Proteomic Analyses of Kidney Glomerular Extracellular Matrix in Health and Disease

Proteomic analyses of kidney glomerular

extracellular matrix in health and disease

A thesis submitted to the University of Manchester for the degree of Doctor of Philosophy in the Faculty of Medical and

Human Sciences

2014

Michael Randles

School of Medicine

Institute of Human Development 2

Contents

List of figures ...... 7

List of tables ...... 9

Abstract ...... 10

Declaration ...... 11

Acknowledgments ...... 12

Rationale for an alternative thesis format ...... 14

Key Abbreviations ...... 15

1 General introduction ...... 16

1.1 Overview of the thesis ...... 16

1.2 The kidney ...... 17

1.3 Glomerular filtration ...... 19

1.3.1 The glomerular filtration barrier ...... 19

1.3.2 Size and charge selective glomerular filtration ...... 22

1.3.3 The albumin retrieval hypothesis ...... 22

1.3.4 The electrokinetic model ...... 23

1.4 Microalbuminuria ...... 24

1.5 Cellular components of the glomerulus ...... 25

1.5.1 The glomerular endothelium ...... 25

1.5.2 Podocytes ...... 26

1.5.3 Mesangial cells ...... 28

1.5.4 Parietal epithelial cells ...... 28

1.6 Glomerular extracellular matrix ...... 31 3

1.6.1 The glomerular basement membrane ...... 31

1.6.2 The mesangial extracellular matrix and Bowman's capsule ...... 36

1.6.3 Regulation of the extracellular matrix by transforming growth factor beta ...... 36

1.6.4 The Matrisome ...... 38

1.7 Podocyte adhesion ...... 38

1.7.1 Integrin biology ...... 39

1.7.2 Integrin α3β1...... 41

1.7.3 CD151 ...... 41

1.7.4 Syndecans ...... 42

1.7.5 Dystroglycan ...... 42

1.7.6 Integrin mediated adhesion complexes ...... 43

1.7.7 The adhesome ...... 46

1.7.8 Slit diaphragm ...... 48

1.8 Mass spectrometry-based proteomics ...... 53

1.9 Thesis aims ...... 55

2 Defining the glomerular matrisome ...... 58

2.1 Introduction ...... 58

2.2 Statements ...... 59

2.3 Global analysis reveals the complexity of the human glomerular extracellular matrix ...... 60

2.3.1 Abstract ...... 61

2.3.2 Introduction ...... 62

2.3.3 Results ...... 64

2.3.4 Materials and Methods ...... 76

2.3.5 Acknowledgments ...... 81

2.3.6 Statement of competing financial interests ...... 81

2.3.7 Supplementary data ...... 82 4

3 Glomerular cell-derived matrices ...... 91

3.1 Introduction ...... 91

3.2 Statements ...... 92

3.3 Glomerular cell crosstalk influences the composition and assembly of extracellular matrix93

3.3.1 Abstract ...... 94

3.3.2 Introduction ...... 95

3.3.3 Results ...... 96

3.3.4 Discussion ...... 110

3.3.5 Methods ...... 112

3.3.6 Acknowledgments ...... 119

3.3.7 Statement of competing financial interests ...... 120

3.3.8 Supplementary data ...... 121

4 Genetic background alters glomerular matrix ...... 141

4.1 Introduction ...... 141

4.2 Statements ...... 142

4.3 Genetic background is a key determinant of glomerular extracellular matrix composition and

organisation ...... 143

4.3.1 Abstract ...... 144

4.3.2 Introduction ...... 145

4.3.3 Results ...... 146

4.3.4 Methods ...... 160

4.3.5 Acknowledgements ...... 166

4.3.6 Conflict of interest ...... 167

4.3.7 Supplementary data ...... 168

5 Podocyte adhesion complexes ...... 177

5.1 Introduction ...... 177 5

5.2 Statements ...... 178

5.3 The influence of extracellular matrix ligand on the assembly of podocyte adhesion complexes

179

5.3.1 Abstract ...... 180

5.3.2 Introduction ...... 181

5.3.3 Results ...... 183

5.3.4 Discussion ...... 203

5.3.5 Materials and Methods ...... 205

5.3.6 Acknowledgements ...... 209

5.3.7 Conflict of interest ...... 209

6 Nephrin associated protein complexes ...... 210

6.1 Introduction ...... 210

6.2 Statements ...... 211

6.3 The nephrin interactome ...... 212

6.3.1 Abstract ...... 213

6.3.2 Introduction ...... 213

6.3.3 Results ...... 214

6.3.4 Discussion ...... 231

6.3.5 Materials and Methods ...... 232

6.3.6 Acknowledgements ...... 238

6.3.7 Conflict of interest ...... 238

7 General discussion ...... 239

7.1 Summary of findings ...... 239

7.2 The glomerular ECM ...... 241

7.2.1 The human glomerular ECM ...... 241

7.2.2 Cell derived ECMs ...... 242 6

7.2.3 Genetic background, sex and the glomerular ECM ...... 242

7.2.4 The use of mass spectrometry to study extracellular matrix ...... 244

7.3 Adhesion signalling complexes ...... 245

7.3.1 Cell-ECM adhesion complexes ...... 245

7.3.2 Cell-cell adhesion complexes ...... 246

7.3.3 The use of mass spectrometry to study adhesion complexes ...... 247

7.4 Conclusion ...... 248

8 General materials and methods ...... 249

8.1 General reagents and equipment ...... 249

8.2 Cell culture media, reagents and plasticware ...... 249

8.3 General Buffers ...... 250

8.4 Western blotting ...... 250

8.5 Image processing ...... 252

8.6 Mass Spectrometry ...... 252

8.6.1 Mass spectrometry reagents and equipment ...... 252

8.6.2 MS data acquisition ...... 253

8.6.3 In-gel proteolytic digestion ...... 253

8.6.4 Offline peptide desalting ...... 254

9 References ...... 256

CD containing movies ...... 278

Word count: 73, 317 7

List of figures

Figure 1.1 Overview of the kidney and the nephron ...... 18

Figure 1.2 Schematic cross section of the glomerulus and the glomerular filtration barrier ...... 21

Figure 1.3 Podocyte slit diaphragm and adhesion genes mutated in nephrotic syndrome ...... 27

Figure 1.4 Common histological changes which occur in glomerular disease ...... 30

Figure 1.5 Glomerular basement membrane components ...... 34

Figure 1.6 Laminin and collagen binding integrins ...... 40

Figure 1.7 Integrin mediated adhesion complex ...... 47

Figure 1.8 Overview of the podocyte slit diaphragm ...... 52

Figure 2.1 Isolation of enriched glomerular ECM...... 65

Figure 2.2 MS analysis of enriched glomerular ECM fractions...... 66

Figure 2.3 Comparison of the glomerular ECM proteome to published glomerular proteomic datasets

...... 68

Figure 2.4 Interaction network analysis of human glomerular ECM...... 70

Figure 2.5 Localisation of glomerular ECM proteins in the Human Protein Atlas (HPA) database. .71

Figure 2.6 Co-localisation of novel and known glomerular ECM proteins...... 73

Figure S2.7 Gene ontology (GO) enrichment analysis of the full MS dataset...... 88

Figure S2.8 Glomerular ECM interaction network analysis...... 89

Figure S2.9 The most connected proteins in the ECM interaction network...... 90

Figure 3.1 Glomerular cells synthesise and organise ECM in vitro ...... 97

Figure 3.2 GEC and podocytes in vitro have differential ECM composition...... 99

Figure 3.3 GEC and podocyte ECM interaction networks...... 101

Figure 3.4 Glomerular ECM in vitro resembles a developmental phenotype...... 103

Figure 3.5 Glomerular cell coculture is associated with altered ECM organisation...... 104

Figure 3.6 Coculture ECM interaction network...... 106

Figure 3.7 The composition of coculture ECM resembles glomerular ECM in vivo ...... 107

Figure 3.8 Model of the glomerular ECM interactome...... 109

Figure 3.9 GEC ECM interaction network analysis...... 136

Figure 3.10 Podocyte ECM interaction network analysis...... 137 8

Figure 3.11 Podocytes and GEC are viable in coculture...... 138

Figure 3.12 The effects of coculture on ECM deposition and cell-cell junctions...... 139

Figure 3.13 Coculture ECM interaction network analysis...... 140

Figure 4.1 Glomerular transcriptomics...... 147

Figure 4.2 Genetic background is a regulator of ECM composition...... 149

Figure 4.3 Topological network analysis of glomerular ECM protein interactions...... 151

Figure 4.4 Network analysis of enriched glomerular ECM proteins...... 152

Figure 4.5 Validation of glomerular ECM proteins regulated by genetic background or sex...... 154

Figure 4.6 Structural abnormalities in glomerular ECM in disease-susceptible FVB mice...... 155

Figure 4.7 GO enrichment map and pathway analysis...... 157

Figure 4.8 Workflow of glomerular ECM enrichment...... 172

Figure 4.9 Unconnected glomerular ECM proteins with enrichment due to strain or sex...... 173

Figure 4.10 Known GBM components do not change in a robust strain- or sex-dependant manner

...... 174

Figure 4.11 GO enrichment analysis of proteomic data-set ...... 175

Figure 5.1 Podocyte attachment to ECM ligands ...... 184

Figure 5.2 Laminin and collagen IV adhesion influence podocyte morphology ...... 186

Figure 5.3 Collagen IV and laminin influence podocyte adhesion complex size and number ...... 188

Figure 5.4 Isolation of adhesion complexes ...... 190

Figure 5.5 Podocyte adhesion complex composition ...... 196

Figure 5.6 GO enrichment analysis ...... 200

Figure 5.7 Podocyte barrier function assessed by electrical cell-substrate impedance sensing (ECIS)

...... 202

Figure 6.1 In silico analysis of the nephrin interactome ...... 216

Figure 6.2 Generation of Nephrin-FLAG and N ∆CT-FLAG cell lines ...... 221

Figure 6.3 Development of nephrin complex isolation assay ...... 224

Figure 6.4 A nephrin signalling complex identified by mass spectrometry ...... 225

Figure 6.5 Nephrin integrin β1 merged interactome ...... 230 9

List of tables

Table 2.1 Human glomerular ECM proteome ...... 82

Table 3.1 Glomerular endothelial cell ECM proteome ...... 121

Table 3.2 Podocyte ECM proteome ...... 126

Table 3.3 Coculture ECM proteome ...... 132

Table 4.1 Mouse glomerular ECM proteome ...... 168

Table 5.1 Adhesome proteins identified by MS ...... 192

Table 5.2 Non-adhesome proteins identified by MS ...... 197

Table 6.1 The nephrin interactome ...... 217

Table 6.2 Novel nephrin signalling proteins ...... 226

Table 8.1 List of antibodies ...... 251

Table 8.2 Chapter specific methods ...... 254 10

Abstract

University of Manchester

Name: Michael Randles

Degree title: Doctor of Philosophy

Thesis title: Proteomic analyses of kidney glomerular extracellular matrix in health and disease

Date: 2014

Glomerular filtration is a vital physiological process removing waste products from the circulation and this process occurs across the glomerular filtration barrier (GFB). The cells and extracellular matrix (ECM), which form this barrier, are exposed to forces during ultrafiltration and special adaptation is required to withstand these forces. Dysfunction in cellular adhesion machinery or ECM assembly within the GFB causes loss of selective glomerular filtration, however, the mechanisms governing these processes are poorly understood. To this end we sought to characterise the glomerular ECM and adhesion machinery using high throughput mass spectrometry (MS)-based proteomics.

MS of human glomerular ECM identified a highly complex extracellular niche, revealing the potential involvement of novel ECM proteins in glomerular development and disease processes. Furthermore we identified that glomerular cells in culture had distinct ECM proteomes and interestingly, coculture experiments demonstrated that the ECM proteome was influenced by cellular crosstalk and had a closer resemblance to glomerular ECM in vivo . Protein network analyses of in vivo and in vitro ECM datasets revealed a common core of highly connected structural ECM proteins that may be important for glomerular ECM assembly. To understand how this ECM proteome altered in disease, we studied mice with mild glomerular dysfunction. Here, transcriptomic and proteomic analyses identified alterations in ECM composition and 3D electron microscopy revealed striking ultrastructural changes in glomerular ECM.

MS-based proteomics was next applied to the analysis of glomerular podocyte adhesion complexes, leading to the discovery that the actin cytoskeletal regulators and trafficking machinery are recruited to adhesions sites in an ECM-ligand dependent manner. Furthermore, these differences functionally altered cell shape and adhesion strength. These same analyses were applied to podocyte cell-cell junctions, revealing an unexpected overlap of cell-ECM and cell-cell adhesion machinery.

Overall, these findings demonstrate for the first time the complexity of the glomerular ECM and adhesion signalling complexes and reinforce the benefits of global, unbiased experimental approaches. In addition the results suggest that glomerular ECM composition, organisation and adhesion signalling are context dependent, and therefore, represent potential therapeutic targets. 11

Declaration

No portion of the work referred to in the thesis has been submitted in support of an application for another degree or qualification of this or any other university or other institute of learning .

The following four notes on copyright and the ownership of intellectual property rights must be included as written below: i. The author of this thesis (including any appendices and/or schedules to this thesis) owns certain copyright or related rights in it (the “Copyright”) and s/he has given The University of

Manchester certain rights to use such Copyright, including for administrative purposes. ii. Copies of this thesis, either in full or in extracts and whether in hard or electronic copy, may be made only in accordance with the Copyright, Designs and Patents Act 1988 (as amended) and regulations issued under it or, where appropriate, in accordance with licensing agreements which the

University has from time to time. This page must form part of any such copies made. iii. The ownership of certain Copyright, patents, designs, trademarks and other intellectual property (the “Intellectual Property”) and any reproductions of copyright works in the thesis, for example graphs and tables (“Reproductions”), which may be described in this thesis, may not be owned by the author and may be owned by third parties. Such Intellectual Property and Reproductions cannot and must not be made available for use without the prior written permission of the owner(s) of the relevant Intellectual Property and/or Reproductions. iv. Further information on the conditions under which disclosure, publication and commercialisation of this thesis, the Copyright and any Intellectual Property and/or Reproductions described in it may take place is available in the University IP Policy

(see http://www.campus.manchester.ac.uk/medialibrary/policies/intellectual-property.pdf ), in any relevant Thesis restriction declarations deposited in the University Library, The University Library’s 12

regulations (see http://www.manchester.ac.uk/library/aboutus/regulations ) and in The University’s policy on presentation of Theses . Acknowledgments

Foremost, I would like to thank my supervisors, Rachel Lennon and Adrian Woolf, for giving me the fantastic opportunity to work on this project. Rachel has an infectious enthusiasm which has helped me enormously throughout my PhD. Furthermore, Rachel has given me numerous opportunities to present my work at national and international conferences, which has been integral to my development. Adrian has had a key contribution to my development as a scientist, both in terms of planning experiments and in communicating ideas. Both Rachel and Adrian have been instrumental in directing this project and giving me good advice throughout my PhD.

Next, I would like to thank Hellyeh Hamidi, who taught me a great deal. Martin Humphries and Patrick

Caswell, who have also given me fantastic advice throughout my PhD. Adam Byron and Jonathan

Humphries (funniest man alive) who were both instrumental in the developing the mass spectrometry approaches that I have used over the course of my PhD. I would also like to thank David Long for his collaborations and for accommodating me in his lab.

I would like to thank Maryline Fresquet for teaching me the art of protein purification. All the members of the Lennon Lab for fantastic input during data meetings: James McCaffrey, Salman Hosawi and

Stephanie Murphy. All members of the Caswell and Humphries lab old and new: Mr Morgan, Guilluame

Jacquemet, Janet Askari, Matthew Jones, Sue Craig, Ewa Koper, Nikki Howay Paul, Joe Robertson,

Edward Horton, Ruth McDowall, Eve Blumson, Angelique Stuani, Dave The Green Machine, Rebecca

Bridgewater, Joe Hetmanski and David Bradley. I would also like to thank Kids Kidney research for funding. I would like to thank Scott Cowan and co for keeping me sane during writing through the medium of football.

I would like to thank my family: Christopher, Shelley, Ethan, Nan, Grandad, Dad and most of all my

Mum who have always supported me.

Finally, I would like to thank my wonderful girlfriend Liz Scott. 13

Rationale for an alternative thesis format

There are several reasons for presenting the thesis in an alternative format. Firstly, the alternative format is good practice to prepare data into paper format, which is a vital skill for early career researchers. Secondly, because data is organised as papers, work can be published more quickly after the completion of my PhD. Finally, my project covers distinct areas of research that are linked by the approach used (i.e using proteomics to study extracellular matrix, cell-extracellular matrix adhesion and cell-cell adhesion), therefore dividing up each area into a separate papers prepares the reader for these different areas of research. 15

Key Abbreviations

ACE Angiotensin converting enzyme ARB Angiotensin receptor blocker B6 C57BL/ 6JOlaHsd BM Basement membrane CRAPome Contaminant Repository for Affinity Purification ECIS Electrical cell-substrate impedance sensing ECM Extracellular Matrix EM Electron microscopy FAK Focal adhesion kinase FGF2 Fibroblast growth factor 2 FSGS Focal segmental glomerulosclerosis FVB FVB/NHanHsd GAG Glycosaminoglycan GAP GTPase activating protein GBM Glomerular basement membrane GDI Guanine nucleotide dissociation inhibitor GEC Glomerular Endothelial cell GEF Guanine-nucleotide exchange factor GEnC Glomerular endothelial cell GFB Glomerular filtration barrier GFR Glomerular filtration rate GO Gene Ontology HPA Human Protein Atlas HSPG Heparan sulphate proteoglycan ILK Integrin linked kinase MMP Matrix metalloproteinase MS Mass spectrometry PAN Puromycin aminonucleoside PCA Principal component analysis PEC Parietal epithelial cell PRIDE PRoteomics IDEntifications SBF-SEM Serial block face scanning electron microscopy TEM Transmission electron microscopy

1 General introduction

1.1 Overview of the thesis

The human glomerular capillaries are fascinating; within the kidney, glomeruli perform an essential filtration process, removing waste products from the circulation, whilst retaining essential plasma components within the circulation. As a result, glomeruli have adapted unique cellular and extracellular matrix (ECM) structures. There is an enormous interest in understanding the biology of the glomerulus, as loss of glomerular function has become a major worldwide healthcare problem, for which there are currently no targeted therapies. Glomerular filtration occurs across a composite three layered capillary wall, termed the glomerular filtration barrier (GFB). Within this structure, adhesion of the two cellular layers to the extracellular niche is crucial; defects in the glomerular adhesion machinery, or ECM, results in loss of glomerular function. The ECM and mechanisms of adhesion within the glomerulus have been studied using candidate based approaches. However, holistic understanding of ECM and adhesion signalling mechanisms within the glomerulus is lacking. In order to address this, global unbiased approaches, such as proteomics, have enormous potential.

The specific proteome of a tissue, cell type, organelle or ECM is a major determinant of its function.

Consequently, mass spectrometry (MS)-based proteomics can generate large data-sets that contain important protein level information about the cell type or organelle studied. Extracting this information from these large data-sets poses a major challenge. Applying bioinformatic approaches, such as hierarchical clustering and protein-protein interaction network analysis can provide holistic understanding of the biological system studied. Furthermore, these analyses can direct hypothesis generation and identify key proteins or signalling networks, and as a result reveal unexpected biology.

Excitingly, approaches to isolate ECMs from tissues and cells 1-3 and adhesion complexes from cells 4-6 are rapidly improving, enabling targeted proteomic studies of ECM and adhesion. The primary focus of this thesis is the use of proteomic approaches to study ECM and adhesion signalling within the glomerulus, with the aim of providing a more comprehensive understanding of glomerular ECM and adhesion signalling mechanisms. 17

1.2 The kidney

Human kidneys have essential roles in homeostasis, including the removal of metabolic waste products from the circulation, the control of intravascular volume and the regulation of extracellular pH.

Additionally, the kidneys secrete the enzyme renin and a number of hormones including erythropoietin and calcitriol. Renin is involved in the renin-angiotensin-aldosterone signalling axis, which controls blood pressure. Erythropoietin is essential for the differentiation of hematopoietic stem cells down the erythroid linage and calcitriol is involved in reabsorption of calcium and bone metabolism. Loss of kidney function with disease compromises these vital biological processes. Chronic kidney disease is a significant worldwide healthcare problem, costing the National Health Service an estimated £1.4 billion per year in the United Kingdom, which is more than lung, skin, colon and breast cancer combined. 7

The individual functional units of the kidney are the nephrons. Nephrons are intricate structures composed of: a capillary network, called a glomerulus; Bowman’s capsule, which encapsulates the glomerular tuft; and a tubular network, where urine is modified and concentrated (Figure 1.1). A healthy adult human kidney contains between 1 million and 1.5 million nephrons. 8 Blood enters the kidney via the paired renal arteries, which branch and supply the glomerular capillaries via the afferent arterioles.

In the glomerular capillaries the blood is filtered, producing an ultrafiltrate that enters Bowman’s space.

Filtered blood exits the glomerulus via the efferent arteriole and the kidney via the renal vein. Under normal physiological conditions the electrolyte composition of the ultrafiltrate is similar to plasma, however, large macromolecules and proteins are absent. This is because the glomerular capillary wall is freely permeable to water and small solutes, but impermeable to larger molecules, such as albumin.

In a 70 kg adult human, glomerular filtration produces approximately 180 litres of ultrafiltrate every day.

In the proximal tubule most of the water, glucose, salts and amino acids are reabsorbed from the ultrafiltrate into the peritubular capillaries, whilst some toxins are actively secreted into the ultrafiltrate.

Ultimately, the human kidneys produce between 1 and 1.5 litres of urine each day.

Figure 1.1 Overview of the kidney and the nephron The renal artery supplies unfiltered blood to the kidney. This blood enters the glomeruli via afferent arterioles where it is filtered producing an ultrafiltrate or 'primary urine' which enters Bowman's space. Filtered blood exits the glomerulus via the efferent arteriole and kidney via the renal vein. The primary urine produced by ultrafiltration is processed by the proximal tubule where most of the water and salts are reabsorbed. In the loop of Henle the urine is concentrated by a countercurrent multiplier system. The urine then passes through the distal tubule which links to the collecting duct system, here K+, Na+, Ca++ and pH undergo additional regulation. Finally, the urine is collected and is propelled out of the kidney into the urinary bladder via the ureter. 19

1.3 Glomerular filtration

Glomerular filtration is the process of producing virtually protein free ultrafiltrate, which is further processed in the tubules to produce urine. Loss of glomerular filtration is common to a range of kidney diseases. Deciphering the mechanism of normal glomerular filtration is prerequisite to understanding the pathogenesis of diseases that affect the glomerulus. The process of glomerular filtration requires the generation of forces within the glomerulus, which the cells and ECM are specially adapted to cope with. As a result, adhesion and ECM within the glomerulus are important topics of research.

1.3.1 The glomerular filtration barrier

Glomerular filtration occurs across the glomerular capillary wall, also known as the GFB, which is the highly specialised interface between the vasculature and the urinary space. This barrier is composed of three major components: the glomerular endothelium, the glomerular basement membrane (GBM) and the glomerular visceral epithelial cells (podocytes) (Figure 1.2). Glomerular filtration is determined by the following parameters: the permeability of the glomerular filtration surface (K), the surface area available for filtration (S), the hydrostatic pressure in the glomerular capillaries (P GC ), the hydrostatic pressure in Bowman’s capsule (P T), the colloid oncotic pressure in the glomerular capillaries ( ∏GC ) and the colloid oncotic pressure in Bowman’s space ( ∏T). The glomerular filtration rate (GFR) can be defined as:

9 GFR = K S [(P GC – P T) – ( ∏GC -∏T)].

In humans, the normal rate of glomerular filtration is > 90 mL/minute. Blood enters and exits the glomerulus via arterioles; therefore, the pressure in the glomerular capillaries is higher than in typical capillaries. Normally, renal autoregulation prevents systemic increases in blood pressure from perturbing GFR. Renal autoregulation is intrinsic to the kidney, leading to vasoconstriction and vasodilatation of the arterioles to maintain GFR. 10, 11 However, a decrease in the surface area available for glomerular filtration, for example with reduced numbers of nephrons or sclerosis of glomeruli, causes a reduction in GFR. In addition, perturbation of the permeability properties of the GFB leads to leakage of plasma proteins, such as albumin, into the urine referred to as proteinuria or albuminuria respectively. 20

The GFB performs a dual role whereby it not only filters blood to remove metabolic waste products from the circulation, but also retains large molecules within the circulation. Water, small solutes and proteins with molecular weights < 40 kDa pass freely into the ultrafiltrate; in fact the permeability of the

GFB to water is orders of magnitude higher than in other capillary beds. 12 In contrast, macromolecules with molecular weights > 40 kDa are highly restricted, for example the albumin (molecular weight 69 kDa) concentration in plasma is 40, 000 mg/L, but the albumin concentration in urine is only 4 mg/L. 13

Reduction in GFR and an increase in proteinuria are risk factors for both renal and cardiovascular disease. 14 It is therefore critical to build an understanding of the mechanisms of glomerular filtration and GFB permeability properties, in order to appreciate the causes of loss of GFB functionality.

Figure 1.2 Schematic cross section of the glomerulus and the glomerular filtration barrier The glomerular capillaries are lined on the inside with fenestrated endothelial cells. Attached to the outside of the glomerular capillaries, with their large cell bodies inside Bowman's space , are the glomerular podocytes. Podocyte foot processes interdigitate to form a highly ordered structure, with specialised cell junctions called slit diaphragms in-between. Both glomerular endothelial cells (GECs) and podocytes attach to the glomerular basement membrane (GBM), a thick extracellular matrix (ECM) that is sandwiched in-between the two cellular layers . Mesangial cells and associated ECM hold capillary loops together by interacting with the GBM and with the glomerular endothelium. Parietal epithelial cells (PECs) and their associated basement membrane form Bowman's capsule. The glomerular filtration barrier (GFB) comprises three major layers: the glomerular endothelium (and glycocalyx), the GBM and podocyte foot processes.

1.3.2 Size and charge selective glomerular filtration

Classically, the mechanism of selective glomerular filtration is thought to be due to the size and charge of filtered molecules. Size selectivity of the barrier is well established; for neutral solutes, glomerular sieving coefficients become smaller with increasing molecular size. 13, 15-17 Electron microscopy of the glomerular podocyte led to the discovery of the podocyte slit diaphragm, an electron dense cell-cell junction. 18, 19 Researchers then hypothesised that podocyte slit diaphragms could act as the size excluding layer of the GFB. Consistent with this hypothesis, the cross-sectional dimension of slit diaphragm pores is approximately the size of an albumin molecule. 18, 19 Furthermore, mutations in podocyte slit diaphragm genes cause proteinuria. 20-22 However, proteinuria can occur in the absence of podocyte morphological changes. 23-25 Indeed, proteinuria can occur regardless of which layer of the

GFB is damaged. 13, 26

Selectivity of the GFB based on charge is supported by tracer studies, which demonstrate that negatively charged molecules are impeded more than neutral molecules when crossing the GFB. 15, 27

However, the exact location of the putative charge barrier is controversial; the GBM, 28, 29 endothelial glycocalyx 30, 31 and the podocyte coating protein podocalyxin 32 have all been proposed as charge barriers. Interestingly, when renal plasma flow is stopped, albumin can rapidly pass into the Bowman's space by diffusion,33 which suggests that flow is required for permselectivity. Another interesting property of the GFB is that despite exquisite permselectivity, preventing the passage of an estimated

250,000 kg of plasma proteins in a human lifetime, 34 the GFB does not clog. Several anti-clogging mechanisms have been proposed including: uptake of plasma proteins by mesangial cells 35 or podocytes, 36 charge repulsion by the GBM, 37 tangential-flow filtration 38 and the gel permeation hypothesis.39, 40

1.3.3 The albumin retrieval hypothesis

Some researchers have proposed that the GFB is more permeable to albumin than is generally accepted and that albumin is in fact reabsorbed in the proximal tubule. This hypothesis is supported by measurements of albumin filtration taken using 2-photon microscopy, which suggest that non- proteinuric rats filter much larger amounts of albumin into Bowman's space than recorded by 23

micropuncture experiments. 41 If this were true , albuminuria would have to be reinterpreted as a marker of tubular dysfunction rather than glomerular dysfunction. However, subsequent studies using 2-photon microscopy found that the GFB was indeed highly impermeable to albumin. 42, 43 In addition, only small

amounts of albumin are detected in the urine when tubular protein adsorption is inactivated ,44, 45

whereas when glomerular structure is compromised large amounts of albumin escape into the urine. 20-

22 23-25

1.3.4 The electrokinetic model

An attractive new model of glomerular filtration, the electrokinetic model, has recently been proposed. 34

This hypothesis is supported by the measurement of an electric field of –0.045 mV per 10 cm H 2O across the GFB of the common mudpuppy. 46 This electric field is thought to be a 'streaming potential' generated by the forced filtration of ionic solutes [Na +, K +, Ca 2+ , Cl – and HCO3–] across the GFB.

Streaming potentials are phenomena produced by the tangential motion of a fluid containing small ions along the charged surface of a filter. The glomerular endothelium coat, or glycocalyx, represents a negatively charged surface, as does the GBM, and both of these structures could be important for the induction of streaming potentials across the GFB. This potential difference may cause electrophoresis of negatively charged molecules, forcing them towards the capillary lumen. This offers an anti-clogging mechanism in the form of active electrophoresis and an explanation for the loss of selectivity when plasma flow is absent.

Although, further studies are required to refine and unify the proposed mechanisms of selective glomerular filtration, numerous studies have now established that all three layers of the GFB are critical for permselectivity and that no one layer is the definitive 'glomerular filter'. It is therefore crucial to study all three layers of the GFB to understand the pathologies associated with proteinuria. 24

1.4 Microalbuminuria

The GFB must remain almost completely impermeable to plasma proteins throughout a human lifetime.

Leakage of plasma proteins into the urine is referred to as proteinuria. In humans this specifically means the escape of > 300 mg/day of plasma proteins into the urine. Proteinuria is a clinical manifestation of glomerular disease, in addition to haematuria and reduced GFR. The combination of these clinical features specifies a variety of clinical syndromes; nephritic syndrome, nephrotic syndrome and chronic renal failure. Nephritic syndrome is characterised by proteinuria, haematuria, oedema and hypertension. Nephrotic syndrome, however, is characterised by severe proteinuria > 3.5 g/day in adults (detectable via dipstick urinalysis), hypoalbuminemia, peripheral oedema and hypercholesterolaemia.47

Some small loss of plasma proteins, for example albumin, does occur in healthy individuals. Typically,

< 30 mg/day of albumin escapes into the urine of a healthy individual. However, this figure is variable, and elevated excretion of albumin between 30 - 300 mg/day, is referred to as microalbuminuria. In individuals with diabetes mellitus microalbuminuria precedes, and may also predict, progression to diabetic nephropathy. 48, 49 In addition, microalbuminuria is an independent risk factor for cardiovascular disease. 14

A series of observations support genetic background as a determinant of albumin excretion. Firstly, in the United States of America, non-Hispanic blacks and Mexican-Americans have a greater incidence of microalbuminuria compared with whites. 50 Secondly, normotensive black individuals excrete lager amounts of albumin compared with white individuals.51 Moreover, these increases in albumin excretion are associated with an increased risk of end-stage renal disease in black individuals compared with

Hispanic and white individuals. 52-55 In addition to racial background, sex also influences the degree of albumin excretion. In both the Netherlands and the United Kingdom it has been reported that men have a higher albumin excretion rate than women. 22, 23 These data suggest that the composition of the GFB may be dependent on racial background and sex. If this is the case, understanding these intrinsic 25

differences may provide insights into the determinants of GFB permeability and facilitate more targeted personalised treatment of glomerular disease. 1.5 Cellular components of the glomerulus

Classically, the glomerulus is thought to contain four major cell types: glomerular endothelial cells

(GECs), podocytes, mesangial cells and parietal epithelial cells (PECs). All of these cells produce, organise and adhere to ECM to form the unique structure of the glomerulus (Figure 1.2). Tight regulation of the ECM and adhesion by glomerular cells is required for normal glomerular function.

1.5.1 The glomerular endothelium

The glomerular endothelium is the first barrier to the passage of fluid and macromolecules during glomerular filtration. It is a continuous endothelium within which each GEC contacts adjacent GECs via cell-cell junctions. The glomerular endothelium contains transcellular cytoplasmic holes, called fenestrae, that are 70-100 nm in diameter and do not possess diaphragms. 56 Fenestrae make up 20-

50% of the glomerular endothelial surface (Figure 1.2), 57, 58 an adaptation that enables high water permeability, which is essential for glomerular filtration. 59 The diameter of these fenestrae is much greater than the diameter of albumin, which suggests that fenestrae have a limited role in the size restriction of albumin.

The glomerular endothelium has an apical cell surface glycocalyx comprising transmembrane proteoglycans, glycoproteins, sialic acids, 60 hyaluronan and adsorbed plasma proteins (Figure 1.2). 26 ,

30, 60, 61 This extracellular structure is a 200 nm thick meshwork that confers negative charge to the glomerular endothelium and has been shown to contribute to the selective passage of albumin across the GFB. 60, 62 This is consistent with either a charge selective role, or in generating streaming potentials as suggested by the electrokinetic model. 30, 60 The endothelial glycocalyx could also be important in glomerular disease; the glycocalyx volume is reduced in individuals with type 1 and type 2 diabetes mellitus, suggesting a possible role for the glycocalyx in the glomerular complications of these diseases. 63-65

1.5.2 Podocytes

The second cellular layer of the GFB is the podocyte. Podocytes are terminally differentiated visceral epithelial cells that reside within the urinary space. They have large cell bodies that are detached from the underlying GBM; instead, podocyte adhesion to the GBM is mediated by long microtubule based primary foot processes and actin rich secondary foot processes (Figure 1.2). Podocyte foot processes interlink and form specialised cell-cell junctions called slit diaphragms. These junctions have a width of

30-45 nm. 18 In 1998 the discovery of nephrin led to major interest in the biology of the glomerular podocyte. NPHS1 , the gene encoding nephrin, is mutated in a severe form of congenital nephrotic syndrome. 66 Nephrin is a 180 kDa transmembrane immunoglobulin superfamily member, which is localised to podocyte slit diaphragms. 67-69 Further studies have led to the discovery that numerous genes encoding slit diaphragm proteins are mutated in genetic forms of nephrotic syndrome (Figure

1.3).

Loss of slit diaphragms and podocyte foot process architecture are major histological features of glomerular diseases. Indeed, minimal change disease is associated with nephrotic syndrome, but a normal appearance of glomeruli at light microscopy. However, with electron microscopy, loss of podocyte slit diaphragms and foot processes can be appreciated (Figure 1.4). Loss of podocyte foot processes may in fact be a mechanism to protect the glomerulus from the loss of podocytes.

Podocytes are unique in that their cell bodies are detached from the underlying GBM, only their foot processes mediate adhesion. When podocytes are stressed they may be stimulated to adhere more tightly to the GBM to prevent detachment. As a consequence, specialised podocyte morphology and function is lost. A plethora of evidence supports podocyte foot processes and slit diaphragms as essential structures for selective glomerular filtration. Therefore, the GBM and podocyte adhesion to the GBM are clearly important areas of research. 27

Figure 1.3 Podocyte slit diaphragm and adhesion genes mutated in nephrotic syndrome List of podocyte genes (in bold) that cause glomerular abnormalities when mutated in humans and/or mice. Genes are sorted into slit diaphragm, ECM and adhesion or actin regulators. Gene names represented as proteins in schematic. Slit diaphragm: NPHS1, nephrin; NPHS2, podocin; TRPC6, short transient receptor potential channel 6; CD2AP, CD2-associated protein; PLCE1, 1-phosphatidylinositol 4,5-bisphosphate phosphodiesterase ε1. Actin regulators: MYH9, myosin heavy chain 9; MYO1E, unconventional myosin Ie; ARHGDIA, Rho GDP-dissociation inhibitor 1; ARHGAP24, Rho-GTPase- activating protein 24; ACTN4, α-actinin-4; INF2, inverted formin-2. ECM and adhesion: ILK, integrin linked kinase; TLN1, talin1; ITGA3, integrin α3; COL4A3, COL4A4, COL4A5, type IV collagen a3, a4 and a5 respectively; LAMB2, laminin β2.

1.5.3 Mesangial cells

In addition to the glomerular cells that form the GFB, there are two other types of resident glomerular cells. Mesangial cells are structural pericyte-like cells, which help maintain the integrity of the GFB.

They sit between glomerular capillary loops directly contacting GECs (Figure 1.2). Their overall contribution to permselectivity is unknown, but thought to be small. 13 Although GFR is controlled predominately at the level of the afferent and efferent arterioles, mesangial cells are involved in fine tuning GFR through contraction and growth factor secretion. 70-73

Mesangial cells are the target for the immune mediated disease IgA nephropathy. IgA nephropathy is caused by the deposition of aberrantly glycosylated IgA in the mesangium. 74, 75 As a result, mesangial cells are activated to proliferate and secrete increased amounts of ECM, causing proteinuria.

Proteinuria is usually associated with dysfunction in GFB components; therefore, proteinuria in IgA nephropathy may provide evidence for crosstalk between the mesangium and GFB components. 76 In a number of pathologies, including diabetic nephropathy, proliferation and expansion of mesangial cells and associated ECM contribute to glomerulosclerosis and reduce GFR by decreasing the surface area available for glomerular filtration (Figure 1.4). 77

1.5.4 Parietal epithelial cells

PECs are resident glomerular cells which together with their basement membrane form Bowman's capsule (Figure 1.2). 78 Podocytes and PECs arise from the same progenitor metanephric mesenchyme population during development. 79 Eventually, PEC and podocyte phenotypes diverge and they begin to express different marker proteins; podocytes express nephrin 67 and WT1 80 , and PECs express Pax-281,

82 and Claudin-1. 83, 84 No glomerular disease has thus far been found to be a PEC specific disease. 85

However, in some glomerular diseases, PECs can become injured or activated causing them to migrate more and produce more ECM (Figure 1.4). PEC activation can lead to a lesion where the glomerular tuft fuses to Bowman’s capsule. In this situation PECs attach to GBM when left bare, for example by podocyte detachment, which could be a common starting point of glomerulosclerosis in a wide range of glomerulopathies. 86, 87 29

Podocytes do not proliferate under normal conditions or in glomerular diseases where podocytes are lost from the glomerular tuft. 88 However, under certain circumstances podocytes can be regenerated, even in the absence of podocyte proliferation, which suggests the existence of a reservoir of podocyte progenitors. 89 PECs have been touted as possible progenitor cells, in addition to cells of the renin linage, 90 because PECs express progenitor cell markers such as a glycosylated isoform of CD133 and

CD24. 91 Moreover, cells lining Bowman's basement membrane can express podocyte markers or show podocyte ultrastructure in normal human glomeruli, these cells are referred to as parietal podocytes 92,

93 or ectopic podocytes. 94 In some cases these cells coexpress markers of differentiated podocytes and

PECs such as WT1, nephrin, Pax-2 and Claudin-1. 95 However, evidence from experimental animal models suggests that these cells are podocytes that have migrated away from the glomerular tuft onto

Bowman's basement membrane rather than PECs differentiating into podocytes. 95-97 30

Figure 1.4 Common histological changes which occur in glomerular disease Histological changes that are common to a range of glomerular diseases include: thickening of the GBM, mesangial proliferation and ECM expansion, podocyte foot process effacement, detachment of podocytes, sclerosis and overall glomerular hypotrophy. In combination these histological changes can result in a complete loss of function within the glomerulus. ECM, extracellular matrix; GBM, glomerular basement membrane; PEC, parietal epithelial cell. 31

1.6 Glomerular extracellular matrix

The ECM is essential for multicellular life providing a structural scaffold with appropriate mechanical properties to support adjacent cells. 98 It comprises a complex network of glycosaminoglycans and fibrous proteins, which are synthesised and secreted by cells. Podocytes and GECs adhere to the

GBM via cell surface receptors. This cell-ECM interface forms a signalling platform that controls all aspects of cell fate decisions including shape, growth, differentiation and survival. 98, 99 In addition to this signalling platform, the ECM modulates cell-cell signalling by sequestering secreted growth factors and cytokines, forming reservoirs for appropriate spatiotemporal release. 98 Thus the composition of ECM within the glomerulus is fundamental, and studying the glomerular ECM is required for a comprehensive understanding of glomerular biology.

1.6.1 The glomerular basement membrane

Basement membranes are thin sheets of ECM with a supramolecular assembly structured around laminin and collagen IV networks. In the glomerulus ECM is organised as the GBM of the capillary walls, the basement membrane of Bowman’s capsule and the loosely associated mesangial ECM between capillary loops (Figure 1.2). The mature GBM is thicker than most basement membranes

(300-350 nm in humans) and is formed by the fusion of two distinct basement membranes, one produced by podocytes the other by GECs .100 The study of human glomerular disease led to the discovery of tissue-restricted isoforms of laminin and collagen IV in the mature GBM , and these are key components of this specialised ECM .101, 102 In a wide range of glomerulopathies the GBM becomes thickened and irregular (Figure 1.4); these changes in the GBM remain poorly understood.

1.6.1.1 Laminins

Laminins are self-assembling heterotrimeric glycoproteins ,103, 104 which are absolutely required for basement membrane formation. 105, 106 All laminin heterotrimers contain one α, one β and one γ chain.

Trimeric laminin has a cruciform shape with one long arm and three short arms (Figure 1.5). The short 32

arms contain the N-terminal (LN) domains of the laminin heterotrimer and the long arms contain the C- terminal globular laminin G-like (LG) domains (Figure 1.5) .107-111 The short arms form the nodes within the laminin network via a three-arm interaction mode; interactions between the N-terminal regions of one α, one β and one γ chain are required for formation of ternary nodes in the network (Figure 1.5) .112-

114 The cell surface receptor binding sites within laminin trimers are mostly localised within the long arm

LG domains, especially LG1-5 of the α chain .107-111

Laminin isoforms within the GBM undergo developmental transitions. The primitive GBM contains laminin α5β1γ1 (laminin-511), the mature GBM however, comprises predominately laminin-521. 101 A complete laminin-521 network is required for a functional GBM; mutations in LAMB2 , the gene encoding the laminin β2 chain, cause Pierson syndrome in humans. Affected individuals have a spectrum of pathology with genotype-phenotype correlation. Truncating mutations cause congenital nephrotic syndrome, microcoria, muscular hypotonia and neurodevelopmental deficit. 115, 116 Lamb2 mutations in mice are also associated with glomerular dysfunction. Mice with null mutations die after 3 weeks of age with severe proteinuria and neuromuscular defects. 117 Interestingly, these mice develop proteinuria before podocyte morphological defects occur, which supports a role of the GBM in restricting macromolecules directly, rather than via modulating cellular phenotype. Over time however, podocyte morphological changes do occur and proteinuria becomes more severe. Furthermore, these animals have accumulation of ectopic laminin chains in the GBM, including α1, α2, α3, β1, β3, and γ2, however, these chains do not compensate for the loss of the β2 chain, possibly due to low expression or the absence of a complete laminin network. 25 The theory that insufficient expression of laminin chains accounts for the observed lack of compensation is supported by the finding that podocyte overexpression of Lamb1 in Lamb2 null mice ameliorates proteinuria. 118

1.6.1.2 Collagen IV

Unlike the laminin network, the collagen IV network is dispensable for basement membrane formation , however , the basement membranes formed lack strength and stability. 119 Collagen IV forms heterotrimers composed of three alpha chain combinations ( α1α1α2, α3α4α5 or α5α5α6). Each alpha chain contains three distinct domains: an N-terminal 7S domain rich in cysteines and lysines, which enables tetramer formation and inter-chain cross -linking; a long collagenous repeat domain, around 33

1400 amino acids in length; and a carboxy terminal non-collagenous domain (NC1), which forms dimers (Figure 1.5) .120 In combination, these interactions lead to the formation of a ‘chicken wire’ meshwork of collagen IV.

A novel chemical bond, not previously identified in biomolecules, the sulfilimine bond (-S=N-), was recently discovered in collagen IV. 121 This bond cross -links lysine/hydroxylysine-211 and methionine-

93 of adjoining protomers in the NC1 domains of both collagen IV α1α1α2 and α3α4α5. This bond may provide additional resistance of the network to mechanical strain. Furthermore , peroxidasin, an enzyme found in basement membranes, catalyses the formation of the sulfilimine bond .122 Ionic bromide is a cofactor required for peroxidasin-catalysed formation of the sulfilimine cross -links in collagen IV networks, 123 which is the first known essential function for ionic bromide in animals.

As with laminin a developmental transition in collagen IV isoforms is critical for GBM maturation. The primitive GBM contains predominantly the ubiquitous collagen IV α1α1α2, whereas the mature GBM contains collagen IV α3α4α5. 102 Mutations leading to a reduction or absence of the α3α4α5 networks cause human Alport syndrome, characterised by a renal phenotype of haematuria, proteinuria and progressive renal failure. 120, 124 The GBM in Alport syndrome has increased collagen IV α1α1α2, but this isoform is unable to compensate for the lack of the α3α4α5 network. As a consequence the GBM develops splits and a typical basket-weave appearance, leading to speculation that mechanical strain cannot be tolerated by the GBM in the absence of collagen IV α3α4α5. Potentially, the reduced numbers of disulphide bonds in the α1α1α2 relative to α3α4α5 network could explain their differing capacities in coping with mechanical strain. This concept is further supported by the observation that reducing mechanical strain in the glomerulus with angiotensin-converting-enzyme (ACE) inhibitors, which lower blood pressure as well as transcapillary filtration pressure, significantly delays disease progression in Alport syndrome. 125-127 34

Figure 1.5 Glomerular basement membrane components Panels display schematics of: laminin heterotrimer, laminin network, collagen IV heterotrimer, collagen IV network and the basic assembly of the glomerular basement membrane (GBM). The laminin-521 network in the mature GBM is located close to the podocyte and glomerular endothelial cell (GEC) surfaces. The collagen IV α3α4α5 network is located deeper within the GBM. Agrin is located close to the podocyte and GEC surfaces and interacts with cell surface receptors and the laminin network. Nidogen and perlecan likely occupy the region between the laminin and collagen IV and act to crosslink these two networks.

1.6.1.3 Additional glomerular basement membrane components

Evidence suggests that the laminin and collagen IV networks are indirectly linked via nidogens 128 and the heparan sulphate proteoglycans, perlecan, 129, 130 and agrin (Figure 1.5) .131 Podocyte specific deletion of agrin from the GBM results in a significant reduction in the negative charge associated with the GBM, however , alone or combined with knockout of perlecan, agrin deletion does not cause proteinuria. The lack of proteinuria in this context suggests the GBM has a limited role in charge selection during glomerular filtration .132 Nidogen 1 and 2 are dumbbell shaped proteins that reportedly bind to both laminin and collagen IV. Mice with knockout of nidogen 1 or 2 are viable and have normal basement membranes. Deletion of both isoforms, however, causes perinatal lethality. 133 This is consistent with a degree of redundancy in their ability to bind collagen IV and laminin. Surprisingly, the

GBM has a normal appearance even in the double ( Nid-1, Nid-2) knockout. This suggests that nidogen is dispensable for the formation of the GBM, but it may be required for the GBM to resist mechanical strain.

1.6.1.4 Orientation of proteins in the glomerular basement membrane

To elucidate the position of ECM proteins within the GBM a systematic analysis of ECM components within basement membranes, with respect to each other and to their receptors, was performed in vivo using super resolution microscopy .134 This investigation found two separate laminin networks, one adjacent to the podocytes the other adjacent to the glomerular endothelium (Figure 1.5). The collagen

IV α3α4α5 network was distributed along the centre of the GBM alongside nidogen, consistent with the putative cross -linking function of nidogens (Figure 1.5). The human GBM is approximately two-fold thicker than the mouse GBM and interestingly this study found increased thickness of the human collagen IV α3α4α5 network, and potentially, an additional layer of laminin-521 closer to the centre of the GBM. 134 36

1.6.2 The mesangial extracellular matrix and Bowman's capsule

In addition to the GBM, mesangial cells produce ECM that holds capillary loops together and PECs synthesise the basement membrane that encapsulates the glomerular tuft. The ECM produced by mesangial cells is composed of mostly basement membrane components, but is not organised into a basement membrane structure. Compositionally, mesangial ECM contains predominately collagen IV

α1α1α2 rather than collagen IV α3α4α5. As a result these collagen chains can be used to distinguish the GBM and mesangial ECM compartments. In addition, the mesangial ECM contains laminins, fibronectin, nidogen, perlecan and decorin. 135 Mesangial cells also interact with the GBM; interactions between mesangial cells and laminin α5LG3-5 in the GBM are required for organisation of glomerular capillary loops. 136

PECs produce the Bowman's basement membrane, which is multilayered and contains collagen IV

α5α5α6, in addition to the ubiquitously expressed collagen IV α1α1α2. 137 The basement membrane of

Bowman's capsule has a defective appearance in hereditary angiopathy with nephropathy, aneurysms, and muscle cramps (HANAC) syndrome. 138 This disease is caused by glycine mutations in COL4A1 the gene that encodes collagen IV α1. Individuals with this disease have a number of extrarenal pathologies which reflect the ubiquitous expression of collagen IV α1α1α2. This disease demonstrates the importance of Bowman's basement membrane and the collagen IV α1α1α2 network within it.

