Identification of Novel Targets in Fibrosis

Identification of novel targets in fibrosis

Johanna Verneau

Centre for Rheumatology and Connective Tissue Diseases University College London

2018

Thesis submitted for the degree of Doctor of Philosophy (PhD)

DECLARATION

I, Johanna Verneau, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis.

Johanna Verneau Date: 22/06/2018

ABSTRACT

Fibrosis is a major pathological feature of many chronic diseases characterised by activation of fibroblasts, accumulation of extracellular matrix (ECM) and persistent inflammation which can lead to impaired organ function and ultimately, to organ failure. To date, there is no effective treatment to delay, halt or reverse fibrosis. Therefore, there is an urgent unmet need for a better understanding of the mechanisms leading to pathological fibrosis to identify potential new targets and biomarkers. The aims of this project are to identify genes altered in fibrosis in 3 major organs (lung, skin and kidney); to shortlist relevant genes using key criteria; and, to explore in vitro and in vivo the function of selected genes under normal and fibrotic conditions. An extensive in silico analysis using published literature and microarray datasets from 1988 to 2015 was performed. Hundreds of genes were identified with a wide variety of functions. Among them, 91 were common for pulmonary, dermal and renal fibrosis while 180 were specific genes altered in lung (60 genes), skin (60 genes) and kidney fibrosis (60 genes). A subset of 12 genes was selected using key criteria including potential drugability and the availability of reagents for further study. Expression of the 12 shortlisted genes was explored in human fibrotic fibroblasts and tissues and their function was examined in vitro and in vivo. Data showed that 3 of the 12 selected genes were significantly associated with fibrotic fibroblasts and tissues: Tetraspanin 13 (TSPAN13), Hyaluronan synthase 2 (HAS2) and Cell migration-inducing protein, hyaluronan binding (CEMIP). TSPAN13 has not previously been explored in fibrotic diseases. Here, TSPAN13 was found up-regulated in fibrosis and appears to be a potent pro-fibrogenic molecule with critical functional activities relevant to fibrosis. HAS2 and CEMIP, both genes involved in the hyaluronic acid (HA) pathway, were up- and down-regulated in fibrotic settings respectively. These data showed that TSPAN13, HAS2 and CEMIP are significantly regulated in fibrotic settings suggesting an important role in fibrosis. Therapeutic antibodies are being developed against the two up-regulated targets, TSPAN13 and HAS2, as potential anti-fibrotic therapies.

IMPACT STATEMENT

This joint IMPACT PhD Studentship between UCL and UCB Pharma, a global biopharmaceutical company with world-class facilities, provided an opportunity for develop academic research to translate into the clinic. The aim of this project was to bring new scientific insights into fibrosis and to develop new target molecules and useful biomarkers for the benefit of patients with fibrotic diseases.

Fibrosis is characterised by an accumulation of scar tissue with in an organ which impair organ function and may ultimately lead to organ failure. Fibrosis can affect all the major organs of the human body resulting in as severe reduction in patients’ quality of life and high mortality. Fibrosis accounts of around 45% of all deaths world-wide thus, the prevention and treatment of this condition is a major healthcare priority. To date antifibrotic therapies are limited and there is a pressing need for new and effective treatments.

The work in this thesis compared fibrosis in three organs: skin, lung and kidney and identified both common and unique factors involved in the fibrotic process. The expression and functional relevance of a short-list of genes were tested in human cells in vitro of which two candidates (Tetraspanin (TSPAN) 13 and Hyaluronic Acid Synthase (HAS) 2) showed to be significantly associated with fibrosis and to be over expressed in human disease. These two proteins are now in an ongoing programme to develop novel therapeutic antibodies for clinical assessment in fibrotic diseases. Thus, this project has to potential to impact directly on human health with the attendant social and economic benefits.

ACKNOWLEDGMENTS

I would like to express my gratitude to my supervisors, Dr Markella Ponticos (UCL), Dr Jill Norman (UCL), Professor David Abraham (UCL) and Professor Tim Johnson (UCB) whose expertise, understanding, help and kindness, has added considerably to my graduate experience. I appreciated their knowledge and skill in many areas, assistance in the laboratory work and in writing reports. I am grateful for their expert guidance and continuous support throughout my PhD.

I would like to express my gratitude towards all the members of the Centre for Rheumatology and Connective Tissue Disease and Centre for Nephrology for the assistance they have provided at all levels of my research project. I have been privileged to work with them.

Importantly, I want to thank the patients from the Royal Free NHS Foundation Trust Hospital for their generous contribution to this project. Patient biopsy materials were critical to the success of this project. This work could not have been completed without the colloboration of UCL and UCB Pharma. Therefore, I must acknowledge them for their funding and for giving me the opportunity to pursue this PhD.

I really want to thank my very special friends for their help and support during challenging times. You should know that your advices and words of encouragement were invaluable.

Thank you to all my family, my parents Philippe and Koulssoum, my two brothers Jonathan and Jeremy, my lovely grandmother Nanima and my other half Nazir, for their unconditional love and support all the way from France!

CONFERENCE ATTENDANCE AND PRIZES

Oral presentations:

2nd Translational Biomarkers International Symposium 2016, Laboratory of Immunopathology Keizo Asami (LIKA-UFPE) Recife, Brasil UCL Three Minute Thesis, University College London, London, UK UK Kidney Week 2016, The ICC, Birmingham, UK

Published abstracts:

Identification of novel mediators in lung fibrosis. American Journal of Respiratory and Critical Care Medicine, 2016; 193: 168 Identification of novel drivers of fibrosis in SSc. Rheumatology, 2016; 55; (Suppl 1)

Poster presentations:

Division of Medicine Graduate Research Day 2014, Royal Free Hospital, London, UK 9th International Workshop on Cardiovascular Biology 2014, Royal College of Physicians, London, UK London Matrix Group Symposium 2015, Aging and the Matrix, Royal Veterinary College, London, UK UCB Pharma Studentship Day 2016, Royal College of Surgeons, London, UK 1st Royal Society of Chemistry Symposium on Fibrotic Diseases, 2015, UCB Pharma, Slough, UK 14th International Workshop on Scleroderma Research 2015, St. John’s College, Cambridge, UK 10th International Workshop on Cardiovascular Biology 2016, Royal College of Physicians, London, UK UCB studentship day 2016, Royal College of Surgeons, London, UK Rheumatology 2016, Scottish Exhibition Conference Centre, Glasgow, Scotland 4th World Scleroderma Congress 2016, Congress Center, Lisbon, Portugal American Thoracic Society International Conference 2016, Moscone Center, San Francisco, California

Prizes:

First prize for the best poster, UCL Division of Medicine Graduate Student Research Day 2015 First prize for the best poster, UCB Pharma PhD Science Day 2016

COMMON ABBREVIATIONS

ACE: Angiotensin Converting Enzyme

ACR: American College of Rheumatology

ADPKD: Autosomal Dominant Polycystic Kidney Disease

αSMA, ACTA2: Alpha Smooth Muscle Actin

CEMIP: Cell Migration Inducing Protein, Hyaluronan Binding (KIAA1199)

CKD: Chronic Kidney Disease

CTGF: Connective Tissue Growth Factor

COL: Collagen

COMP: Cartilage Oligomeric Protein dcSSc: Diffuse Cutaneous Scleroderma

DDR1: Discoidin Domain Receptor Tyrosine Kinase 1

DDR2: Discoidin Domain Receptor Tyrosine Kinase 2

ECM: Extracellular Matrix

EMT: Epithelial-to-Mesenchymal Transition

ESPKD: End-stage Autosomal Dominant Polycystic Kidney Disease

ESRD: End-stage Renal Disease

ERK: Extracellular Signal-Regulated Kinase

EULAR: European League Against Rheumatism

FC: Fold-Change

FN: Fibronectin

GAG: Glycosaminoglycan

GREM1: Gremlin 1, DAN Family BMP Antagonist

GWAS: Genome-Wide Association Study

HSC: Hepatic stellate cell

HSCT: Haematopoietic Stem Cell Transplantation

HA: Hyaluronan or Hyaluronic acid

HAS2: Hyaluronan Synthase 2

HIF1: Hypoxia-Inducible Factor 1

HDAC: Histone Deacetylase

HYAL: Hyaluronidase

HSPB1: Heat Shock 27kDa Protein 1 (HSP27)

IL11: Interleukin 11

IL1β: Interleukin 1β

IL6: Interleukin 6

ILD: Interstitial lung disease

IPF: Idiopathic Pulmonary Fibrosis

ITB1: Integrin 1

ITA5: Integrin 5

ITGAV: Integrin αV

JAG2: Jagged 2

JNK: c-Jun-N-terminal Kinase

KRAS: Kirsten Rat Sarcoma Viral Oncogene Homolog lcSSc: Limited Cutaneous Scleroderma mAb: monoclonal Antibody

MAPK: Mitogen-Activated Protein Kinase

MDK: Midkine

MMPs: Matrix Metalloproteinases

MRSS: Modified Rodnan Skin Score

PAH: Pulmonary Arterial Hypertension

PDGF: Platelet-Derived Growth Factor

PF: Pulmonary Fibrosis

PG: Proteoglycan

PH: Pulmonary Hypertension

PPAR-γ: Peroxisome Proliferator-Activated Receptor-γ

RCT: Randomised Controlled Trial

RA: Rheumatoid Arthritis

ROCK: RhoA and Rho-Associated Kinase

ROS: Reactive Oxygen Species

SSc: Scleroderma or Systemic Sclerosis

STAT3: Signal Transducer and Activator of Transcription 3

STAT4: Signal Transducer and Activator of Transcription 4

TAZ: Transcriptional Co-activator with PDZ-binding motif

TGFβ: Transforming Growth Factor β

TGFβR: Transforming Growth Factor β Receptor

THBS1: Thrombospondin-1

TIMPs: Tissue Inhibitor of Metalloproteinases

TNF-α: Tumor Necrosis Factor-α

TSPAN13: Tetraspanin 13 (NET6)

VEGF: Vascular endothelial growth factor

WIF1: WNT Inhibitory Factor 1

WNT: Wingless/Int

YAP: Yes-Associated Protein

TABLE OF CONTENTS

DECLARATION ...... 2

ABSTRACT ...... 3

IMPACT STATEMENT...... 4

ACKNOWLEDGMENTS ...... 5

CONFERENCE ATTENDANCE AND PRIZES ...... 6

COMMON ABBREVIATIONS ...... 7

TABLE OF CONTENTS ...... 10

LIST OF FIGURES ...... 15

LIST OF TABLES ...... 17

CHAPTER 1 INTRODUCTION ...... 18

PART I ...... 20

1.1 Fibrosis or pathological wound healing ...... 21

1.1.1 Wound healing ...... 20

1.1.2 Pathogenesis of fibrosis ...... 22

1.1.3 Extracellular matrix (ECM) ...... 31

PART II ...... 39

1.2 Fibroproliferative diseases ...... 39

1.2.1 Systemic Sclerosis or scleroderma (SSc) ...... 40

1.2.2 Pulmonary fibrosis (PF)...... 43

1.2.3 Renal fibrosis ...... 47

PART III ...... 51

1.3 Models for fibrosis ...... 51

1.3.1 Pre-clinical in vitro models of fibrosis ...... 51

1.3.2 Pre-clinical in vivo models of fibrosis ...... 52

PART IV ...... 56

1.4 Overall project objectives and specific aims ...... 56

CHAPTER 2 MATERIALS AND METHODS ...... 57

2.1 Patients samples ...... 58

2.2 Cell culture ...... 58

2.3 Gene expression analysis ...... 58

2.3.1 RNA extraction ...... 59

2.3.2 RNA concentration and purity ...... 59

2.3.3 Primers design ...... 59

2.3.4 Real time-quantitative polymerase chain reaction (RT-qPCR)...... 61

2.4 Protein expression analysis ...... 63

2.4.1 Protein extraction ...... 63

2.4.2 Determination of protein concentration ...... 63

2.4.3 SDS-PAGE and Western blotting ...... 64

2.4.4 Densitometry ...... 65

2.5 Immunohistochemistry (IHC) ...... 66

2.6 siRNA gene knockdown ...... 68

2.7 Proliferation assays...... 69

2.8 Scratch wound healing assays ...... 70

2.9 Collagen gel contraction assay ...... 70

2.10 Statistical analysis...... 71

CHAPTER 3 ...... 72

IDENTIFICATION AND SELECTION OF GENES INVOLVED IN FIBROSIS: ...... 72

AN IN SILICO STUDY ...... 72

3.1 Introduction ...... 73

3.2 Methods ...... 73

3.2.1 Identification of genes involved in fibrosis using an extensive literature search73

3.2.2 Identification of genes involved in fibrosis using microarray data ...... 74

3.2.3 Pathways analysis ...... 74

3.3 Results of data mining ...... 75

3.3.1 The in silico data mining identified common and organ-specific altered genes in fibrosis ...... 75

3.3.2 Pathway and gene interaction analyses ...... 77

3.4 Selection of candidate genes: key criteria and review of shortlisted genes ...... 80

3.4.1 CEMIP ...... 81

3.4.2 DDR2 ...... 81

3.4.3 GREM1 ...... 82

3.4.4 HAS2 ...... 82

3.4.5 HSPB1 ...... 83

3.4.6 IL11 ...... 83

3.4.7 ITGαV ...... 83

3.4.8 JAG2...... 84

3.4.9 KRAS ...... 84

3.4.10 MDK ...... 84

3.4.11 TSPAN13 ...... 84

3.4.12 WIF1 ...... 85

3.5 Expression of selected genes in lung, skin and kidney fibroblasts ...... 85

3.5.1 Fibrotic markers: COL1A2, CTGF and αSMA...... 85

3.5.2 Expression of the selected genes ...... 87

3.6 Summary of gene expression data ...... 96

3.7 TGFβ1 effects on selected genes ...... 97

3.7.1 Lung fibroblasts treated with TGFβ1 ...... 98

3.7.2 Skin fibroblasts treated with TGFβ1 ...... 100

3.7.3 Kidney fibroblasts treated with TGFβ1 ...... 100

3.8 Discussion and summary of data ...... 103

CHAPTER 4 TETRASPANIN 13: A NOVEL GENE INVOLVED IN FIBROSIS ...... 106

4.1 Tetraspanins (TSPANs): Structure, function and disease implications ...... 107

4.1.1 The TSPAN family ...... 107

4.1.2 Structure of TSPANs ...... 109

4.1.3 TSPAN functions...... 110

4.1.4 TSPANs and diseases ...... 110

4.1.5 The "TSPAN web" ...... 111

4.1.6 TSPAN signaling pathways and post-translational modifications ...... 111

4.2 TSPAN13 ...... 112

4.3 Methods ...... 113

4.3.1 In vivo studies ...... 113

4.3.2 Analyses ...... 114

4.4 Results...... 117

4.4.1 In vitro studies ...... 117

4.4.1.2 siRNA knock-down of TSPAN13 ...... 120

4.4.2 In vivo studies ...... 130

4.4.2.2 siTspan13 in the DNR mouse model of lung fibrosis ...... 133

4.5 Discussion ...... 140

CHAPTER 5 HA, HAS2 AND CEMIP IN FIBROSIS ...... 143

5.1 HA: description, function and regulation ...... 144

5.1.1 HA ...... 144

5.1.2 HA synthesis: HASs ...... 145

5.1.4 HA degradation: Hyaluronidases ...... 148

5.2 Specific methods ...... 150

5.2.1 mRNA and protein expression ...... 150

5.2.2 HA binding protein (HABP) assay ...... 150

5.2.3 ELISA ...... 151

5.3 Results: HAS2 and CEMIP in fibrosis ...... 152

5.3.1 HAS2 in SSc ...... 152

5.3.2 siRNA knock-down of HAS2 attenuated the fibrotic phenotype of SSc fibroblasts ...... 155

5.3.3 HAS2 in others fibrotic conditions ...... 159

5.4.1 CEMIP in SSc ...... 167

5.4.2 siRNA knock-down of CEMIP had no effect on the fibrotic phenotype of SSc fibroblasts ...... 170

5.4.3 CEMIP in others fibrotic conditions ...... 174

5.5 HA2/CEMIP axis in fibrosis ...... 179

5.5.1 Effect of HAS2 and CEMIP knockdown on CEMIP and HAS2 respectively .. 179

5.5.2 Knock-down of HAS2 reduced HA in SSc lung fibroblasts ...... 179

5.5.3 Potential Tspan13 and HAS2 interactions in HA regulation ...... 182

5.6 Discussion ...... 184

CHAPTER 6 CONCLUSIONS AND FUTURE WORK ...... 189

6.1 Identification of target genes in fibrosis ...... 190

6.2 Selection of candidate target genes in fibrosis ...... 191

6.3 Validation of 3 candidate target genes in fibrosis ...... 192

6.4 TSPAN13 a novel fibrotic target ...... 193

6.5 HAS2 and CEMIP axis ...... 196

6.6 Potential interactions between TSPAN13 and HAS2 ...... 199

6.7 TSPAN13 and HAS2: Relevance in other fibrotic diseases ...... 199

6.8 TSPAN13 and HAS2 - potential biomarkers in fibrosis ...... 200

6.9 Concluding remarks ...... 200

APPENDIX ...... 201

REFERENCES ...... 208

LIST OF FIGURES

Figure 1.1 Wound healing 21 Figure 1.2 Normal and abnormal tissue repair 25 Figure 1.3 Fibrogenesis and major organ systems 39 Figure 1.4 SSc characteristics 41 Figure 3.1 Workflow for the in silico identification of fibrotic genes 76 Figure 3.2 Workflow of the analysis of genes interactions 77 Figure 3.3 STRING analysis of the common genes in fibrosis 78 Figure 3.4 String analysis and network of specific genes in fibrosis 79 Figure 3.5 Expression of COL1A2, CTGF and αSMA mRNA in normal and fibrotic fibroblasts from lung, skin and kidney 86 Figure 3.6 Expression of COL1A2 and CTGF protein in normal and fibrotic fibroblasts 87 Figure 3.7 Expression of CEMIP mRNA in normal and fibrotic fibroblasts 88 Figure 3.8 Expression of DDR2 mRNA in normal and fibrotic fibroblasts 88 Figure 3.9 Expression of GREM1 mRNA in normal and fibrotic fibroblasts 89 Figure 3.10 Expression of HAS2 mRNA in normal and fibrotic fibroblasts 90 Figure 3.11 Expression of HSPB1 mRNA in normal and fibrotic fibroblasts 91 Figure 3.12 Expression of IL11 mRNA in normal and fibrotic fibroblasts 91 Figure 3.13 Expression of ITGαv mRNA in normal and fibrotic fibroblasts 92 Figure 3.14 Expression of JAG2 mRNA in normal and fibrotic fibroblasts 93 Figure 3.15 Expression of KRAS mRNA in normal and fibrotic fibroblasts 94 Figure 3.16 Expression of MDK mRNA in normal and fibrotic fibroblasts 94 Figure 3.17 Expression of TSPAN13 mRNA in normal and fibrotic fibroblast 95 Figure 3.18 Expression of WIF1 mRNA in normal and fibrotic fibroblasts 96 Figure 3.19 Effect of TGFβ1 on fibrotic markers and selected genes in normal and SSc lung fibroblasts 99 Figure 3.20 Effect of TGFβ1 on fibrotic markers and selected genes in normal and SSc skin fibroblasts 101 Figure 3.21 Effect of TGFβ1 on fibrotic markers and selected genes in normal and ADPKD kidney fibroblasts 102 Figure 4.1 Schematic representation of a generic tetraspanin 109 Figure 4.2 Experimental protocol for bleomycin-induced lung fibrosis DNR mice 115 Figure 4.3 Ex-vivo microCT imaging of mouse lungs bleomycin-induced lung fibrosis in DNR mice 116 Figure 4.4 mRNA and protein expression of TSPAN13 in normal and fibrotic human fibroblasts (lung and skin) 118 Figure 4.5 TSPAN13 in normal and fibrotic human lung and skin 119 Figure 4.6 Effect of siTSPAN13 on fibrotic markers in human lung fibroblasts 121 Figure 4.7 siTSPAN13 reduced the migration of SSc lung fibroblasts 123 Figure 4.8 siTSPAN13 reduced the contraction of SSc lung fibroblas 124 Figure 4.9 siTSPAN13 reduced the proliferation of SSc lung fibroblasts 125 Figure 4.10 TSPAN13 in normal and fibrotic kidney tissues 127 Figure 4.11 Expression of COL1A2, CTGF, αSMA and TSPAN13 mRNA in normal and IPF fibroblasts 128 Figure 4.12 Expression of COL1A2, CTGF, αSMA and TSPAN13 mRNA

in human hepatic stellate cells in response to TGFβ1 129 Figure 4.13 Expression of Tspan13, Col1a2 and Ctgf mRNA in WT fibroblasts in response to TGFβ1 131 Figure 4.14 Tspan13 in WT and DNR lung tissues 132 Figure 4.15 Expression of Tspan13 Col1a2, Ctgf and Acta2 mRNA in the lungs of saline- or bleomycin-treated DNR mice 134 Figure 4.16 Protein expression of Tspan13 Col1a2, Ctgf and acta2 after siTSPAN13 in saline- and bleomycin-treated mouse lung 135 Figure 4.17 Collagen deposition measured using PSR stain in bleomycin-treatedlungs after Tspan13-knock-down 137 Figure 4.18 Morphologic changes measured using Haemotoxylin and Eosin stain in saline and bleomycin-treated lungs after Tspan13-knock-down 138 Figure 4.19 Fibrosis in saline- and bleomycin-treated lungs after Tspan13-knock-down 139 Figure 5.1 Synthesis and degradation of HA 146 Figure 5.2 Schematic representation of HASs 146 Figure 5.3 Schematic representation of the structure of full-length CEMIP 149 Figure 5.4 Expression of HAS2 mRNA and protein in normal and SSc human fibroblasts from lung and skin 153 Figure 5.5 HAS2 in normal and fibrotic SSc human lung and skin tissues 154 Figure 5.6 Effect of siHAS2 on fibrotic markers in human lung fibroblasts 156 Figure 5.7 siHAS2 reduced the migration of SSc lung fibroblasts 157 Figure 5.8 siHAS2 reduced contraction (a) and proliferation (b) of SSc lung fibroblasts 159 Figure 5.9 HAS2 in normal and CKD human kidney 161 Figure 5.10 HAS2 in normal human kidney and ADPKD tissue 162 Figure 5.11 Expression of COL1A2, CTGF, αSMA and HAS2 mRNA in normal and ADPKD fibroblasts unstimulated or stimulated with TGFβ1 163 Figure 5.12 Expression of COL1A2, CTGF, αSMA and HAS2 mRNA in normal and IPF lung fibroblasts 164 Figure 5.13 HAS2 in normal human lung and IPF tissues 165 Figure 5.14 Expression of COL1A2, CTGF, αSMA and HAS2 mRNA in human HSCs in response to TGFβ1 166 Figure 5.15 Expression of CEMIP mRNA in normal and fibrotic human fibroblasts (lung and skin) 167 Figure 5.16 CEMIP in normal and fibrotic human lung tissues 168 Figure 5.17 CEMIP in normal and fibrotic human skin tissues 169 Figure 5.18 Effect of siCEMIP on fibrotic markers in lung fibroblasts 171 Figure 5.19 Effect of siCEMIP on the migration of SSc lung fibroblasts 172 Figure 5.20 Effect of siCEMIP on the contraction and proliferation of normal and SSc lung fibroblasts 174 Figure 5.21 Expression of COL1A2, CTGF, αSMA and CEMIP mRNA in normal and ADPKD fibroblasts in response to TGFβ1 175 Figure 5.22 CEMIP in normal human kidney and ADPKD 176 Figure 5.23.Expression of COL1A2, CTGF, αSMA and CEMIP mRNA in normal and IPF lung fibroblasts 177 Figure 5.24 Expression of COL1A2, CTGF, αSMA and CEMIP mRNA in HSC in response to TGFβ1 178

Figure 5.25 Effect of knockdown of HAS2 or CEMIP in normal or SSc lung fibroblasts 180 Figure 5.26 Effect of HAS2 and CEMIP knockdown on secreted HA in normal and SSc lung fibroblasts 180 Figure 5.27 siHAS2 reduced HA production in SSc lung fibroblasts 181 Figure 5.28 HA expression in DNR mouse lungs after Tspan13 knock-down 183 Figure 6.1 Summary of the workflow of the study 191 Figure 6.2 Schematic showing the proposed roles of HAS2 and CEMIP in normal and SSc fibrotic tissues 198

LIST OF TABLES

Table 1.1 Animal models of SSc 53 Table 2.1 RT-qPCR primer sequences for selected human genes 60 Table 2.2 Protocol for RT-qPCR assay 62 Table 2.3 Cycling conditions for RT-qPCR assay 62 Table 2.4 Antibodies used for Western Blotting 66 Table 2.5 Antibodies used for IHC 68 Table 3.1 KEGG analysis of specific genes altered in fibrosis 79 Table 3.2 Genes selected for further study 80 Table 3.3 Expression of selected genes in normal and fibrotic fibroblasts 97 Table 4.1 TSPAN family 108 Table 6.1 Summary of the gene expression analysis on the fibrotic markers and selected genes 192 Table 6.2 In vivo targeting of TSPANs 195 Table A Microarray datasets used for the identification of fibrotic genes 200 Table B Common genes involved in fibrosis (lung; kidney; skin) 201 Table C Specific genes up (+) and down (-) regulated in lung fibrosis 203 Table D Specific genes up (+) and down (-) regulated in skin fibrosis 204 Table E Specific genes up (+) and down (-) regulated in kidney fibrosis 205

CHAPTER 1

INTRODUCTION

Fibrosis or pathological scar formation, is an integral part of the pathophysiological mechanism underlying organ failure and disease progression in a multitude of pathologies (1). Fibrotic diseases are a leading cause of morbidity and mortality and account for up to 45% of deaths in the developed world (1). Although the understanding of the cellular and molecular mechanisms of fibrogenesis has improved greatly in recent years, there are still few approved anti-fibrotic therapies and these are limited to specific conditions and/or have only partial efficacy (2).

In fibrotic diseases, the pathophysiology is characterised by an abnormal wound healing process with the excessive production and deposition of extracellular matrix (ECM) proteins, such as collagens, mainly by activated pro-fibrotic fibroblasts driven by chronic inflammation and tissue injury (3). The imbalance between the formation and degradation of ECM is the end result of a complex cascade of cellular and molecular responses (4).

Although there are differences, fibrotic processes in various organs are thought to share some similar pathological characteristics and biological pathways (1). However the degree to which fibrotic diseases in different organs share common pathways or exhibit tissue- or disease-specific mechanisms remains unexplored. Therefore, this thesis focuses on the identification of common and unique altered gene expression profiles in fibrosis in three different human organs (lung, skin and kidney) using an extensive systematic review of the literature (Chapter 3). Subsequently, the role of selected candidates in fibrosis was explored using in vitro and in vivo approaches (Chapter 4 and 5). In this introduction, normal and pathological wound healing are outlined and the pathogenesis of different fibrotic diseases and the models of fibrosis are described.

PART I

1.1 Fibrosis or pathological wound healing

1.1.1 Wound healing

Wound healing is a complex process in which tissue repair following injury involves dynamic interactions between multiple cell types including fibroblasts and immune cells (5). Damage to tissues can result from various stimuli, including infections, autoimmune reactions, toxins, radiation and mechanical injury. Wound healing is regulated by a network of inflammatory mediators and components of ECM essential for wound closure (6). Although the process of healing in response to an injury depends on the organ injured, it presents common stages and may be physiologically, divided into four phases (Figure 1.1).

(i) Haemostasis and coagulation. Haemostasis is the process of the wound being closed by clotting and occurs immediately after injury with aggregation of platelets at the site of tissue damage (6). This in turn triggers formation of a fibrin clot characterised as a temporary and provisional matrix (6). Platelets degranulate and secrete growth factors (described in the section 1.1.2.3) such as platelet-derived growth factors (PDGFs) and vascular endothelial growth factors (VEGFs) which attract neutrophils, and transforming growth factor beta 1 (TGFβ1), which mediate the inflammatory response and tissue debridement (7,8).

(ii) Inflammation. Inflammation is the second stage of wound healing and begins immediately after injury when the damaged blood vessels leak transudate causing localized swelling. Inflammation both controls bleeding and prevents infection (9). Circulating inflammatory cells are recruited to the injured site and release chemokines, cytokines and growth factors (10). Cells from the monocyte/macrophage lineage are critical players following tissue damage, and depletion of macrophages results in impaired wound healing (11). These cells can regulate inflammation, remove apoptotic cell debris and promote cell proliferation (11).

(iii) Proliferation. Tissue repair is characterized by increased immune cell recrutement which orchestrate immunes responses and lead to tissue cells activation (12). In this phase, release of cytokines and growth factors by infiltrating immune cells cause the activation and transdifferentiation of quiescent fibroblasts into contractile myofibroblasts. Neoangiogenesis is essential for wound repair and is very important during this phase. The new blood vessels provide nutrients and oxygen to support cell growth and tissue repair (13).

Figure 1.1. Wound healing. Wound healing is a dynamic process characterised by 4 steps: coagulation, inflammation, proliferation and remodelling. All steps involve a variety of different cell types, including epithelial cells, fibroblasts, endothelial cells and immune cells. Growth factors, chemokines and cytokines are released from these cells during injury within the wound area. Following tissue injury, the initial response is usually bleeding. The cascade of vasoconstriction and coagulation commences with clotted blood immediately impregnating the wound, leading to haemostasis. An influx of inflammatory cells follows, with the release of cellular mediators. During the proliferation phase, activation of fibroblasts, angiogenesis occur and the deposition of new ECM components ensues leading to tissue remodelling. Adapted from (6).

(iv) Maturation. The fourth and last phase is maturation, also called the wound remodelling phase, and leads new tissue formation. Extensive changes in the ECM include assembly and maturation of collagen fibrils in association with various proteoglycans (PG) and glycosaminoglycans (GAG) including hyaluronic acid or hyaluronan (HA) (14). During the maturation phase, collagen is aligned along tension lines and water is reabsorbed so the collagen fibres are closely apposed (15). Myofibroblasts and others cells involved in wound repair but which are no longer needed are removed by apoptosis and some myofibroblasts revert to quiescent fibroblasts (16). Although initially beneficial, the complex repair process becomes pathogenic when it is not controlled, resulting in substantial deposition of ECM components in which normal tissue is replaced with permanent scar tissue (17).

1.1.2 Pathogenesis of fibrosis

In normal tissue repair, injury leads to a complex series of events which include inflammatory responses, vascular alterations, as well as the activation of tissue resident fibroblasts into myfibroblasts (18). These processes result in the deposition and remodelling of the ECM and the onset of fibrogenenic pathways. Under normal tissue repair, there is resolution of inflammation followed by the termination of the fibrogenic responses which result in ECM remodelling and the restoration of normal tissue architecture (11).

However, in abnormal tissue repair or under pathological conditions such as in fibrosis, there is a persistent and ongoing inflammation (termed chronic inflammation) (19). The continual innate and adaptive immune responses that accompany chronic inflammation and vascular damage induce abnormal wound healing (20). Increased activation of fibroblasts result in ECM synthesis and deposition as well as a decrease of ECM degradation. All these alterations lead to the accumulation of fibrotic ECM within the tissue.

Furthermore, the normal processes involved in resolution are disrupted leading to aberrant wound healing, destruction of normal tissue architecture and impaired tissue function and utlimately organ failure (21) (Figure 1.2). Inflammation, myofibroblast formation, the components of ECM and their regulation, as well as, ECM-cell interactions will be described in greater detail in this section.

1.1.2.1 Inflammation

Inflammation is protective mechanism that eliminates the initial cause of the injury, removes damaged cells and also initiates the repair process. Inflammation occurs to restore tissue integrity and homeostasis. The inflammatory process can be acute or chronic (22).

Acute inflammation is a short term, self-limiting response, mediated by immune cells including neutrophils and leucocytes which migrate into the affected tissue through the capillary wall (23). Chemokines such as monocyte chemoattractant protein-1 (MCP1) and cytokines such as interleukin 1 (IL1) and interleukin 6 (IL6) act on immunes cells as well as tissue resident cells and play a crucial role in cell signaling (23). The initial events in acute inflammation are vascular changes including vasodilation and increased capillary permeability that allow immune cells to move out of the blood vessels and into the interstitial spaces and surrounding tissues (22).

These events initiate the defense mechanisms in managing tissue damage. Many molecules have been identified to be involved in the termination of acute inflammation such as the anti-inflammatory factors TGFβ and interleukin 10 (IL10) (24). Once injury has been delt with, inflammatory pathways are turned off. This resolution phase is very important in human tissue repair and if it is in any way compromised, inflammation persists leading to pathological conditions (25).

In fibrosis, there is a prolonged and ongoing inflammatory response also called chronic inflammation, defined as an immune response that persists for several months in which inflammation, tissue remodelling and repair processes occur simultaneously (22). This persistent inflammation leads to aberrant fibroblasts activation, angiogenesis as well as the recrutment of inflammatory cells within the damaged tissue (26). The fibrotic signaling cascade that occurs during chronic inflammation is regulated by various growth factors, proteolytic enzymes, angiogenic factors and fibrogenic cytokines (27,28). These lead to the deposition of ECM that progressively remodels and destroys normal connective tissue resulting in severely impaired tissue architecture and function and eventually resulting in organ failure (Figure 1.2).

Further elucidation of the molecular and cellular bases for chronic inflammation- associated organ fibrosis is imperative for the development of effective anti-fibrotic therapies. Although inflammation typically precedes fibrosis, it has been demonstrated that fibrosis is not always driven by inflammation, suggesting that some mechanisms that regulate fibrogenesis are distinct from those regulating inflammation (26). This may explain the lack of efficacy of anti-inflammatory compounds in the treatment of fibrotic disease (27). The failure of anti-inflammatory therapies has focused attention on the subsequent fibrotic events. Multiple anti-fibrotic strategies which have been studied in depth and these will be described in section 1.1.2.3.

In order to identify anti-fibrotic strategies, there has been considerable effort to characterize the origin and activation of fibroblasts and myofibroblasts in fibrosis. Activated fibroblasts are the main cell type responsible for the deposition of fibrotic matrix and, therefore, interfering with the activation of fibroblasts may be an effective way to inhibit the fibrotic process (20).

Figure 1.2. Normal and abnormal tissue repair. After tissue damage, vascular injury and inflammation lead to the activation of fibroblasts and ECM deposition to restore the integrity of the tissue. In normal wound healing, the ECM is remodelled, the myofibroblasts either undergo apoptosis or revert to a non-activated state, the epithelium is repaired, and injury resolves. In fibrosis, the normal repair and resolution processes are disrupted and the fibrotic reaction persists, leading to scarring and 25 impaired organ function.

1.1.2.2 Myofibroblasts

Fibroblasts are widely distributed throughout the mesenchyme where they synthesize ECM proteins, such as collagen type I and III, that support normal tissue architecture and function (28). These cells play a major role in embryonic development, injury response and tissue repair (29). After tissue injury, fibroblasts differentiate into contractile and secretory myofibroblast phenotype. This process is driven by a variety of growth factors and cytokines released by platelets, endothelial cells, epithelial cells and inflammatory cells (30). Initially, quiescent fibroblasts transiently differentiate into proto-myofibroblasts, characterized by protein alpha-smooth muscle actin (αSMA)- negative stress fibers, before they differentiate to myofibroblasts (31).

The persistent activation, proliferation and survival of myofibroblasts results in aberrant wound healing and tissue fibrosis (32). Historically, the myofibroblast was characterised by electron microscopy which revealed the presence of prominent cytoplasmic microfilament bundles and peripheral focal adhesions in the fibroblastic cells of granulation tissue (30). Mature myofibroblasts are primarily identified by the expression of the cytoskeletal αSMA incorporated into the stress fibres increasing their contractile activity (33). Myofibroblasts fibre alignment and contraction are essential for normal wound closure, in order to reduce the wound size and to re-establish tissue strength (34).

The force exerted by stress fibers through transmembrane integrins can activate TGFβ1, a key growth factor involved in fibrosis (described in detail in section 1.1.2.3.1) (35). In fibrosis, matrix-producing myofibroblasts fail to undergo apoptosis leading to a persistent fibrotic activity and aberrant ECM accumulation.

1.1.2.2.1 Origin of myofibroblasts

Recent investigations have yielded new insights into the origin of myofibroblasts. Myofibroblasts can arise from a variety of different cell types according to the organ and the type of lesion (36). These include:

Tissue-resident fibroblasts. Local fibroblasts are thought to represent the major source of myofibroblastic cells which become activated in response to stimuli such as TGFβ (37). Quiescent tissue fibroblasts, also called resident fibroblasts, are identified as the most likely source of matrix-producing cells during fibrosis.

Pericytes. There are interstitial cell types implicated in tissue homeostasis and development and been identified as important during fibrotic remodelling (38). Pericytes are perivascular cells attached to the abluminal surface of capillaries to stabilize vascular walls and maintain vascular integrity. In fibrosis, pericytes are activated, detach from capillary walls, and transition to myofibroblasts contributing both to an increase in pro-fibrotic myoifibroblasts and to microvascular dysfunction by destabilising the capillaries (39).

Lipofibroblasts. Lipid interstitial cells or lipofibroblasts contain lipid droplets and contractile filaments, similar to those observed in the myofibroblasts (39). Lineage tracing experiments suggested that these cells differentiate into myofibroblasts. Furthermore, transcriptomic and immunohistochemical analysis showed that pulmonary and liver lipofibroblasts, proliferate and increase the synthesis of ECM proteins in lung and liver fibrosis respectively (39).

Epithelial, endothelial and mesothelial cells. Myofibroblasts may develop through the process of the phenotypic transition of epithelial, endothelial and mesothelial cells into mesenchymal cells called (i) epithelial–mesenchymal transition (EMT) (40) (ii) endothelial–mesenchymal transition (EndoMT) and (iii) mesothelial–mesenchymal transition (MMT), respectively (41,42).

(i) EMT describes the process by which epithelial cells undergo phenotypic changes, including the loss of cell-cell adhesion and apical-basal polarity, and acquire mesenchymal characteristics that confer migratory capacity with increased collagen production (43). It has been shown in vitro, that these transdifferentiation processes are mostly driven by TGFβ1 (44). It is accepted that in lung fibrosis, EMT is an important part of the fibrotic process (40). However, the role of EMT in skin and kidney fibrosis remains controversial (45) where it is thought that myofibroblasts are derived mainly from resident fibroblasts and pericytes, respectively (45). Similar doubts about a role of EMT in liver fibrosis have recently been raised (46).

(ii) EndoMT might be an important source of the mesenchymal cells, which give rise to activated myofibroblasts and contribute to the development of fibrotic diseases (46). In vitro and in vivo studies suggests an important role for EndoMT in the development intestinal, lung and cardiac fibrosis (46). The exact role of EndoMT in the pathogenesis of human fibrotic diseases is not yet completely understood, but it is likely to be involved with the vascular alterations that preceed fibrotic events.

(iiI) MMT is characterised as an epithelial‐to‐mesenchymal transition (EMT)‐like process (39). During MMT, mesothelial cells acquire a fibroblast‐like phenotype, with increased capacity to migrate and participate to the ECM deposition (47). MMT serves as a trigger for peritoneal fibrosis and angiogenesis, via up-regulation of TGFβ1 and VEGF, respectively (48,49).

Other cell types. Myofibroblasts may alo derive from circulating bone marrow-derived, specialized inflammatory cells called fibrocytes and participate in fibrotic lesions in several organs (36). It has been shown that there is loss of adipose tissue in fibrosis and that adipocytes can also differentiate into myofibroblasts (50). Circulating and/or resident mesenchymal stem cells (MSCs) are also precursors of myofibroblasts in a variety of organs and injury situations (37).

1.1.2.3 Growth factors in fibrosis

It is important to understand the molecular basis of the signaling pathways contributing to the activation and maintenance of myofibroblasts in order to modulate their activation. A variety of pro-fibrotic factors, stimulating myofibroblast differentiation, have been well studied including growth factors such as TGFβ, connective tissue growth factor (CTGF), PDGFs and fibroblast growth factors (FGFs) (51–53). Several signal transduction pathways including the best characterised pathway mediated by TGFβ, have been linked to the pathophysiology of fibrosis (54).

1.1.2.3.1 Transforming growth factor beta (TGFβ)

TGFβ superfamily includes the three TGFβ isoforms (TGFβ1, β2 and β3), activins and inhibins, growth differentiation factors (GDFs), bone morphogenetic proteins (BMPs), and anti-mullerian hormone (AMH) (55).

