IDENTIFICATION AND CHARACTERISATION OF ASSOCIATED WITH HYPERGLYCAEMIA SUSCEPTIBILITY AND REDUCED INSULIN SECRETION

Chieh-Hsin Yang

Submitted in total fulfilment of the requirements of the degree of Doctor of Philosophy

February 2018

Department of Medicine

Faculty of Medicine, Dentistry and Health Sciences

The University of Melbourne

Abstract

ABSTRACT

The pathogenesis of type 2 diabetes (T2D) involves a complex interplay of genetic and environmental factors which result in impaired insulin secretion in the presence of insulin resistance. A permissive genetic susceptibility is considered a predominant determinant to the onset of hyperglycaemia, however, it has a complex aetiology that is yet to be fully defined. The aim of this thesis was to identify and characterise genes associated with hyperglycaemia.

A genetically diverse mouse reference population, the Collaborative Cross (CC), was utilised to identify and characterise genetic loci influencing blood glucose concentrations. Therefore, a large-scale phenotypic screening of random blood glucose, insulin sensitivity and body weight across 652 males from 53 inbred CC strains (n=3-15) and 467 females from 48 CC strains (n=3-10) was conducted. The CC mouse population exhibits an abundant diversity in blood glucose levels as well as in insulin sensitivity and body weight. Interesting to note that strains which have blood glucose levels within the top range are rather lean and sensitive to insulin-mediated glucose lowering, indicative of impaired insulin secretion. To identify the genetic loci contributing to this variation in blood glucose concentrations, an unbiased genome-wide association study (GWAS) and quantitative trait loci (QTL) mapping were performed using the random blood glucose data. Two independent loci on 7 were identified to be significantly (p <5x10-8) associated with high blood glucose, moreover, the contributory founder haplotype was demonstrated to be the NZO-derived alleles at both loci according to a haplotype analysis. These implicated loci were then named by the genes nearby peak SNPs as the E2F8 (Chr7: 52.6-56.7 Mb) and Dlg2 locus (Chr7: 98.5-101.55 Mb). Potential candidates in both loci were determined by sequence analyses throughout all genes in the linkage disequilibrium (LD) blocks, moreover, the property of genes and variants were also taken into account during the selection process. Finally, we narrowed the number of gene candidates down to seven for each locus: Gfy, Hsd17b14, Sphk2, E2F8, Ntn5, Abcc8 and Kcnj11 were identified at the E2F8 locus; Dlg2, Ccdc90b, Andkrd42, Pcf11, Ddias, Rab30 and Prcp for the Dlg2 locus.

I Abstract

It was important to find that, to some extent, both E2F8 and Dlg2 loci have been implicated in diabetes-related abnormalities, but the functional defects underlying these deleterious alleles have yet to be fully defined. In order to study the impact of the NZO-derived hyperglycaemia susceptibility alleles on glucose homeostasis, we conducted in vivo and in vitro examinations on the hyperglycaemic CC strains, PIPING and PUB mice, which bear the deleterious alleles from NZO mice at susceptibility loci. Compared with the C57BL/6 mice as a reference strain, the hyperglycaemia susceptible strains are severely glucose intolerant in an oral glucose tolerance test (2 g/kg glucose), due to substantial reduction in insulin secretion in response to glucose and arginine stimulation in vivo. This secretory defect was demonstrated to be independent of abnormalities in islet morphology, -cell mass and pancreatic insulin content, but rather as the result of inherent -cell dysfunction. On the other hand, the genetic predisposition to hyperglycaemia appears to drive the susceptible mice to become more sensitive to dietary fat-induced weight gain and -cell decompensation. As evidenced by the findings that PIPING and PUB mice were susceptible to high fat diet-induced weight gain, which in partly due to increased fat intake but seems independent of higher caloric intake. Furthermore, PIPING and PUB mice developed advanced glucose intolerance on high-fat diet as a result of failure to raise insulin secretion for the increased demand. These results provide evidence that the hyperglycaemia susceptibility loci, E2F8 and Dlg2 loci, have substantial influences on pancreatic -cell function.

Further investigation on the relationship of candidate gene expression with diabetes susceptibility was determined by identifying differential gene expression in primary islets between C57BL/6 (diabetes-resistant) and NZO (diabetes-susceptible) mice. On the basis of results from genetic mapping and expression analysis, E2F8 and Dlg2 genes were prioritised as promising candidates for reduced pancreatic insulin secretion. To determine whether E2F8 and Dlg2 are involved in pancreatic insulin secretion, E2F8 and Dlg2 were knocked down using siRNA and lentiviral transduction of shRNA, respectively, in a mouse pancreatic -cell line, MIN6 cells. The results of insulin secretion in E2F8 knockdown cells showed that down-regulation of E2F8 resulted in reduced insulin secretion in response to glucose as well as arginine and tolbutamide but not to GLP-1 and KCl, suggesting E2F8

II Abstract

+ mediates pancreatic insulin secretion in a K ATP -channel dependent manner. Furthermore, knockdown of Dlg2 expression in MIN6 cells led to a generalised attenuation of insulin secretion regardless of glucose or other non-glucose secretagogues. These data showed the first evidence that the expression of E2F8 and Dlg2 affect insulin secretory function in pancreatic -cells, which can therefore influence individual’s susceptibility to diabetes.

Taken together, this thesis provides evidence of two important loci for hyperglycaemia susceptibility which are linked to impaired insulin secretory function in the pancreatic - cells. Importantly, our results identified a number of novel candidate genes that are likely to alter insulin secretion in the pancreatic islets. Among these, E2F8 and Dlg2 were validated and recognised as novel genes to be involved in the pathogenesis of hyperglycaemia through altering pancreatic insulin secretion. This thesis delineates the use of the CC to identify novel genes for complex traits and understand the molecular control of insulin secretion.

III Declaration

DECLARATION

This is to certify that:

(i) the thesis comprises only my original work towards the PhD except where indicated in the Preface,

(ii) due acknowledgement has been made in the text to all other material used,

(iii) the thesis is less than 100,000 words in length, exclusive of tables, figures, bibliography and appendix.

Signature: ______

Chieh-Hsin Yang

IV Preface

PREFACE

The genome-wide association study using the Collaborative Cross resource was performed in collaboration with Professor Grant Morahan from the University of Western Australia. Dr Salvatore Mangiafico, Ms Rebecca Sgambellone and Dr Fabio Manippa assisted with the phenotypic screening of the Collaborative Cross mice, including random blood glucose measurements and insulin tolerance tests. Real-Time PCR in human islet samples was undertaken by Ms Michaela Waibel from St. Vincent’s Institute (Fitzroy, Victoria, Australia). Dr Maria Stathopoulos and Ms Amy Huang assisted with the pancreas inflation. Insulin immunohistochemistry of pancreatic sections was conducted at Anatomical Pathology, Department of Medicine, University of Melbourne (Parkville, Victoria, Australia).

V Publications

PUBLICATIONS

Unpublished Manuscript

Yang C., Ram R., Mangiafico SP., Waibel M., Thomas H.E., Loudovaris T., Morahan G.*, Andrikopoulos S.*. Identification of Dlg2 and E2F8 As Genes That Cause Impaired Insulin Secretion and Hyperglycaemia using the Collaborative Cross Mouse Population. Manuscript in preparation.

Published Peer-reviewed Journal Article

Stöckli1 J., Fisher-Wellman K.H., Chaudhuri R., Zeng X., Fazakerley D.J., Meoli C.C., Thomas K.C., Hoffman N.J., Mangiafico S.P., Xirouchaki C.E., Yang C., Ilkayeva O., Wong K., Cooney G.J., Andrikopoulos S., Muoio D.M., James D.E.. Metabolomic analysis of insulin resistance across different mouse strains and diets. Journal of Biological Chemistry. 2017 Oct.

VI Published Abstracts and Presentations

PUBLISHED ABSTRACTS AND PRESENTATIONS

Identification and Characterisation of Genetic Susceptibilities to Hyperglycaemia and Reduced Insulin Secretion in Mice and Men Chieh-Hsin Yang, Salvatore P. Mangiafico, Ramesh Ram, Grant Morahan, Sof Andrikopoulos Oral presentation at Australian Diabetes Society, Young Investigator Award Section, Gold Coast, August 2016

Identification and Characterisation of Genetic Susceptibilities to Hyperglycaemia and Reduced Insulin Secretion in Mice and Men Chieh-Hsin Yang, Salvatore P. Mangiafico, Ramesh Ram, Grant Morahan, Sof Andrikopoulos Poster presentation at American Diabetes Association, Annual Scientific Section, New Orleans, June 2016

Identification of Genetic Loci Associated with Glycaemic Variability in Mouse Genome Chieh-Hsin Yang, Salvatore P. Mangiafico, Ramesh Ram, Grant Morahan, Sof Andrikopoulos Poster Presentation at Australian Diabetes Society, Annual Scientific Meeting, Adelaide, August 2015

Identification and Characterisation of Genes Influencing Insulin Secretion and Glycaemic Variability in Mice and Men Chieh-Hsin Yang, Salvatore P. Mangiafico, Ramesh Ram, Grant Morahan, Sof Andrikopoulos Oral Presentation & Recipient of Young Investigator Award at Austin Life Sciences Symposium, Melbourne, October 2015

VII Published Abstracts and Presentations

Identification and Characterisation of Genes Influencing Insulin Secretion and Glycaemic Variability in Mice and Men Chieh-Hsin Yang, Salvatore P. Mangiafico, Ramesh Ram, Grant Morahan, Sof Andrikopoulos Poster Presentation at the Austin Research Week, Melbourne, October 2015

Characterising Novel Mouse Models of Hyperglycaemia in the Gene Mine to Dissect Complex Pathogenesis of Type 2 Diabetes

Chieh-Hsin Yang, Salvatore P. Mangiafico, Ramesh Ram, Grant Morahan, Sof Andrikopoulos Poster Presentation at the Austin Research Week, Melbourne, October 2014

Novel Mouse Models of Hyperglycaemia in the Gene Mine Dissect Complex Pathogenesis of Type 2 Diabetes Chieh-Hsin Yang, Salvatore P. Mangiafico, Ramesh Ram, Grant Morahan, Sof Andrikopoulos Oral presentation & Nominated for the Young Investigator Award at Australian Diabetes Society, Annual Scientific Meeting, Sydney, August 2014

VIII Acknowledgements

ACKNOWLEDGEMENTS

First and foremost I would like to express my gratitude to my supervisor Associate Professor Sofianos Andrikopoulos for his mentorship and unwavering support which have been crucial to the completion of my PhD. Sof, I am grateful that you offered me this life-changing opportunity to do research in your laboratory. It has been truly an amazing journey and I am forever better for having taken the leap of faith. I sincerely appreciate for your supervision and guidance. Working with an outstanding scientist and mentor such as you is invaluable, your motivation, enthusiasm, and immense knowledge keeping me inspired throughout my Ph.D.

Secondly, a special thanks goes to Professor Grant Morahan for his input into this thesis. His knowledge and wise advice have been great help to me throughout this project. I would like to thank Professor Joseph Proietto for sharing his wisdom and thoughts. I would also like to express my thanks to the Ph.D supervisory committee: Dr Xiaofang Wang and Dr Kesha Rana. Thank you for your useful advice which keeping my progress on the right track. To the PhD coordinator Jo Mayall, thank you for being so helpful at all time and always offering great advice for my life during Ph.D years.

Thirdly, my sincere thanks also goes to all the past and present members in the Andrikopoulos Lab, thanks for all your support, encouragement and friendship. To Dr Barbara Fam, thank you for being there to give advice on issues when I was needed and always kindly sharing your wisdom and experiences. To Dr Salvator Mangiafico, thank you for your passionate participation and guidance on this thesis which definitely helped my Ph.D study. To Dr Nicole Wong and Dr Benjamin Lamont, thank you for your suggestions on my experience, especially when I encountered technical difficulties with islets. Thanks to who taught me new techniques and conducted assays which contributed to the successful completion of my Ph.D: Zheng Ruan, Amy Huang, Rebecca Sgambellone, and Dr Maria Stathopoulos. Your assistance definitely made my Ph.D research smoother. To Dr Steve Weng, Dr Chrysovalantou Xirouchaki, Victoria Ntouma, Dr Christos

IX Acknowledgements

Joannides, Christian Haralambous and Dr Evelyn Marin, thank you all for your friendship, support and sense of humour that make the laboratory an enjoyable place.

I recognise that this research would not have been possible without the financial assistance of the Melbourne International Fee Remission Scholarship (MIFRS) and Melbourne International Research Scholarship (MIRS), and express my gratitude to the University of Melbourne for offering me these scholarships.

I would like to thank my partner and best friend Xiang-Rong Lu, thank you for your constant support, patience, understanding and words of encouragement and for keeping me positive and motivated when I faced challenges.

Lastly but most importantly, I would like to thank my dearest family: my father Tsan-Chin, my mother Mei-Hui, my grandmother San-Mei and my siblings Nikki and Vick whose unconditional love and encouragement supported me tremendously throughout my entire Ph.D. Without all of you I would not have completed this thesis. Thank you for believing in me. I dedicate this thesis to you.

X Table of Contents

TABLE OF CONTENTS

ABSTRACT ...... I

DECLARATION ...... IV

PREFACE ...... V

PUBLICATIONS ...... VI

PUBLISHED ABSTRACTS AND PRESENTATIONS ...... VII

ACKNOWLEDGEMENTS ...... IX

TABLE OF CONTENTS ...... XI

LIST OF FIGURES ...... XVIII

LIST OF TABLES ...... XXI

ABBREVIATIONS ...... XXII

CHAPTER 1 LITERATURE REVIEW ...... 2

1.1. Diabetes Mellitus ...... 2 1.1.1. Global Prevalence of Diabetes Mellitus ...... 2 1.1.2. Definition and Classification ...... 2 1.1.2.1. Definition ...... 2 1.1.2.2. Classification ...... 3 1.1.2.2.1. Type 1 Diabetes Mellitus (T1DM) ...... 3 1.1.2.2.2. Type 2 Diabetes Mellitus (T2DM) ...... 4

XI Table of Contents

1.2. Glucose Homeostasis ...... 6 1.2.1. Endogenous Glucose Production ...... 6 1.2.2. Glucose Utilisation ...... 8 1.2.3. Pancreatic Insulin Secretion ...... 10 1.2.3.1. Islets of Langerhans ...... 10 1.2.3.2. Insulin Biosynthesis and Granule Maturation ...... 13 1.2.3.3. Glucose-Stimulated Insulin Secretion (GSIS) ...... 16 1.2.3.4. Non-Glucose Stimulated Insulin Secretion ...... 18 1.2.3.4.1. Amino Acids ...... 18 1.2.3.4.2. Fatty Acids ...... 19 1.2.3.4.3. Incretin Hormones ...... 20 1.2.4. Insulin Action ...... 20 1.2.5. Glucose Counter-Regulation ...... 21 1.2.5.1. Glucagon ...... 21 1.2.5.2. Sympathoadrenal Response ...... 21 1.2.5.3. Cortisol and Growth Hormone ...... 22

1.3. Pathogenesis of Diabetes ...... 22 1.3.1. Insulin Resistance ...... 22 1.3.2. Pancreatic -Cell Dysfunction ...... 24 1.3.2.1. Impaired Pancreatic Insulin Secretory Function ...... 24 1.3.2.2. Reduced -Cell Mass ...... 26

1.4. Genetic Studies of Type 2 diabetes ...... 28 1.4.1. Genetic landscape of T2D ...... 28 1.4.2. Approaches to Identifying T2D Susceptibility Genes ...... 30 1.4.2.1. Linkage Analysis ...... 30 1.4.2.2. Candidate Gene Approach ...... 31 1.4.2.3. Genome-Wide Association Study (GWAS) ...... 33 1.4.2.4. Generation of Congenic Strains ...... 34 1.4.2.5. Gene Targeting Approach ...... 35

XII Table of Contents

1.4.3. Murine Models of Diabetes ...... 36 1.4.3.1. Spontaneous-induced Animal Models ...... 36 1.4.3.2. Polygenic Models of Diabetes Susceptibility ...... 37 1.4.3.2.1. C57BL/6 Mouse ...... 37 1.4.3.2.2. DBA/2 Mouse ...... 39 1.4.3.2.3. 129 Mouse ...... 39 1.4.3.2.4. A/J Mouse ...... 40 1.4.3.2.5. CAST/Ei Mouse ...... 41 1.4.3.2.6. New Zealand Obese (NZO) Mouse ...... 41 1.4.3.2.7. Nonobese diabetic (NOD) Mouse ...... 43 1.4.3.3. Backcross and intercross ...... 43 1.4.3.4. Recombinant Inbred Strains ...... 44 1.4.4. Genetic Reference Population (GRP) designed for Complex Genetic Studies 45 1.4.4.1. Classical Inbred Strain Association ...... 45 1.4.4.2. Hybrid Mouse Diversity Panel (HMDP) ...... 45 1.4.4.3. Heterogeneous Stock ...... 46 1.4.5. The Collaborative Cross Mouse Population ...... 47

1.5. Summary of the literature ...... 49

1.6. Aims of Thesis ...... 52 1.6.1. Specific aims ...... 52

2. CHAPTER 2 MATERIALS AND METHODS ...... 54

2.1. Materials ...... 54 2.1.1. Chemicals and Reagents ...... 54 2.1.2. Pancreatic -Cell Line ...... 56 2.1.2.1. Mouse Insulinoma MIN6 Cells ...... 56 2.1.3. Experimental Animal Models ...... 57 2.1.3.1. Source and Maintenance ...... 57 2.1.3.1.1. The Collaborative Cross Mice (The CC mice) ...... 57

XIII Table of Contents

2.1.3.1.2. Control Inbred Mouse Strains ...... 57 2.1.3.2. Diet ...... 57 2.1.3.2.1. Standard Chow Diet ...... 57 2.1.3.2.2. High Fat Diet ...... 58 2.1.4. EQUIPMENT ...... 58

2.2. Methods ...... 59 2.2.1. Molecular Biology Techniques ...... 59 2.2.1.1. Nucleic Acid Extraction ...... 59 2.2.1.1.1. RNA Extraction from MIN6 Cells ...... 59 2.2.1.1.2. RNA Extraction from Mouse Pancreatic Islets ...... 60 2.2.1.2. DNase Treatment ...... 60 2.2.1.3. Reverse Transcription- cDNA Synthesis ...... 61 2.2.1.3.1. Complementary DNA (cDNA) Preparation from Isolated Human Islets 61 2.2.1.4. Quantitative Real-Time Polymerase Chain Reaction (RT-q-PCR) ...... 62 2.2.1.5. Results Analysis of Real-Time q-PCR ...... 62 2.2.1.6. Assay ...... 63 2.2.2. Cell Biology ...... 67 2.2.2.1. Gene Silencing in MIN6 Cells ...... 67 2.2.2.1.1. Lipofectamine-mediated siRNA Transfection ...... 67 2.2.2.1.2. Lentiviral Transduction of Short Hairpin RNA (shRNA) ...... 69 Optimal Cell Density for Lentivirus Transduction ...... 69 Puromycin Titration (Kill Curve) ...... 69 Hexadimethrine Bromide Dose Determination ...... 71 Lentiviral Transduction with Short Hairpin RNA (shRNA) ...... 73 2.2.2.2. Isolation of Mouse Pancreatic Islets ...... 74 2.2.2.3. Isolation of Human Pancreatic Islets ...... 75 2.2.2.4. Insulin Secretion Assay ...... 75 2.2.3. Physiological Characterisations ...... 76 2.2.3.1. Random Blood Glucose Levels and Body Weights ...... 76

XIV Table of Contents

2.2.3.2. Insulin Tolerance Test (ITT) ...... 76 2.2.3.3. Glucose Tolerance Test (GTT) ...... 77 2.2.3.3.1. Oral Glucose Tolerance Test (OGTT)...... 77 2.2.3.3.2. Intravenous Glucose Tolerance Test (IVGTT) ...... 77 2.2.3.3.3. Intravenous Glucose plus Arginine Tolerance Test ...... 78 2.2.3.4. Pancreatic Insulin Content ...... 78 2.2.3.5. Pancreas Histology ...... 78 2.2.3.6. Fat Pad, Pancreas and Tissue Collection ...... 79 2.2.3.7. Plasma Glucose Determination ...... 79 2.2.3.8. Insulin Concentration Determination ...... 79 2.2.4. Genome-Wide Association Analyses of the Collaborative Cross ...... 79 2.2.4.1. Genotyping and Haplotype Reconstruction ...... 79 2.2.4.2. SNP-wise Association Analyses ...... 80 2.2.4.3. Quantitative Trait Loci Analysis ...... 80 2.2.5. Statistical Analysis ...... 81

3. CHAPTER 3 GENOME-WIDE ASSOCIATION STUDY FOR HYPERGLYCAEMIA USING THE COLLABORATIVE CROSS MOUSE RESOURCE ...... 83

3.1. Introduction ...... 83

3.2. Aim ...... 85

3.3. Methods ...... 85

3.4. Results ...... 86 3.4.1. Metabolic Diversity among the CC Mouse Population ...... 86 3.4.2. Genome-wide Association Mapping for Hyperglycaemia ...... 93 3.4.3. Identification of hyperglycaemia susceptibility genes at the implicated loci . 100

3.5. Discussion ...... 102

XV Table of Contents

4. CHAPTER 4 PHYSIOLOGICAL CHARACTERISATION OF HYPERGLYCAEMIA SUSCEPTIBLE STRAINS OF THE COLLABORATIVE CROSS ...... 108

4.1. Introduction ...... 108

4.2. Aim ...... 111

4.3. Methods ...... 111 4.3.1. Animals ...... 111 4.3.2. Glucose Tolerance Tests ...... 111 4.3.3. High-Fat Diet Study ...... 112

4.4. Results ...... 113 4.4.1. Metabolic characterisation in mice with genetic predisposition of hyperglycaemia ...... 113 4.4.2. The effects of genetic susceptibility of hyperglycaemia on the metabolic response to high-fat diet feeding ...... 126

4.5. Discussion ...... 143

5. CHAPTER 5 IDENTIFICATION OF E2F8 AND DLG2 AS NOVEL DIABETES SUSCEPTIBILITY GENES CAUSING IMPAIRED INSULIN SECRETION ...... 152

5.1. Introduction ...... 152

5.2. Aim ...... 153

5.3. Methods ...... 153

5.4. Results ...... 154 5.4.1. Identification of hyperglycaemia susceptibility genes in primary islets ...... 154 5.4.2. The effect of E2F8 or Dlg2 knockdowns on pancreatic insulin secretion ...... 161

XVI Table of Contents

5.5. Discussion ...... 169

6. CHAPTER 6 SUMMARY, CONCLUSIONS AND FUTURE DIRECTIONS 176

6.1. Summary and Conclusions...... 176

6.2. Future Directions ...... 181

BIBLIOGRAPHY ...... 187

APPENDICES ...... 225

6.3. Appendix I: Standard Laboratory Chow Diet ...... 225

6.4. Appendix II: Composition of High Fat Diet ...... 226

6.5. Appendix III: The Collaborative Cross Strain and Number Used in Phenotypic Screening ...... 228

XVII List of Figures

LIST OF FIGURES

Figure 1.1: Progression of Type 2 Diabetes...... 5 Figure 1.2: Cellular composition and distribution in human and mouse islets of Langerhans...... 12 Figure 1.3: Structure of human proinsulin...... 16 Figure 1.4: Glucose-stimulated insulin secretion in pancreatic -cells...... 18 Figure 2.1 Transfection efficiency of siRNA in MIN6 cells...... 68 Figure 2.2 Determination of an optimal MIN6 cell density for lentivirus transduction. .. 69 Figure 2.3A Dose response of puromycin in MIN6 cells...... 70 Figure 2.3B Dose response of puromycin in MIN6 cells...... 71 Figure 2.4 Cytotoxic effect of hexadimethrine bromide in MIN6 cells...... 72 Figure 2.5 Schematic diagram depicting the features of TRC2 Lentiviral plasmid vector TRC2-pLKO-puro...... 74 Figure 3.5 SNP-wise GWAS for blood glucose levels in males...... 94 Figure 3.6 QTL mapping for blood glucose levels in males...... 95 Figure 3.7 Genome-wide association and QTL mapping for blood glucose in females. .. 96 Figure 3.8 Founder haplotype identity of all CC strains at the E2F8 locus...... 97 Figure 3.10 Schematic illustrating the genomic position and configuration of the mouse E2F8 locus on chromosome 7...... 100 Figure 3.11 Schematic illustrating the genomic position and configuration of the mouse Dlg2 locus on chromosome 7...... 101 Schematic diagram 4.1 Timeline and experimental procedure of high-fat diet study. ... 112 Schematic diagram 4.2 Haplotype structure on chromosome 7:51-102 Mbp in the PIPING and PUB mice...... 113 Figure 4.1 Blood glucose and plasma insulin levels in PIPING, PUB and C57BL/6 mice at 10-12 weeks of age under various feeding states...... 114 Figure 4.2 Body weights and fat pad weights of C57BL/6, PIPING and PUB mice at 10- 12 weeks of age...... 115

XVIII List of Figures

Figure 4.3 Plasma glucose and insulin from OGTT in C57BL/6, PIPING and PUB mice at 10-12 weeks of age...... 117 Figure 4.5 Plasma glucose and insulin from IVGTT in C57BL/6, PIPING and PUB mice at 10-12 weeks of age...... 119 Figure 4.6 Total and incremental insulin secretion in C57BL/6, PIPING and PUB mice during an IVGTT...... 120 Figure 4.7 Plasma insulin secretory response following an IVGTT plus arginine in C57BL/6, PIPING and PUB mice at 10-12 weeks of age...... 121 Figure 4.8 Pancreatic islet number, size and proportion in C57BL/6, PIPING and PUB mice at 20 weeks of age...... 123 Figure 4.8 Pancreatic islet number, size and proportion of C57BL/6, PIPING and PUB mice at 20 weeks of age...... 124 Figure 4.9 Pancreatic -cell mass and insulin content of C57BL/6, PIPING and PUB mice...... 125 Figure 4.10 Change of body weight and fat pad weight of C57BL/6, PIPING and PUB mice fed chow or high-fat diet...... 127 Figure 4.10 Fat pad weights of C57BL/6, PIPING and PUB mice fed chow or high-fat diet...... 128 Figure 4.12 OGTT in conscious C57BL/6, PIPING and PUB mice fed chow or high-fat diet for 8 weeks...... 133 Figure 4.13 OGTTs in 24 weeks old C57BL/6, PIPING and PUB mice fed chow or high- fat diet for 18 weeks...... 134 Figure 4.14 Glucose-stimulated insulin secretion of C57BL/6, PIPING and PUB mice fed chow or high-fat diet for 18 weeks...... 135 Figure 4.15 IVGTTs in 24 weeks old C57BL/6, PIPING and PUB mice fed chow or high- fat diet for 18 weeks...... 136 Figure 4.16 Pancreas weight of C57BL/6, PIPING and PUB mice fed chow or high-fat diet for 18 weeks...... 138 Figure 4.17 Pancreas islets number, size and proportion of C57BL/6, PIPING and PUB mice fed chow or high-fat diet for 18 weeks...... 139

XIX List of Figures

Figure 4.18 Pancreatic -cell mass and insulin content of C57BL/6, PIPING and PUB mice fed chow or high-fat for 18 week...... 141 Figure 4.19 Pancreatic insulin staining of C57BL/6, PIPING and PUB mice fed chow or high-fat diet for 18 weeks...... 142 Figure 4.20 The NZO-derived Dlg2 allele in C57BL/6 mice resulted in impaired insulin secretion...... 145 Figure 5.1 Real-Time PCR analysis of candidate gene expressions in isolated mouse islets from C57BL/6 and NZO mice...... 157 Figure 5.2 Gene expression in primary human islets from diabetic and non-diabetic subjects...... 158 Figure 5.3 Correlation between islet E2F8 gene expression and BMI in non-diabetic and diabetic subjects...... 159 Figure 5.4 Knockdown efficiency of lipofectamine-mediated siRNA transfection targeting E2F8 in MIN6 cells...... 161 Figure 5.5 Glucose-stimulated insulin secretion in MIN6 cells transfected with scramble siRNA or E2F8 siRNA...... 163 Figure 5.6 Insulin secretion in response to non-glucose secretagogues in MIN6 cells transfected with scramble siRNA or E2F8 siRNA...... 164 Figure 5.7 Knockdown efficiency of lentivirus-mediated shRNA targeting Dlg2 in MIN6 cells...... 165 Figure 5.8 Glucose-stimulated insulin secretion in Dlg2 knockdown MIN6 cells ...... 166 Figure 5.9 Insulin secretion in response to non-glucose secretagogues in Dlg2 knockdown MIN6 cells...... 168 Figure 5.10 Putative roles of E2F8 and Dlg2 in glucose-stimulated insulin secretion in the pancreatic -cells...... 174

