Studies on Mature Endothelial Cells and CD34+ Stem Cells

Exploring Mechanisms for Improvement of Cardiovascular Disease; Studies on Mature

+ Endothelial Cells and CD34 Stem Cells

Sherin M. Bakhashab Institute of Cellular Medicine Faculty of Medical Sciences University of Newcastle

A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy

May 2015 Abstract

ABSTRACT

Cardiovascular disease (CVD) is the major cause of morbidity and mortality worldwide and in particular in diabetes mellitus (DM). Although metformin has been shown to reduce CVD in Type-2 DM clinical trials, the underlying cardioprotective mechanism remains unexplored. Objective: to determine the effect of metformin on mature endothelial and stem cells exposed to hypoxia, hyperglycaemia or both conditions combined. Human umbilical vein endothelial cells (HUVEC) were studied by scratch test for migration and flow cytometry for apoptosis after culture in euglycaemia or hyperglycaemia, and/or hypoxia with or without metformin. mRNA was assayed by whole transcript microarrays. Genes of interest were confirmed by quantitative real-time PCR (qRT-PCR), proteins by western blot assay or flow cytometry. Metformin increased cell survival and migration via activation of vascular endothelial growth factor (VEGF) signalling, through upregulation of matrix metalloproteinase 16, and ERK/mitogen-activated protein kinase signalling under hyperglycaemia-hypoxia.

Paracrine secretion of CD34+ cells treated with euglycaemia or hyperglycaemia, and/or hypoxia with or without metformin was assessed by measuring pro-inflammatory cytokines, VEGFA, chemokine (C-X-C Motif) ligand 10 (CXCL10), tissue inhibitor of metalloproteinase 1 (TIMP1) and efficacy to promote in vitro tube formation by HUVECs. Additionally, miR-126 was evaluated in exosomes and exosome-depleted medium, which was found to be increased by metformin. mRNA from treated CD34+ cells was assayed by microarray and genes of interest were validated by qRT-PCR. An anti-inflammatory effect of metformin was detected under euglycaemia and euglycaemia-hypoxia through upregulation of STEAP family member 4. Metformin enhanced angiogenesis via increased VEGFA and miR-126 under hyperglycaemia-hypoxia. Metformin downregulated the expression of angiogenic inhibitors CXCL10 and TIMP1, which were upregulated under hyperglycaemia-hypoxia.

Abstract

In conclusion, our data from HUVEC and CD34+ cells are commensurate with a vascular protective effect of metformin in a model of a diabetic state combined with hypoxia, and add to understanding of the underlying mechanism.

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Dedication

I dedicated this thesis to my parents,

For my mother and the pride that I know she would have felt if she was with us today

For my father enduring love and generous support

Declaration

DECLARATION

The work described in this thesis was undertaken in the laboratory of Visiting Professor Jolanta Weaver at the Institute of Cellular Medicine (Newcastle University) in fulfilment of the degree of Doctor of Philosophy. This thesis is the result of my work, and I have acknowledged the contributions of others. No portion of the work referred to in the thesis has been submitted in support of an application for another degree or qualification of this or any other university or other institution of learning.

Sherin Bakhashab 2015

Acknowledgments

ACKNOWLEDGEMENTS

I would like to extend my sincere gratitude to my supervisor Prof. Jolanta Weaver for giving me the opportunity to perform this research in her laboratory and for passing on her knowledge and guidance to me. Additionally, I am thankful for Prof. Weaver continuous help and support in this project.

I would like to express a special thanks to Dr. Michael Glanville for his great help and advice in molecular techniques. I am particularly grateful to Mrs. Elizabeth Anne Douglas for her help in ordering reagents required for my work. I would like to acknowledge the excellent teamwork by Dr. Fahad Ahmed, Ayat Bashir and other members of our group in Haematological Sciences Laboratory.

As a Joint Supervision Program Ph.D. student at Newcastle University and King Abdulaziz University (KAU), Saudi Arabia, I would like to thank all the people in KAU who supported me with their time and help. I would like especially to thank Prof. Ammar Amin, Supervisor General of the Joint Supervision Program, my KAU supervisors Prof. Abdulrahman Al-Malki, and Dr. Sahira Lary.

I was lucky to be associated with the Centre of Excellence in Genomic Medicine Research (CEGMR), Jeddah, Saudi Arabia, where I was able to perform part of my research. I would therefore like to express my gratitude to Prof. Mohammed Al-Qahtani, the director of CEGMR and Prof. Mamdooh Qari for allowing access to the CEGMR labs and facilities. I am thankful to Dr. Farid Ahmed, for his help in tissue culture and flow cytometry techniques and his guidance in preparing this thesis. I would also like to thank Dr. Hans- Juergen Schulten for his excellent support in microarray technology.

I thank Prof. David Jones and Dr. Gabriele Saretzki for their input and guidance during the internal assessment stages of my Ph.D. I would like to express my appreciation to Prof. Dianne Ford and Prof. Stefan Przyborski for volunteering to be my examiners.

Financial support for this work has been generously provided by Joint Supervision Program, KAU, Jeddah, Saudi Arabia and King Abdulaziz City for Science and Technology (KACST), Saudi Arabia.

Acknowledgments

Finally, I am forever grateful for the open-handed support of my husband, Ali Binladen, and close family members who have shared the excitements and struggles of my Ph.D. years.

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Table of Contents

Table of Contents Chapter 1. Introduction ...... 1

1.1 Diabetes mellitus and cardiovascular disease ...... 2

1.1.1 Definition and classification of diabetes mellitus ...... 2

1.1.2 Complications of diabetes ...... 3

1.2 Vasculogenesis and angiogenesis ...... 3

1.2.1 Origin of vascular endothelium ...... 3

1.2.2 Endothelial cell differentiation and vascular development ...... 5

1.2.3 Molecular mechanisms involved in angiogenesis ...... 7

1.3 Endothelial dysfunction in diabetes ...... 12

1.4 Molecular mechanisms of ischaemic cardiovascular disease ...... 14

1.4.1 Hypoxia ...... 14

1.4.2 Structure and function of HIF-1 ...... 14

1.5 Role of microRNAs in vascular biology ...... 19

1.5.1 MicroRNA biogenesis and mechanism of action ...... 19

1.5.2 Identification and quantification of miRNAs ...... 22

1.5.3 Role of miRNAs in angiogenesis ...... 22

1.6 Human stem cells ...... 24

1.6.1 Stem cell classification based on origin ...... 25

1.6.2 The stem cell niche ...... 25

1.7 CD34+ stem cells ...... 28

1.7.1 CD34+ cells as a therapeutic agent ...... 28

1.7.2 Paracrine function of human CD34+ stem cells ...... 29

1.8 Metformin ...... 31

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Table of Contents

1.8.1 History of metformin ...... 31

1.8.2 Mechanism of action of metformin as anti-diabetic drug ...... 32

1.8.3 Action of metformin on cardiovascular disease ...... 35

1.9 Aims of the thesis ...... 36

Chapter 2. Material and Methods ...... 38

2.1 Cell culture techniques ...... 38

2.1.1 Tissue supply ...... 38

2.1.2 Isolation of HUVEC from umbilical cord...... 38

2.1.3 Incubation of HUVEC with various concentrations of glucose and hypoxia 40

2.1.4 Metformin treatment ...... 41

2.1.5 Isolation of mononuclear cells from umbilical cord blood ...... 42

2.1.6 Isolation of CD34+ stem cells ...... 42

2.1.7 CD34+ cell culture ...... 43

2.1.8 Viable cell count ...... 44

2.1.9 Freezing cells ...... 45

2.1.10 Thawing cells ...... 45

2.2 Purification of exosomes by differential ultracentrifugation ...... 46

2.3 Electron microscopy of CD34+ stem cells and their exosomes...... 48

2.4 Functional assays ...... 48

2.4.1 In vitro scratch assay...... 48

2.4.2 Cell proliferation assay ...... 50

2.4.3 Apoptosis assay ...... 51

2.4.4 In vitro Matrigel tube formation assay ...... 51

2.5 Molecular techniques ...... 53

2.5.1 Total RNA extraction...... 53 ix

Table of Contents

2.5.2 Total RNA and protein extraction ...... 54

2.5.3 SYBR Green real-time polymerase chain reaction...... 56

2.5.4 TaqMan mRNA real-time PCR ...... 59

2.5.5 MicroRNA (miRNA) real-time PCR of CD34+ exosomes and exosome- depleted media ...... 60

2.5.6 Microarray expression analysis ...... 62

2.5.7 Western blot assay ...... 67

2.5.8 MAPK activation dual detection assay ...... 70

2.5.9 Meso Scale Discovery (MSD) assay ...... 71

2.6 Statistical analysis ...... 72

Chapter 3. An in vitro model of cardiovascular disease in diabetes: gene expression in HUVECs under hypoxia and hyperglycaemia ...... 73

3.1 Introduction ...... 73

3.2 Establishment of a hyperglycaemia-hypoxia model and reference genes ...... 74

3.2.1 Validation of a glucose-hypoxia model ...... 74

3.3 Identification of reference genes for expression studies in hypoxia and hyperglycaemia models in HUVEC ...... 77

3.3.1 Introduction ...... 77

3.3.2 Experimental protocol ...... 78

3.3.3 Results ...... 78

3.3.4 Discussion ...... 83

Chapter 4. Vascular functions in mature endothelial cells ...... 85

4.1 Introduction ...... 85

4.2 Experimental approach ...... 85

4.3 Results ...... 87

Table of Contents

4.3.1 Functional assays ...... 87

4.3.2 Microarray analysis of target genes in HUVEC ...... 98

4.3.3 Effect of metformin on gene expression in HUVEC ...... 120

4.3.4 HIF-1α signalling...... 130

4.3.5 VEGF signalling ...... 141

4.3.6 AMPK signalling ...... 154

4.4 Discussion ...... 159

4.4.1 HIF-1α signalling...... 159

4.4.2 VEGF signalling ...... 165

4.4.3 AMPK signalling ...... 172

Chapter 5. Vascular functions in CD34+cells...... 174

5.1 Introduction ...... 174

5.2 Experimental approach ...... 174

5.3 Results ...... 178

5.3.1 Paracrine secretion from human CD34+ cells ...... 178

5.3.2 Microarray analysis of gene expression in CD34+ cells ...... 188

5.3.3 Confirmation of gene expression in CD34+ cells using qRT-PCR ...... 212

5.4 Discussion ...... 218

5.4.1 Effect of metformin on euglycaemia and combined euglycaemia hypoxia ..218

5.4.2 Effect of metformin on hyperglycaemia and combined hyperglycaemia hypoxia 220

5.4.3 Paracrine secretion of miR-126 ...... 225

Chapter 6. General Discussion and Future Directions...... 230

6.1 Concluding remarks ...... 234

6.2 Future work ...... 235 xi

Table of Contents

Chapter 7. Appendices ...... 237

7.1 Appendix I: Forms ...... 237

7.2 Appendix II: Chemicals and reagents ...... 243

7.2.1 Buffers and special medium ...... 243

7.2.2 Antibodies ...... 245

7.2.3 Apparatus ...... 246

7.3 Appendix III: Lists of differentially expressed genes ...... 248

7.4 Appendix VI: Pathway key ...... 263

Chapter 8. References ...... 266

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List of Tables

Table 1.1: The diagnostic criteria of diabetes mellitus...... 2

Table 1.2: miRNAs involved in angiogenesis...... 23

Table 2.1: Oligonucleotide primer sequence for housekeeping genes...... 56

Table 2.2: Oligonucleotide primer sequences for target genes...... 57

Table 2.3: TaqMan assays for CD34+ validated genes...... 60

Table 2.4: TaqMan assays for HUVEC validated genes...... 60

Table 2.5: Reagent volumes for reverse transcription reactions...... 61

Table 2.6: Reagent volumes per replicate for each qPCR...... 61

Table 2.7: Dilutions of primary antibodies...... 69

Table 3.1: Gene expression of target genes used to establish glucose-hypoxia model...... 77

Table 3.2: NormFinder microarray expression stability analysis...... 79 Table 3.3: Transcriptome analysis of five selected reference genes from HUVEC cultured under different conditions...... 80 Table 4.1: Top biological functions involved in HUVEC induced with euglycaemia- hypoxia for 3 hours...... 107 Table 4.2: Top canonical pathways involved in HUVEC induced with euglycaemia-hypoxia for 3 hours...... 108 Table 4.3: Top biological functions involved in HUVEC induced with euglycaemia- hypoxia for 12 hours...... 110 Table 4.4: Top canonical pathways involved in HUVEC induced with euglycaemia-hypoxia for 12 hours...... 111 Table 4.5: Top biological functions involved in HUVEC induced by combined hyperglycaemia-hypoxia for 3 hours...... 116 Table 4.6: Top biological functions involved in HUVEC induced by combined hyperglycaemia-hypoxia for 3 hours...... 117 Table 4.7: Top biological functions involved in HUVEC induced by combined hyperglycaemia and hypoxia for 12 hours...... 118

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List of Tables

Table 4.8: Top canonical pathways involved in HUVEC activated by combined hyperglycaemia and hypoxia for 12 hours...... 119 Table 4.9: The top differentially expressed genes in HUVEC treated with metformin and exposed to euglycaemia or combined euglycaemia hypoxia...... 125 Table 4.10: The top differentially expressed genes in HUVEC treated with metformin and exposed to hyperglycaemia or combined hyperglycaemia hypoxia...... 128 Table 4.11: Effect of metformin on biological functions involved in HUVEC induced by combined hyperglycaemia hypoxia for 1 hour...... 129 Table 4.12: Effect of metformin on biological functions involved in HUVEC induced by combined hyperglycaemia-hypoxia for 3 hours...... 129 Table 4.13: Effect of metformin on biological functions involved in HUVEC induced by combined hyperglycaemia-hypoxia for 12 hours...... 130 Table 4.14: HIF-1 regulated genes among euglycaemia-hypoxia and hyperglycaemia- hypoxia for 1, 3 or 12 hours...... 135 Table 4.15: Summary of the most significant results on the effect of hypoxia, hyperglycaemia, hyperglycaemia-hypoxia and metformin on HUVEC...... 171

Table 5.1: Effect of 3 hours hypoxia on biological functions involved in CD34+ cells. ....193

Table 5.2: Top biological functions involved in CD34+cells cultured in hyperglycaemia. 196 Table 5.3: Top canonical pathways involved in CD34+ cells cultured in hyperglycaemia...... 197 Table 5.4: Effect of metformin on canonical pathways involved in CD34+ cells cultured in combined hyperglycaemia-hypoxia for 3 hours...... 205 Table 5.5: Top canonical pathways involved in CD34+ cells cultured in hyperglycaemia (25.0 mM)...... 211 Table 5.6: Summary of the most significant results on the effect of hypoxia, hyperglycaemia, hyperglycaemia-hypoxia and metformin on CD34+ cells...... 229

Table 7.1: FACS antibodies from BD Biosciences...... 245

Table 7.2: Antibodies used in Westeron blot...... 246 Table 7.3: Top differentially expressed genes on HUVEC induced by chemical hypoxia for 1 hour compared to the control...... 248

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List of Tables

Table 7.4: Top 30 highly expressed genes in HUVEC induced by hypoxia for 3 hours compared to control...... 250 Table 7.5: Top 50 highly expressed genes in HUVEC induced by hypoxia for 12 hours compared to control...... 252 Table 7.6: Differentially expressed genes on HUVEC induced by hyperglycaemia compared to the control...... 254 Table 7.7: Differentially expressed genes in HUVEC induced by hyperglycaemia and hypoxia for 1 hour compared to control...... 255 Table 7.8: Top 30 highly expressed genes in HUVEC induced by hyperglycaemia and hypoxia for 3 hours compared to control...... 256 Table 7.9: Top 50 highly expressed genes in HUVEC induced by hyperglycaemia and hypoxia for 12 hours compared to control...... 258 Table 7.10: Top 30 differentially expressed genes in CD34+cells induced by hyperglycaemia compared to control...... 259 Table 7.11: Top 30 differentially expressed genes in CD34+cells induced by hyperglycaemia and treated with metformin compared to hyperglycaemia...... 260 Table 7.12: Top 30 differentially expressed genes in CD34+cells induced by hyperglycaemia and hypoxia for 3 hours compared to hyperglycaemia...... 261 Table 7.13: Top 30 differentially expressed genes in CD34+cells treated with hyperglycaemia and metformin and then exposed to hypoxia for 3 hours compared to hyperglycaemia and hypoxia...... 262

List of Figures

Figure 1.1: Genesis of the vascular system...... 4 Figure 1.2: VEGF ligands bind to their associated receptors, leading to receptor dimerisation and subsequent signal transduction...... 6

Figure 1.3: The microscopic structure of arteries, veins and capillaries...... 8

Figure 1.4: Angiogenesis process...... 10 Figure 1.5: Endothelium-derived nitric oxide synthesis and its action. Under normal, basal conditions in blood vessels NO is produced from the amino acid L-arginine by the enzymatic action of endothelial nitric oxide synthase (eNOS)...... 12

Figure 1.6: The structure of human HIF-1α gene...... 15

Figure 1.7: Oxygen-dependent regulation of HIF-1 stabilisation and transactivation...... 17

Figure 1.8: miRNA biogenesis and mechanism of action...... 21 Figure 1.9: The pro-angiogenic mechanism of miR-126. miR-126 and Egfl7 are processed into EGFL7 and pre-miR-126...... 24

Figure 1.10: Embryonic and adult stem cell differentiation...... 27

Figure 1.11: Microvesicles formation and release of exosomes...... 30 Figure 1.12: The chemical structure guanidine isoamylene guanidine, and the biguanides metformin, phenformin and buformin...... 31

Figure 1.13: Molecular mechanisms of action of metformin in hepatocytes...... 34 Figure 1.14: Metformin reduces ROS production through inhibition of the mitochondrial − complex І, as well as NADPH oxidase, which generates O2 ...... 36 Figure 2.1: Steps in manipulation and handling of the umbilical cord for the isolation of HUVEC...... 39

Figure 2.2: Experimental design...... 41 Figure 2.3: Schematic representation of CD34+ cells isolation, tissue culture and molecular techniques...... 45

Figure 2.4: Flow chart for exosome purification by differential ultracentrifugation...... 47

Figure 2.5: Plate layout for in vitro scratch assay...... 50

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List of Figures

Figure 2.6: Flow chart for the collection of CD34+cells conditioned media for angiogenesis assay...... 52 Figure 2.7: Analysis of tube length in in vitro tube formation assay using Adobe Acrobat Professional version 8 software...... 53

Figure 2.8: Schematic overview of the glass slide and Affymetrix microarray procedures. 64

Figure 2.9: Sandwich immunoassay of 4 multi-array MSD plate...... 72

Figure 3.1: HUVECs morphology under phase-contrast microscopy...... 74

Figure 3.2: Validation of glucose-hypoxia model...... 76 Figure 3.3: Effect of euglycaemia and hypoxia on gene expression of the selected reference genes...... 81 Figure 3.4: Effect of hyperglycaemia and hypoxia on gene expression of the selected reference genes...... 82

Figure 4.1: PCA mapping for microarray gene expression of HUVEC...... 87

Figure 4.2: Metformin impairs cell migration in HUVEC exposed to only hypoxia...... 89 Figure 4.3: Metformin improves cell migration in HUVEC exposed to hyperglycaemia combined with hypoxia...... 91 Figure 4.4: Hypoxia impairs endothelial cell proliferation. HUVEC were treated with high glucose concentration or normal glucose concentration as a control...... 93 Figure 4.5: Hypoxia coupled with high glucose mediated impairment in endothelial cell survival...... 94 Figure 4.6: Metformin improves cell survival with hyperglycaemia combined with hypoxia...... 97

Figure 4.7: Total RNA analysis using the Agilent Bioanalyzer...... 98 Figure 4.8: The number of differentially expressed genes from transcriptomic analysis of HUVEC...... 100 Figure 4.9: Venn diagram showing the number of differentially expressed genes from transcriptomic analysis of HUVEC using Partek software...... 104

Figure 4.10: Two-dimensional clustering of HUVEC under the effect of hypoxia...... 105 Figure 4.11: Effect of hypoxia on TXNIP mRNA expression in euglycaemia and hyperglycaemia...... 114

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List of Figures

Figure 4.12: Effect of metformin on TXNIP mRNA expression in euglycaemia and hyperglycaemia...... 122 Figure 4.13: HIF-1α signalling pathway in HUVEC exposed to combined hyperglycaemia and hypoxia...... 132 Figure 4.14: Effect of hypoxia on HIF-1α protein expression among euglycaemia and hyperglycaemia...... 133 Figure 4.15: Effect of hypoxia on VEGFA mRNA expression in euglycaemia and hyperglycaemia...... 137 Figure 4.16: Effect of hypoxia and metformin on VEGF 165A protein expression with euglycaemia and hyperglycaemia...... 138 Figure 4.17: Effect of metformin on HIF-1α protein expression in euglycaemic and hyperglycaemic conditions with hypoxia...... 140 Figure 4.18: Hypoxia activates VEGF signalling under euglycaemia and hyperglycaemia...... 143 Figure 4.19: The activity of MAPK pathway under hypoxia, hyperglycaemia and hyperglycaemia combined with hypoxia...... 145 Figure 4.20: Effect of metformin on VEGFA mRNA expression in euglycaemic and hyperglycaemic conditions with hypoxia...... 147 Figure 4.21: Effect of metformin on MMP16 mRNA expression in euglycaemic and hyperglycaemic conditions with hypoxia...... 148 Figure 4.22: Marimastat antagonises the effect of metformin on cell migration in HUVEC exposed to hyperglycaemia combined with hypoxia...... 151

Figure 4.23: Effect of metformin on the activity of MAPK pathway...... 153

Figure 4.24: Hypoxia activates AMPK signalling under euglycaemia...... 156

Figure 4.25: Hypoxia activates AMPK signalling under hyperglycaemia...... 158 Figure 4.26: Effect of hypoxia on FASN expression among euglycaemia and hyperglycaemia...... 158 Figure 4.27: Comprehensive VEGF signalling network of genes and proteins involved in cell migration and survival where metformin treatment is compared to the metformin- untreated condition under combined hyperglycaemia and hypoxia for 12 hours...... 169

Figure 5.1: PCA mapping for microarray gene expression of CD34+ cells...... 176

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List of Figures

Figure 5.2: The number of differentially expressed genes from transcriptomic analysis of CD34+ cells using Partek software...... 177

Figure 5.3: Morphological analysis of exosomes from human CD34+ cells...... 178 Figure 5.4: Expression of pro-angiogenic factor VEGFA and angiogenic inhibitors CXCL10 and TIMP1 in CD34+ cell-derived conditioned media...... 180 Figure 5.5: Pro-angiogenic miR-126 is highly expressed under hypoxia in exosomes of CD34+ cells...... 182 Figure 5.6: Effect of metformin on miR-126 expression under hypoxia and hyperglycaemia from CD34+ cell exosomes and exosome-depleted media...... 184

Figure 5.7: Optimisation of tube length measurement in in vitro angiogenic assays...... 185

Figure 5.8: In vitro angiogenic assays...... 187

Figure 5.9: CD34+ total RNA analysis using the Agilent Bioanalyzer...... 188 Figure 5.10: Two-dimensional clustering of CD34+ cells under the effect of hypoxia, hyperglycaemia and metformin...... 189

Figure 5.11: Anti-inflammatory effect of metformin on CD34+ cells under euglycaemia. 192

Figure 5.12: Effect of metformin on euglycaemia-hypoxia treated CD34+ cells...... 194 Figure 5.13: Effect of hypoxia combined with hyperglycaemia on CD34+ cells: Angiogenesis and triacylglycerol biosynthesis...... 202 Figure 5.14: Effect of hypoxia combined with hyperglycaemia on CD34+ cells: Mitochondrial dysfunction pathway...... 204 Figure 5.15: Effect of metformin on hypoxia combined with hyperglycaemia on CD34+ cells: Angiogenesis and triacylglycerol biosynthesis...... 208 Figure 5.16: Effect of metformin on the mitochondrial function pathway of CD34+ cells treated with hyperglycaemia and hypoxia...... 210 Figure 5.17: Heatmap of the pro-angiogenic and angiogenic inhibitors in CD34+ stem cells...... 213

Figure 5.18: Validation of selected pro-angiogenic factors in CD34+ cells by qRT-PCR. 215 Figure 5.19: Validation of selected angiogenic inhibitors CXCL10 and TIMP1 in CD34+ cells by qRT-PCR...... 216

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List of Figures

Figure 5.20: Validation of selected genes from the mitochondrial dysfunction pathway MT- ND2 and NDUFA4 in CD34+ cells by qRT-PCR...... 217 Figure 5.21: Validation of selected genes from triacylglycerol biosynthesis pathway AGPAT9 and GPAM in CD34+ cells by qRT-PCR...... 218 Figure 5.22: Summary of the effect of metformin on CD34+ cells incubated with hyperglycaemia-hypoxia...... 227

Publications

Publications arising from Thesis

1. Sherin Bakhashab, Sahira Lari, Farid Ahmed, Hans-Juergen Schulten, Manikandan Jayapal, Sajjad Karim, Ayat Bashir, Fahad Ahmed, Abdulrahman Al- Malki, Hasan S Jamal, Mamdooh Gari, Mohammed H. Alqahtani, Ioakim Spyridopoulos1, Jolanta U Weaver. 2nd International Genomic Medical Conference. BMC Genomics 2014, 15(Suppl 2):P23.

2. Bakhashab S, Lary S, Ahmed F, Schulten HJ, Bashir A, Ahmed FW, Al-Malki AL, Jamal HS, Gari MA, Weaver JU: Reference genes for expression studies in hypoxia and hyperglycemia models in human umbilical vein endothelial cells. G3 2014, 4(11):2159-2165.

List of Abbreviations

LIST OF ABBREVIATIONS

2-OG 2-oxoglutarate-dependent dioxygenase ABC ATP-binding cassette ABCB(number) ATP-binding cassette, sub-family B (MDR/TAP), member (number) ABCF2 ATP-binding cassette, sub-family F (GCN20), member 2 ABL1 Abelson Tyrosine-Protein Kinase 1 ACACA or ACC Acetyl-CoA carboxylase α ACAT1 Acetyl-CoA acetyltransferase 1 ACER2 alkaline ceramidase 2 ACTB β-actin ADAM28 ADAM metallopeptidase domain 28 ADM Adrenomedullin ADORA2A Adenosine A2a receptor ADORA2B Adenosine A2b receptor ADRA2A Adrenoceptor alpha 2A ADSSL1 Adenylosuccinate synthase like 1 AGCC Affymetrix GeneChip Command Software AGEs Advanced glycosylation end-products AGPAT9 1-acylglycerol-3-phosphate O-acyltransferase 9 AK4 Adenylate kinase 4 AKAP12 A kinase (PRKA) anchor protein 12 ALDOC Aldolase C, fructose-bisphosphate ALKBH5 AlkB, alkylation repair homolog 5 (E. coli) ALOX12 Arachidonate 12-lipoxygenase ALX1 ALX homeobox 1 AMPK AMP-activated protein kinase AMTN Amelotin ANAPC1 Anaphase promoting complex subunit 1 ANGPT Angiopoietin ANGPTL4 Angiopoietin-like 4 ANKZF1 Ankyrin repeat and zinc finger domain containing 1 ANOVA Analysis of variance ANXA2 Annexin A2 AQP9 Aquaporin 9 AR Aldose reductase ARD1 Arrest-defective-1 ASNS Asparagine synthetase (glutamine-hydrolyzing) AT-1 Angiotensin I receptor ATIC 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase/IMP cyclohydrolase ATP5D ATP synthase, H+ transporting, mitochondrial F1 complex, delta subunit ATR Ataxia telangiectasia and Rad3 related AURKA Aurora kinase A xxii

List of Abbreviations

AVPR1A Arginine vasopressin receptor 1A BARD1 BRCA1 associated RING domain 1 BDNF Brain-derived neurotrophic factor BDNF brain-derived neurotrophic factor bHLH Basic helix-loop- helix BHLHE40 Basic helix-loop-helix family, member e40 BHLHE41 Basic helix-loop-helix family, member e41 BIRC5 Baculoviral IAP repeat containing 5 BLM Bloom syndrome, RecQ helicase-like BLNK B-cell linker BMP6 Bone morphogenetic protein 6 BNIP3 BCL2/adenovirus E1B 19kDa interacting protein 3 BNIP3L BCL2/adenovirus E1B 19kDa interacting protein 3-like BRCA1 Breast cancer 1, early onset BRIP1 BRCA1 interacting protein C-terminal helicase 1 BTG1 B-cell translocation gene 1, anti-proliferative BTLA B and T lymphocyte associated C17orf108 Chromosome 17 open reading frame 108 C3 Complement component 3 C3orf79 Chromosome 3 open reading frame 79 CA2 Carbonic anhydrase II CAC Circulating angiogenic cell CARS Cysteinyl-tRNA synthetase CASP1 Caspase 1, apoptosis-related cysteine peptidase CAV2 Caveolin 2 CBP CREB binding protein CCL-(number) Chemokine (C-C motif) ligand-(number) CCNB1 Cyclin B1 CCND1 Cyclin D1 CCR7 Chemokine (C-C motif) receptor 7 CD(number) Cluster of differentiation (number) CDC6 Cell division cycle 6 CDH5 Cadherin 5, type 2 (vascular endothelium) CDK1 Cyclin-dependent kinase 1 CDKN3 Cyclin-dependent kinase inhibitor 3 cDNA Complementary DNA CDR1 Cerebellar degeneration-related protein 1, 34kDa CDRT1 CMT1A duplicated region transcript 1 CENPE Centromere protein E, 312kDa CHD Coronary heart disease CHEK1 Checkpoint kinase 1 ChREBP Carbohydrate-response-element-binding protein CHRNA5 Cholinergic receptor, nicotinic, alpha 5 (neuronal) CHSY1 Chondroitin sulfate synthase 1 CLEC1B C-type lectin domain family 1, member B

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List of Abbreviations

CLEC2D C-type lectin domain family 2, member D CMA1 Chymase 1, mast cell CoCl2 Cobalt Chloride CORO1A Coronin, actin binding protein, 1A COX6B1 Cytochrome c oxidase subunit VIb polypeptide 1 (ubiquitous) COX7B Cytochrome c oxidase subunit VIIb CP Canonical pathway CREB cAMP-response-element-binding protein CSF1 Colony stimulating factor 1 (macrophage) CSF2RB Colony stimulating factor 2 receptor, beta, low-affinity (granulocyte- macrophage) CST1 Cystatin SN CTPS1 CTP synthase 1 CTSS Cathepsin S CVD Cardiovascular disease CXCL(number) Chemokine (C-X-C Motif) ligand (number) CYB5A Cytochrome b5 type A CYBB Cytochrome b-245, beta polypeptide CYP51A1 Cytochrome P450, family 51, subfamily A, polypeptide 1 CYSLTR2 Cysteinyl leukotriene receptor 2 DAG Diacylglycerol DAGLA Diacylglycerol lipase, alpha DBF4 DBF4 zinc finger DDIT4 DNA-damage-inducible transcript 4 DDX41 DEAD (Asp-Glu-Ala-Asp) box polypeptide 41 DGCR8 DiGeorge syndrome critical region 8 DHCR7 7-dehydrocholesterol reductase DHFR Dihydrofolate reductase DLL4 Delta-like 4 (Drosophila); DM Diabetes Mellitus DPBS Dulbecco’s Phosphate Buffered Saline DPY19L2P2 dpy-19-like 2 pseudogene 2 (C. elegans) DUSP1 Dual specificity phosphatase 1 E2F1 E2F transcription factor 1 EBM-2 Endothelial cells Basal Medium EBP Emopamil binding protein (sterol isomerase) EC Endothelial cell ECM Extracellular matrix EFNA3 Ephrin-A3 Egfl7 Epidermal growth factor-like-domain, multiple 7 EGLN1 egl nine homolog 1 (C. elegans) EGLN3 egl nine homolog 3 (C. elegans) EGR1 early growth response 1 EGR3 early growth response 3 EIF2B3 Eukaryotic translation initiation factor 2B, subunit 3 gamma, 58kDa

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List of Abbreviations

EIF4B Eukaryotic translation initiation factor 4B EIF5A Eukaryotic translation initiation factor 5A ELAM-1 Endothelial-leukocyte adhesion molecule-1 ELANE Elastase, neutrophil expressed ELISA Enzyme-linked immunosorbent assay EMP Endothelium-derived microparticles ENKUR Enkurin, TRPC channel interacting protein ENO2 Enolase 2 (gamma, neuronal) ENPP2 Ectonucleotide pyrophosphatase/phosphodiesterase 2 EPC Endothelial progenitor cell EPCAM Epithelial cell adhesion molecule ERK1/2 Extracellular signal-regulated kinase 1/2 ERO1L ERO1-like (S. cerevisiae) ESC Embryonic stem cell ESPL1 Extra spindle pole bodies homolog 1 (S. cerevisiae) EXOC5 Exocyst complex component 5 F13A1 Coagulation factor XIII, A1 polypeptide F2RL2 Coagulation factor II (thrombin) receptor-like 2 FABP4 Fatty acid binding protein 4, adipocyte FACS Fluorescence-activated flow cytometry FAM13A Family with sequence similarity 13, member A FAM162A Family with sequence similarity 162, member A FANCA Fanconi anaemia, complementation group A FASN Fatty acid synthase FBS Foetal Bovine Serum FBXO5 F-box protein 5 FC Fold change FCER1A Fc fragment of IgE, high affinity I, receptor for; alpha polypeptide FDA Food and Drug Administration FDFT1 Farnesyl-diphosphate farnesyltransferase 1 FDR False Discovery Rate FGF Fibroblast growth factor FIGF c-fos induced growth factor (vascular endothelial growth factor D) FIH-1 Factor inhibiting HIF-1 Flk-1 Fetal liver kinase-1 Flt-1 Fms-like tyrosine kinase-1 FOS FBJ murine osteosarcoma viral oncogene homolog FOXM1 Forkhead box M1 FU Fluorescence unit FYB FYN binding protein GADD45B Growth arrest and DNA-damage-inducible, beta GAPDH Glyceraldehyde 3-phosphate dehydrogenase GART Phosphoribosylglycinamide Formyltransferase GAS5 Growth arrest-specific 5 (non-protein coding) GAX Growth arrest-specific homeobox

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List of Abbreviations

G-CSF Granulocyte-colony stimulating factor GEO Gene Expression Omnibus GFAT Glutamine:fructose-6-phosphate amidotransferase GINS1 GINS complex subunit 1 (Psf1 homolog) GJA4 Gap junction protein, alpha 4, 37kDa GlcNAc N-acetylglucosamine GLMN Glomulin, FKBP associated protein GLRB Glycine receptor, beta GP Glycerol-3-phosphate GP1BA Glycoprotein Ib (platelet), alpha polypeptide GP5 Glycoprotein V (platelet) GP6 Glycoprotein VI (platelet) GPAM Glycerol-3-phosphate acyltransferase, mitochondrial GPAT Glycerol-3-phosphate acyltransferase GPI Glucose-6-phosphate isomerase GUSB Glucuronidase, beta GYS1 Glycogen synthase 1 (muscle) H2AFX H2A histone family, member X HCK Haemopoietic cell kinase HEPES 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid HGF Hepatocyte growth factor HIF-1 Hypoxia inducible factor-1 HILPDA Hypoxia inducible lipid droplet-associated HIST1H1A Histone cluster 1, H1a HIST1H2BH Histone cluster 1, H2bh HIST1H2BM Histone cluster 1, H2bm HIST2H2AA3 Histone cluster 2, H2aa3 HIST2H2AB Histone cluster 2, H2ab HK2 Hexokinase 2 HLA-DQA1 Major histocompatibility complex, class II, DQ alpha 1 HLA-DQB1 Major histocompatibility complex, class II, DQ beta 1 HMGCR 3-hydroxy-3-methylglutaryl-CoA reductase HMGCS1 3-hydroxy-3-methylglutaryl-CoA synthase 1 (soluble) HMMR Hyaluronan-mediated motility receptor HMOX1 Heme oxygenase (decycling) 1 HOXA5 Homeobox A5 HPSE Heparanase HRE Hypoxia response elements HSC Haematopoietic stem cells HSD17B7 Hydroxysteroid (17-beta) dehydrogenase 7 HSD3B1 Hydroxy-delta-5-steroid dehydrogenase, 3 beta- and steroid delta- isomerase 1 HSPA5 Heat shock 70kDa protein 5 (glucose-regulated protein, 78kDa) HUVEC Human umbilical vein endothelial cell ICAM-1 Intercellular adhesion molecule-1

xxvi

List of Abbreviations

IDF International Diabetes Federation IDI1 Isopentenyl-diphosphate delta isomerase 1 IER3IP1 Immediate early response 3 interacting protein 1 IFG Impaired fasting glucose IFNA21 Interferon, alpha 21 IGF Insulin-like growth factor IGF1R Insulin-like growth factor 1 receptor IGHE Immunoglobulin heavy constant epsilon IGHM: Immunoglobulin heavy constant mu IGK Immunoglobulin kappa locus IGT Impaired glucose tolerance IHD Ischaemic heart disease IKZF3 IKAROS family zinc finger 3 (Aiolos) IL-(number) Interleukin (number) INHBA Inhibin, beta A INSR Insulin receptor IPA Ingenuity Pathway Analysis IQCA1P1 IQ motif containing with AAA domain 1 pseudogene 1 IRF4 Interferon regulatory factor 4 ITGB3 Integrin, beta 3 IVT In vitro transcription JMJD6: Jumonji domain containing 6 JUNB jun B proto-oncogene KDM3A Lysine (K)-specific demethylase 3A KDR Kinase-inserted domain containing receptor KIF11 Kinesin family member 11 KIR2DS5 Killer cell immunoglobulin-like receptor, two domains, short cytoplasmic tail, 5 KITLG KIT ligand KLHL20 Kelch-like family member 20 LAT Linker for activation of T cells LDHA Lactate dehydrogenase A LIF Leukaemia inhibitory factor LIPK Lipase, family member K LKB1 Liver kinase B1 LOX Lysyl oxidase LPS Lipopolysaccharide LSD Fisher’s least significant difference LTBP1 Latent transforming growth factor beta binding protein 1 LY9 Lymphocyte antigen 9 LY96 Lymphocyte antigen 96 MALAT1 Metastasis associated lung adenocarcinoma transcript 1 (non-protein) MAP2K1 Mitogen-activated protein kinase kinase 1 MAP3K8 Mitogen-activated protein kinase kinase kinase 8 MAPK Mitogen-activated protein kinases

xxvii

List of Abbreviations

MAPK7 mitogen-activated protein kinase 7 MCD1 Known as RAD21 Homolog (S. Pombe) MCM2 Minichromosome maintenance complex component 2 MCT4 Monocarboxylate transporter 4 MEF2C Myocyte enhancer factor 2C MEIS1 Meis homeobox 1 MET Met proto-oncogene (hepatocyte growth factor receptor) MF Molecular function MI Myocardial infarction MIF Macrophage migration inhibitory factor (glycosylation-inhibiting factor) miRNA microRNA MKI67 Antigen identified by monoclonal antibody Ki-67 MLH1 mutL homology 1 MLLT3 Myeloid/lymphoid or mixed-lineage leukaemia (trithorax homolog, Drosophila); translocated to, 3 MMP Matrix metallopeptidase MNC Mononuclear cell MPP1 Membrane protein, palmitoylated 1, 55kDa MRAS Muscle RAS oncogene homolog mRNA Messenger ribonucleic acid MS4A1 Membrane-spanning 4-domains, subfamily A, member 1 MSC Mesenchymal stem cells MSD Meso Scale Discovery MSH2 mutS homolog 2 MSMO1 Methylsterol monooxygenase 1 MTFP1 Mitochondrial fission process 1 MT-ND2 Mitochondrially Encoded NADH Dehydrogenase 2 mTOR Mammalian target of rapamycin MVB Multivesicular bodies MVK Mevalonate kinase MXD1 MAX dimerization protein 1 NAE1 NEDD8 activating enzyme E1 subunit 1 NCOR2 Nuclear receptor corepressor 2 NCR1 Natural cytotoxicity triggering receptor 1 ND6 NADH dehydrogenase, subunit 6 (complex I) NDRG1 N-myc downstream regulated 1 NDUFA3 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 3, 9kDa NDUFS8 NADH dehydrogenase (ubiquinone) Fe-S protein 8, 23kDa (NADH- coenzyme Q reductase) NFIL3 Nuclear factor, interleukin 3 regulated NF-κB Nuclear factor kappa-light-chain-enhancer of activated B cells NLRP2 NLR family, pyrin domain containing 2 NO Nitric oxide NOS3 nitric oxide synthase 3 (endothelial cell)

xxviii

List of Abbreviations

NQO1 NAD(P)H dehydrogenase, quinone 1 NR4A1 Nuclear receptor subfamily 4, group A, member 1 NRF Nuclear respiratory factor NRN1 Neuritin 1 NSDHL NAD(P) dependent steroid dehydrogenase-like NT5E 5'-nucleotidase, ecto (CD73) NTRK1 Neurotrophic tyrosine kinase, receptor, type 1 NTS Neurotensin NUCKS1 Nuclear casein kinase and cyclin-dependent kinase substrate 1 OCT(number) Organic cation transporter (number) ODC1 Ornithine decarboxylase 1 ODD Oxygen-dependent degradation ORC3 Origin recognition complex, subunit 3 P2RX7 Purinergic receptor P2X, ligand-gated ion channel, 7 P2RY2 Purinergic receptor P2Y, G-protein coupled, 2 P4HA1 Prolyl 4-hydroxylase, alpha polypeptide I P4HA2 Prolyl 4-hydroxylase, alpha polypeptide II PA Phosphatidic acid PAD Peripheral arterial disease PARK2 Parkinson protein 2, E3 ubiquitin protein ligase PAS PER-ARNT-SIM PBS Phosphate Buffered Saline PBS-T Phosphate buffered saline containing-Tween-20 PCA Principle components analysis PCDH18 Protocadherin 18 PCMTD1 Protein-L-isoaspartate (D-aspartate) O-methyltransferase domain containing 1 PCNA Proliferating cell nuclear antigen PDE5A Phosphodiesterase 5A, cGMP-specific PDGF Platelet-derived growth factor PDK1 Pyruvate dehydrogenase kinase, isozyme 1 PECAM-1 Platelet-endothelial cell adhesion molecule PF4 Platelet factor 4 PFAS Phosphoribosylformylglycinamidine synthase PFKFB2 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 2 PFKFB3 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 PFKFB4 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 4 PFKL Phosphofructokinase, liver PGF Placenta growth factor PGK1 Phosphoglycerate kinase 1 PGM1 Phosphoglucomutase 1 PHD Prolylhydroxylase domain PHD-2 Proline-hydroxylase-2 PI Propidium iodide PI3K Phosphatidylinositol-3-kinases

xxix

List of Abbreviations

PIGA Phosphatidylinositol glycan anchor biosynthesis, class A PIK3IP1 Phosphoinositide-3-kinase interacting protein 1 PIK3R1 Phosphoinositide-3-kinase, regulatory subunit 1 (alpha) PIK3R2 Phosphoinositide-3-kinase, regulatory subunit 2 PIK3R3 Phosphoinositide-3-kinase, regulatory subunit 3 (gamma) PKC Protein kinase C PLA2G4C Phospholipase A2, group IVC (cytosolic, calcium-independent) PLA2G7 Phospholipase A2, group VII (platelet-activating factor acetylhydrolase, plasma) PLA2G7 phospholipase A2, group VII (platelet-activating factor acetylhydrolase, plasma) PLAC8 placenta-specific 8 PLAUR Plasminogen activator, urokinase receptor PLC-γ Phosophlipase C-γ PLD2 Phospholipase D2 PLK1 Polo-like kinase 1 PLOD2 Procollagen-lysine, 2-oxoglutarate 5-dioxygenase 2 PLTP Phospholipid transfer protein PPAP2A Phosphatidic acid phosphatase type 2A PPAT Phosphoribosyl pyrophosphate amidotransferase PPBP Pro-platelet basic protein (chemokine (C-X-C motif) ligand 7) PPFIA4 Protein tyrosine phosphatase, receptor type, f polypeptide (PTPRF), interacting protein (liprin), alpha 4 PPP Pentose phosphate pathway PPP1R3C Protein phosphatase 1, regulatory subunit 3C PPP2R5B Protein phosphatase 2, regulatory subunit B, beta PRC1 Protein regulator of cytokinesis 1 PRKAA2 Protein kinase, AMP-activated, alpha 2 catalytic subunit (Alias: AMPKα) PRKACB Protein kinase, cAMP-dependent, catalytic, beta PRKAR2B Protein kinase, cAMP-dependent, regulatory, type II, beta PRKDC Protein kinase, DNA-activated, catalytic polypeptide PROS1 Protein S (alpha) PRR5L Proline rich 5 like PRTN3 Proteinase 3 PSAT1 Phosphoserine aminotransferase 1 PTGS2 Prostaglandin-endoperoxide synthase 2 (prostaglandin G/H synthase and cyclooxygenase) PTPN22 Protein tyrosine phosphatase, non-receptor type 22 PTPRB Protein tyrosine phosphatase, receptor type, B PTPRJ Protein tyrosine phosphatase, receptor type, J PTTG1 Pituitary tumour-transforming 1 PXN Paxillin QC Quality control qRT-PCR Quantitative real time-Polymerase Chain Reaction

xxx

List of Abbreviations

RAB20 RAB20, member RAS oncogene family RAD51 RAD51 recombinase RAR Retinoic acid receptor RBBP8 Retinoblastoma binding protein 8 RBL1 Retinoblastoma-like 1 RDH13 Retinol dehydrogenase 13 REN Renin RFC2 Replication factor C (activator 1) 2, 40kDa RGS5 Regulator of G protein signalling 5 RIN RNA integrity number RISC RNA-induced silencing complex RLIM Ring finger protein, LIM domain interacting RMA Robust multichip average RN5S192 RNA, 5S ribosomal 192 RN5S22 RNA, 5S ribosomal 22 RN5S55 RNA, 5S ribosomal 55 RNASE2 Ribonuclease, RNase A family, 2 (liver, eosinophil-derived neurotoxin) RNU1-11P RNU1-11P // RNA, U1 small nuclear 11, pseudogene RNU5B-1 RNA, U5B small nuclear 1 ROCK1 Rho-associated, coiled-coil containing protein kinase 1 RORA RAR-related orphan receptor A ROS Reactive oxygen species RPA2 Replication protein A2, 32kDa RPL41 Ribosomal protein L41 RPLP0 Ribosomal protein, large, P0 RUNX1T1 Runt-related transcription factor 1; translocated to, 1 (cyclin D-related) S100A12 S100 calcium binding protein A12 S100A8 S100 calcium binding protein A8 S1PR1 Sphingosine-1-phosphate receptor 1 SCARNA1 Small Cajal body-specific RNA 1 SCARNA9L Small Cajal body-specific RNA 9-like SDH Sorbitol dehydrogenase SELE Selectin E SELP Selectin P (granule membrane protein 140kDa, antigen CD62) SERPINB2 Serpin peptidase inhibitor, clade B (ovalbumin), member 2 SH2D1A SH2 domain containing 1A SH2D2A SH2 domain containing 2A SHB: Src homology 2 domain containing adaptor protein B SHP Small heterodimer partner SIRT1 Sirtuin 1 SKP2 S-phase kinase-associated protein 2, E3 ubiquitin protein ligase SLC16A3 Solute carrier family 16 (monocarboxylate transporter), member 3 SLC16A4 Solute carrier family 16, member 4 SLC25A37 Solute carrier family 25 (mitochondrial iron transporter), member 37

xxxi

List of Abbreviations

SLC2A1 Solute carrier family 2 (facilitated glucose transporter), member 1 (GLUT1) SLC2A3 Solute carrier family 2 (facilitated glucose transporter), member 3 (GLUT3) SLC6A9 Solute carrier family 6 (neurotransmitter transporter, glycine) SMAD7 SMAD family member 7 SMC1A Structural maintenance of chromosomes 1A SNAI2 Snail homolog 2 (Drosophila) SNHG10 Small nucleolar RNA host gene 10 (non-protein coding) SNHG3 Small nucleolar RNA host gene 3 (non-protein coding) SNORA14A Small nucleolar RNA, H/ACA box 14A SNORA14B Small nucleolar RNA, H/ACA box 14B SNORA20 Small nucleolar RNA, H/ACA box 20 SNORD116-24 Small nucleolar RNA, C/D box 116-24 SNORD116-6 Small nucleolar RNA, C/D box 116-6 SNORD45C Small nucleolar RNA, C/D box 45C SNX33 Sorting nexin 33 SOD Superoxide dismutase SOST Sclerostin SPAG4 Sperm associated antigen 4 SPANXE SPANX family, member E SPARC Secreted protein, acidic, cysteine-rich (osteonectin) SPC25 SPC25, NDC80 kinetochore complex component, homolog (S. cerevisiae) SPINK7 Serine peptidase inhibitor, Kazal type 7 Spred-1 Sprouty-related, EVH1 domain containing 1 SQLE Squalene epoxidase SREBP-1c Sterol-regulatory-element-binding-prtoein-1c SRP72P2 Signal recognition particle 72kDa pseudogene 2 STC1 Stanniocalcin 1 STC2 Stanniocalcin 2 TAD Transactivation domain TAF1D TATA box binding protein (TBP)-associated factor, RNA polymerase I TAF9B TAF9B RNA polymerase II, TATA box binding protein (TBP)- associated factor, 31kDa TBS Tris-buffered saline TCL1A T-cell leukemia/lymphoma 1A TECR Trans-2,3-enoyl-CoA reductase TEK TEK tyrosine kinase, endothelial TEM Transmission electron microscopy TF Tissue factor TFAM Transcription factor A, mitochondrial TFPI Tissue factor pathway inhibitor (lipoprotein-associated coagulation inhibitor) TFPI2 Tissue factor pathway inhibitor 2

xxxii

List of Abbreviations

TFRC Transferrin receptor TGF Transforming growth factor THBS1 Thrombospondin 1 TIMP (number) Tissue inhibitor of metalloproteinase (number) TK Transketolase TK1 Thymidine kinase 1, soluble TLR4 Toll-like receptor 4 TM4SF1 Transmembrane 4 L six family member 1 TMEM19 Transmembrane protein 19 TMEM45A Transmembrane protein 45A TNFSF18 Tumour necrosis factor (ligand) superfamily, member 18 TNF-α Tumour necrosis factor-α TNIP1 TNFAIP3 interacting protein 1 TOB2 Transducer of ERBB2, 2 TOP2A Topoisomerase (DNA) II alpha 170kDa TORC2 Transducer of regulated CREB binding protein 2 t-PA Tissue-type plasminogen activator TPI1 Triosephosphate isomerase 1 TRIM43 Tripartite motif containing 43 TSPYL1 TSPY-like 1 TUBA4A Tubulin, alpha 4a TUBB1 Tubulin, beta 1 class VI TUBB2C Tubulin, beta 4B class IVb TXNIP Thioredoxin interacting protein UCB Umbilical Cord Blood UCP2 Uncoupling protein 2 (mitochondrial, proton carrier) UGT2B17 UDP glucuronosyltransferase 2 family, polypeptide B17 UGT2B28 UDP glucuronosyltransferase 2 family, polypeptide B28 UNG Uracil-N-Glycosylase uPAR Urokinase plasminogen activator receptor USP18 Ubiquitin specific peptidase 18 UTR Untranslated region UTS2 Urotensin 2 VCAM-1 Vascular cell adhesion molecule-1 VCL Vinculin VE-cadherin Vascular endothelial cadherin VEGF Vascular endothelial growth factor VGLL3 Vestigial like 3 (Drosophila) VHL Von Hippel-Lindau VIP Vasoactive intestinal peptide VLDLR very low density lipoprotein receptor vWF von Willebrand factor WDR52 WD repeat domain 52 WHO World Health Organisation ZDBF2 Zinc finger, DBF-type containing 2

xxxiii

List of Abbreviations

ZNF267 Zinc finger protein 267 ZNF594 Zinc finger protein 594

xxxiv

Chapter 1. Introduction

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality worldwide (Miller and Missov, 2001). Currently, available pharmacological therapies for CVD fail in a significant number of patients. The outcome of CVD management is affected by additional risk factors such as diabetes mellitus (DM), which results in a two- to four-fold increased risk of CVD (Hanson et al., 2002, Nolan et al., 2011). Recent reports from the International Diabetes Federation (IDF) indicate that 8.3% of adults have diabetes, affecting 382 million people worldwide. The incidence of diabetes continues to increase at significant rates and is estimated to rise to 592 million worldwide by 2035 (IDF, 2013). Most disability and premature mortality in patients with diabetes are caused by CVD.

Patients with diabetes and myocardial infarction (MI) have a poor prognosis. Additionally, patients with diabetes have an up to five-fold higher risk for a first MI and a two-fold greater risk for a recurrent MI than people who previously had an MI but non-diabetes (Haffner et al., 1998). Furthermore, the outcome of CVD interventions such as percutaneous coronary intervention and coronary artery bypass graft in patients with DM is much worse in comparison with non-diabetic individuals (Ekezue et al., 2014, Piccolo et al., 2014).

Diabetes associated endothelial dysfunction is a known early step in the adverse sequence of events leading to the development of micro- and macrovascular complications, which results in mortality linked to diabetes in 75% of the cases (Grundy et al., 2002). Vascular complications include both qualitative and quantitative changes in vascular architecture, in particular: abrogation of neovascularization and remodeling of existing vasculature that results in lack of ability to control ischaemic injury. Understanding the mechanisms involved in diabetes associated with impaired vascular repair including cell migration, and cell survival in the setting of short period of ischaemia (60 minutes), intermediate (3 hours) and long period (12 hours) is of significance in order to improve interventions in CVD and in particular CVD in diabetes.

Chapter 1. Introduction

Metformin is the only hypoglycemic therapy to display cardioprotective properties in long- term randomised outcome clinical trials (1998a, Roussel et al., 2010). Despite the wide- spread knowledge of cardioprotective effects of metformin, the underlying mechanism has not been fully explored.

1.1 Diabetes mellitus and cardiovascular disease 1.1.1 Definition and classification of diabetes mellitus Diabetes mellitus is a group of metabolic disorders characterised by hyperglycaemia resulting from defects in insulin secretion, insulin action or both. Diabetes is classified according to the aetiological cause, and the majority of the cases fall into two broad categories, type 1 and type 2. Type 1 DM is caused primarily by autoimmune or idiopathic destruction of β-cells of the pancreas leading to absolute insulin deficiency. Type 2 DM results predominantly from insulin resistance with failure of pancreatic β cells to produce sufficient insulin (WHO, 1999). The diagnosis of diabetes is driven on the basis of the World Health Organisation (WHO) recommendations from 1999 report, that integrate criteria for fasting plasma glucose levels and plasma glucose levels 2 hours after a 75 g oral glucose load (Table 1.1) (WHO, 1999). The Expert Committee on Diagnosis and Classification of Diabetes Mellitus documented an intermediate group of individuals whose glucose levels are higher than normal. These subjects were identified as having impaired fasting glucose (IFG) [individuals who have fasting glucose values above the normal range, but below those diagnostic of diabetes], or impaired glucose tolerance (IGT) [it can be observed in any hyperglycaemic disorder and is itself not diabetes] (Expert Committee, 1997, Genuth et al., 2003) IFG and IGT are indicated as risk factors for diabetes and CVD.

Glucose concentration in venous plasma (mmol/L)

Diabetes mellitus Fasting ≥ 7.0 or 2 hour post-glucose load ≥ 11.1

Impaired fasting glucose Fasting ≥ 6.1 and < 7.0 and 2 hours post-glucose load ≥ 7.8

Impaired glucose tolerance Fasting < 7.0 and 2 hours post-glucose load ≥ 7.8 and < 11.1

Table 1.1: The diagnostic criteria of diabetes mellitus. The table shows the diagnostic criteria of diabetes mellitus and other categories of hyperglycaemia (WHO, 1999). 2

Chapter 1. Introduction

1.1.2 Complications of diabetes Diabetes is associated with long-term vascular complications include micro- and macrovascular disease. DM is considered as a major factor in the development of atherosclerotic macrovascular disorders such as coronary heart disease (CHD), myocardial infarction (MI), peripheral arterial disease (PAD) and stroke being the leading cause of mortality in patients with type 2 DM (Dale et al., 2008). Furthermore, a number of microvascular pathologies occurring in the retina, renal glomerulus and the peripheral nerve are also apparent in diabetic patients and often manifest as retinopathy, nephropathy and neuropathy (Brownlee, 2001, Frank, 2004). The underlying aetiology behind the vascular abnormalities associated with DM is complex. However, one of the main characteristics is aberrant angiogenesis. Microvascular disease, as seen in diabetic retinopathy, are often characterised by excessive angiogenesis. Whereas ischaemic disorders are associated with impaired angiogenesis (Costa and Soares, 2013).

1.2 Vasculogenesis and angiogenesis The cardio-vasculature is the first system to form in the embryo. The de novo organization of endothelial cells (ECs) into vessels in the absence of any pre-existing vascular system is denoted as vasculogenesis and only occurs in the early embryo (Figure 1.1A) (Fishman and Stainier, 1994). Angiogenesis is the continuing expansion of the vascular tree as a result of ECs sprouting from existing vessels, occurs in vascular regions of the embryo and the mature animal (Figure 1.1B), most commonly during wound healing and tumour metastasis in pathological situations (Hanahan and Folkman, 1996).

