Downloaded from the Reactome Database and Used to Filter the Resulting List from the Differential Analysis (Joshi-Tope Et Al., 2005)

Genome-wide and locus-specific approaches to characterize hepatic gene regulation in cancer cachexia

by Kezhuo Zhang

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy

Department of Human Genetics McGill University

Abstract

Cancer-associated cachexia affects 80% of cancer patients and causes 30% of cancer deaths. Many studies have focused on the molecular mechanism of muscle wasting and adipose tissue loss in rodents or human subjects with cancer cachexia. Although the liver is a central player in body energy metabolism, its role in cachexia has rarely been studied. By investigating the gene expression profile of cachectic liver in the C26-induced cachexia mouse model, we aim to discover how this tissue contributes to energy wasting and metabolic regulation in cancer cachexia. The transcriptional profile of cachectic liver shows upregulated expression of genes involved in cholesterol biosynthesis and repressed expression of genes involved in ketogenesis and TCA cycle.

Moreover, chromatin immunoprecipitation (ChIP) coupled with next generation sequencing

(ChIP-seq) provides opportunities to scrutinize the genome-wide epigenetic status of the cachectic liver and identify transcriptional regulatory programs that determine this. The bioinformatic analysis of these active DNA elements could reveal the transcription factors involved in the hepatic response to cachexia. In addition to the well-known cytokine regulators implicated in cancer cachexia (such as TNFα and IL-6), the search for circulating cachectic factors has proceeded slowly. It is necessary to build a comprehensive catalogue of cachectic factors, whose contribution to energy wasting can be evaluated in different organs. We performed a time-series gene expression analysis of cachectic cells C26 and tumor tissue after xenograft transplantation. The bioinformatic analysis has identified a list of cachectic factors, which showed high similarity with

Lewis Lung Carcinoma (LLC), another cachectogenic cells. To better characterize the transcriptional regulators of cholesterol biosynthesis, which is a major metabolic feature of cachectic liver, we developed a novel technology (TALE-AP) to study the DNA locus-specific transcription factor complex by repurposing TALE. We showed that TALE-AP identify SREBP1 as a transcription factor specifically binding to SQLE promoter, which is confirmed by ENCODE 2

ChIP-seq dataset. Other novel transcription factors (such as AEBP1, STAT6, SHOX2, CUX2 and

ZNF226) are also identified and the future studies will validate their binding on SQLE promoter and effects on the gene expression of SQLE.

Résumé

La cachexie cancéreuse affecte 80% des patients atteints de cancer et cause 30% de décès par cancer. De nombreuses études antérieures ont porté sur le mécanisme moléculaire de la perte musculaire et la dégradation des tissus adipeux dans les modèles de rongeurs ou les sujets humains atteints de cachexie cancéreuse. Bien que le foie soit un acteur central du métabolisme de l'énergie corporelle, son rôle dans la cachexie n'a jamais été exploré. En étudiant le profil d'expression génique du foie cachectique, nous cherchons à découvrir comment ce tissu contribue au gaspillage d'énergie et à la régulation métabolique lors de la cachexie. En parallèle, l'immunoprécipitation de la chromatine (ChIP) associée au séquençage à haut débit (ChIP-seq) nous offre la possibilité d'examiner l'état épigénétique du génome du foie cachectique. L'analyse bioinformatique de ces

éléments d'ADN actifs pourrait révéler les facteurs de transcription impliqués dans la réponse hépatique à la cachexie. Outre les régulateurs de cytokines connus impliqués dans la cachexie du cancer (tels que TNFα et IL-6), la recherche de facteurs cachectiques circulants progresse lentement. Il est nécessaire de construire un catalogue complet de facteurs cachectiques, dont la contribution au gaspillage d'énergie pourra être évaluée dans différents organes. Nous avons effectué une analyse d’une série chronologique de l'expression génique de la lignée cellulaire cachectique C26 et du tissu tumoral après xénogreffe. L'analyse bioinformatique a identifié une liste de facteurs cachectiques, qui présente une forte similitude avec les facteurs exprimés dans des lignées associées a la cachexie comme les cellules de Lewis Lung Carcinoma (LLC). Pour mieux caractériser les régulateurs de la transcription de la biosynthèse du cholestérol, qui est une caractéristique métabolique majeure du foie cachectique, nous avons reconverti TALE et développé une nouvelle technologie (TALE-AP), qui nous permet d’étudier le complexe de facteurs de transcription spécifique au locus d'ADN. Nous avons démontré que TALE-AP identifie

SREBP1 comme un facteur de transcription se liant spécifiquement au promoteur SQLE, ce qui est confirmé par l'ensemble de données ENCODE ChIP-seq.

Acknowledgments

I would like to thank Dr Rob Sladek for all of his knowledge and guidance through this project. I would like to thank members of Rob’s lab over the years for their help and support with my projects, most notably Dr Albena Pramatarova, Dr Jarred Chicoine, Dr Yoshihiko Nagai, Huan Chu Pham

Dang and Naghmeh Nikpoor. I would also like to thank Professor Marc Prentki and Anfal Almass for their help with the metabolic studies and Chu for his help with the ChIP studies and TALEN cloning.

I am also indebted to Dr. Aimee Ryan and Ross MacKay for their support and dedication. The

Department of Human Genetics has simply been a wonderful place. Lastly, I would like to thank my family for their endless support and help in the pursuit of all of my passions.

Table of Contents

Abstract ...... 3

Résumé ...... 5

Acknowledgments ...... 6

Table of Contents ...... 7

List of Tables ...... 12

List of Figures ...... 13

LIST OF ABBREVIATIONS ...... 15

DEDICATION ...... 17

Contribution of the Authors ...... 18

Thesis Format ...... 19

Chapter 1 General Introduction and Literature Review ...... 20

1.1 Cachexia ...... 21

1.1.1 The Epidemiology of Cachexia ...... 21

1.1.2 Cancer-associated Cachexia (CAC) ...... 22

1.1.3 The Metabolism of Cancer and Host ...... 23

1.1.4 The Peripheral Tissues in Cancer-associated Cachexia ...... 33

1.1.5 Animal Models ...... 39

1.1.6 Discovery of Cachectic Factors ...... 40

1.1.7 Therapeutic Approaches for Cancer-Associated Cachexia ...... 42

1.2 Transcriptional Regulation: Epigenomic Profiling by ChIP-Seq ...... 43

1.2.1 Epigenetic Regulation: Histone and DNA Modification ...... 44

1.2.2 Metabolism and Transcriptional Regulation ...... 45

1.2.3 Identification of Active Chromatin Regions ...... 46

1.2.4 ENCODE and Histone Marks ...... 47

1.2.5 Transcription factors involved in cachexia progression ...... 48

1.2.6 Transcription Factor Networks Inferred from Active DNA Elements ...... 50

1.2.7 Challenges and Controversies ...... 50

1.3 Locus-specific Transcriptional Regulation ...... 54

1.3.1 Current Methods to Identify Locus-Specific Transcription Factor Complexes ...... 54

1.3.2 Genome-Editing Tools: TALENs and CRISPRs ...... 58

1.3.3 Applications of TALENs in Genome and Chromatin Modification ...... 59

1.3.4 Challenges of Applying Genome Editing Tools ...... 60

1.3.5 Repurposing TALEs for Locus-Specific TF Complex Studies ...... 61

1.4 Objectives of this Study ...... 62

Chapter 2 Materials and Methods ...... 63

2 Summary ...... 64

2.1 Materials and Methods for Chapter 3 ...... 64

2.1.1 C26 Cell Culture and C26 Conditioned Medium (C26-CM) Preparation ...... 64

2.1.2 Gene Expression Analysis of Liver and Tumor ...... 65

2.1.3 Bioinformatic Analysis of Liver Gene Expression Data ...... 65

2.1.4 Bioinformatic Analysis of Tumor Gene Expression Data ...... 66

2.1.5 Isolation of Mouse Primary Hepatocytes ...... 66

2.1.6 C26-CM treatment ...... 68

2.1.7 Cholesterol and Triglyceride Content Measurement ...... 68

2.2 Materials and Methods for Chapter 4 ...... 69

2.2.1 Animal Experiments ...... 69

2.2.2 Preparation of Crosslinked Hepatic Nuclear Extracts ...... 69

2.2.3 Chromatin Immunoprecipitation ...... 70

2.2.4 ChIP-seq Data Analysis ...... 71

2.2.5 Motif analysis ...... 73

2.3 Materials and Methods for Chapter 5 ...... 73

2.3.1 Strategy for Choosing the TALE Binding Site ...... 74

2.3.2 Design and Construction of TALE-HBH ...... 74

2.3.3 Cell Culture ...... 75

2.3.4 Evaluation of TALE-HBH ...... 75

2.3.5 Interference test ...... 76

2.3.6 Protocol Optimization ...... 77

2.3.7 TALE-Affinity Purification (TALE-AP) ...... 78

2.3.8 Sample Preparation for Mass Spectrometry ...... 79

2.3.9 Mass Spectrometry Data Analysis ...... 79

Chapter 3 Gene Expression Profiling of Cachectic Liver and Tumor ...... 80

3 Summary ...... 81

3.1 Introduction ...... 81

3.2 Results ...... 82

3.2.1 Gene Expression Response in Liver and Tumor as Cachexia Progresses ...... 82

3.2.2 Weight Loss of Cachectic Mice and Consistent Changes of Gene Expression in Cachectic Mouse Liver ...... 83

3.2.3 Pathway Enrichment Analysis of Differentially Expressed Genes in Cachectic Liver ...... 84

3.2.4 Metabolic Mapping of Differentially Expressed Genes in Cachectic Liver .... 85

3.2.5 Search for Cachectic Factors Using C26 Cell and Tumor Expression Data .... 86

3.2.6 Metabolism Studies of Mouse Primary Hepatocytes Treated With C26-CM . 87

3.2.7 Summary of the Metabolic Changes in Cachectic Liver ...... 88

3.3 Discussion ...... 88

Chapter 4 Epigenetic Profiling of Cachectic Liver with ChIP-seq ...... 107

4 Summary ...... 108 9

4.1 Introduction ...... 108

4.2 Results ...... 110

4.2.1 Generation of ChIP-seq Data ...... 110

4.2.2 Identification of Upstream Transcriptional Networks Mediating the Gene Expression Changes of Cachectic Liver ...... 110

4.2.3 Differential ChIP-seq Peaks Analysis ...... 111

4.2.4 Integrative Analysis of Multiple Histone Marks and Correlation with Gene Expression ...... 112

4.2.5 Motif Analysis Combined with Microarray Data ...... 112

4.3 Discussion ...... 113

Chapter 5 TALE-AP: A Novel Technology to Identify Locus-Specific Transcription Factor Complexes ...... 129

5 Summary ...... 130

5.1 Introduction ...... 130

5.2 Results ...... 131

5.2.1 TALE-AP Work Flow ...... 131

5.2.2 Initial Evaluation of TALE Protein Stability, DNA Binding Specificity and DNA Purification Yield ...... 132

5.2.3 TALE Effects on Local Gene Expression and Epigenetic Modifications ..... 134

5.2.4 Optimization of TALE-AP Protocol to Reach Mass Spectrometer Sensitivity ...... 135

5.2.5 Identification of Novel Transcription Factors Bound to the SQLE Promoter...... 137

5.3 Discussion ...... 138

Chapter 6 : Discussion, Future Directions and Conclusions ...... 154

6 Summary ...... 155

6.1 Discussion and Overview of Findings ...... 155

6.1.1 Metabolic Changes of Cachectic Liver ...... 158

6.1.2 Cachectic Factors ...... 159 10

6.1.3 Transcription Factor Network ...... 160

6.1.4 Locus-specific Transcription Regulation ...... 161

6.2 Future Directions ...... 164

6.2.1 The Role of Genomics in Understanding Cancer-Associated Cachexia ...... 165

6.2.2 Future Approaches and Applications in Studying Single Chromatin Loci .... 167

6.3 Conclusions ...... 168

References ...... 169

List of Tables

Table 1.1: Estimates of the prevalence of cachexia in diseases ...... 21

Table 1.2: Drugs for treating cancer-associated cachexia and their effects on host cells and

tissues ...... 42

Table 1.3: Summary of Encode histone marks and their putative functions ...... 47

Table 3.1: Differentially expressed transcription factors and co-regulators implicated in

metabolic pathways of cachectic liver ...... 94

Table 3.2: The expression level change of key enzymes in the gluconeogenic pathway using

glycerol and lactate as substrate ...... 98

Table 3.3: Identification of “cachectic factors” ...... 99

Table 3.4: Top 100 upregulated genes in cachectic liver under fed and fasting conditions ...... 103

Table 4.1: Number of sequencing reads and mapping statistics ...... 116

Table 4.2: Top 100 differential H3K27ac ChIP-seq peaks in cachectic liver ...... 122

Table 5.1: Genomic coordinates and sequences of 5 TALE binding sites used in this study .....142

Table 5.2: Summary of designed TALEs regarding to protein expression and binding specificity

test by ChIP-qPCR ...... 145

Table 5.3: Primers used in ChIP-qPCR to test the TALE binding specificity ...... 146

Table 5.4: Parameters optimized in TALE-AP ...... 151

List of Figures

Figure 3.1: Overview of project pipeline to identify gene expression response in cachectic liver

and tumor as cachexia progresses ...... 92

Figure 3.2: Substantial gene expression changes occurred in cachectic liver ...... 93

Figure 3.3: Gene set enrichment analysis (GSEA) of cachectic liver microarray data ...... 95

Figure 3.4: Expression changes for enzymes involved in cholesterol biosynthesis ...... 96

Figure 3.5: Expression changes of enzymes involved in ketogenesis ...... 97

Figure 3.6: Highly expressed secreted protein in C26 and LLC models and the expression levels

of their cognate receptors in liver ...... 100

Figure 3.7: Cholesterol and Triglyceride measurement in C26 conditioned medium-treated

mouse primary hepatocytes ...... 101

Figure 3.8: Summary of metabolic changes in cachectic liver ...... 102

Figure 4.1: Overview of experimental and computational pipeline for identification of cachexia-

responsive epigenetic changes in liver ...... 116

Figure 4.2: Identification and characterization of ChIP-seq peaks in cachectic liver ...... 118

Figure 4.3: Distribution of ChIP-seq peaks relative Transcription start site in cachectic

liver ...... 120

Figure 4.4: Model characterization to identify cachexia-responsive H3k27ac peaks ...... 121

Figure 4.5: Identification of active enhancers responsible for cachectic liver gene expression

changes ...... 126

Figure 4.6: Motif analysis of H3k27ac peaks in cachectic liver ...... 127

Figure 4.7: Motif analysis of differentially active DNA elements in fasted-PBS injected mouse

liver compared to fed-PBS injected group ...... 128

Figure 5.1: Development of TALE-AP to identify de novo locus-specific transcription factors

complex ...... 140

Figure 5.2: Strategy for TALE promoter targeting shown for the SQLE promoter ...... 141

Figure 5.3: Nuclear localization of TALE-LDLR when induced with different concentration of

tetracycline ...... 143

Figure 5.4: Preliminary test of TALEs targeting promoter of LDLR, SREBP2, HMGCR,

HMGCS1 and SQLE ...... 144

Figure 5.5: One mismatch on the first RVD of TALE with the target sequence in the human

genome abolished the binding specificity ...... 147

Figure 5.6: Assessment of TALE regarding to interference with local gene expression and

ChIPed DNA composition ...... 148

Figure 5.7: Distance of TALE binding sites and the interference with local gene expression

shown by TALEs targeting SQLE promoter ...... 149

Figure 5.8: Optimization of TALE-AP efficiency ...... 150

Figure 5.9: DNA purification yield of reChIP ...... 152

Figure 5.10: Identification of novel transcription factors binding the SQLE promoter ...... 153

LIST OF ABBREVIATIONS

ACLY - ATP citrate lyase IGV - Integrative genomic viewer AP - Affinity purification IL-6 - Interleukin 6 BWA- Burrows-Wheeler Aligner IFNg - Interferon gamma C26 - Colon-26 LMF - Lipid mobilization factor CEBPB - CCAAT/enhancer binding protein LLC - Lewis lung cancer beta MACS - Model-based Analysis of ChIP-seq CHF - Congestive heart failure MAC16 - Murine adenocarcinoma 16 ChIP - Chromatin immunoprecipitation MC4R - Melanocortin 4 Receptor ChIP-seq - Chromatin MDH2 - Malate dehydrogenase 2 immunoprecipitation-sequencing MTHFD - Methylenetetrahydrofolate CM - Conditioned medium dehydrogenase COPD - Chronic obstructive pulmonary NGS - Next-generation sequencing disease PBS - Phosphate buffered saline CKD - Chronic kidney disease PFK - Phosphofructokinase CRISPR - Clustered regularly interspaced PIF - Proteolysis inducing factor short palindromic repeats PI3K - Phosphoinositide 3-kinase DNA - Deoxyribonucleic acid qPCR - Quantitative polymerase chain FBS - Fetal bovine serum reaction FOX - Forkhead box RA - Rheumatoid arthritis GSEA - Gene set enrichment analysis RNA - Ribonucleic acid HIV - Human immunodeficiency virus RXR - Retinoid X receptor HK - Hexokinase SHMT2 - Serine hydroxymethyltransferase HNF - Hepatocyte nuclear receptor 2 H3K27ac - Histone 3 lysine 27 acetylation SNP - Single-nucleotide polymorphisms H3K4me1 - Histone 3 lysine 4 mono- SREBP - Sterol regulatory element-binding methylation protein H3K4me3 - Histone 3 lysine 4 tri- STAT3 - Signal transducer and activator of methylation transcription 3 LCN2 - Lipocalin 2 TALEN - Transcription activator-like IDH - Isocitrate dehydrogenase effector nuclease 15

TCA - Tricarboxylic acid ug - microgram TFBS - Transcription factor binding site TNF - Tumor necrosis facto

xvi

DEDICATION

To my family for their unconditional support and love

Contribution of the Authors

Chapter 3: Drs Rob Sladek and Mathieu Miron performed the microarray experiments using the facilities at the McGill University and Genome Quebec Innovation Center. Kezhuo Zhang and

Anfal Almass performed the experiments using mouse primary hepatocytes, including cholesterol and TG measurements. Kezhuo Zhang performed the bioinformatic analysis and wrote the chapter.

Dr Rob Sladek conceived, oversaw and financed the study.

Chapter 4: Dr Albena Pramatarova performed the crosslinking of mouse liver. Kezhuo Zhang and

Huan Chu Pham Dang performed the ChIP-seq experiments. Kezhuo Zhang performed bioinformatic analysis and wrote the chapter. Dr Rob Sladek conceived, oversaw and financed the study.

Chapter 5: Kezhuo Zhang and Huan Chu Pham Dang performed the TALEN cloning and stable cell line construction. Kezhuo Zhang performed the interference test and all experiments for the protocol optimization of TALE-AP. Kezhuo Zhang and Daniel Moses performed the sample preparation for mass spectrometry analysis. Kezhuo Zhang analyzed all data and wrote the chapter.

Dr Rob Sladek conceived, oversaw and financed the study.

Thesis Format

This thesis is in the traditional style and consists of a general introduction and literature review, three chapters of main contributions, and a general discussion. Chapter 3, 4, and 5 contain unpublished results that will be submitted for publication after incorporating additional research results as described in each Chapter and in Chapter 6. The format of the thesis respects the McGill

University Guidelines for Thesis Preparation.

Chapter 1

General Introduction and Literature Review

1.1 Cachexia

Cachexia is a progressive deterioration of nutritional status characterized by the dramatic loss of muscle and adipose tissue, which is commonly seen in patients with late-stage cancer, patients infected with human immunodeficiency virus and other chronic diseases. Previous studies have mainly focused on the molecular mechanisms underlying muscle wasting and adipose tissue loss in cachexia, while the hepatic response to cachexia has been poorly investigated. Moreover, the lack of a systematic catalogue of circulating factors produced by the cancer cells, host tumor stroma and other tissues that lead to metabolic changes in cancer cachexia renders our understanding of the pathogenesis of this process incomplete. In this section, I will review the cachexia studies concerning the epidemiology of cachexia, the metabolism of cachectic host and tumor tissue, the discovery of cachectic factors, therapeutic treatment and common animal models for cancer-associated cachexia.

1.1.1 The Epidemiology of Cachexia

Cachexia literally means “bad condition”, a word derived from the Greek kakos hexis (Gorter,

1991). Cachexia is commonly seen in patients with late-stage cancer, HIV infection and other chronic diseases including chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), chronic kidney disease (CKD), inflammatory disease such as rheumatoid arthritis

(RA) and chronic infections and sepsis (Larkin, 1998). Due to the many multifactorial syndromes and pathogenic mechanisms that can cause cachexia, the diagnostic criteria for cachexia are not well-defined (Garcia et al., 2005). In 2006, researchers and clinicians endeavored to reach agreement on the definition of cachexia, which they defined as weight loss greater than 5% or weight loss greater than 2% showing massive adipose tissue and skeletal muscle loss (Evans et al.,

2008; Tisdale, 2002). According to these diagnostic criteria, the number of patients in the USA

suffering from cachexia associated with COPD, cancer and CKD in 2013 are 600,000, 430,000 and 8,000 respectively (Table 1.1) (von Haehling & Anker, 2010). Among cancer patients, 50-80% of advanced stage malignant tumors are affected by cachexia, which causes anorexia, fatigue and malaise and may eventually lead to death. In total, it has been estimated that nearly 20-30% of cancer deaths are caused by cachexia (Muscaritoli, Bossola, Aversa, Bellantone, & Fanelli, 2006;

B. H. L. Tan & K. C. H. Fearon, 2008).

Table 1.1: Estimates of the prevalence of cachexia in diseases: COPD, CHF, Cancer,

Rheumatoid arthritis and CKD in USA. The prevalence of cachexia is 30% in cancer patients and can reach 50% in CKD. Population assumption (2013-14): USA (319 million).

Prevalence in Prevalence in Number of Disease population (%) patients at risk (%) patients in the USA

CHF 2.0 10 510,000

Cancer 0.5 30 430,000

RA 0.8 10 50,000

CKD 0.1 50 80,000

COPD 3.5 35 600,000

1.1.2 Cancer-associated Cachexia (CAC)

Clinical studies and studies of the molecular mechanisms of CAC have described its pathogenic progression as a three-stage process: precacheixa, cachexia and refractory cachexia (K. Fearon,

Arends, & Baracos, 2013; Muscaritoli et al., 2006). The rate of progression varies among

individuals and can be affected by the cancer types and stage, patient genetic background, severity of insulin resistance and systematic inflammation, food intake and anorexia, the type of treatment and the response to cancer therapy (K. C. H. Fearon, 2008; Larkin, 1998). In a cohort of 390 patients, the prevalence of cachexia was highest in patients with pancreatic cancer (88.9%), followed by gastric (76.5%) and esophageal cancer (52.9%) (K. C. H. Fearon, 2008; L. Sun, Quan,

& Yu, 2015; B. H. Tan & K. C. Fearon, 2008). Despite the high prevalence of CAC, the prevalence of cachexia for different types of cancer has not been well documented at a population level.

Although anorexia and fatigue are the two most common symptoms in CAC and differentiate CAC from starvation, the massive loss of adipose tissue (up to 80%) and muscle cannot be explained completely by changes in food intake (Esper & Harb, 2005; Mantovani, Maccio, Massa, &

Madeddu, 2001). As a result, the underlying mechanisms causing cancer cachexia are not well understood, but are known to involve multiple tissues and organs (such as adipose tissue, muscle, liver and brain) and complex, multifactorial pathogenic mechanisms (Buzby et al., 1980; Costelli et al., 1993). Identification of these mechanisms has involved searching for causal cachectic factors that “melt” muscle and adipose tissue, which started in the 1980s, with the discovery of the macrophage-secreted cytokine TNFα as the first cachectin (Oliff et al., 1987; Tracey et al., 1987).

1.1.3 The Metabolism of Cancer and Host

The high proliferation rate of cancer cells means that they need to reprogram their metabolic pathways in contrast to normal differentiated cells (Ferreira, 2010). In addition, tumor tissue can alter the metabolic functions of host cells and organs bearing through soluble factors secreted by the malignant cells or by non-transformed host cells in the tumor stroma. In this section, I will summarize the main metabolic changes seen in cancers and host cells and tissues; review the molecular pathways driving these changes; and compare the metabolism of CAC and cancer.

1.1.3.1 The Metabolism of Cancer

The Warburg effect was reported by Otto Warburg in 1924, when he discovered that cancer cells utilized glucose in a manner distinct from normal cells (Ferreira, 2010). Under aerobic conditions, normal cells metabolize glucose by glycolysis in the cytosol into pyruvate, which enters the mitochondria and is processed by oxidative phosphorylation to generate energy. Under anaerobic conditions such as hypoxia, very little pyruvate is transported into the mitochondria of normal cells and the majority is metabolized into lactate (Hsu & Sabatini, 2008). However, unlike normal cells, tumor cells favor glycolysis and lactate fermentation, regardless of the oxygen concentration

(Liberti & Locasale, 2016). As a result, four moles of ATP are produced per mole glucose in cancer cells in contrast to 36 moles of ATP per mole glucose by aerobic glycolysis in normal cells. Due to the ATP producing deficiency in tumor mitochondria, tumor glucose uptake measured by 18F- deoxyglucose positron emission tomography (FDG-PET) is significantly higher than for normal cells. Since PI3K signaling through AKT can directly activate glucose transport, hexokinase and phosphofructokinase, constitutive activation of the PI3K/AKT pathway renders cells independent of high concentrations of glucose (Elstrom et al., 2004). The phosphatase and tensin homolog, a well-characterized tumor suppressor that is mutated in many different types of cancer, negatively regulates PI3K/AKT. Recently it has been shown that cancer cells can overcome the lack of available glucose by increasing glycogen storage to maintain cell viability and proliferation (Zois

& Harris, 2016). More recent progress on cancer metabolism will be discussed in the following sections.

Another important nutrient supporting tumor growth and substrate biosynthesis is glutamine, which acts as a source of carbon and nitrogen for biosynthesis of compounds such as pyrimidine nucleotides, glucosamine-6-phosphate and nonessential amino acids (Pavlova & Thompson, 2016).

Harry Eagle first demonstrated that the uptake of glutamine is 10- to 100- fold higher relative to other amino acids in cultured HeLa cells (Eagle, 1955). Several transcription factors including c- myc and the Rb tumor suppressor have been shown to regulate glutamine uptake through transcriptional effects on the glutamine transporters ASCT2, SN2 and the glutaminase GLS1

(Pavlova & Thompson, 2016).

In addition to the ATP required for cellular processes, proliferating cells and tumors need molecular substrates to synthesize macromolecular components such as DNA, cellular membranes and organelles. Therefore, providing enough substrates for these building blocks is critical for cancer cell proliferation. Many of the precursors for synthesizing lipids, protein and nucleic acids are derived from TCA cycle intermediates (DeBerardinis, Lum, Hatzivassiliou, & Thompson,

2008). For example, citrate is transported out of mitochondria and is converted to oxaloacetate and acetyl-CoA, the substrate for cholesterol biosynthesis, ketogenesis and fatty acid synthesis (Zaidi,

Swinnen, & Smans, 2012). Similarly, the efflux of malate from the mitochondria into the cytosol, where it is converted into oxaloacetate and phosphoenolpyruvate, provides substrates for triglyceride synthesis (Q. Liu et al., 2013). This cytosolic efflux of TCA cycle intermediates is known as cataplerosis (Ratnikov et al., 2015); and must be balanced by replenishing these mitochondrial metabolites by conversion of cytosolic substrates through a process called anaplerosis. The four major anaplerotic reactions include formation of oxaloacetate from either pyruvate or aspartate; of α-ketoglutarate from glutamate; and of succinyl-CoA from fatty acid beta- oxidation (Owen, Kalhan, & Hanson, 2002). Therefore, the rate of cell growth can be monitored through the anaplerotic flux. It is worth noting that another major source of anaplerotic substrates is provided by the metabolism of amino acids other than glutamate and aspartate; for example, using amino acids provided by tissue protein breakdown as part of CAC.

1.1.3.2 Recent Advances in Cancer Metabolism

The features of aerobic glycolysis along with the high flux of anaplerosis and cataplerosis in cancer cells mean that tumors need to reprogram their metabolic pathways and nutrients dependence to meet their high growth rate (Vander Heiden, 2011). To systematically profile the nutrient dependence of cancer cells, Jain et al. used mass spectrometry to measure the consumption and release of 219 metabolites in NCI-60 cancer cells grown in vitro (M. Jain et al., 2012); and correlated the consumption of these metabolites with the cell proliferation rate and gene expression profiles across the panel. Their results highlight the importance of glycine consumption and biosynthesis pathways in mitochondria, both of which strongly correlate with the proliferation rate.

Follow-up clinical studies indicated that expression levels of the three genes involved in glycine synthesis (SHMT2, MTHFD2 and MTHFD1L) are associated with breast cancer survival (Nilsson et al., 2014). The biological explanation underlying this finding is the utilization of glycine in de novo purine synthesis, the building component of DNA.

More comprehensive studies in other cancer cell types suggest that different tumors may show different nutrient dependence. For example, a functional screen using a RNAi-based loss-of- function library showed that high expression of phosphoglycerate dehydrogenase (PHGDH) was critical for the proliferation of estrogen receptor (ER)-negative breast cancer cells. PHGDH catalyzes the first step of the conversion of phosphoglycerate into pyruvate in the serine biosynthesis pathway; its beneficial effects on cancer cells are a result of promoting the anaplerotic flux of glutamine into the TCA cycle (Possemato et al., 2011). Interestingly, the genomic region of PHGDH was shown to gain copy number recurrently in breast cancers (Possemato et al., 2011).

The examination of metabolic enzyme isoforms has also uncovered some striking metabolic features of cancer cells. For example, the catalytic enzyme in the last step of glycolysis, only one

isoform of pyruvate kinase (isoenzyme M2, PKM2) is expressed in tumor cells (Christofk et al.,

2008). The switch of PKM2 to PKM1 isoforms in tumor cells could even reverse the Warburg effect. A more extreme example is the mutation of isocitrate dehydrogenase (IDH1 and IDH2) seen in the majority of low grade of gliomas and high grade gliomas (Hartmann et al., 2009). The mutated IDH1 enzyme exhibits very different biochemical properties and catalyzes conversion of isocitrate into 2-hydroxyglutarate (2-HG) instead of 2-oxoglutarate produced by wild type IDH1

(Dang et al., 2009). This is supported by the finding that 2-HG levels are significantly elevated in gliomas containing IDH1 mutations. Although the contribution of IDH1 mutations and 2-HG to cancer progression remains unclear, several studies indicate that 2-HG promotes cancer progression, resulting in 2-HG being called an “oncometabolite”. Considering the structural similarity between 2-HG and alpha-ketoglutarate, it has been proposed that 2-HG will interfere with alpha-ketoglutarate (a-KG)-dependent dioxygenases, some of which play a role in DNA repair and the epigenetic modification of DNA and chromatin (Kondo, Katsushima, Ohka,

Natsume, & Shinjo, 2014). The effect of 2-HG on these a-KG-dependent dioxygenases was systematically tested and it was found that 2-HG is a potent inhibitor of histone demethylases that contain a JmjC domain containing and the TET family of 5-methylcytosine (5mC) hydroxylases

(W. Xu et al., 2011). Besides these epigenetic enzymes, 2-HG also inhibits the activity of prolyl hydroxylase (PHD), which is responsible for the degradation of hypoxia-inducible factor 1-alpha

(HIF1A), a critical transcription factor for cancer survival and metabolism (Zhao et al., 2009). The discovery of 2-HG as a therapeutic target has stimulated the examination of the effect of other intermediary metabolites on tumor growth and the search for other oncometabolites.