XX List of Tables

LIST OF TABLES

Table 2.1 List of TaqMan Gene Expression Assay kits for gene expression in primary islets from mouse and human...... 64 Table 3.1: Blood glucose levels, insulin sensitivity and body weights in male and female CC mice...... 87 Table 3.2 Genome-wide significant SNPs and QTL associated with blood glucose concentration on chromosome 7 ...... 99 Table 4.1 Initial body weight, end body weight, average intake of food and energy, and fluid consumption in C57BL/6, PIPING and PUB fed chow or high-fat diet...... 126 Table 4.2 Blood glucose and plasma insulin concentrations of mice fed chow or high-fat under various fasting conditions at different stage of diet study...... 129 Table 4.3 Distribution of pancreatic islet size (%) of mice fed chow or high-fat diet for 18 weeks...... 140 Table 5.1. Basic characteristics for donors of pancreatic islets. Identity number, body mass index (BMI), age and gender of islets donors in the non-diabetic group and diabetic group...... 160

XXI Abbreviations

ABBREVIATIONS

Radioactive Iodine isotope 125I

A260nm/A280nm Absorbance at 260nm/280nm

ADP Adenosine diphosphate

ATP Adenosine triphosphate

-ME Beta-mercaptoethanol bp Base pairs

BSA Bovine serum albumin

Ca2+ Calcium cAMP Cyclic adenosine monophosphate

CC mice Collaborative Cross mice cDNA Complementary DNA

CPE Carboxypeptidase E

DAG Diacylglycerol dATP Deoxyadenosine triphosphate dCTP Deoxycytidine triphosphate dGTP Deoxyguanosine triphosphate

DM Diabetes mellitus

XXII Abbreviations

DMEM Dulbecco's Modified Eagle's Medium

DMSO Dimethyl Sulfoxide

DNA Deoxyribonucleic acid

DTT DL-dithiothreitol dTTP Deoxythymidine triphosphate

EDTA Ethylenediaminetetraacetic Acid

EGP Endogenous glucose production

ER Endoplasmic reticulum

FBS Fetal Bovine Serum

FFAs Free fatty acids

FPG Fasting plasma glucose

GDM Gestational Diabetes Mellitus

GIP Gastric inhibitory polypeptide

GLP-1 Glucagon-like peptide-1

GLUT Glucose transporter

GPR40 G-protein coupled receptor-40

GRP Genetic reference population

GSIS Glucose-stimulated insulin secretion

GWAS Genome-wide association study

XXIII Abbreviations

HbA1c Glycosylated haemoglobin

HBSS Hank's Balanced Salt Solution

HGP Hepatic glucose production

HIF Hypoxia-inducible transcription factor i.p. Intraperitoneal

IGT Impaired glucose tolerance

INS2 Insulin II

ITT Insulin tolerance test

IVGRTT Intravenous glucose and arginine tolerance test

IVGTT Intravenous glucose tolerance test

+ K ATP channel ATP-sensitive potassium channel kb Kilo base pairs

Kir6.2 Inwardly rectifier potassium channel subunit

KRBB Krebs-Ringer Bicarbonate Buffer

LOD Logarithm of odds

MODY Maturity-Onset Diabetes of the Young

MUGA Mouse Universal Genotyping Array mRNA Messenger RNA

NADPH Nicotinamide adenine dinucleotide hydrogenase

XXIV Abbreviations

OGTT Oral glucose tolerance test

PBS Phosphate Buffered Saline

PCR Polymerase Chain Reaction

PDK1 Phosphoinositide-dependent protein kinase-1

PI-3K Phosphatidylinositol 3-kinase

PKC Protein kinase C

PP Cells Pancreatic polypeptide cells

PPIA Peptidylprolyl Isomerase A

QTL Quantitative trait loci

RIA Radioimmunoassay

RPMI Royal Park Memorial Institute 1640 medium

PPAR Peroxisome proliferator-activated receptor gamma

RRP Readily releasable pool

RT-q-PCR Quantitative Real-Time polymerase chain reaction

SDS Sodium Dodecyl Sulphate

SEM Standard error of the mean

SNAP-25 Synaptosomal-associated protein-25

SNARE Soluble NSF attachment receptor

XXV Abbreviations

SNP Single nucleotide polymorphism

SRP Signal recognition particle

T1DM Type 1 Diabetes Mellitus

T2DM Type 2 Diabetes Mellitus

TCA Tricarboxylic acid

TCF7L2 Transcription factor 7-like 2 gene

TEMED Tetramethylethylenediamine

TGN Trans-Golgi network

XXVI

CHAPTER ONE

LITERATURE REVIEW

Chapter One Literature Review

Chapter 1 Literature Review

1.1. Diabetes Mellitus 1.1.1. Global Prevalence of Diabetes Mellitus

According to World Health Organization (WHO), the global prevalence of diabetes has almost doubled within a thirty-year period from 4.7% among adults age above 18 years in 1980 to 8.8% in 2015 (Mathers & Loncar, 2006). The International Diabetes Federation (IDF) predicts that this figure is projected to rise to 10.4% (equivalent to 642 million cases) by 2040 (International Diabetes Federation, 2015). In addition, prolonged hyperglycaemia arising from diabetes can determine the risk of the occurrence of a number of complications as a result of glucose toxicity, including retinopathy with potential blindness, neuropathy that may lead to renal failure, and at substantial risk of macrovascular and microvascular diseases (Krolewski, Warram, & Freire, 1996). Given the surge in the prevalence of these severe clinical problems, enormous medical and socioeconomic burdens have been placed on healthcare systems globally (Zimmet, Alberti, & Shaw, 2001).

1.1.2. Definition and Classification

1.1.2.1. Definition

Diabetes mellitus (DM) was first documented as an affliction dating back to around 1500 B.C.E. by the ancient Egyptians. It was recognized with frequent urination, excessive drinking, weight loss as well as sweet-taste urine in affected individuals. Accordingly, this ailment was officially named for its symptoms as Diabetes Mellitus, referring to ‘a siphon’ and ‘sweetness in urine’ (Lakhtakia, 2013).

DM is a metabolic disorder characterised by persistent hyperglycaemia due to the presence of excessive circulating glucose that cannot be efficiently cleared by the actions of insulin (American Diabetes Association, 2014). As a result of this disturbance, sugar-laden urine thus occurs as the body tends to dispose excess glucose through frequent urination. Current practicable diagnostic criteria of DM and pathogenic hyperglycaemia were established on

2 Chapter One Literature Review the basis of World Health Organization (WHO) recommendations, as the raised measurements of either fasting plasma glucose (FPG) ≥ 7.0 mmol/L (126 mg/dL) post an at least 8-h fast or 2-h plasma glucose ≥ 11.1 mmol/L (200 mg/dL) during an oral glucose tolerance test (OGTT, 75-g oral glucose) (Alberti & Zimmet, 1998). In addition, the level of glycated hemoglobin (HbA1c) is widely used as an indicator of average glycaemia over a 2- to 3- month period of time, the diagnostic cut-off point of 6.5% is deemed the long- standing hyperglycaemia and associated with an inflection point for diabetes-related complications (Gillett, 2009).

1.1.2.2. Classification

Diabetes mellitus is characterised by chronic hyperglycaemia secondary to insufficient insulin secretion and/or impaired insulin action. According to its pathogenic processes, diabetes can be categorised into four major types: Type 1 Diabetes Mellitus (T1DM), Type 2 Diabetes Mellitus (T2DM), Gestational Diabetes Mellitus (GDM) and Maturity-Onset Diabetes of the Young (MODY) (American Diabetes Association, 2012). However, the majority of diabetes cases fall into two categories as T1DM and T2DM, the main features are described below.

1.1.2.2.1. Type 1 Diabetes Mellitus (T1DM) T1DM also known as juvenile-onset diabetes or insulin-dependent diabetes accounting for about 10 % of all diabetes cases (Gillespie, 2006). The presence of autoantibodies in pancreatic islets is a hallmark of T1DM which arise from a cellular-mediated autoimmune destruction of the -cells by T cells and macrophages infiltration, leading to absolute insulin deficiency (Foulis, McGill, & Farquharson, 1991). Multiple antibodies have been identified as markers of immune destruction of the -cells in T1DM, these are present in 85-90% of individuals upon hyperglycaemia initiation (Pihoker, Gilliam, Hampe, & Lernmark, 2005). The pathogenesis of T1DM is complicated involving the effects of both genetic and environmental factors; importantly, current findings suggest that a substantial portion of T1DM predisposition is genetically determined (Redondo et al., 2001).

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1.1.2.2.2. Type 2 Diabetes Mellitus (T2DM) T2DM is the most pervasive form of diabetes being responsible for approximately 90% of the whole diabetes population (Zimmet et al., 2001). This type of diabetes is typically characterised by persistent hyperglycaemia, that is often accompanied by a temporarily hyperinsulineamia, as a result of an enhanced pancreatic insulin secretion to meet the increasing insulin demand due to peripheral insulin resistance (Reaven, 1997), which ultimately progresses to -cell exhaustion and death (Meier & Bonadonna, 2013). As shown in Figure 1.1, the natural progression of T2DM initiates approximately 10 years before diagnosis. The early pathogenesis of T2DM is prediabetes manifesting as normal glucose tolerance, insulin resistance, and compensatory hyperinsulinemia, with progression to impaired glucose tolerance and/or impaired fasting glucose. Prediabetes progresses to full-blown diabetes once -cell function begins to decline, and persistent hyperglycemia contributes to the macrovascular and microvascular complications that are the major source of morbidity and mortality.

The pathogenesis of T2DM has been demonstrated to be multifactorial, complex and heterogeneous which varies greatly between different ethnic groups and between individuals. Although the specific aetiology is not yet understood, cumulative evidence suggests T2DM has a strong genetic basis with an estimated 30-70% of disease risk from genetics (Doria, Patti, & Kahn, 2008; Lyssenko et al., 2008), whereas its course of development is also substantially influenced by other factors, including age, overnutrition, limited physical activity and sedentary lifestyles (D. O. Smith & LeRoith, 2004).

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Figure 1.1: Progression of Type 2 Diabetes.

In the early pathogenesis of type 2 diabetes, insulin resistance develops as an early insult in concert with the compensatory increased in insulin secretion from pancreatic -cells to maintain normal glycaemia. This prediabetic stage is characterised by impaired fasting glucose and/or glucose intolerance in response to glucose challenges which may begin as early as 10 years before diagnosis. At the diagnosis stage, insulin resistance remains, however, -cell function begins to decline therefore hyperglycaemia and its associated complications occur. Adapt from DeFronzo RA. Med Clin N Am 2004; 88:787–835 (DeFronzo, 2004).

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1.2. Glucose Homeostasis In the normal physiological condition, plasma glucose concentration is tightly regulated and maintained within a narrow range between 3.9-6.7 mmol/L (70 and 120 mg/dl) (Rizza, Gerich, et al., 1980). Although the levels of circulating glucose fluctuate throughout the day, a postprandial concentration usually not exceeding 165 mg/dl and a minimum of ~55 mg/dl following a prolonged fast (60 hours) (Consoli, Kennedy, Miles, & Gerich, 1987) or exercise (Wahren, Felig, & Hagenfeldt, 1978). This narrow range defining normoglycaemia is essential for the continuing glucose supply for the brain and preventing cytotoxic effects from excessive glucose. The brain consumes approximately 120 grams of glucose daily which can account for 60-70% of the whole body glucose metabolism (Grill, 1990; S. C. Huang et al., 1980). Furthermore, the energy requirement of the brain is met exclusively by glucose under physiological conditions; however, the brain itself cannot synthesize nor store sufficient glucose for the high demand. Therefore, a constant supply of glucose from the circulation is of importance for the brain. Glucose transport into the brain is significantly attenuated at plasma glucose concentrations of 1.11 mmol/L below the normal levels (Siesjo, 1988), whereas severe degeneration of cerebral function begins when glucose levels fall below 55 mg/dl (Mitrakou et al., 1991), more severe and prolonged hypoglycaemia can further result in permanent damage and death. Conversely, even a mild elevation in plasma glucose over time may lead to glucotoxicity (Jarrett & Keen, 1976), which is closely related to an increased risk of a wide array of diabetes-associated complications, such as macrovascular and microvascular diseases (Tchobroutsky, 1978; Tominaga et al., 1999). Accordingly, glucose homeostasis is critical to normal physiological functioning and is determined by the dynamic balance of intricate processes predominantly endogenous glucose production and peripheral glucose utilization. These processes are modulated by key hormonal factors including insulin and counter-regulatory hormones on a moment to moment basis.

1.2.1. Endogenous Glucose Production

In the postabsorptive state (after 14-16 hours overnight fast), endogenous glucose production (EGP) becomes prominent for systemic glucose homeostasis to meet the

6 Chapter One Literature Review demands for fuel obligate glucose-consuming cell types, such as brain, neural tissues and red blood cells (Rizza, 2010). Liver and kidney are predominant glucose producing organs in which hepatic glucose production (HGP) contributes to the majority (approximately 80- 90%) of EGP and the remaining is derived from the kidney accounting for 5-20%, this ratio varies with the varying metabolic states (Ekberg et al., 1999). Liver and kidney synthesise glucose due to the presence of glucose-6-phosphatase which mediates the last step in gluconeogenesis/glycogenolysis, converting glucose-6-phosphate to glucose which can then be exported outside the cell (van Schaftingen & Gerin, 2002). EGP is sourced from two mechanisms: gluconeogenesis (glucose synthesis) and glycogenolysis (glycogen breakdown). Under a moderate fast (14 h), half of glucose release from the liver is derived from gluconeogenesis and half from glycogenolysis (Rothman, Magnusson, Katz, Shulman, & Shulman, 1991), while this proportion alters with the prolonged fast as glycogen becomes depleted. As determined by 2H2O incorporate approaches, after 22 h fast the contribution of gluconeogenesis to HGP shifted to 70% and this figure raises up to 90% following 42 h of fasting (Landau et al., 1996). However, the renal glucose release is exclusively derived from gluconeogenesis since there is little amount of glycogen stores in the kidney (Stumvoll, Meyer, Mitrakou, Nadkarni, & Gerich, 1997). In contrast to the liver, the kidney virtually produces comparable amounts of glucose (2.5-3.0 mmol/kg/min) from gluconeogenesis and the relative contribution to overall glucose production owing to renal gluconeogenesis increases even further with prolonged fasting (Cersosimo, Garlick, & Ferretti, 2000). It is evident that glucose production by the liver and kidney are inter-related. This relationship is well reflected on a number of physiologic and pathologic conditions. For example, glucose production by the human kidney increases by 4-fold following prolonged fasting (60 h), while HGP is reduced by 45% (Ekberg et al., 1999). In addition, renal glucose release was found to increase in postprandial states while HGP is remarkably suppressed (~80%), this mechanism facilitates more efficient hepatic glycogen replenishment (Meyer, Dostou, Welle, & Gerich, 2002). Furthermore, a compensatory increase in renal glucose production for the reduced hepatic glucose release is well established in certain pathologic processes, such as hepatic diseases (Gerich, 2002; Joseph et al., 2000), acidosis (Schoolwerth, Smith, & Culpepper, 1988) and diabetes (Woerle,

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Meyer, Popa, Cryer, & Gerich, 2003). This hepatorenal glucose reciprocity ensures an optimal glucose homeostasis by coordinating functions in dual organs.

1.2.2. Glucose Utilisation

The utilisation of glucose alters with the varying metabolic conditions. In the postabsorptive state, glucose released by the liver and kidney is either completely oxidized to generate energy or released back to the circulation as gluconeogenic precursors, such as lactate, alanine, and glutamine for reincorporation into glucose (Perriello et al., 1995). In the basal condition, about half of glucose is oxidized to meet the demand in the brain, approximately 20% of glucose utilization occurs in the splanchnic tissues (liver, gastrointestinal tissues and kidney), another 20% glucose metabolism takes place primarily in the muscle, 5-10% in the blood cells and a small fraction (2-4%) in the adipose tissue (DeFronzo et al., 1981; Gerich, 1993). Insulin is a potent stimulator mediating glucose uptake; however, these tissues (brain, blood cells, renal medulla, and splanchnic tissues) use glucose in an acutely insulin-independent fashion that allows glucose uptake to occur in the postabsorptive state when insulin concentrations are low (< 10 U/ml).

In the postprandial state, the body exerts activates a series of metabolic switches in response to the calorie load, thus the augmentation of circulating glucose level triggers immediate glucose storage and enhanced glycolysis (Ferrannini et al., 1985; McMahon, Marsh, & Rizza, 1989). Approximately 30% of post-digestive glucose is extracted by splanchnic tissues, chiefly by the liver where glucose goes straight to form glycogen (direct pathway) (Petersen et al., 2001), while the remaining glucose is released into the systemic circulation. Of the ingested glucose in the circulation, about 40% is taken up by skeletal muscle to be stored as glycogen (R. Taylor, Price, Katz, Shulman, & Shulman, 1993), and 20% undergoes gluconeogenesis to be incorporated into hepatic glycogen via the indirect pathway (Petersen et al., 2001; R. Taylor et al., 1996). The brain accounts for ~20% of the total circulating glucose uptake postprandially, ~10% goes to the kidney (Meyer et al., 2002) and leaves less than 10% to be used by adipose tissues, blood cells and other tissues (Marin et al., 1992). Once glucose enters the cell, the intracellular glucose is immediately phosphorylated via hexokinase (glucokinase in hepatocytes and beta cells) and then either

8 Chapter One Literature Review stored as glycogen or subjected to glycolysis. Glycolysis converts glucose-6-phosphate into pyruvate which is then reduced to lactate or oxidized through the tricarboxylic acid cycle, producing energy in the form of adenosine triphosphate (ATP).

The metabolic difference in cellular glucose utilization in the postabsorptive and postprandial state is tightly regulated to adapt the tissue demands and maintain systemic homeostasis at all time. This regulation is largely determined by the characteristics and surface density of glucose transporters, GLUTs, and the given plasma glucose concentration. GLUTs are widely expressed and catalyse facilitative diffusion of glucose down its concentration gradient (Mueckler et al., 1985). GLUT1 and GLUT3 are expressed in most tissues throughout the body, but predominantly function in the brain and erythrocytes (Mueckler & Thorens, 2013). High glucose affinity of GLUT1 and GLUT3 ensures a constant and stable glucose uptake by the vital tissues in a state of low insulin concentrations. GLUT2 is the major glucose transporter in hepatocytes, pancreatic -cells and intestinal absorptive cells (Fukumoto et al., 1988; Thorens, Cheng, Brown, & Lodish, 1990). GLUT2 has a low glucose affinity (Km ~17mM) which varies the rate of glucose transport thus acts as a sensor of glucose concentrations under fed and fasting states (Marty, Dallaporta, & Thorens, 2007). The expression of GLUT2 is known to be regulated by glucose and lipids in -cells, and glucolipotoxicity may trigger GLUT2 internalisation (reduced cell surface expression) (Reimer & Ahren, 2002) and may therefore result in impaired glucose-stimulated insulin secretion. GLUT4 is the primary insulin-responsive transporter prominently expressed in adipocytes, skeletal muscle and cardiomyocytes that translocate from intracellular compartments to plasma membrane in response to insulin (S. Huang & Czech, 2007; D. E. James, Brown, Navarro, & Pilch, 1988). Under postabsorptive conditions, GLUT4 is sequestered in the intracellular fraction and its expression has been demonstrated to decline due to low ambient insulin levels (Muretta, Romenskaia, & Mastick, 2008; Yeh, Verhey, & Birnbaum, 1995). Conversely, following a meal ingestion the increased plasma insulin level stimulates intracellular GLUT4 translocation to the plasma membrane, leading to enhanced glucose uptake and preventing postprandial hyperglycaemia (B. B. Kahn, 1996). Genetic ablation of GLUT4 has been shown to markedly attenuate insulin-stimulated glucose uptake by 90% as demonstrated by

9 Chapter One Literature Review hyperinsulineamic euglycaemic clamps in muscle-specific GLUT4 knockout mice (Kaczmarczyk et al., 2003; J. K. Kim et al., 2001; Zisman et al., 2000). In addition, Kim et al. demonstrated that the reduced glucose transport in the muscle lack of Glut4 lead to an increase in muscle glycogen content and enhanced basal glycogen synthase activity by 34% due to increased hexokinase II (Y. B. Kim et al., 2005). The resultant toxic effects from excessive glucose can lead to secondary defects in hepatic and adipose insulin action (J. K. Kim et al., 2001; Kotani, Peroni, Minokoshi, Boss, & Kahn, 2004).

1.2.3. Pancreatic Insulin Secretion

1.2.3.1. Islets of Langerhans

Islets of Langerhans are specialized functional units responsible for the endocrine function of the pancreas. Pancreatic islets comprise of a mixed population of cells that are clusters and scattered throughout the pancreas. To rapidly sense the nutritional levels, islets are highly vascularized receiving a significant fraction (~5-10%) of total pancreatic blood flow, which allows rapid transportation of islet hormones into the circulation. The adult pancreas contains approximately one million islets which accounts for 1-2% of the volume of the pancreas (Saisho et al., 2013) and play a vital role in maintaining whole body glucose homeostasis. It is apparent that pancreatic islets vary widely in size and the typical size lies between 100-200 m, regardless of species (Henderson, 1969).

Five major types of cells have been described in mammalian islets, including the insulin- secreting -cells and the glucagon-producing -cells, the somatostatin-releasing -cells, the pancreatic polypeptide-producing PP cells (S, 1991) and, to a lesser extent, the ghrelin- secreting -cells (Prado, Pugh-Bernard, Elghazi, Sosa-Pineda, & Sussel, 2004; Wierup, Yang, McEvilly, Mulder, & Sundler, 2004). The arrangement of different endocrine cell types within an islet appears to be highly regulated and also physiologically significant to ensure appropriate cellular communication (Cabrera et al., 2006; Hamaguchi, Utsunomiya, Takaki, Yoshimatsu, & Sakata, 2003), as endocrine cells communicate by cell-cell interaction, by paracrine signaling, and by hormone-mediated mechanisms. Our understanding regarding the islet architecture was initially established in rodents, and it has

10 Chapter One Literature Review been recognised as a core-mantle architecture with -cells located centrally and being surrounded by non-beta cells (Figure 1.2b) (GE, 1988). In terms of the proportion of cell populations, the insulin-producing -cells and glucagon-secreting -cells are the most abundant cell types which make up 77% and 18% of murine islet cells, respectively (Figure 1.2b) (Cabrera et al., 2006).

A number of studies on adult human islet architecture have raised the discussion about the interspecies differences in islet cell arrangement between mice and humans (Cabrera et al., 2006). In contrast to rodents, human islets appear to be more heterogeneous as illustrated in Figure 1.2a that do not present a clear -cell core and all cell types are rather intermingled (Brissova et al., 2005). Interestingly, this scattered distribution indeed maintains several functional units that act similarly to the core structure in murine islets. From the perspective of cellular composition, human islets contain relatively less -cells, comprising approximately 55% of all cells, and a greater proportion of -cells than that in the murine islets (about 35% versus 18%) (Abdulreda MH, 2013).

In addition, it has been demonstrated that there is considerable -cell heterogeneity in rodents and human islets that is changed in response to various forms of stress and aging (Campisi & d'Adda di Fagagna, 2007). Katsuta et al. characterised the alterations in -cell subpopulations with age in mice from perinatal to 7-month old, showing that younger mice have markedly larger populations of low insulin containing cells (newly formed -cells) and decreased by 4 months, whereas the high insulin containing cells (both mature and senescent -cells) accounted for a very small portion in the young -cell population and increased by 4 months of age (Katsuta et al., 2012). These changes in -cell population are associated with the functional deteriorations in the aged mice which exhibit an increased in basal insulin secretion and impaired glucose-stimulated insulin secretion. Many studies have reported age-related changes in gene expression which then developed as markers of -cell senescence, such as p16Ink4a, a cyclin-dependent kinase inhibitor encoded by the Cdkn2a locus, which is associated with aging and decreased proliferation (Krishnamurthy et al., 2006; Krishnamurthy et al., 2004); Igf1r, its expression is associated with impaired insulin secretion in response to glucose (Aguayo-Mazzucato et al., 2017); Flattop, in the

11 Chapter One Literature Review absent of flattop expression in -cells of adult mice were found to be non-responsive to glucose (Bader et al., 2016; Van Schravendijk, Kiekens, & Pipeleers, 1992). Given the heterogeneous nature of pancreatic -cells, the replicative capacity of -cells decreases with age in both rodents and humans has been demonstrated (Gregg et al., 2012; Kushner, 2013; Scaglia, Cahill, Finegood, & Bonner-Weir, 1997).

Figure 1.2: Cellular composition and distribution in human and mouse islets of Langerhans.

Top: Diagrams depict the distribution of different endocrine cell types and their abundance in islet of Langerhans in human (Left) and mouse (Right). Bottom: Immunofluorescence staining of insulin, glucagon and somatostatin in human and murine islets. Sections labeled for insulin (INS)-producing -cells (red), glucagon (GCG)-secreting -cells (green) and somatostatin (SST)-releasing -cells (blue) (Abdulreda MH, 2013).

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1.2.3.2. Insulin Biosynthesis and Granule Maturation

Insulin, the primary peptide hormone produced by the pancreatic -cells, is initially synthesized as a precursor “preproinsulin” encoded by the gene insulin. Human and most animals have a single copy of the insulin gene, whereas rodents have two non-allelic insulin genes, namely insulin 1 (Ins1) and insulin 2 (Ins2) (P. Lomedico et al., 1979). Ins2 is demonstrated as the murine homologue of human insulin gene, while Ins1 was a functional retroposon deriving from the partially processed Ins2 mRNA (Soares et al., 1985). Nevertheless, the products derived from the insulin genes across the species undergo a highly conserved process to synthesize mature form insulin.

Insulin biogenesis begins with the synthesis of a 110-amino acid precursor, preproinsulin, at the cytosolic side of the endoplasmic reticulum (ER). The nascent preproinsulin peptide contains a 24-residue hydrophobic signal peptide at the N-terminus, which is rapidly recognised and bound by the cytosolic signal recognition particle (SRP) of the ribonucleoprotein (Egea, Stroud, & Walter, 2005). This interaction leads to a pause of preproinsulin translation known as “elongation arrest,” that facilitates the docking of ribosome-preproinsulin complex to a peptide conducting channel, the Sec61 translocon, on the membrane of the ER (P. T. Lomedico, Chan, Steiner, & Saunders, 1977). This enables the segregation of preproinsulin from the cytosol and mediates its co-translational translocation across the ER membrane into the lumen, where the signal sequence of preproinsulin is excised to yield proinsulin (Patzelt et al., 1978). Within the ER lumen, the oxidizing environment and the presence of ER-resident chaperonins favor posttranslational modification and structure organization of newly synthesized (Sevier et al., 2007), wherein the nascent proinsulin undergoes folding to establish its native structure as well as the formation of three disulfide bonds, including the two interchain disulfide bonds between the A and B chain (A7-B7 and A20-B19) and one within the A chain (A6-A11) (Figure 1.3) (X. F. Huang & Arvan, 1995). Subsequent to the proinsulin formation, the properly folded proinsulin leaves the ER and is transported to the Golgi apparatus for further processing and packaging.

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The Golgi apparatus plays a vital role in defining the subset of proteins to be sorted and transported for appropriate secretion through either the constitutive pathway or the regulated secretory pathway (Kelly, 1985). The constitutive secretory pathway present in all cell types serves as an endomembrane trafficking pathway facilitating either molecule transportation from the trans-compartment of Golgi apparatus to plasma membrane or alternatively enters the recycling endosome system (Palade, 1975). In contrast, only a fraction of highly specialized secretory cells, such as islets of Langerhans and neurosecretory cells, develop the regulated secretory pathway by which substances (eg. hormones and neurotransmitters) are concentrated and stored in secretory granules and released on demand.

In pancreatic -cells, insulin secretion relies almost exclusively on the regulated arm of the secretory pathway that efficiently sorts the vast majority (over 99%) of proinsulin to generate dense-core granules for insulin secretion (Rhodes & Halban, 1987). As proinsulin travels to the trans-Golgi network (TGN), they are diverted into secondary compartments as the clathrin-clad immature secretory granules, which is the principal site for proinsulin conversion to mature insulin (Arvan & Castle, 1992; Orci et al., 1985). A series of events toward the granule maturation involves four key steps: (I) Establishment of an acidic milieu in the granule lumen, (II) Proinsulin conversion to generate the bioactive form insulin and C-peptide via proteolysis, (III) Removal of the coat protein clathrin, and (IV) Formation of insulin crystalline structure and condensation. Studies have indicated that the immature granules display mildly acidic conditions that become more acidic during maturation (Orci et al., 1985). This acidification is attributable to the action of the ATP-dependent proton pump on the granule lumen, which has been demonstrated to be essential for granule maturation since the proinsulin converting enzymes have an acidic pH optimum (Davidson, Rhodes, & Hutton, 1988). The proteolytic cleavage of proinsulin is dependent on the action of two endoproteases PC1/3 and PC2 (Davidson et al., 1988) in concert with carboxypeptidase E (CPE) (Fricker, Evans, Esch, & Herbert, 1986). PC1/3 cleave proinsulin primarily at the B-chain junction, while PC2 was found preferentially to act on the A-chain junction of a proinsulin intermediate des-31, 32 over the intact proinsulin (Rhodes, Lincoln, & Shoelson, 1992). The endoproteolytic cleavage liberates C-peptide

14 Chapter One Literature Review from the proinsulin and exposes basic residues at C-terminus which are subsequently trimmed by CPE to produce native insulin (Guest, Rhodes, & Hutton, 1989). Following proinsulin conversion, the concomitant decrease in clathrin coated protein in immature granules is evident (Orci et al., 1987), and the dissociated clathrin as well as other undesirable components continue to be removed and retrieved through the constitutive pathway (L. Feng & Arvan, 2003).

