And Aryl Hydrocarbon Receptor Repressor (AHRR) Bound Regions in MCF-7 Human Breast Cancer Cells

Genome-wide Mapping and Analysis of Aryl Hydrocarbon Receptor (AHR) and Aryl Hydrocarbon Receptor Repressor (AHRR) Bound Regions in MCF-7 Human Breast Cancer Cells

Yang (Sunny) Yang

A thesis submitted in conformity with the requirements for the degree of Master of Science

Department of Pharmacology and Toxicology University of Toronto

Genome-wide Mapping and Analysis of Aryl Hydrocarbon Receptor (AHR) and Aryl Hydrocarbon Receptor Repressor (AHRR) Bound Regions in MCF-7 Human Breast Cancer Cells Yang (Sunny) Yang

Master of Science

Department of Pharmacology and Toxicology University of Toronto

2015

Abstract

The aryl hydrocarbon receptor (AHR) is a ligand activated transcription factor best known for mediating the toxic actions of environmental contaminants, such as 2,3,7,8-tetrachlorodibenzo-p- dioxin (TCDD). The aryl hydrocarbon receptor repressor (AHRR) is an AHR regulated gene and a negative regulator of AHR. Although the mechanism of AHRR-dependent repression of AHR is not clear, one of the proposed mechanisms is through direct competition or interaction with AHR at DNA sequences termed aryl hydrocarbon response elements (AHREs). This thesis aimed to compare the genome-wide binding profiles of AHR and AHRR in MCF-7 cells treated for 24 h with 10nM TCDD using ChIP-seq. Although AHRE was overrepresented in both AHR- and

AHRR-bound regions, AHRR was shown to bind closer promoter regions than AHR. AHR- or

AHRR-independent regulations of the identified genes were also confirmed. This work is the first genome-wide mapping of AHRR-bound regions which provides insight into potential novel functions for AHRR.

Acknowledgement

The last two years have been a valuable experience to me and will remain an important segment of my life. I would like to direct my gratitude to my supervisor, Dr. Jason Matthews, for his mentorship. Under his supervision, I have developed the necessary skills and independent critical thinking that made this research project possible. I would like to thank my MSc advisor, Dr. David

Riddick, for giving me helpful advices. Also, a big thanks to the rest of my committee members:

Dr. Carolyn Cummins, Dr. Denis Grant and Dr. Amy Ramsey. To all current (Tiffany, Susanna,

Alvin, Laura T., Debbie, David) and past (Shaimaa, Laura M., Francine) members of the Matthews

Lab, I thank you all for supporting me throughout the years and providing me with a friendly environment. And to my housemate, Alastair Mak, thank you for being a great companion to me over the last few years of living together. And last but not least, to my parents and relatives, your continuous support and unconditional love is what keeps me moving forward.

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Table of Content

Abbreviations Acknowledgement Chapter 1: Introduction 1 1) Aryl Hydrocarbon Receptor (AHR) 1 1.1 Structure and Functional Domains 1 1.2 AHR Ligands 2 1.2.1 Natural Ligands 3 1.2.2 Synthetic Ligands 3 1.2.3 Endogenous Ligands 4 1.3 Canonical AHR Signalling Pathway 5 1.3.1 AHR-Chaperone Complex 5 1.3.2 Receptor Activation 6 1.3.3 Aryl Hydrocarbon Receptor Response Elements (AHREs) 6 1.3.4 Co-regulatory Proteins 8 1.3.5 Post-translation modification of AHR 10 1.4 Non-genomic Pathway 11 1.5 Biological Roles of AHR 12 1.5.1 Adaptive Response – Xenobiotic Metabolism 12 1.5.2 Toxic Response 13 1.5.3 Developmental Role 15 1.5.4 Cell Proliferation & Cell Cycle Control 16 1.5.5 Role in the Immune System 18 1.6 Negative Regulation of AHR Signalling 19 2) Aryl Hydrocarbon Receptor Repressor (AHRR) 21 2.1 Discovery 21 2.2 Gene Structure 21 2.3 Protein Structure 21 2.4 AHRR Expression 23 2.4.1 Constitutive Expression 23 2.4.2 Ligand-induced Expression 24 2.5 Repression of AHR Transactivation 25 2.6 Biological Roles 28 2.6.1 Embryonic Development 28 2.6.2 Reproductive Disorders 28 2.6.3 Cancer 28 Chapter 2: Study Rationales and Research Objectives 30 1) Study Rationale 30 2) Research Aims 31 Chapter 3: Materials and Methods 33 1) Materials 33 1.1 Chemicals and Biological Agents 33 1.2 Plasticware 34 1.3 Instruments 34

2) Methods 35 2.1 Maintenance of Cell Lines 35 2.2 Chromatin-immunoprecipitation Sequencing (ChIP-Seq) 36 2.2.1 Seeding & Treatment 36 2.2.2 Cross-linking 36 2.2.3 Sonication 37 2.2.4 Immunoprecipitation 37 2.2.5 ChIP Washes 37 2.2.6 Purification & qPCR 38 2.2.7 Library Preparation 38 2.2.8 Size-selection 38 2.3 Analysis of ChIP-Seq 39 2.3.1 Processing Raw Reads & Mapping to Human Genome 39 2.3.2 Peak Calling 39 2.3.3 Visualization of Peaks 40 2.3.4 Overlap of Peak Regions and Gene Annotation 40 2.3.5 Motif Analysis 41 2.3.6 Transcription Factor Binding Site Analysis & Pathway Analysis 42 2.4 ChIP-qPCR 43 2.5 Luciferase Reporter Gene Assay 44 2.5.1 Creation of Luciferase Reporter Gene Construct 44 2.5.2 Transfection & Treatment 45 2.5.3 Luciferase Reporter Gene Assay & β-gal Normalization 46 2.6 Statistical Analysis 47 Chapter 4: Results 48 1) Genome-wide Mapping of AHR and AHRR Binding Sites in MCF-7 Cells 48 1.1 Determining the AHR- and AHRR-bound regions by ChIP-Seq 48 1.2 Visualization of Peaks 49 1.3 Gene Annotation & Feature Analysis 50 1.4 Overlap Analysis of AHR and AHRR Peaks 53 1.5 Motif Analysis 57 1.5.1 Ligand-induced AHR- and AHRR-bound Regions 57 1.5.2 Overlapped and Unique Regions 63 1.6 Overrepresented Transcription Factor Binding Site or Modules Analysis 68 1.6.1 Ligand-induced AHR- and AHRR-bound Regions 68 1.6.2 Overlapped and Unique Regions 71 1.7 Gene List & Pathway Analysis 76 1.7.1 Ligand-induced AHR- and AHRR-bound Genes 76 1.7.2 Overlapped and Unique Genes 78 2) Validation of Unique AHR-bound and AHRR-bound regions by qPCR 81 2.1 Visualization of candidate unique AHR-bound and AHRR-bound peak regions 81 2.2 ChIP-qPCR of Unique AHR-bound Peak Regions 82 2.3 ChIP-qPCR of Unique AHRR-bound Peak Regions 83 3) Gene Expression Analysis of Unique AHR-bound & AHRR-bound Genes 85 3.1 CYP1B1 Luciferase Reporter Gene Assay as Positive Control 85 3.2 XRCC6 Luciferase Reporter Gene Assay 87

3.3 MAP2K7 Luciferase Reporter Gene Assay 91 3.4 CNR2 Luciferase Reporter Gene Assay 92 3.5 RFTN1 Luciferase Reporter Gene Assay 95 Chapter 5: Discussion 98 1) AHR and AHRR binding profiles exhibit both similar and different characteristics. 98 1.1 Ligand-induced AHR binding patterns 98 1.2 Ligand-induced AHRR binding patterns 101 1.3 Comparison between the AHR and AHRR binding profiles 102 2) AHRR-mediated transcriptional repression can be AHR- and ARNT-independent. 105 3) Not all AHR-mediated gene expression can be repressed by AHRR via competitive binding to the same region. 106 4) Implications for the tumor suppressor role of AHRR 107 5) Novel proposed model for AHRR-mediated repression of AHR 109 6) Limitations of the Study 113 6.1 ChIP-Seq 113 6.2 Complexity of AHR Binding 114 6.3 Time Points 115 7) Future Directions 115 Chapter 6: Conclusion 117 References 118 Appendix 140

List of Tables

Table 1. Q-PCR primers used for the ChIP-qPCR validation of selected unique binding regions extracted from ChIP-seq analysis 43 Table 2. Primers used for cloning of promoter and ChIP regions into reporter gene construct 45 Table 3. Ligand-induced peaks regions identified by MACS2 Peak-caller program using default settings. Q-values cutoff was set at 0.05. 48 Table 4. Top 5 De novo motif discovery (DREME) of TCDD-induced AHR binding sites 58 Table 5. De novo motif discovery (DREME) of Top 500 TCDD-induced AHR binding sites (E-value < 0.05) 59 Table 6. Top 5 De novo motif discovery (DREME) of TCDD-induced AHRR binding sites 59 Table 7. De novo motif discovery (DREME) of Top 500 TCDD-induced AHRR binding sites (E-value < 0.05) 60 Table 8. Top 5 De novo motif discovery (SEME) of TCDD-induced AHR binding sites 60 Table 9. Top 5 De novo motif discovery (SEME) of Top 500 TCDD-induced AHR binding sites 61 Table 10. Top 5 De novo motif discovery (SEME) of TCDD-induced AHRR binding sites 61 Table 11. Top 5 De novo motif discovery (SEME) of Top 500 TCDD-induced AHRR binding sites 62 Table 12. Top 5 De novo motif discovery (DREME) of AHR/AHRR co-bound regions 64 Table 13. Top 5 De novo motif discovery (SEME) of AHR/AHRR co-bound regions 64 Table 14. Top 5 De novo motif discovery (DREME) of unique AHR-bound regions 65 Table 15. Top 5 De novo motif discovery (SEME) of unique AHR-bound regions 66 Table 16. Top 5 De novo motif discovery (DREME) of unique AHRR-bound regions 67 Table 17. Top 5 De novo motif discovery (SEME) of unique AHRR-bound regions 67 Table 18. Top 10 overrepresented transcription factor for the top 500 TCDD-induced AHR peak regions 69 Table 19. Top 10 overrepresented transcription factor for the top 500 TCDD-induced AHRR peak regions 70 Table 20. Top 10 overrepresented modules (in which one partner is the AHRE site) for the top 500 TCDD-induced AHR peak regions 70 Table 21. Top 10 overrepresented modules (in which one partner is the AHRE site) for the top 500 TCDD-induced AHRR peak regions 71 Table 22. Top 10 overrepresented transcription factors for the top 500 TCDD-induced AHR/AHRR co-bound peak regions 72

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Table 23. Top 10 overrepresented modules (in which one partner is the AHRE site) for top 500 TCDD-induced AHR/AHRR co-bound peak regions 72 Table 24. Top 10 overrepresented transcription factor for top 500 unique TCDD-induced AHR-bound peak regions 73 Table 25. Top 10 overrepresented modules (in which one partner is the AHRE site) for the top 500 unique TCDD-induced AHR-bound peak regions 74 Table 26. Top 10 overrepresented transcription factor for top 500 unique TCDD-induced AHRR-bound peak regions 75 Table 27. Top 10 overrepresented modules (in which one partner is the AHR-related factor) for the top 500 unique TCDD-induced AHRR-bound peak regions 75 Table 28. Top 5 canonical pathways and top 5 diseases/functions from IPA analysis of the top 500 TCDD-induced AHR-bound genes 77 Table 29. Top 5 canonical pathways and top 5 diseases/functions from IPA analysis of the top 500 TCDD-induced AHRR-bound genes 77 Table 30. Top 5 canonical pathways and top 5 diseases/functions from IPA analysis of the top 500 AHR/AHRR co-bound genes 79 Table 31. Top 5 canonical pathways and top 5 diseases/functions from IPA analysis of the top 500 unique AHR co-bound genes 79 Table 32. Top 5 canonical pathways and top 5 diseases/functions from IPA analysis of the top 500 unique AHRR co-bound genes 80

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List of Figures

Figure 1. Diagram of the AHR functional domains 2 Figure 2. Examples of natural, synthetic and endogenous AHR ligands and their structures 5 Figure 3. The canonical pathway of AHR signalling. 10 Figure 4. The structural organization of AHR and AHRR. 22 Figure 5. The current proposed model of AHRR-mediated repression of AHR transactivation as proposed by Mimura et al. (1999). 27 Figure 6. ChIP Signal Intensity at CYP1A1 gene for 24 h DMSO-treated and TCDD-treated samples for AHR and AHRR. Scale is the same for all four samples set at 400. 49 Figure 7. Genomic location annotation of TCDD ligand-induced (TCDD vs. DMSO) AHR Peak Regions 51 Figure 8. Genomic location annotation of TCDD ligand-induced (TCDD vs. DMSO) AHRR Peak Regions 51 Figure 9. Histogram of the distance to transcription start site (TSS) for TCDD-induced AHR peak regions (top) and TCDD-induced AHRR peak regions (bottom). 52 Figure 10. Overlap between TCDD (24 h) TCDD-induced AHR peak regions, TCDD (24 h) TCDD-induced AHRR peak regions and TCDD (45 min) ligand-induced AHR peak regions (left) and their corresponding genes (right). 54 Figure 11. Overlap between TCDD (24 h) TCDD-induced AHR peak regions and TCDD (24 h) TCDD-induced AHRR peak regions within the promoter regions (±1000 bp of TSS). 54 Figure 12. Distance to TSS histogram and genomic location annotations for AHR/AHRR co-bound regions (A), unique AHR- (B) and AHRR-bound regions (C). 56 Figure 13. The comparison of the AHRE core consensus sequence in the predicted AHR motifs for the top 500 AHR-bound (A) and AHRR-bound (B) regions with corresponding E- values for matching with the JASPAR core database AHR motif (C). 63 Figure 14. Venn diagram overlap between the top 10 predicted pathways for the top 500 TCDD-induced AHR-bound (left) and AHRR-bound genes (right) 78 Figure 15. ChIP signal intensity of a candidate unique AHR-bound region located near the CNR2 gene. 81 Figure 16. ChIP signal intensity of a candidate unique AHRR-bound region located near the XRCC6 gene. 82 Figure 17. ChIP-qPCR of unique AHR-bound regions (annotated to ACSL1, CNR2, LAMA4 and RFTN1) selected from ChIP-seq analysis. 83 Figure 18. ChIP-qPCR of unique AHRR-bound regions (annotated to MAP2K7, PNPO, RPL22, XRCC6) selected from ChIP-seq analysis. 84

Figure 19. Luciferase activity of pGL3-basic-CYP1B1-promoter with increasing amount (0, 100, 200) of AHR and ARNT expression vector transfected treated with DMSO, TCDD 10 nM and TCDD 100 nM. 86 Figure 20. Luciferase activity of pGL3-basic-CYP1B1-promoter with increasing amount (0, 100, 200) of AHRR expression vector transfected in the presence of 200 ng of AHR/ARNT expression vector treated with DMSO or TCDD 100 nM. 86 Figure 21. Luciferase activity of pGL3-basic XRCC6-promoter with increasing amount (0, 100, 200, 400 ng) of AHRR expression transfection normalized to control (pGL3-basic empty). 88 Figure 22. Luciferase activity of pGL3-basic XRCC6-promoter with increasing amount (0, 100, 200, 400 ng) of AHR and ARNT expression vector transfected with DMSO, TCDD 10 nM or TCDD 100nM treatment. 89 Figure 23. Luciferase activity of pGL3-basic XRCC6-promoter with increasing amount (0, 200, 400 ng) of AHR and ARNT (A) or AHRR (B) expression vector transfected in the presence of high levels of AHRR (A) or AHR/ARNT (B). 89 Figure 24. Luciferase activity of pGL3-promoter XRCC6-ChIP with increasing amount of AHRR expression transfection (0, 100, 200, 400 ng) normalized to control (pGL3-promoter empty). 90 Figure 25. Luciferase activity of pGL3-basic MAP2K7-promoter with increasing amount (0, 100, 200, 400 ng) of AHRR expression transfection normalized to 0 ng of AHRR transfected. 91 Figure 26. Luciferase activity of pGL3-basic MAP2K7-promoter with transfection of increasing amount (0, 100, 200, 400 ng) of AHR and ARNT expression vector with DMSO, TCDD 10 nM or TCDD 100 nM treatments. 92 Figure 27. Luciferase activity of pGL3-promoter CNR2-ChIP with increasing amount (0, 100, 200, 400 ng) of AHR and ARNT expression vectors transfected with DMSO or TCDD 100 nM treatment. 93 Figure 28. Luciferase activity of pGL3-promoter-CNR2-ChIP with increasing amount (0, 100, 200, 400 ng) of AHRR expression transfection normalized to 0 ng of AHRR transfected. 94 Figure 29. Luciferase activity of pGL3-promoter CNR2-ChIP with increasing amount (0, 100, 200, 400 ng) of AHRR expression vector transfected in the presence of high levels (400 ng) of AHR/ARNT expression vector treated with DMSO or TCDD 100 nM. 94 Figure 30. Luciferase activity of pGL3-promoter RFTN1-ChIP with increasing amount (0, 100, 200, 400 ng) of AHR and ARNT expression vector transfected with DMSO or TCDD 100 nM treatment. 96 Figure 31. Luciferase activity of pGL3-promoter-RFTN1-ChIP with increasing amount (0, 100, 200, 400 ng) of AHRR expression transfection normalized to 0 ng of AHRR transfected. 96

Figure 32. Luciferase activity of pGL3-promoter RFTN1-ChIP with increasing amount (0, 100, 200, 400 ng) of AHRR expression vector transfected in the presence of high levels (400 ng) of AHR/ARNT expression vector treated with DMSO or TCDD 100 nM. 97 Figure 33. Novel proposed model of AHRR-mediated repression of AHR divided into three mechanisms 112

Abbreviations

3-MC 3-methylcholanthrene ACSL1 Acyl-CoA Synthetase Long-Chain Family Member 1 AHR Aryl hydrocarbon receptor AHRE Aryl hydrocarbon receptor response element AHRR Aryl hydrocarbon receptor repressor AIP AHR-interacting protein ANKRA2 Ankyrin-repeat protein 2 AP-1 Activator Protein 1 AP-2 Activating Protein 2 ALDH3A1 Aldehyde-3-dehydrogenase ARNT Aryl hydrocarbon receptor nuclear translocator B[a]P Benzo[a]pyrene bHLH-PAS Basic helix-loop-helix-Per-ARNT-Sim CDK Cyclin dependent kinase ChIP Chromatin-immunoprecipitation ChIP-seq Chromatin-immunoprecipitation sequencing CNR2 Cannabinoid Receptor 2 (Macrophage) Coip Co-immunoprecipitation COX-2 cyclooxygenase-2 cPLA2 cytosolic phospholipase A2 CYP1A1 Cytochrome P450 1A1 CYP1B1 Cytochrome P450 1B1 DCC dextran coated charcoal DIM 3,3'-diindolylmethane DMEM Dulbecco's Modified Eagle's Medium DMSO Dimethyl sulfoxide ER Estrogen receptor ERE Estrogen response element FICZ 6-formylindolo[3,2-b]carbazole FOS FBJ Murine osteosarcoma Viral Oncogene Homolog FOX Forkhead Box GATA GATA binding protein GSTA1 Glutathione S-transferase HAH Halogenated aromatic hydrocarobons HAT Histone acetyltransferase HDAC Histone deacetylase HIF Hypoxia inducible factor Hsp90 Heat shock protein 90 ICZ Indolo[3,2-b]carbazole JUN Jun Proto-oncogene LAMA4 Laminin, Alpha 4 LBD Ligand binding domain MAP2K7 Mitogen-Activated Protein Kinase Kinase 7

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MYC V-Myc Avian Myelocytomatosis Viral Oncogene Homolog NC-XRE Nonconsensus Xenobiotic response element NCoA Nuclear receptor coactivator NCOR2 Nuclear receptor corepressor 2 NF-kB Nuclear Factor Kappa B NLS Nuclear localization sequence NQO1 NAD(P)H:quinone oxidoreductase NRIP1 Nuclear receptor-interacting protein 1 PAH Polycyclic aromatic hydrocarbons PAS Period-ARNT Single-minded PCB Polychlorinated diphenyl PCDF Polychlorinated dibenzofuran PKC Protein Kinase C PNPO Pyridoxamine 5’-Phosphate Oxidase P/S/T Proline-serine-threonine Rb Retinoblastoma RFTN1 Raftlin, Lipid Raft Linker 1 RNAPolII RNA polymerase II RPL22 Ribosomal Protein L22 SHP Short heterodimer partner SMARCA4 SWI/SNF related, matrix associated actin dependent regulator of chromatin, subfamily A, member 4 SP1 Specificity protein 1 SUMO Small ubiquitin-like modifier TCDD 2,3,7,8-Tetrachlorodibenzo-p-dioxin TGF-β1 Transforming growth factor beta 1 Th17 T helper 17 cell TIPARP TCDD-inducible poly(ADP-ribose) polymerase TF Transcription factor Treg Regulatory T cell TSS Transcription start site TTS Transcription termination site UGT1A2 UDP-glucoronosyltransferase UTR Untranslated region XRCC6 X-Ray Repair Complementing Defective Repair in Chinese Hamster Cells

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Chapter 1: Introduction

1) Aryl Hydrocarbon Receptor

The aryl hydrocarbon receptor (AHR) is a ligand-activated transcription factor and member of the basic-Helix-Loop-Helix (bHLH)-Period circadian (Per)- Aryl hydrocarbon receptor nuclear translocator (ARNT)-single-minded (Sim) (bHLH-PAS) superfamily of proteins. AHR (also known as the dioxin receptor) mediates the toxicological effects of several environmental contaminants, including 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD; dioxin) (Okey et al., 1994).

In addition to its role in mediating dioxin toxicity, AHR, as part of a heterodimerization complex with ARNT, is an important regulator of metabolism of xenobiotics and endogenous compounds.

AHR has also been implicated in other physiological roles including toxic response, development, cell cycle control and immune response (described in Chapter 1, Section 1.5).

1.1) Structural and Functional Domains

AHR is composed of modular domains, as shown in Figure 1. The bHLH region, located near the

N-terminus of AHR, is essential for nuclear localization and export of AHR (Ikuta et al., 1998;

Ikuta et al., 2000), interaction with the chaperone protein, heat shock protein 90 (Hsp90) (Pongratz et al.,1992), interaction with its dimerization partner, aryl hydrocarbon receptor nuclear translocator (ARNT) (Fukunaga et al., 1995; Gu et al., 2000), and DNA binding (Fukunaga et al.,

1995; Ikuta et al., 1998). The PAS region of AHR contains two domains: PAS A and PAS B.

These domains display high sequence homology to the protein domains found in ARNT (Ema et al., 1992). The PAS A and B domains include sites for interaction with Hsp90 and ARNT

(Antonsson et al., 1995; Fukunaga et al., 1995). An important difference between AHR and ARNT 2

is that the PAS B domain of AHR contains the ligand binding domain (LBD) (Goryo et al., 2007;

Pandini et al., 2009), whereas the PAS B domain of ARNT does not. The C-terminal

transactivation domain is essential for AHR-mediated gene transcription and protein-protein

interaction (Jain et al., 1994; Rowlands et al., 1996; Watts et al., 2005). The transactivation domain

can be separated into three subdomain regions: the acidic region, the glutamine-rich (Q-rich)

region, and the proline-serine-threonine (P/S/T)-rich region (Reen et al., 2002). The acidic region,

rich in aspartate and glutamate, is essential in the transactivation of AHR (Jones and Whitlock,

2001). The Q-rich region is shown to interact with co-regulator proteins important for regulating

gene expression. The P/S/T-rich region may also repress other domains to moderate the

transcriptional response (Kumar et al., 2001).

Hsp90 and ARNT

Dimerization Region Transactivation Domain

bHLH PAS PAS Acidic Q-rich P/S/T N- A B -C

27 88 111 181 275 342 500 600 713 848

DNA binding Domain Ligand binding Domain

Figure 1. Diagram of the AHR functional domains

1.2) AHR Ligands

AHR is a promiscuous receptor that is capable of binding a wide range of structurally diverse

ligands including natural, synthetic and endogenous chemicals (Denison and Nagy, 2003) (Figure

2).

1.2.1) Natural Ligands

A major source of naturally derived AHR ligands for humans comes from our diet. Several herbal extracts (eg. Ginseng, licorice) and some food extracts contains relatively potent AHR ligands

(Jeuken et al., 2003). Indole-3-carbinol, from cruciferous vegetables, can be hydrolyzed in the gastrointestinal tract into an AHR agonist, indolo [3,2-b] carbazole (ICZ), and a partial agonist

3,3’-diindolymethane (DIM) (Gillner et al., 1993; Jellinck et al., 1993). Although high affinity

AHR ligands, these indole-derived compounds do not produce some of the toxicities found in animal models treated with synthetic AHR ligands (Pohjanvirta et al., 2002). Other natural AHR agonists include curcumin (Ciolino et al., 1998) and carotenoids (Gradelet et al., 1996) found in vegetables. On the other hand, dietary AHR antagonists include flavonoids (flavones, flavanol, flavanones and isoflavones), a class of naturally produced plant compounds, found in grapes

(Denison and Nagy, 2003). Resveratrol, a phytoalexin produced by several plants, is also an AHR antagonist and can be found in red wine (Ciolino et al., 1998).

1.2.2) Synthetic Ligands

Many well-known high affinity AHR ligands are synthetic. There are two classes of synthetic chemicals with high affinity for AHR: the halogenated aromatic hydrocarbons (HAHs) and polycyclic aromatic hydrocarbons (PAHs). The HAHs, the more potent of the two classes, are planar and hydrophobic compounds. Examples of HAHs include TCDD, polychlorinated dibenzofurans (PCDFs) and polychlorinated biphenyls (PCBs) (Bandiera et al., 1982; Piskorska-

Pliszczynska et al., 1986; Denison and Nagy, 2003). The binding affinities of HAHs for AHR are very high ranging from the pico-molar (pM) to nano-molar (nM) (Bandiera et al., 1982). Since

TCDD with binding affinities in the pM is the most potent known AHR agonist, it is used in many

AHR-related studies as the prototypical AHR ligand (Bradfield and Poland, 1988; Denison and

Heath-Pagliuso, 1998). HAHs are produced by industrial processes such as incineration (Olie et al., 1977), bleaching of paper, and the manufacturing of some pesticides, herbicides and fungicides

(Gilpin et al., 2003). Due to the high stability and resistance to metabolism, HAHs pose an environment problem as persistent contaminants. Although sources of HAHs are mostly industrial, human exposure is primarily from the consumption of animal foods including meat, fish and dairy products (Startin and Rose, 2003). Levels of these contaminants are detected in all humans

(Schecter and Olson, 1997). Examples of PAHs include 3-methylcholanthrene (3-MC), benzo[a]pyrene (B[a]P), and naphthalene. They have lower potencies compared to HAHs with binding affinities in the nano-molar (nM) to micro-molar (µM) range (Bandiera et al., 1982). PAHs are produced from the incomplete combustion of carbon-containing material such as coal, diesel fuel, tar and plant materials (Bostrom et al., 2002). Since PAHs can be produced from the natural combustion of naturally derived materials such as plants, PAHs can technically be classified as both synthetic and natural AHR ligands (Nikolaou et al., 1984))

1.2.3) Endogenous Ligands

A growing amount of research has been dedicated to examining endogenous AHR ligands. A list of endogenous molecules as possible AHR agonists includes tryptophan derivatives (Rannug et al., 1987; Heath-Pagliuso et al., 1998), arachidonic acid metabolites (Schaldach et al., 1999), heme degradation products (Phelan et al., 1998), cholesterol derivatives (Savouret et al., 2001) and low density lipoproteins (McMillan and Bradfield, 2007). Of this list, the most potent agonist is the 6-formylindolo [3,2-b] carbazole (FICZ) (Rannug et al., 1987). FICZ is formed by the photolysis of tryptophan in the presence of visible and UV light. It can competitively displace

TCDD with AHR with high affinity in the nM range (Rannug et al., 1987; Helferich and Denison,

1991). FICZ was also shown to be a substrate for Phase I enzymes in which it induces a negative feedback loop to regulate its own concentration (Wincent et al., 2009).