The abnormal expression pattern of citrate synthase (CS), aconitase 2 (ACO2), isocitrate dehydrogenase (IDH2), succinate dehydrogenase (SDH), fumarate hydratase (FH) and malate dehydrogenase (MDH2) has been documented in different tumors including pancreatic and ovarian 27

cancer, gastric cancer and prostate cancer (Q. Liu et al., 2013). Molecular mechanism studies to fully characterize these oncometabolites are ongoing, but their effects on transcriptional regulation have gained much attention. In addition to binding to some key TFs (for example, 2-HG to PHD), oncometabolites can serve as substrates for epigenetic modifications of chromatin. In 2014, a comprehensive atlas of histone modifications was built using proteomics techniques, leading to the discovery of 67 novel histone modifications including succinylation, malonylation and crotonylation (Tan et al., 2011). The mutation of SDH, seen in neuroendocrine and renal cancers, results in the accumulation of succinate, which interfere with genome-wide histone succinylation and altered gene expression (Bardella, Pollard, & Tomlinson, 2011).

1.1.3.3 Information Provided by Genomics and Proteomics

The dependence of cancer cells on specific nutrients and their metabolic reprogramming features are highly heterogeneous across different types of tumors. Therefore, it is necessary to investigate metabolic changes systematically in different cancers to identify metabolic vulnerabilities that can potentially be exploited for treatment. In addition to projects studying the consumption and release of metabolites (CORE), several international projects have been initiated including Metabolic Flux

Analysis (MFA), The Cancer Proteome Atlas (TCPA) and The Cancer Genome Atlas (TCGA) (M.

Jain et al., 2012; J. Li et al., 2013; Tomczak, Czerwinska, & Wiznerowicz, 2015; Zamboni, Fendt,

Ruhl, & Sauer, 2009). The CORE project measured the consumption and release profile of 219 metabolites from media across the NCI-60 cancer cell lines; and identified glycine consumption and expression of the mitochondrial glycine biosynthetic pathway as strongly correlated with rates of proliferation across cancer cells. To implement MFA the cells are cultured in C13-labled glucose and the subsequent C13-labeled metabolites are quantified by LC-MS to quantitatively model the metabolism flux in vivo. TCPA has profiled more than 130 proteins across 8167 tumor samples,

including metabolism-related proteins. The longest running of these projects, TCGA, aimed to catalogue the genetic mutation profiles responsible for 33 different types of cancer by combining

NGS with bioinformatic analysis (P. Kim, Cheng, Zhao, & Zhao, 2016). The genomic sequence of 11,000 patients revealed that metabolism-related genes are commonly mutated or have altered expression levels in cancer such as PKM, HK2, IDH1 and HIF1A. By integrating these high- throughput datasets with functional annotation databases such KEGG, COSMIC and DrugBank,

~2000 cancer cell metabolism genes (cmGenes) were discovered, of which 78% show differential gene expression across eight cancer types in the TCGA data. More than 70% of these cmGenes remain to be deeply studied in cancers. Interestingly, pathway enrichment analysis by GSEA showed that cmGenes are enriched for the TCA cycle, butanoate, pyruvate, glycine, serine and threonine metabolism pathways, which confirmed previous studies on the involvement of the TCA cycle and one-carbon metabolism in carcinogenesis (DeBerardinis et al., 2008).

1.1.3.4 Regulation of Metabolism in Tumors

Following the discovery of the widespread occurrence of abnormal metabolism in cancer cells, many signaling pathways have been identified that regulate tumor metabolism. For example, the

Warburg effect is regulated via the PI3K/AKT and AMP-activated protein kinase (AMPK)-LKB1 pathways, as well as by hypoxia-inducible factor (HIF), p53 and MYC (Levine & Puzio-Kuter,

2010). Specifically, PI3K/AKT promotes glycolysis by transactivating expression of glucose kinase and phosphofructokinase 2 (PFK2) and promoting the translocation of glucose transporters to the cell membrane. In addition to upregulating glycolytic enzymes such as PFK2 and glucokinase, HIF1A can activate pyruvate dehydrogenase kinase which suppresses the activity of pyruvate dehydrogenase and then reduces the transport of pyruvate into the TCA cycle (J. W. Kim,

Tchernyshyov, Semenza, & Dang, 2006). Thus, under ambient oxygen conditions, HIF1A can

reinforce the flow of pyruvate derived from glucose into lactate fermentation. Myc acts as a transcription activator of lactate dehydrogenase A (LDHA) and PDK1, the two key enzymes regulating lactate fermentation (Shim et al., 1997). The energy sensor AMPK acts as a negative regulator of mechanistic target of rapamycin (mTOR), which in turn can increase the activity of

HIF1A. Therefore, the loss of AMPK could increase HIF1A activity, which is commonly seen in cancer. Metformin is an AMPK agonist that used as first-line therapy for diabetes. Since this antidiabetic drug could be used to slow down tumor growth by redirecting glycolysis, clinical studies are ongoing to investigate whether metformin can prevent or improve the effectiveness of existing therapies for colon and prostate cancer and other solid tumors (Choi & Park, 2013).

In addition to the loss of tumor suppressors (such as p53) and constitutive activation of oncogenes

(such as PI3K), cancer metabolism can be affected by the tumor microenvironment, (which is characterized by hypoxia, low pH and nutrient deprivation), and by the interaction among these microenvironments and oncogenic signaling pathways (such as between hypoxia and HIF1A)

(Vaupel, Kallinowski, & Okunieff, 1989). Independent of HIF1A, hypoxia inhibits signaling through mTOR, which promotes tumor growth under nutrient deprivation. Moreover, extreme hypoxia (<0.02% O2) could induce the unfolded protein response, which increases cell survival

(Rouschop et al., 2010). Studies of the cause of low pH in tumors are ongoing (for example, to examine the role of excessive glycolysis and poor diffusion of acid inside solid tumors) as are studies to determine how cancer cells survive in acidic environments.

1.1.3.5 Cholesterol and Cancer

Few studies have examined the importance of cholesterol biosynthesis in cancers from the standpoint of genomics and proteomics research; however, the importance of cholesterol for the cancer cell has been recognized by a series of studies published over the last thirty years (Cruz,

Mo, McConathy, Sabnis, & Lacko, 2013). Cholesterol is a major component of the mammalian cell membrane and promotes membrane fluidity; however, excess free cholesterol may lead to increased membrane rigidity, impaired cell signaling by receptors located in lipid rafts and eventually cytotoxicity (Tabas, 1997). As a result, cholesterol homeostasis in the host is maintained tightly through a balance between endogenous cholesterol biosynthesis and exogenous cholesterol transport; and the delivery of dietary cholesterol and removal of excessive cholesterol from peripheral tissues. As a result of the rapid rate of cancer cell division and membrane synthesis, the demand for cholesterol increases during carcinogenesis (Singh, Kumar, & Kapur, 2003). For example, the activity of SREBP2, the key transcriptional regulator of cholesterol biosynthesis, is strongly correlated with cell viability in PC-3 and LNCaP prostate cancer cells (Krycer, Kristiana,

& Brown, 2009).

This finding suggests that strategies to modulate the cholesterol level of tumors with statin could become a promising strategy to treat cancer. Clinical studies have focused on controlling the levels of mevalonate, one of the important intermediates in cholesterol biosynthesis pathway. It has been shown that blockage of mevalonate synthesis by modulating the two key enzymes hydroxyl-3- methylglutaryl CoA reductase (HMGCR) or farnesyl-diphosphate farnesyltransferase (FDFT1) can suppress the breast cancer stem cell and inhibit tumor progression (Ginestier et al., 2012). In glioblastoma, statins could induce apoptosis by suppressing the ERK1/2 pathway (Yanae et al.,

2011). A long-term case-control study of 2656 Danish subjects suggested that the use of statins might also reduce the risk of glioma (Gaist et al., 2013).

However, there are two major questions involving the relationship between cholesterol and cancer pathogenesis that have not been addressed clearly. First, the mechanism through which inhibition of cholesterol biosynthesis suppresses tumor growth remains controversial (Gash, Chambers,

Cotton, Williams, & Thomas, 2017; Hindler, Cleeland, Rivera, & Collard, 2006; M. K. Jain &

Ridker, 2005; Poynter et al., 2005). The direct outcome of reduced mevalonate levels using statins is reduced cholesterol content in cancer cells, preventing them from meeting the high demands of cholesterol for cell membrane synthesis. However, it was found that intermediates of cholesterol biosynthesis such as isoprenoids, sterols and cholesterol itself could interfere with protein modification (Mann & Beachy, 2000). Although not well studied, there are at least five types of lipids which can be covalently attached to proteins: fatty acids, isoprenoids, sterols, phospholipids and glycosylphosphatidyl inositol (GPI) (Eisenhaber, Bork, & Eisenhaber, 1999). For example, biochemical studies indicate that the farnesylation of ras is critical for the interaction with phosphoinositide 3-kinase gamma (PI3Kgamma). The adding of farnesyl pyrophosphate to ras protein is catalyzed by farnesyltransferase (FTase) and treatment using FTase inhibitors in mice with established tumors causes tumor regression (Buss & Marsters, 1995). Cholesterol itself can be attached covalently to the Hedgehog (Hh) protein affecting the diffusion of Hh during development, thus regulating both long- and short-range signaling pathways.

Another controversial issue is the source of cholesterol in tumors (Medes, Thomas, & Weinhouse,

1953). Previous studies highlight the importance of the de novo cholesterol biosynthesis pathway in tumor growth; but in GBM cells, signaling by the mutant EGFR signaling upregulates the expression of LDLR, which imports exogenous cholesterol for tumor survival (Phan, Yeung, &

Lee, 2014). Previous studies have also suggested that while tumor tissue has the capacity for endogenous cholesterol biosynthesis, the fast-growing tumor obtains a large proportion of lipids from the host and not de novo synthesis (Harry, Morris, & McIntyre, 1971). Therefore, it becomes critical to study cholesterol biosynthesis to identify the major source of cholesterol supporting tumor growth. Liver, as the main organ of cholesterol biosynthesis, could provide an exogenous cholesterol source for tumors. This encouraged us to profile the gene expression patterns of the 32

liver and the tumor under cachectic mouse models. Indeed, our results shows elevated mRNA level of cholesterol biosynthesis enzymes in the liver instead of the tumor, which suggests that the tumor may scavenge circulating cholesterol instead of relying on de novo synthesis. Additional studies will be needed to investigate this hypothesis, as the main source of cholesterol may not be the same for different stages and types of tumors.

1.1.4 The Peripheral Tissues in Cancer-associated Cachexia

In this section, I will review the metabolic changes of cachectic host tissues including muscle, adipose tissue, liver and brain. A list of soluble factors secreted by both tumor cells (such as PIF) and host tissues (such as IL-1, IL-6 and TNFα) have been linked to global protein breakdown in muscle, loss of adipose tissue, anorexia and upregulated biosynthetic pathways in liver.

1.1.4.1 Muscle

Wasting of up to 75% of the skeletal muscle can be seen in cachectic patients with 30% weight loss. Muscle protein content is regulated tightly by protein catabolism and protein synthesis

(Melstrom, Melstrom, Ding, & Adrian, 2007), with breakdown occurring through lysosomal, calcium-dependent, caspase-dependent and ubiquitin-proteasome-dependent mechanisms

(Drummond, Dreyer, Fry, Glynn, & Rasmussen, 2009; Lenk, Schuler, & Adams, 2010). In cachexia, activation of the ATP–dependent ubiquitin-proteasome proteolytic pathway is a major cause of muscle wasting (Burger & Seth, 2004; Price et al., 1996). The muscle-specific E3 ubiquitin ligases are induced during cachexia, which are responsible for the specific polyubiquitination of proteins targeted for degradation. Two ubiquitin ligases Fbxo32 (F-Box

Protein 32, also called MAFbx) and MuRF1 can be upregulated at least 7 fold in cancer cachexia animal models (Khal, Wyke, Russell, Hine, & Tisdale, 2005). In addition to protein degradation, the expression of muscle-specific genes can be regulated at the mRNA level. For example, protein 33

levels of the muscle-specific genes actomyosin, actin and myosin are downregulated by cachectic factors including TNFα and IFNg through an RNA-dependent mechanism (D. Cai et al., 2004).

Recent studies have focused on the identification of mediators for the protein degradation pathway in skeletal muscle, which will provide therapeutic targets for cachexia treatment. Recent studies have characterized the role of uncoupling protein family (UCP) in the muscle wasting of cachexia

(Bowen, Schuler, & Adams, 2015). Although the specific isoform of Ucp involved in the cachexia response in muscle has not been identified, injection of TNFα or lipid mobilization factor (LMF) in animal models results in significant upregulation of UCP1, UCP2 and UCP3 (DeJong et al.,

2005). The upregulation of UCP family proteins is associated with increased energy expenditure.

Finally, the key transcriptional regulator of skeletal muscle differentiation and damage repair,

MyoD is downregulated in C2C12 myotubes following TNFα treatment through a pathway mediated by NF-kB. The injection of TNFα or IFNg in mice results in reduction of MyoD mRNA and protein level, leading to muscle dysfunction (Bing et al., 2000; Costelli et al., 1993).

1.1.4.2 Adipose Tissue

The wasting of adipose tissue is attributed to both increased lipolysis and the reduced capacity to synthesize and store lipids (Zhao et al., 2009). It has been shown that cytokines such as TNFα can induce lipolysis by suppressing the cleavage of lipoprotein lipase (LPL), which is a key enzyme hydrolyzing triglyceride in lipoproteins (Zechner, Kienesberger, Haemmerle, Zimmermann, &

Lass, 2009). Mice in which of adipose triglyceride lipase (ATGL) or hormone-sensitive lipase

(HSL) has been knocked-out were resistant to LLC-induced cachexia including loss of white adipose tissue (Argiles, Lopez-Soriano, Busquets, & Lopez-Soriano, 1997; Das et al., 2011). In contrast, the adipose tissue in tumor-bearing mice exhibits reduced capacity for lipid synthesis and storage (Bing et al., 2006). In the MAC16-induced cachexia mouse model, the expression levels

of adipogenic TFs including CCAAT/enhancer binding protein alpha (C/EBPα) and beta (C/EBPβ), peroxisome proliferator activated receptor gamma (PPARγ), and sterol regulatory element binding protein-1c (SREBP-1c) decreased, and was accompanied by reduced expression of their targets, including fatty acid synthase, acetyl CoA carboxylase, stearoyl-CoA desaturase 1 and glycerol-3- phosphate acyl transferase (Jocken et al., 2007).

1.1.4.3 Brain

Cachexia-associated symptoms may be caused by emotional anxiety, depression, physical pain and malfunction of gastrointestinal system, which are attributed to cytokines, neuropeptides, neurotransmitters and the interactions between these molecular pathways (Bing et al., 2000; Plata-

Salaman, 2000). In cachexia, the hypothalamic melanocortin neuropeptide a-MSH can induce anorexia by activating MC3R and MC4R receptors, which are expressed in the hypothalamus

(Krause, James, Ziparo, & Fischer, 1979; Uehara et al., 1992). Blockage of MC4R has been shown to ameliorate the severity of cachexia in the lipopolysaccharide-induced sepsis model (Fetissov et al., 2008). In response to peripheral tumors, cytokines can be produced in several regions of the brain, including the hypothalamus and result in anorexia by inducing anorexigenic neuropeptides.

For example, rats with prostate adenocarcinomas have increased IL-1B levels in the cerebral cortex and the hypothalamus associated with anorexia (Scarlett et al., 2010). A prominent feature of cachexia is increased resting energy expenditure (REE), which can be affected by the central nervous system (CNS) and autonomic nervous system under normal physiological conditions

(Plata-Salaman, 2000). As recent studies have uncovered more details of the role of the CNS in regulating energy balance, thus, providing an alternative to interfere with the pathogenesis of cachexia.

1.1.4.4 Liver

Although the liver coordinates the balance of metabolism between the tumor and the host during cachexia, the role of the liver in cachexia has not been investigated systematically. The value of studying liver metabolism in cachectic subjects is illustrated by the Cori cycle, in which lactate excreted from the tumor is converted into glucose in the liver; and also by the acute phase response

(APR), in which a substantial amount of amino acids are utilized to synthesize proteins in cachectic liver (Plata-Salaman, Ilyin, & Gayle, 1998). Studies have implicated several nuclear receptors in regulating hepatic lipid metabolism (such as LXR, RXR and PPARγ), which modulate fatty acid oxidation, ketogenesis, and fatty acid and cholesterol biosynthesis under physiologic conditions

(Hong & Tontonoz, 2014; Rui, 2014). It has been reported that metabolite flux regulates cell signaling and transcription directly by way of PTM (post-transcriptional modifications) and competitive binding to proteins in the liver and other tissues (Boukouris, Zervopoulos, &

Michelakis, 2016). For example, the intracellular concentration of glucose, amino acids and fatty acids influences the acetylation of metabolic enzymes involved in fatty acid oxidation and gluconeogenesis, such as PEPCK (Jiang et al., 2011). Moreover, metabolites and signaling molecules can directly reprogram transcription through histone modification. For example, the sensor of nutritional status, AMPK has been shown to regulate histone 2B (H2B) GlcNAcylation in response to low energy levels (Bungard et al., 2010; Chawla, Repa, Evans, & Mangelsdorf,

2001). During cachexia, metabolite flux into liver would be extremely different from that in normal physiological conditions, because of substantial breakdown products derived from catabolism of muscle and adipose tissue. Taken together, this evidence suggests that this abnormal nutrient influx into the liver together with tumor-host derived cytokines and hormones are the main causes of the extensive reprogramming of hepatic gene expression in cachexia.

Several secreted proteins, both tumor and host derived, have been identified as possible causes of these metabolic changes in muscle and adipose tissue in cachectic mice (Hambrecht et al., 2005;

Pelletier et al., 2014; Siednienko, Jankowska, Banasiak, Gorczyca, & Ponikowski, 2007; Watchorn et al., 2005; X. Xu, Jiang, Zhang, Bi, & Han, 2015). Most of these factors are secreted cytokines and growth factors including tumor necrosis factor alpha (TNFα), interleukin-6 (IL-6), interleukin-

1 (IL-1), leukemia inhibitory (LIF), ciliary neurotrophic factor (CNTF) and interferon-gamma

(IFNg). For example, TNFα increases lipolysis by stabilizing lipoprotein lipase (LPL) and induces proteolysis through ubiquitin proteasome proteolytic pathways (Schcolnik-Cabrera, Chavez-

Blanco, Dominguez-Gomez, & Duenas-Gonzalez, 2017); however, the mechanism by which

TNFα and other cytokines pass information to transcriptional networks is mostly unknown other than the well-studied downstream targets of cytokines STAT3 and AP-1 (Seto, Kandarian, &

Jackman, 2015). Previous studies have identified STAT3 as a transcriptional mediator of IL-6 in inducing muscle wasting and the hepatic acute phase response by activating gene expression of downstream targets (Q. Xu et al., 2014).

Although many studies have established the function of TFs such as LXR, RXR, PPARα, PPARγ,

HNF4α, CHREBP and SREBP2 in hepatic metabolism, little is known about their contribution to hepatic gene expression in response to cachexia (Bonetto et al., 2011). Moreover, in cachexia there is the potential for overlapping regulation of chronic inflammation and lipid metabolism. This has been identified in atherosclerosis and is partially mediated by LXR and other TFs in smooth muscle cells and macrophages (Kurakula, Hamers, de Waard, & de Vries, 2013). Since cachexia is also characterized by excessive cytokine production and inflammation, it is reasonable to hypothesize that the liver would play an important role in response to hyperlipidemia and hypercholesterolemia in cachexia. In addition, anti-cytokine therapies against IL-6 and TNFα are only partially effective in reversing cachexia, which suggests there are other molecular mechanisms involved (Morley, 37

Thomas, & Wilson, 2006; Sato, Onuma, Yocum, & Ogata, 2003). For example, parathyroid hormone-like hormone (PTHLH) secreted by the tumor has recently been shown to contribute to cachexia through driving the expression of genes involved in thermogenesis in adipose tissue, partially mediated by up-regulating UCP1 and DIO2 (Kir et al., 2014). Therefore, it is informative to assess gene expression changes in cachectic liver and investigate how they contribute to the development of cachexia. More importantly, by studying their transcriptional regulation in the liver, we could discover the upstream cachectic signaling networks modulated by tumor or host- derived cachectic factors, which forms the rationale for the PhD project.

1.1.4.5 Metabolic Features of C26-induced Cachexia: Serum Metabolomics

Since cachectic factors, consumption and release of metabolites in tumor and host are transported by blood, it is informative to profile the metabolomics and proteomics profile in cachectic serum.

An 1H NMR-based approach was used to construct the metabolomics profile of blood taken from the Colon-26 (C26)-induced mouse cachexia model (O'Connell et al., 2008). The NMR results showed significantly increased levels of valine and leucine, which may be due to protein degradation in muscle, but were not associated with increased levels of other amino acids. Mice with CAC also showed decreased plasma glucose levels and increased levels of 3-hydroxybutyrate and lactate, which can be explained by weight loss under cachexia, leading to increased delivery of free acids from adipose tissue and increased energy production by lactate by the tumor tissue.

Finally, the metabolomics data also showed increased LDL and VLDL in mice with cachexia: these results are harder to explain and may be due to increased cholesterol biosynthesis in liver.

However, clinical studies based on a small cohort (n=9) showed decreased LDL levels (72 mg/dL) compared to controls (100 mg/dL) although C-reactive protein (CRP) and other tumor biomarkers

showed changes that were consistent with mouse studies (Fujiwara et al., 2014). Larger sample size based metabolomics studies are needed to elucidate this contradiction.

Proteomics profiling of serum samples obtained from cachectic and non-cachectic prostate cancer and controls by surface enhanced laser desorption/ionization time-of-flight mass spectrometry

(SELDI-TOF-MS) revealed that four proteins are elevated in CAC (ZAG, apoC-II, apoC-III and

GLP-1), which may serve as non-invasive diagnostic biomarkers for cancer cachexia (Felix et al.,

2011).

1.1.5 Animal Models

Several animal models have been developed to study the underlying mechanisms of cachexia. Here we summarize the animal models into three categories by the cachexia-induction methods: tumor- induced, LPS/Bacteria-induced and cytokines-induced cachexia.

Tumor-induced cachexia was initiated by subcutaneously implanting cachectogenic tumor cell lines into mice including Lewis Lung Carcinoma (LLC) and Colon-26 adenocarcinoma (C26) cells

(Aulino et al., 2010). These two commonly used cell lines can result in reproducible weight loss, rapid tumor growth and skeletal muscle loss; and have become standard models to study the molecular mechanism of muscle loss in CAC. LLC was first isolated from C57/BL6 mice and found to induce cachexia; while C26 was established from a mouse colon tumor model and was found to be highly tumorigenic and have low tendency to metastasize (Allport & Weissleder, 2003;

Aulino et al., 2010) . These models have mainly been utilized to identify circulating factors that induce CAC as well as identify metabolic changes in muscle and adipose tissue that occur during

CAC. For example, the gene expression profile of different LLC clones has recently been characterized together with their cachexia-induction capacity, which discovered novel cachectic factors including PTHRP (Kir et al., 2014). 39

Recognizing that cancer and other chronic diseases are associated with prolonged and excessive cytokine production, cachexia animal models have been developed by repeated injection of cytokines or inoculation of cytokine-producing cells (Bennani-Baiti & Walsh, 2011; Deboer,

2009). These cytokines include TNFa, IL-6, LIF, CNTF and IFNg. These animal models provided important information on the clinical aspects of CAC. For example, using animal models we can test if the administration of specific antagonist or antibodies against these cytokines could relieve or even reverse cachexia. Interestingly, these studies revealed that cachexia could be attributed to a set of multiple cytokines instead of a single factor (Chang et al., 2003; Yeh & Schuster, 1999).

Cytokine-induced cachexia can also be useful to study the response and mediators of weight loss muscle and adipose tissue. However, because the cachexia was induced by administration of specific cytokines, it may prevent us from discovering novel cachectic factors.

Another model related to cytokine production is LPS- or bacteria-induced cachexia (Marks, Ling,

& Cone, 2001). LPS is the major component of the cellular wall in gamma-negative bacteria and can be recognized by Toll-like receptor 4 (TLR4) of innate immune cells (Matthys, Heremans,

Opdenakker, & Billiau, 1991). Prolonged administration of LPS in mice can result in anorexia and muscle loss; however, the dose and route of LPS administration needs careful optimization since the infusion of LPS may lead to endotoxin tolerance (Roth, McClellan, Kluger, & Zeisberger,

1994). This model enables us to discover infection-related cachectic factors, which still may be categorized into cytokines. However, because of the optimization procedure involved, the LPS- induced cachexia is not widely used (Braun et al., 2013; Tracey et al., 1988).

1.1.6 Discovery of Cachectic Factors

The first identified cachectic factor was TNFα, which was also named cachectin. Following TNFα, a list of cytokines has been identified to mediate cachexia, which include IL-6, IL-1 and IFNg.

However, it has been difficult to measure the levels of cytokines in cancer cachexia, other than IL-

6, partially because of their short half-life and low protein levels (Noguchi, Yoshikawa,

Matsumoto, Svaninger, & Gelin, 1996). This poses a challenge for the diagnosis of cachexia by detecting the cytokine levels in serum. Since cytokines can suppress the enzyme activity of lipid lipase (LPL), it was proposed to measure the activity and mRNA levels of LPL. The results showed that the difference of LPL enzyme activity and mRNA level between cachectic and normal individuals are not significant, which suggested other mediators were involved (Das & Hoefler,

2013; Fried & Zechner, 1989). Subsequently, some catabolic factors including lipid-mobilizing factor (LMF) and proteolysis-inducing factor (PIF) were discovered to mediate cachexia in peripheral tissues (Argiles, Busquets, Garcia-Martinez, & Lopez-Soriano, 2005). LMF induces adipose tissue degradation through the beta3-adrenoceptor (Sanders & Tisdale, 2004). PIF induces proteolysis by mediators such as AP-1, PPARγ and NF-kB (Todorov, Field, & Tisdale, 1999). In

2014, PTHrP (parathyroid hormone-related protein) was identified as a novel tumor-derived cachectic factor and shown to cause cachexia by increasing UCP1 in adipose tissue, a process called “browning” (Kir et al., 2014). Later, Impl2 (Ecdysone-inducible gene L2, an insulin/IGF antagonist) was demonstrated to cause organ wasting in a Drosophila model (Figueroa-Clarevega

& Bilder, 2015). Another study proved that IGFBP3 contributes to muscle wasting in pancreatic cancer (Huang et al., 2016). We believe that the systematic profiling of cachectic tumor would contribute to finding of novel cachectic factors. However, gene expression profiling of tumor tissue alone cannot identify the causal cachectic factors and we believe that it is necessary to link the gene expression data with cachectic status or cachectogenic capacity of an organ or tissue.

1.1.7 Therapeutic Approaches for Cancer-Associated Cachexia

The treatment for cachexia can be generally divided into three categories: pharmacological treatment, nutritional or dietary intervention and physical exercise (Aoyagi, Terracina, Raza,

Matsubara, & Takabe, 2015). It is worth noting that the best strategy to manage cachexia is still treating the cause of cachexia, the cancer. Here the therapeutic treatment is aiming to ameliorate or reverse the symptoms of cachexia such as anorexia and weight loss (Table 1.2). For example, megestrol acetate (MAGACE) was first synthesized to treat breast cancer and endometrial cancer

(Aoyagi et al., 2015). Subsequent clinical studies showed that it could improve appetite and increase weight in cancer-associated cachexia. The clinical findings also suggested that MAGACE can reduce the level of cytokines including IL-1, IL-6 and TNFα in the serum of cancer patients.

As discussed above, the activation of MC4R receptors can result in anorexia and weight loss.

Knock-out or antagonism of MC4 receptors in mice has shown to protect mice from cancer- associated anorexia and weight loss (Tao, 2010).

Since cachectic patients are progressively losing weight and the calorie deficit for a late-stage cancer cachexia patient is estimated to be 250-400 kcals per day, nutritional intervention has also been suggested as one possible treatment method. The randomized nutritional intervention studies indicated that the home total parenteral nutrition (TPN) can prolong the cachectic patient survival

(Iresjo, Engstrom, & Lundholm, 2016). Recent studies have also revealed multimodal effects of physical exercise on metabolism and energy balance including improving insulin sensitivity, protein synthesis rate, suppression of the inflammatory response, which suggested that physical exercise might benefit cachectic patients (Gullett, Mazurak, Hebbar, & Ziegler, 2011). Indeed, a clinical trial indicated that exercise could improve the physical performance and life quality in late-stage cachexia. Despite these examples, there are still no effective treatments for cancer-

associated cachexia although it causes 30% of cancer patient deaths. We believe that the better understanding of cachectic factors and the interaction between host and tumor will lead to the development of better therapies.

Table 1.2: Drugs for treating cancer-associated cachexia and their effects on host cells and tissues. The main targets of these anti-cachectic agents include TNFα, PIF and energy deficit.

BCAA: Branched-chain amino acids; EPA: Eicosapentaenoic acid.

Effect on Effect on Side effects or Drug Target factor adipose muscle comments

BCAA Serotonin Not reported Not reported Stabilization

Melatonin TNFa Not reported Not reported Reduced incidence of high weight loss

Petoxifyline TNFa No change No change Stabilization

Thalidomide TNFa Not reported Not reported Promotes weight gain in patients with HIV

Fish Oil PIF Stabilization Stabilization Diarrhea, offensive taste

EPA PIF Stabilization Stabilization Nausea

1.2 Transcriptional Regulation: Epigenomic Profiling by ChIP-Seq

In this section, I will review the epigenetic regulation of gene expression, NGS-based technology to study Epigenomics and ENCODE project, ChIP-seq to profile active DNA elements and identification of transcriptional network based on ChIP-seq data. ChIP-seq against active epigenetic markers can be used to profile active chromatin regions in cachectic liver.