1.2.1 Origin of vascular endothelium In 1995, Risau and Flamme (Risau and Flamme, 1995) have shown that vascular, and haematopoietic tissues develop together, immediately after implantation with the formation of blood islands within the primitive yolk sac (Figure 1.1A). These are composed of two cell types: first angioblasts that form the outer layer of ECs covering the blood island; and second haematopoietic stem cells, from which the first embryonic blood cells develop. Angioblasts committed to EC differentiation are found in embryonic mesoderm (Palis et al., 1995) and the adult as well (Asahara et al., 1997). 3

Chapter 1. Introduction

Figure 1.1: Genesis of the vascular system. A) During development, mesodermal stem cells differentiate into hemangioblasts, which are asymmetrically divided to form two cell types: angioblasts that are committed to EC differentiation or haematopoietic stem cells that committed and differentiated to give the whole blood cells. Once stimulated, ECs migrate facilitating the fusion of the blood islands and their remodelling into tubular structures, giving rise to the first primitive vascular plexus. These vascular plexuses remodel into larger vessels, through the process of vasculogenesis in the embryo. B) Angiogenesis is a neovascularization process by which new blood vessels formed from pre-existing ones. Figure adapted from (Lamalice et al., 2007).

Chapter 1. Introduction

1.2.2 Endothelial cell differentiation and vascular development The initiation of vascular development requires both fibroblast growth factor-2 (FGF-2) and vascular EC growth factors as has been documented by Beck and D'Amore, 1997 (Beck and D'Amore, 1997). Three alternatively spliced isoforms of VEGFs, members of the platelet-derived growth factor family, are VEGFA, VEGFB, and VEGFC, which interact with specific tyrosine kinase receptors as illustrated in Figure 1.2 (Breier and Risau, 1996). Growth factor-receptor interactions include those of VEGFR-1 (known as flt-1 or fms-like tyrosine kinase-1) with VEGFA or a related placenta growth factor (PlGF). VEGFR-2 (known alternatively as flk-1, foetal liver kinase-1 or KDR, kinase-inserted domain containing receptor) interacts with both VEGFA and VEGFC. VEGFR-3 (originally designated flk-4) interacts with VEGFC. All VEGFs stimulate receptor autophosphorylation and EC replication and migration. The crucial role of VEGFR-2 in early vasculogenesis was demonstrated in a deficient mouse model revealing that loss of this gene results in embryonic death (Shalaby et al., 1995). The regulation of blood vessel growth depends on the control of VEGF production through changes in the stability of its mRNA and the rate of transcription (Alberts et al., 2002).

Chapter 1. Introduction

Figure 1.2: VEGF ligands bind to their associated receptors, leading to receptor dimerisation and subsequent signal transduction. VEGFR-1 is a key receptor in developmental angiogenesis interacts with VEGFA and placental growth factor (PIGF). VEGFR-2 binds to VEGFA and VEGFC ligands causing phosphorylation of the receptor tyrosine kinase domains and a subsequent cellular signalling cascade that leads to angiogenesis. VEGFR-3 interacts with VEGFC and indirectly enhance angiogenesis. Adapted from (Ellis and Hicklin, 2008).

Chapter 1. Introduction

Functions of endothelial cells

Blood vessels consist of ECs that are in direct contact with the blood. The largest blood vessels are arteries and veins, which have thick, tough wall of connective tissue and layers of smooth muscle cells. The wall is lined by a single layer of ECs, the endothelium, separated from the surrounding outer layers by a basement membrane (Figure 1.3). In the small vessels, the walls of the capillaries and sinusoids consist of ECs and a basement membrane containing a limited number of functionally important pericytes. These cells belong to the connective tissue family that are related to vascular smooth muscle cells (Alberts et al., 2002).

The ECs are metabolically active involved in several regulatory processes in the body. Under normal conditions, ECs exert anti-coagulative process by synthesis of thrombomodulin, tissue factor (TF) pathway inhibitor and tissue-type plasminogen activator (t-PA). Upon damage, ECs release proteins like plasminogen activator inhibitor-1 and von Willebrand factor (vWF) which promotes platelet adhesion and aggregation. Additionally, TF release leads to initiation of the extrinsic blood coagulation pathway (Verstraete, 1995). ECs also have the ability to direct immune cells to lymphoid organs or inflammatory sites via expressing cytokine-inducible cellular adhesion molecules like E-selectin and intercellular adhesion molecule-1 (ICAM-1) and soluble factors such as chemoattractants, cytokines and chemokines (Carlos and Harlan, 1994). Moreover, ECs are involved in vascular remodelling during wound healing, tumour growth and diabetic retinopathy (Folkman, 1995, Hanahan and Folkman, 1996).

1.2.3 Molecular mechanisms involved in angiogenesis

Angiogenesis is a highly pleiotropic process and involves many cellular activities and molecules in the angiogenic cascade. These angiogenic mediators are either soluble, extracellular matrix (ECM), membrane-bound growth factors or components of the ECM (Griffioen and Molema, 2000). Angiogenesis is required in many physiological conditions including wound healing and tissue regeneration. The process is regulated by a tight balance between pro-angiogenic and anti-angiogenic agents involved in the cascade of events (Rousseau et al., 1997). Also, a large number of diseases are associated with angiogenesis. Insufficient angiogenesis occurs in diseases such as tissue damage after reperfusion of ischemic tissue, coronary artery disease, stroke and in chronic wounds, where therapeutic stimulation of angiogenesis with growth factors may improve disease conditions (Carmeliet et al., 1999, Ferrara and Alitalo, 1999). 7

Chapter 1. Introduction

Figure 1.3: The microscopic structure of arteries, veins and capillaries. The walls of blood vessels except the capillaries are composed of three layers or tunics. The inner layer is called the tunica interna, which contains ECs. The ECs are resting on a basement membrane. The middle layer or tunica media is composed of smooth muscle cells and sheets of elastin. The outer layer or tunica externa is composed of collagen fibres. Figure adapted from (Fox, 1993).

Chapter 1. Introduction

In other diseases, like cancer, and diabetic retinopathy, excessive angiogenesis can occur and in these fields anti-angiogenic therapies are used to inhibit new blood vessel formation (Folkman, 1995, Hanahan and Folkman, 1996). Application of therapeutic angiogenesis is a matter of discussion as several studies have indicated that VEGFA and other angiogenic factors can promote atherosclerosis in certain animal models by promoting intra-lesion angiogenesis (Celletti et al., 2001, Heeschen et al., 2001, Moulton et al., 1999).

Recent advances in the identification of molecules that regulate angiogenesis and vascular remodelling resulted in a more complex picture of the angiogenic cascade. This includes the following events (Figure 1.4):

1. Initiation of the angiogenic response:

Angiogenesis is initiated in response to hypoxic or ischaemic conditions. Vascular relaxation mediated by nitric oxide (NO) is essential for ECs to enter the angiogenic cascade (Folkman, 1997). Cytokines from various cells are released in response to hypoxia or ischaemia. Undoubtedly, VEGFA is a major player in early events of angiogenesis based on its ability to induce vasodilation via endothelial NO production (Ziche et al., 1997).

VEGFA production is regulated by hypoxia inducible factor (HIF) which supports an early involvement of VEGF in angiogenesis. Additionally, VEGFR expression is upregulated by hypoxia (Forsythe et al., 1996).

After activation of ECs, proteolytic activities are required for degradation of the basement membrane of ECs by matrix metalloproteinases (MMPs). This allows cell migration, removal of matrix proteins that creates a space in the matrix for generation of endothelial tubules (Stetler-Stevenson, 1999, van Hinsbergh et al., 2006). MMP activity is hindered by the family of tissue inhibitors of metalloproteinases (TIMPs) (Gomez et al., 1997).

Chapter 1. Introduction

Figure 1.4: Angiogenesis process. This process is highly pleiotropic and involves many cellular activities and molecules. Initially, it involves the sprouting of endothelium from established vessels. This process is highly dependent on the VEGF (Ivarsson et al.). Further requirements include digestion of the extracellular matrix by matrix metalloproteinases (MMPs) and activation of the urokinase plasminogen activator receptor (uPAR) system. Signals are conducted to ECs in order to form a tube. In a later phase, mesenchymal cells are recruited, and mesenchymal phenotypes are induced by angiotensin-1 (Ang-1) or transforming growth factor beta (TGF-β). These cells are now developing into pericytes. Figure from (Griffioen and Molema, 2000).

Chapter 1. Introduction

2. Endothelial cell migration and proliferation:

Plasminogen activators convert the ubiquitous plasma protein plasminogen to plasmin. Plasmin specifically degrades fibronectin, laminin and the protein core of proteoglycans. Plasmin is an important protease that is required for mobilisation of the fibroblast growth factor-2 (FGF-2) from ECM. FGF members act as pro-angiogenic molecules. FGF-2 consists of two forms, an 18 kDa low molecular weight and a 22 to 24 kDa high molecular weight form. During angiogenesis, low molecular weight FGF-2 binds to endothelium leading to downregulation of FGF receptor (FGFR) and increased motility, proliferation and proteinase activity. However, high molecular weight FGF-2 acts on endothelial cell proliferation after nuclear translocation in the ECs (Gleizes et al., 1995, Klein et al., 1997). Additionally, VEGF stimulates endothelial cell proliferation besides its effect on angiogenesis initiation (Griffioen and Molema, 2000). During angiogenesis, a number of integrins are expressed on the surface of activated EC mediating adhesive interactions with ECM proteins including fibronectin, fibrinogen, laminin, collagen, and vWF (Eliceiri and Cheresh, 1999).

3. Maturation of vessels:

EC interaction with ECM and mesenchymal cells is important to form a stable vasculature. This is accomplished by the synthesis and secretion of platelet-derived growth factor (PDGF), a mitogen and chemoattractant for mesenchymal cells. Angiopoietins and receptor tyrosine kinase Tie1 and Tie2 play critical roles in the later stages of angiogenesis. They are required for communication of ECs with the surrounding mesenchyme to establish stable cellular and biochemical interactions (Maisonpierre et al., 1997). Tie1 is required for ECs differentiation and the establishment of blood vessel integrity whereas Tie2 is important for vascular network formation (Dumont et al., 1994, Puri et al., 1995, Sato et al., 1995). Angiopoietin-1 (Ang-1) and angiopoietin-2 (Ang-2) are Tie2-specific ligands that activate or antagonise Tie2 signalling in the endothelium, respectively. Ang-1 was identified as a pro-angiogenic protein and the major physiological ligand for Tie2. Ang-2 was found to disrupt blood vessel formation in the developing embryo by antagonising the effects of Ang1 and Tie2. In postnatal neovascularization, Ang-1 may induce maturation and

Chapter 1. Introduction stabilisation of the vascular network whereas Ang-2 may cause destabilisation required for additional sprout formation (Asahara et al., 1998, Maisonpierre et al., 1997).

1.3 Endothelial dysfunction in diabetes Endothelial dysfunction refers to the inability of the endothelium to regulate vascular homeostasis and physiological balance in favour of vasoconstrictive, pro-inflammatory and pro-thrombotic effects that promote atherosclerosis (Xu and Zou, 2009). The term is used to describe reduced nitric oxide (NO) bioavailability through decreased endothelial nitric oxide synthase (eNOS) expression (Tabit et al., 2010). Under normal, basal conditions in blood vessels, NO has an anti-atherogenic effect on the vasculature by regulating blood pressure, inhibiting leukocyte activation, vascular smooth muscle proliferation and platelet aggregation (Figure 1.5) (Moncada and Higgs, 1993).

Figure 1.5: Endothelium-derived nitric oxide synthesis and its action. Under normal, basal conditions in blood vessels NO is produced from the amino acid L-arginine by the enzymatic action of endothelial nitric oxide synthase (eNOS). The activity of eNOS is calcium- and calmodulin- dependent. Adapted from Moncada and Higgs (Moncada and Higgs, 1993).

Chapter 1. Introduction

There is a strong link between endothelial dysfunction to type 1 DM and type 2 DM through impaired endothelial-dependent vasodilation (McVeigh et al., 1992, Johnstone et al., 1993). Even though many mechanisms have been proposed for this relationship, the definitive pathogenesis remains unclear because diabetic patients usually display multiple homeostatic imbalances beside hyperglycaemia (Roberts and Porter, 2013). Insulin resistance usually precedes the development of type 2 diabetes and is accompanied by many risk factors. Insulin activates intracellular signalling pathways important for maintenance of healthy endothelium, the most vasoprotective of which is the phosphoinositide-3 kinase (PI3K)/Akt pathway that enhances eNOS expression and activation (Kuboki et al., 2000, Montagnani et al., 2001). Current evidence of this mechanism is derived from studies showing that insulin-stimulated NO production is reduced by PI3K or Akt inhibitors (Montagnani et al., 2001, Zeng et al., 2000). Animal models of insulin resistance including the obese Zucker rats displayed defective PI3K/Akt signalling that resulted in decreased NO bioavailability (Jiang et al., 1999). Dysfunction of angiogenic competence in DM is correlated to destabilisation of hypoxia inducible factor-1 (HIF-1), which is most likely responsible for the loss of cellular responses that drive the post-ischaemic revascularisation process (Bento and Pereira, 2011). Downregulation of HIF-1α in response to hyperglycaemia is correlated with decreased collateral growth generated by myocardial ischaemia in patients with DM (Catrina et al., 2004). As a consequence of diminished HIF-1α pathway activity in DM, most of the HIF- 1α target genes are reduced. Among these, VEGF and VEGFR-2 are downregulated in ventricles from patients with DM (Chou et al., 2002). Additionally, diabetes is associated with a systemic inflammatory response that may impair endothelial function and contribute to atherosclerosis (Beckman et al., 2002). The hyperglycaemia associated with diabetes contributes to vascular injury through the formation of advance glycation end products (AGE) (Schmidt et al., 1999). The interaction of AGE with surface receptors such as RAGE (receptor for AGE) promotes the production of pro-inflammatory cytokines in vascular endothelial cells (Schmidt et al., 1999). Patients with diabetes have increased circulating levels of inflammatory markers including tumour necrosis factor-α (TNF-α), interleukin-6 (IL-6), and chemokine (C-C motif) ligand 2

Chapter 1. Introduction

(CCL2) (Daniele et al., 2014). In addition, increased levels of inflammatory markers predict cardiovascular risk in diabetic patients (Lowe et al., 2014).

1.4 Molecular mechanisms of ischaemic cardiovascular disease Endothelial cells are among the most dynamic and biologically active cellular components of blood vessels and play a crucial role in the pathophysiology of vascular diseases. A better understanding of the molecular mechanisms involved in diabetes associated with impaired vascular repair in the setting of myocardial infarction (MI) may assist to develop novel biomarkers and therapeutic targets. Hypoxia is one of the critical factors in the pathology of MI.

1.4.1 Hypoxia Hypoxia is a common pathological event that has an extreme impact on ECs during CVD. In ECs, the responses include increased expression of growth factors and their receptors to induce angiogenesis. Conversely, hypoxia regulated genes that can lead to apoptosis in order to maintain homeostasis (Carmeliet et al., 1998). It was found that cell response to hypoxia is mainly regulated by HIF-1α which is rapidly synthesised and degraded under normal conditions but can be stabilised by hypoxia (Epstein et al., 2001, Ivan et al., 2001). Under hypoxic conditions, HIF-1 activates many genes required for cell growth and cell death (Carmeliet et al., 1998). In ECs, hypoxia stimulates the secretion of VEGF and other angiogenic factors through transcriptional regulation by HIF-1, which leads to neovascularisation and protection against ischaemic injury (Brunelle and Chandel, 2002, Iyer et al., 1998, Liu et al., 1995, Mandriota et al., 2000).

1.4.2 Structure and function of HIF-1 HIF-1 is a heterodimeric transcription factor consisting of a β-subunit and an oxygen regulated α-subunit. The HIF-1α protein contains a basic helix-loop-helix (bHLH) motif that bind DNA and causes subunit dimerization (Chapman-Smith et al., 2004, Wang et al., 1995, Yang et al., 2005). The α-subunit contains a PER-ARNT-SIM (PAS) domain with similar functions. Additionally, there is an oxygen-dependent degradation (ODD) domain, which is hydroxylated by proline-hydroxylase-2 (PHD-2); exposing the α-subunit to proteasomal degradation under normoxic conditions. The HIF-1α subunit also contains two 14

Chapter 1. Introduction transactivation domains (TAD), which regulate HIF-1 target genes. The structure of HIF-1α is illustrated in Figure 1.6. CREB binding protein (CBP) and p300, two transcriptional coactivators of HIF-1, interact with the carboxy-terminal transactivation domain (C-TAD) of HIF-1α. Both activators are essential to HIF-1 transcription and are targets to regulate HIF- 1 expression (Lando et al., 2002b).

Figure 1.6: The structure of human HIF-1α gene. The gene contains a basic helix-loop-helix (bHLH) motif and PER-ARNT-SIM (PAS) domain, both of which aid in the formation of heterodimer between the HIF-1α and HIF-1β subunits and the binding to hypoxia response element DNA sequence. HIF-1α contains an ODD that mediates oxygen-regulated stability through the hydroxylation of two proline residues and the acetylation of a lysine. Two transactivation domains (C-TAD and N-TAD) serve as regulatory and transactivation regions. Figure adapted from (Ke and Costa, 2006).

Chapter 1. Introduction

The Regulation of HIF-1α

In normoxia, HIF-1α protein is rapidly degraded resulting in undetectable HIF-1α protein (Wang et al., 1995). Alternatively, in hypoxia HIF-1α becomes stabilised and translocates from the cytoplasm to the nucleus, where it dimerises with HIF-1 β to form the HIF complex that become transcriptionally active (Huang et al., 1996, Kallio et al., 1997). Subsequently, the activated HIF complex associated with hypoxia response elements (HREs) in the regulatory regions of target genes and binds the transcriptional coactivators to induce gene expression (Figure 1.7) (Lando et al., 2002b). The main regulation of the stability and subsequent transactivational function of HIF-1α is by its post-translational modifications, such as hydroxylation, ubiquitination, acetylation and phosphorylation (Brahimi-Horn et al., 2005).

In normoxia, hydroxylation of two proline residues and acetylation of lysine residue in its ODD promote interaction of HIF-1α with the von Hippel-Lindau (VHL) ubiquitin E3 ligase complex (Masson et al., 2001, Srinivas et al., 1999). The VHL complex labels HIF-1α with ubiquitin and, therefore, becomes vulnerable to degradation by the 26S proteasome. Moreover, hydroxylation of an asparagine residue in the C-TAD inhibits the association of HIF-1α with CBP/ p300 and, therefore, inhibits its transcriptional activity (Lando et al., 2002a).

Prolyl hydroxylation by PHD: De novo synthesised cytoplasmic HIF-1α is rapidly hydroxylated by 2-oxoglutarate (2-OG)-dependent dioxygenase, on Pro402 and Pro564 of the prolyl hydroxylase domain (PHD) that is located within the ODD. PHD is a 2-OG- dependent dioxygenase and requires oxygen for hydroxylation as well as Fe2+ and ascorbate as cofactors (Schofield and Zhang, 1999). The requirement of Fe2+ for PHD was demonstrated by applying iron chelators and metal irons such as Co2+, Ni2+ and Mn2+ can stabilise HIF-1α, which diminish the availability of Fe2+ to the enzyme or substituting Fe2+ from Fe2+ binding site. Ascorbate helps to maintain Fe in the ferrous form (Fe2+) and is important for achieving full activity of PHD (Ke and Costa, 2006).

Chapter 1. Introduction

Figure 1.7: Oxygen-dependent regulation of HIF-1 stabilisation and transactivation. In normoxia, two proline residues of HIF-1α (P402 and P564) and asparagine (N803) are hydroxylated by prolyl hydroxylase domain (PHD) and factor inhibiting HIF-1 (FIH-1), 2+ respectively, in O2, 2-oxoglutarate and Fe dependent manner. Hydroxylated HIF-1 α proteins bind to the E3 ubiquitin ligase von Hippel-Lindau (VHL) complex that leads to its degradation by the proteasome. Acetylation of lysine (K532) by arrest-defective 1 (ARD 1) favours the interaction HIF-1α with VHL. Hydroxylated N803 blocks the recruitment of transcriptional coactivator CREP binding protein (CBP) and p300. In hypoxia, the activities of PHD and FIH-1α are inhibited due to lack of oxygen, resulting in inhibition of proline and asparagine hydroxylation. Therefore, there is no VHL binding and HIF-1α is stabilised. Stabilised HIF-1α protein translocate to the nucleus and bind to HIF-1 β. Non- hydroxylated N803 of HIF-1α allows CBP/ p300 recruitment to the target genes resulting in gene transcription. In addition, the expression of ADR1 is decreased under hypoxia, leading to less acetylated HIF-1α. Figure is adapted from (Ke and Costa, 2006).

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Lysine acetylation by arrest-defective-1 (ARD1): Lysine residue 532 (Lys532) located in ODD domain of HIF-1 α is acetylated by an acetyltransferase ARD1. Acetylation of Lys532 favours the interaction of HIF-1α with VHL and thus destabilises HIF-1α (Jeong et al., 2002). Because the activity of acetyltransferases is not influenced by oxygen, ARD1 may be active and acetylate HIF-1α regardless of oxygen conditions. But the mRNA and protein levels of ARD1 were shown to be decreased under hypoxia, which may result in less acetylated HIF-1α in hypoxia than in normoxia (Jeong et al., 2002). Asparagine hydroxylation by factor inhibiting HIF-1 (FIH-1): Under normal oxygen tension, hydroxylation of the asparagine residue 803 (Asn803) in the C-TAD of HIF-1α by FIH-1 prevented the interaction of HIF-1α with CBP/p300 (Hewitson et al., 2002, Lando et al., 2002b, Sang et al., 2002). Hypoxia was shown to abolish the asparagine hydroxylation, which allowed C-TAD of HIF-1α to interact with CBP/p300 leading to transcriptional activation of target genes (Lando et al., 2002a). The transcription of FIH-1 is independent of the oxygen concentration, and it does not influence HIF-1α stability (Metzen et al., 2003). The asparaginyl hydroxylase FIH-1 is a 2-OG-dependent dioxygenase that requires Fe2+ and ascorbate as cofactors and is functionally similar to PHD (Lando et al., 2002a).

HIF-1 regulates oxygen homeostasis:

HIF-1 is a major regulator of oxygen homeostasis within cells. As a transcription factor, it affects and regulates the expression of many genes involved in maintaining homeostasis as oxygen concentrations change (Yoon et al., 2006). HIF-1 activates the expression of these genes by binding to a 50 base pair HRE located in their enhancer and promoter regions (Semenza et al., 1991). Moreover, by using DNA microarrays, it has recently been reported that more than 2% of all human genes are regulated by HIF-1 in arterial ECs, directly or indirectly (Manalo et al., 2005). Angiogenesis: One important function of HIF-1 is to promote angiogenesis; HIF-1 directs the migration of mature ECs toward a hypoxic environment (Carmeliet et al., 1998, Genbacev et al., 1997). This is mediated via HIF-1 regulation of VEGF (Ivarsson et al.) transcription. VEGF is a major regulator of angiogenesis, which promotes endothelial cell migration toward a hypoxic area. During hypoxia, HIF-1 binds the regulatory region of the 18

Chapter 1. Introduction

VEGF gene, inducing its transcription and initiating its expression (Dery et al., 2005, Hewitson and Schofield, 2004, Vaupel, 2004). Thus, ECs eventually aid in the formation new blood vessels to supply the given area with oxygenated blood. Glucose metabolism: HIF-1 also can regulate anaerobic metabolism. When oxygen is available, most cells produce ATP via oxidative phosphorylation. However, in hypoxic environments, there is a shift to anaerobic metabolism to produce cellular energy. HIF-1 is among the principal genes to coordinate this shift, by inducing a variety of glycolytic enzymes and glucose transporters such as aldolase A and pyruvate kinase M, which helps cells efficiently produce energy in hypoxic environments (Carmeliet et al., 1998, Vaupel, 2004). In addition to increasing the expression of these enzymes, HIF-1 decreases mitochondrial oxygen consumption by activating pyruvate dehydrogenase kinase I and arresting the citric acid cycle (Papandreou et al., 2006). Cell proliferation and survival: Hypoxia and HIF-1 induce growth factors, such as insulin-like growth factor-2 (IGF2) and transforming growth factor-α (TGF-α) (Feldser et al., 1999, Krishnamachary et al., 2003). Binding of such growth factors to their receptors activates signal transduction pathways that lead to cell proliferation/survival and stimulate the expression of HIF-1α itself (Semenza, 2003). Cytokines and growth factors, as well as hypoxia in some cell types, can activate signalling pathways mitogen-activated protein kinases (MAPK) and phosphatidylinositol 3-kinases (PI3K), which promote cell proliferation/survival as well as contribute to HIF-1 activity. This leads to increased HIF-1 transcriptional activity of target genes, including those encoding IGF2 and TGF-α (Semenza, 2003).

1.5 Role of microRNAs in vascular biology 1.5.1 MicroRNA biogenesis and mechanism of action MicroRNAs (miRNAs) are small RNAs that play an important role in the negative regulation of gene expression by suppressing protein translation. miRNAs constitute a family of short non-coding RNA molecules of 20 to 25 nucleotides in length that regulate gene expression at the posttranslational level (Ambros, 2004, Bartel, 2004). Moreover, miRNAs have been shown to participate in almost all cellular processes (Bushati and Cohen, 2007). Therefore, their dysregulation inspires different human pathologies including 19

Chapter 1. Introduction

CVD (Chang and Mendell, 2007). In 1993, Ambros and co-workers discovered the first miRNA, lin-4 while studying the developmental timing in Caenorhabditis elegans (C. elegans) (Lee et al., 1993). The human genome is now expected to encode about 1500 miRNAs that are thought to regulate more than 30% of protein-coding genes (Lewis et al., 2005).

The biogenesis of miRNA starts in the nucleus (Figure 1.8) where RNA polymerase II transcribes long primary precursors up to thousands of nucleotides (pri-miRNAs) (Ambros and Lee, 2004). The transcription occurs at the level of genomic regions located within the introns or exons of protein-coding genes or intergenic areas (de Yebenes and Ramiro, 2010). Long, capped and polyadenylated pri-miRNAs are then processed by RNase III endonuclease Drosha and a product of the DiGeorge syndrome critical region 8 (DGCR8), which excises the hairpin structure to generate precursor miRNAs (pre-miRNAs) with 70 nucleotides in length (Ambros, 2001). These precursors are exported to the cytoplasm by Exportin 5 and then the cytoplasmic enzyme Dicer cleaves the pre-miRNA to release the mature miRNA: miRNA* duplex (Ambros, 2003). One strand of the miRNA is then incorporated into a large protein complex named RNA-induced silencing complex (RISC) that binds to the target mRNA (Gregory et al., 2005, Maniataki and Mourelatos, 2005). If the base pairing between miRNA and the 3` untranslated region (3` UTR) of the target mRNA is perfect, the messenger is degraded (in plants), on the other hand if the complementarity is partial the translational repression occurs in animals (Pillai et al., 2007).

Chapter 1. Introduction

Figure 1.8: miRNA biogenesis and mechanism of action. (1) In the nucleus, miRNAs are transcribed by RNA polymerase II to generate primary transcripts (pri-miRNAs). (2) These transcripts are processed by the Drosha/DGCR8 complex resulting in the formation of precursor miRNA (pre-miRNA). (3) Exportin 5 transports the pre-miRNAs into the cytoplasm, where (4) Dicer processes them into nucleotide duplexes that contain the mature miRNA product. (5) One strand of the miRNA duplex incorporates with the RNA-induced silencing complex (RISC), which directs the miRNA to target mRNAs. (6) Partial sequence complementarity between the miRNA and the target mRNA leads to (a) translational repression, whereas complete sequence complementarity lead to (b) translational degradation. Figure from (Oakley and Van Zant, 2007).

Chapter 1. Introduction

1.5.2 Identification and quantification of miRNAs Several techniques have been developed for miRNA quantification. However, most of these involve laborious procedures making the determination of the level of all known miRNAs difficult. At present, the most commonly used technique is based on microarrays (Babak et al., 2004, Liu et al., 2004). Although microarray technology is a widely accepted method, there are some practical issues such as the small size of miRNAs that proposes a challenge for conventional microarray techniques; and some of the employed techniques identify pre- miRNAs rather than mature miRNAs (Barad et al., 2004). Microarrays that detect mature miRNA using oligonucleotides that specifically bind to the mature miRNA sequence have recently been developed. However, the issue of cross hybridisation of related miRNAs remains an issue (Monticelli et al., 2005, Nelson et al., 2004). Indeed, real-time polymerase chain reaction (RT-PCR) is the most sensitive method, but it is time-consuming to be used when the number of miRNAs exceeds 300. Another technical problem also derives from the short length of miRNA. Innovations in mature miRNA detection methods are currently being developed; however there is much room for improvement (Chen et al., 2005, Lu et al., 2005). 1.5.3 Role of miRNAs in angiogenesis The importance of miRNAs in angiogenesis (angiomiRs) and endothelial function was revealed by interrupting the function of Dicer and Drosha. Impaired angiogenesis was detected by knockdown of Dicer or Drosha in vitro in human ECs assayed by endothelial tube formation in Matrigel (Kuehbacher et al., 2007). miRNA profiling of HUVECs revealed that miR-221/222, miR-21, the let-7 family, the miR-17~92 cluster, the miRNA- 23~24 cluster, and miR-126 are highly expressed in ECs (Fasanaro et al., 2008, Harris et al., 2008, Kuehbacher et al., 2007, Poliseno et al., 2006, Suarez et al., 2007). miR-126 is the only miRNA that is specifically expressed in the endothelial lineage and hematopoietic progenitor cells (Fish et al., 2008, Landgraf et al., 2007, Wang et al., 2008). Pro-angiomiRs promote angiogenesis by targeting negative regulators of angiogenic signalling pathways while anti-angiomiRs inhibit angiogenesis by targeting positive regulators of angiogenesis. Specific miRNAs in EC biology are summarised in Table 1.2.

Chapter 1. Introduction

miRs Angiogenic function Relevant References targets miR-126 Required for vascular integrity and Spred-1, (Fish et al., 2008, Wang angiogenesis in vivo PIK3R2 et al., 2008) miR-130a Antagonises the anti-angiogenic GAX, (Chen and Gorski, 2008) activity of GAX and HOXA5 HOXA5 miR-210 Enhances angiogenesis and survival Ephrin-A3 (Fasanaro et al., 2008, response to Hypoxia in vitro Pulkkinen et al., 2008) miR-27b Required for angiogenesis in vitro ? (Kuehbacher et al., 2007) and let7f miR-320 Inhibition of miR-320 improves the IGF-1 (Wang et al., 2009) angiogenesis in diabetic ECs Table 1.2: miRNAs involved in angiogenesis. Key: GAX: growth arrest-Specific homeobox; HOXA5: homeobox A5; IGF-1: Insulin-Like Growth Factor 1; PIK3R2: phosphoinositide-3-kinase, regulatory subunit 2; Spred-1: sprouty-related, EVH1 domain containing 1.

miR-126 is encoded by an intron of the epidermal growth factor-like-domain, multiple 7 (Egfl7) gene, which encodes an EC-derived secreted peptide that acts as an inhibitor of smooth muscle cell migration (Campagnolo et al., 2005, Soncin et al., 2003). The pro- angiogenic action of miR-126 is mediated by promoting MAP kinase and PI3K signalling in response to VEGF and FGF, through targeting negative regulators of these signalling pathways, including the Sprouty-related EVH domain-containing protein (Spred-1) and PI3K regulatory subunit 2 (PIK3R2/p85-β) (Figure 1.9) (Fish et al., 2008, Kuhnert et al., 2008, Wang et al., 2008). Interestingly, miR-126 has been shown to be downregulated in EPCs derived from diabetic patients (Meng et al., 2012). Similarly, reduced plasma levels of miR-126 have been observed in diabetic patients (Zampetaki et al., 2010).

Chapter 1. Introduction

Figure 1.9: The pro-angiogenic mechanism of miR-126. miR-126 and Egfl7 are processed into EGFL7 and pre-miR-126. EGFL7 induces EC migration, whereas, miR-126 enhances VEGF and FGF signalling by suppression of inhibitors of these pathways, PIK3R2 and SPRED1.White and grey boxes illustrate non-coding and protein-encoding regions of Egfl7 respectively. Figure from (Teague et al., 2010).

1.6 Human stem cells Stem cells are undifferentiated cells that are present in the embryonic, foetal and adult stages of life. The major characteristics of stem cells are: a) self-renewal: which is the ability to extensively proliferate, b) clonality: form group of cells that are derived from a single cell, c) potency: the ability to differentiate into different cell types. These properties vary between different stem cells (Kolios and Moodley, 2013). These processes are tightly regulated by multiple factors involving cell-cell and cell-extracellular matrix interactions, as well as the action of specific growth factors and cytokines. Stem cells have been classified by their developmental potential as totipotent (able to give rise to all embryonic and extra-embryonic cell types), pluripotent (able to give rise to all cell types of the embryo proper), multipotent (able to give rise to a subset of cell lineages), oligopotent (able to give rise to a more restricted subset of cell lineages than multipotent stem cells), and unipotent

Chapter 1. Introduction

(able to contribute only one mature cell type) (Smith, 2006). Most stem cells are quiescent, residing in the G0 phase of the cell cycle (Lajtha, 1979). The reason for this extensive dormancy may relate to the requirements for the maintenance of the genetic purity of the essentially immortal stem cells pool (Bonnet, 2002, Graham and Wright, 1997). 1.6.1 Stem cell classification based on origin Embryonic stem cells (ESC) are pluripotent stem cells and give rise during development to all derivatives of the three primary germ layers: ectoderm, endoderm and mesoderm (Figure 1.10) (Yao et al., 2006).

Adult stem cells, also called somatic stem cells, are undifferentiated cells of the postnatal organism which can self-renew and committed differentiation to specialised cell types of the tissue (Figure 1.10) (Blau et al., 2001). However, this classic paradigm of stem-cell differentiation is restricted to its organ-specific lineage and is being challenged by observations that adult stem cells, including haematopoietic stem cells, retain a degree of developmental plasticity that allows them to differentiate into different cell types (Korbling and Estrov, 2003). 1.6.2 The stem cell niche The stem cell niche is a microenvironment that provides signals to stem cells in form of secreted and cell surface molecules to control the rate of stem cell self-renewal, survival and maintenance as well as to determine the fate of stem cell daughters (Jones and Wagers, 2008, Li and Xie, 2005, Scadden, 2006). Additionally, the adhesion between stem cells, stromal cells and ECM reside the stem cells within the niche to self-renewal and survival signals (Song and Xie, 2002, Song et al., 2002).

It has been hypothesised that the presence of low oxygen tensions in stem cell niches is mandatory for their biological roles (Cipolleschi et al., 1993). In principle, cells that undergo aerobic metabolism tolerate to some degree oxidative stress caused by generation of reactive oxygen species that may harm DNA integrity. This is supported by a study in mouse embryonic fibroblasts showing that at 20% O2 there was an accumulation of mutations followed by the onset of senescence then immortalisation, which was accompanied by a further three-fold increase in mutations. However, at 3% O2 the cells did not senesce and the mutation frequency was lower than at the higher oxygen tension

Chapter 1. Introduction

(Busuttil et al., 2003). This suggests that self-renewal and stem cell quiescence may be regulated by gradients of oxygen tension supplied in their niche. Therefore, the dormancy of stem cells favours the hypothesis that these cells are not stimulating HIF or AMPK signallings under hypoxia.

Chapter 1. Introduction

Figure 1.10: Embryonic and adult stem cell differentiation. The blastocyst has two layers of cells, the inner cells mass that will form the embryo, and the outer cell mass called trophoblasts that leads to the formation of the placenta. Embryonic stem cells (ESC) were derived from the inner cell mass of the blastocyst. ESC differentiate into three germ layers: ectoderm, endoderm and mesoderm from which all tissues and organs develop. Figure adapted from (Korbling and Estrov, 2003). GI: gastrointestinal.

Chapter 1. Introduction

1.7 CD34+ stem cells CD34+ stem cells are well-characterised population of stem cells that have been used to reconstitute the haematopoietic system following radiotherapy or chemotherapy. The CD34 cell surface antigen was first identified by using monoclonal antibodies that were specific for haematopoietic progenitor cells (Civin et al., 1984, Tindle et al., 1985). Recently, CD34+ cells have shown to induce therapeutic angiogenesis in animal models and patients with myocardial ischaemia (Mackie and Losordo, 2011, Cogle et al., 2014).

1.7.1 CD34+ cells as a therapeutic agent CD34+ cells play an important role in the treatment of CVD because both endothelial progenitor cells (EPCs) and differentiated endothelial cells express the CD34 antigen (Andrews et al., 1989, Asahara et al., 1997, Baumhueter et al., 1994, Berenson et al., 1988, Ema et al., 1990, Fina et al., 1990). Bone marrow–derived angioblasts, or CD34+ cells, increased neovascularization and improved cardiac function in MI rats (Kawamoto et al., 2001, Kocher et al., 2001). Importantly, injection of isolated CD34+ cells or cultivated EPCs enhance neovascularization (Kalka et al., 2000b) and accelerate the restoration of blood flow in diabetic mice (Schatteman et al., 2000). Preclinical studies indicate that the benefit of human CD34+ cell transplantation after ischaemic injury occurs through an increase in neovascularisation (Kawamoto et al., 2006), though the mechanisms of new vessel formation have not been well identified as yet.

Clinical studies have proved that locally transplanted autologous CD34+ stem cells reduce angina and improve exercise capacity in patients with refractory angina (Losordo et al., 2011) and lower amputation rates in patients with critical limb ischaemia (Losordo et al., 2012). Complete wound closure was observed at an average of 18 weeks with increased vascular perfusion in all patients. A recent clinical study by Tanka et al., (2014) showed that the administration of CD34+ cell therapy in patients with diabetic non-healing wounds increased vascular perfusion and led to complete wound closure (Tanaka et al., 2014).

The major obstacle to the therapeutic use of CD34+ cells was their relatively low density in the circulation (0.03-0.09%) (Sutherland et al., 1994). Therefore, the discovery of agents that help to mobilise these cells from the bone marrow niche into the systemic circulation significantly enhance the CD34+ cell therapy. Treatment of patients with granulocyte- 28

Chapter 1. Introduction colony stimulating factor (G-CSF) increases the number of CD34+ cells in the peripheral blood (To et al., 1997). Another agent, granulocyte macrophage-CSF, has been examined for mobilisation of cells in patients after MI (Zohlnhofer et al., 2006). In addition, other agents such as the CXCR4 chemokine receptor antagonist plerixafor (AMD3100) (Liles et al., 2003), and VEGF (Asahara et al., 1999, Kalka et al., 2000a, Kalka et al., 2000c) have been shown effectively to promote the peripheral mobilisation of CD34+ cells. Furthermore, pathophysiological ischaemic conditions acutely mobilise CD34+ cells from the bone marrow. Both myocardial ischaemia (Massa et al., 2005, Shintani et al., 2001) and peripheral ischaemia (Takahashi et al., 1999) are known to induce endogenous CD34+ cell mobilisation. Moreover, mobilisation of these cells to target regions of ischaemia where they are thought to promote angiogenesis either through the formation of new blood vessels or secretion of angiogenic cytokines that stimulate local endothelial vascular development (Majka et al., 2001).

1.7.2 Paracrine function of human CD34+ stem cells CD34+ stem cells secrete a variety of growth factors, cytokines, and chemokines such as VEGF, IL6, and IL8 that interact with the surrounding microenvironment, and contribute to therapeutic angiogenesis (Janowska-Wieczorek et al., 2001, Mackie and Losordo, 2011, Sahoo et al., 2011). These factors are secreted particularly from activated stem cells that have been aspirated from the bone marrow or mobilised into the circulation. The activated cells play a crucial role to overcome the damage in injured organs by stimulating the cell proliferation and promoting the vascularisation of affected tissues to improve oxygen delivery and metabolic exchange.

In addition activated CD34+ stem cells, secrete membrane-bound nanovesicles known as exosomes. Exosomes are extracellular vesicles that originate intracellularly in multivesicular bodies (MVB) and are secreted out of the cell when MVBs combine with plasma membrane (Figure 1.11) (Chaput and Thery, 2011). They contain proteins, RNAs, and/or miRNA that are important for their extracellular functions. In addition, exosomes are involved in intercellular communication allowing exchange of proteins such as cytokines and lipids between exosome producing cells and target cells (Chaput and Thery, 2011). Exosomes isolated from conditioned media of CD34+ cells had characteristic size (40 to 90

Chapter 1. Introduction nm in diameter), cup-shaped morphology, expressed exosome-marker proteins CD63 and CD34+ cell lineage marker protein, CD34 (Thery et al., 2006). A previous study demonstrated that CD34+ exosomes induce angiogenic activity in isolated endothelial cells and murine models of vessel growth (Sahoo et al., 2011).

Figure 1.11: Microvesicles formation and release of exosomes. Microvesicles are budded directly from the plasma membrane, whereas exosomes are represented by small vesicles of different sizes and are released by fusion of microvesicles with the plasma membrane, from (Raposo and Stoorvogel, 2013).

Chapter 1. Introduction

1.8 Metformin 1.8.1 History of metformin In medieval times, Galega officinalis was used to control the intense urination accompanying DM. The active ingredient in the G. officinalis for the management of diabetic patients that ultimately induced the lowering of blood glucose was proved to be galegine or isoamylene guanidine (Bailey and Day, 1989). Guanidine and certain derivatives have been proved to be too toxic for the treatment of DM, whereas three biguanides became available for diabetes therapy in the 1950s (Figure 1.12). Phenformin and buformin, are potent biguanides that were withdrawn from the pharmacopoeia in the early 1970s due to increased evidence of frequent lactic acidosis and increased cardiac mortality (Bailey and Day, 1989). Metformin, a less lipophilic biguanide, proved to be safer. It was introduced to the United Kingdom in 1958 and the United States in 1995.

Figure 1.12: The chemical structure guanidine isoamylene guanidine, and the biguanides metformin, phenformin and buformin. The biguanides have a shared basis, derived from two

Chapter 1. Introduction linked guanidines (in blue). The pharmacological differences between the biguanides are determined by differences in their non-polar hydrocarbon side chains (in red). According to these non-polar side chains, the biguanides bind to membrane phospholipids and other biological structures.

1.8.2 Mechanism of action of metformin as anti-diabetic drug Metformin is currently the drug of first choice for the treatment of type 2 DM. Metformin is also frequently designated as an insulin-sensitiser, leading to a reduction in insulin resistance and a significant decrease in plasma fasting insulin levels. The improvement in insulin sensitivity by metformin could be attributed to its positive effects on insulin receptor expression and tyrosine kinase activity (Gunton et al., 2003). The primary function of metformin is lowering plasma glucose levels mainly by suppressing hepatic gluconeogenesis, enhancing peripheral glucose utilisation, altering lipid metabolism and enhancing insulin sensitivity (El Messaoudi et al., 2013). The action of metformin in hepatocytes is initiated by the expression of organic cation transporter 1 (OCT1), which facilitates cellular uptake of metformin (Shu et al., 2007). Deletion of the Oct1 [Slc22a1 (solute carrier family 22 member 1)] gene in mice intensely decreased metformin uptake in hepatocytes showing an impaired effect of metformin in reducing blood glucose levels (Shu et al., 2007). An underlying mechanism of the molecular action of metformin in hepatocytes involves the activation of AMP-activated protein kinase (AMPK) (Zhou et al., 2001). AMPK is a conserved serine/threonine protein kinase that plays a crucial role to monitor the systemic and cellular energy status and protects the cellular functions under energy-restricted conditions. AMPK is a heterotrimeric protein consisting of a catalytic α-subunit and two regulatory subunits, β and γ. Moreover, AMPK is activated by an increase in the intracellular AMP/ATP ratio, and activation of AMPK involves AMP binding to regulatory sites on the γ -subunits. This leads to conformational changes that allosterically activate the enzyme and inhibit dephosphorylation of Thr172 within the catalytic α-subunit (Oakhill et al., 2011, Xiao et al., 2011). Activated AMPK switches cells from an anabolic to a catabolic state and this involves phosphorylation of key metabolic enzymes and transcription factors/co-activators modulating gene expression by AMPK (Viollet et al.,

Chapter 1. Introduction

2009). Consequently, glucose, lipid and protein synthesis, as well as cell growth, are inhibited, whereas fatty acid oxidation and glucose uptake are stimulated. There is evidence that AMPK activation by metformin is secondary in hepatocytes to its effect on the mitochondria (Hardie, 2006). The primary effect of metformin (supra- physiological concentration, 10 mM) is inhibition of the mitochondrial respiratory chain complex I (Figure 1.13) (El-Mir et al., 2000, Owen et al., 2000), although the exact mechanism remains unknown, the inhibition is mediated by an AMPK-independent manner (Stephenne et al., 2011). Interestingly, it has been shown that metformin exerts an inhibitory effect on mitochondrial ROS production by impeding the reverse electron flow through the respiratory chain complex I (Batandier et al., 2006).

Chapter 1. Introduction

Figure 1.13: Molecular mechanisms of action of metformin in hepatocytes. Metformin uptake through OCT1 leads to inhibition of respiratory chain complex І in the mitochondria in AMPK- independent manner. This results in a mild decrease in the energy status leading to acute and transient inhibition of the gluconeogenic pathway. Additionally, through AMPK-dependent and - independent regulatory manners, metformin can lead to inhibition of glucose production by suppressing gluconeogenesis gene expression. Independently, the liver kinase B1 (LKB1)- dependent activation of AMPK prompted by ATP depletion reduce hepatic lipogenesis and exert an indirect effect on insulin signalling to control hepatic glucose output. Ac: acetylated. Figure from (Viollet et al., 2012).

The antidiabetic effect of metformin involved AMPK was supported by a study showing that the glucose lowering effect of the drug was decreased in mice lacking hepatic LKB1 (Shaw et al., 2005). LKB1/AMPK signalling has been reported to regulate the phosphorylation and nuclear exclusion of transducer of regulated CREB (cAMP-response- element-binding protein) binding protein 2 (TORC2) (Koo et al., 2005, Shaw et al., 2005). A possible mechanism for the inhibitory action of metformin on TORC2-mediated

Chapter 1. Introduction gluconeogenesis has been proposed by involving an increase of hepatic sirtuin 1 (SIRT1) activity, a NAD+-dependent protein deacetylase (Figure 1.13) (Caton et al., 2010, Liu et al., 2008). Kim et al., revealed that metformin blocks hepatic gluconeogenesis through an AMPK mediated upregulation of the transcriptional repressor orphan nuclear receptor small heterodimer partner (SHP) (Kim et al., 2008). However, Takashima et al., detected that metformin suppressed Kruppel-like factor 15 (KLF15) gene expression [45] leading to inhibition of genes coding for gluconeogenic and amino acid catabolic enzymes (Takashima et al., 2010). In addition, metformin inhibited the gluconeogenesis by allosteric regulation of key enzymes such as fructose-1,6-bisphosphatase when ATP/AMP ratio was decreased (Miller and Birnbaum, 2010). Metformin reduced the hepatic lipid content due to an increase in both fatty acid oxidation and inhibition of lipogenesis through AMPK activation (Figure 1.13) (Zhou et al., 2001). Therefore, metformin through AMPK activation induces the phosphorylation and inactivation of acetyl-CoA carboxylase (ACC), a rate-limiting enzyme for the synthesis of malonyl-CoA which is a precursor for the biosynthesis of fatty acids and inhibitor of mitochondrial fatty acid oxidation (Zhou et al., 2001). In addition, AMPK suppresses the expression of lipogenic genes, for instance, fatty acid synthase (FAS), and ACC by direct phosphorylation of the transcription factors, carbohydrate-response-element-binding protein (ChREBP), and sterol-regulatory-element-binding protein (SREBP)-1c (Zhou et al., 2001, Kawaguchi et al., 2002).

1.8.3 Action of metformin on cardiovascular disease Metformin reduced diabetes-related death in the UKPDS 34 longitudinal trial by 42% (1998a). The mechanisms of such a beneficial effect are not clearly understood. Experimental study suggests that metformin improved cardiac function and reduced the infarct size after MI in Sprague–Dawley rats (Yin et al., 2011). The action of metformin in ECs was studied by Davis et al., through exposure of ECs to 0.05- 0.5 mM concentrations of metformin with high glucose (30 mM) to detect an increase in AMPK-dependent eNOS activation that might improve the vascular function (Davis et al., 2006). A physiological level of metformin (0.01 mM) was found to decrease intracellular ROS production in aortic ECs stimulated with high levels of glucose (30 mM) 35

Chapter 1. Introduction through the inhibition of both NADPH oxidase and the respiratory chain complex I (Figure 1. 14) (Ouslimani et al., 2005).

Figure 1.14: Metformin reduces ROS production through inhibition of the mitochondrial − complex І, as well as NADPH oxidase, which generates O2 (Adapted from (Bost et al., 2012)).

Recently, excessive plasma levels of ICAM-1 and vascular cell adhesion molecule-1 (VCAM-1) were shown to be linked with an increase in cardiovascular events. Interestingly, metformin decreases plasma levels of ICAM-1 and VCAM-1 in Type 2 DM patients (De Jager et al., 2005). From our understanding the mechanism by which the physiological concentration of metformin is improving the endothelial function under hypoxia and combine hyperglycaemia was not thoroughly explored.

1.9 Aims of the thesis We hypothesised that:

Metformin ameliorates the negative effect of hypoxia and hyperglycaemia combined by manipulating pro-angiogenic signallings leading to improved vascular function.

The aim of this thesis was to establish an in vitro model of diabetes and ischaemia that could be used to study the complex molecular mechanisms involved in diabetes-associated 36

Chapter 1. Introduction impaired vascular repair. In addition, we could use this model to study the molecular mechanisms underlying the beneficial effect of metformin on CVD outcomes. Therefore three main research questions were suggested:

a) What are the effects of hyperglycaemia and hypoxia on mature endothelial cell function and molecular pathways?

b) What are the effects of hyperglycaemia and hypoxia on human CD34+ cell function and molecular pathways?

c) What are the effects of metformin on the functions and molecular pathways of mature endothelial cells and human CD34+ cells that were subjected to hypoxia and hyperglycaemia?

Chapter 2. Materials and Methods

Chapter 2. Material and Methods

2.1 Cell culture techniques 2.1.1 Tissue supply The study was approved by Biomedical Ethics Unit, Faculty of Medicine, King Abdulaziz University (approval number: 440-10) and NRES Committee North East- Sunderland, UK (approval number: 12/NE/0044). Following informed consent, umbilical cords were collected from normal deliveries or Caesarean sections, placed in a sterile container containing 50 ml conservation buffer [Appendix II, 7.2.1] and stored at 4°C for up to four hours before being processed in the laboratory for isolation of HUVEC. Umbilical cord blood was collected in 250 ml blood collection bags containing 35 ml citrate- phosphate-dextrose-adenine (Macopharma, Tourcoing, France) for the isolation of CD34+ cells.

2.1.2 Isolation of HUVEC from umbilical cord HUVEC were harvested from umbilical cords by collagenase digestion according to methods described by Jaffe, et al. (Jaffe et al., 1973). The steps of cord manipulation were illustrated in Figure 2.1.

The cord was washed with sterile Dulbecco’s Phosphate Buffered Saline (DPBS) without Ca2+ and Mg2+, and then the ends were cut with sterile surgical scissors. The cannula was introduced at one extremity of the vein (the widest vessel) and tightly maintained with an umbilical cord clamp. Subsequently, the cords were washed with DPBS four times using a 25 mL syringe. The other end was tightly clamped with an umbilical cord clamp, and then 15-20 ml collagenase (0.2%, Roche, Basel, Switzerland) was injected through the cannula. The cord was maintained on a clean aluminium foil plate, covered with aluminium foil and then incubated for 20-30 minutes in DPBS at 37°C.

Chapter 2. Materials and Methods

Figure 2.1: Steps in manipulation and handling of the umbilical cord for the isolation of HUVEC. Umbilical cord was collected in a sterile container with conservation buffer (1). The cord was then placed in a sterile surgical plate (2) and both ends tidily cut with a pair of scissors (3). A butterfly needle was inserted at one end of the umbilical vein and held with an umbilical cord clamp (4 & 5). The cord was then washed twice with 50 ml PBS (6). Subsequently, the other end was clamped, and 15 ml collagenase was injected into the vein (7). The cord was covered with aluminium foil and then incubated for 25 minutes at 37°C (8). After incubation, the vein was washed with 100 ml DPBS and the cells collected in a 50 ml Falcon tubes. The collected cells were centrifuged at 500g for 5 minutes (9). 2x6 well plates/ cord were coated with fibronectin (10). The cell pellet from step 9 was resuspended in 18 ml complete medium and transferred to the fibronectin-coated plates (11 & 12). Cells were incubated at 37°C in a humidified atmosphere of 95% air and 5% CO2.

Chapter 2. Materials and Methods

The vein was then flushed with 100 ml of DPBS, which was collected in two 50 ml Falcon tubes. The tubes were centrifuged at 500 g for 5 minutes. The cell pellet was resuspended in 18 ml of HUVEC culture medium [Appendix II, 7.2.1] which had been pre-warmed to 37°C and transferred into two fibronectin (Corning Limited Life Sciences, Tewksbury, MA) coated 6 well plates (Greiner bio-one, Stonehouse, UK) per cord. Cells were incubated at ° 37 C in a humidified atmosphere of 95% air and 5% CO2 (Galaxy B, Scientific Laboratory Supplies). After 24 hours, the non-adherent cells were removed by aspiration and the culture medium replaced. The culture medium was changed every two days, and confluency was typically achieved in 6-8 days with a “cobblestone appearance” by optical microscopy (~1x106 cells/ well).

When the cells reached confluency, they were sub-cultured using TrypLE Select (Gibco, Paisley, UK) by splitting the cells 1:4 into fibronectin-coated T-75 tissue culture treated flasks with filter cap (Greiner bio-one, Stonehouse, UK).

2.1.3 Incubation of HUVEC with various concentrations of glucose and hypoxia HUVEC cells from passage 2 were cultured in a medium with a physiological glucose concentration of 5.5 mM for use as a control (euglycaemic condition) or with 16.5 mM glucose (Sigma-Aldrich, Dorset, UK) for hyperglycaemic condition. Various glucose concentrations have been used in the published literature, but in our study we aimed to use the concentrations frequently seen in diabetic patients (Altannavch et al., 2004b, Itoh et al., 2003, Omi et al., 2002, Okayama et al., 2002). In cultures using 22 mM glucose, an impaired cellular growth was shown (Altannavch et al., 2004b). While in 16.5 mM glucose no effect on cellular growth was detected, but inflammatory changes were found to be high after 24 hours of treatment (Altannavch et al., 2004b). Parallel cultures of HUVEC in euglycaemic and hyperglycaemic conditions were created from passage 2 when confluency was 50-60%.

After 24 hours incubation with glucose, HUVEC were cultured under normoxia (21% O2) or, chemically induced hypoxia using a final concentration of 150 µM CoCl2 (Sigma- Aldrich, Dorset, UK) (Sultana et al., 1999) to simulate myocardial infarction for 1, 3, and 40

Chapter 2. Materials and Methods

12 hours. A schematic representation of the experimental design was illustrated in Figure 2.2.

Figure 2.2: Experimental design. Human umbilical vein endothelial cells (HUVEC) from passage 2 were cultured either in medium containing euglycemic glucose concentration of 5.5 mM or a hyperglycemic glucose concentration of 16.5 mM. After 24 hours, metformin (0.01 mM) was added to euglycemic and hyperglycemic cultures; then hypoxia was induced by using 150 µM CoCl2 for either 1, 3 or 12 hours. Then RNA was extracted for gene expression analysis performed with microarrays and qRT-PCR. Moreover, protein was extracted to study the variations in target proteins among experimental conditions using Western blot. Apoptosis, cell migration, cell cycle, and cell proliferation functional assays were performed in order to prove the role of studied genes and pathways.

2.1.4 Metformin treatment The physiological levels of metformin (1, 1-Dimethylbiguanidehydrochloride) were applied according to the peak plasma concentration approved by the Food and Drug Administration (FDA) (http://www.fda.gov/ohrms/dockets/dailys/02/May02/053102/800471e6.pdf). After 24 hours’ incubation in euglycaemic or hyperglycaemic conditions, HUVEC were treated

Chapter 2. Materials and Methods with 0.01 mM metformin (Sigma-Aldrich, Dorset, UK) for 12 hours. Hypoxia was then induced by incubation with CoCl2 for either 1, 3 or 12 hours (Figure 2.2).

2.1.5 Isolation of mononuclear cells from umbilical cord blood Light density MNC were separated from UCB using Lymphoprep (1.077 g/ ml, Axis Shield, Oslo, Norway) density gradient centrifugation as follows: the UCB was transferred from the collection bags to sterile 50 ml Falcon tubes (10 ml/ tube). The UCB samples were diluted 1:2 with sterile 1x DPBS, without Ca2+ and Mg2+, and then 30 ml of this cell suspension were carefully layered over 15 ml Lymphoprep in a fresh 50 ml tube. Subsequently, this was centrifuged at 800 g for 20 minutes at room temperature without brake. The MNCs were harvested from the interface layer, transferred to a new centrifuge tube and washed with DPBS to remove excess Lymphoprep solution. The cells were pelleted by centrifugation at 300 g for 10 minutes, and the supernatant removed. The cell pellets were pooled, washed again with DPBS and centrifuged at 200 g for 10-15 minutes. This step is crucial to remove the platelets and increase the purity for the target cells in the subsequent MACS cell separation. The supernatant was discarded, and the cells were resuspended in 50 ml MACS buffer [Appendix II, 7.2.1]. Cell counts were performed by mixing 10 µl of cell suspension with 90 µl DPBS at a ratio of 1:10. Subsequently, 10 µl of the cell suspension were added to a haemocytometer and the cells in four squares counted.

