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PROFILING THE EPIGENETIC LANDSCAPE OF THE

AIRWAY EPITHELIUM IN ASTHMA

Dorota Stefanowicz

B.Sc., Biology, The University of Victoria, 2003

A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF

THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES

(Experimental Medicine)

THE UNIVERSITY OF BRITISH COLUMBIA

(Vancouver)

June 2014

The airway epithelium is the interface between the environment and the submucosa of the lung and thus is the first line of defense against inhaled exogenous agents. In addition to maintaining a structural barrier through homeostasis and repair, the airway epithelium is involved in co- ordination of the mucosal immune response to the inhaled environment. In asthma it is now understood that the airway epithelium is abnormal with dysregulation of genes integral to differentiation, proliferation, and inflammation. Alteration of the chromatin architecture through epigenetic modifications including DNA methylation and histone modifications is reactive to the environment and can establish chromatin states which are permissive or repressive to gene expression. Epigenetic regulation of gene expression is cell specific, as such, it is important to understand epigenetic regulation in cells that are thought to play a central role in asthma.

As epigenetic processes are critical to cellular specificity and disease susceptibility the overarching hypothesis of this thesis is that alterations to the epigenome of asthmatic airway epithelial cells contribute to the dysregulation of genes involved in key epithelial functions.

To investigate this hypothesis, we performed analysis of DNA methylation, expression of epigenetic modifying genes, and histone acetylation and methylation in airway epithelial cells, airway fibroblasts, and peripheral blood mononuclear cells from asthmatic and healthy subjects.

We identified unique signatures of both DNA methylation and expression of epigenetic modifying enzymes in airway epithelial cells and showed that epithelial cells were epigenetically distinct from other cell types. Furthermore, we found that airway epithelial cells from asthmatic subjects displayed changes in DNA methylation, expression of histone kinases, acetylation of ii

lysine 18 on histone 3, and occupancy of this histone modification at genes important to epithelial functions.

Therefore, epigenetic differences between tissue types were more evident and plentiful than within cell types highlighting the importance of the epigenome to cell specificity, yet subtle differences within each tissue were determined and may play a role in disease pathogenesis.

Thus, these findings enhance our understanding of the unique epigenetic landscape which may contribute to the airway epithelial phenotype in health and disease.

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Preface

The studies and methods described in this thesis were approved by the University of British

Columbia‟s Research Ethics Board (#H13-02173) and by the Providence Research Ethics

Committee (#H0-50110), University of British Columbia.

This research project originated as a research grant designed by Dr. Peter Paré, Dr. Darryl

Knight, and Dr. Micheal Kobor, and the research chapters were further designed by Dr. Tillie-

Louise Hackett and myself.

Chapter 1: Introduction

The introduction contains a portion of the book chapter Hackett TL, Warner SM, Stefanowicz D and Knight DA. (2011) Epithelial Cells, in Inflammation and Allergy Drug Design (eds K.

Izuhara, S. T. Holgate and M. Wills-Karp), Wiley-Blackwell, Oxford, UK. doi:

10.1002/9781444346688.ch10. I wrote the genetics section for this book and modified it for use in this chapter. Permission to use this section was obtained from RightsLink.

Chapter 3: DNA Methylation Profiles of Airway Epithelial Cells and Peripheral Blood

Mononuclear Cells from Healthy, Atopic and Asthmatic Children

Chapter 3 is based on the publication Stefanowicz D, Hackett TL, Garmaroudi FS, Günther OP,

Neumann S, Sutanto EN, Ling KM, Kobor MS, Kicic A, Stick SM, Paré PD, Knight DA. DNA methylation profiles of airway epithelial cells and PBMCs from healthy, atopic and asthmatic children. PLoS One. 2012;7(9):e44213. PLoS One employs the Creative Commons Attribution

(CC BY) license whereby authors maintain ownership of the copyright for their article. I was

responsible for data analysis strategy, manuscript writing and a portion of the sample preparation. Tillie-Louise Hackett assisted with cell isolation, data analysis and editing the manuscript, and Anthony Kicic and Steve Stick contributed cells for the study.

Chapter 4: The Landscape of Epigenetic Modifying Enzymes in Airway Epithelial Cells and

Fibroblasts in Health and Disease

For Chapter 4, Dr. Hackett and I designed the study. I performed all experiments, data analysis strategy, data interpretation, writing and a portion of the data analysis. Tillie-Louise Hackett contributed epithelial and fibroblast cells, aided with data analysis strategy, and editing of the chapter. Teal Hallstrand also provided epithelial cells. Yunlong Nie contributed to the data analysis.

Chapter 5: Alterations in Histone Acetylation and Methylation in Airway Epithelial Cells

Derived From Asthmatic and Healthy Subjects

For Chapter 5, Dr. Knight, Dr. Hackett, and I designed the study. I performed all data analysis, interpretation, writing, and the greater part of the experiments. Optimization of chromatin immunoprecipitation was performed by Janet Lee and some immunohistochemistry and color segmentation was performed by Furquan Shaheen and Janet Lee.

Chapter 6: Discussion

I was responsible for interpretation and writing of the summary of data in Chapter 6. Tillie-

Louise Hackett assisted with editing of the chapter.

Table of Contents

Abstract ...... ii

Preface ...... iv

Table of Contents ...... vi

List of Tables ...... xiv

List of Figures ...... xv

List of Abbreviations ...... xviii

Acknowledgements ...... xxii

Dedication ...... xxiii

Chapter 1: Introduction ...... 1

1.1 Overview of thesis ...... 1

1.2 Asthma definition and prevalence ...... 1

1.3 Asthma diagnosis ...... 2

1.4 Airway inflammation in asthma...... 3

1.5 Allergic sensitization ...... 4

1.6 Early phase allergic reaction ...... 6

1.7 Late phase allergic reaction ...... 8

1.8 Airway remodeling in asthma ...... 10

1.8.1 Goblet cell hyperplasia ...... 11

1.8.2 Sub-epithelial fibrosis and thickening of the lamina reticularis ...... 12

1.8.3 Increased airway smooth muscle (ASM) mass ...... 13

1.8.4 Altered epithelial phenotype ...... 13

1.9 Normal epithelial function and wound repair ...... 14

1.10 Abnormal barrier function and wound repair in asthma ...... 16

1.10.1 Epidermal growth factor receptor ...... 16

1.10.2 Transcription factor p63 ...... 17

1.10.3 Signal transducer and activator of transcription 6 ...... 18

1.11 Epidemiology of asthma ...... 18

1.12 Asthma genetics ...... 19

1.13 Epigenetic regulation of gene expression ...... 21

1.13.1 DNA methylation ...... 22

1.13.2 Histone modification ...... 24

1.13.2.1 Histone acetylation...... 24

1.13.2.2 Histone methylation ...... 25

1.13.2.3 Histone phosphorylation ...... 26

1.13.2.4 Histone ubiquitination ...... 27

1.13.3 MicroRNA ...... 27

1.14 Epigenetics and asthma ...... 28

1.14.1 Epigenetic consequences of prenatal and early life environment on asthma

susceptibility ...... 28

1.14.1.1 Nicotine exposure ...... 28

1.14.1.2 Diet ...... 29

1.14.1.3 Air pollution ...... 30

1.14.1.4 Maternal environment ...... 31

1.14.1.5 Psychosocial stress ...... 32

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1.14.2 Epigenetic regulation of airway tissues in asthma ...... 33

1.14.2.1 DNA methylation ...... 33

1.14.2.2 Histone modifications ...... 34

1.15 Current therapies for asthma and epigenetic modulation ...... 36

1.16 Summary of thesis...... 37

Chapter 2: General Methods ...... 40

2.1 Sample collection ...... 40

2.1.1 Airway epithelial cell isolation from bronchial brushings: ...... 40

2.1.2 Peripheral blood mononuclear cell isolation...... 40

2.1.3 Airway epithelial cell isolation from human lung tissue ...... 41

2.1.4 Isolation of airway fibroblasts from human lung ...... 42

2.2 Immunohistochemical staining and analysis ...... 42

2.2.1 Immunohistochemical staining ...... 42

2.2.2 Immunohistochemical analysis ...... 44

2.3 Sodium dodecyl sulfate polyacrylamide gel electrophoresis and immunoblot ...... 44

2.3.1 Protein collection ...... 44

2.3.2 Sodium dodecyl sulfate polyacrylamide gel electrophoresis ...... 45

2.3.3 Immunoblot ...... 45

2.4 Nucleic acid extraction ...... 46

2.4.1 Deoxyribonucleic acid extraction ...... 46

2.4.2 Ribonucleic acid extraction...... 47

2.5 Reverse transcription polymerase chain reaction ...... 47

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Chapter 3: DNA Methylation Profiles of Airway Epithelial Cells and Peripheral Blood

Mononuclear Cells from Healthy, Atopic and Asthmatic Children...... 48

3.1 Introduction ...... 48

3.2 Methods...... 50

3.2.1 Subjects and ethics statement: ...... 50

3.2.2 Airway epithelial cell and peripheral blood mononuclear cell isolation ...... 51

3.2.3 DNA bisulfite conversion and methylation assay ...... 52

3.2.4 Statistical analysis of DNA methylation data ...... 52

3.2.5 Ingenuity pathways analysis (IPA) ...... 53

3.2.6 Quantitative polymerase chain reaction ...... 54

3.3 Results ...... 55

3.3.1 Cell-specific methylation profiles in airway epithelial cells compared to peripheral

blood mononuclear cells ...... 55

3.3.2 Comparison of DNA methylation profiles of airway epithelial cells or peripheral

blood mononuclear cells between disease phenotypes...... 59

3.3.3 Correlation between DNA methylation and gene expression in airway epithelial

cells…...... 61

3.4 Discussion ...... 63

Chapter 4: The Landscape of Epigenetic Modifying Enzymes in Airway Epithelial Cells and Fibroblasts in Health and Disease ...... 70

4.1 Introduction ...... 70

4.2 Methods...... 73

4.2.1 Sample collection ...... 73

4.2.2 Cell culture and RNA isolation ...... 75

4.2.3 Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and

immunoblot ...... 76

4.2.4 Statistical analysis ...... 76

4.3 Results ...... 77

4.3.1 Airway tissue specific expression of epigenetic modifying enzymes ...... 77

4.3.2 DNA methylation enzymes ...... 80

4.3.3 Histone methylation enzymes ...... 80

4.3.4 Histone acetylation enzyme family ...... 83

4.3.5 Histone phosphorylation enzyme family ...... 86

4.3.6 Histone ubiquitination enzyme family ...... 86

4.3.7 Disease specific alterations in gene expression of epigenetic modification enzymes

in airway epithelial cells ...... 87

4.3.8 Disease specific alterations in expression of epigenetic modification enzymes in

fibroblasts ...... 89

4.4 Discussion ...... 91

Chapter 5: Alterations in Histone Acetylation and Methylation in Airway Epithelial Cells

Derived From Asthmatic and Healthy Subjects ...... 99

5.1 Introduction ...... 99

5.2 Methods...... 102

5.2.1 Study subjects ...... 102

5.2.2 Cell culture ...... 103

5.2.3 Immunohistochemical staining of airway sections ...... 104

5.2.4 Chromatin immunoprecipitation ...... 105

5.2.4.1 Real time polymerase chain reaction ...... 106

5.2.4.2 Analysis...... 108

5.2.5 Trichostatin A treatments ...... 109

5.2.6 Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and

immunoblot ...... 109

5.3 Results ...... 111

5.3.1 Global analysis of acetylated histone residues in epithelium from healthy compared

to asthmatic subjects ...... 111

5.3.2 Global analysis of methylated histone residues in epithelium from healthy compared

to asthmatic subjects ...... 113

5.3.3 Gene Specific Analysis of H3K18ac and H3K4me2 in airway epithelial cells from

healthy compared to asthmatic subjects ...... 115

5.3.4 Modulation of ΔNp63, EGFR, and STAT6 with the histone deacetylase inhibitor

Trichostatin A ...... 118

5.4 Discussion ...... 120

Chapter 6: Discussion ...... 126

6.1 Introduction ...... 126

6.2 Airway epithelial cells are an epigenetically distinct cell type ...... 127

6.3 Airway epithelial cells from asthmatic subjects have epigenetic anomalies ...... 130

6.4 Therapeutic implications ...... 134

6.5 Study limitations ...... 136

6.6 Future directions ...... 137

6.7 Final conclusions ...... 138

Bibliography ...... 140

Appendices ...... 163

Appendix A ...... 163

A.1 Differentially methylated CpG sites in airway epithelial cells (AECs) compared to

peripheral blood mononuclear cells (PBMCs)...... 163

A.2 Biological functions of differentially methylated genes in airway epithelial cells

(AECs) compared to peripheral blood mononuclear cells (PBMCs)...... 166

A.3 Differentially methylated sites in healthy airway epithelial cells (AECs) compared to

peripheral blood mononuclear cells (PBMCs)...... 167

A.4 Differentially methylated sites in atopic airway epithelial cells (AECs) compared to

peripheral blood mononuclear cells (PBMCs)...... 170

A.5 Differentially methylated sites in asthmatic airway epithelial cells (AECs) compared

to peripheral blood mononuclear cells (PBMCs)...... 173

A.6 Biological functions of differentially methylated core genes in airway epithelial cells

(AECs) compared to peripheral blood mononuclear cells (PBMCs)...... 176

Appendix B ...... 177

B.1 Table of epigenetic modifier genes including family, gene, full name, and previous

names...... 177

B.2 Distribution of p-values obtained from univariate linear regression modeling of age

and sex on gene expression for all 86 genes in airway epithelial cells (AECs)...... 181

B.3 Distribution of p-values obtained from univariate linear regression modeling of age

on gene expression for all 86 genes in fibroblast cells...... 181

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B.4 P-values from comparison of epigenetic modifier gene expression between epithelial

cells (AEC) and fibroblasts (Fb) from healthy subjects...... 182

B.5 P-values from comparison of epigenetic modifier gene expression between airway

epithelial cells (AECs) from asthmatic and healthy subjects...... 185

B.6 P-values from comparison of epigenetic modifier gene expression between

fibroblasts from asthmatic and healthy subjects...... 188

Appendix C ...... 191

C.1 Quality control for H3K18ac and H3K4me2 chromatin immunoprecipitation...... 191

C.2 Point counting of histone acetylation in airway epithelium from healthy and

asthmatic subjects...... 192

C.3 Point counting of histone methylation in airway epithelium from healthy and

asthmatic subjects...... 193

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List of Tables

Table 3.1 Patient demographics in DNA methylation cohort prior to surgery...... 51

Table 3.2 Patient demographics in gene expression cohort prior to surgery...... 51

Table 3.3 RT-PCR primer sequences for CRIP1, STAT5A and PPIA...... 54

Table 3.4 Differentially methylated CpGs in atopic compared to asthmatic derived airway epithelial cells...... 61

Table 4.1 Subject demographics including phenotype, age, cell type, and sex...... 74

Table 4.2 Antibodies used for immunoblot of cell protein lysate...... 76

Table 5.1 Donor demographics including age, sex, and disease used for immunohistochemistry.

...... 102

Table 5.2 Donor demographics including age, sex, and disease used for chromatin immunoprecipitation and immunoblot...... 103

Table 5.3 Antibodies used for immunohistochemical staining of donor trachea tissue...... 104

Table 5.4 Recipe for PCR cocktail used to analyze ChIP DNA at target genes...... 107

Table 5.5 Primers used in real time PCR analysis of ChIP DNA...... 107

Table 5.6 Cycling conditions for real time PCR of chromatin immunoprecipitated DNA...... 108

Table 5.7 Antibodies used for immunoblot of cell protein lysate...... 110

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List of Figures

Figure 1.1 Cells and mediators involved in the process of allergic sensitization...... 5

Figure 1.2 Early phase inflammation in the airway...... 7

Figure 1.3 Late phase allergic or Th2 inflammation in the airway...... 9

Figure 1.4 Morphological traits of the remodeled airway in asthma...... 10

Figure 1.5 DNA methylation...... 23

Figure 1.6 Histone acetylation...... 25

Figure 1.7 Histone methylation...... 26

Figure 3.1 DNA methylation analysis of atopic asthmatics compared to non-atopic asthmatics individuals in airway epithelial cells and peripheral blood mononuclear cells...... 55

Figure 3.2 DNA methylation profile of airway epithelial cells compared to peripheral blood mononuclear cells...... 57

Figure 3.3 DNA methylation heatmaps of CpG sites in peripheral blood mononuclear cells and airway epithelial cells from healthy, atopic and asthmatic pediatric subjects...... 58

Figure 3.4 Differential methylation between disease phenotypes in airway epithelial cells or peripheral blood mononuclear cells...... 60

Figure 3.5 STAT5A and CRIP1 gene expression and DNA methylation status in airway epithelial cells...... 62

Figure 4.1 Principal component analysis (PCA) of epigenetic modifier enzymes in airway epithelial cells and fibroblasts...... 78

Figure 4.2 Co-expression heatmap of epigenetic modifying genes...... 79

Figure 4.3 Gene expression of DNA methylation family of proteins in airway epithelial cells

(AECs) and fibroblasts (Fb) from healthy subjects...... 80

Figure 4.4 Gene expression of histone methyltransferases (HMT) family of proteins in airway epithelial cells (AEC) and fibroblasts (Fb) from healthy subjects...... 81

Figure 4.5 Gene expression of SET domain containing histone methyltransferases (HMT) family of proteins in airway epithelial cells (AEC) and fibroblasts (Fb) from healthy subjects...... 82

Figure 4.6 Gene expression of histone demethylases (HDM) family of proteins in airway epithelial cells (AEC) and fibroblasts (Fb) from healthy subjects...... 83

Figure 4.7 Gene expression of histone deacetylase (HDAC) family of proteins in airway epithelial cells (AEC) and fibroblasts (Fb) from healthy subjects...... 84

Figure 4.8 Gene expression of histone acetyltransferase (HAT) family of proteins in airway epithelial cells (AEC) and fibroblasts (Fb) from healthy subjects...... 85

Figure 4.9 Gene expression of histone phosphorylation family of proteins in airway epithelial cells (AEC) and fibroblasts (Fb) from healthy subjects...... 86

Figure 4.10 Gene expression of histone ubiquitination family of proteins in airway epithelial cells (AEC) and fibroblasts (Fb) from healthy subjects...... 87

Figure 4.11 Differentially expressed epigenetic modifying genes in asthmatic compared to healthy airway epithelial cells (AECs)...... 88

Figure 4.12 Protein expression of aurora kinase A (AURKA) in airway epithelial cells (AEC) from healthy and asthmatic subjects...... 89

Figure 4.13 Differentially expressed epigenetic modifying genes in asthmatic compared to healthy airway fibroblasts...... 90

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Figure 4.14 Protein expression of arginine methyltransferase 1 (PRMT1) in airway fibroblasts from healthy and asthmatic subjects...... 91

Figure 5.1 Acetylation of specific histone lysine residues in asthmatics airways compared to controls...... 112

Figure 5.2 Methylation of specific histone lysine residues in asthmatics airways compared to controls...... 114

Figure 5.3 Relative occupancy of H3K18ac and H3K4me2 at the ΔNp63 locus...... 116

Figure 5.4 Relative occupancy of H3K18ac and H3K4me2 at the EGFR locus...... 117

Figure 5.5 Relative occupancy of H3K18ac and H3K4me2 at the STAT6 locus...... 118

Figure 5.6 Fold change in H3K18ac with varying concentrations of Trichostatin A (TSA) treatment...... 119

Figure 5.7 Modulation of ΔNp63, EGFR, and STAT6 expression with TSA treatment...... 120

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List of Abbreviations

ACSL3 Acyl-CoA Synthetase Long-Chain family member 3 ACTB Beta-Actin ADAM33 A Disintegrin and Metalloprotease-33 ADCYAP1R1 Adenylate-Cyclase Activating Polypeptide 1 Receptor 1 AEC Airway Epithelial Cell AHR Airway Hyperresponsiveness ALOX12 Arachidonate 12-Lipoxygenase AMCase Acidic Mammalian Chitinase APCs Antigen Presenting Cells apoA-1 Apolipoprotein A-1 ARG Arginase ASH1L Ash1 (Absent, Small, Or Homeotic)-Like ASM Airway Smooth Muscle ATF2 Activating Transcription Factor 2 ATS American Thoracic Society AURK Aurora Kinase

B2M Beta-2-Microglobulin BEBM Bronchial Epithelial Basal Media BEGM Bronchial Epithelial Growth Medium

CARM1 Coactivator-associated Arginine Methyltransferase 1 CCL Chemokine (C-C motif) Ligand CCR Chemokine (C-C motif) Receptor CD23 Fc fragment of IgE, low affinity II, receptor for CDHR3 Cadherin-Related family member 3 CDYL Chromodomain protein, Y-Like ChIP Chromatin Immunoprecipitation CIITA Class II, major histocompatibility complex, Transactivator COX2 Cyclooxygenase-2 CpG Cytosine-Guanine Dinucleotide CREBBP CREB Binding Protein CRIP1 Cysteine-Rich Protein 1 CSRP2BP CSRP2 Binding Protein CXCL Chemokine (C-X-C motif) Ligand

DMEM Dulbecco's Modified Eagle's Medium ΔNp63 Delta N isoform, tumor protein 63 DNA Deoxyribonucleic Acid DNMT DNA Methyltransferase DOT1L DOT1-Like histone H3K79 methyltransferase DUB Deubiquitinating enzyme xviii

DZIP3 DAZ Interacting zinc finger Protein 3

ECM Extracellular Matrix EGFR Epidermal Growth Factor Receptor EHMT2 Euchromatic Histone-lysine N-Methyltransferase 2 ELK4 ETS-domain protein EMT Epithelial-Mesenchymal Transition EP300 E1A binding protein p300 ESCO Establishment of Sister chromatid Cohesion N-acetyltransferase

FBS Fetal Bovine Serum FcɛRI Fc fragment of IgE, high affinity I, receptor for FEV1 Forced Expiratory Volume in 1 second FFPE Formalin Fixed Paraffin Embeded FN Fibronectin FVC Forced Vital Capacity

GAPDH Glyceraldehyde-3-Phosphate Dehydrogenase GATA-3 GATA binding protein 3 GM-CSF Granulocyte-Monocyte Colony-Stimulating Factor GR Glucocorticoid Receptor GRE Glucocorticoid Response Element GWAs Genome-Wide Association

H2A Histone 2A H2B Histone 2B H3 Histone 3 H4 Histone 4 HAT Histone Acetyltransferase HDAC Histone Deacetylase HDM Histone Demethylase HMT Histone Methyltransferase HPRT1 Hypoxanthine Phosphoribosyltransferase 1

IFNɣ Interferon ɣ IgE Immunoglobulin E IL Interleukin IPA Ingenuity Pathways Analysis ISAAC International Study of Asthma and Allergies in Childhood

KAT K(lysine) Acetyltransferase KDM lysine (K)-specific Demethylase KMT lysine (K)-specific Methyltransferase KRT5 Cytokeratin 5

MBD Methyl CpG Binding Domain protein xix

MBP Major Basic Protein MeCP2 Methyl CpG binding Protein 2 MEF Myocyte Enhancer Factor miRNA Micro-Ribonucleic Acid mRNA Messenger Ribonucleic Acid MUC5AC Mucin-5AC MYOD1 Myogenic Differentiation 1 MYSM1 Myb-like, SWIRM and MPN domains 1

NCOA Nuclear receptor Coactivator NEK6 NIMA-related Kinase 6 NF-kB Nuclear Factor kappa-light-chain-enhancer of activated B cells NSD1 Nuclear receptor binding SET Domain protein 1 p63 Tumor Protein 63 PAGE Polyacrylaminde Gel Electrophoresis PAH Polycyclic Aromatic Hydrocarbon PAK1 p21 protein (Cdc42/Rac)-Activated Kinase 1 PBMC Peripheral Blood Mononuclear Cell PBS Phosphate Buffered Saline PC20 Provocative Concentration that results in a 20% decrease in FEV1 PCA Principal Component Anaylsis PCDH1 Protocadherin-1 PCR Polymerase Chain Reaction PEB Protein Extraction Buffer PIC Protease Inhibitor Cocktail PIC2 Phosphatase Inhibitor Cocktail 2 PMSF Phenylmethylsulfonyl Fluoride PPARɣ Peroxisome Proliferator-Activated Receptor-ɣ PPIA Peptidylprolyl Isomerase A PRMT Protein Arginine Methyltransferase

RAST Radioallergosorbent Test RNA Ribonucleic Acid RNF Ring Finger protein RPL13A Ribosomal Protein L13a RPS6KA Ribosomal Protein S6 Kinase, 90kDa, polypeptide RT-PCR Reverse Transcriptase PCR Runx3 Runt-related transcription factor 3

SAT2 Spermidine/Spermine N1-Acetyltransferase family member 2 SDS Sodium Dodecyl Sulfate SET Suppressor of variegation, Enhancer of zeste, and Trithorax SETD SET Domain containing SETDB SET Domain, Bifurcated SIRT7 Sirtuin 7 xx

SMYD3 SET and MYND Domain containing 3 SP1 Specificity Protein 1 STAT Signal Transducer and Activator of Transcription SUV Suppressor of Variegation

TAp63 TA isoform, tumor protein 63 TBS Tris-Buffered Saline TET1 Ten-Eleven Translocation 1 TGF-β2 Transforming Growth Factor Beta 2 Th T Helper Cell Th0 Naïve CD4+ T Lymphocyte Th1 Type 1 T Helper Cell Th2 Type 2 T Helper Cell TIEG1 TGFb Inducible Early Gene-1 TNFα Tumor Necrosis Factor α Treg Regulatory T Cell TSA Trichostatin A TSLP Thymic Stromal Lymphopoietin TSS Transcription Start Site

UB Ubiquitin-conjugating enzyme USP Ubiquitin Specific Peptidase

VCAM Vascular Cell Adhesion Molecule VEGF Vascular Endothelial Growth Factor

WHSC1 Wolf-Hirschhorn Syndrome Candidate 1

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Acknowledgements

I would like to thank my supervisors, Dr. Darryl Knight and Dr. Tillie-Louise Hackett, for being wonderful mentors throughout my studies. In particular, I would like to extend an extra special thanks to Dr. Hackett for the endless hours spent reading, discussing, and editing this thesis.

I also thank my committee members: Dr. Peter Paré, Dr. Andrew Sandford, and Dr. Wendy

Robinson for their valuable input and assistance with this project. I would like to especially acknowledge Dr. Sandford for his support and friendship throughout these many years.

I acknowledge the Natural Sciences and Engineering Research Council of Canada which provided funding for a portion of my studies.

I would like to thank my fellow lab members including Furquan Shaheen, Stephanie Warner,

Jeremy Hirota, Tracee Wee, Hyun-Kyoung Koo, Kevin Lee, Jari Ullah and Janet Lee for their patience, help, and support.

I thank Anna Meredith, Seti Boroomand, and Amrit Samra for their comfort and friendship over these past six years.

I would like to thank Anna, Adam, and Agata Stefanowicz for their support throughout this process.

Lastly, I would like to thank Nathan Elliott Haynes for making me smile every day!

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Dedication

I dedicate this work to Jadwiga Stefanowicz and her coveted supply of pierogi.

Dziękuję babcia!

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Chapter 1: Introduction

1.1 Overview of thesis

It is now apparent that both genetics and the environment contribute to asthma susceptibility [1,

2]. Epigenetic modifications including DNA methylation, histone modifications and miRNA dependent regulation of gene expression are mechanisms by which genetics and the environment can interact to contribute to disease pathogenesis. Epigenetic regulation of gene expression is cell specific, and as such, it is important to understand epigenetic regulation in cells that are thought to play a central role in asthma. Within the conducting airways of the lung the airway epithelium is the first structural barrier to the inhaled environment and is therefore a likely tissue to be influenced by epigenetic modifications. In the airway of asthmatic patients it has long been documented that the airway epithelium exhibits an altered phenotype including loss of ciliated cells, mucus cell metaplasia and defective barrier function [3]. To date it is unknown if these alterations observed in the airway epithelium of asthmatic individuals are epigenetically regulated. Thus, the overarching hypothesis of this thesis is that alterations to the epigenome of airway epithelial cells contribute to the dysregulation of genes involved in key epithelial functions in asthma.

1.2 Asthma definition and prevalence

Asthma is defined as a chronic inflammatory airways disease, characterized by reversible airflow limitation resulting in recurrent symptoms such as tightness of the chest, wheeze, cough, and dyspnea [4]. The symptoms of asthma arise due to airway hyper-responsiveness to stimuli, which results in narrowing of the airways due to constriction of the surrounding airway smooth 1

muscle that is then compounded by bronchial edema and mucus production [4]. This widespread but variable airflow obstruction triggered during an exacerbation is often reversible spontaneously or with treatment [4]. Asthma is a serious and increasingly prevalent world-wide public health problem. Globally, asthma affects around 300 million children and adults leading to an estimated 250 000 deaths per year [5, 6]. In 2012, 8.1% of the Canadian population aged

12 and over reported an asthma diagnosis by a health care professional [7] with direct costs to healthcare estimated at $600 million annually [8]. With asthma prevalence ranging from 1 to

18% of the population in different countries, one can appreciate the urgent need for advancements in our understanding and treatment of asthma.

1.3 Asthma diagnosis

As patients present with symptoms of asthma, confirmation of the diagnosis is made by objective measures of lung function using spirometry [9]. Spirometry measures flow rates and lung volumes during a forced expiratory maneuver to determine the forced expiratory volume in 1 second (FEV1) and the forced vital capacity (FVC) of an individual [9]. FVC is defined as the maximal volume of air delivered during a forceful and complete exhalation from a maximal inspiration whereas FEV1 is the volume exhaled in the first second of an FVC exercise [10].

Either the FEV1/FVC ratio or FEV1% predicted, expressed as a percentage of the FEV1 predicted in the population for a person of a comparable demographic profile, is commonly used as a baseline measurement to diagnose lung function impairment [11-13]. A reduction in both FEV1 and FEV1/FVC relative to predicted values indicates airflow obstruction [13]. By administering short-acting β2-agonists, a pre and post-bronchodilator reading can be obtained to determine reversibility of airflow limitation [13]. Significant airflow reversibility is defined as a ≥12 2

percent increase in FEV1 from baseline after beta agonist bronchodilator administration indicating a diagnosis of asthma [9, 13].

To test for airway hyperresponsiveness (AHR), bronchoprovocation with a constrictor agent such as nebulized histamine, methacholine, hypertonic solutions, or dry air inhalation can be used [13,

14]. Histamine challenge induces bronchoconstriction primarily through the H1 receptor on airway smooth muscle [15, 16], but is not generally used for clinical testing due to the additional systemic inflammatory side effects it also induces [17-19]. Methacholine acts on the M3 muscarinic receptor triggering bronchoconstriction of smooth muscle [16]. The provocative concentration of an agonist that results in a 20% decrease in FEV1 (PC20) is recorded and used to diagnose asthma [14]. Most non-asthmatic subjects have a PC20 of >16mg/mL, whereas asthmatic subjects have a PC20 of <8mg/mL when challenged with either histamine or methacholine indicating an increased bronchoconstrictor response [14].

1.4 Airway inflammation in asthma

Symptomatic attacks of airway inflammation can be the result of several known or unknown sources such as exposure to allergens [20], viruses [21], or air pollutants [22] in asthmatic patients. The inflammatory response in the asthmatic airway is composed of a multitude of cells and is characterized by an immediate or early phase response, and in some individuals a late phase allergic reaction.

1.5 Allergic sensitization

Antigen presenting cells (APCs), consisting mainly of dendritic cells, survey the inhaled environment through extensions which are nested between tight junctions of epithelial cells [23,

24]. Allergens entering the airway interact with APCs directly or through allergen derived proteases which degrade tight junction proteins, thereby disrupting and injuring the epithelial barrier and increasing epithelial permeability [23, 24]. As depicted in Figure 1.1, after initial exposure to an allergen, APCs which reside in the airway mucosa migrate to the regional lymph nodes where they interact with naive CD4+ T lymphocytes (Th0) [25, 26]. The APCs present fragments of the processed allergen through major histocompatibility complex class II molecules to T-cell receptors on the Th0 cell [25, 27]. When exposed to Interleukin (IL) -4, derived from a variety of sources including dendritic cells, Th0 cells commit to a Th2 lineage, the predominant

T cell found in the asthmatic airway [25, 27, 28]. Th2 cells are capable of releasing IL-4 and IL-

13 which results in increased production of immunoglobulin E (IgE) through immunoglobulin class-switch recombination in B cells [25, 27, 28]. Allergen specific IgE is then bound to cells bearing the FcɛRI receptor (mainly mast cells but also macrophages, eosinophils and basophils) within the airways thereby sensitizing them to respond with subsequent exposure [29, 30].

