The Pennsylvania State University

The Graduate School

Huck Institute of Life Sciences

GENOMIC PROFILING OF THE CONSTITUTIVE ANDROSTANE RECEPTOR

IN RODENT MODELS REVEAL NOVEL TUMORIGENESIS TARGETS AND SPECIES

VARIATIONS

A Dissertation in

Cell and Developmental Biology

by

Ben Niu

Ó 2018 Ben Niu

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Doctor of Philosophy

December 2018 ii The dissertation of Ben Niu was reviewed and approved* by the following:

Curtis J Omiecinski Professor of Veterinary and Biomedical Sciences H. Thomas and Dorothy Willits Hallowell Chair Fellow, Academy of Toxicological Sciences Dissertation Advisor Chair of Committee

Istvan Albert Professor of Bioinformatics

Jack Vanden Heuvel Professor of Molecular Toxicology

K. Sandeep Prabhu Professor of Immunology and Molecular Toxicology Co-Leader, Mechanisms of Carcinogenesis Program

Joshua D. Lambert Associate Professor of Food Science Co-Director, Center for Plant and Mushroom Foods for Health

Zhi-Chun Lai Professor of Biology, Biochemistry and Molecular Biology Chair, Cell and Developmental Biology Graduate Degree Program

*Signatures are on file in the Graduate School iii ABSTRACT

The constitutive androstane receptor (CAR; NR1I3) is a member of the nuclear receptor superfamily orchestrating complex roles in xenobiotic drug metabolism, energy regulation and lipid metabolism. CAR is a critical mediator of liver tumor promotion in the mouse. Species differences in these effector pathways remain poorly understood, although several lines of evidence support the concept that mouse CAR and human CAR differentially program the development of liver cancer. To discern the genomic context of these species differences, we used high-resolution ChIP-exo methods and direct and indirect CAR chemical activators, to conduct whole genome interaction analyses for the respective receptors within livers of CAR transgenic mice. Genomic enrichment data for CAR binding were then integrated with transcriptomics.

Comparative genomic binding analysis for mouse and human CAR identified ~1000 genes associated with species-distinctive binding in mouse liver. The results revealed CAR interactions with novel target genes, including Gdf15 and Foxo3, previously characterized as regulators of the carcinogenic process, and identification of species differences in the genomic binding for mouse and human CAR that program altered expression profiles of the proto- oncogenes, Myc and Bmf. The ChIP-exo analyses also enabled the characterization of high- resolution mCAR and hCAR binding motifs across the mouse genome.

Dlk1-Dio3 (Delta like non-canonical notch ligand 1- Iodothyronine Deiodinase 3) imprinted gene cluster have long been of interest as a model for studying imprinting mechanism as well as a crucial region in various stage of development. Dlk1-Dio3 contains 3 paternally expressed protein-coding genes and 3 maternally expressed non-coding genes; disruption of genes in this region may lead to perinatal lethality or late gestational lethality. Altered Dlk1-Dio3 iv gene expression have been implicated in various of cancers; in addition, Dlk1-Dio3 have been associated in induced pluripotency. Recent studies of chronic PB treated rodent hepatocellular carcinogenesis model revealed that lncRNAs and miRNAs in Dlk1-Dio3 region have been induced in a CAR and b-catenin dependent manner, indicating a novel pathway of CAR in mediating tumorigenesis.

Genomic study of CAR binding within the Dlk1-Dio3 locus indicated that CAR may regulate lncRNA Meg3 through direct transactivation. By binding to intergenic germline-derived differentially methylated region (IG-DMR), CAR may coordinately regulate imprinted cluster genes including Rian, Mirg and miRNAs, providing insights into the mechanism of CAR interaction with the Dlk1-Dio3 imprinted gene cluster.

The results obtained attest to CAR’s role as a master regulator of biological signaling processes involved in cell proliferation and tumorigenesis and provide novel insights into species variation and it potential roles in mediating lncRNA and miRNA expression in liver tumor promotion. v TABLE OF CONTENTS

LIST OF FIGURES ...... viii

LIST OF TABLES ...... xi

ACKNOWLEDGEMENTS...... xii

Chapter 1 Literature review ...... 1

Nuclear receptor CAR ...... 1 Activation of CAR ...... 3 Direct activation ...... 3 Indirect activation ...... 5 CAR function ...... 6 CAR in drug metabolism ...... 7 Complex roles of CAR in chemical toxicity ...... 8 CAR in glucose metabolism ...... 10 CAR in lipid metabolism ...... 12 CAR in liver carcinogenesis ...... 14 Splice variants of CAR ...... 16 mCAR variants ...... 17 hCAR variants ...... 17 Species variation of CAR...... 18 Research Objectives ...... 20

Chapter 2 High-resolution, in vivo genome binding interactions of mouse and human constitutive androstane receptors reveal novel gene targets ...... 33

ABSTRACT ...... 33 INTRODUCTION ...... 34 MATERIAL AND METHODS ...... 36 Materials and reagents ...... 36 Animals and treatments ...... 37 Liver extraction and ChIP-exo ...... 38 Frozen tissue embedding and immunohistochemistry ...... 39 Human primary hepatocyte culture and treatments...... 39 RNA extraction and quantitative realtime-PCR analysis ...... 39 Protein extraction and Western blot ...... 40 Data analysis ...... 41 Genome coverage visualization ...... 41 Quantitative differential binding analysis...... 41 Peak annotation and GO analysis...... 42 Motif analysis ...... 43 RNA-seq re-analysis ...... 43 RESULTS ...... 44 Adenovirus transient transgenic mouse characterization ...... 44 vi Genome profiling of mCAR and hCAR DNA interactions ...... 45 Quantitative differential analysis reveals distinct hCAR and mCAR genomic profiles ...... 46 Direct vs indirect CAR activation results in near identical genomic profiles ...... 48 Gene annotations for mCAR and hCAR binding regions ...... 48 Expression analysis on select CAR-binding genes indicates that CAR directly regulates key pathways in hepatocarcinogenesis...... 51 Cross-referencing RNA-seq transcriptomics datasets with identified species differences in CAR-linked oncogenes ...... 53 mCAR prioritizes binding to direct repeat two half-sites motifs whereas hCAR is less stringent in motif recognition ...... 55 DISCUSSION ...... 56 CONCLUSIONS ...... 61

Chapter 3 Genomic CAR profiling and species differential analyses in Dlk1-Dio3 gene cluster...... 88

Abstract ...... 88 Introduction ...... 89 Materials and reagents ...... 91 Animals and treatments ...... 92 Liver extraction and ChIP-exo ...... 93 Data analysis ...... 94 Genome coverage visualization ...... 94 Quantitative differential binding analysis...... 94 Peak annotation and GO analysis...... 95 RNA-seq re-analysis ...... 96 Results ...... 96 CAR showing strong binding sites in Dlk1-Dio3 gene cluster ...... 96 CAR binding on Meg3 promoter region and IG-DMR ...... 97 mCAR vs hCAR differential binding on Dlk1-Dio3 cluster ...... 98 Discussion ...... 99

Chapter 4 Future directions ...... 105

Appendix A Cryosection and immunostaining protocol ...... 111

Perfusion and Fixation ...... 111 Dehydration ...... 112 Cryosectioning ...... 115 Immunostaining ...... 115

Appendix B Chromatin Immunoprecipitation and Sonication protocol ...... 118

Crosslinking and homogenizing ...... 118 Solutions and Reagents ...... 118 Instruments ...... 119 Protocols: ...... 120 Sonication ...... 121 vii Solutions and Reagents ...... 121 Instruments ...... 122 Protocols ...... 122

References ...... 125

viii LIST OF FIGURES

Fig 1-1 Activators and ligands of CAR...... 22

Fig 1-2 Schematic diagram showing CAR activation and function...... 23

Fig 1-3 mCAR and hCAR alternative splicing variants ...... 25

Fig 1-4 mCAR and hCAR amino acid sequence comparison...... 26

Fig 2-1 Adenovirus delivery of YFP-CAR constructs into CAR KO mice...... 63

Fig 2-2 Genomic profiling of CAR using ChIP-exo...... 64

Fig 2-3 Differential analysis between mCAR and hCAR binding profiles...... 66

Fig 2-4 Gene annotation for mCAR and hCAR binding regions...... 67

Fig 2-5 mRNA expression analysis indicates that CAR regulates key genes associated with hepatic carcinogenesis...... 69

Fig 2-6 mRNA expression analysis of CAR-linked oncogenes with species variations...... 71

Fig 2-7 Motif analysis for hCAR and mCAR...... 73

Fig 2-8 Integrated genomic viewer and putative motif locations...... 75 ix

...... 76 x Fig 2-9 IGV showing CAR binding sites and putative motif locations on select genes...... 77

Fig 3-1 UCSC genome browser showing CAR binding peaks for 4 sample groups...... 102

Fig 3-2 Integrated genome viewer (IGV) showing CAR bindings...... 103

Fig 3-3 RNA-seq examination of lncRNA gene expressions...... 104

xi LIST OF TABLES

Table 2-1. The top 500 mCAR and hCAR binding genes as annotated by HOMER...... 50

Table 2-2 Realtime PCR primers used in this paper...... 78

Table 2-3 Cross-referencing RNA-seq transcriptomics datasets with identified species differential binding genes...... 81

Table 3-1 CAR Differential bindings in Dlk1-Dio3 gene cluster ...... 98

xii ACKNOWLEDGEMENTS

First, I’d like to thank all committee members, Dr. Curtis Omiecinski, Dr. Istvan

Albert, Dr. Jack Vanden , Dr. Sandeep Prabhu and Dr Joshua Lambert for their suggestions and patience, and for giving me the opportunity for doctoral defense.

I’d like to thank Dr. Curtis Omiecinski as my advisor for giving me guidance and providing me the platform for finishing my project. In addition to scientific way of solving problems, Dr. Curtis Omiecinski also taught me much in scientific writing, conversation and interpersonal skills.

I’d like to thank Dr. Istvan Albert for the knowledge of bioinformatics and philosophy of data analysis. Without them I would not survive my Ph.D.

I’d like to thank Denise Coslo and Dr. Shengzhong Su for all the chats and works done together. Also I’d like to Jamie, Dr. Tao Chen, Loan, Elizabeth, and all the present and past members in my lab, deep in my heart I have the utmost gratitude and respect for them, and wish them best of futures.

I’d like to thank all my friends, wherever you are now, thank you so much for being part of my life.

Last but not least, I’d like to thank my parents, and my wife, Dr. Yueying Chen. I really appreciate your patience and support. I will make up to you. 1

Chapter 1

Literature review

Nuclear receptor CAR

Constitutive Androstane Receptor (CAR; NR1I3) belongs to the nuclear receptor superfamily. Nuclear receptor are transcription factors, mostly activated by small molecules from endogenous compounds to xenobiotic chemicals (Mangelsdorf and Evans 1995). Nuclear receptors share a modular structure including a modulatory A/B domain, a DNA-binding domain

(DBD), a hinge domain, a ligand-binding domain (LBD) and a variable F domain. The DBDs bind to DNA motifs usually denoted as responsive elements. The LBD is responsible for ligand binding, coregulator interaction and dimerization (Lehmann et al. 1992).

CAR was first identified and described as an orphan member of the nuclear hormone receptor superfamily in 1994 by Dr. Moore’s lab, with initial name MB67, and is primarily expressed in liver (Baes et al. 1994). As the name orphan suggested, these types of nuclear receptors do not yet have identified endogenous ligands as their primary activators. Biochemical studies suggested that CAR binds to retinoid X receptor (RXR) to form a heterodimer which then binds to specific DNA elements to activate expression of target genes. Murine CAR (mCAR) was described in 1997 (Choi et al. 1997) as closely related to human CAR (hCAR), being an orphan receptor and exhibiting constitutive transcriptional activity without the presence of ligands, capable of forming a heterodimer with RXR and sharing a conserved activation functions 2 (AF-

2) domain. Initially, CAR was defined as a xenobiotic receptor regulating detoxification of 2 exogenous/environmental compounds, as well as endogenous metabolites (Reschly and

Krasowski 2006). CAR is primarily expressed in liver, gallbladder and intestine (Bookout et al.

2006).

CAR and RXR heterodimer DNA binding motifs were first characterized as direct repeat elements of hexamer consensus AGGTCA separated by 5bp (DR-5) (Baes et al. 1994; Choi et al.

1997). Later, phenobarbital (PB) responsive element (PBRE) in the 5’ flanking region of rat

Cyp2b2 gene (Trottier et al. 1995; Ramsden et al. 1999) and phenobarbital responsive enhancer module (PBREM) in the 5’ flanking region of mouse Cyp2b10 gene (Honkakoski and Negishi

1997) were identified, containing highly conserved NR1 (RGGTCAggaaAGTACA) and NR2 motifs, consisting of direct repeat elements separated by a 4bp (DR-4) structure. CAR and RXR heterodimers have been shown to bind to NR1, and activate the PBREM (Sueyoshi and Negishi

2001). The PBREM, along with proximal xenobiotic responsive elements (XREM), are the classic DNA motifs that CAR directly binds (Honkakoski et al. 1998). Recent studies have shown that CAR binding DNA motifs are not limited to DR-4 or DR-5 unit. An in vitro study revealed that CAR could also effectively bind to ER (everted repeat) type DNA responsive elements such as ER5, ER6, ER7, ER8 and ER9, with ER8 showing strongest activities (Frank et al. 2003).

CAR can directly bind to the DR1 unit at Cyp7a1 promoter regions, where it competes with

HNF4a (hepatocyte nuclear factor 4 alpha) (Miao et al. 2006). The expanding evidence of CAR binding motifs indicates that CAR likely plays roles in the liver in addition to mediating xenobiotic metabolizing cytochrome P450s. 3 Activation of CAR

Without activation, CAR is normally retained in a cytosolic complex with HSP90 (heat- shock protein 90) and CCRP (CAR cytoplasmic retention protein). Recently, HSP70 has also been characterized to stabilize this complex (Yoshinari et al. 2003; Timsit and Negishi 2014).

Upon activation under normal physiological conditions, such as in vivo or in human or mouse primary hepatocytes, CAR translocates to the nucleus and transactivates downstream genes.

However, in immortalized cell lines such as Cos-1, HepG2, Huh-7, CAR cannot be retained in cytoplasm, and is therefore constitutively activated (Kawamoto et al. 1999).

One unique feature of CAR compared to other nuclear receptors is that it can be either directly or indirectly activated (Omiecinski et al. 2011a). Direct activation is the classical nuclear receptor activation pathway, involving ligand binding for activation. Indirect activation of CAR involves indirect activators, such as PB, inducing CAR mediated activities without being a direct ligand for CAR, meaning PB does not bind to CAR. Both activation methods share the process of

CAR being released from cytoplasmic retention complex, undergoing nuclear translocation, and forming heterodimers with RXR fulfill its transcription function (Mutoh et al. 2009).

Direct activation

The canonical nuclear receptor structure consists of an A/B domain at the N-terminal, a

DNA binding domain (DBD), a hinge domain, a ligand binding domain (LBD) and a F domain at

C-terminal (Steinmetz et al. 2001). Despite not having a A/B domain at its N-terminal, CAR shares the same characteristics with other nuclear receptors in that the LBD goes through conformation changes when binding with ligands to realign and stabilize H12/AF2 domain for 4 recruiting coactivator proteins such as SRC-1 (steroid receptor coactivator 1) and PGC1-a

(peroxisome proliferator-activated receptor gamma coactivator 1-alpha) (Forman et al. 1998;

Shiraki et al. 2003; Shan et al. 2004). However, without the presence of ligands, CAR manifests its unique constitutive activity by forming a charge-charge interaction between H4 and H12/AF2 to maintain an active conformation mimicking ligand docking (Dussault, Lin et al. 2002).

Further studies have recognized species-specific ligands for CAR: TCPOBOP (1,4-bis[2-

(3,5-dichloropyridyloxy)] benzene) as an agonist for mCAR, and CITCO ( 6-(4- chlorophenyl)imidazo[2,1-beta][1,3]-thiazole-5-carbaldehyde-O-(3,4-dichlorobenzyl)oxime ) as as agonist for hCAR (Mackowiak and Wang 2016). Likely due to the relative low sequence identity between mCAR and hCAR in their ligand binding pockets, these ligands are species selective as TCPOBOP can only activate mCAR and not hCAR; and CITCO can only activate hCAR, but not mCAR (Moore et al. 2000; Maglich et al. 2003).

Two endogenous testosterone metabolites, androstenol (5α-androst-16-en-3α-ol) and androstanol (5α-androstan-3α-ol), were among the first identified ligands for mCAR (Forman et al. 1998). Both compounds were categorized as inverse agonists, as they repressed the constitutive activity of mCAR in vitro and disrupted CAR’s interactions with coregulators such as

SRC-1. Later on, the antifungal drug clotrimazole, and PK11195 (peripheral benzodiazepine receptor ligand 1-(2-chlorophenyl-methylpropyl)-3-isoquinoline-carboxamide), were identified as inverse agonists of hCAR, both able to reduce constitutive activities of hCAR (Moore et al. 2000;

Li et al. 2008). One interesting note is, again due to the divergence of mCAR and hCAR LBD sequence, some compounds may exhibit opposite effect for mCAR and hCAR. Meclizine, a histamine H1 receptor antagonist, has been shown to be an agonist for mCAR and an inverse agonist for hCAR (Huang et al. 2004b); a recent high-throughput screening has identified an 5 antineoplastic agent nocodazole to be an agonist for hCAR and an antagonist for mCAR (Lynch et al. 2015).

Indirect activation

Phenobarbital represents a diverse group of compounds known as ‘PB-like inducers’, capable of liver specifically inducing CYP2 family members and other xenobiotic metabolism- related proteins (Waxman 1999; Wei et al. 2000). Although CAR had been earlier characterized to activate PB inducible genes by binding to the PBREM (Sueyoshi and Negishi 2001), subsequent study revealed that PB is not a direct ligand for CAR (Kawamoto et al. 1999; Moore et al. 2000). PB in competition binding assays did not compete with [3H]clotrimazole, a radioactive-labeled known CAR ligand (Moore et al. 2000). Protein phosphatase inhibitor okadaic acid has been demonstrated to inhibit PB activation of Cyp2B expression by repressing

CAR nuclear translocation (Sidhu and Omiecinski 1997; Kawamoto et al. 1999). These results suggested that PB activation of CAR does not involve direct binding of the CAR LBD; rather, an indirect mechanism through phosphorylation was indicated.

Later investigations revealed that dephosphorylation of threonine 38 is crucial for PB induced cytoplasmic CAR translocation into nuclei (Mutoh et al. 2009). Phosphorylation of T38 inactivates DNA-binding of CAR by destabilizing the a-helix between zinc fingers, as well as keeping inactivated CAR sequestered in the cytosol. Mutation of T38 abolished PB-activated translocation of CAR. More recently, the EGF (epidermal growth factor) pathway has been implicated in PB activation of CAR (Mutoh et al. 2013). When PB interacts with EGFR

(epidermal growth factor receptor), the scaffold protein RACK1 (receptor for activated C kinase

1) is dephosphorylated, in turn activating PP2A (protein phosphatase 2A), which then dephosphorylates CAR at Thr38, releasing CAR and initiating its nuclear translocation. A study 6 of the CAR activators, chrysin, baicalein and galangin, demonstrated that these chemicals indirectly activate CAR through inhibition of EGF signaling pathway (Carazo Fernandez et al.

2015). Similarly, polychlorinated biphenyls were also recently identified as EGF inhibitors and

CAR activators (Hardesty et al. 2017).

Further studies suggested that activation of the ERK (extracellular signal-regulated kinase) and MAPK (p38 mitogen-activated protein kinase) pathways are involved in PB induced gene expression (Mackowiak and Wang 2016). These findings further validate that indirect activators of CAR act through multiple events, with a key pathway consisting of inhibition of

EGF-mediated pathways.

CAR function

Upon its first discovery, CAR was identified to mediate the expression of rat Cyp2B and mouse Cyp2b10 through PB induction. Subsequently, a range of drug processing gene pathways have been recognized as CAR-regulated, such as the Phase I xenobiotic metabolizing cytochrome

P450s, including rat Cyp2A, Cyp2C, Cyp3A, mouse Cyp2a5 (Waxman 1999), NADPH-CYP reductase; Phase II xenobiotic transferases, such as sulfotransferases, glutathione S-transferases,

UDP-glucuronosyltransferases; and, Phase III xenobiotic transporters, such as MRP4 and MRP2

(multidrug resistance-associated proteins 4 and 2) (Omiecinski et al. 2011b). Although CAR was established as an import xenobiotic sensor regulating drug metabolism and transport, more recent investigations have pointed out that CAR also plays a major role in various hepatic functions that control physiological and pathophysiological conditions, including energy metabolism, insulin signaling, cell cycle, cell growth cell proliferation, and tumor promotion (Yang and Wang 2014;

Kobayashi et al. 2015). For example, TCPOBOP activation of CAR increases insulin sensitivity 7 and alleviates liver steatosis in both high fat diet (HFD)- and leptin deficiency-induced obese mice (Dong et al. 2009; Gao et al. 2009).

