Circadian Regulation of Hepatic Metabolism by Nuclear Receptors Rev-Erb and Ror

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2017

Circadian Regulation Of Hepatic Metabolism By Nuclear Receptors Rev-Erb And Ror

Yuxiang Zhang University of Pennsylvania, [email protected]

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Recommended Citation Zhang, Yuxiang, "Circadian Regulation Of Hepatic Metabolism By Nuclear Receptors Rev-Erb And Ror" (2017). Publicly Accessible Penn Dissertations. 2665. https://repository.upenn.edu/edissertations/2665

This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/2665 For more information, please contact [email protected]. Circadian Regulation Of Hepatic Metabolism By Nuclear Receptors Rev-Erb And Ror

Abstract Circadian and metabolic physiology are intricately intertwined. Although the liver clock is entrained by the central clock, it also directly controls metabolic gene expression. Hormone responsive nuclear factors are hypothesized to be the major clock components that regulate metabolism, which include Rev-erbs and RORs. In this study, we explored the mechanism of regulation of liver circadian gene expression by Rev- erbs and RORs. We found that Rev-erbalpha modulates the clock and metabolism by different genomic mechanisms. Clock control requires Rev-erbalpha to bind directly to the genome at its cognate sites, where it competes with activating ROR TFs. By contrast, Rev-erbalpha regulates metabolic genes primarily by recruiting the HDAC3 corepressor to sites to which it is tethered by cell type-specific transcription factors. Thus, direct competition between Rev-erbalpha and ROR TFs provides a universal mechanism for self-sustained control of molecular clock across all tissues, whereas Rev-erbalpha utilizes�lineage-determining factors to convey a tissue-specific epigenomic rhythm that egulatesr metabolism tailored to the specific need of that tissue.

In addition, we also investigate the circadian regulation of hepatic metabolism by nuclear receptors RORalpha and RORgamma. We discovered that hepatic depletion of RORs increased expressions of genes involved in lipogenesis as well as the hepatic triglyceride levels, specifically at ZT22 (5 AM) and during feeding, but not at ZT10 (5 PM) nor during fasting. GRO-seq analysis suggested that regulation of lipid metabolism by RORs was mediated by SREBP1-c. Indeed, ROR depletion significantly up-regulated nuclear SREBP1-c protein level specifically at ZT22. Overall, our study reveals a time-specific egulationr of liver lipid metabolism by RORs, and suggests a potential role of chronotherapy targeting circadian factors in the treatment of metabolic disorders.

Degree Type Dissertation

Degree Name Doctor of Philosophy (PhD)

Graduate Group Pharmacology

First Advisor Mitchell A. Lazar

Second Advisor Klaus H. Kaestner

Keywords Circadian Clock, Epigenomics, Liver, Metabolism, Nuclear Receptor, Transcriptional Regulation

Subject Categories Genetics | Pharmacology | Pharmacy and Pharmaceutical Sciences

This dissertation is available at ScholarlyCommons: https://repository.upenn.edu/edissertations/2665 CIRCADIAN REGULATION OF HEPATIC METABOLISM

BY NUCLEAR RECEPTORS REV-ERB AND ROR

Yuxiang Zhang

A DISSERTATION

Pharmacology

Presented to the Faculties of the University of Pennsylvania

In Partial Fulfillment of the Requirements for the

Degree of Doctor of Philosophy

2017

Supervisor of Dissertation

______

Mitchell A. Lazar, M.D., Ph.D.

Willard and Rhoda Ware Professor in Diabetes and Metabolic Diseases

Graduate Group Chairperson

______

Julie A. Blendy, Ph.D.

Professor of Pharmacology

Dissertation Committee

Klaus H. Kaestner, Ph.D., Thomas and Evelyn Suor Butterworth Professor in Genetics

Garret A. FitzGerald, M.D., Robert L. McNeil, Jr., Professor in Translational Medicine and

Therapeutics

Amita Sehgal, Ph.D., John Herr Musser Professor of Neuroscience

To my wife Sheng Zhang, my son Stephen J. Zhang and our parents.

ii ACKNOWLEDGMENT

I would like to thank my mentor Dr. Mitchell A. Lazar, first and foremost, for his guidance and support. His insightful advice always will leads to a deeper scientific thinking, and exciting topics to explore. He has also created a vibrant and collaborative lab environment, in which everyone is happy to share thoughts, reagents and protocol. I would like to thank him and every members of the Lazar lab for the mutual help and encouragement.

I would also like to thank my committee members: Dr. Klaus H. Kaestner who serves as chair, Dr. Garret A. FitzGerald and Dr. Amita Seghal for their advice, constructive criticism and time to join my committee meeting.

I would also like to Dr. Peter F. Davies for his guidance during my Master program study at UPenn. Davies Lab was the first lab I worked for in UPenn and he taught me and helped my family a lot when I first came to U.S. He also encouraged me to pursue a career in science.

I would like to acknowledge the Pharmacology Graduate Group (PGG). I particularly want to thank Dr. Julie A. Blendy for their advice and support, Sarah

Squire, PGG coordinator, for her coordination and organization of the graduate group activities, and support and help during my thesis studying process.

iii I am also grateful to all the people who helped me with the work presented in this dissertation. I would like to thank Dan Feng, Zheng Sun for their helpful advice and experimental guidance when I rotated and started my project in Lazar lab. I would like to thank Bin Fang and Manashree for the bioinformatics analysis. I would like to thank David J. Steger, Matthew J. Emmett, Romeo Papazyan, Sean

M. Armour, Jarrett R. Remsberg, Jennifer Jager, Dongyin Guan, Yong Hoon Kim,

Raymond E. Soccio, Wenxiang Hu, Lindsey Peed, and Erika Briggs for their useful advice and technical assistance, and staffs in the Next-Generation

Sequencing Core at University of Pennsylvania Perelman School of Medicine.

I am indebted to my family for all the love and support while I worked on this dissertation and always. My wife Sheng Zhang has assisted me in every aspect of my life and taken good care of my son Stephen J. Zhang. This work could not be made possible without her efforts. I would also like to thank our parents who supported us and inspired us to pursue our dream.

iv ABSTRACT

CIRCADIAN REGULATION OF HEPATIC METABOLISM

BY NUCLEAR RECEPTORS REV-ERB AND ROR

Yuxiang Zhang

Mitchell A. Lazar

Circadian and metabolic physiology are intricately intertwined. Although the liver clock is entrained by the central clock, it also directly controls metabolic gene expression. Hormone responsive nuclear factors are hypothesized to be the major clock components that regulate metabolism, which include Rev-erbs and

RORs. In this study, we explored the mechanism of regulation of liver circadian gene expression by Rev-erbs and RORs. We found that Rev-erbα modulates the clock and metabolism by different genomic mechanisms. Clock control requires

Rev-erbα to bind directly to the genome at its cognate sites, where it competes with activating ROR TFs. By contrast, Rev-erbα regulates metabolic genes primarily by recruiting the HDAC3 corepressor to sites to which it is tethered by cell type-specific transcription factors. Thus, direct competition between Rev- erbα and ROR TFs provides a universal mechanism for self-sustained control of molecular clock across all tissues, whereas Rev-erbα utilizes lineage-determining factors to convey a tissue-specific epigenomic rhythm that regulates metabolism tailored to the specific need of that tissue.

v In addition, we also investigate the circadian regulation of hepatic metabolism by nuclear receptors RORα and RORγ. We discovered that hepatic depletion of

RORs increased expressions of genes involved in lipogenesis as well as the hepatic triglyceride levels, specifically at ZT22 (5 AM) and during feeding, but not at ZT10 (5 PM) nor during fasting. GRO-seq analysis suggested that regulation of lipid metabolism by RORs was mediated by SREBP1-c. Indeed, ROR depletion significantly up-regulated nuclear SREBP1-c protein level specifically at

ZT22. Overall, our study reveals a time-specific regulation of liver lipid metabolism by RORs, and suggests a potential role of chronotherapy targeting circadian factors in the treatment of metabolic disorders.

vi TABLE OF CONTENTS

ACKNOWLEDGMENT ...... III

ABSTRACT ...... V

TABLE OF CONTENTS ...... VII

LIST OF TABLES ...... XI

LIST OF ILLUSTRATIONS ...... XII

CHAPTER 1 BACKGROUND AND INTRODUCTION ...... 1

1.1 Nuclear Receptors Rev-erbs and RORs Link Circadian to Metabolism ... 2

1.1a Nuclear Receptors in Core Molecular Clock System ...... 2

1.1b Expression and Functions of Rev-erbs and RORs ...... 3

1.1c Post-Translational Modification of Rev-erbs and RORs ...... 4

1.2 Rev-erbs and RORs as Regulators of Central Clock and Brain Function 5

1.3 Role of Rev-erbs and RORs in Peripheral Tissues ...... 6

1.3a Rev-erbs and RORs as Regulators of Liver Metabolism ...... 7

1.3b Rev-erbs and RORs as Regulators of Adipogenesis and Thermogenesis 9

vii 1.3c Rev-erbs and RORs as Regulators of Immune and Inflammation System

...... 10

1.3d Rev-erbs and RORs as Regulator of Muscle Metabolism ...... 11

1.3f Rev-erbs and RORs in Other Tissues: Pancreas, Cancer ...... 12

1.4 Endogenous and Synthetic Ligands for Rev-erbs and RORs ...... 13

1.5 Hypothesis and Objectives ...... 13

CHAPTER 2 MATERIALS AND METHODS ...... 15

CHAPTER 3 DISCRETE FUNCTIONS OF NUCLEAR RECEPTOR REV-

ERBα COUPLE METABOLISM TO THE CLOCK ...... 33

3.1 Abstract ...... 34

3.2 Introduction ...... 34

3.3 Results ...... 36

3.3a Rev-erbα Represses Clock Genes by Competing with RORα at Its

Cognate Sites ...... 36

3.3b Rev-erbα Binds to the Genome Using Both DBD-Dependent and DBD-

Independent Mechanisms ...... 38

3.3c HNF6 Motif is Required for Rev-erbα DBD-Independent Binding ...... 41

3.3d DBD-Independent Rev-erbα Sites Regulate Metabolic Genes in Liver ... 42

viii 3.4 Discussion and Future Directions ...... 45

CHAPTER 4 REDUNDANT REGULATION OF CIRCADIAN HEPATIC

METABOLISM BY RORα AND RORγ ...... 72

4.1 Abstract ...... 73

4.2 Introduction ...... 74

4.3 Results ...... 76

4.3a RORα and RORγ Redundantly Regulate Clock Genes and Metabolic

Genes ...... 76

4.3b RORα and RORγ Liver Specific Deletion Leads to Accumulated

Triglycerides Level and Dysregulation of Lipid Metabolic Genes at ZT22 ...... 77

4.3c RORs Regulation Is Specific to Ad Lib Fed Mice But Not Fasted Mice .... 78

4.3d RORs Regulate Liver Lipid Metabolism Through SREBP pathway ...... 78

4.4 Discussion and Future Directions ...... 80

CHAPTER 5 FUTURE DIRECTIONS ...... 93

5.1 Summary ...... 94

5.2 Future Directions ...... 95

5.2a Overexpression of Rev-erbα and Its DNA Binding Domain In Vivo by AAV

Vectors ...... 95

ix 5.2b Manipulating Cellular Heme Level by Alas1 Deletion in Mice ...... 96

5.2c Mechanisms of Circadian Misalignment Induced Hepatic Steatosis in Mice

...... 97

BIBLIOGRAPHY ...... 101

x LIST OF TABLES

Table 2.1 List of Primers for Gene Expression

Table 2.2 List of Primers of ChIP-PCR

xi LIST OF ILLUSTRATIONS

Figure 3.1 Overlap of Rev-erbα cistrome in various tissues are limited

Figure 3.2 HDAC3 does not dampen circadian rhythmicity of clock genes

Figure 3.3 Common direct targets of Rev-erbα and RORs are enriched for the clock genes

Figure 3.4 Rev-erbα and RORα compete for binding at highly circadian genes including core components of the molecular clock

Figure 3.5 Rev-erbα ChIP-exo peaks are enriched for HNF6 motif.

Figure 3.6 Conditional Rev-erbα allele deletion results in an in-frame DNA binding domain mutant (DBDm).

Figure 3.7 Rev-erbα binds to the genome using both DBD-dependent and DBD- independent mechanism.

Figure 3.8 Rev-erbα and HNF6 co-localize at the functional DBD-independent sites.

Figure 3.9 HDAC3 binding at the DBD-independent sites is attenuated in the

Rev-erbs depleted mice while not affected in the DBDm.

Figure 3.10 SNP associated strain-specific occupancy suggests HNF6-mediated binding of Rev-erbα

Figure 3.11 Binding strength of Rev-erbα in C57BL/6J and 129S1/SvlmJ

Figure 3.12 HNF6-tethered Rev-erbα binding near metabolic genes is decreased in HNF6-KO liver xii Figure 3.13 Sites with reduced Rev-erbα binding in the HNF6 KO are enriched for HNF6 motif

Figure 3.14 Genes regulated exclusively in Rev-erbα KO are enriched for metabolic process

Figure 3.15 DBD-independent Rev-erbα sites regulate metabolic genes in liver

Figure 3.16 Liver-specific depletion of HNF6 in adult mice causes hepatosteatosis

Figure 3.17 HNF6 and Rev-erbα regulate hepatic lipid metabolism through common pathways

Figure 3.18 Model depicting distinct mechanisms of Rev-erbα regulating clock and metabolic genes in liver

Figure 4.1 RORα and RORγ are redundant in regulating expression of clock genes and metabolic genes

Figure 4.2 Transcriptome analysis of RORs single and double mutant mice liver

Figure 4.3 Comparison of liver RORα and RORγ cistromes

Figure 4.4 RORs liver specific depletion leads to accumulated triglycerides after

6 week high fat diet

Figure 4.5 RORs regulation of lipid metabolic genes are restricted around ZT 22

Figure 4.6 RORs regulation of lipid metabolic genes are restricted to fed mice but not to fasted mice at ZT22

Figure 4.7 Differentially expressed nascent transcripts in RORs mutant mice liver

xiii Figure 4.8 Up-regulated and down-regulated eRNAs were enriched in different phase

Figure 4.9 RORs repress nuclear Srebp1c level by activating Insig2 expression around ZT22

Figure 5.1 Schematic of Rev-erbα Over-expression in mouse liver by AAV vectors

Figure 5.2 Ultradian mice model results in hepatic steatosis

xiv

CHAPTER 1

Background and Introduction

1 1.1 Nuclear Receptors Rev-erbs and RORs Link Circadian to Metabolism

Circadian clock machinery, which exists almost in every living organisms and cells, is an adaption to the 24-hour oscillation of changes in internal and environmental factors, including light/dark cycles, time of food availability, temperature, activity/rest cycles, and so on. The master clock in mammals is located in the suprachiasmatic nucleus (SCN) of the hypothalamus. Many physiological processes are subject to circadian oscillations including lipid and carbohydrate metabolism, hormones (insulin, leptin, and cortisol) secretion, blood pressure, coagulation factors and feeding behaviors. Furthermore, disturbance of the circadian rhythms may have clinical implications in the development of dyslipidemia, obesity and cardiovascular events. Indeed, circadian rhythms in both insulin secretion and sensitivity are altered and circadian expressed genes are blunted in obese and type 2 diabetic patients (Boden et al., 1999). In addition, sleep restriction, shift working and night eating conditions have been associated with metabolic syndrome (Karlsson et al., 2001), further supporting the link between circadian oscillations and metabolism.