Figure 1.1 Cells and mediators involved in the process of allergic sensitization. Allergen processing and IgE production involves numerous cells beginning with airway epithelial cells and antigen presenting cells (APCs) followed by T cells, B cells, and mast cells. Modified from Galli et al. [25]. Permission to use original image obtained from Rightslink.

Furthermore, the release of IL-4 by mature Th2 cells acts in an autocrine manner to perpetuate the differentiation of Th0 to Th2 cells and inhibits the formation of a Th1 response [31]. In the presence of IL-12, Th0 cells differentiate into Th1 cells, which release interferon ɣ (IFNɣ) and

IL-2. IFNɣ functions in a similar mechanism to IL-4 in that it is capable of acting in an autocrine manner to propagate Th1 cell growth and inhibit Th2 responses [31]. In the asthmatic airway, the T cell response is skewed towards a Th2 cytokine release for an adaptive immune response which in asthma results in allergy and inflammation. This occurs rather than a Th1 cytokine response which confers innate immunity, the exact mechanisms which cause this shift to Th2 immunity are not fully understood.

1.6 Early phase allergic reaction

The early phase allergic reaction begins within minutes of allergen exposure through cross linking of antigen to FcɛRI -bound IgE on the surface of mast cells (Figure 1.2) [32, 33].

Although mast cells are the predominant cell involved in this interaction, other cells may also participate [33]. Activation of sensitized mast cells results in degranulation and the immediate release of preformed mediators such as histamine, vascular endothelial growth factor A

(VEGFA), tryptase, and other chemotactic factors [34-36]. The release of these mediators causes airway smooth muscle contraction, mucus cell secretion, vasodilation, disruption of the epithelium and recruitment of eosinophils [35]. Newly synthesized mediators are also secreted and include leukotrienes and prostaglandins, which result in further bronchoconstriction, mucus production, and vascular permeabilization [25, 36]. Mast cells produce growth factors, cytokines, and chemokines such as granulocyte-monocyte colony-stimulating factor (GM-CSF), tumor necrosis factor α (TNFα), chemokines (C-C motif) ligand 2 (CCL2), and IL-4, -5, -6, -8, and -13 [25, 35-37]. These contribute to the transition to the late phase response by stimulating, priming, and recruiting leukocytes to the airways [25, 36, 37]. In addition, epithelial cells exposed to TNFα release IL-8 and CCL2 resulting in further recruitment of leukocytes to the airway [25].

Figure 1.2 Early phase inflammation in the airway. Once sensitized, the airway reacts promptly to repeated exposure to allergens. Mast cells release mediators resulting in bronchoconstriction, leukocyte recruitment, vasodilation, increased vascular permeability, and stimulation of goblet and epithelial cells. Epithelial cells also respond directly to allergens by releasing mediators such as thymic stromal lymphopoietin (TSLP). Modified from Galli et al. [25]. Permission to use original image obtained from Rightslink.

Disruption of the epithelial barrier and triggering of protease-activated receptors (PARs) and toll- like receptors (TLRs) expressed on the surface of epithelial cells by allergens activates several proinflammatory genes and chemokines through nuclear factor kB (NF-kB) signaling including thymic stromal lymphopoietin (TSLP), IL-33, IL-1β, GM-CSF, CCL20, and CCL17 [23, 24].

This results in further activation of dendritic cells, skewing towards a Th2 response, and

recruitment of innate immune and Th2 cells to the airways, which contribute to the late phase allergic response and promote inflammation [23, 24]. In addition, a population of regulatory T cells (Tregs), which have the capacity to suppress the Th2 response, interact with epithelial cells through tight junction proteins [38]. Thus, disruption of tight junctions by allergens may result in reduced retention of Treg cells within the airway mucosa therefore enabling a Th2 response

[38]. This positions the airway epithelium as a modulator of both the innate and adaptive immune response.

1.7 Late phase allergic reaction

The late phase reaction typically peaks 6 to 9 hours post allergen exposure and is considered to be a model system to study the chronic inflammation occurring in asthma [25]. Increased expression of vascular cell adhesion molecule (VCAM) and E-selectin by endothelial cells in postcapillary venules is facilitated by mast cell released mediators and allows the adhesion of circulating leukocytes to the endothelium [39]. As depicted in Figure 1.3, chemokines including

IL-3, IL-5, GM-CSF and CCL5 recruit eosinophils, T lymphocytes, neutrophils, and basophils to the airways [35, 39, 40]. In an inflammatory environment, eosinophils release mediators such as major basic protein (MBP), eosinophil cationic protein, and leukotrienes resulting in cytotoxicity, epithelial cell damage, mucus secretion, and airway smooth muscle contraction [37,

39]. MBP also has the capability to stimulate mast cells further perpetuating the inflammatory process seen in the asthmatic airway [37]. Additionally, Th2 cells release proinflammatory cytokines and growth factors such as IL -3, -4, -5, -9, -13, TNFα, and GM-CSF [27, 28]. The effects of these cytokines include goblet cell hyperplasia, differentiation and survival of eosinophils, activation of neutrophils, and proliferation of mast cells [25, 28]. Activated 8

neutrophils release leukotrienes, elastase, and IL-8 which results in bronchconstriction, degradation of basement membrane components, and further recruitment of neutrophils [25].

Figure 1.3 Late phase allergic or Th2 inflammation in the airway. Several hours post allergen exposure, eosinophils, basophils, neutrophils, mast cells, and Th2 cells release an abundance of mediators perpetuating the inflammation in the airway. The result of the late phase of inflammation is further bronchoconstriction, mucus production, epithelial damage, and recruitment of immune cells. Modified from Galli et al. [25]. Permission to use original image obtained from Rightslink.

The release of IL-13 from basophils also contributes to AHR [25]. Epithelial cells release IL-8,

IL-33, and other chemokines and cytokines which recruit leukocytes and augment inflammation

[25, 40, 41]. Repeated cycles of allergen exposure drive the constant activation of inflammatory cell mediators resulting in the chronic inflammation seen in the airways of asthmatic subjects.

1.8 Airway remodeling in asthma

In addition to allergic inflammation, airway wall remodeling is an important component of airway narrowing, and significantly contributes to airway closure during an acute asthma attack.

The principal features of airway remodeling are shown in Figure 1.4 and include goblet cell hyperplasia, sub-epithelial fibrosis, increased smooth muscle mass and significant epithelial damage and fragility [3].

Figure 1.4 Morphological traits of the remodeled airway in asthma. Masson‟s trichrome was used to stain formalin fixed paraffin embedded (FFPE) airway sections from healthy (left) and asthmatic (right) 11 year old males. Collagen is indicated by blue staining, muscle fibers and keratin is stained red, cytoplasm is shown in pink, and cell nuclei are stained black. The airway of the asthmatic subject shows increased smooth muscle mass, subepithelial fibrosis and thickened lamina 10

reticularis, goblet cell hyperplasia resulting in excess mucus production occluding the lumen, and an abnormal epithelial phenotype. Scale bar indicates 100 µm. Image courtesy of F. Shaheen and T.L. Hackett (UBC, Vancouver, Canada).

Traditionally thought to resolve after an attack or with treatment, these remodeling traits are present early in childhood [42] and have been found prior to clinical manifestation or a diagnosis of asthma [43]. These data have now led us to understand that airway remodeling is not a secondary phenomena but rather occurs alongside inflammation [44]. Although current asthma medications, such as glucocorticoids, can relieve inflammation, there is an urgent need to identify strategies for targeted therapy against airway remodeling. Below we list the specific alterations associated with airway remodeling;

1.8.1 Goblet cell hyperplasia

Goblet cells in the epithelium and submucosal glands are responsible for the production of glycoproteins termed mucins, that are the main component of mucus [45]. Mucus lines the epithelial surface trapping particles from the external environment and cellular debris, which can then be removed through mucociliary clearance [45, 46]. Airways from asthmatic subjects display an increase in both the number of goblet cells and the amount of stored mucin, but not goblet cell size [47]. The excess mucin production from goblet cells in asthma consists primarily of the mucin MUC5AC [46, 48] and results in changes to the composition of mucous creating a more viscous material that inhibits ciliary movement and impairs mucus clearance leading to mucous plugs within the airways [46, 49]. Mucin expression can be induced by transforming growth factor beta2 (TGF-β2) [50] or through Th2 cytokines IL-4, IL-13 and IL-9 via a signal transducer and activator of transcription 6 (STAT6) dependent mechanism [51-53].

1.8.2 Sub-epithelial fibrosis and thickening of the lamina reticularis

Another feature of airway remodeling is fibrosis in the sub-epithelial space and a thickened layer of the basement membrane, specifically the lamina reticularis. This has been attributed to the increase in numbers of fibroblasts and myofibroblasts which reside underneath the epithelium and are important for maintenance of the extracellular matrix (ECM) within tissues of the body

[54-56]. The source of these myofibroblasts is not clear but they could originate from three sources; 1) the existing tissue fibroblasts through proliferation, 2) recruitment of progenitor cells

(fibrocytes) from the blood, or 3) epithelial cells transforming into mesenchymal cells through a process termed epithelial-mesenchymal transition (EMT) [57]. In a wound-healing scenario, myofibroblasts secrete a variety of protease inhibitors, chemokines and cytokines in addition to laying down a provisional extracellular matrix to resolve the injury [58, 59]. Once neighboring cells have migrated to cover the wound, contraction at the wound site is complete and granulation tissue has been resolved, myofibroblasts apoptose allowing final remodeling of the

ECM to a repaired tissue state [57-59]. In the asthmatic airway, myofibroblasts persist and continue to produce proinflammatory mediators and deposit extracellular matrix proteins, such as collagens, fibronectin, tenascin, lumican and biglycan resulting in fibrosis and thickening of the lamina reticularis [55, 57, 60]. These alterations in fibroblast and myofibroblast phenotype are therefore thought to play an important role in ECM deposition, particularly fibrillar collagens, which contribute to airway narrowing in asthma.

1.8.3 Increased airway smooth muscle (ASM) mass

The exaggerated airway narrowing seen is asthma is thought to be primarily a result of increased airway smooth muscle (ASM) mass [61]. However, the cause of increased ASM mass seen in the remodeled airways of asthmatic subjects is still not fully understood [62]. Proposed potential mechanisms include hypertrophy, hyperplasia, decreased rates of apoptosis, increased recruitment of stem cells, differentiation of mesenchymal cells, or constitutionally augmented

ASM mass [62, 63]. In severe asthmatics, the increased ASM mass has also been shown to be more secretory as it produces elevated amounts of the chemokines CCL11 and IL-8 [64]. Also,

ASM exposed to serum from an asthmatic individual produces increased amounts of extracellular matrix proteins, which may alter the composition of the airway wall in asthmatics

[65].

1.8.4 Altered epithelial phenotype

Observations of structural changes to the asthmatic airway epithelium have been documented from mild to severe stages of disease. Epithelial shedding has been described as a common characteristic of adult asthma [66, 67] and biopsies obtained from mild/moderate asthmatic children show epithelial loss in children as young as 2 years [42]. This suggests that these changes occur early in the natural progression of asthma and may be due to greater epithelial instability or fragility [42]. There is an increase in the number of basal cells which express the basal cell markers cytokeratin-5 and tumor protein 63 (p63) [68-71] in the airways of asthmatics suggesting a less differentiated or immature phenotype as these cells constitute the resident progenitor cells of the epithelium [69]. Additionally, exposing the asthmatic epithelium to various triggers has identified dysregulation of a variety of processes such as elevated expression 13

of repair markers [72-74], abnormal microbial immune responses [75, 76], increased susceptibility to apoptosis [77], and enhanced secretion of growth factors and pro-inflammatory activity [28, 32, 68].

While all of these remodeling events have been well documented in asthma, the mechanisms for these alterations are unclear. Within the conducting airways of the lung the airway epithelium is the first structural barrier to the inhaled environment and is therefore a likely tissue to be influenced by epigenetic modifications. In this thesis we will focus our attention on the role of epigenetic regulation of genes involved in epithelial differentiation and repair, which are critical to normal mucociliary function.

1.9 Normal epithelial function and wound repair

The primary role of the airway epithelium is not only to provide an effective barrier against the inhaled environment through clearance of particulate matter, bacteria and viruses out of the airways, but also to modulate airway function and immunity by releasing a multitude of mediators [78]. In the large conducting airways, the epithelium consists of a pseudostratified layer composed of three cells: 1) ciliated columnar cells which have cilia that beat in concert, 2) mucus secreting goblet cells, and 3) basal cells which are the progenitor cell population for both ciliated and goblet cells [79]. The formation of tight junctions at the apical margins of columnar cells creates an impermeable epithelial barrier that is further reinforced with structural integrity through cell-cell and cell-extracellular matrix contacts such as adherens junctions, desmosomes and hemidesmosomes [3, 80]. Particles entering the airway cannot breach the normal epithelial barrier and are caught within the mucus which lines the apical surface of the epithelium [80].

The mucociliary escalator, composed of beating cilia moving mucus towards the pharynx, allows for removal of inhaled particles from the airways [80].

In response to injury, the epithelium must react quickly to repair itself. Mechanical injury models of airway epithelial repair have mostly been performed in animal models, which have provided a basis for the understanding of the repair process in the epithelium. First, the epithelial cells surrounding the wound flatten, migrate to the injured site, and transform into a poorly differentiated phenotype [78, 81]. Next, these dedifferentiated cells proliferate and form a multilayered undifferentiated epithelium [78, 81]. Finally, a pseudostratified epithelium can be seen where cell replacement and differentiation has occurred with basal, secretory (goblet) and ciliated cell markers now detectable [81]. This process involves release of soluble factors, remodeling of cell adhesion junctions, and cross-talk between epithelial and mesenchymal cells

[57]. The epithelium can respond to certain stimuli resulting in the secretion of mediators including TNFα, CCL5, and TGFβ [78]. This enables the epithelium to promote inflammation and repair by recruiting cells of the immune system and fibroblasts to the wound site and by inducing proliferation of fibroblasts and differentiation into myofibroblasts [78]. This places the epithelium in a central position in the control of airway function [78]. In the human lung, partial resolution of the insult is seen within one week, with complete resolution at day 14 [82].

Although basal cells are thought to be the main progenitors of the airway epithelium [83], both basal and secretory cells can contribute to the formation of ciliated columnar cells and the establishment of a pseudostratified epithelium [84].

1.10 Abnormal barrier function and wound repair in asthma

The airway epithelium in asthmatic individuals has been shown to be more susceptible to damage and apoptosis [77, 85]. Further, it has been known for many decades that the epithelium of asthmatic individuals contains areas denuded of ciliated cells [86] and “Creola bodies” or groups of shed epithelial cells can be found in the sputum of asthmatic individuals [67, 87-89].

Adherens junctions between epithelial cells are not only important for epithelial integrity by inducing cell polarity and cell-cell contacts; they also initiate tight junction formation which is essential for tissue permeability of ions and solutes [90, 91]. Apical-basal polarity is lacking in the asthmatic epithelium as evidenced by studies showing decreased presence of tight junctional proteins such as zona occludens-1 and adherens junction protein E-cadherin compared to healthy subjects [74, 92, 93]. This not only creates opportunities for allergens and environmental agents to access the subepithelial space, but also results in processes occurring within the epithelial cell that lead to Th2 cell recruitment [93], increased NF-kB activity [94], and possibly a diminished tolerogenic epithelial phenotype [38]. Below we highlight three genes which have dysregulated expression in the epithelium of asthmatic subjects and are involved in mucosal wound repair and inflammation. The reason for dysregulated expression of epidermal growth factor receptor

(EGFR), the transcription factor p63, and STAT6 is unknown, therefore the epigenetic regulation of these genes is the focus of Chapter 5.

1.10.1 Epidermal growth factor receptor

The epithelium in asthmatics is thought to be in a chronic state of repair, unable to complete the injury-repair process. Elevated expression of the EGFR indicates an ongoing repair process in the asthmatic epithelium [95, 96]. The impact of abnormal EGFR expression encompasses many 16

phases of repair, as it is important in migration, proliferation and differentiation of the epithelium

[72, 97]. In healthy epithelium, EGFR is released after injury to promote repair [98], but in epithelium from asthmatic subjects, EGFR expression is not only elevated in areas of shed columnar cells, but also occurs in morphologically intact regions [96, 99]. Surprisingly, this elevated expression of EGFR in asthmatic epithelium does not correlate with increased proliferation and may indicate failure to mount an appropriate proliferative repair response [96].

1.10.2 Transcription factor p63

Previous work in our laboratory has identified elevated numbers of cells expressing the transcription factor p63 in asthmatic epithelium in vivo [69]. p63 plays a central role in regulating epithelial differentiation, adhesion, proliferation and apoptosis [100, 101]. Normally expressed in basal cells of epithelial tissues, an expanded population of p63-positive cells may indicate a defect in cellular differentiation, leading to inappropriate repair and regeneration.

Studies performed by Warner et al. and Arason et al. identified ΔNp63α as the dominant isoform expressed in airway epithelial cells [102, 103]. Modulating the expression of this isoform had effects on multiple genes including EGFR [102]. The relationship between p63 and EGFR is not fully elucidated, although there is evidence of transcriptional regulation of each gene by the other; p63 may regulate EGFR expression [104] and EGFR may also regulate p63 expression

[105]. Recent studies have implicated p63 as a target for and regulator of epigenetic modifications [106, 107].

1.10.3 Signal transducer and activator of transcription 6

The function of the pro-inflammatory transcription factor STAT6 is well known in the context of

T cells as both Th2 cytokines IL-4 and IL-13 signal through STAT6 resulting in Th2 inflammation [108]. In support of this, STAT6 deficient mice are non-responsive to IL-4 or IL-

13, fail to present with a Th2 phenotype, and do not develop antigen-induced AHR or mucus production [109-111]. The expression of STAT6 has also been identified in bronchial biopsies of asthmatic patients [112]. In particular, elevated STAT6 expression occurs in the bronchial epithelium of severe asthmatics, but not mild asthmatics or controls [112, 113]. In an IL-4 and

IL-13 inflammatory milieu, signalling through STAT6 induces target genes including CCL11,

CCL5, and MUC5AC in airway epithelial cells [114-116], further contributing to the inflammation occurring in the airway.

1.11 Epidemiology of asthma

Asthma is not a single-gene disorder, but a complex genetic disease involving the interplay between small variations across multiple genes and environmental factors that lead to the disease phenotype. The genetic contribution to the development of asthma has been extensively studied, yet heritability estimates range from 35-95% [29]. Numerous twin studies have shown increased concordance of asthma and related phenotypes amongst monozygotic compared to dizygotic twins [117]. In addition, the relative risk for first-order relatives of asthmatic individuals is four to five, meaning that the prevalence in family members is elevated compared to the general population [118]. The collective data accumulated from the various studies investigating the genetics of asthma has identified multiple genes of interest involved in biological functions such as inflammation, immunoregulation, and airway remodeling [119]. 18

The environmental component of asthma is also complex as there are a variety of triggers that can cause the AHR in susceptible individuals. Indoor allergens such as house dust mite [120-

123], pet dander [121, 124, 125] and mold [126-128] have been associated with asthma and related traits such as bronchial hyperresponsiveness in multiple studies. Other triggers include irritants encountered in the outdoor environment such as air pollution [129-131] and tobacco smoke [132, 133], medications such as aspirin [134, 135], viral infections [136, 137] and exercise [138, 139].

1.12 Asthma genetics

Multiple approaches have been employed to query the genetic basis of asthma. These include candidate gene association studies, genome-wide linkage studies, genome-wide association studies (GWAS), and sequencing studies [29]. The clinical heterogeneity of the disease has necessitated the use of measurable intermediate and specific phenotypes as these may increase the power of many genetic studies [29, 140]. These genetic studies have identified associations between numerous genes and asthma, or an intermediate phenotype of asthma, with a large subset being preferentially expressed in the airway epithelium. TSLP [141-143], acidic mammalian chitinase (AMCase) [144], IL-33 [140, 143, 145], protocadherin-1 (PCDH1) [146], and most recently cadherin-related family member 3 (CDHR3) [140] are among the many genes currently being investigated for their connection with the airway epithelium in the pathogenesis of asthma.

Important in the host immune response, TSLP, AMCase, and IL-33 have all been found to be overexpressed in the epithelium of asthmatic subjects [147-150]. Investigations of gene variants

in asthmatic populations identified a single nucleotide polymorphism near the TSLP gene which is associated with both asthma and AHR [142]. TSLP, coupled with antigenic stimulation, is capable of initiating both innate and adaptive immune responses resulting in airway inflammation and a proallergic reaction [151-154]. Analysis of genetic polymorphisms in the

AMCase gene has identified associations with asthma [144]. AMCase can act in an autocrine and/or paracrine manner resulting in production of the chemokines CCL2, CCL17, and CXCL8

[155] making it an important part of the immune response to the inhaled environment. IL-33 is thought to act as a danger signal or „alarmin‟ which alerts the immune system to cellular damage

[156] through its functions as both a pro-inflammatory cytokine and an intracellular nuclear factor [157-159]. Within the adaptive immune system, IL-33 is a chemotactic factor for Th2 cells and induces production of Th2 associated cytokines and Th2 polarization [160-163]. The effects IL-33 has on cells of the innate immune system include activation, migration and cytokine production by basophils [161, 164, 165], activation, degranulation and greater survival and adhesion of eosinophils [166, 167], and increase in survival, adhesion, and production of cytokines and chemokines by mast cells [168-172].

Genetic studies of asthma susceptibility have also identified genes involved in epithelial integrity and differentiation. PCDH1 is a structural protein involved in cell adhesion and barrier function, thus the discovery of an association to asthma highlights the importance of the structural barrier.

In multiple populations, genetic variations in the PCDH1 gene have been associated with bronchial hyperresponsiveness and/or asthma [146, 173, 174]. In addition, studies using air- liquid interface cultures found that PCDH1 mRNA and protein levels are strongly upregulated during mucocilliary differentiation suggesting a role in development and repair of the airway

[175]. Although the biological function of CDHR3 is not fully understood, it is a member of the cadherin family of junctional proteins and thus may have an important role in cellular integrity and differentiation [140]. CDHR3 is expressed in the human lung, particularly the bronchial epithelium, and it seems to be especially upregulated during differentiation and development

[176-179].

The identification of numerous epithelial-expressed asthma susceptibility genes through genetic studies has affirmed the importance of the epithelium in contributing to the disease process.

Thus, the airway epithelium not only functions as a structural barrier, but is involved in co- ordination of the mucosal immune response [3]. Although many of the genes involved in the above processes are characterized, the regulation of those genes through epigenetic mechanisms such as histone modifications remains much less well defined.

1.13 Epigenetic regulation of gene expression

Modulation of the level of expression of specific genes in particular cell types is a major source of human diversity and susceptibility to disease that cannot be attributed solely to genetic mechanisms [180-182]. Although the genetic make-up of an individual is identical throughout different tissues of the body, epigenetic modifications are important causes of variation in gene expression in specific tissues [183]. Epigenetic control of gene expression has three general mechanisms: 1) either the DNA itself is chemically altered, 2) the histone proteins that package

DNA into chromatin are modified or 3) the mRNA transcript is regulated by miRNAs.

Epigenetic modifications are an important mechanism through which the environment influences gene expression, serving as a bridge from nurture to nature [184]. The work presented in

Chapter 4 of this thesis investigates the expression of the enzymes regulating epigenetic modifications in the epithelium of asthmatic subjects. The specific epigenetic modifications and the regulatory enzymes involved in these events are outlined below.

1.13.1 DNA methylation

DNA methylation occurs by the addition of a methyl group to the cytosine base in a cytosine- guanine (CpG) dinucleotide resulting in 5-methylcytosine. CpG islands are regions of DNA that span a minimum of 200 base pairs and contain more than 55% CG content, and are thought to occur at approximately 40% of gene promoters [185]. DNA methylation is facilitated by DNA methyltransfterases (DNMTs), and when occurring at gene promoter regions generally results in gene repression by recruiting chromatin remodeling machinery or by blocking regulatory transcription factors binding to the DNA (Figure 1.5) [186]. For example, in malignant T lymphocytes gene silencing of the STAT5A gene promoter through DNA methylation arises through the involvement of methyl CpG binding protein 2 (MeCP2) [187]. MeCP2 binds methylated DNA, specifically a single methyl-CpG pair, and represses gene transcription by interacting with chromatin remodelers such as histone deacetylases [188, 189].

Figure 1.5 DNA methylation. Rendition of DNA showing the nucleotides adenine (A, pink), thymine (T, green), cytosine (C, blue), and guanine (G, yellow/black). DNA can be methylated (red circle) at CpG dinucleotides by DNA methyltransferases (DNMTs) and is generally associated with gene suppression. The mechanism which reverses DNA methylation is not yet understood.

DNA demethylation can be a passive mechanism where a lack of maintenance methylation over multiple cycles of DNA replication results in loss of methylation [190]. The exact mechanisms of active DNA demethylation is less clear, as no known DNA demethylase enzyme has yet been identified. Current research evaluating the ability of ten-eleven translocation 1 (TET1) protein, which can convert 5-methylcytosine into 5-hydroxy-methylcytosine and further to 5- formylcytosine and 5-carboxylcytosine, is promising as this may be a mechanism to reverse

DNA methylation [191-196].

1.13.2 Histone modification

To package DNA each nucleosome consists of 146 base pairs of DNA wrapped around a histone protein octamer consisting of two of each of the following histone proteins: H2A, H2B, H3, and

H4 [197]. These histones are subject to a variety of modifications such as acetylation, methylation, phosphorylation, and ubiquitination which influence the chromosomal architecture and the packaging of DNA [198]. There is growing evidence that combinations or patterns of histone modifications determine the chromatin state, which has been termed the “histone code hypothesis” [199-201]. The impact of histone modification on biological processes such as cellular differentiation has been highlighted in a variety of cells including embryonic stem cells

[202], myofibroblasts [203], keratinocytes [204, 205], and dendritic cells [206].

1.13.2.1 Histone acetylation

Histone acetylation is modulated by histone acetyltransferases (HATs) and histone deacetylases

(HDACs) which add or remove an acetyl group to a lysine residue at the N-terminus of the histone tail, respectively (Figure 1.6) [207]. Histone de/acetylation enables the assembly of co- repressor/activator complexes resulting in repression/expression of the gene present in the corresponding nucleosome [207].

Figure 1.6 Histone acetylation. DNA (black line) is wrapped around histones 2A, 2B, 3, and 4 (hexagons). Histone tails can be acetylated (green starburst) by histone acetyltransferases (HATs) and deacetylated by histone deacetylases (HDACs). Acetylation of histones is generally associated with an open or active chromatin state and gene transcription.

1.13.2.2 Histone methylation

Histone methylation involves the enzymatic addition and removal of a methyl group on histone lysine or arginine residues resulting in mono (me1), di (me2), or tri (me3) methylation by histone methyltransferases (HMTs) and demethylases (HDMs) (Figure 1.7) [207]. Depending on the targeted residue, histone methylation has the capacity to induce or repress the expression of 25

genes present within the corresponding nucleosome [198]. For example, methylation of lysine 4 on histone 3 has been associated with gene induction whereas methylation of lysine 9 or 27 on histone 3 is more commonly associated with gene silencing [198].

Figure 1.7 Histone methylation. Histones tails are methylated (purple starburst) by histone methyltransferases (HMTs) and demethylated by histone demethylases (HDMs). The resulting chromatin state is determined by the particular modified residue as histone methylation can result in opening of the chromatin and gene expression or an inactive condensed chromatin state and gene repression.

1.13.2.3 Histone phosphorylation

Histone phosporylation of serine, threonine, and tyrosine residues on histone tails by protein kinases and dephosphorylation by phosphatases [208] has been linked to a variety of processes including DNA damage responses, chromatin compaction during apoptosis and mitosis, and transcriptional activation [208]. Although the exact mechanism is unclear, histone

phosphorylation may work in concert with histone acetylation to activate gene expression [208,

209].

1.13.2.4 Histone ubiquitination

Histone ubiquitination can result in both transcriptional activation and suppression depending on the targeted histone lysine residue [210]. The addition of ubiquitin to histones is catalyzed by ubiquitin ligases which can mono- or poly-ubiquitinate target residues [210]. Ubiquitination is reversible through ubiquitin specific peptidases known as deubiquitinating enzymes (DUBs)

[210].

1.13.3 MicroRNA

MicroRNAs (miRNAs) are short strands of RNA approximately 22 nucleotides long that have the ability to post transcriptionally regulate gene translation [185]. Sequence similarity between the miRNA and the target mRNA determines the method in which the mRNA is suppressed; perfect or partial complementary annealing of the miRNA to the RNA results in mRNA cleavage or translational repression respectively [185]. Interestingly, the direction of gene regulation may be dependent on the state of the cell. Vasudevan et al. found that certain miRNAs induce translational up-regulation of target mRNAs when the cell has entered cell cycle arrest whereas the same miRNAs suppress translation in proliferating cells [211].

Although miRNAs are an important component of gene regulation, the study of this particular epigenetic mechanism is outside of the scope of this thesis. Instead, we will focus on a

comprehensive analysis of DNA methylation and histone modifications occurring within the epithelium of asthmatics.

1.14 Epigenetics and asthma

While epigenetic mechanisms controlling genes involved in cancer pathogenesis have been the focus of much research over the past 15 years [212], studies investigating the epigenetic phenomena occurring in the pathogenesis of asthma have only recently begun to elucidate the interaction between the epigenome and the environment in disease susceptibility. Below, we describe the literature on epigenetic modifications with relation to asthma.

1.14.1 Epigenetic consequences of prenatal and early life environment on asthma susceptibility

Environmental exposure during gestation and early life are thought to play an important role in disease susceptibility. Many factors including nicotine exposure, diet, air pollution, maternal environment, and psychosocial stress have been shown to influence asthma in offspring.

Although these studies were performed in whole lung tissue or cells from outside of the lung, they provide a basis for understanding the impact of the environment to disease susceptibility.

1.14.1.1 Nicotine exposure

Although observations of multigenerational transmittance of asthma after in utero nicotine exposure have been reported [213], a mechanistic understanding of the processes involved are now being unraveled. Using a rat model, Rehan et al. showed that nicotine exposure during gestation and up to postnatal day 21 resulted in an asthma phenotype in both F1 and F2 offspring 28

[214]. Examination of lungs and gonads of F1 offspring identified changes in both global DNA methylation and histone acetylation status in nicotine-exposed progeny. While H4 acetylation was increased in both testicular and ovarian tissue, there was no change in the lungs [214]. H3 acetylation, however, increased in the lungs and testis but not in the ovaries [214]. Meanwhile, global DNA methylation decreased in the ovaries, increased in the testes, and did not change in the lungs [214]. Co-administration of nicotine and the peroxisome proliferator-activated receptor-ɣ (PPARɣ) agonist rosiglitazone, which has previously been shown to abrogate the effects of perinatal nicotine exposure in rat [215], attenuated lung function impairment and blocked most of the epigenetic changes seen in tissues of F1 progeny of treated dams [214].

1.14.1.2 Diet

Hollingsworth et al., using a murine model, identified the abundance of methyl donors during gestation as another environmental factor that may contribute to asthma susceptibility. Pregnant dams fed a diet rich in methyl donors bore offspring with a more severe asthma phenotype and certain features of this phenotype were paternally transmitted to F2 progeny [216]. Examination of DNA methylation revealed 82 hypermethylated loci in lung tissue of F1 progeny exposed in utero to a methyl-rich as compared to a methyl-poor diet [216]. For a subset of the genes identified, gene expression was analyzed and found to be reduced in lung tissue from pups exposed to a methyl-rich diet in utero [216]. Of these genes, runt-related transcription factor 3

(Runx3), a gene where low expression has previously been associated with airway inflammation

[217], showed lower protein expression in lung tissue of high methyl diet mice [216].

Furthermore, treatment of splenocytes isolated from methyl-rich diet mice with a demethylating

agent reversed the suppression of Runx3 expression indicating epigenetic control of transcriptional regulation [216].