The bioactive insulin consists of two polypeptide chains comprising a 21-residue A-chain and a 30-residue B-chain linked by disulfide bonds. During the granule maturation process, monomeric insulin tends to assemble into hexamers as insulin concentration rises, these hexamers display significantly poorer solubility and therefore aggregate to form crystalline hexameric insulin, resulting in the dense-core structure in the centre of the granules (Greider, Howell, & Lacy, 1969). Thereby, the mature secretory granules can be easily identified by electron microscopy and are characterised as a dense interior surrounded by a clear region with a 300-350nm intracellular membrane delineated compartment (Lange, 1974). The insulin concentration in the mature granules is estimated to be ~40 mM (De Meyts, 2004) which is sustainable for secretion up to days with a half-life of six days (Halban, 1991; Szabat et al., 2016). In addition to insulin, the mature secretory granules contain equal molar quantity of C-peptide and very small amounts (3-5%) of unprocessed proinsulin and its intermediates (Sando, Borg, & Steiner, 1972) as well as islets amyloid polypeptide (amylin) (Nishi, Sanke, Nagamatsu, Bell, & Steiner, 1990). These mature dense-core insulin granules store and populate two different pools proximal to -cell membrane, the readily releasable pool (RRP) and the reserved pool that are responsible for the initial insulin burst and prolonged insulin secretion, respectively (Bratanova-Tochkova et al., 2002).

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Figure 1.3: Structure of human proinsulin.

Proinsulin is a single chain polypeptide that comprises of A-chain (21-amino acids) and B-chain (30-amino acid) and joined with a connecting peptide (C-peptide) which links the C-terminus of the insulin B chain to the N-terminus of the insulin A chain. Depicted is the A chain, B chain and connecting peptide (C-peptide) of insulin (Ize-Ludlow & Sperling, 2005).

1.2.3.3. Glucose-Stimulated Insulin Secretion (GSIS)

Insulin is one of the major intrinsic hormones which is essential for lowering blood glucose concentrations. Insulin secretion is elicited by a wide variety of stimuli, i.e. glucose, amino acids, fatty acids etc.; however, only glucose is able to initiate insulin secretory signaling thus is the primary nutrient secretagogue. Human islets appear to be more sensitive to increments of glucose concentrations than mouse islets. As insulin secretion from human islets initiates at the glucose concentration as low as 3 mM (Henquin, Dufrane, & Nenquin, 2006); in contrast, a higher glucose threshold of 6 mM was shown for mouse islets (Doliba et al., 2012). This difference is due primary to intrinsic islet properties as revealed in diabetic mice transplanted with human islets, in which insulin secretion responded to the normal non-fasting glucose level for humans rather than that of the mice (Ricordi et al.,

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1991). This discordance is thought to be attributable to the difference in fasting blood glucose concentration which is lower in humans (about 5 mM) than in mice (7-10 mM).

Glucose metabolism in the pancreatic -cells is essential for coupling glucose sensing to electrical activity of the -cells and insulin release (Newgard & McGarry, 1995; Prentki, 1996). Glucose-stimulated insulin secretion (GSIS) is characterised by a distinctive biphasic response while dysregulation of this secretory response is demonstrated to be one of the causal factors in the development of type 2 diabetes (Del Prato & Tiengo, 2001). In response to a rise in plasma glucose level, a first phase insulin secretion occurs promptly within 1-2 minutes post glucose load with a spike of release that peaks at 4-5 minutes, followed by a rapid decline to a nadir at about 8 minutes. Subsequently, the second phase begins with a gradually increasing rate of release to a plateau after a further 25-30 minutes (Curry, Bennett, & Grodsky, 1968). The elevated glucose levels trigger glucose uptake via glucose transporter 2 (GLUT2)-mediated facilitated diffusion on the surface of pancreatic -cells. Cytosolic glucose is phosphorylated into glucose-6-phosphate by glucokinase then oxidized through the tricarboxylic acid (TCA) cycle in the mitochondria to generate ATP. The increased concentrations of intracellular ATP results in elevation of ATP/ADP ratio, + this triggers the closure of ATP-sensitive potassium channels (K ATP channel) followed by depolarisation of the plasma membrane, opening of voltage-dependent Ca2+ channels allowing increased influx of calcium [Ca2+], and eventually leading to exocytosis of insulin containing vesicles (Rorsman & Braun, 2013). It has been suggested that secretory granules in pancreatic -cells exist in functionally distinct pools which differ in release competence: a reserve pool (RP) accounting for the vast majority of granules and a readily releasable pool (RRP) accounting for the remaining < 5%. The sequential release of these pools shapes the dynamics of exocytosis. It is hypothesised that the first phase insulin secretion is the result of release of RRP granules and that the second phase of insulin secretion is sustained by the supply of new granules mobilised from the RP (Henquin, 2009; Rorsman & Renstrom, 2003; Straub, Shanmugam, & Sharp, 2004).

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Figure 1.4: Glucose-stimulated insulin secretion in pancreatic -cells. Glucose enters pancreatic -cells through the glucose transporter GLUT2. Metabolism of

+ glucose increases ATP production, closing the K ATP channels (A), depolarising -cell membrane, in turn causes the opening of the voltage-dependent Ca2+ channel (B) and allowing Ca2+ influx. The increased intracellular calcium activates calcium dependent phospholipid protein kinase, in turn leading to exocytosis of insulin granules.

1.2.3.4. Non-Glucose Stimulated Insulin Secretion

1.2.3.4.1. Amino Acids A number of amino acids demonstrate insulinotropic properties that potentiate insulin secretion in a glucose-dependent manner. L-arginine, a cation amino acid, is well established as a potent insulin secretagogue that alters plasma membrane potential by its positive charge, which in turn depolarises the -cell membrane, leading to Ca2+ influx and eventual insulin exocytosis (J. P. Palmer, Benson, Walter, & Ensinck, 1976). In addition, L-arginine potentiated both phases of glucose-stimulated insulin secretion involving glucose-sensitizing effects on potentiation of membrane depolarization and activation of protein kinase A and C (Thams & Capito, 1999). In contrast, glutamine alone does not induce insulin secretion unless in the presence of leucine (Dixon, Nolan, McClenaghan, Flatt, & Newsholme, 2003). Cytosolic glutamine is converted to glutamate by glutaminase, which subsequently enters the TCA cycle in the form of -ketoglutarate, resulting in the elevation of cellular ATP levels and further enhanced insulin secretion (Sener & Malaisse,

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1980). Leucine serves a key role in the activation of the converting enzyme, glutamate dehydrogenase, which converts glutamate to -ketoglutarate. Conversely, in the absence of leucine, glutamine is mobilized to (-aminobutyric acid) GABA and aspartate. Moreover, the indirect insulinotropic effects of free amino acids have also been addressed, i.e. alanine and glutamine, which triggers insulin secretion by augmentation of glucagon secretion from -cells.

1.2.3.4.2. Fatty Acids The influence of free fatty acids (FFAs) on insulin secretion was revealed by the observation that acute elevation of FFAs exposure is concomitant with increased serum insulin levels (Felber & Vannotti, 1964; Pelkonen, Miettinen, Taskinen, & Nikkila, 1968; Yaney & Corkey, 2003). This was also demonstrated by Crespin et al. that FFAs deprivation attenuates glucose-stimulated insulin secretion in pancreatic -cells, which can be restored by infusing long-chain fatty acids (LCFAs), such as oleic, linoleic, and palmitic acids (Crespin, Greenough, & Steinberg, 1969; Stein et al., 1996). The dietary fat lipids can be transformed into triglyceride-rich chylomicrons and cleaved locally in the pancreatic islet capillaries by lipoprotein lipase to 2-monoacylglycerol (2MAG) and LCFAs (Cruz, Kwon, Marshall, McDaniel, & Semenkovich, 2001; Nyren et al., 2012; Winzell, Strom, Holm, & Ahren, 2006), which are found to be typically imported into - cells by the CD36 FA transporter (Khan & Kowluru, 2018). In addition, FFAs have been shownwere found to regulate -cell function by directly acting on -cells through a free fatty acid receptor (FFAR)-1, the activation of G-protein coupled receptors (GPRs), GPR119 and GPR40 (Itoh et al., 2003), stimulating the stimulatory Gs protein, and subsequently, augment insulin secretion via cAMP-dependent pathways (Husted, Trauelsen, Rudenko, Hjorth, & Schwartz, 2017; Steigman, 1976). Moreover, lipid-derived signal molecules, long-chain acyl-Co-A and diacylglycerol (DAG), are major effectors mediating FFAs-induced insulin secretion (Prentki, Joly, El-Assaad, & Roduit, 2002). Long-chain acyl-Co-A acylates key proteins in insulin granule fusion, such as synaptosomal-associated protein-25 (SNAP-25) (Chapman et al., 1996; Gonzalo & Linder, 1998), potentiating exocytosis of insulin granule. On the other hand, DAG was shown to

19 Chapter One Literature Review activate protein kinase C and also bind to synaptic vesicle priming protein Munc-13, leading to increased insulin secretion (Prentki & Matschinsky, 1987; Rhee et al., 2002).

1.2.3.4.3. Incretin Hormones Incretin refers to a group of gut-derived hormones produced by small intestine L-cells in response to nutrient load, including glucagon-like peptide-1 (GLP-1) and gastric inhibitory polypeptide (GIP) (Orskov, 1992). It is evident that GLP-1 potentiated insulin secretion requires the presence of basal glucose level and glucose-mediated signaling in -cells (Ahren, 2009). GLP-1 enhances glucose-induced insulin secretion by the actions mainly + on two aspects: Firstly, GLP-1 reinforced the inhibitory effect of glucose on K ATP channel activity. Second, GLP-1 acts on -cells by directly binding to their specific cell-surface receptor, GLP-1 receptor (GLP-1R) (Mussig, Staiger, Machicao, Haring, & Fritsche, 2010). GLP-1 bound GLP-1R triggers the activation of adenylyl cyclase leading to the elevation of intracellular cAMP and its downstream second messenger pathways (i.e. PKA and EPAC2), thereby augmenting Ca2+-dependent exocytosis of insulin granules (Doyle & Egan, 2007; MacDonald et al., 2002).

1.2.4. Insulin Action

With food ingestion, insulin is secreted by the -cells of the pancreas into the blood circulation, initially reaching the liver via the hepatic portal vein. Insulin mediates its effect through an insulin receptor (IR) dependent signaling cascade comprising a sequence of serine/threonine phosphorylation events. The binding of insulin to the insulin receptor potentiates its intrinsic tyrosine-kinase activity, triggering receptor autophosphorylation, which induces docking and phosphorylation of the insulin receptor substrate that then serves as anchoring sites for the p85/p110 phosphatidylinositol 3-kinase (PI-3K) at the target cell membrane (Boucher, Kleinridders, & Kahn, 2014). This action results in the formation of the phospholipid phosphatidyl 3,4,5-phosphate, leading to the recruitment and interaction between the protein kinase PDK1 and Akt, which in turn activates Akt by phosphorylation at Thr308 and Ser473. This activation of PI3K-AKT signaling is responsible for most of the metabolic actions of insulin, including inhibition of hepatic glucose production (R. J. Perry et al., 2015; Titchenell et al., 2016) while enhancing glucose

20 Chapter One Literature Review uptake into muscle and adipose tissue (Cho, Thorvaldsen, Chu, Feng, & Birnbaum, 2001; Whiteman, Cho, & Birnbaum, 2002) and cell-autonomous activation of hepatic lipogenesis (Leavens, Easton, Shulman, Previs, & Birnbaum, 2009).

1.2.5. Glucose Counter-Regulation

The occurrence of hypoglycaemia arises from an imbalanced glucose flux due to either excessive insulin-mediated glucose disappearance, insufficient endogenous glucose supply, or both (Cryer, 1993). In healthy subjects, progressively declining plasma glucose levels (below a threshold at 3.9 mmol/L) elicit a sequence of counter-regulatory responses counteracting the effects of insulin and enhancing endogenous glucose production into the circulation (N. S. Schwartz, Clutter, Shah, & Cryer, 1987). This regulation requires the action of a number of key hormones as discussed below.

1.2.5.1. Glucagon

Glucagon, a peptide hormone secreted from the pancreatic alpha-cells, functions to raise blood glucose levels through stimulating hepatic glucose production (Gerich et al., 1979). Studies revealed that alpha-cells express glucokinase and ATP-sensitive potassium channels which enable them to directly sense the falling plasma glucose concentrations between meals. Glucagon acts primarily through the G protein-coupled receptors in the livers where it activates adenylyl cyclase followed by an increase in intracellular cAMP levels, resulting in an enhancement of hepatic glucose production due to the increase in amino acid uptake, glycogenolysis and gluconeogenesis (Lecavalier, Bolli, Cryer, & Gerich, 1989; Magnusson, Rothman, Gerard, Katz, & Shulman, 1995; Ramnanan, Edgerton, Kraft, & Cherrington, 2011).

1.2.5.2. Sympathoadrenal Response

Hypoglycaemia elicits catecholamine-mediated (adrenergic) and acetylcholine-mediated (cholinergic) neurotransmission in the nervous system which activates the adrenal medulla and the sympathetic nervous system to secrete catecholamines and acetylcholine, respectively (Gerich, Cryer, & Rizza, 1980; Rizza, Cryer, Haymond, & Gerich, 1980). The

21 Chapter One Literature Review actions of catecholamines are mostly mediated via beta-2 adrenergic receptors. At the liver, catecholamines accelerate glycogenolysis directly through activation of its associated enzymes, and simultaneously augment gluconeogenesis indirectly through increasing availability of gluconeogenic precursors as a result of increased glycogenolysis and lipolysis in skeletal muscles and adipose tissue, respective (De Feo et al., 1991; Lecavalier et al., 1989; Rizza, Cryer, et al., 1980). At the kidney, catecholamines have a more potent effect on renal glucose release than that of hepatic glucose production (Stumvoll et al., 1995).

1.2.5.3. Cortisol and Growth Hormone

In contrast to the immediate actions of glucagon and catecholamines, the effects of cortisol and growth hormone are very much delayed as their actions become evident after several hours of hypoglycaemia. This delayed action can be interpreted as being antagonistic to the action of insulin (Gerich et al., 1980; Rizza, Mandarino, & Gerich, 1982). In response to falling glucose levels, cortisol is secreted from the adrenal cortex to stimulate gluconeogenesis and glycogen synthesis in the liver, and reduce glucose uptake by peripheral tissues (De Feo, Perriello, Torlone, Ventura, Fanelli, et al., 1989). In addition, cortisol can acutely suppress insulin release and further attenuate insulin action over longer periods of time. Likewise, growth hormone is released in the presence of hypoglycaemia. It elevates blood glucose concentration by augmenting glucose production, decreasing glucose utilisation by skeletal muscles and adipose tissue, and accelerating lipolysis (De Feo, Perriello, Torlone, Ventura, Santeusanio, et al., 1989).

1.3. Pathogenesis of Diabetes 1.3.1. Insulin Resistance

Insulin resistance refers to a pathogenic state in which the target tissues have an attenuated response to insulin-mediated signaling transduction, which is one of the major forerunners of T2D driving disease progression (Menke, Casagrande, Geiss, & Cowie, 2015). At the whole body level, an impaired effect of circulating or exogenous insulin on blood glucose lowering leads to defective insulin-stimulated glucose uptake by muscle and adipose tissue,

22 Chapter One Literature Review uncontrolled hepatic glucose output, and dyslipidemia (Reaven, 2005). The cause of insulin resistance in obesity and T2D is known to be heterogeneous and complicated involving the complex interplay of multiple metabolic pathways, which can result from genetic mutations in genes involved in PI3K-AKT pathway (S. I. Taylor et al., 1991), lipotoxicity and inflammation due to obesity (Gregor & Hotamisligil, 2011; Lumeng & Saltiel, 2011), oxidative stress (Furukawa et al., 2004; Urakawa et al., 2003), mitochondrial dysfunction (Abdul-Ghani & DeFronzo, 2008), endoplasmic reticulum (ER) stress (O. K. Kim, Jun, & Lee, 2015) and hyperinsulinemia (Fu, Gilbert, & Liu, 2013; Shanik et al., 2008). At the cellular level, insulin resistance in T2D is manifested in part through defective insulin receptor and post-receptor signaling cascade, most common form is insulin receptor substrate 1 (IRS-1) phosphoinositide (PI) 3-kinase Akt pathway (Boothe et al., 2016; Knutson, Ronnett, & Lane, 1983). A number of rare mutations in the insulin receptor gene have been demonstrated to cause several forms of severe insulin resistance in humans, leading to reduced tyrosine kinase activity, altered affinity of insulin binding or decreased receptor mRNA expression (Cochran, Musso, & Gorden, 2005; C. R. Kahn et al., 1976; S. I. Taylor et al., 1991). In contrast, human polymorphisms in insulin receptor substrate (IRS) proteins (Burguete-Garcia et al., 2010; Haruta et al., 1995; Martinez-Gomez et al., 2011) and PI3-kinase (Almind et al., 2002; Baier, Wiedrich, Hanson, & Bogardus, 1998) are observed with higher frequency in patients with T2D. It has been clearly suggested that impaired IRS-1 signaling causes defective insulin action not only in obese diabetic patients but also observed in lean subjects with T2D (Bajaj & Defronzo, 2003; Miyazaki, He, Mandarino, & DeFronzo, 2003), in obese individuals with normal glucose tolerant (Cusi et al., 2000), as well as in the insulin resistant, normal glucose tolerant subjects with two T2D parents (S. R. Kashyap et al., 2004; Pratipanawatr et al., 2001). The disruption of insulin signaling has been associated with decreased insulin-stimulated IRS-1 tyrosine phosphorylation (Pratipanawatr et al., 2001), while the enhanced serine phosphorylation of IRS-1 is a characteristic of attenuation of insulin signal in T2D (Aguirre et al., 2002; Qiao, Goldberg, Russell, & Sun, 1999). However, insulin resistance in humans who do not harbor such mutations can occur in many other contexts which are often in concomitant with obesity, such as ER stress, inflammation, lipotoxicity, oxidative stress, mitochondrial dysfunction and hyperinsulinemia (Hotamisligil, 2010; Houstis, Rosen, & Lander, 2006;

23 Chapter One Literature Review

A. M. James, Collins, Logan, & Murphy, 2012; Olefsky & Glass, 2010). The association of obesity with insulin resistance has been established for decades as demonstrated in adult humans that every 5% weight gain is associated with an approximate 20% increase in the risk of insulin resistance (Everson et al., 1998). The elevated levels of plasma free fatty acids (FFAs) are prevailing in obese subjects with insulin resistance (Boden, 2011), and excess free fatty acids have been shown to attenuate insulin signaling via activation of protein kinase C (PKC) and its associated serine phosphorylation of IRS-1 in skeletal muscle (Boden & Carnell, 2003). The chronic elevation of free fatty acids causes lipotoxicity which triggers insulin resistance through the induction of oxidative stress (Schrauwen & Hesselink, 2004) and malonyl-CoA-induced alterations in MAP kinase (Saha & Ruderman, 2003). Oxidative stress has been extensively associated with obesity and T2D which has been proven in both humans as well as experimental animal models (Anderson et al., 2009; Furukawa et al., 2004; Houstis et al., 2006). Increased reactive oxygen species (ROS) production in muscle and mitochondrial dysfunction are evident in mice that are rendered obese and diabetic by 16 weeks of high-fat (36% fat) and high- sucrose diet (50% carbohydrate from sucrose) feeding (Bonnard et al., 2008). The cumulative ROS in the obese subjects activate JNK (Kamata et al., 2005; H. Liu, Nishitoh, Ichijo, & Kyriakis, 2000) and IKK (Storz & Toker, 2003), which phosphorylate the serine 302, 307 and 612 on insulin receptor substrate 1 (IRS-1), resulting in dissociation with insulin receptor (IR), accelerated IRS-1 degradation, suppressed PI3K binding and insulin resistance (Gual, Le Marchand-Brustel, & Tanti, 2005).

1.3.2. Pancreatic -Cell Dysfunction

1.3.2.1. Impaired Pancreatic Insulin Secretory Function

Glucose-stimulated insulin secretion is tightly regulated by many components in vivo, such as glucose metabolism, mitochondrial function, fatty acids and hormones (GLP-1, estrogen, growth hormones); however, reduced insulin secretion resulting from -cell dysfunction is the decisive defect of T2D (Abdul-Ghani, Tripathy, & DeFronzo, 2006; Ashcroft & Rorsman, 2012). It is believed that pulsatile patterns of insulin secretion are essential for normal regulation of hepatic glucose production (Porksen, 2002) and rapid bursts after

24 Chapter One Literature Review meals to maximise the efficiency of nutrient utilisation and clearance (Polonsky, Sturis, & Van Cauter, 1998). Of particularly importance is that first phase insulin secretion is thought to prime the tissues for the coming nutrients. Therefore, defects in the first phase insulin secretion have been suggested to be critical determinants of impaired glucose tolerance early in the course of T2D progression (Gerich, 1998; Perley & Kipnis, 1967). This is also supported by a well-known cross-sectional study by Brunzell et al. showing that early phase insulin secretion was profoundly attenuated when the fasting blood glucose rose >100 mg/dL, and was totally absent when fasting glucose levels went up above 115 mg/dL (Brunzell et al., 1976). In addition, studies in normoglycemic individuals with first-degree relatives of T2D showed a near-total elimination of oscillations in pancreatic insulin secretion (O'Rahilly, Turner, & Matthews, 1988; Schmitz et al., 1997). Importantly, it was shown that the defects of first phase insulin secretion can be restored following a period of intensive glucose control (Vague & Moulin, 1982), suggesting this aberrant -cell function contributes to the earliest form of acquired -cell dysfunction. Furthermore, the current concept is that this -cell defect in first phase secretion not only occurs at the time of transition from normoglycaemia to impaired glucose tolerance but is the critical pathogenic event that contributing to the progression to overt T2D.

The pathogenesis of -cell dysfunction in T2D has been intensively investigated. Several potential cellular mechanisms have been proposed to be detrimental, including glucose toxicity, impaired proinsulin biosynthesis, lipotoxicity and -cell exhaustion. An abnormally high glucose level can lead to devastating -cell dysfunction. Hyperglycaemia has been demonstrated to alter one or more key aspects of -cell physiology, gene expression of key enzymes in metabolic pathways (Weyer, Bogardus, Mott, & Pratley, 1999), and to impair proinsulin transcription (Jonas et al., 1999; Olson, Qian, & Poitout, 1998). There is mounting experimental evidence in both animal models and humans to demonstrate that -cell exhaustion is a leading cause of -cell dysfunction (Greenwood, Mahler, & Hales, 1976; Leahy, Bonner-Weir, & Weir, 1992; S. H. Song, Rhodes, Veldhuis, & Butler, 2003). Conversely, it is also supported by the observation that the period of - cell rest leads to an improvement in -cell function (Laedtke et al., 2000). In addition, in

25 Chapter One Literature Review the context of excessive fatty acids (lipotoxicity), overproduction of metabolites from fatty acids such as ceramides and precursors for oxidative stress are fatal to -cell function and viability (McGarry & Dobbins, 1999; Robertson, Harmon, Tran, & Poitout, 2004). Importantly, a combination of both a high glucose level and excess fatty acids (glucolipotoxicity) has been thought to be required for impaired -cell function as the presence of hyperglycaemia is required for the production of a mitochondrial metabolic product of glucose, malonyl-CoA, which is essential to inhibit fatty acid oxidation (Poitout & Robertson, 2002; Prentki et al., 2002). Furthermore, recent genetic studies have revealed that progressive -cell dysfunction in T2D is affected by genetic factors. It was shown that most risk variants for T2D were identified as regulators in pancreatic -cells acting through impairing insulin secretory function (Florez, 2008; McCarthy, 2010; McCarthy & Hattersley, 2008). For example, the T2D susceptibility gene Kcnq1 (potassium voltage- gate channel, KQT-like subfamily, member 1) is implicated in reduced -cell function and decreased insulin secretion (Dayeh et al., 2014; van Vliet-Ostaptchouk et al., 2012). Reduced expression of Prox1 (prospero homeobox 1) by cis-regulatory variants results in impaired insulin secretion which conferred susceptibility to T2D (Lecompte et al., 2013). Other diabetes susceptibility genes such as Kcnj11 (potassium inwardly rectifying channel, subfamily J, member 11), GCK (glucokinase) and HNF1 (hepatocyte nuclear factor 4 alpha) contain common variants which are recognised to influence -cell insulin secretion (Bonnefond, Froguel, & Vaxillaire, 2010).

1.3.2.2. Reduced -Cell Mass

Adequate -cell mass is essential to ensuring that sufficient amount of insulin is secreted. It has been suggested that loss of approximately 65% -cell mass can cause diabetes as revealed in patients who underwent partial pancreatectomy (Kendall, Sutherland, Najarian, Goetz, & Robertson, 1990; Meier et al., 2012). In addition, the progression from a state of -cell adaptation to diabetes is inevitably associated with a decline in functional -cell mass (Buchanan, 2003; Ferrannini & Mari, 2004; S. E. Kahn, 2003). At the cellular level, the amount of -cell mass has been proposed to be determined by the sum of four major mechanisms: -cell neogenesis (development from precursor cells), hyperplasia (enhance

26 Chapter One Literature Review mitotic division of differentiated-cell), hypertrophy (increase in size of existing cells) and apoptosis (programmed cell death) (Bonner-Weir & Weir, 2005; Dor, Brown, Martinez, & Melton, 2004; Halban, 2004; Rhodes, 2005). It is well established in the mouse and human that a deficit in functional -cell mass is associated with reduced insulin secretion and T2D. As revealed by pancreatic autopsy analyses in various populations that significant reductions in the amount of pancreatic -cells are prevailing in patients with T2D and impaired fasting glucose (prediabetes) compared with nondiabetic individuals (Kloppel, Lohr, Habich, Oberholzer, & Heitz, 1985; Maedler, 2008; Rahier, Guiot, Goebbels, Sempoux, & Henquin, 2008). Butler et al. characterised the fluctuations of -cell mass in the course of the development of T2D, demonstrating a 50% increase in -cell volume in obese, non-diabetic subjects, while this figure declined by 40% and 63% in obese people with impaired fasting glucose and T2D, respectively. This reduction in -cell mass is not solely driven by obesity as there is evidence to show that diabetic lean subjects had a 41% deficit in -cell volume compared with non-diabetic lean subjects. In addition, these reductions in -cell mass were associated with a 3-fold and 10-fold increase in apoptosis in obese T2D and lean T2D cases, respectively (Butler et al., 2003). A number of insults have been proposed to contribute to -cell demise in T2D, such as cytokine-mediated oxidative stress and inflammation, high concentrations of glucose and free fatty acids, protein misfolding in the endoplasmic reticulum (ER stress) (Donath & Shoelson, 2011; Maedler, Oberholzer, Bucher, Spinas, & Donath, 2003; Robertson & Harmon, 2006). Chronic hyperglycaemia results in -cell exhaustion because of the persevering increase in insulin demand that precedes the -cell demise (Ferrannini, 2010; Talchai, Xuan, Lin, Sussel, & Accili, 2012). At the molecular level, the potent role of IRS-2 signaling has been established as a metabolic hub coupling the cellular conditions to -cell survival (Hennige et al., 2003). Tyrosine phosphorylation of IRS-2 resulting in activation of downstream signaling pathways which leads to increased -cell growth (Dickson & Rhodes, 2004; Hennige et al., 2003; Lin et al., 2004; Rhodes & White, 2002), whereas serine/threonine phosphorylation promotes IRS-2 degradation ultimately resulting in -cell apoptosis (Hennige et al., 2003; Withers et al., 1998). For instance, chronic hyperglycaemia causes the activation of the mammalian Target of Rapamycin (mTOR) in -cells, which triggers

27 Chapter One Literature Review serine/threonine phosphorylation of IRS-2 and the subsequent increased -cell apoptosis (Briaud et al., 2005).

1.4. Genetic Studies of Type 2 diabetes 1.4.1. Genetic landscape of T2D

It is clear that the development of T2D is the result of the interaction between environmental factors and a permissive genetic component. There is compelling evidence that the individual risk of T2D is substantially influenced by heritable factors (Willemsen et al., 2015). The evidence that T2D may be genetically driven is supported by two major findings. First, Asians or Pima Indians have at least a two-fold increased risk of T2D compared to European natives which suggested the ethnic differences in the disease prevalence. Second, there is strong family history of T2D as many studies showed that subjects with one type 2 diabetic parent are found to have 40% lifetime risk of developing T2D, while this figure rises to 70% if both parents are affected (van Tilburg, van Haeften, Pearson, & Wijmenga, 2001). In contrast to the general population risk of 5-10%, siblings of a diabetic proband have a nearly four-fold increased risk for T2D (Hemminki, Li, Sundquist, & Sundquist, 2010). This is also supported by twin studies with a greater than 70% concordance rates of T2D in monozygotic twins and 20%-30% among dizygotic twins (Kaprio et al., 1992). Furthermore, the higher prevalence of T2D in specific ethnic groups such as Pima Indian of Arizona and Mexican Americans all lend support to the existence of genetic components for T2D (Elbein, 2007; Haffner, 1998; Permutt & Hattersley, 2000). Overall, estimates have demonstrated that 30-70% of T2D risk can be ascribed to heritable factors (Almgren et al., 2011; Morris, 2018; Poulsen, Kyvik, Vaag, & Beck-Nielsen, 1999).