Natural Ligands

Indolo [3,2-b] carbazole Curcumin Synthetic

Ligands

2,3,7,8-tetrachlorodibenzo-ρ-dioxin Benzo[a]pyrene

Endogenous Ligands

6-formylindolo [3,2-b] carbazole Bilirubin

Figure 2. Examples of natural, synthetic and endogenous AHR ligands and their structures

1.3) Canonical AHR Signalling Pathway

1.3.1) AHR-Chaperone Complex

In the absence of ligand, AHR is sequestered in the cytoplasm in a complex formed by the interaction with chaperone proteins: 90kDa heat shock protein (Hsp90) (Perdew, 1988) and the aryl hydrocarbon receptor interacting protein (AIP; XAP2; ARA9). Hsp90 is required for the

6 proper folding and stabilization of AHR in a high affinity ligand-binding conformation (Pongratz et al., 1992; Coumailleau et al., 1995). AIP enhances the stability of AHR and retention of AHR in the cytoplasm by interacting with the carboxyl-terminal of the Hsp90 and masking the nuclear localization sequence (NLS) (Petrulis et al., 2002; Meyer and Perdew, 1999). Another chaperone protein in the complex is the p23, which may play a role in ligand responsiveness and in AHR activation (Kazlauskas et al., 1999).

1.3.2) Receptor Activation

AHR ligands enter the cell via diffusion due to their lipophilic planar properties (Denison and

Nagy, 2003). Ligand binding causes a conformational change in AHR resulting in the exposure of the NLS. This leads to the nuclear translocation of the AHR and the dissociation of the chaperone proteins from the complex (Okey et al., 1980). In the nucleus, ligand-bound AHR heterodimerizes with ARNT (Pollenz et al., 1994; Hord and Perdew, 1994). The AHR-ARNT heterodimer complex seeks out and binds with high affinity specific DNA recognition sequences termed the aryl hydrocarbon response elements (AHREs; xenobiotic response elements (XREs) or dioxin response elements (DREs)) that are located in the regulatory regions of target genes, including cytochrome

P450 CYP1A1, CYP1B1 and CYP1A2 (Probst et al., 1993; Bacsi and Hankinson, 1996; Tang et al., ). The canonical mechanism of AHR activation is illustrated in Figure 3.

1.3.3) Aryl Hydrocarbon Receptor Response Elements (AHREs)

The consensus sequence of the AHRE was determined to be 5’-TnGCGTG-3’ and GCGTG being the pentanucleotide which is essential for the AHR-ARNT complex to bind to the DNA (Shen and

Whitlock, 1992; Lusska et al., 1993; Swanson et al., 1995). AHR interacts with the half-site recognition sequence of the 5’-GC-3’ portion of the core consensus sequence motif while ARNT

7 interacts with the 5’-GTG-3’ portion (Swanson et al. 1995). ARNT has been reported to homodimerize and bind with low affinity to the E-box palindromic core sequence 5’-CACGTG-

3,’ but not the AHRE sequence (Sogawa et al., 1995; Huffman et al., 2001). However, a target gene regulated by the ARNT homodimer has yet to be identified (Sogawa et al., 1995; Card et al.,

2005). Based on the genome-wide analysis of AHR and ARNT binding, the majority of their binding regions were found to be overlapping, supporting the importance of the heterodimerization complex for binding to the DNA (Lo and Matthews, 2012). Recent chromatin immunoprecipitation coupled to next generation sequencing (ChIP-seq) studies of TCDD-induced AHR binding profiles in MCF-7 cells revealed that AHR binds at promoter regions but also to sequences > 10000 bp away from the transcription start sites (TSS). These findings support the notion that AHR regulates target genes through long-range chromatin remodeling or chromatin looping mechanisms in a manner similar to that reported for estrogen receptor alpha (Fullwood et al., 2009). Although

AHREs are enriched in AHR binding sites, not all of the binding sites necessarily contain an AHRE

(Ahmed et al., 2009; Sartor et al., 2009; De Abrew et al., 2010, Dere et al., 2011; Lo et al., 2012).

These results suggest that AHRE may not be an absolute requirement for AHR binding and that an alternative binding sequence or mechanism may also be essential. A novel enhancer element called the AHRE-II (5’CATG(N6)CWTG3’) may also facilitate AHR binding to DNA (Boutros et al., 2004; Sogawa et al., 2004); however, these sites have not been overrepresented in genome- wide AHR binding studies (Dere et al., 2011; Lo et al., 2012). More recently, another response element called the nonconsensus XRE (NC-XRE) was found to mediate AHR-dependent gene expression (Huang and Elferink, 2012). In that study, AHR was shown to interact with NC-XRE independently without ARNT, suggesting an alternative DNA binding partner for AHR. Since the

AHRE was not present for all AHR binding regions, interest has been developed for finding novel consensus AHR recognition sequences.

1.3.4) Co-regulatory Proteins

The binding of the ligand-AHR-ARNT complex to an AHRE promotes the recruitment of various co-regulator proteins to modulate the transcription of AHR target genes (Hankinson, 2005).

SWI/SNF Related, Matrix Associated Actin Dependent Regulator of Chromatin, Subfamily A,

Member 4 (SMARCA4), a component of the ATP-dependent chromatin remodeling complex, was found to be involved in the CYP1A1 gene transcription using transient transfection and ChIP assays, suggesting specific recruitment of SMARCA4 by the AHR-ARNT complex (Wang and

Hankinson, 2002). Another family of coactivators that interacts with the AHR-ARNT complex in mediating transcription is the nuclear coactivator (NCoA) family (Beischlag et al., 2002).

Interactions between these NCoA proteins and the mouse Ahr-Arnt complex were shown using co-immunoprecipitation (co-ip). These co-regulator proteins contain nuclear receptor interacting domains and have intrinsic histone acetyltransferase (HAT) activity. HATs acetylate lysine residues on histones, which results in the remodeling of the chromatin to allow access of other proteins to bind to the DNA. However, it is worth noting that HATs can also have non-histone substrates including transcriptional activators. One such example is the CBP/p300 coactivator family, another family of coactivators that contain HAT domains, found to be involved in the

AHR-ARNT complex mediated transcription through its interaction with ARNT (Kobayashi et al.,

1997). AHR was also found to be required for p300-mediated transcription, which may expand

AHR’s role in cell cycle control (Tohkin et al., 2000).

Nuclear receptor interacting protein (NRIP1; RIP140) is involved in the gene transcription of nuclear receptors target genes. NRIP1 acts mainly as a negative regulator of hormone-dependent

9 nuclear receptor activity (Augereau et al., 2006). NRIP1 was shown to enhance AHR-mediated gene transcription (Kumar et al., 1999). However, another group failed to find NRIP1 recruitment to the regulatory region of CYP1A1 (Matthews et al., 2005). Whether NRIP1 act as a coactivator or corepressor may by dependent on the presence or absence of ERα at the regulatory region of

AHR target genes (Madak-Erdogan and Katzenellenbogen, 2012).

AHR interacts with RNA polymerase II (RNAPolII) and basal transcription factors including transcription factor IIB (TFIIB) (Swanson and Yang, 1998) and TFIIF (Rowlands et al., 1996;

Hankinson, 2005).

Although AHR is known to be a transcriptional activator, it may also act as a transcriptional repressor. Nuclear corepressor 2 (NCOR2) is a repressor of nuclear receptor activity possibly through the interaction with histone deacetylase (HDAC) (Nagy et al., 1997). NCOR2 was reported to interact with AHR-ARNT complex in the inhibition of CYP1A1 gene expression

(Nguyen et al., 1999). However, this repression appears to be species-specific and that the human

NCOR2 action on CYP1A1 promoter may be due to other factors outside of AHR signalling pathway (Rushing and Denison, 2002). Another corepressor, the short heterodimer partner (SHP), a nuclear receptor lacking a DNA binding domain, was also shown to inhibit AHR-dependent transcription by preventing the AHR-ARNT complex from binding to the DNA (Klinge et al.,

2001). Nevertheless, studies of corepressors regulating AHR activity remain limited.

AHR Ligand

Co-regulatory Proteins

HSP90 Heterodimerization AHR ARNT Basal HSP90 Transcription AHR Factors AHR ARNT AHRE Target Gene AIP P23 Nuclear Translocation ↑CYP1A1 T Negative AHRR ↑CYP1B1 HSP90 HSP90 Feedback ↑AHRR, etc… AIP P23 AHR 26s

Figure 3. The canonical pathway of AHR signalling. After ligand enters the cell and binds to the AHR, the cytoplasmic AHR, which is bound to a chaperone complex, translocates to the nucleus. In the nucleus, AHR dissociates from the chaperone complex and heterodimerizes with ARNT. Then, the ligand-AHR-ARNT complex binds to AHREs in the regulatory region of AHR target genes mediating changes in gene expression through the recruitment of co-regulatory proteins and basal transcription factors. AHR is regulated through a negative feedback mechanism by AHRR and through 26S proteasome-mediated degradation.

1.3.5) Post-translational modification of AHR

There are three post-translational modifications known to play a role in regulating the activity of

AHR: phosphorylation, ubiquitination and SUMO (small ubiquitin-like modifier)-ylation.

Phosphorylation of AHR at S36, after nuclear localization, was found to be important for AHR binding to AHREs. However, phosphorylation of the NLS of cytosolic AHR was shown to inhibit the ligand-dependent nuclear import of AHR (Ikuta et al., 2004). These different observations

11 suggest that phosphorylation plays different roles within different stages of the AHR signalling pathway. As described earlier, ubiquitination promotes proteosomal degradation of AHR, and this process can be enhanced by TCDD (Ma and Baldwin, 2000). On the other hand, SUMOylation stabilizes AHR by inhibiting its ubiquitination (Xing et al., 2012). SUMOylation was also found to repress transcriptional activity of AHR, which could be reversed by TCDD treatment, suggesting that TCDD may activate transcriptional activity by down-regulating SUMOylation.

The ability of TCDD to affect ubiquitination and SUMOylation of AHR may partly explain the

TCDD-induced temporal changes in the protein levels of AHR.

1.4) Non-genomic Pathway

The canonical pathway for AHR leads to DNA binding and gene expression changes in AHR target genes. However, the canonical pathway does not fully explain the toxic and physiological responses seen in some cases. Exploring pathways beyond the classical model of AHR signalling can be significant in uncovering the mechanism of toxic effects such as inflammation (Matsumura,

2009). The non-involvement of ARNT in this pathway suggests that the non-genomic pathway can occur without translocation of the ligand-bound AHR to the nucleus for DNA binding (Dong and

Matsumura, 2008). Protein Kinase C (PKC), protein kinase A and Src activation following TCDD treatment were seen in variety of cells (Matsumura, 2009). TCDD was shown to induce production of proinflammatory cytokines and chemokines (Vogel et al., 2007). Activation of the cytosolic phospholipase A2 (cPLA2) and cyclooxygenase-2 (COX-2) by these signalling molecules leads to the activation of the inflammatory pathway (Sciullo et al., 2008). The significance of the nongenomic pathway is further supported by the fact that the occurrence of some toxic endpoints, such as wasting syndrome, were shown to be reduced by the suppression of cPLA2, Cox-2 or Src

(Matsumura, 2009).

1.5) Biological Roles of AHR

AHR is most widely known as a regulator of xenobiotic metabolism. However, new studies suggest that AHR regulates a wide range of other physiological processes including cell proliferation/differentiation, apoptosis, cell cycle control, immune response, fetal development and vasculature development (Section 1.5.3-1.5.5).

1.5.1) Adaptive Response – Xenobiotic Metabolism

The most well-known biological role of AHR is in the regulation of xenobiotic metabolizing enzymes. When a xenobiotic ligand binds to the AHR, it leads to the transcriptional activation of the “AHR gene battery” which is a group of genes that function in metabolizing and detoxifying xenobiotic ligands (Rowlands and Gustafsson, 1997). The “AHR gene battery” include genes encoding Phase I and II enzymes (Nebert et al., 1990). Examples of some of these AHR target genes are CYP1A1, CYP1A2, CYP1B1, glutathione S-transferase (GSTA1), aldehyde-3- dehydrogenase (ALDH3A1), NAD(P)H: quinone oxidoreductase (NQO1) and UDP- glucoronosyltransferase (UGT1A2) (Nebert et al., 2000). Although these detoxifying enzymes are thought to be beneficial, they can act as a double-edge sword in bioactivating a chemical to a more reactive metabolite. An example of this is the metabolism of benzo[a]pyrene (B[a]P) by Phase I enzymes to B[a]P-7,8-epoxide. This metabolite is then metabolized by epoxide hydrolase to

B[a]P-7,8-dihydrodiol which is then metabolized again by Phase I enzymes to B[a]P-7,8- dihydrodiol-9,10-epoxide. The resulting genotoxic metabolite is capable of forming DNA adducts leading to deleterious effects (Grover and Sims, 1968; Jiang et al., 2007). However, the toxic metabolite can be detoxified through conjugation by Phase II enzymes. Further evidence of AHR’s important bioactivation role in B[a]P carcinogenicity was shown in Ahr knockout mouse model

13 where the Ahr-null mice were found to be resistant to B[a]P induced carcinogenesis (Shimizu et al., 2000).

1.5.2) Toxic Response

While the adaptive response is seen as beneficial, the side effect of the adaptive response can be manifested into a toxic response. Both HAHs and PAHs have been shown to induce a toxic response and have carcinogenic potential. Some of the observed toxic endpoints include tumour promotion, teratogenesis, thymic involution, immunosuppression and wasting syndrome (Poland and Knutson, 1982). Although the receptor affinity for TCDD is similar in different species, the toxic potency and endpoints varies significantly among different species. For example, the lethal dose (LD50) for acute TCDD exposure can vary from 1 μg/kg in guinea pig to 5000 μg/kg in hamster (Poland and Knutson, 1982).

Tumour Promotion

TCDD-mediated carcinogenesis is one of the major focuses of AHR-related toxicity due to several findings linking TCDD exposure to cancer (Huff et al., 1994). Chronic dietary exposure of TCDD in female Sprague-Dawley rat resulted in an increased number of hepatocellular carcinomas and squamous cell carcinomas of the lung (Kociba et al., 1978). TCDD was shown to be a potent promoter of hepatocarcinogenesis (Pitot et al., 1980). It is important to note that TCDD is a tumour promoter, not a tumour initiator, since it lacks direct DNA-damaging capabilities (eg. covalent binding to DNA) (Dragan and Schrenk, 2000). Mechanisms proposed for the carcinogenic effects of TCDD include bioactivation of carcinogens by Phase 1 Enzymes (eg. CYP1A1 and CYP1B1)

(Huff et al., 1994), lipid peroxidation induced DNA breaks (Wahba et al., 1988) and transcriptional regulation of cytokines and growth factors (Marlowe and Puga, 2005).

Teratogenesis

TCDD was also found to be a teratogen, capable of inducing cleft palate in mice (Courtney and

Moore, 1971). The mechanism of TCDD-induced cleft palate was proposed to be through the alteration of transforming growth factor β (TGF- β) expression in the palatal epithelial cells

(Abbott et al., 2003). In mice treated with TCDD, palatal shelves grew normally but did not fuse

(Pratt et al., 1984).

Thymic involution

TCDD-induced thymic involution was proposed to be due to the result of decreased response to mitogens in the thymic epithelium (Poland and Glover, 1980).

Immunosuppression

TCDD may also affect the immune system by suppressing B lymphocyte activity and antibody production in response to T cell-dependent and independent antigens (Esser et al., 2009).

Wasting Syndrome

An acute lethal dose of TCDD can result in lethal wasting syndrome. This can be seen by a decrease in gluconeogenesis and food intake. Multiple species of animals treated with a lethal dose developed weight loss with a depletion of adipose tissue. TCDD-induced lethality is not entirely attributable to wasting syndrome since parenterally fed rats prevented weight-loss but not their death (Poland and Knutson, 1982). Although dietary manipulations cannot prevent the lethality due to TCDD in rat, they can potentially postpone or hasten death (Tuomisto et al., 1999).

Human Toxic Endpoints

Epidemiological studies of human exposure through industrial accidents and pesticide provided us with valuable information on the toxic effects of TCDD and dioxin-like compounds in humans.

One of the most characteristic and frequently observed endpoints of high-dose TCDD toxicity in humans is chloracne (May, 1973). Other effects on human health include cardiovascular mortality

(Steenland et al., 1999; Humblet et al., 2008), altered luteinizing and follicle stimulating hormones

(Egeland et al., 1994), endometriosis (Eskenazi et al., 2002), diabetes (Henriksen et al., 1997), immune system disruption (Sweeney and Mocarelli, 2000), and increased risk of cancer (Fingerhut et al., 1991; Steenland et al., 1999; Sweeney and Mocarelli, 2000; Arisawa et al., 2005; Pesatori et al., 2009, Boffetta et al., 2011; Warner et al., 2011). However, some inconsistencies still exist among the results of these studies.

1.5.3) Developmental Role

Early evidence of AHR’s role in development came from studies in invertebrates. In drosophila fruit flies, Spineless, a homolog of AHR, was found to be involved in homeosis, transforming one organ into another (Struhl, 1982; Burgess and Duncan, 1990; McMillan and McGuire, 1992;

Duncan et al., 1998). In C. elegans, the Ahr-1, an ortholog of mammalian AHR, was found to be important to neuronal development (Huang et al., 2004). Spineless, another ortholog, may also be in important in neuronal development (Kim et al., 2006). Studies in vertebrates using Ahr knockout mouse model resulted in more convincing evidence regarding AHR’s involvement in development

(Gonzalez et al., 1995). Abnormal phenotypes observed in these Ahr-null mice include abnormal fetal vascular structures (Lahvis et al., 2000), decreased early body weights (Schmidt et al., 1996), decreased liver size (Fernandez-Salguero et al., 1995; Schmidt et al., 1996; Lahvis et al., 2000), and decreased female reproductive success (Abbott et al., 1999; Lahvis et al., 2000).

1.5.4) Cell Proliferation & Cell Cycle Control

An increasing amount of evidence suggests a role of AHR signalling in cell cycle regulation. AHR- mediated control of cell cycle depends on the absence or presence of an exogenous ligand.

Immediate Early Genes

Early evidence linked AHR to the upregulation of immediate early genes regulating cell proliferation (Schwarz et al., 2000). Immediate-early response genes that are activated by AHR ligand, include V-Myc Avian Myelocytomatosis Viral Oncogene Homolog (MYC) and members of the FBJ Murine Osteosarcoma Viral Oncogene Homolog (FOS) and Jun Proto-oncogene (JUN) families. These genes are important for cell cycle progression and dysregulation of their expression can lead to carcinogenesis, implicating AHR as a tumour promoter. Ligand-mediated AHR activation may lead to the activation of the c-MYC gene in human breast cancer cells in which

AHR and RelA forms a complex which binds to a Nuclear Factor Kappa B (NF-κB) binding element on the c-MYC promoter (Kim et al., 2000). AHR agonists also induce the expression of another group of immediate-early genes, members of the FOS and JUN families. Increase in

Activator Protein 1 (AP-1) DNA-binding activity was observed in multiple live cell types (Puga et al., 1992; Enan et al., 1998), but a decrease in AP-1 expression was found in other cells such as

LPS-activated B cells (Suh et al., 2002). This suggests that AHR-mediated regulation of AP-1 is cell type-dependent.

Ligand-independent Mechanisms

AHR interacts with both cyclin dependent kinases (CDKs) (Barhoover et al., 2010) and retinoblastoma (Rb) (Ge and Elferink, 1998), both of which are important in cell cycle control.

Ahr-null mice displayed hyperproliferation of hair follicles and liver blood vessels, but increased

17 apoptosis in liver tissues (Gonzalez and Fernandez-Salguero, 1998). Mouse embryonic fibroblasts

(MEFs) from Ahr-null mice exhibit decreased proliferation rates, increased apoptosis and accumulation of cells in the G2/M phase (Elizondo et al., 2000). The increased apoptosis may be through the TGF-β1 signalling (Schulte-Hermann et al., 1995). Ahr-null mice exhibited increased levels of plasmin and transglutaminase II enzymes, which are needed in the activation of TGF-β1

(Sutter et al, 1991; Zaher et al., 1998). AHR may also control cell proliferation through a CDK- independent pathway via interaction of AHR with p300 to promote DNA synthesis (Tohkin et al.,

2000). The absence of AHR was shown to prolong the cell cycle in vitro (Ma and Whitlock, 1996).

Knockdown of AHR caused reduced proliferation rates and delayed G1-S phase transition

(Abdelrahim et al., 2003). Direct interaction of AHR with Rb may lead to increased cell cycle progression by promoting the phosphorylation of Rb by CDK4/cyclin D1(CCND1) complex (Puga et al., 2000; Elferink et al., 2001). Generally, in the absence of exogenous ligand, AHR was shown function endogenously by promoting cell cycle progression and cell proliferation.

Ligand-dependent Mechanisms

Exogenous AHR ligands, such as TCDD, were shown to inhibit cell proliferation but its mechanism is unclear (Puga et al., 2002). TCDD-induced AHR activation was found to decrease proliferation rates (Gierthy and Crane, 1984), inhibit DNA synthesis (Hushka and Greenlee, 1995), block S phase progression (Laiosa et al., 2003) and trigger G1 phase arrest (Laiosa et al., 2003;

Jin et al., 2004). Another proposed mechanism of TCDD-induced AHR-mediated cell cycle arrest is through the interaction between AHR and Rb (Ge and Elferink, 1998). The TCDD-induced G1 arrest was found to be dependent on ARNT (Huang and Elferink, 2005), suggesting the cell cycle arrest may be partially attributable to the binding of AHR to E2F-dependent genes. Chromatin immunoprecipitation (ChIP) assay showed that TCDD-induced AHR recruitment to E2F-

18 dependent promoters led to the displacement of p300 coregulator protein from these promoters and repression of S phase-specific genes (Marlowe et al., 2004). Additionally, TCDD-bound AHR was found to be incapable of interacting with CDK4, leading to reduction in the phosphorylation of Rb, which in turn leads to G1 arrest (Barhoover et al., 2010). In summary, through protein- protein interaction and competitive DNA binding, ligand-activated AHR inhibits cell cycle progression. Although ligand-activated AHR was shown to prevent cell proliferation and can possibly be anti-carcinogenic, AHR can be a tumour promoter through inhibition of apoptosis

(Dragan and Schrenk, 2000). Inhibition of apoptosis by AHR was proposed to be through inhibition of p53, a tumor suppressor protein (Worner and Schrenk, 1996; Paajarvi et al., 2005).

Another mechanism is through AHR binding to E2F1 and inhibiting E2F1-induced apoptosis

(Marlowe et al., 2008). Overall, whether ligand-activated AHR has both a proliferative and inhibitory effect on cell cycle regulation ultimately depend on multiple factors including the type of cells, the ligand used in the treatment and the gene expression patterns.

1.5.5) Role in the Immune System

Growing amounts of evidence suggest that AHR also plays a role in the immune system. Ahr-null mice exhibit reduced number of lymphocytes in the spleens and lymph nodes (Fernandez-Salguero et al., 1995). AHR was shown to be required for optimal immune response against Listeria monocytogenes infection (Shi et al., 2007) and for enhanced neutrophil recruitment during influenza virus infection (Teske et al., 2008). In rodent models, TCDD treatment led to thymus involution, increased susceptibility to infection, and suppression of both humoral and cell- mediated immune responses (Vos et al., 1973; Holsapple et al., 1991). The immunosuppression by AHR ligands correlated with their binding affinity for AHR suggesting that this immunosuppression is indeed AHR-dependent (Vecchi et al., 1983).

A recent study discovered that AHR also affects regulatory T cells (Tregs), an important component of the regulation of the immune system. Ligand-activated AHR was shown to promote

Treg differentiation by upregulating transcription factor FoxP3, enhancing TGFβ signalling or indirectly though dendritic cells (Quintana et al., 2008). AHR also plays a role in T helper 17 cells

(Th17) development. While TCDD induced Treg development, FICZ interfered with Treg development and boosted Th17 differentiation. This suggests that the regulation of Treg and Th17 by AHR is dependent on the specific ligand (Quintana et al., 2008). Immunogenicity of dendritic cells was also shown to be negatively regulated by AHR (Nguyen et al., 2010). Recently, a possible role of AHR in the development of plasmacytoid dendritic cells was also uncovered (Liu et al.,

2014).

1.6) Negative Regulation of AHR Signalling

There are four proposed mechanisms for the negative regulation of AHR signalling. The first one is through the ubiquitin-proteasome pathway, which serves as a mechanism to control the activity of activated AHR (Roberts and Whitelaw, 1999; Ma and Baldwin, 2000). Inhibitors of the 26S proteasome were shown to block the TCDD-induced turnover of AHR, suggesting that the degradation is mediated by the 26S proteasome. Inhibition of the proteasomal degradation of AHR led to an increase in the level of AHR-ARNT complex bound to the DNA, sustained level of AHR in the nucleus and superinduction of the CYP1A1 gene by TCDD. Cycloheximide treatment blocked the degradation implicating a labile factor controlling the stability of ligand-activated

AHR (Ma and Baldwin, 2002). The rapid turnover of AHR following its activation is one of the regulatory mechanisms for AHR signalling.

The second pathway is through the aryl hydrocarbon receptor repressor (AHRR) (Mimura et al.,

1999). The AHRR-mediated repression of AHR activity is explained in detail in later sections of this chapter (See Chapter 1, Section 2).

The third pathway is through the TCDD-inducible poly(ADP-ribose) polymerase (TIPARP), which is an AHR target gene. Research from our laboratory has provided insights into the TIPARP- mediated repression of AHR. TIPARP was shown to function as a transcriptional repressor of

AHR (MacPherson et al., 2013). Knockdown of TIPARP showed a decreased AHR protein degradation and enhanced expression of AHR target genes such as CYP1A1 and CYP1B1

(MacPherson et al., 2013). TIPARP-mediated repression of AHR differs from that of AHRR in that TIPARP led to changes in the AHR protein levels (MacPherson et al., 2014). Based on these evidence, it was hypothesized that TIPARP directly interacts with AHR by ADP-ribosylating it and targeting it for proteosomal degradation (MacPherson et al., 2013; MacPherson et al., 2014).

Lastly, AHR antagonists negatively regulate AHR activity. Several flavonoids act as an AHR antagonist including kaempferol, quercetin, myricetin and luteolin. Resveratrol was also found to have antagonist activity on the AHR and was able to block AHR ligand-mediated increases in

CYP1A1 and Il-1β expression (Casper et al., 1999).