In humans, folic acid is commonly used during pregnancy to diminish the risk of neural tube defects [218]. Folate, however, is a methyl donor and recent studies have found associations between maternal folate intake and asthma outcomes in children. High gestational folate intake in the first trimester was associated with increased respiratory tract infections and wheeze up to

18 months of age [219]. Second trimester folate supplementation was associated with an increased risk of asthma at age 3 [220]. In another study, late pregnancy supplementation with folic acid was found to be associated with an elevated risk of childhood asthma at 3.5 years and with persistent asthma at age 5 [221].

Rastogi et al. recently identified a unique DNA methylation profile in obese children with asthma [222]. Analyzing DNA methylation in peripheral blood mononuclear cells (PBMCs),

215 loci were consistently differentially methylated in obese asthmatic compared to normal weight asthmatic, non-asthmatic obese, and healthy control children [222]. Differentially methylated genes were primarily involved in Th cell differentiation and macrophage activation, processes which have previously been associated with obesity-related asthma [222].

1.14.1.3 Air pollution

Prenatal exposure to environmental factors such as diesel exhaust and polycyclic aromatic hydrocarbons (PAHs) may also impact the development of asthma [223, 224] through epigenetic mechanisms. In a study of transplacental PAH exposure, Perera et al. found that maternal PAH exposure influenced the methylation status of over 30 DNA sequences in umbilical cord white

blood cells, of which 6 had known 5‟CpG islands [225]. The authors focused on acyl-CoA synthetase long-chain family member 3 (ACSL3) as the elevated DNA methylation status of this gene showed the strongest inverse correlation to the level of gene expression [225] and its chromosomal location is associated with regions of asthma susceptibility loci [226, 227].

Furthermore, methylation of ACSL3 was associated not only with maternal PAH exposure but also with parental report of asthma symptoms in children before age 5 [225].

A recent study of DNA methylation and air pollution from Russia also highlights the impact of environment on disease. Rossnerova et al. examined differences in DNA methylation in whole blood of asthmatic and non-asthmatic children from two distinct regions – Ostrava: urban and heavily polluted and Prachatice: rural and unpolluted [228]. Previous data on this cohort identified distinct phenotypes of asthma in children living in these two regions [229, 230]. The authors discovered that DNA methylation patterns differed between both asthmatic and non- asthmatic children from the two regions with lower methylation occurring in children from

Ostrava [228]. In addition, ACSL3 was found to be borderline significant when comparing asthmatic to non-asthmatic children from Ostrava but not in Prachatice [228] supporting a role for ACSL3 in air pollution exposure and asthma. The authors also noted a gradient towards elevated DNA methylation with high cotinine levels due to parental smoking, longer gestation, higher birth weight and longer breastfeeding times [228].

1.14.1.4 Maternal environment

Associations with IL-2 DNA methylation at birth and asthma and atopy in early life have also been identified [231]. Demethylation of a particular CpG site in the promoter-enhancer region of

the IL2 gene is necessary for IL-2 gene transcription [232]. Using cord blood cells from a cohort of children, Curtin et al. found that increasing DNA methylation of this particular CpG site at birth was associated with severe asthma exacerbations and hospital admissions after the first year of life [231]. However, the authors did not analyze the cell composition of cord blood mononuclear cells therefore the changes observed in IL-2 methylation may have been impacted by cell variability. An association between maternal atopy or cat ownership and IL-2 gene methylation was also identified [231] indicating possible in utero epigenetic mechanism whereby the maternal environment influences the epitype of the offspring. Additionally, Gunawardhana et al. identified 70 differentially methylated loci in peripheral blood from infants exposed to maternal asthma in utero [233] further demonstrating the plasticity of the epigenome in development.

1.14.1.5 Psychosocial stress

Psychosocial stress may also contribute to asthma susceptibility. A study of Puerto Rican children identified an association between increasing methylation of a particular CpG site in the adenylate-cyclase activating polypeptide 1 receptor 1 (ADCYAP1R1) promoter and increased odds of asthma [234]. Interestingly, the authors also found an association between asthma and increased exposure to violence [234]. In those exposed children, increased methylation of the same ADCYAP1R1 CpG site was associated with exposure to violence [234] although further research will be necessary to fully elucidate the mechanisms involved.

1.14.2 Epigenetic regulation of airway tissues in asthma

There have been numerous studies elucidating the role of epigenetic modifications in inflammatory cell development and phenotype. These studies identified that histone methylation, acetylation and DNA methylation are integral to the proper differentiation and response of Th cells by regulating IL4, IL13, IFNɣ, and GATA binding protein 3 (GATA-3)

[235-242]. Yet, in terms of the role of epigenetics in asthma, very little work has focused specifically on the airway epithelium, which is surprising given the prominent identification of epithelial genes in genetic studies.

1.14.2.1 DNA methylation

A recent study of house dust mite induced AHR provided insight into epigenetic mechanisms at work. Using a murine model, Shang et al. found that house dust mite exposure not only resulted in an increase in global DNA methylation but also hydroxymethylation in lung tissue of treated mice [243]. This indicates that there may be an imbalance in the DNA methylation/demethylation process occurring in house dust mite induced AHR. The authors identified 6 differentially methylated genes between lung tissue from house dust mite exposed and control mice where DNA methylation was inversely correlated to gene expression [243].

Examination of isolated smooth muscle cells revealed concordance in DNA methylation for only a subset of the genes identified in lung tissue [243].

The disintegrin and metalloprotease-33 (ADAM33) has been identified as an asthma susceptibility gene in several studies [244-247]. ADAM33 is expressed by multiple cell types

[244, 248] and is important in cell interaction, adhesion, and remodeling [249]. In fibroblasts,

ADAM33 gene expression is regulated by histone methylation and acetylation rather than DNA methylation [250] whereas in epithelial cells, the ADAM33 gene is repressed due to DNA hypermethylation within its promoter in both healthy and asthmatic individuals [251].

Important to epithelial repair processes, expression of the glycoprotein fibronectin (FN) is diminished in airway epithelial cells from asthmatic patients [252]. Supplementing these cells with FN resulted in enhanced wound repair whereas knockdown in healthy epithelial cells inhibited wound closure [252]. Since DNA hypermethylation of the FN promoter has previously been identified in hepatoma tissues [253], Kicic et al. set out to determine if epigenetic mechanisms were involved in FN gene regulation in epithelial cells. They treated asthmatic epithelial cells with either the DNA methylation inhibitor azacytidine, the histone deacetylase inhibitor Trichostatin A (TSA), or both, but found no effect on FN gene expression [252]. This thesis addresses differential DNA methylation profiles in airway epithelial cells from asthmatic subjects and is presented in Chapter 3.

1.14.2.2 Histone modifications

Elevated gene expression of protein arginine methyltransferases 1, 2, and 3 (PRMT1, PRMT2, and PRMT3), all histone methyltransferases, has been identified in lungs from an antigen induced pulmonary inflammation rat model of asthma [254]. Within the epithelium, PRMT1 was the most significantly upregulated and was expressed in both alveolar and airway epithelial cells

[254]. Inhibition of PRMT1 in an IL4 stimulated human epithelial cell line resulted in diminished expression of the chemokines eotaxin1 and CCR3 [254]. As PRMTs can methylate

proteins other than histones, it is not known whether this result was due to an epigenetic effect or methylation of another protein involved in eotaxin1 expression.

Histone methylation has been implicated in regulation of vascular endothelial growth factor

(VEGF) in smooth muscle cells. Airway smooth muscle cells from asthmatic subjects hypersecrete VEGF, which contributes to angiogenesis and inflammation [255]. Clifford et al. found a lack of the repressive histone mark H3K9me3 as well as presence of H3K4me3, an expressive histone mark, at the VEGF promoter in smooth muscle cells from asthmatic patients

[256]. Interestingly, neither DNA methylation nor histone acetylation at the VEGF promoter was different in smooth muscle cells from asthmatic and nonasthmatic patients [256].

The role of histone acetylation in the pathogenesis of asthma remains unclear. HAT activity is increased in asthmatic bronchial biopsies [257, 258]. However there are contrasting studies regarding HDACs within the asthmatic epithelium. Although HDAC activity is diminished in asthmatic bronchial biopsies [257], the specific HDACs involved are debatable. Ito et al. showed that, at the protein level, both HDAC1 and HDAC2 are decreased in bronchial biopsies from asthmatic patients [257]. More recently, Butler et al. identified increased HDAC1 gene expression only in severe asthmatic bronchial biopsies and no difference in HDAC2 expression

[259]. Furthermore, neither HDAC1 nor HDAC2 were found to be different in the epithelium of severe asthmatics by immunohistochemistry in two separate studies [259, 260]. In a murine model of asthma, Choi et al. found that treatment with the HDAC inhibitor Trichostatin A (TSA) attenuated AHR and reduced airway occlusion by mucus [261]. Similarly, Banerjee et al. also found that TSA reduces AHR, but they found that TSA exerted no effect on antigen-induced

airway inflammation [262]. In Chapter 5, we investigate changes in both global and gene- specific histone modifications in airway epithelial cells from asthmatic subjects.

1.15 Current therapies for asthma and epigenetic modulation

Although no cure for asthma exists, treatments which manage symptoms are available and are classified into two groups: controllers and relievers [4]. These medications can be dispensed in a variety of ways, however, inhaled therapy is the most advantageous as it results in direct delivery to the tissue of interest – the airways – with less risk of systemic side effects [4]. Reliever medications, including rapid-acting inhaled β2-agonists, are used only as needed to reverse the bronchoconstriction experienced during an asthma attack [4]. Controller medications target the underlying inflammation occurring within the airways of asthmatics and, therefore, need to be used on a daily basis [4]. Of the varieties of controller medications available, glucocorticoids are the most effective [4] and exert both suppressive and expressive effects on pro- and anti- inflammatory genes respectively [263].

HAT and HDAC activity are intertwined with steroid responsiveness. Glucocorticoids have both repressive and expressive effects on gene transcription. In the case of anti-inflammatory genes, glucocorticoids diffuse across the cell membrane, enter the cell cytoplasm, and bind glucocorticoid receptors (GRs) [263]. This results in the release of GRs from chaperone proteins, allowing this complex to enter the nucleus via nuclear import proteins [263]. Once in the nucleus, GRs are acetylated, homodimerize and bind to glucocorticoid response elements

(GREs) in the promoters of target genes [263-265]. GRs then recruit HATs resulting in acetylation of the surrounding histones and recruitment of chromatin remodelers leading to gene

expression [263, 266]. Interestingly, Ito et al. identified acetylation of lysines 5 and 16 on histone 4 with GR mediated anti-inflammatory gene expression, whereas pro-inflammatory gene expression mediated by interleukin 1β resulted in acetylation of lysines 8 and 12 on histone 4

[266]. GRs can also suppress pro-inflammatory gene expression. HDAC2 deacetylates GRs allowing GRs to cross-talk with transcription factors such as NFκB which activate pro- inflammatory gene transcription [265, 267]. GRs can inhibit HATs present at the activated gene or recruit HDAC2, which removes the activating histone acetylation marks, reversing expressive chromatin remodeling and resulting in gene suppression [266]. Of note, peroxynitrite, a product of both oxidative and nitrative stress, results in degradation of HDAC2, and oxidative stress alone can indirectly inactivate HDAC2 leading to degradation [268-270]. Thus, causes of this kind of stress, such as smoking, can have serious implications to the response to glucocorticoids.

Therapies that impede degradation of HDAC2, such as theophylline, are under investigation in diseases such as asthma and COPD where corticosteroid resistance occurs [270-275].

1.16 Summary of thesis

Efforts to elucidate the epigenetic contribution to the pathogenesis of asthma have illuminated both the complexity of epigenetic gene regulation and the sensitivity of the epigenome to the environment. Epigenetic mechanisms are cell-specific yet epigenetic events occurring in the asthmatic airway epithelium, the first line of contact to the external environment, are not well documented. As epigenetic processes are influential to cellular specificity and disease susceptibility the overarching hypothesis of this thesis is that alterations to the epigenome of asthmatic airway epithelial cells contribute to the dysregulation of genes involved in key epithelial functions. 37

The first aim of this work was to identify cell specific and disease specific differences in DNA methylation and this is presented in Chapter 3. We identified differences in DNA methylation in epithelial cells and peripheral blood mononuclear cells (PBMCs) from asthmatic, atopic and healthy pediatric patients. We identified 57 CpG sites across 47 genes which were differentially methylated in airway epithelial cells versus PBMCs. This variation in DNA methylation was maintained regardless of atopic, asthmatic, or healthy patient phenotype. When we examined

DNA methylation between the various phenotypes within each tissue, we found differential methylation only when epithelial cells from atopic and asthmatic subjects were compared. Thus, although epigenetic differences between tissue types are more evident and plentiful, subtle differences within each tissue can be determined and may play a role in disease pathogenesis.

The second aim of this thesis was to characterize cell specific and disease specific expression of five families of epigenetic modifying enzymes and this work is presented in Chapter 4. We found a clear segregation of epithelial cells from fibroblasts based on expression of these epigenetic enzymes. We identified significant differences in the expression of 43 epigenetic modifying enzymes, at least one gene per family, in airway epithelial cells compared to fibroblasts. In addition, disease specific changes were found for two histone kinases in epithelial cells and three genes, two HATs and one HMT, in fibroblasts from asthmatic compared to healthy subjects.

The dysregulation of these epigenetic genes may impact the function of these cells in disease pathogenesis.

The last aim of this study is presented in Chapter 5 and was to characterize the global histone acetylation and methylation status in the epithelium of asthmatic subjects and to identify the impact of these variations on genes involved in epithelial functions. We identified an increase in

the acetylation of lysine 18 on histone 3 (H3K18ac) and trimethylation of lysine 9 on histone 3

(H3K9me3) in the airway epithelium of asthmatic as compared to healthy subjects. We found increased occupancy of H3K18ac around the transcription start site of three genes dysregulated in the epithelium of asthmatics which included ΔNp63, EGFR, and STAT6. However, we were unable to modify the expression of these genes with the use of an HDAC inhibitor.

Chapter 2: General Methods

2.1 Sample collection

2.1.1 Airway epithelial cell isolation from bronchial brushings:

Airway epithelial cells (AECs) were obtained by either trans-laryngeal, non bronchoscope brushing [71, 276] or via a portable „bronchoscope directed‟ sampling methodology [277]. The study was approved by the Princess Margaret Hospital for Children Ethics Committee (Perth,

Australia) and Providence Health Care Research Ethics Committee of The University of British

Columbia. Written informed consent was obtained from the parents of the children prior to sampling. Briefly, children were anaesthetized and an unsheathed nylon cytology brush was used to gently rub against the airway epithelial surface. Once removed, the brush tip was cut off and placed into 5ml of collection media and vortexed to release cells in suspension. AECs were grown in Bronchial Epithelial Growth Medium (BEGM, Lonza, Walkersville, MD) containing

100U/mL penicillin and 100ug/mL streptomycin, at 37°C in a humidified 95% air / 5% CO2 atmosphere and collected at passage 2 and 3.

2.1.2 Peripheral blood mononuclear cell isolation

For each subject 10mL of whole blood was also obtained at the time of AEC isolation, and peripheral blood mononuclear cells (PBMCs) were isolated as previously described [278, 279].

Briefly, blood was diluted with an equal amount of RPMI 1640 medium containing heparin (Life

Technologies). PBMCs were then isolated on a ficoll density gradient (Lymphoprep, Axis-

Shield, Oslo, Norway) by centrifugation then washed three times in RPMI medium supplemented with 2% fetal calf serum before collection. The study was approved by the 40

Princess Margaret Hospital for Children Ethics Committee (Perth, Australia) and Providence

Health Care Research Ethics Committee of the University of British Columbia. Written informed consent was obtained from the parents of the children prior to sampling.

2.1.3 Airway epithelial cell isolation from human lung tissue

AECs were obtained from de-identified donor lungs donated for research and not suitable for transplantation from asthmatic and non-asthmatic donors though the International Institute for the Advancement of Medicine (Edison, NJ, USA). The study was approved (#H0-50110) by the

Providence Research Ethics committee, The University of British Columbia. AECs were isolated by Protease digestion as previously described [69, 280]. Briefly, human donor lungs were washed in Histidine-Tryptophan-Ketoglutarate solution (Custodiol, Brantford, ON,

Canada) after surgical removal and transported on ice within 16-18 hours. The large airways were blunt-dissected to the 4th or 5th generation and cut into 2-4cm sections. To remove mucus, blood and purulent material, the remaining sections were washed in cold Ca2+ and Mg2+ free phosphate buffered saline (PBS, Thermo Scientific, Waltham, MA, USA) for 1 hour at 4ºC.

Segments were then placed in 100ml of Bronchial Epithelial Basal Media (BEBM; Lonza, Basel,

Switzerland) containing 1.1mg/ml Pronase for 16 hours at 4ºC. The sections were then washed with BEBM by pipetting with a 1ml tip to aid cell dissociation and the harvested cell suspension was passed through a 70µm nylon mesh (Becton-Dickinson, Franklin Lakes NJ). 10% fetal bovine serum (FBS, Gibco, Burlington, ON, Canada) was added to the strained cell suspension and incubated for 10 minutes allowing neutralization of Pronase. After incubation, cells were collected by 1000 RPM centrifugation at room temperature, resuspended in 5ml BEGM containing an antibiotic and antimycotic (Gibco) and seeded in 25cm2 flasks containing 2x106 41

cells in 3ml media. Cultures were maintained to passage 2 or 3 in a 37°C humidified environment (95% air / 5% CO2) at which point they were seeded into plates for experimental use.

2.1.4 Isolation of airway fibroblasts from human lung

As described above airway tissue was isolated and additionally used for fibroblast isolation as previously described [281]. Briefly, the airway section was diced into approximately 2-3mm3 cubes and 5-6 pieces were placed per well of a 6-well tissue culture plate (BD Biosciences,

Mississauga, ON, Canada). Each well was filled with 1ml of Dulbecco's Modified Eagle's medium (DMEM) (Invitrogen, Burlington, ON, Canada) supplemented with 10% FBS (Gibco), 2 mM L-glutamine, and 1% antibiotic/antimycotic solution (Gibco) and cultured at 37°C in 95% air and 5% CO2. Tissue debris and non-adherent cells were removed by gentle washing with fresh media every three days. By day 7 to 10, fibroblasts had outgrown from the tissue to form a confluent layer and tissue pieces were removed. Cells were collected using trypsin/EDTA

(Invitrogen), centrifuged, resuspended in media, and seeded into 75cm2 flasks containing 1x106 cells in 11ml media for expansion. Cultures were maintained to passage 2 or 3 at which point they were seeded into plates for experimental use.

2.2 Immunohistochemical staining and analysis

2.2.1 Immunohistochemical staining

Tissue sections were obtained from de-identified donor lungs used for AEC isolation (General

Methods 2.1.3). Once blunt-dissection of the airways was complete, segments of trachea (3-

5mm) were removed and stored in formalin for paraffin-embedding. For immunohistochemical 42

staining, 5µm sections of donor trachea were first deparaffinized by soaking in xylene twice for

5 minutes each and then rehydrated with graded ethanol (3 minutes each of 100, 95, 70% EtOH) followed by a 3 minute wash in Tris-Buffered Saline (TBS). Antigen retrieval was performed by placing slides in 1x citrate buffer (Dako, Mississauga, ON, Canada) at 120ºC and 30 psi for 15 minutes. Slides were left to cool for 15 minutes before washing with TBS for 10 minutes.

Endogenous peroxidase was quenched with 3% H2O2 for 10 minutes followed by a 10 minute wash in TBS. Non-specific interactions were blocked by incubating for 20 minutes with a goat or horse serum block for primary antibodies raised in rabbit or mouse respectively. Primary antibodies were diluted in the appropriate blocking serum (goat or horse) and incubated overnight at 4°C. As multiple antibodies were used, a table of all primary antibodies, the clone, and concentration is provided in the appropriate chapter. After removing the primary antibody, slides were washed three times for 10 minutes each in TBS. Sections were then incubated with a biotinylated goat anti-rabbit or goat anti-mouse secondary antibody (1:100, Vector Laboratories,

Burlingame, CA, USA) for 60 minutes at room temperature. Secondary antibody was removed and slides were washed three times for 10 minutes each in TBS. Streptavidin-Horseradish

Peroxidase (Dako) was added for 10 minutes for signal amplification followed washing three times for 10 minutes each with TBS. The brown chromogen 3,3-diaminobenzidine (Dako) was added for visualization of staining. Slides were then washed with TBS three times for 10 minutes each. Counterstaining was performed by adding Harris Hematoxylin Solution (Sigma,

Oakville, ON, Canada) for 1 minute followed by washing in tap water for 1 minute. Finally, sections were dehydrated with graded ethanol (70, 95, 100% EtOH for 3 minutes each), dried thoroughly, and coverslipped with Cytoseal 60 mounting medium (Richard-Allan Scientific,

Kalamazoo, MI, USA). 43

2.2.2 Immunohistochemical analysis

To determine the expression of each protein of interest five to seven images were obtained from each donor airway using the Nikon Eclipse 700 (Nikon Instruments, Melville, NY, USA), a 60x objective, and SPOT Advanced software (Diagnostic Instruments, Sterling Heights, MI, USA).

To quantify staining, color segmentation and point counting of nuclei was performed with

ImagePro Plus software (Media Cybernetics, Rockville, MD, USA).

For color segmentation, the epithelium was traced manually for each image. Colors indicating positive staining were set at a threshold and a measurement of this area was obtained. A measurement of the total traced area (epithelium) was then recorded. These values were used to calculate % positive area of airway epithelium (area of positive stain/total area) as previously described [57, 68].

For point counting calculations, first, nuclei with positive staining were manually tagged with a marker for each image. Next, a second marker was used to identify unstained nuclei. These values were then used to calculate total nuclei and % positive nuclei/total nuclei for each image.

2.3 Sodium dodecyl sulfate polyacrylamide gel electrophoresis and immunoblot

2.3.1 Protein collection

To isolate protein from AECs and airway fibroblasts, cells were rinsed with PBS then lysed using a modified RIPA protein extraction buffer (PEB) containing protease inhibitor cocktail

(PIC), phosphatase inhibitor cocktail 2 (PIC2), and phenylmethylsulfonyl fluoride (PMSF)

(Sigma). These components were combined at 1ml PEB, 20ul PIC, 3ul PMSF, and 10ul PIC2

and 120ul of the mixture was added to each well of a 6-well plate. Protein lysates were then collected using a cell scraper.

2.3.2 Sodium dodecyl sulfate polyacrylamide gel electrophoresis

For denaturing gels, sodium dodecyl sulfate (SDS) containing loading buffer was added to protein lysates and boiled for 5 minutes. SDS is an anionic detergent which linearizes and imparts a negative charge on the protein. The loading buffer contained 20% SDS, Tris, glycerol,

β-mercaptoethanol and bromophenol blue. Samples were then loaded onto a stacking gel overtop of a SDS-polyacrylamide separating gel. The stacking gel consisted of dH20, 40% polyacrylamide, ammonium persulfate, Temed, and a stacking buffer of Tris-base and 20% SDS.

Depending on the molecular weight of each protein of interest a 10-18% SDS-polyacrylamine gel was used to ensure proper separation of proteins. The separating gel was composed of the same ingredients as the stacking gel with the substitution of a separating buffer that contained more Tris-base for the stacking buffer. Each gel contained one lane dedicated to a molecular weight standard ladder (SeeBlue Plus 2 Prestained Standard, Invitrogen). Electrophoresis was performed on gels bathed in running buffer containing glycine, SDS and Tris-base. Gels were run at 100mV until samples ran through the stacking gel followed by 150mV for approximately

1 hour or until appropriate separation of protein was achieved.

2.3.3 Immunoblot

Protein was transferred from the SDS-polyacrylaminde gel to a nitrocellulose membrane

(Osmonics, Thermo Scientific) overnight at 35mV in transfer buffer containing glycine and Tris- base on ice. To ensure that all protein had transferred, the membrane was then stained with 45

Ponceau to visualize protein, then rinsed with dH2O to remove the Ponceau stain, and placed in

1x TBS/Casein Blocking Buffer (BioRad, Mississauga, ON, Canada) for 2 hours to block non- specific binding of antibodies. Depending on the protein being analyzed primary antibodies were diluted in blocking buffer containing 0.1% Tween-20 (Thermo Scientific), and incubated with the membrane on a rocking platform overnight at 4°C. Before application of secondary antibody, membranes were washed 3 times for 10 minutes at room temperature (RT) with TBS containing 0.1% Tween-20. Depending on the primary antibody used, either goat anti-mouse IR-

800 (Vector Laboratories) or goat anti-rabbit Alexa 680 (Invitrogen) secondary antibodies were diluted to 1/2500 in blocking buffer containing 0.1% Tween-20 and 0.2% SDS, and incubated with the membrane, for 2 hours at RT. Membranes were then washed twice in TBS containing

0.1% Tween-20, followed by a final wash in TBS for 20 minutes. The protein of interest was then normalized to an appropriate loading control run on the same gel. Details regarding primary and secondary antibodies will be provided in each chapter. Imaging of the labeled membrane was performed on the LI-COR Odyssey system (LI-COR Biotechnology, Lincoln, NE, USA) by scanning both infrared 700 and 800 channels at a start intensity of 5.0. Odyssey software 1.1

(LI-COR Biotechnology) was used to calculate the density of the bands whereby an area would be selected around each band to measure the density of the signal within that area for that channel. This would be repeated for each band on the blot using an identical area.

2.4 Nucleic acid extraction

2.4.1 Deoxyribonucleic acid extraction

DNA was extracted from 1x106 cells using the Flexi Gene DNA Kit and QIAamp columns according to the manufacturer‟s instructions (Qiagen, Valencia, CA, USA). DNA concentration 46

and purity was determined using the NanoDrop 8000 UV-Vis Spectrophotometer (Thermo

Scientific).

2.4.2 Ribonucleic acid extraction

RNA was collected from 1x106 cells using the RNeasy Mini Kit (Qiagen). Briefly, cells were collected using a cell scraper in 350ul Buffer RLT and homogenized using a QIAshredder spin column followed by standard column extraction according to the manufacturer‟s protocol

(Qiagen). RNA concentration and purity was determined using the NanoDrop 8000 UV-Vis

Spectrophotometer (Thermo Scientific).

2.5 Reverse transcription polymerase chain reaction cDNA was synthesized from 500ng RNA using random hexanucleotide primers and Multiscribe

Reverse Transcriptase (Life Technologies, Burlington, ON, Canada) or using the RT2 First

Strand Kit for Qiagen based PCR technology. RT-PCR was performed to determine gene expression by loading cDNA, 2x SybrGreen Master Mix, gene-specific primers, and water onto a

MicroAmp® Optical 384-Well Reaction Plate and sealing with MicroAmp® Optical Adhesive

Film (Life Technologies). Plates were centrifuged for 1 minute at 1000g to remove bubbles and placed in a thermocycler (ABI 7900 or ViiA7, Life Technologies). Cycling conditions were

95°C for 10 minutes followed by 40 cycles of 95°C for 15 seconds and 60°C for 1 minute.

Samples for which the threshold cycle exceeded 35 were omitted from analysis as these were considered unreliable readings. Target gene expression was calculated using the delta Ct method: 2(CtHousekeeping Gene – CtTarget Gene) * 10000.

Chapter 3: DNA Methylation Profiles of Airway Epithelial Cells and

Peripheral Blood Mononuclear Cells from Healthy, Atopic and Asthmatic

Children.

3.1 Introduction

Asthma and atopy are two of the most common chronic inflammatory conditions in the western world [6]. While atopy affects up to half of the adult population, only 20% of these patients develop asthma [6]. Although the allergic inflammatory mechanisms in both diseases are similar, it is clear that genetic, environmental and epigenetic factors are important in the pathogenesis of atopy and asthma. Many studies have investigated the role of both environmental and genetic factors in the development of atopy and asthma, however there is little understanding of the epigenetic impact.

Epigenetic control of gene expression has two general mechanisms: either the DNA itself is chemically altered by the addition of a methyl group to a cytosine base in a cytosine-guanine

(CpG) dinucleotide by a DNA methyltransferase (DNMT), or the histone proteins that package

DNA into chromatin are chemically modified. DNA methylation generally results in gene silencing or repression by either changing the conformation of chromatin (facilitated by methyl

CpG binding domain proteins (MBDs) and histone deacetylases), or by simply blocking

Chapter 3 is based on the publication Stefanowicz D, Hackett TL, Garmaroudi FS, Günther OP, Neumann S, Sutanto EN, Ling KM, Kobor MS, Kicic A, Stick SM, Paré PD, Knight DA. DNA methylation profiles of airway epithelial cells and PBMCs from healthy, atopic and asthmatic children. PLoS One. 2012;7(9):e44213. PLoS One employs the Creative Commons Attribution (CC BY) license whereby authors maintain ownership of the copyright for their article.

transcriptional machinery from accessing the DNA. Thus, although the genetic make-up of an individual is identical throughout different tissues of the body, DNA methylation is important for the regulation of unique gene expression profiles in each tissue or cell type [183].

Epigenetic modifications to DNA have been described as the potential link between environmental effects and clinical phenotypes [282]. For example, in genetically identical individuals phenotypic discordance is not apparent in early life, however environmental exposures throughout the individuals life result in epigenetic disparity, which influence factors such as disease susceptibility [183]. Hollingsworth et al. have demonstrated that epigenetic modifications can also be inherited by using a murine model in which a maternal diet, rich in methyl donors, was shown to increase the severity of allergic airway disease in offspring [216].

Furthermore, this effect was demonstrated to be inherited transgenerationally as offspring of male pups (which were exposed to a high methyl diet in utero) also had increased severity of allergic airway disease [216].

The tracheo-bronchial epithelium is the interface between the environment and the submucosa and represents the first line of defense against inhaled exogenous agents. There is now substantial evidence that the airway epithelium of subjects who have asthma is abnormal, including increased expression of epithelial growth factor receptor (EGFR) at denuded and damaged sites [42, 88, 95, 99, 283], altered expression of adhesion proteins E-cadherin and zonula occluden-1 [284] and elevated numbers of basal cells marked by cytokeratin-5 [69, 70].

Cultured asthmatic epithelial cells have also been shown to be more vulnerable to oxidant- induced stress and display aberrant expression of heat-shock proteins and proinflammatory transcription factors [68, 71, 112, 285, 286]. Thus, it is important to identify if airway epithelial-

specific DNA methylation patterns are altered in diseased epithelium in order to fully understand the disease process. To allow us to understand the methylation signature of the airway epithelium, we compared DNA methylation of airway epithelial cells (AECs) with peripheral blood mononuclear cells (PBMCs), as they are a minimally invasive and readily available source of biological material.

The objectives of this study were first to identify the DNA methylation signature of AECs compared to PBMCs. The second objective was to identify epigenetic differences within AECs and PBMCs from non-atopic asthmatic, atopic asthmatic, atopic and healthy children.

3.2 Methods

3.2.1 Subjects and ethics statement:

This study is part of ongoing research investigating the phenotype of AECs from mild asthmatic and atopic children [71]. Initially, 25 children undergoing elective surgery for non-respiratory conditions were recruited as part of the DNA methylation study. For validation, the next 44 children were recruited in sequence as part of the gene expression study. Children were characterized as healthy non-atopic non-asthmatic, atopic non-asthmatic, non-atopic asthmatic and atopic asthmatic. Asthma was defined as physician-diagnosed asthma plus documented wheeze by a physician in the past 12 months. Positive responses to relevant questions on the

International Study of Asthma and Allergies in Childhood (ISAAC) [287] and American

Thoracic Society (ATS) [288] respiratory questionnaires as reported by the parents or subject were used to corroborate the diagnoses and negative responses were used to validate absence of respiratory symptoms. Atopic status was determined by positive radioallergosorbent (RAST) or

skin prick tests to common allergens (e.g. grass pollens, mold, animal dander, peanut, house dust mite). The recruited children had no record of any infections in the 3 months prior to surgery and were not taking glucocorticoids. All parents/legal guardians gave written informed consent for this study, which was approved by the Princess Margaret Hospital for Children Ethics

Committee. Subject demographics are presented in Table 3.1 for the DNA methylation group and Table 3.2 for the gene expression group.