The three TGFβ isoforms are synthesized as large precursor proteins (pro-TGFβ) forming homo-dimeric complexes in the endoplasmic reticulum, and are subsequently cleaved near the carboxy terminus, to yield mature 112-amino acid polypeptides, which share 60–80% conservation across the three isoforms (56). The different isoforms have different effects in the context fibrosis with both TGFβ1 and TGFβ2 inducing fibrotic effects while TGFβ3 has been shown to have an anti-fibrotic role (55).

TGFβ is a dimeric cytokine produced from various cells in an inactive form in which the amino-terminal part (also termed latency-associated peptide (LAP)) is non-covalently associated with the mature carboxy terminal peptide.

Upon proteolytic cleavage, the bioactive TGFβ is released from the LAP. Active TGFβ signals through two serine/threonine receptors. TGFβ ligands form receptor complexes with one of 7 type I receptors (also termed activin-like kinase or ALK receptors) in combination with one of 5 type II receptors (ActRIIA, ActRIIB, TGFBRII, BMPRII, and AMHRII) (57). The TGFβ pathway does not operate as a single entity but as part of a signaling network composed of multiple pathways contributing to the complexity of fibrotic mechanisms.

TGFβ signaling regulates a number of physiological and pathological cellular processes, including proliferation, differentiation, apoptosis and migration (55). TGFβ1 is the major pro-fibrotic isoform. In this thesis only TGFβ1 was used and therefore only this isoform will be discussed here (58). TGFβ1 is considered as a key molecule in the inflammatory responses as well as in wound healing, tissue repair and fibrosis. It regulates production of collagens and other ECM proteins (8). TGFβ1 maintains fibroblasts in a persistently activated state via interaction with specific TGFβ receptors (TGFβRs) (59). TGFβ1/TGFβRs interactions activate a multitude of intracellular signaling pathways (reviewed in (60)) including the SMAD-dependent (canonical) and SMAD-independent (non-canonical) pathways (60).

Much emphasis has been placed on inhibiting TGFβ1 and its downstream targets (SMADs and non-SMADs) as an ideal anti-fibrotic strategies (described in Part II). However, treatment directed at pharmacological blockade of TGFβ1 has not yet translated into successful therapy for humans (61). This may be due to the pleiotropic effects of TGFβ1 and the capacity of TGFβ1 to induce both pro-fibrotic effects and protective effects during wound healing and fibrosis. TGFβ1 can also operate indirectly via other mediators such as PDGF, CTGF and cytokines including IL1 and IL6, which together enhance ECM deposition by matrix-producing cells leading to fibrosis (62).

1.1.2.3.2 Connective tissue growth factor (CTGF)

CTGF (also known as CCN2) is a matricellular protein belonging to the CCN protein family, a group of immediate-early response genes that encode secreted and regulatory proteins associated with the ECM (63). CTGF regulates diverse biological processes including cell adhesion, proliferation and differentiation (64).

CTGF is absent or expressed at basal levels in normal adult cells but it is induced during normal wound re@pair including during granulation tissue formation, re- epithelialisation, angiogenesis, matrix formation and tissue remodelling (28,65).

TGFβ1 is thought to be the primary stimulus for CTGF expression although other stimuli can regulate expression in fibrotic conditions including TNFα, PPARγ, hypoxia, biomechanical and shear stress (63). CTGF has been reported to interact with a variety of molecules after being secreted from cells including cytokines and growth factors (66). Extensive data have shown that CTGF interacts particularly with the TGFβ and MAPK signaling pathways (28,67). A variety of CTGF ‘receptors’ have been reported including low density lipoprotein receptor-related protein 1 and 6 (LRP1; LRP6) (68) and several different integrins (eg. αVβ3) (69). CTGF has also been reported to interact with matrix proteins such as fibronectin (FN) (66). CTGF increased FN synthesis induced by TGFβ (70).

CTGF involvement in cancer and fibrosis, including in myofibroblast activation, collagen synthesis and angiogenesis, is well-documented (66,71,72). Numerous in vitro and in vivo studies showed that CTGF induces fibrosis by modulating signaling pathways and transcription factors including p38, ERK1/2, MAPK, SMAD and NFκB (67,73,74). Increasing evidence showed that CTGF levels in plasma, serum and urine may be promising biomarkers in fibrotic disorders and that CTGF is a potential target for anti- fibrotic therapy (described in section 1.2.2.2.1).

1.1.2.3.3 Platelet-derived growth factors (PDGFs)

PDGFs, are potent mitogens and chemoattractant for fibroblasts and smooth muscle cells (75). Five different PDGF polypeptide chains A,B,C and D combine to generate five PDGF isoforms: PDGF AA, AB, BB, CC and DD (76). Members of the PDGF family play an important role during embryonic development and contribute to the maintenance of connective tissue in adults. They are also involved in many cellular processes including wound healing. These isoforms act via two receptor tyrosine kinases (TKs), PDGF receptor α and β (PDGFRα and PDGFRβ) which form three different dimeric receptors: αα, ββ and αβ (77).

Receptor dimerization is a key event in activation since it brings the intracellular parts of the receptors close to each other promoting autophosphorylation and activating the kinase. The receptors bind and activate key molecule including signal transducers and activators of transcription (STATs), which are translocated to the nucleus where they act as transcription factors; the regulatory subunit p85 of the phosphatidylinositol 3′- kinase (PI3K); Ras; and the Erk MAP-kinase pathway (78).

It has been shown that PDGF-mediated signaling is highly associated with human diseases including cancer, atherosclerosis, and fibrosis (75). Recent findings indicate that inhibition of PDGF signaling in particular PDGFRs, presents an attractive therapeutic approach for fibrosis via TK inhibitors (TKI) including Imatinib and Nintedanib (as noted in Part II).

1.1.2.3.4. Fibroblast growth factors (FGFs)

Twenty-two members of the FGF family have been identified in humans which signal through four distinct receptors (FGFR1–4) (79). FGFs are produced by many cells including fibroblasts, endothelial cells and smooth muscle cells (79). As their name indicates, FGFs stimulate fibroblast proliferation but recently it has been shown that they can also stimulate epithelial cell proliferation. Within the FGF family some members can be considered ‘pro-fibrotic’ while others have ‘anti-fibrotic’ effects (80). For instance, FGF-7 stimulates proliferation in lung epithelial cells and has been studied as a potential therapy for lung injury (81). Findings in human lung fibroblasts suggest that FGF-1 can induce apoptosis of fibroblasts and inhibit differentiation into myofibroblasts (82). Further, Kim et al. showed that in lung fibrosis, FGF-1 inhibits TGFβ1-mediated EMT through suppression of TGFβR1 transcription (80). In addition, in vivo overexpression of FGF-1 diminished TGFβ1-induced lung fibrosis, supporting an anti-fibrotic role for FGF-1 (80).

Although targeting mediators such as growth factors described in this section, is a key anti-fibrotic strategy, accumulation of ECM is the key feature of fibrosis and a parallel approach would be to target the altered ECM.

1.1.3 Extracellular matrix (ECM)

The ECM is the non-cellular component present in all tissues and organs, and provides not only essential physical scaffolding for the cells but also provides crucial biochemical and biomechanical cues that are required for tissue morphogenesis, differentiation and homeostasis (83). Remodelling of ECM by myofibroblasts is crucial for wound repair, but if dysregulated, can result in pathological fibrosis (84).

Understanding the mechanisms of dyregulation of ECM production and turnover and the consequences of altered matrix composition is central to a more complete understanding of the pathogenesis of fibrosis.

The ECM is a complex mixture of components of which the most studied are collagens, FN, laminin and other proteoglycans (PGs) which have core glycoproteins with large glycosaminoglycans (GAGs) side-chains. ECM components regulate a variety of cellular functions including cell-cell and cell-matrix interactions, cell proliferation, and migration (29,85).

1.1.3.1 Collagens

Collagens are the most abundant proteins in mammals (∼30% of total protein mass) with 28 different collagens identified (reviewed in detail in (93)). The structure of each member contains at least one triple-helical domain. This common structural feature can range from most of the structure (96% for collagen type I) to less than 10% (collagen type XII). The collagen prolyl 4-hydroxylases (P4Hs), enzymes residing within the endoplasmic reticulum, are responsible of the biosynthesis of collagens (86). The P4Hs catalyze the formation of 4-hydroxyproline in collagens and the reaction products, 4- hydroxyproline residues, serve to stabilize the collagen triple helices under physiological conditions (87). Collagens are deposited in the ECM where they form supramolecular assemblies (88). Collagen family members can be divided into several groups according to the structure they form.

Fibrillar collagens. The fibrillar collagens (types I, II, III, V, XI, XXIV and XXVII) are the most abundant collagens in vertebrates where they play a structural role, contributing to the molecular architecture, shape, and mechanical properties of tissues (89). Among the fibrillar collagens, type I is mainly found in the skin, tendon, internal organs and bone (90). The fibrillar collagens type II, III and V are found in cartilage, reticular fibers, hair and placenta, respectively (90). Collagen I is the most abundant collagen in the human body and forms large collagen fibres. Collagen I is synthesised by several cell types including smooth muscle cells and fibroblasts.

When stimulated by growth factors including TGFβ, CTGF and FGFs, cells synthesize small subunits of collagen, known as procollagen (91). These subunits are then transported out of the cell where they form long chains and mature type I collagen fibers (91).

Non-fibrillar collagens. The non-fibrillar collagens (IV, VI, VII, and XII) are a family of structurally-related short-chain collagens that do not form large fibril bundles (92). Found in basement membranes, they serve as a molecular bridge between different types of matrix molecules in different tissues and are involved in remodelling and fibrosis (93).

Collagens have many functions in cell physiology, they play structural roles and contribute to mechanical properties, organization, and shape of tissues (94). Collagens also participate in cell-matrix interactions via several receptor families (95). They are ligands of integrins, cell-adhesion receptors that lack intrinsic kinase activities. Collagens I–III are also ligands of the dimeric discoidin domain receptors DDR1 and DDR2, that possess TK activity (96). DDR2 will be describe in detail in Chapter 3, section 3.4.2.

1.1.3.2 Fibronectin (FN)

Fibronectin (FN) is a 230–270 kDa glycoprotein ubiquitously present in connective tissue (97) and is composed of an array of multiple repeated modular structures with a series of FN type I (FNI) repeats, FN type II (FNII) repeats, conserved FN type III (FNIII) repeats and alternatively-spliced FNIII repeats (EDA) (98). FN contains the alternatively spliced segments EDA (EIIIA) or EDB (EIIIB). EDA-FN has been shown to be essential for the differentiation of myofibroblasts. It functions both as a regulator of cellular processes and an important scaffolding protein to maintain and direct tissue organization and ECM composition (97).

Increased FN expression has an important role in a range of fibrotic diseases (99,100). Excessive deposition of FN occurs in fibrotic tissues and precedes the collagen deposition. Types I, III, and IV collagen, depend on a previously assembled FN matrix for incorporation into the ECM (101). Further research into the mechanisms that regulate FN-matrix assembly will help to understand how this can be manipulated to prevent aberrant deposition that contributes to pathological conditions.

1.1.3.3 Laminin

The laminins, multifunctional protein present in the ECM, are an important and biologically active part of basal laminae, influencing cell differentiation, migration, and adhesion (102). Laminins are a large family (16 members) of heterotrimers each consisting of an α-, a β- and a γ-subunit joined together through a long coiled-coil domain. Laminins have been implicated in fibrosis, for instance in human and mouse fibrotic lungs, the α3 laminin subunit is lacking with increased inflammation leading fibrosis (110). Recently it has been shown that laminin and the basement membrane, collagen IV were increased in serum of patients with liver fibrosis.

Col IV and laminin are related to degrees of fibrosis and severity of hepatitis, and may reflect hepatic basement membrane metabolism. Therefore, they could be potential biomarkers of the disease (103).

1.1.3.4 Other proteoglycans (PGs)

There are mutiliple PGs involved in wound healing and fibrosis (104). Here, only PGs with an important role in fibrogenesis and which are markedly overexpressed in fibrotic tissues will be discussed (92).

Hyaluronic acid (HA). HA is a major ECM PG. Aside from its role as a hydrating, space-filling polymer, HA regulates numerous different cellular functions and is known to have a role in wound healing and inflammation (105). Importantly, HA deposition is increased in multiple fibrotic diseases (106). HA will be described in detail in Chapter 5, section 5.1.

Versican. Versican is a PG that has important roles in embryogenesis, cell–matrix interactions during adhesion, migration and inflammatory responses (107). Versican was first identified as a fibroblast PG and forms large complexes with HA and other components of the provisional matrix during wound healing (108). It is involved in cardiovascular disorders, cancer and fibrosis (109). Targeting the HA/versican content of provisional matrices in a variety of diseases including fibrosis, is becoming an attractive strategy for intervention (110).

Syndecans. The syndecans are a gene family of four transmembrane heparan sulfate PGs that bind diverse components of the cellular microenvironment (111). They interact with a large variety of ligands including FGFs, TGFβ1 and FN (112). Among the syndecans, syndecan-1 is involved in wound healing, inflammation and vascular biology and is increased in fibrosis (113). Furthermore, syndecan-1 is involved in molecular pathways that are dysregulated during cancer and fibrosis and are related to cell proliferation, apoptosis, angiogenesis, tumour invasion and metastasis (111,113).

Levels of soluble syndecan-1 in serum were found to be associated with the development of proliferative vasculopathy in SSc patients (111). It could potentially be an attractive target for anti-fibrotic treatment with anti-syndecan-1 antibodies.

Vitronectin. Vitronectin is a versatile and multifunctional PG, first detected in the serum and later identified in a variety of tissues (114). It plays a key role in cell attachment to the surrounding matrix and may participate in cell differentiation, proliferation, and morphogenesis (115).

Vitronectin expression was found increased in several fibrotic tissues (115).

Fibrillin. Fibrillin is essential for the formation of elastic fibers found in connective tissue. To date, 3 forms of fibrillin have been described (116). Fibrillin 1 and 2 are well documented whereas fibrillin 3 was discovered more recently and is less well characterized. Fibrillin 1 is expressed throughout life, whereas fibrillin 2 and 3 are thought to be primarily present during development (117). Fibrillins 1 and 2 are ubiquitous PGs that self-polymerize into filamentous microfibrils and are secreted into the ECM by fibroblasts (117). Fibrillin assemblies (microfibrils) can modulate local TGFβ and BMP signals that regulate ECM formation and remodeling (117). Fibrillin 1 expression is increased in many fibrotic tissue including skin and heart (118,119).

1.1.3.5 ECM turnover

In fibrosis, there is both an increase of the ECM synthesis and a reduction of the ECM turnover. ECM turnover is primarily regulated by the matrix metalloproteinases (MMPs) and the plasminogen activators (PA) which are in turn regulated by their intrinsic inhibitors, the tissue inhibitors of metalloproteinases (TIMPs) and plasminogen activator inhibitors (PAIs), respectively (120–122).

1.1.3.5.1 MMPs and TIMPs

The MMPs are a family of more than 25 zinc-dependent endopeptidases that have been implicated in a variety of physiological and pathological processes (123) including cell migration and leukocyte activation (124). They have been largely studied for their role in cancer metastasis (125).

In wound healing, MMPs were initially thought to only function in the resolution phase, particularly during scar resorption, however, recent evidence suggests that they also influence other stages of the wound healing response, such as inflammation where they regulate the activity of inflammatory cytokines and chemokines (124). Therefore, MMPs play a role in fibrosis both through direct and indirect effects. Importantly, MMPs are involved in the degradation of ECM components generating biologically-active fragments which may be pro- or anti-fibrotic (126).

Increased and/or decreased MMPs activity has been implicated in fibrosis (127). For example, increased MMP3 leads to activation of growth factors and cytokines. MMP3 can process latent TGFβ1 to its active form, and also can promotes fibrosis by inducing epithelial cells to undergo EMT (128).

On the other hand, reduced levels of MMP1 were found in fibrotic tissues reducing ECM turnover (129). Furthermore, it has been shown that over-expression of human MMP1 reduces collagen deposition and fibrosis in various animal models (130). These findings indicated that MMP1 can breakdown the excess collagen matrix and similar data have been reported for MMP2 and 8 (123).

The TIMPs are endogenous inhibitors of MMPs which play an important role in tissue remodelling (121). Under physiological conditions, the TIMPs appear to limit MMP activity and spare the ECM from degradation in restricting inflammation, ECM accumulation and fibrosis following injury (131,132). There are four homologous members of the TIMP family (TIMP 1,2,3 and 4) which are capable of inhibiting all known MMPs, however, the efficacy of MMP inhibition varies with each TIMP (121). For example, TIMP1 inhibits many MMPs but not MMP14, 15, 16, 19, and 24 (133). The diverse functions of TIMPs in regulating matrix turnover are dependent on the TIMP, the specific tissue and local tissue environment (121). There is evidence to support a role for TIMP1, TIMP2 and TIMP3 in the inhibition of ECM proteolysis by various MMPs (121).

1.1.3.5.2 Plasminogen activators (PAs) and plasminogen activator inhibitors (PAI)

PAI-1 is a serine protease inhibitor (serpin) identified as an inhibitor of PA. There are 2 main PAs: urokinase (uPA) and tissue plasminogen activator (tPA) which play significant roles in the proteolytic degradation of ECM proteins and the maintenance of tissue homeostasis (122,134). In fibrosis, expression of PAI-1 was found significantly elevated (135). This is thought to contribute to accumulation of matrix proteins in the wound area leading to fibrosis. Also, in vivo knock-out experiments showed that the absence of PAI-1 protects different organs from fibrosis in response to injury (122).

1.1.3.6 ECM-cell interactions

During fibrogenesis, a complex interplay occurs between cells and the ECM mediated via key molecules responsible for cell-matrix interactions. Families of adhesion molecules and ECM receptors such as integrins, collagen receptors such as integrins, DDRs and tetraspanins (TSPANs), are crucial in cell signaling in response to the ECM (136,137).

1.1.3.6.1 Integrins

Integrins are a family of transmembrane cell adhesion molecules (CAMs) which play a key role in mediating cell-matrix interactions (138). Integrins are non-covalent α/β heterodimers of which there are 24 known members in humans,composed from 18 different α subunits and 8 β subunits (139). Integrins relay signals from the ECM to the cell and from the cell to the ECM.

Extracellular signals, result in a wide range of downstream effects on cell adhesion, migration, proliferation, differentiation and apoptosis. Signaling downstream of integrins is mediated by the focal adhesion kinase (FAK), a cytosolic protein which can interact directly with the integrin cytoplasmic tail (140), thereby allowing integrins to link to the actin cytoskeleton and promote signaling to regulate cell activation, fusion, motility, and signaling (141).

In fibrosis, integrins have been implicated in transmitting signals from the altered ECM and modulation of members of the integrin family has been shown to impact on fibrosis (142). In particular, in vitro and in vivo studies have shown an important role for integrin αv (ITGαV) in fibrotic diseases (143) (described in Chapter 3, section 3.4.7).

It is well established that integrin αvβ1 is a major fibroblast αv integrin and can to mediate TGFβ1 activation. Expression of αvβ1 and αvβ5 were found upregulated during fibrosis and both heterodimers have been reported to activate TGFβ1 signaling in vitro (144,145). Integrin αvβ6 has also been shown to play an important role in the progression of lung, liver and kidney fibrosis (146). It has been suggested that inhibition of specific αv integrin subunits might allow a targeted and efficient approach to TGFβ pathway inhibition, providing potential anti-fibrotic effects.

1.1.3.6.2 Tetraspanins (TSPANs)

Tetraspanins (also called TM4SF proteins) are a family of widely expressed four transmembrane domain proteins. It has been shown that TSPANs associate with integrins and also with a variety of growth factors, and intracellular signaling molecules which are important in a range of physiological and pathological processes (147). It is proposed that TSPANs might influence cell behaviours such as migration by modulating integrin signaling, compartmentalisation of integrins on the cell surface, direction of intracellular trafficking and recycling of integrins (148). TSPANs will be described in detail in Chapter 4, section 4.1.1.

1.1.3.6.3 Discoidin domain receptor tyrosine kinase (DDRs)

DDR1 and DDR2, are non-integrin collagen receptors that are members of the tyrosin kinase receptor family. They bind a number of different collagen types and play important roles in embryonic development (149). DDRs are unique in that their collagen-binding extracellular domain exists both as a cell surface-anchored and as a soluble protein in the ECM (136). By interacting with components of the ECM, the cell surface-anchored receptors facilitate cellular functions including cell migration, cell survival, proliferation and differentiation, as well as tissue remodelling (150).

Dysregulated DDR1 or DDR2 function is associated with progression of cancer and fibrosis (151). Targeting the DDRs using established kinase inhibitors has been found to inhibit DDR kinase activity (152). Thus, DDRs have been considered as emerging potential molecular targets in fibrosis. DDR2 will be described in detail in Chapter 3, section 3.2.4.

PART II

1.2 Fibroproliferative diseases

In this thesis, dermal, pulmonary and renal fibrosis will be explored using SSc, IPF and CKD as fibrotic disease models, respectively. As described in Part I, fibrosis can be defined as the accumulation of excess ECM as a result of abnormal tissue repair. It is a complex, multi-factorial interplay of events that ultimately result in organ failure. Almost all fibroproliferative diseases share common pathological processes such as vascular damage, inflammation and fibrosis (1).

The choice of these diseases was based on the fact that they are characterised by severe fibrosis with no effective therapy; on the urgent need for a better understanding of the fibrotic process through comparison of several organ and diseases; on the strong paradigms of fibrotic mechanisms in these disorders; and, importantly, the availability of human samples and reagents.

Figure 1.3. Fibrogenesis and major organ systems. All major organ systems of the body can undergo fibrosis resulting in organ-specific fibrotic diseases including SSc, IPF and CKD. Adapted from (159).

1.2.1 Systemic Sclerosis or scleroderma (SSc)

SSc is one of the most lethal rheumatic diseases, it is defined by vascular dysfunction, impaired angiogenesis, inflammation, autoimmunity and fibrosis (153). Fibrosis in SSc accounts for much of the morbidity and mortality associated with this disease (153).

SSc has a worldwide distribution with a prevalence of 0.07% (in the USA, 276/million adults compared to 88/million in the UK) with a female preponderance: the female to male ratio is reported as 4-5:1 (154).

Although SSc can affect all ages, the usual age of onset is 30-50 years. SSc in children is rare and it has been shown that juvenile SSc (jSSc) differs from the SSc in adults (155). The exact causes of SSc are still unknown although genetic and environmental factors are thought to be involved (156). The pathogenesis of SSc is complex and remains poorly understood. The diagnosis of SSc and disease subsets is based primarily on extent of skin and internal organ involvement and on the circulating autoantibody profiles which are present in more than 95% of SSc patients and are considered as serological hallmarks of SSc (157).

1.2.1.2 Clinical features of SSc

In 2016, an update of the 2009 EULAR recommendations resulted in 16 recommendations being developed (compared to 14 in 2009) that address treatment of several SSc-related organ complications. New criteria were built upon the previous ones incorporating key elements: proximal scleroderma, sclerodactyly, digital pits, pulmonary fibrosis, Raynaud's phenomenon, and scleroderma-specific autoantibodies (158). These new criteria allowed an important improvement in the ability to classify and stage the disease as early, moderate, limited and severe.

1.2.1.3 Subsets of SSc

Based on extent of skin involvement, two different disease subsets have been characterised: limited cutaneous (lcSSc; the most common form which occurs in 60% of SSc patients) and diffuse cutaneous (dcSSc) (159) (Figure 1.4).

LcSSc. The older term for lcSSc is CREST syndrome (Calcinosis, Raynaud's disease, (O)Esophageal dysmotility, Sclerodactyly, Telangiectasia). In lcSSc, the skin of patients is mainly affected (160) where thickening of the skin occurs at the distal joints and the face (161).

DcSSc. In contrast, patients with dcSSc tend to have a more rapid onset of disease that is characterized by more extensive skin thickening, extending beyond the extremities, face, limbs and trunk, and often accompanied by major organ involvement including kidney, lungs and many other organs (162).

Figure 1.4. SSc characteristics. SSc is a complex rheumatic disease characterised by autoimmunity and inflammation, vasculopathy and fibrosis of the skin and internal organs. Two different disease subsets have been characterised: limited cutaneous (lcSSc; the most common form accounting for 60% of SSc patients) and diffuse cutaneous (dcSSc).

1.2.1.4 Organ-based complications in SSc

The term scleroderma arose from the cutaneous manifestations such as skin fibrosis, the hallmark of this disease (163). Skin fibrosis is defined as excess deposition and accumulation of ECM in the dermis. Although SSc is a clinically heterogeneous disorder, almost all patients have skin involvement with the loss of cutaneous elasticity, tightness followed by thickening and hardening of the skin (the dermis and/or subcutaneous tissues) (164). As such, skin manifestations are critical in the initial diagnosis of SSc.

In SSc, renal involvement such as SRC remains an important cause of morbidity and mortality (165). SRC occurs in 5-10% of SSc patients and is characterized by an acute, usually symptomatic malignant hypertension, oliguria, thrombotic microangiopathy and acute renal failure (166).

Athough SRC is defined as rare, it is the most severe complication in SSc. Despite the advent of angiotensin-converting inhibitor therapy, SRC remains a life-threatening complication. FIbrotic and vascular pulmonary manifestations of SSc, including interstitial lung disease (ILD) and pulmonary hypertension (PH) are also severe complications in SSc and are the leading cause of mortality in these patients. A third of patients have pulmonary fibrosis and/or PH, which together account for 60% of SSc- related deaths (167). Lung involvment in SSc (SSc-ILD and SSc-PH) will be descibed in detail in Chapter 1, section 1.2.2.1.1.

1.2.1.6 Potential therapies in SSc

The treatment of SSc is based on organ involvement and requires a multidisciplinary approach (159). Recent developments and findings have led to a better understanding of the pathogenesis of SSc (168). Morbidity and mortality are high among patients with SSc, particularly among those with lung involvement, although various systemic anti- fibrotic therapies have been used for fibrotic disorders, however, showed unsatisfactory results (169).

B-cell depletion

Some patients with SSc showed a dermal gene expression signature consistent with the presence of active B cells (170). This observation led to a clinical trial using Rituximab (RTX), an anti-CD20 antibody, in SSc (171). RTX depletes both circulating B cells and dermal B cells and it has been observed that RTX reduces collagen deposition in skin and lung in patients with SSc (172).

Stem cells

Stem cell transplantation has emerged as a novel rescue therapy for SSc and also for a variety of other refractory autoimmune diseases (173). The strategy involves the ablation of self-reactive immune cells by chemotherapy and the regeneration of a new self-tolerant immune system by transplanted stem cells. Thousands of patients worldwide have received haematopoietic stem cell transplantation (HSCT) as treatment for severe, irreversible autoimmune diseases, with promising results (174). Three randomized trials, exploring the safety and efficacy of autologous HSCT in SSc have been performed (175). Although the results are encouraging, the application of stem cell transplantation remains an area of active investigation.

Cytokine inhibition

Novel therapies blocking key cytokines that drive the fibrotic response are being developed (176). There is evidence supporting important roles for IL6 in the pathogenesis of SSc (177). Recently, Khanna et al. showed the efficacy of tocilizumab, an IL-6 receptor inhibitor, in patients with SSc in a phase II study (treatement for 48 weeks in an open-label extension phase of the faSScinate study with weekly 162 mg subcutaneous tocilizumab). This study revealed skin score improvement and FVC stabilisation in SSc patients (178). Denton et al showed that therapeutic IL6 blockade reversed TGFβ pathway activation in dermal fibroblasts and attenuated their fibrotic phenotype. These data highlights the potential of IL-6 as a therapeutic target in SSc (179).

More recently fresolimumab (GC-1008), a human high-affinity neutralizing monoclonal IgG4 antibody that targets all three isoforms of TGFβ was tested in an open-label trial. Fresolimumab showed a trend to reverse skin fibrosis reducing of TGFβ-regulated gene expression in patients with early dcSSc (180).

A broader approach to cytokine inhibition is provided by inhibitors such as Imatinib (Gleevec), a first-generation TKI. Imatinib inhibits the PDGFRα and β, the discoidin domain receptors (DDR1 and DDR2), c-kit, c-Abl and TGFβR signaling pathways (181). Clinical trial assessing the safety and efficacy of imatinib in the treatment of dcSSc showed improvements in skin thickening (182).

All together these data demonstrate that there are numerous strategies for SSc treatment but there is no effective anti-fibrotic therapy for patients. Treating SSc remains challenging despite advances in medical therapeutics for other rheumatologic diseases. This could be explained by the heterogeneous multisystem nature of SSc and its complex pathophysiology.

1.2.2 Pulmonary fibrosis (PF)

Pulmonary fibrosis (PF) refers to scarring of the lung tissue. It is a chronic lung disease in which there is an extensive remodelling of tissue architecture and accumulation of ECM within the lungs leading to cough, shortness of breath and decreased exercise tolerance (183).

PF is associated with many human diseases including SSc (ILD and PH; Chapter 1, section 1.2.1.4), IPF, non-specific interstitial pneumonia (NSIP) (184), pneumonitis and fibrotic sarcoidosis, a systemic granulomatous lung disease in which 20% of patients may develop lung fibrosis (185). In this project, SSc and IPF were selected as models of lung fibrosis.

1.2.2.1 SSc-PF

PF associated with SSc (SSc-PF) represents the most frequent cause of death in SSc with 10-year survival ranging from 29 to 69% (186). The pathogenesis of SSc-PF is not well understood. It is presumed to be related to abnormal interactions between endothelial cells, lymphocytes/monocytes and fibroblasts leading to an accumulation of ECM. This results in increasingly fibrotic lungs, reducing air-exchanges and contributing to respiratory failure (175). Thus, early detection of PF in SSc is imperative for the optimal management of these patients.

Diagnosis is based on clinical exam, pulmonary function tests, and evaluation of lung involvement by high-resolution computed tomography (CT) (187). To date, the treatment of SSc-PF is limited to targeting inflammatory pathways with corticosteroids or other immunosuppressive therapy.

1.2.2.1.1 Potiential therapies for SSc-PF

B-cell depletion

RTX (mentioned in the section 1.2.1.6) also reduces collagen deposition in lung fibrosis in patients with dcSSc (188). The comparison of RTX-treated versus untreated matched control SSc patients from the EUSTAR cohort demonstrated reduced progression of lung fibrosis, supporting the therapeutic concept of B cell inhibition in SSc-PF (189). RTX has been now considered for patients with IPF (190,191).

There are data to suggest that more broader immunosuppression with cyclophosphamide (CYC) and mycophenolate mofetil (MMF) are effective in the management of SSc-PF (192). Treatment of SSc-PF with either CYC for 1 year or MMF for 2 years, both resulted in significant improvement in lung function over the 2- year study (192). In particular, MMF was usef(179,193)(179)ul in terms of tolerability and should be positioned as one of the first treatment options for SSc-PF (194). These drugs have been also considered for patients with IPF (190,191).

Cytokine inhibition.

Tocilizumab has some beneficial effects on lung dcSSc (195). A phase 2 clinical trial investigating the efficacy and safety of tocilizumab, revealed a clinically significant delay in deterioration of lung function in SSc (188).

Further, it has been shown that serum IL6 levels increased in SSc-PF and appear to be predictive of early disease progression in patients with mild PF (196), suggesting the potential of IL6 as a surrogate marker for clinical outcome in this disease.

Furthermore, low-dose oral imatinib was assessed in the treatment of SSc-PF in a cohort of SSc patients unresponsive to CYC therapy and found to stabilise lung function in a large proportion of patients (197).

Lung transplantation is a viable and potentially life-saving approach for managing with patients with SSc with end-stage PF (183). However, SSc patients are considered poor candidates for transplantation, since multiple comorbidities (including gastroesophageal reflux, dysmotility, renal impairment, skin complications) increase the risk of procedure-related death (198).

1.2.2.2 SSc associated pulmonary arterial hypertension (SSc-PAH)

Pulmonary hypertension (PH) can occur in all forms of SSc and is associated with early mortality. Importantly, SSc patients have the highest prevalence of PH among patients with a cardiovascular disease (CVD) (199). The most common etiology of PH in SSc is pulmonary arterial hypertension (PAH). PH in SSc can also be due to pulmonary veno- occlusive disease, left ventricular systolic or diastolic dysfunction and pulmonary fibrosis (199). The prevalence of SSc-PAH has been estimated to be 8–12% (200) and. PAH remains the leading cause of mortality in SSc (199). PAH can develop any time during the course of SSc and is more common in lcSSc compared to dcSSc (199).

The pathogenesis of SSc-PAH seems to be one of injury of the pulmonary vascular endothelium with subsequent apoptosis, perivascular inflammation and dysregulated angiogenesis leading to arterial obliteration and narrowing due to fibrosis (201). Typical features include dyspnea, fatigue and chest pain and telangiectasias are also associated with PAH in SSc (199).

1.2.2.2.1 Potential targeted therapies for SSc-PAH

Therapies in SSc-PAH fall into 3 groups: (1) prostacyclin analogues and prostacyclin receptor agonists (eg. epoprostenol). Epoprostenol is an intravenous medication approved for the treatment of PAH (202); (2) endothelin receptor antagonists (ERA eg. bosentan). Bosentan is used to treat people with moderate PAH and to reduce the number of digital ulcers (203,204); (3) phosphodiesterase-5 inhibitors (PDE-5i eg. sildenafil). Sildenafil has emerged as an effective first-line oral therapeutic agent for patients with symptomatic PAH (205).

Recently, a stimulator of the soluble guanylate cyclase (sGC) Riociguat was identified as a new therapeutic target for PAH. A Phase II study is investigating Riociguat in patients with PAH associated with idiopathic interstitial pneumonias (PH-IIP) and SSc- PAH (NCT02138825) (206). Riociguat has recently been shown to inhibit TGFβ signaling and have anti-fibrotic effects (eg. reduced skin thickening, myofibroblast differentiation and accumulation of collagen) in preclinical experimental mice model of fibrosis. Data showed that Riociguat also ameliorated lung and gastrointestinal tract fibrosis (206,207). These exicting findings suggest a role for Riociguat for the treatment of fibrotic diseases, especially for the treatment of SSc.

1.2.2.3 Idiopathic pulmonary fibrotis (IPF)

IPF is a progressive, chronic and ultimately fatal lung disease associated with dyspnoea, cough and impaired quality of life (208). About 5 million people are affected world-wide and males are more affected than females (209). Diagnosis are based on computerised tomography (CT) scan or lung biopsy (210). IPF is the most severe form of PF and has the worst prognosis with a mean life expectancy of 3.8 years from diagnosis (169). Despite extensive investigation, the pathogenesis of IPF is poorly understood and the cause of IPF remains unknown. However, environmental factors (eg. gases, smoke, chemicals or dusts) or to genetic predisposition (familial IPF) have been linked to IPF. As in many fibrotic diseases, myofibroblasts within the lungs are the main effector cells in IPF (29).

Injury to alveolar epithelial cells activates pulmonary fibroblasts to myofibroblasts producing ECM (211). Also, EMT process is known to be important in fibrosis in IPF to increase the population of pathological myofibroblasts and ECM deposition in lungs (212).

1.2.2.2.2 Treatment for IPF

Over the last 2 decades the understanding of disease pathogenesis has evolved and 2 anti-fibrotic drugs, pirfenidone and nintedanib, are approved for the treatment of IPF (213).

Pirfenidone was recently approved by the US Food and Drug Administration (214). It is a pyridone with both anti-inflammatory and anti-fibrotic effects which has been shown reduce lung function decline, decrease mortality and improve progression-free survival for IPF patients (215,216).

Nintedanib (BIBF 1120), is a TKI identified as a ‘triple KI’ with activity against the PDGF, VEGF and FGF receptor kinases. It has been extensively studied as a potential angiogenesis inhibitor in various neoplastic disorders (217). The mechanism of action is still not fully elucidated but it is hypothesised that it potently inhibits PDGFR, VEGFR and FGFR1–3 (80). Nintedanib was tested in IPF in phase III clinical trials (two replicate phase III INPULSIS® studies) (218) and shown to slow disease progression with a favorable long-term safety profile (187).

In addition to these 2 anti-fibrotics drugs, a recent phase II clinical trial (NCT01262001) sponsored by FibroGen, using an anti-CTGF antibody demonstrated that FG-3019 was well-tolerated in patients with IPF (219). Furthermore, lung fibrosis improvements were positively correlated with lung function improvements in IPF patients (219). Despite recent advances diagnosis and treatment of IPF remain challenging. In addition to pharmacological therapy, treatment of comorbidities, pulmonary rehabilitation and palliative care are all necessary to for optimal management of this disease (183).

1.2.3 Renal fibrosis

Renal fibrosis status as a hallmark and common outcome across all kinds of progressive CKD (220). The process of renal fibrosis is characterized by an excessive deposition of ECM in the interstitial compartment, leading to scar formation (220). Renal fibrogenesis is a dynamic pathophysiologic process that involves microvascular and tubular cell injury, apoptosis, infiltration of inflammatory cells, interstitial fibroblasts activation and ECM accumulation within the kidney (221).

As in other fibrotic diseases, myofibroblasts are widely recognized as the major matrix- producing cells in the fibrotic kidney. However, tubular epithelial cells, which are the main constituent of renal parenchyma, often localize at the epicentre of damage and are especially vulnerable to damage after kidney injury (221). Microvascular damage and hypoxia are though to be also key drivers of the fibrotic response. These events lead to impaired kidney function and eventually, to end-stage renal disease (ESRD) and ultimatly to renal failure, requiring renal replacement therapy (dialysis or transplantation).

1.2.3.1 Chronic kidney disease (CKD)

CKD is characterised by replacement of normal tissue architecture by excessive ECM (222). CKD is increasing in prevalence and is estimated to affect nearly hundreds of millions of people world-wide (220).

CKD presents a serious public health problem due of the associated morbidity, premature mortality, and high healthcare costs (223). Hypertension and diabetes are the most common causes of CKD (222) although CKD can arise as a result of multiple etiologies including inflammation (eg. glomerulonephritis), infection (pyelonephritis) (224), ischemia, genetic mutation (eg. autosomal dominant polycystic disease or ADPKD) or obstruction (eg. kidney stones).

Regardless of etiology, CKD is defined by glomerulosclerosis and progressive tubulointerstitial fibrosis with tubulointerstitial fibrosis providing the best prognostic indicator of progression to ESRD. A multitude of factors and pathways have been implicated in the pathogenesis of CKD. As in many fibrotic pathologies, TGFβ1 has been shown to be the pre-eminent fibrogenic cytokine (225).

The cellular architecture of the kidney is complex with a wide variety of different cell types and numerous cell types are involved in the fibrotic response. Many cellular sources have been reported to contribute to the renal myofibroblast pool in diseased kidneys including resident kidney fibroblasts, pericytes, tubular epithelial cells and endothelial cells (226). Fibrotic lesions in the kidney were generally thought to be irreversible but recent studies in a kidney fibrosis model suggested that there is some reversibility of renal fibrosis however the degree to which it can be reversed clearly depends on the extent of tissue damage (220).

1.2.3.2 Autosomal dominant polycystic kidney disease (ADPKD)

As mentioned earlier, CKD can be acquired or inherited (223). In this study the genetic disease named autosomal dominant polycystic kidney disease (ADPKD) was used as an example of CKD (227). The choice of this model of CKD was driven by the availability of the human ADPKD kidney cells and tissues. CKD tissues and cells are not generally available as the diseased kidney is not removed and biopsy specimens are limited.

ADPKD is the most common monogenic cause of ESRD (228) and represents the fourth most common cause of ERSD in Western countries (229). It is estimated to affect more than 12 million individuals world-wide (229). The main diagnostic tool are family history and clinical symptoms. Although genetic testing remained relatively limited, there is an increase of molecular genetic diagnostics for ADPKD (230). ADPKD is caused by mutations in either PKD1 (polycystic kidney disease-1; 85% of cases) or PKD2 (polycystic kidney disease-2; 15% of cases) encoding PC1 (polycystin-1) and PC2 (polycystin-2), respectively (227).

Mutations lead to bilateral renal cysts, urinary tract infection, haematuria, hypertension and progressive renal failure due to progressive enlargement of cysts and fibrosis leading progressive loss of renal function (227).

Half of the patients with ADPKD will progress to ESRD and need renal replacement therapy. As in fibrotic diseases in other organs clinical-proven therapies for ADPKD are limited (229). The current strategies are based on the inhibtion of cysts formation rather than fibrosis. Mechanisms contributing to renal fibrosis in ADPKD are complex and incompletely understood.

1.2.3.3 Potential therapies for CKD

Traditionally the frontline treatment for CKD is regulation of blood pressure using angiotensin converting enzyme inhibitors (ACEi) and angiotensin receptor blockers (ARB) (231). To date, there have been only a few trials of anti-fibrotic and anti- inflammatory drugs in CKD (232).