2.1.6 Isolation of CD34+ stem cells CD34+ cells were isolated from fresh MNCs by positive selection using the CD34 MicroBead kit according to the manufacturer’s instructions (Miltenyi Biotec, Bergisch Gladbach, Germany). Magnetic labelling: MNCs isolated by density gradient separation [2.1.5] were pelleted at 300 g for 10 minutes at room temperature and the supernatant removed. The cells were resuspended in MACS buffer to a final volume of 300 µl per 108 cells. Then the cells were labelled with 100 µl FcR blocking reagent and 100 µl CD34 microbeads per 108 cells. This was followed by mixing of the cell suspension and incubation for 30 minutes at 2-8°C in dark. The cells

Chapter 2. Materials and Methods were then washed with 20 ml MACS buffer and centrifuged at 300 g for 10 minutes. Finally, the cells were resuspended in MACS buffer by scaling 500 µl per 108 cells, before being applied to the LS column using MidiMACS separator. Magnetic separation: An LS column was placed in the magnetic field of the MidiMACS Separator and rinsed with 3 ml MACS buffer. The cell suspension was then applied to the column and allowed to flow through. The column was washed three times with 3 ml MACS buffer. The unlabelled cells (CD34- cells) passed the column and were collected. The column was then removed from the separator and placed in a 15 ml tube. The bound cells (CD34+ cells) were eluted from the column by flushing with 5 ml MACS buffer with pressure using the plunger. To increase the yield, the CD34- cell suspension was applied to a new LS column and allowed to pass through. The column was washed three times with 3 ml MACS buffer. Subsequently, the column was removed and placed onto the tube with the previously eluted CD34+ cells, and flushed with 5 ml MACS buffer using the plunger. To increase the purity of CD34+ cells, a third column was placed in the magnetic field of the MACS Separator. The CD34+ cell suspension was applied to a new column and was washed three times with 3 ml MACS buffer. The column was removed and placed onto a new tube for collection of CD34+ cells by flushing with 5 ml MACS buffer, as above. A viable cell count was performed by pipetting 10 µl of the CD34+ cell suspension onto a haemocytometer. Additionally, the purity of CD34+ cells was verified by flow cytometry (FACSCanto II, BD, Bioscience, San Jose, CA). Surface staining of cell suspension: The purified CD34+ cells were labelled with anti CD34-PE and anti CD45-FITC antibodies (5 µl/ 105 cells), for 30 minutes at 4oC protected from light. Labelled cells were then washed with wash machine (BD, Bioscience, San Jose, CA) and analysed using an FACSCanto II flow cytometer. The purity of CD34+ cells was above 95%.

2.1.7 CD34+ cell culture The isolated CD34+ cells [2.1.6] were centrifuged at 500 g for 10 minutes, and the supernatant discarded. The cell pellet was resuspended in X-Vivo 10 medium (Lonza,

Chapter 2. Materials and Methods

Basel, Switzerland) containing 100 ng/ ml Flt3-L, 100 ng/ ml SCF and 0.25% human serum albumin or M199 complete medium for CD34+ cells [Appendix II, 7.2.1]. The cells were seeded at a density of 1x106 per well of a 6-well plate (Greiner bio-one, Stonehouse, UK), and treated with 0.01 mM metformin and incubated for 24 hours. Untreated cells were grown as control cultures. Hyperglycaemia studies CD34+ cells were incubated in a medium with physiological glucose concentration of 5.5 mM for use as a control or 16.5 mM and 25.0 mM glucose to simulate hyperglycaemia, for 24 hours. Hypoxia studies + After 24 hours, CD34 cultures were incubated in 21% O2 (normoxia) or 4% O2 (hypoxia) in a hypoxia incubator (HeracellTM 150i, ThermoScientific) for 3 hours (Figure 2.3). Hyperglycaemia combined with hypoxia studies CD34+ cells were incubated in a media containing 16.5 mM glucose for 24 hours and then transferred to the 4% hypoxia incubator for 3 hours.

2.1.8 Viable cell count Determination of viable cell numbers was performed by trypan-blue exclusion. The cell suspension was diluted 1:1 with 0.4% solution of trypan-blue (Lonza Basel, Switzerland) dissolved in with 1% (v/v) glacial acetic acid. An aliquot of the stained cell suspension was placed on a haemocytometer and viable cells, which exclude trypan-blue, were scored at a 100 x magnification under transmission light microscope.

Chapter 2. Materials and Methods

Figure 2.3: Schematic representation of CD34+ cells isolation, tissue culture and molecular techniques. CD34+ cells were isolated from umbilical cord blood and grown for 24 hours under euglycaemia or hyperglycaemia in the presence or absence of metformin. Then after 24 hours, 4% hypoxia was induced for 3 hours. Total RNA was extracted from the cells for subsequent molecular techniques. Exosomes were purified from the conditioned media. The purity of exosomes were characterised and assessed using transmission electron microscopy (TEM).

2.1.9 Freezing cells Cells were grown to a confluency of 90%, washed with warm DPBS and trypsinised. Subsequently, cells were washed using their respective complete cell culture medium and pelleted down at 500 g for 5 minutes. The pellets were resuspended in ice-cold freezing medium [Appendix II, 7.2.1], transferred to Cryotubes (NUNC, Thermo Scientific, Waltham, MA) and frozen at -80°C for 24 hours before being transferred to a liquid nitrogen tank for long-term storage.

2.1.10 Thawing cells Frozen Cryotubes were thawed in a water bath at 37°C and immediately transferred to a 15 ml tube. Two ml culture medium was added to the cell suspension and mixed gently. This 45

Chapter 2. Materials and Methods was followed by addition of 8 ml of culture medium and centrifugation at 500 g for 5 minutes. The cell pellet obtained was resuspended in 10 ml culture medium and transferred to T75 flasks. Cells were then incubated at 37°C in a humidified atmosphere of 95% air and

5% CO2.

2.2 Purification of exosomes by differential ultracentrifugation Exosomes were purified from CD34+ cell culture conditioned media from different conditions as described previously (Thery et al., 2006). Briefly, the cells and conditioned media were separated by centrifugation at 300 g for 10 minutes. The supernatants were further clarified from dead cells and cell debris by successive centrifugations at 2,000 g for 20 minutes at 4°C and then 10,000 g for 30 minutes at 4oC (Figure 2.4). The supernatants were ultracentrifuged at 100,000 g (Beckman Coulter, High Wycombe, UK) for 70 minutes at 4°C to pellet the exosomes. The pellets were washed with DPBS to remove any contaminating proteins. The purified exosomes were resuspended in 50 µl DPBS. Exosomes can be stored in -80°C for up to one year.

Chapter 2. Materials and Methods

Figure 2.4: Flow chart for exosome purification by differential ultracentrifugation. Several steps of centrifugation and ultracentrifugation were involved to purify the exosomes. After the first three centrifugation steps, the cell pellets were discarded. The next two ultracentrifugation steps were to collect the exosomes.

Chapter 2. Materials and Methods

2.3 Electron microscopy of CD34+ stem cells and their exosomes Cells were fixed in 2% glutaraldehyde in sodium cacodylate buffer overnight at 4°C; post- fixed in 1% osmium tetroxide (Agar Scientific, UK), and progressively dehydrated in a graded acetone 25-100%. The cells were impregnated with epoxy resin (Taab, UK) in acetone graded from 25% to 75% for one hour each and then embedded in 100% resin at 60°C for 24 hours. Survey sections of 1µm were cut and stained with 1% Toluidine blue in 1% Borax. Ultrathin sections approximately 80 nm were cut from the resin block using a diamond knife (Diatome, Switzerland) on an RMC MT-XL ultramicrotome. The sections were picked up on a 3 mm copper grids (Gilder Grids, UK), and stained with 2% uranyl acetate and lead citrate (Leica, UK). Exosomes were fixed in 2% glutaraldehyde in sodium cacodylate buffer overnight at 4°C. Approximately 10 µl of the exosome suspension was picked up on a copper grid that had been carbon-coated and glow-discharged to aid adsorption. After a few seconds, excess solution was removed by blotting with filter paper and the grid was washed with two drops (20 µl) of water. A drop (about 10 µl) of 2% uranyl acetate was then put on the grid for a few seconds before the excess was removed as above with filter paper, and the grid was allowed to dry. The grids were examined using Philips CM 100 Compustage (FEI) transmission electron microscope, and digital images were collected using AMT CCD camera (Deben, Suffolk, UK). This technique was gratefully performed by Dr. White and Mrs. Davey, EM Research Services, Newcastle University.

2.4 Functional assays 2.4.1 In vitro scratch assay HUVEC was subcultured after passage 2 onto a fibronectin-coated 24 well plate (1x105 cells/ well). Cells were incubated with normal glucose (5.5 mM) or high glucose (16.5 mM) in the presence or absence of physiological metformin (0.01mM) for 24 hours. Once confluent monolayer was formed, scratch lines were created using a 1000 µl pipette tip and then the wells were gently rinsed with DPBS to remove the detached cells. The medium was replaced with medium containing different concentrations of glucose and metformin

(Figure 2.5) and then the cells were exposed to chemical hypoxia (150 µM CoCl2) for 24

Chapter 2. Materials and Methods hours. As a negative control, HUVEC were treated with marimastat, an MMP inhibitor (Sigma-Aldrich, Dorset, UK) to a final concentration of 10 µM in DMSO (Scott et al., 1998) or 0.1 µM sunitinib (Huang et al., 2010). For a positive control, cells were incubated with EBM-2 medium containing VEGF growth factor. Subsequently, the cells were incubated in a 5 % CO2 chamber (OkoLab) for 24 hours that was connected to the camera (Hamamatsu Orca ER). Cell migration was examined by phase-contrast microscopy (Nikon Tie) and was focus maintained using software based autofocus (NIS Elements V4.13). Images were acquired every hour, and three independent biological experiments were performed at which each condition was assessed in duplicate. The scratch area in each image was measured using NIS Elements software. Cell migration is presented as a percentage of closure calculated using the following equation:

where Area (0) is the area of scratch pre-migration, Area (t) is an area of the scratch after migration at time t.

Chapter 2. Materials and Methods

Figure 2.5: Plate layout for in vitro scratch assay. HUVEC were incubated with high glucose concentration (16.5 mM) and the physiological metformin concentration (0.01mM) for 24 hours and then after scratch the cells exposed to chemical hypoxia for 24 hours. In parallel cells were treated with10 µM marimastat or 1 µM sunitinib as negative control and EBM-2 containing VEGFA as a positive control.

2.4.2 Cell proliferation assay The Apoptosis, DNA Damage, and Cell Proliferation Kit (BD, Bioscience, San Jose, CA) was designed with the inclusion of fluorescent antibodies specific for incorporated BrdU, phosphorylated H2AX (γH2AX) and cleaved PARP. These probes along with optimised protocols enable multi-colour flow cytometric analysis of proliferation, DNA damage and apoptosis, respectively, by individual cells within samples. HUVEC were incubated with high glucose concentration (16.5 mM) for 24 hours, and then with the physiological metformin concentration (0.01mM) for 12 hours. Subsequently, the cells were exposed to chemical hypoxia (150 µM CoCl2) for 3 or 12 hours. Cells were in vitro labelled with BrdU for 24 hours. After labelling, cells were optionally stained with cell surface markers. Samples were then fixed, permeabilized, and treated with DNase according to the manufacturer’s instructions as the DNase treatment aids to expose the

Chapter 2. Materials and Methods

BrdU epitopes. Following this treatment, cells were simultaneously stained with fluorochrome-labelled anti-BrdU, cleaved PARP, and H2AX. DAPI staining was performed at this step to determine DNA content. Cells were resuspended in staining buffer and analysed by acquiring 25,000 events using FACSCanto II flow cytometer.

2.4.3 Apoptosis assay HUVEC were treated in passage 2 when reaching 60% confluency with normal glucose (5.5 mM) or high glucose (16.5 mM) in the presence or absence of physiological metformin (0.01 mM) for 48 hours and parallel cultures were exposed to chemical hypoxia (150 µM

CoCl2) for 3, 12, and 24 hours. The effect of supra-physiological concentration of metformin 1.0 mM on apoptosis was performed under normal and high glucose concentrations. Metformin and culture media were replaced every 12 hours. Positive control for apoptosis assay was created by treating the cells with 14 µM sunitinib. The cells were detached by trypsin and then washed twice with 1x DPBS. Annexin V APC staining assay (BD, Bioscience, San Jose, CA) was applied to measure the apoptosis. The cells (1x105) were resuspended in 100 µl 1x Binding Buffer in 5 ml fluorescence-activated cell sorting (FACS) tube (BD, Bioscience, San Jose, CA). Subsequently, 2 µl of APC annexin V and 5 µl of 7-AAD were added to the cell suspension. The cells were mixed gently using the vortex and incubated for 15 minutes at 25°C protected from light. Then 300 µl of 1x Binding Buffer were added to each tube. Labelled cells were analysed by acquiring 10,000 events using FACSAria III flow cytometer.

2.4.4 In vitro Matrigel tube formation assay HUVEC (2.0 x104 cells) were serum starved overnight in M199 complete medium with 0.25% FBS. The cells were seeded with 70 µl EBM-2 medium containing 14 µM sunitinib (VEGFA inhibitor, Sigma-Aldrich, Dorset, UK) (Huang et al., 2010) as a negative control, or with 70 µl CD34+ cell-derived conditioned medium from 2.0 x105 CD34+ cells into µ- plate angiogenesis 96-well plates (Ibidi, Martinsried, Germany) that had been coated with 10 µl of growth factor reduced Matrigel® matrix (BD, Bioscience, San Jose, CA, Figure ° 2.6). Subsequently, the cells were incubated at 37 C and 5 % CO2 (OkoLab, NA, Italy) for

Chapter 2. Materials and Methods

24 hours in a chamber that was connected to the camera (Hamamatsu Orca ER). Tube formation was examined by phase-contrast microscopy (Nikon Eclipse Tie, Tokyo, Japan) and the focus was maintained using software based autofocus (NIS Elements V4.0, Nikon, Tokyo, Japan). Images were acquired every hour. All conditions in each experiment were assessed in duplicate, and tube length was measured as the mean summed length of capillary-like structures in 2 wells. Three independent experiments were performed for each condition.

Figure 2.6: Flow chart for the collection of CD34+cells conditioned media for angiogenesis assay.

The tube length was measured using Adobe Acrobat Professional version 8 software by measuring long tubes first and then the small branches to cover the whole image (Figure 2.7). The analysis was conducted blinded.

Chapter 2. Materials and Methods

Figure 2.7: Analysis of tube length in in vitro tube formation assay using Adobe Acrobat Professional version 8 software. The tube length was measured as the mean summed length of capillary-like structures (Red lines) in 2 wells by measuring long tubes first and then the small branches to cover the whole image. The yellow flags on the image show the lengths of the tubes marked by the red lines.

2.5 Molecular techniques 2.5.1 Total RNA extraction Total RNA from HUVEC and CD34+ cells was extracted using the RNeasy Mini kit (QIAGEN, Hilden, Germany) according to the manufacturer’s instructions. The culture media were aspirated, and then the cells washed with DPBS. HUVEC were detached by using RTL lysis buffer containing β-mercaptoethanol (Sigma-Aldrich, Dorset, UK, 6 µl to 600 µl RTL buffer). The volume of RTL buffer depends on the surface area of the vessel in attached cells or number of cells in suspended cells. For HUVEC grown in T75 flasks, 1200 µl of RTL buffer was added to lyse the cells, following which two RNeasy Mini columns were used to isolate the RNA. One volume of 70% ethanol (Sigma-Aldrich, Dorset, UK) was added to the cell lysate and mixed well by vortexing for 1 min. RNeasy Min spin column was placed in a 2 ml collection tube, and 700 µl of the sample was transferred directly to the column. The lid of the column was closed to be centrifuged in a microfuge (IEC MicroCL 17 centrifuge, Thermo Scientific, Waltham, MA) at 8,000 g for 15 sec. The flow-through was discarded and the remaining lysate was transferred to the column and centrifuged at the same speed for 15 sec. 350 µl of RW1 buffer was added to the column after discarding the flow and centrifuged at 8,000 g for 15sec. On column 53

Chapter 2. Materials and Methods

DNase digestion with RNase-Free DNase set (QIAGEN, Hilden, Germany) was utilised to remove genomic DNA. Before use, 10 µl of DNase I was dissolved in 70 µl RDD buffer. Subsequently, DNase I mix was directly placed into the column and incubated at room temperature for 15 min, and then another volume of 350 µl of RW1 buffer was added. The column was centrifuged at 8,000 g for 15 sec, and then the flow-through was discarded. Approximately 500 µl of RPE buffer was added to the column after adding four volumes of absolute ethanol to the buffer. The column was centrifuged at 8,000 g for 15 sec, replaced in a new collection tube and then additional 500 µl of RPE buffer was added. In this step longer centrifugation (for 2 minutes) was applied to the column at 8,000 g to ensure that no ethanol was carried over during RNA elution. The RNA was eluted by placing the columns in a clean Eppendorf tube, adding 30 µl of RNase-free water directly to the column, followed by centrifugation at 8,000 g for 1 min. For CD34+ cells, 350 µl of RTL buffer was added to the cell pellets. The cell lysates from three different cord blood samples per condition were pooled before proceeding to an RNeasy Mini spin column. The quality and quantity of RNA were assessed using NanoDrop 2000c Spectrophotometer (Thermo Scientific, Waltham, MA). The integrity of RNA samples was also measured via Agilent RNA 6000 Nano Kit and then assessed by using Agilent 2100 Bioanalyzer (Santa Clara, CA). 2.5.2 Total RNA and protein extraction Total RNA and protein were extracted using the mirVana PARIS (Ambion, Grand Island, NY) according to the manufacturer’s protocol. The culture media were aspirated, and then the cells washed with DPBS. HUVEC were detached by adding ice-cold Disruption buffer (625 µl for T25 flask). The cell lysates were mixed vigorously by vortex in order to obtain homogenous lysate. Subsequently the cell lysate was divided into two tubes, one for RNA isolation and the other for protein analysis. The portion of cell lysate for protein analysis was incubated on ice for 10 minutes before being used. The protein samples were stored in -80oC. The portion of cell lysates for RNA isolation was mixed with an equal volume of 2x Denaturing Solution previously containing 375 µl of β-mercaptoethanol at 25°C. The mixture was incubated on ice for 5 minutes. Acid-Phenol: chloroform equal to the total

Chapter 2. Materials and Methods volume of the mixture was added and mixed by vortex for 30-60 seconds. The aqueous and organic phases were separated by centrifugation at 8000 g for 5 minutes. The aqueous phase was carefully removed without disturbing the lower phase or the interphase and being transferred to a clean tube. 1.25 volumes of absolute ethanol were added to the aqueous phase and mixed thoroughly. For each sample, a filter cartridge was placed into one of the collection tubes supplied with the kit. The lysate/ ethanol mixture was transferred to the filter cartridge up to 700 µl at a time. The filter cartridges were centrifuged at 10,000 g for 30 seconds, and then the flow-through was discarded from the collection tube. Afterwards, 700 µl of miRNA wash solution 1 previously containing 21 ml absolute ethanol was applied to the filter cartridge. The filter cartridges were centrifuged at 10,000 g for 15 seconds, and then the flow-through was discarded from the collection tube. Moreover, 500 µl of wash solution 2/3 (working solution mixed with 40 ml absolute ethanol) was applied to the filter cartridge and drawn through as in the previous step. A second 500 µl of wash solution 2/3 was applied to the filter cartridge. After the flow- through was discarded from the last wash, the filter cartridge was replaced in the same collection tube and centrifuged at 10,000 g for 1 minute to remove the residual fluid from the filter. Finally, the filter cartridge was transferred into a clean collection tube and then 100 µl of preheated 95°C elution solution was applied to the centre of the filter. The assembly was centrifuged at 10,000 g for 30 seconds to recover the RNA. The eluate (RNA) was collected and stored at -80°C. RNA concentrations were verified on the NanoDrop Spectrophotometer, and the quality of total RNA was assessed using Agilent 2100 Bioanalyzer Nano Chips (Santa Clara, CA). Protein concentration measured by using Pierce Microplate BCA protein assay (Thermo Scientific, Waltham, MA) according to the manufacturer’s protocol. The concentration was scanned using enzyme-linked immunosorbent assay (ELISA) plate reader (Multiskan Ascent, Thermo Labsystems, Waltham, MA).

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2.5.3 SYBR Green real-time polymerase chain reaction Real-time PCR oligonucleotide primer design Primers were designed using the NCBI database http://www.ncbi.nlm.nih.gov/nucleotide, using the optimal parameters of between 40-60% G-C content, and melting temperature between 55 and 60°C. The resulting amplicon sequence was tested for species and gene specificity using http://www. ncbi.nlm.nih.gov/BLAST. All individual oligonucleotides were supplied by Metabion (Steinkirchen, Germany) in solution form at a 100pmol/ µl concentration. The working stocks of oligonucleotide pairs were adjusted to 10pmol/ µl. Primers for reference genes: ribosomal protein, large, P0 (RPLP0), glyceraldehyde 3- phosphate dehydrogenase (GAPDH), glucuronidase, beta (GUSB), transferrin receptor (TFRC), and β-actin (ACTB) are stated in Table 2.1. The specifications of primers used in experiments as target genes for validation of the models: HIF1α, VEGFA, regulator of G- protein signalling 5 (RGS5), and VCAM-1 are summarised in Table 2.2.

  ° Gene Specific Oligonucleotide primer sequence 5 to 3 Primer Product Tm ( C) GC primers Length (bp) Size (bp) % RPLP0 Fw CCATTCTATCATCAACGGGTACAA 24 75 62 41.7 RPLP0 Rw TCAGCAAGTGGGAAGGTGTAATC 23 63 47.8 GAPDH Fw GCCATCAATGACCCCTTCAT 20 81 58 50.0 GAPDH Rw GCCATGGAATTTGCCAT 17 50 47.1 GUSB Fw CTACATCGATGACATCACCGTCAC 24 80 65 50.0 GUSB Rw TGCCCTTGACAGAGATCTGGTAA 23 63 47.8 TFRC Fw GTCGCTGGTCAGTTCGTGATT 21 80 61 52.4 TFRC Rw AGCAGTTGGCTGTTGTACCTCTC 23 65 52.2 ACTB Fw CCCTGGCACCCAGCAC 16 71 58 75.0 ACTB Rw GCCGATCCACACGGAGTAC 19 62 63.2 Table 2.1: Oligonucleotide primer sequence for housekeeping genes.

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Gene Specific ’ ’ Primer Product ° Oligonucleotide primer sequence 5 to 3 Tm ( C) GC% Primers Length (bp) Size (bp) HIF1α Fw TGTGACCATGAGGAAATGAG 20 56.97 45.00 157 HIF1α Rw CCTTCCATGTTGCAGACTTT 20 57.80 45.00 VEGFA Fw AGAAGGAGGAGGGCAGAAT 19 57.85 52.63 153 VEGFA Rw ATCAGGGGCACACAGGAT 18 58.83 55.56 RGS5 Fw AGGGCCAAGGAGATTAAGAT 20 56.98 45.00 162 RGS5 Rw GAGTTTGTCCAGGGAATCAC 20 56.96 50.00 VCAM1 Fw GACTTGCAGCACCACAGGCT 20 57.46 60.00 213 VCAM1 Rw GGAAAAGAGTAGATCTCCACCTGGA 25 55.27 48.00 Table 2.2: Oligonucleotide primer sequences for target genes.

Reverse transcription reaction First-strand cDNA kit (GE Healthcare, Little Chalfont, UK) was employed to synthesise cDNA following the manufacturer’s guidelines. A solution of 20 µl RNA in RNase-free water with total RNA concentration of 25 ng/ µl were heated to 65°C for 10 minutes. This was followed by addition of 11 µl of the bulk first-strand cDNA reaction mix, 1 µl of DTT solution, and 1 µl of random hexadeoxynucleotide primers to the heat-denatured RNA. The sample was incubated for 1 hour at 37°C in a thermal cycler (Eppendorf, Hamburg, Germany). Validation of primers used in SYBR Green qRT-PCR using end-point PCR End-point PCR was conducted using KAPA2G Robust HotStart ReadyMix (Kapa Biosystems, Wilmington, MA). The standard reaction setup was as follows: to each 1 µl sample of cDNA or water the following were added and mixed: 1 µl (10 pmol/l) forward primer, 1 µl (10 pmol/l) reverse primer, 12.5 µl 2x KAPA2G Robust HotStart ReadyMix, and 9.5 µl PCR grade water. The sample was then amplified using the following cycle profile: initial denaturation of 5 minutes at 95°C followed by 40 cycles consisting of 30 seconds at 95°C, 30 seconds at 60°C, and 15 seconds at 72°C, using an Eppendorf Mastercycler Pro (Eppendorf, Hamburg, Germany). A final incubation was conducted for 5 minutes at 72°C followed by incubation at 4°C.

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To resolve and detect the PCR products agarose gel electrophoresis was performed using ethidium bromide [Appendix II, 7.2.1] as the staining agent. All gels were 2% agarose (GE Healthcare, Little Chalfont, UK) and were prepared by heating 2 g of agarose in 100 ml 1xTBE [Appendix II, 7.2.1] in a microwave. Once dissolved, ethidium bromide (stock of 10 mg/ ml) was added to give a final concentration of 0.05 mg/ ml. The gel was allowed to set in a cast (BioRad Sub-Cell GT, Hercules, CA) with 600 ml 1x TBE running buffer. PCR product (5 µl) was mixed with 2 µl of the gel loading buffer [Appendix II, 7.2.1], loaded onto the gel and fractionated by electrophoresis at 15 volts/cm for 25 to 30 minutes ensuring the wells were positioned at the cathode. GelPilot 100bp Plus ladder (QIAGEN, Hilden, Germany) was used as the molecular weight ladder. The amplicons were visualised using UV transilluminator (Uvitec Cambridge, UK), and digital images were captured. Quantitative Real-time Polymerase Chain Reaction (qRT-PCR) The StepOne Plus Real-Time PCR system (Life Technologies, Paisley, UK) was used for quantification of gene transcripts. Samples were analysed in triplicate using 96 well plates (Life Technologies, Paisley, UK). Each 20 µl reaction mixture contained 1.0 µl of diluted cDNA (equivalent to 25 ng RNA) was prepared as previously described, 10 µl of 2X Fast SYBR® Green Master Mix (Life Technologies, Paisley, UK), and 10 pmol/L of each gene- specific primer. The amplifications were performed under the following conditions: 95°C for 20 seconds, followed by 40 cycles of 95°C for 3 seconds, 60°C for 30 seconds and 72°C for 15 seconds. Data were collected at the end of the extension step (72°C). At the end of 40 cycles, melting curve analysis was performed to ensure specificity of amplified product.

The Comparative threshold cycle (∆∆Ct) method (Livak and Schmittgen, 2001) was used to quantify the relative expression of the target genes, which were normalised to a reference gene, RPLP0 (Bakhashab et al., 2014). The StepOneTM Software v2.2.2 was used to analyse the data.

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2.5.4 TaqMan mRNA real-time PCR Reverse transcription reaction Approximately 100 ng of total RNA from each sample was converted to cDNA using the SuperScript® VILOTM cDNA synthesis kit (Life Technologies, Paisley, UK) in a final volume of 20 µl. The cDNA product was diluted 1:1 with Nuclease-free water (Ambion, Life Technologies, Paisley, UK). The reaction volume was scaled up to 100 µl when needed. A solution of 20 µl containing RNA in RNase-free water with total RNA concentration 50 ng, 4 µl 5X VILOTM Reaction Mix, and 2 µl 10X SuperScript® Enzyme mix was incubated at 25°C for 10 minutes. The mixture was then incubated at 42°C for 60 minutes followed by 85°C for 5 minutes. TaqMan real-time PCR The cDNA product was diluted 1:1 with Nuclease-free water (Ambion, Life Technologies, Paisley, UK). The genes of interest were quantified by hydrolysis probe real-time PCR, performed with TaqMan® Universal master mix II (Life Technologies, Paisley, UK) in a final volume of 20 µl. The reaction mixture was prepared by mixing 10 µl TaqMan® Universal master mix II, with Uracil-N-Glycosylase (UNG), 1 µl TaqMan® assay (Table 2.3 and Table 2.4), 20X (Life Technologies, Paisley, UK), 1 µl cDNA, and 8 µl RNase-free water. All samples were processed in triplicate and mean Ct values were calculated. Three independent reverse transcriptions were tested for each gene. The real-time PCR was assayed on a 7900 HT Fast Real-Time PCR System and StepOne Plus Real-Time PCR system (Life Technologies, Paisley, UK) under the following conditions: UNG incubation at 50°C for 2 minutes, Polymerase activation at 95°C for 10 minutes, and 50 cycles of 15 seconds at 95°C and 1 minute at 60°C in MicroAmp Optical 96-well reaction plates (Life

Technologies, Paisley, UK). The Comparative Ct method (∆∆Ct) was used to quantify the relative expression of the target genes, which were normalised to the reference gene, RPLP0. The SDS 2.3 software was used for analysis of the data.

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Assay ID Cat. No. Gene Hs00985639_m1 4331182 IL6 Hs00234140_m1 4331182 CCL2 Hs00174575_m1 4331182 CCL5 Hs00174092_m1 4331182 IL1A Hs01573680_m1 4331182 GPAM Hs00262010_m1 4331182 AGPAT9 Hs01124251_g1 4331182 CXCL10 Hs00927900_m1 4331182 SELP Hs00800172_s1 4331182 NDUFA4 Hs00171558_m1 4331182 TIMP1 Hs02596874_g1 4331182 MT-ND2 Hs00174103_m1 4331182 IL8 Hs00900070_m1 4331182 HGF Human RPLP0 EC 4326314E RPLP0 Table 2.3: TaqMan assays for CD34+ validated genes.

Assay ID Cat. No. Gene Hs01006900_g1 4331182 TXNIP Hs00234676_m1 4331182 MMP16 Hs00900055_m1 4331182 VEGFA Hs01005622_m1 4331182 FASN Human RPLP0 EC 4326314E RPLP0 Table 2.4: TaqMan assays for HUVEC validated genes.

2.5.5 MicroRNA (miRNA) real-time PCR of CD34+ exosomes and exosome-depleted media Reverse transcription reaction Volumes of 5 µl of CD34+ exosomes or exosome-depleted media were heated to 95°C for 10 minutes followed by 4°C hold. The simple heat treatment releases the RNA from exosomes and inactivates nucleases, thus exposing the template for downstream reverse transcription and PCR. TaqMan® MicroRNA Reverse Transcription Kit (Life Technologies, Paisley, UK) was used to prepare the reverse transcription reactions (Table 2.5). After that 10 µl of the reverse transcription master mix was transferred to each tube containing the preheated 5 µl samples and incubated at 16°C for 30 minutes. The mixture then was incubated at 42°C for 30 minutes followed by 85°C for 5 minutes.

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Reagent Volume (L) 10x RT Buffer 1.50 100mM dNTPs 0.15 5x TaqMan RT Primer 3.00 RNase Inhibitor 0.19 Reverse Transcriptase 1.00 Nuclease-free water 4.16 Total 10.00 Table 2.5: Reagent volumes for reverse transcription reactions.

TaqMan real-time PCR Quantification of exosome miRNA cDNAs was achieved by hydrolysis probe real-time PCR using the TaqMan® Fast Advanced Master Mix (Life Technologies, Paisley, UK, Table 2.6) and assayed on a 7900 HT Fast Real-time PCR system.

Reagent Volume (µl) 2X TaqMan Fast Advanced Master Mix 10 20X TaqMan Assay 1 Nuclease-free water 7 RT product 2 Total 20 Table 2.6: Reagent volumes per replicate for each qPCR.

Expression of miR-126 was determined using the TaqMan miR-126 assay ID: 002228 (Life Technologies, Paisley, UK). The real-time PCR was run under the following conditions: incubation at 50°C for 2 minutes, Polymerase activation at 95°C for 20 seconds, and 50 cycles of 1 second at 95°C and 20 seconds at 60°C in MicroAmp Optical 96-well reaction plates (Life Technologies, Paisley, UK). All samples were run in triplicate, and mean values were calculated. Three independent reverse transcription reactions were tested for each miRNA and sample. Cy0 method was used to quantify expression of miRNA using the following equation: -Cq R0 = RCq x 2

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Where R0 and RCq are initial fluorescence and fluorescence at Cq (quantification cycle) values, respectively (Guescini et al., 2008). The advantage of Cy0 method is to minimize the variation introduced due to slight inhibition. This inhibition may be caused by carry- over of reagents during the RNA extraction. Such inhibition leads to shifting of the amplification curve to the right; this generates higher Cq values than those found under optimal amplification conditions and thus underestimating the target amount. Two-tailed paired t-test was used to analyse the data in all conditions without metformin vs. 5.5 mM glucose (control); alternatively the conditions treated with metformin were compared with parallel untreated condition.

2.5.6 Microarray expression analysis Microarray technology has had a great impact on gene expression research over the last decade. However, microarrays are the most frequently used technology for genome-wide expression profiling and among various microarray platforms, the Affymetrix GeneChips technology is the most commonly one. In medical research, expression profiling by microarrays is used as a promising tool for better understanding of diseases and identification of new therapeutic targets.

Principle and limitations The principle of microarray experiment which is different from the classical northern blot assay in that mRNA from target cell is used to generate a labelled sample which is hybridised to complementary DNA (cDNA) sequences immobilized on solid surface on a grided array (Schena et al., 1995). The advantage of microarray is based on the fact that thousands of transcripts can be detected and quantified simultaneously. Recently, the DNA microarray technology has been developed rapidly by using powerful robots for arraying, new surface technology for glass slides and new labelling protocols and dyes.

Many different microarray systems have been developed, but the most common are two systems according to the arrayed material: cDNA and oligonucleotides microarrays (Figure 2.8). Oligonucleotide arrays with short 20-25 mers are synthesised in situ by photolithography on silicon wafers (high-density oligonucleotide arrays from Affymetrix, 62

Chapter 2. Materials and Methods http://www.affymetrix.com) (Wodicka et al., 1997). The disadvantage of short oligonucleotides that may result in less specific hybridisation and reduced sensitivity was resolved by optimisation of hybridisation conditions or by producing longer oligonucleotides 50-100 mers (Kane et al., 2000). However, in situ-synthesised oligonucleotides arrays are quite expensive and demand labour intensive preparation.

In all techniques, mRNA from cells or tissues is extracted, converted to cDNA, labelled and hybridised to the DNA elements on the array surface and detected by phospho-imaging or fluorescence scanning. In order to increase the sensitivity and reduce the amount of RNA required, an amplification strategy using in vitro transcription was introduced that yields up to 50 µg of labelled cRNA from 1 µg of mRNA (Luo et al., 1999).

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Figure 2.8: Schematic overview of the glass slide and Affymetrix microarray procedures. In glass slide experiments, two different samples are isolated, RNA is extracted, and cDNA is synthesised, and then is used for in vitro transcription (IVT) with Cy3 (green) or Cy5 (red) labeled nucleotides. The two labelled cRNA samples are mixed and hybridised to a glass slide array, which is scanned with a laser, followed by computer analysis of the intensity image. In Affymetrix arrays, one sample is used for total RNA extraction, and then cDNA synthesis. The cDNA is used in an IVT reaction to generate biotinylated cRNA. After fragmentation, this cRNA is hybridised to microarrays, washed and stained with PE-conjugated streptavidin, and subsequently scanned on a laser scanner (From (Staal et al., 2003)).

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The data of a microarray experiment constituted of a set of gene list generated by pairwise comparison of two samples or by comparing numerous samples to a common control. Replication has been shown to reduce the potential of false positive results and to establish a high degree of confidence in data (Mills et al., 2001). Moreover, verification of results by alternative techniques such as Northern blot or quantitative real-time reverse transcription (qRT)-PCR can assist to detect the variability of the experimental system (Bustin, 2000). The complexity of the data sets generated by microarray experiments makes the use of specialised data-analysis software essential. Several tools have been developed by commercial suppliers such as GeneSpring from Agilent Technologies http://www.agilent.com, Partek Genomics Suite from Partek Inc. http://www.partek.com/pgs and Ingenuity Pathway Analysis (IPA) from Qiagen http://www.ingenuity.com/products/ipa. IPA is a web-based software for the analysis, and interpretation of data derived from omics experiments, such as RNAseq, and microarrays including miRNA and SNP, metabolomics, proteomics. It is powerful algorithms to identify regulators, relationships, mechanisms, functions, and pathways relevant to changes observed in an analysed dataset (IPA datasheet 2014). RNA sample preparation and microarray hybridisation Microarray experiments were performed utilizing the Affymetrix (Santa Clara, CA) Human Gene 1.0 ST arrays according to manufacturer’s instructions with minor modifications (Merdad et al., 2014, Bakhashab et al., 2014). For each sample, 250 ng of total RNA was processed using the Affymetrix GeneChip Whole Transcript Sense Target Labelling Assay. For the sample preparation, we used Ambion WT Expression kit (Life Technologies, Paisley, UK), and GeneChip WT Terminal Labelling and Controls Kit (Affymetrix). 10,000 ng of cRNA was used for the second cycle cDNA reaction, and 5500 ng of the second cycle cDNA was fragmented and then labelled using GeneChip WT Terminal labelling and controls kit. One µL of the fragmented and 1 µL of the fragmented and subsequently labelled cDNA were subjected to 3% agarose gel electrophoresis to determine fragmentation size that usually ranged between 40 and 70 bp. Hybridisation cocktails are containing fragmented, end-labelled cDNA were applied to the Gene Chip Human Gene 1.0 ST arrays. Hybridisation was performed for 17 ± 1 hour in the GeneChip Hybridisation

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Oven 640 at 45°C and 60 rpm. Three different biological replicates were hybridised for each experimental condition resulting in a total of 66 microarray experiments for HUVEC and 20 microarrays for CD34+ cells. Arrays were washed and stained using GeneChip Fluidics Station 450, FS450_007 Fluidics profile. The arrays were scanned using Affymetrix GeneChip® scanner 3000 7G. Gene expression analysis Affymetrix GeneChip Command Software (AGCC) was used for array scanning, raw data acquisition and to generate .dat and .cel files. Affymetrix CEL files were imported to Partek Genomic Suite version 6.6 (Partek Inc., MO, USA). The data were normalised using RMA (Robust Multichip Average) normalisation. Principal component analysis (PCA) was performed on all probes to visualise high-dimensional data. By default, expression values were filtered for statistical significance using Benjamini and Hochberg’s False Discovery Rate (FDR), with an FDR-unadjusted p-value < 0.05. Analysis of variance (ANOVA) test was performed using p-values < 0.05 and cut off fold change (FC) ≥ 1.5. We employed the two most common criteria for differentially expressed genes, FC cutoff of ≥ 1.5 and p <0.05 as declared by Peart et al. (Peart et al., 2005) and Raouf et al. (Raouf et al., 2008). By using stringent criteria (FC cutoff of ≥ 2 and p <0.05) many important differentially expressed genes encountered for EC response to hypoxia and metformin were omitted. Therefore, applying the FC cutoff of ≥ 1.5 and p <0.05 in our microarray analysis explored more genes, that enabled better understanding of the dynamic picture of the transcriptome and the biological response than the more stringent conditions. Two-dimensional average linkage hierarchical clustering was performed for the differentially expressed genes using Spearman’s correlation as a similarity matrix. The microarray data generated in this study are in compliance with MIAME (http://www.mged.org/Workgroups/MIAME/miame.html) guidelines. The complete dataset and associated experimental information were submitted to NCBI’s Gene Expression Omnibus (GEO) and were accessible through accession numbers GSE46262 and GSE46263. Ingenuity pathway analysis (IPA) software version 9 (Ingenuity, Redwood City, CA) was employed to enable exploring the Canonical Pathways that may be increased or decreased based on activation or inhibition of molecules within that pathway.

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Additionally, IPA assisted in detecting the interactive molecular and cellular functions affected in each condition.

2.5.7 Western blot assay Separation of protein samples and their immunological detection via western blotting was accomplished using Novex® NuPAGE® SDS-PAGE Gel System from Invitrogen.

 NuPAGE® 10% Bis-Tris Pre-Cast Gels, 1.5 mm, 12 wells, for separating small to mid-size molecular weight proteins  NuPAGE® 3-8% Tris-Acetate Pre-Cast Gels, 1.5 mm, 15 wells, for separating large molecular weight proteins  NuPAGE® LDS Sample Buffer  NuPAGE® Reducing Agent  NuPAGE® Antioxidant  NuPAGE® MOPS SDS Running Buffer for NuPAGE® Bis-Tris Gels  NuPAGE® Tris-Acetate SDS Running Buffer for NuPAGE® Tris-Acetate Gels  NuPAGE® Transfer Buffer for blotting of NuPAGE® Pre-Cast Gels

SDS-gel electrophoresis (SDS-PAGE) For fractionation 1x SDS-PAGE running buffer was prepared by adding 50 ml of 20x NuPAGE® MOPS SDS running buffer to 950 ml deionised water for running NuPAGE Novex 10% Bis-Tris gels or 50 ml of 20x NuPAGE® Tris-Acetate SDS running buffer to 950 ml deionised water for running NuPAGE® Novex 3-8% Tris-Acetate Gels (Life Technologies, Paisley, UK). NuPAGE® Bis-Tris Gels with NuPAGE® MOPS SDS Running Buffer was used to resolve proteins with molecular weight between 14 and 200 kDa. While Tris-Acetate Gels with NuPAGE® Tris-Acetate SDS Running Buffer was applied to resolve higher molecular weight proteins between 36 and 400 kDa under denaturing conditions.The upper buffer chamber was prepared by adding 500 µl of NuPAGE® antioxidant to 200 ml of 1x running buffer. The remaining volume was employed to the lower chamber. Prior to loading, the samples, consisting of 10 µg of total protein, were mixed with 5 µl of LDS sample buffer and 2 µl of reducing agent, and then heated at 70°C for 10 minutes. The gels were fitted to the electrophoresis module, and the buffer chambers were filled with the respective running buffers. The samples and the

Chapter 2. Materials and Methods protein ladder (10-245 kDa, AppliChem, Darmstadt, Germany) were loaded and fractionated using the following conditions: Gel Type Voltage Run Time NuPAGE® Novex 10% Bis-Tris Gels with MOPS SDS running buffer 200 V 50 minutes NuPAGE® Novex 3-8% Tris-Acetate Gels 150 V 1 hour Transfer and detection of proteins on membranes 1x Transfer buffer: Reagent Volume (ml) NuPAGE Transfer Buffer (20x) 50 NuPAGE Antioxidant 1 Methanol 100* Deionised water 849 Total 1000 *In case of transferring the protein from 2 gels, 200 ml methanol was added then the volume of water adjusted accordingly.

Proteins were separated by SDS-PAGE and transferred to nitrocellulose membrane (Life Technologies, Paisley, UK) using a Trans-Blot Cell (BioRad) wet blot system. The nitrocellulose membrane was rinsed in 1x Transfer buffer. Sheets of filter paper (Life Technologies, Paisley, UK) were soaked in this buffer. Then the membrane was placed on the acrylamide gel and two sheets of filter paper placed on each side. Proteins were transferred to freshly prepared 1x NuPAGE transfer buffer at 30 volts for 90 minutes. Probing with antibodies  10x Tris-buffered saline (TBS): Tris-base 24.2 g NaCl 80 g Deionised water 1 L Adjust the pH to 7.6.  1x wash buffer (TBS-T): 0.1% Tween 20 in 1x TBS.  Blocking buffer: ready-made (LiCOR, Lincoln, NE), was diluted 1:2 with 1x TBS for washing membrane.  Blocking buffer (LiCOR) for incubation with antibodies was diluted 1:2 with 1x TBS-T [20 µl Tween to 10 ml 1x TBS (0.2%), then 10 ml blocking buffer].

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Membranes were incubated in approximately 5 ml blocking buffer with 1x TBS and blocked for at least 1 hour at room temperature or overnight at 4°C with gentle shaking to prevent non-specific antibody binding. After washing off the blocking buffer, membranes were washed with 3 ml blocking buffer diluted with 1x TBS-T. Then the membranes were incubated with antibodies [Appendix II, Table 7.2] in 3 ml blocking buffer for at least 1 hour or overnight at 4°C under gentle agitation. Different dilutions were used in this step depending on the quality of antibodies (Table 2.7). Subsequently, the blots were washed with 5 ml 1x TBS-T for 15 minutes and then three times for 5 minutes with wash buffer. The blots were incubated with a 1:10000 dilution of the secondary antibody in 5 ml of blocking buffer diluted with 1x TBS-T for 1 hour in special dark container (Li-COR) at room temperature with gentle agitation. After that the membrane was rinsed with TBS-T twice for 5 minutes then twice for 15 minutes. The membrane was rinsed twice briefly in 5 ml 1x wash buffer (1x TBS) followed by 15 minutes wash in 10 ml 1x wash buffer at room temperature with gentle shaking. The bands were visualised using the infrared imager Odyssey (Li-COR, Lincoln, NE) and ACTB was used as loading control. The previously bound antibodies were removed by incubation in RestoreTM Plus Western Blot Stripping Buffer (ThermoScientific, Waltham, MA) for 15 minutes at room temperature and subsequent rinsing with 1x TBS for 3 times.

Antibody Ratio ACTB 1: 3000 AMPKα 1:1000 HIF-1α 5:3000 mTOR 6:3000 Phospho- AMPKα 1.5:3000 Phospho-mTOR 6:3000 Secondary antibody 1:10000 VEGF 165A 1:1000 VEGFA 1:1000 Table 2.7: Dilutions of primary antibodies.

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2.5.8 MAPK activation dual detection assay The regulation of a large number of cellular processes (such as cell proliferation/ differentiation, cell survival and apoptosis) is dependent on the activation of MAPK signaling pathway, which can be measured by the phosphorylation of ERK1/2. FlowCellect™ MAPK Activation Dual Detection kit (Millipore, Darmstadt, Germany) includes two antibodies, a phospho-specific anti-phospho-ERK1/2 (Thr202/Tyr204, Thr185/Tyr187)-PE and an anti-ERK1/2-Alexa Fluor® 647 conjugated antibody to measure total levels of ERK. HUVEC were treated in passage 2 when reaching 60% confluency with normal glucose (5.5 mM) or high glucose (16.5 mM) in the presence or absence of physiological metformin (0.01mM) for 48 hours and parallel cultures were exposed to chemical hypoxia (150 µM

CoCl2) for 12 hours. Metformin and culture media were replaced every 12 hours. Cells were incubated with EBM-2 medium (PromoCell, Heidelberg, Germany) containing VEGF as a positive control or with EBM-2 medium containing 7 µM sunitinib (VEGF inhibitor) as a negative control. Subsequently, 2x105 cells were fixed by an equal volume of ice-cold fixation buffer for 20 minutes on ice according to the manufacturer’s instructions. Fixed cells were centrifuged at 670 g for 5 minutes in a 4°C centrifuge, and the supernatant was discarded. Cells were washed with 1 ml of 1x Wash buffer and centrifuged at 670 g for 5 minutes. The cell pellets were resuspended in 200 µl of 1x Wash Buffer. The cells were centrifuged at 670 g for 5 minutes in a 4°C centrifuge, and the supernatant was discarded. Then the cells were permeabilized by adding 100 µl of ice-cold permeabilisation buffer and incubated on ice for 20 minutes. Cells were centrifuged at 670 g for 5 minutes in a 4°C centrifuge, and the supernatant was discarded. The cells were then washed with 200 µl of 1x wash buffer. For multiplexing, the cells were resuspended in 95 µl of 1x assay buffer, 2.5 µl of anti-phosph-ERK1/2-PE and 2.5 µl of anti-ERK1/2-Alexa Fluor 647 to each sample and incubated for one hour on ice protected from light. Additional 100 µl of 1x wash buffer was added to the diluted antibodies and then centrifuged at 670 g for 5 minutes in a 4°C centrifuge. Finally, the cells were resuspended with 200 µl of 1x cold Assay Buffer. Labelled cells were analysed by acquiring 14,000 events using BD FACSAria III flow cytometer (BD, Bioscience, San Jose, CA).

Chapter 2. Materials and Methods

2.5.9 Meso Scale Discovery (MSD) assay MSD cytokine assays measure up to ten cytokines in an MSD 96-well multi-array plate. The assays employ a standard sandwich immunoassay format where capture antibodies are coated in a single spot on the bottom of the wells (Figure 2.9). The MSD assay was used to detect the levels of the proinflammatory panel consisting of interleukin-1β (IL-1β), IL6, IL8, and TNF-α; TIMP1, chemokine (C-X-C Motif) ligand 10 (CXCL10) and VEGFA secreted from primary CD34+ cells into the culture medium. CD34+cells were treated with euglycaemia (5.5 mM) or hyperglycaemia (16.5 mM) in the presence and absence of 0.01 mM metformin for 48 hours then exposed to 4% hypoxia for 3 hours. The culture supernatant was collected from 5 x 105 cells and centrifuged briefly to pellet the cell debris or artefacts. Assays were performed using: K15025C human Pro-inflammatory II 4-Plex, K151A0H Custom V-PLEX Human Biomarkers, K151JFC human TIMP-1 Kit (Meso Scale Discovery, Rockville, MD, USA) in accordance with the manufacturer’s protocol. This was performed by dispensing 25 µl of diluent 2 into each well of the MSD plate, then the plate was sealed with an adhesive plate seal and incubated for 30 minutes with vigorous shaking (700 rpm) at room temperature. Subsequently, 25 µl of the sample or calibrator was dispensed into the appropriate wells of the MSD plate. The plate was sealed and incubated for two hours with vigorous shaking (700 rpm) at room temperature. The wells were washed three times with phosphate buffered saline containing 0.05% Tween-20 (PBS– T). After that, 25 µl of the 1X Detection Antibody Solution was dispensed into each well. The plate was sealed and incubated for two hours with vigorous shaking at room temperature. The wells were washed three times with PBS–T and then150 µl of 2X Read Buffer T were added. Plates were read with MSD Sector Imager 2400 and data were analysed by MSD Discovery Workbench version 2.0. All conditions were assayed in three independent biological samples and performed in duplicate.

Chapter 2. Materials and Methods

Figure 2.9: Sandwich immunoassay of 4 multi-array MSD plate. Cytokine capture antibody is pre-coated on specific spots on 4 multi-array MSD plate (MSD handout).

2.6 Statistical analysis Results are presented as mean ± SEM, and statistical analysis was performed using one- way ANOVA followed by post-hoc analysis Fisher’s least significant difference (LSD) test for qRT-PCR, western blot, in vitro scratch assay, cell proliferation, apoptosis assay, in vitro Matrigel tube formation assay, and MSD assay. A two-tailed Student’s t-test between any two experimental groups was applied for miRNA qRT-PCR, MAPK activation dual detection assay, apoptosis assay. Calculations were performed using IBM SPSS software version 21.0 (SPSS Inc, NY). A p-value < 0.05 was considered statistically significant.

Chapter 3. In vitro model of cardiovascular disease in diabetes

Chapter 3. An in vitro model of cardiovascular disease in diabetes: gene expression in HUVECs under hypoxia and hyperglycaemia

3.1 Introduction Impairment of endothelial repair is recognized as an early event in the development of CVD (Ross, 1993) therefore, in vitro studies involving ECs obtained from HUVEC are important to study molecular mechanisms involved in CVD. In patients with diabetes, there are several risk factors involved in acute MI of which hypoxia, hyperglycaemia and the combination of both are fundamental and thus require in-depth investigation with emphasis on clinically relevant conditions and time intervals.

The event of an acute MI/ acute ischaemia takes approximately 20 minutes to develop, whilist complete necrosis of all myocardial cells requires at least 2-4 hours or longer (Thygesen et al., 2007). Therefore, hypoxic model systems using short-term cultures between 1 and 3 hours are of paramount importance to appropriately simulate the time frame of an acute ischaemia.

To mimic the diabetic condition, moderate hyperglycaemia (16.5 mM glucose) was used. We avoided the commonly used excessive high glucose concentrations (22 mM) that have been shown to impair cellular growth in tissue culture. With 16.5 mM glucose concentration no such effect was observed (Altannavch et al., 2004a). A 16.5 mM glucose concentration is known not only to simulate a diabetic state but is also associated with hyperglycaemia-mediated vascular inflammation through increased expression of endothelial adhesion molecules, such as endothelial-leukocyte adhesion molecule-1 (ELAM-1), VCAM-1 and ICAM-1 (Altannavch et al., 2004a, Takami et al., 1998).

Chapter 3. In vitro model of cardiovascular disease in diabetes

3.2 Establishment of a hyperglycaemia-hypoxia model and reference genes

3.2.1 Validation of a glucose-hypoxia model Preliminary experiments were performed in order to establish the glucose-hypoxia model in endothelial cells. HUVEC were isolated from the umbilical cord using collagenase. The cells were grown as confluent monolayers with cobblestone morphology. HUVECs are large, flat and polygonal as illustrated in Figure 3.1.

Figure 3.1: HUVECs morphology under phase-contrast microscopy. (A) HUVEC were observed under phase-contrast microscopy with 40x and (B) 100x magnifications.

The cells were treated in passage 2 with hyperglycaemia (16.5 mM) or euglycaemia (5.5 mM) as a control. After 48 hours, hypoxia was induced with CoCl2 (150 µM) for different intervals 1, 3, and 12 hours, concurrently parallel cultures without hypoxia were set up. The competence of the model was evaluated by assessing the mRNA expression of HIF1 α and HIF1 α downstream genes; angiogenic growth factor VEGFA and RGS5. The HIF1α transcription factor mediates the adaptive responses to hypoxia (Wang et al., 1995). VEGF gene expression is dramatically induced in response to hypoxia through HIF1α regulation (Liu et al., 1995, Forsythe et al., 1996). Previous studies have also demonstrated increased expression of RGS5 in hypoxia at both mRNA and protein levels in endothelial cells (Jin et al., 2009). Hypoxia-induced RGS5 expression is mediated by the transcription factor

Chapter 3. In vitro model of cardiovascular disease in diabetes

HIF1α (Jin et al., 2009). Additionally, the expression of VCAM-1 was reported to increase in response to glucose (Altannavch et al., 2004a); therefore, it has been used to determine the hyperglycaemic effect in our model. The genes were measured using qRT-PCR compared to the control, and quantified by the

ΔΔCT method (Fleige et al., 2006). The data were normalised to GUSB, a well-established reference gene (n = 1). Under hypoxic conditions, elevation in HIF1 α mRNA expression was observed together with the downstream target genes (VEGFA, and RGS5) as illustrated in Figure 3.2. HIF1α expression appeared to be elevated under high glucose concentration with normoxic and hypoxic conditions. VEGFA mRNA was dramatically increased under hypoxia and hyperglycaemia combined with hypoxia to reach its maximal levels after 12 hours.

Furthermore, the expression of RGS5 mRNA was up-regulated when endothelial cells were incubated under hypoxia. RGS5 mRNA reached the maximal level at 12 hours as shown in Table 3.1. RGS5 appeared to be elevated under hyperglycaemia and hyperglycaemia combined with hypoxia to reach its maximal detection level at 3 hours (Table 3.1). Similarly, VICAM1 gene expression appeared to be highly increased under hyperglycaemia but decreased under hyperglycaemia combined with hypoxia, but upregulated in HUVEC exposed to hypoxia for 1 hour (Table 3.1).

Chapter 3. In vitro model of cardiovascular disease in diabetes

Figure 3.2: Validation of glucose-hypoxia model. In passage 2 HUVEC were treated with high glucose concentration 16.5 mM or a physiological concentration of 5.5 mM as a control. After 48 hours, hypoxia was induced with 150 µM CoCl2 (n = 1) for variable intervals 1, 3 or 12 hours. The gene expression of HIF1α, VEGFA, RGS5, and VCAM1 were measured using qRT-PCR and were analysed by the ΔΔCT method.

Chapter 3. In vitro model of cardiovascular disease in diabetes

Gene 5.5 mM 5.5 mM 5.5 mM 5.5 mM 16.5 16.5 mM 16.5 mM 16.5 mM glucose glucose + glucose + glucose + mM glucose + glucose + glucose + (control) CoCl2 1 h CoCl2 3 h CoCl2 12 glucose CoCl2 1 h CoCl2 3 h CoCl2 12 h h HIF1 α 1.0 + 1.7 + 2.0 - 2.6 + 1.6 + 1.2 + 3.1 - 4.6 VEGFA 1.0 + 1.2 + 4.2 + 6.4 + 1.2 + 1.5 + 2.2 + 5.0 RGS5 1.0 1.0 + 1.2 + 1.4 + 1.5 - 1.4 + 1.70 - 1.2 VCAM1 1.0 + 1.9 1.0 - 3.4 + 3.1 - 2.6 + 1.3 - 7.1 Table 3.1: Gene expression of target genes used to establish glucose-hypoxia model. Real-Time PCR was used to measure the gene expression of HIF-1α, VEGFA, RGS5, and VCAM1 by the ΔΔCT method and normalised by the GUSB housekeeping gene. (+) means upregulated, (-) means downregulated.

These results validate our model of hypoxia and/ or hyperglycaemia for our studies on gene expression profiling.

3.3 Identification of reference genes for expression studies in hypoxia and hyperglycaemia models in HUVEC

3.3.1 Introduction

Accurate normalisation of gene expression data generated by qRT-PCR experiments is a prerequisite for reliable results. This is commonly done using reference genes. Reference genes, otherwise known as housekeeping genes, have a stable expression level across different experimental conditions in the tissues or cells under investigation (Dheda et al., 2004, Huggett et al., 2005). qRT-PCR is the technique of choice to validate gene expression data generated by microarray experiments (Mutch et al., 2001, Provenzano and Mocellin, 2007). Therefore, pilot studies are frequently conducted to establish reference genes for varying in vivo and in vitro conditions.

To date, no suitable reference genes for HUVEC cultures under hypoxia and/or hyperglycaemia have been established.

Chapter 3. In vitro model of cardiovascular disease in diabetes

3.3.2 Experimental protocol HUVEC were harvested from four independent umbilical cords by collagenase digestion as described previously in section 2.1.2. Subsequently, RNA was extracted and prepared for microarray hybridisation (Section 2.4.1 and 2.4.6); this was followed by validation of reference genes using SYBR Green real-time PCR (2.4.3). Gene Expression Analysis Affymetrix .CEL files were imported into Partek Genomics Suite version 6.6, and the data was normalised using RMA normalisation. A list of genes with gene expression between - 1.2 and 1.2 FC was generated using ANOVA. Ten candidate reference genes which have been previously utilised in different studies under hypoxic conditions (Foldager et al., 2009, Bruge et al., 2011, Tan et al., 2012), were selected from our microarray data and then ranked by employing NormFinder software (Andersen et al., 2004). NormFinder generated different stability values for a given set of candidate genes; a lower value implies a higher stability in gene expression. Statistical analysis Mean expression values of each triplicate were calculated using the 2-∆CT method, a modification of the 2-∆∆CT method described earlier (Livak and Schmittgen, 2001), wherein

∆Ct = Ct(sample) - Ct(control) and control is the sample without treatment. Expression values are presented as mean ± SEM. The comparisons of gene expression levels between qRT-PCR results were performed using paired t-test. Calculations were performed using SPSS software. A p-value < 0.05 was considered statistically significant.