Average Age Phenotype Number Sex (M/F) (range) Healthy 7 4/3 7.28 (4.6-10.1) Atopic 9 8/1 6.78 (4.5-10.9) Atopic 4 4/0 8.4 (3.3-14.6) Asthmatic Non-Atopic 5 4/1 5.96 (2.4-10.5) Asthmatic

Table 3.1 Patient demographics in DNA methylation cohort prior to surgery. Subjects are identified as healthy, atopic, atopic asthmatic, or non-atopic asthmatic.

Average Age Phenotype Number Sex (M/F) (range) Healthy 15 6/9 5.07 (1.2-12.9) Atopic 14 7/7 7.86 (2.2-16.5) Atopic 15 8/7 7.74 (1.3-14.1) Asthmatic

Table 3.2 Patient demographics in gene expression cohort prior to surgery. Subjects are identified as healthy, atopic, or atopic asthmatic.

3.2.2 Airway epithelial cell and peripheral blood mononuclear cell isolation

AECs and PBMCs were isolated as described in General Methods 2.1.1 and 2.1.2.

DNA and RNA was extracted as described in General Methods 2.4.1 and 2.4.2. 51

3.2.3 DNA bisulfite conversion and methylation assay

Bisulfite treatment of 250ng of genomic DNA was performed using the EZ DNA Methylation-

Gold Kit (Zymo Research, Orange, CA). DNA methylation of 1505 CpG sites across 807 genes were analyzed using the Illumina GoldenGate Methylation Cancer Panel I, according to the manufacturer‟s protocol (Illumina, San Diego, CA). The GoldenGate Methylation Cancer Panel

I has previously been validated with other technologies such as pyrosequencing [289] and methylation-specific PCR [290, 291]. We use the target ID which is a locus specific identifier to refer to CpG sites. This ID contains information about the gene, distance to transcription start site, and the reference strand (e.g. STAT5A_E42_F).

3.2.4 Statistical analysis of DNA methylation data

A Kruskal-Wallis test with a Dunn‟s Multiple Comparison test between disease phenotype groups for sex (p=0.22) and age (p=0.84) was conducted and as we found no significant differences, we did not adjust for these parameters in the subsequent analyses. To avoid gender bias, all CpG sites on the X chromosome (n=84) were removed from the analysis as well as probes containing single nucleotide polymorphisms (n=272). Probes with detection P-values >

0.05 in more than 10% of samples (n=122) were also removed leaving 1027 CpG sites across

671 genes. Remaining loci with detection P-values > 0.05 were imputed using the K-nearest neighbour algorithm (0.5% of data points) [292]. Raw data was then background corrected and adjusted for color bias using the Bioconductor methylumi package [293] and batch effects using distance weighted discrimination [294]. For each sample, the M-value: M = log2 ((Cy5+1)/

(Cy3+1)) was calculated, where Cy5 and Cy3 were the methylated and unmethylated sequences, 52

respectively [295]. These data were then standardized by z-score transformation. Briefly, each value was standardized by subtracting the sample mean and then dividing by the sample standard deviation [296]. Differentially methylated sites between two groups (PBMCs versus AECs and phenotypic categories) were determined using a t-test [297]. The false discovery rate (FDR) of differentially expressed sites between two subject categories was then calculated [298] and used to control for multiple comparisons. Restrictions of 5% q-value and a z-score difference of 2 were employed to limit data to the most relevant results. Batch effect correction, z-score transformation, and t-test computations were performed in MATLAB (MathWorks Inc., MA).

All preprocessing was performed in R version 2.12.0 (http://www.R-project.org) [299]. Raw data and normalized data have been submitted to the Gene Expression Omnibus database accession # GSE37853.

3.2.5 Ingenuity pathways analysis (IPA)

The genes containing CpG sites which showed differential DNA methylation between AECs and

PBMCs were classified by IPA (Ingenuity® Systems, www.ingenuity.com) using the biological functions application. As the Illumina GoldenGate Cancer Panel I is an array based on cancer related genes, we used our dataset as the reference set for pathways analysis to avoid bias towards cancer associated pathways. A right-tailed Fisher‟s exact test was used to calculate a p- value, which determined if the association between the genes identified and the biological pathway was a result of chance alone.

3.2.6 Quantitative polymerase chain reaction

Gene expression of cysteine-rich protein 1 (CRIP1) and signal transducer and activator of transcription 5A (STAT5A) was determined via two-step reverse transcriptase polymerase chain reactions (RT-PCR) as described in General Methods 2.5. Specific reagents include random hexanucleotide primers and Multiscribe Reverse Transcriptase (Life Technologies) and 2X

Sybrgreen Master Mix (Life Technologies). Primers were obtained from GeneWorks

(Hindmarsh, SA, Australia), sequences are provided in Table 3.3. Expression was normalized to

PPIA. A Kruskal-Wallis and a post hoc Dunn‟s multiple comparison test was performed using

GraphPad Prism Version 4.0 for Windows (GraphPad Software, San Diego California USA, www.graphpad.com). A p-value of less than 0.05 was deemed significant.

Gene Forward Primer (5’-3’) Reverse Primer (3’-5’) CRIP1 CGGAGCCGTCATGCCCAAGT CCGATGCCAGTCCTTGCCCA STAT5A CCTGGACTTTTCTGAAGGGGCTCA ATCCCGGGCTCTGGAAATCCCA PPIA TGAGCACTGGAGAGAAAGGA CCATTATGGCGTGTAAAGTCA

Table 3.3 RT-PCR primer sequences for CRIP1, STAT5A and PPIA.

3.3 Results

3.3.1 Cell-specific methylation profiles in airway epithelial cells compared to peripheral blood mononuclear cells

We first determined whether there were any differences in the epigenetic signatures of PBMCs and AECs from non-atopic and atopic asthmatic subjects. As demonstrated in Figure 1, we found no difference in the DNA methylation signatures between atopic asthmatics and non- atopic asthmatics for any of the CpG sites interrogated in either AECs (Figure 3.1A) or PBMCs

(Figure 3.1B). Thus, although our sample size is small, for the population used, atopic asthmatic and non-atopic asthmatic subjects were grouped for subsequent analyses.

Figure 3.1 DNA methylation analysis of atopic asthmatics compared to non-atopic asthmatics individuals in airway epithelial cells and peripheral blood mononuclear cells. Volcano plots of airway epithelial cells (AECs) (A) and PBMCs (B), analyzed for 1027 CpG loci in 671 genes, from atopic and non-atopic asthmatic subjects. Y-axis represents the q-values (-log10) for all of the CpG sites analyzed and the x-axis is the z-score difference (log2). Dashed lines indicate cut-offs for significance. These results show no differences between atopic and non-atopic asthma.

To determine if a cell specific DNA methylation profile could be obtained from patient-matched

PBMCs and AECs, we initially compared the DNA methylation status of all 1027 CpG loci 55

across 671 genes between PBMCs and AECs from all individuals (n = 25). We were able to construct a cell specific signature consisting of 80 CpG sites across 67 genes which had differential DNA methylation in AECs compared to PBMCs (Figure 3.2A and 3.2B). Appendix

A.1 contains a list of all CpG sites identified, the Z-score difference and q-value. These genes were classified by IPA, which identified 19 biological functions including cell-to-cell signaling and interaction, cell death, cellular movement, antigen presentation, and cellular compromise

(Figure 3.2C). Appendix A.2 shows the 67 annotated genes involved in each biological function.

We then evaluated the DNA methylation profiles of PBMCs and AECs for each disease phenotype and found 96, 71, and 89 differentially methylated CpG sites across 83, 58, and 77 genes in the healthy, atopic and asthmatic groups respectively (Figure 3.3A, B, and C). A full list of differentially methylated CpG sites for each disease phenotype can be found in the supplementary data (Appendix A3, A4, and A5). Many of the CpG sites identified were similar between the three phenotypes as detailed in the Venn diagram demonstrating overlap between each group (Figure 3.3D). This analysis also identified CpG sites that are differentially methylated between AECs and PBMCs that were unique for each phenotype; 6 for asthmatics, 8 for atopics, and 13 for healthy controls. For all individuals, 57 CpG sites across 47 genes from the original 80 CpG sites were differentially methylated in AECs compared to PBMCs irrespective of disease. The IPA analysis of these AEC specific genes identified 19 overrepresented biological and molecular functions, the top three being cell-to-cell signaling and interaction, cellular development, and cellular compromise as detailed in Figure 3.3E and

Appendix A6.

Figure 3.2 DNA methylation profile of airway epithelial cells compared to peripheral blood mononuclear cells. DNA methylation for 1027 CpG sites was assessed in AECs compared to PBMCs from all subjects. A. Volcano plot of CpG sites interrogated with red and blue points indicating significantly over- and under- methylated sites. Q-values are shown on the y-axis (-log10) and z-score difference on the x-axis (log2). Dashed lines indicate cut-offs for significance. B. Heatmap illustrating z-scores of 80 differentially methylated loci in AECs compared to PBMCs. Columns represent subjects and rows represent CpG sites while red/blue indicates more/less methylated. C. The molecular and cellular functions of the 67 genes classified by IPA. The x-axis shows functions while the y-axis shows –log (p-value). 57

Figure 3.3 DNA methylation heatmaps of CpG sites in peripheral blood mononuclear cells and airway epithelial cells from healthy, atopic and asthmatic pediatric subjects. AECs and PBMCs were analyzed for 1027 CpG loci in 671 genes from healthy (A), atopic (B), and asthmatic (C) subjects. Heatmaps of z-scores for AECs and PBMCs are shown with individuals (columns) 58

and differential CpG sites (rows). Increased methylation is shown in red and decreased methylation in blue. D. Venn diagram showing overlap of differentially methylated sites between healthy, atopic and asthmatic subjects. Numbers in black indicate total number of CpG sites while numbers in red/blue indicate more/less methylated in AECs (compared to PBMCs). E. The molecular and cellular functions in the 47 genes classified by IPA. The x-axis shows functions while the y-axis shows –log (p-value).

3.3.2 Comparison of DNA methylation profiles of airway epithelial cells or peripheral blood mononuclear cells between disease phenotypes.

We next sought to determine if there were differences in the DNA methylation profiles of AECs from asthmatics compared to healthy controls. We found no differentially methylated CpG sites in this comparison (Figure 3.4A). We also analyzed differences in DNA methylation between atopic and healthy subjects and again found no differentially methylated CpG sites (Figure

3.4B). As only a portion of atopic individuals develop symptoms of asthma, we next examined whether this dichotomy was associated with differential DNA methylation status of AECs in these two cohorts. Comparison of AECs from asthmatic and atopic children showed eight differentially methylated CpG sites from eight different genes (Figure 3.4C). The q-values and

Z-score differences for these eight differentially methylated CpG sites are presented in Table 3.4.

Figure 3.4 Differential methylation between disease phenotypes in airway epithelial cells or peripheral blood mononuclear cells. Volcano plots of CpG sites interrogated with red and blue points indicating significantly over- and under- methylated sites. Q-values are shown on the y-axis (-log10) and z-score difference on the x-axis (log2). Dashed lines indicate cut-offs for significance. Within AECs, differences in DNA methylation were assessed in healthy subjects compared to atopics (A) and asthmatics (B) as well as atopic subjects compared to asthmatics (C). The same comparisons were performed in PBMCs (D, E, and F).

When the DNA methylation signatures of PBMCs from asthmatic, atopic and healthy individuals were compared, we found no CpG sites that were differentially methylated between the subject groups (Figure 3.4D, E and F).

z-score CpG Site Protein Function difference q-value (log2) LIM domain protein involved in cell adhesion CRIP1_P874_R -1.14 0.01 and differentiation Growth factor receptor involved in FGFR1_P204_F -1.11 0.01 mitogenesis and differentiation Transcriptional activator involved in cell STAT5A_E42_F -1.01 0.01 differentiation and proliferation Calcium binding protein involved in cell S100A2_P1186_F 1.03 0.02 cycle progression and differentiation ITGA2_P26_R Integrin involved in cell adhesion -1.07 0.02 Transcription factor involved in mitogenesis EGR4_E70_F -1.31 0.02 and differentiation Transcriptional inhibitor involved in cell ID1_P880_F -1.03 0.04 growth and senescence IGSF4C_E65_F Cell adhesion protein -1.09 0.04

Table 3.4 Differentially methylated CpGs in atopic compared to asthmatic derived airway epithelial cells. Z-score difference is presented as atopic relative to asthmatic derived AECs.

3.3.3 Correlation between DNA methylation and gene expression in airway epithelial cells.

To determine the impact of DNA methylation on gene expression in AECs, we chose to examine two genes. A methylation change in the promoter of CRIP1 was selected as it had the most significant q-value and z-score difference for DNA methylation between atopic and asthmatic

AECs (Table 3.4) and a methylation change in exon 1 of STAT5A was selected because it had a significant q-value and was also identified as a unique methylation CpG site in the analysis between asthmatic AECs verses asthmatic PBMCs (Appendix A5). We found that STAT5A

gene expression was significantly reduced in AECS from asthmatic when compared to atopic and healthy subjects (Figure 3.5A). These data strongly support our findings of higher DNA

Figure 3.5 STAT5A and CRIP1 gene expression and DNA methylation status in airway epithelial cells. Airway epithelial cells (AECs) from atopic, healthy and asthmatic individuals were analyzed for STAT5A (A) and CRIP1 (C) mRNA expression using RT-PCR. Results are expressed as gene expression normalized to PPIA (y-axis). DNA methylation status is shown as M-values for STAT5A_E42_F (B) and CRIP1_P874_R (D) for the three phenotypes. * indicates differential methylation as detailed in Table 3.4.

methylation for the STAT5A_E42_F CpG site in asthmatic compared to atopic derived AECs

(Figure 3.5B). Similar to STAT5A_E42_F, the CRIP1_P874_R CpG site had higher methylation in AECS from asthmatic compared to atopic subjects (Figure 3.5D). However, in contrast to STAT5A gene expression, CRIP1 gene expression was elevated in AECs from asthmatic when compared to both healthy and atopic subjects (Figure 3.5C).

3.4 Discussion

In the current study, we characterized the methylation status of over 1000 CpG sites in AECs and

PBMCs obtained from asthmatic, atopic and healthy children. We demonstrate a signature set of

57 CpG sites that are differentially methylated in AECs as compared to PBMCs regardless of disease phenotype. Our findings confirm that DNA methylation plays a role in tissue or cell specialization. We also identified 8 genes with differentially methylated CpG sites in AECs derived from asthmatic compared to AECs obtained from atopic children. Although gene markers in PBMCs have been identified through gene expression analysis for multiple diseases including aspirin-exacerbated respiratory disease and asthma [300, 301], we did not identify any differences in DNA methylation when comparing PBMCs from the three subject groups.

Characterization of DNA methylation profiles has been performed on epithelium derived from several tissues, such as ovary, prostate, breast and lung but these studies have been focused on cancer pathogenesis and cancer derived epithelial cell lines [302-306]. We identified 80 CpG sites in 67 genes which were differentially methylated in AECs compared to PBMCs. By analyzing AEC DNA methylation signatures by phenotype, we identified 57 CpG sites in 47 genes that were differentially methylated in AECs compared to PBMCs irrespective of disease

status. These genes were identified by pathways analysis to be important in several cellular functions including cell cycle, cell signaling and cell metabolism. Such cell-specific patterns are important in understanding the role of DNA methylation in cellular function and disease. Many of the CpG methylation differences we identified between AECs and PBMCs were to be expected, as many are within the promoter region or exon 1 of genes that are specific for specialized cell functions. For example, we demonstrate that exon 1 of cytokeratin 5 (KRT5), a cytoskeletal protein, is less methylated in AECs compared to PBMCs (Appendix A1 and A6).

KRT5 is a well documented marker of basal epithelial cells, it was therefore to be expected that this epithelial protein is repressed, and thus more methylated, in PBMCs. In contrast, the promoter of CD2, a cell adhesion molecule expressed in certain lymphocytes, is less methylated in PBMCs compared to AECs (Appendix A1 and A6). These findings support the notion that

DNA methylation is an important regulator of cell or tissue functions through the regulation of gene expression as previously described [307, 308]. In support of our findings is a report by

Yang et al. of a cell specific DNA methylation profile for a disintegrin and metalloprotease 33

(ADAM33) [251]. DNA methylation analysis of the promoter region revealed hypermethylation of ADAM33 in epithelium and hypomethylation in fibroblasts which strictly regulated gene expression [251]. In addition, using a murine model, Hollingsworth et al. identified an association between in utero supplementation with methyl donors and both allergic airways disease as well as differential methylation of the runt-related transcription factor 3 (Runx3) gene

[216]. In our study, we identified elevated methylation of the Runx3 gene at three separate CpG loci when we compared AECs to PBMCs from healthy subjects (Figure 3.3A, Appendix A3) but only two CpG sites in atopic and asthmatic subjects (Figure 3.3B and C, Appendix A4 and A5).

Similarly, we identified differential methylation in the apolipoprotein A-1 (apoA-1) gene; we 64

found decreased methylation in AECs as compared to PBMCs from all of our subjects (Figure

3.2B, Appendix A1) but, when we then compared AECs to PBMCs by phenotype, we found decreased methylation of apoA-1 only in the asthmatic and atopic individuals (Figure 3.3B and

C, Appendix A4 and A5). ApoA-1 has recently been identified as a potential new therapeutic target for airway inflammation in asthma [309]. Using an apoA-1 mimetic peptide, Yao et al. were able to inhibit airway inflammation and hyperreactivity and attenuate manifestations of airway remodeling in a murine asthma model [309]. The alterations in methylation of Runx3 and apoA-1 which we identified in atopic and asthmatic subjects may well indicate a role for these genes in airway inflammation, however further validation is required.

We also identified differentially methylated CpG sites which were unique to each phenotype. Of interest, of the 6 unique differentially methylated CpG sites identified in asthmatic derived AECs compared to PBMCs (Figure 3.3D), one of these was the STAT5A_E42_F CpG site which we also found to be differentially methylated in asthmatic compared to atopic derived AECs, potentially highlighting the importance of this gene. Comparison of DNA methylation between

AECs from atopic and asthmatic subjects yielded 8 genes that were differentially methylated of which the most significant was CRIP1. Our data suggest that atopy results from an epigenetically different profile from asthma rather than an intermediate phenotype. Support for this notion has come from GWAS studies in which some of the major asthma associated genes are epithelial in origin and do not segregate with allergy [145]. The effect of DNA methylation on STA5A and CRIP1 gene expression by disease status was further investigated. It is worth noting that we only found differential methylation based on atopy in AECs but not PBMCs, which are the source of monocytes associated with atopy and allergic inflammation (eosinophils,

basophils, mast cells, and Th2 cells). As reported by Southam et al., in an allergic asthma murine model it has been shown that CD34+45+IL-5Rα+ cells, which are thought to be the earliest eosinophil lineage committed progenitor cell, give rise to eosinophil colonies only when isolated from the lungs of allergic but not control mice [310]. These data suggest that changes in local eosinophil numbers after allergen challenge may be caused, at least in part, by local lineage commitment and subsequent differentiation of progenitor cells via an IL-5 dependent mechanism. In future studies, comparisons of tissue resident monocytes would potentially reveal epigenetic modifications in allergic subjects compared to non-atopic subjects.

STAT5A is a member of the STAT family of transcription factors and is activated by a variety of cytokines and hormones. When activated by IL2, IL7, or TSLP, STAT5A is a strong promoter of Th2 cell differentiation and response [311, 312]. In an asthmatic mouse model, STAT5 phosphorylation was found to be elevated in OVA-induced splenocytes [313]. In mammary epithelial cells, activated STAT5 is required for tissue-specific gene expression via histone acetylation and chromatin remodeling of gene specific loci [314], while in the airway, STAT5 can be activated by Neuregulin-1, resulting in epithelial cell proliferation [315]. STAT5A knockout mice displayed enhanced Th1 responses as well as decreased airway eosinophil recruitment

[316]. In this study we demonstrate that, in asthmatic compared to both healthy and atopic derived AECs, STAT5A gene expression is decreased, which is concordant with our finding of increased STAT5A methylation (Figure 3.5C and D). These data promote the notion that DNA methylation is a regulator of STAT5A gene expression in AECs. Based on the previous work on

STAT5A, we would anticipate higher expression of this transcription factor in asthmatic derived

AECs. However, since we did not perform experiments measuring STAT5A activation, which

could potentially modulate its effects, it remains to be seen how gene expression due to DNA methylation interacts with the cellular cytokine and hormonal milieu to impact STAT5A function. Therefore, altered DNA methylation in the STAT5A gene could have implications in allergic airways disease but more studies are necessary to elucidate the precise role of STAT5A in epithelial functions.

Due to its double zinc-finger motifs (LIM domains), CRIP1 is involved in many cellular processes including motility, adhesion, and structure via its interaction with the cytoskeletal protein actin [317]. CRIP1 can also shuttle to the nucleus where it can facilitate protein interactions important for transcriptional regulation [318, 319]. In a transgenic mouse model,

CRIP1 over-expression impacted host defense by skewing towards a Th2 phenotype, as well as increasing host susceptibility to toxins from pathogens and viral infection [320]. Within

PBMCs, CRIP1 has previously been identified as playing a role in the acute-phase immune response [321] but within epithelial cells its function is not as clear. In fibroblasts, CRIP1 is important in stress response being induced by growth-inhibitory signals and cytotoxic stress resulting in suppression of cell death and proliferation while elevating cellular attachment and metabolic activity [322]. Despite the lack of concordance between DNA methylation and gene expression, our findings of increased CRIP1 gene expression in the epithelium of asthmatics provides us with a new potential candidate gene that could be involved in many important epithelial functions. As the Illumina GoldenGate array focuses on CpG sites within the promoter region and/or exon 1 of genes and not the gene body, methylation is likely to influence gene expression. However, it has been reported that DNA methylation can be overruled by histone modifications resulting in expression of a gene even if it is methylated and vice versa repress a

gene when it is not methylated [323-326]. In addition, agglomerative epigenetic aberrations including hypermethylation and hypomethylation have been described in several cancer studies

[327-331]. These events affect large chromosomal regions where clusters of genes are silenced or activated as a result of a global epigenetic state. In long-range epigenetic silencing (LRES), although the majority of genes spanning these zones are hypermethylated, all of the genes in the region are transcriptionally repressed whether their promoters are methylated or not [329, 332].

Thus, it is possible that regulation of CRIP1 gene expression is more complex, potentially involving other factors such as histone modifications and global epigenetic states.

Asthma is classically characterized by chronic airway inflammation, variable airway obstruction, airway remodeling and hyperresponsiveness [80]. Allergic asthma involves the adaptive immune response which is triggered upon allergen exposure resulting in a T-helper type 2 (Th2) biased inflammatory response [333]. In contrast, the mechanisms involved in non-allergic asthma are less well defined and are thought to involve an innate immune response that may be activated by environmental factors such as air pollution or oxidative stress. We thus wanted to identify if differential epigenetic hallmarks could be found between these two asthma phenotypes, yet we found that PBMCs or AECs from non-atopic and atopic asthmatics shared the same epigenetic signature across the 1027 CpG sites examined. Therefore, even though our sample size was small, this suggests that the biological processes involved in these two asthma phenotypes are not influenced by DNA methylation of the genes examined in this study thus we grouped the asthmatic subjects as one phenotype and used this for all subsequent analyses.

This study has several limitations. While a small sample size for each disease phenotype studied is likely to influence our ability to detect differential CpG methylation, we were able to

determine a number of differentially methylated CpG sites between asthmatic and atopic AECs.

Additionally, we used the Illumina GoldenGate Methylation Cancer Panel I, which was designed to screen candidate genes originally identified in cancers and thus focuses on cancer related pathways. Even so, we were able to identify pathways within this cancer-centric structure that were relevant to our study. The inability to determine the entire epigenetic profile of the airway epithelium may result in potentially missing many disease specific epigenetic modifications.

With regards to disease, our study also focused on pediatric subjects with asthma and atopy.

Therefore we cannot determine the epigenetic component of atopy and asthma with increasing age, which may identify a stronger epigenetic fingerprint. Also, all subjects included in the study displayed mild disease therefore it is difficult to generalize our results to all subtypes and severities of asthma. However our findings do indicate that epigenetic changes are present early in disease pathogenesis and highlight the importance of understanding these mechanisms of gene regulation further. Although the individuals within the study were well characterized for asthma and atopy we did not have enough power in the study cohort to stratify the groups by specific allergens, which may also influence the epigenetic profile of the airway epithelium.

In summary, we have characterized a cell specific DNA methylation signature for AECs and

PBMCs that is maintained regardless of the presence of asthma or atopy. Our data also highlights the importance of determining the effects of DNA methylation in the airway epithelium in discriminating atopy and asthma rather than using PBMCs. We conclude that future studies are required to determine the complete epigenetic signature of the airway epithelium in individuals with atopy and asthma to elucidate candidate genes which may be involved in the pathogenesis of disease and amenable to targeted therapies.

Chapter 4: The Landscape of Epigenetic Modifying Enzymes in Airway

Epithelial Cells and Fibroblasts in Health and Disease

4.1 Introduction

Asthma is a chronic condition of the airways that affects around 300 million people worldwide

[5]. Part of the pathophysiology of asthma consists of remodeling of the airway wall that includes goblet cell metaplasia, subepithelial fibrosis, increased smooth muscle mass and angiogenesis; all of which contribute to airway narrowing and closure during an asthmatic episode. While not present at birth, airway remodeling can be identified early in disease even before a clinical diagnosis of asthma is made [3, 42, 43, 58]. In the airway mucosa, the first structural barrier to the inhaled environment is the airway epithelium formed from the endoderm.

In asthma, the airway epithelium has been described to have an altered phenotype with loss of cell-cell junctions, exuberant inflammatory cytokine release, altered epithelial cell cycle and increased numbers of basal cells. [42, 68]. In the sub-epithelium, resident fibroblasts formed from the mesoderm have been shown to exhibit an invasive and synthetic phenotype when isolated from asthmatic subjects [54-56, 334, 335]. How these alterations in cellular phenotype occur early in disease is unknown but it is clear that allergic asthma involves both a strong genetic and environmental component.

Epigenetic regulation is an important mechanism for tissue and cell specific gene regulation which can impact an individual‟s susceptibility to disease [336]. An epigenetic trait is defined as a “stably heritable phenotype resulting from changes in chromatin without alterations in the

DNA sequence” [337]. For efficient packaging into the nucleus, DNA is wrapped around an

octamer of histone proteins H2A, H2B, H3, and H4 (two of each) forming a nucleosome, which is connected to linker DNA by the H1 histone protein [198, 336, 338]. Covalent modification of the histone N-terminal tails are an epigenetic method of regulating gene expression [198, 336].

These post-translational modifications include acetylation, methylation, phosphorylation, and ubiquitination, which result in changes to the chromatin environment and thus gene regulation

[198, 336]. Histone acetylation and phosphorylation have been found to be associated with a more open chromatin structure and gene expression, whereas histone methylation and ubiquitination can work both in a gene repressive and expressive manner depending on the target residue [198, 207-210]. The enzymes responsible for the addition / removal of these modifications include: histone acetyltransferases (HATs) / deacetylases (HDACs), protein kinases / phosphatases, histone methyltransferases (HMTs) / demethylases (HDMs), and ubiquitin ligases / deubiquitinating enzymes (DUBs) [198, 210]. A second epigenetic mechanism for gene regulation is DNA methylation, which is facilitated by DNA methyltransferases (DNMTs) that add a methyl group to cytosine bases within cytosine-guanine

(CpG) dinucleotides, forming 5-methylcytosine (5-mC) [336]. When occurring at the gene promoter, this is generally associated with gene suppression, as methylation can block transcription factors from binding to the DNA or can recruit proteins such as methyl CpG binding domain proteins (MBDs) that can initiate alterations to the conformation of the chromatin structure by interacting with chromatin remodeling complexes [336, 339].

Abnormal epigenetic control of gene expression has been identified in both fibroblasts and epithelial cells in numerous pathologies [340-345]. However, very little is known about the expression and regulation of epigenetic modifying enzymes in asthma. Abnormal HAT and

HDAC activity has been documented in bronchial biopsies from asthmatic patients [257, 258], but the specific family members which are dysregulated in activity are not clear. One study of bronchial biopsies from asthmatic subjects identified diminished levels of HDAC1 and 2 [257], meanwhile another study found elevated HDAC1 and no difference in HDAC2 [259].

Furthermore, immunohistochemical analysis of asthmatic biopsies in two further studies found no differences in HDAC1 or HDAC2 in the airway epithelium of asthmatics compared to controls [259, 260]. We have additionally identified unique DNA methylation patterns in airway epithelial cells (AECs) from asthmatic subjects [346] yet research on the variability of the enzymes responsible for these changes is lacking. In a mouse model of asthma, DNMT1 was found to be expressed at lower levels in the lung and tracheal tissues compared to control animals [347], highlighting a potential anomaly in the DNA methylation machinery in allergic disease. Lastly, HMTs were identified in an antigen-induced pulmonary inflammation murine model of asthma [254]. In whole lung tissue of these mice, elevated expression of the histone arginine methyltransferases PRMT1, 2, and 3 was identified, whereas in the epithelium alone,

PRMT1 was most significantly upregulated [254].

A more complete understanding of the landscape of epigenetic modification enzyme families involved in DNA and histone modifications, with specific focus on the airway tissue, is required if we are to understand epigenetic alterations in diseases such as asthma. The aim of this study was to determine the expression profile of epigenetic modifier enzymes in airway epithelial and fibroblast cells, to determine if the epigenetic landscape of enzymes expressed is tissue specific.

Secondly, by comparing cells from healthy and asthmatic subjects we hoped to identify a profile of histone modifying enzymes that are distinct to the disease. As epigenetic mechanisms are

important for cell specific gene expression and disease susceptibility, we hypothesized that expression of DNA and histone modifying enzymes would be different between AECs and fibroblasts and between healthy and disease states.

4.2 Methods

4.2.1 Sample collection

AECs isolated by pronase digestion and airway fibroblasts obtained by outgrowth technique, were collected from donated human lungs as described in the General Methods Chapters 2.1.3 and 2.1.4. Endobronchial airway brushings from patients were also used to obtain AECs as previously described [348, 349]. Briefly, brushings were obtained from subsegmental airways using a sterile nylon cytology brush (Olympus BC9C-26101, Olympus, Tokyo, Japan). Cells were then placed in ice-cold sterile phosphate-buffered saline, centrifuged, washed, and resuspended in bronchial epithelial growth medium (BEGM) containing an antibiotic and antimycotic. Cultures were at 37°C in a humidified 95% air / 5% CO2 atmosphere. This study was approved by the University of Washington ethics committee and subject demographics are provided in Table 4.1.

Cell Used for Used For ID Phenotype Ageº Sex Type PCR Immunoblot EA1 A 8 AEC F X X EA2 A 21 AEC F X X EA3 A 23 AEC F X

EA4 A 29 AEC F X

EA5 A 23 AEC F X X EA6 A 23 AEC F X X EA7 A 25 AEC M X X

Cell Used for Used For ID Phenotype Ageº Sex Type PCR Immunoblot EA8 A 11 AEC M X X EA9 A 11 AEC M X

EA10 A 23 AEC M X

EA11 A 10 AEC M X

EH1 H 19 AEC F X X EH2 H 20 AEC F X X EH3 H 42 AEC F X X EH4 H 23 AEC F X

EH5 H 26 AEC F X

EH6 H 22 AEC F X

EH7 H 24 AEC F X

EH8 H 20 AEC M X X EH9 H 22 AEC M X

EH10 H 11 AEC M X X EH11 H 24 AEC M X X EH12 H 12 AEC M X X EH13 H 29 AEC M X

FA1 A 11 Fb M X X FA2 A 25 Fb M X X FA3 A 23 Fb M X X FA4 A 20 Fb M X X FH1 H 14 Fb M X X FH2 H 24 Fb M X X FH3 H 12 Fb M X X FH4 H 11 Fb M X X

Table 4.1 Subject demographics including phenotype, age, cell type, and sex. Airway epithelial cells (AECs) and airway fibroblasts (Fb) were collected from healthy (H) and asthmatic (A) subjects. Subject ID is denoted as E (epithelial) or F (fibroblast), followed by disease phenotype. ºThere were no differences for age between all groups by one-way ANOVA; p=0.31 and p=0.71 for samples used for PCR and immunoblot, respectively.

