Mine measured at circumneutral pH levels ranging from 6.8 and 8.6 (Blake et al.

2017). Thus, the pH utilized in our study is within the range of the pH of the water detected near uranium mine sites. While pH may be playing a significant role in the uptake discrepancy between our study and previous studies, further research investigating uranium uptake specifically into developing zebrafish over a pH range is necessary to explain the observed differences. Here, we utilize the same exposure conditions on dechorionated zebrafish for our uptake experiment as well as for the neurobehavioral assays in order to

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directly correlate nominal water exposure concentration, uptake concentration, and developmental neurobehavioral toxicity. In our uptake study, the uranium concentration

per embryo remained constant between the 48 and 120 hpf time points demonstrating that

the uranium did not bioaccumulate. Our result is in contrast to previous studies where adult zebrafish accumulated uranium or a mixture of 233U-depleted uranium over several days of waterborne exposure (Barillet et al. 2007; Simon et al. 2018). This is not surprising, as the primary uptake route for metals in fish is via the gills (Bucher et al. 2016; Bywater et al.

1991), and zebrafish do not have fully functional gills until 14 days post fertilization

(Rombough 2002). The difference in life stages between studies, and their concomitant physiological and behavioral traits, may explain the lack of bioaccumulation in our developing zebrafish. Overall, our results suggested that 1) dechorionated zebrafish embryos were able to take up uranium from a waterborne exposure, 2) uptake was rapid and detectable by 48 hpf at exposure concentrations ≥ 300 µg/L UA, and 3) uranium did not bioaccumulate in zebrafish between 48 and 120 hpf.

Behavior Assessments

Embryonic Photomotor Response (EPR)

Following uptake estimation, dechorionated embryos were exposed to 0, 0.1, 3, 10, 30,

100, 300, 1,000, and 3,000 µg/L UA from 6 to 120 hpf to assess developmental toxicity.

To assess if subteratogenic UA might have caused behavioral changes, we examined the

embryonic photomotor response (EPR) at 24 hpf and the larval photomotor response (LPR)

at 120 hpf. None of the UA exposure concentrations used in our study were associated with

an EPR profile that was statistically different from the control embryos in any of the three

intervals of the assay (Figure D.2). Similar to the control embryos, the UA-exposed

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zebrafish responded to the first light flash (Excitation Interval) and failed to respond to the

second flash (Refractory Interval). It is possible that either insufficient uranium was taken

up into the 24-hpf zebrafish embryos to cause behavioral effects, or that uranium exposure

does not alter 24-hpf behavior of zebrafish. Since we measured uranium uptake only at 48

hpf and 120 hpf, we cannot confirm which of these hypotheses are true. Future work quantifying uranium uptake at earlier developmental time points will help explain this result.

The zebrafish EPR assay has been previously utilized to investigate the toxicity of a wide variety of compounds including neuroactive drugs and chemicals (Carbaugh et al. 2020;

Geier et al. 2018b; Kokel et al. 2011). Additionally, the developmental neurotoxicity of environmentally relevant concentrations of metals has also been evaluated (Olivares et al.

2016). While gallium exposure does not alter 24-hpf photomotor activity, indium and

arsenic exposures are associated with hypoactivity relative to controls in the Excitation

Interval of the assay. We highlight that the EPR assay is highly predictive of teratogenic

outcomes later in life (Reif et al. 2016) indicating that the absence of an altered EPR

response from uranium exposure in our study suggests the lack of long-term detrimental

effects. Of note, however, is that this assay has a low sensitivity for detecting chemicals

known to be neuroactive in mammals (Carbaugh et al. 2020). Other studies have found

conflicting results between the EPR assay and other neurobehavioral assays for a specific

chemical, demonstrating the utility of multi-endpoint developmental assessments in

identifying potential neurotoxicity (Geier et al. 2018a). Thus, by presenting the negative

results for this assay in our study, we emphasize the importance of evaluating more than

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one behavior endpoint to truly capture the potential of a chemical to alter neurobehavioral

functioning (Audira et al. 2018).