1.1a Nuclear Receptors in Core Molecular Clock System

On molecular level, the cellular circadian clock is maintained by a complex circuitry of transcriptional/post-translational regulatory loops. This loop starts from the positive limb Bmal1 and Clock, which activate transcription of Per and Cry, as well as the nuclear receptors Rev-erb and ROR. Post-translational regulators Per

2 and Cry, could repress Bmal1 and Clock activity in feedback. On the other hand,

Rev-erb and ROR are transcriptional repressor and activator respectively that regulate Bmal1 and Clock transcription, serving as the other arm of the feedback loop circuit. It has been demonstrated that all isoforms of Rev-erbs and RORs could bind to the RORE motif in the promoter of Bmal1 and regulate Bmal1 expression (Guillaumond et al., 2005). The competition between the two factors also leads to a transcriptional activity shift that could contribute to the robustness of Bmal1 transcriptional oscillation (Guillaumond et al., 2005). In addition, there is also an RORE near the transcriptional start site of Rev-erbα. Thus, Rev-erbα could also be regulated by Rev-erbs and RORs, which adds another layer of complexity of feedback circuit (Adelmant et al., 1996; Raspe et al., 2002).

1.1b Expression and Functions of Rev-erbs and RORs

Rev-erbs comprise two members, Rev-erbα and Rev-erbβ, which display rhythmic expression in many mammalian tissues such as the liver, white and brown adipose tissue, muscle, brain, and cell types such as endothelial cells

(ECs), vascular smooth muscle cells and macrophages (Lazar et al., 1989;

Dumas et al., 1994; Migita et al., 2004). Recent studies on the simultaneous disruption of Rev-erbα and β have revealed a redundant function in the molecular clock (Bugge et al., 2012; Cho et al., 2012). ROR has three isoforms, RORα,

RORβ, and RORγ. RORα and RORγ are expressed in a number of tissues

(Forman et al., 1994), whereas RORβ mRNA is restricted to the central nervous

3 system. RORα does not show clear circadian variation in the tissue it is expressed, while RORγ exhibits an oscillatory expression pattern in liver, brown adipose tissue, and kidney, but not in skeletal muscle and thymus (Guillaumond et al., 2005; Jetten and Joo, 2006). In general, nuclear receptors are composed of N-terminal activation domain (AF1), a DNA-binding domain (DBD), a central hinge region, and a C-terminal ligand-binding domain (LBD) with another ligand- dependent activation function domain (AF2) observed in several nuclear receptors, including RORs. However, Rev-erbs lack the AF2 ligand-dependent transactivation domain, and thus constitutively repress transcription through recruitment of nuclear receptor co-repressor (NCoR) and HDAC3 (Yin and Lazar,

2005), while RORs mainly activate transcription through recruitment of coactivators SRC2 or Med1 (Atkins et al., 1999; Stashi et al., 2014). Both Rev-erb and ROR bind as a monomer to the RORE consisting of a 6bp core motif

(A/G)GGTCA flanked by an A/T-rich 5’ sequence, and Rev-erb can also bind cooperatively as a homodimer to a RevDR2 element composed of a direct repeat of the core motif spaced by two nucleotides (Harding and Lazar, 1995).

1.1c Post-Translational Modification of Rev-erbs and RORs

Besides their oscillation in mRNA abundance, Rev-erbs could also be regulated on the post-translational level. Rev-erbα can be phosphorylated by GSK3b at its

N-terminal, and phosphorylation protects Rev-erbα from ubiquitin-mediated degradation. Lithium is a potent inhibitor of GSK3b activity, and thus promotes

4 Rev-erbα degradation and activates Bmal1 transcription (Yin et al., 2006; Yin et al., 2010). More recently it was found that cyclin-dependent kinase 1(Cdk1) could also phosphorylate Rev-erbα and target it for ubiquitination and subsequent degradation by the F-box protein Fbxw7. Moreover, targeted hepatic disruption of

Fbxw7 altered circadian expression of core clock genes and perturbed whole- body lipid and glucose levels (Zhao et al., 2016).

RORα could also be regulated by translational phosphorylation by protein kinase

C (PKC), which inhibit RORα transcriptional activity (Duplus et al., 2008). On the other hand, ROR could also be regulated by small ubiquitin-like modifiers

(SUMOs). SUMylation of RORα by both SUMO-1 and SUMO-2 on the 240th lysine residue at the hinge region of human proein, which enhance its transcriptional activity. PIAS family members increased SUMOylation of RORα, whereas SENP2 specifically removed SUMO from RORα (Hwang et al., 2009).

1.2 Rev-erbs and RORs as Regulators of Central Clock and Brain Function

Numerous studies have investigated the role of Rev-erbs and RORs in the regulation of central clock. Ueli Schibler and collaborators first evidenced a critical role of Rev-erbα in the clock machinery in vivo showing that Rev-erbα deficient mice display markedly altered circadian rhythms and a shorter period length (~0.5h) than wild-type mice (Preitner et al., 2002). In addition, heterozygous and homozygous staggerer mutant mice, which are deficient in

5 RORα, had shorter average locomotor activities but still sustain rhythmicity (Sato et al., 2004; Akashi and Takumi, 2005). Nonetheless, staggerer mutant also showed locomotor difficulties owing to cerebellar abnormalities, and had less overall activity of compared to the wild-type animals, which might interfere the interpretation of circadian phenotypes of staggerer mice (Akashi and Takumi,

2005).

In addition to its regulation of core clock, Rev-erbα could also regulate dopaminergic and hippocampus-dependent behaviors. Rev-erbα null mice were deficient in short-term, long-term, and contextual memories and also showed defective hippocampal function (Jager et al., 2014). Rev-erbα could also regulate midbrain dopamine production and mood-related behavior in mice, potential by repressing tyrosine hydroxylase gene transcription via competition with nuclear receptor-related 1 protein (NURR1), which is another nuclear receptor crucial for dopaminergic neuronal function (Chung et al., 2014).

1.3 Role of Rev-erbs and RORs in Peripheral Tissues

To function as a pacemaker and synchronizer for the peripheral clocks, the intrinsic timekeeping signal from the central clock has to be transmitted to the peripheral tissues via hormones, nervous system or behaviors, such as feeding.

The peripheral clock components, particularly hormone and nutrients responsive nuclear receptors, Rev-erb and ROR, not only control clock genes to maintain

6 the robustness and integrity of the clock in each tissue, but also control a number of genes as clock output in different tissues. Their roles in tissue specific function have been studied extensively.

1.3a Rev-erbs and RORs as Regulators of Liver Metabolism

Studies have shown that Rev-erbs regulate whole body metabolism via the control of cholesterol and bile acid metabolism in liver. In addition, Rev-erbα- deficient mice displayed elevated serum and liver mRNA levels of apoC-III and increased serum VLDL triglycerides (Coste and Rodriguez, 2002; Raspe et al.,

2002) through RORE in the apoC-III gene promoter. In rats, Rev-erbα has also been shown to repress apoA-1 gene expression and plasma HDL-cholesterol levels, which also has an RORE in its promoter. Both apoA-1 and apoC-III are also regulated by RORα, illustrating the cross talk of these nuclear receptors on common target genes.

The genome wide binding sites, or cistrome, of Rev-erbα has been defined in the liver using the chromatin-immunopreciptation followed by high-throughput sequencing (ChIP-seq). It binds to thousands of genomic locations and repress their transcription at ZT10, when its expression is maximal. Those binding sites are mostly gone at ZT22 due to the very low level of Rev-erbα protein. Pathway analysis of those binding sites revealed that fatty acid biosynthesis pathways are enriched near those binding sites, supported by the hepatic steatosis phenotype

7 in the Rev-erbα null mice (Bugge et al., 2012). Besides, similarly to the redundant function in the core clock, Rev-erbα and Rev-erbβ are also redundant in the maintenance of systematic and hepatic lipid homeostasis, demonstrated by the severer dysregulation of serum and hepatic triglycerides level in the mice deficient in both genes (Bugge et al., 2012; Cho et al., 2012).

RORα and RORγ also regulate liver lipid, bile acid, and glucose pathway, but often through different pathways compared to Rev-erb regulation. It has been reported that RORγ could regulate rhythmic expression of several lipid metabolic genes, including the Insig2a, Elovl3 and Cyp8b1, by enhancing their expression at ZT20-4 (Takeda et al., 2014b). In addition, both RORγ and RORα deficient mice exhibit decreased gluconeogenesis and improved insulin sensitivity and glucose tolerance during daytime specifically (Takeda et al., 2014a; Kadiri et al.,

2015). However, reports on RORα regulation of hepatic triglyceride are not consistent. While some studies report RORα deficient staggerer mice had a decreased serum and liver triglycerides and lipogenic gene expression (Lau et al.,

2008; Kang et al., 2011), some others reported it had increased liver triglyceride accumulation and lipogenic gene expression (Wada et al., 2008). The differences in the experimental settings such as euthanizing time or feeding status at the time of euthanizing might be the factors that caused that inconsistency.

1.3b Rev-erbs and RORs as Regulators of Adipogenesis and

Thermogenesis

Rev-erbα mRNA is induced during adipocyte differentiation of 3T3-L1 cells, and promote adipocytes differentiation (Chawla and Lazar, 1993; Fontaine et al.,

2003; Wang and Lazar, 2008). However, mice lacking Rev-erbα has normal adipose tissue mass, if not more (Chomez et al., 2000; Delezie et al., 2012), possibly due to the compensation effects from other redundant factors such as

Rev-erbβ. Interestingly, in BAT, Rev-erbα controls the circadian thermogenesis process. It is rhythmically expressed, peaking at ZT10 in mice, which is in the opposite phase as body temperature rhythm. Mice lakcing Rev-erbα were shown to have an attenuated nadir in temperature oscillation owing to derepression of uncoupling protein 1 (UCP1), which is a direct target of Rev-erbα in BAT

(Gerhart-Hines et al., 2013). The role of Rev-erbβ has not been explored in the

BAT.

RORα deficient mice are resistant to age-induced WAT and BAT hypertrophy and exhibited a significantly lower total body fat index compared with WT littermates, while RORγ deficient mice developed adipose hypertrophy to a similar extend as WT littermates (Kang et al., 2011). It is further demonstrated that those RORα deficient mice had increased Ucp1 expression in both brown

9 adipose and subcutaneous white adipose tissue, (Lau et al., 2015), as well as decreased glyceroneogenesis (Kadiri et al., 2015).

1.3c Rev-erbs and RORs as Regulators of Immune and Inflammation

System

The role of Rev-erbs in the immune system was mainly explored in macrophages.

It was shown to mediate the circadian gating of the LPS-induced endotoxic response (Gibbs et al., 2012). Notably, recent cistrome profiling of Rev-erbα and

Rev-erbβ and global run-on sequencing that quantifies nascent 5’ ends in macrophage revealed that Rev-erbα repress macrophage genes by repressing eRNA transcription at a distance (Lam et al., 2013). Further, it could block pro- inflammatory signals in macrophages by repressing the Toll-like receptor 4

(TLR4) gene, which triggers the innate immune response to lipopolysaccharide

(LPS) associated with Gram-negative bacteria (Fontaine et al., 2008). It could also directly repress Ccl2 expression as well as CCL2-activated signals ERK and p38 in peritoneal macrophages (Sato et al., 2014).

Interleukin 17 (IL-17) producing T helper 17 (Th17) cells are a major driver of autoimmune diseases and its cell lineage is dependent on RORγt. IL-17A expression is directly regulated by RORs through their interaction with ROREs in the Il17 promoter (Yang et al., 2008). In addition, RORγ-deficient mice lack lymph nodes and peyer’s patches indicating that it is essential for lymph node

10 development (Kurebayashi et al., 2000; Sun et al., 2000), and displayed a diminished susceptibility to auto-immune and inflammatory diseases such as allergen-induced lung inflammation and collagen-induced arthritis (Jaradat et al.,

2006; Tilley et al., 2007).

1.3d Rev-erbs and RORs as Regulator of Muscle Metabolism

Rev-erbα and Rev-erbβ are required for muscle cell differentiation and regulate genes involved in lipid absorption in C2C12 myocytes (Downes et al., 1995;

Ramakrishnan et al., 2005). In addition, overexpression of Rev-erbα in C2C12 myocytes was shown to increase mitochondrial content and activity by modulating the AMP-activated protein kinase (AMPK) pathway. Notably, loss of

Rev-erbα function in mice skeletal muscle was shown to reduce mitochondrial content and function, leading to impaired exercise capacity (Woldt et al., 2013). It is shown that Rev-erbα could inhibit autophagy of mitochondria in this tissue and thus increase mitochondrial content and oxidative capacity of myocytes, by directly repress the autophagy and mitophagy genes.

A study using the exogenous dominant negative RORα expression in skeletal muscle C2C12 cells demonstrated that RORs directly activated muscle genes involved in lipid homeostasis such as Srebp1, Fasn, and Scd1 (Lau et al., 2004).