4.2.2 Cell culture and RNA isolation

AECs and airway fibroblasts were grown in 6-well plates until 80% confluence, at which point

RNA was collected as described in General Methods 2.4.2. For RT-PCR: cDNA was synthesized using the RT2 First Strand Kit (Qiagen) as described in General Methods 2.5. cDNA was then combined with 2x RT2 SYBR Green Mastermix (Qiagen) and RNase-free water and distributed onto a manufacturer optimized 384-well Human Epigenetic Chromatin Modification Enzymes

PCR Plate (PAHS-085E-4, Qiagen) pre-loaded with primers targeting 84 genes encoding epigenetic enzymes and 5 housekeeping genes as per manufacturer‟s protocol. A complete list of the genes that were analyzed is available in Appendix B.1. To identify expression of

CREBBP and EP300, cDNA was also combined with 2x RT2 SYBR Green Mastermix, RNase- free water, and primers targeting CREBBP (PPH00324F-200, Qiagen), EP300 (PPH00319A-

200, Qiagen), and ribosomal protein L13a (RPL13A, PPH01020B-200, Qiagen) and loaded onto

384-well reaction plates. All RT-PCR plates once loaded were processed as described in

General Methods 2.5. All samples were run in duplicate and target gene expression was normalized to the housekeeping gene RPL13A. Of the five available housekeeping genes, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and beta-actin (ACTB) were removed because they have previously been associated with asthma in epithelial cells [350]. Of the three remaining genes, one-way ANOVA (two-tailed) of RPL13A, hypoxanthine phosphoribosyltransferase 1 (HPRT1), and beta-2-microglobulin (B2M) (p=0.35, 0.31, 0.03 respectively) revealed that RPL13A was not associated with any of our groups and had the largest p-value. It was thus was used as the housekeeping gene for this study.

4.2.3 Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and immunoblot

Protein was collected from AECs and airway fibroblasts in culture and SDS-PAGE and immunoblot was performed as described in General Methods 2.3. Information for specific antibodies used is provided in Table 4.2.

Data for AURKA and PRMT1 were normalized to β-tubulin. A two-tailed paired t-test was performed, a p-value of less than 0.05 was considered significant.

Catalogue Primary Antibody Epitope Host Company Number Dilution AURKA Mouse Cell Signaling 12100 1/500 PRMT1 Mouse Cell Signaling 2453 1/1000 β-tubulin Mouse Millipore 05-661 1/2000

Table 4.2 Antibodies used for immunoblot of cell protein lysate.

4.2.4 Statistical analysis

Principal component analysis (PCA) was performed by transforming gene expression data to a new set of variables (principal components). The first two generated principal components explained 63.7% of the variance in the dataset. Student t-tests were performed for comparisons of gene expression between cell types and between health and disease. Bonferroni corrected p- values were generated by multiplying the p-values by the number of tests performed (n=86) and are provided in supplementary data contained in appendices. However, co-expression analysis performed by ordering gene expression per gene by mean absolute correlation with all other

genes identified strong co-expression between our genes, thus Bonferroni correction can be considered as too rigorous for our dataset. Parallel to this, we calculated the effective number of independent variables as described by Li and Ji [351] based on the co-expression of our genes and therefore also provide p-values corrected with this factor in addition to the Bonferroni correction p-values in the supplementary data. We did not correct for either age or sex the univariate linear regression modeling for these variables on gene expression showed that they impact few genes in our study (Appendix B.2 and B.3). All statistical analysis and figures were generated using the R software version 3.0.2 [299] and genes are presented in order of abundance in each family.

4.3 Results

4.3.1 Airway tissue specific expression of epigenetic modifying enzymes

We first began by identifying patterns of gene expression in both AECs and airway fibroblasts for the five families of epigenetic modifying enzymes. The principal component analysis (PCA) of epigenetic modifier enzyme expression variation for AECs (orange) and airway fibroblasts

(blue), with (circles) and without (triangles) asthma, is shown in Figure 1A. We observed that

51.71% of the variation across both cell types could be accounted for by the first principal component, and PC2 accounted for 11.98% of the variation. Therefore, samples of similar cell type cluster separately along PC1 and PC2 and account for the main components of variance. As the two cell types were easily segregated based on epigenetic modifier enzyme gene expression we next proceeded to determine cell-specific gene expression profiles.

Figure 4.1 Principal component analysis (PCA) of epigenetic modifier enzymes in airway epithelial cells and fibroblasts. Gene expression levels of 86 genes were used to construct the PCA plot. Epithelial cells (AECs) are shown in orange and fibroblasts (Fb) are shown in blue. Asthmatic samples are identified with a circle, healthy samples with a triangle. Principal component 1 (PC1) and principal component 2 (PC2) combined explained 63.7% of the total variation in the data.

Co-expression analysis within healthy subjects from AECs and fibroblasts showed that the epigenetic modifier enzyme genes which we examined were heavily co-expressed (Figure 4.2).

Therefore, although we provide a Bonferroni corrected p-value for all t-tests performed in the appendix (Appendix B.4-6), this stringent correction is clearly not appropriate considering so many genes are co-expressed. We also provide a p-value corrected for the effective number of

independent variables, which was found to be 20.9, in the appendix (Appendix B.4-6). Although we highlight genes which pass these corrections in the results, to explore the full potential of this unique dataset, we focus on the uncorrected p-values for each epigenetic modifier gene family for the remainder of this chapter.

Figure 4.2 Co-expression heatmap of epigenetic modifying genes. Gene expression from both epithelial and fibroblast cells from healthy individuals was used to analyze degree of co-expression of 86 genes involved in epigenetic mechanisms. Genes are listed on the x- and y- axis, blue indicates positive co-expression and pink indicates negative co-expression of genes. 79

4.3.2 DNA methylation enzymes

We first began our investigation into each of the epigenetic modifier families by looking at the three main genes involved in the addition of a methyl group to DNA, and MBD2, a gene which binds methyl-CpGs. Using a student‟s t-test, we found that out of these genes, only DNMT1 expression was differentially expressed, with lower expression in AECs (orange) compared to airway fibroblasts (blue) (p=0.016, Figure 4.3). As DNMT1 is crucial for maintenance of DNA methylation marks in daughter cells following cell division [336], lower expression may indicate lower proliferation in epithelial cells in culture.

Figure 4.3 Gene expression of DNA methylation family of proteins in airway epithelial cells (AECs) and fibroblasts (Fb) from healthy subjects. AECs are shown in orange whereas fibroblasts are shown in blue. A Student‟s t-test was performed, * indicates p < 0.05.

4.3.3 Histone methylation enzymes

Next, we characterized the expression of the histone methylation enzyme family. We have separated the histone methyltransferases (HMTs) into two groups: those with and without a SET domain. Although both groups of HMTs result in histone methylation, the SET domain HMT subgroup contains a conserved sequence motif originating from the Drosophila Suppressor of variegation (Su(var)3-9), Enhancer of zeste (Ez), and Trithorax genes, which forms an active

methyltransferase site [352, 353]. We identified differential expression of 9 HMTs all of which had lower expression in AECs compared to airway fibroblasts in healthy subjects (Figure 4.4).

Of these, PRMT2 maintained significance after correction for the effective number of independent variables (p=0.047, Appendix B.4).

Figure 4.4 Gene expression of histone methyltransferases (HMT) family of proteins in airway epithelial cells (AEC) and fibroblasts (Fb) from healthy subjects. AECs are shown in orange whereas fibroblasts are shown in blue. A Student‟s t-test was performed, * indicates p < 0.05, ** indicates p < 0.01.

We also found that 12 of the 17 SET domain containing HMTs showed decreased expression in

AECs compared to fibroblasts in healthy subjects (Figure 4.5). After both Bonferroni and

effective number of independent variable corrections, SETD7 remained significant (0.021 and

0.003 respectively, Appendix B.4). As histone methylation can result in both gene expression and suppression, further research at a gene-specific level would need to be performed to identify the ramifications of this finding.

Figure 4.5 Gene expression of SET domain containing histone methyltransferases (HMT) family of proteins in airway epithelial cells (AEC) and fibroblasts (Fb) from healthy subjects. AECs are shown in orange whereas fibroblasts are shown in blue. A Student‟s t-test was performed, * indicates p < 0.05, ** indicates p < 0.01, and *** indicates p < 0.001. 82

Interestingly, we only found one histone demethylation enzyme that was differentially expressed between AECs and fibroblasts, with lower KDM4A expression in epithelial cells from healthy individuals (p=0.007, Figure 4.6). KDM4A acts on both H3K9 and H3K36 therefore it may function in a repressive or expressive manner depending on interactions with other histone modifications and the position in the gene sequence [354, 355].

Figure 4.6 Gene expression of histone demethylases (HDM) family of proteins in airway epithelial cells (AEC) and fibroblasts (Fb) from healthy subjects. AECs are shown in orange whereas fibroblasts are shown in blue. A Student‟s t-test was performed, ** indicates p < 0.01.

4.3.4 Histone acetylation enzyme family

We then characterized the expression profiles of enzymes involved in histone acetylation. We identified diminished expression of the HDAC4, 5, and 7, all members of the class II HDAC subgroup, in AECs compared to fibroblasts from healthy subjects (p=0.045, 0.009, and 0.003 respectively, Figure 4.7).

Figure 4.7 Gene expression of histone deacetylase (HDAC) family of proteins in airway epithelial cells (AEC) and fibroblasts (Fb) from healthy subjects. AECs are shown in orange whereas fibroblasts are shown in blue. A Student‟s t-test was performed, * indicates p < 0.05, ** indicates p < 0.01.

Additionally, when we compared AECs to fibroblasts from healthy individuals, we identified 7 differentially expressed HATs. These were expressed at lower levels in epithelial cells compared to fibroblasts and include ATF2, KAT6A, KAT7, CREBBP, KAT5, CSRP2BP, and KAT2B

(Figure 4.8). KAT2B survived both Bonferroni and effective number of independent variable corrections (p=0.03 and 0.004 respectively, Appendix B.4).

Figure 4.8 Gene expression of histone acetyltransferase (HAT) family of proteins in airway epithelial cells (AEC) and fibroblasts (Fb) from healthy subjects. AECs are shown in orange whereas fibroblasts are shown in blue. A Student‟s t-test was performed, * indicates p < 0.05, ** indicates p < 0.01, and *** indicates p < 0.001.

4.3.5 Histone phosphorylation enzyme family

Within the family of enzymes responsible for histone phosphorylation, two of the seven genes we analyzed were differentially expressed between cell types. NEK6 was expressed at lower and

AURKC at higher levels in AECs compared to fibroblasts (p=0.004 and 0.017 respectively,

Figure 4.9). Both of these kinases can phosphorylate histones and therefore may be involved in gene activation.

Figure 4.9 Gene expression of histone phosphorylation family of proteins in airway epithelial cells (AEC) and fibroblasts (Fb) from healthy subjects. AECs are shown in orange whereas fibroblasts are shown in blue. A Student‟s t-test was performed, * indicates p < 0.05, and ** indicates p < 0.01.

4.3.6 Histone ubiquitination enzyme family

The last group of enzymes we examined was the histone ubiquitination family. We measured the expression of 9 genes from this family and found that 8 genes were differentially expressed between epithelial and fibroblast cells (Figure 4.10). RNF20 and DZIP3 remained significant after Bonferroni and effective number of independent variables corrections (Appendix B.4).

Both ubiquitin ligases and deubiquitinating enzymes showed lower levels of gene expression in

AECs compared to fibroblasts in our cohort. As the function of histone ubiquitination can result in both repressive and permissive chromatin states [356], the impact of altered expression of these enzymes would need to be evaluated in a gene specific manner.

Figure 4.10 Gene expression of histone ubiquitination family of proteins in airway epithelial cells (AEC) and fibroblasts (Fb) from healthy subjects. AECs are shown in orange whereas fibroblasts are shown in blue. A Student‟s t-test was performed, * indicates p < 0.05, ** indicates p < 0.01, *** indicates p < 0.001, and **** indicates p< 0.0001.

4.3.7 Disease specific alterations in gene expression of epigenetic modification enzymes in airway epithelial cells

We then wanted to identify if there were any phenotype specific changes to gene expression of our epigenetic modifier families in AECs. We compared the expression of the 86 genes in AECs derived from asthmatic and healthy subjects (Appendix B.5). We discovered differential expression of the kinases AURKC and AURKA, which can activate gene transcription through 87

histone phosphorylation [208]. Specifically, AURKC showed lower and AURKA higher expression in AECs from asthmatics (p=0.03 and 0.01 respectively, Figure 4.11).

Figure 4.11 Differentially expressed epigenetic modifying genes in asthmatic compared to healthy airway epithelial cells (AECs). Healthy subjects are shown in light orange whereas asthmatic subjects are shown in dark orange. A Student‟s t-test was performed, * indicates p < 0.05.

To identify if the observed changes in AURKA expression in AECs correspond to level of protein, we performed SDS-PAGE and immunoblot. AURKA was significantly elevated in

AECs from asthmatic (0.025 ± 0.003) as compared to healthy subjects (0.017 ± 0.002, p=0.04,

Figure 4.12), which aligns with our findings of increased AURKA gene expression in asthmatic

AECs.

Figure 4.12 Protein expression of aurora kinase A (AURKA) in airway epithelial cells (AEC) from healthy and asthmatic subjects. AURKA protein expression normalized to β-tubulin in AECs from healthy (light orange) and asthmatic (dark orange) subjects. A Student‟s t-test was performed, * indicates p < 0.05.

4.3.8 Disease specific alterations in expression of epigenetic modification enzymes in fibroblasts

We also wanted to investigate if any differences existed in our 86 genes between healthy and asthmatic derived fibroblasts (Appendix B.6). Both CREBBP and PRMT1 showed lower levels of gene expression in fibroblasts from asthmatic compared to healthy subjects (p=0.02 and 0.04 respectively, Figure 4.13). Expression of the histone acetyltransferase KAT2B was also increased in fibroblasts from asthmatic donors (p=0.03, Figure 4.13).

Figure 4.13 Differentially expressed epigenetic modifying genes in asthmatic compared to healthy airway fibroblasts. Healthy subjects are shown in light blue whereas asthmatic subjects are shown in dark blue. A Student‟s t-test was performed, * indicates p < 0.05.

To determine if the decreased gene expression of PRMT1 resulted in a change in protein expression we performed SDS-PAGE and immunoblot. There was no significant difference in

PRMT1 protein expression in airway fibroblasts between healthy (0.046 ± 0.02) and asthmatic subjects (0.031 ± 0.003, p= 0.54, Figure 4.14).

Figure 4.14 Protein expression of arginine methyltransferase 1 (PRMT1) in airway fibroblasts from healthy and asthmatic subjects. PRMT1 protein expression normalized to β-tubulin in fibroblasts from healthy (light blue) and asthmatic (dark blue) subjects. A Student‟s t-test was performed.

4.4 Discussion

This is the first study to evaluate the gene expression levels of epigenetic histone and DNA modifier enzymes in epithelial and fibroblast cells derived from human airway tissue. We demonstrate that AECs and fibroblasts follow a similar gene expression profile for abundant and less abundant genes in each functional modifier family. There was significantly higher expression for 42 and lower expression for 1 of these enzymes, respectively, in fibroblasts compared to AECs from healthy individuals. Further, we demonstrate that AURKA and

AURKC genes are differentially regulated in epithelial cells and KAT2B, CREBBP, and PRMT1 in fibroblasts when derived from asthmatic compared to healthy subjects. In addition, we identified a corresponding increase in AURKA protein expression in AECs derived from asthmatic as compared to healthy subjects, further supporting our findings. Even though airway epithelial cells and fibroblasts reside in close proximity within the airway mucosa, the function of each cell is very different. These data therefore support the notion that epigenetic modulation 91

of gene expression may be important for cell type specificity, and potentially for susceptibility to diseases such as asthma.

Previous literature regarding DNA methylation has focused heavily on the methylation status of genes, and the consequent changes in the expression of RNA and protein. Multiple studies have documented differential DNA methylation in relation to tissue and cell specificity, and how this is altered in diseases [346, 357-359]. Yet very few studies have focused on the global expression of enzymes responsible for DNA methylation expression. In our study, DNMT3a and 3b were the least expressed of all the DNA methylation family genes that we examined. DNMT3a and

3b are responsible for de novo DNA methylation in a cell, and DNMT1 is a maintenance methyltransferase which binds preferentially to hemimethylated DNA and transmits DNA methylation patterns to daughter cells during DNA replication [360]. In addition, we found that although AECs express more DNMT1 than the other DNMTs, it is significantly less than in fibroblasts in our healthy subjects. Enrichment of DNMT1 has previously been associated with an undifferentiated state in epidermal progenitor cells [361]. DNMT1 expression was elevated in these cells in proliferative conditions and a significant number of epidermal differentiation genes were methylated which then became demethylated during differentiation [361]. In our study, both airway epithelial and fibroblast cells were collected from culture at 80% confluency, therefore high DNMT1 expression may have developed to maintain the proliferative state in our model. Fibroblasts are inherently plastic cells with the capability to reprogram or differentiate into numerous distinct phenotypic populations [362]. Compared to epithelial cells which can only be grown in culture to approximately passage 4 [69], airway fibroblasts are also highly proliferative, and in culture they can continue proliferating up to passage 17 [363]. Therefore,

enrichment of DNMT1 in fibroblasts may be due to their elevated proliferative potential and plasticity and, therefore, management of numerous transformative genes during DNA replication in this cell population.

The outcome of an epigenetic change can be variable depending on the particular modification that occurs. Methylation of lysine and arginine residues on histone tails is facilitated by enzymes which are quite specific to both residue and site yet the outcome can activate or repress transcription [198]. Similarly, ubiquitination of histones H2A and H2B can result in both gene transcription or silencing depending on the target residue [210]. We found that 21 of 30 HMTs and 8 of 9 members of the histone ubiquitination family were expressed at lower levels in AECs compared to fibroblasts in healthy subjects. However, KDM4A was the only differentially expressed histone demethylase showing lower levels in AECs compared to fibroblasts. The actions of KDM4A oppose methyltransferases which methylate H3K9 and H3K36 residues

[207]. Interestingly, we found that the HMTs, including SUV39H1, EHMT2, SETDB1, ASH1L,

SETD2, and NSD1, which methylate the residues H3K9 and H3K36 were also diminished in

AECs compared to fibroblasts in healthy subjects. In addition, KDM4A and the six HMTs listed above were all positively co-expressed when we performed co-expression analysis. This may indicate possible co-regulation of the histone methylation family members regulating these two residues as all enzymes involved are expressed at lower levels in epithelial cells or it may be that fibroblasts preferentially use this epigenetic mechanism over others to regulate gene expression.

Three of the six histone demethylases that we examined (KDM1A, 5B, and 5C) target demethylation of H3K4 [207]; however many of the HMTs which create this activating mark showed diminished levels in AECS. We found decreased expression of SETD1A, SETD7,

KMT2A, and SMYD3, all of which methylate H3K4 and are positively co-expressed, in our epithelial compared to fibroblast samples from healthy subjects. This suggests there may be an imbalance in the regulation of this activating mark in AECs, potentially indicating decreased cellular transcriptional activity in AECs compared to fibroblasts.

Histone acetylation and phosphorylation is generally associated with transcriptional activity and is mediated by HATs and HDACs and histone kinases and phosphatases [198, 208]. In the histone phosphorylation family of enzymes, NEK6 had significantly lower expression and

AURKC had greater expression in AECs compared to fibroblasts from healthy subjects. Histone phosphorylation has been related to the regulation of genes involved in proliferation yet histone 3 phosphorylation, particularly on serine 10 (H3S10), is a mark of chromatin compaction during meiosis and mitosis [208]. Although AURKC is expressed at very low levels in somatic tissues

[364], both AURKC and NEK6 have been implicated in transcriptional activation via phosphorylation of H3S10 and H3, respectively [365-367]. Thus, differences in expression of these genes between our cell types may indicate a cell-specific preference within fibroblasts to use NEK6 over other kinases to propagate histone phosphorylation marks and may again be a signal of the proliferative capacity of fibroblasts.

Within the histone acetylation family we found 3 HDACs and 7 HATs with lower gene expression in AECs compared to fibroblasts from healthy individuals. HATs are not residue specific and have been described as promiscuous in their substrate specificity [207], thus our findings may indicate overall subdued transcriptional activity in AECs but further work investigating specific genes would need to be done to identify functional effects. Interestingly, we identified 3 HDACs that were expressed at lower levels in AECs, which comprise 75% of the

class IIa HDAC family of enzymes. The class IIa HDAC family is important in inhibiting tissue- specific gene expression and developmental and differentiation programs [368, 369]. This

HDAC subfamily works primarily through repression of genes targeted by the transcription factor MEF2, which is an important component of fibroblast differentiation [370]. Higher expression of a majority of the class IIa HDAC family in mesenchymal cells may be a reflection of their considerable plasticity. Therefore, in a cell culture model, where both epithelial and mesenchymal cell types are in an undifferentiated state, fibroblasts may require increased management of developmental and differentiation genes and processes due to their greater capacity to undergo reprogramming.

Differential expression of two aurora kinase family members in AECs from asthmatic subjects indicates dysregulation of the histone phosphorylation machinery. In the context of disease, aurora kinases have been linked to spermatogenic arrest, chromosomal instability, and tumorigenicity in pathologies such as infertility, chronic inflammation, and a wide range of cancers [371-373]. Both AURKA and AURKC are capable of phosphorylating H3S10, a site implicated in both gene activation and cell division [208, 374, 375]. In a murine model of wound repair, rapid and sustained phosphorylation of H3S10 was associated with wound healing in intestinal epithelial cells [376]. Further, although the mechanism is not fully clear, phosphorylation of H3S10 is a critical component of chromatin compaction during mitosis [377].

The aurora kinase family members show peaks of expression throughout the mitotic process and each have distinct subcellular localizations within mitotic cells [374]. Our finding of decreased

AURKC gene expression and increased AURKA gene and protein expression in AECs from asthmatic as compared to healthy subjects may indicate aberrant regulation of cell processes,

such as migration, proliferation, and inflammation, known to be dysregulated in asthma.

Asthmatic AECs are mitotically dyssynchronous [378], show defects in cell cycle regulation

[73], and exhibit abnormal proliferation and delayed wound repair [71, 252, 379]. Dysregulation of the aurora kinases may result in an imbalance of histone phosphorylation and, consequently, affect the ability of the cell to condense chromatin, proliferate and heal wounds. Interestingly, there is research supporting cross regulation of the aurora family of kinases as AURKC expression was found to be inversely correlated to AURKB expression [380]. The opposing direction of these kinases in our study may be an example of cross regulation or a compensatory process within this family.

We also identified dysregulation of two histone acetyltransferases in fibroblasts from asthmatics.

KAT2B and CREBBP expression was increased and decreased, respectively, in fibroblasts from asthmatic compared to healthy subjects. KAT2B and CREBBP have been implicated in the epigenetic regulation of COX2 gene expression [342]. COX2 is an activator of the potent antifibrotic prostaglandin E2, and in a study using a murine model of asthma, inhibition of

COX2 worsened airway fibrosis [381]. Furthermore, fibroblasts significantly upregulate COX2 production in response to stretch, IL-1β, and TNF-α [382, 383], which are important in asthmatic airway remodeling and inflammation. Thus, the dysregulation of KAT2B and CREBBP that we found in fibroblasts from asthmatic subjects may play a role in the fibrosis seen in the remodeled airway.

We also found decreased gene expression of the histone arginine methyltransferase PRMT1 in fibroblasts from asthmatic as compared to healthy subjects. This was surprising as PRMT1 and two other PRMTs were shown to be elevated in lung tissue from a murine model of antigen-

induced pulmonary inflammation resulting in enhanced expression of chemokines [254]. This conflicting result may be due to the use of whole lung tissue in the murine study whereas we used homogeneous cultures of specific cell populations. The discrepancy may also be due to the ovalbumin model used; Sun et al. sensitized animals with ovalbumin/alum followed by ovalbumin challenge which produces robust pulmonary inflammation in rodents yet is rarely implicated in human asthma [384]. However, in vitro and in vivo studies of pulmonary arterial smooth muscle cells found that decreased PRMT1 expression leads to reduced cellular methylation and increased proliferation [385]. In an inflammatory milieu, fibroblasts derived from asthmatic subjects show augmented proliferation [386]. Thus, it is possible that our finding of decreased PRMT1 gene expression in fibroblasts derived from asthmatic subjects may influence the proliferative capacity of fibroblasts and fibrosis seen in the airways of asthmatics.

While we found many cell specific and some disease specific changes in the enzymes involved in epigenetic chromatin modification, there are some limitations to our study. The discussion is focused on differentially expressed genes without correcting for multiple comparisons. We provide both Bonferroni and effective number of independent variable corrected p-values in the appendix and highlight genes which pass correction in the results. This approach may increase the number of false positives, however, this is an exploratory study with a small sample size and therefore overcorrecting this data may lead to loss of biologically relevant findings. Thus, these findings will need to be replicated to verify differences in gene expression. However, 5 genes were identified following correction for the effective number of independent variables and, of these, 4 survived Bonferroni correction. In addition, we used a cell culture model that does not necessarily represent the complexity of cell – cell interactions known to be integral to airway

mucosa homeostasis. However, a cell culture model allowed us to identify differences in the chromatin modification families in relatively undifferentiated epithelial and fibroblast cells under controlled conditions. Although we examined gene and protein expression of the epigenetic modifiers associated with asthma, we did not assess the activity of these enzymes, which has been shown to differ in disease. Lastly, we did not look at the targeted chromatin changes as a result of the differential expression of the epigenetic modifying enzymes and further studies would need to be performed to solidify the functional effects of the cell and disease specific changes we described in our cohort.

To summarize, we identified cell specific variation in gene expression in each of the families of epigenetic modifying enzymes in AECs and fibroblasts. While the pattern of expression was similar in both cell types, epithelial cells showed diminished expression of 42 of the 86 genes analyzed. This may be a reflection of the greater proliferative capacity and plasticity of fibroblasts compared to AECs in culture. These data provide insight into the cell-specific variation in epigenetic regulation which may impact the functions of different cell types. We identified disease specific dysregulation of the histone kinases AURKA and AURKC in AECs, which may play a role in processes important in the pathogenesis of asthma such as proliferation and inflammation. In addition, the acetyltransferases KAT2B and CREBBP and methyltransferase PRMT1 showed aberrant gene expression in fibroblasts derived from asthmatic compared to healthy subjects. These genes may impact fibrotic and proliferative mechanisms and therefore may impact the remodeling process occurring in the asthmatic airway.

These findings provide further evidence of cell-specific and disease-specific epigenetic landscapes which may be relevant to cell fate and disease susceptibility.

Chapter 5: Alterations in Histone Acetylation and Methylation in Airway

Epithelial Cells Derived From Asthmatic and Healthy Subjects

5.1 Introduction

There is compelling evidence that the airway epithelium of asthmatics is abnormal [78]. It has been demonstrated that the asthmatic epithelium has increased susceptibility to injury and structural damage leading to increased numbers of shed epithelial cells termed Creola bodies [78,

387]. It is thought that this primary disruption of the airway epithelium leads to an altered epithelial repair and ongoing inflammatory process.

Abnormal expression of genes involved in repair and inflammation has been reported in the epithelium of asthmatic patients. The expression of epidermal growth factor receptor (EGFR), important for migration, proliferation, and differentiation, all integral components of the repair process, is elevated in the epithelium of childhood-onset and adult asthmatics [72, 96, 97, 99].

The expression of EGFR is increased in both damaged and intact regions of the airway epithelium of subjects who have asthma [96, 99]. This overexpression of EGFR indicates either that there is an unresolved repair process or that the epithelium is locked in a repair phenotype

[96] contributing to the inflammatory and remodeling process. Furthermore, there is an increase in the number of basal cells expressing the transcription factor p63 in the asthmatic epithelium

[69, 70]. In particular, the ΔNp63 isoform is heavily expressed in the epithelium compared to the larger TAp63 isoform [102]. This transcription factor is essential for differentiation, adhesion, and proliferation [100, 101], and thus, much like EGFR, is an important factor in epithelial repair [102, 103]. The increased number of cells expressing p63 in the asthmatic

epithelium may signify an inappropriate cellular differentiation program in asthma, which would directly affect epithelial homeostasis and repair [102]. Another transcription factor, signal transducer and activator of transcription 6 (STAT6), is also overexpressed in the epithelium of severe asthmatics [112, 113]. In T cells, STAT6 is crucial to IL-4 and IL-13 signaling resulting in skewing towards a Th2 lineage [108]. In the epithelium, signaling through STAT6 can activate the chemokines CCL11 and CCL5 [114, 115], which result in the recruitment of eosinophils, basophils, T cells, and leukocytes to the epithelium. This upregulation of STAT6 is thought to contribute to the chronic inflammation seen in the airway mucosa of asthmatic patients. Together these studies support the rationale that alterations in the inflammatory and repair mechanisms of the airway epithelium contribute to chronic inflammation and airway remodeling in asthma. Further, as the airway epithelium is the first point of contact with inhaled environmental agents, it is well positioned for environmental factors to influence gene expression and ultimately susceptibility to disease through epigenetic modifications [184].

The sequence of the human genome is essentially the same in all cells of the body within a specific individual, yet the epigenome differs from tissue to tissue [183]. The DNA of each cell is packaged into nucleosomes where 146 base pairs of DNA wrap around an octamer of histone proteins [197]. This octamer consists of two of each of the core histones 2A, 2B, 3, and 4 [197].

An important mechanism for altering the chromatin structure regulating gene expression is covalent modification of the amino acid residues of the histone N-terminal tails [198, 336]. The acetylation of lysine residues on histone tails has been positively associated with gene transcription [198, 207]. Histone acetyltransferases (HATs) add, whereas histone deacetylases

(HDACs) remove the acetyl mark from histone tails [198]. Methylation of histone tails can be

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both activating and suppressive of gene expression depending on the particular residue [198].

Methyl groups are added to lysine or arginine residues by histone methyltransferases (HMTs) and removed by histone demethylases (HDMs) [198, 207].

The role of epigenetics in the pathogenesis of asthma remains unclear. There is evidence of altered HAT and HDAC activity and expression in the airways of asthmatics. In bronchial biopsies from asthmatic subjects, HAT activity was shown to be elevated and HDAC activity reduced [257, 258]. Although research investigating the exact HDACs involved is inconsistent, expression of certain inflammatory genes has been associated with acetylation of histone lysine residues in an epithelial cell line [265]. In addition, histone methylation is a key regulator of many genes involved in chronic inflammation and epithelial mesenchymal transition [343, 344,

388, 389]. Thus, histone methylation may also play a role in the regulation of epithelial genes in asthma.

The impact of altered histone modification on the regulation of genes associated with repair and inflammation in the epithelium of asthmatic subjects is unclear. Therefore, we hypothesized that epigenetic changes including histone acetylation and methylation contributed to the abnormal expression of EGFR, ΔNp63, and STAT6 in the epithelium of asthmatic subjects and thus influence the pathogenesis of the disease. The aims of this study were i) to identify global changes in histone modifications in the asthmatic epithelium, ii) to identify gene-specific alterations in histone modifications in airway epithelial cells (AECs) of asthmatics, and iii) to modify the expression of those genes by modulating histone modification.

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5.2 Methods

5.2.1 Study subjects

Tissue sections and airway epithelial cells (AECs) were obtained from human non-transplantable donor lungs from the International Institute for the Advancement of Medicine (Edison, NJ,

USA). The study was approved by the Providence Research Ethics committee, University of

British Columbia (#H0-50110). Relevant information for each sample used for immunohistochemistry, chromatin immunoprecipitation, and immunoblot is listed in Tables 5.1 and 5.2.

ID Ageº (yrs) Sex Disease A1 8 F Asthma A2 11 M Asthma A3 15 F Asthma A4 11 M Asthma A5 26 F Asthma A6 25 M Asthma H1 20 M None H2 4 F None H3 22 M None H4 14 M None H5 20 F None H6 24 M None

Table 5.1 Donor demographics including age, sex, and disease used for immunohistochemistry. ºThere were no significant differences between age for disease groups; unpaired two-tailed t-test p=0.77

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Used for ID Ageº (yrs) Sex Disease Used for ChIP Immunoblot A1 14 F Asthma X A2 20 M Asthma X A3 21 F Asthma X A4 11 M Asthma X A5 25 M Asthma X H1 20 M None X X H2 19 F None X X H3 20 F None X X H4 11 M None X X H5 12 M None X X

Table 5.2 Donor demographics including age, sex, and disease used for chromatin immunoprecipitation and immunoblot. ºThere were no significant differences between age for disease groups; unpaired two-tailed t-test p=0.59

5.2.2 Cell culture

AECs from asthmatic (n=5) and healthy (n=5) subjects were collected by pronase digestion as described in General Methods 2.1.1. Cells were grown in 6-well plates in culture at 37°C in 95% air and 5% CO2 and used at passage 2 for all experiments.

