The Role of the Set7 Lysine Methyltransferase in the Development of Diabetic Complications” Is No More Than 100,000 Words in Length

The Role of the Set7 Lysine Methyltransferase in

the Development of Diabetic Complications

Hanah V. Rodriguez C.

MSc. (Biotechnology)

Submitted in total fulfilment of the requirements of the degree of

Doctor of Philosophy

2017

Department of Chemistry and Biotechnology

School of Science Faculty of Science, Engineering and Technology

Swinburne University of Technology

ABSTRACT

Diabetes is characterised by chronic hyperglycaemia, which results in long-term vascular and renal damage through various pathways. The contribution of epigenetic modifications to the development of cardiovascular and renal disease in diabetes has been widely reported. Set7, a mono-methyltransferase, has been implicated in the transcriptional regulation of genes that contribute to inflammation and fibrosis through histone and non- histone protein lysine methylation. However, the role of this enzyme in the development of vascular complications of diabetes in vivo remains poorly understood. In the present work, mouse models of Set7 deficiency were used to examine the function of this enzyme in metabolic regulation and in the development of diabetes-accelerated atherosclerosis (DAA) and diabetic nephropathy (DN). Genetic deletion of Set7 in mice had no significant effect in glucose tolerance and other metabolic parameters such as circulating lipids or response to high fat feeding. Given its contribution to pro-inflammatory and pro- fibrotic gene activation in response to high glucose, Set7 has been proposed as a potential target to reduce the burden of diabetic complications. To test this hypothesis, diabetes was induced in Set7-deficient mice on an Apolipoprotein E (ApoE) knock-out background (ApoE-/-), and vascular and renal damage was assessed after 10 weeks. The deletion of Set7 in these animals attenuated the diabetes-induced increase in atherosclerotic plaque area in the aortic arch. Similarly, diabetic Set7-deficient animals (Set7-/-/ApoE-/-) had lower levels of albuminuria and attenuated structural features of DN, such as mesangial expansion and glomerular deposition of collagen I and IV, when compared to diabetic ApoE-/- mice. This protective phenotype was associated with the attenuation of diabetes-driven gene expression changes in the aorta and kidneys of diabetic Set7-/-/ ApoE-/- when compared to ApoE-/- mice as determined by RNA sequencing (RNA-seq). Transcriptome profiling by RNA-seq and Gene Set Enrichment Analysis (GSEA) identified transcription factors such as GA-binding protein (GABP) and Host Cell Factor C1 (HCFC1) that participate in diabetes-induced, Set7-dependent gene expression changes in the vasculature and kidney. GABP and HCFC1 are associated with the transcriptional regulation of nuclear-encoded mitochondrial genes, implicating Set7 in the regulation of mitochondrial function in diabetes. Additionally, GSEA identified the transcription factor TCF21 as a regulator of Set7-dependent gene expression changes in the diabetic kidney. Co-immunoprecipitation studies showed that the Set7 and TCF1 proteins interact and indicate that TCF21 may represent a novel non-histone methylation i

target for Set7. Furthermore, pharmacological inhibition of Set7 in vitro attenuated gene expression changes caused by inflammatory and fibrotic stimuli in cultured vascular (endothelial and smooth muscle cells) and renal (mesangial and proximal tubules cells, and podocytes) cell populations. Overall, the results presented here suggest that targeting Set7 may represent a strategy for developing athero- and reno-protective therapies in diabetes.

DECLARATION

I, Hanah V. Rodriguez C., declare that the thesis entitled “The Role of the Set7 Lysine Methyltransferase in the Development of Diabetic Complications” is no more than 100,000 words in length. This thesis contains no material that has been submitted previously, in whole or in part, for the award of any other academic degree or diploma, and has not been previously published by another person. The thesis comprises only my original work except where indicated in the Preface.

Hanah V. Rodriguez C.

November 2017

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PREFACE

The work presented in this thesis was performed in the Epigenetics in Human Health and Disease Laboratory with assistance from the Diabetic Complications Laboratory, both part of the Department of Diabetes, Central Clinical School, Monash University. The following experiments were performed fully or partially by someone other than the candidate:

• Animal technicians Ms. Samantha Sacca, Ms. Elisha Lastavec, Ms. Megan Haillay and Ms. Deanna Smith from the Diabetic Complications Laboratory, performed every day monitoring of animals, glucose tolerance tests, diabetes induction (streptozotocin injections) and monitoring (weekly blood glucose readings and weighing) and metabolic caging. • Ms. Amy Huang from the Beta Cell Biology Laboratory, University of Melbourne, assisted with pancreatic islet isolation from mice. • Dr. Jun Okabe from the Epigenetics in Human Health and Disease Laboratory, assisted with the generation of overexpression vectors as well as shRNA constructs and lentiviral transduction. • Dr. Christos Tikellis from the Diabetic Complications Laboratory performed en face determination of aortic plaque area. • Dr. Bryna Chow from the Diabetic Complications Laboratory assisted with the determination of urinary albumin excretion and immunohistochemistry for the assessment of glomerular structural changes. • Dr. Mark Ziemann from Epigenetics in Human Health and Disease Laboratory, performed library preparation and read alignment for RNA sequencing experiments. • Dr. Haloom Rafehi from Epigenetics in Human Health and Disease Laboratory, performed bioinformatics and statistical analyses from RNA sequencing results. • Mr. Sameer Jadaan from Epigenetics in Human Health and Disease Laboratory, performed the culture of human microvascular endothelial cells (HMEC-1) and TNFa and (R)-PFI-2 exposure.

The following publications were produced directly from work presented in this thesis:

Review article: Rodriguez H, Rafehi H, Bhave M, El-Osta A. 2017. Metabolism and chromatin dynamics in health and disease. Molecular Aspects of Medicine 54: 1-15

Conference proceedings: Rodriguez H, Okabe J, Bhave M, El-Osta A. 2015. Contrasting roles of the lysine methyltransferase Set7 in pancreatic b cells. 6th Annual ASMR Victorian Student Research Symposium, Oral Presentation, Melbourne, Australia 15 May 2015.

Rodriguez H, Ziemann M, Rafehi H, Okabe J, Cooper M, Bhave M, El-Osta A. 2015. The role of the lysine methyltransferase Set7 in the transcriptional regulation of pancreatic b cells. 6th Biennial National Australian Epigenetics Alliance Conference, Poster Presentation, Hobart, Australia, 12-14 November 2015

Rodriguez H, Chow B, Ziemann M, Jandeleit-Dahm K, Cooper M, Bhave M, El-Osta A. 2016. Genetic deletion of the Set7 lysine methyltransferase attenuates renal damage in a mouse model of diabetic nephropathy. 2016 Australian Diabetes Society Annual Scientific Meeting, Oral Presentation (Young Investigator Award Finalist), Gold Coast, Australia, 24 – 26 August 2016.

Rodriguez H, Rafehi H, Ziemann M, Okabe J, Chow B, Bhave M, Cooper M, El-Osta A. 2017. Genetic deletion of the Set7 lysine methyltransferase attenuates renal damage in a mouse model of diabetic nephropathy. 2017 Australian Diabetes Society Annual Scientific Meeting, Oral Presentation. Awarded the Pincus Taft Young Investigator Award, Perth, Australia, 30 August – 1 September 2017.

ACKNOWLEDGEMENTS

They say it takes a village to raise a child – the same can be said about completing a PhD. Despite it largely being an individual effort, I would like to thank the many people without whom this thesis would not be in front of you today.

I am thankful to my supervisors: Prof. Mrinal Bhave for recognising potential in me and Prof. Sam El-Osta and Prof. Mark Cooper for accepting me into the Epigenetics and Diabetic Complications laboratories. I could not have done it without your guidance at different steps along the way. I am indebted to members of the group who helped me with experiments: Dr. Haloom Rafehi and Dr. Mark Ziemann for bioinformatics analyses that made a major contribution to this thesis. Dr. Jun Okabe for help with every protocol imaginable, new or old. I am particularly thankful for the help provided by Ms. Samantha Sacca, Ms. Elisha Lastavec, Ms. Megan Haillay and Ms. Deanna Smith with animal work, Mr. Sameer Jadaan for setting up HMEC-1 experiments, and Dr. Chris Tikellis, Dr. Bryna Chow and Ms. Amy Huang for lending their expert hands to my cause.

It is no secret that the PhD journey is full of ups and downs and I am grateful to have counted with the support of great people in the lab to celebrate the ups and get through the downs. Haloom (a PhD student at the start of my journey and a great post doc by the end) has been a tremendous source of encouragement and a role model as a young successful female scientist. Dr. Harikrishnan Kaipananickal – or just Hari – for being the voice of wisdom and experience. I truly believe sharing the same office with him taught me much about science and life, it also kept me sane during the past few years.

I would not be here without the support of my Mum who has given everything she has and more for me to have the opportunities I had. I am grateful for my loving family and long-time friends, who have always believed in me and encouraged me from far away. A special thank-you to my in-laws for adopting me into their family and supporting me in this whole process, they always make me feel super smart. Last but not least my husband, for believing in me more than I believe in myself and for agreeing to marry someone in the middle of her PhD. He makes my life better in ways a doctorate never will. It is to this neat group of people that I dedicate this thesis.

~ Gracias

TABLE OF CONTENTS

Abstract i Declaration iii Preface iv Acknowledgements vi Table of contents vii List of figures xiii List of tables xvi List of abbreviations xviii

1. Literature review 1

1.1. Introduction 2 1.2. Normal glucose metabolism 4 1.3. Diabetes mellitus 7 1.4. Reactive oxygen species and diabetic complications 8 1.4.1 Polyol pathway flux 10 1.4.2. Formation of AGE precursors 10 1.4.3. Activation of protein kinase C 10 1.4.4. Hexosamine pathway flux 11 1.4.5. Other sources of Reactive Oxygen species 11 1.5. Vascular complications of diabetes 12 1.5.1. Macrovascular complications: Atherosclerosis and cardiovascular 12 disease 1.5.2. Diabetic nephropathy 14 1.5.3. Diabetic retinopathy and neuropathy 16 1.6. Epigenetics 17 1.6.1. DNA methylation 17 1.6.2. Histone modifications 18 1.6.2.1. Histone acetylation 20 1.6.2.2. Histone methylation 20 1.6.2.3. Other epigenetic modifications 21 1.7. The Set7 lysine methyltransferase 22

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1.8. Epigenetic modifications in metabolic disease 28 1.8.1. Epigenetic reprogramming and metabolic disease 29 1.8.2. Hyperglycaemic memory 32 1.8.3. Oxidative stress and epigenetic gene regulation in diabetes 32 1.8.4. Epigenetic mechanisms underlying macrovascular diabetic 32 complications 1.8.5. Epigenetic mechanisms underlying diabetic nephropathy 34 1.9. Set7 knock-out mouse models 37 1.10. High throughout sequencing 40 1.11. Summary of the literature and project aims 41

2. Materials and Methods 43

2.1. Materials 44 2.1.1. Instruments and equipment 44 2.1.2. General reagents 45 2.1.3. Cell lines and tissue culture reagents 48 2.1.4. Antibodies 50 2.2. General experimental methods 51 2.2.1. Animal ethics 51 2.2.2. General tissue culture 51 2.2.3. Gene expression studies 52 2.2.4. Protein analysis 56 2.2.5. Lentiviral gene transduction 58 2.3. Experimental methods specific to Chapter 3 61 2.3.1. Body composition analysis 61 2.3.2. Metabolic caging 61 2.3.3. Blood and plasma measurements 61 2.3.4. Glucose tolerance test 62 2.3.5. Pancreatic islet isolation 62 2.4. Experimental methods specific to Chapters 4 and 5 63 2.4.1. Measurement of general metabolic parameters 64 2.4.2. En face determination of aortic arch plaque area 65 2.4.3. Determination of urinary albumin excretion 65 2.4.4. Renal proximal tubule cell (PTC) isolation from mice 66

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2.5. Experimental methods specific to Chapter 6 66 2.5.1. Generation of a mammalian TCF21 expression vector 66 2.5.2. Immunoprecipitation 67 2.6. RNA sequencing and bioinformatics analysis 68 2.6.1. Library construction and RNA sequencing 68 2.6.2. Read alignment and differential gene expression 68 2.6.3. Data visualisation 68 2.6.4. TCF21 motif analysis 69 2.6.5. Gene Set Enrichment Analysis (GSEA) 69 2.6.6. Fisher’s exact test 70 2.7. Statistical analysis 70

3. Metabolic characterisation of Set7-deficient mice 71

3.1. Abstract 72 3.2. Introduction 73 3.3. Results 75 3.3.1. Set7 expression is reduced in tissues from Set7+/- Set7-/- mice 76 3.3.2. Set7 deficiency does not alter body composition 77 3.3.3. Set7 deficiency does not alter blood cell composition or plasma lipid 80 concentrations 3.3.4. Genes associated with insulin secretion are down-regulated in Set7- 81 deficient b cells 3.3.5. Set7-/- mice have normal islet numbers and architecture 84 3.3.6. Set7-deficient mice are normal glucose tolerance 85 3.4. Discussion 87 3.4.1. Set7 is a ubiquitous protein and its expression is reduced in Set7 87 knock-out mice 3.4.2. Set7 participates in the transcriptional regulation of several b cell 88 genes but is not required for glucose tolerance 3.4.3. Set7 is not necessary for maintaining metabolic homeostasis but may 89 participate in adipose tissue differentiation 3.5. Conclusions 91

4. The effect of genetic Set7 deletion in the development of diabetes- 92 accelerated atherosclerosis (DAA)

4.1. Abstract 93 4.2. Introduction 94 4.3. Results 96 4.3.1. Diabetes induction causes metabolic changes regardless of genotype 96 4.3.2. Set7 deletion attenuates diabetes-induced increases in plaque area 96 4.3.3. Diabetes induces gene expression changes in the aortas of ApoE-/- 97 mice 4.3.4. Genes up-regulated by diabetes are strongly associated with immune 100 responses and mitochondrial function 4.3.5. Set7 deletion attenuates diabetes-induced gene expression changes in 107 the aortas of ApoE-/- mice 4.3.6. Pharmacological inhibition of Set7 in cultured endothelial cells and 117 smooth muscle cells attenuates gene expression changes induced by high glucose, TNFa and TGFb1 treatment 4.4. Discussion 121 4.4.1. Set7 deletion confers atheroprotection by attenuating diabetes- 121 induced changes in gene expression 4.4.2. Set7-dependent regulation of genes involved in mitochondrial 123 function may contribute to vascular damage in diabetes 4.5. Conclusion 126

5. The effect of genetic Set7 deletion in the development of diabetic 127 nephropathy

5.1. Abstract 128 5.2. Introduction 129 5.3. Results 131 5.3.1. Diabetes induction causes metabolic changes regardless of Set7 131 genotype 5.3.2. Set7 deletion attenuates the diabetes-induced increase in albuminuria 131 5.3.3. Set7 deletion attenuates diabetes-induced glomerular structural 132 damage

5.3.4. Diabetes induces widespread gene expression changes in the kidneys 134 of ApoE-/- mice 5.3.5. Genes up-regulated by diabetes are associated with inflammation 137 and fibrosis 5.3.6. Set7 deletion attenuates diabetes-induced gene expression changes in 143 the kidneys of diabetic ApoE-/- mice 5.3.7. Set7-mediated transcriptional regulation in the diabetic kidney is 149 associated with mitochondrial function and microRNA regulation pathways 5.3.8. Pharmacological inhibition of Set7 in cultured renal cells attenuates 153 gene expression changes induced by high glucose and TGFβ1 treatment 5.4. Discussion 157 5.4.1. Set7 deletion mitigates renal damage by attenuating diabetes-induced 157 changes in gene expression 5.4.2. Set7 mediates the expression of genes involved in mitochondrial 159 function in diabetes 5.4.3. Set7 participates in TFGβ1-driven pro-fibrotic responses via several 160 pathways 5.4.4. Set7 may associate with the transcription factor Tcf21 to regulate 161 gene expression changes in the diabetic kidney 5.5. Conclusions 161

6. Interaction between Set7 and the transcription factor 21 164

6.1. Abstract 165 6.2. Introduction 166 6.3. Results 168 6.3.1. TCF21 regulates the expression of diabetic nephropathy-associated 169 genes in cultured human podocytes 6.3.2. TCF21 gene targets are deregulated in the kidneys of diabetic 170 ApoE-/- mice 6.3.3. TCF21 gene targets are regulated by Set7 in the diabetic kidney 173 6.3.4. Set7 interacts with TCF21 177 6.4. Discussion 181 6.4.1. TCF21 may mediate diabetes-induced gene expression changes in 181 the kidney

6.4.2. TCF21 may represent a novel lysine methylation target for Set7 182 6.5. Conclusions 184

7. General discussion and conclusions 185

7.1. General discussion 186 7.1.1. Genetic deletion of Set7 attenuates renal and vascular damage in a 187 mouse model of diabetic complications 7.1.2. Set7 participates in the regulation of mitochondrial function in 189 diabetes 7.1.3. Set7 mediates transcriptional changes that regulate the smooth 190 muscle cell phenotype 7.1.4. Set7 associates with the transcription factor TCF21 to regulate gene 191 expression 7.2. Conclusions and future directions 192

Bibliography 198

Appendices 243

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LIST OF FIGURES

Figure 1.1. Glucose-stimulated insulin secretion by pancreatic β cells 5 Figure 1.2. Unified theory for the pathobiology of diabetic complications 9 Figure 1.3. Schematic representation of the structure of chromatin 19 Figure 1.4. Protein structure of the human Set7 methyltransferase 23 Figure 1.5. Epigenetic modifications mediate diabetes-induced gene 36 expression changes underlying the development of diabetic complications Figure 1.6. Structure of the mouse (Mus musculus) Setd7 gene 38 Figure 1.7. Strategy for the generation of a mouse heterozygous for a Setd7 39 knock-out allele Figure 2.1. Experimental outline for diabetic complications studies (Chapters 64 4 and 5) Figure 3.1. Expression levels of Set7 and other histone methyltransferase 76 enzymes in mouse tissues Figure 3.2. Set7 expression in pancreatic tissue 77 Figure 3.3. Body weight gain in high fat fed mice 78 Figure 3.4. Set7 deficiency in pancreatic β cells causes gene expression 83 changes in vitro and in vivo Figure 3.5. Pancreatic islet structure in Set7+/+ and Set7-/- mice 84 Figure 3.6. Morphometric details of pancreatic islets from Set7+/+ and Set7-/- 85 animals Figure 3.7. Intraperitoneal glucose tolerance test in chow- and high-fat fed 86 Set7+/+ and Set7-/- animals Figure 4.1. Atherosclerotic plaque area measured in the aortic arch of control 97 and diabetic ApoE-/- and Set7-/-/ApoE-/- mice Figure 4.2. MA plot representing genome-wide changes in gene expression in 98 response to diabetes in the aortas of ApoE-/- mice Figure 4.3. Figure 4.7. qRT-PCR validated gene expression changes 109 identified by RNA-seq Figure 4.4. Two-dimensional (contour) plot showing the effect of Set7 113 deletion in diabetic aortas

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Figure 4.5. Pharmacological inhibition of Set7 attenuates the high glucose 119 and TNFa-induced increase in expression of pro-inflammatory genes in human microvascular endothelial cells (HMEC-1) Figure 4.6. Gene silencing and pharmacological inhibition of Set7 attenuates 120 the high glucose and TGFβ1-induced increase in expression of pro- inflammatory and pro-fibrotic genes in mouse smooth muscle cells (mSMCs) Figure 5.1. Urinary albumin excretion (albuminuria) in control and diabetic 132 ApoE-/- and Set7-/-/ApoE-/- mice Figure 5.2. Histological assessment of diabetes-induced renal structural 133 damage in the glomeruli of control and diabetic ApoE-/- and Set7-/-/ApoE-/- mice Figure 5.3. MA plot representing genome-wide changes in gene expression in 135 response to diabetes in the kidneys of ApoE-/- mice Figure 5.4. Multidimensional Scaling (MDS) Plot of RNA-seq samples of 144 control and diabetic ApoE-/- and Set7-/-/ApoE-/- kidneys Figure 5.5. Comparison of genes deregulated by diabetes in ApoE-/- and 145 Set7-/-/ApoE-/- kidneys Figure 5.6. Two-dimensional (contour) plot showing the effect of Set7 146 deletion in the diabetic kidney Figure 5.7. qRT-PCR validated gene expression changes identified by RNA- 148 seq Figure 5.8. Genetic deletion and pharmacological inhibition of Set7 154 attenuates the high glucose and TGFβ1-induced increase in expression of pro- inflammatory and pro-fibrotic genes in mouse renal Proximal Tubule Cells (PTCs) Figure 5.9. Pharmacological inhibition of Set7 attenuates the high glucose 155 and TGFβ1-induced increase in expression of pro-inflammatory and pro- fibrotic genes in Normal Human Mesangial cells (NHMCs) and human podocytes Figure 5.10. Pharmacological inhibition of Set7 attenuates the high glucose 156 and TGFβ1-induced increase in expression of smooth muscle-related genes in NHMCs Figure 6.1. Control and TCF21 KD human podocytes in culture 169

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Figure 6.2. High glucose and TGFb1-induced gene expression changes in 170 control and TCF21 KD cultured human podocytes Figure 6.3. Sequence logo representing the TCF21 DNA binding motif 172 Figure 6.4. Two-dimensional plot showing the effect of Set7 deletion in the 173 expression of TCF21 target genes Figure 6.5. qRT-PCR validated gene expression changes identified by RNA- 177 seq Figure 6.6. Generation of a pCGN-TCF21 vector for expression in 178 mammalian cells Figure 6.7. Co-immunoprecipitation experiments showed physical interaction 179 between TCF21 and Set7 Figure 7.1. H3K4me1 at promoters and enhancers of differentially expressed 195 genes in the kidneys of diabetic ApoE-/- and Set7-/-/ApoE-/- mice

LIST OF TABLES

Table 1.1. Regulation of gene expression by Set7-mediated H3K4 methylation 25 Table 1.2. Non-histone substrates of Set7 27 Table 1.3. Histone-modifying enzymes associated with the regulation of genes 30 involved in glucose metabolism and diabetes Table 1.4. Published models of Set7 knock out mice 37 Table 2.1. Equipment and instruments used 44 Table 2.2. General reagents and chemicals used 45 Table 2.3. Commercial reagent kits used 47 Table 2.4. Buffers and solutions prepared 47 Table 2.5. Reagents and chemicals used for tissue culture 48 Table 2.6. Cell lines used and basic culture media composition 49 Table 2.7. Primary and secondary antibodies used 50 Table 2.8. Mouse and human cDNA primers used for qRT-PCR 55 Table 3.1. Body composition analysis by EchoMRI 79 Table 3.2. Metabolic caging data 79 Table 3.3. Basic blood cell count and plasma parameters 81 Table 4.1. General physiological parameters in control and diabetic ApoE-/- 96 and Set7-/-/ApoE-/- mice after 10 weeks of study Table 4.2. RNA-seq identified gene expression changes conferred by diabetes in 99 the aortas of ApoE-/- mice Table 4.3. GSEA identified major pathways positively enriched in ApoE-/- 103 diabetic aortas Table 4.4. GSEA identifies major pathways negatively enriched in ApoE-/- 104 diabetic aortas Table 4.5. GSEA identified transcription factors associated with genes up- 106 regulated and down-regulated by diabetes in ApoE-/- mice aortas Table 4.6. RNA-seq identifies gene expression changes conferred by Set7 108 deletion in the aortas of diabetic ApoE-/- mice Table 4.7. GSEA identified major pathways positively enriched by Set7 110 deletion in aortas of diabetic ApoE-/- mice Table 4.8. GSEA identified major pathways negatively enriched by Set7 111 deletion in aortas of diabetic ApoE-/- mice

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Table 4.9. GSEA identifies transcription factors associated with genes up- 116 regulated and down-regulated by Set7 deletion in ApoE-/- mice aortas Table 5.1. Kidney weight and body weight of control and diabetic ApoE-/- and 131 Set7-/-/ApoE-/- mice after 10 weeks of study Table 5.2. RNA-seq identified gene expression changes conferred by diabetes in 136 the kidneys of ApoE-/- mice Table 5.3. GSEA identified major pathways positively enriched in ApoE-/- 138 diabetic kidneys Table 5.4. GSEA identified major pathways negatively enriched in ApoE-/- 140 diabetic kidneys Table 5.5. GSEA identified transcription factors associated with genes up- 142 regulated and down-regulated by diabetes in ApoE-/- mice kidneys Table 5.6. RNA-seq identified gene expression changes conferred by the 147 deletion of Set7 in the kidneys of diabetic ApoE-/- mice Table 5.7. GSEA identified major pathways positively enriched by Set7 150 deletion in kidneys of diabetic ApoE-/- mice Table 5.8. GSEA identified major pathways negatively enriched by Set7 151 deletion in kidneys of diabetic ApoE-/- mice Table 5.9. GSEA identified transcription factors associated with up-regulated 152 and down-regulated genes by Set7 deletion in the kidneys of diabetic ApoE-/- mice Table 6.1. Fisher’s exact test demonstrated statistical associations between 171 TCF21 gene targets and genes deregulated by diabetes Table 6.2. Fisher’s exact test demonstrated statistical associations between 172 TCF21 gene targets containing a TCF21 binding motif and genes deregulated by diabetes Table 6.3. Summary of GSEA results for TCF21 motifs located at gene 174 promoters, enhancers or body Table 6.4. Hypergeometric tests demonstrated REACTOME gene sets 175 significantly associated with gene expression changes mediated by Set7 and TCF21 in the diabetic kidney Table 6.5. TCF21-dependent genes deregulated by diabetes in the kidneys of 176 ApoE-/- mice

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LIST OF ABBREVIATIONS

Abbreviation Full name ACL ATP-citrate lyase AGEs Advanced glycation end-products AR Aldose reductase ATP Adenine triphosphate bHLH Basic helix-loop-helix bp Base pair ChIP Chromatin immunoprecipitation CKD Chronic kidney disease CVD Cardiovascular disease DAA Diabetes-accelerated atherosclerosis DAG Diacylglycerol DCCT Diabetes control and complications trial DNA Deoxyribonucleic acid DNMT DNA methyltransferase ECM Extracellular matrix edgeR Empirical analysis of digital gene expression data in R ENCODE Encyclopedia of DNA elements EDIC Epidemiology of diabetes interventions and complications eNOS Endothelial nitric oxide synthase, constitutive ESRD End stage renal disease FAD Flavin adenine dinucleotide FDR False discovery rate GEO Gene expression omnibus GLPs Glucagon-like peptides GSEA Gene set enrichment analysis GSIS Glucose-stimulated insulin secretion GWAS Genome-wide association studies H3K27me3 Trimethylation of lysine 27 on histone H3 H3K4me1 Monomethylation of lysine 4 on histone H3 H3K4me2 Dimethylation of lysine 4 on histone H3 H3K4me3 Trimethylation of lysine 4 on histone H3

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H3K9/14Ac Acetylation of lysines 9 and 14 on histone H3 H3K9me3 Trimethylation of lysine 9 on histone H3 HAT Histone acetyltransferase HbA1c Glycated haemoglobin HDAC Histone deacetylase HFD High fat diet HMEC Human microvascular endothelial cell HMT Histone methyltransferase HTS High throughput sequencing ipGTT Intraperitoneal glucose tolerance test IUGR Intrauterine growth restriction Kbp Kilo base pair KD (gene) Knock-down KMT Lysine methyltransferase KO (gene) Knock-out lncRNA long non-coding RNA log2FC log to the base of 2 of the fold change logConc log to the base of 2 of the mean read concentration across all samples miRNA microRNA MODY Maturity onset diabetes of the young MORN Membrane occupation and recognition nexus mRNA Messenger RNA MSigDB Molecular signature database NADPH Nicotinamide adenine dinucleotide ncRNA non-coding RNA NES Normalised enrichment score NHMC Normal human mesangial cell NO Nitric oxide oxLDL Oxidised LDL cholesterol PARP Poly (ADP-ribose) polymerase PDGF-B Platelet-derived growth factor B PKC Protein kinase C PRMT Protein arginine methyltransferase

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PTC Proximal tubule cell RAGE Receptor for advanced glycation end-products RAS Renin-angiotensin system RNA Ribonucleic acid RNA-seq RNA sequencing ROS Reactive oxygen species SAH S-adenosyl homocysteine SAM S-adenosyl methionine SET Su(var) 3-9, enhancer of zeste, trithorax SMC Smooth muscle cell STZ Streptozotocin SUMO small ubiquitin-related modifier T1D Type 1 diabetes T2D Type 2 diabetes TFBS Transcription factor binding site TSS Transcriptional start site TGFb1 Transforming growth factor b 1 TNFa Tumour necrosis factor a UDP-GlcNac Uridine N-acetyl glucosamine UKPDS United Kingdom prospective diabetes study UUO Unilateral ureteral obstruction VSMC Vascular smooth muscle cell

xx 1 | Literature Review

CHAPTER 1

LITERATURE REVIEW

1 1 | Literature Review

1.1. INTRODUCTION

The process of maintaining glucose homeostasis involves the interaction between multiple organs and tissues. Alterations in various aspects of glucose sensing as well as insulin secretion or action can result in the development of disease, the most common of which is diabetes mellitus. The term diabetes covers a number of pathologies that result in sustained levels of hyperglycaemia. Diabetes can be classified in type 1, which results from an absolute deficiency in the production of insulin or type 2, characterised by peripheral organ resistance to the action of insulin (Alberti and Zimmet, 1998). The chronic nature of diabetes makes it a major public health issue with a major proportion of public health expenditure destined to the treatment of its complications (Zimmet et al., 2014).

Complications of diabetes largely result from direct tissue damage due to long-term exposure to hyperglycaemia. Tissues in which glucose uptake is mediated by insulin- independent glucose transporters, such as the retina, renal glomeruli and the vasculature, are highly susceptible to hyperglycaemia-induced damage as intracellular glucose concentrations rise in parallel to circulating glucose (Giacco and Brownlee, 2010). The current paradigm in the pathophysiology of diabetic complications suggests that high levels of intracellular glucose increase the generation of reactive oxygen species (ROS) which, in turn, affects several metabolic pathways resulting in the increased production of inflammatory and fibrotic mediators that drive pathological changes (Brownlee, 2001).

The early development of vascular diabetic complications is characterised by endothelial dysfunction. The vascular endothelial layer is critical for preserving normal haemostasis as it secretes a series of mediators that prevent thrombosis and maintain blood flow. Hyperglycaemia decreases the production and bioavailability of one of these mediators, nitric oxide (NO), resulting in changes to the endothelium that promote a pro- inflammatory and pro-thrombotic state (Lin et al., 2002; Mitchell et al., 2008). Some of these changes include increased and sustained expression of adhesion molecules that promote leukocyte binding as well as the accumulation of oxidised low-density lipoproteins (oxLDL). Over time, atherosclerosis develops in the vascular wall and this represents the major pathological event behind macrovascular complications of diabetes (Libby et al., 2009; Libby et al., 2011).

2 1 | Literature Review

Similarly, increased renal production of pro-inflammatory and pro-fibrotic mediators causes damage to the renal glomerular filtration barrier and tubular compartment. Haemodynamic factors, such as hypertension, are also involved in the development of chronic diabetic kidney disease which can ultimately result in renal failure. Indeed, despite improvement in glucose-lowering treatments, diabetes remains the leading cause of end-stage renal disease (ESRD) (ADA, 2014).

Landmark clinical trials have demonstrated that exposure to hyperglycaemia has long- lasting cellular effects and that adequate glycaemic control is critical for preventing the development of diabetic complications. These studies highlighted that periods of hyperglycaemia can induce persistent cellular changes and this phenomenon was named hyperglycaemic or metabolic memory (Pirola, 2010). Further studies have demonstrated that persistent epigenetic modifications are a mechanism underlying hyperglycaemic memory, as these modifications result in persistent gene expression changes (Reddy et al., 2015). A major epigenetic mediator of these changes is the lysine methyltransferase Set7. This enzyme catalyses the monomethylation of lysine residues on the tail of histone proteins, promoting a chromatin structure that favours transcription. In vitro experiments have demonstrated that Set7 mediates the up-regulation of pro-inflammatory and pro- fibrotic mediators in response to stimuli from the diabetic milieu and suggest that Set7 may represent a therapeutic target for the prevention of vascular diabetic complications (Brasacchio et al., 2009; El-Osta et al., 2008; Okabe et al., 2012). Animal studies also suggest that Set7 mediates tissue damage in vivo (Chen et al., 2014; Elkouris et al., 2016; Sasaki et al., 2016), however the role of this enzyme in the development of macro- and microvascular complications of diabetes such as atherosclerosis and nephropathy remains largely unknown.

This chapter will review the molecular mechanisms behind the development of vascular complications of diabetes and their epigenetic component as well as summarise the current knowledge regarding Set7-mediated regulation of gene expression. It will also address the role of epigenetic modifications in the transcriptional regulation of metabolic programs.

3 1 | Literature Review

1.2. NORMAL GLUCOSE METABOLISM

Glucose constitutes the main source of energy for the human body. In addition, intermediate products of glucose utilisation pathways are a main carbon source for many biosynthetic reactions (Aronoff et al., 2004). Circulating glucose derives from: 1) dietary carbohydrates, 2) release from glycogen in a process called glycogenolysis, and 3) de novo production using other carbon sources (e.g. amino acids) during gluconeogenesis (Giugliano, 2008). When the supply of dietary glucose is sufficient, glycogenolysis and gluconeogenesis are inhibited, while the opposite occurs when there is little or no glucose intake (i.e. fasting, starvation). These responses to circulating glucose concentration are tightly regulated by a complex network involving the secretion of hormones and other molecules (Aronoff et al., 2004; Giugliano, 2008). Two hormones that play a major role in regulating blood glucose concentrations are insulin and glucagon, produced by the endocrine pancreas. The pancreas, an organ of the digestive system, is composed of two distinct compartments: the exocrine or acinar, and the endocrine pancreas. Pancreatic acinar tissue is responsible for synthesizing zymogens (precursors of digestive enzymes) while the endocrine pancreas consists of the islets of Langerhans, which comprise 1-2% of the organ, and secrete hormones related to glucose homeostasis. The islets of Langerhans contain five different cell types: β, α, δ, PP and ε cells that produce insulin, glucagon, somatostatin, pancreatic polypeptide and ghrelin respectively (Babu et al., 2007; Gittes, 2009).

The release of insulin from pancreatic β cells is tightly regulated in response to glucose in a process called glucose-stimulated insulin secretion (GSIS). As summarised in Figure 1.1, circulating glucose enters the β cells via the glucose transporter GLUT2 where it is phosphorylated by glucokinase and enters the glycolytic pathway. Both GLUT2 and glucokinase have low affinity and high capacity thus being able to deal with high glucose concentrations creating a metabolic glucose sensing process (Leturque et al., 2005).

Glucose metabolism leads to an increase of intracellular adenosine triphosphate (ATP) and raise the ATP/ADP ratio which, in turn, causes the closure of ATP-sensitive K+ channels in the plasma membrane. The closure of these channels leads to depolarisation of the plasma membrane and subsequent opening of voltage-dependent Ca2+ channels. The influx of Ca2+ increases its intracellular concentration and this causes the exocytosis of insulin-containing vesicles (Komatsu et al. 2013).

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Figure 1.1. Glucose-stimulated insulin secretion by pancreatic β cells Glucose enters the cell through the GLUT2 glucose transporter, it is then phosphorylated by glucokinase and enters the glycolytic pathway ending with increased production of ATP. An increase in the intracellular ATP/ADP ratio causes the closure of ATP-sensitive K+ channels leading to membrane depolarisation and opening of Ca2+ channels. Increased concentrations of Ca2+ within the cell stimulate the exocytosis of insulin-containing vesicles. Insulin secretion is also stimulated by incretins and insulin itself binding to its receptor. IR: insulin receptor, G-6-P: gluce-6-phosphate.

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Insulin secretion is also indirectly stimulated by the release of other hormones of gastrointestinal origin called incretins. Some of these incretins include the glucagon-like peptides (GLPs) 1 and 2 and the glucose-dependant insulinotropic polypeptide (GIP) produced in the gastrointestinal tract after meals. They bind to specific G-protein-coupled receptors in the membrane of pancreatic β cells triggering a cascade of events that ultimately lead to exocytosis of insulin secretory vesicles (Kieffer and Habener, 1999; Komatsu et al., 2013). Free fatty acids, neuropeptides, amino acids and insulin itself have also been shown to regulate insulin secretion, although these mechanisms are poorly characterised (Komatsu et al., 2013).

The process of GSIS also promotes transcription of the insulin gene in order to replenish the secretory vesicles. Insulin gene expression is directly stimulated by the binding of a series of transcription factors, such as Pdx1, MafA and NeuroD1/E47, to its promoter (Aramata et al., 2005; Barrow et al., 2006). Pdx1 is a homeobox transcription factor that is important in pancreatic embryonic development and is responsible for activation of many β cell genes including those involved in the process of GSIS (Andrali et al., 2008; Babu et al., 2007).

Insulin is initially synthesised as preproinsulin and is processed in the endoplasmic reticulum where it is cleaved and folded to form proinsulin. The endoplasmic reticulum oxidoreductin-1-like β (Ero1lβ) plays a key role in this process by mediating disulphide bond formation (Frand and Kaiser, 1999; Khoo et al., 2011; Tu and Weissman, 2002). Proinsulin is cleaved by proprotein convertases PC1/3 (PCSK1) and PC2 (PCSK2) to release mature insulin and C-peptide (Steiner, 1998). This process is necessary for adequate insulin secretion (Zhu et al., 2002). In fact, PCSK1 gene polymorphisms are associated with obesity and glucose intolerance (Bell et al., 2004; Benzinou et al., 2008; Farooqi et al., 2007; Heni et al., 2010).

The mature insulin protein (5.8 KDa) has one A chain (21 amino acids) and one B chain (30 amino acids) linked by disulphide bridges (Bi et al., 1984; De Meyts, 2004). It promotes rapid glucose uptake from blood into skeletal muscle and adipose tissue, through binding to its receptor on the cell surface. This stimulates glucose utilisation and inhibits gluconeogenesis, promoting cell growth and metabolism (Cheatham and Kahn, 1995).

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1.3. DIABETES MELLITUS

The term diabetes refers to a disorder in the metabolism of carbohydrates, fat and protein due to insulin deficiency or impaired action or both, that is characterised by persistent hyperglycaemia (Alberti and Zimmet, 1998; ADA, 2003). Diabetes is now regarded as a global pandemic, there has been a steep increase in the number of cases in the last 20 years. It is now estimated that there are over 400 million people worldwide suffering from the disease and this figure is expected to rise to almost 650 million by the year 2040 (Ogurtsova et al., 2017). Most of the morbidity and mortality associated with diabetes is related to complications derived from chronic hyperglycaemia, making diabetes a major public health burden as it requires life-long care. The economic burden of diabetes in the developed world is steadily rising. Globally, approximately 12% of total health expenditure is estimated to be dedicated to the treatment of diabetes and its complications (Ogurtsova et al., 2017; Zimmet et al., 2014).

Diabetes is a heterogeneous disease and can be classified according to its aetiology and clinical stage (ADA, 2003). Type 1 diabetes (T1D), formerly referred to as insulin- dependent diabetes, is an autoimmune disorder that causes the destruction of pancreatic β cells resulting in a marked or absolute deficiency in insulin secretion. It is one of the most common autoimmune diseases and it is characterised by a selective immune T-cell attack of β cells and, although its causes are not clear, it is thought to involve genetic susceptibility and an environmental trigger (Culina et al., 2013)

Genetic factors determining susceptibility to T1D include polymorphisms in human leukocyte antigen (HLA) genes as well as the insulin gene region in loci referred to as IDDM1 and IDDM2 (from Insulin-Dependent Diabetes Mellitus), respectively (Barrett et al., 2004; Fox et al. 2000). Together, IDDM1 and IDDM2 polymorphisms account for over 50% of cases of familial inheritance of T1D (Kelly et al., 2003; MacFarlane et al., 2009). The exact role of environmental factors in the development of T1D remains undefined. Environmental triggers such as viruses (e.g. coxsackie, rubella) are able to elicit the onset of disease in people who are genetically susceptible but the mechanisms behind this effect are not clear. It appears that exposure to these agents stimulates an immune response that promotes the development of the disease (Kelly et al., 2003)

On the other hand, type 2 diabetes (T2D), the most common form of the disease, refers to a defect in insulin action and/or secretion frequently related to insulin resistance. The 7 1 | Literature Review

onset of T2D is slow and noticeable symptoms are often not present for years despite the presence of persistent hyperglycaemia (Alberti and Zimmet, 1998). Several factors contribute to the pathogenesis of this disease including reduced glucose uptake by skeletal muscle, β cell dysfunction, impaired liver insulin action, impaired secretion of adipokines (hormones secreted by adipose tissue) and impaired glucose sensing by the central nervous system. Although the aetiology of T2D is not completely understood, it is known to be closely linked to lifestyle factors like obesity and physical inactivity (Lin and Sun, 2010). A state of glucose intolerance and subsequent hyperglycaemia that develops during pregnancy is known as gestational diabetes. If often resolves at the end of the pregnancy but can also progress to T2D (ADA, 2003).

There are rare cases of diabetes involving genetic defects. Mutations in genes encoding transcription factors involved in pancreatic development and insulin secretion result in insulin insufficiency and early onset of diabetes. These forms of the disease are referred to as maturity-onset diabetes of the young (MODY), examples include mutations of the pancreatic transcription factor Pdx1 and glucokinase genes that cause MODY4 and MODY2 respectively (Chakrabarti and Mirmira, 2003).

1.4. REACTIVE OXYGEN SPECIES AND DIABETIC COMPLICATIONS

Regardless of its aetiology, the persistent hyperglycaemia that characterises the diabetic state is responsible for the development of serious complications. With the number of diabetic patients expected to rise by more than 50% in most parts of the world in the next 15 years, there is an important focus in trying to reduce the burden of diabetic complications in terms of loss of life and productivity as well as increasing healthcare costs (Zimmet et al. 2014).

The current paradigm for the development of diabetic complications involves the overproduction of reactive oxygen species (ROS). (Brownlee, 2001; Giacco and Brownlee, 2010). According to the unifying mechanism for the pathobiology of diabetic complications, or Brownlee hypothesis, an increase in intracellular glucose results in overproduction of mitochondrial ROS that induce DNA damage, this in turn activates poly(ADP-ribose) polymerase (PARP). The resulting accumulation of ADP-ribose

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polymers on the enzyme glyceraldehyde-3 phosphate dehydrogenase (GAPDH) inhibits its activity, leading to the accumulation of all metabolites upstream in the glycolytic pathway. This process initiated by ROS overproduction causes cell damage via four major pathways: 1) increased flux through the polyol pathway, 2) increased formation of advanced glycation end-products (AGEs), 3) activation of protein kinase C (PKC), and 4) increased flux through the hexosamine pathway (Fig. 1.2) (Brownlee, 2001; Giacco and Brownlee, 2010).

Figure 1.2. Unified theory for the pathobiology of diabetic complications Increases in intracellular concentrations of glucose result in overproduction of mitochondrial ROS which in turn activates the enzyme PARP, inhibiting GAPDH and resulting in the accumulation of all glycolytic intermediate metabolites upstream of this enzyme. These events lead to cellular damage by four major mechanisms: increased flux through the polyol pathway, increased production of AGEs and its receptors, activation of PKC and the Nuclear Factor κB (NFκB) pathways and increased flux through the hexosamine pathway.

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1.4.1. Polyol pathway flux

Hyperglycaemia causes a rapid increase in intracellular glucose concentrations in cells that rely on insulin-independent glucose transporters. Under these conditions, aldose reductase (AR), the enzyme at the centre of the polyol pathway, reduces glucose to sorbitol in a NADPH-dependent manner. NADPH is an essential cofactor for the regeneration of the antioxidant molecule glutathione, thus increased AR activity increases susceptibility to oxidative stress by reducing the amount of glutathione in the cell (Chung et al., 2003). Consistent with this theory, diabetic mice overexpressing AR have increased susceptibility to the development of atherosclerosis (Vikramadithyan et al., 2005).

1.4.2. Formation of AGE precursors

AGEs form as a result of non-enzymatic covalent reactions (Maillard process) between reducing sugars and proteins (Yamagishi et al., 2015). AGEs cause cellular damage by three mechanisms: 1) modifying intracellular proteins altering their function (Giardino et al., 1994; Shinohara et al., 1998), 2) diffusing out of the cell and modifying extracellular matrix proteins, disrupting cell-matrix signalling (Charonis and Tsilbary, 1992), and 3) entering the bloodstream and modifying circulating proteins. Modified circulating proteins can bind to AGE receptors (RAGE), promoting the production of inflammatory mediators such as leukocyte adhesion molecules and growth factors (Ai et al., 2013; Lu et al., 1998b; Schmidt et al., 1995; Vlassara et al., 1995). ROS formation also results in the increase of RAGE and RAGE ligands further augmenting the inflammatory state (Yao and Brownlee, 2010). Furthermore, AGEs are degraded slowly and can exist in circulation even after glucose levels return to normal causing persistent cellular injury (Yamagishi et al., 2015).

1.4.3. Activation of protein kinase C

The protein kinase C (PKC) family is composed of several isoforms that are involved in signal transduction. These enzymes are activated by diacylglycerol (DAG), a molecule that is greatly increased by high intracellular glucose levels (Koya and King, 1998). The increase in active PKC results in a reduction in endothelial nitric oxide synthase (eNOS) and the overexpression of inflammatory and fibrotic mediators that lead to cellular dysfunction and damage (Koya et al., 1997; Kuboki et al., 2000; Studer et al., 1993).

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1.4.4. Hexosamine pathway flux

Glucose entering glycolysis is converted to glucose-6-phosphate which in turn generates fructose-6-phosphate. Although most of the fructose-6-phosphate enters the glycolytic pathway, a proportion enters the hexosamine pathway and is ultimately converted to uridine diphosphate N-acetyl glucosamine (UDP-GlcNac) (Hart, 2015). UDP-GlcNac modification of serine and threonine residues of proteins, including transcription factors, can regulate gene expression. For example, glycosylation of the transcription factor Sp1 results in increased expression of the plasminogen activator inhibitor (PAI) 1, a known mediator of cellular damage in diabetes, in both endothelial and smooth muscle cells (Chen et al., 1998; Du et al., 2000).

1.4.5. Other sources of Reactive Oxygen Species

Mitochondrial-derived ROS only represent a fraction of all glucose-induced ROS production within the cell. There are several enzymatic pathways of ROS generation that include NADPH oxidases, cytochrome P-450 (CYTP450) xanthine oxidase (XO), myeloperoxidase (MPO) and, in certain conditions, nitric oxide synthase (NOS) (Taniyama and Griendling, 2003). The different sources of ROS all have a contribution towards the development and progression of vascular complications of diabetes (Fakhruddin et al., 2017).

The NOX family of NADPH oxidases are homologs of the gp91phox (NOX2) subunit first described in phagocytes as a critical component of the respiratory burst response against microorganisms (Fakhruddin et al., 2017). Different NOX family members are responsible for ROS production in different tissues, for example NOX1 is highly expressed in the endothelium and smooth muscle while NOX4 is highly expressed in the kidney (Bedard and Krause, 2007). NOX enzymes are a major source of ROS in response to hyperglycaemia, constituting an important driver of vascular diabetic complications. Indeed, genetic deletion or pharmacological inhibition of NOX1 and 4 attenuate vascular and renal damage, respectively, in mouse models of diabetes (Gray et al., 2013; Jha et al., 2014).

Cytochrome 450 enzymes are involved in xenobiotic metabolism that can catalise uncoupled NADPH oxidation to form ROS. Their role in ROS production has been extensively studied in the context of the metabolism of chemicals and induction of DNA

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damage (Dostalek et al., 2008). However, enzymes belonging to this family are expressed in tissues that are targets for diabetic complications. For example, cytochrome P450 of the 4A (CYP4A) family is highly expressed in the kidney and its up-regulation during diabetes is associated with podocyte injury (Eid et al., 2009).

Xanthine oxidase (XO) forms part of the xanthine oxidoreductase complex involved in purine metabolism. Although the involvement of XO-derived ROS in diabetic complications remains poorly understood, have been implicated in pathological conditions such as ischemia-reperfusion injury (Harrison, 2002). Myeloperoxidase (MPO) is highly expressed in phagocytic cells and it is involved in the production of reactive oxygen and nitrogen species, contributing to vascular pathology (Taniyama and Griendling, 2003).

1.5. VASCULAR COMPLICATIONS OF DIABETES

Long-term complications of diabetes include retinopathy (retinal damage and visual impairment), nephropathy (renal damage) and neuropathy (nerve damage) as well as cardiovascular disease (Nathan, 1993). Retinopathy, nephropathy and neuropathy are considered microvascular complications of diabetes while cardiovascular events associated with the formation of atherosclerosis are considered macrovascular complications (Fowler, 2008).

1.5.1. Macrovascular complications: Atherosclerosis and cardiovascular disease

The association between diabetes and macrovascular disease has been recognised for over 30 years with the observation that people with diabetes have a significantly higher risk of death from cardiovascular events (Ducimetiere et al., 1980; Laing et al., 2003; Pyörälä, 1979). Indeed, cardiovascular disease (CVD) is a major cause of death in people with both type 1 and 2 diabetes and it accounts for a large proportion of healthcare expenditure in people with diabetes (ERFC, 2010; Ogurtsova et al., 2017).

The major pathological process behind cardiovascular disease is atherosclerosis, a progressive narrowing of arterial walls. Inflammation is a key process in the development of atherosclerosis, linking multiple pathways of cellular damage in the atherosclerotic vessel (Libby et al., 2009). Alterations in the function of vascular endothelial and smooth muscle cells are hallmarks of atherosclerosis. The endothelium secretes molecules that maintain vascular homeostasis by regulating blood flow and preventing leukocyte

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adhesion and thrombosis. Vascular endothelial cells constitutively express the enzymes eNOS (NOS3) and cyclooxygenase 1 (COX1) that generate the vasoactive molecules nitric oxide (NO) and prostacyclin (PGI2) respectively (Mitchell et al., 2008). Both NO and PGI2 are vasodilators and inhibitors of platelet activation, thus preventing thrombus formation in the healthy vessel (Mitchell et al., 2008; Radomski et al., 1990; Wang et al., 1998). Diabetes is characterised by endothelial dysfunction which starts with a decrease in production and bioavailability of NO (Lin et al., 2002; Paneni et al., 2013; Tessari et al., 2010). In addition to maintaining a relaxed vessel, preventing platelet aggregation and leukocyte adhesion, NO prevents the oxidation of low density lipoprotein (LDL) (Davignon and Ganz, 2004).

Endothelial dysfunction is also characterised by increased production of vasoconstrictors like endothelin and angiotensin II and prostanoids such as thromboxane A2, as well as ROS-producing NADPH oxidases (Cosentino et al., 2003; Creager et al., 2003; Di Marco et al., 2013). In fact, an increase in NADPH oxidases (NOX) 1 and 4 has been associated with the development of macrovascular and renal complications of diabetes (Gray et al., 2013; Jha et al., 2014). The combination of these factors results in expression of adhesion and chemotactic molecules that lead to the recruitment of immune cells and inflammation of the vessel wall (Di Marco et al., 2013). A key part of this process is the activation of the nuclear factor κB (NFκB). The family of NFκB transcription factors are key transcriptional mediators of inflammation and consist of several subunits that works as homo- or heterodimers: p65, RelB, c-Rel, p50 and p52 (Barnes and Karin 1997; Gilmore, 2006). In basal conditions, NFκB activity is inhibited by the action of a set of proteins called inhibitors of κB (IκB) which sequester it to the cytoplasm of the cell (Baker et al. 2011). Several environmental stimuli can lead to the degradation of IκB and activation of NFκB including ROS and increased AGE binding to RAGE in the dysfunctional endothelium (Barnes and Karin 1997).