The genetic architecture of T2D risk has been characterised to be heterogeneous which may arise from defects in one or multiple molecular pathways. Less than 5% of non- autoimmune diabetes is due to monogenic defects. In these cases of MODY that mutations in single genes are often of high penetrance, such as mutations in the hepatocyte nuclear factor-1A (HNF1A) and the GCK gene (Vaxillaire & Froguel, 2008). Forguel et al. reported a linkage between the glucokinase locus (chromosome 7p) and MODY, with an estimated

28 Chapter One Literature Review

45-95% of the 16 French families presenting linkage to glucokinase (GCK), a key enzyme of glucose metabolism (Froguel et al., 1992). However, more predominant cases of T2D appear to be attributable to the interaction of multiple genes scattered across the genome, each of which contributes modestly and identification of these genes that confer susceptibility to T2D has proven problematic. Intensive efforts have been made in order to unravel the genetic identity of T2D, whereas only a few have been confirmed to be reproducibly associated with susceptibility to the disease. For example, common variants in the transcription factor 7-like 2 gene (TCF7L2) have been robustly replicated in subjects of different ethnicities for the association with an increased risk of T2D (Duggirala et al., 1999; Grant et al., 2006; Tong et al., 2009; Zeggini & McCarthy, 2007). TCF7L2 encodes a transcription factor in the Wnt signaling pathway and the risk alleles have been shown to increase the level of TCF7L2 protein expression by 5-fold in -cells which was associated with impaired insulin secretion, incretin effects and enhanced rate of hepatic glucose output (Lyssenko et al., 2007). Many common polymorphisms in the KCNJ11 and ABCC8 genes have been associated with diabetes susceptibility (Haghverdizadeh, Sadat Haerian, Haghverdizadeh, & Sadat Haerian, 2014; Haghvirdizadeh et al., 2015). The KCNJ11 and + ABCC8 genes encode components of the K ATP channels, the inward-rectifier potassium ion channel (Kir6.2) and sulfonylurea receptor 1 (SUR1), respectively, thus variants in + these genes may disrupt the potentiation activity of K ATP channel resulting in defective insulin secretion (Abujbara et al., 2014). Variants in these genes can lead to either severe insulin deficiency or hypoglycaemia due to hyperinsulinaemia, which is dependent on + whether the mutations cause aberrant opening or closing of the K ATP channel. Another gene that has been implicated in diabetes susceptibility is peroxisome proliferator-activated receptor gamma (PPAR), which encodes a transcription factor involved in adipocyte differentiation that is an attractive candidate since it is a molecular target of the thiazolidinedione class of anti-diabetic drug. A missense variant (Pro12Ala) in the 2 isoform of PPAR has been demonstrated to increase diabetes risk by 25% (Altshuler et al., 2000). Of importance, the T2D susceptible genes are likely to be those involved in pancreatic -cell function, insulin production and -cell survival, whereas a few participated in insulin signalling, energy expenditure and appetite behavior. To date, at

29 Chapter One Literature Review least 83 susceptibility loci for T2D has been identified using the advanced genetic resources such as genome-wide association study, whereas these findings only account for a small fraction (approximately 10%) of overall heritable risk as a result of the complex and heterogeneous nature of T2D aetiology (X. Wang et al., 2016).

1.4.2. Approaches to Identifying T2D Susceptibility Genes

Several approaches have been developed to elucidate genetic defects that lead to diseases. Linkage analysis, candidate gene approach, genome-wide association study, target gene study and generation of congenic strains are commonly employed for identification and elucidation of the genetic regions contributing to T2D.

1.4.2.1. Linkage Analysis

Linkage analysis was the primary strategy used for the genetic mapping of Mendelian and complex traits within relatively small family-based studies. In linkage analysis, a genome- wide set of a few hundred markers spaced throughout the genome (millions of bases apart) is genotyped in families with multiple affected relatives. Markers that segregate with disease in affected family members with increased frequency are recognised and localised to the genomic region containing a susceptibility genes. This approach has the advantage of being an unbiased, comprehensive search across the genome for susceptibility alleles, and has been successfully applied to identify genes for many monogenic disorders.

The most prominent cases are CAPN10 (calpain 10) and TCF7L2. CAPN10 was the first T2D gene to be identified by linkage analysis (Hanis et al., 1996; Horikawa et al., 2000) which encodes a cysteine protease that is involved in several cellular processes such as cell signalling, apoptosis, exocytosis, mitochondrial metabolism, cytoskeletal remodeling and other intracellular functions. Polymorphisms in CAPN10 gene have been associated with the increased risk of T2D in large meta-analyses across several populations (Bodhini et al., 2011; Y. Song, Niu, Manson, Kwiatkowski, & Liu, 2004; Tsuchiya et al., 2006; Weedon et al., 2003), however consistent associations did not always presence in all populations or in large human GWAS studies, suggesting some doubt on the broad applicability of this gene. Calpain-10 has an important role in insulin resistance, insulin secretion as well as -

30 Chapter One Literature Review cell survival (Johnson, Theisen, Johnson, & Wheelock, 2004; Zhou et al., 2003). Emerging evidence suggests that calpain-10 facilitates GLUT4 externalization through actin cytoskeleton rearrangements. It appears to be a determinant of fuel sensing and insulin exocytosis acting on mitochondria and plasma membrane, respectively (Panico, Salazar, Burns, & Ostrosky-Wegman, 2014).

TCF7L2 was identified as a diabetes susceptibility gene that confers the strongest effect on T2D risk yet found (Tong et al., 2009). This linkage signal at TCF7L2 was first mapped in a Mexican-American cohort (Duggirala et al., 1999) and later replicated in independent linkage studies in various ethnic groups, including Icelandic, United States and Danish populations (Cauchi et al., 2007; Florez, 2007; Grant et al., 2006). Multiple sequence variants in TCF7L2 gene have been associated with diabetes susceptibility, among these the SNPs (rs12255372n and rs7903146) are associated with decreased insulin secretion in American subjects with impaired glucose tolerance (Florez et al., 2006). Several lines of evidence revealed that these variants in TCF7L2 gene affect pancreatic insulin secretion and -cell proliferation and thereby increase the T2D risk (Z. Liu & Habener, 2008; Lyssenko et al., 2007).

Linkage analysis has been shown to be successful in identifying rare variants of large effect for monogenic diseases, while its application on complex disorders with polygenic aetiology is limited. In addition, the poor resolution was a major problem as only a few hundred markers were usually typed, and thereby the resulting linkage regions could span greater than 20cM.

1.4.2.2. Candidate Gene Approach

In candidate gene studies, targets are previously identified genes that are already suspected of contributing to T2D if abnormal, based on a priori knowledge of the gene's biological function. Typically, genes that play a role in pathways involved in glucose metabolism, insulin secretion, insulin receptor and post-receptor signalling are considered reasonable candidates for contributing to the genetics of diabetes. The candidate gene approach to conducting genetic association studies focuses on significant association between diabetes

31 Chapter One Literature Review mellitus and functional variations in pre-specified genes of interest through focused sequencing efforts. By comparing sample of T2D patients with a matched control group, the increased frequency of a certain polymorphism in affected group can be identified to be contributory to the disease.

So far, more than 250 genes have been investigated for their role in T2D by candidate gene approach, including the previously described PPAR, KCNJ11, ABCC8, TCF7L2, CAPN10 and HNF1A as well as GCGR (the glucagon receptor), IRS1 and IRS2. Insulin receptor substrate IRS1 and IRS2 genes encode proteins that compose of important components in insulin signal transduction. Variants in these genes were found to be associated with decreased insulin sensitivity. It is evidence that a 25-35% decrease in insulin sensitivity was found in two cohorts of obese children to be associated with Arg972Gly and the Asp1057Gly variants in IRS-1 and IRS-2, respectively (Le Fur, Le Stunff, & Bougneres, 2002). In addition, lean subjects with polymorphism at 972 residue of IRS-1 had a lower insulin sensitivity than the subjects who did not have the polymorphism (Clausen et al., 1995). Clausen et al. also showed that the codon-972 IRS-1 gene variant was associated with a 50% reduction in insulin sensitivity, suggesting this may interact with obesity in the pathogenesis of common insulin-resistant disorder. On the other hand, GCGR gene encodes glucagon receptor which is important for mediating glucose levels via glucagon signalling. A missense mutation at codon 40 (Gly40Ser) of the GCGR gene leading to the substitution of a serine for a glycine residue in the encoded protein. This mutation has been associated with reduced insulin secretion (Shiota, Kasamatsu, Dib, Chacra, & Moises, 2002), decreased tissue sensitivity to glucagon (Hager et al., 1995) , increased central adiposity (Siani et al., 2001) and T2D mainly in European whites and Australians (Barbato et al., 2003; Hager et al., 1995).

However, other than few successful cases, most gene candidates were found to have minor to no association with T2D. The possible explanation for this include small sample sizes, a strong genetic heterogeneity across ethnic groups, variation in environmental exposures, and gene-environment interaction. In addition, this approach will not lead to the identification of entirely new genes or pathways involved in T2D.

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1.4.2.3. Genome-Wide Association Study (GWAS)

Linkage analysis and candidate gene studies were commonly used approaches which have brought the identification of many T2D-associated gene and loci, however their contribution to overall heritable risk of T2D remains very limited. A considerable breakthrough in understanding the genetic basis of T2D was facilitated by the advent of genome-wide association study (GWAS), which scan the entire genome for common genetic variation. A number of significant advances in genomic technology has made it possible to examine variants throughout the entire genome: The development of high- throughput genotyping technologies for single-nucleotide polymorphisms (SNPs) enabled rapid identification of sequence variants across the whole genome. Completion of the Project and availability of Hapmap data allowed the discovery of a large number of haplotype markers (International HapMap, 2005). Moreover, the development of analytical tools to assist in the association analysis and interpretation of huge amounts of data. These together contributed to great advances in analytical power of GWAS to scan millions of SNPs across the genome and facilitate rapid implementation to a wide variety of diseases (Imamura & Maeda, 2011). In GWAS, the entire genome of each individual in control or affected populations is taken into account for association analysis by screening a large number of common SNPs. By determining genetic variants if one type of the variant (one allele) is significantly more frequent in the affected group of people compared to the control subjects, this variant will be considered to be associated with the trait of study. Thus, the associated SNPs are then considered to mark a region of human genome which influences the risk of disease.

GWAS have been implemented in diverse human populations (Chan et al., 2009; Fortes, 2015; Imamura & Maeda, 2011) and substantial progress has been made to identify more than 100 loci associated with T2D predisposition that informed novel genes, pathway, and mechanisms of diabetes pathogenesis (McCarthy, 2017a). The first GWAS for T2D was performed in a French cohort composed of 661 cases of T2D and 614 non-diabetic subjects. This study analysed a total of 392,935 SNPs for association with T2D and identified novel and reproducible association signals at SLC30A8 and HHEX, and also validated the well- known association at TCF7L2 (Sladek et al., 2007). SLC30A8 gene encodes a zinc

33 Chapter One Literature Review transporter in pancreatic -cells that serves to transport zinc from the cytoplasm into insulin secretory granules (Chimienti, Devergnas, Favier, & Seve, 2004). HHEX gene encodes a transcription factor involved in pancreatic development (Bort, Martinez-Barbera, Beddington, & Zaret, 2004). These findings in the initial GWAS were able to be replicated in the following GWAS, for example the Icelandic company deCODE Genetics confirmed the association between T2D risk with SLC30A8 and HHEX and also discovered a novel association with CDKAL1 (Steinthorsdottir et al., 2007). One of the most successful cases in the human GWAS is the identification of TCF7L2 that was intensively replicated in different cohorts with the largest effect size (Bodhini, Radha, Dhar, Narayani, & Mohan, 2007; Cauchi et al., 2007; Chauhan et al., 2010; Grant et al., 2006; Groves et al., 2006; Sanghera et al., 2008; U. Smith, 2007). Various T2D GWAS carried out by many fruitful collaborating studies have yield substantial progress in diabetes genetics. The data from the GWAS provide invaluable insight into the genetic architecture of complex disease. Given the evidence that many T2D susceptibility genes are found to be active in the -cells or may be involved in insulin secretion support the notion that -cell dysfunction is central to the pathogenesis of diabetes (Florez, 2008; McCarthy & Zeggini, 2009). In fact, very few of these genes are likely to act on insulin sensitivity and genes involved in the insulin signaling pathway rarely identify in GWAS studies for T2D. It was reported by Voight et al. in an attempt to identify the most affected signaling pathway by T2D risk genes, the indices of-cell function (HOMA-B) and insulin sensitivity (HOMA-IR) from 37,000 subjects were used for association analyses (Voight et al., 2010). This study revealed that ten risk loci (MTNR1B, SLC30A8, THADA, TCF7L2, KCNQ1, CAMK1D, CDKAL1, IGF2BP2, HNF1B and CENTD2) were associated with reduced -cell function, while only three loci (PPARG, FTO and KLF14) were implicated in aberrant insulin sensitivity.

1.4.2.4. Generation of Congenic Strains

Individuals that are genetically identical except at one locus and the linked region of chromosome are considered as congenic. Congenic inbred strains are commonly utilised to investigate the phenotypic effect of one genetic segment on the trait of interest and are generated by mating two inbred mouse strains of mice, followed by repeated back-crossing

34 Chapter One Literature Review the descendants with a recipient strain for at least 10 generations. The congenic mice containing different segments of the target QTL can be generated, which are allowed to break one linkage region into several individual components for physiological characterisation, therefore achieving fine-mapping of a given genetic area and also separating multiple QTL lying under the same initial logarithm of the odds (LOD) peak. The resultant congenic strains are compared to the pure recipient strain to determine whether they are phenotypically different as a result of a particular chromosomal region. In addition, this approach can also apply to identify the critical genetic locus contributing to the trait of interest, if selection was for a phenotype.

1.4.2.5. Gene Targeting Approach

Genetic engineering becomes the most popular approach to manipulate the mouse genome for a broad range of interests. It provides a great platform to study the effect of specific gene products and its mechanisms under certain circumstances. Many genes involved in glucose metabolism and insulin action have been investigated individually either by transgenic or knockout in an attempt to gain insights into the pathogenesis of diabetes, genetic manipulation on insulin receptor, insulin receptor substrate (IRS1/IRS2), glucose transporter (GLUT4), phosphoenolpyruvate carboxykinase (PEPCK) and fructose-1,6- bisphosphatase (FBPase) are examples.

PEPCK is an essential enzyme that catalyses the conversion of oxaloacetate into phosphoenolpyruvate during gluconeogenesis. The PEPCK transgenic rat was generated to study the effect of increased EGP on the pathogenesis of T2D (Rosella et al., 1995). The PEPCK transgenic was characterised to develop mild obesity with elevated triglyceride levels, overt peripheral insulin resistance and glucose intolerance (Lamont et al., 2003; Mangiafico et al., 2011; Rosella et al., 1995). This model has been employed as a unique platform to investigate the metabolic consequences of chronic excess glucose supply and obesity (Joannides, Mangiafico, Waters, Lamont, & Andrikopoulos, 2017; Mangiafico et al., 2011). Another example is FBPase that is considered a good target for treating T2D via attenuating gluconeogenesis and EGP (van Poelje et al., 2006; van Poelje, Potter, & Erion, 2011). FBPase is a rate determining enzyme responsible for catalysing the hydrolysis of

35 Chapter One Literature Review fructose-1,6-bisphosphate to form fructose-6-phosphate. This enzyme was found to be associated with T2D in -cells as evidenced by upregulation in islets isolated from T2D patients and C57BL/KsJ-db/db mice (Biden, Robinson, Cordery, Hughes, & Busch, 2004; Kebede et al., 2008). In addition, a -cell specific FBPase transgenic mouse model was studied and revealed that increased FBPase levels in pancreatic -cells led to decreased glucose utilisation, glucose oxidation and ATP levels indicative of metabolic deceleration, which can protect from β-cell dysfunction and glucose intolerance under chronic nutrient overload (M. Stathopoulos, S. Andrikopoulos unpublished observations).

Undoubtedly, this approach has let to great advance in biomedical research and allows investigators to elucidate the influence of single genes or mutations on diabetes. However, concern remains that genetic engineering causing the artificial perturbation may potentially lead to unexpected effects.

1.4.3. Murine Models of Diabetes

1.4.3.1. Spontaneous-induced Animal Models

Spontaneous diabetic rodents have been preserved as valuable models that are commonly used in widespread of diabetic-related studies, due to the rodents resembling human patients in genetic etiologies. In terms of the different etiologies, they can be further divided into monogenic and polygenic. Animals with spontaneous mutations in leptin signaling are the most widely used monogenic models of diabetes susceptibility, such as the obese (ob/ob) mice with a mutation in the Leptin (Lep) gene (Lavine, Voyles, Perrino, & Recant, 1977), and the diabetic (db/db) mice (Hummel, Dickie, & Coleman, 1966) as well as the Zucker fatty (fatty, fa) rats (Phillips et al., 1996) harboring mutations in the Leptin receptor (Lepr) gene develop overt obesity-induced T2D. Due to the mutation in Leptin gene, ob/ob mice develop obesity as early as 4-6 weeks of age in company with hyperphagia, transient hyperglycameia, glucose intolerance, and elevated plasma insulin (Coleman & Hummel, 1973; Zhang et al., 1994). The diabetic homozygotes db/db mice are characterised by obesity, hyperphagic, and consistently develop severe diabetes with marked hyperglycaemia (400 mg/dl) until death at 5-8 months (Hummel et al., 1966). In addition,

36 Chapter One Literature Review the male mice homozygous for the fat spontaneous mutation at the Carboxypeptidase E (Cpe) locus (Cpefat/Cpefat) on a C57BLKS/J genetic background exhibit early onset hyperinsulinemia followed by a slowly developing obesity at 8-12 weeks of age with diabetes phenotype. These rodent models have been extensively utilised in the studies of T2D, obesity, leptin signaling and in the drug discovery to test glucose lowering agents, insulin sensitizers, insulin secretagogues, and anti-obesity agents (Reed & Scribner, 1999).

As obesity and diabetes in humans are rarely caused by a single gene mutation, monogenic models may not mimic the human conditions; on the other hand, selective inbreeding also being utilized in developing of diabetic rodents, for example, selective inbreeding with large body size (the KK mouse) (Nakamura & Yamada, 1967), abnormal glucose tolerance (GK rat) (Goto, Kakizaki, & Masaki, 1976) and agouti coat color (NZO mice) generated diabetic rodents caused by defects in multiple susceptible loci instead . Spontaneous diabetic animals model that have a strong genetic disposition to diabetes on homogeneous genetic background, which allows reproducible results that is essential for biomedical studies but it unlike heterogeneity seen in humans (Y. W. Wang et al., 2013).

1.4.3.2. Polygenic Models of Diabetes Susceptibility

1.4.3.2.1. C57BL/6 Mouse The C57BL/6 strain was generated by Little in 1921 from a mating of female no. 57 and male no. 52 from Latherrop’s stock (Abe et al., 2004). The C57BL/6 strain has become the most widely used inbred strain, accounting for 14% of all such uses with more than 25,000 articles on Pubmed documenting its use, and also the most commonly used reference strain in studies of diabetes. C57BL/6 is demonstrated to be a strain with intermediate susceptibility to diabetes in which both protective and diabetes susceptibility alleles have been observed. In addition, C57BL/6 mouse exhibits numerous aspects of the diabetic phenotype typically seen in obese humans, including insulin resistance and hyperinsulinemia. It is well-documented that an increased diabetes susceptibility in the C57BL/6 mice when fed a diet enriched with fat and carbohydrate (Rebuffe-Scrive, Surwit, Feinglos, Kuhn, & Rodin, 1993; Surwit et al., 1995; Surwit, Kuhn, Cochrane, McCubbin, & Feinglos, 1988; West, Boozer, Moody, & Atkinson, 1992). It was demonstrated by the

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Surwit laboratory that feeding a high-fat and high-carbohydrate diet led to a nearly 50% increase in fasting plasma glucose and a 10.4-fold rise in plasma insulin in C57BL/6 mice, suggestive of diet-induced insulin resistance and hyperglycaemia (Surwit et al., 1988). An in vivo study that assessed insulin sensitivity and insulin secretion in C57BL/6 mice during high-fat feeding revealed that long-term feeding resulted in defective acute insulin secretion that is central to diet-induced glucose intolerance, although total insulin secretion remain sufficient to compensate for insulin resistance (Ahren & Pacini, 2002). Kaku et al. (Kaku, Fiedorek, Province, & Permutt, 1988) and others (Goren, Kulkarni, & Kahn, 2004) also showed that C57BL/6 mice are inherently less glucose tolerant as compared to a number of inbred strains (129X1, AKR/J, C3H/HeJ, C57L/J, C57BLKS/6 DBA/2J, and SWR/J). While, in an insulin tolerance test, the C57BL/6 strain is more insulin sensitive than DBA/2 or 129X1. Furthermore, a reduced insulin secretion in the C57BL/6 strain has been well characterised in several in vivo studies (Kaku et al., 1988; Kayo, Fujita, Nozaki, E, & Koizumi, 2000; Kooptiwut et al., 2002; Rossmeisl, Rim, Koza, & Kozak, 2003; Toye et al., 2005) as well as in perifusion experiments (S. K. Lee, Opara, Surwit, Feinglos, & Akwari, 1995; Wencel et al., 1995). This defect was primarily due to an impaired second- phase secretion of insulin in response to glucose. In terms of -cell mass in C57BL/6 strain, a study by Bock et al. examined the pancreatic histology across several strains and concluded that lean C57BL/6 mice have a relatively low islet mass and -cell mass and smallest islets, while they have a high number of islets (Bock, Pakkenberg, & Buschard, 2005).

The genes involved in the glucose tolerance of the C57BL/6 strain have been studied and demonstrated that variation in each of the glucose-associated phenotypes is linked to their distinct loci (Toye et al., 2005). For example, a QTL on chromosome 13 contains the nicotinamide nucleotide transferase (Nnt) gene in which a spontaneous deletion mutation is associated with lower gene expression in the C57BL/6 islets, resulting in reduced insulin secretion and glucose intolerance (Aston-Mourney et al., 2007; H. Freeman, Shimomura, Horner, Cox, & Ashcroft, 2006). Nnt is an inner mitochondrial membrane enzyme that couples the transport of protons from the cytosol to the mitochondrial matrix. Freeman et al. showed that knockdown of Nnt expression in the MIN6 cells impaired glucose-

38 Chapter One Literature Review stimulated insulin secretion (H. Freeman et al., 2006). The same group also demonstrated that two mice with missense mutations in Nnt from an N-ethyl-N-nitrosourea (ENU) mutagenesis screen had impaired insulin secretion. Additionally, the reduced insulin secretion of C57BL/6 can be rescued by transgenic expression of the 129S6/SvEvTac allele of the Nnt gene (H. C. Freeman, Hugill, Dear, Ashcroft, & Cox, 2006).

Despite the reduced insulin secretion and mild glucose intolerance of the C57BL/6 strain, C57BL/6 mice are relatively resistant to obesity-induced diabetes when compared to other strains. Interestingly, Coleman found that C57BL/6 mice are able to be rendered obese and severely insulin resistant when genetically manipulated with the Lepob/ob mutation, but they do not develop overt diabetes due to a substantial increase in insulin arising from a dramatic expansion of -cell mass (Coleman, 1992).

1.4.3.2.2. DBA/2 Mouse The DBA strain was the first inbred strain of mice, which was named after its coat-colour alleles dilute (d), brown (b), and non-agouti (a), DBA. The DBA/2 is one of the two DBA substrains that is commonly used in diabetes research as a more diabetes susceptible strain (Kooptiwut et al., 2002; Wiltshire et al., 2003). DBA Leprdb/db mice (Coleman, 1992) as well as DBA/2 Lepob/ob mice (Chua et al., 2002) develop severe diabetes. In response to a high-fat feeding, the DBA/2 mice are more glucose tolerant with the lowest glucose levels in a survey of commonly used inbred mouse strains (Toye et al., 2005; West et al., 1992). In addition, the plasma insulin levels in DBA/2 strain was the highest among all strains due to hypersecretion of insulin (Kaku et al., 1988), which has been attributed to increased islet glucose utilisation and GLUT2 levels (Kooptiwut et al., 2002).

1.4.3.2.3. 129 Mouse The 129 strain is the common ancestor of many distinct substrains in existence today and the ES cell lines used in generating gene-targeted mice were mostly derived from substrains of the 129/Sv strain in the early era of engineered mouse models (Simpson et al., 1997). This strain has relative low insulin levels and is more glucose tolerant than other strains regardless of chow or high-fat-diet feeding, indicative of enhanced insulin sensitivity (Almind & Kahn, 2004; Almind, Kulkarni, Lannon, & Kahn, 2003). The 129 strain is

39 Chapter One Literature Review considered diabetes-resistant in the context of genetic obesity as the 129-derived alleles are capable of conferring protection to C57BLKS/J-Leprdb/db mice (Kaku, Province, & Permutt, 1989). Moreover, it has been observed that only transient hyperglycaemia was found in the 129 mice with homozygous db allele, it can be attributable to the compensatory increase in insulin secretion and its associated elevation of pancreatic insulin content (Leiter, Coleman, Eisenstein, & Strack, 1980). In a study of C57BL/6 x 129 F2 mice, five loci for hyperglycaemia or hyperinsulinemia were identified in which the 129 allele was associated with the lower insulin or glucose levels (Almind & Kahn, 2004). In addition, the 129 strain is also protected from developing severely insulin resistance. Kulkarni et al. revealed that the effect of knockout of insulin receptor (Insr) and insulin receptor substrate-1 (Irs1) on 129/Sv background have only mild hyperinsulinemia with little insulin resistance as compared to wild-type 129/Sv mice (Kulkarni et al., 2003). In contrast, C57BL/6 and DBA/2 mice bearing the same mutations exhibit marked hyperinsulinemia with overt insulin resistance and diabetes (Almind et al., 2003; Kulkarni et al., 2003).

Importantly, it is also shown that the diabetes-resistant 129 mice harbour genes contributing to diabetes susceptibilities. It is evidence that heterozygosity for only a null allele of the Insr gene leads to a more profound hyperinsulinemia on the 129 background than C57BL/6 (Kido, Philippe, Schaffer, & Accili, 2000). The genetic mapping in an Insr+/- C57BL/6 x 129 F2 population demonstrated that at least one diabetogenic locus was derived from the 129 strain. Additional evidence of diabetes promoting alleles in the 129 strain come from Irs2-/- mice in a C57BL/6 x 129/Sv mixed background which have reduced Pdx1 expression and develop overt diabetes (Kushner et al., 2002), however Irs2- /- mice on the pure C57BL/6 background have preserved Pdx1 expression (Suzuki et al., 2003). These findings suggested that alleles from the 129 background contribute to the downregulation of Pdx1.

1.4.3.2.4. A/J Mouse The A/J strain is a classic mouse model of diabetes-resistance that has the lowest glucose levels among the inbred strains. When fed a high-fat diet, the A/J mice are resistant to diet- induced obesity, insulin resistance, and glucose intolerance as compared to the C57BL/6 strain and other inbred strains. (Rebuffe-Scrive et al., 1993; Surwit et al., 1995; Surwit et

40 Chapter One Literature Review al., 1988)125. This protective effect is partly due to enhanced insulin secretion and a sustained second-phase insulin secretion (S. K. Lee et al., 1995; Wencel et al., 1995). It is interesting to note that even on chow diet the A/J mice have reduced glucose levels (Surwit et al., 1988; Surwit, Seldin, Kuhn, Cochrane, & Feinglos, 1991) and do not expand -cell mass when fed high-fat diets (S. K. Lee et al., 1995).

1.4.3.2.5. CAST/Ei Mouse The CAST/Ei strain is a wild derived inbred strain which has relatively high plasma glucose levels. This mouse strain has been used extensively in obesity and atherosclerosis research (Mehrabian, Wen, Fisler, Davis, & Lusis, 1998). Interestingly, CAST/Ei mice are unusually lean, however, when fed a high-fat diet they developed hyperinsulinemia. Three loci on chromosome 2 were identified using an F2 deriving from CAST/Ei and C57BL/6 which are associated with plasma insulin levels (Mehrabian et al., 1998). In addition, various glucose and insulin levels were also observed in congenic strains where CAST/Ei alleles have been integrated into the C57BL/6 background (Estrada-Smith et al., 2004).

1.4.3.2.6. New Zealand Obese (NZO) Mouse The NZO strain is a model of spontaneous polygenic obesity and insulin resistance that was formerly bred for agouti coat colour (Haskell, Flurkey, Duffy, Sargent, & Leiter, 2002). NZO mice exhibit a polygenic syndrome of obesity, hyperinsulineamia, and hyperglycaemia which resembles the natural progression of type 2 diabetes in humans (Ortlepp et al., 2000; Veroni, Proietto, & Larkins, 1991). In this strain, marked insulin resistance is manifested in reduced insulin-stimulated glucose uptake by muscle and adipose tissue, which is accompanied by increased endogenous glucose production from the liver (Veroni et al., 1991). Importantly, impaired first-phase insulin secretion in response to a glucose bolus is the prominent defect contributing to glucose intolerance in NZO mice (Veroni et al., 1991). During diabetes progression, initial -cell hyperplasia is not sustained, thereafter the -cell decompensation becomes evident in the male NZO mice which is accompanied by reduced -cell mass and degranulation (Junger, Herberg, Jeruschke, & Leiter, 2002). However, not all male NZO mice develop decompensation,

41 Chapter One Literature Review only in those who acquire obesity before the onset of diabetes (Leiter et al., 1998; Reifsnyder, Churchill, & Leiter, 2000; Reifsnyder & Leiter, 2002).