1.2.1 Epigenetic Regulation: Histone and DNA Modification

It has been known for decades that besides direct binding of TFs to the DNA sequence itself, transcription can be regulated by histone protein post-translation modifications (PTMs) such as methylation and acetylation; and DNA modifications such as methylation and hydroxylation

(Esteller et al., 2002; Jones & Takai, 2001). For example, the acetylation of histone lysine residues would remove their positive charge, which abolishes the electrostatic interaction between the histone and negatively-charged DNA (D. Y. Lee, Hayes, Pruss, & Wolffe, 1993; X. J. Yang &

Seto, 2008). Another mechanism involves the recruitment of protein “readers” of histone PTMs.

For example, scetylated lysines can be recognized by bromodomain- and the tandem PHD domain- containing proteins, which include histone acetyl transferases (HATs) and HAT-associated proteins, p300-associated factors and bromodomain-containing protein 9 (X. J. Yang & Seto,

2008). It is worth noticing that bromodomains are found in a large number of transcription factors and chromatin remodeling complexes, such as the chromatin structure remodeling complex (RSC) and SWItch/Sucrose Non-Fermentable (SWI/SNF) remodeling complexes (Smolle & Workman,

2013). Although the molecular mechanism by which histone tail modifications regulate transcription has been determined, the mechanisms by which the histone core modifications affect nucleosome structure and dynamics remain largely unknown (Tessarz & Kouzarides, 2014).

Epigenetic regulation may also involve the recruitment of transcriptional coactivators and long non-coding RNA transcripts (lncRNAs) on active DNA enhancer elements, which recruit and stabilize the transcriptional machinery (K. C. Wang & Chang, 2011). For example, the lncRNA

HOTAIR can bind polycomb repressive complex 2 (PRC2) by the 3’ domain and

LSD1/CoREST/REST complex by the 5’ domain, thus tethering the two complexes to chromatin with H3K27me and H3K4me2 modifications (Tsai et al., 2010). The aberrant transcription

regulation can cause cancer and dysregulate metabolism by activating or repressing gene expression.

1.2.2 Metabolism and Transcriptional Regulation

Recent studies have revealed the tight link between metabolism and epigenetic regulation such as

PTMs in histones and TFs (Choudhary, Weinert, Nishida, Verdin, & Mann, 2014). For example, the generation of nuclear acetyl-CoA catalyzed by ATP citrate lyase strongly correlates with the total histone acetylation levels (Wellen et al., 2009). Therefore, cells can respond to metabolic changes that cause physiological fluctuations in acetyl-CoA levels by modifying the histone acetylation status. Indeed, supplementing glucose in stationary-phase yeast will cause increased histone acetylation dependent on the histone acetyltransferase Gcn5 and Esa1 (Ginsburg, Govind,

& Hinnebusch, 2009). A recently-constructed comprehensive map of histone modifications catalogued ~100 different histone PTMs, most of which were related to intermediary metabolites such as crotonylation, succinylation and malonylation (Xie et al., 2012). Interestingly, as a result of the structural similarity between some of these metabolites (e.g. Acetyl-CoA, Succinyl-CoA,

Crotonyl-CoA), cells may employ the same histone-modifying enzyme to attach the chemical group to the histone. For example, the transcriptional co-activator p300 is capable of catalyzing histone crotonylation in addition to histone acetylation (Sabari et al., 2015).

In addition to the effects of metabolic intermediates on epigenetic status, metabolic regulators and energy sensors can act directly as transcription co-regulatory proteins. The potential moonlighting function of metabolic enzymes in transcriptional regulation has been recently highlighted by

Aristeidis et al. (Boukouris et al., 2016), based on their demonstration of nuclear localization of metabolic enzymes such as Hexokinase (HK), Phosphofructokinase (PFK), Isocitrate dehydrogenase (IDH), Malate dehydrogenase 2 (MDH2), ATP citrate lyase (ACLY) and Carnitine

O-acetyltransferase (CRAT). While the precise roles of these enzymes in nuclear biology and chromatin regulation remains to be elucidated, other metabolic regulators have recently been shown to have clear roles in gene regulation. For example, stresses such as hypoxia and nutrient deprivation have been shown to cause AMPK translocation from the cytoplasm to the nucleus, where it phosphorylates H2B at serine 36 and transactivates AMPK-responsive gene expression

(Bungard et al., 2010). Another example is the recently discovered nuclear function of mTOR

(Kantidakis, Ramsbottom, Birch, Dowding, & White, 2010). For several years, mTOR was known to shuttle between cytoplasm and nucleus in a nutrient- and rapamycin-sensitive manner (Mayer,

Zhao, Yuan, & Grummt, 2004). ChIP assays showed that TOR binds the promoters of genes involved in amino acid metabolism, amino acid transporters and rDNA, which is consistent with its established physiological role in regulating protein metabolism (H. Li, Tsang, Watkins, Bertram,

& Zheng, 2006). ChIP-seq in mouse hepatocytes also demonstrated that the targets of mTOR are enriched for regulatory elements of genes involved in the TCA cycle and lipid biosynthesis.

Interestingly, the nuclear receptor ERRα binds a motif that is also enriched in the genomic binding targets of mTOR; and validation studies demonstrate the functional crosstalk between mTOR and

ERRα (Chaveroux et al., 2013). This study highlights the importance of re-examining novel functions of metabolic regulators and enzymes in the nucleus, de novo discovery by high- throughput sequencing and the investigation of TF cooperation through bioinformatic analysis.

1.2.3 Identification of Active Chromatin Regions

ChIP-seq was developed to study TF binding sites on chromatin, and combines chromatin immunoprecipitation with high-throughput sequencing (Furey, 2012). In the ChIP-seq procedure, the protein-DNA interaction is fixed by crosslinking and then DNA-protein complexes are purified with specific antibodies against the protein of interest and the bound DNA is sequenced. Several

technologies have been built on ChIP-seq by optimizing at different steps such as ChIP-exo,

Tandem ChIP-seq and combinatorial-iChIP (Perreault & Venters, 2016; Soleimani, Palidwor,

Ramachandran, Perkins, & Rudnicki, 2013). ChIP-exo relies on the DNA exonuclease to digest the flanking DNA nearby by the TFBS (transcription factor binding site), thus improving the resolution of ChIP-seq to ~10 bases (Perreault & Venters, 2016). Tandem ChIP-seq aims to study the cooperation of two TFs by performing tandem ChIP; however, it suffers severely from the low

DNA yield of the repeated ChIP purifications (Soleimani et al., 2013). ChIP experiments to detect histone modifications will have DNA yields relative to input of 0.3-1%, while the DNA yields for

TFs drop dramatically to 0.01-0.1%. To overcome the yield issue, combinatorial-iChIP was developed to determine the genomic co-occupancy of histone marks by adding barcoded DNA to the purified sample in each round of ChIP. Other techniques have been designed to analyze the

DNA, RNA and protein purified from ChIP-seq since it can reveal the lncRNA scaffold and protein complexes involved in transcriptional regulation. In addition to ChIP-seq to study genome-wide transcriptional regulation: DNase hypersensitive-sequencing (DHS-seq), Formaldehyde-Assisted

Isolation of Regulatory Elements (FAIRE-SEQ) and self-transcribing active regulatory region sequencing (STARR-seq) (Arnold et al., 2013; Crawford et al., 2006; Park, 2009; Waki et al.,

2011). For example, the newly developed ATAC-seq (ATAC: Assay for Transposase-Accessible

Chromatin) can detect active chromatin regions using inputs with as few as 103-104 cells using a protocol that takes one day (Buenrostro, Wu, Chang, & Greenleaf, 2015).

1.2.4 ENCODE and Histone Marks

The Encyclopedia Of DNA Elements (ENCODE) Project is an international consortium that aims to characterize the functionality of the human genome in selected tissues and cell lines

(Consortium, 2011). The second phase of the project generated 1640 datasets (including ChIP-seq,

DHS-seq and RNA-seq) across 147 different cell types, yielding a set of 30 publications across several journals. Datasets from the ENCODE project suggest that 80.4% of the human genome is functional in at least one cell type, which indicates the potential function of “junk DNA”

(Consortium et al., 2007). Moreover, ENCODE has systematically profiled the histone modifications present in active DNA regions, leading to the development of genome-wide maps of chromatin state in many cells and tissues. For example, H3k4me3 has become widely used as histone mark for active promoters and H3k4me1 as a mark for active enhancers (Table 1.3). More recently, H3k27ac has been shown to better predict active DNA regions and outperforms H3k4me3 and H3k4me1 (Creyghton et al., 2010). This provides guidelines for the PhD projects to select correct and informative histone marks to identify active DNA element.

1.2.5 Transcription factors involved in cachexia progression

Several TFs have been identified as mediator of cachectic factors (such as TNFα and IL-6) in muscle and adipose tissue. For example, TNFα administration leads to upregulation of Ucp in muscle, which leads to muscle degradation and increased energy expenditure. In the MAC16- induced mouse cachexia model, the main adipogenic TFs (including C/EBPα, PPARγ and SREBP-

1c) have decreased expression levels in adipose tissue. However, the TF network mediating the hepatic response to cachexia remained unknown. Previous studies have identified a list of upregulated TFs in cachectic liver including STAT3, CEBPD and HNF4α. The ideal follow-up experiments will build the genome-wide binding profile of these TFs; however, to perform ChIP- seq on these TFs (~50) is time-consuming and not cost-effective. The identification of active DNA elements by performing ChIP-seq against active histone marks provides us another better alternative. First, these active DNA elements are more comprehensive picture of the regulatory program in resting and cachectic tissues compared to DNA elements associated with specific TFs.

Second, we can build a de novo and unbiased TF network by analyzing these genome-wide active

DNA elements.

Table 1.3: Summary of Encode histone marks and their putative functions. This is based on

12 histone marks profiled in 46 cell types which also include the transcriptional activity data.

H3k4me1 and H3k27ac have been used as marks of active regulatory elements (Gerstein et al.,

2012).

Histone modification Signal Putative functions or variant characteristics Associated with regulatory elements with H2A.Z Peak dynamic chromatin Associated with enhancers and distal H3K4me1 Peak / Region elements Associated with promoters and H3K4me2 Peak enhancers Associated with promoters and H3K4me3 Peak transcription start sites Active regulatory elements with H3K9ac Peak preference for promoters

H3K9me1 Region Preference for the 5’end of genes

Repressive marks associated with H3K9me3 Peak / Region heterochromatin and repetitive elements Repressive marks established by H3K27me3 Region polycomb complex. Elongation marks associated with H3K36me3 Region transcribed regions. Transcription associated marks with H3K79me2 Region preference for 5’end of genes

H4K20me1 Region Preference for 5’ end of genes

H3K27ac Peak Active regulatory elements

1.2.6 Transcription Factor Networks Inferred from Active DNA Elements

ENCODE has provided some guidelines to characterize the functional properties of DNA regions based on the associated histone code. Therefore, we can identify active DNA elements based on the panel of histone codes generated from ENCODE project and use these to identify TFs with overrepresented binding sites in the regions identified by our ChIP assays. In addition, de novo motif discovery approaches will be used to identify DNA motifs that are enriched in these regions; these are the likely binding sites for TFs whose binding affinities are not yet documented in databases like Transfac and Jaspar. Recently several motif analysis packages have been developed to identify TF networks based on ChIP-seq datasets including MDscan, HOMER, ChIPMunk and

MEME (Bailey et al., 2013; Castro-Mondragon, Rioualen, Contreras-Moreira, & van Helden, 2016;

Heinz et al., 2010; Patel & Stormo, 2014).

1.2.7 Challenges and Controversies

Although ChIP-seq could provide global genomic binding profiles of TFs and histone modification marks, there are still some challenges and controversies for both experimental studies and bioinformatics analysis (Furey, 2012). First, when dealing with a biological system including both static and perturbed systems, without much prior information concerning transcription regulation, the choice of TFs for ChIP studies is hard to make. Taking this doctoral project as an example, cachectic liver showed altered expression of more than 55 transcription factors and co-activators compared to sham-injected liver, including STAT3, CEPBB, SREBP2 and other metabolic regulators. Without knowing which TF is the most important for hepatic function in cachexia, we may just choose STAT3 since based on published data STAT3 is involved in cytokine response or

CEPBB since it is involved in regulating the acute-phase response (Bonetto et al., 2011). However,

the pitfall for choosing STAT3 as a focus for ChIP-seq is that these results could only provide information regarding STAT3 binding or STAT3-interacting proteins, which greatly limits the power of ChIP-seq. Therefore, to choose general marks which represent active DNA regions could reveal the global pictures of transcription regulation in cachectic liver. The conclusions of

ENCODE have served as guidelines to make that decision although controversies exist (Creyghton et al., 2010; D. Wang et al., 2011). For example, it is common to choose H3K4me1-positive and

H3K4me3-negative as a set of histone marks to perform ChIP-seq to map active enhancers.

However, Creyghton discovered that H3K27ac outperforms H3K4me1 in this setting and other studies have proposed using p300 binding as a feature to determine active enhancers (Creyghton et al., 2010; May et al., 2011). The identification of active enhancers is ongoing and the guidelines to determine these by ChIP-seq are also evolving. At the beginning of this thesis project, we planned to combine the histone marks H3K4me1, H3K4me3 and H3k27ac together with ChIP-seq data for CBP and RNA polymerase as indicators of active DNA regions in cachectic liver.

However, genes bearing active histone marks do not necessarily show upregulated gene expression.

Creyghton’s group integrated ChIP-seq data for the histone H3K27ac and H3K4me1 marks with microarray data of ES cells and found that the average percentile of genes with H3K27ac marks among the rank of all expressed genes is ~55% which is only slightly higher than 50% (Creyghton et al., 2010).

Second the quality of ChIP-seq data depends highly on the specificity of the antibodies used; however, the systematic evaluation of antibodies used initially by ENCODE and other projects to detect specific histone modifications demonstrated that significant cross-reactivity of commercial antibodies exists (Peach, Rudomin, Udeshi, Carr, & Jaffe, 2012). For example, the quantitative assessment of ChIP-grade antibodies by Peach et al. revealed that the antibody against H3K4me2

(CST9726) possesses cross-reactivity against H3K4me1 and relatively lower cross-reactivity 51

against H3K79me1 and H3K79me2; while a commonly used antibody against H3K4me3

(CST9751) could cross-react with H3K4me1 and H3K4me2 (Peach et al., 2012). Therefore, while many of the ENCODE chromatin maps were prepared using a computationally efficient set of antibodies, the integration of multiple histone ChIP-seq datasets for a larger set of histone marks may actually be necessary to alleviate the problem of cross-reactivity (Zentner & Henikoff, 2013).

Third, computational analysis of ChIP-seq data remains challenging (Pepke, Wold, & Mortazavi,

2009). The first challenging task is peak calling, which identifies the genomic region with a significantly higher level of TF binding relative to background. The most appropriate negative background sample for ChIP-seq remains controversial, and ChIP-seq against IgG, mock ChIP or simulation in silico have all been proposed as appropriate methods to model the background TF or histone binding (Liang & Keles, 2012). Different methods for providing a negative control may have unpredictable effects on peak calling; and moreover the different peak calling methods may perform differently depending on the methods used to fragment chromatin (Shen et al., 2013). Two peak calling algorithms are preferred for sonication-based ChIP-seq data. Model-based Analysis of ChIP-seq (MACS) and Hypergeometric Optimization of Motif Enrichment (HOMER).

Generally, for narrow peaks such as H3K4me3 peaks and TF peaks, both algorithms work well; however, HOMER is recommended for calling broad peaks including H3K9me2 peaks. In 2017, a systematic evaluation of several peak calling algorithms (GEM, MACS2, MUSIC, BCP,

Threshold-based method (TM) and ZINBA) indicated that three features will determine an optimal algorithm: the incorporation of input sample, windows of varying sizes, statistical testing methods of ranking peaks (Poisson test or binomial test) (Thomas, Thomas, Holloway, & Pollard, 2017).

The evaluation suggested that BCP and MACS outperformed other calling algorithms. The second challenging task is to compare ChIP-seq data across multiple samples (i.e. differential peak analysis). Although several packages have been developed (such as DIME, DBChIP and ChIPDiff), 52

the differential analysis was based on comparing peak intensity between samples after peak calling which is difficult to define for some wide and diffuse peaks (e.g. H3K36me3). Compared to these packages, MAnorm was based on the assumption that intensities of most ChIP-seq peaks are equal between samples, which provided a basis for rescaling ChIP-seq peaks for comparison (Shao,

Zhang, Yuan, Orkin, & Waxman, 2012). The differential peak analyses by MAnorm are robust and sensitive for ChIP-seq data of histone marks. Therefore, in my doctoral project, we applied the MAnorm algorithm to perform differential peaks analysis and compared that with results from

MACS.

Fourth, the most common strategy used to build TF networks based on ChIP-seq data for histone modification marks is to perform transcription factor binding site (TFBS or motif) analysis. Similar strategies have been used for DHS-seq data in the ENCODE project to reveal cell type-specific transcriptional regulation networks (Thurman et al., 2012). By analyzing DNaseI footprinting data across 41 cell types, ENCODE built a TF regulatory network connecting 475 TFs (Neph et al.,

2012). The resulting networks had biological plausibility, for example, the data identified a network composed of TAL1/SCL, PU.1, ELF1, HES1, MYB, GATA2 and GATA1 that is essential for hematopoietic lineage cells. Despite this, there have been very few successful examples where this strategy identified disease-specific TF networks. Through tracking the published research, motif analysis of ChIP-seq data against histone modifications or DHS-seq is routinely used to build a general TF network or cell-type-specific TF network. To build a TF network based on these datasets is always challenging, particularly to “determine which TFs are involved in the perturbation of the biological system”. Using cachectic liver as an example, one could anticipate that there are a set of TFs regulating the hepatic response to cachexia rather than a single TF. The number of ChIP-seq peaks for a specific histone mark can be as high as 20,000 (Mikkelsen et al.,

2007). Due to the large number of TFs involved in the cachectic liver response, the percentage of 53

peaks bearing a specific TF can be as low as 5-10% even for the TF binding the largest number peaks. In this case, it will be difficult to distinguish the low enrichment of TF from the background

(J. Wang et al., 2012). Therefore, to identify the cachexia-responsive ChIP-seq peaks becomes very challenging.

1.3 Locus-specific Transcriptional Regulation

In this section, I will review the current methods to identify locus-specific transcription factor complex, genome-editing tools of transcription activator-like effector (TALE) and rationale of repurposing TALE for locus-specific chromatin proteomics studies. Besides the ChIP-seq method mentioned above, the identification of locus-specific TFs will reveal the upstream transcriptional regulators mediating hepatic response to cachexia.

1.3.1 Current Methods to Identify Locus-Specific Transcription Factor Complexes

Since the gene expression data of cachectic liver suggests significant upregulation of cholesterol biosynthesis, we aim to investigate the transcription regulation of metabolic enzymes in cholesterol biosynthesis pathway such as SQLE, HMGCS and HMGCR. However, it is still challenging to systematically identify DNA locus-specific TF complexes. Similarly, whole genome sequencing of tumor samples has discovered many mutations located in regulatory regions or altering copy number. In some cases, these have been linked to specific TFs, such as the TERT promoter mutation found in ~83% of glioblastomas (GBM) that affects GABP binding (MacArthur et al.,

2017; Tak & Farnham, 2015); however, identification of the function of most of these regulatory mutations has been daunting. In order to investigate the function of these regulatory SNPs, the first step is to accurately associate SNPs to relevant genes for further function characterization. While

SNPs could regulate the gene expression locally or distantly, most GWAS have simply considered

the effect of regulatory SNPs to be caused by changes in expression of the nearest genes. This may lead researchers in the wrong direction, as demonstrated by the many experiments that studied the function of FTO in Type 2 diabetes based on the discovery of noncoding variations in linkage disequilibrium blocks located in the first and second introns of FTO that were associated with obesity and T2D (Morris et al., 2012). However, in 2014 and 2015, Marcelo and Kellis showed that these SNPs function by forming a distant chromatin loop with another gene (IRX3) that can also affect cell metabolism (Smemo et al., 2014). Therefore, pinpointing the target gene that regulatory SNPs function through is an important first step (L. Chen, Jin, & Qin, 2016; Faye,

Machiela, Kraft, Bull, & Sun, 2013). This can be accomplished by techniques such as chromosome conformation capture coupled with NGS (3C-seq and Hi-C), which have been used to build genome-wide chromatin interaction maps that may be informative to link regulatory SNPs to known genes (Tanizawa & Noma, 2012). These strategies are based on preparing restriction enzyme digests of whole nuclei; in which the physically interacting DNA can be ligated together, purified and sequenced. However, these strategies initially suffered from the poor resolution of the chromatin interaction map (~1M base), which has improved to ~kbp by combining the original Hi-

C protocol with a nuclear ligation assay (Belton et al., 2012).

Once an accurate map of regulatory elements has been obtained, the second step is to identify how the specific TFs and other chromatin proteins are involved in the gene regulatory program. There are two general ways to achieve this goal. 1) Bioinformatics “motif” analysis can predict the TFBS in these SNP-containing regions; however, since motif analysis tends to generate false positive results these results are frequently validated by electrophoretic mobility shift assays (EMSA) and transient transfection studies (C. Y. Chen, Chang, Hsiung, & Wasserman, 2014; Cheng et al.,

2017). 2) Laboratory technologies have been developed to directly identify locus-specific DNA- binding proteins including DNA-affinity chromatography, proteomics of isolated chromatin 55

(PICH), engineered DNA-binding molecule-mediated ChIP (enChIP), and targeted chromatin purification (TChIP) (Dejardin & Kingston, 2009; Dunham, Mullin, & Gingras, 2012; Fujita et al.,

2013; Pourfarzad et al., 2013). Two technologies, PICH and enChIP aim to purify the native DNA- protein complex (Dejardin & Kingston, 2009), although enChIP still needs to introduce CRISPR into the cells. In contrast, TChIP relies on the insertion of Tet operator binding sequence at the locus of interest, which may interfere with the local chromatin environment (Pourfarzad et al.,

2013).

DNA affinity chromatography was the earliest strategy used to identify TFs that bind specific transcriptional regulatory regions (Bos, Bohmann, Tsuchie, Tjian, & Vogt, 1988; W. Lee, Mitchell,

& Tjian, 1987). The technique involves immobilization of a DNA sequence of interest (either an oligonucleotide duplex or a short PCR amplicon) on a matrix and incubation of a cellular (nuclear) extract with the matrix. After extensive washing and elution, proteins bound to the DNA sequence can be characterized by western blot or mass spectrometry. Using DNA affinity chromatography,

Kadonaga first identified TFs such as SP1 and AP-1 (Bos et al., 1988; W. Lee et al., 1987); however, the in vitro incubation step in this technology tends to result in a high nonspecific protein background, rendering validation difficult.

In 2009, Dejardin and Kingston developed a more complex hybridization-based technology, called

PICH, which uses hybridization to a locked nucleic acid (LNA) probe to isolate native chromatin regions of interest. In their initial study, PICH identified 210 proteins associated with HeLa cell telomeres. It is worth noting that each HeLa cell contains 92 telomeres and each telomere harbours multiple binding sites for the LNA capture probe. Although replacing DNA hybridization with

LNA improved the hybridization efficiency, this technology has not been used for any single-copy

DNA element. Recently Farzin et al. developed a novel approach, called TChIP, that purified

single regulatory elements by insertion of a bait DNA “Tet operator” (TetO) into the chromatin region of interest (Pourfarzad et al., 2013). Co-expression of the bacterial TetR protein, which specifically recognizes TetO, could be used to purify the DNA flanking the TetO site in the modified cells. In theory, if TetO insertion does not interfere with local gene expression, TChIP generates a proteomics profile of native chromatin proteins; however, the extra step of inserting

TetO into specific genomic regions makes it time consuming.

In general, there are two reasons that make locus-specific proteomics analysis difficult (Wierer &

Mann, 2016). First, compared to regular proteomics studies, locus-specific proteomics aims to purify proteins bound to specific short DNA regions that are present at only two copies per cell.

Considering the stochastic and dynamic properties of the DNA-TF interaction, not all the DNA regions of interest are bound by TFs in a population of cells at a given time point. The poor purification efficiency (~0.1%) of chromatin regions aggravates this situation; and increasing the exposure of the TF-DNA macromolecular complex to the antibody for affinity purification is a major experimental challenge. Second, purification of a single copy DNA-protein complex is like finding a needle in a haystack. The high complexity of human genome (~3 billion bases) suggests that any purification method will capture other off-target DNA elements with high sequence similarity to the region of interest. This means that it is important to choose an extremely specific bait to recognize the DNA of interest and prevent high background. The success of all single locus proteomics assays is limited by the sensitivity of mass spectrometry: as a result, all the technologies described above need to start with several billion cells. As the mass spectrometer sensitivity improves (for example, the Orbitrap Fusion can detect proteins at attomolar levels) (Tu et al., 2016), we anticipate that affinity purification approaches can be used to uncover TF complexes with smaller numbers of cells.

1.3.2 Genome-Editing Tools: TALENs and CRISPRs

Four major nucleases have been developed as tools for locus-specific genome editing to date:

Meganuclease, zinc finger nuclease, transcription factor-like effector nuclease (TALENs) and

CRISPR-associated nuclease Cas9. The DNA recognition mechanisms for these four tools are different. CRISPR-Cas9 achieves DNA recognition specificity via a short guide RNA which pairs with the targets. Meganuclease, ZFN and TALENs are similar to TFs in that they recognize DNA achieved by site-specific DNA-protein interactions. At the beginning of this doctoral project, the

DNA recognition code of TALENs had just been discovered and we realized it could be repurposed to study locus-specific transcription regulation.

TAL effectors are proteins that are secreted by Xanthomonas bacteria via their type III secretion system when they infect plants. The DNA binding domain contains a repeated highly conserved

33–34 amino acid sequence with divergent 12th and 13th amino acids. These two positions, referred to as the Repeat Variable Diresidue (RVD), are highly variable and show a strong correlation with specific nucleotide recognition: A (NI), G/A (NN), C (HD) and T (NG) (Cong,

Zhou, Kuo, Cunniff, & Zhang, 2012). The DNA recognition code was discovered by analyzing the RVD sequences of eight TALENs and their known DNA targets and validated by experiments on other TALEs (Hax2, Hax3 and Hax4) (Boch et al., 2009). Later a more systematic evaluation of the TALEN recognition code was evaluated by systematic evolution of ligands by exponential enrichment (SELEX) for a larger panel of synthetic TALEs (76 proteins, >250 examples for each

RVD) (Miller et al., 2015). Bioinformatics analysis confirmed the rule and the possible off-targets for each RVD were quantitatively identified. For example, a small proportion of the NI RVD could bind C, which is context-dependent although the off-target effects are trivial. Moreover, in addition to the four canonical RVDs, an expanded list of RVDs was generated and the DNA recognition

base was tested, which found NH could bind G better than NN. The straightforward relationship between RVD amino acid sequence and DNA recognition can be used to engineer specific DNA- binding proteins by selecting a combination of repeat segments containing the appropriate RVDs

(H. B. Lee, Sundberg, Sigafoos, & Clark, 2016).

In addition to the DNA recognition code, a set of other design rules have been developed and implemented in TALEN engineering software (including PROGNOS, TALENoffer and E-

TALEN) (Booher & Bogdanove, 2014; Grau, Boch, & Posch, 2013; Heigwer et al., 2013). For example, the first several RVDs are important for determining DNA binding specificity and most naturally occurring TALEN catalogued have between 12 and 27 full repeats. This has been paralleled by innovations in laboratory technology. The nuclease domain of TALEN has been deleted and the mutant TALEN (TALE), which is still able to recognize DNA sequences specifically, has been engineered at its C-terminus to fuse to different protein domains for customized application. Using these tools TALENs are easy to program to target any regions of interest. Moreover, compared to ZFN, both TALEN and CRISPR have demonstrated high specificity and less off-target effects (Hruscha et al., 2013; Veres et al., 2014).

1.3.3 Applications of TALENs in Genome and Chromatin Modification

The most intuitive application of TALENs was to perform gene mutagenesis. The well-known examples include HIV co-receptor CCR5 knockout in T cells PM1 and genetic knockout mice of

Pibf1 and Sepw1 (Deng et al., 1996; Sung et al., 2013). Recently the methyl-CpG binding protein

2 (MECP2) knock-out rhesus and cynomolgus model has been developed by microinjection of

MECP2-targeting TALEN plasmids into rhesus and cynomolgus zygotes, demonstrating the effectiveness of this technology in nonhuman primates (H. Liu et al., 2014). To avoid the long-

term effects of TALEN plasmids integrated into host cells, delivery of TALEN-expressing mRNA or TALEN protein directly were used (J. Liu, Gaj, Patterson, Sirk, & Barbas, 2014).

In addition to creating gene-specific knockout models, TALENs have been used to study epigenetic regulation by fusing the core DNA-binding repeat to transcription activation domains including VP16, VP64 or repressor domains such as KRAB (Polstein et al., 2015). TALE-VP16 targeting the HEK293 NTF3 gene promoter can induce NTF3 mRNA levels by ~ 30-fold (Niu,

Zhang, & Chen, 2014). TALE fusion proteins have also been engineered using epigenetic regulators. For example, the fusion of TALEN to p300 creates a chimeric TALE-p300 that can be used to perform gene-specific histone acetylation (Hilton et al., 2015). ChIP-seq of TALE-p300 indicated that the off-target TALE binding can be minimized; and that TALE-p300 showed increased histone acetylation at the target site. Later, DNA methyl transferase was fused to TALEN

(Valton et al., 2012). Moreover, large-scale screening protocols have been developed based on

TALEs with epigenetic modification or nuclease functions (Reyon et al., 2012). The easy-to- program properties and high specificity encouraged us to explore the possibility that

TALEN/CRISPR could be used as bait to isolate DNA-locus specific TF complexes.

1.3.4 Challenges of Applying Genome Editing Tools

ZFN, an earlier genome-editing technology than TALENs and CRISPR-Cas9, has been used to correct genetic mutations for diseases such as X-linked severe combined immune deficiency

(SCID), sick-cell disease and hemophilia B (Mukherjee & Thrasher, 2013). A clinical trial using

ZFN to disable HIV co-receptor CCR5 was also ongoing (Tebas et al., 2014). TALEN and

CRISPR have been used to correct a dystrophin frameshift mutation responsible for Duchenne muscular dystrophy in patient-derived induced pluripotent stem cells (H. L. Li et al., 2015). While the follow up experiments validated the successful expression of full-length dystrophin the

evaluation of CRISPR technology in clinical trials is still at an early stage. One of the challenges to address in clinical studies is the optimization of Adeno-associated virus (AAV) vector, which aimed to minimize the immune interactions after delivery of foreign nucleic acids, deliver plasmids or ribonuclear proteins to target cells and trespass cellular barriers (Gray et al., 2011).