Conversely, the glomerular basement membrane is largely unaffected in individuals with HANAC syndrome, which emphasises that the collagen IV α3α4α5 is the important collagen IV network within the GBM.

1.6.3 Regulation of the extracellular matrix by transforming growth factor

beta

Named originally for its ability to induce a transformed phenotype in cultured cells, 139, 140 transforming growth factor beta (TGF-β) is a dimeric cytokine. TGF-β comprises three isoforms (-β1, -β2 and -β3), that are part of a larger family of 33 proteins, 141 which include bone morphogenetic proteins, growth 37

and differentiation factors, activin and nodal. TGF-β signalling can stimulate or inhibit growth in a context dependent manner and is required for normal development. 142, 143 Moreover, TGF-β is involved in diverse cellular processes including differentiation, wound healing and angiogenesis. In addition to its role in normal processes, increases and decreases in production and/or activation of TFG-β lead to numerous disease sates including cancers, atherosclerosis and fibrotic disease of the kidney, liver, and lung. Fibrosis is defined as an excessive deposition of extracellular matrix (ECM), which leads to the destruction of organ architecture and impairment of organ function. 144

TGF-β proteins have widespread expression; nearly every cell in the human body secretes TGF-β and expresses receptors for it, however, TGF-β activity is tightly regulated. TGF-β is generated and secreted in a latent form and activation is localized to sites where TGF-β is released from latency.

Large aminoterminal prodomains aid folding and dimerisation, furin cleaves this prodomain, but even after secretion it remains associated with TGF-β through non-covalent interactions conferring latency to

TGF-β. In addition, this latency associated peptide (LAP) targets TGF-β for storage in the extracellular matrix, either in complex with latent TGF binding proteins (LTBPs) or fibrillins. 145, 146 TGF-β activation is regulated by thrombospondin-1 147, 148 and by binding of αV integrins to the prodomain leading to exertion of force upon this domain. 149 _ENREF_9

Once activated TGF-β binds to serine/threonine type II receptors; at the cell surface two type II receptors and two type I receptors form an active TGF-β family receptor. 150 Different type I/II receptor combinations allow TGF-β ligands to induce different signalling pathways, with different ligands having preference for different receptor combinations. The signalling complexity is enhanced by a plethora of accessory binding proteins which can enhance or modify ligand binding specificity. Activation of type

I/II heterodimers by TGF-β ligands causes the recruitment and phosphorylation of Smad proteins, in vertebrates there are eight Smad proteins. Smad2 and Smad3 are receptor Smads, which are phosphorylated through an interaction with activated type I receptors. These proteins bind to Smad4 and this complex tanslocates into the nucleus where it regulates gene expression. In contrast, Smad6 and Smad7 are inhibitory Smads which perform an autoregulation mechanism, controlling ligand induced signalling. 151 38

TGF-β is one of the most potent regulators of the ECM. TGF-β stimulates fibroblasts and other cell types to produce fibronectin, collagens I,III,V,VI, Proteoglycans, tenascin, osteonectin, osteopontin and thrombospondin, in addition to inhibitors of ECM degrading enzymes such as plasminogen-activator inhibitor type 1 and tissue inhibitor of metalloprotease. Moreover, it also represses the expression of

ECM degrading enzymes: collagenase, heparinase, and stromelysin. 152 Overall, active TGF-β increases ECM deposition and reduces ECM turnover, hence when dysregulated TGF-β is a driver of fibrosis in a number of disease states. As such TGF-β is potentially a major therapeutic target in fibrotic diseases such as scleroderma, 153 diabetic nephropathy 154 and renal fibrosis. 155

1.6.4 The Matrisome

Recent predictions suggest that the human ECM proteome (matrisome) contains 1065 components, expressed in a tissue dependent manner. 1 MS-based proteomic analyses of aorta, lung and colon tissue have revealed tissue restricted matrisomes containing 103-143 proteins. 1, 156 A small number of

ECM proteins have been identified as key determinants of glomerular function, however, it remains possible that many more may contribute. Global analysis of glomerular ECM will build upon the previously studied proteins and may provide unexpected insights to glomerular biology.

1.7 Podocyte adhesion

For most cells, including the cells of the glomerulus, adhesion is absolutely required for cell survival.

Adhesion sites do not act as mere anchoring points for cell attachment, rather they form a signalling nexus that controls all aspects of cell fate decisions. 157 Podocytes and endothelial cells express, like all adherent cells, transmembrane adhesion receptors. Adhesion receptors contain extracellular domains, which bind to specific ECM ligands or other cell surface receptors, and cytoplasmic domains that recruit effector proteins. Recruitment of proteins to the cytoplasmic domains of adhesion receptors leads to the formation of macromolecular signalling complexes. It is through these complexes that the

ECM controls cellular fate. 98, 99 Both cell-ECM and cell-cell contacts regulate the actin cytoskeleton through direct mechanical linkage and the recruitment of effector proteins. 157, 158 Exquisite control of the actin cytoskeleton is required to produce and maintain the elegant morphology of podocyte foot 39

processes. 159 Thus to understand the mechanisms of diseases which affect podocyte morphology, a comprehensive understanding of both the glomerular ECM and adhesion is required.

1.7.1 Integrin biology

A major family of proteins responsible for cell-ECM adhesion are the integrins . Integrins are αβ - heterodimers that propagate signals from outside the cell to the cell interior, whilst also transducing intracellular signals to the extracellular environment. Integrins form 24 different αβ combinations, 99 which have different affinities for ECM ligands and recruit different proteins to their cytoplasmic domains. 160 All integrins link to the actin cytoskeleton, with the exception of α6β4 which links to intermediate filaments. 99 Laminin binding integrins include: α1β1, α2β1, α3β1, α6β1, α10 β1, α7β1 and

α6β4, whereas the collagen binding integrins are: α1β1, α2β1, α10 β1, α11 β1 and αXβ2 (Figure 1.6) .161

Furthermore, integrin α3β1, α6β4 have a preference for laminin-511 and 521, which are present in the

GBM. 107 In contrast, α1β1 and α2β1 have higher affinities for collagen IV and collagen I respectively. 162,

163

Upon integrin engagement of the ECM, integrins cluster and become activated. Conformational changes in integrins are central to the regulation of their activity. Integrins adopt either a low affinity bent conformation, a primed, active high affinity extended conformation or a ligand occupied state. 164

Integrins lack intrinsic enzymatic activity; therefore, propagation of signals by integrins occurs via the recruitment of a number of adaptor and effector proteins into sites known as focal adhesions. At least

232 protein components are recruited to adhesion complexes in a cell type and context dependent manner, demonstrating the potential for adhesion signalling to bring about different cellular outcomes. 165 Furthermore , global analysis of adhesion complexes using MS suggest an even larger number of proteins may be recruited to focal adhesions. 4-6 These proteins include: scaffolds, adaptors, actin remodelling proteins, signalling proteins, guanine-nucleotide exchange factors (GEFs) , GTPase activating proteins (GAPs), Guanine nucleotide dissociation inhibitors (GDIs), serine threonine and tyrosine kinases and phosphatases. Furthermore, integrin association and crosstalk with other transmembrane receptors , such as syndecans and growth factor receptors, increase the potential for cellular regulation by adhesion complexes .166, 167 40

Figure 1.6 Laminin and collagen binding integrins The α1, α2, α10 and α11 integrin subunits all contain an inserted A-domain. This family of integrins bind collagen and laminin. α1β1 and α2β1 have preference for collagen IV and collagen I respectively. Non A-domain containing laminin binding integrins include: α6β4, α 3β1, α 6β1 and α 7β1.

1.7.2 Integrin ααα3βββ1

The laminin binding integrin α3β1 heterodimer is the most highly expressed integrin on the podocyte cell surface, and it is an indispensible link between podocyte foot processes and the GBM. 168-170 In humans homozygous mutations in ITGA3, the gene encoding integrin α3, leads to congenital nephrotic syndrome with defects in the GBM , interstitial lung disease and epidermolysis bullosa. 171 In addition, a mutation in ITGA3 causing a gain of glycosylation which prevents α3β1 dimer formation causes fatal interstitial lung disease and congenital nephrotic syndrome. 172 This phenotype is recapitulated in mice lacking the integrin α3 subunit, which die within the first day of life due to developmental defects in the lungs and kidneys. 173 These defects include loss of podocyte foot processes and disorganised thickened GBMs. The renal phenotype caused by mutations ITGA3 is probably due to loss of integrin

α3β1 from the podocyte cell surface, rather than other glomerular cells, because podocyte specific deletion of Itga3 in mice results in the same GBM and podocyte phenotype as the total knock out .174

Human mutations affecting integrin-β1 have not been described to date and this may be due to embryonic lethality since integrin-β1 forms at least 12 heterodimers. However , the role of this integrin has been studied with podocyte specific deletion of Itgb1 in the mouse. This resulted in a severe phenotype of proteinuria from birth and renal failure by 3 weeks featuring both glomerular and tubular pathologies. 175, 176

1.7.3 CD151

Humans with mutations in CD151 develop haematuria and proteinuria progressing to end stage kidney disease , in addition to pretibial epidermolysis bullosa, sensorineural deafness, and beta thalassaemia minor. 177 CD151 is a tetraspanin that binds tightly to integrin α3β1.178 In mice, deletion of Cd151, both globally and specifically in podocytes, causes early proteinuria, abnormalities of the GBM , loss of podocyte foot processes, glomerulosclerosis and renal failure. This phenotype, however, is dependent on the genetic background of the mice, with Cd151 -knockout mice on the FVB background displaying the pathological phenotype. 174, 179, 180 In contrast, Cd151 -knockout mice on the C57BL/6 background do not spontaneously develop renal failure , but when challenged with induced hypertension they develop 42

significant proteinuria. Furthermore, treatment of the susceptible Cd151 -knockout FVB strain with ACE inhibitors ameliorates progression of renal failure. In addition to in vivo experiments, in vitro experiments show that podocytes lacking CD151 lose their resistance to shear stress when cultured on laminin. 179 It is therefore likely that CD151 increases the strength of podocyte integrin α3β1-laminin-

521 adhesion complexes. This evidence supports a crucial role for integrin α3β1 as a major adhesion receptor, and in combination with CD151, a complex necessary for podocytes to withstand mechanical forces within the glomerulus.

1.7.4 Syndecans

In addition to the integrin family of adhesion receptors, transmembrane heparan sulphate proteoglycan receptors, such as the syndecan family, are key regulators of cell-ECM interactions. 167 Cooperation of integrins and syndecans in adhesion formation has been shown on a variety of ECM ligands including fibronectin, vitronectin and laminin. 181-184 Syndecans regulate integrin trafficking to the cell surface, 185 a process used by cells to regulate adhesion formation and disassembly. 186-188 In addition to modulating integrin dynamics, syndecans facilitate growth factor binding to their receptors. 189, 190 In podocytes null for EXT1 , a key molecule required for heparan sulphate glycosaminoglycan assembly, adhesion complexes are reduced in size, the actin cytoskeleton is rearranged and cell surface syndecan 4 is upregulated. 191 However, mice null for Ext1 specifically in podocytes do not develop significant proteinuria, despite some podocyte abnormalities, including a degree of foot process effacement. 192 In podocytes, autocrine signalling by the soluble vascular endothelial growth factor receptor, sFLT1, causes actin rearrangements in podocytes and this is associated with phosphorylation of both syndecan 1 and 4. 193 Thus , there is accumulating evidence that syndecans contribute to cell-matrix adhesion and signalling in podocytes.

1.7.5 Dystroglycan

Dystroglycan is a cell surface adhesion receptor that comprises a highly glycosylated extracellular

α− dystroglycan subunit and a non-covalently linked intracellular β− dystroglycan subunit . The

α− dystroglycan subunit binds to laminins and β− dystroglycan links to the actin cytoskeleton via an 43

interaction with utrophin. 194, 195 Dystroglycan is expressed on podocytes 196 and the expression pattern is altered in glomerular pathologies. 197, 198 As a result it seemed likely that dystroglycan was important for podocyte adhesion, however, defective glycosylation of α− dystroglycan which abrogates

α− dystroglycan-laminin interactions does not cause proteinuria, only mild podocyte foot process effacement .199 Furthermore, podocyte specific deletion of dystroglycan in mice causes only mild GBM thickening. 200 These data suggest that dystroglycan is not a critical adhesion receptor in podocytes.

1.7.6 Integrin mediated adhesion complexes

1.7.6.1 Talin 1

A number of proteins link integrins to the actin cytoskeleton. One such protein is talin-1, a 270 kDa protein comprising an N-terminal globular head and flexible rod domain. The head domain contains binding sites for integrin-β subunit cytoplasmic tails, F-actin, focal adhesion kinase (FAK) and

PIPK1 γ90. The rod domain contains additional binding sites for integrin, actin , the Rap1 effector RIAM and multiple vinculin binding sites. 201 Finally , the C-terminal domain contains helices responsible for talin-1 dimerisation. Binding of talin-1 to the cytoplasmic tail of β-integrins triggers a conformational change in the extracellular domain of integrins, which amplifies the affinity of the integrin for the ECM.

Talin-1 dependent recruitment of further proteins to active integrin triggers the formation of focal adhesions. 202

Talin-1 expression in podocytes is required for the specialised actin morphology of foot processes.

Podocyte specific Tln1 -knockout mice develop proteinuria and die within 10 weeks. 203 These mice, however, do not have major defects in integrin β1 activation or podocyte adhesion. Nevertheless, the actin cytoskeleton is perturbed and podocyte foot processes are lost. These data show that talin-1, a protein known to be important in focal adhesion formation and linkage to the actin cytoskeleton in vitro , is important for relaying signals from integrins to the actin cytoskeleton in podocytes in vivo .203

1.7.6.2 Vinculin

Another key adaptor protein involved in integrin mediated adhesion complex formation is vinculin.

Vinculin is a 123 kDa protein that is recruited by talin to focal adhesions and binds to the actin cytoskeleton. 204 Vinculin comprises an N-terminal head, proline rich neck and a C-terminal tail domain. 205, 206 Cytoplasmic vinculin assumes an autoinhibited inactive conformation ,207 but following recruitment to adhesion sites by talin, vinculin is extended by forces applied through the actin cytoskeleton, revealing an open active state. 208 This conformational change allows vinculin to directly interact with a number of proteins including α-actinin, Arp2/3, actin and paxillin. 209, 210 Considering vinculin is an important link between integrins and the actin machinery this protein may have a key role in force sensing by podocytes via integrin α3β1.

1.7.6.3 Paxillin

Paxillin is another component of focal adhesions that acts as a scaffolding protein. It contains multiple protein-binding modules, many of which are regulated by phosphorylation. Paxillin localises to focal adhesions when phosphorylated, demonstrating the importance of phosphorylation in controlling adhesion signalling .211, 212 Paxillin is an important molecular adaptor; its N-terminus controls most of its signalling activity and provides docking sites for vinculin, FAK, Src and Crk1/2. Moreover, paxillin brings about spatiotemporal control of Rho family small GTPases by recruiting numerous GEFs and

GAPs. 213 Although paxillin is known to be an important focal adhesion protein, it has yet to be studied in the glomerulus.

1.7.6.4 Focal adhesion kinase

Another highly studied focal adhesion protein is FAK, a non-receptor tyrosine kinase, which is recruited to focal adhesions by talin and paxillin. 214 FAK has a number of roles at focal adhesion sites including recruitment of p130Cas, Crk1/2 and Src-family kinases. 215 Global deletion of FAK in mice is lethal in embryogenesis, causing a profound migration defect.216 The importance of FAK in podocytes was highlighted by the observation that FAK is phosphorylated upon podocyte injury. 217 Surprisingly , podocyte-specific deletion of FAK in mice leads to a normal phenotype; however, these mice are 45

protected from proteinuria and podocyte injury after experimental podocyte insult. 217 Additionally, FAK inhibition reduces podocyte injury in a mouse model of glomerular injury. 217

A role for FAK has also been found in Alport syndrome where ectopic laminins, α1 and α2, accumulate in the GBM. Laminin α2 causes phosphorylation of FAK at Y397 and this phosphorylation associates with the upregulation of the matrix metalloproteinases (MMPs) 9 and 10 , in addition to GBM defects. 218

FAK inhibition reduced proteinuria, MMP levels and GBM defects in this context .218 These data support a role for FAK in glomerular dysfunction.

1.7.6.5 Integrin linked kinase/PINCH/parvin

Integrin linked kinase (ILK) binds directly to the integrin β1 cytoplasmic tail and is important for signal transduction at adhesion sites. 219 ILK was originally identified as a kinase, but increasing evidence suggests that it is a pseudokinase. 220-225 In fact, the C-terminal kinase homology domain of ILK mediates multiple protein–protein interactions at adhesion sites, including interactions with α/β/γ -

Parvin. 226, 227 ILK also contains 5 ankyrin domains that mediate interactions with PINCH-1/2. 228-230

Kindlin 2 is another ILK interacting protein, which is expressed in podocytes. Kindlin 2 localises to focal adhesions and through ILK/PINCH/parvin , or migfilin–filamin binds to the actin cytoskeleton. 231-233 It is through this scaffolding role that ILK orchestrates focal adhesion signalling. The ILK/PINCH/parvin complex influences the actin cytoskeleton, 234 in addition to negatively regulating cell contractility. 235

A number of studies have shown that the ILK/PINCH/parvin axis is vital during development; total loss of ILK or PINCH in mice is lethal in embryogenesis, due to failure in epiblast polarisation .214, 216 Indeed, this axis is important for adhesion signalling in the glomerulus. The interaction between ILK and α- parvin is required for kidney development; mutations in ILK in the α-parvin binding site causes renal agenesis. 236 Furthermore, a similar phenotype is observed when α-parvin is genetically deleted in mice. 236 Podocyte-specific loss of ILK in mice causes GBM defects, loss of slit diaphragms and podocyte foot process effacement .237, 238 In addition to the role of ILK at cell-ECM adhesion sites, ILK interacts with nephrin at podocyte slit diaphragms.238 Therefore, ILK represents a potential link between cell-cell and cell-ECM adhesion signalling. Finally, increased expression of ILK is observed in 46

a variety of glomerular diseases. 239, 240 This evidence strongly supports a role for ILK in adhesion signalling in podocytes.

1.7.7 The adhesome

Overall , cell adhesion to the GBM occurs at a complex cell-matrix interface (Figure 1.7). A wide range of proteins localise to focal adhesions. Through the recruitment of these proteins integrins transmit information regarding the extracellular environment to the cell interior. A process that is undoubtedly vital for glomerular cells. The cellular adhesome has been predominantly investigated in the context of fibroblast-fibronectin adhesion complexes, but less so for epithelial cells .165 Similar analyses in glomerular cells will help to build our understanding about the key cellular components that are involved in cell-matrix adhesion in the glomerulus. 47

Figure 1.7 Integrin mediated adhesion complex The ECM and the cell cytoskeleton are physically linked through integrin mediated adhesion complexes. Top panel; simplified adhesion complex view displaying two major linkages from integrins to the actin cytoskeleton: talin/vinculin/paxillin and ILK/PINCH/parvin. Bottom panel; a predicated ECM- 48

adhesion complex protein-protein interaction network. Nodes (circles) represent proteins and edges (grey lines) represent reported protein-protein interactions. ILK, Integrin linked kinase. 1.7.8 Slit diaphragm

In addition to cell-ECM adhesion, cell-cell adhesion is also critical for the GFB. Slit diaphragms are the unique cell-cell junctions that connect the entire length of adjacent podocyte foot processes, forming a zipper-like substructure containing pores .18 The calculated cross-sectional dimensions of these pores is approximately the size of an albumin molecule; therefore, consistent with the observations from tracer studies , using ferritin and dextrans , that the slit diaphragm contributes to the retention of macromolecules within the circulation. 241 35 Interestingly, these pores increase in size with proteinuria, perhaps providing an explanation for the increased transit of proteins across the GFB in pathologies where foot processes appear normal .19

Podocytes change dramatically in human glomerular disease; their actin-rich foot processes flatten and slit diaphragms are lost. 159 Similarly , these changes are observed in animal studies of puromycin aminonucleoside (PAN) induced nephrotic syndrome. 242 Whilst these dramatic morphological changes are associated with a profound GFB defect, remarkably these changes seem to completely reverse , for example in the subset of patients with nephrotic syndrome who respond to treatment with glucocorticosteroids.

The first junctions to form in podocytes are apical and have been described as tight junctions. 243 During glomerular development the junctional complexes descend toward the GBM and widen to become the mature slit diaphragm. These are highly specialised and unique junctions and many studies have investigated the components that localise to slit diaphragms . The tight junction components: zona occludens protein (ZO-1) ,244 JAM-A, occludin and cingulin are associated with slit diaphragms and PAN nephrosis increases the expression of these tight junction components .245 Additionally, classical components of adherens junctions including cadherin-3 and α-, β-, γ-catenins 246 and the gap junction protein connexin-43 247 localise to the podocyte slit diaphragm . Moreover, connexin-43 is upregulated the early phase of PAN nephrosis. 247 Taken together, these findings suggest, not surprisingly, that there is context dependent composition of slit diaphragms.

1.7.8.1 Neph family proteins

The slit diaphragm is a hybrid of adherens and tight junction components, but it also contains unique proteins (Figure 1.8). Nephrin and the homologues Neph-1, Neph-2 and Neph-3 are members of the immunoglobulin superfamily of cell adhesion receptors that are involved in the development of specialised junctions in neurons and at the slit diaphragm. 248 Orthologs of these proteins are expressed in Drosophila nephrocytes, 249 which have diaphragm structures with very similar composition to the mammalian slit diaphragm. 250 Further back in evolution , C. Elegans express orthologs of NEPH1 and nephrin, called SYG-1 and SYG-2 respectively. SYG-1 and SYG-2 are required for synapse formation and specificity. Investigation of the crystal structures of these orthologs revealed SYG-1 homodimers with a conserved binding interface and an unusual, angled geometry in the heterophilic SYG1/2 complex. 251 The crystal structures of nephrin and Neph1-3 remain unresolved, however, there is evidence for these proteins interacting in both a heterophilic and homophilic manner (Figure 1.8). 252-255

Nephrin and Neph-1 are required for slit diaphragm formation. Mutations in nephrin cause human congenital nephrotic syndrome , with a high incidence in Finland , although many mutations have now been described in individuals with later onset of disease and from diverse ethnic backgrounds .256

Infants require albumin infusions to maintain intravascular volume and ultimately proceed to removal of their kidneys prior to dialysis and transplantation. This disease phenotype is mimicked in the mouse , where deletion of Nphs1 leads to early proteinuria and death within 24 hours. These mice do not form slit diaphragms or normal podocyte foot processes .257 Neph1 deletion in mice is also associated with perinatal lethality with proteinuria and podocyte foot process effacement. 258 As yet no human mutations in NEPH1 have been described , but it is more widely expressed than nephrin; therefore , mutations may be incompatible with life.

1.7.8.2 Slit diaphragm signalling

Neph family of proteins are thought of as the signature slit diaphragm proteins, but there are additional proteins that are vital for slit diaphragm signalling including podocin, CD2AP and FAT atypical cadherin-1. Podocin is a stomatin family protein that is mutated in patients with early onset nephrotic syndrome .259 Moreover, podocin is important for the recruitment of proteins to the slit diaphragm complex and for facilitating signalling .260 CD2AP is an adaptor protein that is required for maintaining 50

the integrity of the GFB in mice 261 and humans, 262 in addition to the balance of receptor tyrosine kinase signalling in podocytes. 263 FAT atypical cadherin-1 also regulates GFB formation and mice lacking this component have significant glomerular defects, in addition to eye and brain abnormalities. 244

Phosphorylation of nephrin and Neph-family proteins by Src family kinases is key to signal transduction at the slit diaphragm. Indeed, deletion of the Src family kinase member, Fyn , results in GFB dysfunction. 264 Phosphorylation of nephrin by Fyn leads to the recruitment of a number of proteins including the adaptor proteins Nck1/2, 265, 266 Crk1/2, 267 CrkL ,268 Grb2 269, 270 and phosphoinositide 3-OH kinase (PI3-kinase) .271, 272 Following recruitment, Nck1/2 binds phosphorylated nephrin which initiates actin reorganisation via the actin nucleation factor N-WASP. 273 The receptor Robo2 also links to

Nck1/2 and is expressed in podocytes. 274 Robo2 inhibits actin reorganisation and it appears to negatively regulate nephrin and Nck1/2 signalling. Crk1/2 is recruited to phosphorylated nephrin via p130Cas. Deletion of Crk1/2 reduces podocyte foot process effacement in a glomerular injury model. 267

The p85 regulatory subunit of PI3-kinase also interacts with nephrin leading to downstream activation of Akt 271 and subsequently to actin reorganisation. 272 Emphasising the importance of signalling via Akt, deletion of the Akt2 causes podocyte dysfunction .275

Regulation of the podocyte actin cytoskeleton is key to maintaining barrier integrity. Actin reorganisation in podocytes is likely to relate directly to the dramatic podocyte foot process effacement, which is seen across the spectrum of glomerular disease. The actin cross -linking protein alpha-actinin-

4 has been associated with human nephrotic syndrome. Moreover, mutations in ACTN4 are associated with adult onset focal segmental glomerulosclerosis (FSGS) .276 These are gain of function mutations with the mutant alpha-actinin-4 protein binding filamentous actin more tightly than the wild-type protein.

This indicates that balanced actin regulation is important for normal podocyte function. Another class of actin regulators are the GTPases, which are in turn regulated by GEFs, GAPs and GDIs. Podocyte specific deletion of the GTPase RhoA does not cause barrier defects. 277 However , RhoA is activated in humans with mutations in the formin INF2, a common cause of adult onset FSGS. 278 The small

GTPase Rac1, which is required for lamellipodia formation, has a role in podocyte biology. Rac1 is not essential for glomerular development , but over expression leads to GFB dysfunction either with constitutive activation of Rac1 279 or RhoGDI-alpha knockout. 280 However , a number of studies have also shown that Rac1 activation protects the GFB from injury. 268, 281 Thus, it is likely that a fine balance 51

of Rac1 activity is required for GFB stability. The GTPase CDC42 is linked to the formation of filopodia and its absence results in early barrier dysfunction .277, 282 This may be due to links with apical-basal polarity proteins, which are also required for slit diaphragm formation. 283

The dynamic regulation of this specialised cell junction undoubtedly requires quality control and recycling of components . In rat glomeruli turnover rates of slit diaphragm proteins is high and regulated by atypical protein kinase C (aPKC). 284 Accordingly, the endocytic pathway components dynamin, synaptojanin and endophilin have been shown to be important for maintaining barrier function. 285 The correct localisation of proteins is also key; Neph1 and nephrin localisation at the plasma membrane requires the endocytic protein unconventional myosin-Ic. 286 The long tailed myosin, unconventional myosin-Ie may also contribute to endocytosis in podocytes. 287, 288 Finally, mutations in the transient receptor potential cation channel TRPC6 have been associated with adult onset FSGS, indicating the importance of calcium signalling at the slit diaphragm .289, 290

The slit diaphragm includes components that make the connections between adjacent podocyte foot processes extracellularly, in addition to the more dynamic network of proteins that assemble intracellularly (Figure 1.8). To identify novel components of this complex, unbiased approaches with

MS have led to the discovery of proteins including IQGAP , MAGI-2, CASK, spectrins and alpha- actinin .291 As methods to isolate and analyse silt diaphragms improve, more unexpected components may be identified. A recent bioinformatic analysis of the cadherin adhesome has predicted an assembly of 170 components, which provides an indication of the probable scale of nephrin signalling complexes .292 52

Figure 1.8 Overview of the podocyte slit diaphragm Slit diaphragms form a physical link between adjacent podocyte foot processes. A number of adaptor, signalling and cytoskeleton proteins are recruited to slit diaphragms. Top panel; proteins that are known to localise to podocyte slit diaphragms. Bottom panel; protein-protein interaction network view of known slit diaphragm components. The network is annotated with processes that slit diaphragm proteins are known to regulate. Nodes (circles) represent proteins and edges (grey lines) represent reported protein-protein interactions.

1.8 Mass spectrometry-based proteomics

To date candidate based approaches have advanced our understanding of glomerular biology, with laminins, nidogens, perlecan, agrin and type XVIII collagen being the most well studied glomerular

ECM components. Understanding the function of these molecules has been advanced through studies of genetic mutations in humans and animals in addition to targeted studies of these proteins at the level of tissue expression, single molecule structure and analysis of interactions with other biological molecules. These are irreplaceable scientific approaches, however, systems biology attempts to systematically study all concurrent processes in a tissue by the global measurement of differentially perturbed states and this approach has enormous potential to complement targeted studies.

Immunoaffinity-based approaches such as immunofluorescence microscopy, flow cytometry, enzyme- linked immunosorbent assay (ELISAs) and immunoblotting are central to the study of proteins in biological systems. One caveat of these approaches is that they rely on antibody affinity, antigen accessibility and the availability of reagents. Furthermore, these approaches do not enable the identification and quantification of hundreds of proteins in a single experiment or the discovery of novel proteins as the target protein is predetermined. As a result alternative techniques are required to enable the field of systems biology.

Analysis of the transcriptome with microarrays and RNA sequencing experiments enable global quantification of gene expression, and these approaches are employed for systems level analyses.

These analyses are frequently utilized as surrogates for protein abundance. However, although powerful, these techniques do not represent the true presence and abundance of proteins within a biological sample because protein expression is regulated through post-transcriptional mechanisms, such as synthesis and degradation. 293 In contrast proteomics allows the unbiased study of protein composition and can therefore be used to examine multiple concurrent processes occurring within a biological system.

Ideally, researchers would like to identify and quantify all proteins in biological sample under different experimental or disease conditions. However, the desire to achieve maximal coverage including low abundance proteins, and to probe the huge array of protein modifications necessitates highly sensitive analytical techniques. Mass spectrometry (MS) enables the analysis of proteins in a global manner. 294

The analytical instrument, a mass spectrometer, contains: an ion source, which generates charged gaseous analytes; a mass analyzer, which measures the mass-to-charge ratio (m/z) of analytes; and a detector that records the number of ions at each m/z value.

For analysis of proteins, the process of generating ions in the gas phase is most commonly achieved by either electrospray ionization (ESI) 295 or matrix-assisted laser desorption/ionization (MALDI). 296

These processes overcome the propensity of large molecules (such as proteins/peptides) to fragment when ionized, and are referred to as 'soft ionization techniques. ESI ionizes the analytes out of a solution and is therefore readily coupled to prior liquid-based separation tools, for example, chromatography. MALDI sublimates and ionizes proteins or peptides out of a dry, crystalline matrix via laser pulses. MALDI-MS is normally used to analyze relatively simple protein/peptide mixtures, whereas integrated liquid-chromatography ESI-MS systems (LC-MS) are preferred for the analysis of complex samples. An additional benefit of ESI is the production of multiply charged ions extending the m/z range of the mass analyser. The development of ESI in 1989 led to John B. Fenn sharing the 2002

Nobel prize in chemistry.

Following the generation of ions in the gaseous phase, ions are captured in mass analyzers. These devices operate by measuring the trajectories of ions in an electric field or by trapping ions for further manipulation. Examples of mass analyzers include quadrupole mass filters, time of flight instruments, fourier transform ion cyclotron analyzers and the Orbitrap. These analyzers can be stand alone or, more commonly, assembled in tandem to take advantage of their relative strengths.

Biological samples are complex and frequently contain thousands of proteins. In order to detect, and ideally quantify, all of the proteins present in a biological sample, proteins are digested to peptides

(frequently using enzymatic trypsin digestion) and these peptides are analyzed. By breaking down a 55

complex protein into its building blocks there is more chance of assigning a unique identification (ID).

An analogy here is the accurate weighing of all staff at a University. It would not be possible to give a unique ID based on their total body weight alone due to the limitation of accuracy and precision of the device used to weigh each individual and the large number of individuals. However, if each individual was asked to remove their coat, shoes and bag and each of these items were weighed separately, then the probability of assigning a unique ID to each member of staff would increase. To assign a unique ID to peptides, tandem MS (MS/MS), employs two mass analyzers in series. In the first analyzer precursor peptides are selected and analyzed. The exact mass of this precursor peptide is determined and then it is then fragmented along its backbone, usually by collision-induced dissociation

(CID) with an inert gas. These fragment ions are then detected in the second analyzer, giving rise to characteristic spectra. The fragment and precursor m/z measurements can be compared to protein database search engines. These databases have libraries of peptides predicted in silico from theoretical digests of the complete genome sequence of the organism of interest and therefore the identity of a peptide is thus inferred. The next challenge is to determine the original proteins from which the identified peptides have arisen. This is trivial in the case of peptides that are unique to a given protein, but pose a complex problem for peptides with sequences that are shared between proteins. As a result a range of software are now available for processing MS proteomic data and the most popular include; Mascot, 297 SEQUEST 298 and X!Tandem. 299

1.9 Thesis aims

In health the podocyte coordinates signals from the slit diaphragm and cell-ECM interactions. As our understanding of mechanisms that control cell adhesion in the glomerulus develops, then insight into the effects of disease will improve. The ultimate goal will be to develop targeted therapies to prevent and repair defects in the GFB and to restore glomerular function. Currently the glomerular ECM and podocyte adhesion have not been studied in a global manner.

The principal aim of this thesis is to identify, validate and characterise components of the glomerular

ECM and podocyte adhesion complexes in health and disease. This will be achieved by using mass spectrometry based proteomics and imaging techniques to:

1. Catalogue the components of the human glomerular ECM

Prior knowledge of the normal composition of the human glomerular ECM is prerequisite to understanding the changes that occur in the glomerular ECM during disease. Although glomeruli have been studied with MS, I hypothesise that an ECM enrichment strategy coupled with MS-based proteomics will enable the identification of a more comprehensive glomerular specific ECM proteome.

In turn this data-set will provide the framework for a more comprehensive understanding of glomerular

ECM biology.

2. Investigate the relative contribution of glomerular cells to the ECM

The glomerular basement membrane is a crucial structure within the glomerulus, but isolation of the

GBM is currently technically unfeasible. To study the GBM, GEC and podocyte cell-derived ECMs

(CDMs) are an alternative option. Proteomic analysis of GEC and podocyte CDMs has the potential to identify cell specific differences in ECM production, reveal the origin of GBM components, and therefore provide new insights into the regulation of the GBM.

3. Characterise the influence of genetic background, sex and degree of microalbuminuria on the

composition and organisation of glomerular ECM

Genetic background and sex determine the degree of albuminuria exhibited by an individual, but the reasons for this remain poorly understood. This project will utilise electron microscopy and proteomics to explore the ECM as a possible cause of sex- and genetic-dependent variation in microalbuminuria. I hypothesise that this analysis will identify structural and compositional differences within the glomerular

ECM, which may contribute to microalbuminuria.

4. Investigate podocyte cell-matrix adhesion complexes

The sophisticated function of glomerular filtration relies on podocyte adhesion at the interface with the

ECM. Adhesion underpins the regulation of the actin cytoskeleton, which in turn generates the labyrinthine podocyte foot process architecture. Using proteomics to identify differences between adhesion complexes formed by podocytes attached to GBM ligands, collagen IV and laminin, may provide important insights into normal podocyte adhesion signalling. Moreover, these analysis may discern possible mechanism pertaining to the pathogenesis of Alport and Pierson syndromes. I 57

hypothesise that this analysis will pinpoint fundamental differences in adhesion signalling on different

ECM ligands.

5. Investigate podocyte cell-cell adhesion complexes

In addition to cell-ECM adhesion, cell-cell adhesion is also critical within the glomerulus. Podocyte slit diaphragms are unique cell-cell junctions that are crucial for podocyte function. Nephrin is a key component of the slit diaphragm, but both low expression and biophysical properties have made this molecule challenging to study. The generation of cell lines expressing different nephrin constructs, coupled with proteomic approaches will be applied to the study of nephrin induced signalling complexes. Furthermore, these analyses may identify novel signalling pathways regulated by slit diaphragms. 58

2 Defining the glomerular matrisome

2.1 Introduction

The extracellular matrix (ECM) is a scaffold that creates the complex structure of the glomerulus, in addition to forming a signalling platform that orchestrates cell behaviour. Indeed, the glomerular ECM is critical for glomerular function as it is frequently altered in glomerular disease. Candidate based studies of the glomerular ECM have revealed tissue restricted isoforms of laminin ( α5β2γ1) and collagen IV

(α3α4α5), which are crucial components of the glomerular basement membrane (GBM). The ECM in the glomerulus can be segregated on the basis of collagen IV chain expression: collagen IV α3α4α5 predominates within the GBM, collagen IV α1α1α2 is highly expressed within the mesangium and collagen IV α5α5α6 is expressed within Bowman's capsule. Furthermore, a handful of other ECM proteins including agrin, nidogen and perlecan have been studied in detail in the glomerulus. However, recent data have revealed that the human genome encodes 1065 ECM proteins, 1 which suggests the

ECM may be far more complex than previously appreciated. A more detailed understanding of the composition of the human glomerular ECM will facilitate a better understanding of glomerular disease processes. 59

2.2 Statements

Author contributions to data generation and analysis presented as figures of this paper are indicated in figure legends.

Rachel Lennon planned the study and designed experiments. Adam Byron, Jonathan D. Humphries and Michael J. Randles contributed to the study design and analysis. Paul E. Brenchley, Roy Zent and

Martin J. Humphries contributed to the study design.

Michael J. Randles performed Gene Ontology enrichment analysis of the human glomerular ECM proteomic dataset, performed in gel-proteolytic digestion, offline peptide desalting of human glomerular

ECM samples, analysis of peptide intensity for relative quantification of glomerular ECM proteins by mass spectrometry, and evaluated the effectiveness of the ECM enrichment approach used in this study.

Rachel Lennon isolated enriched glomerular ECM fractions, performed Western blotting and immunofluorescence imaging and cross-referenced the proteomic dataset with the Human Protein

Atlas. Adam Byron built the database of human ECM protein-protein interactions and performed network topological analysis. Alex Carisey performed colocalisation analyses of immunofluorescence images. David Knight aided with mass spectrometry data acquisition.

The manuscript is written in the style of a Journal of the American Society of Nephrology research article by Rachel Lennon, Adam Byron, Jonathan D. Humphries and Michael J. Randles. Roy Zent and

Martin J. Humphries contributed to the preparation of the manuscript and the manuscript was critically assessed by all authors . Author guidelines restrict the size of the article to 3000 words or less

(excluding title page, methods, figure legends, tables, and references) .

2.3 Global analysis reveals the complexity of the human

glomerular extracellular matrix

Rachel Lennon, 1,2 Adam Byron, 1, * Jonathan D. Humphries, 1 Michael J. Randles, 1,2 Alex Carisey, 1

Stephanie Murphy, 1,2 David Knight, 3 Paul E. Brenchley, 2 Roy Zent, 4,5 and Martin J. Humphries. 1

1Wellcome Trust Centre for Cell-Matrix Research, Faculty of Life Sciences, University of Manchester,

Manchester, UK; 2Faculty of Medical and Human Sciences, University of Manchester, Manchester, UK;

3Biological Mass Spectrometry Core Facility, Faculty of Life Sciences, University of Manchester,

Manchester, UK; 4Division of Nephrology, Department of Medicine, Vanderbilt University Medical Center,

Nashville, TN, USA; and 5Veterans Affairs Hospital, Nashville, TN, USA.

*Present address: Edinburgh Cancer Research UK Centre, Institute of Genetics and Molecular Medicine,

University of Edinburgh, Edinburgh, UK.

Running title: Proteome of the glomerular matrix

Corresponding author:

Dr Rachel Lennon, Wellcome Trust Centre for Cell-Matrix Research, Michael Smith Building, University of Manchester, Manchester M13 9PT, UK.

Phone: 0044 (0) 161 2755498. Fax: 0044 (0) 161 2755082.

Email: [email protected] 61

2.3.1 Abstract

The glomerulus contains unique cellular and extracellular matrix (ECM) components, which are required for intact barrier function. Studies of the cellular components have helped to build understanding of glomerular disease; however, the full composition and regulation of glomerular ECM remains poorly understood. Here, we employed mass spectrometry–based proteomics of enriched ECM extracts for a global analysis of human glomerular ECM in vivo and identified a tissue-specific proteome of 144 structural and regulatory ECM proteins. This catalogue includes all previously identified glomerular components, plus many new and abundant components. Relative protein quantification demonstrated a dominance of collagen IV, collagen I and laminin isoforms in the glomerular ECM together with abundant collagen VI and TINAGL1. Protein network analysis enabled the creation of a glomerular ECM interactome, which revealed a core of highly connected structural components. More than half of the glomerular ECM proteome was validated using co-localisation studies and data from the Human Protein

Atlas. This study yields the greatest number of ECM proteins, relative to previous investigations of whole glomerular extracts, highlighting the importance of sample enrichment. It also demonstrates that the composition of glomerular ECM is far more complex than previously appreciated and suggests that many more ECM components may contribute to glomerular development and disease processes. 62

2.3.2 Introduction

The glomerulus is a sophisticated organelle comprising unique cellular and extracellular matrix (ECM) components. Fenestrated capillary endothelial cells and overlying podocytes are separated by a specialised glomerular basement membrane (GBM), and these three components together form the filtration barrier. Mesangial cells and their associated ECM, the mesangial matrix, exist between adjacent capillary loops and maintain the three-dimensional organisation of the capillary bundle. In turn the parietal epithelial cells and ECM of Bowman’s capsule enclose this network of capillaries. Cells adhere to ECM proteins via adhesion receptors, and these interactions are required to maintain intact barrier function of the glomerulus. 300, 301

In addition to operating as a signalling platform, ECM provides a structural scaffold for adjacent cells and has a tissue-specific molecular composition. 302, 303 Candidate-based investigations of glomerular

ECM have focused on the GBM and have shown it resembles the typical basal lamina found in multicellular organisms, containing a core of glycoproteins (collagen IV, laminins and nidogens) and heparan sulphate proteoglycans (agrin, perlecan and collagen XVIII). 304 Mesangial and parietal cell

ECMs have been less well investigated nonetheless, they are also thought to contain similar core components in addition to other glycoproteins, including fibronectin. 305, 306 Thus the glomerulus consists of a combination of condensed ECM within the GBM and Bowman’s capsule and loose ECM supporting the mesangial cells.

The ECM compartments in the glomerulus are thought to be distinct and to exhibit different functional roles. The GBM is integral to the capillary wall and therefore functionally linked to glomerular filtration. 304 Mutations of tissue-restricted isoforms of collagen IV ( COL4A3, COL4A4 and COL4A5 ) and laminin ( LAMB2 ), which are found in the GBM, cause significant barrier dysfunction and ultimately renal failure. 307, 308 Less is understood about the functions of mesangial and parietal cell ECMs, although expansion of the mesangial compartment is a histological pattern seen across the spectrum of glomerular disease. 309

Compositional investigation of the distinct glomerular ECM compartments is limited by the technical difficulties of separation. Early investigations of GBM constituents used the relative insolubility of ECM proteins to facilitate separation from cellular proteins in the glomerulus, but did not separate the GBM from mesangial and parietal cells ECMs. 310, 311 Recently studies incorporating laser microdissection of glomerular sections have been coupled with proteomic analyses. 312, 313 These studies report both cellular and ECM components and typically require pooled material from glomerular sections to improve protein identification. The ability of laser microdissection to separate glomerular ECM compartments has not yet been tested, however this approach will be limited by the amount of protein it is possible to retrieve. To achieve good coverage of ECM proteins within a tissue, proteomic studies need to combine a reduction in sample complexity with maximal protein quantity. Currently the inability to separate glomerular ECM compartments in sufficient quantity is a limitation that prohibits proteomic studies of these structures, however for other tissues proteomic analysis of ECM has been achieved by enrichment of ECM combined with sample fractionation. 314

Whilst the composition of the ECM in other tissues has been addressed using proteomic approaches, 314 studies of glomerular ECM to date have employed candidate-based technologies.

These studies have identified key molecular changes during development and disease and highlighted the compositional and organisational dynamics of glomerular ECM. Nonetheless, the extracellular environment within the glomerulus is the setting for a complex series of interactions between both structural ECM proteins and ECM-associated proteins, such as growth factors 315-317 and proteases, 318 which together provide a specialised niche to support glomerular cell function. Therefore, to interrogate this complexity effectively, a systems-level understanding of glomerular ECM is required. To address the need for a global analysis of the extracellular environment within the glomerulus, we used mass spectrometry (MS)–based proteomics of glomerular ECM fractions to define the human glomerular

ECM proteome. 64

2.3.3 Results

2.3.3.1 Isolation of glomerular ECM

Using purified isolates of human glomeruli (Figure 2.1A), we developed a fractionation approach to collect glomerular ECM proteins (Figure 2.1B). Prior to homogenisation and solubilisation, extracted glomeruli appeared acellular, suggestive of successful ECM enrichment (Figure 2.1A). Western blotting confirmed enrichment of ECM proteins (collagen IV, laminin) and depletion of cytoplasmic and nuclear proteins (nephrin, actin, lamin B1) in the ECM fraction (Figure 2.1C). Glomerular ECM fractions from three adult male human kidneys were then extracted and analysed by MS.

2.3.3.2 Gene ontology enrichment analysis of ECM fractions

All proteins identified by MS were allocated to categories according to their gene ontology (GO) assignment, and the enrichment of GO terms in the dataset was assessed using GO enrichment analysis. The network of enriched GO terms clustered into three sub-networks, and the largest, most confidently identified sub-network was for ECM-related GO terms (Figure 2.2A). A large majority of other proteins were included in two additional sub-networks representing mitochondrial and cytoskeletal GO terms, and the presence of these proteins in the ECM fractions is likely to be due to the strength of their inter-molecular interactions with ECM proteins. Spectral counting was used to determine the enrichment of ECM in each of the four fractions collected from isolated human glomeruli. There was 38% enrichment of extracellular proteins in the glomerular ECM fractions (Figure

2.2B), and this compares favourably with the 12–30% enrichment of ECM proteins reported in comparable proteomic studies. 314 65

Figure 2.1 Isolation of enriched glomerular ECM. A: Human glomeruli were isolated by differential sieving, yielding >95% purity. Prior to homogenisation, glomeruli appeared acellular (right panel). B: A proteomic workflow for the isolation of enriched glomerular ECM by fractionation (see methods for details). C: Coomassie staining and Western blotting (WB) of fractions 1–3 and the ECM fraction probing for the extracellular proteins with pan-collagen IV and pan-laminin probes and the intracellular proteins nephrin, actin and lamin B1. M, molecular weight marker. Rachel Lennon generated data for this figure. 66

Figure 2.2 MS analysis of enriched glomerular ECM fractions. A: Gene ontology (GO) enrichment analysis of the full MS dataset. Nodes (circles) represent enriched GO terms and edges (grey lines) represent overlap of proteins between GO terms. Node colour indicates the significance of GO term enrichment; node diameter is proportional to the number of proteins assigned to each GO term; edge weight is proportional to the number of proteins shared between connected GO terms. The full list of GO terms are detailed in Figure S2.7. B: All four protein fractions were analysed by MS, and spectral counting was used to determine the enrichment of ECM proteins (as identified by GO analysis). The mean ECM enrichment was 38% from three biological replicates. C: Relative quantification for the ten most abundant ECM proteins detected by MS. Relative protein abundance was calculated using peptide intensity as described in the Methods. Gene names are shown for clarity, and collagen IV and laminin isoforms are combined as one value. D: Western blotting (WB) confirmed enrichment of TINAGL1 and collagen VI in glomerular ECM. Michael Randles generated data parts A, B and C. David Knight contributed to part C. Rachel Lennon generated data part D. 67

2.3.3.3 Defining the glomerular ECM proteome

Using the GO classification of extracellular region proteins and cross-referencing with the human matrisome project ,302 we identified 144 extracellular proteins in the glomerular ECM, including all previously known glomerular ECM components (Table S2.1). Only proteins identified in at least two of the three biological replicate analyses were included in the dataset, and identified ECM proteins were further categorised as basement membrane, other structural ECM proteins or ECM-associated proteins. There are a number of published glomerular proteomic studies 319-323 and a comparison was made to protein identifications in our dataset (Figure 2.3). Of the two published studies for which complete MS datasets were available, 320, 323 neither study enriched for glomerular ECM or analysed more than one biological replicate. We detected all of the 15 ECM proteins reported by Yoshida et al. 320 (which represents 8% of ECM proteins detected in this study) and 76% of the 91 ECM proteins reported by Cui et al. 323 (which represents 39% of ECM proteins detected in this study), based on identification in one biological replicate, as reported in these published studies. Furthermore, we identified 12 times as many ECM proteins as Yoshida et al. 320 and twice as many ECM proteins as Cui et al. 323 Therefore, our study yielded the greatest number of ECM proteins from glomeruli to date, likely owing to the use of effective ECM enrichment prior to state-of-the-art MS analysis. 68

Figure 2.3 Comparison of the glomerular ECM proteome to published glomerular proteomic datasets. The glomerular ECM proteome identified in this study was compared to other glomerular proteomic studies for which full datasets were available (Cui et al. ; Yoshida et al.). Numbers of proteins in each intersection set of the area proportional Euler diagram are in bold italics. ECM proteins were categorised as basement membrane, other structural ECM or ECM-associated proteins and were coloured and arranged accordingly. Nodes (circles) are labelled with gene names for clarity. ECM proteins detected in any of the three biological replicates reported in this study were included in the comparison with other proteomic datasets; these published datasets each reported one biological replicate. Large node size indicates proteins detected in at least two biological replicates in this study. Michael Randles performed this analysis and generated this figure. 69

2.3.3.4 Creation of a protein interaction network

To visualise the components of glomerular ECM as a network of interacting proteins, the identified proteins were mapped onto a curated protein interaction database to generate an interaction network

(Figure 2.4A). Interestingly, basement membrane and structural ECM proteins were involved in more interactions with other proteins in the network than were ECM-associated proteins (Figure 2.4B).