3.3.3 Results RNA quality analyses showed RNA Integrity Values (RIN) of 9.1-10. The algorithm assigns a RIN number score from 1 to 10, where level 10 represents completely intact RNA, and 1 represents a highly degraded RNA. Expression levels of candidate reference genes by microarray Microarray data (.CEL files) from three independent biological replicates were analysed using Partek Genomics Suite version 6.6. Agglomerative average was performed, and genes showing expression between -1.2 and 1.2 FC were considered stable. Therefore, a list of

Chapter 3. In vitro model of cardiovascular disease in diabetes reference genes was generated by filtering genes that are stable under each condition (p > 0.05). We identified 9235 genes with no significant variation across all conditions. To narrow down the number of candidate reference genes for all eight different conditions tested in our study, we selected ten common reference genes to identify their stability values by using the NormFinder software (Table 3.2). Under hypoxia as well as under hyperglycaemia and hyperglycaemia combined with hypoxia for different time intervals (1, 3, and 12 hours); RPLP0 was predicted to be the most stable expressed reference gene. Based on NormFinder analysis we selected RPLP0, ACTB, GAPDH, GUSB, and TFRC; for further qRT-PCR validation (Table 3.3).

Gene name Hypoxia Hyperglycaemia/ hypoxia Lower log2- Upper log2- Stability value Stability value intensity intensity RPLP0 0.008 0.004 13.05 13.31 GAPDH 0.008 0.005 13.81 14.05 RPLP2 0.009 0.005 13.03 13.18 ACTB 0.010 0.005 13.28 13.56 HPRT1 0.015 0.008 9.14 9.77 GUSB 0.016 0.008 8.92 9.65 TFRC 0.018 0.012 10.63 11.40 ATP5F1 0.019 0.010 7.20 7.94 B2M 0.020 0.012 10.44 11.58 RPL13A 0.023 0.028 9.86 11.18 Table 3.2: NormFinder microarray expression stability analysis. Gene expression in the microarray was calculated by log2-intensitiy values of the genes. NormFinder transforms the log to linear scale and ranks the candidate genes according to their expression stability where a lower value indicates a higher stability in gene expression. The stability of genes was ranked into two groups, hypoxia for different time points (1, 3, 12 hours) and hyperglycaemia/ hyperglycaemia combined with hypoxia for different time points (1, 3, 12 hours).

Chapter 3. In vitro model of cardiovascular disease in diabetes

Gene Hypoxia1h p 1h FC Hypoxia Hypoxia3h p 3h FC Hypoxia Hypoxia12h p 12h FC Hypoxia Glucose p Glucose FC High Hypoxia1h Glucose + p H Glucose + FC High Hypoxia3h Glucose + p Hypoxia3h Glucose + FC High Hypoxia12h Glucose + p Hypoxia12h Glucose + FC High

------

ypoxia1h

value value value value High value High value High value High

ACTB 0.870 1.014 0.653 -1.039 0.154 1.132 0.428 1.070 0.320 1.089 0.519 1.056 0.343 1.085

GAPDH 0.962 1.002 0.877 -1.007 0.033 -1.101 0.410 -1.036 0.849 1.008 0.301 -1.046 0.051 -1.092

GUSB 0.993 -1.004 0.937 1.035 0.842 1.090 0.869 1.074 0.919 -1.044 0.926 1.041 0.879 -1.068

RPLP0 0.910 -1.006 0.731 -1.018 0.471 -1.038 0.535 -1.033 0.394 -1.045 0.416 -1.043 0.928 1.005

TFRC 0.836 1.049 0.890 -1.033 0.754 -1.076 0.997 1.001 0.828 -1.052 0.557 -1.147 0.886 1.034

Table 3.3: Transcriptome analysis of five selected reference genes from HUVEC cultured under different conditions. Reference gene expression from three independent microarray hybridisations was analyzed by Partek genomic suite version 6.6. Gene expression for all conditions was compared against a control sample that is under euglycaemia and normoxia. Genes are showing expression between -1.2 and 1.2 FC and p > 0.05 were considered stable. ACTB: actin, beta; GAPDH: glyceraldehyde-3-phosphate dehydrogenase; GUSB: glucuronidase, beta; RPLP0: ribosomal protein, large, P0; TFRC: transferrin receptor. The NCBI reference sequence (RefSeq) for: ACTB: NM_001101; GAPDH: NM_002046; GUSB: NM_000181; RPLP0: NM_001002; TFRC: NM_003234. Key: FC: fold change.

Chapter 3. In vitro model of cardiovascular disease in diabetes

TFRC and RPLP0 mRNA expression were stable in HUVEC induced by hypoxia Expression values of the five chosen reference genes were validated in four independent cDNA preparations. Under hypoxic conditions, the expression of ACTB was found to be stable after 1 hour of hypoxia compared to the control, but significantly downregulated after 3 and 12 hours of hypoxia compared to the control (p = 0.01 and p = 0.009 respectively, Figure 3.3). The expression of GUSB was found to be inhibited (p = 0.05) while GAPDH was upregulated with a borderline significance (p = 0.05) after 12 hours of hypoxia compared to the control (5.5 mM glucose). In contrast, TFRC and RPLP0 displayed stable expression under hypoxia in all intervals (1, 3 and 12 hours) compared to the control.

Figure 3.3: Effect of euglycaemia and hypoxia on gene expression of the selected reference genes. Fold change in gene expression analysed by the 2-∆CT method. Data are mean ± SEM, n = 4, *P ≤ 0.05, **P < 0.01 vs. control. Expression of GUSB and GAPDH was shown borderline significance after 12 hours of hypoxia while ACTB was significantly changed after 3 and 12 hours of hypoxia. In contrast, the gene expression of RPLP0 and TFRC was found to be stable compared to control.

Chapter 3. In vitro model of cardiovascular disease in diabetes

GUSB, TFRC, RPLP0, and ACTB mRNA expression was stable in HUVEC induced by hyperglycaemia and hypoxia When HUVEC were treated under hyperglycaemic conditions (16.5 mM glucose) for 24 hours, the five selected reference genes demonstrated stable expression (Figure 3.4). Under hyperglycaemia combined with hypoxia, gene expression of GUSB, TFRC, RPLP0, and ACTB turned out to be stable (Figure 3.4) in all time intervals measured (1, 3 and 12 hours). In contrast, an increase in gene expression of GAPDH was revealed with a borderline significance (p = 0.05) after 12 hours of hypoxic incubation compared to the control.

Figure 3.4: Effect of hyperglycaemia and hypoxia on gene expression of the selected reference genes. Fold change in gene expression analysed by the 2-∆CT method. Data are mean ± SEM, n = 3- 4, *P < 0.05 vs. control. Expression of GUSB, TFRC, RPLP0 and ACTB was found to be stable while GAPDH was increased with borderline significance after 12 hours of hypoxia compared to the control.

Chapter 3. In vitro model of cardiovascular disease in diabetes

3.3.4 Discussion

Better understanding of the cellular and molecular responses to hypoxia and hyperglycaemia can lead to improved treatment of CVD in patients with diabetes. Myocardial infarction due to occlusion of one of the coronary arteries is frequently observed in such patients (Michiels, 2004). Translational cardiovascular research in vitro requires an experimental design, which reflects clinical conditions in patients to identify the affected genes.

In our in vitro studies, we investigated the conditions that are known to be associated with acute myocardial infarction under diabetic and non-diabetic conditions. We induced hypoxia using a hypoxia-mimetic agent, CoCl2. Earlier studies have shown that CoCl2 is able to generate a similar transcriptional response to reduced oxygen tension using hypoxia incubators (Poulios et al., 2006, Vengellur et al., 2003). The CoCl2 method has the advantage of being fast and inexpensive and provides a more stable hypoxia that is not possible with hypoxic chambers (Wu and Yotnda, 2011). Our experiments show typical hypoxia response genes such as HIF1a, and VEGFA to be over-expressed in hypoxia induced by CoCl2 making our results comparable to low O2 tension experiments (Liu et al., 1999, Wang et al., 2007).

Different exposure times to hypoxic and/or hyperglycaemic conditions were selected to simulate both early and late cellular and molecular responses following ischaemia (Thygesen et al., 2007). Of notice, a qRT-PCR study on HUVEC cultured for 48 hours under different hypoxic conditions (1% O2, 5% CO2, with balanced N2, or 150 µM CoCl2) has demonstrated that human ATP synthase, H-transporting, mitochondrial F0 complex, subunit B1 (ATP5F1), RPLP0, and ribosomal protein, large, P2 (RPLP2) were suitable reference genes (Hatipoglu et al., 2009). In contrast, we applied shorter incubation times (1, 3, and 12 hours) and identified two reference genes, namely RPLP0 and TFRC as the most stably expressed under both euglycaemia and hyperglycaemia, in the presence or absence of hypoxia. In our results two commonly perceived reference genes, GAPDH and ACTB

Chapter 3. In vitro model of cardiovascular disease in diabetes displayed significant differences in their expression levels under various hypoxic conditions. These results match with an earlier study on bovine endothelial cells maintained in a humidified chamber flushed with 3% O2, where GAPDH showed unstable expression levels with a significant increase after 2-4 hours and a maximum level at 18 h of hypoxia (Graven et al., 1994). From 9235 genes that were found stable in all studied conditions in microarray experiments, we analysed ten common reference genes using NormFinder. The NormFinder results showed RPLP0 as the most stable reference gene that is in concordance with our qRT-PCR data. However, GAPDH and ACTB identified using the NormFinder as highly stable reference genes in hyperglycaemia and hypoxia combined were not found in our validation by qRT-PCR. This difference in reference genes identified by microarray data using NormFinder and qRT-PCR could be due to non-concordance of transcripts used in the microarray probe set and qRT-PCR (Dallas et al., 2005). The TFRC and RPLP0 identified by us have been previously reported as reference genes in other cell types including dexamethasone exposed breast cancer cells and CD19-positive chronic lymphocytic leukaemia cells (Abruzzo et al., 2005, Majidzadeh et al., 2011). Therefore, our data indicates that TFRC and RPLP0 are suitable reference genes to normalise gene expression levels in qRT-PCR experiments in HUVEC exposed to hypoxia, hyperglycaemia or hypoxia and hyperglycaemia combined.

Chapter 4. Vascular functions in mature endothelial cells

4.1 Introduction Ischaemic heart disease (IHD) is characterised by deprivation of blood and oxygen supply that reaches the heart muscle. Patients with DM have an increased risk of coronary heart disease (CHD), compared to those with normal fasting glucose (Alexander et al., 2000). Endothelial cells (EC) are among the most biologically active cellular components of blood vessels and play a critical role in the pathophysiology of CVD and diabetes. The link between diabetes and CVD is complex and multifactorial (Muhlestein et al., 2003, Nielson and Lange, 2005). A critical adaptation to hypoxia is angiogenesis, which consists of the formation of new blood vessels extending from the pre-existing vasculature (Carmeliet, 2003). This phenomenon occurs through the activation of ECs by a multistep process including changes in the cellular and extracellular matrix (Bouis et al., 2006). The transcription factor HIF-1 functions as a master regulator of oxygen haemostasis by regulating angiogenesis and vascular remodelling (Kelly et al., 2003). In ischaemic cardiovascular pathologies, activation of angiogenesis is beneficial. Therefore, identifying the interplay of molecular signals and events that occur in hypoxia and lead to the formation of functional new blood vessels is a challenging yet important issue.

4.2 Experimental approach To address the hypothesis that metformin improves vascular function by increasing the production of angiogenic factors in HUVEC and cell survival after hypoxia, functional studies were performed including cell migration, cell proliferation, and apoptosis followed by gene expression profiling of hypoxia with euglycaemia (5.5 mM glucose) or hyperglycaemia (16.5 mM) for either 1, 3 or 12 hours time intervals in the presence and absence of metformin (0.01 mM). Primary HUVEC were isolated from three different fresh umbilical cords in order to study the biological and genetic variations. The HIF-1α, VEGF, and AMPK signalling pathways were of particular interest in the analysis of microarray data. We compared the HUVEC transcriptome in different experimental conditions each 85

Chapter 4. Vascular functions in mature endothelial cells with three independent biological replicates resulting in 66 experiments from which raw cell intensity (CEL) files were imported into Partek Genomic Suite version 6.6 and normalised using RMA. Quality Control (QC) was considered to identify outliers, batch effects, overly noisy experiments, and experimental design problems. The QC was computed by using PCA mapping for visualising the high dimensional data and outliers of variances within samples (Figure 4.1). The normalised data were then used to identify differentially expressed genes. ANOVA was performed using p-values < 0.05 and cut off FC ≥ 1.5. Each condition was compared to the control of 5.5 mM glucose, and lists of differentially expressed genes were created. In order to detect the effect of metformin, gene lists were created by pairwise comparison of the conditions with versus without metformin treatment. Afterwards, the gene lists were imported into IPA software to identify affected canonical pathways, molecular and cellular functions. Independent assessment of microarray results was performed using qRT-PCR, western blot, and flow cytometry for key findings in our experiments.

Chapter 4. Vascular functions in mature endothelial cells

Figure 4.1: PCA mapping for microarray gene expression of HUVEC. HUVEC were treated with various glucose concentrations (5.5 or 16.5 mM) and then exposed to either normoxia or hypoxia using 150 µM CoCl2 for different time intervals of 1, 3 or 12 hours. These conditions were compared to the control 5.5 mM glucose with normoxia. Each sample is represented by one spot, and the colours of spots depend on the sample attributes. The spheres denoted the distribution of the samples among each condition. Plots were created using the Partek Genomics Suite software package. The outliers are outside the 95 % confidence interval.

4.3 Results 4.3.1 Functional assays Cell migration

Cell migration is impaired by hypoxia combined with hyperglycaemia The scratch assay was applied on HUVEC treated with euglycaemic (5.5 mM, Figure 4.2A) or hyperglycaemic (16.5 mM, Figure 4.3A) conditions to determine the effect of hypoxia on endothelial cell migration. Euglycaemia and hypoxia had no effect on cell migration of ECs compared to control (Figure 4.2B). However, hyperglycaemia significantly accelerated 87

Chapter 4. Vascular functions in mature endothelial cells the gap closure after 6 (p < 0.001), 12 (p < 0.001), and 18 hours (p < 0.001, Figure 4.3B), but not after 24 hours (plateau phase). Hypoxia inhibited the rate of cell migration when combined with hyperglycaemia after 6 (p < 0.001), 12 (p < 0.001), 18 (p < 0.001), and 24 hours (p < 0.01, Figure 4.3C) compared to hyperglycaemia.

Metformin promotes cell migration under hyperglycaemia combined with hypoxia A physiological metformin concentration decreases the rate of gap closure at 18 h (p < 0.01) and 24 hours of euglycaemia-hypoxia (p < 0.001, Figure 4.2B). However, metformin accelerated the rate of cell migration significantly when HUVEC were treated with hyperglycaemia and combined with hypoxia at 24 hours (p < 0.01, Figure 4.3C). In control assays, HUVEC were treated with the VEGF inhibitor sunitinib (Figure 4.2B and 4.3C).

Chapter 4. Vascular functions in mature endothelial cells

Figure 4.2: Metformin impairs cell migration in HUVEC exposed to only hypoxia. (A) HUVEC were incubated for 24 hours with euglycaemia in the presence or absence of physiological metformin concentration (0.01mM). At the time point when a confluent monolayer was formed, scratch lines were created and then the wells were gently rinsed with DPBS to remove the detached 89

Chapter 4. Vascular functions in mature endothelial cells cells. The media were replaced with those containing different glucose concentrations and metformin, and then the cells were exposed to chemical hypoxia for 24 hours. Subsequently, the cells were incubated in a 5 % CO2 chamber for 24 hours that was connected to CCD camera. Images were acquired every hour, and three independent biological experiments were performed at which each condition was assessed in duplicate. Each image is a composite image that generated by NIS Elements software. The scratch area in each image was measured using NIS Elements software. (B) Hypoxia had no significant effect on cell migration under euglycaemia, whereas metformin inhibited scratch recovery after 18 h. Sunitinib (0.1 µM) was used as a negative control. ##P < 0.01, ###P < 0.001 compared pairwise, i.e., the metformin-treated versus metformin-untreated condition. The scale bar is 100 µm.Key: met: metformin; sun: sunitinib.

Chapter 4. Vascular functions in mature endothelial cells

Figure 4.3: Metformin improves cell migration in HUVEC exposed to hyperglycaemia combined with hypoxia. (A) HUVEC were incubated for 24 hours with euglycaemia or hyperglycaemia in the presence or absence of physiological metformin concentration (0.01mM). At the time point when a confluent monolayer was formed, scratch lines were created and then the wells were gently rinsed with DPBS to remove the detached cells. The medium was replaced with that containing different glucose concentrations and metformin, and then the cells were exposed to chemical hypoxia for 24 hours. Subsequently, the cells were incubated in a 5 % CO2 chamber for 24 hours that was connected to a CCD camera. Images were acquired every hour, and three independent biological experiments were performed at which each condition was assessed in duplicate. Each image is a composite image that generated by NIS Elements software. The scratch area in each image was measured using NIS Elements software. (B) Hyperglycaemia accelerated gap closure till 18 hour, whereas hypoxia significantly decreased the gap closure (C). Metformin treatment increased the rate of cell migration. Sunitinib (0.1 µM) was used as a negative control of cell migration. Results are expressed as mean ± SEM and were analysed by one-way ANOVA 91

Chapter 4. Vascular functions in mature endothelial cells followed by LSD, **P < 0.01, ***P < 0.001 compared to the control. ##P < 0.01 compared pairwise, i.e., the metformin-treated versus metformin untreated-condition. The scale bar is 100 µm. Key: met: metformin; sun: sunitinib.

Cell proliferation

To evaluate the effect of hypoxia and hyperglycaemia on HUVEC proliferation, the BrdU proliferation assay was used and the levels of incorporated BrdU cells were measured using flow cytometry. Euglycaemia-hypoxia was associated with a time-dependent decrease in cell proliferation by 41.4 % (p < 0.05) after 3 hours and by 77.9 % (p < 0.001) after 12 hours of hypoxia compared to the control of 5.5 mM glucose (Figure 4.4). Hyperglycaemia showed a similar effect on the rate of cell proliferation compared with the control. However, hyperglycaemia combined with hypoxia drastically inhibited cell proliferation by the rate of 71.6 % (p < 0.001) after 3 hours hypoxia and by 78.7 % (p < 0.001) after 12 hours compared to the control. Physiological metformin concentration (0.01 mM) did not affect cell proliferation in HUVEC exposed to hypoxia neither under euglycaemia nor hyperglycaemia (data not shown). However, a supra-physiological metformin concentration (1.0 mM) non- significantly decreased cell proliferation under hyperglycaemia.

Chapter 4. Vascular functions in mature endothelial cells

Figure 4.4: Hypoxia impairs endothelial cell proliferation. HUVEC were treated with high glucose concentration or normal glucose concentration as a control. After 24 hours, euglycaemic and hyperglycaemic cultures were exposed to hypoxia for 3 or 12 hours. Endothelial cell proliferation was assessed using the BrdU incorporation assay. Results are representative of three independent biological replicates and expressed as mean ± SEM and were analysed by one-way ANOVA followed by LSD test., *P < 0.05, **P < 0.01, ***P < 0.001 compared to the control.

Chapter 4. Vascular functions in mature endothelial cells

Apoptosis

HUVEC were treated with euglycaemia or hyperglycaemia in the presence or absence of physiological metformin (0.01mM) for 48 hours and then parallel cultures were exposed to hypoxia (150 µM CoCl2) for 3, 12, or 24 hours. The effect of supra-physiological concentration of metformin 1.0 mM on apoptosis was performed under normal and high glucose concentrations. A positive control for the apoptosis assay was created by treating the cells with 14 µM sunitinib. Annexin V assay was used to measure the proportion of apoptotic cells that was assessed by flow cytometry.

No effect on apoptosis after shorter hypoxia (3 and 12 hours) was detected neither under euglycaemic nor hyperglycaemic conditions (Figure 4.5). After 24 hours of hypoxia there was a statistically non-significant increase (1.3-fold, p > 0.05) in the apoptotic cells under euglycaemic conditions; and a statistically significant increase (1.4-fold, p = 0.039) in the apoptotic cells under hyperglycaemia.

Figure 4.5: Hypoxia coupled with high glucose mediated impairment in endothelial cell survival. HUVEC were treated with euglycaemia or hyperglycaemia for 48 hours, and parallel cultures were exposed to chemical hypoxia for 3, 12, or 24 hours. Then apoptosis was assessed by Annexin V staining and flow cytometry analysis. Results are representative of 3 independent

Chapter 4. Vascular functions in mature endothelial cells biological replicates and expressed as mean ± SEM and were analysed by one-way ANOVA followed by LSD test., *P < 0.05, compared to the control.

Physiological (0.01 mM) metformin concentration exhibited no effect on apoptosis under euglycaemia combined with hypoxia for 3, 12, or 24 hours (Figure 4.6 B, C, and D) or under hyperglycaemia combined with hypoxia for 3 or 12 hours (Figure 4.6F, and G). In contrast, metformin significantly reduced the apoptotic cells (-1.3-fold, p = 0.045) under hyperglycaemia exposed to hypoxia for 24 hours (Figure 4.6H). A supra-physiological metformin concentration (1.0 mM) non-significantly enhanced apoptosis (1.3-fold, p > 0.05) in HUVEC treated with hyperglycaemia (Figure 4.6E) but had no effect under euglycaemia (Figure 4.6A).

Chapter 4. Vascular functions in mature endothelial cells

Figure 4.6: Metformin improves cell survival with hyperglycaemia combined with hypoxia. HUVEC were treated with (A) 5.5 or (E) 16.5 mM glucose for 48 hours in the presence or absence 97

Chapter 4. Vascular functions in mature endothelial cells of a physiological metformin concentration and parallel cultures were exposed to chemical hypoxia for (B and F) 3, (C and G) 12, or (D and H) 24 hours. The effect of supra-physiological concentration of metformin 1.0 mM on apoptosis was performed. Apoptosis was assessed by Annexin V staining and flow cytometry analysis. Results are representative of 2-3 independent experiments and expressed as mean ± SEM and were analysed by paired t-test, *P < 0.05, compared pairwise, the conditions treated with and without metformin. Key: glu: glucose; hyp: hypoxia; met: metformin.

4.3.2 Microarray analysis of target genes in HUVEC The quality of RNA extracted from HUVEC was assessed using the Agilent Bioanalyzer system. Results from the Agilent Bioanalyzer were generated in the form of an electropherogram. The integrity of the RNA sample was assessed by the RIN tool, a dedicated algorithm available in the Agilent 2100 Export software. The RIN of the samples varied between 9.1 and 10. The high RIN observed in our electropherograms indicate good quality RNA populations (Figure 4.7).

Figure 4.7: Total RNA analysis using the Agilent Bioanalyzer. The electropherogram shows the total RNA pattern analysed with the RNA 6000 Nano assay. Two RNA peaks are shown: 18S ribosomal RNA and 28S ribosomal RNA. FU: fluorescence unit; S: seconds.

In the microarray experiments the number of differentially expressed genes with a p-value < 0.05 and a cutoff FC of 1.5 varied considerably between the conditions (Figure 4.8). The lowest numbers of differentially expressed genes were detected after 1 hour of hypoxia exposure either under euglycemia with (206 genes) or without metformin (129 genes) and hyperglycaemia with (134 genes) or without metformin (122 genes). In contrast, the highest 98

Chapter 4. Vascular functions in mature endothelial cells numbers of differentially expressed genes were detected in after 12 hours of hypoxia exposure either under euglycemia with (2120 genes) or without metformin (2064 genes) and hyperglycaemia with (1645 genes) or metformin (1470 genes). Of notice, also the frequency of up and downregulated genes varied substantially between the conditions. The highest proportion of upregulated genes (86%) was revealed under hyperglycaemia with metformin, whereas the lowest proportion (16%) was detected under euglycemia with metformin. Venn diagrams created by Genomic Suite software display the proportions of differentially expressed intersecting and non-intersecting genes after 1, 3, or 12 hours under different conditions assayed (Figure 4.9). Remarkably, although the comparable lowest numbers of differentially expressed genes were detected after 1 hour of hypoxia either under euglycemia or hyperglycaemia, the majority of the genes did not intersect with the respective 3 and 12 hours conditions (Figure 4.9A and B). The proportion of intersecting genes in the pairwise comparison of conditions treated with or without metformin under euglycaemia and hyperglycaemia ranged between 14% under euglycemia after 1 hour hypoxia, and 70% under euglycemia after 12 hours hypoxia (Figure 4.9C and D).

Chapter 4. Vascular functions in mature endothelial cells

Figure 4.8: The number of differentially expressed genes from transcriptomic analysis of HUVEC. Expression profiling of hypoxia with euglycaemia (5.5 mM glucose) and hyperglycaemia (16.5 mM) in vitro were employed on HUVEC for different time intervals of 1, 3, or 12 hours in the presence and absence of metformin (0.01 mM). Raw CEL files of 66 experiments from three independent biological replicates were imported into Partek Genomic Suite version 6.6. ANOVA test was performed using a p-value < 0.05 and cut off FC of 1.5. Each condition was compared to the control of 5.5 mM glucose.

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(A)

(B)

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(C)

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(D)

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Figure 4.9: Venn diagram showing the number of differentially expressed genes from transcriptomic analysis of HUVEC using Partek software. Expression profiling of hypoxia with (A) euglycaemia (5.5 mM glucose) and (B) hyperglycaemia (16.5 mM) in vitro were employed on HUVEC for different time intervals of 1, 3, and 12 hours in the absence and presence of metformin (0.01 mM) (C and D). Raw CEL files of 66 experiments from three independent biological replicates were imported into Partek Genomic Suite version 6.6. ANOVA test was performed using a p-value < 0.05 and cut off FC of 1.5. Each condition was compared to the control of 5.5 mM glucose. Gene expression of HUVEC treated with chemical hypoxia

An overview of the gene expression pattern and affected canonical pathways in HUVEC following induction with chemical hypoxia is shown in Figure 4.10.

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Figure 4.10: Two-dimensional clustering of HUVEC under the effect of hypoxia. Results were generated by transcriptomic analysis of 3 biological replicates using Affymetrix microarray after treatment of HUVEC with CoCl2 for 1, 3, and 12 hours. The heatmap was generated using Partek software where increased gene expression is indicated by red shades, decreased expression by blue shades, and no change by grey shades. MF: molecular function; CP: canonical pathway.

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Effect of combined euglycaemia hypoxia 1 hour

After 1 hour of hypoxia, 129 genes passed the cutoff FC of 1.5 and p-value < 0.05 (Table 7.3 in Appendix III) compared to the control (5.5 mM glucose with normoxia). Among the top molecular and cellular functions associated with altered genes were cardiovascular system development and function, cell morphology, and cellular development that were activated (p = 9.09E-06), whereas cell death decreased and survival increased (p = 4.46E- 04). The top canonical pathways were: retinoic acid receptor (RAR) activation (p = 8.25E- 03), and mammalian target of rapamycin (mTOR) signalling pathway (p = 9.2E-03).

Effect of combined euglycaemia hypoxia 3 hours

The response to hypoxia after 3 hours increased as the number of differentially expressed genes were 443 (cutoff FC 1.5, and p-value < 0.05) as demonstrated in Table 7.4 in Appendix III. Among the top molecular and cellular functions associated with altered genes were cardiovascular system development and function, carbohydrate metabolism, and cell survival that were activated, while apoptosis was inhibited as summarised in Table 4.1 according to the p-value.

Molecular P-value Activation Molecules Number of function z-score molecules Cell Death and 8.00E-07 – 54 Survival 7.88E-03

Apoptosis 2.36E-03 -2.520 ABL1, ADM, ADORA2A, ADORA2B, 42 ALDOC, ALKBH5, ANGPTL4, BHLHE40, BHLHE41, BNIP3, CD274, CDKN3, DDIT4, DDX41, DLL4, DUSP1, DUSP6, EGLN3, FAM162A, GAS5, HK2, HMOX1, INHBA, JUNB, KDM3A, MAP2K1, MAP3K8, MAPK7, MLLT3, MTFP1, NDRG1, NFIL3, PDK1, PIK3IP1, PIK3R1, SLC2A1, SLC2A3, SNX33, STC1, TNIP1, TXNIP, VEGFA

Cell survival 7.36E-04 +3.511 ABCB6, ABL1, ACER2, ADORA2A, ADORA2B, BHLHE40, BNIP3, DUSP1, 27 DUSP6, EFNA3, EGLN3, HK2, HMOX1, MAP2K1, MAP3K8, MAPK7, NDRG1, NFIL3, NRN1, PDK1, PIK3IP1, PIK3R1, PPFIA4, SLC2A1, STC1, TXNIP, VEGFA 106

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Molecular P-value Activation Molecules Number of function z-score molecules Carbohydrate 3.73E-05 - 20 metabolism 7.88E-03

Glycolysis 3.73E-05 ENO2, HK2, PFKFB3, PGM1, PIK3R1, 7 SLC16A3, SLC2A1

Transport of D- 1.02E-04 +0.869 HK2, MAP2K1, PIK3R1, PPP1R3C, 7 glucose SLC2A1, SLC2A3, TXNIP

Metabolism of 7.01E-03 +2.125 ADORA2A, ADORA2B, ALDOC, 13 carbohydrate CHSY1, DUSP6, HK2, PFKFB3, PGM1, PIGA, PIK3R1, PPP1R3C, SLC16A3, SLC2A1 Cardiovascular 3.50E-05 - 30 system 7.88E-03 development and function

Development of 3.50E-05 +1.477 ABL1, ADM, ADORA2A, ADORA2B, 22 blood vessels ANGPTL4, CCL28, DLL4, EGLN1, EGLN3, HMOX1, INHBA, JUNB, KLHL20, MAP2K1, MAPK7, PFKFB3, PIK3R1, PTPRB, SHB, STC1, VEGFA, VLDLR

Endothelial cell 8.78E-05 +1.093 ADM, ANGPTL4, DLL4, HMOX1, 12 development INHBA, KLHL20, MAP2K1, MAPK7, PFKFB3, STC1, VEGFA, VLDLR

Angiogenesis 1.06E-04 +1.159 ABL1, ADM, ADORA2A, ADORA2B, 19 ANGPTL4, CCL28, DLL4, EGLN1, EGLN3, HMOX1, INHBA, JUNB, KLHL20, MAPK7, NDRG1, PIK3R1, PTPRB, RORA, VEGFA Table 4.1: Top biological functions involved in HUVEC induced with euglycaemia-hypoxia for 3 hours. The biological functions were generated by analysis of the gene list using IPA software. The activation z-score used in the calculation of significant changes in gene expression in different samples and conditions. It is calculated from the dataset and indicates activation or inhibition of the biological function as (+) means activation while (-) means inhibition. Key: ABCB6: ATP-binding cassette, sub-family B (MDR/TAP), member 6; ABL1: Abelson Tyrosine-Protein Kinase 1; ACER2: alkaline ceramidase 2; ADM: adrenomedullin; ADORA2A: adenosine A2a receptor; ADORA2B: adenosine A2b receptor; ALDOC: aldolase C, fructose-bisphosphate; ALKBH5: alkB, alkylation repair homolog 5 (E. coli); ANGPTL4: angiopoietin-like 4; BHLHE40: Basic helix-loop-helix family, member e40; BHLHE41: Basic helix-loop-helix family, member e41; BNIP3: BCL2/adenovirus E1B 19kDa interacting protein 3; CCL28: chemokine (C-C motif) ligand 28; CD274: CD274 molecule; CDKN3: cyclin-dependent kinase inhibitor 3; CHSY1: chondroitin sulfate synthase 1; DDIT4: DNA-damage-inducible transcript 4; DDX41: DEAD (Asp-Glu-Ala-Asp) box polypeptide 41; DLL4: delta-like 4 (Drosophila); DUSP1: dual specificity phosphatase 1; DUSP6:

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Chapter 4. Vascular functions in mature endothelial cells dual specificity phosphatase 6; EFNA3: ephrin-A3; EGLN1: egl nine homolog 1 (C. elegans); EGLN3: egl nine homolog 3 (C. elegans); ENO2: enolase 2 (gamma, neuronal); FAM162A: family with sequence similarity 162, member A; GAS5: growth arrest-specific 5 (non-protein coding); HK2: hexokinase 2; HMOX1: heme oxygenase (decycling) 1; INHBA: inhibin, beta A; JUNB: jun B proto-oncogene; KDM3A: lysine (K)-specific demethylase 3A; KLHL20: kelch-like family member 20; MAP2K1: mitogen-activated protein kinase kinase 1; MAP3K8: mitogen-activated protein kinase kinase kinase 8; MAPK7: mitogen-activated protein kinase 7; MLLT3: myeloid/lymphoid or mixed-lineage leukemia (trithorax homolog, Drosophila); translocated to, 3; MTFP1: mitochondrial fission process 1; NDRG1: N-myc downstream regulated 1; NFIL3: nuclear factor, interleukin 3 regulated; NRN1: neuritin 1; PDK1: pyruvate dehydrogenase kinase, isozyme 1; PFKFB3: 6- phosphofructo-2-kinase/fructose-2,6-biphosphatase 3; PGM1: phosphoglucomutase 1; PIGA: phosphatidylinositol glycan anchor biosynthesis, class A; PIK3IP1: phosphoinositide-3-kinase interacting protein 1; PIK3R1: phosphoinositide-3-kinase, regulatory subunit 1 (alpha); PPFIA4: protein tyrosine phosphatase, receptor type, f polypeptide (PTPRF), interacting protein (liprin), alpha 4; PPP1R3C: protein phosphatase 1, regulatory subunit 3C; PTPRB: protein tyrosine phosphatase, receptor type, B; RORA: RAR-related orphan receptor A; SHB: Src homology 2 domain containing adaptor protein B; SLC2A1 (GLUT1): solute carrier family 2 (facilitated glucose transporter), member 1; SLC2A3 (GLUT3): solute carrier family 2 (facilitated glucose transporter), member 3; SLC16A3: solute carrier family 16 (monocarboxylate transporter), member 3; SNX33: sorting nexin 33; STC1: stanniocalcin 1; TXNIP: thioredoxin interacting protein; VEGFA: vascular endothelial growth factor A; VLDLR: very low density lipoprotein receptor.

HIF-1α signalling was detected as the top canonical pathway in the condition of 3 hours hypoxia, followed by G-protein coupled receptor signalling, AMPK pathway and VEGF signalling as listed in Table 4.2.

Ingenuity Canonical Pathways P-value Molecules

HIF-1α Signalling 1.73E-05 ↑VEGFA, ↑EGLN1, ↑SLC2A1, ↑PIK3R1, ↑EGLN3, ↑MAPK7, ↑SLC2A3

G-Protein Coupled Receptor Signalling 4.27E-03 ↑DUSP1, ↑DUSP6, ↑PIK3R1, ↑ADORA2B, ↑MAP3K8, ↑MAP2K1, ↑ADORA2A

AMPK Signalling 4.37E-03 ↑PFKFB3, ↑SLC2A1, ↑PFKFB4, ↑PIK3R1, ↑AK4

VEGF Signalling 3.55E-02 ↑VEGFA, ↑PIK3R1, ↑MAP2K1 Table 4.2: Top canonical pathways involved in HUVEC induced with euglycaemia-hypoxia for 3 hours. Key: AK4: adenylate kinase 4; for the name of the genes refer to Table 4.1 legend. The arrow ↑ indicates upregulated gene.

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Effect of combined euglycaemia hypoxia 12 hours

After 12 hours of hypoxia, 2064 genes were differentially expressed (cutoff FC 1.5 and p- value < 0.05). The top 50 highly expressed genes were represented in Table 7.5 Appendix III. Among the top biological functions influenced after 12 hours of hypoxia are: DNA damage (5.96E-28 - 5.80E-04) and cell death (8.39E-13 - 5.84E-04) which were increased while cell survival decreased. These functions with number of molecules differentially expressed are summarised in Table 4.3 according to the p-value. The top canonical pathways related to these functions have been underlined in Table 4.4.

Molecular function P-value Activation z- Number of molecules score

Cell cycle 5.96E-28 - 302 7.31E-04

Segregation of chromosomes 5.96E-28 -1.012 48

Cell cycle progression 2.08E-21 -2.631 191

Cell Cycle, DNA Replication, 1.44E-19 -2.732 41 Recombination, and Repair

Cellular assembly and 5.96E-28 - -2.669 164 organisation 5.80E-04

DNA replication, 5.96E-28 - 243 recombination and repair 6.45E-04

Metabolism of DNA 7.44E-12 -2.083 79

Repair of DNA 1.12E-11 -3.697 56

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Molecular function P-value Activation z- Number of molecules score

Cell death and survival 8.39E-13 - 446 5.84E-04

Apoptosis 2.10E-06 +0.813 322

Cell survival 3.21E-06 -2.861 181

Cell viability 2.87E-05 -2.212 163 Table 4.3: Top biological functions involved in HUVEC induced with euglycaemia-hypoxia for 12 hours. The biological functions were generated by analysing differentially expressed gene sets using IPA software. The activation z-score used in the calculation of significant changes in gene expression in different samples and conditions. It is calculated from the dataset and indicates activation or inhibition of the biological function as (+) means activation while (-) means inhibition.

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Ingenuity Canonical P-value Molecules Pathways

Role of BRCA1 in 1.99E-12 ↓BRCA1, ↓RFC3, ↓FANCB, ↓FANCL, ↓CHEK2, ↓MLH1, DNA Damage ↓BARD1, ↓MDC1, ↓RBBP8, ↓BLM, ↓BRIP1, ↓FANCD2, Response ↓RFC5, ↓RFC2, ↓ATR, ↓FANCG, ↓FANCA, ↓RBL1, ↓BRCA2, ↓RAD51, ↓RFC4, ↓PLK1, ↓MSH2, ↓CHEK1

Superpathway of 2.54E-10 ↓EBP, ↓HMGCS1, ↓NSDHL, ↓ACAT2, ↓HMGCR, ↓FDFT1, Cholesterol ↓MSMO1, ↓SQLE, ↓IDI1, ↓DHCR24, ↓MVK, ↓HSD17B7, Biosynthesis ↓ACAT1, ↓CYP51A1, ↓DHCR7

Cell Cycle Control of 9.73E-10 ↓RPA2, ↓CHEK2, ↓MCM2, ↓CDK2, ↓ORC6, ↓DBF4, Chromosomal ↓CDC7, ↓CDC45, ↓MCM3, ↓MCM6, ↓MCM4, ↓MCM5, Replication ↓CDC6, ↓ORC3

Mitotic Roles of Polo- 4.55E-09 ↓KIF23, ↓CHEK2, ↓ESPL1, ↓SMC1A, ↓CDC25A, ↓PRC1, Like Kinase ↓CDC20, ↓CCNB2, ↓KIF11, ↓CDC25C, ↓ANAPC1, ↓ANAPC10, ↓CDC7, ↓PLK4, ↓PTTG1, ↓ANAPC7, ↓PLK1, ↓CDK1, ↓CCNB1, ↓FBXO5, ↓CDC25B AMPK Signalling 1.95E-03 ↓CHRNA5, ↓MAPK14, ↓PIK3R3, ↓MTOR, ↑PFKFB3, ↓PRKAR2B, ↓PFKFB2, ↓MRAS, ↑AK4, ↓HMGCR, ↓PPAT, ↓FASN, ↑INSR, ↑SLC2A1, ↑PIK3R1, ↑GYS1, ↑PFKFB4, ↑PRKAA2, ↓NOS3, ↓ACACA HIF1α Signalling 1.78E-02 ↓MAPK14, ↓PIK3R3, ↑SLC2A3, ↑VEGFA, ↓MRAS, ↑EGLN3, ↑FIGF, ↑SLC2A1, ↑PIK3R1, ↓HIF1α, ↑EGLN1, ↑LDHA, ↑MAPK7, ↓NOS3 VEGF Signalling 3.31E-02 ↓PIK3R3, ↓VCL, ↑VEGFA, ↓MRAS, ↓HIF1α, ↑FLT1, ↑PXN, ↑FIGF, ↓EIF2B3, ↓NOS3, ↑PIK3R1, ↑MAP2K1 Table 4.4: Top canonical pathways involved in HUVEC induced with euglycaemia-hypoxia for 12 hours. Key: ACACA: acetyl-CoA carboxylase alpha; ACAT1: acetyl-CoA acetyltransferase 1; ACAT2: acetyl-CoA acetyltransferase 2; AK4: adenylate kinase 4; ANAPC1: anaphase promoting complex subunit 1; ANAPC7: anaphase promoting complex subunit 7; ANAPC10: anaphase promoting complex subunit 10; ATR: ataxia telangiectasia and Rad3 related; BARD1: BRCA1 associated RING domain 1; BLM: Bloom syndrome, RecQ helicase-like; BRCA1: breast cancer 1, early onset; BRCA2: breast cancer 2, early onset; BRIP1: BRCA1 interacting protein C-terminal helicase 1; CCNB1: cyclin B1; CCNB2: cyclin B2; CDC6: cell division cycle 6; CDC7: cell division cycle 7; CDC20: cell division cycle 20; CDC25A: cell division cycle 25A; CDC25B: cell division cycle 25B; CDC25C: cell division cycle 25C; CDC45: cell division cycle 45; CDK1: cyclin-dependent kinase 1; CDK2: cyclin-dependent kinase 2; CHEK1: checkpoint kinase 1; CHEK2: checkpoint kinase 2; CHRNA5: Cholinergic Receptor, Nicotinic, Alpha 5; CYP51A1: cytochrome P450, family 51, subfamily A, polypeptide 1; DBF4: DBF4 zinc finger; DHCR7: 7- dehydrocholesterol reductase; DHCR24: 24-dehydrocholesterol reductase; EBP: emopamil binding protein (sterol isomerase); EGLN3: egl-9 family hypoxia-inducible factor 3; EIF2B3: eukaryotic translation initiation factor 2B, subunit 3 gamma, 58kDa; ESPL1: extra spindle pole bodies homolog 1 (S. cerevisiae); FANCA: Fanconi anaemia, complementation group A; FANCB: Fanconi anemia, complementation group B; FANCD2: Fanconi anaemia, complementation group D2; FANCG: Fanconi anemia, complementation group G; FANCL: Fanconi anaemia, complementation group L; FASN: fatty acid synthase; FBXO5: F-box protein 5; FDFT1: farnesyl-diphosphate 111

Chapter 4. Vascular functions in mature endothelial cells farnesyltransferase 1; FIGF: c-fos induced growth factor (vascular endothelial growth factor D); FLT1: fms-related tyrosine kinase 1; GYS1: glycogen synthase 1; HIF-1α: hypoxia inducible factor- 1 alpha; HMGCR: 3-hydroxy-3-methylglutaryl-CoA reductase; HMGCS1: 3-hydroxy-3- methylglutaryl-CoA synthase 1 (soluble); HSD17B7: hydroxysteroid (17-beta) dehydrogenase 7; IDI1: isopentenyl-diphosphate delta isomerase 1; INSR: insulin receptor; KIF11: kinesin family member 11; KIF23: kinesin family member 23; LDHA: lactate dehydrogenase A; MAPK14: mitogen-activated protein kinase 14, MAP2K1: mitogen-activated protein kinase kinase 1; MCD1: known as RAD21 Homolog (S. Pombe); MCM2: minichromosome maintenance complex component 2; MCM3: minichromosome maintenance complex component 3; MCM4: minichromosome maintenance complex component 4; MCM5: minichromosome maintenance complex component 5; MCM6: minichromosome maintenance complex component 6; MLH1: mutL homology 1; MRAS: muscle RAS oncogene homolog; MSH2: mutS homolog 2; MSMO1: methylsterol monooxygenase 1; mTOR: Mammalian Target Of Rapamycin; MVK: mevalonate kinase; NOS3: nitric oxide synthase 3; NSDHL: NAD(P) dependent steroid dehydrogenase-like; ORC3: origin recognition complex, subunit 3; ORC6: origin recognition complex, subunit 6; PFKFB3: 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3; PIK3R3: phosphoinositide-3- kinase, regulatory subunit 3 (gamma); PLK1: polo-like kinase 1; PLK4: polo-like kinase 4; PPAT: phosphoribosyl pyrophosphate amidotransferase; PRC1: protein regulator of cytokinesis 1; PRKAA2: protein kinase, AMP-activated, alpha 2 catalytic subunit; PRKAR2B: protein kinase, cAMP-dependent, regulatory, type II, beta; PTTG1: pituitary tumour-transforming 1; PXN: paxillin; RAD51: RAD51 recombinase; RBBP8: retinoblastoma binding protein 8; RBL1: retinoblastoma-like 1; RFC2: replication factor C (activator 1) 2, 40kDa; RFC3: replication factor C (activator 1) 3, 38kDa; RFC4: replication factor C (activator 1) 4, 37kDa; RFC5: replication factor C (activator 1) 5, 36.5kDa; RPA2: replication protein A2, 32kDa; SLC2A1: solute carrier family 2 (facilitated glucose transporter), member 1; SQLE: squalene epoxidase; SMC1A: structural maintenance of chromosomes 1A; VCL: vinculin; VEGFA: vascular endothelial growth factor A. The arrow ↓ indicates downregulated gene.

The pathway analysis revealed molecular pathways involving several signalling molecules and thus contributing to our understanding of the molecular mechanism implicated in ischaemia. Amongst the pathways were HIF-1α signalling, AMPK signalling, mTOR signalling, and VEGF signalling (described in Sections 4.3.4, 4.3.5 and 4.3.6). We found that expression of HIF-1α reached its maximum after 3 hours coinciding with its known protective cellular function under hypoxic conditions. HIF-1α mRNA was found to be downregulated after 12 hours (– 1.64-fold, p = 1.39E-06). Moreover, the activity of EGLN1 (1.97, 2.53-fold) and EGLN3 (3.72, 6.19-fold) prolylhydroxylases increased significantly by time; after 3 and 12 hours of hypoxia respectively; triggering the hydroxylation and then degradation of HIF-1α. EGLN1 and EGLN3 play a role in cell apoptosis (Lee et al., 2005, Tambuwala et al., 2010). Although expression of the angiogenic factor VEGFA was augmented (5.15-fold, p = 4.51E-13), the angiogenic inhibitors ANGPTL4 (9.37-fold, p =

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3.42E-20) and TIMP3 (3.45 fold, p = 7.03E-03) were upregulated reaching the observed maximum expression after 12 hours.

Gene expression of HUVEC under hyperglycaemic condition

When HUVEC were treated with a high glucose concentration of 16.5 mM for 24 hours gene expression changes were minor compared to the effect of hypoxia under euglycaemic conditions. The microarray data analysis demonstrated 164 differentially expressed genes (Table 7.6 Appendix III) compared to control (5.5 mM glucose with normoxia). Thioredoxin interacting protein (TXNIP) was highly overexpressed (5.44-fold, p = 1.69E- 10) under hyperglycaemia compared to euglycaemia. TXNIP is a glucose inducible gene as it was reported previously to be overexpressed in both diabetic animals and humans (Parikh et al., 2007, Schulze et al., 2004). Moreover, TXNIP overexpression impaired EC angiogenic function and VEGF signalling (Dunn et al., 2014). IPA analysis showed that TXNIP induces cell cycle arrest in sub-G1 phase (p = 7.30E-04) as well as apoptosis (p = 7.28E-03). These functions were studied thoroughly using functional assays that have been described in Section 4.3.1. Furthermore, TXNIP gene expression was assessed by qRT-PCR showing that hyperglycaemia was associated with elevation of mRNA level (19.9-fold, p < 0.001) compared to euglycaemia (Figure 4.11). There was no significant change in the expression of TXNIP under hyperglycaemia combined with hypoxia compared to hyperglycaemia alone. Thus, no pathways were significantly identified since < 3 genes were differentially expressed per pathway.

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Figure 4.11: Effect of hypoxia on TXNIP mRNA expression in euglycaemia and hyperglycaemia. HUVEC were treated with high glucose concentration or in control conditions with normal glucose concentration. After 24 hours, hypoxia was induced for either 1, 3 or 12 hours. The variation in mRNA levels of TXNIP was assessed by qRT-PCR on three independent biological replicates. Results are presented as mean ± SEM and were analysed using one-way ANOVA followed by LSD test. The effect of hypoxia was revealed by comparing the conditions against the control. *P < 0.05, **P < 0.01, ***P < 0.001.

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Gene expression of HUVEC under combined hyperglycaemia and hypoxia DM is associated with high incidence of CVD leading to mortality and disability than other onsets. Therefore, we applied in vitro models that resemble the behaviour of CVD and diabetes in HUVEC under hyperglycaemia and hypoxia in vitro. These cells were incubated for 24 hours with hyperglycaemia (16.5 mM) and then subjected to CoCl2 induced hypoxia for either 1, 3, 12 hours. The data were analysed by one-way ANOVA with FC cutoff of 1.5 and FDR-unadjusted p-value < 0.05.

Effect of combined hyperglycaemia and hypoxia for 1 hour Results showed that 122 genes passed the cutoff 1.5 and p-value < 0.05 after 1 hour of hypoxia compared to the control 5.5 mM glucose with normoxia (Table 7.7 Appendix III). Among the top associated molecular and cellular functions were cardiovascular system development and function, cellular movement that was activated (7.35E-05), whereas cell death decreased, and survival increased (p = 1.02E-05). However, the top canonical pathways were: RAR activation (p = 9.25E-03), and mTOR signalling (p = 1.05E-02). The early response to hypoxia in both euglycaemia and hyperglycaemia after 1 hour exposure was initiated by upregulation of downstream genes of HIF-1α: ADM, ANGPTL4, BHLHE40, and VEGFA with similar fold changes. In addition; combined hyperglycaemia hypoxia was associated with induction of TXNIP mRNA (5.75-fold, p = 7.13E-11). The expression of TXNIP assayed by qRT-PCR is presented in Figure 4.12.

Effect of combined hyperglycaemia and hypoxia for 3 hours The response to hypoxia increased after 3 hours as reflected by the number of differentially expressed genes rose to 255 compared to control (5.5 mM glucose with normoxia). The most 30 differentially expressed genes are illustrated in Table 7.8 in Appendix III. According to the created gene list, the most significant molecular functions detected by IPA software (Table 4.5) were elucidated depending on the p-value. Additionally, the top canonical pathways were: HIF-1α signalling (p = 6.48E-06), AMPK signalling (p = 2.28E- 03), RAR activation (p = 7.08E-03), and mTOR signalling (p = 9.12E-03) as summarised in Table 4.6.

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Molecular P-value Activation Molecules Number of function z-score molecules Cell death and 4.24E-06 - 46 survival 1.35E-02

Cell death of 1.17E-03 -1.422 ADM, DUSP6, HMOX1, MAPK7, 5 vascular VEGFA endothelial cells

Cell viability 1.51E-03 +2.243 ABCB6, ABL1, ACER2, BHLHE40, 22 BNIP3, DUSP1, DUSP6, EFNA3, EGLN3, HK2, HMOX1, MAPK7, NRN1, PCDH18, PDK1, PIK3IP1, PIK3R1, PPFIA4, SLC2A1, STC1, TXNIP, VEGFA Carbohydrate 1.42E-05 - 20 metabolism 7.57E-03

Glycolysis 1.42E-05 ENO2, HK2, PFKFB3, PGM1, PIK3R1, 7 SLC16A3, SLC2A1

Transport of D- 3.32E-04 +1.578 HK2, PIK3R1, PPP1R3C, SLC2A1, 6 glucose SLC2A3, TXNIP Cardiovascular 2.70E-05 - 28 system 1.35E-02 development and function

Angiogenesis 3.66E-03 +1.173 ABL1, ADM, ANGPTL4, CCL28, 14 EGLN1, EGLN3, HMOX1, JMJD6, JUNB, KLHL20, MAPK7, PIK3R1, RORA, VEGFA

Development of 1.50E-03 +0.419 ADM, ANGPTL4, EGLN1, HMOX1, 10 cardiovascular KLHL20, MAPK7, PFKFB3, STC1, tissue VEGFA, VLDLR Table 4.5: Top biological functions involved in HUVEC induced by combined hyperglycaemia-hypoxia for 3 hours. The biological functions were generated by analysing differentially expressed gene sets using IPA software. The activation z-score indicates activation or inhibition of the biological function as (+) means activation while (-) means inhibition. Key: PCDH18: protocadherin 18; JMJD6: jumonji domain containing 6; for the rest of genes refer to Table 4.1 legend.

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Ingenuity Canonical P-value Molecules Pathways ↑SLC2A3, ↑VEGFA, ↑EGLN1, ↑EGLN3, ↑MAPK7, ↑SLC2A1, HIF-1α Signalling 6.48E-06 ↑PIK3R1

AMPK Signalling 2.28E-03 ↑PFKFB3, ↑PFKFB4, ↑AK4, ↑SLC2A1, ↑PIK3R1

RAR Activation 7.08E-03 ↑DUSP1, ↑VEGFA, ↑TNIP1, ↑RDH13, ↑PIK3R1 mTOR Signalling 9.12E-03 ↑PRR5L, ↑VEGFA, ↑DDIT4, ↑PIK3R1, ↑HMOX1 Table 4.6: Top biological functions involved in HUVEC induced by combined hyperglycaemia-hypoxia for 3 hours. Key: DDIT4: DNA-damage-inducible transcript 4; PRR5L: proline rich 5 like; RDH13: retinol dehydrogenase 13; TNIP1: TNFAIP3 interacting protein 1; for the rest of genes refer to Table 4.1 legend. The arrow ↑ indicates upregulated gene compared to the control (euglycaemia with normoxia).

Effect of combined hyperglycaemia and hypoxia for 12 hours The number of differentially expressed genes after hyperglycaemia combined with hypoxia for 12 hours was 1470 with FC cutoff of 1.5 and unadjusted p-value < 0.05. The most 50 differentially expressed genes are represented in Table 7.9 Appendix III. The top biological functions influenced after 12 hours of hypoxia were the cellular growth and proliferation, cell cycle, DNA replication, recombination and repair, cell death and survival. These functions with number of molecules differentially expressed have been summarised according to the lowest p-value in Table 4.7. The top related canonical pathways to these functions were: growth arrest and DNA-Damage-Inducible 45 (GADD45) signalling (p = 8.33E-06), purine nucleotide de novo biosynthesis II (p = 5.3E-05), 5-aminoimidazole ribonucleotide biosynthesis I (p = 7.86E-05), cell cycle: G2/M DNA damage checkpoint regulation (p = 2.06E-04), and AMPK signalling (p = 6.88E-04) as underlined in Table 4.8. HIF-1α and VEGF signalling were non-significantly altered under combined hyperglycaemia hypoxia for 12 hours.

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Molecular function P-value Activation z- Number of molecules score

Cellular growth and 1.10E-11 – 6.19E-03 302 proliferation

Cellular growth and proliferation 1.10E-11 -3.060 293

Colony formation of cells

8.28E-04 -1.765 42

Cell cycle 8.96E-10 - 146 6.19E-03

Cell cycle progression 8.96E-10 -3.047 103

Check point control 3.36E-06 -2.577 17

DNA replication, 8.82E-09 - 115 recombination and repair 5.35E-03

Metabolism of DNA 2.29E-05 -1.146 41

Cell death and survival 1.81E-07 - 270 6.17E-03

Apoptosis 1.81E-07 -0.870 131

Cell survival 2.20E-06 -0.780 117

Cell viability 2.54E-06 -0.403 109 Table 4.7: Top biological functions involved in HUVEC induced by combined hyperglycaemia and hypoxia for 12 hours. The biological functions were generated by analysing differentially expressed gene sets using IPA software. The activation z-score used in the calculation of significant changes in gene expression in different samples and conditions. It is calculated from the dataset and indicates activation or inhibition of the biological function as (+) means activation while (-) means inhibition.

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Ingenuity Canonical P-value Molecules Pathways GADD45 Signalling 8.33E-06 ↓PCNA, ↓CCNE2, ↑GADD45B, ↓ATR, ↓CCND1, ↓CDK1, ↓CCNB1

Purine Nucleotides De 5.3E-05 ↑ADSSL1, ↓PFAS, ↓PPAT, ↓ATIC, ↓GART Novo Biosynthesis II 5-aminoimidazole 7.86E-05 ↓PFAS, ↓PPAT, ↓GART Ribonucleotide Biosynthesis I Cell Cycle: G2/M DNA 2.06E-04 ↓CDC25B, ↓PRKDC, ↑ABL1, ↓TOP2A, ↓CCNB2, ↓ATR, Damage Checkpoint ↓CDK1, ↓CHEK1, ↓CCNB1 Regulation AMPK Signalling 6.88E-04 ↓PRKACB, ↑PFKFB3, ↑SLC2A1, ↑PIK3R1, ↑PPP2R5B, ↑PFKL, ↓PFKFB2, ↑GYS1, ↓PRKAR2B, ↑PFKFB4, ↓FASN, ↓ACACA, ↑AK4, ↑INSR, ↓PPAT Table 4.8: Top canonical pathways involved in HUVEC activated by combined hyperglycaemia and hypoxia for 12 hours. Key: ABL1: Abelson Tyrosine-Protein Kinase 1; ACACA: acetyl-CoA carboxylase α; ADSSL1: adenylosuccinate synthase like 1; AK4: adenylate kinase 4; ATIC: 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase/IMP cyclohydrolase; ATR: ataxia telangiectasia and Rad3 related; CCNB1: cyclin B1; CCNB2: cyclin B2; CCND1: cyclin D1; CCNE2: cyclin E2 CDC25B: cell division cycle 25B; CDK1: cyclin- dependent kinase 1; CHEK1: checkpoint kinase 1; FASN: fatty acid synthase; GADD45B: growth arrest and DNA-damage-inducible, beta; GART: phosphoribosylglycinamide formyltransferase; GYS1: glycogen synthase 1 (muscle); INSR: insulin receptor; PCNA: proliferating cell nuclear antigen; PFAS: phosphoribosylformylglycinamidine synthase; PFKFB2: 6-phosphofructo-2- kinase/fructose-2,6-biphosphatase 2; PFKFB3: 6-phosphofructo-2-kinase/fructose-2,6- biphosphatase 3; PFKFB4: 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 4; PFKL: phosphofructokinase, liver; PIK3R1: phosphoinositide-3-kinase, regulatory subunit 1 (alpha); PPAT: phosphoribosyl pyrophosphate amidotransferase; PPP2R5B: protein phosphatase 2, regulatory subunit B', beta; PRKACB: protein kinase, cAMP-dependent, catalytic, beta; PRKAR2B: protein kinase, cAMP-dependent, regulatory, type II, beta; PRKDC: protein kinase, DNA-activated, catalytic polypeptide; SLC2A1 (GLUT1): solute carrier family 2 (facilitated glucose transporter), member 1; TOP2A: topoisomerase (DNA) II alpha 170kDa. The arrows indicate: ↓ downregulated gene while ↑ upregulated gene.