Another major concern in applying TALEN/CRISPR in clinical studies is their possible off-target effects. In the lab, off-target effects can be identified and solved in several ways although reliable strategies to eliminate them still do not exist. First, experiments can be designed to determine if the off-target interactions are significantly related to the gene or pathway of interest: for example, by using ChIP-seq it can be performed to identify and quantify the genome-wide TALEN binding sites; or by measuring binding to the top off-target sites predicted in silico using ChIP-qPCR (H.

Zhang et al., 2013). Second, even when the TALEN has detectable off-target effects, the biological conclusions can be validated with other molecular biology methods or with another TALEN targeting the same or nearby regions.

1.3.5 Repurposing TALEs for Locus-Specific TF Complex Studies

TALEs could provide an excellent platform for developing technologies to isolate locus-specific

TF complexes for three reasons. First, it has been reported that TALEs can bind to DNA independent of the local epigenetic features (Scott, Kupinski, Kirkham, Tuma, & Boyes, 2014).

This makes TALEs advantageous over CRISPR since the DNA of interest may be a repressor- binding element and CIRSPR may not recognize the repressed DNA efficiently. Second, the naïve

CRISPR protein could still activate local gene expression modestly (~2 fold), which means it may interfere with the composition of the TF complex surrounding the CRISPR binding site or other transcriptional regulatory elements (Qi et al., 2013). Third, CRISPR are composed of two components: cas9 protein and single-guide RNA (sgRNA), whose stoichiometry needs to be

precisely controlled to ensure locus-specific targeting. The purification of a single component

TALE may be more specific compared to CRISPR.

1.4 Objectives of this Study

This thesis focuses on characterizing the hepatic transcriptional response to cachexia using mouse models. By studying gene expression changes in the liver as cachexia develops, we will identify transcriptional changes in the cachectic liver and moreover, develop a comprehensive catalogue of potential cachectic factors by studying the time-course of gene expression changes in the tumor as cachexia develops. The hepatic transcription factor network mediating this response to cachexia will be characterized using ChIP-seq coupled with bioinformatic analysis of published datasets, to identify genome-wide active chromatin regions in cachectic liver. The sequence analysis of these active DNA regions in cachectic liver will reveal the upstream TF network involved in regulating this response at a transcriptional level. To validate predictions of the bioinformatics analysis and better dissect the transcriptional regulation of cholesterol biosynthesis, one of the prominent metabolic changes in cachectic liver, we developed a novel technology to identify locus-specific

TF complexes. Taken together, the computational and laboratory approaches developed in this thesis will provide a systematic strategy to characterize complex transcriptional responses in mammalian cell lines and tissues.

Chapter 2

Materials and Methods

2 Summary

This chapter will introduce the materials and methods used for the experiments and bioinformatics analysis in the subsequent chapters of this thesis.

2.1 Materials and Methods for Chapter 3

2.1.1 C26 Cell Culture and C26 Conditioned Medium (C26-CM) Preparation

Cell culture: Murine C26 colon adenocarcinoma cells (CRL-2638) were obtained from the

American Type Culture Collection (ATCC, Manassas VA). C26 cells were cultured in RPMI-1640

(Invitrogen) supplemented with 10% fetal bovine serum (FBS, Invitrogen, product 11058-021)) and antibiotics (100× penicillin and streptomycin; Invitrogen, product 15140-163). The cells were trypsinized and passed at a ratio of 1:5 twice weekly.

Conditioned Medium (CM) Collection: Conditioned medium from C26 cells (ATCC CRL-

2638) was prepared following the protocol of Lokireddy et al. (Lokireddy et al., 2012). C26 cells are cultured in Dulbecco's minimum Eagle's medium (DMEM, 1×, high glucose; Invitrogen, product 10313-039), supplemented with FBS, Penicillin-streptomycin, at 37°C in 5% CO2. When the cells reached ~80% confluence, the growth medium was removed, and the cells were washed twice with sterile 37°C PBS. Then the cells were then split 1:2 at 40% confluence in DMEM with no serum plus antibiotics and glutamine. It is essential to remove the FBS completely to prepare conditioned medium since it has been reported that FBS contains myostatin which can induce cells to produce high levels of IL-6 (Bonetto et al., 2012). After 24 hours, the medium was collected and centrifuged in 50 ml falcon tubes at 4500 rpm for 15 minutes at 4°C. The supernatant was passed through a 0.22-micron filter in a sterile environment. Aliquots of the filtered medium were

stored at -80°C for up to one month. As a control to C26-CM, HEK 293T cells (CRL-3216) were cultured and the same procedure was performed to prepare HEK 293-CM.

2.1.2 Gene Expression Analysis of Liver and Tumor

Ten adult CD2/F1 mice were inoculated in the right flank with 1 x 106 C26 cells in 0.2 mL PBS and 10 were injected with vehicle alone (0.2 mL PBS) (Aulino et al., 2010). The C26 cells were tested for mycoplasma contamination prior to injection into mice. The mice were housed one per cage to allow measurement of their body mass and food consumption. The mice were provided with free access to normal rodent chow until 30 days following the tumor or PBS-injection (the evening prior to sacrifice), when they were subjected to either fasting or feeding with three replicates per group. The liver was collected in Trizol at day 31 after tumor or PBS-injection. We also collected samples of the cultured C26 cells and of tumor xenograft tissue at day 14 and 31.

Microarray studies were performed using 10 µg of total RNA isolated from livers of 6 cachectic and 6 sham injected CD2/F1 mice together with tumor tissue (including tissue from 3 fasted and

3 fed mice in each group). The RNA was labeled and hybridized to Affymetrix MOE430A arrays according to the manufacturer's instructions (Affymetrix, Santa Clara CA) (Woo et al., 2004).

2.1.3 Bioinformatic Analysis of Liver Gene Expression Data

To identify gene expression changes responsive to cachexia, we compared the liver microarray data of tumor-injected mice with sham-injected mice. Two Bioconductor packages were used to analyze the data: GCRMA for normalizing microarray data and Limma for differential gene expression analysis (Diboun, Wernisch, Orengo, & Koltzenburg, 2006; Gharaibeh, Fodor, & Gibas,

2008). A moderated t-statistic was calculated using the eBayes function, which combines information across genes to increase the stability of the analysis (Smyth, 2004). Differentially expressed genes were filtered using the topTable function. The multiple testing problem was 65

addressed by controlling the false discovery rate (FDR) with Benjamini and Hochberg’s method

(Benjamini, Drai, Elmer, Kafkafi, & Golani, 2001; Benjamini & Hochberg, 1995; Reiner,

Yekutieli, & Benjamini, 2003). The adjusted p-values represent the expected proportion of false discoveries among the rejected hypotheses. The resulting gene list was analyzed with GSEA (Gene

Set Enrichment Analysis) to check for pathway enrichment (Subramanian, Kuehn, Gould, Tamayo,

& Mesirov, 2007). Principal component analysis (PCA) was performed in R to cluster the sample based on gene expression changes (Stacklies, Redestig, Scholz, Walther, & Selbig, 2007).

Heatmaps were generated in R using the heatmap2 function (Zhao, Guo, Sheng, & Shyr, 2014).

2.1.4 Bioinformatic Analysis of Tumor Gene Expression Data

The same analytic strategy was used for tumor gene expression data; however, to identify those cachectic factors, we compared the expression data of tumor tissue at day 31 with those at day 14 and C26 cells to search for genes whose expression level gradually increased from cell line to day

14 and day 14 to day 31. A reference list of secreted proteins was downloaded from the Reactome database and used to filter the resulting list from the differential analysis (Joshi-Tope et al., 2005).

Microarray data for LLC cells were downloaded from the GEO database with (Accession number

GSE57797) and re-analyzed using the same strategy as C26 microarray data (Kir et al., 2014). The list of C26 cachectic factors were compared to those identified in LLC cells, which aimed to identify common cachectic factors from both two cachectic cell lines.

2.1.5 Isolation of Mouse Primary Hepatocytes

Male C57BL/6NCrl mice were provided by Dr. Marc Prentki at 10 weeks age and originate from the NIH strain of the C57BL/6 mice (Nocito et al., 2015). Mice were euthanized with carbon dioxide and primary hepatocytes were isolated according to the following protocol (Bhogal et al.,

2011). First, the liver was exposed and inferior vena cava was cannulated with a 27 1/2G needle

attached to a peristaltic pump. The portal vein was opened and the liver perfused for ~ 5 minutes with warm Krebs perfusion buffer via the inferior vena cava. Perfusion and dissociation media were warmed in a 43°C bath during the procedure and the pH was adjusted to 7.4 just prior to procedure. The perfusate temperature of 43°C ensures that the final temperature of the solution reaching the liver is 37°C to guarantee the best enzymatic activity; however, lower temperatures do not affect the final yield. Krebs buffer solution (118 mM sodium chloride, 4.8 mM potassium chloride, 25 mM sodium bicarbonate, 1.2 mM potassium phosphate, 1.2 mM magnesium sulfate, pH 7.2) was supplemented with 0.5 mM ethylene glycol tetraacetic acid (EGTA) and 5 mM glucose for approximately 5 minutes using a peristaltic pump at a rate of 4 mL per minute. After the liver became pale, the Krebs infusion was switched to disassociation media. The disassociation media was perfused 3-4 minutes until the liver looked puffy. The time for disassociation should be optimized against the size of mice to make sure the viability of primary hepatocytes. Next, the liver was dissected free of the gall bladder, removed from the mouse and transferred to a sterile petri dish. A cell culture scraper was used to disperse the liver cells in plating media (DMEM with

5mM glucose, phenol red and 3.7g/L NaHCO3, 1 mM sodium pyruvate, 2mM glutamine, 10%

FBS, 1nM insulin, 100nM Dexamethasone) in the culture hood (Isom, Secott, Georgoff,

Woodworth, & Mummaw, 1985). The cells were passed through a 70um filter and centrifuged at

300g for 3 minutes to remove cell debris. The supernatant was aspirated and the cells gently resuspended in 10 mL plating media and 10 mL Percoll solution (buffered in advance 1 mL 10X

DPBS, 9 mL Percoll). The cell suspension was centrifuged at ~200g for 5 minutes to pellet the live cells, which were then washed twice with 20 mL plating media. After resuspending the pellet in 20 mL plating media, the cells were counted and viability was assessed with Trypan Blue. The cells were plated and transferred into maintenance medium (DMEM with 5mM glucose, phenol

red and 3.7 g/L NaHCO3, 1 mM sodium pyruvate, 2mM glutamine, 0.2% BSA, 1nM insulin,

100nM Dexamethasone) 2 hours after plating (Yee et al., 1994).

2.1.6 C26-CM treatment

Eighteen hours after the primary hepatocytes had been plated on the plate, we switched the maintenance medium into maintenance medium supplemented with C26 conditioned medium (1:5 dilution) (Lokireddy et al., 2012; Silva et al., 2015). For the next 72 hours, we changed the medium every 24 hours. The primary hepatocytes were collected at 12, 24, 48 72 hours by washing the plate three times with cold-PBS, scraping the cells in 5ml PBS per 10cm plate and storing the cell suspension at -80°C for metabolism studies. Parallel experiments using HEK 293 conditioned medium were used as a negative control.

2.1.7 Cholesterol and Triglyceride Content Measurement

Cholesterol including cholesterol esters was measured with the Amplex Red Cholesterol Assay

Kit (Thermo Fisher Scientific) according to the manufacturer’s instructions (Amundson & Zhou,

1999). In brief, the collected suspension of primary hepatocytes suspended in PBS was thawed and gently sonicated on ice. The cell lysate was incubated for 30 min at 37°C with horseradish peroxidase (HRP; 2 U/ml), cholesterol oxidase (2 U/ml), without or with cholesterol esterase (0.2

U/ml), in the presence of Amplex Red reagent (300 mM) in 0.1 M potassium phosphate buffer (pH

7.4) containing 50 mM NaCl, 5 mM cholic acid, and 0.1% Triton X-100. Standard curves were obtained according to the manufacturer’s instructions. Total cholesterol and cholesterol esters

(total minus free) were determined from the standard curves. Fluorescence was measured on a

Victor 34 2030 Multilabel Plate Reader (Perkin-Elmer, Waltham, MA, USA). Tissues were snap frozen in liquid nitrogen and powdered with a mortar and pestle.

The triglyceride content was measured with Triglyceride kit (GPO Trinder, Sigma) according to the manufacturer’s instructions (Kakuma et al., 2000). In brief, 1 ml of kit Reagent A was pipetted into cuvettes and either 30 µl of sample lysate or 30 µl H2O (blank samples) was added. The mixed solution was shaken and incubated for 15 minutes. The absorbance was measured at 540 nm and the TG content was calculated using the standard curve.

2.2 Materials and Methods for Chapter 4

2.2.1 Animal Experiments

Adult CD2/F1 mice were either injected C26 cells as described in Chapter 2 (Klimek et al., 2010).

In brief, C26 cells are cultured in RPMI-1640 supplemented with 10% fetal bovine serum and antibiotics (penicillin and streptomycin). The cells were trypsinized, washed with complete medium and resuspended in PBS. Animals were inoculated in the right flank with 1 x 106 C26 cells in 0.2 mL PBS or with vehicle alone (0.2 mL PBS). The mice were housed one per cage to allow measurement of their body mass and food consumption. By 31 days, these animals showed marked wasting (~10% reduction in body mass) and were sacrificed and their livers were removed and processed for ChIP-seq studies.

2.2.2 Preparation of Crosslinked Hepatic Nuclear Extracts

In the following protocol, detergents, buffers and salts were obtained from Sigma-Aldrich Canada

(Oakville, ON) unless indicated otherwise. Mouse livers were dissected, weighed, washed quickly in PBS and then rapidly minced using a razor blade into 2-3 mm cubes. PBS (10 mL per gram tissue) was added to the minced tissue and the resulting mixture passed once through a 21-gauge needle. The tissue suspension was crosslinked by adding 1.5% Formaldehyde and mixing on a

Nutator for 15 minutes at 25C; quenched by adding glycine (0.125 M final concentration) for 10

minutes also at 25C; and disaggregated with a Dounce homogenizer (Raha, Hong, & Snyder, 2010;

Schmidt et al., 2009). The cells were pelleted; resuspended in a hypotonic IPB buffer (Hepes 10 mM (pH 7.4), NaCl 10 mM, Nonidet P-40 1%, 2mM EDTA, 50 mM NaF, 1mM PMSF, 1mM

Phenylarsine oxide, 5 mM Na Orthovanadate with 1x Roche Complete Protease Inhibitor Cocktail)

(Kidder, Hu, & Zhao, 2011; Lefrancois et al., 2009); and incubated for 15 minutes at 4C. Nuclear lysates were prepared by resuspending the nuclei for 15 minutes at 4°C in nuclei lysis IPB++ buffer

(0.1% SDS, 0.1% Na Deoxycholate, Hepes 10 mM (pH 7.4), NaCl 10 mM, Nonidet P-40 1%,

2mM EDTA, 50 mM NaF, 1mM PMSF, 1mM Phenylarsine oxide, 5 mM Na Orthovanadate with

1x Roche Complete Protease Inhibitor Cocktail) (O'Geen, Echipare, & Farnham, 2011). The lysates were sonicated with a Branson 450 Sonicator (3 mm tip, 10 cycles with a 10 second ON and 30 seconds OFF for each pulse cycle, 35% power, on ice) and the nuclear lysate was aliquoted, snap-frozen and stored at -80°C (Fan, Lamarre-Vincent, Wang, & Struhl, 2008).

2.2.3 Chromatin Immunoprecipitation

ChIP assays were performed using 500µL hepatic lysate which represents 50 mg of hepatic tissue based on published protocols for ChIP-chip and ChIP-seq assays (Rey et al., 2011). The lysate was precleared by adding 4µL of rabbit serum and 50uL of protein A or protein G Dynabeads

(Invitrogen, Carlsbad, CA) and mixing on a rotator for 2 hours at 4°C (Zwart et al., 2013). The beads were removed; the supernatant was mixed with target antibody (the amount was individually optimized from 3-10ug for Abcam antibodies ab4174 (H2A.Z), ab8580 (H3K4me3), ab8895

(H3K4me1) and ab5131 (Pol2a-phospho S5) (Simonet, Dulermo, Schott, & Palladino, 2007; J.

Sun et al., 2004). Following incubation, the immunoprecipitate was washed five times (LiCl IP washing buffer (Tris 100 mM (pH 7.5), LiCl 500mM, Triton X-100 1%, SDS 0.1) and twice in TE

(10 mM Tris (pH 7.4), 1 mM EDTA) buffer). The immunoprecipitate was eluted in elution buffer

(Tris 50 mM (pH 8), EDTA 10 mM, SDS 1%) by incubation at 65°C for 1 hour and vortex very

10-15mins (Adli & Bernstein, 2011). The beads were spun at 12000rpm for 30 seconds at 4°C and then placed on a magnetic stand to aspirate the supernatant. 5M NaCl was added to the supernatant to achieve a final NaCl concentration of 0.2M and the formaldehyde crosslinks were reversed by incubating the beads at 65°C overnight. The eluate was treated with proteinase K (Sigma) and

RNase A (Sigma); and DNA was purified with a Qiaquick PCR Purification Kit (Qiagen Canada,

Toronto, Ontario) and eluted in 50µl H2O or 10mM Tris-HCl (pH 8) pre-warmed to 55°C.

2.2.4 ChIP-seq Data Analysis

Data Trimming: Since the base quality is encoded in phred33, the sequencing reads were trimmed starting from the 3’ end using a phred score cutoff = 30. The Illumine adapter sequence was removed from all reads and any final trimmed reads less than 32bp long were discarded. The trimming was performed with the Trimmomatic package (Bolger, Lohse, & Usadel, 2014).

Read Mapping and Quality Control: The filtered reads were mapped to mouse genome mm9 with BWA (H. Li & Durbin, 2009). The reads which can be mapped to multiple loci were filtered and only unique mapping reads were retained. The parameter MAPQ was set to 20 to remove low- quality mapping reads. The mapped reads with the same 5’ alignment position were regarded as duplicates and removed using the Picard software. The final mapped sequencing reads were stored in a Binary Alignment Map file (.bam), which were visualized using the Integrative Genomics

Viewer (IGV) (Robinson et al., 2011).

Peak Calling: ChIP-seq peaks were called with MACS for each ChIP-seq dataset stored as bam file (Wilbanks & Facciotti, 2010). For wide ChIP-seq peaks, such as histone peaks, the lower bound estimate of mfold is set to 10. The upper bound estimate of mfold can be chosen between

15 and 100: in this analysis we used the default value of 30. In total, MACS identified ~20,000

H3k27ac peaks and ~60,000 H3k4me3 peaks in 4 groups of mouse liver, which were consistent with previously reported numbers. We also used MACS (version 2) to perform differential ChIP- seq peak analysis between cachectic liver and sham-injected liver. The callpeak module of MACS2 generated pileup tracks for each individual ChIP-seq bam file and bdgdiff module was used to find the differentially enriched peaks between two samples.

Peak Annotation: The resulting peaks were annotated and analyzed with the annotatePeaks module of HOMER which was designed to perform known and de novo motif discovery (Heinz et al., 2010). The distribution analysis of peaks relative to transcription start site (TSS) was performed for each ChIP-seq datasets and compared with that of ENCODE, which ensured the genomic distribution of ChIP-seq peaks was normal. The peaks were annotated according to the nearest genes and the resulting gene lists were submitted for pathway enrichment analysis by GSEA (Gene

Set Enrichment Analysis) to identify the enriched pathways (Subramanian et al., 2007).

Differentially ChIP-seq Peaks Analysis: The MAnorm package was used to detect the differential ChIP-seq peaks between cachectic and non-cachectic liver, which generated active genomic regions only present in cachectic liver (Shao et al., 2012). The bedtools (bamtobed module) were used to convert bam file of mapped reads to bed files, which were used as input files for MAnorm analysis. MAnorm rescales the common peaks intensity to be equal between groups.

The parameter readshift length was generated in the MACS analysis and ranged between 150 and

170. For example, for ChIP-seq peaks against H3K4me3, the shift length was estimated to be 164.

Annotation and pathway enrichment for the differentially detected peaks was performed as mentioned above. The M-value generated by MAnorm represented the quantitative measurement of differential binding between two samples. To find the transcriptionally upregulated genes, we integrated the gene expression data from Chapter 2 with the M-value of ChIP-seq peaks.

2.2.5 Motif analysis

Two methods were used to select relevant peaks for motif analysis. First, we combined the

H3K27ac (Positive), H3K4me1 (Positive) and H3K4me3 (Negative) datasets together and defined these regions as potential enhancers. The distance between enhancers and nearby promoters should be at least 1000bp. Second, we integrated the H3K27ac peaks with gene expression data, which selects the DNA regions with active H3K27ac marks and upregulated gene expression. Unbiased motif analysis was performed using HOMER to identify statistically overrepresented motifs using default parameters to search known and de novo TFBS (Heinz et al., 2010). To overcome the false positive results of motif analysis, we used MDscan to discover the enriched k-mer sequence in

ChIP-seq peaks. The traditional motif discovery algorithms including MDscan, MEME-ChIP were developed and optimized to process limited regions of sequence in ChIP-seq peaks (X. S. Liu,

Brutlag, & Liu, 2002). Therefore, the 200bp DNA sequence flanking the ChIP-seq summit of

MACS peaks was extracted and used as input.

2.3 Materials and Methods for Chapter 5

The TALE-AP protocol consists of (1) designing the TALE, (2) evaluating and assessing the

TALE expression and DNA-specificity; and (3) optimizing TALE-AP protocols for proteomics studies. The TALE design needs to consider how to choose the TALE binding sites and to engineer the TALE N- and C-terminal backbone; while TALE assessment involves testing for interference with local gene expression and histone modifications. In the following protocol, detergents, buffers and other chemicals were obtained from Sigma-Aldrich Canada (Oakville, ON) unless indicated otherwise.

2.3.1 Strategy for Choosing the TALE Binding Site

For each specific genomic region, we chose TALE target binding sites based on three criteria: 1)

The location of known epigenetic features, DNAse hypersensitive sites and TF binding sites

(TFBS). This information is available in the ENCODE database, which collects data for TF binding sites, DHSs and histone ChIP-seq on a genome-wide basis. We tried to avoid these TFBS and not interfere with epigenetic modifications. 2) Unique predicted target binding site: We used the Target Finder 2 software to design the TALE (Doyle et al., 2012). The TALEs returned by the software were compared to the human genome using BLAST (Altschul et al., 1997); to minimize off-target potential, we selected only TALEs whose 2nd hit in the BLAST results had at least 2 mismatches. 3) A/G/C/T composition. The preferred order was T > C >> A > G. We tried to minimize the number of G residues (Juillerat et al., 2015). Since the 5’ binding site sequence contributes more to the TALE binding specificity, we prioritized target sites with TC content at the 5’ end.

2.3.2 Design and Construction of TALE-HBH

Three hexamers targeting nucleotide positions 1-6, 7-12 and 13-18 of the genomic target site were assembled from the NI, NG, NH and HD monomer library using the Golden Gate TALEN and

TAL Effector Kit 2.0 (Cong et al., 2012). These hexamers were ligated together through specifically designed BsaI restriction enzyme sites. We choose TALEs that were 18 nucleotides in length based on the off-target prediction by BLAST. Our TALE protein backbone was designed to preserve TALE function and was based on the previously described N1 and C3 truncations at the N- and C-termini with restriction enzymes BamHI (NEB) and BsiWI (NEB) (Miller et al.,

2011). We added a HBH tag to the TALE C-terminus and subcloned the assembled TALE-HBH

into the BamHI and XhoI restriction enzyme sites of pcDNA5-FRT-TO (Invitrogen), and the full- length TALE-HBH-tagged constructs were verified by Sanger sequencing with 6 different primers.

2.3.3 Cell Culture

Flp-In T-REx 293 cells (Invitrogen, product R78007) were grown in Dulbecco's minimum Eagle's medium (DMEM, 1×, high glucose; Invitrogen, product 10313-039), supplemented with FBS

(Invitrogen, product 11058-021), Penicillin-streptomycin (100×; Invitrogen, cat. no. 15140-163),

Zeocin (50 µg/ml, Invitrogen) and Blasticidin (10 µg/ml, Invitrogen). Stable cell lines were constructed according to the manufacturer's protocol (Invitrogen). Briefly, 15 million Flp-In T-

REx 293 cells were transfected in 10 cm plates with 5 µg DNA comprised of pcDNA5-FRT-TO-

TALE-HBH and pOG44 (Invitrogen) expression vectors with a mass ratio between 1:4 and 1:7

(Mateo, Garcia-Lecea, Cadenas, Hernandez, & Moncada, 2003). Forty-eight hours following transfection, stable recombinants were selected by adding Hygromycin (500 µg/ml, Invitrogen) and Blasticidin (10 µg/ml, Invitrogen) to the culture medium for 14 days. After we obtained stable recombinants, the stable cell line was grown in maintenance medium containing 250 µg/ml hygromycin and 10 µg /ml blasticidin. Expression of the TALE-HBH fusion protein was induced by adding tetracycline to the culture medium. The final working concentration of tetracycline ranged from 10 ng/ml to 1000 ng/ml.

2.3.4 Evaluation of TALE-HBH

Each TALE-HBH construct was evaluated to test the fusion protein stability and DNA binding specificity. Flp-In T-REx 293 cells with stably integrated TALE-HBHs were cultured in 10cm dishes and induced with 100ng/ml tetracycline for 36 hours. The cells were crosslinked with 1% formaldehyde at room temperature for 10 minutes lysed on ice in RIPA buffer (167mM NaCl, 1.0%

IGEPAL, 0.5% sodium deoxycholate, 0.1% SDS, 50 mM Tris, pH 8.0) supplemented with protease inhibitors (Roche, cOmplete™ Protease Inhibitor Cocktail) and purified with 10ul of prewashed M280 streptavidin magnetic beads (Invitrogen) per 10cm plate. A fraction of the input cell lysate and the flow-through wash buffers were kept to monitor the purification efficiency.

After purification, the beads were divided into two aliquots. One aliquot was used to isolate crosslinked DNA to check the TALE binding specificity by quantitative PCR (qPCR). For this aliquot, DNA-protein crosslinks were reversed by incubation in ChIP reverse crosslinking buffer

(50mM Tris PH8, 10mM EDTA, 1% SDS, 0.5M NaCl) at 65°C for 16 hours and the DNA was isolated with a QIAquick PCR purification column. Primers flanking each TALE binding site were used in qPCR reactions to evaluate TALE DNA-binding specificity. Another aliquot of beads was boiled in SDS gel loading buffer for 5 minutes to elute proteins, which were detected by western blot with Flag M2 antibody (Sigma, 1:4000) to check the protein stability and depletion efficiency.

2.3.5 Interference test

To investigate if the TALE-HBH construct alters expression of the target gene in Flp-In T-REx

293 cells, we induced expression of the TALE-HBH as mentioned above and then isolated RNA from the induced cells using Trizol Reagent (Invitrogen). In brief, 2 micrograms of total RNA extracted from the cultured cells was used to synthesize cDNA using Superscript III reverse transcriptase with oligo-dT primers (Invitrogen) according to the manufacturer’s instructions.

Real-time quantitative PCR (qPCR) was performed using Platinum SYBR Green SuperMix-UDG

(Invitrogen) together with experimental or control primers. Reactions were performed in triplicate per condition using the Step One Plus Real-Time PCR system (Applied Biosystems). Relative mRNA expression was shown as fold change compared to control.

Chromatin immunoprecipitation (ChIP) against H3K4me3 (Antibody: Diagenode C15410003) and H3K27ac (Antibody: Abcam ab4729) was performed to test if the TALE-HBH binding interfered with histone modification. Briefly, ~40 million cells were crosslinked with 1% formaldehyde at room temperature for 10minutes; and their nuclei were isolated and fragmented by sonication with a Branson Sonicator (Low power setting, 10 seconds ON/30 second OFF, 18 cycles) to generate chromatin fragments ~500bp in length. The chromatin was centrifuged at

13,000 rpm for 10 minutes at 4°C and the supernatant was collected for ChIP. A fraction of nuclear lysate was kept for later analysis by SDS-PAGE. Protein A/G beads pre-coupled with antibody were used to purify DNA-protein complexes, which was eluted with 100 ul elution buffer (50mM

Tris-HCl PH 8, 1% SDS, l0 mM EDTA). The eluate was reverse crosslinked at 65°C overnight, followed by digestion with RNAse A (2 ul, 1 hour at 37°C) and Protease K (1ul of 20 ug/ul Protease

K, 2-3 hours at 55°C). DNA was purified with QIAquick PCR purification column (Qiagen).

Quantitative PCR (qPCR) was used to quantify the DNA as described above.

2.3.6 Protocol Optimization

Several parameters are critical to achieve optimal isolation of DNA-protein complexes, including:

(1) crosslinking conditions (formaldehyde concentration, crosslinking time, crosslinking on cells or nuclei); (2) nuclear lysis conditions (lysis buffer composition, including concentration of NaCl and SDS); (3) sonication conditions (scaling the sonication for ~700 million cells); (4) the need for reChIP (perform a second round TALE-AP on the flow-through of the first round); and (5) titration of the tetracycline concentration used to induce expression of the TALE-HBH fusion protein (10 ng/ml to 1000 ng/ml). The optimized protocol was generated by modifying the following basic protocol: Cells were lysed in nuclear extraction buffer (NEB: 20 mM HEPES (4-

(2-hydroxyethyl)-1-piperazineethanesulfonic acid) PH 7.4, 10 mM NaCl, 1% Triton-X 100).

Nuclei were suspended in nuclear lysis buffer (IPB++: 140 mM NaCl, 1% Triton-X 100, 10 mM

Tris PH8, 0.1% SDS, 0.1% sodium deoxycholate). The nuclear extract was used for TALE-affinity purification (TALE-AP) by incubating the extract with prewashed streptavidin magnetic beads overnight at 4°C. The results of optimization were checked by qPCR as described above.

2.3.7 TALE-Affinity Purification (TALE-AP)

Cells induced with 10ng/ml tetracycline induction for 36 hours and then harvested by scraping.