Recently, evidence was provided for a role of RORα in PI3K-Akt signaling. Akt1/2 expression was up-regulated in skeletal muscle of RORαsg/sg mice and this

11 correlated with an increase in the level of insulin-induced Akt phosphorylation,

Glut4 expression, and glucose uptake (Lau et al., 2011). RORγ has been reported to regulate the expression of genes associated with lipid and carbohydrate metabolism as well as the production of reactive oxygen species

(Raichur et al., 2007).

1.3f Rev-erbs and RORs in Other Tissues: Pancreas, Cancer

Rev-erbs and RORs have also been shown to have a role in pancreas function.

Rev-erbα mRNA is expressed in islets and oscillates with a circadian rhythm similar to that of liver. Intriguingly, Rev-erbα was found to regulate lipogenic genes in mouse islets (Vieira et al., 2012). Islet isolated at the time of peak Rev- erbα expression have higher levels of glucose-stimulated insulin secretion, and

Rev-erbα regulates gene expression as well as insulin processing, exocytosis, and proliferation in primary β cell and β cell lines (Vieira et al., 2012). Rev-erbβ is also expressed in both α cells and β cells although its role in islets remains to be investigated (Chuang et al., 2008). All ROR members are expressed in rat pancreas islets, with the highest expression in α-cells (Muhlbauer et al., 2013).

RORα and RORγ, but not RORβ have circadian mRNA expression in rat pancreas, but the their functional role in islet have not been investigated. Rev- erbs and RORs could also be detected in other tissues including cancer, and has been involved cellular stress response and cytotoxicity processes in cancer (Zhu et al., 2006; De Mei et al., 2015).

1.4 Endogenous and Synthetic Ligands for Rev-erbs and RORs

The original orphan nuclear receptors Rev-erbs and RORs have been adopted recently, with heme identified as endogenous ligands for Rev-erbs, and cholesterol and cholesterol derivatives as the putative ligands for RORs.

Melatonin was initially suggested as a ligand for RORs but later turned out not directly activate RORs (Slominski et al., 2016). Besides, several synthetic agonists have been identified for Rev-erbs and RORs. Rev-erb agonists have been shown to disrupt rhythmic wheel-running behavior and circadian hypothalamic gene expression in mice, and can also alter whole-body metabolism (Solt et al., 2012), while ROR inverse agonists inhibited the differentiation of splenocytes that were cultured under conditions to produce

TH17 cells and this inhibition was associated with inhibited expression of several cytokines, including IL17A, IL17F, IL21 and IL22 (Solt et al., 2011). However, it is still not clear whether the phenotypes caused by the compounds treatment are through targeting Rev-erbs and RORs, or off-target effects. Future studies for those compounds using Rev-erbs or RORs null mice as negative control are needed to demonstrate the functional specificity of those compounds.

1.5 Hypothesis and Objectives

In mammals, tissue specific metabolic processes in peripheral organs are synchronized with robust circadian rhythms to coordinated with the daily cycles of

13 light and nutrient availability. Circadian disruption has been associated with various diseases including metabolic dysfunction and cardiovascular disorders.

Nuclear receptors Rev-erbs and RORs are hypothesized to be the link connecting the circadian clock with metabolism. In this dissertation, we explore the role of nuclear receptors Rev-erbs and RORs in the circadian regulation of hepatic metabolism. First we hypothesize that Rev-erbα and RORα compete with each other to control the clock genes and circadian genes transcription. To address this hypothesis, in Chapter 3, we profile Rev-erbα and RORα in mouse liver genome at different times of the day and revealed that Rev-erb compete with ROR at the sites where it directly binds to DNA, but they could also be tethered to the chromatin by lineage determining factors. It is found that Rev-erb controls core clock gene expression through the direct binding sites, and regulate metabolic processes through the tethering sites. In Chapter 4, we examine how

RORs regulate the liver circadian metabolism. Chapter 5 summarizes the work and discussed the future directions.

CHAPTER 2

Materials and Methods

Animal studies

Male wild type inbred C57BL/6 and 129S1/SvlmJ mice were purchased from

Jackson Labs. The Rev-erbα knockout mice were obtained from B. Vennström and backcrossed seven or more generations with C57BL/6 mice (9). The

HDAC3fl/fl mice were described previously (9). Mice carrying floxed alleles at

Rev-erbα or Rev-erbβ loci were obtained from MCI/ICS (Mouse Clinical

Institute—Institut Clinique de la Souris, Illkirch-Graffenstaden; http://www.ics- mci.fr/), as reported previously (12, 23). RORα floxed mice were obtained from

MCI/ICS, and RORγ floxed mice were obtained from Jackson Laboratory. The

HNF6fl/fl mice were on a C57BL/6 background and have been described previously (Zhang et al., 2009). Mice were housed on a temperature-controlled specific-pathogen free facility with 12:12-hour light–dark cycle (lights on at 07:00, lights off at 19:00).

Experiments were carried out on 12–16-week-old male mice. The Penn Vector

Core generated the AAV vectors (AAV8-Tbg-GFP and AAV8-Tbg-Cre). We injected each AAV vector intravenously at 1.5 × 1011 genome copies per mouse and characterized the mice at 3–4 weeks after AAV injection for chow diet fed mice and at 5 weeks after injection for HFD fed mice. HFD containing 60 kcal % fat was purchased from Research Diets Inc. (D12492i). We harvested tissues at specified time without restricting the mice to food or water.

16 We performed glucose tolerance tests at 3 weeks after AAV injection, and after fasting for 16 hours, beginning at 5 p.m. We performed insulin tolerance tests at

4 weeks after AAV injection, and after fasting for 6 hours, beginning at 9 a.m. For the glucose tolerance test, we injected glucose intraperitoneally at 1.5 g per kg body weight. For the insulin tolerance test, we intraperitoneally injected insulin

(Novolin R) into the mice at 0.6 U per kg body weight. We measured blood glucose concentration using a glucometer (OneTouch) by sampling from the tail.

All animal studies were performed with an approved protocol from the University of Pennsylvania Perelman School of Medicine Institutional Animal Care and Use

Committee.

ChIP

Mouse liver, brain (ventral tegmental area), and epididymal white adipose tissue

(eWAT) were harvested, minced and cross-linked in 1% formaldehyde for 20min, followed by quenching with 1/20 volume of 2.5M glycine solution for 5 min, and two washes with 1×PBS. Nuclear extracts were prepared by Dounce homogenization in ChIP buffer (50mM Tris-HCl pH 7.5, 140mM NaCl, 1mM

EDTA, 1% Triton X-100, 0.1% NaDOC). Chromatin fragmentation was performed by sonication in lysis buffer (50mM Tris-HCL ph 8.0, 0.1%SDS, 10mM EDTA), using the Bioruptor (Diagenode). Proteins were immune-precipitated in ChIP buffer, and crosslinking was reversed overnight at 65°C in SDS buffer (50mM

Tris-HCL, 10mM EDTA, 1% SDS, pH 8), and DNA isolated using

17 phenol/chloroform/isoamyl alcohol. Precipitated DNA was analyzed by quantitative PCR or high-throughput sequencing.

ChIP-Re-ChIP

ChIP-Re-ChIP was performed in essentially the same way as ChIP, except that the first elution was carried out in a reducing elution buffer (1% SDS, 10 mM

DTT), shaking at 800 rpm for 15 min at 65°C. The Rev-erbα ChIP eluate was then diluted in ChIP dilution buffer with 0.1 mg/ml BSA and 2ug/ml lamda DNA-

HindIII digest, divided into two, and subjected to HNF6, Rev-erbα, or IgG ChIP.

Quantitative PCR

Quantitative PCR was performed with Power SYBR Green PCR Mastermix and the PRISM 7500 instrument (Applied Biosystems), and analysis was performed by the standard curve method. Primers for gene expression and ChIP-PCR used were listed in Table 2.1 and Table 2.2.

ChIP-seq

ChIP experiments were performed independently on liver samples from individual mice harvested at indicated times. DNA was amplified according to ChIP

Sequencing Sample Preparation Guide provided by Illumina, using adaptor oligo and primers from Illumina, enzymes from New England Biolabs (NEB) and PCR

Purification Kit and MinElute Kit from QIAGEN. Deep sequencing was performed

18 by the Functional Genomics Core of the Penn Diabetes Endocrinology Research

Center using the Illumina HiSeq2000, and sequences were obtained using the

Solexa Analysis Pipeline.

ChIP-seq data processing

Sequencing reads of biological replicates were pooled and aligned to the mm9 genome using bowtie (Langmead et al., 2009), followed by peak calling. Genome browser tracks of ChIP-seq data were generated using Homer v4.7 (Heinz et al.,

2010) and visualized in IGV (Robinson et al., 2011). For the comparison of Rev- erbα cistromes in multiple tissues, and HNF6 and RORs cistromes in liver, peaks higher than 1 read per million (rpm) were used, and motifs mining was performed using Homer in 200bp regions surrounding the peak centers. RORa and RORg liver combined cistromes were determined by pooling their peaks found at ZT10 and ZT22. To eliminate potential bias from different reading depths, 20 million reads were selected by random sampling from Rev-erbα ChIP-seq data in WT and DBDm and subjected to downstream analysis. Rev-erbα ChIP-seq in aKO mice was also analyzed using the same number of reads to eliminate false positive binding. Similarly, 10 million randomly sampled reads from HDAC3

ChIP-seq in WT, Rev-erb DBDm, and Rev-erbαKO/βKD mouse livers were used for the peak height quantification. Among Rev-erbα peaks that are great than

1rpm and at least 3 times stronger than their counterpart in Rev-erbα KO, DBD- dependent Rev-erbα sites were selected using the cutoff: tag count fold change

19 WT/DBDm >3 or tag count difference WT-DBDm > 3 rpm; DBD-independent Rev- erbα sites were selected using cutoff: tag count fold change WT/DBDm < 1.5 and

DBDm/WT < 1.5. DBD-dependent Rev-erbα sites with reduced HDAC3 binding in

DBDm liver (HDAC3 WT/DBDm > 1.5 and WT>1rpm) were defined as DBD- dependent Rev-erbα/HDAC3 sites. DBD-independent Rev-erbα sites with intact

HDAC3 binding in DBDm liver (HDAC3 WT/DBDm < 1.5 and WT>1rpm) were defined as DBD-independent Rev-erbα/HDAC3 sites. All ChIP-seq peaks were annotated by Homer using the nearest mapping within 50kb of gene TSSs. The functional Rev-erbα binding sites were defined as binding sites with up-regulated eRNA (tag count KO/WT fold change > 2.5) as determined by GRO-seq in wildtype and Rev-erbα KO liver at ZT10 (Fang et al., 2014). ChIP-seq data are available in GEO (GSE67973, GSE83791).

SNP mediated strain-specific binding

Rev-erbα ChIP-seq reads from 129 mice were aligned to the mm9 reference genome using GSNAP (Wu and Nacu, 2010), allowing mismatches at ~5.4 million SNPs specific to 129 genome (Keane et al., 2011). To minimize potential bias introduced by different IP efficiency in B6 and 129 mice, Rev-erbα peaks identified in two strains were pooled and their peak heights were quantile normalized prior to the analysis of strain-specific binding. Rev-erbα peaks where

Fimo (Keane et al., 2011) predicted HNF6 motif score was altered by the SNP,

20 from one strain to the other, were selected, and their log2 fold change of peak heights in two strains were calculated.

ChIP-exo

The ChIP-exo portion of the protocol was performed as described (Rhee and

Pugh, 2011) with modifications making the assay compatible with the Illumina sequencing platform. Briefly, it was performed in essentially the same way as

ChIP-seq, except that after the end repair, adapter ligation and nick repair, the

DNA was digested by the lambda (M0262, NEB) and RecJf exonucleases

(M0264, NEB), while it is still on the agarose beads. Then, the DNA samples were eluted from the beads and are analyzed by high-throughput sequencing.

ChIP-exo data processing

Rev-erbα ChIP-exo reads from two biological replicates were pooled and aligned to the mouse genome (mm9) using bowtie (Langmead et al., 2009) with parameters ‘-n 1 and -m 1’. Genome browser track of aligned reads were generated using Homer with the parameter -fragLength 10 and visualized in IGV.

ChIP-exo peak call on the forward and reverse strands was performed using

MACE-1.0 with default parameter (Wang et al., 2014). The most prevalent ChIP- exo peaks were selected from within published Rev-erbα ChIP-seq peaks (Fang et al., 2014). Two approximate exo-peaks on different DNA strands (a peak pair) with distance from -50bp to +150bp (center of negative strand peak – center of

21 positive strand peak), was found using the windowBed feature of bedtools-2.21

(Quinlan and Hall, 2010). Peak pairs with a distance less than +/-2 bp from the most prevalent distance of all peak pairs were selected. Motif search was performed in 30bp regions surrounding the peak pair centers using Homer. HNF6

ChIP-exo (Wang et al., 2014) data were reprocessed using the same protocol.

ChIP-exo data are available in GEO (GSE67973).

Global Run-on Sequencing

Nuclei Extraction. Mouse liver was harvested at indicated time and washed with cold swelling buffer (10 mM Tris pH 7.5, 2 mM MgCl2, 3 mM CaCl2, 2 U/ml

Superase-In). Nuclear extract was prepared by dounce homogenization in cold swelling buffer and filtered using the cell strainer (100 µm, BD Biosciences).

Lysate was centrifuged at 400 g for 10 min, then resuspended in lysis buffer

(swelling buffer with 10% glycerol and 1% Igepal) and incubated on ice for 5 min.

Nuclei were washed twice with lysis buffer, and resuspended at the concentration of 10x107 nuclei/ml in freezing buffer (50 mM Tris pH 8.3, 40% glycerol, 5 mM

7 MgCl2, 0.1 mM EDTA). For each library, run-on was performed on 4x10 nuclei.

Nuclear Run-On. Nuclei were mixed with an equal volume of run-on buffer (10 mM Tris pH 8.0, 5 mM MgCl2, 1 mM DTT, 300 mM KCl, 200 U/ml Superase-In, 1%

Sarkosyl, 500 µM ATP, GTP and Br-UTP, 2 µM CTP) and incubated for 5 min at

30°C. Nuclear RNA was extracted with Trizol (Invitrogen) and treated with DNase

22 (Ambion). RNA was hydrolyzed using fragmentation reagents (Ambion) for 13 min at 70°C and purified through a Micro Bio-Spin Column P-30 (Bio-Rad). RNA was treated with T4 polynucleotide kinase (New England Biolabs) for 1h at 37°C, then with an additional T4 polynucleotide kinase for 1h more, and inactivated for

5 min at 75°C.