2) Aryl Hydrocarbon Receptor Repressor (AHRR)

2.1) Discovery

The aryl hydrocarbon receptor repressor (AHRR) is a bHLH-PAS protein that was discovered due to its structural similarity to AHR (Mimura et al., 1999). The group isolated mouse cDNA clones that encode a polypeptide with high similarity to bHLH/PAS proteins, especially AHR. In transient transfection experiment in mammalian cells, AHRR was found to inhibit AHR-dependent transactivation of a luciferase reporter gene and, therefore, it was named the “AHR repressor”

(Mimura et al., 1999). AHRR was established as an AHR target gene that contained an AHRE found to be functional as an inducible enhancer (Mimura et al., 1999). AHRR was then cloned in other species, including human (Watanabe et al., 2001), rat (Korkalainen et al., 2004) and different species of fish (Karchner et al., 2002; Evans et al., 2005).

2.2) Gene Structure

The AHRR gene is located in chromosome 13 in mouse and chromosome 5 in human. The human

AHRR gene contains 12 exons (1st exon is non-coding), resulting in a protein with 715 amino acids. The regulatory region upstream of the transcriptional start site does not have a TATA box and contains three AHREs, one NF-κB site and three GC boxes (Baba et al., 2001). These regulatory sequences are located within intron 1 (Cauchi et al., 2003).

2.3) Protein Structure

Structurally, AHRR is very similar with AHR showing high amino acid similarity in the N- terminal end of the protein where the bHLH and PAS A domains are located. In the structural comparison between AHR and AHRR, bHLH and PAS A domains were shown to have around

80% and 60% amino acid similarity respectively (Mimura et al., 1999) (Figure 4). Considering the roles of bHLH and PAS A domains in DNA binding and heterodimerization with ARNT, AHRR with such highly identical N-terminal domains was found to also heterodimerize with ARNT and bind to AHREs. In the study that first characterized AHRR, it was proposed that AHRR-mediated

AHR repression is through the competition between AHR and AHRR for binding to ARNT, as well as the competition of the AHRR-ARNT complex against AHR-ARNT complex for binding to the AHREs (Mimura et al., 1999). Despite the similarity in the N-terminal region, the structure of AHRR differs from AHR and ARNT towards the C-terminal direction. AHRR lacks the PAS B and Q-rich domains. Furthermore, AHRR does not have a ligand-binding domain and appears to act in a ligand-independent manner (Mimura et al., 1999; Baba et al., 2001).

bHLH PAS Transactivation A B AHR Domain

848

80% 60%

bHLH PAS

A AHRR

695

Figure 4. The structural organization of AHR and AHRR. The amino acid homology at the bHLH domain and PAS A for AHR and AHRR is 80% and 60% respectively. The structure of the two proteins differs increasingly towards the carboxyl terminal.

2.4) AHRR Expression

2.4.1) Constitutive Expression

Constitutive expression of AHRR mRNA was studied in human and animal tissues. Compared to

AHR, AHRR seemed to be more tissue-specific. In human adult tissues, the highest level of AHRR mRNA, in descending order of magnitude, was found in the testis, lung, ovary and spleen

(Tsuchiya et al., 2003; Yamamoto et al., 2004). In rat tissues, AHRR expression was highest in the heart and testes and relatively low in other tissues (Korkalainen et al., 2004). Female rats have lower constitutive AHRR expression than male rat, suggesting a gender-specific difference in

AHRR expression (Nishihashi et al., 2006). In mice, the high AHRR expression was found in the heart and brain (Bernshausen et al., 2006).

Interestingly, in some tissues with high AHR level (eg. liver), AHRR expression was low and could be detected only after induction with strong AHR ligands (Mimura et al., 1999; Korkalainen et al., 2004; Bernshausen et al., 2006). Another notable observation was the decrease in the

CYP1A1 expression in some tissue with high constitutive AHRR expression such as testis and ovary (Yamamoto et al., 2004). It was suggested that the high AHRR level may be protecting sensitive organs from CYP1A1-mediated generation of toxic metabolites (Haarmann-Stemmann and Abel, 2006).

AHRR expression in human cell lines was also examined. Constitutive AHRR expression was high in primary human fibroblasts, HeLa, and OMC-3 cells, whereas it was low in ACHN, A549, HT-

1197 and NEC14 cells (Tsuchiya et al., 2003). It was reported that in human tissues and cell lines, the predominant active form of AHRR discovered (AHRRΔ8) lacks exon 8 of the human AHRR ortholog (AHRR715) (Karchner et al., 2009).

2.4.2) Ligand-induced Expression

Inducibility of AHRR was tested on mouse and rat animal models using different AHR ligands.

Administration of 100 μg/kg TCDD to two strains of rats with different TCDD sensitivity resulted in peak AHRR mRNA level after 1 day of treatment. TCDD-induced AHRR expression increase was found in the kidney, spleen and heart but the induction profile of AHRR did not parallel that of CYP1A1. No observed differences in the two strains suggest that AHRR is not a significant contributing factor to TCDD sensitivity but may contribute to tissue-specific responsiveness

(Korkalainen et al., 2004).

Treatment of 10 mg/kg of B[a]P in mice resulted in significantly increased AHRR expression in liver, lung, spleen and ovary. However, tissues with the highest constitutive AHRR expression levels did not see a significant increase, suggesting that AHR ligand-inducibility of AHRR expression is tissue-specific.

Treatment with TCDD and 3-MC did not enhance AHRR expression in human cell lines with low

AHRR expression. Cell lines shown to exhibit dose-dependent ligand-induced increases in AHRR expression include the MCF-7, HepG2, LS-180 and OMC-3 (Tsuchiya et al., 2003). In MCF-7 breast cancer cells, the mRNA level of AHRR peaked after 2h of TCDD treatment and the AHRR protein levels were only detected after 24 h of treatment (MacPherson et al., 2014). As demonstrated in ChIP assays, AHR recruitment to AHR target genes was found to peak at 45-60 min after TCDD treatment in mouse hepatic tissue (Lo et al., 2011). This suggests that, for ligand- induced AHRR expression, there is a lag period between AHR recruitment to AHRR and the subsequent increase in AHRR protein level.

2.5) Repression of AHR Transactivation

Localization of AHRR to the nucleus suggests that it might have a role in either regulating transcription or DNA replication (Mimura et al., 1999). The structural similarity of AHRR and

AHR led to the proposed function of AHRR as a negative regulator of AHR signalling. Using co- immunoprecipitation (co-ip) assay, AHRR coimmunoprecipitated efficiently with anti-ARNT antibody, suggesting ARNT as a heterodimer partner for AHRR. Association of AHRR with

ARNT neither required nor was enhanced by AHR ligand (Mimura et al., 1999). Transfection using AHRR expression vector showed dose-dependent repressive effect of AHRR on both the basal and 3-MC-induced AHRE-driven luciferase activity (Mimura et al., 1999). Several other studies using reporter constructs in transfected cells demonstrated strong evidence for AHRR as a transcriptional repressor of AHR (Karchner et al., 2002; Evans et al., 2005; Oshima et al., 2007).

Overexpression of AHRR also reduced the expression of known AHR target genes such as

CYP1A1 (Mimura et al., 1999; Haarmann-Stemmann et al., 2007) and c-MYC (Yang et al., 2005).

These studies led to the conclusion that AHRR is a ligand-independent nuclear repressor of AHR transcription.

The exact mechanism of AHR transactivation by AHRR is still unclear. It is proposed that AHRR competes with AHR for binding to ARNT and competition between AHR-ARNT and AHRR-

ARNT complexes for binding to the DNA. Since AHRR is an AHR target gene, this creates a negative feedback loop regulation (Figure 5). The molecular mechanism of the transcriptional inhibitory activity of AHRR is found to be through the recruitment of co-repressors: ankyrin-repeat protein 2 (ANKRA2), histone deacetylase 4 (HDAC4) and HDAC5 (Oshima et al., 2007). The inhibitory activity of AHRR was shown to be sensitive to HDAC inhibitors, such as Trichostatin

A, suggesting that AHRR binds to an AHRE and recruits HDAC proteins to the site. HDAC

26 proteins are known to mediate transcriptional repression by removing the acetyl group from histones leading to a decrease in the space between the nucleosome and DNA around it, which decreases the accessibility for the transcription factors necessary to mediate transcription (De

Ruijter et al., 2003). Small ubiquitin modification (SUMOylation) of AHRR and ARNT was also found to be important for efficient transcriptional repression (Oshima et al., 2009). For AHRR,

SUMOylation usually increases the transcriptional repressor activity and is required for the interaction between AHRR and the corepressors, ANKRA2, HDAC4 and HDAC5 (Oshima et al.,

2009). ARNT with AHRR, but not AHR, mutually enhances SUMOylation of the partner which may enhance the repression activity of the AHRR-ARNT heterodimer.

There are, however, new studies refuting the simple model of AHRR-mediated repression proposed by Mimura et al., (1999) and suggesting that the mechanism is more complex than was originally proposed. To examine this competition mechanism, one group used overexpression of

ARNT and a DNA-binding mutant of AHRR (Evans et al., 2008). However, overexpression of

ARNT did not reverse the AHRR dependent repression of AHR, suggesting that ARNT is not a target for AHRR (Evans et al., 2008). The repressive ability of the AHRR DNA-binding mutant were only slightly affected, suggesting that the competition for AHREs does not account for all of the repression (Evans et al., 2008). It was suggested that AHRR-mediated repression of AHR is through a protein-protein interaction, rather than AHRR directly competing with AHR for DNA binding. In rats, the CYP1A1 and AHRR expression were not always inversely correlated

(Bernshausen et al., 2006). Furthermore, CYP1A1 superinduction in AHRR-null mice was tissue specific (Hosoya et al., 2008). Together, these studies suggest that the repression is tissue-specific.

In primary human skin fibroblasts, where AHRR is supposedly overexpressed, the repressed CYP1 activity was originally thought to be due to AHRR expression (Tigges et al., 2013). In this study,

27 the use of trichostatin A to inhibit the histone deacetylase action led to higher mRNA CYP1A1 levels but not CYP1 activity. Using AHRR-/- mouse embryoinic fibroblasts (MEFs), despite the large increase in mRNA, the CYP1A enzyme activity was not significantly induced (Tigges et al.,

2013). Also, observations from a previous study in our lab showed that siRNA-mediated knockdown of AHRR in MCF-7 cells did not increase TCDD-induced CYP1A1 mRNA levels, suggesting a cell-type dependent difference (MacPherson et al., 2014). However, it is possible that the time-point in which the mRNA levels were examined may not be optimal or that the knockdown may not be sufficient to attenuate the repression action of AHRR. Although it is generally accepted that AHRR is a negative regulator of AHR transcriptional activity, new studies are suggesting a broader role for AHRR that is yet to be uncovered.

AHRE AHRR Gene TCDD 2

AHR AHRR 1 ARNT 3

AHR ARNT AHRR ARNT 4

AHRE

Figure 5. The current proposed model of AHRR-mediated repression of AHR transactivation as proposed by Mimura et al. (1999). (1) When TCDD induces the translocation of AHR to the nucleus, it heterodimerizes with ARNT to bind to the AHRE. (2) Since AHRR is an AHR target gene, the protein level of AHRR increases. (3) AHRR then competes with AHR for binding to the heterodimerization partner. (4) Subsequently, the AHRR-ARNT complex competes against the AHR-ARNT complex in binding to the AHRE, effectively repressing the AHR-mediated gene expression changes.

2.6) Biological Roles

2.6.1) Embryonic Development

AHRR expression in fish and amphibian embryos suggests a possible development role (Evans et al., 2005; Roy et al., 2006; Zimmermann et al., 2008). Knockdown of an AHRR paralog in zebrafish resulted in developmental abnormalities (Jenny et al., 2009). However, in AHRR knockout mice, the embryonic development appears to be normal (Hosoya et al., 2008). This suggests that the developmental role of AHRR may be species-specific and possibly an evolutionary adaption for some fish inhabiting highly contaminated environments (Hahn et al.,

2009).

2.6.2) Reproductive Disorders

Another potential role of AHRR is in human reproduction. Since AHR signalling was found to play a possible role in reproduction, AHRR, being a regulator of AHR, may also be involved.

Although there were inconsistencies for AHRR-linked female reproductive abnormalities in the literature, some studies have found some interesting results. One study reported no association between AHR polymorphism and occurrence of uterine endometriosis (Watanabe et al., 2001), while two other studies did (Tsuchiya et al., 2005; Kim et al., 2007). On the other hand, the male reproductive defects were also linked to AHRR polymorphism including micropenis (Fujita et al.,

2002) and male infertility (Watanabe et al., 2004; Merisalu et al., 2006).

2.6.3) Cancer

There have been several studies suggesting AHR’s role in carcinogenesis. AHR was shown to be tumour promoter (Pitot et al., 1980; Huff et al., 1994; Dragan and Schrenk, 2000; Marlowe and

Puga, 2005). Taking this into consideration, AHRR, a putative repressor of AHR, may have tumour

29 suppressing capabilities. Transfection of AHRR into MCF-7 breast cancer cells led to a slower growth rate and a decreased expression of important tumor-related genes: E2F, cyclin E1 and

PCNA (Kanno et al., 2006). A follow-up study suggested that the AHRR-mediated growth inhibition in MCF-7 cells may be partly due to a direct interaction between AHRR and ERα

(Kanno et al., 2008). Inhibition of breast cancer cell growth by AHRR was also seen in ERα- negative breast cancer cells, MCF-10F (Schlezinger et al., 2006). Later studies revealed that AHRR acts as a tumor suppressor gene in multiple types of cancers (Zudaire et al., 2008). The chromosomal region containing AHRR is frequently deleted in several types of human cancers

(Zudaire et al., 2008). AHRR promoter is also hypermethylated in several different tumour cell lines and tumours from different organs, resulting in decreased AHRR expression when compared to normal tissue. Furthermore, AHRR knockdown with siRNA increases growth and invasiveness of human lung cancer cells and anchorage-independent growth of normal non-malignant human mammary epithelial cells (Zudaire et al., 2008). Although it has been proposed that the tumour- suppressing function of AHRR may be due to its inhibition on AHR’s tumorigenic potential, it is possible that the reverse may also be true. In AHR-null mice, AHRR expression was shown to be increased in spleen and thymus, implying that AHR may even repress AHRR expression in some cases (Bernshausen et al., 2006). This leads to an intriguing possibility that AHR tumorigenic potential could be related to a lower expression of AHRR.

Chapter 2: Study Rationale and Research Objectives

1) Study Rationale

Although AHR signalling has been extensively studied, the regulation of AHR is still not well understood. AHR is an important receptor in toxicology due to its ability to mediate the toxicity of chemical compounds, both natural and synthetic. Several studies have also suggested that AHR plays a role in a variety of biological processes including development, cell cycle control, and the immune system. As a result, further understanding in the regulation of AHR signalling is of great importance. One of the current models for the regulation of AHR signalling is repression through

AHRR resulting in a simple negative feedback loop through the competition of AHRR for ARNT to form a complex which subsequently competes for AHR-ARNT binding sites (Mimura et al.,

1999). Genome-wide mapping of AHR and ARNT binding regions has already been documented in previous ChIP-seq studies (Lo and Matthews, 2012). However, genome-wide mapping of

AHRR binding regions has yet to be performed. According to the proposed model by Mimura et al. (1999), AHR and AHRR should be expected to share most, if not all, binding regions. This study sought to reevaluate the model by performing high resolution genome-wide mapping of

AHR and AHRR binding regions and comparing the binding patterns between the two transcription factors. Several previous studies have shown inconsistencies in which AHRR represses AHR, implicating clear limitations for such repression (Bernshausen et al., 2006; Evans et al., 2008; Hosoya et al., 2008; Tigges et al., 2013; MacPherson et al., 2014). Therefore, a goal of this thesis is to uncover certain limitations in the binding competition/interaction-based repression model and open up the possibility of exploring AHR-independent roles of AHRR in the biological system. Although AHRR is known to bind to AHREs in promoter regions of classical

AHR target genes (Mimura et al., 1999), there has not been any study to support this on a genome- wide scale. To address these questions, chromatin immunoprecipitation sequencing was performed for both AHR and AHRR to characterize and compare their DNA binding profiles. MCF-7 cell line was chose as the model as a follow-up to a previous study in our lab that has already characterized the AHRR induction profiles and the specificity of the anti-AHRR antibody in this particular cell line (MacPherson et al., 2014). Computational analysis of these binding regions can provide insights into the extent in which common binding, as well as unique binding, occur between the two transcription factors. Results from this work can increase our understanding of

AHR signalling and provide new insight into the mechanisms of AHRR activity.

2) Research Aims:

2.1) Perform a genome-wide mapping and comparative analysis of AHR and AHRR binding regions using ChIP-Seq.

Genome-wide mapping of AHR and AHRR binding sites were performed using ChIP-seq and compared using several computational methods. Analysis of the binding sites provided valuable information on where the proteins interact on the genome. Using a series of analysis programs, key features of the genomic interaction were also elucidated. Since no studies have produced a genome-wide mapping of AHRR binding sites, these data provide valuable insights into differences and similarities between the genomic binding profiles of AHR and AHRR.

2.2) Determine and validate the unique AHR-bound and AHRR-bound regions.

From the overlap analysis of the ChIP-seq data, a list of unique binding sites for AHR and AHRR was determined. Although ChIP-seq data are usually very sensitive, binding sites derived from the data needs to be validated by performing a ChIP-quantitative polymerase chain reaction (ChIP-

32 qPCR) using appropriate primers and ChIP samples. This ensures that these binding sites are truly unique sites and not an artifact of the computational process. Further genomic analysis of these overlapping and unique regions would provide further insights into AHR and AHRR interactions.

2.3) Determine whether AHRR can regulate gene expression independently of AHR and vice-versa.

Recruitment of AHR or AHRR to a region detected by ChIP-seq does not necessarily constitute regulation of the closest associated gene. Therefore, performing a reporter gene assay is fundamental in connecting the changes in recruitment of the proteins to the DNA and subsequent changes in transcriptional activity of a closest associated gene. This would also provide insight into whether AHRR can regulate gene expression independently of AHR and vice-versa.

Chapter 3: Materials and Methods

1) Materials

1.1) Chemicals and Biological Agents

All cell lines used (MCF-7, COS-1) were cultured in a mixture of Dulbecco’s Modified Eagle’s

Medium (DMEM; Sigma-Aldrich, St. Louis, MO, USA) with 1g/L of glucose, fetal bovine serum

(FBS; Sigma-Aldrich) and penicillin/streptomycin antibiotic mixture (PEST; Sigma-Aldrich)

(Referred to as DMEM complete media in later sections). Dextran coated charcoal stripped FBS

(DCC-FBS) was made in-house using DCC (C6241; Sigma-Aldrich). Another media used is the

DMEM phenol red free media supplemented with DDC-FBS, PEST and GlutaMAX (Life

Technologies, Carlsbad, CA, USA). Phosphate buffered saline (PBS) for cell culturing was purchased from Sigma-Aldrich.

Chemicals used in the experiments include dimethyl sulfoxide (DMSO; Sigma Aldrich) and

2,3,7,8-tetrachlorodibenzo-ρ-dioxin (TCDD; Accustandard, New Haven, CT, USA).

Antibodies used for ChIP-seq include: AHR (H-211; Santa Cruz Biotechnology, Dallas, Texas,

USA), AHRR (HPA019614; Sigma-Aldrich) and normal rabbit immunoglobin (sc-2027; Santa

Cruz). Protein A-Agarose Fast Flow (Invitrogen, Life Technologies, Carlsbad, CA, USA) was used for ChIP-seq experiments. Bovine Serum Albumin (BSA) solution was made from powder purchased from Sigma-Aldrich. Purification of PCR products for ChIP-seq experiments were performed using Buffer PB (Qiagen, Venlo, Limberg, Netherlands) and EZ-10 Spin Column PCR

Purification Kit (Bio Basic, Markham, ON, CAN). SsoFast EvaGreen Supermix (Bio-Rad) was used for all quantitative real-time PCR reactions. The pfu-Turbo polymerase (Agilent, Santa Clara,

CA, USA), restriction enzymes (New England Biolabs, Ipswich, MA, USA), T4 DNA ligase (New

England Biolabs) was used for cloning and creation of the reporter gene constructs. For the isolation of reporter gene constructs, DNA miniprep kit (Geneaid, New Taipei City, Taiwan) and maxiprep kit (Qiagen) were used. All primers used for real-time PCR and the design of reporter gene constructs were synthesized by Integrated DNA Technologies (IDT, SickKids, Toronto, ON,

CAN). Lipofectamine 2000 transfection (Invitrogen) and Opti-MEM (Gibco) were used in transfection. The ONE-Glo luciferase assay system was used to detect firefly luciferase reporter gene expression (Promega, Madison, WI, USA).

1.2) Plasticware

Plasticware used for cell culture was purchased from Sartedt (Numbrecht, Germany) including the

T-25, T-75, and T-175 tissue culture flasks. The 12-well and 96-well clear flat bottom plates were purchased form BD Biosciences-Falcon. The 96-wel black flat bottom plates used for luciferase assays were purchased from Corning. The 1.5mL microcentrifuge tubes (Axygen, Corning Inc.,

Corning, NY, USA) and 1.7 mL low-binding microcentrifuge tubes (Progene) were purchased from Ultident Scientific. 96-well non-skirted PCR plates (BIOplastics, Landgraaf, Netherlands) were purchased from D-Mark Biosciences (Toronto, ON, CAN).

1.3) Instruments

For cell culture, HERAcell 150 incubators were used. Centrifugation were done with Centrifuge

5415D (Eppendorf, Hamburg, Germany) and Centrifuge 5702 (Eppendorf). Cell counting was done on the TC20 Automated Cell Counter (Bio-Rad, Hercules, CA, USA). Sonication for ChIP- seq was done on Bioruptor® (Diagenode, Denville, NJ, USA). The rotator used for ChIP-seq experiment was from Mini LabRoller. Real-time PCR amplification of ChIP DNA and designing

35 reporter gene construct was performed on Chromo4 Real-time PCR detector (Bio-Rad, Hercules,

CA, USA). B-galactosidase levels were determine using a ThermoScientific 96 well Multiskan EX

Photometer. Luciferase levels from reporter gene constructs were measured using GLO-max 96- well microplate luminometer (Promega). ChIP samples were sequenced using HiSeq 2000

(Illumina, San Diego, CA, USA) sequencer.

2) Methods

2.1) Maintenance of Cell Lines

MCF-7

MCF-7 is an immortalized human breast adenocarcinoma cell line purchased from ATCC (HTB-

22; Manassas, VA, USA). 1 million cells were suspended in 1 mL of 90% FBS/10% DMSO mixture and placed in liquid nitrogen for long-term storage. To initiate the growth of cells, they were thawed quickly at 37°C and transferred into T-25 coated tissue culture flask with 1:1 mixture of DMEM (1g/mL glucose) supplemented with 10% FBS and 1% PEST. Cells were subcultured

1:3 every 2-3 days or upon reaching 90% confluence. The steps included aspirating media, rinsing with PBS to wash residual media, adding 2 mL of trypsin. After trypsinization of the cell for 2-3 minutes (needs to be monitored carefully), the complete media was added to neutralize the trypsin and appropriate volume of cells was transferred to a new T-175 culture flask with new 25mL of complete media. Cells were placed in incubator set at 37°C and 5% CO2.

COS-1

COS-1 is an immortalized fibroblast-like cell line derived fronm African Green monkey kidney tissue. The COS-1 cells were cryopreserved and propagated using the same method as MCF-7 cells. Cells were subcultured 1:5 or 1:6 every 2-3 days or upon reaching 90% confluence by the same methods described earlier with an exception of a longer trypsinization period of 3-5 minutes.

2.2) Chromatin-immunoprecipitation Sequencing (ChIP-Seq)

2.2.1) Seeding & Treatment

MCF-7 cells were seeded at a density of 3 million cells per 10 cm dish containing 10 mL of complete media. After 24 h, the media of every dish was changed to a phenol-red free DMEM media. After another 24 h, the cells were treated with either 10 μl of DMSO (vehicle control) or

10 nM of TCDD for 24 h. Since the protein level of AHRR was undetectable in MCF-7 cells until

24 h after TCDD treatment in a previous study in our lab, the 24 h time-point was chosen to ensure that the protein level was sufficient for the recruitment to the DNA to be captured in the ChIP-seq process (MacPherson et al., 2014).

2.2.2) Cross-linking

After treatment, the protein-chromatin complexes were cross-linked with 1% formaldehyde

(Sigma-Aldrich) at room temperature for 10 min with constant rocking. Then, the cross-link was quenched with 125 mM glycine for 5 min with constant rocking. The media was aspirated and the cells were washed twice with PBS. 750 μL of PBS/0.1% Tween 20 mixture was added to each dish and cells were scraped and pelleted by centrifugation for 3 min at 10000 rpm at 4°C.

2.2.3) Sonication

The following steps should be done at 4°C or on ice at room temperature. The supernatant was resuspended in 500 μL of TSE I buffer (50 mM Tris-Base [pH 8.0], 150 mM of NaCl, 1 mM

EDTA, 1% Triton X-100) along with 1X protease inhibitor cocktail (PIC, Sigma Aldrich). Cells were then sonicated to chromatin fragments approximately 200 - 400 bp in length. Sonication was done at 4°C in the Bioruptor® sonicator at high intensity with 30s on/ 30s off cycles for 15 min and repeated again for 15 min. Solubilized chromatin was then separated from cellular debris by centrifugation at 13000 rpm for 10 min at 4°C. The supernatant was transferred to a new low- binding microcentrifuge tube.

2.2.4) Immunoprecipitation

30 μL of 50% slurry of Protein-A Agarose beads was added to the chromatin and incubated at 4°C for 2 h to remove non-specific binding. The agarose beads were pelleted by centrifugation and 150

μL chromatin was aliquoted into low-binding microcentrifuge tubes containing 20 μL BSA. 4 μg of IgG, AHR (Santa Cruz, H-211) or AHRR (Sigma-Aldrich, HPA019614) antibody was added to each tube and the fragmented chromatin was immunoprecipitated overnight at 4°C with constant rotation. Protein-A Agarose beads were also pre-blocked for 24 h with BSA to prevent non-specific binding to beads. The next day, 30 μL of the pre-blocked beads was added to each sample followed by incubation on a rotator for 2 h at 4°C. This step allowed the antibody-protein-DNA complexes to bind to the beads which could be isolated at later steps.

2.2.5) ChIP Washes

The Protein A beads were pelleted at 5000 rpm and washed with buffers for 10 min each. The beads were washed with 1 mL of different buffers of the following order: 3 times with TSEI, 2

38 times with TSEII (20 mM Tris-base, 500 mM NaCl, 2 mM EDTA, 1% Triton X-100, 0.1% SDS),

2 times with LiCl (Tris-Base, 250 mM LiCl, 1 mM EDTA, 1% NP-40, 1% sodium-deoxycholate) and 2 times with TE (10 mM Tris-Base, 1 mM EDTA). After the final wash, the protein-DNA complexes were eluted in 110 μL of elution buffer (TE + 1% SDS) for 30 min at room temperature on rotator. Then, cross-links were reversed by overnight incubation at 64°C.

2.2.6) Purification & qPCR

The next morning, eluted DNA was bound using 5X volume of Buffer PB (Qiagen) as the binding buffer (instead of the one in the Bio Basic kit) and purified using EZ-10 Spin Column PCR

Purification Kit (Bio Basic). During the final step, the DNA was eluted using 50 μL of elution buffer. ChIP DNA was quantified by qPCR with primers for the enhancer regions of known AHR and AHRR target genes: CYP1A1 and CYP1B1. This step was implemented to confirm the quality of the ChIP samples. 6 replicates of ChIP samples were acquired from multiple rounds of ChIP experiments and best 3 replicates were further processed in preparation for ChIP-sequencing.