CAR in drug metabolism

In addition to above-mentioned drug processing genes from phase I drug metabolizing genes, phase II transferases and phase II drug transporters, CAR has also been found to activate

CYP2A6 (Wortham et al. 2007) and to some extent CYP1A1 and CYP1A2 (Yoshinari et al.

2010). Aside from the mouse drug transporters, Mrp2 and Mrp4, human MDR1 (multidrug resistance protein 1) has been shown to be up-regulated by CAR (Burk et al. 2005), and OATP1,

(organic anion-transporting polypeptide 1) is also inducible by CAR (Osabe et al. 2008).

A recent genome-wide CAR study using HepaRG cell lines provided transcriptomic insights into CAR-targeted gene expression in human cells (Li et al. 2015). HepaRG is an immortal human liver cell line exhibiting human primary hepatocyte-like features such as induction of the majority of P450 enzymes, transporter efflux and liver-enriched transcriptional factors, which are usually absent in immortal human liver cells derived from human hepatocellular carcinomas, such as HepG2 and Huh7 (Parent et al. 2004). In Li et al. study,

CITCO was shown to differentially regulate 135 genes and PB was shown to regulate 133 genes, all in a CAR-dependent manner. Selective drug metabolizing genes detected includes CYP3A43,

CYP3A7, CYP3A5, CYP2C9, CYP2C18, ALDH3A1, AKR1B10, SLC5A12, Ces2a and Por.

An RNA-seq study for the CAR ligand, TCPOBOP, and the PXR ligand, PCN, in treated mouse livers revealed that TCPOBOP mediated 2125 differential expressed genes, 1978 of which were TCPOBOP-exclusively induced, with no significant differential expression from PCN (Cui 8 and Klaassen 2016). Among genes perturbed by TCPOBOP or PCN, 127 were drug-processing genes, including 119 phase I enzymes, 35 phase II enzymes and 18 transporters.

Therefore, together with PXR, CAR provides a major regulatory role mediating the expression of xenobiotic metabolizing enzymes and transporters, and functions as a critical driver for drug and other xenobiotic compound disposition in vivo (Yan and Xie 2016). In these respects, estimates are that CAR contributes to regulate approximately 75% of the overall metabolism of clinically used drugs and detoxification of environmental chemicals. Hence, CAR has long been a gene of interest in industrial drug discovery for investigating drug-induced cytotoxicity and drug-drug interactions.

Complex roles of CAR in chemical toxicity

Although CAR plays major role in metabolizing xenobiotic compounds, it also mediates in some cases liver injury induced by drugs and foreign compounds. One leading cause for acute liver failure is overdose of acetaminophen (APAP, also known as N-acetyl-p-aminophenol or paracetamol) (Zhang et al. 2002). At high doses, cytochrome P450 enzymes including CYP1A2,

CYP2E and CYP3A isoforms biotransform APAP to a reactive quinone form, NAPQI (N-acetyl- p-benzoquinone imine), a highly reactive oxygen species which binds to cellular macromolecules and results in potentially lethal centrilobular necrosis (Zhang et al. 2002). Under non-toxic APAP level, treating WT mice with PB or TCPOBOP would lead to elevated ALT (alanine aminotransferase) levels and hepatic necrosis at 24 hours; however, treating CAR-null mice with

PB or TCPOBOP results in no such hepatotoxicity. PB or TCPOBOP activation of CAR showed induction of Cyp1a2, Cyp3a11 and GSTPi, with modest repression of Cyp2e; CAR-null mice further demonstrated resistance to APAP toxicity, which was associated with absence of the 9 induction of APAP-metabolizing enzymes. These studies established mouse CAR as a central mediator of APAP induced hepatotoxicity.

In addition to APAP, TCPOBOP and PB pretreated WT mice induce hepatotoxicity with the presence of cocaine; however, this hepatotoxicity is not found in CAR-null mice (Wei et al.

2000). Cocaine could selectively induce Cyp2b10 over Cyp2c and Cyp3a (Pellinen et al. 1996);

CAR-null mice again demonstrated reduced hepatotoxicity (Kobayashi et al. 2015). These results support a conclusion that CAR is a central mediator of cocaine induced hepatotoxicity.

Recent studies have investigated CAR’s controversial role in alcohol-induced hepatotoxicity. Absence of CAR in mice caused significantly increased susceptibility to chronic alcohol-induced liver injury, through fat accumulation and elevating hepatocyte apoptosis, apparently involving production of reactive oxygen species and hydroxyethyl radicals (Chen et al.

2011). However, TCPOBOP pretreatment strongly enhances hepatotoxicity by both acute and chronic alcohol intake in wild-type mice, but not in CAR null mice. Gene expression analyses indicated that CAR pre-activation and alcohol infusion synergistically decreased the expression of alcohol metabolizing enzymes such as alcohol dehydrogenase, aldehyde dehydrogenase, catalase and CYP2e1. Although the mechanism of CAR activation and alcohol synergistically repressing gene expression was unclear, these results suggest that CAR is a regulator in alcoholic liver injury and that risk of hepatotoxicity may be enhanced by alcohol acting synergistically with

CAR activation (Chen et al. 2011).

CAR also offers a protective role in chemical induced toxicities, particularly in bilirubin clearance. 6,7-Dimethylesculetin, a compound present in a traditional Chinese tea used in jaundice treatment, Yin Chin, activates CAR in primary hepatocytes from both WT and 10 humanized CAR mice, and accelerates bilirubin clearance, protecting against hyperbilirubinemia

(Huang et al. 2004a). Another study demonstrated that CAR and PXR contribute to the protective response to the hydrophobic bile acid LCA (lithocholic acid) (Zhang et al. 2004). In this study,

CAR was shown to predominantly mediate induction of CYP3a11 and Mrp3 to detoxicate bile acid induced toxicities. Together, these studies established CAR’s central role in regulating bile acid metabolism.

As a key regulator in xenobiotic compound metabolism, CAR is also recognized as a critical mediator in xenobiotic-induced liver injury. These characteristics of CAR provide an interesting prospective in understanding the complex roles of nuclear receptors in an integrated xenobiotic metabolism network.

CAR in glucose metabolism

Recent studies illustrated that CAR is not only a xenobiotic sensor; it also has expansive roles in regulating endogenous physiological functions, including glucose metabolism, cholesterol metabolism and energy homeostasis.

FoxO1 (forkhead box O1) is a crucial transcription factor promoting gluconeogenesis that is also repressed by insulin impacting the gluconeogenesis pathway (Moreau et al. 2008). G6pase and PEPCK are two key enzymes contributing to gluconeogenesis in glucose metabolism.

Without the presence of insulin, FoxO1 binds to IRS (insulin response sequence) on the promoter region of G6pase (glucose-6-phosphatase) and PEPCK (or PCK1, phosphoenolpyruvate carboxykinase 1) and activates their gene expression. When insulin is present, FoxO1 is phosphorylated by insulin, resulting in FoxO1 being retained in the cytoplasm. Therefore, 11 gluconeogenesis through G6pase and PEPCK, are repressed in an insulin dependent manner

(Kobayashi et al. 2015). PGC1-a, mentioned earlier as a coactivator of CAR, also plays a role in gluconeogenesis. PGC1-a expression is induced by glucagon. Through interacting with HNF4a

(hepatocyte nuclear factor 4 alpha), a key liver specific transcription factor, in binding to G6pase and PEPCK promoter regions, PGC1-a promotes gluconeogenesis with glucagon induction

(Kobayashi et al. 2015).

Past studies have demonstrated that short term and chronic treatment with PB leads to reduced level of PEPCK in WT mice, but not in CAR null mice (Kodama et al. 2004). This repression effect is regulated by CAR, as it physically interacts with FoxO1 to inhibit binding of

FoxO1 to IRS, consequently repressing expression of gluconeogenesis, thereby promoting genes such as PEPCK. In addition, activated CAR also binds to the DR-1 motif present in PEPCK promoter regions, resulting in binding competition with HNF4a (Miao et al. 2006). Furthermore,

PB activated CAR could compete with HNF4a with coactivator interactions, such PGC1-a and

GRIP-1 (glucocorticoid receptor-interacting protein 1), facilitating the repression of PEPCK and

G6pase expression. A more recent study revealed that CAR suppresses gluconeogenesis by promoting the ubiquitination and degradation of PGC1-α (Gao et al. 2015).

Interestingly, FoxO1 likely also acts as a coactivator for CAR for activating CYP2B6

(Kodama et al. 2004). In addition, CAR is proposed to regulate the metabolism of thyroid hormones, predominant regulators of the basal metabolic rate (Maglich et al. 2004). Fasting mice induced increased Cyp2b10, suggested to be contributed by PGC1-a induction of CAR

(Kobayashi et al. 2015). Although the cross-talk regulation of CAR, PGC1-a, HNF4a and other 12 related metabolic pathways are still not well studied, it is clear that CAR is nonetheless crucial in mediating glucose metabolism, including gluconeogenesis.

A recent study illustrated that in addition to repressing gluconeogenesis genes, CAR could also induce expression of glycolytic genes, such as glycolytic enzyme Gpi1 (glucose phosphate isomerase 1) and glycolysis rate-limiting enzyme Pklr (pyruvate kinase L/R), and glucose transporter genes such as Glut1, Glut3, and Glut4 (glucose transporter 1,3,4). The induction of CAR glycolytic genes is explained by CAR induction of c-Myc, which in turn activates glycolytic genes and glucose transporters (Osthus et al. 2000; Blanco-Bose et al. 2008).

Together with gluconeogenesis repression and glycolysis activation, activated CAR seems to be promoting energy uptake in glucose metabolism.

CAR in lipid metabolism

The LXR-SREBP (sterol regulatory element binding protein 1) pathway plays a crucial role in mediating lipogenesis, through LXR transactivation of genes related to fatty acid biosynthesis and lipid uptake (Horton et al. 2002). Again, CAR cross-talk with LXR causes mutual repression through coactivator competition of binding. As a result, LXR targeted lipogenic genes, such as Srebp1, Scd1, Acc1 and Fas, are repressed (Zhai et al. 2010). Several studies have shown that activation of CAR contributes to lower hepatic triglyceride levels, and loss of CAR leads to basal accumulation of triglycerides in the liver (Yan et al. 2015). As well,

CAR directly binds to SREBP1, suggesting that CAR could potentially directly cross-talk with

SREBP1 in lipogenesis (Roth et al. 2008).

13 In addition to CAR repression of lipogenesis by cross-talk with LXR, CAR likely inhibits lipogenesis through interaction with other components in lipogenesis. Insig-1 (Insulin-induced gene-1) is a cholesterol sensor that binds to endoplasmic reticulum. When sterols are sufficient,

Insig-1act to suppresses the proteolytic activation of SREBPs (Yang et al. 2002). Sulfotransferase

2B1b (Sult2B1b) could inhibit SREBP1 expression and liver lipogenesis through enzymatic deactivation of LXR ligands (Falany 1997). CAR could directly binds to both of these genes and consequently suppresses SREBP1 and liver lipogenesis (Yan et al. 2015).

Interestingly, CAR activation is also reported to down regulate apoA-I (apolipoprotein A-

1) in apoA-I transgenic mice, resulting in decreases VLDL secretion (Sberna et al. 2011) and cholesterol levels in plasma (Masson et al. 2008). Similarly, CAR could also lower VLDL secretion and plasma cholesterol level in Ldlr null mice through upregulation of Vldlr.

Recent studies showed that either PB or TCPOBOP induces the expression of cholesterogenic genes, such as squalene epoxidase, Dhcr7 (7-dehydroxycholesterol reductase), oxidosqualene lanosterol cyclase, HMG-CoA synthase, HMG-CoA reductase, 24- hydroxycholesterol reductase and Cyp51a1, in a CAR-dependent manner (Ueda et al. 2002;

Kobayashi et al. 2015), demonstrating activation of CAR enhances the expression of liver cholesterogenic genes. Therefore, CAR activation induces the expression of some cholesterogenic genes in live, and, as a result, liver cholesterol contents are elevated when CAR is activated.

However, total cholesterol levels in plasma were decreased in a CAR-dependent manner when treated with TCPOBOP, suggesting that other than directly enhancing liver cholesterol synthesis, there might be an undiscovered CAR-involved mechanism that controls plasma cholesterol levels

(Kobayashi et al. 2015).

14 In summary, CAR plays a central role in mediating lipid homeostasis and serves as a potential drug target for prevention and treatment of metabolic diseases.

CAR in liver carcinogenesis

Liver cancer ranks second in terms of cancer related death around the globe (Bray et al.

2013). Hepatocellular carcinoma accounts for 90% of liver cancers (Ringelhan et al. 2018); it is also the fifth most frequent neoplasm (Heindryckx et al. 2009). The main risk factors for HCC development are relatively well established, including chronic hepatitis B and C virus infection, and alcohol abuse. Many experimental mouses model have been developed for studying HCC, including chemically induced models, xenograft models and genetically modified mouse models

(Heindryckx et al. 2009). Two-stage, chemically induced models involving initiation by a genotoxic compound, followed by promotion, is often employed in HCC studies. The genotoxic carcinogen, DEN (N-nitrosodiethylamine), has been used classically as direct tumor initiation agent, and the non-genotoxic PB is often used as a subsequent promotion agent. Activation of cytochrome P450s with the presence of PB could enhance effect of DEN; in addition, PB may induce oxidative stress and cause hypermethylation in tumor suppressor gene promoter regions, facilitating tumorigenesis (Heindryckx et al. 2009).

CAR activation has long been associated with liver hepatomegaly. TCPOBOP treated mice demonstrated that after 3h single treatment of TCPOBOP, mouse liver is enlarged by 30%; after 18h liver size increased 2-fold (Kazantseva et al. 2016). One study demonstrated that Mdm2

(Mouse double minute 2 homolog) is directly activated upon TCPOBOP induced CAR activation

(Huang et al. 2005). Mdm2 stimulates cell proliferation, as well as suppresses p53-dependent apoptosis. Another recent study in an chemically-induced mouse HCC model has reported that 15 activation of CAR through TCPOBOP increases YAP activity (Kowalik et al. 2011). Liver size is controlled through YAP (Yes-associated protein) in mitogen-induced Hippo signaling pathway.

Without growth stimuli, YAP is phosphorylated and sequestered in cytoplasm; once the Hippo signaling pathway is repressed, YAP is dephosphorylated and released into nucleus where it activates cell growth and cell division. In this model, YAP activation accompanies significantly reduction of microRNA 375 level. miRNA 375 is a negative regulator of YAP. The exact role of

CAR here demands further investigation.

Recent studies revealed that CAR is closely associated with Wnt/b-catenin signaling pathway. Wnt signaling pathway has been well characterized for involvement in tumorigenesis. b-catenin, subunit of cadherin complex, is a downstream effector of Wnt signaling pathway and it plays a crucial role in liver development and injury regeneration. b-catenin mutations are present in approximately 30% of human liver tumors (Heindryckx et al. 2009). However, b-catenin mutations alone do not initiate hepatocarcinogenesis; it only causes hepatomegaly. A recent study demonstrated that CAR activation with partial b-catenin activation could induce liver carcinogenesis (Dong et al. 2015). Also, CAR activation-induced liver tumorigenesis is lost in liver-specific b-catenin knock out mice. It has been indicated that CAR activation could induce b- catenin, therefore overcome growth inhibition.

Myc is another critical transcription factor that cooperates with other oncogenes in carcinogenesis, as well as to inhibit tumor suppressor genes (Blanco-Bose et al. 2008). A variety of tumors have been associated with Myc mutations. One study indicated that upon TCPOBOP activation of CAR, Myc mediates liver proliferation through directly transactivating FoxM1, a transcription factor associated with the development and progression of many cancers, including 16 lung, prostate, pancreatic, glioblastoma and HCC (Blanco-Bose et al. 2008). FoxM1 overexpression could suppress p21 and p53, therefore suppress cell arrest.

CAR has also been shown to induce expression of Gadd45b (anti-apoptotic growth arrest and DNA damage-inducible 45b) (Yamamoto and Negishi 2008). Gadd45b could repress MKK7

(mitogen-activated protein kinase 7), which in turn represses JNK1, an apoptosis inducing protein kinase. Mediating through upregulation of Gadd45b, CAR may repress cell death via the MKK7-

JNK pathway. Recent studies have revealed that CAR can directly bind to Gadd45b complex, affecting Gadd45b’s regulation of MKK6 and the p38 MAPK pathway (Hori et al. 2018).

Connexin 32 or gap junction beta 1 (Gjb1) has also been shown as another factor involved in PB-induced tumorigenesis. Connexin 32 knock out mice displayed complete resistance to carcinogenesis by PB induction (Moennikes et al. 2000).

These results all suggest that CAR plays an essential role in PB/TCPOBOP induced hepatocellular carcinogenesis, through various pathways involving promoting cell growth and cell proliferation, as well as inhibition of cell arrest and apoptosis.

Splice variants of CAR

In addition to the predominant transcripts of hCAR and mCAR in human and mouse liver, variants of hCAR and mCAR has been identified and characterized. These variants have been produced through alternative RNA splicing (Omiecinski et al. 2011b). They display distinctive properties in terms of structure, expression levels, variants-specific ligands, potential coregulator interaction and function. 17 mCAR variants

mCAR2 was discovered along with mCAR(Choi et al. 1997). mCAR2 share the same sequence comparing to mCAR1 from exon 1 up to exon 6. Then an out-of-frame 107-bp deletion occurs starting from exon 7 to exon 8, resulting 6 new amino acids in mCAR2 instead of the remaining 78 residues in mCAR1 (Choi et al. 1997). This deletion in C-terminal region is in ligand binding domain, contacting both the heterodimerization interface (Forman et al. 1989) and the AF-2 transactivation domain (Danielian et al. 1992). Due to the absence of heterodimerization interface, mCAR2 does not bind to RXR or recognize DNA binding element of CAR. In the same manner, mCAR2 does not exhibit transactivation function nor does it affect transactivation by hCAR or mCAR1 (Choi et al. 1997).

hCAR variants

Two predominant splice variants of hCAR, termed CAR2 and hCAR3, have been characterized (Auerbach et al. 2003). hCAR2 and hCAR3 together could account for up to one third of the total CAR transcript pool in human hepatocytes (Omiecinski et al. 2011a). hCAR2 contains a 12 bp insertion at the start of exon 7, whereas hCAR3 consists of a 15 bp insertion at the start of exon 8 (Auerbach et al. 2003). Both insertions of hCAR2 and hCAR3 are located in ligand binding domain of hCAR. Later investigations revealed that both hCAR2 and hCAR3 possess unique functional biology in that they are not constitutively active, unlike hCAR1 (Auerbach et al. 2005; Auerbach et al. 2007).

The four amino acids insertion in hCAR2 alters its ligand binding domain, moderating constitutive activity, ligand binding ability and transactivation activity. The 18 ubiquitous plasticizer, DEHP (di(2-ethylhexyl) phthalate), was demonstrated as a highly potent activator of hCAR2, although not a strong agonist for hCAR1 or hCAR3 (DeKeyser et al. 2009). DiNP (di-isononyl phthalate) was also identified as a highly potent and selective ligand activator of hCAR2 (DeKeyser et al. 2011).

The five amino acids insertion in hCAR3 is responsible for the relative absence of hCAR3 constitutive activity. However, the insertion does appear affect the structure of ligand binding pocket of reference hCAR1 (Auerbach et al. 2005); suggesting that hCAR3’s ligand selectivity profile may be similar to hCAR1. A recent study further demonstrated that the five amino acids insertion of hCAR3 can be engineered into other species’ CAR3 while still maintaining species-specific ligand binding function, and therefore utilized for selective detection of CAR ligands for other species (Omiecinski et al. 2011a).

Species variation of CAR

Nuclear receptors usually share a conserved structure in terms of a high degree of sequence conservation. For instance, between human and mouse ortholog nuclear receptor genes, the amino acids sequence identity is typically higher than 95% and 85% in DBD and LBD, respectively (di Masi et al. 2009). However, while hCAR and mCAR share approximately 93.9% amino acid identity for their full sequence, their LBDs only have approximately 70% amino acid identity, suggesting an unusual divergence. Synonymous and nonsynonymous substitution rates are traditionally used to measure selection (Yang and Bielawski 2000). It is defined by the ratio between non-synonymous amino acids replacements caused by nucleotide substitutions and 19 synonymous amino acids replacements. This ratio analysis suggests that the natural selection favors CAR ligand binding domain sequence diversity, possibly in order to adopt to cross-species ligand recognition variation.

mCAR and hCAR ligands demonstrate species selectivity. For example, TCPOBOP is a selective mCAR ligand, and greatly induces mCAR downstream genes while not altering the comparative hCAR activities. In contrast, CITCO is a potent hCAR selective agonist, with no mCAR induction capability. In terms of antagonists, endogenous metabolites androstanol and androstanol are mCAR selective antagonists; clotrimazole and PK11195 are potent hCAR antagonists, capable of strongly inhibiting the constitutive activity of hCAR and repressing coactivator recruitment of hCAR (Li et al. 2008).