NRO-RNA Immunoprecipitation. Anti-BrdU agarose beads (Santa Cruz) were rotated for 1h in blocking buffer (0.25x SSPE, 38 mM NaCl, 1 mM EDTA, 0.05%

Tween- 20, 0.1% PVP, and 0.1% BSA). Then NRO-RNA was rotated with beads for 1h, followed by washes twice in binding buffer (0.25x SSPE, 38 mM NaCl, 1 mM EDTA, 0.05% Tween-20), twice in low salt buffer (0.2x SSPE, 1 mM EDTA,

0.05% Tween-20), once in high salt buffer (0.2x SSPE, 1 mM EDTA, 0.05%

Tween-20, 138 mM NaCl), and twice in TET buffer (TE pH 7.4, 0.05% Tween-20).

BrdU-labeled RNA was eluted from the beads four times for 10 min with 100 µl elution buffer preheated to 42°C. RNA was precipitated by adding 300 mM NaCl,

1 µl of glycogen and 2.5 volumes of ethanol and incubated overnight at 20°C.

RNA was resuspended in water (with 1 U/µl Superase-In and 0.05% Tween-20), denatured for 3 min at 65°C, and treated with poly(A)-polymerase (New England

Biolabs) for 30 min at 37°C.

cDNA Library Preparation. Synthesis of cDNA from RNA was performed as described previously (Wang et al., 2011) using oNTI223 primer (5’ -/5Phos/GA

23 TCG TCG GAC TGT AGA ACT CT/idSp/CAA GCA GAA GAC GGC ATA CGA

TTT TTT TTT TTT TTT TTT TTV N-3’) where the ‘‘5Phos’’ indicates 50 phosphorylation, ‘‘idSp’’ indicates the abasic dSpacer furan, and ‘‘VN’’ indicates degenerate nucleotides. The reaction was treated with exonuclease I (Fermentas) for 15 min at 37°C, followed by 100 mM NaOH for 20 min at 98°C, and neutralized with 100 mM HCl. cDNA was denatured at 70°C for 3 min, and run on

10% TBE-urea gel, then products from 105-400 nucleotides size were excised and eluted from shredded gel pieces for 4h in TE + 0.1% Tween and precipitated by adding 1 ml of glycogen, 300 mM NaCl and 2 volumes of ethanol and incubated overnight at −20°C. cDNA was resuspended into circularization reaction and circularized with CircLigase (Epicenter) for 1 hr at 60°C, denatured for 10 min at 80°C, and relinearized by adding relinearization mix (25 mM KCl,

500 mM DTT) and APE I (15U; New England Biolabs). Linearized DNA was amplified by PCR using Phusion Hot Start II Kit, according to manufacturer’s instructions. The oligonucleotide primers oNTI200 (5’ -CAA GCA GAA GAC GGC

ATA-3’ ) and oNTI201 (5’ -AAT GAT ACG GCG ACC ACC GAC AGG TTC AGA

GTT CTA CAG TCC GAC G-3’ ) were used for amplification. The PCR product was run on a 10% TBE gel and products from 150-305 nucleotides size were eluted from shredded gel pieces for 4h in TE + 0.1% Tween + 150 mM NaCl and purified using ChIP DNA Clean and Concentrator Kit (Genesee Scientific).

Libraries were sequenced on an Illumina HiSeq2000 with sequencing primer 5’ -

CGA CAG GTT CAG AGT TCT ACA GTC CGA CGA TC-3’.

GRO-Seq Data Processing. GRO-seq sequencing reads were mapped to the mouse genome (mm9) using Bowtie v0.12.8 (Langmead et al., 2009) with parameters ‘-n 1 -l 20 -m 1 –f’. Uniquely mapped reads were extended to 150bp in the 5’ to 3’ direction and used for downstream analysis. Bedgraph files were generated using HOMER v4.3 (Heinz et al., 2010) and visualized using

Integrative Genomics Viewer (Robinson et al., 2011).

Gene Quantification. GRO-seq signal in gene bodies progressively decreases from 5’ to 3’ in general due to the nature of mRNA transcription (Hah et al., 2013).

In addition, GRO-seq signal can accumulate at the TSS of low- or nontranscribed genes due to Pol2 pausing (Hah et al., 2013). To minimize the influence of these issues, nascent transcription of genes was measured using GRO-seq reads mapped to the sense strand of the gene in a 10kb window (+2kb to +12kb relative to the TSS) within the Refseq annotated gene body. Smaller genes between 2kb and 12kb in length were quantified using smaller window size, from

+2kb to the transcription end site (TES). For genes shorter than 2kb, the entire gene body was used for the quantification. The mapped reads within each gene quantification window were counted using HOMER and expressed as read per kb per ten million reads (RPKTM). Genes with transcription levels greater than 5

RPKTM were considered as being actively transcribed.

25 De Novo Identification of eRNAs. GRO-seq reads mapped to the plus and minus DNA strand were separated and subjected to downstream processes.

Unlike gene transcripts, eRNA transcripts are normally short and form sharp peaks that were identified by HOMER (0.1% FDR). Candidate peaks were then filtered against local background in ± 10kb window surrounding the peak using a

3-fold cutoff. Peaks passing the filtering step from each DNA strand were subsequently pooled for further analysis. Bidirectional eRNA loci were identified by searching for paired eRNA peaks from opposite DNA strands, such that the plus strand peak center had a greater genomic coordinate than the minus strand peak center, and the distance between the 5’ ends of the peak pair was < 1kb.

Unpaired peaks were further divided into inter- and intragenic peaks. Intergenic peaks and the subset of intragenic peaks located on the antisense strand of

Refseq genes were defined as uni-directional eRNAs. Based on a comparison of

ChIP-seq signal density for numerous liver TFs at eRNA loci, the center of each bidirectional eRNA locus was set to the middle point of the 50 ends of the paired peaks, whereas the center of each uni-directional eRNA locus was set to 200bp upstream from the 5’ end of the eRNA peak. Bi- and unidirectional eRNA loci identified in each GRO-seq sample were merged for subsequent analysis. Only those loci identified in 2 or more samples were selected. Finally sites located within 300bp of Refseq TSSs were excluded.

26 Quantification of eRNAs. eRNA level was quantified by calculating GRO-seq reads mapped within ± 500bp of eRNA locus center using HOMER, and normalized to RPKTM. For unidirectional intergenic eRNAs, tag count was performed on the DNA strand where the eRNA peak was identified. For intragenic eRNAs, tag count was performed on the antisense strand of the coding gene.

Western blot and gene expression analysis

For the western blot of the total lysates, we lysed tissues or cells in radio- immuno-precipitation assay (RIPA) buffer supplemented with phosphatase and protease inhibitors, resolved the samples by Tris-glycine SDS-PAGE, transferred the samples to a nitrocellulose membrane and blotted them with the indicated antibodies. For qRT-PCR, we extracted total RNA using TRIzol (Invitrogen) and a

High Pure RNA tissue kit (Roche). We performed qRT-PCR with a High Capacity

RT kit, a SYBR Green PCR Master Mix and the PRISM 7500 instrument (ABI) using the absolute quantification method with standard curves. 36B4 (Arbp) was used as housekeeping control.

Microarray Analysis

Liver samples were taken from 12 week old male RORafl/flgfl/fl mice 2 week after injection of AAV-Cre or AAV-GFP virus (4 mice per group). Total RNA was extracted from each liver sample using the RNeasy Mini Kit (Qiagen). Each RNA

27 sample was processed with the Ambion WT Expression Kit and the GeneChip

WT Terminal Labeling and Controls Kit (Affymetrix), and hybridized to the Mouse

Gene 2.0 ST Array (Affymetrix). The array was then read by GCS3000 laser scanner (Affymetrix), and microarray data analysis was carried out using Partek®

Genomics Suite software (Copyright, Partek Inc.). Subsequent data analysis was performed using BioConductor. ROR target genes were selected using threshold p<0.01, expression fold change < -1.3 and compared with Rev-erbα target genes previously identified using equivalent cutoffs (Lam et al., 2013). For comparison between Rev-erbα target genes and DBDm target genes, p<0.05 and expression fold change > 1.3 were used, and only those containing at least one Rev- erbα binding site within 50kb of their TSSs were compared. The procedures were performed by the Penn Digestives Disease Center Morphology Core. Microarray data are available in GEO (GSE67973).

Mass spectrometric identification of Rev-erbα

Rev-erbα antibody (Cell signaling Technology #2124) was covalently linked to

AminoLink™ Coupling Resin (Thermo). Tissue was lysed in RIPA with protease inhibitors and sonicated. Cleared lysates were incubated with 100 µl of resin overnight at 4°C, washed in RIPA, eluted in 10% NH4OH, and dried. Samples were prepared for MS as previously described (37). After reduction/alkylation, samples were digested with Lys-C (Wako) followed by trypsin (Promega) at 37°C.

Samples were resuspended in 1% acetic acid, and desalted with C18 stage tips

28 as previously described (38). EASY-nanoLC (Thermo) was configured with a 75

µm ID x 17 cm Reprosil-Pur C18-AQ (3 µm; Dr. Maisch GmbH, Germany) nano- column and coupled with an Orbitrap Fusion mass spectrometer (Thermo). Full scan MS spectrum (m/z 360−1600) was performed in the Orbitrap with a resolution of 120,000 (at 200 m/z). Fragmentation was performed with higher- energy collisional dissociation (HCD) and a maximum injection time of 120 msec.

MS/MS data were collected in centroid mode in the ion trap mass analyzer. Peak area was extracted from raw files by using Proteome Discoverer v1.4 and peptides were identified using MASCOT.

Hepatic triglyceride assay

Liver samples were homogenized in the TissueLyser (Qiagen) with steel beads in tissue lysis buffer (140mM NaCl, 50mM Tris and 1% Triton-X, pH8.0).

Triglyceride concentration in the lysates was then quantified using LiquiColor

Triglyceride Procedure No. 2100 (Stanbio).

Oil Red O staining

5µM frozen sections were prepared from snap-frozen liver tissues. The sections were stained in 0.5% Oil Red O in propylene glycerol overnight for lipid and then in hematoxylin for nucleus for 5 seconds. The procedures were performed by the

Penn Digestives Disease Center Morphology Core.

29 Antibodies

Rev-erbα antibody was purchased from Cell Signaling Technology (#2124).

HDAC3 antibodies were purchased from Abcam (ab7030). HNF6 antibody was purchased from Santa Cruz Biotechnology (sc13050). RORα antibody from

Santa Cruz Biotechnology (sc6062). RORγ antibody was purchased from Santa

Cruz Biotechnology (sc28559). Rabbit IgG was from Santa Cruz Biotechnology

(sc2027).

Constructs and gene transductions

The adeno-associated viruses encoding Cre recombinase (AAV-Cre), GFP

(AAV-GFP) and flag-tagged Rev-erbα were obtained from the Vector Core of the

Penn Diabetes Research Center. The Cre, GFP or flag-taggered Rev-erbα gene was inserted into the AAV2/8 vector containing the liver specific Tbg promoter.

Each 12 week old male floxed mouse received 1.5 ×1011 particles of virus through tail-vein injection. The adenoviruses encoding shRNA targeting the b- galactosidase (TGCACCTGGTAAATCTTAT) or the Rev-erbβ gene

(GCACTAAGGACCTTA ATAATG) were constructed using the BLOCK- iTadenoviral RNAi expression system from Invitrogen (#K4941-00) and subsequently amplified and purified by the Vector Core. Each mouse received

11 5.7×10 particles of virus through tail vein injection.

Table 2.1 List of Primers for Gene Expression

RT-qPCR Primers Site Name Forward Primer Reverse Primer Arbp TCCAGGCTTTGGGCATCA CTTTATCAGCTGCACATCACTCAGA HNF6 AGACCTTCCGGAGGATGTG TGGACGTCTGTGAAGACCAG Bmal1 TAGGATGTGACCGAGGGAAG TCAAACAAGCTCTGGCCAAT Npas2 GACGCAACCAGCTTTAACCAAT CCAGACACGTTACCATCAGGAG Cry1 AGCGCAGGTGTCGGTTATGAGC ATAGACGCAGCGGATGGTGTCG Per1 GCCGCTTACAGCAGTCTAATGA GGCAGTTTCCTATTGGTTGGTC E4bp4 GCTCTTTTGTGGACGAGCAT ACCGAGGACACCTCTGACAC RORα TGTTTGATTGATCGGACCAG CTTGGACATCCCGACCAAACT RORγ ACTACGGGGTTATCACCTGTGAG GTGCAGGAGTAGGCCACATTAC Lpl TTTTCTGGGACTGAGGATGG GCCAGCTGAAGTAGGAGTCG Scd1 GCCCCTACGACAAGAACATT CATGCAGTCGATGAAGAACG Acacb ATTGCCAAGAGAATCCCTGT TCCACTCCAGGGTAAGGTTG Cd36 CCACAGTTGGTGTGTTTTATCC TCAATTATGGCAACTTTGCTT Fasn AAGTTCGACGCCTCCTTTTT TCTCGGGATCTCTGCTAAGG Elovl5 GAACATTTCGATGCGTCACT GGATGTAATTGTCCAGGAGGA Acss3 AATGTCGCAAAGTAACAGGCG GTGGGTCTTGTACTCACCACC AAGAGGCCAAACAA- ACCCTGAATTTTCTG- Plin2 AAGAGCCAGGAGACCA GTTGGCACTGTGCAT