Cells were washed three times with cold PBS and then lysed in nuclear extraction buffer (NEB:

20 mM HEPES pH 7.4, 10 mM NaCl, 1% Triton-X 100). Without delay, the cell extract was poured into crosslinking buffer (final formaldehyde concentration 2%); and the crosslinking reaction was performed at room temperature on a rotor for 20 minutes. The crosslinked nuclei were collected by centrifugation at 4000 rpm for 10 minutes at 4°C. Nuclei were washed three times with excess pre-chilled PBS and pelleted by centrifugation. The nuclei could be frozen at this step or lysed directly. Nuclei were lysed by sonication in nuclei lysis buffer (IPB++: 140 mM NaCl, 1%

Triton-X 100, 10 mM Tris PH8, 0.1% SDS, 0.1% sodium deoxycholate). We used a Branson

Sonifier (Branson 450 with regular probe) to fragment the isolated chromatin into ~2 kbp fragments (sonication conditions: 18 cycles of 10 seconds ON / 30 seconds OFF at Low power setting). Then sonicated chromatin was cleared by centrifugation at 13,000 rpm for 10 minutes at

4°C. The supernatant was used for affinity purification and a fraction of the supernatant was saved as input for ChIP-qPCR and western blot. Streptavidin magnetic beads were prepared by washing with PBS three times and then incubated with chromatin extract. The TALE-AP binding reaction was performed at 4°C overnight. Then we collected the beads by gentle centrifugation and a magnetic rack. The beads were washed extensively five times with nuclei lysis buffer and seven times low-salt buffer (25 mM Tris pH 7.4, 100 mM NaCl, 0.025% SDS). Proteins bound to the

streptavidin magnetic beads were analyzed by Western blotting or mass spectrometry. TALE-AP will be discussed in depth in Chapter 5.

2.3.8 Sample Preparation for Mass Spectrometry

We used an in-gel digestion protocol for mass spectrometry sample processing. In brief, beads were centrifuged at 2500xg for 30 seconds and the supernatant, consisting of the residual wash buffer, was removed from the beads. For each sample, we mixed the beads with 50 µL 2x Laemmli sample buffer supplemented with 0.733 mg biotin. Samples were incubated at 37°C for 15 minutes, followed by boiling at 98°C for 10 minutes. Samples were centrifuged at 3200xg for 1 minute. The supernatant was removed, boiled at 98°C for 5 minutes and cleared by centrifugation at 3200xg for 30 seconds. The upper ~90% of liquid from the supernatant was loaded onto a Bio-Rad precast acrylamide gel and the sample was electrophoresed into the stacking gel. Each gel was cut into 8 small pieces and digested with trypsin. Following in-solution digestion, approximately half of the digested proteins were analyzed by LC-MS/MS on the Velos Orbitrap (Michalski et al., 2011).

2.3.9 Mass Spectrometry Data Analysis

Protein identification was performed by the Mascot search engine (Matrix Science) and X!

Tandem and MS/MS based peptide and protein identifications will be validated by Scaffold and protein probabilities assigned by Protein Prophet (Brosch, Swamy, Hubbard, & Choudhary, 2008).

Proteins that cannot be differentiated by MS/MS analysis alone were grouped to satisfy the principles of parsimony. Data was then loaded into Scaffold. The proteomics results of each

TALE-HBH could be compared with two negative controls: Flp-In T-REx 293 cells with a stably integrated pcDNA-FRT-TO backbone and or that expressed a TALE targeting a gene that is not involved in cholesterol biosynthesis (TALE-SLC5A2-HBH).

Chapter 3

Gene Expression Profiling of Cachectic Liver and Tumor

3 Summary

Although liver plays an important role in all aspects of human metabolism, its role in cachexia has not been studied completely. Comprehensive identification of cachectic factors is also necessary to understand the underlying mechanism of weight loss during cachexia. In this chapter, we performed gene expression profiling of both liver and tumor tissues from the C26-induced cachexia mouse model. Cachectic liver showed significant upregulation of genes involved in cholesterol biosynthesis in contrast to repressed expression of genes involved in TCA cycle and ketogenesis. By integrating the gene expression datasets from the C26 and LLC-induced cachexia model, we identified a list of cachectic factors that may be important for cachexia progression. By combining gene expression data of tumor and liver, we identified pairs of tumor-derived “ligands” and hepatic “receptors” including Bmp2 and Bmp2r, Areg and Egfr, IL6 and IL6ra, which may be potential tumor-secreted regulators of the metabolic changes seen in cachectic liver. Metabolism studies of mouse primary hepatocytes treated with conditioned media from C26 cells showed increased hepatocyte cholesterol and triglyceride content, providing further support for tumor- derived signals in mediating this process.

3.1 Introduction

Cachexia is a progressive deterioration of nutritional status that is associated with marked loss of adipose tissue and muscle which cannot be explained completely by changes in food intake

(Giacosa, Frascio, Sukkar, & Roncella, 1996). Several cachectic factors including TFNa and IL-

6 have been identified to explain the weight loss, but no systematic investigation of cachectic factors has yet been performed in either humans or rodents. Although the liver plays a critical role in metabolism and energy expenditure, our understanding of the hepatic response to cachexia is incomplete (Tessitore, Bonelli, & Baccino, 1987). In this chapter, we aim to characterize the

hepatic response to cachexia, including the identification of novel cachectic factors, by profiling the gene expression of the cachectic liver as cachexia progresses. To achieve this goal, we propose to use expression microarrays to study changes in hepatic gene expression in the established murine C26-adenocarcinoma-induced cachexia model (Klimek et al., 2010). The mice were sacrificed 31 days after being injected with C26 tumor cells, by which time their weight had decreased by 8% compared to animals that had received sham injections. Our microarray data showed that mice transplanted with C26 adenocarcinomas had large changes in hepatic gene expression with more than 2000 genes showing greater than 2-fold expression changes compared to control (all with an adjusted p-value < 2x10-6). The expression levels of the TFs Hnf4α, Pparα,

Srebf1 and PGC-1α changed significantly, consistent with their established role in the regulation of liver metabolism (Schrem, Klempnauer, & Borlak, 2002). Interestingly, we identified ten additional TFs which have not been linked to metabolism, but whose expression also changed significantly in liver. In cachectic tumor-injected mice, expression of genes in the gluconeogenesis pathways is increased to provide enough energy for tumor proliferation. However, fatty acid oxidation, providing acetyl-CoA for gluconeogenesis is decreased, which may be partly be explained by the depletion of adipose tissue in these animals. Consistent with the increased biosynthesis of cholesterol seen in C26 cachectic mice, the mRNA level of Srebf2 increased in tumor-injected mice as did the expression of Sqle, Hmgcs1, Hmgcr involved in cholesterol biosynthesis.

3.2 Results

3.2.1 Gene Expression Response in Liver and Tumor as Cachexia Progresses

Adult CD2/F1 mice were either injected with C26 cells (1 million cells in 0.2ml PBS) or vehicle alone (0.2 ml PBS) (Figure 3.1). The animals were provided with free access to chow and water;

and weighed and observed closely as the tumor grew. On the afternoon of day 30, the animals were either fasted overnight or provided with food. On day 31, the tumors and livers were harvested:

~100 mg of liver and tumor tissue was homogenized in Trizol reagent and the remainder of the tissue was processed for ChIP-seq studies (see Chapter 4), or snap frozen and stored at -80°C. In addition, to identify the cachectic factors secreted by tumor, we collected samples of the cultured

C26 cells prior to injection and samples of the tumor xenograft tissue at day 14 and 31. RNA was isolated from the liver and tumor tissue were isolated and processed for microarray analysis using

Affymetrix Mouse430 V2 Chips. We used the GCRMA package for microarray data normalization and the Limma and eBayes packages to identify differentially expressed genes. FDR was used to adjust p-values to control the false discovery rate with Benjamini and Hochberg’s method

(Benjamini & Hochberg, 1995; Hochberg & Benjamini, 1990); and Toptable was used to filter the gene based on fold change. By combining the gene expression changes for the tumor and liver, we aimed to identify tumor-derived cachectic factors that function in liver as cachexia progressed.

3.2.2 Weight Loss of Cachectic Mice and Consistent Changes of Gene Expression in

Cachectic Mouse Liver

In our study, the C26-injected mice lost ~10% weight when they were sacrificed at day 31 post injection (Figure 3.2A). Principal component analysis (PCA) was performed on the normalized microarray data (Raychaudhuri, Stuart, & Altman, 2000) and showed that the effects of the tumor on liver gene expression even override those of fasting. Expression profiles from the tumor- inoculated and PBS-injected mice are well separated by the first and second principal components

(PC1 explains 37.1% of the variance and PC1 and PC2 combined explain 51.2% of the variance).

However, the differences between fasting and fed expression profiles are less marked in the cachectic tumor-inoculated mice and the cachectic liver of fed and fasting mice clustered together

in the PCA analysis (Figure 3.2B). In cachectic liver, 1587 genes are differentially expressed more than 2 fold (all with an adjusted p-value < 2x10-6). The microarray analysis identified 1056 upregulated and 531 downregulated transcripts (p-value < 2x10-6, fold change > 2) in cachectic liver under fed condition. Many of them are common in the comparison between tumor-fasting Vs

PBS-fasting group, as shown by the heatmap (Figure 3.2C). It is interesting that 80 out of these

1056 genes can be classified as receptors and 49 out of these 80 receptors may respond to tumor- derived ligands based on the gene expression data of cachectic tumor. Consistent with these extensive gene expression changes, we identified a list of differentially expressed TFs and coregulators implicated in cell metabolic pathways. Besides TFs that have well-known roles in metabolism (such as HNF4α, SREBP2 and STAT3) (Alder et al., 2014; Duncan, Navas, Dufort,

Rossant, & Stoffel, 1998) ; we identified several TFs and transcriptional coactivators (including

Cebpd, Cited2, Onecut1 and Nrip1) that were upregulated in cachectic liver but have not been linked to metabolic regulation (Table 3.1).

3.2.3 Pathway Enrichment Analysis of Differentially Expressed Genes in Cachectic

Liver

The resulting list of differentially regulated genes was analyzed with GSEA (Gene Set Enrichment

Analysis) to check for pathway enrichment (Figure 3.3). Activation of several of these pathways has been reported in cachectic livers including the acute phase response. However, we also find that energy-generating pathways are suppressed in cachectic liver, including TCA cycle and oxidative phosphorylation (Figure 3.3B) (Vander Heiden, Cantley, & Thompson, 2009). In contrast, the cholesterol biosynthesis pathway is upregulated in cachectic liver (Figure 3.3A).

Consistent with these results, expression of the master transcriptional regulator Srebp2 in cholesterol biosynthesis was increased by 2-fold (Miserez, Cao, Probst, & Hobbs, 1997). Similar

expression changes also existed in cachectic livers when the mice are fasted (data not shown), which suggests the potent cholesterol biosynthesis induction effects by the tumor can overcome the inhibition of cholesterol biosynthesis caused by fasting. We also found that the tumor graft host response and liver EGF pathways are upregulated (Figure 3.3C and D), which suggested that the cachectic livers are under the effects of excessive cytokine and growth factor stimulation

(Natarajan, Wagner, & Sibilia, 2007). This encourages us to investigate the expression changes of secreted proteins in the tumor tissue, some of which may exert their effects on the liver (discussed below).

3.2.4 Metabolic Mapping of Differentially Expressed Genes in Cachectic Liver

Since gene set enrichment analysis (GSEA) has shown that the expression of genes involved in cholesterol biosynthesis in cachectic liver is upregulated, we next aimed to investigate the metabolic changes systematically in cachectic liver. We focused on cholesterol biosynthesis, ketogenesis and gluconeogenesis. The mRNA levels for Fdft1, Hmgcs1, Sqle and Srebp2, the master regulators for cholesterol biosynthesis (Brown & Goldstein, 1997) were increased in cachectic liver independent of fasting status. Consistent with previous studies, cholesterol biosynthesis is repressed in the liver of fasting mice compared to fed mice (Figure 3.4) (Bucher,

Mc, Gould, & Loud, 1959). Although both cholesterol and fatty acid biosynthesis use acetyl-CoA as substrate, we observed repression of genes involved in ketogenesis (Figure 3.5).

Gluconeogensis can use glycerol from the host adipose tissue and lactate produced from the tumor as substrate (Marko, Gabrielli, & Caruso, 2004). The mRNA level of two key enzymes for gluconeogenesis using glycerol (glycerol kinase and Gpdh1) were decreased in cachectic livers

(Table 3.2) (Lundholm, Edstrom, Karlberg, Ekman, & Schersten, 1982). Consistent with the well- known Cori-cycle function of the liver, expression of lactate dehydrogenase was increased (Cori,

1981). Expression of the rate-limiting enzyme in gluconeogenesis, Pepck, was also increased in cachectic liver; however, the mRNA levels of G6pd, which converts glucose-6-phosphate into glucose in the last step of gluconeogenesis, decreased by 2.6-fold (p<0.05) (Ellwood, Michaelis,

Emberland, & Bhathena, 1985).

3.2.5 Search for Cachectic Factors Using C26 Cell and Tumor Expression Data

We hypothesize that during cachexia, the tumor expresses more “cachectic factors” that can function to regulate metabolism in peripheral tissues including the liver (Schmitt et al., 2007).

Therefore, we compared the expression data of tumors at day 31 to those at day 14 since at day 14 mice have not started to lose weight. In total, there were 359 genes up-regulated more than two fold (all with adjusted p-values p < 10-6) in the tumors of cachectic mice. A selected list of potential cachectic factors, obtained by filtering these genes for transcripts that encode secreted proteins, is shown in Table 3.3.

The upregulated genes included several growth factors such as Amphiregulin (Areg), heparin- binding EGF-like growth factor (HbEGF) and betacellulin (Btc). We also saw the upregulation of

PTHLH, which has already been shown to be a master regulator in fat browning during cachexia

(Kir et al., 2014). Cytokines such as IL-6 and IL-11 were also upregulated. To investigate if these secreted tumor proteins could function in liver, we focused on the expression pattern of a selected list of genes in C26 cells grown in culture, and from tumors sampled on day 14 and 31 post injection into mice (Figure 3.6A). These genes include growth factors, cytokines and chemokines.

LLC-induced cachexia is another widely used mouse model to identify cachectic factors, and could potentially involve similar cachectic factors (Kir et al., 2014). By searching for the highly expressed transcripts for secreted proteins shared between LLC and C26 cells, we aimed to find general mechanisms mediating cachexia progression. We re-analyzed the microarray data for LLC 86

cells (GEO accession number GSE57797) using the same strategy as for the C26 dataset. Kir et al have isolated two subtypes of LLC clones, which exhibit different capacity to induce cachexia in mice (Kir et al., 2014). We found some proteins, including Ereg, Areg, Btc and Bmp2, are also highly expressed in LLC clones with a higher capacity to induce cachexia (Figure 3.6A). We combined the comparative LLC-C26 analysis, with expression data for protein receptors in the cachectic liver to narrow down the list of factors that may be active in this condition (Figure 3.6B).

For example, growth factors including Ereg and Areg are highly expressed in C26 tumor tissue in cachectic mice and the liver expression of EGFR is upregulated in the same animals. In total we identified four ligand-receptor pairs that include EREG/AREG/BTC/HBEGF-EGFR/ERB3,

BMP2-BMPR2, NPPB-ACVR1B and IL6/IL33-IL6RA.

3.2.6 Metabolism Studies of Mouse Primary Hepatocytes Treated With C26-CM

Since the main metabolic changes in cachectic liver include the upregulation of genes involved in cholesterol biosynthesis, we investigated whether C26-conditioned medium could stimulate cholesterol biosynthesis in mouse liver. We isolated mouse primary hepatocytes, treated them with

C26-conditioned medium for up to three days and measured their cholesterol and TG content. We found that C26-CM stimulates cholesterol biosynthesis (with peak increases of > 3-fold after 48h of exposure) (Figure 3.7A).; and that TG content was significantly higher in C26 CM-treated mouse hepatocytes (Figure 3.7B). In addition, within 10 hours of plating the mouse primary hepatocytes, we noticed differences in the spreading efficiency of control and C26 CM-treated hepatocytes (data not shown). Primary hepatocytes treated with C26 CM spread on the plate much faster, suggesting the growth-promoting effects of C26 CM on mouse hepatocytes.

3.2.7 Summary of the Metabolic Changes in Cachectic Liver

Expression profiles from cachectic liver showed changes in the expression of genes implicated in metabolic pathways involved in cholesterol biosynthesis, ketogenesis and the TCA cycle (Figure

3.8). Since cholesterol biosynthesis is highly dependent on having adequate NADPH levels and since the pentose phosphate pathway is the only pathway producing NADPH in mammals, we checked the mRNA levels in this pathway. Consistent with the upregulation of cholesterol biosynthesis, we observed upregulated expression of pentose phosphate pathway genes (Ledda-

Columbano et al., 1985; Wamelink, Struys, & Jakobs, 2008). In mammalian cells, acetyl-CoA, the central substrate of metabolism can either used in the TCA cycle to produce ATP or as a substrate for ketogenesis, gluconeogenesis and cholesterol biosynthesis (McGarry, Mannaerts, & Foster,

1977; Wieland & Weiss, 1963). Surprisingly, cholesterol biosynthesis is upregulated in contrast to the repression of the other two pathways (Figure 3.8).

3.3 Discussion

Cachexia is a progressive deterioration of nutritional status that is associated with marked loss of adipose tissue and muscle. Nearly half of cancer patients are affected by cachexia, which causes anorexia, fatigue and malaise and may eventually lead to their death (Creagh-Brown & Shee, 2009).

Previous studies have mainly focused on the pathogenic mechanisms of protein breakdown in muscle and triglyceride breakdown in adipose tissue during cachexia (Aoyagi et al., 2015). For example, the ActRIIB pathway has been shown to be involved in muscle wasting; and blockage of this pathway with antagonists was able to reverse cancer-induced atrophy of the heart (J. L.

Chen et al., 2014; Zhou et al., 2010). Since adipose triglyceride lipase is a key regulator of CAC by promoting loss of adipose tissue (Das et al., 2011), it is tempting to speculate that drugs targeting adipose lipase will ameliorate cancer cachexia and its devastating effects.

The liver plays a central role in mammalian glucose, protein and lipid metabolism. As tumor cells use glucose as their primary source of energy, changes in hepatic metabolism in cachectic subjects will occur to satisfy the tumor cells’ needs (Rajendran et al., 2004). Cytosolic factors secreted by the tumor and by the immune system have been implicated in rewiring hepatic metabolism, but the detailed transcriptional changes and TFs involved remain unknown (Tisdale, 2002). It has been documented that the weight and volume of liver and spleen increased during cachexia in contrast to the loss of muscle and adipose tissue, which is consistent with the upregulated activity of the acute phase response and other biosynthetic pathways in cachectic liver. Cachectic factors such as

TNFα stimulated increased total liver protein synthesis (as part of the acute phase response). In this chapter, we systematically profiled the hepatic gene expression changes occurring during cachexia in the C26-induced cachexia model. Our microarray data showed that mice transplanted with C26 adenocarcinomas had large changes in hepatic gene expression. In total, more than 1587 genes showed expression changes greater than 2-fold (all with adjusted p-value p < 2x10-6) compared to controls. Pathway enrichment analysis showed significantly upregulation of cholesterol biosynthesis genes in contrast to repression of genes involved in the TCA cycle, electron transport chain, fatty acid biosynthesis and gluconeogenesis. Strikingly, these metabolic changes in cachectic liver are independent of feeding or fasting. Overall, the synthesis of 1 mole of cholesterol is a substrate and energy consuming bioprocess that requires 26 moles of ATP, 16 moles of NADPH and 18 moles of acetyl-CoA (Kang, Kim, Johng, & Paik, 1995). We speculate that cholesterol biosynthesis could be deployed by the tumor as a novel molecular pathway for energy wasting during cachexia since cholesterol cannot be used as energy substrate once synthesized.

Consistent with the extensive gene expression changes in cachectic liver, we observed that the expression levels of the TFs Hnf4a, Ppara, Srebf1 and Pgc1a changed significantly, reflecting 89

their established role in the regulation of liver metabolism. Interestingly, we identified ten additional TFs which have not been linked to metabolism, but whose expression also changed significantly in liver. These include Cited2, Onecut1 and Nrip1, which function as coregulatory proteins in transcriptional regulation. This encouraged us to investigate the transcriptional regulation in cachectic liver in detail with epigenomics approaches in the next chapter.

To identify potential circulating cachectic factors, we profiled the gene expression of cultured C26 cells and tumor xenograft tissue before cachexia developed and in end-stage cachexia. We identified a list of cachectic factors whose expression is upregulated as cachexia progressed, including Pgf, Pthlh, Areg, Ereg, Vegfa, Bmp2, IL-11 and IL-6. We also reanalyzed the expression data of LLC cells and found that these C26-derived cachectic factors are also significantly upregulated in high-cachexia LLC cells (Klimek et al., 2010). By combining gene expression data from the tumor and liver, we identified several pairs of “ligands” and “receptors” including Bmp2 and Bmp2r, Areg and Egfr, IL6 and IL6ra, which suggests the distal effects of cachectic factors on liver.

Finally, we studied metabolism changes of mouse primary hepatocytes treated with C26-CM.

Consistent with the microarray data, it indicated that cholesterol and triglyceride content in primary hepatocytes treated with C26-CM is upregulated. We hypothesized that cholesterol biosynthesis may be deployed by the tumor to regulate whole body energy expenditure during cachexia. Cholesterol biosynthesis inhibitors such as statins have proven to be effective in treating glioma (Gaist et al., 2013). Therefore, to characterize the transcriptional regulation of cholesterol biosynthesis including key enzymes such as SQLE, HMGCS and MVK in detail may provide additional therapeutic targets for cachexia reversal. However, until now locus-specific TF

identification remains challenging. In the last chapter, we aimed to develop a novel technology to identify DNA-specific TF complex using the SQLE promoter as an example.

Cachec&c liver Cachec&c liver C26 cell line C26 cell line Vs. Control Vs. Control (Feeding) (Fas&ng) Day 14 tumor &ssue Day 14 tumor &ssue Diﬀeren&ally (Feeding) (Fas&ng) expressed genes (DEGs) Day 31 tumor &ssue Day 31 tumor &ssue DEGs in DEGs in (Feeding) (Fas&ng) cachec&c liver cachec&c liver (Fas&ng) (Feeding) Progressive DEGs paLerns Pathways: GSEA Cachec&c factors Cachec&c factors (Feeding) (Fas&ng) Cachec&c liver Cachec&c liver pathways pathways (Feeding) (Fas&ng) LLC model

Network construc&on Tumor-related cachec&c in cachec&c liver factors

Infer the “Communica&on”

Figure 3.1: Overview of project pipeline to identify gene expression response in cachectic liver and tumor as cachexia progresses. Left: liver tissue from control and tumor-injected mice was isolated and used for microarray analysis with Affymetrix Mouse430 V2 Chips. Two

Bioconductor packages were used to analyze the data: GCRMA for normalizing microarray data and Limma for differential gene expression analysis. Right: In parallel, cultured C26 cells and tumor tissues were collected and used for gene expression profiling. The result from C26 studies were compared with published expression data from LLC, another cachexia-inducing cell line.

A C Color Key Color Key Color Key Color Key Color Key 24 C26 PBS 23 ) −1 0 1 2 −1 0 1 2 A" −2 0 1 2 B" Row Z−Score gm −2 0 1 2 −2 0 1 2 Row( Z−Scorea 22 RowCell Cell$Z−Score Day 14 Day4$ RowDayDay 31 Z−$Score1$ RowL$ Z−Score H$ Pros1 Pros1 Insl3 Insl3 Pgf Pgf Pgf

Weight Cxcl1 21 Cxcl1 Inhba Inhba $ Inhba Il6 Il6 Btc Btc Btc Btc $ Btc Nppb Nppb Pthlh Pthlh Pthlh Pthlh Pthlh $ Days 0Tgfb15 10 15 20 25 30 Tgfb1 Hbegf Hbegf Hbegf Dll1 Dll1 B Il11 Il11 $ Il11 8 Inhbb Inhbb Il33 Il33 Bmp2 Bmp2 $ Bmp2 6 Areg PS Areg PS Ereg Ereg Ereg 4 Vegfa PS Vegfa $ Il11 Il11 Areg Areg Areg Inhba Inhba 2 Vegfa Vegfa $ Vegfa Cxcl13 TS Cxcl13 0 Il1rn TS TS Il1rn Il33 Il33 Il33 Pgf Pgf $ PC2 -2 Crlf1 TFTFTF Crlf1 Tgfb1 Tgfb1 Tgfb1 Ereg PS PF TS EregTF Dll1 Dll1 $ Dll1 -4 HbegfPF HbegfColor Key Color Key -6 Bmp2PF Bmp2 Inhbb Inhbb $ Inhbb PsapPF Psap Il6 Il6 $ Il6 -8 −1 0 1 2 −2 0 1 2

L L L L L L RowL Z−L ScoreL L L L L L L L L L L L L L L L L L L L L L L L L L L L H H H H H H H H H H H -1 0 1 2 Row Z−Score H H H H H H H H H H H H H H H H H H H H H H

-10 -5Cell Cell Cell 5 10

Cell Cell Cell 0 Row Z-score Pros1 C" PC1 Insl3 Pgf Feed14 Feed14 Feed14 Feed31 Feed31 Feed31 Feed14 Feed14 Feed14 Feed31 Feed31 Feed31 Cxcl1 Inhba b Il6 Expression"level"of"Ligand"in" Btc Expression"level"of"Receptors" Btc Nppb Pthlh Figure 3.2: Substantial Ligand" gene expression changes occurred in cachectic liver. (A)Weight Receptor"" Pthlh Tumor" Tgfb1 in"Liver" Hbegf Ligand Expression level in Tumor Receptor Dll1 Expression level in Liver Il11 Inhbb $ 6 Il33 Bmp2 change of mice inoculated on Day 1 with PBS or Colon-26 (C26) adenocarcinoma cells (10 cells Areg Ereg 3.73 Vegfa $ Ereg EREG$ 3.73$ Il11 Areg EGFR$ Inhba 3.24$ AREG$Areg 13.00 Egfr Cxcl13 3.24 Vegfa 13.00$ Il1rn Il33 per mouse). By Day 30, the weight of the C26-injected mice was 7.8% less than the PBS-injected $ Pgf $ BTC$Btc 5.97 5.97$ Erbb3 Crlf1 1.62 Tgfb1 ERBB3$ Ereg 1.62$ $HBEGFHbegf$ 2.29 2.29$ Hbegf Dll1 Bmp2 Inhbb mice (P<0.005). (B) Principal component analysis of expression data from 12 mouse liver samples Psap $ $ Il6 L L L L L L L L L L L L Cell Cell Cell Bmp2 3.97 Bmpr2 1.84 H H H H H H H H H H H in 4 groups: PBS-injectedBMP fed (PF),2$$ PBS-injected fasted (PS), tumor-injected3.97 fed$ (TF), tumor- Feed14 Feed14 Feed14 BMPR2$Feed31 Feed31 Feed31 1.84$ NppbNPPB$ $ 12.29 12.29$ Acvr1b ACVR1B$ 2.78 2.78$ injected fasted (TS). PC1 explained 37.1% of the variance and PC2 explained 14.1%. (C) One- ILIL6 ?6$$ 10.98$ IL6RA$ 6.82$ 10.98 IL6ra 6.82 way hierarchical clustering ofIL33 genes showing significantly changes in cachectic liver.

Table 3.1: Differentially expressed transcription factors and co-regulators implicated in metabolic pathways of cachectic liver. Besides the well-known TFs Hnf4a, Srebf2 and Stat3, several novel TFs are identified including Cebpd, Cited2, Onecut1 and Nrip1 in cachectic liver.

Tumor (Fed) Gene Adjusted P-value Name or Function PBS (Fed)

-12 CCAAT/enhancer binding Cebpd 13.0 ↑ 2.3 x 10 proteins -12 Cited2 7.6 ↓ 7.4 x 10 Coregulatory protein

-10 Signal transducer and activator Stat3 3.5 ↑ 1.6 x 10 of transcription

-8 Carbohydrate response element Mlxipl 2.8 ↓ 3.9 x 10 binding protein

-7 One cut domain family Onecut1 4.0 ↓ 2.8 x 10 member 1

-7 Sterol regulatory element Srebf2 2.0 ↑ 4.6 x 10 binding factor -7 Ppargc1a 4.8 ↑ 7.1 x 10 Coregulatory protein

-6 Hnf4a 1.8 ↑ 4.7 x 10 Hepatic nuclear factor 4 alpha

-6 Sterol regulatory element Srebf1 1.9 ↓ 1.9 x 10 binding factor -3 Nrip1 1.4 ↓ 2.6 x 10 Coregulatory protein

A C Cholesterol biosynthesis Graft Versus Host Disease 0.4 SQLE 0.4 0.3

Score HMGCR 0.2 Score 0.2 FDPS 0.1 MVK 0.0 0.0 Enrichment

DHCR24 Enrichment DHCR7

B D TCA cycle & Respiratory electron transport Liver Cancer EGF Up A" C" Tumor&&&& SDHD PBS& 0.4 0.4 IDH1 Score Score CYCS 0.2 0.2 NDUFA12 0.0 0.0 ATP5J Enrichment Enrichment PDHA1

Figure 3.3: Gene set enrichment analysis (GSEA) of cachectic liver microarray data. GSEA B" Tumor&&PBS&&& analysis of cachectic liver microarray data reveals that the expression of genes related to cholesterol biosynthesis is significantly upregulated (A), while that of genes involved in the TCA cycle and respiratory electron transport are inhibited (B). We also observed enrichment of genes in signaling pathways involved in graft host response (C) and liver EGF pathways (D), both of which are upregulated.

Enzyme and Tumor (Fed) Tumor (Fasted) Fasted (PBS) Acety-CoA Regulators PBS (Fed) PBS (Fasted) Fed (PBS)

FDFT1 2.0↑** 4.6↑** 1.7↓** HMGCS1 & HMGCS1 1.8↑** 5.2↑** 2.5↓** HMGCR Enzymes SQLE 5.0↑** 41.0↑** 8.0↓** Mevalonate SREBP2 2.0↑** 2.1↑** 1.1↓ FDFT1 LXR Beta 1.1↓ 1.1↓* 1.1↑ Squalene Regulators LXR Alpha 1.0 1.0 1.1↓

SQLE

Lanosterol

Cholesterol

Figure 3.4: Expression changes for enzymes involved in cholesterol biosynthesis. In cachectic mice, the cholesterol biosynthesis pathway is upregulated and the key transcriptional regulator

SREBP2 is also over-expressed. Several important enzymes involved are shown, including FDFT1,

HMGCS1 and SQLE. (**p < 10-6, *p < 0.05, all others, p = n.s. All p-values adjusted by FDR.)