Larval Photomotor Response (LPR)

In addition to EPR, we conducted a second photomotor behavior assay at 120 hpf to

evaluate UA-induced alterations in larval swimming activity in response to rapid light-dark

transitions. A normal LPR consists of low total movement in the light phase and 5 – 10 fold more movement in the dark phase. Any alterations to the normal LPR upon UA exposure could indicate impacts to sensory or neuromuscular development. Figure D.3A depicts results from the three alternating light-dark phases of the assay, and reveals that there is a clear sustained hypoactivity from UA exposure across all cycles despite the decreasing maximum activity of the control zebrafish. Embryos exposed to 10, 30, 100, and 3,000 µg/L UA exhibited statistically significant decrease (hypoactive) in swimming

(p < 0.01; Relative Ratio ≤ -10%) in the light phase relative to control fish, while embryos exposed to 1,000 µg/L UA were hyperactive in the light phase (p < 0.01; Relative Ratio ≥

10%). The 0.1, 3, and 300 µg/L UA concentrations displayed a trend toward hypoactivity, but they were not statistically significant (Figure D.3B). Zebrafish exposed to 3, 30, and

100 µg/L UA were significantly hypoactive in the dark phase of the assay (p < 0.01;

Relative Ratio ≤ -10%). Zebrafish exposed to all other concentrations displayed a trend toward hypoactivity. Despite low uptake of uranium at the 30, 300, and 3,000 µg/L UA exposure concentrations (0.003, 0.020, and 0.460 µg uranium/g wet weight, respectively at 120 hpf) (Figure D.1), we still detected a behavioral impact at 120 hpf at all three concentrations, which underscores the value of including subteratogenic endpoints in toxicity evaluations.

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The UA-induced altered LPR profile not only shows that some aspect of zebrafish sensory

or neurodevelopment has been misregulated at 120 hpf, but is indicative of potential long-

term cognitive effects as seen in recent studies conducted by our group (Garcia et al. 2018;

Knecht et al. 2017). Previous uranium work in other vertebrate models are also consistent with this hypothesis. While several studies have found alterations to both locomotion and neurocognitive behaviors in adult rodents from uranium exposure (Dinocourt et al. 2015), only a handful of studies have focused on the effects of developmental uranium exposure.

Rodents exposed to uranium during development displayed altered locomotor activity

(Lestaevel et al. 2016), a deficit in spatial working memory (Dinocourt et al. 2017; Houpert et al. 2007), and delayed hyperactivity that lasted up to 9 months of age (Houpert et al.

2007). One study also found that prenatal uranium exposure caused a depressive-like

behavior phenotype in postnatal rats (Legrand et al. 2016). The results of these studies not

only demonstrate that uranium can target the central nervous system, but also emphasize

the long-term detrimental impacts of developmental uranium exposure.

The altered LPR response diagnosed in our study is the first time a behavioral outcome has

been identified in developing zebrafish exposed to uranium. While the larval swimming

behavior disruption may be due to altered sensory system development, the potential for

the uranium exposure to cause misregulation of neurogenesis is not unlikely. Uranium-

induced impairment of nervous system development has been hypothesized in previous

rodent studies (Dinocourt et al. 2017), however more research is needed to gain an in-depth

mechanistic understanding of uranium neurotoxicity. As a first step, we have identified an

aberrant behavioral response in the high-throughput zebrafish model which also has a high

physiological and genetic similarity to humans. Future work conducting gene expression

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analyses in developing zebrafish exposed to uranium will give us insight into the

mechanisms of uranium toxicity. Further, assessment of multiple behavioral endpoints at

later life stages of zebrafish will determine whether long-term neurocognitive health effects exist from exposure to uranium during development.

Comparison of UA with other metals

Similar to UA, we also exposed developing zebrafish to lead, cadmium, and iron, and

assessed their impact on development and swimming behavior at 120 hpf. All three of these

metals have been detected at elevated levels in water sources near uranium mine sites.

Exposure to a broad concentration range of lead or iron resulted in a hypoactive larval

photomotor response at 120 hpf in both the light and dark phases of the assay (Figure

D.4A). Exposure to cadmium resulted in a hypoactive LPR in the dark phase of the assay

and did not induce an altered LPR in the light. The lowest effect level (LEL) to cause the

adverse effect was 759 µg/L for lead (dark phase), 0.183 µg/L cadmium (dark phase), and

1,046 µg/L for iron (light phase). We note that the LPR results for all three metal exposures

are similar to the LPR hypoactivity associated with UA exposure (LEL = 3 µg/L). At all

the exposure concentrations for UA, lead, cadmium, and iron, we did not detect significant

teratogenicity (Figure D.4B). Fewer than five animals per concentration (<16%) of UA,

lead, and iron had any teratogenic effects, while cadmium exposure was associated with a

higher rate of mortality (up to ~23% per concentration; not statistically significant) relative

to other metals. These results not only highlight zebrafish as a sensitive model for exposure assessment, but also emphasize the importance of conducting both teratogenic and subteratogenic studies when determining the toxicological impact of chemical exposure.