In addition, mCAR and hCAR show very interesting species variations in terms of tumor promotion. As discussed earlier, in rodent models, PB is recognized as a non-genotoxic carcinogen. The DEN and PB two-stage mouse model is a widely used as a hepatocellular carcinoma rodent model. However, although PB activates hCAR in human hepatocytes in a fashion similar to mCAR, long-term retrospective epidemiological studies strongly suggest no significant increase of HCC in long-term PB-dosed patients (Ferko et al. 2003; Omiecinski et al.

2011b). In a recent DEN/PB two stage tumor initiation / tumor promotion study comparing WT mice and humanized hCAR/hPXR transgenic mice, PB-treated hCAR/hPXR mice displayed significantly less pronounced tumor response, in terms of tumor volume fraction and tumor multiplicity (Braeuning et al. 2014). The underlying mechanisms of mCAR and hCAR variation are still not well understood. These discrepancies of mCAR and hCAR in modulation of tumorigenesis raise critical toxicological questions regarding to the relevance of rodent model tumorigenesis in pharmaceutical drug testing. 20 Research Objectives

CAR has been regarded principally as a central xenobiotic metabolism modulator, although more recently its important roles in energy regulation, lipid homeostasis and tumorigenesis have become increasingly appreciated. However, as a transcription factor, detailed genomic profiling of CAR has not been achieved, due mainly to the lack of availability of a specific CAR antibody qualified for chromatin immunoprecipitation. To circumvent this issue, we developed an adenovirus-based transgenic mice model, to unveil in vivo genomic profiling of

CAR for the first time. The understanding of CAR’s role in glucose and lipid metabolism have gradually increased over time, however, CAR’s role in carcinogenesis, although heavily inferred, has not been well elucidated. The mechanisms underlying differences between mCAR and hCAR in their abilities to program PB-induced tumorigenesis across species is also poorly understood.

The hypothesis of Chapter 2 is that CAR, as a nuclear receptor and transcriptional regulator, directly regulates genes associated with carcinogenesis through binding to cis- regulatory elements for those genes. A further hypothesis is that mouse CAR and human CAR exhibit substantial differences in direct binding and transactivation of genes associated with carcinogenesis, contributing to species variation in tumorigenic potential. Results from high- resolution ChIP-exo profiling indeed allowed the identification of novel CAR gene targets associated with carcinogenesis, including Gdf15, Foxo3, Shc1; as well as differential genomic interaction between mCAR and hCAR, in genes such as Myc and Bmf. These results substantiate that CAR plays an important role in carcinogenesis through direct regulation of cancer-related genes.

21 The Dlk1-Dio3 imprinted gene cluster has received scientific attention as a model for studying mechanisms of imprinting as well as for the contribution of this genomic region in various stages of development. Recent studies in chronic PB-treated rodent hepatocellular carcinogenesis models revealed that lncRNAs and miRNAs encoded in the Dlk1-Dio3 region were induced in a CAR- and b-catenin dependent manner, implying a novel pathway for CAR in mediating tumorigenesis; the exact mechanism being unknown.

The hypothesis of Chapter 3 is that CAR regulates lncRNA for Meg3, Rian and Mirg in

Dlk1-Dio3 imprinted gene clusters through direct interaction with cis-regulatory elements. Based on the data generated and detailed in Chapter 2, intensive interactions of CAR within the Meg3 promoter region were revealed, particularly in the intergenic differentially methylated region (IG-

DMR), which serves as a central control element for multiple genes within the imprinted domain.

These results lend evidence for CAR’s interaction with the cluster and help inform the mechanistic basis of CAR as a mediator of the Dlk1-Dio3 gene cluster in tumor promotion.

22

Fig 1-1 Activators and ligands of CAR.

23

Fig 1-2 Schematic diagram showing CAR activation and function. CAR could be activated through binding with ligand such as TCPOBOP and CITCO. PB could indirect activate CAR through inhibition of EGF pathway. Both activations lead to dephosphorylation of CAR, releasing CAR from CAR retention complex in the cytoplasm. CAR then translocates into nucleus, forming heterodimer with RXR, binding to PBREM and transactivating downstream genes. 24

25 Fig 1-3 mCAR and hCAR alternative splicing variants A. Structure scheme of mCAR1 (reference form) and mCAR2. Comparing to mCAR1, mCAR2 does not contain exon 8, exon 9 and a portion of exon 7. B. Structure scheme of hCAR1

(reference form) and hCAR2 and hCAR3. hCAR2 has a 12bp insertion at the start of exon 7. hCAR3 has a 15bp insertion at the start of exon 8. 26

10 20 30 40 50 mCAR mtamltletmaseeeygprncvvcgdratgyhfhaltcegckgffrrtvs X XX hCAR ------masred-elrncvvcgdqatgyhfnaltcegckgffrrtvs 10 20 30 40

60 70 80 90 100 mCAR ktigpicpfagrcevskaqrrhcpacrlqkclnvgmrkdmilsaealalr X X X X hCAR ksigptcpfagscevsktqrrhcpacrlqkcldagmrkdmilsaealalr 50 60 70 80 90

110 120 130 140 150 mCAR rarqaqrraekaslqlnqqqkelvqillgahtrhvgpmfdqfvqfkppay XX X X hCAR rakqaqrraqqtpvqlskeqeelirtllgahtrhmgtmfeqfvqfrppah 100 110 120 130 140

160 170 180 190 200 mCAR lfmhhrpfqprgpvlpllthfadintfmvqqiikftkdlplfrsltmedq XXXXX X X hCAR lfihhqplptlapvlplvthfadintfmvlqvikftkdlpvfrslpiedq 150 160 170 180 190

210 220 230 240 250 mCAR isllkgaaveilhislnttfclqtenffcgplcykmedavhagfqyefle X X X X X XXXX X hCAR isllkgaaveichivlnttfclqtqnflcgplrytiedgarvgfqvefle 200 210 220 230 240

260 270 280 290 300 mCAR silhfhknlkglhlqepeyvlmaatalfspdrpgvtqreeidqlqeemal X X XX X X X hCAR llfhfhgtlrklqlqepeyvllaamalfspdrpgvtqrdeidqlqeemal 250 260 270 280 290

310 320 330 340 350 mCAR ilnnhimeqqsrlqsrflyaklmglladlrsinnaysyelqrleelsamt X X XX X X X X X X X X hCAR tlqsyikgqqrrprdrflyakllgllaelrsineaygyqiqhiqglsamm 300 310 320 330 340

mCAR pllgeics X hCAR pllqeics

Fig 1-4 mCAR and hCAR amino acid sequence comparison. Sequence differences between hCAR and mCAR are visualized using EMBOSS needle

Pairwise Sequence Alignment. The DBD of mCAR and hCAR is shown in bold. X represents non-conservative amino acids replacements.

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Chapter 2

High-resolution, in vivo genome binding interactions of mouse and human

constitutive androstane receptors reveal novel gene targets

ABSTRACT

The constitutive androstane receptor (CAR; NR1I3) is a member of the nuclear receptor superfamily orchestrating complex roles in metabolism and liver tumor promotion. Species differences in these effector pathways remain poorly understood, although several lines of evidence suggest that mouse and human CAR differentially program the development of liver cancer. To discern these differences at the genomic level, we used high-resolution ChIP-exo approaches, with both direct and indirect chemical activators, to conduct whole genome interaction analyses for rodent and human CAR in livers of transgenic mouse models. Genomic binding data were integrated with transcriptomic expression analyses. Newly identified were the

CAR target genes, Gdf15 and Foxo3, genes characterized as important regulators of the carcinogenic process. In addition, approximately 1000 genes exhibited species-distinctive CAR binding events, including Myc and Bmf, proto-oncogenes that also demonstrate CAR-dependent differences in expression. Further, use of ChIP-exo methodology enabled characterization of high-resolution binding motifs for the respective receptors. Overall, the results obtained provide new insights into the important role that CAR contributes as a key regulator of numerous signaling pathways in mammalian organisms, and provides a genomic context that specifies 34 species variation in biological processes under CAR’s control, including liver cell proliferation and tumor promotion.

INTRODUCTION

The constitutive androstane receptor (CAR) is a nuclear receptor member of subfamily 1 group I (NR1I3) of the nuclear receptor superfamily. Typically, nuclear receptors function as transcription factors that are activated by direct ligands (Krasowski et al. 2005) and generally share a common structure, an N-terminal A/B domain, a DNA binding domain (DBD), a hinge domain, a ligand binding/ heterodimerization domain (LBD) and an F domain at the C-terminal

(Steinmetz et al. 2001). Upon ligand activation, the LBD would utilize AF-2 (active function 2) to mediate co-regulator interactions (Kojetin et al. 2015). However, CAR is distinguished from other nuclear receptors in that it lacks an A/B domain and is constitutively active in the absence of ligand due to a charge-charge interaction between LBD helixes that mimic an active AF-2 conformation, and therefore is capable of co-activator protein interactions (Dussault et al. 2002a).

CAR was initially recognized characterized as a xenobiotic modulator. Classified as a

Class II nuclear receptor, another unusual property of CAR is its retention in the cytosol in a phosphorylated state, but upon xenobiotic sensing, it undergoes dephosphorylation, translocates into the nucleus, and subsequently binds specific DNA motifs to coordinately regulate transcription of target genes. Targets include genes that encode drug metabolizing functions, such as the Phase I cytochrome P450s, Phase II transferases and Phase III transporters (Omiecinski et al. 2011a; Mutoh et al. 2013; Yang and Wang 2014). With respect to its role in xenobiotic regulation, CAR contributes to the metabolism of approximately 75% of clinically used drugs and is a major determinant in the detoxification of environmental chemicals (Yang and Wang 2014). 35 Consequently, CAR is established as a gene of toxicological importance in drug discovery and in the development of industrial chemicals destined for regulatory approval.

More recent evidence demonstrates CAR’s additional roles in regulating energy and lipid metabolism, cell proliferation and carcinogenesis (Yang and Wang 2014). For example, CAR is reported to suppress gluconeogenesis through its targeting of genes such as phosphoenolpyruvate carboxykinase (PEPCK) and glucose-6-phosphatase (G6PC), and functions to reverse induced- obesity in rodent models by targeting genes such as sterol regulatory element binding protein 1

(SREBP1) (Gao and Xie 2012). Studies of liver tumorigenesis promoted by phenobarbital (PB) and 1,4-bis[2-(3,5-dichloropyridyloxy)] benzene (TCPOBOP) revealed that CAR is essential for hepatic tumor progression in initiation-promotion models (Yamamoto et al. 2004; Huang et al.

2005). Further studies have demonstrated that CAR induces Growth arrest and DNA-damage- inducible beta (GADD45B), and MYC, while altering microRNA expression profiles related to hepatocarcinogenesis (Columbano et al. 2005; Blanco-Bose et al. 2008; Shizu et al. 2012).

However, the exact roles for CAR’s participation in the carcinogenic process are not well understood, and the subject of some controversy in that chronic human exposures to PB, a known

CAR activator, have not been associated with any detectable increase in liver cancer incidence

(La Vecchia and Negri 2014; Braeuning and Schwarz 2016). Adding to its overall complexity, another unique feature of CAR among the nuclear receptors is that its activation pathways proceed through both direct ligand interactions and through indirect signaling mechanisms

(Omiecinski et al. 2011a; Negishi 2017). The potential receptor conformational alterations inherent in these respective activation pathways may result in differences in CAR genomic targeting, or in biological/ signaling outcomes, areas that have not been well characterized. We hypothesized that differing DNA interaction profiles for the respective species CAR proteins, and perhaps variable activation states, underlie the transcriptional programs driving differences in

CAR’s biological function. 36 Due to technical limitations and poor availability of a ChIP (chromatin immunoprecipitation)-grade antibody, high-resolution genomic mapping for CAR has not been performed in vivo. In this investigation, we deployed ChIP-exo, a modified ChIP-seq method incorporating exonuclease to trim crosslinking ChIP DNA (Rhee and Pugh 2011), combined with

CAR-fusion proteins and a novel delivery system, to conduct high resolution, genome scale profiling for mCAR and hCAR in vivo, using transgenic mouse models. With a view toward better definition of the role of CAR in carcinogenesis, we identified novel CAR genomic interactions, referenced to published RNA-seq transcriptomic datasets. Further, we assessed

CAR’s genomic interactions in the presence of direct vs. indirect activators, performed DNA motif analyses, assessed species differences in CAR genomic binding profiles and identified specific target genes that may contribute to species differences in liver tumor promotion.

MATERIAL AND METHODS

Materials and reagents

TCPOBOP (>99%) was synthesized by the Environmental Health Laboratory in the

Department of Environmental and Occupational Health Safety at the University of Washington

(Seattle, WA). Phenobarbital was purchased from Sigma/Aldrich (St. Louis, MO). 6-(4- chlorophenyl: imidazo[2,1-b]thiazole-5-carbaldehyde O-(3,4-dichlorobenzyl)oxime (CITCO) was purchased from BIOMOL Research Laboratories (Plymouth Meeting, MA). Rabbit anti-GFP antibody (ab290) and chicken anti-GFP antibody (ab13970) were from Abcam (Cambridge, MA).

Full length human CAR and mouse CAR cDNAs were sub-cloned into the pEYFP-c1 plasmid

(Clontech/Takara, Mountain View, CA) to generate YFP-hCAR and YFP-mCAR N-terminal fusion protein constructs. All constructs were validated by DNA sequencing. Adenovirus (AV) 37 constructs containing YFP-hCAR, YFP-mCAR and YFP-empty were produced in the Adeno X

Expression System (Clontech/Takara) and amplified to high titer by SignaGen (Rockville, MD).

Animals and treatments

All animal care and experimental procedures complied with protocols approved by the

Institutional Animal Care and Use Committee of The Pennsylvania State University. Wild-type

(WT) C57BL/6 mice were purchased from Charles River Laboratories (Wilmington, MA).

Breeding pairs of CAR (-/-) /PXR (-/-) double-knockout mice in the C57BL/6 background were a kind gift of Dr. Wen Xie (University of Pittsburg, Pittsburg, PA), and their derivation was described previously (Saini et al. 2004). CAR (-/-) knockout mice were generated by crossing WT

C57BL/6 mice with CAR (-/-) /PXR (-/-) double-knockout mice. The mice were maintained under a standard 12h light, 12h dark cycle at constant temperature (23±1°C) with 45-65% humidity. A total of 6, 8-week-old male CAR (-/-) knockout mice were infected with YFP-hCAR

AV (1x10^12 virus particles/ ml, approximately 100ul diluted injection volume per mouse) through tail-vein injection. At 72 and 94h, the mice were administrated with two successive doses of PB (75mg/kg, in saline) (n=3), or CITCO (5mg/kg, in DMSO) (n=3), through IP injection.

Another 6, 8-week-old male CAR (-/-) knockout mice were infected with YFP-mCAR AV constructs (1x10^12 virus particles/ ml) through tail-vein injection. At 72 and 94h, two successive doses of PB (75mg/kg, in saline) (n=3), or TCPOBOP (2mg/kg, in DMSO) (n=3), were administrated. After 96h, all AV-infected mice were harvested for liver tissues after CO2 asphyxiation-induced euthanasia. For non-adenovirus infected mice, 8-week-old male CAR (-/-) knockout mice (n=6) and WT mice (n=6) were administrated with single dose of DMSO (4ml/kg)

(n=3), or TCPOBOP (2mg/kg, in DMSO) (n=3), through IP injection. After 24h, liver tissue extractions were performed following CO2 asphyxiation-induced euthanasia. 38 Liver extraction and ChIP-exo

Briefly, fresh YFP-hCAR or YFP-mCAR AV infected CAR (-/-) knockout mouse liver

(~1.0g) was immediately minced and cross-linked with 1% formaldehyde (diluted from 16% formaldehyde solution methanol-free 1 ml ampules (Thermo Scientific)) for 10 min followed by quenching with 0.125M glycine for 5 min. Liver tissues were then washed 3X with ice-cold PBS, and subjected to Dounce homogenization followed by 100nm filtration. Nuclei lysates of liver tissues were prepared by incubating homogenized cells for 15 min in cell lysis buffer (10mM Tris pH 8.0, 10mM NaCl, 0.2% IGEPAL) on ice, and then incubating precipitates from previous steps with nuclear lysis buffer (50mM Tris, pH8.0, 10mM EDTA, 0.5% SDS) for 20 min on ice. Nuclei lysates were aliquoted to 700ul each in 10ml sonication tubes (Diagenode, Denville, NJ) and sonicated using a Bioruptor 300 instrument (Diagenode, Denville, NJ) for 36 cycles of 30s on,

30s off, to achieve an average chromatin fragmentation size of 100bp to 200bp. Subsequent ChIP- exo procedures and sequencing were performed following previously published protocols (Rhee and Pugh 2012). Chromatin samples were diluted 2.5-fold with 0.6% v/v Triton X-100 and immunoprecipitated using sepharose antibody-conjugated magnetic beads. All YFP-empty, YFP- hCAR and YFP-mCAR construct-infected samples utilized anti-GFP antibody Ab290 (Abcam,

Cambridge, MA), followed by DNA polishing, A-tailing, Illumina adaptor ligation (ExA2), and subsequent digestion on the beads using lambda and recJ exonuclease. Following single-stranded

DNA elution, a primer was annealed to EXA2 and extended with phi29 DNA polymerase, then

A-tailed. Exonuclease treated ends were then ligated with a second Illumina sequencing adaptor; the products PCR-amplified and gel-purified. 39 Frozen tissue embedding and immunohistochemistry

YFP-hCAR and YFP-mCAR AV infected CAR (-/-) knockout mice were perfused with

PBS (Gibco, Gaithersburg, MD) prior to liver extraction. After overnight incubation in 4% paraformaldehyde, livers were dehydrated with a sucrose solution for 48h. Then livers were embedded in OCT-filled cryomolds and snap-frozen in liquid nitrogen-cooled 2-methyl butane.

Frozen liver tissues were sectioned using a Leica CM1950 Cryostat (Leica Biosystems,

Germany). Sectioned liver tissues were immunostained with anti-GFP antibody (ab13970,

Abcam) and Alexa 488 conjugated antibody (103-545-155, Jackson ImmunoResearch, West

Grove, PA), according to the manufacturers’ protocols. Fluorescent imagining was performed using a Nikon Eclipse TE2000-S system equipped with a Nikon Digital Sight DS-Ri1 camera

(Nikon, Japan).

Human primary hepatocyte culture and treatments

Human primary hepatocytes (HPH) in 6-well plates were procured from the Liver Tissue

Cell Distribution System at the University of Pittsburgh, Pittsburg, PA, funded by National

Institutes of Health Contract HHSN276201200017C. The HPH culture protocols were detailed previously (Olsavsky et al. 2007; Page et al. 2007). HPH were treated with DMSO or 3µm

CITCO 24h prior to total RNA extraction.

RNA extraction and quantitative realtime-PCR analysis

Mouse liver total RNA was extracted from WT, CAR (-/-) knockout, YFP-hCAR, YFP- mCAR or YFP-empty AV infected CAR (-/-) knockout mouse livers using TRIzol (Qiagen,

Germantown, MD), following the manufacturer’s protocol. Extraction methods for HPH total 40 RNA were described previously (Olsavsky et al. 2007; Page et al. 2007). Concentrations of total

RNA were measured using a NanoDrop 1000 spectrophotometer (ThermoScientific, Waltham,

MA). Total RNA was converted to cDNA using a High Capacity cDNA Reverse Transcription

Kit (Applied Biosystems, Foster City, CA), following the manufacturer’s instructions. qPCR reactions were performed using a CFX96 Real Time system (Bio-Rad, Hercules, CA) as previously described (Hao et al. 2016). Each sample was assessed in duplicate. The primer sequences are listed in Suppl. Table S1. mRNA relative expression levels were calculated using the ΔΔCt method as previously described (Page et al. 2007), and all target genes were normalized to Gapdh as reference. Primer information is provided in Suppl. Table S1.