Topological network analysis confirmed that basement membrane and other structural ECM proteins formed a highly connected “core” sub-network, whereas ECM-associated proteins were less clustered in the network (Figure S2.8, S2.9). These data suggest that structural ECM proteins mediate multiple sets of protein–protein interactions and thus have important roles in the assembly and organisation of glomerular ECM.

2.3.3.5 Localisation of glomerular ECM proteins

Validation of protein expression was performed by searching the Human Protein Atlas (HPA) database 324 for the 144 glomerular ECM proteins identified in this study (Figure 2.5A, 2.5B).

Glomerular immunostaining was not available for 20 proteins, including the known glomerular ECM proteins collagen IV α3, α4, α5, and laminin α5. Protein expression was confirmed for 78 ECM components (63% of proteins with available HPA data) and was reported as negative for 46 ECM components, although there were notable false negatives, including collagen IV α2, collagen XVIII and laminin β1. Immunohistochemistry relies upon antibody specificity, and therefore combining expression data from antibody-based investigations with MS data has the potential to significantly increase the number of protein identifications. Indeed, our proteomic dataset increased the number of ECM proteins detected in the glomerulus by 59% compared to HPA data alone. We further evaluated the HPA database to determine the pattern of immunostaining as glomerular basement membrane (GBM), mesangial matrix, Bowman’s capsule or a combination of compartments. The majority of components were present in more than one ECM compartment (Figure 2.5B), suggesting a common core of protein components between the glomerular ECM compartments.

Figure 2.4 Interaction network analysis of human glomerular ECM. A: Protein interaction network constructed from enriched glomerular ECM proteins identified by MS. Nodes (circles) represent proteins and edges (grey lines) represent reported protein–protein interactions. ECM proteins were categorised as basement membrane, other structural ECM or ECM- associated proteins and were coloured and arranged accordingly. Nodes are labelled with gene names for clarity. B: Distribution of degree (number of protein–protein interactions per protein) for basement membrane, other structural ECM or ECM-associated proteins. Data points are shown as circles; 71

outliers are shown as diamonds. **, P < 0.01; NS, P ≥ 0.05. Adam Byron performed this analysis and generated this figure.

Figure 2.5 Localisation of glomerular ECM proteins in the Human Protein Atlas (HPA) database. A: The HPA was searched for glomerular ECM proteins identified in at least two biological replicates in this study. Glomerular immunostaining was reviewed (+, detected; −, not detected; N/A, data not available in the HPA; left panel) and localisation was determined as glomerular basement membrane (GBM), mesangial matrix (MM), Bowman’s capsule (BC) or a combination of these ECM compartments (right panel). B: ECM proteins were categorised as basement membrane, other structural ECM or ECM-associated proteins and were coloured and arranged accordingly. Proteins not detected or without data in glomeruli in the HPA are shown separately (right panel). Nodes (circles) are labelled with gene names for clarity. Node diameter (proteins localised in glomeruli in the HPA only; left panel) is proportional to the intensity of glomerular immunostaining in the HPA. Rachel Lennon and Michael Randles contributed data to this figure parts A and B. 72

2.3.3.6 Co-localisation of novel glomerular ECM proteins

In order to confirm the expression of new glomerular ECM proteins, which were either abundant in our

MS analysis or not detected in the HPA database, we conducted co-localisation studies. Collagen VI and TINAGL1 (also known as TIN -Ag-RP, lipocalin-7, oxidized LDL-responsive gene 2 and androgen- regulated gene 1) were amongst the most abundant proteins detected in this study (Figure 2.2C), and these were validated by Western blotting (Figure 2.2D). Collagen VI was also present in the highly connected sub-network of ECM proteins (Figure S1.8, S1.9). Immunohistochemical and correlation intensity analysis of human renal cortex revealed co-localisation of TINAGL1 with collagen IV α1, which has a mesangial pattern of immunostaining, whereas collagen VI overlapped both with collagen IV α1 and with collagen IV α3 and laminin, which both have a GBM pattern of immunostaining (Figure 2.6A,

C). MS analysis also detected nephronectin and vitronectin. Correlation intensity analysis demonstrated that nephronectin localised in the GBM and mesangial matrix, whereas vitronectin localised in the mesangial matrix alone (Figure 2.6B, D). This objective and quantitative method of co- localisation allows protein expression to be correlated with reliable markers predominating in distinct

ECM compartments. With antibodies of suitable specificity, this approach could be extended to map the full proteome into glomerular ECM compartments. 73

Figure 2.6 Co-localisation of novel and known glomerular ECM proteins. A and B: Immunohistochemistry of human renal cortex was used to examine the co-localisation of collagen VI, TINAGL1, nephronectin and vitronectin with laminin, collagen IV α3 (A) and collagen IV α1 (B). C and D: Bar charts show intensity correlation quotients calculated from immunohistochemistry images (n = 6–10 images for each analysis), demonstrating co-localisation of laminin, collagen VI and nephronectin with collagen IV α3 (C) and co-localisation of collagen VI, TINAGL1, nephronectin and vitronectin with collagen IV α1 (D). Rachel Lennon generated data parts A and B. Alex Carisey generated parts C and D. 74

2.3.3.7 Discussion

We employed an ECM enrichment strategy coupled with an unbiased proteomics approach to confirm the presence of all known glomerular ECM proteins, in addition to many potentially novel components.

Moreover, using immunohistochemistry, we have confirmed the MS identification of TINAGL1, collagen

VI, nephronectin and vitronectin within specific glomerular compartments. These findings demonstrate that the composition of glomerular ECM is far more complex than previously appreciated and imply that many more ECM components may contribute to glomerular development and disease processes.

This unbiased, global approach to define the glomerular ECM proteome demonstrated that this specialised ECM has a core of highly connected extracellular components. In adult human glomeruli,

144 ECM proteins, including all previously described components, were identified. In addition, we found many more structural and regulatory ECM proteins, revealing the complexity of the glomerular

ECM. This proteome is comparable in size to ECM profiles recently reported for vasculature-, lung- and bowel-derived ECMs. 302, 325, 326 In our analysis, several novel glomerular proteins were highly abundant, including collagen VI and TINAGL1. Collagen VI is a structural component that forms microfibrils and is important for muscle function 327, 328 but its role in the glomerulus has not been investigated. Our study demonstrated that collagen VI is present in a highly connected core network of proteins in the glomerular ECM, and it is localised within both the GBM and mesangial matrix. TINAGL1 was also abundantly expressed in glomerular ECM, where it predominantly localised to the mesangial matrix.

TINAGL1 is a glycoprotein and structurally related to TINAG, a tubular basement membrane component, which is the antigenic target in autoimmune anti-TBM disease. 329 TINAGL1 has a proteolytically inactive cathepsin domain, 330 and it has been shown to have a role in angiogenesis; 331 however, its function within the glomerulus is undefined. Both of these highly abundant proteins may have roles in barrier function or glomerular disease.

To assess the relative abundance of ECM proteins, we used two distinct methods of quantification, peptide intensity analysis 332 and spectral counting, and both approaches gave very similar results.

Peptide intensity analysis revealed collagen IV and laminin isoforms to be the most abundant proteins in the glomerular ECM, and both analyses revealed abundant collagen VI and TINAGL1. There were 75

some differences between the methods for the quantification of collagen isoforms, but the stoichiometry of different proteins is difficult to determine by any global proteomic methodology, including antibody-based techniques, which rely on probe immunoaffinity. To perform absolute protein quantification by MS, labelled peptide standards for each protein could be used; however, this would be a significant undertaking for a large protein dataset and would not permit the discovery of unknown

ECM proteins. Therefore, the findings presented in this study provide relative patterns of glomerular

ECM protein enrichment and pave the way for further in-depth study of the ECM components identified.

In comparison to other large-scale glomerular proteomic studies, which have identified up to a total of

1800 proteins, 320, 323 this investigation had the greatest yield of ECM proteins, thus highlighting the importance of sample fractionation for improving protein identification. However, the large-scale requirement for the analysis prohibited the separation of glomerular ECM components, and therefore protein localisation was determined by immunohistochemistry. Whilst laser microdissection studies have the ability to precisely define the anatomical structure for analysis, the total number of proteins identified in the most recent of these studies, using a variety of tissue samples, ranged from 114 to

340, which is fewer proteins than the large-scale glomerular studies and with significantly fewer ECM proteins compared to this study. 312, 333, 334 Future developments in technologies, which couple tissue imaging with improved sensitivity of molecular analysis, may allow direct compositional analysis of

ECM within distinct tissue compartments. 335, 336

The profile of glomerular ECM we have described is likely to represent a snapshot of a highly dynamic extracellular environment, changing under the influence of cellular, physical and chemical environmental cues. Nonetheless, the datasets provide a valuable resource for further investigation of the composition and complexity of glomerular ECM. Our methodology can now be applied to the comparison of glomerular ECMs in vivo in the context of development or disease. Systems -level analysis will be integral to the downstream interrogation of data in order to identify informative, predictive networks of ECM composition and function. Combined with the methodologies and datasets described herein, such analyses will enable the construction of a dynamic glomerular ECM interactome and help to build understanding of how this network may alter during glomerular development and disease.

2.3.4 Materials and Methods

2.3.4.1 Antibodies

Monoclonal antibodies used were against actin (clone AC-40; Sigma-Aldrich, Poole, UK), nephronectin

(ab64419; Abcam, Cambridge, UK) TINAGL1 (ab69036; Abcam), vitronectin (ab11591; Abcam) and collagen IV chain-specific antibodies (provided by B. Hudson, Vanderbilt University Medical Center,

Nashville, TN, USA). Polyclonal antibodies used were against pan–collagen IV (ab6586; Abcam), pan- laminin (ab11575; Abcam), pan-collagen VI (ab6588; Abcam), lamin B1 (ab16048; Abcam) and nephrin

(ab58968; Abcam). Secondary antibodies against rabbit IgG conjugated to TRITC and mouse or rat

IgG conjugated to FITC (Jackson ImmunoResearch Laboratories, Inc., West Grove, PA, USA) were used for immunofluorescence; secondary antibodies conjugated to Alexa Fluor 680 (Life Technologies,

Paisley, UK) or IRDye 800 (Rockland Immunochemicals, Glibertsville, PA, USA) were used for

Western blotting.

2.3.4.2 Western blotting

See General Materials and Methods.

2.3.4.3 Isolation of human glomeruli

Normal renal cortex from human donor kidneys technically unsuitable for transplantation was used with full ethical approval (reference 06/Q1406/38). Three adult male donors between 37 and 63 years were chosen to reduce the influence of age and sex. Normal renal function was determined by pre- nephrectomy serum creatinine values. At 4 oC, renal cortex (2.5 g) was finely diced and pressed onto a

250-m sieve (Endecotts, London, UK) using a 5-ml syringe plunger. Glomeruli were rinsed through sieves (250-m and 150-m) with cold PBS and separated from tubular fragments by collection on both 150 and 100-m sieves. Retained glomeruli were retrieved into 10 ml PBS and washed a further three times with PBS and interval centrifugation. Glomerular purity was consistently >95% as determined by counting whole glomeruli and non-glomerular fragments using phase-contrast light microscopy.

2.3.4.4 Isolation of enriched glomerular ECM

This was adapted from previously published methods 337 and utilised to reduce the complexity of protein samples for MS analysis by removing cellular components and enriching for ECM proteins. All steps were carried out at 4 oC to minimise proteolysis. Pure glomerular isolates from three human kidneys were incubated for 30 minutes in extraction buffer (10 mM Tris, 150 mM NaCl, 1% (v/v) Triton X-100,

25 mM EDTA, 25 g/ml leupeptin, 25 g/ml aprotinin and 0.5 mM AEBSF) to solubilise cellular proteins, and samples were then centrifuged at 14000 × g for 10 minutes to yield fraction 1. The remaining pellet was incubated for 30 minutes in alkaline detergent buffer (20 mM NH 4OH and 0.5%

(v/v) Triton X-100 in PBS) to further solubilise cellular proteins and to disrupt cell–ECM interactions.

Samples were then centrifuged at 14000 × g for 10 minutes to yield fraction 2. The remaining pellet was incubated for 30 minutes in a deoxyribonuclease (DNase) buffer (10 g/ml DNase I (Roche,

Burgess Hill, UK) in PBS) to degrade DNA. The sample was centrifuged at 14000 × g for 10 minutes to yield fraction 3, and the final pellet was re-suspended in reducing sample buffer (50 mM Tris-HCl, pH

6.8, 10% (w/v) glycerol, 4% (w/v) sodium dodecyl sulfate (SDS), 0.004% (w/v) bromophenol blue, 8%

(v/v) β-mercaptoethanol) to yield the ECM fraction. Samples were heat denatured at 70 oC for 20 minutes.

2.3.4.5 MS data acquisition

See General Materials and Methods.

2.3.4.6 MS data analysis

Tandem mass spectra were extracted using extract_msn (Thermo Fisher Scientific) executed in

Mascot Daemon (version 2.2.2; Matrix Science, London, UK). Peak list files were searched against a modified version of the IPI Human database (version 3.70; release date, 4 March 2010) , containing ten additional contaminant and reagent sequences of non-human origin , using Mascot (version 2.2.03;

for precursor and fragment ions were 0.4 Da and 0.5 Da, respectively. MS datasets were validated using rigorous statistical algorithms at both the peptide and protein level 338, 339 implemented in Scaffold

(version 3.00.06; Proteome Software, Portland, OR, USA). Protein identifications were accepted upon assignment of at least two unique validated peptides with ≥ 90% probability, resulting in ≥ 99% probability at the protein level. These acceptance criteria resulted in an estimated protein false discovery rate of 0.1% for all datasets.

2.3.4.7 MS data access

The MS proteomics data have been deposited to the ProteomeXchange Consortium

(http://proteomecentral.proteomexchange.org ) through the PRoteomics IDEntifications partner

340 repository with the dataset identifier PXD000456.

2.3.4.8 MS data quantification and statistical analysis

MS quantification and proteomic data analyses were performed as previously 341, 342 with modifications as described below. Relative protein abundance was calculated using the unweighted spectral count of a given protein normalised to the total number of spectra observed in the entire sample and to the molecular weight of that protein (normalised spectral count). Mean normalised spectral counts were calculated using data from enriched glomerular ECM samples from three human kidneys. Peptide intensity was used as an alternative method of protein quantification, as described by Silva et al. 343

Orbitrap MS data were entered into Progenesis LCMS (Non Linear Dynamics Ltd, Newcastle upon

Tyne, UK) and aligned. Spectra were extracted using extract_msn (Thermo Fisher Scientific, Waltham,

MA, USA) executed in Mascot Daemon (version 2.4; Matrix Science, London, UK) and imported back into Progenesis to acquire intensity data. Peptides assigned to proteins with fewer than three unique peptides per protein were removed, and median normalised intensity was calculated for all peptides.

Peptides with multiple entries were reduced to the most intense median peptide, and the three peptides with the most intense median values were retained and summed. The sum value is used to compare approximate stoichiometries between proteins in the same sample. 343

2.3.4.9 Functional annotation and enrichment analysis

Proteins identified in at least two of the three biological replicates were included for further analysis.

344, 345 Gene ontology (GO) annotations were downloaded using the online resource DAVID. The GO cellular compartment annotation chart (GOTERM_CC_FAT) was selected and the protein list was

346 347 imported into Cytoscape (version 2.8.1) and analysed using the Enrichment Map plugin. The following criteria were used to generate the network: P value < 0.005, FDR (Benjamini–Hochberg) cut- off < 0.01, similarity cut-off > 0.6. The network was then analysed using the Markov Cluster Algorithm

(MCL) to generate distinct clusters. Proteins annotated in the extracellular region cluster were further

1 cross-referenced with the human matrisome project, and cytoplasmic proteins annotated as extracellular region (ACTN1, ACTN2, ACTN4, CALM1, CSNK2B, FLNA, GLIPR2, HINT2, HSPD1,

RNH1, TLN1, TTN, TUB4A4, and VCL) were excluded. The final list of 144 proteins was termed the glomerular ECM proteome, and this was used for further analysis.

2.3.4.10 Protein interaction network analysis

346 Protein interaction network analysis was performed using Cytoscape (version 2.8.1). Proteins identified in at least two biological replicates were mapped onto a merged human interactome built from the Protein Interaction Network Analysis platform Homo sapiens network (release date, 28 June

348 2011) and Mus musculus network (release date, 28 June 2011), the ECM interactions database

MatrixDB (release date, 26 August 2010), 10 and a literature-curated database of integrin-based

349 adhesion–associated proteins. Topological parameters were computed using the NetworkAnalyzer plug-in. 350

2.3.4.11 Immunohistochemistry and image analysis

Formalin-fixed, paraffin-embedded tissue blocks were sectioned at 5 m. Sections were dewaxed and treated with recombinant proteinase K (Roche Diagnostics, IN, USA) for 15 minutes. Sections were blocked with 5% (v/v) donkey serum (Sigma) and 1.5% (v/v) BSA (Sigma) for 30 minutes and with primary antibodies overnight at 4 oC. Sections were washed three times with PBS, incubated with secondary antibodies, mounted with polyvinyl alcohol mounting medium (Fluka 10981, Sigma) and imaged using a BX51 upright microscope (Olympus, Southend on Sea, UK) equipped with a 20× UPlan 80

Fln 0.50 objective and controlled through MetaVue software (Molecular Devices, Wokingham, UK).

Images were collected using a CoolSnap HQ camera (Photometrics, Tucson, AZ, USA) and separate

DAPI/FITC/Cy3 filters (U-MWU2, 41001, 41007a, respectively; Chroma, Olching, Germany) to minimise bleed-through between the different channels. Images were processed and analysed using

Fiji/ImageJ software (version 1.46r; National Institutes of Health, Bethesda, MD, USA). Raw images were subjected to signal re-scaling using linear transformation for display in the figures. For calculation of the Pearson’s correlation coefficient, a region of interest was drawn and a threshold was set to restrict analysis to single glomeruli. The coefficient was measured using the Intensity Correlation

Analysis (ICA) plugin for Fiji/ImageJ 351 and the subsequent plotting step were performed using

MATLAB (version R2012a; MathWorks, Natick, MA, USA).

2.3.4.12 Analysis of Human Protein Atlas immunohistochemistry data

The Human Protein Atlas (HPA) (version 11.0) was searched for the 144 ECM proteins identified in this study, and glomerular immunohistochemistry was reviewed. Proteins were categorised as being positive (strong, moderate, weak), negative or not available according to the HPA report. Proteins with positive immunostaining were further allocated to one or more ECM compartments (GBM, mesangial matrix, Bowman’s capsule) according to our assessment of the histological pattern of staining.

Negative immunostaining controls were not present for analysis in the HPA database, limiting our independent evaluation of protein expression. In addition, the HPA report indicated protein expression in cells of the glomerulus, rather than ECM compartments. Both of these factors could influence false negative or false positive reporting.

2.3.4.13 Statistical analysis

All measurements are shown as mean ± standard error of the mean. Box plots indicate 25 th and 75 th percentiles (lower and upper bounds, respectively), 1.5× interquartile range (whiskers) and median

(black line). Numbers of protein–protein interactions were compared using Kruskal–Wallis one-way analysis of variance tests with post-hoc Bonferroni correction. GO enrichment analyses were compared using modified Fisher’s exact tests with Benjamini–Hochberg correction. P values <0.05 were deemed significant. 81

2.3.5 Acknowledgments

This work was supported by a Wellcome Trust Intermediate Fellowship award to R.L. (ref: 090006) and

Wellcome Trust grant 092015 to M.J.H. R.Z. is supported by VA Merit Award 1I01BX002196-01,

DK075594, DK069221, DK083187 and an American Heart Association Established Investigator Award.

The mass spectrometer and microscopes used in this study were purchased with grants from the

Biotechnology and Biological Sciences Research Council, Wellcome Trust and the University of

Manchester Strategic Fund. Mass spectrometry was performed in the Biological Mass Spectrometry

Core Facility, Faculty of Life Sciences, University of Manchester, and we thank Stacey Warwood for advice and technical support. We thank Julian Selley for bioinformatic support. Microscopy was performed in the Bioimaging Core Facility, Faculty of Life Sciences, University of Manchester. The collagen IV chain–specific antibodies were kindly provided by the Billy Hudson research group,

Department of Medicine, Vanderbilt Medical Center.

2.3.6 Statement of competing financial interests

No competing interests . 82

2.3.7 Supplementary data

Table 2.1 Human glomerular ECM proteome

Uniprot ID Gene name Alias Abundance (nSC) Category

O00468 AGRN Agrin 3.075 Glycoprotein

P39059 COL15A1 Collagen alpha-1 (XV) 0.052 Collagen chain

P39060 COL18A1 Collagen alpha-1 (XVIII) 5.23 Collagen chain

P02462 COL4A1 Collagen alpha-1 (IV) 12.515 Collagen chain

P08572 COL4A2 Collagen alpha-2 (IV) 17.709 Collagen chain

Q01955 COL4A3 Collagen alpha-3 (IV) 8.155 Collagen chain

P53420 COL4A4 Collagen alpha-4 (IV) 8.961 Collagen chain

P29400 COL4A5 Collagen alpha-5 (IV) 3.778 Collagen chain

Q14031 COL4A6 Collagen alpha-6 (IV) 0.589 Collagen chain

P23142 FBLN1 Fibulin-1 0.029 Glycoprotein

P35555 FBN1 Fibrillin-1 1.403 Glycoprotein

P02751 FN1 Fibronectin 1.893 Glycoprotein

Q86XX4 FRAS1 Extracellular matrix protein 0.039 Glycoprotein FRAS1

Q96RW7 HMCN1 Hemicentin-1 0.007 Glycoprotein

P98160 HSPG2 Perlecan 7.012 Proteoglycan

P24043 LAMA2 laminin subunit alpha-2 0.043 Glycoprotein

O15230 LAMA5 laminin subunit alpha-5 9.482 Glycoprotein

P07942 LAMB1 laminin subunit beta-1 0.977 Glycoprotein

P55268 LAMB2 laminin subunit beta-2 14.745 Glycoprotein

P11047 LAMC1 laminin subunit gamma-1 8.818 Glycoprotein

P14543 NID1 Nidogen-1 10.247 Glycoprotein

Q14112 NID2 Nidogen-2 1.249 Glycoprotein

Q0UJW2 TINAG Tubulointerstitial nephritis 12.873 Glycoprotein antigen 83

Q6PCB0 VWA1 von Willebrand factor A 1.171 Glycoprotein domain-containing protein 1

Q9BXN1 ASPN Asporin 1.199 Proteoglycan

P21810 BGN Biglycan 2.268 Proteoglycan

Q99715 COL12A1 Collagen alpha-1 (XII) 0.073 Collagen chain

P02452 COL1A1 Collagen alpha-1 (I) chain 0.253 Collagen

P08123 COL1A2 Collagen alpha-2 (I) chain 0.594 Collagen

P02461 COL3A1 Collagen alpha-1 (III) chain 0.143 Collagen

P12109 COL6A1 Collagen alpha-1 (VI) 12.685 Collagen chain

P12110 COL6A2 Collagen alpha-2 (VI) 5.889 Collagen chain

P12111 COL6A3 Collagen alpha-3 (VI) 11.145 Collagen chain

P07585 DCN Decorin 0.067 Proteoglycan

Q07507 DPT Dermatopontin 0.397 Glycoprotein

Q9Y6C2 EMILIN1 EMILIN-1 0.699 Glycoprotein

P02671 FGA Fibrinogen alpha chain 0.954 Glycoprotein

P02675 FGB Fibrinogen beta chain 1.965 Glycoprotein

P02679 FGG Fibrinogen gamma chain 2.002 Glycoprotein

P08493 MGP Matrix Gla protein 0.557 Glycoprotein

Q6UX19 NPNT Nephronectin 3.707 Glycoprotein

B1ALD8 POSTN Periostin, osteoblast 0.191 Glycoprotein specific factor

Q8IVN8 RPESP RPE-spondin 1.364 Glycoprotein

Q15582 TGFBI Transforming growth 0.369 Glycoprotein factor-beta-induced protein

Q9GZM7 TINAGL1 Tubulointerstitial nephritis 20.01 Glycoprotein antigen-like 1

P04004 VTN Vitronectin 9.015 Glycoprotein

Q9BVH8 VWA5B2 von Willebrand factor A 0.024 Glycoprotein domain-containing protein 5B2

E2QRD0 VWA8 von Willebrand factor A 0.076 Glycoprotein domain-containing protein 8

P01019 AGT Angiotensinogen 0.327 Protease inhibitor

P02768 ALB Serum albumin 11.448 Secreted

P02760 AMBP protein AMBP 8.382 Secreted 84

E9PDK5 ANXA11 Annexin A11 0.371 Annexin

P07355 ANXA2 Annexin A2 1.621 Annexin

P08758 ANXA5 Annexin A5 0.283 Annexin

P20073 ANXA7 Annexin A7 0.062 Annexin

P02743 APCS Serum amyloid P- 19.195 Secreted component

P02647 APOA1 Apolipoprotein A-I 4.906 Apolipoprotein

P06727 APOA4 Apolipoprotein A-IV 5.896 Apolipoprotein

Q6Q788 APOA5 Apolipoprotein A-V 0.055 Apolipoprotein

B0YIW2 APOC3 Apolipoprotein C-III variant 3.204 Apolipoprotein 1

P02649 APOE Apolipoprotein E 16.039 Apolipoprotein

P20160 AZU1 Azurocidin 0.194 Protease

P61769 B2M Beta-2-microglobulin 2.157 Secreted

Q6UXH0 C19orf80 Hepatocellular carcinoma- 0.256 Secreted associated gene TD26

P02747 C1QC Complement C1q 0.352 Complement subcomponent subunit C

P01024 C3 Complement C3 4.768 Complement (Fragment)

P01031 C5 Complement C5 0.559 Complement

P13671 C6 Complement component 6 0.458 Complement precursor

P10643 C7 Complement component 0.445 Complement C7

P07357 C8A Complement component 0.151 Complement C8 alpha chain

P07358 C8B Complement component 0.276 Complement C8 beta chain

P07360 C8G Complement component 6.669 Complement C8 gamma chain

Q8WUY1 C8orf55 UPF0670 protein C8orf55 0.134 Secreted

P02748 C9 Complement component 2.713 Complement C9

P00918 CA2 Carbonic anhydrase 2 1.506 Enzyme

P13987 CD59 CD59 glycoprotein 3.179 Cell surface

Q5VYL6 CFHR5 Complement factor H- 0.477 Complement related 5

P10909 CLU Clusterin 4.587 Secreted 85

P09543 CNP CNPI of 2',3'-cyclic- 0.743 Enzyme nucleotide 3'- phosphodiesterase

P53621 COPA Coatomer subunit alpha 0.128 Secreted

Q96IY4 CPB2 Carboxypeptidase B2 0.198 Enzyme

P01034 CST3 Cystatin-C 1.315 Protease inhibitor

P07339 CTSD Cathepsin D 0.11 Protease

P08311 CTSG Cathepsin G 1.188 Protease

A5JHP3 DCD Dermcidin isoform 2 0.075 Secreted

P59665 DEFA1 Neutrophil defensin 1 15.387 Secreted

Q03001 DST Dystonin 0.002 Plakin

P08246 ELANE Neutrophil elastase 0.779 Protease

P00740 F9 Coagulation factor IX 0.821 Coagulation

Q5D862 FLG2 Filaggrin-2 0.041 Cytoskeleton

P17948 FLT1 Flt1 of Vascular endothelial 0.037 Secreted growth factor receptor 1

Q5SZK8 FREM2 FRAS1-related 0.071 Cell surface extracellular matrix protein 2

Q92820 GGH Gamma-glutamyl 0.146 Enzyme hydrolase

Q5JWF2 GNAS Guanine nucleotide- 0.262 Cell surface binding protein G (s) subunit alpha

P06744 GPI Glucose-6-phosphate 0.482 Enzyme isomerase

P22352 GPX3 Glutathione peroxidase 3 8.232 Enzyme

P06396 GSN Gelsolin 1.039 Cytoskeleton

F5GXA6 HLAC HLA class I 0.267 Cell surface histocompatibility antigen, Cw-7 alpha chain

P00738 HP Haptoglobin 0.163 Secreted

P02790 HPX Hemopexin 1.231 Secreted

Q53GQ0 HSD17B12 Estradiol 17-beta- 0.17 Enzyme dehydrogenase 12

P01861 IGHG4 Ig gamma-4 chain C region 1.806 Secreted

P58166 INHBE Inhibin beta E chain 0.536 Secreted

P19827 ITIH1 Inter-alpha-trypsin inhibitor 0.032 Protease inhibitor heavy chain H1

Q06033 ITIH3 Inter-alpha-trypsin inhibitor 0.035 Protease inhibitor heavy chain H3 86

P01042 KNG1 Kininogen-1 0.674 Protease inhibitor

P09382 LGALS1 Galectin-1 4.983 Secreted

P17931 LGALS3 Galectin-3 2.965 Secreted

Q08380 LGAS3BP Galectin-3-binding protein 0.55 Secreted

P11150 LIPC Hepatic triacylglycerol 0.265 Enzyme lipase

P98164 LRP2 Low-density lipoprotein 0.101 Cell surface receptor-related protein 2 (megalin)

P16126 LYZ Lysozyme C 1.547 Secreted

P14780 MMP9 Matrix metalloprotein ase-9 0.111 Protease

P05164 MPO Myeloperoxidase 0.868 Enzyme

Q8WX17 MUC16 Mucin-16 0.001 Cell surface

P30086 PEBP1 Phosphatidylethanolamine- 0.506 Protease inhibitor binding protein 1

P62937 PPIA Peptidyl-prolyl cis-trans 4.879 Enzyme isomerase A

P15888 PRELP Prolargin 0.198 Secreted

Q92954 PRG4 Proteoglycan 4 0.016 Secreted

O95084 PRSS23 Serine protease 23 0.299 Protease

Q5SQ09 PTGDS Prostaglandin D2 synthase 0.564 Enzyme 21kDa

Q5VY30 RBP4 Retinol binding protein 4, 20.718 Secreted plasma

Q86YZ3 S100A18 Hornerin 0.012 Cytoplasmic

P06703 S100A6 Protein S100-A6 1.59 Cell surface

P05109 S100A8 Protein S100-A8 3.701 Secreted

P02735 SAA1 Serum amyloid A protein 0.675 Secreted

Q13214 SEMA3B Semaphorin-3B 0.032 Secreted

P01009 SERPINA1 Alpha-1-antitrypsin 5.59 Protease inhibitor

P01011 SERPINA3 Alpha-1-antichymotrypsin 0.481 Protease inhibitor

P05154 SERPINA5 Plasma serine protease 2.071 Protease inhibitor inhibitor

P01008 SERPINC1 Antithrombin-III 0.583 Protease inhibitor

P07093 SERPINE2 Glia-derived nexin 0.164 Protease inhibitor

P05155 SERPING1 Plasma protease C1 0.89 Protease inhibitor inhibitor

P50454 SERPINH1 Serpin H1 0.401 Protease inhibitor 87

Q9Y5W8 SNX13 Small inducible cytokine 0.782 Cytoplasmic B14 precursor

Q00796 SORD Sorbitol dehydrogenase 2.804 Enzyme

Q13103 SPP2 Secreted phosphoprotein 1.527 Secreted 24

P02787 TF Serotransferrin 0.096 Secreted

P21980 TGM2 protein -glutamine gamma- 1.302 Enzyme glutamyltransferase 2

P35625 TIMP3 Metalloprotein ase inhibitor 0.928 Protease inhibitor 3

O75888 TNFSF13 Gamma of Tumor necrosis 0.135 Secreted factor ligand superfamily member 13

P02766 TTR Transthyretin 3.88 Secreted

E9PEA4 UMOD Uromodulin 26.901 Secreted

A8K0G1 WNT7B Protein Wnt 0.058 Secreted 88

Figure S2.7 Gene ontology (GO) enrichment analysis of the full MS dataset. Nodes (circles) represent enriched GO terms and edges (grey lines) represent overlap of proteins between GO terms. Node colour indicates the significance of GO term enrichment ; node diameter is proportional to the number of proteins assigned to each GO term; edge weight is proportional to the number of proteins shared between connected GO terms. The network from Figure 2.2 is annotated (left panel); nodes are numbered according to their GO term (right panel). Michael Randles generated data for this figure. 89

Figure S2.8 Glomerular ECM interaction network analysis. Proteins identified by MS and classified as extracellular region according to Gene Ontology annotation were converted to a protein-protein interaction network model. The interaction network was clustered, and topological parameters were computed. Self-interactions were excluded from the analysis. Nodes are coloured according to their clustering coefficient, and node diameter is proportional to number of interaction partners (degree). Nodes are labelled with gene names. Proteins classified as basement membrane are displayed with red node borders; other structural ECM proteins are displayed with orange node borders; and ECM-associated proteins are displayed with green node borders. Adam Byron generated data for this figure. 90

Figure S2.9 The most connected proteins in the ECM interaction network. The ten proteins with the highest number of neighbours (degree) in the glomerular ECM interaction network are plotted as a bar chart. The most connected proteins (known as hubs), which incorporated the top 20% of the degree distribution, are indicated by dark blue bars. Self-interactions were excluded from the analysis. Proteins are labelled with gene names. Adam Byron generated data for this figure . 91

3 Glomerular cell-derived matrices

3.1 Introduction

The glomerular basement membrane (GBM) is a key extracellular matrix (ECM) structure within the glomerulus. Perturbation of GBM structure leads to loss of selective glomerular filtration. Currently, there is limited knowledge about how the GBM is formed during development, regulated in health and disrupted in disease. Examination of the GBM has been limited to candidate-based approaches, as it is currently technically unfeasible to separate the GBM and mesangial ECM compartments during ECM isolation from the glomerulus. It is established that the GBM is synthesised jointly by podocytes and glomerular endothelial cells (GECs), with both cell types contributing to the laminin networks. 352 In contrast, the mature collagen IV network ( α3α4α5) is produced solely by podocytes. 353 However, the

GBM contains more than just type IV collagen and laminins, and the relative contributions of podocytes and GECs to the plethora of other ECM proteins within the GBM are unknown. Establishing the origins of ECM proteins within the GBM in a global manner will enable better hypothesis generation regarding

ECM regulation within the glomerulus. 92

3.2 Statements

Author contributions to data generation and analysis presented as figures of this paper are indicated in figure legends.

Rachel Lennon, Adam Byron, Michael J. Randles and Jonathan D. Humphries planned the study and designed experiments. Roy Zent and Martin J. Humphries contributed to the study design.

Michael J. Randles characterised podocyte and glomerular endothelial cells with transmission electron microscopy, performed coculture experiments, assessed podocyte and glomerular endothelial cell viability in coculture, performed conditioned media experiments, quantified ECM deposition using transmission electron microscopy, and assessed ECM and cell junctions in coculture with immunofluorescence.

Rachel Lennon isolated enriched podocyte, GEC and coculture ECM fractions, performed Western blotting and immunofluorescence imaging and performed hierarchical clustering analysis. Adam Byron built the database of human ECM protein-protein interactions and performed network topology analysis. Jonathan D. Humphries performed QSpec analysis and Gene Ontology enrichment analysis.

Aleksandr Mironov aided with electron microscopy data acquisition. Hellyeh Hamidi generated the

GFP-podocyte cell line by lentiviral transduction. Shelley Harris performed RT-PCR and q-PCR. Peter

W. Mathieson , Moin A. Saleem and Simon C. Satchell provided human podocyte and glomerular endothelial cell lines.

The manuscript is written in the style of a Journal of the American Society of Nephrology research article by Rachel Lennon, Adam Byron, Michael J. Randles, and Jonathan D. Humphries. Roy Zent and

Martin J. Humphries contributed to the preparation of the manuscript and the manuscript was critically assessed by all authors. Author guidelines restrict the size of the article to 3000 words or less

(excluding title page, methods, figure legends, tables, and references) .

3.3 Glomerular cell crosstalk influences the composition and

assembly of extracellular matrix

† † Adam Byron ,1, *, Michael J. Randles ,1,2 , Jonathan D. Humphries ,1 Aleksandr Mironov ,1 Hellyeh

Hamidi, 1 Shelley Harris, 2 Peter W. Mathieson ,3 Moin A. Saleem ,3 Simon C. Satchell ,3 Roy Zent ,4,5

Martin J. Humphries ,1 and Rachel Lennon .1,2

1Wellcome Trust Centre for Cell-Matrix Research, Faculty of Life Sciences, University of Manchester,

Manchester, UK; 2Faculty of Medical and Human Sciences, University of Manchester, Manchester, UK;

3Academic Renal Unit, Faculty of Medicine and Dentistry, University of Bristol, Bristol, UK; 4Department of Medicine, Vanderbilt University Medical Center , Nashville, TN, USA; and 5Veterans Affairs Hospital,

Nashville, TN, USA .

*Present address: Edinburgh Cancer Research UK Centre, Institute of Genetics and Molecular

Medicine, University of Edinburgh, Edinburgh, UK.

† These authors contributed equally to this work.

Running title: Glomerular extracellular matrix

Corresponding author:

Dr Rachel Lennon, Wellcome Trust Centre for Cell-Matrix Research , Michael Smith Building, University of Manchester, Manchester M13 9PT, UK .

Phone: 0044 (0) 161 2755498. Fax: 0044 (0) 161 2755082 .

Email: [email protected] 94

3.3.1 Abstract

The glomerular basement membrane (GBM) is a specialised extracellular matrix (ECM) compartment within the glomerulus and is known to contain tissue-restricted isoforms of collagen IV and laminin. It is integral to the capillary wall and therefore functionally linked to glomerular filtration. While the composition of the GBM has been investigated with global and candidate-based approaches, the relative contributions of glomerular cell types to the production of ECM is not well understood. To enable the characterisation of specific cellular contributions to the GBM, we analysed ECM isolated from podocytes and glomerular endothelial cells in vitro using mass spectrometry–based proteomics.

These analyses identified cell type–specific differences in ECM composition, indicating distinct contributions to glomerular ECM assembly. Coculture of podocytes and endothelial cells resulted in an altered composition and organisation of ECM compared to monoculture ECMs from single cell types, suggesting a role for cell–cell crosstalk in the production of glomerular ECM. This was supported by the identification of basement membrane–like ECM deposition between cocultured cells using electron microscopy. Importantly, compared to monoculture, the coculture ECM proteome better resembled a tissue-derived glomerular ECM dataset, indicating its relevance to GBM in vivo . Protein network analyses revealed a common core of 35 highly connected structural ECM proteins that may be important for glomerular ECM assembly. Overall, these findings demonstrate the complexity of the glomerular ECM and suggest that both ECM composition and organisation are context dependent. 95

3.3.2 Introduction

The glomerular basement membrane (GBM) is integral to the glomerular capillary wall, and it directly contributes to barrier function .354, 355 The ultrastructural appearance of the GBM shows it is unusually thick (300–350 nm) as a result of two basement membranes fusing during glomerular development. 356

This specialised extracellular matrix (ECM) supports two distinct cell types: glomerular endothelial cells

(GEC) on the inner capillary wall and podocytes, which form a mesh-like covering of the outer surface.

However , there is limited understanding about the relative contribution of each cell type to the regulation of ECM synthesis and organisation in the GBM .

Some unique ECM isoforms, essential for normal barrier function, characterise the mature glomerulus.

For example, collagen IV networks are predominantly composed of α1 and α2 chains; however, at the capillary-loop stage of glomerular development there is an isoform switch to the expression of the collagen IV α3α4α5 network in the GBM. 357, 358 This network is thought to provide the tensile strength necessary to withstand filtration forces. 358, 359 Similar changes in laminin isoforms have been reported, switching from a combination of laminin α1β1γ1 (laminin-111) and 511 to a predominance of laminin-

521 in the mature GBM. 360 The importance of glomerular ECM composition for normal barrier function is highlighted by human genetic mutations, which result in the absence of the collagen IV α3α4α5 network in Alport syndrome 307 and the absence of laminin-521 in Pierson syndrome. 308 However, there is a much wider spectrum of inherited and acquired glomerular disease with associated abnormalities in the GBM, which are not explained by defects in collagen IV or laminin. 361, 362 Therefore, building understanding of ECM composition in an unbiased manner could help to identify additional genetic defects and to appreciate mechanisms of ECM regulation in the glomerulus.

These questions can be initially addressed with studies in vitro since cell-based investigations allow the dissection of molecular pathways in a manner not feasible in animal studies. Conditionally immortalised human glomerular cells have been derived to facilitate these investigations; 363, 364 however, their ability to synthesise and organise ECM has not been fully investigated. In vivo , these cells are highly specialised, and ex-vivo they may lose critical paracrine and environmental cues required to maintain a differentiated state. 365 To test this hypothesis and to inform mechanisms of ECM regulation in the 96

glomerulus, global investigation of cell-derived ECM represents a powerful, unbiased analytical approach.

Using an ECM enrichment strategy coupled to mass spectrometry (MS)–based proteomics, we recently described a glomerular ECM proteome derived from isolated human glomeruli. 366 To extend this global analysis, we analysed cell-derived ECM from GEC and podocytes to determine their relative contribution to ECM production and regulation in the glomerulus.

3.3.3 Results

3.3.3.1 Human glomerular cells synthesise and organise ECM in vitro

To investigate ECM production by glomerular cells either side of the GBM, we prepared monocultures of human GEC and podocytes in vitro . Both cell types synthesised and deposited ECM (Figure 3.1A), and this did not appear disrupted by the removal of cells prior to ECM extraction (Figure 3.1A; right panels). Ultrastructural analysis of both glomerular cell types revealed a thin, basal layer of ECM

(Figure 3.1B). In addition, GEC and podocytes exhibited distinct morphology (Figure 3.1B, C). GEC had more caveolae, which were visible as small (50–100-nm diameter) invaginations of the plasma membrane (Figure 3.1B, panel i), and which are known to be particularly abundant in endothelial cells. 367 GEC also had longer focal adhesions, observed as electron-dense sites of cell contact with the

ECM (Figure 3.1B, panel ii) and also had more glycogen granules (Figure 3.1B, panel iii). In contrast, podocytes had wider cell–cell junctions (29 ± 3.4 nm) (Figure 3.1B, panel v), more lysosomes (Figure

3.1B, panel vi), and greater cell width (6.1 ± 0.49 m) (Figure 3.1B, C). 97

Figure 3.1 Glomerular cells synthesise and organise ECM in vitro . A: Immunocytochemistry of human GEC and podocytes demonstrated expression of laminin and collagen IV in vitro . These ECM proteins remained detectable following removal of either cell type (denuded ECM; right panel). B: Ultrastructural analysis of GEC and podocytes revealed morphological differences. GEC had more caveolae (arrows; i) and glycogen deposits (arrows; iii), whereas podocytes had smaller focal adhesions (asterisks; iv, cf. ii), wider cell junctions (arrow; v), more lysosomes (asterisks; vi) and greater cell width. Both cell types deposited a thin basal layer of ECM adjacent to focal adhesions. C: Quantification of morphological differences between GEC (n = 11) and podocytes (n = 31 ). Graphs display mean ± SEM and results of statistical analysis using unpaired 98

Student’s t-tests. Rachel Lennon generated data for this figure part A and B. Michael Randles generated data for this figure part B and C. 3.3.3.2 Glomerular cells in vitro exhibit differential ECM protein synthesis

To collect ECM proteins for proteomic analysis, a fractionation workflow was adapted from previous studies (Figure 3.2A). 342, 368 Cell-derived ECMs were extracted from both GEC and podocyte monocultures and analysed by MS. We identified 127 extracellular proteins in GEC samples and 142 extracellular proteins in podocyte samples (Tables S3.1 and S3.2). There was considerable (55%) overlap between proteins detected in GEC and podocyte ECMs, but cell-type specific proteins were also identified (Figure 3.2B). Furthermore, 44% of proteins in the recently described in vivo glomerular

366 ECM proteome were identified in cell-derived ECMs (Figure 3.2B). Statistical analysis of all identified proteins in the cell-derived ECM samples revealed differential ECM protein synthesis between GEC and podocytes (Figure 3.2C). While Gene Ontology (GO) enrichment analysis highlighted cell-type specific differences between the ECMs. Specifically, there was significant enrichment of vascular development processes in GEC ECM, whereas cell adhesion processes were enriched in podocyte

ECM (Figure 3.2D). These data indicate a functional distinction between GEC and podocyte ECMs.

Moreover, the expected association of GEC with endothelial cellular functions suggests that this analysis has the capability to identify proteins with tissue-specific functions.

In contrast to the compositional differences highlighted by the GO analysis, previously identified glomerular ECM components were detected to a similar extent in ECMs from both cell types (Figure

3.2B), suggesting that the ECMs are qualitatively closely related. To examine putative interactions between the identified ECM proteins, interaction networks were generated from a curated protein interaction database. ECM protein interaction networks had similar topologies, with highly connected subnetworks of basement membrane and other structural ECM proteins (Figures 3.3, S3.9, S3.10).

Interestingly, basement membrane proteins had a higher number of protein–protein interactions than

ECM-associated proteins, suggesting that these molecules play key roles in glomerular ECM organisation (Figure 3.3B, 3.3D). These findings are very similar to the tissue-derived glomerular ECM interactome, which identified a core of highly connected structural components. 366 Overall, these data suggest that qualitative differences between GEC and podocyte ECMs do not affect the core organisation of the ECM. 99

Figure 3.2 GEC and podocytes in vitro have differential ECM composition. A: A proteomic workflow for the isolation of ECM from glomerular cells in vitro . B: Cell-derived ECM fractions were prepared from human GECs (n = 3) and human podocytes (n = 3) and analysed by MS. The Venn diagram compares identified ECM proteins with a glomerular ECM dataset, derived from isolated human glomeruli. Percentage overlaps of set pairs are indicated. C: QSpec statistical analysis of all GEC and podocyte proteins identified by MS demonstrates the differential relative abundance of ECM proteins between both datasets. Blue boxes indicate zones of significant differential relative abundance (false discovery rate <5%). D: Gene Ontology (GO) enrichment analysis of GEC and podocyte datasets revealed enrichment of distinct biological process terms. The top three biological processes for each cell type are shown. Rachel Lennon generated data for this figure parts A and B. Jonathan Humphries generated data for this figure parts C and D.

100

101

Figure 3.3 GEC and podocyte ECM interaction networks. A, C: Protein interaction networks constructed from GEC (A) or podocyte (C) ECM proteins identified by MS. ECM proteins were classified as basement membrane, other structural ECM or ECM- associated proteins, and were coloured and arranged accordingly. Nodes are labelled with gene names, and relative node positions are preserved in both interaction networks. B, D: Distributions of degree (number of protein -protein interactions per protein) for basement membrane, other structural ECM or ECM-associated proteins for the GEC (B) or podocyte (D) ECM interaction networks. Data points are shown as circles; outliers are shown as diamonds. **, P < 0.01; *, P < 0.05; NS ≥ 0.05. Adam Byron generated data for this figure.

3.3.3.3 Glomerular cell-derived ECM resembles a developmental phenotype

To assess quantitative differences between the compositions of the ECMs, predicted GBM components were analysed by unsupervised hierarchical clustering. A striking enrichment of collagen

IV chains was identified in glomerular ECM as compared to cell-derived ECMs (Figure 3.4A). We found chains of both isoforms in glomerular ECM ,366 with a predominance of α1 and α2 chains, which may reflect the mesangial ECM contribution. 369 In cell-derived ECMs, we identified α1 and α2 chains by MS, but minimal detection of α3, α4, or α5 chains (Figure 3.4A). These data were confirmed by Western blotting of ECM samples (Figure 3.4B). Taken together, these findings suggest that ECM derived from glomerular cells in culture resembles a developmental ECM, 370 and this concept is further supported by the low relative abundance of laminin β2, a predominant isoform in the mature glomerulus (Figure

3.4A). Whilst RT-PCR analysis revealed that both glomerular cell types expressed mRNA for COL4A1 ,

3, 4, and 5 (Figure 3.4C), q -PCR demonstrated the relative abundance of mRNA was significantly less than the amount isolated from human renal cortex (Figure 3.4D, 3.4E). It is conceivable that glomerular cells have the capacity to synthesise both the collagen IV α1 α 2 α 1 chains and the mature α3 α 4 α 5 chains , but require the appropriate environmental context. 102

103

Figure 3.4 Glomerular ECM in vitro resembles a developmental phenotype. A: Hierarchical clustering analysis of the abundance of basement membrane proteins identified in GEC, podocyte and glomerular ECMs identified a cluster of proteins with increased relative abundance in glomerular ECM. The heat map displays relative protein abundance (normalised spectral count), and the associated dendrogram displays clustering on the basis of uncentered Pearson correlation. Proteins are labelled with gene names for clarity. B: The low abundance of collagen IV α3, α4 and α5 in GEC and podocyte ECMs was confirmed by Western blotting (WB) using chain-specific antibodies to detect monomers (~25 kD) and dimers (~50 kD). C: RT-PCR of RNA pellets from GEC and podocytes confirmed expression of COL4A1, COL4A3, COL4A4 and COL4A5 transcripts. M, molecular weight marker. D, E: Quantitative PCR also confirmed the expression of COL4A1 and COL4A3 in podocytes and GECs. The relative mRNA abundance was calculated and compared with mRNA extracted from human fibroblasts and human renal cortex. With the lowest mRNA expression, human fibroblasts were used for normalisation, and glyceraldehyde-3-phosphate dehydrogenase was used as the endogenous mRNA control. These data represent n=3 replicates. Rachel Lennon generated data parts A and B. Shelley Harris generated data part C, D and E.