NFκB activates the transcription of early genes involved in inflammatory and immune responses. Hyperglycaemia causes the up-regulation the intercellular adhesion molecule 1 (ICAM-1), vascular cell adhesion molecule 1 (VCAM-1) and monocyte chemoattract protein 1 (MCP-1) in vascular endothelial cells via activation of NFκB (Kim et al., 1994; Morigi et al., 1998; Piga et al., 2007). NFκB activation in vascular smooth muscle cells (VSMCs) also occurs in response to hyperglycaemia resulting in increased expression of

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cellular adhesion molecules and contributing to the recruitment of inflammatory cells (Braun et al., 1999; Hattori et al., 2000; O'Brien et al., 1993).

Once monocytes adhere to the arterial wall they differentiate into macrophages and migrate to the intima layer of the vessel where they take up oxidised LDL (oxLDL) particles and become foam cells (Insull, 2009; Libby et al., 2002). Pro-inflammatory signalling from macrophages and oxLDL recruit T cells to the lesion where they produce cytokines that act on endothelial and smooth muscle cells as well as macrophages, thereby augmenting the inflammatory process (Libby et al., 2002). oxLDL molecules in the intima also promote cytokine release directly contributing to inflammation (Stemme et al., 1995). Along with persistent inflammation, the progression of atherosclerosis involves the production of fibrotic mediators that promote smooth muscle cell proliferation leading to the increase in extracellular matrix characteristic advanced lesions (Creager et al., 2003).

Atherosclerotic plaques can limit blood flow locally and cause ischemia, or they can rupture promoting the formation of thrombi that may travel through the vascular tree and obstruct blood flow at distal sites with consequences such as myocardial infarction or stroke (Libby et al., 2011).

1.5.2. Diabetic nephropathy

Chronic kidney disease (CKD) can develop as a major complication of diabetes. In fact, diabetes constitutes the lead cause of end stage renal disease (ESRD) (ADA, 2004). Diabetic nephropathy is characterised by overt proteinuria, defined as urinary protein excretion of more than 500mg in 24 hours, which can develop after years of milder forms of proteinuria called microalbuminuria (urinary albumin excretion of 30-299 mg in 24 hours) and macroalbuminuria (urinary albumin excretion of 300-499 mg in 24 hours) (Fowler, 2008).

Renal glomeruli constitute the filtering unit of the kidney. They are comprised of endothelial cells surrounded by podocytes, which together with the basement membrane constitute the glomerular filtration barrier. As with pericytes in the retina, renal podocyte injury and loss are critical in the pathological progression of diabetic nephropathy and represent the mechanism through which proteins leak into the urinary compartment (Orasanu and Plutzky, 2009). The major pathological features of diabetic nephropathy

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are increase in glomerular basement membrane thickness and mesangial expansion, accumulation of extracellular matrix (ECM) components and hypertrophy of glomerular and tubular compartments (Fowler, 2008; Sharma and Ziyadeh, 1995). Hyperglycaemia is required for the development of this phenotype as it increases the production of ROS and AGEs and activates the PKC and polyol pathways, as summarised in Section 1.4. This results in the generation of pro-inflammatory and pro-fibrotic mediators that, in combination with haemodynamic alterations, drive kidney damage (Gallagher and Suckling, 2016; Mason and Wahab, 2003; Navarro-Gonzalez and Mora-Fernandez, 2008). Hyperglycaemia stimulates the production of transforming growth factor β1 (TGFβ1), a potent pro-fibrotic cytokine that promotes the expression of ECM proteins like collagen and connective tissue growth factor (CTGF) (Schena and Gesualdo, 2005; Sharma and Ziyadeh, 1995). Various kidney cell types produce TGFβ1 in this context and are, in turn, also targets for its pro-fibrotic action. Glomerular mesangial, endothelial and proximal tubule cells, as well as podocytes respond to stimuli from the diabetic milieu by upregulating the expression of ECM proteins (Mason and Wahab, 2003; Tang and Lai, 2012). However, although targeting of TGFβ1 signalling represents a promising option for the treatment of kidney disease (and other fibrotic conditions), complete blockage of this pathway has deleterious effects as it plays an essential role the regulation of cell proliferation and limiting inflammation (Schnaper et al., 2009)

Haemodynamic factors also play an important role in the development of diabetic nephropathy. In this context, abnormal signalling of the renin-angiotensin system (RAS) is a major contributor to pathological changes (Kobori et al., 2013). These changes are mainly due to local imbalance of the RAS and give rise to glomerular hyperfiltration seen in early stages of diabetic nephropathy (Carey and Siragy, 2003; Cooper, 2001). Additionally, the increased renal expression of angiotensin II also has non- haemodynamic effects such as stimulating cytokine production, cell proliferation and ECM accumulation further contributing to disease progression (Gallagher and Suckling, 2016)

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1.5.3. Diabetic retinopathy and neuropathy

Retinopathy is one of the most common complications of diabetes. The prevalence of retinopathy in adult diabetic patients is close to 35% and its severity is closely associated to the length and magnitude of hyperglycaemia (Yau et al., 2012). Diabetic retinopathy initiates as local microvascular damage involving vessel leakage and/or rupture and capillary degeneration that ultimately leads to extensive new vessel formation (proliferative stage) and loss of retinal function (Calcutt et al. 2009; Frank 2004).

Diabetic retinopathy is characterised by damage to the pericytes, cells that associate with the endothelial layer to form the retinal capillaries. Damage and subsequent loss of pericytes is closely linked to the progression of diabetic retinopathy and is caused by multiple pathways altered in diabetes, discussed in greater detail in Section 1.4 (Arboleda- Velasquez et al., 2015). Subclinical levels of inflammation involving increased leukocyte adhesion and cytokine production are also observed in diabetic retinopathy and this is thought to contribute to the maintenance and propagation of vascular damage (Tarr et al., 2013). In addition to increased oxidative stress and inflammation, fibrotic signalling also plays a large role in the development and progression of retinopathy. For example, TGFb1, platelet-derived growth factor B (PDGF-B) and vascular endothelial growth factor (VEGF) are key determinants of the progression of diabetic retinopathy from early (non-proliferative) to proliferative stages (Arboleda-Velasquez et al., 2015; Hammes et al., 2002).

The progression of diabetic retinopathy is also characterised by neuronal damage which resembles nerve damage observed in diabetic neuropathy. Although the molecular mechanisms underlying nerve damage by hyperglycaemia are not well understood, they include increased oxidative stress, polyol accumulation and AGEs as described in Section 1.4 (Fowler, 2008). Axonal thickening and neural loss are characteristic of diabetic neuropathy and are preceded by thickening of the basement membrane and loss of pericytes (Beckman and Creager, 2016). Neuropathy is associated with considerable morbidity in diabetic patients as it contributes to foot ulcerations that account for 80% of amputations (Boulton et al., 2005).

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1.6. EPIGENETICS

The term epigenetics refers to inheritable, reversible changes in gene expression that are not determined by DNA sequence itself (Egger et al., 2004). The concept of epigenetic modifications began as an explanation for the differences in gene expression among diverse cell types in an organism, as well as the timely activation and repression of genes during embryonic development. With time, and especially after the introduction of genome sequencing, epigenetic modifications started being regarded as a key aspect in gene regulation, not only during embryonic development but also throughout adulthood (Jenuwein & Allis 2001). Epigenetic modifications are now appreciated as a critical component in the maintenance of normal gene expression patterns and in the development of disease (Devaskar and Raychaudhuri, 2007; Kobow and Blümcke, 2011; Martín- Subero and Esteller, 2011). The most widely studied epigenetic modifications are DNA methylation and histone protein modifications.

1.6.1. DNA methylation

DNA methylation was the first epigenetic mark to be investigated, especially in association with genomic imprinting (Bird, 1986; Li et al., 1993; Razin and Riggs, 1980). In mammalian cells, it involves the addition of a methyl group to carbon 5 (C5) of cytosine and it is generally associated with gene silencing (Razin and Riggs, 1980). This was initially evidenced by experiments using a demethylating drug named 5-azacytidine (5-azaC). Treatment of cultured cells with 5-azaC results in a global reduction of methylated sites that is associated with an overall increase in gene expression (Razin and Cedar, 1991).

DNA methylation patterns are maintained through cell division by the action of enzymes called DNA methyltransferases (DNMTs). These enzymes recognise methylated sites in the parental DNA strand and add a methyl group to the corresponding site on the newly synthesised strand during DNA replication (Egger et al., 2004; Razin and Cedar, 1991). DNA and histone methylation reactions require the cofactor S-adenosylmethionine (SAM), and products of this reaction are S-adenosylhomocysteine (SAH) and the methylated substrate. SAM derives from the condensation of the amino acid methionine and ATP as part of the one-carbon metabolism cycle (Anderson et al., 2012; Kaelin and McKnight, 2013).

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1.6.2. Histone modifications

In eukaryotic cells, DNA is wrapped around and in complex with histones and other proteins in units called nucleosomes. A nucleosome consists of four core histones: H3, H4, H2A and H2B with 147bp of DNA wrapped around them (Fig. 1.3) (Kouzarides 2007). DNA organised and packed in this way is termed chromatin and it can exist in two states: loose euchromatin, characteristic of areas of active gene transcription, and compact heterochromatin, found in transcriptionally inactive areas. Posttranslational modifications in the tails of histone proteins can modify chromatin structure as well as mediate the recruitment of co-regulatory proteins, modulating the accessibility of the transcriptional machinery to DNA. The most common histone modifications are acetylation and methylation although phosphorylation, ubiquitination and sumoylation have also been described. The enzymes responsible for writing or erasing such modifications have a high substrate and product specificity and their activity is closely linked to transcriptional activation or repression (Rodriguez et al., 2017). The close relationship between specific histone tail modifications and their effect in transcription led to the proposal of the ‘histone code’ hypothesis. This hypothesis states that histone modifications complement the genetic code by serving as binding sties for effector proteins and influencing chromatin accessibility and transcription factor binding (Jenuwein and Allis, 2001). Moreover, accumulating evidence suggests that transcriptional changes observed during the development of pathological states are mediated by histone modifications (Rodriguez et al., 2017). Besides regulating transcription, histone modifications also play a role in DNA repair by allowing chromatin unwinding and recruitment of protein complexes involved in repair and cell cycle arrest (Barski et al., 2007; Kouzarides, 2007) .

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Transcriptional activationTranscriptional activation Transcriptional repressionTranscriptional repression H2AK5H2AK5acetylationacetylation H3R8 methylationH3R8 methylation H2BK5/12/15/20H2BK5/12/15/20acetylationacetylation H3K9 methylationH3K9 methylation H3K4 methylationH3K4 methylation H3K27 methylationH3K27 methylation H3K9/14/18/23/27 acetylationH3K9/14/18/23/27 acetylation H4R3 methylationH4R3 methylation H3R17 methylationH3R17 methylation H4K20 methylationH4K20 methylation H3K36 methylationH3K36 methylation H4K59 methylationH4K59 methylation H3K79 methylationH3K79 methylation H4R3 methylationH4R3 methylation

H2A H2B H2A H2B H4K5/8/12/16H4K5/8/12/16acetylationacetylation H2A H2B H2A H2B

H3 H4 H3 H4 H3 H4 H3 H4

Acetylation

H2A H2B Methylation -

H3 H4 activating H2A H2B H2A H2B AcetylationMethylation Methylation-

H3 H4 H3 H4 repressive

Figure 1.3. Schematic representation of the structure of chromatin DNA is wrapped around a histone octamer made up of four core histones: H3, H4, H2A and H2B. Modifications of the histone tails cause changes in the conformation of chromatin which can lead to transcriptional activation or repression. Some modifications such as lysine acetylation, are associated with chromatin relaxation and active gene transcription while others promote heterochromatin formation and transcriptional repression.

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1.6.2.1. Histone acetylation

Histone acetylation involves the addition of an acetyl group to the e-amino group of lysine residues near the N-terminus of the protein. It was the first modification of histones to be discovered and was soon after associated with transcriptional activation (Allfrey et al., 1964; Phillips, 1963; Pogo et al., 1966; Verdone et al., 2005). Histone lysine acetylation neutralises the residue’s positive charge, reducing the interaction between the protein and the negatively charged DNA and leading to ‘opening’ of chromatin (Dion et al., 2005; Hong et al., 1993). Lysine residues in histone tails subject to acetylation include lysine 9 (K9), K14, K18, 23, and K27 on histone H3 and K5, K8, K12 and K16 on histone H4 (Fig. 1.3) (Kouzarides, 2007). The acetylation of histones, particularly around gene promoter regions, is highly dynamic and turnover rates can be as short as several minutes (Barth and Imhof, 2010). Acetylation turnover rates depend on the specific lysine being modified and residues in its vicinity and are correlated with gene activation, i.e. actively transcribed genes show high promoter histone acetylation turnover rates and vice versa (Evertts et al., 2013; Waterborg, 2002).

The enzymes responsible for adding the acetylation mark onto histone tails are called histone acetyltransferases (HATs) and the removal of such marks is mediated by histone deacetylases (HDACs) (Brownell and Allis, 1996; de Ruijter et al., 2003). The highly dynamic nature of histone acetylation can be explained by the finding that both HATs and HDACs are enriched at the promoters of actively transcribed genes. Moreover, the highest levels of HAT and HDAC binding are found at the promoters of genes with the highest levels transcriptional activity (Wang et al., 2009b). This is consistent with the notion that highly transcribed genes have a higher acetylation turnover rate (Waterborg, 2002). In addition to directly modulating chromatin accessibility, acetylated lysine residues on histone tails also serve as binding sites for bromodomain (BRD) proteins which can further recruit transcriptional regulators (Filippakopoulos and Knapp, 2014; Sanchez and Zhou, 2009).

1.6.2.2. Histone methylation

Methyl groups can also be added to residues on the tails of histone proteins. Histone lysine methylation is carried out by histone methyltransferases (HMTs) and it was thought to be a permanent mark until the discovery of the first lysine demethylase, lysine demethylase

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1 (LSD-1) (Shi et al., 2004). Most HMTs share a conserved SET (Su(var), Enhancer of zeste, Trithorax, from the Drosophila proteins in which it was initially described) domain responsible for catalytic activity and cofactor binding. These enzymes add one or more methyl groups, donated by the cofactor SAM, to the ε-amino group of target lysines resulting in mono-, di- or trimethylation (Martin and Zhang, 2005; Mentch et al., 2015). HMTs have high substrate specificity, as opposed to HATs, and they often only methylate one lysine residue on a particular histone. Several histone lysine residues are subject to methylation and their location and type of reaction (mono-, di- or trimethylation) determines the effect in transcription. Methylation of lysines K9 and K27 on histone H3, and K20 and K59 on histone H4 are associated with transcriptional repression whereas methylation of H3K4, H3K36 and H3K79 are characteristic of transcriptional activation (Fig. 1.3) (Martin and Zhang, 2005).

Activating lysine methylation marks are differentially observed between parts of a gene. For example, monomethylated H3K4 (H3K4me1) is preferentially enriched at gene enhancers while trimethylated H3K4 (H3K4me3) is enriched at gene promoters. Furthermore, levels of H3K4me1 and H3K4me2 correlate well with transcriptional levels (Barski et al. 2007; Heintzman et al. 2007).

Arginine residues within histone tails are also subject to methylation and protein arginine methyltransferases (PRMTs) are responsible for this modification. They transfer the methyl group from the cofactor SAM to the guanidine group of arginine in histone and non-histone protein substrates (Kouzarides 2007; Lee et al. 2005).

1.6.2.3. Other epigenetic modifications

The tails of histone proteins are subject to a wider range of modifications that have an impact in transcription (Bannister and Kouzarides, 2011). The enzyme PADI4 catalyses the deimination of arginine and monomethyl arginine to citrullin, thus antagonising arginine methylation (Cuthbert et al., 2004; Wang et al., 2004b). Serine and Threonine residues on histones H2A, H2B and H4 are targets for b-N-acetylglucosamine (O- GlcNAc) transferase suggesting that O-GlcNAcylation can be considered part of the histone code (Sakabe et al., 2010). Histones can also be ADP-ribosylated by enzymes of the PARP family and this reversible modification has been associated with promoting chromatin relaxation (Hassa et al., 2006). Ubiquitination, particularly

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monoubiquitination, of histone proteins appears to play a diverse role in gene transcription by mediating interactions with histone modifying enzymes (Lee et al., 2007; Wang et al., 2004a). Similarly, small-ubiquitin-related modifier (SUMO) proteins can modify sites on histone H4. The function of histone sumoylation is not completely understood but it is thought to be associated with transcriptional repression (Seeler and Dejean, 2003; Shiio and Eisenman, 2003).

Non-coding RNAs (ncRNAs) are considered part of the epigenetic machinery regulating gene expression as they can form RNA scaffolds that recruit DNA and histone modifying proteins to sites of transcription (Holoch and Moazed, 2015; Peschansky and Wahlestedt, 2014). Furthermore, increasing evidence suggests that covalent modifications to RNA molecules themselves can directly regulate gene expression. The most common mRNA modification is N6-methyladenosine (m6A), a reversible modification akin to those on DNA and histone tails, that has been associated with transcript stability (Fu et al., 2014).

1.7. THE SET7 LYSINE METHYLTRANSFERASE

The SET domain-containing histone methyltransferase Set7 was first isolated from HeLa cells as a 45KDa polypeptide linked to histone methyltransferase activity. The human Set7 protein has 366 amino acids and its sequence is characterised by the presence of the evolutionary conserved SET motif, which is required for enzymatic activity, and three MORN (Membrane Occupation and Recognition Nexus) repeats (Wang et al., 2001) (Fig. 1.4a). The Set7 amino acid sequence is highly conserved among mammals and the human and murine sequences share 95% homology (Fig. 1.4b). The gene encoding Set7 is only found in vertebrates suggesting this protein has a specific role in higher eukaryotes (Nishioka et al., 2001; Wang et al., 2001).

The Set7 secondary structure is characterised by two major domains: 1) the N terminal domain, consisting mainly of beta sheets and encompassing the three MORN repeats, and 2) the C terminal SET-containing domain consisting of both beta sheets and alpha helices, where most of the highly conserved regions are located (Kwon et al., 2003). Crystal structure and mutational analyses suggest that sequences near the N terminus of the protein, along with basic residues around target lysines, are likely to be involved in

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substrate specificity of the enzyme, while the C terminus is involved in the creation of the active site (Wilson et al., 2002; Xiao et al., 2003).

Figure 1.4. Protein structure of the human Set7 methyltransferase 1.4a. Schematic representation of the structure of the human Set7 protein showing the location of the three MORN repeats (M1-M3) and SET domain as well as cofactor (SAM) and substrate binding sites. 1.4b. Alignment of the murine (NP_542983.3) and human (NP_085151.1) Set7 protein sequences showing conserved residues in dark grey and amino acid differences in light grey. SAM (SAMB) and substrate binding (SB) sites are identical between both sequences, as is most of the sequence in SET domain (underlined red).

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Set7 specifically monomethylates lysine 4 on the tail of histone 3 (H3K4) as shown in vitro by histone methyltransferase (HMT) activity assays using recombinant human Set7 peptides (Wang et al., 2001; Wilson et al., 2002; Xiao et al., 2003). However, some studies have suggested Set7 may also have dimethylase activity (Deering et al., 2009; Kwon et al., 2003). An amino acid substitution at position 245 (from tyrosine to alanine) in the human Set7 sequence greatly reduces methyltransferase activity of the enzyme onto unmodified lysine residues while it increases it on mono- and dimethylated substrate lysines (Wilson et al., 2002; Xiao et al., 2003). This suggests that product specificity is highly dependent on the conserved amino acid sequence around the enzyme’s active site.

Many studies have shown that Set7 mediates transcriptional activation through histone lysine methylation (Table 1.1). As described above, modifications of histone H3 tails have different effects on gene transcription. H3K4 methylation by Set7 has a direct activating effect by promoting an open chromatin conformation, and indirect effect by 1) inhibiting the binding of the NuRD deacetylation complex to the histone H3 tail, 2) preventing the methylation of H3K9 by Suv39h1, and 3) facilitating acetylation by the p300 HAT (Nishioka et al., 2001; Wang et al., 2013a).

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Table 1.1. Regulation of gene expression by Set7-mediated H3K4 methylation Function Tissue/ Experimental model References organ HMEC-1 (human (Okabe et al., Vascular microvascular endothelial) 2012) endothelium cells BAEC (bovine aortic (Brasacchio et al., Regulation of endothelial) and HMEC-1 2009) inflammatory gene cells expression THP-1 (human) monocytic (Li et al., 2008) Monocytes cells Peripheral Blood Monocytes (Paneni et al., from T2D patients, HAEC 2014) cells Pancreatic β βTC3 (mouse) β cells, mouse (Fujimaki et al., cells islets 2015) Liver Bile duct ligation in rats (Sheen-Chen et al., 2014) Regulation of db/db (diabetic) mice (Chen et al., 2014) fibrotic gene Unilateral ureteral obstruction (Sasaki et al., expression in mice, mouse and rat kidney 2015) Kidney cell lines Rat Mesangial Cells (Guo et al., 2016; Sun et al., 2010; Yuan et al., 2016) Regulation of Brain T98G (human) glioblastoma (Martens et al., collagenase gene cells 2003) expression Regulation of insulin Pancreatic β βTC3 (mouse) β cells (Deering et al., gene expression cells 2009) Modulation of the In vitro HMT assays, LNCaP (Ko et al., 2011) androgen receptor Prostate (human prostate cancer) cells (Gaughan et al., 2011) Activation of gene C2C12 (mouse) myoblasts (Tao et al., 2011) expression during Muscle Mouse smooth muscle and (Tuano et al., muscle Sca1+ cells 2016) differentiation Regulation of Liver HCV-infected Huh7.5.1 (Han et al., 2015) interferon (IFN) (human hepatoma) cells signalling pathways Regulation of Skeletal L6 (rat) skeletal muscle cells (Ciccarelli et al., glucose-induced muscle 2016) gene expression Human bone marrow cells, (Ma et al., 2016) Bone marrow Nalm6 and REH cells (human leukemia) Human Acute Myeloid (Zipin-Roitman et Regulation of cell Leukemia (AML) cells al., 2017) growth and MCF7 and MDA-MB-231 (Zhang et al., tumorigenesis Breast (human) breast cancer cells 2016b) Breast cancer cell lines, human (Montenegro et breast tissue al., 2016) Digestive Gastric cancer cell lines (Akiyama et al., tract 2016)

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Set7 also monomethylates non-histone proteins including the TATA box binding protein- associated factor 10 (TAF10), the tumour suppressor p53, estrogen receptor alpha (ERα), and the mammalian DNA methyltransferase 1 (DNMT1) (Chuikov et al., 2004; Estève et al., 2009; Kouskouti et al., 2004; Subramanian et al., 2008). Known non-histone substrates of Set7 are summarised in Table 1.2.

Set7 targets a motif characterised by basic residues around the methylated lysine, this determines the formation of a tridimensional structure that allows Set7 binding and action (Xiao et al., 2003). Based on this motif, it is possible to predict putative non-histone targets of Set7 (Keating et al., 2014). This capacity of Set7 and other HMTs to act on non-histone substrates means they can directly influence the activity of a target protein without modifying its expression.

The role of Set7 in the regulation of cellular processes through non-histone protein lysine methylation is complex. Monomethylation of transcription factors may enhance their activity promoting gene expression. For example, methylation of K372 on the p53 tumour suppressor increases this protein’s stability leading to upregulation of its transcriptional targets (Chuikov et al., 2004). A synergistic effect has also been suggested between Set7- mediated H3K4me1 and transcription factor methylation. For example, H3K4me1 at the promoter of the gene encoding the p65 subunit of the NFkB transcription factor (RELA) promotes gene expression, at the same time methylation of K37 on the p65 protein enhances its transactivating activity (Ea and Baltimore, 2009; El-Osta et al., 2008). On the other hand, methylation by Set7 may result in decreased protein stability, as is the case with the transcriptional mediator Smad7 where K70 monomethylation promotes protein ubiquitination and proteasome degradation (Elkouris et al., 2016).

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Table 1.2. Non-histone substrates of Set7 Substrate Target residue Reference Transcription factors Tata box binding associated factor K189 (Kouskouti et al., 2004) (TAF10) p53 tumour suppressor K372 (Chuikov et al., 2004) Estrogen receptor alpha (ERα) K302 (Subramanian et al., 2008) NFκB subunit p65 K37 (activating) (Ea and Baltimore, 2009) K314, K315 (Yang et al., 2009) (supressing) E2F1 K185 (Kontaki and Talianidis, 2010) Retinoblastoma protein (pRb) K810 (Carr et al., 2010; Munro et al., 2010) STAT3 K140 (Yang et al., 2010) STAT1 K685 (Keating et al., 2014) Tat (HIV) K51 (Pagans et al., 2010) Androgen receptor (AR) K630 (Gaughan et al., 2011) K632 (Ko et al., 2011) Farnesoid X receptor (FXR) K206 (Balasubramaniyan et al., 2012) FoxO3 K275 (Calnan et al., 2012) Yes-associated protein (Yap) K494 (Oudhoff et al., 2013) Sox2 K119 (Fang et al., 2014) Hypoxia-inducible factor alpha K32 (HIF1α), K29 (Liu et al., 2015) (HIFα) (HIF2α) Pdx1 K123, K131 (Maganti et al., 2015) Msx2 interacting nuclear target K2076 (Dhalayan et al., 2011) (MINT) Interferon regulatory factor 1 K126 (Dhalayan et al., 2011) (IRF1) Centromere protein C1 (CENPC1) K414 (Dhalayan et al., 2011) ZDHC8 K300 (Dhalayan et al., 2011) β catenin K180 (Shen et al., 2015) LIN28A pluripotency factor K135 (Kim et al., 2014) Serum response factor (SRF) - (Tuano et al., 2016) PPARg coactivator 1a (PGC1a) K779 (Aguilo et al., 2016a) Ying yang 1 (YY1) K173, K411 (Zhang et al., 2016a) Smad7 K70 (Elkouris et al., 2016) Gli3 K436, K595 (Fu et al., 2016) Enzymes Sirtuin 1 (SIRT1) K232, K235, (Liu et al., 2011; Son et al., 2016) K236, K238 DNA methyltransferase 1 K142 (Estève et al., 2009) (DNMT1) Dual specificity protein kinase K708 (Dhalayan et al., 2011) (TTK) p300/CBP associated factor K78, K89, (Masatsugu and Yamamoto, 2009) (PCAF) K638, K671, K672, K692 Suv39h1 K105, K123 (Wang et al., 2013a) Flap endonuclease 1 (FEN1) K377 (Thandapani et al., 2017) Continues on next page

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Table 1.2 Continues from previous page Others ADP-ribosyltransferase diphtheria K508 (Kassner et al., 2013) toxin-like 1 (ARTD1) MeCP2 K347 (Dhalayan et al., 2011) Peroxisome proliferator-activated K1006 (Dhalayan et al., 2011) receptor binding protein (PPARBP) A kinase anchor protein (AKA6) K604 (Dhalayan et al., 2011) Cullin 1 K73 (Dhalayan et al., 2011) Lysine residues in bold are preferentially methylated

1.8. EPIGENETIC MODIFICATIONS IN METABOLIC DISEASE

1.8.1. Epigenetic reprogramming and metabolic disease

Epigenetic modifications are subject to regulation by products from intermediary metabolism and can, in turn, regulate cellular metabolic programs. Acetyl-CoA is required for all cellular acetylation reactions thus, the levels of this metabolite influence the activity of HATs (Rodriguez et al., 2017). Most of the Acetyl-CoA needed for acetylation reactions is generated by the enzyme ATP-citrate lyase (ACL) from mitochondrial citrate (Evertts et al., 2013; Kaelin and McKnight, 2013; Wellen et al., 2009). Silencing of ACL leads to changes in chromatin structure and alterations in the expression of genes that are associated with glucose metabolism in peripheral tissues (Wellen et al., 2009). Similarly, HDACs of the sirtuin family are dependent on NAD+ and, in fact, changes in cellular NAD+ concentration affect histone acetylation levels (Bendale et al., 2013; Feldman et al., 2012; Nakahata et al., 2008). Other HDACs (HDAC 1, 2, 3 and 8) are inhibited by the metabolite b-hydroxybutyrate further demonstrating that HAT and HDAC activity, and consequently histone acetylation, is subject to regulation by intracellular metabolites (Shimazu et al., 2013).

Histone and DNA methyltransferases require the cofactor SAM which is generated by condensation of the amino acid methionine and ATP (Anderson et al., 2012; Kaelin and McKnight, 2013). Changes in the overall levels of SAM have been associated with altered methylation states (Bleich et al., 2014; Mentch et al., 2015; Shyh-Chang et al., 2013). Furthermore, changes in SAM concentration have also been correlated with dietary intake of methionine and other components of the one-carbon metabolism cycle such as B vitamins, folate and choline (Anderson et al., 2012; Mentch et al., 2015). Similarly,

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demethylation reactions also require products of intermediary metabolism as cofactors. The enzyme LSD-1 requires FAD+ and the JmjC class of histone demethylases are dioxygenases that require a-ketoglutarate and iron (FeII) for enzymatic activity (Loenarz and Schofield, 2011; Shi et al., 2004).

Evidence suggests that environmental exposures in utero can induce epigenetic modifications that influence metabolic pathways later in life. Important epidemiological studies have revealed that people born to mothers who lived through famine have an increased risk of developing type 2 diabetes later in life (Thurner et al., 2013). Animal models have served to elucidate some of the mechanisms behind this reprogramming phenomenon. In a rat model of intrauterine growth restriction (IUGR), undernourished offspring had lower birth weights and an increased risk of developing diabetes due to epigenetic silencing of the Pdx1 gene (Park et al., 2008). Pdx1 is a key transcription factor for pancreatic development and it is required for adequate insulin secretion in adulthood. β cells from IUGR animals are characterised by excessive DNA methylation and repressive histone marks at the Pdx1 locus (Park et al., 2008).

Parental over nutrition also induces epigenetic changes associated with metabolic alterations. Mice born to parents fed high fat diets have increased body weight and reduced insulin sensitivity which is associated with changes in DNA methylation as well as histone acetylation and methylation at the sites of genes involved in energy metabolism (Dunn and Bale, 2009; Gniuli et al., 2008; Ng et al., 2010; Strakovsky et al., 2011; Suter et al., 2012; Suter et al., 2014). Furthermore, some of these acquired phenotypes can be transmitted to following generations via epigenetic modifications in germ cells that are resistant to the epigenetic resetting that normally occurs in gametes during early development (Fullston et al., 2013; van Otterdijk and Michels, 2016).

A summary of histone modifying enzymes and their association with metabolic disease is presented in Table 1.3 below.

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Table 1.3. Histone-modifying enzymes associated with the regulation of genes involved in glucose metabolism and diabetes. Modified from (Rodriguez et al., 2017) Enzyme Associated function/disease Reference Histone lysine methyltransferases SUV39H1 Vascular inflammation, diabetes (Villeneuve et al., 2008) SET7 β cell function, insulin secretion (Deering et al., 2009; Maganti et al., 2015) Hyperglycaemic memory, diabetic (El-Osta et al., 2008; Keating et complications al., 2014; Li et al., 2008; Okabe et al., 2012) Histone arginine methyltransferases PRMT1 Hepatic glucose metabolism (Choi et al., 2012) Diabetic retinopathy (Kim et al., 2015) PRMT4 (CARM1) Glycogen metabolism in skeletal (Wang et al., 2012) muscle Diabetic retinopathy (Kim et al., 2015) Hepatic glucose metabolism (Tsai et al., 2013) Chronic vascular inflammation (Harris et al., 2016) PRMT6 Hepatic glucose metabolism (Han et al., 2014) Histone acetyltransferases P300/CBP Insulin secretion (Mosley et al., 2004) Diabetic complications (Kaur et al., 2006) GCN5 Glucose metabolism (Lerin et al., 2006) SRC-1 Glucose metabolism (Louet et al., 2010; Spencer et al., 1997; Tannour-Louet et al., 2014) PCAF Hepatic glucose metabolisma (Sun et al., 2014) CLOCK Hepatic glucose metabolism (Ishikawa-Kobayashi et al., 2012) OGA Diabetic vascular disease (Makino et al., 2015) Insulin signalling (Forsythe et al., 2006) Histone demethylases KDM1A (LSD1) Hyperglycaemic memory, diabetic (Brasacchio et al., 2009) complications JMJD5 Glycolysis (Wang et al., 2014a) Histone deacetylases HDAC1 Insulin secretion (Mosley and Ozcan, 2004) HDAC2 Hepatic lipid and carbohydrate (Knutson et al., 2008) HDAC3 metabolism Hepatic glucose metabolism (Chou et al., 2012) Adipose tissue inflammation (Zhang et al., 2011) HDAC4, 5 Hepatic glucose metabolism (Mihaylova et al., 2011) β cell development and function (Lenoir, 2011) Glucose homeostasis (McGee et al., 2008; Raichur et al., 2012) HDAC7 Hepatic glucose metabolism (Mihaylova et al., 2011) HDAC9 β cell development and function (Lenoir, 2011) HDAC6 Hepatic glucose metabolism (Winkler et al., 2012) SIRT1 Energy metabolism, metabolite (Li, 2013) sensing SIRT2 Hepatic glucose metabolism (de Oliveira et al., 2012) SIRT3 Metabolic syndrome (Hirschey et al., 2011)

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1.8.2. Hyperglycaemic memory

The Diabetes Control and Complications Trial (DCCT), which took place from 1983 to 1989, aimed to study the effect of intensive glycaemic control compared to conventional treatment in patients with type 1 diabetes. This seminal study showed that patients under intensive control indeed had improved glycaemic and HbA1c levels and this was associated with reduced incidence of albuminuria and risk of developing neuropathy and retinopathy (DCCT, 1993). At the end of the study period, all participants were treated with the intensive control regime. A follow-up study called Epidemiology of Diabetes Interventions and Complications (EDIC) assessed the long-term effects of intensive control on the original DCCT cohort. During the course of the EDIC trial, all participants regardless of their treatment group during the DCCT, achieved similar levels of HbA1c. Results showed that patients who had received intensive treatment during the DCCT and EDIC had improved kidney function and decreased risk of retinopathy progression and cardiovascular disease compared to those who had been on the conventional treatment arm during the DCCT (DCCT/EDIC, 2000; DCCT/EDIC 2005). The effect of tight glycaemic control in patients with type 2 diabetes was assessed during the UK Prospective Diabetes Study (UKPDS). The initial endpoint study (10-year treatment) revealed a significant reduction in the risk of developing microvascular but not macrovascular complications (UKPDS, 1998). However, a 10-year follow-up study demonstrated that intensive treatment also reduced the risk of developing macrovascular complications (Holman et al., 2008; Stratton et al., 2000). The conclusions from these studies suggested that previous periods of hyperglycaemia cause long-lasting effects that result in the development of complications despite glycaemic normalisation. This effect was termed hyperglycaemic memory or the legacy effect (Chalmers and Cooper, 2008; DCCT/EDIC, 2000).

Recent studies using blood monocytes and lymphocytes isolated from DCCT participants showed there were specific differences in DNA methylation as well as histone acetylation and methylation at the sites of pro-inflammatory genes between patients on the intensive and conventional treatment groups (Chen et al., 2016; Miao et al., 2014). Similarly, lymphocytes from subjects with type 1 diabetes have higher levels of the H3K9me2 mark in genes associated with inflammation and autoimmunity compared to healthy individuals (Miao et al., 2008). Furthermore, the expression of RELA (p65) and some of its target genes is increased mononuclear cells derived from type 2 diabetes patients in a 31 1 | Literature Review

H3K4me1-dependent manner (Paneni et al., 2015). These observations implicate a role for epigenetic mechanisms mediating the phenomenon of hyperglycaemic memory.

1.8.3. Oxidative stress and epigenetic gene regulation in diabetes

As discussed in Section 1.4, the generation of mitochondrial ROS is behind the deregulation of several pathways ultimately leading to the development of diabetic complications. Persistent ROS signalling is thought to be a key mechanism for the establishment of hyperglycaemic memory (Ihnat et al., 2007). Increased levels of intracellular glucose in endothelial cells promote the recruitment of the Set7 lysine methyltransferase to the RELA gene promoter increasing H3K4me1 levels. This is prevented by overexpressing antioxidant enzymes (El-Osta et al., 2008). Furthermore, mice that are deficient in antioxidant uncoupling protein 2 (UCP2) and glyoxalase (GLO1) have increased levels of H3K4me1 at the RelA promoter in response to hyperglycaemia (El-Osta et al., 2008). P66Shc is a mitochondrial adaptor protein that is upregulated in response to hyperglycaemia and is implicated in the generation of ROS. This upregulation is maintained despite normalisation of glucose levels and this is associated with increased histone acetylation and decreased DNA methylation at its gene promoter (Paneni et al., 2012; Zhou et al., 2011). Postprandial increases in blood glucose observed in subjects with type 2 diabetes stimulate ROS production even more than sustained hyperglycaemia (Monnier, 2006). This observation, together with persistent epigenetic changes that occur in response to hyperglycaemia, provides a potential new regulatory mechanism for hyperglycaemic memory. This highlights the importance of effective diabetes management treatments that focus not only on HbA1c but also target glucose fluctuations.

1.8.4. Epigenetic mechanisms underlying macrovascular diabetic complications

Histone modifications mediate changes in the gene expression profile of vascular endothelial cells in response to high glucose, resulting in increased expression of pro- inflammatory genes (Muniandy et al., 2009; Pirola et al., 2011). In this context, Set7 is a key mediator of cellular responses to hyperglycaemia. This enzyme promotes the methylation of histone and non-histone proteins to activate the expression of pro- inflammatory genes (Keating and El-Osta, 2013a; Li et al., 2008; Okabe et al., 2012). Set7 may act as a hyperglycaemic sensor, translocating into the nucleus in response to

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high glucose levels to influence gene expression (Keating and El-Osta, 2013b; Okabe et al., 2012). High glucose stimulation of endothelial cells induces the recruitment of Set7 to the RELA gene promoter and subsequent increase in the levels of monomethylated H3K4. Importantly, this effect is maintained after the cells are returned to normal glucose conditions (El-Osta et al., 2008; Okabe et al., 2012). Persistent activation of NFκB was also described in vivo using a mouse model of hyperglycaemic memory where transient hyperglycaemia resulted in increased expression of NFκB target genes Hmox1 and Cxcl2 (IL-8) for up to seven days (Okabe et al., 2012). The role of Set7 as an activator of NFκB was also evidenced in cultured monocytes where knock-down of the enzyme resulted in a decrease in monocyte adhesion and reduced expression of 25% of all pro-inflammatory genes induced by tumour necrosis factor α (TNF-α), an inflammatory cytokine (Li et al. 2008).

Other chromatin remodelling events that occur at the RELA promoter and are associated with increased pro-inflammatory gene expression include the recruitment of LSD1 and decrease binding of SUV39H1, which lead to a reduction in repressive histone methylation marks (Brasacchio et al., 2009; Villeneuve et al., 2008). NFκB also promotes the expression of its pro-inflammatory targets by mediating the recruitment of HATs (Miao et al., 2004). In fact, high glucose treatment of vascular endothelial cells confers a specific hyperacetylation pattern that is directly correlated to transcriptional activation (Pirola et al., 2011).

NFκB activation is also increased in VSMCs in response to high glucose (Hattori et al., 2000). These cells play a key role in the progression of macrovascular disease as atherogenic stimuli induce their transdifferentiation into macrophage-like cells (Feil et al., 2014). Angiotensin II (AngII) has an atherogenic effect in VSMCs as it increases the production of ROS and upregulates the expression of pro-inflammatory mediators (Brasier et al., 2002). In vitro experiments suggest that some of these actions of AngII are mediated by histone modifications as exposure of VSMCs to AngII results in increased levels of trimethylated H3K4 and H3K36, and expression of several long non-coding RNA genes (Leung et al., 2013).

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1.8.5. Epigenetic mechanisms underlying diabetic nephropathy

Accumulating evidence indicates that pathological gene expression changes observed during the progression of diabetic nephropathy are mediated by histone modifications. TGFβ1 is produced in the kidney in response to hyperglycaemia and is a key mediator of kidney damage in diabetes by promoting the expression of pro-fibrotic genes, partially by inducing changes in chromatin structure that facilitate transcriptional activation. TGFβ1 stimulates the expression of plasminogen activator inhibitor 1 (PAI-1) by recruiting p300/CBP and Set7 to its gene promoter (Sun et al., 2010; Yuan et al., 2013). Other TGFβ1 targets such as collagen 1 and connective tissue growth factor (CTGF) are also regulated by Set7-mediated histone methylation (Sun et al., 2010). Increased ROS production and TGFβ1 signalling also induce HDAC2 expression and this is associated with increased expression of α-smooth muscle actin (α-SMA) and fibronectin in the diabetic kidney (Noh et al., 2009). In fact, HDAC inhibition has been shown to decrease oxidative stress and attenuate renal damage in mouse models of diabetic nephropathy (Advani et al., 2011; Gilbert et al., 2011; Shimazu et al., 2013). TGFβ1 signalling also induces H2A and H2B ubiquitination but the role of these modifications in the regulation of gene expression remains poorly understood (Gao et al., 2013). Hyperglycaemia also causes endoplasmic reticulum stress which promotes the upregulation of CCL2 (MCP-1) in a histone methylation-dependent mechanism mediated by Set7 (Chen et al., 2014). Similarly, hyperglycaemia causes the myocardin-related transcription factor A (MRTF- A) to recruit HATs and HMTs to its target genes, such as collagen, increasing histone acetylation and H3K4me3 levels at their promoters and promoting fibrotic gene expression in the kidney (Xu et al., 2015).

Increased TGFβ1 signalling is characteristic of other fibrotic pathologies, and epigenetic mechanisms are implicated in this process. Set7 mediates the up-regulation of pro-fibrotic genes such as those encoding collagen 1 and fibronectin in a mouse model of unilateral ureteral obstruction (UUO) (Sasaki et al., 2015). Furthermore, Set7 levels are directly correlated to the degree of fibrosis in human samples from IgA and membranous nephropathies (Sasaki et al., 2015). Additionally, Set7-mediated histone methylation regulates TGFβ1 gene expression in the liver of rats after bile duct ligation (Sheen-Chen et al., 2014). These observations strongly suggest that epigenetic modifications, particularly histone methylation by Set7, play a key role in mediating gene expression changes that drive the development of fibrosis in diverse disease environments. 34 1 | Literature Review

During the progression of diabetes there is an increase in production of the enzyme 12/15 lipooxygenase which mediates lipid oxidation. Oxidised lipids such as 12(S)- hydroxyeicosatetraenoic acid cause kidney damage, partially through increasing the levels of Set7 in renal mesangial cells resulting in the up-regulation of pro-fibrotic genes (Yuan et al., 2015). Increased levels of the fatty acid palmitate are associated with alterations in insulin signalling in renal podocytes by inducing a H3K36me2-mediated increase in FOXO1 gene expression (Kumar and Tikoo, 2015).

MicroRNAs have been widely implicated in the development and progression of diabetic nephropathy. Several microRNAs, such as miR21, miR192, miR200-c, miR216a and miR217, are highly expressed in the diabetic kidney and promote the up-regulation of ECM components contributing to glomerular hypertrophy characteristic of diabetic nephropathy (Kato et al., 2007; Kato et al. 2009; Kato et al., 2010; Kato et al., 2011; McClelland et al., 2015; Park et al., 2013). MicroRNAs are detectable in tissues and bodily fluids such as blood and urine and have been previously proposed as biomarkers for cancer (Mitchell et al., 2008; Weber et al., 2010). Circulating and urinary microRNA signatures may provide valuable information as biomarkers of nephropathy progression in type 1 and type 2 diabetic subjects (Delić et al., 2016; Pezzolesi et al., 2015).

The mechanisms behind diabetes-induced epigenetic and gene expression changes that contribute to the development and progression of vascular diabetic complications are summarised in Figure 1.5.

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Growth Factors ROS AGEs Diabetes Oxidized HG lipids

Epigenetic responses

Biomarker Memory DNAme Histone PTMs miRNAs Potential

Pathological gene expression

Inflammation Fibrosis MCP-1 VCAM-1 CTGF PAI-1 MnSOD Collagens

Diabetic Diabetic Atherosclerosis Nephropathy Retinopathy

Figure 1.5. Epigenetic modifications mediate diabetes-induced gene expression changes underlying the development of vascular diabetic complications Stimuli from the diabetic milieu such as hyperglycaemia (HG), increased production of growth factors, advanced glycation end products (AGEs), reactive oxygen species (ROS) and oxidised lipids induce changes in the epigenetic states of genes associated with inflammation and fibrosis. These pathological gene expression changes contribute to the development and progression of nephropathy, retinopathy and atherosclerosis in diabetes. Persistent epigenetic marks that result in sustained gene expression changes are thought to underlie the phenomenon of hyperglycaemic memory. Furthermore, epigenetic markers such as microRNAs (miRNAs) are strongly associated with disease development and progression and have been proposed as predictive and diagnostic biomarkers.

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1.9. SET7 KNOCK-OUT MOUSE MODELS

In vitro experiments have provided valuable insights into the mechanism of action of Set7 and suggested its involvement in specific pathways. However, cell culture techniques do not accurately mimic the context that cells and tissues encounter within the body. For this reason, there was a need for an animal model of Set7 deficiency for in vivo studies. The mouse Setd7 gene (Genbank accession number NC_000069.6) is over 45.5Kb long and has eight exons that encode the 366 amino-acid-protein (Fig. 1.6). Knowledge of the structure of the mouse Setd7 gene has enabled the design of targeting strategies for the generation of knock-out animals. Based on this information, several knockout mouse models where expression of Setd7 is inhibited (Set7 KO mice) have been developed and used with diverse outcomes (Table 1.4).

Table 1.4. Published models of Set7 knock out mice Knock-out approach Deletion KO phenotype Reference site 1 Constitutive, global KO via Exon 2 Embryonic lethality in (Kurash et Cre/Lox recombination. half of -/- mice. Animals al., 2008) that survived to adulthood appeared normal with impairment in p53 activation. 2 Constitutive, global KO via Exons No phenotype observed. (Lehnertz et Cre/Lox recombination. 4-8 No impairment in p53 al., 2011) activation. 3 Constitutive, global KO via Exon 2 No phenotype observed. (Campaner Cre/Lox recombination. No impairment in p53 et al., 2011) activation. 4 Conditional, intestinal epithelial Exon 2 Differences in intestinal (Oudhoff et cell-specific KO. Cre/Lox crypt structure. al., 2013) recombination using a mouse expressing Cre under the Villin promoter 5 Inducible, β cell-specific KO. Exon 2 Impaired glucose (Maganti et Cre/Lox recombination tolerance. al., 2015) specifically in β cells upon tamoxifen treatment using a MIP- CreERT mouse (mouse insulin promoter upstream of Cre gene fused with the hormone-binding domain of the estrogen receptor). 6 Constitutive, global KO via Exon 4 No phenotype observed. (Elkouris et Cre/Lox recombination. KO animals are protected al., 2016) against bleomycin- induced pulmonary fibrosis.

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Figure 1.6. Structure of the mouse (Mus musculus) Setd7 gene (NC_000069.6) Chromosomal location and structure of the mouse Setd7 gene from the UCSC Genome Browser (https://genome.ucsc.edu). The murine Setd7 gene has 8 exons (E1-E8) and 7 introns, the start and stop codons are located in exons 1 and 8 respectively. Untranscribed regions (UTRs) are 323bp on the 5’ end and 5989bp at the 3’ end (not shown).

The mouse model used for this project was developed by El-Osta in 2011. The approach used to generate such model was the conditional deletion of exon 2 from the Setd7 gene which encodes the first MORN repeat, this leads to a frame-shift resulting in a premature stop codon.

Briefly, the process of generating this Setd7 knock-out mouse model was as follows (Fig. 1.7):

• Construction of a targeting vector to create a recombinant Setd7 locus. This vector contained 1) homology regions (7.2 Kb in total), 2) two loxP sites flanking exon 2, 3) a neomycin cassette (for positive selection) flanked by FRT (flippase recognition targets) sites, 4) Diphteria toxin A (DTA) negative selection marker.

• Generation of a recombinant Setd7 locus by homologous recombination in mouse embryonic stem (ES) cells. ES cells were transfected using electroporation with the plasmid containing the targeting vector. Recombinant colonies were identified, cloned and amplified. The presence of DTA as a negative selection marker reduces the isolation of non-homologous recombined ES cell clones. Screening homologous recombination was performed by PCR and southern blot.

• Generation of animals with a Setd7 recombined locus. ES clones were injected into C57BL/6J blastocysts, implanted into pseudo-pregnant females and allowed to develop to term. Resulting animals are chimeras.

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• Excision of the neomycin selection cassette via Flp-FRT recombination to generate “floxed” mice in which exon 2 is flanked by loxP sites. This was done in vivo to breed chimeras with Flp-expressing deleter mice. Resulting animals were bred with wild type C57BL/6j mice to generate a pure line of heterozygous mice carrying the floxed Setd7 allele. Screening for Flp-mediated recombination was performed by PCR and southern blot.

• Deletion of the loxP flanked region to generate mice heterozygous for the Setd7 knock- out allele. This was also done in vivo by breeding “floxed” recombined animals with Cre- expressing deleter mice. Screening for Cre-mediated recombination was performed by PCR and southern blotting.

Figure 1.7. Strategy for the generation of a mouse heterozygous for a Setd7 knock-out allele A targeting vector containing loxP sites flanking exon 2 of the Setd7 gene was generated by homologous recombination in mouse embryonic stem cells. Recombinant clones were injected into C57BL/6J blastocysts and implanted into pseudopregnant females. The Neomycin selection cassette was excised by crossing resulting chimeras with Flp-deleter mice to generate animals with loxP sites flanking exon 2. Breeding of these ‘floxed’ animals with Cre-expressing mice, generated animals heterozygous for a Setd7 KO allele. E2: exon 2; FRT: flippase recognition targets for Flp-mediated recombination; loxP: sites for Cre-mediated recombination.

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1.10. HIGH THROUGHPUT SEQUENCING

Sequencing of the human genome was first completed in 2001, closely followed by the genomes of several model organisms. This was achieved using the Sanger DNA sequencing technology with an estimated cost of up to one billion dollars (Reuter et al., 2015). Since then, high throughput sequencing (HTS) technologies have been developed and the time and cost of sequencing have decreased dramatically (Reuter et al., 2015). Lower turnaround times and costs have led to the widespread use of HTS and the development of diverse applications.