The genetic basis of diabetes in the NZO strain has been demonstrated to be complex in which the loci contributing to obesity and diabetes susceptibility have been identified on almost every chromosome (Reifsnyder & Leiter, 2002). Intensive effort has been made using genetic cross, QTL mapping and congenic lines in order to dissect the complex genetic component contributing to obesity and diabetes in NZO mice (Giesen, Plum, Kluge, Ortlepp, & Joost, 2003; Leiter et al., 1998; Ortlepp et al., 2000; Plum et al., 2000; Reifsnyder & Leiter, 2002; B. A. Taylor, Wnek, Schroeder, & Phillips, 2001), however, it has been challenging to fully characterise and understand the genetic identity. Polymorphisms of the leptin receptor have been identified which may partly contribute to leptin resistance and may interact with other obesity-promoting alleles to accelerate the development of obesity of these mice (Igel, Becker, Herberg, & Joost, 1997; Kluge et al., 2000). Genetic studies carried out in a (NZO x Swiss Jackson Laboratory (SJL)) x SJL backcross panel identified a major locus, Nidd/SJL, on chromosome 4 from the SJL background that confers the hypoinsulinemia and hyperglycaemia in obese NZO mice (Plum et al., 2002; Plum et al., 2000). Genetic mapping was also done in female NZO x F1 backcross mice which identified that two NZO-derived alleles at Nob1 and Nob2 loci are responsible for a major effect on obesity and hyperinsulinemia (Kluge et al., 2000). Furthermore, Leiter et al. identified three NZO-derived diabetes susceptibility loci from an F2 panel deriving from NZO and NON. Among these loci, two were demonstrated to have influences on blood glucose (Leiter et al., 1998). A subsequent study in an F1 x NON backcross mice identified an important NZO-derived diabetogenic locus that influenced body weight, plasma glucose and hyperinsulinemia (Reifsnyder et al., 2000). Collectively, complex interactions between multiple QTL and the maternal genotype take important parts in the increased diabetes susceptibility in NZO strain, illustrating the complexity of factors that may influence diabetes-related QTL.

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1.4.3.2.7. Nonobese diabetic (NOD) Mouse The NOD strain is a mouse model for type 1 diabetes, in which hyperglycaemia as a result of autoimmune deconstruction of pancreatic -cells leading to absolute insulin deficiency (Delovitch & Singh, 1997). The genetic etiology of diabetes in this strain is complex and now known at least 20 different loci are synergistically contributed to diabetes (Ikegami, Fujisawa, & Ogihara, 2004). Many genetic loci have been associated with diabetes susceptibility in the NOD strain, among these several insulin dependent diabetes (Idd) loci have been dissected into multiple distinct loci that serve critical roles in systemic immune response (Ikegami et al., 2004; Lyons et al., 2001). For example, the Idd1 is an important locus linking to the major histocompatibility complex class II (Serreze & Leiter, 1994).

1.4.3.3. Backcross and intercross

In mouse genetics, gene mapping is common to breed together the two inbred strains in which divergent phenotype has been identified. The consequent F1 offspring are further bred to their parental strain(s) (backcross) or to each other (intercross) to generate an F2 offspring. A backcross generates mice with either homozygous for one parental genotype or heterozygous, whereas an F2 from an intercross has all three possible genotypes at any locus. The backcross mice have more power to identify the major dominant QTL. In contrast, fewer F2 individuals is able to demonstrate a higher degree of power to obtain a profile of all the QTL for a trait (Darvasi, 1998). The number of recombination events is known to be the key factor in determining mapping resolution. It has been suggested that increasing the recombination within the sample by intercrossing for additional 10 generations, known as advanced intercross lines (AILs), improves mapping accuracy by approximately 5-fold from the corresponding F2 sample (Darvasi & Soller, 1995). This approach has been successfully applied in the detection of QTL affecting glucose and insulin levels (Ehrich et al., 2005) and also resolved multiple linked QTL influencing high- density lipoprotein-cholesterol concentrations into individual QTL (X. Wang et al., 2003). Furthermore, involvement of additional strains in the founding crosses is also a common strategy for gene mapping. For instance, a heterogenous F2 deriving from (BALB/c x C57BL/6) F1 mice mated to (C3H x DBA/2) F1 mice were used in a mapping study for levels of hormones related to diabetes (Harper, Galecki, Burke, Pinkosky, & Miller, 2003).

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However, a major disadvantage of backcross and F2 samples is that there is no possibility to have biological replication as each individual animal is unique.

1.4.3.4. Recombinant Inbred Strains

Recombinant inbred (RI) strains are generated by intercrossing the inbred strains followed by 20 generations of brother-sister mating of the F2 progeny to establish inbred strains (Crow, 2007). The resulting RI strain carries a unique mosaic of alleles that incorporate an essentially permanent set of recombination events between inherited from two or multiple inbred strains. In contrast to an F2 mouse, a RI panel is homozygous at all loci which only requires genotyping once because the genetically identical strain is able to be propagated. Therefore, this feature allows the genetic and phenotypic data obtained on multiple occasions across time and institute to be correlated to identify common loci influencing multiple phenotypes. Due to the efforts of Benjamin Taylor at the Jackson Laboratory, there has been great progress made in establishing numerous panels of RI strains from commonly used strains varying in susceptibility to many different diseases. Mapping traits using these RI panels simply require phenotyping the panel of strains and comparing the phenotypic differences between strains with that of the marker genotypes. RI strains have been used extensively in genetic mapping for a wide variety of traits (Paigen, 2003). Mapping resolution of RI panels is critically determined by the number of recombination events contained within the panel, including the recombination within each strain and the number of strains. Therefore, large number of strains have been generated for several RI panels which enable to achieve high resolution mapping (Peirce, Lu, Gu, Silver, & Williams, 2004; Williams et al., 2004). In addition, concerted efforts of previously established genome data and numerous available markers have made it possible to fine-map the existing panels (Williams, Gu, Qi, & Lu, 2001).

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1.4.4. Genetic Reference Population (GRP) designed for Complex Genetic Studies

1.4.4.1. Classical Inbred Strain Association

The concept of association studies in mouse strains was initially established using a set of commonly used laboratory strains (Cervino, Darvasi, Fallahi, Mader, & Tsinoremas, 2007; Grupe et al., 2001; McClurg et al., 2007). This led to the first mouse GWAS which was carried out by Pletcher et al. using 25 inbred strains to identify 11 loci associated with high- density lipoprotein (HDL) cholesterol levels (Pletcher et al., 2004). In contrast to the typical genetic cross, the inbred strains association approach enables a greater mapping resolution down to ~2 Mb because these inbred strains are more genetically distant from their founders therefore include more recombination events. The advantages of using established inbred strains in GWAS are as follows: (1) Do not require breeding steps as the classic inbred strains are commercially available. (2) A larger amount of variations are included in the genetic analysis. (3) Robust reproducibility. (4) The homozygosity in the inbred strain enhances the power of the association approach. Unfortunately, it has been demonstrated that applications of this approach is limited due to a failure to deal with population structure which gives rise to false-positive associations (Bennett et al., 2010; Kang et al., 2008). Therefore, most of the reported associations were shown to be false positives (Manenti et al., 2009; Payseur & Place, 2007).

1.4.4.2. Hybrid Mouse Diversity Panel (HMDP)

The HMDP is an advanced design of the classical inbred strain association, which is composed of a set of approximately 100 well-charactersied inbred strains including 70 recombinant inbred strains and 30 classic inbred strains (Bennett et al., 2010). Using this combined population of classic inbred strains and recombinant inbred strains in the HMDP provides many advantages making it ideal for systems-level analyses of complex traits, including (1) obtaining superior mapping resolution (approximately 2Mb interval size) from using inbred strains (2) retaining properties of using inbred strains, including lower cost of obtaining and breeding the animals; reproducibility since genetically identical

45 Chapter One Literature Review animals are generated (3) provide improved statistical mapping power from employing recombinant inbred strains (Lusis et al., 2016). In addition, the effect of population structure is corrected while performing association using a mixed model algorithm (EMMA). However, the application of the resource is very much limited by the number of available inbred strains, resulting in an upper limit on the statistical power of the HMDP. Bennett et al. reported that the use of these 100 inbred strains in the HMDP panel (8-10 animals per strain) has ~80% power to detect loci which account for approximately 10% of the variance of the trait. The HMDP approach has been applied to the genetic studies for a variety of metabolic traits, including insulin resistance (Parks et al., 2015), obesity and gut microbiota composition in response to different diets (Parks et al., 2013), bone density (Farber et al., 2011), and lipid levels (Bennett et al., 2010).

1.4.4.3. Heterogeneous Stock

The heterogeneous stock was generated as an alternative strategy for high resolution genetic mapping of small effect QTLs which employ outbred mice, such as heterogeneous stock mice (Valdar et al., 2006) and Diversity Outbred mice (Svenson et al., 2012). The mice in the heterogeneous stock are descended from eight inbred strains as founders (A/J, AKR/J, BALBc/J, CBA/J, C3H/HeJ, C57BL/6J, DBA/2J and LP/J), following more than 60 generations of pseudorandom breeding, the resulting offspring have equal genome content from each parental strain with an average recombination distance of 1.7 cM (Demarest, Koyner, McCaughran, Cipp, & Hitzemann, 2001; Mott, Talbot, Turri, Collins, & Flint, 2000). This breeding approach is able to generate nearly unlimited number of genetically unique animals, enabling large association studies to be conducted for studying weak genetic effects. An association study can be conducted on a sample of 1,000-2,000 outbred mice which allows a high mapping resolution of ~3 Mb for a typical QTL. However, unlike inbreed strains, the animals in the heterogeneous stock are genetically unique, therefore there is no phenotypic data available and reproducibility advantages offered as that of the inbred strains. Thus, studies that use heterogeneous stock animals require genotyping of each animal included in the study. The heterogeneous stock mice have enable the identification of genetic loci for many important traits. For instance, one large study employing a population of over 2,000 outbred mice used an extensive high-

46 Chapter One Literature Review throughput phenotyping system for 101 traits, including common-disease-related traits, haematology, behavioural traits, biochemistry and immunology-related traits (Valdar et al., 2006). This study enable the identification of genetic loci for almost all traits of measure. Furthermore, an alternative source of the outbred mice is getting from the commercially available outbred stock. Importantly to find that a number of commercial outbred stocks have desirable properties for fine-mapping because of the large number of generations since the founding of the stock. In fact, it has been shown that some outbred stocks enable the mapping resolution of interval sizes of less than 100 kb (Yalcin et al., 2010).

1.4.5. The Collaborative Cross Mouse Population

The Collaborative Cross (CC) is a massive panel of newly generated RI strains (ultimately over 1,000 RI lines will be produced) derived from an eight-way breeding scheme that is designed not only for fine-mapping of complex traits but also as a resource to support systems-genetics (Churchill et al., 2004; Threadgill, 2006). This mouse population was derived from eight, well-selected parental strains including five classical inbred strains (A/J, C57BL/6J, 129Sv/ImJ, NOD/LtJ, NZO/H1J) and three wild-derived strains (CAST/EiJ, PWK/PhJ and WSB/EiJ), representing the three major Mus musculus subspecies: M. m. musculus, M. m. domesticus, and M. m. castaneous. Genetic analysis demonstrated that this setting captures almost 90% of the known genetic variation present in mice originating from M. musculus and that the captured variation is randomly distributed across the mouse genome (A. Roberts, Pardo-Manuel de Villena, Wang, McMillan, & Threadgill, 2007; Yalcin et al., 2011). Among these founders, C57BL/6, A/J and 129S1/Sv have been sequenced previously (Mouse Genome Sequencing et al., 2002); NOD, CAST and WSB have established high-density polymorphism maps available (Frazer et al., 2007). The involvement of wild-derived strains introduces rare genetic variants which ensures the maximal genetic diversity in the resultant CC population compared with other strategies (Keane et al., 2011). Furthermore, the inclusion of the NZO and NOD mouse strains also introduced genetic variants presenting the susceptibility to common disorders such as Type 1 and Type 2 diabetes, obesity and insulin resistance (Leiter, 2005; Veroni et al., 1991).

47 Chapter One Literature Review

The generation of the CC strains was initiated through a full reciprocal cross among the eight founder strains which led to the production of 56 unique G1 progenies and followed by two stages of intercrosses to generate octo-parental mice from which inbred CC lines were developed. Subsequently, the CC lines emerged followed by an advanced inbreeding through 23 generations of sibling-mating (Churchill et al., 2004). Consequently, each CC line originates from an independent breeding funnel is genetically unique representing a genetically random mosaic of eight parental animals. Currently, over 500 RI CC lines have been established and available for a wide variety of studies.

The CC mouse panel retains the advantages of typical RI mouse strains and also overcomes limitations of existing RI panels, such as lack of genetic variation and small number of available lines, which have been markedly improved in the CC resource (Threadgill, Miller, Churchill, & de Villena, 2011). In terms of genetic diversity, it has been demonstrated that each CC line contains about 135 unique recombinant events and a single nucleotide polymorphism (SNP) every 100-200 bp, these contribute to high degree of phenotypic diversity that can be observed in the early generation of inbreeding (Morahan, Balmer, & Monley, 2008). More important, the use of the CC strains do not require genotyping because the genotypes are available from public databases. The CC and the incipient CC lines (pre-CC) have successfully produced QTL to a host of complex traits, such as energy balance traits (Mathes et al., 2011), hematological parameters (Kelada et al., 2012), host susceptibility to pathogens (Durrant et al., 2011; Graham et al., 2015; Gralinski et al., 2015) and metabolic traits (Atamni, Botzman, Mott, Gat-Viks, & Iraqi, 2016; Leist et al., 2016). In addition, it has been suggested that genetic mapping with these CC strains enabled a locus exerting a modest effect to be detected and mapped with approximately 4 cM resolution (Flint, Valdar, Shifman, & Mott, 2005). These studies demonstrated that the CC mouse population present an abundance of phenotypic diversity and it is a powerful resource with great potential to precisely identify QTLs for most inheritable trait of interest.

48 Chapter One Summary of Literature

1.5. Summary of the literature Type 2 Diabetes (T2D) has emerged as one of the leading health problems worldwide that threatens public health at unprecedented rates. This increased prevalence of diabetes mellitus is considered a major health problem worldwide that requires urgent action for prevention and development of effective therapies. It is characterised by hyperglycaemia arising from progressive deterioration of -cell function in the setting of insulin resistance. The pathophysiology of -cell dysfunction in diabetes is known to be heterogeneous and multifactorial, involving a complex interplay between permissive genetic components and detrimental environmental factors including an increasing consumption of Western diet and physical inactivity (Andrikopoulos, 2010; DeFronzo, 2004). There is ample evidence that diabetes has a strong genetic basis (Hemminki et al., 2010; Meigs, Cupples, & Wilson, 2000), and that islet -cell dysfunction is inherited (Elbein, Hasstedt, Wegner, & Kahn, 1999). Therefore, understanding the genetic basis of -cell dysfunction will inform us of specific therapeutic interventions that can correct the physiologic imbalance driving diabetes.

Studying the genetics of diabetes is challenging because of the heterogeneity and polygenic origin. In the past decade, technological advances in GWAS have led to the discovery of SNPs marking more than 150 loci that are implicated in T2D risk (Mohlke & Boehnke, 2015). Despite the success of human GWAS, current findings account for merely 10% of the overall heritability of T2D and most variants identified having no known function and only a small effect on risk (DIAbetes Genetics Replication and Meta-analysis Consortium et al., 2014; Morris et al., 2012). Human studies have fundamental limitations that hamper the demonstration of the imputed variants, as well as having confounding environmental factors.

Recombinant inbred mouse populations have been developed as valuable resources with many advantages, including the accessibility of disease-relevant samples, a better control of environmental factors and well-established techniques available for genetic modification and functional characterisation of candidate genes (Skarnes et al., 2011). Studies on disease like T2D which has a complex eatiology using advanced genetic resource may be a solution. Therefore, a sophisticated mouse genetic reference population, the Collaborative Cross (CC), was designed and generated specifically for

49 Chapter One Summary of Literature mapping complex traits (Churchill et al., 2004; Morahan et al., 2008). Each CC strain was derived from a strict breeding program involving eight parental strains that capture over 90% of the common variation of the mouse genome. The CC resource provides an appropriate model system with high genetic diversity, detailed genomic characterisation, balanced allele frequencies and evenly distributed recombination events, which together facilitate high resolution gene mapping and systems genetics studies (Aylor et al., 2011; Collaborative Cross, 2012). In addition, the CC has been successfully implemented to study a number of complex traits and phenotypes (Boutilier et al., 2017; Chick et al., 2016; Chitsazan et al., 2016; Ferguson et al., 2015; Nachshon et al., 2016) and proven to recapitulate human disease conditions (Weerasekera, Balmer, Ram, & Morahan, 2015). However, few studies have been conducted using the CC mouse resource to the study of glycaemia-associated traits (Abu-Toamih Atamni, Ziner, Mott, Wolf, & Iraqi, 2017; Atamni, Mott, Soller, & Iraqi, 2016; Nashef et al., 2017a). Of these, Hanifa et al. investigated gene-environment interactions using the CC mice fed a high-fat diet; a female-sex specific QTL (Chr8: 32.02-34.52 Mbp; logP=5.9) was associated with glucose tolerance (Abu-Toamih Atamni et al., 2017). However, the physiological implications of the imputed QTL and putative gene candidates were not explored.

Despite intensive efforts, advances in the fields of genomic sciences have given rise to the concept of personalized medicine as a more effective strategy than conventional treatment algorithms for T2D (McCarthy, 2017b), which has been successfully implemented to develop more effective treatment formula for monogenic types of diabetes such as MODY and neonatal diabetes (Hattersley & Patel, 2017). However, the application to T2D has been challenging due to the limited understanding of the genetic components governing diabetes progression. It has been demonstrated by several clinical trials that lifestyle modification and pharmacological interventions can effectively reduce the incidence of T2D in high-risk subjects (Knowler et al., 2002; Knowler et al., 2005). However, it was also shown that T2DM patients exhibit significant inter-individual variation in glycaemic control, glycated haemoglobin level and responsiveness to anti-diabetes agents.

Hence new approaches to improve our understanding of the genetic basis of glucose regulation is critical. Understanding the genetic basis of T2D can lead to considerable

50 Chapter One Summary of Literature progress in the development of advanced strategies for diagnosis and treatment, thereby the application in T2D is considered invaluable to uncover novel therapeutic targets and inform specific signalling pathways. The aim of this thesis was to use the CC resource to identify genes that mediated elevated blood glucose concentrations, and to further provide comprehensive understanding of the role of susceptibility genes in pancreatic -cell function.

51 Chapter One Aims of Thesis

1.6. Aims of Thesis The overall aim of this thesis is to identify the genetic causes of hyperglycaemia by utilising the Collaborative Cross (a genetic reference population) and to characterise the effects of identified gene(s) on glucose homeostasis and pancreatic insulin secretion in vivo and in vitro.

1.6.1. Specific aims

1. Identification of hyperglycaemia susceptibility loci  The genome-wide association study and linkage analysis were performed with the random blood glucose data from a cohort of 1,119 mice of the Collaborative Cross (CC) mouse population at 8-10 weeks of age.

2. Determination of the effects of the hyperglycaemia susceptibility loci on glucose homeostasis and pancreatic insulin secretion.  Two CC strains carrying the hyperglycaemia susceptibility loci were characterised in order to evaluate the physiological effects of the hyperglycaemia susceptibility loci on glucose homeostasis and pancreatic -cell function.  To investigate whether the inherent hyperglycaemia susceptibility predispose individuals to the high-fat diet-induced metabolic abnormalities, these two hyperglycaemia susceptible CC strains were fed a high-fat diet for 20 weeks and examined for body weight gain, glucose tolerance and -cell function.

3. Identification and investigation of candidate gene(s) contributing to the impaired insulin secretion in Type 2 diabetes.  The expression of candidate genes were examined in pancreatic islets and compared between diabetes resistant and susceptible mice and humans in order to explore the correlation of candidate genes with diabetes susceptibility.  Gene specific knockdown by RNA interference in a pancreatic -cell line (MIN6 cells) to determine the effect of hyperglycaemia susceptibility genes (E2F8 and Dlg2) on insulin secretory function in response to glucose as well as a number of secretogogues.

52

CHAPTER TWO

MATERIALS AND METHODS

Chapter Two Materials and Methods

2. Chapter 2 Materials and Methods

2.1. Materials 2.1.1. Chemicals and Reagents

The following chemicals and reagents were purchased from their respective companies:

Arginine, Albumin from Bovine Serum,-mercaptoethanol (-ME), D-Glucose, DL- dithiothreitol (DTT), Dimethyl Sulfoxide (DMSO), Ethylenediaminetetraacetic Acid (EDTA), Formalin, Glycine, Hank's Balanced Salt Solution (HBSS), HEPES, Hexadimethrine Bromide, Histopaque-1077, Phosphate Buffered Saline (PBS), Sodium Dodecyl Sulphate (SDS), Sodium Hydroxide (NaOH), Sodium Pyruvate, Tris-HCl, Trizma® Base and Tween-20 were purchased from Sigma Aldrich (St Louis, MO, USA). Ammonium Persulphate (APS) were purchased from Univar N.V. Product (Amsterdam). Glycerol was purchased from ICN Biochemicals, Inc. (Aurora, OH, USA). Glycogen (molecular biology grade) and Pierce ECL Western Blotting Substrate were purchased from Thermo Fisher Scientific (Boehringer, Munich, Germany). Ethanol and Methanol were purchased from Chem-Supply (Adelade, SA, Australia). Bromophenol Blue, Propan- 2-ol and Sucrose were purchased from BDH Chemicals (QLD, Australia). Chloroform was purchased from M&B (VIC, Australia). Dulbecco's Modified Eagle's Medium (DMEM) high glucose with pyruvate (11.1 mM glucose, Catalogue number: 11995), DMEM low glucose with pyruvate (5.5 mM glucose, Catalogue number: 11885), Opti-MEM® I Reduced Serum Medium, Fetal Bovine Serum (FBS), Penicillin & Streptomycin (10,000 U/mL) and Trypan Blue Stain (0.4%) were manufactured by GIBCO BRL and purchased from Invitrogen, Life Technologies (Carlsbad, CA, USA). 0.5% Trypsin-EDTA (10X) was purchased from Thermo Fisher Scientific, Life Technologies (Carlsbad, CA, USA). Puromycin dihydrochloride (Catalogue number: ant-pr-1) in HEPES buffer was purchased from InvivoGen (San Diego, CA, USA). RPMI media 1640 (with L-glutamine, without

NaHCO3) manufactured by GIBCO was purchased from Invitrogen, Life Technologies (Carlsbad, CA, USA). MISSION® shRNA Lentiviral Transduction particles were

54 Chapter Two Materials and Methods manufactured by Sigma-Aldrich (St. Louis, MO, USA). Collagenase P from Clostridium histolyticum and cOmplete™ Protease Inhibitor Cocktail were manufactured by Roach and purchased from Sigma Aldrich (St Louis, MO, USA). Chemical salts used in preparation of Krebs-Ringer Bicarbonate Buffer (KRBB) were purchased from their respective companies: Calcium Chloride Dihydrate (CaCl2·2H2O) was purchased from Univar N.V.

(Amsterdam); Magnesium Sulfate Heptahydrate (MgSO4·7H2O), Potassium Chloride (KCl) and Potassium Dihydrogen Phosphate (KH2PO4) were purchased from BDH Prolabo

Chemicals (QLD, Australia); Sodium Chloride (NaCl) and Sodium Bicarbonate (NaHCO3) were purchased from Sigma Aldrich (St Louis, MO, USA). Silencer® Select Pre-designed siRNA and Silencer® Select Negative siRNA No.1 and No.2 were manufactured by Ambion and purchased from Life Technologies (Carlsbad, CA, USA). Lipofectamine® RNAiMAX and BLOCK-iT™ Alexa Fluor™ Red Fluorescent Control were purchased by Invitrogen, Life Technologies (Carlsbad, CA, USA). MISSION® TRC shRNA Lentiviral Transduction Particles were purchased from Sigma Aldrich (St Louis, MO, USA). RNeasy® Mini Kit was purchased from QIAGEN (Hilden, Germany). TRIzol® Reagent, TURBO DNA-free™ Kit [TURBO DNase (2 unit/l), 10X TURBO DNase Buffer, DNase Inactivation Reagent] and Nuclease-Free Water (0.2 M filter) were purchased from Ambion, Life Technologies (Carlsbad, CA, USA). SuperScript® III Reverse Transcriptase (200 units/l), 5x First-Strand Buffer, Random Primers (3 g/l), 0.1 M DTT, 10 mM dNTP Mix, RNaseOUT™ Recombinant Ribonuclease Inhibitor (40 units/l) were purchased from Invitrogen, Life Technologies (Carlsbad, CA, USA). TaqMan® Universal PCR Master Mix, TaqMan® Gene expression assay, Eukaryotic 18S Gene Expression Assay were manufactured by Applied Biosystems and purchased from Life Technologies (Foster City, CA, USA). Tetramethylethylenediamine (TEMED) was purchased from MP Biomedical (OH, USA). BSA standard for protein assay (2 mg/ml) and Bio-Rad Protein Assay Dye Reagent Concentrate were purchased from Bio-Rad Laboratories (Richmond, CA, USA). Rat Insulin Radioimmunoassay kit (RIA) manufactured by Millipore (Merck) was purchased from Abacas (Billerica, MA, USA); Mouse Insulin ELISA was purchased from ALPCO Diagnostics (Salem, NH, USA). Analox Glucose Reagent and Glucose Standards (5.0 mmol/L, 8.0 mmol/L and 25.0 mmol/L glucose) were purchased from

55 Chapter Two Materials and Methods

Analox Instruments Ltd (Hammersmith, London, UK). Glucose Intravenous Infusion BP (50 %), Saline (0.9% W/V solution) were purchased from Astra Pharmaceuticals Pty. Ltd. (North Ryde, NSW, Australia). Insulin Actrapid® was purchased from Novo Nordisk (Bagsvaerd, Denmark). Heparin was purchased from David Bull Laboratories (Mulgrave, VIC, Australia). Sodium Pentobarbitone (Lethabarb) was purchased from Boehringer Ingelheim (Artamon, NSW, Australia). Bovine Albumin fraction V Fatty Acids was purchased from ICN Biochemicals Inc. (Aurora, OH, USA). Hexadimethrine Bromide (≥94%, titration) and Theophylline anhydrous (≥99%, powder) were purchased from Sigma Aldrich (St Louis, MO, USA).

2.1.2. Pancreatic -Cell Line

2.1.2.1. Mouse Insulinoma MIN6 Cells

The mouse pancreatic insulinoma cell line, MIN6 cells were obtained from Dr. Ross Laybutt, Garvan Institute of Medical Research (Darlinghurst, NSW, Australia), at passage number 29. The MIN6 cells used in the assays were between passage 29 to 40 and were cultured in 1X DMEM (Catalogue number: 11995) containing 25 mmol/L glucose, supplemented with 10 % (vol/vol) FBS, 100 units/ml penicillin, 100 g/ml streptomycin and 15 mM HEPES (pH 7.4) at 37 °C with 5 % CO2 in a humidified incubator. For optimal culture condition, culture medium was replaced every second day during the culture. Regular subculture of MIN6 cells was conducted weekly by dividing one in five when the culture reached around 80-90% confluence. To subculture MIN6 cells, culture medium were aspirated from culture vessels (T75 flasks) and cells were rinsed once with 5 mL of warm 1X PBS. Subsequently, cells were dissociated by adding 1 mL of working strength (0.125 %) trypsin-EDTA then incubated at 37 °C for 5 minutes. Following the incubation, trypsinisation was terminated by adding 5 mL of complete DMEM into flasks and the detached cells were then mixed evenly by gentle pipetting. The diluted cells in complete

DMEM were then transferred into fresh T75 flasks and cultured in 5% CO2 incubator at 37°C.

56 Chapter Two Materials and Methods

2.1.3. Experimental Animal Models

2.1.3.1. Source and Maintenance

2.1.3.1.1. The Collaborative Cross Mice (The CC mice) The CC mice used in this study were bred and maintained by Geniad Pty Ltd. at the Animal Resource Centre (Murdoch, WA, Australia). The CC mice were transferred to the BioResources Facility at Austin Health (Heidelberg, VIC, Australia) after weaning. On arrival, mice were allowed to acclimatize for at least 1 week prior to procedures. All CC mice included in this study were at inbreeding generation F15 or beyond with an average of 21 generations of inbreeding. All mice were fed ad libitum a standard rodent chow diet (9% fat, 22% protein, and 69% carbohydrate by weight) unless otherwise state. Drinking water was accessed freely at all time during the study. Mice were separated by gender and housed in groups of 2-3 per cage and maintained with controlled temperature at 19-22 °C and 12-h light/dark cycle. All experimental procedures were performed in accordance with protocols approved by the Austin Health Animal Ethics Committee (AEC No. 2012/4844 and 2014/5196).