31 Table 2.2 List of Primers of ChIP-PCR

ChIP-qPCR Primers Site Name Forward Primer Reverse Primer Arbp GAGGTGGCTTTGAACCAGAG TCTTTGTCTCTGTCTCGGAAAA Insulin GGACCCACAAGTGGAACAAC GTGCAGCACTGATCCACAAT Bmal1 AGCGGATTGGTCGGAAAGT ACCTCCGTCCCTGACCTACT Npas2 CTGTCTTGGCTAGGGGTTTG GAGACAGGCGAGAATCCAAG DBD dependent GAGATTGGGTCACCACAGGT GTGACCCAGCCACCTATGAC site 1 DBD dependent ATTCCCTACTGCCCCAATTC TTGTCCCACTCAAACAGCAG site 2 DBD dependent TGTCTCCTCCCACAGGAAAA CACTGCCCTCTCTCCTTTTG site 3 DBD independent TAGGGCTCTGCAGATTAGCC GCCCATGTGAAAATGGATGT site 1 DBD independent CCCTGGAAGTATAAGCGAGACA GGGTTCAGGGTTCCATTCTT site 2 DBD independent CATGCAATCGCTGACAGAAT AAGGGGTATGGAGTCGGAGT site 3 Better HNF6 motif TCCCAGTGTGAAGGTTTTGTT GCTTGATGATGTGGCTGAGA in C57BL/6J site 1 Better HNF6 motif TTTCCGTGTTGTGTGTTTTCA AAGCGCTAATTGAGCAAATCA in C57BL/6J site 2 Better HNF6 motif GACGATTTGCCATGTTTCCT CATGTTCTCTCTGCCGAGGT in C57BL/6J site 3 Better HNF6 motif in 129S1/SvlmJ TGATAAATCCCTGGCCTTTG GCAGAGCTGCTTGGAAAGTC site 1 Better HNF6 motif in 129S1/SvlmJ TTCCGGGAGAAACTGTAAGC GGGTATCGATTTGACCCGTA site 2 Better HNF6 motif in 129S1/SvlmJ GGATGCAGAAGCAGCAATAA CTGCTGACACTGAACCAGGA site 3 Same HNF6 motif in both strains site TGAGACCAAGGCTCAAAAGAA ACAAAGTGCACAGCTTGGAA 1 Same HNF6 motif in both strains site CAGTGGCAATGCTTCTGTGT TTGCAGCTGGACTCTGAGC 2 Same HNF6 motif in both strains site TGAACCCTGTAAAGCAGACCA CCCGTACCGTTCCTGTAGTC 3

CHAPTER 3

Discrete Functions of Nuclear Receptor Rev-erbα Couple

Metabolism to the Clock

Published in Science, 2015, 348 (6242):1488-92 and Genes &

Development, 2016, 30 (14):1636-44

The text, figures, and legends in this chapter were the work of Yuxiang Zhang with the following exceptions. Bin Fang, Manashree Damle, Zhenghui Li conducted most of the bioinformatics analysis. Matthew J. Emmett provided the

Rev-ebα DNA binding domain mutant mice. Zheng Sun assisted with gene expression assays in HDAC3 liver specific deletion mice. Dan Feng assisted with the ChIP-seq experiments in liver and eWAT, and provided RORsfl/fl mice. Sean

M. Armour and Jarrett R. Remsberg performed the mass spectrometry experiments. Jennifer Jager performed the ChIP-seq experiments in Brain. David

J. Steger assisted with the ChIP-seq and ChIP-exo experiments. Dongyin Guan assisted with the transcriptome analysis in HNF6 liver specific deletion mice. 33 3.1 Abstract

Circadian and metabolic physiology are intricately intertwined, as illustrated by

Rev-erbα, a transcription factor (TF) that functions both as a core repressive component of the cell autonomous clock and as a regulator of metabolic genes.

Here we show that Rev-erbα modulates the clock and metabolism by different genomic mechanisms. Clock control requires Rev-erbα to bind directly to the genome at its cognate sites, where it competes with activating ROR TFs. By contrast, Rev-erbα regulates metabolic genes primarily by recruiting the HDAC3 corepressor to sites to which it is tethered by cell type-specific transcription factors. Thus, direct competition between Rev-erbα and ROR TFs provides a universal mechanism for self-sustained control of molecular clock across all tissues, whereas Rev-erbα utilizes lineage-determining factors to convey a tissue-specific epigenomic rhythm that regulates metabolism tailored to the specific need of that tissue.

3.2 Introduction

Circadian rhythmicity is a common feature of nearly all physiological processes

(Feng and Lazar, 2012). Each cell of the body contains a molecular clock comprised of transcription factors that act on one another in interlocking feedback loops that generate near-24 hour oscillations (Bass and Takahashi,

2010). A core component of the molecular clock, the nuclear receptor Rev-erbα, is expressed with a circadian rhythm (Bugge et al., 2012) and represses BMAL1,

34 a positive regulator of clock output genes (Preitner et al., 2002). Rev-erbα represses many genes, often to regulate metabolism in a circadian and tissue- dependent manner (Feng et al., 2011; Gerhart-Hines et al., 2013; Woldt et al.,

2013). Thus, Rev-erbα is central to complex interactions between the core clock and metabolism.

Rev-erbα recruits the NCoR-HDAC3 complex to actively repress Bmal1 transcription (Yin and Lazar, 2005). It could also compete with another nuclear receptor ROR for the DNA binding site, which contains RevDR2/RORE motifs bound by both receptors (Forman et al., 1994; Giguere et al., 1994; Harding and

Lazar, 1995). The a and g isoforms of ROR are most abundant in liver (Solt and

Burris, 2012), and are expressed in a circadian manner with a peak at ZT18, antiphase to Rev-erbα (Takeda et al., 2012), although the circadian variation of

RORα is modest and of unclear biological significance (Takeda et al., 2012).

Liver specific deletion of RORα and γ has been shown to markedly dampen the circadian oscillation of core clock genes in liver (Takeda et al., 2012). In this study, I aim to investigate the mechanism of circadian regulation of hepatic metabolism by Rev-erbα by analyzing its own cistrome and compare to the cistromes of RORs.

35 3.3 Results

3.3a Rev-erbα Represses Clock Genes by Competing with RORα at Its

Cognate Sites

Since Rev-erbα is a core clock component but also has tissue-specific functions, we were interested in comparing its cistromes in different mouse tissues including liver, brain, and epididymal adipose tissue. The majority of Rev-erbα binding sites were tissue-specific (Figure 3.1A), and gene ontology analyses were consistent with specialized functions of Rev-erbα (Figure 3.1B). However, a common Rev-erbα cistrome included binding at clock genes in all tissues, consistent with its universal function in the core clock (Figure 3.1B) (Preitner et al., 2002; Bugge et al., 2012; Cho et al., 2012).

RevDR2 and RORE were the most enriched motifs at Rev-erbα binding sites shared among tissues (Figure 3.1A), consistent with earlier reports that the function of Rev-erbα as a repressive component of the molecular clock involves binding to two RORE motifs that function in the transcriptional regulation of the

Bmal1 gene (Yin and Lazar, 2005). Rev-erbα recruits the NCoR-HDAC3 complex to actively repress Bmal1 transcription (Yin and Lazar, 2005), and liver specific deletion of HDAC3 induced Bmal1 expression (Figure 3.2B) at ZT10, consistent with previous reports (Yin and Lazar, 2005; Feng et al., 2011). However, the loss of HDAC3 did not dampen circadian rhythmicity of Bmal1 nor other clock components as much as the loss of Rev-erbα itself, suggesting an additional

36 mechanism (Figure 3.2A, B).

Another, non-mutually exclusive mechanism posits competition with the activating nuclear receptor ROR for the DNA binding site, which contains

RevDR2/RORE motifs bound by both receptors (Forman et al., 1994; Giguere et al., 1994; Harding and Lazar, 1995). To determine target genes common to

RORs and Rev-erbα, we compared gene expressions in livers depleted of RORα and g, at their peak time of expression, with gene expression from Rev-erbα knockout livers (9) (Figure 3.3A). Intriguingly, genes regulated both by Rev-erbα and the RORs included clock genes such as Bmal1, Npas2, Cry1, and E4bp4, and were expressed with large circadian amplitudes, consistent with the model that Rev-erbα and RORs are both critical regulators of the clock (Figure 3.3B).

By contrast, Rev-erbα-specific genes had modest circadian rhythms and were enriched for liver metabolic processes (Figure 3.3C).

Although RORa expression was similar at ZT10 and ZT22, there was a marked difference between RORa binding to ROREs at the clock genes Bmal1 and

Npas2 at these times (Figure 3.4A). Deletion of Rev-erbα enhanced RORα recruitment to these sites at ZT10, and this was potentiated by loss of Rev-erbβ

(Figure 3.4B), consistent with lower binding of RORα at ZT10 being due to competition with Rev-erbs. Conversely, hepatic overexpression of Rev-erbα reduced RORα recruitment to Bmal1 and Npas2 sites at ZT22 (Figure 3.4C). 37 Genome-wide, ~44% of RORα binding sites overlapped with Rev-erbα, and these were more likely to be circadian than RORα-specific sites (Figure 3.4D).

Moreover, oscillating RORα binding sites were enriched near common target genes of RORs and Rev-erbα (Figure 3.4E), further suggesting that RORα and

Rev-erbα compete for binding at highly circadian genes including core components of the molecular clock.

3.3b Rev-erbα Binds to the Genome Using Both DBD-Dependent and DBD-

Independent Mechanisms

To understand why Rev-erbα and ROR tended to compete near clock genes but not Rev-erbα-specific genes, we performed ChIP-exonuclease followed by high- throughput sequencing (ChIP-exo) (Rhee and Pugh, 2011) in mouse liver at

ZT10 to better resolve Rev-erbα binding. At clock genes that regulated by Rev- erbα and RORs, exemplified by Bmal1 and Cry1, the RevDR2/RORE motif was detected at ChIP-exo peaks (Figure 3.5A, left). However, Rev-erbα ChIP-exo peaks were most commonly enriched for the motif bound by liver-lineage determining TF hepatocyte nuclear factor 6 (HNF6) (Figure 3.5B). As exemplified by Cyp2b13 and Slc45a3, these Rev-erbα binding sites co-localized with HNF6 in mouse liver (Figure 3.5A, right). Overall, the HNF6 motif was found at 1,108

Rev-erbα ChIP-exo sites (Figure 3.5C), the vast majority of which were also detected by HNF6 ChIP-exo in liver (Wang et al., 2014). Indeed, enhancer RNAs

(eRNAs) at these sites bound by Rev-erbα and HNF6 had a robust circadian 38 expression pattern (Figure 3.5D), and were markedly up-regulated in livers depleted of Rev-erbα, indicating active repression of enhancer function at these sites (Figure 3.5E) (Fang et al., 2014).

To test whether the binding of Rev-erbα on the genome can be indirect, we utilized a mouse model with a conditional deletion of the Rev-erbα DNA-binding domain (DBD). These mice have been previously studied as a model of Rev- erbα deletion (Cho et al., 2012; Lam et al., 2013), but the targeting strategy is predicted to lead to in-frame deletion of the DBD (Figure 3.6A) and Rev-erbα immunoblot of mouse liver after Cre-recombination revealed an abundant species at the approximate molecular weight of the protein lacking the DBD

(Figure 3.6B). The identity of this protein as full-length Rev-erbα lacking its DBD was confirmed by mass spectrometric analysis of Rev-erbα immunoprecipitates from recombined liver extracts (Figure 3.6C). Thus, this model is actually a knock-in of a DBD mutation, rather than a complete knockout of the Rev-erbα protein. We studied the function of this Rev-erbα DBD mutant in mice whose livers were also depleted of Rev-erbβ to eliminate its compensatory effects

(Bugge et al., 2012; Cho et al., 2012).

ChIP-seq analysis of Rev-erbα in livers expressing only the Rev-erbα DBD mutant ("DBDm") revealed a comparable level of binding at a subset of wild type sites ("DBD-independent sites"), while binding was dramatically reduced at many 39 other sites ("DBD-dependent sites") (Figure 3.7A, B). HNF6 ChIP-seq signals

(Faure et al., 2012) were more enriched at Rev-erbα DBD-independent sites than at the DBD-dependent sites (Figure 3.7C), suggesting that HNF6 might tether

Rev-erbα to the DNA even in the absence of Rev-erbα DBD domain.

DBD-dependent sites were enriched in RORE as well as dimeric RevDR2 motifs, in agreement with direct DNA binding (Figure 3.7D). These motifs are also recognized by ROR, and indeed the binding of RORα at these sites decreased markedly at ZT10 when Rev-erbα competition is strongest (Figure 3.7E).

RevDR2 were depleted in DBD-independent sites while ROREs still exist in a minority of sites (Figure 3.7D), suggesting RORE may facilitate, although not required for, DBD-independent binding. The HNF6 motif was markedly enriched at DBD-independent sites (Figure 3.7D), and Rev-erbα binding at Rev- erbα/HNF6-exo sites was comparable between wild type and DBDm mice, following the same pattern as DBD-independent sites (Figure 3.7F). The simultaneous binding of Rev-erbα and HNF6 at these sites was confirmed by

ChIP-re-ChIP experiments in wild type liver, whereas HNF6 and Rev-erbα were not co-localized at DBD-dependent sites (Figure 3.8A, B). Enhancer RNA transcription showed circadian oscillation in phase ZT22, at DBD-dependent and

-independent sites (Figure 3.8C), suggesting active repression of Rev-erba in both cases. In agreement with Rev-erbα functioning by recruiting the corepressor complex, HDAC3 binding at Rev-erbα sites was reduced in Rev-erb-depleted 40 livers (Figure 3.9A). In addition, the HDAC3 ChIP-seq signal in the DBDm was reduced at DBD-dependent sites but not at sites that are DBD-independent

(Figure 3.9B), suggesting active repression by Rev-erbα via recruitment of

HDAC3.

3.3c HNF6 Motif is Required for Rev-erbα DBD-Independent Binding

To determine whether HNF6 is required for Rev-erbα DBD-independent binding, we performed ChIP-seq for Rev-erbα in liver of 129S1/SvlmJ mice, and compared this result with that obtained in the C57BL/6J mice. The two strains differ by ~5.4 million single nucleotide polymorphisms (SNPs), and SNPs were predicted to cripple the HNF6 motif at 107 sites in C57BL/6J mice and 71 sites in

129S1/SvlmJ mice. Remarkably, Rev-erbα binding was markedly diminished at the sites in which the SNPs disrupt either RevDR2/RORE or HNF6 motif in

C57BL/6J or 129S1/SvlmJ mice (Figure 3.10A, B). Specific examples of strain- dependent binding of Rev-erbα at HNF6 sites are shown in Figs. 3A-B, and differential binding in the two mouse strains was confirmed by ChIP-PCR for

HNF6 and Rev-erbα (Figure 3.11A, B). Interestingly, while the HNF6 motif was most significantly associated with strain specific Rev-erbα binding by the same analysis, we also found significant association with motifs for several other TFs that play important roles in liver function, suggesting involvement of other partners in Rev-erbα binding in the absence of RORE and RevDR2 (Figure

3.10C).