One such application is transcriptome sequencing or RNA-seq (Nagalakshmi et al., 2008). The transcriptome includes all transcripts present in a cell under specific conditions, thus RNA-seq is used to generate and compare gene expression profiles (Mortazavi et al., 2008; Wang et al., 2009a). Analysing the results from RNA-seq experiments requires bioinformatics analysis during which sequence reads are aligned to a reference genome to identify the transcripts present and determine their abundance (Wang et al., 2009a). Identifying changes in the transcriptome has helped elucidate some of the molecular mechanisms behind the development of cardiovascular and renal diseases (Churko et al., 2013; Mimura et al., 2014)

The rapid uptake of HTS technologies as well as their increased reliability have enabled the formation of global projects driven by consortia. One such project is ENCODE (Encyclopedia Of DNA Elements). The ENCODE project is a large-scale venture that aims to characterise all functional elements in the human genome to enable researchers to better interpret the human genome sequence (ENCODE Consortium, 2004). In order to achieve this, ENCODE has built an extensive dataset based on HTS applications including RNA-seq, chromatin immunoprecipitation (ChIP)-seq, and methylome sequencing (ENCODE Consortium, 2011). While ENCODE data is mostly based on cultured cells experiments, the Roadmap Epigenomics Project aims use data generated from HTS experiments to profile the epigenome of human tissues (Romanoski et al., 2015). Genome-wide datasets like these are a valuable tool for researchers worldwide, they provide useful information for identifying mechanisms of gene regulation under particular experimental conditions. It is expected that this type of initiative will provide a comprehensive account of gene expression regulation in the human body.

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1.11. SUMMARY OF THE LITERATURE AND PROJECT AIMS

Diabetes Mellitus is a metabolic disease characterised by persistent hyperglycaemia which can lead to serious long-term complications. Vascular complications of diabetes include cardiovascular disease (macrovascular) and diabetic nephropathy, neuropathy and retinopathy (microvascular). A unifying theory for the mechanism behind hyperglycaemia-induced cellular damage in diabetes states that increased oxidative stress activates a series of pathways that lead to the increased production of inflammatory mediators that cause tissue injury. Increasing evidence suggests these pathways are subject to epigenetic regulation, particularly histone methylation by the Set7 lysine methyltransferase. However, gene regulation in vivo is complex and most of the evidence linking Set7 and hyperglycaemia-induced inflammatory gene expression derived from in vitro studies. It will be important to validate such findings using in vivo models.

Set7 is also associated with mediating fibrotic gene expression in vitro and in different in vivo models of disease. However, whether Set7 regulates the expression of fibrotic genes in an in vivo model of diabetes has not been determined. It will be useful to determine the extent of the involvement of Set7 in pathways that lead to the development of diabetic complications. Furthermore, Set7-mediated gene expression regulation in the context of diabetes has been primarily studied in regards to histone methylation. However, this enzyme can also methylate non-histone proteins (such as transcription factors) affecting their function, adding an extra layer of complexity to its ability to regulate gene expression. Identifying non-histone protein mechanisms of gene regulation by Set7 will provide a more comprehensive view of the role of this enzyme during the development of vascular diabetic complications.

The maintenance of metabolic homeostasis is a complex process that involves different tissues and requires careful regulation of gene expression programs. Such fine control is possible because of epigenetic regulation. Environmental changes are known to affect gene expression by influencing the concentration of metabolites that are required for enzymatic reactions leading to epigenetic modifications. Moreover, several epigenetic modifiers have been associated with defining metabolic transcriptional programs in both adulthood and during development. However, the involvement of Set7 in such pathways has not been studied. Given the relevance of this enzyme for the development of diabetic complications and the potential implications for new therapeutic approaches, it will be

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valuable to study Set7-mediated gene regulation in the context of overall metabolic homeostasis.

This project aims to validate Set7 as a target for reducing the burden of diabetic vascular complications. In order to achieve this, the following specific aims were proposed:

1) To assess the effect of Set7 knock-out in metabolic homeostasis in a mouse model, with a focus on glucose metabolism.

2) To assess the effect of Set7 knock-out in a mouse model of diabetes-accelerated atherosclerosis.

3) To assess the effect of Set7 knock-out in a mouse model of diabetic nephropathy.

4) To identify novel Set7 gene targets with a focus on histone methylation- independent mechanisms.

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CHAPTER 2

MATERIALS AND METHODS

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2.1. MATERIALS

2.1.1. Instruments and equipment

All experimental equipment and instruments used throughout this thesis are presented in Table 2.1.

Table 2.1. Equipment and instruments used Equipment Supplier 7500 Fast RealTime PCR System Thermo Fisher Scientific, Waltham MA, USA QuantStudio 3 and 5 Real Time PCR System Thermo Fisher Scientific, Waltham MA, USA AirClean 600 PCR workstation AirClean Biological Safety Cabinet Class II CCL Pty Ltd Biological Safety Cabinet Class II ESCO, Singapore, Singapore Bioruptor XL Sonicator Diagenode, Denville NJ, USA Q800R2 Sonicator QSonica, Newtown CT, USA C1000™ thermal cycler Bio-Rad, Hercules CA, USA SimpliAmp™ thermal cycler Thermo Fisher Scientific, Waltham MA, USA MCE-202 MultiNA Microchip Shimadzu, Kyoto, Japan Electrophoresis System Qubit 2.0 and 3.0 Fluorometers Thermo Fisher Scientific, Waltham MA, USA NanoDrop™ 2000 Spectrophotometer Thermo Fisher Scientific, Waltham MA, USA Countess® II Automated Cell Counter Thermo Fisher Scientific, Waltham MA, USA Microcentrifuge 5424 Eppendorf, Hamburg, Germany EchoMRI™ body composition analyser EchoMRI, Houston, USA Accucheck glucose meter and testing strips Roche, Basel, Switzerland Cobas b101 POC system Roche, Basel, Switzerland HeraCell Vios 160i tissue culture incubators Thermo Fisher Scientific, Waltham MA, USA Allegra® XR-12 and XR-15 centrifuges Beckman Coulter, Brea CA, USA Odyssey infrared imager Li-Cor, Lincoln NE, USA Novex XCell SureLock II electrophoresis Invitrogen, Carlsbad CA, USA tank Transfer tank Bio-Rad, Hercules CA, USA Trans-Blotâ Turbo™ Transfer System Bio-Rad, Hercules CA, USA Heat block Ratek Thermomixer Eppendorf, Hamburg, Germany Rocking platform Ratek, Victoria, Australia Sysmex CBC analyser XS100i Sysmex, Kobe, Japan SensoCard glucometer and testing strips Point of Care Diagnostics, Artarmon, Australia AccuChek glucometer and testing strips Roche, Basel, Switzerland Cobas Integra 400 analyser Roche, Basel, Switzerland Bullet Blender® Gold Next Advance, Averill Park NY, USA Continues on next page

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Table 2.1. Continues from previous page Polytron-PT MR2100 tissue homogeniser Kinematica, Littau/Lucerne, Switzerland Microplate reader Bio-Rad, Hercules CA, USA RM2135 Microtome Leica, Wetzlar, Germany BX-61 fluorescence microscope Olympus, Tokyo, Japan BX-50 brightfield microscope Olympus, Tokyo, Japan QIAxpert spectrophotometer Qiagen, Venlo, The Netherlands Mouse and rat tail cuff blood pressure system IITC Life Science, Woodland Hills CA, USA

2.1.2. General reagents

All reagents including chemicals, prepared solutions and commercially available reagent kits are presented in Tables 2.2, 2.3 and 2.4 below.

Table 2.2. General reagents and chemicals used Reagent Supplier 3-(N-Morpholino) propane-sulfonic acid Astral Scientific, Taren Point, Australia (MOPS) 3,3′-diaminobenzidine Sigma-Aldrich, St. Louis MO, USA tetrahydrochloride/H2O2 (DAB) Ampicillin Sigma-Aldrich, St. Louis MO, USA Bicine Astral Scientific, Taren Point, Australia Bis-Tris Sigma-Aldrich, St. Louis MO, USA Bovine serum albumin (BSA) Sigma-Aldrich, St. Louis MO, USA Bovine Serum Albumin (BSA) Standard Thermo Fisher Scientific, Waltham MA, Ampules, 2mg/mL USA Bradford reagent (B6916) Sigma-Aldrich, St. Louis MO, USA Collagenase P Roche, Basel, Switzerland Complete™ Mini protease inhibitor tablets Roche, Basel, Switzerland Dithiothreitol Invitrogen, Carlsbad CA, USA D-Glucose Sigma-Aldrich, St. Louis MO, USA DynaBeads Protein A Invitrogen, Carlsbad CA, USA Enhanced Chemiluminescence (ECL) reagent Sigma-Aldrich, St. Louis MO, USA Ethyl alcohol Sigma-Aldrich, St. Louis MO, USA Ethylene diamine tetraacetic acid (EDTA) Sigma-Aldrich, St. Louis MO, USA 0.5M solution Formaldehyde 37% solution Sigma-Aldrich, St. Louis MO, USA Ficoll-Paque PLUS GE Healthcare, Little Chalfont, UK Glycine Sigma-Aldrich, St. Louis MO, USA Hank’s Balanced Salt Solution Sigma-Aldrich, St. Louis MO, USA Immobilon®-FL PVDF membrane Millipore, Billerica MA, USA Luria-Bertani (LB) medium and agar BHRI media services LB broth with agar (Lennox) Sigma-Aldrich, St. Louis MO, USA LB broth base Invitrogen, Carlsbad CA, USA Magnesium chloride (MgCl2) Sigma-Aldrich, St. Louis MO, USA MAX Efficiencyä E. coli Stbl2ä Thermo Fisher Scientific, Waltham MA, USA. NuPAGE® 4X sample buffer Invitrogen, Carlsbad CA, USA Continues on next page

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Table 2.2. Continues from previous page NuPAGE® Novex Bis-Tris 4-12% precast Invitrogen, Carlsbad CA, USA gels One-Shotä TOP10 chemically competent E. Thermo Fisher Scientific, Waltham MA, coli cells USA. Pepsin Sigma-Aldrich, St. Louis MO, USA Phenylmethylsulfonyl (PMSF) Sigma-Aldrich, St. Louis MO, USA Potasium chloride (KCl) Sigma-Aldrich, St. Louis MO, USA ProLong® Gold Antifade reagent with DAPI Invitrogen, Carlsbad, CA, USA Protease inhibitor 100X Biotool, Houston, TX, USA Proteinase K Sigma-Aldrich, St. Louis MO, USA Puromycin Sigma-Aldrich, St. Louis MO, USA RNA 6000 ladder Ambion, Carlsbad, USA RNase A Sigma-Aldrich, St. Louis MO, USA SeeBlue® Plus2 prestained protein molecular Invitrogen, Carlsbad CA, USA weight market Super Optimal Broth with Catabolite Invitrogen, Carlsbad CA, USA repression (SOC) medium Sodium bicarbonate (NaHCO3) Sigma-Aldrich, St. Louis MO, USA Sodium carbonate (Na2CO3) Sigma-Aldrich, St. Louis MO, USA Sodium chloride (NaCl) Amresco, Solon OH, USA Sodium dodecyl sulphate (SDS) Amresco, Solon OH, USA Streptozotocin Sigma-Aldrich, St. Louis MO, USA Sulphuric acid (H2SO4) BDH, Dorset, UK Tris-EDTA (TE) buffer 100X Sigma-Aldrich, St. Louis MO, USA Tris base Sigma-Aldrich, St. Louis MO, USA Tris-HCl Sigma-Aldrich, St. Louis MO, USA Triton X-100 Fluka Chemika, Buchs, Switzerland TRIzol® reagent Life Technologies, Carlsbad CA, USA Tween-20 Sigma-Aldrich, St. Louis MO, USA UltraPure® Formamide Invitrogen, Carlsbad CA, USA XbaI restriction enzyme New England BioLabs, Ipswich MA, USA BamHI restriction enzyme New England BioLabs, Ipswich MA, USA Zirconium oxide beads, 0.5mm and 1mm Next Advance, Averill Park NY, USA

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Table 2.3. Commercial reagent kits used Reagent Supplier DNA 2500 reagent kit for MultiNA Shimadzu, Kyoto, Japan Direct-zol™ RNA Mini prep kit Zymo Research, Irvine CA, USA High capacity cDNA reverse transcriptase kit Applied Biosystems, Foster City CA, USA Lipofectamine™ 3000 transfection reagents Invitrogen, Carlsbad CA, USA Mouse Albumin ELISA quantitation set Bethyl Laboratories, Montgomery TX, USA Mouse Ultrasensitive Insulin ELISA Alpco, Salem NH, USA Wizard SV Plus Minipreps Promega, Madison WI, USA PureYield™ Plasmid Midiprep Systems Promega, Madison WI, USA QuantiNova® SYBR® Green PCR kit Qiagen, Venlo, The Netherlands Qubit dsDNA HS assay kit Thermo Fisher Scientific, Waltham MA, USA RNA reagent kit for MultiNA (Separation Shimadzu, Kyoto, Japan buffer, marker solution) VECTASTAIN Elite ABC Staining Kit Vector laboratories, Burlingame, CA, USA NEBNextÒ Poly(A) mRNA Magnetic New England BioLabs, Ipswich MA, USA Isolation Module NEBNextÒ UltraÔ RNA Library Prep Kit New England BioLabs, Ipswich MA, USA for IlluminaÒ

Table 2.4. Buffers and solutions prepared Buffer Composition PAGE and Immunoblotting Whole cell protein 20mM HEPES, 25% glycerol, 520mM KCl, 5mM MgCl2, extraction buffer 0.1mM EDTA, 1mM DTT, 0.5mM PMSF, 0.2% NP-40, proteinase inhibitor MOPS running buffer 50mM MOPS, 50mM Tris, 0.1% SDS, 1mM EDTA, pH 7.7 Transfer buffer 25mM bicine, 25mM Bis-Tris, 1mM EDTA, pH 7.2 Basic stripping buffer 0.2N NaOH Acid stripping buffer 25mM glycine, 1% SDS, pH 2 Tris buffered saline with 50mM Tris, 150mM NaCl, 0.1% Tween 20 Tween (TBST) Buffer K 50mM Tris-HCl, 250mM NaCl, 10% glycerol, 0.5% NP40 Albumin ELISA Wash buffer (base) 9mM Tris, 41mM Tris-HCl, 138mM NaCl, 2.7mM KCl, pH 8 Sample conjugate diluent Wash buffer + 1% BSA + 0.05% Tween-20 Coating buffer 15mM Na2CO3, 35mM NaHCO3, pH 9.6 Blocking buffer Wash buffer + 1% BSA Stop solution 0.18M H2SO4 General use Phosphate buffered saline 137mM NaCl, 2.7mM KCl, 4.3mM Na2HPO4, 1.47mM (PBS) KH2PO4, pH 7.4 Tris buffered saline (TBS) 50mM Tris, 150mM, pH 7.5

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2.1.3. Cell lines and tissue culture reagents

Cell and tissue culture reagents used are presented in Table 2.5. Table 2.6 describes major characteristics of the cells lines used and basic culture media conditions.

Table 2.5. Reagents and chemicals used for tissue culture Reagent Supplier Cell lines Normal Human Mesangial Cells (NHMCs) Lonza, Basel, Switzerland Human podocytes Gift from Dr. Jay Jha (Jha et al., 2014) Mouse Smooth Muscle Cells (SMC) Gift from Dr. Jun Okabe (Tuano et al., 2016) Mouse Proximal Tubule Cells (PTC) Primary isolation (Section 2.4.4) Human Embryonic Kidney (HEK) 293FT Gift from Dr. Jun Okabe (Tuano et al., 2016) MIN6 mouse b cells Gift from Dr. Alaina Natoli (Siebel et al., 2013) Reagents Antibiotic-Antimycotic 100X Gibco, Gran Island, NY, USA Clonetics™ REGM™ Renal Epithelial Cell Lonza, Basel, Switzerland Growth Medium Bullet Kit Clonetics™ MsGM™ Mesangial Cell Growth Lonza, Basel, Switzerland Medium Bullet Kit Collagen type I Sigma-Aldrich, St. Louis MO, USA Dimethylsulfoxide (DMSO) Sigma-Aldrich, St. Louis MO, USA Dulbecco’s Modified Eagle Medium Gibco, Grand Island, NY, USA (DMEM) HyClone™ Fetal Bovine Serum (FBS) GE Healthcare, Little Chalfont, UK GlutaMAX 100X Gibco, Grand Island, NY, USA Hexadimethrine Bromide (Polybrene) Sigma-Aldrich, St. Louis MO, USA Insulin-Transferrin-Selenium (ITS) Gibco, Grand Island, NY, USA Molecular, Cellular and Developmental Gibco, Grand Island, NY, USA biology (MCDB) 131, no glutamine Minimum Essential Medium (MEM) Gibco, Grand Island, NY, USA MEM Non-Essential Amino Acids 100X Gibco, Grand Island, NY, USA Penicillin/Streptomycin 100X Gibco, Grand Island, NY, USA Phosphate buffered saline (no Ca2+, Mg2+) BHRI media service, Gibco Roswell Park Memorial Institute (RPMI) Gibco, Grand Island, NY, USA 1640 medium, no glucose Roswell Park Memorial Institute (RPMI) Gibco, Grand Island, NY, USA 1640 medium, 11mM glucose (R)-PFI-2 Cayman Chemicals, Ann Arbor, MI, USA Sodium Pyruvate 100mM Gibco, Grand Island, NY, USA Transforming Growth Factor (TGF) b1 R&D Systems, Minneapolis, MN, USA Trypsin/EDTA 0.5% (10X) Gibco, Grand Island, NY, USA MISSION® shRNA-expressing plasmids for Sigma-Aldrich, St. Louis MO, USA lentiviral transduction

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Table 2.6. Cell lines used and basic culture media composition Cell line Characteristics Growth medium MIN6 Mouse b cells, DMEM 4g/L glucose, 1X GlutaMAX, transformed, adherent 50µM 2-mercaptoethanol, 1X Antibiotic/Antimycotic (100 units/mL penicillin, 100µg/mL streptomycin, 0.25µg/mL amphotericin B), 15% FBS Normal Human Renal mesangial cells, Mesangial cell basal medium (MsBM) Mesangial Cells primary, adherent with supplements (NHMCs) (Gentamicyn/Ampicillin), 10% FBS Human Podocytes Renal glomerular RPMI 1640 5mM glucose, 1X podocytes (visceral Antibiotic/Antimycotic, 1X ITS epithelial cells), supplement (10mg/l insulin, 5.5mg/l transformed, adherent transferrin, 7µg/l sodium selenite), 10% FBS Mouse Smooth Vascular smooth muscle DMEM 4g/L glucose, 1X Muscle cells cells, transformed, Antibiotic/Antimycotic, 10% FBS (mSMCs) adherent 293FT cells Human embryonic kidney DMEM 4g/L glucose, 1X cells, transformed, low- Antibiotic/Antimycotic, 10mM adherence sodium pyruvate, Non-Essential Amino Acids, 10% FBS Mouse Proximal Mouse renal tubular Renal Epithelial Cell Basal Medium Tubule Cells (PTCs) epithelial cells, primary (REBM) and supplements (hEGF, (from renal cortex), hydrocortisone, epinephrine, insulin, adherent triiodothyronine, transferrin, gentamycin/ampicillin), 10% FBS Human Human, transformed, MCDB131 5.5mM glucose, 1X Microvascular adherent GlutaMAX, 1X Endothelial Cells Antibiotic/Antimycotic, 10% FBS (HMEC-1)

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2.1. Antibodies

A list of antibodies used for immunoblotting, immunohistochemistry (including immunofluorescence) and immunoprecipitation are presented in Table 2.7.

Table 2.7. Primary and secondary antibodies used Antibody Supplier Application Primary antibodies Anti-Histone H3 unmodified Cell Signalling Technology Immunoblotting (mouse) Danvers MA, USA Anti-H3K4me1 (rabbit) Abcam, Cambridge, UK Immunoblotting Anti-Collagen I (rabbit) Biodesign International, Saco, Immunohistochemistry ME, USA

Anti-Collagen IV (rabbit) Southern Biotechnology, Immunohistochemistry Birmingham, AL, USA

Anti-Glucagon (mouse) Sigma-Aldrich, St. Louis MO, Immunofluorescence USA Anti-GAPDH Abcam, Cambridge, UK Immunoblotting Anti-Insulin (mouse) Sigma-Aldrich, St. Louis MO, Immunofluorescence USA Anti-Set7/9 (rabbit) Cell Signalling Technology, Immunoblotting Danvers MA, USA Anti-Set7/9 (rabbit) Novus Biologicals, Littelton CO, Immunofluorescence USA Anti-Tcf21 (rabbit) Novus Biologicals, Littelton CO, Immunoblotting USA Anti-HA clone 3F10 (rat) Roche, Basel, Switzerland Immunoblotting Anti-HA agarose (clone HA-7) Sigma-Aldrich, St. Louis MO, Immunoprecipitation USA Anti-FLAG M2 (mouse) Sigma-Aldrich, St. Louis MO, Immunoblotting USA Anti-FLAG M2 affinity gel Sigma-Aldrich, St. Louis MO, Immunoprecipitation USA Secondary antibodies AlexaFluor® 488 Goat anti- Invitrogen, Carlsbad CA, USA Immunofluorescence rabbit IRDye® 800CW Goat anti- Li-Cor, Lincoln NE, USA Immunoblotting mouse IRDye® 680RD Goat anti- Li-Cor, Lincoln NE, USA Immunoblotting rabbit Donkey anti-rabbit HRP GE Healthcare, Little Chalfont, Immunoblotting conjugated UK Goat anti-mouse HRP Dako, Glostrup, Denmark Immunoblotting conjugated Goat anti-rat HRP conjugated Millipore, Temecula, CA, USA Immunoblotting

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2.2. GENERAL EXPERIMENTAL METHODS

2.2.1. Animal Ethics

All animal experiments relevant to thesis were approved by the Alfred Medical Research and Education Precinct (AMREP) Animal Ethics Committee under project numbers E/1456/2014/B, E/1504/2014/B and T/1419/2014/B (Approval letters are presented in Appendix 4).

2.2.2. General tissue culture

Cell expansion and sub-culture

All cell lines, with the exception of podocytes, were cultured in tissue culture incubators at 37°C and 5% CO2. Growth medium contained 10% FBS and conditional media used for stimulation experiments contained 2% FBS. Human podocytes were initially expanded in culture at 33°C with growth medium (permissive conditions). To prepare these cells for stimulation experiments, they were seeded onto tissue culture plates and ~60% confluent cells were incubated at 37°C with 2% FBS (non-permissive conditions) for 10-12 days. During this time, cells differentiate and adopt the arborized pattern characteristic of mature podocytes. Differentiated podocytes were not sub-cultured.

For sub-culturing, cells were washed with PBS (without Ca2+ and Mg2+) twice and detached from flasks or dishes with 0.05% Trypsin/EDTA (Gibco, USA) and incubating at 37°C for approximately 5 minutes. Once cells were fully detached, the trypsin solution was inactivated with the addition of growth medium containing FBS and cells were collected in 15mL or 50mL plastic tubes. Cells were centrifuged at 1,200rpm for 3 minutes, the supernatant was discarded and cells were resuspended in fresh growth medium. Cells were counted manually with a haemocytometer or using the Countess® II Automated Cell Counter (Thermo Fisher, USA) when required and seeded onto culture dishes for experiments (seeding density varied according to the experiment) or into culture flasks for expansion.

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Stimulation experiments

To assess cellular responses to inflammatory and fibrotic stimuli and study the effect of pharmacological inhibition of Set7, cultured cells were subjected to stimulation experiments. High glucose and Transforming Growth Factor (TGF) b1 stimulation experiments were performed in NHMCs, Podocytes and mouse SMCs. HMEC-1 cells were stimulated with high glucose and Tumour Necrosis Factor (TNF) α. Cells were seeded onto 6-well (or 24-well for NHMCs) plates at a density of 150 x 104 cells per well and cultured normally in growth medium until they reached approximately 70% confluence. The medium was then replaced with conditional medium (2% FBS) with normal (5mM) or high (30mM) glucose and/or 5ng/mL TGFb1 or 10ng/mL TNF α and incubated for 24 or 48 hours. For Set7 inhibition experiments, cells were pre-incubated with (R)-PFI-2 dissolved in DMSO for 24 hours before stimulation and was also added to the conditional media. DMSO was used as a control for (R)-PFI-2 treated cells.

2.2.3. Gene expression studies

In order to determine the expression levels of genes of interest in cells and animal tissues, RNA was isolated followed by cDNA synthesis and quantitative real-time polymerase chain reaction (qRT-PCR).

RNA isolation and quantification

For RNA purification from kidney, liver, muscle, aorta and adipose tissues, small pieces of fresh or frozen tissue were placed in a 1.5mL microcentrifuge tube containing 500µL of TRIzol® (Life Technologies, USA) and homogenised with a Polytron tissue homogeniser (Kinematica, Switzerland) at high speed. Alternatively, zirconium oxide beads were added to the tissues in TRIzol® and tubes were placed in the Bullet Blender® Gold (Next Advance, USA) and homogenised for 5 minutes at high speed. For RNA isolation from cultured adherent cells, the medium was removed from the cells and they were washed with ice-cold PBS followed by the addition of TRIzol® (500µL per well of a 6-well plate). Cells were scraped and the lysates incubated at room temperature for 10 minutes on a rotating wheel. Cell lysates or tissue homogenates were mixed with an equal amount of absolute ethanol and the mixture placed in a Direct-zol™ RNA Mini prep column (Zymo Research, USA) for purification of RNA following the manufacturer’s instructions. Briefly, the sample mixture was centrifuged in the column at 15,000rpm for 52 2 | Materials and Methods

1 minute and flowthrough discarded. The RNA bound to the column membrane was washed twice with pre-wash and wash buffers provided in the kit by centrifugation at 15,000rpm for 1 minute. RNA was eluted with 30-50µL nuclease-free water.

Purified RNA was quantified using a NanoDrop 2000 (Thermo Scientific, USA) or Qiaxpert spectrophotometer (Qiagen, The Netherlands) on a 2µL sample volume. Concentrations and purity of RNA samples were calculated based on their absorbance at

260 (1 A260 = 40µg/mL RNA) and 280nm. A 260/280 ratio of 2 is suggestive of good quality RNA (Wilfinger et al., 1997). For RNA samples intended for sequencing, RNA concentration and quality was also assessed using the MultiNA microchip electrophoresis system (Shimadzu, Japan) using the RNA kit according to the manufacturer’s instructions. Briefly, 3µL of sample and RNA 6000 ladder were mixed with 3µL of RNA marker solution and denatured at 65°C for 5 minutes. Samples and RNA separation buffer containing SYBRÒ Green II and formamide, were loaded onto the MultiNA. RNA quality was determined based on the ratio between the 28S and 18S ribosomal RNA peaks, a ratio of 2 is indicative of high quality RNA. RNA samples were stored at -80°C.

Complementary DNA (cDNA) synthesis cDNA was prepared using the High Capacity cDNA reverse transcriptase kit (Applied Biosystems, USA). A 2X reverse transcription master mix containing MultiScribe™ reverse transcriptase, RT buffer, random primers and deoxyribonucleotides was prepared according to the manufacturer’s instructions. A total RNA amount of 1-2µg was used in a final volume of 20µL. Reverse transcription was performed using the C1000™ (Bio- Rad, USA) or SimpliAmp™ (Thermo Fisher) thermal cyclers with the following parameters: 10 minutes at 25°C, 120 minutes at 37°C and 5 minutes at 85°C. The resulting cDNA was diluted up to 60-80µL with nuclease-free water and stored at -20°C.

Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) qRT-PCR amplification was performed using the QuantiNova® SYBR® Green PCR kit (Qiagen) following the manufacturer’s instructions. A reaction mix containing 2X QuantiNova SYBR green master mix (with ROX reference dye added at a 1% final concentration) and primers targeting the gene of interest was prepared (primer design explained below). A volume of 2µL of sample was used in a total reaction volume of

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13µL. Samples were assayed in duplicate in 96-well reaction plates using the Applied Biosystems 7500 Fast Real-Time PCR or QuantStudio 3 systems, or in 384-well plates using the Applied Biosystems QuantStudio 5 system. The parameters used for amplification are as follows: 95°C for 5 minutes followed by 40 cycles of 95°C for 15 seconds and 60°C for 30 seconds. Gene expression levels were calculated using the 2-∆∆CT method by normalisation to the expression of the endogenous control H3F3A.

Primers for gene expression analysis by qRT-PCR were designed to 1) yield a product between 100 and 200bp in length, 2) span at least 1 intron in the corresponding genomic sequence where possible, and 3) have melting temperatures close to 60°C. The BLAST- like alignment tool (BLAT) from the UCSC genome browser (genome.ucsc.edu) was used to assess the specificity of the primers.

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Table 2.8. Mouse and human cDNA primers used for qRT-PCR Target Forward primer Reverse primer Gene Mouse primers Acta2 AGCAGAACAGAGGAATGCAGTGGAA CCTCCCACTCGCCTCCCAAACAA GAGAC GGAGC Bmper GGTGCGCTGTGTTGTTCATT TTCTCTCACGCACTGTGTCC Ccl2 GTCTGTGCTGACCCCAAGAAG TGGTTCCGATCCAGGTTTTTA Col4a1 GGCGGTACACAGTCAGACCAT GGAATAGCCGATCCACAGTGA Ctgf GCGCCTGTTCTAAGACCTGT AGGTGTCCGGATGCACTTTT Ero1lb TCACTCGGCAGGAAATCGTT CTCTTTGCCCAGTGTCCAAG Fn1 AGACCATACCTGCCGAATGTAG GAGAGCTTCCTGTCCTGTAGAG Gck CGCGAAAGCCGCAGTGAGGA ACCGCTCCTTGAAGCTCGGGT H3f3a GAGCTCCAGCCGAAGGAGAAG CAGTACCAGGCCTGTAACGATGAG Hsp90aa1 GTTACGAGAGCCTGACGGAC ACAATGGTCAGGGTTCGGTC Icam1 CTTCTCTCCGGACTCACCTG GGTATTTCCGGGTGGAGACT Ins2 ACCATCAGCAAGCAGGAAGG GCTTGACAAAAGCCTGGGTG Isl1 CGGCAATCAAATTCACGACCA GGCGCATTTGATCCCGTACA MafA CAAGGAGGAGGTCATCCGAC CTCTCCAGAATGTGCCGCTG Ncf1 CCCTGAGCCCAACTATGCA CCACAGCAGTGTAGGCCTTGA NeuroD1 GCTCCAGGGTTATGAGATCGT CATCTGTCCAGCTTGGGGGA Pcks2 TGGGCTAGACTTGAACGTGG GAGGGTATGGGTAGGGGTCA Pcsk1 TCGCCTTCTTTTGCGTTTGG CCTCCGAGGATGGCTTTTGT Pdx1 TGAAATCCACCAAAGCTCAC CCGAGGTCACCGCACAATCT Rela TCTCACATCCGATTTTTGATAACC CGAGGCAGCTCCCAGAGTT Setd7 CGCTCAGCCACCAGGAGCAC GTCCAGGTGCCCTTCCACGG Slc2a2 AAGGAAGAGGCATCGACTGA TGCCAGCTGTCTGAAAAATG Slc30a8 AGTTGATGGCGTGATCTCCG TTGAGCAATTCCTGTCCGCA Tcf21 GGCCAACGACAAGTACGAGA GTTTGCCGGCCACCATAAAG Tcf7l2 CAAGAGGCAAGATGGAGGGC TGAGGGTTTGTCTGCTCTGG Tmem64 CATCGTGCTTAATGTGGCGG TTGCAGACCACATGAGCGAT Vcam1 TGAAAGGACGTTGATGCAGA ATGCAGCCAAAGAAATCCAC Human primers ACTA2 ACCCTGAAGTACCCGATAGAACAT CAACACGAAGCTCATTGTAGAAAGA CCL2 CAAAGCAGGGCTCGAGTTG CCTGGGACTAGACTTGATGTCTCA CNN1 CTGGCTGCAGCTTATTGATG CTGAGAGAGTGGATCGAGGG COL4A1 CAATATGAAAACCGTAAAGTGCCTT CAGCAAGTAGAGGTCAATGAAGCA ATA CTGF GCGGCTTACCGACTGGAA GGAACAGGCGCTCCACTCT FN1 AGAACAGTGGCAGAAGGAATATCTC CCCGCTGGCCTCCAA H3F3A GGTGTCTTCAAAAAGGCCAA GCGAGAAATTGCTCAGGACT RELA CTCATCCCATCTTTGACAATCGT TGCACCTTGTCACACAGTAGGAA SETD7 TACCGCACGGGTTCTGCACAG CCTCCAGGGTGCTGCCATCAA TAGLN CTCATGCCATAGGAAGGACC GTCCGAACCCAGACACAAGT TCF21 TTCAGGTCACTCTCGGGTTT TGAGGCAGATCCTGGCTAAC VEGF CGAGGGCCTGGAGTGTGT CGCATAATCTGCATGGTGATG Genes presented in this table were selected for analysis based on their relevance to epigenetic regulation, glucose tolerance and diabetic vascular complications. Their role is discussed in the relevant results chapter.

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2.2.4. Protein analysis

In order to determine the expression levels of proteins in cells and tissues, protein extracts were obtained from cells and tissues, separated by gel electrophoresis and proteins of interest detected by immunoblotting. Additionally, immunohistochemistry was used to assess tissue localisation of specific proteins and as a method of quantification.

Whole cell protein extraction and quantification

For tissue samples from kidney, liver, muscle and adipose tissues, small pieces of fresh or frozen tissue were placed in a 1.5mL microcentrifuge tube and washed twice with ice- cold PBS. Tissue samples were homogenised in cold whole cell protein extraction buffer (500µl per 10mg of tissue) as described previously. For cultured adherent cells, the medium was removed and cells washed with ice-cold PBS, scraped from the plates and collected in a 15mL conical tube. Samples were centrifuged at 1,200rpm for 3 minutes, supernatant removed and pellet resuspended in cold whole cell extraction buffer. Cells were then lysed by passing through a 21G needle on a syringe. Homogenates were incubated at 4°C on a rotating wheel for 20 minutes and then centrifuged at 4°C for 20 minutes at 13,000rpm. The resulting supernatant contains the whole cell protein extract.

Sample protein concentrations were determined using the Bradford protein assay as previously described (Bradford, 1976; Ernst and Zor, 2010). Equal volumes of diluted sample (1:500 for tissues, 1:300 for cells) and Bradford reagent (Sigma-Aldrich, USA) were mixed into wells of a 96-well plate and the absorbance was measured at 595nm and 450nm in a microplate reader (Bio-Rad, USA). A BSA standard curve between the 0 to 25mg/mL range was generated to calculate protein concentrations based on the 595/450nm ratio.

Sodium Dodecyl Sulphate-Polyacrylamide Gel Electrophoresis (SDS-PAGE) and Immunoblotting

Protein extract samples (40µg in a 17µl volume) were prepared for PAGE by mixing with 0.5µl 1M DTT and 5.5µL 4X LDS loading buffer followed by incubation at 95°C for 5 minutes. Samples were loaded onto NuPAGE® Novex Bis-Tris 4-12% precast gels (Life Technologies, USA), along with 5µl of SeeBlue® Plus2 prestained protein molecular weight marker (Life Technologies). Gels were run in 1X MOPS running buffer at 130V

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for approximately 2 hours. Prior to transferring, Immobilon®-FL PVDF membranes were washed in absolute methanol and kept in transfer buffer until use. Protein transfer was carried out at 4°C for 2 hours at 100V in 1X transfer buffer with constant agitation. Alternatively, a semi-dry transfer was performed using the Trans-Blotâ Turbo™ Transfer System (Bio-Rad). Transferred membranes were then blocked with 3% BSA in PBS or Odyssey® blocking buffer for 1 hour at room temperature on a rocking platform. Blocked membranes were incubated with the diluted primary antibody and incubated overnight at 4°C on a rocking platform. Primary antibodies were diluted in blocking buffer plus 0.1% Tween-20 as follows: anti-Set7/9 (Cell Signalling Technology, USA) 1:1,000; anti-GAPDH (Abcam, UK) 1:3,000; anti-HA (Roche, Switzerland) 1:2,000; anti-FLAG M2 (Sigma-Aldrich) 1:2,000; anti-Tcf21 (Novus Biologicals) 1:500. The membranes were then washed 4 times for 5 minutes each in TBST and incubated with the corresponding diluted secondary antibodies (1:20,000) for 30-60 minutes at room temperature on a rocking platform. Following this, the membranes were again washed 4 times for 5 minutes each in TBST and rinsed one time in TBS to remove residual Tween- 20. The membranes were read using the Odyssey® infrared imaging system (Li-Cor, USA) which allows for the detection of two different primary antibodies at the same time. The images obtained were analysed using the Image Studio™ software (Li-Cor) and protein concentrations were calculated by normalisation against the expression of the endogenous control (Glyceraldehyde 3-phosphate dehydrogenase, GAPDH). Alternatively, blotting was performed using Immobilon®-P membranes and developed by chemiluminescence by incubating the membrane briefly with enhanced chemiluminescence (ECL) reagent (Sigma-Aldrich). This was performed using the protocol described with the following changes: blocking was performed with 3% BSA in TBST, HRP-conjugated secondary antibodies were used, blots were visualised and imaged using the Chemidoc imaging system (Bio-Rad).

Immunohistochemistry

Tissues were excised and fixed in 10% neutral buffered formalin (3.7-4% formaldehyde in PBS) and sent for processing and paraffin-embedding at the Histopathology Laboratory of the Alfred Pathology Service (Alfred Hospital, Melbourne, Australia). Paraffin tissue sections of 4-5µm were cut using a RM2135 microtome (Leica, Germany), dried overnight at 37°C and stored at room temperature until used for staining. Tissue sections

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were dewaxed by successive washes with xylene and ethanol. For immunohistochemical staining, dewaxed samples were washed twice in PBS and quenched with 3% hydrogen peroxide for 20 minutes, washed and blocked again with 10% normal serum before incubating with the diluted primary antibodies overnight at 4°C in a humidified chamber. Sections to be incubated with collagen IV primary antibody (Southern Biotechnology, USA) were also digested with 0.4% pepsin in 0.01 mol/L HCl at 37°C for 10 min before the blocking step. All sections were then incubated with the appropriate biotinylated secondary antibody followed by horseradish peroxidase-conjugated streptavidin (Vector Laboratories, USA). Sections were visualised with 3,3′-diaminobenzidine tetrahydrochloride/H2O2 (DAB). Finally, sections were counterstained with Mayer’s haematoxylin, dehydrated and coverslipped. All sections were examined under a BX- 50 brightfield microscope (Olympus, Japan) and digitised with a high-resolution camera. All digital quantification (Image-Pro Plus, v6.0) and assessments were performed in a blinded manner.

For immunofluorescence staining, tissue sections were subjected to antigen retrieval by incubating in antigen retrieval buffer (10mM sodium citrate, 0.05% Tween-20, pH 6) at 100°C for 20 minutes. For this protocol, only one blocking step with 1% BSA was used followed by overnight incubation at 4°C in a humidified chamber with primary antibodies (Rabbit anti-insulin 1:500; rabbit anti-glucagon 1:500; rabbit anti-Set7/9, Novus Biologicals, 1:50). Fluorescent anti-rabbit AlexaFluor® 488 secondary antibodies were incubated for 30 minutes. After washing, slides were allowed to dry and a drop of ProLong® Gold mounting medium with DAPI was applied to the section and covered with a coverslip. Once dry, sections were visualised using the Olympus BX61 fluorescence microscope and images were analysed with the ImageJ software when needed.

2.2.5. Lentiviral gene transduction

Lentivirus-mediated delivery of shRNA constructs was used to decrease the expression of genes on interest in cultured cells. This allowed the generation of stable knock-down (KD) cell lines: Set7 KD MIN6, Set7 KD mouse SMCs, Tcf21 KD podocytes.

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Cloning of shRNA-containing vectors

MISSION® pLKO.1-Puro vectors (vector map presented in Appendix 1) containing the desired shRNA sequence (custom-ordered from Sigma-Aldrich) were transformed into MAX Efficiencyä E. coli Stbl2ä competent cells (Invitrogen) (Mouse Set7, mSet7, targeting sequence: CCGTGTTCAGAGATACCAAAT; human Set7, hSet7, targeting sequence: CCAGATCCTTATGAATCAGAA; human TCF21, hTcf21, targeting sequence: CCCGAGAGTGACCTGAAAGAA). Briefly, 10ng of plasmid DNA were added to 25µL of competent cells and incubated on ice for 25 minutes. The cells were then heat-shocked for 30 seconds at 42°C and placed on ice for 2 minutes. S.O.C. medium (Invitrogen) (180µL) was added and cells were incubated at 37°C with shaking for 1 hour. The cells in medium were plated onto LB agar plates containing 100µg/mL ampicillin and incubated overnight at 37°C. Transformant colonies were picked and inoculated into 2mL of LB medium containing 100µg/mL ampicillin and incubated overnight at 37°C. 1.5mL of bacterial cell culture medium were used for plasmid DNA isolation using the Wizard SV Plus Minipreps (Promega, USA) while 500µL of culture medium was taken into 50mL of LB medium and further incubated overnight at 37°C for plasmid DNA isolation using the PureYield™ Plasmid Midiprep system (Promega) according to the manufacturer’s instructions. Briefly, cells were pellet by centrifugation, resuspended in 300µL Cell Resuspension Solution and lysed with 300µL Cell Lysis Buffer and 10µL Alkaline Protease, the reaction was then neutralised with 500µL Neutralisation Solution. The lysates were centrifuged at top speed and the supernatant transferred to Wizard DNA binding columns. Columns were washed twice with Column Wash solution and DNA was eluted with 50µL nuclease-free water. For the Midiprep system, 50mL of bacterial culture was pelleted and resuspended with 3mL Cell Resuspension Solution, lysed with 3mL of Cell Lysis Solution and neutralised with 5mL Neutralisation Solution. Lysates were cleared by centrifugation (4,000rpm for 3 min) using a clearing column and the flowthrough was placed into DNA binding columns and centrifuged. Binding columns were washed once with Endotoxin Removal Wash and once with Column Wash Solution, dried and DNA was eluted with 400µL nuclease-free water. Eluted DNA was quantified using a NanoDrop 2000 or QIAxpert spectrophotometers and stored at -20°C. Targeting sequences were verified by Sanger DNA sequencing at the Monash Micromon facility (Melbourne, Australia).

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Generation of lentiviral particles

The human embryonic kidney-derived cell line 293FT was used as packaging cell line. Cells were seeded onto 35mm culture dishes at a density of 100 x 104 cells per dish (1 dish per shRNA construct) and incubated overnight at 37°C and 5% CO2. Cell transfection was performed using Lipofectamine™ 3000 reagents (Invitrogen) according to the manufacturer’s instructions. Briefly, plasmid DNA containing the shRNA constructs was diluted in P3000 reagent in OptiMEM medium and ViraPower™ packaging mix (Invitrogen) was added. DNA-lipid complexes were formed by mixing the diluted DNA with Lipofectamine™ 3000 in OptiMEM medium and incubating at room temperature for 5 minutes. The culture medium was removed from the 293FT cells and replaced with 750µL of OptiMEM followed by the addition of 250µL of the DNA- lipid complexes. Cells were incubated for 1 hour at 37°C after which 1mL of growth medium containing 10% FBS was added to the dishes. Cells were again incubated for 1 hour at 37°C and 1mL of growth medium containing 20% FBS was added before incubating the cells overnight. Cell culture medium was replaced with fresh 10% FBS growth medium and the cells were further incubated overnight at 37°C. For virus collection, cell culture medium containing the virus was removed and filtered through a 0.45µm low-protein binding filter and centrifuged at 1,200rpm for 3 minutes. Viral supernatants were used immediately or frozen at -80°C. More growth medium was added to the cells; they were incubated overnight at 37°C and the virus-containing supernatant was collected again.

Transduction of lentiviral particles

MIN6, podocytes and smooth muscle cells to be transduced were seeded onto 100mm culture dishes at a density of 150 x 104 cells/dish (1 dish per shRNA construct including a Non-Target shRNA and No-Virus controls) and incubated overnight at 37°C and 5%

CO2. Polybrene (Sigma-Aldrich) was added to the cells at a final concentration of 6µg/mL and dishes were incubated for 30 minutes at 37°C. Culture medium was partially removed from the cells (leaving only 2 mL per dish) and 1 mL of virus-containing supernatant was added slowly. The cells were then incubated at 37°C for 1 hour with gentle mixing every 20 minutes. After the incubation, growth medium was added was the cells were incubated 24 or 48 hours at 37°C after which puromycin was added to the cell culture medium at a

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final concentration of 1µg/mL. Cells were subjected to puromycin selection for 4-5 days and harvested for RNA isolation in order to assess knock-down efficiency.

2.3. EXPERIMENTAL METHODS SPECIFIC TO CHAPTER 3

Set7 wildtype (Set7/+), heterozygous (Set7+/-) and homozygous (Set7-/-) male mice of four weeks of age were allocated to receive either a normal chow diet or a high fat diet (23% fat and 46% calories from fat, Specialty Feeds, Glen Forest, Australia) for 12 weeks. Animals of both groups were subjected to an intraperitoneal glucose tolerance test at 12 weeks of age, metabolic caging at 14 weeks of age and were humanely euthanised at 16 weeks of age by CO2 overload. The strategy for generation of this mouse model is described in Chapter 1, Section 1.9.

2.3.1. Body composition analysis

Fat and lean mass content were determined using the EchoMRITM body composition analyser (EchoMRI, USA). For this purpose, the animals were weighed, placed in provided plastic cylinders and placed in the analyser. All animals were returned to their cages after the measurements were taken.

2.3.2. Metabolic caging

Animals were individually placed in metabolic cages with a pre-weighed amount of food and access to water for 24 hours at the end of which the amount of food and water consumed was determined. Urine produced during this time was also collected and the volume measured.

2.3.3. Blood and plasma measurements

Blood was collected via cardiac puncture and anticoagulated with EDTA (5mM total concentration) in a 1.5mL tube after the animals were euthanized. Whole blood was used for complete blood count using the Sysmex CBC analyser XS100i (Sysmex, Japan). Remaining blood was centrifuged at 6,000rpm for 6 minutes; plasma was separated and stored at -80°C for later use. Plasmatic glucose, cholesterol (total, LDL and HDL) and triglycerides were measured using the Cobas Integra 400 analyser (Roche, Switzerland). Plasma insulin concentrations were determined using the Mouse Ultrasensitive Insulin 61 2 | Materials and Methods

ELISA kit (Alpco, USA) according to the manufacturer’s instructions. Standards, controls and samples (25µL) were added to the wells of 96-well plates pre-coated with anti-insulin antibodies followed by the addition of 75µL of Conjugate solution. Plates were incubated for 2 hours at room temperature with shaking and then washed 6 times. 100µL of TMB substrate was added to each well and plates were incubated for 30 minutes at room temperature with shaking. The colour reaction was terminated with the addition to 100µL of stop solution to each well. The absorbance at 450nm was measured on a plate spectrophotometer and a standard curve was used to calculate the concentration of insulin present in the samples.

2.3.4. Glucose tolerance test

Following a 6-hour fast, animals were weighed and glucose levels were determined from a small blood sample taken from a tail-tip cut using a SensoCard glucometer (POC diagnostics, Australia). The animals were then administered a 2g/kg dose of glucose via intraperitoneal injection. Blood glucose readings were repeated and blood was collected 15, 30, 60 and 120 min after the glucose injection. Plasma was separated from whole blood and kept at -80°C for further analysis.

2.3.5. Pancreatic islet isolation

10-week-old animals were euthanized and their abdomen exposed. The bile duct was clamped at its entrance to the duodenum and a solution of 0.6mg/mL Collagenase P

(Roche) in Hank’s Balanced Salt Solution (Sigma-Aldrich) containing 1M CaCl2 and 2M HEPES was injected into the bile duct. The inflated pancreata were carefully excised, placed in a 50mL conical tube and kept on ice until they were all ready for digestion at 37°C for 5.5 minutes. After digestion, tubes were immediately placed on ice and filled with cold HBSS to stop the reaction and shaken to ensure disruption of the tissue. The homogenate was filtered through a 500µm mesh and the tissue was allowed to drop by gravity; the supernatant containing fat and other impurities was discarded. The pellet was washed once with RPMI 1640 medium (11mM glucose) by centrifuging at 1,000rpm for 2 minutes. 10mL Ficoll-Paque PLUS (GE Healthcare, UK) was added and overlaid with 5mL of RPMI. The top medium phase and interphase (containing the islets) were collected into a new tube and washed once. The medium solution containing the islets was placed onto a 3cm petri dish and islets were hand-picked under a stereomicroscope

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and placed into a new petri dish containing 11mM glucose RPMI 1640 medium with 10%

FBS. The islets were incubated at 37°C, 5% CO2 overnight and collected in TRIzol® for RNA isolation.

2.4. EXPERIMENTAL METHODS SPECIFIC TO CHAPTERS 4 AND 5

In order to study the role of Set7 in the development of vascular complications of diabetes, diabetes-induced renal and vascular damage was assessed in Set7-/-/ApoE-/- and ApoE-/- mice. To generate the Set7-/-/ApoE-/-, Set7-/- mice were back-crossed with the ApoE-/- mouse strain for 10 generations. Initially, female ApoE-/- mice were mated with male Set7-/- mice to generate F1 generation heterozygotes (Set7+/-/ApoE+/-). These animals were mated to obtain the F2 generation genotypes: Set7+/-/ApoE-/- and Set7-/-/ApoE+/- which were then mated to produce the F3 generation with genotypes Set7-/-/ApoE-/- and Set7+/+/ApoE-/-. Male and female Set7-/-/ApoE-/- were mated to generate the double- knockout animals used for these studies.

Six-week-old Set7-/-/ApoE-/- male mice were allocated to control or diabetic groups. Animals in the diabetic group received an intraperitoneal injection of streptozotocin (Sigma-Aldrich) in citrate buffer once daily for five days at a dose of 55mg/kg. Mice in the control group were given citrate buffer (vehicle) injections. Animals were placed in metabolic cages 9 weeks after the induction of diabetes and euthanized the following week by CO2 overload. A summary of experimental procedures is presented in Figure 2.1.

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ApoE-/- or Set7-/-ApoE-/- STZ (or vehicle) Metabolic injection caging Age (Weeks) 6 15 16 Euthanasia Duration of Diabetes: 10 weeks

Euthanasia and tissue harvest

Kidney Aorta

RNA IHC RNA Plaque area RNA-seq RNA-seq qRT-PCR qRT-PCR

Structural damage scores

Figure 2.1. Experimental outline for diabetic complications studies (Chapters 4 and 5) Animals were culled 10 weeks after the induction of diabetes and tissues were harvested. Kidney tissue was divided for paraffin embedding and histological analysis (immunohistochemistry, IHC) and for molecular analyses (snap-frozen in liquid nitrogen for RNA isolation). The aorta was removed and cleaned and the arch was used for determination of plaque area. The remaining aorta was snap-frozen for RNA isolation.

2.4.1. Measurement of general metabolic parameters

Animals were individually placed in metabolic cages 9 weeks after the induction of diabetes. Urine was collected, its volume measured, and stored for subsequent analysis. A blood sample was taken at the end of the metabolic caging period via submandibular bleeding and used for determination of glycated haemoglobin (HbA1c) with the Cobas b101 POC system (Roche). Blood glucose readings were taken weekly from blood samples from a tail-tip cut using the SensoCard glucometer (POC diagnostics). Animals were weighed and glucose level readings taken weekly, these measurements were used to monitor the progression of diabetes. Systolic blood pressure was determined using a 64 2 | Materials and Methods

non-invasive tail cuff blood pressure machine (IITC Life Science, USA). Briefly, mice were placed in clear plastic restrainers in the machine’s warming chamber (<30°C) and their tails fitted through the cuff on the Doppler probe. Blood pressure measurement were taken 5 times for each animal and the best 3 values (based on blood pressure traces given by the machine’s analysis software) were recorded, an average of these was used for analysis.