Protein extraction and Western blot

Nuclear protein and cytoplasmic protein from YFP-hCAR, YFP-mCAR and YFP-empty

AV infected CAR (-/-) knockout mice livers were extracted using NE-PER Nuclear and

Cytoplasmic Extraction Reagents with Protease inhibitor cocktail set I (Merck Millipore,

Billerica, MA) following the manufacturer’s instructions. Protein concentrations were measured with the Pierce BCA Protein Assay Kit (Thermo Fisher Scientific, Waltham, MA) using a Tecan

Infinite M200pro fluorometer absorption function (Tecan, Mannedorf, Switzerland). 30 µM of nuclear or cytoplasmic proteins were loaded onto 10-well precast 10% SDS-PAGE gels (mini-

PROTEAN TGX gel, Bio-Rad, Hercules, CA). After denaturing SDS-PAGE separation, proteins were transferred to a 0.2 µm PVDF membrane using the Trans-Blot Turbo Transfer system (Bio-

Rad, Hercules, CA) with the Trans-Blot Turbo Transfer Kit (Bio-Rad, Hercules, CA), on the 1 mini TGX preprogrammed setting. Subsequent western blot procedures were performed as previously described (Omiecinski et al. 2011a). Rabbit anti-CYP2B450 was generously provided by Dr. Gonzalez from the Laboratory of Metabolism, National Cancer Institute/NIH. Rabbit anti- 41 GFP antibody ab290 was purchased from Abcam. Chicken anti β-actin polyclonal antibody sc-

81178 was purchased from Santa Cruz (Santa Cruz Biotechnology, Dallas, TX). All antibodies were diluted 1:1000 prior to incubation.

Data analysis

Sample sequencing was performed on an Illumina NextSeq500 system using a 40bp paired-end sequencing setting. Reads were mapped to the mouse genome (mm10) using BWA-

MEM (version 0.7.9a) (Li and Durbin 2009; Li 2013). Each biological replicate contained at least

20 million unique reads.

Genome coverage visualization

For visualization, biological replicates reads files were merged into a single file for each sample. Sample reads were separated by forward/reverse strand and respectively converted to genome coverage format bedgraph files; then forward strand and reverse strand bedgraph files were combined and converted by IGV tools for IGV browser visualization. Red areas represent forward strand reads coverage, whereas blue area represents reverse strand reads coverage.

Quantitative differential binding analysis

42 Biological replicate reads files were merged and then peaks were called using the

MACS14 default setting (p-value cut off 1E-5) against the YFP-empty infected control sample

(Zhang et al. 2008). MACS14 peaks from all four samples were merged into one peak file using the bedtools merge function (Quinlan and Hall 2010) and filtered for blacklist regions

(Consortium 2012). The merged peak files represent all potential enrichment regions for both mCAR and hCAR. Count matrixes of potential enrichment regions coverage were generated for all biological replicates, then further analyzed using the R corrplot package and the R

Bioconductor DESeq2 package. The R corrplot package was used to calculate and plot for

Pearson correlation coefficients between two replicates. The DESeq2 utilized a negative binomial-based generalized linear model to examine following differential binding tests: mCAR vs hCAR, mCAR TCPOBOP vs PB, and hCAR CITCO vs PB. The Principal Component

Analysis (PCA) data were generated using the DESeq2 package. Hierarchy clustering was performed by submitting the count matrix to ClustVis (Metsalu and Vilo 2015), with average method for both row and column clustering. Enrichment regions with significant differential binding between test groups (fold change >= 2, q-value < 0.05) were associated to the closest

TSS site within 10kb region and annotated using HOMER (Heinz et al. 2010).

Peak annotation and GO analysis

Based on the highest FRiP score (fraction of reads in peaks), three replicates from mCAR samples and three replicates from hCAR samples were chosen for the stringent peak calling processes. Peak calling processes were performed based on the official specification for the

ENCODE TF ChIP-seq processing pipeline, using the Irreproducibility Discovery Rate (IDR) method. Briefly, three replicates from mCAR or hCAR were merged, randomly shuffled into two 43 pseudo replicates, and pseudo replicates were called for peaks using MACS2 and analyzed for

IDR consistency R script. Following the IDR framework guideline, the IDR output thresholds,

0.01 for mCAR and 0.0025 for hCAR, were selected. IDR analysis-generated peaks were filtered with blacklist region, then associated with the closest TSS within a 10kb region and annotated by

HOMER. GO analysis was performed using the HOMER functional enrichment analysis and the

DAVID Functional annotation tool (Huang da et al. 2009b; Huang da et al. 2009a). The mCAR and hCAR top 500 genes ranked by MACS p-value were submitted for GO analysis.

Motif analysis

ChIP-exo peak-pairs for each treatment as well as merged mCAR and hCAR samples were generated following previously published methods (Rhee and Pugh 2011). Briefly, peak- pairs of 5′ ends of ChIP-exo reads were determined using the Genetrack algorithm (Albert et al.

2008), with fine grain peak-calling parameters: sigma = 5, exclusion zone = 10. Peak-pairs were merged using the bedtools merge function. The top 500 or 1000 merged peak-pairs overlapping with primary binding sites were used for de novo motif discovery using the MEME algorithm

(Bailey et al. 2009). Motifs for mCAR and hCAR differential binding sites were also generated by MEME algorithm using the same settings. Myc, Ran, Ikbke, Cyp2b10 and Ces2a were selected for a putative motif location analysis, using the FIMO algorithm (Find Individual Motif

Occurences), to scan identified motifs on CAR binding regions near transcription start sites.

RNA-seq re-analysis

To cross-reference our differential genomic species mapping results with transcriptomic data, we utilized a set of published RNA-seq files consisting of C57BL/6 wild type mice and 44 humanized CAR-transgenic (hCAR-TG) mice (Cheng et al. 2017). WT_Day60_CornOil (n=3),

WT_Day60_TCPOBOP (n=3), hCAR-TG_Day60_CornOil (n=3) and hCAR-TG_Day60_CITCO

(n=3) samples (NCBI Gene Expression Omnibus database GSE98666) were re-analyzed specifically for mCAR and hCAR species difference comparisons. Reads from each sample replicate were mapped to the mm10 mouse genome using HISAT2; then uniquely mapped reads were processed with featureCounts and DESeq2 for differential gene expression analysis.

Differential expression analyses were performed between the C57BL/6 wild type mouse control and TCPOBOP, and the humanized CAR-transgenic (hCAR-TG) mice control and CITCO (fold change >= 2, q-value < 0.05).

RESULTS

Adenovirus transient transgenic mouse characterization

To overcome technical difficulties resulting from the lack of commercial CAR ChIP- grade antibodies, we developed an adenovirus-based system to direct the expression of YFP-CAR fusion constructs in transgenic mouse system livers, as depicted in Fig. 2-1A. mCAR and hCAR

N-terminal YFP fusion constructs were created, along with YFP-only vehicle constructs serving as controls, and deployed in high titer adenoviral delivery vectors. A commercially available

ChIP-grade anti-GFP antibody was used to immunocapture YFP-CAR fusion proteins bound to their DNA targets with high specificity, allowing genomic mapping of the CAR-DNA interactions. The efficacy of the adenovirus-delivered transgenic mouse strategy was verified through immunofluorescence visualization, validation of known CAR target gene mRNA expression and with protein expression analysis. Fluorescence imaging of both YFP-mCAR and

YFP-hCAR expression in cyrostat liver sections showed that CAR was highly expressed in 45 mouse liver hepatocytes subsequent to adenovirus delivery (Fig. 2-1 B). Although the extent of nuclear translocation appeared to differ between the activated human and mouse constructs, successful transcriptional activation of known target genes for each construct was validated independently. Real-time PCR analyses of liver RNA was conducted in WT and CARKO (-/-) mice, transduced with YFP-empty vector as well as YFP-mCAR and YFP-hCAR constructs.

Both YFP-mCAR and YFP-hCAR infected mice exhibited high levels of YFP mRNA expression.

Following transduction with the CAR viral vectors, both indirect CAR activator PB and CAR ligand activators TCPOBOP or 6-(4-chlorophenyl: imidazo[2,1-b]thiazole-5-carbaldehyde O-

(3,4-dichlorobenzyl)oxime (CITCO), induced mouse Cyp2b10 mRNA expression in CARKO mice at comparable levels to WT mice (Fig. 2-1C). Mouse liver western blotting showed YFP- expressed proteins, with the anticipated correct size differences between the YFP-empty and YFP fusion vectors, YFP-mCAR and YFP-hCAR. CAR activation by PB induced Cyp2b10 protein expression in YFP-mCAR and YFP-hCAR mice, but not in YFP-empty mice (Fig. 2-1D). These results demonstrated that the adenovirus delivery system used effectively expressed the YFP-

CAR constructs in mouse liver, and that the resulting fusion proteins retained activity as transcriptional regulators of canonical CAR target genes in the presence of receptor activators.

Genome profiling of mCAR and hCAR DNA interactions

Livers from adenovirus infected YFP-mCAR and YFP-hCARmice with direct and indirect activators treatments (PB treated n=3, TCPOBOP treated n=3; PB treated n=3, CITCO treated n=3; respectively) were extracted and processed through the ChIP-exo pipeline with paired-ended sequencing. On average, a replicate sample consisted of 27.5 million uniquely mapped reads, with the lowest replicate exhibiting 21 million unique reads. The Integrated

Genome Viewer (IGV) was used to display ChIP genomic reads coverage for several known 46 CAR-binding genes: Gstm2, Gstm3, Cyp2b10 and Ces2a (Fig.2-2 A). The visualization revealed fine structures for CAR binding events. For Cyp2b10, in addition to a well characterized ~2kb upstream PBREM, several CAR binding sites were mapped through the Cyp2b10 promoter region, with the strongest binding localized at ~10kb upstream. Initial peak calling analysis across the entire mouse genome was assessed using macs14 default settings and identified mCAR binding at an average of 18,000 binding loci, whereas hCAR exhibited 48,000 binding loci. Peak distances to Transcription Start Site (TSS) were calculated for each hCAR/mCAR treatment and showed that the majority of the detected CAR binding peaks centered on TSSs (Fig.2-2 B).

Quantitative differential analysis reveals distinct hCAR and mCAR genomic profiles

Previous reports assessing the overall correspondence of transcription factor binding with gene expression levels concluded positive correlations of TF binding signals to the respective levels of gene expression (Ouyang et al. 2009; Cheng et al. 2012; Cheng and Gerstein 2012).

Therefore, delineating gene binding site differences between mCAR and hCAR is likely relevant as a predictor of transcriptional effects and likely formative in contributing towards our understanding of species variation in downstream CAR signaling.

To quantitatively analyze the difference between genomic binding profile of hCAR and mCAR, we analyzed the genomic coverage for each of the selected ChIP replicates using featureCounts software and used the resulting genomic coverage output for subsequent statistical study. Pearson correlation analysis was carried out for all replicates (Fig.2-3 A). For the hCAR replicates coverage, correlation coefficients approached 1 between any two replicates; for mCAR,

5 samples exhibited very high correlation coefficients between each other (r > 0.95), with an exception for mCAR_PB_3, which displayed a lower coefficient relative to other mCAR samples

(0.42 < r < 0.45). Overall, the results demonstrated marked positive correlations within the hCAR 47 replicates and within the mCAR replicates, indicating that the respective replicate binding profiles were highly similar. However, between the hCAR and mCAR replicates, the coefficients were much lower (0.05 < r < 0.20), suggesting distinctions in the binding profiles between hCAR and mCAR.

DESeq2 package from R Bioconductor uses a negative binomial distribution model to calculate variance-mean dependence in count data. The package is often used for RNA-seq comparative expression analyses, however the same model can be applied to analyze differential genome coverage count data generated from ChIP-seq. In this respect, DESeq2 analysis was performed to compare differential genome coverage, or binding profiles, between mCAR and hCAR replicates. Among 59508 loci examined, DESeq2 revealed a small number of 6311 loci that showed statistically significant differential binding profiles for mCAR and hCAR (adjusted

P-value < 0.05). The top 1000 loci, ranked by adjusted P-value, were analyzed using principal component analysis (PCA) (Fig 2-3 B). Each red dot represents an mCAR replicate and blue dots represent hCAR replicates. Separation between the mCAR and hCAR replicate groups is evident, indicating differential DNA binding between mCAR and hCAR.

Hierarchy cluster analysis was also conducted for the mCAR vs hCAR DESeq2 results, using 1048 significant differential binding loci (loci within ±10kb of TSS, differential genome coverage fold change >= 2, adjust P-value <=0.05). The red color scale indicates increased locus binding events, compared to blue, indicating decreased binding events. The hierarchy clustering analysis indicates that the hCAR replicates and the mCAR replicates cluster separately, demonstrating differential DNA binding interactions exhibited by the respective species’ receptors. 674 binding loci were associated with stronger mCAR bindings, whereas 331 loci were related to stronger hCAR bindings, as shown in Fig 2-3 D. A comprehensive list of these gene- binding interactions is provided in Suppl. Table S2. An IPA disease and function analysis of differential binding associated genes (Fig 2-3 E) illustrated that the diseases and functional 48 annotations corresponding to the strongest mCAR binding gene regions were largely associated with biological terms: cell proliferation, apoptosis, and cell death; whereas for hCAR, the strongest binding gene regions were annotated with terms that were energy metabolism-related.

Together, these results point to interesting species-driven distinctions underlying mCAR vs hCAR DNA binding interactions that likely drive differences in the receptors’ biological functions.

Direct vs indirect CAR activation results in near identical genomic profiles

Applying Pearson correlation analysis for all samples, highly positive correlation coefficients (r > 0.95, except for mCAR_PB_3) were obtained between direct (TCPOBOP /

CITCO) and indirect CAR activator (PB) replicates (Fig 2-3 A), indicating remarkably high similarity between the DNA binding profiles resulting from direct vs indirect CAR activator treatments. DESeq2 differential analyses were conducted for TCPOBOP vs PB in mCAR infected

CAR -/- mice and CITCO vs PB in hCAR infected CAR -/- mice. In contrast to the differential

DNA binding profile results obtained for mCAR vs hCAR, when comparing the profiles for PB vs TCPOBOP treatments (for mouse), or PB vs CITCO (for human), only relatively few genes exhibited differential binding patterns. These results strongly support a conclusion that the genomic CAR binding profiles comparing direct and indirect activators for both mCAR and hCAR are remarkably similar.

Gene annotations for mCAR and hCAR binding regions

Since the direct- and indirectly-activated CARs exhibited almost identical genomic binding interactions, efforts were then focused toward high confidence peak calling and 49 annotation. Replicates possessing the highest quality parameters for mCAR and hCAR were selected from the sample pool, regardless of activator. Specifically, three replicates possessing the highest FRiP (Fraction of Reads in Peaks) scores were pooled within mCAR and hCAR samples, followed by peak calling using the IDR method. Peaks were mapped to the nearest identified TSS of annotated genes, within ±10kb, although we realize this is likely a stringent threshold, in that enhancers or other transcriptional regulators may map further distant from a TSS (Landt et al.

2012). The resulting analysis identified 6364 unique genes associated with hCAR, and 2839 unique genes annotated for mCAR, with 2661 of the genes overlapping (Fig 2-4 A; full gene lists in Additional File 2). 94% of the mCAR annotated genes overlapped with hCAR genes, consistent with the genome browser visualization data (Fig. 2-2 A), since in most loci, hCAR and mCAR showed similar binding patterns. A possible explanation for hCAR mapping to almost twice the number of annotated genes relative to mCAR is that hCAR replicates generally exhibited higher signal-to-noise ratios than mCAR replicates, likely skewing those weaker binding peaks below the selected annotation thresholds.

GO Term analysis was conducted for the top 500 annotated genes using DAVID functional annotation (Huang da et al. 2009b; Huang da et al. 2009a) (Fig 2-4 B). The top three enriched GO DIRECT biological process terms for mCAR and hCAR were the same, i.e., lipid metabolic, oxidation-reduction and cholesterol metabolic processes; terms consistent with previously reported roles for CAR as a regulator of phase I cytochrome P450s in drug metabolic pathways and coordinator of energy and lipid metabolism. Glucose metabolic processes were present in both top 10 lists, indicating CAR’s major role in regulating glucose metabolism as well. Drug transmembrane transport processes and glutathione metabolic processes demonstrated the phase II and III regulatory functions of CAR. Gene lists from the top 500 CAR binding genes involved in select biological processes are shown in Table 1. Overall, GO analysis indicated that 50 the most enriched functionalities of CAR lie in drug and energy metabolism, consistent with

previous determinations.

Top 500 mCAR and hCAR binding genes categorized by selected GO terms

GO term ID Term mCAR top 500 binding genes hCAR top 500 binding genes Cyp2b10, Mgst1, Gstm1, Tcf3, Cyp2d9, Mgst1, Abcc3, Gstm3, Ppm1f, Nr1i2, Cyp2c55, Nfe2l2, Fbp1, Gstm3, Abcb11, Abcc2, Cyp2b10, Tcf3, Abcc4, Gstm1, GO:0042493 response to drug Abcc3, Cyp2d22, Nr1i2, Ppm1f Abcb4, Abcb11

Nr5a2, Thrsp, Dhrs4, G6pc, Scp2, Akr1b7, Cyp2d9, Cyp7a1, Cers2, Cyp17a1, Hmgcr, Pon1, Erlin1, Ttr, Cyp2b10, Dbi, Cyp2b10, Acox2, Pgap2, Cecr5, Por, Cyp2d22, Serpina6, Cyp2c55, C3, Cyp2c66, Acot3, Akr1b7, Pon1, Gpd1, Plch2, Cyp26a1, Cyp2d9, Ppard, Gpat4, Cyp26a1, Apoa2, Acaa1b, Ttr, Decr2, Cps1, Ces1d, Il1rn, Apof, Gpcpd1, Cyp2c55, Serpina6, C3, Apof, Fabp6, Cyp17a1, Stat5a, Qk, Pck1, St6galnac6, G6pc, Impad1, Lrp1, Etnk2, Scarb1, lipid metabolic GO:0006629 Crat, Apoc3, Pla2g12a, Lipe, Ptges, Ptgds, Ppard, Ptges, Hsd17b4, Lpin2, process Insig2, Acox2, Agpat2, Cyp4v3, Hsd17b4, Cyp27a1, Erlin1, Cps1, Ptgr2, Ces1d, Impad1, Apoa2, Dhrs9, Cers2, Cyp27a1, Tm7sf2, Hacd3, Pck1, Fdx1, Thrsp, Acsm1, Cln8, Cpt1a, Lrp5, Etnk2 Cln8, Gpcpd1, Hmgcr, Por, Abhd5, Cyp2g1, Hsd17b2, Acsm1, Cyp3a13, Gpd1, Decr2, Lrp5, Pcx, Pla2g12a, Scd1, Dhrs9, Stat5a Galm, Lrp5, Cpt1a, Car5a, Gapdh, Pdk4, Pck1, Pdhb, G6pc, Pgm1, Serp1, glucose metabolic G6pc, Fbp1, Serp1, Pck1, Pgm1, Dlat, Pdk2, Pcx, Dlat, Akt1, Lrp5, Gpd1 GO:0006006 process Gpd1, Pdk2

Lrp5, Cebpb, Lifr, St6gal1, Smad3, Erbb3, Smad6, Serpinf2, Eif4g1, Fosl2, Grn, Pid1, Nkx2.5, Irs2, Zmiz1, Ptgds, Ptges, Bcl6, Abcc4, Agt, Cebpb, Por, Hmgcr, Xdh, Birc5, Fgf18, Bcl6, Nr2f2, Hilpda, Bak1, Shc1, Cacul1, Rarb, Stat5a, regulation of cell Klf9, Mlxipl, Hhex, Tcf3, Stat5a, Crip2, Rnf10, Nfkbia, Eppk1, Lrp5, Slc9a3r1, GO:0042127 proliferation Atf5, Ptgfr, Ppard, Cnot8, Cacul1, Cela1, Hilpda, Sdc4, Morc3, Trp53inp1, Xirp1, Arg1, Rarb, Nfkbia, Wnt11, Mafg, Cers2, Zmiz1, Tcf3, Mst1, Mlxipl, Klf9, Lims2, Agt, Por, Slc9a3r1, Grn, Podn, Akt1, Ern1, Podn, Nr2f2, Jup, Birc5, Hmgcr, Ccr7, Shc1, Nr5a2 Pdgfc, Smad3, Tob2, Ppard, Ptges

Table 2-1. The top 500 mCAR and hCAR binding genes as annotated by HOMER. Listed Go terms were selected to represent CAR’s role in drug metabolism, lipid

metabolism, glucose metabolism and cell proliferation. 51 Expression analysis on select CAR-binding genes indicates that CAR directly regulates key pathways in hepatocarcinogenesis

One of the goals of our study was to identify novel gene involvements for CAR in carcinogenesis-related pathways, relationships less well elucidated than other CAR regulatory functions, such as drug and energy metabolism. In these respects, we generated a list of genes from those that overlapped as annotated CAR binding genes with genes associated with common cancer-related pathways, including Wnt/β-catenin, TGF-β pathway, Jak/STAT, PI3K/Akt, p53, apoptosis, etc., and then selected genes whose expression levels were altered in published CAR- related gene expression studies (Ochsner et al. 2016). For the selected endpoints, we assessed transcriptomics data (Fig 2-5), including realtime-PCR mRNA experiments conducted here together with re-analysis of published RNA-seq data (GSE98666). Cyp2b10 or CYP2B6 mRNA served as a positive control for CAR inducibility. Among those CAR-binding genes that were also associated with carcinogenesis pathways, Gdf15 (Growth/differentiation factor 15) mRNA expression was remarkable, exhibiting greater than a 15-fold induction when comparing

TCPOBOP and DMSO treated WT mice; although unchanged in CAR -/- mice. GDF15 is involved in inflammatory and apoptotic pathways (Zimmers et al. 2005) and recently associated with liver carcinogenesis through activation of the GSK-3β / β-catenin pathway (Xu et al. 2017).