3.3.3.4 Glomerular cell coculture results in altered ECM organisation

We hypothesised that the developmental phenotype could be due to a lack of functional interaction between cell types. To investigate the role of cell–cell crosstalk in ECM synthesis, GEC and podocytes were grown in coculture. Cells were viable in coculture and over 14 days the proportion of each cell type did not alter significantly (Figure S3.11). We found that cells synthesised ECM (Figure 3.5A,

Figure S3.12) and ultrastructural analysis revealed a basal layer of ECM (Figure 3.5B, panel i), as previously seen in monoculture (Figure 3.1B). In addition, ECM was deposited between cells in coculture (Figure 3.5Bii–iv), and this organisation resembled a basement membrane, with parallel bundles of ECM proteins. Our phenotypic characterisation of cells in monoculture (Figure 3.1B, C) enabled the identification of ECM between adjacent GEC and podocytes as well as adjacent cells of the same type. Quantification of ECM thickness demonstrated significantly more ECM between cells in coculture compared to monoculture (Figure 3.5Bv). Furthermore, the thickness of the ECM between cocultured cells was in a range comparable to the thickness of the GBM in vivo .371, 372 To investigate whether GEC–podocyte contact or a soluble mediator was required for this basement membrane–like assembly of ECM, we cultured monocultures in conditioned medium from either the same or the other cell type (Figure 3.5C) . Interestingly, ECM was deposited in significant amounts between GEC monocultures in podocyte-conditioned medium (Figure 3.5Civ) , and this increase was significant compared to control-conditioned medium (Figure 3.5Cv). These data suggest that a secreted molecule released by podocytes contributes to the organisation of ECM between glomerular cells. 104

Figure 3.5 Glomerular cell coculture is associated with altered ECM organisation . A: GEC and podocytes were combined in cell suspension and seeded as cocultures. The ECM proteins collagen IV and laminin were detected by immunocytochemistry. B: Ultrastructural analysis of cocultured GEC and podocytes revealed a striking difference in ECM organisation compared to monocultured cells. Cocultured cells deposited basal ECM (i) and, unlike monoculture, deposited ECM between cells, as indicated by asterisks (i–iv). Based on characterisation of monoculture cells, this was not restricted to podocytes adjacent to GEC (ii) but was also evident with adjacent cells of the same type (iv). Quantification of ECM deposited between cells (v). C: Podocytes in GEC or control conditioned media and GECs in control conditioned media did not exhibit ECM deposition or organisation between cells (i–iii); GECs cultured in podocyte conditioned media deposited and organised ECM between cells (iv). Quantification of ECM deposited between cells (v). Pod CM, podocyte conditioned medium; GEC CM, GEC conditioned medium. Data points are shown as circles, n = 15-29. **, P < 0.0001; NS, P ≥ 0.05. Rachel Lennon generated data for this figure part A and B i-iv. Michael Randles generated data for this figure parts B v and C i-v. 105

3.3.3.5 Glomerular cell crosstalk alters ECM composition

To capture basal ECM in addition to the ECM deposited between cells, coculture ECM was extracted using a modified glomerular fractionation workflow. Samples were analysed by MS, which identified

123 extracellular proteins (Table S3.3). Once again, network analysis identified a highly connected subnetwork of basement membrane proteins (Figure 3.6, S3.13), indicating that the interconnected network topology of core structural components of the glomerular ECM was maintained in coculture.

Indeed basement membrane proteins were similarly identified in coculture and monoculture ECMs

(Figure 3.7A), but more differences were observed in other structural ECM proteins (Figure 3.7B) and

ECM-associated proteins (Figure 3.7C). We did not observe an increase in the abundance of mature collagen IV chains α3, α4, and α5 in ECM isolated from coculture (Figure 3.7A), a finding confirmed by

Western blotting (Figure 3.7D). However, collagen VI, which was abundant in tissue-derived glomerular

ECM 366 and absent in monoculture ECM, was enriched in coculture ECM (Figure 3.7B, 3.7D). In contrast, tenascin C, which was abundant in podocyte ECM, was less abundant in coculture and absent in glomerular ECM (Figure 3.7A, 3.7D), and CYR61, an inducer of angiogenesis, 373 was most abundant in GEC ECM, but less abundant in coculture and glomerular ECMs (Figure 3.7B, 3.7D). In summary, glomerular cell coculture results in altered ECM organisation and composition as compared to monoculture, regulating the synthesis and incorporation of ECM molecules in both a positive

(collagen VI) and negative (tenascin C, CYR61) manner. 106

Figure 3.6 Coculture ECM interaction network. A: Protein interaction network constructed from coculture ECM proteins identified by MS. ECM proteins were classified as basement membrane, other structural ECM or ECM-associated proteins, and were coloured and arranged accordingly. Nodes are labelled with gene names. ) B: Distributions of degree (number of protein -protein interactions per protein) for basement membrane, other structural ECM or ECM-associated proteins for the coculture ECM interaction network. Data points are shown as circles; outliers are shown as diamonds. **, P < 0.01; NS , P ≥ 0.05. Adam Byron generated data for this figure. 107

Figure 3.7 The composition of coculture ECM resembles glomerular ECM in vivo . A-C: Hierarchical clustering analysis of the abundance of basement membrane proteins (A), other structural ECM proteins (B) and ECM-associated proteins (C) identified in glomerular, coculture, podocyte and GEC ECMs. Heat maps display relative protein abundance (normalised spectral count), and associated dendrograms display clustering on the basis of uncentered Pearson correlation. Proteins are labelled with gene names for clarity. Arrowheads indicate candidates selected for Western blotting analysis. D: Western blotting (WB) confirmed the expression of selected structural ECM components identified by MS. Rachel Lennon generated data for this figure. 108

3.3.3.6 Building the glomerular ECM interactome

To investigate the molecular architecture of glomerular ECM in vivo and in vitro , a protein interaction network was generated to compare the monoculture and coculture ECM proteomes and the tissue- derived glomerular ECM proteome (JASN-2013-03-233). A total of 232 proteins were mapped to the interactome, which involved 511 reported protein–protein interactions (Figure 3.8). By clustering proteins according to their detection in different ECMs, we identified a core subnetwork of 35 proteins detected in cell- and tissue-derived ECM datasets. Intriguingly, these 35 proteins, which represented

15% of proteins in the network, were involved in 259 protein–protein interactions, which represented

51% of interactions in the network, demonstrating a substantial clustering of network connectivity in the core subnetwork (Figure 3.8). These findings suggest that these core proteins play key roles in mediating the majority of structural and functional interactions in glomerular ECM in vitro and in vivo .

Interestingly, the core ECM components included 50% of the basement membrane proteins (as compared to 9% of the ECM-associated proteins) in the interactome, supporting the central role of these structural components in ECM organisation. All other basement membrane proteins (apart from von Willebrand factor A domain–containing 1) were detected either in cell-derived ECM or tissue- derived glomerular ECM, but not in both, suggesting distinct structural ECM subnetworks in vitro and in vivo , which may reflect environmental context–dependent synthesis of glomerular ECM. 109

Figure 3.8 Model of the glomerular ECM interactome. All three in vitro ECM MS datasets and the in vivo glomerular ECM MS dataset were combined and mapped onto a database of human protein–protein interactions to create an interaction network model. The network comprised 232 glomerular ECM proteins (nodes; circles) and 511 protein–protein interactions (edges; grey lines). Proteins were clustered according to their identification in one or more ECM; the Venn diagram sets indicate in which ECMs each protein was identified. A core subnetwork of 35 proteins, which were detected in all four glomerular ECM preparations (central intersection set; inset), were involved in 51% (259) of protein–protein interactions in the network. ECM proteins were categorised as basement membrane, other structural ECM or ECM-associated proteins, and were coloured accordingly. Nodes are labelled with gene names for clarity. Adam Byron generated data for this figure. 110

3.3.4 Discussion

To investigate the relative cellular contribution to glomerular ECM synthesis, we focused on the cells either side of the GBM and characterised the ECM proteomes of GEC and podocytes in vitro . We found: (1) both GEC and podocytes synthesised and organised ECM in culture, the composition of which overlapped considerably with the tissue-derived glomerular ECM proteome; (2) the cell-derived

ECMs contained distinct components, with the enrichment of proteins involved in vascular development in GEC ECM and cell adhesion in podocyte ECM; (3) cell-derived ECM resembles a developmental phenotype; (4) glomerular cell crosstalk affects the composition of glomerular ECM; and (5) a core subnetwork of structural ECM proteins appears central to glomerular ECM assembly.

In addition to considerable overlap of ECM components, MS analysis of cell-derived ECMs revealed the enrichment of proteins involved in distinct cellular roles: vascular development in GEC ECM and cell adhesion in podocyte ECM. This enrichment of cell type–appropriate biological processes shows that the analysis of ECM isolated in vitro is a suitable approach for the investigation of the relative contribution of different cell types to glomerular ECM assembly.

There is growing evidence that cellular crosstalk within the glomerulus is critical for function. 315, 374 GEC and podocyte coculture was used to determine whether crosstalk altered the composition and organisation of cell-derived ECM. Ultrastructural analysis revealed the production of ECM between adjacent cells, which had a striking resemblance to a basement membrane. In addition, we observed

ECM between GEC in monoculture when these cells were cultured in medium conditioned by podocytes. Both observations provide evidence that the assembly and organisation of ECM requires crosstalk between glomerular cells and suggests that soluble mediators (eg. growth factors or ECM components) released by podocytes contribute to this effect.

The use of a coculture approach in vitro and hierarchical clustering analysis of MS data identified patterns of protein enrichment in common between the proteomes of the coculture ECM and the tissue- derived glomerular ECM (JASN-2013-03-233). Many of these similarities did not extend to monoculture-derived ECMs, suggesting that crosstalk between GEC and podocytes provides cues necessary to generate ECM that better resembles glomerular ECM in vivo . For example, components 111

such as collagen VI and TINAGL1 were enriched in coculture and glomerular ECM, whereas the expression of other components was reduced as compared to monoculture ECM. Interestingly, proteins associated with vascular development, such as CYR61 373 and HRTA1, 375 were downregulated in coculture ECM compared to GEC ECM. This suggests that paracrine factors may influence cell differentiation and fate. Tenascin C, which was highly expressed in podocyte ECM, was downregulated in coculture ECM and absent in glomerular ECM. This ECM ligand has roles in neurite guidance, 376 vascular morphogenesis and response to wounding. 377 The biological relevance of these molecules for glomerular function has yet to be established; however, our in vitro data suggest that these molecules could have temporally restricted expression in the glomerular ECM.

Among the basement membrane proteins detected, collagen IV chain α3, α4, and α5 and laminin β2 were present in low abundance in cell culture, indicating that cell-derived ECM resembles a developmental ECM phenotype. The expression of these ECM molecules was not substantially altered between monoculture and coculture, which contrasted strongly with the enrichment of these proteins to tissue-derived glomerular ECM (JASN-2013-03-233). Therefore, cell crosstalk between GEC and podocytes was not sufficient to recapitulate expression of the mature isoforms of these ECM molecules. We speculate that the developmental ECM phenotype could be due to the absence of environmental cues such as flow, tension, or specific cell–cell crosstalk in our existing cell culture system. Further investigation and incorporation of these environmental stimuli could lead to improved cell culture systems.

This unbiased, global investigation demonstrates the complexity of the glomerular ECM proteome.

Using a combination of strategies in vitro we were able to provide insight into the relative contribution of glomerular cells to ECM production and show that this may be regulated by cell–cell crosstalk. Network analyses revealed distinct and overlapping subnetworks of ECM proteins and a core set of components that may play key roles in the structure and function of glomerular ECM. These networks can now be extended to investigate ECM composition, organisation, and regulation in the context of glomerular development and disease.

112

3.3.5 Methods

3.3.5.1 Antibodies

Monoclonal antibodies used were against β-catenin (610154; BD Biosciences), cleaved caspase-3

(9664; Cell Signalling) PECAM1 (89C2; Cell Signalling), TINAGL1 (ab69036; Abcam, Cambridge, UK), tenascin C (ab108930; Abcam), and collagen IV chains (provided by B. Hudson, Vanderbilt University

Medical Center, Nashville, TN, USA). Polyclonal antibodies used were against pan-collagen VI

(ab6588; Abcam), CYR61 (ab24448; Abcam) and ZO1 (5406; Cell Signalling). Secondary antibodies against rabbit IgG conjugated to TRITC and mouse or rat IgG conjugated to FITC (Jackson

ImmunoResearch Laboratories, Inc., West Grove, PA, USA) were used for immunofluorescence; secondary antibodies conjugated to AlexaFluor 680 (Life Technologies, Paisley, UK) or IRDye 800

(Rockland Immunochemicals, Glibertsville, PA, USA) were used for Western blotting.

3.3.5.2 Western blotting

See general materials and methods.

3.3.5.3 Glomerular cell culture

Conditionally immortalised human podocytes 363 and GEC 364 were grown in monoculture or coculture on uncoated tissue culture plates. Podocytes between passage 10 and 16 were cultured for 14 days at

37 oC in RPMI-1640 medium with glutamine (R-8758; Sigma, St. Louis, MO, USA) supplemented with

10% (v/v) FCS (Life Technologies) and 5% (v/v) ITS (I-1184; Sigma; 1 ml/100 ml). GEC were grown in endothelial basal medium-2 (CC-3156; Lonza, Slough, UK) containing 5% (v/v) FCS and EGM-2

BulletKit growth factors (CC-4147; Lonza) , excluding VEGF, for 5 days at 37 oC. For coculture, proliferating cells in suspension were combined, seeded, and cultured in GEC medium for 14 days at

37 oC. For experiments with conditioned medium, feeder podocytes and GEC were grown in GEC medium (as above) for 12 days. Test podocytes and GEC were grown in parallel at 37 oC for 12 days.

Every 48 hours , medium from test cells was removed and replaced with 50% (v/v) fresh medium and

50% (v/v) conditioned medium from feeder cells. Before addition to test cells, conditioned medium was centrifuged at 1600 rpm for 4 minutes to remove cells and cell debris . 113

3.3.5.4 Non-glomerular cell culture

HEK 293T and human foreskin fibroblasts were cultured until confluent in Dulbecco's Modified Eagle

Medium (DMEM) supplemented with 10% foetal bovine serum (Gibco) .

3.3.5.5 Lentiviral production and transduction

Podocytes stably expressing GFP were produced by lentiviral transduction. HEK 293T cells were transfected with three plasmids obtained from Addgene (psPAX2 Addgene ID 12260, pMD2.G

Addgene ID 12259 and pWPXL Addgene ID 12257) using polyethyleneimine (Sigma-Aldrich).

Conditioned medium containing viruses was collected after 5 days following several media changes including an 8 hr incubation with 1M sodium butyrate-containing media to promote virus production.

Conditioned media was then used immediately to infect conditionally immortalised podocytes or stored at -80°C. To each well, one ml of normal culture media supplemented with 8 g.ml-1 of polybrene, and

1 ml of 293T conditioned media containing the virus was added. The day after the transduction, the medium was replaced by fresh media. Positive GFP-expressing podocytes were selected using fluorescence-activated cell sorting.

3.3.5.6 Isolation of cell-derived ECM from monocultures

ECM was isolated as previously described. 342, 368 Fifteen-centimeter plates (Corning BV Life Sciences,

Amsterdam, The Netherlands) were seeded with glomerular cells, and culture medium was exchanged alternate daily for fresh medium supplemented with 50 g/ml ascorbic acid to stabilise ECM fibrils.

Differentiated cells were washed with cold PBS, and ECM was denuded of cells by lysis with 20 mM

NH 4OH and 0.5% (v/v) Triton X-100 in PBS, followed by digestion with 10 g/ml DNase I (Roche) in

PBS containing 1 mM calcium and 0.5 mM magnesium. Denuded ECM was scraped into reducing sample buffer and heat denatured at 70 oC for 20 minutes. Three biological replicates were analysed for podocyte and GEC monocultures. 114

3.3.5.7 Isolation of cell-derived ECM from cocultures

To recover ECM deposited between cocultured cells, the method of ECM enrichment from tissue

(described above) was adapted. Differentiated cells were washed with cold PBS. Cells and ECM were scraped into extraction buffer (10 mM Tris, 150 mM NaCl, 1% (v/v) Triton X-100, 25 mM EDTA, 25

g/ml leupeptin, 25 g/ml aprotinin and 0.5 mM AEBSF) and incubated for 30 minutes , followed by centrifugation at 14000 × g for 10 minutes. The resulting pellet was re-suspended in alkaline detergent buffer for 5 minutes before centrifuging at 14000 × g for 10 minutes. The pellet was incubated in

DNase buffer for 30 minutes and then centrifuged at 14000 × g for 10 minutes before re -suspension in reducing sample buffer . Samples were heat denatured at 70 oC for 20 minutes. Three coculture biological replicates were analysed.

3.3.5.8 Collagenase treatment

To release collagen NC1 epitopes for Western blotting, glomerular isolates were initially treated with

500 g/ml collagenase in PBS (CLSPA grade; Worthington Biochemical, Lakewood, NJ, USA) overnight at 37 oC. For cell-derived ECM, cells were washed twice in PBS and scraped into cold PBS, followed by incubation with collagenase (50 g/ml) in PBS for 2 hours at 37 oC. ECM samples treated with collagenase were not used for MS analysis but prepared in parallel for collagen IV analysis by

Western blotting under non-reducing conditions.

3.3.5.9 MS data access

The MS proteomics data have been deposited to the ProteomeXchange Consortium

(http://proteomecentral.proteomexchange.org ) through the PRoteomics IDEntifications partner

340 repository with the dataset identifier PXD000643.

3.3.5.10 MS data acquisition

See General Materials and Methods.

115

3.3.5.11 MS data analysis

Tandem mass spectra were extracted using extract_msn (Thermo Fisher Scientific) executed in

Matrix Science). 297 Carbamidomethylation of cysteine was set as a fixed modification; oxidation of methionine and hydroxylation of proline and lysine were allowed as variable modifications. Only tryptic peptides were considered, with up to one missed cleavage permitted. Monoisotopic precursor mass values were used, and only doubly and triply charged precursor ions were considered. Mass tolerances for precursor and fragment ions were 0.4 Da and 0.5 Da, respectively. MS datasets were validated using rigorous statistical algorithms at both the peptide and protein level 338, 339 implemented in Scaffold

3.3.5.12 MS data quantification and statistical analysis

Relative protein abundance was calculated using the unweighted spectral count of a given protein normalised to the total number of spectra observed in the entire sample and to the molecular weight of that protein (normalised spectral count) . Mean normalised spectral counts were calculated using data from triplicate GEC monoculture ECM, podocyte monoculture ECM, and coculture ECM samples. For comparison, equivalent data were extracted from the recently published tissue-derived glomerular ECM proteome. Statistical analysis of the relative abundance of ECM proteins was performed using

QSpec. 378 Proteins with Bayes factors ≥ 10 and natural-logarithm-transformed fold changes ≥ 1.5 were selected as differentially expressed. These parameters were chosen to provide a conservative false discovery rate estimate of <5% in accordance with the modelled data of Choi et al .378

116

3.3.5.13 Hierarchical clustering analysis

Agglomerative hierarchical clustering was performed using Cluster 3.0 (C Clustering Library, version

1.37) .379 Quantitative data (mean normalised spectral counts) were used for hierarchical clustering.

Protein hits were hierarchically clustered on the basis of uncentered Pearson correlation, and distances between hits were computed using a complete-linkage matrix. Clustering results were visualised using

Java TreeView (version 1.1.1) 380 and MultiExperiment Viewer (version 4.1.01) .381

3.3.5.14 Functional annotation and enrichment analysis

Proteins identified in at least two of the three biological replicates were included for further analysis.

Gene ontology (GO) annotations were downloaded using the online resource DAVID. 382, 383 The GO cellular compartment annotation chart (GOTERM_CC_FAT) was selected, and proteins annotated in the extracellular region cluster were further cross-referenced with the human matrisome project 302 , and cytoplasmic proteins annotated as extracellular region (ACTN1, ACTN2, ACTN4, CALM1, CSNK2B,

FLNA, GLIPR2, HINT2, HSPD1, RNH1, TLN1, TTN, TUB4A4, and VCL) were excluded. This selection enabled the definition of the GEC, podocyte, and coculture ECM proteomes.

3.3.5.15 Protein interaction network analysis

348 2011) and Mus musculus network (release date, 28 June 2011), the ECM interactions database

MatrixDB (release date, 26 August 2010), 10 and a literature-curated database of integrin-based

349 adhesion–associated proteins. Topological parameters were computed using the NetworkAnalyzer plug-in. 350

3.3.5.16 Cellular immunofluorescence

primary antibodies. Coverslips were mounted and images were collected using a CoolSnap HQ camera (Photometrics, Tucson, AZ, USA) and separate DAPI/FITC/Cy3 filters (U-MWU2, 41001,

41007a , respectively; Chroma, Olching, Germany) to minimise bleed -through between the different channels. For analysis of cell-cell junctions and 3D ECM models, images were acquired on a Delta

Vision (Applied Precision) restoration microscope using a 60x objective and the [ Sedat ] filter set

(Chroma [ 89000 ]). The images were collected using a Coolsnap HQ (Photometrics) camera with a Z optical spacing of 0.2 m. For cell-cell junction images, raw images were deconvolved using the

Softworx software and maximum intensity projections of these deconvolved images are shown in the results. For 3D models of ECM organisation raw image Z-stacks were processed using fiji image J software. Acquired z-stacks were segmented and features traced using TrakEM2, 384 the resulting model was visualised using the 3D viewer plug-in (ImageJ_3D_Viewer.jar).

3.3.5.17 Isolation of RNA

Cells were removed from a confluent 75-cm 2 tissue culture flask using 1 ml RNA Protect (Qiagen,

Crawley, UK). RNA was isolated from the cell pellet using the RNAeasy plus kit (Qiagen) according to the manufacturer’s instructions. RNA quality and concentration were measured using a NanoDrop

2000 spectrophotometer.

3.3.5.18 RT-PCR

Approximately 1 g of RNA was reverse-transcribed using SuperScript III First-Strand Synthesis

SuperMix (Life Technologies, Paisley, UK) according to the manufacturer’s instructions. Specific oligonucleotide primers were designed based on genomic sequences obtained from the University of

California, Santa Cruz, Genome Browser (http://genome.ucsc.edu/) . All intronic sequence was removed , and primers were designed to recognise sequences spanning an exon–exon boundary using

Primer3 software (version 0.4.0) (http://frodo.wi.mit.edu/primer3/). PCR amplification for each polymorphism was performed using 100 ng cDNA, 0.5 µmol/l each primer, 0.75 mmol/l each deoxynucleoside triphosphate (dNTP) and 0.5 units of Taq polymerase , with varying concentrations of

Mg 2+ and with or without 10% (v/v) DMSO, in a total reaction volume of 20 µl PCR buffer (supplied with the Taq polymerase). Cycling conditions were as follows: 94 °C for 3 minutes, followed by 32 or 40 118

cycles of 94 °C for 30 seconds, variable annealing temperatures for 30 seconds, 72 °C for 30 seconds, followed by a terminal extension step of 72 °C for five minutes. Genomic DNA was used as a control , and water for injection was used as a no -template control. Amplification of the product was confirmed by gel electrophoresis using a 2% (w/v) agarose gel and visualised using ethidium bromide. Molecular masses were compared to a standard base -pair ladder. RT-PCR primers used were as follows:

Primer Sequence (5' to 3') Mg 2+ Cycles Product size Temp

(mM) (base pairs) (°C)

COLL4A1 CDNA F GTTGGTCTACCGGGACTCAA 3 32 204 60

COLL4A1 CDNA R GGCCTATTCCTGGAACTCCT 3 32 204 60

COLL4A3 CDNA F AGGATTTCGTGGTCCAACAG 3 35 207 62

COLL4A3 CDNA R CCTCGTTCCCCTTTACTTCC 3 35 207 62

COLL4A4 CDNA F GAACAAAAGGTGACCCAGGA 5 32 223 60

COLL4A4 CDNA R ATCCCCTTTTTCTCCAGCAT 5 32 223 60

COLL4A5 CDNA F AAAGGAGAGCCTGGTGGAAT 5 32 220 60

COLL4A5 CDNA R CCGGCTGGGTTATAGTCTGA 5 32 220 60

BETA ACTIN CDNA F GCCGTCTTCCCCTCCATC 3.5 32 322 65

BETA ACTIN CDNA R CCCCAGCCATGTACGTTGCTA 3.5 32 322 65

3.3.5.19 q-PCR and relative abundance

For a single reaction 4µl 5X VILO™ Reaction Mix, 2µl 10X SuperScriptR Enzyme Mix, RNA (up to 2.5

µg) and DEPC-treated water to 20 µl were combined and incubated on ice. The tube contents were gently mixed, centrifuged and placed in a thermal cycler. The tubes were incubated at 25°C for 10 minutes followed by 60 minutes at 42°C and 5 minutes at 85°C. cDNA and primer/probe mixes for target gene and endogenous control were briefly vortexed and centrifuged. TaqMan Gene Expression assays for target genes COL4A1 (Hs00266237_ml) and COL4A3 (Hs01022542_ml) and endogenous control GAPDH (Hs03929097_gl) were used. 3 replicates were prepared for each of the target genes and one for the endogenous control. The plate was covered with an optical lid centrifuged and placed in the TaqMan 7500. The samples were heated to 50°C for 5 minutes followed by heating to 95°C for

10 minutes. Thermal cycling conditions were as follows; 95°C for 15 seconds followed by 60°C for 1 minute for 40 cycles. Relative abundance ( ∆∆ Ct) was calculated using the system software via the comparative CT method . 119

3.3.5.20 Transmission electron microscopy and ECM quantification

Samples were fixed for at least 1 hour in a mix of 2% formaldehyde and 2.5% glutaraldehyde in 0.1 M sodium cacodylate buffer (pH 7.4). The samples were postfixed with reduced osmium (1% OsO 4 and

1.5% K 4Fe(CN) 6) for 1 hour, then with 1% tannic acid in 0.1 M sodium cacodylate buffer for 1 hour, and finally with 1% uranyl acetate in water overnight. The specimens were dehydrated with alcohols, infiltrated with TAAB LV resin and polymerised for 12 hours at 60 oC. Specimens were then detached from the aclar coverslips and glued together with the resin by an additional 24 hours of polymerisation.

Ultrathin 70 -nm sections were cut with a Leica Ultracut S ultramicrotome and placed on formvar/carbon -coated slot grids. The grids were observed in a Tecnai 12 Biotwin transmission electron microscope at 80 kV. Electron microscopy data from three separate experiments were screened for ECM deposition between cells using Fiji/ImageJ software (version 1.46r; National

Institutes of Health, Bethesda, MD, USA). ECM thicknesses between cells were quantified in three regions per observation and reported as mean values.

3.3.5.21 Statistical analysis

(black line). Numbers of protein–protein interactions and ECM thicknesses were compared using

Kruskal–Wallis one-way analysis of variance tests with post-hoc Bonferroni correction. GO enrichment analyses were compared using modified Fisher’s exact tests with Benjamini–Hochberg correction. P- values <0.05 were deemed significant.

3.3.6 Acknowledgments

This work was supported by a Wellcome Trust Intermediate Fellowship award to R.L. (ref: 090006) and

Wellcome Trust grant 092015 to M.J.H. R.Z. is supported by VA Merit Award 1I01BX002196-01,

DK075594, DK069221, DK083187 , and an American Heart Association Established Investigator

Award. The mass spectrometer and microscopes used in this study were purchased with grants from the Biotechnology and Biological Sciences Research Council, Wellcome Trust, and the University of 120

Manchester Strategic Fund. Mass spectrometry was performed in the Biological Mass Spectrometry

Core Facility, Faculty of Life Sciences, University of Manchester, and we thank David Knight and

Stacey Warwood for advice and technical support. We thank Julian Shelley for bioinformatic support.

Microscopy was performed in the Bioimaging and Electron Microscopy Core Facilities, Faculty of Life

Sciences, University of Manchester. The collagen IV chain–specific antibodies were kindly provided by the Billy Hudson research group, Department of Medicine, Vanderbilt Medical Center.

3.3.7 Statement of competing financial interests

No competing interests . 121

3.3.8 Supplementary data

Table 3.1 Glomerular endothelial cell ECM proteome

Uniprot ID Gene name Alias Abundance Category

(nSC)

O00468 AGRN Agrin 0.635 Glycoprotein

P39060 COL18A1 Collagen alpha-1(XVIII) chain 1.458 Collagen

P02462 COL4A1 Collagen alpha-1(IV) chain 2.787 Collagen

P08572 COL4A2 Collagen alpha-2(IV) chain 2.535 Collagen

Q01955 COL4A3 Collagen alpha-3(IV) chain 0.1 Collagen

P53420 COL4A4 Collagen alpha-4(IV) chain 0.001 Collagen

P29400 COL4A5 Collagen alpha-5(IV) chain 0.357 Collagen

P20908 COL5A1 Collagen alpha-1(V) chain 0.202 Collagen

P05997 COL5A2 Collagen alpha-2(V) chain 0.362 Collagen

Q02388 COL7A1 Collagen alpha-1(VII) chain 0.01 Collagen

P27658 COL8A1 Collagen alpha-1(VIII) chain 1.921 Collagen

P23142 FBLN1 Fibulin-1 0.11 Glycoprotein

P35555 FBN1 Fibrillin-1 1.705 Glycoprotein

P02751 FN1 Fibronectin 10.912 Glycoprotein

P98160 HSPG2 Perlecan 6.387 Proteoglycan

Q16787 LAMA3 Laminin alpha-3 chain 0.026 Glycoprotein

variant 1

Q16363 LAMA4 Laminin subunit alpha-4 0.445 Glycoprotein

O15230 LAMA5 Laminin subunit alpha-5 0.406 Glycoprotein

P07942 LAMB1 Laminin subunit beta-1 0.908 Glycoprotein

P55268 LAMB2 Laminin subunit beta-2 0.075 Glycoprotein

Q13751 LAMB3 Laminin subunit beta-3 0.048 Glycoprotein

P11047 LAMC1 Laminin subunit gamma-1 0.772 Glycoprotein

Q13753 LAMC2 Laminin subunit gamma-2 0.08 Glycoprotein

Q9H8L6 MMRN2 Multimerin-2 1.58 Glycoprotein

P14543 NID1 Nidogen-1 0.614 Glycoprotein

Q14112 NID2 Nidogen-2 0.131 Glycoprotein

Q9HB63 NTN4 Netrin-4 0.249 Glycoprotein

P24821 TNC Tenascin 0.215 Glycoprotein

Q6PCB0 VWA1 von Willebrand factor A domain- 0.419 Glycoprotein

containing protein 1 122

P02452 COL1A1 Collagen alpha-1(I) chain 0.077 Collagen

P29279 CTGF Connective tissue growth factor 5.519 Glycoprotein

O00622 CYR61 Protein CYR61 26.44 Glycoprotein

O43854 EDIL3 EGF-like repeat and discoidin I-like 0.187 Glycoprotein

domain-containing protein 3

Q53RD9 FBLN7 Fibulin-7 0.215 Glycoprotein

P35556 FBN2 Fibrillin-2 1.505 Glycoprotein

Q14314 FGL2 Fibroleukin 0.941 Glycoprotein

Q5D862 FLG2 Filaggrin-2 0.13 Cytoskeleton

P17936 IGFBP3 Insulin-like growth factor-binding 0.066 Glycoprotein

protein 3

P25493 IGFBP5 Insulin-like growth factor-binding 0.159 Glycoprotein

protein 5

Q16270 IGFBP7 Insulin-like growth factor-binding 1.315 Glycoprotein

protein 7

Q14766 LTBP1 Latent-transforming growth factor beta- 0.067 Glycoprotein

binding protein 1

Q14767 LTBP2 Latent-transforming growth factor beta- 0.025 Glycoprotein

binding protein 2

P55081 MFAP1 Microfibrillar-associated protein 1 0.225 Glycoprotein

P08493 MGP Matrix Gla protein 9.374 Glycoprotein

B1ALD8 POSTN Periostin, osteoblast specific factor 2.566 Glycoprotein

Q92926 PXDN Peroxidasin homolog 0.277 Glycoprotein

P78539 SRPX Sushi repeat-containing protein SRPX 0.053 Glycoprotein

Q15582 TGFBI Transforming growth factor-beta-induced 2.818 Glycoprotein

protein ig-h3

P07996 THBS1 Thrombospondin-1 10.386 Glycoprotein

Q6ZMP0 THSD4 Thrombospondin type-1 domain- 1.317 Glycoprotein

containing protein 4

Q9GZM7 TINAGL1 Tubulointerstitial nephritis antigen-like 0.96 Glycoprotein

P13611 VCAN Versican core protein 0.182 Proteoglycan

P04004 VTN Vitronectin 1.827 Glycoprotein

E2QRD0 VWA8 von Willebrand factor A domain- 0.013 Glycoprotein

containing protein 8 123

P01023 A2M Alpha-2-macroglobulin 0.29 Protease inhibitor

O75173 ADAMTS4 A disintegrin and metalloproteinase with 0.189 Protease

thrombospondin motifs 4

P02771 AFP Alpha-fetoprotein 0.147 Secreted

B7Z8Q2 AHSG Alpha-2-HS-glycoprotein 3.799 Secreted

P02768 ALB Serum albumin 9.425 Secreted

Q9BY76 ANGPTL4 Angiopoietin-related protein 4 1.683 Secreted

P04083 ANXA1 Annexin A1 0.364 Annexin

E9PDK5 ANXA11 Annexin A11 0.068 Annexin

P07355 ANXA2 Annexin A2 2.821 Annexin

P08758 ANXA5 Annexin A5 0.047 Annexin

B0YIW2 APOC3 Apolipoprotein C-III variant 1 0.163 Secreted

P02649 APOE Apolipoprotein E 1.066 Apolipoprotein

Q5SRP5 APOM Apolipoprotein M 0.302 Apolipoprotein

P61769 B2M Beta-2-microglobulin 0.445 Secreted

E9PGA6 C1QTNF Complement C1q tumor necrosis factor- 0.652 Secreted

related protein 3

P01024 C3 Complement C3 (Fragment) 0.123 Complement

Q76M96 CCDC80 Coiled-coil domain-containing protein 80 0.054 Secreted

Q6YHK3 CD109 CD109 antigen 0.171 Cell surface

P16070 CD44 CD44 antigen 0.636 Cell surface

P13987 CD59 CD59 glycoprotein 4.482 Cell surface

Q5XG92 CES4A Isoform 4 Carboxylesterase 8 0.124 Enzyme

P01040 CSTA Cystatin-A 2.022 Protease inhibitor

A5JHP3 DCD Dermcidin isoform 2 6.513 Secreted

Q03001 DST Dystonin 0.005 Plakin

P00488 F13A1 Coagulation factor XIII A chain 0.032 Coagulation

P00734 F2 Prothrombin (Fragment) 0.309 Coagulation

P12259 F5 Coagulation factor V 0.167 Coagulation

P09038 FGF2 Heparin-binding growth 1.818 Secreted

factor 2

P02774 GC Vitamin D-binding protein 0.08 Secreted

Q99988 GDF15 Growth/differentiation factor 15 4.169 Secreted

P06396 GSN Gelsolin 0.982 Cytoskeleton 124

Q96QV1 HHIP Hedgehog-interacting protein precursor 1.873 Cell surface

F5GXA6 HLAC HLA class I histocompatibility antigen, 0.324 Cell surface

Cw-7 alpha chain

Q92743 HTRA1 Serine protease HTRA1 2.716 Protease

P83110 HTRA3 Serine protease HTRA3 0.134 Protease

C9JAF2 IGF2 Insulin-like growth factor 2 isoform 2 0.421 Secreted

P05161 ISG15 Interferon-induced 17 kDa protein 0.69 Secreted

P19823 ITIH2 Inter-alpha-trypsin inhibitor heavy chain 0.279 Protease inhibitor

P09382 LGALS1 Galectin-1 4.089 Secreted

O00182 LGALS9 Galectin-9 0.798 Secreted

Q08380 LGAS3BP Galectin-3-binding protein 0.251 Secreted

Q9Y4K0 LOXL2 Lysyl oxidase homolog 2 0.754 Secreted

P21741 MDK Midkine 1.574 Secreted

Q8WX17 MUC16 Mucin-16 0.004 Cell surface

Q15354 orf2 Platelet-derived growth factor beta 0.736 Secreted

preproprotein

P07237 P4HB Protein disulfide-isomerase 0.401 Enzyme

P04085 PDGFA Platelet derived growth 0.532 Secreted

factor A

P00750 PLAT Tissue-type plasminogen activator 0.142 Secreted

P00749 PLAU Urokinase-type plasminogen activator 0.086 Secreted

P00747 PLG Plasminogen 0.111 Secreted

P62937 PPIA Peptidyl-prolyl cis-trans isomerase A 0.437 Enzyme

O95084 PRSS23 Serine protease 23 1.62 Protease

P35030 PRSS3 Trypsin-3 0.079 Protease

P26022 PTX3 Pentraxin-related protein PTX3 0.574 Secreted

Q6ZRP7 QSOX2 Sulfhydryl oxidase 2 0.043 Enzyme

P60903 S100A10 Protein S100-A10 0.879 Secreted

P31949 S100A11 Protein S100-A11 2.078 Cytoplasm

Q96FQ6 S100A16 Protein S100-A16 1.122 Cytoplasm

Q86YZ3 S100A18 Hornerin 0.223 Cytoplasm

P06703 S100A6 Protein S100-A6 4.869 Cell surface

P05109 S100A8 Protein S100-A8 1.124 Secreted

B4E219 SEMA3C Semaphorin-3C 0.035 Secreted 125

P01009 SERPINA1 Alpha-1-antitrypsin 0.216 Protease inhibitor

P05154 SERPINA5 Plasma serine protease inhibitor 0.27 Protease inhibitor

P01008 SERPINC1 Antithrombin-III 0.991 Protease inhibitor

P05121 SERPINE1 Plasminogen activator 38.709 Protease inhibitor

inhibitor 1

P50454 SERPINH1 Serpin H1 0.778 Protease inhibitor

Q9UQE7 SMC3 Structural maintenance chromosomes 0.079 Nucleus

protein 3

Q9Y5W8 SNX13 Small inducible cytokine B14 precursor 0.518 Cytoplasm

P48307 TFPI2 Tissue factor pathway 0.567 Secreted

inhibitor 2

P61812 TGFB2 Transforming growth factor beta-2 0.221 Secreted

P21980 TGM2 Protein-glutamine gamma- 7.415 Enzyme

glutamyltransferase 2

Q9Y3A2 UPT11L Probable U3 small nucleolar RNA- 0.768 Secreted

associated protein 11 126

Table 3.2 Podocyte ECM proteome

Uniprot Gene name Alias Abundance (nSC) Category

O00468 AGRN Agrin 0.294 Glycoprotein

P39060 COL18A1 Collagen alpha-1(XVIII) 2.203 Collagen

chain

P02462 COL4A1 Collagen alpha-1(IV) chain 2.02 Collagen

P08572 COL4A2 Collagen alpha-2(IV) chain 3.327 Collagen

Q01955 COL4A3 Collagen alpha-3(IV) chain 0.001 Collagen

P53420 COL4A4 Collagen alpha-4(IV) chain 0.039 Collagen

P29400 COL4A5 Collagen alpha-5(IV) chain 0.017 Collagen

P20908 COL5A1 Collagen alpha-1(V) chain 0.036 Collagen

P05997 COL5A2 Collagen alpha-2(V) chain 0.025 Collagen

Q02388 COL7A1 Collagen alpha-1(VII) chain 0.064 Collagen

P27658 COL8A1 Collagen alpha-1(VIII) chain 0.743 Collagen

P23142 FBLN1 Fibulin-1 0.8 Glycoprotein

P35555 FBN1 Fibrillin-1 1.147 Glycoprotein

P02751 FN1 Fibronectin 20.621 Glycoprotein

Q96RW7 HMCN1 Hemicentin-1 0.321 Glycoprotein

P98160 HSPG2 Perlecan 5.194 Proteoglycan

Q16787 LAMA3 Laminin subunit alpha-3 0.518 Glycoprotein

O15230 LAMA5 Laminin subunit alpha-5 0.559 Glycoprotein

P07942 LAMB1 Laminin subunit beta-1 0.543 Glycoprotein

P55268 LAMB2 Laminin subunit beta-2 0.023 Glycoprotein

Q13751 LAMB3 Laminin subunit beta-3 1.655 Glycoprotein

P11047 LAMC1 Laminin subunit gamma-1 0.615 Glycoprotein

Q13753 LAMC2 Laminin subunit gamma-2 0.995 Glycoprotein

P14543 NID1 Nidogen-1 0.371 Glycoprotein

Q14112 NID2 Nidogen-2 0.03 Glycoprotein

P24821 TNC Tenascin 13.744 Glycoprotein

Q07092 COL16A1 Collagen alpha-1(XVI) chain 0.1 Collagen

P02452 COL1A1 Collagen alpha-1(I) chain 0.333 Collagen

P29279 CTGF Connective tissue growth 0.503 Glycoprotein

factor

Q96CG8 CTHRC1 Collagen triple helix repeat- 1.065 Glycoprotein

containing protein 1

O00622 CYR61 Protein CYR61 6.014 Glycoprotein 127

O43854 EDIL3 EGF-like repeat and 0.05 Glycoprotein

discoidin I-like domain-

containing protein 3

P35556 FBN2 Fibrillin-2 0.257 Glycoprotein

P02675 FGB Fibrinogen beta chain 0.136 Glycoprotein

P10915 HAPLN1 Hyaluronan and 3.07 Proteoglycan

proteoglycan link protein 1

P18065 IGFBP2 Insulin-like growth factor 0.108 Glycoprotein

binding protein 2

P17936 IGFBP3 Insulin-like growth factor- 0.586 Glycoprotein

binding protein 3

P25493 IGFBP5 Insulin-like growth factor- 0.458 Glycoprotein

binding protein 5

Q16270 IGFBP7 Insulin-like growth factor- 0.341 Glycoprotein

binding protein 7

Q14767 LTBP2 Latent-transforming growth 0.103 Glycoprotein

factor beta-binding protein 2

Q8N2S1 LTBP4 Latent-transforming growth 0.36 Glycoprotein

factor beta-binding protein 4

O00339 MATN2 Matrilin-2 3.792 Glycoprotein

P55081 MFAP1 Microfibrillar-associated 0.069 Glycoprotein

protein 1

Q08431 MFGE8 Lactadherin 0.091 Glycoprotein

Q6UX19 NPNT Nephronectin 0.653 Glycoprotein

B1ALD8 POSTN Periostin, osteoblast specific 0.062 Glycoprotein

factor

Q92926 PXDN Peroxidasin 1.017 Glycoprotein

Q15582 TGFBI Transforming growth factor- 18.156 Glycoprotein

beta-induced protein

P07996 THBS1 Thrombospondin-1 6.558 Glycoprotein

Q6ZMP0 THSD4 Thrombospondin type-1 0.651 Glycoprotein

domain-containing protein 4

Q9GZM7 TINAGL1 Tubulointerstitial nephritis 1.307 Glycoprotein

antigen-like

P13611 VCAN Versican core protein 1.614 Proteoglycan

P04004 VTN Vitronectin 2.164 Glycoprotein

P01023 A2M Alpha-2-macroglobulin 0.929 Protease inhibitor 128

Q9UHI8 ADAMTS1 A disintegrin and 0.331 Protease

metalloproteinase with

thrombospondin motifs 1

Q8WXS8 ADAMTS14 Matrix metalloproteinase 14 0.185 Protease

preproprotein

Q9UNA0 ADAMTS5 A disintegrin and 0.197 Protease

metalloproteinase with

thrombospondin motifs 5

Q6UY14 ADAMTSL4 ADAMTS-like protein 4 0.505 Protease

P15121 AKR1B1 Aldose reductase 0.933 Enzyme

Q9BY76 ANGPTL4 Angiopoietin-related 0.168 Secreted

protein 4

P07355 ANXA2 Annexin A2 1.651 Annexin

P09525 ANXA4 Annexin IV 0.177 Annexin

P08758 ANXA5 Annexin A5 0.155 Annexin

E5RJR0 ANXA6 Annexin VI 0.085 Annexin

P04114 APOB Apolipoprotein B-100 0.047 Apolipoprotein

P02649 APOE Apolipoprotein E 1.039 Apolipoprotein

Q5SRP5 APOM Apolipoprotein M 0.199 Apolipoprotein

E9PGA6 C1QTNF Complement C1q and tumor 2.161 Secreted

necrosis factor protein

P01024 C3 Complement C3 0.22 Complement

P01031 C5 Complement C5 0.025 Complement

Q76M96 CCDC80 Coiled-coil domain- 0.745 Secreted

containing protein 80

Q6YHK3 CD109 CD109 antigen 0.064 Cell surface

P16070 CD44 CD44 antigen 0.506 Cell surface

P13987 CD59 CD59 glycoprotein 2.322 Cell surface

Q5XG92 CES4A Carboxylesterase 8 0.114 Enzyme

P10909 CLU Clusterin 0.081 Secreted

P01040 CSTA Cystatin-A 2.122 Protease inhibitor

P07858 CTSB Cathepsin B 0.387 Protease

P07339 CTSD Cathepsin D 0.042 Protease

Q9UBR2 CTSZ Cathepsin Z 0.082 Protease

A5JHP3 DCD Dermcidin 2 3.988 Secreted

P24655 DNASE1 Deoxyribonuclease-1 0.707 Enzyme

Q03001 DST Dystonin 0.018 Plakin

Q14213 EBI3 Interleukin-27 subunit beta 0.339 Secreted 129

Q9UM22 EPDR1 Mammalian ependymin- 1.027 Secreted

related protein 1

P00488 F13A1 Coagulation factor XIII A 0.11 Coagulation

chain

P00734 F2 Prothrombin 0.275 Coagulation

P12259 F5 Coagulation factor V 0.489 Coagulation

P09038 FGF2 Heparin-binding growth 1.484 Secreted

factor 2

P12034 FGF5 Fibroblast growth factor 5 0.451 Secreted

P20930 FLG Filaggrin 0.022 Cytoskeleton

Q5D862 FLG2 Filaggrin-2 0.106 Cytoskeleton

Q10471 GALNT2 Polypeptide N- 0.055 Secreted

acetylgalactosaminyltransfer

ase 2

Q99988 GDF15 Growth/differentiation 3.986 Secreted

factor 15

Q92820 GGH Gamma-glutamyl hydrolase 0.053 Enzyme

O60565 GREM1 Gremlin-1 2.584 Secreted

P06396 GSN Gelsolin 2.986 Secreted

Q96QV1 HHIP Hedgehog-interacting 0.075 Secreted

protein precursor

P19823 ITIH2 Inter-alpha-trypsin inhibitor 0.425 Protease inhibitor

heavy chain H2

P09382 LGALS1 Galectin-1 4.331 Secreted

O00214 LGALS8 Galectin-8 0.099 Secreted

O00182 LGALS9 Galectin-9 0.254 Secreted

Q08380 LGAS3BP Galectin-3-binding protein 0.125 Secreted

Q08397 LOXL1 Lysyl oxidase homolog 1 0.268 Secreted

Q9Y4K0 LOXL2 Lysyl oxidase homolog 2 1.989 Secreted

Q96JB6 LOXL4 Lysyl oxidase homolog 4 0.402 Secreted

P02780 LTF Lactoferrin 0.049 Secreted

P08582 MFI2 Melanotransferrin 0.057 Cell surface

Q13251 NOG Noggin 0.293 Secreted

Q15354 orf2 Platelet-derived growth 0.584 Secreted

factor beta 2 preproprotein

O15460 P4HA2 Prolyl 4-hydroxylase subunit 0.029 Enzyme

alpha-2

P07237 P4HB Protein disulfide-isomerase 0.546 Enzyme 130

P04085 PDGFA Platelet-derived growth 0.248 Secreted

factor subunit A

P00749 PLAU Urokinase-type plasminogen 0.285 Secreted

activator

P00747 PLG Plasminogen 0.039 Secreted

Q5JXB8 PLOD1 Procollagen-lysine, 2- 0.082 Enzyme

oxoglutarate 5-dioxygenase1

P62937 PPIA Peptidyl-prolyl cis-trans 1.73 Enzyme

isomerase A

Q92954 PRG4 Proteoglycan 4 0.038 Secreted

O95084 PRSS23 Serine protease 23 3.327 Protease

P35030 PRSS3 Trypsin-3 0.422 Protease

P60903 S100A10 Protein S100-A10 1.097 Secreted

P31949 S100A11 Protein S100-A11 2.541 Cytoplasm

Q99584 S100A13 Protein S100-A13 2.742 Secreted

Q96FQ6 S100A16 Protein S100-A16 0.996 Cytoplasm

Q86YZ3 S100A18 Hornerin 0.094 Cytoplasm

P06703 S100A6 Protein S100-A6 4.386 Cell surface

P05109 S100A8 Protein S100-A8 0.243 Secreted

Q13214 SEMA3B Semaphorin-3B 0.31 Secreted

B4E219 SEMA3C Semaphorin-3C 0.044 Secreted

P01009 SERPINA1 Alpha-1-antitrypsin 0.133 Protease inhibitor

P05154 SERPINA5 Plasma serine protease 0.951 Protease inhibitor

inhibitor

P01008 SERPINC1 Antithrombin-III 0.563 Protease inhibitor

P05121 SERPINE1 Plasminogen activator 17.2 Protease inhibitor

inhibitor 1

P50454 SERPINH1 Serpin H1 1.819 Protease inhibitor

P61812 TGFB2 Transforming growth factor 0.144 Secreted

beta-2

P21980 TGM2 Protein-glutamine gamma- 2.108 Enzyme

glutamyltransferase 2

P50591 TNFSF10 Tumor necrosis factor ligand 1.285 Secreted

superfamily member 10

O75888 TNFSF13 Tumor necrosis factor ligand 0.24 Secreted

superfamily member 13

P41273 TNFSF9 Tumor necrosis factor ligand 0.687 Secreted

superfamily member 9 131

Q9Y3A2 UPT11L Probable U3 small nucleolar 1.076 Secreted

RNA-associated protein 11

Q9H1J7 WNT5B Protein Wnt-5b 0.76 Secreted

A8K0G1 WNT7B Protein Wnt 0.249 Secreted 132

Table 3.3 Coculture ECM proteome

Uniprot Gene name Alias Abundance (nSC) Category O00468 AGRN Agrin 0.231 Glycoprotein P39060 COL18A1 Collagen alpha-1(XVIII) chain 1.737 Collagen P02462 COL4A1 Collagen alpha-1(IV) chain 1.822 Collagen P08572 COL4A2 Collagen alpha-2(IV) chain 4.994 Collagen Q01955 COL4A3 Collagen alpha-3(IV) chain 0.088 Collagen P53420 COL4A4 Collagen alpha-4(IV) chain 0.001 Collagen P29400 COL4A5 Collagen alpha-5(IV) chain 0.001 Collagen P20908 COL5A1 Collagen alpha-1(V) chain 0.087 Collagen P05997 COL5A2 Collagen alpha-2(V) chain 0.226 Collagen Q02388 COL7A1 Collagen alpha-1(VII) chain 0.044 Collagen P27658 COL8A1 Collagen alpha-1(VIII) chain 0.224 Collagen P23142 FBLN1 Fibulin-1 0.086 Glycoprotein P35555 FBN1 Fibrillin-1 4.281 Glycoprotein P02751 FN1 Fibronectin 18.42 Glycoprotein P98160 HSPG2 Perlecan 4.313 Proteoglycan Q16787 LAMA3 Laminin subunit alpha-3 0.055 Glycoprotein Q16363 LAMA4 Laminin subunit alpha-4 0.046 Glycoprotein O15230 LAMA5 Laminin subunit alpha-5 0.179 Glycoprotein P07942 LAMB1 Laminin subunit beta-1 0.957 Glycoprotein P55268 LAMB2 Laminin subunit beta-2 0.083 Glycoprotein Q13751 LAMB3 Laminin subunit beta-3 0.273 Glycoprotein P11047 LAMC1 Laminin subunit gamma-1 1.341 Glycoprotein Q13753 LAMC2 Laminin subunit gamma-2 0.128 Glycoprotein Q9H8L6 MMRN2 Multimerin-2 0.241 Glycoprotein P14543 NID1 Nidogen-1 0.714 Glycoprotein Q14112 NID2 Nidogen-2 1.038 Glycoprotein P24821 TNC Tenascin 2.661 Glycoprotein Q6PCB0 VWA1 von Willebrand factor A domain- 0.444 Glycoprotein containing protein 1