2.2.7) Library Preparation

After the best 3 replicates of ChIP samples were chosen, library preparation for the sample was performed next. Using MicroPlex Library Preparation Kit (Diagenode), appropriate amount of reaction materials were added according to the manufacturer’s instructions.

2.2.8) Size-selection

After amplification, the samples were loaded on a gel, and the desired bands of 300-500 bp in size were cut out and extracted using Gel Extraction Kit. The samples were sent to TCAG Agilent 2200

TapeStation to assess for sample quality and concentration. To ensure unbiased amplification, real- time PCR was also performed on the samples.

2.3) Analysis of ChIP-Seq

2.3.1) Processing Raw Reads & Mapping to Human Genome

The ChIP samples were sequenced at TCAG (SickKids, Toronto). The Illumina-sequencing raw reads were output as FASTQ files. Quality control of FASTQ files were done by performing quality-trimming and removal of adapters. FASTQ files are large data files containing all the DNA sequences from ChIP samples. For downstream analysis, they need to be organized and mapped to a certain location on the genome. FASTQ files were mapped to the human genome assembly

(hg19 version) using Bowtie2 (Langmead and Salzberg, 2012). This resulted in 3 BAM files representing the 3 biological replicates for each control/treatment group.

2.3.2) Peak Calling

ChIP sample replicates (in BAM format) were merged using SAMTools (Li et al., 2009). There are no standardized method of analyzing ChIP-seq biological replicates, but the most common method is by pooling the replicates into one. An essential step in the ChIP-seq analysis process is the peak-calling step. Peak-calling is required for finding the regions that are changed between treatment and control with statistical significance. Therefore, a peak is essentially an identified binding region in which fragments of DNA mapped are significantly higher in the treatment sample than in the control sample. Peak-calling was performed using the pooled BAM files as input for Model-based Analysis for ChIP-Seq (MACS2) program using default settings (Zhang et al., 2008). The q-value cut-off for peak-calling was set at 0.05. For the peak-calling of AHR and

AHRR, the pooled TCDD-treated sample data was inputted as the treatment and the pooled

DMSO-treated sample data was inputted as the control. The peak-called results were outputted as

BED format, a common format for downstream analysis programs. Although my current study

40 focused on the 24 h time points, the 45 min TCDD-treated AHR ChIP-seq (performed by a previous member of the lab) data was also analyzed and included in the overlap comparison analysis. In order to remove the artifact signals (regions with high ChIP signals such as near centromeres, telomeres, satellite repeats) (Carroll et al., 2014), the ENCODE consortia blacklisted regions (ENCODE Project Consortium, 2012) were filtered out using BEDTools (Quinlan and

Hall, 2010). All of the ChIP-seq data were subjected to the same peak-calling and filtering process.

Other peak-calling algorithms were also used for validation (data not shown) including HOMER

(Heinz et al., 2010) and CisGenome (Ji et al., 2008), giving different number of peaks yet similar overall binding patterns. For the purpose of this study, only data generated by MACS were used in all further downstream analysis.

2.3.3) Visualization of Peaks

The peak-caller program also generated a bedgraph (BDG) file containing ChIP signal intensities that can be inputted in some programs for visualization purposes. The main tool used for visualization of peaks was the Integrative Genomic Viewer (IGV) (Thorvaldsdóttir et al., 2012).

It was also used to search and visualize motifs in conjunction with the location of the binding regions and genes.

2.3.4) Overlap of Peak Regions and Gene Annotation

Overlap analysis and manipulation of the BED files were done using BEDTools (Quinlan and Hall,

2010). The minimum overlap was set as default at one base pair, to ensure that overlaps would not be missed. Although fractional overlap could be implemented, it may result in a loss of overlaps due to differences in length of peak regions. The Hypergeometric Optimization of Motif

EnRichment (HOMER) Analysis Suite was used for peak annotations of genomic features (Heinz

41 et al., 2010). The peaks were annotated to the closest transcription start site (TSS) of a gene. The list of genes were also used in comparison as a stricter way of identifying unique regions. Regions in uniquely bound genes would most likely be true unique regions. Distance of peaks to the TSS for every set of data were graphed as histograms and peak features were displayed as pie graphs.

Co-bound regions were determined by overlapping 24 h TCDD-induced AHR-bound regions, 24 h TCDD-induced AHRR-bound regions and 45 min TCDD-induced AHR-bound regions. Unique

AHRR-bound regions were found by starting with 24 h TCDD-induced AHRR-bound regions and overlap-filtering against the 24 h TCDD-induced AHR-bound regions, and those remaining regions were then overlap-filtered against the 45 min TCDD-induced AHR-bound regions. Finally, to ensure that these AHRR-bound regions were truly unique, the regions were also overlap-filtered with the solo-peakcalled TCDD AHR-bound regions (not shown). This was to remove any trace of AHR-bound region from the unique AHRR-bound dataset. Unique AHR regions were found by taking the overlap of 24 h and 45 min TCDD-induced AHR-bound regions, and then filtering through overlapping against 24 h TCDD-induced AHRR-bound regions, and finally filtering against solo-peakcalled TCDD AHRR regions (not shown). Genomic location and feature analyses were performed separately for these extracted regions.

2.3.5) Motif Analysis

Three main motif analysis tools were used. Multiple Em for Motif Elicitation (MEME) Suite was used for motif analysis (Bailey et al., 2009). De novo motif discovery was performed using

MEME-ChIP, which is a program that combined multiple tools in the suite to specifically target large ChIP datasets (Machanick and Bailey, 2011). Discriminative Regular Expression Motif

Elicitation (DREME), part of the MEME Suite, was one of the main algorithms used for motif discovery (Bailey, 2011). The cutoff of the E-value used for predicted motif was 0.05. The

42 predicted motifs were also compared with the JASPAR core vertebrate database of known motifs

(Sandelin et al., 2004). Another de novo motif discovery algorithm was used in my analysis called

SEME1.0 (Sampling with Expectation Maximization for Motif Elicitation) (Zhang et al., 2013).

The output of this program also included position and sequence rank preferences for the predicted motifs. The Hypergeometric Optimization of Motif EnRichment (HOMER) motif discovery program was the third tool used (data not included). The output position weight matrix file was designed into logos and matched with JASPAR database using STAMP with default settings

(Mahony and Benos, 2007). For this study, the DREME and SEME program were the main tools used for motif analysis. Motif discovery analyses were performed for the identified TCDD-induced

AHR and AHRR peak regions at 24 h, as well as co-bound and unique regions. The analyses were also repeated with the top 500 peak regions to evaluate whether the calculated motifs changed for the higher ranked regions.

2.3.6) Transcription Factor Binding Site Analysis & Pathway Analysis

The datasets were also inputted to the Genomatix Overrepresented Transcription Factor Analysis program or analysis. Although this analysis is similar to the de novo motif analysis, it uses a different approach and may provide information that is otherwise missed. A database called

MatBase was used for this analysis, which contains information on currently known transcription factors (eg. Sequences of binding sites) and their corresponding weight matrices. The overrepresentation was calculated against the genomic background and listed by z-scores. The list of closest annotated genes were also analyzed on Ingenuity Pathway Analysis engine (Qiagen) to predict the most relevant pathways and biological functions. Like the motif analysis, all subsets of regions were used in these analyses.

2.4) ChIP - Quantititative Polymerase Chain Reaction (ChIP-qPCR)

A list of unique regions were generated from the ChIP-seq analysis. 4 regions were picked based on their fold enrichment, q-value and closest gene annotated. Regions annotated to uncharacterized genes were not selected. Primers were designed for the regions of interest by using Primer3Plus and NCBI Primer-blast program (Table 1). ChIP DNA was quantified by qPCR using SsoFast

EvaGreen SYBR supermix (Bio-Rad). Samples were run in triplicates on the Chromo4 Real-Time

PCR detector (Bio-Rad). The procedure was set according to the following. First, there was an initial denaturation step of 3 min at 95°C, which also activated the SYBR Green enzyme. Then it was followed by 45 cycles of 95°C for 5 s (denaturation step) and then 60°C for 20 s (annealing and elongation step). Plate was read after every cycle. Results were obtained from Opticon Monitor

3 software and inputted into Excel spreadsheets. Fold changes were calculated by comparing cycle threshold (Ct) values. Percent recruitment values were calculated using the difference between the

Ct values of each treatment condition compared to the Ct value of each input. This was then transformed using the equation (2ΔCT x 100) to calculate a percentage value compared to 5% total input. The readings were normalized to 5% total input and reported as percent recruitment relative to 5% total chromatin input (results were also normalized to 100% input).

Table 1. Q-PCR primers used for the ChIP-qPCR validation of selected unique binding regions extracted from ChIP-seq analysis

Chrom Start End Closest AHR-only or Primer sequence 5’-3’ Gene AHRR-only Chr22 42017193 42017286 XRCC6 AHRR-only AGGTAGAAGCTGGTTGGGGA TACTCGGTCCCAATCAACGC Chr19 7968539 7968637 MAP2K7 AHRR-only TCAGGAGCGATCGGGAATTG GATGACGCCACCTAGAGCTC Chr1 24223771 24223848 CNR2 AHR-only GGCGAGAGAAAGGCAGATCA AAGACGGTAAAGGGTCGCTG Chr3 16551179 16551271 RFTN1 AHR-only AGCCATGGTAAAGTGCCCTC CATCTGGGTGATCGCCTCTC

2.5) Luciferase Reporter Gene Assay

2.5.1) Creation of Luciferase Reporter Gene Construct

Previously, the selected unique bound regions for AHR and AHRR were validated by ChIP-qPCR.

The first step of the luciferase reporter gene assay was the cloning of the ChIP binding regions into the luciferase reporter plasmid (pGL3). Primers were designed to introduce the MluI and BglII restriction enzyme sites into the PCR products. The regions of interest were PCR amplified from genomic DNA from MCF-7 cells with the following set of primers (Table 2). Both of the selected

AHRR-only binding ChIP regions were located within the promoter regions of the genes. In order to investigate whether AHRR can regulate the expression of the gene with only the ChIP region, both the promoter region and AHRR-bound ChIP region of an AHRR-only gene (XRCC6) were cloned into the appropriate luciferase vector (pGL3-basic and pGL3-promoter respectively). The promoter region sequence of XRCC6 and MAP2K7 were found on the SwitchGear promoter database. However, for the AHR-only bound regions (annotated to CNR2 and RFTN1), the two selected regions were not found within the promoter region of the genes. These regions were cloned into a pGL3-promoter vector, which contains an SV40 promoter, to observe whether they could be enhancer regions for AHR. Amplification of the PCR products were done using Pfu

Turbo polymerase (Agilent). Standard manufacturer-recommended amplification procedure was used. Temperature gradient was used to optimize PCR reaction. The PCR products with the correct band size were run on a 1% agarose gel and extracted using the Gel Extraction Kit (Geneaid). The vectors were pre-digested with MluI and BglII and treated with shrimp alkaline phosphatase to dephosphorylate the overhangs of the vector to prevent self-re-annealing. Then, the PCR products were cloned into pGL3-basic and pGL3-promoter vectors via ligation for 1 h at room temperature and then at 4°C overnight. 10 µL of the ligation mixture was used for overnight transformations

45 on ampicillin LB agar plates. Colonies were picked and minipreped to screen for the insert (region of interest) by performing a double restriction enzyme digest reactions with MluI and BglII. The successfully screened candidate samples were then sent to sequencing for further validation. After the sequencing was confirmed, the selected minipreped sample were retransformed and maxipreped using Qiagen Plasmid Maxi Kit. The maxipreped samples were re-sequenced for the final confirmation before they were used for transfection.

Table 2. Primers used for cloning of promoter and ChIP regions into reporter gene construct

Chrom Start End Closest AHR-only Promoter Primer sequence 5’-3’ Gene or AHRR- or ChIP (Forward & Reverse) only region Chr22 42016612 42017621 XRCC6 AHRR- Promoter CAAAACGCGTACTCTC only TTCTCCACTCGGCTT CAATGAGATCTCAAAC GCAGAC Chr22 42017103 42017377 XRCC6 AHRR- ChIP CAAAACGCGTCATGTG only CTTACAGTCCTGACGT CAAAAGATCTCAACAC AAATGGATAACGGCCC Chr19 7967704 7969326 MAP2K7 AHRR- Promoter CAAAACGCGTTTCCGA only CAAGCCGGTAACTC CAAAAGATCTAGCATC AGGTCAACAGGGAC Chr1 24223513 24223945 CNR2 AHR-only ChIP CAAAACGCGTAAGTTC TCAGTTTTAGTCTCTA CAAAAGATCTTGGGGT TTCACTATTTTGGCCA Chr3 16551002 16551570 RFTN1 AHR-only ChIP CAAAACGCGTCTTAGT CCCTCTCTGTGGTGCC CAAAAGATCTGTACAA CAGTGCCTGGCAAAGG

2.5.2) Transfection & Treatment

COS-1 cells expresses low levels of endogenous AHR (Levine et al., 2000) and ARNT (Long et al., 1999); therefore, COS-1 can serve as a low-background model well-suited for the luciferase reporter gene experiments. Approximately 80,000 COS-1 cells were plated in 12-well plates using

DMEM complete media. Transfection was done 24 h after plating using Lipofectamine 2000 (Life

Technologies) and OptiMEM (Life Technologies) transfection media. Empty luciferase reporter vectors (pGL3-basic empty and pGL3-promoter empty) were transfected in cells as a negative control. 200 ng of luciferase reporter gene constructs were transfected in the cells along with 50 ng of CH100-β-Gal, used to normalize for transfection efficiency. Differing levels (0, 100, 200,

400 ng) of expression vectors (pcDNA3) for human AHR (pcDNA3-hAHR), ARNT (pcDNA3- hARNT), and AHRR (pcDNA3-hAHRRΔ8) (MacPherson et al., 2014) were transfected into the

COS-1 cells. pSG5 empty vector was used for balancing the total amount of DNA transfected to 1

µg. For every transfection per well, 50 µL OptiMEM with 1 µL Lipofectamine were added to 50

µL OptiMEM with 1 µg of plasmid and incubated for 20 min. Approximately, 100 µL mixture was added to each well in a drop-wise manner. After approximately 6 h, the cells were dosed with either DMSO, 10 nM TCDD or 100 nM TCDD. CYP1B1 luciferase vector, developed from a previous study in the lab, was used as a positive control (MacPherson et al., 2009).

2.5.3) Luciferase Reporter Gene Assay & β-gal Normalization

On the next day, the cells were rinsed with PBS and lysed using passive lysis buffer (Promega).

250 µL of 1X passive lysis buffer was added to each well and incubated for 10 min with constant shaking. The luciferase activity was determined using the ONE-Glo (Promega) luciferase system following manufacturer’s recommendation. After lysis, 25 µL of the sample (in duplicates) and 25

µL of ONE-Glo were added to black flat bottom 96-well plates. Luciferase activity was measured using the GLO-max luminometer using the default ONE-Glo setting. β-galactosidase levels were measured for normalization of the luciferase activity results. 20 µL of lysed sample, 100 µL of β- gal buffer (0.6 M Na2HPO4, 0.04 M NaH2PO4, 0.01 M KCl, 1 mM MgSO4, 0.3% β- mercaptoethanol), 25 µL of 4 mg/ml ortho-Nitrophenyl-β-galactoside (ONPG) were added each

47 well of a clear flat bottom 96-well plate. The plate was incubated at 37°C for 30 min to 2 h, or until it turned to a light yellow colour. The plate reading was done on a Multiskan EX Photometer

(ThermoScientific) at 420 nm. The readings were transferred to an Excel spreadsheet for analysis.

The luciferase activity readings were normalized to the β-gal readings. The results were then normalized to 0 ng transfection of expression vectors and/or to DMSO control samples.

2.6) Statistical Analysis

Peak-calling statistics were generated directly from the MACS2 program. Statistics from other downstream analysis programs to predict motifs and pathways were also generated from the respective programs. All statistical significance was assessed with default settings at p<0.05 unless stated otherwise. ChIP-qPCR and luciferase reporter gene assays were performed with three biological replicates. For each biological replicates, three (for ChIP-qPCR) and two (for luciferase assay) technical replicates were measured. The average of the technical replicates were calculated for each biological replicates, which was then used for statistical significance calculation. For each

ChIP-qPCR and luciferase reporter gene experiment, the mean ± the standard error of the means

(SEM) of the three biological replicates were calculated. Two-way ANOVAs (unless otherwise specified) were performed followed by a Bonferroni post-hoc test using GraphPad Prism 5

(GraphPad Software Inc., La Jolla, CA, USA). Statistical significance was assessed at P<0.05. The number of asterisks denotes statistical p-value of less than 0.05 (*), 0.01 (**), 0.001 (***), 0.0001

(****).

Chapter 4: Results

1) Genome-wide Mapping of AHR and AHRR Binding Sites in MCF-7 cells

1.1) Determining the AHR- and AHRR-bound regions by ChIP-Seq

In order to determine the AHR and AHRR genomic binding regions, we performed ChIP-seq in

MCF-7 cells. Since the protein level of AHRR was undetectable in MCF-7 cells until 24 h after

TCDD treatment (MacPherson et al., 2014), the 24 h time-point was chosen to ensure that AHRR protein levels was sufficient to be captured for ChIP-seq. Using the peak-calling function of the

MACS2 program and a default q-value cutoff of 0.05, I identified 3915 AHR-bound regions and

2811 AHRR-bound regions in MCF-7 cells when 24 h TCDD treatment samples were compared against DMSO control samples (Table 3). I also included AHR-bound regions from other ongoing studies in our laboratory that were obtained from MCF-7 cells treated with 10 nM TCDD for 45 min. I also analyzed the data with other peak callers, such as HOMER and CisGenome. However,

I only used the MACS2 output results for all downstream analyses, since it was the most widely used of the three algorithms and gives the most balanced output.

Table 3. Ligand-induced peaks regions identified by MACS2 Peak-caller program using default settings. Q-values cutoff was set at 0.05.

Antibody Treatment Control MACS2 Peaks Used (q-value < 0.05) AHR TCDD 24h DMSO 24h 3915 AHRR TCDD 24h DMSO 24h 2811 AHR TCDD 45min DMSO 45min 20954

1.2) Visualization of Peaks

Visualization of the peak signal intensities was done on the Integrative Genomic Viewer (IGV) using the bedGraph file output from the MACS2 peak-caller. The ChIP signal intensities were visualized for the four different ChIP samples (DMSO-AHR, DMSO-AHRR, TCDD-AHR,

TCDD-AHRR). As expected, recruitment to the CYP1A1 and CYP1B1 genes were among the highest. High recruitment of AHR was found in several of the other classical AHR target genes such as CYP1A2 and NQO1 (data not shown). Regions of special interest, as well as user-inputted motifs were manually searched and visualized using IGV. Based on the ChIP intensities for

CYP1A1 region, ligand-induced recruitment of AHRR was comparable to that of AHR (Figure 6).

It is important to note that this diagram does not necessarily reflect the actual recruitment level differences between AHR and AHRR and should be used for visualization purposes only. Since these signal intensities were drawn from separate peak calls, they were normalized to their own

DMSO control and to their own sequencing depth. Therefore, ChIP-qPCR is still a necessity for the validation of recruitment levels.

DMSO 400 AHR

0 TCDD 400 AHR

0 DMSO 400 AHRR

0 TCDD 400 AHRR

Figure 6. ChIP Signal Intensity at CYP1A1 gene for 24 h DMSO-treated and TCDD-treated samples for AHR and AHRR. Scale is the same for all four samples set at 400.

1.3) Gene Annotation & Feature Analysis

The closest genes to the peak regions were annotated using the HOMER AnnotatePeaks program

(Heinz et al., 2010). The genomic locations of each peak region (classified as either intergenic, intron, promoter-TSS, TTS (transcription termination site), exon, non-coding, 3’-UTR

(untranslated regions), 5’-UTR, other) were also annotated based on their location to the nearest gene. The promoter-TSS regions were defined as -1 kb to +100 bp from TSS. The genomic location annotations were summarized in Figures 7-10. The most notable difference in the AHR and AHRR binding location preference was the high promoter-TSS binding by AHRR (26.2% in the AHRR peak regions compared to only 2.6% in the AHR regions) (Figure 7 and 8). There were fewer annotated regions found in the intron and intragenic regions for AHRR (33% and 33% respectively) compared to AHR (42% and 52% respectively). Also, there were more AHRR-bound regions (3.1%) within the 5’-UTR compared with AHR-bound regions (0.2%). The distance profiles relative to TSS were depicted as histograms for the AHR- and AHRR-bound regions.

AHRR-bound regions mapped much closer to the TSS than did AHR-bound regions (Figure 9).

The histogram showed that AHRR bound almost exclusively near promoter regions, whereas

AHR-bound regions were more dispersed relative to the TSS. In agreement with previous studies,

AHR had a slight preference for promoter regions with the highest peak density situated at the TSS

(Lo and Matthews, 2012), but this was much lower compared with AHRR. This analysis also supported the above annotated genomic location analysis, since a higher percentage of the AHRR- bound regions were annotated to the promoter-TSS and 5’-UTR compared with those of AHR

(Figures 7-8).

Intron Other 42.55% 0.77% Promoter-TSS 2.63% Non-coding TTS 0.28% 1.05% Exon 3'UTR 0.82% 0.26%

5'UTR 0.23%

Intergenic 52.18%

Figure 7. Genomic location annotation of TCDD ligand-induced (TCDD vs. DMSO) AHR Peak Regions

Promoter-TSS Exon 26.22% TTS 1.67% 1.67% Non-coding 0.92% Intron 3'UTR 33.19% 0.50% Other 4.48% 5'UTR 3.06%

Intergenic 32.76%

Figure 8. Genomic location annotation of TCDD ligand-induced (TCDD vs. DMSO) AHRR Peak Regions

TCDD-induced AHR Binding Regions 100 90 80

70 60

40 Frequency 30 20 10 0

9000

-9000

18000 27000 36000 45000 54000 63000 72000 81000 90000 99000

-63000 -99000 -90000 -81000 -72000 -54000 -45000 -36000 -27000 -18000 Distance to TSS

TCDD-induced AHRR Binding Regions 600

500

400

300

Frequency 200

100

9000

-9000

63000 27000 36000 45000 54000 72000 81000 90000 99000

18000

-45000 -90000 -81000 -72000 -63000 -54000 -36000 -27000 -18000 -99000 Distance to TSS

Figure 9. Histogram of the distance to transcription start site (TSS) for TCDD-induced AHR peak regions (top) and TCDD-induced AHRR peak regions (bottom).

1.4) Overlap Analysis of AHR and AHRR Peaks

BEDtools was used to find the overlaps between AHR and AHRR TCDD-induced peak regions at

24 h, as well as the AHR TCDD-induced regions at 45 min (Figure 10). The 24 h TCDD-induced

AHR-bound regions were highly overlapped with those of 45 min TCDD treatment (~90%), which was expected as they were derived from the same protein. Overlap of all three datasets yielded 969 peak regions, while there were 994 AHRR-bound peak regions outside of any overlap. The peak regions were annotated to the nearest genes (Figure 10). As expected, there was a nearly a complete overlap of genes (95%) between AHR at 24h and with those at 45 min after TCDD treatment.

Interestingly, only 504 AHRR-bound genes were not among the list of AHR-bound genes at both

24 h and 45 min time-points. Moreover, there were more annotated genes overlapped (1064) among the three datasets than there were overlapping peak regions (969). This indicates that, in some cases, AHR and AHRR bind at different genomic locations relative to the same gene. Since

AHRR binding regions were found to be promoter-focused (Figures 8 and 9), it is possible that

AHRR can specifically compete with AHR at promoter regions. To test this, AHR-bound and

AHRR-bound regions bound within 1000 bp of the TSS were extracted resulting in 155 AHR- bound regions and 1018 AHRR-bound promoter regions. These promoter regions for AHR and

AHRR were then overlapped (Figure 11). Only around half of the AHR-bound promoter regions overlapped with AHRR-bound promoter regions, indicating that the promoter regions account for some, but not all of the overlaps.

Overlap of TCDD-induced Peak Regions Overlap of Nearest Gene

AHR 24h AHRR 24h AHR 24h 3915 2811 2647 AHRR 24h 417 112 2417 994 504 2524 969 843 1461 1064

839

16618 5077

AHR 45min AHR 45min Gene Annotation 8441 20954

Figure 10. Overlap between TCDD (24 h) TCDD-induced AHR peak regions, TCDD (24 h) TCDD- induced AHRR peak regions and TCDD (45 min) ligand-induced AHR peak regions (left) and their corresponding genes (right).

AHRR 24h 1018 Promoter AHRR 24h 990 Annotated AHR 24h Peak Regions AHR 24h 155 Promoter 150 Annotated Genes Peak Regions Genes

72 83 935 64 86 905

Gene Annotation

Figure 11. Overlap between TCDD (24 h) TCDD-induced AHR peak regions and TCDD (24 h) TCDD-induced AHRR peak regions within the promoter regions (±1000 bp of TSS).

The AHR and AHRR co-bound, unique AHR-bound and unique AHRR-bound subsets of regions were identified using BEDTools. Analysis of the distance to the TSS and genomic location annotation showed vast differences among the three datasets. Based on the distribution, the pattern of the AHR/AHRR co-bound regions was more similar to that of AHR-bound rather than AHRR- bound regions since they were similarly dispersed as AHR and not as promoter-focused as AHRR

(Figure 12a). The promoter-TSS regions only accounted for 6.3% of the co-bound regions, further suggesting that the overlapping or AHR/AHRR co-bound regions occur away from promoter regions. This also suggests that AHRR-bound regions outside the promoter region account for a larger portion of the shared regions between AHR and AHRR. Unique AHR-bound regions did not show promoter binding preferences with many regions scattered away from the TSS (Figure

12b). This indicates that unique AHR-bound regions, not bound by AHRR, are most likely to be located away from the TSS. In contrast, the unique AHRR-bound regions showed (Figure 12c) substantial promoter preference. In addition, 46% of the unique AHRR-bound regions were within the promoter-TSS region, representing the highest annotated genomic location for this dataset. By extending the parameters to ±2000 bp from the TSS, approximately 70% of the unique AHRR- bound regions fell within those parameters, indicating the extremely high preference for sequences close to TSS.

A) Co-bound Regions 60 60% 48.19% 50 50% 42.21% 40 40%

30 30%

Frequency 20 20% 10 10% 6.30% 1.44%0.72%0.41%0.31%0.41% 0 0%

66000 11000 22000 33000 44000 55000 77000 88000 99000

-88000 -77000 -66000 -55000 -44000 -33000 -22000 -11000 -99000 Distance to TSS

B) Unique AHR-bound Regions

30 60% 54.54%

25 50% 42.13% 20 40% 15 30%

Frequency 10 20% 5 10% 1.22%0.80%0.75%0.28%0.14%0.14% 0 0%

11000 22000 33000 44000 55000 66000 77000 88000 99000

-88000 -77000 -66000 -55000 -44000 -33000 -22000 -11000 -99000 Distance to TSS

C) Unique AHRR-bound Regions 350 50% 45.92% 300 45% 40% 250 35% 200 30% 22.16% 25% 150 20.44% 20% Frequency 100 15% 10% 5.74% 50 2.32% 5% 1.91% 0.91%0.60% 0 0%

11000 22000 33000 44000 55000 66000 77000 88000 99000

-77000 -99000 -88000 -66000 -55000 -44000 -33000 -22000 -11000 Distance to TSS

Figure 12. Distance to TSS histogram and genomic location annotations for AHR/AHRR co-bound regions (A), unique AHR- (B) and AHRR-bound regions (C).