Our ChIP-exo data identified two major CAR binding sites within 5 kb of the Gdf15 gene’s TSS.

RNA-seq expression analyses also demonstrated that Gdf15 mRNA levels are increased ~15-fold in WT mice following TCPOBOP treatment, and ~5-fold with CITCO treatment in humanized

CAR mice. Together, these results strongly imply that mCAR directly regulates Gdf15 at the transcriptional level. Although not as remarkable, we conducted mRNA expression assays on 3 human primary hepatocyte donor cases, and the results were consistent with a trend of increased

GDF15 expression with CITCO induction. 52 In addition, Prkar2a (cAMP-dependent protein kinase type II-alpha regulatory subunit) and Bcar1 (Breast cancer anti-estrogen resistance protein 1, p130cas) exhibited strong CAR binding sites within ± 5 kb of each gene’s TSS. Expression analyses for the respective genes indicated that both were selectively induced in WT mice but not CAR -/- mice (Fig. 2-5 A). Shc1

(SHC-transforming protein 1) showed a similar trend, exhibiting increased CAR dependent expression. These results are likely of biological importance, since cAMP-activated protein kinases (PKA) play many roles, including an interplay in the progression of various tumors

(Caretta and Mucignat-Caretta 2011), and the PKA regulatory subunit RII alpha is specifically reported to block apoptosis in pathological processes (Benetti and Roizman 2004). Similarly,

BCAR1 belongs to the CAS family of adaptor proteins that contribute to signaling pathways involved in cell adhesion, migration and apoptosis (Cabodi et al. 2010). As well, SHC1 activates cell proliferation and positively regulates the cell cycle through signaling in the EGF pathway

(Zheng et al. 2013). Together, the ChIP-exo results advance mCAR’s novel role in promoting tumorigenesis through activation of adaptor proteins such as Shc1 and Bcar1, and the PKA kinase subunit, Prkar2a.

Of interest, several genes exhibiting strong CAR-binding sites demonstrated repressed expression levels. Foxo3 (Forkhead box protein O3), functioning in antitumor activities (Deng et al. 2018), was significantly repressed with respect to mRNA level in realtime-PCR assays conducted with TCPOBOP treated WT mice WT mice, but not in CAR -/- mice. RNA-seq expression results indicated that Foxo3 mRNA expression was reduced by 30% in TCPOBOP- treated mice compared with controls (Fig 2-5 A, Fig 2-5 B). These results imply that mCAR may promote tumorigenesis by suppressing Foxo3. IRS2 (Insulin receptor substrate 2) and IGF1

(Insulin-like growth factor I) have roles in the insulin signaling pathway, promoting cell proliferation (Reiss et al. 2012; White 2014). Here we show that Igf1 mRNA expression levels decreased in TCPOBOP-treated WT mice, and Irs2 displayed a trend toward decreased 53 expression in TCPOBOP-treated WT mice. In sum, these results suggest that CAR may function to repress hepatocarcinogenesis by affecting the insulin signaling pathway, adding to the complexity of understanding CAR’s role in carcinogenesis.

For each of genes alluded to above, we examined expression level of their human ortholog genes in three human primary hepatocytes cases (Fig 2-5 C). GDF15, FOXO3, IRS2 and

IGF1 all exhibited similar mRNA expression trends upon exposure to the hCAR-specific activator, CITCO. However, SHC1, PRKAR2A and BCAR1 did not show consistent trend changes in HPH. Potential explanations for these differences include actual species differences in effect between mCAR and hCAR, or the large interindividual differences inherent in humans that the limited donor samples were unable to reflect.

Cross-referencing RNA-seq transcriptomics datasets with identified species differences in CAR-linked oncogenes

Earlier, we characterized genomic profiles of differential binding loci for mCAR and hCAR. Functional annotation of these loci using IPA suggested that mCAR exhibits stronger binding to regions associated with regulation of cell proliferation and apoptosis. Using RNA-seq data for both WT mice and CAR-humanized mice, we cross-examined both genomic and transcriptomic data with the aim of more specifically identifying species differences in the respectively regulated genes.

For the RNA-seq analyses, we set the expression fold-change >= 2 or <= -2 and adjusted p-value to <= 0.05 as the threshold for screening significantly perturbed genes. Under this threshold, we identified 1837 mCAR-specifically perturbed genes and 93 hCAR-specifically perturbed genes. Within those gene lists, 48 overlapped with mCAR stronger-binding genes and 3 overlapped with hCAR stronger-binding genes. Detailed gene lists were shown in Suppl. Table 54 S2. Again, we focused on carcinogenesis-related genes. MYC is a well-studied transcription factor regulating cell cycle and apoptosis. It has been reported as a downstream effector of mCAR in liver proliferation (Blanco-Bose et al. 2008). Our ChIP-exo data showed that mCAR binds stronger than hCAR on Myc genes (Tab. S2). Expression analyses with realtime-PCR and RNA- seq demonstrated that Myc mRNA was significantly upregulated in TCPOBOP-treated WT mice by mCAR, in contrast to the modest Myc induction in humanized CAR mice with CITCO treatment. Given that Myc plays a key role in regulating cell cycle and proliferation, these respective mCAR and hCAR differences in Myc regulation may underpin their differences as biological contributors to hepatocarcinogenesis.

The ChIP-exo data also identified Ikbke and Ran as possessing stronger mCAR binding avidity than hCAR, results that corresponded well with the respective hepatic transcript expression levels obtained in WT but not in CAR -/- mice (Fig 2-6 A). IKBKE (inhibitor of nuclear factor kappa B kinase subunit epsilon) was reported as an oncogene and is overexpressed in various tumors (Hutti et al. 2009; Zubair et al. 2016), whereas RAN (ras-related nuclear protein) is suggested to promote tumor cell survivor by regulating cell cycle (Barres et al. 2010).

In addition, Bmf (Bcl2 modifying factor), a BCL2 family protein functioning as an apoptotic activator (Pinon et al. 2008), is repressed in TCPOBOP-treated WT mice by 65% based on realtime PCR assays, and by 70% in corresponding RNA-seq analysis. However, in humanized

CAR mice, CITCO treatment repressed Bmf expression by only 25% (Fig 2-6 B). Although highly donor-dependent, the data in Fig 2-6 C demonstrated reasonable correspondence in primary cultures of human hepatocytes to the inducibility effects noted in mouse liver. Together, these gene targets provide potential insights into the differences between mouse and human with respect to CAR’s regulatory role in carcinogenesis. Specifically, compared to hCAR, mCAR displayed stronger genomic interactions and greater capability of upregulating expression of the proto-oncogenes, Myc, Ikbke and Ran, and repressing tumor suppressor Bmf. 55 mCAR prioritizes binding to direct repeat two half-sites motifs whereas hCAR is less stringent in motif recognition

To investigate CAR-binding motifs with higher biological relevance, we used reference transcriptome to filter sites with more functional binding potential. Primary binding sites were defined with peak p-values ranked in the top 500 of binding sites associated with specific genes; as well, we selected only those genes present both in genomic annotations and in the reference transcriptome. We used primary binding sites for the de novo motif analysis. The best characterized binding element for CAR is the PBREM, consisting of two DR4 units serving as

CAR-binding sites (Sueyoshi et al. 1999). Each DR4 unit contains two canonical hexamer half- sites AG(T/G)TCA (Handschin and Meyer 2000). Here, our data showed the preferred mCAR motif, AGGTCANNNNAG(T/G)TCA, displaying the DR4 feature with a slightly weaker hexamer half-site on the left (Fig 2-7 A). In contrast, the preferred hCAR motif, AG(G/T)TCA, showed only a single hexamer half-site, i.e., a degenerate DR4 unit. The single hexamer motif in hCAR’s 4-color plot visually formed a single lane in the center, whereas mCAR’s two hexamer direct repeat motif structure shows double lanes (Fig 2-7 A). We also examined motifs generated from weaker binding sites, expanding the input sequence to the top 1000 sites. At this level, the mCAR motif exhibited only a degenerate single hexamer AG(T/G)TCA, the same as with hCAR

(Fig 2-7 B). This disparity of DNA binding sequences was further substantiated by comparing motifs from mCAR and hCAR differential binding regions characterized earlier with DESeq2

(Fig 2-7 C). Motifs from mCAR’s stronger binding sites displayed two strong hexamer half-sites,

AGGTCANNNNAG(T/G)TCA, rather than one weak / one strong half-site as seen in the primary binding motif. The hCAR stronger binding site motif exhibited the single hexamer,

AG(T/G)TCA, once again. These results suggest that across the genome, mCAR has a stronger tendency to recognize a direct repeats with a two half-site structure, but as binding weakens, mCAR binds to degenerate single half-sites. In contrast, hCAR DNA binding sites were less 56 dependent on the two half-site structures. Recently, several reports demonstrated that DNA binding site sequences can modulate nuclear receptor activities (Meijsing et al. 2009; Zhang et al.

2011). Consistent with this view, the motif interactions exhibited by the mCAR and hCAR top binding sites might then specify the genomic basis for species variation for these receptors, findings that lend new insights into differences in the genomic interactions of rodent vs human

CAR.

Subsequently, we compared motifs from direct activator-treated samples with indirect activator-treated samples (Fig 2-7 D). Both hCAR and mCAR exhibited similar motif preferences between direct activator, i.e., CITCO/TCPOBOP, and indirect activator, PB, suggesting that sequence recognition of CAR was not influenced by activation pathways. To verify the credibility of characterized motifs, we used FIMO to scan the mCAR motif AGGTCANNNNAG(T/G)TCA for putative binding locations on CAR binding sites we visualized for established CAR binding genes such as Cyp2b10 and Ces2a (Fig 2-9 ) We successfully characterized clusters of putative

CAR binding sites that were consistent with CAR peaks visualized on the genome browser, particularly within the PBREM of Cyp2b10, a classic CAR-binding element. FIMO motif scans were also applied to Gdf15, Myc, Ikbke and Ran, and showed putative mCAR motifs locations in differential binding sites (Fig 2-8). Therefore, motif analyses supported findings presented earlier regarding differential binding analysis results viewed in terms of binding strength and binding location, strengthening a conclusion that CAR binding to DNA elements is largely unaffected by the presence of ligand.

DISCUSSION

In this study, in vivo genome-wide profiling analyses were conducted to detail binding interactions for the mouse and human CAR transcription factors. These experiments were 57 performed using a novel adenoviral delivery system targeting mouse livers, coupled with high resolution ChIP-exo assays. CAR is unusual in the nuclear receptor superfamily in that although constitutively active, CAR is sequestered in a cytosolic binding complex. Release and subsequent nuclear translocation of the transcriptional regulator occurs either through direct activation with specific ligands, or indirect activation through an EGFR-mediated signaling pathway (Mutoh et al. 2013). Our differential analysis of both mCAR and hCAR genomic binding profiles demonstrated that in terms of binding locations as well as binding signal intensities, direct vs. indirect CAR activation resulted in almost identical DNA binding profiles. Of the 59,508 genomic total binding sites detected in direct/indirect activation differential analysis, only 56 sites

(0.09%) showed significant differences for mCAR, whereas only 2 sites (0.003%) exhibited significant differences for hCAR. Further, motif analysis revealed that the consensus binding sites for the direct-/indirect-activated hCAR, or for the direct-/indirect-activated mCAR, were indistinguishable (Fig 2-6 D). However, differing gene expression profiles for CAR were reported previously for direct and indirect activation (Li et al. 2015). Along these lines, one consideration is that CAR’s conformation may undergo change in the presence of a direct ligand, potentially affecting the receptor’s interaction with transcriptional co-regulators. In these respects, other nuclear receptors are reported to realign and stabilize their H12/AF2 domain upon ligand binding, thereby directing coactivator recruitment (Shan et al. 2004). CAR is unique among the nuclear receptors in that its constitutive activity results from a charge-charge interaction between H4 and

H12/AF2, maintaining an active conformation that mimics ligand docking (Dussault et al.

2002b). However, this constitutive activation activity is suggested as somewhat inferior with respect to strength of coactivator recruitment, compared with direct ligand-binding activation

(Dussault et al. 2002b). Another important consideration is that the indirect activation pathway proceeds through EGFR signaling modulation, which itself may affect other gene expression programs (Mutoh et al. 2013; Negishi 2017). 58 Consistent with CAR’s known regulatory role in drug metabolism and energy homeostasis, gene ontology analysis indicated that most CAR binding genes were enriched in these biological processes. For example, the ChIP-exo results showed strong binding events associated with CAR in genes including carboxylesterase 2 (Ces2a), ATP binding cassette subfamily C member 3 (MRP3 or Abcc3), cytochrome p450 oxidoreductase (Por), phosphoenolpyruvate carboxykinase (PEPCK or Pck1), glucose-6-phosphatase catalytic subunit

(G6pc), stearoyl-CoA desaturase-1 (Scd1) and sterol regulatory element binding transcription factor 1 (SREBP1 or Srebf1). These binding profiles strongly suggest that CAR directly regulates these genes at the transcriptional level, profiles supported by gene expression reports. As one goal of this study was to provide genomic insights into the differential contributions of rodent vs human CAR in cell proliferative and tumorigenic processes, IPA disease and function analysis indicated that regions exhibiting significantly stronger mCAR binding signals linked to cell proliferation and apoptosis functions, contrasting to hCAR with higher associations with energy / metabolic functions.

Visualization of ChIP-exo data on the genome browser (Fig 2-2) illustrated that the mCAR and hCAR samples yielded overall matching binding patterns in most scenarios. Further, the hexamer half-site consensus sequences, characterized from the top 1000 primary binding sites, were both AG(G/T)TCA, for mCAR and hCAR. These results indicated that the mCAR and hCAR interacting genomic profiles are similar in mouse liver. Likely contributing to this result is the highly conserved DNA binding domain (DBD) existing between mCAR and hCAR, demonstrating a similarity of amino acid sequences between the respective species’ is 95.6%, with only 4 non-conservative amino acid replacements occurring in the 91aa length of the DBD

(di Masi, De Marinis et al. 2009).

Of particular note, differential binding analysis for mCAR and hCAR identified ~1000 genes associated with species distinctive binding in mouse liver (Fig 2-3 D). To characterize 59 these binding differences, we used DESeq2 to normalize samples with their signal tag numbers and then quantitatively measured the differences at each of the binding sites. Of all the binding regions examined, 5.9% of the binding peaks exhibited significant mCAR vs. hCAR differences

(binding FC > 2, q-value < 0.05). Previous reports suggest significant effector pathway differences programmed through mCAR and hCAR, particularly in cell proliferation and tumor promotion, raising questions for the human relevancy of chemical testing performed in rodent models (Ross et al. 2010). By cross-referencing the ChIP-exo data with published RNA-seq data, and validating selected results with realtime-PCR mRNA assays, several cancer-related genes were identified both as likely CAR transcriptionally regulated and as exhibiting species differences. With respect to cell proliferation, several genes possessed sites exhibiting both stronger binding for mCAR as well as associated CAR-dependent expression level changes induced by CAR ligands. In these respects, the oncogene Myc is well documented for its roles in tumorigenesis in multiple tissues (Stine et al. 2015). Overexpression of MYC induces hepatocellular cancer in mouse models (Shachaf et al. 2004) and dysregulation of MYC is frequently observed in clinical HCC samples (Wang et al. 2014). Our ChIP-exo data demonstrated that mCAR bound to a site +2kb downstream of the mouse Myc TSS, whereas hCAR did not. RNA-seq expression levels of Myc also demonstrate similar species variation.

Following ligand activation of mCAR in mice, Myc expression increases by ~10-fold in WT mice, whereas in mice humanized with hCAR, Myc expression only increases by ~2-fold (Fig 2-

6). Of related interest, the tumor suppressor FOXO3 is reported to suppress MYC by activating

ARF (Stine et al. 2015). Our data indicate that Foxo3 is repressed by CAR, an event that would then facilitate CAR’s activation of Myc. Given that Myc is reported as a downstream target of

CAR in liver proliferation (Blanco-Bose et al. 2008), differential regulation of Myc is likely to play a central role in species variability of CAR response in rodent tumorigenesis. 60 In addition, Gdf15, a gene associated with cell proliferation, was identified as bound by both species’ CAR and transcriptionally activated. Gdf15 is overexpressed in clinical liver cancer tissues, inducing proliferation of liver cancer stem cells through activating AKT/GSK-3β/β- catenin pathways (Xu et al. 2017). The Wnt/β-catenin pathway has been extensively studied with respect to hepatic carcinogenesis (Cui et al. 2001; Wong et al. 2001). In the current investigation,

ChIP-exo data revealed several peaks with strong binding signals in the Gdf15 promoter region, and putative binding motifs were characterized using local analysis of consensus sequences. Both

RNA-seq results and real-time PCR assays (Fig 2-5) demonstrate that the expression level of

Gdf15 is increased markedly, up to ~15-fold, with CAR ligand treatment. The real-time PCR assays further illustrated that induction of Gdf15 was CAR-dependent, as CAR -/- mice show no induction. These results suggest that regulation of Gdf15 regulation is of high relevance with respect to CAR’s role in promoting mouse liver carcinogenesis. In addition to Gdf15, other novel cancer-related genes that were identified as CAR-regulated include Shc1, Prkar2a, and Bcar1; and, Foxo3, a gene with apoptotic-repressing function. These interactions reveal apparently complex mechanisms of CAR that underscore its importance as a regulator of the carcinogenic process.

Adding complexity, our ChIP-exo investigation identified apparent intensive genomic cross-talk of CAR with other nuclear and soluble receptors. CAR exhibited significant binding to various receptors’ promoter regions, including Hnf4a (HNF4-α), Rarb (RARβ), Rxrb (RXRβ),

Nr1i2 (PXR), Ppard (PPARβ/δ), Esr1 (ERα), Nr2f2 (COUP-TFII), Nr0b2 (SHP), as well as Ahr

(AHR), and Nr1i3 (CAR) itself. Although genomic binding does not necessarily denote a direct link to biological function, the concept of nuclear receptor cross-talk at the genomic level is fascinating, in particular for cases where CAR’s genomic binding events are further correlated as directly regulating expression. In these respects, several of the aforementioned CAR-binding genes, specifically RARβ, SHP and AHR, RNA-seq data demonstrate expression level 61 responsiveness to CAR activators (Cui and Klaassen 2016). In this context, expression of both mouse and human CAR is modulated through AHR interaction (Patel et al. 2007). Consistent with these concepts, recent studies examining roles of nuclear receptors suggest important roles for their recognition as dynamic scaffolding proteins, capable of fine-tuning transcriptional signaling outcomes through modification by ligands, co-regulators and even DNA binding sites sequences (Nwachukwu and Nettles 2012).

CONCLUSIONS

Using in vivo high-resolution profiling of mCAR and hCAR genomic interactions in transgenic liver models, coupled with transcriptomic data analyses, this investigation identified several novel CAR target genes established as key regulators of cell proliferation and carcinogenesis. Comparative analysis of mCAR and hCAR genomic binding revealed species differences in their respective interaction profiles and regulation of several oncogenes that serve to underlie biological variation in xenobiotic response. These findings substantiate the importance of CAR as a general transcription activator in the hepatic environment and its role as a critical regulator not only of xenobiotic and energy metabolism, but also general coordinator of cell proliferation, apoptosis and liver cancer development.

62

63 Fig 2-1 Adenovirus delivery of YFP-CAR constructs into CAR KO mice. (A) Scheme of Adenovirus delivery system. Each adenovirus YFP-CAR fusion construct was injected into CAR KO mice 4 days prior to terminal surgery. CAR activator treatments were initiated 24h and again at 2h before liver extraction. (B) Fluorescence imaging of liver cryostats in adenovirus infected mice. All samples were treated with PB. DAPI staining was performed for nuclei visualization. Upper panels show anti-YFP IHC merged with DAPI in YFP-mCAR infected mice. Lower panels show cryostat imaging of YFP fluorescence merged with DAPI in

YFP-hCAR infected mice. (C) RT-qPCR analysis of mice liver mRNA levels. Left: YFP expression levels of YFP-mCAR and YFP-hCAR infected mice were compared to wild type

(WT) mice (3). Student t-tests were performed for each sample versus WT untreated. Right:

Expression of Cyp2b10 compared to both untreated WT mice and YFP-empty infected CARKO.