2.4.2. En face determination of aortic arch plaque area

The aortas were removed from the mice and cleaned of excess fat under a dissecting microscope. Aortas were then divided into arch and thoracic/abdominal. Aortic arch segments were subsequently stained with Sudan IV-Herxheimer’s solution (0.5% wt/vol) and pinned flat onto wax. Images were acquired with a dissecting microscope equipped with a camera. Aortic arch plaque area was quantitated as the percentage area of aorta stained red using the Adobe Photoshop v7.0 software. The remaining section of aorta was snap-frozen for molecular studies.

2.4.3. Determination of urinary albumin excretion

The amount of albumin present in urine (albuminuria) was determined as a measure of glomerular filtration barrier integrity. Albuminuria was determined in urine samples (from metabolic caging) by ELISA using the Mouse Albumin ELISA Quantitation Set (Bethyl Laboratories, USA). Flat-bottom 96-well plates were coated with anti-albumin antibody by adding 100µL of diluted antibody to each well and incubating at room temperature for 1 hour before washing the plate 5 times with wash buffer. The wells were blocked by adding 200µL of Blocking Solution and incubating at room temperature for 30 minutes. The plates were washed 5 times and 100µL of diluted standards or urine samples were added to each well in duplicate. Dilutions were determined for each cohort of samples and were in the range of 1:300 for samples from control mice and 1:60 for samples from diabetic mice. Plates were incubated at room temperature at 1 hour and washed 5 times. 100µL of HRP-linked detection antibody was added to each well at a 1:40,000 dilution and plates were incubated at room temperature for 1 hour. The plates were washed and 100µL of TMB Substrate Solution was added to each well. The plates were developed in the dark at room temperature for 15 minutes and the reaction stopped with the addition of 100µL of Stop Solution to each well. The absorbance at 450nm was

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measured on a plate spectrophotometer and a standard curve was generated to calculate the concentration of albumin in the samples.

2.4.4. Renal Proximal Tubule Cell (PTC) isolation from mice

Set7-/-/ApoE-/- and ApoE-/- mice were euthanised and bled via cardiac puncture as described above. Kidneys were removed with the capsule still intact (4-6 mice per genotype), placed into 50mL conical tubes containing ice-cold PBS and kept on ice until all kidneys were harvested. Inside a biological safety cabinet (Class II, for tissue culture), kidneys were decapsulated, bisected and the medulla separated from the cortex. Renal cortices were minced using a sterile scalpel blade while kept on ice and transferred to a 50mL conical tube. Minced tissue was washed 3 times with cold PBS by centrifugation at 1,500rpm at 4°C for 5 minutes. After the final wash, the tissue suspension was sequentially passed through a 100µm and 70µm cell strainers using cold PBS as needed. Flow-through containing the PTCs was collected and cells were washed 2 times with PBS before resuspending with complete growth medium containing 10% FBS (Table 2.6). Resuspended cells were plates in Collagen I-coated tissue culture flasks and incubated at

37°C, 5% CO2 for 48 hours without disruption. After this period, cells are ready for adherent culture and expansion for later experiments. PTCs were used for stimulation experiments after 2 to 3 passages (original phenotype is lost after 4-5 passages).

2.5. EXPERIMENTAL METHODS SPECIFIC TO CHAPTER 6

In order to explore protein-protein interactions between Set7 and Tcf21, recombinant human Set7 and Tcf21 peptides were produced as follows:

2.5.1. Generation of a mammalian TCF21 expression vector

Primers were designed to amplify the complete human TCF21 coding sequence (Genbank accession number NM_003206.3) from cDNA obtained from cultured human podocytes (Forward primer: GGTCTAGAGCTATGTCCACCGGCTCCCTCA, Reverse primer: AGGATCCTTATCAGGACGCGGTGGTTCCA). The resulting 540bp PCR product was cloned into the XbaI and BamHI restriction sites of a pCGN plasmid expression vector (Tanaka and Herr, 1990) by overnight ligation using T4 DNA ligase (Promega) and transformation into One-Shotä TOP10 chemically competent E. Coli cells (Thermo

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Fisher) as described in section 2.2.5. Plasmid DNA was isolated from transformant colonies as described in Section 2.2.5 and restriction digestion analysis was used to select recombinant plasmid DNA. For this, plasmid DNA was digested with XbaI and BamHI for 2 hours at 37°C and separated by agarose gel electrophoresis. The release of a 540bp fragment confirmed the presence of the Tcf21 insert. The sequence and orientation of the Tcf21 insert was verified by Sanger DNA sequencing at the Monash Micromon facility (Melbourne, Australia). The pCGN vector contains a Hemagglutinin (HA) tag sequence upstream of the multiple cloning site, hence this plasmid is referred to as pCGN-TCF21- HA hereafter.

2.5.2. Immunoprecipitation

293FT cells were transfected with 6µg of pCGN-TCF21-HA or pLVX-SET7-FLAG (previously described in Okabe et al., 2012, and Tuano et al., 2016) DNA per well on a 60mm plate using Lipofectamine 3000 as described in Section 2.2.5. For co- immunoprecipitation (Co-IP) experiments, 3µg of plasmid DNA from each vector were co-transfected. After 48 hours, cells were washed with cold PBS and lysed with buffer K followed by sonication at high amplitude for 25 seconds in a Q800R2 water bath sonicator (QSonica, USA). The lysates were incubated for 10 minutes at 4°C and centrifuged at 13,000rpm for 20 minutes at 4°C. The protein-containing supernatants were incubated with anti-HA agarose (Sigma-Aldrich) or anti-FLAG M2 affinity gel (Sigma-Aldrich) for 2 hours (for Co-IP) or overnight (for recombinant peptide elution) at 4°C. Peptide-bound beads were washed 3 times with buffer K, elution of the recombinant HA-TCF21 peptide was achieved by incubating the washed beads with 50mM glycine pH 2.5 solution for 5 minutes, immediately followed by the addition of 1 drop of 1M Tris-HCl to neutralise the reaction. FLAG-SET7 was eluted by incubating the washed beads with diluted FLAG peptide (Sigma-Aldrich) at 4°C for 20 minutes. For Co-IP experiments, the peptide-bound washed beads were directly mixed with LDS protein sample buffer and 1µL 1M dithiothreitol (DTT) and incubated at 95°C for 5 minutes. IP material was analysed by immunoblotting as described in section 2.2.4

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2.6. RNA-SEQUENCING AND BIOINFORMATICS ANALYSIS

2.6.1. Library construction and RNA sequencing

RNA was isolated from kidney cortex and whole aorta as described in Section 2.2.3. NEBNext® Poly(A) mRNA Magnetic Isolation Module (New England BioLabs, USA) was used to enrich mRNA from 1 µg of total RNA. Barcoded libraries were generated using the NEBNext® Ultra™ Directional RNA Library Prep Kit for Illumina® (New England BioLabs) following the manufacturer’s instructions. Briefly, mRNA was enriched using Oligo d(T)25 beads and fragmented in the presence of divalent cations under high temperature. mRNA fragments were used for cDNA synthesis and purified, subjected to end-repair and adaptor ligation. Ligation reactions were purified and libraries enriched by PCR. Libraries were quantified on the MultiNA bioanalyzer (Shimadzu) and pooled to equimolar ratios for sequencing. Deep sequencing was performed using Illumina Hiseq 2500 using version 4 kits for 100 cycles at the Australian Genome Research Facility (Melbourne, Australia).

2.6.2. Read alignment and differential gene expression

Sequence reads underwent quality trimming with Fastx-Toolkit. Trimmed reads were mapped to the mouse genome using STAR aligner. Tags aligning to genes were counted using FeatureCounts with Ensembl annotations. Changes in gene expression were determined using edgeR (bioconductor). Genes with less than 10 reads average across all samples were excluded from the analysis. The P value threshold for False Discovery Rate (FDR) filtering for family wise error rate control was set to 0.05. Default parameters were used and the prior.df was set to 20.

2.6.3. Data visualisation

All plots were generated in R using default plotting functions unless otherwise specified. Heatmaps were produced using ComplexHeatmaps. MA plots were generated as scatterplots of the logFC against the logCPM (Concentration). Multidimensional Scaling (MDS) plots were generated using the filtered gene matrix (genes with less than an average of 10 reads per gene across all samples were removed) by calculating the Euclidean distances between samples and applying Classical (Metric) Multidimensional Scaling with two dimensions. Correlation plots to visualise the correlations of gene

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expression between different datasets from the same study, the log2FC for one dataset was plotted against the log2FC of the other. A linear model was plotted on each and the Pearson product-moment correlation coefficient (r) was calculated. Contour maps were generated using Two-Dimensional Kernel Density Estimation.

2.6.4. TCF21 motif analysis

The DNA binding motif for Tcf21 was obtained from JASPAR (mouse reference genome mm10). The genome-wide position weight matrix (PWM) scanner was used to identify TCF21 binding sites in the mouse genome (settings: lowest logs-odd score: -10000; log- odds scaling factor: 100; p-value cut-off 0.00001; bg base composition: 0.29, 0.21, 0.21, 0.29; search strand: both; reference position: 7; non-overlapping matches: true). Results were downloaded as a bed file.

Bed files were annotated to the gene promoters, the gene body or enhancers from the Ensembl mouse genome mm10, release 78. Gene promoter were defined as 2Kb either side of the transcriptional start site (TSS). Kidney enhancer regions were obtained from the ENCODE project and the regions were expanded out 2Kb either side of the enhancer. Annotation was performed using bedtools intersect. A motif was annotated if 10% of its base pairs intersect with the region of intersect. The annotated files were filtered to include only the top 5000 motifs (ranked by the score provided by PWMScan). Fisher’s method for combing p-values was used to determine the relationship between TCF21 motifs and gene expression.

2.6.5. Gene Set Enrichment Analysis (GSEA)

GSEA was used for pathway and transcription factor analysis (Subramanian et al., 2005). Transcripts from microarray and NGS expression data were assigned a score based on the negative log10 of the p value multiplied by the sign of the fold change (–logP*signFC). GSEA was run using classical scoring with 1000 permutations. Only gene sets between 30 and 5000 genes were included in the analysis. Pathways were obtained from the Molecular Signatures Database (MSigDB) (version 4). For transcription factor analysis, gene sets of transcription factor target genes were generated from ENCODE (Encyclopedia of DNA elements) transcription factor binding site (TFBS) ChIP-seq bed files. TFBS bed file represents a list of genome regions targeted by transcription factor

69 2 | Materials and Methods

binding. Target genes were defined as possessing a TFBS within 3kb either side of the TSS.

2.6.6. Fisher’s exact test

Fisher’s exact test was used to assess whether two variables were statistically likely to occur together. For example, in order to determine whether increases in variable 1 and variable 2 were associated, the following matrix was generated:

Variable 1 up-regulated Variable 1 not up- (FDR p val <0.05) regulated* Variable 2 up-regulated Number of genes Number of genes (FDR p val <0.05) Variable 2 not up- Number of genes Number of genes regulated* *includes transcripts reduced in expression as well as those that do not change in expression.

This matrix was generated for both up- and down-regulated variables suspected of being associated. Fisher’s exact test was applied in R using this matrix. Results were provided as odds ratios and a p value of <0.05 was considered significant.

2.7. Statistical analysis

Statistical significance was determined by one or two-way ANOVA with Tukey’s post hoc testing for multiple comparisons (when applicable) using GraphPad Prism 7. Results were considered statistically significant with a p value less than 0.05. All results are presented as the group mean ± Standard Error of the Mean (SEM).

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CHAPTER 3

METABOLIC CHARACTERISATION OF

SET7 DEFICIENT MICE

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3.1. ABSTRACT

Products of intermediary metabolism are required for post-translational protein modifications that affect protein function and stability. Histone protein acetylation and methylation have the ability to influence gene expression, thus directly linking metabolic pathways with gene regulation. Conversely, histone-modifying proteins have been implicated in the regulation of genes involved in metabolic regulation. The histone demethylase JmjC2A and methylase MLL3 are associated with adipogenesis and their genetic deletion causes a clear metabolic phenotype in mouse models. The lysine methyltransferase Set7 is implicated in the transcriptional regulation of the insulin gene transcription and the maintenance of glucose homeostasis. However, whether Set7 plays a role in the regulation of major metabolic pathways remains unknown. To address this, physiological and molecular analyses were performed using mice with a genetic deletion of Set7. This methyltransferase was found to regulate gene expression in pancreatic β cells, however, the deletion of the Set7 gene did not affect glucose homeostasis in the animals studied. Furthermore, body weight, body composition and plasma lipid levels were not affected by Set7 deletion in either normal or high fat fed animals. These results suggest that, despite participating in the regulation of metabolic genes, Set7 is not necessary for maintaining metabolic homeostasis in adult mice.

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3.2. INTRODUCTION

There is increasing interest in the relationship between epigenetics and metabolism, particularly regarding the influence of substrate availability on methylation and demethylation reactions. Both DNA and histone methylation reactions require the methyl donor S-adenosyl methionine (SAM) which is generated from the amino acid methionine and ATP through several metabolic pathways, thus linking intermediary metabolism to histone methylation (Kaelin and McKnight, 2013).

Histone methyltransferases (HMTs) and lysine demethylases (KDMs) have been implicated in the regulation of key metabolic pathways (Lee et al., 2008a; Inagaki et al., 2009; Tateishi et al., 2009). The JmjC-domain-containing histone demethylase 2A (JmjC2A) removes mono and dimethyl groups from histone H3 lysine 9 (H3K9), an important heterochromatin mark (Martin and Zhang, 2005). JmjC2A knockout (JmjC2a-/-) mice develop a metabolic syndrome characterized by obesity and dyslipidaemia (Inagaki et al., 2009; Tateishi et al., 2009). These mice have alterations in brown fat and skeletal muscle metabolism, and their phenotypic and biochemical characterization revealed that JmjC2A is responsible for peroxisome proliferator- activated receptor γ (PPARγ) activation following β-adrenergic stimulation (Tateishi et al., 2009). On the other hand, mice expressing an inactive version of the H3K4 methyltransferase MLL3, have less white fat and fibroblasts that are less responsive to adipogenic stimuli (Lee et al., 2008). Furthermore, the PPARγ coactivator 1α (PGC1α) is a key transcription factor for cellular metabolic adaptation that is subject to extensive posttranslational regulation (Aguilo et al., 2016; Lerin et al., 2006). It is inactive when unmethylated, but methylation of lysine residue 779 by Set7 results in PGC1α activation and promotes the expression of genes needed for cellular responses to energy depletion (Aguilo et al., 2016b; Lerin et al., 2006).

Additionally, Set7 is involved in the regulation of insulin secretion from pancreatic β cells at a transcriptional level, potentially implicating this lysine methyltransferase in the regulation of glucose metabolism. The β cell-specific transcription factor Pdx-1 maintains an “open” chromatin state by mediating the Set7-dependent methylation of H3K4 and recruiting other histone modifiers such as p300 to its target genes (Babu et al., 2007; Deering et al., 2009). A recent study suggests that Set7 directly methylates Pdx1 to enhance its transcriptional activity and shows that mice with β-specific Set7 deficiency

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have impaired glucose tolerance (Maganti et al., 2015). This study provides pre-clinical evidence that Set7 participates in the regulation of glucose homeostasis. However, very little is known about the consequences of global Set7 deletion in metabolic regulation. Additionally, there have been conflicting reports on the effects of Set7 deletion in different mouse models (Chapter 1, Section 1.9). In this sense, it is important to determine the effect of the genetic Set7 deletion in the experimental model at hand.

The work in this chapter was aimed at characterizing the Set7 knockout (Set7-/-) mouse model to determine whether this enzyme is involved in regulating metabolic pathways, with special attention to glucose metabolism. It includes molecular analyses and immunostaining of tissues to assess Set7 expression and metabolic profiling of the animals, i.e. body measurements, determination of plasma metabolites. The results presented here provide important information to determine the suitability of this mouse model for studying diabetic complications.

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3.3. RESULTS

3.3.1. Set7 expression is reduced in tissues from Set7+/- Set7-/- mice

Histone modifying enzymes are ubiquitously expressed, although their expression levels vary in different tissues. To confirm the global deletion of Set7 in the mouse model generated for this project (described in Chapter 1, section 1.9), tissues from wildtype (Set7+/+), heterozygous (Set7+/-) and homozygous (Set7-/-) mice were used to examine the expression of Set7 mRNA and protein. Set7 is expressed in skeletal muscle, white adipose tissue (fat), liver and kidneys of wildtype animals and, as expected, not expressed in tissues from homozygous mice. Set7 mRNA and protein were reduced in heterozygous animals by approximately 50% (Fig. 3.1A). Set7 mRNA levels were highest in skeletal muscle, followed by kidney, white adipose tissue and liver. A similar pattern was observed for Set7 protein in these tissues (Fig. 3.1A). The expression levels of other histone modifiers such as the lysine demethylase 1 LSD1 and H3K9 methyltransferase Suv39h1 were not altered by Set7 deletion in the tissues assessed (Fig. 3.1B).

Studies by Deering et al. (2009) show that Set7 is also expressed in the pancreas, where it localises specifically to the islets of Langerhans. The results presented here show that Set7 mRNA was significantly reduced in pancreatic islets isolated from Set7+/- and was not detected in Set7-/- (Fig. 3.2A). Immunofluorescent staining of paraffin-embedded pancreatic tissue showed that Set7 is specifically found in pancreatic islets and not in exocrine tissue and confirmed the absence of the enzyme in islets of Set7-/- animals (Fig. 3.2B).

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+/+ +/- -/- +/+ +/- -/- +/+ +/- -/- +/+ +/- -/- A Set7 25 Setd7 mRNA GAPDH Sk Muscle Fat Liver Kidney 20 1.5

1.0 10 Relative expressio n 5 0.5 Fold Chang e

0 Sk Muscle Fat Liver Kidney 0.0 Sk Muscle Fat Liver Kidney

B Suv39h1 mRNA 15 2.5 LSD1 mRNA

2.0 10 1.5

1.0 5 0.5 Relative expressio n Relative expressio n

0 0.0 Sk Muscle Fat Liver Kidney Sk Muscle Fat Liver Kidney

Set7+/+ Set7+/- Set7-/-

Figure 3.1. Expression levels of Set7 and other histone methyltransferase enzymes in mouse tissues RNA and whole-cell protein were isolated from tissues from 16-week-old Set7+/+, Set7+/- and Set7-/- mice. A) Set7 mRNA by qRT-PCR (left) and protein by semi-quantitative Immunoblotting (right). Set7 is expressed in wildtype skeletal muscle, fat, liver and kidneys at both RNA (left) and protein (right) levels, with a significant reduction in heterozygous animals. Set7 is not detected in homozygous tissues. B) The mRNA levels of the HMT Suv39h1 (left) and lysine demethylase LSD1 (right) were unchanged in tissues of Set7-deficient mice. n=4 for RNA and 3 for protein per genotype.

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A B

1.5 Setd7 mRNA e

1.0 fold chang

A 0.5 mRN

0.0 Set7+/+ Set7+/-

Set7 / DAPI

Figure 3.2. Set7 expression in pancreatic tissue Set7 mRNA and protein levels were decreased in islets isolated from Set7+/- mice and undetected in Set7-/- islets. (A) qRT-PCR for Set7 mRNA in isolated pancreatic islets, (B) Immunofluorescent staining of formalin-fixed, paraffin-embedded pancreatic sections from Set7+/+, Set7+/- and Set7-/- animals using anti-Set7 antibodies. Nuclei are counterstained with DAPI. Magnification: upper panel 200X, lower panel 400X. n=3 per group

3.3.2. Set7 deficiency alters body composition without changes in plasma lipids

Deficiency of certain histone-modifying enzymes in animal models can have a profound effect in metabolic regulation and result in phenotypic alterations (Inagaki et al., 2009; Lee et al., 2008; Tateishi et al., 2009). Set7+/+, Set7+/- and Set7-/- mice were subjected to metabolic characterisation in 10order to investigate a potential role for Set7 in metabolic regulation. To further assess the metabolic phenotype of Set7-deficient mice, they were also placed on a high fat diet (HFD) (45% calories from fat) for 12 weeks. High-fat feeding induces insulin resistance and obesity in mice, making it a valuable model for studying impaired glucose tolerance and type 2 diabetes (Wang and Liao, 2012; Winzell and Ahrén, 2004). Mice fed a HFD diet have a higher demand for insulin and an increase in circulating free-fatty acids, both of which can cause β dysfunction (Priyadarshini et al., 2017). This combination of factors creates metabolic stress that may reveal phenotypic characteristics that are not evident in normal conditions (i.e. normal chow). For example, the response to HFD can uncover obesity prone or resistant phenotypes (Choi et al., 2016).

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Animals on high fat diet gained weight at the same rate (Fig. 3.3) and were approximately 10% heavier than their normal chow counterparts at the experimental end-point (Table 3.1). They also had increased body weight, fat content (Table 3.1) and plasma cholesterol levels compared to chow-fed animals, independently of their genotype (Table 3.3). These results suggest the high fat diet caused weight gain due to fat accumulation in all animals independently of Set7 expression.

40 Weight gain on High Fat Diet

Body Weight (g) Body Weight 10 Set7+/+ Set7+/- Setd7-/-

0 0 1 2 3 4 5 6 7 8 9 10 11 Weeks on HFD

Figure 3.3. Body weight gain in high fat fed mice Animals on the high fat diet group were weighed weekly to track the gain of weight over the 12 weeks of high fat feeding. Weight gain in these animals was independent of their genotype. n= 6- 9 per genotype.

Body composition was determined by EchoMRI (as described in Chapter 2, Section 2.5.1) before euthanasia. Chow-fed Set7-/- animals had a modest but statistically significant increase in the proportion of fat mass compared to their Set7+/+ counterparts (12.07±1.04 vs. 8.86±1.17 as percentage of body weight, respectively, P=0.049) as well as a decrease in lean mass (86.13±1.19 vs. 90.21±1.17 as percentage of body weight, respectively, P=0.023) despite having similar body weight (Table 3.1). There were no significant differences in body composition between high-fat fed wildtype and Set7-deficient mice, although there was a trend towards higher fat content due to Set7 deletion.

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Data obtained from metabolic caging showed no significant differences in food and water intake or urine output between all three genotypes in both diet groups (Table 3.2). Hence the difference in body composition is not likely due to differences in appetite and food intake but rather the result of their developmental process.

Table 3.1. Body composition analysis by EchoMRI Set7+/+ Set7+/- Set7-/- Normal Chow Body weight (g) 30.39 ± 0.59 28.50 ± 0.73 31.19 ± 0.50 Fat mass (g) 2.70 ± 0.42 2.64 ± 0.23 3.94 ± 0.37* Lean mass (g) 27.39 ± 0.48 25.43 ± 0.62* 26.83 ± 0.55 Fat/lean mass ratio 9.86 ± 1.48 10.35 ± 0.86 14.36 ± 1.36* Fat mass (% body weight) 8.86 ± 1.17 9.14 ± 0.75 12.07 ± 1.04* Lean mass (% body weight) 90.21 ± 1.17 89.43 ± 0.79 86.13 ± 1.19* High Fat diet Body weight (g) 33.83 ± 1.01 33.44 ± 0.86 33.88 ± 1.26 Fat mass (g) 5.79 ± 0.89 6.27 ± 0.92 7.97 ± 0.93 Lean mass (g) 26.36 ± 0.49 26.43 ± 0.51 26.47 ± 1.03 Fat/lean mass ratio 22.00 ± 3.37 23.81 ± 3.48 30.60 ± 3.66 Fat mass (% body weight) 16.86 ± 2.13 18.35 ± 2.16 23.34 ± 2.26 Lean mass (% body weight) 78.16 ± 2.04 79.38 ± 2.26 78.35 ± 2.55 *p<0.05 vs. Set7+/+, n= 12-14 per genotype chow, 6-9 per genotype HFD.

Table 3.2. Metabolic caging data Set7+/+ Set7+/- Set7-/- Normal Chow Food intake (g) 2.59 ± 0.28 2.99 ± 0.22 2.48 ± 0.26 Water intake (ml) 3.05 ± 0.53 2.87 ± 0.32 3.64 ± 0.48 Urine output (ml/24hrs) 0.62 ± 0.14 0.61 ± 0.11 0.96 ± 0.19 High Fat diet Food intake (g) 1.78 ± 0.37 0.96 ± 0.12 0.87 ± 0.13 Water intake (ml) 1.72 ± 0.57 2.98 ± 0.51 2.99 ± 0.84 Urine output (ml/24hrs) 0.59 ± 0.23 0.49 ± 0.13 0.96 ± 0.19 n= 6-9 per genotype

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3.3.3. Set7 deficiency does not alter blood cell composition or plasma lipid concentrations

Blood was collected via cardiac puncture at the time of euthanasia to perform basic blood cell count and plasmatic determination of glucose, insulin and lipids as described in Chapter 2, Section 2.5.3 (Table 3.3). Chow-fed Set7+/- animals had a lower count of white blood cells (WBC) and higher haemoglobin content when compared to wild type animals. These differences were not statistically significant when compared to Set7-/- animals. Chow control Set7+/- and Set7-/- mice had higher blood glucose levels compared to wild type animals (15.79±0.67 and 16.82±1.01 vs. 13.34±0.43, P=0.007 and 0.008 respectively). However, this was not associated with lower plasmatic insulin levels. Additionally, there were no significant differences in plasma levels of cholesterol (total, HDL and LDL) or triglycerides between wild type and Set7-deficient animals. Besides a modest reduction in haemoglobin in Set7-/- mice (138.38±2.5 vs. 151.25±3.90 in Set7+/+, P= 0.017), there were no differences between genotypes in blood count or lipid parameters measured in the high-fat fed group. Taken together, these results show that Set7 may be implicated in glucose metabolism. However, Set7 deficiency does not cause detectable changes in lipid metabolism.

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Table 3.3. Basic blood cell count and plasma parameters Set7+/+ Set7+/- Set7-/- Blood cell count Normal Chow WBC (x102/µl) 9.98 ± 0.79 7.12 ± 0.79* 8.13 ± 0.89 RBC (x106/µl) 9.49 ± 0.23 10.08 ± 0.11* 9.66 ± 0.11 Haemoglobin (g/l) 135.5 ± 2.4 150.7 ± 1.5* 141.1 ± 2.4 Platelets (x105/µl) 10.76 ± 1.06 10.82 ± 0.54 10.62 ± 0.39 High Fat diet WBC (x102/µl) 11.08 ± 1.44 11.21 ± 0.94 9.00 ± 1.13 RBC (x106/µl) 9.97 ± 0.31 9.43 ± 0.26 9.95 ± 0.12 Haemoglobin (g/l) 151.25 ± 3.90 141.25 ± 4.04 138.38 ± 2.5* Platelets (x105/µl) 11.70 ± 0.36 11.81 ± 0.84 14.14 ± 1.04 Biochemical parameters Normal Chow Glucose (mmol/l) 13.34 ± 0.43 16.82 ± 1.01* 15.79 ± 0.67* Insulin (ng/ml) 1.24 ± 0.09 1.32 ± 0.22 1.79 ± 0.27 Total cholesterol (mmol/l) 2.53 ± 0.14 2.71 ± 0.12 2.41 ± 0.09 HDL-c (mmol/l) 2.16 ± 0.12 2.34 ± 0.09 2.05 ± 0.08 LDL-c (mmol/l) 0.24 ± 0.02 0.24 ± 0.01 0.23 ± 0.02 Triglycerides (mmol/l) 1.13 ± 0.14 1.20 ± 0.14 1.26 ± 0.13 High Fat diet Glucose (mmol/l) 17.69 ± 1.37 17.41 ± 1.00 16.11 ± 0.62 Insulin (ng/ml) 2.35 ± 0.20 2.65 ± 0.11 3.14 ± 0.36 Total cholesterol (mmol/l) 3.37 ± 0.39 3.17 ± 0.39 3.11 ± 0.35 HDL-c (mmol/l) 2.71 ± 0.24 2.50 ± 0.20 2.57 ± 0.22 LDL-c (mmol/l) 0.45 ± 0.12 0.37 ± 0.09 0.41 ± 0.10 Triglycerides (mmol/l) 1.03 ± 0.02 1.02 ± 0.07 1.00 ± 0.08 *p<0.05 vs. Set7+/+, blood cell count: n=13-15 per genotype normal chow, 5-8 per genotype HFD; plasma parameters: n=6-8 per genotype.

3.3.4. Genes associated with insulin secretion are down-regulated in Set7-deficient β cells

Several studies have shown that Set7 may influence insulin secretion by regulating gene expression in pancreatic β cells (Deering et al., 2009; Maganti et al., 2015). In order to further investigate the role of Set7 in this cell type, cultured β cells as well as pancreatic islets isolated from Set7+/+, Set7+/- and Set7-/- mice were used for gene expression studies. Following lentivirus-mediated Set7 knock-down (Set7KD, ~30% of normal Set7 expression), MIN6 cells were harvested for gene expression analysis. Set7 KD led to a significant decrease in the expression of the genes involved in glucose-stimulated insulin secretion such as those encoding the glucose transporter GLUT2 (Slc2a2), glucokinase (Gck), proprotein convertase (Pcsk1) and Ero1-like protein B (Ero1lb) (described in Chapter 1, section 1.2) (Fig. 3.4A). However, the expression of the Ins2 gene remained 81 3 | Metabolic characterisation

unchanged, while Pdx1 mRNA was increased after Set7 KD. These results suggest that Set7 may regulate the expression of genes involved in insulin secretion but this may not initially affect insulin gene transcription.

To explore whether Set7 deficiency led to gene expression changes in β cells in vivo, pancreatic islets isolated from Set7+/+, Set7+/- and Set7-/- mice were also used for gene expression analysis. mRNA levels were determined for β cell-specific genes involved in glucose sensing, insulin synthesis and encoding transcription factors (Fig. 3.4B). Consistent with the experimental results in Set7KD MIN6 cells, there was a significant down-regulation of the Slc2a2, Gck and Ero1lb mRNAs in islets from Set7+/- and Set7-/- animals. Genes encoding transcription factors such as Pdx1, Isl1, NeuroD1, enzymes like Pcsk1 and the Slc30a8 zinc transporter as well as the insulin gene (Ins2) were also downregulated in Set7-deficient islets. These observations suggest that permanent Set7 deficiency in vivo affects insulin gene transcription. This may result from decreased levels of transcription factors that contribute to activation of the insulin gene promoter such as Pdx1, NeuroD1, MafA, Isl1, Tcf7l2 as well as other components of the glucose- stimulated insulin secretion (GSIS) process such as GLUT2 (Slc2a2), glucokinase, proprotein convertase 2 and the zinc transporter Slc30a8 (Chapter 1, Section 1.2).

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A MIN6 cells 2.0 Control * * Set7KD 1.5

1.0

fold chang e *** * * 0.5 *** mRN A ****

0.0 Setd7 Pdx1 MafA Ins2 Slc2a2 Gck Ero1lb Isl1 Pcsk1 Pcsk2

B Isolated pancreatic islets

1.5 Set7+/+ Set7+/- Set7-/-

1.0 * * * * * fold chang e * * * 0.5 * * * **** * mRN A

0.0 **** Setd7 Slc2a2 Gck Ins2 Pcsk1 Slc30a8 Tcf7l2 Isl1 MafA NeuroD1 Pdx1 Glucose Insulin synthesis Transcription factors sensing and secretion

Figure 3.4. Set7 deficiency in pancreatic β cells causes gene expression changes in vitro and in vivo A) Set7KD was achieved in MIN6 murine β cell line by lentivirus-mediated shRNA delivery. Control and Set7KD cells were cultured under the same conditions and RNA was isolated (n= 3 independent experiments) for determination of the expression levels of β cell-specific genes involved in insulin secretion. B) Pancreatic islets were isolated from Set7+/+, Set7+/- and Set7-/- mice n=3-4 (independent isolations) and used for RNA isolation in order to assess the expression of β cell-specific genes *P<0.05, **P<0.001, ***P<0.0001 vs. control/ Set7+/+

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3.3.5. Set7-/- mice have normal islet numbers and architecture

Given the gene expression differences observed in Set7-deficient β cells and islets, the number, size and shape of islets was assessed in the pancreas of Set7+/+ and Set7-/- mice. In order to do this, paraffin-embedded pancreas sections were stained for insulin or glucagon by immunofluorescence. Islets from all mice studied had the expected structure, with a large core of insulin-producing β cells surrounded by less abundant glucagon- producing α cells (Fig. 3.5). For quantification and measurement of islet size, sections stained for insulin were used. Islets were defined as any area of more than five insulin- positive cells and counted in 10 serial sections 100µm apart. Relative islet area was measured using the area tool from the ImageJ software. The results showed that there was no significant difference in islet number or size between wild type and Set7-/- mice, suggesting that Set7 is not required for normal endocrine pancreas development and that any potential effect in glucose homeostasis would be to reduced function rather than number of pancreatic β cells

Figure 3.5. Pancreatic islet structure in Set7+/+ and Set7-/- mice Immunofluorescent staining of formalin-fixed, paraffin-embedded pancreatic sections from Set7+/+ and Set7-/- animals using anti-insulin (green; A and B, D and E) and anti-glucagon (green; C and F) antibodies. Nuclei are counterstained with DAPI (blue). Magnification: A and D 100X; B-F 400X. Images are representative of 3 animals per group.

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A B 80 150 Set7+/+ 60 Set7 -/- 100 40

50 20 Number of islets Number of islets

0 0 Set7+/+ Set7-/-

0-2500 >90000 2501-5000 5001-10000 10001-2000020001-3000030001-4000040001-5000050001-6000060001-7000070001-8000080001-90000 Islet size (pixels) Figure 3.6. Morphometric details of pancreatic islets from Set7+/+ and Set7-/- animals Immunofluorescence staining (anti-insulin) was used to assess the number (A) and relative size (B) of the islets in pancreatic tissue sections. There was no change in these parameters between the genotypes. n=3 animals per group.

3.3.6. Set7-deficient mice have normal glucose tolerance

The results above suggest that Set7 is involved in the transcriptional regulation of β cells and implicate this enzyme in the regulation of glucose homeostasis. In order to investigate this pre-clinical role, intraperitoneal glucose tolerance tests (ipGTT) were performed as described in the methods chapter (Section 2.3.4).

Upon the glucose challenge, chow-fed Set7+/- and Set7-/- animals had higher blood glucose levels when compared to their wild type counterparts. However, this increase was not statistically significant (Fig. 3.7). Indeed, blood glucose levels returned to basal levels after 120 minutes in all chow-fed mice indicating Set7 deficiency does not cause a major disturbance in glucose tolerance.

Animals fed a high fat diet for 12 weeks became glucose intolerant and had a much higher blood glucose peak (Figure 3.7). As with the chow-fed group, there were no genotype- dependent differences in glucose tolerance in high-fed mice.

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Normal Chow High Fat Diet

50 50

40 40

30 30

20 20

10 10 Blood Glucos e (mmol/l) Blood Glucos e (mmol/l)

0 0 0 15 30 60 120 0 15 30 60 120 Time (min. after glucose injection) Time (min. after glucose injection)

400 400

300 300

200 200

100 100 Blood glucose (% of basal ) Blood glucose (% of basal ) 0 0 0 15 30 60 120 0 15 30 60 120 Time (min. after glucose injection) Time (min. after glucose injection)

+/+ +/- -/- Set7 Set7 Set7

Figure 3.7. Intraperitoneal glucose tolerance test in chow- and high-fat fed Set7+/+, Set7+/- and Set7-/- animals An ip injection of 2g/Kg of glucose as performed and blood glucose measured from the tail tip at 15, 30, 60 and 120 minutes later. Blood glucose levels are expressed as absolute values in mmol/l (top) and relative to basal levels (bottom). n=6-9 per genotype

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3.4. DISCUSSION

3.4.1. Set7 is a ubiquitous protein and its expression is reduced in Set7 knock out mice

Set7 mRNA and protein were found expressed in all tissues tested. As expected, Set7 levels (both mRNA and protein) were reduced in approximately 50% in tissues from Set7+/- mice and were not detected in Set7-/- tissues. Set7 is highly expressed in skeletal muscle, fat and kidney and was detected at low levels in the liver. Similar expression patterns have been reported, highlighting the possibility that Set7 participates in global cellular regulation processes and that it might be particularly relevant in metabolically active tissues like skeletal muscle and fat (Campaner et al., 2011; Tao et al., 2011). Indeed, Set7 has been implicated in the process of skeletal and smooth muscle cell differentiation (Tao et al., 2011; Tuano et al., 2016). This enzyme was also found to be highly expressed specifically in pancreatic islets as opposed to pancreatic exocrine tissue (Deering et al., 2009). The islet-specific pattern of Set7 expression in the pancreas was confirmed in the present study by immunofluorescent staining in pancreata from Set7+/+ and Set+/- mice. It was also suggested that the expression of Set7 mRNA and protein in different mouse tissues are not correlated, suggesting potential post-transcriptional regulatory events affecting Set7 expression (Deering et al., 2009). This was not evident in the present model where the levels of Set7 mRNA as determined by qRT-PCR correlated to those of Set7 protein assessed by immunoblotting in mouse skeletal muscle, fat, liver and kidney.

There are conflicting reports on the effect of Set7 deletion in mice. The first Set7-/- mouse model was generated to study the enzyme’s role in regulating the activity of the tumour suppressor p53 (Kurash et al., 2008). The deletion of Set7 was found to be embryonically lethal in about 50% of pups, however adult Set7-/- were reportedly healthy and fertile (Kurash et al., 2008). In contrast, independent studies using different Set7-/- mouse lines reported that mice with a deletion of Set7 were born at a normal Mendelian ratio from Set7+/- parents and developed normally to adulthood (Campaner et al., 2011; Lehnertz et al., 2011). The Set7-/- mouse model described here is related to the one described by Kurash et al. (2008) where deletion of exon 2 on the Setd7 gene was generated by loxP and Cre-recombinase excision. The Set7-/- mice described in this chapter were fertile and

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viable. Their birth ratios from Set7+/- crosses were not assessed; however, offspring were obtained from inbred Set7-/- crosses in similar numbers as with Set7+/+ crosses.

3.4.2. Set7 participates in the transcriptional activation of several β cell-specific genes but is not required for glucose tolerance

Set7 has been implicated in numerous pathways either by promoting the expression of target genes through H3K4 methylation or regulating protein activity was well as stability (summarised in Tables 1.1 and 1.2). The experiments described in this chapter aimed to study the involvement of Set7 in metabolic regulation with a focus on glucose homeostasis.

Previous reports have shown that reduced levels of Set7 in the mouse βTC3 cell line resulted in decreased expression of genes important for insulin secretion including those encoding insulin, GLUT2 (Slc2a2) and MafA (Deering et al., 2009). In a recent study, islets derived from β cell-specific Set7 knock-out mice showed reduced expression of Pdx1, MafA, GLUT2 and glucokinase mRNAs (Maganti et al., 2015). Similar results were observed in the present study in islets derived from Set7+/- and Set7-/- mice as well as Set7 knock-down MIN6 β cells. Set7 deficiency in both models resulted in the reduced expression of Slc2a2, Gck and Pcsk1. However, there were differences in the gene expression patterns between the in vivo and in vitro models. Set7+/- and Set7-/- islets have reduced expression of Ins2, Pdx1 and Isl1 whereas in Set7 knock-down β cells Pdx1 and Isl1 were up-regulated and Ins2 remained unchanged. This reflects differences in gene expression changes following acute (shRNA knock-down) or chronic (genetic knock-out) Set7 deficiency. Differences in the expression levels of some of the Set7-dependent genes where gene down-regulation was more pronounced in Set7+/- than Set7-/- islets (Fig. 3.4B, Slc2a2, Gck, Pcsk1, Slc30a8) may be due to compensatory mechanisms present in Set7-/- islets. Additionally, it is important to consider the high variability in expression levels observed for some of these candidate genes.

Genes studied here are directly related to insulin synthesis and secretion; for example, GLUT2, glucokinase and the transcription factors Pdx1 and MafA are necessary for the transcriptional activation of the insulin gene and Ero1-like protein B is important for adequate folding of the insulin molecule (Andrali et al., 2008; Khoo et al., 2011). Their down-regulation indicates that Set7 may be important for the process of glucose-regulated

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insulin secretion. A recent study using an inducible, β cell-specific Set7 genetic deletion showed that mice lacking Set7 have an impaired insulin response to a glucose challenge (Maganti et al., 2015). However, the results presented here show that Set7 deletion did not lead to impaired glucose tolerance. This suggests the existence of compensatory mechanisms in Set7-/- mice and highlights the relevance of acute versus chronic as well as global versus tissue-specific Set7 depletion.

Set7 may be required for glucose tolerance in conditions of metabolic stress (i.e. insulin resistance) where the demand for insulin is higher. To explore this hypothesis, glucose tolerance tests were also performed in mice fed a high fat diet, a model known to induce metabolic stress and promote insulin resistance (Wang and Liao, 2012). There was no significant difference in glucose tolerance between high fat-fed wildtype and Set7-/- mice, demonstrating that Set7 is not required for the regulation of blood glucose levels.

3.4.3. Set7 is not necessary for maintaining metabolic homeostasis but may participate in white adipose tissue differentiation

Several studies have implicated histone-modifying proteins in gene regulation during adipogenesis (de Oliveira et al., 2012; Hanzu et al., 2013; Kuzmochka et al., 2014; Musri et al., 2010; Okamura et al., 2010; Wang et al., 2013b). Mice with genetic deletion of histone demethylase JmjC2A or histone methylase MLL3 have abnormal metabolic phenotypes characterised by increased or reduced adiposity respectively (Inagaki et al., 2009; Lee et al., 2008; Tateishi et al., 2009). Additionally, a recent study demonstrates that Set7 methylates the key metabolic regulator peroxisome proliferator-activated receptor-g coactivator-1 α (PGC1α) and may thus play a role in regulating metabolic responses (Aguilo et al., 2016).

Consistent with a role for Set7 in transcriptional regulation in metabolically active tissues, Set7+/- and Set7-/- animals have increased fat mass when compared to their wildtype counterparts (Table 3.1). However, there were no genotype-driven differences in plasma concentrations of the lipids measured to suggest an altered metabolic phenotype. All mice had similar body weight as well as comparable levels of circulating cholesterol and triglycerides. Furthermore, under a high fat diet, all animals gained weight in a similar manner and Set7 deficiency did not affect fat deposition in this model. This increase in fat mass without any apparent differences in glucose or lipid metabolism suggests that

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Set7 could be involved in adipose tissue development. Indeed, a recent study described a role for Set7 in brown adipocyte differentiation through regulating the expression of several thermogenic genes without changes in lipid accumulation (Son et al., 2016). In this context, Set7 indirectly enhances the expression of UCP1 (uncoupling protein 1) and PGC1α via interacting with Sirtuin 1 to modulate p53 acetylation (Son et al., 2016). Set7 is also involved in muscle cell differentiation by regulating the expression of mature cell markers (Tao et al., 2011; Tuano et al., 2016). This raises the possibility that the deletion of Set7 results in gene expression changes during white adipose tissue differentiation that lead to fat accumulation but no change in cell function.

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3.5. CONCLUSIONS

Overall, the results presented in this chapter support a role for Set7 in the transcriptional regulation of β cell-specific genes including those encoding insulin, the glucose transporter GLUT2 and glucokinase. However, they do not implicate Set7 in the maintenance of normal glucose homeostasis. Moreover, Set7 does not play a major role in the regulation of metabolic balance as evidenced by the absence of any genotype- dependent differences in plasma metabolites and body measurements in mice.

Set7 mediates gene expression changes in response to hyperglycaemia and has been proposed as a potential target for the treatment of diabetic complications. The results presented here demonstrate that Set7 deficiency does not affect β cell function and overall metabolic homeostasis. This motivated the generation of a novel Set7 knock-out mouse model to study the role of this enzyme in the development of vascular complications of diabetes. Results from these studies are presented in the following chapters.

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CHAPTER 4

THE EFFECT OF GENETIC SET7 DELETION IN THE

DEVELOPMENT OF DIABETES-ACCELERATED

ATHEROSCLEROSIS

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4.1. ABSTRACT

Chronic hyperglycaemia causes cells in the vascular wall to adopt a pro-inflammatory phenotype. Vascular endothelial and smooth muscle cells increase the production of chemotactic and adhesion molecules such as macrophage chemotactic protein 1 (MCP1/CCL2), vascular cell adhesion molecule 1 (VCAM1) and intercellular adhesion molecule 1 (ICAM1). This results in leukocyte adhesion and tissue migration and contributes to the development of vascular damage. The Set7 lysine methyltransferase regulates the expression of pro-inflammatory molecules in endothelial cells in response to high glucose. This enzyme is also associated with vascular damage in diabetic subjects; however, the role of Set7 in the development of diabetes-accelerated atherosclerosis (DAA) in vivo remains poorly understood. The aim of this study was to assess the effect of the genetic deletion of Set7 in the development of DAA. For this purpose, control and diabetic ApoE-/- and Set7-/-/ApoE-/- mice were used. ApoE-/- mice spontaneously develop atherosclerosis in a process that is significantly accelerated by diabetes. Set7 deletion attenuated plaque formation in the aortic arch of ApoE-/- mice 10 weeks after the induction of diabetes. This was associated with the attenuation of diabetes-induced increases in the expression of pro-inflammatory genes such as Vcam1, Icam1 and Ccl2. Gene expression profiling by RNA sequencing and Gene Set Enrichment Analysis (GSEA) revealed that the atheroprotective effect conferred by Set7 deletion was associated with changes in the expression of genes involved in mitochondrial function, cell proliferation and lipid deposition. They also implicate Set7 in regulatory networks involving the transcription factors HCFC1 (Host cell factor C1), GABP (GA-binding protein) and THAP11 (THAP domain-containing 11) and CREB1 (cAMP-response element-binding protein) in diabetes. Furthermore, pharmacological inhibition of Set7 using (R)-PFI-2 attenuated high glucose and TNFa-induced pro-inflammatory gene expression changes in cultured endothelial cells, as well as changes in the expression of smooth muscle cell markers following exposure to TGFb1. Overall, these results suggest that Set7 contributes to atherogenesis in diabetes and may constitute a therapeutic target to reduce the burden of macrovascular complications of diabetes.

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4.2. INTRODUCTION

Diabetes significantly increases the risk of developing cardiovascular disease and death from events such as myocardial infarction and stroke (Chapter 1, section 1.5.1). Although the exact mechanisms behind this increased risk are not clear, dyslipidaemia and hyperglycaemia are major players as they contribute to the development of atherosclerosis (Dorman et al., 1984; Kannel and McGee, 1979; Turner et al., 1998). Trials evaluating the effect of glycaemic control in the development of complications in diabetic patients showed that intensive control did not initially appear to significantly reduce the risk of cardiovascular disease in type 1 and type 2 diabetes (DCCT, 1995; UKPDS, 1998). However, long-term follow up studies showed that early intensive glycaemic control did improve cardiovascular outcomes in both groups (DCCT/EDIC, 2005; Holman et al., 2008). These observations highlighted a memory effect, whereby good glycaemic control had a protective effect against the development of vascular complications of diabetes.

Blood leukocytes from type 1 diabetic subjects who received intensive glycaemic control treatment display distinct histone methylation and acetylation patterns compared to those placed on conventional treatment (Miao et al., 2014). Increased levels of the activating histone mark H3K4me1 were found at promoters of pro-inflammatory genes such as RELA (p65) in blood leukocytes from type 2 diabetic subjects (Paneni et al., 2015). Furthermore, this effect was associated with increased expression of the lysine methyltransferase Set7 (Paneni et al., 2013). These findings implicate epigenetic mechanisms, particularly Set7-mediated histone methylation, in the memory effect observed in earlier epidemiological studies.

In vitro experiments demonstrated that Set7 is recruited to the promoters of pro- inflammatory genes in endothelial cells in response to rising intracellular glucose levels and oxidative stress (El-Osta et al., 2008). This leads to a persistent increase in the levels of H3K4me1 associated with sustained pro-inflammatory gene expression profile (El- Osta et al., 2008; Okabe et al., 2012). Set7-mediated persistent gene activation in response to transient hyperglycaemia has also been shown in vivo (Okabe et al., 2012). However, the role of Set7 in vivo in the vasculature under chronic high glucose exposure remains to be elucidated.

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This chapter describes experiments aimed at defining the role of Set7 in the development of diabetes accelerated atherosclerosis (DAA). They also sought to examine whether Set7 inhibition may be a valid strategy for therapeutic intervention to reduce the burden of macrovascular complications of diabetes. Atherosclerosis-prone ApoE-/- mice were used for this study. These mice develop aortic fatty streaks by 3 months of age and present fibrous atherosclerotic plaques by 5 months (Meir and Leitersdorf, 2004). In this study, diabetes accelerated this process, with established plaque in the aortic arch observed 10 weeks after the induction of diabetes.

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4.3. RESULTS

4.3.1. Diabetes induction causes metabolic changes regardless of genotype

As shown in Table 4.1 below, the induction of diabetes was associated with increased blood glucose and glycated haemoglobin as well as decreased body weight in diabetic ApoE-/- and Set7-/-/ApoE-/- compared to their respective controls. There were no changes in tail cuff blood pressure measurements as a result of diabetes. Importantly, the deletion of Set7 had no effect in these parameters indicating that the burden of diabetes was similar between the two genotype groups.

Table 4.1. General physiological parameters in control and diabetic ApoE-/- and Set7-/-/ApoE-/- mice after 10 weeks of study Parameter ApoE-/- Set7-/- / ApoE-/- Control Diabetic Control Diabetic Blood glucose 13.58 ± 1.02 29.54 ± 1.96*** 13.97 ± 0.93 28.28 ± 1.91*** (mmol/ml) Body weight (g) 30.67 ± 1.03 24.44 ± 1.18* 29.07 ± 0.55 25.77 ± 0.72** Glycated 4.20 ± 0.07 10.73 ± 0.80*** 4.25 ± 0.08 10.65 ± 0.51*** haemoglobin (%) Systolic Blood 105.6 ± 1.4 104.6 ±1.6 104.7 ± 0.8 105.9 ±0.7 Pressure (mmHg) *P<0.01, **P<0.001, ***P<0.0001 vs. respective control

4.3.2. Set7 deletion attenuates diabetes-induced increases in plaque area

Diabetic ApoE-/- mice rapidly develop atherosclerotic plaques in a process that resembles human pathology (Meir and Leitersdorf, 2004). Set7 is implicated in mediating pro- inflammatory gene expression in endothelial cells, a major feature in diabetic cardiovascular disease and a key event in early atherogenesis (Okabe et al., 2012). In order to study the role of Set7 in the development of atherosclerosis in diabetes, plaque formation was investigated in diabetic ApoE-/- and Set7-/-/ApoE-/- mice. Set7 deletion in control ApoE-/- mice significantly attenuated the diabetes-induced increase in plaque area (Fig. 4.1). Moreover, plaque area was also reduced by Set7 deletion in control ApoE-/- animals suggesting that Set7 may also regulate atherogenic pathways independent of hyperglycaemia.

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Ctrl Diab p=0.004 p=0.05 10 -/- p=0.006

ApoE

- -/

% plaque are a 2

E /Apo - -/ 0

Ctrl Diab Ctrl Diab

7 Set ApoE-/- -/- -/- Set7 /ApoE

Figure 4.1. Atherosclerotic plaque area measured in the aortic arch of control and diabetic ApoE-/- and Set7-/-/ApoE-/- mice The aortas were removed from the mice and cleaned of excess fat under a dissecting microscope. They were then divided into arch and thoracic/abdominal. Aortic arch segments were subsequently stained with Sudan IV-Herxheimer’s solution (0.5% wt/vol) and pinned flat onto wax. Images were acquired with a dissecting microscope equipped with a camera. Aortic arch plaque area was quantitated as the percentage area of aorta stained red using the Adobe Photoshop v7.0 software. n=9 ApoE-/-, 10-13 Set7-/-/ApoE-/-.