Q99715 COL12A1 Collagen alpha-1(XII) chain 0.174 Collagen Q07092 COL16A1 Collagen alpha-1(XVI) chain 0.053 Collagen P02452 COL1A1 Collagen alpha-1(I) chain 0.378 Collagen P12109 COL6A1 Collagen alpha-1(VI) chain 0.07 Collagen P29279 CTGF Connective tissue growth factor 0.943 Glycoprotein Q96CG8 CTHRC1 Collagen triple helix repeat-containing 0.361 Glycoprotein protein 1

O00622 CYR61 Protein CYR61 1.556 Glycoprotein Q53RD9 FBLN7 Fibulin-7 0.089 Glycoprotein P35556 FBN2 Fibrillin-2 0.61 Glycoprotein P10915 HAPLN1 Hyaluronan and proteoglycan link 0.104 Proteoglycan protein 1

Q16270 IGFBP7 Insulin-like growth factor-binding 0.648 Glycoprotein protein 7 133

Q14766 LTBP1 Latent-transforming growth factor 0.231 Glycoprotein beta-binding protein 1

Q14767 LTBP2 Latent-transforming growth factor 0.136 Glycoprotein beta-binding protein 2

Q8N2S1 LTBP4 Latent-transforming growth factor 0.181 Glycoprotein beta-binding protein 4

P55081 MFAP1 Microfibrillar-associated protein 1 0.114 Glycoprotein

P55001 MFAP2 Microfibrillar-associated protein 2 2.949 Glycoprotein

Q08431 MFGE8 Lactadherin 1.085 Glycoprotein P08493 MGP Matrix Gla protein 1.218 Glycoprotein B1ALD8 POSTN Periostin, osteoblast specific factor 3.839 Glycoprotein

Q92926 PXDN Peroxidasin 1.03 Glycoprotein P78539 SRPX Sushi repeat-containing protein 0.216 Glycoprotein SRPX

Q15582 TGFBI Transforming growth factor-beta- 3.304 Glycoprotein induced protein ig-h3

P07996 THBS1 Thrombospondin-1 6.865 Glycoprotein Q6ZMP0 THSD4 Thrombospondin type-1 domain- 1.207 Glycoprotein containing protein 4

Q9GZM7 TINAGL1 Tubulointerstitial nephritis 2.771 Glycoprotein antigen-like 1

P13611 VCAN Versican core protein 0.52 Proteoglycan P04004 VTN Vitronectin 0.68 Glycoprotein Q9BVH8 VWA5B2 von Willebrand factor A domain- 0.064 Glycoprotein containing protein 5B2

O75173 ADAMTS4 A disintegrin and metalloproteinase 0.131 Protease with thrombospondin motifs 4

P15121 AKR1B1 Aldose reductase 0.968 Enzyme P02768 ALB Serum albumin 6.296 Secreted P04075 ALDOA Fructose-bisphosphate aldolase A 0.681 Enzyme

Q9BY76 ANGPTL4 Angiopoietin-related protein 4 1.926 Secreted P04083 ANXA1 Annexin A1 0.464 Annexin E9PDK5 ANXA11 Annexin A11 0.389 Annexin P07355 ANXA2 Annexin A2 7.769 Annexin P09525 ANXA4 Annexin IV 0.568 Annexin P08758 ANXA5 Annexin A5 0.52 Annexin E5RJR0 ANXA6 Annexin VI 0.217 Annexin P20073 ANXA7 Annexin A7 0.432 Annexin P01024 C3 Complement C3 (Fragment) 0.054 Complement P27797 CALR Calreticulin 0.123 Secreted Q6YHK3 CD109 CD109 antigen 0.414 Cell surface P16070 CD44 CD44 antigen 0.902 Cell surface 134

P13987 CD59 CD59 glycoprotein 5.531 Cell surface P53621 COPA Coatomer subunit alpha 0.092 Secreted O75718 CRTAP Cartilage-associated protein 0.2 Secreted P07858 CTSB Cathepsin B 0.65 Protease Q9UBR2 CTSZ Cathepsin Z 0.174 Protease Q03001 DST Dystonin 0.025 Plakin P00734 F2 Prothrombin 1.198 Coagulation P12259 F5 Coagulation factor V 0.08 Coagulation P09038 FGF2 Heparin-binding growth factor 2 0.977 Secreted Q5D862 FLG2 Filaggrin-2 0.048 Cytoskeleton Q99988 GDF15 Growth/differentiation factor 15 1.14 Secreted Q5JWF2 GNAS Guanine nucleotide-binding protein 1.198 Cell surface G(s) subunit alpha

P35052 GPC1 Glypican 1 0.161 Secreted P06396 GSN Gelsolin 0.305 Cytoskeleton Q00341 HDLBP Vigilin 0.14 Cytoplasm Q96QV1 HHIP Hedgehog-interacting protein 0.043 Cell surface F5GXA6 HLAC HLA class I histocompatibility 0.807 Cell surface antigen, Cw-7 alpha chain

Q53GQ0 HSD17B12 Estradiol 17-beta-dehydrogenase 12 0.174 Enzyme

Q92743 HTRA1 Serine protease HTRA1 0.439 Protease Q13123 IK Protein Red 0.248 Nucleus P19823 ITIH2 Inter-alpha-trypsin inhibitor heavy 0.186 Protease chain H2 inhibitor

Q32P28 LEPRE1 Prolyl 3-hydroxylase 1 0.157 Secreted P09382 LGALS1 Galectin-1 18.629 Secreted P17931 LGALS3 Galectin-3 2.704 Secreted O00214 LGALS8 Galectin-8 0.236 Secreted O00182 LGALS9 Galectin-9 0.92 Secreted P49257 LMAN1 Protein ERGIC-53 0.34 Cytoplasm Q08397 LOXL1 Lysyl oxidase homolog 1 0.122 Secreted Q9Y4K0 LOXL2 Lysyl oxidase homolog 2 1.332 Secreted Q96JB6 LOXL4 Lysyl oxidase homolog 4 0.079 Secreted P02780 LTF Lactoferrin 0.255 Secreted P16126 LYZ Lysozyme C 0.56 Secreted O15460 P4HA2 Prolyl 4-hydroxylase subunit alpha-2 0.225 Enzyme

P07237 P4HB Protein disulfide-isomerase 1.458 Enzyme P00750 PLAT Tissue-type plasminogen activator 0.066 Secreted

P00747 PLG Plasminogen 0.151 Secreted Q5JXB8 PLOD1 Procollagen-lysine, 2-oxoglutarate 5- 0.166 Enzyme dioxygenase 1

O00469 PLOD2 Procollagen-lysine,2-oxoglutarate 5- 0.104 Enzyme dioxygenase 2 135

O60568 PLOD3 Procollagen-lysine,2-oxoglutarate 5- 0.474 Enzyme dioxygenase 3

Q15165 PON2 Serum paraoxonase 2 0.214 Enzyme P62937 PPIA Peptidyl-prolyl cis-trans isomerase A 9.244 Enzyme

O95084 PRSS23 Serine protease 23 1.166 Protease P06703 S100A6 Protein S100-A6 2.462 Cell surface P05109 S100A8 Protein S100-A8 3.389 Secreted P01008 SERPINC1 Antithrombin-III 0.725 Protease inhibitor

P05121 SERPINE1 Plasminogen activator inhibitor 1 23.723 Protease inhibitor

P50454 SERPINH1 Serpin H1 9.45 Protease inhibitor

Q9UQE7 SMC3 Structural maintenance of 0.289 Secreted chromosomes protein 3

P61812 TGFB2 Transforming growth factor beta-2 0.792 Secreted

P21980 TGM2 Protein-glutamine gamma- 15.244 Enzyme glutamyltransferase 2

Q9Y3A2 UPT11L U3 small nucleolar RNA-associated 0.476 Secreted protein 11

136

Figure 3.9 GEC ECM interaction network analysis. Proteins identified by MS and classified as extracellular region according to Gene Ontology annotation were converted to a protein -protein interaction network model. The interaction network was clustered, and topological parameters were computed. Self-interactions were excluded from the analysis. Nodes are coloured according to their clustering coefficient, and node diameter is proportional to number of interaction partners (degree). Nodes are labelled with gene names. Proteins classified as basement membrane are displayed with red node borders; other structural ECM proteins are displayed with orange node borders; and ECM-associated proteins are displayed with green node borders. Adam Byron generated data for this figure. 137

Figure 3.10 Podocyte ECM interaction network analysis. Proteins identified converted to a protein by MS and classified as extracellular region according to Gene Ontology annotation were converted to a protein -protein interaction network model. The interaction network was clustered, and topological parameters were computed. Self-interactions were excluded from the analysis. Nodes are coloured according to their clustering coefficient, and node diameter is proportional to number of interaction partners (degree). Nodes are labelled with gene names. Proteins classified as basement membrane are displayed with red node borders; other structural ECM proteins are displayed with orange node borders; and ECM-associated proteins are displayed with green node borders. Adam Byron generated data for this figure. 138

Figure 3.11 Podocytes and GEC are viable in coculture. A: We generated podocytes with stable expression of green fluorescent protein (GFP) as described in the Methods. Both GFP-podocytes and GECs were viable in coculture at day 1, day 7 and day 14 and there was minimal detection of the apoptosis marker, cleaved caspase 3. B: Quantification of the proportion of GFP-positive cells: 49.2 %, 47.1 % and 45.8 % of cells are GFP-podocytes at day 1, day 7 and day 14 respectively. There was a small increase in apoptosis in both cell types from day 1 to day 14. Numbers of apoptotic GFP-podocytes were greater than GECs at day 1; podocyte = 1.3 % and GEC = 0.7 %; day 7 podocyte = 2.5 % and GEC = 1.4 %; day 14 podocyte = 3.6 % and GEC = 1.8 %. Boxes highlight apoptotic podocytes (yellow) and GECs (red). n = 3 replicates with > 300 cells counted per replicate. Scale bar represents 500 m. Michael Randles generated data for this figure. 139

Figure 3.12 The effects of coculture on ECM deposition and cell-cell junctions. A: 3D models of ECM deposition were derived as described in the Concise Methods section of the main manuscript. After 14 days of monoculture (left panel) GEC cultures had more obvious detection of laminin as compared to podocyte cultures in which collagen IV-alpha 1 detection was more evident. In cocultures of GEC and GFP-expressing podocytes, both collagen and laminin were equally detected (right panel). We did not detect collagen IV-alpha 3 in the ECM of monoculture or cocultured cells. B: Immunofluorescence staining of cell-cell adhesion molecules; beta catenin was detected at cell-cell junctions between podocytes and GEC (top panel). PECAM1 (lower panel) was detected exclusively at GEC cell-cell junctions (silver arrows) and ZO1 was localised to podocyte cell-cell junctions (gold arrows). With these junctional markers we did not observe differences in the appearance of cell-cell junctions between monoculture and coculture. In addition we did not detect changes in the localisation 140

of the podocyte cell-junction markers nephrin and podocin (data not shown). Scale bars represent 20 m. Michael Randles generated data for this figure.

Figure 3.13 Coculture ECM interaction network analysis. Proteins identified converted to a protein by MS and classified as extracellular region according to Gene Ontology annotation were converted to a protein -protein interaction network model. The interaction network was clustered, and topological parameters were computed. Self-interactions were excluded from the analysis. Nodes are coloured according to their clustering coefficient, and node diameter is proportional to number of interaction partners (degree). Nodes are labelled with gene names. Proteins classified as basement membrane are displayed with red node borders; other structural ECM proteins are displayed with orange node borders; and ECM-associated proteins are displayed with green node borders. Adam Byron generated data for this figure. 141

4 Genetic background alters glomerular matrix

4.1 Introduction

Having defined normal human glomerular, podocyte and glomerular endothelial cell (GEC) extracellular matrix (ECM) proteomes, we next investigated how the glomerular ECM changes when the glomerular filtration barrier (GFB) becomes abnormally permeable to plasma proteins, such as albumin.

Understanding factors which predispose individuals to disease is a prerequisite for effective preventative medicine.

The level of the plasma protein albumin detected in urine of healthy human beings varies with both racial background 20, 21 and sex. 22, 23 Normally the albumin excretion rate of an individual is < 30 mg/day.

When this level increases to 30 - 300 mg/day it is referred to as microalbuminuria. Microalbuminuria is an indicator of glomerular dysfunction as well as an independent risk factor for cardiovascular disease.

The degree of microalbuminuria may reflect the permeability properties of the entire vasculature of an individual. However, the GFB is a highly specialised structure, which has many unique features that are not shared with the rest of the vasculature. As a result, it is important to specifically study the structure and composition of the GFB. The glomerular ECM forms an integral part of the GFB, so we aimed to identify structural and compositional differences in glomerular ECM which associate with microalbuminuria. 142

4.2 Statements

Author contributions to data generation and analysis presented as figures of this paper are indicated in figure legends.

Michael J. Randles, Rachel Lennon, Adrian S. Woolf and David A. Long planned the study, designed experiments and wrote the manuscript.

Michael J. Randles generated all figures presented in this manuscript, reanalysed microarray data, optimised glomerular ECM enrichment from mouse glomeruli, performed Western blotting, ECM enrichment, mass spectrometry sample preparation, bioinformatic analysis, immunofluorescence, quantification of immunofluorescence, network analysis, pathway analysis, screened exome data for mutations, performed transmission electron microscopy and serial block face scanning electron microscopy data analysis.

Thomas Denny and Jennifer L . Huang isolated glomeruli using Dynabead perfusion, Aleksandr

Mironov and Toby Starborg aided with electron microscopy data acquisition and interpretation, David

Knight aided with MS data acquisition. Adam Byron, Jonathan D. Humphries, Ron Korstanje and Martin

J. Humphries contributed to the study design and analysis and contributed to the preparation of the manuscript.

The manuscript is written in the style of a Journal of the American Society of Nephrology research article by Michael J. Randles, Rachel Lennon, Adrian S. Woolf and David A. Long and was critically assessed by all authors of the paper. Author guidelines restrict the size of the article to 3000 words or less (excluding title page, methods, figure legends, tables, and references) . 143

4.3 Genetic background is a key determinant of glomerular

extracellular matrix composition and organisation

Michael J . Randles ,1,2 Adrian S . Woolf ,2 Adam Byron ,1,* Jonathan D . Humphries ,1 Thomas Denny ,1,2

Jennifer L . Huang ,3 David Knight, 1 Aleksandr Mironov, 1 Toby Starborg, 1 Ron Korstanje, 4 Martin J .

Humphries 1, David A . Long 3 and Rachel Lennon 1,2 .

1Wellcome Trust Centre for Cell-Matrix Research, Faculty of Life Sciences, University of Manchester,

Manchester, M13 9PT, UK; 2Institute of Human Development, Faculty of Medical and Human

Sciences, University of Manchester, Manchester, M13 9PT UK; 3Developmental Biology and Cancer,

Institute of Child Health, University College London, London, WC1N 1EH, UK, 4The Jackson

Laboratory, Bar Harbor, Maine, ME 04609 USA.

*Present address: Edinburgh Cancer Research UK Centre, Institute of Genetics and Molecular

Medicine, University of Edinburgh, Edinburgh, EH4 2XR UK.

Running title: Genetic background alters matrix

Corresponding author:

Dr Rachel Lennon

Wellcome Trust Centre for Cell-Matrix Research, Michael Smith Building,

University of Manchester, Manchester M13 9PT, UK

Phone: 0044 (0) 161 2755498 Fax: 0044 (0) 161 2755082

Email: [email protected] 144

4.3.1 Abstract

Diseases affecting kidney glomeruli often feature altered histological patterns of extracellular matrix

(ECM) proteins. Despite this, the potential complexities of the glomerular ECM niche in both health and disease are currently understood only at a rudimentary level. To explore whether genetic background and sex determine glomerular ECM, we investigated two mouse strains, FVB/NHanHsd (FVB) and

C57BL/6JOlaHsd (B6), using RNA microarrays of isolated glomeruli combined with proteomic glomerular ECM analyses. These analyses, undertaken in healthy young adult animals, revealed unique strain- and sex-dependent glomerular ECM signatures, which correlated with previously established variations in levels of albuminuria and predisposition to progressive nephropathy.

Differences in protein abundance were validated by quantitative fluorescence immunohistochemistry, and the collective differences were not explained by mutations in known ECM or glomerular disease genes. Furthermore, as assessed by 2D and 3D electron microscopy, striking ultrastructural changes in glomerular basement membranes were documented in FVB mice. Pathway analysis of merged transcriptomic and proteomic datasets identified potential ECM regulatory pathways involving inhibition of matrix metalloproteases, liver X receptor/retinoid X receptor, nuclear factor erythroid 2-related factor

2, notch and cyclin-dependent kinase 5. These pathways may therefore alter ECM and confer susceptibility to disease. As well as discovering these previously-unrecognised differences between mouse strains, our study highlights a core set of structural ECM proteins, which form multiple protein- protein interactions, and which are conserved from mouse to man. 145

4.3.2 Introduction

Loose extracellular matrix (ECM) networks surround all cells, and epithelial sheets sit on basement membranes (BMs) containing condensed ECM networks. ECM affects tissue rigidity, 385 cell signalling 6,

386, 387 and tissue development 98, 388 and it is implicated in genetic and acquired diseases. 22, 389-391

Proteomic approaches are being undertaken for global analyses of the complex niche represented by

ECM 1, 392, 393 and such studies are beginning to help unravel ECM roles in health and disease. 394

Glomerular disease often features altered histological ECM patterns. Candidate-based investigations of glomerular ECM demonstrate the presence of tissue-restricted isoforms of collagen IV 102 and laminin. 101 Moreover, mutations of genes encoding these proteins cause glomerular disease. 29

Glomerular ECM aberrations are, however, currently only understood at a rudimentary level although they are potential therapeutic targets. 29 We recently exploited mass spectrometry-based proteomics to characterise healthy human glomerular ECM in an unbiased manner, 395 identifying 144 proteins, demonstrating the niche’s complexity. Furthermore, we identified a core of highly connected ECM proteins which may be required for BM assembly. 396

As well as ECM changes, glomerular disease usually features loss of macromolecular barrier integrity manifest as increased albuminuria. In overtly healthy human populations, albuminuria varies with racial background, 51, 55 and sex. 397, 398 This is recapitulated in inbred mouse strains: for example, young adult

FVB mice have eightfold greater albuminuria than B6 mice 399 , 400 and within each of these strains, males have twofold greater albuminuria than females. 400 The albuminuria range between female B6 and male FVB mice corresponds, after factoring for weight, to human albuminuria of 30-300 mg/day.

Notably, there are no significant differences in systemic blood pressure between FVB and B6 mice , but the former have significantly fewer glomeruli/kidney. 400 Furthermore, FVB mice have more severe disease than B6 mice in nephropathy models. 180, 401

To explore whether genetic background and sex determine glomerular ECM, we investigated the two mouse strains using RNA microarrays of isolated glomeruli combined with proteomic analyses of glomerular ECM and we examined ECM structure with electron microscopy (EM). 146

4.3.3 Results

4.3.3.1 RNA profiling

We undertook principal component analysis (PCA) of all detected transcripts in microarrays from glomeruli isolated from B6 and FVB mice. 400 We found the determinants of sample variation were strain and sex (PC-1: 94.4%; PC-2: 3.6% of the total variation) (Figure 4.1A). Using Gene Ontology (GO) classification, a subset of ECM transcripts were selected and a further PCA revealed that the strains could still be distinguished (PC-1, 91.6%; PC-2, 6.3% of the total variation) , but there was less variation between sexes (Figure 4.1A). Similar separation of datasets were documented using unsupervised hierarchical clustering (Figure 4.1B) where the ECM transcript data segregated into clusters relating to

B6 and FVB strains and within the strains, sexes clustered together. Upregulated gene clusters were categorised by GO enrichment analysis and the top five enriched terms were annotated onto the clustered heat map (Figure 4.1B). This revealed enrichment of ‘neuronal development’ and ‘peptidase inhibitor activity’ in FVB glomeruli. Conversely, GO ECM terms, including ‘adhesion’ and ‘receptor binding’, were enriched to B6 glomeruli. Thus, the glomerular transcriptome, including ECM transcripts, is influenced by strain and, to a lesser extent, by sex . 147

Figure 4.1 Glomerular transcriptomics. A: Principal component analysis of all transcripts from whole glomerular microarray (left panel) and ECM transcripts (right panel). B6 females, blue dots; B6 males, gray dots; FVB females, orange dots; FVB males, red dots. Orthogonality between the principal components is highlighted by dashed quadrilaterals B: Unsupervised hierarchical clustering of ECM transcripts. The heat map displays Z- transformed normalised intensities, and the associated dendrogram displays clustering on the basis of 148

Euclidean distance. The top five enriched GO biological processes and molecular function terms are annotated onto the B6 and FVB clusters. Michael Randles contributed data to this figure. 4.3.3.2 ECM enrichment

Next, to analyse glomerular ECM using mass spectrometry (MS)–based proteomics. we developed a workflow for ECM isolation, adapted from our human glomerular studies 395 (Figure S4.8A). We harvested mouse glomeruli using Dynabead perfusion 400 and found this approach yielded significantly more glomeruli/kidney versus sieving (Figure S4.8B). Glomeruli were fractionated to produce an enriched ECM fraction (Figure 4.2A and Figure S4.8A), with enrichment confirmed by GO analysis of

MS identifications (Figure 4.2B). Western blotting also confirmed ECM enrichment with increased laminin and collagen IV , but reduced cellular proteins in the predicted ECM fraction (Figure 4.2C).

4.3.3.3 ECM proteomes

Triplicate enriched ECM fractions from the four groups (i.e. FVB and B6 male and females) were analysed by MS (Table S4.1). Identified proteins were classified as ‘ECM’ as previously defined .395, 396

A combination of the mouse matrisome resource 1 and DAVID GO 344 was used to classify identified proteins as ‘BM’, ‘other structural ECM’ or ‘ECM-associated’. PCA analyses of ECM proteomic datasets found the main cause of sample variation was strain rather than sex (PC-1, 85.6 %; PC-2,

4.7% of the total variation) (Figure 4.2D), in keeping with RNA microarrays (Figure 4.1A, B). The proteomic data were used to generate a glomerular ECM protein-protein interaction network (Figure

4.2E) that demonstrated sharing of most (58%) glomerular ECM proteins between the four groups.

These proteins formed the most highly connected subnetwork, making 92% of reported protein-protein interactions in the entire network (Figure 4.2E). This subnetwork comprises almost all (96%) of the BM and 52% of other structural ECM proteins identified in the dataset and likely represents components essential for glomerular ECM assembly and/or maintenance (Figure 4.2E). Using topological network analysis of all ECM proteins identified by MS, BM proteins were found to make significantly more protein-protein interactions than ECM-associated proteins (Figure 4.3A), aligning with proteomic data from human glomerular ECM. 395 Moreover, 60% of BM and other structural ECM proteins were shared in human and mouse datasets (Figure 4.3B) and of all protein-protein interactions from both human and mouse datasets, the mouse-human shared interactome contained 74% of interactions. 149

Figure 4.2 Genetic background is a regulator of ECM composition. A: Glomeruli isolated by Dynabead perfusion and magnetic particle concentration. Dynabeads can be seen in glomeruli (brown dots). (i) isolated glomerulus prior to detergent extraction, (ii) glomerulus post detergent extraction. Scale bar represents 50 µm. B: Isolated fractions were analysed by MS. Fractions 1-3 represent the supernatants from the extraction protocol, the ECM fraction represents the final ECM enriched fraction. Identified proteins were grouped by GO cellular compartment and visualised with MeV. Height of the bar represents the number of proteins in each GO compartment identified in the indicated fraction. C: Western blot analysis confirming ECM enrichment D: Principal component analysis of glomerular ECM proteins identified by MS. B6 females, blue dots; B6 males, grey dots; FVB females, orange dots; FVB males, red dots. E: Protein-protein interaction network constructed from enriched glomerular ECM proteins identified by MS. Nodes (circles) represent proteins and edges (white lines) represent reported protein–protein interactions. ECM proteins were categorised as basement membrane, other structural ECM or ECM-associated proteins and were coloured accordingly. The Venn diagram sets indicate in which ECMs each protein was identified. *Protein abundance was determined by normalised spectral counting. Jenifer Huang and Thomas Denny contributed data to this figure part A. Michael Randles contributed data to this figure B-E. 150

4.3.3.4 Enriched ECM proteins

Comparing relative abundance of different classifications of ECM proteins between mouse groups, there were no significant differences (Figure 4.3C-E). However, together with the core set of structural

ECM proteins, distinct strain- and sex-dependent sub-networks were apparent (Figure 4.2E), and these proteins are predominately ECM-associated proteins, suggesting a role in ECM regulation. To highlight specific ECM components that may influence glomerular phenotype in a strain- or sex-dependent manner, two additional networks were generated. The first displays coloured nodes for proteins that always increased in B6 versus FVB groups, or always increased in FVB versus B6 groups (Figure

4.4A). The second displays coloured nodes for proteins always increased in female versus male groups, or always increased in male versus female groups (Figure 4.4B). Vitronectin, tenascin C, alpha-1 and alpha-2 collagen I and alpha-1 and alpha-2 collagen IV form a sub-cluster of proteins in the core of both ECM networks. The sub-network of B6 enriched proteins close in topology included tenascin C and alpha-1 and alpha-2 collagen I . The sub-network of FVB enriched proteins close in topology to the core network included fibrinogen, vitronectin, fibrillin-1, netrin 4 and fibroblast growth factor-2 (FGF2). Of note, transcript and protein levels of major GBM components, alpha-3,4,5 collagen

IV, laminin-521, perlecan, agrin and nidogen 1 and 2 did not show robust sex- or strain-related changes (Figure 4.4C, 4.5A and B Figure S4.10). 151

Figure 4.3 Topological network analysis of glomerular ECM protein interactions. A: Topological analysis of the number of protein-protein interactions (degree) made by the distinct categories of ECM proteins. ****, P < 0.0001 **, P < 0.005; NS, P ≥ 0.05. B: Overlap of mouse and human basement membrane and other structural ECM proteins. Protein interaction network constructed from enriched glomerular ECM proteins identified by MS. Nodes (circles) represent proteins and edges (gray lines) represent reported protein–protein interactions. ECM proteins were categorised as basement membrane or other structural ECM and were coloured accordingly. Nodes are labelled with gene names for clarity. Protein abundance differences in C: basement membrane, D: other structural ECM and E: ECM associated proteins in each of the groups of animals. Michael Randles contributed data to this figure. 152

Figure 4.4 Network analysis of enriched glomerular ECM proteins. A, B: Protein interaction network constructed from enriched glomerular ECM proteins identified by MS. Nodes (circles) represent proteins and edges (gray lines) represent reported protein–protein interactions. Nodes are labelled with gene names for clarity. A: Nodes are coloured if a protein is enriched to FVB (red) or B6 (blue) in all comparisons of FVB groups against B6 groups. B: Nodes are coloured if a protein is enriched to males (grey) or females (gold) in all comparisons of male groups against female groups. Proteins validated in this study are highlighted with black arrows. ECM proteins with no known protein-protein interactions within the dataset are shown in Figure S4.9 . C: Comparison of selected ECM proteins at protein and transcript level. F, females; M, males. Michael Randles contributed data to this figure. 153

Using the proteomics dataset, whole glomerular microarray and published literature, we selected a subset of ECM proteins with altered abundance for further analyses. These included certain molecules altered in a strain-dependent manner i.e. netrin 4, collagen I, tenascin C and fibroblast growth factor 2

(FGF2) (Figure 4.4C). Netrin 4 RNA and protein were more abundant in FVB versus B6 mice. FGF2 protein was detected in FVB glomerular ECM , but not in B6 ECM and, notably, Fgf2 transcripts were below the level of detection (Figure 4.4C). Tenascin C and alpha-1 and -2 collagen I RNA and proteins were more abundant in B6 versus FVB. In both strains, the matrix metalloproteases proteins meprin alpha and meprin beta were more abundant in males than females (Figure 4.4C). Changes in protein abundance were validated using quantitative fluorescence immunohistochemistry (Figure 4.5A and B).

Here we found that laminin and collagen IV were equally detected across all four groups , whereas

FGF2, netrin 4, meprins and collagen I were detected in ranges consistent with MS and transcript analyses.

4.3.3.5 BM ultrastructural variation

Transmission EM (TEM) and serial block face scanning EM (SBF-SEM) were used to examine glomerular BMs (GBMs) (Figure 4.6A). GBMs were significantly thicker in FVB than B6 mice (Figure

4.6B, C and D). This was confirmed with SBF-SEM using 3D reconstructions (Movie S4.1). Moreover,

SBF-SEM visualised reproducible sub-podocyte regions of expanded GBM in FVB mice (Figure 4.6E and F, and Movies S4.4-5), only rarely observed in B6 GBMs (Figure 4.6E and Movies S4.2-3). In association with the expanded regions, there was splitting of the GBM and cellular interposition within the GBM in FVB mice (Figure 4.6G). There were no significant differences in podocyte foot process width between the groups (Figure 4.6C). 154

Figure 4.5 Validation of glomerular ECM proteins regulated by genetic background or sex. A: Representative fluorescence immunohistochemistry images. Dapi staining in blue, scale bar represents 25 mm. B: Quantification of normalised mean pixel intensity of glomeruli, measured using fiji image J software. F, females; M, males. Points represent individual glomerular measurements n > 60 from each biological replicate, n = 3 mice. Box plots indicate 25th and 75th percentiles (lower and upper bounds, respectively), 1.5× interquartile range (whiskers) and median (middle line). Michael Randles contributed data to this figure. 155

Figure 4.6 Structural abnormalities in glomerular ECM in disease-susceptible FVB mice. A: Schematic diagram of the glomerular filtration barrier. B-D: The GBM is significantly thicker in FVB compared with B6 mice, whereas in both strains podocyte cytoarchitecture is preserved, with normal foot processes (TEM images). B: Quantification of GBM thickness. C: Quantification of podocyte foot process width. E: The GBM of FVB mice contain significantly more sub-podocyte GBM expansions compared with B6 mice (SBF-SEM images). Arrows highlight sub-podocyte GBM expansions. F: Quantification of sub-podocyte GBM expansions (E). G: Representative images of GBM splitting and cell interposition (arrows) in FVB female mice (TEM images). GBM, glomerular BM. * P < 0.05, **** P < 0.001, n = 15 glomeruli from n = 3 mice. Scale bars represent 500 nm. F, female; M, male; E, 156

Endothelial cells; P, podocytes. Michael Randles contributed data to this figure A-G. Tobias Starborg contributed data to this figure part E. 4.3.3.6 Biological pathways and altered ECM

Accessible exome data from B6 and FVB mice were interrogated for the 4586 genes predicted from the microarray to be influenced by strain (Anova p<0.01) together with known ‘glomerular’ and ECM genes.

No frame-shift, truncation or read through mutations were found in any of these genes. Therefore to gain insight into ECM regulatory pathways, we performed GO enrichment and pathway analyses on proteomic and microarray datasets. As we found strain to be the major determinant of glomerular ECM composition, GBM thickness and frequency of expanded regions, we concentrated on the strain- specific differences and analysed the datasets in a global manner. Components and GO terms selectively enriched in a strain-dependent manner were hierarchically clustered to produce a heat-map and associated dendrograms. In this analysis, the FVB data associated with terms for ‘inflammation’,

‘wound healing’ and ‘cell adhesion’, whereas ‘collagen’ and ‘epithelial development’ terms associated with the B6 strain (Figure S4.11). Using GO enrichment map view for the whole microarray, we found few terms enriched to B6 glomeruli and these included an ECM cluster and terms relating to post- translation modifications (Figure 4.7A). However, FVB glomeruli had many enriched terms (Figure

4.7A). Three clusters relating to ‘peroxisome’, ‘fatty acid metabolism and lipid biosynthesis’ and

‘organelles’, would be consistent with altered lipid processing. Similarly, clusters relating to ‘coenzyme biosynthesis’ and ‘mitochondrial biological processes’, would be consistent with altered mitochondrial biology. Using pathway prediction tools to compare ECM transcripts and ECM proteins, we identified

‘LXR/RXR signalling’ ‘NRF2' and 'mTOR signalling pathways’, which were predicted to be more active in FVB versus B6 glomeruli (Figure 4.7B and 4.7C). Conversely, ‘notch signalling’ and ‘CDK5 signalling’, were predicted to be active in B6 glomeruli (Figure 4.7B and 4.7C). In FVB glomeruli, inhibition of ‘matrix metalloproteases’ was predicted with several approaches (Figure 4.1B, 4.6B and

4.7C). These analyses reveal potential novel pathways, which may regulate glomerular ECM and confer susceptibility to glomerular disease. 157

Figure 4.7 GO enrichment map and pathway analysis. A: GO enrichment map of terms significantly enriched in FVB or B6 glomerular microarray. B: Pathway analysis of canonical pathways enriched in whole glomerular microarray. C: Canonical pathways enriched in Glomerular ECM proteomic data-set. Nodes (circles) represent terms/canonical pathways significantly enriched in the microarray/proteomic data-sets, edges (lines) represent overlap of genes/proteins within terms. Node size relates to the number of genes/proteins which are allocated to a given term and node colour relates to the enrichment to either FVB (warm colours) or B6 (cool colours). Michael Randles contributed data to this figure. 158

4.3.3.7 Discussion

Using RNA microarrays of isolated glomeruli and proteomic analyses of glomerular ECM, the latter validated by quantitative fluorescence immunohistochemistry, we observed unique strain- and sex- dependent glomerular ECM signatures correlating with previously established variations in albuminuria and predisposition to progressive nephropathy. Furthermore, as assessed by 2D and 3D EM strategies, striking ultrastructural changes in glomerular basement membranes were documented in

FVB mice. The regions were homogeneous and distinct from the sub-podocyte immune deposits seen in membranous nephropathy and membranoproliferative glomerulonephritis. These findings were not explained by mutations in known ECM or glomerular genes. Equally important, as well as discovering these previously-unrecognised differences between mouse strains, our study highlights a core set of structural ECM proteins, which form multiple protein-protein interactions, and which are conserved from mouse to man

Amongst changes in composition, we found netrin 4 was enriched in glomerular ECM from high albuminuria/nephropathy-susceptible FVB mice. Netrin 4 may be involved in BM assembly 402 and epithelial cells adhere to netrin 4 via α1β2 and α3β1 integrins, 403 both of which are expressed by podocytes. However, the regulation of this ECM component and its requirement for GBM assembly has not been examined. FGF2 was also upregulated in FVB datasets and this growth factor, enriched in

BMs, has tissue-specific roles including angiogenesis, cell proliferation, survival, migration and differentiation. 404 FGF2 signals via the FGF receptor tyrosine kinase and FGFR signalling is critical for the growth and patterning of all renal lineages. 404 In the glomerulus, FGF2 is expressed by podocytes and mesangial cells. FGF signalling is important for podocyte morphology as podocytes lacking FGF signalling fail to rearrange their actin cytoskeleton. 404 Increased FGF2 expression has also been associated with animal nephropathy models, suggesting that regulation of this pathway requires tight control. 404 Our data support a pathological role for increased FGF2 signalling in the glomerulus, and suggests it may influence ECM synthesis.

Components enriched to B6, low albuminuria/nephropathy-resistant, mice included tenascin C and collagen I. Tenascin C is a glycoprotein that modulates the functions of fibronectin 405 and it is involved 159

in neuronal guidance. 406 Collagen I is the predominant structural component of bone and cartilage and a functional role in the glomerulus has yet to be found. Both collagen I and tenascin C are associated with wounding and organ fibrosis, however, our data suggest these proteins may be required at low levels in the glomerular ECM. In our analysis we found few changes related to sex, although meprin metalloproteases were enriched to males of both strains. Meprins were originally identified in kidney and intestine and have been shown to have roles in angiogenesis, cancer, inflammation, fibrosis and neurodegenerative diseases process. 407 Meprin alpha and meprin beta are unique in their ability to process and release both C- and N-propeptides from type I procollagen, 408 in addition to many other potential ECM substrates identified by MS. 409 Our data support a role for these ECM regulatory proteins as sex-associated modifiers of glomerular barrier function.

Having identified changes in ECM composition, we used serial EM sectioning to interrogate structure and we identified GBM defects, which correlated with barrier dysfunction. GBM changes are found in a range of human diseases, however, the homogenous expansions we observed were unlike immune- complex deposits. However, the defects were similar to those seen in mice with global and podocyte specific deletion of adhesion and matrix proteins, although these phenotypes are often dependent on the genetic background of the mice 27, 410, 411 . We speculate that weakened or split regions of GBM are sensed by podocytes and/or endothelial cells leading to increased synthesis of ECM in an attempt to repair the damage. Alternatively GBM expansions are weak points in the capillary walls leading to splitting at these points. We observed these structural changes despite a lack of major alteration in known GBM components; collagen IV alpha-3,4,5 and laminin-521, perlecan, agrin and nidogen 1 and

2. In future studies it would be important to investigate whether changes in netrin 4 or meprins, which were increased in the GBM of FVB and males respectively, contribute to the structural changes we have observed.

Having identified altered composition and organisation of ECM, the question of cause and effect remained. We therefore screened whole exome data from FVB and B6 mice and did not find any mutations in known ECM or glomerular genes. Notably, our previous studies in have shown that young adult FVB mice have significantly fewer glomeruli per kidney compared with B6 mice. 400 It has been postulated that congenital nephron deficit predisposes to arterial hypertension and progressive kidney injury ,412 however, there were no significant differences in systemic arterial pressure between the two 160

adult strains used in this study .400 It remains possible that altered intraglomerular haemodynamics are present in FVB mice and these contribute to the altered ECM we report.

To appreciate regulatory pathways associated with our observations, we performed systems-level analysis. With global microarray data we found enrichment of GO terms relating to mitochondrial dysfunction, activation of NRF2 and lipid metabolism with FVB mice. NRF2 is an antioxidant transcription factor and it increases the expression of ROS detoxification genes, which have been associated with diabetic kidney disease. 413 In the B6 strain, notch signalling was active and this pathway is key for glomerular patterning. 414 In addition, notch intracellular domain expression is increased in glomerular epithelial cells in diabetic nephropathy and in focal segmental glomerulosclerosis. 415, 416 Analysis of the proteomics data predicted CDK5 pathway activity in B6 mice and the Cyclin I-CDK5 complex safeguards podocytes against apoptosis via MAPK signalling. 417 By combining microarray and proteomic data we identified activity of the LXR/RXR pathway with the FVB strain. LXR/RXR are nuclear hormone receptors that act as transcription factors to regulate the expression of genes involved in cholesterol and fatty acid metabolism. 418 In the kidney, LXRs are specifically expressed in renin-producing juxtaglomerular cells and LXRs were shown to regulate renin expression in vivo , suggesting crosstalk between the RAAS and lipid metabolism. 419 Overall our systems-level analysis identified novel pathways, which may lead to changes in ECM and therefore could be the targets of therapeutic intervention to restore glomerular barrier function.

4.3.4 Methods

4.3.4.1 Antibodies

Monoclonal antibodies used were against actin (clone AC-40; Sigma-Aldrich, Poole, UK) mtHSP70

(MA3-028; Fisher scientific), nidogen (MAB1946; Millipore), tenascin C (MAB2138; R&D), meprin beta

(MAB28951; R&D). Polyclonal antibodies used were against lamin B1 (ab16048; Abcam, Cambridge,

UK), pan–collagen IV (ab6586; Abcam), pan-laminin (ab11575; Abcam), nephrin (ab58968; Abcam), fibroblast growth factor 2 (ab106245; Abcam), netrin 4 (AF1132; R&D) , meprin alpha (AP5858a;

Abgent), and collagen I (2150-1410; AbD Serotec). Secondary antibodies conjugated to Alexa Fluor

680 (Life Technologies, Paisley, UK) or IRDye 800 (Rockland Immunochemicals, Glibertsville, PA, 161

USA) were used for western blotting. Secondary antibodies conjugated to Alexa Fluor 488 or 594 (Life

Technologies) were used for Immunohistochemistry.

4.3.4.2 Global microarray

Eighteen-week-old male and female B6 and FVB mice (n = 3 in each group) were anesthetised and perfused with 1x1 08 Dynabeads (Invitrogen, Paisley, UK) through the left ventricle of the heart. 420

Kidneys were removed, decapsulated, minced, and digested, 420 and the glomeruli containing

Dynabeads were gathered by a magnetic particle concentrator and RNA prepared using RNeasy kit

(Qiagen, Crawley, UK). RNA quality was assessed on the Bioanalyser 2100 (Agilent Technologies,

Palo Alto, CA); subsequent cDNA and cRNA synthesis was performed and hybridised to mouse

MOE430 2.0 GeneChips . Signal values for transcripts on the Affymetrix array were calculated using the

MAS 5.0 algorithm to generate .chp files and these were exported to GeneSpring 9.0 (Agilent

Technologies, Wokingham, Berkshire, UK) for further analysis. The MAS 5.0–generated values were log2 transformed, normalised to the median within each array (to control for array loading), and these values were then baseline transformed to the median value of each transcript. Transcripts were filtered to exclude genes where expression did not reach a threshold value for reliable detection (based on the relaxed Affymetrix MAS 5.0 probability of detection; Pp0.1) in at least 1 of the 12 arrays assessed. To determine genes modified by strain and sex, a two-way analysis of variance analysis was performed.

Transcripts that were significantly altered by either strain or sex ( P < 0.01 after applying the Benjamini and Hochberg false discovery multiple testing correction) were used in subsequent analysis. Genes encoding extracellular matrix (ECM) proteins were selected from the microarray in the same manner as proteins were selected as ECM, as described below.

4.3.4.3 Isolation of murine glomeruli and enrichment of glomerular ECM

The glomeruli of eighteen-week-old male and female B6 and FVB mice ( n=3 in each of four groups) were isolated through the Dynabead-based isolation method .420 All steps were carried out at 4 oC to minimise proteolysis. Pure glomerular isolates from three mouse kidneys were incubated for 30 minutes in extraction buffer (10 mM Tris, 150 mM NaCl, 1% (v/v) Triton X-100, 25 mM EDTA, 25 g/ml leupeptin, 25 g/ml aprotinin and 0.5 mM AEBSF) to solubilise cellular proteins, and samples were then centrifuged at 14000 × g for 10 minutes to yield fraction 1. The remaining pellet was incubated for 162

30 minutes in alkaline detergent buffer (20 mM NH 4OH and 0.5% (v/v) Triton X-100 in PBS) to further solubilise cellular proteins and to disrupt cell–ECM interactions. Samples were then centrifuged at

14000 × g for 10 minutes to yield fraction 2. The remaining pellet was incubated for 30 minutes in a deoxyribonuclease (DNase) buffer (10 g/ml DNase I (Roche, Burgess Hill, UK) in PBS) to degrade

DNA. The sample was centrifuged at 14000 × g for 10 minutes to yield fraction 3, and the final pellet was re-suspended in reducing sample buffer (50 mM Tris-HCl, pH 6.8, 10% (w/v) glycerol, 4% (w/v) sodium dodecylsulfate (SDS), 0.004% (w/v) bromophenol blue, 8% (v/v) β-mercaptoethanol) to yield the ECM fraction. Samples were heat denatured at 70oC for 20 minutes.

4.3.4.4 Western blotting

See General Materials and Methods.

4.3.4.5 MS data acquisition

See General Materials and Methods.

4.3.4.6 MS data analysis and data deposition

Tandem mass spectra were extracted using extract_msn (Thermo Fisher Scientific) executed in

Mascot Daemon (version 2.2.2; Matrix Science, London, UK). Peak list files were searched against a modified version of the Uniprot mouse database (version 3.70; release date, 3 May 2011) , containing ten additional contaminant and reagent sequences of non-mouse origin , using Mascot (version 2.2.06;

Matrix Science) (Perkins et al , 1999). Carbamidomethylation of cysteine was set as a fixed modification; oxidation of methionine and hydroxylation of proline and lysine were allowed as variable modifications. Only tryptic peptides were considered, with up to one missed cleavage permitted.

Monoisotopic precursor mass values were used, and only doubly and triply charged precursor ions were considered. Mass tolerances for precursor and fragment ions were 0.4 Da and 0.5 Da, respectively. MS datasets were validated using rigorous statistical algorithms at both the peptide and protein level 421 , 422 implemented in Scaffold (version 3.6 .5; Proteome Software, Portland, OR, USA).

Protein identifications were accepted upon assignment of at least two unique validated peptides with

≥ 90% probability, resulting in ≥ 99% probability at the protein level. These acceptance criteria resulted 163

in an estimated protein false discovery rate of 0.1% for all datasets. The MS proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org )

340 through the PRoteomics IDEntifications partner repository with the dataset identifier PDX000811 and

DOI 10.6019/PXD000811.

4.3.4.7 MS data quantification and statistical analysis

4.3.4.8 Functional annotation and enrichment analysis

Proteins identified in at least two of the three biological replicates were included for further analysis.