Tumor (Fed) Tumor(Fasted) Fasted (PBS) Acetyl CoA Enzyme PBS (Fed) PBS(Fasted) Fed (PBS) ACAT2 ACAT2 2.1 ↓** 1.8↓** 1.8↓**

HMGCS2 5.1↓** 11.6↓** 2.1↑** Acetoacetyl CoA

HMGCS2 3-HBDH type 1 9.0↓** 10.0↓** 1.0 HMG-CoA

3-HBDH type 2 4.6↓** 2.3↓** 2.3↓**

Acetoacetate

3-HBDH

D-3- Acetone hydroxybutyrate

Figure 3.5: Expression changes of enzymes involved in ketogenesis in cachectic liver. mRNA level of enzymes in ketogenesis decreased in cachectic mice under both fed and fasting conditions in contrast to upregulated cholesterol biosynthesis. Several key enzymes involved are listed including ACAT2 and HMGCS2. (**p < 10-6, *p < 0.05, all others, p = n.s. All p-values adjusted by FDR)

Table 3.2: The expression level change of key enzymes in the gluconeogenic pathway using glycerol and lactate as substrate. Gluconeogenesis starting from glycerol decreased in cachectic mice, which may be due to the depletion of adipose tissue in these animals. Gluconeogenesis from lactate increased in the cachectic mice, which can be explained by excessive lactate produced in the tumor. Note that glucose-6-phosphatase decreased in both fed and fasted cachectic mice compared to PBS-injected mice. (**p < 10-6, *p < 0.05, all others, p = n.s.; P-values adjusted for multiple testing using FDR correction)

Tumor (Fed) Tumor (Fasted) Fasted (PBS) Enzyme PBS (Fed) PBS (Fasted) Fed (PBS)

Glycerol kinase 1.8↓** 2.6↓** 1.3↑* Substrate - glycerol GPDH 1 1.7↓** 2.0↓** 1.0 Lactate 1.8↑** 1.6↑** 1.2↓* Substrate - Lactate dehydrogenase PEPCK 3.9↑** 1.1↓ 4.0↑** Fructose-1,6- Rate-limiting 1.0 1.0 1.0 bisphosphatase enzymes Glucose-6- 2.6↓* 4.9↓** 1.0 phosphatase

Table 3.3: Identification of cachectic factors. The list of cachectic factors was derived from analysis of gene expression data in C26 cell lines, xenograft tumor tissue at day 14 and 31 post transplantation. We hypothesized that the cachectic factors upregulated from day 14 to 31 may be important for cachexia progression. Cell: C26 cell line; 14F: fed tumor tissue at day 14; 14S: fasting tumor tissue at day 14; 31F: fed tumor tissue at day 31; 31S: fasting tumor tissue at day 31.

Gene 14S-Cell 31S-14S 14F-Cell 31F-14F Pthlh 0.84 5.56 2.28 4.39 Areg -4.56 5.05 -2.96 3.74 Nppb -0.22 4.17 0.90 3.62 IL11 -1.63 4.35 -0.78 3.58 IL6 -1.25 3.90 -1.37 3.46 Inhba -3.28 2.46 -3.67 2.64 Btc 1.53 2.38 1.48 2.58 Ereg -2.76 2.79 -2.18 1.96 Cxcl1 2.12 2.42 2.55 1.47 Hbegf -3.29 2.31 -1.69 1.21 Vegfa -2.79 1.80 -2.34 1.10 Crlf1 -0.29 0.93 -0.23 1.00 Pdgfa -0.64 0.98 -0.34 0.98 Jag1 -0.67 0.37 -0.46 0.93 IL7 -0.36 0.88 -0.28 0.92

Color Key Color Key Color Key Color Key Color Key A C26 Day 14 Day 31 LLC: Lower cachectic LLC: Higher cachectic

−1 0 1 2 Pgf −1 0 1 2 Row Z−Score A" −2 0 1 2 −2 0 1 2 B" −2 0 1 2 Row Z−Scorea Inhba RowCell Cell$Z−Score Day 14 Day4$ RowDayDay 31 Z−$Score1$ Row Z−Score Btc L$ H$ Pros1 Pros1 Insl3 Pthlh Insl3 Pgf Pgf Pgf Cxcl1 Cxcl1 Inhba Inhba $ Inhba Il6 Hbegf Il6 Btc Btc Btc Btc $ Btc Nppb IL11 Nppb Pthlh Pthlh Pthlh Pthlh Bmp2 Pthlh $ Tgfb1 Tgfb1 Hbegf Hbegf Hbegf Dll1 Ereg Dll1 Inhbb Il11 Il11 $ Il11 Areg Inhbb Il33 Il33 Bmp2 Bmp2 $ Bmp2 Areg Vegfa Areg Vegfa Ereg Ereg Ereg IL33 Vegfa $ Il11 Il11 Areg Areg Areg Inhba Inhba Tgfb1 Vegfa Cxcl13 Cxcl13 Vegfa $ Vegfa Il1rn Dll1 Il1rn Il33 Il33 Il33 Pgf Pgf $ Inhbb Tgfb1 Crlf1 Crlf1 Tgfb1 $ Tgfb1 Ereg IL6 Ereg Dll1 Dll1 Dll1 Hbegf HbegfColor Key Color Key Bmp2 Bmp2 Inhbb Inhbb $ Inhbb Psap Psap Il6 Il6 $ Il6 −-1 0 1 2 −2 0 1 2

L L L L L L RowL Z−L ScoreL L L L L L L L L L L L L L L L L L L L L L L L L L L L H H H H H H H H H H H Row Z−Score H H H H H H H H H H H H H H H H H H H H H H Cell Cell Cell

Cell Cell Cell Row Z-score Pros1 C" Insl3 Pgf Feed14 Feed14 Feed14 Feed31 Feed31 Feed31 Feed14 Feed14 Feed14 Feed31 Feed31 Feed31 Cxcl1 Inhba b B Il6 Expression"level"of"Ligand"in" Btc Expression"level"of"Receptors" Btc Nppb Pthlh Ligand" Receptor"" Pthlh Ligand Expression level in TumorTumor"Receptor Expression level in Liver Tgfb1 in"Liver" Hbegf Ligand Expression level in Tumor Receptor Dll1 Expression level in Liver Il11 Inhbb Il33 Bmp2 $ Ereg 3.73 Areg Ereg 3.73 Vegfa $ Ereg EREG$ Areg 13.00 3.73$ Egfr 3.24 Il11 Areg EGFR$ Inhba 3.24$ AREG$Areg 13.00 Egfr Cxcl13 3.24 Vegfa Btc 5.97 13.00$Erbb3 1.62 Il1rn Il33 $ Pgf $ BTC$Btc Hbegf 2.29 5.97 5.97$ Erbb3 Crlf1 1.62 Tgfb1 ERBB3$ Ereg 1.62$ $HBEGFHbegf$ 2.29 2.29$ Hbegf Dll1 Bmp2 Inhbb Bmp2 3.97 Bmpr2 1.84 Psap $ $ Il6 L L L L L L L L L L L L Cell Cell Cell Bmp2 3.97 Bmpr2 1.84 H H H H H H H H H H H BMP2$$ Nppb 12.29 3.97$Acvr1b 2.78 Feed14 Feed14 Feed14 BMPR2$Feed31 Feed31 Feed31 1.84$ IL6 NppbNPPB$ $ 10.98 12.29 12.29$IL6ra 6.82 Acvr1b ACVR1B$ 2.78 2.78$ IL33 ILIL6 ?6$$ 10.98$ IL6RA$ 6.82$ 10.98 IL6ra 6.82 IL33 Figure 3.6: Highly expressed secreted proteins in C26 and LLC models and the expression

levels of their cognate receptors in liver. (A) Heatmap showing the expression pattern of

secreted proteins in C26 tumor cells grown in culture or harvested from tumor Day 14 or31 post

injection into mice. LLC cell lines possess different capacity to induce cachexia, which are

labeled as LLC/Lower cachectic and LLC/Higher cachectic. (B) List of ligand-receptor pairs

showing increased ligand expression in tumor and corresponding receptor level upregulation in

liver of cachectic animals.

100

7.5

/ml) 5.0 ug

Cholesterol ( 2.5

Control 0.0 C26-CM hepatocytes 12h 24h 48h 72h

0.6 ) 0.4 g/ml m TG (

0.2

Control 0.0 C26-CM hepatocytes 12h 24h 48h 72h

Figure 3.7: Cholesterol and Triglyceride measurement in C26 conditioned medium-treated mouse primary hepatocytes. Primary hepatocytes are collected at 12, 24, 48 and 72 hours, after treatment with C26 conditioned medium. Cholesterol content was measured with Thermo Amplex red kit (A) and triglyceride content was measured with SIGMA TG GPO-Trinder (B). Error bars indicate the standard deviation (SD) from triplicate experiments.

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Figure 3.8: Summary of metabolic changes in cachectic liver. Cholesterol biosynthesis is significantly upregulated together with the pentose phosphate pathway, providing NADPH for cholesterol synthesis. In contrast, the TCA cycle and ketogenesis are suppressed.

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Table 3.4: Top 100 upregulated genes in cachectic liver under fed and fasting conditions. The differential expressed genes in fed and fasting conditions are significantly consistent. The correlation coefficient of expression level changes is 0.74 for the top 100 genes and 0.71 for the top 500 genes (adjusted p-value p < 0.05).

ID Gene logFC(Fed) logFC(Fasting) 1427747_a_at Lcn2 10.54 9.15 1449326_x_at Saa2 9.22 9.14 1428942_at Mt2 8.73 3.39 1432517_a_at Nnmt 8.62 7.30 1434719_at A2m 8.36 10.66 1420447_at Sult1e1 8.22 8.36 1420438_at Orm2 8.19 9.55 1417017_at Cyp17a1 7.36 4.85 1449525_at Fmo3 7.35 3.83 1419669_at Prtn3 6.73 6.83 1418918_at Igfbp1 6.68 3.03 1450788_at Saa1 6.52 6.59 1419394_s_at S100a8 6.27 7.06 1415977_at Isyna1 6.24 5.88 1451612_at Mt1 6.09 3.91 1424599_at Fgl1 6.06 4.23 1448756_at S100a9 5.83 7.22 1429273_at Bmper 5.52 6.20 1450611_at Orm3 5.47 5.27 1417507_at Cyb561 5.39 5.30 1416576_at Socs3 5.33 5.39 1426501_a_at Tifa 5.18 4.33 1449824_at Prg4 5.14 5.47 1416125_at Fkbp5 4.97 2.15 103

1428776_at Slc10a6 4.96 4.78 1457123_at Nrg4 4.88 5.04 1421564_at Serpina3c 4.66 5.75 1416474_at Igdcc4 4.64 5.33 1417268_at Cd14 4.61 4.22 1417065_at Egr1 4.56 3.69 1450826_a_at Saa3 4.55 3.63 1457644_s_at Cxcl1 4.52 7.35 1450530_at B3galt1 4.51 4.12 1425150_at Acnat2 4.39 2.41 1428306_at Ddit4 4.36 3.02 1423233_at Cebpd 4.27 2.01 1460212_at Gnat1 4.27 4.53 1451204_at Scara5 4.20 5.13 1453851_a_at Gadd45g 4.15 2.93 1436370_at Gucy2c 3.92 3.99 1426858_at Inhbb 3.81 3.36 1419005_at Crybb3 3.81 4.01 1418457_at Cxcl14 3.79 6.87 1448741_at Slc3a1 3.79 4.15 1428780_at Tha1 3.77 3.57 1448950_at Il1r1 3.75 1.56 1460197_a_at Steap4 3.63 4.57 1425829_a_at Steap4 3.63 4.81 1419647_a_at Ier3 3.62 2.49 1427167_at Armcx4 3.60 4.12 1415899_at Junb 3.59 3.08 1451416_a_at Tgm1 3.54 4.22 1449360_at Csf2rb2 3.54 3.39 1417266_at Ccl6 3.49 3.02 1416933_at Por 3.40 0.78

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1429185_at Arhgef26 3.39 1.90 1421363_at Cyp2c39 3.38 1.69 1438377_x_at Slc13a3 3.37 4.96 1450505_a_at Fam134b 3.34 2.83 1436544_at Atp10d 3.34 2.89 1452445_at Slc41a2 3.321 4.44 1417290_at Lrg1 3.32 2.24 1419100_at Serpina3n 3.32 2.50 1448555_at Rpap3 3.31 3.03 1421430_at Rad51b 3.30 3.03 1449106_at Gpx3 3.30 3.47 1425745_a_at Tacc2 3.29 1.73 1448754_at Rbp1 3.25 4.61 1415938_at Spink3 3.23 3.75 1417441_at Dnajc12 3.22 2.06 1433966_x_at Asns 3.20 4.69 1451095_at Asns 3.20 3.72 1418288_at Lpin1 3.18 2.66 1459971_at Kcnt2 3.17 2.60 1450424_a_at Il18bp 3.14 2.78 1431213_a_at Gm3579 3.12 0.84 1460241_a_at St3gal5 3.04 0.66 1450970_at Got1 3.04 2.42 1448290_at Reg3b 3.02 4.77 1426812_a_at Fam129b 2.98 2.37 1451382_at Chac1 2.97 2.61 1418174_at Dbp 2.97 3.13 1437675_at Slc8a1 2.93 1.83 1416953_at Ctgf 2.93 1.18 1435936_at Slc13a5 2.89 3.81 1446368_at 9130221J18Rik 2.88 4.37

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1448529_at Thbd 2.84 2.58 1452418_at Gm20186 2.83 2.09 1415993_at Sqle 2.81 6.15 1417399_at Gas6 2.79 2.82 1426601_at Slc37a1 2.79 2.13 1436195_at Fam211a 2.79 2.43 1424758_s_at Serpina10 2.78 3.37 1426246_at Pros1 2.77 2.81 1416255_at Gja4 2.76 2.13 1419059_at Apcs 2.75 3.93 1433833_at Fndc3b 2.75 3.13 1438667_at Sybu 2.75 2.08 1449007_at Btg3 2.74 3.59 1419144_at Cd163 2.73 2.31

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Chapter 4

Epigenetic Profiling of Cachectic Liver with ChIP-seq

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4 Summary

Previous studies have characterized the gene expression profile of cachectic liver, but the underlying TF network mediating these transcription changes remains unknown. In this chapter, we adopted an epigenomic approach to systematically identify active DNA elements genome-wide and used bioinformatics analysis to uncover the TFs involved. To identify the active DNA regions in cachectic liver, we performed ChIP-seq against three commonly used histone modification marks (H3K4me1, H3K4me3 and H3K27ac), CTD-phosphorylated RNA polymerase II and CBP in cachectic and sham-injected liver. Pathway enrichment analysis indicated that the active genes were enriched for cytokine-cytokine receptor interaction, the insulin response pathway and JAK-

STAT signaling. Motif analysis suggested that STAT3, FOX and ETS family TFs were involved in reprogramming hepatic gene expression during cachexia.

4.1 Introduction

Many of the soluble mediators implicated in cancer cachexia act through cytosolic signaling pathways that alter gene transcription. Detailed studies have shown altered NF-kB and AP-1 activity associated with muscle wasting and altered Stat3 and AP-1 activity in response to secreted mediators of cachexia (Argiles et al., 2005). Despite extensive literature showing the effects of

TFs such as Hnf4α, Chrebp/Mlxipl, Onecut1, Srebf1, Srebf2, Esrrα, Fxr/Nr1h4, Pparα and Pparγ and coactivators such as Nr0b2, Ppargc1a, Cited2 and Nrip1 on hepatic metabolism (Dentin et al.,

2006; Nair et al., 2006; F. Yang et al., 2007), little is known about their contribution to the changes in gene expression seen in cancer cachexia. Since the liver plays a central role in regulating energy metabolism in tumor cachexia, we studied changes in liver gene expression in the established colon-26 (C26) adenocarcinoma model. This model has been used to identify the molecular effects of IL-1, IL-6, PTHrP and PIF in cancer cachexia (Seto et al., 2015). Our studies of mice with

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transplanted C26 adenocarcinomas revealed large changes in the hepatic expression of TFs and coregulators that regulate metabolic processes form the basis for the present research proposal.

The investigation of protein-DNA interactions through chromatin immunoprecipitation (ChIP) has facilitated great advances in our understanding of TFs (Biddie, John, & Hager, 2010; Tang et al.,

2011), by allowing us to assess the activity of transcriptional regulatory elements bound by individual TFs and transcriptional coactivators in living cells at a genome-wide scale. The union of ChIP and microarray technology allows for genome-wide mapping of protein-DNA interactions by the hybridization of ChIP fragments to a genomic microarray (ChIP-chip) (Buck & Lieb, 2004).

However, with the rapid development of next-generation sequencing platforms, the coupling of

ChIP to high throughput sequencing (ChIP-Seq) has largely superseded ChIP-chip in recent years

(Visel et al., 2009). Data generated from ChIP-Seq provides greater genomic coverage, higher resolution, for particular cells or tissues of interest. The ChIP-seq signals generate less background, and is more dynamic than that obtained from the ChIP-chip method (Kaufmann et al., 2010).

We identified TFs with overrepresented binding sites in the regions identified by our ChIP assays using TFBS analysis for known binding motifs (Jothi, Cuddapah, Barski, Cui, & Zhao, 2008). In addition, de novo motif discovery approaches were used to identify DNA motifs that are enriched in these regions; these are the likely binding sites for TFs whose binding affinities are not yet documented in databases like Transfac and Jaspar (Grant, Bailey, & Noble, 2011; Sandelin,

Alkema, Engstrom, Wasserman, & Lenhard, 2004; Wingender, Dietze, Karas, & Knuppel, 1996).

A similar analysis was performed on the set of genes with differential expression under cachexia.

The promoter regions of these genes were mined for enriched sites for known or unknown factors to identify the upstream TF network that regulates liver gene expression in cachexia. We identified promoters and enhancers responsible for hepatic gene expression changes. ChIP-Seq was used to identify genomic active regions in the liver of cachectic mice. ChIP was performed against active 109

histone marks (including H3K4me1 and H3k27Ac), CBP and RNA polymerase II. These histone modification marks along with CBP and RNA polymerase II have been extensively used to identify mammalian genomic enhancers and actively-transcribed regions in ENCODE and other projects.

4.2 Results

4.2.1 Generation of ChIP-seq Data

A detailed description of the methods used to collect cachectic liver samples and the ChIP-seq protocols is presented in the “Methods” section. In brief, adult CD2/F1 mice were injected with either PBS or C26 cells. At day 30 (the day before sacrifice), each group of mice were divided into two categories: fed and fasting. The mice are sacrificed 31 days after injection and the liver was harvested. The analysis compared four groups of mice: PBS-Fed, PBS-Fasting, Tumor-Fed and

Tumor-Fasting.

4.2.2 Identification of Upstream Transcriptional Networks Mediating the Gene

Expression Changes of Cachectic Liver

In the C26 model, hepatic gene expression has been reprogrammed in response to cachexia on a genome wide basis. To identify the active enhancers mediating this response in cachectic liver, we used ChIP-seq against H3k27Ac, H3k4me3, CBP and RNAPol2 in 4 groups of mice: (Tumor/PBS injection)*(Fasting/Fed) (Figure 4.1). These histone modification marks have been widely used to profile active genomic enhancers.

The sequencing reads obtained from ChIP-seq for each histone mark were trimmed, checked for quality control and mapped to mouse genome mm9 with BWA. Some of the statistic numbers about number of sequencing reads and mapping percentage are listed in Table 4.1. For example, we generated 212 million sequencing reads in cachectic liver for ChIP-seq against H3K27ac and

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92.10% of the sequencing reads could be mapped uniquely to the mm9 genome. ChIP-seq peaks were called with MACS and visualized with IGV (Robinson et al., 2011). The resulting peaks were annotated and analyzed with HOMER which was designed to perform known and de novo motif discovery. In addition, the MAnorm package was used to detect the differential ChIP-seq peaks between cachectic and non-cachectic liver, which generated active genomic regions only present in cachectic liver.

Examples of ChIP-seq signals visualized using IGV are shown for the genes OSMR and IL13RA.

For both genes, the observed RNAPol2 reads are higher in cachectic liver, consistent with the upregulation of gene expression observed in the microarray datasets (Figure 4.2A). However, in

OSMR the H3k27ac peak height and width does not change between the cachectic liver and control; while IL13RA has two cachexia-unique H3k27ac peaks (Figure 4.2B). These results provide examples of the limitations of using differential histone ChIP-seq data to detect changes in transcriptional activity. In total, MACS identified ~20,000 H3k27ac peaks and ~60,000 H3k4me3 peaks in 4 groups of mouse liver (Figure 4.2C). The distribution of peaks relative to the transcription start site (TSS) was consistent with ENCODE datasets (Figure 4.3) (Consortium et al., 2007). One strategy is to find the differential H3k27ac peaks and use it for further analysis.

Another strategy will be intersecting H3k27ac peaks with H3k4me3.

4.2.3 Differential ChIP-seq Peaks Analysis

MAnorm was used to identify differentially expressed H3k27ac peaks in the cachectic liver, and these were compared to the microarray data (Figure 4.4). MAnorm rescales the peak intensity of cachectic liver and control under fed conditions to be equal (Figure 4.4A). The before-rescaling plot shows the necessity to do scaling between samples since the median value and regression line indicates the sample peak variation. The genes with higher M value in cachectic liver group

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represent the higher H3k27ac signal. In total, there are 2902 genes with M>1 in cachectic fed mouse liver (Figure 4.4B). These genes are enriched on the list of hepatic upregulated gene list in cachectic liver. The top 100 differential H3K27ac ChIP-seq peaks in cachectic liver are shown

(Table 4.2).

4.2.4 Integrative Analysis of Multiple Histone Marks and Correlation with Gene

Expression

Next, we sought to integrate the H3k4me3 and H3k27ac peak datasets. By correlating the presence/absence of histone marks with gene expression changes, we discovered that H3k27ac together with H3k4me3 could better mark the active genomic regions (Figure 4.5A). In addition, it indicates that the location of H3k27ac does not affect the contribution of TFs bound in the peak to gene activation. We used DAVID to group the downstream targets genes bearing cachexia- unique H3k27ac peaks (Figure 4.5B) (Huang da, Sherman, & Lempicki, 2009). Genes involved in cytokine/chemokine-receptor interactions were enriched among these genes, which suggests that liver responds to excessive cytokines produced by the tumor or host, and which is consistent with the GSEA analysis of cachectic liver microarray expression data (Figure 3.3C).

4.2.5 Motif Analysis Combined with Microarray Data

Next we tried to identify the TF network involved in regulating the liver response to cachexia by performing motif analysis on these ChIP-seq peaks. H3k27ac ChIP-seq peaks whose nearest genes were upregulated were used for motif analysis. The top enriched motifs in these regions compared to background were FOX:Ebox, FOX and ETS TFs (Figure 4.6A). In addition, we found that the

Fox and ETS binding sites were neighbors with an average spacing 10bp, which suggest they may cooperate with each other (Figure 4.6B). Microarray data from the cachectic liver showed that expression of FOXQ1, FOXA3, FOXO1 and ETS1 is upregulated significantly (p-value < 10-6,

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Figure 4.6C). These contrasted the CEBPB, GRE and RXR motifs identified by comparing the active DNA elements of fasted PBS-injected liver to those of fed PBS-injected mice liver (Figure

4.7).

4.3 Discussion

Previously, we have identified extensive gene expression changes in cachectic liver and shown that many metabolism-related TFs (such as STAT3, CEBPB, PGC1a and SREBP2) are upregulated. This encouraged us to characterize the transcriptional regulation network systematically across the genome in cachectic liver. One possible approach would be to investigate the genome-wide DNA binding profile of these TFs (~50) by ChIP-seq, which is time-consuming and expensive. Another possible approach would be to identify the active DNA elements in the genome of cachectic liver and use bioinformatics analysis of these elements to reveal the TF network involved in hepatic response to cachexia. There are two advantages for the second strategy:

First, it has been suggested that one histone mark H3K27ac is sufficient to distinguish active regulatory regions from repressed and poised regions. The antibody and experiment protocol of

ChIP-seq against H3K27ac have been optimized by several groups and consortia, which ensure its data quality. Second, the bioinformatic analysis of active DNA elements obtained by the epigenomic strategy has the potential to reveal the global and unbiased transcriptional network mediating the hepatic response to cachexia. However, the computational analysis on ChIP-seq data of specific TFs may limit the TF network to those pre-selected TFs and associated TFs.

In this chapter, we performed ChIP-seq in cachectic liver and sham-injected liver tissue against three commonly used histone modification marks (H3K4me1, H3K4me3 and H3K27ac), RNA polymerase CTD phosphorylation form and CBP. The ENCODE project has established these marks as standards to identify active promoters, enhancers and transcribed regions. In total, we

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identified ~70,000 active promoters, of which ~8,000 promoters were significantly upregulated in cachectic livers. Among 20,000 active enhancers across all samples, ~3,000 enhancers are unique in cachectic liver. The pathway enrichment analysis indicated that the active genes are enriched for cytokine-cytokine receptor interaction, insulin response pathway and JAK-STAT signaling.

Previous gene expression profiling of cachectic liver identified a list of receptors responding to peripheral tumor and host. The epigenomic profiling indicated that these receptors are under transcriptional activation such IL13RA. Members of the JAK-STAT family are downstream targets of several cytokine signaling pathways, whose role in weight loss has been studied in cachectic muscle (Bonetto et al., 2011). It will be informative to profile the STAT family TF binding sites in cachectic liver and investigate if they are directly involved in regulating the metabolic changes.

Although fasting affects the hepatic gene expression changes, some cachexia-related epigenetic changes are independent of feeding and fasting. This highlights the profound effects of soluble cachectic factors such as TNFα, IL-6, epidermal growth factors and BMP2 on hepatic gene expression. Through known and de novo TFBS mining, we identified a list of TFs that may be responsible reprogramming the gene expression in response to these factors and the changes in circulating metabolic substrate levels. Besides STAT3, which is well known to regulate gene expression downstream of inflammatory cytokines, we found that ETS family TF binding sites were enriched in the active enhancers. In addition, the composite motif analysis revealed that ETF family factors cooperate with FOX TFs (Figure 4.6). This is supported by the gene expression data, which suggests that ETS1 may cooperate with FOX (FOXQ1, FOXA3 and FOXO1) to regulate the hepatic response to cachexia. These contrast the CEBPB, GRE and RXR motifs that are over represented in the active DNA elements in fasted liver, which suggests the biological relevance of FOX:ETS motif in hepatic response to cachexia (Figure 4.7). 114

We next correlated list of upregulated genes from the microarray studies with changes in their epigenomic status in cachectic liver. We hypothesized that genes with significant changes in expression and epigenomic status would be important for the hepatic response to cachexia (Table

4.2). The first candidate on this list is LCN2. A recent study shows that bone-derived LCN2 can be secreted to circulatory system and promotes anorexia by binding to MC4R of neurons in the hypothalamus (Mosialou et al., 2017). In cachectic liver, the gene expression of LCN2 increased by 625-fold and the number of ChIP-seq reads against H3K27ac increased by 8-fold. Considering the common occurrence of anorexia during cachexia, it will be interesting to investigate the function of liver-derived LCN2 and other top members of this list in cachexia.

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Experimental component Computational component

Cachec&c Control liver- liver-Feed Feed Diﬀeren&al peak Control liver- Cachec&c discovery: MAnorm Peak annota&on/ GO: HOMER Fas&ng liver-Fas&ng

Mo&f analysis: Peak calling: HOMER MACS/FindPeaks Mouse Liver: Chroma&n

immunoprecipita&on Microarray Diﬀeren&ally expressed Genes (DEG) QC metric: HOMER

Sequencing Library prepara&on Alignment: BWA

Raw reads trimming HiSeq 2000 and QC: Fastx

Figure 4.1: Overview of experimental and computational pipeline for identification of cachexia-responsive epigenetic changes in liver. Left: cachectic liver under fed and fasted conditions were collected and used for ChIP-seq. Four histone marks and chromatin binding proteins were chosen: H3k27ac, H3k4me3, RNAPol2 and CBP. Right: The sequencing reads were mapped to mouse genome mm9 with BWA. ChIP-seq peaks were called with MACS. The resulting peaks were annotated and analyzed with HOMER to perform known and de novo motif discovery. The MAnorm algorithm was used to detect the differential ChIP-seq peaks.

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Table 4.1: Number of sequencing reads and mapping statistics. The number of sites with

H3K4me3, H3K27ac and CBP chromatin marks are shown for the cachectic liver and control samples. After trimming, more than 80% of reads were mapped uniquely to the mouse genome

(build mm9).

Chromatin Mark Cachectic Liver % Mapped Control % Mapped

H3K4m3 47161844 83.20% 44994071 87.20%

H3K27ac 212574029 92.10% 187227660 94.60%

CBP 184390549 87.60% 178932806 84.20%

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A C p < 10-6

2020 15

10 -6

10 p < 10 seq Peaks (MACS) - changes in cachectic liver 5 level Number of ChIP mRNA

00 PBS Tumor PBS Tumor TF PF TS PS TF PF TS PS OSMR IL13RA H3K27ac H3K4me3

B OSMR

Tumor-Feed: RNAPol2

PBS-Feed: RNAPol2

Tumor-Feed: H3K27ac

PBS-Feed: H3K27ac

IL13RA1

Tumor-Feed: RNAPol2

PBS-Feed: RNAPol2

Tumor-Feed: H3K27ac

PBS-Feed: H3K27ac

Figure 4.2: Identification and characterization of ChIP-seq peaks in cachectic liver. ChIP-

seq signal for the OSMR and IL13RA are shown. RNAPol2 reads are higher in cachectic liver (B),

consistent with the increased gene expression shown by microarray (A). However, in OSMR the

H3k27ac read count does not change between cachectic liver and control. Near IL13RA, there are

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two cachexia-unique H3k27ac peaks. (C) MACS identified ~20,000 H3k27ac peaks and ~60,000

H3k4me3 peaks in the 4 groups of mouse liver samples.

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H3K4me3 H3K27ac

Figure 4.3: Distribution of ChIP-seq peaks in cachectic liver classified by genic/intergenic compartment. Consistent with previous reports, H3K4me3 is more enriched in promoter regions compared to H3K27ac. However, H3K27ac marks active enhancer regions and are more enriched in intergenic regions.

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A Before Rescaling After Rescaling

Median: 0.288 Median: 0.0272 M M

A A B 11462 Genes M>0 Cachectic Liver-Fasting 12611 Genes Cachectic Liver-Feed Cachectic CachecticLiver Liver-Fasting-Fasted 2902 Genes M>1 Cachectic CachecticLiver Liver-Feed-Fed 2738 Genes

371 Genes M>2 421 Genes

32 Genes M>3 41 Genes

0 1 2 3 4 5 6 Enrichment Score

Figure 4.4: Model characterization to identify cachexia-responsive H3k27ac ChIP-seq peaks.

(A) MAnorm was used to identify the differential peaks in cachectic liver under fed conditions.

The MAnorm pipeline rescales the common peaks intensity to be equal between groups. Peaks 0 1 2 3 4 5 6 with M>0 are cachexia-unique. (B) Enrichment score of genes with cachexia-unique H3k27ac

peaks in the cachectic liver list of upregulated genes.

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Table 4.2: Top 100 differential H3K27ac ChIP-seq peaks in cachectic liver. Note: Some genes possess multiple active H3K27ac peaks. The M-value of these 100 ChIP-seq peaks are greater than

2.32, which represent the number of ChIP-seq reads of H3K27ac in cachectic liver are 5-fold more than that in sham-injected liver.