4.3.3. Diabetes induces gene expression changes in the aortas of ApoE-/- mice

Chronic hyperglycaemia induces a pro-inflammatory and pro-fibrotic state in the vasculature characterised by the up-regulation of a group of genes related to immune responses (adhesion, chemotaxis) and extracellular matrix (ECM) deposition (collagens, growth factors) (Libby et al., 2009). To characterise the genome-wide expression signature in the vasculature, RNA was extracted from control and diabetic ApoE-/- mice aortas 10 weeks after the induction of diabetes and differential gene expression was determined by RNA-seq. Using a False Discovery Rate (FDR) threshold of <0.05, there were 370 genes deregulated by diabetes (red), out of which 282 were up-regulated and 88 were down-regulated (Fig. 4.2). The top 40 genes altered in expression by diabetes (sorted by FDR p value) are presented in Table 6.1. Genes encoding the anti- inflammatory Heat Shock Protein 70 (Hsp1a and Hsp1b) were down-regulated. Hsp70 expression is induced by stimuli that promote cellular stress and it is thought to mediate the attenuation of inflammatory responses (Ferat-Osorio et al., 2014; Lee and Corry, 1998; Srivastava et al., 2016). Furthermore, polymorphisms in the HSPA1A and HSPA1B 97 4 | Atherosclerosis

genes are associated with increased risk of nephropathy and severity of foot ulcers in diabetic patients (Buraczynska et al., 2009; Mir et al., 2009). On the other hand, genes associated with cellular immunity such as toll-like receptor Tlr7 and lymphocyte marker Cd48 were up-regulated by diabetes. Toll-like receptor activation promotes leukocyte adhesion and lipid accumulation contributing to vascular damage (Curtiss and Tobias, 2009). However, some members of this family of receptors, such as TLR7, attenuate inflammatory responses derived from macrophages, exerting a beneficial effect in the vasculature (Salagianni et al., 2012). Indeed, TLR7 is abundantly expressed in human atherosclerotic plaques and high expression levels are associated with improved patient outcomes (Karadimou et al., 2017). CD48 mediates inflammatory responses by immune cells caused by oxidised LDL (OxLDL) in the vasculature, contributing to the development of atherosclerosis (Dong et al., 2011). These observations highlight the involvement of the immune responses in the signaling pathways that drive vascular damage in diabetes.

Up: 282 LogFC

Down: 88

Log(concentration)

Figure 4.2. MA plot representing gene expression changes in response to diabetes in the aortas of ApoE-/- mice RNA-seq identified changes in gene expression in whole aorta tissue conferred by 10 weeks of diabetes in ApoE-/- mice. MA plots show the correlation between transcript read abundance and their fold change (log-transformed). Red dots represent differentially expressed genes FDR<0.05.

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Table 4.2. RNA-seq identified gene expression changes conferred by diabetes in the aortas of ApoE-/- mice Gene symbol Log2FC FDR p val Description Hamp2 2.4 9.05 x10-07 Hepcidin Antimicrobial Peptide Azgp1 1.15 1.53 x10-06 Alpha-2-Glycoprotein 1, Zinc-Binding Sgk2 2.61 1.53 x10-06 Serine/Threonine Kinase 2 Cyp2f2 2.61 1.53 x10-06 Cytochrome P450, family 2, subfamily f, polypeptide 2 Slc7a8 3.35 1.93 x10-05 Solute Carrier Family 7 Member 8 Tpsb2 1.83 2.95 x10-05 Tryptase Beta 2 Lrrn4cl 1.99 4.22 x10-05 LRRN4 C-Terminal Like Slc7a10 2.75 4.22 x10-05 Solute Carrier Family 7 Member 10 Gck -1.64 1.11 x10-04 Glucokinase Hhip 1.54 1.27 x10-04 Hedgehog-interacting protein Cpa3 1.67 1.27 x10-04 Carboxypeptidase A3 Siglec1 1.91 1.64 x10-04 Sialic Acid Binding Ig-Like Lectin 1 Ccbe1 1.92 1.64 x10-04 Collagen and calcium binding EGF domains 1 Ms4a4a 1.05 1.65 x10-04 Membrane Spanning 4-Domains A4A Fmo4 1.27 2.62 x10-04 Flavin Containing Monooxygenase 4 Lyz1 1.65 3.20 x10-04 Lysozyme 1 Slc2a5 -1.57 6.95 x10-04 Solute Carrier Family 2 Member 5 Phyhip -1.1 6.95 x10-04 Phytanoyl-CoA 2-Hydroxylase Interacting Protein Gm10069 -0.75 6.95 x10-04 Predicted gene 10069, long non-coding RNA Hhipl1 1.31 6.95 x10-04 HHIP Like 1 Mcpt4 1.67 9.88 x10-04 Mast cell protease 4 Ahsg 0.96 1.15 x10-03 Alpha 2-HS Glycoprotein Fer1l6 -1.36 1.16 x10-03 Fer-1 Like Family Member 6 Gm23196 -1.17 1.16 x10-03 Predicted gene, 23196 Hspa1b -2.55 1.51 x10-03 Heat Shock Protein Family A (Hsp70) Member 1B Kcnk13 0.96 1.56 x10-03 Potassium Two Pore Domain Channel Subfamily K Member 13 Lyve1 1.67 2.99 x10-03 Lymphatic Vessel Endothelial Hyaluronan Receptor 1 Gdf10 2.07 3.16 x10-03 Growth Differentiation Factor 10 Tlr7 1.57 3.22 x10-03 Toll-like receptor 7 Stil -0.98 3.64 x10-03 SCL/TAL1 Interrupting Locus Ifi27l2a 2.78 3.88 x10-03 Interferon, alpha-inducible protein 27 like 2A Hpd 0.88 3.91 x10-03 4-Hydroxyphenylpyruvate Dioxygenase Endou 1 3.91 x10-03 Endonuclease, polyU-specific Cd48 1.1 5.01 x10-03 Cluster of Differentiation 48/ B-lymphocyte activation marker Hspa1a -2.28 5.07 x10-03 Heat Shock Protein Family A (Hsp70) Member 1A Arhgdig 0.94 5.07 x10-03 Rho GDP Dissociation Inhibitor Gamma Esrrb 1.28 5.07 x10-03 Estrogen Related Receptor Beta Ms4a7 1.36 5.07 x10-03 Membrane Spanning 4-Domains A7 Cma1 1.38 5.07 x10-03 Chymase 1 Fndc1 2.14 5.13 x10-03 Fibronectin Type III Domain Containing 1

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4.3.4. Genes up-regulated by diabetes are strongly associated with immune responses and mitochondrial function

Genome-wide expression analysis, like the one performed by RNA sequencing, provides a ranked list of differentially expressed genes between two conditions (in this case between control and diabetic ApoE-/- aortas). Although some information can be obtained by assessing the gene rank (those with the strongest statistical value or fold change), determining meaningful biological insights from such data requires further analysis. Gene Set Enrichment Analysis (GSEA) is used to assess whether members of a gene set are likely to occur near the top or bottom of the gene rank list. Genes Sets are curated collections of genes based on prior biological knowledge about biochemical pathways (for example, Gene Ontology, GO; BioCarta pathway collections) or based on experimental results from published expression profiles (Subramanian et al., 2005). In the studies presented in this thesis, gene sets used for analysis are taken from the Molecular Signature Database (MSigDB). Unlike other curated gene sets, MSigDB sets reflect an unbiased representation of a transcriptional state associated with a biological condition without relying on any prior knowledge (Liberzon et al., 2011).

MSigDB contains several gene set collections depending on the origin of the data within them. For example, the C2 collection (curated gene sets) contains gene sets derived from pathway databases such as KEGG (Kyoto Encyclopedia of Genes and Genomes) (Liberzon et al., 2011). KEGG pathways link genomic and functional information by grouping genes into interacting networks (Kanehisa and Goto, 2000). C2 also includes gene sets based on experimentally determined gene expression signatures between two conditions such as a gene knock-out or drug treatment (Liberzon et al., 2011). The MSigDB collection C4 contains gene sets that were assembled from computational analysis by mining large datasets of cancer-associated genes (Liberzon et al., 2011; Segal et al., 2004). A number of ‘modules’ were generated to group genes associated with a particular biological function in different cancer types (Segal et al., 2004). For example, MODULE_75 contains genes involved in immune response.

It is important to note that gene sets from MSigDB have an unclear naming convention. They begin with the investigator who generated the data set and can end with either ‘UP’ or ‘DN’. They can also include two conditions separated by ‘VS’: the first is the ‘control’ and the second is the ‘treatment’. ‘UP’ refers to genes that are up-regulated in the control

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compared to the treatment while ‘DN” refers to genes down-regulated in the control compared to the treatment. Additionally, some gene sets refer to ‘UP’ or ‘DN’ according to regulation by transcription factors. For example, the gene set STEIN_ESRRA_TARGETS_UP includes genes that are up-regulated by the transcription factor ESRRA (Estrogen related receptor alpha).

Results from GSEA provide gene sets with an Enrichment Score (ES). The ES represents the strength of the association between that gene set and the genes in the top or bottom of the ranked list of differentially expressed genes (Subramanian et al., 2005). The ES is normalised (Normalised Enrichment Score, NES) when using an entire database (such as MSigDB) to adjust for the size of the gene sets and account for multiple hypothesis testing. A False Discovery Rate (FDR) calculation is then performed to estimate the statistical strength of the association by providing the probability of a given NES being a false positive (Subramanian et al., 2005). GSEA results presented throughout this thesis are considered statistical significant based on a FDR adjusted p-value below 0.05 (FDR<0.05). This implies a false positive probability of 5% of all significant tests.

For the studies in this chapter, GSEA was used to determine key gene expression signatures induced by diabetes in the aortas of ApoE-/- mice. A gene set was defined as positively enriched if a significant proportion of genes within that set are increased in expression in response to diabetes, whereas negative enrichment indicates that the genes in the set are mostly down-regulated by diabetes.

Diabetes was associated with 1,288 positively enriched and 547 negatively enriched gene sets (FDR<0.05). This was consistent with the considerably larger number of genes up- regulated versus down-regulated by diabetes.

Table 4.3 shows the top 40 most positively enriched gene sets (sorted by NES). Gene sets related to immune responses and inflammation were strongly associated with gene up- regulation by diabetes. This is consistent with the increase in inflammatory mediators by endothelial and smooth muscle cells caused by diabetes and subsequent recruitment of immune cells. Genes related to adipogenesis and mitochondrial function were also induced by diabetes. Indeed, increasing lipid deposition and formation of mitochondrial reactive oxygen species are well-described mechanisms of blood vessel damage in diabetes that contribute to the development of atherosclerosis (Hulsmans et al., 2012;

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Insull, 2009). Mitochondrial genes altered in expression include those whose expression is induced by the Estrogen-related receptor alpha (ESRRA). ESRRA is a transcription factor that associates with PPARg co-activator 1 alpha (PGC1a) to regulate mitochondrial activity (Schreiber et al., 2004).

The top 40 most negatively enriched gene sets associated with gene down-regulation by diabetes are presented in Table 4.4. These included genes common to cardiovascular pathologies such as unstable atherosclerotic plaques and cardiomyopathies, as well as muscle function and ECM. Targets of the transcription factor SRF (Serum Response Factor) were also down-regulated by diabetes in the aorta. SRF is a transcription factor that regulates cell growth and is key for muscle development and differentiation (Clark and Graves, 2014).

Overall, these GSEA results suggest that the processes driving atherosclerosis in diabetes involve increases in genes that promote inflammation as well as a decrease in the expression of genes maintaining cellular function, particularly within the vessel’s smooth muscle layer.

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Table 4.3. GSEA identified major pathways positively enriched in ApoE-/- diabetic aortas MSigDB Gene Set NES FDR p val MODULE_84 (immune inflammatory response) 8.83 <10-05 MCLACHLAN_DENTAL_CARIES_UP 7.59 <10-05 BOQUEST_STEM_CELL_DN 7.53 <10-05 MODULE_75 (immune response) 7.46 <10-05 MOOTHA_HUMAN_MITODB_6_2002 7.08 <10-05 MODULE_46 (cancer module) 7.05 <10-05 SMID_BREAST_CANCER_NORMAL_LIKE_UP 7.04 <10-05 MODULE_45 (whole blood genes) 7.03 <10-05 BURTON_ADIPOGENESIS_6 6.91 <10-05 MODULE_64 (membrane receptors) 6.83 <10-05 MOOTHA_MITOCHONDRIA 6.73 <10-05 REACTOME_TCA_CYCLE_AND_RESPIRATORY_ELECTRON_ 6.71 <10-05 TRANSPORT WALLACE_PROSTATE_CANCER_RACE_UP 6.67 <10-05 WONG_MITOCHONDRIA_GENE_MODULE 6.54 <10-05 MODULE_5 (lung genes) 6.42 <10-05 REACTOME_RESPIRATORY_ELECTRON_TRANSPORT_ATP_ 6.38 <10-05 SYNTHESIS_BY_CHEMIOSMOTIC_COUPLING_AND_HEAT_P RODUCTION_BY_UNCOUPLING_PROTEINS_ MITOCHONDRION 6.35 <10-05 MEISSNER_BRAIN_HCP_WITH_H3K4ME3_AND_H3K27ME3 6.32 <10-05 MCLACHLAN_DENTAL_CARIES_DN 6.20 <10-05 MODULE_88 (metabolic and xenobiotic response genes) 6.16 <10-05 MEMBRANE 5.96 <10-05 CHEN_METABOLIC_SYNDROM_NETWORK 5.91 <10-05 KEGG_OXIDATIVE_PHOSPHORYLATION 5.89 <10-05 BURTON_ADIPOGENESIS_5 5.81 <10-05 MODULE_27 (cancer module) 5.76 <10-05 STEIN_ESRRA_TARGETS_UP 5.75 <10-05 MODULE_44 (Thymus genes) 5.74 <10-05 REACTOME_RESPIRATORY_ELECTRON_TRANSPORT 5.72 <10-05 MOOTHA_VOXPHOS 5.70 <10-05 GSE13485_CTRL_VS_DAY7_YF17D_VACCINE_PBMC_DN 5.68 <10-05 MODULE_55 (cancer module) 5.68 <10-05 MODULE_152 (oxidative phosphorylation and ATP synthesis) 5.67 <10-05 MOOTHA_PGC 5.63 <10-05 INTEGRAL_TO_MEMBRANE 5.59 <10-05 INTEGRAL_TO_PLASMA_MEMBRANE 5.55 <10-05 INTRINSIC_TO_PLASMA_MEMBRANE 5.55 <10-05 INTRINSIC_TO_MEMBRANE 5.51 <10-05 MODULE_93 (oxidoreductases) 5.48 <10-05 PLASMA_MEMBRANE 5.48 <10-05 JAATINEN_HEMATOPOIETIC_STEM_CELL_DN 5.47 <10-05

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Table 4.4. GSEA identifies major pathways negatively enriched in ApoE-/- diabetic aortas MSigDB Gene Set NES FDR p val LIM_MAMMARY_STEM_CELL_UP -5.32 <10-05 LIU_PROSTATE_CANCER_DN -4.89 <10-05 DACOSTA_UV_RESPONSE_VIA_ERCC3_DN -4.55 <10-05 NUCLEUS -4.51 <10-05 KEGG_ARRHYTHMOGENIC_RIGHT_VENTRICULAR_CARDI -4.33 <10-05 OMYOPATHY_ARVC PASINI_SUZ12_TARGETS_DN -4.26 <10-05 SCGGAAGY_V$ELK1_02 -4.13 <10-05 MILI_PSEUDOPODIA_HAPTOTAXIS_UP -4.11 <10-05 SHEN_SMARCA2_TARGETS_UP -4.09 <10-05 PAPASPYRIDONOS_UNSTABLE_ATEROSCLEROTIC_PLAQUE -4.08 <10-05 _DN V$SRF_C -4.08 <10-05 KEGG_HYPERTROPHIC_CARDIOMYOPATHY_HCM -3.96 <10-05 GINESTIER_BREAST_CANCER_ZNF217_AMPLIFIED_DN -3.93 <10-05 BERTUCCI_MEDULLARY_VS_DUCTAL_BREAST_CANCER_DN -3.93 <10-05 CHICAS_RB1_TARGETS_CONFLUENT -3.89 <10-05 NUCLEAR_PART -3.88 <10-05 RCGCANGCGY_V$NRF1_Q6 -3.85 <10-05 KEGG_DILATED_CARDIOMYOPATHY -3.78 <10-05 WONG_ADULT_TISSUE_STEM_MODULE -3.77 <10-05 ACEVEDO_FGFR1_TARGETS_IN_PROSTATE_CANCER_MODEL_ -3.63 <10-05 DN V$SRF_Q6 -3.52 <10-05 V$SRF_Q5_01 -3.50 <10-05 REACTOME_METABOLISM_OF_RNA -3.44 <10-05 ACTAYRNNNCCCR_UNKNOWN -3.41 <10-05 GRAESSMANN_APOPTOSIS_BY_DOXORUBICIN_DN -3.38 6.77 x10-05 YAO_TEMPORAL_RESPONSE_TO_PROGESTERONE_CLUSTER_1 -3.37 6.51 x10-05 6 MODULE_1 (ovary genes) -3.36 6.27 x10-05 EXTRACELLULAR_MATRIX_PART -3.34 6.04 x10-05 REACTOME_MUSCLE_CONTRACTION -3.33 5.83 x10-05 MODULE_66 (cancer genes) -3.25 5.64 x10-05 MICROTUBULE_CYTOSKELETON -3.23 5.46 x10-05 PROTEINACEOUS_EXTRACELLULAR_MATRIX -3.22 5.29 x10-05 MODULE_100 (cancer module) -3.22 5.13 x10-05 MODULE_137 (CNS genes) -3.22 4.98 x10-05 REACTOME_MRNA_PROCESSING -3.21 4.83 x10-05 REACTOME_TRANSLATION -3.21 4.70 x10-05 SENESE_HDAC1_TARGETS_DN -3.21 4.57 x10-05 V$SRF_Q4 -3.20 4.45 x10-05 DACOSTA_UV_RESPONSE_VIA_ERCC3_COMMON_DN -3.19 4.34 x10-05 KEGG_VASCULAR_SMOOTH_MUSCLE_CONTRACTION -3.18 6.36 x10-05 JOHNSTONE_PARVB_TARGETS_3_DN -3.18 6.20 x10-05

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The potential association of transcription factors with gene deregulation caused by diabetes in the aorta was determined by transcription factor analysis. This involved generating custom gene sets of gene promoters associated with transcription factor binding sites from publicly available ENCODE (Encyclopedia of DNA Elements) chromatin immunoprecipitation coupled with sequencing (ChIP-seq) data. GSEA was used to determine correlations between these gene sets and changes induced by diabetes. There were 1,067 positively enriched and 220 negatively enriched gene sets (FDR<0.05).

Table 4.5 summarises the top 30 most positively and negatively enriched transcription factor gene sets. The transcriptional regulators most associated with gene up-regulation in diabetes were ubiquitous, non-specific factors related to transcriptional regulation such as the methyltransferase Ezh2 and RNA polymerase II (POL2RA). Gene targets of NFkB p65 subunit (RELA), a key mediator of inflammatory responses (Baker et al., 2011), were also up-regulated by diabetes. On the other hand, HCFC1 (Host cell factor C1) was associated with gene down-regulation by diabetes. This transcription factor regulates diverse cellular functions by associating to chromatin and chromatin-modifying proteins as well as other transcription factors such as GA-binding protein (GABP) and Ying-Yang 1 (YY1) (Yu et al., 2010). These associations were similar to those observed in the diabetic kidney (Chapter 5, Section 5.3.5).

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Table 4.5. GSEA identified transcription factors associated with genes up-regulated and down- regulated by diabetes in ApoE-/- mice aortas. Positively enriched Negatively enriched TF Gene Set NES FDR TF Gene Set NES FDR p val p val EZH2 7.74 <10-05 HCFC1 (HEPG2) -9.37 <10-05 PHOSPHOT487 (Spindle neuron) EZH2 (Hepatocyte) 6.38 <10-05 HCFC1 (HELA-S3) -9.05 <10-05 TAF1 (GM12878) 6.18 <10-05 GATAD1 (HEPG2) -8.94 <10-05 TAF1 (GM12891) 5.99 <10-05 GABPA (HEPG2) -8.88 <10-05 EZH2 (Astrocyte) 5.88 <10-05 HCFC1 (MCF-7) -8.78 <10-05 POLR2A 5.86 <10-05 HCFC1 (GM12878) -8.76 <10-05 (GM12891) C11ORF30 (K562) 5.70 <10-05 THAP11 (HEPG2) -8.70 <10-05 NBN (GM12878) 5.37 <10-05 YY1 (Ishikawa) -8.69 <10-05 EZH2 5.36 <10-05 YY1 (GM12878) -8.67 <10-05 (Keratinocyte) POLR2A 5.33 <10-05 GABPA (MCF-7) -8.53 <10-05 PHOSPHOS5 (GM12878) EZH2 (Lung 5.33 <10-05 GABPA (HEPG2) -8.48 <10-05 fibroblast) CBFB (GM12878) 5.20 <10-05 GABPA (SK-N-SH) -8.45 <10-05 EZH2 (Dermal 5.00 <10-05 YY1 (GM12892) -8.36 <10-05 fibroblast) POLR2APHOSPHO 4.98 <10-05 GABP (HEPG2) -8.26 <10-05 S5 (GM12891) ZGPAT (HEPG2) 4.94 <10-05 KAT8 (HEPG2) -8.25 <10-05 CEBPB (GM12878) 4.90 <10-05 SIX5 (A549) -8.14 <10-05 MYC (GM12878) 4.86 <10-05 YY1 (SK-N-SH) -8.13 <10-05 POLR2A 4.83 <10-05 YY1 (A549) -8.12 <10-05 (GM12878) GTF2F1 (HEPG2) 4.81 <10-05 GABPB1 (HEPG2) -8.11 <10-05 POLR2APHOSPHO 4.81 <10-05 YY1 (HEPG2) -8.09 <10-05 S5 (HL-60) POU2F2 4.80 <10-05 SIX5 (GM12878) -8.07 <10-05 (GM12878) POLR2APHOSPHO 4.78 <10-05 YY1 (H1-HESC) -8.05 <10-05 S5 (GM12892) EZH2 (H1-HESC) 4.72 <10-05 ELF1 (HEPG2) -7.99 <10-05 STAT5A 4.70 <10-05 GABPA (GM12878) -7.94 <10-05 (GM12878) FOXM1 (GM12878) 4.67 <10-05 YY1 (HCT116) -7.90 <10-05 NFIC (GM12878) 4.39 <10-05 ZFX (K562) -7.88 <10-05 POU2F2 4.38 <10-05 YY1 (GM12891) -7.88 <10-05 (GM12891) ATF2 (GM12878) 4.38 <10-05 GABPA (K562) -7.85 <10-05 RELA (GM18951) 4.14 <10-05 ZBTB40 (GM12878) -7.83 <10-05 Terms in brackets refer to the cell type used to generate the transcription factor dataset.

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4.3.5. Set7 deletion attenuates diabetes-induced gene expression changes in the aortas of ApoE-/- mice.

Results of plaque area analysis (section 4.3.2) suggest that Set7 ameliorates vascular damage in this model of diabetes-accelerated atherosclerosis. In order to investigate the gene expression changes behind this protective phenotype, RNA was also extracted from diabetic Set7-/-/ ApoE-/- mice 10 weeks after the induction of diabetes. Differential gene expression was determined by RNA-seq and compared to the profile generated for diabetic ApoE-/- mice. The deletion of Set7 in control ApoE-/- aortas was associated with the deregulation of 12 genes (1 up-regulated and 11 down-regulated, FDR p value<0.05). These represent genes that are regulated by Set7 in the vasculature independently of diabetes. Set7 deletion in diabetic ApoE-/- resulted in the up-regulation of 63 genes and down-regulation of 2 genes (FDR p val<0.05). The top 40 genes deregulated by Set7 deletion in diabetic ApoE-/- aortas (sorted by FDR adjusted p value) are presented in Table 4.6.

Genes associated with a healthy smooth muscle cell contractile phenotype were enriched in diabetic vessels with Set7 deletion. These included those encoding myosin chains (Myh6, Myh7, Myl4) and myosin-associated proteins (Mybphl, Mybpc3). The gene encoding YY1-associated muscle lncRNA, Yam1, was also up-regulated by Set7 deletion. This protein is regulated by the transcription factor YY1, a target for Set7 methylation, and it is involved in muscle cell differentiation (Flynn and Chang, 2014). Piro (Gm10800), a gene recently identified as a part of the receptor activator NFkB ligand (RANKL) pathway was also up-regulated by diabetes in a Set7-dependent manner (Oh et al., 2015). These observations suggest that Set7 regulates the expression of genes involved in smooth muscle cell function and that the protective effects following Set7 deletion involve preservation of smooth muscle cell phenotype as well as attenuation of inflammation in endothelial cells.

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Table 4.6. RNA-seq identifies gene expression changes conferred by Set7 deletion in the aortas of diabetic ApoE-/- mice Gene symbol Log2FC FDR p val Description Sln 2.78 1.86 x10-05 Sarcolipin Lars2 5.93 3.20 x10-04 Leucyl-TRNA Synthetase 2, Mitochondrial Yam1 6.17 3.20 x10-04 YY1-Associated Myogenesis RNA 1 Mir6236 6.19 3.20 x10-04 microRNA 6236 n-R5-8s1 4.55 3.20 x10-04 Nuclear encoded rRNA 5.8S 1 Ttn 4.2 4.18 x10-04 Titin Gm15564 6.11 4.63 x10-04 Predicted gene 15564, antisense lncRNA Pkhd1 2.38 4.63 x10-04 Polycystic Kidney and Hepatic Disease 1 Slc12a3 3.06 4.85 x10-04 Solute Carrier Family 12 Member 3 Corin 2.03 4.85 x10-04 Corin, Serine Peptidase Xirp2 2.86 5.14 x10-04 Xin Actin Binding Repeat Containing 2 Tnni3 2.7 8.37 x10-04 Troponin I3, Cardiac Type Lrp2 2.83 1.08 x10-04 Low density lipoprotein-related protein 2 Csrp3 2.54 1.28 x10-04 Cysteine and Glycine-Rich Protein 3 Obscn 3.33 1.52 x10-04 Obscurin, Cytoskeletal Calmodulin and Titin- Interacting RhoGEF Mybphl 2.06 1.58 x10-04 Myosin Binding Protein H Like Gm10069 0.72 1.62 x10-04 Predicted gene 10069, antisense lncRNA Abca13 2.95 1.62 x10-04 ATP-Binding Cassette, Sub-Family A (ABC1), Member 13 Nrap 1.95 1.70 x10-03 Nebulin Related Anchoring Protein Smyd1 1.72 1.88 x10-03 SET and MYND Domain Containing 1 Slc34a1 2.44 2.02 x10-03 Solute Carrier Family 34 Member 1 Kcnj3 1.76 2.76 x10-03 Potassium Voltage-Gated Channel Subfamily J Member 3 Gm10718 2.76 3.29 x10-03 Predicted gene 10718, protein coding Setd7 -1.47 3.49 x10-03 SET Domain Containing Lysine Methyltransferase 7 Myl4 2.65 3.49 x10-03 Myosin Light Chain 4 Gm10801 4.96 3.49 x10-03 Predicted gene 10801, protein coding Gm17535 2.02 3.83 x10-03 Predicted gene 17535, protein coding Gm26870 4.11 4.34 x10-03 Predicted gene 26870, lincRNA Gm10722 4.72 4.34 x10-03 Predicted gene 10722, protein coding Myh6 4.63 4.34 x10-03 Myosin Heavy Chain 6 Mybpc3 2.8 4.82 x10-03 Myosin Binding Protein C Mb 2.79 4.82 x10-03 Myoglobin Myh7 2.11 4.91 x10-03 Myosin Heavy Chain 7 Ckm 1.95 4.91 x10-03 Creatine Kinase, M-Type Ckmt2 2.19 4.91 x10-03 Creatine Kinase, Mitochondrial 2 Tbx20 2.51 5.17 x10-03 T-Box 20 Gm21738 4.75 5.18 x10-03 Predicted gene 21738, protein coding Stil 0.95 5.28 x10-03 SCL/TAL1 Interrupting Locus Gm11168 3.19 5.28 x10-03 Predicted gene 11168, protein coding Gm10800 4.98 5.28 x10-03 Predicted gene 10800 – PGRN-induced receptor-like protein during osteoblastogenesis (PIRO)

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Consistent with the proposed role of Set7 as a mediator of inflammation in the vasculature, Set7 deletion also attenuated the expression of Ccl2 (MCP1), Vcam1, RelA (p65) and Acta2 (aSMA) in diabetic animals. The changes in the expression of these genes were validated in independent aorta tissue samples by qRT-PCR (Fig. 4.3).

2.0

1.5

1.0 ## # ## ## ## 0.5 *** mRNA fold change mRNA

0.0 Setd7 Vcam1 Mcp1 RelA Acta2 ApoE-/- control Set7-/-/ApoE-/- control ApoE-/- diabetic Set7-/-/ApoE-/- diabetic

Figure 4.3. qRT-PCR validated gene expression changes identified by RNA-seq mRNA expression levels of pro-inflammatory genes previously associated with vascular disease were investigated in aortic tissue samples from control and diabetic ApoE-/- and Set7-/-/ApoE-/- mice. The changes in gene expression validate the fold changes obtained by RNA-seq. n=6-7 mice per group, ***p<0.001 vs. respective control; #p<0.05, ##p<0.01, ####p<0.0001 vs. diabetic ApoE-/-.

GSEA was used to investigate gene expression signatures resulting from Set7 deletion in the aortas of diabetic ApoE-/- mice. There were 462 positively enriched gene sets and 321 negatively enriched gene sets associated with Set7 deletion in this context (FDR<0.05). The top 40 gene sets (sorted by NES) in each direction are presented in Tables 4.7 and 4.8.

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Table 4.7. GSEA identified major pathways positively enriched by Set7 deletion in aortas of diabetic ApoE-/- mice MSigDB Gene Set NES FDR p val GOBERT_OLIGODENDROCYTE_DIFFERENTIATION_UP 4.88 <10-05 ROSTY_CERVICAL_CANCER_PROLIFERATION_CLUSTER 4.86 <10-05 GSE15750_DAY6_VS_DAY10_TRAF6KO_EFF_CD8_TCELL_UP 4.39 <10-05 CHANG_CYCLING_GENES 4.36 <10-05 GNF2_CCNA2 4.33 <10-05 GNF2_CDC20 4.25 <10-05 DACOSTA_UV_RESPONSE_VIA_ERCC3_DN 4.23 <10-05 MODULE_54 (cell cycle) 4.09 <10-05 DUTERTRE_ESTRADIOL_RESPONSE_24HR_UP 4.09 <10-05 GNF2_CCNB2 4.05 <10-05 GNF2_CDC2 4.05 <10-05 GSE15750_DAY6_VS_DAY10_EFF_CD8_TCELL_UP 4.04 <10-05 JOHNSTONE_PARVB_TARGETS_3_DN 4.03 <10-05 REACTOME_PEPTIDE_CHAIN_ELONGATION 3.91 <10-05 GNF2_HMMR 3.90 <10-05 FEVR_CTNNB1_TARGETS_DN 3.88 <10-05 KEGG_RIBOSOME 3.83 <10-05 GNF2_RRM1 3.79 <10-05 GEORGES_TARGETS_OF_MIR192_AND_MIR215 3.78 <10-05 GNF2_PCNA 3.73 <10-05 CAIRO_HEPATOBLASTOMA_CLASSES_UP 3.72 <10-05 BENPORATH_CYCLING_GENES 3.70 <10-05 KEGG_HYPERTROPHIC_CARDIOMYOPATHY_HCM 3.70 <10-05 REACTOME_3_UTR_MEDIATED_TRANSLATIONAL_ REGULATION 3.69 <10-05 REACTOME_INFLUENZA_VIRAL_RNA_TRANSCRIPTION_REPLICA 3.68 <10-05 TION STRUCTURAL_MOLECULE_ACTIVITY 3.66 <10-05 CELL_CYCLE_PROCESS 3.65 <10-05 GOLDRATH_EFF_VS_MEMORY_CD8_TCELL_UP 3.65 <10-05 GOLDRATH_ANTIGEN_RESPONSE 3.64 <10-05 KOBAYASHI_EGFR_SIGNALLING_24HR_DN 3.63 <10-05 MORF_TPT1 3.63 <10-05 CROONQUIST_IL6_DEPRIVATION_DN 3.63 <10-05 REACTOME_NONSENSE_MEDIATED_DECAY 3.62 <10-05 GNF2_CENPF 3.61 <10-05 MORI_LARGE_PRE_BII_LYMPHOCYTE_UP 3.60 <10-05 SOTIRIOU_BREAST_CANCER_GRADE_1_VS_3_UP 3.60 <10-05 KEGG_DILATED_CARDIOMYOPATHY 3.59 <10-05 REACTOME_CELL_CYCLE 3.58 <10-05 GNF2_RRM2 3.57 <10-05 PUJANA_BRCA2_PCC_NETWORK 3.54 <10-05

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Table 4.8. GSEA identified major pathways negatively enriched by Set7 deletion in aortas of diabetic ApoE-/- mice MSigDB Gene Set NES FDR p val BURTON_ADIPOGENESIS_6 -5.21 <10-05 BURTON_ADIPOGENESIS_5 -5.15 <10-05 WAKABAYASHI_ADIPOGENESIS_PPARG_RXRA_BOUND_8D -4.48 <10-05 LANDIS_ERBB2_BREAST_TUMORS_324_DN -4.40 <10-05 REACTOME_TCA_CYCLE_AND_RESPIRATORY_ELECTRON_ -4.37 <10-05 TRANSPORT MOOTHA_MITOCHONDRIA -4.34 <10-05 MOOTHA_PGC -4.28 <10-05 RUAN_RESPONSE_TO_TNF_DN -4.26 <10-05 MITOCHONDRION -4.26 <10-05 WONG_MITOCHONDRIA_GENE_MODULE -4.26 <10-05 MOOTHA_HUMAN_MITODB_6_2002 -4.25 <10-05 SWEET_LUNG_CANCER_KRAS_DN -4.03 <10-05 MODULE_152 (oxidative phosphorylation and ATP synthesis) -3.99 <10-05 WEST_ADRENOCORTICAL_TUMOR_DN -3.93 <10-05 HSIAO_LIVER_SPECIFIC_GENES -3.78 <10-05 ACEVEDO_LIVER_CANCER_DN -3.76 <10-05 REACTOME_RESPIRATORY_ELECTRON_TRANSPORT_ATP_ -3.75 <10-05 SYNTHESIS_BY_CHEMIOSMOTIC_COUPLING_AND_HEAT_P RODUCTION_BY_UNCOUPLING_PROTEINS_ WAKABAYASHI_ADIPOGENESIS_PPARG_RXRA_BOUND_WIT -3.73 <10-05 H_H4K20ME1_MARK HOSHIDA_LIVER_CANCER_SUBCLASS_S3 -3.71 <10-05 MODULE_75 (immune response) -3.70 <10-05 KEGG_OXIDATIVE_PHOSPHORYLATION -3.69 <10-05 KEGG_PEROXISOME -3.68 <10-05 LEE_BMP2_TARGETS_UP -3.67 <10-05 MCBRYAN_PUBERTAL_BREAST_4_5WK_DN -3.62 <10-05 GERHOLD_ADIPOGENESIS_UP -3.56 <10-05 SABATES_COLORECTAL_ADENOMA_DN -3.56 <10-05 REACTOME_RESPIRATORY_ELECTRON_TRANSPORT -3.50 <10-05 LANDIS_ERBB2_BREAST_PRENEOPLASTIC_DN -3.47 <10-05 BOQUEST_STEM_CELL_UP -3.47 <10-05 LANDIS_BREAST_CANCER_PROGRESSION_DN -3.46 <10-05 MODULE_62 (cancer module) -3.46 <10-05 MODULE_93 (oxidoreductases) -3.40 2.91 x10-05 RUAN_RESPONSE_TO_TNF_TROGLITAZONE_DN -3.37 2.82 x10-05 OXIDOREDUCTASE_ACTIVITY -3.37 2.74 x10-05 SMID_BREAST_CANCER_BASAL_DN -3.35 2.66 x10-05 MODULE_55 (cancer module) -3.34 2.52 x10-05 KEGG_PPAR_SIGNALLING_PATHWAY -3.34 2.45 x10-05 SANSOM_APC_TARGETS_DN -3.32 2.39 x10-05 MODULE_43 (energy pathways) -3.23 4.71 x10-05 STEIN_ESRRA_TARGETS_UP -3.19 4.28 x10-05

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Gene sets related to cell growth and muscle dysfunction were associated with gene up- regulation by Set7 deletion in diabetes (Table 4.7, shown in bold). Genes sets associated with mitochondrial function and adipogenesis were down-regulated by the deletion of Set7 in diabetes (Table 4.8, shown in bold).

Figure 4.4 contains a visual representation of some of the gene expression changes conferred by Set7 deletion in the aortas of diabetic ApoE-/- mice based on GSEA results. Overall, Set7 deletion attenuated gene expression changes induce by diabetes, this was more evident for genes that were up-regulated by diabetes (Fig. 4.4A). These include genes associated with immune responses belonging to the MSigDB gene set MODULE 75 (Fig. 4.4B). Many of these genes encode chemokines and their receptors, such as Ccl2 and Ccr2, which play an important role in atherosclerosis development and progression (van der Vorst et al., 2015). For example, overproduction of MCP-1 and increased signalling through its receptor, CCR2, in diabetes are major contributors to the development of complications such as atherosclerosis and nephropathy (Boring et al., 1998; Seok et al., 2013). The recruitment of monocytes through this pathway is critical for early atherogenesis; however, it is not required for lesion progression at later stages of the disease (Aiello et al., 2010; Boring et al., 1998; Guo et al., 2005). The diabetes- induced up-regulation of ESRRA targets was also attenuated by Set7 deletion (Fig. 4.4D). These include genes associated with mitochondrial function such as the cytochrome P450 component Cyp1a1, which has been shown to mediate vascular damage in atherosclerosis (De Caterina and Madonna, 2009; Iwano et al., 2005). On the other hand, genes associated with muscle cell function whose expression is altered in hypertrophic cardiomyopathy (KEGG pathway Hypertrophic Cardiomyopathy), were down-regulated by diabetes and their expression increased by Set7 deletion (Fig. 4.4C). These genes include Tgfb2, a member of the transforming growth factor family required for tissue remodelling and cardiovascular development (Sanford et al., 1997). TGFb2 is found in macrophages and vascular smooth muscle cells within atherosclerotic lesions (Evanko et al., 1998). Furthermore, disruption of the TGFb signalling axis in immune cells accelerated the development of atherosclerosis (Robertson et al., 2003).

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A Effect of Set7 KO in gene expression changes in diabetic aortas: All genes Gene density Up High

NC O in diabetes Set7 K

Low Down NC Up Diabetes

Effect of Set7 KO in gene expression B changes in diabetic aortas: MODULE 75 (Immune Response) Gene density Up High

NC O in diabetes Set7 K

Low Down NC Up Diabetes Figure 4.4. Two-dimensional contour plot showing the effect of Set7 deletion in diabetic aortas The abundance of gene transcripts that were differentially expressed between control and diabetic ApoE-/- aortas (Diabetes effect, X-axis) was compared to the abundance of transcripts that are differentially expressed between diabetic ApoE-/- and Set7-/-/ApoE-/- (Set7 knock-out effect in diabetes, Y-axis). Red areas represent high abundance of transcripts whereas blue areas represent less abundant transcripts. The location of a transcript along the axes represents whether its expression was up-regulated (Up), down-regulated (Down) or unchanged (NC) in that experimental condition. A) All genes, B) Module 75 gene set, C) Kegg Hypertrophic Cardiomyopathy gene set, D) ESRRA targets gene set. Continues on next page

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Effect of Set7 KO in gene expression C changes in diabetic aortas: KEGG HYPERTROPHIC CARDIOMYOPATHY HCM Gene density Up High

NC O in diabetes Set7 K

Low Down NC Up Diabetes Effect of Set7 KO in gene expression D changes in diabetic aortas: STEIN ESRRA TARGETS UP Gene density Up High

NC O in diabetes Set7 K

Low Down NC Up Diabetes

Figure 4.4. Two-dimensional contour plot showing the effect of Set7 deletion in diabetic aortas Continues from previous page

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The potential involvement of transcription factors in the gene expression changes caused by Set7 deletion in diabetic aortas was assessed using transcription factor analysis. There were 330 positively enriched and 884 negatively enriched gene sets (FDR<0.05). Table 4.9 summarises the results of GSEA-ENCODE transcription factor analysis; it includes the top 30 most positively and most negatively enriched transcription factor gene sets.

Genes associated with the transcription factor YY1 were down-regulated by Set7 deletion, as opposed to the up-regulation observed as a result of diabetes. Targets of the transcription factor CREB1 (cyclic AMP-response element-binding protein 1) were also up-regulated by the deletion of Set7. Down-regulation of CREB is observed in the vasculature of aging, hypertensive and atherosclerotic rodent models and contributes to plaque formation in these settings (Schauer et al., 2010). This suggests that Set7 deletion prevents, at least partially, the pathological CREB down-regulation induced by diabetes.

The diabetes-induced down-regulation of genes associated with the transcription factors HCFC1 and GABPA was also attenuated by Set7 deletion. Moreover, the transcription factor THAP11 (Thanatos associated protein-domain containing 11) is also associated with gene down-regulation by Set7 deletion. This transcription factor associates with HCFC1 in diverse contexts to regulate gene expression (Dejosez et al., 2010; Parker et al., 2012). This suggests that Set7 participates in a network involving several transcription factors that regulates the transcription of mitochondrial genes. In this sense, Set7 deletion may be atheroprotective, at least partly, by attenuating pathways of tissue damage by mitochondrial dysfunction.

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Table 4.9. GSEA identifies transcription factors associated with genes up-regulated and down- regulated by Set7 deletion in ApoE-/- mice aortas. Positively Enriched Negatively enriched TF Gene Set NES FDR TF Gene Set NES FDR p val p val EZH2 (Hepatocyte) 4.46 <10-05 GATAD1 (HEPG2) -5.14 <10-05 TAF1 (Ishikawa) 4.10 <10-05 KAT8 (HEPG2) -5.09 <10-05 TAF1 (PFSK-1) 4.06 <10-05 HCFC1 (MCF-7) -4.90 <10-05 YY1 (H1-HESC) 4.05 <10-05 HCFC1 -4.87 <10-05 (GM12878) YY1 (SK-N-SH) 3.95 <10-05 HCFC1 (HELA- -4.83 <10-05 S3) POLR2APHOSPHO 3.90 <10-05 GABPA (K562) -4.81 <10-05 S5 (Neural cell) TAF1 (Neural cell) 3.86 <10-05 KDM5A (A549) -4.78 <10-05 ZFX (K562) 3.86 <10-05 DIDO1 (K562) -4.77 <10-05 REST (Neural cell) 3.84 <10-05 HCFC1 (HEPG2) -4.75 <10-05 GTF2E2 (K562) 3.84 <10-05 GABPA -4.72 <10-05 (GM12878) ZZZ3 (K562) 3.83 <10-05 GABPA (A549) -4.71 <10-05 E2F4 (K562) 3.82 <10-05 SIX5 (A549) -4.70 <10-05 TAF1 (H1-HESC) 3.81 <10-05 ZHX2 (HEPG2) -4.67 <10-05 YY1 (K562) 3.81 <10-05 SIX5 (GM12878) -4.66 <10-05 CREB1 (HEPG2) 3.78 <10-05 GABPA (HELA- -4.65 <10-05 S3) POLR2APHOSPHO 3.78 <10-05 GABPA (K562) -4.62 <10-05 S5 (K562) POLR2A (HEK293) 3.75 <10-05 ZBTB11 (K562) -4.62 <10-05 CREB3L1 (K562) 3.74 <10-05 ZBTB40 (K562) -4.53 <10-05 CREB1 (H1-HESC) 3.70 <10-05 ELF1 (GM12878) -4.52 <10-05 TAF1 (SK-N-SH) 3.68 <10-05 ZNF518A -4.51 <10-05 (HEK293) POLR2A (H1-HESC) 3.65 <10-05 NR2C2 (K562) -4.49 <10-05 SMAD5 (K562) 3.62 <10-05 ZBTB40 (HEPG2) -4.49 <10-05 SUPT5H (K562) 3.62 <10-05 TAF1 (GM12891) -4.49 <10-05 THRAP3 (K562) 3.61 <10-05 GABPA (MCF-7) -4.48 <10-05 E2F4 (HELA-S3) 3.61 <10-05 GABPA (HEPG2) -4.47 <10-05 POLR2A (HGPS) 3.60 <10-05 TAF1 (GM12878) -4.47 <10-05 CREB1 (K562) 3.60 <10-05 YY1 (HCT116) -4.45 <10-05 POLR2A (H54) 3.59 <10-05 GABPA (A549) -4.44 <10-05 CREB1 (HEPG2) 3.56 <10-05 ELF1 (HEPG2) -4.43 <10-05 CHD1 (H1-HESC) 3.55 <10-05 THAP11 (HEPG2) -4.43 <10-05 Terms in brackets refer to the cell type used to generate the transcription factor dataset.

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4.3.6. Pharmacological inhibition of Set7 in cultured endothelial and smooth muscle cells attenuates gene expression changes induced by high glucose, TNFa and TGFβ1 treatment

The vascular endothelium reacts to chronic high glucose exposure by producing pro- inflammatory mediators that promote leukocyte adhesion and tissue migration (Piga et al., 2007). For this reason, endothelial cells play an important role in the initiation of atherosclerosis. Tumour Necrosis Factor a (TNFa) is a potent inflammatory cytokine that activates endothelial cells promoting the expression of adhesion molecules and chemokines that contribute to atherogenesis (Huang and Vita, 2006; Jersmann et al., 2001; Modur et al., 1996). Production of TNFa is increased by diabetes resulting in increased systemic inflammation and endothelial dysfunction that lead to vascular damage (Anderson et al., 2004; Behi et al., 2008; Dandona et al., 1998; Makino et al., 2005).

Smooth muscle cells (SMCs) are a major component of the aortic wall and undergo changes in response to diabetic stimuli that contribute to the progression of atherosclerosis (Gomez and Owens, 2012). One of the factors that influences SMC phenotype in diabetes is the Transforming Growth Factor β1 (TGFβ1). In response to this cytokine, vascular SMCs up-regulate the expression of differentiation markers and certain components of the ECM (Liu et al., 2007; Muto et al., 2007; Schmidt et al., 2006).

The results presented in this chapter so far suggest that Set7 mediates diabetes-driven gene expression changes that result in vascular damage. Given the relevance of endothelial and smooth muscle cells in the development of vascular disease and to investigate the effects of pharmacological inhibition of Set7, in vitro experiments were performed using the potent and selective Set7 inhibitor, (R)-PFI-2 (Fig. 4.5A) (Tuano et al., 2016). This compound binds to the substrate binding pocket on the Set7 protein as well as to the co-factor S-adenosyl methionine (SAM) and is a powerful competitive inhibitor of enzyme activity (Barsyte-Lovejoy et al., 2014).

Human microvascular endothelial cells (HMEC-1) were exposed to normal (5.5mM) or high glucose (30mM) and TNFa (10ng/ml), for 24 hours in the presence or absence of (R)-PFI-2 (20µM) and harvested for RNA isolation. Set7 inhibition in these cells significantly attenuated the TNFa-induced increase in IL8, VCAM1, ICAM1 and CCL2

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but not RELA and IL6 (Fig. 4.5B). This is consistent with previous in vitro experiments where silencing of Set7 attenuates pro-inflammatory gene expression changes in response to high glucose in endothelial cells (El-Osta et al., 2008; Okabe et al., 2012).

Mouse SMCs (mSMCs) were exposed to normal or high glucose with and without TGFβ1 (5ng/ml) for 48 hours in the presence or absence of (R)-PFI-2 (20µM) and harvested for RNA isolation. Additionally, Set7 was silenced in these cells using lentiviral-mediated shRNA delivery as described in Chapter 2, Section 2.2.5. Non-target control and Set7 KD cells were also exposed to normal and high glucose and TGFβ1 as above. High glucose plus TGFβ1 treatment increased the expression of smooth muscle cell markers aSMA (Acta2), transgelin (Tagln) and calponin (Cnn1) as well as matrix component fibronectin (Fn1). These gene expression changes were attenuated by treatment with (R)-PFI-2 for all genes except Fn1 (Fig. 4.6A). Similar results were observed with Set7 silencing where high glucose and TGFβ1-induced increases in the expression of Acta2 and Tagln were attenuated (Fig. 4.6B).

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(R)-PFI-2

B CCL2 (MCP-1) 4 VCAM1 2.0 **** **** ****

3 **** **** 1.5 ##

2 #### 1.0 fold chang e fold chang e ***

1 0.5 ## #### mRN A mRN A

0 0.0 NG HG TNF HG+ NG HG TNF HG+ NG HG TNF HG+ NG HG TNF HG+ TNF TNF TNF TNF DMSO (R)-PFI-2 DMSO (R)-PFI-2

IL8 ICAM1 4 2.0 **** **** **** **** **** ## 3 **** 1.5 ##

2 1.0 fold chang e fold chang e #### 1 0.5 mRN A mRN A

0 0.0 NG HG TNF HG+ NG HG TNF HG+ NG HG TNF HG+ NG HG TNF HG+ TNF TNF TNF TNF DMSO (R)-PFI-2 DMSO (R)-PFI-2

2.0 RELA (p65) 3 IL6 **** **** 1.5 **** 2

1.0 fold chang e fold chang e 1 0.5 mRN A mRN A

0.0 0 NG HG TNF HG+ NG HG TNF HG+ NG HG TNF HG+ NG HG TNF HG+ TNF TNF TNF TNF DMSO (R)-PFI-2 DMSO (R)-PFI-2

Figure 4.5. Pharmacological inhibition of Set7 attenuates the high glucose and TNFa- induced increase in expression of pro-inflammatory genes in human microvascular endothelial cells (HMEC-1) Normal HMEC-1 cells pre-treated with (R)-PFI-2 (A) were exposed to high glucose and TNFa for 24 hours. qRT-PCR was performed to assess the expression level of pro-inflammatory genes associated with vascular damage in diabetes (B). n= 3, ***p<0.001, ****p<0.0001 vs. respective NG control; ##p<0.01, ####p<0.0001 vs. respective DMSO control

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A Mouse Smooth Muscle Cells: (R)-PFI-2 treatment

Acta2 ( Smooth Muscle Actin) Fn1 (Fibronectin) 3 3

**** *** ** 2 2

#### fold chang e fold chang e 1 1 mRN A mRN A

0 0 NG HG HG+ NG HG HG+ NG HG HG+ NG HG HG+ TGF 1 TGF 1 TGF 1 TGF 1 DMSO (R)-PFI-2 DMSO (R)-PFI-2

Tagln (Transgelin/SM22) Cnn1 (Calponin) 8 1.5

6 **** 1.0 4 ##

fold chang e ** # fold chang e 0.5 2 mRN A mRN A

0 0.0 NG HG HG+ NG HG HG+ NG HG HG+ NG HG HG+ TGF 1 TGF 1 TGF 1 TGF 1 DMSO (R)-PFI-2 DMSO (R)-PFI-2

B Mouse Smooth Muscle Cells: Set7 KD

Acta2 ( Smooth Muscle Actin) Fn1 (Fibronectin) 8 2.5

**** 6 2.0 ## **** 1.5 4 fold chang e fold chang e 1.0 2 0.5 mRN A mRN A

0 0.0 NG HG HG+ NG HG HG+ NG HG HG+ NG HG HG+ TGF 1 TGF 1 TGF 1 TGF 1 Non-Target Set7 KD Non-Target Set7 KD

Tagln (Transgelin/SM22) Cnn1 (Calponin) 100 **** 8 80 #### 60 6 **** **** **** 40 20 4 * 5 fold chang e fold chang e 4 3 2 mRN A

mRN A 2 1 0 0 NG HG HG+ NG HG HG+ NG HG HG+ NG HG HG+ TGF 1 TGF 1 TGF 1 TGF 1 Non-Target Set7 KD Non-Target Set7 KD Figure 4.6. Gene silencing and pharmacological inhibition of Set7 attenuates the high glucose and TGFβ1-induced increase in expression of pro-inflammatory and pro-fibrotic genes in mouse smooth muscle cells (mSMCs) Normal mSMCs pre-treated with (R)-PFI-2 as well as Non-Target control and Set7 KD transfected cells were exposed to high glucose and TGFβ1 for 48 hours. qRT-PCR was performed to assess the expression level of pro-fibrotic and smooth muscle-associated genes. n= 3, **p<0.01, ***p<0.001, ****p<0.0001 vs. respective NG control; #<0.05, ##p<0.01, ####p<0.0001 vs. respective DMSO or Non-Target control.