Vitamin D. Vitamin D deficiency is common in CKD patients. Progression of CKD is associated with low vitamin D levels and serum levels of vitamin D appear to have an inverse correlation with kidney function (233). Also, in animal models of CKD, treatment with vitamin D alone or in combination with renin-angiotensin-aldosterone system (RAAS) blockade reduces proteinuria, glomerulosclerosis and tubulointerstitial fibrosis (234). These data emphasise the potential role of vitamin D in CKD and its clinical use (235).

Cytokine blockade. Fresolimumab (GC-1008, Genzyme) a human monoclonal antibody (mAb) that inactivates all forms of TGFβ, has been used in SSc, also showed promising results in CKD. In a phase I multicenter in patients with treatment-resistant primary focal segmental glomerulosclerosis (FSGS), a single-dose infusion was well tolerated (188,236). A subsequent phase II multicenter, double-blind, randomized study of fresolimumab in patients with steroid-resistant primary FSGS was recently completed, but the results are yet to be reported (ClinicalTrials.gov: NCT01665391) (237).

Pirfenidone. It is an anti-fibrotic and anti-inflammatory agent which acts by reducing lung fibrosis through downregulation of the production of growth factors and procollagens I and II (213). In the kidney, the effects of pirfenidone in suppressing renal fibrosis have been shown in several experimental models (described in Section 1.3.5.3) (238–240). A phase II trial study demonstrated that pirfenidone significantly slowed renal functional decline rate in patients with renal fibrosis (241).

1.2.3.4 Potential therapies for ADPKD

Current treatment strategies in ADPKD, not relevant to others CKD, include reducing cyclic adenosine monophosphate levels, cell proliferation (eg. fibroblasts) and fluid secretion (242). Many clinical trials have been undertaken to study the effect of diverse drugs on the growth of renal and hepatic cysts, and on deterioration of renal function. Drugs tested in clinical trials included: mTOR inhibitors (eg. sirolimus), somatostatin analogues (eg. octreotide), and most recently, vasopressin V2 receptor antagonist (tolvaptan) (242).

PART III

1.3 Models for fibrosis

The use of preclinical models (in vitro and in vivo) for fibrosis is essential to support the validation of new targets; the testing and optimization of new treatments; and, to provide insight into the mechanisms of fibrosis. There are established in vivo models for fibrosis for preclinical development and numerous compounds have shown efficacy in limiting development of fibrosis using animal models, however few of these compounds have replicated these beneficial effects in clinical trials (17).

The lack of adequate and reliable systems to study fibrogenesis has in vitro impeded the development of anti-fibrotics drugs and because there are not covering the complexity of the fibrotic response in vivo in human and it is very difficult to establish the right animal models for a specific disease. Despite these limitations, in vivo and in vitro assay models are essential for a better understanding of a disease and also for testing of potential anti-fibrotic therapies.

1.3.1 Pre-clinical in vitro models of fibrosis

1.3.1.1 Primary fibroblasts cells

In vitro studies are necessary for the understanding of a disease mechanisms, the identification of new candidate targets and also the testing of potential anti-fibrotic compounds in a reproducible, cheap and rapid manner (243). Primary human fibroblasts, directly derived from healthy and fibrotic tissue, provide a good reflection of fibrosis in a in vivo situation. However, when seeded on a plastic in culture, isolated fibroblasts spontaneously activate and turn into myofibroblast-like cells (244).

This spontaneous in vitro activation triggers a differential gene expression profile in comparison with the in vivo counterpart process, which may not reflect the pathophysiological mechanisms manifested during the fibrogenesis process. Several in vitro models of fibrosis suitable for high-throughput screening (HTS) of anti-fibrotic molecules utlising monolayer cultures have been reported (245).

Among such models, in vitro models of TGFβ1-induced fibrosis in which normal fibroblasts are treated with TGFβ1, are commonly used because as this key fibrotic mediator promotes the differentiation of fibroblasts into myofibroblasts with ECM production and fibrotic phenotype (246).

1.3.1.1 Other in vitro models

Cell lines. Cell lines appeared as an alternative to primary cells and offer advantages, such as ease of use, unlimited supply and high interlaboratory reproducibility of results. However, cell lines may lose differentiated functionality and morphology, thus questioning their in vivo relevance Nevertheless, a variety of cell lines from rodent and human origin has been developed and are abundantly used in order to study fibrosis.

Co-culture. Although useful, cultures consisting of only one cell type are limiting the study of fibroblasts activation and fibrosis. The interactions between different cell types are critical for disease progression. Therefore, co-cultures, joining 2 cell types, have been developed (eg. epithelial cells with fibrotic fibroblasts). The co-culture systems have been improved by applying a number of strategies, including seeding between 2 layers of ECM compounds favoring the 3D architecture of cells. It is anticipated that other recent advances such as bioprinting, spheroids, decellularized organs and also the use of microfluidic bioreactors will lead to valid 3D in vitro models of fibrosis (247).

A widely used in vitro fibrosis model is the culture of monolayer of primary fibroblasts from healthy and fibrotic patients. Such are primary cell cultures, with low passage, were used in this project, where healthy/SSc skin, healthy/SSc/IPF lung and healthy and ADPKD kidney fibroblasts were used as well as TGFβ1-treated fibroblasts.

1.3.2 Pre-clinical in vivo models of fibrosis

Animal models have been used to study numerous aspects of disease and provide the opportunity to investigate the role of particular genes or efficacy of therapeutics in a complex physiological system without initial risk to humans.

The most widely animal model used is rodent. Most rodent models with over 95% of the mouse genome is similar to that in humans (248). Mice can relatively easily be genetically engineered for selective tissue-specific expression or deletion and despite some disadvantages, they replicate some human disease more accurately. In this project, mouse models were used and are described below.

1.3.2.1 In vivo models of SSc

Multiple mouse models have been developed and used to study SSc (249). These include wild-type mice treated with exogenous stimuli (eg. bleomycin-induced lung or skin fibrosis); transgenic models (eg. the TβRIIΔk models); and, knock-out mice (eg. the Cav1 model) (Table 1.1).

Table 1.1. Animal models of SSc. Different mouse models of SSc classified into 3 groups (inducible models; transgenic models and knock-out models) with their pathophysiologic features indicated as S, L, K for skin, lung and kidney fibrosis, respectively.

Bleomycin-induced fibrosis. Among wild-type models where fibrosis was induced, the most widely used and established model is the bleomycin-induced SSc mouse model, which is used to replicate dermal or pulmonary fibrosis (250,251). Bleomycin is a glycopeptide antibiotic produced by Streptomyces verticillus, which is used to treat Hodgkin’s lymphoma, squamous cell carcinoma and testicular cancer (252). Studies have shown that older mice are more susceptible than younger mice to pro-fibrotic stimuli including bleomycin (212).

Local subdermal injections of bleomycin in mice induces collagen synthesis at the injection site over 4 weeks (253,254). Intratracheal or oropharangeal administration leads to lung fibrosis leads to hyperplasia of alveolar epithelial cells, infiltration of inflammatory cells, septal thickening, enlarged alveoli, and extensive fibrosis (217). C57BL/6J male mice (8–12 weeks) have been the predominant animal model, as this particular strain is highly susceptible to lung injury following intratracheal bleomycin administration (255).

In contrast, the BALB/c or SV129 strains appear more resistant to bleomycin-induced pulmonary fibrosis (256). Two of the initiating events in bleomycin-induced fibrosis are inflammation and vascular changes at the injection site resulting in oxidative stress, collagen synthesis and ECM accumulation (257). Therefore, this model replicates some of the most important features of SSc.

TGFβRI and TβRIIΔk. As described in Chapter 1, section 1.3.3, TGFβ1 is a key factor in fibrosis (258). Two models have been developed based on the regulation of this pro- fibrotic factor, the type I TGFβ receptor transgenic model (TGFβRI) and the kinase- deficient type II TGFβ receptor transgenic model (TβRIIΔk) (259).

The TGFβRI model specifically up-regulates TGFβRI expression in fibroblasts and there are an increased collagen deposition and vascular changes similar to that observed in SSc (259). This model suggests that altered TGFβRI expression which leads to a chronic expression of TGFβ in fibroblasts, could cause the pathology observed in SSc.

The TβRIIΔk model was developed using a fibroblast-specific transcriptional enhancer upstream of the COL1A2 collagen gene which allows for the specific expression of a kinase-deficient mutant TGFβRII in fibroblasts and which results in fibrosis in the lung and skin. This model was used in this project and will be described in more detail in Chapter 4, section 4.3.11.

1.3.2.2 In vivo models for IPF

To better understand the pathogenesis of IPF, multiple animal models have been developed and provide powerful tools for studying fibrotic lung diseases (260), although none of the currently accepted mouse models of PF described in section 1.3.2.1 including silica (260, 261), radiation (262), bleomycin (253) and transgenic animals, completely mimic human IPF (261).

1.3.2.3 In vivo models of CKD

There are multiple rodent models of CKD including spontaneous, genetic and surgically- or chemically-induced models (262). The most commonly used mouse models are described below.

Unilateral ureteral obstruction (UUO). UUO leads to a reduction of the renal blood flow and glomerular filtration rate within 24 hours in the obstructed kidney. UUO is a widely used interstitial fibrosis model, due to the rapid disease course (7-28 days) characterised by tubular atrophy and interstitial inflammation and fibrosis (263). UUO is a reliable model with high success rate and good reproducibility and is amenable to intervention. However, this model lacks functional readouts in that serum creatinine is normal, and there is no proteinuria.

Aristolochic acid and folic acid. Both aristolochic acid and folic acid can induce interstitial fibrosis (264). A single dose of aristolochic acid I (4.7 mg/kg) results in moderate acute kidney failure in the early stages and subsequent renal fibrosis whereas multiple low doses of aristolochic acid (3 mg/kg, once every 3 days for 6 weeks) leads to substantial tubulointerstitial fibrosis at 12 weeks. Administration of high doses of folic acid in mice induces tubular necrosis and patchy interstitial fibrosis which evolves to a progressive fibrosis. The advantage of these models, compared to the UUO model, is that renal function can be assessed as a measure of CKD.

Although many of these models do not exactly mirror human diseases, they provide an opportunity to investigate disease-specific mechanisms and molecular pathogenesis, to optimize translational research and assess potential novel therapies.

PART IV

1.4 Overall project objectives and specific aims

Although the current understanding of the cellular and molecular mechanisms of fibrosis has improved and numerous candidates and pathways have been put forward as drivers of the fibrotic process (265), the identification of common and unique mechanisms resulting in fibrosis is incomplete. Also, there are is still a lack of clinically effective therapies for patients.

The severity of each organ involvement and response to therapy are extremely heterogeneous among patients with fibrotic disorders including SSc, IPF or CKD and it remains a challenge to identify those patients at risk of more rapid progression to end- stage disease and organ failure. There are therefore two unmet clinical needs: i) the identification of novel targets and development of effective anti-fibrotic therapies; and ii) the identification and validation of novel biomarkers for patients stratification. The overarching objective of this thesis was to identify common and unique genes in skin lung and kidney fibrosis with a view to defining novel potential therapeutic targets.

The specific aims were to:

1- Identify common and specific genes associated with lung, skin and kidney fibrosis using an in silico approach through an extensive search of the literature and published gene expression array data. 2- Select potential target genes and validate expression of these genes in fibrosis in vitro and in vivo. 3- Explore the function of selected genes in normal and fibrotic human cells in vitro and in established mouse models of fibrosis.

CHAPTER 2

MATERIALS AND METHODS

This chapter describes the general methods used in this project. Specific methods are described in the relevant chapters.

2.1 Patients samples

Primary human skin and lung fibroblasts were derived from healthy volunteers and SSc patients. For human lung samples, specimens were obtained from healthy individuals, SSc and IPF patients. For human skin samples, 3-6 mm3 punch biopsies were taken from the forearm from healthy individuals and SSc patients. Informed consent and ethical approval was obtained from all subjects who contributed medical data and biological specimens. Normal human kidney tissue and fibroblasts were from kidneys donated for research but found to be anatomically unsuitable for transplantation (Ethical approval 05/Q0508/6). ADPKD fibroblasts were used as model of renal fibrosis because of the non-availability of CKD or SRC cells or tissues. ADPKD were obtained from the polycistic kidney disease (PKD) Charity Biobank.

2.2 Cell culture

Tissues biopsies were minced into small fragments ~1mm2. The explants were plated sparsely into tissue culture flasks (Corning) and cultured in Dulbecco’s-modified Eagle’s medium (DMEM; Life Technologies) supplemented with 10% foetal bovine serum (FBS; Life Technologies) at 37°C in a humidified atmosphere of 5% CO2. For experiments, cells were grown to confluence and serum-starved (DMEM + 0.2% FBS) overnight.

To investigate basal compared to activated levels of gene and protein expression, cells were treated with TGFβ1 (2 ng/ml; Sigma Aldrich) for 24 and 48 hours as it has been shown by our group that after TGFβ1 stimulation (2 ng/ml) for 24 hours fibroblasts express significantly higher levels of COL1A2 and CTGF compared to untreated control fibroblasts (266).

2.3 Gene expression analysis

RNA was extracted from cells and tissue samples using the RNeasy Mini Kit (Qiagen) which allows efficient purification of total RNA from small amounts of starting material. RNeasy technology simplifies total RNA isolation by combining the stringency of guanidine-isothiocyanate lysis with the speed and purity of silica-membrane purification.

2.3.1 RNA extraction

Total RNA was extracted from the cells and tissues following the instructions of the RNeasy Mini Kit extraction protocol (Qiagen). Briefly, samples were first lysed with 350µl RLT buffer. Cell (1x106) lysates were homogenized using a sterile syringe and needle (0.9 mm). Ethanol (350µl) was added to the lysate to provide optimal binding conditions. The lysate was applied to the RNeasy Mini spin column. Total RNA binds to the membrane, contaminants are efficiently washed away, and high-quality RNA is eluted in RNase-free water (50 µl). All binding, wash, and elution steps were performed by centrifugation at 13000xg for 30 seconds at room temperature (RT). Purified RNA was stored at –80°C in 50 µl RNase-free water.

2.3.2 RNA concentration and purity

The concentration and the purity of all RNA samples were measured using NanoDrop™ 8000 (Thermo Fisher Scientific) at 260 and 280nm. While nucleic acids absorb at many wavelengths, peak absorbance of UV light is at 260nm.

Thus, the amount of light absorbed can be used to determine the concentration of RNA by applying the Beer‐Lambert law, A = ɛcl, where A=absorbance, ԑ=extinction coefficient, c=concentration and l=path length, which draws a direct correlation between absorbance and concentration (267).

The NanoDrop™ 8000 calculates the concentration in ng/ul. The ratio of absorbance (A) at 260 nm and 280 nm is used to assess the purity of RNA. A ratio of ~2.0 is generally accepted as “pure” for RNA. A ratio less than 2.0 indicates contamination with protein or other contaminants that absorb strongly at 280 nm. All RNA samples used for downstream applications had an A260/280 ratio of ~2.0.

2.3.3 Primers design

Two primer pairs were designed for each gene of interest using Primer-BLAST software (NCBI). Secondary structures including hairpins and homo- and heterodimers, were assessed with the OligoAnalyzer 3.1 software available from Integrated DNA Technologies (IDT) who synthesised all the oligonucleotides. GC content of 40-60% ensures stable binding of primer and template and reduces the probability of primer- dimer formation.

The GC content and the melting temperature (Tm) for each primer in the salt conditions of the RT-qPCR reaction were also evaluated. For primer optimisation, a range of primer concentrations was tested with serial dilutions of RNA. The efficiency of the primers sets was determined by running a standard curve (in duplicate). The slope of the standard curve (Ct vs. concentration) was related to the efficiency. Primer pairs with the highest efficiency were selected for use (Table 2.1)

GENE FORWARD PRIMER REVERSE PRIMER

TBP AGTGACCCAGCATCACTGTTT GGCAAACCAGAAACCCTTGC

CTGF GACCTGGAAGAGAACATTAAGAAGG TCGGTATGTCTTCATGCTGGTG

COL1 TGCTTGCAGTAACCTTATGCCTA CAGCAAAGTTCCCACCGAGA

ΑSMA CCGACCGAATGCAGAAGGAG ACAGAGTATTTGCGCTCCGAA

GREM1 CCGCACTGACAGTATGAGCC GGATGGCACCTTGGGACC

HSPB1 GGATGGCGTGGTGGAGATC GGGGGCAGCGTGTATTTCC

WIF1 AAGGTTGGCATGGAAGACAC TTAAGTGAAGGCGTGTGCTG

JAG2 AGGTGGAGACGGTTGTTACG TTGCACTGGTAGAGCACGTC

KRAS TAGACACAAAACAGGCTCAGGA GCATCCTCCACTCTCTGTCTTG

MDK GAACTGGGGTGCGTGTGATG TCCCTTCCCTTTCTTGGCTTTG

IL-11 GCGGACAGGGAAGGGTTAAAG CCAGGCGGCAAACACAGTTC

TSPAN13 GTGCTCGAAATGACATCCAGAG ACTTTTAACACAGCTAGCCAGACA

HAS2 GCCGGTCGTCTCAAATTCATC TCACAATGCATCTTGTTCAGCTC

DDR2 AGGGAGACTGTAGCCTCATTTC TGGTGTTGGAACTGGAGTCTCT

CEMIP GGCGACCATCCCTGACAATTCC CTGCCTTTGAAGCCAACGAAT

Table 2.1. RT-qPCR primer sequences for selected human genes

2.3.4 Real time-quantitative polymerase chain reaction (RT-qPCR)

Quantitative real-time PCR (qPCR) is an extensively used method for gene expression and quantification analysis. The principle of the method is based on classical PCR, where incorporation of fluorescent dyes or probes and fluorescent signal measurement enables quantification of starting DNA during amplification (220).

SYBR green was used for all RT-qPCR assays in this thesis. SYBR green binds strongly to double-stranded DNA and in the bound state the level of fluorescence increases 100-fold. As the reaction progresses, enough amplified product accumulates to yield a detectable fluorescence signal. To account for differences in the amount of the starting material, fluorescence of the target gene was normalised to that of a “housekeeping” gene. “Housekeeping” genes are typically ubiquitously expressed genes that are expressed at relatively constant levels. However, the expression of some “housekeeping” genes can alter in disease or under certain experimental conditions (268).

For this project, the chosen “housekeeping” gene was human TATA-box binding protein (TBP) for which expression level is known to remain stable under normal and pathological conditions (269). For RT-qPCR, a 1-step method was followed based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). In this protocol, total RNA is transcribed to cDNA followed by a PCR reaction in the same tube. Briefly, total RNA (100ng) was added in a 12µl total reaction volume. RT-qPCR assays were performed in a Corbett Rotor Gene 6000 cycler (Qiagen) in 200μl reaction tubes in a 72-well rotor (Corbett Research) (Table 2.2). The RT-qPCR reaction consisted 40 cycles with specific cycling conditions and at the end of the run samples were held at 4°C (Table 2.3). Fluorescence measurements were carried out at each annealing step. The total reaction time was approximately 1.5 hours.

The two most commonly used methods of analysing data from RT-qPCR experiments are absolute quantification and relative quantification. Absolute quantification determines the copy number relating the PCR signal to a standard curve. For this study, relative quantification was used as this is widely used by other investigators to analyse changes in gene expression in a given sample relative to a reference sample (such as an untreated control sample) (223). Analysis was carried out using fluorescence intensity and Ct values were compared between samples. The 2-ΔΔCT method was used (where Ct is the cycle number at which the fluorescence signal crosses threshold) to analyse the relative changes in gene expression (224).

REAGENT VOLUME (µL)

RNA 2

FORWARD AND REVERSE PRIMERS 0.24 (10 PMOLES EACH)

RT ENZYME 0.12

2X QUANTIFAST RT-QPCR MASTER 6 MIX

RNASE FREE WATER 3.64

TOTAL 10

Table 2.2. Protocol for RT-qPCR assay.

STEP TIME TEMPERATURE

REVERSE- 10 minutes 50°C TRANSCRIPTION 5 minutes 95°C

DENATURATION 10 seconds 95°C

ANNEALING 30 seconds 60°C

EXTENSION 30 seconds 60°C

Table 2.3. Cycling conditions for RT-qPCR assay.

2.4 Protein expression analysis

2.4.1 Protein extraction

Various detergents, salts, and buffers may be used to lyse cells and solubilize proteins. There are many different lysis buffers and buffer choice is generally determined by the protein of interest (270). RIPA buffer (Sigma), is known to be highly effective for protein extraction from a variety of cell types because it contains three non-ionic and ionic detergents (271). RIPA buffer was used for this study and was supplemented with protease (Complete mini; Roche) and phosphatase inhibitors (Cocktails 2 and 3; Sigma). This mixture contains individual components with specific inhibitory properties including sodium orthovanadate which inhibits a number of protein tyrosine phosphatases, sodium molybdate which inhibits acid and phosphoprotein phosphatases and imidazole which inhibits alkaline phosphatases. Cells were washed 3 times with ice-cold phosphate (PBS) then incubated in ice-cold RIPA buffer (150µl per well) during 5 minutes. Cells were scraped and the protein extracts were snap- frozen in liquid nitrogen and stored at -80oC prior to use.

2.4.2 Determination of protein concentration

A variety of protein assays (eg. Bradford, Lowry or BCA) are used to determine protein concentration by comparing the assay response of a sample to that of a standard of known concentration (272). Protein samples and protein standards are processed in the same manner by mixing with assay reagent and using a spectrophotometer to measure the absorbance. Total protein concentration was measured using the BCA protein assay (Thermo Fisher Scientific) in a 96-well plate format.

Bovine serum albumin (BSA; Thermo Fisher Scientific) standards (0.125 – 1 mg/ml) were used to create a standard curve. BCA reagents A and B were mixed at a ratio of 50: 1 to give a green working reagent (WR). WR (200 µl) was added to the protein samples (25µl) and the standards (25µl).

The plate was incubated in the dark for 30 minutes at 37oC and the absorbance measured at 562nm in a Mithras LB 940 Plate Reader. The total protein concentration in each sample was calculated by comparison with the regression line of the standard curve and multiplying by the dilution factor of the samples. Within each experiment, equal amounts of protein (10-50µg) were used.

2.4.3 SDS-PAGE and Western blotting

Polyacrylamide gel electrophoresis (PAGE) and Western blotting are widely used analytical techniques to detect specific proteins in cell lysates or tissue homogenates (273). Protein samples are separated by electrophoresis and transferred to a membrane for immunodetection of the protein of interest. In PAGE, buffers contain sodium dodecyl sulfate (SDS) a strongly anionic detergent. Addition of SDS to the proteins ensures that all proteins have the same electrical charge.

Treatment with a reducing agent (eg. -mercaptoethanol or dithiothreitol) disrupts disulphide bonds. Thus, the proteins become denatured and negatively charged. Application of an electrical current across the gel causes the proteins to move towards the positively-charged electrode through the acrylamide mesh of the gel. Smaller proteins migrate faster through the mesh and the proteins are separated according to size measured in kilodaltons (kDa) (274).

Samples with equal amounts of protein (13 µl) were heat denatured at 70°C for 10 minutes in NuPage LDS Sample Buffer (5 µl) and NuPage Reducing Agent (2 µl) (Thermo Fisher Scientific) prior to electrophoresis (final volume 20 µl). The denatured proteins were separated by molecular weight in NuPage Bis-Tris precast gradient (10%) polyacrylamide gels (Thermo Fisher Scientific) and run in 1x MOPS SDS Running Buffer (Thermo Fisher Scientific) for 50 minutes at 200V.

The SeeBlue® Plus2 Pre-Stained Standard protein ladder (Supplier) was used to visualize molecular weight ranges (X-Y kDa) during electrophoresis and quickly evaluate Western transfer efficiency. Wet transfer was performed to transfer the separated proteins to a nitrocellulose membrane (0.2µm pore size; GE Healthcare Life Sciences). Proteins were transferred by sandwiching the gel between a membrane, filter paper and sponges soaked in NuPage Transfer Buffer (Thermo Fisher Scientific) containing 20% methanol for 3 hours at 30V or overnight at 12V.

Efficient transfer was confirmed using Ponceau S staining solution (Sigma). Ponceau S is a negative stain which binds to the positively charged amino groups of the protein. It also binds non-covalently to non-polar regions in the protein (275). This stain is useful because it does not appear to have a deleterious effect or interfere with subsequent antibody binding and detection and is therefore one method of choice for visualising proteins Western blots.

To remove the stain, blots were washed 3 times for 5 minutes each with distilled water. Blocking agents are used to reduce non-specific binding of detection reagents to the membrane. Blocking is often done using 5% BSA, non-fat dried milk or 5% casein diluted in distilled water. Here, we used purified casein because it minimises cross- reaction with assay components compared to serum or milk solutions (276).

Membranes were incubated in casein blocking buffer 5% (W/V; Sigma) for 1 hour at RT on a roller followed by incubation with the primary antibodies diluted in casein blocking buffer (Table 2.4) overnight on a roller at 4oC. HRP-conjugated secondary antibodies diluted in casein blocking buffer were added for 1 hour with gentle agitation at RT. This was followed by 3,5 minutes, PSB washes at RT and blots were incubated in chemiluminescent detection reagent (high sensitivity ECL; GE Healthcare) for 5 minutes at RT prior to image acquisition. The signal was captured on Amersham Hyperfilm ECL (GE Healthcare). Films were processed manually using RG X-Ray Developer RTU and RG X-Ray Fixer RTU (Champion Photochemistry).

2.4.4 Densitometry

Relative protein expression was measured by densitometry, a quantitative measurement of optical density (OD). The developed films were scanned and processed in Image J, a commercially-available software which is commonly used to compare the density of bands on Western blots (277). A relative value was calculated based on the OD of a given sample compared to the OD of the ‘housekeeping’ proteins GAPDH or Tubulin. The ratios of the protein-of-interest to GAPDH from the replicate experiments were used to calculate averages and standard errors.

ANTIBODY COMPANY CAT. NUMBER SPECIES DILUTION

GAPDH Abcam ab51841 Mouse 1: 50000

TUBULIN Abcam ab6046 Rabbit 1: 10000

CTGF Santa Cruz sc-14939 Goat 1: 1000

COL1A Millipore AB758 Goat 1: 1000

Α-SMA DAKO M0851 Mouse 1: 1000

TSPAN13 Abcam ab121262 Rabbit 1: 250

TSPAN13 Proteintech 13570-1-AP Rabbit 1: 250 # HAS2 Abcam ab140671 Goat 1: 1000

HAS2 Santa Cruz sc-34068 Goat 1: 1000

CEMIP Santa Cruz ab98947 Goat 1: 1000

IL11 Santa Cruz H-169 Goat 1: 1000

DDR2 Santa Cruz N-20 Goat 1: 1000

INTEGRIN ΑV Cell Signaling 4749 Rabbit 1: 1000

INTEGRIN Β1 Cell Signaling 4749 Rabbit 1: 1000

INTEGRIN Α5 Cell Signaling 4749 Rabbit 1: 1000

AKT Cell Signaling 9272 Rabbit 1: 1000

P-AKT Cell Signaling 9271 Rabbit 1: 1000

P38 Cell Signaling 9212 Rabbit 1: 1000

P-P38 Cell Signaling 4511 Rabbit 1: 1000

ERK-1,2 Cell Signaling 9102 Rabbit 1: 1000

P-ERK-1,2 Cell Signaling 9101 Rabbit 1: 1000

ERK-5 Cell Signaling 3372 Rabbit 1: 1000

P-ERK-5 Santa Cruz sc-16564 Goat 1: 1000

ASK1 Cell Signaling 8662 Rabbit 1: 1000

P-ASK1 Cell Signaling 3764 Rabbit 1: 1000

SMAD2/3 Cell Signaling 3102 Rabbit 1: 1000

P -SMAD2/3 CELL SIGNALING 8828 RABBIT 1: 1000

Table 2.4. Antibodies used for Western Blotting.

2.5 Immunohistochemistry (IHC)

Immunohistochemistry (IHC) is a powerful microscopy-based technique for visualizing cellular localisation of proteins in tissue sections. The strength of IHC is the visual output that reveals the presence and distribution of the target-protein in different cell types, biological states, and/or subcellular localization within complex tissues (233). Human tissue specimens were collected and preserved in formalin (10%; CellStor). Tissues were dehydrated, embedded in paraffin wax and sectioned (4-6µm). Sections were mounted on glass slides for staining. All processing and sectioning was performed by M.Mohadani, C & C Laboratories.

Sections were dewaxed for 10 minutes in xylene and transferred through xylenes and graded ethanols to water. After washing in running water for 5 minutes, antigen retrieval treatment was performed by incubation in sodium citrate buffer (0.01M, 0.05% Tween 20, pH6.0) for 10 minutes in a microwave oven at 750 W, followed by cooling in running tap-water for 10 minutes. To eliminate the endogenous peroxidase activity found in many tissues, sections were incubated with hydrogen peroxide H2O2 (0.3%) in methanol for 10 minutes at room temperature.

The main cause of non-specific background staining is non-immunological binding of the specific immune sera to tissue sections. This form of background staining can be reduced by blocking the sites with normal serum. Non-specific binding of immunoglobulins (Ig) was blocked by treatment for 20 minutes with 10% normal serum from the same species as the secondary antibody.

The sections were rinsed 3 times for 5 minutes each with PBS. IHC was performed using the avidin-biotin method as previously described (278). Sections were incubated for 15 minutes with Avidin, a large glycoprotein which can be labelled with peroxidase or fluorescein and has a very high affinity for biotin, rinsed with PBS and then incubated for an equivalent period with Biotin, a low molecular weight vitamin which can be conjugated to a variety of biological molecules such as antibodies.

After 3 5 minute PBS washes, sections were incubated with primary antibodies (Table 2.5) diluted in antibody diluent (Dako) overnight at 4°C in a humidified atmosphere. Antibody dilution was optimised for the strongest signal and lowest background by testing several dilutions. Isotype-matched IgG controls, used at the same concentration as the primary antibody, were used to demonstrate specificity of primary antibodies.

After 3 5 minute washes with PBS, secondary antibodies were applied at a dilution of 1: 200 in Dako diluent for 30 minutes. After 3 5 minutes washes with PBS, slides were incubated with Vectastain Avidin Biotin complex (ABC)-horse radish peroxidase (HRP) conjugate (Vector Laboratories) for 30 minutes.

After 3 5 minutes washes with PBS, binding of the complex was visualised by a few minutes incubation (the incubation time was depending on the intensity of the staining) with 3,3' diaminobenzidine tetrahydrochloride (DAB; Vector Laboratories) which resulted in formation of a brown precipitate at the antigen site (279). The sections were counterstained with Mayer's haematoxylin for 2 minutes and washed for 5 minutes in running water. Sections were dehydrated through graded ethanols to xylene and mounted in DPX (Sigma). Sections were viewed and photographed on an Axio Scope microscope using AxioVision software (Zeiss).

ANTIBODY COMPANY CAT.NUMBER SPECIE DILUTION

TSPAN13 Abcam ab121262 Rabbit 1: 20

#HAS2 Abcam ab140671 Goat 1: 200

CEMIP Santa Cruz ab98947 Goat 1: 200

Table 2.5. Antibodies used for IHC

2.6 siRNA gene knockdown siRNAs, also known as short interfering RNAs or silencing RNAs, are a class of double-stranded RNA molecules which operate in the RNA interference (RNAi) pathway (280). siRNA interferes with the expression of specific genes with complementary nucleotide sequences by degrading mRNA or blocking protein translation. siRNA target sequences to human genes were obtained from Dharmacon (sigenome SMARTpool reagent) and used according to the manufacturer's instructions. Briefly, primary human fibroblasts were transfected using Oligofectamine™ Transfection Reagent (Invitrogen) which forms stable complexes with oligonucleotides, permitting efficient transfection into eukaryotic cells. Oligofectamine™ Reagent is suitable for nuclear and cytoplasmic targets and for transfection of a wide variety of cell lines (281).

Cells were grown to 30–50% confluence and were transfected with siRNAs at concentrations of 50, 100, 150 and 200 nM for 48 hours. Scrambled control siRNA (K- 002800; Dharmacon) was used to assess specificity and was transfected at the same concentrations. After transfection, cells were washed with PBS, some were lysed for further protein and gene expression analysis and some were trypsinised and plated for functional assays (proliferation, migration and collagen contraction assay).

2.7 Proliferation assays

Many methods have been developed to measure cell proliferation including those based on direct counting of viable cells, measurement of metabolic activity and cellular DNA content (282). Measurement of mitochondrial metabolic rate using MTS (3-(4,5- dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium) to indirectly reflect viable cell numbers has been widely applied. However, metabolic activity may be changed by different conditions or chemical treatments which can cause considerable variation in results reported from these assays. In the present study, MTS and crystal violet staining assays (CVS) which binds to proteins and DNA, were compared. For both assays, cells (1×104 cells/well) were seeded in 96-well plates and cultured overnight at 37°C. For the MTS assay, CellTiter-Blue® (20 μL/well) (Promega) was added and the plates were incubated under standard cell culture conditions for 1 hour (283). The plate was shaken for 10 seconds, and fluorescence recorded at 490 nm in a Mithras LB 940 Plate Reader. The background (average OD490 of the wells without cells) was subtracted from the OD490 of each well on the plate. Samples were tested in triplicate.

For the CVS, 50 µl of 0.5% crystal violet staining solution was added to each well and incubated for 20 minutes at RT on a bench rocker. The plate was washed 3 times in a stream of tap-water. After washing, the water was removed and the plate was air-dried for at least 4 hours at room temperature. Methanol (200 µL) was added to each well, and the plates incubated for 20 minutes at RT on a bench rocker. OD of each well was recorded at 570 nm with the Mithras LB 940 Plate Reader. The background (average OD570 of the wells without cells) was subtracted from the OD570 of each well on the plate. Samples were tested in triplicate.

2.8 Scratch wound healing assays

Scratch assays to assess cell migration were performed as described previously (284). The assay involves creating a "scratch" in a confluent cell monolayer, capturing the images immediately after wounding and over a time-course to evaluate cell migration to repopulate the denuded area (285). Fibroblasts were seeded in a 96-well plate and grown to confluence. Scratch wounding of the monolayer was performed using a 96- well floating-pin transfer device with a pin diameter of 1.58 mm (Essen). Each pin of the array was placed in the top corner of a well, the device pushed down into the cell layer and pulled across the plate.

Post-wounding, plates were rinsed with PBS to remove any cell debris and returned to the incubator for 24 and 48 hours. Migration was assessed in the presence of mitomycin C (5 μl/ml; Sigma) to inhibit proliferation and quantify only the migration. Migration was assessed by measuring the change in scratch area over time. Images of the scratch zone were taken after 24 and 48 hours at 20X magnification. Image J was used to calculate the extent of wound closure as percentage of wound area covered by cells as a measure of fibroblast migration.

2.9 Collagen gel contraction assay

Collagen gel contraction assays provide a model of tissue contraction and show cell- populated collagen hydrogel contraction over time. This model allows for investigation of the influence of specific agents on ECM contraction (286). Briefly, 24-well tissue culture plates were pre-coated with sterile 2% BSA in PBS (1ml/well) by incubation at 37°C overnight, and were then washed 3 times with PBS. Trypsinized fibroblasts were suspended in DMEM (Sigma) and mixed with collagen solution (one part 0.2 M N-2- hydroxyethylpiperazine -N'-2-ethanesulfonic acid (HEPES), pH8.0; four parts collagen (3 mg/ml; Nutragen) and five parts of DMEM) at a final concentration of 8x104 cells/ml in 1.2 mg/ml collagen. Collagen-cell suspension was added to each well (1 ml/well) and the plate was incubated for 1 hour at 37°C. After polymerization, 1 ml DMEM (with 0.2% or 10% FBS) was added to each well and the gels were detached from the well by gently running the tip of a 200μL pipet tip around the edge of the gel. Plates were incubated at 37°C and images and weights of the gels were taken after 24 and 48 hours. Gel contraction was quantified by reduction in gel weight. The gels with 10% FBS were weighed and compared to control gels in 0.2% FBS. Gels were blotted to remove excess liquid and weighed on an analytical balance.

2.10 Statistical analysis

Statistical analyses were made for multiple comparisons using analysis of variance (ANOVA) and Student’s t-test. The t-test and ANOVA are widely used statistical methods to compare group means (287). P-values <0.05 were considered to be statistically significant and were represented in the figures with p value absolute number. Unpaired t-tests were performed since all the sample groups were independent, and the 2-tailed version was used indicating that the means are not expected to be equal under the null hypothesis. Data are mean + SEM. GraphPad prism and Excel were used for statistical analyses and preparation of graphs. The graphs represent data from at least 3 independent experiments with a minimum of duplicate.

CHAPTER 3

IDENTIFICATION AND SELECTION OF GENES INVOLVED IN FIBROSIS: AN IN SILICO STUDY

3.1 Introduction

One of the most important areas of medical research is the identification of disease causing genes. Identification of altered genes can improve the knowledge, diagnosis and the treatment of diseases (288). Microarray technology is an effective and sensitive method, used for large-scale genotyping, comparative genomic hybridization and gene expression profiling (289). For instance, Alone et al. used microarray data in in various cancers and showed that gene expression profiles were different depending on the organ affected (290,291). This integrative analysis of cancer transcriptomes led to identify specific but also common genes associated with a specific type of cancer (290).

In fibrosis, effective treatments are limited and there is an urgent need for identification of novel targets for anti-fibrotic therapy. Therefore, the same approach was used to identify genes differentially expressed in fibrosis which might lead to the discovery of new potential targets. Numerous different analyses are commonly used to assess expression data eg. t-tests, ANOVAs, Gene Ontology (GO) annotation, p-value cutoffs, array normalization and Fold-Change (FC) (292). In the study undertaken in this thesis, analysis was based on applying an arbitrary FC>2. Also, up- and down- regulated genes were all included for the identification. To complement the microarray analysis, an extensive literature search was performed. Published data from PubMed were used for data-mining purposes to identify genes associated with fibrosis in three major organs (lung, skin and kidney).

3.2 Methods

3.2.1 Identification of genes involved in fibrosis using an extensive literature search

A literature search was carried out of published articles and reviews available in PubMed (https://www.ncbi.nlm.nih.gov/pubmed). Published papers were eligible for inclusion if they were full-length articles in English and whose topic was fibrosis or fibrotic diseases. Papers from 1988 to 2015 were reviewed to identify genes involved in lung, skin and kidney fibrosis.

The year 1988 was selected as the starting point as this was the time where electronic publications on fibrosis were available (there were 2418 papers published on fibrosis in 1988) and subsequently, a large amount of research on fibrosis was documented. The search term: “fibrosis” identified a total of 202,865 papers. Application of the key terms “lung fibrosis”, “skin fibrosis” and “kidney fibrosis” highlighted: 49705, 14470 and 16013 publications, respectively. As all the literature searches were carried out manually, it was necessary to narrow down the numbers of papers before starting to examine the data in detail.

The rationale of the survey was to identify genes regulated in fibrosis so the keywords “fibrosis” and “gene” were used and returned 26620 papers. The terms “lung fibrosis gene”, “skin fibrosis gene”, “kidney fibrosis gene” identified 5028, 1320 and 2686 papers, respectively. The term “lung fibrosis” highlighted papers with a specific focus on fibrotic lung disorders such as ILD. The term “skin fibrosis” returned papers describing known or new molecules involved in fibrotic dermal conditions of diverse etiologies. The term “kidney fibrosis” was used to obtain all articles relating to fibrotic renal conditions. Collectively, these terms allowed to obtain a large list of genes involved in fibrosis as well as to perform an extensive comparaison to select the ones common to all 3 organs. Importantly, from this survey, some genes appeared to be organ-specific and were compiled as a separate list of specific genes.

3.2.2 Identification of genes involved in fibrosis using microarray data

Eighteen published microarray GEO DataSets produced between 2004 and 2013 (https://www.ncbi.nlm.nih.gov/gds) were examined to identify genes altered in lung, skin and kidney fibrosis (Appendix, Table A). Athough array datasets were published before and after these dates, the data were not accessible in GEO DataSets.

3.2.3 Pathways analysis

For all the identified genes, pathway analysis was carried out using several online platforms including STRING, DAVID (Database for Annotation, Visualization and Integrated Discovery), WebGestalt and KEGG (Kyoto encyclopedia of genes and genomes). All these sofware provides a comprehensive set of functional annotation tools in order to study of large list of genes and to highlight pathways and potential interactions between genes. STRING is a database of known and predicted protein- protein interactions. The interactions include direct and indirect associations, derived from computational predictions, from knowledge transfer between organisms, and from interactions aggregated from other databases.

3.3 Results of data mining

The literature search, conducted in PubMed, and the data mining of array data, from GEO DataSet, were complementary and both yielded common and organ-specific genes (Figure 3.1).