Chromosome Summit Gene M-Value LogFC chr2 32242369 Lcn2 2.98 9.29 chr6 121569046 A2m 2.26 7.31 chr6 121566926 A2m 2.07 7.31 chr11 105804630 Cyb561 2.85 5.32 chr8 42294192 Fgl1 2.60 5.27 chr9 23116193 Bmper 2.54 4.88 chr9 23026566 Bmper 2.43 4.88 chr9 23110585 Bmper 2.38 4.88 chr9 23026110 Bmper 2.36 4.88 chr9 23109645 Bmper 2.34 4.88 chr9 23212807 Bmper 2.25 4.88 chr9 23065945 Bmper 2.24 4.88 chr9 23214543 Bmper 2.02 4.88 chr5 104057445 Slc10a6 2.31 4.21 chr13 51961270 Gadd45g 2.70 3.72 chr13 56397080 Cxcl14 3.44 3.11 chr13 56393480 Cxcl14 2.83 3.11 chr13 56400071 Cxcl14 2.53 3.11 chr13 56422085 Cxcl14 2.12 3.11 chr11 117749291 Tha1 3.52 3.06 chr11 117771952 Tha1 2.12 3.06 chr1 174830435 Apcs 2.10 2.40 chr1 174832519 Apcs 2.05 2.40 chr7 25269851 Cadm4 2.47 2.37 122

chr7 25259016 Cadm4 2.38 2.37 chr1 142313473 Kcnt2 2.13 2.33 chr3 27561776 Fndc3b 2.01 2.32 chr1 65451006 Pth2r 2.39 2.03 chr2 32720478 Fam129b 2.32 1.97 chr6 51590506 Snx10 3.04 1.87 chr6 51629489 Snx10 2.04 1.87 chr4 62998626 Orm1 2.50 1.83 chr4 62999054 Orm1 2.45 1.83 chr7 3247706 Nlrp12 2.33 1.74 chr7 3248843 Nlrp12 2.23 1.74 chr1 136077782 Chi3l1 2.30 1.66 chr1 60665063 Raph1 2.31 1.62 chr9 104251610 Acpp 3.49 1.61 chr9 104259943 Acpp 2.67 1.61 chr9 104257243 Acpp 2.30 1.61 chr9 104257801 Acpp 2.25 1.61 chr12 84883821 Dcaf4 2.29 1.51 chr12 84883560 Dcaf4 2.24 1.51 chr1 193510907 Lpgat1 2.06 1.50 chr10 21654011 Sgk1 2.04 1.49 chr10 21656054 Sgk1 2.02 1.49 chr19 38594489 Plce1 2.08 1.46 chr7 148148092 Ifitm1 2.14 1.45 chr14 11581367 Fhit 2.98 1.42 chr14 11575209 Fhit 2.47 1.42 chr14 11580172 Fhit 2.43 1.42 chr19 4476160 Syt12 2.36 1.42 chr19 4474320 Syt12 2.12 1.42 chr9 22288524 Bbs9 2.67 1.39 chr9 22379946 Bbs9 2.50 1.39

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chr9 22293061 Bbs9 2.50 1.39 chr9 22285320 Bbs9 2.07 1.39 chr14 47998813 Lgals3 2.05 1.38 chr13 39019851 Slc35b3 2.42 1.37 chr10 13544576 Aig1 2.22 1.35 chr10 13580541 Aig1 2.19 1.35 chr11 50482281 Adamts2 2.94 1.32 chr11 50502221 Adamts2 2.67 1.32 chr11 50467073 Adamts2 2.31 1.32 chr2 164181479 Slpi 2.16 1.27 chr8 12882616 Mcf2l 3.11 1.21 chr8 12878855 Mcf2l 2.41 1.21 chr8 12872369 Mcf2l 2.33 1.21 chr4 124709289 Gnl2 2.86 1.21 chr4 124712894 Gnl2 2.85 1.21 chr3 105973417 Chi3l3 2.06 1.15 chrX 160197993 1700045I19Rik 2.70 1.13 chrX 160203071 1700045I19Rik 2.42 1.13 chr9 122223126 Ano10 2.01 1.12 chr4 35352108 3110043O21Rik 2.16 1.08 chr19 17503348 Rfk 2.74 1.03 chr19 17509250 Rfk 2.30 1.03 chr19 17496961 Rfk 2.08 1.03 chr6 90568169 Slc41a3 2.22 1.01 chr6 125522178 Vwf 2.12 1.00 chr6 125522167 Vwf 2.04 1.00 chr2 10058167 Itih2 2.05 0.91 chr10 86385445 Stab2 2.24 0.89 chr8 91892297 Sall1 3.63 0.89 chr8 91885703 Sall1 2.79 0.89 chr8 91671247 Sall1 2.65 0.89

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chr8 91977003 Sall1 2.49 0.89 chr8 92039370 Sall1 2.41 0.89 chr8 91476630 Sall1 2.41 0.89 chr8 91487667 Sall1 2.18 0.89 chr8 91978638 Sall1 2.07 0.89 chr3 65648131 Ccnl1 3.01 0.87 chr3 65646345 Ccnl1 2.52 0.87 chr3 65661742 Ccnl1 2.33 0.87 chr3 65646331 Ccnl1 2.03 0.87 chr5 30836980 Cib4 3.67 0.86 chr5 30825126 Cib4 2.44 0.86

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1.01.0

0.8 0.8

0.60.6

0.40.4 Percentile 0.2 0.2

0.00.0

H3K4me3am3 bk27interH3K27ac H3K27acck27 dk27intraIntergenic & H3K4me3 H3K27ac & H3K4me3

Jak-STAT pathway

Hematopoietic cell lineage

Insulin pathway Pathways Pathway in cancer

Cytokine-Receptor interaction

0.0 2.0 4.0 6.0 -Log(P-value)

Figure 4.5: Identification of active enhancers responsible for cachectic liver gene expression changes. (A) Gene expression of microarray data generated in cachectic liver for genes bearing different marks of histone modifications. Box-plots show genes with isolated H3k4me3 or

H3k27ac marks and the combination of both marks in different genomic locations. Solid bars of boxes display the 25-75% of ranked genes with the mean indicated by a horizontal line. (B)

Pathway enrichment analysis of genes with intergenic H3k27ac and H3k4me3 marks unique in cachectic liver.

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A A$ # of targets Rank Motif Name P-value with motif Fox:Ebox(Forkhead,bHLH)/Panc1-Foxa2- 1 10-8 287 ChIP-Seq(GSE47459)/Homer FOXA1(Forkhead)/MCF7-FOXA1-ChIP- 2 10-8 238 Seq(GSE26831)/Homer FOXA1(Forkhead)/LNCAP-FOXA1-ChIP- 3 10-8 291 Seq(GSE27824)/Homer

Foxa2(Forkhead)/Liver-Foxa2-ChIP- 4 10-7 210 Seq(GSE25694)

5 Fli1(ETS)/CD8-FLI-ChIP-Seq(GSE20898) 10-5 253

B C B$ 40 C$ Fold change TFs TFs$ (p < 10Fold$-6) 30 change$ FOXQ1 2.77 $ 20 FOXA3FOXQ1& 2.37 2.77& 10 Frequency FOXO1FOXA3& 2.00 2.37& Frequency& 0 FOXA1FOXO1& 4.49 2.00& -150 -100 -50 0 50 100 150 FOXA2 ETS1& 2.23 2.00& Spacing&length&between&FOX/ETS&binding&sites& Spacing length between FOX/ETS binding sites ETS1 2.00

Figure 4.6: Motif analysis of H3k27ac peaks in cachectic liver. (A) Top 5 motifs of TF binding sites enriched in H3k27ac and H3k4me3 peaks of cachectic liver identified by HOMER. (B)

Spacing between predicted FOX and ETS binding sites. FOX and ETS binding sites on H3k27ac peaks are predicted by HOMER. (C) Gene expression changes of TFs with motifs overrepresented in H3k27ac peaks of cachectic liver (p-value < 10-6).

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Figure 4.7: Motif analysis of differentially active DNA elements in fasted-PBS injected mouse liver compared to fed-PBS injected group.

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Chapter 5

TALE-AP: A Novel Technology to Identify Locus-Specific Transcription

Factor Complexes

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5 Summary

Although previous ChIP-seq studies have identified a list of TFs that may mediate the hepatic response to cachexia, the functional exploration of these TFs will be challenging. One option will be to investigate the genome-wide binding profile of these TFs (~50) by ChIP-seq which will be expensive and time-consuming. Moreover, the epigenomic profiling of cachectic liver does not reveal the underlying transcriptional regulation mechanism of cholesterol upregulation. In this chapter, we developed a novel technology by repurposing TALE to identify locus-specific TF complexes using affinity purification and mass spectrometry of chromatin proteins. Our proof-of- principle studies indicated that Srebp1, Stat6, Hnf4a, Shox2, Cux2 and other nuclear receptors such as Nr2e3 and Nr2f1 potentially regulated the transcription of Sqle.

5.1 Introduction

To elucidate the molecular mechanism of transcription regulation, it is necessary to build a comprehensive atlas of the location of chromatin-proteins. In general, the technologies to address this question are either based on NGS or mass spectrometry. ChIP-seq can map the genome-wide binding sites of a specific TF. Although reChIP has been developed to study the combinatorial binding of two TFs using a single sample, the application is limited by the low yield of DNA after two rounds of ChIP (Shankaranarayanan, Mendoza-Parra, van Gool, Trindade, & Gronemeyer,

2012). ChIP-MS could identify the protein interaction network of a specific TF, which relied on mass spectrometry to analyze the protein instead of DNA after ChIP (C. I. Wang et al., 2013).

However, these methods cannot provide comprehensive information about the transcription regulation of a specific DNA locus. The study of locus-specific TF and coactivator content remains challenging. PICH was developed in 2009, which used LNA to purify a specific chromatin region by hybridization. The low hybridization efficiency between the LNA probe used in PICH and the

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chromatin region of interest hinders its further application in other DNA locus instead of telomere

(Dejardin & Kingston, 2009). At the beginning of this thesis project, TALEs have been engineered to bind specific DNA loci, minimize off-target bindings, transactivate gene expression and perform epigenetic modification precisely (Cong et al., 2012). These encouraged us to repurpose TALEs for the DNA-locus proteomics study (TALE-AP). In TALE-AP, a TALE targeting a specific DNA region will be stably expressed in cells in order to purify the chromatin and associated proteins. In developing TALE-AP, we set up several benchmark experiments, which includes the interference test of TALE with local gene expression and epigenetic modification, establishing chromatin shearing conditions to ensure the identification of the “local” chromatin-protein profile (< 2kbp) and optimization of the TALE-AP protocols. We believe the systematic mapping of proteomics profile on individual DNA loci will help elucidate the function of specific regulatory SNPs, which are identified in cancer genomics and GWAS (MacArthur et al., 2017).

5.2 Results

5.2.1 TALE-AP Work Flow

To develop locus-specific TF complex purification assay, we made use of TALE, a class of proteins that can be designed to target any specific locus of interest. Each module targeting A, G,

C or T consists of 33-34 amino acids and we can assemble modules based on the sequence we proposed to target. In our assay, stably expressed TALEs were crosslinked to chromatin, allowing purification of the TFs located nearby with streptavidin magnetic beads (Figure 5.1). We tested 3 different affinity tags fused to the core TALE (N1-C3) protein: 3xFlag on the N-terminus and

RGS-His6-Biotinylation tag-RGS-His6 (HBH) on the C-terminus (I. Kim, Mi, & Rao, 2004). We chose the RGS-His6 tag because it allowed large-scale purification of the isolated complexes with

Nickel-affinity columns and a biotinylation tag because the strong streptavidin-Biotin interaction

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would allow for stringent washing of the streptavidin-bound complexes. The HBH tag would also allow us to use tandem affinity purification to reduce the background if required. The combination of His6-Flag and His6-Biotin tags was shown to be successful in chromatin proteomics studies previously (Pourfarzad et al., 2013). We used Flp-In T-REx 293 cells as a model since the expression level of the TALE construct was inducible by tetracycline in these cells, which may reduce the background of the affinity purification. In addition, generation of stable cell lines expressing different TALE proteins in this system is simple and reproducible since the host cell contains a unique integration site for the TALE. ENCODE data was used to choose appropriate

TALE targeting regions to avoid known TF binding sites. The TALEN Targeter 2.0 software was used to design the TALE DNA binding domain, which was selected using heuristic criteria (NG composition in the first 5 RVD sequences, 18 RVDs). TALE site sequences were aligned against the human genome using BLAST to check for off-target effects (Figure 5.2). The second strongest

BLAST hit should have at least two mismatches. Genomic coordinates and sequences of five

TALE binding sites used in this study are shown in Table 5.1.

5.2.2 Initial Evaluation of TALE Protein Stability, DNA Binding Specificity and DNA

Purification Yield

We first checked the subcellular localization of the TALE-HBH as this would play a critical role in designing our affinity purification protocol. For example, if a considerable amount of TALE was located in the cytoplasm, we may achieve better purification by isolating the nuclei first and crosslinking the chromatin in the isolated nuclei to decrease the proteomics background. In addition, due to the TALE binding specificity, the nuclear localization may only show several spots in the nucleus. Indeed, the majority of TALE-LDLR, when induced with tetracycline, was localized in the nuclei. Surprisingly, when induced at 500ng/ml tetracycline, there were only

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several imaging locations of TALE-LDLR in each nucleus. In some cells, there were two locations, which suggested the high specificity of TALE binding. However, if induced at high concentration

1 mg/ml tetracycline, the TALE localized to multiple spots (Figure 5.3). This suggested that using the lowest possible tetracycline concentration was important for specificity.

Although TALEs have been extensively used for genome editing and regulation of gene expression, the TALE protein expression and stability has not been tested systematically in Flp-In T-REx 293 cells, which may be important considering the fact that the repeats RVD may destabilize the protein.

Indeed when we started the project in Hepa1c1c7 cell, we observed degraded peptides on western blot gel. After testing several cell lines, we chose Flp-In T-REx 293 cells since we did not observe the degradation peptide detected by Anti-Flag for most of the TALE proteins we constructed. The

TALE protein was purified with M280 Streptavidin Magnetic beads and detected by an Anti-Flag antibody (Figure 5.4). The Western blot indicated that TALE protein was not degraded as only one band existed. We constructed 14 TALE clones in addition to the FRT control (Table 5.2).

Eleven clones could stably express TALE proteins with the optimized C-term TALE truncation and tag and only five of them were shown on western (Figure 5.4). We next investigated if the

TALEs were bound to their cognate targets by ChIP-qPCR: 5/11 of the TALE-HBH could target the specific DNA of interest and the DNA purification yield was at least 0.3%. For simplicity, only the primers used in ChIP-qPCR for these 5 specific TALEs were listed in Table 5.3.

It has been reported the 5’ sequence of TALE binding sites contributes more to the specificity than the downstream site sequence. To confirm this, we constructed a TALE-SQLEm (TALE-SQLE mutant) fusion protein whose binding site contained 1 mismatch at the first RVD compared to

TALE-SQLE. ChIP-qPCR result indicated that TALE-SQLEm bound much less to the SQLE

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promoter compared to TALE-SQLE. The DNA yield was around 0.01% (n=3 replicates), which was 30-fold less than TALE-SQLE (Figure 5.5).

5.2.3 TALE Effects on Local Gene Expression and Epigenetic Modifications

We tested the interference of TALE binding with local gene expression by RT-qPCR (Figure 5.6).

In most cases, TALE binding did not interfere with local mRNA transcription (less than 1.1-fold change, n=3, p=n.s.); however, one TALE, TALE-SREBP2, slightly increased the SREBP2 mRNA level (1.15-fold, p<0.05). In addition, we have tested a second TALE targeting SQLE that showed effects on the local gene expression. This SQLE TALE bound to ENCODE annotated

DHS and TFBS and it significantly reduced the gene expression of SQLE, which may be accounted by the TF binding competition with TALE (Figure 5.7). However, the further distance between the TALE binging sites and the region of interest would decrease the DNA purification yield, which was partially due to DNA breakdown by sonication.

Next, we evaluated whether the TALE binding would alter histone modifications, since the perturbation of the local TF binding profile and epigenetic features may not necessarily be detected by changes in gene expression measured by RT-qPCR. Based on results from the ENCODE project, we concentrated on two histone modifications that have been widely used to indicate the active chromatin status: 1) the H3K27ac modification marks active enhancers; and 2) the H3K4me3 modification marks active promoters and enhancers. We performed ChIP-qPCR using antibodies against H3K27ac and H3K4me3 for Flp-In T-REx 293 cells with 5 stably integrated TALE fusion proteins (Figure 5.6). Compared to the negative control (Mock IP), the histone H3K4me3 modification showed less than 1.05-fold change (p<0.05); however, the change of H3K27ac modification status was not statistically significant for any of the five TALEs. Based on these

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results, we conclude that these TALEs do not interfere strongly with local histone marks of active transcriptional regulatory elements.

5.2.4 Optimization of TALE-AP Protocol to Reach Mass Spectrometer Sensitivity

Most chromatin proteomics studies, including TChIP, ChIP-MS and PICH, are based on the ChIP- seq protocol to identify DNA targets of transcriptional regulatory proteins, which involves chromatin crosslinking, fragmentation, purification and elution (Wierer & Mann, 2016). Since our studies will focus on the protein content of chromatin, the ChIP-seq protocol will need to be adapted in several aspects. For example, in order to reduce the proteomics background, chromatin crosslinking should be performed in isolated instead of in cells as done in the ChIP protocol. In addition, the chromatin proteomics studies used harsher crosslinking conditions (e.g. Wang et al. used 3% formaldehyde) and longer chromatin fragments (~3 kbp for chromatin proteomics vs. 500 bp in ChIP-seq protocols) (C. I. Wang et al., 2013). Longer DNA fragments, obtained by gentler sonication conditions, could improve the DNA purification yield; however, this may purify some proteins not bound to the locus of interest.

The protocol parameters that we targeted for optimization included the tetracycline concentration used to induce TALE expression, as well as crosslinking, nuclei lysis, sonication, and purification conditions (Table 5.4). We first optimized the tetracycline concentration, since we were concerned that high TALE expression would be the most serious cause of off-target effects and would significantly decrease the yield of specific DNA. Previous proteomics studies with Flp-In T-REx

293 cells used final tetracycline concentrations ranging from 100 ng/ml to 0.1 mg/ml, which induce high expression of the genes of interest for better mass spectrometry signals. In our experiments, we induced TALE expression using ~10 ng/ml tetracycline; this concentration was shown in our experiments to optimize the DNA yield for the next steps. Compared to cells induced with 100

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ng/ml tetracycline, the DNA yield using 10 ng/ml tetracycline was almost two fold higher when keeping other conditions constant.

Figure 5.8 shows the effects of the formaldehyde concentration and the purification tag on DNA yield. Over-crosslinked chromatin may prevent the antibody or beads from binding to the TALE, which will decreased the purification yield, while under-fixation may lead to lower recovery of

TFs that interact weakly with other chromatin proteins or DNA. Previously locus-specific chromatin studies have generally used 3% formaldehyde in order to increase the DNA yield (C. I.

Wang et al., 2013). Indeed 3% formaldehyde crosslinking conditions can improve the DNA purification yield (Figure 5.8). In our experiments, we chose 2% formaldehyde for crosslinking and we did not observe a significant difference with 3% formaldehyde. Another alternative to streptavidin beads is to use the Flag antibody to affinity purify the Flag-tagged TALE, since this antibody possesses high specificity and has been used extensively in proteomics studies. We compared the performance of the Flag and HBH tags and showed that the HBH-tagged TALE could achieve ~2 fold higher yield of DNA than the Flag-tagged TALE.

Other important parameters included prelysis of nuclei before affinity purification, AP under denaturing conditions and re-AP. High concentrations of salt or detergent (500mM NaCl or 1%

SDS) has been used to prelysed crosslinked nuclei for better proteomics results in ChIP-AP, however, we realized the Prelysis slightly decreased the DNA yield. Optimization of the denaturing condition was obtained by modifying the nuclear lysis buffer to contain a final concentration of 6M guanidine HCl. After a series of optimization experiments, the final purification yield for different TALEs ranged from 1% to 3%, with the highest yield being obtained using the optimized protocol parameters shown in Table 5.4. Of the 5 TALEs tested, TALE-SQLE achieved the highest yield with 3% relative to input.

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Another interesting result from the reChIP optimization experiments was that we should be more cautious when comparing ChIP-seq or ChIP-qPCR results quantitatively across multiple samples.

After the first round of TALE-AP, we collected the flow through, which we suspected could contain a considerable amount of crosslinked TALE-chromatin complex remaining even though our western blot results showed that the majority (~98%) of the TALE protein had been depleted using an excess of streptavidin beads. We performed the second round of TALE-AP on the flow through. Surprisingly, the specific DNA purified in the second-round purification was still around

50% of that purified from the first round. Taking TALE-SQLE as an example, in the preliminary experiment, the first round purification retrieved ~0.3% SQLE promoter relative to input, while the second round purification still purified ~0.15% target DNA (Figure 5.9). This suggested that the first round IP in ChIP-qPCR or ChIP-seq protocols may not deplete the majority of crosslinked protein-DNA complex even though the majority of proteins had been purified. When quantitatively comparing ChIP-seq or qPCR results across multiple samples, the real difference may be hidden because of the inefficiency of the first round of IP.

5.2.5 Identification of Novel Transcription Factors Bound to the SQLE Promoter.

Currently the sensitivity limit of the Velos Orbitrap LC-MS is in the attomolar range (Tu et al.,

2016). Based on our optimized purification yield, this suggested we needed to start with ~109 cells to detect TFs that bind at single copy to the target locus. We harvested 20 15-cm plates of Flp-In

T-REx 293 cells stably transfected with a TALE-HBH targeting the SQLE promoter, which were induced by 10 nM tetracycline for 36 hours. We followed the lysis and purification protocol to purify the SQLE promoter chromatin-TF complex, which were identified by mass spectrometry.

Two negative controls were used, including the affinity purification with streptavidin beads using

Flp-In T-REx 293 cells and Flp-In T-Rex 293 cells stably expressing a TALE-HBH targeting the

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SLC5A2 promoter. The MS results identified three categories of protein: 1) RNA processing and

RNA binding proteins including RBM3 and RRP7A; 2) General TFs such as GTF2F1 and GTF2F2; and 3) Sequence-specific TFs (Figure 5.10). Interestingly, SREBP1 was identified by mass spectrometry, which is consistent with results from the ENCODE project showing that SREBP1 binds to the SQLE promoter (Figure 5.10). Several papers have link SREBP1 with the transcriptional regulation of SQLE. Our MS experiment also identified other site-specific TFs including HNF4A, STAT6 and nuclear receptors that may play a role in SREBP regulation.

5.3 Discussion

Although ChIP-seq has been developed to study the binding site of TF across the genome, to study the locus-specific transcriptional regulation remains challenging. Until the start of this project, the most successful method to study the native chromatin-TFs complex is PICH; however, since PICH relied on the hybridization of chromatin of interest to a complementary designed LNA probe, the purification yield is extremely low. By 2011, the DNA recognition rule of TALEN has been decoded and there are several strategies optimized to design and clone TALEN to target genes of interest. We proposed to repurpose TALENs for locus-specific TF purification, which used HBH-

TALE as bait to target SQLE promoter and purified chromatin from the locus with streptavidin beads. As the big challenge for this technique is to optimize the purification yield, we systematically optimize the expression level of the affinity-tagged TALE; the DNA-TF crosslinking conditions; the sonication methods to fragment locus of interest; the amount of streptavidin beads for purification and the reverse crosslinking conditions. The MS results indicated that SREBP1 was bound to the promoter of SQLE, which was consistent with the ChIP- seq results for SREBP1 from ENCODE project (Consortium, 2011). We anticipated this

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technology could be used to characterize other cachexia-responsive hepatic promoters or enhancers, GWAS-identified SNP and other important DNA elements (Wierer & Mann, 2016).

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Chroma/n Protein1 Chroma/n Protein2 TFA TFA TFC TFD TF B

T G T T A A G G A C A G G A A T T A T Chroma'n Region of Interest Epitope Tag

Chroma/n Chroma/n Protein2 Protein1

Epitope TFD Tag TFC TFA

TF TFA T G T T A A G G A C A G G A A T T A T B

Figure 5.1: Development of TALE-AP to identify de novo locus-specific transcription factors complex. TALE-HBH targeting a gene of interest was integrated stably into Flp-In T-REx 293 cells. ~109 cells were harvested and lysed to collect nuclei, which were immediately crosslinked with formaldehyde. The chromatin was then fragmented by sonication and used for purification with streptavidin magnetic beads. Proteins were eluted by boiling in SDS-loading buffer and processed with in gel digestion prior to LC/LC-Mass spectrometry.

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Figure 5.2: Strategy for TALE promoter targeting is shown for the SQLE promoter.

ENCODE chromatin states (H3K27ac) and DNAse hypersensitive sites (DHS) were used to choose TALE binding regions to avoid known TF binding sites and active chromatin. The TALEN targeter 2.0 software was used to design TALE, which were selected using heuristic criteria (NG composition in the first 5 RVD sequences, 18RVDs). TALE site sequences would be BLASTed against the human genome to minimize off-target effects. The second strongest BLAST hit should have at least 2 mismatches. Finally, the TALEs would be used for preliminary testing including protein stability, IP efficiency, nuclear localization and binding specificity.

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Table 5.1: Genomic coordinates and sequences of five TALE binding sites used in this study.

In order to avoid interference with TF binding and epigenetic modifications, we tried to avoid DHS regions, active chromatin regions marked by H3K27ac peaks. Therefore, we listed the distance of

TALE binding sites to H3K27ac summits.

Distance to TALE Name TALE binding sites Coordinate H3K27ac peaks chr22:41832773- TALE-SREBP2 TGGCCTGTTAACCCTTCACT 450bp 41832792 chr19:11085519- TALE-LDLR TATCAGCATGACTCAGTCCT 500bp 11085538 chr5:75336766- TALE-HMGCR TCTCTCCCGCGCTAGTAACT 350bp 75336785 chr8:124997626- TALE-SQLE TCCTGGACACCGTGTAACCT 500bp 124997645

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Merge: DAPI and Anti-Flag (TALE-LDLR)

Tetracycline: 0.5µg/ml Tetracycline: 1µg/ml

Figure 5.3: Nuclear localization of TALE-LDLR when induced with different concentration of tetracycline. We used DAPI for nuclei imaging and an Anti-Flag antibody to stain TALE-

LDLR. When induced at 500ng/ml tetracycline, TALE-LDLR located in several regions in each nucleus. In some cells, there were two imaging locations, which suggested the high specificity of

TALE binding. However, if induced at high concentration 1µg/ml tetracycline, the TALE seemed to localize to multiple locations. Scale bar: 10 µm.

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A B Mock IP 0.3

LDLR SREBP2 HMGCR HMGCS1 SQLE TALE-SQLE IP - - - - - Control TALE TALE TALE TALE TALE 0.2 Flow through

IP 0.1 DNA yield (% Input)

0.0 C Off-target site TALE binding site

0.9

0.6

DNA yield 0.3

0.0 -2500 -1800 -1550 -1100 -300 0 1150 1535 Distance to TALE binding site

Figure 5.4: Preliminary test of TALEs targeting promoter of LDLR, SREBP2, HMGCR,

HMGCS1 and SQLE. (A) Protein purification yield by streptavidin magnetic beads. The pcDNA5-FRT-TO backbone was used as control. (B) DNA binding specificity of TALE-SQLE shown by ChIP-qPCR. Relative to input, TALE-SQLE ChIPed DNA yield was ~0.3% compared to ~0.005% in mock IP and negative primer qPCR. (C) To better isolate the TF complexes bound to the region of interest, we assessed the DNA yield in the TALE targeting nearby regions; most of the DNA purified were within ~1000bp of the TALE binding site. Error bars indicate the standard deviation (SD) from triplicate experiments.

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Table 5.2: Summary of designed TALEs regarding to protein expression and binding specificity test by ChIP-qPCR. In total, 14 TALEs clones were constructed with the optimized

C-term TALE truncation and tag. Of these, 11 clones stably express TALE proteins and 5 TALEs passed our stringent criteria for binding specificity (which required that the TALE bound to its specific target at least 100 fold more than the negative FRT control). For example, TALE-LDLR2 could specifically bind to LDLR promoter; however, it was not evaluated further since the enrichment of DNA showed by ChIP-qPCR is only ~27 fold higher than the negative control.

TALE clone Target Gene Expression QPCR FRT Control Yes Yes SLC5A2-HBH SLC5A2-Control Yes Yes TALE-FDPS FDPS Yes No TALE-FDPS2 FDPS Yes No TALE-LDLR LDLR Yes Yes TALE-LDLR2 LDLR2 No No TALE-SREBP2 SREBP2 Yes Yes TALE-SREBP2-1 SREBP2 No No TALE-HMGCR-2 HMGCR No No TALE-HMGCR HMGCR Yes Yes TALE-HMGCS1 HMGCS1 Yes No TALE-HMGCS1-2 HMGCS1 Yes No TALE-HMGCS1-3 HMGCS1 Yes No TALE-SQLE1 SQLE Yes No TALE-SQLE SQLE Yes Yes

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Table 5.3: Primers used in ChIP-qPCR to test the TALE binding specificity. The same primers would be used in DNA yield optimization of TALE-AP protocol. Negative primers targeting random chromatin regions were not listed in the table.

Primer Name Primer Sequence

TALE-SREBP2-Forward GAGGTGCCAGAGATTGAGGA

TALE-SREBP2-Reverse TCTCCCAGCCTCCTTACAAA

TALE-HMGCS1-Forward CGAGGAAGTGGTGTGAGAGA

TALE-HMGCS1-Reverse TAGTCCCAATTGGTCGGAGA

TALE-LDLR-Forward AGAGCCCTGTGAGCTAGTTA

TALE-LDLR-Reverse GAGGACTGAGTCATGCTGATAAA

TALE-SQLE-Forward GGCTGTCTCACTCCGAGACT

TALE-SQLE-Reverse AAACCCAAGAGGTGGTCCTT

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0.3

0.2

0.1 DNA yield relative to input (%)

0.0

Negative Primer SQLE Primer Negative Primer SQLE Primer

TALE-SQLEm TALE-SQLE

Figure 5.5: One mismatch on the first RVD of TALE with the target sequence in the human genome abolished the binding specificity. TALE-SQLEm: the TALE-SQLE mutant with one mismatch with the target sequence. TALE-SQLE targets the region in chr8:124997626-124997645.

The DNA binding specificity was determined by ChIP-qPCR. TALE-SQLEm binds to target site with ~30 fold less than TALE-SQLE.