In addition, the 1HAE human airway epithelial cell line was grown and maintained in

Dulbecco‟s Modified Eagle Medium (DMEM, Gibco) containing 10% Fetal Bovine Serum

(FBS; Gibco) and an antibiotic and antimycotic (Gibco). Cells were grown in 6-well plates in a

37°C humidified environment (95% air / 5% CO2) and used at passage 4.

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5.2.3 Immunohistochemical staining of airway sections

Formalin fixed, paraffin embedded airway tissue sections were used for immunohistochemical staining of histone modifications. Both color segmentation and point-counting analyses were performed for each image and data obtained from each analysis are presented as % positive staining of airway epithelium and % positive nuclei, respectively. A two-tailed unpaired t-test was performed to determine differences in % positive area of airway epithelium and % positive nuclei between asthmatic and healthy subjects, a p-value of less than 0.05 was considered significant. Please see General Methods 2.2 for a complete description of the immunohistochemical staining and analysis protocol. Antibody information is listed in Table

5.3.

Catalogue Primary Antibody Epitope Host Company Block Number Dilution H3K14ac Rabbit Abcam ab52946 50% GS 1/100 in TBS + 25% GS H3K18ac Rabbit Abcam ab1191 50% GS 1/200 in TBS + 25% GS H3K27ac Rabbit Abcam ab4729 50% GS 1/400 in TBS + 25% GS H4K8ac Rabbit Abcam ab15823 100% GS 1/700 in TBS + 50% GS H4K12ac Rabbit Active Motif 39165 100% GS 1/400 in TBS + 50% GS H4K16ac Rabbit Santa Cruz sc-8662R 10% GS 1/100 in TBS + 5% GS H3K4me3 Rabbit Abcam ab8580 100% GS 1/1200 in TBS + 50% GS H3K4me2 Rabbit Abcam ab7766 100% GS 1/1500 in TBS + 50% GS H3K9me3 Rabbit Abcam ab8898 100% GS 1/1500 in TBS + 50% GS H3K27me3 Mouse Abcam ab6002 50% HS 1/100 in TBS + 25% HS H3K36me3 Rabbit Abcam ab9050 100% GS 1/200 in TBS + 50% GS

Table 5.3 Antibodies used for immunohistochemical staining of donor trachea tissue. GS indicates goat serum, HS indicates horse serum.

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5.2.4 Chromatin immunoprecipitation

AECs were maintained in culture and grown to 80% confluence before extraction of samples for chromatin immunoprecipitation (ChIP) experiments. ChIP was performed using the EpiTect

ChIP Kit (Qiagen) according to the manufacturer‟s protocol. Briefly, proteins were fixed to

DNA by adding 1% formaldehyde to each well and incubating at 37ºC for 10 minutes. Stop buffer was then added to quench the formaldehyde and stop the cross-linking process. The plate was then placed on ice and each well was washed twice with ice-cold PBS. Cells were harvested by adding ice-cold cell harvesting buffer containing protease inhibitor cocktail (PIC) and using a cell scraper to collect cells. To pellet the fixed cells, the cell suspension was centrifuged at

800xg for 10 minutes at 4ºC and the supernatant was removed from the tube and discarded. The cell pellet was resuspended in immunoprecipitation (IP) Lysis buffer containing PIC and incubated on ice for 15 minutes with gentle mixing every 5 minutes.

To shear chromatin, the cell lysate was sonicated on ice using a Sonic Dismembrator Model 100

(Fisher Scientific). Sonication was cycled at 30 seconds on, and 30 seconds off, for a total of 25 minutes. Lysates were pipetted gently after every sonication round to avoid precipitation and chromatin loss. Samples were then centrifuged at 14,000xg for 10 minutes at 4 ºC to pellet cell debris and the supernatant containing chromatin was transferred to a new tube. To pre-clear the samples, IP Buffer A containing PIC and protein A beads was added to the sample and incubated for 50 minutes at 4ºC with rotation. Samples were then centrifuged at 4,000xg for 1 minute at

4ºC to collect beads and isolate supernatant. The supernatant was aliquotted equally into fractions for immunoprecipitation.

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To obtain the input fraction of DNA, 1% of one fraction per sample was removed and set aside for DNA purification. To each fraction, 2ug of either H3K18ac (ab1191, Abcam), H3K4me2

(GAH-3203, Qiagen) or IgG (Qiagen) antibody was added and incubated overnight at 4ºC with rotation. Next, protein A beads were added to each fraction and incubated for 1 hour at 4ºC with rotation then centrifuged 4,000xg for 1 minute at 4ºC. Supernatants were discarded and the beads were washed by incubating with wash buffers for 4 minutes on a rotating platform, centrifuging, and removing supernatants five times. To reverse cross-link and purify DNA, elution buffer and proteinase K were added to bead pellets and input fraction and incubated at

45ºC for 30 minutes. DNA extraction beads were added to each fraction, incubated at 95ºC for

10 minutes, and centrifuged at 14,000xg for 1 minute. Supernatant containing DNA was transferred to another tube. Beads were washed with Elution buffer, centrifuged at 14,000xg for

1 minute, and supernatants were pooled. DNA was purified using spin columns and eluted in a final volume of 100ul.

5.2.4.1 Real time polymerase chain reaction

Purified ChIP DNA was analyzed by quantitative real time PCR for enrichment of target genes.

Reagents were mixed with ChIP DNA as shown in Table 5.4 and loaded in triplicate onto a

MicroAmp® Optical 384-Well Reaction Plate and sealed with MicroAmp® Optical Adhesive

Film (Life Technologies). Primers used in PCR reactions are listed in Table 5.5. Plates were centrifuged for 1 minute at 1000g to remove bubbles and placed in a thermocycler (ViiA7, Life

Technologies). Cycling conditions were as described in Table 5.6. Cycle threshold (Ct) values were recorded and the presence of a single melt temperature was confirmed. Samples for which

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the threshold cycle exceeded 35 were omitted from analysis as these were considered unreliable readings.

Component Volume (ul) RT2 SYBR Green qPCR Master Mixº 6 ddH2O 4.52 Assay Primers (10uM) 0.48 ChIP DNA 1 Total 12

Table 5.4 Recipe for PCR cocktail used to analyze ChIP DNA at target genes. ºRT2 SYBR Green qPCR Master Mix obtained from Qiagen (330520).

Catalogue Assay Forward Primer Reverse Primer ID Gene Source Number Positionº (5’-3’) (3’-5’) GPH110001 GAPDH GAPDH Qiagen +330 - - C(+)01A GPH110003 SAT2 SAT2 Qiagen -1074 - - C(+)01A GPH110002 MYOD1 MYOD1 Qiagen -376 - - C(+)01A In- TGTAAATCGT GAGGCCTCTC ΔNp63-I p63 - -313 house GGTGGTGGTG CCATCTCATT ΔNp63- In- CCTGTCTGTCT GAGGCGGGAC p63 - -218 II house CCTGGGTTT TCTTCTCTTT GPH101192 EGFR-I EGFR Qiagen -393 - - 3(-)01A GPH101192 EGFR-II EGFR Qiagen 932 - - 3(+)01A STAT6- GPH101725 STAT6 Qiagen -294 - - I 4(-)01A STAT6- GPH101725 STAT6 Qiagen 608 - - II 4(+)01A

Table 5.5 Primers used in real time PCR analysis of ChIP DNA. Two sets of primers were obtained for each gene to enhance the likelihood of obtaining a signal from ChIP DNA. Primers for ∆Np63 were designed in-house as they were unavailable from the manufacturer. ºAssay start position relative to transcription start site (TSS). 107

Cycles Duration Temperature (ºC) 1 10 minutes 95 15 seconds 95 40 1 minute 60 1 minute 95 1 30 seconds 55 30 seconds 95

Table 5.6 Cycling conditions for real time PCR of chromatin immunoprecipitated DNA.

5.2.4.2 Analysis

To calculate occupancy of H3K18ac, H3K4me2, or IgG at target genes, DNA from each fraction was amplified by real time PCR for each locus of interest and Ct values were normalized to the

Input DNA fraction for the same assay according to the manufacturers protocol (Qiagen). This accounts for differences in sample starting amounts and preparation. To normalize each fraction to the input fraction, the following formula was applied:

ΔCt [normalized IP] = (Ct [IP] - (Ct [Input] - Log2 (Input Dilution Factor)))

To calculate Relative Occupancy (linear conversion of the ΔCt [normalized IP]) for each fraction, the following formula was used:

Relative Occupancy (%) = 2 (-ΔCt [normalized IP]) *100

A two-tailed unpaired t-test was performed to determine differences in relative occupancy between asthmatic and healthy AECs for each locus, a p-value of less than 0.05 was considered significant.

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As the two histone modifications examined in this study are activating marks, confirmation of the specificity of the immunoprecipitation was demonstrated by analyzing occupancy at genes known to be expressed (positive locus) and repressed (negative locus) as per manufacturer‟s protocol. Both H3K18ac and H3K4me2 had high occupancy at the glyceraldehyde-3-phosphate dehydrogenase locus (GAPDH, transcriptionally active euchromatin), and low occupancy at the spermidine/spermine N1-acetyltransferase family member 2 (SAT2, heterochromatin) and myogenic differentiation 1 loci (MYOD1, transcriptionally inactive euchromatin) indicating antibody specificity (Appendix C.1).

5.2.5 Trichostatin A treatments

For drug treatment experiments, AECs and 1HAEs were grown in 6-well plates to 80% confluence and treated with the HDAC inhibitor Trichostatin A (TSA, T8552, Sigma) for 24hrs before protein was collected and used for sodium dodecyl sulfate polyacrylamide gel electrophoresis and immunoblot. 1HAEs were treated with control media or TSA at the following concentrations: 10, 100, 500, and 1000ng/ul whereas AECs were treated with control media or TSA at 100ng/ul.

5.2.6 Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and immunoblot

Protein was collected from AECs in culture and SDS-PAGE and immunoblot was performed as described in General Methods 2.3. Specific antibody information is provided in Table 5.7.

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Data for H3K18ac were normalized to total H3 and presented as fold change over untreated control. A one-way ANOVA and a post hoc Dunnett‟s multiple comparison test was performed.

A p-value of less than 0.05 was deemed significant.

Data for ΔNp63 and STAT6 were normalized to β-tubulin whereas EGFR was normalized to

HSP-90, both of which have high molecular weights, to maintain resolution of the gel at a high molecular weight. All data are presented as fold change over untreated control. A two-tailed paired t-test was performed, a p-value of less than 0.05 was considered significant.

Catalogue Primary Antibody Epitope Host Company Number Dilution H3 Mouse Abcam ab10799 1/500 H3K18ac Rabbit Abcam ab1191 1/3000 ΔNp63 Mouse Santa Cruz sc-8341 1/500 EGFR Mouse R&D Systems 1095 1/500 STAT6 Rabbit Epitomics 1505-1 1/500 β-tubulin Mouse Millipore 05-661 1/2000 HSP-90 Mouse BD Biosciences 610418 1/1000

Table 5.7 Antibodies used for immunoblot of cell protein lysate.

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5.3 Results

5.3.1 Global analysis of acetylated histone residues in epithelium from healthy compared to asthmatic subjects

To determine if there were differences in global histone acetylation between healthy and asthmatic epithelial tissue, we compared histone acetylation of 6 lysine residues on histones 3 and 4. We identified a significant increase in % positive area of airway epithelium for acetylation of lysine 18 on histone 3 (H3K18ac) in asthmatic compared to healthy subjects

(p=0.02, Figure 5.1). However, when we counted the number of positive nuclei for H3K18ac, we found no difference between asthmatic and healthy epithelium (Appendix C.2). This suggests that more histones are acetylated at this particular residue in each positively stained cell within the epithelium from asthmatic subjects, which may affect the transcriptional activity of more genes as compared to healthy subjects. We found no differences in global histone acetylation or the amount of positively stained nuclei for H3K14ac, H3K27ac, H4K8ac,

H4K12ac, and H4K16ac in epithelium from asthmatic compared to healthy subjects (Figure 5.1,

Appendix C.2).

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Figure 5.1 Acetylation of specific histone lysine residues in asthmatics airways compared to controls. 112

Airway sections from asthmatic and healthy patients were fixed, embedded in paraffin and then sectioned for immunohistochemical analysis. Sections were stained for H3K14ac, H3K18ac, H3K27ac, H4K8ac, H4K12ac or H4K16ac. Scale bar is equal to 50 μm. The expression of H3K14ac, H3K18ac, H3K27ac, H4K8ac, H4K12ac, and H4K16ac within the epithelium was quantified for both healthy (white) and asthmatic (blue) subjects. Data are expressed as % of positive area of airway epithelium ± SEM (n=5). A two-tailed unpaired t-test was performed, * indicates p<0.05.

5.3.2 Global analysis of methylated histone residues in epithelium from healthy compared to asthmatic subjects

As histone methylation has well defined roles in regulating many genes, we next investigated whether global histone methylation differed between healthy and asthmatic epithelium. We found no difference in global histone methylation of the activating marks H3K4me3/me2, the silencing mark H3K27me3, nor the differentiation associated mark H3K36me3 (Figure 5.2).

Additionally, we did not identify any differences in positively stained nuclei for these methylated histone lysine residues (Appendix C.3). However, we did find a significant increase in the amount of staining in the airway epithelium for histone 3 lysine 9 trimethylation (H3K9me3) in asthmatic compared to healthy subjects (p=0.015, Figure 5.2). This was mirrored by an increase in the number of nuclei positive for H3K9me3 in the epithelium from asthmatic subjects

(p=0.001, Appendix C.3). Although there is more global H3K9me3 in the epithelium of asthmatics, this may be a reflection of the increased number of cells containing this repressive histone mark and not necessarily more of the H3K9me3 mark in each cell. Thus, the gene- specific effect may not be as pronounced as that of H3K18ac where there was more staining yet the same amount of stained cells in asthmatic as compared to healthy subjects. Therefore, we focused on H3K18ac as a potential modulator of genes overexpressed in the epithelium of asthmatics.

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Figure 5.2 Methylation of specific histone lysine residues in asthmatics airways compared to controls. Airway sections from asthmatic and healthy patients were fixed, embedded in paraffin and then sectioned for immunohistochemical analysis. Sections were stained for H3K4me2 and 3, H3K9me3, H3K27me3, or H3K36me3. Scale bar is equal to 50 μm. The expression of H3K4me2 and me3, H3K9me3, H3K27me3, and H3K36me3 within the epithelium was quantified for both healthy (white) and asthmatic (blue) subjects. Data are expressed as % of positive area of airway epithelium ± SEM (n=5). A two- tailed unpaired t-test was performed, * indicates p<0.05. 114

5.3.3 Gene Specific Analysis of H3K18ac and H3K4me2 in airway epithelial cells from healthy compared to asthmatic subjects

We performed chromatin immunoprecipitation to identify if the activating mark H3K18ac was enriched in regions with genes known to be upregulated in asthma. Specifically, we targeted regions surrounding the transcription start site (TSS) of ΔNp63, EGFR, and STAT6 primarily because it has been shown that the acetylation of H3K18 is augmented predominantly around this region of active genes and not the promoter or transcribed regions [199]. We designed two sets of primers for each gene to enhance our likelihood of obtaining a signal as sonication results in sheared DNA of approximately 500 base pairs. As dysregulation of acetylation of the

H3K18ac residue has been identified in other diseases to occur alongside H3K4me2 [390-394], we also investigated this activating epigenetic modification to determine if any changes were unique to H3K18ac or possibly part of a larger epigenetic phenomenon involving other histone modifications.

We determined that the relative occupancy of H3K18ac at the both ΔNp63-I and ΔNp63-II loci is significantly increased in AECs from asthmatic subjects compared to controls (Figure 5.3A and B). The relative occupancy of H3K18ac at the ΔNp63-I locus in AECs from asthmatic donors was 8.21% ± 1.54, whereas healthy donors showed a relative occupancy of 4.22% ± 0.75

(p=0.05, Figure 5.3A). For ΔNp63-II, AECs derived from asthmatic subjects showed 8.81% ±

1.78 while healthy donors displayed 4.41% ± 0.51 relative occupancy of H3K18ac (p=0.04,

Figure 5.3B). However, we found no difference in relative occupancy of H3K4me2 at the

ΔNp63-I or ΔNp63-II sites in AECs from asthmatic subjects compared to controls (Figure 5.3A and B respectively).

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Figure 5.3 Relative occupancy of H3K18ac and H3K4me2 at the ΔNp63 locus. Chromatin immunoprecipitation for H3K18ac, H3K4me2, and IgG was performed on AECs from asthmatic and healthy subjects. Real time PCR targeting the ΔNp63-I (A) and the ΔNp63-II (B) loci was used to quantify presence of each histone modification and negative control at the site of interest. Data are expressed as relative occupancy at the target locus (%) ± SEM (n=5). A two-tailed unpaired t-test was performed to determine differences in relative occupancy between asthmatic and healthy AECs for each locus, * indicates p<0.05.

Next, we investigated whether H3K18ac and H3K4me2 were differentially present surrounding the EGFR transcription start site in AECS from healthy compared to asthmatic donors. There was significantly more H3K18ac but not H3K4me2 occupancy upstream of the EGFR TSS in asthmatic versus healthy AECs (Figure 5.4A). The relative occupancy of H3K18ac in asthmatic derived AECs was 11.92% ± 0.95 as compared to 7.21% ± 1.04 for healthy subjects at the

EGFR-I locus (p=0.03, Figure 5.4A). In contrast, we found no difference in occupancy of

H3K18ac or H3K4me2 between AECs from healthy or asthmatic subjects downstream of the

EGFR TSS (Figure 5.4B).

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Figure 5.4 Relative occupancy of H3K18ac and H3K4me2 at the EGFR locus. Chromatin immunoprecipitation for H3K18ac, H3K4me2, and IgG was performed on AECs from asthmatic and healthy subjects. Real time PCR targeting the EGFR-I (A) and the EGFR-II (B) loci was used to quantify presence of each histone modification and negative control at the site of interest. Data are expressed as relative occupancy at the target locus (%) ± SEM (n=5). A two-tailed unpaired t-test was performed to determine differences in relative occupancy between asthmatic and healthy AECs for each locus, * indicates p<0.05.

As demonstrated in Figure 5.5A, we found a difference in occupancy of H3K18ac and no change in H3K4me2 upstream of the STAT6 TSS between our groups. In AECs from asthmatic subjects, the relative occupancy of H3K18ac at the STAT6-I locus was greater than healthy subjects (18.76% ± 0.73 versus 12.07% ± 1.50, p=0.004). However, we found no difference in occupancy of H3K18ac or H3K4me2 at the STAT6-II locus in a comparison of healthy or asthmatic derived AECs (Figure 5.5B).

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Figure 5.5 Relative occupancy of H3K18ac and H3K4me2 at the STAT6 locus. Chromatin immunoprecipitation for H3K18ac, H3K4me2, and IgG was performed on AECs from asthmatic and healthy subjects. Real time PCR targeting the STAT6-I (A) and the STAT6-II (B) loci was used to quantify presence of each histone modification and negative control at the site of interest. Data are expressed as relative occupancy at the target locus (%) ± SEM (n=5). A two-tailed unpaired t-test was performed to determine differences in relative occupancy between asthmatic and healthy AECs for each locus, * indicates p<0.05.

5.3.4 Modulation of ΔNp63, EGFR, and STAT6 with the histone deacetylase inhibitor

Trichostatin A

As the limited HAT inhibitors available are known to have low cell permeability and potency, rather than diminishing H3K18ac in asthmatic AECs, we chose to investigate the functionality of acetylated H3K18ac in healthy AECs using a histone deacetylase inhibitor. First, to ensure the histone deacetylase inhibitor Trichostatin A (TSA) could alter the acetylation of H3K18ac, we optimized the use of TSA in an epithelial cell line. By performing a dose response experiment in

1HAE cells, we found that treatment with 100ng/ul TSA for 24 hrs resulted in significantly more acetylation of the H3K18 protein compared to baseline untreated (Figure 5.6).

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Figure 5.6 Fold change in H3K18ac with varying concentrations of Trichostatin A (TSA) treatment. 1HAE cells were treated with TSA at 0, 10, 100, 500, and 1000ng/ul for 24 hrs. Protein was collected and SDS-PAGE immunoblot was performed to quantify H3K18ac and total H3. Data are presented as fold change H3K18ac normalized to H3 ± SEM (n=3). A one-way ANOVA and a post hoc Dunnett‟s multiple comparison test was performed. * indicates p<0.05.

Given that TSA was able to elevate the amount of H3K18ac in our cell line, we treated AECs from healthy donors with 100ng/ul TSA for 24 hrs to determine if we could alter the expression of ΔNp63, EGFR, and STAT6 through changes in histone acetylation. However, we did not observe changes in protein expression of the three target genes ΔNp63, EGFR, and STAT6 in

AECs with TSA treatment (Figure 5.7).

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Figure 5.7 Modulation of ΔNp63, EGFR, and STAT6 expression with TSA treatment. AECs from healthy subjects were treated with TSA (100ng/ul) for 24 hrs. Protein was collected and SDS-PAGE immunoblot was performed to quantify the change in ΔNp63, EGFR, and STAT6 protein between control untreated and TSA treated samples. Data are presented as fold change normalized to loading control (β-tubulin for ΔNp63 and STAT6, and hsp90 for EGFR) ± SEM (n=5). A two-tailed paired t-test was performed for control versus treatment for each gene.

5.4 Discussion

The study goals were to identify specific histone modifications altered in the epithelium of asthmatic subjects and to determine if genes important in the pathogenesis of asthma are affected by these epigenetic changes. From a global approach, we identified an increase in the amount of acetylation of lysine 18 on histone 3 in epithelium from asthmatic as compared to healthy subjects with no change in the number of nuclei positive for H3K18ac staining. In addition, we found an increase in both the amount of staining and number of positive nuclei for trimethylation of lysine 9 on histone 3 in the epithelium of asthmatic versus healthy subjects. As acetylation of histone lysine residues is generally associated with a permissive chromatin structure and gene expression, we directed our focus to genes previously identified to be overexpressed in the epithelium of asthmatics. We found increased occupancy of acetylated H3K18 but no difference

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in dimethylation of H3K4me2 around the transcription start site of ΔNp63, EGFR, and STAT6 in

AECs derived from asthmatics compared to healthy samples.

Previous studies have implicated global changes in both H3K18ac and H3K4me2 in relation to cancer pathogenesis. Specifically, low global levels of these histone modifications have been identified in studies of prostate [392], kidney [394], pancreatic [393], and lung cancer [394] as predictors of poor prognosis. However, studies investigating global histone modifications in esophageal squamous cell carcinoma indicate that high global levels of H3K18ac and H3K4me2 are associated with tumor grade and low levels of H3K18ac are correlated to better prognosis

[391]. As tumor grade increases, the carcinoma cells become more poorly differentiated [395], thus it is possible that the genes affected by H3K18ac and H3K4me2 in esophageal squamous cell carcinoma may contribute to halting the cellular differentiation program. Although the clinical outcome resulting from differential levels of these histone modifications may vary depending on specific disease pathology, there is growing evidence to support a mechanistic role for H3K18ac and H3K4me2 in biological processes including differentiation.

This research identified elevated global levels of the permissive and repressive histone marks

H3K18ac and H3K9me3 in the epithelium of asthmatic as compared to healthy subjects.

Interestingly, while global H3K18ac was elevated within the epithelium of asthmatics, we found no change in the number of positively stained cells, suggesting that there are more histones bearing an acetylated lysine 18 residue in each positively stained cell, which may therefore influence the epigenetic regulation of an increased amount of genes. On the other hand,

H3K9me3 was elevated both globally and in the number of positively stained cells within the epithelium of asthmatic subjects. H3K9me3 staining was also limited to the lower layer of the

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epithelium and thus our findings may be a reflection of the increase in basal cell numbers seen in the epithelium of asthmatics, although further work is needed to verify this inference. As global analysis of histone modifications does not provide information about affected genes, we focused our efforts into elucidating which specific genetic targets H3K18ac was potentially influencing.

Previous research has identified that the epithelium of asthmatic subjects contains more cells expressing the transcription factor p63 and also shows overexpression of EGFR and STAT6.

Since H3K18ac has been shown to peak surrounding the TSS of active genes, we amplified two regions around the TSS for each gene target. We interrogated these candidates and found elevated occupancy of H3K18ac but not H3K4me2 in AECs from asthmatic compared to healthy subjects for at least one amplified region around the TSS of each gene.

Recently, Qian et al. identified the necessity of HDAC2 in the regulation of ΔNp63 expression

[107]. This mechanism is thought to be dependent on the interaction between HDAC2 and the

DEC1 transcription factor, which regulates ΔNp63 expression and has implications for growth suppression and cellular differentiation [107]. Although the authors did not clarify the exact epigenetic mechanism in terms of histone acetylation, they did find that treatment of mammary epithelial cell lines with TSA resulted in the induction of ΔNp63 [107]. We identified elevated occupancy of H3K18ac at two regions upstream of the ΔNp63 TSS in AECs from asthmatic compared to healthy donors. ΔNp63 is the most prevalent p63 isoform expressed in human airway epithelial cells with the ability to regulate multiple genes involved in epithelial functions such as proliferation and differentiation [102]. The finding of elevated histone acetylation at the

ΔNp63 TSS provides insight into the possible mechanism of ΔNp63 gene regulation. As HAT

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activity is known to be increased in asthmatic epithelium [257, 258], it is possible that ΔNp63 may be a target of this epigenetic aberration.

The role of histone modifications in EGFR gene regulation is still not fully clear but there are studies that have begun elucidating the mechanisms involved. Multiple studies reported modulation of EGFR expression in response to histone deacetylase inhibitors, yet the epigenetic process may be dependent on the transcription factor involved. Chou et al. discovered a complex interaction between the histone deacetylase HDAC3, the histone acetyltransferase CBP, and the transcription factor SP1 in regulating EGFR expression [396]. In human colon carcinoma cells, deacetylation of the SP1 protein by HDAC3 results in increased binding of SP1 to the EGFR promoter [396]. The subsequent association of CBP with this SP1/HDAC3 complex results in histone acetylation and EGFR expression [396]. Of interest, the authors also suggested that priming of the EGFR gene was due to histone methylation, as they found enrichment of

H3K4me2 at the EGFR promoter [396]. Another study in breast cancer epithelial cells found that EGFR is repressed by binding of the TIEG1/HDAC1 complex to SP1 sites on the EGFR promoter resulting in suppression of histone acetylation [397]. Our finding of increased occupancy of acetylated H3K18 upstream of the EGFR TSS in AECs derived from asthmatic as compared to healthy subjects indicates an epigenetic mechanism may be a regulatory component of the overexpression of epithelial EGFR seen in other studies of asthma. Evidence of epigenetic regulation of EGFR has significant implications in the setting of asthma given its role in epithelial functions such as differentiation and proliferation and may provide a novel therapeutic modality in this disease.

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STAT6 may interact with histone acetyltransferases and demethylases to facilitate transcriptional activation of STAT6 target genes [398-400] but the mechanism by which STAT6 itself is regulated by histone modification is not understood. Within the epithelium, STAT6 is an integral transcription factor in IL-13 mediated pathology and eotaxin gene activation [51, 115,

401]. Although the importance of STAT6 in the inflammatory process is recognized and STAT6 levels are elevated in the asthmatic epithelium [112], regulatory mechanisms controlling expression of this gene have yet to be elucidated. We identified elevated occupancy of H3K18ac upstream of the STAT6 TSS in AECs from asthmatic compared to healthy donors. As histone acetylation is commonly associated with gene expression, our finding may provide an epigenetic mechanism, histone acetylation, for the increase in STAT6 seen in asthma.

Interestingly, we were not able to significantly modulate the levels of ΔNp63, EGFR, or STAT6 with the HDAC inhibitor TSA. In order to reduce the elevated histone acetylation we identified in AECs of asthmatic subjects, we would need an appropriate HAT inhibitor which could block the addition of acetyl groups to H3K18. Currently, difficulties encountered with HAT inhibitors include low cell-permeability, low potency, and a lack of specificity [402]. Instead, we chose to focus on HDAC inhibition as previous literature has identified modulation of both EGFR and

ΔNp63 gene expression in response to these drugs. Since our healthy epithelial samples displayed lower levels of H3K18ac at our target genes, we hoped to observe an increase in the expression of these genes with TSA treatment. Our finding of unchanged protein expression for all three genes after TSA treatment may indicate that although TSA was able to induce H3K18 hyperacetylation in a cell line, the mechanism in primary airway epithelial cells may be different.

TSA is a potent HDAC inhibitor which functions primarily on class I and class II and not class

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III deacetylases [403]. Recently, SIRT7 was identified as an H3K18ac specific class III histone deacetylase [404]. Interestingly, SIRT7 exerted its effects only on a particular group of genes which contained an ELK4 transcription factor binding site [404]. In addition, our time point for sample collection may also contribute to the lack of observed changes. We collected protein 24 hrs after TSA treatment; this may have been enough time for a change in histone acetylation to appear but may not have allowed time for production of target proteins. Therefore, this data indicates that although TSA may affect global H3K18 acetylation, gene-specific events may be regulated by another layer of control in different environments.

In summary, we have identified elevated levels of acetylated lysine 18 on histone 3 in AECs from asthmatic as compared to healthy subjects. This does not exclude differences in various histone modifications at the gene-specific level, but it does provide a starting point for a possible epigenetic target for overexpressed genes in epithelial cells in asthma. This is especially relevant as we identified elevated occupancy of H3K18ac at three genes important in the pathogenesis of asthma, specifically ΔNp63, EGFR, and STAT6. However, we could not modulate the expression of these genes through the use of TSA which may suggest a more complex mechanism of control in AECs, and the need for development of more specific therapeutic agents for use as epigenetic modulators in disease.

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Chapter 6: Discussion

6.1 Introduction

Previously thought to be a simple, inert barrier, the airway epithelium is now known to be capable of orchestrating complex biological processes which coordinate the host response to the external milieu. Recognition of the airway epithelium as a central mediator in the pathogenesis of asthma has resulted in a necessity to understand the cellular mechanisms that are aberrant in this disease phenotype. Although previous studies have found numerous irregularities in the expression of genes involved in disease processes, the contribution of epigenetic mechanisms to the regulation of these genes is unknown. The architecture of chromatin is integral to the regulation of gene expression and is determined by modifications to the surrounding histones and

DNA. The acetylation, methylation, phosphorylation, and ubiquitination of histone tail residues have the potential to greatly alter the accessibility of the cell‟s transcriptional machinery to the

DNA. Further, DNA methylation can both interrupt binding of transcription factors and recruit chromatin remodelers resulting, in general, in gene silencing. Thus it is important to understand the epigenetic landscape in diseases such as asthma.

Epigenetics has been described as the bridge between genotype and phenotype [405].

Throughout the development of a multicellular organism an assortment of cell types are generated, each genetically homogenous yet with unique gene expression profiles and biological functions. The epigenetic landscape is essential in determining the fate of these cells [406] by histone modification and DNA methylation patterns that regulate the expression of genes integral to cellular development and differentiation [307, 405]. Furthermore, because the epigenome is

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adaptable, it has the capability to respond to and be modified by environmental factors [307].

The outcome of this interaction depends on the environmental stressor and can be a normal physiological response or deregulation of the epigenome producing an abnormal phenotype [307,

407].

Asthma is a complex disease whereby the interaction between genes and the environment is known to contribute to disease susceptibility [408]. Thus the interplay between epigenetics and environmental stimuli has brought focus to the role of this emerging field in the pathogenesis of asthma. Indeed, dysregulation of epigenetic mechanisms in asthma has been identified in a variety of cells but most studies have been performed in tissues from outside of the lung [409].

While dysregulation of enzymes involved in histone acetylation was identified in the airways of asthmatics, there is still disagreement on the exact enzymes responsible [257-260].