The data shown are the average from three biological replicates normalized to GAPDH. Student t-tests were performed for each sample versus WT untreated. (D) Nuclear lysate western blot of mice liver. Adenovirus containing YFP-empty, YFP-mCAR and YFP-hCAR constructs infected

CAR KO mice through tail-vein injection. All three samples were treated with PB prior to harvest. In anti-YFP blotting, size differences between YFP-empty, YFP-mCAR and YFP-hCAR constructs could be visualized. All samples were obtained from mice treated with 75 mg/kg PB.

64

Fig 2-2 Genomic profiling of CAR using ChIP-exo. (A) Visualization of CAR enrichment on selected genes. The Integrative Genomics

Viewer (IGV) displayed YFP-CAR proteins with their direct/indirect activator binding locations on selected known CAR binding genes: Cyp2b10 (upper left), Gstm2 and Gstm3 (upper right), and Ces2a (lower left). Mapped reads were separated by strands, with forward strand reads on the upper track (red) and reverse strand reads on the bottom track (blue). From top track to bottom track: hCAR PB, hCAR CITCO, mCAR PB, mCAR TCPOBOP, and the Refseq gene track.

Track length is 20kb. (B) Peak distance to TSS plot showing relative distribution of CAR binding peaks to TSS.

65

66 Fig 2-3 Differential analysis between mCAR and hCAR binding profiles. (A) Pearson correlation analysis for all replicates. Correlation coefficients between mCAR and hCAR replicates were much lower than replicates within the same species, indicating species variances in the respective genomic profiles. (B) PCA analysis for all replicates. Teal dots represent hCAR replicates, orange dots represent mCAR replicates. PCA analysis showed clear separation between mCAR and hCAR replicates. (C) Hierarchy cluster analysis. 1048 significant mCAR versus hCAR differential binding loci are shown in the clustering. Clustering for all replicates indicated species variation, and consistency within each species. (D) Summary of differential binding loci associated genes. Approximately 1000 genes exhibit differential mCAR and hCAR binding loci, whereas relatively few genes exhibit variation comparing direct vs indirect activation for hCAR or mCAR replicates. (E) IPA Disease and function analysis for mCAR and hCAR differential binding associated genes. The top 10 disease and function terms are shown, ranked by p-value.

67

Fig 2-4 Gene annotation for mCAR and hCAR binding regions. (A) Venn diagram for total mCAR annotated genes and hCAR annotated genes.

Annotated mCAR binding genes (n=2839) exhibit 2661 genes that overlap with hCAR binding genes (n=6364). Binding peaks were annotated to the nearest gene TSS within ±10kb region. (B)

GO biological process analysis for the top 500 mCAR and hCAR binding genes. Top 10 GO biological process terms from DAVID GO DIRECT analysis are listed. mCAR and hCAR GO analysis indicated that the top binding genes for CAR are enriched in metabolic pathways, particularly in oxidation-reduction such as cytochrome P450, and lipid metabolism and glucose metabolism. 68

69 Fig 2-5 mRNA expression analysis indicates that CAR regulates key genes associated with hepatic carcinogenesis. (A) Realtime PCR assay of selected genes with respect to their mRNA expression levels in mouse liver. Realtime PCR results showed that selected genes exhibit mRNA expression level perturbation in a mCAR dependent manner. Error bars represented standard deviation of biological replicates (n=3) in each treatment. p-values were calculated by a two-sided student t test. (B) RNA-seq examination of selected genes. RNA-seq validated selected genes expression changes in WT mice. Shc1, Prkar2a and Foxo3 expression did not exhibit perturbations in humanized CAR mice. Each column represents the fold-change value calculated by DESeq2, between treated (n=3) and untreated (n=3) samples. Asterisks (*) indicate p-values from DESeq2 analysis. (C) Realtime PCR assay of selected genes mRNA level expression in human primary hepatocytes. Realtime PCR showing selected gene mRNA expression changes for 3 human primary hepatocyte donors. GDF15, IGF1 IRS2 and FOXO3 all showed remarkable trend changes consistent with their mouse ortholog genes.

70

71 Fig 2-6 mRNA expression analysis of CAR-linked oncogenes with species variations. (A) Realtime PCR assay of selected genes mRNA level expression in mouse liver.

Realtime PCR mRNA expression analysis showed that selected genes exhibiting species variation in binding were perturbed significantly in WT mice but not in CAR -/- mice. Error bars represent standard deviation of biological replicates (n=3) in each treatment. p-values were calculated by a two-sided student t test. (B) RNA-seq showing mRNA expression species variations of selected genes. All selected genes show significant expression changes in WT mice with no significant changes in humanized CAR mice, indicating species variation at the transcription level in the rodent model. Each column represents a fold-change value, calculated by DESeq2 comparing treated (n=3) and untreated (n=3) samples. Asterisks (*) indicate p-values from DESeq2. (C)

Realtime PCR assay of selected genes mRNA level expression in human primary hepatocytes.

Realtime PCR showing selected gene mRNA expression changes within human primary hepatocytes. Due to the limited availability of human donor specimens, statistical evaluation of the data was not conducted.

72

73 Fig 2-7 Motif analysis for hCAR and mCAR. (A) hCAR and mCAR motifs from the top 500 primary binding sites. The top 500 primary binding sites clearly illustrated two hexamer half-sites DR4 structural motifs for mCAR, whereas hCAR exhibited only one hexamer. Motifs and 2-bits motif logos were generated using

MEME suites. 4-color plot represents ±50bp genome sequences, centered on the top 500 primary binding sites. The color scheme for the 4-color plot is the same as with motif logos. (B) hCAR and mCAR motifs from the top 1000 primary binding sites. mCAR and hCAR motifs from the top 1000 primary binding sites were very similar, with the mCAR motif degenerated to one hexamer halfsite. (C) Motifs characterization of mCAR and hCAR differential binding sites.

Differential binding sites analysis revealed that hCAR has high preference for single hexamer motifs, whereas mCAR preferred a complete two-halfsites structure. (D) Motif comparison between direct / indirect activators. Binding motifs resulting from direct- or indirect-activated

CAR were largely equivalent for both mCAR and hCAR, indicating that different modes of receptor activation do not appear to alter CAR binding profiles.

74

75 Fig 2-8 Integrated genomic viewer and putative motif locations. (A) IGV screenshots showing differential binding peaks and putative motif locations.

CAR binding regions on Myc and Ikbke are shown using IGV, indicating preferential binding of mCAR on peaks highlighted by red rectangles. Mapped reads are separated by strands, with forward strand reads on the upper track (green) and reverse strand reads on the bottom track (red).

From top to bottom tracks: hCAR, mCAR, Refseq gene and putative mCAR motif locations using

FIMO default settings. Track heights are normalized according to mCAR and hCAR total reads number within peaks. Genome tracks were 10k in length. (B) IGV screenshots showing peaks and putative motif locations on Gdf15. Four putative motif locations by FIMO are indicated with red arrows. (C) Putative mCAR motifs and their coordinates. q-values were calculated by FIMO. The mCAR motif logo from top 500 primary binding sites is shown on top of the putative binding sequences.

76

77 Fig 2-9 IGV showing CAR binding sites and putative motif locations on select genes. Cyp2b10 and Ces2a are two genes with established CAR direct binding. The middle panel shows a zoom-in view of PBREM upstream of Cyp2b10 TSS, with FIMO able to accurately point out putative mCAR motifs exactly at the NR1 and NR2 binding sites inside PBREM. B.

Putative mCAR motifs coordinates. Putative mCAR motifs coordinates and q-value were calculated by FIMO.

78

Forward Primer Reverse Primer Species Source AAGTACAACCCACTTCGGAATG GAAAGAAGGAACACAGGGTAGTC mus musculus PrimerBank 14211984a1 CTGGCAATGCCTGAACAACG GGTCGGGACTTGGTTCTGAG mus musculus PrimerBank 6753968a1 GAGGAGGATAACGATCCAAGGG TGCTCGTCAGTTTTGACAATCTT mus musculus PrimerBank 22550094a1 CCAAAGCCCTCTATGACAATGT CTTGAGGCGGTTACCAGGC mus musculus PrimerBank 6753524a1 CTGGGGGAACCTGTCCTATG TCATTCTGAACGCGCATGAAG mus musculus PrimerBank 9789951a1 AGCTGGACCAGAGACCCTTT GCAACACTCATCCACAATGC mus musculus qPrimerDepot CTGCGTCCTCTCCCAAAGTG GGGGTCATGGGCATGTAGC mus musculus PrimerBank 3661525a1 AGAGCTCCTCGAGCTGTTTG ACGGAGTCGTAGTCGAGGTC mus musculus qPrimerDepot ACCACTAACTACCTGTGGCAT CCTCCCCGGATTTCTTGTTTC mus musculus PrimerBank 22766829a1 GCGATGGCTACTACATCCAA GGGATGTTTTCACACACTCGT mus musculus qPrimerDepot GATCGCCAGAAAGCTTCAGT ACCCCTTCCCTGTTTTCTTG mus musculus qPrimerDepot ACCCCACGTTCCTCTTCCA CAGCAGGCGCAAGAACTG mus musculus Denise paper TCCACTCACGGCAAATTCAACG TAGACTCCACGACATACTCAGC mus musculus Denise paper GCCAAAGACCCTGTGAATCAG GTATTGTTTGAAGCGCAACTCG homo sapiens PrimerBank 322302754c3 GACCCTCAGAGTTGCACTCC GCCTGGTTAGCAGGTCCTC homo sapiens PrimerBank 153792494c1 TGTGCCCCTCCTTAAATCACT ACTTCGCCAGACTCTATGATGT homo sapiens PrimerBank 47157329c3 ATGGGCAGTACGAGAACAGC GGCCAGGTCGTGGTCTATG homo sapiens PrimerBank 282398126c2 TCACGCACCAATTCTAACGC CACGGCTTGCTTACTGAAGG homo sapiens PrimerBank 146260266c2 GCTCTTCAGTTCGTGTGTGGA GCCTCCTTAGATCACAGCTCC homo sapiens PrimerBank 163659898c1 CGGTGAGTTCTACGGGTACAT TCAGGGTGTATTCATCCAGCG homo sapiens PrimerBank 38683859c1 GCTGCTTAGACGCTGGATTT CACCGAGTCGTAGTCGAGGT homo sapiens qPrimerDepot TGCCTGAGGATGAGTTCCTG CGATGCACAATGCCGTTCT homo sapiens PrimerBank 300937400c1 GGTGGTACTGGAAAAACGACC CCCAAGGTGGCTACATACTTCT homo sapiens PrimerBank 219879807c1 AAGGTGTCATGCTGCCTTGT AAGGTTGTGCAGGAAGAGGA homo sapiens qPrimerDepot ACCCCACGTTCCTCTTCCA CAGCAGGCGCAAGAACTG mus musculus (Omiecinski et al. 2011a) TCCACTCACGGCAAATTCAACG TAGACTCCACGACATACTCAGC mus musculus (Omiecinski et al. 2011a) CCCATCACCATCTTCCAGGAG GTTGTCATGGATGACCTTGGC homo sapiens (Chen et al. 2014) AGACGCCTTCAATCCTGAC GCGGATTTGTCTTGGTGAAGG homo sapiens (Chen et al. 2014) AGAAGAACGGCATCAAGGTGAACT GGACTGGTAGCTCAGGTAGTGGTTG hs/mm (Drepper et al. 2010)

Table 2-2 Realtime PCR primers used in this paper. A portion of realtime PCR primers are from PrimerBank (Spandidos et al. 2010) and

qPrimerDepot (Cui et al. 2007).

79

mRNA stronger binding genes Upregulated mRNAs Downregulated mRNAs Symbol Gene Name Symbol Gene Name Abcc2 ATP-binding cassette, sub-family C (CFTR/MRP), member 2(Abcc2) Bmf BCL2 modifying factor(Bmf) Atp2b2 ATPase, Ca++ transporting, plasma membrane 2(Atp2b2) Aspdh aspartate dehydrogenase domain containing(Aspdh) C1qtnf6 C1q and tumor necrosis factor related protein 6(C1qtnf6) Atoh8 atonal bHLH transcription factor 8(Atoh8) Dcaf4 DDB1 and CUL4 associated factor 4(Dcaf4) F11 coagulation factor XI(F11) Gmds GDP-mannose 4, 6-dehydratase(Gmds) Col27a1 collagen, type XXVII, alpha 1(Col27a1) Hid1 HID1 domain containing(Hid1) Fgb fibrinogen beta chain(Fgb) Pdzk1 PDZ domain containing 1(Pdzk1) Ier2 immediate early response 2(Ier2) Setdb1 SET domain, bifurcated 1(Setdb1) Pdilt protein disulfide isomerase-like, testis expressed(Pdilt) Aldh3b3 aldehyde dehydrogenase 3 family, member B3(Aldh3b3) Scarb1 scavenger receptor class B, member 1(Scarb1) Akr1b10 aldo-keto reductase family 1, member B10 (aldose reductase)(Akr1b10) Slc22a7 solute carrier family 22 (organic anion transporter), member 7(Slc22a7) Alpl alkaline phosphatase, liver/bone/kidney(Alpl) Ankle1 ankyrin repeat and LEM domain containing 1(Ankle1) Ahr aryl-hydrocarbon receptor(Ahr) Cxcl10 chemokine (C-X-C motif) ligand 10(Cxcl10) Cyp2c29 cytochrome P450, family 2, subfamily c, polypeptide 29(Cyp2c29) Dnah1 dynein, axonemal, heavy chain 1(Dnah1) Fam109a family with sequence similarity 109, member A(Fam109a) Fam110a family with sequence similarity 110, member A(Fam110a) Fam50b family with sequence similarity 50, member B(Fam50b) Glrx5 glutaredoxin 5(Glrx5) Gpx4 glutathione peroxidase 4(Gpx4) Hnrnpab heterogeneous nuclear ribonucleoprotein A/B(Hnrnpab) Ikbke inhibitor of kappaB kinase epsilon(Ikbke) Lif leukemia inhibitory factor(Lif) Mybl1 myeloblastosis oncogene-like 1(Mybl1) Myc myelocytomatosis oncogene(Myc) Nrg4 neuregulin 4(Nrg4) Pla2g6 phospholipase A2, group VI(Pla2g6) Pcdh20 protocadherin 20(Pcdh20) Rarb retinoic acid receptor, beta(Rarb) Rrm2 ribonucleotide reductase M2(Rrm2) Rnf145 ring finger protein 145(Rnf145) Spsb4 splA/ryanodine receptor domain and SOCS box containing 4(Spsb4) Spon2 spondin 2, extracellular matrix protein(Spon2) Samd4 sterile alpha motif domain containing 4(Samd4) Tlr1 toll-like receptor 1(Tlr1)

80

hCAR stronger binding genes Upregulated mRNAs Downregulated mRNAs ID Gene Name ID Gene Name Ppp1r3g protein phosphatase 1, regulatory (inhibitor) subunit 3G(Ppp1r3g) Btg2 B cell translocation gene 2, anti-proliferative(Btg2) Cdkn1a cyclin-dependent kinase inhibitor 1A (P21)(Cdkn1a)

81 Table 2-3 Cross-referencing RNA-seq transcriptomics datasets with identified species differential binding genes. RNA-seq upregulation or downregulation genes (treated with CAR activator versus control). Cut off thresholds are > 2 or < 0.5 fold changes.

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88 Chapter 3

Genomic CAR profiling and species differential analyses in Dlk1-Dio3 gene

cluster

Abstract

The Dlk1-Dio3 imprinted gene cluster has received scientific attention as a model for studying mechanisms of imprinting as well as for the contribution of this genomic region to various stage of development. The Dlk1-Dio3 gene locus contains 3 paternally expressed protein- coding genes and 3 maternally expressed non-coding genes. Disruption of genes in this region are reported to lead to perinatal lethality or late gestational lethality. Altered Dlk1-Dio3 gene expression has also been implicated in various cancers. In addition, the Dlk1-Dio3 region has been associated with induced pluripotency. Recent studies in chronic PB-treated rodent hepatocellular carcinogenesis models revealed that lncRNAs and miRNAs encoded in the Dlk1-

Dio3 region were induced in a CAR- and b-catenin dependent manner, implying a novel pathway for CAR in mediating tumorigenesis.

Genomic study of CAR in Dlk1-Dio3 region indicated that CAR may regulate lncRNA

Meg3 through direct transactivation. By binding to an intergenic germline-derived differentially methylated region (IG-DMR), CAR may coordinately regulate imprinted cluster genes, including

Rian, Mirg and miRNAs, providing insights into the mechanism of CAR interaction with the

Dlk1-Dio3 imprinted gene cluster.

89 Introduction

As their name implies, long noncoding RNAs (lncRNA) are noncoding RNAs with lengths greater than 200nt. They have recently received great research interest for their newly discovered important roles in biology regulation, involving many aspects. Originally thought to be “junk” noncoding sequences, evidenceshas emerged demonstrated that these noncoding sequences correlate with chromatin signatures, such as DNase1 hypersensitivity; H3K9ac,

H3K4me3 and H3K36me3 histone modifications; and/or dependency of transcription factor expression level and binding (Kung et al. 2013). Since the first characterized epigenetic regulatory function of lncRNA (Brannan et al. 1990), a variety of roles have been ascribed to lncRNAs, including regulation of allelic expression, such as X chromosome inactivation and imprinting (da Rocha et al. 2008; Lee 2011), regulation of developmental processes, such as he control of pluripotency to lineage specification (Deuve and Avner 2011; Ng et al. 2012), and regulation in carcinogenesis including the up-regulation of oncogenes and suppression of tumor suppressor genes (Huarte et al. 2010; Prensner et al. 2011). New discoveries regarding lncRNAs are advancing rapidly, together with expansion of our understanding of lncRNA’s role and mechanisms of action.

The cluster of genes located between the delta-like homolog 1 gene and the type III iodothyronine deiodinase gene (Dlk1-Dio3) were first described as containing the linked and reciprocally imprinted genes, Dlk1 and Meg3 (maternally expressed 3, originally named Gtl2, gene-trap line 2) (Schmidt et al. 2000; da Rocha et al. 2008). Imprinted genes are usually involved in developmental processes such as regulating growth, differentiation, as well as postnatal neurological and metabolic function modulation (Wilkinson et al. 2007).

90 The Dlk1-Dio3 gene cluster consists of three protein-coding genes, Dlk1, RTL and Dio3, that are all paternally expressed. However, the long non-coding genes, Meg3, Rian and Mirg, in this region are all maternally expressed (da Rocha et al. 2008; Benetatos et al. 2014). Dlk1 plays regulatory roles in differentiation of several tissues and in adipogenesis (Moon et al. 2002). Rtl

(retrotransposon-like gene) is expressed mainly in embryonic stages in certain tissues, including placenta (Brandt et al. 2005). It is crucial for normal placental development (Sekita et al. 2008).

Dio3 (type 3 iodothyronine deiodinase) is capable of degrading thyroid hormone into inactive metabolites and therefore may play a protective role in tissue against excessive thyroid hormone

(Galton et al. 1999).

Meg3 is a lncRNA gene reciprocally imprinted with Dlk1. It is involved in normal cell biology and development; Meg3 deletion causes perinatal death in mice (Benetatos et al. 2014).

Meg3 can also interact with key proteins such as p53, Mdm2, Rb and Vegf (vascular endothelial growth factor), modulating cell proliferation, differentiation and survival (Benetatos et al. 2011).

Rian (RNA imprinted and accumulated in nucleus, also called Meg8 in human) is another lncRNA playing a regulatory role in the development of diverse organs in embryonic stage

(Benetatos et al. 2012). Mirg (MicroRNA containing gene) has been recently identified as a maternally expressed non-coding RNA, which overlaps with several micro RNAs (Hagan et al.

2009). In addition, the Dlk1-Dio3 cluster contains one C/D box snoRNA gene cluster and 47 clustering miRNAs; the functions of these miRNAs are largely not well understood (Liu et al.

2010).

Recently, studies have revealed that PB activation of tumorigenesis in mouse models perturbs gene expressions of the Dlk1-Dio3 gene cluster (Lempiainen et al. 2013). In response to chronic PB treatment, hepatic expression of lncRNAs and miRNAs, especially from the Dlk1- 91 Dio3 gene cluster, showed an accumulative perturbation with time; the expression levels of these non-coding RNAs were not pronounced after immediate PB treated, but exhibited significant changes starting from 14 days. The lncRNA perturbations were also shown as CAR- and β- catenin dependent. In a follow up study, chlordane, another CAR indirect activator with different structure tthan PB, produced a similar effect in inducing lncRNAs from Dlk1-Dio3 and physiological hepatic changes compared to PB. In addition, among 6 non-genotoxic hepatic carcinogen treatments with different mode of actions, only PB-treated mice showed Meg3 and

Rian up-regulation, both of which are Dlk1-Dio3 lncRNAs. These results suggest a CAR dependent activation of Dlk1-Dio3 genes in promoting hepatocellular carcinogenesis.