3.3.1 The in silico data mining identified common and organ-specific altered genes in fibrosis

3.3.1.1. Literature search

This literature survey highlighted 26620 published papers and identified 443 genes involved in fibrosis. Application of the terms ‘organ fibrosis’ and ‘genes’ gave for lung fibrosis: 5028 papers and 244 genes; for skin fibrosis:1320 papers and 135 genes; and, for kidney fibrosis: 2686 papers and 155 genes (Figure 3.1).

3.3.1.2 Array data

Eighteen microarray datasets were analysed: lung fibrosis (6 arrays), skin fibrosis (8 arrays) and kidney fibrosis (4 arrays) (Appendix, Table A). Array data were included regardless of disease etiology. The first selection of relevant genes was based on published list of genes with a FC <2 (and the top 20-50 genes up- or down-regulated). This identified 160, 227 and 153 genes for lung, skin and kidney, respectively (Figure 3.1).

3.3.1.3 Identification of common and specfic genes

From the combination of the literature seach and array analyses, 91 common genes were identified and classified in 8 different groups according to their function using GEO and KEGG. As expected, many of the genes such as collagens, CTGF and TGFβ1, key mediators involved in fibrosis, were highly reported (244,293). Of these 91 common genes, some were up- and some were down-regulated; 19 were known pro- fibrotic genes eg. Collagens; 15 were associated with inflammation and immune system; 17 were enzymes; 14 were growth factors; 12 were associated with signal transduction; 10 were receptors or transmembrane proteins and transmembrane proteins; and 4 were described as anti-fibrotic (Appendix, Table B).

Figure 3.1. Workflow for the in silico identification of fibrotic genes. Common and specific genes implicated in fibrosis in human lung, skin and kidney were identified using PubMed and GEO databases. FC: Fold-Change. * An arbitrary cut-off of the top 60 specific genes for each organ was used.

To obtain the specific genes for each organ, the 91 common genes were subtracted from the 160, 227 and 153 genes identified for each organ: 69 genes specific to lung fibrosis, 136 specific to skin fibrosis and 62 specific to kidney fibrosis. To narrow down the number of specific genes, only the top 60 genes, up- and down-regulated, were selected for further analysis. Common genes and specific genes for lung, skin and kidney fibrosis are listed in Appendix Table B-E, respectively.

Figure 3.2. Workflow of the analysis of genes interactions using STRING and pathways using KEGG.

3.3.2 Pathway and gene interaction analyses

Network and pathway analyses were performed with the lists of the common and specific genes using online tools. The data from the different platforms (DAVID, STRING and WebGestalt) produced similar results. Therefore, only the results obtained with STRING are shown in this chapter (Figure 3.2). Analysis of the common genes involved in lung, skin and kidney fibrosis, revealed a complex network of gene interactions (Figure 3.3). Functional analyses indicated that common gene sets characterized by the highest statistical significance, converged on several pathways including pathways in: cancer, focal adhesion, TGFβ1 signaling and ECM-receptor interaction. A number of unexpected pathways such as Malaria were also highlighted with high p-value.

Figure 3.3. STRING analysis of the common genes involved in fibrosis (in lung, skin and kidney)

Pathway analyses of specific genes (Figure 3.4) revealed a distinct cluster in skin, where the network obtained for the specific genes altered only in skin fibrosis showed one distinct group involving important fibrotic pathways such as ECM-receptor interaction protein pathways. In contrast, the networks obtained for the specific genes altered in lung and kidney fibrosis did not show distinct clusters (Figure 3.4). Furthermore, the top 10 pathways involved in lung, skin and kidney are shown in Table 3.1. Overall, pathway analyses highlighted many expected pathways such as TGFβ1 signaling but also interesting pathways such as infectious diseases (eg. Pertussis, African trypanosomiasis or Hepatitis C) (Table 3.1). The network analyses showed that most of both the common and specific genes identified were related to cancer, cardiovascular diseases and infectious diseases. Also, this analysis showed that ECM signaling and interactions in fibrosis were significantly highlighted in the STRING analysis of common genes. Unfortunately these analyses did not facilitate a rational reduction in the number of genes to take forward thus it was necessary to apply other selection criteria to define a feasible number of genes for further study.

Figure 3.4. String analysis and network of specific genes altered fibrosis (in lung, skin and kidney fibrosis). Compared to lung and kidney, skin (in the center) presented a cluster of genes (in red). All the pathways from these analysis are given in the Table 3.1.

Lung Skin Kidney

Drug metabolism Right ventricular Pertussis Cardiomyopathy

Complement and Leucocyte transendothelial Arachidonic acid coagulation migration

Pertussis ECM-receptors interactions Fc epsilon RI

TGFβ Cell adhesion molecules T cell receptor

Tyrosine metabolism Focal adhesion Natural killer cell

Pathways in cancer Pathways in cancer Metabolic pathways

Osteoclast African trypanosomiasis Influenza A differentiation

Neutrophin Hypertrophic MAPK cardiomyopathy

Insulin Amoebiasis Cytokine

Hepatitis C Citrate cycle Pancreatic secretion

Table 3.1. KEGG analysis of specific genes altered in lung, skin and kidney fibrosis. The top 10 pathways are shown in this table. 79

3.4 Selection of candidate genes: key criteria and review of shortlisted genes

This data mining approach was used to identify common and specific genes in fibrosis lung, skin and kidney and provides a large resource for future studies. To reduce the genes identified to a workable number, key selection criteria were jointly developed by all investigators involved in the project (UCL and UCB Pharma). The selection also considered the function of the genes; potential drugability (defined by the accessibility of the drug to specific cellular compartment eg. receptor, soluble protein); and, the availability of analytical reagents including commercially-available antibodies and siRNA. Importantly, the number of publications for each gene (common and specific) was also examined with the view that genes with fewer publications are more likely to be novel. Using all these criteria, a shortlist of 12 candidate genes was compiled (Table 3.2).

GENE NAME

CEMIP Cell migration-inducing protein; KIAA1199

DDR2 Discoidin domain receptor tyrosine kinase 2

GREM1 Gremlin 1

HAS2 Hyaluronan synthase 2

HSPB1 Heat shock 27 kDa protein 1; HSP27

IL11 Interleukin 11

ITGαV Integrin alpha v

JAG2 Jagged 2

KRAS Kirsten rat sarcoma viral oncogene homolog

MDK Midkine; Neurite growth-promoting factor 2

TSPAN13 Tetraspanin 13; NET6

WIF1 Wnt inhibitory factor 1

Table 3.2. Genes selected for further study.

These 12 genes cover a wide variety of functions and include cytokines and receptors. Although some selected genes have previously been implicated in fibrosis eg. HAS2 and ITGαV, some are novel and have not previously been associated with fibrosis eg. TSPAN13 and CEMIP. Presented below is a brief description of the known functions of each of the 12 selected genes.

3.4.1 CEMIP

CEMIP (also known as KIAA1199) is described as a “deafness gene of unknown function” and appears to be essential for normal auditory function (294). CEMIP is a likely target gene of the Wnt pathway (295). The protein also has a role in HA binding and depolymerisation of HA. In vivo, CEMIP is increased in many human cancers including: gastric, colorectal and breast cancer. It has been shown that CEMIP plays a critical role in maintaining an aggressive phenotype of tumour cells and suppression of CEMIP-related motility of tumour cells contributes to reduced metastasis in colorectal cancer (296). CEMIP was shown to be over-expressed by synovial fibroblasts and synovial tissues from OA and RA patients (297). Although, CEMIP has roles in other pathological disorders, it has not been previously implicated in fibrosis. CEMIP will be described in detail in Chapter 5, section 5.1.4.2.

3.4.2 DDR2

DDR2 belongs to the DDR family of receptor tyrosine kinases (RTK) of which there are 2 members, DDR1 and DDR2. DDRs bind both fibrillar and non-fibrillar collagens (298). DDR2 plays a key role in the communication of cells with their microenvironment and in the regulation of cell growth, differentiation, and metabolism (299). COL1-mediated DDR2 activation promoted breast cancer cell invasion in vitro and increased metastasis in vivo (300). Interestingly, DDR2 was expressed in 71% of invasive ductal breast cancers and breast cancer patients show a 6-fold increase of DDR2 in tumour tissues compared to normal breast tissue (300).

DDR2 participated in hypoxia-induced breast cancer metastasis through regulation of cell migration, invasion and EMT suggesting DDR2 may serve as an accessible therapeutic target for treatment (301). Expression of DDR2 was increased in the cartilage of OA patients and in a mouse model of OA (302). Knock-down of DDR2 in mice resulted in reduced bone growth and impaired dermal wound healing due to reduced fibroblast proliferation (303).

Zhao et al. showed in vivo and in vitro, that targeting of DDR2 prevents myofibroblast activation and neovessel formation during pulmonary fibrosis (304). Thus DDR2 seems also to be a good potential target for anti-fibrotic therapies.

3.4.3 GREM1

GREM1 is a member of the bone morphogenic protein (BMP) antagonist DAN family which has 7 members (305). This protein has a critical role in regulating organogenesis, body patterning, and tissue differentiation via the TGFβ1/Smad3 pathway. GREM1 has been implicated as both a promoter and an inhibitor of cell proliferation driven by BMP4 and other mitogens in a diverse range of cell types (306). Deletion of GREM1 in mouse embryonic fibroblasts increased cell proliferation and migration (307). Interestingly, GREM1 was over-expressed in fibrotic disorders including IPF (308), pulmonary sarcoidosis (309) and diabetic nephropathy (310). Recently, it has been shown that GREM1 was a pro-fibrotic marker and in dermal fibroblasts, its effect was mediated through canonical TGFβ signaling (311). These data suggest a potential role of GREM1 in lung, skin and kidney fibrosis.

3.4.4 HAS2

HAS2 is a multipass transmembrane protein responsible for the polymerization of HA in the ECM (312). HA has a role in space-filling, lubrication of joints and provision of a matrix through which cells can migrate. HA is a major component of most ECMs, particularly in tissues in which there is rapid cell proliferation and migration (313).

Aberrant expression of the HAS2 gene has been implicated in the pathology of malignancy, PAH, OA, asthma, thyroid dysfunction, and large organ fibrosis (314,315). Targeted over-expression of HAS2 in α-SMA-expressing cells induced a severe fibrotic phenotype in the lung (increased inflammation and number of myofibroblasts). Conversely, conditional KO of HAS2 reduced pulmonary fibrosis (211). A significant increase of HA and HAS2 was observed in keloid tissue compared with normal skin (340). Recently, it has been shown that TGFβ1-induced EMT in mammary epithelial cells depends on HAS2 (317). HAS2 seems to have an important role in the fibrotic process and might be a potential target for anti-fibrotic therapy. HAS2 will be described in detail in Chapter 5, section 5.1.2.1.

3.4.5 HSPB1

HSPB1, a chaperone of the sHSP (small heat shock protein) family, has multiple functions including roles in cell migration, adhesion and differentiation (318). HSPB1 has been implicated in vascular disease and in many cancers (319). It is also involved in the response to renal injury and fibrosis and in neuro-degenerative and cardiovascular diseases, highlighting its potential role as a biomarker and a therapeutic target (320). Vidyasagar et.al showed that in the kidney, HSPB1 levels increased in UUO-induced fibrosis and demonstrated a role for HSPB1 in TGFβ1-induced EMT and chronic tubulointerstitial fibrosis (321). However, it has also been shown that HSPB1 inhibits EndoMT to suppress pulmonary fibrosis and lung tumorigenesis (321). These data suggest that HSPB1 has an important role in fibrosis with potential organ-specific differences acting as both a pro- and anti-fibrotic factor.

3.4.6 IL11

IL11 is the dominant IL6 family cytokine and is released by many cell types including fibroblasts (322). IL11 receptor expression and STAT3 activation were found to be increased in prostate carcinoma and gastrointestinal tumorigenesis (323,324). In fibrotic diseases, it has been shown that IL11 is induced by TGFβ1 and by IL17 and is involved in human vascular smooth muscle cell (VSMC) proliferation and inflammation (325). Also, IL11 was increased in chronic skin lesions of atopic dermatitis patients and had a role in airway inflammation and remodelling (325). Recently, it has been shown that IL11 was a crucial determinant of cardiac fibrosis (326). Taken together these data suggest this cytokine has important roles in tissue remodelling and fibrosis (326).

3.4.7 ITGαV

ITGαV is a member of the integrin superfamily (327) described in the Chapter 1, section 1.1.3.6.1.In the αV family, it has been shown that integrin αVβ3 mediates cell adhesion and migration on a variety of ECM proteins (328). A variant in the ITGαV gene has been associated with susceptibility to RA (rs3768777-G allele) (329). Henderson et al. showed that targeting of αv integrin identifies a core molecular pathway that regulates fibrosis in several organs (330) and αv integrins are considered as key regulators of tissue fibrosis (138). For instance, in the kidney, myofibroblast- specific deletion of ITGαV was protective in renal fibrosis. Also, blockade of αv integrins attenuated liver and lung fibrosis (143).

3.4.8 JAG2

JAG2 is a Notch signaling pathway-dependent protein. Notch pathways are intercellular signaling pathways that are essential for embryonic development (331). JAG2 encodes a transmembrane receptor that is critical for various cell fate decisions and is one of several ligands that activates Notch and related receptors. The Jagged/Notch pathway may selectively mediate the fibrogenic properties of TGFβ1 (332). Jagged ligand/Notch receptor signaling pathway is also important in cancer (333). JAG2 has been identified as a regulator of the expression of cytokines that can promote anti-tumour immunity (334).

3.4.9 KRAS

KRAS, a Kirsten ras oncogene homolog from the mammalian ras gene family, encodes a protein that is a member of the small GTPase superfamily (335). Ras proteins play important roles in cell physiology and tare ubiquitously expressed in all cell lineages and organs (336). KRAS expression was altered in many cancers (337–339). Renal KRAS expression increases by 70% in a rat model of renal fibrosis and silencing KRAS expression markedly inhibited renal fibrosis (340). These findings suggest an important role of this protein in kidney fibrosis and need to be explored in dept in other fibrotic organs.

3.4.10 MDK

MDK is a heparin-binding growth factor and promotes cell growth, migration and angiogenesis through the ERK and PI3K/AKT pathways (341). MDK also regulates migration of inflammatory leukocytes and osteoclast differentiation (341). It has been shown that MDK has a role in both oncogenesis and tissue repair (342,343). The expression of MDK was increased in the diabetic and ischemic kidney and in cardiac hypertrophy and remodelling (Mk-Tg mice) (344). Increased serum levels of MDK were found in RA patients (345). All these findings suggest a key role for MDK in chronic diseases and a potential role in fibrotic disorders.

3.4.11 TSPAN13

TSPAN13 is a cell-surface protein belonging to the transmembrane 4 superfamily, also known as the tetraspanin (TSPAN) family (346). TSPANs have a role in the regulation of cell growth and motility.

They are involved in membrane compartmentalization and interact with proteins such as integrins (347). TSPAN13 has been suggested to be a novel breast cancer suppressor gene (348). In addition, this protein may have an important role in the progression of prostate cancer (349). TSPAN13 has not been previously implicated in fibrosis, however, other members of the TSPAN family such as CD151, have been shown to be involved in wound healing and fibrosis (350). TSPANs family including TSPAN13, will be described in more detail in Chapter 4, section 4.5.

3.4.12 WIF1

WIF1 is a lipid-binding protein that binds to Wnt proteins, which are extracellular signaling molecules that play a role in embryonic development (351). WIF1 is considered as a biomarker for colorectal cancer diagnosis, both in tissue and serum (352). It has also been shown that WIF1 regulates tumour invasion through suppressing EMT and thus, may play an important role in controlling metastatic disease in prostate cancer (352). Also it has been shown that Wnt/β-catenin signaling was involved in fibrogenesis and promoted myofibroblast differentiation and activation (353). Decreased WIF1 expression was found in skin biopsies from subsets of SSc patients (354). Expression (mRNA and protein) of these 12 selected genes was evaluated in normal and fibrotic fibroblasts from lung, skin and kidney to identify the genes most highly regulated in the 3 organs and define a smaller number of genes for further study.

3.5 Expression of selected genes in lung, skin and kidney fibroblasts

At least 3 different normal and fibrotic primary fibroblast lines from SSc patients (lung and skin fibroblasts), ADPKD patients (kidney fibroblasts) and the respective, healthy controls were used.

3.5.1 Fibrotic markers: COL1A2, CTGF and αSMA

The fibrotic phenotype was confirmed in the lung, skin and kidney by looking at the gene expression of the fibrotic markers including COL1A2, CTGF and αSMA was confirmed in the fibrotic cells. Once, satisfied that the fibroblasts were indeed fibrotic, the expression of selected genes was assesed in these cells.

COL1A2, CTGF and αSMA gene expression was measured by RT-qPCR in primary human fibroblasts from normal and diseased lung, skin and kidney. The data showed that there was a significant difference in the gene expression of COL1A2, CTGF and αSMA in normal and fibrotic cell lines from all 3 organs.The relative gene expression of these 3 fibrotic markers was significantly higher in the skin, lung and kidney fibroblasts from SSc and ADPKD patients compared to controls (Figure 3.5). Protein expression was also assessed (Western blotting) and showed a marked increase in COL1A2 and CTGF in fibrotic lung, skin and kidney fibroblasts (Figure 3.6).

Figure 3.5 Expression of COL1A2, CTGF and αSMA mRNA in normal and fibrotic fibroblasts from lung, skin and kidney. Fibroblasts were cultured in 6 well plates to confluence and serum-starved overnight. Total RNA was extracted following the instructions of the RNeasy Mini Kit (Qiagen). Expression of COL1A2, CTGF and αSMA mRNA was investigated by RT-qPCR using a 1-step method based on the Quantifast SYBR Green RT- qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as endogenous control. Blue: normal; red: fibrotic; n=3 patients per condition and each sample tested in duplicate. P-values <0.05 were considered to be statistically significant.

Figure 3.6. Expression of COL1A2 and CTGF protein in normal and fibrotic fibroblasts from lung, skin and kidney. Fibroblasts were cultured in 6 well plates to confluence and serum-starved overnight. Protein lysates were heat denatured at 70°C for 10 minutes in NuPage LDS Sample Buffer and NuPage Reducing Agent (Thermo Fisher Scientific) prior to electrophoresis. COL1A2 and CTGF protein expression was tested by western blot and COL1A2 and CTGF antibodies (Millipore and Santa Cruz, respectively) were used at 1:1000 dilution. Human GAPDH was used as endogenous control. n=3 patients per condition and each sample tested in duplicate for SSc and n=2 patients per condition and each sample tested in duplicate for ADPKD.

3.5.2 Expression of the selected genes

Expression of the short-listed genes in normal and fibrotic fibroblasts was measured by RT-qPCR.

3.5.2.1 CEMIP in lung, skin and kidney fibroblasts

In skin and lung fibroblasts from SSc patients, CEMIP was significantly down-regulated compared to controls. In contrast, CEMIP was significantly up-regulated in ADPKD fibroblasts compared to controls. CEMIP mRNA was highly expressed in normal skin fibroblasts but expression was lower in normal lung and kidney fibroblasts (Figure 3.7).

3.5.2.2 DDR2 in lung, skin and kidney fibroblasts

DDR2 mRNA expression in kidney fibroblasts from ADPKD patients was significantly elevated compared to the controls. However, expression was significantly down- regulated in lung and skin fibroblasts from SSc patients compared to their normal counterparts.

The level expression of DDR2 mRNA was higher in ADPKD fibroblasts compared to SSc lung and skin fibroblasts Normal fibroblasts from the 3 organs showed different levels of basal expression (Figure 3.8).

Figure 3.7. Expression of CEMIP mRNA in normal and fibrotic fibroblasts. Fibroblasts were cultured in 6 well plates to confluence and serum-starved overnight. Total RNA was extracted following the instructions of the RNeasy Mini Kit (Qiagen). Expression of CEMIP mRNA was investigated by RT-qPCR using a 1-step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as endogenous control. Blue: normal; red: fibrotic; n=3 patients per condition and each sample tested in duplicate. P-values <0.05 were considered to be statistically significant.

Figure 3.8. Expression of DDR2 mRNA in normal and fibrotic fibroblasts. Fibroblasts were cultured in 6 well plates to confluence and serum-starved overnight. Total RNA was extracted following the instructions of the RNeasy Mini Kit (Qiagen). Expression of DDR2 mRNA was investigated by RT-qPCR using a 1-step method based on the Quantifast SYBR Green qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as endogenous control. Blue: normal; red: fibrotic; n=3 patients per condition and each sample tested in duplicate. P-values <0.05 were considered to be statistically significant.

3.5.2.3 GREM1 in lung, skin and kidney fibroblasts

Expression of GREM1 was significantly down-regulated in SSc lung fibroblasts compared to normal lung fibroblasts.. In contrast to SSc lung fibroblasts, expression was unchanged in SSc skin fibroblasts and ADPKD fibroblasts compared to their normal counterparts. Levels of GREM1 mRNA were very different in normal fibroblasts from the different organs: there was markedly more GREM1 mRNA in skin than in lung and kidney fibroblasts. However there was considerable variability in expression in normal fibroblasts in all samples (Figure 3.9).

Figure 3.9. Expression of GREM1 mRNA in normal and fibrotic fibroblasts. Fibroblasts were cultured in 6 well plates to confluence and serum-starved overnight. Total RNA was extracted following the instructions of the RNeasy Mini Kit extraction protocol (Qiagen). Expression of GREM1 mRNA was investigated by RT-qPCR using a 1-step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as endogenous control. Blue: normal; red: fibrotic; n=3 patients per condition and each sample tested in duplicate. P-values <0.05 were considered to be statistically significant. ns= non-significant.

3.5.2.4 HAS2 in lung, skin and kidney fibroblasts

HAS2 was identified as a common gene involved in lung, skin and kidney fibrosis (Appendix, Table B). RT-qPCR showed that the expression of HAS2 was significantly up-regulated in skin and lung fibroblasts from SSc patients and kidney fibroblasts from ADPKD patients compared to their respective normal controls. The expression level of HAS2 mRNA was higher in skin and lung fibrotic fibroblasts compared to ADPKD fibroblasts. HAS2 expression in ADPKD fibroblasts was highly variable. (Figure 3.10).

Figure 3.10. Expression of HAS2 mRNA in normal and fibrotic fibroblasts. Fibroblasts were cultured in 6 well plates to confluence and serum-starved overnight. Total RNA was extracted following the instructions of the RNeasy Mini Kit (Qiagen). Expression of HAS2 mRNA was investigated by real-time RT-qPCR using a 1-step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as endogenous control. Blue: normal; red: fibrotic; n=3 patients per condition and each sample tested in duplicate. P-values <0.05 were considered to be statistically significant.

3.5.2.5 HSPB1 in lung, skin and kidney fibroblasts

The expression of HSPB1 mRNA was significantly down-regulated in SSc skin fibroblasts but there was no change in the expression of HSPB1 in fibrotic lung and kidney fibroblasts compared to controls. There was considerable variability in the expression levels in SSc lung fibroblasts compared to SSc skin and ADPKD kidney (Figure 3.11). The level of expression of HSPB1 was high in normal and fibrotic fibroblasts from all 3 organs. HSPB1 mRNA was highest in skin then lung then kidney.

3.5.2.6 IL11 in lung, skin and kidney fibroblasts

The RT-qPCR results confirmed that the expression of IL11 mRNA was significantly up-regulated in skin and lung fibroblasts from SSc patients compared to controls. IL11 expression was low in normal fibroblasts from all 3 organs. IL11 expression was not altered in fibroblasts from ADPKD patients. IL11 was more highly expressed in lung and skin fibrotic fibroblasts than in ADPKD fibroblasts (Figure 3.12).

Figure 3.11. Expression of HSPB1 mRNA in normal and fibrotic fibroblasts. Fibroblasts were cultured in 6 well plates at confluence and serum-starved overnight. Total RNA was extracted following the instructions of the RNeasy Mini Kit (Qiagen). Expression of HSPB1 mRNA was investigated by RT-qPCR using a 1-step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as endogenous control. Blue: normal; red: fibrotic; n=3 patients per condition and each sample tested in duplicate. P-values <0.05 were considered to be statistically significant. ns= non- significant. .

Figure 3.12. Expression of IL-11 mRNA in normal and fibrotic fibroblasts. Fibroblasts were cultured in 6 well plates to confluence and serum starved overnight. Total RNA was extracted following the instructions of the RNeasy Mini Kit (Qiagen). Expression of IL11 mRNA was investigated by RT-qPCR using a 1-step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as endogenous control. Blue: normal; red: fibrotic; n=3 patients per condition and each sample tested in duplicate. P-values <0.05 were considered to be statistically significant. ns= non- significant.

3.3.5.2.7 ITGαV in lung, skin and kidney fibroblasts

The ITGαV subunit mRNA was significantly up-regulated in lung SSc fibroblasts compared to controls. However, the expression was not altered in skin fibroblasts from SSc patients and was significantly reduced in ADPKD fibroblasts compared to controls. There was marked variation in ITGαV expression in SSc skin fibroblasts compared to lung and kidney fibroblasts. There was also considerable variation in expression in normal kidney fibroblasts.The level of expression of ITGαV was higher in normal skin compared to normal lung fibroblasts and kidney fibroblasts (Figure 3.13).

Figure 3.13. Expression of ITGαv mRNA in normal and fibrotic fibroblasts. Fibroblasts were cultured in 6 well plates to confluence and serum starved overnight. Total RNA was extracted following the instructions of the RNeasy Mini Kit (Qiagen). Expression of IL11 mRNA was investigated by RT-qPCR using a 1-step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as endogenous control. Blue: normal; red: fibrotic; n=3 patients per condition and each sample tested in duplicate. P-values <0.05 were considered to be statistically significant. ns= non- significant.

3.5.2.8 JAG2 in lung, skin and kidney fibroblasts

Fibrotic fibroblasts from lung and kidney did not show any significant difference in JAG2 mRNA expression compared to their respective controls. SSc lung fibroblasts showed a trend to decrease but this did not reach statistical significance due to the variability between the controls samples. JAG2 expression was significantly decreased in SSc skin fibroblasts compared to controls. The level of expression of JAG2 was similar in normal skin and kidney fibroblasts and higher in normal lung fibroblasts (Figure 3.14). Normal fibroblasts from all three organs. showed a high variability in JAG2 expression levels.

Figure 3.14. Expression of JAG2 mRNA in normal and fibrotic fibroblasts. Fibroblasts were cultured in 6 well plates to confluence and serum-starved overnight. Total RNA was extracted following the instructions of the RNeasy Mini Kit (Qiagen). Expression of JAG2 mRNA was investigated by RT-qPCR using a 1-step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as endogenous control. Blue: normal; red: fibrotic; n=3 patients per condition and each sample tested in duplicate P-values <0.05 were considered to be statistically significant. ns= non- significant.

3.5.2.9 KRAS in lung, skin and kidney fibroblasts

Expression of KRAS mRNA in SSc skin fibroblasts was significantly up-regulated compared to controls. There was no difference between SSc lung and ADPKD fibroblasts and their respective controls. KRAS expression was higher in normal lung and skin fibroblasts compared to kidney fibroblasts (Figure 3.15).

3.5.2.10 MKD in lung, skin and kidney fibroblasts

The expression of MDK mRNA was significantly reduced in lung and skin fibroblasts from SSc patients compared to normal controls. In contrast, ADPKD fibroblasts expressed significantly more MDK than control fibroblasts. There were differences in the difference in the expression in fibroblasts from the different organs, MDK mRNA was expressed at higher levels in normal skin and lung fibroblasts than in kidney fibroblasts (Figure 3.16).

Figure 3.15. Expression of KRAS mRNA in normal and fibrotic fibroblasts. Fibroblasts were cultured in 6 well plates to confluence and serum-starved overnight. Total RNA was extracted following the instructions of the RNeasy Mini Kit (Qiagen). Expression of KRAS mRNA was investigated by RT-qPCR using a 1-step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as endogenous control. Blue: normal; red: fibrotic; n=3 patients per condition and each sample tested in duplicate. P-values <0.05 were considered to be statistically significant. ns= non- significant.

Figure 3.16. Expression of MDK mRNA in normal and fibrotic fibroblasts. Fibroblasts were cultured in 6 wells plate at confluence and serum starved overnight. Total RNA was extracted following the instructions of the RNeasy Mini Kit extraction protocol (Qiagen). Expression of MDK mRNA was investigated by RT-qPCR using a 1-step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as endogenous control. (Blue: normal; red: fibrotic; n=3 patients per condition and each sample tested in duplicate). P-values <0.05 were considered to be statistically significant.

3.5.2.11 TSPAN13 in lung, skin and kidney fibroblasts

The expression of TSPAN13 was significantly up-regulated in lung and skin fibroblasts from SSc patients compared to normal controls. In contrast, ADPKD fibroblasts expressed significantly less TSPAN13 mRNA than control fibroblasts. TSPAN13 mRNA was expressed at higher levels in normal kidney fibroblasts than in normal skin and lung fibroblasts which basal expression was very low. (Figure 3.17).

Figure 3.17 Expression of TSPAN13 mRNA in normal and fibrotic fibroblasts. Fibroblasts were cultured in 6 well plates to confluence and serum starved overnight. Total RNA was extracted following the instructions of the RNeasy Mini Kit (Qiagen). Expression of TSPAN13 mRNA was investigated by RT-qPCR using a 1-step method based on the Quantifast SYBR Green qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as endogenous control. Blue: normal; red: fibrotic; n=3 patients per condition and each sample tested in duplicate. P-values <0.05 were considered to be statistically significant.

3.5.2.12 WIF1 in lung, skin and kidney fibroblasts

Expression of WIF1 mRNA was low in normal fibroblasts in all 3 organs but was significantly down-regulated in lung and skin SSc fibroblasts compared to controls. However, there was no change in expression in ADPKD fibroblasts compared to controls.(Figure 3.18). There was variability in WIF1 mRNA expression in normal lung, skin and kidney fibroblasts.

Figure 3.18. Expression of WIF1 mRNA in normal and fibrotic fibroblasts. Fibroblasts were cultured in 6 well plates to confluence and serum-starved overnight. Total RNA was extracted following the instructions of the RNeasy Mini Kit (Qiagen). Expression of WIF1 mRNA was investigated by RT-qPCR using a 1-step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as endogenous control. Blue: normal; red: fibrotic n=3 patients per condition and each sample tested in duplicate P-values <0.05 were considered to be statistically significant. ns= non- significant.

3.6 Summary of gene expression data

The summary of the gene expression data is shown in the Table 3.3. Of the 12 genes selected and tested by RT-qPCR, 5 genes were highly regulated (up- or down- regulated) in fibrotic fibroblasts from all 3 organs and these were selected for further analysis. These are shown in bold in the table (CEMIP, DDR2, HAS2, IL11 and TSPAN13).

GENE SSC LUNG SSC SKIN ADPKD KIDNEY

CEMIP Down Down Up

DDR2 Down Down Up

GREM1 Down Ns Ns

HAS2 Up Up Up

HSPB1 Ns Down Ns

IL11 Up Up Ns

ITGAV Up Ns Down

JAG2 Ns Down Ns

KRAS Ns Up Ns

MDK Down Down Up

TSPAN13 Up Up Down

WIF1 Down Down Ns

Table 3.3. Expression of selected genes in normal and fibrotic fibroblast. Relative gene expression of the 12 selected genes in SSc lung and skin fibroblasts and ADPKD fibroblasts compared to control fibroblasts (n=3 cell lines per condition and each sample tested in duplicate). Down: significantly down-regulated compared to normal; Up: significantly up- regulated compared to control; Ns: non-significant change in expression between fibrotic and normal cells.

3.7 TGFβ1 effects on selected genes

As described in the Chapter 1 (section 1.1.2.3.1) TGFβ1 is a key cytokine in the pathogenesis of SSc and other fibrotic conditions (355). In addition to differences in basal expression of the selected genes in normal and SSc fibroblasts, the effect of TGFβ1 stimulation of normal fibroblasts (TGFβ1 will activated them) and fibrotic fibroblasts (TGFβ1 may enhance the fibrotic response in these cells already activated) on the expression of the 5 selected genes highlighted was examined. Normal and fibrotic lung, skin and kidney fibroblasts were treated with TGFβ1 (2ng/ml) for 24 hours and expression of the TSPAN13, HAS2, CEMIP, IL11 and DDR2 measured by RT- qPCR.

3.7.1 Lung fibroblasts treated with TGFβ1

After 24 hours stimulation with TGFβ1, COL1A2 expression was significantly increased in treated normal lung fibroblasts compared to untreated cells. Expression was also higher in SSc fibroblasts after stimulation but this did not reach statistical significance. After stimulation with TGFβ1, there was a significant increase in CTGF mRNA in both normal and SSc lung fibroblasts. There was also a significant increase in αSMA expression in normal fibroblasts after stimulation although this was unchanged in SSc fibroblasts with and without TGFβ1 treatment. Among the 5 selected genes, expression of TSPAN13 and HAS2 and IL11 was significantly higher in normal and fibrotic fibroblasts stimulated with TGFβ1. The expression of DDR2 was not changed after stimulation in either normal or SSc cells. CEMIP expression was significantly down- regulated after stimulation in both normal and fibrotic cells (Figure 3.19).

Figure 3.19. Effect of TGFβ1 on fibrotic markers (a) and selected genes (b) in normal and SSc lung fibroblasts. Normal and SSc pulmonary fibroblasts were grown to confluence, serum-starved overnight, treated with vehicle (-) or TGFβ1 (2ng/ml) (+) for 24 hours. Total RNA was extracted using the RNeasy Mini Kit (Qiagen). Expression of COL1A2, CTGF, αSMA and selected genes mRNA was investigated by RT-qPCR using a 1-step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Human TATA- box binding protein (TBP) was used as endogenous control. Blue: normal; red: SSc; n=3 patients per condition and each sample tested in duplicate. P-values <0.05 were considered to be statistically significant. ns= non-significant.

3.7.2 Skin fibroblasts treated with TGFβ1

After 24 hours of TGFβ1 stimulation, COL1A2 expression was increased in normal fibroblasts, although there was a trend to increase in treated SSc fibroblasts this did not reach statistical significance. CTGF was significantly up-regulated in normal and SSc skin fibroblasts after TGFβ1 treatment. αSMA was markedly increased in normal fibroblasts treated with TGFβ but was unaffected in treated SSc fibroblasts compared to untreated cells. Among the 5 selected genes, TSPAN13, IL11, and HAS2 expression was significantly higher in normal stimulated with TGFβ1 compared to untreated cells, DDR2 was unadffected and CEMIP was significantly down-regulated. In SSc, TGFβ1 significantly up-regulated TSPAN13 and IL11, there was no effect on the expression of HAS2 or DDR2 and CEMIP was significantly down-regulated (Figure 3.20).

3.7.3 Kidney fibroblasts treated with TGFβ1

After 24 hours of TGFβ1 stimulation COL1A2, CTGF and αSMA expression was significantly up-regulated in normal kidney fibroblasts. However, in ADPKD fibroblasts treated with TGFβ1, there was no difference in these 3 genes expression compared to untreated. In normal kidney fibroblasts treated with TGFβ1,TSPAN13 and HAS2 were significantly increased, DDR2 and IL11 were unaffected and CEMIP was reduced TGFβ1 had no effect on the expression of the 5 selected genes in ADPKD fibroblasts (Figure 3.21).

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Figure 3.20. Effect of TGFβ1 on fibrotic markers (a) and selected genes (b) in normal and SSc skin fibroblasts. Normal and SSc dermal fibroblasts were grown to confluence, serum- starved overnight, treated with vehicle (-) or TGFβ1 (2ng/ml) (+) for 24 hours. Total RNA was extracted using the RNeasy Mini Kit (Qiagen). Expression of COL1A2, CTGF, αSMA and selected genes mRNA was investigated by RT-qPCR using a 1-step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as endogenous control. Blue: normal; red: SSc; n=3 patients per condition and each sample tested in duplicate. P-values <0.05 were considered to be statistically significant. ns= non-significant.

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Figure 3.21. Effect of TGFβ1 on fibrotic markers (a) and selected genes (b) in normal and ADPKD kidney fibroblasts. Normal and ADPKD fibroblasts were grown to confluence, serum- starved overnight, treated with TGFβ1 (2ng/ml) for 24 hours (+). Total RNA was extracted using the RNeasy Mini Kit (Qiagen). Expression of COL1A2, CTGF, αSMA and selected genes mRNA was investigated by RT-qPCR using a 1-step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as endogenous control. Blue: normal; red: ADPKD; n=3 patients per condition and each sample tested in duplicate. P-values <0.05 were considered to be statistically significant. ns= non- significant.

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3.8 Discussion and summary of data

Mining of existing published data can provide important informations for research on disease mechanisms. Published literature (1988-2015) and transcriptomic microarray data (2004-2013) were used in this study to identify common and organ-specific genes implicated in fibrosis in human lung, skin and kidney. This study identified 91 genes common to fibrosis in the lung, skin and kidney, which had a wide variety of functions. In terms of organ-specific genes, this analysis selected 60 genes specific to fibrosis in each of the three organs.

Analysis of pathways and protein interactions using several online platforms, STRING, KEGG and DAVID, revealed, both, expected (eg. TGFβ signaling) and novel pathways unknown in fibrosis. Since all 3 online tools gave similar data, STRING was more extensively studied.

Since the number of identified genes was too large for validation, key criteria were applied to edit the gene lists and to select a manageable number of candidates. These criteria included the highest FC, limited numbers of previous studies reported, the drugability, and the availability of analytical reagents. Twelve genes which met all the criteria were selected for further study.

Expression of the 12 selected genes in normal and fibrotic primary fibroblasts (from healthy controls and from SSc and ADPKD patients) were examined by RT-qPCR. Also, TGFβ1 treatment was used to activate normal fibroblasts and potentially enhance fibrosis in fibrotic fibroblasts characterised by increased COL1A2, CTGF and αSMA mRNA expression. Of the 12 genes selected, 5 were significantly regulated in fibrotic cells:

TSPAN13 was significantly up-regulated in SSc lung and skin fibroblasts compared to normal controls. In contrast, ADPKD fibroblasts expressed less TSPAN13 than normal kidney fibroblasts. TGFβ1 treatment up-regulated TSPAN13 in normal and fibrotic lung and skin fibroblasts and also in normal kidney fibroblasts. TGFβ1 had no effect on TSPAN13 expression in ADPKD fibroblasts.

HAS2 was significantly up-regulated in fibrotic fibroblasts from lung, skin and kidney compared to controls. Also, HAS2 was up-regulated after TGFβ1 treatment in normal and fibrotic lung. In normal skin and kidney fibroblasts TGFβ1 up-regulated HAS2, however, there was no increase in expression in SSc skin or ADPKD fibroblasts, in response to TGFβ1.

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CEMIP expression was down-regulated in SSc lung and skin fibroblasts compared to normal controls. In contrast, the level of expression was increased in ADPKD. TGFβ1 down-regulated CEMIP in normal and SSc lung and skin fibroblasts as well as normal kidney fibroblasts. In contrast, TGFβ1 had no effect on CEMIP expression in ADPKD fibroblasts.

IL11 was significantly elevated in SSc lung and skin fibroblasts compared to controls. There was no change in the expression of IL11 in normal and ADPKD kidney fibroblasts. TGFβ1 treatment of normal lung and skin fibroblasts increased IL11 expression. In SSc skin fibroblasts, TGFβ1 increased IL11 expression. In SSc lung fibroblasts there was a trend to increase this did not reach statistical significance. TGFβ1 had no effect in IL11 in normal and ADPKD fibroblasts.

DDR2 was down-regulated in SSc lung and skin fibroblasts compared to controls. In contrast it was significantly up-regulated in ADPKD fibroblasts. There was no changes in DDR2 expression after TGFβ1 treatment in normal or fibrotic lung, skin and kidney fibroblasts.

In general, patterns of expression of the 5 candidates genes were similar in SSc skin and lung fibroblasts, in both, untreated and in response to TGFβ1. TGFβ1 treatment of normal skin and lung fibroblasts induced an expression pattern similar to that seen in SSc. In contrast, gene expression of ADPKD fibroblasts tend to be opposite to SSc eg. CEMIP increased in SSc but decreased in ADPKD and TSPAN13 was reduced in SSc but increased in ADPKD. One potential explanation for these differences in ADPKD fibroblasts might be that the underlying genetic defect in ADPKD confers some unique fibrotic characteristics. Interestingly, treatment of normal kidney fibroblasts with TGFβ1, an accepted model of CKD of non-genetic origin induced similar expression pattern to that seen in SSc. Among the 5 genes tested, TSPAN13, HAS2 and CEMIP showed the most striking changes in gene expression in fibrotic cells and were selected for further analysis:

TSPAN13 is novel candidate in fibrosis although it is known that TSPANs interact with integrins which are key mediators in the fibrotic process (356). Little is know about TSPAN13 apart from its potential role in cancer (348) and it has not previously been implicated in fibrosis. The role of TSPAN13 is explored further in Chapter 4.