The importance of epigenetic mechanisms to both cellular specificity and disease susceptibility led us to hypothesize that alterations to the epigenome of airway epithelial cells contribute to the dysregulation of genes involved in key epithelial functions. Our principal findings were identification of epithelial cell specific epigenotypes, epigenetic alterations in airway epithelial cells specific to asthma, and epigenetic variations specific to genes involved in epithelial functions.

6.2 Airway epithelial cells are an epigenetically distinct cell type

By obtaining other biological tissues, epithelial specific changes in epigenetic modulators of gene expression were identified. From our study of DNA methylation presented in Chapter 3, differential methylation of 80 CpG sites across 67 genes in AECs compared to PBMCs from

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healthy subjects was identified (Figure 3.2B). By combining all phenotypes, this list was narrowed down to a signature set of 57 differentially methylated CpG sites in 47 genes in AECs as compared to PBMCs regardless of disease phenotype. In Chapter 4, the expression of 86 genes involved in epigenetic modification of both histones and DNA was compared in AECs and fibroblasts. Although these two cells reside in adjacent positions in the airway mucosa, they are formed from different embryonic tissues and perform vastly different structural and functional roles in the airway. Significant changes in epigenetic modifiying enzyme families were identified with 43 differentially expressed genes in AECs compared to airway fibroblasts from healthy subjects (Appendix B.4). When a PCA plot with all phenotypes was constructed, a clear distinction could be seen as all AEC samples clustered distinctly separate from all fibroblasts independent of disease phenotype (Figure 4.1).

These findings confirm the importance of the epigenetic landscape to cell fate and specialization programs. The available literature on epigenetics and cellular differentiation has predominantly been performed in stem cell models [202, 410-412]. Although from these studies many epigenetic mechanisms of early development and differentiation have been elucidated, a comprehensive understanding of the epigenetic status in a cell from fully developed tissue is not clear. Recently, Marconett et al. performed an elegant study analyzing gene transcription and chromatin states of primary human lung epithelial cell differentiation [413]. The authors used an in vitro model to differentiate alveolar type 2 (AT2) cells into alveolar type 1 (AT1) cells, collecting transcriptomic and epigenomic data at the beginning, middle, and end of the differentiation process. Using this approach, they identified multiple transcription factors integral to the alveolar epithelial differentiation process and correlated their binding sites to the

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chromatin structure, specifically histone methylation and acetylation, of target genes [413].

Changes to both DNA methylation and histone modifications have also been found in studies of human mammary epithelial differentiation [414, 415]. Additionally, in a model of human intestinal epithelial maturation, both histone methylation and acetylation were modulated during cellular differentiation [416]. Interestingly, it was the combination of chromatin structure and transcription factors present at a locus which dictated the expression of genes in cells undergoing a proliferation or differentiation program [416]. In terms of specific enzymes responsible for epigenetic alterations, data regarding the epithelium is mostly limited to studies of processes occurring in cancer. The histone demethylases KDM5B and KDM6B and methyltransferases

EZH1, EZH2, and WHSC1 have been implicated in controlling epithelial mesenchymal transition by changing the status of histone 3 methylation [343, 344, 417, 418]. Further, numerous studies found that the HDAC family of epigenetic modifiers may be important to epithelial differentiation and proliferation processes [419-423].

The data obtained in this thesis not only generated a profile of epigenetic phenomena occurring within a basal epithelial cell, but also identified unique epigenetic traits differing from phenotypically distinct cells. Construction of a baseline profile specific to the basal airway epithelial cell for both DNA methylation and epigenetic modifying enzymes is integral to the interpretation and understanding of the epigenetic landscape of a healthy airway epithelium. In addition, our findings of 80 differentially methylated CpG sites in AECs compared to PBMCs from healthy subjects and 43 differentially expressed epigenetic modifier genes in AECs compared to fibroblasts from healthy subjects reinforce the involvement of epigenetics in cell specialization. For both DNA methylation and epigenetic modifying enzymes, when combining

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all phenotypes, the experimental results were able to produce an epithelial cell-specific DNA methylation profile and a marked segregation of cell types based on epigenetic modifying enzyme expression.

6.3 Airway epithelial cells from asthmatic subjects have epigenetic anomalies

Once a baseline in healthy subjects was established for epigenetic queries, we focused on disease centric alterations in our cells. We identified disease specific differences in DNA methylation, gene expression of epigenetic modification enzymes, global histone acetylation, and gene specific histone alterations. As many of these experiments had not been performed in AECs derived from asthmatic subjects prior to our study, the insight obtained from our data was novel.

The abnormal epigenetic processes that we observed provide a potential mechanism to explain some of the anomalies in gene expression and susceptibility occurring in the pathogenesis of asthma.

Studies of DNA methylation concerning asthma have uncovered interesting findings yet have mostly been performed on cell sources distant to the lung, and many have used murine models of the disease [216, 222, 228, 233, 424-428]. In these studies, epigenetic regulation of genes such as ALOX12, ARG1 and 2, CD23, which are important in processes such as inflammation, nitric oxide production, and T cell differentiation, were found to be associated with chronic wheeze, childhood asthma, and obesity induced asthma [222, 424, 425]. Our findings differed from these studies and may be a reflection of our use of an airway epithelial cell source with specific functions in the lung tissue. The epigenetic regulation of genes in each tissue and cell type would be unique, as discussed in section 6.2, and disparate DNA methylation between cell types

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is to be expected. However, identification of epigenetic alterations to genes involved in essential biological processes aberrant in asthma across cell types is informative as to the epigenetic malleability, and therefore therapeutic potential, of a specific gene. Our finding of STAT5A hypermethylation and decreased expression of STAT5A in AECS from our asthmatic cohort

(Table 3.4, Figure 3.5) provides a potential target for epigenetic modulation. Recently, the

STAT5A binding sequence was found to be enriched at genomic locations bearing the activating histone modifications H3K9ac and H3K14ac in undifferentiated alveolar epithelial cells [413].

In addition, the STAT5A binding motif was identified as a proliferation- not differentiation- specific motif in human intestinal epithelial cells at sites enriched for another activating histone mark H3K4me2 [416]. Hence aberrant methylation of the STAT5A gene in AECs of asthmatics may be participating in maintaining the “immature” and undifferentiated cellular state seen in the asthmatic airway.

We identified differential expression of epigenetic modifying enzymes in both epithelial cells and fibroblasts from asthmatic as compared to healthy subjects. Although previous studies found contradictory expression of HDAC1 and HDAC2 in the airways of asthmatics [257, 259, 260], neither of these two histone deacetylases showed aberrant expression in asthmatic AECs or fibroblasts in our study. Our data support both Butler et al. and Bergeron et al. who found no differences, by immunohistochemistry, in HDAC1 or HDAC2 expression in the epithelium of asthmatics [259, 260]. As we did not measure the activity of either HATs or HDACs, which have been shown to be elevated and diminished respectively in the airways of asthmatics [257,

258], we cannot conclude that these enzymes are not contributing to abnormal processes in the epithelium of asthmatics. We discovered that HDAC1 and HDAC2 were the greatest and lowest

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expressed members of the HDAC family in both AECs and fibroblasts (Figure 4.7), which may shape the interpretation of future data regarding enzymatic activity.

Our investigation of dysregulated epigenetic modifying enzymes in asthma yielded novel results as we identified dysregulation of histone phosphorylation enzymes in AECs of asthmatics. The expression of histone kinases has yet to be reported in AECs of asthmatics. We found that both

AURKA gene and protein expression and AURKC gene expression were differentially expressed in AECs from asthmatic as compared to healthy subjects (Figure 4.11-4.12). This finding not only has implications for gene-specific histone architecture, but also for chromosomal condensation and mitosis. Both AURKA and AURKC can phosphorylate H3S10 – a site associated with gene activation and cell division [208, 374, 375, 377]. During cell division, the subcellular and temporal localization of each enzyme is unique as AURKA is associated with the centrosomes of interphase and the spindle poles of metaphase cells and AURKC with the centrosomes of anaphase cells [364, 374, 429, 430]. Our findings support previous work by

Freishtat et al. showing that the asthmatic epithelium is intrinsically mitotically dyssynchronous

[378] as inappropriate expression of AURKA and AURKC may alter the normal mitotic process.

In addition to disease-specific changes in the epithelium, we identified differential expression of three epigenetic modifying enzymes in fibroblasts. We found that the histone acetyltransferases

KAT2B and CREBBP were both dysregulated in fibroblasts from asthmatic as compared to healthy subjects (Figure 4.13). Interestingly, both of these HATs have been associated with upstream signaling leading to anti-fibrotic actions [342, 381]. In addition, we also found a decrease in gene expression of the histone arginine methyltransferase PRMT1 in fibroblasts from asthmatics compared to healthy donors (Figure 4.13). Elevated levels of PRMTs have

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previously been identified in lung tissue from murine models of pulmonary inflammation [254].

This contrast from our data may be due to our use of human cells in vitro with no stimulation, whereas the previous study used whole lung tissue and antigen stimulation to generate inflammation. Therefore, the resulting elevation in expression of PRMT1 may have been due to a reaction to stimulus and not an inherent epigenetic anomaly. In line with our study, decreased

PRMT1 expression was associated with increased proliferation of pulmonary arterial smooth muscle both in vitro and in vivo [385]. Thus, we identified potential epigenetic irregularities which may affect proliferation and differentiation and therefore, be a contributing factor to the remodeling process occurring in the airways of asthmatics.

Although our study did not identify changes in gene expression of HATs or HDACs in AECs from asthmatic as compared to healthy subjects, we did not test the activity of these epigenetic modifying enzymes and abnormal activity of the histone acetylation machinery in the airways of asthmatics has been reported [257, 258]. We identified a target histone lysine residue, H3K18, which showed a global increase in acetylation status in the epithelium of asthmatics (Figure 5.1).

This does not imply that differences at a gene-specific level for other histone modifications do not exist, as it is possible that global signals for other modifications are similar but the genes affected are different in asthmatics. Rather, our data indicate that there is an elevated amount of the permissive H3K18ac mark which provides a candidate epigenetic target for gene-specific studies. The importance of H3K18ac was further imparted when we identified elevated occupancy of this histone modification at the ΔNp63, EGFR, and STAT6 genes (Figure 5.3-5.5).

Abnormal expression of each of these genes has previously been identified in studies of the epithelium in asthma and may contribute to the aberrant repair and remodeling program seen in

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disease pathogenesis [69, 96, 99, 112, 113]. However, epigenetic processes regulating the expression of these genes are not fully understood. Histone acetylation is a potential mechanism in the regulation of both ΔNp63 and EGFR expression, but the particular histone modifications involved were not elucidated [107, 396, 397]. Surprisingly, we were not able to significantly alter the expression of EGFR, ΔNp63, or STAT6 by treatment with the HDAC inhibitor TSA

(Figure 5.7). These findings are contradictory with the literature where treatment with HDAC inhibitors was shown to modulate the expression of EGFR and ΔNp63 [107, 396]. This may be due to the use of cell lines and carcinoma cells in the previous studies whereas we used primary

AECs from healthy subjects for these experiments. There may be a different epigenetic process to control histone acetylation, possibly through SIRT7, in primary AECs.

Our data expands on these previous observations by indicating an abnormal histone acetylation program and identifying a novel specific epigenetic target which may disrupt the balance required for normal gene expression. These findings begin to clarify the unique epigenetic landscape which may contribute to the regulation of genes in the airway epithelium in asthma.

6.4 Therapeutic implications

By virtue of the reversibility of epigenetic phenomena, the modulation of epigenetic mechanisms is an attractive target for therapeutic intervention. Indeed, pharmacological agents directed against epigenetic processes are currently in use for a limited number of pathologies, however, there are impediments to the development and implementation of these drugs.

Targeting DNA methylation by either cytidine analogs or non-nucleoside analogs has been a focus of anti-cancer therapies for over a decade [431, 432]. Nucleoside analogs such as 5-

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azacytidine have been approved for the treatment of myeloid malignancies and clinical trials are ongoing for other cancers [431, 432]. Recently, Wu et al. demonstrated modulation of airway inflammation through administration of 5-azacytidine in an asthma mouse model [433]. The effect of cytidine or non-nucleoside analogs is a general non-specific depression of DNA methylation and it is difficult to predict which particular genes will be affected in different tissue types [432].

Our finding of dysregulated AURKA and AURKC expression in AECs from asthmatic as compared to healthy subjects may be a novel target for asthma therapeutics. Aurora kinases are overexpressed in many cancers, and there has been much progress in the development and clinical trials of drugs targeting the aurora kinase family [434]. The majority of the aurora kinase inhibitors which have been developed are not only toxic but are non-specific [434].

However, there is an AURKA inhibitor which decreases activity of pro-proliferative signaling pathways [435] giving it potential as an anti-tumor drug; it is currently undergoing clinical trials

[436, 437].

To decrease the acetylation of H3K18ac, which we identified in AECs from asthmatic as compared to healthy subjects, a HAT inhibitor would need to be selected. Research investigating inhibitors of histone acetylation has identified potential candidates including mimetic, small- molecule enzyme, and protein-interaction inhibitors [438]. However, challenges such as low cell permeability, low potency, lack of specificity, and poor bioavailability have created difficulties in validating the use of these inhibitors [402, 438].

A comprehensive understanding of the epigenetic landscape of the epithelium in asthma is necessary to interpret the potential reaction of these cells to therapeutic interventions targeting 135

the epigenome. This is especially applicable to non-specific pharmacological agents, which may have wide-spread epigenetic consequences. Our findings highlight potential targets for therapeutic intervention although future work is necessary to develop compounds which accurately target epigenetic mechanisms at particular genes known to be dysregulated in disease.

6.5 Study limitations

The complexity of the biological processes ongoing in the airways is very difficult to replicate in a laboratory setting. By using primary AECs isolated from human lungs, we attempted to maintain a relevant system for our studies, but because the use of cell culture was a necessity, our data only presents a simplified view of the biological activity of the epithelium. Also, previous studies have shown epigenetic alterations as a result of early childhood exposure to multiple environmental factors which associate with asthma status. Our study was not a longitudinal design and we had very limited information on our subjects in regards to environmental exposures in early life. Thus, we cannot clarify whether the alterations observed in this study are inherited or imprinted, or as a result of an environmental exposure during gestation or early life. Our data and possible analyses were limited by the relatively small sample size. Although we were able to uncover differences between our populations, we were unable to stratify by other factors and examine interactions. Lastly, the current view of epigenetics is that of a combinatorial process where multiple epigenetic events may occur to enable transcriptional control. We performed studies on a limited number of epigenetic modifications and therefore cannot make conclusions about combinations or interactions of epigenetic phenomena at our gene targets.

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6.6 Future directions

Replication of in vitro experiments using freshly isolated cells which have not been exposed to cell culture conditions is very desirable. This would limit the possibility of alterations to the epigenome through sample handling and storage and therefore may represent the internal airway milieu more accurately. If the amount of starting material required to perform certain experiments requires cell expansion through cell culture techniques, then using either a coculture method with epithelial cells and fibroblasts or an air-liquid interface differentiating the epithelium may produce data more representative of the in vivo state. Furthermore, obtaining data from these models at various time points with different stimuli (wounding, inflammatory agents, allergens) may allow a more comprehensive analysis of the epigenetic response and any anomalies within the asthmatic subject. An investigation of multiple epigenetic processes in this type of setting would allow interpretation of possible combinatorial epigenetic phenomena, such as bivalent or poised states, which may be contributing to transcriptional control.

Studies elucidating the epigenetic process controlling STAT5A are needed. We showed elevated

DNA methylation and diminished expression in AECs from asthmatic subjects, but we do not know if DNA methylation is a result of recruitment of DNMTs to the site by HMTs and/or

HDACs or the opposite – recruitment of HMTs and/or HDACs by DNMTs. We identified this differential DNA methylation in cultured basal cells without stimulus and it is possible our findings are due to an exposure in early life. Longitudinal data profiling the methylation of this gene from subjects with a more comprehensive record of exposures would allow us to interpret a possible cause/source for our findings.

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We identified abnormal expression of enzymes integral to histone phosphorylation and overabundance of H3K18ac in the region of the transcription start site of three genes (ΔNp63,

EGFR, STAT6) involved in wound repair, in asthmatic AECs. However, in the case of the aurora kinases, we did not further investigate the possible effect of changes to the histone target in asthmatic AECs. Studies including identification of target genes through chromatin immunoprecipitation and overexpression/inhibition of AURKA and AURKC activity would clarify the role of this histone modification in the asthmatic airway. In addition, using TSA, we could not modulate the protein expression of ΔNp63, EGFR and STAT6 that showed enrichment for H3K18ac. It is possible that the HDAC SIRT7 is controlling the acetylation status of H3K18 for those genes. Future experiments using a SIRT7 inhibitor would further elucidate the possible epigenetic mechanisms occurring which regulate these genes.

6.7 Final conclusions

In conclusion, the work presented in this thesis contributes to the current knowledge of the epigenetic landscape and cell fate, and identified epigenetic anomalies in the epithelium of asthmatic subjects. Specifically, identification of unique signatures of both DNA methylation and expression of epigenetic modifying enzymes in AECs and showed that AECs were epigenetically distinct from other cell types. This further supports the argument of using specific cell types in studies of epigenetic phenomena as cell specificity is determined by a unique epigenetic profile. Furthermore, this work found that AECs from asthmatic subjects displayed changes in DNA methylation, expression of histone kinases, acetylation of H3K18, and enrichment of this mark at genes important to epithelial functions. Although further research

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needs to be completed to gain a more comprehensive understanding of the mechanisms at work, these data support a role for epigenetic mechanisms in asthma susceptibility.

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Appendices

Appendix A

Supplementary data for Chapter 3.

A.1 Differentially methylated CpG sites in airway epithelial cells (AECs) compared to peripheral blood mononuclear cells (PBMCs). We identified 80 CpG sites which are differentially methylated between AECs and PBMCs. Z- score difference is presented as AEC relative to PBMC.

z-score q- CpG Site difference value (log2) S100A2_P1186_F -2.03 <0.001 FRK_P36_F -1.91 <0.001 TRIM29_E189_F -1.89 <0.001 DDR1_P332_R -1.83 <0.001 RARRES1_P426_R -1.78 <0.001 NBL1_P24_F -1.76 <0.001 LY6G6E_P45_R -1.56 <0.001 HOXA5_E187_F -1.46 <0.001 HOXA5_P479_F -1.45 <0.001 SERPINB5_P19_R -1.41 <0.001 NID1_P677_F -1.41 <0.001 KRT5_E196_R -1.40 <0.001 LIG3_P622_R -1.40 <0.001 IL2_P607_R -1.40 <0.001 SFN_E118_F -1.39 <0.001 TGFB3_E58_R -1.38 <0.001 ACVR1_P983_F -1.33 <0.001 TRIM29_P135_F -1.30 <0.001 TRIP6_P1090_F -1.30 <0.001 SNCG_P53_F -1.29 <0.001 MC2R_P1025_F -1.27 <0.001 163

z-score q- CpG Site difference value (log2) SPDEF_P6_R -1.25 <0.001 TMPRSS4_E83_F -1.25 <0.001 NOS3_P38_F -1.24 <0.001 KLK11_P103_R -1.24 <0.001 PRSS8_E134_R -1.21 <0.001 FGF1_E5_F -1.21 <0.001 MET_E333_F -1.18 <0.001 SNCG_E119_F -1.13 <0.001 GLI2_P295_F -1.11 <0.001 PLAT_P80_F -1.10 <0.001 CXCL9_E268_R -1.09 <0.001 PTK6_E50_F -1.09 <0.001 CTGF_P693_R -1.09 <0.001 MST1R_P87_R -1.05 <0.001 APOA1_P261_F -1.04 <0.001 S100A2_E36_R -1.03 <0.001 RIPK1_P868_F -1.03 <0.001 FGF1_P357_R -1.01 <0.001 RUNX3_E27_R 1.01 <0.001 TIE1_E66_R 1.05 <0.001 CRIP1_P874_R 1.14 <0.001 LTA_P214_R 1.14 <0.001 TM7SF3_P1068_R 1.16 <0.001 PPARG_P693_F 1.17 <0.001 DLC1_E276_F 1.17 <0.001 MT1A_P600_F 1.19 <0.001 SLC22A3_P634_F 1.19 <0.001 PECAM1_P135_F 1.22 <0.001 ICAM1_P386_R 1.23 <0.001 ERCC3_P1210_R 1.24 <0.001 TBX1_P520_F 1.25 <0.001 PSCA_E359_F 1.27 <0.001 RUNX3_P247_F 1.27 <0.001

164

z-score q- CpG Site difference value (log2) RUNX3_P393_R 1.30 <0.001 MPL_P62_F 1.31 <0.001 S100A4_P194_R 1.33 <0.001 AIM2_P624_F 1.37 <0.001 SPI1_P48_F 1.38 <0.001 CD34_P339_R 1.42 <0.001 LAT_E46_F 1.44 <0.001 GP1BB_E23_F 1.47 <0.001 CD86_P3_F 1.51 <0.001 IL10_P348_F 1.53 <0.001 EVI2A_E420_F 1.53 <0.001 OSM_P188_F 1.56 <0.001 RARA_P1076_R 1.56 <0.001 LTB4R_E64_R 1.59 <0.001 PADI4_E24_F 1.62 <0.001 OSM_P34_F 1.66 <0.001 CD34_P780_R 1.68 <0.001 LMO2_E148_F 1.75 <0.001 RAB32_P493_R 1.76 <0.001 LTB4R_P163_F 1.81 <0.001 CD2_P68_F 2.09 <0.001 TBX1_P885_R 2.20 <0.001 TNFSF8_P184_F 2.22 <0.001 AFF3_P122_F 2.36 <0.001 TNFSF8_E258_R 2.87 <0.001 GP1BB_P278_R 3.08 <0.001

165

A.2 Biological functions of differentially methylated genes in airway epithelial cells (AECs) compared to peripheral blood mononuclear cells (PBMCs). We identified 67 genes which contain the 80 differentially methylated CpG sites between AECs and PBMCs, these genes are classified into 19 overrepresented biological functions.

Category Genes Cell-To-Cell PPARG, CTGF, ICAM1, CXCL9, CD2, IL10, APOA1, S100A4, Signaling and PSCA, NOS3, SPI1, DDR1, FGF1, TNFSF8, LTB4R, IL2, LTA, LAT, Interaction CD86, OSM, PECAM1, SERPINB5, CD34 PPARG, RUNX3, ICAM1, CD2, IL10, DLC1, SPI1, TNFSF8, RIPK1, Cell Death IL2, LTA, ERCC3, TGFB3, OSM CTGF, ICAM1, GLI2, CD2, APOA1, S100A4, RARRES1, NOS3, SPDEF, SPI1, LTB4R, IL2, RARA, OSM, SERPINB5, PLAT, Cellular PPARG, RUNX3, CXCL9, NBL1, IL10, ACVR1, TRIP6, SNCG, Movement DLC1, S100A2, DDR1, TIE1, FGF1, MET, LTA, TGFB3, PTK6, CD86, PECAM1, MST1R, TBX1, CD34 Antigen RUNX3, CXCL9, ICAM1, CD2, IL2, APOA1, IL10, CD86, SPI1 Presentation Cellular CD2,IL2,S100A4,CD86 Compromise PPARG, RUNX3, ICAM1, CD2, APOA1, IL10, LMO2, SPI1, DDR1, Cellular FGF1, TNFSF8, MET, MPL, IL2, LTA, RARA, LAT, HOXA5, OSM, Development PECAM1, CD86 Cellular PPARG, LTB4R, ICAM1, CD2, IL2, IL10, LTA, RARA, OSM, CD86, Growth and SPI1 Proliferation PPARG, RUNX3, GLI2, CD2, IL10, LMO2, SPDEF, SPI1, FGF1, Gene TNFSF8, MET, RIPK1, IL2, ERCC3, RARA, HOXA5, TGFB3, OSM, Expression TRIM29, TBX1 Protein PPARG, APOA1, IL10, IL2, LTA, CD86, SFN, NOS3, SPI1 Synthesis Carbohydrate IL2, IL10, APOA1, OSM, FGF1 Metabolism Cell MPL, CD2, IL2, IL10, LAT, CD86, GP1BB, SPI1 Morphology Cellular Function and PPARG, ICAM1, IL10, IL2, LMO2, LAT, CD86, SPI1 Maintenance Molecular APOA1, IL10, NOS3, MC2R Transport Cell Signaling ICAM1, IL10, RARA, NOS3, TBX1

166

Category Genes Small Molecule ICAM1, IL2, IL10, OSM, NOS3, CD34, FGF1 Biochemistry Amino Acid NOS3, CD34 Metabolism Cell Cycle PPARG, PADI4 Cellular Assembly and CD2, IL10, LAT, CD86, MST1R Organization DNA Replication, IL10, MST1R Recombination and Repair

A.3 Differentially methylated sites in healthy airway epithelial cells (AECs) compared to peripheral blood mononuclear cells (PBMCs). We identified 96 CpG sites which are differentially methylated between healthy AECs and PBMCs. Z-score difference is presented as AECs relative to PBMCs.

z-score q- CpG Site difference value (log2) TRIM29_E189_F -2.12 <0.001 S100A2_P1186_F -2.09 <0.001 FRK_P36_F -2.05 <0.001 DDR1_P332_R -2.04 <0.001 NBL1_P24_F -1.76 <0.001 LIG3_P622_R -1.72 <0.001 RARRES1_P426_R -1.72 <0.001 HOXA5_P479_F -1.70 <0.001 LY6G6E_P45_R -1.63 0.001 KLK11_P103_R -1.63 <0.001 TRIP6_P1090_F -1.61 <0.001 CTGF_P693_R -1.53 0.017 SERPINB5_P19_R -1.48 <0.001 MC2R_P1025_F -1.44 <0.001 167

z-score q- CpG Site difference value (log2) SFN_E118_F -1.44 <0.001 HOXA5_E187_F -1.40 <0.001 GLI2_P295_F -1.38 <0.001 KRT5_E196_R -1.37 <0.001 TRIM29_P135_F -1.37 <0.001 ACVR1_P983_F -1.32 <0.001 SNCG_E119_F -1.32 <0.001 IL2_P607_R -1.32 <0.001 CXCL9_E268_R -1.32 <0.001 SNCG_P53_F -1.29 <0.001 NPR2_P618_F -1.26 <0.001 NID1_P677_F -1.26 <0.001 HHIP_P578_R -1.25 <0.001 MET_E333_F -1.25 <0.001 NOS3_P38_F -1.25 0.001 CSF3_E242_R -1.24 <0.001 SPDEF_P6_R -1.24 <0.001 LCN2_P141_R -1.20 <0.001 PRSS8_E134_R -1.20 <0.001 EPHA2_P340_R -1.19 <0.001 TMPRSS4_E83_F -1.18 <0.001 PTK6_E50_F -1.15 <0.001 TGFB3_E58_R -1.14 <0.001 GRB7_E71_R -1.13 <0.001 ZIM3_P451_R -1.10 <0.001 PLAT_P80_F -1.10 <0.001 IGFBP5_P9_R -1.10 <0.001 RIPK1_P868_F -1.10 <0.001 S100A2_E36_R -1.08 <0.001 CALCA_E174_R -1.08 <0.001 NOS2A_E117_R -1.08 <0.001 MST1R_P87_R -1.07 <0.001 CARD15_P302_R -1.06 <0.001

168

z-score q- CpG Site difference value (log2) FGF1_E5_F -1.05 <0.001 FASTK_P598_R -1.05 <0.001 DSC2_E90_F -1.04 <0.001 MMP14_P208_R -1.03 <0.001 ERG_E28_F -1.02 <0.001 CD82_P557_R -1.02 <0.001 DLC1_P695_F 1.10 0.013 MT1A_P600_F 1.13 <0.001 MPL_P62_F 1.13 <0.001 JAK2_P772_R 1.16 <0.001 ITK_E166_R 1.19 <0.001 LTA_P214_R 1.19 <0.001 ERCC3_P1210_R 1.19 <0.001 TBX1_P520_F 1.21 <0.001 PECAM1_P135_F 1.24 <0.001 SPI1_P48_F 1.26 <0.001 CD34_P339_R 1.29 <0.001 TM7SF3_P1068_R 1.29 <0.001 RUNX3_E27_R 1.31 <0.001 CRIP1_P874_R 1.32 0.002 ICAM1_P386_R 1.33 0.001 PSCA_E359_F 1.40 <0.001 S100A4_P194_R 1.40 <0.001 IL10_P348_F 1.40 <0.001 DLC1_E276_F 1.42 <0.001 AIM2_P624_F 1.42 <0.001 SLC22A3_P634_F 1.47 0.002 PADI4_E24_F 1.48 <0.001 RUNX3_P393_R 1.51 <0.001 LAT_E46_F 1.51 <0.001 PPARG_P693_F 1.52 <0.001 OSM_P34_F 1.52 <0.001 RARA_P1076_R 1.53 <0.001

169

z-score q- CpG Site difference value (log2) RUNX3_P247_F 1.56 <0.001 EVI2A_E420_F 1.57 <0.001 OSM_P188_F 1.58 <0.001 GP1BB_E23_F 1.60 <0.001 LTB4R_E64_R 1.60 <0.001 CD86_P3_F 1.76 <0.001 CD2_P68_F 1.80 <0.001 LMO2_E148_F 1.81 <0.001 RAB32_P493_R 1.82 <0.001 CD34_P780_R 1.82 <0.001 LTB4R_P163_F 1.88 <0.001 TNFSF8_P184_F 1.92 <0.001 AFF3_P122_F 2.05 <0.001 TBX1_P885_R 2.51 <0.001 TNFSF8_E258_R 2.94 <0.001 GP1BB_P278_R 3.16 <0.001

A.4 Differentially methylated sites in atopic airway epithelial cells (AECs) compared to peripheral blood mononuclear cells (PBMCs). We identified 71 CpG sites which are differentially methylated between atopic AECs and PBMCs. Z-score difference is presented as AECs relative to PBMCs.

z-score q- CpG Site difference value (log2) HOXA5_E187_F -1.52 0.001 FRK_P36_F -1.52 <0.001 RARRES1_P426_R -1.50 <0.001 S100A2_P1186_F -1.46 <0.001 LY6G6E_P45_R -1.40 <0.001 MC2R_P1025_F -1.35 <0.001 HOXA5_P479_F -1.35 0.002

170

z-score q- CpG Site difference value (log2) TRIM29_E189_F -1.34 <0.001 NBL1_P24_F -1.34 <0.001 KRT5_E196_R -1.30 <0.001 DDR1_P332_R -1.29 <0.001 NID1_P677_F -1.27 <0.001 FGF1_E5_F -1.26 <0.001 SFN_E118_F -1.26 0.001 TGFB3_E58_R -1.25 <0.001 FER_E119_F -1.18 0.003 EGR4_E70_F -1.18 0.007 ACVR1_P983_F -1.17 <0.001 PRSS8_E134_R -1.17 <0.001 TMPRSS4_E83_F -1.14 <0.001 FGF1_P357_R -1.13 <0.001 LIG3_P622_R -1.13 0.002 SERPINB5_P19_R -1.08 <0.001 IL2_P607_R -1.08 <0.001 TRIM29_P135_F -1.07 <0.001 NBL1_E205_R -1.07 <0.001 RIPK1_P868_F -1.07 <0.001 SNCG_E119_F -1.06 0.001 APOA1_P261_F -1.05 0.003 ZMYND10_E77_R -1.05 0.001 SNCG_P53_F -1.05 0.002 CXCL9_E268_R -1.03 <0.001 ID1_P880_F -1.02 0.005 FOSL2_E384_R -1.02 0.014 TIE1_E66_R 1.00 <0.001 SLC22A3_P634_F 1.02 0.001 MT1A_P600_F 1.03 <0.001 LTA_P214_R 1.07 0.003 CD86_P3_F 1.07 <0.001 CSF1R_E26_F 1.09 0.001