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B 0.25 Control TALE-SQLE 0.20

0.15

0.10 DNA yield (% Input) 0.05

0.00

H3k27ac H3k4me3

Figure 5.6: Assessment of TALE regarding to interference with local gene expression and ChIPed DNA composition. (A) Interference of TALE binding resulted in only small changes in expression of the adjacent gene when evaluated by RT-qPCR, except for TALE-SREBP2 which slightly increased SREBP2 mRNA level (p<0.05). (B) Evaluation of the interference of TALE binding with local histone modifications shown for TALE-SQLE. ChIP-qPCR indicated that TALE-SQLE binding did not change the local histone modification status for H3k4me3 and H3k27ac. Error bars indicate the standard deviation (SD) from triplicates.

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0.9

0.6

0.3 SQLE mRNA level relative to control

0.0

TALE-SQLE-A: TALE-SQLE-B: TALE-SQLE-C: TALE-SQLE-D: TALE-SQLE-E: TALE-SQLE-F: -1200bp -400bp -270bp -150bp +20bp +300bp

Figure 5.7: Distance of TALE binding sites and the interference with local gene expression shown by TALEs targeting SQLE promoter. As the TALE binding site gets closer to the

H3K27ac peak, it slightly repressed the SQLE gene expression. When the TALE was ~400bp away from the peak, the TALE did not interfere with the gene expression.

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Primer: Off-target

0.75 Primer: TALE-SQLE

0.50

0.25 DNA yield (% Input)

0.00

1% HCHO 3% HCHO 3% HCHO Streptavidin IP Flag IP Streptavidin IP

Primer: Off-target

3.00 Primer: TALE-BS

2.00

DNA yield (% Input) 1.00

0.00 TALE-SREBP2 TALE-HMGCR TALE-HMGCS1 TALE-SQLE

Figure 5.8: Optimization of TALE-AP efficiency. (A) The optimization parameters included crosslinking, nuclei lysis, sonication, tetracycline concentration and purification conditions. One example showed the effects of the formaldehyde concentration and purification tag on DNA yield. (B) The final purification yield for different TALEs. TALE-SQLE achieved the highest yield with 3% relative to input. Primer TALE-BS: Primer flanking the TALE binding site.

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Table 5.4: Parameters optimized in TALE-AP. The final protocol did not include pre-lysis of nuclei, denaturing AP and Re-AP since none of these increased the DNA purification yield. The crosslinking condition was 2% formaldehyde for 10 minutes at room temperature. We used a

Branson 450 Sonifier with 0.1% SDS and 0.1% sodium deoxycholate as a nuclear lysis buffer.

Parameter Optimization Result

Crosslinking 2% Formaldehyde 10mins

Pre-lysis of Nuclei No

Sonication 0.1% SDS / Branson

Tetracycline 10ng/ml

Flag or streptavidin Streptavidin

Denaturing or non-denaturing AP Non-denaturing

Re-AP No

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3.003

2.002 group 1 Cholesterol

1.001 DNA yield relative to input (%)

0.000

First AP12hour Second APDay 1 First FTDay 2 Second FTx Time TALE-SQLE Affinity purification

Figure 5.9: DNA purification yield of reChIP. A second round of affinity purification (AP) was performed on the flow through of the first AP. The DNA yield was determined by qPCR. The second AP still retrieved ~0.13% DNA compared to ~0.32% in the first AP. FT: flow through.

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AA Uniq Total Iden%fied proteins Descrip%on pep%de pep%de Srebp1a 1 1 Sterol regulatory element-binding transcrip%on factor 1 Aebp1 1 4 Adipocyte enhancer-binding protein 1 Stat6 1 2 Signal transducer and ac%vator of transcrip%on 6 Hnf4a 1 1 Hepatocyte nuclear factor 4-alpha Kdm5b 2 4 Lysine-specific demethylase 5B Hdac2 2 4 Histone deacetylase 2 Zfp42 1 2 Zinc finger protein 42 Shox2 2 2 Short stature homeobox protein 2 Cux2 1 1 Homeobox protein cut-like 2 Sp140 1 1 Nuclear body protein SP140 Npas1 1 1 Neuronal PAS domain-containing protein 1 Znf226 1 3 Zinc finger protein 226 Nr2e3 1 1 Photoreceptor-specific nuclear receptor Nr2f1 1 1 COUP transcrip%on factor 1

SQLE

H3K27Ac

DHS

SREBP1

Figure 5.10: Identification of novel transcription factors bound to the SQLE promoter. (A)

Summary of selected proteins and their identified peptides. In addition to RNA processing proteins and general TFs (data not shown), we identified several specific TFs including the lipid regulators

SREBP1. (B) ENCODE data has previously shown the binding of SREBP1 on the promoter of

SQLE.

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Chapter 6 : Discussion, Future Directions and Conclusions

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6 Summary

In this chapter, I will summarize the main discovery of this doctoral project and propose the future directions.

6.1 Discussion and Overview of Findings

While the molecular mechanism of muscle wasting and adipose tissue loss in cachexia has been investigated thoroughly, the role of liver in cachexia has not been well characterized, even though it is a central player in many of the pathways that provide basic metabolic building blocks for tumor growth and progression. Compared to the loss of mass in peripheral tissues such as adipose tissue and muscle that is seen in CAC, liver and spleen are the two organs that gain size (Tracey et al., 1988). Previous studies have shown liver responds to injury, inflammation and cachexia by the secretion of cytokines, C-reactive protein, complement factors and fibrinogen as part of the hepatic acute phase response (Oldenburg et al., 1993). The first goal of this thesis project was to systematically evaluate hepatic gene expression under cachectic conditions. The second goal of this thesis was to catalogue potential secreted cachectic factors, through which the tumor alters the metabolism of host tissue, as these could be good candidates to be translated into clinical studies.

The major cachectic factors currently being investigated include TNFα and IL-6 (identified in the

1990s) and tumor-derived PTHrP (identified in 2015) (Kir et al., 2014). There are several reasons why the identification of additional cachectic factors is challenging. First, there are not many cachexia models, especially animal models for CAC and most data is based on only two cachectogenic cancer cells (C26 and LLC). Second, the cachectogenic capacity of C26 and LLC are difficult to quantify, therefore it is challenging to correlate their gene expression pattern with cachectogenic capacity, which is the standard method to identify the cachectic factors (Tisdale,

2003). Third, cachectic factors can be derived from the tumor and the host. It has been suggested 155

that the host-derived cytokines such as TNFα, IL-12 and IFNg interact with tumor to promote cachexia. Fourth, the target tissue of cachectic factors can be adipose tissue, muscle, liver and brain, which render follow-up functional studies to identify molecular mechanisms difficult. A recent successful example is the discovery of PTHrP as a cachectic factor. In 2015, Kir isolated several LLC clones with varying cachexia-induction capacity and PTHrP was upregulated in the highly cachectogenic cells. The functional studies showed that PTHrP promoted fat browning under cachexia. In this thesis, we profiled the gene expression of C26 cell line and tumor tissue 14 and 31 days post xenograft transplantation. We expected the time-series gene expression data of

C26 will revealed the underlying cachectic factors as cachexia progresses. Interestingly, when integrating our C26-derived cachectic factors with that of LLC, we found those highly-upregulated factors are overlapping between them even if these two cells lines are very different. These factors can be generally divided into growth factors (Epiregulin, Amphiregulin and Betacellulin),

Cytokines and hormone-related proteins (PTHrP and BMP2). These similarities suggest that some molecular mechanisms may be common in different cachexia situations. As we discussed above, it remains challenging to unravel the target tissue of cachectic factors. We proposed the evaluating the gene expression of cachectic factors’ receptors may be a feasible way. Specifically, in this thesis, we checked the expression data for all the receptors of C26-derived cachectic factors in cachectic liver and found several interesting signaling axes including EREG/AREG/BTC-EGFR and BMP-BMPR2.

When this thesis project was started, the application of ChIP-seq to study transcription factor networks was a relatively novel approach. Results from the ENCODE projects provided us clues to systematically catalogue the active chromatin regions in cachectic liver, which then can be analyzed to build the TF network in silico. However, the identity of the best epigenetic marker for active regulatory DNA elements remains controversial. Based on a series of ENCODE 156

publications, the presence of H3K4me1 marks and absence of H3K4me3 marks have been used in epigenetics studies to identify enhancers. Later the comparison between H3K27ac and H3K4me1 indicated that H3K27ac could be used as a better predictive marker for enhancers, while H3K4me1 represented the poised enhancer. Other studies have also tried to use p300 and CBP binding to predict active enhancers. In this thesis project, we chosen H3K4me3, H3K4me1, H3K27ac and

CBP as epigenetic markers for active promoters and enhancers in cachectic liver and examined combinations of these histone marks to determine which set of histone markers provide a better prediction for enhancers. In addition, it is challenging to build TF networks based on ChIP-seq against histone modifications because of the large number of histone binding events across the genome (~60,000 for H3K4me3) and hundreds of TFs involved. Each of the TFs only occupies a small percentage of the active histone peaks, making it difficult to distinguish the TF binding signals from the background.

Encouraged by the bioinformatic analysis results from ChIP-seq, I started to ask if there were better ways to identify the upstream transcriptional regulators using experiments instead of in silico prediction since TFBS prediction using ChIP-seq and DHS data tends to generate false positive results. Recent advances in GWAS and cancer genomics have discovered many disease-related genetic variants whose effects are difficult to identify at a molecular level, partially because many

SNPs are located in enhancers, promoters and other chromatin regulatory regions. For example, the TF GABP selectively binds to mutant noncoding regions around the TERT promoter to induce multiple cancers (Bell et al., 2015). Therefore, we realized that it would be important to develop a technology to study locus-specific TF complexes. The traditional method to solve this challenge is DNA affinity chromatography, in which the DNA of interest is immobilized on the matrix and used to purify DNA binding factors from cell or tissue extracts so that they can be analyzed and identified by western blot or mass spectrometry. Another method (PICH) has been developed 157

recently, which relied on the hybridization between LNA and the specific native chromatin

(Dejardin & Kingston, 2009). Since the DNA and proteins are crosslinked in cells, the hybridization between LNA and DNA is not efficient. As the genome-editing tools developed, we realized that TALEN could be repurposed to study locus-specific transcription regulation. We performed the proof-of-concept projects using gene expression data of cachectic liver to identify targets for locus-specific characterization. We designed TALEs to target the promoters of cholesterol biosynthesis related enzymes, purified these promoter-specific TF complexes and analyzed them by mass spectrometry. Taken together, these results (1) identified key metabolic changes in cachectic liver (including the upregulation of genes involved in cholesterol biosynthesis and suppression of genes in the TCA cycle and electron transport chain); (2) identified a network of TFs mediating hepatic response to cachexia; and (3) provided proof-of-principle for a laboratory approach to directly identify the TFs involved in this response though isolating and characterizing locus-specific transcription factor complexes.

6.1.1 Metabolic Changes of Cachectic Liver

In Chapter 2, we profiled the gene expression of cachectic liver under fed and fasting conditions, which were compared against sham-injected liver. The major gene expression changes in cachectic liver are consistent between the fed and fasting groups of mice. We showed that gluconeogenesis, and TCA cycle is suppressed in contrast to cholesterol biosynthesis, which is significantly upregulated. Consistent with the increased size of liver under cachexia conditions, the acute phase response (which synthesizes large amount of proteins) and cholesterol biosynthesis may explain part of the excessive energy expenditure seen in CAC. Considering the high cost of cholesterol biosynthesis, therapies that intervene this process may provide a way for reversing cachexia. Our expression studies of cachectic liver suggest that the central metabolite Acetyl-CoA was shunted

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from TCA cycle and gluconeogenesis into the pentose phosphate pathway and cholesterol biosynthesis. It will be interesting to check if restoring the mitochondrial function of cachectic liver can relieve the cachexia syndrome. Recently, a statin has proved to be effective in treating glioma by inhibiting cholesterol biosynthesis (Koyuturk, Ersoz, & Altiok, 2004). The enhancement of mitochondrial function was also considered as a promising therapeutic strategy for cancer (Bhat,

Kumar, Chaudhary, Yadav, & Chandra, 2015). Although the truncated TCA cycle and repressed electron chain were observed in the cachectic liver and not the C26 tumor tissue, it may be worth investigating whether clinical strategies to treat cancer by enhancing mitochondrial function could also be used to improve mitochondrial function in the cachectic liver and deplete the supply of

Acetyl-CoA available to supply substrates for tumor growth, thus providing an alternative to treating CAC by strategies that simply reduce tumor size.

6.1.2 Cachectic Factors

Although some cytokines, including TNFα, IL-6 and IFNg are linked to the loss of muscle and adipose tissue in cachexia, treating patients with CAC by neutralizing these serum cytokines has not been effective in reversing or alleviating the cachexia syndrome (Cahlin et al., 2000; Seto et al., 2015; Strassmann, Fong, Kenney, & Jacob, 1992; Truyens et al., 1995; Van Zee et al., 1992;

Wieser, Moschen, & Tilg, 2013). We cannot rule out the possibility that relatively high cytokine concentrations in the tumor microenvironment, the half-life of antibodies in the serum and penetrance of antibody into tumor tissue, may all contribute to the failure of these therapeutic strategies; however, systematic profiling of cachectic factors could provide additional comprehensive targets for therapy. Therefore, in Chapter 2 we profiled the gene expression of C26 cells and C26 xenografts in mice. By comparing the gene expression data of C26 at different stages of the experiment, we identified a list of cachectic factors that included cytokines, hormones,

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growth factors and novel cachectic factors such as PTHRP, BMP, EGF, IL11 and IL33. We compared the list of cachectic factors with data obtained from LLC cells and found that many of the cachectic factors are shared by both cachectic cell lines, which indicated that these cachectic factors may be functionally important for other cachectic cells and tissues.

To further dissect the function of each cachectic factor, it is necessary to pinpoint the tissue where they have effects. One direct way is to check the expression level of each cachectic factor’s receptor. We have combined the expression data of liver with C26 tumor data and identified the pairs of ligand-receptors that may link tumor and liver together by distant regulation. The resulting list contains BMP-BMPR2, IL11-IL6RA/OSMR and EGF-EGFR. It is worth noting that the crosstalk between these signaling axes may exist in the cachectic liver. For example, it has been shown that OSMR functions as an essential co-receptor for EGFR, which was synergistically activated by OSM and EGF in gliomas, leading to STAT3 activation of (Jahani-Asl et al., 2016).

6.1.3 Transcription Factor Network

NGS has proven to be a powerful technique in functional genomics by providing an approach to characterize active DNA elements on a genome-wide basis. The coupling of a biochemical assay with NGS established several key technologies including ChIP-seq, DHS-seq, ATAC-seq and

STARR-seq (Pindyurin, de Jong, & Akhtar, 2015). To systematically identify the active DNA regions driving the transcriptional changes observed in cachectic liver, we performed ChIP-seq for histone marks of active promoters and enhancers, based on the consensus recommendations from

ENCODE project investigators, which combines the H3K4me1-positive and H3K4me3-negative chromatin marks as standards to characterize potential enhancers. H3K27ac was then found to distinguish “poised” and active enhancer (Creyghton et al., 2010). Approaches to better identify active enhancers are ongoing and may benefit from characterization of other chromatin

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components. For example, enhancer RNAs (eRNAs) represent a class of short non-coding RNAs transcribed from enhancer regions (Hah, Murakami, Nagari, Danko, & Kraus, 2013; Lam, Li,

Rosenfeld, & Glass, 2014). Although the function of eRNAs remains elusive, active enhancers have been shown to transcribe high levels of eRNAs.

To characterize the chromatin states across the genome in cachectic liver, we performed ChIP-seq against H3K4me1, H3K4me3 and H3K27ac. Consistent with previous studies, we showed that

H3K27ac was a good indicator for active chromatin regions. Genes that are epigenetically upregulated are enriched for cytokines response, insulin pathways and STAT targets, which highlights the influence of cytokines on liver function in CAC, the occurrence of insulin resistance in cachexia and the reported involvement of STAT3 in the hepatic response to cachexia (Tisdale,

2002; Watchorn, Waddell, Dowidar, & Ross, 2001). More importantly, through integrative analysis of de novo motif discovery of these active DNA elements and transcriptome data in cachectic liver, we identified a list of TFs mediating the hepatic response to cachexia (including

Stat3, Hnf4α, and Fox and Ets family TFs). Previous studies have highlighted the importance of

FOX TFs in liver metabolism and development. For example, the pioneer factor FoxA cooperates with C/EBPb and HNF4a to maintain accessible nucleosomes at liver-specific enhancers by displacing linker histone H1 (Iwafuchi-Doi et al., 2016). In addition to activating mTORC1, Akt can phosphorylate and inhibit FoxO1 which in turn is involved in the activation of G6pc and Pck1.

Future work on these TFs will provide a more detailed view of how hepatic gene expression changes are reprogrammed by cachectic factors.

6.1.4 Locus-specific Transcription Regulation

We developed a novel technology (TALE-AP) to characterize locus-specific TF complexes in a native chromatin context and showed that TALE-AP can be used to identify novel and known TFs

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located in the SQLE promoter, including Srebp1 and Hnf4α. To demonstrate the feasibility of this approach, we evaluated TALE protein expression and stability for each construct: this is necessary since we noticed some TALE proteins are not stable in cells and western blot shows evidence of

TALE degradation. TALEs passing these preliminary tests were evaluated for DNA binding specificity and efficiency.

In this study, we evaluated ~20 TALEs and identified five TALEs that bound DNA specifically and showed minimal interference with gene expression and modification at the target site. This shows that careful experimental design, based on a straightforward algorithm for selecting the

TALE binding site, will provide TALE constructs that do not interfere with the local chromatin environment. On the one hand, TALEs binding at increasing distances from the DNA site of interest are less likely to interfere with TF binding; on the other, increasing the distance to the

DNA site of interest will decrease the DNA purification yield as the sonication may introduce breaks between the TALE binding site and DNA element. Our results show that incorporating information from published ChIP-seq, DHS and TFBS datasets can balance this trade-off, by identifying DNA elements with known functions in a wide range of cell types that can be used to effectively design better TALE target sites.

Although several technologies have been developed to study locus-specific TF and chromatin complexes, each has specific drawbacks. For example, DNA affinity chromatography reconstitutes

DNA-protein complexes in vitro by incubating cell or tissue extracts with immobilized DNA, which results in high false positive results from non-specific binding caused by high concentrations of TFs in the protein extract (Kadonaga, 1991). Recently PICH was developed to study the proteomics profile of telomeres. Compared to DNA affinity purification, PICH utilized biotinylated LNA probes to purify the DNA of interest (Dejardin & Kingston, 2009). The rationale

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for switching from DNA to LNA is that the binding affinity between LNA-DNA hybrids is stronger than DNA-DNA duplexes. Despite this, the purification yield of PICH is still low, requiring ~2 billion cells, and cannot be applied to a single copy locus. Finally, protocols such as

TChIP, which requires insertion of an extra DNA element at the DNA site of interest, may interfere with local chromatin structures and disturb the native chromatin context (Pourfarzad et al., 2013).

Realizing the DNA purification yield of a specific locus is the key bottleneck for TALE-AP, we systematically optimized the protocol at each step (including crosslinking, cell lysis, sonication and affinity purification). The routine ChIP-seq protocol for histone proteins has a DNA purification yield of ~0.3%, which is lower if the ChIP-seq is performed for TFs since their binding with DNA is much weaker compared to the histone-DNA interaction and since the antibodies available for TFs are generally not as well optimized as those for histone proteins (Egelhofer et al.,

2011). Our optimization experiments achieved a DNA purification yield of ~3%, which allowed us to perform the proteomics study with less than 1,000 million cells. However, possibly due to the stochastic properties of TF binding to DNA, it seems there is an upper limit to the DNA purification yield. We also noticed the first round of ChIP could not deplete the TF-bound DNA and the DNA yield from the second round affinity purification could be as high as 50% of the first round. This warns us to be more cautious when interpreting the quantitative comparison of ChIP- seq results across samples. For example, the DNA purification yield of TALE-SQLE in the second round is around 1% compared to 3% of that in the first round of AP.

There are several drawbacks associated with TALE-AP. First, it is difficult to ensure that the TALE does not have off-target effects. We have assessed the off-target effects by quantifying the top

BLAST hits of the TALE using ChIP-qPCR and shown that the TALE does not bind off-target sites predicted by BLAST. For example, the ChIP-qPCR analysis indicated that TALE-SQLE

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bound to the predicted off-target sites ~100 fold less than to the Sqle promoter, which provides an estimate for the background binding efficiency to other genomic regions. Arguably, it would be better to test this on a genome-wide basis using ChIP-seq studies to assess TALE binding; particularly given the lower cost of high-throughput sequencing; however, it is not clear how these results should be interpreted for DNA off-targets that comprise a small fraction (1-2%) of the identified sites. Second, consistent with the other single-locus technologies mentioned above,

TALE-AP still requires ~700 million cells for affinity purification. Future improvements in mass spectrometry could greatly lower the number of cells required for locus-specific studies and recently the ThermoFisher Fusion has been achieved ~1 attomole detection sensitivity for some proteins (Tu et al., 2016). On the other hand, the TALE-AP approach may identify false positive interactions. As the mass spectrometry detection sensitivity increases (i.e. as our ability to identify true interacting proteins increases), this problem could be addressed by comparing the proteomics results for multiple TALEs that target the same region of interest (since the overlapping protein profile will better reflect the bona fide proteomic profile at the specific locus). This innovation is important as interacting proteins identified in the TALE-AP assay need to be confirmed by other techniques (such as EMSA and ChIP-qPCR), which scale poorly. Despite these potential limitations, we believe that efficient protocols for locus-specific chromatin studies will prove essential in directly identifying the TFs, transcriptional coregulators and epigenetic modulators involved in CAC and other complex genomic regulatory programs.

6.2 Future Directions

In this thesis, we investigated the transcriptomes of cachectic liver and C26 xenografts in order to better understand the regulation of metabolic processes that occurred in CAC. The bioinformatics analysis revealed a list of ligand-receptor pairs through which secreted cachectic factors from the

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tumor could act through their cognate receptor to influence metabolic changes in the liver. Future studies will try to identify the specific cachectic factors affecting hepatocytes and the molecular pathways that are involved in their effects in the liver, especially the factors that upregulate cholesterol metabolism in CAC. To better identify the transcriptional regulators of CAC in the liver, we developed TALE-AP to study locus-specific transcription complexes that are recruited to gene promoters and enhancers; and optimized this protocol to study promoters involved in hepatic cholesterol biosynthesis. Taken together, we expect that our expression studies and chromatin profiles, along with our new technique to perform in-depth characterization gene regulatory regions, will identify new cachectic factors secreted from tumors and the transcriptional regulators of their effect in the liver of mice with CAC.

6.2.1 The Role of Genomics in Understanding Cancer-Associated Cachexia

Our list of cachectic factors identified by expression profiles of C26 xenografts derived as cachexia progressed showed a significant overlap with the potential cachectic factors previously seen in the

LLC model of CAC. These cachectic factors include growth factors (AREG, EREG and BTC), cytokines (IL-6, IL-11 and IL-33) and hormones (PTHrP and BMP2); however, the function of most of these in regulating metabolism remains unknown. A major challenge is to identify the host tissues these cachectic factors affect: our integrated analysis of gene expression of liver and tumor suggests that they may support signalling between the tumor and the distant liver. We have shown that treatment with C26-CM leads to significant upregulation of cholesterol biosynthesis in mouse primary hepatocytes. An important next step will be to identify the specific cachectic factor causing this effect, which we propose to achieve using three approaches. First, we will use CRISPR to knock out the specific cachectic factor candidate in C26 cells and will determine if the conditioned medium of these C26 cells can still upregulate cholesterol biosynthesis. Second, we

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will use attempt to use antibodies to neutralize the specific candidate in C26-CM (Kir et al., 2014).

Third, we will treat the primary hepatocytes with conditioned medium collected from CHO or

HEK 293 cells overexpressing individual candidate cachectic factors. After identifying the causative cachectic factors leading to upregulation of cholesterol biosynthesis in cachectic liver, we can validate the discovery in vivo by neutralizing these factors with antibodies.

Since cachexia involves integrated metabolic changes involving multiple tissues and organs, it will be important to examine the gene expression profiles of liver, adipose tissue, brain and tumor systematically in the future. Although these tissues are usually thought to be targets affected by cachectic factors secreted by the tumor, it may be possible that these tissues also produce cachectic factors. For example, LCN2, a hormone secreted by bone, has been shown recently to promote anorexia by binding to MC4R in hypothalamic neurons (Mosialou et al., 2017). In the cachectic liver, our ChIP-seq data showed 8-fold increased signal at the LCN2 promoter; and we observed

625-fold upregulation of LCN2 gene expression. This suggests that LCN2 or other factors secreted by the liver may also be a cause of anorexia in CAC.

Although we chose H3K27ac, H3K4me1 and H3K4me3 as chromatin marks to identify active regulatory regions, the epigenetic markers for enhancers remain controversial. Recently a comprehensive atlas of histone modifications was built using proteomics techniques, leading to the discovery of 67 novel histone modifications including succinylation, malonylation and crotonylation (Tan et al., 2011), which used intracellular pools of succinyl-CoA, malonyl-CoA and crotonyl-CoA as substrates. The significant metabolic changes seen in cachectic liver, may affect metabolic intermediates such as succinyl-CoA, leading to the genome-wide reprogramming of histone succinylation, which is similar to the effects of intracellular acetyl-CoA levels on histone acetylation at genes important for cell growth and metabolism (L. Cai, Sutter, Li, & Tu, 2011; Xie

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et al., 2012). Therefore, it may be informative to perform ChIP-seq against histones that have been modified by succinylation, malonylation and crotonylation to identify dysregulated transcriptional and chromatin regulatory regions in the cachectic liver. It is worth noting that lysine succinylation induces more substantial changes to a protein's chemical properties than lysine methylation and acetylation. At a physiological pH (7.4), succinylation, acetylation and monomethylation at lysine residues will respectively change the side chain charge from +1 to −1, from +1 to 0 and not at all

(Z. Zhang et al., 2011). Therefore, histone succinylation may affect the chromatin activity and gene expression more strongly than histone acetylation and methylation at some DNA regions.

6.2.2 Future Approaches and Applications in Studying Single Chromatin Loci

We developed the TALE-AP technology to profile locus-specific transcription complexes and directly identify TFs that regulate the liver response to CAC. We anticipate that the TALE-AP technology could be used to study other locus-specific TF complexes; for example, to effects the effects of SNPs identified by GWAS for complex traits or mutations found in cancer genomics studies. The technique could also answer basic questions related to the recruitment of co-factors, general transcription factors and other chromatin components to a specific transcriptional regulatory element. For example, recent studies have highlighted the long-range DNA interaction, involvement of lncRNA and eRNA in transcriptional machinery (Ernst et al., 2011; Hah et al.,

2013; Lam et al., 2014). Coupled with NGS, TALE-AP could be used to study locus-specific DNA recruitment of lncRNA and eRNA and their effects on gene activation.

Although we have systematically optimized the TALE-AP protocol to allow us to identify locus- specific TF complexes with ~ 500 million cells. It will be critical to decrease the cell number to make this technology useful. Recently CRISPR/Cas9-APEX has been proposed to study locus- specific chromatin proteomics, which has the potential to start with less number of cells. The

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catalytically dead RNA-guided nuclease (dCas9) was fused to the engineered ascorbate peroxidase

APEX2 to biotinylated proteins proximal to defined genomic loci for subsequent enrichment and identification with mass spectrometry (Myers, Wright, Zhang, & Carr, 2017). Since multiple copies of a specific TF could be biotinylated by APEX2 on a single locus, we may need fewer cells to reach the sensitivity of the mass spectrometer. Similar to CRISPR-APEX2, we have adapted TALE-AP by fusing TALE to the BioID2 biotin ligase that can be used to biotinylated

Lys residues of proteins near the TALE-BioID2 fusion protein for purification and characterization by mass spectrometry (D. I. Kim et al., 2016; Varnaite & MacNeill, 2016).

FLASH and other modern high-throughput and robotic cloning technologies have made the high- throughput TALE construction possible (Reyon et al., 2012). If the TALE-AP protocol can be scaled down to use a smaller number of cells, we propose that TALE-AP can be used to study locus-specific TF complex on a large-scale to reveal transcription regulation mechanisms that act simultaneously on individual promoters and enhancers across the genome. In addition, recent discoveries in transcriptional regulation have highlighted the importance of noncoding RNA

(including enhancer and lncRNAs), DNA topology and cooperation of multiple TFs on an active

DNA element: TALE-AP may provide a good strategy to efficiently isolate specific chromatin-TF complexes, which can be subsequently characterized comprehensively by RNA-seq, DNA-seq and mass spectrometry. This integrated multi-platform approach could be applied to profile locus- specific TF complexes on a broad scale, to simultaneously characterize specific enhancers, promoters and disease-related regulatory regions.

6.3 Conclusions

In this doctoral project, we profiled the gene expression of cachectic liver in mice and its epigenetic status across the genome. Pathway enrichment analysis indicates that expression of enzymes

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involved in cholesterol biosynthesis is significantly upregulated in cachectic mouse liver in contrast to the suppressed expression of genes involved in the TCA cycle, respiratory electron transport and ketogenesis. The hepatic response to CAC is accompanied by differential expression of several metabolism-related TFs including Hnf4α, Stat3, Onecut1 and Pgc1α. To identify the de novo transcriptional network mediating these changes, we performed ChIP-seq against H3K4me3,

H3K4me1, H3K27ac, CBP and RNAPol2 in cachectic liver. Consistent with the gene expression changes, we observed that the genes bearing active histone marks are enriched in several functional categories (including cytokine-receptor interaction, EGF signaling pathway, STAT3 targets and insulin resistance), which demonstrates the extensive effects of tumor and / or host-derived cachectic factors on the liver in CAC. Bioinformatics (“motif”) analysis for the ChIP-seq data implicated several TFs (including STAT3 and FOX and ETS family TFs) in this response. Future work on these TFs, including targeted ChIP-seq and ChIP-PCR studies, will elucidate their genomic binding targets how they mediate the hepatic response to cachexia.

Since increased expression of cholesterol biosynthesis genes is the main metabolic change in cachectic liver, we aimed to study the transcription regulation of several key enzymes in the cholesterol biosynthesis pathway including Sqle, Hmgcs1 and Hmgcr. We developed a novel technology, called TALE-AP, by repurposing the programmable TALE DNA binding domain to purify the locus-specific chromatin proteins. We characterized the TALE-AP protocol by measuring and optimizing the protein stability TALE-HBH and determining how the constructs interfered with histone modifications and the expression of nearby genes. These preliminary tests can provide rules for designing TALEs in future applications of TALE-AP. Using TALE-AP, we identified Srebp1 as a TF specifically binding to the SQLE promoter, which is confirmed by results from ENCODE ChIP-seq datasets. Our TALE-AP studies for the SQLE promoter also identified

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novel transcription factors (such as Aebp1, Stat6, Shox2, Cux2 and Znf226); and future studies will be required to validate their binding on SQLE promoter and their effects on SQLE expression.

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