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4.4. DISCUSSION

The results in this chapter demonstrate that Set7 deletion attenuates gene expression changes induced by diabetes in the vasculature, attenuating the development of atherosclerosis in the mouse model studied.

4.4.1. Set7 deletion confers atheroprotection by attenuating diabetes-induced changes in gene expression

Chronic hyperglycaemia stimulates cells within the vascular wall to produce inflammatory and fibrotic mediators that trigger vascular damage. In vitro experiments have shown that silencing of the Set7 gene in endothelial cells attenuates hyperglycaemia- driven changes in gene expression that cause vascular damage, such as the up-regulation of IL8 and CCL2 (El-Osta et al., 2008; Okabe et al., 2012; Paneni et al., 2015). Consistent with these observations, pharmacological inhibition of Set7 using (R)-PFI-2 attenuated the high glucose and TNFa-induced up-regulation of IL8, CCL2, VCAM1 and ICAM1 in HMEC-1 cells. Set7 and NFkB target genes are up-regulated in leukocytes from type 2 diabetic patients, suggesting that this enzyme is involved in the progression of vascular damage in diabetes (Paneni et al., 2015). Set7 can modulate the activity of p65 (RELA) by lysine methylation (Ea and Baltimore, 2009; Yang et al., 2009). The results presented in Figure 4.5 highlight that Set7 regulates the expression of p65 target genes (CCL2, VCAM1 and ICAM1) without changes in p65 (RELA) expression levels in endothelial cells. This further supports a role of Set7 in mediating pro-inflammatory gene expression changes in endothelial cells and demonstrates that gene expression changes similar to Set7 silencing can be achieved by pharmacological inhibition.

Despite extensive in vitro studies in endothelial cells, the role of Set7 has not been investigated in an in vivo model of chronic hyperglycaemia. The results presented in this chapter show for the first time that the genetic deletion of Set7 attenuates vascular damage using an in vivo model of diabetes. Atherosclerotic plaque formation in the aortic arch of diabetic Set7-/-/ApoE-/- was reduced compared to ApoE-/- mice. Transcriptome sequencing revealed that gene expression changes induced by diabetes were attenuated by Set7 deletion, consistent with the atheroprotection observed in mice. These changes included attenuated expression of Ccl2, Vcam1, RelA and Acta2, genes previously associated with the development of vascular disease and known to be regulated by Set7

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(El-Osta et al., 2008; Keating et al., 2014; Tuano et al., 2016). Importantly, the changes in expression of these pro-inflammatory and pro-fibrotic genes, as well as previously described Set7 targets, induced by diabetic stimuli (high glucose, TGFβ1, TNFa) were attenuated by treatment with a pharmacological Set7 inhibitor.

RNA-seq also identified novel Set7-dependent, diabetes-inducible genes in the vasculature. One such gene was the Progranulin (PGRN)-induced receptor-like protein during osteoblastogenesis (Piro) (predicted gene Gm10800). Piro was recently identified as a progranulin-induced component of the RANK/RANKL pathway (Oh et al., 2015). Progranulin is a glycoprotein with anti-inflammatory properties involved in diverse physiological and pathological processes like wound healing and immune responses (Liu, 2011; Yin et al., 2010). This protein attenuates inflammatory responses in macrophages and endothelial cells suggesting that it may have an atheroprotective effect (Hwang et al., 2013; Yin et al., 2010). The results presented here suggest that Set7 down-regulates genes like Piro in response to diabetes, reducing anti-inflammatory responses and contributing to vascular damage. This may represent another pathway through which Set7 deletion confers atheroprotection in diabetes.

Smooth muscle cells (SMCs) are highly plastic and play a key role in the development of vascular diseases. A shift from a quiescent, contractile phenotype to a synthetic phenotype defined by high expression of ECM proteins is characteristic of atherosclerotic lesions (Gomez and Owens, 2012; Tabas et al., 2015). The expression of many SMC markers under physiological and pathological conditions is regulated by the serum response factor (SRF) in a process mediated by chromatin modifications (Manabe and Owens, 2001; McDonald et al., 2006; Tuano et al., 2016). Indeed, Gene Set Enrichment Analysis (GSEA) revealed that SRF gene targets are deregulated in the aortas of diabetic ApoE-/- mice, implicating SRF in SMC dysfunction and plaque formation in diabetes.

Set7 has been implicated in the regulation of SMC genes via H3K4me1 and SRF- dependent mechanisms (Tao et al., 2011; Tuano et al., 2016). RNA-seq data revealed that the expression of the muscle long non-coding RNA Yam1 was attenuated by Set7 deletion in diabetic aortas. The expression of Yam1 is directly regulated by the transcription factor YY1, a mediator of muscle differentiation whose activity is influenced by Set7-mediated lysine methylation (Zhang et al., 2016a). The results presented here suggest that Set7

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plays a role in transcriptional regulation of smooth muscle cells not only during development but also in pathological processes such as atherosclerotic plaque formation.

Another interesting finding derived from transcriptomic data analysis was the association between Set7 and the transcription factor CREB1 (CAMP-responsive element binding protein). A set of genes that were up-regulated by Set7 deletion in diabetic ApoE-/- aortas include CREB1 targets. Given that Set7 deletion was associated with a vasoprotective phenotype in this setting, it is hypothesised that CREB1-associated gene up-regulation might be protective. Indeed, a decrease in CREB1 expression and activation is observed in models of hyperlipidaemia in an oxidised LDL (oxLDL) dependent manner (Schauer et al., 2010). Furthermore, an increase in reactive oxygen species (ROS) production caused by high glucose in primary smooth muscle cells (SMCs) decreases CREB1 function, thereby promoting SMC migration (Watson et al., 2001). These observations implicate CREB1 loss as a mechanism underlying atherogenesis in diabetes. However, the role for CREB1 remains controversial as several studies have shown that CREB1 activation promotes inflammation and fibrosis in response to diabetic stimuli (Funakoshi et al., 2002; Ono et al., 2004; Reddy et al., 2002; Schroer et al., 2002).

4.4.2. Set7-dependent regulation of genes involved in mitochondrial function may contribute to vascular damage in diabetes

Mitochondrial dysfunction contributes to the development of atherosclerosis in diabetes. Hyperglycaemia increases the production of reactive oxygen species (ROS) from both cytoplasmic and mitochondrial origin, resulting in the activation of several pathways that lead to cellular damage (summarised in Chapter 1, Section 1.4). At the same time, overproduction of mitochondrial ROS can induce mitochondrial DNA (mtDNA) damage, further increasing ROS production and forming a deleterious positive feedback loop that exacerbates mitochondrial dysfunction (Madamanchi and Runge, 2007). Mitochondrial dysfunction activates atherogenic pathways including inflammation and apoptosis that contribute to vascular damage in diabetes (Fetterman et al., 2016; Hulsmans et al., 2012; Yu and Bennett, 2014). Furthermore, mtDNA damage precedes the onset of atherosclerosis and directly contributes to vascular damage (Yu et al., 2013).

Set7-mediated up-regulation of pro-inflammatory genes in endothelial cells is driven by an increase in mitochondria-derived ROS following hyperglycaemia (El-Osta et al.,

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2008). Leukocytes from subjects with type 2 diabetes have increased expression of Set7 and its pro-inflammatory targets, which directly correlates with increased oxidative stress and endothelial dysfunction (Paneni et al., 2015). In the present study, GSEA from RNA- seq data revealed that Set7 deletion in diabetic ApoE-/- aortas attenuated the expression of nuclear-encoded genes associated with mitochondrial function, including ESRRA (estrogen related receptor alpha) targets. Furthermore, GSEA ENCODE-TFBS analysis revealed that these gene expression changes were associated with the transcription factors GABPA, HCFC1 and THAP11.

GABP is a transcription factor that regulates the expression of nuclear-encoded mitochondrial genes. This regulatory process involves HCFC1 (Host Cell Factor C1), a modulator of chromatin structure (Machida et al., 2009). These two proteins form a complex with members of the PPARg co-activator 1 (PGC1) family, including PGC1α, to promote the expression of genes involved in mitochondrial respiration (Vercauteren et al., 2008). Additionally, GABP has also been associated with the regulation of TGFb1 expression in vascular SMCs in early atherosclerotic lesions, further implicating it in the atherogenic process (Dhaouadi et al., 2014). Moreover, the expression of HCFC1 is regulated by SREBP-1, a transcription factor involved in lipid metabolism that is associated with the pathogenesis of atherosclerosis (Motallebipour et al., 2009). HCFC1 often co-localises with GABP, YY1 and THAP11 at gene promoters (Michaud et al., 2013). The transcription factor THAP11 (Thanatos-associated protein domain-containing 11) binds to HCFC1 to promote the expression of genes important for energy production and cell growth (Dejosez et al., 2010; Parker et al., 2012). Additionally, Set7 is known to regulate the activity of PGC1α and YY1 (Ying-Yang 1) (Aguilo et al., 2016a; Zhang et al., 2016a). Taken together, these observations and the results from the transcriptome analysis presented here suggest that Set7 participates in a transcriptional complex that regulates the expression of nuclear-encoded mitochondrial genes. In this sense, Set7 may contribute to mitochondrial dysfunction that leads to vascular damage in diabetes.

The results presented here provide insight into the diabetes-induced changes in gene expression that promote vascular damage. However, accurate interpretation of gene expression profiles of atherosclerotic vessels is challenging because of cellular diversity (Tuomisto et al., 2005). The vascular wall contains endothelial and smooth muscle cells as well as leukocytes as atherosclerotic lesions develop. Moreover, some cell types also

124 4 | Atherosclerosis

undergo differentiation changes such as macrophage transformation into foam cells (Tabas et al., 2015). Identifying the mechanisms underlying pathological gene expression is essential for understanding the pathogenesis of atherosclerosis. Techniques such as laser capture microdissection (LCM) or single-cell sequencing may help accurately determine cell-specific gene expression profiles during the progression of atherosclerosis.

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4.5. CONCLUSION

The results presented in this chapter show for the first time that the genetic deletion of Set7 is atheroprotective in a mouse model of diabetes-accelerated atherosclerosis. Transcriptome profiling provided a comprehensive view of gene expression changes in the aorta as a result of diabetes and implicated Set7 in this process. However, given the heterogeneous nature of the tissues used, other techniques are needed to accurately determine the contribution of diverse vascular cell populations to the development and progression of atherosclerosis in diabetes. Cell culture studies using a specific Set7 inhibitor, (R)-PFI-2, prevents gene expression changes induced by inflammatory and fibrotic stimuli in endothelial and smooth muscle cells. This suggests that pharmacological inhibition of Set7 represents an option for developing treatments aimed at reducing the burden of macrovascular disease in diabetes.

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CHAPTER 5

THE EFFECT OF GENETIC SET7 DELETION IN THE

DEVELOPMENT OF DIABETIC NEPHROPATHY

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5.1. ABSTRACT

Chronic hyperglycaemia promotes the production of pro-inflammatory and pro-fibrotic mediators that lead to the development of chronic kidney disease. Increasing evidence implicates epigenetic modifications, particularly histone methylation, in this process. Set7, a lysine methyltransferase that monomethylates histone and non-histone proteins, has been associated with increased expression of pro-fibrotic genes in various models of kidney disease. The aim of the experiments presented in this chapter was to further define the role of Set7 in the development of diabetic nephropathy and evaluate it as a target for therapeutic intervention. For this purpose, diabetes-induced structural and functional kidney damage was studied in ApoE-/- and Set7-/-/ApoE-/- male mice compared to nondiabetic controls 10 weeks after the induction of diabetes. ApoE-/- mice were used as they develop obvious renal disease under diabetic conditions. Set7 genetic deletion conferred renal protection as evidenced by attenuated albuminuria, mesangial expansion and glomerular deposition of collagen I and IV. Transcriptome profiling by RNA sequencing (RNA-seq) revealed that diabetes caused widespread gene expression changes in the kidney that were attenuated in Set7-/-/ApoE-/- animals. Gene expression changes associated with Set7 were confirmed using qRT-PCR. Furthermore, treatment of cultured renal cells with (R)-PFI-2, a selective Set7 inhibitor, attenuated high glucose and TGFb1- mediated increases in pro-inflammatory and pro-fibrotic gene expression. Additionally, Gene Set Enrichment Analysis (GSEA) derived from the RNA-seq data revealed that Set7 was associated with gene regulation in diabetes by the estrogen receptor-related alpha (ESRRA) and GA-binding protein (GABP) transcription factors, known transcriptional regulators of mitochondrial function. Many of the Set7-dependent genes in the diabetic kidney were also associated with the transcription factor Tcf21, a key mediator of kidney development and podocyte function, suggesting a potential interaction with Set7. Collectively, the results in this chapter suggest that targeting Set7 may represent a strategy for developing therapies aimed at reducing the burden of diabetic nephropathy.

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5.2. INTRODUCTION

The diabetic milieu induces pathological gene expression changes in the kidney, many of which are mediated by changes in chromatin structure. A major driver of pathological renal changes during diabetes is the transforming growth factor (TGF) β1 (Sharma and Ziyadeh, 1995). TGFβ1 expression increases with hyperglycaemia, promoting the expression of many pro-fibrotic genes. TGFβ1-mediated expression of plasminogen activator inhibitor (PAI-1) under high glucose conditions involves the recruitment of the p300/CBP acetyltransferase and Set7 methyltransferase to its gene promoter (Sun et al., 2010; Yuan et al., 2013). TGFβ1 targets such as collagen I, connective tissue growth factor (CTGF) and p21 are also regulated by Set7-mediated histone methylation (Guo et al., 2016; Sun et al., 2010). The macrophage chemotactic protein 1 (MCP1) is a key inflammatory molecule contributing to renal damage in diabetic nephropathy. Endoplasmic reticulum stress during the progression of diabetes promotes the expression this chemokine in a Set7-mediated, H3K4me1-dependent mechanism (Chen et al., 2014). Diabetes also causes an increase in oxidised lipids which have a tissue damaging effect. Recent studies show that activation of the 12/15-Lipoxygenase following diabetic stimuli increases pro-fibrotic signalling in renal mesangial cells in a Set7-dependent mechanism (Yuan et al., 2016).

TGFβ1-mediated gene expression changes are a common feature of fibrotic diseases. Set7 is also involved in the up-regulation of pro-fibrotic genes in kidneys of mice after unilateral ureteral obstruction (UUO) and liver of rats after bile duct ligation (Sasaki et al., 2016; Sheen-Chen et al., 2014). Furthermore, the levels of Set7 correlate well to the degree of fibrosis in human samples from IgA nephropathy and membranous nephropathy (Sasaki et al., 2016). Moreover, the attenuation of diabetes-induced renal pro- inflammatory and pro-fibrotic gene expression conferred by losartan (an angiotensin II receptor antagonist used for treatment of diabetic patients with hypertension) in mice is partially mediated by a reduction in the levels of permissive histone marks like H3K4me1 at gene promoters (Reddy et al., 2014). These results suggest that histone modifications, specifically methylation by Set7, mediate the development of fibrosis and inflammation in different disease contexts.

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The Set7-mediated up-regulation of several pro-inflammatory and pro-fibrotic genes by hyperglycaemia have described previously (Brassachio et al., 2009; Chen et al., 2014; Fujimaki et al., 2015; Li et al., 2008; Okabe et al., 2012; Paneni et al., 2014; Sasaki et al., 2015; Sheen-Chen et al., 2014; Sun et al., 2010). However, in vivo models of diabetes are needed to understand the role that Set7 plays in the development of complications such as diabetic nephropathy. Furthermore, genome wide approaches to gene expression analysis would be advantageous as they allow for a comprehensive view of gene regulation and represent a resource for discovery.

Apolipoprotein E knock-out (ApoE-/-) mice have been used to create diabetic nephropathy models as they develop a more severe disease that closely resembles human pathology (Lassila et al., 2004). This is thought to be mediated by an increase in the generation of advanced glycation end-products (Lassila et al., 2004). This chapter describes experiments aimed to further define the role of Set7 in the development of diabetic nephropathy and evaluate it as a target for developing reno-protective therapies in diabetes. The animals used for this purpose were ApoE-/- and Set7/ApoE double knock- out (Set7-/-/ApoE-/-) mice on the C57Bl/6 background in which insulin-deficient diabetes was induced by streptozotocin injections.

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5.3. RESULTS

5.3.1. Diabetes induction causes metabolic changes regardless of Set7 genotype

As discussed in Chapter 4 (Section 4.3.1), general physiological parameters such as blood glucose, glycated haemoglobin and body weight were similar between diabetic ApoE-/- and Set7-/-/ApoE-/- mice. Diabetes induction in experimental mouse models induces kidney hypertrophy (Segev et al., 1997; Soler et al., 2012). Kidney weight was determined in these animals to assess whether the deletion of Set7 had an effect in diabetes-induced kidney hypertrophy. Ten weeks of diabetes increased kidney weight in all animals; however, there was no significant difference in the kidney to body weight ratio between control and diabetic ApoE-/- and Set7-/-/ApoE-/- mice (Table 5.1)

Table 5.1. Kidney weight and body weight of control and diabetic ApoE-/- and Set7-/-/ApoE-/- mice after 10 weeks of study Parameter ApoE-/- Set7-/- / ApoE-/- Control Diabetic Control Diabetic Body weight (g) 30.67 ± 1.03 24.44 ± 1.18* 29.07 ± 0.55 25.77 ± 0.72** Kidney weight/ 0.75 ± 0.02 0.92 ± 0.03** 0.72 ±0.02 0.90 ± 0.06* Body weight (%) *P<0.01, **P<0.001 vs. respective control. n= 9 ApoE-/-, 13 Set7-/-/ApoE-/-

5.3.2. Set7 deletion attenuates the diabetes-induced increase in albuminuria

The glomerular filtration barrier is composed of podocytes, the glomerular basement membrane and endothelial cells. Damage to any of these layers results in the excretion of albumin in urine or albuminuria (Brinkkoetter et al., 2013). Albuminuria is a key feature of diabetic kidney disease and is often used to evaluate kidney function and as a predictor of cardiovascular risk in diabetic subjects (Gerstein et al., 2001). To study the changes in kidney function caused by diabetes in ApoE-/- and Set7-/-/ApoE-/- mice, animals were placed in a metabolic cage for 24 hours during which urine samples were collected for albumin measurements (Chapter 2, Section 2.3.2). The results in Figure 5.1 show that after 10 weeks of diabetes, Set7-/-/ApoE-/- mice had lower levels of albuminuria than their ApoE-/- counterparts (76.4±15.5 vs. 118.3±8.2 µg/24 hrs).

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150 ***

100 # * g/24hrs)

( µ 50

Urinary Albumin Excretion Urinary 0 Ctrl Diab Ctrl Diab

ApoE-/- Set7-/-/ApoE-/-

Figure 5.1. Urinary albumin excretion (albuminuria) in control and diabetic ApoE-/- and Set7-/-/ApoE-/- mice Urinary albumin excretion levels in control and diabetic ApoE-/- and Set7-/-/ApoE-/- mice were determined by ELISA in 24-hour urine samples collected 9 weeks after induction of diabetes. Diabetes increased urinary albumin and this was attenuated in animals lacking Set7. n=8-9 ApoE-/-, 11-12 Set7-/-/ApoE-/-Set7. #p<0.05 vs. diabetic ApoE-/-

5.3.3. Set7 deletion attenuates diabetes-induced glomerular structural damage

The diabetic kidney is characterised by a marked increase in TGFβ1 which promotes the expression of pro-fibrotic molecules and deposition of certain extracellular matrix (ECM) components (Sharma and Ziyadeh, 1995). Deposition of ECM proteins including Collagen I and IV on the glomerular basement membrane and tubulointerstitial spaces increases with the progression of diabetes (Mason and Wahab, 2003). Mesangial expansion is also observed during the progression of diabetic nephropathy and this feature is directly associated with the worsening of kidney function (Ziyadeh et al., 2000).

Given the apparent improvement in kidney function in diabetic ApoE-/- mice with Set7 deletion, histological analyses were performed to determine whether Set7 deletion attenuated key histological features of diabetic nephropathy. Slides of paraffin-embedded kidney tissue were prepared and used for staining with Periodic acid-Schiff to measure mesangial area as well as for immunohistochemistry to determine the levels of collagen I and collagen IV. The results, summarised in Figure 5.2, showed that Set7-/-/ApoE-/- mice had reduced mesangial area (Fig. 5.2A) and lower levels of glomerular collagen IV (Fig. 5.2B) and I (Fig. 5.2C) deposition compared to diabetic ApoE-/- mice.

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A Ctrl Diab 40 ***

-/- 30 ##

ApoE

- -/ 10

Mesangial area (% ) E

/Apo 0 -

-/ Ctrl Diab Ctrl Diab 7 Set ApoE-/- Set7-/-/ApoE-/- B Ctrl Diab 20

-/- 15 *

ApoE

10 #

- -/ 5

Collagen I V (% glomeruli ) E

/Apo 0 - -/ Ctrl Diab Ctrl Diab

-/- 7 Set ApoE Set7-/-/ApoE-/-

C Ctrl Diab 20 *

-/- 15

ApoE

10 ##

- -/ 5

Collagen I (% glomeruli ) E

/Apo 0 -

-/ Ctrl Diab Ctrl Diab 7 Set -/- -/- -/- ApoE Set7 /ApoE Figure 5.2. Histological assessment of diabetes-induced renal structural damage in the glomeruli of control and diabetic ApoE-/- and Set7-/-/ApoE-/- mice Set7 deletion was associated with reduced mesangial area (A) and glomerular deposition of collagen IV (B) and I (C). *p<0.05 vs. control ApoE-/-, ##p<0.01 vs. diabetic Set7-/-/ApoE-/-; n=6 per group

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5.3.4. Diabetes induces widespread gene expression changes in the kidneys of ApoE-/- mice

The diabetic kidney is characterised by increased expression of ECM proteins and other molecules that contribute to the inflammatory and fibrotic state. To characterise the genome-wide expression signature in kidney cortical tissue, RNA was extracted from control and diabetic ApoE-/- mice 10 weeks after the induction of diabetes and differential gene expression was determined by RNA-seq.

Figure 5.3 shows that diabetes induced significant changes in gene expression. Using a false discovery rate adjusted p value (FDR p val) threshold of <0.05, there were 4,900 genes deregulated by diabetes. Of the 4,900 differentially expressed genes, 2,683 were up-regulated while 2,217 were down-regulated by diabetes. The top 40 genes deregulated by diabetes (sorted by FDR p value) are presented in Table 5.2. Diabetes greatly induced the up-regulation of the cyclin-dependent kinase inhibitor 1 (p21) gene (Cdkn1a) as well as that of members of the aldehyde dehydrogenase family (Aldh1a1, Aldh1a7). Up- regulation of p21 expression is characteristic of diabetic nephropathy and it mediates many of the pathological changes characteristic of this disease. It is responsible for driving mesangial expansion and glomerular hypertrophy (Al-Douahji et al., 1999; Griffin and Shankland, 2004; Kuan et al., 1998). This cell cycle protein also mediates TGFb1-induced apoptosis in podocytes playing a key role in podocyte loss during the progression of the disease (Wada et al., 2005). Furthermore, p21 also contributes to tubular cell damage in the diabetic kidney by decreasing their regenerative capacity, promoting apoptosis and stimulating the production of inflammatory and fibrotic mediators (Fan et al., 2011; Kitada et al., 2012; Kitada et al., 2014). Both Aldh1a1 and Aldh1a7 have been shown to be up-regulated in diabetic mouse kidneys (Kolmers et al., 2014). Dysregulation of retinoic acid metabolism, including overproduction of aldehyde dehydrogenase Aldh1, has also been reported in diabetic mouse kidneys (Starkey et al., 2010). This may be associated with overall energy metabolism changes that occur in the kidney as a result of chronic hyperglycaemia (Imasawa et al., 2017).

On the other hand, the genes encoding the membrane transporters Slc22a28 and Slc22a30 and the cytochrome P450 member Cyp4a12a were down-regulated by diabetes. Down- regulation of Slc22a28 and Slc22a30 has been described in kidneys of type 1 and type 2 diabetes mouse models (Komers et al., 2014; Zhang et al., 2017); however, the effect of

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these gene expression changes remains unknown. Cyp4a12 is a member of the cytochrome P450 family that participates in the w-hydroxylation of arachidonic acid to form 20-hydroxyeicosatetraetonic acid (20-HETE) (Capdevila et al., 2002; Powell et al., 1998). 20-HETE is highly abundant in the kidney where it is involved in blood pressure regulation (Lasker et al., 2000). It also induces glomerular injury, contributing to the progression of diabetic nephropathy (Gangadhariah et al., 2015).

Up: 2,683 Up: 666 LogFC

Down: 2,217 Down: 200

Log(concentration) Log(concentration)

Figure 5.3. MA plot representing genome-wide changes in gene expression in response to diabetes in the kidneys of ApoE-/- mice RNA-seq identified changes in gene expression in renal cortical tissue conferred by 10 weeks of diabetes in ApoE-/- mice. MA plots show the correlation between transcript read abundance (X- axis) and their fold change (Y-axis) (log-transformed). Red dots represent differentially expressed genes (FDR<0.05).

Up: 518 Up: 275

135 LogFC

Down: 563 Down: 191

Log(concentration) Log(concentration) 5 | Diabetic nephropathy

Table 5.2. RNA-seq identified gene expression changes conferred by diabetes in the kidneys of ApoE-/- mice Gene symbol Log2 Adj P Description FC value Cdkn1a 3.91 2.36 x10-81 Cyclin-Dependent Kinase Inhibitor 1A Eda2r 2.78 7.42 x10-59 Ectodysplasin A2 Receptor Psrc1 2.45 6.86 x10-40 Proline and Serine Rich Coiled-Coil 1 Ephx1 2.01 5.65 x10-33 Epoxide Hydrolase 1 Cbr3 1.9 4.43 x10-26 Carbonyl Reductase 3 Dscaml1 1.99 2.26 x10-25 Down Syndrome Cell Adhesion Molecule Like 1 Ugt1a10 3.4 9.15 x10-25 UDP Glucuronosyltransferase Family 1 Member A10 Fam212b 2.04 1.10 x10-23 Family With Sequence Similarity 212 Member B Mgmt 1.53 1.74 x10-20 O-6-Methylguanine-DNA Methyltransferase Aldh1a1 2.22 1.33 x10-19 Aldehyde Dehydrogenase 1 Member A1 Calml3 1.99 4.67 x10-19 Calmodulin Like 3 Crabp1 2.15 5.45 x10-19 Cellular Retinoic Acid Binding Protein 1 Ankrd34b -1.38 2.89 x10-18 Ankyrin Repeat Domain 34B Ltc4s 2.04 4.48 x10-18 Leukotriene C4 Synthase Hsd3b2 -1.55 5.29 x10-18 Hydroxy-Delta-5-Steroid Dehydrogenase, 3 Beta- And Steroid Delta-Isomerase 2 C1qtnf3 -1.54 7.84 x10-18 C1q Tumor Necrosis Factor Related Protein 3 Sulf2 1.5 4.44 x10-18 Sulfatase 2 Aldh1a7 2.14 1.06 x10-16 Aldehyde Dehydrogenase 1 Member A7 Plcd4 1.35 6.34 x10-16 Phospholipase C Delta 4 Phlda3 2.57 9.40 x10-16 Pleckstrin Homology Like Domain Family A Member 3 Tmem43 1.06 1.04 x10-15 Transmembrane Protein 43 Angptl7 -1.43 2.15 x10-15 Angiopoietin Like 7 Nefm 1.3 3.83 x10-15 Neurofilament, Medium Polypeptide Cox6b2 1.45 4.64 x10-15 Cytochrome C Oxidase Subunit 6B2 Akr1c12 1.36 4.80 x10-15 Aldo-keto Reductase Family 1, Member C12 Gabrb3 -1.65 7.78 x10-15 Gamma-Aminobutyric Acid Type A Receptor Beta3 Subunit Ugt1a2 4.69 8.00 x10-15 UDP Glucuronosyltransferase Family 1, Member A2 Gdf15 1.5 1.27 x10-14 Growth Differentiation Factor 15 Zfp365 1.02 1.47 x10-14 Zinc Finger Protein 365 Cyp4a12a -2.18 2.33 x10-14 Cytochrome P450, Family 4, Subfamily A, Polypeptide 12A Cxcl10 1.6 5.15 x10-14 C-X-C Motif Chemokine Ligand 10 Ifit2 1.3 7.98 x10-14 Interferon Induced Protein With Tetratricopeptide Repeats 2 Slc22a30 -1.3 1.16 x10-14 Solute Carrier Family 22, Member 30 Gm11974 1.42 1.39 x10-13 Snhg15 – Small Nucleolar Host Gene 15 Gtse1 1.37 1.97 x10-13 G2 And S-Phase Expressed 1 Pde6a -1.71 3.37 x10-13 Phosphodiesterase 6A Etv1 -1.63 3.48 x10-13 ETS Variant 1 Serpine2 1.29 4.05 x10-13 Serpin Peptidase Inhibitor, Clade E Member 2 Gpnmb 1.64 4.36 x10-13 Glycoprotein Nmb, Osteoactivin. Sepp1 -1.00 6.42 x10-13 Selenoprotein P, Plasma, 1 136 5 | Diabetic nephropathy

5.3.5. Genes up-regulated by diabetes are associated with inflammation and fibrosis

Gene Set Enrichment Analysis (GSEA) was used to determine key gene expression signatures induced by diabetes as explained in Chapter 4, section 4.3.4. A summary of positively and negatively enriched gene sets from the Molecular Signature Database (MSigDB) indicated that diabetes was associated with 3,509 positively enriched gene sets and 1,493 negatively enriched gene sets (FDR<0.05). The top 40 most positively enriched gene sets (sorted by Normalised Enrichment Score, NES) are summarised in Table 5.3.

Genes related to ECM and immunological responses were strongly enriched due to diabetes. These gene sets (shown in bold) represented 5 of the top 40 most positively enriched, highlighting their relevance to the pathological remodelling processes that occur in the diabetic kidney. Moreover, the expression of kidney-specific genes was also affected by diabetes. For example, genes up-regulated in kidneys of GLIS2 knockout mice were positively enriched (KIM_GLIS2_TARGETS_UP, shaded in Table 5.3). GLIS2 is a member of the Krüppel-like zinc finger protein family that is essential for the preservation of kidney function. Knock-out of the Glis2 gene in mice results in serious inflammation and fibrosis leading to renal failure (Kim et al., 2008).

137 5 | Diabetic nephropathy

Table 5.3. GSEA identified major pathways positively enriched in ApoE-/- diabetic kidneys MSigDB Gene Set NES FDR p val CHEN_METABOLIC_SYNDROM_NETWORK 8.01 <10-05 MODULE_84 (immune and inflammatory responses) 7.89 <10-05 SCHUETZ_BREAST_CANCER_DUCTAL_INVASIVE_UP 7.47 <10-05 MODULE_5 (lung genes) 7.36 <10-05 RODWELL_AGING_KIDNEY_UP 7.33 <10-05 MODULE_75 (immune response) 6.81 <10-05 MODULE_46 (cancer module) 6.76 <10-05 MODULE_118 (cell line expressed genes) 6.69 <10-05 CHICAS_RB1_TARGETS_CONFLUENT 6.49 <10-05 MODULE_6 (trachea genes) 6.48 <10-05 KIM_GLIS2_TARGETS_UP 6.44 <10-05 MODULE_2 (dorsal root ganglia genes) 6.39 <10-05 LINDGREN_BLADDER_CANCER_CLUSTER_2B 6.34 <10-05 MODULE_47 (ECM and collagens) 6.29 <10-05 MODULE_64 (membrane receptors) 6.28 <10-05 BOQUEST_STEM_CELL_UP 6.22 <10-05 MODULE_1 (ovary genes) 6.18 <10-05 POOLA_INVASIVE_BREAST_CANCER_UP 6.17 <10-05 MCLACHLAN_DENTAL_CARIES_UP 6.09 <10-05 MODULE_45 (whole blood genes) 6.05 <10-05 MODULE_88 (metabolic and xenobiotic response genes) 5.92 <10-05 BOQUEST_STEM_CELL_CULTURED_VS_FRESH_UP 5.81 <10-05 TARTE_PLASMA_CELL_VS_PLASMABLAST_UP 5.79 <10-05 MODULE_53 (cell line expressed genes) 5.73 <10-05 CUI_TCF21_TARGETS_2_UP 5.72 <10-05 MEISSNER_BRAIN_HCP_WITH_H3K4ME3_AND_H3K27ME3 5.70 <10-05 ICHIBA_GRAFT_VERSUS_HOST_DISEASE_D7_UP 5.66 <10-05 PICCALUGA_ANGIOIMMUNOBLASTIC_LYMPHOMA_UP 5.64 <10-05 WIELAND_UP_BY_HBV_INFECTION 5.63 <10-05 MODULE_38 (placenta genes) 5.62 <10-05 LEE_BMP2_TARGETS_UP 5.59 <10-05 WALLACE_PROSTATE_CANCER_RACE_UP 5.58 <10-05 STRUCTURAL_MOLECULE_ACTIVITY 5.58 <10-05 BERENJENO_TRANSFORMED_BY_RHOA_UP 5.57 <10-05 KEGG_RIBOSOME 5.56 <10-05 GOLDRATH_ANTIGEN_RESPONSE 5.54 <10-05 NAKAYAMA_SOFT_TISSUE_TUMORS_PCA2_UP 5.53 <10-05 MCLACHLAN_DENTAL_CARIES_DN 5.49 <10-05 LIU_VAV3_PROSTATE_CARCINOGENESIS_UP 5.47 <10-05 SERVITJA_ISLET_HNF1A_TARGETS_UP 5.46 <10-05

138 5 | Diabetic nephropathy

Despite a similar number of up- and down-regulated genes by diabetes, the number of gene sets that were negatively enriched in diabetes was one third (655) of those positively enriched (1,872). The 40 most negatively enriched gene sets are summarised in Table 5.3.

Twenty different microRNA (miR) gene sets (shown in bold) were identified in the top 40 most negatively enriched gene sets. These gene sets include targets of miR21 and the miR30 family. A reduction in the levels of miR30 facilitates podocyte damage and it is characteristic of diabetic nephropathy (Shi et al., 2013; Wu et al., 2014). Targets of miR21 were also associated with genes down-regulated by diabetes. miR21 is a negative regulator of transcription and its activation in the kidney as a result of diabetes contributes to the development of renal fibrosis (McClelland et al., 2015). Targets of the podocyte- enriched transcription factor Tcf21 were also down-regulated in response to diabetes (shown shaded in Table 5.4). Tcf21 has been shown to be critical for kidney development and is associated with maintenance of podocyte function in rodent models (Maezawa et al., 2014). Other negatively enriched gene sets included responses to DNA damage and apoptosis. This indicates that diabetes alters the normal processes of cell repair and renewal, which is consistent with the fibrotic phenotype that diabetic kidneys adopt.

139 5 | Diabetic nephropathy

Table 5.4. GSEA identified major pathways negatively enriched in ApoE-/- diabetic kidneys MSigDB Gene Set NES FDR p val DACOSTA_UV_RESPONSE_VIA_ERCC3_DN -8.55 <10-05 DACOSTA_UV_RESPONSE_VIA_ERCC3_COMMON_DN -6.71 <10-05 SHEN_SMARCA2_TARGETS_UP -6.68 <10-05 GABRIELY_MIR21_TARGETS -5.90 <10-05 BUYTAERT_PHOTODYNAMIC_THERAPY_STRESS_UP -5.57 <10-05 PILON_KLF1_TARGETS_DN -5.39 <10-05 DAZARD_RESPONSE_TO_UV_NHEK_DN -5.12 <10-05 GRAESSMANN_APOPTOSIS_BY_DOXORUBICIN_DN -5.04 <10-05 TGTTTAC,MIR-30A-5P,MIR-30C,MIR-30D,MIR-30B,MIR- -5.00 <10-05 30E-5P MILI_PSEUDOPODIA_HAPTOTAXIS_UP -4.92 <10-05 GCM_RAB10 -4.88 <10-05 ATGTTAA,MIR-302C -4.78 <10-05 TGAATGT,MIR-181A,MIR-181B,MIR-181C,MIR-181D -4.76 <10-05 TTTGCAC,MIR-19A,MIR-19B -4.55 <10-05 GCACTTT,MIR-17-5P,MIR-20A,MIR-106A,MIR- -4.54 <10-05 106B,MIR-20B,MIR-519D GCM_MYST2 -4.46 <10-05 TGCTGCT,MIR-15A,MIR-16,MIR-15B,MIR-195,MIR- -4.45 <10-05 424,MIR-497 DACOSTA_UV_RESPONSE_VIA_ERCC3_XPCS_DN -4.44 <10-05 CAGTATT,MIR-200B,MIR-200C,MIR-429 -4.41 <10-05 DIAZ_CHRONIC_MEYLOGENOUS_LEUKEMIA_UP -4.39 <10-05 SCGGAAGY_V$ELK1_02 -4.38 <10-05 ACCAAAG,MIR-9 -4.36 <10-05 TGCACTG,MIR-148A,MIR-152,MIR-148B -4.35 <10-05 TBK1.DF_DN -4.35 <10-05 TGCACTT,MIR-519C,MIR-519B,MIR-519A -4.33 <10-05 GGCACTT,MIR-519E -4.33 <10-05 CAGTGTT,MIR-141,MIR-200A -4.17 <10-05 GCCATNTTG_V$YY1_Q6 -4.16 <10-05 ATTCTTT,MIR-186 -4.15 <10-05 MARTORIATI_MDM4_TARGETS_FETAL_LIVER_DN -4.14 <10-05 TTGCACT,MIR-130A,MIR-301,MIR-130B -4.11 <10-05 AGCACTT,MIR-93,MIR-302A,MIR-302B,MIR-302C,MIR- -4.10 <10-05 302D,MIR-372,MIR-373,MIR-520E,MIR-520A,MIR- 526B,MIR-520B,MIR-520C,MIR-520D OSMAN_BLADDER_CANCER_UP -4.09 <10-05 MONNIER_POSTRADIATION_TUMOR_ESCAPE_UP -4.08 <10-05 TACTTGA,MIR-26A,MIR-26B -4.05 <10-05 TTTGTAG,MIR-520D -4.04 <10-05 HAMAI_APOPTOSIS_VIA_TRAIL_UP -4.02 <10-05 AAAGGGA,MIR-204,MIR-211 -3.98 <10-05 TAATGTG,MIR-323 -3.98 <10-05 CUI_TCF21_TARGETS_2_DN -3.81 <10-05

140 5 | Diabetic nephropathy

The potential association of transcription factors with gene deregulation caused by diabetes in the kidney was determined by transcription factor analysis. ENCODE (Encyclopedia of DNA elements) transcription factor binding gene sets and GSEA were used to determine correlations between these gene sets and changes induced by diabetes as discussed in Chapter 4 (Section 4.3.4). There were 79 positively enriched gene sets and 1,105 negatively enriched gene sets (FDR<0.05). Table 5.5 summarises the results of GSEA-ENCODE transcription factor analysis, it includes the top 30 positively and negatively enriched gene sets.

The transcriptional regulators most associated with gene up-regulation in diabetes included ubiquitous factors such as the methyltransferases Ezh2 and Suz12, components of the Polycomb Repressor Complex 2 (PRC2), as well as CBX2 and RING1, which are associated with a repressive chromatin state (Schwartz and Pirrotta, 2013). This suggests that genes that are normally associated with repressive histone marks are up-regulated by diabetes. On the other hand, the transcription factors YY1, GABPA and HCFC1 are highly represented among the most negatively enriched transcription factor gene sets. YY1 (Ying-Yang 1) is a transcription factor involved in cell proliferation that has been described as a target for Set7 methylation (Zhang et al., 2016a). GABPA (GA-binding protein transcription factor alpha subunit) and HCFC1 (Host cell factor C1) are involved in the transcriptional regulation of nuclear-encoded mitochondrial genes (Machida et al., 2009; Rosmarin et al., 2004). Moreover, GABP shares sequence identity with the nuclear respiratory factor 2 (NRF2), a protein target for methylation by Set7 (He et al., 2015).

141 5 | Diabetic nephropathy

Table 5.5. GSEA identified transcription factors associated with genes up-regulated and down- regulated by diabetes in ApoE-/- mice kidneys Positively Enriched Negatively Enriched TF Gene Set NES FDR TF Gene Set NES FDR p val p val EZH2 (Neuron) 7.07 <10-05 KMT2B (HEPG2) -9.47 <10-05 EZH2 (Hepatocyte) 6.64 <10-05 YY1 (Ishikawa) -9.33 <10-05 EZH2 5.61 <10-05 ZFX (K562) -9.09 <10-05 (Keratinocyte) EZH2 (Dermal 5.16 <10-05 ASH2L (GM12878) -9.07 <10-05 fibroblast) EZH2 (Lung 5.15 <10-05 YY1 (SK-N-SH) -8.72 <10-05 fibroblast) EZH2 (Astrocyte) 5.08 <10-05 POLR2A (H54) -8.53 <10-05 EZH2 (Umbilical 4.85 <10-05 RBBP5 (K562) -8.52 <10-05 vein endothelial cell) EZH2 (HEPG2) 4.67 <10-05 YY1 (GM12891) -8.49 <10-05 EZH2 (Mammary 4.49 <10-05 YY1 (H1-HESC) -8.47 <10-05 epithelial cell) EZH2 (B cell) 4.11 <10-05 YY1 (GM12878) -8.39 <10-05 EZH2 (H1-HESC) 4.02 <10-05 TAF1 (Ishikawa) -8.37 <10-05 POLR2A (A549) 3.49 <10-05 PHF8 (H1-HESC) -8.34 <10-05 SMC3 (HELA) 3.33 <10-05 YY1 (HEPG2) -8.30 <10-05 CBX2 (H1-HESC) 3.33 <10-05 HCFC1 (HEPG2) -8.16 <10-05 RNF2 (H1_HESC) 3.32 <10-05 HCFC1 (MCF7) -8.10 <10-05 ZNF747 (HEK293) 3.30 <10-05 TAF1 (Neural cell) -7.97 <10-05 EZH2 (Myotube) 3.20 <10-05 PHF8 (K562) -7.95 <10-05 SUZ12 (K562) 3.16 <10-05 HCFC1 -7.94 <10-05 (GM12878) TCF12 (SK-N-SH) 3.10 6.05 x10-05 GABPA (SK-N- -7.92 <10-05 SH) CBX2 (K562) 3.07 5.78 x10-05 YY1 (HCT116) -7.90 <10-05 EZH2 (Myoblast) 3.06 5.52 x10-05 GABPA -7.86 <10-05 (GM12878) CTCF (HCT116) 3.01 5.29 x10-05 YY1 (GM12892) -7.86 <10-05 SUZ12 (H1-HESC) 2.96 8.52 x10-05 K562_TAF1 (K562) -7.80 <10-05 RAD21 (IMR-90) 2.93 1.14 x10-04 KAT8 (HEPG2) -7.79 <10-05 RCOR1 (K562) 2.92 1.10 x10-04 YY1 (NT2-D1) -7.78 <10-05 SUZ12 (NT2-D1) 2.83 1.43 x10-04 PHF8 (A549) -7.76 <10-05 EZH2 (GM12878) 2.78 1.80 x10-04 ELF1 (HEPG2) -7.72 <10-05 MCM3 (K562) 2.77 1.74 x10-04 GABPA (HEPG2) -7.71 <10-05 RAD21 (HCT116) 2.75 1.94 x10-04 YY1 (A549) -7.70 <10-05 RING1 (H1-HESC) 2.73 2.13 x10-04 TAF1 (SK-N-SH) -7.69 <10-05 Terms in brackets refer to the cell type used to generate the transcription factor dataset.

142 5 | Diabetic nephropathy

5.3.6. Set7 deletion attenuates diabetes-induced gene expression changes in the kidneys of diabetic ApoE-/- mice

The results described earlier in the chapter suggest that genetic deletion of Set7 has a reno-protective effect in diabetic ApoE-/- mice. To tease out the global gene expression changes that underpin this protective phenotype, RNA was also extracted from diabetic Set7-/-/ApoE-/- mice 10 weeks after the induction of diabetes. Differential gene expression was determined by RNA-seq and compared to the profile generated for diabetic ApoE-/- mice.

The deletion of Set7 in control ApoE-/- mice caused modest gene expression changes in the kidney; 271 genes were up-regulated while 191 were down-regulated (FDR <0.05). These represent genes that are regulated by Set7 in the kidney, independently of diabetes. Interestingly, the gene expression profile of diabetic Set7-/-/ApoE-/- closely resembled that of control ApoE-/- kidneys (Fig. 5.4). In fact, diabetes in Set7 deficient animals only caused the deregulation of 866 genes in the kidney (666 up- and 200 down-regulated), as opposed to the almost 5,000 genes deregulated in ApoE-/- mice.

143 5 | Di a b eti c n e p hr o p at h y

6e+05 4e+05 2e+05 C o or di n at e 2 0e+00 −2e+05 −4e+05

−2000000 −1000000 −500000 0 500000 C o or di n at e 1

C o ntr ol Di a b eti c C o ntr ol Di a b eti c A p o E -/- A p o E -/- S et 7 -/-/ Apo E -/- S et 7 -/-/ Apo E -/-

Figure 5.4. Multidi mensional Scaling ( M DS) plot of R N A -se q sa m ples of co ntrol a n d diabetic Apo E -/- a n d Set7 -/-/ Apo E -/- ki d neys The M DS sho ws the si milarities in gene expression profile bet ween sa mples used for R N A -s e q. Each dot represents one individual sa mple. Sa mples clearly cluster according to their genotype and experi mental group. n=5 sa mples per group per genotype were used for R N A -s e q

1 4 4 5 | Diabetic nephropathy

- - / Diabetes in ApoE Diabetes in

n=2,878 n=494

Diabetes in Set7-/-/ApoE-/-

Figure 5.5. Comparison of genes deregulated by diabetes in ApoE-/- and Set7-/-/ApoE-/- kidneys The scatter plot compares the fold change (log-transformed) of genes that are deregulated by diabetes in ApoE-/- (Y-axis) and in Set7-/-/ApoE-/- (X-axis) kidneys. The middle panel shows 2,878 genes that are significantly up- or down-regulated (FDR<0.05) by diabetes only in the presence of Set7. Genes in the right panel (494) are those whose induction or reduction by diabetes is attenuated in the absence of Set7.

Figure 5.5 shows that a subset of 2,878 genes that were significantly up- or down- regulated by diabetes did not significantly change in expression in the absence of Set7 (blue, middle panel). Another group of 494 genes deregulated by diabetes in ApoE-/- mice are also altered in expression in the absence of Set7, but the magnitude of these changes was smaller (red, right panel). Genes in green (left panel) represent those affected by Set7 deletion in control animals (independent of diabetes). This suggests that while diabetes causes widespread gene expression changes, these were attenuated by Set7 deletion.

The gene expression profiles of kidney cortical tissue from diabetic ApoE-/- and Set7-/- /ApoE-/- were compared to investigate putative Set7 targets in the diabetic kidney. Overall, Set7 deletion was strongly associated with the attenuation of gene expression changes induced by diabetes (Fig. 5.6). There were 1,081 differentially expressed genes in this comparison; 518 were up-regulated and 563 were down-regulated. The top 40 differentially expressed genes (sorted by FDR p value) are summarised in Table 5.6.

145 5 | Diabetic nephropathy

Effect of Set7 KO in gene expression changes in diabetic kidneys: All genes Gene density Up High

NC Set7 KO in diabetes

Low Down NC Up Diabetes

Figure 5.6. Two-dimensional contour plot showing the effect of Set7 deletion in the diabetic kidney The abundance of gene transcripts that are differentially expressed between control and diabetic ApoE-/- kidneys (Diabetes effect, X-axis) was compared to the abundance of transcripts that are differentially expressed between diabetic ApoE-/- and Set7-/-/ApoE-/- (Set7 knock-out effect in diabetes, Y-axis). Red areas represent high gene density whereas blue areas represent a low number of genes. The location of a transcript along the axes represents whether its expression was up-regulated (Up), down-regulated (Down) or unchanged (NC) in that experimental condition. This plot reveals that the expression of a large proportion of genes that are up-regulated by diabetes is strongly attenuated by the deletion of Set7.