2.1.3.1.2. Control Inbred Mouse Strains The commercial mouse strain C57BL/6 was purchased at 6-weeks of age from the Walter and Eliza Hall Institute (WEHI, Kew, VIC, Australia); NZO/HILtJ mice were bred in the BioResources Facility and transferred to the experimental area after weaning. All mice were fed ad libitum a standard rodent chow diet unless otherwise state with free access to clean drinking water throughout the duration of the study.

2.1.3.2. Diet

2.1.3.2.1. Standard Chow Diet A standard rodent chow diet, irradiated WEHI mice cubes (Product code 8720610), was manufactured by Rigley Agricultural Products, Barastoc (Pakenham, VIC, Australia). This chow contains 9 % fat, 22 % protein, and 69 % carbohydrate by weight. The chow diet contained 13.5 MJ kg−1 digestible energy, with 2.7 MJ or 20% coming from protein, 9.5

57 Chapter Two Materials and Methods

MJ or 70% from carbohydrate and 1.4 MJ or 10% from fat. Refer to Appendix I for composition data of the standard chow diet.

2.1.3.2.2. High Fat Diet The high fat diet (Product number: SF04-030) used in this thesis was manufactured by Specialty Feeds (Glen Forest, WA, Australia). This high fat diet is a maize starch modification of research diet SF04-001 and contains 23.5% fat, 22.6% protein and 53.9% carbohydrate. Total digestible energy was calculated as 19 MJ kg-1 with 43% calculated digestible energy from lipids and 21% from protein. The fat content of this diet was primarily derived from 207 g/kg lard with minor portions from 29 g/kg soya bean oil, by weight. Refer to Appendix II for full composition data of this high fat diet.

2.1.4. EQUIPMENT

Whole blood glucose concentrations were measured using an Accu-Chek Performa blood glucose meter and glucose test strips manufactured by Roche (Switzerland). Plasma glucose was assessed using a GM7 Micro-Stat glucose analyser manufactured by Analox Instruments Ltd (Hammersmith, London, UK).

The Sunrise plate reader manufactured by TECAN Group Ltd (Switzerland) was used for colorimetric absorbance based assays. The radioactivity of tracer-labelled insulin in radioimmunoassay was counted using the COBRAII Auto Gamma counter manufactured by PACKARD (Meriden, CT, USA).

Tissue samples were homogenised utilising the Plytron 3100 homogenizer from Kinematica (Switzerland). Sonication of samples was performed using the MICROSON XL 2000 Ultrasonic Liquid Processor manufactured by MISONIX Incorporated (Farmingdale, NYC, USA).

A Nikon Eclipse TE2000-E fluorescent microscope (Japan) and Olympus CK2 Inverted microscope (Olympus Optical, Tokyo, Japan) were utilised for cell culture experiments. Islet picking was performed using an Olympus Model SZX12 stereomicroscope (Olympus Optical,

58 Chapter Two Materials and Methods

Tokyo, Japan). Blood vessel cannulation and bile duct inflation were conducted using a Leica MZ6 modular stereomicroscope (Leica Microsystems, Germany).

MyCycler Personal Thermal Cycler manufactured by Bio-Rad Laboratories (Richmond, CA, USA) was utilised for synthesising complimentary DNA (cDNA). Quantitative Real- Time PCR (qRT-PCR) was performed using Applied Biosystems ViiA™ 7 Real-Time PCR System and MicroAmp™ Optical 384-well reaction plates manufactured by Applied Biosystems (Thermo Fisher Scientific, CA, USA). ViiA 7 software (version 1.2.4) from Applied Biosystems was used for Real-Time PCR data analysis.

RNA quality and concentration was determined using a NanoDrop Lite Spectrophotometer manufactured by Thermo Scientific (Carlsbad, CA, USA).

2.2. Methods 2.2.1. Molecular Biology Techniques

2.2.1.1. Nucleic Acid Extraction

2.2.1.1.1. RNA Extraction from MIN6 Cells The culture medium was aspirated from all wells containing cells, followed by two washes with 1X PBS to remove the remaining medium, cell debris and metabolic wastes on the cell surface. TRIzol reagent (Invitrogen, Catalogue number: 15596-018) was placed (200 l per well in 24-well plates) in order to lyse cells directly in wells and mixed by repeated pipetting. The homogenised lysate was collected in a new eppendorf tube and incubated at room temperature for 5 minutes to permit complete dissociation of nucleoprotein complexes. Subsequently, 40 l of chloroform was added and mixed vigorously for 15 seconds followed by a 3 minute incubation at room temperature. Phase separation of the TRIzol-chloroform mixture was achieved by centrifuging at 12,000 Xg for 15 minutes at 4°C. The upper aqueous phase where the RNA was located was transferred to a new eppendorf tube then processed for RNA precipitation by incubating overnight with 100 l of 100% isopropanol containing 5 g RNase-free glycogen at -80°C. Centrifugation was performed at 12,000 X g for 10 minutes at 4°C to pellet the RNA. The pellet was washed

59 Chapter Two Materials and Methods in 200 l 75% ethanol followed by centrifugation at 7500 X g for 5 minutes at 4°C. The RNA pellet was air-dried then dissolved in nuclease-fee water (Ambion) and stored at - 80°C. The RNA purity and concentration was determined by measuring the absorbance at 260 nm and 280 nm using a NanoDrop Lite Spectrophotometer from Thermo Scientific (Carlsbad, CA, USA).

2.2.1.1.2. RNA Extraction from Mouse Pancreatic Islets Total RNA from mouse isolated islets was prepared using the RNeasy® Mini Kit (QIAGEN, Hilden, Germany) as this product enabled us to enrich high-quality RNA from small quantities of primary islets. Prior to RNA extraction, the isolated islets were allowed to recover overnight in an optimum culture medium RPMI containing 11.1 mM glucose. The recovered islets were hand-picked into eppendorf tubes and washed once with 1X PBS before proceeding to RNA extraction. Islets (approximately 150 islets in each sample) were re-suspended in 350 l Buffer RLT (guanidine-thiocyanate-containing lysis buffer supplemented with 1% -mercaptoethanol) followed by vigorous vortexing for 1 minute. Islet homogenisation was achieved with QIAshredder homogenizer (QIAGEN) by loading the lysate into a QIA shredder spin column and centrifuging for 2 minutes at 13,000 rpm. The homogenised lysate in the flow-through was then mixed with an equal volume of 70% (vol/vol) ethanol and transferred into an RNeasy spin column for RNA purification. After centrifugation at 10,000 rpm for 15 seconds the flow-through was discarded and the column was subjected to three-wash steps as follows: the RNeasy spin column was washed once with 700 l Buffer RW1 followed by two washes with 500 l Buffer RPE. Centrifugation was performed in between the washes and the flow-through was discarded after each centrifugation. Total RNA was eluted by the addition of 30 l nuclease-free water directly onto the spin column membrane followed by centrifugation at 10,000 rpm for 1 minute. The purified RNA was stored at -80°C. RNA quality and concentration was determined as described in section 2.1.4.

2.2.1.2. DNase Treatment

Following RNA extraction the contaminating DNA from RNA preparation was removed by DNase treatment. TURBO DNA-free DNase Treatment Kit (Ambion) was employed

60 Chapter Two Materials and Methods and the TURBO DNase included an engineered version of wild type DNase I with greater DNA affinity and catalytic efficiency than the conventional DNase I. Each reaction consisted of 1 g (~90 ng/l RNA) of islet-derived RNA or RNA from MIN6 cells, 1X TURBO DNase Buffer and 0.5 l (1 unit) DNA-free recombinant DNase I to make up to a volume of 13.5 l with nuclease-free water. The mixture was incubated at 37 °C for 20 minutes on heat blocks and the reaction was terminated by adding 2 l of DNase Inactivation Reagent. The RNA sample was allowed to incubate with the DNase Inactivation Reagent for 5 minutes at room temperature, mixing occasionally. Followed by the incubation, samples were subjected to centrifugation at 10,000 X g for 90 seconds minutes. The clear supernatant containing RNA was transferred to a fresh tube and stored at -80 °C.

2.2.1.3. Reverse Transcription- cDNA Synthesis

Complementary DNA (cDNA) was synthesised using SuperScript III Reverse Transcription System (Ambion). In a 20 l reaction, the DNase-treated RNA samples were incubated with 1 l random primers (0.5 g/l) and 1l dNTP Mix (10 mM each dATP, dGTP, dCTP and dTTP) at 65 °C for 5 minutes on heat blocks. Subsequently, the mixture was chilled on ice for 1 minute followed by the addition of 4 l of 5X First Strand Buffer, 1 l of 0.1 M DTT, 1 l of RNaseOUT Recombinant Ribonuclease Inhibitor (40 units/l) and 1 l of SuperScript III reverse transcriptase (200 units/l). Reverse transcription was carried out in a thermocycler (My Cycler Personal Thermal Cycler) with the following cycling conditions: 25 °C for 5 minutes, 50 °C for 45 minutes, 70 °C for 15 minutes and 4 °C for 5 minutes. A control for the genomic DNA contamination was prepared as a reaction without the addition of reverse transcriptase. The resulting cDNA samples were stored at - 80 °C for the subsequent quantitative Real-Time PCR analysis.

2.2.1.3.1. Complementary DNA (cDNA) Preparation from Isolated Human Islets Purified islets were snap-frozen in aliquots of 1000-3000 IEQ following a culture period of 24-48 hours. Extraction and purification of RNA was performed using ISOLATE II RNA Micro Kit (Bioline) according to the manufacturer’s instructions. The purified RNA

61 Chapter Two Materials and Methods was quantified by absorbance at 260 nm using a Nanodrop 2000. 500µg RNA per sample was reverse transcribed in a 20µL reaction using the High Capacity cDNA Reverse Transcription MasterMix (ThermoFisher Scientific/Life Technologies). Complementary DNA was diluted 1:20 with nuclease-free water for real-time PCR.

2.2.1.4. Quantitative Real-Time Polymerase Chain Reaction (RT-q- PCR)

Quantitative Real-Time PCR was conducted using TaqMan® Gene Expression System purchased from Applied Biosystems (Life Technologies, Scoresby, VIC, Australia). Target gene expression was performed in duplicate on the target cDNA. Real-Time PCR was performed in the ViiA™ 7 Real-Time PCR thermocycler system using the TaqMan® universal PCR master mix reagent kit (Applied Biosystems) and gene-specific TaqMan® Gene Expression Assays (Applied Biosystems, Scoresby, Victoria, AU) as listed in Table 2.1. Reactions were set up in a two-notch type 384-well PCR plate (Interpath, VIC, Australia) and each reaction was performed in a 10 l volume containing: 1X TaqMan® Universal PCR Master Mix, 1X TaqMan Gene Expression Assay and 2 l cDNA template (1 ng/l of mouse islet cDNA, 5 ng/l of cDNA derived from MIN6 cells or 1.25 g/l of human islet cDNA). Three housekeeping genes, 18S rRNA (18S), insulin II (INS2) and peptidylprolyl isomerase A (PPIA), were utilised as internal controls to evaluate the relative amount of target gene expression levels. Real-Time PCR was conducted using the following cycling conditions: 50 °C for 2 minutes, 95 °C for 10 minutes, followed by 40 cycles of (95 °C for 15 seconds then 60 °C for 1 minute).

2.2.1.5. Results Analysis of Real-Time q-PCR

ViiA™ 7 software (version 1.2.4) was employed to analyse real-time PCR data using the comparative Ct (ΔΔCt) method and the results were reported as fold change from the controls. Islet gene expression in mouse and human islets were calculated as relative to the levels of housekeeping genes, INS2 and PPIA, respectively. Relative gene expression was based on relative quantification of the diabetic group to non-diabetic group. Target gene

62 Chapter Two Materials and Methods expression in MIN6 cells was normalised to a housekeeping gene PPIA, and the relative gene expression was then quantified as fold change from the control treatment group.

2.2.1.6. Protein Assay

The Bio-Rad Protein Assay was utilised to determine pancreatic protein content based on the Bradford-dye binding method (Bradford, 1976). The standard protocol for microfiber plates was followed. The protein dye reagent was prepared by diluting 1 part of Dye

Reagent Concentrate with 4 parts distilled H2O then filtered through a filter (Whatman #1 filter, Bio-Rad) to remove particulates. A five-point standard curve was created by a 1:1 serial dilution from 0.5 mg/ml bovine serum albumin (Bio-Rad) with distilled H2O to achieve the following concentrations: 62.5 g/ml, 125 g/ml, 250 g/ml and 500 g/ml BSA. The reaction was set up as 10 l of each standard and test sample mixing with 200l of working strength dye reagent in a 96-well microplate, followed by a 5 minute incubation at room temperature. The absorbance was measured by using the Sunrise plate reader (TECAN) at 595 nm.

63 Chapter Two Materials and Methods

Table 2.1 List of TaqMan Gene Expression Assay kits for gene expression in primary islets from mouse and human.

Species Gene Symbol Gene Name Probe ID Dye/Quencher

Mouse 18S Eukaryotic 18S rRNA 4319413E VIC® -MGB

INS2 Insulin II Mm00731595_gH FAM-MGB

PPIA Peptidylprolyl isomerase A Mm02342429_g1 FAM-MGB

GFY Golgi associated olfactory signaling regulator Mm04243347_g1 FAM-MGB

SNRNP70 Small nuclear ribonucleoprotein U1 subunit 70 Mm00456329_m1 FAM-MGB

HSD17B14 Hydroxysteroid 17-beta dehydrogenase 14 Mm01197302_g1 FAM-MGB

SPHK2 Sphingosine kinase 2 Mm00445021_m1 FAM-MGB

NTN5 Netrin-5 Mm01205606_m1 FAM-MGB

ABCC8 ATP binding cassette subfamily C member 8 Mm00803450_m1 FAM-MGB

KCNJ11 Potassium voltage-gated channel subfamily J member 11 Mm00440050_s1 FAM-MGB

E2F8 E2F transcription factor 8 Mm01204160_m1 FAM-MGB

DLG2 Discs large homolog 2 Mm01318470_m1 FAM-MGB

DDIAS DNA damage induced apoptosis suppressor Mm00546937_m1 FAM-MGB

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Continue Table 2.1 List of TaqMan Gene Expression Assay kits for gene expression in primary islets from mouse and human.

Species Gene Symbol Gene Name Probe ID Dye/Quencher

Mouse CCDC90B Coiled-coil domain containing 90B Mm02343991-m1 FAM-MGB

ANKRD42 Ankyrin repeat domain-containing protein 42 Mm00551320_m1 FAM-MGB

PCF11 Pre-mRNA cleavage complex II protein Pcf11 Mm01324032_m1 FAM-MGB

RAB30 Ras-related protein Rab-30 Mm00512814_m1 FAM-MGB

PRCP Proline carboxypeptidase Mm00804502_m1 FAM-MGB

Human PPIA Peptidylprolyl isomerase A Hs04194521_s1 FAM-MGB

RPLPO Ribosomal protein lateral stalk subunit P0 Hs9999902_m1 FAM-MGB

E2F8 E2F transcription factor 8 Hs00226635_m1 FAM-MGB

ABCC8 ATP binding cassette subfamily C member 8 Hs01093752_m1 FAM-MGB

SNRNP70 Small nuclear ribonucleoprotein U1 subunit 70 Hs01091627_m1 FAM-MGB

KCNJ11 Potassium voltage-gated channel subfamily J member 11 Hs00265026_s1 FAM-MGB

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Continue Table 2.1 List of TaqMan Gene Expression Assay kits for gene expression in primary islets from mouse and human.

Species Gene Symbol Gene Name Probe ID Dye/Quencher

Human SPHK2 Sphingosine kinase 2 Hs01016543_g1 FAM-MGB

DDIAS DNA damage induced apoptosis suppressor Hs00376649_m1 FAM-MGB

DLG2 Discs large homolog 2 Hs00265843_m1 FAM-MGB

ANKRD42 Ankyrin repeat domain-containing protein 42 Hs02889709_m1 FAM-MGB

PCF11 Pre-mRNA cleavage complex II protein Pcf11 Hs00391974_m1 FAM-MGB

RAB30 Ras-related protein Rab-30 Hs00205577_m1 FAM-MGB

PRCP Proline carboxypeptidase Hs00234607_m1 FAM-MGB

CCDC90B Coiled-coil domain containing 90B Hs00991733_g1 FAM-MGB

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2.2.2. Cell Biology

2.2.2.1. Gene Silencing in MIN6 Cells

2.2.2.1.1. Lipofectamine-mediated siRNA Transfection A cationic lipid-mediated delivery of Silencer® Select Pre-designed siRNA (Ambion, Life Technologies) was employed for E2F8 gene knockdown. An optimal concentration of siRNA for lipofectamine-mediated tranfection in MIN6 cells was determined using the BLOCK-iT™ Alexa Fluor™ Red Fluorescent Control oligonucleotides (Catalogue number: 14750-100) (Invitrogen, Life Technologies), an Alexa Fluor (red fluorescent)-labelled non- target siRNA (BLOCK-iT Alexa Fluor® Red Fluorescent Control, ThermoFisher). MIN6 cells were plated one day prior to siRNA transfection at density of 5 x 104 cells/well in 96- well plates. Three concentrations of siRNA were examined as follows: 0 (untreated control), 25nM, 50 nM and 100 nM of Alexa Fluor-labelled non-target siRNA. Transfection efficiency was assessed 16-hour post transfection by directly observing the red fluorescent signals unsing fluorescent microscopy (Nikon Eclipse TE2000-E, Japan). Fluorescence signal was detected using a standard filter for rhodamine fluorophores at 555 nm. As shown in Figure 2.1, nuclear localisation of the oligo (non-targeted siRNA, red fluorescent) was observed in all three treatments but not in the untreated group. Moreover, almost 100% of cells have taken up the oligo as observed in all groups with Alexa Fluor siRNA transfection, and cells retained a normal morphology as noticed in bright-field images. A medium concentration of 50 nM was determined to be used for the subsequent siRNA transfection in MIN6 cells.

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Figure 2.1 Transfection efficiency of siRNA in MIN6 cells. MIN6 cells at density of 2.5 x 105 cells /ml (5 x 104 cells/well in 96-well plates) were transfected with Alexa Fluor-labelled control siRNA at 0, 25 nM, 50 nM or 100 nM using Lipofectamine RNAiMAX transfection reagent (4 l/ml). Alexa Fluor-labelled control siRNA (red fluorescence) was visualised 16-hour post transfection. Representatives present merging of bright-filed and red fluorescence images by using image processing program, Image J. Images were taken at 40X magnification.

Thus, a pre-designed, gene specific siRNA duplexes against E2F8 (siRNA ID: s99361, sense sequence: 5’-GCU CGGCUAUCGUAAACUUtt-3’; antisense: 5’-AAGUUUAC GAUAGCCGAGCtt-3’) or scrambled control siRNA (Silencer® Select Negative Control No.1 siRNA, Cat no. 4390843) was transfected at a final concentration of 50 nM with 4 l/ml Lipofectamine RNAiMAX transfection reagent (Invitrogen, Life Technologies) by following the manufacturer’s protocol for forward transfection. Firstly, culture medium was aspirated from each well and cells were rinsed once with warmed 1X PBS (500 l/well). Subsequently, culture medium was replaced with 80 l of siRNA transfection medium, containing 1X DMEM, 11.1 mM glucose, 10 % (vol/vol) FBS and 15 mM HEPES (pH 7.4). Secondly, siRNA working stock (10 M) was diluted in Opti-MEM® I Reduced Serum Medium (Gibco, Life Technologies) to make a desired concentration of 0.25mol/ml with addition of 0.4 l of Lipofectamine. Subsequently, the siRNA- Lipofectamine mixture was incubated at room temperature for 15 minutes with occasional mixing. Following the incubation, 20l/well of the siRNA-Lipofectamine mixture was added into each well and the culture vessels were gently swirled to mix. Cells were cultured at 37 °C for 72 hours in a CO2 humidified incubator. E2F8 gene knockdown was determined 72 hours post transfection using a Real-Time PCR.

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2.2.2.1.2. Lentiviral Transduction of Short Hairpin RNA (shRNA) Optimal Cell Density for Lentivirus Transduction MIN6 cells were plated at density of 2x104, 4x104, 6x104 and 105 cells/well in a 96-well plate 24-hour prior to examination. An optimal of 60% confluency was observed in the wells plating 4 x105 cells/ml (6 x104 cells/well) as shown in Figure 2.2.

Figure 2.2 Determination of an optimal MIN6 cell density for lentivirus transduction. MIN6 cells were plated at various densities in a 96-well plate: 105 cells/ml, 2x105 cells/ml, 3x105 cells/ml, 4x105 cells/ml and 5x105 cells/ml (equivalent to 2x104, 4x104, 6x104 and 105 cells/well, respectively). Images were taken 20-hour post seeding. Magnification: 40X.

Puromycin Titration (Kill Curve) To determine an optimal puromycin concentration for screening lentiviral-positive MIN6 transductants, a puromycin titration test was performed with seven puromycin concentrations: 0.5 g/ml, 1 g/ml, 2 g/ml, 4 g/ml, 6 g/ml, 8 g/ml and 10 g/ml. MIN6 cells without lentiviral transduction were plated at density of 6 x 104 cells/well in 96-well plates, supplemented with 200 l/well of complete culture medium. Next day, medium was replaced with conditional medium containing 0, 0.5 g/ml, 1 g/ml, 2 g/ml, 4 g/ml, 6 g/ml, 8 g/ml and 10 g/ml puromycin. Cell viability was examined daily for signs of visual toxicity by examining cell morphology under microscope. During the period of examination, routine change of conditional medium was conducted every second day

69 Chapter Two Materials and Methods for up to two weeks. The minimum concentration of 1 g/ml puromycin that caused complete cell death over the course of seven days was identified for MIN6 cells stable clone selection. As shown in Figure 2.3A and B, significant cell death was observed after 24-hours of puromycin incubation at concentration of 2 g/ml and above, moreover, 48- hours post incubation there was no viable cell could be found in these groups. However, 0.5 g/ml puromycin was not sufficient to cause 100% cell death within seven days. In contrast, 1 g/ml puromycin treatment was demonstrated to cause nearly 50% cell death after 72-hours of incubation and merely 100% cells were dead post five days of incubation (Figure 2.3A). Accordingly, 1 g/ml puromycin was determined as the optimal concentration to be used during stable clone selection.

Figure 2.3A Dose response of puromycin in MIN6 cells. MIN6 cells (6 x 104 cells/well) were treated with puromycin at the following concentrations: 0 (untreated), 0.5, 1, 2, 4, 6, 8, 10 g/ml. Cell viability was examined under the microscope at 24, 48, 72, 96 and 120 hours of puromycin treatments. Images are untreated group and MIN6 cells treated with 0.5, 1 and 2 g/ml puromycin at different time points. Magnification: 40X.

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Figure 2.3B Dose response of puromycin in MIN6 cells. MIN6 cells (6 x 104 cells/well) were treated with puromycin at the following concentrations: 0, 0.5, 1, 2, 4, 6, 8, 10 g/ml. Cell viability was examined under the microscope at 24, 48, 72, 96 and 120 hours of puromycin treatments. Images are MIN6 cells treated with 4, 6, 8 and 10 g/ml puromycin at different time points. Magnification: 40X

Hexadimethrine Bromide Dose Determination Hexadimethrine bromide was supplemented to enhance lentiviral transduction of MIN6 cells. To determine an appropriate concentration for MIN6 cells, four doses of hexadimethrine bromide were tested as followed: 2 g/ml, 4 g/ml, 8 g/ml, 16 g/ml. MIN6 cells without lentiviral transduction were plated at density of 6 x 104 cells/well in 96-well plates one day prior to hexadimethrine bromide treatment. Complete medium supplemented with four different doses of hexadimethrine bromide was administered to the seeded cells in the next day, and cells were examined 24- and 48-hour post hexadimethrine bromide treatment for cell viability. The maximum concentration of hexadimethrine bromide that does not cause cell death after a 48-hour incubation was determined. As shown in Figure 2.4, hexadimethrine bromide had no cytotoxic effect on MIN6 cells at the dose ≤ 8 mg/ml, however, substantial reduction in cell number was observed at 16 mg/ml hexadimethrine bromide treated cells. Based on these data a recommended concentration of 8 mg/ml hexadimethrine bromide was determined to be used during lentiviral transduction.

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Figure 2.4 Cytotoxic effect of hexadimethrine bromide in MIN6 cells. MIN6 cells (6 x 104 cells/well) were treated with hexadimethrine bromide at the following concentrations: 0 (untreated), 2, 4, 8, 16 mg/ml. Cell viability was examined under the microscope at 24 and 48 hours of treatment. Magnification: 40X.

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Lentiviral Transduction with Short Hairpin RNA (shRNA) Dlg2 knockdown cells were generated via lentiviral-based stable shRNA transduction using MISSION TRC lentiviral particles expressing shRNA targeting Dlg2 (Sigma-Aldrich, St. Louis, MO, USA). The lentiviral vector used in this study contained specific sequence target Dlg2 and a puromycin resistance gene (puroR) for mammalian selection as illustrated in Figure 2.5. The sequence for the Dlg2 shRNA (TRCN0000024872) utilised in this study was 5’-CCGGCCCATGAAGGATCGAATCAATCTCGAGATTGATTCGATCCTTC ATGGGTTTTT-3’. Lentiviral transduction and generation of stably transduced cell lines were performed according to the manufacturer’s instructions. MIN6 cells were plated at a density of 6 x 104 cells/well in a 96-well plate to accommodate a confluency of 60-70% upon transduction. The following day, cells were infected with lentiviral particles at a multiplicity of infection (MOI) of 20 in complete medium containing 8 g/ml hexadimethrine bromide (Sigma-Aldrich) for 24 hours. The cells treated with hexadimethrine bromide without lentiviral transduction was employed as a control for this experiment. Following transduction, the stably transduced cells were selected and cultured in complete DMEM medium with the addition of 1 g/ml of puromycin until cells were grown to 80 % confluence. All dealings including lentivirus and its related materials were carried out in accordance with protocols approved by the Gene Technology Regulations Committee (NLRD 2016/032).

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Name Description U6 U6 Promoter cppt Central polypurine tract hPGK Human phosphoglycerate kinase eukaryotic promoter puroR Puromycin resistance gene for mammalian selection WPRE Woodchuck hepatitis post- transcriptional regulatory element SIN/3’LTR 3’ self-inactivating long terminal repeat f1 ori fi origin of replication ampR Ampicillin resistance gene for bacterial selection pUC ori pUC origin of replication 5’ UTR 5’ long terminal repeat Psi RNA packaging signal RRE Rev response element Figure 2.5 Schematic diagram depicting the features of TRC2 Lentiviral plasmid vector TRC2-pLKO-puro. Data source: MISSION shRNA Plasmid DNA, Product information from Sigma-Aldrich.

2.2.2.2. Isolation of Mouse Pancreatic Islets

Primary islets were isolated from 10- to 12-week-old mice by Collagenase digestion using a modified method of Lacy and Kostianovsky (Lacy & Kostianovsky, 1967) and Gotoh (Gotoh, Maki, Kiyoizumi, Satomi, & Monaco, 1985). The digestion buffer was prepared with 0.6 mg/mL of Collagenase P in HBSS supplemented with 20 mM HEPES (pH 7.4) and 2 mM CaCl2. The mouse was sacrificed by cervical dislocation and the abdomen was opened for the perfusion procedure. For the perfusion, the mouse bile duct was clamped at its point of entry to the duodenum and 3 mL of digestion buffer was perfused into pancreas by intraductal injection using a 30 gauge needle. The inflated pancreas was excised from the animal and topped up the reaction volume to 5 mL with digestion buffer followed by a 6 minute incubation at 37 °C. Once the pancreatic tissue was digested, collagenase was inactivated immediately by the additional of 25 mL of ice-cold complete HBSS supplemented with 0.2 % BSA and passed through a 50 micron mesh. Samples were then washed once in ice-cold HBSS containing 0.2 % BSA and centrifuged at 1,200 rpm for 2 minutes at room temperature. The supernatant was poured off and excess fluid in the tube

74 Chapter Two Materials and Methods was drained completely before moving on to islet purification. Islets were then purified using Histopaque-1077 density gradient (a solution of polysucrose and sodium diatrizoate adjusted to a density of 1.077 ± 0.001 g/ml) with RPMI-1640 layered on top, followed by centrifugation at 2,000 rpm for 15 minutes at room temperature. Islets were collected from the interface into 40 mL of RPMI-1640 and subsequently pelleted by centrifugation at 1,000 rpm for 2 minutes at room temperature. Supernatant was aspirated by using a 20 mL syringe and the islet pellet was then resuspended, hand-picked under a stereoscopic microscope (Olympus Model SZX12, Olympus Optical, Tokyo, Japan) into a petri dish with warmed complete RPMI-1640 medium containing 11.1 mmol/L glucose, 10 % FBS, 100 units/ml penicillin and 100 g/ml streptomycin. Isolated islets were cultured in a 37°C humidified atmosphere of 95 % air: 5 % CO2.