HAS2 and CEMIP are both play a role in HA regulation which is one of the major component of the ECM (297). HAS2 is responsible for polymerisation of HA while CEMIP depolymerises HA (297).

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HAS2 is known to be involvedin fibrosis (357) but CEMIP has not previously been implicated. Studies of these two genes are described in Chapter 5.

In summary, the initial search revealed hundreds of genes. A serious of selection step was applied to reduce the number of genes and 12 were validated. Based on the expression studies of these 12 genes, 3 were selected for in detailed analysis. Furthermore, the data mining exercise has provided an important resource for future studies in pulmonary, dermal and renal fibrosis.

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CHAPTER 4 TETRASPANIN 13: A NOVEL GENE INVOLVED IN FIBROSIS

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4.1 Tetraspanins (TSPANs): Structure, function and disease implications

4.1.1 The TSPAN family

Tetraspanins (also called transmembrane superfamily 4 proteins (TM4SF)) are a family of widely expressed four transmembrane domain proteins (358). The tetraspanin family was first characterised at the beginning of the 1990s and 33 TSPANs proteins have been identiied in humans (359). The proteins are divided into 4 subgroups: the CD family, the CD63 family, the uroplakin (UPK) family, and the retinal degeneration slow (RDS) family (360) (Table 4.1).

TSPANs are found on all human cells both on the plasma membrane and intracellular membranes, with the majority of TSPANs found predominantly at the cell surface, with the exception of CD63 which is mainly localised to the surface of intracellular vesicles. TSPANs have been reported to regulate a variety of cellular processes including membrane organization, protein trafficking, cell fusion, cell-cell interactions, migration, and apoptosis (361). Recent evidence suggests that some TSPANs can directly recruit signaling proteins (362). Tetraspanins have been implicated in a variety of pathologies (Table 4.1) and in particular, in cancer. CD37 and CD151 are involved in cancer metastasis (363) and targeting these TSPANs has recently been shown to be beneficial in treating metastatic cancers (364).

However, the role of TSPANs in fibrosis has not been determinated and little is known about the function of TSPAN13 in normal physiology. Although, no data were found on TSPAN13 in fibrosis, TSPAN13 has been reported to be altered in pathological conditions (eg. prostate and breast cancer) (348,365).

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GENE ALIAS DISEASE IMPLICATION REF CD FAMILY TSPAN1 TSP1 Colorectal and gastric cancer (366) TSPAN2 TSP2 Oligodendrogenesis (367) TSPAN4 TSP4 Not known Glioblastoma; gastric cancer; bipolar TSPAN8 CO029 (368–370) disorder TSPAN9 NET5 Not known TSPAN16 TM4B Not known TSPAN18 TSP18 Not known TSPAN19 TSP19 Not known Lung and renal carcinoma; HIV1 and TSPAN24 CD151 (371–374) Papilloma virus infection; fibrosis CD53 TSPAN25 Not known

Leukaemia; Toxoplasma gondii TSPAN26 CD37 (375) infection Renal cell carcinoma TSPAN27 CD82 (376,377) Hepatitis C infection Cancer (breast, lung, prostate, TSPAN28 CD81 (378,379) melanoma, brain, and lymphoma) Breast and gastric cancers; TSPAN29 CD9 (376,380,381) leukaemia TSPAN33 TSP33 Not known CD63 TSPAN3 TSP3 Not known TSPAN6 TSP6 Not known TSPAN7 CD23 HIV infection Osteoclastogenesis; prostaste TSPAN13 NET6 (348,349,382,383) cancer; breast cancer Myocardial fibrosis; breast and liver TSPAN30 CD63 (384–386) cancers TSPAN31 SAS Not known RD FAMILY TSPAN5 TSP5 Gastric cancer (387) TSPAN10 OCULOSPANIN Not known TSPAN11 CD151 like Not known TSPAN14 TSP14 Not known TSPAN15 NET7 Venous thromboembolism (388) TSPAN17 TSP17 Metastatic sarcomas (389) TSPAN22 PRPH2 Retinopathy (390) TSPAN23 TSP23 Not known UROPLAKIN Colorectal and lung cancer; TSPAN12 NET2 (391,392) Retinopathy TSPAN20 UPK1B Bladder cancer (393) TSPAN21 UPK1A Bladder cancer (394) TSPAN32 TSSC6 Venous thromboembolism (395)

Table 4.1. TSPAN family. The TSPAN family members and disease involvement. The 33 members of the TSPAN family identified in humans are shownwith known aliasesand disease implications.

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4.1.2 Structure of TSPANs

Despite the important roles of TSPANs in human physiology, little is known about the molecular mechanisms underlying their various functions. TSPANs are small integral membrane proteins that protrude 3–5 nm from the plasma membrane. The structure of TSPANs (200-350-aminoacids) is widely conserved and they have several transmembrane domains (396). The proteins contain short N-terminal and C-terminal cytoplasmic tails, a small extracellular loop (EC1 domain) and a large extracellular loop (EC2 domain) (Figure 4.1). The four transmembrane domains are involved in both intramolecular and intermolecular interactions (397).

The EC2 loop is required for interactions between TSPANs and other proteins. Despite the conserved cysteine motifs, the EC2 domain contains also a highly variable region that is frequently involved in specific interactions between TSPANs and various non- tetraspanin partners. It is typical for TSPANs to undergo extensive post-translational modification. Covalent attachment of palmitate to intracellular cysteine residues is implicated in mediating TSPAN-TSPAN interactions and assembly of TSPAN-enriched domains that can support signaling (140,398).

Figure 4.1. Schematic representation of a generic tetraspanin. TSPANs have 4 transmembrane (TM) domains and 2 extracellular loops,a small loop EC1 between the first and second TM domains, and a large loop, EC2, between the third and fourth TM domains. TSPANs also have an intracellular loop (between the second and third TM domains), and cytoplasmic N- and C- termini.

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Furthermore, nearly all TSPANs display extensive N-linked glycosylation at extracellular sites (359). This glycosylation is likely to have functional relevance, as shown with CD82 and CD9, which only influence motility or apoptosis when glycosylated (399). A variety of glycosylation patterns has been observed in the proteins in different cell lines but it is not known whether these differences have any impact on TSPAN function (400).

4.1.3 TSPAN functions

Although TSPANs are abundant proteins in many cell types and several TSPANs display broad tissue expression (eg. CD9, CD63, CD81, CD82 and CD151), others are restricted to haematopoietic cells (CD37, CD53). TSPANs are implicated in the immune response eg. CD81 and CD37 (346), and also in epithelial, vascular development and egg-sperm fusion (401). Two TSPANs, CD151 and CD9, have been shown to regulate endothelial cell migration and angiogenesis in vitro and in vivo (402,403). Recent studies have revealed that CD9 is also critically involved in osteoclast differentiation (380). TSPANs are well known regulators of chemotaxis and migration in several cell types (404) and also directly interact with MMPs and regulate their cell surface localization, trafficking, lysosomal degradation, and proteolytic activity (405). Several TSPANs are expressed on the surface of keratinocytes including CD151, CD9, and CD81, and have a role in migration during wound healing and co-localize with α3 and β1 integrins (403).

4.1.4 TSPANs and diseases

TSPAN proteins have been identified in a variety of diseases including cancers (Table 4.1). Increasing evidence suggests that intracellular pathogens including viruses and bacteria, use TSPANs to enter cells during the course of infection. Recently, it has been shown that in hepatocytes CD81 is a gateway for pathogens such as hepatitis C virus and Plasmodium; it also confers susceptibility to Listeria infection (406). It is thought that TSPANs may have an important role in the uptake and spread of pathogens (407).

TSPANs also regulate the progression and metastasis of various types of cancers through their ability to regulate cell migration and invasion. For instance, CD9 and CD82, CD151 and TSPAN8 promote metastasis in breast, pancreatic, colorectal and non-small-cell lung cancers (408). CD81 which is expressed in most types of cancer, including breast, lung, prostate, melanoma,brain cancer and lymphoma, also contributes to tumour growth and metastasis .

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A key role of TSPANs in disease appears to be to regulate the trafficking and function of other membrane proteins such as integrins, cell-surface αβ heterodimers, known to be key adhesion receptors for ECM (409). Despite recent progress in the understanding of the physiological importance of TSPANs and the potential implication in several pathologies, the molecular function of these proteins is not well understood.

4.1.5 The "TSPAN web"

Although the mechanisms are not fully described, TSPANs are known to function as scaffolding proteins in the plasma membrane of eukaryotic cells. TSPANs can bind to one another and also to numerous partner proteins, forming a "tetraspanin web" or tetraspanin-enriched microdomains (TEMs), which serve as structural and functional units within the plasma membrane and provide a scaffold for the transmission of external stimuli to intracellular signaling components (350).

A single TSPAN molecule can associate with different proteins depending on the cell type. TSPANs can interact with integrins to regulate cell physiology in health and disease (397). The first association described between a TSPAN and an integrin was CD9 with the α2β3 integrin in human platelets (410). Association of certain integrins eg. α3β1, α4β1 and α6β1, with TSPANs was observed in all cells in which the proteins are co-expressed (410). However, some integrins eg. α2β1, α5β1 and α6β4, form complexes with TSPANs only in particular cell types (411). It has been shown that at the plasma membrane, integrin-TSPAN signaling complexes are partitioned into specific microdomains proximal to cholesterol-rich lipid rafts (412). TEMs cluster host cell membrane proteins including integrins and it has been shown that TEMs are required for viral entry (eg. Human cytomegalovirus (HCMV)) (413).

4.1.6 TSPAN signaling pathways and post-translational modifications

Two TSPANs, CD81 and CD9, are known to associate with phosphatidylinositol 4- kinase (PI4K), ERK, p38 or Jun N-terminal kinase (JNK) pathways leading to either cell proliferation or apoptosis (414). CD151, which is associated laminin-binding integrins, activates Rac pathways leading to regulation of the actin cytoskeleton and cell motility (415). The C-terminal tails of CD151 contain PDZ-domain-binding motifs essential for signaling and adhesion-strengthening functions (402).

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TSPANs eg. CD9, CD151 and CD81, are post-translationally modified but little is known about the functional consequences of these post-translational modifications. The proteins can be palmitoylated at membrane proximal cysteine residues which regulates protein-protein interactions and modulates TEM formation (416). Palmitoylation deficient forms of CD151 and TSPAN12 have only weak association with integrins and ADAM10 respectively, resulting in diminished phosphorylation of AKT. These data indicate that palmitoylation-mediated disruption of TEMs can reduce downstream signaling responses. Recent work has shown that overexpression of a palmitoylation-deficient form of CD82 diminishes PKC membrane stabilization, reducing ERK1/2 activation, and downstream leukemia colony formation (417). Collectively, these studies demonstrate that TSPAN palmitoylation contributes significantly to the regulation of downstream cellular signaling.

Recently, the role of CD82 glycosylation with respect to acute myeloid leukemia cell homing was examined (418). Three putative N-glycosylation sites of CD82 were identiﬁed using proteomics and glycomics (419) and it was shown that mutation of the glycosylation sites within CD82, to inhibit glycosylation, resulted in increased acute myeloid leukemia (AML) cell homing to the bone marrow.

TSPANs may also be ubiquitinated at cytoplasmic sites (420). Protein ubiquitination is important for regulating cellular signaling by selectively targeting proteins for degradation (420).

4.2 TSPAN13

The TSPAN13 is the main focus of this project. The TSPAN13 gene is located on chromosome 7p21.1 and encodes a 204 amino acid protein with a predicted molecular weight of 24 kDa (421). TSPAN13 is part of a locus that was reported to be deleted in Wilms' tumours, an embryonic tumour arising from undifferentiated renal mesenchyme, but to date no TSPAN13 mutations relevant to disease have been identified (422). TSPAN13 was identified as an interaction partner of the α1 subunit of the N-type CaV2.2 channel (423). Interactions were located between domain IV of CaV2.2 and the transmembrane segments S1 and S2 of TSPAN13. Electrophysiological analysis revealed that TSPAN13 specifically modulates the efficiency of coupling between voltage sensor activation and pore opening of the channel and accelerates the voltage- dependent activation and inactivation of the Ca2+ current through CaV2.2 (423). This suggests that TSPAN13 might regulate CaV2.2 Ca2+ channel activity in defined synaptic membrane compartments and thereby influence transmitter release.

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A few reports suggest that TSPAN13 may be implicated in variety of cancers. Arencibia et al. performed gene expression analysis of prostate cancer samples compared to matched normal tissue from the same patient using a cancer-related microarray (424). The data showed that TSPAN13 was up-regulated >2-fold in 75% of the samples analyzed. Also, IHC analysis of prostate cancer tissue showed that TSPAN13 was overexpressed in 80% of the samples analyzed (424). In another study, microarray analysis of urinary RNAs to identify candidate genes in prostate cancer, revealed a strong correlation of TSPAN13 mRNA with prostate cancer (29). In breast cancer, it has been shown that aberrant expression of TSPAN13 correlated with epidermal growth factor receptor tyrosine kinase 2 (HER2) and estrogen receptor status (348). This effect of TSPAN13 on proliferation and invasion, suggest that TSPAN13 may be a novel breast cancer suppressor gene (348). TSPAN13 expression was also shown to be related to dysregulation of cell proliferation in paediatric glioma tissue leading to an increase of the tumour growth (425).

Apart from these limited examples there is little information on TSPAN13 in disease. In Chapter 3, TSPAN13 was identified as a gene involved in fibrosis and TSPAN13 mRNA expression was significantly up-regulated in fibrotic fibroblasts compared to their normal counterparts. Here these findings are extended to explore in more detail the role of TSPAN13 in fibrosis in vitro and in vivo.

4.3 Methods

4.3.1 In vivo studies

4.3.1.1 Mouse model of pulmonary fibrosis

All animal experiments were performed under Home Office Project Licence 704587. Dominant-negative mutant TGFβ receptor (DNR) transgenic mice (32) were used in this study. DNR transgenic mice express a kinase-deficient human type II TGFβ receptor (TβRIIΔk) in fibroblasts (TβRIIΔk-fib), using a lineage-specific expression cassette subcloned from the pro-α2(I) collagen gene. The generation and characterization of TGFβ RIIΔk-fib transgenic mice have been described previously (426). Mice expressing this construct show an increase in TGFβ activity and develop dermal and pulmonary fibrosis (426).

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An in-house colony of DNR mice were genotyped by PCR of genomic DNA extracted from ear clips using primers specific for the β-galactosidase reporter gene (16) (5′- CGGATAAACGGAACTGGAAA-3′ and 5′-TAATCACGACTCGCTGTATC-3′) to yield a 500-bp product. Amplification was undertaken by 35 cycles of 60 seconds of annealing at 58 °C, 2.6 mM Mg2+, and 60 seconds of extension at 72°C. The sample size would be ideally 8 mice per group, however in this pilot study, only limited numbers of DNR mice aged 6-8 weeks, were available and 4 mice per group were used.

4.3.1.2 Bleomycin treatment and the administration of siRNA

DNR mice (1 male and 3 females in each group) were 6–8 weeks old and weighed 20- 25 g. DNR mice develop fibrosis but administration of bleomycin enhances fibrosis in these mice (426). Fifty microlitres of unbuffered 0.9% sterile saline or 0.12 U bleomycin sulfate (Sigma) in 50μl sterile saline per mouse were instilled via the oropharyngeal route through a cannula under isoflurane (2.5% in oxygen) anaesthesia on day 0. Animals were monitored daily.

DNR mice were divided into 4 experimental groups (n = 4 mice/group): (1) Saline + non-specific siRNA (siControl, 50ug per mouse; Dharmacon); (2) Saline + siRNA for Tspan13 (siTspan13; Dharmacon); (3) Bleomycin + siControl; (4) Bleomycin + siTspan13. siTspan13 and siControl were instilled every 3 days for 21 days. After 24 days, animals were sacrificed and the lungs removed for analysis (protein and RNA analysis, histology and micro-CT) (as illustrated in Figure 4.2). Lungs were immersed in 10% formal saline (CellStor), RNALater (Qiagen), or snap-frozen in liquid nitrogen and stored at -80oC.

4.3.2 Analyses

4.3.2.1 mRNA and protein expression

RNA was extracted from lung tissue samples using the RNeasy Mini Kit (Qiagen). Briefly, tissues (25-30g) were lysed with 350µl RLT buffer containing β- mercaptoethanol (β-ME) (10µl β-ME/ml RLT). Tissues were disrupted by rapid agitation in the presence of stainless steel beads (Qiagen) in a TissueLyser LT homogenizer (Qiagen).

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Figure 4.2. Experimental protocol for bleomycin-induced lung fibrosis in DNR mice. Mice treated with either control siRNA (siControl) or Tspan13 siRNA (siTSPAN13).

After homogenization, tissue lysate was applied to a RNeasy Mini spin column and ethanol (350µl) was added to the lysate for optimal binding. Total RNA binds to the membrane, contaminants are efficiently washed away, and high-quality RNA is eluted in RNase-free water (50 µl). All binding, wash, and elution steps were performed by centrifugation at 13000xg for 30 seconds at room temperature. Purified RNA was stored at –80°C in RNase-free water.

Proteins were extracted from lung tissue samples using RIPA buffer (Sigma). RIPA buffer was supplemented with protease (Complete mini; Roche) and phosphatase inhibitors (Cocktails 2 and 3; Sigma). Briefly, lung tissues (25-30g) were lysed with 350µl RIPA buffer and disrupted in a TissueLyser LT homogenizer (Qiagen). After homogenization, samples were heat-denatured at 70°C for 10 minutes in NuPage LDS Sample Buffer and NuPage Reducing Agent (ThermoFisher) prior to electrophoresis. Gene and protein expression were examined by RT-qPCR and Western blotting as described in Chapter 2 section 2.3 and 2.4, respectively.

4.3.2.2 Histology

Lungs were dissected (Figure 4.2; Histology and IHC) and immersed in 10% neutral- buffered formalin in CellStor™ pots (CellStor). Tissues were embedded, sections were cut and stained with H&E or Masson’s Trichrome by M.Nohadani (C & C Laboratory Services, London, UK). PSR staining and IHC were performed as described in Chapter 2, section 2.5.

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4.3.2.3 Ex-vivo microCT imaging of mouse lungs

Lungs were dissected (Figure 4.2) and fixed in 10% neutral-buffered formalin in CellStor™ pots (CellStor) for 1 hour at room temperature, rinsed 3 times in PBS and stored in 70% ethanol at 4 oC for processing. Tissues were dehydrated through a series of graded alcohols (2 hours each in 35%,70%, 80%, 90%, then overnight in 100% ethanol) followed by 2 hours in bis(trimethylsilyl)amine (hexamethuldis lazane, HMDS) in a fume cupboard and then air-dried overnight.

Lungs were scanned using a SkyScan 1172 high resolution microCT (Bruker-MicroCT) with the help of David Pearce (Centre for Inflammation and Tissue Repair, UCL). The X-ray source was set to 34kV and source current to 169µA. Medium camera pixel size 2kx1k was used to get the whole lung into the field-of-view at a resolution of 12.6µm. Lungs were placed securely in a foam cuff on the stick mount for the scanner and scanned through 180o with a rotational angle of 0.44. The lungs must be secure and not move as this will affect the scan quality. Raw scans were reconstructed using NRecon Software, adjusted for thermal shift, without beam hardening but with ring artefact reduction of 10. The dynamic range used for reconstruction was 0.0015-0.05. Data Viewer, Image J and Inform software were used for scoring fibrosis (Figure 4.3).

Figure 4.3. Ex-vivo microCT imaging of mouse lungs bleomycin-induced lung fibrosis in DNR mice.

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4.4 Results

4.4.1 In vitro studies

4.4.1.1 TSPAN13 in SSc

RT-qPCR data showed that the expression of TSPAN13 mRNA was significantly up- regulated in SSc lung and skin fibroblasts compared to controls (Figure 4.4). Expression of TSPAN13 mRNA was higher in skin fibroblasts than lung fibroblasts. TGFβ1 treatment also increased TSPAN13 mRNA in fibroblasts from normal and SSc lung and skin (data shown in Chapter 3, section 3.4). TSPAN13 protein expression was examined by Western blotting which showed that, in parallel with the mRNA levels, protein expression was higher in lung and skin SSc fibroblasts compared to normal fibroblasts (Figure 4.4).

TSPAN13 protein expression in human lung and skin (normal and SSc) was also examined by IHC. Expression of TSPAN13 appeared to be elevated in SSc fibrotic lung compared to normal tissue (Figure 4.5a). TSPAN13 was highly expressed in SSc lung tissues mostly in the fibrotic ECM. Expression of TSPAN13 was increased also in SSc skin compared to normal tissue with staining of the stroma of the dermis (Figure 4.5b). Overall, TSPAN13 appeared to be expressed at lower levels than in fibrotic skin compared to fibrotic lungs.

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Figure 4.4. mRNA(a) and protein (b) expression of TSPAN13 in normal and fibrotic human fibroblasts (lung (left) and skin (right)). Fibroblasts were cultured in 6-well plates to confluence and serum-starved overnight. Total RNA was extracted using the RNeasy Mini Kit (Qiagen). Expression of TSPAN13 mRNA was investigated by real-time RT-qPCR using a 1- step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen) (upper panel). Human TATA-box binding protein (TBP) was used as endogenous control. Blue: normal; Red: SSc; n=3 patients per condition and each sample tested in duplicate. TSPAN13 protein expression was tested by Western blot and an anti-TSPAN13 antibody (Abcam) was used at 1:250 dilution. Human tubulin was used as endogenous protein control. n=3 patients per condition (lower panel). Relative protein expression was assessed by densitometry (c). P- values <0.05 were considered to be statistically significant.

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Figure 4.5. TSPAN13 in normal and fibrotic human lung (a) and skin (b). Human tissues were obtained from healthy individual (normal; upper panel) and SSc patients (lower panel). Sections were dewaxed for 10 minutes in xylene and rehydrated through xylenes and graded ethanols to water. Antigen retrieval was performed by incubation in sodium citrate buffer (0.01M, 0.05% Tween 20, pH6.0). IHC was performed as previously described (Chapter 2, section 2.5) using the Vectastain avidin-biotin complex (ABC) method (Vector Laboratories). Sections were incubated with primary anti-TSPAN13 antibody (Abcam) diluted at 1:20 in antibody diluent (Dako) overnight at 4°C in a humidified atmosphere. An IgG control was done for each condition (IgG). TSPAN13 expression was visualised with DAB (Vector Laboratories). Sections were counterstained with Mayer's haematoxylin. Sections were dehydrated through graded ethanols to xylene and mounted in DPX (Sigma). Sections were viewed and photographed with the Hamamatsu NanoZoomer Digital Pathology (NDP) scanner and NDP.view2 softwares. Representative sections of n=4 patients.

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4.4.1.2 siRNA knock-down of TSPAN13

As TSPAN13 mRNA and protein expression was significantly up-regulated in SSc lung and skin fibroblasts, the potential role of TSPAN13 in the fibrotic phenotype displayed by SSc fibroblasts was examined. TSPAN13 was knocked-down in normal and SSc fibroblasts using siRNA as described in Chapter 2, section 2.6. The expression of key fibrotic markers COL1A2, CTGF and αSMA which are over-expressed in SSc fibroblasts, was assessed to determine whether reducing TSPAN13 expression affected the expression of these markers. In SSc lung fibroblasts, TSPAN13 siRNA (siTSPAN13) reduced the expression of TSPAN13 mRNA (55% reduction; Figure 4.7a) and protein (66% reduction; Figure 4.7b). Expression of COL1A2, CTGF and αSMA was elevated in SSc lung and skin fibroblasts compared to normal fibroblasts where TSPAN13 knock-down did not affect COL1A2, CTGF and αSMA expression. However, these increased expression in SSc was significantly reduced by siTSPAN13 compared to treatment with the control scrambled siRNA (siControl) (Figure 4.6).

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Figure 4.6. Effect of siTSPAN13 on fibrotic markers in human lung firoblasts. mRNA (a) and protein (b) expression of TSPAN13, COL1A2, CTGF and αSMA after knocking-down TSPAN13 in normal (blue) and SSc (red) human lung fibroblasts. Fibroblasts were cultured in 6-well plates to confluence and serum-starved overnight. siControl (-) and siTSPAN13 (+) were used at the concentration of 50nM for 48 hours. Total RNA was extracted using he RNeasy Mini Kit (Qiagen). Expression of TSPAN13, COL1A2, CTGF and αSMA mRNA was assessed by real-time RT-qPCR using a 1-step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as endogenous control (n=3 patients and each sample tested in duplicate). (b) TSPAN13, CTGF, COL1A2 and αSMA protein expression was evaluated by Western blot with antibodies to TSPAN13 (Abcam ; 1:250 dilution) and CTGF (Santa Cruz; 1:1000 dilution), COL1A2 (Millipore; 1:1000 dilution) and αSMA (DAKO; 1:1000 dilution). Human GAPDH was used as endogenous control. n=3 patients per condition and each sample tested in duplicate. Here only one patient is shown. Relative protein expression was assessed by densitometry (graphs on the lower panel). P-values <0.05 were considered to be statistically significant.

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4.4.1.2.1 siTSPAN13 reduced the migration of SSc lung fibroblasts in a scratch wound assay

Activated fibroblast migration is an important function in fibrosis as it is essential for fibroblast accumulation at fibrotic foci (84). The scratch wound assay is an in vitro method mimicking cell migration into wound areas (427). SSc and normal fibroblasts treated with siTSPAN13 or siControl were cultured to confluence and serum-starved overnight. A linear scratch was made across each monolayer using a micropin device (this method is described in Chapter 2, section 2.8). The cells were cultured in DMEM containing 10% FCS with mitomycin C (5µg/ml) to inhibit cell proliferation (428). This is crucial to establish any effect is due to the cell migration rather than cell proliferation. Images were taken 24 and 48 hours post-scratch and the area covered by cells was analysed with Image J. After 48 hours, normal fibroblasts had completely closed the scratch wound, hence this time was selected as the end-point for analysis (Figure 4.7). SSc fibroblasts treated with siTSPAN13 showed a significant decrease in migration 48 hours after scratch wounding compared to SSc fibroblasts treated with siControl. These data suggests a pro-migratory role for TSPAN13 in lung fibroblasts (Figure 4.7).

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Figure 4.7. siTSPAN13 reduced the migration of SSc lung fibroblasts. Fibroblasts were seeded (1×104 cells/well) in a 96-well plate, grown to confluence and serum-starved overnight. siControl (blue) and siTSPAN13 (red) was added to the cells for 48 hours. Scratch wounding of the monolayer was performed using a 96-well floating-pin transfer device with a pin diameter of 1.58 mm (Essen). Post-wounding, plates were rinsed twice with PBS to remove any cell debris and incubated for 24 and 48 hours. Migration was assessed in the presence of mitomycin C (5 μg/ml; Sigma) to inhibit proliferation and quantify only migration. Migration was assessed by measuring the change in scratch area over time. Images of the scratch were taken after 24 and 48 hours at 20X magnification and representative images of n=3 patients are shown (upper panel). Image J was used to calculate the extent of wound closure as percentage of wound area covered by cells as a measure of the fibroblast migration (lower panel). n=3 patients per condition and each sample tested in duplicate. P-values <0.05 were considered to be statistically significant.

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4.4.1.2.2. siTSPAN13 reduced the contraction of lung fibroblasts from SSc patients

Contraction of the ECM is another important feature of activated fibroblasts (myofibroblasts) and fibrosis (94). To investigate the role of TSPAN13 in lung and skin fibroblast contractility, a floating gel collagen contraction assay was performed (Figure 4.8). Collagen lattices containing fibroblasts pre-treated with siTSPAN13 or siControl were cultured for 24 hours in the presence of 0.2 or 10%FBS. The quantification of gel contraction is performed by measuring the gel diameter or gel weight (contraction of the collagen by fibroblasts increases the density of collagen fibrils and expels water leading to a reduction in gel weight) (429). In this study, gels were weighed and also imaged to obtain gel areas. SSc fibroblast-seeded gels pre-treated with siControl were significantly smaller and lighter than the normal fibroblast-seeded gels pre-treated with siControl. In contrast, SSc fibroblast-seeded gels treated with siTSPAN13 showed a significant reduction in contractility with a significant increase in gel weight compared to siControl-treated SSc fibroblast-seeded gels. There was no difference in the contraction of normal fibroblasts with siTSPAN13 or siControl. Together, these results suggest that TSPAN13 is involved in contraction of SSc lung fibroblast-seeded collagen lattices.

Figure 4.8. siTSPAN13 reduced the contraction of SSc lung fibroblasts. 24-well plates were pre-coated with sterile 2% BSA in PBS (1ml/well) by incubation at 37°C overnight, and were then washed 3 times with PBS. Trypsinized fibroblasts (pre-treated with siTSPAN13 (+) or siControl (-) for 48 hours) were suspended in DMEM (Sigma) and mixed with collagen solution (1 part 0.2 M N-2-hydroxyethylpiperazine-N'-2- ethanesulfonic acid (HEPES), pH8.0; 4 parts collagen (3 mg/ml; Nutragen) and 5 parts DMEM) at a final concentration of 8x104 cells/ml in 1.2 mg/ml collagen. Collagen-cell suspension (1 ml) was added to each well and was incubated for 1 hour at 37°C. After polymerization, 1 ml of DMEM (with 0.2% or 10% FBS) was added to each well and the gels were detached from the well by gently running a 200μL pipet tip around the edge of the gel. Plates were incubated at 37°C for 24 hours and imaged using AxioVision microscope (Zeiss). After 24 hours gel contraction was quantified by reduction in gel 124 weight (upper panel; Blue: normal; Red: SSc) and images were taken (lower panel) Representative images of n=3 per condition and each sample tested in duplicate). P- values <0.05 were considered to be statistically significant. 4.4.1.2.3. siTSPAN13 reduced the proliferation of lung fibroblasts from SSc patients

The proliferation of activated fibroblasts is pivotal to the development of fibrosis (430). In this study, Crystal violet (CV) assays (Chapter 2, section 2.7) were used to assess cell proliferation. As mentioned in section 2.7, fibroblast proliferation was quantified by measuring absorbance. The results revealed increased proliferation of SSc lung fibroblasts compared to normal lung fibroblasts (Figure 4.10).

Furthermore, siTSPAN13 significantly reduced the proliferation of SSc lung fibroblasts compared to the siControl SSc lung fibroblasts (Figure 4.10). However, there was no difference in proliferation of normal lung fibroblasts with siTSPAN13 or siControl. Taken together, these results suggest that TSPAN13 may play a role in the proliferation of lung fibroblasts during fibrosis (Figure 4.9).

Figure 4.9. siTSPAN13 reduced the proliferation of SSc lung fibroblasts. Cells (1×104 cells/well) were transfected with siTSPAN13 (100nM) for 48 hours and were seeded in 96-well plates and cultured overnight at 37°C. Crystal violet staining solution (0.5%; 50 µL/well) was added to each well and incubated for 20 minutes at room temperature on a rocker. The plate was washed 3 times in running tap-water. The plate was drained and air-dried for at least 4 hours at room temperature. Methanol (200 µL) was added to each well, and the plates incubated for 20 minutes at room temperature on a rocker. OD at 570 nm of each well was measured on a Mithras LB 940 Plate Reader. n=3 patients per condition and each sample tested in duplicate.Blue: normal; Red: SSc. P-values <0.05 were considered to be statistically significant.

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4.4.1.3 TSPAN13 in other fibrotic conditions

Data showed that TSPAN13 was up-regulated in SSc lung and skin tissues and fibroblasts in lung and skin compared to normal controls. Also siRNA to TSPAN13 reduced ECM production and suppressed the fibrotic phenotype of SSc fibroblasts. The expression of TSPAN13 was examined in other fibrotic conditions including renal fibrosis, IPF and liver fibrosis.

4.4.1.3.1 TSPAN13 in kidney fibrosis

TSPAN13 in chronic kidney disease (CKD)

IHC showed that TSPAN13 protein was over-expressed in human CKD compared to normals (Figure 4.10a). Staining for TSPAN13 was confined to distal tubular and collecting duct epithelial cells in the normal kidney tissue. In CKD tissues, TSPAN13 staining was up-regulated and was more widespread. TSPAN13 expression was also apparent in glomeruli and appeared to localise to the mesangial matrix.

TSPAN13 in ADPKD

Fibrosis is one of the major components of progression of ADPKD (228) (Chapter 1, section 1.2.3.1). Expression of COL1A2, CTGF, αSMA and TSPAN13 mRNA was examined in normal and ADPKD human kidney fibroblasts (Chapter 3, section 3.3.5.1). RT-qPCR data showed that the markers of fibrosis were significantly up-regulated in ADPKD fibroblasts compared to normal fibroblasts (Chapter 3, Figure 3.6). However, TSPAN13 was significantly down-regulated in ADPKD fibroblasts compared to normal fibroblasts (Chapter 3, section 3.3.5.2.1.2). The expression of TSPAN13 protein was examined in normal kidney and ADPKD patient tissues. As shown in Figure 4.10 in normal human kidney, expression of TSPAN13 was limited to epithelial cells of the distal tubule and collecting duct (Figure 4.10b). In ADPKD tissue, overall staining was reduced compared to normal and localised to the cyst-lining epithelial cells in ADPKD (Figure 4.10b). Together, RT-qPCR and IHC data showed that TSPAN13 was down- regulated in ADPKD tissues and fibroblasts compared to normal and overexpressed in CKD of other etiologies and normal human kidney fibroblasts treated with TGFβ1, suggesting that the genetic mutation in ADPKD may lead to fibrosis via different mechanisms than occur in CKD of non-genetic disease origin.

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Figure 4.10. TSPAN13 in normal and fibrotic kidney tissues. Tissues were obtained from healthy individual (normal) kidneys donated for transplantation, from CKD patient biopsies (a) and ADPKD kidney tissue (b) obtainded from patients undergoing nephrectomy. Sections were dewaxed for 10 minutes in xylene and rehydrated through xylenes and graded ethanols to water. Antigen retrieval was performed by incubation in sodium citrate buffer (0.01M, 0.05% Tween 20, pH6.0). IHC was performed as previously described (Chapter 2, section 2.5) using the Vectastain avidin-biotin complex (ABC) method (Vector Laboratories). Sections were incubated with primary anti-TSPAN13 antibody (Abcam) diluted at 1:20 in antibody diluent (Dako) overnight at 4°C in a humidified atmosphere. An IgG isotype control was performed for each condition (IgG). TSPAN13 expression was visualised DAB (Vector Laboratories). Sections were counterstained with Mayer's haematoxylin. Sections were dehydrated through graded ethanols to xylene and mounted in DPX (Sigma). Sections were viewed and photographed with the Hamamatsu NanoZoomer Digital Pathology (NDP) scanner and NDP.view2 softwares.Representative images of n=3 kidneys.

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4.4.1.3.2 TSPAN13 in IPF

Many lung diseases result in fibrotic remodelling (431). IPF is the most common of the idiopathic interstitial pneumonias with unknown aetiology (432). The expression of COL1A2, CTGF, αSMA and TSPAN13 mRNA was examined in normal human lung fibroblasts and fibroblasts from IPF patients. RT-qPCR data showed that all the genes tested were significantly up-regulated in IPF fibroblasts compared to normals (Figure 4.11). COL1A2 mRNA expression was markedly elevated in IPF fibroblasts. CTGF and αSMA were also higher in IPF fibroblasts than controls although to a less extent than COL1A2. TSPAN13 mRNA was barely detectable in normal lung fibroblasts but it was significantly higher in IPF fibroblasts (Figure 4.11) suggesting a role for TSPAN13 in lung fibrosis.

Figure 4.11. Expression of COL1A2, CTGF, αSMA and TSPAN13 mRNA in normal and IPF fibroblasts. Normal and IPF lung fibroblasts were cultured in 6-well plates to confluence and, serum-starved overnight. Expression of COL1A2, CTGF, αSMA and TSPAN13 mRNA was investigated by RT-qPCR using a 1-step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as an endogenous control. n=3 patients and each sample was tested in duplicate. (Blue: normal; Red: IPF). P-values <0.05 were considered to be statistically significant.

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4.4.1.3.3 TSPAN13 in liver fibrosis

Liver fibrosis is the excessive accumulation of ECM proteins that occurs in most types of chronic liver diseases. Advanced liver fibrosis can result in cirrhosis, liver failure and portal hypertension and often requires liver transplantation (433). Activated hepatic stellate cells (HSC), have been identified as the major collagen-producing cells in the injured liver (434). As HSC from patients with fibrotic livers were not available, in this study, normal HSC were stimulated with TGFβ1, as a model of liver fibrosis, in order to examine the expression of TSPAN13. COL1A2, CTGF, αSMA and TSPAN13 were significantly up-regulated in human HSC in response to TGFβ1 compared to unstimulated HSC (Figure 4.12) suggesting that TSPAN13 may be implicated in fibrotic liver disease.

Figure 4.12. Expression of COL1A2, CTGF, αSMA and TSPAN13 mRNA in human hepatic stellate cells in response to TGFβ1. HSC were grown to confluence, serum starved overnight and treated with TGFβ1 (2ng/ml) for 24 hours. Total RNA was extracted using the RNeasy Mini Kit extraction protocol (Qiagen). Expression of COL1A2, CTGF, αSMA and TSPAN13 mRNA was investigated by RT-qPCR using a 1-step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as an endogenous control. n=3 cell lines per condition and each sample was tested in duplicate. (Blue;stimulated cells; Red: non-stimulated cells). P-values <0.05 were considered to be statistically significant.

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4.4.2 In vivo studies

4.4.2.1 Tspan13 expression in WT and fibrotic mice lungs

In order to study Tspan13 in vivo in a mouse model of fibrosis, Tspan13 was first examined in vitro in normal mouse (WT) fibroblasts treated with TGFβ1. Preliminary data showed that Tspan13 mRNA was up-regulated in WT fibroblasts treated with TGFβ1 (Figure 4.13). Also, blockade of Tspan13 using siRNA knock-down in these treated fibroblasts reduced Col1a2 and Ctgf expression (Figure 4.14). These data were similar with the data obtained in human fibroblasts treated with TGFβ1. This confirmed that the effect of Tspan13 was not species specific and that the expression of Tspan13 was enhanced in fibrotic conditions.

The preliminary data in vitro was encouraging and Tspan13 was also explored in vivo in 3 groups of mice: WT, DNR mice treated with saline and DNR mice treated with bleomycin. DNR mice mimic features of SSc with vascular alterations and fibrosis with lung complications (440). The rational of this experiment was based on data generated by Denton et al. who demonstred that DNR mice is a good model of mild fibrosis and that DNR model treated with bleomycin develop more severe fibrosis. Therefore, before knocking-down Tspan13, Tspan13 expression was evaluated by IHC on lung sections in these models (Figure 4.14). Data showed that Tspan13 was not present in WT lung section, however, vehicle-treated DNR lung showed many fibrotic areas where Tspan13 was expressed mostly in fibroblasts and epithelial cells.

Furthermore, Tspan13 was markedly increased in the lung of DNR mice treated with bleomycin compared to the other groups (Figure 4.14). Also, there were more fibrotic areas and lesions in the DNR mice with bleomycin than the DNR mice treated with saline. These findings strongly support that this model as a good model to explore fibrosis and in which to perform the siRNA knock-down experiments.

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Figure 4.13. Expression of Tspan13, Col1a2 and Ctgf mRNA in WT fibroblasts in response to TGFβ1. Normal mouse fibroblasts were grown to confluence, serum starved overnight and treated with TGFβ1 (2ng/ml) for 24 hours. siControl and siTSPAN13 were added to the cells for 48 hours.Total RNA was extracted using the RNeasy Mini Kit extraction protocol (Qiagen). Expression of Tspan13, Col1a2 and Ctgf mRNA was investigated by RT-qPCR using a 1-step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Mouse TATA-box binding protein (TBP) was used as an endogenous control. n=2 mice per per condition and each sample was tested in duplicate. (Blue: non-stimulated cells; Red: stimulated cells). P-values <0.05 were considered to be statistically significant.