The mechanism of CAR activation inducing Dlk1-Dio3 gene clusters are not well understood. Here, we utilized the genomic profiling data detailed in Chapter 2 to investigate the genomic regulation of CAR on Dlk1-Dio3 regions

Materials and reagents

TCPOBOP (>99%) was synthesized by the Environmental Health Laboratory in the

Department of Environmental and Occupational Health Safety at the University of Washington

(Seattle, WA). Phenobarbital was purchased from Sigma/Aldrich (St. Louis, MO). 6-(4- chlorophenyl: imidazo[2,1-b]thiazole-5-carbaldehyde O-(3,4-dichlorobenzyl)oxime (CITCO) was purchased from BIOMOL Research Laboratories (Plymouth Meeting, MA). Rabbit anti-GFP antibody (ab290) and chicken anti-GFP antibody (ab13970) were from Abcam (Cambridge, MA).

Full length human CAR and mouse CAR cDNAs were sub-cloned into the pEYFP-c1 plasmid

(Clontech/Takara, Mountain View, CA) to generate YFP-hCAR and YFP-mCAR N-terminal fusion protein constructs. All constructs were validated by DNA sequencing. Adenovirus (AV) 92 constructs containing YFP-hCAR, YFP-mCAR and YFP-empty were produced in the Adeno X

Expression System (Clontech/Takara) and amplified to high titer by SignaGen (Rockville, MD).

Animals and treatments

All animal care and experimental procedures complied with protocols approved by the

Institutional Animal Care and Use Committee of The Pennsylvania State University. Wild-type

(WT) C57BL/6 mice were purchased from Charles River Laboratories (Wilmington, MA).

Breeding pairs of CAR (-/-) /PXR (-/-) double-knockout mice in the C57BL/6 background were a kind gift of Dr. Wen Xie (University of Pittsburg, Pittsburg, PA), and their derivation was described previously (Saini et al. 2004). CAR (-/-) knockout mice were generated by crossing WT

C57BL/6 mice with CAR (-/-) /PXR (-/-) double-knockout mice. The mice were maintained under a standard 12h light, 12h dark cycle at constant temperature (23±1°C) with 45-65% humidity. A total of 6, 8-week-old male CAR (-/-) knockout mice were infected with YFP-hCAR

AV (1x10^12 virus particles/ ml, approximately 100ul diluted injection volume per mouse) through tail-vein injection. At 72 and 94h, the mice were administrated with two successive doses of PB (75mg/kg, in saline) (n=3), or CITCO (5mg/kg, in DMSO) (n=3), through IP injection.

Another 6, 8-week-old male CAR (-/-) knockout mice were infected with YFP-mCAR AV constructs (1x10^12 virus particles/ ml) through tail-vein injection. At 72 and 94h, two successive doses of PB (75mg/kg, in saline) (n=3), or TCPOBOP (2mg/kg, in DMSO) (n=3), were administrated. After 96h, all AV-infected mice were harvested for liver tissues after CO2 asphyxiation-induced euthanasia. For non-adenovirus infected mice, 8-week-old male CAR (-/-) knockout mice (n=6) and WT mice (n=6) were administrated with single dose of DMSO (4ml/kg)

(n=3), or TCPOBOP (2mg/kg, in DMSO) (n=3), through IP injection. After 24h, liver tissue extractions were performed following CO2 asphyxiation-induced euthanasia. 93 Liver extraction and ChIP-exo

Briefly, fresh YFP-hCAR or YFP-mCAR AV infected CAR (-/-) knockout mouse liver

(~1.0g) was immediately minced and cross-linked with 1% formaldehyde (diluted from 16% formaldehyde solution methanol-free 1 ml ampules (Thermo Scientific)) for 10 min followed by quenching with 0.125M glycine for 5 min. Liver tissues were then washed 3X with ice-cold PBS, and subjected to Dounce homogenization followed by 100nm filtration. Nuclei lysates of liver tissues were prepared by incubating homogenized cells for 15 min in cell lysis buffer (10mM Tris pH 8.0, 10mM NaCl, 0.2% IGEPAL) on ice, and then incubating precipitates from previous steps with nuclear lysis buffer (50mM Tris, pH8.0, 10mM EDTA, 0.5% SDS) for 20 min on ice. Nuclei lysates were aliquoted to 700ul each in 10ml sonication tubes (Diagenode, Denville, NJ) and sonicated using a Bioruptor 300 instrument (Diagenode, Denville, NJ) for 36 cycles of 30s on,

30s off, to achieve an average chromatin fragmentation size of 100bp to 200bp. Subsequent ChIP- exo procedures and sequencing were performed following previously published protocols (Rhee and Pugh 2012). Chromatin samples were diluted 2.5-fold with 0.6% v/v Triton X-100 and immunoprecipitated using sepharose antibody-conjugated magnetic beads. All YFP-empty, YFP- hCAR and YFP-mCAR construct-infected samples utilized anti-GFP antibody Ab290 (Abcam,

Cambridge, MA), followed by DNA polishing, A-tailing, Illumina adaptor ligation (ExA2), and subsequent digestion on the beads using lambda and recJ exonuclease. Following single-stranded

DNA elution, a primer was annealed to EXA2 and extended with phi29 DNA polymerase, then

A-tailed. Exonuclease treated ends were then ligated with a second Illumina sequencing adaptor; the products PCR-amplified and gel-purified. 94 Data analysis

Sample sequencing was performed on an Illumina NextSeq500 system using a 40bp paired-end sequencing setting. Reads were mapped to the mouse genome (mm10) using BWA-

MEM (version 0.7.9a) (Li and Durbin 2009; Li 2013). Each biological replicate contained at least

20 million unique reads.

Genome coverage visualization

For visualization, biological replicates reads files were merged into a single file for each sample. Sample reads were separated by forward/reverse strand and respectively converted to genome coverage format bedgraph files; then forward strand and reverse strand bedgraph files were combined and converted by IGV tools for IGV browser visualization. Red areas represent forward strand reads coverage, whereas blue area represents reverse strand reads coverage.

Quantitative differential binding analysis

Biological replicate reads files were merged and then peaks were called using the

MACS14 default setting (p-value cut off 1E-5) against the YFP-empty infected control sample

(Zhang et al. 2008). MACS14 peaks from all four samples were merged into one peak file using the bedtools merge function (Quinlan and Hall 2010) and filtered for blacklist regions

(Consortium 2012). The merged peak files represent all potential enrichment regions for both mCAR and hCAR. Count matrixes of potential enrichment regions coverage were generated for 95 all biological replicates, then further analyzed using the R corrplot package and the R

Bioconductor DESeq2 package. The R corrplot package was used to calculate and plot for

Pearson correlation coefficients between two replicates. The DESeq2 utilized a negative binomial-based generalized linear model to examine following differential binding tests: mCAR vs hCAR, mCAR TCPOBOP vs PB, and hCAR CITCO vs PB. The Principal Component

Analysis (PCA) data were generated using the DESeq2 package. Hierarchy clustering was performed by submitting the count matrix to ClustVis (Metsalu and Vilo 2015), with average method for both row and column clustering. Enrichment regions with significant differential binding between test groups (fold change >= 2, q-value < 0.05) were associated to the closest

TSS site within 10kb region and annotated using HOMER (Heinz et al. 2010).

Peak annotation and GO analysis

Based on the highest FRiP score (fraction of reads in peaks), three replicates from mCAR samples and three replicates from hCAR samples were chosen for the stringent peak calling processes. Peak calling processes were performed based on the official specification for the

ENCODE TF ChIP-seq processing pipeline, using the Irreproducibility Discovery Rate (IDR) method. Briefly, three replicates from mCAR or hCAR were merged, randomly shuffled into two pseudo replicates, and pseudo replicates were called for peaks using MACS2 and analyzed for

IDR consistency R script. Following the IDR framework guideline, the IDR output thresholds,

0.01 for mCAR and 0.0025 for hCAR, were selected. IDR analysis-generated peaks were filtered with blacklist region, then associated with the closest TSS within a 10kb region and annotated by

HOMER. GO analysis was performed using the HOMER functional enrichment analysis and the 96 DAVID Functional annotation tool (Huang da et al. 2009b; Huang da et al. 2009a). The mCAR and hCAR top 500 genes ranked by MACS p-value were submitted for GO analysis.

RNA-seq re-analysis

To cross-reference our differential genomic species mapping results with transcriptomic data, we utilized a set of published RNA-seq files consisting of C57BL/6 wild type mice and humanized CAR-transgenic (hCAR-TG) mice (Cheng et al. 2017). WT_Day60_CornOil (n=3),

WT_Day60_TCPOBOP (n=3), hCAR-TG_Day60_CornOil (n=3) and hCAR-TG_Day60_CITCO

(n=3) samples (NCBI Gene Expression Omnibus database GSE98666) were re-analyzed specifically for mCAR and hCAR species difference comparisons. Reads from each sample replicate were mapped to the mm10 mouse genome using HISAT2; then uniquely mapped reads were processed with featureCounts and DESeq2 for differential gene expression analysis.

Differential expression analyses were performed between the C57BL/6 wild type mouse control and TCPOBOP, and the humanized CAR-transgenic (hCAR-TG) mice control and CITCO (fold change >= 2, q-value < 0.05).

Results

CAR showing strong binding sites in Dlk1-Dio3 gene cluster

The UCSC genome browser illustrated CAR binding sites on the Dlk1-Dio3 gene clusters

(Fig 3-1). Through MACS2 peak calling and irreversible discover rate (IDR) stringent filters, 29 hCAR binding peaks and 11 mCAR binding peaks were identified with high confidence in the

Dlk1-Dio3 regions. It is important to point out that the differences inmCAR vs hCAR peak 97 numbers should not be interpreted as absolute differences in mCAR vs. hCAR genomic interactions within Dlk1-Dio3 clustering regions. Rather, they represent differences in the binding peaks that passed the stringent peak calling thresholds. Dlk1-Dio3 gene clusters span over 800kb.

Several hCAR and mCAR peaks reside between the 90kb region between Meg3 and Dlk1, and more than 60% of the peaks are located within the 350kb Dio3 side of the region. Strikingly, there are no CAR binding sites in the miRNA cluster region, containing 47 miRNAs.

CAR binding on Meg3 promoter region and IG-DMR

Meg3 is a crucial lncRNA within the Dlk1-Dio3 region that interact with key cancer related proteins such as p53, Mdm2 and Rb, regulating cell proliferation, cell cycle and apoptosis.

Previous studies have revealed that upon PB activation, Meg3 levels are up-regulated with time in a CAR-dependent manner. Here, the integrated genome viewer showed that there are several

CAR binding sites in the Meg3 promoter region, indicating that CAR may directly up-regulate

Meg3 through binding to several DNA motifs upstream of Meg3 (Fig 3-2).

Upstream of Meg3 transcription start sites lies an -10 to -15kb IG-DMR (intergenic germline-derived differentially methylated region) that have been characterized as a key control element for imprinted Dlk1-Dio3 gene cluster (Lin et al. 2003). For both mCAR and hCAR, several binding sites are located flanking the IG-DMR region, with one peak in the middle of the

IG-DMR region. IG-DMR region has been characterized as a control element for Meg3 lncRNA expression. The fact that CAR binding sites overlap with IG-DMR strongly suggests a direct transactivation regulatory role of CAR over Meg3. 98 mCAR vs hCAR differential binding on Dlk1-Dio3 cluster

Using DESeq differential analysis tool from R Bioconductor package, a differential

binding analysis comparing mCAR vs hCAR, as well as direct activation vs indirect activation

have been performed on Dlk1-Dio3 region, shown in the following Table.

binding regions in significant differential Differential Test binding region annotation Dlk1_Dio3 cluster binding TSS of Mir1247; 73kb mCAR vs hCAR 2 upstream of Dio3 mCAR, direct vs indirect 0 40 hCAR, direct vs indirect 0

direct vs indirect 0

Table 3-1 CAR Differential bindings in Dlk1-Dio3 gene cluster Consistent with Chapter 2’s findings, the genomic profiles of direct activation of CAR

through CITCO and TCPOBOP were largelyidentical to those detected for the indirect activation

of CAR through PB. Among total 40 CAR binding regions, there are no regions showing

differential binding patterns (enrichment fold change > 2) between direct activation and indirect

activation.

However, for mCAR vs hCAR, there are 2 binding sites exhibiting differential binding

events (enrichment fold change > 2) (Fig 3-3). In both binding sites, hCAR displays higher

binding affinity to DNA. Both sites are located close to Dio3, with one binding at -73kb upstream

of Dio3, the other one at -1kb upstream of Dio3, which also overlaps with the TSS of miRNA

Mir1247. Since Dio3 end of the Dlk1-Dio3 region is less characterized, it is unclear at this time

what the impact is of two mCAR vs hCAR differential binding sites. 99

Meg3, Rian and Mirg expression levels were calculated from RNA-seq data from 5 days of CITCO-treated hCAR humanized mice and from TCPOBOP-treated WT mice, all compared to corn oil-treated respective mouse controls. Interestingly, for mCAR, all three lncRNAs from the activated samples demonstrated reduced expression level or trend-decreases compared to control samples, whereas hCAR CAR-activated samples showed increased trends. Considering that previously published results showed that chronic PB activation up-regulated lncRNA levels

(initial expression levels were very low); it is possible that the lncRNA level from short term treatment might not reflect the CAR induction of lncRNA in Dlk1-Dio3. There might be an underlying mechanism causing short term lncRNA levels to display the opposite effects compared to the longterm results.

Discussion

Since the discovery of Dlk1-Dio3 gene cluster, this region has been an area of great scientific interest for its function in development and carcinogenesis, as well for its candidacy for understanding mechanisms of imprinting(Benetatos et al. 2012). Recently, using a chronic PB- treated mouse model, it wasshown that lncRNA Meg3, Rian and Mirg, along with miRNAs in this region, could be induced accumulatively through time in a CAR-dependent manner, contributing to tumor promotion.

Our results showed clearly that both mCAR and hCAR exhibited strong binding sites over the Dlk1-Dio3 region. Combined with previously published results, these results suggest that

CAR might directly regulate these lncRNAs through transactivation. Since these lncRNAs are involved in development, pluripotency and carcinogenesis in various stages of development in 100 different tissues, the idea that CAR, upon xenobiotic activation, may impinge on the lncRNA system could tremendously expand the traditional understanding of a xenobiotic sensor.

One point of interest worth highlighting is that CAR binding regions overlap with IG-

DMR, upstream of Meg3. DNA methylation is an epigenetic modification that plays an important role in modulating the monoallelic behavior in the majority of imprinted genes. Since many imprinted genes are clustered, it has been suggested that epigenetically modifying the gene cluster domains would serve to coordinate the regulation of more than just one imprinted gene. For example, the Dlk1 has a completely unmethylated CpG island promoter on the paternal allele, with a DMR (differentially methylated region) which is hypomethylated on the maternal allele and partially methylated on the paternal allele, allowing Dlk1 exclusively paternally expressed

(Takada et al. 2002). Even though the results do not show CAR binding regions in proximity of the Rian and miRNA cluster, CAR binding on the domain controlling IG-DMR suggest that CAR may directly modulate or facilitate other transcription factors to regulate the expressions of those genes. In addition, CAR binding sites were enriched in the Dio3 end of the Dlk1-Dio3 cluster.

Those binding sites may contribute to regulating genes close to Dlk1 through chromatin folding.

They may also serve as an indicator for potential cis regulatory functions in the DNA elements.

There are only two mCAR and hCAR differential binding sites, which are both more than

400kb away from the lncRNAs in the region. Previously studies demonstrated that lncRNAs showed species variation of induction between mCAR and hCAR (Pouche et al. 2017). Further studies needed to be carried out to elucidate biological meaning of these two differential binding sites.

101 One previous study used an in silico approach in an attempt to identify CAR binding locations in the Dlk1-Dio3 region, yielding 26 potential binding sites through stringent filtering.

Unfortunately, our binding results showed that none of the 26 potential binding sites overlapped with ChIP-exo binding profiles, highlighting the challenge of using in silico models for accurate prediction.

Overall the results presented here provide novel insights into the potential mechanism of

CAR as a direct regulator of lncRNAs in Dlk1-Dio3 regions. These results could expand our understanding of CAR regulatory approaches; in addition to directly transactivating genes, through competing/interacting with other transcription factors and competing with coactivators,

CAR could potentially regulate lncRNAs and therefore mediate a plethora of other genes. These results help enhance the understanding of the Dlk1-Dio3 region itself, although the exact mechanistic implications of the CAR binding sites enriched in Dio3 region will require further investigation.

102

Fig 3-1 UCSC genome browser showing CAR binding peaks for 4 sample groups. hCAR samples treated with direct ligand CITCO and indirect activator PB are shown in blue. mCAR samples treated with direct ligand CITCO and indirect activator PB are shown in black.

103

Fig 3-2 Integrated genome viewer (IGV) showing CAR bindings. Both mCAR and hCAR bind on Meg3 promoter regions and IG-DMR (intergenic germline-derived differentially methylated region).

104

Fig 3-3 RNA-seq examination of lncRNA gene expressions. Each column represents the fold-change value calculated by DESeq2, between treated

(n=3) and untreated (n=3) samples. Asterisks (*) indicate p-values from DESeq2 analysis.

105

Chapter 4

Future directions

In chapter 2 the goal of these studies was to identify novel gene involvements for CAR in carcinogenesis-related pathways, an association that is poorly understood relative to other CAR regulatory functions, such as drug and energy metabolism. In these respects, we generated a list of genes from those that overlapped as annotated CAR binding genes with genes associated with common cancer-related pathways, including for example, Wnt/β-catenin, TGF-β pathway,

Jak/STAT, PI3K/Akt, p53, cellular proliferation and apoptosis.

Among those genes, the identification of Gdf15 (Growth/differentiation factor 15) was remarkable. Gdf15 mRNA expression exhibited greater than a 15-fold induction compared with

TCPOBOP- and DMSO- treated WT mice; although unchanged in CAR -/- mice. GDF15 is involved in inflammatory and apoptotic pathways (Zimmers et al. 2005) and was recently associated with liver carcinogenesis through activation of the GSK-3β / β-catenin pathway (Xu et al. 2017). Our ChIP-exo data identified two major CAR binding sites within 5 kb of the Gdf15 gene’s TSS. RNA-seq expression analyses also demonstrated that Gdf15 mRNA levels are increased ~15-fold in WT mice following TCPOBOP treatment, and ~5-fold with CITCO treatment in humanized CAR mice. Together, these results strongly imply that mCAR directly regulates Gdf15 at the transcriptional level.

106

Further, strong evidence indicates that Gdf15 directly regulates b-catenin (Xu et al.

2017). Clinical data show that Gdf15 is overexpressed in liver cancer tissues; and, knockdown of

Gdf15 significantly inhibits the growth and metastasis of liver cancer stem cells through suppression of the AKT/GSK-3β/β-catenin pathway (Xu et al. 2017). In addition, the zonal expression pattern of Gdf15 in liver tissue correlates to that of CAR and b-catenin (Halpern et al.

2017). As well, b-catenin expression is considered crucial for PB-induced mouse liver tumorigenesis (Dong et al. 2015). All of these observations strongly support the concept that

Gdf15 may be the missing link between CAR, b-catenin and hepatocarcinogenesis.

As such, this is area warrants additional investigation. Direct promoter / transcriptional activation assays should be conducted to prove direct transactivation and more detailed functional biology analyses are required. Along these lines, conducting in vitro cell proliferation assays in cultured primary hepatocytes, from CAR +/- mice, in Gdf15 +/- mice and in b-catenin +/- mice, should be assessed. Directed CRISPR gene editing technologies could be applied to produce the requisite mice. Long term two-stage initiation/ PB promotion experiments should also be performed to assess development of HCC in the mouse genetic models, and genetic combinations thereof, to ultimately assess the contributions of these genes in tumorigenesis.

In addition to the GDF15 findings, Prkar2a (cAMP-dependent protein kinase type II- alpha regulatory subunit) and Bcar1 (Breast cancer anti-estrogen resistance protein 1, p130cas) exhibited strong CAR binding sites, as well as demonstrating corresponding CAR dependent gene expression perturbations. Shc1 (SHC-transforming protein 1) showed a similar trend, exhibiting increased CAR dependent expression. SHC1 reportedly activates cell proliferation and positively regulates the cell cycle through signaling in the EGF pathway (Zheng et al. 2013). These results 107 are likely of biological importance, since cAMP-activated protein kinases (PKA) play many roles, including an interplay in the progression of various tumors (Caretta and Mucignat-Caretta 2011), and the PKA regulatory subunit RII alpha is specifically reported to block apoptosis in pathological processes (Benetti and Roizman 2004). Similarly, BCAR1 belongs to the CAS family of adaptor proteins that contribute to signaling pathways involved in cell adhesion, migration and apoptosis (Cabodi et al. 2010). As well, Foxo3 (Forkhead box protein O3), functions in antitumor activities (Deng et al. 2018) and displayed strong CAR-binding sites near its TSS. Foxo3 was significantly repressed by CAR activators. These results imply that mCAR may promote tumorigenesis by suppressing Foxo3.