1.5) Motif Analysis

1.5.1) Ligand-induced AHR- and AHRR-bound Regions

De novo motif discovery was performed for AHR-bound and AHRR-bound regions, generated from MACS using the DREME and SEME algorithms. The top 5 discovered motifs (Tables 4 and

6; refer to Appendix for top 10 motifs) with corresponding E-values for AHR- and AHRR-bound regions were listed. Motif discovery was also repeated for the top 500 peak regions in each data set to test whether the motif prediction would change with higher ranked peak regions (Tables 5 and 7). For both AHR and AHRR, the AHRE core consensus sequence (5’-GCGTG-3’) was highly ranked yet there were some notable differences between the two datasets in other motifs discovered. The Forkhead Box (FOX) motifs were detected much higher in AHR than AHRR.

However, using the top 500 peak regions as input dramatically dropped the statistical significance of this FOX motif, indicating that the FOX motif may be more important for lower ranked regions.

Ultimately, when the top 500 ranked regions were used as input, the AHR motif was still the highest predicted motif for AHR and AHRR. The FOX motif was also predicted for the AHRR dataset but not to the same extent seen in the AHR dataset. Interestingly, the estrogen receptor

(ESR) motif was predicted for AHR, supporting the established cross-talk between AHR-ER

(Ahmed et al., 2009). Using the SEME algorithm, the predicted motifs were found to be similar to those predicted from the DREME algorithm (Figures 8-11). The DREME analysis resulted in a quadranucleotide version (5’-CGTG-3’) of the AHRE core consensus sequence, while the SEME resulted in a perfect full pentanucleotide core (5’-GCGTG-3’). This difference may be attributed to the differences in their motif calculation algorithms, revealing the importance of using different algorithms to analyze the same datasets. In the SEME analysis, the full AHRE core consensus

58 sequence (5’-GCGTG-3’) was predicted as a highly ranked motif for both the AHR (2nd) and

AHRR (1st) datasets (Tables 8 and 10). Consistent with the DREME analysis, the AHR motif was ranked first for both the top 500 AHR-bound and AHRR-bound regions (Tables 9 and 11). The most notable predicted non AHRE motifs identified by both DREME and SEME that were common to both the AHR and AHRR datasets, were the FOX and the Specificity Protein 1 (SP1) motifs (Tables 4-11). The Two notable motifs found highly ranked for AHR, but not in AHRR, were the Activator Protein 1 (AP1) and the GATA binding protein (GATA) motifs. Several other motifs, such as the E2F transcription factor, Early Growth Response (EGR1), and the Signal

Transducer and activation of transcription 3 (STAT3) motifs were more significantly identified for

AHRR-bound regions.

Table 4. Top 5 De novo motif discovery (DREME) of TCDD-induced AHR binding sites

Rank Motif Found E-value Known Similar Motifs

1 2.3e-105 FOXA1, Foxa2, FOXD1, FOXI1, FOXF2, FOXP2, Foxd3, FOXO3, Foxo1, FOXP1

2 6.2e-093 Bhlhe40, HIF1A::ARNT, Ahr:Arnt

3 1.3e-025 FOSL2, NFE2::MAF, JUNB, Bach1::Mafk, Nfe2l2, JUND, JUN, FOSL1, JUN::FOS, FOS

4 5.5e-024 GATA, Gata1, Gata4, Mecom, GATA2, TAL::GATA1

5 1.8e-022 ESR1, TFAP2A, TFAP2C

Table 5. De novo motif discovery (DREME) of Top 500 TCDD-induced AHR binding sites (E-value < 0.05)

Rank Motif Found E-value Known Similar Motifs (<0.05) 1 1.2e-020 Ahr::Arnt, Bhlhe40, HIF1A::ARNT

2 2.8e-006 FOXI1, Foxd3, FOXA1, Foxa2, FOXD1, Foxq1, HNF1B, FOXO3, FOXP2, FOXF2

3 3.3e-002 JUN::FOS, NFE2::MAF, Nfe2l2, Bach1::Mafk, BATF::JUN, FOSL2, FOS, JUNB, JUN, JUND

Table 6. Top 5 De novo motif discovery (DREME) of TCDD-induced AHRR binding sites

Rank Motif Found E-value Known Similar Motifs

1 4.5e-059 Arnt::Ahr, HIF1A::ARNT

2 3.1e-018 FOXI1, FOXF2, Foxd3, FOXD1, Foxq1, FOXA1

3 3.6e-018 SP1, KLF5, EGR1, EGR2, Klf, SP2, E2F4, E2F6

4 5.9e-013 EBF1, STAT3

5 5.4e-010

Table 7. De novo motif discovery (DREME) of Top 500 TCDD-induced AHRR binding sites (E- value < 0.05)

Rank Motif Found E-value Known Similar Motifs (<0.05)

1 2.4E-021 Ahr::Arnt, Bhlhe40, HIF1A::ARNT

2 1.5E-002 FOXI1, Foxa2, FOXA1, Foxd3, FOXO3, FOXP2, FOXD1, FOXF2, Foxg1, Foxo1

Table 8. Top 5 De novo motif discovery (SEME) of TCDD-induced AHR binding sites

Rank Motif Found Best match in JASPAR Other matches 1 MA0148.1_FOXA1 Foxa2, (E val: 2.0295e-13) Foxd3

2 MA0006.1_Arnt_Ahr (E val: 1.2426e-04)

3 MA0006.1_Arnt_Ahr TLX::NFI (E val: 1.3161e-03) C, Sox2

4 MA0099.2_AP1 NFE2L2, (E val: 2.3589e-07) Fos, ESR1, PPARG 5 MA0079.2_SP1 RREB1, (E val: 1.4710e-13) Klf4

Table 9. Top 5 De novo motif discovery (SEME) of Top 500 TCDD-induced AHR binding sites

Rank Motif Found Best match in JASPAR Other matches 1 MA0006.1_Arnt_Ahr (E val: 1.8412e-05)

2 MA0148.1_FOXA1 Foxa2 (E val: 4.6296e-14)

3 MA0035.2_Gata1 (E val: 2.5561e-04)

4 MA0079.2_SP1 (E val: 2.4863e-09)

5 MA0004.1_Arnt Mycn, (E val: 4.3217e-07) USF1

Table 10. Top 5 De novo motif discovery (SEME) of TCDD-induced AHRR binding sites

Rank Motif Found Best match in JASPAR Other matches 1 MA0006.1_Arnt_Ahr Egr1, (E val: 5.3483e-04) TP53, RAP1 2 MA0277.1_AZF1 GLN3, (E val: 3.8761e-07) HMGA1,

Mecom 3 MA0079.2_SP1 Klf4, ( E val: 5.4035e-13) Egr1

4 MA0047.1_Foxa2 Foxd3, (E val: 8.5365e-13) FOXI1

5 MA0277.1_AZF1 Foxd3 (E val: 5.1991e-07)

Table 11. Top 5 De novo motif discovery (SEME) of Top 500 TCDD-induced AHRR binding sites

Rank Motif Found Best match in JASPAR Other matches 1 MA0006.1_Arnt_Ahr (E val: 1.2551e-04)

2 MA0045.1_HMG- MEF2A I_Y (E val: 2.1820e-05) 3 MA0079.2_SP1 Egr1, ( E val: 6.7746e-13) Klf4

4 MA0047.1_Foxa2 Foxd3, (E val: 1.1912e-09) FOXI1

5 MA0277.1_AZF1 Foxd3 (E val: 6.4272e-07)

The predicted AHR motif in the AHR and AHRR datasets significantly matched with the AHR motif from the JASPAR core database (Figure 13). Although the 5’-GCGTG-3’ predicted motif in the AHRR dataset was very similar to that of the AHR dataset, the guanine bases were more emphasized within and around the pentanucleotide core sequence. Another notable difference between the predicted AHR motif for the AHR and AHRR datasets was the cytosine within the pentanucleotide core sequence. This cytosine base was not as highly enriched in the AHRR dataset compared to the AHR dataset. Additionally, at two bases to the left of the GCGTG pentanucleotide core within their motif, there was a thymine base for AHR and a guanine base for AHRR. The predicted motif found for top 500 AHR-bound regions matches the early depictions of the AHRE, the extended AHRE (5’-TnGCGTG-3’) (Swanson et al., 1995), where the thymine base is

63 presented in addition to the pentanucleotide core. Overall, both motif discovery algorithms predicted an AHRE-like core consensus sequence as the top sequence motif for both the AHR- and AHRR-bound peak regions.

A B

E val: 1.8412e-05 E val: 1.2551e-04

Figure 13. The comparison of the AHRE core consensus sequence in the predicted AHR motifs for the top 500 AHR-bound (A) and AHRR-bound (B) regions with corresponding E-values for matching with the JASPAR core database AHR motif (C).

1.5.2) Overlapped and Unique Regions

Since it is essential to also explore both co-bound and unique AHR- and AHRR-bound regions, I used the same de novo motif discovery method for each of those datasets.

AHR/AHRR Co-bound Regions

Compared with the motifs identified in the previous analysis, similar motifs were discovered for

AHR/AHRR co-bound regions with DREME including the FOX, GATA and AP1 motifs (Table

12). In both DREME and SEME, the AHR motif was found to be undisputedly the most important predicted motif for the co-bound regions, followed by the FOX motif (Tables 12-13). This reaffirms the competition or interaction of AHR and AHRR at AHRE sites.

Table 12. Top 5 De novo motif discovery (DREME) of AHR/AHRR co-bound regions

Rank Motif Found E-value Known Similar Motifs

1 2.6e-045 Ahr:Arnt

2 5.5e-017 FOXD1, FOXA1, Foxa2, FOXO3, FOXP2, FOXF2, Foxo1, FOXP1, SRY, Foxd3 3 1.8e-009 GATA3, Gata1, Gata4, Mecom, GATA2, TAL1::GATA1

4 9.3e-009 JUND, FOSL1, JUNB, Bach1::Mafk, FOSL2, NFE2::MAF, FOS, Nfe2l2, JUN::FOS, JUN 5 2.1e-005 FEV, Ets1, FLI1, Erg, EHF, ELF1, STAT1, SPIB, ELF5, Ecl6

Table 13. Top 5 De novo motif discovery (SEME) of AHR/AHRR co-bound regions

Rank Motif Found Best match in JASPAR Other matches 1 MA0006.1_Arnt_Ahr (E val: 1.2305e-04)

2 MA0148.1_FOXA1 Foxa2 (E val:5.5511e-16)

3 MA0041.1_Foxd3 Foxa2 (E val: 2.1581e-07)

4 MA0047.1_Foxa2 HNF1B, (E val: 5.5577e-09) HNF1A, Foxd3

5 MA0079.2_SP1 Klf4 (E val: 1.3228e-06)

Unique AHR Regions

Instead of the AHR motif, motif discovery of the unique AHR-bound regions using both DREME and SEME resulted with the FOX motif as being the top ranked with strong statistical scores

(Tables 14-15). The predicted AHR motif differed greatly from the conventional AHR motif pattern seen in earlier analyses where this AHR motif was a heavily distorted version (Table 15).

This suggests that the conventional AHR motif with the pentanucletide core may not be a good predictor of binding for these regions. Surprisingly, using SEME, a perfect FOX motif was predicted, especially FOXA1, suggesting that FOXA1 plays a major role for these unique AHR- bound regions (Table 15). Overall, the FOX motif was found to be the most significant for unique

AHR binding sites, followed by what seemed to be an AHR motif.

Table 14. Top 5 De novo motif discovery (DREME) of unique AHR-bound regions

Rank Motif Found E-value Known Similar Motifs

1 1.3e-083 Foxa2, FOXA1, FOXD1, FOXI1, FOXF2,FOXP2, Foxd3, FOXO3, foxo1, FOXp1 2 3.0e-053 Ahr::arnt, bhlhe40, PPARG

3 2.4e-019 PPARG::RXR, ESRRA, NR2C2, ESR1, Esrrb, RORA_1, Rxra, Nr1h3::Rxra, ESR2, NR1H2::RXR 4 1.3e-017 GATA3, Gata1, Mecom, Gata4, GATA2, TAL1::GATA1

5 3.3e-015 JUN::FOS, Bach1::Mafk, NFE2::MAF, Nfe2l2, FOSL2, JUNB, JUND, FOSL1, FOS, JUN

Table 15. Top 5 De novo motif discovery (SEME) of unique AHR-bound regions

Rank Motif Found Best match in JASPAR Other matches 1 MA0148.1_FOXA1 Foxa2, (E val:0.0000e))

2 MA0006.1_Arnt_Ahr Sox2 (E val: 9.0744e-05)

3 MA0359.1_RAP1 RREB1, (E val: 1.9655e-05) Foxd3, Egr1 4 MA0003.1_TFAP2A EBF1, (E val: 3.5114e-08) PLAG1

5 MA0079.2_SP1 (E val: 2.0589e-07)

Unique AHRR Regions

Motif discovery of the unique AHRR regions using DREME (Table 16) still resulted in the AHR motif as first ranked but to a lesser extent than one found for the co-bound regions (Table 12).

Interestingly, several other motifs were found, including the STAT, EGR and E2F motifs.

However, SEME analysis resulted in different motif patterns, namely the dinucleotide repeats of

GA, CA and GC (Table 17). Although both analyses predicted different variations of motifs, a consistent observation amongst these predicted motifs was the pronounced presence of guanine

(or cytosine) bases. The affinity of AHRR for GC-rich regions was also implicated in earlier analyses, suggesting that it may be crucial for the binding of AHRR (Figure 10). Another striking observation was the disappearance of the FOX motif amongst the ranks of motifs that were consistently seen in earlier motif analyses. This indicates that FOX may be the key to the differences in the binding seen between AHR and AHRR. Overall, motif analysis for unique

AHRR provided valuable insights and hints to uncover the differences in the binding patterns between AHR and AHRR.

Table 16. Top 5 De novo motif discovery (DREME) of unique AHRR-bound regions

Rank Motif Found E-value Known Similar Motifs

1 9.8e-016 Arnt::Ahr, HIF1A::ARNT

2 2.0e-009 STAT3, STAT1, E2F4, E2F6, E2F1

3 1.0e-006 EGR1, SP1, KLF5, SP2, Klf4, EGR2, E2F4, E2F6, E2F3, E2F1 4 7.9e-005

5 1.3e-003 PPARG, USF1, Pax2, PAX5, Bhlhe40, USF2, Arnt::Ahr

Table 17. Top 5 De novo motif discovery (SEME) of unique AHRR-bound regions

Rank Motif Found Best match in JASPAR Other matches 1 MA0277.1_AZF1 Foxa2, (E val:0.0000e))

2 MA0359.1_RAP1 Sox2 (E val: 9.0744e-05)

3 MA0079.2_SP1 RREB1, (E val: 1.9655e-05) Foxd3, Egr1 4 MA0402.1_SWI5 EBF1, (E val: 3.5114e-08) PLAG1

5 MA0277.1_AZF1 (E val: 2.0589e-07)

1.6) Overrepresented Transcription Factor Binding Site or Modules Analysis

1.6.1) Ligand-induced AHR- and AHRR-Bound Regions

Overrepresented transcription factor binding site analysis was performed for the top 500 AHR- and AHRR-bound regions (Table 18-19). I used the overrepresentation analysis tool that is part of the Genomatix Bioinformatic Suite of programs. This tool searches for known transcription factor binding sites within genomic sequences that occur in those sequences at a higher rate than would occur in randomly chosen genomic DNA sequences. Because genome-wide ChIP-seq data was inputted, a genomic rather than a promoter background was used for all datasets. The predicted transcription factors were ranked by calculated Z-scores. Overrepresented transcription factor modules (selecting AHR-related factors as one of the transcription factor partners) were also analyzed for the AHR and AHRR ligand-induced peak regions (Table 20-21). The transcription factor modules analysis provides insights into possible transcription factor partners that may interact or coregulate genes with AHR and AHRR. Although it performs similar functions as de novo motif discovery, it presents a different algorithm which may result in a different conclusion.

As expected the AHRE and AHRE-related sites were among the highest predicted transcription factors for both AHR-bound and AHRR-bound regions. However, other predicted transcription factors were markedly different between the two datasets. Interestingly, the highest ranked overrepresented transcription factor site identified in the AHRR dataset was the ZF5 POZ domain zinc finger proteins, which is known to mediate transcriptional repression (Numoto et al., 1993).

Some other overrepresented transcription factors found more significantly in the AHRR dataset than those of AHR included the nuclear respiratory factor 1 (NRF1), EGR/nerve growth factor induced protein C (EGR) & related factors and E2F-myc activator/cell cycle regulator (E2F). This

69 was consistent with the results from the de novo motif discovery (Table 6) and support possible interactions or coregulation of target genes by AHRR and other families of transcription factors.

Since these transcription factors were not highly predicted in the AHR dataset, it suggests that

AHRR may interact with them independently of AHR. Although the FOX TF family was not found amongst the overrepresented TFs, overrepresentation of FOX and GATA site was observed in the module analysis with high Z and distance scores in the AHR dataset (Table 20). This indicates that the FOX and GATA may be important regulators for AHR. Both GATA3 and FOXA1 are critically important for robust estrogen receptor-mediated signalling (Carroll et al., 2005; Eeckhoute et al.,

2007). In comparison, the GATA and FOX family of transcription factors were not highly predicted for the AHRR dataset, suggesting that FOX and GATA may be key to the differences in

AHR and AHRR binding. Taken together, for the ligand-induced binding regions, the AHR & related family of TFs was high ranked for AHR and AHRR. However, the differences in AHR and

AHRR in the predicted overrepresented transcription factors suggests independent actions.

Table 18. Top 10 overrepresented transcription factor for the top 500 TCDD-induced AHR peak regions

Nr. of Input Over TF Z-Score Rank Description Seq. with representation Families (genome) Match (genome) 1 AHR-arnt heterodimers and AHR- V$AHRE related factors 266 4.76 34.2 2 V$AP2 Activator Protein 2 197 3.07 24.19 3 V$AP1R MAF and AP1 related factors 349 1.69 15.15 4 V$AP1 AP1, Activating protein 1 231 1.96 15.15 5 V$ERE Estrogen response elements 129 2.4 12.34 6 V$PAX9 PAX-9 binding sites 83 2.96 11.48 7 Vertebrate homologues of V$HES enhancer of split complex 158 1.84 10.27 8 V$NRF1 Nuclear respiratory factor 1 36 2.63 9.14 9 V$HIF Hypoxia inducible factor 173 1.75 8.83 10 CTCF and BORIS gene family, transcriptional regulators with 11 V$CTCF highly conserved zinc finger 128 1.77 8.4

Table 19. Top 10 overrepresented transcription factor for the top 500 TCDD-induced AHRR peak regions

Nr. of Over Z-Score Rank TF Families Description Input Seq. representation (genome) with Match (genome) 1 V$NRF1 Nuclear respiratory factor 1 142 19.08 94.86 2 V$ZF5 ZF5 POZ domain zinc finger 146 15.89 88.48 3 AHR-arnt heterodimers and AHR- V$AHRE related factors 373 8.99 67.65 4 EGR/nerve growth factor induced V$EGR protein C & related factors 278 5.34 58.42 5 E2F-myc activator/cell cycle V$E2F regulator 336 4.03 50.21 6 V$SP1 GC-Box factors SP1/GC 275 4.84 47.34 7 BED subclass of zinc-finger V$BED proteins 178 7.36 46.26 8 CTCF and BORIS gene family, transcriptional regulators with 11 highly conserved zinc finger V$CTCF domains 264 5.06 41.25 9 C2H2 zinc finger transcription V$ZF02 factors 2 237 3.74 40.01 10 O$MTEN Core promoter motif ten elements 136 8.59 37.7

Table 20. Top 10 overrepresented modules (in which one partner is the AHRE site) for the top 500 TCDD-induced AHR peak regions

Over Modules with Distance Z-Score Rank Description representation V$AHRE Score (genome) (genome) 1 SOX/SRY-sex/testis determining and related HMG V$AHRE V$SORY box factors 2.246 4.71 34.64 2 V$AHRE V$AP1R MAF and AP1 related factors 1.797 8.81 33.89 3 V$AHRE V$AP1 AP1, Activating protein 1 1.955 13.63 33.86 4 Krueppel like transcription V$AHRE V$KLFS factor 2.434 6.55 33.17 5 V$AHRE V$GATA GATA binding factors 2.763 8.64 30.73 6 V$AHRE V$FKHD Fork head domain factors 3.218 6.17 30.66 7 C2H2 zinc finger transcription V$AHRE V$ZF02 factors 2 2.011 7.95 30.07 8 V$AHRE V$AP2 Activator protein 2 3.274 9.93 28.47 9 cAMP-responsive element V$AHRE V$CREB binding proteins 3.35 6.32 26.42 10 CTCF and BORIS gene family, transcriptional regulators with V$AHRE V$CTCF 11 highly conserved zinc finger 2.868 8.13 26.09

Table 21. Top 10 overrepresented modules (in which one partner is the AHRE site) for the top 500 TCDD-induced AHRR peak regions

Over Modules with Distance Z-Score Rank Description representation V$AHRE Score (genome) (genome) 1 V$AHRE V$NRF1 Nuclear respiratory factor 1 2.441 82.25 187.49 2 EGR/nerve growth factor induced protein C & related V$AHRE V$EGR factors 3.289 38.88 151.51 3 V$AHRE V$ZF5 ZF5 POZ domain zinc finger 2.659 48.38 144.18 4 E2F-myc activator/cell cycle V$AHRE V$E2F regulator 2.005 24.5 114.24 5 C2H2 zinc finger V$AHRE V$ZF02 transcription factors 2 2.687 28.39 110.5 6 V$AHRE V$SP1 GC-Box factors SP1/GC 2.061 27.57 104.33 7 Krueppel like transcription V$AHRE V$KLFS factors 2.183 18.88 99.45 8 BED subclass of zinc-finger V$AHRE V$BED proteins 2.038 40.56 97.62 9 V$AHRE V$MAZF Myc associated zinc fingers 2.108 37.19 88.22 10 V$AHRE V$AP2 Activator protein 2 2.823 27.61 79.1

1.6.2) Overlapped and Unique Regions

AHR/AHRR Co-bound Regions

I repeated the same overrepresentation of transcription factor binding site analysis for the top 500

AHR/AHRR co-bound regions. As expected, the AHRE was the top ranked site in this dataset

(Table 22). The predicted TFs and modules for co-bound regions (Tables 22 and 23) were more similar with those predicted for AHR-bound regions (Tables 18-20) than AHRR-bound regions

(Tables 19-21). These observations were consistent with the distance to TSS profile of co-bound region (Figure 12a) in which the binding pattern was more identical to that of AHR (Figure 9).

Like the motif discovery of co-bound regions (Tables 12-13), the AHRE was found to be most important in the overrepresented transcription factor binding site analysis of these same regions

(Table 22).

Table 22. Top 10 overrepresented transcription factors for the top 500 TCDD-induced AHR/AHRR co-bound peak regions

Nr. of Input Over TF Z-Score Rank Description Seq. with representation Families (genome) Match (genome) 1 AHR-arnt heterodimers and AHR- V$AHRE related factors 280 6.47 42.09 2 V$AP2 Activator Protein 2 173 3.91 28.7 3 V$PAX9 PAX-9 binding sites 83 3.94 14.56 4 V$AP1R MAF and AP1 related factors 273 1.67 12.46 5 CTCF and BORIS gene family, transcriptional regulators with 11 highly conserved zinc finger V$CTCF domains 133 2.32 12.12 6 V$AP1 AP1, Activating protein 1 176 1.9 12.06 7 Vertebrate homologues of enhancer V$HES of split complex 141 2.14 11.78 8 Members of ZIC-family, zinc V$ZIC finger protein of the cerebellum 109 2.65 11.22 9 V$NRF1 Nuclear respiratory factor 1 39 3.29 10.83 10 V$HIF Hypoxia inducible factor 140 1.97 9.66

Table 23. Top 10 overrepresented modules (in which one partner is the AHRE site) for top 500 TCDD-induced AHR/AHRR co-bound peak regions

Over Modules with Distance Z-Score Rank Description representation V$AHRE Score (genome) (genome) 1 V$AHRE V$AP2 Activator Protein 2 2.814 15.53 39.18 2 V$AHRE V$AP1R MAF and AP1 related factors 1.717 11.59 38.84 3 Krueppel like transcription V$AHRE V$KLFS factors 3.403 8.5 37.9 4 V$AHRE V$AP1 AP1, Activating protein 1 1.944 17.53 37.45 5 V$AHRE V$MYOD Myoblast determining factors 3.198 10.31 35.15 6 SOX/SRY-sex/testis determining and related HMG V$AHRE V$SORY box factors 2 5.4 34.78 7 C2H2 zinc finger transcription V$AHRE V$ZF02 factors 2 1.734 9.86 32.4 8 V$AHRE V$FKHD Fork head domain factors 2.376 7.33 31.7 9 Twist subfamily of class B V$AHRE V$HAND bHLH transcription factors 2.181 9.17 31.21 10 CTCF and BORIS gene family, transcriptional regulators with 11 highly V$AHRE V$CTCF conserved zinc finger domains 4.072 10.96 30.81

Unique AHR Regions

For the top 500 unique AHR regions, the most notable change was the drop in the top ranking of the AHRE (Table 24). Other predicted transcription factor sites remained relatively consistent with those predicted with the full AHR dataset. Similarly, the identified transcription factor modules were also comparable to those from the full AHR dataset (Table 25). For example, the Forkhead

Box family of transcription factors was also identified as an important enriched site in the unique

AHR-bound regions. Overall, this analysis complemented the de novo motif analysis in which the

AHRE showed reduced importance in the unique AHR-bound regions.

Table 24. Top 10 overrepresented transcription factor for top 500 unique TCDD-induced AHR- bound peak regions

Nr. of Input Over TF Z-Score Rank Description Seq. with representation Families (genome) Match (genome) 1 V$AP1 AP1, Activating protein 1 230 2.26 17.24 2 V$AP2 Activator Protein 2 135 2.63 16.45 3 V$AP1R MAF and AP1 related factors 304 1.78 14.89 4 V$ERE Estrogen response elements 108 2.57 11.97 5 AHR-arnt heterodimers and V$AHRE AHR-related factors 135 2.31 10.29 6 V$GATA GATA binding factors 368 1.44 9.76 7 cAMP-responsive element V$CREB binding proteins 306 1.32 6.93 8 Zinc finger BED domain- V$ZBED containing protein 52 2.03 6.51 9 Grainyhead-like transcription V$GRHL factors 111 1.51 5.97 10 V$HIF Hypoxia inducible factor 122 1.57 5.83

Table 25. Top 10 overrepresented modules (in which one partner is the AHRE site) for the top 500 unique TCDD-induced AHR-bound peak regions

Over Modules with Distance Z-Score Rank Description representation V$AHRE Score (genome) (genome) 1 SOX/SRY-sex/testis determining and related HMG V$AHRE V$SORY box factors 1.786 3.58 20.84 2 V$AHRE V$AP1 AP1, Activating protein 1 2.492 8.26 16.73 3 V$AHRE V$FKHD Fork head domain factors 2.377 3.89 14.76 4 V$AHRE V$OCT1 Octamer binding protein 2.9 4 13.39 5 V$AHRE V$LHXF Lim homeodomain factors 2.389 4.1 13.24 6 V$AHRE V$AP1R MAF and AP1 related factors 2.841 4.5 13.06 7 cAMP-responsive element V$AHRE V$CREB binding proteins 3.201 3.92 12.48 8 Paralog hox genes 1-8 from the V$AHRE V$HOX four hox clusters A, B, C, D 2.704 3.59 12.13 9 Homeodomain transcription V$AHRE V$HOM factors 1.946 3.2 12.09 10 Grainyhead-like transcription V$AHRE V$GRHL factors 2.975 6.74 11.45

Unique AHRR Regions

For unique AHRR-bound regions, the ranking for the AHRE was reduced to the 5th ranked overrepresented site compared with the full AHRR dataset. While the Z-score was very high for the AHRE, several other transcription factors displayed higher z-scores including the ZF5 POZ domain zinc finger, nuclear respiratory factor 1 (NRF1), E2F, EGR, BED zinc-finger (BEDF) and

SP1 (Table 26). Unique AHRR regions exhibited an absence of commonly overrepresented transcription factors for the other AHR datasets, such as AP1, AP2, ERE, PAX9 and GATA (Table

26). The module analysis displayed extremely high Z-scores, emphasizing the importance of the interactions between these transcription factor sites and the AHRE (Table 27). These observations suggest that at the sites with AHREs, they are likely to be accompanied by transcription factor binding sites for NRF1, ZF5, E2F, EGR, SP1. Overall, unique AHRR regions showed lower dependence of the AHRE in these regions, relative to other TF binding sites.