GO annotations were downloaded using the online resource DAVID (Huang et al , 2009a)(Huang et al ,

2009b) . The GO cellular compartment annotation chart (GOTERM_CC_FAT) was selected, and proteins annotated in the extracellular region cluster were further cross-referenced with the mouse matrisome project ,1 and annotated as extracellular region. These ECM proteins were manually further divided into basement membrane, other structural and ECM-associated proteins. Proteins with no evidence of extracellular localisation, were removed from the ECM dataset . This selection enabled the definition of the B6 female, B6 male, FVB female and FVB male glomerular ECM proteomes, as presented in Table S2 .

4.3.4.9 Principal component analysis

Principal component analysis was performed using the commercial software package MATLAB

(version 7.14.0).

164

4.3.4.10 Hierarchical clustering analysis

Z-transformed mean normalised intensities or normalised spectral counts were used for hierarchical clustering of microarray data and proteomic data respectively. Agglomerative hierarchical clustering was performed using MultiExperiment Viewer (version 4.8.1) .381 Protein hits were hierarchically clustered on the basis of Euclidean distance, and distances between hits were computed using a complete-linkage matrix. Clustering results were visualised using MultiExperiment Viewer (version

4.8.1).

4.3.4.11 Protein interaction network analysis

Protein interaction network analysis was performed using Cytoscape (version 2.8.1) (Shannon et al,

2003). Proteins identified in at least two biological replicates were mapped onto a merged human, mouse and rat interactome built from Protein Interaction Network Analysis platform Homo sapiens network (release date, 10 December 2012), Mus musculus network (release date, 10 December 2012) and the Rattus norvegicus network (release date, 10 December 2012) ,348 the ECM interactions database MatrixDB (release date, 20 April 2012), 349 and a literature-curated database of integrin-based adhesion–associated proteins. 423 For networks where enrichment is presented Z transformed normalised spectral counts were used. Topological parameters were computed using the

NetworkAnalyzer plug-in .350

4.3.4.12 GO enrichment and Ingenuity pathway analysis

To generate Figure 7A and Figure E6A, GO annotations were downloaded using the online resource

DAVID 344 and loaded into the Cytoscape plug-in, Enrichment map v1.2, 347 with the following settings:

P-value cut-off 0.005, Q-value cut-off 0.05 and overlap coefficient 0.6. The generated enrichment maps were clustered using the Markov cluster algorithm . For Figures 7B and 7C datasets were uploaded to

IPA (Ingenuity® Systems, www.ingenuity.com), the 'overlapping canonical pathways' tool was used to identify pathways predicted to be active in B6 or FVB. The data was then visualised using Cytoscape

(version 2.8.1) .

165

4.3.4.13 Immunohistochemistry and image analysis

Aldrich Ltd, Gillingham, UK) for 30 minutes and with primary antibodies overnight at 4 oC. Sections were washed three times with PBS, incubated with secondary antibodies, mounted with polyvinyl alcohol mounting medium (Fluka 10981, Sigma-Aldrich Ltd, Gillingham, UK) and imaged using Delta Vision

(Applied Precision) restoration microscope using a 60x objective. The images were collected using a

Coolsnap HQ (Photometrics) camera with a Z optical spacing of 0.2 m. Raw images were then deconvolved using the Softworx software and projections of these deconvolved images are shown in the results . Images were also collected using a 20x objective and 3D Histech Pannoramic 250 Flash slide scanner. Images were processed and analysed using Fiji/ImageJ software (version 1.46r;

National Institutes of Health, Bethesda, MD, USA) and Pannoramic Viewer

(http://www.3dhistech.com/ ). Raw images were subjected to signal re-scaling using linear transformation for display in the figures. For calculation of mean pixel intensity, a region of interest was drawn around glomeruli and the mean pixel intensity in the selected region measured . This was performed on secondary only controls and on the test samples. The signal from secondary only control samples was subtracted from test samples. Mean pixel intensity measurements were acquired from >

60 glomeruli per biological replicate.

4.3.4.14 Electron microscopy

We used transmission electron microscopy (TEM) and serial block-face scanning electron microscopy

(SBFSEM) to investigate glomerular ultrastructure. Tissue samples were fixed for at least 1 hour in a mix of 2% formaldehyde and 2.5% glutaraldehyde in 0.1 M sodium cacodylate buffer (pH 7.4). The samples were postfixed with reduced osmium (1% OsO 4 and 1.5% K 4Fe(CN) 6) for 1 hour, then with 1% tannic acid in 0.1 M sodium cacodylate buffer for 1 hour, and finally with 1% uranyl acetate in water overnight. The specimens were dehydrated with alcohols, infiltrated with TAAB LV resin and polymerised for 12 hours at 60 oC. Ultrathin 70-nm sections were cut with a Leica Ultracut

S ultramicrotome and placed on formvar/carbon-coated slot grids. The grids were observed in a Tecnai

12 Biotwin transmission electron microscope at 80 kV. Electron microscopy data were screened for 166

ECM characteristics between using Fiji/ImageJ software (version 1.46r; National Institutes of Health,

Bethesda, MD, USA). GBM thickness were quantified in five regions per observation and reported as mean values. For serial block face SEM (SBF-SEM) the polymerised samples were trimmed, glued to aluminum pins and sputter coated with gold/palladium (60 seconds at standard settings). Automated sectioning (60nm) was completed in a Gatan 3View. The images were taken with FEI Quanta 250 FEG

SEM at 3.8kV accelerating voltage and 0.4 Torr chamber pressure with a pixel size of 12nm.

4.3.4.15 Mouse strain SNP analysis

The Jackson laboratory phenome resource was used in order to screen for small nucleotide polymorphisms (SNPs) in ECM proteins identified in this study, and transcripts identified by whole glomerular microarray. The SNP/genotype variation query tool (http://phenome.jax.org/ ) was used with the following criteria: Choose data sets>>strains and filtering options>>C57BL/6J, FVB/NJ>> polymorphism between certain strains>>all examined calls must be nonblank. We screened genes encoding ECM proteins identified by MS in this study, transcripts identified by whole glomerular microarray in addition to genes associated with glomerular disease.

4.3.4.16 Statistical analysis

All measurements are shown as mean ± standard error of the mean. Box plots indicate 25th and 75th percentiles (lower and upper bounds, respectively), 1.5× interquartile range (whiskers) and median

(black line). Bar chart error bars represent standard error of the mean. Analysis of thickness of GBM, podocyte foot process width and frequency of GBM sub-podocyte outpockets were compared using one-way analysis of variance with post-hoc Bonferroni correction. Numbers of protein–protein interactions and normalised fluorescence intensity were compared using Kruskal–Wallis one-way analysis of variance tests with post-hoc Bonferroni correction.

4.3.5 Acknowledgements

This work was supported by a Wellcome Trust Intermediate Fellowship award (090006) to R.L., a Kids

Kidney Research grant awarded to RL and A.S.W to support a PhD studentship for M.J.R, a Kidney

Research UK Senior Non-Clinical Fellowship (SF1/2008) and Medical Research Council New 167

Investigator Award (MR/J003638/1) both to D.A.L and Wellcome Trust grant (092015) to M.J.H. The mass spectrometer and microscopes used in this study were purchased with grants from the

Biotechnology and Biological Sciences Research Council, Wellcome Trust and the University of

Manchester Strategic Fund. Mass spectrometry was performed in the Biomolecular Analysis Core

Facility, Faculty of Life Sciences, University of Manchester, and we thank Stacey Warwood for advice and technical support and Julian Shelley for bioinformatic support.

4.3.6 Conflict of interest

The authors declare no conflicts of interests . 168

4.3.7 Supplementary data

Table 4.1 Mouse glomerular ECM proteome

Uniprot Gene name B6 females B6 males FVB females FVB males Classification (nSC) (nSC) (nSC) (nSC) Q60847 Col12A1 0.509 0.328 0.508 0.179 Collagens

B7ZNH7 Col14A1 0.181 0.288 0.429 0.000 Collagens

A2AJY2 Col15A1 0.999 0.905 0.831 1.057 Collagens

P39061 Col18A1 1.114 1.084 1.192 0.700 Collagens

P11087 Col1A1 1.121 1.271 0.950 0.282 Collagens

Q01149 Col1A2 0.727 1.001 0.693 0.301 Collagens

Q8BLW4 Col3A1 0.305 0.672 0.267 0.000 Collagens

P02463 Col4A1 0.720 0.683 0.619 0.605 Collagens

B2RQQ8 Col4A2 1.350 1.377 1.244 1.357 Collagens

Q9QZS0 Col4A3 0.677 0.809 0.668 0.764 Collagens

Q9QZR9 Col4A4 1.441 1.414 1.377 1.552 Collagens

Q63ZW6 Col4A5 0.500 0.452 0.536 0.538 Collagens

B1AVK5 Col4A6 0.366 0.379 0.341 0.158 Collagens

Q3U962 Col5A2 0.000 0.000 0.214 0.000 Collagens

Q04857 Col6A1 1.819 1.969 2.244 1.825 Collagens

Q02788 Col6A2 2.280 2.008 2.226 2.150 Collagens

Q99K31 Col6A3 0.170 0.163 0.180 0.263 Collagens

A6H584 Col6a5 0.000 0.000 0.131 0.136 Collagens

Q6PCM6 Agrn 2.333 2.104 2.417 2.417 Glycoproteins

Q91VF5 Emid1 0.000 0.664 0.509 0.425 Glycoproteins

Q99K41 Emilin1 1.193 1.069 1.583 0.900 Glycoproteins

Q9WVH9 Fbln5 0.417 0.000 0.566 0.391 Glycoproteins

A2AQ53 Fbn1 0.146 0.224 0.259 0.278 Glycoproteins

Q8K0E8 Fgb 1.090 1.342 2.318 1.999 Glycoproteins

Q8VCM7 Fgg 0.852 1.241 1.969 1.157 Glycoproteins

Q3UH17 Fn1 1.874 1.555 1.912 2.053 Glycoproteins

Q80T14 Fras1 0.048 0.046 0.071 0.000 Glycoproteins

P70389 Igfals 0.000 0.000 0.689 0.292 Glycoproteins

Q3U492 Kcp 0.000 0.000 0.113 0.144 Glycoproteins

P57016 Lad1 0.653 1.238 0.897 0.662 Glycoproteins

P19137 Lama1 0.764 0.519 0.571 0.351 Glycoproteins 169

Q60675 Lama2 0.721 0.421 0.513 0.561 Glycoproteins

P97927 Lama4 0.649 0.437 0.922 0.587 Glycoproteins

Q61001 Lama5 1.833 1.848 1.842 1.962 Glycoproteins

P02469 Lamb1 1.983 1.696 1.765 1.689 Glycoproteins

Q61292 Lamb2 3.116 2.822 2.748 3.123 Glycoproteins

P02468 Lamc1 2.512 2.454 2.329 2.515 Glycoproteins

P21956 Mfge8 1.435 1.465 1.339 1.763 Glycoproteins

A6H6E2 Mmrn2 0.536 0.735 0.767 0.617 Glycoproteins

P10493 Nid1 2.196 2.130 2.172 2.209 Glycoproteins

Q3TPN0 Nid2 1.673 1.650 1.713 1.581 Glycoproteins

Q9JI33 Ntn4 0.457 0.435 0.890 0.663 Glycoproteins

Q9EPX2 Papln 0.331 0.401 0.673 0.462 Glycoproteins

B2RX13 Pxdn 0.478 0.984 0.793 0.573 Glycoproteins

A1L353 Tgfbi 1.101 0.609 0.830 0.000 Glycoproteins

Q3UTY6 Thsd4 0.000 0.000 0.166 0.000 Glycoproteins

Q91XG7 Tinag 1.823 1.795 2.295 1.684 Glycoproteins

A3KFW2 Tinagl1 1.265 1.365 1.758 1.270 Glycoproteins

Q80YX1 Tnc 0.706 0.519 0.459 0.309 Glycoproteins

O54796 Tnxb 0.000 0.000 0.105 0.000 Glycoproteins

P29788 Vtn 0.709 0.922 1.354 1.254 Glycoproteins

Q8R2Z5 Vwa1 0.464 0.677 0.614 0.434 Glycoproteins

O35598 Adam10 0.000 0.364 0.541 0.349 ECM Regulators

Q769J6 Adamts13 0.000 0.000 0.122 0.000 ECM Regulators

Q9DBB9 Cpn2 0.000 0.000 0.464 0.651 ECM Regulators

Q9R118 Htra1 0.000 0.461 0.706 0.000 ECM Regulators

O35632 Hyal2 0.582 0.469 0.577 0.669 ECM Regulators

Q61702 Itih1 0.652 0.856 0.652 0.507 ECM Regulators

Q61703 Itih2 0.204 0.288 0.221 0.000 ECM Regulators

A6X935 Itih4 0.216 0.306 0.246 0.373 ECM Regulators

Q8BJD1 Itih5 0.397 0.331 0.521 0.183 ECM Regulators

P28825 Mep1A 0.425 0.964 0.269 0.774 ECM Regulators

Q61847 Mep1B 0.664 1.259 0.396 1.007 ECM Regulators

P28665 Mug1 0.338 0.222 0.189 0.875 ECM Regulators

Q61838 Pzp 0.000 0.000 0.141 0.177 ECM Regulators

P07758 Serpina1A 0.454 0.551 0.540 0.776 ECM Regulators

P07759 Serpina3K 0.666 1.300 1.192 1.654 ECM Regulators

P70124 Serpinb5 0.994 0.000 0.000 0.000 ECM Regulators

Q3TWG9 Serpinh1 2.695 2.781 2.777 2.590 ECM Regulators

Q9JLF6 Tgm1 0.356 0.493 0.351 0.478 ECM Regulators 170

P21981 Tgm2 1.601 1.478 2.011 1.637 ECM Regulators

Q54AE5 Timp3 0.000 0.000 1.162 0.814 ECM Regulators

Q792Z1 Try10 0.000 0.000 0.000 0.871 ECM Regulators

P07356 Anxa2 1.894 1.618 1.501 1.821 ECM-associated

Q61176 Arg1 0.596 0.803 0.000 0.736 ECM-associated

Q02105 C1Qc 0.000 0.000 1.239 0.000 ECM-associated

Q8VHY0 Cspg4 0.177 0.000 0.116 0.078 ECM-associated

Q61543 Glg1 0.253 0.168 0.278 0.281 ECM-associated

Q9QZF2 Gpc1 0.439 0.668 0.000 0.000 ECM-associated

Q9D0F3 Lman1 0.869 0.000 0.000 0.505 ECM-associated

Q9WVQ1 Magi2 2.186 1.689 2.241 1.700 ECM-associated

P41317 Mbl2 0.775 0.000 1.197 0.000 ECM-associated

Q3U5S6 Sdc4 0.000 1.940 0.000 0.000 ECM-associated

P28653 Bgn 0.758 0.727 0.752 0.000 Proteoglycans

Q9QUP5 Hapln1 0.000 0.504 0.778 0.810 Proteoglycans

Q05793 Hspg2 1.794 1.659 1.717 1.772 Proteoglycans

Q9JK53 Prelp 0.623 0.706 0.930 0.000 Proteoglycans

P31230 Aimp1 0.000 0.599 0.555 0.000 Secreted Factors

P07724 Alb 0.914 1.035 1.570 1.179 Secreted Factors

Q9R045 Angptl2 0.549 0.000 0.758 0.514 Secreted Factors

Q8R0Z6 Angptl6 0.437 0.799 0.356 0.000 Secreted Factors

Q00623 Apoa1 0.000 1.642 1.505 1.891 Secreted Factors

Q810I7 Apoa4 0.000 0.947 0.000 0.000 Secreted Factors

P08226 Apoe 0.899 1.396 1.052 0.000 Secreted Factors

P01027 C3 2.067 1.953 2.796 2.143 Secreted Factors

P01029 C4A 0.938 1.241 1.554 1.783 Secreted Factors

P01029 C4B 0.938 1.241 1.554 1.783 Secreted Factors

B2MWM9 Calr 0.000 0.000 0.376 0.469 Secreted Factors

Q4LDF6 Cfhr2 0.000 0.000 0.323 0.000 Secreted Factors

Q8CIE6 Copa 0.275 0.146 0.198 0.245 Secreted Factors

Q61147 Cp 1.208 1.344 1.129 1.091 Secreted Factors

P15655 Fgf2 0.000 0.000 2.024 1.711 Secreted Factors

Q5M9M0 Flt3L 1.382 1.843 2.573 1.583 Secreted Factors

P46412 Gpx3 3.243 4.262 4.597 3.253 Secreted Factors

Q58E61 Igh 0.631 0.860 0.000 0.000 Secreted Factors

Q6PJA7 Igh-1A 0.000 0.000 0.690 0.000 Secreted Factors

Q6PJA7 Igh-1B 0.000 0.000 0.690 0.000 Secreted Factors

P01872 Igh-6 0.703 0.972 0.809 0.613 Secreted Factors

P01869 Ighg1 0.000 0.000 0.432 0.526 Secreted Factors 171

Q9JJ00 Plscr1 0.620 0.000 0.502 0.765 Secreted Factors

P06281 Ren1 0.888 0.829 0.651 0.000 Secreted Factors

P06281 Ren2 0.888 0.829 0.651 0.000 Secreted Factors

O88592 Sod3 0.000 1.126 0.800 0.000 Secreted Factors

Q3TTV6 Tfip11 0.000 0.000 0.408 0.000 Secreted Factors

Q8CC88 Vwa8 0.344 0.529 0.149 0.395 Secreted Factors 172

Figure 4.8 Workflow of glomerular ECM enrichment. A: A proteomic workflow for the isolation of enriched glomerular ECM by fractionation (see methods for further details). B: Mouse glomeruli were isolated by Dynabead perfusion and magnetic concentration. The Dynabead approach yielded significantly greater numbers of glomeruli, **** P < 0.001, (18744 ± 1590, n = 5) than glomerular isolation by differential sieving (2148 ± 238, n = 5) left panel. Glomerular purity was not significantly different between Dynabead isolation (97.00 ± 0.59, n = 5) and sieving isolation (95.02 ± 0.57, n = 5) methods, right panel. Rachel Lennon and Michael Randles generated data for this figure part A. Jenifer Huang and Michael Randles contributed to data part B. 173

Figure 4.9 Unconnected glomerular ECM proteins with enrichment due to strain or sex. Nodes represent proteins and are labelled with gene names for clarity. A: Nodes are coloured if a protein is enriched to FVB (red) or B6 (blue) in all comparisons of FVB groups against B6 groups. B: Nodes are coloured if a protein is enriched to males (grey) or females (gold) in all comparisons of male groups against female groups. Michael Randles generated data for this figure. 174

Figure 4.10 Known GBM components do not change in a robust strain- or sex-dependant manner. Comparison of selected GBM components at the protein and transcript level. F, females; M, males. Michael Randles generated data for this figure. 175

Figure 4.11 GO enrichment analysis of proteomic data-set A: Unsupervised hierarchical clustering of GO terms against proteins, B: GO enrichment map of terms significantly enriched in FVB or B6 glomerular ECM. Nodes (circles) represent GO terms significantly enriched in the proteomic data set, edges (lines) represent overlap of proteins within terms. Node size relates to the number of proteins which are allocated to a given term and node colour relates to the enrichment to either FVB (red) or B6 (Blue). Michael Randles generated data for this figure. 176

Movie S4.1. Serial block face scanning electron microscopy of a glomerulus from a female B6 mouse. This magnification allows visualization of the entire glomerulus and assembly of serial sections into a movie provides a 3D reconstruction.

Movie S4.2. Serial block face scanning electron microscopy of a glomerulus from a female B6 mouse. Increased magnification view of capillary loops in a B6 female mouse. Arrows indicate regions of expanded basement membrane.

Movie S4.3. Serial block face scanning electron microscopy of a glomerulus from a male B6 mouse. Increased magnification view of capillary loops in a B6 male mouse. Arrows indicate regions of expanded basement membrane.

Movie S4.4. Serial block face scanning electron microscopy of a glomerulus from a female FVB mouse. Increased magnification view of capillary loops in a FVB female mouse. Arrows indicate regions of expanded basement membrane.

Movie S4.5. Serial block face scanning electron microscopy of a glomerulus from a male FVB mouse. Increased magnification view of capillary loops in a FVB male mouse. Arrows indicate regions of expanded basement membrane. 177

5 Podocyte adhesion complexes

5.1 Introduction

Glomerular extracellular matrix (ECM) undergoes changes in appearance during a range of glomerulopathies. These changes may modify the structure and permeability properties of the glomerular filtration barrier (GFB) directly, in addition to affecting the GFB by influencing the surrounding cells. Cells interpret the stiffness, composition and topology of the extracellular environment through the engagement of adhesion receptors. Sites of cell adhesion to the ECM contain macromolecular signalling complexes termed focal adhesions. These adhesion sites are required for the survival of adherent cells, in addition to being key determinants of cell morphology and behaviour.

Podocytes are a crucial component of the GFB, they have unique cellular morphology which is dependent on adhesion signalling at the cell-cell and cell-ECM interface. Podocytes utilise integrin

α3β1 to engage laminin α5β1γ1 (laminin-511) during development and laminin-521 in the mature glomerular basement membrane (GBM). This interaction is thought to be the major linkage of podocytes to the GBM. In addition, podocytes express α1β1 and α2β1 integrins, which can bind to laminin and collagen, but to the latter with higher affinity. The major ECM substrates in the GBM are laminin and collagen IV, however, the differences in adhesion signalling complexes formed by cells on these different substrates has not been studied. Understanding the effect of ECM ligand on adhesion signalling may provide insights into the pathogenesis of a number of diseases where adhesion or ECM proteins are mutated or otherwise dysregulated. 178

5.2 Statements

Author contributions to data generation and analysis presented as figures of this paper are indicated in figure legends.

Michael J. Randles, Rachel Lennon, Hellyeh Hamidi and Adrian S. Woolf planned the study and designed experiments. Adam Byron, Jonathan D. Humphries and Martin Humphries contributed to the study design.

Michael J. Randles generated all figures presented in this manuscript, performed Western blotting, immunofluorescence, live cell imaging, image analysis and quantification, electrical cell-substrate impedance sensing, adhesion complex isolation, mass spectrometry sample preparation, bioinformatic and network analysis.

These data are still preliminary and are organised as a Journal of the American Society of Nephrology research article for the purpose of this thesis only. Author guidelines restrict the size of the article to

3000 words or less (excluding title page, methods, figure legends, tables, and references) .

Michael J. Randles wrote the manuscript and it was critically assessed by Rachel Lennon and Adrian

S. Woolf. 179

5.3 The influence of extracellular matrix ligand on the assembly

of podocyte adhesion complexes

Michael J. Randles, 1,2 Hellyeh Hamidi, 1 Jonathan D. Humphries, 1 Adam Byron, 3 Martin J. Humphries, 1

Adrian S. Woolf, 2 and Rachel Lennon. 1,2

1Wellcome Trust Centre for Cell-Matrix Research, Faculty of Life Sciences, University of Manchester,

Manchester, UK; 2Faculty of Medical and Human Sciences, University of Manchester, Manchester, UK;

3Edinburgh Cancer Research UK Centre, Institute of Genetics and Molecular Medicine, University of

Edinburgh, Edinburgh, EH4 2XR UK .

Running title: Podocyte adhesion complexes

Corresponding author:

Dr Rachel Lennon, Wellcome Trust Centre for Cell-Matrix Research, Michael Smith Building, University of Manchester, Manchester M13 9PT, UK.

Phone: 0044 (0) 161 2755498. Fax: 0044 (0) 161 2755082.

Email: [email protected] 180

5.3.1 Abstract

Epithelial cells adhere to basement membranes in vivo , and these are composed of laminin and collagen IV, in addition to a large number of lower abundance proteins. These adhesion sites enable the transduction of information from outside to inside the cell and vice versa, thereby controlling cell phenotype. In the glomerulus specialised epithelial cells called podocytes adhere to a unique basement membrane, which together with glomerular endothelial cells form a barrier that is adapted for plasma filtration. Adhesion within this structure is essential; loss or downregulation of integrin adhesion receptors, laminin or collagen IV within the glomerulus causes loss of selective filtration function. Here, we study podocyte adhesomes on the basement membrane substrates, collagen IV and laminin, in order to gain insight into how these signalling complexes control cell fate. Using mass spectrometry

(MS)-based proteomics we identified changes in adhesion complexes formed on collagen IV compared with laminins. Firstly, we find that collagen IV adhesion complexes are larger and enriched with adaptor, scaffolding, actin binding and cytoskeletal proteins compared with laminin adhesion sites. In contrast, laminin adhesion complexes are less stable in nature with greater abundance of proteins involved in endocytosis and trafficking. Furthermore, these differences influence the podocyte actin cytoskeleton and cell shape; adhesion to collagen IV causes podocytes to form a rounded cell morphology. In contrast, laminin promotes the formation of elongated podocytes with numerous projections. These data support a role for adhesion sites in controlling podocyte morphology and suggest a role for laminin adhesion sites in podocyte foot process formation. 181

5.3.2 Introduction

Adhesion of cells to each other and to the extracellular substratum is required for multicellular existence. A major family of receptors that mediate cellular adhesion to the extracellular matrix (ECM) are the integrins. Integrins are heterodimeric transmembrane proteins that form a physical link between the ECM and the cell cytoskeleton. 99 Integrins form 24 different heterodimers each with preferential

ECM binding partners. 161 Integrins are differentially expressed throughout human tissues and specific integrins are important for glomerular function. Mutations in ITGA3 , coding for the integrin α3 subunit, causes severe interstitial lung disease, epidermolysis bullosa and congenital nephrotic syndrome, reflecting the tissue-restricted expression of this gene .171 Integrin α3β1 is a laminin adhesion receptor, which is highly expressed by the glomerular podocyte. 168, 170 Additionally, mutations in LAMB2, which result in the absence of the laminin-521 network from the glomerular basement membrane (GBM), cause a form of congenital nephrotic syndrome, called Pierson syndrome. 115, 116 Taken together these data suggest that podocyte adhesion to the laminin network within the GBM via integrin α3β1 is required for a normal glomerular filtration.

Mutations in COL4A3, COL4A4 or COL4A5 , which can lead to loss or downregulation of the collagen

IV α3α4α5 network in the GBM causes Alport syndrome. Alport syndrome is associated with progressive nephrotic syndrome and sensorineural hearing loss. 120 Collagen IV is thought to be required for the structural strength and stability in basement membranes; therefore, one would predict a weaker GBM to be formed in the absence of collagen IV α3α4α5. 119 This notion is consistent with the splitting of the GBM observed in individuals with Alport syndrome.

Super resolution microscopy has provided an additional clue to the mechanism of progressive nephrotic syndrome in Alport syndrome. When collagen IV α3α4α5 is lost through the deletion of

Col4a3 in mice, the collagen IV α1α1α2 network within the GBM is upregulated, but it is unable to compensate. 134 Collagen IV α3α4a5 is normally distributed in the centre of the GBM, too far away from integrins on the podocyte cell surface to make meaningful interactions. In contrast, in Alport syndrome the upregulated collagen IV α1α1α2 network is distributed throughout the GBM, theoretically close enough to contact adhesion receptors on the podocyte cell surface. 134 Podocytes express integrins 182

α1β1 and α2β1, which bind to collagen IV. 162, 163 Therefore, it is possible that signalling by α1β1 and

α2β1 integrins in the presence of an ectopic collagen IV α1α1α2 network may contribute to the progression of Alport syndrome. This hypothesis is further supported by the global deletion of Itga1 the gene encoding integrin α1. Mice null for integrin α1 are overtly normal and do not have proteinuria, suggesting that it is not important in the glomerulus in normal conditions. However, Alport mice that are also null for integrin α1 develop less severe proteinuria and podocyte foot process effacement is attenuated .424 In order to test the hypothesis that podocyte engagement of collagen IV α1α1α2 is pathogenic in the context of Alport syndrome, we utilised MS-based proteomic analysis of podocyte adhesion signalling complexes induced by either collagen IV α1α1α2, laminin-511 or laminin-521. This analysis will also enable the identification of the normal composition of podocyte adhesion complexes when adhered to mature and developmental laminin substrates.

Currently, 232 different protein components have been shown to be recruited to focal adhesions in a cell type and context dependent manner. 165, 425 Adhesion complexes have been studied using global

MS-based proteomic approaches. These studies have elegantly demonstrated that different integrins that bind to the same ligand differentially recruit proteins to adhesion sites, such as VCAM binding integrins α4β1 and a chimeric α4β1 variant with α5 cytoplasmic domain, 160 in addition to fibronectin binding α5β1 and αVβ3 integrins. 6 However, a proteomic comparison of adhesion complexes formed on different ECM ligands has yet to be performed. This question is important for a number of cell types and ligands, but has specific importance for the glomerular podocyte as adhesion plays a major role in podocytopathies. Using MS-based proteomics of adhesion complexes we aim to unravel the pathogenesis of diseases where podocyte adhesion is perturbed, we begin by investigating the affect of ECM ligand on podocyte adhesion signalling. 183

5.3.3 Results

5.3.3.1 Podocyte attachment to ECM ligands

We first used flow cytometry to determine which integrins podocytes express in vitro . We found that podocytes express: the laminin binding integrins, α3β1 (and its associated tetraspanin, CD151) and

α6; integrins that preferentially bind to collagen, α1β1 and α2β1; and integrins α4 and αVβ3, which bind to a range of ligands such as fibronectin and osteopontin (Figure 5.1A). Therefore, podocytes in vitro express the relevant receptors to study podocyte adhesion to collagen IV and laminin.

During development podocytes adhere to a GBM that contains laminin-511 and collagen IV α1α1α2, but the mature glomerulus contains predominately laminin-521 and collagen IV α3α4α5. 101, 102 Using attachment assays we found that podocytes attach to collagen IV α1α1α2. This will be referred to as collagen IV for all subsequent experiments presented as figures in this paper. There was also attachment to laminin-511 and laminin-521 with similar kinetics. Additionally, podocytes attached to fibronectin, but with lower avidity (Figure 5.1B and C) . Apotransferrin was used as a control ligand.

Cells attach to apotransferrin via the transferrin receptor, rather than using integrins. However, cells that attach to apotransferrin are unable to spread and therefore remain phase bright (Figure 5.1C). 184

Figure 5.1 Podocyte attachment to ECM ligands A: Podocytes express: integrin α3β1, which binds specifically to laminin; CD151, a tetraspanin that is associated with integrin α3β1; integrin α1β1, which binds to laminin and collagen with preference for collagen IV; integrin α2β1, which binds to laminin and collagen with preference for collagen I; and integrin αVβ3, which binds to fibronectin, vitronectin, von Willebrand factor, latency associated peptide- transforming growth factor beta, fibrillin, fibrinogen, thrombospondin, tenascin and osteopontin. B: Podocytes attachment assays. Podocytes attach to fibronectin, collagen IV, laminin-511 and laminin- 521. For attachment assays cells were allowed to attach for 30 minutes in serum free media. C: Podocytes attach and spread on fibronectin, collagen IV, laminin-511 and laminin-521. Podocytes attach to apotransferrin, but do not spread so remain phase bright. For adhesion assays podocytes were allowed to adhere to plates coated with 5 µg/ml of ECM substrate for 180 minutes in serum free media. MFI, median fluorescence intensity. Rachel Lennon produced data figure part A. Hellyeh Hamidi produced data figure part B. Michael Randles produced data figure part C. 185

5.3.3.2 ECM ligand determines podocyte cell shape, cytoskeletal morphology, focal adhesion size and

number

We observed a striking variation in podocyte morphology dependent on ECM ligand (Figure 5.1C).

Podocytes attached and spread onto collagen IV and laminin, but established distinct cell shapes; collagen IV induced a more rounded (higher circularity) cell shape, as opposed to laminin, which caused a stretched out, elongated phenotype (Figure 5.1C, 5.2A, B). In addition, adhesion to both laminin isoforms enhanced podocyte protrusive activity compared with adhesion to collagen IV where podocytes exhibited limited protrusive activity (Figure 5.2E and Movies S5.1-3). However, podocytes were spread equally well regardless of ECM ligand, as average cell area was not significantly different between ECM ligands (Figure 5.2C). In addition to cell shape, the actin cytoskeleton was affected by

ECM ligand. Adhesion to collagen IV caused a concentration of actin close to the centre of the podocyte, with short actin stress fibres (Figure 5.2A). This was markedly different when podocytes adhere to laminin, where they generated actin stress fibres running parallel to the longest edge of the podocyte. Indeed, the average length of podocyte actin stress fibres on laminin was significantly longer than on collagen IV (Figure 5.2A, D).

We next assessed podocyte focal adhesion formation on collagen IV and laminin. Focal adhesion kinase (FAK; PTK2) is a non-receptor tyrosine kinase that localises to focal adhesions. When recruited to focal adhesions FAK is phosphorylated at tyrosine 397 (P-FAK397). We observed that large P-

FAK397 positive adhesions were formed in podocytes when plated onto fibronectin. Conversely, these structures were much smaller and punctate in podocytes spread onto collagen IV or laminin (Figure

5.3A). Overall, levels of P-FAK397 were significantly lower in podocytes on basement membrane ligands compared with fibronectin, but there was no significant difference between collagen IV, laminin-

511 or laminin-521 (Figure 5.3A, B). FAK, talin and paxillin recruitment to integrins is an early event in integrin mediated adhesion formation. 426 Talin subsequently recruits vinculin, 204 we therefore used vinculin as a marker of mature focal adhesions. Vinculin and paxillin staining revealed large adhesive structures in podocytes adhered to collagen IV, but only small punctate adhesion structures in podocytes adhered to laminins (Figure 5.4E). Quantification of vinculin straining revealed significantly more and larger mature focal adhesions were formed by podocytes on collagen IV compared with laminin (Figure 5.4C-E). 186

Figure 5.2 Laminin and collagen IV adhesion influence podocyte morphology A: Podocyte actin morphology on different ECM ligands. Podocytes form stress fibres along the longest edge of podocytes attached to laminin, whereas they form shorter stress fibres from the centre of the cell to the cell periphery when attached to collagen IV. B: Podocytes are more circular in shape when attached to collagen compared with laminin. C: Podocyte cell area is not significantly different on different ECM ligands. D: Podocyte stress fibres are significantly longer when podocytes adhere to laminin compared with collagen IV. E: The colour-coded shape outlines indicate representative protrusive activities recorded over 100 minute period (5 minute intervals). For all experiments podocytes were allowed to adhere to plates coated with 5 µg/ml of ECM substrate for 180 minutes in serum free media before fixation and imaging. For measurements of protrusive activity live cell imaging was performed between 180 - 280 minutes of cell spreading. For all experiments n > 30 cells per experiment, n = 4 experiments; scale bar represents 20 µm; ****, p < 0.0001; NS, not significant; FN, 187

fibronectin; COL4, collagen IV; LAM511, laminin-511; LAM521, laminin-521. Michael Randles generated data for this figure. 188

Figure 5.3 Collagen IV and laminin influence podocyte adhesion complex size and number A: P-FAK Y397 staining of podocytes adhered to ECM ligands. B: Levels of P-FAK Y397 when podocyte adhere to different ECM ligands C: Vinculin positive focal adhesions are significantly larger in podocytes adhered to collagen IV compared with laminin isoforms. D: Significantly more vinculin positive adhesions are formed on collagen IV compared with laminin isoforms. E: Podocyte adhesion complexes assessed by vinculin and paxillin staining. For all experiments podocytes were allowed to adhere to plates coated with 5 µg/ml of ECM substrate for 180 minutes in serum free media: n > 30 cells per experiment; n = 4 experiments; scale bar represents 20 µm; ****, p < 0.0001; **, p < 0.01; ***, P < 0.001; NS, not significant; FN, fibronectin; COL4, collagen IV; LAM511, laminin-511; LAM521, laminin-521. Michael Randles generated data for this figure. 189

5.3.3.3 Isolation of podocyte cell-matrix adhesion complexes

To investigate how adhesion to collagen IV or laminin isoforms controlled podocyte morphology we isolated focal adhesions from podocytes spread onto ECM substrates. Podocytes were allowed to attach to either collagen IV, laminin-511 or laminin-521 and form adhesion complexes. Subsequently, adhesion complexes were stabilised using a reversible crosslinker and podocyte cells bodies and nuclei were removed by detergent lysis and hydrodynamic force (Figure 5.4A). Adhesion complex isolation was confirmed by immunofluorescence and Western blotting; known focal adhesion proteins talin and vinculin were isolated in adhesion complexes, but proteins that do not localise to focal adhesions such as Bcl-2 homologous antagonist/killer (BAK) and Heat shock 70 kDa protein (HSP70) were not detected in adhesion complexes (Figure 5.4B and C). In transferrin control complexes, focal adhesions do not form; therefore, known focal adhesion proteins were not detected by Western blotting

(Figure 5.4C).

Analysis of adhesion and transferrin complexes by MS revealed that transferrin and the transferrin receptor were abundant in transferrin complexes, but had low abundance in adhesion complexes

(Figure 5.4D). In contrast, integrins were enriched in adhesion complexes including, as expected, collagen IV and laminin binding integrins α1, α2 and α3 (Figure 5.4D). Furthermore, integrin β1 and syndecan 4 were also detected in adhesion complexes, but with higher abundance in collagen IV adhesion complexes compared with laminin adhesion complexes (Figure 5.4D). Finally, known focal adhesion proteins such as talin, vinculin and ILK were more abundant in isolated adhesion complexes compared with transferrin complexes (Figure 5.4E) . 190

Figure 5.4 Isolation of adhesion complexes A: Isolation of focal adhesions workflow. B: Immunofluorescence of isolated adhesion complexes. C: Western blotting of adhesion proteins (talin and vinculin) and non-adhesion proteins (BAK, HSP70). D: MS analysis of isolated complexes. Integrins were enriched in isolated focal adhesion complexes and transferrin and the transferrin receptor were enriched in transferrin complexes. E: Known adhesion components were enriched in adhesion complexes. Protein abundance was determined using peptide intensity in Progenesis and represented by heat maps; black represents low abundance, yellow represents high abundance. Scale bar represents 20 µm. TCL, podocyte total cell lysate; APOT, transferrin complexes; COL4, collagen IV adhesion complexes; LAM511, laminin-511 adhesion complexes; LAM521, laminin-521 adhesion complexes. Michael Randles generated data for this figure. 191

5.3.3.4 Podocyte cell-matrix adhesion complex composition

Unsupervised hierarchical clustering of MS data revealed that samples segregated into two major clusters, one containing the transferrin control samples the other containing adhesion complexes

(Figure 5.5A). The cluster containing the adhesion complexes split into two further distinct clusters, a cluster containing collagen IV adhesion complexes and a cluster containing both the laminin-511 and

521 adhesion complexes. Within the laminin cluster however, laminin-511 and 521 adhesion complexes could not be distinguished (Figure 5.4A). Indeed, compared with each other only very few proteins, which included laminin β1 and laminin β2, were significantly enriched to laminin-511 and laminin-521 adhesion complexes respectively. This suggests that the current analysis is sensitive enough to identify differences between collagen IV and laminin adhesion complexes, but not between laminin-511 and laminin-521 adhesion complexes. Similar results were achieved with principal component analysis (PCA) (Figure 5.5B).

We identified 64 proteins listed in the current version of the integrin adhesome. 165 These 64 adhesome proteins were mapped onto a network of experimentally observed protein-protein interactions and the proteins coloured to represent enrichment to either collagen IV or laminin adhesions (Figure 5.5C nodes with borders Table 5.1). One core complex that links focal adhesions to the actin cytoskeleton is the talin/vinculin/paxillin axis, which was enriched in collagen IV adhesions, in addition to actin binding proteins filamin-A and vasodilator-stimulated phosphoprotein (VASP) (Figure 5.5C). Enrichment of these proteins in collagen IV adhesion complexes suggests that podocytes form more stable interactions between integrins and the actin cytoskeleton compared with podocytes on laminin. In contrast the ILK/PINCH(LIMS1)/parvin axis was not enriched in collagen IV or laminin adhesion complexes. Interestingly, some adhesome proteins were enriched in laminin adhesions, including protein kinase C alpha, SH3 domain-containing kinase-binding protein 1 (SH3KBP1), dynamin-2, caveolin-1, low density lipoprotein receptor-related protein (LRP), kinectin and raver1 (Figure 5.5C). 192

Table 5.1 Adhesome proteins identified by MS

Uniprot ID Gene name Alias Fold change Adhesome collagen IV / Functional Category laminin

O95425 SVIL Supervillin -1.4 Actin regulation

Q14847 LASP1 Lasp-1 -1.3 Actin regulation

Q9BR76 CORO1B Coronin 1B -1.0 Actin regulation

P07737 PFN1 Profilin 1.0 Actin regulation

O15144 ARPC2 Arp2/3 1.1 Actin regulation

Q7Z2X0 NEXN Nelin 1.2 Actin regulation

P23528 CFL1 Cofilin 1.2 Actin regulation

P35579 MYH9 Myosin-9 1.3 Actin regulation

P50552 VASP Vasp 1.8 Actin regulation

Q8N8S7 ENAH Mena 1.9 Actin regulation

P21333 FLNA Filamin 2.1 Actin regulation

Q03135 CAV1 Caveolin -3.5 Adaptor

Q96B97 SH3KBP1 Cin85 -2.9 Adaptor

P26038 MSN Moesin -2.2 Adaptor

P08670 VIM Vimentin -2.2 Adaptor

P35241 RDX Radixin -1.6 Adaptor

Q9BRJ7 NUDT16L1 Syndesmos -1.4 Adaptor

P15311 EZR Ezrin -1.2 Adaptor

Q7L3E0 PALLD Palladin -1.1 Adaptor

P21291 CSRP1 Crp1 1.0 Adaptor

P48059 LIMS1 Pinch-1 1.0 Adaptor

Q15942 ZYX Zyxin 1.1 Adaptor

O60504 SORBS3 Vinexin 1.1 Adaptor

P60709 ACTB Actin 1.1 Adaptor

Q13418 ILK Ilk 1.2 Adaptor

Q8IZP0 ABI1 Abl1 1.2 Adaptor

P49023 PXN Paxillin 1.2 Adaptor

Q9NVD7 PARVA Parvin-A 1.2 Adaptor

Q96AC1 FERMT2 Kindlin-2 1.3 Adaptor 193

Q93052 LPP Lpp 1.3 Adaptor

Q14192 FHL2 Fhl2 1.4 Adaptor

P46109 CRKL Crkl 1.4 Adaptor

Q9Y490 TLN1 Talin 1.4 Adaptor

Q9UGI8 TES Tes 1.5 Adaptor

P35240 NF2 Merlin 1.7 Adaptor

Q15654 TRIP6 Trip6 1.9 Adaptor

Q13136 PPFIA1 Lip-1 2.1 Adaptor

P18206 VCL Vinculin 2.2 Adaptor

Q9BPW3 TGFB1I1 Hic-5 2.3 Adaptor

Q8WUP2 FBLIM1 Migfilin 2.7 Adaptor

Q07954 LRP1 Lrp-1 -4.5 Adhesion receptor

Q86UP2 KTN1 Kinectin -2.3 Adhesion receptor

P26006 ITGA3 Cd49C -1.4 Adhesion receptor

P31431 SDC4 Syndecan -1.1 Adhesion receptor

P05556 ITGB1 Cd29 2.7 Adhesion receptor

P56199 ITGA1 Cd49A 4.8 Adhesion receptor

P17301 ITGA2 Cd49B 12.1 Adhesion receptor

P27797 CALR Calregulin -2.0 Chaperone

P20936 RASA1 P120Gap -1.4 GAP

Q92974 ARHGEF2 Arhg2 -2.0 GEF

Q14155 ARHGEF7 Beta-Pix -1.1 GEF

O75962 TRIO Trio -1.0 GEF

P50570 DNM2 Dynamin -2.7 GTPase

P15154 RAC1 Rac1 1.1 GTPase

P01112 HRAS H-Ras 1.2 GTPase

P17655 CAPN2 Calpain 2 -1.6 Protease

P07384 CAPN1 Calpain -1.5 Protease

Q8IY67 RAVER1 Raver1 -2.6 RNA or DNA regulation

P40763 STAT3 Stat3 -1.1 RNA or DNA regulation

P17252 PRKCA Pkc -4.4 Serine/threonine kinase

Q05397 PTK2 Fak -1.3 Tyrosine Kinase

Q9H792 PEAK1 Peak1 -1.2 Tyrosine kinase

P41240 CSK Csk 2.0 Tyrosine Kinase 194

P18031 PTPN1 Ptp-1B 1.3 Tyrosine phosphatase 195

196

Figure 5.5 Podocyte adhesion complex composition A: Unsupervised hierarchical clustering of podocyte adhesion complexes. B: Principal component analysis of podocyte adhesion complexes. PC1 and PC2 accounted for 81 % of the total sample variation. Black dots, transferrin complexes; blue dots, collagen IV complexes; orange dots, laminin- 511 complexes; red dots, laminin-521 complexes. C: Protein-protein interaction network of adhesome components detected by MS. Nodes (circles) represent proteins, the size of the node is proportional to the degree (number of protein-protein interactions) of the protein. Edges (grey lines) represent known experimentally observed protein-protein interactions. Nodes that are blue in colour have 1.5 fold greater abundance in collagen IV adhesion complexes compared with laminin adhesion complexes, nodes that are red in colour have 1.5 fold greater abundance in laminin adhesion complexes compared with collagen IV adhesion complexes. Gene names are used as protein labels for clarity. Black borders indicate integrin adhesome proteins. ApoT, transferrin complexes; COL4, collagen IV complexes; LAM511, laminin-511 complexes; LAM521, laminin-521 complexes. Michael Randles generated data for this figure.