171

z-score q- CpG Site difference value (log2) S100A4_P194_R 1.10 0.005 LAT_E46_F 1.15 <0.001 SPI1_P48_F 1.18 <0.001 GP1BB_E23_F 1.19 <0.001 MPL_P62_F 1.19 0.001 AIM2_P624_F 1.21 0.003 EVI2A_P94_R 1.23 0.005 TBX1_P520_F 1.24 0.001 TM7SF3_P1068_R 1.25 0.003 ICAM1_P386_R 1.29 <0.001 RUNX3_P247_F 1.30 0.003 RARA_P1076_R 1.30 0.001 RUNX3_P393_R 1.35 0.003 ERCC3_P1210_R 1.36 0.001 CD34_P780_R 1.38 0.001 RAB32_P493_R 1.45 <0.001 CD34_P339_R 1.50 <0.001 PADI4_E24_F 1.56 0.001 OSM_P34_F 1.57 0.002 EVI2A_E420_F 1.62 0.001 LTB4R_E64_R 1.64 <0.001 IL10_P348_F 1.66 <0.001 OSM_P188_F 1.74 <0.001 LMO2_E148_F 1.77 <0.001 LTB4R_P163_F 1.82 <0.001 CD2_P68_F 1.92 <0.001 TNFSF8_P184_F 1.93 <0.001 AFF3_P122_F 1.94 <0.001 TBX1_P885_R 1.96 <0.001 TNFSF8_E258_R 2.63 <0.001 GP1BB_P278_R 2.99 <0.001

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A.5 Differentially methylated sites in asthmatic airway epithelial cells (AECs) compared to peripheral blood mononuclear cells (PBMCs). We identified 89 CpG sites which are differentially methylated between asthmatic AECs and PBMCs. Z-score difference is presented as AECs relative to PBMCs.

z-score q- CpG Site difference value (log2) S100A2_P1186_F -2.47 0.027 TRIM29_E189_F -2.21 <0.001 FRK_P36_F -2.15 <0.001 DDR1_P332_R -2.13 <0.001 NBL1_P24_F -2.13 <0.001 RARRES1_P426_R -2.06 <0.001 IL2_P607_R -1.74 <0.001 TGFB3_E58_R -1.67 <0.001 LY6G6E_P45_R -1.65 <0.001 SERPINB5_P19_R -1.65 <0.001 NID1_P677_F -1.65 <0.001 SPDEF_P6_R -1.55 <0.001 KRT5_E196_R -1.53 <0.001 SNCG_P53_F -1.53 <0.001 NOS3_P38_F -1.52 <0.001 PLAT_P80_F -1.49 0.005 SFN_E118_F -1.47 0.015 ACVR1_P983_F -1.46 0.003 TRIP6_P1090_F -1.46 <0.001 HOXA5_E187_F -1.45 <0.001 TRIM29_P135_F -1.45 <0.001 TMPRSS4_E83_F -1.40 <0.001 MET_E333_F -1.39 <0.001 LIG3_P622_R -1.38 <0.001 HOXA5_P479_F -1.35 <0.001 NOS2A_E117_R -1.34 <0.001 AXL_P223_R -1.32 <0.001 S100A2_E36_R -1.31 <0.001 PTK6_E50_F -1.31 <0.001

173

z-score q- CpG Site difference value (log2) FGF1_E5_F -1.29 <0.001 PRSS8_E134_R -1.27 <0.001 APOA1_P261_F -1.18 <0.001 CARD15_P302_R -1.18 <0.001 MST1R_P87_R -1.18 <0.001 MMP14_P208_R -1.18 <0.001 GRB7_E71_R -1.17 <0.001 NPR2_P618_F -1.16 <0.001 MMP3_P16_R -1.16 <0.001 KLK11_P103_R -1.14 <0.001 GLI2_P295_F -1.13 <0.001 EPHA2_P340_R -1.11 <0.001 CD82_P557_R -1.09 <0.001 MC2R_P1025_F -1.07 <0.001 ACVR1B_P572_R -1.06 <0.001 SNCG_E119_F -1.03 <0.001 FGF1_P357_R -1.01 <0.001 RUNX3_P247_F 1.06 <0.001 JAK2_P772_R 1.06 <0.001 EMR3_E61_F 1.07 <0.001 ICAM1_P386_R 1.07 0.001 RUNX3_P393_R 1.12 <0.001 MMP2_P197_F 1.13 <0.001 SLC22A3_P634_F 1.14 <0.001 PPARG_P693_F 1.16 <0.001 ERCC3_P1210_R 1.18 <0.001 TIE1_E66_R 1.18 <0.001 LTA_P214_R 1.19 <0.001 DLC1_E276_F 1.26 <0.001 TBX1_P520_F 1.29 <0.001 STAT5A_E42_F 1.37 <0.001 MT1A_P600_F 1.37 <0.001 OSM_P188_F 1.39 0.001

174

z-score q- CpG Site difference value (log2) CD34_P339_R 1.43 <0.001 EVI2A_E420_F 1.44 <0.001 AIM2_P624_F 1.47 0.002 S100A4_P194_R 1.49 0.003 IL10_P348_F 1.50 0.001 MPL_P62_F 1.53 0.002 LTB4R_E64_R 1.54 <0.001 PECAM1_P135_F 1.56 <0.001 CRIP1_P874_R 1.60 <0.001 LAT_E46_F 1.63 <0.001 GP1BB_E23_F 1.63 0.002 SPI1_P48_F 1.64 <0.001 LMO2_E148_F 1.68 0.003 CD86_P3_F 1.68 <0.001 PSCA_E359_F 1.68 <0.001 LTB4R_P163_F 1.73 0.002 PADI4_E24_F 1.79 <0.001 RARA_P1076_R 1.79 0.003 CD34_P780_R 1.83 0.003 OSM_P34_F 1.85 <0.001 RAB32_P493_R 1.96 <0.001 TBX1_P885_R 2.17 0.002 CD2_P68_F 2.49 <0.001 TNFSF8_P184_F 2.68 <0.001 AFF3_P122_F 2.90 <0.001 TNFSF8_E258_R 2.95 <0.001 GP1BB_P278_R 3.09 0.002

175

A.6 Biological functions of differentially methylated core genes in airway epithelial cells (AECs) compared to peripheral blood mononuclear cells (PBMCs). We identified 47 core genes which contain 57 differentially methylated CpG sites between AECs and PBMCs, these genes are classified into 19 overrepresented biological functions.

Category Genes Cell-To-Cell ICAM1, CD2, IL10, SPI1, DDR1, FGF1, TNFSF8, LTB4R, IL2, Signaling and LTA, LAT, CD86, OSM, CD34 Interaction RUNX3, ICAM1, CD2, IL10, LMO2, SPI1, DDR1, FGF1, Cellular TNFSF8, MPL, IL2, LTA, RARA, LAT, HOXA5, TGFB3, OSM, Development CD86, SFN, CD34 Cellular CD2, IL2, S100A4, CD86 Compromise Cellular Growth RUNX3, ICAM1, CD2, IL10, SPI1, FGF1, LTB4R, IL2, RARA, and Proliferation LTA, TGFB3, CD86, OSM RUNX3, ICAM1, CD2, IL10, SPI1, TNFSF8, IL2, LTA, ERCC3, Cell Death LAT, TGFB3, OSM, CD86 RUNX3, CD2, IL10, LMO2, SPI1, FGF1, TNFSF8, IL2, ERCC3, Gene Expression RARA, HOXA5, TGFB3, OSM, TRIM29, TBX1 Protein Synthesis IL2, IL10 RUNX3, ICAM1, CD2, IL10, SPI1, MPL, IL2, LTA, LAT, Cell Morphology HOXA5, CD86, GP1BB, CD34 Cellular Function TNFSF8, RUNX3, ICAM1, CD2, IL10, IL2, LMO2, LTA, LAT, and Maintenance CD86, OSM, SPI1 Cellular Movement RUNX3, ICAM1, CD2, IL2, IL10, LTA, S100A4, OSM, CD86 Carbohydrate IL2, IL10, OSM, FGF1 Metabolism Small Molecule IL2, IL10, OSM, SNCG, SFN, FGF1 Biochemistry Antigen ICAM1, CD2, IL2, IL10, SPI1 Presentation Cell Signaling RARA, TBX1 Cellular Assembly CD2, LAT, CD86 and Organization Drug Metabolism IL2, IL10, OSM, SNCG, SFN Molecular ICAM1, CD2, IL2, IL10, LTA, LAT, S100A4, OSM, MT1A, Transport MC2R, FGF1 Lipid Metabolism SNCG, SFN Cell Cycle OSM, FGF1

176

Appendix B

Supplementary data for Chapter 4.

B.1 Table of epigenetic modifier genes including family, gene, full name, and previous names.

Family Gene Name Previous Names DNA DNMT, MCMT, DNMT1 DNA methyltransferase 1 Methylation AIM, CXXC9 DNMT3A DNA methyltransferase 3A

DNMT3B DNA methyltransferase 3B ICF

methyl-CpG binding domain protein MBD2 DMTase, NY-CO-41 2 Histone KDM1A lysine (K)-specific demethylase 1A AOF2, LSD1 Demethylation KDM5B lysine (K)-specific demethylase 5B JARID1B

KDM5C lysine (K)-specific demethylase 5C JARID1C

KDM4A lysine (K)-specific demethylase 4A JMJD2A

KDM4C lysine (K)-specific demethylase 4C JMJD2C

KDM6B lysine (K)-specific demethylase 6B JMJD3

Histone coactivator-associated arginine CARM1 PRMT4 Methylation methyltransferase 1 DOT1-like histone H3K79 DOT1L KMT4 methyltransferase euchromatic histone-lysine N- EHMT2 G9A, BAT8 methyltransferase 2 lysine (K)-specific methyltransferase KMT2A MLL 2A PRMT1 protein arginine methyltransferase 1 HRMT1L2

PRMT2 protein arginine methyltransferase 2 HRMT1L1

PRMT3 protein arginine methyltransferase 3 HRMT1L3

PRMT5 protein arginine methyltransferase 5 HRMT1L5, SKB1

PRMT6 protein arginine methyltransferase 6 HRMT1L6

PRMT7 protein arginine methyltransferase 7

HRMT1L3, PRMT8 protein arginine methyltransferase 8 HRMT1L4 177

Family Gene Name Previous Names Histone CLLD8, KMT1F, SETDB2 SET domain, bifurcated 2 Methylation CLLL8, C13orf4 SET and MYND domain containing ZNFN3A1, SMYD3 3 ZMYND1, KMT3E Histone Ash1 (Absent, Small, Or Homeotic)- ASH1L KMT2H, ASH1 Methylation Like (SET) lysine (K)-specific methyltransferase KMT2C MLL3 2C lysine (K)-specific methyltransferase KMT2E MLL5 2E nuclear receptor binding SET domain KMT3B, STO, NSD1 protein 1 ARA267 KMT2F, SET1, SETD1A SET domain containing 1A SET1A SETD1B SET domain containing 1B KMT2G, SET1B

KMT3A, HYPB, SETD2 SET domain containing 2 SET2 SETD3 SET domain containing 3 C14orf154

SETD4 SET domain containing 4 C21orf27, C21orf18

SETD5 SET domain containing 5

SETD6 SET domain containing 6

SETD7 SET domain containing 7 KMT7, SET7

SETD8 SET domain containing 8 KMT5A, SET8

SETDB1 SET domain, bifurcated 1 KMT1E, ESET

suppressor of variegation 3-9 SUV39H1 KMT1A homolog 1 SUV420H suppressor of variegation 4-20 KMT5B, CGI85 1 homolog 1 Wolf-Hirschhorn syndrome NSD2, MMSET, WHSC1 candidate 1 TRX5 Histone AURKA aurora kinase A STK15, STK6 Phosphorylation STK12, AIM1, AURKB aurora kinase B AIK2, ARK2 AURKC aurora kinase C STK13, AIE2, AIK3

NEK6 NIMA-related kinase 6

178

Family Gene Name Previous Names Histone p21 protein (Cdc42/Rac)-activated PAK1 p65-PAK Phosphorylation kinase 1 ribosomal protein S6 kinase, 90kDa, RPS6KA3 MRX19, CLS, RSK2 polypeptide 3 ribosomal protein S6 kinase, 90kDa, RPS6KA5 MSK1, RSKL polypeptide 5 Histone DZIP3 DAZ interacting zinc finger protein 3 hRUL138 Ubiquitination Myb-like, SWIRM and MPN MYSM1 domains 1 BAP1, DING, RNF2 ring finger protein 2 HIPI3, RING1B RNF20 ring finger protein 20 BRE1A

UBE2A ubiquitin-conjugating enzyme E2A RAD6A

UBE2B ubiquitin-conjugating enzyme E2B RAD6B, HR6B

USP16 ubiquitin specific peptidase 16

USP21 ubiquitin specific peptidase 21

USP22 ubiquitin specific peptidase 22 USP3L

Histone CREB2, CREBP1, ATF2 activating transcription factor 2 Acetylation HB16 CDYL chromodomain protein, Y-like

class II, major histocompatibility CIITA MHC2TA complex, transactivator KAT14, ATAC2, CSRP2BP CSRP2 binding protein CRP2BP establishment of sister chromatid ESCO1 EFO1, CTF cohesion N-acetyltransferase 1 establishment of sister chromatid ESCO2 RBS, EFO2 cohesion N-acetyltransferase 2 HAT1 histone acetyltransferase 1 KAT1

KAT2A K(lysine) acetyltransferase 2A GCN5, GCN5L2

KAT2B K(lysine) acetyltransferase 2B PCAF

HTATIP, TIP60, KAT5 K(lysine) acetyltransferase 5 ESA1 KAT8 K(lysine) acetyltransferase 8 MYST1, MOF

MYST2, ORC1, KAT7 K(lysine) acetyltransferase 7 HBO1 179

Family Gene Name Previous Names Histone MYST3, ZNF220, KAT6A K(lysine) acetyltransferase 6A Acetylation RUNXBP2 KAT6B K(lysine) acetyltransferase 6B MYST4, MORF

KAT13A, SRC1, NCOA1 nuclear receptor coactivator 1 RIP160 KAT13B, ACTR, NCOA3 nuclear receptor coactivator 3 AIB1, RAC3, SRC3,

TRAM1 PRIP, RAP250, NCOA6 nuclear receptor coactivator 6 ASC2, AIB3 CREBBP CREB binding protein CBP, RSTS, KAT3A

p300, RSTS2, EP300 E1A binding protein p300 KAT3B Histone HDAC1 histone deacetylase 1 RPD3L1 Deacetylation HDAC10 histone deacetylase 10

HDAC11 histone deacetylase 11

HDAC2 histone deacetylase 2 RPD3, YAF1

HDAC3 histone deacetylase 3 RPD3-2, SMAP45

AHO3, BDMR, HDAC4 histone deacetylase 4 HDACA HDAC5 histone deacetylase 5 NY-CO-9

HDAC6 histone deacetylase 6 CPBHM

HDAC7 histone deacetylase 7

CDLS5, RPD3, HDAC8 histone deacetylase 8 MRXS6 MITR, HDAC7B, HDAC9 histone deacetylase 9 HDRP

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B.2 Distribution of p-values obtained from univariate linear regression modeling of age and sex on gene expression for all 86 genes in airway epithelial cells (AECs). We identified 7 and 14 genes with age and sex, respectively, as significant covariates in AECs.

B.3 Distribution of p-values obtained from univariate linear regression modeling of age on gene expression for all 86 genes in fibroblast cells. We identified 1 gene with age as a significant covariate in fibroblast cells.

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B.4 P-values from comparison of epigenetic modifier gene expression between epithelial cells (AEC) and fibroblasts (Fb) from healthy subjects. The mean expression for epigenetic modifier genes is shown for fibroblasts and epithelial cells. P-values are derived from Student‟s t-test and are shown as uncorrected, corrected for the effective number of independent variables (ENIV), and Bonferroni corrected for all 86 genes analyzed.

Mean p-value p-value Gene Mean Fb p-value AEC (ENIV) (Bonferroni) ASH1L 395.777 202.679 0.004 0.084 0.689 ATF2 1114.916 547.503 0.027 0.562 1 AURKA 1011.822 167.103 0.073 1 1 AURKB 416.400 96.406 0.077 1 1 AURKC 10.501 17.606 0.018 0.366 1 CARM1 1647.452 664.608 0.009 0.191 1 CDYL 375.402 504.762 0.183 1 1 CIITA 6.078 33.443 0.073 1 1 CREBBP 475.570 157.860 0.005 0.101 0.832 CSRP2BP 126.906 75.828 0.014 0.287 1 DNMT1 1446.592 348.036 0.017 0.345 1 DNMT3A 160.508 110.375 0.216 1 1 DNMT3B 20.192 4.579 0.068 1 1 DOT1L 144.007 90.262 0.249 1 1 DZIP3 280.525 42.504 1.06E-10 2.22E-09 1.83E-08 EHMT2 657.345 185.230 0.039 0.808 1 EP300 548.774 295.237 0.067 1 1 ESCO1 195.883 201.835 0.877 1 1 ESCO2 272.319 67.089 0.076 1 1 HAT1 946.757 379.290 0.052 1 1 HDAC1 846.391 781.262 0.646 1 1 HDAC10 102.914 29.485 0.057 1 1 HDAC11 104.173 81.984 0.478 1 1 HDAC2 4.257 2.887 0.179 1 1 HDAC3 519.906 548.013 0.725 1 1 HDAC4 300.180 88.704 0.046 0.952 1 HDAC5 18.367 5.455 0.009 0.197 1 HDAC6 207.600 164.751 0.227 1 1

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Mean p-value p-value Gene Mean Fb p-value AEC (ENIV) (Bonferroni) HDAC7 807.430 364.133 0.003 0.071 0.587 HDAC8 265.744 188.505 0.109 1 1 HDAC9 56.281 17.525 0.107 1 1 KAT2A 340.727 341.274 0.992 1 1 KAT2B 70.332 20.793 1.84E10-4 0.004 0.032 KAT5 220.791 84.151 0.019 0.397 1 KAT6A 843.733 459.611 0.038 0.789 1 KAT6B 4.493 0.986 0.194 1 1 KAT7 583.486 213.660 0.012 0.255 1 KAT8 290.496 285.583 0.889 1 1 KDM1A 818.726 628.506 0.060 1 1 KDM4A 570.951 296.426 0.008 0.161 1 KDM4C 67.980 30.897 0.054 1 1 KDM5B 444.441 492.997 0.534 1 1 KDM5C 16.126 14.368 0.806 1 1 KDM6B 189.224 265.582 0.287 1 1 KMT2A 525.820 188.106 0.041 0.850 1 KMT2C 211.020 117.474 0.151 1 1 KMT2E 829.241 304.481 0.009 0.188 1 MBD2 2471.561 2380.155 0.756 1 1 MYSM1 350.000 217.688 0.013 0.282 1 NCOA1 4.470 1.255 0.078 1 1 NCOA3 494.016 359.628 0.271 1 1 NCOA6 259.137 270.288 0.807 1 1 NEK6 1709.525 429.229 0.005 0.094 0.777 NSD1 560.731 304.685 0.015 0.315 1 PAK1 461.095 426.403 0.710 1 1 PRMT1 3279.689 1049.189 0.005 0.099 0.815 PRMT2 2783.166 931.116 0.002 0.047 0.391 PRMT3 247.546 200.947 0.300 1 1 PRMT5 687.656 354.776 0.085 1 1 PRMT6 28.174 6.911 0.049 1 1 PRMT7 285.199 92.347 0.031 0.655 1 PRMT8 7.845 12.994 0.588 1 1 183

Mean p-value p-value Gene Mean Fb p-value AEC (ENIV) (Bonferroni) RNF2 446.131 233.691 0.024 0.506 1 RNF20 529.495 246.498 2.37E10-4 0.005 0.041 RPS6KA3 270.754 259.314 0.853 1 1 RPS6KA5 60.841 61.064 0.992 1 1 SETD1A 341.212 201.439 0.012 0.249 1 SETD1B 445.599 119.137 0.036 0.750 1 SETD2 829.882 477.611 0.045 0.939 1 SETD3 819.622 603.083 0.136 1 1 SETD4 188.042 94.404 0.026 0.542 1 SETD5 1026.214 662.501 0.117 1 1 SETD6 67.174 66.958 0.991 1 1 SETD7 1497.289 842.047 1.21E10-4 0.003 0.021 SETD8 727.139 363.605 0.011 0.237 1 SETDB1 211.228 92.616 0.025 0.521 1 SETDB2 350.433 72.275 0.007 0.136 1 SMYD3 416.849 141.802 0.011 0.224 1 SUV39H1 69.555 26.190 0.014 0.282 1 SUV420H1 599.632 554.744 0.570 1 1 UBE2A 1669.412 564.001 0.015 0.307 1 UBE2B 3544.696 1501.536 0.025 0.524 1 USP16 505.903 197.913 0.016 0.331 1 USP21 303.354 178.094 0.127 1 1 USP22 2247.743 975.546 0.004 0.076 0.623 WHSC1 1535.151 396.203 0.022 0.467 1

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B.5 P-values from comparison of epigenetic modifier gene expression between airway epithelial cells (AECs) from asthmatic and healthy subjects. The mean expression for epigenetic modifier genes is shown for AECs from healthy and asthmatic subjects. P-values are derived from Student‟s t-test and are shown as uncorrected, corrected for the effective number of independent variables (ENIV), and Bonferroni corrected for all 86 genes analyzed.

Mean Mean p-value p-value Gene p-value Healthy Asthma (ENIV) (Bonferroni) ASH1L 202.679 218.802 0.654 1 1 ATF2 547.503 497.876 0.465 1 1 AURKA 167.103 251.477 0.013 0.278 1 AURKB 96.406 153.286 0.172 1 1 AURKC 17.606 10.746 0.029 0.599 1 CARM1 664.608 793.061 0.373 1 1 CDYL 504.762 563.232 0.604 1 1 CIITA 33.443 17.995 0.326 1 1 CREBBP 157.860 134.079 0.337 1 1 CSRP2BP 75.828 75.643 0.992 1 1 DNMT1 348.036 360.495 0.836 1 1 DNMT3A 110.375 130.363 0.526 1 1 DNMT3B 4.579 5.374 0.582 1 1 DOT1L 90.262 102.495 0.586 1 1 DZIP3 42.504 94.208 0.096 1 1 EHMT2 185.230 285.805 0.104 1 1 EP300 295.237 295.204 0.999 1 1 ESCO1 201.835 180.584 0.550 1 1 ESCO2 67.089 72.724 0.848 1 1 HAT1 379.290 360.542 0.738 1 1 HDAC1 781.262 887.785 0.500 1 1 HDAC10 29.485 49.537 0.082 1 1 HDAC11 81.984 106.509 0.222 1 1 HDAC2 2.887 2.853 0.963 1 1 HDAC3 548.013 519.430 0.674 1 1 HDAC4 88.704 110.462 0.258 1 1 HDAC5 5.455 6.496 0.501 1 1 HDAC6 164.751 197.341 0.319 1 1

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Mean Mean p-value p-value Gene p-value Healthy Asthma (ENIV) (Bonferroni) HDAC7 364.133 440.164 0.286 1 1 HDAC8 188.505 213.262 0.495 1 1 HDAC9 17.525 19.344 0.703 1 1 KAT2A 341.274 315.606 0.679 1 1 KAT2B 20.793 21.702 0.900 1 1 KAT5 84.151 95.827 0.560 1 1 KAT6A 459.611 422.005 0.458 1 1 KAT6B 0.986 1.207 0.208 1 1 KAT7 213.660 227.631 0.665 1 1 KAT8 285.583 256.395 0.442 1 1 KDM1A 628.506 648.662 0.796 1 1 KDM4A 296.426 331.786 0.506 1 1 KDM4C 30.897 45.612 0.091 1 1 KDM5B 492.997 506.889 0.885 1 1 KDM5C 14.368 12.643 0.668 1 1 KDM6B 265.582 320.470 0.405 1 1 KMT2A 188.106 193.714 0.872 1 1 KMT2C 117.474 116.432 0.964 1 1 KMT2E 304.481 330.734 0.727 1 1 MBD2 2380.155 2616.747 0.362 1 1 MYSM1 217.688 247.737 0.409 1 1 NCOA1 1.255 1.548 0.494 1 1 NCOA3 359.628 329.469 0.564 1 1 NCOA6 270.288 275.429 0.895 1 1 NEK6 429.229 567.087 0.174 1 1 NSD1 304.685 310.024 0.921 1 1 PAK1 426.403 357.756 0.391 1 1 PRMT1 1049.189 1244.181 0.121 1 1 PRMT2 931.116 903.314 0.838 1 1 PRMT3 200.947 173.170 0.356 1 1 PRMT5 354.776 295.431 0.195 1 1 PRMT6 6.911 9.925 0.286 1 1 PRMT7 92.347 119.527 0.269 1 1 PRMT8 12.994 8.989 0.495 1 1 186

Mean Mean p-value p-value Gene p-value Healthy Asthma (ENIV) (Bonferroni) RNF2 233.691 248.654 0.724 1 1 RNF20 246.498 212.875 0.578 1 1 RPS6KA3 259.314 244.427 0.665 1 1 RPS6KA5 61.064 64.137 0.807 1 1 SETD1A 201.439 218.741 0.601 1 1 SETD1B 119.137 129.319 0.560 1 1 SETD2 477.611 478.259 0.993 1 1 SETD3 603.083 602.614 0.996 1 1 SETD4 94.404 98.068 0.822 1 1 SETD5 662.501 623.749 0.702 1 1 SETD6 66.958 60.471 0.783 1 1 SETD7 842.047 885.209 0.804 1 1 SETD8 363.605 409.055 0.500 1 1 SETDB1 92.616 98.214 0.738 1 1 SETDB2 72.275 93.283 0.411 1 1 SMYD3 141.802 143.855 0.945 1 1 SUV39H1 26.190 40.409 0.101 1 1 SUV420H1 554.744 526.506 0.731 1 1 UBE2A 564.001 602.036 0.657 1 1 UBE2B 1501.536 1357.817 0.447 1 1 USP16 197.913 227.833 0.324 1 1 USP21 178.094 202.733 0.493 1 1 USP22 975.546 1200.402 0.160 1 1 WHSC1 396.203 481.618 0.260 1 1

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B.6 P-values from comparison of epigenetic modifier gene expression between fibroblasts from asthmatic and healthy subjects. The mean expression for epigenetic modifier genes is shown for airway fibroblasts from healthy and asthmatic subjects. P-values are derived from Student‟s t-test and are shown as uncorrected, corrected for the effective number of independent variables (ENIV), and Bonferroni corrected for all 86 genes analyzed.

Mean Mean p-value p-value Gene p-value Healthy Asthma (ENIV) (Bonferroni) ASH1L 395.777 373.839 0.764 1 1 ATF2 1114.916 901.815 0.313 1 1 AURKA 1011.822 427.797 0.176 1 1 AURKB 416.400 151.700 0.128 1 1 AURKC 10.501 7.741 0.065 1 1 CARM1 1647.452 1742.365 0.855 1 1 CDYL 375.402 244.772 0.122 1 1 CIITA 6.078 6.766 0.807 1 1 CREBBP 475.570 261.960 0.023 0.476 1 CSRP2BP 126.906 135.861 0.666 1 1 DNMT1 1446.592 921.373 0.146 1 1 DNMT3A 160.508 102.500 0.149 1 1 DNMT3B 20.192 11.685 0.237 1 1 DOT1L 144.007 121.576 0.680 1 1 DZIP3 280.525 277.436 0.933 1 1 EHMT2 657.345 608.584 0.784 1 1 EP300 548.774 319.188 0.088 1 1 ESCO1 195.883 171.569 0.499 1 1 ESCO2 272.319 107.342 0.144 1 1 HAT1 946.757 487.937 0.100 1 1 HDAC1 846.391 642.498 0.206 1 1 HDAC10 102.914 118.960 0.695 1 1 HDAC11 104.173 111.009 0.839 1 1 HDAC2 4.257 2.318 0.071 1 1 HDAC3 519.906 436.107 0.494 1 1 HDAC4 300.180 255.361 0.568 1 1 HDAC5 18.367 24.658 0.100 1 1 HDAC6 207.600 189.359 0.688 1 1

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Mean Mean p-value p-value Gene p-value Healthy Asthma (ENIV) (Bonferroni) HDAC7 807.430 829.065 0.875 1 1 HDAC8 265.744 237.090 0.626 1 1 HDAC9 56.281 105.473 0.150 1 1 KAT2A 340.727 316.572 0.743 1 1 KAT2B 70.332 98.127 0.034 0.713 1 KAT5 220.791 189.708 0.451 1 1 KAT6A 843.733 690.912 0.334 1 1 KAT6B 4.493 3.629 0.765 1 1 KAT7 583.486 454.189 0.289 1 1 KAT8 290.496 320.803 0.460 1 1 KDM1A 818.726 627.832 0.270 1 1 KDM4A 570.951 450.180 0.308 1 1 KDM4C 67.980 48.666 0.402 1 1 KDM5B 444.441 312.538 0.105 1 1 KDM5C 16.126 13.512 0.747 1 1 KDM6B 189.224 141.393 0.457 1 1 KMT2A 525.820 406.756 0.280 1 1 KMT2C 211.020 153.880 0.351 1 1 KMT2E 829.241 755.087 0.680 1 1 MBD2 2471.561 1961.960 0.236 1 1 MYSM1 350.000 291.027 0.358 1 1 NCOA1 4.470 5.283 0.593 1 1 NCOA3 494.016 366.840 0.312 1 1 NCOA6 259.137 278.865 0.661 1 1 NEK6 1709.525 1776.006 0.850 1 1 NSD1 560.731 431.370 0.155 1 1 PAK1 461.095 453.345 0.951 1 1 PRMT1 3279.689 1983.247 0.043 0.890 1 PRMT2 2783.166 2535.134 0.549 1 1 PRMT3 247.546 178.914 0.159 1 1 PRMT5 687.656 452.456 0.250 1 1 PRMT6 28.174 26.523 0.853 1 1 PRMT7 285.199 201.106 0.332 1 1 PRMT8 7.845 2.220 0.555 1 1 189

Mean Mean p-value p-value Gene p-value Healthy Asthma (ENIV) (Bonferroni) RNF2 446.131 317.814 0.132 1 1 RNF20 529.495 458.226 0.161 1 1 RPS6KA3 270.754 222.988 0.444 1 1 RPS6KA5 60.841 46.342 0.542 1 1 SETD1A 341.212 339.734 0.975 1 1 SETD1B 445.599 220.881 0.095 1 1 SETD2 829.882 712.314 0.465 1 1 SETD3 819.622 697.126 0.536 1 1 SETD4 188.042 144.518 0.209 1 1 SETD5 1026.214 812.168 0.325 1 1 SETD6 67.174 65.051 0.850 1 1 SETD7 1497.289 1464.133 0.917 1 1 SETD8 727.139 690.165 0.802 1 1 SETDB1 211.228 175.744 0.390 1 1 SETDB2 350.433 355.071 0.957 1 1 SMYD3 416.849 416.739 0.999 1 1 SUV39H1 69.555 65.873 0.914 1 1 SUV420H1 599.632 520.822 0.416 1 1 UBE2A 1669.412 1293.800 0.254 1 1 UBE2B 3544.696 2863.930 0.331 1 1 USP16 505.903 407.431 0.388 1 1 USP21 303.354 254.174 0.541 1 1 USP22 2247.743 1998.106 0.626 1 1 WHSC1 1535.151 1189.347 0.511 1 1

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Appendix C

Supplementary data for Chapter 5.

C.1 Quality control for H3K18ac and H3K4me2 chromatin immunoprecipitation. Enrichment of transcriptionally active euchromatin at the GAPDH locus and minimal occupancy at the MYOD1 (transcriptionally inactive euchromatin) and SAT2 (heterochromatin) loci for both histone modifications indicates antibody specificity (n=10).

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C.2 Point counting of histone acetylation in airway epithelium from healthy and asthmatic subjects. Sections were stained for H3K14ac, H3K18ac, H3K27ac, H4K8ac, H4K12ac or H4K16ac. The amount of nuclei stained for H3K14ac, H3K18ac, H3K27ac, H4K8ac, H4K12ac and H4K16ac within the epithelium was quantified for both healthy (white) and asthmatic (blue) subjects. Data are expressed as % positive nuclei ± SEM (n=5). A two-tailed unpaired t-test was performed to determine differences in histone acetylation between asthmatic and healthy AECs.

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C.3 Point counting of histone methylation in airway epithelium from healthy and asthmatic subjects. Sections were stained for H3K4me2 and me3, H3K9me3, H3K27me3, or H3K36me3. The amount of nuclei stained for H3K4me2 and me3, H3K9me3, H3K27me3, and H3K36me3within the epithelium was quantified for both healthy (white) and asthmatic (blue) subjects. Data are expressed as % positive nuclei ± SEM (n=5). A two-tailed unpaired t-test was performed to determine differences in histone methylation between asthmatic and healthy AECs, ** indicates p<0.01.

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