Table 26. Top 10 overrepresented transcription factor for top 500 unique TCDD-induced AHRR- bound peak regions

Nr. of Input Over TF Z-Score Rank Description Seq. with representation Families (genome) Match (genome) 1 V$ZF5 ZF5 POZ domain zinc finger 252 37.5 205.37 2 V$NRF1 Nuclear respiratory factor 1 245 42.01 203.66 3 E2F-myc activator/cell cycle V$E2F regulator 415 7.24 97.97 4 EGR/nerve growth factor induced V$EGR protein C & related factors 340 7.41 81.74 5 V$BED BED subclass of zinc-finger proteins 254 11.89 74.95 6 V$SP1 GC-Box factors SP1/GC 361 7.39 74.52 7 AHR-arnt heterodimers and AHR- V$AHRE related factors 362 10.12 73.04 8 CTCF and BORIS gene family, transcriptional regulators with 11 V$CTCF highly conserved zinc finger domains 359 8.53 72.46 9 Huntington’s disease gene regulatory V$HDBP region binding proteins 141 24.1 71.21 10 O$MTEN Core promoter motif ten elements 212 15.04 66.06

Table 27. Top 10 overrepresented modules (in which one partner is the AHR-related factor) for the top 500 unique TCDD-induced AHRR-bound peak regions

Over Modules with Distance Z-Score Rank Description representation V$AHRE Score (genome) (genome) 1 V$AHRE V$NRF1 Nuclear respiratory factor 1 3.101 168.59 366.21 2 V$AHRE V$ZF5F ZF5 POZ domain zinc finger 2.371 113.09 322.99 3 E2F-myc activator/cell cycle V$AHRE V$E2FF regulator 1.685 48.9 220.5 4 EGR/nerve growth factor induced V$AHRE V$EGRF protein C & related factors 3.191 45.56 168.71 5 V$AHRE V$SP1F GC-Box factors SP1/GC 2.603 38.37 138.9 6 C2H2 zinc finger transcription V$AHRE V$ZF02 factors 2 2.009 33.41 123.74 7 V$AHRE V$CTCF CTCF and BORIS gene family 2.675 39.05 122.99 8 Krueppel like transcription V$AHRE V$KLFS factors 1.875 24.18 122.02 9 BED subclass of zinc-finger V$AHRE V$BEDF proteins 2.294 52.77 120.95 10 Activator-, mediator- and TBP- dependent core promoter element for RNA polymerase II transcription from TATA-less O$XCPE V$AHRR promoters 1.804 70.86 110.1

1.7) Gene List & Pathway Analysis

1.7.1) Ligand-induce AHR- and AHRR-bound Genes

To determine which pathways and cellular functions may be regulated by AHR and AHRR, the top 500 closest genes annotated to the AHR-bound and AHRR-bound regions were analyzed using

Ingenuity Pathway Analysis (IPA) software. The top 5 canonical pathways and top 5 diseases and functions were summarized (Table 28-29). As expected, the AHR signalling pathway was the highest ranked pathway for both the AHR and AHRR datasets. Four of the top 10 predicted canonical pathways were common to both datasets: AHR signalling, ERK/MAPK signaling, hepatocyte growth factor (HGF) signaling and the molecular mechanism of cancer (Figure 14).

However, large differences in the identification of other predicted pathways with a wide range of cellular functions were observed. Interestingly, the xenobiotic metabolism pathway was only significant for the AHR, but not for the AHRR list of genes. The differences between the predicted pathways emphasize that despite the fact that AHR and AHRR exhibit considerable functional overlap, they also exhibit unique target gene regulations and functions. Some common predicted top diseases/functions between AHR and AHRR datasets included cellular growth/death, lipid metabolism and development (Table 28-29). Pathways involved in development were more significant in the AHR dataset, whereas cell cycle control was notably increased in the AHRR dataset.

Table 28. Top 5 canonical pathways and top 5 diseases/functions from IPA analysis of the top 500 TCDD-induced AHR-bound genes

Top Canonical Pathways p-value Top Diseases and Functions Score Aryl Hydrocarbon Receptor 1.32E-07 Cellular Development, Cellular Growth Signaling and Proliferation, Digestive System ERK/MAPK Signaling 1.37E-04 Development and Function 37 Xenobiotic Metabolism 3.01E-04 Cell Death and Survival, Energy Signaling Production, Lipid Metabolism 37 Embryonic Development, Organismal HGF Signaling 3.06E-04 Development, Tissue Development 33 Molecular Mechanisms of 5.20E-04 Cellular Development, Hematological Cancer System Development and Function, Hematopoiesis 29 Cellular Development, Hematological System Development and Function, Hematopoiesis 23

Table 29. Top 5 canonical pathways and top 5 diseases/functions from IPA analysis of the top 500 TCDD-induced AHRR-bound genes

Top Canonical Pathways p-value Top Diseases and Functions Score Aryl Hydrocarbon Receptor Lipid Metabolism, Small Molecule Signalling 1.52E-04 Biochemistry, Endocrine System Disorders 37 Molecular Mechanisms of Cell Cycle, Cellular Growth and Cancer 2.75E-04 Proliferation, Cell Morphology 35 ERK/MAPK Signalling 2.21E-03 Hematological Disease, Immunological EIF2 Signalling 3.91E-03 Disease, Cell Death and Survival 31 Cell-To-Cell Signaling and Interaction, Telomerase Signalling 4.46E-03 Cellular Movement, Hematological System Development and Function 23 Cell Cycle, Cellular Assembly and Organization, DNA Replication, Recombination, and Repair 18

AHR AHRR

 Xenobiotic Metabolism Signaling  Aryl Hydrocarbon  EIF2 Signaling  Prolactin Signaling Receptor Signaling  Telomerase Signaling  HER-2 Signaling in Breast Cancer  ERK/MAPK Signaling  mTOR Signaling  VEGF Family Ligand-Receptor  HGF Signaling  VDR/RXR Activation Interactions  Molecular Mechanisms of  EGF Signaling  Bupropion Degradation Cancer  PPARα/RXRα Activation  Acetone Degradation I (to Methylglyoxal)

Figure 14. Venn diagram overlap between the top 10 predicted pathways for the top 500 TCDD- induced AHR-bound (left) and AHRR-bound genes (right)

1.7.2) Overlapped and Unique Genes

AHR/AHRR Co-bound Genes

The top 500 genes for the AHR/AHRR co-bound genes were also analyzed by IPA. Interestingly, the first ranked pathway was HGF signalling, followed by AHR signaling (Table 30). This indicates that the list of bound genes includes and also extends beyond those known for the canonical AHR signaling pathway. It was also worth noting that several of the top functions were related to cellular growth and development, indicating that AHRR’s role in development may be related to its interactions with AHR.

Table 30. Top 5 canonical pathways and top 5 diseases/functions from IPA analysis of the top 500 AHR/AHRR co-bound genes

Top Canonical Pathways p-value Top Diseases and Functions Score HGF Signaling 2.78E-04 Organismal Development, Cellular Movement, Cellular Growth and Aryl Hydrocarbon Receptor Proliferation 42 Signaling 4.56E-04 Organ Morphology, Reproductive System HER-2 Signaling in Breast Development and Function, Cancer 31 Cancer 9.08E-04 Cancer, Cellular Growths and VEGF Family Ligand-receptor Proliferation, Organismal Functions 27 interactions 9.08E-04 Cellular Development, Cellular Growth Molecular Mechanisms of and Proliferation, Embryonic Cancer 1.19E-03 Development 27 Cellular Movement, Cancer, Organismal Injury and Abnormalities 22 Unique AHR-bound Genes

For the top 500 unique AHR-bound genes, the AHR signaling pathway was still the top ranked pathway, but with a lower statistical significance compared with the top 500 of all AHR-bound genes (Table 31). This suggests that unique AHR-bound genes extend beyond those known to be within the canonical AHR signalling pathway. Interestingly, the top predicted function was energy production and lipid metabolism, suggesting that these are AHR-regulated pathways that are independent and resistant to the AHRR-dependent repression by competition for specific DNA sequences.

Table 31. Top 5 canonical pathways and top 5 diseases/functions from IPA analysis of the top 500 unique AHR co-bound genes

Top Canonical Pathways p-value Top Diseases and Functions Score Aryl Hydrocarbon Receptor Energy Production, Lipid Metabolism, Signaling 1.86E-03 Small Molecule Biochemistry 40 VDR/RXR Activation 4.93E-03 Tissue Development, Cancer, Mechanisms of Viral Exit from Cardiovascular Disease 34 Host Cells 9.61E-03 Cell Death and Survival, Cellular Development, Hematological System nNOS Signaling in Neurons 1.43E-02 Development and Function 32 Dopamine-DARPP32 Feedback Cancer, Organismal Injury and in cAMP Signaling 1.56E-02 Abnormalities, Free Radical Scavenging 26 Cancer, Organismal Injury and Abnormalities 19

Unique AHRR-bound Genes

Interestingly, the AHR signal pathway was not amongst the pathways predicted for the unique

AHRR-bound genes (Table 32). The wide range of pathways predicted may be due to AHRR’s ability to interact with multiple transcription factors. Multiple cancer-related pathways were implicated for these unique AHRR-bound genes including glioma invasiveness signalling and lung cancer signalling. A wide range of predicted biological functions included infectious disease, cancer and DNA repair. Overall, unique AHRR-bound genes were shown to be part of a wide range of pathways, supporting the notion that AHRR has broader signalling roles that do not necessarily overlap with AHR.

Table 32. Top 5 canonical pathways and top 5 diseases/functions from IPA analysis of the top 500 unique AHRR co-bound genes

Top Canonical Pathways p-value Top Diseases and Functions Score Actin Nucleation by ARP- Infectious Disease, Cancer, Organismal WASP complex 1.71E-03 Injury and Abnormalities 41 Glioma Invasiveness Signaling 1.88E-03 Cell Death and Survival, Skeletal and Non-Small Cell lung Cancer Muscular System Development and Signaling 3.67E-03 Function, Protein Synthesis 34 Small Cell Lung Cancer Cellular Movement, Auditory Disease, Signaling 5.68E-02 Auditory and Vestibular System Development and Function 34 Protein Ubiquitination Pathway 5.91E-03 Gene Expression, Behavior, Digestive System, Development and Function 21 Infectious Disease, DNA Replication, Recombination and repair, gene expression 15

2) Validation of Unique AHR-bound and AHRR-bound regions by qPCR

2.1) Visualization of the candidate unique AHR-bound and AHRR-bound peak regions

The ChIP signal intensities at the candidate unique AHR-bound and AHRR-bound peak regions were also visualized on Integrative Genomics Viewer (Figure 15-16). Visualization of the signal intensities for the candidate peak regions is a quick and convenient method to confirm the results of the overlap analysis. Visual examples of the ChIP signal intensities were given for a unique

AHR-bound peak region, Cannabioid receptor 2 (CNR2) (Figure 15) and a unique AHRR-bound peak region, X-ray repair cross-complementing protein 6 (XRCC6) (Figure 16).

DMSO 50 AHR 0

TCDD 50 AHR 0

DMSO 50 AHRR

TCDD 50 AHRR 0

Figure 15. ChIP signal intensity of a candidate unique AHR-bound region located near the CNR2 gene. Scales are equivalent between samples.

DMSO 50 AHR

0 TCDD 50 AHR

DMSO 50 AHRR

TCDD 50 AHRR

Figure 16. ChIP signal intensity of a candidate unique AHRR-bound region located near the XRCC6 gene. Scales are equivalent between samples.

2.2) ChIP-qPCR of Unique AHR-bound Regions

The unique AHR-bound regions were chosen from a subset of overlapping AHR-bound regions from 45 min and 24 h datasets that did not overlap with the AHRR-bound regions. These unique regions were not annotated to the same genes as AHRR-bound regions. I chose 4 unique AHR- bound regions for further study. They were annotated to the following closest genes, Acyl-CoA

Synthetase Long-Chain Family Member 1 (ACSL1), CNR2, Laminin Alpha 4 (LAMA4) and Raftlin

Lipid Raft Linker 1 (RFTN1). Using ChIP-qPCR, I confirmed the TCDD-dependent recruitment of AHR but not AHRR to all selected unique AHR gene regions (Figure 17). Of the four regions,

CNR2 and RFTN1 were chosen to be candidates for the reporter gene assay.

CNR2 ACSL1

0.03 ** 0.10 ** DMSO 24h DMSO 24h TCDD 24h 0.08 TCDD 24h 0.02 0.06

0.04 % Total of % Total of 0.01 0.02

0.00 0.00

IgG IgG AHR AHR AHRR AHRR ChIP Antibody ChIP Antibody LAMA4 RFTN1 0.08 0.15 ** DMSO 24h DMSO 24h TCDD 24h *** TCDD 24h 0.06 0.10

0.04

% Total of % Total of 0.05 0.02

0.00 0.00

IgG AHR IgG AHR AHRR AHRR ChIP Antibody ChIP Antibody

Figure 17. ChIP-qPCR of unique AHR-bound regions (annotated to ACSL1, CNR2, LAMA4 and RFTN1) selected from ChIP-seq analysis. Normalized to total (input). Asterisks indicate statistically significant differences compared to the DMSO sample (P<0.05, 0.01, 0.001) using a two-way ANOVA followed by a Bonferroni post-test.

2.3) ChIP-qPCR of Unique AHRR-bound Peak Regions

Similar to the unique AHR-bound regions, the unique AHRR-bound regions were chosen from a subset of AHRR-bound regions that did not overlap with AHR-bound regions identified after 45 min or 24 h of TCDD treatment. These unique regions were not annotated to the same genes as any AHR-bound regions. I chose 4 unique AHRR-bound regions for further analysis. The 4 regions were annotated to the following closest genes: Mitogen-activated Protein Kinase Kinase 7

(MAP2K7), Pyridoxamine 5’-Phosphate Oxidase (PNPO), Ribosomal Protein L22 (RPL22) and

XRCC6. Using ChIP-qPCR, I confirmed significant TCDD-induced AHRR recruitment but not

AHR recruitment to each of the four gene regions (Figure 18). Out of the four regions, the XRCC6 and MAP2K7 unique AHRR-bound regions were chosen for the reporter gene assay.

MAP2K7 PNPO 0.020 0.015 * * DMSO 24h DMSO 24h TCDD 24h TCDD 24h 0.015 0.010

0.010

% Total of 0.005 % Total of 0.005

0.000 0.000 IgG IgG AHR AHR AHRR AHRR ChIP Antibody ChIP Antibody

RPL22 XRCC6 0.020 0.025 DMSO 24h DMSO 24h ** TCDD 24h *** TCDD 24h 0.015 0.020 0.015 0.010 0.010

% Total of % Total of 0.005 0.005

0.000 0.000

IgG IgG AHR AHR AHRR AHRR ChIP Antibody ChIP Antibody

Figure 18. ChIP-qPCR of unique AHRR-bound regions (annotated to MAP2K7, PNPO, RPL22, XRCC6) selected from ChIP-seq analysis. Normalized to total (input). Asterisks indicate statistically significant differences compared to the DMSO (P<0.05, 0.01, 0.001) using a two-way ANOVA followed by a Bonferroni post-test.

3) Gene Expression Analysis of Unique AHR-bound & AHRR- bound Genes

3.1) CYP1B1 Luciferase Reporter Gene Assay as Positive Control

For a positive control, pGL3-basic-CYP1B1-promoter was also used with varying levels of AHR,

ARNT and AHRR expression vectors (MacPherson et al., 2009).

3.1.1) CYP1B1 gene expression, as a positive control, exhibited both AHR/ARNT- dependent increase which was further induced with TCDD treatment.

Luciferase activity was measured for transfection using pGL3-basic-CYP1B1-promoter luciferase vector and increasing amount of AHR/ARNT expression vectors. As expected, AHR/ARNT increased the luciferase activity even without the addition of TCDD. TCDD further induced the luciferase activity, demonstrating the classical role of AHR in mediating the effects of TCDD

(Figure 19).

3.1.2) CYP1B1 gene expression displayed a classical AHRR-mediated repression model in an AHRR gene dose-dependent manner.

Luciferase activity with a transfection of increasing amount of AHRR expression vector transfected along with pGL3-basic-CYP1B1 -promoter, 200 ng AHR/ARNT expression vector was measured. The cells were treated with either DMSO or TCDD 100 nM. Increasing levels of transfected AHRR exhibited a gene dose-dependent repression of both AHR/ARNT-mediated basal and TCDD-induced luciferase levels (Figure 20). This experiment demonstrated the classical

AHRR-mediated repression of AHR-target gene CYP1B1.

pGL3-basic CYP1B1-promoter Figure 19. Luciferase activity of pGL3-basic-CYP1B1-promoter 15 DMSO with increasing amount (0, 100, *** TCDD 10nM 200) of AHR and ARNT *** TCDD 100nM expression vector transfected 10 *** treated with DMSO, TCDD 10 *** nM and TCDD 100 nM. The luciferase activity was 5 normalized to the luciferase activity of control (0 ng transfection of AHR and ARNT

Normalized to DMSO AHR/ARNT 0ng toAHR/ARNT DMSO Normalized 0 expression vector). A two-way ANOVA was performed followed by a Bonferroni post-test. Asterisks indicate statistically AHR/ARNT 0ng AHR/ARNT 100ng AHR/ARNT 200ng significant differences (P<0.001) Transfection when compared to the DMSO control. There was also an

extremely significant interaction between AHR/ARNT transfection and TCDD treatment (p<0.0001).

pGL3-basic CYP1B1-promoter Figure 20. Luciferase activity of 2.5 pGL3-basic-CYP1B1-promoter *** DMSO with increasing amount (0, 100, TCDD 100nM 2.0 200) of AHRR expression vector transfected in the 1.5 presence of 200 ng of AHR/ARNT expression vector 1.0 treated with DMSO or TCDD 100 nM. A two-way ANOVA 0.5 was performed followed by a

Normalized to DMSO AHRR 0ng toAHRR DMSO Normalized 0.0 Bonferroni post-test. Asterisks indicate statistically significant differences (P<0.001) when compared to DMSO. There was also a significant interaction between AHRR transfection and

AHR/ARNT 200ng + AHRR 0ng TCDD treatment (p<0.0001). AHR/ARNT 200ng + AHRRAHR/ARNT 100ng 200ng + AHRR 200ng Transfection

3.2) XRCC6 Luciferase Reporter Gene Assay

To study the AHRR-dependent regulation of XRCC6 gene, I PCR-amplified and cloned the

XRCC6 promoter region (-682 to +318, from SwitchGear) into pGL3-basic luciferase vector and the AHRR-bound ChIP region that was identified near the XRCC6 gene into the pGL3-promoter luciferase vector.

Luciferase activity with a transfection of increasing amount of AHRR expression vector along with pGL3-basic-XRCC6-promoter luciferase vector was measured (Figure 21). Empty pGL3- basic vector was transfected as a negative control showing no background. The luciferase activity was decreased with increasing amount of AHRR expression vector transfected showing a gene dose-dependent decrease.

Luciferase activity with a transfection of increasing amount of AHR and ARNT expression vectors along with the pGL3-basic-XRCC6-promoter luciferase vector was measured (Figure 22). The cells were treated with either DMSO, TCDD 10 nM or TCDD 100 nM. No significant change was seen either with increasing amount of AHR/ARNT transfected or with increasing concentrations of TCDD.

Luciferase activity with a transfection of increasing amount of AHR and ARNT expression vectors, along with a constant level of pGL3-basic-XRCC6-promoter luciferase vector and AHRR expression vector, was measured. The cells were treated with either DMSO or TCDD 100 nM.

The control represented transfection with only the pGL3-basic-XRCC6-promoter vector. Here,

AHRR was shown to repress the luciferase activity regardless of the amount of AHR/ARNT expression vectors transfected or the dose of TCDD (Figure 23a). However, increasing amount of

AHRR expression vector with constant levels of AHR/ARNT significantly repressed the luciferase activity in similar manner between DMSO and TCDD treatment (Figure 23b). These experiments

88 showed that the AHR/ARNT complex was unable to affect or competitively reverse AHRR- mediated repression.

Luciferase activity with a transfection of increasing amount of AHRR expression vector along with pGL3-promoter-XRCC6-ChIP luciferase vector was measured to evaluate whether the ChIP region would be sufficient on its own for the repression. Empty pGL3-basic vector was transfected as a negative control. No change in reporter gene activity was observed after transfection of increasing concentrations of AHRR expression vector indicating that solely the ChIP region was not sufficient for repression (Figure 24). Since AHRR affected the luciferase activity of the reporter plasmid with the promoter but not the ChIP region, regions surrounding the AHRR-bound region for XRCC6 may be essential for the repression.

pGL3-basic XRCC6-prom Figure 21. Luciferase activity of pGL3-basic XRCC6-promoter 1500 with increasing amount (0, 100, 200, 400 ng) of AHRR expression * transfection normalized to 1000 ** control (pGL3-basic empty). The negative control represented the *** transfection with a pGL3-basic 500 empty vector. One-way ANOVA

Normalized to Control Normalized was performed along with Bonferroni post-test. Asterisks 0 indicate statistically significant

differences (P<0.05, 0.01, 0.001) Control AHRR 0ng with the Bonferroni post-test when AHRR 100ng AHRR 200ng AHRR 400ng Transfection compared to the luciferase activity level with 0 ng of AHRR expression vector transfection.

pGL3-basic XRCC6-prom Figure 22. Luciferase activity 2.0 of pGL3-basic XRCC6- DMSO promoter with increasing TCDD 10nM 1.5 TCDD 100nM amount (0, 100, 200, 400 ng) of AHR and ARNT expression 1.0 vector transfected with DMSO, TCDD 10 nM or 0.5 TCDD 100nM treatment. The luciferase activity was

Normalized to DMSO AHR/ARNT 0ng toAHR/ARNT DMSO Normalized 0.0 normalized to the luciferase activity of control (0 ng transfection of AHR and ARNT AHR/ARNT 0ng AHR/ARNT 100ngAHR/ARNT 200ngAHR/ARNT 400ng expression vectors). No Transfection significant differences were

found using a two-way ANOVA

coupled with Bonferroni multiple comparison post-test.

A pGL3-basic XRCC6-prom B pGL3-basic XRCC6-prom

1.5 DMSO 1.5 DMSO TCDD 100nM TCDD 100nM

1.0 1.0 *** ** ** **** *** ** ** **** 0.5 *** *** 0.5

Normalized to Control Normalized

Normalized to DMSO AHRR 0ng AHRR to DMSO Normalized 0.0 0.0

Control

400 AHR/ARNT + AHRR 0ng AHRR 400ng + AHR/ARNT 0ng 400 AHR/ARNT + AHRR 400200ng AHR/ARNT + AHRR 400ng AHRR 400ng + AHR/ARNTAHRR 400ng 200ng + AHR/ARNT 400ng Transfection Transfection

Figure 23. Luciferase activity of pGL3-basic XRCC6-promoter with increasing amount (0, 200, 400 ng) of AHR and ARNT (A) or AHRR (B) expression vector transfected in the presence of high levels of AHRR (A) or AHR/ARNT (B). Cells were treated with DMSO and

TCDD 100 nM. The luciferase activity was normalized to the luciferase activity of DMSO-treated luciferase vector only (A) or DMSO-treated 0 ng AHRR + 400 ng AHR/ARNT (B). Two-way ANOVA was performed. There was no significant interaction between the treatment and vector transfection for both sets of data. Asterisks indicate statistically significant differences (P<0.01,0.001,0.0001) with the Bonferroni post-hoc test when compared to the luciferase activity level with control (no expression vector transfected) (A) or 0 ng AHRR expression vector transfection (B).

pGL3-promoter XRCC6-ChIP

to Control Normalized

Control 0ng AHRR 100ng AHRR 200ng AHRR 400ng AHRR Transfection

Figure 24. Luciferase activity of pGL3-promoter XRCC6-ChIP with increasing amount of AHRR expression transfection (0, 100, 200, 400 ng) normalized to control (pGL3-promoter empty). The pGL3-promoter empty vector was used as the negative control. However, one-way ANOVA with Bonferroni post-test did show statistically significant differences amongst the different levels of AHRR transfection. No gene dose-dependent change was observed.

3.3) MAP2K7 Luciferase Reporter Gene Assay

AHRR-bound region at MAP2K7 was also found within the promoter region of the gene. The

MAP2K7 promoter (-552 to +1050, from SwitchGear) was cloned into the pGL3-basic luciferase vector.

Luciferase activity with a transfection of increasing amount of AHRR expression vector along with pGL3-basic-MAP2K7-promoter luciferase vector was measured. The luciferase activity was significantly decreased with a high amount of AHRR expression vector transfected (Figure 25).

Luciferase activity with a transfection of increasing amount of AHR and AHRR expression vector along with pGL3-basic-MAP2K7-promoter luciferase vector was measured. The cells were treated with either DMSO, TCDD 10 nM or TCDD 100 nM. The luciferase activity was increased even at the lowest level of transfection with the AHR and ARNT expression vectors. Subsequent increases in AHR and ARNT expression vectors did not affect the luciferase activity. Increasing concentrations of TCDD also did not significantly affect the luciferase activity, although a weak trend was observed (Figure 26).

pgL3-basic MAP2K7_prom Figure 25. Luciferase activity of

1.5 pGL3-basic MAP2K7-promoter with increasing amount (0, 100, 200, 400 ng) of AHRR expression transfection 1.0 normalized to 0 ng of AHRR transfected. One-way ANOVA * was performed resulting in 0.5 significant differences (p=0.0278). Asterisks indicate statistically significant differences (P<0.05) 0.0 with the Bonferroni post-test when

Normalized to DMSO AHRR 0ng AHRR to DMSO Normalized compared to the luciferase activity level with no AHRR expression vector transfection. AHRR 0ng AHRR 100ng AHRR 200ng AHRR 400ng Transfection

pgL3-basic MAP2K7_prom 3 DMSO TCDD 10nM TCDD 100nM 2

Normalized to DMSO AHR/ARNT 0ng AHR/ARNT to DMSO Normalized

AHR/ARNT 0ng AHR/ARNT 100ngAHR/ARNT 200ngAHR/ARNT 400ng Transfection

Figure 26. Luciferase activity of pGL3-basic MAP2K7-promoter with transfection of increasing amount (0, 100, 200, 400 ng) of AHR and ARNT expression vector with DMSO, TCDD 10 nM or TCDD 100 nM treatments. The luciferase activity was normalized to the luciferase activity of control (DMSO, no expression vector transfected). Two-way ANOVA was performed along with a Bonferroni post-test. Transfection of 100 ng, 200 ng or 400 ng AHR/ARNT significantly increases luciferase activity compared to no transfection. There were no significant differences between the treatments although a slight trend can be observed.