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4.3.3 Effect of metformin on gene expression in HUVEC Metformin is a biguanide and currently the first-line drug recommended for the treatment of type 2 DM. Moreover, metformin is the only anti-diabetic drug that has a cardioprotective effect as supported by data from the clinical outcome trial UKPDS 34 (1998a). Metformin lowers plasma glucose levels by suppressing hepatic gluconeogenesis, enhancing peripheral glucose utilisation, altering lipid metabolism and enhancing insulin sensitivity (El Messaoudi et al., 2013). An underlying mechanism behind these functions involves AMPK activation (Zhou et al., 2001). Metformin activates AMPK in the liver and other tissues as a consequence of the transient reduction in cellular energy induced through mild inhibition of the mitochondrial respiratory chain complex 1. This attenuates intracellular ATP concentration by increasing the AMP to ATP ratio (Stephenne et al., 2011). Physiological metformin concentrations range between 0.01 mM and 0.04 mM (Sum et al., 1992, Wiernsperger, 1999). However, in vitro anticancer research employs higher concentrations of metformin (Erices et al., 2013).

In order to prove our hypotheses that metformin activates HIF-1α signalling, VEGF signalling and AMPK signalling, HUVEC were incubated under hyperglycaemia or euglycaemia for 24 hours and then treated with physiological metformin concentration (0.01 mM) for 12 hours. This was followed by induction of chemical hypoxia for different time intervals 1, 3 or 12 hours. Euglycaemic and hypoxic cultures were used as controls for the experiments.

Effect of metformin on euglycaemia and combined euglycaemia-hypoxia

Each condition was compared pairwise with the metformin-treated condition and differentially expressed gene sets were generated by Partek software (Table 4.9). After analysing the gene lists with IPA, we found that the top biological functions altered under euglycaemia and metformin were cell progression arrest (p = 3.20E-25, 51 genes), decreased cell proliferation (p = 1.32E-06, 60 genes), and increased cell death (p = 2.87E- 06, 56 genes). After 1 hour of euglycaemia-hypoxia, arrest in cell cycle progression was detected (p = 1.21E-02, 11 genes). Whereas after 3 hours of euglycaemia-hypoxia, binding of vascular ECs was predicted to be activated (p =1.75E-07) due to upregulation of three

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Chapter 4. Vascular functions in mature endothelial cells genes, namely intercellular adhesion molecule 1 (ICAM1), KIT ligand (KITLG), and selectin E (SELE). Furthermore, cell morphology (p = 1.57E-06) due to overexpression of ICAM1, KITLG, and SELE, and underexpression of TXNIP, membrane protein, palmitoylated 1, 55kDa (MPP1), protein tyrosine phosphatase, and non-receptor type 22 (PTPN22). Atherosclerosis signalling is the top canonical pathway affected (p = 1.11E-04) through upregulation of the following genes: phospholipase A2, group IVC (cytosolic, calcium-independent) (PLA2G4C), SELE, ICAM1. After 12 hours of hypoxia, lipid metabolism (p = 3.43E-06) was associated with alterations of three genes: UDP glucuronosyltransferase 2 family, polypeptide B17 (UGT2B17), UDP glucuronosyltransferase 2 family, polypeptide B28 (UGT2B28), and KITLG, as well as cell cycle progression (p = 5.91E-04) associated with alterations of two genes: CD274 molecule (CD274), and KITLG. Hence, no pathways were significantly identified since < 3 genes were differentially expressed per pathway (data not shown).

Effect of metformin on hyperglycaemia and combined hyperglycaemia-hypoxia

The most differentially expressed genes affected by metformin under hyperglycaemia exposed to hypoxia are summarised in Table 4.10. Microarray results had shown no change in TXNIP expression levels upon treatment with physiological metformin. This was validated by qRT-PCR assay that demonstrated that metformin exhibited no effect on TXNIP mRNA expression under hyperglycaemia or following exposure to hypoxia in euglycaemia and hyperglycaemia in HUVEC (Figure 4.12).

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Figure 4.12: Effect of metformin on TXNIP mRNA expression in euglycaemia and hyperglycaemia. HUVEC were treated with high glucose concentration or in control conditions with normal glucose concentration. After 24 hours, metformin (0.01 mM) was added to euglycaemic and hyperglycaemic cultures for 12 hours, then hypoxia was induced for either 1, 3 or 12 hours. The variation in mRNA levels of TXNIP was assessed by qRT-PCR on three independent biological replicates. Results are presented as mean ± SEM and were analysed using one-way ANOVA followed by LSD test. The effect of metformin was identified by comparing the conditions pairwise, i.e. the condition without metformin versus metformin-treated condition. *P < 0.05, **P < 0.01, ***P < 0.001.

The gene list was imported to IPA software to find out the correlation between these genes and the studied pathways as explained thoroughly in Sections 4.3.4, 4.3.5, and 4.3.6. Moreover, metformin had undetectable effect on the biological functions under hyperglycaemia; however, metformin altered significantly biological functions under hyperglycaemia exposed to hypoxia either for 1, 3 or 12 hours (Tables 4.11, 4.12 and 4.13). Metformin stimulates adhesion of ECs by upregulation of ICAM1 and SELE under hyperglycaemia exposed to hypoxia for 1 and 3 hours. However, ICAM1 and SELE were detected to be non-significantly expressed in HUVEC treated with metformin and hyperglycaemia-hypoxia for 12 hours. In previous studies, it was found that VEGF as a proinflammatory cytokine stimulates the expression of ICAM1 and SELE that are mediated mainly through nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB)

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Chapter 4. Vascular functions in mature endothelial cells activation (Detmar et al., 1998, Melder et al., 1996, Kim et al., 2001). Additionally, there was a relation between leukocyte-endothelial cells adhesion molecules and angiogenesis as the endothelial markers were identified to play a role in leukocyte recruitment that was involved in neovascularization (Ferrara, 1995). Furthermore, it was observed by Griffioen and Molema (2000) that angiogenic stimuli often downregulate adhesion molecules involved in leukocyte-endothelial cell interactions (Griffioen and Molema, 2000), which explain the non-significant expression of adhesion molecules after prolonged hypoxia.

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Gene name Gene P-value FC control + P-value FC hypoxia P-value FC hypoxia P-value FC hypoxia 12h Symbol metformin 1h + 3h + + metformin metformin metformin phosphoserine PSAT1 5.81E-01 -1.20 8.90E-01 -1.05 1.32E-01 1.64 1.95E-02 2.19 aminotransferase 1 asparagine synthetase ASNS 8.84E-01 1.05 9.59E-01 -1.02 2.12E-01 1.49 4.09E-02 1.93 (glutamine-hydrolyzing) KIT ligand KITLG 1.69E-01 1.32 5.47E-02 1.47 1.45E-03 1.95 3.17E-03 1.85 enkurin, TRPC channel ENKUR 5.00E-01 1.17 4.09E-01 1.21 5.75E-01 1.14 3.41E-02 1.65 interacting protein CD274 molecule CD274 4.72E-01 1.16 1.28E-01 1.36 1.73E-01 1.32 1.62E-02 1.64 vestigial like 3 VGLL3 7.15E-01 1.09 1.10E-01 1.46 6.52E-01 1.11 4.22E-02 1.63 (Drosophila) glycine receptor, beta GLRB 4.83E-01 1.11 1.57E-03 1.65 1.18E-02 1.48 2.46E-03 1.62 TSPY-like 1 TSPYL1 9.73E-01 -1.01 4.53E-01 1.16 4.34E-01 1.16 1.95E-02 1.59 WD repeat domain 52 WDR52 4.26E-01 1.15 8.35E-01 1.04 2.15E-01 1.24 1.25E-02 1.56 zinc finger protein 594 ZNF594 5.84E-01 -1.09 1.30E-01 1.28 7.61E-01 1.05 1.12E-02 1.54 dpy-19-like 2 pseudogene DPY19L2P2 1.26E-01 1.24 9.65E-01 -1.01 5.15E-01 -1.09 3.06E-03 1.54 2 (C. elegans) solute carrier family 6 SLC6A9 3.59E-01 1.20 8.69E-01 1.03 8.52E-01 1.04 3.52E-02 1.54 (neurotransmitter transporter, glycine) interferon, alpha 21 IFNA21 3.75E-01 1.13 3.47E-01 -1.14 5.28E-01 -1.09 2.99E-03 -1.52 signal recognition SRP72P2 7.25E-01 -1.05 3.07E-01 -1.16 2.13E-03 1.59 4.95E-03 -1.53 particle 72kDa pseudogene 2 tripartite motif TRIM43 2.59E-01 1.12 1.78E-01 1.14 3.86E-01 1.09 4.55E-05 -1.55 containing 43 UDP UGT2B17 7.10E-01 -1.08 5.79E-01 -1.11 2.95E-01 -1.23 2.68E-02 -1.56 glucuronosyltransferase 2 family, polypeptide B17 SPANX family, member SPANXE 3.54E-01 -1.18 7.01E-01 -1.07 9.61E-02 -1.35 1.46E-02 -1.57 E chromosome 3 open C3orf79 5.93E-01 1.09 6.72E-01 1.07 2.84E-01 -1.19 8.53E-03 -1.57 reading frame 79

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Gene name Gene P-value FC control + P-value FC hypoxia P-value FC hypoxia P-value FC hypoxia 12h Symbol metformin 1h + 3h + + metformin metformin metformin histone cluster 1, H2bh HIST1H2BH 1.05E-01 -1.25 6.08E-02 -1.30 1.61E-01 -1.21 1.37E-03 -1.59 IQ motif containing with IQCA1P1 6.91E-01 -1.10 5.78E-01 -1.14 8.45E-01 1.05 4.98E-02 -1.61 AAA domain 1 pseudogene 1 RNA, 5S ribosomal 22 RN5S22 2.57E-01 -1.43 4.65E-02 -1.89 2.68E-01 1.42 1.19E-01 -1.64 gap junction protein, GJA4 1.70E-01 1.42 2.77E-01 1.32 9.08E-01 1.03 4.76E-02 -1.67 alpha 4, 37kDa UDP UGT2B28 8.48E-01 1.03 7.40E-01 1.06 1.43E-01 -1.29 3.33E-03 -1.70 glucuronosyltransferase 2 family, polypeptide B28 CMT1A duplicated CDRT1 3.03E-01 1.27 1.69E-01 1.37 3.18E-01 1.26 1.45E-02 -1.78 region transcript 1 lipase, family member K LIPK 2.90E-02 -1.52 6.88E-01 1.08 6.65E-01 -1.08 2.21E-03 -1.84 cystatin SN CST1 3.30E-02 -3.48 1.79E-01 -2.16 4.07E-02 -3.30 2.32E-01 -1.99 Table 4.9: The top differentially expressed genes in HUVEC treated with metformin and exposed to euglycaemia or combined euglycaemia hypoxia. HUVEC were incubated with normal glucose concentration 5.5 mM and 0.01 mM metformin then chemical hypoxia was induced by 150 µM CoCl2. Affymetrix .CEL files were imported to Partek Genomic Suite version 6.6 and normalised using RMA. Differentially expressed gene sets were generated using one-way ANOVA, FDR-unadjusted p-value < 0.05 and FC cutoff of 1.5 compared pairwise, i.e. the condition treated with and without metformin. Subsequently, the most influenced genes were compared among different conditions of hypoxia. The signs in the FC column denote (-) downregulated, (+) upregulated genes.

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Gene name Gene Symbol P-value FC P-value FC 16.5mM P-value FC 16.5mM P-value FC 16.5 mM 16.5mM Glucose + Glucose + Glucose + Glucose + hypoxia 1h + hypoxia 3h + hypoxia 12h + metformin metformin metformin metformin small nucleolar RNA, SNORA20 5.67E-01 1.26 6.07E-01 -1.23 5.45E-01 -1.28 1.23E-03 4.01 H/ACA box 20 small nucleolar RNA, C/D SNORD45C 2.21E-01 1.73 3.28E-01 -1.55 3.62E-01 -1.50 1.26E-02 3.16 box 45C metastasis associated lung MALAT1 7.76E-01 -1.12 2.77E-01 1.53 2.88E-02 2.38 8.78E-03 2.87 adenocarcinoma transcript 1 (non-protein) Rho-associated, coiled-coil ROCK1 3.13E-01 1.46 8.58E-01 1.07 9.38E-02 1.90 1.15E-02 2.68 containing protein kinase 1 cerebellar degeneration- CDR1 7.24E-01 1.13 1.71E-01 1.63 5.47E-03 2.80 1.20E-02 2.52 related protein 1, 34kDa small nucleolar RNA, C/D SNORD116-6 3.36E-01 1.50 8.19E-01 1.10 9.02E-01 -1.05 4.32E-02 2.40 box 116-6 small nucleolar RNA, SNORA14A 7.73E-01 1.08 7.07E-01 1.11 9.95E-01 1.00 2.68E-03 2.38 H/ACA box 14A exocyst complex EXOC5 7.91E-01 1.08 6.16E-01 1.15 5.95E-02 1.70 4.99E-03 2.25 component 5 protein-L-isoaspartate (D- PCMTD1 4.68E-01 1.25 5.93E-01 1.18 7.61E-02 1.75 1.45E-02 2.19 aspartate) O- methyltransferase domain containing 1 small Cajal body-specific SCARNA1 9.95E-01 1.00 3.66E-01 -1.42 2.07E-01 -1.63 4.99E-02 2.16 RNA 1 TATA box binding TAF1D 1.36E-04 2.67 3.01E-01 1.28 8.99E-01 1.03 2.52E-03 2.13 protein (TBP)-associated factor, RNA polymerase I NADH dehydrogenase, ND6 3.71E-01 -1.34 4.49E-01 1.28 6.04E-02 1.86 2.54E-02 2.11 subunit 6 (complex I) immediate early response IER3IP1 7.37E-01 1.14 9.94E-01 1.00 4.37E-02 2.25 8.13E-02 2.01 3 interacting protein 1 small nucleolar RNA, SNORA14B 5.91E-02 1.68 7.83E-01 -1.08 8.53E-01 -1.05 1.30E-02 1.99 H/ACA box 14B small nucleolar RNA host SNHG3 1.05E-01 1.42 1.09E-01 1.41 6.62E-01 1.10 2.19E-03 1.99 gene 3 (non-protein coding) 126

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Gene name Gene Symbol P-value FC P-value FC 16.5mM P-value FC 16.5mM P-value FC 16.5 mM 16.5mM Glucose + Glucose + Glucose + Glucose + hypoxia 1h + hypoxia 3h + hypoxia 12h + metformin metformin metformin metformin RNU1-11P // RNA, U1 RNU1-11P 1.51E-01 1.51 8.25E-01 -1.06 1.28E-01 -1.55 2.04E-02 1.97 small nuclear 11, pseudogene interleukin 8 IL8 3.77E-01 1.30 5.37E-02 1.79 1.49E-01 1.54 2.84E-02 1.94 Tumour necrosis factor TNFSF18 3.27E-01 1.29 4.84E-01 1.20 4.04E-01 1.24 1.41E-02 1.92 (ligand) superfamily, member 18 small nucleolar RNA host SNHG10 5.36E-01 1.17 9.32E-01 1.02 4.22E-01 1.23 1.47E-02 1.92 gene 10 (non-protein coding) ribosomal protein L41 RPL41 9.90E-01 -1.00 7.90E-02 1.48 5.86E-02 1.53 4.77E-03 1.91 RNA, U5B small nuclear RNU5B-1 5.93E-01 -1.17 7.42E-01 -1.10 3.91E-01 -1.29 3.25E-02 1.91 1 small nucleolar RNA, C/D SNORD116-24 2.19E-01 1.46 8.03E-01 1.08 7.70E-01 -1.09 3.99E-02 1.90 box 116-24 ring finger protein, LIM RLIM 7.47E-01 -1.10 6.02E-01 1.17 4.99E-02 1.85 4.99E-02 1.85 domain interacting RNA, 5S ribosomal 192 RN5S192 2.23E-01 1.35 7.50E-01 -1.08 3.91E-01 -1.23 1.54E-02 1.83 eukaryotic translation EIF4B 3.32E-01 1.29 6.46E-01 1.13 2.14E-01 1.38 2.48E-02 1.81 initiation factor 4B RNA, 5S ribosomal 55 RN5S55 7.56E-01 1.07 5.48E-01 -1.14 5.37E-01 1.15 9.80E-03 1.81 small Cajal body-specific SCARNA9L 4.94E-01 1.21 6.05E-01 1.16 4.89E-01 -1.21 4.21E-02 1.78 RNA 9-like cytochrome c oxidase COX7B 3.96E-01 1.18 8.21E-01 1.05 4.23E-01 1.17 5.53E-03 1.76 subunit VIIb matrix metallopeptidase MMP16 2.84E-01 1.29 4.90E-01 1.18 8.78E-01 -1.04 2.34E-02 1.73 16 (membrane-inserted) histone cluster 1, H1a HIST1H1A 1.40E-01 -1.28 1.25E-01 -1.30 1.91E-01 -1.25 1.45E-03 -1.75 trans-2,3-enoyl-CoA TECR 8.14E-02 -1.40 2.94E-02 -1.53 4.97E-02 -1.46 4.14E-03 -1.77 reductase histone cluster 2, H2ab HIST2H2AB 1.34E-01 -1.39 1.33E-01 -1.39 9.95E-02 -1.44 1.14E-02 -1.77 gap junction protein, GJA4 8.40E-01 1.05 8.35E-01 -1.05 7.69E-01 -1.08 2.90E-02 -1.77 alpha 4, 37kDa 127

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Gene name Gene Symbol P-value FC P-value FC 16.5mM P-value FC 16.5mM P-value FC 16.5 mM 16.5mM Glucose + Glucose + Glucose + Glucose + hypoxia 1h + hypoxia 3h + hypoxia 12h + metformin metformin metformin metformin uncoupling protein 2 UCP2 4.74E-01 -1.20 2.74E-01 -1.33 1.65E-01 -1.43 2.28E-02 -1.82 (mitochondrial, proton carrier) NLR family, pyrin NLRP2 9.31E-01 1.02 5.82E-01 -1.11 8.81E-01 -1.03 2.82E-03 -1.84 domain containing 2 chromosome 17 open C17orf108 6.01E-01 1.12 8.86E-01 -1.03 9.30E-01 -1.02 1.92E-03 -2.05 reading frame 108 histone cluster 1, H2bm HIST1H2BM 1.11E-01 -1.51 2.30E-01 -1.36 1.67E-01 -1.42 3.76E-03 -2.16 amelotin AMTN 8.02E-01 1.07 9.38E-01 1.02 8.53E-01 -1.05 2.00E-03 -2.54 Table 4.10: The top differentially expressed genes in HUVEC treated with metformin and exposed to hyperglycaemia or combined hyperglycaemia hypoxia. HUVEC were incubated with high glucose concentration 16.5 mM and 0.01 mM metformin then chemical hypoxia was induced by 150 µM CoCl2. Affymetrix .CEL files were imported to Partek Genomic Suite version 6.6 and normalised using RMA. Differentially expressed gene sets were generated using one-way ANOVA, FDR-unadjusted p-value < 0.05 and FC cutoff of 1.5 compared pairwise, i.e. the condition treated with and without metformin. Subsequently, the most influenced genes were compared among different conditions of hypoxia. The signs in the FC column denote (-) downregulated, (+) upregulated genes.

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Biological P-value Activation z-score Molecules Number of function molecules adhesion of 9.78E-06 ↑ICAM1, ↑SELE 2 endothelial cells

Cell cycle 1.10E-05 ↓CDC25A, ↓CDC7, ↓FANCG, 4 checkpoint ↓MCM7 control cell death 1.76E-03 -0.445 ↓CDC25A, ↓CDC7, ↑FABP4, 13 ↓FANCG, ↓GAS5, ↓GINS1, ↑ICAM1, ↓MCM7, ↓NQO1, ↓PTPN22, ↑SELE, ↓ZNF443 Table 4.11: Effect of metformin on biological functions involved in HUVEC induced by combined hyperglycaemia hypoxia for 1 hour. The biological functions of the genes were generated by using IPA software. The activation z-score used in the calculation of significant changes in gene expression in different samples and conditions. It is calculated from the dataset and indicates activation or inhibition of the biological function as (+) represents activation while (-) represents inhibition. The arrow ↑ indicates upregulated gene and ↓ indicates downregulated gene. Key: CDC7: cell division cycle 7; CDC25A: cell division cycle 25A; FABP4: fatty acid binding protein 4, adipocyte; FANCG: Fanconi anaemia, complementation group G; GAS5: growth arrest- specific 5 (non-protein coding); GINS1: GINS complex subunit 1 (Psf1 homolog); ICAM1: intercellular adhesion molecule 1; MCM7: minichromosome maintenance complex component 7; NQO1: NAD(P)H dehydrogenase, quinone 1; PTPN22: protein tyrosine phosphatase, non-receptor type 22 (lymphoid); SELE: selectin E; ZNF443: zinc finger protein 443.

Biological function P-value Molecules Number of molecules adhesion of endothelial cells 9.70E-05 ↑ICAM1, ↑SELE 2 hydrolysis of fatty acid 1.56E-04 ↑FABP4, ↑PLA2G4C 2 Table 4.12: Effect of metformin on biological functions involved in HUVEC induced by combined hyperglycaemia-hypoxia for 3 hours. Key: PLA2G4C: phospholipase A2, group IVC (cytosolic, calcium-independent); for genes names and details refer to the legend in Table 4.11.

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Biological function P-value Activation z-score Molecules Number of molecules

Carbohydrate 7.13E-04 ↑IL8, ↑LY96 2 metabolism

(binding of lipopolysaccharide)

Angiogenesis of 5.31E-03 ↑CTSS, ↑IL8 2 endothelial cells

Cell migration 3.04E-03 +2.622 ↑ALX1, ↑CTSS, 6 ↑IL8, ↑MMP16,

↑ROCK1, ↑TFPI2 Table 4.13: Effect of metformin on biological functions involved in HUVEC induced by combined hyperglycaemia-hypoxia for 12 hours. Key: ALX1: ALX homeobox 1; CTSS: cathepsin S; IL8: interleukin 8; LY96: lymphocyte antigen 96; MMP16: matrix metallopeptidase 16 (membrane-inserted); ROCK1: Rho-associated, coiled-coil containing protein kinase 1; TFPI2: tissue factor pathway inhibitor 2. For details refer to the legend in Table 4.11.

4.3.4 HIF-1α signalling Transcription factor HIF-1α is a crucial determinant of oxygen-dependent gene regulation. HIF-1 is specifically activated by hypoxia and induces target genes which influence energy metabolism (Semenza et al., 1994), cell proliferation (Carmeliet et al., 1998), vascular development (remodelling and angiogenesis) (Liu et al., 1995, Rose et al., 2002) and vascular tone (Kourembanas et al., 1998). Regulation of HIF-1, which is a dimer of HIF-1α and HIF-1β, is mainly mediated by protein stabilisation and transactivation of HIF-1α (Salceda and Caro, 1997, Jiang et al., 1996). In normoxia, HIF-1α is immediately targeted for ubiquitination by hydroxylation of two specific proline residues and then degraded by the proteasome pathway (Jaakkola et al., 2001). In hypoxia, hydroxylation and proteasomal degradation are suppressed leading to a dramatic increase of HIF-1α protein. Subsequently, HIF-1α translocates to the nucleus, dimerises with HIF-1β, and activates target genes by binding of HIF-1 to HRE (Ivan et al., 2001).

Microarray expression analysis of HUVEC exposed to euglycaemia-hypoxia and combined hyperglycaemia-hypoxia showed no change in HIF-1α mRNA levels after 1 and 3 hours.

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However, a significant decrease was detected at 12 hours of hypoxia in both euglycaemia (- 1.64-fold, p = 1.4E-06), and hyperglycaemia (-1.91-fold, p = 4.4E-09) as demonstrated in Figure 4.13.

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Figure 4.13: HIF-1α signalling pathway in HUVEC exposed to combined hyperglycaemia and hypoxia. Genes differentially expressed under (A) hyperglycaemia with hypoxia 3 hours versus euglycemia, while (B) under hyperglycaemia with hypoxia 12 hours versus euglycaemia. Green shades indicate downregulated whereas red shades indicate upregulated. Pathway key is illustrated in Appendix VI and genes names are listed in Table 4.14.

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Therefore, protein expression was evaluated by western blot assay revealing a significant increase in HIF-1α protein after 1 hour of hypoxia (3.35-fold, p < 0.01) but no significant increase in combined hyperglycaemia-hypoxia (Figure 4.14). Since the accumulation of HIF-1α protein induces the activity of prolyl hydroxylase (PHD) (synonyms EGLN1 and EGLN3) for post-translational modification to hydroxylate proline residue in HIF. Then ubiquitin-ligase recognises hydroxylated proline residue in HIF-1α to undergo proteasomal degradation (Bruick and McKnight, 2001).

Figure 4.14: Effect of hypoxia on HIF-1α protein expression among euglycaemia and hyperglycaemia. HUVEC were treated with hyperglycaemic or euglycaemic concentration as a control. After 24 hours, hypoxia was induced for either 1, 3 or 12 hours. Protein expression levels of HIF-1α were assessed by Western blot assay on three independent biological replicates, and ACTB was used for normalisation. Results are presented as mean ± SEM and were analysed using one-way ANOVA followed by LSD test. *P < 0.05, **P < 0.01 compared to control.

A HIF-1α network generated by IPA analysis showed upregulation of ADM, ANGPTL4, BHLHE40, and VEGFA genes after 1 hour of hypoxia in both euglycaemia and hyperglycaemia. While after 3 hours of hypoxia, 36 genes implicated in the HIF-1α network were differentially expressed under euglycaemia and 33 genes under hyperglycaemia. After 12 hours of hypoxia, the number of differentially regulated genes within the HIF-1α network increased to 67 genes under euglycaemia and 55 genes under hyperglycaemia (Table 4.14).

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Gene Gene name Euglycaemia with hypoxia Hyperglycaemia with Symbol (FC) hypoxia (FC) 1 hour 3 hours 12 1 hour 3 hours 12 hours hours ABCF2 ATP-binding cassette, sub-family F -1.59 (GCN20), member 2 ADM adrenomedullin 1.86 2.43 3.39 2.2 2.46 3.67 ADORA2B adenosine A2b receptor 1.62 AKAP12 A kinase (PRKA) anchor protein 12 1.66 1.58 ALDOC aldolase C, fructose-bisphosphate 2.12 4.56 2.11 4.30 ANGPTL4 angiopoietin-like 4 1.64 8.09 9.37 1.93 7.64 9.29 ANKZF1 ankyrin repeat and zinc finger domain 2.63 3.31 2.99 3.00 containing 1 ASNS asparagine synthetase (glutamine- 3.24 hydrolyzing) AURKA aurora kinase A -2.28 BHLHE40 basic helix-loop-helix family, member 2.37 2.70 2.71 2.45 2.62 2.92 e40 BHLHE41 basic helix-loop-helix family, member 2.67 2.24 2.09 e41 BIRC5 baculoviral IAP repeat containing 5 -1.70 BNIP3 BCL2/adenovirus E1B 19kDa 2.48 3.40 2.45 3.02 interacting protein 3 BNIP3L BCL2/adenovirus E1B 19kDa 1.76 1.57 interacting protein 3-like BRCA1 breast cancer 1, early onset -2.32 CARS cysteinyl-tRNA synthetase 1.55 1.91 CCND1 cyclin D1 -1.66 -1.60 CORO1A coronin, actin binding protein, 1A -1.54 CTPS1 CTP synthase 1 -3.23 -2.31 DLL4 delta-like 4 (Drosophila) 1.96 2.14 1.87 EGLN1 egl nine homolog 1 (C. elegans) 1.97 2.53 1.95 1.98 EGLN3 egl nine homolog 3 (C. elegans) 3.72 6.19 2.97 5.12 EIF5A eukaryotic translation initiation factor -1.54 5A ENO2 enolase 2 (gamma, neuronal) 3.22 6.16 3.43 5.15 ERO1L ERO1-like (S. cerevisiae) 1.94 2.57 1.74 1.88 FAM13A family with sequence similarity 13, 1.88 2.32 1.77 2.15 member A FAM162A family with sequence similarity 162, 2.86 4.80 3.23 3.54 member A FLT1 fms-related tyrosine kinase 1 1.66 GADD45B growth arrest and DNA-damage- 1.99 1.97 inducible, beta GPI glucose-6-phosphate isomerase 1.50 1.58 H2AFX H2A histone family, member X -1.57 HIF-1α hypoxia inducible factor 1, alpha -1.64 -1.90 subunit (basic helix-loop-helix transcription factor) HILPDA hypoxia inducible lipid droplet- 3.24 2.85 3.26 2.37 associated HK2 hexokinase 2 5.46 4.47 4.58 3.36 HMOX1 heme oxygenase (decycling) 1 1.71 3.97 1.70 4.09 KDM3A lysine (K)-specific demethylase 3A 2.84 3.72 2.82 2.78 KLHL20 kelch-like family member 20 1.62 2.11 1.50 2.13

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Gene Gene name Euglycaemia with Hyperglycaemia with Symbol hypoxia (FC) hypoxia (FC) 1 3 12 1 3 12 hour hours hours hour hours hours LDHA lactate dehydrogenase A 1.51 LOX lysyl oxidase 2.79 2.80 MEF2C myocyte enhancer factor 2C -1.84 -1.58 NAE1 NEDD8 activating enzyme E1 subunit 1 -2.14 -1.95 NDRG1 N-myc downstream regulated 1 1.53 2.33 2.05 NOS3 nitric oxide synthase 3 (endothelial cell) -1.79 NRN1 neuritin 1 2.06 3.41 1.94 2.84 NT5E 5'-nucleotidase, ecto (CD73) -1.62 NUCKS1 nuclear casein kinase and cyclin-dependent -1.59 -1.51 kinase substrate 1 P4HA1 prolyl 4-hydroxylase, alpha polypeptide I 2.24 4.51 2.44 3.74 P4HA2 prolyl 4-hydroxylase, alpha polypeptide II 1.77 3.25 1.61 3.10 PDGFA platelet-derived growth factor alpha -1.68 polypeptide PDK1 pyruvate dehydrogenase kinase, isozyme 1 2.10 2.33 2.00 2.55 PFKFB3 6-phosphofructo-2-kinase/fructose-2,6- 3.75 2.97 3.41 2.62 biphosphatase 3 PFKFB4 6-phosphofructo-2-kinase/fructose-2,6- 7.62 7.57 7.11 5.49 biphosphatase 4 PFKL phosphofructokinase, liver 1.53 PGK1 phosphoglycerate kinase 1 1.51 PLAC8 placenta-specific 8 5.41 PLAUR plasminogen activator, urokinase receptor -1.52 PLOD2 procollagen-lysine, 2-oxoglutarate 5- 1.77 1.52 1.70 dioxygenase 2 PPFIA4 protein tyrosine phosphatase, receptor type, f 1.56 2.57 1.76 3.05 polypeptide (PTPRF), interacting protein (liprin), alpha 4 RAB20 RAB20, member RAS oncogene family 2.33 2.70 2.47 2.07 SLC16A4 solute carrier family 16, member 4 -1.70 SLC25A37 solute carrier family 25 (mitochondrial iron 1.64 1.72 transporter), member 37 SLC2A1 solute carrier family 2 (facilitated glucose 3.15 3.50 3.00 3.53 transporter), member 1 SLC2A3 solute carrier family 2 (facilitated glucose 3.23 3.57 3.07 3.12 transporter), member 3 SMAD7 SMAD family member 7 2.59 2.71 SPAG4 sperm associated antigen 4 1.61 2.92 1.66 3.73 STC2 stanniocalcin 2 3.90 5.27 3.90 5.91 TAF9B TAF9B RNA polymerase II, TATA box -1.84 -2.08 binding protein (TBP)-associated factor, 31kDa TFAM transcription factor A, mitochondrial -1.58 TMEM19 transmembrane protein 19 -2.19 TMEM45A transmembrane protein 45A 1.96 5.44 2.10 4.07 TPI1 triosephosphate isomerase 1 1.60 1.71 VEGFA vascular endothelial growth factor A 1.80 3.79 5.15 1.61 3.47 4.72 ZDBF2 zinc finger, DBF-type containing 2 1.58 ZNF267 zinc finger protein 267 -1.53 Table 4.14: HIF-1 regulated genes among euglycaemia-hypoxia and hyperglycaemia-hypoxia for 1, 3 or 12 hours. The gene list was created by importing Affymetrix CEL files to Partek Genomic Suite version 6.6. The data was normalised using RMA normalisation. The list of differentially expressed genes was generated using one-way ANOVA, with p-value < 0.05 and FC 135

Chapter 4. Vascular functions in mature endothelial cells cutoff of 1.5. The signs in the FC column denote (-) downregulated genes are highlighted in green, and (+) upregulated genes are highlighted in red in comparison to the control condition.

A pro-angiogenic response to hypoxia was initiated after 1 hour under euglycaemia and hyperglycaemia by upregulation of VEGFA according to the microarray results as shown in Table 4.14. Moreover, VEGF constitutes the critical molecule in initiation of angiogenesis based on its ability to induce vasodilatation via NO, and to increase endothelial cell permeability (Ziche et al., 1997). VEGF also affects endothelial cell proliferation, partly attributable to NO and cGMP-mediated activation of the MAPK family (Griffioen and Molema, 2000). That expression of VEGFA is under the control of HIF strengthens the suggestion of an early involvement of VEGFA in the angiogenic response (Griffioen and Molema, 2000). Considering hypoxia-induced changes in glucose metabolism (Mobasheri et al., 2005, Yu et al., 2008), our data revealed enhanced expression of solute carrier family 2 (facilitated glucose transporter), member 1 (SLC2A1) known as glucose transporter-1 (GLUT-1) and solute carrier family 2 (facilitated glucose transporter), member 3 (SLC2A3) or GLUT3 within 3 hours (refer to Table 4.14). Moreover, hypoxia acting through HIF-1 stimulates GLUT-1 transcription. GLUT-1 is a ubiquitously expressed transmembrane glycoprotein that mediates Na+ independent transport of glucose into cells. According to one proposed model, hypoxia is associated with a transient fall in intracellular ATP levels that may augment glucose transporter function (Behrooz and Ismail-Beigi, 1999).

VEGFA mRNA overexpressed under euglycaemia-hypoxia and combined hyperglycaemia-hypoxia Gene expression of VEGFA was validated by qRT-PCR showing that hyperglycaemia did not lead to changes in mRNA under normoxia, but the expression increased with incremental time exposure to hypoxia (Figure 4.15). VEGFA mRNA levels were significantly upregulated after 3 hours of hypoxia with both euglycaemia by 7.22-fold (p = 0.017) and hyperglycaemia by 7.4-fold (p = 0.015) compared to euglycaemia. Moreover, the transcription levels of VEGFA were found to increase after 12 hours of hypoxia by 9.42-fold (p = 0.002) under euglycaemia and by 8.37-fold (p = 0.005) under

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Figure 4.15: Effect of hypoxia on VEGFA mRNA expression in euglycaemia and hyperglycaemia. HUVEC were treated with high glucose concentration or normal glucose concentration as a control. After 24 hours, hypoxia was induced for either 1, 3 or 12 hours. The variation in mRNA levels of VEGFA was assessed by qRT-PCR on three independent biological replicates. Results are presented as mean ± SEM and were analysed using one-way ANOVA followed by LSD test., *P < 0.05, **P < 0.01 compared to control.

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VEGF 165A protein expression As an anti-VEGFA antibody detected many extra protein non-specific bands in western blot assay, we instead studied VEGF 165A the most predominant and potent isoform in ECs (Soker et al., 1997) in detail. The level of VEGF 165A protein was significantly upregulated after 12 hours of hypoxia when HUVEC were treated with hyperglycaemia (6.83-fold, p < 0.05 compared to hyperglycaemia) but not after 1 and 3 hours as illustrated in Figure 4.16. VEGF 165A protein expression was found not to be affected by combined euglycaemic/hypoxic conditions.

Figure 4.16: Effect of hypoxia and metformin on VEGF 165A protein expression with euglycaemia and hyperglycaemia. HUVEC were treated with hyperglycaemia or euglycaemia as a control. After 24 hours, metformin (0.01mM) was added to euglycaemic and hyperglycaemic cultures, and then hypoxia was induced for either 1, 3 or 12 hours. The variation in protein 138

Chapter 4. Vascular functions in mature endothelial cells expression levels of VEGF 165A in three independent biological replicates was assessed by Western blot. Results are presented as mean ± SEM and were analysed one-way ANOVA followed by LSD test. *P < 0.05 compared to hyperglycaemia. #P < 0.001 compared pairwise, the condition treated with and without metformin.

Effect of metformin on HIF 1α pathway genes Transcriptome analysis demonstrated that a physiological concentration of metformin (0.01mM) exerts no effect on HIF 1α signalling when HUVEC are exposed to hypoxia for 1, 3 or 12 hours either under euglycaemia or hyperglycaemia.

Western blot assay revealed that metformin (0.01mM) leads to a significant downregulation of HIF-1α protein levels after 1 hour of hypoxia (-7.6-fold, p < 0.001) but exhibited no effect on combined hyperglycaemia and hypoxia (Figure 4.17). Therefore, metformin promotes the degradation of HIF-1α induced by 1 hour hypoxia under euglycaemic conditions. However, in hyperglycaemic conditions, hypoxia induced low levels of HIF-1α did not show any further degradation upon treatment with metformin.

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Figure 4.17: Effect of metformin on HIF-1α protein expression in euglycaemic and hyperglycaemic conditions with hypoxia. HUVEC were treated with hyperglycaemic (16.5 mM) or euglycaemic (5.5 mM) glucose concentrations. After 24 hours, metformin (0.01mM) was added to euglycaemic and hyperglycaemic cultures and then hypoxia was induced for either 1, 3 or 12 hours. The variation in protein expression levels of HIF-1α was assessed by western blot from three independent biological replicates. Results are presented as mean ± SEM and were analysed using one-way ANOVA followed by LSD test. ***P < 0.001 compared pairwise, without metformin versus metformin-treated conditions.

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4.3.5 VEGF signalling VEGF ligands and their receptors are key regulators of vasculogenesis and angiogenesis. VEGFA and its receptor VEGFR2 play a critical function in physiological and pathological angiogenesis through transduction pathways regulating proliferation and migration of ECs (Ferrara and Davis-Smyth, 1997, Shibuya et al., 1999). Furthermore, VEGFA exerts its activity on surrounding ECs via paracrine and autocrine mechanisms (Kerbel, 2008). VEGF receptor tyrosine kinases have been shown to stimulate a diverse array of signalling pathways, including phospholipase C (PLC)-γ, phosphatidylinositol 3′-kinase (PI3K), and Src (Ferrara et al., 2003, Cross et al., 2003). Moreover, activation of MAPK pathway by VEGF appears to be a key event in determining the survival of endothelial cell (Gupta et al., 1999).

Microarray studies on HUVEC induced by hypoxia demonstrated early expression of VEGFA under euglycaemia and hyperglycaemia after 1 hour as explained in section 4.3.5. Several key signalling cascades, including the extracellular signal–regulated kinase 1/2 (ERK1/2), and PI3K/ Akt pathways have been implicated in the modulation of VEGF induced angiogenic and proliferation processes via VEGFR1 (Cross and Claesson-Welsh, 2001). The mRNA of VEGFR1 (FLT-1) was upregulated (1.7-fold, p = 7.36E-03) under euglycaemia combined with hypoxia for 12 hours but not under hyperglycaemia combined with hypoxia. A number of VEGFR1 downstream genes were significantly differentially expressed under euglycaemia/ hypoxia after 12 hours and hyperglycaemia/ hypoxia (Figure 4.18). Among these genes was the mitogen-activated protein kinase kinase 1 (MAP2K1) in the ERK1/2 MAP kinase pathway with 1.7-fold (p = 5.37E-09) upregulated under euglycaemia-hypoxia, and 1.6-fold (p = 6.79E-08) upregulated under hyperglycaemia- hypoxia. Moreover, gene expression of phosphoinositide-3-kinase and regulatory subunit 1 (PIK3R1) which is a PI3K/ Akt signalling molecule, was increased under hypoxia with euglycaemia to 1.9-fold (p = 2.25E-07) and hyperglycaemia to 1.7-fold (p = 7.37E-06). In contrast, PIK3R3 was detected to be downregulated in hypoxia (12 hours) by -2.5-fold (p = 2.09E-09).

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Gene Symbol Gene Name FC EIF2B3 eukaryotic translation initiation factor 2B, subunit 3 gamma, 58kDa -2.6 FIGF c-fos induced growth factor (vascular endothelial growth factor D) 1.5 FLT1 fms-related tyrosine kinase 1 1.7 HIF-1α hypoxia inducible factor 1, alpha subunit (basic helix-loop-helix transcription factor) -1.6 MAP2K1 mitogen-activated protein kinase kinase 1 1.6 MRAS muscle RAS oncogene homolog -1.8 NOS3 nitric oxide synthase 3 (endothelial cell) -1.8 PIK3R1 phosphoinositide-3-kinase, regulatory subunit 1 (alpha) 1.9 PIK3R3 phosphoinositide-3-kinase, regulatory subunit 3 (gamma) -2.5 PXN paxillin 1.6 VCL vinculin -1.5 VEGFA vascular endothelial growth factor A 5.2 142

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Gene Symbol Gene Name FC EIF2B3 eukaryotic translation initiation factor 2B, subunit 3 gamma, 58kDa -2.4 HIF-1α hypoxia inducible factor 1, alpha subunit (basic helix-loop-helix transcription factor) -1.9 MAP2K1 mitogen-activated protein kinase kinase 1 1.6 PIK3R1 phosphoinositide-3-kinase, regulatory subunit 1 (alpha) 1.7 PXN paxillin 2.1 VEGFA vascular endothelial growth factor A 4.7 Figure 4.18: Hypoxia activates VEGF signalling under euglycaemia and hyperglycaemia. Differentially expressed genes in VEGF signalling under (A) euglycaemia with hypoxia 12 hours versus control, and (B) hyperglycaemia with hypoxia 12 hours versus control. Red and green shades refer to up- and downregulated genes, respectively. Pathway key is illustrated in Appendix VI. Tables below each pathway list the respective gene names and FC.

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Activation of the MAPK pathway by VEGF mediates the anti-apoptotic activity of VEGF (Gupta et al., 1999). The activity of MAPK signalling pathway, was measured by phosphorylation of ERK1/2 using the FlowCellect™ MAPK Activation Dual Detection Kit that includes two antibodies, a phospho-specific Anti-phospho-ERK1/2 (Thr202/Tyr204, Thr185/Tyr187)-PE and an Anti-ERK1/2-Alexa Fluor® 647 conjugated antibody to measure total levels of ERK. No significant change in the total ERK1/2 protein (Figure 4.19A) was detected. However, a significant decrease in phosphorylation of ERK1/2 was observed after either 12 hours hypoxia to 11.95 % (p = 0.004), hyperglycaemia to 33.77 % (p = 0.05), or combined hyperglycaemia-hypoxia to 7.51 % (p = 0.001, Figure 4.19B). The altered functions were studied by applying functional assays for apoptosis, cell proliferation and cell migration as outlined in Section 4.3.1.

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Figure 4.19: The activity of MAPK pathway under hypoxia, hyperglycaemia and hyperglycaemia combined with hypoxia. HUVEC were treated in passage 2 with euglycaemia (5.5 mM) or hyperglycaemia (16.5 mM) for 48 hours and parallel cultures were exposed to chemical hypoxia (150 µM CoCl2) for 12 hours. (A) Represents the total ERK1/2 proteins that were detected by an anti-ERK1/2-Alexa Fluor 647 conjugated antibody. (B) Represents phospho-ERK1/2 proteins that were detected by anti-phospho-ERK1/2 (Thr202/Tyr204, Thr185/Tyr187)-PE. Percentage of phosphorylation indicates the relative intensity of phospho-protein/ total protein. The variation in protein expression levels was assessed by flow cytometry on three independent biological replicates. Results are presented as mean ± SEM and were analysed using paired t-test. *P < 0.05, **P < 0.01, ***P < 0.002 compared to the control.

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Effect of metformin on VEGF signalling

Previous studies stated that metformin at concentrations of 0.5-3 mM suppresses VEGFA mRNA expression in HUVEC in a dose-dependent manner as well as cell migration (Soraya et al., 2012). These supra-physiological concentrations are not approved for the treatment of diabetic patients and thus were not considered for application in the present study.

The performed microarray experiments demonstrated that metformin treatment mediated no effect on VEGF signalling in HUVEC exposed to hypoxia/euglycaemia. In contrast, after 12 hours in combined hypoxia/hyperglycaemia, metformin treatment significantly upregulated CXCL8 to 1.9-fold (p = 2.84E-02), lymphocyte antigen 96 (LY96) to 1.6-fold (p = 2.01E-03), MMP16 (1.7-fold, p =2.34E-02), and ROCK1 to 2.7-fold (p = 1.15E-02) that were known to enhance cell migration. Furthermore, metformin led to a significant overexpression of tissue factor pathway inhibitor 2 (TFPI2) to 1.6-fold (p = 2.07E-04) when exposed to hyperglycaemia combined with hypoxia for 12 hours that was reported previously to regulate EC migration (Ivanciu et al., 2007). Whilst, this effect was abolished without metformin leading to downregulation of LY96 to -1.7-fold (p = 5.4E-04), and TFPI2 to -1.7-fold (p = 1.4E-05). FABP4 mRNA levels were downregulated under 12 hours hypoxia in euglycaemia (-1.6- fold, p = 2.4E-02) and hyperglycaemia (-1.6-fold, p = 2.9E-02). Conversely, the expression of FABP4 was significantly induced by adding metformin to HUVEC cultured in hypoxia/hyperglycaemia of 12 hours (1.6-fold, p = 2.53E-02).

Effect of metformin on VEGFA mRNA expression Metformin treatment exhibited no effect on VEGFA mRNA expression under combined euglycaemia-hypoxia and hyperglycaemia-hypoxia as assayed by microarray and the qRT- PCR experiments (Figure 4.20) when compared to the parallel condition without metformin.

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Figure 4.20: Effect of metformin on VEGFA mRNA expression in euglycaemic and hyperglycaemic conditions with hypoxia. HUVEC were treated with high glucose concentration or euglycaemic concentration as a control. After 24 hours, metformin (0.01mM) was added to euglycaemic (5.5 mM glucose) and hyperglycaemic (16.5 mM glucose) cultures, then hypoxia was induced for either 1, 3 or 12 hours. The variation in mRNA expression levels of VEGFA was assessed by qRT-PCR on three independent biological replicates. Results are presented as mean ± SEM and were analysed using one-way ANOVA followed by LSD test and compared pairwise, i.e., metformin-treated condition versus untreated condition.

Effect of metformin on VEGF 165A protein expression Metformin treatment exhibited no significant effect on VEGF 165A protein expression under euglycaemic/hypoxic conditions as detected by western blot assays. In contrast, metformin treatment with hyperglycaemia and hypoxia combined for 1 hour significantly increased VEGF 165A protein level (7.65-fold, p = 0.012), whereas after 12 hours of hypoxia the level decreased (-5.2-fold, p = 0.019) compared to the parallel metformin- untreated condition (Figure 4.16).

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Effect of metformin on MMP16 mRNA expression MMP16 was selected to be validated by qRT-PCR showing that hyperglycaemia increased the mRNA level (1.4-fold, p = 0.023) compared to control. There was a decrease in MMP16 expression under hyperglycaemia exposed to hypoxia for 12 hours (-1.9-fold, p < 0.001) compared to hyperglycaemia under normoxia as illustrated in Figure 4.21. However, metformin significantly increased the mRNA level of MMP16 by 1.1-fold, p = 0.049 under combined hyperglycaemia-hypoxia for 12 hours compared to the metformin-untreated parallel condition.

Figure 4.21: Effect of metformin on MMP16 mRNA expression in euglycaemic and hyperglycaemic conditions with hypoxia. HUVEC were treated with high (16.5 mM glucose) or normal concentrations (5.5 mM glucose) as a control. After 24 hours, metformin (0.01mM) was added to euglycaemic and hyperglycaemic cultures; then hypoxia was induced for either 1, 3 or 12 hours. The variation in RNA expression levels of MMP16 was assessed by qRT-PCR on three independent biological replicates. Results are presented as mean ± SEM and were analysed using one-way ANOVA followed by LSD test. *P < 0.05, **P < 0.01, ***P < 0.001 compared to the control or hyperglycaemia or pairwise, i.e., the conditions treated with and without metformin.

To confirm the MMP16 mediated mechanism behind the metformin effect on cell migration, we employed marimastat, a MMP16 inhibitor, and confirmed inhibition of migration induced by metformin in combined hyperglycaemia and hypoxia at 18 (p < 0.01) 148

Chapter 4. Vascular functions in mature endothelial cells and 24 hours (p < 0.001) as shown in Figure 4.22A and C. Marimastat had no effect on cell migration of ECs treated with metformin under euglycaemia and hypoxia when compared to the parallel marimastat-untreated condition (Figure 22A and B).

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Figure 4.22: Marimastat antagonises the effect of metformin on cell migration in HUVEC exposed to hyperglycaemia combined with hypoxia. (A) HUVEC were incubated with normal glucose (5.5 mM) or high glucose (16.5 mM) in the presence or absence of physiological metformin concentrations (0.01 mM) for 24 hours. Scratch lines were created on confluent monolayers, and then the wells were gently rinsed with DPBS to remove detached cells. Culture media were applied with or without metformin containing either 5.5 or 16.5 mM glucose concentrations. Then cells were exposed to chemical hypoxia and incubated for 24 hours in a 5 % CO2 chamber that was equipped with a CCD camera. Images were acquired every hour, and three independent biological experiments were performed in duplicate for each condition. Each image is a composite image that generated by NIS Elements software. The scratch area in each image was measured using NIS Elements software. (B) Marimastat had no significant effect on cells treated with metformin and exposed to hypoxia. (C) The effect of metformin was abolished by marimastat treatment. Results are expressed as mean ± SEM and were analysed by one-way ANOVA followed by LSD, ††P < 0.01, †††P < 0.001 compared with marimastat treated versus untreated-condition. The scale bar is 100 µm. Key: mar: marimastat; met: metformin.

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Effect of metformin on activation of ERK1/2 protein

The effect of metformin on activation of the MAPK pathway downstream signalling of VEGF was assessed. We detected no significant change in the expression of total ERK1/2 protein in all conditions assayed (Figure 4.23A, B, C, and D). However, a significant decrease to 32.52 % (p = 0.007, Figure 4.23E) of phopho-ERK1/2 to total ERK was detected under euglycemia/normoxia,whereas under combined hyperglycaemia-hypoxia for 12 hours an increase by 33.63 % (p = 0.024, Figure 4.23H) of phospho-ERK to total ERK was revealed. No change in the activity of ERK1/2 was observed under combined euglycemia/hypoxia or hyperglycaemia/normoxia (Figure 4.23F, and G).

The effect of metformin on functions influenced by activation of VEGF signalling and the MAPK pathway have been described in Section 4.3.1. Furthermore, we have shown by using sunitinib (a VEGF inhibitor) an inhibition of metformin stimulatory effect on migration (Figure 4.2B, and 4.3C) and an increase in apoptosis following the use of sunitinib with the control (Figure 4.6).

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Figure 4.23: Effect of metformin on the activity of MAPK pathway. HUVEC were treated with normal glucose (5.5 mM) or high glucose (16.5 mM) in the presence or absence of physiological metformin (0.01mM) for 48 hours and parallel cultures were exposed to chemical hypoxia (150 µM CoCl2) for 12 hours. Total ERK1/2 proteins were detected by an anti-ERK1/2-Alexa Fluor® 647 153

Chapter 4. Vascular functions in mature endothelial cells conjugated antibody under following different conditions (A) 5.5 mM glucose, (B) 5.5 mM glucose combined with 12 hours hypoxia, (C) 16.5 mM glucose, and (D) 5.5 mM glucose combined with 12 hours hypoxia. ERK1/2 phosphorylation was detected by anti-phospho-ERK1/2 (Thr202/Tyr204, Thr185/Tyr187)-PE under variable conditions , namely (E) 5.5 mM glucose, (F) 5.5 mM glucose combined with 12 hours hypoxia, (G) 16.5 mM glucose, and (H) 5.5 mM glucose combined with 12 hours hypoxia. The variation in protein expression levels was assessed by flow cytometry on three independent biological replicates. Results are presented as mean ± SEM and were analysed using paired t-test. *P < 0.05 compared pairwise, the conditions treated with versus without metformin.

4.3.6 AMPK signalling AMP-activated protein kinase is a sensor of cellular energy that is activated by diminished ATP. AMPK exists as heterotrimeric complexes comprised of the catalytic α and regulatory β and γ subunits. AMPK complexes are activated by phosphorylation at a specific threonine residue (Thr-172) on α subunit. This is triggered by hypoxia that causes an increase in the AMP: ATP ratio, which is amplified by adenylate kinase (AK) (Hardie and Hawley, 2001). Activated AMPK is involved in switching on ATP-generating catabolic pathways while switching off ATP-requiring anabolic processes (Hardie, 2005). The microarray data demonstrated that hypoxia activated AMPK signalling either under euglycaemia or hyperglycaemia after 3 and 12 hours. Although AMPKα mRNA level was observed to be upregulated under euglycaemia and hypoxia for 12 hours 2.05-fold (p = 8.25E-03, Figure 4.24), downstream genes were influenced by 3 and 12 hours of hypoxia. Induction of ECs with euglycaemia-hypoxia significantly increased AK4 (15.9-fold, p = 2.11E-15) expression that in turn raised the AMP level. Activated AMPK enhanced the expression of catabolic enzymes PFKFB3 (3.0-fold, p = 1.2E-11), PFKFB4 (7.6-fold, p = 5.3E-17). Gene expression of the anabolic enzyme glycogen synthase (GYS1) was found to be upregulated (1.9-fold, p = 1.61E-06). It was previously reported that HIF-1 promoted glycogen accumulation through regulating protein phosphatase 1, regulatory subunit (PPP1R3C) expression under hypoxia (Shen et al., 2010). Our data demonstrated an increase in the expression of PPP1R3C (2.3-fold, p = 4.15E-04).

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Gene Name Gene symbol FC acetyl-CoA carboxylase α ACACA -1.9 adenylate kinase 4 AK4 15.9 cholinergic receptor, nicotinic, alpha 5 (neuronal) CHRNA5 -2.5 fatty acid synthase FASN -1.6 glycogen synthase 1 (muscle) GYS1 1.9 3-hydroxy-3-methylglutaryl-CoA reductase HMGCR -1.9 insulin receptor INSR 3.0 mitogen-activated protein kinase 14 MAPK14 -1.5 muscle RAS oncogene homolog MRAS -1.8 mammalian target of rapamycin (serine/threonine kinase) MTOR -1.5

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Gene Name Gene symbol FC nitric oxide synthase 3 (endothelial cell) NOS3 -1.8 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 2 PFKFB2 -2.0 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 PFKFB3 3.0 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 4 PFKFB4 7.6 phosphoinositide-3-kinase, regulatory subunit 1 (alpha) PIK3R1 1.9 phosphoinositide-3-kinase, regulatory subunit 3 (gamma) PIK3R3 -2.5 phosphoribosyl pyrophosphate amidotransferase PPAT -1.7 protein kinase, AMP-activated, alpha 2 catalytic subunit PRKAA2 2.1 protein kinase, cAMP-dependent, regulatory, type II, beta PRKAR2B -1.6 solute carrier family 2 (facilitated glucose transporter), member 1 SLC2A1 3.5 Figure 4.24: Hypoxia activates AMPK signalling under euglycaemia. Differentially expressed genes in AMPK signalling under euglycaemia combined with hypoxia for 12 hours versus control were described in the table below the pathway. Red and green shades refer to up- and downregulation respectively. Pathway key is illustrated in Appendix VI.

AMPK signalling was detected by IPA software to be activated under combined hyperglycaemia hypoxia 12 hours through the upregulation of AK4 (11.8-fold, p = 8.7E- 14), catabolic enzymes PFKFB3, PFKFB4, PFKL, and downregulation of anabolic enzymes ACACA, FAS (Figure 4.25). Conversely, mTOR expression was inhibited by 1 hour hypoxia in euglycaemia or hyperglycaemia by enhanced expression of its inhibitor DNA-damage-inducible transcript 4 (DDIT4) (2.11-fold, p = 1.20E-03; 1.86-fold, p = 6.29E-03 respectively). mTOR was known to regulate cell growth and proliferation by stimulating many anabolic pathways, including the biosynthesis of proteins, lipids and organelles, and by inhibiting catabolic pathways (Guertin and Sabatini, 2007). It is known that downregulation of mTOR is mediated by increased expression of the hypoxia-inducible gene DDIT4 leading to inhibition of mTOR anabolic growth pathway (Schneider et al., 2008, Sofer et al., 2005). The inhibition of mTOR also persisted after 3 and 12 hours of hypoxia. It has been reported that activation of AMPK after hypoxia-induced energy stress leads to enhanced expression of DDIT4 that is known to control the mTOR pathway (Schneider et al., 2008).