2.2.2.3. Isolation of Human Pancreatic Islets

Human pancreata were obtained from heart-beating, brain-dead donors, with informed consent from next of kin, following the research approval from HREC committee at the St Vincent’s Hospital Melbourne (HREC-011-04). Human islets were purified by intraductal perfusion and digestion of the pancreas with collagenase followed by purification using ficoll density gradients (Ricordi, Lacy, Finke, Olack, & Scharp, 1988). Human islets were cultured in Connaught Medical Research Laboratories (CMRL) 1066 medium (Invitrogen) supplemented with 10% human serum albumin, 100 U/ml penicillin, 100 mg/ml streptomycin and 2 mM L-glutamine (complete CMRL), in a 37°C, 5% CO2 humidified incubator.

2.2.2.4. Insulin Secretion Assay

Silencing RNA (siRNA) transfected or lentiviral transduced MIN6 cells grown in 96-well plates were pre-incubated overnight in complete DMEM containing 5.5 mmol/L glucose prior to the assay. Insulin secretion was performed in KRBB (108 mM NaCl, 4.9 mM KCl,

2.3 mM CaCl2·2H2O, 1.2 mM MgSO4·7H2O, 1.2 mM KH2PO4, 25.7 mM NaHCO3 and 10mM HEPES, pH 7.4) supplemented with 0.2 % (w/v) bovine serum albumin (BSA). Glucose-stimulated insulin secretion (GSIS) was conducted using a static incubation protocol as follows: The cultured cells were washed three times with glucose-free KRBB

75 Chapter Two Materials and Methods and pre-incubated in KRBB containing 2 mmol/L glucose for 90 minutes at 37°C. Following the pre-incubation, cells were washed two times with glucose-free KRBB and incubated for an additional 1 hour in 200 l of KRBB containing low glucose (2 mmol/L, basal), high glucose (20 mmol/L, stimulated) or secretagogues in separate wells. Secretagogues were prepared in KRBB containing either low or high glucose at the stated concentration: 20 mM Arginine, 275 M Tolbutamide and 10 nM GLP-1. KCl (30 mM) utilised in this assay was prepared in glucose-free KRBB. Following the incubation, cultured medium was collected from each well for insulin analyses. The cells were scraped off and harvested in 200 l glucose-free KRBB in a 1.5 mL Eppendorf tube. The harvested cells were sonicated using MICROSON XL 2000 Ultrasonic Liquid Processor (MISONIX Incorporated) followed by centrifugation at 15,000 Xg for 15 minutes and the supernatant was collected for measurement of total insulin content using a mouse insulin ELISA (ALPCO Diagnostics, NH, USA).

2.2.3. Physiological Characterisations

2.2.3.1. Random Blood Glucose Levels and Body Weights

Random blood glucose (RBG) and body weight were measured in a total of 1,119 mice consisting of 652 males from 53 strains (n=3-15) and 467 females from 48 strains (n=3-10) at 8-10 weeks of age. Phenotyping was performed in a total of 1,119 mice consisting of 652 males from 53 strains (n=3-15) and 467 females from 48 strains (n=3-10) at 8-10 weeks of age. These measurements were taken at the same time of the day between 9 am - 11 am. Random blood glucose was sampled from tail tipping and measured on an Accu-Chek® Performa glucometer (Roche, Switzerland).

2.2.3.2. Insulin Tolerance Test (ITT)

An insulin tolerance test (ITT) was conducted on 8- to 10-week old mice in a non-fasted state. Mice were weighted and anesthetised with intraperitoneal (i.p.) injection of sodium pentobarbitone (12 mg/kg body weight). Mice were rested on a heat mat for 20 minutes and subsequently taken basal blood glucose from tail bleeding using a glucometer. 0.75 I.U./Kg Actrapid insulin was administrated via an i.p. injection and blood glucose

76 Chapter Two Materials and Methods concentrations were measured at 15, 30, 45, 60 and 120 minutes following insulin administration. To better reflect the change of glucose levels in response to insulin, the blood glucose concentrations during ITTs were presented as percentage of its baseline glucose levels, and subsequently, the area under the curve of percentage of basal glucose was calculated as a measurement of insulin sensitivity.

2.2.3.3. Glucose Tolerance Test (GTT)

2.2.3.3.1. Oral Glucose Tolerance Test (OGTT) Glucose tolerance was assessed by an oral glucose tolerance test (OGTT) on 10-12 weeks old mice following a 6-hour fast. Mice were anesthetised with an i.p. injection of 12mg/kg sodium pentobarbitone and then underwent a catheter insertion into the right carotid artery as well as a tracheotomy to aid with breathing. After 20 minutes of rest, mice were administered an oral bolus of glucose (2 g/kg) via a gavage needle (20-gauge, 38 mm long curved, with a 21/4 mm ball end; Able Scientific, Canning Vale, Western Australia, Australia), and 200 µl of blood was sampled from the catheter at 0 (basal, prior to gavage) and 15, 30, 60, and 120 minute after glucose load. The blood sample was centrifuged immediately to separate plasma from blood cells. Plasma was collected and stored at -20 °C for plasma glucose and insulin analyses. The red blood cells were resuspended with heparinized saline and reinfused into the mouse prior to the collection of the next blood sample to prevent anaemic shock.

2.2.3.3.2. Intravenous Glucose Tolerance Test (IVGTT) The intravenous glucose tolerance test (IVGTT) was performed on 10-12 weeks old mice following an overnight fast (16-hour fast, 5 pm – 9 am). Mice were weighed and anesthetised with i.p. injection of sodium pentobarbitone (12 mg/kg body weight). After 20 minutes of rest, a silastic cathether filled with heparinised saline (25 Uml-1 heparin, 0.9% saline) was inserted into the left carotid artery. Mice were kept warm on a heat mat after the procedure and allowed to rest for 20 minutes. Following the basal blood sample (about 200 l), a bolus of glucose (1g/kg) was given through the carotid artery. Blood samples were collected at 2, 5, 10, 15 and 30 minutes following the glucose bolus. The blood samples were immediately spun down at maximal speed for plasma collection for

77 Chapter Two Materials and Methods each time point. The pelleted red blood cells were then resuspended in an equal volume of heparinised saline and infused back to mice via the cathether. Plasma samples were stored at -20 °C for glucose and insulin analyses.

2.2.3.3.3. Intravenous Glucose plus Arginine Tolerance Test Pancreatic capacity for insulin secretion was evaluated by an intravenous glucose tolerance test in combination with arginine at the dose of 1g/kg of glucose and arginine, respectively. The same procedure for IVGTT was followed for this test.

2.2.3.4. Pancreatic Insulin Content

A portion of mouse pancreas was weighed and homogenised in an ice-cold acid-ethanol solution (0.21 M HCl in 70% ethanol) using an Ultra-Turrax homogeniser (Janke & Kunkel, Staufen, Germany). Subsequent to an overnight incubation at 4°C, the homogenates were centrifuged at 16,000 X g for 15 minutes and supernatants were collected. Pellets were resuspended in the acid-ethanol solution for second extraction following the same protocol. The resulting pancreas extracts from the two fractions were pooled and neutralised with 1M Tris-Base (pH 11.07) prior to assay for insulin by rat specific insulin radioimmunoassay kit (Millipore). Protein concentrations were determined by the Bio-Rad Protein Assay Kit (Bio-Rad, Hercules, CA, USA).

2.2.3.5. Pancreas Histology

Mouse pancreas was carefully excised and fixed overnight in 4% (w/v in PBS) formaldehyde, dehydrated and embedded in paraffin. Three non-consecutive sections were sampled from each pancreas (100 m apart), 5 m thick sections were prepared for immunostaining. Sections were stained for insulin using a guinea pig anti-porcine insulin antibody (DAKO, Carpentaria, CA) to identify -cells and counterstained with haematoxylin and eosin (H&E) to aid morphological visualization of islets. Slides were scanned at 40X magnification. Islets were outlined manually on the digital images, and islet area, number and insulin intensity were analysed with ImageScope software (Aperio Technologies, CA, USA). Cell mass of pancreatic -cell was determined as the product of wet pancreas weight and the ratio of insulin positive/total pancreas area.

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2.2.3.6. Fat Pad, Pancreas and Tissue Collection

Mice were sacrificed with an overdose of sodium pentobarbitone (60 mg/kg) and the subcutaneous, infra-renal, epididymal fat pads were excised and weighted. Pancreas was removed and weighted. Following excision, all tissues were placed in a 1.5 mL Eppendorf tube and immediately frozen in liquid nitrogen and stored at -80°C.

2.2.3.7. Plasma Glucose Determination

A GM7 Analox glucose analyser was employed to determine glucose concentration in plasma samples using a glucose oxidase method. Maximum rate of oxygen uptake was determined as it is proportional to the amount of glucose content in the sample. Prior to the glucose reaction, the glucose analyser was calibrated with an 8 mmol/L glucose standard.

2.2.3.8. Insulin Concentration Determination

Plasma insulin concentrations and MIN6 cell insulin secretion assay samples were determined by a mouse specific insulin ELISA (ALPCO Diagnostics, NH, USA) using a sandwich type immunoassay. A rat insulin radioimmunoassay kit (RIA) manufactured Millipore was utilised to determine insulin concentration of islet insulin secretion assay and pancreatic insulin content. The assay employed the 125I-labelled insulin and rat insulin antiserum to determine the level of insulin by the double antibody method.

2.2.4. Genome-Wide Association Analyses of the Collaborative Cross

2.2.4.1. Genotyping and Haplotype Reconstruction

Each strain in the Collaborative Cross (CC) was genotyped at inbreeding generation N16 and beyond using microarray technology, the Mouse Universal Genotyping Array (MUGA), and its subsequent derivatives MegaMUGA and GigaMUGA Illumina arrays (Didion et al., 2014). The MegaMUGA array genotypes 77,808 SNPs spaced evenly throughout the genome, including 26 and 23 SNP markers located in the Y and mitochondria chromosomes, respectively. The genotype on MegaMUGA for the CC founders was extracted from the University of North Carolina CC web site:

79 Chapter Two Materials and Methods http://csbio.unc.edu/CCstatus/index.py?run=GeneseekMM. Subsequently, the consensus calls for each of the eight inbred founders was analysed and identified 68,903 SNPs that are robustly homozygous, and there is at least one founder carrying the non-reference allele. Therefore, each CC strain was genotyped for these 68,903 SNPs on MegaMUGA and these SNPs were positioned with reference to the NCBI mm9/build37 assembly.

Founder haplotype reconstruction and haplotype estimation for each CC strain were achieved using HAPPY in conjunction with 68,903 homozygous genotype data of the eight founder strains (Mott et al., 2000). The maximum-likelihood probability for genotype sets of two alleles were considered individually using the “hdesign method” in HAPPY. Therefore, the genomic intervals of the CC strains can be readily assigned to each of the eight founders according to the haplotype analysis (Ram, Mehta, Balmer, Gatti, & Morahan, 2014). The haplotype data of all the CC strains are accessible online at the site of GeneMiner to test against phenotypes. The GeneMiner UTL: www.sysgen.org/GeneMiner.

2.2.4.2. SNP-wise Association Analyses

A SNP-based genome-wide association study (GWAS) was utilised to identify genes associated with blood glucose levels. Random blood glucose levels obtained from 53 strains of males and 48 strains of females (N ≥ 3) were subjected to this test superlatively. This GWAS was performed using the raw genotyping data obtained from the MegaMUGA genotyping array. In addition, a simple fast association analysis with 77,808 well-annotated SNP markers was implemented (Ram & Morahan, 2017). SNPs with P<1x10-5 were outputs, however, only signals that passed the threshold of P<5x10-8 was considered as significant. The results of SNP-wise association study was presented as a GWAS style Manhattan plot. All SNPs were positioned with reference to the NCBIm37/mm9 assembly on the UCSC Genome Browser.

2.2.4.3. Quantitative Trait Loci Analysis

Linkage style analysis coupled inferred founder haplotypes was performed with the random blood glucose concentrations from a total of 1,119 CC mice consisting of males from 53 strains and females from 48 strains (N ≥ 3). Males and females were analysed separately

80 Chapter Two Materials and Methods following the same method as below. Linkage analysis identified quantitative trait loci (QTL) and inferred contributory founder haplotypes for the trait of study. A linear mixed model (R/QTL regression) was employed to evaluate the maximum-likelihood estimate (derived LOD score) for each genomic position (Broman & Sen, 2009). The imputed founder haplotype was determined by deriving coefficients (log odds ratio) of the fit from the logistic regression model (Gatti et al., 2014). A genome-wide significant threshold of 5x10-8 was determined by a one-way ANOVA chi-square test. The confidence interval of each associated region was defined by a 2-LOD drop interval from the peak position. The founder coefficients (logarithm of odds) for QTL were presented with the corresponding

P-value, which was estimated by an ANOVA test –log10 (P). The results of QTL mapping displayed as LOD-score QTL plots. Genetic loci were positioned with reference to the NCBIm37/mm9 assembly on the UCSC Genome Browser.

2.2.5. Statistical Analysis

All data are presented as mean ± SEM and a P-value < 0.05 was considered significant. Correlations between traits were determined by the Pearson correlation coefficient. Statistical significance between two variables was assessed using unpaired one-tailed Student’s t-test, and one-way ANOVA with Tukey’s post hoc test was used to determine significant differences between multiple comparisons. All analyses were performed using GraphPad Prism 6 (GraphPad Software Inc, La Jolla, CA, USA).

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CHAPTER THREE

GENOME-WIDE ASSOCIATION STUDY FOR HYPERGLYCAEMIA USING THE COLLABORATIVE CROSS MICE

Chapter Three Genome-Wide Association Study for Hyperglycaemia

3. Chapter 3 Genome-wide association study for hyperglycaemia using the Collaborative Cross mouse resource

3.1. Introduction The unprecedented increase in the prevalence of diabetes mellitus has long been considered one of the major health problems worldwide that requires urgent actions towards disease prevention and development of effective therapies. Type 2 Diabetes (T2D) accounts for the majority of cases (more than 90%) of the diabetes epidemic (Scully, 2012), which is characterised by hyperglycaemia resulting from progressive deterioration of -cell dysfunction in the presence of insulin resistance. The pathophysiology of hyperglycaemia is known to be heterogeneous and multifactorial, involving the complex interplay between a permissive genetic environment and detrimental environmental factors such as an increasing consumption of Western-style diet and obesity (Andrikopoulos, 2010; DeFronzo, 2004). There is ample evidence that diabetes has a strong genetic basis (Hemminki et al., 2010; Meigs et al., 2000), and islet -cell dysfunction is also inherited (Elbein et al., 1999). Therefore, understanding the genetic causes of hyperglycaemia will inform specific therapeutic interventions that correct the physiologic imbalance driving the pathogenesis of diabetes.

Studying the genetics of diabetes is challenging because of the heterogeneity and polygenic origin. In the past decade, there has been great progress made in technological advances in genome-wide association studies (GWAS). Since the initial GWAS for T2D were reported in 2007 (Diabetes Genetics Initiative of Broad Institute of et al., 2007; Scott et al., 2007; Wellcome Trust Case Control, 2007), which have led to the discovery of single nucleotide polymorphisms (SNPs) marking more than 150 loci that are implicated in T2D risk (Mohlke & Boehnke, 2015). Despite the success of human GWAS, current findings account for merely 10% of overall heritability of T2D with most variants identified having no known function and only a small effect on risk (Morris et al., 2012). Human studies

83 Chapter Three Genome-Wide Association Study for Hyperglycaemia have fundamental limitations that hamper the demonstration of the impute variants, as well as confounding environmental factors.

Recombinant inbred mouse populations have been developed as valuable resources with many advantages, including the availability of disease-relevant samples, a better control of environmental factors and well-established techniques available for genetic modification and functional characterisation of candidate genes (DIAbetes Genetics Replication and Meta-analysis Consortium et al., 2014; Skarnes et al., 2011). Here, we studied hyperglycaemia in a sophisticated mouse genetic reference population (GRP), termed the Collaborative Cross (CC), which comprises over 100 genetically diverse inbred strains and was designed specifically for mapping complex traits (Churchill et al., 2004; Morahan et al., 2008). Each CC strain was derived from a strict breeding program involving eight parental strains that capture over 90% of the common variation of the mouse species. The CC resource provides an appropriate model system with high genetic diversity, detailed genomic characterization, balanced allele frequencies and evenly distributed recombination events, which together facilitate high resolution gene mapping and systems genetics studies (Aylor et al., 2011; Collaborative Cross, 2012). The CC has been successfully used to study a variety of phenotypes and complex traits (Atamni, Botzman, et al., 2016; Chick et al., 2016; Gralinski et al., 2015; Kelada et al., 2012; Leist et al., 2016; Nachshon et al., 2016) and proven to recapitulate human disease conditions (Graham et al., 2015). Few studies have been conducted using the CC mouse resource to the study of glycaemia-associated traits (Abu-Toamih Atamni et al., 2017; Atamni, Mott, et al., 2016; Nashef et al., 2017a). Of these, Hanifa et al. investigated gene-environment interaction using the CC mice fed a high-fat diet; a female-sex specific QTL (Chr8: 32.02-34.52 Mbp; logP=5.9) was identified to be associated with glucose tolerance (Abu-Toamih Atamni et al., 2017). However, the physiological implications of the imputed QTL and putative gene candidates were not explored. Therefore, the aim of our study was to use the CC resource to identify genetic variants that mediate elevated blood glucose concentrations.

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3.2. Aim The aim of this chapter was to identify genetic loci influencing blood glucose level using the Collaborative Cross (CC) resource.

1. To conduct phenotypic screening for glycaemic traits across the inbred CC strains. 2. To identify the genetic causes which are contributory to hyperglycaemia by performing genome-wide association analysis.

3.3. Methods Methods utilised in this chapter are described in Chapter 2.

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3.4. Results 3.4.1. Metabolic Diversity among the CC Mouse Population

To determine the variation in glycaemia in the CC mice, random blood glucose concentrations were assessed in a cohort of 1,119 mice at 8-10 weeks of age. These mice included males from 53 strains and females from 48 CC strains (Refer to Appendix III for the complete list of strains used in this study). As shown in Figure 3.1, a wide range in blood glucose levels was observed: there was a 2.86-fold (from 7.8 mmol/L to 22.3 mmol/L) and 2.13-fold (from 6.7 mmol/L to 14.3 mmol/L) difference among males and females, respectively. It is important to note that the variability of the glucose measurements within each strain is fairly tight as demonstrated by small standard deviations (SD): The mean SD in male mice was 2.13 with a median SD at 1.71 ranging from 4.8 to 0.59, while in the female cohort, the mean SD is 1.42 ranging from 2.62 to 0.21 with a median SD at 1.24. A higher blood glucose level was noted in males with an average of 11.9 ± 0.3 mmol/L versus 9.7 ± 0.2 mmol/L than in females (Table 3.1). It is worth noting that four strains were identified as hyperglycaemic (indicated by striped columns in Figure 3.1) with blood glucose levels in the upper 2.5 % (beyond two standard deviations of the mean) of the whole population regardless of gender. These hyperglycaemia susceptible strains were PIPING, DET3, PEF2 and PUB mice, which displayed non-fasting hyperglycaemia with blood glucose concentrations at 22.3 ± 2.7, 19 ± 1.2, 16.9 ± 1.5 and 16.7 ± 0.9 mmol/L, respectively, compared with the level of whole population.

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Table 3.1: Blood glucose levels, insulin sensitivity and body weights in male and female CC mice.

Males Females Trait Mean ± SEM Min Max Mean ± SEM Min Max Random blood glucose 11.9 ± 0.4 7.8 22.3 9.7 ± 0.2 6.7 14.3 (mmol/L)

Insulin sensitivity (AUC during ITTs) 9175 ± 214.69 3749 13830.7 3703 ± 210.27 1176 7716

Body weight (g) 23.3 ± 0.77 18 57.6 19 ± 0.41 14.7 31.4

Values presented as mean ± SEM. Min, minimum value within a group for trait measured; Max, maximum value within a group for trait measured. Insulin sensitivity was calculated as the area under the curve (AUC) of the percentage of baseline glucose levels overtime during ITTs.

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Figure 3.1 Random blood glucose levels across the CC strains in males and females. Non-fasted glucose concentrations were determined in 8-10-week-old male (■) and female (□) mice fed standard chow diet. Hyperglycaemic mice ( ) and control C57BL/6 mice (■) are highlighted as indicated. Data presented as mean ± SEM (n≥ 3 each strain). The red dash lines indicate the average measurements in males and females, respectively.

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As glucose dysregulation is often associated with insulin resistance and obesity in humans to an extent that insulin cannot be efficiently utilised to lower blood glucose (B. B. Kahn & Flier, 2000), we determined whether the observed blood glucose variation was due to differences in insulin sensitivity and/or body weight in the same cohort of mice. The degree of insulin resistance was assessed by insulin tolerance tests (ITTs) and presented as area under the curve of glucose excursion (% of baseline) throughout 120 minutes of the tests. As shown in Figure 3.2 and Figure 3.3, the responses to exogenous insulin challenge and body weights differed widely between the CC strains in both genders. However, the results also revealed that hyperglycaemia in the previously identified susceptible strains were unlikely to be due to insulin resistance nor overweight, as they were relatively insulin- sensitive and lighter in body weight compared to that of the whole CC population, and this is particularly evident within the males.

The relationship between these three traits was analysed and shown in Figure 3.4. Random blood glucose levels exhibited a significant negative correlation with the degree of insulin resistance in the male population (r= -0.4165; p= 4 x 10-4) but not in females (r= -0.1465; p= 0.16) (Figure 3.4, top). However, there was no significant correlation between blood glucose levels and body weight in either gender (r= 0.1484, p= 0.13 in male; r= 0.0686, p= 0.32 in female) (Figure 3.4, middle). As expected, body weight was positively correlated with insulin resistance in both male (r= 0.2463; p= 0.028) and female mice (r= 0.4584; p= 5 x 10-4) (Figure 3.4, bottom). These results suggested that insulin resistance and obesity were not the predominant contributors to hyperglycaemia in this CC mouse population.

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Figure 3.2 Degree of insulin resistance across the CC strains in males and females.

Degree of insulin resistance was determined in non-fasted, 8-10-week-old male (■) and female (□) mice fed standard chow diet. Results are presented as area under the curve (AUC) of percentage of basal glucose during an ITT. Hyperglycaemic mice ( ) and control C57BL/6 mice (■) are highlighted as indicated. Data expressed as mean ± SEM (n ≥ 3 each strain). The red dash lines indicate the average measurements in males and females, respectively.

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Body weight was determined in non-fasted, 8-10-week-old male (■) and female (□) mice fed standard chow diet. Hyperglycaemic mice ( ) and control C57BL/6 mice (■) are highlighted as indicated. Data presented as mean ± SEM (n ≥ 3 each strain). The red dash lines indicate the average measurements in males and females, respectively.

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3.4.2. Genome-wide Association Mapping for Hyperglycaemia

The wide variation in blood glucose level in the CC population is the result of diverse genetic background. To identify genetic loci influencing blood glucose concentrations, SNP-based GWAS analysis and QTL mapping were performed with the random blood glucose measurements of males from 53 strains and females from 48 CC strains, respectively. The GWAS identified four genome-wide significant (p <5 x 10-8) SNPs associated with high blood glucose levels in males (Figure 3.5). These SNPs were mapped to two adjacent loci on chromosome 7 as revealed by QTL analysis (Figure 3.6A). The most significant locus was identified at position 52.6-56.7 Mb on chromosome 7 (LOD=15.4) and designated by the nearest gene as the E2F8 locus. Likewise, the peak SNP (rs253243259, p=2.19e-15) and QTL (LOD: 15.4) were recaptured in the female population to be associated with elevated blood glucose levels (p= 9.39e-8 for rs253243295) (Figure 3.7A and 3.7B). Furthermore, a second locus was identified approximately 40 Mb downstream of the first locus at 98.5-101.55 Mb (LOD=8.4). Moreover, two significant SNPs at this locus (rs32123098, p=3.83e-08 and rs243982980, p=2e-08) both residing in the disks large homolog 2 (Dlg2) gene were identified, making it a high-confidence candidate for this locus, and therefore this region was designated as the Dlg2 locus. Details of these significant associations are summarized in Table 3.2.

To determine the identity of the contributory haplotype and the founder strain(s), founder haplotype analysis was conducted and the results demonstrated clear segregation from NZO alleles at both implicated regions as shown in Figure 3.6B and Figure 3.7C. These results suggested that the deleterious effects could be attributable to variants derived from the NZO genome, a well-documented diabetes susceptible strain. Moreover, the inferred haplotypes for all tested strains in our phenotypic screening showed that the CC strains which exhibited the higher blood glucose levels inherited the detrimental NZO allele(s) at either one or both loci on chromosome 7 (Figure 3.8 and Figure 3.9), including the hyperglycaemia susceptible PUB and PIPING mice.

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Figure 3.5 SNP-wise GWAS for blood glucose levels in males. Manhattan plot showing the association of 77,808 SNPs with blood glucose concentration. The x-axis presents the genomic location and the y-axis presents the genome-wide significance of each SNP as –log10 (p) values. Thresholds of GWAS significances are indicated as lines drawn at p <5x10-5 (99.9% confidence, blue line) and p <5x10-8 (99.99% confidence, red line).

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Figure 3.6 QTL mapping for blood glucose levels in males. (A) LOD-score plot depicting the genomic position of significant peaks for high blood glucose by linkage analysis. The x-axis showed mouse genome in physiological order; significance (LOD) scores is presented on the y-axis. (B) Locus plot (upper) for genome- wide significant association of blood glucose level at chromosome 7. Founder coefficient plot (lower) illustrating haplotype segregation from eight founder strains on chromosome 7, where high significance is conferred. The red line and blue dotted line indicate threshold of 95% and 63% significance, respectively.

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Figure 3.7 Genome-wide association and QTL mapping for blood glucose in females. (A) Manhattan plot showing the association of SNPs with blood glucose concentration. Significant thresholds of p <5x10-5 (99.9% confidence) and p <5x10-8 (99.99% confidence) are indicated by blue and red lines, respectively. (B) LOD-score plot depicting the genomic position of significant peaks for higher blood glucose levels by linkage analysis. (C) Locus plot (upper) and founder coefficient plot (lower) illustrating significant associations and founder haplotypes segregation at chromosome 7, respectively. X-axis shows genome position; y-axis presents statistical significance as LOD scores.

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Figure 3.8 Founder haplotype identity of all CC strains at the E2F8 locus. Founder haplotypes are presented with different colours as shown in the colour scheme on top of the graph. Horizontal axis shows mouse genomic region at 52.8-56.5 Mb on chromosome 7. The vertical label lists the CC strains in an order of relative blood glucose level from lower glucose (top) to hyperglycaemic (bottom) strains.

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Figure 3.9 Founder haplotype identity of all CC strains at the Dlg2 locus. Founder haplotypes are presented with different colours as shown in the colour scheme on top of the graph. Horizontal axis shows mouse genomic region at 98.6-103.3 Mb on chromosome 7. The vertical label lists the CC strains in an order of relative blood glucose level from lower glucose (top) to hyperglycaemic (bottom) strains.

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Table 3.2 Genome-wide significant SNPs and QTL associated with blood glucose concentration on chromosome 7 SNP-wide Association Analysis

Sex SNP Marker Nearby Gene Position (bp) Ref. Allele Risk Allele p-Value SNP ID Avg. cM SNP Type

Male UNC12729115 Trpm4 Chr7:52,594,130 G A 9.94E-08 rs256453820 29.3 Intronic Upstream variant

Male UNC12784582 E2F8 Chr7:56,127,520 C A 2.19E-15 rs253243259 31.2 Exonic missense

Female UNC12784582 E2F8 Chr7:56,127,520 C A 9.39E-08 rs253243259 31.2 Exonic missense

Male UNC13364773 DLG2 Chr7:98,989,101 T G 3.83E-08 rs32123098 51.3 Intronic Non-coding transcript variant

Male UNC13368440 DLG2 Chr7:99,261,374 C T 2.00E-08 rs243982980 51.4 Intronic Non-coding transcript variant Quantitative Trait Loci Interval Founder No. of #Sanger #Sanger Sex Locus Interval Position (bp) Length LOD #Sanger SVs Haplotype Gene(s) SNPs/gene Indels/gene (Mb) Male E2F8 NZO/HILtJ Chr7:52,631,000-56,700,000 4.1 > 96 15.4 214/29 164/41 2

Female E2F8 NZO/HILtJ Chr7:52,631,000-56,700,000 4.1 > 96 7.3 214/29 164/41 2

Male DLG2 NZO/HILtJ Chr7:98,500,000-101,550,000 3.05 7 8.7 226/7 466/7 6

Position was based on genome build NCBI37/mm9 assembly; Ref. allele, reference allele for the given SNPs refer to the genotype in the C57BL/6 mouse genome; Indels, insertion or deletions; cM, Centimorgan.