We used a stringent dataset filtering process to identify proteins specifically isolated in collagen and laminin adhesion complexes that are not known adhesome components. Specifically, proteins were required to be identified by at least 3 quantifiable peptides, detected ≥ 2 biological replicates and ≥ 1.5 fold enriched in adhesion complexes compared with transferrin control samples. This list was cross- referenced with the Contaminant Repository for Affinity Purification (CRAPome) an online database of common contaminants is MS experiments. As a result we identified 56 additional, possibly novel adhesion components (Figure 5.5C nodes without borders, Table 5.2). Gene Ontology (GO) 344, 345 enrichment analysis demonstrated that these proteins were involved in vesicle-mediated transport, cytoskeletal organisation and membrane organisation ( p < 1x10 -3), supporting a role for these proteins at adhesion sites. Proteins were subsequently categorised into subnetworks based on GO terms. The generated sub-networks revealed that the majority of proteins were involved in actin cytoskeletal organisation, followed by endocytosis and trafficking, kinases, phosphatases and GTPases (Figure

5.6). Proteins involved in actin cytoskeletal organisation were enriched in collagen IV adhesions, whereas proteins involved in endocytosis and trafficking were enriched in laminin adhesion complexes

(Figure 5.6). 197

Table 5.2 Non-adhesome proteins identified by MS

Uniprot ID Gene Alias Fold change Annotation name Collagen IV / laminin

B4E3K1 TJP1 Tight junction protein 1 1.0 Cell-cell junction

Q5VXL0 TJP2 Tight junction protein 2 1.5 Cell-cell junction

Q8NEW9 CTNNB1 Beta catenin 1.6 Cell-cell junction

Q8NAA8 MX1 Myxovirus -3.8 Cytoplasm

Q6P1R0 EIF3A Eukaryotic translation -2.2 Cytoplasm

initiation factor 3, subunit A

Q5MD60 MAP4K4 Mitogen-activated protein -1.8 Cytoplasm

kinase kinase kinase kinase

Q9UDL5 STAT1 Signal transducer and -1.8 Cytoplasm

activator of transcription 1,

91kDa

Q13625 TP53BP2 Tumor protein p53 binding -1.7 Cytoplasm

protein, 2

Q4ZFX1 FARSB Phenylalanyl-tRNA -1.4 Cytoplasm

synthetase, beta subunit

P27708 CAD Carbamoyl-phosphate -1.3 Cytoplasm

synthetase 2

Q8WYR5 GFPT1 Glutamine-fructose-6- 1.6 Cytoplasm

phosphate transaminase 1

Q8N8U8 PHLDB2 Phosphatidylinositol-specific 2.8 Cytoplasm

phospholipase C

Q07065 CKAP4 Cytoskeleton-associated -4.3 Cytoskeleton

protein 4

Q9Y2B6 NUDC Nuclear distribution gene C -2.9 Cytoskeleton

homolog

Q9UEY7 ADD3 Adducin 3 -2.1 Cytoskeleton

Q9BYH6 CASK Calcium -1.7 Cytoskeleton

Q9NRC3 DYNC1H1 Dynein, cytoplasmic 1, -1.7 Cytoskeleton

heavy chain 1

Q2TA90 KIF5B Kinesin family member 5B -1.5 Cytoskeleton 198

Q86TR8 CDC42BPB CDC42 binding protein -1.4 Cytoskeleton

kinase beta

Q5VXR7 SIPA1L2 Signal-induced proliferation- -1.4 Cytoskeleton

associated 1 like 2

Q9BPZ9 PDLIM1 PDZ and LIM domain 1 -1.2 Cytoskeleton

Q53QM2 ACTR3 ARP3 actin-related protein 3 -1.1 Cytoskeleton

homolog

Q9UNU4 SIPA1L1 Signal-induced proliferation- 1.0 Cytoskeleton

associated 1 like 1

Q5XG82 PDLIM7 PDZ and LIM domain 7 1.0 Cytoskeleton

Q7Z5I4 RAI14 Retinoic acid induced 14 1.1 Cytoskeleton

Q8IUV1 SIPA1L3 Signal-induced proliferation- 1.1 Cytoskeleton

associated 1 like 3

Q9P210 KANK2 KN motif and ankyrin repeat 1.1 cytoskeleton

domains 2

P46940 IQGAP1 IQ motif containing GTPase 1.1 Cytoskeleton

activating protein 1

Q6LAL4 MYLK Myosin light chain kinase 1.3 Cytoskeleton

Q5T097 UTRN Utrophin 1.3 Cytoskeleton

Q5W9G1 MYO18A Myosin XVIIIA 1.3 Cytoskeleton

Q8NDD7 UACA Uveal autoantigen with 1.4 Cytoskeleton

coiled-coil domains and

ankyrin repeats

Q14315 FLNC Filamin C, gamma 1.5 Cytoskeleton

Q99439 CNN2 Calponin 2 1.8 Cytoskeleton

Q53FI1 LCP1 Lymphocyte cytosolic 1.8 Cytoskeleton

protein 1

A6NLB8 TLN2 Talin 2 2.2 Cytoskeleton

Q96RQ8 IL4I1 Interleukin 4 induced 1 -2.3 Extracellular

Q53T69 HADHA Hydroxyacyl-Coenzyme A -1.6 Mitochondrial

dehydrogenase

Q9H3F5 HADHB Hydroxyacyl-Coenzyme A -1.1 Mitochondrial

dehydrogenase

O95758 PTBP3 Polypyrimidine tract-binding -3.9 Nucleus

protein 3 199

P48307 TFPI2 Tissue factor pathway -3.9 Nucleus

inhibitor 2

Q8TEM1 NUP210 Nucleoporin 210kDa -2.1 Nucleus

Q5TH92 LUZP1 Leucine zipper protein 1 1.3 Nucleus

O00299 CLIC1 Chloride intracellular -1.9 Plasma membrane

channel 1

Q5W116 NT5E 5'-nucleotidase, ecto -1.5 Plasma membrane

Q59GW5 TRIM25 Tripartite motif-containing -1.1 Plasma membrane

Q9ULF2 PARP14 Polypyrimidine tract-binding 1.0 Plasma membrane

protein 3

Q8NAD0 TNS3 Tensin 3 3.1 Plasma membrane

Q96CI7 AP2A1 Adaptor-related protein -3.3 Vesicle

complex 2, alpha 1 subunit

Q6N0A0 CLTC Clathrin, heavy chain -2.7 Vesicle

Q96EL6 AP2B1 Adaptor-related protein -2.6 Vesicle

complex 2, beta 1 subunit

Q9NTK2 COPB1 Coatomer protein complex, -2.3 Vesicle

subunit beta 1

Q13122 SND1 Staphylococcal nuclease -1.9 Vesicle

and tudor domain containing

A6NKA3 COPE Coatomer protein complex, -1.7 Vesicle

subunit epsilon

Q05D76 EEA1 Early endosome antigen 1 -1.4 Vesicle

Q9BY09 SNTB2 Syntrophin, beta 2 1.1 Vesicle 200

Figure 5.6 GO enrichment analysis GO enrichment analysis of MS dataset. Proteins were categorised using GOTERM_BP_FAT and presented as interaction networks. Nodes (circles) represent proteins, edges (grey lines) represent known experimentally observed protein-protein interactions. Nodes that are blue in colour have 1.5 fold greater abundance in collagen IV adhesion complexes compared with laminin adhesion complexes, nodes that are red in colour have 1.5 fold greater abundance in laminin adhesion complexes compared with collagen IV adhesion complexes. Gene names are used as protein labels for clarity. Michael Randles generated data for this figure. 201

5.3.3.5 Podocyte adhesion strength is enhanced on collagen IV compared with laminin

In order to test the functional outcome of the observed differences in podocyte morphology and adhesion complexes due to ECM ligand, podocyte barrier properties were assessed. Electrical cell- substrate impedance sensing (ECIS) is a technique that can be used to infer changes in the spaces under and between cells in a confluent monolayer. The initial phase of the experiment (1-3 hours) measures an increase in resistance over time as cells attach to the electrodes and form an insulating monolayer. In the second phase, higher resistances infer smaller spaces under and between cells; therefore, indicating stronger cell-cell or cell-ECM adhesion. We first tested whether there was any difference in insulator properties of the different ECM ligands, which revealed that collagen IV, laminin-

511, laminin-521, or a 1:1 combination of laminin-511 and collagen IV did not result in a detectable difference in resistance measured by ECIS (Figure 5.7). This data suggested that these ECM ligands do not have significantly different insulator properties in our experimental setup.

Using ECIS we discovered that podocyte adhesion to collagen IV enhanced monolayer resistance properties by 3-fold compared with adhesion to laminin-511 or 521 (Figure 5.7). Moreover, podocytes exhibited around 1.2-fold increase in monolayer resistance when adhered to laminin-521 compared with laminin-511 (Figure 5.4). In addition, when a 1:1 combination of collagen IV and laminin-511 was used as a substrate, the resistance of podocytes during the initial spreading phase followed a profile similar to collagen IV, but over 10 hours the resistance gradually dropped to match podocyte monolayers adhered to laminins (Figure 5.7). This may suggest that laminin can induce protrusive activity or reduce adhesion strength in the presence of collagen IV. 202

Figure 5.7 Podocyte barrier function assessed by electrical cell-substrate impedance sensing (ECIS) Measurements of resistance of ECM ligands alone or ECM ligands with podocytes adhered. For all experiments n = 4. Michael Randles generated data for this figure. Norm. resistance, normalised resistance. 203

5.3.4 Discussion

We used MS-based proteomics of isolated adhesion complexes from podocytes to investigate the influence of ECM ligand on adhesion complex assembly. The principal findings of this study were: (1) podocyte cell shape and actin cytoskeletal morphology are markedly different on collagen IV and laminin, with collagen IV causing a rounded morphology and laminin inducing projections and elongated morphology; (2) podocytes form larger adhesion complexes on collagen IV compared with laminin; (3) compositionally collagen IV and laminin adhesion complexes are distinct, collagen IV adhesion complexes are enriched for adaptor and actin binding proteins, whereas laminin adhesion complexes recruit endocytic and trafficking machinery; (4) and podocyte adhesion to collagen IV induces a higher resistance monolayers compared with adhesion to laminin, indicative of closer cell- cell and cell-ECM contacts. Although these data remain preliminary, there are already a number of interesting observations that generate further questions regarding podocyte adhesion biology.

Collagen IV is a major component of the GBM, however, it is likely that under normal conditions podocytes do not attach to collagen IV, but instead attach to laminin. Podocytes form microtubule- based primary and actin rich secondary foot processes. These processes interdigitate with those of their neighbours, forming an elaborate cytoarchitecture. It is these, rather than the podocyte cell body, which mediate adhesion to the GBM. Establishment and maintenance of foot processes is vital; loss of podocyte foot processes causes nephrotic syndrome. However, the mechanisms that establish podocyte foot processes are not fully understood. Interestingly, several similarities have been observed between podocytes and neuronal cells. These similarities include both protein expression 427 ,428 and morphology; neuronal cells also produce microtubule-based thick processes with branching morphology and thin actin-based projections in the form of dendritic spines. One key signal controlling neuronal morphology is the ECM , with laminin being an important guidance molecule.429

Attachment of podocytes to laminin leads to protrusive activity and small adhesive structures that are enriched with endocytic machinery suggesting fast turnover of adhesions. Conversely, this behaviour is absent when podocytes adhere to collagen IV. It is attractive to propose that adhesive signals from laminin which promote protrusive activity in podocytes in vitro represents signalling in vivo that 204

orchestrates foot process formation. One potential candidate signalling protein is protein kinase C alpha, which was over 4-fold enriched in laminin compared with collagen IV adhesion complexes.

During development podocytes adhere to laminin-511, whereas the mature GBM contains laminin-521.

We observed similar podocyte morphologies and protrusive activities when podocytes attach to these different laminins. Further investigation of adhesion complexes formed on laminin-511 compared with laminin-521 using MS revealed that the protein composition of these adhesion complexes could not be distinguished. However, it remains possible that some subtle differences in laminin-511 compared with laminin-521 may alter podocyte behaviour and further studies are still required.

In contrast to laminin adhesion complexes, collagen IV adhesion complexes were much larger and enriched for adaptors and actin binding proteins. The enrichment of proteins such as talin and vinculin indicate a stronger mechanical link between the podocyte actin cytoskeleton and collagen IV adhesion sites. Incidentally, adhesion of podocytes to collagen IV caused higher resistance podocyte monolayers compared with adhesion to laminin, consistent with stronger adhesion formation. This observation may in fact represent an equivalent of podocyte foot process effacement through enhanced adhesion to a collagen IV. Indeed, foot process effacement is associated with a transition from wide slit diaphragm podocyte cell-cell junctions to tight junction like contacts, 430-434 again theoretically increasing the resistance of a podocyte monolayer. This mechanism may have relevance for Alport syndrome, where podocytes encounter an ectopic collagen IV α1α1α2 network. In addition, it is tantalising to hypothesise that when podocytes are stressed, regardless of the cause, they upregulate collagen IV and adhesion receptors as a compensation mechanism. Nonetheless, adhesion must be finely regulated: adhere too tightly to the GBM and foot process architecture is lost; too loosely and podocytes may detach from the surface of the GBM. Overall this study highlights the importance of

ECM ligand for podocyte morphology and identifies potential mechanisms for the establishment and loss of podocyte foot processes. 205

5.3.5 Materials and Methods

5.3.5.1 Glomerular cell culture

Conditionally immortalised human podocytes 363 were grown in uncoated tissue culture plates.

Podocytes between passage 10 and 16 were cultured for 14 days at 37 oC in RPMI-1640 medium with glutamine (R-8758; Sigma-Aldrich) supplemented with 10% (v/v) FCS (Life Technologies) and 5% (v/v)

ITS (I-1184; Sigma; 1 ml/100 ml).

5.3.5.2 Flow cytometry

For standard flow cytometric analysis, podocytes were washed with phosphate buffered saline without cations (PBS-) and detached from culture surfaces with 1x trypsin-EDTA at 37 oC. The dissociated cells were harvested by centrifugation (1000 rpm, 4 min). The cell pellet was resuspended in 0.1% (w/v)

BSA/0.1% (w/v) sodium azide in PBS- (0.1/0.1 solution). Cells were then incubated with primary antibody, diluted in 0.1/0.1 solution, at 4 oC for 30 min. Following 2 washes with 0.1/0.1 solution and centrifugation steps, cells were incubated with appropriate species-specific FITC-conjugated secondary antibody at 4 oC for 30 min. Cells were then washed three times with 0.1/0.1, resuspended in PBS- and analysed on a Dako CYAN, FACS machine.

5.3.5.3 ECM ligands

Recombinant human laminin-511 and laminin-521 were a kind gift from Professor Karl Tryggvason, and can be purchased from BioLamina. Fibronectin from bovine plasma and collagen type IV from human cell culture was purchased from Sigma-Aldrich (Poole, UK) .

5.3.5.4 Attachment assay

centrifugation (1000 rpm for 4 minutes at room temperature). To remove cell-bound extracellular matrix, cells were washed three times with PBS -, and left to incubate in serum free RPMI for 30 minutes at 37°C and 5% (v/v) CO 2. Subsequently, cells were pelleted and washed with serum free

RPMI and plated on ligand-coated dishes.

5.3.5.5 Adhesion complex isolation

Podocytes were attached to ligand coated plates for 180 minutes following attachment assay.

Thereafter, podocytes were cross-linked with 6 mM DTBP diluted in Advanced Dulbecco's Modified

Eagle Medium (DMEM5) (Sigma-Aldrich) for 3 minutes and quenched with Tris-HCl pH 8.5, followed by

1 minute incubation with extraction buffer (10 mM Tris, 150 mM NaCl, 1% (v/v) Triton X-100, 25 mM

EDTA, 25 g/ml leupeptin, 25 g/ml aprotinin and 0.5 mM AEBSF). Adhesion complexes were subjected to 30 seconds high pressure water wash to removed nuclei and were collected in reducing sample buffer (50 mM Tris-HCl, pH 6.8, 10% (w/v) glycerol, 4% (w/v) sodium dodecylsulfate (SDS),

0.004% (w/v) bromophenol blue, 8% (v/v) β-mercaptoethanol). Adhesion complex samples were fractionated by SDS-PAGE and used either for Western blotting or visualised with InstantBlue to be used for in-gel proteolytic digestion.

5.3.5.6 Antibodies

Monoclonal antibodies used were against human paxillin (clone 349; BD Biosciences), CD151 (11G5a;

Abcam), integrin α6 (GoH3; Abcam), integrin α1 (FB12; Millipore) , integrin α2 (P1E6; Abcam), integrin

α3 (ab24696; Abcam), integrin α 4 (HP2/1; Abcam), integrin α vβ3 (LM609; Millipore) . Mouse polyclonal vinculin (hVin1; Sigma-Aldrich). Polyclonal rabbit pY397FAK (44-624G; Invitrogen), BAK (B5897;

Sigma-Aldrich). Alexa Fluor 488 Phalloidin molecular probe was used to detect actin filaments.

Secondary antibodies conjugated to Alexa Fluor 488 or 594 (Life Technologies) were used for

Immunohistochemistry; secondary antibodies conjugated to AlexaFluor 680 (Life Technologies,

Paisley, UK) or IRDye 800 (Rockland Immunochemicals, Glibertsville, PA, USA) were used for

Western blotting.

207

5.3.5.7 Western blotting

See General Materials and Methods.

5.3.5.8 MS data acquisition

See General Materials and Methods.

5.3.5.9 MS data analysis

Tandem mass spectra were extracted using extract_msn (Thermo Fisher Scientific) executed in

Mascot Daemon (version 2.4; Matrix Science, London, UK). Peak list files were searched against a modified version of the Uniprot mouse database (version 3.70; release date, 3 May 2011) , containing ten additional contaminant and reagent sequences of non-mouse origin , using Mascot (version 2.2.06;

Protein identifications were accepted upon assignment of at least two unique validated peptides with

≥ 90% probability, resulting in ≥ 99% probability at the protein level. These acceptance criteria resulted in an estimated protein false discovery rate of 0.1% for all datasets.

5.3.5.10 MS data quantification

Relative protein abundance was calculated using peptide intensity. Orbitrap MS data were entered into

Progenesis LCMS (Non Linear Dynamics Ltd, Newcastle upon Tyne, UK) and automatically aligned.

Spectra were extracted using extract_msn (Thermo Fisher Scientific, Waltham, MA, USA) executed in

Mascot Daemon (version 2.4; Matrix Science, London, UK) and imported back into Progenesis to acquire intensity data. Alignment of chromatograms was carried out using the automatic alignment 208

algorithm, followed by manual validation and adjustment of the aligned chromatograms. All features were used for peptide identifications. Progenesis created the peak list file that was exported and searched in Mascot. Results were loaded in Scaffold (Proteome Software Inc, version 3.6.5) and peptide and protein identification threshold was set to 95% and 99% confidence respectively. Data was exported from Scaffold as a spectrum report, and imported into Progenesis to assign peptide identifications to features. Peptide and protein data were then exported from Progenesis as .csv files to be analysed in Excel.

5.3.5.11 Protein interaction network analysis

Protein interaction network analysis was performed using Cytoscape (version 2.8.1) (Shannon et al ,

2003). Proteins identified in at least two biological replicates were mapped onto a merged human, mouse and rat interactome built from Protein Interaction Network Analysis platform Homo sapiens network (release date, 10 December 2012), Mus musculus network (release date, 10 December 2012) and the Rattus norvegicus network (release date, 10 December 2012) ,348 the ECM interactions database MatrixDB (release date, 20 April 2012), 349 and a literature-curated database of integrin-based adhesion–associated proteins. 423 For networks where enrichment is presented Progenesis normalised intensity data was used. Topological parameters were computed using the NetworkAnalyzer plug-in. 350

5.3.5.12 Immunofluorescence and image analysis

Cells on coverslips were washed with PBS and then fixed with 4% (w/v) paraformaldehyde. Cells were permeabilized with 0.5% (v/v) Triton X-100 and blocked 3% (w/v) BSA in PBS before incubation with primary antibodies. Coverslips were mounted and images were collected using a CoolSnap HQ camera (Photometrics, Tucson, AZ, USA) and separate DAPI/FITC/Cy3 filters (U-MWU2, 41001,

41007a , respectively; Chroma, Olching, Germany) to minimise bleed -through between the different channels. For analysis of cell-cell junctions and 3D ECM models, images were acquired on a Delta

Vision (Applied Precision) restoration microscope using a 60x objective and the [ Sedat ] filter set

(Chroma [ 89000 ]). The images were collected using a Coolsnap HQ (Photometrics) camera with a Z optical spacing of 0.2 m. Images collected were viewed and analysed with Fiji. 435 To calculate the area of immunostained marker per cell, images were edited to remove additional cells and artefacts outside the required cell area, compiled to form a hyperstack, background subtracted using a rolling ball radius 209

of 30, 'threshold' function used to select immunostained marker and to convert images to black-white binary images, and 'analyze particles' function used to calculate total black pixel area.

5.3.5.13 Electrical cell-substrate impedance sensing

Serum free RPMI-1640 medium with glutamine (R-8758; Sigma, St. Louis, MO, USA) was equilibrated to

o match the CO 2 levels (5%) and temperature (37 C) of the experiment for three hours. ECIS array wells were coated with 10mM cysteine solution and washed three times with sterile H 2O. Arrays were subsequently coated with protein of choice diluted in sterile NaCl (150 mM) for 1 hour at room temperature. Equilibrated medium was then added to coated wells and the arrays calibrated using test cards. Podocytes were trypsinised as normal, and resuspended in equilibrated medium. 25,000 cells/well were added to each well of the array. Data was acquired at a single frequency (4000 Hz for trans-epithelial resistance).

5.3.6 Acknowledgements

This work was supported by a Wellcome Trust Intermediate Fellowship award (090006) to R.L., a Kids

Kidney Research grant awarded to R .L. and A.S.W to support a PhD studentship for M.J.R and

Wellcome Trust grant (092015) to M.J.H. The mass spectrometer and microscopes used in this study were purchased with grants from the Biotechnology and Biological Sciences Research Council,

Wellcome Trust and the University of Manchester Strategic Fund. Mass spectrometry was performed in the Biomolecular Analysis Core Facility, Faculty of Life Sciences, University of Manchester, and we thank David Knight and Stacey Warwood for advice and technical support and Julian Shelley for bioinformatic support.

5.3.7 Conflict of interest

The authors declare no conflicts of interests . 210

6 Nephrin associated protein complexes

6.1 Introduction

Adhesion of cells to the extracellular matrix (ECM) is critical for the normal function of the glomerulus.

Likewise, interactions between cells via secreted factors and cell-cell contact is essential. Podocyte foot processes are the outer layer of the glomerular filtration barrier (GFB) and are linked to one another via unique cell-cell junctions, called slit diaphragms. Loss of podocyte foot processes and slit diaphragms are functionally linked to nephrotic syndrome, though the reasons for this remain poorly understood. The slit diaphragm is a hybrid junction containing components of both tight junctions and adherens junctions. Though, it is the expression of neuronal junction proteins nephrin, Neph-1, -2, -3 and podocin that makes the slit diaphragm unique. Nephrin is an immunoglobulin superfamily member that binds to a number slit diaphragm proteins and regulates the actin cytoskeleton. Downregulation of nephrin and other slit diaphragm proteins is common to a range of glomerulopathies. Thus, comprehensive understanding of nephrin signalling could reveal general mechanisms of slit diaphragm loss and podocyte foot process effacement during glomerulopathy. 211

6.2 Statements

Author contributions to data generation and analysis presented as figures of this paper are indicated in figure legends.

Michael J. Randles, Hellyeh Hamidi, Rachel Lennon and Adrian S. Woolf planned the study and designed experiments. Adam Byron, Jonathan D. Humphries and Martin J. Humphries contributed to the study design.

Michael J. Randles generated all of the figures displayed within the manuscript, performed Western blotting, immunofluorescence, image analysis, transfection, lentiviral transduction, nickel chromatography, attachment assays, nephrin complex isolation, mass spectrometry sample preparation, bioinformatic and network analysis.

Hellyeh Hamidi performed literature mining for nephrin interacting proteins, cloning and lentiviral transduction.

3000 words or less (excluding title page, methods, figure legends, tables, and references) .

Michael Randles wrote the manuscript and it was critically assessed by Rachel Lennon and Adrian S.

Woolf. 212

6.3 The nephrin interactome

Michael J. Randles, 1,2 Hellyeh Hamidi, 1 Jonathan D. Humphries, 1 Adam Byron, 3 Martin J. Humphries, 1

Adrian S. Woolf, 2 and Rachel Lennon. 1,2

1Wellcome Trust Centre for Cell-Matrix Research, Faculty of Life Sciences, University of Manchester,

Manchester, UK; 2Faculty of Medical and Human Sciences, University of Manchester, Manchester, UK;

3Edinburgh Cancer Research UK Centre, Institute of Genetics and Molecular Medicine, University of

Edinburgh, Edinburgh, EH4 2XR UK .

Running title: Podocyte adhesion complexes

Corresponding author:

Dr Rachel Lennon, Wellcome Trust Centre for Cell-Matrix Research, Michael Smith Building, University of Manchester, Manchester M13 9PT, UK.

Phone: 0044 (0) 161 2755498. Fax: 0044 (0) 161 2755082.

Email: [email protected] 213

6.3.1 Abstract

Podocytes are an integral part of the human glomerular filtration barrier. They are fascinating cells, which form an elaborate morphology of extended processes. Interestingly, between adjacent processes podocytes form unique cell-cell junctions, called slit diaphragms, which are required for the restriction of plasma proteins across the glomerular filter. At these junctions nephrin is a key cell adhesion and signalling molecule. Nephrin is encoded by NPHS1 and loss of nephrin causes congenital nephrotic syndrome of the Finnish type in human beings. This highlights the importance of nephrin for maintaining an intact filtration barrier, however, a comprehensive understanding of nephrin signalling is lacking. Here we undertake meta-analysis of published data regarding nephrin, followed by mass spectrometry (MS)-based proteomics of nephrin-nephrin signalling complexes. We highlight known functions of nephrin, such as actin cytoskeletal organisation, and identify novel nephrin signalling proteins that are involved in this process. Unexpectedly, we identify 29 focal adhesion proteins, which localise to nephrin signalling complexes. This data suggests that close crosstalk between cell-cell and cell-ECM signalling complexes govern podocyte barrier function.

6.3.2 Introduction

Podocyte slit diaphragms are intriguing cell-cell junctions. They are absolutely required for selective glomerular filtration; loss of slit diaphragms causes proteinuria. 436 Slit diaphragms are hybrid junctions that link adjacent podocyte foot processes via both tight and adherens junction components. 245, 246, 437

However, the slit diaphragm is highly complex and also contains the neuronal-junction proteins nephrin, Neph-1, -2, -3 and podocin. 67-69, 438 Nephrin is often thought of as the 'signature molecule' of the glomerular podocyte and the slit diaphragm. 21, 439 Without nephrin slit diaphragms do not even form, instead tight junction contacts develop. 430, 431 Furthermore, individuals with mutations in nephrin develop severe congenital nephrotic syndrome. 66 Nephrin is a single pass transmembrane protein, extracellularly it contains a type III fibronectin domain followed by eight C2-type immunoglobulin repeats. The nephrin extracellular domain is predicted to be extremely rigid due to the absence of linker residues between the immunoglobulin repeats .251 Through this extracellular domain nephrin can interact with both nephrin and Neph1 at slit diaphragms. 252-255 214

Nephrin not only acts as a structural molecule at the slit diaphragm, it is also involved in signalling.

Intracellularly, nephrin contains a short cytoplasmic tail that can be tyrosine phosphorylated ( Tyr1191,

Tyr1208 and Tyr1232) . Src family kinases are important for regulating the status of nephrin phosphorylation. Fyn binds to and phosphorylates nephrin, whereas Yes causes a reduction in the phosphorylation of nephrin. 264 Nephrin phosphorylation status determines its associations with other slit diaphragm proteins such as podocin. 440 In addition, nephrin regulates the podocyte actin cytoskeleton through interactions with NCK1/NCK2 and the actin nucleation complex, N-WASP/Arp2/3. 265, 266

Tyrosine phosphorylated nephrin recruits NCK1/2, which then facilitates the release of N-WASP and activation of the Arp2/3 complex, which is required for actin nucleation and polymerisation. 265, 273

Moreover, phosphorylated nephrin interacts with PI3K and AKT enhancing the potential for nephrin induced signalling events. 271, 272

Incoming signals at the slit diaphragm include mechanical force resulting from filtration 441 and exposure to both paracrine and autocrine modulators. Many questions still surround the role of nephrin in slit diaphragm signalling. In order to study nephrin signalling complexes we first performed a comprehensive in silico analysis of nephrin and its interaction partners. Secondly , we generated podocytes overexpressing nephrin or nephrin lacking its cytoplasmic domain, to be used for interaction and functional studies. Finally, these cells were utilised to isolate nephrin signalling complexes for analysis by MS. The aim of this study is to gain holistic understanding of nephrin signalling complexes.

6.3.3 Results

6.3.3.1 An nephrin in silico interactome

We used a literature mining approach in order to identify proteins that directly interact with nephrin. We found 54 proteins that were reported to interact with nephrin. Each nephrin interactor was allocated a confidence score based on: the number of publications, number of different techniques and perceived accuracy of the technique(s) used to identify the interaction (Figure 6.1A, Table 6.1). 442 These 54 proteins were mapped onto a human protein-protein interaction database forming a highly connected network. Within which Src family kinases (Src, Fyn, Lyn Was, Yes), and adaptors (NCK1/2, CRK1/2 215

CD2AP) were highly connected, suggesting an important role for these proteins in nephrin signalling.

Gene Ontology (GO) enrichment analysis revealed that proteins containing SH3 and PDZ protein motifs were significantly enriched in the nephrin interaction network, in addition to biological processes including: cellular adhesion, tight junction, adherens junction, focal adhesion, cytoskeletal binding, cytoskeletal organisation and actin filament based process ( p < 0.0005). Protein interaction subnetworks, based on these enriched terms were generated (Figure 6.1B-D), again highlighting Src family kinases as critical interaction nodes. Nephrin interacting proteins are shared between, tight, adherens and focal adhesion complexes, which supports the existence of a core adhesion machinery at all types of junctions, including slit diaphragms.

216

Figure 6.1 In silico analysis of the nephrin interactome A: Literature mining identified 54 reported nephrin interactors. Nodes (circles) represent proteins Edges (grey lines) represent known experimentally observed protein-protein interactions. B: Enriched Gene Ontology INTERPRO domains . C: Enriched GOTERM_BP_FAT Cell adhesion. D: Enriched GOTERM_BP_FAT Cytoskeletal organisation. Hellyeh Hamidi generated data figure part A. Michael Randles generated data parts B-D.

217

Table 6.1 The nephrin interactome

Human Gene Alias Interaction detection Confidence UniProt ID name method(s) Score

P06241 FYN Tyrosine-protein kinase Fyn anti-bait coIP|anti-tag coIP|GST pull 0.9

down|in vivo|in vitro|protein kinase

assay

Q9Y5K6 CD2AP CD2-associated protein anti-bait coIP|GST pull down|anti-tag 0.88

coIP|cosedimenation|in vivo

P16333 NCK1 NCK In vitro|GST pull down|anti-bait 0.88

coIP|anit-tag coIP|fluorescnce

microscopy|in vivo

O43639 NCK2 NCK adaptor protein 2 In vitro|GST pull down|anti-bait 0.88

coIP|anit-tag coIP|fluorescnce

microscopy|in vivo

Q9NP85 NPHS2 Podocin cosedimentation through density 0.88

gradient|in vivo|GST pull down|anti-bait

coIP|anti-tag coIP

P17252 PRKCA Protein kinase C alpha type anti-tag coIP|in vivo|protein kinase 0.88

assay|in vitro|anti-bait coIP

O60500 NPHS1 Nephrin surface plasmon resonance|in 0.87

vitro|anti-tag coIP|in vivo

P27986 PIK3R1 Phosphatidylinositol 3-kinase 85 anti-tag coIP|in vivo|in vitro|GST pull 0.85

kDa regulatory subunit alpha down

Q96J84 KIRREL Kin of IRRE-like protein 1 anti-tag coIP|anti-bait coIP|in vivo|in 0.84

vitro

O43707 ACTN4 Alpha-actinin-4 GST pull down|in vivo|anti-bait coIP 0.83

P32121 ARRB2 Beta-arrestin-2 anti-tag coIP|in vivo|anti-bait coIP 0.83

P09382 LGALS1 Galectin-1 in vitro|in vivo|fluorescnce 0.78

microscopy|anti-bait coIP|electron

microscopy|surface plasmon

resonance

P46108 CRK Adapter molecule crk GST pull down|fluorescence 0.77

microscopy|in vivo

O94850 DDN Dendrin GST pull down|anti-tag coIP|anti-bait 0.77

coIP|in vivo|in vitro 218

P46940 IQGAP1 Ras GTPase-activating-like GST pull down|fluorescence 0.77

protein IQGAP1 microscopy|in vivo

Q12791 KCNMA1 Calcium-activated potassium yeast two-hybrid screen|GST pull 0.77

channel subunit alpha-1 down|anti-bait coIP|fluorescence

microscopy|in vivo

Q96QZ7 MAGI1 Membrane-associated yeast two hybrid screen|anti-tag 0.77

guanylate kinase, WW and PDZ coIP|GST GST pull down|in

domain-containing protein 1 vivo|fluorescence microscopy

P63027 VAPM2 Vesicle-associated membrane yeast two hybrid screen|GST pull 0.77

protein 2 down|in vivo|anti-bait coIP

O60716 CTNND1 Catenin delta-1 anti-bait coIP|GST pull 0.75

down|cosedimentation|in vivo

P19174 PLCG1 Phospholipase C-gamma-1 GST pull down|in vivo|in vitro|anti-tag 0.75

coIP|fluorescence microscopy

P12931 SRC Proto-oncogene tyrosine-protein anti bait coIP|in vivo 0.75

kinase Src

P35968 VEGFR2 Vascular endothelial growth anti-bait coIP|anti-tag 0.75

factor receptor 2 coIP|fluorescence microscopy|in vivo|in

vitro

P07947 YES1 Tyrosine-protein kinase Yes anti-bait coIP|in vivo 0.75

P23528 CFL1 Cofilin-1 in vivo|anti-bait coIP|anti-tag 0.74

coIP|fluorescnece microscopy

P21333 FLNA Filamin-A fluorescence microscopy|anti-tag 0.74

coIP|anti-bait coIP|in vivo

O15357 INPPL1 Phosphatidylinositol-3,4,5- fluorescence microscopy|anti-tag 0.74

trisphosphate 5-phosphatase 2 coIP|anti-bait coIP|in vivo

Q70E73 RAPH1 Ras-associated and pleckstrin fluorescence microscopy|anti-tag 0.74

homology domains-containing coIP|anti-bait coIP|in vivo

protein 1

O14936 CASK Peripheral plasma membrane affinity chromatography 0.73

protein CASK technology|GST pull down|in vivo

Q8IZU9 KIRREL3 Kin of IRRE-like protein 3 anti-tag coIP|anti-bait coIP|in vivo 0.73

O00159 MYO1c Myosin-Ic anti-bait coIP|GST pull down|in vivo 0.73

Q05586 NMDAR1 N-methyl-D-aspartate receptor anti-bait coIP|protein cross-linking with 0.73

subunit NR1 a bifunctional reagent|in vivo

Q13153 PAK1 Serine/threonine-protein kinase fluorescence microscopy|anti-tag 0.73

PAK 1 coIP|in vivo

Q8TEW0 PARD3 Partitioning defective 3 homolog anti-tag coIP|anti-bait coIP|in vivo 0.73 219

P78352 PSD95 Disks large homolog 4 anti-bait coIP|protein cross-linking with 0.73

a bifunctional reagent|in vivo

Q8TE54 SLC26A7 Anion exchange transporter anti-tag coIP|in vivo|anti-bait coIP 0.73

Q9Y210 Trpc6 transient receptor potential anti-tag coIP|anti-bait coIP|in vivo 0.73

cation channel, subfamily C,

member 6, FSGS2

P22223 CDH3 Cadherin-3 GST pull down|cosedimentation|in vivo 0.7

Q13418 ILK Integrin-linked protein kinase fluorescence microscopy|anti-bait 0.67

coIP|in vivo

Q96B97 SH3KBP1 SH3 domain-containing kinase- anti-tag coIP|fluorescence 0.67

binding protein 1 microscopy|in vivo

Q03135 CAV1 Caveolin-1 anti-bait coIP|in vivo 0.65

Q6UWL6 KIRREL2 Kin of IRRE-like protein 2 anti-tag coIP|in vivo 0.65

P07948 LYN Tyrosine-protein kinase Lyn in vivo|anti-bait coIP 0.65

Q86UL8 MAGI2 Membrane-associated GST pull down|in vivo 0.65

guanylate kinase, WW and PDZ

domain-containing protein 2

Q9NRD5 PICK1 PRKCA-binding protein anti-tag coIP|in vivo 0.65

P18031 PTPN1 Tyrosine-protein phosphatase GST pull down|in vivo 0.65

non-receptor type 1

Q13813 SPTA2 Spectrin alpha chain GST pull down|in vivo 0.65

Q01082 SPTB2 Spectrin beta chain GST pull down|in vivo 0.65

Q07157 TJP1 Tight junction protein ZO-1 GST pull down|in vivo 0.65

P42768 Was Wiskott-Aldrich syndrome anti-tag coIP|in vivo 0.65

protein

P02768 ALB Serum albumin affinity chromatography technology 0.63

P06733 ENO1 alpha-enolase two hybrid pooling approach 0.63

Q81Q28 TenA Transcriptional activator TenA two hybrid pooling approach 0.63

P56945 BCAR1 Breast cancer anti-estrogen fluorescence microscopy|in vivo 0.55

resistance protein 1

P0CG48 UBC Polyubiquitin-C in vivo 0.52

220

6.3.3.2 Generation of nephrin expressing cell lines

In order to gain an holistic understanding of nephrin signalling complexes and add experimental data to our generated list of existing nephrin interactors, we utilised a MS-based proteomics approach. It is well established that podocytes in cell culture express low levels of nephrin, although the reasons for this remain unclear. 443 For studies of nephrin signalling complexes, high expression of nephrin is required. Tools have been developed to study nephrin signalling, such as chimeric CD16/7/nephrin- cytoplasmic domain. 268 However, we aimed to use full length nephrin in our studies. Firstly, using a lentiviral approach we generated podocyte cell lines that stably express nephrin with cytoplasmic FLAG tag (Nephrin-FLAG). Additionally, we generated a control podocyte cell line expressing Nephrin-FLAG with a truncated cytoplasmic domain (N ∆CT-FLAG), which lacks amino acids 1079-1241 of the cytoplasmic tail rendering it unable to interact with intracellular binding partners; therefore, establishing an excellent control for future experiments. Expression of Nephrin-FLAG and N ∆CT-FLAG was confirmed by Western blotting and immunofluorescence. Western blotting confirmed the expected 20 kDa shift in molecular weight of Nephrin-FLAG compared with N ∆CT-FLAG (Figure 6.2A).

Furthermore, immunofluorescence demonstrated localisation of Nephrin-FLAG to the plasma membrane and colocalisation with β catenin at cell-cell contacts (Figure 6.2B, C). In contrast, N ∆CT-

FLAG localised predominantly to the cytoplasm (Figure 6.2B, C).

221

Figure 6.2 Generation of Nephrin-FLAG and N ∆∆∆CT-FLAG cell lines Podocyte cell lines stably expressing Nephrin-FLAG or N ∆CT-FLAG were produced by lentiviral transduction. A: Western blot confirming the expression of Nephrin-FLAG and N ∆CT-FLAG. Non-trans, non-transduced podocytes. Michael Randles generated data figure parts A, C. Hellyeh Hamidi generated data figure part B. 222

6.3.3.3 Isolation of nephrin signalling complexes

In order to isolate nephrin-nephrin complexes at the podocyte cell surface, we generated recombinant

HIS tagged extracellular domain nephrin (Nephrin-ECD) using a mammalian expression system

(Figure 6.3A). This purified protein was then used as ligand in Nephrin-FLAG and N ∆CT-FLAG podocyte attachment assays. Wild type podocytes, N ∆CT-FLAG and Nephrin-FLAG podocytes attached to Nephrin-ECD with increasing affinity (Figure 6.3B). Experiments where podocytes were pre-incubated with an antibody to the extracellular domain of nephrin prior to attachment to Nephrin-

ECD demonstrated that the attachment of Nephrin-FLAG and N ∆CT-FLAG podocytes to Nephrin-ECD was mediated, at least in part, by nephrin-nephrin interactions (Figure 6.3B). However, antibody blocking was unable to completely prevent the attachment of podocyte to Nephrin-ECD, which suggests the involvement of other cell surface adhesion receptors.

Analysis of podocyte morphology revealed that the actin cytoskeleton of N ∆CT-FLAG podocytes attached to Nephrin-ECD was not typical of cells attached to ECM ligand; they lacked organised actin structures (Figure 6.3C). Furthermore, Nephrin-FLAG podocytes attached to Nephrin-ECD displayed a distinct actin morphology compared with N ∆CT-FLAG podocytes. Nephrin-FLAG podocytes displayed actin ruffles around the cell periphery, but actin stress fibres were absent (Figure 6.3C). Nephrin- nephrin signalling complexes were isolated using a similar approach to the isolation of focal adhesions. 5, 6 Nephrin-FLAG podocytes were allowed to attach to Nephrin-ECD to generate nephrin- nephrin signalling complexes. Complexes formed at the basolateral surface of spread podocytes were subsequently stabilised with reversible crosslinker and podocyte cells bodies and nuclei were removed by detergent lysis and hydrodynamic force. We used Nephrin-FLAG-apotransferrin, N ∆CT-FLAG- apotransferrin and N ∆CT-FLAG-Nephrin-ECD complexes as control conditions. Staining for FLAG in cells attached to Nephrin-ECD revealed a concentration of Nephrin-FLAG at the base of podocytes in both whole cells and isolated complexes (Figure 6.3D). In order to confirm nephrin complex isolation we used Western blotting. FLAG and known nephrin interactors NCK1/2, α catenin, Src and CD2- associated protein were selectively enriched in Nephrin-FLAG-Nephrin-ECD complexes compared with all control complexes (Figure 6.3E). In contrast, the transferrin receptor was selectively enriched in apotransferrin complexes (Figure 6.3E). 223

224

Figure 6.3 Development of nephrin complex isolation assay A: Nephrin-ECD was generated by transfection of 293-EBNA cells, media was harvested and Nephrin- ECD purified by nickel chromatography. Purification was confirmed by Western blotting and coomassie staining. B: Attachment of podocytes expressing Nephrin-FLAG and N ∆CT-FLAG to Nephrin-ECD. C: Actin cytoskeletal morphology of Nephrin-FLAG and N∆CT-FLAG attached to Nephrin-ECD D: Immunofluorescence of Nephrin-FLAG and N ∆CT-FLAG before and after isolation of nephrin-nephrin complexes. E: Isolation of nephrin interacting proteins in Nephrin-FLAG-Nephrin-ECD complexes. N∆CT-FLAG was used as a control cell line and apotransferrin was used a control ligand. Podocytes were allowed to adhere to plates coated with 5 µg/ml of Nephrin-ECD for 180 minutes in serum free media before fixation and imaging. For blocking experiments 10 µg of sheep anti-nephrin (AF4269; R&D) was used. NFL, Nephrin-FLAG; Non-trans, non transduced podocytes. Michael Randles generated data for this figure.

6.3.3.4 MS of nephrin-nephrin adhesion complexes

MS analysis of isolated nephrin-nephrin complexes identified 24 of the 54 known nephrin interacting proteins. Additionally, filtering the dataset for proteins that were detected in ≥ 2 biological replicates and were ≥ 1.5 fold enriched in Nephrin-FLAG-Nephrin-ECD complexes compared with control samples, identified 63 potentially novel nephrin interacting proteins (Figure 6.4, Table 6.2). These proteins may not represent direct nephrin interactors, but also more peripheral components of nephrin signalling complexes. The potentially novel nephrin signalling proteins detected by MS were annotated by GO as: actin cytoskeleton organisation and establishment of cell polarity ( p < 5x10 -5), which is consistent with known nephrin signalling processes. Of these novel proteins, 30 make protein-protein interactions with previously identified nephrin interacting proteins; therefore, their recruitment to nephrin signalling complexes can be rationalised through these interactions. 15 additional focal adhesion proteins were identified in this analysis including talin1, vinculin, zyxin, VASP and syndecan 4 suggesting greater overlap of focal adhesion and nephrin signalling complexes than previously thought.

Importantly, integrins were not detected in nephrin signalling complexes by MS. Incidentally, 29 nephrin signalling proteins ( in silico nephrin interactors plus proteomic identifications) are also members of the integrin adhesome. 165

225

Figure 6.4 A nephrin signalling complex identified by mass spectrometry A: Merged nephrin in silico interactome and nephrin signalling proteome identified by MS. Nodes (circles) represent proteins, black node borders indicate proteins that were identified by MS. Nodes that are white in colour are not in silico direct nephrin interactors. Edges (grey lines) represent known experimentally observed protein-protein interactions. Michael Randles generated data for this figure.

226

Table 6.2 Novel nephrin signalling proteins

Uniprot Gene Alias Annotation Notes ID name

Q99996 AKAP9 A-kinase anchor Cytoplasm Binds to type II regulatory subunits of protein protein 9 kinase A

O60610 DIAPH1 Diaphanous Cytoplasm Required for the assembly of F-actin structures, homolog 1 Rho-dependent

Q8WTR2 DSP Protein Cytoplasm Ser/Thr and Tyr-phosphatase phosphatase 19

P31150 GDI1 Rab GDP Cytoplasm Regulates the GDP/GTP exchange reaction of dissociation most Rab proteins inhibitor alpha

P50395 GDI2 Rab GDP Cytoplasm Regulates the GDP/GTP exchange reaction of dissociation most Rab proteins inhibitor beta

Q13098 GPS1 COP9 Cytoplasm Regulator of the ubiquitin Suppresses G- signalosome protein- and mitogen-activated protein kinase- complex subunit mediated signal transduction 1

P09914 IFIT1 Interferon- Cytoplasm Interferon-induced antiviral RNA-binding protein induced protein with tetratricopeptide repeats 1

P05161 ISG15 Ubiquitin-like Cytoplasm Ubiquitin-like protein protein ISG15

P45985 MAP2K4 Mitogen- Cytoplasm MAP kinase signal transduction pathway activated protein kinase kinase 4

P20591 MX1 Interferon- Cytoplasm Interferon-induced dynamin-like GTPase induced GTP- binding protein

P20592 MX2 Interferon- Cytoplasm Interferon-induced dynamin-like GTPase induced GTP- binding protein

P43034 PAFAH1B1 Platelet- Cytoplasm Activation of Rho GTPases and actin activating factor polymerisation in response to calcium influx acetylhydrolase IB subunit alpha

Q8IXQ6 PARP9 Poly [ADP- Cytoplasm PARP1-dependent DNA damage repair ribose] polymerase 9

Q16512 PKN1 Serine/threonine Cytoplasm PKC-related kinase involved in regulation of the -protein kinase actin cytoskeleton N1

P49792 RANBP2 E3 SUMO- Cytoplasm E3 SUMO-protein ligase protein ligase RanBP2 227

Q9NZJ4 SACS Sacsin Cytoplasm Regulator of the Hsp70 chaperone machinery

Q8IY81 FTSJ3 Pre-rRNA Nucleus Methyltransferase processing protein FTSJ3

Q9BVP2 GNL3 Guanine Nucleus Maintenance of proliferative capacity of stem nucleotide- cells. binding protein- like 3

Q86V48 LUZP1 Leucine zipper Nucleus Detected in soma and dendrites of neurons protein 1

O00566 MPHOSPH1 U3 small Nucleus Component of the 60-80S U3 small nucleolar 0 nucleolar ribonucleoprotein ribonucleoprotei n protein MPP10

P16070 CD44 CD44 Plasma membrane Mediates cell-cell and cell-matrix interactions through its affinity for hyaluronic acid

O14745 SLC9A3R1 Na(+)/H(+) Plasma membrane Links plasma membrane proteins with exchange ezrin/moesin/radixin family to the actin regulatory cytoskeleton cofactor

Q8TE54 SUT2 Anion exchange Plasma membrane Maintenance of the electrolyte and acid-base transporter homeostasis in the kidney

Q9H270 VPS11 Vacuolar protein Vesicle Vesicle-mediated protein trafficking sorting- associated protein 11

Q9H269 VPS16 Vacuolar protein Vesicle Vesicle-mediated protein trafficking sorting- associated protein 16

Q9P253 VPS18 Vacuolar protein Vesicle Vesicle-mediated protein trafficking sorting- associated protein 18

P26231 CTNNA1 Catenin alpha-1 Cell-cell junction Association of catenins to cadherins produces a complex which is linked to the actin filament network

Q9Y5S2 CDC42BPB Serine/threonine Cell-cell Downstream effector of CDC42 and plays a role -protein kinase junction/Cytoskeleton in the regulation of cytoskeleton reorganisation MRCK beta

Q96EY1 DNAJA3 DnaJ homolog Cytoplasm/Cell-cell Modulates apoptotic signal transduction junction

P61160 ACTR2 Actin-related Cytoskeleton ATP-binding component of the Arp2/3 complex protein 2 involved in regulation of actin polymerisation

P35611 ADD1 Alpha-adducin Cytoskeleton Membrane-cytoskeleton-associated protein promotes the assembly of the spectrin-actin network

P40121 CAPG Macrophage- Cytoskeleton Calcium-sensitive protein which reversibly capping protein blocks the barbed ends of actin filaments 228

Q9UJU6 DBNL Drebrin-like Cytoskeleton Adapter protein that binds F-actin, plays a role protein in receptor-mediated endocytosis

O43491 EPB41L2 Band 4.1-like Cytoskeleton Actin cytoskeletal organisation protein 2

P58107 EPPK1 Epiplakin Cytoskeleton Cytoskeletal

Q14315 FLNC Filamin-C Cytoskeleton Involved in reorganising the actin cytoskeleton

P06396 GSN Gelsolin Cytoskeleton Calcium-regulated actin-modulating protein

P35579 MYH9 Myosin-9 Cytoskeleton Cytoskeleton reorganisation, focal contacts formation, role in cytokinesis, cell shape

P60660 MYL6 Myosin light Cytoskeleton Regulatory light chain of myosin polypeptide 6

Q15746 MYLK Myosin light Cytoskeleton Calcium/calmodulin-dependent myosin chain kinase contraction, regulates tight junctions by modulating ZO-1, triggers TRPC5 channel activity

Q9UM54 MYO6 Unconventional Cytoskeleton Reverse-direction motor protein, moves towards myosin-VI the minus-end of actin filaments

Q13459 MYO9B Unconventional Cytoskeleton Actin-based motor, also acts as a GTPase myosin-IXb activating protein on Rho

Q9P0K7 RAI14 Ankycorbin Cytoskeleton Associated with actin cytoskeleton structures in cell-cell adhesion sites and stress fibres

P06753 TPM3 Tropomyosin Cytoskeleton Stabilising cytoskeleton actin filaments alpha-3 chain

Q13753 LAMC2 Laminin gamma Extracellular ECM protein 2 chain

Q08380 LGALS3BP Galectin-3- Extracellular Promotes integrin-mediated cell adhesion binding protein

Q03169 TNFAIP2 Tumor necrosis Extracellular Inflammation and angiogenesis factor alpha- induced protein 2

Q99575 POP1 Ribonucleases Extracellular/Nucleus Component of ribonuclease P P/MRP protein subunit POP1

P15311 EZR Ezrin Focal adhesion connections of major cytoskeletal structures to the plasma membrane

O14976 GAK Cyclin-G- Focal adhesion Associates with cyclin G and CDK5, act as an associated auxilin homolog that is involved in the uncoating kinase of clathrin-coated vesicles

P48059 LIMS1 PINCH Focal adhesion Adapter protein in a cytoplasmic complex linking beta-integrins to the actin cytoskeleton

Q7L3E0 PALLD Palladin Focal adhesion Organisation of actin cytoskeleton

O00151 PDLIM1 PDZ and LIM Focal adhesion Actin cytoskeletal organisation domain protein 1 229

Q9NR12 PDLIM7 PDZ and LIM Focal adhesion Actin cytoskeletal organisation domain protein 7

P07737 PFN1 Profilin Focal adhesion Involved in the polymerisation of actin

P49023 PXN Paxillin Focal adhesion LIM domain protein

Q9Y490 TLN1 Talin 1 Focal adhesion Actin binding focal adhesion protein

Q9H2D6 TRIO triple functional Focal adhesion Binding and stabilising filamentous-actin domain protein

P50552 VASP Vasodilator- Focal adhesion Actin filament elongation stimulated phosphoprotein

Q15942 ZYX Zyxin Focal adhesion LIM domain, targets ENA/VASP family members to focal adhesions

P18206 VCL Vinculin Focal adhesion/Cell- Actin binding protein cell junction

P08670 VIM Vimentin Focal adhesion Class-III intermediate filament protein /Cytoskeleton

P31431 SDC4 Syndecan 4 Focal adhesion Cell surface proteoglycan, focal adhesion /Plasma membrane protein

6.3.3.5 Slit diaphragm and focal adhesion crosstalk

Within the merged proteomic and nephrin in silico interactome (Figure 6.4), focal adhesion was a significantly enriched GO term ( p < 1x10 -12 ), with only actin cytoskeleton, adherens junction, basolateral plasma membrane and cell junction terms being more significantly enriched. This analysis led us to hypothesise crosstalk between integrin signalling and nephrin signalling, a concept that has yet to be appreciated. In order to test this hypothesis we merged the experimentally observed nephrin interactome with the experimentally observed integrin β1 interactome. Proteins that directly interact with nephrin or integrin β1 are referred to as 'one hop interactors', whereas proteins that are within one interaction partner are referred to as 'two hop interactors'. The resulting network was highly complex; nephrin and integrin β1 share ten one hop interactors, but 71 proteins are nephrin and integrin β1 two hop interactors, forming 145 protein-protein interactions between the two complexes (Figure 6.5A). The most highly connected proteins in the merged interactome were: Src (49), FAK (PTK2) (34), Fyn (32),

Paxillin (26), ELAVL1 (25), SHC1, PIK3R1 and PRKCA (24 each) (Figure 6.5B).This analysis highlights the potential for extensive crosstalk between nephrin and integrin β 1 signalling complexes. 230

Figure 6.5 Nephrin integrin βββ1 merged interactome A: Merged nephrin integrin β1 interactome. Nodes (circles) represent proteins and edges (grey lines) represent known experimentally observed protein-protein interactions. B: Most highly connected proteins in the merged interactome, number of protein-protein interaction partners (degree). Michael Randles generated data for this figure. 231

6.3.4 Discussion

This analysis has utilised previously published data and MS-based proteomics to build a more comprehensive understanding of nephrin signalling. We have identified 54 nephrin direct interactors from published studies. Analysis of the biological roles of these proteins, confirmed regulation of the actin cytoskeleton as a major nephrin signalling process. Nonetheless, the nephrin interactome and more peripheral signalling complex is likely to be highly dynamic; therefore, we aimed to develop a system where nephrin signalling could be analysed in different contexts. We successfully generated cells lines that express nephrin. Subsequently, we isolated nephrin-nephrin signalling complexes using the extracellular domain of nephrin as a ligand. One striking finding was the overlap of nephrin and focal adhesion signalling complexes. This data suggests crosstalk between these two complexes, which may be important for foot process formation and assembly.