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To the best of our knowledge, mixed metal exposures using the metals tested in this study have not been investigated in zebrafish (or other aquatic) behavioral models. To prepare for environmentally relevant mixed metal exposures, we first needed to compare the toxicity effects of each metal alone in the same biological model system. Thus, we evaluated the morphological and behavioral impact of the exposure to uranium, lead, cadmium, and iron in this study. While an increasing number of studies are evaluating developmental toxicity of individual metals in zebrafish (Roy et al. 2015; Zhu et al. 2012), there is only limited research making direct comparisons between them. Unlike the lack of teratogenicity that we observed from exposure to cadmium and lead in our study, a previous study reported that exposures to 25 µmol/L cadmium (4,583 µg/L) and 2.5 µmol/L lead

(948.32 µg/L) were associated with both morphological malformations and behavioral effects in developing zebrafish (Tu et al. 2017). The exposure concentration difference for cadmium between our study and Tu et al. likely accounts for toxicity differences.

Additionally, while we conducted a static exposure over the entire course of the exposure period, Tu et al. renewed the exposure solutions every 24 hours. This difference in experimental design likely accounts for the lack of significant teratogenicity from our lead exposures. The disparity reiterates the importance of toxicity comparisons using the same experimental platform. Indeed, we expect to observe varying results in behavior depending on which metal mixtures are investigated and at what exposure concentrations. While metal mixture toxicity studies are scarce in the literature, some individual metals have been shown to interfere with the cellular uptake and bioaccumulation of other metals when fish are exposed to metal mixtures. When juvenile Prochilodus lineatus (streaked prochilod) were exposed to a mixture consisting of zinc, manganese, and iron, the tissue distribution

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of the metals was altered compared to each metal alone (de Oliveira et al. 2018).

Futhermore, binary combinations of copper with zinc and copper with cadmium have been

shown to cause synergistic effects on Gobiocypris rarus (Chinese rare minnow) larvae

mortality (Zhu et al. 2011). Any altered tissue distribution or mortality caused by individual metals when presented as a mixture, could also have synergistic behavioral consequences.

We plan to follow up our study by exposing zebrafish embryos to mixtures of the individual metals presented in this manuscript.

The metals tested in this study are found in unregulated water sources in the southwestern

U.S. in the same region as abandoned uranium mine sites, possibly acting as co-

contaminants. Several metals such as calcium, manganese, magnesium, nickel, uranium,

zinc, and iron have been detected at concerning levels in both groundwater, and flora and

fauna near uranium mine sites in Portugal (Pereira et al. 2014). Additionally, Wang et al.

reported elevated concentrations of uranium in combination with thorium, iron, zinc,

lithium, and cobalt in surface waters near uranium mine and milling sites in Northern

Guangdong Province, China (Wang et al. 2012), demonstrating that the co-occurrence of

metals is not unique to just the U.S. Along with uranium, these trace metals have the ability

to be taken up by crops grown around uranium mining areas potentially leading to exposure

of humans and subsequent health effects from the elevated levels of metals (Neves et al.

2012). In unregulated water sources in the Navajo Nation of northeastern Arizona and

southeastern Utah, an area with a long history of uranium mining, cadmium was detected

at levels of up to 11 µg/L whereas the U.S. EPA guideline is 5 µg/L (Credo et al. 2019).

The 75th percentile of iron concentrations was 2,100 µg/L, with 38.6% of the samples

testing over the National Drinking Water Standard of 300 µg/L, and the 75th percentile of

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lead concentrations was 6.3 µg/L, with 2.6% testing over the Standard of 15 µg/L (Hoover et al. 2018). The concentration range utilized to test lead, cadmium, and iron in developing zebrafish in our study included environmentally relevant concentrations, and we detected significant behavioral toxicity at these concentrations suggesting the potential for adverse health impacts from human exposure.

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D.5. Conclusions

Uranium toxicity is an important issue for animals and humans living in close proximity to abandoned uranium mine sites. The current study demonstrates both the sensitivity and utility of developmental zebrafish to model human toxicity by our ability to detect subteratogenic impacts of depleted uranium on neurobehavioral outcomes. The photomotor behavior effect that we identified would be missed with a more traditional toxicology approach, which would have read 3,000 µg/L UA, and its corresponding low level of uptake, as putatively benign. Traditionally, the nervous system has not been considered a primary target of uranium toxicity, but our results should inform further research on the potential neurobehavioral effects of exposure to uranium. Logical next steps will be to 1) assess these same animals for gene expression changes that might be anchored to the LPR phenotype, forming a basis for mechanistic understanding of how uranium exerts developmental toxicity, and 2) determine whether any cognitive and social behavioral deficits from developmental exposure are manifest in adulthood.