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Figure 4.14. Tspan13 in WT and DNR lung tissues. Tissues were obtained from WT mice (upper panel) and from DNR mice (lower panel) lungs. Sections were dewaxed for 10 minutes in xylene and rehydrated through xylenes and graded ethanols to water. Antigen retrieval was performed by incubation in sodium citrate buffer (0.01M, 0.05% Tween 20, pH6.0). IHC was performed as previously described (Chapter 2, section 2.5) using the Vectastain avidin-biotin complex (ABC) method (Vector Laboratories). Sections were incubated with primary anti- Tspan13 antibody (Abcam) diluted at 1:20 in antibody diluent (Dako) overnight at 4°C in a humidified atmosphere. An IgG isotype control was performed for each condition (IgG). Tspan13 expression was visualised DAB (Vector Laboratories). Sections were counterstained with Mayer's haematoxylin. Sections were dehydrated through graded ethanols to xylene and mounted in DPX (Sigma). Sections were viewed and photographed with the Hamamatsu NanoZoomer Digital Pathology (NDP) scanner and NDP.view2 softwares. Representative images of n=3 mice per group.

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4.4.2.2 siTSPAN13 in the DNR mouse model of lung fibrosis

4.4.2.2.1 siTSPAN13 significantly reduced Tspan13 gene expression in the lungs of DNR mice treated with bleomycin

Age-matched DNR (6-8 weeks; 20-25g; 4 mice per group) were instilled with saline or bleomycin via the oropharyngeal route on day 0. siTspan13 (+) and siControl (-) were instilled oropharyngeally every 3 days for 21 days from day 0. Animals were sacrificed and lungs were removed 3 days after the last dose of siRNA (day 24). Expression of Tspan13, Col1a2, Ctgf and Acta2 mRNA were analysed in the lungs of saline- and bleomycin-treated (Figure 4.15). Col1a2, Ctgf and Acta2 gene expression was elevated in the bleomycin group compared to the saline group. In the lungs of DNR mice treated with bleomycin and Tspan13 siRNA there was a significant reduction of Tspan13 compared to the siControl-treated lungs. However, there was no change in the expression of Col1a2, Ctgf and Acta2 between the 2 groups which may be due to the small sample size and a large inter-sample variability (Figure 4.15).

4.4.2.2.2 siTSPAN13 significantly reduced Col1a2 protein expression in the lungs of DNR mice treated with bleomycin.

The Western blot data showed that siTspan13 reduced Tspan13 gene expression in the lungs of DNR mice treated with bleomycin but although this did not reach stastistical significance (Figure 4.16). In the DNR lungs treated with siTspan13, Col1a2 protein expression was significantly reduced, as well as Ctgf and Acta2 which showed a trend to decrease but this did not reach statistical significance (Figure 4.16).

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Figure 4.15. Expression of Tspan13 Col1a2, Ctgf and Acta2 mRNA in the lungs of saline- or bleomycin-treated DNR mice administered Tspan13 siRNA or control scrambled siRNA. Saline (50µl) or bleomycin (0.12units in 50µl saline/mouse) was administered via the oropharyngeal route to DNR mice (aged 6-8 weeks; body weight 20-25g) at day 0. Starting at day 0, siTspan13 (+) and siControl (-) (50ug per mouse) was instilled oropharyngeally every 3 days for 21 days. At day 24, animals were sacrificed and the lungs removed for analysis and 25mg were used for mRNA extraction. Expression of Tspan13, Col1a2, Ctgf and acta2 mRNA were investigated by real-time RT-qPCR using a 1-step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Mouse TATA-box binding protein (TBP) was used as an endogenous control. n=4 mice/group (4 females and 1 male) and each sample was tested in duplicate. Blue: saline-treated DNR mice; Red: bleomycin-treated DNR mice. P-values <0.05 were considered to be statistically significant.

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Figure 4.16. Protein expression of Tspan13 Col1a2, Ctgf and acta2 after siTspan13 in saline- and bleomycin-treated mouse lung. Oropharyngeal saline or bleomycin administration (0.12 units in 50 µl sterile saline/mouse) was performed at day 0. SiTspan13 (+)and siControl (-) (50 µg/mouse) was instilled via the oropharyngeal route every 3 days for 21 days. Animals were sacrificed and lungs were harvested at day 24. Tspan13 Col1a2, Ctgf and acta2 protein expression was evaluated by Western blotting (upper panel) using antibodies to Tspan13 (Proteintech; 1:250 dilution) and Ctgf (Santa Cruz; 1:1000 dilution), Col1a2 (Millipore; 1:1000 dilution) and Acta2 (DAKO; 1:1000 dilution). Tubulin was used as endogenous control. Relative protein expression was measured by densitometry (lower panel). n=3 mice per group. Blue: saline-treated DNR mice; Red: bleomycin-treated DNR mice. P-values <0.05 were considered to be statistically significant.

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4.4.2.2.3 Tspan13 siRNA reduced fibrillar collagen in bleomycin-treated DNR mouse lungs

Administration of siTspan13 to bleomycin-treated DNR mice altered the expression of fibrotic ECM markers including Col1a2 (mRNA and protein) in the mouse lungs. Using PSR and polarised light, the presence of mature collagen fibrils was examined to determine whether siTspan13 changed fibrillar collagen content. Lung sections from DNR mice were stained with PSR. Under bright field microscopy, the PSR staining showed that the bleomycin-treated DNR mouse lungs showed more fibrotic foci and expressed more collagen fibres (in red) compared to the saline-treated group. In bleomycin-treated DNR mouse lungs siTspan13 reduced collagen staining compared to the siControl group (Figure 4.17). Examination of the sections under polarised light showed that Tspan13 siRNA significantly reduced fibrillar collagen (in yellow) in bleomycin-treated DNR mouse lungs (Figure 4.17). Furthermore, other histological analyses including Hematoxylin and eosin (H&E) (Figure 4.18a) and Masson’s Trichrome staining (Figure 4.18b) confirmed the PSR observations and showed more marked fibrotic areas and collagen deposition in the DNR lungs in response to bleomycin compared to DNR lungs treated with saline. Also, bleomycin-treated DNR mouse lungs treated with siTspan13 showed a reduction in the collagen staining (in blue) compared to the siControl group (Figure 4.18).

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Figure 4.17. Collagen deposition measured using PSR stain in saline- and bleomycin- treated lungs after Tspan13-knock-down. Sections (5μm) were prepared from formalin-fixed paraffin-embedded tissues and stained with PSR. After deparaffinization and rehydration, the sections were incubated in 0.1% (w/v) Sirius red F3BA (C.I.35780) in saturated picric acid solution for 1 hour at room temperature; sections were then rinsed with distilled water followed by staining with Weigert’s haematoxylin. Differentiation was assesed in 1% HCl, followed by alkalization with tap water. The sections were dehydrated and mounted in DPX. Under bright- field microscopy (PSR), collagen fibres stained a deep pink/red colour (arrow). To evaluate fibrillar collagen, the sections were examined under polarised light. Under these conditions, fibrillar collagen appears yellow (arrow). Sections were imaged using an Olympus Model microscope (original magnification 20X). The graph shows quantitative analysis of lung fibrillar collagen using Image J (lBlue: saline; Red: bleomycin). n=4 mice per group and representative images of n=5 fields/section. P-values <0.05 were considered to be statistically significant. 137

Figure 4.18. Haemotoxylin and Eosin and Masson's trichrome stainings in saline and bleomycin-treated lungs after Tspan13-knock-down. Sections (5μm) were prepared from formalin-fixed paraffin-embedded tissues and stained with H&E by M.Nohadani (C & C Laboratory Services, London, UK). Hematoxylin has a blue color and stains the nucleus. Eosin is pink and stains cytoplasm and ECM (upper panel). Masson's trichrome stains (lower panel) collagen fibres in blue colour (arrow) and cytoplasm in light red. Sections were imaged using an Olympus Model microscope. n=4 mice per group and representative images of n=5 fields/section.

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4.4.2.2.4 Tspan13 siRNA effect on the extent of fibrosis in bleomycin-treated DNR mouse lungs.

Assessment of bleomycin-induced lung fibrosis in DNR mice was performed by micro- CT (section 4.3.2.3). Briefly, DNR lungs were dissected and dehydrated through a series of graded alcohols and air-dried overnight. Lungs were scanned using a SkyScan 1172 high resolution microCT (Bruker-MicroCT).

Data Viewer, Image J and Inform software were used for scoring fibrosis. Fibrosis was significantly increased in bleomycin-treated DNR mouse lungs compared to saline- treated DNR mouse lungs. Overall, although fibrosis foci were elevated in bleomycin- treated lungs treated with siControl compared to those treated with siTspan13, there was no difference in the the fibrosis score between the saline- or bleomycin-treated lungs treated with siTspan13 compared to mice treated with siControl (Figure 4.19).

Figure 4.19. Fibrosis in saline- and bleomycin-treated lungs after Tspan13-knock- down. Lungs were dissected and fixed for 1 hour at room temperature, rinsed 3 times in PBS and stored in 70% ethanol at 4 oC for processing. Tissues were dehydrated through a series of graded alcohols followed by 2 hours in HMDS in a fume cupboard and then air- dried overnight. Lungs were scanned using a SkyScan 1172 high resolution microCT (Bruker-MicroCT) scanned through 180o. Raw scans were reconstructed using NRecon Software. Data Viewer, Image J and Inform software were used for scoring fibrosis. n=4 mice per group (Blue: saline; Red: bleomycin). P-values <0.05 were considered to be statistically significant.

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4.5 Discussion

Among the 12 candidate genes selected after the in silico data mining described in Chapter 3, TSPAN13 was highlighted as a novel gene involved in fibrosis which met all the key criteria applied for shortlisting (Chapter 3 section 3.4).

Little is known about this member of the TSPAN family. One study suggested that TSPAN13 was a regulator of CaV2.2 Ca(2+) channel activity in the synaptic membrane. A diversity of TSPANs is expressed in osteoclast precursors, and cell fusion during osteoclastogenesis is regulated by cooperation of distinct TSPAN family proteins including TSPAN13 (365). Iwai et al. showed that inhibition of TSPAN13 in a monocyte/macrophage-lineage osteoclast precursor cell line augmented osteoclastogenesis (365). This study suggested that targeting TSPAN members may have therapeutic potential in diseases such as rheumatoid arthritis and osteoporosis. TSPAN13 is also postulated to play a role in prostate and breast cancer (349). However, to date, there are no publications on TSPAN13 in fibrotic diseases and more generally, there is only a limited knowledge about TSPAN13 biology and function. The present in vitro and in vivo studies provide strong evidence that TSPAN13 is involved in fibrosis.

The in vitro studies clearly showed that TSPAN13 was differentially regulated in fibrotic fibroblasts. TSPAN13 mRNA and protein was significantly up-regulated in SSc fibroblasts from both lung and skin compared to normal fibroblasts. In addition, in SSc lung and skin tissues, TSPAN13 protein expression appeared to be elevated compared to healthy normal tissue with a higher level of expression of TSPAN13 in SSc lung compared to SSc skin. These novel findings suggest a potential role for TSPAN13 in SSc fibrosis. These data were confirmed by IHC staining which showed an increased TSPAN13 expression in SSc and IPF fibrotic tissues compared to normals.

TGFβ1, a key cytokine driving fibrosis (27), stimulated the expression of fibrotic markers such as COL1A2 and CTGF but also significantly increased the gene and protein expression of TSPAN13 in normal lung, skin and kidney fibroblasts treated with TGFβ1. This indicated that TGFβ1 can induce TSPAN13.

Also, preliminary data showed that knocking-down TSPAN13 in SSc lung fibroblasts reduced the activation of SMAD2/3, p38 and ERK pathways (data not shown). These data support an association between TSPAN13 and the TGFβ pathways where TSPAN13 might be required for TGFβ signaling and co-ordination. To elucidate the role of TSPAN13 in SSc fibroblasts,

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siRNA knock-down of TSPAN13 was performed in vitro on these cells. siTSPAN13 attenuated the fibrotic phenotype of SSc fibroblasts compared to siControl treated fibroblasts. In addition to a significant reduction of key fibrotic mediators COL1A2, CTGF and αSMA (mRNA and protein levels), knock-down of TSPAN13 reduced the migration, contraction and proliferation of SSc lung fibroblasts.

These in vitro analyses examining the impact of deletion of TSPAN13 in fibrotic fibroblasts from SSc patients highlighted for the first time a role for this transmembrane protein in SSc. It was of interest to determine whether TSPAN13 is involved more generally in other fibrotic diseases. Gene expression and protein analyses showed similar data in IPF, where TSPAN13 was significantly up-regulated in fibrotic lung fibroblasts from IPF patients compared to healthy control subjects. Diseased fibroblasts from other fibrotic organs such as CKD kidney and NASH liver were not available therefore TGFβ1-treated normal kidney fibroblasts and HSC were usedas models to test TSPAN13 expression. The data showed that TSPAN13 was increased by TGFβ1 stimulation in both normal kidney fibroblasts and HSC suggesting that TSPAN13 is also implicated in renal and liver fibrosis through TGFβ1. Together these findings suggest that TSPAN13 is involved in organ fibrosis.

Further studies were carried out to explore the role of Tspan13 in vivo in a mouse model of lung fibrosis. Two models of lung fibrosis (bleomycin-induced lung fibrosis and DNR model) are well established (250) and are used to better understand lung fibrosis and to develop novel anti-fibrotic treatments (255). DNR mice develop a moderate fibrosis and vascular alterations. DNR mice treated with bleomycin display a non- reversible fibrosis and a more severe pulmonary fibrosis and lung complications. As the DNR-bleomycin model showed a significant level of fibrosis, it is a useful model to measure any ameliorating effects using siRNA or in future, mAbs.

Histological analyses (H&E, PSR and Masson’s Trichrome staining) showed more marked fibrotic areas and collagen deposition in the DNR lungs in response to bleomycin compared to DNR lungs treated with saline. To explore the role of Tspan13 in lung fibrosis Tspan13 was knocked-down in the DNR mouse model using siRNA. Firstly, data showed that lungs from DNR mice treated with bleomycin expressed more Tspan13 than lungs of DNRs mice administrated instilled with saline (RT-qPCR and Western blot). IHC data confirmed these findings, Tspan13 expression was elevated in lung sections of DNR mice compared to WT where Tspan13 was almost absent. Thus this model may be a useful tool for future studies into the mechanisms of TSPAN13 action.

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Although only small numbers of mice were available for this study (n=4/group) lungs of bleomycin-treated DNR mice receiving siTspan13 showed a significant reduction of Tspan13 showed

Expression of fibrotic markers was also reduced. Col1a2 gene and protein expression were signifcantly decreased; Ctgf and Acta2 (mRNA and protein levels) were also reduced although these did not reach statistical significance. Also, staining of lung sections of bleomycin-treated DNR mice treated with siTspan13 revealed a decreased expression of Tspan13. Lung fibrosis is known to be focal and conventional histological analysis of sections taken in random areas of the lung may may over or under represent fibrosis. Micro-CT analysis permits an assessment of the whole organ. The scans showed that fibrotic areas were not uniformly dispersed throughout the DNR lungs and as expected there were more fibrotic lesions in the DNR treated with bleomycin than the DNR treated with saline. However scoring the extent of fibrosis did not show any difference between the siTspan13 and siControl groups. In terms of the microCT analysis, one potential issue is that in this study lungs were not inflated prior to fixation leading to a restriction of the lungs and a reduced visualisation of lung anatomy for the scanning. Thus in future studies, a lung inflation step should be included to ensure to better resolution by micro-CT. Although the initial in vivo findings are promising and suggest a role for TSPAN13 in lung fibrosis, the experiment needs to be repeated with an increased group size to improve statistical power. In this study, time constraints and the low numbers of mice available, meant that it was only possible to test one siRNA concentration (50µg per mouse) and one dosing regime (1 dose every 3 days). These were selected based on advice from collaborators at UCB Pharma who have optimized siRNA doses in a mouse model of lung fibrosis. However, a siRNA dose response should be performed to determine the optimal concentration. Further, we need to test different dosing regimes, in particular prophylactic versus therapeutic dosing. It will also be important to test the effect of inhibition of TSPAN13 in different organs to establish whether there is broad efficacy.

In summary, TSPAN13 is over-expressed in cells and tissues from diverse fibrotic diseases. Knock-down of TSPAN13 expression attenuated the fibrotic phenotype in SSc lung fibroblasts in vitro and suppressed expression of fibrotic markers in vivo in a mouse model of pulmonary fibrosis suggesting that TSPAN13 has a key role in the pathogenesis of fibrosis. Furthermore, data from this thesis showed TSPAN13 involvement in a variety of fibrotic disorders and in diverse organs including lung, skin, kidney and liver.

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CHAPTER 5

HA, HAS2 AND CEMIP IN FIBROSIS

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The extensive in silico analysis and literature survey detailed in Chapter 3 identified common and unique genes whose expression is altered in 3 organs (lung, skin and kidney) which undergo scarring and fibrosis in disease. As described in Chapter 3, a subset of genes was selected using key criteria and validated in fibrotic fibroblasts and tissues. Two of the shortlisted genes shown to be significantly associated with fibrosis: Hyaluronan synthase 2 (HAS2) and Cell Migration-Inducing Protein (CEMIP also known as KIAA1199), are both involved in the hyaluronic acid or hyaluronan (HA) pathways, known to be a important component of ECM (435).

5.1 HA: description, function and regulation

5.1.1 HA

The importance of the ECM in the complex processes of wound healing is that it provides architectural support for the tissues and a platform for cells and molecules that regulates inter- and intra-cellular signaling (85). The ECM is made up of secreted molecules that constitute the cell microenvironment and is composed of glycoproteins, collagens and glycosaminoglycans (GAGs) including HA (436). HA is found throughout the human body in almost all biological fluids and tissues, with the highest amounts present in the ECM of soft connective tissues (312). HA is a linear non-sulphated GAG composed of tandem repeats of a D-glucuronic acid and D-N-acetylglucosamine disaccharide motif linked by alternating β-1,4 and β-1,3 glycosidic bonds (437). HA can basically be divided into high and low molecular weight molecules, HMW and LMW respectively (438). These 2 forms will be described in this chapter, section 5.4.1. It has been estimated that an adult human contains approximately 15 g of HA and that about one-third turns over daily (439). HA turnover in most tissues is rapid (eg. a half-life of ∼1 day in epidermal tissues).

HAS2 is involved in several key processes, including early EMT in development and morphogenesis, cell proliferation and migration, regeneration, joint lubrication, matrix organization and wound repair (312). During wound healing, HA accumulates in the wound bed and modulates fibroblast migration and production of cytokines and chemokines (6). In disease, HA participates in many important processes including tumour growth and elevated extracellular amounts of HA are correlated with several types of malignancies potentially due to decoupled synthesis and degradation (440). Elevated HA is used as a tumour marker for prostate, liver and colorectal cancer and it may also be used to monitor the progression of the disease (441,442).

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Recently it has been shown that in breast cancer, elevated HA in the peri-tumor stroma and increased HA receptor expression are prognostic for poor outcome and are associated with disease recurrence (443).

Regulation of HA synthesis and degradation is crucial to maintenance of ECM homeostasis (312). HA metabolism is regulated by a balance between polymerisation and depolymerisation. HA is produced at the plasma membrane by HA synthases (HAS), multi-pass transmembrane proteins and HA is degraded by the action of plasma membrane-associated enzymes called hyaluronidases (HYAL) and other HA receptors including CD44 (444) , a heparin sulphate proteoglycan also known as extracellular matrix receptor III (445). Synthesis and degration of HA (Figure 5.1) and will be described in detail in the section 5.1.2 and 3.

5.1.2 HA synthesis: HASs

In vertebrates, three isoforms: HAS1, HAS2, and HAS3 have been identified (56). HAS2 is present on chromosome 8q24.12, while HAS1 and HAS3 have been identified at loci 19q13.4 and 16q22.1, respectively. HAS are members of the glycosyl transferase family 2 proteins and share similar amino acid sequences and structural conformations with 7 TM (Figure 5.2), however, each isozyme presents different spatial and temporal expression patterns. HASs catalyse the polymerization of the HA from its intracellular UDP-esterified precursor, resulting in glucuronic acid and N- acetylglucosamine dissacharide motifs (437) and the 3 isozymes yield HA chains of different length and concentration (446). While HAS1 and HAS2 are able to produce large HA (up to 2000kDa), HA produced by HAS3 has a lower molecular mass (100– 1000kDa).

5.1.2.1 HAS2

It has been shown that HAS2 is the major HAS producing HA (447) and that HAS2 seems to be crucial for tissue morphogenesis and embryogenesis: HAS2 null mice present defects in cardiac development and vascular abnormalities. In contrast, HAS1 and HAS3 null mice are normal (448).

Aberrant expression of HAS2 has been implicated in the pathology of malignancy, pulmonary arterial hypertension (PAH), osteoarthritis (OA), asthma, thyroid dysfunction, and fibrosis (449).

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Figure 5.1 Synthesis and degradation of HA. HASs produce HA in the plasma membrane of different sizes and at different rates. HA is anchored to the cell surface through CD44 and HYAL and localized to lipid rafts in the cell membrane The HYALs hydrolyze HA through endocytosis orchestred on the plasma membrane by HYAL and CD44 (receptor for HA). HA is then internalised and degraded in endosomes and further degraded in lysosomes. Adapted from (521).

Figure 5.2. Schematic representation of HASs. HASs presents 7 transmembrane (TM) domains and 3 extracellular loops, 3 intracellular loop and N- and C- termini.

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HAS2 has been identified as modulator of tumour microenviroment and is implicated in progression of cancer associated fibroblast (CAF)-mediated oral squamous cell carcinoma (OSCC) (450). CAF express higher levels of HAS2 compare to normal fibroblasts. In addition, HAS2 expression correlates with αSMA-positive myofibroblasts in OSCC patients, particularly at advanced clinical stages. Further, inhibiting HAS2 with siRNA markedly attenuated CAF-induced invasion and EMT (450). In breast cancer, HAS2 and HA are overexpressed in breast cancer cell lines and invasive duct cancer tissues, compared with the non-malignant breast cell lines and normal breast tissues (451). Deletion of HAS2 inhibited breast tumour cell proliferation in vivo and in vitro. These data suggested that HAS2 may be a potential prognostic marker and therapeutic target in breast cancer (452). In fibrosis, it has been shown that TGFβ1 induces HAS2 expression and that inhibition of HA synthesis reduces TGFβ1-driven fibroblast proliferation and differentiation to myofibroblasts (246).

Over-expression of HAS2 in mesenchymal cells in mice regulates the invasiveness of fibroblasts and promotes severe lung fibrosis (211). Furthermore, deleting HAS2 in mouse mesenchymal cells increased the cellular senescence of fibroblasts in bleomycin-induced mouse lung fibrosis in vivo (357). In this study a model was proposed whereby over-expression of HAS2 promotes an invasive phenotype resulting in severe fibrosis and conversely, down-regulation of HAS2 promotes resolution (357). These studies suggested that targeting HAS2 to induce fibrotic fibroblast senescence could be an approach to resolve tissue fibrosis (357).

Steadman et al. showed that fibroblast differentiation in response to TGFβ1 is mediated by HA and that CD44 (a HA receptor which can also interact with other ligands such as collagens) and the epidermal growth factor receptor (EGFR) are involved in this process (453). The cell-surface glycoprotein CD44 is involved in a multitude of important physiological functions including cell proliferation, haematopoiesis, and lymphocyte activation (67). Also, CD44 is involved in many diseases including cancer, arthritis, bacterial and viral infections, interstitial lung disease, renal fibrosis, and wound healing (72). HAS2 siRNA studies in fibroblasts suggested that HAS2-dependent production of HA facilitates TGFβ1-dependent fibroblast differentiation through promoting CD44 interaction with EGFR (246).

Another in vitro study suggested that both HAS2 transcriptional induction and subsequent HAS2-driven HA synthesis may contribute to kidney fibrosis via phenotypic modulation of renal proximal tubular epithelial cells (447).

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Together these data suggest an important role for HAS2 in fibrosis. In dermal fibroblasts, it has been shown that the differentiation to myofibroblasts was associated with HAS2 and inhibition of HA synthesis significantly attenuated TGFβ1-mediated differentiation (454).

5.1.4 HA degradation: Hyaluronidases

HYALs are endoglycosidases that degrade HA by cleaving the β-N-acetyl-D- glucosaminidic linkages in HA chains. In humans, 6 HYALs have been identified: HYAL1, 2, 3, 4, PH-20, and HYALP1, all having a high degree of homology (446). Among them, in mammals, HYAL1 and HYAL2 are expressed in most of tissues. Only HYAL1 is present in mammalian plasma and urine and found at high levels in major organs such as liver, kidney, spleen, and heart (446). HYAL1 and HYAL2 are known to act in concert to degrade high molecular weight HA (HMW). HYAL2 on the cell membrane cleaves the HA, the fragments are endocytosed and transferred to lysosomes where there are fully degraded by the action of beta-glucuronidase and beta-N-acetylglucosaminidase (Figure 5.1) (455).

HYALs are known to be involved in development but they also have a role in disease including in a variety of cancers (456). For example, HYAL1, PH-20, HYAL2 and HYAL3 were found to be over-expressed in breast and colorectal cancer tissues (457). Furthermore, HYALs have also been shown to have a beneficial role in surgery and aesthetic medicine. Recently, it was demonstrated that recombinant human HYAL (rHuPH20) temporarily degrades HA, allowing subcutaneous delivery of drug including Rituximab, the first therapeutic mAbs approved in oncology for patients with follicular lymphoma (458). HYALs may also have therapeutic potential in fibrotic diseases since it is known that the degradation of dermal HA by infiltration of HYAL reduces ECM production and may therefore potentially slow or halt fibrosis.

5.1.4.2 CEMIP

Recently, a novel mediator CEMIP, was described as essential for an endogenous HA degradation in human skin fibroblasts (294). CEMIP is an 153 kDa protein which was first described as an inner-ear protein expressed in Deiters’ cells and the fibrocytes. It has beeen shown that CEMIP alteration can lead to the non-syndromic hearing loss (459). CEMIP is expressed in a wide range of normal human tissues, with the highest level of expression in brain (460).

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The protein has 4 parallel -helix repeat (PbH1) domains; one G8 domain consisting of 5 β-strand pairs and one α-helix, having a role in extracellular ligand binding and HA catalysis and processing; two GC domains consisting of 7 β-strands and 2 α-helices; and 7 predicted N-glycosylation sites. The N-terminal portion of 30 amino acids is predicted to be a cleavable signal sequence for endoplasmic reticulum targeting (461) and this portion of CEMIP is the signal sequence essential for cellular HA depolymerisation (461). Another domain in the N-terminus of CEMIP is called G8, containing 8 conserved glycine residues and 5 repeated β-strand pairs. Most G8- containing proteins are predicted to be integral membrane proteins with signal peptides and/or transmembrane segments (462). Based on the structure and function of G8- associated domains and proteins, it is reasonable to predict that G8 may be involved in extracellular ligand binding and enzymatic activity (463) (Figure 5.3).

Figure 5.3. Schematic representation of the structure of full-length CEMIP. Functional domains of CEMIP are represented here: SS, predicted N-terminal signal sequence; PbH1, PbH1 domain; GC, GC domain; G8, G8 domain. Numbers indicate the positions of the residues relative to the N-terminus of the full length CEMIP.

The signaling pathway(s) regulating CEMIP is not clear but some studies indicate that it is a target gene of canonical Wnt/β-catenin signaling (295). CEMIP has been shown to be involved in normal and pathological conditions that are marked by aberrant cell migration and proliferation and HA depolymerisation (464). Increased degradation of HA in synovial fibroblasts from patients with OA or rheumatoid arthritis (RA) was correlated with increased levels of CEMIP and was abrogated by the knockdown of CEMIP (464).

Yoshida et al. demonstrated that the murine homologue of human CEMIP (mKiaa1199) selectively catabolised HA via the clathrin-coated pit pathway (465). In a recent study, CEMIP-deficient mice were generated to explore the function of CEMIP in the central nervous system (460) and showed a deficit in memory function in CEMIP-deficient mice compared to WT. Hippocampal HA was increased in CEMIP-deficient mice, accompanied by a significant increase in total HA (460).

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CEMIP was also identified by Shimoda et al. as a key driver in endochondral ossification through HA metabolism. HMW HA was found accumulated in the lengthened hypertrophic zone in CEMIP-deficient mice bone compared to WT (466). CEMIP is differentially expressed in human tissues and cells including dermal fibroblasts in normal skin and over-expressed by synovial fibroblasts and tissues from arthritic joints (467).

CEMIP has been suggested to play a critical role in cancer progression. In gastric cancer, CEMIP was found to be highly expressed and has been associated with prognosis and lymph node metastasis (468). Elevated CEMIP expression in gastric cancer patients was noted in advanced stages and associated with poorer prognosis, indicating a potential prognostic value (468). CEMIP mRNA was detected in the plasma of approximately 80% of individuals with colorectal adenomas or cancers (469). It was found up-regulated in invasive breast cancer specimens, in invasive MDA-MB-231 breast cancer cells and in invasive DU145 prostate cancer cells (470).

In addition, in pancreatic ductal adenocarcinoma (PDAC), the expression pattern of CEMIP correlated with clinicopathological variables and patient outcome (470). CEMIP mRNA expression was higher in most PDAC cells compared to controls. CEMIP knock- down using siRNA in PDAC cells resulted in decreased cell migration and proliferation. Over-expression of CEMIP significantly enhanced the migration and invasion of PDAC cells. Increased CEMIP expression was associated with an increased level of LMW HA in the conditioned medium. Taken together, these recent data suggested that CEMIP over-expression predicts poor prognosis in PDAC (470,471).

In the present study, HAS2 and CEMIP was examined in human fibrotic fibroblasts and tissues to test the expression of these proteins within differents organs and explore their role in fibrosis.

5.2 Specific methods

5.2.1 mRNA and protein expression

Gene and protein expression were analysed using RT-qPCR, Western blotting and IHC described in Chapter 4, section 4.3.2.

5.2.2 HA binding protein (HABP) assay

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Normal and fibrotic human lung fibroblasts (1×104 cells/well of 8-well chamber slides (Sigma)) were stained with biotinylated hyaluronic acid binding protein (HABP, Millipore) which forms a stable complex with HA (472). Typically, HABP is isolated from bovine nasal cartilage (Millipore). Briefly, the cells treated with siControl, siHAS2 and siCEMIP (100nM for 48 hours) were trypsinised, seeded in 8-well chamber slides, grown to 60% confluence and serum-starved overnight.

Cells were washed 3 times with PBS and fixed in 4% paraformaldehyde (PFA) for 10 minutes on ice. All following washes were done 3 times for 5 minutes with PBS. After washing, 1% BSA in PBS was added to the cells for 30 minutes at room temperature. Cells were washed and incubated with HABP (1:100 dilution) for 30 minutes at room temperature. After washing, cells were incubated with fluorescent labeled secondary antibody (Anti-Biotin antibody Texas Red®; dilution 1: 500) for 30 minutes at room temperature in the dark. The cells were washed, the gasket was removed from chamber slides and cells mounted in Vectashield mounting medium containing with DAPI (Vector Laboratories). The slide was sealed with coverslip (60 mm x 40 mm) and cells visualized using fluorescence microscopy (Axiophot, Zeiss).

5.2.3 ELISA

The HA Quantikine ELISA kit (R&D Systems) was used to measure HA in conditioned medium collected over 24 hours from normal and diseased human fibroblasts. At confluence, cell culture supernatants were collected, clarified by centrifugation to remove any floating debris, aliquoted and stored in freezer for futre use. 50 µL of the culture supernatants were placed into the wells of the plate. Assay Diluent (50 µL) was added to each well and 50 µL Standard, control, or sample were added to each well. The plate was covered with a plate sealer, and incubated at room temperature for 2 hours on a horizontal orbital microplate shaker. The plate was washed 5 times with PBS and 100 µL Substrate Solution was added to each well. The plate was covered with foil and incubated at room temperature for 30 minutes on the benchtop shaker. Finally, 100 µL Stop Solution was added to each well and the absorbance of each well was measured at 540 nm using a Mithras LB 940 Plate Reader. HA concentration was calculated from a standard curve of the measured absorbance. Standards were prepared according to the manufacturer’s instructions.

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5.3 Results: HAS2 and CEMIP in fibrosis

5.3.1 HAS2 in SSc

HAS2 was identified as a common gene up-regulated in fibrosis in the 3 selected organs (Appendix, Table B). RT-qPCR data showed that the expression of HAS2 mRNA was significantly up-regulated in SSc lung and skin fibroblasts compared to normal cells (Figure 5.4).

Expression of HAS2 mRNA was higher in normal skin fibroblasts than normal lung fibroblasts. HAS2 mRNA was also increased in normal and SSc lung and skin fibroblasts after stimulation of TGFβ1 (data shown in Chapter 3, section 3.4). HAS2 protein expression was examined by Western blot which showed that, in parallel with the mRNA levels, protein expression was higher in lung and skin SSc fibroblasts compared to normal fibroblasts (Figure 5.4).

HAS2 protein expression was examined by IHC in normal human and in SSc skin and lung tissues (Figure 5.5) The SSc tissue showed extensive fibrosis and focal fibrotic lesions within the lung parenchyma disrupting the normal architecture of the lung (Figure 5.5a). Expression of HAS2 appeared to be markedly elevated in lung fibrotic tissue compared to normal tissue where only few positive cells stained were observed (Figure 5.5a). In fibrotic skin, HAS2 was highly expressed compared to normal skin. HAS2 staining was strong and elevated in the stroma and ECM within the skin section (Figure 5.5b).

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Figure 5.4. Expression of HAS2 mRNA and protein in normal and SSc human fibroblasts from lung and skin. Fibroblasts were cultured to confluence in 6-well plates and serum- starved overnight. Total RNA was extracted using the RNeasy Mini Kit (Qiagen). Expression of HAS2 mRNA was investigated by real-time RT-qPCR using a 1-step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as an endogenous control. Upper panel: HAS2 mRNA in lung (left) and skin (right), Blue: Normal; Red; SSc. n=3 patients per condition and each sample tested in duplicate. Protein extracts were extracted from confluent, quiescent fibroblasts in 6-well plates. Lysates were heat denatured at 70°C for 10 minutes in NuPage LDS Sample Buffer and NuPage Reducing Agent (Thermo Fisher Scientific) prior to electrophoresis. HAS2 protein expression was evaluated by Western blot with HAS2 antibody (Abcam; 1:1000). Human GAPDH was used as an endogenous control. Lower panel: HAS2 protein expression in lung (left) and skin (right), n=3 patients per condition and each sample tested in duplicate. Relative protein expression was assessed by densitometry (c). P-values <0.05 were considered to be statistically significant.

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Figure 5.5 HAS2 in normal and fibrotic SSc human lung (a) and skin (b) tissues. Human tissues were obtained from healthy individuals (upper panel) and SSc patients. Sections were dewaxed for 10 minutes in xylene and transferred through xylene and graded ethanols to water. After washing in running water for 5 minutes, antigen retrieval was performed by incubation in sodium citrate buffer (0.01M, 0.05% Tween 20, pH6.0). IHC was performed as previously described (Chapter 2, section 2.5) using the Vectastain avidin-biotin complex (ABC) method (Vector Laboratories). Sections were incubated with primary HAS2 antibody (Abcam) diluted 1:200 in antibody diluent (Dako) overnight at 4°C in a humidified atmosphere. An IgG control was done for each condition (right panels). HAS2 expression was visualised with DAB (Vector Laboratories) which resulted in formation of a brown precipitate (left panels). The sections were counterstained with 154 Mayer's haematoxylin. Sections were dehydrated through graded ethanols to xylene and mounted in DPX (Sigma). Sections were viewed and photographed with the Hamamatsu NanoZoomer Digital Pathology (NDP) scanner and NDP.view2 software. Representative images of n=3 patients. 5.3.2 siRNA knock-down of HAS2 attenuated the fibrotic phenotype of SSc fibroblasts

5.3.2.1 HAS2 knock-down reduced fibrotic markers in SSc lung fibroblasts

As HAS2 mRNA and protein expression were significantly up-regulated in SSc lung and skin fibroblasts, the potential role of HAS2 in the fibrotic phenotype of SSc fibroblasts was examined. Data from lung and skin SSc fibroblasts were similar therefore only data from SSc lung are shown, HAS2 was knocked-down using siRNA as described in Chapter 2, section 2.6. The expression of key fibrotic markers COL1A2, CTGF and αSMA, was assessed to determine whether reducing HAS2 expression affects expression of these markers in SSc fibroblasts. In SSc lung fibroblasts, HAS2 siRNA (siHAS2) significantly reduced the expression of HAS2 mRNA (44% reduction) (Figure 5.6a) and protein (59% reduction) (Figure 5.6b). Expression of COL1A2, CTGF and αSMA was elevated in SSc lung fibroblasts compared to normal cells and was significantly reduced following treatment with siHAS2 compared to treatment with control scrambled siRNA (siControl) (Figure 5.6b). The normal cells showed less intra- group variation than the SSc cells.

5.3.2.2 siHAS2 reduced the migration of SSc lung fibroblasts

To assess the migration of normal and SSc fibroblasts, a linear scratch was made across each monolayer in the presence mitomycin C (5ug/ml)) to inhibit cell proliferation (428) as described in Chapter 2, section 2.8. After 48 hours, normal fibroblasts had completely filled the scratch area, hence this time was selected as the end-point for analysis. SSc fibroblasts treated with siControl showed more rapid migration then normal cells. siHAS2 did not have an effect in normal fibroblasts, however it significantly decreased the migration of SSc fibroblasts compared to cells treated with siControl (Figure 5.7). These data suggests a pro-migratory role for HAS2 in SSc lung fibroblasts (Figure 5.7).

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Figure 5.6. Effect of siHAS2 on fibrotic markers in human lung fibroblasts. Fibroblasts were cultured in 6 well plates to confluence and serum-starved overnight. siControl (-) and siHAS2 (+) were added at the concentration of 50nM for 48 hours. (a) Total RNA was extracted using the RNeasy Mini Kit (Qiagen). Expression of HAS2, COL1A2, CTGF and αSMA mRNA was assesed by real-time RT-qPCR using a 1-step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as an endogenous control. Blue: Normal fibroblasts; Red: SSc fibroblasts. n=3 patients and each sample tested in duplicate). (b) Protein extracts were prepared from fibroblasts and lysates were heat denatured at 70°C for 10 minutes in NuPage LDS Sample Buffer and NuPage Reducing Agent (Thermo Fisher Scientific) prior to electrophoresis. HAS2, CTGF, COL1A2 and αSMA protein expression was evaluated by Western blot with antibodies to HAS2 antibody (Abcam; 1:250 dilution), CTGF (Santa Cruz; 1:1000 dilution), COL1A2 (Millipore; 1:1000 dilution) and αSMA (DAKO; 1:1000 dilution). Human GAPDH was used as an endogenous control. A representative sample from n=3 patients and each sample tested in duplicate. P-values <0.05 were considered to be statistically significant.

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Figure 5.7 siHAS2 reduced the migration of SSc lung fibroblasts. Fibroblasts from SSc lung were seeded (1×104 cells/well) in a 96-well plate, grown to confluence and made quiescent overnight. Cells were transfected with the siHAS2 (100nM) for 48 hours. Scratch wounding of the monolayer was performed using a 96-well floating-pin transfer device with a pin diameter of 1.58 mm (Essen). Post-wounding, plates were rinsed twice with PBS and incubated for 24 and 48 hours. Migration was assessed in the presence of mitomycin C (5 μl/ml; Sigma) to inhibit proliferation and quantify only the migration. Migration was assessed by measuring the change in scratch area over time. Images of the scratch zone were taken after 24 and 48 hours (upper panel). Image J was used to calculate the extent of wound closure as a percentage of wound area covered by cells as a measure of the fibroblasts migration (lower panel). Blue: SSc fibroblasts treated with siControl; Red: SSc fibroblasts treated with siHAS2. n=3 patients and each sample tested in duplicate. P-values <0.05 were considered to be 5.3.2.3statistically siHAS2 significant. reduced the contraction of SSc lung fibroblasts

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To investigate the role of HAS2 in lung fibroblast contractility, a floating gel collagen contraction assay was performed as described in Chapter 2, section (Figure 5.8a). Collagen lattices containing lung fibroblasts (which were previously transfected with siHAS2 (100Nm) for 48 hours) were cultured for 24 hours. The gels were weighed and imaged. In terms of gel weight, the gels cultured in 10% FBS were significantly decreased compared to the control group cultured in 0.2% FBS indicating greater contractility in the higher concentration of FBS. SSc lung fibroblast-seeded gels treated with siControl were significantly smaller and lighter than the normal fibroblast-seeded gels treated with siControl illustrating the enhanced contractility of SSc cells compared to normal cells. In contrast, SSc fibroblast-seeded gels treated with siHAS2 showed a significant reduction in contractility with a significant increase in gel weight compared to siControl-treated SSc fibroblast-seeded gels. There was no difference in the contraction of normal fibroblasts treated with siHAS2 or siControl. Together, these results suggest that HAS2 also plays a role in contraction of disease lung fibroblasts.