3.4) CNR2 Luciferase Reporter Gene Assay

AHR-bound region at CNR2 was not found to be in the promoter region of the gene, but rather in the intragenic region. The CNR2 ChIP region was cloned into pGL3-promoter luciferase vector to examine whether it could act as an enhancer. Although the closest TSS is that of CNR2, it does not necessarily mean that this region regulates the expression of this gene.

Luciferase activity with transfection of increasing amount of AHR and ARNT expression vectors was measured. The cells were treated with either DMSO or TCDD 100 nM. The luciferase activity

93 was increased with the increased transfection of AHR and ARNT expression vectors and further induced in an AHR/ARNT dose dependent manner after TCDD treatment (Figure 27).

Luciferase activity with a transfection of increasing amount of AHRR expression vector along with the pGL3-promoter-CNR2-ChIP luciferase vector was measured. The CNR2-mediated luciferase activity was not significantly changed with increasing amount of AHRR expression vector (Figure 28).

Luciferase activity with a transfection of increasing amount of AHRR expression vector along with pGL3-basic-CNR2-promoter luciferase vector and high amount of AHR and AHRR expression vectors was measured. The cells were treated with either DMSO or TCDD 100 nM.

The transfection of AHRR expression vector neither changed the basal nor the TCDD-induced luciferase activity (Figure 29).

Figure 27. Luciferase activity of pGL3-promoter CNR2-ChIP pGL3-promoter CNR2-ChIP

5 DMSO with increasing amount (0, 100, 200, 400 ng) of AHR and ARNT TCDD 100nM ** 4 expression vectors transfected

with DMSO or TCDD 100 nM 3 treatment. The luciferase * activity was normalized to the

2 * luciferase activity of control

(DMSO-treated and 0 ng 1 transfection of AHR and ARNT

0ng to AHR/ARNT Normalized expression vector). Asterisks 0 indicate statistically significant differences (P<0.05, 0.01) with Bonferroni multiple comparison when compared to DMSO. There AHR/ARNT 0ng AHR/ARNT 100ngAHR/ARNT 200ngAHR/ARNT 400ng was also a significant interaction Transfection between the amount of AHR/ARNT transfected and TCDD treatment (p=0.0015).

pGL3-promoter CNR2-ChIP

1.5 Figure 28. Luciferase activity of pGL3-promoter-CNR2-ChIP with increasing amount (0, 100, 200, 400

1.0 ng) of AHRR expression transfection normalized to 0 ng of 0.5 AHRR transfected. No significant change with increased AHRR

Normalized to AHRR 0ng to AHRR Normalized transfection was observed. 0.0

AHRR 0ng AHRR 100ng AHRR 200ng AHRR 400ng Transfection

pGL3-promoter CNR2-ChIP 2.0 DMSO TCDD 100nM * 1.5 * * *

1.0

0.5 to DMSO Normalized

0.0

AHR/ARNT 400ng + AHRR 0ng AHR/ARNT 400ng AHR/ARNT+ AHRR 100ng 400ng AHR/ARNT+ AHRR 200ng 400ng + AHRR 400ng Transfection Figure 29. Luciferase activity of pGL3-promoter CNR2-ChIP with increasing amount (0, 100, 200, 400 ng) of AHRR expression vector transfected in the presence of high levels (400 ng) of AHR/ARNT expression vector treated with DMSO or TCDD 100 nM. Two-way ANOVA was performed. There was a significant difference between TCDD treatment and DMSO across all samples (p=0.0003). Asterisks indicate statistically significant differences (P<0.05) when compared to DMSO with Bonferroni post-test. No significant interaction was observed between AHRR transfection and the TCDD-induced increase.

3.5) RFTN1 Luciferase Reporter Gene Assay

Like the CNR1 AHR-bound region, the AHR-bound region at RFTN1 was also not found in the promoter region of the gene and was found within the intragenic regions. The RFTN1 ChIP region was cloned into pGL3-promoter luciferase vector to examine whether it could act as an enhancer.

Luciferase activity with a transfection of increasing amount of AHR and AHRR expression vectors along with the pGL3-promoter-RFTN1-ChIP luciferase vector was measured. The cells were treated with either DMSO or TCDD 100 nM. The luciferase activity was significantly increased in the presence of increasing amounts of AHR/ARNT expression vectors (Figure 30). However,

TCDD did not significantly affect the luciferase activity when compared to DMSO control.

Luciferase activity with a transfection of increasing amount of AHRR expression vector along with pGL3-promoter-RFTN1-ChIP luciferase vector was measured. The luciferase activity was not significantly changed in the presence of increasing amounts of AHRR (Figure 31).

Luciferase activity with a transfection of increasing amount of AHRR expression vector along with pGL3-basic-RFTN1-promoter, 400 ng AHR expression, and 400 ng ARNT expression vector was measured. The cells were treated with either DMSO or TCDD 100 nM. This was done to examine whether AHRR could repress the previously established AHR-mediated increase in basal level of RFTN1 reporter gene activity. No significant change from DMSO was found (Figure 32).

pGL3-promoter RFTN1-ChIP

2.5 DMSO

TCDD 100nM 2.0

1.5

1.0

0.5

0.0

Normalized to DMSO AHR/ARNT 0ng AHR/ARNT to DMSO Normalized

AHR/ARNT 0ng AHR/ARNT 100ngAHR/ARNT 200ngAHR/ARNT 400ng Transfection

Figure 30. Luciferase activity of pGL3-promoter RFTN1-ChIP with increasing amount (0, 100, 200, 400 ng) of AHR and ARNT expression vector transfected with DMSO or TCDD 100 nM treatment. The luciferase activity was normalized to the luciferase activity of control (0 ng transfection of AHR and ARNT expression vector). A two-way ANOVA was performed. Luciferase activity increase was significantly associated with increasing AHR/ARNT transfection (p<0.0001).

pGL3-promoter RFTN1-ChIP Figure 31. Luciferase activity of 1.5 pGL3-promoter-RFTN1-ChIP with increasing amount (0, 100, 200, 400 ng) of AHRR expression transfection 1.0 normalized to 0 ng of AHRR transfected. One-way ANOVA was performed. No significant changes

0.5 were observed.

Normalized to AHRR 0ng to AHRR Normalized

0.0

AHRR 0ng AHRR 100ng AHRR 200ng AHRR 400ng Transfection

pGL3-promoter RFTN1-ChIP

2.0 DMSO TCDD 100nM 1.5

1.0

0.5

0ng to AHRR Normalized

0.0

AHR/ARNT 400ng + AHRR 0ng AHR/ARNT 400ng AHR/ARNT+ AHRR 100ng 400ng AHR/ARNT+ AHRR 200ng 400ng + AHRR 400ng Transfection

Figure 32. Luciferase activity of pGL3-promoter RFTN1-ChIP with increasing amount (0, 100, 200, 400 ng) of AHRR expression vector transfected in the presence of high levels (400 ng) of AHR/ARNT expression vector treated with DMSO or TCDD 100 nM. A two-way ANOVA was performed. No significant differences were observed with increased AHRR transfection or TCDD treatment.

Chapter 5: Discussion

1) AHR and AHRR binding profiles exhibit both similar and different characteristics.

1.1) Ligand-induced AHR binding patterns

Ligand-induced AHR-bound regions were mapped using ChIP-seq. Using MACS2 peak-caller program with default settings, 3915 and 20954 ligand-induced peak regions were detected with 24 h and 45 min TCDD (10nM) treatment respectively (Table 3). A previous study in our lab identified 2594 AHR-bound regions with 45 min TCDD treatment in MCF-7 cells using a different peak-caller program, CisGenome (Lo and Matthews, 2012). The difference in the number of identified peaks may be due to the peak-calling algorithms used, in which the MACS algorithm generally outputs more peaks than that of CisGenome. Other factors that may have contributed to this difference include the different library preparation methods and mapping algorithms used (Lo and Matthews, 2012). Using multiple peak-calling programs, more peak regions were consistently found at 45 min of TCDD treatment when compared with 24 h. This same observation was also seen for AHR in mouse liver tissue in which there were significant more enriched regions found with 2 h than with 24h TCDD treatment (Dere et al., 2011). Nearly all of the results from my downstream analyses of the ligand-induced AHR-bound regions are consistent with the results obtained by Lo and Matthews (2012). Visualization of the ChIP signal intensity shows strong increases in the intensity levels with TCDD treatment at classical AHR-target genes such as

CYP1A1 (Figure 6), CYP1B1 and CYP1A2. The genomic location distribution of the peak regions exhibits around 52% intergenic regions and 43% intron regions (Figure 7). Only 2.6% of the peak regions are within promoter-TSS region, suggesting that binding preference of AHR for promoters

99 is a relatively weak one. The distance to TSS analysis suggests that, while the highest densities are near the TSS, in agreement with our previous work, AHR-bound regions are generally widespread, binding as far as 100 kb away from the TSS (Figure 9, Lo and Matthews (2012)). This disperse binding pattern suggests that AHR may be able to mediate gene expression from a long range potentially through the remodeling of the chromatin, such as chromatin looping (Fullwood et al.,

2009), and potentially intrachromosomal and interchromosomal interactions (Hu et al., 2008).

Both the motif discovery studies and overrepresentation of transcription factor binding site (Table

18) analysis are in agreement with the similar study reported by Lo and Matthews (2012).

Interestingly, the Forkhead Box motif ranks higher than the AHR motif in both DREME and

SEME program, implicating that Forkhead Box family of proteins has an essential role in AHR signalling (Table 4 and 8). However, using the top 500 peaks clearly shows that the AHR motif is still the most important (Table 5 and 9). Nevertheless, the importance of Forkhead Box proteins to

AHR signalling is evident. In a previous study in our lab, FOXA1 has been found to be essential for TCDD-induced recruitment of AHR to the CCNG2 gene (Ahmed et al., 2012). FOXA1 has also been shown to be important in estrogen receptor binding (Hurtado et al., 2011). FOXA proteins have pioneering transcription activity allowing them to bind and open condensed chromatin to permit access for other factors. Like FOX, GATA proteins are also reported to act as pioneer transcription factors (Zaret and Carroll, 2011). The role of pioneer transcription factors, such as FOX and GATA, on AHR signalling is still unclear and will require further investigation.

Interestingly, I also found a motif containing the estrogen response element (ERE) (Table 4 and

18), supporting the well-established crosstalk between AHR and estrogen receptor signalling (Safe et al., 2000; Ahmed et al., 2009). Another group of motifs found was the JUN- and FOS-related group of motifs (part of the AP-1 complex), which may be related to or influence the role of AHR

100 in cell proliferation/apoptosis (Puga et al., 1992, Puga et al., 2000). This is also highlighted in the overrepresented TF analysis with a high rank for the AP-1 (Table 18). The second top ranked overrepresented TF is AP-2, which plays a critical role in regulating gene expression during early development and may be associated with the developmental role of AHR shown in previous studies (Gonzalez et al., 1995; Lahvis et al., 2000; Huang et al., 2004). The module analysis shows similar predicted overrepresented transcription factors: AP-1, AP-2 and FOX (Table 20). Also of interest, the highest ranked module is the SOX/SRY-sex/testis determining and related HMG box factors, implying a possible role for AHR in sex determination, reproductive function and neuronal development. This is supported by a previous study that suggested that the SOX-related neuronal development is mediated by AHR (Gohlke et al., 2009). Furthermore, Ahr-null mice exhibit low sperm counts, reduced fertility and reduced testosterone levels (Baba et al., 2008).

As expected, the top canonical pathways predicted from the closest annotated genes of AHR- bound regions comprise the AHR signalling pathway, which was predicted significantly higher than any other pathways, followed by ERK/MAPK signalling pathway and xenobiotic metabolism signalling (Table 28). Analysis of the top biological functions and diseases from the list of genes is suggestive of AHR’s role in lipid metabolism, development, cell death and survival (Table 28), which have been previously described as possible biological functions of AHR (Nishiumi et al.,

2008; Schmidt et al., 1996; Evans et al., 2005; Puga et al., 2002). Overall, the analyses of the

AHR-bound regions are in strong agreement with previous ChIP-seq analyses for AHR (Lo and

Matthews, 2012) and provide further insights into the complexity of AHR signalling.

101

1.2) Ligand-induced AHRR binding patterns

Although genomic AHR binding profiles have been previously studied, my work is the first to report genome-wide binding profiles for AHRR. Using the MACS2 peak-caller program with default settings, 2811 ligand-induced peak regions were detected with 24 h treatment of 10 nM

TCDD in MCF-7 cells. Similar to the AHR-bound regions, visualization of the AHRR-bound regions shows strong recruitment of AHRR to the classical AHR target gene such as CYP1A1,

CYP1B1 and CYP1A2 (Figure 6). The most striking discovery was the high promoter-focused binding of AHRR. Analysis of the genomic location distribution of the ligand-induced peak regions revealed that approximately 26% of the AHRR-bound regions were within the promoter-

TSS region (designated as -1000 bp to +40 bp from TSS) (Figure 8). This binding pattern was also illustrated in the histogram generated for the distance to TSS (Figure 9). De novo motif discovery using both DREME and SEME predicted a motif with the complete AHRE core consensus sequence 5’-GCGTG-3.’ Using SEME, the predicted AHR motif had an interesting pattern that is similar to the AHRE core sequence with an emphasis on the guanine base, resulting in a more GC- rich version of the AHRE (Table 10). This GC-rich pattern may be attributed to the binding of a zinc finger protein (Berg, 1992), which is consistent with the overrepresented TF binding site analysis, in which ZF5 POZ domain zinc finger is ranked with a high z-score (Table 19). ZF5 is a transcriptional repressor shown to repress the mouse c-myc gene (Numoto et al., 1993). This suggests that the transcriptional repression function of AHRR may be related to possible interactions with the ZF5 transcriptional repressor. Other significant motifs identified in the analysis included the SP1, E2F family and the FOX family of proteins. Although the AHRE was highly ranked in the overrepresented transcription factor binding site analysis, several other transcription factor sites were also overrepresented with similarly high scores including nuclear

102 respiratory factor 1, EGR/nerve growth factor, E2F-myc activator/ cell cycle regulator and so on

(Table 19). The response element recognized by the E2F family of proteins was consistently overrepresented in multiple analyses, suggesting potential interactions between of AHRR with

E2F transcription factors. This is especially interesting because the E2F transcription factors play essential roles in the regulation of cell proliferation (Helin et al., 1998). A range of biological functions were predicted for the analysis of the AHRR-bound genes, including cell cycle control, lipid metabolism, development and even DNA repair. The molecular mechanisms of cancer was the 2nd most highly ranked pathway after AHR signalling, suggesting an important role for AHRR in cancer (Table 29) (Zudaire et al., 2008).

1.3) Comparison between the AHR and AHRR binding profiles

The discovery of AHRR provided a popular mechanistic model for the negative regulation of AHR signalling. Although not a focus of this thesis, more recent work suggests that TIPARP is also a prominent and possibly dominant repressor of AHR activity (MacPherson et al., 2014). In the simple model, the AHRR-mediated repression is thought to be due to a competition between

AHRR and AHR for binding to ARNT and subsequently AHREs. This competition-based model implies that AHR and AHRR have very similar, if not the same, binding preferences. The high similarity in the amino acid sequence in bHLH domain (which contains a DNA-binding domain) compared to that of AHR, suggests that the AHR and AHRR are likely to bind in a similar fashion

(Mimura et al., 1999). A major goal of my study is to provide a comparison between the binding preferences of AHR and AHRR in a genome wide manner. Interestingly, the overlap analysis shows that AHRR can potentially bind to non-AHR-bound regions. Moreover, there are more overlapped annotated genes than overlapped binding regions, suggesting that AHR and AHRR may bind close to the same genes but not to the same sequences (Figure 10). This result contradicts

103 with that of the simple competitive model as it suggests that AHRR-mediated repression may not necessarily require competition with AHR for the same AHREs. Although 1064 co-bound genes were identified, there were 504 unique AHRR-bound genes, even after processing the overlap with a larger set of AHR-bound genes (at 45 min treatment) (Figure 11). Therefore, with these observations, it is likely that AHRR does not bind to all the AHR-bound regions. The genomic location analysis of the AHR- and AHRR- bound regions revealed even more striking differences.

While AHR-bound regions were distributed over a large distance with some preferences for TSS,

AHRR-bound regions were promoter-focused with extremely high enrichment close to the TSS.

This substantial difference in binding preferences between AHR and AHRR further supports an

AHR-independent role for AHRR. However, visualization of ChIP signal intensity near the classical AHR-target gene, CYP1A1, CYP1B1 and CYP1A2, revealed that AHR and AHRR bind at the same location (Figure 6). Based on these results, the simple competition model, proposed by

Mimura et al. (1999), may only be true in a context and promoter-specific manner. Since AHRR prefers to bind to promoters, we postulated that the repression of AHR by AHRR was more likely to occur within promoter regions. However, despite the promoter-centric binding of AHRR, regions outside of the promoter may in some cases also be important for the competition-based repression (Tables 11 and 12a). In the overrepresented TF binding site analysis, AHRE sites were highly ranked in both AHR-bound and AHRR-bound regions. However, vast differences in the overrepresentation of other transcription factors and modules were evident. Some of the highly- ranked overrepresented transcription factors with high z-score in AHRR dataset were ranked much lower in the AHR dataset, indicating potentially distinct functional preferences of AHRR and AHR for other transcription factors. Although the gene analysis of both datasets (top 500 genes) showed the AHR signalling as the top canonical pathway, there were some notable differences. The

104 xenobiotic metabolism signalling pathway, which was the top ranked pathway for AHR, was not amongst the pathways found in the AHRR dataset. Other than a few xenobiotic metabolism related genes that has been studied extensively in the past such as CYP1A1 and CYP1B1, it is possible that

AHRR-mediated repression may not extend to the other genes in the xenobiotic metabolism pathway normally regulated by AHR.

Analysis of different subsets of overlapping and unique AHR- and/or AHRR-bound regions showed substantial difference in their binding profiles. While co-bound regions display a binding pattern that is as expected (Figure 12a), unique AHR-bound and AHRR-bound regions showed nearly opposite binding patterns. Unique AHR-bound regions were more dispersed throughout the genome and were generally located even further from the promoter (Figure 12b), whereas unique

AHRR-bound regions were almost exclusively within promoter regions (Figure 12c). A comparison of binding distribution clearly shows that AHR and AHRR differentially bind across the genome, but it does not explain why. In order to explore these differences further, de novo motif discovery was performed. AHR/AHRR co-bound regions were highly dependent on the

AHRE with the AHRE motif ranking far higher than any of the other motifs (Tables 12-13). This is supportive of the classical model proposed by Mimura et al. (1999), in which AHR and AHRR competes for AHREs. Interestingly, for the unique AHR-bound regions, the FOX motif was the most important motif, ranking above the AHRE motif with the top 500 regions (Table 14-15).

With SEME, a perfect FOX motif, specifically the FOXA1, is calculated from this subset of regions, suggesting that FOXA1 may be the key to the difference in binding between AHR and

AHRR. In addition, the FOX motif was not found in the unique AHRR-bound regions (Table 16-

17). For the pathway analysis, the top 10 predicted pathways for AHR and AHRR were also compared and overlapped. While AHR and AHRR shared common pathways, a wide range of

105 pathways and biological functions were predicted for AHRR (Table 29). Moreover, the AHR signaling pathways was not predicted in the unique AHRR genes (Table 32). This suggests that

AHRR exhibits cellular functions that are independent of AHR.

We speculate that AHRR interacts with other different partners leading to the overall difference in binding. Based on these analyses, there seems to be clear similarities and differences between the binding properties of AHR and AHRR. Although ChIP-seq can provide information on where a transcription factor binds on the genome, the binding does not necessarily equate to regulation of gene expression. Therefore, luciferase reporter gene assays were also performed in this study on selected regions to examine changes in gene expression.

2) AHRR-mediated transcriptional repression can be AHR- and ARNT-independent.

When a transcription factor binds to a certain region in the genome, it does not necessarily mean that it will regulate the closest gene (MacQuarrie et al., 2011). So, it is important to examine whether a unique AHRR-bound region can affect gene expression independently of AHR. The

COS-1 cells were chosen for the luciferase reporter gene assays since they express low levels of endogenous AHR (Levine et al., 2000) and ARNT (Long et al., 1999). Transfection of increasing amount of AHRR expression vector led to dose-dependent decreases in the luciferase activity of the reporter gene construct containing the XRCC6 promoter (Figure 21). Remarkably, the ability of AHRR to repress independently of AHR or ARNT suggests that AHRR has an intrinsic repressor function. Transfection of the AHR and ARNT expression vectors with TCDD treatment did not affect the luciferase activity, indicating that XRCC6 is a unique AHRR target gene (Figure

22). Because the AHRR-bound region identified by ChIP-seq was insufficient on its own to be

106 repressed by AHRR (Figure 24), additional sequences that may be recognized by other transcription factors may be necessary for optimal repression. In addition, AHRR was able to repress MAP2K7 when transfected at high levels (Figure 25). However, MAP2K7 gene expression is affected by AHR transfection (Figure 26), indicating that MAP2K7 region may be targeted by both AHR and AHRR. Overall, the series of transfection experiments established that AHRR can repress gene expression in an AHR-independent manner, which may be promoter-specific.

3) Not all AHR-mediated gene expression can be repressed by AHRR via competitive binding to the same region.

Another goal of this study was to examine the limitations of AHRR-mediated repression of AHR.

AHR-bound regions annotated to the closest genes, CNR2 and RFTN1, were cloned into a luciferase reporter gene construct. Using luciferase experiments, both the CNR2 and RFTN1 ChIP regions were increased with transfection of AHR and ARNT (Figures 27 and 30). AHRR did not affect the basal nor the AHR-mediated increase in gene expression, suggesting that the CNR2 ChIP region is a unique AHR-bound enhancer region that cannot be competitively targeted by AHRR

(Figure 28-29). For RFTN1, however, AHR/ARNT increased its expression while TCDD treatment did not seem to affect it (Figures 30 and 32). Similar to the CNR2 region, AHRR did not repress the AHR-mediated increase in basal gene expression of RFTN1 enhancer (Figure 31-32).

From these transfection experiments, we are able to provide an example of a ligand-dependent unique AHR target enhancer, CNR2, and a ligand-independent unique AHR target enhancer,

RFTN1. It is important to note that while these AHR-bound enhancer regions are located close to the transcription start sites of CNR2 and RFTN1, it does not necessarily mean that they affect the gene expression of CNR2 and RFTN1. The nature of AHR binding and gene regulation is so

107 complex that it would not be surprising for such AHR-bound enhancer regions to be mediating the gene expression of genes located much further away.

4) Implications for the tumor suppressor role of AHRR

Other than repressing AHR, the function of AHRR is largely unknown. Here, we have shown similarities and differences in the binding properties of AHR and AHRR. The analysis strongly implies an alternate function for AHRR. AHRR has been implicated as a tumour suppressor for multiple type of cancers (Zudaire et al., 2008). This link between AHRR and cancer is also implicated in several of our analyses. De novo motif discovery analysis and overrepresented transcription factor binding site analysis for AHRR revealed the presence of transcription factors that are associated with cell cycle regulation. One such group included the E2F family, known to play a crucial role in the regulation of cell cycle during G1/S transition and involvement in cancer.

E2F is also targeted by a tumor suppressor called the retinoblastoma protein (Rb) (Nevins et al.,

2001). Rb binds to the E2F1 transcription factor and prevents it from interacting with the transcription machinery. It is possible that AHRR can act in a similar way as Rb in interacting with

E2F1. Based on our analysis, there is a potential interaction between AHRR and E2F family of proteins in the regulation of the cell cycle. In the study by Zudaire et al. (2008), AHRR was deleted in several tumours. Deletion of AHRR may cause the E2F regulation of cell cycle to be dysregulated leading to potential carcinogenesis. It is also possible that AHRR is a co-regulator of or works in concert with Rb. To further support this, an E2F1-bound peak region occurs near the

XRCC6 and MAP2K7 gene according to past E2F1 ChIP-seq data in MCF-7 cells (Cao et al.,

2011). We have shown that AHRR can repress XRCC6 and MAP2K7 gene expression independently of AHR and ARNT. When the E2F1-bound regions from the study by Cao et al.

108 were overlapped with the AHRR-bound regions from our study, nearly 1000 regions were identified (data not shown). However, a previous study has also shown that AHR can be recruited to E2F-dependent promoters, which may explain AHR’s involvement in cell cycle regulation

(Marlowe et al., 2004). Therefore, we propose a multi-mechanistic model for the role of AHRR as a tumour suppressor in which AHRR can act through both an AHR-independent and AHR- dependent manner. AHRR may directly interact with cancer-related transcription factors, such as

E2F1, without AHR. Alternatively, AHRR may compete with AHR for common interaction partners. Other candidate interaction partners with AHRR included the ZF5 POZ domain zinc finger, EGR factors, Nrf1 and Zinc Finger BED domain-containing protein (ZBED). ZF5, a zinc finger protein, is a transcriptional repressor of MYC and thymidine kinase promoters (Numoto et al., 1993). EGR1 is a zinc finger transcription factor, implicated to play a part in the control of cell growth, survival and transformation (Thiel and Cibelli, 2002). Nrf1 is a transcription factor that functions by activating metabolic genes regulating cellular growth and respiration. ZBED1 functions as a transcription factor that binds to promoter regions of several genes related to cell proliferation (Yamashita et al., 2007). Many of the transcription factor candidates predicted to interact with AHRR are proposed to be involved in the regulation of cell proliferation. Taking this into consideration, we propose that AHRR acts as a repressor that interacts with several transcription factors involved in the control of cell proliferation and that AHR is only one of the many partners. Therefore, mutations or the deletion of AHRR may lead to the dysregulation of several pathways involved in cell growth that could ultimately result in increased carcinogenesis.

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5) Novel proposed model for AHRR-mediated repression of AHR

It is clear from our analyses that AHRR does not bind to all AHR-bound regions on the genome, demonstrating the limitations of the competition-based model. Here, we propose a novel mechanism of AHRR-mediated repression based on a pseudo competition or interaction model.

This model includes some aspects of the classical model proposed by Mimura et al. (1999) in which AHRR competes against AHR for binding to an AHRE in the regulatory region of an AHR target gene (Figure 33a). This classical mechanism likely occurs at classical AHR target genes:

CYP1A1, CYP1B1 and CYP1A2. After induction with an AHR ligand, AHR increases the gene expression of AHRR, leading to increase in AHRR protein level. However, here is where this model deviates from the classical model. Other than ARNT, AHRR may be able to heterodimerize with an unknown partner that forms a complex also capable of binding AHRE or a similar sequence. From previous studies and our own observations, it is evident that AHRR-mediated repression of AHR is not dependent on ARNT (Evans et al., 2008). Our luciferase assays suggest that AHRR has an intrinsic repressor function independent of ARNT (Figure 21, 25). This leads me to believe that there may be other partner(s) that AHRR dimerizes with. Since AHR is known to interact with the 5’-GC-3’ half-site sequence of the AHRE core sequence and ARNT interacts with the other 5’-GTG-3’ portion (Swanson et al., 1995), AHRR interactions with half-sites should theoretically be similar due to the high homology in the DNA-binding domain compared to AHR.