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Gene Name Gene symbol FC acetyl-CoA carboxylase alpha ACACA -1.7 adenylate kinase 4 AK4 11.8 fatty acid synthase FASN -1.5 glycogen synthase 1 (muscle) GYS1 1.9 insulin receptor INSR 2.7 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 2 PFKFB2 -1.7 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 PFKFB3 2.6 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 4 PFKFB4 5.5 phosphofructokinase, liver PFKL 1.5

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Gene Name Gene symbol FC phosphoinositide-3-kinase, regulatory subunit 1 (alpha) PIK3R1 1.7 phosphoribosyl pyrophosphate amidotransferase PPAT -1.8 protein phosphatase 2, regulatory subunit B', beta PPP2R5B 1.6 protein kinase, cAMP-dependent, catalytic, beta PRKACB -1.7 protein kinase, cAMP-dependent, regulatory, type II, beta PRKAR2B -1.6 solute carrier family 2 (facilitated glucose transporter), member 1 SLC2A1 3.5 Figure 4.25: Hypoxia activates AMPK signalling under hyperglycaemia. Differentially expressed genes in AMPK signalling under hyperglycaemia combined with hypoxia 12 hours versus control were described in the table below the pathway. Red and green shades refer to up- and downregulation respectively. Pathway key is illustrated in Appendix VI.

The fatty acid synthase (FASN) gene was selected from the AMPK pathway for validation of its expression by qRT-PCR (Figure 4.26). Hypoxia exhibited no effect on mRNA levels of FASN after 1 and 3 hours neither under euglycaemia nor hyperglycaemia. However, hypoxia significantly downregulated FASN after 12 hours coupled with euglycaemia (-2.5- fold, p < 0.001) or with hyperglycaemia (-3.0-fold, p < 0.001) which is compatible with microarray results.

Figure 4.26: Effect of hypoxia on FASN expression among euglycaemia and hyperglycaemia. HUVEC were treated with high glucose concentration or euglycaemic concentration as a control. After 24 hours, hypoxia was induced for either 1, 3 or 12 hours. The variation in RNA levels of FASN was assessed by qRT-PCR on three independent biological replicates. Results are presented as mean ± SEM and were analysed using one-way ANOVA followed by LSD test., ***P < 0.001 compared to control.

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Effect of metformin on AMPK signalling Our microarray data revealed that physiological metformin concentration conferred no effect on mRNA levels of AMPK and mTOR signalling in HUVEC treated with euglycaemia or hyperglycaemia exposed to hypoxia at the studied time intervals (1, 3, and 12 hours). Effect of hypoxia, hyperglycaemia and metformin at the protein level of AMPKα, phospho- AMPKα (Thr172), mTOR and phospho-mTOR (S2448) were studied but the results were difficult to interpret due to extra non-specific bands. Further standardisation of the Western blot assays for phospho-proteins detection is needed.

4.4 Discussion The data in this study identified and characterised transcriptomic signatures and pathways associated with hypoxia, hyperglycaemia or hyperglycaemia and hypoxia combined at different time intervals in HUVEC. This study aids our understanding of the pathophysiology of ischaemic heart diseases and diabetes, by identifying novel biomarkers and therapeutic targets for treating these diseases. In addition, we studied the cellular and molecular mechanisms of the protective role of metformin on ECs that are stressed by hypoxia and hyperglycaemia.

Our data demonstrate congruent effects using a physiological concentration of metformin (0.01 mM) as anticipated in patients treated with metformin. Most notably, we have used realistic hyperglycaemia (16.5 mM) which can be observed in diabetic patients as opposed to studies that use extremely high glucose levels. We specifically avoided high metformin concentrations (supra-physiological in clinical terms) and very high glucose levels, in order to render our data translational to clinical practice.

4.4.1 HIF-1α signalling Our results indicate that HIF-1α mediated regulation of multiple genes encoding cytokines/growth factors, receptor tyrosine kinases, G protein–coupled receptors, and associated signalling proteins providing a broad molecular basis for endothelial cell

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Chapter 4. Vascular functions in mature endothelial cells activation. The response to hypoxia was variable between euglycaemia and hyperglycaemia among a group of genes that are vital for cell death and cell survival, angiogenesis and glycolysis. Our western blot assays demonstrate that hypoxic and euglycaemic conditions stabilised HIF-1α protein levels under 1 hour but not under 3 or 12 hours’ incubation. Furthermore, our finding of unstable HIF-1α protein in hyperglycaemia with hypoxia is not new. Other groups have shown similar results in human dermal fibroblasts, human dermal microvascular ECs, and vascular smooth muscle cells, augmenting that hyperglycaemia interferes with hypoxia-dependent stabilisation of HIF-1α in a glucose dose-dependent manner (Catrina et al., 2004, Fadini et al., 2006b, Gao et al., 2007). Thangarajah et al., recently demonstrated that hyperglycaemia decreases the transactivation capacity of HIF-1α to mediate hypoxia-stimulated VEGF expression (Thangarajah et al., 2009). This decreased HIF-1α mediated gene transactivation capacity was due to its impaired binding to the coactivator p300. The same study revealed that the impaired binding between HIF-1α and p300 is caused by a covalent modification of p300 by methylglyoxal that is a degradation product of triose phosphates. Transcriptomic data and apoptosis assays showed that during early phase of hypoxia (1 and 3 hours) the response for cell survival and repair persisted in HUVEC under euglycaemia and hyperglycaemia as HIF-1 α signalling genes were observed to be similarly expressed. This suggests that MI therapy should occur in less than 3 hours when the effect is in balance with the repair. However, two genes improving cell survival, and angiogenesis were only differentially expressed under euglycaemia and hypoxia for 3 hours namely: ADORA2B and DLL4. Previous studies have shown a key role of ADORA2B in the cardioprotective response (Eckle et al., 2008, Eckle et al., 2012). However, Cardiac ADORA2B was induced with HIF activation, and HIF-dependent cardio-protection was abolished in Adora2B -/- mice (Eckle et al., 2008). Furthermore, DLL4 was identified as a ligand responsible for the activation of Notch1 and Notch4, and differently expressed in arterial endothelium, suggesting a potential role for DLL4 in arteriogenesis (Shutter et al., 2000). Notch and DLL4 gene expression in human ECs are modulated by VEGF (Liu et al.,

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2003). VEGF induced Notch1/DLL4 signalling enhances cell survival and induces endothelial cell differentiation.

However, under euglycaemia and prolonged hypoxia (12 hours), cell injury may arise by downregulation of genes involved in cell survival, proliferation and DNA repair. In our model of euglycaemia-hypoxia, these genes are as follow ABCF2, AURKA, BIRC5, BRCA1, CORO1A, EIF5A, H2AFX, HIST1H1C, NOS3, PLAUR, SLC16A4, and TFAM. Previous study have been reported that ABCF2 is implicated in ATP-dependent transport of various molecules and therefore, contributes to tissue differentiation and survival (Moitra et al., 2011, Wenzel et al., 2007). Another gene is important for cell survival, AURKA that markedly regulate several key molecules of proliferative signalling pathways, including the c-Myc, n-Myc, NFκB, β-catenin and cyclin B1 cascades (Dar et al., 2009, Otto et al., 2009, Qin et al., 2009, Sasayama et al., 2005, Tomita et al., 2009, Yang et al., 2004). Therefore, decreased mRNA levels are associated with poor survival as described before in patients with glioblastoma (Lehman et al., 2012). Additionally, the angiogenic and anti-apoptotic effect of BIRC5 is suppressed. It has been earlier suggested that BIRC5 enhances survival of injured cells and angiogenic reactions in ischaemia (Ma et al., 2007, Yamagishi et al., 2002, Conway et al., 2003).

In our study downregulation of BRCA1 could suggest poor EC survival and enhanced atherosclerotic lesion. This is in concordance with previous in vivo studies that detected a cardioprotective and anti-inflammatory role of BRCA1 (Shukla et al., 2011, Teoh et al., 2013, Singh et al., 2013). Moreover, BRCA1 was detected to exert multiple functions involving DNA repair, transcriptional regulation, ubiquitination and cell cycle control (Greenberg, 2008). Kang et al. (2006) stated that BRCA1 plays a critical role in the transcriptional regulation of VEGF expression in hypoxia (Kang et al., 2006). Moreover, in the present study CORO1A was detected to be downregulated in HUVEC under hypoxia leading to enhance apoptosis. As previously, CORO1A was demonstrated to be essential for cell survival of naïve T cell by activating Ca2+ release from intracellular stores (Mueller et al., 2008). Taken all together, downregulation of these genes is in line with the reduction in cell survival observed in our apoptosis assays.

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Downregulation of EIF5A in our results indicates growth arrest that is compatible with the results of the cell proliferation assay. Furthermore, EIF5A was detected to be the only cellular protein that contains the unique polyamine-derived amino acid, hypusine. The correlation between hypusine synthesis and cell growth suggest an important role for hypusine in cell proliferation (Cooper et al., 1982, Chen, 1983, Gerner et al., 1986). Another groups demonstrated that inhibitors of deoxyhypusine synthase and deoxyhypusine hydroxylase two important enzymes required for hypusine synthesis, cause growth arrest in various mammalian cells (Park et al., 1994, Hanauske-Abel et al., 1994). Moreover, loss of EIF5A gene (Schnier et al., 1991), or disruption of the deoxyhypusine synthase gene (Sasaki et al., 1996, Park et al., 1998), results in cessation of cell growth. However, the reduction in cell proliferation was detected under combined euglycaemia with hypoxia could be due to downregulation of H2AFX. This is in concordance with previous work that confirmed the importance of H2AFX and its C-terminal phosphorylation (γ- H2AX) in the DNA damage response and DNA repair mechanisms (Fernandez-Capetillo et al., 2004, Celeste et al., 2003, Bassing and Alt, 2004). Economopoulou et al. (2009) discovered that hypoxia induced the generation of γ-H2AFX in ECs in vitro and mice (Economopoulou et al., 2009). H2AFX deficiency reduces EC proliferation under hypoxia in vitro and mice. Additionally, hypoxia-induced neovascularisation during retinopathy, hind limb ischemia, or during tumour angiogenesis was significantly reduced in H2afx−/− mice (Economopoulou et al., 2009). This confirms that H2AFX aids ECs to maintain their proliferation and neovascularisation capacity under hypoxia. Moreover, NOS3 that is also known as eNOS was found to be downregulated after 12 hours of hypoxia in our experiments. NOS3 is responsible for the synthesis of NO through the conversion of L-arginine to L-citrulline (Oess et al., 2006). Another study supports that hypoxia in HUVEC reduces NO release and NOS3 expression and activity (Ostergaard et al., 2007). NO plays a major role in the control of vascular tone (Moncada et al., 1997) and angiogenesis (Morbidelli et al., 2003). The experimental models used by Murohara et al (1998) showed, at first, that the oral administration of L-arginine after induction of severe hind limb ischaemia in rabbits significantly triggered angiogenesis and secondly, impaired

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Chapter 4. Vascular functions in mature endothelial cells angiogenesis after surgical induction of hind limb ischaemia was observed in mice lacking NOS3 (Murohara et al., 1998). In our study, TFAM was decreased under 12 hours hypoxia, might be associated with mitochondrial dysfunction. TFAM was detected to stimulate mtDNA transcription and nuclear respiratory factors, NRF1 and NRF2 that coordinate nuclear and mitochondrial gene expression (Scarpulla, 2006, Fernandez-Marcos and Auwerx, 2011). Mitochondrial biogenesis is mainly regulated by peroxisome proliferator-activated receptor γ coactivator 1α (PGC1α) (Fernandez-Marcos and Auwerx, 2011). PGC1 α is a transcriptional coactivator that is induced during elevated energy demands and regulates TFAM. Other genes that involved in angiogenesis and glycolysis are upregulated, FLT1, LDHA, PGK1 to supply the energy required for tissue repair. We found that FLT1 was upregulated by hypoxia that is consistent with findings of previous reports (Gerber et al., 1997, Li et al., 2005b). FLT1 is a VEGF endothelial cell-specific receptor tyrosine kinase that plays a key role in physiological and pathological angiogenesis. The expression of genes encoding glycolytic enzymes; LDHA and PGK1 were induced in HUVEC exposed to hypoxia, as observed in other studies (Firth et al., 1994, Firth et al., 1995, Semenza et al., 1994). Moreover, we found that hypoxia reduced the expression of SLC16A4 a monocarboxylate transporter (MCT4) that facilitates the uptake and excretion of monocarboxylates, like lactate and pyruvate (Enerson and Drewes, 2003). In addition, hypoxia downregulated the expression of PLAUR the urokinase receptor leading to decrease in cellular migration (Higazi et al., 1995).

Among the important genes differentially expressed in HUVEC treated with hyperglycaemia-hypoxia for 12 hours were: NT5E, PDGFA, PFKL, and PLAC8 as described in Table 4.14. NT5E (CD73) is an ectoenzyme mainly expressed in endothelial and epithelial cells and a subset of lymphocytes, especially in regulatory T cells. The ectoenzyme is a part of the extracellular ATP metabolism that catalyses the conversion of AMP to adenosine. Adenosine is a highly anti-inflammatory molecule, as it increases endothelial function and mediates suppressive effects on leukocytes (Jalkanen and Salmi, 2008, Stagg and Smyth, 2010). The results of the present study showed that the expression

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Chapter 4. Vascular functions in mature endothelial cells of NT5E was diminished, leading to enhancement of the inflammatory reaction required for ischaemia-reperfusion injury (Mold and Morris, 2001). In our results, PDGFA mRNA level was significantly downregulated under hypoxia combined with hyperglycaemia leading to inhibition of cell migration and proliferation as reported in previous studies (Appelmann et al., 2010, Hellberg et al., 2010). PDGF produced by ECs, is a main growth factor for vascular smooth muscle cells and a potent vasoconstrictor (Berk et al., 1986). It consists of a disulfide-linked dimer of two related peptides, A and B, which are products of two different genes (Starksen et al., 1987). Kourembanans et al., discovered that hypoxia significantly increased PDGFB mRNA in HUVEC but not PDGFA (Kourembanas et al., 1990). Expression of the gene encoding the glycolytic enzyme PFKL was stimulated in HUVEC exposed to hypoxia and pre-treated with hyperglycaemia that is consistent with previous studies on hypoxia (Semenza et al., 1994). In the present study overexpression in PLAC8 was detected under hyperglycaemia-hypoxia for 12 hours. This could be associated with enhanced apoptosis as described under the apoptosis functional assay (section 4.3.1). In previous study upregulation of PLAC8 in two human T-leukaemic cell lines and untransformed human peripheral blood lymphocytes is accompanied by pro-apoptotic effects (Mourtada-Maarabouni et al., 2013). Therefore, PLAC8 may represent an entirely novel target for therapy of CVD in diabetic patients.

Effect of metformin on HIF-1α signalling In our study metformin treatment significantly downregulated the protein level of HIF-1α when HUVEC were exposed to hypoxia for 1 hour. Recent studies in HCT116 colon cancer cells and human hepatocellular carcinoma Bel-7402/5-flurouracil cells demonstrated similar effects of metformin (1 mM) in reducing HIF-1α stability and expression of HIF target genes (Wheaton et al., 2014, Ling et al., 2014). Furthermore, our results displayed that metformin exhibited no effect on mRNA or on the protein expression of HIF-1α in cells treated with hyperglycaemia and hypoxia at all-time intervals assayed. However, the critical role of HIF-1 in ischaemia-induced vascular remodelling and the evidence that diabetes impairs HIF-1 activation in response to

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Chapter 4. Vascular functions in mature endothelial cells ischaemia, raises interest in developing therapeutic strategies to increase HIF-1 stability as a means to restore the repair responses to hypoxia. We did not observe change in NOS3 expression in HUVEC treated with metformin (0.01 mM) under the different studied conditions due to the short-time exposure to metformin (12 hours). This was confirmed by anthor group detecting an increase in NOS3 expression only after 48 hours of metfromin treatment (0.005-0.05 mM) by ~ 15% in pulmonary artery ECs isolated from fetal lambs with utero pulmonary hypertension (Teng et al., 2013).

4.4.2 VEGF signalling VEGF signalling plays a key role in the vascular remodelling by enhancing cell migration and angiogenesis (Asahara et al., 1995). Accelerated atherosclerosis and impaired angiogenesis in response to ischemia are elaborated as major macrovascular complications in DM (Zou et al., 2004). Therefore, in this study we report the effect of metformin on cell survival and cell migration under conditions of hyperglycaemia and combined hyperglycaemia hypoxia with detailed genetic studies on VEGF pathway. We have confirmed the cardioprotective effect of metformin at several levels. We have shown for the first time that physiological concentrations of metformin improved survival and cell migration of EC under hyperglycaemia exposed to hypoxia through activation of VEGF pathway signalling. In our study, an exposure of EC to hyperglycaemia lead to a marked increase in cell migration when compared to euglycaemia and reduction in cell migration when treated with hyperglycaemia and hypoxia. This is consistent with a previous study (Shigematsu et al., 1999). Furthermore in our study metformin inhibited cell migration under euglycemia/hypoxia whilst improving migration under combined hyperglycaemia-hypoxia. Esfahanian et al., has reported similar inhibiting effect of metformin on EC migration under euglycaemia although at supra-physiological metformin (0.5-3.0 mM) concentrations (Esfahanian et al., 2012). Our microarray studies demonstrated that the following genes: MMP16, FABP4, ROCK1, TFPI2, CXCL8, and LY96 which play part in the cell migration were upregulated by

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Chapter 4. Vascular functions in mature endothelial cells metformin under hyperglycaemia and hypoxia combined for 12 hours. The role in migration was confirmed in our experiments using inhibitors of VEGF and MMPs. MMP16 is a membrane-type MT3-MMP that contributes to angiogenesis by degrading extracellular matrix components, thus promoting cell migration and bioavailability of growth factors (Hotary et al., 2000, Shofuda et al., 2001). Park et al., demonstrated that upon secretion, VEGF becomes bound to the extracellular matrix (ECM) and acts in a paracrine fashion (Park et al., 1993). The interaction of VEGF with matrix proteins is mediated through the carboxy-terminal region, also known as an ECM-binding domain (Houck et al., 1991, Houck et al., 1992). The regulation of VEGF in the extracellular environment has been implicated in the angiogenic switch (Bergers et al., 2000). A preceding study found that inhibition of MMP16 with anti-MMP16 significantly reduces the VEGFA-enhanced tube formation in ECs (Plaisier et al., 2004). This is in agreement with our results from the scratch assay where the pro-angiogenic effect of metformin in combined hyperglycaemia hypoxia was inhibited by marimastat, an MMP inhibitor. Additionally, the downstream mediator of VEGF/ VEGFR2 (Elmasri et al., 2012); FABP4 was increased by the treatment with metformin under hyperglycaemia-hypoxia for 12 hours enhancing cell survival and cell migration. However, FABP4 knockdown HUVEC demonstrated decreased cell migration as well as increased apoptosis (Elmasri et al., 2012) whilst VEGF knockdown was lethal (Carmeliet et al., 1996, Ferrara et al., 1996). In a previous study, RhoA/ROCK signalling was shown to promote endothelial migration in response to VEGFA through the modulation of the actin cytoskeleton organisation (Rolfe et al., 2005). Therefore, ROCK activation appears to be a key event in the initiation of the angiogenic process by mediating an increase in endothelial permeability and migration. Whilst ROCK1 inhibitor suppressed cell migration as shown recently (Patel et al., 2014). On the other hand, the pro-inflammatory chemokine CXCL8 has shown pro-angiogenic properties by preserving VEGFA expression and secretion (Mizukami et al., 2005, Martin et al., 2009). Anti-CXCL8 was reported to reduce EC migration as compared to controls suggesting that CXCL8 functions as an important autocrine growth and angiogenic factor (Li et al., 2005a).

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A Kunitz-type serine proteinase inhibitor TFPI-2 (Sprecher et al., 1994) was detected to be overexpressed in migrating EC (Ivanciu et al., 2007). Previous studies have shown that VEGF mediated the induction of TFPI-2 through ERK1/2 activation (Xu et al., 2006, Kast et al., 2003). However, as TFPI-2 was detected to control cell migration (Ivanciu et al., 2007) thus, it acted as an important regulator to prevent excessive and uncontrolled cell migration. Taken together, our results demonstrated that metformin enhanced cell survival (apoptosis assay) under combined hyperglycaemia hypoxia through VEGF and VEGF downstream signalling ERK/MAPK. This was confirmed by using a VEGF inhibitor (sunitinib) in an apoptosis assay (Section 4.3.1 under apoptosis). It is known that VEGF activates the phosphorylation of the extracellular signal-regulated kinase 1/2 (ERK1/ ERK2) leading to activation of the mitogen-activated protein kinase (MAPK) pathway which is a key step in determining the survival of EC (Gupta et al., 1999). ERK (ERK1 and ERK2) is activated upon phosphorylation by MEK (MEK1 and MEK2), which is itself activated when phosphorylated by Raf (Raf-1, B-Raf, and A-Raf) (Dhillon et al., 2007). We found that the activity of the ERK/ MAPK pathway was inhibited by hypoxia, hyperglycaemia and hyperglycaemia combined with hypoxia for 12 hours as measured by MAPK activation dual detection assay. A previous study demonstrated that the level of phospho-ERK1/2 in astrocytes was elevated after 1 hour ischaemia and reached a maximal level after 4 hours ischaemia, before decreasing at 5 hours resulting in enhanced ischaemia-induced cell death (Jiang et al., 2002). In another study activation of ERK/ MAPK signalling was required for cardioprotection from ischaemia-reperfusion injury in vivo through antagonism of apoptotic regulatory pathways (Yue et al., 2000). In our experiments we were able to demonstrate that a physiological metformin concentration activated ERK/ MAPK signalling in HUVEC exposed to hyperglycaemia combined with hypoxia, augmenting cell survival, therefore metformin may have properties to enhance cardioprotection from ischaemia-reperfusion, however, we have not studied the reperfusion phase in our study. Waltenberger, et al., reported that the serum level of VEGFA was significantly elevated in diabetic patients, and the cellular response to VEGFA was attenuated due to possible defect in downstream signal transduction (Waltenberger et al., 2000). Data from our FACS

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Chapter 4. Vascular functions in mature endothelial cells analysis demonstrated inhibition of VEGF dependent intracellular signalling in HUVEC exposed to hypoxia under euglycaemia or hyperglycaemia despite the fact that a significant upregulation in VEGFA expression was measured by microarray and qRT-PCR. This is in agreement with the previous study performed in myocardial tissue from diabetic patients with chronic ischaemic heart disease (Sasso et al., 2005). From our data, one could conclude that the effect of metformin is only manifest in “stressed” HUVEC i.e. where hyperglycaemia mimics the diabetic state and under hypoxia. A summary of our results is displayed in Figure 4.27 and Table 4.15.

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Figure 4.27: Comprehensive VEGF signalling network of genes and proteins involved in cell migration and survival where metformin treatment is compared to the metformin-untreated condition under combined hyperglycaemia and hypoxia for 12 hours. The network was created by IPA software rendering VEGF signal transduction pathways. The genes from microarray expression study that are represented with red shades are upregulated and green shades are downregulated, MMP16 was validated by qRT-PCR. The activity of ERK1/2 was assessed by MAPK activation dual detection assay flow cytometry. The red shade on functional assays denoted

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Chapter 4. Vascular functions in mature endothelial cells activation and green shade denoted inhibition. Solid lines denoted direct interaction, interrupted lines denoted indirect interaction.

The lack of beneficial effect of metformin in “unstressed” HUVEC is related to the fact that the VEGF related genes as expected were essentially unaltered in euglycaemia-hypoxia. In line with these findings are clinical studies that confirm the effect of metformin in non- diabetic individuals offers no benefit in cardiovascular outcome (Lexis et al., 2014, Preiss et al., 2014). In conclusion, our in vitro study let us suggest that the cardioprotective effect of metformin in the diabetic state appears to be mediated by activation of VEGF signalling cascades.

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Euglycaemia- Euglycaemia- Hyperglycaemia Hyperglycaemia Hyperglycaemia- Hyperglycaemia- Biological hypoxia hypoxia + + Met hypoxia hypoxia + Met significance Met Cell migration No effect Inhibited Accelerated Inhibited Accelerated Cell proliferation decreased No effect No effect No effect Inhibited No effect Apoptosis No effect No effect No effect No effect Increased after Reduced after 24h 24h hypoxia hypoxia HIF1α protein Stabilised at Decreased at No change No change No change No change HIF1α is 1h hypoxia 1h hypoxia transcription factor activated by hypoxia and induces target genes for angiogenesis Activity of Decreased No effect Decreased No effect Decreased Increased Mediates the MAPK pathway anti- (% ERK1/2 apoptotic phosphorylation) effect of VEGF MMP16 gene No change No change Upregulated No change Downregulated at Upregulated at Cell expression 12h hypoxia 12h hypoxia migration ROCK1 gene No change No change No change No change Downregulated at Upregulated at Cell expression 12h hypoxia 12h hypoxia migration Table 4.15: Summary of the most significant results on the effect of hypoxia, hyperglycaemia, hyperglycaemia-hypoxia and metformin on HUVEC.

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4.4.3 AMPK signalling Our microarray data demonstrate that AMPK signalling was significantly activated by hypoxia either under euglycaemia or hyperglycaemia as well as suppressing anabolic downstream genes. In contrast, hyperglycaemia had no effect on AMPKα gene expression in EC. This is in accordance with multiple reports that demonstrated that hypoxia activates AMPK (Laderoute et al., 2006, Liu et al., 2006, Nagata et al., 2003). Anabolic downstream genes of AMPKα such as FASN, ACACA, and glycerol-3-phosphate acyltransferase (GPAT) were inhibited in combined euglycaemia-hypoxia and hyperglycaemia-hypoxia for 12 hours. As upon activation, AMPK downregulates by phosphorylation, several anabolic enzymes such as 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) (Carling et al., 1989, Clarke and Hardie, 1990, Hawley et al., 1996, Moore et al., 1991, Weekes et al., 1994), ACACA (Carling et al., 1989, Clarke and Hardie, 1990, Davies et al., 1990, Hawley et al., 1996, Moore et al., 1991, Weekes et al., 1994), and glycogen synthase (Carling and Hardie, 1989); which diminishes metabolite flux through synthetic pathways that consume ATP. AMPK activation also accelerates β-oxidation of fatty acid, which promotes ATP production (Ruderman et al., 1999, Velasco et al., 1997). Activated AMPK is known to enhance catabolic pathways through the PFKFB3. This molecule was detected previously to be important for the synthesis of fructose 2, 6-bisphosphate that is an allosteric activator of 6-phosphofructo-1-kinase, a rate-limiting enzyme in glycolysis (Yalcin et al., 2009). Moreover, NOS3 was found to be downregulated after 12 hours of hypoxia downstream AMPK signalling through Akt as discussed in Section 4.4.1. Previously, Nagata et al., investigate that activation of AMPK signalling in ECs has a role in regulating angiogenesis through the maintenance of pro-angiogenic Akt signalling under hypoxia (Nagata et al., 2003). AMPK is the candidate target mediating beneficial metabolic effects of metformin in hepatocytes (Zhou et al., 2001). Owen et al., suggested that metformin (0.05 mM) can act as an inhibitor of complex 1 of the respiratory chain in isolated mitochondria leading to activation of AMPK by decreasing cellular energy charge (Owen et al., 2000). We have shown that metformin exhibited no effect on AMPK signalling in ECs exposed to hypoxia

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Chapter 5. Vascular functions in CD34+ cells

Chapter 5. Vascular functions in CD34+cells

5.1 Introduction CD34+ cell therapy of ischaemic CVD is relatively novel. The mechanism by which CD34+ cells appear to promote angiogenesis is still unclear; although direct participation in vessel formation and paracrine secretion of angiogenic factors were supported (Mackie and Losordo, 2011). A recent study demonstrated that injection of CD34+ stem cells to the infarcted myocardium improves myocardial perfusion in acute myocardial infarction patients in a dose-dependent manner (Cogle et al., 2014). Moreover, circulating CD34+ cell number is used as an independent outcome indicator for CVD in diabetes (Fadini et al., 2009, Fadini et al., 2006a). It was reported that paracrine secretion was impaired in CD34+ cells derived from diabetic patients (Jarajapu et al., 2014). There are several lines of evidence that demonstrate that autologous CD34+ cells derived from diabetic patients are deficient in tissue repair as compared to those from non-diabetic or healthy volunteers (Caballero et al., 2007, Tan et al., 2010). For this reason, in the present study CD34+ cells have been used to investigate the effect of hypoxia, hyperglycaemia and combined hyperglycaemia hypoxia, in the presence or absence of metformin in order to further understand the underlying mechanism of impaired vascular regeneration in diabetes and beneficial effects of metformin.

5.2 Experimental approach The prime aim of this part of the work was to test the hypothesis that metformin enhances the paracrine production of angiogenic factors in CD34+ cells under hypoxia and hyperglycaemia conditions. Therefore, studies were performed exploring the effect of metformin on cytokine secretion, functional studies (tube formation) and then microarray gene expression profiling of CD34+ cells were performed under euglycaemia (5.5 mM glucose), euglycaemia-hypoxia, hyperglycaemia (16.5 and 25.0 mM glucose), or hyperglycaemia (16.5 mM)-hypoxia for 3 hours, in the presence or absence of physiological doses of metformin (0.01 mM). 174

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Paracrine secretion of CD34+ cells

The paracrine function of CD34+ cells was assessed after separating the conditioned medium and exosomes. Key pro-angiogenic growth factors and cytokines in conditioned media were measured by MSD assay as described in Materials and Methods 2.5.9. A candidate pro-angiogenic miR-126 was also assessed to evaluate the role of metformin in inducing angiogenesis via enhancing miR-126 release (described in 2.5.5). Subsequently, an in vitro Matrigel tube formation assay was performed on HUVECs using CD34+ conditioned media from the different experimental conditions as explained previously in 2.4.5.

Microarray assay

RNA extracted from three independent biological replicates (three different cord blood (CB) per condition) were pooled due to low cell number as CD34+ cells are rare population in the CB with an average percentage of 1.33% (Nimgaonkar et al., 1995). We compared the CD34+ cells transcriptome in 10 different experimental conditions each processed in two technical replicates resulting in 20 array experiments as described previously in Section 2.1.7. Then the raw CEL files were imported into Partek Genomic Suite version 6.6 and normalised using RMA. The QC was computed by using PCA mapping for visualising high-dimensional data and outliers of variances within samples (Figure 5.1). ANOVA test was performed using p-values < 0.05 and cut off FC of 1.5. Each treated condition either with hypoxia, hyperglycaemia, and/or metformin was compared with the parallel untreated condition and the numbers of differentially expressed genes have been illustrated in Figure 5.2. In order to detect the effect of metformin, gene lists were created by pairwise comparison of the conditions with versus without metformin treatment.

Afterwards, the gene lists were imported into IPA software to identify affected canonical pathways, molecular and cellular functions. Independent validation of microarray results was performed using qRT-PCR.

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Figure 5.1: PCA mapping for microarray gene expression of CD34+ cells. CD34+ cells were treated with various glucose concentrations (5.5, 16.5, or 25 mM) and then exposed to either normoxia (21%) or 4% hypoxia for 3 hours. These conditions were compared to the control 5.5 mM glucose with normoxia. Each sample is represented by one spot, and the colour of spots depends on the sample attributes. The spheres denote the distribution of the samples among each condition. Plots were created using the Partek Genomics Suite software package. The outliers are outside the 95% confidence interval.

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Figure 5.2: The number of differentially expressed genes from transcriptomic analysis of CD34+ cells using Partek software. Expression profiling of hypoxia with euglycaemia (5.5 mM glucose) and hyperglycaemia (16.5 mM or 25 mM) in vitro were employed in CD34+ cells for 3 hours in the presence and absence of metformin (0.01 mM). CD34+ cells transcriptome in 10 different experimental conditions each processed in two technical replicates resulting in 20 array experiments was compared. Then the raw CEL files were imported into Partek Genomic Suite version 6.6. ANOVA test was performed using a p-value < 0.05 and cut off FC of 1.5. Each treated condition either with hypoxia, hyperglycaemia, and/ or metformin was compared with the parallel untreated condition.

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5.3 Results 5.3.1 Paracrine secretion from human CD34+ cells Morphological analyses of exosomes from human CD34+ cells

In order to determine the role of exosomes in the paracrine secretion of CD34+ cells, we examined to what extent CD34+ cells secrete exosomes using transmission electron microscopy. Electron micrographs identified several multivesicular bodies (MVB) in the cytoplasm of CD34+ cells, carrying bilipid membrane-bound vesicles. The MVB membrane invaginated, initiating the biogenesis of exosomes (Chaput and Thery, 2011) and fused to the plasma membrane to release the vesicles to the media (Figure 5.3). Exosomes from CD34+ cells were similar to previous descriptions in size (40 to 90 nm in diameter), and cup-shaped (Thery et al., 2006).

Figure 5.3: Morphological analysis of exosomes from human CD34+ cells. Transmission electron micrograph of CD34+ cell: A) cytoplasm with multivesicular bodies enclosing bilipidic layer bound exosomes (arrow); B) exosomes are secreted out of the cell (40 to 90 nm).

Secretion of pro-angiogenic factors and angiogenic inhibitors by CD34+ cells

To address whether purified human CD34+ cells release factors that affect angiogenesis under experimental conditions, we evaluated the protein expression of pro-inflammatory cytokines; IL-1β, IL-6, IL-8 and TNFα; pro-angiogenic factor VEGFA, and angiogenic 178

Chapter 5. Vascular functions in CD34+ cells inhibitors TIMP1 and CXCL10 in exosomes from CD34+ using MSD assay; however, resulting data were very low, not within the standard curve range. For this reason, the CD34+-derived conditioned media (CM) were collected to measure cytokines and angiogenic factors using MSD assay. We did not observe significant variation in the expression of selected pro-inflammatory cytokines under all studied conditions (data not shown). However the pro-angiogenic factor VEGFA showed a significant increase in CM collected from CD34+ cells treated with hyperglycaemia (2.0-fold, p < 0.001) and hyperglycaemia combined with hypoxia for 3 hours (2.04-fold, p < 0.001) compared to 5.5 mM glucose. The CM from CD34+ cells treated with metformin displayed augmented levels of VEGFA either under euglycaemia (1.6-fold, p = 0.008), euglycaemia combined with hypoxia (1.4-fold, p = 0.037), and hyperglycaemia combined with hypoxia (1.2-fold, p = 0.037) compared pairwise with metformin-untreated condition (Figure 5.4A).

Compared to 5.5 mM glucose, angiogenic inhibitors exhibited significantly higher expression levels in the CM from CD34+ cells induced by hyperglycaemia and hyperglycaemia exposed to hypoxia, CXCL10 (2.0-fold, p < 0.001; 2.1-fold, p < 0.001 respectively) and TIMP1 (2.1-fold, p = 0.003; 2.4-fold, p < 0.001 respectively) (Figure 5.4B and C). Metformin elevated the concentration of CXCL10 in CD34+ CM under euglycaemia (1.6-fold, p = 0.002), euglycaemia combined with hypoxia (1.4-fold, p = 0.015), and hyperglycaemia combined with hypoxia (1.2-fold, p = 0.007). Furthermore, metformin increased the levels of TIMP1 only under euglycaemia (2.1-fold, p = 0.013), and euglycaemia combined with hypoxia (2.0-fold, p = 0.013) compared with parallel metformin-untreated condition, but exhibited no effect under hyperglycaemia and hyperglycaemia combined with hypoxia (Figure 5.4C).

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Figure 5.4: Expression of pro-angiogenic factor VEGFA and angiogenic inhibitors CXCL10 and TIMP1 in CD34+ cell-derived conditioned media. The culture supernatant was collected from 5 x 105 CD34+cells treated with euglycaemia (5.5 mM) or hyperglycaemia (16.5 mM) in the presence and absence of 0.01 mM metformin for 48 hours and then exposed to 4% hypoxia for 3 hours. The levels of (A) VEGFA, (B) CXCL10, and (C) TIMP1 were assayed on three independent biological replicates using the MSD technique. Results are presented as ± SEM and were statistically analysed using one-way ANOVA followed by LSD test. The effect of hypoxia and hyperglycaemia was revealed by comparing the conditions against the control, and the effect of

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Chapter 5. Vascular functions in CD34+ cells metformin was identified by comparing the conditions pairwise, i.e., the condition without metformin versus with metformin. *P < 0.05, **P < 0.01, ***P < 0.001.

miR-126 expression in CD34+ cell exosomes and exosome-depleted media

The expression of the pro-angiogenic miR-126 (Zhang et al., 2011) was assayed in CD34+ exosomes and exosome-depleted media as a measure of significant paracrine activity. Hypoxia significantly upregulated the expression of miR-126 isolated from exosomes (3.9- fold, p < 0.001) compared to control (Figure 5.5A). However, the expression of miR-126 decreased in exosomes of CD34+ cells treated with hyperglycaemia (-1.7-fold, p = 0.004 with 16.5 mM glucose, -2.5-fold, p < 0.001 with 25 mM glucose). No significant change had been shown in the levels of miR-126 under hyperglycaemia with hypoxia.

In CD34+ exosome-depleted media (Figure 5.5B), the levels of miR-126 were highly expressed under hypoxia (8.2-fold, p = 0.002) compared to control. Hyperglycaemia significantly upregulated the expression of miR-126 (16.5 mM, 2.7-fold, p = 0.002) and combined hyperglycaemia-hypoxia (3.6-fold, p < 0.001), whereas hyperglycaemia suppressed miR-126 (25 mM, -5.0-fold, p = 0.002).

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Figure 5.5: Pro-angiogenic miR-126 is highly expressed under hypoxia in exosomes of CD34+ cells. Volumes of 5 µl of CD34+ (A) exosomes or (B) exosome-depleted media were heated to 95°C for 10 minutes followed by 4°C hold. miRNA expression was measured by qRT-PCR TaqMan miRNA assay, data were quantified by the Cy0 method, n = 3. **P < 0.01, ***P < 0.001 compared to 5.5 mM glucose.

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The effect of metformin on miR-126 expression in CD34+ cell exosomes and exosome- depleted media

The effect of metformin on miR-126 expression levels in CD34+ exosomes revealed upregulation of miR-126 under euglycaemia (1.3-fold, p = 0.014) and combined hyperglycaemia (16.5 mM)-hypoxia (1.7-fold, p = 0.002), while metformin resulted in downregulation of miR-126 under euglycaemia-hypoxia (-1.4-fold, p = 0.002) (Figure 5.6A) compared pairwise to metformin-untreated condition.

However, in CD34+ exosome-depleted medium metformin treatment increased the expression miR-126 under euglycaemia (3.2-fold, p = 0.008) but inhibited expression under euglycaemia-hypoxia (-2.5-fold, p = 0.004) and hyperglycaemia (25 mM, -1.7-fold, p = 0.02) compared pairwise to metformin-untreated condition (Figure 5.6B).

The combination of the levels of miR-126 expression from exosomes and exosome- depleted media showed that the distribution is more in exosomes than released miRNA in exosome-depleted media as the percentage of miR-126 in exosomes varies between 51 to 87 % among different experimental conditions (Figure 5.6C). The total expression of miR- 126 was upregulated under the effect of metformin in euglycaemia (1.5-fold, p = 0.0105), and combined hyperglycaemia (16.5 mM)-hypoxia (1.46-fold, p = 0.0006), whereas downregulated under euglycaemia-hypoxia (-1.7, p = 0.0006) compared pairwise to metformin-untreated condition.

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Figure 5.6: Effect of metformin on miR-126 expression under hypoxia and hyperglycaemia from CD34+ cell exosomes and exosome-depleted media. Volumes of 5 µl of CD34+ (A) exosomes or (B) exosome-depleted media were heated to 95°C for 10 minutes followed by 4°C hold. (C) The distribution of miR-126 in exosomes and exosome depleted media. miRNA expression was measured by qRT-PCR TaqMan miRNA assay, data was quantified by Cy0 method, n = 3. *P < 184

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0.05, **P < 0.01, ***P < 0.001 compared pairwise, i.e., the condition with metformin versus without metformin.

Angiogenesis model in vitro

Tube formation was evaluated in HUVEC which had been cultured for 24 hours with CD34+ CM from cells treated with euglycaemia, euglycaemia and hypoxia 3 hours, hyperglycaemia, or hyperglycaemia and hypoxia in the presence and absence of metformin. To detect the best timing to analyse tube formation with Adobe Acrobat 8 Professional software, the time curve of the total tube length was measured at three different time points (0, 6, and 12 hours). The maximum tube length was observed after 6 hours of HUVEC incubation with CD34+ CM from cells under euglycaemia (control condition), therefore this was the optimum time for quantifying the tube length in all other conditions (Figure 5.7). EBM-2 medium containing the VEGF inhibitor sunitinib was used as a negative control for the assay (Figure 5.8A).

Figure 5.7: Optimisation of tube length measurement in in vitro angiogenic assays.

Effect of CD34+ cell-derived CM on in vitro angiogenesis

The tube length was unchanged in HUVEC cultured with CD34+ CM from cells treated with euglycaemia-hypoxia, hyperglycaemia, or hyperglycaemia-hypoxia compared to HUVEC incubated with CD34+ CM under euglycaemia (Figure 5.8A and B). 185

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The effect of metformin on in vitro angiogenic function of CD34+ cell-derived CM Tube length was significantly greater by 30.7% (p = 0.04) in HUVEC incubated with CD34+ CM derived from cells treated with hyperglycaemia and hypoxia in the presence of metformin than in parallel condition without metformin (Figure 5.8A highlighted with red and B). This followed by 1.2-fold increase in VEGFA and 1.2-fold increase in CXC10 and no change in TIMP-1 as shown above. This suggests that metformin mediated to a greater extent the paracrine secretion of pro-angiogenic factors versus inhibitors (as shown above) and thus on balance enhanced the in vitro angiogenic activity from the CD34+ CM with hyperglycaemia and hypoxia.

Metformin treatment did not significantly change tube length when HUVEC were incubated with CD34+ CM from cells treated with euglycaemia, euglycaemia-hypoxia or hyperglycaemia compared to the parallel untreated condition.

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Figure 5.8: In vitro angiogenic assays. (A) HUVEC (2.0 x104) were platted on Matrigel with CM from 2.0 x105 CD34+ cells treated with euglycaemia, euglycaemia and hypoxia 3 hours, 187

Chapter 5. Vascular functions in CD34+ cells hyperglycaemia or hyperglycaemia and hypoxia in the presence and absence of metformin. EBM-2 medium containing sunitinib (14 µM) was used as a negative control for the assay. Highlighted image showing the greatest tube length in HUVEC incubated with CD34+ CM-derived from cells treated with hyperglycaemia and hypoxia in the presence of metformin (B) Tube length was measured 6 hours later and expressed as a percentage of 5.5 mM glucose CM-treated HUVEC (n = 3). *P < 0.05 compared pairwise, i.e., condition with metformin versus without metformin.

5.3.2 Microarray analysis of gene expression in CD34+ cells The quality of RNA extracted from CD34+ was assessed using the Agilent Bioanalyzer system. The RIN of the samples varied between 7.0 and 9.7 (Figure 5.9).

Figure 5.9: CD34+ total RNA analysis using the Agilent Bioanalyzer. The electropherogram shows the total RNA pattern analysed with the RNA 6000 Nano assay. Two RNA peaks are shown: 18S ribosomal RNA and 28S ribosomal RNA. FU: fluorescence unit; S: seconds.

In the microarray experiments the number of differentially expressed genes with a p-value < 0.05 and a cutoff FC of 1.5 varied considerably between the conditions (Figure 5.2). The lowest numbers of differentially expressed genes were detected after 3 hours of hypoxia exposure under euglycaemia with metformin (21 genes) and hyperglycaemia (25 mM) with metformin (13 genes). In contrast, the highest number of differentially expressed genes was detected after 3 hours of hypoxia exposure under hyperglycaemia (16.5 mM) (1006 genes). Of note, also the frequency of up and downregulated genes varied substantially between the conditions. The highest proportion of upregulated genes (86%) was revealed after 3 hours hypoxia under euglycaemia with metformin, whereas the lowest proportion (35%) was detected under hyperglycaemia (16.5 mM).

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An overview of the gene expression pattern and affected biological functions in CD34+ cells following induction with hypoxia and hyperglycaemia is shown in Figure 5.10.

Figure 5.10: Two-dimensional clustering of CD34+ cells under the effect of hypoxia, hyperglycaemia and metformin. Results were generated by transcriptomic analysis of 2 technical replicates using Affymetrix microarray after exposure of CD34+ cells to 4% hypoxia for 3 hours either under euglycaemia or hyperglycaemia in the presence or absence of metformin. The Heatmap was generated by using Partek software, the differentially expressed genes with red shades reflect upregulated genes, whereas blue shades reflect downregulated genes and grey shades reflect not affected genes. The influenced biological functions for each group of genes were detected using IPA software.

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The effect of metformin on CD34+ cells under euglycaemia

The transcriptomic analysis of CD34+ cells showed that 313 genes passed the cutoff FC of 1.5 and p-value < 0.05 under euglycaemia and metformin compared to the control (5.5 mM glucose with normoxia). The gene list was imported to IPA software to detect the correlation of these genes to canonical pathways and biological functions. The analysis reveals that among the top associated molecular and cellular functions were inflammatory response (p = 1.52E-06), and binding of EC (1.56E-06) that were predicted to be inhibited. The inhibitory effect of metformin on inflammatory response and binding of EC was mediated through downregulation of following genes (Figure 5.11): complement component 3 (C3, -1.51-fold, p = 1.85E-05), chemokine (C-C motif) ligand 2 (CCL2, - 1.68-fold, p = 3.18E-03), CCL5 (-1.81, p = 4.66E-04), CD226 molecule (CD226, -1.85, p = 1.29E-02), IL-1α (-3.66-fold, p = 2.35E-05), IL-6 (-2.21-fold, p = 1.72E-04), IL-8 (-1.55, p =1.13E-02), integrin, beta 3 (ITGB3, -1.51-fold, p = 2.06E-03).

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Gene Gene Name FC Family symbol asp (abnormal spindle) homolog, microcephaly associated ASPM -1.52 other (Drosophila) complement component 3 C3 -1.51 peptidase chemokine (C-C motif) ligand 2 CCL2 -1.68 cytokine chemokine (C-C motif) ligand 5 CCL5 -1.81 cytokine CD24 molecule CD24 -1.61 other centromere protein E, 312kDa CENPE -1.51 other transmembrane C-type lectin domain family 2, member D CLEC2D -1.59 receptor chymase 1, mast cell CMA1 1.58 peptidase cathepsin L1 CTSL1 -1.50 peptidase glomulin, FKBP associated protein GLMN -1.62 other Heparanase HPSE -1.60 enzyme immunoglobulin heavy constant epsilon IGHE 1.57 other transmembrane interleukin 18 receptor 1 IL18R1 -1.56 receptor interleukin 1, alpha IL1A -3.66 cytokine interleukin 26 IL26 1.53 cytokine

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Gene Name Gene FC Family symbol interleukin 36, gamma IL1F9 -1.56 Cytokine interleukin 6 (interferon, beta 2)* IL6 -2.21 cytokine interleukin 8* IL8 -1.55 cytokine integrin, beta 3 (platelet glycoprotein IIIa, antigen CD61) ITGB3 -1.51 transmembrane receptor met proto-oncogene (hepatocyte growth factor receptor) MET -1.63 kinase antigen identified by monoclonal antibody Ki-67 MKI67 -1.61 other nuclear receptor corepressor 2 NCOR2 1.77 transcription regulator pro-platelet basic protein (chemokine (C-X-C motif) ligand 7) PPBP -1.69 cytokine S100 calcium binding protein A8 S100A8 1.71 other serpin peptidase inhibitor, clade B (ovalbumin), member 2 SERPINB2 -4.62 other Sclerostin SOST 1.56 other SPC25, NDC80 kinetochore complex component, homolog (S. SPC25 -1.50 other cerevisiae) thrombospondin 1 THBS1 -1.50 other tumour necrosis factor, alpha-induced protein 6* TNFAIP6 -2.36 other Figure 5.11: Anti-inflammatory effect of metformin on CD34+ cells under euglycaemia. CD34+ cells were treated with euglycaemia in the presence and absence of metformin for 24 hours. A set of differentially expressed genes was generated by comparing euglycaemia with metformin versus euglycaemia. The comprehensive network was created by IPA software. Green shades indicate downregulated whereas red shades indicate upregulated genes. Pathway key is illustrated in Appendix VI. *Genes assessed by cytokine assays for paracrine secretion.

The effect of metformin on CD34+ cells under euglycaemia and hypoxia

According to the transcriptomic analysis, 100 genes passed the cutoff FC of 1.5 and p-value < 0.05 under euglycaemia exposed to hypoxia for 3 hours compared to the control (5.5 mM glucose with normoxia). Among the top associated molecular and cellular functions (Table 5.1) were inflammatory response (p = 1.09E-11), cellular movement (p = 2.82E-11), cell to cell signalling and interaction (p = 4.69E-11), and cellular growth and proliferation (p = 3.92E-08). However, 21 genes were differentially expressed after 3 hours exposure to hypoxia with metformin treatment compared to the parallel untreated condition. The top molecular functions related were cell death and survival (p = 7.23E-04), and cellular growth and proliferation (p = 7.23E-04). The genes related to these functions are IL-5 (1.52-fold, p = 7.05E-03), serine peptidase inhibitor, Kazal type 7 (putative) (SPINK7,

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1.60-fold, p = 1.48E-02), and transducer of ERBB2, 2 (TOB2, -1.53-fold, p = 4.62E-02). The effect of metformin is minor as it reverses the inflammatory response, cell movement and adhesion to normal as demonstrated in Figure 5.12.

Predicted Functions Activation Number p-Value Activation Molecules Annotation z-score Molecules State Inflammatory 1.09E-11 Decreased -2.725 ↓AQP9, ↓C3, ↓CCL2, ↓CCL20, 19 Response ↓CCL5, ↓CCR7, ↓CD14, ↓CXCL5, ↓FOS, ↓IL-1α, ↓IL-6, ↓IL-8, ↓mir- 21, ↓PLA2G7, ↓PPBP, ↓RGS1, ↑S100A8, ↓TNFAIP6, ↓UTS2 Cellular 2.82E-11 Decreased -2.274 ↓AQP9, ↓C3, ↓CCL2, ↓CCL20, 14 Movement ↓CCL5, ↓CCR7, ↓CXCL5, ↓IL-1α, ↓IL-8, ↓PLA2G7, ↓PPBP, ↓RGS1, ↑S100A8, ↓UTS2 Cell-To-Cell 4.69E-11 Decreased -3.289 ↓C3, ↓CCL2, ↓CCL5, ↓CCR7, 20 Signalling ↓CD14, ↓CLEC1B, ↓CTSL1, and ↓CXCL5, ↓DUSP1, ↓FOS, ↓IL-1α, Interaction ↓IL-6, ↓IL-8, ↓KRT18, ↓MET, ↓ mir-21, ↓MS4A1, ↓NCOR2, ↑PPBP, ↑S100A8 Cellular 3.92E-08 Decreased -2.195 ↓CCL2, ↓CCL5, ↓DUSP1, ↓FOS, 11 Growth and ↓IL-1α, ↓IL-6, ↓IL-8, ↓MET, ↓mir- Proliferation 21, ↓TFPI2, ↓UTS2 Table 5.1: Effect of 3 hours hypoxia on biological functions involved in CD34+ cells. The biological functions were generated by analysis of the gene list using IPA software. The activation z-score was used in the calculation of significant changes in gene expression in different samples and conditions. It is calculated from the dataset and indicates activation or inhibition of the biological function as (+) indicates activation while (-) indicates inhibition. The arrow ↑ indicates gene is upregulated and ↓ indicates gene is downregulated. Key: AQP9: aquaporin 9; C3: complement component 3; CCL2: chemokine (C-C motif) ligand 2; CCR7: chemokine (C-C motif) receptor 7; CD14: CD14 molecule; CLEC1B: C-type lectin domain family 1, member B; CTSL1: cathepsin L1; CXCL5: chemokine (C-X-C motif) ligand 5; DUSP1: dual specificity phosphatase 1; FOS: FBJ murine osteosarcoma viral oncogene homolog; IL-1α: interleukin 1 alpha; KRT18: keratin 18; MET: met proto-oncogene (hepatocyte growth factor receptor); mir-21: microRNA 21; MS4A1: membrane-spanning 4-domains, subfamily A, member 1; NCOR2: nuclear receptor corepressor 2; PLA2G7: phospholipase A2, group VII (platelet-activating factor acetylhydrolase, plasma); PPBP: pro-platelet basic protein (chemokine (C-X-C motif) ligand 7); RGS1: regulator of G-protein signalling 1; S100A8: S100 calcium binding protein A8;TFPI2: tissue factor pathway inhibitor 2; TNFAIP6: tumour necrosis factor, alpha-induced protein 6; UTS2: urotensin 2.

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Figure 5.12: Effect of metformin on euglycaemia-hypoxia treated CD34+ cells. CD34+ cells were treated with euglycaemia and hypoxia for 3 hours in the presence and absence of metformin 194

Chapter 5. Vascular functions in CD34+ cells for 24 hours. A set of differentially expressed genes was generated by comparing (A) euglycaemia and hypoxia versus euglycaemia or (B) euglycaemia and metformin exposed to hypoxia versus euglycaemia and hypoxia without metformin. The comprehensive network was created by IPA software. Green shades indicate downregulated, red shades indicate upregulated genes and grey unchanged genes. Pathway key is illustrated in Appendix VI.

The effect of metformin on CD34+ cells under hyperglycaemia

Microarray data analysis revealed that 370 genes passed the cutoff FC of 1.5 and p-value < 0.05 under hyperglycaemia compared to the control (5.5 mM glucose with normoxia). Approximately 35% of the genes are upregulated, and 65% are downregulated, and the uppermost 30 differentially expressed genes are listed in Table 7.10 in Appendix III. The top molecular functions were inflammatory response (p = 1.32E-12), cell to cell signalling and interaction (p = 3.84E-17), cellular growth and proliferation (p = 1.21E-12), cell migration (p = 8.30E-11) which were all predicted to be inhibited (Table 5.2). The main canonical pathways were summarised in Table 5.3.