5.3.2.4 siHAS2 reduced the proliferation of SSc lung fibroblasts

The proliferation of activated fibroblasts is pivotal in the development of fibrosis (430). In this study, the crystal violet (CV) assay, described in Chapter 2, section 2.7, was used to assess fibroblast proliferation. The results revealed increased rates of proliferation of SSc lung fibroblasts compared to normal lung fibroblasts (Figure 5.8b). Normal fibroblasts proliferation was not changed with siHAS2 treatment. However, siHAS2 significantly reduced the proliferation of SSc lung fibroblasts compared to the siControl SSc lung fibroblasts (Figure 5.8b) suggesting a role for HAS2 in the proliferation of fibroblasts in the fibrotic process.

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Figure 5.8. siHAS2 reduced contraction (a) and proliferation (b) of SSc lung fibroblasts. (a) 24-well tissue culture plates were pre-coated with sterile 2% BSA in PBS (1ml/well) at 37°C overnight, and were then washed 3 times with PBS. Trypsinized fibroblasts pretreated with siControl (-) or siHAS2 (+) for 48 hours (100Nm), were suspended in DMEM (Sigma) and mixed with collagen solution (1 part 0.2 M N-2-hydroxyethylpiperazine-N'-2-ethanesulfonic acid (HEPES), pH8.0; 4 parts collagen (3 mg/ml; Nutragen) and 5 parts of DMEM) at a final concentration of 8x104 cells/ml in 1.2 mg/ml collagen. Collagen-cell suspension (1 ml) was added to each well and the plate was incubated for 1 hour at 37°C. After polymerization, 1 ml DMEM (with 0.2% or 10% FBS) was added to each well and the gels were detached from the well by gently running a 200μL pipet tip around the edge of the gel. Plates were incubated at 37°C. Gel contraction was quantified by reduction in gel weight (upper panel). Images of the gels were taken after 24 hours (lower panel). (b) Cells were transfected with siHAS2 (100nM) for 48 hours and were seeded in 96-well plates and cultured overnight at 37°C. Crystal violet staining solution (50 µL; 0.5% CV) was added to each well and incubated for 20 minutes at room temperature on a bench rocker. The plate was washed 3 times in a stream of tap-water. After washing, the water was removed and the plate was air-dried for at least 4 hours at room temperature. Methanol (200 µL) was added to each well, and the plates incubated for 20 minutes at room temperature on a bench rocker. OD at 570 nm was measured on a Mithras LB 940 Plate Reader. Blue: Normal fibroblasts; Red: SSc fibroblasts; -: scrambled control siRNA; +: siHAS2. n=3 patients and each sample tested in duplicate. P-values <0.05 were considered to be statistically significant.

5.3.3 HAS2 in others fibrotic conditions

The data showed that HAS2 was significantly up-regulated in tissues and fibroblasts in lung and skin from SSc patients compared to their normal counterparts. Also, HAS2 reduced ECM production and attenuated the fibrotic phenotype of SSc fibroblasts. These results prompted further investigation into the expression of HAS2 in other fibrotic conditions (Renal fibrosis, IPF, and liver fibrosis).

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5.3.3.1 HAS2 in CKD and ADPKD

5.3.3.1.1 HAS2 in CKD

As described in Chapter 1,section 1.1.3), CKD is characterised by progressive fibrosis leading to a gradual loss of kidney function over time and ultimately leading to organ failure (222). Compared to normal human kidney, biopsy sections from patients with CKD showed alterations in kidney morphology with fibrotic foci and more ECM (Figure 5.9). IHC staining for HAS2 protein expression revealed increased HAS2 in CKD compared to normal human kidney (Figure 5.9). HAS2 expression was mainly localized in the cortex of the kidney and was elevated around the vessels of CKD patients (Figure 5.9).

5.3.3.1.2 HAS2 in ADPKD

The protein expression of HAS2 was examined in normal human kidney and ADPKD kidney sections by IHC. Overall HAS2 staining was similar in human ADPKD tissues compared to normal kidney (Figure 5.10). The expression of COL1A2, CTGF, αSMA and HAS2 mRNAs was examined in normal human kidney fibroblasts and fibroblasts from ADPKD patients, untreated or treated with TGFβ1. RT-qPCR showed that the expression of the fibrotic markers and HAS2 were significantly up-regulated in untreated ADPKD fibroblasts compared to untreated normal kidney fibroblasts (Figure 5.11). CTGF mRNA expression was higher than COL1A2 or αSMA in ADPKD kidney fibroblasts compared to normals. After TGFβ1 stimulation, there was a significant increase in the expression of these 3 fibrotic markers and HAS2 in normal kidney fibroblasts treated with TGFβ, however, expression levels were still lower than those in ADPKD fibroblasts. Also, there was substantial intra-group variation in the ADPKD fibroblast groups which were already fibrotic and therefore activated. This may have masked any differences between stimulated and non-stimulated cells (Figure 5.11).

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Figure 5.9. HAS2 in normal and CKD human kidney.Tissues were obtained from kidneys donated for transplantation from healthy individuals (normal; upper panel) and from CKD patient biopsies (lower panel). Sections were dewaxed for 10 minutes in xylene and transferred through xylenes and graded ethanols to water. After washing in running water for 5 minutes, antigen retrieval was performed by incubation in sodium citrate buffer (0.01M, 0.05% Tween 20, pH6.0). IHC was performed as described in Chapter 2, section 2.5, using Vectastain avidin-biotin complex (ABC) (Vector Laboratories). Sections were incubated with primary HAS2 antibody (Abcam) diluted at 1:200 in antibody diluent (Dako) overnight at 4°C in a humidified atmosphere. An IgG control was done for each condition (right panel). HAS2 expression was visualised with DAB (Vector Laboratories). Sections were counterstained with Mayer's haematoxylin, dehydrated, mounted in DPX (Sigma) and imaged with the Hamamatsu NanoZoomer Digital Pathology (NDP) scanner and NDP.view2 software. Representative images of n=3 patients.

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Figure 5.10. HAS2 in normal human kidney and ADPKD tissue. Tissues were obtained from kidneys donated for transplantation from healthy individuals (normal; upper panel) and from ADPKD patients (lower panel). Sections were dewaxed for 10 minutes in xylene and transferred through xylenes and graded ethanols to water. After washing in running water for 5 minutes, antigen retrieval was performed by incubation in sodium citrate buffer (0.01M, 0.05% Tween 20, pH6.0). IHC was performed as described in Chapter 2, section 2.5, using Vectastain avidin-biotin complex (ABC) (Vector Laboratories). Sections were incubated with primary HAS2 antibody (Abcam) diluted at 1:200 in antibody diluent (Dako) overnight at 4°C in a humidified atmosphere. An IgG control was done for each condition (right panels). HAS2 expression was visualised with DAB (Vector Laboratories) which resulted in formation of a brown precipitate. The sections were counterstained with Mayer's haematoxylin. Sections were dehydrated, mounted in DPX (Sigma) and imaged with the Hamamatsu NanoZoomer Digital Pathology (NDP) scanner and NDP.view2 software. Representative images of n=3 patients.

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Figure 5.11. Expression of COL1A2, CTGF, αSMA and HAS2 mRNA in normal and ADPKD fibroblasts unstimulated or stimulated with TGFβ1. Fibroblasts were cultured in 6- well plates to confluence and serum-starved overnight. TGFβ1 (2ng/ml) was added for 24 hours and total RNA was extracted using the RNeasy Mini Kit (Qiagen). Expression of COL1A2, CTGF, αSMA and HAS2 mRNA were investigated by real-time RT-qPCR using a 1- step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as an endogenous control. Blue: Normal fibroblasts; Red: ADPKD fibroblasts; -: untreated; +: treated with TGFβ1. n=3 patients and each sample tested in duplicate. P-values <0.05 were considered to be statistically significant.

5.3.3.2 HAS2 in IPF

IPF, the most common of the idiopathic interstitial diseases is described in Chapter 1,section 1.1.2. The progressive nature of the disease leads to a rapid decline in lung function cause by an abnormal tissue remodelling with elevated pro-fibrotic factors including collagens (473).

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The expression of COL1A2, CTGF, αSMA and HAS2 mRNAs was examined in normal human lung fibroblasts and fibroblasts from IPF patients. RT-qPCR showed that COL1A2, CTGF, αSMA and HAS2 gene expression was significantly up-regulated in IPF fibroblasts compared to normal lung fibroblasts (Figure 5.12). The fibrotic marker COL1A2 showed a very pronounced increase in expression in IPF fibroblasts compared to more modest increases in CTGF and αSMA (Figure 5.13). IPF cells also showed a marked increase in HAS2.

Figure 5.12. Expression of COL1A2, CTGF, αSMA and HAS2 mRNA in normal and IPF lung fibroblasts. Fibroblasts were cultured in 6-well plates to confluence and serum-starved overnight. Total RNA was extracted using the RNeasy Mini Kit (Qiagen). Expression of COL1A2, CTGF, αSMA and HAS2 mRNA was investigated by real-time RT-qPCR using a 1- step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as an endogenous control. Blue: Normal lung fibroblasts; Red: IPF fibroblasts. n=3 patients and each sample was tested in duplicate. P- values <0.05 were considered to be statistically significant.

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IHC showed that HAS2 expression was absent or low in the normal lung where only few epithelial cells were stained. In contrast, HAS2 expression was significantly increased in IPF tissues (Figure 5.13) particularly in fibrotic areas of the sections where it appears that the main cell type stained were fibroblasts embedded in the interstitial ECM. However this would need to be confirmed by co-staining with fibroblast markers (eg. αSMA; vimentin or TE-7) by immunofluoresence (474,475).

Figure 5.13 HAS2 in normal human lung and IPF tissues. Tissues were obtained from healthy individual (normal; upper panel) and from IPF patients (right panel). Sections were dewaxed for 10 minutes in xylene and transferred through xylenes and graded ethanols to water. After washing in running water for 5 minutes, antigen retrieval was performed by incubation in sodium citrate buffer (0.01M, 0.05% Tween 20, pH6.0). IHC was performed as described in Chapter 2, section 2.5, using Vectastain avidin-biotin complex (ABC) (Vector Laboratories). Sections were incubated with primary HAS2 antibody (Abcam) diluted at 1:200 in antibody diluent (Dako) overnight at 4°C in a humidified atmosphere. An IgG control was done for each condition (lower panels). HAS2 expression was visualised with DAB (Vector Laboratories). Sections were counterstained with Mayer's haematoxylin. Sections were dehydrated, mounted in DPX (Sigma) and imaged with the Hamamatsu NanoZoomer Digital Pathology (NDP) scanner and NDP.view2 software. Representative images of n=3 patients.

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5.3.3.3 HAS2 in liver fibrosis

In the present study, normal primary HSCs were stimulated with TGFβ1 to mimic a fibrotic environment (476) to examine the expression of the fibrotic markers and HAS2. The basal expression level of COL1A2 and CTGF in both conditions (stimulated and non-stimulated) was markedly higher than the expression of αSMA or HAS2. Despite variation within the treated cells, COL1A2, CTGF, αSMA and HAS2 mRNAs were all significantly up-regulated in TGFβ1-treated HSC compared to unstimulated cells (Figure 5.14).

Figure 5.14 Expression of COL1A2, CTGF, αSMA and HAS2 mRNA in human HSCs in response to TGFβ1. HSC were grown to confluence, serum-starved overnight and either left untreated (-; Blue) or treated with TGFβ1 (2ng/ml) (+; Red) for 24 hours.Total RNA was extracted using the RNeasy Mini Kit (Qiagen). Expression of COL1A2, CTGF, αSMA and HAS2 mRNA was measured by real-time RT-qPCR using a 1-step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as an endogenous control. n=3 per condition and each sample tested in duplicate. P-values <0.05 were considered to be statistically significant.

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5.4.1 CEMIP in SSc

CEMIP mRNA was measured by RT-qPCR in normal and SSc fibroblasts (from lung and skin). CEMIP was markedly higher in normal skin than normal lung fibroblasts compared to fibrotic fibroblasts from both organs (Figure 5.15). Although several commercially-available antibodies were tested none of them produced specific signal in Western blots even after extensive optimization (varying the antibodies concentrations, the use of different blocking buffers and incubation times). Thus, it was not possible to compare CEMIP protein expression by Western blotting. Immunocytochemistry would be a way to test CEMIP protein expression. Interestingly despite the problems with antibodies for Western blotting an antibody worked in formalin fixed, paraffin embedded tissue sections and therefore IHC was performed to examine the expression of CEMIP protein in normal and SSc lung. Extensive fibrotic area were observed SSc lung compared to normal (Figure 5.16). CEMIP expression was more highly expressed in normal lung tissues compared to SSc lung tissues, where only few fibroblast-like cells appeared positive for CEMIP in areas where the SSc dermis appeared to be markedly thickened compared to normal dermis (Figure 5.16). CEMIP was found more highly expressed in normal skin tissues both around the vessels and also in the ECM compared to skin tissues from SSc patients (Figure 5.17).

Figure 5.15. Expression of CEMIP mRNA in normal and SSc human fibroblasts (lung and skin). Fibroblasts from normal lung and skin and SSc lung and skin were cultured in 6-well plates to confluence and serum-starved overnight. Total RNA was extracted using the RNeasy Mini Kit (Qiagen). Expression of CEMIP mRNA was investigated by real-time RT-qPCR using a 1-step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as an endogenous control. Blue: Normal fibroblasts; Red: SSc fibroblasts. n=3 patients and each sample tested in duplicate. P-values <0.05 were considered to be statistically significant.

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Figure 5.16. CEMIP in normal and fibrotic human lung tissues. Human tissues were obtained from healthy individuals (normal; upper panel) and SSc patients (lower panel). Sections were dewaxed and after washing in running water for 5 minutes, antigen retrieval was performed by incubation in sodium citrate buffer (0.01M, 0.05% Tween 20, pH6.0). IHC was performed as described in Chapter 2, section 2.5, using the Vectastain avidin-biotin complex (ABC) method (Vector Laboratories). Sections were incubated with primary CEMIP antibody (Abcam) diluted at 1:200 in antibody diluent (Dako) overnight at 4°C in a humidified atmosphere. An IgG control was done for each condition (right panels). CEMIP expression was visualised with DAB (Vector Laboratories) which resulted in formation of a brown precipitate. The sections were counterstained with Mayer's haematoxylin. Sections were dehydrated through graded ethanols to xylene and mounted in DPX (Sigma). Sections were viewed and photographed with the Hamamatsu NanoZoomer Digital Pathology (NDP) scanner and NDP.view2 software. Representative images of n=3 patients.

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Figure 5.17. CEMIP in normal and fibrotic human skin tissues. Human tissues were obtained from healthy individuals (upper panel) and SSc patients (lower panel). Sections were dewaxed for 10 minutes and after washing in running water for 5 minutes, antigen retrieval was performed by incubation in sodium citrate buffer (0.01M, 0.05% Tween 20, pH6.0). IHC was performed as previously described using the Vectastain avidin-biotin complex (ABC) method (Vector Laboratories). Sections were incubated with primary CEMIP antibody (Abcam) diluted at 1:200 in antibody diluent (Dako) overnight at 4°C in a humidified atmosphere. An IgG control was done for each condition (right panels). CEMIP expression was visualised with DAB (Vector Laboratories) which resulted in formation of a brown precipitate. The sections were counterstained with Mayer's haematoxylin. Sections were dehydrated, mounted in DPX (Sigma) and imaged with the Hamamatsu NanoZoomer Digital Pathology (NDP) scanner and NDP.view2 software. Representative image of n=3 patients.

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5.4.2 siRNA knock-down of CEMIP had no effect on the fibrotic phenotype of SSc fibroblasts

5.4.2.1 CEMIP knock-down did not alter the expression of fibrotic markers

CEMIP mRNA (RT-qPCR) and protein expression (IHC) were significantly down- regulated in SSc lung and skin fibroblasts compared to normal cells. The function of CEMIP in normal and SSc fibroblasts was explored using siRNA knock-down as described in Chapter 2, section 2.6, and expression of CEMIP, COL1A2, CTGF and αSMA mRNA was tested by RT-qPCR. The expression of CEMIP was higher in normal lung fibroblasts compared to SSc fibroblasts. Although CEMIP mRNA was reduced in normal lung fibroblasts treated with siCEMIP compared to the cells treated with siControl this change did not reach statistical significance (Figure 5.1). There was no change in levels of CEMIP mRNA in SSc lung fibroblasts treated with siCEMIP. Expression of COL1A2, CTGF and αSMA mRNA was elevated in SSc lung fibroblasts compared to normals but none of these were significantly reduced following siCEMIP treatment compared to the control scrambled siRNA (siControl) (Figure 5.18).

5.4.2.2 siCEMIP had no effect on the migration of lung fibroblasts from SSc patients.

The effect of CEMIP knockdown on lung fibroblast migration was assessed as described for HAS2 knockdown (Chapitre 2, section 5.3.2.2). There was no difference between the migration ability of SSc fibroblasts treated with either with siControl or siCEMIP after 48 hours. In both conditiions, the SSc lung fibroblasts migrated over the scratch and fibroblasts closed the gap after 48 hours (Figure 5.19).

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Figure 5.18. Effect of siCEMIP on fibrotic markers in human lung fibroblasts. Fibroblasts were cultured in 6-well plates to confluence and serum-starved overnight. siControl and siCEMIP were used at the concentration of 100nM for 48 hours. Total RNA was extracted using the RNeasy Mini Kit (Qiagen). Expression of CEMIP, COL1A2, CTGF and αSMA mRNA was assessed by real-time RT-qPCR using a 1-step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as an endogenous control. Blue: Normal fibroblasts treated with siControl (-); Red: SSc fibroblasts treated with siCEMIP (+). n=3 patients and each sample tested in duplicate. P-values <0.05 were considered to be statistically significant.

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Figure 5.19 Effect of siCEMIP on the migration of SSc lung fibroblasts. Fibroblasts were seeded (1×104 cells/well) in a 96-well plate and grown to confluence and transfected with siControl (blue) or siCEMIP (red) for 48 hours. Scratch wounding of the monolayer was performed using a 96-well floating-pin transfer device with a pin diameter of 1.58 mm (Essen). Post-wounding, plates were rinsed twice with PBS to remove any cell debris and returned to the incubator for 24 and 48 hours. Migration was assessed in the presence of mitomycin C (5 μl/ml; Sigma) to inhibit proliferation and quantify only the migration. Migration was assessed by measuring the change in scratch area over time. Images of the scratch zone were taken after 24 and 48 hours (upper panel). Image J was used to calculate the extent of wound closure as percentage of wound area covered by cells as a measure of fibroblast migration (lower panel). P-values <0.05 were considered to be statistically significant.

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5.4.2.3 siCEMIP had no effect on the contraction of lung fibroblasts from SSc patients

Fibroblast contractility was assessed in a floating gel collagen contraction assay (described in Chapter 2, section 2.9). SSc fibroblasts treated with siCEMIP or siControl were embedded in collagen gels and contractility was assessed. The gels with SSc cells were smaller and lighter compared to the gels containing nornal lung fibroblasts However, there was no change in the contractility between normal or SSc fibroblasts treated with siCEMIP compared to siControl (Figure 5.20a). These results suggest that knock-down of CEMIP has no effect on the contractility of lung fibroblasts in fibrotic conditions.

5.4.2.4 siCEMIP had no effect on the proliferation of from SSc patients

The proliferation of normal and SSc lung fibroblasts was tested using crystal violet (CV) assay, described in Chapter 2, section 2.7. Briefly, SSc and normal fibroblasts treated with siCEMIP and siControl were seeded in 96-well plates, grown for 48 hours. CV was added to each well and the absorbance read at 570 nm. The results confirmed that SSc lung fibroblasts proliferation was higher than normal lung fibroblasts (Figure 5.20b). siCEMIP had no effect on proliferation of normal or SSc lung fibroblasts compared to the equivalent cells treated with siControl (Figure 5.20b). These results suggest that knckdown of CEMIP has no effect on the proliferation of lung fibroblasts.

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Figure 5.20 Effect of siCEMIP on the contraction and proliferation of normal and SSc lung fibroblasts. (a) 24-well tissue culture plates were pre-coated with sterile 2% BSA in PBS (1ml/well) at 37°C overnight, and were then washed 3 times with PBS. Trypsinized normal (Blue) or SSc (Red) lung fibroblasts pre-treated with siControl (-) or siCEMIP (+) for 48 hours, were suspended in DMEM (Sigma) and mixed with collagen solution (1 part 0.2 M N-2- hydroxyethylpiperazine-N'-2-ethanesulfonic acid (HEPES), pH8.0; 4 parts collagen (3 mg/ml; Nutragen) and 5 parts of DMEM) at a final concentration of 8x104 cells/ml in 1.2 mg/ml collagen. Collagen-cell suspension (1 ml) was added to each well and the plate was incubated for 1 hour at 37°C. After polymerization, 1 ml DMEM (with 0.2% or 10% FBS) was added to each well and the gels were detached from the well by gently running the tip of a 200μL pipet tip around the edge of the gel. Plates were incubated at 37°C. After 24 hours, gel contraction was quantified by reduction in gel weight (upper panel) and images of the gels were taken (lower panel). (b) Cells were transfected with siCEMIP (100nM) for 48 hours and were seeded in 96-well plates and cultured overnight at 37°C. Crystal violet staining solution (50 µL 0.5% CV) was added to each well and incubated for 20 minutes at room temperature on a bench rocker. The plate was washed 3 times in a stream of tap-water. After washing, the water was removed and the plate was air-dried for at least 4 hours at room temperature. Methanol (200 µL) was added to each well, and the plates incubated for 20 minutes at room temperature on a bench rocker. OD of each well at 570 nm was measured on a Mithras LB 940 Plate Reader. P-values <0.05 were considered to be statistically significant.

5.4.3 CEMIP in others fibrotic conditions

5.4.3.1 CEMIP in ADPKD

The expression of COL1A2, CTGF, αSMA and CEMIP mRNA was examined in normal human kidney fibroblasts and fibroblasts from ADPKD patients untreated or treated with TGFβ1. RT-qPCR data confirmed that the expression of the fibrotic markers as well as CEMIP was significantly up-regulated in ADPKD fibroblasts compared to normal cells (Figure 5.21). After TGFβ1 stimulation, a significant increase was observed for the 3 fibrotic markers in normal kidney fibroblasts.

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ADPKD fibroblasts treated with TGFβ1 did not show a difference in the expression of COL1A2, CTGF, αSMA or CEMIP between the untreated and treated cells (Figure 5.21).Expression of CEMIP was examined in normal kidney and in ADPKD tissues by IHC. In the normal kidney CEMIP expression was largely confined to distal tubular epithelia. In ADPKD, alongside the changes in the kidney architecture, there was increased CEMIP expression with a positive staining around the blood vessels, within the fibrotic ECM and in some cyst-lining epithelia (Figure 5.22). This finding was consistent with the RT-qPCR data which showed elevated levels of CEMIP in ADPKD fibroblasts compared to normal kidney fibroblasts.

Figure 5.21. Expression of COL1A2, CTGF, αSMA and CEMIP mRNA in normal and ADPKD fibroblasts in response to TGFβ1. Fibroblasts were cultured in 6-well plates to confluence and serum-starved overnight. Normal (Blue) and ADPKD kidney fibroblasts (Red) were untreated (-) or treated with TGFβ1 (2ng/ml) (+) for 24 hours and total RNA was extracted using the RNeasy Mini Kit (Qiagen). Expression of COL1A2, CTGF, αSMA and CEMIP mRNAs were investigated by real-time RT-qPCR using a 1-step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as an endogenous control. n=3 cell lines per condition and each sample tested in duplicate. P-values <0.05 were considered to be statistically significant.

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Figure 5.22. CEMIP in normal human kidney and ADPKD. Human tissues were obtained from healthy donors (normal; upper panel) and ADPKD patients (lower panel). Sections were dewaxed for 10 minutes in xylene and transferred through xylenes and graded ethanols to water. After washing in running water for 5 minutes, antigen retrieval was performed by incubation in sodium citrate buffer (0.01M, 0.05% Tween 20, pH6.0). IHC was performed as described in Chapter 2 section 2.5, using the Vectastain avidin-biotin complex (ABC) method (Vector Laboratories). Sections were incubated with CEMIP antibody (Abcam) diluted at 1:200 in antibody diluent (Dako) overnight at 4°C in a humidified atmosphere. CEMIP expression was visualised with DAB (Vector Laboratories) which resulted in formation of a brown precipitate. An IgG control was done for each condition (right panels). The sections were counterstained with Mayer's haematoxylin. Sections were dehydrated through graded ethanols to xylene and mounted in DPX (Sigma). Sections were viewed and photographed with the Hamamatsu NanoZoomer Digital Pathology (NDP) scanner and NP.view2 software. Representative image of n=3 patients.

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5.4.3.2 CEMIP in IPF

COL1A2, CTGF and αSMA mRNA expression was significantly up-regulated in IPF fibroblasts compared to normal with markedly increased expression of COL1A2 indicating a robust fibrotic phenotype.(Figure 5.23). CEMIP mRNA was significantly up- regulated in IPF fibroblasts compared to normal cells. This observation was surprising compared to the data obtained from SSc lung fibroblasts where CEMIP was down- regulated compared to normal lung fibroblasts.

Figure 5.23. Expression of COL1A2, CTGF, αSMA and CEMIP mRNA in normal and IPF lung fibroblasts. Normal lung fibroblasts (blue) and IPF fibroblasts (red) were cultured in 6- well plates to confluence and serum-starved overnight. Total RNA was extracted following the instructions of the RNeasy Mini Kit extraction protocol (Qiagen). Expression of COL1A2, CTGF, αSMA and CEMIP mRNA was investigated by real-time RT-qPCR using a 1-step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as an endogenous control. n=3 patients and each sample tested in duplicate. P-values <0.05 were considered to be statistically significant.

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5.4.3.3 CEMIP in liver fibrosis

CEMIP expression was evaluated in normal primary HSC either untreated or stimulated with TGFβ1. In these fibroblast-like cells from the liver, TGFβ1 significantly up- regulated the expression of the fibrotic markers, COL1A2, CTGF and αSMA, compared to untreated HSC (Figure 5.24). In contrast, CEMIP expression was significantly down- regulated in TGFβ1-treated HSC compared to untreated cells (Figure 5.24). The expression level of COL1A2 and CTGF was higher than the expression of αSMA or CEMIP (Figure 5.24).

Figure 5.24. Expression of COL1A2, CTGF, αSMA and CEMIP mRNA in HSC in response to TGFβ1. HSC were grown to confluence, serum-starved overnight and either left untreated (- ; Blue) or treated with TGFβ1 (2ng/ml) (+; Red) for 24 hours. Total RNA was extracted using the RNeasy Mini Kit (Qiagen). Expression of COL1A2, CTGF, αSMA and CEMIP mRNA was investigated by real-time RT-qPCR using a 1-step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as an endogenous control. n=3 patients per condition and each sample tested in duplicate. P-values <0.05 were considered to be statistically significant.

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5.5 HA2/CEMIP axis in fibrosis

It is known that HA is regulated by several enzymes including HAS2 and CEMIP (297) and that the balance between the polymerisation and depolymerisation of HA is generated by HAS2 and CEMIP, respectively (470). This may suggest that an imbalance between these 2 processes could play a role in fibrosis and that regulation of HAS2 and CEMIP is co-ordinated. To begin to address these questions, HAS2, CEMIP and HA were measured in normal and SSc lung fibroblasts in which HAS2 and CEMIP expression was reduced by siRNA knockdown.

5.5.1 Effect of HAS2 and CEMIP knockdown on CEMIP and HAS2 respectively

HAS2 and CEMIP expression was examined in fibroblasts treated with siRNA for HAS2 and CEMIP. RT-qPCR data showed that knocking-down HAS2 had no effect on the expression of CEMIP mRNA in normal lung fibroblasts. However, in SSc lung fibroblasts, siHAS2 induced a significant increase of CEMIP expression (Figure 5.27). In the same way, HAS2 gene expression was tested after knocking-down CEMIP in normal and SSc lung fibroblasts. The data showed that siCEMIP had no effect on HAS2 in normal and SSc lung fibroblasts (Figure 5.25). Together the data suggest that, in fibrosis, CEMIP may be regulated by HAS2.

5.5.2 Knock-down of HAS2 reduced HA in SSc lung fibroblasts

HA was measured in the conditioned medium of normal and SSc lung fibroblasts after treatment with siHAS2 and siCEMIP. ELISA analysis of the conditioned media revealed that secreted HA was significantly increased in SSc lung fibroblasts treated with siControl compared to siControl-treated normal lung fibroblasts. Knock-down of HAS2 had no effect on secreted HA in normal lung fibroblasts but suppressed HA levels in the conditioned medium of SSc lung fibroblasts. In parallel, HA was measured also in conditioned meduim from normal and SSc lung fibroblasts treated with siCEMIP had no effect on secreted HA in normal fibroblats. There was a slight increase in HA in SSc lung fibroblasts treated with siCEMIP but this did not reach stastical significance (Figure 5.26).

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Figure 5.25. Effect of knockdown of HAS2 or CEMIP in normal or SSc lung fibroblasts. Normal (blue) and SSc lung fibroblasts (red) were cultured in 6-well plates to confluence, serum-starved overnight and treated with siControl (-), siCEMIP or siHAS2 (+) 100nM for 48 hours. Total RNA was extracted using the RNeasy Mini Kit (Qiagen). Expression of HAS2 and CEMIP mRNA was assessed by real-time RT-qPCR using a 1-step method based on the Quantifast SYBR Green RT-qPCR protocol (Qiagen). Human TATA-box binding protein (TBP) was used as an endogenous control. n=3 patients and each sample tested in duplicate. P- values <0.05 were considered to be statistically significant.

Figure 5.26. Effect of HAS2 and CEMIP knockdown on secreted HA in normal and SSc lung fibroblasts. Normal (Blue) and SSc (Red) fibroblasts were cultured in 6-well plates to confluence and serum-starved overnight. Cells were treated with siControl (-), siHAS2 (+, right graph) and siCEMIP (+; left graph) 100nM for 48 hours. Cell culture supernatants were collected, clarified by centrifugation to remove any floating debris. 50 µL of the culture supernatants were placed into the wells of the plate. Standards were prepared according to the manufacturer’s instructions. (50 uL) were collected and placed into the wells of the plate of the HA Quantikine ELISA kit (R&D Systems). Assay Diluent (50 µL) was added to each well and 50 µL Standard, control, or sample were added to each well. The plate was incubate at room temperature for 2 hours,washed 5 times with PBS and 100 µL Substrate Solution was added to each well. The plate was incubated at room temperature for 30 minutes in the dark. Finally, 100 µL Stop Solution was added to each well and the absorbance of each well was measured at 540 nm using a Mithras LB 940 Plate Reader. P-values <0.05 were considered to be statistically significant. 180

Cell-associated HA in normal and SSc lung fibroblasts (fixed prior staining) was also assessed using a HABP assay in which HA is visualised by binding of biotinylated HABP (483). As expected, HA was more strongly expressed in SSc than normal fibroblasts (Figure 5.27). Knockdown of either HAS2 or CEMIP had no effect on HA in normal fibroblasts compared to siControl. In SSc fibroblasts, siHAS2 markedly reduced HA to levels comparable to normal fibroblasts. In contrast, siCEMIP had no effect on HA in SSc fibroblasts (Figure 5.27).

Figure 5.27. siHAS2 reduced HA production in SSc lung fibroblasts. Human lung fibroblasts (1×104 cells/well) were seeded in 8-well chamber slides (Sigma), were grown to 30% confluence and serum-starved overnight. Cells were washed and fixed in 4% paraformaldehyde for 10 minutes. After washing, 1% BSA in PBS was added to the cells for 30 minutes. Cells were washed and incubated with biotinylated HABP (Millipore) which forms a stable complex with HA (472) (1:100 dilution) for 30 minutes at room temperature. After washing, cells were incubated with Texas Red®-labelled anti-biotin antibody (diluted 1:500) for 30 minutes at room temperature in the dark. Cells were covered with Vectashield mounting medium containing with DAPI (Vector Laboratories), sealed with a coverslip and visualized and photographed on a fluorescence microscope (Axioskop Hbo 50;Zeiss). Magnification: 40X.

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5.5.3 Potential Tspan13 and HAS2 interactions in HA regulation

To test the hypothesis that HA (Chapter 5, section 5.1) could be regulated by Tspan13 and might be modified by siTspan13, immunofluroresence was used to visualize HA depostion using HA binding protein (HABP) in the DNR mice lung sections. In the absence of bleomycin, very little HA was observed within the siControl and siTspan13 lung. In contrast, HA was observed in abundance in the lungs treated with bleomycin compared to the saline groups. Also, in the bleomycin group siTSPAN13 treatment appeared to reduce HA compared to the group treated with siControl (Figure 5.28).

HA was quantified and measured by image J and although there was a leading to a reduction of HA in the bleomycin-treated DNR mouse lung with siTspan13, it did not reach statistical significance. These data suggest that siTspan13 in bleomycin-treated lung could potentially reduce the HA production. Tspan13 within the Tspans web might interact with HAS2 and/or CD44 receptors leading to a reduction of HA polymerisation in fibrosis

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Figure 5.28. HA expression in DNR mouse lungs after Tspan13 knock-down. Lung sections (5μm) were prepared from formalin-fixed paraffin-embedded tissues and stained with HABP (upper panel) as described in section 4.3.2.4. Briefly, tissue sections were washed and incubated with HABP (1:100 dilution) for 30 minutes at room temperature. After washing, cells were incubated with fluorescent labeled secondary antibody (Anti-Biotin antibody Texas Red®; dilution 1:500). Sections were cells visualized and photographied using fluorescence microscopy (Axiophot, Zeiss). The graph shows quantitative analysis of lung HA using Image J (lower panel). Representative images of n=5 fields per section and n= 4 mice. Arrows showed HA in red. P-values <0.05 were considered to be statistically significant.

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5.6 Discussion

The in silico analysis (Chapter 3, section 3.2) highlighted HAS2 and CEMIP as genes involved in fibrosis. These 2 candidates are implicated in the regulation of HA which has important roles in both, normal and pathological conditions. A large number of studies have shown that HA deposition is up-regulated in many fibrotic diseases and in most human cancers where HA concentrations are usually markedly higher in cancer tissues compared to normal (312). HA may support tumour formation by stimulating growth and proliferation of tumour cells (477). Also, HA actively promotes tumour cell adhesion migration and metastasis (478). Tumour cells may take advantage of HA-rich ECM matrices to invade more easily into the surrounding tissues. In fibrosis, HA has been identified as a being a major component of the ECM and a biomarker of fibrotic conditions (106). The role and the metabolic pathways for the synthesis and degradation of HA are based on the regulation of enzyme activities including HAS2 (which polymerises HA) and CEMIP (which depolymerises HA) (297).

The role of HAS2 in diseases such as cancer and fibrosis has been well studied. For instance, Li et al, found that overexpression of HAS2 by myofibroblasts produced an aggressive phenotype leading to severe lung fibrosis in vivo (211). Also, fibroblasts isolated from transgenic mice overexpressing HAS2 showed a greater capacity to invade matrix and conditional deletion of HAS2 in mesenchymal cells reduced myofibroblast accumulation, and reduced lung fibrosis (211). In the same study, using a blocking antibody to CD44, the authors showed that the progressive fibrosis was inhibited in the absence of CD44 in mice in vivo. Furthermore, they showed that IPF fibroblasts isolated from patients exhibited an invasive phenotype dependent on HAS2 and CD44 (211). In futur studies, these models could be used to assess the exact role of HAS2 in vivo using siRNA technology or neutralizing antibodies against HAS2.

Although, HAS2 is known in the fibrotic field, it is the first time that HAS2 was explored SSc. HAS2 gene and protein expression were significantly up-regulated in several fibrotic diseases including lung and skin cells and tissues from SSc patients. Exciting data using siRNA revealed that knockdown of HAS2 in SSc fibroblasts reduced the expression of COL1A2 and CTGF and also attenuated the migration, proliferation and contraction of fibrotic fibroblasts. In addition, HAS2 was examined in other fibroproliferative diseases including IPF, CKD and ADPKD. In these fibrotic disorders, HAS2 was significantly overexpressed regardless of the organ affected.

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In contrast to HAS2, CEMIP hasn’t been studied in fibrosis. Data showed that CEMIP was significantly reduced in SSc fibrotic fibroblasts and tissues from SSc lung and skin compared to normal. Also, normal fibroblasts treated with TGFβ, showed a significant decrease. The interactions of CEMIP and TGFβ need to be explored. siRNA for CEMIP was used to investigate the role of CEMIP in fibroblasts. No changes were observed between the cells treated with siControl or siCEMIP, in the expression of fibrotic markers of ECM or in the fibrotic phenotype. Furtheremore, CEMIP expression was also examined in other fibrotic diseases including ADPKD and IPF. Both, RT-qPCR and IHC results, showed that CEMIP was significantly up-regulated in ADPKD and IPF fibroblasts compared to normal. In the lung, CEMIP was up-regulated in SSc-PF but down-regulated in IPF. Conversely, CEMIP expression was down-regulated, as in SSc skin and lung, in normal kidney fibroblasts as well as in HSC stimulated with TGFβ1. These important findings revealed the complexity of fibrosis which shares similarity but at the same time may have different gene signature depending of the disease of the organ affected. In other words, fibrosis in different organs and in different diseases shares many common pathways but there are also likely to be organ- and disease- specific mechanisms.

CEMIP expression was reduced in some fibrotic organs (eg. SSc skin and lung; Section 5.4.1) but was increased in others (eg. IPF lungs or ADPKD kidney compared to normal; Section 5.4.3). Understanding this kind of differential regulation is crucial to developing therapeutic treatments for fibro-proliferative diseases. For example, an anti- CEMIP antibody could be effective to treat fibrosis in IPF patients but would likely be detrimental for SSc patients with lung involvement. Here, knockdown of CEMIP in normal lung fibroblasts did not appear to have any effect on fibrotic markers or on the fibrotic phenotype of fibroblasts (Section 5.4.2.1). Furthermore, in prostate cancer, data showed that CEMIP is a likely target gene of the Wnt/β-catenin signaling pathway which is well documented (462). It would be necessary to explore this pathway in fibrosis in order to investigate the signaling of CEMIP in fibrosis which is so far unknown.

The role of HA and CEMIP in chronic inflammatory disease including in Crohn's disease suggested that high-molecular-weight HA (HMW-HA), when depolymerized to low-molecular-weight HA (LMW-HA) fragments by CEMIP, becomes proinflammatory and pro-fibrotic and contributes to maintenance of gut inflammation and fibrosis (479).

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The data revealed (Chapter 5, section 5), a reduced expression of CEMIP in fibrotic SSc cells and tissues as well as the knocking-down of CEMIP in normal fibroblasts did not induce a fibrotic phenotype and raised the question of what effect restoring CEMIP expression levels would have on the fibrotic phenotype.

This question led to attempts to create an overexpression vector for CEMIP. However generation of such a vector proved challenging. A variety of strategies using different cloning and transfection reagents were tried to clone CEMIP but without success. As an alternative, three gBlocks Gene Fragments for CEMIP (Integrated DNA Technologies) were ordered. These fragments are known to be ideal for affordable and easy gene construction or modification.

The fragments are double-stranded DNA sequence of 1000, 1600 and 1600 bp in length, respectively, which overlap to allow for direct recombinase cloning using Infusion kit (Clontech). Unfortunately, this approach was also unsuccessful and needs to be repeated. Similarly, optimisation of PCR (different cycle conditions and primer pairs) did not improve success. The failure to generate a vector may be related to the large size of CEMIP (4.2kb). A number of specialist companies (eg. Origen and Thermofisher) predict problems in cloning large genes. An attractive but expensive alternative is have the CEMIP gene synthesised commercially. The hypothesis that knock-in CEMIP in fibrotic cells could reduce the fibrotic phenotype remains to be explored.

It is known that an imbalance in the synthesis and degradation of HA causes the accumulation of HA with different molecular weights in the ECM which is commonly observed in disease including in arthritis and cancers (480). The molecular weight of HA is important for its biological effects and functional properties. The size of HA can vary from (<1000bp) to LMW (<200bp). Among the 3 HASs, HAS1 and HAS3 synthesize LMW- HA fragments and HAS2 generates HMW-HA fragments (481). In its HMW form, HA has a role in the hydration of the tissues and in regenerative repair (482). It has been shown that different sized HA fragments have different biological effects. HMW-HA tends to be anti-inflammatory promoting pro-resolving gene transcription including IL10 and also attenuates angiogenic and immune responses (483). Conversely, LMW-HA is pro-inflammatory and angiogenic (475) increasing TNFα, IL12 and CD80 expression and the secretion of NO and TNFα (438). These effects are mediated via surface receptors including CD44 and TLR which bind HMW HA and LMW HA, respectively (447).