Often when slit diaphragms are lost, cell-ECM adhesion is affected as podocytes lose their foot processes and their cell bodies adhere to the substratum. 22 Vice versa, when cell-ECM adhesion is perturbed podocytes lose their foot processes. 22 These observations suggest that coordinated signalling of cell-cell and cell-ECM adhesion complexes is required for the generation and maintenance of podocyte foot processes. This is not surprising as both of these adhesion complexes regulate the actin cytoskeleton, which ultimately generates podocyte foot processes. Interestingly, isolation of nephrin-nephrin signalling complexes followed by MS revealed considerable overlap of this complex with focal adhesion complexes. In certain cells and situations some focal adhesion proteins have already been shown to localise to cell-cell junctions. For example, vinculin has been shown to localise to cadherin-based adhesion sites where it is involved in force sensing. 444-446 Furthermore, the focal adhesion protein integrin linked kinase (ILK) has been shown to interact with nephrin. 238 However, there is currently no evidence for other focal adhesion proteins such as talin, paxillin, PINCH, zyxin or focal adhesion kinase localising to cell-cell contact sites. 202, 444, 447 We identified both talin1 and paxillin at nephrin signalling complexes. In addition to paxillin and zyxin, we detected other LIM domain containing focal adhesion proteins, PDLIM1, PDLIM7 and LIMS1 (PINCH). These proteins are involved in force sensing at focal adhesions. 448 Slit diaphragms are likely to be involved in force sensing and

LIM domain proteins could be important for this process. 232

Analysis of crosstalk between nephrin and integrin signalling complexes can now be studied in the context of mechanical regulation (flow), or stimulation with paracrine and autocrine modulators.

Improved understanding of podocyte cell adhesion may lead to the identification of therapies to prevent or repair injury to this highly sophisticated filter.

6.3.5 Materials and Methods

6.3.5.1 Glomerular cell culture

Conditionally immortalised human podocytes 363 were grown in monoculture in uncoated tissue culture plates. Podocytes between passage 10 and 16 were cultured for at 33 oC in RPMI-1640 medium with glutamine (R-8758; Sigma-Aldrich) supplemented with 10% (v/v) FCS (Life Technologies) and 5% (v/v)

ITS (I-1184; Sigma; 1 ml/100 ml) until transduced with Nephrin-FLAG or N ∆CT-FLAG. Subsequently,

Nephrin-FLAG and N ∆CT-FLAG expressing podocytes were grown with the same medium for 14 days at 37 oC before experiments.

6.3.5.2 293-EBNA culture

293-EBNA cells were grown to 80% confluency in a T25 tissue culture flask (Corning Incorporated,

New York, USA) containing Dulbecco’s Modified Eagle’s Medium (DMEM4) (Sigma-Aldrich) supplemented with 10% foetal bovine serum (Gibco).

6.3.5.3 Antibodies

Goat polyclonal antibody to the ECD of nephrin (N-20; Santa Cruz) was used for Western blot. Sheep polyclonal antibody to the ECD of nephrin was used for immunofluorescence and FACs (AF4269;

R&D). Mouse monoclonal FLAG antibody (MN2; Sigma-Aldrich), transferrin receptor (H68.4;

Invitrogen). Rabbit polyclonal NCK1/2 (06-288; Millipore), alpha-catenin (3236; Cell signalling), Src

(2108; Cell signalling), CD2AP (H-290; Santa Cruz). Alexa Fluor® 594 phalloidin molecular probe was used to detect actin filaments. Secondary antibodies conjugated to Alexa Fluor 488 or 594 (Life

Technologies) were used for Immunohistochemistry; secondary antibodies conjugated to AlexaFluor 233

680 (Life Technologies, Paisley, UK) or IRDye 800 (Rockland Immunochemicals, Glibertsville, PA,

USA) were used for Western blotting.

6.3.5.4 Western blotting

See General Materials and Methods.

6.3.5.5 Building the nephrin interactome

Resource databases BIND, IntAct, STRING, Biogrid were screened for nephrin interactors. Followed by searching PubMed using the query: nephrin OR nphs1 OR nphn OR cnf AND interact, which returned 165 publications. All reported interactions were manually curated and interactions scored.

6.3.5.6 Protein interaction scoring

Confidence scoring of reported protein-protein interactions was based on Human Integrated

Protein-Protein Interaction rEference. 442

6.3.5.7 MS data acquisition

See General Materials and Methods.

6.3.5.8 MS data analysis

Tandem mass spectra were extracted using extract_msn (Thermo Fisher Scientific) executed in

Monoisotopic precursor mass values were used, and only doubly and triply charged precursor ions 234

were considered. Mass tolerances for precursor and fragment ions were 0.4 Da and 0.5 Da, respectively. MS datasets were validated using rigorous statistical algorithms at both the peptide and protein level 421 , 422 implemented in Scaffold (version 3.6 .5; Proteome Software, Portland, OR, USA).

Protein identifications were accepted upon assignment of at least two unique validated peptides with

≥ 90% probability, resulting in ≥ 99% probability at the protein level. These acceptance criteria resulted in an estimated protein false discovery rate of 0.1% for all datasets.

6.3.5.9 MS data quantification

Relative protein abundance was calculated using peptide intensity. Orbitrap MS data were entered into

Progenesis LCMS (Non Linear Dynamics Ltd, Newcastle upon Tyne, UK) and automatically aligned.

Spectra were extracted using extract_msn (Thermo Fisher Scientific, Waltham, MA, USA) executed in

Mascot Daemon (version 2.4; Matrix Science, London, UK) and imported back into Progenesis to acquire intensity data. Alignment of chromatograms was carried out using the automatic alignment algorithm, followed by manual validation and adjustment of the aligned chromatograms. All features were used for peptide identifications. Progenesis created the peak list file that was exported and searched in Mascot. Results were loaded in Scaffold (Proteome Software Inc, version 3.6.5) and peptide and protein identification threshold was set to 95% and 99% confidence respectively. Data was exported from Scaffold as a spectrum report, and imported into Progenesis to assign peptide identifications to features. Peptide and protein data were then exported from Progenesis as .csv files to be analysed in Excel.

6.3.5.10 Protein interaction network analysis

Protein interaction network analysis was performed using Cytoscape (version 2.8.1) (Shannon et al ,

2003). Proteins identified in at least two biological replicates were mapped onto a merged human, mouse and rat interactome built from Protein Interaction Network Analysis platform Homo sapiens network (release date, 10 December 2012), Mus musculus network (release date, 10 December 2012) and the Rattus norvegicus network (release date, 10 December 2012) ,348 the ECM interactions database MatrixDB (release date, 20 April 2012), 349 and a literature-curated database of integrin-based adhesion–associated proteins. 423 For networks where enrichment is presented Progenesis normalised intensity data was used. Topological parameters were computed using the NetworkAnalyzer plug-in .350 235

6.3.5.11 Fluorescence-activated cell sorting (FACS)

Podocytes were washed with phosphate buffered saline without cations (PBS-) and detached from culture surfaces with 1x trypsin-EDTA at 37 oC. The dissociated cells were harvested by centrifugation

(1000 rpm, 4 min). The cell pellet was resuspended in serum-free DMEM. Cells were then incubated with primary antibody at 10 g/ml, diluted in serum-free DMEM, at room temperature for 30 min.

Following 2 washes with 0.1/0.1 solution and centrifugation steps, cells were incubated with appropriate species-specific FITC-conjugated secondary antibody at 4 oC for 30 min. Cells were then washed three times with serum-free DMEM. Cells expressing the receptor of interest at the cell surface were sorted using BD Biosciences ARIA, FACS machine and the selected cells were placed in T25 flasks containing RPMI-1640 medium with glutamine (R-8758; Sigma, St. Louis, MO, USA) supplemented with 10% (v/v) FCS (Life Technologies) and 5% (v/v) ITS (I-1184; Sigma; 1 ml/100 ml) .

6.3.5.12 Plasmid amplification and purification

Plasmid were amplified and purified using QIAGEN Plasmid Plus Maxi Kit (QIAGEN) according to manufacturer's recommendation.

6.3.5.13 Cloning

NephrinforpWPXLd construct was purchased from GenScript (Piscataway, NJ). Nephrin DNA sequence was amplified by PCR (PlatinumR PCR SuperMix, life technologies) (forward primer, GAT

CCT GAT TAT AAA GAT GAT GAT GAT AAA TAA CA; reverse primer, GAC TAA TAT TTC TAC TAC

TAC TAT TTA TTG TAT) and FLAG sequence was amplified by PCR (PlatinumR PCR SuperMix, life technologies) (forward primer, GAT CCG ATT ATA AAG ATG ATG ATG ATA AAT AAC A; reverse primer, GCT AAT ATT TCT ACT ACT ACT ATT TAT TGT AT). Nephrin and FLAG PCR fragments were then ligated into pWPXLd vector (Addgene ID 12257) previously digested using the restriction enzymes MluI and NdeI (New England Biolabs).

N∆CT-FLAG was amplified by PCR (PlatinumR PCR SuperMix, life technologies) using Nephrin-FLAG pWPXLd construct as template (forward primer, GTT TAA ACA TGG CTC TGG GCA CAA CAC TGC; 236

reverse primer, CCT GCT GCT GCT GTC TAA CGC TAG TTG CGC ACG CGT). N ∆CT-FLAG PCR fragment was then ligated into pWPXLd vector previously digested using the restriction enzyme HindIII

(New England Biolabs).

NephrinECD-Fc was purchased from GenScript. Fc tag was removed through cloning, NephrinECD

DNA sequence was amplified by PCR (PlatinumR PCR SuperMix, life technologies) (forward primer,

GCT AGC CCA GCT GGC TAT CCC TGC AAG C; reverse primer, GCC AAC AGA GCC TCC ATC

TAG CGG CCG C). NephrinECD and HIS oligo PCR fragments were then ligated into pCEP-Pu vector 449 previously digested using the restriction enzymes NheI and NotI (New England Biolabs).

6.3.5.14 Lentiviral production and transduction

Podocytes stably expressing Nephrin-FLAG and N ∆CT-FLAG were produced by lentiviral transduction.

HEK 293T cells were transfected with three plasmids obtained from Addgene (psPAX2 Addgene ID

12260, pMD2.G Addgene ID 12259 and pWPXL Addgene ID 12257) using polyethyleneimine (Sigma-

Aldrich). Conditioned medium containing viruses was collected after 5 days following several media changes including an 8 hr incubation with 1M sodium butyrate-containing media to promote virus production. Conditioned media was then used immediately to infect conditionally immortalised podocytes or stored at -80°C. To each well, one ml of normal culture media supplemented with 8

g.ml-1 of polybrene, and 1 ml of 293T conditioned media containing the virus was added. The day after the transduction, the medium was replaced by fresh media.

6.3.5.15 Lipofectamine 2000 Transfection

2 µg of Nephrin-ECD-pCEP-Pu DNA was incubated with 0.2 ml Opti-MEM (Invitrogen Ltd, Paisley, UK) whilst in a separate tube 0.2 ml Opti-MEM was incubated with 10µl Lipofectamine 2000 for 10 minutes.

The contents of each tube were then combined and incubated for 20 minutes creating the transfection mix. Media was removed from 293-EBNA cells and the cells were washed with Opti-MEM. Following this 3.6ml of Opti-MEM was added to the tube containing the transfection mix which was subsequently added to the 293-EBNA cells . Cells and transfection mix were incubated for a further 24 hours prior to 237

selection with DMEM containing 10% foetal calf serum and 300 µg/ml puromycin (Invitrogen Ltd,

Paisley, UK) .

6.3.5.16 Purification of Nephrin-ECD

Nephrin-ECD expressing 293-EBNA cells were grown for 10 days in serum free DMEM. Conditioned media was collected and Nephrin-ECD was purified using a HisTrap excel column (GE Healthcare,

Chalfont St Giles, UK). Conditioned media was passed through the column overnight followed by gradient elution of HIS tagged protein from the column with 10 - 500 mM imidazole , 20 mM Tris, 400 mM NaCl.

6.3.5.17 Attachment assay

For cell adhesion assays, cells were trypsinised for 10 minutes, followed by pipetting cells up and down to break up cell clumps. Cells were left to trypsinise for another 5 minutes, and then pelleted by centrifugation (1000 rpm for 4 minutes at room temperature). To remove cell-bound extracellular matrix, cells were washed three times with PBS -, and left to incubate in serum free RPMI for 30 minutes at 37°C and 5% (v/v) CO 2. Subsequently, cells were pelleted and washed with serum free

RPMI and plated on ligand-coated dishes.

6.3.5.18 Isolation of nephrin-nephrin complexes

Nephrin-FLAG and N ∆CT-FLAG podocytes were attached to nephrin coated plates (5 µg / ml) for 180 minutes following attachment assay. Thereafter, podocytes were cross-linked with 6 mM DTBP diluted in Advanced Dulbecco's Modified Eagle Medium (DMEM5) (Sigma-Aldrich Ltd, Gillingham, UK) for 3 minutes and quenched with Tris-HCl pH 8.5, followed by 1 minute incubation with extraction buffer (10 mM Tris, 150 mM NaCl, 1% (v/v) Triton X-100, 25 mM EDTA, 25 g/ml leupeptin, 25 g/ml aprotinin and 0.5 mM AEBSF). Nephrin complexes were subjected to 30 seconds high pressure water wash to removed nuclei and were collected in reducing sample buffer (50 mM Tris-HCl, pH 6.8, 10% (w/v) glycerol, 4% (w/v) sodium dodecylsulfate (SDS), 0.004% (w/v) bromophenol blue, 8% (v/v) β- mercaptoethanol). Nephrin complex samples were fractionated by SDS-PAGE and used either for

Western blotting or visualised with InstantBlue to be used for in-gel proteolytic digestion. 238

6.3.5.19 Immunofluorescence and image analysis

41007a , respectively; Chroma, Olching, Germany) to minimise bleed -through between the different channels. For analysis of cell-cell junctions and 3D ECM models, images were acquired on a Delta

Vision (Applied Precision) restoration microscope using a 60x objective and the [ Sedat ] filter set

(Chroma [ 89000 ]). The images were collected using a Coolsnap HQ (Photometrics) camera with a Z optical spacing of 0.2 m. Images collected were viewed and analysed with Fiji. 435

6.3.6 Acknowledgements

This work was supported by a Wellcome Trust Intermediate Fellowship award (090006) to R.L., a Kids

Kidney Research grant awarded to RL and A.S.W to support a PhD studentship for M.J.R, and

Wellcome Trust grant (092015) to M.J.H. The mass spectrometer and microscopes used in this study were purchased with grants from the Biotechnology and Biological Sciences Research Council,

6.3.7 Conflict of interest

The authors declare no conflicts of interests . 239

7 General discussion

7.1 Summary of findings

Work presented in this thesis has applied global mass spectrometry (MS)-based proteomics to the study of extracellular matrix (ECM) and adhesion machinery within the glomerulus.

The analysis of normal human glomerular ECM in Chapter 2 established:

• The human glomerular ECM proteome is far more complex than previously thought.

• Within this ECM niche all previously known components were identified in addition to novel

components.

• The Human Protein Atlas (HPA) online resource validated 54 % of the MS dataset, enhancing

confidence in identified novel components, but also highlighting that MS combined with

immunodetection techniques has the capacity to increase resolution.

Through the analysis of glomerular podocyte- and glomerular endothelial cell (GEC)-derived ECMs in

Chapter 3 it was determined that:

• Cells either side of the glomerular basement membrane (GBM) produce distinct ECMs that

reflect their linage.

• There was significant overlap of podocyte- and GEC-ECMs, including developmental forms of

collagen IV and laminin, suggesting a dedifferentiated state of these cells.

• Coculture of glomerular cells led to basement membrane-like deposits between cells,

highlighting the requirement for glomerular cell crosstalk in GBM assembly.

• Through network merging, a core set of human glomerular ECM proteins was established and

these may well be essential for basement membrane assembly.

Chapter 4 details the analysis of glomerular ECM from FVB/NHanHsd (FVB) and C57BL/6JOlaHsd

(B6) mice. These animals were analysed as they exhibit a range of albuminuria relevant to human sub- 240

clinical microalbuminuria, with FVB mice developing a greater degree of albuminuria compared with B6 mice. Here analysis revealed:

• Thickened GBMs with frequent sub-podocyte protrusions in FVB mice.

• MS analysis revealed the variable nature of the glomerular ECM, and identified candidate

proteins whose expression was influenced in a stain- or sex-specific manner.

• Pathway analysis identified diverse cellular pathways that were differentially regulated in these

mice, suggesting a multifactorial cause of microalbuminuria.

Having analysed the glomerular ECM the next challenge was to analyse the glomerular adhesion machinery. In Chapter 5, MS was used to study glomerular podocyte adhesomes on two major GBM components, laminin and collagen IV. Adhesion of podocytes to laminin is essential for glomerular filtration barrier integrity. In contrast, collagen IV has a structural role and is not normally in contact with podocytes. Findings in Chapter 5 established:

• Distinct podocyte collagen IV and laminin adhesomes, comprising 64 known adhesome

components, in addition to 56 potentially novel components.

• Compositional differences in adhesion complexes manifest as cell shape and actin

cytoskeletal changes and differences in adhesion strength.

• Data from this study suggest that adhesion to collagen IV or laminin promotes foot process

effacement or formation respectively.

Finally, Chapter 6 addresses adhesion at the podocyte cell-cell interface, again using a proteomic approach. Nephrin is a key component of unique podocyte cell-cell junctions, called slit diaphragms. A nephrin-centric analysis of slit diaphragm signalling was undertaken. The findings in Chapter 6 were:

• Meta-analysis of known direct nephrin interacting proteins revealed distinct biological roles for

nephrin, such as cell-adhesion and actin cytoskeletal organisation.

• Isolation and analysis of nephrin-nephrin signalling complexes identified 63 proteins that were

not previously known to be nephrin signalling proteins.

• 29 focal adhesion proteins were found to interact with nephrin highlighting the overlap of slit

diaphragm and focal adhesion complexes.

• Consequent meta-analysis emphasised the potential for focal adhesion and slit diaphragm

crosstalk. 241

In the following sections, limitations, key questions and future work pertaining to the entire thesis are discussed.

7.2 The glomerular ECM

7.2.1 The human glomerular ECM

The glomerular ECM represents a complex structural niche, which is important for glomerular function.

There are at least 1065 ECM proteins encoded in the human genome; 1 therefore, performing tissue staining for every ECM protein to confirm glomerular expression is unfeasible. Moreover, tissue staining is limited by antibody availability and affinity. In contrast, the progression of MS technologies has made it possible to identify and quantify thousands of proteins in a single experiment. 450

In order to define identified proteins as ECM, a bioinformatic approach was used and this was based on Gene Ontology (GO) classification, 344, 345 the human Matrisome resource 1 and manual curation. One caveat of this approach is that bona fide novel ECM proteins that have not previously been described in the ECM, are removed from the dataset. However, manual curation enabled both the inclusion and exclusion of misassigned proteins. Moreover, it is possible to update the glomerular ECM proteome by reanalysing the MS data retrospectively as more ECM proteins are discovered.

Compared with previous studies of glomeruli using global approaches, 319-323 the application of an ECM enrichment strategy and multiple biological replicates in Chapter 2 enabled the identification of a more comprehensive list of glomerular ECM proteins. These data represent an important advance towards a systems level understanding of the glomerular ECM in health. Over one hundred novel candidate ECM proteins that may have roles in glomerular development and disease processes were identified. It is likely that many of these proteins represent novel therapeutic targets. This optimised approach for the analysis of glomerular ECM can be applied to animal disease models or prospective human renal biopsy tissue, and these analysis will undoubtedly identify ECM biomarkers, or specific ECM proteins that could be targeted therapeutically.

242

7.2.2 Cell derived ECMs

Chapter 3 of this thesis focused on the GBM, using MS-based proteomics of cell-derived ECMs from podocytes and GECs to address the cellular origins of GBM proteins. Podocytes and GECs produce distinct ECMs that reflect their in vivo roles. These data inform cell culture models of glomerular cells and form a valuable resource for understanding the extracellular niche in the glomerulus. Furthermore, these data have revealed novel cell types specific roles for ECM molecules. A drawback of this dataset is that 80 glomerular ECM proteins were not detected in either podocyte- or GEC-derived ECMs.

These proteins may be expressed by mesangial cells rather than GECs and podocytes. Alternatively, these proteins may not be expressed due to factors that were absent in the culture system, such as flow and crosstalk with other glomerular cells.

In the glomerulus paracrine signalling from podocytes to GECs is critical. For example, vascular endothelial growth factor (VEGF) produced by podocytes signals via VEGFR-2 receptors on GECs ; loss of this signalling results in global defects in the glomerular vasculature. 451, 452 In Chapter 3, crosstalk between glomerular cells was for the first time proven to be required for appropriate ECM production. Controlled perturbation of crosstalk represents a potential therapy for the treatment of diseases in which the glomerular ECM is dysregulated. Identifying the factor(s) involved in podocyte-to-

GEC crosstalk is the next challenge. Many candidates already exist, but additional MS experiments of podocyte conditioned media could identify additional possibilities. Identification of factors involved in this crosstalk may also represent novel therapeutic targets.

7.2.3 Genetic background, sex and the glomerular ECM

This thesis aimed to discover structural and compositional changes in the glomerular ECM, which associate with microalbuminuria. In order to address this aim, in Chapter 4 a proteomic workflow was applied to study inbred mouse stains with varying degrees of microalbuminuria. B6 female/male and

FVB female/male glomeruli were analysed because these mice establish a range of albuminuria relevant to human microalbuminuria. 400

243

A variety of histological glomerular ECM changes are described in the literature, but which changes actually cause changes in albumin permeability are not well characterised. Data in Chapter 4 demonstrated that GBMs increase in thickness with albuminuria. Another morphological feature which associated with albuminuria was sub-podocyte expansions of ECM. Expansions were frequently associated with splits in the GBM, suggesting they arise as cells attempt to repair damaged areas of

GBM. Despite an increase in GBM thickness and ECM expansions with albuminuria, the known major

GBM proteins laminin-521, collagen IV α3α4α5, perlecan, agrin and nidogen did not change in abundance at the protein and transcript level. This is evidence that novel GBM proteins are involved in selective glomerular filtration.

The basement membrane protein netrin 4 was more abundant in the glomerular ECM of animals with albuminuria. In addition, fibroblast growth factor 2 (FGF2) a growth factor that is expressed by both podocytes 453 and mesangial cells, 454 increased with albuminuria. Induction of this growth factor could be a key signalling event in the increased ECM production and in a manner similar to TGF-β, and may represent a target for treating glomerular specific ECM expansion. Furthermore, FGF2 is involved in cell motility. 455 It has become established that podocyte motility is increased with injury 456 therefore increased abundance of FGF2 may increase albuminuria through increasing podocyte motility rather than through ECM remodelling. Targeting FGF2 may be a possible therapeutic approach in a wide range of podocytopathies. Sub-podocyte expansions may be composed of netrin 4, but we were unable to confirm this with standard fluorescence microscopy. These types of lesion are not unique to our study and have been observed in mouse models of glomerular disease where adhesion is perturbed. For example, mice that are null for integrin α2, 411 Ext1 -knockout mice, 192, 457, 458 Actn4 and

Itga3 -knockout mice .173, 410 _ENREF_21 Cd151. 174, 179, 180 These findings support the existence of

intrinsic differences in ECM and adhesion machinery as contributors to albuminuria. Therefore studying adhesion signalling mechanisms within podocytes may help to dissect the mechanisms which lead to

GBM expansion and defects.

244

A hypothesis not addressed in this study is whether the arrangement of proteins within the GBM could explain the defects observed GBMs. Data in Chapter 4 demonstrate that expression levels of collagen

IV and laminin are similar in FVB and B6 glomeruli, however, the localisation of these proteins within the GBM may be altered. Linking to data in Chapter 5 of this thesis, adhesion of podocytes to collagen

IV compared with laminin leads to increased recruitment of talin1 and vinculin, suggestive of a stronger link between collagen IV adhesion complexes and the actin cytoskeleton. Therefore, altered organisation of ECM proteins within the GBM could cause altered podocyte traction forces and splitting of the GBM. Therapies modulating integrin function have been suggested for the treatment of scleroderma 153 and focal segmental glomerulosclerosis 459, 460 . Modulation of integrins, or specific integrins, may well represent a protective therapy to preserve GBM structure and filtration barrier integrity.

Meprins ( α and β) were upregulated with albuminuria and upregulated in males compared with females. These metalloproteases cleave type I procollagen, 408 in addition to many other ECM substrates. 409 Both meprin proteins have been implicated in inflammation and fibrosis. 407 Interestingly actinonin, a meprin inhibitor, protects male but not female rats from acute kidney injury. 461 This data in combination with data from this thesis suggest that meprins could potentially represent a therapeutic target for glomerular injury in human males, although sex-specific expression in humans has yet to be confirmed.

7.2.4 The use of mass spectrometry to study extracellular matrix

ECM proteins present analytical difficulties to proteomic strategies because of their biochemical properties. ECM proteins interact with many other proteins and are often highly covalently crosslinked resulting in low solubility. Progress in the isolation and proteomic analysis of ECM presented here, represents one of the many efforts to progress towards a systems-level understanding of ECM biology. 2, 392, 462, 463 Incorporation of an ECM enrichment methodology relying on the insolubility of the

ECM, vastly increased the signal over the noise and enabled the detection of a large dynamic range of

ECM protein concentrations. As a result generating the largest list of ECM proteins from glomerular tissue to date. However, despite the improvement in ECM isolation technique, pure ECM preparations from tissue were not achieved. 245

Another major challenge when performing high throughput analyses is extracting the important information from these large datasets. Here, to pursue a better understanding of the glomerular ECM, network analysis was performed in Chapters 2, 3 and 4. Analysis of interaction networks based on previously reported protein–protein interactions, can provide insights into the functional roles of the identified proteins. For example, proteins in subnetworks with many common interaction partners are more likely to function together. ECM is not only a structural scaffold that defines the physical properties of a tissue, but a complex signalling nexus; understanding how the ECM influences cells is the next challenge.

Glomerular cells are intimately linked to their microenvironment. Indeed, the cellular cytoskeleton of glomerular cells is linked, via integrins, to the ECM. 464 These sites of cellular adhesion to the ECM provide an informational link between the microenvironment and the cell interior. Integration of extracellular and adhesion signalling proteomic datasets will lead to a better system-level understanding of ECM regulation and dynamics. Ultimately, this will enable therapies to be designed that carefully manipulate this informational link to preserve filtration barrier function after insult.

7.3 Adhesion signalling complexes

7.3.1 Cell-ECM adhesion complexes

In vivo podocyte adhesion to laminin via integrin α3β1 is vital, 115, 116, 171 however, adhesion to collagen

IV is unlikely to occur in normal situations, but may occur in some pathological situations, such as

Alport syndrome. 134, 424 _ENREF_13 Interestingly, data in Chapter 5 demonstrated both cell shape and actin morphology were determined by adhesion to collagen IV or laminin. Podocytes appeared rounded and did not undergo protrusive activity when adhered to collagen IV, whereas laminin did induce protrusive activity in podocytes.

Adhesomes have predominately been studied in the context of adhesion to fibronectin, where cells use

αVβ3 and α5β1 integrins, 5, 6, 448 as such the adhesion machinery of podocytes on basement membrane ligands may have distinct compositions. In concurrence, 56 potentially novel adhesion components 246

were identified in Chapter 5. Proteins involved in actin cytoskeletal binding and regulation were enriched in collagen IV adhesions, including talin1 and vinculin. This led to the hypothesis that podocyte adhesion complexes link more strongly to the actin cytoskeleton when formed on collagen IV compared with laminin. In support of this, electrical cell-substrate impedance sensing (ECIS) suggested stronger adhesion formation when podocytes adhere to collagen IV compared with laminin.

Conversely, endocytic machinery is enriched in laminin adhesion complexes. Hence, laminin adhesions may be more dynamic, enabling process formation. This may be functionally linked to faster rates of endocytosis and recycling of integrin α3β1 compared with integrins α1β1 and α1β2.

Experiments in mice have demonstrated that deletion of the collagen adhesion receptor integrin α2 in

Alport mice is protective, 424 and data in this study suggests that targeting integrin α1 and α2 with small molecules or blocking antibodies may be a possible approach to treating Alport syndrome.

7.3.2 Cell-cell adhesion complexes

In addition to interactions between cells and the microenvironment, cell-cell interactions are also crucial in the glomerulus. Expanding on global analyses performed on cell-matrix adhesion complexes in

Chapter 5, data in Chapter 6 explored the feasibility of using MS to study cell-cell adhesion signalling complexes. Using nephrin as a focal point of the podocyte slit diaphragm signalling, nephrin signalling complexes were isolated and analysed by MS. This required the generation of Nephrin-ECD protein,

Nephrin-FLAG and N ∆CT-FLAG cell lines, which provide powerful tools for subsequent investigations of nephrin signalling. Proteomic analyses revealed unanticipated overlap of nephrin and focal adhesion complexes. Specific focal adhesion proteins may have unexpected roles at slit diaphragms. For example, talin1 knockout mice have proteinuria with actin cytoskeletal defects, but do not have perturbed integrin β1 activation. 203 Therefore, it is possible that loss of talin1 from slit diaphragms could contribute to this process. Chapter 6 proposes crosstalk between slit diaphragm and focal adhesion complexes, highlighting an unexpected and potentially important biological regulation process. Both focal adhesion and slit diaphragms are involved in actin cytoskeletal organisation and mechanotransduction. 21, 465, 466 With the global datasets generated it will be fascinating to perform knock-down experiments of proteins that have been identified as solely focal adhesion or cell-cell junction, in addition to those that may be involved in crosstalk. Coupling these knockdown experiments 247

to functional readouts such as the ECIS and podocyte actin cytoskeletal morphology will enable better understanding of the complex signalling networks generated in Chapters 5 and 6.

7.3.3 The use of mass spectrometry to study adhesion complexes

Adhesion signalling complexes are indeed complicated. They comprise a plethora of adaptors, which link cell surface receptors to the cytoskeleton, and numerous enzymes and signalling proteins that together mediate adhesion signalling. 165 Ultimately, many signalling networks are activated through adhesion complexes. Because of this complexity MS-based proteomics, which can identify and quantify thousands of proteins in a single experiment, 450 is a powerful tool for studying adhesion signalling. Strangely, one of the major challenges to the analysis of adhesion signalling complexes by

MS is the exquisite sensitivity of modern MS instruments. They detect thousands of proteins in a single experiment over a very large dynamic range of protein concentrations. This enables the detection of many adhesion components, but also many low abundance contaminant proteins. In order to identify novel adhesion components, appropriate controls are required.

Throughout this thesis apotransferrin is used as a control ligand. Cells attach to apotransferrin via the transferrin receptor and not the adhesion receptor of interest. The cells are then exposed to the same experimental conditions, including crosslinking and MS sample preparation. In theory, different proteins are recruited to the transferrin receptor compared with the adhesion receptor of interest, plus the same degree of contamination. This apotransferrin dataset can then be used as 'background'. One caveat for this control is that proteins detected specifically in apotransferrin samples may also be novel adhesion proteins, and will be lost from the dataset during filtering. In addition to this control, the Contaminant

Repository for Affinity Purification (CRAPome) can be cross referenced for common contaminants.

Gene Ontology definitions can help build confidence in generated datasets, although these definitions are often misassigned and manual annotation is required. Additionally, comparing ones data with published proteomic datasets and resources such as the integrin adhesome can also be beneficial.

Unfortunately, resources for studying specialised cell-junctions such as the slit diaphragm are currently unavailable.

248

To understand the complex interplay of ECM and adhesion networks integration of global datasets with functional readouts of cell behaviour, poses the next challenge. Furthermore, the complex biological systems studied herein are likely to be dynamic and context dependent. For this reason, it will be interesting to study these complexes in different contexts, such as mechanotransduction, growth factor stimulation and perturbation of novel components or key nodes within interaction networks.

7.4 Conclusion

The aim of this thesis was to use MS-based proteomics to study the glomerular extracellular environment in a global manner. These analyses are important for hypothesis generation as well as enabling holistic understanding of glomerular ECM and adhesion signalling. Although these studies are not hypothesis driven, and the results are very unpredictable, what is truly exciting about this experimental approach is the potential for identifying unexpected biology. For example, MS of adhesion and nephrin complexes identified proteins with diverse cellular roles, their localisation at adhesion sites unexpected. Furthermore, unanticipated overlap of slit diaphragm and integrin complexes suggests crosstalk, which could be important for glomerular barrier function. As these datasets are powerful resources for the research community, deposition into proteome databases such as the PRoteomics

IDEntifications (PRIDE) is an important archiving process.

Overall the data generated in this thesis forms a platform for further studies of glomerular ECM and adhesion signalling and raise many interesting questions. These optimised approaches for the analysis of ECM and adhesion signalling can now be readily applied to study human glomerular disease, for example the glomerular basement membrane nephropathy Alport syndrome. 249

8 General materials and methods

Listed here are methods that are applicable to all Chapters of the thesis. Chapter specific methods and their locations are indicated in table 8.2.

8.1 General reagents and equipment

Reversible cross-linker dimethyl 3,3'-dithiobispropionimidate (DTBP) was purchased from Thermo

Scientific. DTBP has a spacer arm of 11.9 Å and crosslinks primary amines. Novex 4-12% Tris-Glycine

Mini Gel 1.5 mm, 10 Well SDS-PAGE gels; 20x NuPAGE MES SDS running buffer; XCell SureLock

Mini-Cell Gel Electrophoresis Chamber and XCell II blot module were purchased from Life

Technologies. Precision Plus Protein All Blue Standards was purchased from Bio-Rad Laboratories.

Protein stain InstantBlue was purchased from Expedeon. Nitrocellulose blotting membrane for Western blotting was purchased from Whatman. 10x Western blotting blocking buffer was purchased from

Sigma.

8.2 Cell culture media, reagents and plasticware

Cell culture media and reagents, conditionally immortalised podocytes 363 were grown in RPMI-1640 medium with glutamine (R-8758; Sigma, St. Louis, MO, USA) supplemented with 10% (v/v) FCS (Life

Technologies) and 5% (v/v) ITS (I-1184; Sigma; 1 ml/100 ml), conditionally immortalised glomerular endothelial cells 364 (GECs) were grown in endothelial basal medium-2 (CC-3156; Lonza, Slough, UK) containing 5% (v/v) FCS and EGM-2 BulletKit growth factors (CC-4147; Lonza) , excluding VEGF .

Dulbecco's Modified Eagle Medium (DMEM), Porcine trypsin solution with EDTA were purchased from

Sigma-Aldrich. Fetal calf serum (FCS) was purchased from BioSera. Plasticware used for cell culture were purchased from Corning. 250

8.3 General Buffers

PBS -: Dulbecco's phosphate buffered saline without divalent cations.

PBS-T: PBS - containing 0.1% (v/v) Tween-20.

PBS +: phosphate buffered saline solution with divalent cations Mg 2+ and Ca 2+

SDS sample buffer: 2x buffer – 0.1 M Tris-HCl pH 6.8, 5% (w/v) glycerol, 2% Sodium dodecyl-sulfate

(SDS), 0.01% Bromophenol Blue and 5% (v/v) 2-β-mercaptoethanol; 5x buffer – 0.2 M Tris-HCl pH 6.8,

30% (v/v) glycerol, 7% (w/v) SDS, 0.01% Bromophenol Blue, 10% (v/v) 2-β-mercaptoethanol

TAE: 50x buffer – 2 M Tris-acetate, 50 mM EDTA pH 8.0

TBS: 10 mM Tris-HCl pH 7.4, 150mM NaCl

TBS-T: TBS containing 0.1% (v/v) Tween-20

Western blotting buffer: 25 mM Tris (Thermo Fisher) pH 8, 192 mM glycine (Thermo Fisher) and

10% (v/v) Methanol (Thermo Fisher)

8.4 Western blotting

Following SDS-PAGE, resolved proteins were transferred to nitrocellulose membrane (Whatman,

Maidstone, UK). Membranes were blocked with casein blocking buffer (Sigma-Aldrich) and probed with primary antibodies diluted in blocking buffer containing 0.05% (v/v) Tween 20. Membranes were washed with TBS-T and incubated with species-specific fluorescent dye–conjugated secondary antibodies diluted in blocking buffer containing 0.05% (v/v) Tween 20. Membranes were washed in the dark, and then scanned using the Odyssey infrared (IR) imaging system (LI-COR Biosciences,

Cambridge, UK) to visualise bound antibodies. Primary and secondary antibodies are listed in table

8.1. 251

Table 8.1 List of antibodies

Ab specificity Ab name and description Source/supplier: Working dilution

CD2AP H-290, rabbit polyclonal Santa Cruz 1:500 (WB)

NCK1/2 06-288, rabbit polyclonal Millipore 1:500 (WB)

PY397FAK 44-624G, polyclonal rabbit Ab Invitrogen 1:1000 (IF)

Paxillin Clone 349, monoclonal mouse Ab BD Biosciences 1:500 (IF)

Talin 8D4, monoclonal mouse Ab Sigma-Aldrich 1: 5000 (WB)

Vinculin hVin1, polyclonal mouse Ab Sigma-Aldrich 1:5000 (WB); 1:500

(IF)

Transferrin receptor H68.4, monoclonal mouse Ab Invitrogen 1:500 (WB)

Mitochondrial HSP70 JG1, monoclonal mouse Ab Pierce Antibodies 1:2000 (WB)

BAK B5897, polyclonal rabbit Ab Sigma-Aldrich 1:1000 (WB)

Cleaved caspase-3 9664, monoclonal rabbit Ab Cell Signalling 1:500 (IF)

Alpha-catenin 3236, polyclonal rabbit Ab Cell Signalling 1:200 (IF)

Beta-catenin 610154, monoclonal mouse Ab BD Biosciences 1:500 (IF)

CD151 11G5a, monoclonal mouse Ab Abcam 1:100 (FC)

Nephrin N-20, goat polyclonal Ab Santa Cruz 1:2000 (WB)

Nephrin AF4269, sheep polyclonal Ab R&D 1:500 (IF)

Nephrin Ab58968, polyclonal rabbit Ab Abcam 1:1000 (WB)

PECAM1 89C2, monoclonal mouse Ab Cell Signalling 1:500 (IF)

ZO1 5406, polyclonal rabbit Ab Cell Signalling 1:500 (IF)

Transferrin receptor H68.4, monoclonal mouse Ab Invitrogen 1:500 (WB)

Actin Clone AC-40, mouse monoclonal Sigma-Aldrich 1: 1000 (WB)

Collagen IV chain-specific Chain-specific rat monoclonal Abs A kind gift from Billy 1:5000 (WB);

a3 a1 antibodies Hudson, Vanderbilt 1:1000 (IF)

University Medical Center,

Nashville, TN, USA

Collagen IV Ab6586, polyclonal rabbit Ab Abcam 1:1000 (WB); 1:500

(IF)

Meprin alpha AP5858a, polyclonal rabbit Ab Abgent 1:1000 (IF)

Meprin beta MAB28951, monoclonal rat Ab R&D 1:500 (IF)

Nephronectin Ab64419, polyclonal rabbit Ab Abcam 1:100 (IF)

Netrin 4 AF1132, goat polyclonal Ab R&D 1:500 (IF)

TINAGL1 Ab69036, polyclonal mouse Ab Abcam 1:100 (IF)

Tenascin C Ab108930, monoclonal rabbit Ab Abcam 1:100 (IF)

Vitronectin Ab11591, monoclonal mouse Ab Abcam 1:500 (IF)

CYR61 Ab24448, polyclonal rabbit Ab Abcam 1:1000 (WB) 252

α1 FB12, monoclonal mouse Ab Millipore 1:100 (FC)

α2 P1E6, monoclonal mouse Ab Abcam 1:100 (FC)

α3 P1B5, monoclonal mouse Ab Abcam 1:100 (FC)

αvβ3 LM609, monoclonal mouse Ab Millipore 1:100 (FC)

α6 GoH3, monoclonal mouse Ab Abcam 1:100 (FC)

Lamin B1 Ab16048, polyclonal rabbit Ab Abcam 1:1000 (WB)

Rat, goat, mouse or rabbit Alexa Fluor 488, 594 or 647 conjugated Life Technologies 1:200 (IF)

IgG monoclonal donkey antibodies

Rat, goat, mouse or rabbit FITC, TRITC or Cy5 conjugated Jackson ImmunoResearch 1:200 (IF)

IgG monoclonal donkey antibodies Laboratories

Rat, goat, mouse or rabbit 680-conjugated or 800-conjugated Rockland 1:5000 (WB)

IgG polyclonal donkey Ab Immunochemicals

Ab, antibody; WB, Western blot; IF, immunofluorescence; FC, flow cytometry

8.5 Image processing

Images collected were viewed and analysed with Fiji. 435 To calculate the area of immunostained marker per cell, images were edited to remove additional cells and artefacts outside the required cell area, compiled to form a hyperstack, background subtracted using a rolling ball radius of 30, 'threshold' function used to select immunostained marker and to convert images to black-white binary images, and

'analyze particles' function used to calculate total black pixel area.

8.6 Mass Spectrometry

8.6.1 Mass spectrometry reagents and equipment

Acetonitrile (CHROMASOLV Plus), formic acid, dithiothreitol (DTT) and iodoacetamide (IA) were purchased from Sigma-Aldrich; sequencing grade trypsin was purchased from Promega; R3 beads were purchased from Applied Biosystems; the protein standard containing carboxymethylated peptides from 6 non-human proteins [cytochrome c ( Bos taurus ), lysozyme ( Gallus gallus domesticus ), alcohol dehydrogenase ( Saccharomyces cerevisiae ), bovine serum albumin ( Bos taurus ), apo-transferrin ( Bos 253

taurus ), β-galactosidase ( Escherichia coli )] was purchased from Dionex. Perforated V-bottom 96-well plates were purchased from Proxeon and 96 -well collection plates were purchased from Thermo

Fisher. 96 well plate with 0.2 µm PVDF membrane was purchased from Corning.

8.6.2 MS data acquisition

Protein samples were resolved by SDS-PAGE and visualized by Coomassie staining. Gel lanes were sliced and subjected to in-gel trypsin digestion as described previously. 4 Liquid chromatography– tandem MS analysis was performed using a nanoACQUITY UltraPerformance liquid chromatography system (Waters, Elstree, UK) coupled online to an LTQ Velos mass spectrometer (Thermo Fisher

Scientific, Waltham, MA, USA) or coupled offline to an Orbitrap Elite analyser (Thermo Fisher

Scientific, Waltham, MA, USA) for experiments incorporating analysis with Progenesis. Peptides were concentrated and desalted on a Symmetry C 18 preparative column (20 mm length, 180 m inner diameter, 5 m particle size, 100 Å pore size; Waters). Peptides were separated on a bridged ethyl hybrid C 18 analytical column (250 mm length, 75 m inner diameter, 1.7 m particle size, 130 Å pore size; Waters) using a 45-min linear gradient from 1% to 25% (v/v) acetonitrile in 0.1% (v/v) formic acid at a flow rate of 200 nl/min. Peptides were selected for fragmentation automatically by data-dependent analysis.

8.6.3 In-gel proteolytic digestion

In-gel proteolytic digestion was carried out as described previously. 4 Gel lanes were cut into slices, and each slice cut into ~1 mm 3 pieces. Gel pieces were destained twice with 50 % acetonitrile and 50 % ammonium bicarbonate solution for 30 minutes to remove protein stain, dehydrated by immersing in acetonitrile followed by vacuum centrifugation for 30 minutes. Subsequently, gel pieces were reduced in 10 mM DTT, alkylated in 55 mM IA and washed with alternating washes of ammonium bicarbonate and acetonitrile. Next, gel pieces were dehydrated and digested with sequencing grade trypsin (12.5 ng/ µl). Peptides from gel slices were collected in one wash of 99.8% (v/v) acetonitrile, and 0.2% (v/v) formic acid and one wash of 50% (v/v) acetonitrile and 0.1% (v/v) formic acid. Peptides were desiccated in a vacuum centrifuge and resuspended in 50 µl of 5% (v/v) acetonitrile and 0.1% (v/v) formic acid . 254

8.6.4 Offline peptide desalting

Firstly, 1 mg of R3 beads were placed in each well of a 96 well plate with 0.2 um PVDF membrane. R3 beads were washed with 50% (v/v) acetonitrile followed by 0.1 % (v/v) formic acid. Peptide sample were then resuspended in 5% (v/v) acetonitrile and 0.1% (v/v) formic acid to allow peptide binding to beads, washed twice with 0.1 % (v/v) formic acid, and peptides eluted in 50 % (v/v) acetonitrile, 0.1 %

(v/v) formic acid. Peptides were desiccated in a vacuum centrifuge and resuspended in 10 µl of 5%

(v/v) acetonitrile and 0.1% (v/v) formic acid.

Table 8.2 Chapter specific methods

Method Location

293-EBNA culture See Chapter 6

Analysis of Human Protein Atlas immunohistochemistry data See Chapter 2

Attachment and adhesion assays See Chapters 5 and 6

Building the nephrin interactome See Chapter 6

Cellular immunofluorescence See Chapter 3, 5 and 6

Cloning See Chapter 6

Collagenase treatment See Chapter 3

Electrical cell-substrate impedance sensing See Chapter 5

Electron microscopy See Chapter 3 and 4

Flow cytometry See Chapter 5

Fluorescence-activated cell sorting (FACS) See Chapter 6

Functional annotation and enrichment analysis See Chapters 2, 3, 4

Global microarray See Chapter 4

Glomerular cell culture See Chapters 3, 5 and 6

GO enrichment and Ingenuity pathway analysis See Chapter 4

Hierarchical clustering analysis See Chapters 3 and 4

Immunohistochemistry and image analysis See Chapters 2 and 4

Isolation of cell-derived ECMs See Chapter 3

Isolation of enriched glomerular ECM See Chapters 2 and 4

Isolation of glomeruli See Chapter 2 and 4

Isolation of nephrin-nephrin complexes See Chapter 6

Isolation of RNA See Chapter 3 255

Lentiviral production and transduction See Chapters 3 and 6

Lipofectamine 2000 Transfection See Chapter 6

Mouse strain SNP analysis See Chapter 4

MS data access See Chapters 2, 3 and 4

MS data analysis See Chapters 2, 3, 4, 5 and 6

MS data quantification and statistical analysis See Chapters 2, 3, 4, 5 and 6

Non-glomerular cell sulture See Chapters 3 and 6

Plasmid amplification and purification See Chapter 6

Principal component analysis See Chapters 4 and 5

Protein interaction network analysis See Chapters 2, 3, 4, 5 and 6

Protein interaction scoring See Chapter 6

Purification of Nephrin-ECD See Chapter 6 q-PCR and relative abundance See Chapter 3

RT-PCR See Chapter 3

Statistical analysis See Chapter 2, 3, 4, 5

256

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