146 5 | Diabetic nephropathy

Table 5.6. RNA-seq identified gene expression changes conferred by the deletion of Set7 in the kidneys of diabetic ApoE-/- mice Gene symbol Log2 FDR p val Description FC Setd7 -0.89 1.12 x10-07 SET Domain Containing Lysine Methyltransferase 7 Cdca3 -1.18 1.12 x10-07 Cell Division Cycle Associated 3 Wdfy1 1.35 6.95 x10-07 WD Repeat And FYVE Domain Containing 1 Mat1a -2.41 8.07 x10-06 Methionine Adenosyltransferase 1A Gm10254 0.85 3.05 x10-05 Pseudogene 10254 Dio2 2.65 3.70 x10-05 Deiodinase, Iodothyronine, Type II Mum1l1 0.86 5.89 x10-05 Melanoma Associated Antigen (Mutated) 1-Like 1 Nnat 1.5 5.94 x10-05 Neuronatin Fam221b -1.16 5.95 x10-05 Family With Sequence Similarity 221 Member B Hsd3b2 0.88 7.62 x10-05 Hydroxy-Delta-5-Steroid Dehydrogenase, 3 Beta- And Steroid Delta-Isomerase 2 Cidea 4.75 7.62 x10-05 Cell Death-Inducing DFFA-Like Effector A Gm15743 -0.82 8.66 x10-05 Antisense lncRNA Taco1os -0.61 9.59 x10-05 Translational Activator of Mitochondrially Encoded Cytochrome C Oxidase I, Opposite Strand (Antisense lncRNA) Ucp1 5.81 9.59 x10-05 Uncoupling Protein1 C1qtnf3 0.86 9.97 x10-05 C1q And Tumor Necrosis Factor Related Protein 3 Spink8 -0.93 1.15 x10-05 Serine Peptidase Inhibitor, Kazal Type 8 (Putative) Ces1g 2.12 2.00 x10-04 Carboxylesterase 1G Gm13202 -0.78 2.0 x10-04 Pseudogene 13202 Trdn 1.04 2.00 x10-04 Triadin Npr3 0.73 2.00 x10-04 Natriuretic Peptide Receptor 3 Slc5a10 -0.94 2.04 x10-04 Solute Carrier Family 5 Member 10 (sodium/glucose cotransporter) D630023F18Rik 0.97 2.37 x10-04 Predicted gene Ahsg -2.53 2.37 x10-04 Alpha-2-HS-Glycoprotein Tat -1.74 2.37 x10-04 Tyrosine Aminotransferase Uox -1.48 2.58 x10-04 Urate Oxidase (Pseudogene) Hpx -1.89 2.77 x10-04 Hemopexin Rbm12b1 0.87 3.47 x10-04 RNA Binding Motif Protein 12 B1 Mug1 -0.85 3.76 x10-04 Murinoglobulin 1 Gm10076 -0.76 5.21 x10-04 Pseudogene 10076 Kbtbd8 0.75 5.55 x10-04 Kelch Repeat And BTB Domain Containing 8 Cps1 -1.86 5.55 x10-04 Carbamoyl-Phosphate Synthase 1 Fga -1.27 5.55 x10-04 Fibrinogen Alpha Chain Ugt2a3 0.84 5.79 x10-04 UDP Glucuronosyltransferase Family 2 Member A3 Mif -0.7 6.73 x10-04 Macrophage Inhibitory Factor Reep6 -0.83 6.73 x10-04 Receptor Accessory Protein 6 Ugt1a1 -0.65 7.12 x10-04 UDP Glucuronosyltransferase Family 1 Member A1 Gm2962 -1.24 7.82 x10-04 Pseudogene 2962 Tm6sf2 -0.95 7.82 x10-04 Transmembrane 6 Superfamily Member 2 Lats2 0.73 7.82 x10-04 Large Tumor Suppressor Kinase 2 Slc51b -0.66 7.82 x10-04 Solute Carrier Family 51 Beta Subunit

147 5 | Diabetic nephropathy

Genes up-regulated by Set7 deletion in the diabetic kidney include the mediator of inflammation C1qtnf3, apoptosis-related Cidea and antioxidant Ucp1. Cidea (Cell death- inducing DFFA-like) and Ucp1 (Uncoupling protein 1) participate in transcriptional regulation associated with energy metabolism (Viswakarma et al., 2007; Zhou et al., 2003). Alterations in metabolic programming are associated with the progression of diabetic nephropathy (Imasawa et al., 2017). Among the genes down-regulated with the knock-out of Set7 is the pro-inflammatory Mif (Macrophage inhibitory factor). MIF expression is increased in diabetic kidneys, where it promotes inflammation and contributes to the progression of nephropathy (Bruchfeld et al., 2016; Sanchez-Niño et al., 2009; Wang et al., 2014b). Consistent with the proposed role of Set7 as a mediator of inflammation and fibrosis, Set7 deletion also attenuated the diabetes-induced up- regulation of pro-inflammatory and pro-fibrotic genes such as Ccl2 (MCP1), Vcam1 Icam1, Ncf1 (p47phox), Fn1, Col4a2 and Acta2 (aSMA). The changes in the expression of these genes were validated in independent kidney cortex samples by qRT-PCR (Fig. 5.7).

4 ApoE-/- control Set7-/-/ApoE-/- control ApoE-/- diabetic Set7-/-/ApoE-/- diabetic **** 3 **** * **** **** *** * 2 ## ** #

1 mRNA fold change mRNA

0 Ccl2 Vcam1 Icam1 Fn1 Col4a2 Acta2 Ncf1 Inflammation Fibrosis Oxidative stress *p<0.05, ***p<0.0001 vs. ApoE-/- control, #p<0.05 vs. ApoE-/- diabetic n=5 per group Figure 5.7. qRT-PCR validated gene expression changes identified by RNA-seq mRNA expression levels of pro-inflammatory and pro-fibrotic genes previously associated with diabetic nephropathy in renal cortical tissue of control and diabetic ApoE-/- and Set7-/-/ApoE-/- mice. The changes in gene expression validate the fold changes obtained by RNA-seq. n=6-7 mice per group, *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001 vs. respective control; #p<0.05, ##p<0.01 vs. diabetic ApoE-/-.

148 5 | Diabetic nephropathy

5.3.7. Set7-mediated transcriptional regulation in the diabetic kidney is associated with mitochondrial function and microRNA regulation pathways

GSEA was used to determine key gene expression signatures induced by the deletion of Set7 in diabetic ApoE-/- mice kidneys. A summary of positively and negatively enriched pathways indicates that Set7 deletion was associated with 1,061 positively enriched gene sets and 802 negatively enriched gene sets (FDR<0.05).

The 40 most positively enriched gene sets (sorted by NES) are summarised in Table 5.7. They include 19 miRNA gene sets (shown in bold), including miR21 and the miR30 family which are associated with fibrosis in diabetic nephropathy (McClelland et al., 2015; Wu et al., 2014). This indicates that the deletion of Set7 prevents the diabetes- induced down-regulation of micro RNA target genes. Gene targets of the transcription factor Tcf21 that were down-regulated by diabetes, were also up-regulated in diabetic Set7-/-/ApoE-/- kidneys (shaded in Table 5.4). This suggests a potential role for Set7 in Tcf21-mediated transcriptional regulation in the kidney. The top 40 gene sets most negatively enriched by the deletion of Set7 in diabetes are summarised in Table 5.8. Nine of these gene sets were associated with mitochondrial function (shown in bold). They include genes that encode components of the mitochondrial electron transport chain, mitochondrial membrane transporters and enzymes involved in energy metabolism. These observations suggest that Set7 deletion in diabetes attenuates the increased expression of mitochondrial genes and may result in a decrease in the production of ROS.

149 5 | Diabetic nephropathy

Table 5.7. GSEA identified major pathways positively enriched by Set7 deletion in kidneys of diabetic ApoE-/- mice MSigDB Gene Set NES FDR p val DACOSTA_UV_RESPONSE_VIA_ERCC3_DN 9.22 <10-05 CUI_TCF21_TARGETS_2_DN 8.12 <10-05 MILI_PSEUDOPODIA_HAPTOTAXIS_UP 7.66 <10-05 GABRIELY_MIR21_TARGETS 7.47 <10-05 PILON_KLF1_TARGETS_DN 7.20 <10-05 HAMAI_APOPTOSIS_VIA_TRAIL_UP 7.18 <10-05 DACOSTA_UV_RESPONSE_VIA_ERCC3_COMMON_DN 6.87 <10-05 SHEN_SMARCA2_TARGETS_UP 6.81 <10-05 SENGUPTA_NASOPHARYNGEAL_CARCINOMA_WITH_LMP 5.92 <10-05 1_UP CAGTATT,MIR-200B,MIR-200C,MIR-429 5.82 <10-05 TGTTTAC,MIR-30A-5P,MIR-30C,MIR-30D,MIR-30B,MIR- 5.63 <10-05 30E-5P TGAATGT,MIR-181A,MIR-181B,MIR-181C,MIR-181D 5.54 <10-05 JOHNSTONE_PARVB_TARGETS_3_DN 5.53 <10-05 ATGTTAA,MIR-302C 5.45 <10-05 TTTGCAC,MIR-19A,MIR-19B 5.44 <10-05 TGCTTTG,MIR-330 5.42 <10-05 SCHLOSSER_SERUM_RESPONSE_DN 5.37 <10-05 CATTTCA,MIR-203 5.30 <10-05 DAZARD_RESPONSE_TO_UV_NHEK_DN 5.26 <10-05 RODRIGUES_THYROID_CARCINOMA_ANAPLASTIC_UP 5.19 <10-05 JOHNSTONE_PARVB_TARGETS_2_DN 5.16 <10-05 TTGTTT_V$FOXO4_01 5.15 <10-05 TGCTGCT,MIR-15A,MIR-16,MIR-15B,MIR-195,MIR- 5.13 <10-05 424,MIR-497 BUYTAERT_PHOTODYNAMIC_THERAPY_STRESS_UP 5.12 <10-05 TAGCTTT,MIR-9 5.05 <10-05 NUYTTEN_NIPP1_TARGETS_UP 5.04 <10-05 GSE39820_CTRL_VS_TGFBETA1_IL6_IL23A_CD4_TCELL_UP 5.01 <10-05 OSMAN_BLADDER_CANCER_UP 4.92 <10-05 TTGCACT,MIR-130A,MIR-301,MIR-130B 4.89 <10-05 CAGCTTT,MIR-320 4.78 <10-05 TGCACTT,MIR-519C,MIR-519B,MIR-519A 4.77 <10-05 GSE39820_CTRL_VS_TGFBETA3_IL6_IL23A_CD4_TCELL_UP 4.75 <10-05 GCM_RAB10 4.73 <10-05 ATATGCA,MIR-448 4.72 <10-05 ATTCTTT,MIR-186 4.71 <10-05 ATACTGT,MIR-144 4.69 <10-05 GCACTTT,MIR-17-5P,MIR-20A,MIR-106A,MIR-106B,MIR- 4.66 <10-05 20B,MIR-519D SENESE_HDAC3_TARGETS_UP 4.59 <10-05 TGCACTG,MIR-148A,MIR-152,MIR-148B 4.54 <10-05 GTATTAT,MIR-369-3P 4.51 <10-05

150 5 | Diabetic nephropathy

Table 5.8. GSEA identified major pathways negatively enriched by Set7 deletion in kidneys of diabetic ApoE-/- mice MSigDB Gene Set NES FDR p val GRADE_COLON_CANCER_UP -6.15 <10-05 CHR19P13 -6.13 <10-05 ENK_UV_RESPONSE_KERATINOCYTE_UP -5.99 <10-05 MODULE_114 (protein biosynthesis and ribosomes) -5.59 <10-05 CHR19Q13 -5.52 <10-05 WONG_MITOCHONDRIA_GENE_MODULE -5.48 <10-05 MOOTHA_HUMAN_MITODB_6_2002 -5.41 <10-05 MODULE_88 (metabolic and xenobiotic response) -5.38 <10-05 MODULE_151 (cancer module) -5.37 <10-05 MODULE_55 (cancer module) -5.36 <10-05 KIM_ALL_DISORDERS_OLIGODENDROCYTE_NUMBER_C -5.26 <10-05 ORR_UP REACTOME_RESPIRATORY_ELECTRON_TRANSPORT_ -5.24 <10-05 ATP_SYNTHESIS_BY_CHEMIOSMOTIC_COUPLING_AN D_HEAT_PRODUCTION_BY_UNCOUPLING_PROTEINS_ MODULE_23 (metabolism and xenobiotics) -5.21 <10-05 MOOTHA_MITOCHONDRIA -5.20 <10-05 GNF2_CCNB2 -5.17 <10-05 GNF2_CDC20 -5.10 <10-05 MODULE_83 (cancer module) -5.08 <10-05 MORF_ACTG1 -5.06 <10-05 ROSTY_CERVICAL_CANCER_PROLIFERATION_CLUSTER -5.06 <10-05 SOTIRIOU_BREAST_CANCER_GRADE_1_VS_3_UP -5.05 <10-05 MODULE_152 (oxidative phosphorylation and ATP synthesis) -5.01 <10-05 SPIELMAN_LYMPHOBLAST_EUROPEAN_VS_ASIAN_UP -4.98 <10-05 GSE15750_DAY6_VS_DAY10_TRAF6KO_EFF_CD8_TCELL_ -4.94 <10-05 UP GNF2_CCNA2 -4.93 <10-05 KAECH_DAY8_EFF_VS_DAY15_EFF_CD8_TCELL_UP -4.91 <10-05 HSIAO_HOUSEKEEPING_GENES -4.90 <10-05 GNF2_HMMR -4.88 <10-05 MORF_NME2 -4.87 <10-05 REACTOME_RESPIRATORY_ELECTRON_TRANSPORT -4.86 <10-05 MOOTHA_VOXPHOS -4.85 <10-05 MITOCHONDRION -4.85 <10-05 MODULE_60 (heart genes) -4.82 <10-05 KEGG_OXIDATIVE_PHOSPHORYLATION -4.80 <10-05 KIM_BIPOLAR_DISORDER_OLIGODENDROCYTE_DENSIT -4.79 <10-05 Y_CORR_UP GSE15750_DAY6_VS_DAY10_EFF_CD8_TCELL_UP -4.75 <10-05 STRUCTURAL_CONSTITUENT_OF_RIBOSOME -4.75 <10-05 MODULE_62 (cancer module) -4.74 <10-05 DAIRKEE_TERT_TARGETS_UP -4.72 <10-05 CUI_TCF21_TARGETS_2_UP -4.32 <10-05

151 5 | Diabetic nephropathy

The potential association of transcription factors with Set7 deletion in the diabetic kidney was determined by transcription factor analysis. This involved comparing genes that were differentially expressed between diabetic ApoE-/- and Set7-/-/ApoE-/- kidneys with ChIP- seq data sets (genome wide transcription factor binding sites) from ENCODE. There were 261 positively enriched gene sets and 552 negatively enriched gene sets (FDR<0.05). Table 5.9 summarises the results of GSEA-ENCODE transcription factor analysis, which includes the top 20 most positively and negatively enriched gene sets.

Table 5.9. GSEA identified transcription factors associated with up-regulated and down- regulated genes by Set7 deletion in the kidneys of diabetic ApoE-/- mice Positively Enriched Negatively Enriched TF Gene Set NES FDR p TF Gene Set NES FDR p val val NFE2L1 (K562) 8.74 <10-05 ZNF747 (HEK293) -6.53 <10-05 CHAMP1 (K562) 7.23 <10-05 POLR2A (K562) -6.28 <10-05 POLR2A (H54) 7.11 <10-05 POLR2A (A549) -5.82 <10-05 RBBP5 (K562) 7.05 <10-05 JUNB (K562) -5.46 <10-05 ZNF318 (K562) 6.58 <10-05 SMC3 (HELA-S3) -5.39 <10-05 CBX5 (GM12878) 6.22 <10-05 POLR2A (HEPG2) -5.34 <10-05 RBBP5 (H1-HESC) 6.19 <10-05 ZNF175 (HEK293) -5.26 <10-05 GATA3 (SH-SY5Y) 5.97 <10-05 POLR2A (HELA-S3) -5.01 <10-05 FOXA1 (HEK293T) 5.96 <10-05 KDM6A (H1-HESC) -4.91 <10-05 TCF7 (GM12878) 5.83 <10-05 PAF1 (HEPG2) -4.76 <10-05 ASH2L (GM12878) 5.66 <10-05 ZBTB33 (MCF-7) -4.75 <10-05 REST (Neural cell) 5.60 <10-05 POLR2A (MCF10-A) -4.74 <10-05 POLR2A (HELA-S3) 5.54 <10-05 MXI1 (HELA-S3) -4.74 <10-05 POLR2A (HGPS) 5.38 <10-05 RCOR1 (HEPG2) -4.68 <10-05 THRAP3 (K562) 5.23 <10-05 ZNF169 (HEK293) -4.65 <10-05 FOXK2 (HEK293T) 5.18 <10-05 BHLHE40 (K562) -4.53 <10-05 COPS2 (K562) 5.18 <10-05 MYC (HEPG2) -4.49 <10-05 TAF1 (PFSK-1) 5.17 <10-05 ESRRA (GM12878) -4.48 <10-05 TAF1 (H1_HESC) 5.11 <10-05 MAZ (K562) -4.42 <10-05 HOMEZ (HEPG2) 5.10 <10-05 RCOR1 (K562) -4.38 <10-05 KMT2B (HEPG2) 5.08 <10-05 ZNF493 (HEK293) -4.34 <10-05 CTBP1 (HEK293T) 5.07 <10-05 ZNF143 (K562) -4.30 <10-05 KDM5B (K562) 5.01 <10-05 SP1 (HEPG2) -4.30 <10-05 NFRKB (HEK293T) 5.00 <10-05 JUND (K562) -4.28 <10-05 STAT3 (GM12878) 4.76 <10-05 ZBTB7A (HEK293) -4.27 <10-05 AF1 (SK-N-SH) 4.60 <10-05 SP3 (HEL293) -4.18 <10-05 CBX8 (A549) 4.51 <10-05 ZNF274 (HEK293) -4.12 <10-05 JUNB (GM12878) 4.47 <10-05 HDGF (K562) -4.11 <10-05 POLR2A 4.32 <10-05 CTCF (HEPG2) -4.10 <10-05 (Erythroblast) GABPA (GM12878) 4.31 <10-05 MXI1 (K562) -4.10 <10-05 Terms in brackets refer to the cell type used to generate the transcription factor dataset.

152 5 | Diabetic nephropathy

Transcription factors most associated with gene up-regulation by Set7 deletion in diabetes include GABPA (GA-binding protein). This transcription factor is involved in the transcriptional regulation of mitochondrial genes (discussed in Chapter 4, Section 4.4.2). GABPA was also associated with gene down-regulation as a result of diabetes (Table 5.4), suggesting that it might be involved in Set7-mediated gene expression changes in diabetes. STAT3 (signal transducer and activator of transcription 3) gene targets were also up-regulated in diabetic kidneys from Set7-/-/ApoE-/- when compared to ApoE-/-. STAT3 mediates inflammatory responses that contribute to the progression of diabetic nephropathy (Chuang and He, 2010; Lu et al., 2009). On the other hand, gene sets associated with the transcription factor ESRRA (Estrogen Related Receptor Alpha) were negatively enriched by the deletion of Set7 in diabetes. ESRRA has been shown to interact with PGC1α to regulate mitochondrial function (Schreiber et al., 2004), further suggesting a role for Set7 in regulating the expression of nuclear-encoded genes mitochondrial genes.

5.3.8. Pharmacological inhibition of Set7 in cultured renal cells attenuates gene expression changes induced by high glucose and TGFβ1 treatment

The results discussed above indicate that Set7 mediates some of the renal gene expression changes induced by diabetes. TGFβ1 is a potent pro-fibrotic cytokine that stimulates the production of ECM components (Border and Noble, 1994). Production of TGFβ1 is elevated in the kidneys of human and experimental diabetic nephropathy contributing to the glomerular and tubular hypertrophy characteristic of this disease (Sharma et al., 1997; Park et al., 1997; Reeves and Andreoli, 2000; Yamamoto et al., 1993; Wolf and Zidayeh, 1999). The renal cortex comprises several cell types including endothelial cells, mesangial cells, podocytes and proximal tubule cells (PTCs). Hypertrophy of the mesangial and tubular compartment as well as podocyte loss observed in diabetic kidneys, are driven by the overproduction of TGFβ1.

In vitro experiments were used to investigate the effects of pharmacological inhibition of Set7 in specific renal cell populations by using the selective inhibitor of Set7, (R)-PFI- 2 (Tuano et al., 2016). PTCs isolated from the renal cortex of ApoE-/- and Set7-/-/ApoE-/- as well as primary normal human mesangial cells (NHMCs) and human podocytes were exposed to either normal glucose (5mM), high glucose (30mM) or high glucose plus TGFβ1 (5ng/ml) for 48 hours in the presence or absence of (R)-PFI-2 (10-20µM).

153 5 | Diabetic nephropathy

Treatment with (R)-PFI-2 did not change the expression levels of Set7, consistent with published observations that changes in gene expression are dependent on Set7 methyltransferase activity (Fig. 5.8A) (Tuano et al., 2016). High glucose and TGFβ1 exposure significantly increased the expression of Acta2 and Fn1 in PTCs in a Set7- dependent manner, as both (R)-PFI-2 treatment and Set7 deletion attenuated this effect (Fig. 5.8B,C). Exposure to (R)-PFI-2 attenuated the high glucose and TGFβ1-induced increases in the expression of pro-inflammatory (CCL2/MCP1) and pro-fibrotic genes (ACTA2/SMA, COL4A1, CTGF) in cultured NHMCs and podocytes (Fig. 5.9).

A 1.5 Set7 mRNA DMSO (R)-PFI-2 1.0 fold chang e 0.5 mRN A

0.0 PTCs NHMCs Podocytes B C Fn1 (Fibronectin) 15 Acta2 ( Smooth Muscle Actin) 8 **** 6 **** **** 10 #### #### #### **** **** **** 4 fold chang e fold chang e 5 2 mRN A mRN A

0 0 NG HG HG+ NG HG HG+ NG HG HG+ NG HG HG+ NG HG HG+ NG HG HG+ TGF TGF TGF TGF TGF TGF ApoE-/- Set7-/-/ApoE-/- ApoE-/- + (R)-PFI-2 ApoE-/- Set7-/-/ApoE-/- ApoE-/- + (R)-PFI-2

Figure 5.8. Genetic deletion and pharmacological inhibition of Set7 attenuates the high glucose and TGFβ1-induced increase in expression of pro-inflammatory and pro-fibrotic genes in mouse renal Proximal Tubule Cells (PTCs) PTCs were isolated from the renal cortex of ApoE-/- and Set7-/-/ApoE-/- and cultured in vitro for one week before being exposed to high glucose and TGFβ1. ApoE-/- were also assayed in the presence of (R)-PFI-2 (10µM). (R)-PFI-2 treatment did not alter Set7 mRNA expression (A). qRT-PCR was performed to assess the expression level of key pro-fibrotic genes (B,C). n= 3, ****p<0.0001 vs. respective NG control; ###p<0.001 vs. respective ApoE-/- control

154 5 | Diabetic nephropathy

A Normal Human Mesangial Cells (NHMCs)

CTGF CCL2 (MCP-1) 5 1.5 **** 4

1.0 ## 3 ### ** fold chang e

2 fold chang e 0.5 1 mRN A mRN A

0 NG HG HG+ NG HG HG+ NG HG HG+ NG HG HG+ TGF 1 TGF 1 TGF 1 TGF 1

DMSO (R)-PFI-2 DMSO (R)-PFI-2

3 COL4A1 2.0 ACTA2 ( SMA) **** **** 1.5 2 #### **** 1.0 fold chang e

fold chang e #### 1 0.5 #### ### mRN A mRN A

0 0.0 NG HG HG+ NG HG HG+ NG HG HG+ NG HG HG+ TGF 1 TGF 1 TGF 1 TGF 1

DMSO (R)-PFI-2 DMSO (R)-PFI-2

B Human Podocytes

5 CTGF CCL2 (MCP-1) **** 2.0 **** 4 1.5 3 # 1.0 fold chang e fold chang e 2 0.5 1 mRN A mRN A

0 0.0 NG HG HG+ NG HG HG+ NG HG HG+ NG HG HG+ TGF 1 TGF 1 TGF 1 TGF 1

DMSO (R)-PFI-2 DMSO (R)-PFI-2

6 COL4A1 3 ACTA2 ( Smooth Muscle Actin) **** *

4 2 fold chang e fold chang e 2 1 mRN A mRN A

0 0 NG HG HG+ NG HG HG+ NG HG HG+ NG HG HG+ TGF 1 TGF 1 TGF 1 TGF 1 DMSO (R)-PFI-2 DMSO (R)-PFI-2 Figure 5.9. Pharmacological inhibition of Set7 attenuates the high glucose and TGFβ1- induced increase in expression of pro-inflammatory and pro-fibrotic genes in Normal Human Mesangial cells (NHMCs) and human podocytes Cultured NHMCs and human podocytes were exposed to high glucose and/or TGFβ1 for 48 hours in the presence or absence of (R)-PFI-2 (20µM) followed by RNA isolation. qRT-PCR was performed to assess the expression level of key pro-inflammatory and pro-fibrotic genes. n= 4 for NHMCs, n=3 for podocytes, *p<0.05, ***p<0.001, ****p<0.0001 vs. respective NG control; #p<0.05, ##p<0.01, ###p<0.001, ####p<0.0001 vs. respective DMSO control.

155 5 | Diabetic nephropathy

Additionally, exposure of NHMCs to (R)-PFI-2 also attenuated the expression of the high glucose and TGFβ1-responsive smooth muscle-related genes CNN1 (Calponin) and TAGLN (Transgelin/SM22) (Fig. 4.10). This is consistent with recent studies showing Set7-mediated transcriptional activation of these genes during smooth muscle cell differentiation (Tuano et al., 2016).

CNN1 (Calponin) 2.0 TAGLN (Transgelin, SM22) 4 **

1.5 3 *** ## 1.0 2 fold chang e fold chang e #### 0.5 1 mRN A mRN A

0.0 0 NG HG HG+ NG HG HG+ NG HG HG+ NG HG HG+ TGF 1 TGF 1 TGF 1 TGF 1 DMSO (R)-PFI-2 DMSO (R)-PFI-2

Figure 5.10. Pharmacological inhibition of Set7 attenuates the high glucose and TGFβ1- induced increase in expression of smooth muscle-related genes in NHMCs Cultured NHMCs were exposed to high glucose and/or TGFβ1 for 48 hours in the presence or absence of (R)-PFI-2 followed by RNA isolation. qRT-PCR was performed to assess the expression level of key pro-inflammatory and pro-fibrotic genes. n= 4 for NHMCs, **p<0.01, ***p<0.001 vs. respective NG control; ##p<0.01, ####p<0.0001 vs. respective DMSO control

156 5 | Diabetic nephropathy

5.4. DISCUSSION

The results in this chapter show that Set7 deletion attenuates gene expression changes induced by diabetes in the kidney leading to a reduction in histological (mesangial expansion, glomerular collagen deposition) and functional (albuminuria) features of diabetic nephropathy. Furthermore, analyses of RNA-seq data highlighted the involvement of Set7 in pathways not previously related to this enzyme and suggest Tcf21 as a novel Set7 interacting partner.

5.4.1. Set7 deletion mitigates renal damage by attenuating diabetes-induced changes in gene expression

Diabetes is known to promote the production of inflammatory and fibrotic mediators. In the kidney, chronic hyperglycaemia promotes the production of TGFβ1 which, in turn, activates the transcription of pro-fibrotic genes (Sharma and Ziyadeh, 1995). Increased deposition of extracellular matrix (ECM) proteins along with a persistent inflammatory response induce glomerular and tubular damage, ultimately leading to a decline in renal function (Ziyadeh et al., 2000). Accordingly, RNA-seq from kidney cortical tissue from control and diabetic ApoE-/- mice showed diabetes was associated with the up-regulation of genes involved in ECM production and inflammation.

Differential gene expression and pathway analyses in control and diabetic ApoE-/- kidneys support the notion that TGFβ1 drives the fibrotic program during the development of diabetic nephropathy. This further strengthens the confidence in both the pre-clinical model and the bioinformatic tools used. Moreover, our data highlights the diversity of pathways leading to fibrosis in the diabetic kidney. A successful approach to developing new therapies aimed at limiting the progression of diabetic nephropathy should consider this diversity when assessing potential targets.

Gene expression profiles from control and diabetic ApoE-/- kidneys resulted in the up- regulation of 2,683 and down-regulation of 2,217 genes, revealing that diabetes causes genome-wide gene expression changes in the kidney. This suggests that the pathophysiological changes observed during the progression of diabetes are not only the result of the up-regulation of pro-inflammatory and pro-fibrotic mediators, but also the down-regulation key proteins that help maintain normal tissue homeostasis. These gene expression changes appear to be largely mediated by a change in chromatin state. GSEA

157 5 | Diabetic nephropathy

analysis of the RNA-seq data together with ENCODE transcription factor binding site (TFBS) data revealed that genes up-regulated by diabetes are bound by histone modifiers associated with transcriptional repression. Genes bound by the Polycomb Repressor Complex 2 (PRC2) subunits Enhancer of Zeste 2 (Ezh2) and Suppressor of Zeste (Suz12) are strongly associated with gene up-regulation in response to diabetes in the kidney. PRC2 is responsible for di- and trimethylation of H3K27 resulting in transcriptional repression (Czermin et al., 2002). This suggests that these genes are normally suppressed by PRC2 and diabetes induces changes in chromatin structure that result in their up- regulation. Histone modifications are highly dynamic and, in this sense, diabetes could induce the displacement of repressor complexes by activating proteins that establish permissive histone marks such as H3K27Ac or H3K4me.

Contrary to the perception of Set7 acting as a transcriptional activator mainly through its role as a H3K4 methyltransferase, Set7 deletion in the diabetic kidney lead to a similar number of up- and down-regulated genes (518 vs. 563). This highlights the complexity of transcriptional regulatory cascades and emphasises the ability of Set7 to regulate gene expression through non-histone dependent pathways. In this sense, the global gene expression profiles generated during this study increase the current understanding of the effect of diabetes in the kidney and the involvement of Set7 in this process.

Set7 is known to regulate the expression of inflammatory and fibrotic mediators in renal cells in response to high glucose and TGFβ1 through histone methylation (Chen et al., 2014; Guo et al., 2016; Sun et al., 2010). In the context of kidney disease, Set7 has been implicated in the transcriptional regulation of several TGFβ1 gene targets in renal cells such as Ctgf, Pai1, Col1 and Col4 (Chen et al., 2014; Sun et al., 2010; Yuan et al., 2016). It has also been associated with the expression of NFκB targets such as Ccl2 (MCP-1), Vcam1, Icam1 and the NFκB subunit p65 (Rela) in endothelial cells (Li et al., 2008; Okabe et al., 2012). The genetic deletion of Set7 attenuated the diabetes-induced up- regulation of pro-inflammatory Ccl2 and Icam1, as well as pro-fibrotic Fn1 and Col4a2 in total renal cortical tissue. Set7 deletion did not affect the expression of the Ctgf gene, a described H3K4me1-dependent Set7 target in mesangial cells (Sun et al., 2010). Additionally, TFGβ1-driven CTGF gene up-regulation was not attenuated by Set7 inhibition in cultured human podocytes. However, the up-regulation of CTGF in response to TFGβ1 was attenuated in Normal Human Mesangial Cells in the presence of (R)-PFI- 2, a selective Set7 inhibitor. This suggests that the transcriptional regulation of this gene 158 5 | Diabetic nephropathy

by Set7 is specific to mesangial cells and the abundance of RNA from diverse sources in the renal cortical samples masks mesangial cell-specific gene expression.

Importantly, the gene expression changes observed are consistent with renoprotection observed in mice. This was evidenced by the attenuation of histological features of nephropathy such as glomerular deposition of collagen I and IV and expansion of the mesangial compartment. The functionality of the glomerular filtration barrier was also better preserved in these animals as shown by reduced levels of urinary albumin excretion when compared to diabetic ApoE-/- mice.

5.4.2. Set7 mediates the expression of genes involved in mitochondrial function in diabetes

Diabetes increases the expression of nuclear-encoded components of the mitochondrial electron transport chain in the kidney (Wada et al., 2002). Moreover, expression levels of genes involved in oxidative phosphorylation and electron transport are associated with the progression of diabetic nephropathy (Huang et al., 2006). However, the mechanisms behind this effect of diabetes remain poorly characterised.

Pathway analysis of RNA-seq data revealed that the deletion of Set7 in ApoE-/- prevents the diabetes-induced up-regulation of genes involved in mitochondrial function. One recent study supports a role for Set7 in mitochondrial regulation and generation of reactive oxygen species (ROS) (He et al., 2015). This suggests the possibility that Set7 participates in the diabetes-induces up-regulation of these genes and that the protective effect conferred by the deletion of this methyltransferase involves a reduction in the generation of ROS.

Transcription factor analysis of the RNA-seq data showed that gene targets of the transcription ESRRA (Estrogen Receptor Related alpha) were up-regulated by diabetes and that this was attenuated by Set7 deletion. ESRRA interacts with the PPARg coactivator 1 alpha (PGC1α) to regulate genes involved in oxidative phosphorylation and mitochondrial biogenesis (Schreiber et al., 2004). Set7 was recently shown to methylate PGC1α at lysine residue 779, promoting the transcription of its target genes (Aguilo et al., 2016). Similarly, genes bound by the GA-binding protein subunit alpha (GABPA) were down-regulated by diabetes and this was attenuated by Set7 deletion. GABP has been associated with a myriad of functions including proliferation, apoptosis and energy

159 5 | Diabetic nephropathy

metabolism (Rosmarin et al., 2004). Importantly, GABP regulates the expression of nuclear-encoded mitochondrial genes including components of the electron transport chain such as the cytochrome C oxidase subunits IV (Carter and Avadhani, 1994), Vb (Sucharov et al., 1995), VIIa (Seelan et al., 1996) and ATP synthase b subunit (Villena et al., 1998). Furthermore, the DNA binding activity of GABP (mediated by the alpha subunit) and subsequent gene activation are inhibited by pro-oxidant conditions (Martin et al., 1996). This is consistent with the down-regulation of GABPA targets in response to diabetes.

These findings support a role for Set7 in regulating mitochondrial function and energy metabolism indirectly by affecting the activity of the transcription factors PGC1α, ESRRA and GABPA.

5.4.3. Set7 participates in TGFβ1-driven pro-fibrotic responses via several pathways

Pathway analysis of the RNA-seq data showed that Set7 deletion recovers the expression of miR21 targets that are down-regulated by diabetes in the kidney. miR21 plays an important role in the development of diabetic nephropathy and other fibrotic diseases, as it represses genes that participate in pathways limiting the onset of fibrosis (Zhong et al., 2011). Thus, diabetes-induced up-regulation of miR21 results in increased expression of pro-fibrotic genes and worsening renal function. Indeed, the expression of miR21 is directly correlated to the progression of diabetic nephropathy in humans (McClelland et al., 2015). miR21 participates in the regulation of expression of pro-fibrotic genes partially by its interactions with SMAD3 and SMAD7, pro- and anti-fibrotic effectors of the TGFβ1 signalling pathway, respectively. miR21 up-regulation leads to activation of SMAD3 and down-regulation of SMAD7 (McClelland et al., 2015). The expression of Set7 in the kidney in response to TGFβ1 is mediated by the effector protein SMAD3 (Sasaki et al., 2016). Furthermore, Set7-mediated methylation of anti-fibrotic SMAD7 was recently described as a mechanism for SMAD7 degradation (Elkouris et al., 2016). These associations suggest that Set7 and miR21 are part of a common axis that regulates gene expression changes in response to TGFβ1 signalling.

Mesangial expansion is an early feature of diabetic nephropathy. Hypertrophic mesangial cells are characterised by increased production of smooth muscle cell markers and ECM components like fibronectin and collagens. This effect was evident in the results

160 5 | Diabetic nephropathy

presented both in vivo (Section 5.3.3) and in vitro (Section 5.3.7). Mesangial expansion as well as increased glomerular deposition of collagens were observed, accompanied by increased expression of genes encoding fibronectin and collagen IV. These pathological changes were attenuated by the deletion of Set7, suggesting the enzyme plays an important role in the transcriptional regulation of mesangial cells and early diabetic nephropathy pathogenesis. Furthermore, cultured mesangial cells had marked responses to TGFβ1 stimulation, which were inhibited by (R)-PFI-2. Mesangial cells share many characteristics with smooth muscle cells (SMCs) and are closely related to SMCs of the renal arterioles (Brunskill et al., 2011). Set7 was recently shown to regulate the expression of SMC differentiation markers Transgelin (TAGLN), Calponin 1 (CNN1) and a-Smooth Muscle Actin (ACTA2) directly via H3K4me1 or via its interaction with the transcription factor SRF (Serum Response Factor) (Tuano et al., 2016). In fact, transgelin appears to be a marker of mesangial cell proliferation following injury (Daniel et al., 2012). These genes were also up-regulated in response to diabetic stimuli in mesangial cells, an effect that was attenuated by Set7 inhibition (Section 5.3.7).

These observations implicate Set7 with cellular proliferation and fibrosis. Such programs are common to several disease contexts and suggest that targeting Set7 may offer a therapeutic option in pathological settings beyond diabetic kidney disease.

5.4.4. Set7 may associate with the transcription factor Tcf21 to regulate gene expression changes in the diabetic kidney

Gene Set Enrichment Analysis (GSEA) of the RNA-seq data from control and diabetic ApoE-/- and Set7-/-/ApoE-/- mice revealed that gene targets of the transcription factor Tcf21 (Pod-1/Epicardin/Capsulin) were differentially deregulated in diabetes in a Set7- dependent manner. Genes that are down-regulated by Tcf21 knock-out in murine glomeruli (from E18.5 Tcf21-/- mouse embryos) were also down-regulated in the kidneys of ApoE-/- mice in response to diabetes. The expression of these genes was recovered in ApoE-/-/Set7-/- animals.

Tcf21 is a basic Helix-Loop-Helix (bHLH) transcription factor highly expressed in glomerular podocytes in rodents and humans (Lindenmeyer et al., 2010). The expression of Tcf21 is reduced in the early stages of diabetic nephropathy, coinciding with the onset of albuminuria (Makino et al., 2006). Furthermore, mice with podocyte-specific

161 5 | Diabetic nephropathy

deficiency of Tcf21 have compromised podocyte function and develop a more severe proteinuric phenotype when diabetes is induced (Maezawa et al., 2014).

Increasing attention is being paid to Set7 methylation of non-histone proteins. There have been almost 40 published non-histone protein substrates for Set7-mediated monomethylation (Chapter 1, Table 1.2). Lysine methylation of non-histone proteins can have different effects. For example, Set7-mediated methylation of the NFkB subunit p65 on lysine 37 stabilises the protein leading to enhanced expression of NFkB gene targets (Ea and Baltimore, 2009). On the other hand, methylation of lysines 314 and 315 on the same protein lead to its nuclear exclusion conferring gene repression (Yang et al., 2009). Similarly, Set7-mediated methylation of SMAD7 on lysine 70, decreases the protein stability by allowing its poly-ubiquitination and subsequent degradation (Huang et al., 2009).

While these observations suggest that Set7 and Tcf21 interact, further experiments are required to determine whether Tcf21 is a substrate for Set7-mediated methylation and whether this regulates gene expression in the kidney. This would constitute the first report of interaction between these two proteins and the potential identification of a novel regulatory network in the diabetic kidney that contributes to the development of diabetic nephropathy. This potential interaction is explored in the next chapter.

16 2 5 | Diabetic nephropathy

5.5. CONCLUSIONS

The results presented in this chapter show for the first time that the genetic deletion of Set7 attenuates renal damage in a mouse model of diabetic nephropathy. Furthermore, it provides a comprehensive view of gene expression changes that occur in the kidney as a result of diabetes and the involvement of Set7 in this process. The results presented here also show that inhibition of Set7 using a selective pharmacological inhibitor in different renal cell populations recapitulates changes in gene expression observed in an animal model of diabetic nephropathy. Overall, the results presented here support targeting Set7 as a strategy for developing treatments aimed at reducing the burden of diabetic nephropathy.

163 6 | TCF21 interaction

CHAPTER 6

INTERACTION BETWEEN SET7 AND THE

TRANSCRIPTION FACTOR 21

164 6 | TCF21 interaction

6.1. ABSTRACT

The transcription factor 21 (TCF21/Pod-1/Capsulin/Epicardin) is involved in the regulation of cell fate decisions in the heart and lungs. In the kidney, it is highly expressed in glomeruli where it is required to maintain podocyte homeostasis. Gene expression profiling of control and diabetic ApoE-/- and Set7-/-/ApoE-/- mouse kidneys by RNA sequencing (RNA-seq) revealed that a subset of genes that are deregulated by diabetes and dependent on Set7 are targets of TCF21. Bioinformatics and statistical analyses were performed comparing the gene expression profile generated by RNA-seq with existing data sets from mouse Tcf21-/- glomeruli deposited in the Gene Expression Omnibus (GEO). This analysis showed a negative association between Set7 expression and diabetes-induced changes in TCF21 target genes. Interaction between Set7 and TCF21 was demonstrated by co-immunoprecipitation experiments using a recombinant overexpression system. Furthermore, lysine residue 61 on the TCF21 protein was identified as a putative Set7 methylation target. These observations implicate TCF21 in the development of diabetic nephropathy and suggest that Set7 participates in this process. Moreover, the results presented here propose TCF21 as a novel non-histone Set7 methylation target.

165 6 | TCF21 interaction

6.2. INTRODUCTION

The Set7 lysine methyltransferase is associated with transcriptional regulation mediated by histone dependent (H3K4me1) and independent mechanisms (summarised in Chapter 1, Section 1.7). Many non-histone proteins have been identified and validated as Set7 targets including transcription factors and enzymes (Chapter 1, Table 1.2). Many Set7- induced gene expression changes are not associated with histone monomethylation, highlighting the importance of identifying non-histone Set7 methylation targets for understanding the complexities of transcriptional regulation. As presented in Chapter 5, gene expression profiling of control and diabetic ApoE-/- and Set7-/-/ApoE-/- mice kidney identified the transcription factor TCF21 (Pod-1/Capsulin/Epicardin) as a potential Set7 interacting partner and non-histone methylation target.

TCF21 is a basic helix-loop-helix (bHLH) transcription factor first identified as a regulator of morphogenesis in the developing embryo (Lu et al., 1998a). It is a cell- specific Class B bHLH protein that forms heterodimers with other bHLH proteins and binds to the consensus E-box sequence CANNTG (Lu et al., 1998a). It is expressed in murine mesodermal precursors and is most abundant in lungs, kidneys and heart of adult mice and humans (Lu et al., 1998a; Quaggin et al., 1998). Tcf21 null mice have severely hypoplastic lungs and kidneys, do not develop a spleen and die shortly after birth (Lu et al., 2000; Quaggin et al., 1999). These animals also have hypoplastic gonads, demonstrating that TCF21 is required for sex determination (Cui et al., 2004). This is partly explained by the direct regulation of TCF21 expression by SRY (Sex determining region Y) (Bhandari et al., 2011). Additionally, mice lacking both TCF21 and the related bHLH, MyoR, specifically lack groups of facial muscles (Lu et al., 2002).

In the developing kidney, TCF21 is required for mesenchymal-epithelial interactions. TCF21 is expressed in visceral epithelial cells of the glomeruli, podocytes, from the start of epithelial cell differentiation until they undergo terminal differentiation (Quaggin et al., 1998). Indeed, the expression of TCF21 appears to be restricted to this cell type in adult kidneys (Lindenmeyer et al., 2010; Quaggin et al., 1998). Podocytes are highly differentiated cells wrapped around the glomerular capillaries that are characterised by long interdigitating foot processes. Together with the glomerular basement membrane and the fenestrated endothelium of glomerular capillaries, the slit diaphragms formed by the podocytes constitute the glomerular filtration barrier (Pavenstädt et al., 2003). Due to

166 6 | TCF21 interaction

their highly-differentiated state, podocytes are non-proliferative, although some evidence suggests that resident renal progenitors may be able to regenerate lost podocytes (Barisoni et al., 2000; Lasagni et al., 2015). Disease states such as diabetes alter podocyte foot process structure ultimately resulting in loss of the slit diaphragm. As a consequence, proteinuria and glomerulosclerosis develop, which ultimately lead to loss of renal function (Brinkkoetter et al., 2013). For this reason, the preservation of podocyte numbers and function is of great interest for the treatment of renal disease.

Deletion of Tcf21 from late podocyte progenitors in mice resulted in reduced number of glomerular endothelial and mesangial cells leading to severe proteinuria and glomerular lesions in 40% of the animals (Maezawa et al., 2014). Non-proteinuric mice with a podocyte-specific deletion of Tcf21 were susceptible to diabetes-induced kidney damage and had higher death rates compared to control animals (Maezawa et al., 2014). Furthermore, expression of TCF21 appears to be elevated at very early stages of diabetic nephropathy but it is significantly decreased by the time of albuminuria onset (Makino et al., 2006).

This chapter describes efforts to determine whether TCF21 and Set7 interact and to study this as a possible mechanism of renal damage in the development of diabetic nephropathy.

167 6 | TCF21 interaction

6.3. RESULTS

6.3.1. TCF21 regulates the expression of diabetic nephropathy-associated genes in cultured human podocytes

TCF21 is highly expressed in renal podocytes; however, the role of this transcription factor in these terminally differentiated cells is poorly understood. In order to determine whether TCF21 plays a role in transcriptional responses to diabetic stimuli in human cultured podocytes, shRNA-mediated silencing of TCF21 was performed as described in Chapter 2, Section 2.2.5. Control and TCF21 KD cells were cultured under non- permissive conditions (5mM glucose RPMI medium, 2% Fetal Bovine Serum, 37°C) to promote differentiation (Fig. 6.1). Under these conditions, podocytes stop proliferating and adopt an arborised pattern with elongated foot processes (Shankland et al., 2007). Initial observable structural differences between Non-target control and TCF21KD cells (Fig. 6.1, Day 0) were no longer evident as cells differentiated. This may be due to the acute effect of TCF21KD, after which compensatory mechanisms may offset this phenotype.

Differentiated podocytes were exposed to normal (5mM) or high glucose (25mM) and TGFb1 (5ng/ml) for 48 hours and harvested for gene expression analysis. Silencing of TCF21 augmented the high glucose and TGFb1-induced up-regulation of genes associated with diabetic nephropathy such as CCL2 (MCP-1), CDKN1A (cyclin- dependent kinase inhibitor 1A, p21) and VEGFA (Vascular endothelial growth factor A), while attenuating ACTA2 gene expression (Fig. 6.2). Podocytes are the major site of VEGF production in the kidney and the expression of this growth factor is exacerbated by diabetes (Tufro and Veron, 2012). This up-regulation, as well as altered VEGF and nitric oxide (NO) signalling contribute to the development of diabetic nephropathy (Hoshi et al., 2002; Tufro and Veron, 2012). The cyclin-dependent kinase inhibitor p21 is also up-regulated in the diabetic kidney where it is associated with the development of hypertrophy (Griffin and Shankland, 2004). In podocytes, p21 mediates TGFb1-induced apoptosis, further contributing to the progression of nephropathy (Wada et al., 2005). Increased expression of the VEGFA and CDKN1A genes, as well of the pro-inflammatory CCL2, following TCF21 KD in podocytes suggests that this transcription factor is required for maintaining podocyte health under diabetic conditions. However, the high

168 6 | TCF21 interaction

glucose and TGFb1-induced increase in ACTA2 expression was reduced by TCF21 KD, demonstrating the complex regulatory role of this protein.

Control TCF21 KD

Day 0 33ºC

Day 3 37ºC

Day 10 37ºC

Figure 6.1. Control and TCF21 KD human podocytes in culture Human podocytes were cultured under permissive conditions (10% Fetal Bovine Serum, 33°C) to allow them to proliferate. Cells were transfected with lentiviral particles containing shRNA targeting TCF21 (TCF21 KD) or non-targeting shRNA (control cells) and cultured under non- permissive conditions (2% Fetal Bovine Serum, 37°C) for 10-14 days to promote differentiation. Differentiated podocytes were exposed to high glucose (25mM) and TGFb1 (5ng/ml) for 48 hours. Magnification: 100X

169 6 | TCF21 interaction

CDKN1A (p21) CCL2 (MCP-1) 2.0 3 #### #### ## 1.5 *** # 2

1.0 fold chang e fold chang e 1 0.5 mRN A mRN A

0.0 0 Non-Target TCF21 KD Non-Target TCF21 KD

VEGFA ACTA2 ( Smooth Muscle Actin) 2.0 5 # *** ### * **** 4 **** 1.5 **** ## 3 **** 1.0 fold chang e fold chang e 2

0.5 1 mRN A mRN A

0.0 0 Non-Target TCF21 KD Non-Target TCF21 KD

NG (5mM) TGF 1 (5ng/ml) HG (25mM) HG + TGF 1 Figure 6.2. High glucose and TGFb1-induced gene expression changes in control and TCF21 KD cultured human podocytes Non-target control and TCF21 KD cultured human podocytes were exposed to high glucose and/or TGFβ1 for 48 hours followed by RNA isolation. qRT-PCR was performed to assess the expression level of key pro-inflammatory and pro-fibrotic genes. n= 3, *p<0.05, ***p<0.001, ****p<0.0001 vs. respective NG control; #p<0.05, ##p<0.01, ###p<0.001, ####p<0.0001 vs. respective Non-Target.

6.3.2. TCF21 gene targets are deregulated in the kidneys of diabetic ApoE-/- mice

As discussed in Chapter 5, transcriptome profiling of control and diabetic ApoE-/- mouse kidneys revealed that genes associated with transcriptional regulation by TCF21 are deregulated by diabetes. Given the proposed role of this transcription factor in regulating gene expression in response to stimuli from the diabetic milieu, statistical analyses were performed to identify commonalities between TCF21-dependent genes and the transcriptiome profile of the diabetic kidney. Fisher’s exact test was performed to determine whether TCF21 target genes were more likely to be deregulated by diabetes than other genes. TCF21 target genes were considered to be those up- or down-regulated in Tcf21-/- mouse glomeruli from the Gene Expression Omnibus (GEO) (Cui et al., 2005). These genes were compared to those up- or down-regulated by diabetes in ApoE-/- mouse kidneys based on RNA-seq (presented in Chapter 5). The results, summarised in Table

170 6 | TCF21 interaction

6.1, show that genes down-regulated in Tcf21-/- glomeruli are 1.92 times more likely to be suppressed by diabetes (odds ratio 1.92). Similarly, genes up-regulated in Tcf21-/- are 2.92 times more likely to be activated by diabetes.

Table 6.1. Fisher’s exact test demonstrated statistical associations between TCF21 gene targets and genes deregulated by diabetes Odds ratio P value Genes suppressed in diabetic ApoE-/- kidneys Down-regulated in 1.92 <10-05 Down-regulated by TCF21-/- more Tcf21-/- glomeruli likely to be suppressed in diabetes Up-regulated in Tcf21-/- 0.48 7x10-05 Up-regulated by TCF21-/- unlikely to glomeruli be suppressed in diabetes Genes activated in diabetic ApoE-/- kidneys Down-regulated in 1.04 0.69 Down-regulated by TCF21-/- unlikely Tcf21-/- glomeruli to be activated in diabetes Up-regulated in Tcf21-/- 2.92 <10-05 Up-regulated by TCF21-/- more likely glomeruli to be activated in diabetes

As a bHLH heterodimer, TCF21 binds to an E-box motif identified as CAGCTG (Fig. 6.3) (Sazonova et al., 2015). Binding motifs were predicted using the JASPAR database (http://jaspar.genereg.net), which contains non-redundant binding sites for a collection of transcription factors across different species allowing functional annotation of genomic data (Mathelier et al., 2016).

The identified TCF21 binding motif was intersected with gene promoters, enhancers and gene body regions (includes promoters) using a genome-wide position weight matrix (PWM) scanner as described in methods Section 2.6.4. TCF21-regulated genes (based on gene expression changes in Tcf21-/- glomeruli) were then assessed for the presence of a TCF21 binding motif in order to define them as direct TCF21 targets. Fisher’s exact test was used again to determine whether these genes were associated with diabetes-induced gene deregulation. Consistent with this hypothesis, the results of this analysis showed diabetes-induced deregulation of TCF21 gene targets (Table 6.2).

171 6 | TCF21 interaction

Figure 6.3. Sequence logo representing the TCF21 DNA binding motif The sequence logo for the CAGCTG TCF21 binding motif was obtained from the JASPAR database. Sequence logos represent the degree of conservation of bases at each position. The more frequently encountered a base is at a given position, the higher its letter will be because it is more conserved at that position. Different bases encountered at the same position are scales according to how frequently they are encountered. This TCF21 motif shows a high degree of conservation.