41 To further test the hypothesis that HNF6 could mediate Rev-erbα binding at the tethering sites, we induced hepatocyte-specific knockout of HNF6 in 10-week-old

C57Bl/6 mice by tail vein injection of AAV8-Tbg-Cre into HNF6fl/fl mice, resulting in efficient deletion of HNF6 in the liver (Figure 3.12A). Loss of HNF6 leads to significant attenuation of Rev-erbα binding at the DBD-independent sites, which have been shown to co-localize with HNF6 by Rev-erbα-HNF6 ChIP-re-ChIP

(Figure 3. 8A, B) in the previous results, while little effects were observed on the

DBD-dependent sites (Figure 3.12B). Remarkably, among those tethering sites,

Rev-erbα binding near metabolic targets that overlapped with HNF6 binding was significantly decreased, exemplified by Acacb and Cyp2b13 (Figure 3.12C). By contrast, direct binding of Rev-erbα was preserved at clock-related genes, exemplified by Bmal1 and Npas2 (Figure 3.12D). At the genome-wide level,

HNF6 KO reduced a population of Rev-erbα binding sites (Figure 3.13A). These sites were highly enriched for the HNF6 motif (>85%), with no significant enrichment for other motifs (Figure 3.13B).

3.3d DBD-Independent Rev-erbα Sites Regulate Metabolic Genes in Liver

The preserved binding of HDAC3, mediated by Rev-erbα DBD mutant, at metabolic genes suggested that hepatic expression of these genes might also be intact relative to livers of mice in which the Rev-erbα protein is deleted. To test this, we compared the gene expression changes in mice lacking Rev-erbα in liver with published results using the DBDm mouse model used here, in which Rev- 42 erbα is converted to the DBD mutant and Rev-erbβ is also deleted (Cho et al.,

2012). Circadian clock genes were derepressed in both situations, demonstrating that that regulation of these genes required direct binding at RevDR2/RORE sites by Rev-erbα. Overall only ~25% of Rev-erbα target genes that were derepressed in Rev-erbα KO mice were also de-repressed in the DBDm mice

(“DBD-dependent Rev-erbα targets”) (Figure 3.14A). Genes de-repressed specifically in Rev-erbα KO mice (“DBD-independent Rev-erbα targets”) showed circadian expression peaking at ZT22 (Figure 3.14B), and were enriched for lipid metabolic functions (Figure 3.14C), suggesting that Rev-erbα regulates circadian lipid metabolic genes independent of its DBD. In support of this, DBD- independent Rev-erbα/HDAC3 sites were enriched near DBD-independent Rev- erbα targets, whereas DBD-dependent Rev-erbα/HDAC3 sites, where Rev-erbα and HDAC3 binding markedly reduced in DBDm mice, were more enriched near

DBD-dependent Rev-erbα targets (Figure 3.14A). Examples of the deletion- specific regulation of metabolic genes are shown in Figure 3.15A and B.

Consistent with preserved metabolic gene expression in the DBDm mice livers, these mice did not display hepatosteatosis as is characteristic of the mice with complete deletion of Rev-erbα (Figure 3.15C, D, E) (Feng et al., 2011; Bugge et al., 2012).

We further tested whether HNF6 depletion will also affect the hepatic lipid metabolism. Indeed, the loss of HNF6 resulted in fatty liver, demonstrated by Oil 43 Red O staining (Figure 3.16A), with marked accumulation of hepatic triglycerides in mice fed on chow diet and exacerbation of high fat-diet HFD-induced steatosis

(Figure 3.16B). Consistent with our hypothesis and the hepatosteatotic phenotype, many lipogenic genes repressed by Rev-erbα and its corepressor/HDAC3 complex, including Lpl, Scd1, Acacb, Cd36, Fasn (Feng et al.,

2011; Bugge et al., 2012; Sun et al., 2012; Sun et al., 2013) were up-regulated in the HNF6 KO liver (Figure 3.16C). To test whether the regulation of lipid metabolism by HNF6 and Rev-erbα involved a common pathway, we performed microarray analysis with HNF6 KO liver from mice fed chow diet. Using a cut-off of >1.5-fold change and p-value <0.05, we identified 101 up-regulated genes and

80 down-regulated genes. The up-regulated genes were enriched for oxidation- reduction, lipid biosynthesis, lipid storage and other metabolic processes and the down-regulated genes included inflammatory genes and monosaccharide metabolic genes (Figure 3.17A). Notably, gene set enrichment analysis (GSEA) showed that up-regulated genes in HNF6 KO liver were over-represented by the genes up-regulated in Rev-erbα KO liver (Figure 3.17B). Moreover, the lipid metabolic genes were enriched in the genes up-regulated commonly in both

HNF6 and Rev-erbα KO (Figure 3.17C), while the circadian clock genes were enriched in genes exclusively up-regulated in the Rev-erbα KO (Figure 3.17D), consistent with the hypothesis that HNF6 tethered Rev-erbα is targeted to metabolic genes but not the clock genes.

3.4 Discussion and Future Directions

These findings demonstrate that Rev-erbα has a DBD-independent function that contributes to its regulation of liver metabolism. Other nuclear receptors, including estrogen receptor and glucocorticoid receptor, have DBD-independent activities through protein-protein interactions with other TFs, either directly or indirectly (Glass and Saijo, 2010; Faure et al., 2012). In liver, Rev-erbα is tethered to chromatin by hepatic lineage determining TFs (Figure 3.18), and this mechanism of binding explains much of the non-overlapping cistromes of Rev- erbα in different tissues as well as the large proportion of binding sites without the RevDR2/RORE motif. In liver, the tethered cistrome is more enriched for genes with specialized function in hepatic metabolism, whereas the DBD- dependent cistrome is enriched for circadian clock genes and common to multiple tissues.

Circadian clocks and metabolism are tightly connected (Feng and Lazar, 2012), and Rev-erbα has emerged as a transcriptional link from circadian rhythms to metabolism in multiple tissues (Everett and Lazar, 2014). Our findings delineate a molecular hierarchy that governs how the clock is wired with metabolism. Direct competition between Rev-erb and ROR provides a universal mechanism for self- sustained control of molecular clock across all tissues. On top of this basic landscape, circadianly expressed Rev-erb utilizes lineage-determination factors

45 to convey a tissue-specific epigenomic rhythm that, through corepressor and

HDAC3, regulates metabolism tailored to the specific need of that tissue. These two modes of action may bestow on Rev-erbα the ability to stabilize the circadian oscillations of clock gene, while coupling liver metabolism to environmental and metabolic changes, perhaps through its endogenous ligand heme (Yin et al.,

2007). This raises the possibility that synthetic ligands which specifically affect

Rev-erbα interaction with NCoR/HDAC3 without disrupting DNA-binding could modulate liver metabolism with lesser effects on the integrity of the circadian clock.

While the mechanisms of Rev-erβ regulation of liver circadian metabolism have been elucidated, how ROR contribute to the liver metabolism regulation still remains to be shown, which will be addressed in the next chapter.

Figure 3.1 Overlap of Rev-erbα cistrome in various tissues are limited. (A) Overlap of Rev- erbα cistromes in liver (9), brain and epididymal adipose tissue (eWAT). Most significantly enriched known motifs (abundance>10%) in common and tissue specific cistromes are shown. (B)

GO analysis of different group of binding sites.

Figure 3.2 HDAC3 does not dampen circadian rhythmicity of clock genes. mRNA expression of Bmal1, Cry1, and E4bp4 over a 24-hour period in (A) wild type, Rev-erbα knockout and (B) HDAC3 KO mouse liver. The expression at

ZT10 is highlighted in the upper right corner of the plot. Data are expressed as mean± SEM (* Student’s t-test, p < 0.05, n= 4 or 5 per group).

Figure 3.3 Common direct targets of Rev-erbα and RORs are enriched for the clock genes. (A) Heat map showing expression fold changes of genes deactivated by RORα/γ double KO (DKO/WT< -1.3, p<0.01) and derepressed by

Rev-erbα KO (aKO/WT>1.3, p<0.01). (B) Mean relative GRO-seq transcription

(left) throughout 24-hour light dark cycle, as well as oscillation amplitudes (right) of RORs/Rev-erbα common targets (red) and Rev-erbα specific targets (blue).

Time points were duplicated for clearer visualization. (C) GO analysis of the

49 genes that are commonly regulated by Rev-erbα and RORs, and the genes that are specifically regulated by Rev-erbα.

Figure 3.4 Rev-erbα and RORα compete for binding at highly circadian genes including core components of the molecular clock. (A) ChIP-PCR validation of cyclic RORα binding at clock genes promoters at ZT10 or ZT22 in wild type mice liver, in comparison with an unbound control at Arbp. Data are expressed as mean± SEM (Student’s t-test, *p<0.05, n=4). (B) RORα binding at clock and control genes promoters at ZT10, in wild type (WT), Rev-erbα KO

(aKO), Rev-erbβ knockdown (bKD), and aKO/bKD mice liver, interrogated by

ChIP-PCR. Data are expressed as mean± SEM (* Student’s t-test, p<0.05, n=4).

(C) RORα binding at clock and control genes promoters at ZT10 or ZT22 in Rev- erbα overexpression mice liver, interrogated by ChIP-PCR. Data are expressed as mean± SEM (* Student’s t-test, p<0.05, n=6 or 7). (D) Circadian binding of 51 RORα at sites overlapped or not overlapped with Rev-erbα cistrome (N.S.

Student’s t-test, p>0.05). (E) Percentage of common or Rev-erbα specific target genes containing high confidence oscillating RORα binding sites (ZT22>2 reads per million (rpm), ZT22/ZT10>1.5) within 50kb of TSSs (P value from hypergeometric test).

Figure 3.5 Rev-erbα ChIP-exo peaks are enriched for HNF6 motif. (A)

Genome browser view of Rev-erbα and HNF6 ChIP-exo signals under Rev-erbα

ChIP-seq peaks near clock and metabolic genes. Blue bars indicate locations of

RevDR2/RORE and HNF6 motif. (B) Highly enriched known motifs found in Rev- erbα ChIP-exo peak pairs 22-26bp apart. (C) Heat map showing 5’-end tag densities of Rev-erbα ChIP-exo centered at HNF6 motifs within 1,108 peak pairs.

Red and blue indicate tag density on the plus and minus strand, respectively. (D)

Mean relative eRNA transcription (22) at Rev-erbα/HNF6-exo sites throughout

24-hour light dark cycles. Data were double plotted for clearer visualization. (E) eRNA tag density (22) centered at Rev-erbα/HNF6-exo sites near Rev-erbα target genes, in Rev-erbα KO and wild type liver. 53

Figure 3.6 Conditional Rev-erbα allele deletion results in an in-frame DNA binding domain mutant (DBDm). (A) Illustration depicting floxed exons 3 and 4 encoding the DNA binding domain of the Rev-erbα locus. Numbers highlighted in

54 red represent the nucleotide codon phases at exon-exon junctions. Deletion of exons 3 and 4 maintains the open reading frame through splicing of exon 2 to exon 5. The peptide sequence highlighted (from S6C) is unique to the conditional

Rev-erbα DBDm, with amino acids encoded in exons 2 and 5 colored black and red respectively. (B) Western blot of Rev-erbα in the livers of wild type mice,

Rev-erbα KO mice, wild type control mice (floxed mice injected with AAV-GFP), and DBDm mice (floxed mice injected with AAV-Cre), “*” denotes non-specific band. (C) Rev-erbα peptides detected by mass spectrometry in wild type control and the DBDm livers. Red arrows indicate peptides discovered in wild type control liver mapping to the DNA binding domain of Rev-erbα, which are absent in the

DBDm. The blue arrow indicates a unique peptide found only in the DBDm livers as a result of the new open reading frame joining exons 2 and 5. Note the third threonine (amino acid 203) of the highlighted DBDm peptide is a 1 amino acid substitution from alanine resulting from the new exon splice junction between exons 2 and 5.

Figure 3.7 Rev-erbα binds to the genome using both DBD-dependent and

DBD-independent mechanism. (A) Heat map showing Rev-erbα ChIP-seq tag densities (at ZT10) in wild type, DBD mutant (DBDm), and Rev-erbα KO (aKO) mice, at DBD-dependent and -independent sites identified among 5,792 high confidence Rev-erbα peaks (peak height >1rpm, WT/Rev-erbα KO>3). (B)

Binding strength of Rev-erbα at selected DBD-dependent and DBD-independent sites at ZT10 as determined by ChIP-PCR. Data are expressed as mean±SEM (*

56 Student’s t-test, p < 0.05, n=4 per group). (C) HNF6 tag density at Rev-erbα

DBD-dependent and -independent sites. (D) The percent of sites containing

RevDR2, RORE, or HNF6 motif in DBD-dependent sites and DBD-independent sites (*** hypergeometric test p<1e-3). (E) Circadian binding of RORα at Rev- erbα DBD-dependent sites but not DBD-independent sites (*** Student’s t-test p<1e-3). (F) Difference in Rev-erbα binding strengths between WT and DBDm mice at three groups of binding sites.

Figure 3.8 Rev-erbα and HNF6 co-localize at the functional DBD- independent sites. (A) Sequential ChIP of Rev-erbα followed by either HNF6 or

IgG ChIP in wild type and DBDm mouse liver at ZT10. Data are expressed as mean± SEM (* Student’s t-test, p<0.05, n=3 or 4 per group). (B) Sequential ChIP of HNF6 followed by either Rev-erbα or IgG ChIP in wild type and DBDm mouse liver at ZT10. Data are expressed as mean±SEM (* Student’s t-test, p<0.05, n=3 58 or 4 per group). (C) eRNA oscillation at DBD-dependent sites and DBD- independent sites throughout 24-hour light dark cycle. Time points were duplicated for better visualization.

Figure 3.9 HDAC3 binding at the DBD-independent sites is attenuated in the

Rev-erbs depleted mice while not affected in the DBDm. (A) Comparison of

HDAC3 binding in WT and Rev-erb αKO/βKD mice at DBD-dependent and DBD- independent Rev-erbα sites (*** Student’s t-test p<1e-3). (B) Binding of HDAC3 at DBD-dependent and -independent sites in WT and DBDm liver (N.S. Student’s t-test, p>0.05, *** Student’s t-test p<1e-3) .