Functions p-Value Predicted Activation Molecules Number of Annotation Activation z-score Molecules State Cell-To-Cell 1.80E-17 Decreased -2.27 ↓AVPR1A, ↓BDNF, ↑BLNK, 57 Signalling ↑BTLA, ↓CCL2, ↑CCR7, ↓CD14, and ↓CD226, ↓CD24, ↓CD36, Interaction ↑CD3E, ↑CD79A, ↓CLEC1B, ↓CTSL1, ↓CXCL2, ↓FCER1A, ↓GP1BA, ↓GP5, ↓GP6, ↑HCK, ↓HLA-DQA1, ↓HLA-DQB1, ↓HPSE, ↑IGHM, ↑IGK, ↓IL-18, ↓IL-1α, ↓IL-6, ↓IL-8, ↑IRF4, ↓ITGA2B, ↓ITGB3, ↓KDR, ↓LTBP1, ↓MET, ↑MS4A1, ↑NCOR2, ↑NCR1, ↓NTS, ↓PF4, ↑PLD2, ↓PPBP, ↓PRKCA, ↓PROS1, ↓PTGS2, ↓PTPRJ, ↑S100A12, ↑S100A8, ↓SELP, ↑SH2D1A, ↑TCL1A, ↓THBS1, ↓TLR4, ↑TLR7, ↑TNFRSF17, ↓VIP, ↓VWF

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Functions p-Value Predicted Activation Molecules Number of Annotation Activation z-score Molecules State Cellular 1.21E-12 Decreased -1.40 ↑BLNK, ↑BTLA, ↓CCL2, 44 Growth and ↑CCR7, ↓CD14, ↓CD226, Proliferation ↓CD24, ↓CD36, ↑CD3E, ↑CD79A, ↓CLECL1B, ↓CXCL2, ↓FYB, ↑HCK, ↓HLA-DQA1, ↓HLA-DQB1, ↑IGHM, ↑IGKC, ↑IKZF3, ↓IL-18, ↓IL-1α, ↓IL- 5RA, ↓IL-6,IL-8, ↓INHBA, ↑IRF4, ↓ITGA2B, ↓ITGB3, ↓LTBP1, ↑LY9, ↑MS4A1, ↓PDE5A, ↓PF4, ↓PTGS2, ↓PTPRJ, ↓RUNX1T1, ↑SH2D1A, ↑SNAI2, ↑TCL1A, ↓THBS1, ↓TLR4, ↑TLR7, ↑TNFRSF17, ↓VIP Inflammatory 1.59E-12 Decreased -1.18 ↑BLNK, ↑BTLA, ↓CCL2, 39 Response ↑CCR7, ↓CD14, ↓CD226, ↑CD3E, ↑CD79A, ↓CLEC1B, ↓CTSL1, ↑HCK, ↓HLA-DQA1, ↓HLA-DQB1, ↓HPSE, ↑IGHM, ↓IL-18, ↓IL-1α, ↓IL-6, ↓IL-8, ↑IRF4, ↓LTBP1, ↓MET, ↑NCOR2, ↑NCR1, ↓PF4, ↑PLD2, ↓PRKCA, ↓PROS1, ↓PTGS2, ↓PTPRJ, ↑S100A12, ↑S100A8, ↑SH2D1A, ↑TCL1A, ↓THBS1, ↓TLR4, ↑TLR7, ↑TNFRSF17, ↓VIP

Cellular 2.60E-11 Decreased -3.31 ↓AQP9, ↓BDNF, ↑BTLA, 44 Movement ↓CCL2, ↑CCR7, ↓CD14, ↓CD226, ↑CD36, ↓CD3E, ↓CLEC1B, ↓CTTN, ↓CXCL2, F13A1, ↓FCER1A, ↓FYB, ↓GP1BA, ↓GP6, ↑HCK, ↓IL-18, ↓IL-1α, ↓IL-6, ↓IL-8, ↓INHBA, ↓ITGA2B, ↓ITGB3, ↓KDR, ↓PF4, ↓PLA2G7, ↓PPBP, ↓PRKCA, ↓PTGS2, ↓PTPRJ, ↓S100A10, ↑S100A12, ↑S100A8, ↓SELP, ↑SH2D1A, ↓THBS1, ↓TLR4, ↑TLR7, ↑TNFSF8, ↓UTS2, ↓VIP, ↓VWF Table 5.2: Top biological functions involved in CD34+cells cultured in hyperglycaemia. The affected biological functions were generated by analysis of the gene list using IPA software. The activation z-score was used in the calculation of significant changes in gene expression in different samples and conditions. It is calculated from the dataset and indicates activation or inhibition of the 196

Chapter 5. Vascular functions in CD34+ cells biological function as (+) indicates activation while (-) indicates inhibition. The arrow ↑ indicates gene is upregulated and ↓ indicates gene is downregulated. Key: AQP9: aquaporin 9; AVPR1A: arginine vasopressin receptor 1A; BDNF: brain-derived neurotrophic factor; BLNK: B-cell linker; BTLA: B and T lymphocyte associated; CCL2: chemokine (C-C motif) ligand 2; CCR7: chemokine (C-C motif) receptor 7; CD14: CD14 molecule; CD226: CD226 molecule; CD3E: CD3e molecule, epsilon (CD3-TCR complex); CD79A: CD79a molecule, immunoglobulin-associated alpha; CLEC1B: C-type lectin domain family 1, member B; CTSL1: cathepsin L1; CXCL2: chemokine (C- X-C motif) ligand 2; F13A1: coagulation factor XIII, A1 polypeptide; FCER1A: Fc fragment of IgE, high affinity I, receptor for; alpha polypeptide; FYB: FYN binding protein; GP1BA: glycoprotein Ib (platelet), alpha polypeptide; GP5: glycoprotein V (platelet); HCK: haemopoietic cell kinase; HLA-DQA1: major histocompatibility complex, class II, DQ alpha 1; HPSE: heparanase; IGHM: immunoglobulin heavy constant mu; IGK: immunoglobulin kappa locus; IKZF3: IKAROS family zinc finger 3 (Aiolos); IL-18: interleukin 18; INHBA: inhibin, beta A; IRF4: interferon regulatory factor 4; ITGA2B: integrin, alpha 2b (platelet glycoprotein IIb of IIb/IIIa complex, antigen CD41); ITGB3: integrin, beta 3 (platelet glycoprotein IIIa, antigen CD61); KDR: kinase insert domain receptor (a type III receptor tyrosine kinase); LTBP1: latent transforming growth factor beta binding protein 1; LY9: lymphocyte antigen 9; MET: met proto-oncogene (hepatocyte growth factor receptor); MS4A1: membrane-spanning 4-domains, subfamily A, member 1; NCOR2: nuclear receptor corepressor 2; NCR1: natural cytotoxicity triggering receptor 1; NTS: neurotensin; PDE5A: phosphodiesterase 5A, cGMP-specific; PF4: platelet factor 4; PLA2G7: phospholipase A2, group VII (platelet-activating factor acetylhydrolase, plasma); PLD2: phospholipase D2; PPBP: pro-platelet basic protein (chemokine (C-X-C motif) ligand 7); PRKCA: protein kinase C, alpha; PROS1: protein S (alpha); PTGS2: prostaglandin-endoperoxide synthase 2 (prostaglandin G/H synthase and cyclooxygenase); PTPRJ: protein tyrosine phosphatase, receptor type, J; RUNX1T1: runt-related transcription factor 1; translocated to, 1 (cyclin D-related); S100A12: S100 calcium binding protein A12; SELP: selectin P (granule membrane protein 140kDa, antigen CD62); SH2D1A: SH2 domain containing 1A; SNAI2: snail homolog 2 (Drosophila); TCL1A: T-cell leukaemia/lymphoma 1A; THBS1: thrombospondin 1; TLR4: toll-like receptor 4; TNFRSF17: tumour necrosis factor receptor superfamily, member 17; UTS2: urotensin 2; VIP: vasoactive intestinal peptide; VWF: von Willebrand factor.

Ingenuity P-value Molecules Canonical Pathways Atherosclerosis 3.2E-07 ↓IL8, ↓IL1A, ↓IL18, ↓CCL2, ↓SELP, ↓CD36, ↓ALOX12, ↑S100A8, ↓IL6, Signaling ↓PLA2G7 Dendritic Cell 8.9E-05 ↑TLR4, ↓IL1A, ↓IL18, ↓HLA-DQA1, ↑HLA-DOB, ↓HLA-DQB1, ↓IL6, Maturation ↓PLCL1, ↓HLA-DRB5, ↑CCR7 T Helper Cell 6.6E-04 ↓IL18, ↓HLA-DQA1, ↑HLA-DOB, ↓HLA-DQB1, ↓IL6, ↓HLA-DRB5 Differentiation IL-6 Signalling 2.1E-03 ↓IL8, ↓IL1A, ↓IL18, ↓CD14, ↓IL6, ↓TNFAIP6 Table 5.3: Top canonical pathways involved in CD34+ cells cultured in hyperglycaemia. The arrow ↑ indicates gene is upregulated and ↓ indicates gene is downregulated. For genes key refer to Table 5.2 legend.

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Metformin treatment of CD34+ cells under hyperglycaemia led to differentiate expression of 65 genes compared to hyperglycaemia (16.5 mM glucose). Approximately, 40% of the genes are upregulated, and 60% are downregulated. The top differentially expressed genes are listed in Table 7.11 in Appendix III. The top molecular functions influenced were cell death and survival (p = 1.29E-02), cell proliferation and growth (p = 3.25E-03), and lipid metabolism (p = 1.63E-03). The genes attributed to cell death and survival are killer cell immunoglobulin-like receptor, two domains, short cytoplasmic tail, 5 (KIR2DS5) 1.75-fold, p = 2.27E-02, ubiquitin specific peptidase 18 (USP18) 1.63-fold, p = 3.10E-03; whereas genes related to, cell proliferation and growth are epithelial cell adhesion molecule (EPCAM) -2.06-fold, p = 9.93E-03, USP18, and hydroxy-delta-5-steroid dehydrogenase, 3 beta- and steroid delta-isomerase 1 (HSD3B1) -1.64-fold, p = 2.73E-02; and lipid metabolism are diacylglycerol lipase, alpha (DAGLA) 1.56-fold, p = 4.15E-02, and HSD3B1. Thus, no pathways were significantly identified since < 3 genes were differentially expressed per pathway (data not shown).

The effect of metformin on CD34+ cells under hyperglycaemia with hypoxia

There were 1006 differentially expressed genes identified under hyperglycaemia exposed to hypoxia for 3 hours compared to hyperglycaemia (16.5 mM). Fifty-two % of the genes are upregulated whereas 48% are downregulated, and the top significantly 30 differentially expressed genes are listed in Table 7.12 in Appendix III. The most affected functions were cell cycle (p = 1.95E-19), cellular movement (p = 2.03E-09), and cellular proliferation (p = 1.39E-07) which were predicted by IPA software to be activated whereas chemotaxis of vascular endothelial cells (p = 6.44E-03) was inhibited. Downregulation of pro-angiogenic cytokines IL-8 (-2.12-fold, p = 3.55E-04) and hepatocyte growth factor (HGF, -1.64-fold, p = 2.33E-03) was detected, while upregulation of angiogenic inhibitors TIMP1 (2.36-fold, p = 6.34E-06), TIMP3 (2.15-fold, p = 2.29E-02), and CXCL10 (1.89-fold, p = 2.04E-02) was detected (Figure 5.13). The functional effect of these factors on angiogenesis was studied using in vitro Matrigel tube formation assay as described in Section 5.3.1. Amongst the thoroughly studied pathways: triacylglycerol biosynthesis (Figure 5.13), and mitochondrial

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Chapter 5. Vascular functions in CD34+ cells dysfunction (Figure 5.14). Genes participating in triacylglycerol biosynthesis were: glycerol-3-phosphate acyltransferase, mitochondrial (GPAM, 13.44-fold, p = 1.91E-06), phosphatidic acid phosphatase type 2A (PPAP2A, 1.76-fold, p = 1.50E-04), and 1- acylglycerol-3-phosphate O-acyltransferase 9 (AGPAT9, -1.63-fold, p = 5.02E-05).

Potential genes involved in mitochondrial dysfunction are: cytochrome c oxidase subunit VIb polypeptide 1 (ubiquitous) (COX6B1, 2.08-fold , p = 4.17E-03), Mitochondrially Encoded NADH Dehydrogenase 2 (MT-ND2, 15.2-fold, p = 1.12E-09), NADH dehydrogenase (ubiquinone) Fe-S protein 8, 23kDa (NADH-coenzyme Q reductase) (NDUFS8, 1.51-fold, p = 3.17E-02), ATP synthase, H+ transporting, mitochondrial F1 complex, delta subunit (ATP5D, 1.86-fold, p = 2.33E-04), COX8A (2.15-fold, p = 2.10E- 04), NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 3, 9kDa (NDUFA3, 1.55- fold, p = 1.32E-02), cytochrome b5 type A (CYB5A, 1.92-fold, p = 4.25E-03), Parkinson protein 2, E3 ubiquitin protein ligase (PARK2, -1.52-fold, p = 2.22E-03), and COX5B (2.19-fold, p = 2.28E-04).

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Gene Name Gene symbol FC Family adenosine A2b receptor ADORA2B 1.55 G-protein coupled receptor 1-acylglycerol-3-phosphate O-acyltransferase 9 AGPAT9 -1.63 enzyme arachidonate 12-lipoxygenase ALOX12 1.66 enzyme amylase, alpha 2A (pancreatic) AMY2A -1.62 enzyme annexin A2 ANXA2 1.96 other aryl hydrocarbon receptor nuclear translocator-like ARNTL -1.56 transcription regulator ATP synthase, H+ transporting, mitochondrial F1 ATP5D 1.86 transporter complex, delta subunit baculoviral IAP repeat containing 5 BIRC5 1.56 other breast cancer 2, early onset BRCA2 1.53 transcription regulator B and T lymphocyte associated BTLA -1.73 other carbonic anhydrase II CA2 1.61 enzyme cyclin B2 CCNB2 2.40 other chemokine (C-C motif) receptor 1 CCR1 -1.80 G-protein coupled receptor

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Gene Name Gene symbol FC Family CD244 molecule, natural killer cell receptor 2B4 CD244 -1.80 transmembrane receptor CD276 molecule CD276 1.62 other CD38 molecule CD38 -1.92 enzyme CD79a molecule, immunoglobulin-associated alpha CD79A -1.68 transmembrane receptor cholinergic receptor, muscarinic 3 CHRM3 -1.50 G-protein coupled receptor cathepsin S CTSS -1.71 peptidase chemokine (C-X-C motif) ligand 10 CXCL10 1.89 cytokine cytochrome b-245, beta polypeptide CYBB -1.75 enzyme diacylglycerol kinase, alpha 80kDa DGKA -1.55 kinase dihydrofolate reductase DHFR 1.81 enzyme E2F transcription factor 1 E2F1 1.59 transcription regulator ectonucleotide pyrophosphatase/phosphodiesterase 2 ENPP2 -1.84 enzyme FBJ murine osteosarcoma viral oncogene homolog FOS -1.52 transcription regulator glycerol-3-phosphate acyltransferase, mitochondrial GPAM 13.44 enzyme hepatocyte growth factor (hepapoietin A; scatter factor) HGF -1.64 growth factor histone cluster 1, H3a HIST1H3A 2.07 other histone cluster 1, H4a HIST1H4A 2.07 other histone cluster 2, H2aa3 HIST2H2AA 1.51 other 3/HIST2H2A A4 insulin-like growth factor 1 receptor IGF1R -1.69 transmembrane receptor immunoglobulin heavy constant mu IGHM -2.38 transmembrane receptor inhibitor of kappa light polypeptide gene enhancer in B- IKBKG -1.69 kinase cells, kinase gamma interleukin 1 receptor, type I IL1R1 -1.53 transmembrane receptor interleukin 8 IL8 -2.12 cytokine interferon regulatory factor 4 IRF4 -1.51 transcription regulator interferon regulatory factor 8 IRF8 -1.52 transcription regulator potassium voltage-gated channel, subfamily H (eag- KCNH2 1.59 ion channel related), member 2 potassium inwardly-rectifying channel, subfamily J, KCNJ2 -1.67 ion channel member 2 KIAA0101 KIAA0101 1.66 other linker for activation of T cells LAT 2.05 kinase mitogen-activated protein kinase 9 MAPK9 -1.56 kinase antigen identified by monoclonal antibody Ki-67 MKI67 2.11 other

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Gene Name Gene symbol FC Family mRNA turnover 4 homolog (S. cerevisiae) MRTO4 1.71 other membrane-spanning 4-domains, subfamily A, member 1 MS4A1 -2.93 other myosin, heavy chain 10, non-muscle MYH10 1.51 other peroxisomal trans-2-enoyl-CoA reductase PECR -1.53 enzyme phospholipid transfer protein PLTP 1.57 enzyme protein phosphatase 1, catalytic subunit, alpha isozyme PPP1CA 1.55 phosphatase ras homolog family member H RHOH -1.58 enzyme ribonuclease H1 RNASEH1 1.57 enzyme S100 calcium binding protein A8 S100A8 -5.24 other SET binding factor 2 SBF2 -1.66 other splicing factor 3b, subunit 5, 10kDa SF3B5 1.63 other SH2 domain containing 1A SH2D1A -1.56 other solute carrier family 3 (activators of dibasic and neutral SLC3A2 1.68 transporter amino acid transport), member 2 suppressor of cytokine signalling 1 SOCS1 1.55 other SP110 nuclear body protein SP110 -1.80 other signal transducer and activator of transcription 6, STAT6 -1.50 transcription interleukin-4 induced regulator tec protein tyrosine kinase TEC -1.70 kinase tissue factor pathway inhibitor (lipoprotein-associated TFPI -1.61 other coagulation inhibitor) thymocyte selection associated THEMIS -1.56 other TIMP metallopeptidase inhibitor 1 TIMP1 2.36 other TIMP metallopeptidase inhibitor 3 TIMP3 2.14 other thymidine kinase 1, soluble TK1 1.73 kinase tumour necrosis factor (ligand) superfamily, member 10 TNFSF10 -1.69 cytokine tubulin, alpha 1a TUBA1A 1.53 other tubulin, alpha 1c TUBA1C 1.88 other tubulin, alpha 4a TUBA4A 1.69 other TXK tyrosine kinase TXK -1.97 kinase thymidylate synthetase TYMS 1.51 enzyme vascular endothelial growth factor B VEGFB 1.60 growth factor Figure 5.13: Effect of hypoxia combined with hyperglycaemia on CD34+ cells: Angiogenesis and triacylglycerol biosynthesis. CD34+ cells were treated with hyperglycaemia for 24 hours and then exposed to hypoxia for 3 hours. A set of differentially expressed genes was generated by comparing hyperglycaemia and hypoxia versus hyperglycaemia. Pro-angiogenic factors and inhibitors were detected from the gene list created by Genomic Suite software that was then uploaded to the IPA. The two genes involved in triacylglycerol biosynthesis were deregulated in this condition. Green shades indicate downregulated, and red shades indicate upregulated genes. Pathway key is illustrated in Appendix VI. CP: canonical pathway, Fx: biological function.

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Gene Name Gene FC Family symbol aminoacyl tRNA synthetase complex-interacting AIMP2 1.53 other multifunctional protein 2 Aly/REF export factor THOC4 2.15 transcription regulator cyclin-dependent kinase inhibitor 3 CDKN3 1.91 phosphatase cytochrome c oxidase subunit Vb COX5B 2.19 enzyme cytochrome c oxidase subunit VIb polypeptide 1 (ubiquitous) COX6B1 2.08 enzyme cytochrome c oxidase subunit VIIIA (ubiquitous) COX8A 2.15 enzyme cytochrome b5 type A (microsomal) CYB5A 1.92 enzyme dimethylarginine dimethylaminohydrolase 1 DDAH1 1.73 enzyme eukaryotic translation initiation factor 1A, Y-linked EIF1AY 2.20 translation regulator eukaryotic translation initiation factor 4A3 EIF4A3 1.53 enzyme erythrocyte membrane protein band 4.9 (dematin) EPB49 1.83 other Kruppel-like factor 1 (erythroid) KLF1 1.71 transcription regulator latrophilin 2 LPHN2 2.25 G-protein coupled receptor 203

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Gene Name Gene FC Family symbol maternal embryonic leucine zipper kinase MELK 1.53 kinase MTND2 ND2 15.22 enzyme NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 3, NDUFA3 1.55 enzyme 9kDa NADH dehydrogenase (ubiquinone) Fe-S protein 8, 23kDa NDUFS8 1.51 enzyme (NADH-coenzyme Q reductase) OTU domain, ubiquitin aldehyde binding 1 OTUB1 1.60 enzyme parkinson protein 2, E3 ubiquitin protein ligase (parkin) PARK2 -1.52 enzyme phosphodiesterase 5A, cGMP-specific PDE5A -1.61 enzyme regulator of chromosome condensation 1 RCC1 1.79 other ribonucleotide reductase M2 RRM2 2.03 enzyme S100 calcium binding protein A12 S100A12 -2.45 other thyroid hormone receptor interactor 13 TRIP13 2.37 transcription regulator tubulin tyrosine ligase-like family, member 7 TTLL7 -1.67 other tubulin, beta 4B class IVb TUBB2C 1.76 other ubiquitin-conjugating enzyme E2C UBE2C 1.75 enzyme ubiquitin-conjugating enzyme E2L 3 UBE2L3 1.64 enzyme Figure 5.14: Effect of hypoxia combined with hyperglycaemia on CD34+ cells: Mitochondrial dysfunction pathway. CD34+ cells were treated with hyperglycaemia for 24 hours and then exposed to hypoxia for 3 hours. Differentially expressed genes were generated by comparing hyperglycaemia and hypoxia versus hyperglycaemia. Mitochondrial dysfunction pathway was detected from the significantly related gene list created by Genomic Suite software that was then uploaded to the IPA. Green shades indicate downregulated, red shades indicate upregulated genes and grey indicate unchanged genes. Pathway key is illustrated in Appendix VI. CP: canonical pathway, Fx: biological function.

In summary, 317 differentially expressed genes were identified under hyperglycaemia treated with metformin and then exposed to hypoxia for 3 hours compared to the parallel hyperglycaemia-hypoxia metformin-untreated condition. Forty-two % of the genes were upregulated whereas 58% were downregulated. The 30 most differentially expressed genes have been shown in Table 7.13 in Appendix III. The most affected functions are cellular movement (p = 3.19E-05), DNA replication and repair (p = 1.41E-04), lipid metabolism (p = 1.41E-04) and haematological system development and function (p = 2.67E-04). The main canonical pathways were demonstrated in Table 5.4. Metformin reverses the effect of hypoxia on CD34+ cells treated with hyperglycaemia resulting in that the pro-angiogenic factors were not affected and that the angiogenic inhibitors TIMP1 (Johnson et al., 1994) (-

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1.68-fold, p = 3.90E-04), and CXCL10 (Glaser et al., 2004) (-2.01-fold, p = 1.28E-02) were downregulated as shown in Figure 5.15. Moreover, metformin significantly suppressed the mitochondrial dysfunction (p = 2E-03) and triacylglycerol biosynthesis (p = 7E-03). Genes that were significantly downregulated in mitochondrial pathway are: MT-ND2 (-11.9-fold, p = 2.79E-09), COX6B1 (-1.83-fold, p = 1.27E-02), ATP5D (-1.85-fold, p = 2.40E-04), COX8A (-1.90-fold, p = 7.99E-04), NDUFA3 (-1.59-fold, p = 9.53E-03), CYB5A (-1.59- fold, p = 2.55E-02), COX5B (-1.82-fold, p = 1.68E-03) (Figure 5.16). Furthermore, genes involved in triacylglycerol biosynthesis were inhibited including GPAM (-10.76-fold, p = 4.31E-06), and AGPAT9 (1.69-fold, p = 2.73E-05).

Canonical Pathways P-value Molecules Mitochondrial Dysfunction 2.0E-03 ↓COX6B1, ↓ATP5D, ↓COX8A, ↑MAPK9, ↓NDUFA3, ↓CYB5A, ↓COX5B, ↓MT-ND2

Dendritic Cell Maturation 5.2E-03 ↑IKBKG, ↓ICAM1, ↓HLA-DRB4, ↓TYROBP, ↓HLA-DRB3, ↓FCER1G, ↑MAPK9

Induction of Apoptosis by HIV1 5.4E-03 ↓DAXX, ↓MAP2K7, ↑IKBKG, ↑MAPK9 CD40 Signalling 6.8E-03 ↓MAP2K7, ↑IKBKG, ↓ICAM1, ↑MAPK9 Triacylglycerol Biosynthesis 7.1E-03 ↓GPAM, ↑AGPAT9, ↑ELOVL6

Interferon Signalling 7.6E-03 ↓SOCS1, ↓IFITM1, ↓IFI35 Role of MAPK Signalling in the 7.9E-03 ↓CXCL10, MAP2K7, PLA2G10, ↑MAPK9 Pathogenesis of Influenza Type I Diabetes Mellitus Signalling 9.5E-03 ↓SOCS1, ↓MAP2K7, ↑IKBKG, ↓FCER1G, ↑MAPK9 Table 5.4: Effect of metformin on canonical pathways involved in CD34+ cells cultured in combined hyperglycaemia-hypoxia for 3 hours. The arrow ↑ indicates gene is upregulated and ↓ indicates gene is downregulated. For genes’ key refer to tables in Figures 5.14 and 5.15.

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Gene Name Gene FC Family symbol adenosine A2b receptor ADORA2B -1.25 G-protein coupled receptor 1-acylglycerol-3-phosphate O-acyltransferase 9 AGPAT9 1.69 enzyme arachidonate 12-lipoxygenase ALOX12 -1.01 enzyme amylase, alpha 2A (pancreatic) AMY2A 1.25 enzyme annexin A2 ANXA2 -1.68 other aryl hydrocarbon receptor nuclear translocator-like ARNTL 1.35 transcription regulator ATP synthase, H+ transporting, mitochondrial F1 complex, ATP5D -1.85 transporter delta subunit baculoviral IAP repeat containing 5 BIRC5 -1.13 other breast cancer 2, early onset BRCA2 -1.04 transcription regulator B and T lymphocyte associated BTLA 1.06 other carbonic anhydrase II CA2 -1.18 enzyme cyclin B2 CCNB2 -1.05 other chemokine (C-C motif) receptor 1 CCR1 1.07 G-protein coupled receptor CD244 molecule, natural killer cell receptor 2B4 CD244 1.53 transmembrane receptor

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Gene Name Gene symbol FC Family CD276 molecule CD276 -1.09 other CD38 molecule CD38 1.10 enzyme CD79a molecule, immunoglobulin-associated alpha CD79A -1.09 transmembrane receptor cholinergic receptor, muscarinic 3 CHRM3 1.38 G-protein coupled receptor cathepsin S CTSS 1.27 peptidase chemokine (C-X-C motif) ligand 10 CXCL10 -2.01 cytokine cytochrome b-245, beta polypeptide CYBB 1.04 enzyme diacylglycerol kinase, alpha 80kDa DGKA 1.48 kinase dihydrofolate reductase DHFR -1.37 enzyme E2F transcription factor 1 E2F1 -1.21 transcription regulator ectonucleotide pyrophosphatase/phosphodiesterase 2 ENPP2 -1.03 enzyme FBJ murine osteosarcoma viral oncogene homolog FOS -1.45 transcription regulator glycerol-3-phosphate acyltransferase, mitochondrial GPAM - enzyme 10.76 hepatocyte growth factor (hepapoietin A; scatter factor) HGF 1.11 growth factor histone cluster 1, H3a HIST1H3A -1.54 other histone cluster 1, H4a HIST1H4L -1.47 other histone cluster 2, H2aa3 HIST2H2AA3 -1.22 other histone cluster 2, H2be HIST2H2BE -1.53 other insulin-like growth factor 1 receptor IGF1R 1.32 transmembrane receptor immunoglobulin heavy constant mu IGHM -1.14 transmembrane receptor inhibitor of kappa light polypeptide gene enhancer in B-cells, IKBKG 1.64 kinase kinase gamma interleukin 1 receptor, type I IL1R1 -1.12 transmembrane receptor interleukin 8 IL8 -1.25 cytokine interferon regulatory factor 4 IRF4 -1.09 transcription regulator interferon regulatory factor 8 IRF8 1.15 transcription regulator potassium voltage-gated channel, subfamily H (eag-related), KCNH2 -1.14 ion channel member 2 potassium inwardly-rectifying channel, subfamily J, member 2 KCNJ2 1.25 ion channel KIAA0101 KIAA0101 -1.04 other linker for activation of T cells LAT -1.52 kinase mitogen-activated protein kinase 9 MAPK9 1.63 kinase antigen identified by monoclonal antibody Ki-67 MKI67 1.22 other mRNA turnover 4 homolog (S. cerevisiae) MRTO4 -1.37 other membrane-spanning 4-domains, subfamily A, member 1 MS4A1 1.04 other myosin, heavy chain 10, non-muscle MYH10 1.07 other peroxisomal trans-2-enoyl-CoA reductase PECR 1.28 enzyme

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Gene Name Gene symbol FC Family phospholipid transfer protein PLTP -1.20 enzyme protein phosphatase 1, catalytic subunit, alpha isozyme PPP1CA -1.34 phosphatase ras homolog family member H RHOH 1.35 enzyme ribonuclease H1 RNASEH1 -1.15 enzyme S100 calcium binding protein A8 S100A8 1.24 other SET binding factor 2 SBF2 1.11 other splicing factor 3b, subunit 5, 10kDa SF3B5 -1.61 other SH2 domain containing 1A SH2D1A 1.08 other solute carrier family 3 (activators of dibasic and neutral amino SLC3A2 -1.48 transporter acid transport), member 2 suppressor of cytokine signalling 1 SOCS1 -1.73 other SP110 nuclear body protein SP110 1.46 other signal transducer and activator of transcription 6, interleukin-4 STAT6 1.17 transcription regulator induced tec protein tyrosine kinase TEC 1.37 kinase tissue factor pathway inhibitor (lipoprotein-associated TFPI 1.42 other coagulation inhibitor) thymocyte selection associated THEMIS 1.34 other TIMP metallopeptidase inhibitor 1 TIMP1 -1.68 other TIMP metallopeptidase inhibitor 3 TIMP3 -1.18 other thymidine kinase 1, soluble TK1 -1.08 kinase tumour necrosis factor (ligand) superfamily, member 10 TNFSF10 1.18 cytokine tubulin, alpha 1a TUBA1A -1.14 other tubulin, alpha 1c TUBA1C -1.20 other tubulin, alpha 4a TUBA4A -1.17 other tubulin, beta 4B class IVb TUBB2C -1.12 other TXK tyrosine kinase TXK 1.30 kinase thymidylate synthetase TYMS 1.05 enzyme vascular endothelial growth factor B VEGFB -1.74 growth factor Figure 5.15: Effect of metformin on hypoxia combined with hyperglycaemia on CD34+ cells: Angiogenesis and triacylglycerol biosynthesis. CD34+ cells were treated with hyperglycaemia and metformin for 24 hours and then exposed to hypoxia for 3 hours. Differentially expressed genes were generated by comparing CD34+ cells treated with hyperglycaemia and metformin and then exposed to hypoxia versus hyperglycaemia and hypoxia. Pro-angiogenic factors and inhibitors were detected from the significantly related gene list created by Genomic Suite software that was then uploaded to the IPA. Additionally, the effect of metformin on genes involved in triacylglycerol biosynthesis were considered. Green shades indicate downregulated, and red shades indicate upregulated genes. Pathway key is illustrated in Appendix VI. CP: canonical pathway, Fx: biological function.

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Gene Name Gene symbol FC Family aminoacyl tRNA synthetase complex-interacting AIMP2 -1.15 other multifunctional protein 2 transcription Aly/REF export factor THOC4 -1.47 regulator cyclin-dependent kinase inhibitor 3 CDKN3 1.25 phosphatase cytochrome c oxidase subunit Vb COX5B -1.82 enzyme cytochrome c oxidase subunit VIb polypeptide 1 (ubiquitous) COX6B1 -1.83 enzyme cytochrome c oxidase subunit VIIIA (ubiquitous) COX8A -1.90 enzyme cytochrome b5 type A (microsomal) CYB5A -1.59 enzyme dimethylarginine dimethylaminohydrolase 1 DDAH1 1.10 enzyme eukaryotic translation initiation factor 1A, Y-linked EIF1AY 1.61 translation regulator eukaryotic translation initiation factor 4A3 EIF4A3 -1.25 enzyme erythrocyte membrane protein band 4.9 (dematin) EPB49 -1.08 other transcription Kruppel-like factor 1 (erythroid) KLF1 -1.13 regulator

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Gene Name Gene symbol FC Family latrophilin 2 LPHN2 -1.06 G-protein coupled receptor maternal embryonic leucine zipper kinase MELK 1.26 kinase NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 3, NDUFA3 -1.59 enzyme 9kDa NADH dehydrogenase (ubiquinone) Fe-S protein 8, 23kDa NDUFS8 -1.30 enzyme (NADH-coenzyme Q reductase) OTU domain, ubiquitin aldehyde binding 1 OTUB1 -1.58 enzyme parkinson protein 2, E3 ubiquitin protein ligase (parkin) PARK2 1.38 enzyme phosphodiesterase 5A, cGMP-specific PDE5A 1.42 enzyme regulator of chromosome condensation 1 RCC1 -1.58 other ribonucleotide reductase M2 RRM2 1.05 enzyme S100 calcium binding protein A12 S100A12 -1.24 other thyroid hormone receptor interactor 13 TRIP13 -1.24 transcription regulator tubulin tyrosine ligase-like family, member 7 TTLL7 1.54 other tubulin, beta 4B class IVb TUBB2C -1.12 other ubiquitin-conjugating enzyme E2C UBE2C -1.05 enzyme ubiquitin-conjugating enzyme E2L 3 UBE2L3 -1.36 enzyme Figure 5.16: Effect of metformin on the mitochondrial function pathway of CD34+ cells treated with hyperglycaemia and hypoxia. CD34+ cells were treated with hyperglycaemia and metformin for 24 hours and then exposed to hypoxia for 3 hours. A set of differentially expressed genes was generated by comparing CD34+ cells treated with hyperglycaemia and metformin and then exposed to hypoxia versus hyperglycaemia and hypoxia. Mitochondrial function pathway was detected from the significantly related gene list created by Genomic Suite software that was then uploaded to the IPA. Green shades indicate downregulated, red shades indicate upregulated genes and grey unchanged genes. Pathway key is illustrated in Appendix VI. CP: canonical pathway, Fx: biological function.

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Induction of CD34+ cells with a higher concentration of glucose (25 mM) expressed 609 genes differentially when compared to euglycaemia (5.5 mM). Fifty-five % of these genes were upregulated, and 45% were downregulated. The main affected biological functions are as follows: inflammatory response (p = 3.47E-15) that was activated, but in contrast, cardiovascular system development and function such as binding and adhesion of EC, and angiogenesis (p = 8.31E-07) were inhibited. The main canonical pathways were summarized in Table 5.5.

Ingenuity P-value Molecules Canonical Pathways Atherosclerosis ↓IL8, ↓CD40LG, ↓IL1A, ↓ICAM1, ↓CD36, ↓ALOX12, ↓IL6, ↓IL18, Signalling 5.4E-05 ↓CCL2, ↓SELP, ↓IL1RN, ↓CSF1, ↑RBP4

IL-6 Signalling ↓IL8, ↓ABCB1, ↓IL1A, ↓IL1RL1, ↓IL6, ↑PIK3R3, ↓FOS, ↓IL18, ↓IL1RN, 1.4E-03 ↓CD14, ↓TNFAIP6 p38 MAPK ↓IL1A, ↓IL18, ↓DDIT3, ↓DUSP1, ↓IL1RN, ↓IL1RL1, ↓IRAK2 Signalling 2.9E-02 Dendritic Cell ↓CD40LG, ↓IL1A, ↓ICAM1, ↓HLA-DQA1, ↓HLA-DQB1, ↓IL6, Maturation 4.0E-02 ↑PIK3R3, ↓IL18, ↓IL1RN, ↓HLA-DRB5 Table 5.5: Top canonical pathways involved in CD34+ cells cultured in hyperglycaemia (25.0 mM). The arrow ↑ indicates gene is upregulated and ↓ indicates gene is downregulated. Key: IL-8: interleukin 8; CD40LG: CD40 ligand; ICAM-1: intercellular adhesion molecule 1; CD36: CD36 molecule; ALOX12: arachidonate 12-lipoxygenase; CCL2: chemokine (C-C motif) ligand 2; SELP: selectin P; CSF1: colony stimulating factor 1; RBP4: retinol binding protein 4, plasma; ABCB1: ATP-binding cassette, sub-family B (MDR/TAP), member 1; PIK3R3: phosphoinositide-3-kinase, regulatory subunit 3; FOS: FBJ murine osteosarcoma viral oncogene homolog; TNFAIP6: tumour necrosis factor, alpha-induced protein 6; DDIT3: DNA-damage-inducible transcript 3; DUSP1: dual specificity phosphatase 1; IRAK2: interleukin-1 receptor-associated kinase 2; HLA-DQA1: major histocompatibility complex, class II, DQ Alpha 1; HLA-DQB1: major histocompatibility complex, class II, DQ beta 1; HLA-DRB5: major histocompatibility complex, class II, DR beta 5.

The effect of metformin on hyperglycaemia with 25 mM concentration was minor, as the number of differentially expressed genes was only 13 when compared with hyperglycaemia and metformin untreated condition (data not shown). Of the 13 genes, 69% were upregulated, and 31% were downregulated. Therefore, this gene list was too small to annotate biological functions and canonical pathways. 211

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5.3.3 Confirmation of gene expression in CD34+ cells using qRT-PCR In order to confirm the results of the microarray experiments, 13 genes exhibiting highly significant differences in expression or those with critical biological functions were validated by qRT-PCR.

Seven pro-angiogenic factors CCL2, CCL5, HGF, IL-1a, IL-6, IL-8, SELP and two angiogenic inhibitors CXCL10 and TIMP1 were selected and compared to results from the microarray experiments (Figure 5.17). Induction of CD34+ cells with 3 hours euglycaemia- hypoxia suppressed the expression of CCL2 (-3.2-fold, p < 0.001), CCL5 (-4.2-fold, p < 0.001), HGF (-1.3-fold, p = 0.045), IL1a (-16.7-fold, p < 0.001), IL-6 (-20.0-fold, p < 0.001), IL-8 (-4.3-fold, p < 0.001), and SELP (-1.3-fold, p = 0.035) when compared to euglycaemia (5.5 mM). Furthermore, hyperglycaemia either with 16.5 or 25.0 mM glucose inhibited the expression of CCL2 (-3.4-fold; -100.0-fold, p < 0.001), CCL5 (-2.1-fold; -9.1- fold, p < 0.001), HGF (-2.5-fold, p < 0.001; -1.3-fold, , p = 0.03), IL-1a (-11.1-fold; -33.3- fold, p < 0.001), IL-6 (-16.7-fold; -100.0-fold, p < 0.001), IL-8 (-3.1-fold; -50.0-fold, p < 0.001), and SELP (-6.7-fold; -50.0-fold, p = 0.035) respectively compared to euglycaemia (Figure 5.18A, B, C, D, E, F and G). Conversely, hyperglycaemia combined with hypoxia had no effect on the mRNA levels of CCL2, CCL5, HGF, IL-6, IL-8, and SELP; whereas IL-1a was downregulated (-25.0-fold, p = 0.014) compared to hyperglycaemia (16.5 mM). Metformin treatment of CD34+ cells under euglycaemia resulted in suppression of CCL2 (- 2.1-fold, p < 0.001), CCL5 (-2.8-fold, p < 0.001), IL-1a (-8.3-fold, p < 0.001), IL-6 (-20.0- fold, p < 0.001), IL-8 (-2.5-fold, p < 0.001), and SELP (-1.3-fold, p = 0.025) compared to parallel metformin-untreated condition. Under euglycaemia combined with hypoxia, metformin upregulated the expression of CCL2 (1.4-fold, p = 0.037), and downregulated the expression of HGF (-1.3-fold, p = 0.039) but exhibited no effect on CCL5, IL-1a, IL-6, IL-8, and SELP compared parallel untreated condition with metformin. Moreover, CD34+ cells treated with hyperglycaemia (16.5 mM) and metformin resulted in increase of the mRNA levels of HGF (2.0-fold, p < 0.01), and IL-6, (4.0-fold, p = 0.01) while displaying no effect on CCL2, CCL5, IL-1a, IL-8, and SELP compared to metformin-untreated condition.

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Figure 5.17: Heatmap of the pro-angiogenic and angiogenic inhibitors in CD34+ stem cells. Results were generated by transcriptomic analysis of 10 different experimental conditions each processed in two technical replicates resulting in 20 array experiments using Affymetrix microarray after exposure of CD34+ cells to 4% hypoxia for 3 hours either under euglycaemia or hyperglycaemia in the presence or absence of metformin. The heatmap was generated by using Partek software, the differentially expressed genes with red shading indicating upregulated genes, whereas blue shading indicating downregulated genes, and grey shading indicating non-affected genes. Key: 1A: euglycaemia, 1B: euglycaemia with metformin, 2A: euglycaemia with hypoxia, 2B: euglycaemia with hypoxia + metformin, 3A: hyperglycaemia (16.5 mM), 3B: hyperglycaemia (16.5 mM) with metformin, 4A: hyperglycaemia (16.5 mM) with hypoxia, 4B: hyperglycaemia (16.5 mM) with hypoxia + metformin, 5A: hyperglycaemia (25 mM), 5B: hyperglycaemia (25 mM) with metformin.

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Figure 5.18: Validation of selected pro-angiogenic factors in CD34+ cells by qRT-PCR. CD34+ cells were treated with 4% hypoxia for 3 hours either under euglycaemia or hyperglycaemia in the presence or absence of metformin. The variation in mRNA levels of (A) CCL2, (B) CCL5, (C) HGF, (D) IL-1a, (E) IL-6, (F) IL-8, and (G) SELP was assessed by qRT-PCR on three independent technical replicates. Results are presented as ± SEM and were statistically analysed using one-way ANOVA followed by LSD test. The effect of hypoxia and hyperglycaemia was revealed by comparing the conditions against the control, whereas the effect of hyperglycaemia combined with hypoxia was detected by comparing the condition to hyperglycaemia. The effect of metformin was identified by comparing the conditions pairwise, i.e., the condition without metformin versus with metformin. *P < 0.05, **P < 0.01, ***P < 0.001.

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Confirmation of angiogenic inhibitors before and after metformin treatment

Induction of CD34+ cells with hyperglycaemia combined with hypoxia significantly upregulated the expression of angiogenic inhibitors CXCL10 (3.92-fold, p = 0.007) and TIMP1 (1.4-fold, p = 0.001) compared to hyperglycaemia (16.5 mM); however, metformin downregulated expression of CXCL10 significantly (-3.3-fold, p = 0.011), and expression of TIMP1 non-significantly to normal level (-1.3-fold, p > 0.05) compared to parallel untreated-condition (Figure 5.19). Euglycaemia combined with hypoxia and hyperglycaemia either in presence or absence of metformin exhibited no effect on the mRNA levels of CXCL10 and TIMP1.

Figure 5.19: Validation of selected angiogenic inhibitors CXCL10 and TIMP1 in CD34+ cells by qRT-PCR. For further details refer to Figure 5.18 legend.

Confirmation of mitochondrial dysfunction genes before and after metformin treatment Two selected genes from the mitochondrial dysfunction pathway, MT-ND2 and NDUFA4 were chosen as they were highly overexpressed according to analysis by DNA microarray hybridisation of mRNA under combined hyperglycaemia and hypoxia, whereas metformin highly downregulated these genes. However, no significant differences were detected by qRT-PCR in any condition studied (Figure 5.20).

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Figure 5.20: Validation of selected genes from the mitochondrial dysfunction pathway MT- ND2 and NDUFA4 in CD34+ cells by qRT-PCR. For further details refer to Figure 5.18 legend.

Confirmation of triacylglycerol biosynthesis genes before and after metformin treatment Genes implicated in triacylglycerol biosynthesis AGPAT9 and GPAM showed downregulation of mRNA under hyperglycaemia (16.5 mM) to -1.6-fold, p = 0.044, and - 2.0-fold, p = 0.002 respectively compared to euglycaemia (Figure 5.21). Conversely, AGPAT9 was upregulated (1.5-fold, p = 0.008) under hyperglycaemia with 25.0 mM glucose and metformin significantly decreased expression to normal levels (-1.4-fold, p = 0.016) compared to the parallel metformin-untreated condition. Hyperglycaemia-hypoxia significantly raised the expression of GPAM to normal unaffected levels (1.9-fold, p = 0.004) compared to hyperglycaemia. In addition, metformin elevated the mRNA levels of GPAM under hyperglycaemia to normal levels (1.9-fold, p = 0.007) compared to the metformin-untreated condition.

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Figure 5.21: Validation of selected genes from triacylglycerol biosynthesis pathway AGPAT9 and GPAM in CD34+ cells by qRT-PCR. For further details refer to Figure 5.18 legend.

5.4 Discussion There is considerable evidence that the pathophysiological mechanisms leading to the vascular complications and tissue damage in diabetes arise from the interplay between inflammatory and metabolic abnormalities (Goldberg, 2009). There is a reciprocative correlation between inflammatory reaction and angiogenesis by increased recruitment of inflammatory cells through the formation of new blood vessels and release of pro- angiogenic cytokines, chemokines and growth factors (Kim et al., 2013). A range of pro- inflammatory cytokines expressed by vascular wall cells has been identified, comprising IL-1β, IL-1α, TNF-α, IL-6 and other factors important for recruitment of monocytes such as CCL2, that participate in the progression of CVD (Libby, 2006). Therefore, in this study we have focused the research on paracrine secretion of selected pro-inflammatory, pro- angiogenic factors, and angiogenic inhibitors that then was validated using in vitro tube formation assay. Furthermore, gene expression profiling of CD34+ cells was performed highlighting on angiogenic, metabolic changes correlated with vascular function. Subsequently, genes of interest were validated by qRT-PCR.

5.4.1 Effect of metformin on euglycaemia and combined euglycaemia hypoxia Metformin showed an inhibitory effect under euglycaemia on pro-inflammatory factors CCL2, CCL5, CD226, IL-1α, IL-6, IL-8, and ITGB3. We anticipated that the anti- 218

Chapter 5. Vascular functions in CD34+ cells inflammatory action of metformin could be through the upregulation of STEAP4 family member (STEAP4, 1.9-fold, p = 8.3E-04) as it was previously detected to suppress IL-6 and IL-8 expression in fibroblast-like synoviocytes in vitro (Tanaka et al., 2012).

Gene expression data from CD34+ cells exposed to euglycaemia-hypoxia for 3 hours displayed significant downregulation of genes coding for pro-inflammatory cytokines as listed in Table 5.1, leading to inhibition of the inflammatory response according to in silico prediction of affected pathways.

A minor effect of metformin was measured on cells treated under euglycaemia-hypoxia as the expression of pro-inflammatory cytokines was restored to normal levels compared to the parallel metformin-untreated condition. Metformin has no further inhibitory effect on inflammatory response under hypoxia.

However, non-significant variations in the protein levels of IL-1β, IL-6, IL-8, and TNFα were detected from CD34+-CM collected from cells treated with euglycaemia and metformin or euglycaemia-hypoxia in the presence or absence of metformin. The lack of significant variations at the protein level is consistent with reduced gene expression as discussed above. These data suggest that hypoxia is a beneficial in our cells as it reduces the expression and release of pro-inflammatory cytokines. This is consistent with cardiac rehabilitation following MI when patients are being asked to exercise to the point of hypoxia (shortness of breath). In fact, low oxygen tension in the range of 3-5% (hypoxia) is the preferable environment for stem cells to reside quiescent in their niches and maintain pluripotency with no effect on proliferation (Ezashi et al., 2005). In addition, we have confirmed that transducer of ERBB2, 2 (TOB2, 2.2-fold, p = 1.58E-03) is upregulated. This gene has previously been found to act as an anti-proliferative agent, inhibiting cell cycle progression from G0/G1 to S phases in stem cells (Chen et al., 2015).

It has been previously demonstrated that metformin inhibited the increase in TNF production in human monocytes induced with lipopolysaccharide (LPS) but has no effect on unstimulated cells (Arai et al., 2010). Isoda et al., showed that the physiological dose of metformin inhibited IL-1β, IL-6 and IL-8 expression in smooth muscle cells, ECs, or macrophages in a concentration-dependent manner (Isoda et al., 2006). These findings 219

Chapter 5. Vascular functions in CD34+ cells proposed an anti-inflammatory mechanism for metformin through inhibition of NF-κB activation (Isoda et al., 2006). Moreover, the inhibition of NF-κB signalling is proposed to be through the overexpression of STEAP4 as previously reported STEAP4-deficient macrophages enhanced NF-κB signalling and increased IL-6 expression (Inoue et al., 2012).

Metformin induced secretion of the angiogenic factor VEGFA under euglycaemia or euglycaemia-hypoxia that was balanced by an increase in the angiogenic inhibitors CXCL10 and TIMP1. The effect of these secreted factors was validated using in vitro Matrigel angiogenic assay, which showed no variations among angiogenic activity on HUVEC cultured with CD34+-CM collected from cells treated with euglycaemia, euglycaemia-hypoxia in the presence and absence of metformin. Our observations demonstrate for the first time that the physiological metformin concentration has no noticeable effect on the angiogenic activity of CD34+ induced by euglycaemia and euglycaemia-hypoxia. In clinical practice, a study revealed that metformin exhibits no effect on several surrogate markers of CVD in non-diabetic patients with high cardiovascular risk treated with other cardioprotective agents such as statins (Preiss et al., 2014). Therefore, we believe that metformin has an anti-inflammatory effect but no additional cardioprotective effect in the non-diabetic state. Furthermore, confirmation is needed before metformin can be recommended for cardiovascular benefit in non-diabetic patients.

5.4.2 Effect of metformin on hyperglycaemia and combined hyperglycaemia hypoxia Pro-inflammatory response

Our microarray data demonstrate that hyperglycaemia (16.5 and 25.0 mM) and hyperglycaemia-hypoxia downregulate gene expression of pro-inflammatory and pro- angiogenic cytokines, chemokines and receptors in CD34+ stem cells as mentioned before in Table 5.2, and Figure 5.13. Additionally, paracrine secretion of the most common inflammatory cytokines IL-1β, IL-6, IL-8, and TNFα displayed no variations in protein 220

Chapter 5. Vascular functions in CD34+ cells levels. The possible explanation is that CD34+ stem cells are quiescent in terms of cytokine paracrine function but can be stimulated by the circulating inflammatory factors produced by other mature cells.

As reported before, hepatic and adipose mature cells produce pro-inflammatory cytokines, including TNFα, IL-6, and IL-1β that are transported through circulation to affect further sites including endothelium, skeletal and cardiac muscles, kidneys and circulating leukocytes (Arkan et al., 2005, Cai et al., 2005).

In type 2 DM increased glucose levels, and free fatty acids lead to elevatation of the levels of circulatory IL1-dependent pro-inflammatory factors that probably reflect the activation of innate immune cells (Larsen et al., 2007). Furthermore, this notion has been approved by the discovery of adipose tissue macrophages that produce a significant amount of inflammatory factors (Xu et al., 2003). In addition, another study showed that hyperglycaemia shifted the differentiation of bone marrow precursor cells by reducing the potential to generate EPC and favouring the development of macrophages (Loomans et al., 2009). It has been reported previously that macrophages migrate to hypoxic areas that stimulate expression of pro-angiogenic and pro-inflammatory genes in an attempt to repair the damage (Burke et al., 2003). Together these data demonstrate the significant role of macrophages in inflammatory response under hyperglycaemia and hyperglycaemia-hypoxia that could potentially stimulate CD34+ stem cells to respond to vascular repair (angiogenesis and cell migration) under these conditions.

Metformin exhibited no significant effect on the mRNA levels of pro-inflammatory cytokines and chemokines studied under hyperglycaemia and hyperglycaemia-hypoxia: CCL2, CCL5, IL-1α, IL-6, IL-8, HGF, and SELP. Although there was a slight increase in the expression of HGF and IL-6 under hyperglycaemia with metformin versus untreated condition, but the expression was still below the normal unaffected level. In addition, no significant alteration in the protein levels of the classical inflammatory cytokines, IL-1β, IL-6, IL-8, and TNFα studied from CD34+-CM was measured. The undetected effect of metformin is due to hyperglycaemia and hyperglycaemia-hypoxia suppressing the gene expression of inflammatory cytokines. Additionally, metformin exhibited no significant

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Chapter 5. Vascular functions in CD34+ cells change on the expression of STEAP4 under hyperglycaemia and combined hyperglycaemia- hypoxia.

Angiogenic regulation

Paracrine secretion of the angiogenic inhibitors TIMP1 and CXCL10 was increased under hyperglycaemia and combined hyperglycaemia-hypoxia, however, metformin was detected to increase the release of CXCL10 significantly only under hyperglycaemia-hypoxia but had no effect on TIMP1 secretion when compared pairwise with metformin-untreated condition. Our microarray and qRT-PCR results from CD34+ cells treated with hyperglycaemia-hypoxia for 3 hours showed that the TIMP1 and CXCL10 mRNA levels were upregulated, whereas metformin downregulated these inhibitors. There was no change in the expression of TIMP1 and CXCL10 in CD34+ cells treated with hyperglycaemia. The variations in results under hyperglycaemia-hypoxia with metformin obtained in CXCL10 gene expression and paracrine secretion could be that chemokines are relatively resistant to inactivation and have a long half-life (Rossi and Zlotnik, 2000). Therefore, longer incubation of CD34+ cells with metformin is required to exert its effect on paracrine secretion and decrease the release of TIMP1 and CXCL10 or increase its clearance. In concordance with our results, Chung et al., demonstrated that TIMP1 expression increased at gene and protein levels in diabetic internal mammary artery samples compared to control (Chung et al., 2006). Another study demonstrated high levels of CXCL10 in serum from diabetic patients (Shimada et al., 2001).

Furthermore, the concentration of pro-angiogenic factor VEGFA in the CD34+ cell-CM was identified to be high under hyperglycaemia, and hyperglycaemia-hypoxia compared to the control. These results were counterbalanced by the increase of anti-angiogenic factors that did not affect the tube formation of ECs incubated with CD34+ cell-CM from cells treated with hyperglycaemia or combined hyperglycaemia-hypoxia. It is of interest that the diabetic patients with chronic coronary heart disease exhibit an increase in VEGF myocardial expression but a decrease in its receptors lead to downregulation of its signal transduction (Sasso et al., 2005).

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Metformin stimulated the release of VEGFA from CD34+ cells incubated with hyperglycaemia-hypoxia. At the same time metformin enhanced the release of angiogenic inhibitor CXCL10 significantly, however, metformin increased in vitro tube formation showing that the pro-angiogenic factors effectively overcome the angiogenic inhibitors. On the other hand, metformin has no effect on VEGFA secretion from CD34+ cells incubated under hyperglycaemia.

Mitochondrial dysfunction

To the best of our knowledge, to date there is no previous study on mitochondrial dysfunction using physiological metformin concentration under hyperglycaemia-hypoxia- induced stem cells. Previous studies have shown that supra-physiological metformin concentration is an inhibitor of respiratory chain complex 1 (MT-ND2) in hepatocytes and oocytes resulting in decreased ROS production (El-Mir et al., 2000, Detaille et al., 2002). Another study demonstrated a decrease in intracellular ROS production through inhibition of both NADPH oxidase and the respiratory chain complex I in aortic ECs stimulated with high levels of glucose (30 mM) and physiological concentration of metformin (0.01 mM) (Ouslimani et al., 2005).

Mitochondria are complex organelles that are involved in diverse, vital functions including cellular metabolism, growth, differentiation and cell survival. The critical role of mitochondria is to regulate ATP synthesis by oxidative phosphorylation, generation of ROS, stimulation of apoptosis, and regulation of cytoplasmic and mitochondrial matrix calcium (Brand and Nicholls, 2011). Impairment in any of these processes can be termed mitochondrial dysfunction. There has been growing evidence that a correlation between altered mitochondrial function and the aetiology of DM exists (Perry et al., 2013). A recent study in ECs isolated from the coronary arteries of diabetic mice showed fragmented mitochondria and increased ROS production (Makino et al., 2010).

Our microarray data analysis demonstrated that most of the genes implicated in the mitochondrial dysfunction pathway were upregulated under hyperglycaemia-hypoxia: MT- ND2, COX6B1, NDUFS8, ATP5D, COX8A, NDUFA3, CYB5A, and COX5B. In contrast, metformin opposes the effect of hyperglycaemia-hypoxia by downregulating MT-ND2, 223

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COX6B1, ATP5D, COX8A, NDUFA3, CYB5A, and COX5B. Additionally, MT-ND2 and NDUFA4 were assessed by qRT-PCR displaying no changes in mRNA levels under all studied conditions. These differences in the results obtained from microarray data and qRT- PCR could be due to non-concordance of transcripts used in the microarray probe set and qRT-PCR (Dallas et al., 2005).

Triacylglycerol biosynthesis

Triacylglycerol (TAG) is the major storage form of energy in animals. Glycerol-3- phosphate acyltransferases (GPAT) isoforms located in mitochondria (GPAT1, alias GPAM) and endoplasmic reticulum (GPAT3, alias AGPAT9) catalyse the initial and rate- limiting step in the de novo synthesis of TAG and glycerolphospholipids (Coleman and Lee, 2004). GPAT esterifies fatty acids to glycerol-3-phosphate forming lysophosphatidic acid. There is a frequent association between insulin resistance and TAG accumulation in the liver and other tissues as lowering of liver TAG content in rodent models results in improvement of hepatic and peripheral insulin resistance (An et al., 2004, Neschen et al., 2005). Our microarray results showed a significant increase in GPAM under hyperglycaemia-hypoxia in CD34+ treated cells. The risk of GPAM overexpression was studied previously in vivo using an adenoviral construct to overexpress GPAM (Ad- GPAM) (Nagle et al., 2007). After 5-7 days, Ad-GPAM rats developed fatty liver, hyperlipidaemia, and hepatic/systemic insulin resistance in the absence of obesity or a lipogenic diet (Nagle et al., 2007). ECs can store TAG that are often elevated in insulin resistance syndrome and diabetes contributing to overexposure to free fatty acids (Figard et al., 1986). The dyslipidemia associated with insulin resistance, represent important risk factors for CVD (Reaven, 2011). However, metformin treatment of CD34+ cells under hyperglycaemia-hypoxia displayed a significant decrease in the expression of GPAM that may reduce the TAG storage. In type 2 DM, various tissues including skeletal muscle, liver, β-cells and ECs, metformin promoted fat oxidation and suppresed de novo synthesis of TAG by inhibiting GPAT (enzyme) via AMPK activation (Boden, 2003, El-Assaad et al., 2003, Ruderman et al., 2003). Thus, the effects metformin on CD34+ cells is not cell specific but similar to the effect on other tissues.

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5.4.3 Paracrine secretion of miR-126 Accumulating evidence supports that exosomes are not just by-products resulting from cell activation but form a novel type of cell-cell communication. Recently, it has been demonstrated that miRNAs containing vesicles alter in number and composition depending on the disease state (VanWijk et al., 2003). miR-126 is highly enriched in ECs and governs the maintenance of vascular integrity, angiogenesis, and wound repair (Wang et al., 2008). In the viable endothelium, miR-126 is expressed resulting in suppression of two VEGF signalling inhibitors, including SPRED-1 and PI3KR2 (Fish et al., 2008).

In our study, the expression of miR-126 was reduced with hyperglycaemia in CD34+ exosomes and exosome-depleted media in glucose-dependent manner. This result is in agreement with published data illustrating reduced miR-126 content in plasma from patients with type 2 DM (Zampetaki et al., 2010). Furthermore, miR-126 has been shown to be down-regulated in EPCs derived from patients with type 2 diabetes (Meng et al., 2012). Notably, a recent study in CD34+ cells showed reduced release of miR-126 after high glucose (25 mM) exposure or in patients with type 2 DM (Mocharla et al., 2013).

Although euglycaemia-hypoxia elevated the miR-126 levels in the exosomes and exosome- depleted media, this response exhibited no effect on the tube formation of ECs grown under the same conditions of CD34+-CM. This is in agreement with recent study, which demonstrated an increase in miR-126 expression after 1 hour of cardiac myocytes being exposed to hypoxia (Shi et al., 2013).

On the other hand, hyperglycaemia-hypoxia suppressed the expression of miR-126 without any change observed in the in vitro tube formation assay. All together none of euglycaemia-hypoxia, hyperglycaemia or hyperglycaemia-hypoxia treated CD34+-CM stimulated the in vitro tube formation. Indeed, the repertoire of pro- and anti-angiogenic molecules secreted to the CM were counterbalanced as explained before in section 5.4.2 under angiogenic regulation.

In our study, metformin had shown to decrease the release of miR-126 under euglycaemia- hypoxia and to increase it under combined hyperglycaemia-hypoxia. It is postulated that the increased miR-126 levels in the CM may contribute to the augmented tube formation under 225

Chapter 5. Vascular functions in CD34+ cells hyperglycaemia-hypoxia thus, explaining part of the key role of metformin in improving the vascular function. This is in concordance with Meng et al., results in which EPCs from diabetic patients were transfected with miR-126 expressing lentiviral vector that lead to increasing the expression of miR-126. The upregulation of miR-126 improved angiogenic process whereas, adverse results occurred with miR-126 inhibitor (Meng et al., 2012).