IRS2 (Insulin receptor substrate 2) and IGF1 (Insulin-like growth factor I) contributes to the insulin signaling pathway, promoting cell proliferation (Reiss et al. 2012; White 2014). In this thesis research, as indicated in Chapter 2, Igf1 mRNA expression levels decreased in TCPOBOP- treated WT mice, and Irs2 displayed a trend toward decreased expression in TCPOBOP-treated

WT mice. These results suggest that CAR may function to repress hepatocarcinogenesis by affecting the insulin signaling pathway, adding to the complexity of understanding CAR’s role in carcinogenesis.

In summary, these newly identified cancer-related CAR target genes are all promising future study directions for understanding various roles of CAR in different pathways, especially in tumorigenesis. Investigation designs for further studying CAR interactions with these genes would be similar to that of Gdf15.

Another potential future direction involves substantiating the novel CAR-binding targets with more evidence of direct regulation. Although ChIP-exo binding profiling coupled with 108 expression analyses are powerful tools, and many of the identified genomic binding sites linked to genes that were already well-established by transcriptomics and biological investigations as

CAR-dependent, additional in vitro and in vivo studies are still needed to solidify the genomics results. For example, although the ChIP-exo binding data were derived directly from in vivo methodologies, in vitro transactivation assays with wild type CAR, or gel shift assays, could further verify the CAR and DNA interactions. These assays are also applicable for further studies of species variation. Proteomic investigations of the affected pathways results would be additionally informative.

The concept of receptor cross-talk is also fascinating and warrants further investigation.

How do hepatic transcription factors cross-talk, in particular nuclear receptors, which have program overlapping biological functions? Through the coupling of direct activator- or indirect activator-treated mice with ChIP-seq or ChIP-exo against other transcription factors, or by probing histone modification of DNA methylation, additional biological insights would be revealed to help clarify our understanding of how CAR interacts with other transcription factors.

Another future area of inquiry relates to sexual dimorphism. Sexual dimorphism has long been associated with PB activation and CAR (Hernandez et al. 2006; Lodato et al. 2017).

Studying CAR’s biological impact in both genders would help facilitate our understanding of the differences that this receptor contributes between males and females in xenobiotic response, energy metabolism and carcinogenesis.

A number of studies have already documented the long-term effect of PB induced mice in

HCC models (Moennikes et al. 2000; Ferko et al. 2003; Braeuning et al. 2014; Dong et al. 2015); for example, only chronic PB treatment could induce lncRNA in Dlk1-Dio3. There might be 109 genomic profile differences of CAR between short-term studies and long-term studies. If possible, performing a long-term genomic profiling investigation in PB-exposed animal models should help clarify issues related to timing of exposures on cancer development.

Last but not the least, to ultimately understand species differences between mCAR and hCAR, investigations will need to proceed directly in human systems. In these respects, genomic profiling using highly defined human primary hepatocyte cultures, even if only for short-term treatment, should provide valuable insight.

110 Benetti L, Roizman B. 2004. Herpes simplex virus protein kinase US3 activates and functionally overlaps protein kinase A to block apoptosis. Proc Natl Acad Sci U S A 101: 9411-9416. Braeuning A, Gavrilov A, Brown S, Wolf CR, Henderson CJ, Schwarz M. 2014. Phenobarbital- mediated tumor promotion in transgenic mice with humanized CAR and PXR. Toxicol Sci 140: 259-270. Cabodi S, del Pilar Camacho-Leal M, Di Stefano P, Defilippi P. 2010. Integrin signalling adaptors: not only figurants in the cancer story. Nat Rev Cancer 10: 858-870. Caretta A, Mucignat-Caretta C. 2011. Protein kinase a in cancer. Cancers (Basel) 3: 913-926. Deng Y, Wang F, Hughes T, Yu J. 2018. FOXOs in Cancer Immunity: Knowns and Unknowns. Semin Cancer Biol doi:10.1016/j.semcancer.2018.01.005. Dong B, Lee JS, Park YY, Yang F, Xu G, Huang W, Finegold MJ, Moore DD. 2015. Activating CAR and beta-catenin induces uncontrolled liver growth and tumorigenesis. Nat Commun 6: 5944. Ferko A, Bedrna J, Nozicka J. 2003. [Pigmented hepatocellular adenoma of the liver caused by long-term use of phenobarbital]. Rozhl Chir 82: 192-195. Halpern KB, Shenhav R, Matcovitch-Natan O, Toth B, Lemze D, Golan M, Massasa EE, Baydatch S, Landen S, Moor AE et al. 2017. Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 542: 352-356. Hernandez JP, Chapman LM, Kretschmer XC, Baldwin WS. 2006. Gender-specific induction of cytochrome P450s in nonylphenol-treated FVB/NJ mice. Toxicol Appl Pharmacol 216: 186-196. Lodato NJ, Melia T, Rampersaud A, Waxman DJ. 2017. Sex-Differential Responses of Tumor Promotion-Associated Genes and Dysregulation of Novel Long Noncoding RNAs in Constitutive Androstane Receptor-Activated Mouse Liver. Toxicol Sci 159: 25-41. Moennikes O, Buchmann A, Romualdi A, Ott T, Werringloer J, Willecke K, Schwarz M. 2000. Lack of phenobarbital-mediated promotion of hepatocarcinogenesis in connexin32-null mice. Cancer Res 60: 5087-5091. Reiss K, Del Valle L, Lassak A, Trojanek J. 2012. Nuclear IRS-1 and cancer. J Cell Physiol 227: 2992-3000. White MF. 2014. IRS2 integrates insulin/IGF1 signalling with metabolism, neurodegeneration and longevity. Diabetes Obes Metab 16 Suppl 1: 4-15. Xu Q, Xu HX, Li JP, Wang S, Fu Z, Jia J, Wang L, Zhu ZF, Lu R, Yao Z. 2017. Growth differentiation factor 15 induces growth and metastasis of human liver cancer stem-like cells via AKT/GSK-3beta/beta-catenin signaling. Oncotarget 8: 16972-16987. Zheng Y, Zhang C, Croucher DR, Soliman MA, St-Denis N, Pasculescu A, Taylor L, Tate SA, Hardy WR, Colwill K et al. 2013. Temporal regulation of EGF signalling networks by the scaffold protein Shc1. Nature 499: 166-171. Zimmers TA, Jin X, Hsiao EC, McGrath SA, Esquela AF, Koniaris LG. 2005. Growth differentiation factor-15/macrophage inhibitory cytokine-1 induction after kidney and lung injury. Shock 23: 543-548.

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Appendix A

Cryosection and immunostaining protocol

Perfusion and Fixation

Reagents:

PBS

0.9% NaCl Saline (30ml per mouse)

4% Paraformaldehyde (PFA) (20ml per mouse, plus 1X50ml conical tube)

Key Instrument:

BD Vacutainer blood collection needle

10ml Syringe (with proper lock to the needle), 2 per mouse

1. CO2 euthanize mice.

Note: Cardiac puncture blood collecting is probably less preferred in this scenario.

2. Open abdominal cavity.

3. Put needle through the hepatic portal vein (or upper part of inferior vena cava, past kidney). Attach the needle with saline syringe to start liver perfusion. 112 Note: get rid of bubbles in the syringe, or else they will clog capillaries.

4. Slowly inject saline with syringe for about 10ml or until the color of liver appear to be bloodless.

5. Hold the needle in the vein, meanwhile change saline syringe with 10ml 4% PFA syringe. No bubbles allowed.

6. Slowly perfuse with 4% PFA for 10ml. Liver should be stiffer than before.

7. Extract liver, wash off the blood on the surface in a PBS petri dish.

8. Cut the liver to smaller pieces to fit in the cryomold. Put them in the 50 conical tube with 4% PFA. Incubate overnight at RT.

Dehydration

Reagents:

15% Sucrose in PBS (1X50ml conical tube)

30% Sucrose in PBS (2X50ml conical tube)

9. After overnight immersion liver piecies with 4% PFA, dry them with kimwipes and wash them with PBS. After wash, dry them with kimwipes. 113 Note: Might need to wash more times or soak in PBS longer to remove 4% PFA from tissue. Though the dehydration process will have plenty of time to achieve that.

10. Put liver pieces in 15% Sucrose. RT 5h, make sure they do not float.

11. Dry with kimwipes. Transfer liver to a 30% Sucrose conical tube. Incubate at RT for

8h or until all pieces sink.

12. Dry with kimwipes and transfer liver to a new 30% Sucrose conical tube. Incubate overnight.

Note: Not sure how necessary this step is. Could possibly skip this step.

Snap Freezing

Reagents:

2-methylbutane

Liquid nitrogen

Dry ice

OCT

Key Instruments:

Styrofoam to contain liquid nitrogen and dry ice

Cryomold

Forceps

114 13. Cool 2-methylbutane. Prepare liquid nitrogen in styrofoam. Pour 2-methylbutane in a cut open aluminum can with counterweight put in or a metal beaker for 2/3 full. Use forceps, put the metal container with 2-methylbutane in the liquid nitrogen. Liquid nitrogen should be at the same level with 2-methylbutane. Wait 10 min for 2-methylbutane to cool down. Meanwhile continue to do step 14.

Note: Using dry ice instead of liquid nitrogen is fine, but the snap freezing effect could be suboptimal.

14. Prepare tissue in cryomold for snap freezing. Use kimwipes to dry liver pieces. Pour

OCT into the cryomold for half full, then put liver pieces in with desired orientation, finally add more OCT to completely cover the tissue.

15. Using forceps to hold the cryomold, immerse cryomold into 2-methylbutane. Do not release forceps. When cryomold is frozen, quickly take it out and put it in a styrofoam box with dry ice.

Note: The total freezing time for each sample should be less than 50 seconds. Longer time will cause OCT to crack.

16. Long term store cryomolds in -80C. To reduce drying (even if we dehydrate it already??), the cryomolds should be wrapped in aluminum foil; then put in a sealed bag or tube.

115 Cryosectioning

17. Prior to cryosectioning, take the cryomolds from -80C to -20C for 2h.

18. The blade should be the one for soft tissue. Change the temperature of the tissue to -

15C, the whole station -15C. Generally the blade angle should be between 4 to 5.

Note: Temperature too low will cause the cut liver tissue to break or roll. The glass cover should be closed tightly to the anti-roll platform, otherwise the slice will not attach to the platform.

19. Trim the tissue at 40 um, cut the tissue at 10um.

Note: I have not tried thinner cut. Might work.

20. Collect the slice using superfrost slide with the plus side. Dry the slide in RT or 37C.

30min to 1.5h is fine.

21. At this point, the slides could be stored in -80C freezer.

Immunostaining

Reagents:

TBS 116 Triton X-100

Goat Serum (or serum from the same species as secondary antibody)

BSA powder

Abcam ab13970 chicken anti-GFP antibody (Primary Antibody)

Jackson Immunoresearch Alexa488 goat anti-chicken antibody (Secondary Antibody)

Life technology diamond mounting medium with DAPI

Permeabilization buffer: TBS + 2% Triton X-100

Wash buffer: TBS + 0.025% Triton X-100

Blocking buffer: TBS + 2% Serum + 1% BSA

Incubation buffer: TBS+ 0.3% Triton + 1%BSA

Key Instruments:

Liquid blocker

Wet chamber: e.g. a Styrofoam box with lid, put napkins in the bottom and add water.

22. Wash slides. Use TBS to wash the slides 2 X 5mins.

23. Dry the slides with kimwipes and use liquid blocker to circle the tissue slices.

Note: After applying liquid blocker, whenever finished washing, drain excess water on the blocker region by lightly tapping it with kimwipes. Make sure the whole blocker region circling the tissue slice is clear of water. Reapply liquid blocker if it’s failing (before using liquid blocker, make sure it’s dry wherever you want to use). If you don’t do this, the liquid blocker will not work, tissue will dry out.

117 24. Incubate with Permeabilization buffer for 2h at RT.

25. Use blocking buffer to block the slide for 1 h in RT.

Note: All incubations from this step should be done in a wet chamber.

26. Thaw primary antibody from -20C, use 1:1000 in incubation buffer. Add primary antibody buffer on the slides. Incubate overnight at cold room.

Note: One slide should use less than 200ul incubation buffer. It really depends on the tissue area on the slides, but the moral is you don’t need too much. Primary antibody could potentially be further diluted, though I haven’t tried that out.

27. Wash slides 3 times for 10mins each in wash buffer.

28. Thaw secondary antibody from -20C, use 1:500 in incubation buffer. Add secondary antibody buffer on the slides. Incubate 1h at room temperature.

Note: secondary antibody contains fluorophore, so keep it in the dark even when washing.

29. Wash slides 3 times for 10mins each in wash buffer.

30. Add one drop of DAPI mounting medium per tissue on the slides. Put on cover glass.

Note: DAPI mounting medium is stored in 4C. Take out in room temperature 30min before use. 118 Appendix B

Chromatin Immunoprecipitation and Sonication protocol

Note: for this protocol the liver weight should be around 1.0g empirically. Too much or too less cells might affect sonication results drastically. To achieve higher consistency across samples, it may be a good idea to use the same weight of liver before processing.

Crosslinking and homogenizing

Solutions and Reagents

Fresh formaldehyde 1% (w/v), methanol free, Room Temperature

For each liver sample, prepare 10ml 1% formaldehyde.

Dilute 16% Pierce Formaldehyde Ampules (1ml) into PBS to make fresh 1% formaldehyde.

Alternatively 1% formaldehyde could be made fresh with paraformaldehyde powder and

PBS. Perform weighing and stirring inside fume hood. Heat PBS to 60°C, then add in paraformaldehyde powder. Paraformaldehyde should not take long to dissolve in PBS solution; however in H2O, adjusting pH is necessary for paraformaldehyde to dissolve.

Do not use stock 37% formaldehyde with methanol to make 1% formaldehyde solution.

Also methanol increases membrane permeability, which could lead to over-crosslinking.

119

Glycine 1.25M, Room Temeprature

Use 1.1ml 1.25M Glycine for each sample.

Glycine 1.25M should be long term stored in 4°C

PBS, Ice Cold

Instruments

Timer

Razor blades for each sample

Labeled 15ml conical tubes for each sample

10ml serological pipettes with pipette aids; 1ml pipette.

Dounce Homogenizers, with “A” label

200um filter nets

Funnels

120 Protocols:

1. Make sure 1% formaldehyde and 1.25M glycine are on room temperature.

2. After liver extraction, use razor blade to cut up liver into very small pieces (diameter less than 2mm). Transfer minced liver to a 15ml conical tube.

This step should be performed quickly to preserve nuclear DNA protein interactions.

3. Crosslink liver tissue by adding 10ml 1% formaldehyde to each conical tubes with liver. Incubate 10mins at room temperature. Put tubes on a rocker or inverse tubes constantly.

Crosslinking incubation time is crucial to the whole ChIP. 10mins here is enough;

15mins is too long. One could try use shorter duration, for example, 9 or 8 minutes.

4. Quench crosslinking by adding 1.1ml glycine (1.25M) to formaldehyde. Incubate

5mins at room temperature. Put tubes on a rocker or inverse tubes constantly.

5. Use pre-chilled centrifuge to spin down liver tissue. Centrifuge at 4°C 800g, 3min.

6. Wash liver tissue. Use serological pipettes to transfer supernatant to waste flask with bleach. Add 10ml cold PBS to each tube, vortex, put on the rocker for 3mins, then centrifuge at

4°C 800g for 3mins.

The liver tissue contains adenovirus product, thus all waste supernatants should be bleach treated.

121 7. Repeat step 6 for 2 more times. Put conical tubes with liver tissue on ice. Do not add

PBS now.

8. Homogenize tissue using Dounce homogenizer. Prechill a douncer homogenizer on ice. Take a 1ml barrier pipett tip and use scissors to cut the tip, about 5mm away from the tip end.

Use this tip to pipette liver tissue into Dounce homogenizer. Add 10ml cold PBS and do 20~30 strokes to homogenize tissue. Set up a funnel with 200um filter on top of a 50ml conical tube on ice. Filter homogenized tissue; then use another 10ml cold PBS on the filter to wash down more cells. Wash Dounce homogenizers and filters for reuse.

9. Centrifuge all 50ml conical tubes at 4°C 1,500g for 5mins. Remove the supernatant, the cell pellets could be stored at -80°C or used immediately for sonication.

Sonication

Solutions and Reagents

Cell Lysis Buffer (CLB): 10mM Tris pH 8.0

10mM NaCl

0.2% IGEPAL

122 Nuclear Lysis Buffer (NLB): 50mM Tris pH 8.0

10mM EDTA

0.5% SDS

Dilution Buffer: 50mM Tris pH 8.0

10mM EDTA

Protease Inhibitor (PI) 100X

Instruments

Bioruptor

It takes about 25mins for water circulation to reach 4°C. Turn on all parts of the machine and start cooling process on the water cooler at step 5 below.

Protocols

1. For each sample, 3ml CLB and 2.4ml NLB is needed. Add PI to ice cold CLB and

NLB aliquots accordingly.

2. Thaw liver cell pellet by adding 3ml CLB and PI mix to each tube. Transfer cell solutions into 15ml conical tubes.

Pipetting up and down helps thawing. 123

3. Lyse cell membrane by incubating with CLB on ice for 15mins. Vortex each tubes every 5mins.

4. Centrifuge at 4°C 1,500g for 5min. Discard supernatant.

5. Lyse nuclear membrane by adding 2.4ml NLB and PI mix to each tube and incubating on ice for 20min. Vortex each tubes every 5mins. Keep all samples on ice for subsequent steps.

6. For now each sample volume should be 3ml or more. Split them evenly into four sonication tubes.

7. Choose Bioruptor setting to 12 cycles, 30s on 30s off, Strong power. Put four sonication tubes into sonication chamber and press Start.

Make sure the lid for each sonication tube is tight.

8. After 12min, take out all tubes and vortex briefly; then put them back and start another cycle. Repeat this step once more. Each sample should be sonicated for 36 cycles (36mins) in total.

For optimizing sonication steps, one can set up different break time and collect

10ul~20ul samples for analysis at each break time.

9. Transfer all supernatant into two 1.7ml tubes and centrifuge at 4°C 14,000g fro 5mins.

Transfer supernatants to new tubes. Sample are now ready for the immunoprecipitation or storage in -80°C for further usage. Samples sent to Pugh lab are from this step. 124

For examining sonication results, one can take 10ul ~ 20ul and perform reverse- crosslinking and purification:

1. Dilute to 100ul with dilution buffer

2. Add NaCl to 300mM; heat tubes to 65°C on heated shaker for 2h or overnight.

3. Add 0.5ul RNAse A and incubate 10min at 37°C.

4. Add 1ul Proteinase K and incubate 45min at 45°C.

5. Use DNA gel purification kit or phenol chloroform extraction to purify DNA.

6. Perform DNA electrophoresis on 3% agarose gel and check results. Ideally majority of

DNA fragments should be 200~300bp, at least less than 500bp.

For YFP-tag the antibody used is rabbit anti-YFP antibody ab290 from ABCAM. For each sample, Pugh lab use 3ul 5ug/ul antibody solution, that is, 15ug for each sample. According to Pugh lab, it’s best to use the same antibody with the same lot number to do one batch of ChIP pull-downs experiment. 125

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Vita

Ben Niu

Education:

Ph.D. Cell and Development Biology 2009-2018 Pennsylvania State University

B.S. Biology 2005-2009 Tsinghua University

Select Publications:

Niu, B., Coslo, D.M., Bataille, A.R., Albert, I., Pugh, BF. and Omiecinski, C.J. High- resolution, in vivo genome binding interactions of mouse and human constitutive androstane receptors reveal novel gene targets. Nucleic Acids Research. (Accepted)

Hao, R., Su, S., Wan, Y., Shen, F., Niu, B., Coslo, D.M., Albert, I., Han, X. and

Omiecinski, C.J. Bioinformatic analysis of microRNA networks following the activation of the constitutive androstane receptor (CAR) in mouse liver. Biochim Biophys Acta, 1859, 1228-

1237.

Select Awards:

Bristol-Myers Squibb graduate student fellowship 2012 - 2014

Select Activities:

Society of Toxicology conferences poster presentation 2015, 2016

Society of Toxicology membership 2015 - 2018