Figure 3.10 SNP associated strain-specific occupancy suggests HNF6- mediated binding of Rev-erbα. (A) Heat map showing log2 fold changes of

Rev-erbα binding in 129 and B6 mice. The left column in the heat map contains

141 Rev-erbα peaks where RevDR2/RORE motif scores are higher in 129 mice and lower in B6 mice, due to the SNPs (illustrated in the left panel). Similarly, the right column contains 101 SNP-bearing Rev-erbα peaks with better

RevDR2/RORE in the B6 genome. P value was calculated using Student’s t-test.

(B) Same analysis as in A, focusing on SNPs disrupting HNF6 motif under Rev- erbα peaks. (C) Heat map showing -log10 p values for other motifs that are enriched in DBD-independent Rev-erbα peaks.

Figure 3.11 Binding strength of Rev-erbα in C57BL/6J and 129S1/SvlmJ. (A-

B) Binding strengths of HNF6 (A) and Rev-erbα (B) are correlated with HNF6 motif at selected Rev-erbα sites at ZT10, as determined by ChIP-PCR. Data are expressed as mean± SEM (* Student’s t-test, p < 0.05, n=4 per group).

Figure 3.12 HNF6-tethered Rev-erbα binding near metabolic genes is decreased in HNF6-KO liver. (A) Western blot for HNF6 of total liver lysates 3 weeks after AAV injection. GFP, treatment with AAV-Tbg-GFP; Cre, treatment with AAV-Tbg-Cre, b-Tubulin was used as a loading control. (B) Binding strength of Rev-erbα at selected DBD-independent and DBD-dependent sites at ZT10 as validated by ChIP-PCR. Data are expressed as mean±SEM (* Student’s t-test, p

< 0.05, n=4 per group). (C) and (D) Genome browser view of Rev-erbα ChIP-seq

64 peaks in the widltype control and HNF6 KO liver, and HNF6 ChIP-seq peaks near (C) metabolic genes Acacb and Cyp2b13, and (D) clock genes Bmal1 and

Npas2. The Rev-erbα peaks that overlapped with HNF6 peaks and Rev-erbα

DBD-dependent binding sites are highlighted.

Figure 3.13 Sites with reduced Rev-erbα binding in the HNF6 KO are enriched for HNF6 motif. (A) Scatterplots of the Rev-erbα high confidence cistrome in the wildtype control (x-axis) and HNF6 KO (y-axis). The cut-off for differentially regulated sites is fold change >3. The down-regulated sites are plotted in red, and no up-regulated sites were found. (B) Enrichment of HNF6,

HNF4A, CEBP, FOXA1, DR2 and RORE motif in the down-regulated sites and unchanged sites.

Figure 3.14 Genes regulated exclusively in Rev-erbα KO are enriched for metabolic process. (A) Top panel shows the number of DBD-dependent and - independent Rev-erbα target genes identified using microarrays in Rev-erbα KO and DBDm mice. Bar graph shows ratios of DBD-independent and -dependent

Rev-erbα/HDAC3 binding sites located near two groups of Rev-erbα target genes. (B) Circadian average mRNA expression of Rev-erbα DBD-dependent and -independent target genes mRNA levels throughout 24-hour cycle were normalized to the peak expression of the day. Time course microarray data from were duplicated for better visualization. (C) Gene ontology analyses for Rev-erbα

DBD-dependent and -independent target genes. 67

Figure 3.15 DBD-independent Rev-erbα sites regulate metabolic genes in liver. (A) mRNA expression of lipid metabolic genes normalized to Arpp, measured by RT-qPCR, in livers of Rev-erbα KO mice and wild type mice at

ZT10. (B) mRNA expression of lipid metabolic genes normalized to Arpp, measured by RT-qPCR, in livers of Rev-erbα DBDm (Rev-erbα/β double floxed mice injected with AAV-Tbg-Cre) or control mice (floxed mice injected with AAV-

Tbg-GFP) at ZT10. Data are expressed as mean± SEM (* Student’s t-test, p<0.05, n=4 per group). (C) Hepatic triglyceride (TG) levels in the same mice as in B. (D) Hepatic TG levels in mice as in C (* Student’s t-test, p<0.05, n=4 per group). (E) Oil Red O staining of livers from wild type control mice (Rev-erbα/β floxed mice injected with AAV-GFP), and DBDm mice (Rev-erbα/β floxed mice injected with AAV-Cre). 68

Figure 3.16 Liver-specific depletion of HNF6 in adult mice causes hepatosteatosis. (A) Oil Red O staining of livers from the control mice and

HNF6 KO mice fed on normal chow diet at a magnification of 20X. (B) Hepatic triglyceride (TG) measurement of the livers from the control mice and HNF6 KO mice fed on normal chow diet and high fat diet (HFD). mg per g, mg triglyceride per g of liver weight. Data are expressed as mean±SEM (* Student’s t-test, p <

0.05, n=3 per group for chow diet, n=7 per group for HFD). (C) Relative mRNA expression level of lipid metabolic genes known to be Rev-erbα and HDAC3 targets in the control and HNF6 KO mice. Data are expressed as mean±SEM (*

Student’s t-test, p < 0.05, n=4 per group). 69

Figure 3.17 HNF6 and Rev-erbα regulate hepatic lipid metabolism through common pathways. (A) Heatmap and gene ontology (GO) analysis of differentially regulated genes in the HNF6 KO. (B) GSEA results for the enrichment of genes up-regulated by HNF6 KO in Rev-erbα KO liver. (C) and (D)

Gene ontology (GO) analysis of the (C) genes up-regulated in both HNF6 KO and Rev-erbα KO liver and (D) genes up-regulated exclusively in Rev-erbα KO.

Figure 3.18 Model depicting distinct mechanisms of Rev-erbα regulating clock and metabolic genes in liver. Grey refers to other potential TFs that may be involved in the tethering of Rev-erbα.

CHAPTER 4

Redundant Regulation of Circadian Hepatic Metabolism by

RORα and RORγ

The text, figures, and legends in this chapter were the work of Yuxiang Zhang with the following exceptions. Romeo Papazyan provided the SCAP liver specific deletion samples and performed the Srebp1c western blot experiments. Bin Fang and Manashree Damle assist with the bioinformatics analysis. Jennifer Jager performed the GRO-seq experiments. Dan Feng assisted with the RORs ChIP- seq experiment. Zheng Sun assisted with the metabolic assays in RORs liver specific deletion mice.

4.1 Abstract

The liver circadian clock is tightly connected to metabolism. Although the liver clock is entrained by the central clock, it also directly controls metabolic gene expression. Hormone responsive nuclear factors are hypothesized to be the major clock components that regulate metabolism. Here, we investigate the circadian regulation of hepatic metabolism by nuclear receptors RORα and

RORγ. RORα and γ double depletion was induced by tail-vein injection of AAV-

TBG-Cre into adult RORαfl/flγfl/fl mice. Mice were assayed for lipid triglyceride and expression of genes involved in lipogenesis. Transcriptional mechanisms of the regulated genes were investigated by global run-on assay followed high- throughput sequencing (GRO-seq). We discovered that hepatic depletion of

73 4.2 Introduction

In Chapter 3, we showed that Rev-erbα modulates the clock and metabolism by different genomic mechanisms. Clock control requires Rev-erbα to bind directly to the genome at its cognate sites, where it competes with activating ROR TFs.

By contrast, Rev-erbα regulates metabolic genes primarily by recruiting the

HDAC3 corepressor to sites to which it is tethered by cell type-specific transcription factors. However, clock could control the hepatic metabolism not only through Rev-erbs, but also through other factors, such as its closely related nuclear receptors RORs. RORs could compete with Rev-erbs for common binding and maintain the circadian rhythm of target genes, and could also regulate other metabolic processes at different phases. Therefore, in this chapter, we aim to address the mechanism of RORs regulation of hepatic metabolism.

ROR has three members, RORα, RORβ, and RORγ. Among them, only RORα and RORγ are highly expressed in the liver, while RORα are mainly expressin the central nervous system. As nuclear receptors, RORs also are composed of

N-terminal activation domain (AF1), a DNA-binding domain (DBD), a central hinge region, and a C-terminal ligand-binding domain (LBD). But unlike Rev-erbs,

RORs possess the ligand-dependent activation function domain (AF2) which allows it to interact with coactivators to activate target transcription.

The role of RORs regulation of hepatic metabolism has been investigated before.

74 RORα and RORγ could also regulate liver lipid, bile acid, and glucose pathway, but maybe through different pathways compared to Rev-erb regulation. It has been reported that RORγ could regulate rhythmic expression of several lipid metabolic genes, including the Insig2a, Elovl3 and Cyp8b1, by enhancing their expression at ZT20-4 (Takeda et al., 2014b). In addition, both RORα and RORγ deficient mice exhibited decreased gluconeogenesis and improved insulin sensitivity and glucose tolerance during daytime specifically (Takeda et al.,

2014a; Kadiri et al., 2015). However, reports on RORα regulation of hepatic triglyceride are not consistent. While some studies report RORα deficient staggerer mice had a decreased serum and liver triglycerides and lipogenic gene expression (Lau et al., 2008; Kang et al., 2011), some others reported it had increased liver triglyceride accumulation and lipogenic gene expression (Wada et al., 2008). The differences in the experimental settings such as euthanizing time or feeding status at the time of euthanizing might be the factors that caused that inconsistency. In this study, we aim to elucidate the RORs regulation of hepatic lipid metabolism and address the mechanism of RORs regulation of their target genes in the mice deficient for RORs in the liver.

75 4.3 Results

4.3a RORα and RORγ Redundantly Regulate Clock Genes and Metabolic

Genes

RORα, RORγ and RORs double deletion were induced by injecting AAV-Tbg-Cre in RORαfl/fl or RORγfl/fl or double floxed mice. The depletion of RORs was confirmed by qRT-PCR (Figure 4.1A). Notably, RORs targeted clock genes

Bmall1 and clock were only significantly regulated in the double mutant mice

(Figure 4.1B), suggesting redundant function of clock gene regulation of RORα and γ. Interestingly, it is also found that several lipid metabolic genes including

Acacb, Fasn, Elovl6, Gpam and Acly were also under redundant regulation of

RORα and RORγ (Figure 4.1C). To systematically analyze the function of RORα and RORγ in the mice liver, we compared the microarray heatmaps of single mutant and double mutant mice liver. Consistent with previous results, double mutant mice liver exhibited significant regulation of RORs target genes, while few genes were regulated in the single mutant liver (Figure 4.2A). Among the down- regulated are some of the well-known ROR target genes, including Bmal1, Cry1,

Npas2, Cyp8b1 and others, those genes were enriched for circadian rhythm

(Figure 4.2B). The up-regulated genes include Srebf1 and some other genes regulated by Srebp1c, including Fasn, Scd1, Elovl6, Acly (Figure 4.2A). Gene ontology analysis of those genes showed enrichment for fatty acids metabolism and PPAR signaling pathway, suggesting a that RORs might be involved in the regulation of hepatic lipid metabolism.

76 To further confirm the functional redundancy of RORα and RORγ in the liver, we profiled their cistrome by ChIP-seq analysis. It is found that most of the RORα and RORγ binding sites overlapped (Figure 4.3A), and the gene near the overlapping sites were over-represented by circadian rhythm, insulin signaling pathway, and other metabolic pathways (Figure 4.3B), which are consistent with its regulation of clock genes and Srebp1c pathway. Example of RORα and RORχ binding were shown in Figure 4.3C, where RORα and RORγ bind to the same sites that disappeared in the RORs double mutant mice livers.

4.3b RORα and RORγ Liver Specific Deletion Leads to Accumulated

Triglycerides Level and Dysregulation of Lipid Metabolic Genes at ZT22

Based on the microarray analysis, we characterized the lipid metabolism in the

RORs double mutant mice. We observed a trend of increase in hepatic triglycerides in RORs double mutant mice fed on chow diet, and more significant increase in mutant mice fed on 6-week high fat diet (Figure 4.4A), which was confirmed by the Oil Red O staining (Figure 4.4B).

To elucidate the mechanism of RORs regulation of the circadian rhythm of lipid metabolic genes, we harvested the mice liver every 4 hours and the circadian expression of the lipid metabolic genes repressed by RORs identified from microarray. Interestingly, it is found that the up-regulation of those genes are restricted to ZT18~ZT26 (Figure 4.5), suggesting those genes are regulated by

77 RORs only during this time window, and are dominantly regulated by other factors outside this time frame.

4.3c RORs Regulation Is Specific to Ad Lib Fed Mice But Not Fasted Mice

Since feeding behavior is tightly regulated by the circadian clock and have a robust rhythm (Turek et al., 2005), we further investigate the feeding or fasting status of the mice would affect RORs regulation of those metabolic genes. To address this, we harvest the mice either on ad lib feeding condition or fasting for

16 hours (Figure 4.6A) after 6 weeks of high fat diet. We found the difference in hepatic triglycerides was diminished after fasting in mice (Figure 4.6B). Similarly,

RORs regulation of Srebf1 and targets genes of Srebp1c was also minimized in the fasted mice (Figure 4.6C), suggesting RORs regulation of this pathway requires feeding and a robust Srebp1c signaling.

4.3d RORs Regulate Liver Lipid Metabolism Through SREBP pathway

To further characterize the mechanism of regulation of genes differentially expressed in RORs mutant mice, we performed the global run-on assay to measure the nascent transcription from the gene body and enhancers (eRNAs).

As a result, we identified 541 up-regulated eRNAs and 762 down-regulated eRNAs (Figure 4.7A). Motif analysis showed that the down-regulated eRNAs were enriched for STAT1 motif and nuclear receptor motif including RORE, consistent with the canonical role of RORs as transcription activators. Notably,

78 the up-regulated eRNAs were enriched for Zinc Finger, and bHLH motif, which include the SREBP1 motif (Figure 4.7B). Therefore, we hypothesized that RORs regulated the hepatic lipid metabolism through an SREBP1c-dependent mechanism.