In summary, we found evidence that in the diabetic state combined with hypoxia, metformin exerts its effect in CD34+ cells mainly via increasing VEGFA and miR126, enhancing angiogenesis (tube formation), and inhibiting the respiratory chain and lipogenesis. Consequently, the beneficial effect of metformin in acute hypoxia- hyperglycaemia is possibly by improving the angiogenic properties of CD34+ cells (Figure 5.22 and Table 5.6).

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Figure 5.22: Summary of the effect of metformin on CD34+ cells incubated with hyperglycaemia-hypoxia. CD34+ cells were treated with hyperglycaemia and metformin for 24 hours and then exposed to hypoxia for 3 hours. Differentially expressed genes from microarray data were generated by comparing CD34+ cells treated with hyperglycaemia-hypoxia versus euglycaemia (A), and hyperglycaemia-hypoxia with metformin versus metformin untreated- 227

Chapter 5. Vascular functions in CD34+ cells condition (B). Green shades indicate downregulation, red shades indicate upregulation and grey shades indicate unchanged gene expression. In vitro tube formation (angiogenesis) green shades indicate inhibition, whereas red shades indicate activation. Key: ADORA2B: adenosine A2b receptor; ALOX12: arachidonate 12-lipoxygenase; ANXA2: annexin A2; BIRC5: Baculoviral IAP repeat containing 5; CTSS: cathepsin S; CXCL10: chemokine (C-X-C Motif) ligand 10; DGKA: diacylglycerol kinase, alpha 80kDa; E2F1: E2F transcription factor 1; ENPP2: ectonucleotide pyrophosphatase/phosphodiesterase 2; HGF: hepatocyte growth factor; IL8: interleukin 8; MT-ND2: mitochondrially Encoded NADH Dehydrogenase 2; MYH10: myosin, heavy chain 10, non-muscle ; TFPI: tissue factor pathway inhibitor; TIMP1: tissue inhibitor of metalloproteinase 1; VEGFA: vascular endothelial growth factor A.

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Euglycaemia Euglycaemia- Euglycaemia- Hyperglycaemia Hyperglycaemia Hyperglycaemia- Hyperglycaemia- Biological + Met hypoxia hypoxia + + Met hypoxia hypoxia + Met significance Met Secretion of Increased No change Increased Increased No change Increased Increased Pro- VEGFA angiogenic cytokine

Expression Upregulated Upregulated Downregulated Downregulated No change No change Upregulated AngiomiR of miR-126 in exosomes In vitro No change No change No change No change No change No change Increased angiogenesis

Gene No change No change No change No change No change Upregulated Downregulated Angiogenic expression inhibitor of CXCL10 Gene No change No change No change No change No change Upregulated Downregulated Angiogenic expression inhibitor of TIMP1 Table 5.6: Summary of the most significant results on the effect of hypoxia, hyperglycaemia, hyperglycaemia-hypoxia and metformin on CD34+ cells.

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Chapter 6. General Discussion and Future Directions

In this project, we aimed to investigate the cardioprotective mechanism of metformin using an in vitro model system consisting of immature human CD34+ stem cells and primary, terminally differentiated mature endothelial cells. Our in vitro systems resemble diabetes macrovascular complications that are mainly represented by atherosclerotic disease and manifested as MI. Atherosclerosis is a complex inflammatory response involving interaction between inflammatory cells (neutrophils, lymphocytes, monocytes and macrophages), ECs, vascular smooth muscles, and ECM that ultimately leads to formation of atherosclerotic plaques (Forbes and Cooper, 2013). The precise initiating event is unknown; however, endothelium dysfunction is thought to be an important contributor.

One of the strengths of the conducted in vitro study is its flexibility to use different hypoxic time intervals that cannot be applied easily to in vivo models. We have used realistic hyperglycaemia (16.5 mM) that can frequently be observed in diabetic patients (UKPDS 33, (1998b) and avoided extremely high concentrations that may have resulted in artificial findings. The study considered the effect of metformin in the physiological concentration used in patients treated with metformin.

On basis of observations from other studies we hypothesised at the beginning of our project that metformin plays an important role in endothelial repair after induction with hypoxia, hyperglycaemia or combined hyperglycaemia-hypoxia, specifically by mediating cell survival, migration, and pro-angiogenic properties through the activation of angiogenesis promoting signallings.

In chapter 3 we have shown that TFRC and RPLP0 are, among the tested reference genes, the most suitable genes to normalise gene expression levels in qRT-PCR experiments in HUVEC exposed to hypoxia, hyperglycaemia or combined hyperglycaemia and hypoxia for all different time intervals (1, 3, and 12 hours).

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In chapter 4 we presented findings of functional assays and the transcriptomic signature and pathways associated with hypoxia, hyperglycaemia and combined hyperglycaemia- hypoxia at different time intervals in HUVEC. Our transcriptomic data and apoptosis functional assays showed that during the early phase of hypoxia (1 and 3 hours) cell survival and repair respones are preserved in HUVEC under euglycaemia and hyperglycaemia most probably through HIF-1α signalling. This explains the benefit to start MI therapy during the early phase of hypoxia. However, under prolonged hypoxia (12 hours), cell survival is compromised by downregulation of genes required for cell survival, proliferation and DNA repair either under euglycaemia or hyperglycaemia. We observed a significant increase of apoptosis under prolonged hypoxia and hyperglycaemia that could be attributed to the overexpression of PLAC8 according to HIF-1α network analysis. PLAC8 was previously detected to be associated with pro-apoptotic effects in leukaemic cells and lymphocytes (Mourtada-Maarabouni et al., 2013). In another study, PLAC8 was highly expressed in neonatal endothelial colony-forming cells (ECFCs) from gestational diabetes mellitus pregnancies, and PLAC8 expression correlated with maternal hyperglycaemia. Knockdown of PLAC8 in these ECFCs improved proliferation and senescence defects (Blue et al., 2015). We have detected that metformin treatment decreased the protein level of HIF-1α under 1 hour euglycaemia-hypoxia, but exhibited no effect under hyperglycaemia-hypoxia at different time intervals of 1, 3, and 12 hours. We have shown that metformin exerts its beneficial protective effects in HUVEC only under combined hyperglycaemia-hypoxia but not under euglycaemic-hypoxia. The inhibition of endothelial cell migration, proliferation and increased apoptosis under hyperglycaemia combined with hypoxia is correlated with inactivation of VEGF signalling and inhibition of the ERK/ MAPK pathway, detected at the transcriptomic and protein level respectively. Metformin invoked a likely pro-angiogenic effect by improving cell migration through upregulation of VEGF downstream genes MMP16, FABP4, ROCK1, TFPI-2, CXCL8 and LY96. Furthermore, we demonstrated by using sunitinib (VEGF inhibitor) and marimastat (MMP inhibitor) an inhibition of the metformin stimulatory effect on cell migration. A recent study supports our finding by investigating the effect of MMP16 on

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Chapter 6. General Discussion and Future Directions cell migration in human cardiomyocyte progenitor cells using MMP16 blocking antibody or siRNA knockdown of MMP-16 (Liu et al., 2012). Scratch assays showed that blocking MMP-16 by MMP-16 antibody treatment or RNAi diminished cell migration. The role of ROCK1 in CVD was detected in an in vivo study by generating heterozygous of Rock1 (+/−) mice and performing carotid artery ligations that reduced neointima formation compared with wild-type mice (Noma et al., 2008). These data suggest that specific expression of ROCK1 may play an important role in EC function during angiogenesis. Additionally, Rho/ ROCK signalling was reported previously to be important for VEGF-dependent in vivo angiogenesis and in vitro tube formation (Hoang et al., 2004). Blocking ROCK1/2 using Y- 27632 pharmacological inhibitor was detected to suppress the VEGF-mediated in vitro angiogenesis (Bryan et al., 2010). Future studies will be aimed at verifying other mediators of metformin’s effect by using specific inhibitors of ROCK1 and CXCL8. Metformin suppressed apoptosis in HUVEC treated with hyperglycaemia and hypoxia by activating the VEGF downstream ERK/ MAPK pathway and through upregulation of FABP4. This was confirmed by using a VEGF inhibitor in an apoptosis assay with the control condition. The anti-apoptotic effect of metformin was observed at 24 hours of hypoxia. The cells survived during 1 and 3 hours of hypoxia and then at 12 hours of hypoxia the expression of apoptotic genes was enhanced as detected from microarray data. Additionally, we have shown that hypoxia activated AMPK signalling both under euglycaemia and hyperglycaemia leading to enhancement of catabolic downstream genes such as in glycolysis. We found that metformin exhibited no effect on AMPK signalling in ECs exposed to hypoxia neither under euglycaemia nor hyperglycaemia. This could be due to the stimulatory effect of hypoxia on AMPK signalling under euglycaemia and hyperglycaemia.

In chapter 5 we presented a novel methodological approach to test the effect of combined hyperglycaemia and hypoxia on CD34+ cells in the presence or absence of metformin. The effect was studied on paracrine secretion of selected pro-inflammatory, pro-angiogenic factors and angiogenic inhibitors that were assessed functionally by an in vitro Matrigel tube formation assay. Furthermore, whole transcriptome microarray analysis was

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Chapter 6. General Discussion and Future Directions performed specifically to analyse expression profiles of vascular related genes under these conditions.

We detected that hypoxia is a non-stimulatory factor under euglycaemia as these stem cells remained quiescent through the expression of the anti-proliferative gene TOB2 (Ikematsu et al., 1999). The anti-inflammatory effect of metformin under euglycaemia can be attributed to the upregulation of the STEAP4 gene that is known to have anti-inflammatory action (Tanaka et al., 2012).

We observed that hyperglycaemia and combined hyperglycaemia-hypoxia did not stimulate gene expression and paracrine secretion of pro-inflammatory cytokines. This observation could be due to the fact that CD34+ cells are quiescent and are mainly activated by members of TNF superfamily produced by injured cells and interferons secreted by inflammatory cells (King and Goodell, 2011, Trumpp et al., 2010). Therefore, the limited effect of metformin under this condition might be due to suppression of an inflammatory response under hyperglycaemia and hyperglycaemia-hypoxia.

We observed that paracrine secretion of angiogenic inhibitors CXCL10 and TIMP-1 was increased under hyperglycaemia and hyperglycaemia-hypoxia. Furthermore, metformin increased the release of CXCL10 but had no effect on TIMP1. Transcriptome analysis displayed overexpression of CXCL10 and TIMP1 under hyperglycaemia-hypoxia, but no change in expression levels of these genes was observed under hyperglycaemia alone. Metformin was detected to suppress the gene expression of these inhibitors under hyperglycaemia-hypoxia; however, it exhibited no effect under hyperglycaemia. The discordance between chemokine protein and mRNA levels may reflect the short half-life of CXCL10 mRNA, which is approximately 30 minutes (Shanmugam et al., 2006) and the chemokine protein long half-life (Rossi and Zlotnik, 2000). Although the paracrine secretion of VEGFA from CD34+ cell-derived CM in our observation was high under hyperglycaemia and combined hyperglycaemia-hypoxia, the effect of high VEGFA protein levels was counterbalanced by the increase in anti-angiogenic factors and as a result no effect on in vitro tube formation was detected compared to the control condition.

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Metformin stimulated the release of VEGFA from CD34+ cells incubated with hyperglycaemia-hypoxia and this effect significantly induced tube formation in our results. The secretion level of miR-126, which is known to be associated with angiogenesis, was studied in the exosomes and exosome-depleted medium to determine its effect on tube formation. Although euglycaemia-hypoxia resulted in the elevation of miR-126 levels in the exosomes and exosome-depleted media, this elevation was correlated with no variation in tube formation of ECs grown under the same conditions using CD34+-CM. In contrast, hyperglycaemia-hypoxia suppressed the expression of miR-126 without any change observed in the in vitro tube formation. CM from metformin-treated CD34+ under hyperglycaemia-hypoxia had increased levels of miR-126, which may contribute to the enhanced tube formation compared to the parallel metformin-untreated condition.

We observed upregulation of mitochondrial dysfunction pathway genes under hyperglycaemia-hypoxia, whereas addition of metformin counteracted these effects by downregulation of these genes. Upregulation of these genes is known to be associated with increased ROS production. Moreover, metformin treatment of CD34+ cells under hyperglycaemia-hypoxia resulted in a decrease of the expression of GPAM, the rate- limiting enzyme in TAG biosynthesis, and this may decrease the risk for TAG accumulation.

6.1 Concluding remarks The results reported in this thesis serve to highlight the important regulatory and pro- angiogenic roles of metformin that are likely to be implicated in cell-mediated endothelial repair. One of the main outcomes of the study was to demonstrate for the first time that a physiological metformin concentration improved cell survival and migration of EC exposed to combined hyperglycaemia and hypoxia and that this effect is possibly mediated by activation of VEGF signalling cascades.

The benefit of metformin as a potent angiogenic stimulator in adult human CD34+ stem cells is prominently attributed to increasing VEGFA and miR-126 paracrine secretion and

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Chapter 6. General Discussion and Future Directions decreased mRNA levels of CXCL10 and TIMP1 under hyperglycaemia-hypoxia. To conclude, this project adds to the understanding of:

1. The mechanism of hypoxia under euglycaemia and hyperglycaemia showing the effect on functional assays and variation on gene expression of HIF-1α, VEGFA, and AMPK signallings in mature ECs.

2. The mechanism of hypoxia under euglycaemia and hyperglycaemia demonstrating the differences in in vitro tube formation assay, paracrine secretion and gene expression variations on pro-inflammatory cytokines, angiogenic regulation, mitochondrial dysfunction and TAG biosynthesis in CD34+ stem cells.

3. The vascular protective mechanism of metformin is mainly through VEGFA in both mature ECs and CD34+ stem cells.

6.2 Future work 1. Confirm western blot results for VEGFA in protein samples extracted from HUVEC or conditioned media using enzyme-linked immunosorbent assay (ELISA).

2. Evaluate the activity of tyrosine kinase receptors specific for the angiogenic factor VEGFA, VEGFR1 and VEGFR2 required for angiogenesis through measuring the total and phospho-proteins using ELISA immunoassay.

3. Confirm the negative effect of metformin on AMPK signalling by assessing the activity of AMPKα total and phospho-proteins.

4. A comprehensive analysis of the molecular and cellular effects of PLAC8, FABP4, and RhoA/ROCK inhibition in ECs from diabetic patients or mouse models that may assist in the development of more specific and effective therapeutics.

5. Develop three-dimensional (3-D) tissue cultures in order to design physiologically relevant in vitro models of living tissues. Almost all cells in vivo exist in an extracellular matrix consisting of 3-D architecture and interact with other cells through biochemical and mechanical signals. The importance of 3-D cell culture 235

Chapter 6. General Discussion and Future Directions scaffolds and models offers a paradigm that precisely simulates biological phenomena of cell morphology, proliferation, differentiation and migration (Qutub and Popel, 2009). Additionally, 3-D culture systems can be used to study disease models and reduce the need for animal models (Harma et al., 2010). Noticeably, 3- D cell culture has significant applications in tissue engineering, regenerative medicine, and drug delivery.

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Chapter 7. Appendices 7.1 Appendix I: Forms I. Ethical approval

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II. GEO submission

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7.2 Appendix II: Chemicals and reagents 7.2.1 Buffers and special medium 1. Conservation buffer and transport of umbilical cords

50ml of Dulbecco’s phosphate buffered saline (DPBS) without Ca++ and Mg++ containing 200 U/ml penicillin, 200 µg/ml streptomycin, and 2.5 µg/ml fungizone (GE Healthcare, Little Chalfont, UK).

2. Collagenase

0.2% Collagenase H (Roche, Basel, Switzerland) from Clostridium Histolyticum was dissolved in DPBS and mixed for 10 minutes in the dark. The solution was filter sterilised using 0.20 micron filters (Nalgene, Rochester, NY) prior to use then stored in -20o C as aliquots.

3. Fibronectin-coating

Fibronectin (BD Bioscience, San Jose, CA) was prepared by dilution 1-5 μg/ml in DPBS and filter sterilised using 0.20 micron filters (Nalgene, Rochester, NY) prior to use. The tissue culture plates and flasks were coated with fibronectin by adding the appropriate amount to cover the vessel then sucked and left to dry in the laminar flow for 30-60 minutes.

4. HUVEC media

M199 (GE Healthcare, Little Chalfont, UK) was supplemented with 1% (v/v) 200 mM L- glutamate (Gibco, Life Technologies, Paisley, UK), 200 U/ml penicillin, 200 µg/ml streptomycin, and 2.5 µg/ml fungizone (GE Healthcare, Little Chalfont, UK), 20 % (v/v) heat inactivated Foetal Bovine Serum (FBS) (GE Healthcare, Little Chalfont, UK), 1.5% (v/v) 1 M 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES, GE Healthcare,

Little Chalfont, UK), and 1.8 % (v/v) NaHCO3 7.5% (w/v) (GE Healthcare, Little Chalfont, UK).

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5. MACS buffer

DPBS 1 L FBS 2% EDTA 2 mM

6. M199 for growing CD34+ cells

M199 (GE Healthcare, Little Chalfont, UK) was supplemented with 1% (v/v) 200mM L- glutamate (Gibco, Life Technologies, Paisley, UK), 100 U/ml penicillin/100 g/ml streptomycin, 2.5 µg/ ml fungizone (GE Healthcare, Little Chalfont, UK), 0.25% (v/v) human serum albumin (Octapharma, Manchester, UK), 1.5% (v/v) 1 M HEPES (GE

Healthcare, Little Chalfont, UK), 1.8 % (v/v) NaHCO3 7.5% (w/v) (GE Healthcare, Little Chalfont, UK), 100 ng/ ml Flt3-L, 100 ng/ ml stem cell factor (SCF, PerproTech, Rocky Hill, NJ).

7. Freezing medium

Freezing medium containing 20% (v/v) heat inactivated FBS (GE Healthcare, Little Chalfont, UK), 10% (v/v) dimethylsulfoxide (DMSO) (Sigma-Aldrich, Dorset, UK), and 70% (v/v) culture medium.

8. 10 X Tris-Base EDTA (TBE) buffer

108.0g trizma-base (Sigma-Aldrich, Dorset, UK), 55.0g orthoboric acid (Sigma-Aldrich, Dorset, UK), and 9.3g ethylenediaminetetraacetate (Thermo Fischer Scientific, Waltham,

MA) were added together and made up to 1 litre with distilled H2O. For electrophoresis gel preparation and associated running buffer 1.0 x TBE was used.

9. Loading buffer

50 mg Bromophenol Blue (Thermo Fischer Scientific, Waltham, MA) and 50 mg Xylene cyanol (Sigma-Aldrich, Dorset, UK) were added to 10 ml glycerol (Thermo Fischer

Scientific, Waltham, MA) and completed to 20 ml with distilled H2O.

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10. Ethidium bromide

A 100mg pellet of ethidium bromide (Sigma-Aldrich, Dorset, UK) was added to 10ml of distilled H2O to give a 10mg/ml working solution.

11. Preparation of 3% agarose gel 3g of agarose (GE Healthcare, Little Chalfont, UK) were dissolved in 100 ml 1xTBE by heating in a microwave. Once dissolved 5µl ethidium bromide was added. The gel was allowed to set then covered in a cell (BioRad) with 1x TBE running buffer.1 µl of the fragmented ssDNA or labeled fragment was loaded in the wells with 2µl Bromophenol blue and run at 50 volt for 5 minutes and then the voltage raised to 100 for 1 hour ensuring the wells were positioned at the negative terminal. The resulting gel was then observed under UV transilluminator (Uvitec) and photographs were taken where appropriate.

The DNA molecular weight marker used was a 50 base-pair ladder (GE Healthcare, Little Chalfont, UK).

7.2.2 Antibodies

No. Antibody Target 1 FITC mouse anti-human CD45 Leukocyte 2 FITC mouse IgG1, κ Isotype control Control 3 PE mouse anti-human CD34 Haematopoietic progenitor cells, vascular endothelium 4 PE mouse IgG1, κ Isotype control Control Table 7.1: FACS antibodies from BD Biosciences.

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No. Antibody Cat number Company 1 Mouse monoclonal anti- HIF1α Ab16066 Abcam, UK 2 Mouse monoclonal anti-VEGFA Ab68334 Abcam, UK 2 Mouse monoclonal anti-VEGF 165A Ab69479 Abcam, UK 3 Mouse monoclonal anti-beta actin Ab8226 Abcam, UK 4 Rabbit polyclonal anti-mTOR Ab83495 Abcam, UK 5 Rabbit polyclocal anti-mTOR (phospho S2448) Ab51044 Abcam, UK 6 Rabbit monoclonal anti-AMPKα 2603 Cell Signalling 7 Rabbit monoclonal anti-AMPKα (phospho Thr172) 2535 Cell Signalling 8 IRDye® 680RD goat (polyclonal) anti-mouse IgG 926-68070 Li-COR 9 IRDye® 800CW goat (polyclonal) anti-rabbit IgG 926-32211 Li-COR Table 7.2: Antibodies used in Westeron blot. 7.2.3 Apparatus

No. Apparatus Company Application 1 Tissue culture centrifuge AccuSpin 3R, Thermo Fisher Cell culture Scientific, Waltham, MA 2 CO2 incubator MCO-18AC, Sanyo Cell culture Galaxy B, Scientific Laboratory Supplies 3 Microscopes TMS, Nikon Cell visualisation Labrolux II, Leitz Cell counting 4 Refrigerated centrifuge 3K30, Sigma-Aldrich, Dorset, UK Protein/ RNA isolation

Rotor Nr.12154-H 5 Plate reader Multiskan Ascent, Thermo Protein quantification Labsystems, Milford, MA 6 SDS-PAGE Gel tank XCell SureLock, Life Technologies, Protein fractionation Paisley, UK 7 Western Transfer Tank Mini Protean II, BioRad, Hercules, CA Protein transfer 8 Power Supply Model 200/2.0, BioRad, Hercules, CA Electrophoresis 9 Microfuge IEC MicroCL 17 centrifuge, Thermo Protein/ RNA isolation Scientific, Waltham, MA 10 Heating Block Dri-Block DB-2A,Techne, Stafordshire, UK 11 BD FACSCanto II BD Bioscience, San Jose, CA Flow cytometry 12 Thermal cycler Eppendorf, Hamburg, Germany Reverse transcription, 2720 Thermal Cycler, Life traditional PCR Technologies, Paisley, UK 13 UV transilluminator Uvitec, Cambridge, UK PCR

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No. Apparatus Company Application 14 qPCR machine 7900HT Fast Real Time PCR System, Real-time PCR Applied Biosystems StepOne Plus Real Time, Applied Biosystems 15 Infrared imager Odyssey Classic, LiCOR, Lincoln, NE Western blot imager 16 Thermomixer Eppendorf, Hamburg, Germany Microarray 17 Beckman L7-80 Beckman coulter, High Wycombe, UK, Ultracentrifugation Ultracentrifuge 50 Ti rotor 18 Bottle, with cap assembly, Beckman coulter, High Wycombe, UK Ultracentrifugation polycarbonate,10.4 ml, 16x 76mm 19 Tissue culture 6 well plate Greiner bio-one, Stonehouse, UK Tissue culture 20 Tissue culture 24 well plate Greiner bio-one, Stonehouse, UK Tissue culture 21 T75 tissue culture treated flasks Greiner bio-one, Stonehouse, UK Tissue culture

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7.3 Appendix III: Lists of differentially expressed genes Gene name Gene symbol RefSeq p-value FC basic helix-loop-helix family, BHLHE40 AB004066 2.80E-08 2.37 member e40 DNA-damage-inducible DDIT4 BC007714 1.20E-03 2.11 transcript 4 ankyrin repeat domain 37 ANKRD37 AY296056 4.31E-05 1.97 arrestin domain containing 3 ARRDC3 AK291753 4.05E-03 1.96 adrenomedullin ADM BC015961 5.14E-04 1.86 transfer RNA glutamine 52 TRNAQ52P ENST00000462706 9.88E-05 1.79 (anticodon UUG) pseudogene vascular endothelial growth VEGFA AF022375 9.08E-04 1.78 factor A transfer RNA cysteine 31 TRNAC31P ENST00000465944 3.86E-03 1.72 (anticodon GCA) pseudogene angiopoietin-like 4 ANGPTL4 AF202636 8.66E-04 1.64 late cornified envelope 2C LCE2C ENST00000368783 2.37E-02 1.61 uncharacterized LOC158696 LOC158696 NR_026935 9.45E-03 1.57 lysozyme-like 1 LYZL1 AY358694 1.17E-02 1.57 dual specificity phosphatase DUSP1 BC022463 9.59E-06 1.54 RNA, Ro-associated Y4 RNY4P18 ENST00000391107 4.78E-02 1.54 pseudogene 18 developmental pluripotency DPPA3P2 BC062480 2.37E-02 1.54 associated 3 pseudogene 2 amphiregulin AREG BC009799 4.43E-03 1.53 Table 7.3: Top differentially expressed genes on HUVEC induced by chemical hypoxia for 1 hour compared to the control. The gene list was created by importing Affymetrix .CEL files to Partek Genomic Suite version 6.6. The data was normalised using RMA normalisation. Differentially expressed gene list was generated using one way ANOVA, FDR-unadjusted p-value < 0.05. The differentially expressed genes passes the fold change cutoff of 1.5.

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Gene name Gene Symbol RefSeq p-value FC angiopoietin-like 4 ANGPTL4 AF202636 4.41E-19 8.09 6-phosphofructo-2-kinase/fructose- PFKFB4 ENST00000232375 4.74E-17 7.62 2,6-biphosphatase 4 adenylate kinase 4 AK4 CR456830 4.79E-11 7.40 hexokinase 2 HK2 BC064369 1.40E-15 5.46 stanniocalcin 2 STC2 BC000658 7.89E-05 3.90 stanniocalcin 1 STC1 U25997 1.95E-05 3.86 vascular endothelial growth factor VEGFA AF022375 1.85E-10 3.79 A 6-phosphofructo-2-kinase/fructose- PFKFB3 AF056320 2.93E-14 3.75 2,6-biphosphatase 3 egl nine homolog 3 (C. elegans) EGLN3 AK025273 6.91E-08 3.72 hypoxia inducible lipid droplet- HILPDA AF144755 3.85E-11 3.24 associated solute carrier family 2 (facilitated SLC2A3 AB209607 2.65E-09 3.23 glucose transporter), member 3 enolase 2 (gamma, neuronal) ENO2 M22349 1.99E-08 3.22 solute carrier family 2 (facilitated SLC2A1 AK292791 2.21E-13 3.15 glucose transporter), member 1 chromosome 1 open reading frame C1orf51 AK098755 2.10E-09 3.12 51 very low density lipoprotein VLDLR NM_003383 2.74E-03 3.03 receptor family with sequence similarity FAM162A AF250321 3.62E-11 2.86 162, member A AK304276 // KDM3A // lysine (K)- KDM3A AK304276 1.44E-07 2.84 specific demethylase 3A DNA-damage-inducible transcript DDIT4 BC007714 2.54E-05 2.76 4 ankyrin repeat domain 37 ANKRD37 AY296056 3.05E-08 2.73 basic helix-loop-helix family, BHLHE40 AB004066 9.55E-10 2.70 member e40 basic helix-loop-helix family, BHLHE41 BC025968 2.83E-09 2.67 member e41 protein phosphatase 1, regulatory PPP1R3C BC012625 7.08E-05 2.66 subunit 3C chemokine (C-C motif) ligand 28 CCL28 AF110384 8.22E-06 2.64 ankyrin repeat and zinc finger ANKZF1 AK095304 1.40E-11 2.63 domain containing 1 MAX interactor 1 MXI1 BC035128 1.11E-10 2.58 sprouty homolog 1, antagonist of SPRY1 BC063856 4.39E-03 2.50 FGF signalling (Drosophila) BCL2/adenovirus E1B 19kDa BNIP3 AF002697 9.15E-10 2.48 interacting protein 3

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Gene name Gene Symbol RefSeq p-value FC adrenomedullin ADM BC015961 2.95E-06 2.43 BEN domain containing 5 BEND5 AK021650 1.21E-02 2.42 family with sequence similarity FAM115C AK090395 7.54E-07 2.38 115, member C Table 7.4: Top 30 highly expressed genes in HUVEC induced by hypoxia for 3 hours compared to control. The gene list was created by importing Affymetrix .CEL files to Partek Genomic Suite version 6.6. The data was normalised using RMA normalisation. Differentially expressed gene list was generated using one way ANOVA, FDR-unadjusted p-value < 0.05. The differentially expressed genes passes the fold change cutoff of 1.5.

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Gene name Gene Symbol RefSeq p-value FC adenylate kinase 4 AK4 CR456830 2.11E-15 15.86 angiopoietin-like 4 ANGPTL4 AF202636 3.42E-20 9.37 6-phosphofructo-2- PFKFB4 ENST00000232375 5.30E-17 7.57 kinase/fructose-2,6- biphosphatase 4 very low density lipoprotein VLDLR NM_003383 4.50E-06 6.21 receptor egl nine homolog 3 (C. EGLN3 AK025273 1.72E-11 6.19 elegans) enolase 2 (gamma, neuronal) ENO2 M22349 9.98E-14 6.16 transmembrane protein 45A TMEM45A BC040355 1.34E-10 5.44 stanniocalcin 2 STC2 BC000658 3.45E-06 5.27 vascular endothelial growth VEGFA AF022375 4.51E-13 5.15 factor A family with sequence FAM162A AF250321 1.04E-16 4.80 similarity 162, member A phosphoglucomutase 1 PGM1 BC019920 1.18E-16 4.73 aldolase C, fructose- ALDOC BC003613 1.56E-12 4.56 bisphosphate prolyl 4-hydroxylase, alpha P4HA1 BC034998 3.13E-13 4.51 polypeptide I hexokinase 2 HK2 BC064369 8.75E-14 4.47 stanniocalcin 1 STC1 U25997 4.21E-06 4.40 BCL2/adenovirus E1B 19kDa BNIP3P1 ENST00000550043 4.62E-09 4.34 interacting protein 3 pseudogen heme oxygenase (decycling) 1 HMOX1 BC001491 5.30E-10 3.97 family with sequence FAM189A2 BX641153 7.38E-09 3.93 similarity 189, member A2 fibroblast growth factor 11 FGF11 BC032502 2.23E-14 3.83 potassium channel KCTD16 AB037738 1.07E-06 3.77 tetramerisation domain containing 16 lysine (K)-specific KDM3A AK304276 6.41E-10 3.72 demethylase 3A BEN domain containing 5 BEND5 AK021650 3.66E-04 3.67 chromosome 1 open reading C1orf51 AK098755 6.19E-11 3.66 frame 51 chemokine (C-C motif) ligand CCL28 AF110384 3.53E-08 3.60 28 solute carrier family 2 SLC2A3 AB209607 3.28E-10 3.57 (facilitated glucose transporter), member 3 arrestin domain containing 3 ARRDC3 AK291753 8.97E-07 3.54

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Gene name Gene Symbol RefSeq p-value FC solute carrier family 2 SLC2A1 AK292791 1.37E-14 3.50 (facilitated glucose transporter), member 1 TIMP metallopeptidase TIMP3 BC014277 7.03E-03 3.45 inhibitor 3 neuritin 1 NRN1 BC042019 4.62E-04 3.41 BCL2/adenovirus E1B 19kDa BNIP3 AF002697 1.70E-13 3.40 interacting protein 3 adrenomedullin ADM BC015961 3.52E-09 3.39 ankyrin repeat and zinc ANKZF1 AK095304 1.82E-14 3.31 finger domain containing 1 family with sequence FAM117B ENST00000392238 1.05E-08 3.29 similarity 117, member B prolyl 4-hydroxylase, alpha P4HA2 ENST00000401867 5.05E-13 3.25 polypeptide II adenosine A2a receptor ADORA2A BC013780 4.52E-05 2.97 6-phosphofructo-2- PFKFB3 AF056320 1.19E-11 2.97 kinase/fructose-2,6- biphosphatase 3 insulin receptor INSR ENST00000302850 2.74E-11 2.96 sperm associated antigen 4 SPAG4 AF262992 1.84E-12 2.92 integrin, alpha 11 ITGA11 AF109681 3.62E-02 2.89 hypoxia inducible lipid HILPDA AF144755 9.00E-10 2.85 droplet-associated zinc finger, SWIM-type ZSWIM5 ENST00000359600 3.54E-12 2.84 containing 5 MAX interactor 1 MXI1 BC035128 7.26E-12 2.83 chromosome 1 open reading C1orf21 AF312864 2.14E-04 2.81 frame 21 lysyl oxidase LOX AF039291 9.55E-04 2.79 alkaline ceramidase 2 ACER2 BC092487 1.37E-12 2.79 neuron-derived neurotrophic NDNF BC019351 3.29E-06 2.74 factor inhibin, beta A INHBA J03634 1.18E-04 2.73 DNA-damage-inducible DDIT4 BC007714 3.09E-05 2.73 transcript 4 golgin A8 family, member A GOLGA8A AF163441 5.99E-08 2.72 basic helix-loop-helix family, BHLHE40 AB004066 8.90E-10 2.71 member e40 Table 7.5: Top 50 highly expressed genes in HUVEC induced by hypoxia for 12 hours compared to control. The gene list was created by importing Affymetrix .CEL files to Partek Genomic Suite version 6.6. The data was normalised using RMA normalisation. Differentially expressed gene list was generated using one way ANOVA, FDR-unadjusted p-value < 0.05. The differentially expressed genes passes the fold change cutoff of 1.5.

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Gene name Gene Symbol RefSeq p-value FC thioredoxin interacting protein TXNIP S73591 1.69E-10 5.44 RNA, Ro-associated Y3 RNY3P6 ENST00000390895 4.06E-02 1.74 pseudogene 6 transfer RNA cysteine 31 TRNAC31P ENST00000465944 5.02E-03 1.69 (anticodon GCA) pseudogene olfactory receptor, family 1, OR1S2 ENST00000302592 1.37E-04 1.66 subfamily S, member 2 RNA, Ro-associated Y4 RNY4P18 ENST00000391107 2.53E-02 1.63 pseudogene 18 RNA, 5S ribosomal 346 RN5S346 ENST00000363541 1.36E-03 1.57 leukocyte immunoglobulin-like LILRA1 AF025529 1.97E-03 1.57 receptor, subfamily A (with TM domain), member 1 RNA, Ro-associated Y4 RNY4P2 ENST00000363640 8.80E-03 1.52 pseudogene 2 coiled-coil domain containing 75 CCDC75P1 ENST00000469247 4.40E-02 -1.50 pseudogene 1 centrosomal protein 170kDa CEP170P1 ENST00000450306 3.01E-02 -1.50 pseudogene 1 ribosomal protein L7 pseudogene RPL7P48 ENST00000461513 4.33E-03 -1.50 48 eukaryotic translation initiation EIF4A1P2 ENST00000545933 3.10E-02 -1.53 factor 4A1 pseudogene complement factor H-related 1 CFHR1 BC107771 4.35E-02 -1.54 high mobility group nucleosomal HMGN2P27 ENST00000427352 1.45E-02 -1.54 binding domain 2 pseudogene RNA, 5S ribosomal 513 RN5S513 ENST00000364648 1.03E-02 -1.55 small nucleolar RNA, C/D box SNORD15B NR_000025 4.75E-02 -1.59 15B basic transcription factor 3 BTF3P10 ENST00000412607 7.87E-03 -1.60 pseudogene 10 aldehyde dehydrogenase 6 ALDH6A1 ENST00000553458 3.00E-02 -1.64 family, member A1 Rho-associated, coiled-coil ROCK1P1 BC041849 2.95E-02 -1.67 containing protein kinase 1 pseudogene immunoglobulin (CD79A) IGBP1P1 AY168620 1.73E-02 -1.68 binding protein 1 pseudogene 1 transfer RNA glutamine 54 TRNAQ54P ENST00000463811 2.72E-02 -1.90 (anticodon UUG) pseudogene zinc finger protein 578 ZNF578 AK299106 3.34E-02 -1.91 coiled-coil and C2 domain- LOC100653119 ENST00000469549 9.43E-04 -2.18 containing protein 2A-like

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Gene name Gene Symbol RefSeq p-value FC TATA box binding protein TAF1D AK057183 1.77E-03 -2.19 (TBP)-associated factor, RNA polymerase I, D, 41kDa RNA, 5S ribosomal 191 RN5S191 ENST00000362585 2.07E-02 -2.42

RNA, 5S ribosomal 242 RN5S242 ENST00000364171 3.25E-02 -3.69 Table 7.6: Differentially expressed genes on HUVEC induced by hyperglycaemia compared to the control. The gene list was created by importing Affymetrix .CEL files to Partek Genomic Suite version 6.6. The data was normalised using RMA normalisation. Differentially expressed gene list was generated using one way ANOVA, FDR-unadjusted p-value < 0.05. The differentially expressed genes passes the fold change cutoff of 1.5. The signs in the fold change column means (-) downregulated, (+) upregulated.

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Gene name Gene Symbol RefSeq p-value FC thioredoxin interacting protein TXNIP S73591 7.13E-11 5.75 basic helix-loop-helix family, member BHLHE40 AB004066 1.21E-08 2.45 e40 adrenomedullin ADM BC015961 1.83E-05 2.22 arrestin domain containing 3 ARRDC3 AK291753 2.33E-03 2.04 angiopoietin-like 4 ANGPTL4 AF202636 2.03E-05 1.93 DNA-damage-inducible transcript 4 DDIT4 BC007714 6.29E-03 1.86 ankyrin repeat domain 37 ANKRD37 AY296056 6.09E-04 1.74 transfer RNA cysteine 31 (anticodon TRNAC31P ENST00000465944 6.20E-03 1.67 GCA) pseudogene dual specificity phosphatase 6 DUSP6 BC005047 3.19E-05 1.66 dual specificity phosphatase 1 DUSP1 BC022463 8.85E-07 1.64 olfactory receptor, family 1, subfamily OR1S2 ENST00000302592 2.30E-04 1.63 S, member 2 small Cajal body-specific RNA 23 // SCARNA23 NR_003007 3.33E-02 1.62 Xp22.11 vascular endothelial growth factor A VEGFA AF022375 5.06E-03 1.61 CD177 molecule CD177 AF146747 2.44E-02 1.60 olfactory receptor, family 4, subfamily OR4A47 BC140738 8.21E-04 1.58 A, member 47 dpy-19-like 2 (C. elegans) DPY19L2 AY358792 7.89E-03 1.53 small nucleolar RNA, H/ACA box 41 SNORA41 ENST00000482103 1.42E-02 -1.50 RNA, 5S ribosomal 156 RN5S156 ENST00000410250 2.47E-03 -1.53 olfactory receptor, family 6, subfamily OR6C72P ENST00000451403 2.62E-02 -1.60 C, member 72 pseudogene TATA box binding protein (TBP)- TAF1D AK057183 2.85E-02 -1.70 associated factor, RNA polymerase I, D, 41kDa RNA, 5S ribosomal 513 RN5S513 ENST00000364648 1.95E-03 -1.71 solute carrier family 9, subfamily B SLC9B1 AY461581 2.80E-03 -1.78 (NHA1, cation proton antiporter 1), member 1 Table 7.7: Differentially expressed genes in HUVEC induced by hyperglycaemia and hypoxia for 1 hour compared to control. The gene list was created by importing Affymetrix .CEL files to Partek Genomic Suite version 6.6. The data was normalised using RMA normalisation. Differentially expressed gene list was generated using one way ANOVA, FDR-unadjusted p-value < 0.05. The differentially expressed genes passes the fold change cutoff of 1.5. The signs in the fold change column means (-) downregulated, (+) upregulated.

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Gene name Gene Symbol RefSeq p-value FC angiopoietin-like 4 ANGPTL4 AF202636 1.22E-18 7.64 adenylate kinase 4 AK4 CR456830 3.90E-11 7.51 6-phosphofructo-2-kinase/fructose-2,6- PFKFB4 ENST00000232375 1.62E-16 7.11 biphosphatase 4 hexokinase 2 HK2 BC064369 5.29E-14 4.58 thioredoxin interacting protein TXNIP S73591 5.75E-09 4.38 chromosome 1 open reading frame 51 C1orf51 AK098755 4.52E-12 4.15 stanniocalcin 2 STC2 BC000658 8.00E-05 3.90 vascular endothelial growth factor A VEGFA AF022375 1.12E-09 3.47 enolase 2 (gamma, neuronal) ENO2 M22349 5.90E-09 3.43 6-phosphofructo-2-kinase/fructose-2,6- PFKFB3 AF056320 3.21E-13 3.41 biphosphatase 3 hypoxia inducible lipid droplet-associated HILPDA AF144755 3.40E-11 3.26 family with sequence similarity 162, member FAM162A AF250321 1.49E-12 3.23 A very low density lipoprotein receptor VLDLR NM_003383 2.10E-03 3.13 solute carrier family 2 (facilitated glucose SLC2A3 AB209607 7.98E-09 3.07 transporter), member 3 solute carrier family 2 (facilitated glucose SLC2A1 AK292791 8.52E-13 3.00 transporter), member 1 ankyrin repeat and zinc finger domain ANKZF1 AK095304 3.14E-13 2.99 containing 1 egl nine homolog 3 (C. elegans) EGLN3 AK025273 3.01E-06 2.97 stanniocalcin 1 STC1 U25997 4.88E-04 2.89 DNA-damage-inducible transcript 4 DDIT4 BC007714 1.37E-05 2.88 lysine (K)-specific demethylase 3A KDM3A AK304276 1.66E-07 2.82 ankyrin repeat domain 37 ANKRD37 AY296056 4.29E-08 2.69 basic helix-loop-helix family, member e40 BHLHE40 AB004066 2.20E-09 2.62 BCL2/adenovirus E1B 19kDa interacting BNIP3P1 ENST00000550043 3.54E-05 2.53 protein 3 pseudogene family with sequence similarity 115, member FAM115C AK090395 1.90E-07 2.53 C chemokine (C-C motif) ligand 28 CCL28 AF110384 2.42E-05 2.48 RAB20, member RAS oncogene family RAB20 BC026025 2.71E-07 2.47 adrenomedullin ADM BC015961 2.40E-06 2.46 sprouty homolog 1, antagonist of FGF SPRY1 BC063856 5.15E-03 2.45 signaling (Drosophila) BCL2/adenovirus E1B 19kDa interacting BNIP3 AF002697 1.36E-09 2.45 protein 3 prolyl 4-hydroxylase, alpha polypeptide I P4HA1 BC034998 2.72E-07 2.44 Table 7.8: Top 30 highly expressed genes in HUVEC induced by hyperglycaemia and hypoxia for 3 hours compared to control. Refer to legend in table 7.7.

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Gene name Gene Symbol RefSeq p-value FC adenylate kinase 4 AK4 CR456830 8.65E-14 11.81 angiopoietin-like 4 ANGPTL4 AF202636 3.97E-20 9.29 thioredoxin interacting protein TXNIP S73591 9.56E-13 7.59 very low density lipoprotein receptor VLDLR NM_003383 3.24E-06 6.42 stanniocalcin 2 STC2 BC000658 9.97E-07 5.91 6-phosphofructo-2-kinase/fructose- PFKFB4 ENST00000232375 1.92E-14 5.49 2,6-biphosphatase 4 nuclear protein, transcriptional NUPR1 AF069073 1.04E-04 5.43 regulator, 1 placenta-specific 8 PLAC8 ENST00000311507 1.86E-02 5.41 enolase 2 (gamma, neuronal) ENO2 M22349 2.54E-12 5.15 egl nine homolog 3 (C. elegans) EGLN3 AK025273 3.64E-10 5.12 ChaC, cation transport regulator CHAC1 BC019625 1.46E-04 4.74 homolog 1 (E. coli) vascular endothelial growth factor A VEGFA AF022375 2.39E-12 4.72 phosphoglucomutase 1 PGM1 BC019920 8.20E-16 4.35 aldolase C, fructose-bisphosphate ALDOC BC003613 4.96E-12 4.30 potassium channel tetramerisation KCTD16 AB037738 2.75E-07 4.15 domain containing 16 chromosome 1 open reading frame 51 C1orf51 AK098755 5.56E-12 4.11 heme oxygenase (decycling) 1 HMOX1 BC001491 3.07E-10 4.09 transmembrane protein 45A TMEM45A BC040355 1.55E-08 4.07 stanniocalcin 1 STC1 U25997 1.81E-05 3.88 prolyl 4-hydroxylase, alpha P4HA1 BC034998 1.69E-11 3.74 polypeptide I sperm associated antigen 4 SPAG4 AF262992 2.44E-15 3.73 adrenomedullin ADM BC015961 6.96E-10 3.67 family with sequence similarity 162, FAM162A AF250321 1.45E-13 3.54 member A solute carrier family 2 (facilitated SLC2A1 AK292791 1.08E-14 3.53 glucose transporter), member 1 family with sequence similarity 189, FAM189A2 BX641153 1.10E-07 3.37 member A2 hexokinase 2 HK2 BC064369 5.01E-11 3.36 arrestin domain containing 3 ARRDC3 AK291753 2.17E-06 3.34 asparagine synthetase (glutamine- ASNS BC008723 5.01E-04 3.24 hydrolyzing) fibroblast growth factor 11 FGF11 BC032502 2.09E-12 3.20

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Gene name Gene Symbol RefSeq p-value FC solute carrier family 2 (facilitated SLC2A3 AB209607 5.55E-09 3.12 glucose transporter), member 3 golgin A8 family, member A GOLGA8A AF163441 3.12E-09 3.12 DNA-damage-inducible transcript 4 DDIT4 BC007714 4.27E-06 3.10 prolyl 4-hydroxylase, alpha P4HA2 ENST00000401867 1.66E-12 3.10 polypeptide II protein tyrosine phosphatase, PPFIA4 AK295757 1.70E-11 3.05 receptor type, f polypeptide (PTPRF), interacting protein (liprin), alpha 4 BCL2/adenovirus E1B 19kDa BNIP3 AF002697 3.89E-12 3.02 interacting protein 3 ankyrin repeat and zinc finger ANKZF1 AK095304 2.84E-13 3.00 domain containing 1 phosphoglycerate dehydrogenase PHGDH AF171237 5.17E-04 2.95 golgin A8 family, member A GOLGA8A AF163441 6.86E-09 2.93 chemokine (C-C motif) ligand 28 CCL28 AF110384 1.42E-06 2.92 basic helix-loop-helix family, member BHLHE40 AB004066 1.31E-10 2.92 e40 BCL2/adenovirus E1B 19kDa BNIP3P1 ENST00000550043 4.08E-06 2.89 interacting protein 3 pseudogene neuritin 1 NRN1 BC042019 2.44E-03 2.84 adenylosuccinate synthase like 1 ADSSL1 AK096124 4.20E-16 2.82 lysyl oxidase LOX AF039291 9.35E-04 2.80 lysine (K)-specific demethylase 3A KDM3A AK304276 2.27E-07 2.78 SH3 domain containing 21 SH3D21 AK056459 3.06E-11 2.78 phosphoserine aminotransferase 1 PSAT1 BC004863 2.89E-03 2.78 insulin receptor INSR ENST00000302850 2.52E-10 2.73 SMAD family member 7 SMAD7 AF010193 3.32E-03 2.71 BEN domain containing 5 BEND5 AK021650 5.56E-03 2.67 Table 7.9: Top 50 highly expressed genes in HUVEC induced by hyperglycaemia and hypoxia for 12 hours compared to control. Refer to legend in table 7.7

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Gene Name Gene Symbol p-value FC lysine (K)-specific demethylase 5D KDM5D 2.32E-11 -3.56 selectin P (granule membrane protein 140kDa, antigen SELP 3.27E-09 -5.01 CD62) gamma-aminobutyric acid (GABA) A receptor, epsilon GABRE 3.31E-09 -2.76 integrin, beta 3 (platelet glycoprotein IIIa, antigen CD61) ITGB3 3.71E-09 -6.70 chromosome Y open reading frame 15B CYorf15B 3.74E-09 -5.06 coagulation factor XIII, A1 polypeptide F13A1 4.77E-09 -3.50 polycystic kidney and hepatic disease 1 (autosomal PKHD1L1 6.50E-09 -6.43 recessive)-l KIAA0087 KIAA0087 1.40E-08 2.48 chromosome Y open reading frame 15A CYorf15A 2.26E-08 -4.54 DEAD (Asp-Glu-Ala-Asp) box polypeptide 3, Y-linked DDX3Y 2.29E-08 -4.16 ubiquitously transcribed tetratricopeptide repeat gene, Y- UTY 2.94E-08 -3.89 linked heparanase HPSE 3.39E-08 -2.81 nexilin (F actin binding protein) NEXN 9.83E-08 -3.21 major histocompatibility complex, class II, DQ beta 1 HLA-DQB1 1.03E-07 -7.01 ubiquitin specific peptidase 9, Y-linked USP9Y 1.19E-07 -3.83 major histocompatibility complex, class II, DQ beta 1 HLA-DQB1 1.44E-07 -8.74 galectin-related protein HSPC159 1.55E-07 -5.74 epithelial cell adhesion molecule EPCAM 2.04E-07 -2.96 zinc finger protein, Y-linked ZFY 2.10E-07 -3.23 dynamin 3 DNM3 2.35E-07 -2.06 eukaryotic translation initiation factor 1A, Y-linked EIF1AY 3.09E-07 -4.85 phosphodiesterase 3A, cGMP-inhibited PDE3A 3.98E-07 -2.52 olfactory receptor, family 3, subfamily A, member 2 OR3A2 4.25E-07 2.31 snail homolog 2 (Drosophila) SNAI2 4.51E-07 2.16 glycoprotein Ib (platelet), alpha polypeptide GP1BA 6.25E-07 -2.70 testis-specific transcript, Y-linked 10 (non-protein coding) TTTY10 6.58E-07 -2.58 latent transforming growth factor beta binding protein 1 LTBP1 6.69E-07 -2.94 C-type lectin domain family 1, member B CLEC1B 1.06E-06 -4.25 potassium channel, subfamily K, member 17 KCNK17 1.17E-06 2.55 immunoglobulin lambda joining 3 IGLJ3 1.76E-06 2.31 Table 7.10: Top 30 differentially expressed genes in CD34+cells induced by hyperglycaemia compared to control. Refer to legend in table 7.7.

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Gene Name Gene Symbol p-value FC small nucleolar RNA, H/ACA box 70C (retrotransposed) SNORA70C 1.41E-03 1.95 HFM1, ATP-dependent DNA helicase homolog (S. cerevisiae) HFM1 1.73E-03 -1.62 eyes shut homolog (Drosophila) EYS 2.10E-03 -1.53 small Cajal body-specific RNA 16 SCARNA16 2.13E-03 -1.51 ribosomal protein S6 pseudogene 6 RPS6P6 2.91E-03 1.79 small nucleolar RNA, H/ACA box 71C SNORA71C 2.99E-03 -1.59 transmembrane protein 45A TMEM45A 3.00E-03 -1.52 ubiquitin specific peptidase 18 USP18 3.10E-03 1.63 small nucleolar RNA, C/D box 94 SNORD94 3.94E-03 -1.51 microfibrillar associated protein 5 MFAP5 5.58E-03 -1.54 small nucleolar RNA, H/ACA box 68 SNORA68 5.69E-03 -1.60 CD177 molecule CD177 5.72E-03 -1.86 nuclear transport factor 2-like LOC128322 6.53E-03 1.58 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 4, NDUFA4 6.77E-03 -1.53 9kDa family with sequence similarity 72, member D FAM72D 7.17E-03 -1.64 SPANX family, member E SPANXE 7.19E-03 1.58 small Cajal body-specific RNA 6 SCARNA6 8.70E-03 -1.89 heat shock protein 90kDa alpha (cytosolic), class A member 6 HSP90AA6P 9.46E-03 2.34 chromosome 4 open reading frame 11 C4orf11 9.60E-03 1.58 epithelial cell adhesion molecule EPCAM 9.93E-03 -2.06 golgin A6 family, member A GOLGA6A 9.94E-03 1.89 small nucleolar RNA, C/D box 15A SNORD15A 1.04E-02 -1.52 coiled-coil domain containing 122 CCDC122 1.22E-02 -1.51 small Cajal body-specific RNA 1 SCARNA1 1.24E-02 -1.86 small nucleolar RNA, H/ACA box 21 SNORA21 1.32E-02 -1.57 golgin A6 family, member A GOLGA6A 1.38E-02 1.72 small nucleolar RNA, H/ACA box 52 SNORA52 1.40E-02 -1.64 glutathione S-transferase alpha 3 GSTA3 1.42E-02 1.64 small nucleolar RNA, H/ACA box 23 SNORA23 1.48E-02 -1.62 small nucleolar RNA, H/ACA box 54 SNORA54 1.55E-02 -1.74 Table 7.11: Top 30 differentially expressed genes in CD34+cells induced by hyperglycaemia and treated with metformin compared to hyperglycaemia. Refer to legend in table 7.7.

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Gene Name Gene Symbol p-value FC mitochondrially encoded NADH dehydrogenase 2 ND2 1.12E-09 15.22 non-SMC condensin I complex, subunit H NCAPH 1.46E-09 2.24 polo-like kinase 1 PLK1 1.54E-09 4.71 cyclin A2 CCNA2 1.94E-09 2.57 serine/arginine-rich splicing factor 4 SRSF4 1.15E-08 -1.73 protein regulator of cytokinesis 1 PRC1 1.17E-08 2.17 TIMP metallopeptidase inhibitor TIMP3 1.24E-08 2.14 lysine (K)-specific demethylase 5D KDM5D 2.59E-08 1.86 TPX2, microtubule-associated, homolog (Xenopus TPX2 6.63E-08 2.64 laevis) cell division cycle 20 homolog (S. cerevisiae) CDC20 8.42E-08 3.08 olfactory receptor, family 2, subfamily L, member OR2L13 9.20E-08 -1.93 13 prohibitin PHB 9.25E-08 1.99 ZW10 interactor ZWINT 9.82E-08 1.93 X-ray repair complementing defective repair in XRCC2 1.44E-07 1.61 Chinese hamster cells 2 brain expressed, X-linked 4 BEX4 1.47E-07 -1.76 protein kinase, membrane associated PKMYT1 1.61E-07 2.14 tyrosine/threonine 1 golgin A8 family, member B GOLGA8B 1.62E-07 -1.64 Holliday junction recognition protein HJURP 1.67E-07 2.55 S100 calcium binding protein A8 S100A8 2.10E-07 -5.24 golgin A8 family, member B GOLGA8B 2.71E-07 -1.61 budding uninhibited by benzimidazoles 1 homolog BUB1B 2.76E-07 1.97 beta (yeast) SET binding factor 2 SBF2 3.03E-07 -1.66 ELL associated factor 2 EAF2 3.54E-07 -1.66 small Cajal body-specific RNA 7 SCARNA7 3.61E-07 -2.34 small nucleolar RNA, H/ACA box 38B SNORA38B 4.35E-07 -11.90 (retrotransposed) CD38 molecule CD38 4.45E-07 -1.92 family with sequence similarity 118, member A FAM118A 4.49E-07 -1.96 high-mobility group nucleosomal binding domain 2 HMGN2 4.69E-07 1.87 proline rich 11 PRR11 5.13E-07 2.64 oxysterol binding protein-like 6 OSBPL6 5.33E-07 2.35 Table 7.12: Top 30 differentially expressed genes in CD34+cells induced by hyperglycaemia and hypoxia for 3 hours compared to hyperglycaemia. Refer to legend in table 7.7.

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Gene Name Gene Symbol p-value FC mitochondrially encoded NADH ND2 2.79E-09 -11.94 dehydrogenase 2 serine/arginine-rich splicing factor 4 SRSF4 3.32E-09 1.87 brain expressed, X-linked 4 BEX4 1.43E-07 1.76 dihydrouridine synthase 4-like (S. cerevisiae) DUS4L 2.41E-07 1.60 mitochondrial ribosomal protein S21 MRPS21 2.99E-07 1.52 endoplasmic reticulum protein 29 ERP29 6.47E-07 -1.74 prohibitin PHB 1.40E-06 -1.67 chromosome 11 open reading frame 58 C11orf58 1.48E-06 1.91 NOP14 nucleolar protein homolog (yeast) NOP14 1.50E-06 1.52 deoxynucleotidyltransferase, terminal, DNTTIP1 1.97E-06 1.91 interacting protein 1 family with sequence similarity 98, member A FAM98A 2.03E-06 1.52 dynactin 3 (p22) DCTN3 2.25E-06 -2.14 solute carrier family 25, member 32 SLC25A32 2.47E-06 2.02 heterogeneous nuclear ribonucleoprotein H3 HNRNPH3 4.21E-06 1.72 (2H9) glycerol-3-phosphate acyltransferase, GPAM 4.31E-06 -10.76 mitochondrial zinc finger protein 391 ZNF391 4.55E-06 1.59 chromosome 11 open reading frame 51 C11orf51 4.61E-06 2.36 USO1 vesicle docking protein homolog (yeast) USO1 4.64E-06 1.55 peroxisomal D3,D2-enoyl-CoA isomerase PECI 4.69E-06 1.78 small Cajal body-specific RNA 7 SCARNA7 6.13E-06 1.87 small nucleolar RNA, H/ACA box 38B SNORA38B 7.26E-06 6.17 (retrotransposed) pleckstrin homology domain containing, PLEKHA5 7.54E-06 1.53 family A member 5 high-mobility group nucleosomal binding HMGN2 7.62E-06 -1.59 domain 2 sorting nexin 12 SNX12 7.67E-06 -1.76 chromosome 19 open reading frame 43 C19orf43 7.71E-06 -1.80 synaptotagmin binding, cytoplasmic RNA SYNCRIP 1.02E-05 1.58 interacting protein chromosome 6 open reading frame 47 C6orf47 1.04E-05 2.15 transducer of ERBB2, 2 TOB2 1.22E-05 4.48 heterogeneous nuclear ribonucleoprotein A3 HNRNPA3 1.30E-05 1.88 tRNA splicing endonuclease 15 homolog (S. cerevisiae) TSEN15 1.34E-05 -1.60 Table 7.13: Top 30 differentially expressed genes in CD34+cells treated with hyperglycaemia and metformin and then exposed to hypoxia for 3 hours compared to hyperglycaemia and hypoxia. Refer to legend in table 7.7.

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7.4 Appendix VI: Pathway key

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