Rarely do single metals exist as sole contaminants near mine land use sites, but they are present as mixtures of metals. This study reveals the adverse neurobehavioral impacts of uranium and its potential co-contaminants lead, cadmium, and iron, on developing zebrafish without causing significant teratogenicity. Organisms including humans that live around uranium mine sites are at a high risk of environmental exposure to both uranium and trace metals, and very little is known about the adverse health effects associated with exposure. Our results provide a benchmark for conducting developmental zebrafish toxicity studies with metal mixtures, and reveal a benefit of using a sensitive animal to model the potential human health effects of exposure to environmentally relevant metals.

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Figure D.1. Uranium uptake by developing zebrafish. Concentrations of uranium detected by ICP-MS analyses in 48 hpf and 120 hpf zebrafish exposed from 6 hpf to 30, 300, or 3,000 µg/L UA. As exposure concentration increased, the concentration detected in zebrafish also increased. The UA did not bioaccumulate between 48 hpf and 120 hpf. A two-way ANOVA followed by a Sidak’s post-hoc multiple comparison test was used to compare samples. Graph represents mean ± SEM of at least 3 independent replicates (ns = not significant, **p ≤ 0.01).

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Figure D.2. UA exposure did not affect embryonic photomotor response (EPR). Dechorionated zebrafish embryos (n=32-68) were exposed to 0, 0.1, 3, 10, 30, 100, 300, 1,000, and 3,000 µg/L UA from 6 hpf, and the EPR assay was conducted at approximately 24 hpf. Normal zebrafish embryos exhibit baseline activity in IR light prior to the first light flash (Background), an immediate burst of tail bending in response to the first light flash (Excitation) that rapidly decays to immobility, and little or no movement in response to the second light flash (Refractory). The K-S test (p < 0.05, delta > 50%) was used to determine that there were no alterations from normal behavior before and after the two light pulses in UA-exposed zebrafish. Any dead or malformed animals were excluded from the analysis.

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Figure D.3. UA exposure altered larval photomotor response (LPR). Developmental exposure of zebrafish (n=32-68) to UA was associated with hypoactivity in the light (quiescent) and dark (active) periods. (A) LPR assay consisted of three 3-minute periods of alternating light and dark periods. Dechorionated zebrafish embryos exposed to 10, 30, 100, 300, and 3,000 µg/L UA had a significant decrease (p < 0.05, Relative Ratio ≤ -10%) in photo-motor activity in the light phase, and embryos exposed to 3, 30, and 100 µg/L UA had a significant decrease in the dark phase (p < 0.05, Relative Ratio ≥ 10%). (B) Table shows area under the curve (AUC), AUC Relative Ratio (%), p value (calculated by the K- S test), and the detected significant effect, if any, calculated during the third light-dark cycle of the assay.

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Figure D.4. Comparison of developmental toxicity of UA with lead, cadmium, and iron. (A) Developmental exposure of zebrafish to lead and iron was associated with hypoactivity in the light (quiescent) and dark (active) phases, and exposure to cadmium resulted in hypoactivity in the light phase. The lowest effect levels (LEL) in the dark and light phases, and the lowest sample sizes of all concentrations for each chemical are indicated in the table. (B) Developmental exposures to uranyl acetate, lead, cadmium, and iron did not cause teratogenic effects till 120 hpf. “Any effect” includes all 18 developmental endpoints described in the Teratogenicity Assessments section of the Methods (Abbreviations: MO24 = mortality at 24 hpf, MORT = mortality at 120 hpf).

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Table D.1. Chemicals tested with sampling time point and purpose (Abbreviations: EM – Embryo media, hpf – hours post fertilization, DMSO – dimethyl sulfoxide).

Form Exposure CAS Contr Time Metal of concentrations Purpose number ol point metal (µg/L) 48 and 0, 30, 300, 3,000 EM Body burden 120 hpf Uranyl Acetate 541-09-3 238U 0, 0.1, 3, 10, 30, 100, 300, 1,000, EM 3,000 0, 379, 759, Lead (II) 1,707, 3,414, acetate 6080-56-4 Pb2+ Teratogenici 6,828, 12,897, trihydrate ty and 25,415 24 and behavior 0, 0.183, 1, 1.83, 120 hpf Cadmium 10108-64- 1% assessment Cd2+ 5, 18.3, 183, chloride 2 DMSO s 1830 0, 162, 412, Iron (III) 1,046, 2,660, 7705-08-0 Fe3+ chloride 5,977, 12,133, 16,220