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This was confirmed in synovial fluids from patients with OA or RA which contain lower molecular weight HAs than normal subjects, leading to decreased synovial viscosity and increased inflammatory responses in these patients (297).

The mechanisms of regulation of HAS2 and CEMIP and the effect of different molecular weight HA in fibrosis are yet to be fully elucidated. Recent findings by Nagaoka et al. showed that TGFβ1 was an effective stimulator of HMW HA production (>1000 kDa) by up-regulating HAS2 genes and down-regulating CEMIP in skin and in arthritic synovial fibroblasts (297) and that the response was consistent across 3 different lines of skin fibroblasts (Detroit 551, HS27, and NHDF-Ad cells).

In addition they showed that treatment with other growth factors implicated in fibrosis eg. EGF, or PDGF-BB, resulted in an accumulation of LMW HA (>200 kDa). Furthermore, they demonstrated that siRNA-mediated knock-down of CEMIP in Detroit 551 cells resulted in production of only HMW HA. Thus the expression levels of HAS2 and/or CEMIP could determine the molecular sizes of newly-produced HA. The different molecular sizes of newly synthesized HA under growth factor stimulation may be related to molecular size-dependent HA functions under physiological and pathological conditions.

To test this hypothesis, the molecular weight distribution of HA in normal and fibrotic lung fibroblasts (cell lysates and conditioned medium) was assessed using an electrophoretic method described by Cowman et al. (484). Briefly, the method involves electrophoretic separation of HA in a 0.5% agarose gel, followed staining with a cationic dye (Stains-All (Sigma)) to visualise the HA in normal and SSc skin and lung fibroblasts (conditioned media and cell lysates) to observe HMW and LMW of HA.

The preliminary data suggest that SSc cells produced more HA compared to the normal fibroblasts however it was not possible to determine the size of HA in the stained gel because of experimental and technical issues. Further optimisation of the method is required (choice of HYAL, gel preparation, running and transfer conditions).

Another interesting study would be to explore the expression of other factors in the HA pathway including other members of HAS family, HYALs and HA receptors as well as the downstream signaling pathways in fibrosis. The main HA receptor is CD44 and, the role of CD44-HA interaction in various pathophysiologies has been studied (485).

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HA binding to cell surface CD44 triggers a variety of signaling events, including complex formation between CD44 and co-receptors such as c-Met, EGFR, and TGFβ receptors, and activation of downstream effectors such as Akt, PI3K, PP2A, ERK1/2, and Ras/Raf/Rac. Also, it has been shown that HA–CD44 signaling drives proliferation, invasion, cytoskeletal rearrangement, and angiogenesis, which lead not only to normal cell functions such as fibroblast migration, wound healing, and immune cell function but also to tumour growth and progression (486).

Accumulation of HA is a characteristic of tissue fibrosis in many organs and the interactions between HA with fibroblast CD44 appear critical for the recruitment of fibroblasts to sites of injury, for cell survival in severe fibrosis and for TGFβ-dependent differentiation of fibroblasts to myofibroblasts (453). Strategies to block HAS2 have been explored. It has been shown in keloid keratinocytes that treatment with 4- methylumbelliferone (4MU) caused a significant reduction in HAS2 expression and cells migration (316). Therefore, inhibition of HAS2 expression using 4MU may represent a novel strategy for treatment of keloid scarring. A similar approach could could be used in SSc fibrotic fibroblasts to block HAS2 activity and further explore its role in the fibrotic process.

The data presented in this chapter has shown altered expression of HAS2, CEMIP and HA in a variety of fibrotic settings. HAS2 and CEMIP are involved in HA regulation and, both, are altered in fibrosis, the expression of both genes and proteins were analysed by RT-qPCR and Western Blot and siRNA methods were used to investigate the role of HAS2 and CEMIP in the fibrotic phenotype.

Also, ELISA data showed as expected that HA is significantly up-regulated in SSc fibroblasts from lung and skin compared to normals. Knocking-down HAS2 significantly reduced the HA secretion by the SSc fibroblasts and conversely knocking-down CEMIP trend to increase HA in the medium. These preliminary results suggested a potential co-ordinated role of these 2 HA regulators in SSc. Furthermore, immunofluoresence findings using a HABP assay used to test siHAS2 on HA synthesis, provided support that knocking-down HAS2 significantly reduced cell associated-HA in SSc lung fibroblasts compared to normals. Taken together, these data suggest the mechanisms of HA deposition in fibrosis through HAS2, CEMIP and even potentially TSPAN13 and will be discussed in the Chapter 6.

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CHAPTER 6 CONCLUSIONS AND FUTURE WORK

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The objectives and specific aims of this project were: (i) to identify common and specific genes associated with lung, skin and kidney fibrosis using an in silico approach; (ii) to select and validate novel targets in terms of expression in fibrosis in vitro and in vivo; and, (iii) to explore the function of the selected genes (TSPAN13 and HAS2) in normal and fibrotic human cells in vitro and in an established mouse model of fibrosis. Overall these objectives were achieved and in addition, the in silico analyses generated a wealth of data which will provide the basis for future projects.

6.1 Identification of target genes in fibrosis

As is the case in many other disorders identifying new targets and biomarkers is desperately needed in order to better define, diagnose and treat fibrosis. The main objective of this thesis was to discover and investigate common and tissue-specific drivers of fibrosis in human skin, lung and kidney. The data generated highlighted the complex nature of fibrotic diseases with diverse etiologies revealing similarities as well as differences and unique signatures of gene expression in the organs affected. Through an extensive in silico mining analysis using Pubmed (microarray data and published literature 1988-2015) a extensive list of more than 1000 genes involved in fibrosis was compiled. This list of genes was edited into a shorter lists of 91 genes common to skin, lung and kidney fibrosis and 60 genes unique to fibrosis of each of the 3 organs (Figure 6.1).

Analysis of such a large heterogeneous gene list compiled from published studies is challenging not least because of differences in experimental design (eg. studies on different tissue or cells types, methodologies in sample preparation). Similarly, mining of large-scale microarray data sets is complex due to use of different platforms and analysis tools for data management and clustering in different studies. This emphasises the need going forward, for systematic quality control, as well as for standardization of tissue samples, cells and analyses, to facilitate comparison of data from different studies.

The in silico mining exercise undertaken in this thesis, generated an extensive dataset of fibrosis-associated genes providing a large number of candidate targets for further study. The data is also amenable to pathway analyses potentially revealing novel interactions for subsequent functional investigation. Overall the data provide a valuable resource to further the understanding of the fibrotic process.

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Figure 6.1.Summary of the workflow of the study. This workflow was used to identify and select candidate genes involved in skin, lung and kidney fibrosis. FC (fold-change).

6.2 Selection of candidate target genes in fibrosis

The number of genes was reduced manually using a “fold change” cut-off (FC<2). In order to edit the list further key criteria were also applied (listed in Chapter 3, section 3.4). Among these criteria, “drugability” was one of the most important for the selection. Membrane proteins are unquestionably the most studied cellular components for drug development, not only because they are attractive and accessible targets for drug design but also because they provide an interface between the extracellular environment and intracellular compartment and are involved in multiple functions including cell activation, differentiation, apoptosis and signaling (487). Multiple pathway analysis platforms, including STRING were also used in order to sort and classify potential gene targets and reduce the number of genes taken forward for validation (Chapter 3). In general clustering of genes did not prove to be particularly useful in reducing the gene list number or identifying novel pathways that may be involved in fibrosis. However, it did identify a skin-specific cluster of genes which may be interesting to follow up in the future. Although time-consuming, these combined approaches narrowed down the gene list to a subset of 12 genes, which was considered to be, an experimentally manageable number.

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6.3 Validation of 3 candidate target in fibrosis

Expression of the 12 short-listed genes (listed in Chapter 3, Table 3.2) was tested in primary normal and fibrotic human fibroblasts from diverse organs (lung, skin and kidney). In vitro and in vivo studies provided strong evidence that 3 of the 12 selected genes were significantly associated with a variety of fibrotic diseases namely, TSPAN13 (Chapter 4) and HAS2 and CEMIP (Chapter 5) which were validated in human fibrotic tissue sections. A summary of the gene expression analysis on fibrotic markers and the 3 candidates genes is shown in Table 6.1.

SKIN LUNG KIDNEY GENE SSc N+TGFβ SSc IPF N+TGFβ ADPKD N+TGFβ

COL1A2 Up Up Up Up Up Up Up

CTGF Up Up Up Up Up Up Up

αSMA Up Up Up Up Up Up Up

TSPAN13 Up Up Up Up Up Down Up

HAS2 Up Up Up Up Up Up Up

CEMIP Down Down Down Up Down Up Down

Table 6.1. Summary of the gene expression analysis on the fibrotic markers and selected genes. SSc: fibroblasts from systemic sclerosis or scleroderma; N+TGFβ: normal fibroblasts treated with TGFβ ; IPF: fibroblasts from idiopathic pulmonary fibrosis; ADPKD: fibroblasts from autosomal dominant polycystic kidney disease; up: significantly up-regulated, down: significantly down-regulared.

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Immunohistochemical analysis of human tissue sections (skin, lung and kidney from normal subjects and from patients) confirmed the RT-qPCR and Western blot results and revealed that TSPAN13 and HAS2 were up-regulated in fibrotic tissues (Chapter 4 and 5). The functional relevance of these targets was investigated in vitro using gene manipulation strategies in well-established fibrogenic assays which showed a pro- fibrotic role for TSPAN13 and HAS2.

6.4 TSPAN13 a novel fibrotic target

The few studies available on TSPAN13 in human cells describe a role in cancer (351). Although the function of TSPAN13 has not been previously explored in fibrotic diseases, the work in this thesis provides novel and compelling evidence that TSPAN13 may be a new fibrotic mediator in several fibrotic diseases including SSc, IPF and CKD. Furthermore, depletion of TSPAN13 in vitro (human lung fibroblasts) and in vivo mouse model) leads to attenuated lung fibrosis.

Interestingly, in contrast to the data in skin and lung and in normal and CKD kidney, TSPAN13 was significantly reduced in ADPKD fibroblasts and tissues. This observation may relate to the underlying genetic mutation in ADPKD but points to potential differences in the mechanisms leading to fibrosis in different disease etiologies. In ADPKD, mutations in PKD1 or PKD2 lead to ciliary abnormalities and changes in localisation of membrane proteins/membran organisation. It is possible that such changes result in reduced expression of TSPAN13. Further research will need to take place to understand the role of reduced TSPAN13 in fibrosis associated with ADPKD. This example highlights the complexity of fibrosis depending on the etiology of the disease as well as the organ affected.

Taken together the findings in this thesis suggest that TSPAN13 may be a novel mediator in fibrosis and the mechanisms of action need to be explored. Current understanding of TSPAN13 biology and function in either normal tissue homeostasis or in pathological conditions is limited but there may be similarities with other better characterised TSPANs. It is well established that TSPANs interact with other TSPANs, integrins and adhesion molecule receptors, thereby regulating the function of partner proteins. Preliminary data using Western blotting for a panel of integrins after knocking- down TSPAN13 in lung SSc fibroblasts showed that siTSPAN13 reduced ITGβ1 and ITGαV protein expression suggesting that in fibrosis, TSPAN13 may regulate specific integrins.

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Based on the biology of other TSPANs, TSPAN13 may also regulate or interact with growth factor receptors and intracellular signaling molecules. The role of TSPAN13 in ECM remodelling might be linked to a potential association of TSPAN13 with specific integrins (ITGβ1 and ITGαV) which activate known ‘fibrotic’ intracellular signaling cascades leading to increased synthesis and accumulation of ECM.

Membrane microdomains including tetraspanin-enriched microdomains (TEMs) (Chapter 4, section 4.1.5), have been identified as a platform for many processes including signal transduction and have been suggested to play a role in a variety of physiological and pathological conditions including cancers (409,488). As TSPANs associate with a large number of cell surface proteins, it has been a challenging task to define the composition of TEMs. However approaches using mass spectrometry, flow cytometry, immuno-precipitation and Western blotting with antibodies directed against a panel TSPANs and integrins, have characterised the TEMs involved in cancers (489). Similar approaches could be adopted to characterise the TEMs involved in fibrosis in order to better define their functions.

TSPAN13 within the TEMs and with the association of integrins and other potential partners would potentially facilitate the formation of multimolecular complexes, regulating fibroblast activation, migration, contraction and signaling thus favoring ECM accumulation leading to fibrosis.

In addition, data from this project suggests that there may be a link between TSPAN13 and TGFβ signaling. Preliminary Western blot data showed that siRNA knock-down of TSPAN13 in fibrotic lung fibroblasts, reduced Smad2/3, ERK and p38 phosphorylation suggesting the depletion of TSPAN13 reduces TGFβ signaling. It would be of interest to treat TSPAN13 knock-down cells with TGFβ to detemine whether the cell response to TGFβ is affected. It was been shown that CD151 interact with TGFβ receptors and that activate intracellular signaling pathways including Smads, ERK and p38, all involved in pro-fibrotic genes transcription and therefore to ECM synthesis in fibrosis. In the same way, the association of TSPAN13 with TGFβ receptors and fibrotic pathways has to be elucidated.

The preliminary in vivo data in a mouse model of pulmonary fibrosis strongly suggests the knockdown of TSPAN13 is beneficial at least in lung fibrosis. These studies need to be repeated to test both prophylactic and therapeutic regimes in fibrosis in different organs. Furthermore, tissue- or cell-type specific gene deletion strategies including CRISPR/Cas9 (490) could be employed to investigate the role of TSPAN13 in more detail.

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Given the expression of TSPAN13 in other tissues including skin, kidney, brain, liver as well as gastrointestinal tract (https://www.proteinatlas.org /ENSG00000106537- TSPAN13/tissue), targeted therapies may be required to prevent unwanted side- effects. To date, there is only been a few examples of targeting of TSPANs in vivo in humans (409,491). Recent evidence suggests that targeting TSPANs could be therapeutically beneficial in a variety of diseases (Table 6.2).

As mentioned earlier drugability was one of the selection criteria. TSPANs on the cell membrane are good candidates for blockade using mAbs which block lateral interactions between TSPANs and their partners leading to disruption of TEMs and dysregulation of intracellular signaling (350). Given their multiple roles in supporting tumor survival and immune evasion, it is not surprising that anti-CD37 therapeutics are being developed as novel treatments for hematological malignancies (491).

TSPAN DISEASES IN VIVO TARGETING REFREF

CD151 Tumour growth and mAb blocks primary tumour growth in (492,493) metastasis mice

Angiogenesis and Wound healing defective in CD151-null wound healing mice (494)

TSPAN8 Tumour metastasis; mAb blocks tumour growth and (495) Angiogenesis angiogenesis in mice

CD37 B-cell leukemia mAb for chronic lymphocytic leukemia in human Lymphoma growth (364) Phase1/2

CD9 Tumour metastasis mAb prevents colon and gastric cancer (381) Lymphangiogenesis growth in mice

Table 6.2. In vivo targeting of TSPANs

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Using the same rationale and targeting strategy, the utilitiy of mAb to TSPAN13 as a potential effective anti-fibrotic treatment is being explored. As an initial proof of concept a panel of commercially available antibodies against TSPAN13 are being tested in in vitro cell-based assays to determine whether any of these antibodies attenuate the fibrotic phenotype. In parallel, UCB Pharma is developing mAbs against the extracellular domain (EC2) which by analogy with other TSPANs, is potentially the functional domain of TSPAN13. This region is important in determining TSPANs function and EC2 domains have been shown to modulate different cellular events through their interaction with partners including integrins (353).

Thus targeting TSPAN13 EC2 domains may prove to be a valuable therapeutic strategy. TSPAN13 antibodies will be tested initially in vivo, in established mouse model of lung fibrosis and further in dermal fibrosis although this could be extended to models of fibrosis in other organs.

In vitro, RNA profiling comparing normal and fibrotic fibroblast global transcriptomes in the presence of neutralising TSPAN13 mAb will reveal characteristic gene signatures and patterns of gene expression dependent upon TSPAN13. This will reveal interactions of TSPAN13 with other partners and also will improved the understanding of TSPAN13 biology and its role both in normal homeostasis and in fibrosis. Also, the secretome could be also analysed comparing the media from untreated and treated fibrotic fibroblasts with neutralising TSPAN13 mAbs which will provide cues on the effect of TSPAN13 in fibrosis.

6.5 HAS2 and CEMIP axis

HAS2 has previously been shown to be associated with cancer and some fibrotic disorders (357). However, a role for HAS2 in SSc has not been reported. Data generated in this thesis revealed that HAS2 is significantly increased in fibrotic fibroblasts and tissues from SSc lung and skin and ADPKD kidney. HAS2 is thought to contribute to the fibrotic environment through enhanced HA production (Chapter 5, section 5.5).

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Knock-down of HAS2 in fibrotic fibroblasts from SSc lung and skin. ameliorated the fibrotic phenotype. Therefore targeting HAS2 may be an attractive approach to attenuate and/or resolve tissue fibrosis. Conversely, reduced expression of CEMIP in some fibrotic conditions (Chapter 5, section 5.4.1) led to the hypothesis that overexpression in fibrotic fibroblasts would reduce HA production and potentially normalize the fibrotic feature. However, from a therapeutic perspective overexpression of a gene is more challenging that blocking the activity of a protein. Of note, examining the role of CEMIP in fibrosis also proved difficult to investigate experimentally. Thus in terms of manipulating the HAS2/CEMIP axis therapeutically the focus is on HAS2. Based on the data obtained in this thesis a potential mechanism for the mode of action of HA in fibrosis is proposed (Figure 6.2). Regulation of HA synthesis and degradation is crucial to maintenance of ECM homeostasis (496). HA deposition is regulated by a balance between polymerisation and depolymerisation by HAS2 and CEMIP, respectively.

All the data in Chapter 5 corroborate that in SSc, HAS2 expression is increased and CEMIP expression is reduced potentially leading to an imbalance between polymerisation and depolymerisation. The dysregulation of these two processes lead to an increased HA in the ECM and ultimately, to fibrosis (Figure 6.2). Further detailed studies are required to better understand the role and mechanisms of HAS2 and CEMIP in the regulation of HA in the context of progressive scarring but restoring the imbalance between the two proteins could be an anti-fibrotic strategy.

The biological effects of HA in tissue homeostasis and the response to injury also need to be clarified. Therefore, mechanisms of HA synthesis and degradation in fibrosis, which lead to the generation of different sized HA fragment need to be determined. Determining HA size is challenging and there is much controversy in the field about the biological effects of different HA polymers.

Different sized HA fragments have been reported to have different biological effects, for example some reports suggest that HMW-HA tends to be anti-inflammatory (483), while others show that LMW-HA is pro-inflammatory (475), particularly in cancer. It appears that HA size (HMW-HA and LMW-HA) and location (intracellular, extracellular, as well as sub-cellular compartments) dictate the effect of HA (497).

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Figure 6.2. Schematic showing the proposed roles of HAS2 and CEMIP in normal(a) and SSc fibrotic tissue (b). In normal conditions, HA is regulated by HAS2 and CEMIP which polymerise and depolymerise HA, respectively. However, in fibrosis, there is increased HAS2 and decreased CEMIP leading to a dysregulation of HA synthesis and degradation. Thus, HA accumulates in the ECM and contributes to fibrosis.

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Based on the data in Chapter 5, the hypothesis is that increased HAS2 expression and reduced CEMIP expression, might lead to more HA polymerisation and, therefore, production of HMW-HA. Preliminary data, using agarose gel eletrophoresis, suggested that HA was increased in SSc fibrotic cells and tissues compared to their normal counterparts, however, the molecular weights of HA in this setting have still to be determined.

Of interest, a recent study in a mouse model of endometrial fibrosis, which examined the effect of exogenous HMW-HA and LMW-HA injected into the uterine cavity (498), showed that HMW-HA attenuated the degree of endometrial fibrosis and the expression of fibrosis markers (TGFβ1, CTGF, collagens) in the endometrium (498) suggesting that HMW-HA is anti-fibrotic. In order to resolve this apparent conflict on the implication of LMW-HA, HMW-HA in fibrosis, a similar in vivo experiment could be performed in established mouse models of lung, skin and kidney fibrosis. The dissection of HA regulation and signaling pathways may also help elucidate its biological functions in normal and fibrotic conditions.

Overall, our data suggest that the HA/HAS2/CEMIP axis is altered in fibrosis. Based on the evidence presented in this thesis, HAS2 was also selected by UCB Pharma for the generation of therapeutic mAbs against this target which will be tested in vitro and in vivo in pre-clinical models of fibrosis.

6.6 Potential interactions between TSPAN13 and HAS2

TSPAN13 and HAS2 are two potential pro-fibrotic mediators which were selected independently in a unbiased approach. Since both are membrane proteins, there may be potential relationships between these two proteins which need to be elucidated. Preliminary data in a mouse model of lung fibrosis showed that siTSPAN13 reduced HA production which might suggest a possible shared regulatory mechanisms. Pull- downs and proteomics analyses could be used to show if there are interactions (direct or indirect) between TSPAN13 and HAS2.

6.7 TSPAN13 and HAS2 - Relevance in other fibrotic diseases

In this thesis, the in vitro studies were performed on primary human fibroblasts and HSCs. The use of primary human cells is a key aspect of pre-clinical fibrosis research since immortalised cell lines may have lost relevant physiological characteristics (499).

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However it is known that multiple cell types contribute to the fibrotic response (500) and it would be of interest to study the expression and role of TSPAN13 and HAS2 in other normal and diseased cells types such as epithelial cells, immune cells, endothelial cells and stem cells. Immunohistochemical staining of tissue sections showed that TSPAN13 and HAS2 are expressed in both connective tissue cells and epithelial cells. Co-localisation studies using cell-specific markers are required to determine which cell types express these proteins in fibrotic tissue. Fibrotic disorders can occur across other major organ systems and as a result of diverse aetiologies (265). TSPAN13 and HAS2 were explored human HSCs untreated vs TGF-treated as model of liver fibrosis.

It would be of interest to examine the role of TSPAN13 and HAS2 in fibrotic liver diseases including NASH, the most common of chronic liver disease in the western world (501) and in other fibrotic diseases including myocardial fibrosis, a significant global health problem associated with nearly all forms of heart disease (502), gastrointestinal tract fibrosis (eg. Crohn's disease) (503), and ocular fibrosis (eg. macular and retinal fibrosis) (504).

6.8 TSPAN13 and HAS2 - potential biomarkers in fibrosis

The research aim of this project was to bring new scientific insights to organ fibrosis and to identify potential new therapeutic targets and useful biomarkers to be developed for new therapies and for the diagnosis and management of patients. The question whether TSPAN13 and HAS2 may be useful biomarkers for diagnosis of fibrosis and for patient stratification could be assessed by examining whether TSPAN13 and HAS2 are present in microparticles and/or exosomes from biological fluids (serum, urine, broncheolar lavage) of patients with fibrotic diseases including the cohorts of the Royal Free NHS Foundation Trust Hospital.

6.9 Concluding remarks

Overall, the overarching objectives as well as the specific aims of this project were achieved and provided an extensive list of common and unique genes, involved in fibrosis in skin, lung and kidney as well as strong evidence suggesting a key role for TSPAN13 and HAS2 in several fibrotic diseases. Based on the data generated in this exiting project, the future objective is to develop and explore the properties of the novel mAbs directed against TSPAN13 and HAS2 as potential anti-fibrotic therapies and to examine the role of these proteins as candidate biomarkers. It is hoped that TSPAN13 and HAS2 mAbs may ultimately be translated for clinical use.

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APPENDIX

Table A. Microarray datasets used for the identification of fibrotic genes.

ORGAN DATASET TITLE REF

GSE1724 Gene expression profiling reveals novel TGFβ1 (505) targets in adult lung fibroblasts GSE24206 Validated gene expression signatures of Idiopathic (506) Pulmonary Fibrosis GSE40839 Expression data from fibroblasts cultured from normal (507) Lung and fibrotic human lung tissue GSE48149 Lung tissues in systemic sclerosis have gene (508) expression patterns unique to pulmonary fibrosis GSE10667 Gene expression proﬁles of acute exacerbations of (509) Idiopathic Pulmonary Fibrosis GDS3705 Progressive pulmonary sarcoidosis (510)

GSE22459 Fibrosis with inflammation at one year predicts (511) transplant functional decline GEOD42303 TIMP2 and TIMP3 have divergent roles in early renal (512) tubulointerstitial injury Kidney GSE36496 Gene expression changes induced by unilateral (513) ureteral obstruction in mice 12787392 Altered expression of immune modulator and (514) structural genes in neonatal unilateral ureteral obstruction GEOD41524 Genome-wide analysis demonstrates an important (515) role for expression of ECM and tissue remodelling genes in Dupuytren's disease GEOD32413 Stable gene expression of serial skin biopsies defines (516) patient subsets in dsSSc GEOD27165 A pro-fibrotic gene expression program induced by (517) Egr-1 in skin fibroblasts Skin MEXP-32 Transcription profiling of human skin biopsies from (170) individuals with a diagnosis of dcSSc vs biopsies from normal individuals GEOD9285 Gene expression profiling of SSc skin (354) GEOD47616 The effects of treatments in dermal fibroblasts (518) 16736506 Gene profiling of Scleroderma skin reveals robust (519) signatures of disease that are imperfectly reflected in the transcript profiles of explanted fibroblasts PMC1613194 Matrix contraction by dermal fibroblasts requires (520) TGF/ALK 5 and MEK/ERK

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Table B. Common genes involved in fibrosis (lung; kidney; skin).

COMMON GENES IDENTIFIED IN FIBROSIS

Pro-fibrotic FSP1 Fibroblast specific protein 1 ACTA2 Actin, alpha 2, smooth muscle EFNB2 Ephrine b2 TSP1 Thromspodine 1 HSP47 Heat shock protein 47 VIM Vimentin FBN1 Fibrillin 1 COL1A2/COL3A1Collagen 1A2/collagen 3A1 CTGF connective tissue growth factor FN1 Fibronectin CD44 Hyaluronic acid receptor HAS2 Hyaluronan synthase 2 OPN Osteopontin HTR2A/B Serotonine receptors A and B COMP Cartilage oligomeric matrix protein BGN Biglycan MARCKS Myristoylated alanine-rich protein kinase C substrate

Inflammation TGFβ R Transforming growth factor β receptors TGFB1 Transforming growth factor 1 CCL11 chemokine C-C motif ligand 11 CCL3 chemokine C-C motif ligand 3 CCL2 chemokine C-C motif ligand 2 Il13 Interleukin 13 Il 4 Interleukin 4 Il6 Interleukin 6 CD154 CD40 ligand ACVR1 Activin A receptor type I FST Follistatin

Immune system CD30 Tumor necrosis factor receptor Superfamily member 8 CD90 Thy-1 cell surface antigen CD19 CD19 molecule Bcl2 B-cell CLL/lymphoma 2

Enzymes FAP Fibroblast activation protein α ROCK1/2 Rho-associated coiled-coil containing protein kinase1 NOX4 NADPH oxidase 4 ILK Integrin-linked kinase CK2 Casein kinase 2 P4Hβ Prolyl 4-hydroxylase, beta polypeptide COX2 Cyclooxygenase 2 GSK-3β Glycogen synthase kinase 3β PAI-1 Plasminogen activator inhibitor 1 MMP1 Metalloproteinase 1 MMP2 Metalloproteinase 2 MMP9 Metalloproteinase 9 TIMP1 Tissue inhibitors of MMP1 TACE TNF-α-converting enzyme ARK5 AMPK-related protein kinase 5 TG2 Transglutaminase 2

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Growth factors PDGFRA/B Platelet-derived growth factor receptors A/B VEGFA Vascular endothelial growth factor A VEGFR Vascular endothelial growth factor receptor FGF2 Fibroblast growth factor FGFR2 Fibroblast growth factor receptor 2 CTGF Connective tissue growth factor Snai1 Snail family zinc finger 1 AGT Angiotensinogen EGR1 Early growth response protein 1 IGF-1 Insulin-like growth factor 1 IGFBP5 Insulin-like growth factor binding protein 5 IGFBP3 Insulin-like growth factor binding protein 3 EDN1 Endothelin 1

Signal transduction Sox 9 SRY (sex determining region Y)-box 9 STAT3 Signal transducer and activator of transcription 3 STAT1 signal transducer and activator of transcription 1 Sp1 Sp1 transcription factor Nfkb1 Nuclear factor of kappa light polypeptide JNK mitogen-activated protein kinase 8 Smad 3 Smad 2 Wnt3a Wingless-type MMTV integration site family mb3A MAPK1 Mitogen-activated protein kinase 1 Twist1 Twist family bHLH transcription factor 1 HIF1α Hypoxia inducible factor 1, alpha

Receptors ITGA5 Integrin, alpha 5 (fibronectin receptor, alpha) ALK5 Transforming growth factor, beta receptor 1 ENG Endoglin PPAR-γ Peroxisome proliferator-activated receptor gamma E Cadherin Cadherin 1 DDR2 Discoidin domain receptor tyrosine kinase 2 ICAM 1 Intercellular adhesion molecule 1 CAV1 Caveoline 1 EGFR Epidermal growth factor recepto FASLG Fas ligand

Anti- fibrotic SAP Serum amyloid P BMP 7 Bone morphogenetic protein 7 COL18A1 Endostatin HGF Hepatocyte growth factor

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Table C. Specific genes up (+) and down (-) regulated in lung fibrosis

GENE -/+ REF Pro-fibrotic ESM1 endothelial cell-specific molecule1 + Renzoni 2004 FCN1 ficolin collagen domain containing1 + Hsu 2011 SEMA7A semaphorin 7A, GPI membrane + Gan2012 ELN elastin + Lindah 2013

Inflammation IL11 interleukin 11 + Lindahl 2013 SOCS1 suppressor of cytokine signaling 1 + Renzoni 2004 SCGB1A1 secretoglobin family 1A mb1 + Hsu 2011 CRLF1 cytokine receptor-like factor 1 + Lindahl 2013

Immune system CD24 + Hsu 2011 LAMP3 lysosomal-associated membrane protein 3 + Hsu 2011 SFTPA1 surfactant protein A1 + Hsu 2011 CTAGE1 cutaneous T-cell lymphoma-associated antigen 1 - Lockstone 2010

Enzymes XYLT1 Xylosyltransferase 1 + Lindahl 2013 NBEA neurobeachin + Hsu 2011 FASN fatty acid synthase + Hsu 2011 MAOA monoamine oxidase A + Hsu 2011 PGC progastricsin + Hsu 2011 SOD2 superoxide dismutase 2, mitochondrial - Lindahl 2013 CASP1 caspase 1, apoptosis-related cysteine peptidase - Lindahl 2013

Growth factors ALK2 activin A receptor, type 2 + Renzoni 2004 BDNF Brain-derived neurotrophic factor + Renzoni 2004 GRB10 Growth factor receptor-bound protein 10 + Renzoni 2004 NGF Nerve growth factor + Renzoni 2004 MDK midkine (neurite growth-promoting factor 2) + Meltzer 2011 INHBA Inhibin, beta A + Lindahl 2013

Signal transduction Nkx2-5 NK2 homeobox 5 + Hu 2010 MKL1 megakaryoblastic leukemia + Renzoni 2004 PAWR Prostate apoptosis response-4 (Par-4) - Renzoni 2004 CLDN18 claudin 18 + Hsu 2011

Protein secreted/coding DSP desmoplakin + Lindahl 2013 ID1 inhibitor of DNA binding 1 + Lindahl 2013 PLN phospholamban + Lindahl 2013 GAL galanin + Lindahl 2013 PODN podocan + Hsu 2011 MGP matrix Gla protein + Hsu 2011 MLPH melanophilin + Hsu 2011 SPOCK1 sparc/osteonectin/Testican-1 + Hsu 2011 KRT15 keratin 15 + Konishi 2009 SYNE1 spectrin repeat containing, nuclear envelope 1 - Lockstone 2010 ING5 inhibitor of growth family, member 5 - Lindahl 2013 PTX3 pentraxin 3 /TNF-inducible gene 14 protein (TSG-14) - Lockstone 2010 DSTN destrin (actin depolymerizing factor) - Lockstone 2010 OFD1 oral-facial-digital syndrome 1 - Lockstone 2010 CEMIP cell migration inducing protein, hyaluronan binding - Lindahl 2013

Receptors/ Transmembrane proteins BDKRB2 Bradykinin receptor B2 + Renzoni 2004 PLAUR Urokinase receptor + Renzoni 2004 DTR diphtheria toxin receptor + Renzoni 2004 DARC Duffy blood group, atypical chemokine receptor + Hsu 2011 VIPR1 vasoactive intestinal peptide receptor 1 + Hsu 2011

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TSPAN13 tetraspanin 13 + Lindahl 2013 RXRA retinoid X receptor, alpha - Lindahl 2013 AGER advanced glycosylation end product-specific receptor - Konishi 2009 OR2A7 olfactory receptor, family 2, subfamily A, member 7 - Lockstone 2010 THR THRB thyroid hormone receptor, beta - Lockstone 2010

Table D. Specific genes up (+) and down (-) regulated in skin fibrosis

GENE -/+ REF Pro-fibrotic LUM lumican + Gardner 2006 ; SDC1 syndecan 1 + Pendergrass 2012 CHI3L1 chitinase 3-like 1 + Chen 2005 FMOD fibromodulin + Forrester 2013 NOV nephroblastoma overexpressed - Forrester 2013

Immune system + Bhattacharyya 2011 IL11 interleukin 11 + Fang 2013 BST2 bone marrow stromal cell antigen 2 + Forrester 2013 CRLF1 cytokine receptor-like factor 1

Enzymes + Gardner 2006 NNMT nicotinamide N-methyltransferase - Forrester 2013 PBK PDZ binding kinase - Forrester 2013 TOP2A topoisomerase (DNA) II alpha - Forrester 2013 CTSK cathepsin K - Bhattacharyya 2011 ENPP2 ectonucleotide pyrophosphatase/phosphodiesterase 2 - Bhattacharyya 2011 PDE5A phosphodiesterase 5A

Growth factors VGF nerve growth factor inducible + Bhattacharyya 2011

Signal transduction SFRP4 secreted frizzled-related protein 4 + Forrester 2013 CHN1 chimerin 1 + Forrester 2013 TSPAN13 tetraspanin 13 + Bhattacharyya 2011 LMCD1 LIM and cysteine-rich domains 1 + Bhattacharyya 2011 CALB2 calbindin 2 + Bhattacharyya 2011 APPL2 adaptor protein, phosphotyrosine + Fang 2013 DDIT3 DNA-damage-inducible transcript 3 + Fang 2013 WIF1 WNT inhibitory factor 1 - Whitfield 2013 LPHN2 latrophilin 2 - Forrester 2013 OSR2 odd-skipped related transciption factor 2 - Bhattacharyya 2011 CLDN8 claudin 8 - Gardner 2006 CLDN11 claudin 11 - Bhattacharyya 2011

Protein secreted/coding LBH limb bud and heart development - Bhattacharyya 2011 ASPN asporin - Gardner 2006 CALR calreticulin + Whitfield 2013 FKBP1A FK506 binding protein 1A + Whitfield 2013 ARRB2 arrestin, beta 2 + Whitfield 2013 MAPRE1 microtubule-associated protein 1 + Whitfield 2013 ACTN1 actinin, alpha 1 + Whitfield 2013 NID2 nidogen 2 + Whitfield 2013 CADM1 cell adhesion molecule 1 + Forrester 2013 SPON1 spondin 1, extracellular matrix protein + Forrester 2013 ANGPTL4 angiopoietin-like 4 + Forrester 2013 DACT1 dishevelled-binding antagonist of catenin1 + Forrester 2013 CILP2 cartilage intermediate layer protein 2 + Forrester 2013 RBP4 retinol binding protein 4, plasma + Forrester 2013 SPAG4 sperm associated antigen 4 + Forrester 2013 PDLIM3 PDZ and LIM domain 3 + Forrester 2013 CPLX1 complexin 1 + Bhattacharyya 2011 CST6 cystatin E/M + Bhattacharyya 2011

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PRC1 protein regulator of cytokinesis 1 + Bhattacharyya 2011 ABHD6 abhydrolase domain containing 6 + Gardner 2006 SGCG sarcoglycan, gamma + Gardner 2006 VAT1 vesicle amine transport 1 + Fang 2013 SGCD sarcoglycan, delta + Fang 2013 RCN2 reticulocalbin 2 - Fang 2013 LAMA2/4 laminin, alpha 2 /4 - Fang 2013; POSTN periostin, osteoblast specific factor - Bhattacharyya 2011 LTBP4 latent TGF binding protein 4 - Fang 2013 TFP1 transferrin pseudogene 1 - Whitfield 2013 KIF11 kinesin family member 11 - Forrester 2013 CEMIP cell migration inducing protein HA binding - Forrester 2013 PLEKHA4 pleckstrin homology domain family A - Bhattacharyya 2011

Receptors KISS1R KISS1 receptor + Forrester 2013 OXTR oxytocin receptor + Forrester 2013

Table E. Specific genes up (+) and down (-) regulated in kidney fibrosis

GENE -/+ REF Pro-fibrotic SEMA4C semaphorin 4C + Zeng 2011 NRP2 neuropilin 2 + Schramek 2009 LUM lumican + Silverstein 2003

Inflammation TOLLIP toll interacting protein - Park 2010

Immune system ZAP70 zeta-chain (TCR) associated protein + Wu 2012 kinase LCP2 lymphocyte cytosolic protein2 + Wu 2012 C8G complement 8 gamma polypeptide - Wang 2013 IL1B interleukin 1, beta + Silverstein 2003

Enzymes CBR3 carbonyl reductase 3 + Wu 2012 PLA2G4A phospholipase A2 group4A + Wu 2012 NUCKS1 nuclear casein kinase 1 + Wang 2013 DPYS dihydropyrimidinase - Wang 2013 PRODH2 proline dehydrogenase - Wang 2013 FTCD formimidoyltransferase - Wang 2013 DIO1 deiodinase, iodothyronine 1 - Wang 2013 TSTv thiosulfate sulfurtransferase - Wang 2013 DMGDH dimethylglycine dehydrogenase - Wang 2013 SARDH sarcosine dehydrogenase - Wang 2013 G6PC glucose-6-phosphatase, catalytic subunit - Wu 2012 Signal transduction CEBPE CCAAT/enhancer binding protein + Wu 2012 BRCA1 breast cancer 1 + Wang 2013 PBRM1 polybromo 1 + Wang 2013 XPO1 exportin 1 + Wang 2013 EHF ets homologous factor + Wang 2013 AFF4 AF4/FMR2 family, mb 4 + Wang 2013 FOXP1 forkhead box P1 + Park 2010 FOXP3 forkhead box P3 + Si H 2008 FOXA2/F2 forkhead box A2/F2 + Wang 2013 AGFG1 ArfGAP with FG repeats 1 + Si H 2008 NFIL3 nuclear factor, interleukin 3 regulated + Si H 2008 SRY sex determining region Y + Si H 2008 PTTG1IP pituitary tumor-transforming 1 interac + Si H 2008 GABPA GA binding protein transcription factor IRF1 + Si H 2008

206

interferon regulatory factor1 + Si H 2008 ELK4 ETS-domain protein + Si H 2008 PBX1 preB-cell leukemia homeobox1 + Silverstein 2003

Protein secreted/coding AQP7 aquaporin7 - Silverstein 2003 CDKN3 cyclin-dependent kinase inhibitor 3 - Silverstein 2003 KAP Kidney androgen-regulated protein - Silverstein 2003 CNN1 calponin 1, basic, smooth muscle + Silverstein 2003 DES desmin + Silverstein 2003 DNM1 dynamin 1 + Silverstein 2003 HSPB1 heat shock 27kDa protein 1 + Wu 2012 GLIPR2 GLI pathogenesis-related2 + Wang 2013 GMFB glia maturation factor b + Wang 2013 CACYBP calcyclin binding protein + Wang 2013 TRIM2 tripartite motif containing2 + Wang 2013 MTPN myotrophin + Wang 2013 SBNO1 strawberry notch homolog 1 + Wang 2013 VAMP7 vesicle-associated membrane 7 + Wu 2012 CDHR2 cadherin-related family mb 2 - Wu 2012 AQP1/7 aquaporin1/7 - Wu 2012 FHL1 four and a half LIM domains 1 - Wu 2012 UMOD uromodulin - Wu 2012 CALML4 calmodulin-like 4 - Wu 2012 TENM4 teneurin transmembrane protein 4 - Wu 2012 NEK2 nima related kinase 2 + Wu 2012 NEK7 nima related kinase 2 + Wu 2012 Wu 2012 Receptors PRLR prolactin receptor - Wang 2013

207

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