Table 6.2. Fisher’s exact test demonstrated statistical associations between TCF21 gene targets containing a TCF21 binding motif and genes deregulated by diabetes Odds ratio P value Genes suppressed in diabetic ApoE-/- kidneys Down-regulated in 1.62 0.03 Down-regulated by Tcf21-/- with Tcf21-/- glomeruli with enhancer motif are more likely to be enhancer motif suppressed in diabetes Down-regulated in 1.84 2x10-05 Down-regulated by Tcf21-/- with gene Tcf21-/- glomeruli with body motif are more likely to be gene body motif suppressed in diabetes Down-regulated in 1.36 0.35 Down-regulated by Tcf21-/- with Tcf21-/- glomeruli with promoter motif are unlikely to be promoter motif suppressed in diabetes Genes activated in diabetic ApoE-/- kidneys Up-regulated in Tcf21-/- 3.52 4x10-03 Up-regulated by Tcf21-/- with enhancer glomeruli with enhancer motif are more likely to be activated in motif diabetes Up-regulated in Tcf21-/- 2.36 2x10-04 Up-regulated by Tcf21-/- with gene glomeruli with gene body motif are more likely to be body motif activated in diabetes Up-regulated in Tcf21-/- 2.21 0.08 Up-regulated by Tcf21-/- with gene glomeruli with body motif are unlikely to be activated promoter motif in diabetes

172 6 | TCF21 interaction

6.3.3. TCF21 gene targets are regulated by Set7 in the diabetic kidney

Gene Set Enrichment Analysis (GSEA) revealed that changes in the expression of TCF21-dependent genes conferred by diabetes were attenuated by Set7 deletion in the kidneys of ApoE-/- mice (Chapter 5, section 5.3.7). Importantly, Set7 deletion did not alter Tcf21 mRNA expression in diabetic ApoE-/- mouse kidneys. Figure 6.4 shows that a large proportion of TCF21 target genes were down-regulated by diabetes and up-regulated by Set7 deletion.

CUI TCF21 TARGETS 2 DN (Genes downregulated in TCF21 KO glomeruli) Gene density Up High

NC Set7 KO in diabetes

Low Down NC Up

Diabetes Figure 6.4. Two-dimensional contour plot showing the effect of Set7 deletion in the expression of TCF21 target genes The abundance of TCF21-dependent differentially expressed transcripts between control and diabetic ApoE-/- kidneys (Diabetes effect, X-axis) was compared to the abundance of TCF21- dependent transcripts that are differentially expressed between diabetic ApoE-/- and Set7-/-/ ApoE-/- (Set7 knock-out effect in diabetes, Y-axis). Red areas represent high number of transcripts whereas blue areas represent low numbers of transcripts. This plot reveals that the expression of a large proportion of TCF21 target genes that are down-regulated by diabetes is increased by the deletion of Set7.

173 6 | TCF21 interaction

GSEA was used to determine whether genes with a TCF21 binding motif were altered in expression in diabetic kidneys based on the RNA-seq data generated from control and diabetic ApoE-/- and Set7-/-/ApoE-/- kidneys. A summary of the results for this analysis is presented in Table 6.3. This analysis shows genes with a TCF21 motif in the gene body were generally suppressed (Normalised Enrichment Score, NES, -5.93) by diabetes and this was reversed by Set7 deletion (NES 3.36), while TCF21 motifs located at gene promoters were not correlated with altered gene expression. Genes with TCF21 binding motifs at enhancers were weakly associated with gene suppression by diabetes but this effect was not dependent on Set7.

Table 6.3. Summary of GSEA results for TCF21 motifs located at gene promoters, enhancers or body Size NES FDR ID p val TCF21 motif-gene body 3568 -5.93 <10-05 Diabetes in ApoE-/- TCF21 motif-gene body 3.36 <10-05 Diabetes in Set7-/-/ApoE-/- TCF21 motif-enhancer 1009 -2.55 <10-05 Diabetes in ApoE-/- TCF21 motif-enhancer -2.21 6x10-04 Diabetes in Set7-/-/ApoE-/- TCF21 motif- promoter 1310 0.89 0.58 Diabetes in ApoE-/- TCF21 motif-promoter 1.07 0.34 Diabetes in Set7-/-/ApoE-/-

A hypergeometric (HGM) test was performed to explore the association of genes regulated by TCF21 and Set7 with annotated biological pathways. This was done by dividing the data into genes that are up- or down-regulated (from the RNA-seq data, defined as absolute logFC greater than 0 and FDR<0.05) and comparing them to REACTOME gene sets (Table 6.4). These genes were found to be significantly associated with signal transduction, including signalling through the epidermal growth factor (EGF), fibroblast growth factor (FGF), platelet-derived growth factor and nerve growth factor (NGF) receptors as well was immune responses. The EGF receptor (EGFR) signalling pathway is critical for kidney development and function and its dysregulation is associated with various forms of chronic kidney disease, including diabetic nephropathy (Harskamp et al., 2016; Zhang et al., 2014). Similarly, NGF and its receptor TRKA (Tropomyosin receptor kinase A) as well as the PDGF receptor are implicated in renal damage in diabetes (Antonucci et al., 2009; Fragiadaki et al., 2012; Suzuki et al., 2011; Uehara et al., 2004). While the role of FGF receptor signalling in diabetic nephropathy is poorly understood, elevated levels of FGF-2, FGF-21 and FGF-23 have been associated

174 6 | TCF21 interaction

with poor clinical outcomes in patients with diabetic kidney disease (Kohara et al., 2017; Mao et al., 2016; Titan et al., 2011; Vasko et al., 2009).

Table 6.4. Hypergeometric tests demonstrated REACTOME gene sets significantly associated with gene expression changes mediated by Set7 and TCF21 in the diabetic kidney Adjusted FDR p-value REACTOME Gene Set Diabetes Diabetes Set7 KO in Set7 KO in ApoE-/- Down ApoE-/- Up diabetes Down diabetes Up Signalling by EGFR in 0.0004 0.8432 1.0000 0.0010 cancer Signalling by FGFR in 0.0016 0.5646 1.0000 0.0019 disease Downstream signalling of 0.0083 0.6027 1.0000 0.0045 activated FGFR Signalling by ERBB2 0.0235 0.6027 1.0000 0.0047 Signalling by FGFR 0.0057 0.6451 1.0000 0.0068 Signalling by PDGF 0.0035 0.0429 1.0000 0.0090 NGF signalling via TRKA 0.0010 0.4194 1.0000 0.0131 Signalling by NGF 0.0001 0.2405 0.8018 0.0171 Immune System 0.0027 0.0000 1.0000 0.0268 Signalling by insulin 0.0014 0.8365 1.0000 0.0304 receptor P value <0.05 indicates an association between the gene set and gene expression changes in the particular comparison group

The top 30 genes significantly deregulated by diabetes in ApoE-/- kidneys that are also TCF21-dependent and contain TCF21 binding motifs are presented in Table 6.5. These included genes downregulated by diabetes such as Timp3, an inhibitor of metalloproteinases whose loss is associated with the worsening of renal disease (Basu et al., 2012; Fiorentino et al., 2013a; Fiorentino et al., 2013b; Kassiri et al., 2009) and Hsp90aa1, a member of the heat shock protein family associated with diabetic nephropathy through gene expression profiling of human healthy and diabetic glomeruli (Fu et al., 2015). The calcium signalling regulators Tmem64 and Trpc1 were also down- regulated by diabetes. Tmem64 (Transmembrane protein 64) mediates calcium signalling as part of the receptor activator of NFkB ligand (RANKL) and is associated with mitochondrial production of reactive oxygen species (ROS) (Kim et al., 2013). Decreased expression of the transient receptor potential channel 1 gene (Trpc1) is observed in chronic kidney disease and its dysregulation has been proposed as molecular pathway of renal damage in diabetes (Niehof and Borlak, 2008). Furthermore, polymorphisms in the TRPC1 gene are associated with nephropathy in diabetic subjects (Chen et al., 2013;

175 6 | TCF21 interaction

Zhang et al., 2009) Up-regulated genes included Fn1, a key ECM component contributing to the development of diabetic nephropathy and Bmper, a negative regulator of the bone morphogenic pathway (Brunskill and Potter, 2010). These diabetes-induced changes in gene expression were attenuated by Set7 deletion. Changes in the expression of some of these genes were validated by qRT-PCR in renal samples from ApoE-/- and Set7-/-/ ApoE-/- mice (Fig. 6.5).

Table 6.5. TCF21-dependent genes deregulated by diabetes in the kidneys of ApoE-/- mice Gene symbol Log2FC Adj P value Description Timp3 -1.22 5.27 x10-09 Tissue inhibitor of metalloproteinase 3 Il33 0.84 1.05 x10-07 Interleukin 33 Fndc3a -0.66 1.07 x10-07 Fibronectin type III domain containing 3A Fn1 1.21 1.19 x10-07 Fibronectin 1 Fam208a -0.94 2.41 x10-07 Family with sequence similarity 208 member A Tmem64 -0.81 1.04 x10-06 Transmembrane protein 64 Pdlim4 0.80 1.36 x10-06 PDZ and LIM domain 4 Hsp90aa1 -0.84 1.67 x10-06 Heat Shock Protein 90 Alpha Family Class A Member 1 Dclk1 0.68 2.48 x10-06 Doublecortin Like Kinase 1 Samd8 -0.72 5.90 x10-06 Sterile Alpha Motif Domain Containing 8 Plod2 -0.75 8.99 x10-06 Procollagen-Lysine,2-Oxoglutarate 5-Dioxygenase 2 Lbp 0.87 1.82 x10-05 Lipopolysaccharide Binding Protein Cbl -1.03 1.83 x10-05 Casitas B-Lineage Lymphoma Proto-Oncogene Pltp 1.19 1.91 x10-05 Phospholipid Transfer Protein Bmper 0.77 2.26 x10-05 BMP Binding Endothelial Regulator Cd36 -0.85 2.34 x10-05 CD36 Molecule Sorbs1 -0.92 2.39 x10-05 Sorbin And SH3 Domain Containing 1 Ncor1 -0.63 3.99 x10-05 Nuclear Receptor Corepressor 1 Prkar1b 0.76 4.44 x10-05 Protein Kinase CAMP-Dependent Type I Regulatory Subunit Beta Arid5b -0.79 7.17 x10-05 AT-Rich Interaction Domain 5B Ulk2 -0.54 7.55 x10-05 Unc-51 Like Autophagy Activating Kinase 2 Usp9x -0.83 8.18 x10-05 Ubiquitin Specific Peptidase 9, X-Linked Trpc1 -0.58 9.36 x10-05 Transient Receptor Potential Cation Channel Subfamily C Member 1 Il13ra1 -0.71 1.09 x10-04 Interleukin 13 Receptor, Alpha 1 Csgalnact1 -0.69 1.80 x10-04 Chondroitin Sulfate N- Acetylgalactosaminyltransferase 1 Il6st -0.59 1.90 x10-04 Interleukin 6 Signal Transducer Mtus1 -0.50 2.54 x10-04 Microtubule Associated Tumor Suppressor 1 Dock5 -0.57 3.75 x10-04 Dedicator of Cytokinesis 5 Edem3 -0.57 3.89 x10-04 ER Degradation Enhancing Alpha-Mannosidase Like Protein 3 Card10 -0.63 4.08 x10-04 Caspase Recruitment Domain Family Member 10

176 6 | TCF21 interaction

-/- -/- -/- 5 ApoE control Set7 /ApoE control ApoE-/- diabetic Set7-/-/ApoE-/- diabetic * 4

** 3 ****

2 * * **

mRNA fold change mRNA * * 1

0 Hsp90aa1 Tmem64 Trpc1 Bmper Fn1

Figure 6.5. qRT-PCR validated gene expression changes identified by RNA-seq mRNA expression levels of TCF21-dependent genes in renal cortical tissue of control and diabetic ApoE-/- and Set7-/-/ApoE-/- mice. The changes in gene expression validate the fold changes obtained by RNA-seq. n=5 mice per group, *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001

6.3.4. Set7 interacts with TCF21

The results described in this chapter suggest a role for TCF21 in the regulation of diabetes-induced gene expression changes. Furthermore, they support a potential interaction between this transcription factor and Set7. In order to test this hypothesis, a human TCF21 overexpression vector was generated by cloning the TCF21 coding sequence into a pCGN plasmid backbone as described in Chapter 2, Section 2.5.1 (Fig. 6.6). Cultured 293FT cells overexpressing human HA-tagged TCF21 and FLAG-tagged Set7 were harvested for protein isolation and immunoprecipitation 48 hours after plasmid transfection.

177 6 | TCF21 interaction

A B 1 2 3 4 5 6 7 8 9 10 11

Figure 6.6. Generation of a pCGN-TCF21 vector for expression in mammalian cells (A) A plasmid vector for transient ectopic expression of human TCF21 in mammalian cells was generated by cloning the TCF21 coding sequence into the XbaI and BamHI sites of a pCGN vector (see Appendix 2). (B) Recombinant plasmids were identified by agarose gel electrophoresis following restriction digestion. Lane 1 contains the amplified 540bp PCR product as control (arrow) and lanes 2-11 contain plasmid DNA isolated from 10 different transformant colonies; all except lanes 3, 9 and 11 represent a recombinant plasmid containing the TCF21 insert. (C) The human TCF21 sequence (grey) encoding the full-length TCF21protein was inserted downstream of a sequence encoding an HA-tag. (D) The underlined lysine residue (K61) represents a potential site for methylation by Set7.

178 6 | TCF21 interaction

Immunoprecipitation using anti-HA agarose was able to pull down recombinant FLAG- tagged Set7, as evidenced by a ~48KDa band after immunoblotting with an anti-FLAG antibody (Fig. 6.7). Conversely, a band at ~25KDa corresponding to HA-tagged recombinant TCF21 was observed after blotting FLAG-precipitated lysates with anti-HA and anti-TCF21 antibodies (Fig. 6.7). These observations demonstrate a protein-protein interaction between Set7 and TCF21.

Lysate IP:FLAG IP:HA Lysate IP:FLAG IP:HA Lysate IP:FLAG IP:HA IP: FLAG IP: HA FLAG-SET7 - + - + - + - + - + - + - + - + - + + - - HA-TCF21 - + - + - + - + - + - + - + - + - + - + +

50 * 50

37 37

25 * 25

KDa KDa

IB: FLAG IB: HA IB: TCF21 IB: HA IB: FLAG IB: TCF21

Figure 6.7. Co-immunoprecipitation experiments showed protein interaction between TCF21 and Set7 Recombinant HA-tagged TCF21 and FLAG-tagged Set7 were expressed in 293FT cells, protein lysates were used for immunoprecipitation with anti-HA and anti-FLAG agarose. Immunoblotting with an anti-FLAG antibody (left panel) showed that immunoprecipitation with anti-HA agarose was able to pull down FLAG-tagged Set7 (blue asterisk). Conversely, immunoblotting with an anti-HA and anti-TCF21 antibodies showed that immunoprecipitation with anti-FLAG agarose was able to pull down HA-tagged TCF21 (red asterisk). IP: Immunoprecipitation antibody, IB: Immunoblotting antibody.

179 6 | TCF21 interaction

Set7 monomethylates lysine residues in non-histone proteins (discussed in Chapter 1, Section 1.7). Analysis of the human TCF21 amino acid sequence revealed a putative site for Set7 methylation on lysine residue 61 (Fig. 6.6D). This site was predicted using the following Set7 recognition and methylation motif (target lysine shown in bold):

[G/R/H/K/P/S/T]-[K>R]-[S>K/Y/A/R/T/P/N]-K-[Q/N> other amino acids, but not FYWPL]-[A/Q/G/M/S/P/T/Y/N]

These results suggest that TCF21 may constitute a novel non-histone protein target for Set7 methylation.

180 6 | TCF21 interaction

6.4. DISCUSSION

6.4.1. TCF21 may mediate diabetes-induced gene expression changes in the kidney

Despite its importance in kidney differentiation and development, little is known about the role of TCF21 during the progression of diabetic nephropathy. Genetic deletion of TCF21 in mice results in hypoplasia of the kidneys and several other developmental abnormalities that are lethal to newborn pups (Quaggin et al., 1998). Mice with a podocyte-specific deletion of TCF21 are normal and viable; however, approximately 40% of these mice developed a phenotype characterised by reduced expression of key proteins involved in podocyte function (podocin, nephrin), albuminuria and glomerulosclerosis (Maezawa et al., 2014). The rest of the mice did not develop renal damage or albuminuria but developed massive proteinuria, more severe renal damage and had lower survival rates than wildtype animals upon induction of diabetes (Maezawa et al., 2014). This is the only study to date to suggest that TCF21 is required for maintenance of podocyte function in adults and that its loss may be related to renal damage in diabetes. Consistent with this hypothesis, silencing of TCF21 in cultured human podocytes augmented basal and high glucose and TGFb1-induced increases in the expression of pro-inflammatory CCL2 (MCP-1) and pro-fibrotic CDKN1A (p21) and VEGFA. Furthermore, bioinformatics and statistical analyses of gene expression profiles of control and diabetic ApoE-/- mouse kidneys revealed that a number of genes deregulated by diabetes are TCF21 targets. The gene encoding the bone morphogenic protein endothelial regulator, Bmper, was identified as a Set7 target through analysis of RNA-seq data. The changes in expression of this gene as a result of diabetes and Set7 deletion were validated by qRT- PCR in independent samples. Bioinformatics analysis revealed that the Bmper gene contains a TCF21 binding motif (Table 6.5) and was also identified as a TCF21 target in renal glomeruli using gene expression profiling by microarray (Maezawa et al., 2014). As a regulator of the BMP pathway, Bmper is required for vascular development and angiogenesis (Dyer et al., 2015; Heinke et al., 2013; Willis et al., 2013). Furthermore, loss of BMPER in humans is associated with a severe skeletal dysfunction with kidney involvement (Zong et al., 2015).

TCF21 is also involved in cardiac fibroblast differentiation from progenitor epicardial- derived cells (EPDCs) (Acharya et al., 2012). These progenitor cells also give rise to coronary artery smooth muscle cells (CASMCs). Loss of TCF21 in mice leads to

181 6 | TCF21 interaction

premature smooth muscle cell differentiation characterised by elevated expression of SMC markers (Braitsch et al., 2012). These observations demonstrate a critical role for TCF21 in cell fate specification involving fibroblasts and smooth muscle cell lineages. Furthermore, Genome-wide association studies (GWAS) have identified TCF21 as a coronary artery disease (CAD) associated gene (Schunkert et al., 2011). Gene expression profiling of TCF21-deficient human CASMCs revealed that genes downstream of this transcription factor are associated with pathways related to vascular disease (Nurnberg et al., 2015). Moreover, TCF21 binds to regions in the genome that are also associated with CAD, as identified by GWAS (Sazonova et al., 2015). These studies suggest a novel mechanistic link between CAD susceptibility genes and the development of disease. It is proposed that EPDCs recapitulate their embryonic programs during disease (Winter and Gittenberger-de Groot, 2007). Additionally, regeneration and neovascularisation following myocardial injury is thought to require the activation of fetal-like gene programs that promote cell growth (Dirkx et al., 2013; Sim et al., 2015). Given the role of TCF21 in these processes during development, it would be valuable to acquire a further understanding of the mechanisms behind TCF21-dependent regulation of fibrotic pathways.

6.4.2. TCF21 may represent a novel lysine methylation target for Set7

Diabetes-induced changes in the expression of TCF21 targets were attenuated by Set7 deletion in the kidneys of ApoE-/- mice without a change in Tcf21 expression. This suggests that TCF21-mediated gene regulation in diabetes involves Set7. Given the ability of Set7 to methylate non-histone proteins, potential protein-protein interactions were assessed. Co-immunoprecipitation experiments show interaction between TCF21 and Set7. Furthermore, based on a predicted Set7 lysine methylation motif, a putative site for methylation was identified on TCF21.

The role of TCF21 in the regulation of cell fate determination is widely appreciated. However, the mechanisms through which this occurs remain poorly understood. TCF21 forms complexes with several proteins and these interactions may help elucidate its mechanism of gene regulation in different settings. TCF12 is a described TCF21 heterodimer partner and they cooperate to promote gene transcription (Arab et al., 2011; Hidai et al., 1998; Lu et al., 1998a; Tandon et al., 2013). Consistent with this role, TCF12 was found to be associated with gene up-regulation by diabetes in ApoE-/- mice kidney

182 6 | TCF21 interaction

by GSEA ENCODE transcription factor analysis (Chapter 5, section 5.3.5). On the other hand, TCF21 interacts with the Estrogen Receptor alpha (ERa) and inhibits its transcriptional activity (Ao et al., 2016). Similarly, TCF21 also suppresses the expression as well as transactivation of the androgen receptor (AR) (Hong et al., 2005). Histone deacetylase 2 (HDAC2) has also been identified as a TCF21-interacting protein and TCF21-dependent suppression of ERa and AR activity depends on the recruitment of HDAC1/2 (Ao et al., 2016; Hong et al., 2005; Tandon et al., 2013).

Interestingly, the level and activity of HDAC2 are increased in the kidneys of rodent models of type 1 and type 2 diabetes (Noh et al., 2009). HDAC2 directly contributed to renal damage in these models by promoting extracellular matrix (ECM) accumulation and epithelial to mesenchymal transition (EMT) (Noh et al., 2009). Indeed, HDAC inhibition mitigated diabetes-induced renal damage by attenuating inflammation and fibrosis in rat and mouse models of streptozotocin-induced diabetes as well as db/db mice (Advani et al., 2011; Gilbert et al., 2011; Noh et al., 2009).

It would be of interest to explore whether the interaction between TCF21 and Set7 involves other partners such as HDAC2 or TCF12 by performing further co- immunoprecipitation experiments. Moreover, given that the transcriptional effect of TCF21 is mediated by its interacting partners, screening methods to detected novel interactions or complex formation would also provide useful information. Affinity purification followed by mass spectrometry is extensively used to map protein interactions. This technique could help define TCF21 interacting partners as well as provide information regarding complex conformation and post-translational modifications (Smiths and Vermeulen, 2016).

TCF21 is subject to post-translational modifications that may regulate its activity. It is phosphorylated in serine residues S37, S48 and S67 but the effect of these modifications on protein stability and activity remains unknown (Tandon et al., 2013). TCF21 is also subject to sumoylation of lysine residue K64, which promotes the recruitment of HDAC 1/2 contributing to the suppression of ERa activity (Ao et al., 2016). There are no previous reports of post-translational methylation, thus the work presented here support the notion that Set7 interacts with TCF21 as a mechanism regulating its transcriptional activity.

183 6 | TCF21 interaction

6.5. CONCLUSIONS

The results presented in this chapter support the hypothesis that Set7 deletion attenuates diabetes-induced changes in the expression of TCF21 target genes. Diabetic kidneys are characterised by increased TGFb1 activation, which promotes Set7 expression and nuclear localization (Sasaki et al., 2016; Yuan et al., 2016). In this sense, it is possible that an increase in Set7 could reduce TCF21 activity in the diabetic kidney and the mechanism involving K61 methylation remains to be determined. This may result in dysregulation of genes required for podocyte maintenance and advancing renal damage. Further work is needed to confirm this hypothesis, including experimental validation of Set7-mediated methylation of TCF21.

184 7 | Discussion

CHAPTER 7

GENERAL DISCUSSION AND CONCLUSIONS

185 7 | Discussion

7.1. GENERAL DISCUSSION

Vascular complications of diabetes represent a major cause of morbidity and mortality for both type 1 and type 2 diabetic subjects. Considering that diabetes affects over 400 million people worldwide, this constitutes a major public health issue (Ogurtsova et al., 2017).

The molecular basis for the development of vascular diabetic complications involves an increase in cellular production of reactive oxygen species (ROS). This activates pathways that promote inflammation and fibrosis which in turn drive tissue damage (Brownlee, 2001; Giacco and Brownlee, 2010). Gene expression changes that occur as part of this process are mediated in part by epigenetic mechanisms, particularly histone modifications. The lysine methyltransferase Set7 participates in the transcriptional up- regulation of pro-inflammatory and pro-fibrotic genes in response to hyperglycaemia (Brasacchio et al., 2009; El-Osta et al., 2008; Okabe et al., 2012; Sun et al., 2010). Given this role of Set7, the overall aim of this this thesis was to study Set7-mediated transcriptional regulation in the development of diabetic complications.

In vivo mouse models were used for different studies aimed at addressing the specific aims stated in Chapter 1, Section 1.11. Set7 knock-out mice (both homozygous and heterozygous for the Set7 deletion) were generated and used to assess the effect of Set7 deletion in metabolic homeostasis, addressing the project’s first specific aim. These results were presented in chapter 3 and, in summary, they demonstrate that the genetic deletion of Set7 does not affect overall metabolic homeostasis despite having an effect in the expression of a group of pancreatic b cell genes. The knowledge that adult mice lacking Set7 are viable and do not have altered glucose metabolism allowed for the design of a novel mouse model to study the role of Set7 in the development of diabetic complications which has been referred throughout the thesis as Set7/ApoE double knock- out (Set7-/-/ApoE-/-) mice. Diabetes was induced in these animals and the effect of Set7 deletion in the development of diabetes-accelerated atherosclerosis (DAA) (Chapter 4) and diabetic nephropathy (Chapter 5) was assessed to address specific aims two and three, respectively.

The results presented in Chapter 4 indicate that Set7 knock-out in diabetic animals attenuates vascular damage defined as a reduction in atherosclerotic plaque area.

186 7 | Discussion

Similarly, results in Chapter 5 demonstrate that genetic deletion of Set7 attenuates renal damage in diabetes as evidenced by reduced albuminuria and glomerular deposition of extracellular matrix (ECM) proteins. In both cases, the use of transcriptome sequencing provided an unbiased approach to studying gene expression changes induced by diabetes and mediated by Set7. This led to the identification of novel Set7 targets and provided novel insights into gene deregulation by diabetes in the kidneys and vasculature. Furthermore, this approach allowed the identification of a novel potential non-histone Set7 methylation target, the transcription factor Tcf21. The interaction between Set7 and Tcf21was explored in Chapter 6 and contributed to addressing specific aim four.

The major findings of this thesis are summarised and discussed below.

7.1.1. Genetic deletion of Set7 attenuates renal and vascular damage in a mouse model of diabetic complications

Set7 was first identified as a histone methyltransferase that catalyses the monomethylation of lysine 4 on histone H3 (H3K4) (Nishioka et al., 2001; Wang et al., 2001). Monomethylated H3K4 is associated with a permissive chromatin state and transcriptional activation (Kouzarides, 2007). Set7 participates in the transcriptional activation of genes in several pathways including cell proliferation and differentiation, tumorigenesis, insulin secretion and inflammation through histone dependent mechanisms (Chapter 1, Table 1.1). Its role in regulating the expression of pro- inflammatory genes implicates Set7 in gene regulation in diabetes. The diabetic milieu promotes endothelial dysfunction which is characterised by increased expression of pro- inflammatory mediators that promote leukocyte adhesion and migration such as VCAM- 1, ICAM-1 and MCP-1 (Kim et al., 1994; Morigi et al., 1998; Piga et al., 2007). Set7 mediates the high glucose-induced up-regulation of these genes in cultured endothelial cells via its interaction with the p65 subunit of NFkB, suggesting that this enzyme could play a role in the development of vascular diabetic complications (El-Osta et al., 2008; Keating et al., 2014; Okabe et al., 2012; Paneni et al., 2015). Furthermore, Set7 is up- regulated in leukocytes from type 2 diabetic subjects and this is correlated with increased levels of pro-inflammatory molecules in circulation (Paneni et al., 2015). Indeed, as presented in Chapter 4, pharmacological inhibition of Set7 with (R)-PFI-2 attenuated the increase in expression of VCAM1, ICAM1, IL8 and CCL2 in human endothelial cells exposed to high glucose and TNFa. This is consistent with previous published studies

187 7 | Discussion

and supports the application of the pharmacological Set7 inhibitor to attenuate pro- inflammatory gene expression changes.

Set7 also promotes the expression of pro-fibrotic mediators in renal cells in response to high glucose and TGFb1 (Sun et al., 2010). RNA interference-mediated silencing of Set7 attenuates inflammation in the kidneys of db/db mice, a model of type 2 diabetes, by decreasing the expression of MCP-1 (Chen et al., 2014). Moreover, Set7 levels are increased in association with the degree of tissue fibrosis in human nephropathies (Sasaki et al., 2016). The experimental results presented in Chapter 5 showed that pharmacological inhibition of Set7 attenuated the high glucose and TGFb1-induced increase in pro-inflammatory and pro-fibrotic genes (CCL2, COL4A1, CTGF) in different renal cell populations.

Despite accumulating evidence for the role of Set7 mediating inflammatory and fibrotic gene expression changes in vitro, little was known about the role of this enzyme in the development of diabetic complications in vivo. The results presented here show for the first time in an in vivo model of diabetic complications that the genetic deletion of Set7 is athero- and renoprotective.

Diabetes induced widespread gene expression changes in the kidneys and aortas of ApoE-/- mice. There were 4,900 differentially expressed genes in the kidney and 370 in the aorta between control and diabetic animals. In both cases, Set7 deletion attenuated these gene expression changes. This was associated with a reduction in albumin excretion, mesangial expansion and glomerular deposition of collagen in the kidney and a significant reduction in plaque formation in the aortic arch. Set7 has long been associated with inflammatory pathways related to NFkB activation and fibrosis induced by TGFb1. This was consistent with many of the gene expression changes attenuated by Set7 deletion in diabetic tissues. However, genes associated with Set7 regulation in diabetes were associated with a myriad of pathways, including mitochondrial function, suggesting that Set7 participates in many other networks that regulate transcription.

Set7 is also known to negatively regulate the antioxidant NRF2 (Nuclear respiratory factor 2), a key protective factor in the diabetic retina (He et al., 2015). Set7 mediates the high glucose-induced up-regulation of Keap1, a negative regulator of NRF2, in retinal endothelial cells by increasing H3K4me1 at its promoter (Mishra et al., 2014).

188 7 | Discussion

Furthermore, these histone methylation changes were observed in the retinas of diabetic rats where they persisted even in animals that were subsequently placed on good glycaemic control (Mishra et al., 2014). These observations suggest Set7 in the transcriptional regulation of inflammatory and fibrotic genes that drive the development and progression of other microvascular complications of diabetes such as retinopathy.

7.1.2. Set7 participates in the regulation of mitochondrial function in diabetes

As a major site of ROS production, mitochondria play a key role in the pathology of diabetic complications. Set7 has been shown to regulate ROS signalling by negative regulation of NRF2, a key transcriptional activator of antioxidant response genes (He et al., 2015). On the other hand, Set7-mediated transcriptional activation of NFkB target genes is sensitive to oxidative stress (El-Osta et al., 2008). Furthermore, a recent study suggests that Set7 is involved in mitochondrial biogenesis and regulation of energy metabolism by methylating the PPARg co-activator 1 alpha (PGC1α) (Aguilo et al., 2016a).

Nuclear-encoded mitochondrial genes were deregulated by diabetes in the kidney and vasculature and many of the gene expression changes were attenuated by the deletion of Set7. Mitochondrial genes that were up-regulated by diabetes in a Set7-dependent manner were associated with the transcription factor ESRRA (Estrogen related receptor α), an effector protein that acts with PGC1α to regulate mitochondrial biogenesis and antioxidant responses (Schreiber et al., 2004). Set7-dependent genes down-regulated by diabetes and associated with mitochondrial function were associated with the transcription factors GABP (GA-binding protein) and HCFC1 (Host cell factor C1). The subunit alpha of GABP (GABPA) binds to the promoters of nuclear-encoded mitochondrial genes, including components of the electron transport chain, to activate their transcription, often in partnership with the chromatin modulator HCFC1 (Carter and Avadhani, 1994; Michaud et al., 2013; Seelan et al., 1996; Sucharov et al., 1995; Villena et al., 1998). GABPA binding to DNA is reduced in conditions of increased oxidative stress which is consistent with the down-regulation of GABP target genes in diabetes (Martin et al., 1996). These observations suggest that Set7 may negatively regulate transcription factors like NRF2 and GABP and promote the action of others like PGC1α and ESRRA. Taken together, the results presented here support a role for Set7 in the regulation of genes involved in mitochondrial function.

189 7 | Discussion

7.1.3. Set7 mediates transcriptional changes that regulate the smooth muscle cell phenotype

Smooth muscle cells (SMCs) are a major component of the vascular wall and changes in their phenotype are associated with vascular disease. SMCs are highly plastic and can modulate their phenotype according to environmental cues. Some of the stimuli that induce phenotypic switching in vascular SMCs are associated with disease, particularly atherosclerosis (Owens et al., 2004). The contribution of SMC phenotype switching to the progression of atherosclerosis is not completely understood. Switching from a quiescent, contractile phenotype to a synthetic, de-differentiated one contributes to disease development and progression. However, increased SMC proliferation followed by differentiation in later stages of the disease contribute to the fibrous cap and help plaque stabilisation (Gomez and Owens, 2012).

The expression of SMC markers such as smooth muscle actin (aSMA), transgelin (TAGLN) and h1-calponin (CNN1) are characteristic of differentiated cells and reduced during phenotypic switching. The promoters of these genes contain common regulatory elements like CC(A/T-rich)6GG (CArG) motifs and TGFb1 response elements (Spin et al., 2012). Transcriptional regulation of these genes involves chromatin remodelling events that allow or prevent the binding of the serum response factor (SRF) to their promoters (Spin et al., 2012). Set7 participates in SMC differentiation by regulating SRF binding to CArG elements both by methylating SRF and by H3K4 methylation of promoter chromatin (Tuano et al., 2016).

GSEA revealed that genes with promoter SRF motifs were down-regulated by diabetes in the aortas of ApoE-/- mice. This is consistent with SMCs undergoing transcriptional changes due to phenotypic switching and many of these gene expression changes were attenuated by Set7 deletion. Moreover, Set7 silencing and pharmacological inhibition attenuated the expression of SMC markers upon high glucose and TGFb1 stimulation in mouse SMCs and human mesangial cells. These observations are in agreement with the work from Tuano et al. (2016) and support a role for Set7 in the transcriptional regulation of SMC differentiation in the context of vascular complications of diabetes.

190 7 | Discussion

7.1.4. Set7 associates with the transcription factor TCF21 to regulate gene expression

TCF1, also known as Pod-1, Capsulin and Epicardin, is a member of the basic helix-loop- helix family of transcription factors. It plays an important role in cell fate determination, particularly in the heart, vasculature and kidneys (Quaggin et al., 1998). Transcriptome sequencing and GSEA identified TCF21 target genes in the kidney whose expression is deregulated in diabetes in a Set7-dependent manner. These genes were involved in signalling pathways associated with cell growth and proliferation. TCF21 gene targets down-regulated by diabetes were associated with transcriptional up-regulation by Set7 deletion and vice-versa. This inverse relationship suggested that Set7 negatively regulates TCF21. The interaction between these two proteins was demonstrated by co- immunoprecipitation experiments, constituting the first report of an interaction between Set7 and TCF21. Furthermore, the results presented here identify TCF21 as a potential novel non-histone substrate for lysine methylation mediated by Set7.

TCF21 participates in the maintenance of a healthy phenotype in podocytes, a cell type that is key in the development of kidney disease (Maezawa et al., 2014). In this sense, these observations have implications for understanding the transcriptional changes that underlie the development of diabetic kidney disease. Moreover, because of the described role of TCF21 in cell fate specification of epicardial and SMC precursors it is possible that this transcription factor also contributes to the pathological changes behind cardiovascular disease. (Acharya et al., 2012; Braitsch et al., 2012). This idea is further supported by associations studies that link TCF21 to coronary artery disease (CAD) risk and transcriptional profiling of coronary artery SMCs lacking TCF21 (Nurnberg et al., 2015; Schunkert et al., 2011).

191 7 | Discussion

7.2. CONCLUSIONS AND FUTURE DIRECTIONS

The results presented here demonstrate that the genetic deletion of Set7 confers renal and vascular protection in a mouse model of diabetic complications. It constitutes the first report of in vivo silencing of Set7 to assess the development of both diabetic nephropathy and diabetes-accelerated atherosclerosis. This supports the hypothesis that Set7 represents a potential target for developing athero- and renoprotective therapies in diabetes.

In vitro experiments show that pharmacological inhibition of Set7 using (R)-PFI-2 is effective at attenuating gene expression changes induced by diabetic stimuli in renal and endothelial cells. In order to further validate Set7 as a therapeutic target, it will be useful to duplicate the study presented here using (R)-PFI-2 as a Set7 inhibitor. To date, there are no reports of (R)-PFI-2 use in vivo; however, this drug is highly selective and effective at low doses, making suitable for animal studies (Barsyte-Lovejoy et al., 2014). Recent studies identified the anti-histamine drug cyproheptadine as an effective Set7 inhibitor (Fujiwara et al., 2016; Takemoto et al., 2016). Using this drug is an attractive option because it is approved for clinical use as treatment for allergic reactions. Pharmacological inhibition of Set7 using cyproheptadine was recently reported to attenuate inflammation associated with cancer-induced bone pain in mice (Hang et al., 2017). Moreover, cyproheptadine treatment has been used in animal models of malignancies where it has been effective at reducing tumour burden (Mao et al., 2008; Zhao et al., 2001). Given Set7 has been extensively associated with tumorigenesis (Akiyama et al., 2016; Ma et al., 2016; Montenegro et al., 2016; Zhang et al., 2016b; Zipin-Roitman et al., 2017), it is possible that some of this effect could be related to cyproheptadine’s action as a Set7 inhibitor.

The role of Set7 in gene expression regulation in endothelial cells has been widely studied and the effect of Set7 silencing in attenuating inflammatory pathways is well recognised. However, the endothelium only represents a fraction of the vascular wall. The results discussed here suggest that Set7 regulation of smooth muscle cell transcription plays an important role in the development of vascular disease. It would be interesting to expand on the knowledge of Set7-mediated gene expression changes that drive smooth muscle cell differentiation and to determine how that process is deregulated in diabetes.

192 7 | Discussion

Set7 was identified as an important mediator of gene expression changes induced by diabetes in the kidney and vasculature. However, the mechanism behind these changes remains largely unexplored. Set7 promotes transcription by increasing the levels of monomethylated H3K4 at gene promoters and enhancers. To explore the role of this histone mark on diabetes-induced gene expression changes in the kidney, a heatmap was generated to represent H3K4me1 profiles at promoters and enhancers of genes that were differentially expressed in control and diabetic ApoE-/- and Set7-/-/ApoE-/- kidneys using data from the ENCODE project obtained from the Gene Expression Ommibus (GEO) (Fig. 7.1).

These analyses identified four clusters based on changes in mRNA expression. Clusters 1-3 group 582 genes that were down-regulated by diabetes in ApoE-/- kidneys whose changes in expression where attenuated in the absence of Set7. Genes in these clusters included C1qtnf3, Hsph1 and Crkl. C1qtnf3 encodes an anti-inflammatory molecule also known as cartonectin/CTRP3 that is down-regulated in the kidney in various murine models of diabetes (Komers et al., 2014; Rubin et al., 2016). Low plasma concentrations of this protein are associated with inflammation and insulin resistance, and it has been suggested as a biomarker for early diagnosis of type 2 diabetes (Ban et al., 2014; Qu et al., 2015). The heat shock protein family member Hsph1 is involved in stress responses and is associated with anti-oxidant effects (Ashton et al., 2013; Yan et al., 2013). This gene has also been found down-regulated in the kidneys of diabetic mice, although its role in the development of diabetic nephropathy remains unknown (Komers et al., 2014). The Crkl proto-oncogene and adaptor protein is necessary for the development and maintenance of podocyte foot processes, thus its deficiency may underlie podocyte loss in early diabetic nephropathy (George et al., 2014; George et al., 2012). Moreover, a genetic deletion resulting in loss of CRKL expression drives abnormal renal development in people with a congenital condition known as DiGeorge syndrome (Lopez-Rivera et al., 2017).

Cluster 4 represents 477 genes that were up-regulated by diabetes in the kidneys of ApoE-/- mice and their expression changes attenuated in diabetic Set7-/-/ApoE-/- kidneys. Examples of genes in this cluster include Ugt1a2, Cxcl10 and Agt. Ugt1a2 encodes a member of the UDP-glucuronosyl transferase family highly expressed in the kidney that mediates substrate conjugation to glucuronic acid to enhance their excretion (Buckley and Klaassen, 2007). Members of this enzyme family are involved in early pathological 193 7 | Discussion

changes observed in podocytes in hyperglycaemic conditions (Jain et al., 2011). Moreover, this gene has been associated with the progression of diabetic nephropathy in mouse models by transcriptome profiling (Rubin et al., 2016). Cxcl10 encodes a pro- inflammatory cytokine known to be deregulated in the diabetic kidney (Kelly et al., 2013). Indeed, elevated plasma Cxcl10 levels are associated with the presence of nephropathy in patients with type 2 diabetes (Wong et al., 2007). Furthermore, Cxl10 up-regulation may play a detrimental role in health outcomes for renal transplant recipients by exacerbating inflammatory responses to infection (Kariminik et al., 2016). The Agt gene encodes angiotensinogen, a precursor for angiotensin II, the principal effector of the renin-angiotensin system (RAS). Urinary angiotensinogen has been proposed as a marker of the onset and progression of nephropathy as it reflects renal RAS activation (Satirapoj et al., 2014; Urushihara and Kagami, 2011; Yamamoto et al., 2007). AGT gene polymorphisms are associated with diabetic nephropathy and constitute a genetic risk factor for the development of this condition in diabetic patients (Rogus et al., 1998; Shaikh et al., 2014). Other genes in this cluster, such as the membrane translocase Timm23 and cytochrome c oxidase subunits Cox8a and Cox6a, encode structural components of mitochondria that are essential for their function (Mootha et al., 2003; Yoshikawa et al., 2011; Zhang et al., 2012).

This analysis suggests that a large number of the Set7-mediated gene expression changes described in Chapter 5 are histone-dependent. A limitation of the studies presented in this thesis is the lack of direct information regarding chromatin structure. Given the role of Set7 as a histone methyltransferase, it will be necessary to determine the status of histone methylation at Set7-dependent genes. Chromatin immunoprecipitation (ChIP) would allow the identification of changes in H3K4me1 levels at the sites of Set7 targets to experimentally validate this hypothesis in these animals. Furthermore, coupled with sequencing, ChIP can be a valuable tool to identify genomic regions where H3K4me1 is gained or lost across the genome in response to diabetes and Set7 deletion (or inhibition). Indeed, these experiments will be performed in order to complete these studies for publication. Furthermore, chromatin structure is dynamically regulated and exploration of other histone marks would also be necessary to acquire a more detailed picture of chromatin dynamics during the development of diabetic complications.

194 7 | Discussion

Figure 7.1. H3K4me1 at promoters and enhancers of differentially expressed genes in the kidneys of diabetic ApoE-/- and Set7-/-/ApoE-/- mice A heatmap was generated to represent the H3K4me1 profile of Set7 and diabetes-dependent genes based on transcriptome analysis of ApoE-/- and Set7-/-/ApoE-/- kidneys and using data derived from the ENCODE project. This shows clustering of genes according to their expression levels in diabetic kidneys and the presence or absence of Set7. Changes in the expression of H3K4me1- dependent genes deregulated by diabetes were attenuated in the absence of Set7. Although H3K4me1 occurs at promoters, enhancers or both at the sites of diabetes-dependent genes in the kidney, it was most commonly observed at promoters. GEO Accession number: GSM769023

195 7 | Discussion

Because of the positive transcriptional effect of H3K4me1, Set7 is often considered a transcriptional activator. However, the data presented here suggest that Set7 is also associated with gene suppression. One way in which this occurs is by modulating transcription factor activity. The use of transcriptome profiling allowed the identification of TCF21 as a novel Set7 interacting partner, however, the mechanism by which this interaction regulates gene expression remains poorly characterised. The results presented here raise the hypothesis that an increase in Set7 levels (or increased nuclear localisation) in diabetes could lead to increased TCF21 methylation, reducing protein stability. Set7- mediated methylation has been previously associated with protein destabilization. For example, monomethylation of lysine residue K70 of SMAD7 by Set7 leads to increased ubiquitination of the protein and subsequent proteasome degradation (Elkouris et al., 2016). This proposed hypothesis needs to be empirically validated. Site-directed mutagenesis studies can be performed to determine whether amino acid substitutions at K61 affect methylation rates in vitro, thus confirming this residue as the methylation target. Mass spectrometry is a commonly used methodology to characterise post- translational protein modifications. This technique would be useful in determining the exact location of the methylation site as well as confirm that the target residue is only monomethylated. Additionally, the effect of this modification on TCF21 will also need to be assessed. Possibilities to be tested include increased recognition by ubiquitination complexes and subsequent proteasome degradation, reduced DNA or heterodimer partner binding ability and nuclear exclusion.

Transcriptome profiling by RNA-seq of kidney and aorta tissues led to the identification of global gene expression changes caused by diabetes and mediated by Set7. However, accurate interpretation of gene expression profiles from tissues is challenging because of their heterogeneous nature. New high throughput sequencing (HTS) techniques allow RNA sequencing at the single cell level. One such technique referred to as Drop sequencing, is able to generate RNA profiles at the single cell level from thousands of cells simultaneously to identify transcriptionally distinct cell populations (Klein et al., 2015; Macosko et al., 2015). This may represent a valuable tool for evaluating the effect of diabetes in specific cell types and estimating their contribution to the development of complications. Moreover, it may allow the identification of cell types that are more susceptible to Set7 inhibition which may result in a more targeted therapeutic approach.

196 7 | Discussion

Vascular complications of diabetes are a burden in patients with diverse disease etiologies. Because of the increased prevalence of T2D over other types of the disease, this group of patients would greatly benefit from improved treatments aimed at attenuating complications (Ogurtsova et al., 2017). The studies presented here were only performed in a model of type 1 diabetes, thus although the changes observed are expected to be similar in other models, it would be of great value to validate the findings in a model of type 2 diabetes. Moreover, different types of mouse models of diabetes recapitulate different, but not all, aspects of human disease particularly in the case of diabetic nephropathy (Betz and Conway, 2016). Furthermore, transcriptional responses in mice and humans differ and studies have shown that these differences can be significant in the case of inflammatory diseases (Seok et al., 2013). Given these limitations, candidate targets as well as novel regulatory pathways derived from the studies presented here should be validated in human cohorts.

In conclusion, this thesis presents transcriptome sequencing as a valuable tool in understanding the molecular mechanisms behind the development of complications in diabetes. Moreover, it demonstrates that Set7 participates in several pathways that lead to tissue damage in diabetes and that genetic deletion of Set7 in mice is athero- and reno- protective. These observations validate Set7 inhibition as an approach for developing therapies aimed at reducing the burden of diabetic complications.

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242 Appendices

APPENDICES

243 Appendices

Appendix 1

MISSIONâ pLKO.1-puro vector map (Sigma-Aldrich)

Vector used for transfection of shRNA and production of lentiviral particles

Information from Sigma-Aldrich

244 Appendices

MISSIONâ pLKO.1-puro Non-Target shRNA vector map (Sigma-Aldrich)

Vector used as control for shRNA transfections

cPPT hPGK shRNA puroR U6

SIN/3’ LTR RRE SHC016 (non-target shRNA) f1 ori (Ψ) Psi 7,086 bp

RSV/5' LTR

ampR pUC ori

Information from Sigma-Aldrich

245 Appendices

Appendix 2 pCGN vector map (Addgene)

Addgene plasmid # 53395. Information from Addgene.

Based on vector generated by Whinship Herr (1990) Full human TCF21-coding cDNA was inserted between the XbaI and BamHI sites (highlighted in red).

246 Appendices

Appendix 3 pLVX-EF1a-IRES-Puro vector map (Clontech)

pLVX-EF1α-IRES-Puro Multiple Cloning Site (MCS)

Information from Clontech.

Full human SET7-coding cDNA was inserted between the EcoRI and XbaI sites.

247 Appendices

Appendix 4

Notification of approval of animal ethics applications by the Alfred Medical Research and Education Precinct Animal Ethics Committee. Approved projects are:

E/1456/2014/B (Application no. 4330) E/1504/2014/B (Application no. 4627) T/1419/2014/B (Application no. 4252)

248 Appendices

MEMORANDUM

TO: Jun Okabe

FROM: AEC Secretary – Judy Nash

DATE: Thursday, 15 May 2014

SUBJECT: AEC APPLICATION 4330 – APPROVAL

Dear Jun,

The AMREP Animal Ethics Committee has considered and approved your application: Characterising the SETD7KO mice colony. APPROVAL 15/25/2014 TITLE: DATE: NO. OF ANIMALS SPECIES/STRAIN: APPROVED: Mouse Setd7 KO 540 Mouse C57BL6/J 180 APPLICATION NO. 4330 Closure Date: 15/05/2017

AEC APPROVED PROJECT NUMBER: E/1456/2014/B SCIENTIFIC LICENCE: BakerIDI Heart & Diabetes Institute COMMITTEE B

Obtaining Animals: All requests for animals and services related to this project must be made through the Precinct Animal Centre. Advanced planning will assist the PAC in supplying animals and services. Animals requiring special housing conditions, e.g. PC2, must be clearly identified on your orders.

Facility Use: Use of animals in this project must be in compliance with the Australian Code for the care and use of animals for scientific purposes, 8th edition and AMREP AEC Policy and Guidelines. Introduction of new colonies and species need to be applied for through the AMREP AEC Committee.

OH&S Issues: As the responsible investigator please ensure that all personnel who may be exposed to potentially biohazardous material are informed of any risks and how best to avoid them. This includes notifying PAC management of such issues.

Reports: You are required to complete an annual progress report on this application for the AEC committee. On completion of the project you are required to submit to the AEC a final report. You are required to complete an annual report for the Bureau of Animal Welfare. You are required to maintain experimental records as defined in the Australian Code for the care and use of animals for scientific purposes, 8th edition. These records must be made available when requested.

The AEC may undertake an inspection or ask for a progress report at any time during this project.

Please note: Approval is subject to finalisation of any outstanding OH&S requirements. Work should not commence until the OH&S department has approved your application. Please contact the OHS Dept (Adrian Quintarelli x 8532 1123 [email protected]) if you have queries regarding OH&S requirements.

Yours sincerely,

AMREP AEC SECRETARY On Behalf of the AMREP AEC 4330 JO

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MEMORANDUM

TO: Karin Jandeleit-Dahm

FROM: AEC Secretary – Judy Nash

DATE: Thursday, 20 November 2014

SUBJECT: AEC APPLICATION 4627 – APPROVAL

Dear Karin,

The AMREP Animal Ethics Committee has considered and approved your application: The role of set7 in diabetes associated APPROVAL 20/11/2014 TITLE: atherosclerosis DATE: NO. OF ANIMALS SPECIES/STRAIN: APPROVED: Mouse APOE -/- (KO) 126 Mouse setd7/apoE DKO 126 Mouse Setd7- SM22 126 Mouse Setd7 Flox 44 Mouse Tie2-cre 44 Mouse SM22(tTA) 44 Mouse alpha MHC Cre recombinase Tg 44 Mouse alpha MHC Set7-/- 126 Mouse alpha MHC Set7-/-ApoE-/- 126 Mouse SM22Set7- /-ApoE-/- 126

APPLICATION NO. 4627 Closure Date: 20/11/2017

AEC APPROVED PROJECT NUMBER: E/1504/2014/B SCIENTIFIC LICENCE: BakerIDI Heart & Diabetes Institute COMMITTEE B

4627 KJD

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