Moreover, our ChIP-seq data suggest that AHR and AHRR bind to a similar AHRE motif. The

PAS A domain, containing part of the dimerization region, of AHR and AHRR share only 60% amino acid homology (Mimura et al., 1999), suggesting that the dimerization partner may be similar but not necessarily the same. Based on our current knowledge of the bHLH-PAS family of

110 proteins, proteins with high similarity with ARNT, such as ARNT2 or ARNTL, would be the likely candidates. Other than competition, AHR and AHRR may also be interacting through protein- protein interactions (Evans et al., 2008). Another possible explanation for the phenomena in which

AHRR can bind to non-AHR-bound regions is that AHRR can potentially tether with other transcription factors, likely ones with high promoter preferences.

The second mechanism of the novel repression model is derived from the common binding sites for AHR and AHRR despite their different binding preferences. Based on previous ChIP-seq analysis (Lo and Matthews, 2012) and our current analysis (Figure 9), AHR is proposed to mediate gene expression from a long-range by chromatin looping or remodeling mechanisms (Figure 11).

These enhancer regions in which AHR binds to are located throughout the genome, making it difficult to determine and predict which particular gene expression these enhancers affect.

Although AHRR is promoter-focused, the promoter regions did not account for all of the co-bound regions in our analyses (Figure 13), suggesting that AHRR also binds to AHR-bound regions located away from the TSS. Therefore, we propose that for some genes AHRR, in a complex with either ARNT or an unknown factor, competes directly for enhancer regions of AHR. This prevents

AHR from accessing the regulatory region of a target gene through chromatin looping mechanism

(Figure 33b). The third proposed mechanism is similar to the second one with a few exceptions.

Instead of the AHRR complex competing directly for binding, it binds at a promoter region of an

AHR target gene and indirectly blocks AHR from accessing the same region from a distant enhancer region through the recruitment of histone deacetylases (Figure 33c). Unlike the first two mechanisms, which are supported by our ChIP-seq analysis, this mechanism has not been validated in this work. Since we cannot disregard the long-range regulation by AHR, it is possible that AHR can also interact with AHRR-bound promoter regions from a distant enhancer. The inability to

111 confirm this third mechanism represents one of the limiting factors of our study. Even if genome- wide gene expression data were provided, the association of AHR binding and gene expression would still pose a challenge. I believe a way to truly bridge the binding of transcription factors, like AHR, and gene regulation would be through computational modeling of dynamic chromatin structure, which can integrate chromatin remodeling with the analysis of transcription factor binding. However, this is not yet feasible with current technology and would take enormous amount of computational power and deeper understanding of the 3-dimensional chromatin structure. Although our study provides valuable insights in terms of the competition or interaction at the same binding sites, it does not solve all of the mysteries surrounding the AHR-AHRR interactions.

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A AHRR ARNT Basal Transcription Machinery AHRR ?

AHR ARNT

AHRE Promoter AHR Target Gene B AHRE? AHR ARNT AHRR ? Co-regulatory Proteins AHRR ARNT Basal Transcription Machinery

Promoter AHR Target Gene

C AHRE? AHR ARNT Co-regulatory AHRR ? Proteins

Basal AHRR ARNT Transcription Machinery

Promoter AHRE-like AHR Target Gene

Figure 33. Novel proposed model of AHRR-mediated repression of AHR divided into three mechanisms: (a) the classical competition model in which AHRR directly competes for binding or interact with AHR at AHREs within the regulatory regions of genes, (b) AHRR competes or interact with AHR at a distal enhancer region situated far away from the target gene and subsequently blocking the chromatin looping mechanism required for AHR to access the promoter of the target gene, (c) AHRR directly binds to the promoter of an AHR target gene and blocking the long-range AHR-mediated promoter-enhancer interaction for a target gene.

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6) Limitations of the Study

6.1) ChIP-Seq

ChIP sequencing is a powerful approach that integrates next-generation sequencing into the genome-wide profiling of DNA-binding proteins. In recent years, ChIP-seq has become increasingly popular due the large amount of data that can be generated. Compared to its predecessor, ChIP-Chip, ChIP-seq provides advantage in terms of high resolution, lower background and greater genome coverage (Park, 2009). Even though ChIP-seq is a powerful analytical technique, unwanted artifacts will still be produced. Often for genomic sequencing- based research, the limiting factors are related to the computational component of the analysis.

Alignment algorithms needs to be continually improved to provide accurate and time-efficient mapping of sequences to the reference genome. Errors can occur during the alignment process, which could lead to incorrect mapping. Especially for ChIP-seq, the peak-calling algorithm is of great importance. Different peak-calling programs employ different algorithms for the identification of significant binding events, each with its advantages and disadvantages.

Furthermore, there has not been a consensus regarding the best method for analyzing biological

ChIP-seq replicates. The methods widely used include pooling all the replicates, irreproducible discovery rate, one best replicate and majority rule (Yang et al., 2014). Although according to the

ENCODE consortium guideline (Landt et al., 2012) which suggests that two biological replicates are sufficient, it is generally accepted that more replicates leads to lower variability and maximizes consistency (Yang et al., 2014). However, the cost of ChIP-seq also needs to be taken into consideration if more biological replicates are used. In my analysis, I used the most popular method, which is pooling the three replicates into one prior to subjecting them to MACS for peak- calling. A disadvantage of pooling replicates is that information from individual replicates will be

114 lost in the process (Yang et al., 2014). Currently, the best method for ChIP-seq analysis remains controversial in terms of peak-calling or replicate analysis. A validation step, such as the ChIP- qPCR used in our study, may ameliorate this potential problem.

The quality of the antibody also determines the quality of the ChIP data. The quality of commercial antibody can be highly variable, even among different batches of the same antibody, which could present a problem in ChIP experiments. While there are reliable antibodies for AHR, the same could not be said for AHRR. A previous study in our lab has confirmed the anti-AHRR antibody we have used in this study by a primary characterization through immunoblotting and a secondary characterization through siRNA-mediated knockdown (MacPherson et al., 2014). However, a potential issue could still arise due the difference in quality between the anti-AHR and anti-AHRR antibodies. Differences in the quality could lead to a bias when comparing ChIP-seq data derived from different antibodies.

The greatest limitation is of the ChIP technology itself due the inability to distinguish between direct and indirect DNA binding (Gordân et al., 2009). While motif discovery is informative, it does not necessarily mean that the particular transcription factor binds directly to that sequence of

DNA. Distinguishing between direct and indirect TF-DNA interaction cannot be achieved directly from ChIP data.

6.2) Complexity of AHR Binding

The complexity of AHR binding is demonstrated not only in our current study but also in previous

ChIP-seq study for AHR (Lo and Matthews, 2012). Based on the binding patterns of AHR, it may be able to mediate gene expression by binding to enhancer regions and distally access promoters of target genes through chromatin remodeling or looping strategy. My analyses suggest that AHR may even act a pioneering factor for its own binding by mediating chromatin remodeling

115 potentially through interacting with pioneering factors like Forkhead Box proteins shown in the motif analysis (Tables 4-5, 8-9, 14-15). Pioneer factors function as adaptor molecules that open up the local chromic and establish competence for other factors to bind to regulatory sites. Such a complex binding pattern makes it difficult to map binding with specific gene regulations as they can be located far away from the genes. This also suggests that unique AHR-bound regions could potentially mediate the same genes as AHRR even though they are bound far away from each other. This formed the basis for the third mechanism of our novel proposed model (Figure 33c), a mechanism that is difficult to verify yet may be essential for the understanding AHR-AHRR interactions.

6.3) Time Points

Another limitation is the lack of multiple time points. While there are two time points for AHR

(45 min and a 24 h), there is only one time point for AHRR at 24 h. We can only speculate that there is a slow increase in AHRR binding after TCDD treatment and spiking around 24 h.

Performing ChIP for AHRR at an earlier time point may not be appropriate due to undetectably low levels of AHRR (MacPherson et al., 2014). However, later time points such as 36 h or 48 h could be informative. The use of multiple time points for both AHR and AHRR would allow us to understand the temporal changes that occur following treatment.

7) Future Directions

In order to completely profile genome-wide AHR and AHRR binding, more time points are needed. This would provide information on the duration of AHRR binding and whether the binding can persist for a longer period. The first recommended step is to perform immunoblotting at later time points to characterize the level of AHRR over a longer time course. Based on the results,

116 additional time points can be chosen for a new set of ChIP-seq experiments. In addition, negative controls such as IgG or diluted total chromatin input, can be used to determine the ligand- independent binding of AHRR. Other than MCF-7 cells, other cell lines can be examined in the future. For example, HeLa cells expresses high constitutive levels of AHRR and may present a different scenario compared to MCF-7 cells (Tsuchiya et al., 2003). Another informative experiment to add as a future direction is RNA sequencing (RNA-seq) to profile the gene expression changes. Although it may be difficult to associate AHR binding to gene expression changes, this may not be the case for AHRR. Our ChIP-seq results suggest that AHRR prefers to bind at promoter regions while luciferase reporter gene data suggest that AHRR repress gene expression at these regions. RNA-seq is necessary to confirm whether this holds true for other

AHRR-bound regions and would provide a broader overview of AHRR-mediated gene regulation.

Knockdown models and mRNA level measurements can be used for further validation of gene expression regulation. In this study, only one type of ligand, TCDD, was used. Therefore, profiling the binding of AHR and AHRR using other AHR ligands can be informative. Additionally, exploring the interaction of AHRR and potential co-regulatory partners identified in our study may also be an interesting future research direction. Currently, little is known about co-regulatory proteins for AHRR and requires further research by examining protein-protein interactions using techniques such as co-immunoprecipitation assays or yeast two-hybrid screening potentially coupled with knockdown experiments.

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Chapter 6: Conclusion

AHR and AHRR share some binding preferences including binding to a similar AHRE sequence; however, there are vast differences in AHR and AHRR binding properties especially in terms of the distribution of the binding sites. AHRR, unlike AHR, binds much closer to the TSS, exhibiting strong preferences for promoters. De novo motif discovery and transcription factor binding site analysis shows that AHR and AHRR bind to a similar AHRE sequence with some minor variations. Common predicted motifs include Forkhead Box and SP1. However, there are also vast differences in the predictions of other transcription factor motifs. While AHR-bound regions displayed AP-1 and GATA motifs, AHRR-bound regions predicted a variety of GC-rich motifs such as E2F. FOXA1 was differentially predicted between unique AHR-bound and unique AHRR- bound regions, suggesting that FOXA1 is key to the observed binding differences. Luciferase reporter gene assays suggest that AHRR has an intrinsic repressor function independent of AHR or ARNT. Additionally, gene expression mediated by AHR bound enhancers may not be repressed by AHRR, indicating possible limitations in the ability of AHRR to regulate AHR activity.

Therefore, by comparing the binding profiles of AHR and AHRR and taking into consideration

AHR’s potential to mediate gene expression from a long-range, we propose that AHRR repression occurs through a more complex model as outlined in the preceding section. This study is the first to characterize the differences between the genome-wide binding profiles of AHR and AHRR and provides further insight into AHRR-mediated repression of AHR signaling and provides additional support for AHRR signaling mechanisms that are independent of AHR and vice versa.

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Appendix

Table A1. Top 100 TCDD-induced 24h AHR Peak Regions (MACS2) (Refer to the Excel spreadsheet for full list)

Fold Rank chr start end length Enrichment -log(q-value) Gene Name 1 chr15 75055722 75056319 598 9.73 195.18 CYP1A2 2 chr15 75018275 75019093 819 7.41 168.21 CYP1A1 3 chr3 1.57E+08 1.57E+08 534 9.01 161.87 PA2G4P4 4 chr3 1.57E+08 1.57E+08 565 6.2 154.75 LINC00886 5 chr12 58926625 58927418 794 9.04 151.03 LRIG3 6 chr15 1.01E+08 1.01E+08 667 7.51 150.7 ASB7 7 chr2 38303886 38304993 1108 6.61 148.98 CYP1B1 8 chr15 99440172 99440533 362 9.11 141.12 PGPEP1L 9 chr16 87823641 87823996 356 5.93 140.25 KLHDC4 10 chr13 96890992 96891596 605 7.24 133.12 HS6ST3 11 chr3 1.58E+08 1.58E+08 353 9.69 132.44 MFSD1 12 chr21 47167821 47168146 326 7.84 128.79 AL592528.1 13 chr12 1.1E+08 1.1E+08 313 8.92 128.57 FOXN4 14 chr9 1.1E+08 1.1E+08 300 8.6 128.08 RAD23B 15 chr15 93188230 93188581 352 7.65 125.06 FAM174B 16 chr20 50351623 50351994 372 4.85 123.74 ATP9A 17 chr15 74935379 74936073 695 10.99 122.6 CLK3 18 chr20 46619543 46619836 294 5.62 122.46 RP11-347D21.3 19 chr20 55605709 55606009 301 11.92 121.5 BMP7 20 chr8 1.28E+08 1.28E+08 330 11.33 120.43 FAM84B 21 chr7 1.58E+08 1.58E+08 375 6.53 120.27 AC011899.9 22 chr5 1.01E+08 1.01E+08 397 10.07 120.21 ST8SIA4 23 chr17 5787731 5788054 324 5.79 119.86 LOC339166 24 chr5 1.73E+08 1.73E+08 753 10.98 118.77 MIR8056 25 chr11 68147823 68148294 472 4.98 116.19 LRP5 26 chr15 74934662 74935127 466 4.23 115.85 CLK3 27 chr16 72961652 72961971 320 8.1 115.38 ZFHX3 28 chr1 2.17E+08 2.17E+08 302 5.72 114.73 ESRRG 29 chr16 87840578 87840996 419 10.26 114.38 MIR6775 30 chr2 39444095 39444393 299 9.23 114.03 CDKL4 31 chr3 64487126 64487534 409 4.35 113.8 RP11-14D22.5 32 chr6 45678252 45678604 353 5.36 112.98 CLIC5 33 chr9 97581760 97582054 295 5.36 112.26 MIR2278 34 chr13 25691564 25691936 373 4.84 111.53 PABPC3

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35 chr6 17012872 17013340 469 7.57 111.24 STMND1 36 chr10 1.28E+08 1.28E+08 350 9.94 110.88 FANK1-AS1 37 chr5 1.43E+08 1.43E+08 610 6.64 110.39 MIR5197 38 chr20 15042730 15043011 282 6.56 110.15 MACROD2-AS1 39 chr3 1.08E+08 1.08E+08 541 4.67 110.04 LINC00635 40 chr5 373925 374235 311 4.81 109.47 C5orf55 41 chr6 1.4E+08 1.4E+08 306 5.95 109.36 LOC100132735 42 chr11 78770961 78771740 780 4.93 109.12 MIR708 43 chr17 59308404 59308671 268 7.68 108.88 TBX2 44 chr4 5367481 5367828 348 9.34 108.06 C4orf6 45 chr6 16965068 16965429 362 9.55 107.97 STMND1 46 chr17 6280518 6280806 289 8.74 107.62 AIPL1 47 chr2 38323394 38323778 385 4.06 106.98 CYP1B1 48 chr20 49294888 49295374 487 5.12 106.64 FAM65C 49 chr2 1.78E+08 1.78E+08 270 5.35 105.81 NFE2L2 50 chr2 2.35E+08 2.35E+08 368 12.17 103.41 ARL4C 51 chr1 1.89E+08 1.89E+08 318 8.14 103.28 BRINP3 52 chr15 51659201 51659861 661 5.1 103.07 GLDN 53 chr8 1.29E+08 1.29E+08 455 4.4 103.03 PVT1 54 chr1 2.32E+08 2.32E+08 617 25.43 102 DISC2 55 chr11 76483615 76483984 370 6.44 101.07 TSKU 56 chr8 69644151 69644422 272 10.98 100.95 RP11-600K15.1 57 chr20 31779343 31779714 372 19.11 99.81 BPIFA4P 58 chr5 1.78E+08 1.78E+08 405 4.31 98.53 N4BP3 59 chr2 1.11E+08 1.11E+08 295 8.07 98.46 ACOXL 60 chr18 11005622 11005950 329 9.84 97.62 PIEZO2 61 chr8 1.2E+08 1.2E+08 454 11.51 97.37 TNFRSF11B 62 chr3 1.61E+08 1.61E+08 300 4.94 96.84 RP11-3P17.4 63 chr20 51962808 51963364 557 5.15 96.55 TSHZ2 64 chr3 8498113 8498449 337 4.09 95.06 LMCD1-AS1 65 chr3 1.93E+08 1.93E+08 298 5.32 95.05 MB21D2 66 chr17 78734465 78734803 339 9.18 94.87 RPTOR 67 chr3 1.08E+08 1.08E+08 299 11.28 94.24 LINC00635 68 chr12 1.16E+08 1.16E+08 315 5.47 92.93 MIR620 69 chr8 94130842 94131128 287 6.02 92.89 C8orf87 70 chr3 63777520 63778029 510 8.74 92.78 C3orf49 71 chr13 28531025 28531388 364 6.26 91.92 ATP5EP2 72 chr20 55734495 55734817 323 4.17 91.71 BMP7 73 chr2 2.01E+08 2.01E+08 398 5.93 91.3 SPATS2L 74 chr3 4928875 4929226 352 11.05 91.13 BHLHE40-AS1 75 chr5 1.73E+08 1.73E+08 319 4.72 90.96 LOC285593

142

76 chr2 55180429 55180715 287 8.09 90.89 RTN4 77 chr9 1.08E+08 1.08E+08 281 6.42 90.81 SLC44A1 78 chr18 21207359 21207662 304 9.8 90.25 ANKRD29 79 chr14 97966462 97966834 373 5.27 89.3 LOC101929241 80 chr8 1.29E+08 1.29E+08 305 4.16 88.82 MIR1208 81 chr6 11390277 11390583 307 4.18 88.64 NEDD9 82 chr9 1.37E+08 1.37E+08 329 5.62 88 MIR4669 83 chr2 2.21E+08 2.21E+08 324 10.09 87.8 MIR4268 84 chr6 65484851 65485437 587 7.49 87.65 LOC441155 85 chr20 49306474 49306796 323 3.82 87.13 PARD6B 86 chr20 52480668 52481131 464 4.35 87.1 SUMO1P1 87 chr16 82170999 82171299 301 7.73 86.81 MPHOSPH6 88 chr14 34682670 34682931 262 8.82 86.76 SPTSSA 89 chr8 1.29E+08 1.29E+08 271 6.32 86.23 MIR1204 90 chr3 1.76E+08 1.76E+08 279 4.75 85.76 RP11-71G7.1 91 chr3 8498645 8498903 259 5.53 84.34 LMCD1-AS1 92 chr9 97545630 97545924 295 3.57 83.61 C9orf3 93 chr6 1.07E+08 1.07E+08 354 16.31 83.45 PRDM1 94 chr11 69706382 69706703 322 9.63 83.37 FGF3 95 chrX 1.31E+08 1.31E+08 318 8.6 83.04 OR13H1 96 chr8 92219838 92220128 291 11.23 82.34 SLC26A7 97 chr7 1.28E+08 1.28E+08 317 5.25 82.12 LINC01000 98 chr1 16934829 16935126 298 3.73 80.87 NBPF1 99 chr8 1.02E+08 1.02E+08 346 4.13 80.86 GRHL2 100 chr8 1.35E+08 1.35E+08 283 4.13 80.86 ST3GAL1

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Table A2. Top 100 TCDD-induced 24h AHRR Peak Regions (MACS2) (Refer to the Excel spreadsheet for the full list)

Fold Rank chr start end length Enrichment -log(q-value) Gene Name 1 chr3 1.57E+08 1.57E+08 514 6.23 133.96 LINC00886 2 chr8 1.45E+08 1.45E+08 315 12.12 119.02 PLEC 3 chr15 75055830 75056316 487 9.48 112.51 CYP1A2 4 chr16 31085532 31085798 267 9.09 109.93 ZNF668 5 chr15 75018739 75019106 368 5.44 105.16 CYP1A1 6 chr13 25691589 25691925 337 6.7 102.31 PABPC3 7 chr21 47167819 47168126 308 6.35 100.86 AL592528.1 8 chr11 68147966 68148313 348 5.52 99.81 LRP5 9 chr2 64978585 64978907 323 8.46 88.05 SERTAD2 10 chr3 14270146 14270536 391 20.58 87.89 LSM3 11 chr16 87823692 87824028 337 6.6 85.41 KLHDC4 12 chr5 373978 374226 249 4.43 83.62 C5orf55 13 chr15 1.01E+08 1.01E+08 634 6.33 81.51 ASB7 14 chr5 1.78E+08 1.78E+08 339 6.66 80.58 N4BP3 15 chr1 2.02E+08 2.02E+08 340 10.7 80.05 ELF3 16 chr12 83080189 83080435 247 4.63 75.74 TMTC2 17 chr14 77371434 77371690 257 6.7 75.72 C14orf166B 18 chr2 1.11E+08 1.11E+08 356 12.4 73.42 ACOXL 19 chr12 48297806 48298023 218 9.92 72.53 VDR 20 chr2 1.28E+08 1.28E+08 261 9.27 71.03 SFT2D3 21 chrX 13687567 13687817 251 11.35 70.9 TCEANC 22 chr11 66795217 66795532 316 9.54 69.86 SYT12 23 chr20 3792405 3792691 287 4.11 69.62 LOC101929125 24 chr7 1.58E+08 1.58E+08 324 6.4 69.33 AC011899.9 25 chr3 64487154 64487489 336 6 68.91 RP11-14D22.5 26 chr2 38323520 38323754 235 3.47 67.28 CYP1B1 27 chr4 5367529 5367821 293 9.7 65.84 C4orf6 28 chr5 81046782 81047055 274 5.17 65.78 SSBP2 29 chr11 78618684 78619090 407 12.38 65.44 NARS2 30 chr7 1.12E+08 1.12E+08 262 3.7 65.33 IFRD1 31 chr7 2772916 2773213 298 6.65 65.24 GNA12 32 chr20 15042731 15042999 269 9.17 65.12 MACROD2-AS1 33 chr1 33282878 33283139 262 6.06 64.8 S100PBP 34 chr7 2394282 2394609 328 3.25 64.48 EIF3B 35 chr20 18118416 18118653 238 5.25 63.74 PET117 36 chr19 45245224 45245442 219 6.87 63.64 BCL3

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37 chr3 1.08E+08 1.08E+08 385 9.03 63.23 LINC00635 38 chr6 16965090 16965428 339 9.03 63.23 STMND1 39 chr15 81097366 81097805 440 7.43 62.28 KIAA1199 40 chr7 1.41E+08 1.41E+08 263 26.85 61.68 MRPS33 41 chr15 75018327 75018583 257 5.02 61.27 CYP1A1 42 chr3 8498217 8498465 249 4.59 61 LMCD1-AS1 43 chr2 1.72E+08 1.72E+08 275 5.84 60.25 GAD1 44 chr7 1.28E+08 1.28E+08 270 6.83 59.58 LINC01000 45 chr8 92219816 92220124 309 15.97 59.58 SLC26A7 46 chr2 28617521 28617781 261 4.74 59.37 FLJ31356 47 chr14 75667359 75667635 277 7.47 59.05 TMED10 48 chr5 70220764 70221033 270 6.13 59 SMN2 49 chr7 47615583 47615908 326 13.95 58.26 TNS3 50 chr2 38304108 38304978 871 3.41 58.24 CYP1B1 51 chr1 16693648 16693893 246 7.35 58.2 SZRD1 52 chr7 1.58E+08 1.58E+08 309 8.24 58.14 AC011899.9 53 chr14 23449932 23450192 261 6.5 57.16 AJUBA 54 chr19 38423063 38423323 261 5.81 57.05 SIPA1L3 55 chr15 74782302 74782518 217 9.12 56.78 UBL7-AS1 56 chr13 20368819 20369044 226 3.52 56.6 PSPC1 57 chr17 70657740 70658033 294 8.9 56.36 LINC00511 58 chr14 24899162 24899433 272 4.2 56.31 KHNYN 59 chr4 2798523 2798764 242 13.62 56.03 SH3BP2 60 chr14 53619824 53620202 379 5.97 55.93 DDHD1 61 chr17 7381321 7381598 278 6.95 55.56 ZBTB4 62 chr1 2.17E+08 2.17E+08 260 5.04 55.42 ESRRG 63 chr15 42349363 42349604 242 7.69 55.16 PLA2G4E 64 chr15 74934746 74935078 333 3.46 53.94 CLK3 65 chr1 16934861 16935122 262 4.17 53.54 NBPF1 66 chrX 40033459 40033703 245 4.52 53.5 BCOR 67 chr3 1.94E+08 1.94E+08 242 7.83 52.82 OPA1-AS1 68 chr20 20529493 20529737 245 13.14 52.72 RALGAPA2 69 chr10 1.04E+08 1.04E+08 248 5.55 52.61 ELOVL3 70 chr8 1.25E+08 1.25E+08 547 7.5 52.57 RNF139-AS1 71 chr17 73641587 73641867 281 3.7 52.47 SMIM6 72 chr5 80467726 80467992 267 6.4 52.35 RNU5E-1 73 chr11 17043205 17043432 228 5.51 51.88 PLEKHA7 74 chr2 39444115 39444382 268 6.03 51.8 CDKL4 75 chr19 46696525 46696760 236 5.29 51.71 DKFZp434J0226 76 chr9 97581821 97582071 251 4.96 51.65 MIR2278 77 chr19 30162263 30162560 298 7.15 51.58 PLEKHF1

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78 chr9 99328770 99329076 307 6.35 51.56 CDC14B 79 chr14 36402276 36402513 238 18.67 51.25 BRMS1L 80 chr12 1.21E+08 1.21E+08 249 4.65 51.18 CABP1 81 chr11 1.19E+08 1.19E+08 277 11.68 51.09 BCL9L 82 chr5 1.38E+08 1.38E+08 324 5.26 51.01 CTNNA1 83 chr10 1.28E+08 1.28E+08 265 8.42 50.85 FANK1-AS1 84 chr11 85955828 85956375 548 8.34 50.85 EED 85 chr9 2235770 2236031 262 17.21 50.82 SMARCA2 86 chr21 43518090 43518340 251 6.63 50.66 C21orf128 87 chr7 35065369 35065648 280 11.01 50.52 DPY19L1 88 chr15 99443064 99443319 256 10.3 50.26 PGPEP1L 89 chr16 80752448 80752696 249 7.63 50.21 CDYL2 90 chrX 1.23E+08 1.23E+08 318 7.63 50.21 XIAP 91 chr6 90348106 90348415 310 9.23 50.1 LYRM2 92 chr16 87840560 87840995 436 8.26 49.95 MIR6775 93 chr6 1.4E+08 1.4E+08 277 8.26 49.95 LOC100132735 94 chr7 99935895 99936174 280 6.79 49.86 PMS2P1 95 chr3 1.51E+08 1.51E+08 222 4.72 49.73 GPR171 96 chr12 56511873 56512121 249 4.45 49.45 ZC3H10 97 chr4 84473401 84473669 269 19.88 49.33 AGPAT9 98 chr3 1.61E+08 1.61E+08 234 8.17 49.05 RP11-3P17.4 99 chr7 1.29E+08 1.29E+08 256 5.36 49.01 SMKR1 100 chr17 10023136 10023381 246 9.72 48.93 GAS7