In the wild type mice, it is observed that SREBP1c target genes expression has a circadian rhythm that peaks around ZT14~18. To further validate our hypothesis that RORs directly regulate clock genes and indirectly repress lipid metabolic gene, we interrogated the phase distribution of the eRNAs down-regulated or up- regulated in RORs mutant mice liver. As shown in Figure 4.8A and B, the down- regulated eRNAs were enriched around phase ZT21-24, while the up-regulated eRNA were enriched around phase ZT15-18, consistent with the phase of the clock genes directly regulated by RORs (ZT21-24), such as Npas2, and the phase of lipid metabolic genes regulated by Srebp1c (ZT15-18), such as Thrsp

(Figure 4.8C). As expected, RORs deficient liver has elevated level of active form of Srebp1c in the nuclear (Figure 4.9A). However, the precursor form of Srebp-1c expression and mRNA level remained unchanged in RORs deficient liver, indicating that the regulation of Srebp-1c was more like on the post-translational level (Figure 4.9A, B). Indeed, it is found that Insig2, which can block Srebp1c signaling by retaining the Scap-Srebp1c complex in the ER, was directly activated by RORs, with strong RORs binding sites near the Insig2 transcription

79 start site (Figure 4.9C, D), suggesting RORs might repress the Srebp1c signaling by activating Srebp1c repressor Insig2 expression.

4.4 Discussion and Future Directions

Studies in RORα or RORγ null mice have suggested a role of RORs in the control of energy homeostasis and the regulation of lipid and glucose metabolism.

However, since the regulation of energy and metabolism homeostasis is a complex process that involves multiple interrelated pathways in many organs involving endocrine, immune, and nervous systems, as well as the circadian clock and gut microbiome, it is difficult to determine whether the metabolic changes observed in ROR-deficiency mice are direct regulation of RORs or a result of systematic changes. Therefore, our study could elucidate the tissue specific function of RORs in the liver.

Previous report on RORs regulation of liver metabolism using the RORs total body deficient mice has generated contradicting results. Some studies reported decreased while others reported increased hepatic triglyceride level in the RORα deficient staggerer mice, which suffered from deficient in cerebellum development (Lau et al., 2008; Kang et al., 2011; Wada et al., 2008). RORg null mice also displayed down-regulation of several lipid metabolic genes, but paradoxically, the nuclear active form of SREBP-1c also got significantly up-

80 regulated, which is usually associated with up-regulation of lipogenic process

(Takeda et al., 2014b). Here, we showed that RORs regulation of lipid metabolism varies depending on the time of the day as well as the feeding status.

Around ZT22, it could repress the lipid metabolic process through inhibition on the SREBP pathway while the mice were fed. But around ZT10 or during fasting, its regulation on the lipid metabolism is diminished, or reversed by indirectly impact through activation of clock genes.

SREBP activities are regulated on both transcriptional and post-translational level.

SREBP is regulated by LXR transcriptionally. Then, newly synthesized SREBPs are inserted into the endoplasmic reticulum (ER) membrane as inactive precursors, where it binds to the sterol-sensing protein SREBP cleavage activating protein (SCAP). Upon activation such as insulin signaling, SREBP-

Scap complex traffics to the Golgi via COPII vesicles where SREBP N-terminal bHLH-LZ transcription factor domain is cleaved and translocate into the nuclei as active form to control target gene transcription. Insig proteins, including Insig1 and Insig2 are endoplasmic reticulum proteins that block the processing of

SREBP by binding to SCAP, and thus prevent SCAP from escorting SREBPs to the Golgi. In our study, we found that RORs activate expression of Insg2 in the liver specifically around ZT22, and thus indirectly inhibit SREBP activity at that time.

81 To further validate this discovery, we will delete the RORs genes in the liver of the SCAP liver-specific deficient mice, by breeding their LoxP sites together, to test the dependence of RORs regulation of lipid genes on the SREBP signaling pathway. We expected in the absence of SCAP, RORs would no longer repress the expression of those lipid metabolic genes. We will also try to over-express

Insig2 gene by AAV vector in the RORs deficient liver, to test whether it could restore the expression of lipid genes back to normal level.

Figure 4.1 RORα and RORγ are redundant in regulating expression of clock genes and metabolic genes. Ratio of gene expression in Cre over GFP for (A)

RORα and RORγ,(B) clock genes Bmal1 and Cry1, and (C) lipid metabolic genes including Acacb, Fasn, Elovl6, Gpam, Acly in RORs single or double mutant liver.

Figure 4.2 Transcriptome analysis of RORs single and double mutant mice liver. (A) Heatmap of the ROR single mutant and double mutant. The color indicate the log2 fold change ratio of Cre/GFP. The cut-off used here is fold change over 1.3 with 15% FDR. (B) Gene ontology analysis of the down- regulated genes and up-regulated genes.

Figure 4.3 Comparison of liver RORα and RORγ cistromes. (A) Scatter plot of

RORα and RORγ ChIP-seq signals. All the binding sites have been filtered by the

KO sample (KO/WT>3). (B) Gene ontology analysis of the genes closest to the binding sites. (C) Screenshot of the RORα and RORγ binding sites near the well characterized target genes of RORs in the WT samples and KO samples.

Figure 4.4 RORs liver specific depletion leads to accumulated triglycerides after 6 week high fat diet. (A) Hepatic triglyceride measurement of the liver from the control mice and RORs mutant mice fed a normal chow diet and 6-weeks high fat diet. (mg/g) Milligrams of triglycerides per gram of liver weight. (B) Oil

Red O staining of livers from the control mice and RORs mutant mice fed 6- weeks high fat diet at a magnification of 20×.

Figure 4.5 RORs regulation of lipid metabolic genes are restricted around

ZT 22. mRNA expression of Fasn, Acly, Acacb, and Spot14 over a 24-hour period in wild type and RORs mutant mouse liver. Data are expressed as mean±SEM. RU, relative unit after normalized to housekeeping gene Arbp mRNA expression

Figure 4.6 RORs regulation of lipid metabolic genes are restricted to fed mice but not to fasted mice at ZT22. (A) Feeding schedule before the mice dissection. (B) Hepatic triglyceride measurement of the liver from the fed and fasted control mice and RORs mutant mice fed on 6-weeks high fat diet. (mg/g)

Milligrams of triglycerides per gram of liver weight. (C) mRNA expression of lipid metabolic genes at ZT22 normalized to Arbp, measured by RT-qPCR, in fed or fasted mice.

Figure 4.7 Differentially expressed nascent transcripts in RORs mutant mice liver. (A) Scatter plot of eRNAs tag count in WT and RORs mutant mouse

89 liver. Differentially expressed eRNAs are marked by different colors. (B) De novo motif analysis of the down-regulated and up-regulated eRNAs.

Figure 4.8 Up-regulated and down-regulated eRNAs were enriched in different phase. (A) Fraction of eRNA in each circadian phase that up-regulated in RORs double mutant liver. (B) Fraction of eRNA in each circadian phase that down-regulated in RORs double mutant liver. (C) Genome browser view of circadian eRNA transcription near the TSS of Naps2 and Thrsp in the wild type mice, and their regulation in WT and RORs mutant mice at ZT22.

Figure 4.9 RORs repress nuclear Srebp1c level by activating Insig2 expression around ZT22. (A) Western blot of precursor Srebp1c, active form of nuclear Srebp1c, and loading control Pol II in the liver of RORs floxed mice injected with AAV-GFP or AAV-Cre, harvested at ZT22 or ZT10. (B) mRNA expression of Srebf1 at ZT22 and ZT10. (C) mRNA expression of Insig2 over a

24-hour period in wild type and RORs mutant mouse liver. (D) Genome browser view of RORa and RORg binding near the Insig2 TSS.

CHAPTER 5

Future Directions

The text, figures, and legends in this chapter were the work of Yuxiang Zhang with the following exceptions. Zheng Sun assisted with the metabolic assays in ultradian mouse model and performed the Oil Red O staining in those mice.

93 5.1 Summary

Through the dissertation, we explored the mechanisms of circadian regulation of hepatic metabolism by nuclear receptors Rev-erbs and RORs. In Chapter 3, we showed that Rev-erbα modulates the clock and metabolism by different genomic mechanisms. Clock control requires Rev-erbα to bind directly to the genome at its cognate sites, where it competes with activating ROR TFs. By contrast, Rev- erbα regulates metabolic genes primarily by recruiting the HDAC3 corepressor to sites to which it is tethered by cell type-specific transcription factors. Thus, direct competition between Rev-erbα and ROR TFs provides a universal mechanism for self-sustained control of molecular clock across all tissues, whereas Rev-erbα utilizes lineage-determining factors to convey a tissue-specific epigenomic rhythm that regulates metabolism tailored to the specific need of that tissue.

In Chapter 4, we discovered that hepatic depletion of RORs increased expressions of genes involved in lipogenesis as well as the hepatic triglyceride levels, specifically at ZT22 (5 AM) and during feeding, but not at ZT10 (5 PM) nor during fasting. GRO-seq analysis suggested that regulation of lipid metabolism by RORs was mediated by SREBP1-c. Indeed, ROR depletion significantly up- regulated nuclear SREBP1-c protein level specifically at ZT22. Overall, our study reveals a time-specific regulation of liver lipid metabolism by RORs, and suggests a potential role of chronotherapy targeting circadian factors in the treatment of metabolic disorders.

5.2 Future Directions

Future study will focus on validating the previous finding with the DBD-dependent and independent functions of Rev-erbα, studying the ligand regulation on those different mechanisms, and further establishing the link between circadian clock, nuclear receptors and liver metabolism by studying the mechanism of circadian misalignment induced hepatic steatosis.

5.2a Overexpression of Rev-erbα and Its DNA Binding Domain In Vivo by

AAV Vectors

In Chapter 3, we reported that Rev-erbα could utilize two mechanisms to regulate its target gene expression. The DBD-dependent mechanism maintains the circadian expression of core clock genes, while the DBD-independent mechanism involves regulation of hepatic metabolism. It has been demonstrated that Rev-erbα regulation of clock genes mainly involves completion with RORs at its direct binding sites instead of recruitment of NCoR and HDAC3. Indeed, it has reported that Rev-erbα DBD alone is able to form homodimers and bind to the

Rev-DR2 consensus site (Harding and Lazar, 1995). Because of this, we hypothesize that DNA binding domain alone could be sufficient to repress the clock genes transcription in vivo. To test this hypothesis, we have generated the

AAV-vectors to over-express wild type Rev-erbα, Rev-DBD, Rev-DBD with

95 additional nuclear localization signal sequences, and Rev-erbα DBD-null mutant

(Figure 5.1). We propose to over-express them in wild-type mouse liver, or in the liver of Rev-arbs deficient mice, and measure the circadian expression of Rev- erbα target genes.

Since the Bmal1 circadian expression were flattened by Rev-erbs deletion in mice liver, we expected the over-expression of wild-type Rev-erbα could help

Bmal1 gene to regain circadian expression. In addition, because the repression of Bmal1 mainly involve DNA binding function of Rev-erbα, we hypothesized that the over-expression of Rev-erbα DNA binding domain could also bind to clock gene promoters and enhancers and rescue the Bmal1 circadian expression. The result will shed light on the possibility on regulate Rev-erbα DBD-depend target genes including clock genes, without disturbing the DBD-independent targets such as metabolic genes.

5.2b Manipulating Cellular Heme Level by Alas1 Deletion in Mice

In Chapter 3, we discussed that since Rev-erbα could regulate its target by two discrete mechanisms, the Rev-erbα ligands, such as its endogenous ligand

Heme, which specifically affect Rev-erbα interaction with NCoR/HDAC3 without disrupting DNA-binding, could modulate liver metabolism with lesser effects on the integrity of the circadian clock. To test this hypothesis, we propose to reduce the cellular Heme level by deplete Alas1 proteins, which is the key enzyme in the

96 Heme synthesis pathway, by generating the Alas1 floxed mutant mice using the

CRISPR/Cas9 technology.

We will inject AAV-TBG-Cre in the mice or cross these mice with various cre lines to induce Alas1 deletion in the major metabolic organs, such as liver and adipose tissues. We hypothesize that Heme level will be reduced in the mutant mice and play a role in regulating Rev-erbs trans-repression activity of the metabolic target genes, without affecting the integrity of the cellular clock circuit.

5.2c Mechanisms of Circadian Misalignment Induced Hepatic Steatosis in

Mice

In human, the circadian misalignment has been associated with cardiovascular diseases and metabolic disorders (Morris et al., 2015; Morris et al., 2016). The mechanism of this still remains to be elucidated, but one possibility is that misalignment between the circadian clock gene expression and environmental/behavioral cycles leads to dysfunction of metabolic homeostasis.

Since nuclear receptors Rev-erbs and RORs are of the most important clock factors that link to metabolism, we hypothesized that circadian misalignment might affect the liver metabolism through disruption of the normal functions of

Rev-erbs and RORs.

97 To test this hypothesis, we first used a mice model to mimic the circadian misalignment. We put the mice on an ultradian 7-hour light: 7-hour dark schedule, which mimic the schedule of shift-working (Figure 5.2A). We found that the ultradian schedule resulted in increased body weight (Figure 5.2B) and accumulated hepatic triglycerides (Figure 5.2 C, D). For the next step, we propose to measure the food intake behavior of the ultradian mouse. We also plan to characterize the transcriptome profile of the mice on ultradian schedule throughout the 24-hour day, focusing on the expression of clock genes, as well as the metabolic genes targeted by Rev-erbs and RORs. We will compare the result of the food intake rhythm and clock gene expression rhythm to check if the misalignment exist between the two rhythms. In addition, we will also examine the Rev-erbs and RORs binding to their target genes, and test whether Rev-erbs and RORs circadian cistromes are also misaligned with the feeding behavior and thus contribute to the metabolic dysfunction.

Figure 5.1 Schematic of Rev-erbα Over-expression in mouse liver by AAV vectors. (A) Schematic of the over-expression vector for Rev-erbα, DBD domain,

DBD domain with two NLS sequences, and the DBDm. Red color indicates the

NLS signal. HF is short for HA-Flag tag protein. (B) Western blot for HA-tag in livers over-expressing the vectors. (C) Western blot for Rev-erbα using an antibody only detecting the C-terminal peptides.

Figure 5.2 Ultradian mice model results in hepatic steatosis. (A) Schedule and diet treatment in the ultradian mice model. (B) Body weight at the end of the study. (C) Hepatic Triglyercerides level at the end of the study. (D) Oil Red O staining of the circadian and the ultradian mice liver.

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