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3.4.3. Identification of hyperglycaemia susceptibility genes at the implicated loci

To identify gene candidates at these hyperglycaemia susceptibility loci, nucleotide sequence variants and their properties were examined using publically available genome databases. As shown in Figure 3.10, the E2F8 locus is a gene-dense region containing more than 96 protein coding genes within a 4.1 Mb region. Based on the result of the haplotype analysis, genes containing variants which are unique to the NZO mouse genome were likely to be detrimental and therefore identified as candidates. Thereby, the number of candidate genes was narrowed to 29 genes after the first stage of screening. Subsequently, these sequence variants which potentially have functional effects were considered in our selection process, the ENCODE (Encyclopedia of DNA Elements) SNP database was utilized to determine variants in regulatory motifs (Encode Project Consortium, 2012; Encode Project Consortium et al., 2007). This analysis revealed seven genes which were predicted to mediate functional alterations at the E2F8 locus, therefore these genes were prioritised to be studied: Gfy, Hsd17b14, Sphk2, E2F8, Ntn5, Abcc8 and Kcnj11.

Figure 3.10 Schematic illustrating the genomic position and configuration of the mouse E2F8 locus on chromosome 7.

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Schematic diagram of the genomic position ranging from 52,631,000-56,700,000bp on chromosome 7. The 4.1-Mb locus is composed of more than 96 protein-coding genes. The position of the E2F8 locus is shown using a display from the UCSC genome browser.

As illustrated in Figure 3.11, the Dlg2 locus harboured only seven known genes, including Dlg2, Ccdc90b, Andkrd42, Pcf11, Ddias, Rab30 and Prcp. In addition, sequence analysis revealed that all these seven genes contained variants specific to the NZO mouse genome and hence deemed as candidates. Importantly, the GWAS identified two significant SNPs at this locus (rs32123098, p=3.83e-08 and rs243982980, p=2e-08) both residing in the intronic region of the disks large homolog 2 (Dlg2) gene, making it a high-confidence candidate.

Figure 3.11 Schematic illustrating the genomic position and configuration of the mouse Dlg2 locus on chromosome 7. Schematic diagram of the genomic position ranging from 98,500,000-101,550,000bp on chromosome 7. The 3-Mb locus is composed of 7 protein-coding genes. The position of the Dlg2 locus is shown using a display from the UCSC genome browser.

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3.5. Discussion Despite many years of effort, the identity and contribution of genetic defects in diabetes remains to be fully elucidated. In an effort to define the contributory genetic component leading to type 2 diabetes (T2D), we investigated genetic loci influencing glucose homeostasis using a genetically diverse mouse population, the Collaborative Cross (CC), and further highlight the significance of these regions in the pathogenesis of hyperglycaemia. Our results revealed a wide variation in blood glucose levels in the CC mouse population at an early age (8-10 weeks of age), suggesting the divergent genetic background accounts for the differences in blood glucose. The GWAS results fell into two significant loci on chromosome 7 associated with elevated blood glucose levels. Moreover, mouse strains carrying the NZO allele(s), such as PIPING, PUB, DET3 and PEF2 mice, all exhibited hyperglycaemia with the blood glucose levels in the upper 2.5% of the whole population irrespective of gender, suggesting the substantial effect of these loci on glucose homeostasis.

It is worth noting that the higher blood glucose in the CC population was associated with enhanced insulin sensitivity, indicative of -cell dysfunction. It is widely accepted that insulin resistance is not essential for the onset of hyperglycaemia unless islet -cells failed to compensate for the increased demand of insulin during the progression towards T2D (van Haeften et al., 2000). Moreover, -cell dysfunction emerges in the early pathogenesis of hyperglycaemia (Ferrannini et al., 2005; Tfayli, Lee, & Arslanian, 2010) and is genetically predetermined as demonstrated by Pascoe et al. that non-diabetic subjects who are genetically susceptible to diabetes exhibited 39% decline in -cell function compared with those who do not have diabetes risk alleles (Pascoe et al., 2008; Polonsky, Sturis, & Bell, 1996). Therefore, it was concluded that the blood glucose variation among the CC mice could be due to genetically predetermined alterations in pancreatic -cell function. On the other hand, we also observed that blood glucose was not related to obesity, whereas insulin resistance was associated with increased body weight in the CC population. It is of interest to highlight that less than 25% obese people develop diabetes (Benjamin, Valdez, Geiss, Rolka, & Narayan, 2003; Warram, Martin, Krolewski, Soeldner, & Kahn, 1990), and that the EPIC-InterAct study revealed that over 15% of T2D incidence occurred among

102 Chapter Three Genome-Wide Association Study for Hyperglycaemia individuals of normal weight and over 50% were non-obese at baseline (InterAct et al., 2012). These findings together support our hypothesis that genetic predisposition per se has substantial effects (in addition to obesity) on determining blood glucose. Furthermore, numerous prospective studies have demonstrated that insulin secretory function in healthy subjects with raised blood glucose (fasting glucose level between 5.6 and 6.3 mmol/L), albeit still within normal glycaemia range, were substantially decreased by 50-70% (p <0.02) (Ahren, 2007; Brunzell et al., 1976; Ferrannini et al., 2005; Tfayli et al., 2010). This indicated raised blood glucose levels could be a strong predictor of reduced -cell function, and therefore our GWAS results for blood glucose may inform specific genetic regulation by which pancreatic -cell progressed from vulnerable to full-blown failure.

Our genome-wide scan for random blood glucose has led to the identification of two significant loci on chromosome 7 with highly significant association with elevated blood glucose levels. The E2F8 locus is identified proximal to a previously reported region (chr7: 56,255,370-62,555,370) for fasting plasma glucose in a completely independent mouse population, the Hybrid Mouse Diversity Panel (HMDP) (Parks et al., 2015). In contrast, using the CC mouse population enabled us to fine-map this region to a smaller interval (4.1 versus 6.3Mb) with an enhanced statistical significance (p= 2.19e-15 versus 1.52e-07). In addition, a number of human GWAS have reported associations with T2D (Diabetes Genetics Initiative of Broad Institute of et al., 2007; Replication et al., 2014; Scott et al., 2007; Timpson et al., 2009; Zeggini et al., 2008; Zeggini et al., 2007), cholesterol (Global Lipids Genetics et al., 2013; Teslovich et al., 2010), and obesity-related traits (Comuzzie et al., 2012; Locke et al., 2015) within the human orthologue of this region. A well- established diabetes susceptible region, ABCC8/KCNJ11 locus, was identified as part of the E2F8 locus and often imputed as causal to these metabolic abnormalities and particularly in -cell dysfunction (Ashcroft, Harrison, & Ashcroft, 1984; Gloyn et al., 2004; Koster, Marshall, Ensor, Corbett, & Nichols, 2000; Miki et al., 1998). In fact, our laboratory has previously reported that genetic variants in the Abcc8 gene partly contributed to impairment of early-phase insulin secretion in the NZO mice (Andrikopoulos et al., 2016).

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Furthermore, sequence analysis revealed a significant peak SNP locating in intron 5 of Trpm4 gene at chromosome 7:52,594,130 (rs256453820; p=9.94e-08) to be associated with hyperglycaemia (Table 3.2). Trpm4 encodes the transient receptor potential (TRP) melastatin-like subfamily member 4 (TRPM4), which mediates the flux of monovalent cations (such as Na+ and K+) across the plasma membrane into the cytoplasm, but not to Ca2+ or Mg2+. It has been demonstrated in the rat pancreatic -cell line INS-1 that the impermeability to Ca2+ of TRPM4 results in the accumulation of intracellular Ca2+, which exerts the depolarization of the plasma membrane (Cheng et al., 2007; Ma, Bjorklund, & Islam, 2017). While, TRPM4 inhibition via expressing a dominant negative TRPM4 construct has been demonstrated to attenuate insulin secretion and Ca2+ signals in response to glucose as well as an arginine vasopressin stimulation (Marigo, Courville, Hsu, Feng, & Cheng, 2009).

More importantly, the peak SNP (rs253243259; p=3.82e-15) located in exon 8 of E2F8 gene, which represents a nonsynonymous polymorphism from C allele (wildtype) to a deleterious A allele (AAG → AAT), leading to an amino acid substitution from lysine to asparagine at residue 401 (Lys401Asn). This lysine possesses a positively charged epsilon- amino group in the GAP-43-like domain (between residues 400 and 630) of E2F8 gene, whereas this positive charge was abolished when substitution by asparagine (an uncharged amino acid). This GAP-43-like domain has 27% protein homology (16% amino acid identity) to the GAP-43, a known nonreceptor activator of heterotrimeric G proteins (Strittmatter, Valenzuela, & Fishman, 1994). Moreover, human E2F8 has been suggested to act as a guanine nucleotide exchange factor (GEF) for activating heterotrimeric G protein in a yeast study (Hagemann, Narzinski, & Baranski, 2007). Despite this amino acid substitution, whether this mutation alters protein property or its downstream signalling is unknown. Hence, E2F8 was deemed as a novel candidate gene which may link variants in this locus to elevated blood glucose levels.

The human homologous of Dlg2 locus on chromosome 11q (Chr11:82844368-85006039; NCBI Build 36.1 hg18) has been suggested in a small African-American cohort to associate with the disposition index, a measure of the relationship between pancreatic -cell function and insulin sensitivity (N. D. Palmer et al., 2010). The Dlg2 gene was attributed as a

104 Chapter Three Genome-Wide Association Study for Hyperglycaemia positional candidate gene containing multiple SNPs associated with DI. Apart from this, the Dlg2 locus was also linked to body weight in women (Johansson et al., 2010) and childhood obesity in the Hispanic population (Comuzzie et al., 2012). Our results showed two SNPs (rs32123098, p=3.83e-08 and rs243982980, p=2e-08) residing in the intronic region of Dlg2 gene to be associated with hyperglycaemia, and these variants may influence pancreatic insulin secretion as suggested by evidence that considerable contribution from regulatory noncoding regions determined diabetes progression and - cell dysfunction (Arnes & Sussel, 2015; Fadista et al., 2014; Moran et al., 2012).

As revealed by haplotype analysis, the deleterious alleles were contributed to by the polygenic diabetic model, the NZO mouse. It is well-established that the development of glucose intolerance and diabetes in the NZO mouse is driven by multiple genes, understanding the genetic profile of the NZO mice could be very informative. Since the first genome-wide scan conducted in the NZO mouse by Leiter et al. in 1998 (Leiter et al., 1998), considerable efforts have been made utlising QTL analysis and the congenic mouse lines generated from crossing phenotypically divergent inbred lines (ie. the lean SJL mouse, the small mouse (SM), and nonobese nondiabetic (NON) mouse strains) in order to uncover the genetic causes underlying its complex metabolic aberrations (Giesen et al., 2003; Ortlepp et al., 2000; Plum et al., 2000; Reifsnyder & Leiter, 2002; B. A. Taylor et al., 2001). However, the identification of adverse genetic components in the NZO has been challenging and yet to be fully elucidated, hence new approaches to improve our understanding of the complex genetic basis of glucose regulation is crucial. The recombinant inbred (RI) CC mouse population provided significant power for genome- wide association analysis. The phenotypic property and utility of the CC mouse population in metabolic traits has been evaluated previously (Atamni, Mott, et al., 2016; Mathes et al., 2011; Nashef et al., 2017b). A recent CC study reported a female-specific QTL on chromosome 8 associated with IPGTT (Abu-Toamih Atamni et al., 2017). However, the functional interpretation for this locus is lacking.

Given that a fasting blood glucose level is one of the most common parameters people use in clinical practice to diagnose diabetes in humans, it would be interesting to investigate whether fasting blood glucose and random blood glucose (postprandial) are regulated by

105 Chapter Three Genome-Wide Association Study for Hyperglycaemia similar genetic machinery using this genetic diverse mouse panel. The source of glucose during fasting and postprandial states is essentially different as random blood glucose (postprandial) is mainly derived from food ingestion, while endogenous (mainly hepatic) glucose production supplies the majority of circulating glucose during the fasting state. GWAS for random blood glucose might be associated with pancreatic -cell function in response to glucose ingestion, however, GWAS for fasting blood glucose levels might identify genes associated with hepatic function and basal insulin secretion. It is particularly interesting to find that the hyperglycaemia susceptibility E2F8 locus was previously identified to be associated with fasting plasma glucose in the Hybrid Mouse Diversity Panel (Parks et al., 2015). In addition, it is also observed that the hyperglycaemia susceptible CC strains identified by higher random blood glucose levels also exhibited fasting hyperglycaemia after six-hour food deprivation. Together, this suggests that the genetic regulation of fasting blood glucose and postprandial blood glucose may partly share the common genetic machinery. In conclusion, our study provides proof-of-principle work of rapid gene identification for complex traits, blood glucose concentrations, and highlights the potential influence of the E2F8 and Dlg2 locus on islet -cell function and glucose homeostasis.

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CHAPTER FOUR

PHYSIOLOGICAL CHARACTERISATION OF HYPERGLYCAEMIA SUSCEPTIBLE STRAINS OF THE COLLABORATIVE CROSS

Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

4. Chapter 4 Physiological Characterisation of Hyperglycaemia Susceptible Strains of the Collaborative Cross

4.1. Introduction Glucose homeostasis is tightly maintained by coordinating the glucose flux through the action of insulin and insulin secretion by the pancreatic islets. In the course of development of Type 2 diabetes (T2D), it has become evident that by the time hyperglycaemia occurs, a decrease in the ability of tissues to respond to insulin (insulin resistance) and an inability of -cells to produce sufficient insulin to compensate for the increasing demand (impaired -cell function) have already been established (S. E. Kahn, 2003). However, comprehensive evidence has demonstrated that -cell dysfunction leading to decreased glucose-stimulated insulin release is the major determinant of hyperglycaemia in T2D. The United Kingdom Prospective Diabetes Study (UKPDS) evaluated Homeostasis Model Assessment (HOMA) in a cohort of subjects with recently diagnosed T2D demonstrating an ongoing decline in -cell function without a change in insulin sensitivity (Holman, 1998; Matthews, Cull, Stratton, Holman, & Turner, 1998). In the UKPDS and other studies (Carnevale Schianca, Rossi, Sainaghi, Maduli, & Bartoli, 2003; Larsson, Berglund, & Ahren, 1995; Polonsky et al., 1996; van Haeften et al., 2000) showed that the defect in pancreatic -cell function is an early event which commences years prior to the onset of hyperglycaemia independent of insulin resistance. In fact, there is evidence that subjects with impaired fasting glucose (≥6.1 mmol/L) have normal or even enhanced insulin- stimulated glucose disposal relative to the normal glucose tolerant population (Abdul- Ghani, Jenkinson, Richardson, Tripathy, & DeFronzo, 2006; Wasada et al., 2004). In addition, numerous studies have demonstrated that a significant decline (50-70% reduction, p<0.02) in pancreatic insulin secretory function is evident in subjects who are at risk of developing diabetes that display raised blood glucose despite remaining within the normoglycaemic range (Ahren, 2007; Brunzell et al., 1976; Ferrannini et al., 2005; Tfayli et al., 2010; Zhu et al., 2007). This suggested that -cell dysfunction occurs well before the

108 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci occurrence of insulin resistance which significantly contributes to the early progression of diabetes. Furthermore, this decline in -cell function has been shown to be primarily functional incapable to secret insulin rather than a reduction in the number of insulin secreting cells as an early event of disease progression.

In keeping with the fact that T2D has a strong genetic component, it has been examined that up to 70 % of -cell function is attributable to heritable factors (Elbein et al., 1999). In addition, a longitudinal study performed on the Pima Indians, a population with extremely high prevalence of T2D, demonstrated a progressive decline in pancreatic insulin secretion (by 78%) in those who were destined to progress to diabetes, indicating that -cell function is genetically predetermined and which takes significant part in determining diabetes progression (Weyer et al., 1999). Many other studies carried out in subjects who are at high risk of developing T2D, including first-degree relatives of patients with T2D (Ehrmann et al., 1995; Elbein, Wegner, & Kahn, 2000), women with a history of gestational diabetes (Buchanan et al., 1999; Ryan et al., 1995; Ward et al., 1985), subjects with impaired glucose tolerance (Cavaghan, Ehrmann, Byrne, & Polonsky, 1997; Larsson & Ahren, 1996) and elderly people (Dunaif & Finegood, 1996; S. E. Kahn et al., 1992), demonstrated that reduced -cell function is an early event in the pathogenesis of T2D which is associated with genetically determined propensity to -cell decompensation.

On the other hand, it is well established that T2D arises from a complex interplay between an individual’s genetic makeup and the adverse milieu, particularly the increasing prevalence of obesity appears to be highly associated with the epidemic rise of T2D (Everson et al., 1998; Zimmet et al., 2001). However, the gene-by-environment interaction contributing towards the causation of T2D is difficult to be decisively validated in humans. Fortunately, the laboratory rodent models have proven useful to reflect human physiology and obesity progression due to the genetic homology (Islam & Loots du, 2009; Peltonen & McKusick, 2001). In the context of obesity, murine models can be rendered obese and insulin resistant by feeding high-energy-dense diets rich in saturated fatty acids in a few weeks (Ding, Guo, & Su, 2015; H. Y. Lee, Jeong, Choi, & International Mouse Phenotyping, 2014). The high-fat diet consumption in mouse models has been linked to

109 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci obesity in concert with insulin resistance, hyperinsulineamia, and raised plasma lipids that model human T2D (Surwit et al., 1988; Winzell & Ahren, 2004). The different pathologic responses to the high-fat diet between inbred mouse strains has been emphasised and reviewed (Fontaine & Davis, 2016; Kahle et al., 2013; Kobayashi et al., 2014; Mull, Berhanu, Roberts, & Heydemann, 2014) that differences in genetic background results in strain specific differences in HFD susceptibility to glucose intolerance and hyperglycaemia. Thus, the high-fat fed mouse models are employed as invaluable tools to investigate the effect of hereditary predisposition during the natural progression of human T2D.

As described in the previous chapter that the hyperglycaemia susceptibility loci on chromosome 7 were identified for significant association with elevated blood glucose, demonstrating substantial influence on determining individual’s disposition to hyperglycaemia in the absence of insulin resistance or obesity. On the basis of these findings, the current study aims to test the hypothesis that the hyperglycaemia that develops in the susceptible mouse strains is associated with defective -cell function, and this inherent -cell defect predisposes the affected ones to metabolic degenerations in response to the high-fat diet. Thus, we assessed the effect of hyperglycaemia-associated loci in two hyperglycaemia susceptible CC strains, the PIPING and PUB mice, which are genetically unique inbred strains sharing the detrimental alleles from the parental NZO mice. The assessments were focusing largely on pancreatic -cell function and on glucose tolerance and islet histological parameters. The C57BL/6 mouse strain was employed as a reference strain to be compared with the hyperglycaemia susceptible CC strains in the physiological characterisations for the following reasons: The C57BL/6 inbred strain is a popular choice for researchers conducting metabolic studies because it is evident that C57BL/6 mouse exhibits numerous aspects of the diabetic phenotype typically seen in obese humans, including insulin resistance and hyperinsulinemia (Surwit et al., 1988). In addition, C57BL/6 is one of the parental strains used to generate the CC population, and was shown to have an intermediate blood glucose level, insulin sensitivity and body weight among both male and female cohort of the CC mice in the phenotypic screening across 53 CC strains.

110 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

4.2. Aim The aim of this chapter was to investigate the impact of the hyperglycaemia-associated loci on glucose homeostasis and its response to nutrient overload utilising the susceptible CC strains fed a high-fat diet.

1. To determine the physiological effects of the hyperglycaemia susceptibility loci on glucose homeostasis and pancreatic -cell function. 2. To investigate whether the inherent hyperglycaemia susceptibility predispose individuals to the high-fat diet-induced metabolic abnormalities.

4.3. Methods General methods utilised in this chapter are described in Chapter 2. The methods outlined below are specific to experiments performed in this chapter.

4.3.1. Animals

Male mice were utilised in the physiological characterisation and dietary study. The C57BL/6 mouse strain was employed as a reference strain in comparison to the hyperglycaemia susceptible CC strains in all experiments.

4.3.2. Glucose Tolerance Tests

Glucose tolerance and insulin secretion were evaluated in anesthetized mice at 10-12 weeks of age by performing OGTTs (2g/kg glucose) or IVTTs with glucose (1g/kg glucose) or glucose plus arginine (1g/kg each) as described in chapter 2, section 2.2.3.3.1, 2.2.3.3.2 and 2.2.3.3.3.

The effect of high-fat feeding on glucose tolerance was determined by conducting OGTTs (2g/kg glucose) in conscious mice fed either chow or high-fat diet for 8 weeks. The insulin secretory function was investigated in mice following 18 weeks of high-fat diet feeding or chow fed control utilising OGTTs (2g/kg glucose) or IVGTTs (1g/kg glucose) as described in Chapter 2.

111 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

4.3.3. High-Fat Diet Study

The procedure for the dietary study is illustrated in Schematic diagram 4.1. Prior to the dietary study, mice were kept on the standard rodent chow diet containing 9% fat until 6 weeks of age. Mice were randomly assigned to two dietary groups and fed either the control chow diet or 43% high-fat diet ad libitum for 18 weeks. During this period of time, mice had free access to clean water and food. Body weight, food intake and fluid consumption were monitored weekly during the course of dietary study. An insulin tolerance test (ITT) was conducted on mice following the 3-weeks of dietary treatments. Subsequently, mice were recovered and proceeded to oral glucose tolerance tests (OGTT) after 8 weeks on either chow or high-fat diet. Following 18 weeks of dietary treatment, an OGTT or IVGTT was conducted on mice at 24 weeks of age to evaluate glucose tolerance and glucose- stimulated insulin secretion. Following the GTTs, mice were sacrificed and pancreata were collected for histological examination and insulin content analysis. Three fat depots, subcutaneous, infrarenal and epididymal fat, were weighted and collected. Plasma glucose and insulin concentrations were determined as described in chapter 2, section 2.2.3.7 and 2.2.3.8.

Schematic diagram 4.1 Timeline and experimental procedure of high-fat diet study.

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4.4. Results 4.4.1. Metabolic characterisation in mice with genetic predisposition of hyperglycaemia

Identification of hyperglycaemia susceptible strains in the CC mouse population

To investigate how these hyperglycaemia susceptibility loci influenced blood glucose homeostasis, we performed in-depth characterisation of two hyperglycaemic CC strains, the PIPING and PUB mice, comparing them with the most commonly used reference strain, the C57BL/6 mouse. The hyperglycaemia susceptible PIPING and PUB strains are genetically unique strains which were selected as they both shared the detrimental allele with NZO mice at E2F8 locus, and only PIPING mice carried the NZO allele at the Dlg2 locus, while PUB mice had the wild-type Dlg2 allele which was inherited from C57BL/6 strain (Schematic diagram 4.2). Blood glucose and plasma insulin levels and body weights were determined at 10-12 weeks of age. PUB and PIPING mice were identified as “hyperglycaemic” as demonstrated by significantly (p<0.05) increased blood glucose concentrations in both fed and 6-hour fasted states, compared with C57BL/6. Interestingly, after an overnight fast, blood glucose levels remained elevated in the PUB mice but were normalized in PIPING mice (Figure 4.1A). In addition, as shown in Figure 4.1B hyperglycaemia was associated with significantly lower plasma insulin concentrations (p<0.05) in PIPING and PUB mice, suggesting a potential defect in insulin secretion. The body weights of PIPING and PUB mice were significantly lower compared with C57BL/6 regardless of the fasting period (Figure 4.2A), while there was no difference in total fat pad mass among these three strains as presented by percentage of body weight (Figure 4.2B).

Schematic diagram 4.2 Haplotype structure on chromosome 7:51-102 Mbp in the PIPING and PUB mice.

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115 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

Reduced insulin secretion leads to hyperglycaemia in the susceptible strains

An OGTT with 2 g/kg glucose was conducted to assess glucose tolerance and glucose- induced insulin secretion. The results revealed profound glucose intolerance in both hyperglycaemic strains (PIPING and PUB mice) compared with C57BL/6 mice (Figure 4.3A). Plasma insulin concentrations showed a substantial reduction in insulin secretion in PIPING and PUB mice compared with C57BL/6 mice, which was characterised by significantly lower levels at all time points during the OGTT (Figures 4.3B). Glucose intolerance in the PIPING and PUB mice was also reflected by significantly higher total area under the glucose curve (AUC glucose) and incremental AUC (iAUCglucose) (Figure 4.4 A and B). When compared with C57BL/6 mice, significant reduction in AUC plasma insulin concentration (AUC insulin) as well as incremental AUC (iAUC insulin) indicated impaired insulin secretion in the hyperglycaemic strains (Figure 4.4 C and D).

Insulin secretory capacity was then determined by the intravenous glucose tolerance test (IVGTT), in PIPING and PUB mice compared to C57BL/6 mice. As shown in Figure 4.5A and 4.5B, glucose-stimulated insulin secretion was significantly decreased in PIPING and PUB mice compared with C57BL/6 despite a greater level of glucose stimulus. Total insulin secretion and incremental insulin secretion as presented by area under the curve of plasma insulin (AUC insulin) and incremental AUCinsulin, respectively, were reduced significantly in PIPING and PUB mice compared with C57BL/6 (Figure 4.6).

To further define the causes of impaired insulin secretion, secretory response to intravenous arginine, which acts to secrete insulin by direct depolarisation of the -cell membrane, was determined. Plasma insulin excursion following a bolus of arginine plus glucose (1 g/kg body weight) revealed a substantial decline in insulin secretion in both hyperglycaemic strains compared with C57BL/6 (Figure 4.7A). A blunt response in PUB mice was noted, whereas PIPING mice showed significant reduced insulin secretion at first time point at 2 minute followed by a small increment at 15-minute time point. Overall, PIPING and PUB mice both had reduced insulin response to arginine, and the defect was more profound in

PUB as demonstrated by decreased AUC insulin and iAUC insulin (AUC insulin 177.5 ± 39.3 vs 16.7 ± 2.6 ng/ml x 30 min; C57BL/6 vs. PUB p <0.05; n=3-5) (Figure 4.7 B and C).

116 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

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Figure 4.3 Plasma glucose and insulin from OGTT in C57BL/6, PIPING and PUB mice at 10-12 weeks of age. Plasma glucose excursions (A) and insulin concentrations (B) of C57BL/6 (○), PIPING (■) and PUB (▲) mice during an OGTT. Data presented as mean ± SEM (C57BL/6 n=7, PIPING n=11, PUB n=14). *p<0.05 PIPING versus C57BL/6; # p<0.05 PUB versus C57BL/6.

117 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

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Figure 4.4 Total and incremental area under the curve of plasma glucose and insulin during an OGTT. Total AUC and incremental AUC (iAUC) of plasma glucose (A, B) and plasma insulin (C, D) in 10-12 weeks old C57BL6 (□), PIPING (■) and PUB (■) mice. Data presented as mean ± SEM (C57BL/6 n=7; PIPING n=11; PUB n=14). * p <0.05, ** p < 0.01 PIPING versus C57BL/6; # p<0.05, ## p<0.01 PUB versus C57BL/6.

118 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

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119 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

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Figure 4.6 Total and incremental insulin secretion in C57BL/6, PIPING and PUB mice during an IVGTT. Total AUC (A) and incremental AUC (iAUC) of plasma insulin levels (B) of C57BL/6 (□), PIPING (■) and PUB (■) mice at 10-12 weeks of age following an IVGTT. Data presented as mean ± SEM of ng/ml of insulin during IVGTTs (C57BL/6 n=5; PIPING n=5; PUB n=7). **p<0.01 PIPING versus C57BL/6; ## p<0.01 PUB versus C57BL/6.

120 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

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Figure 4.7 Plasma insulin secretory response following an IVGTT plus arginine in C57BL/6, PIPING and PUB mice at 10-12 weeks of age. Plasma insulin excursions (A) of C57BL/6 (○), PIPING (■) and PUB (▲) mice during the 30 minutes of IVGTTs. Total area under the curve (AUC) for plasma insulin secretion (B) and incremental AUC (C) in C57BL/6 (□), PIPING (■) and PUB (■) mice. Data presented as mean ± SEM (C57BL/6 n=5, PIPING n=3, PUB n=3). *p<0.05 PIPING versus C57BL/6; # p<0.05 PUB versus C57BL/6.

121 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

Histological examination of pancreatic islets in hyperglycaemia susceptible mice

To further determine whether the impaired insulin secretion in hyperglycaemic PIPING and PUB mice was due to defective islet morphogenesis, pancreatic histology and total insulin content were examined. Pancreatic histological analysis revealed that hyperglycaemic mice had comparable number of islets to C57BL/6 mice (Figure 4.8A). In terms of islet size, there was no reduction in islet area (Figure 4.8B) or proportion (Figure 4.8C) in pancreas from the hyperglycaemic strains, PIPIGN and PUB mice, compared with C57BL/6 mice. By contrast, increased islet size and proportion were observed and these were associated with a significant rise in the proportion of large islets in PIPING and PUB mice (Figure 4.8D). In addition, -cell mass (Figure 4.9A) and pancreatic insulin content (Figure 4.9B) were not reduced in these hyperglycaemic strains compared with C57BL/6. Taken together, our data suggest that the hyperglycaemia in PUB and PIPING strains was not due to the loss of -cells in pancreas.

122 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

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123 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

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C 5 7 P IP IN G P U B Figure 4.8 Pancreatic islet number, size and proportion of C57BL/6, PIPING and PUB mice at 20 weeks of age. Islet proportion by area (C) and percentage of small, medium and large islets (D) in pancreas of C57BL/6 (□), PIPING (■) and PUB (■) mice at 20 weeks of age. Data presented as mean ± SEM (C57BL/6 n=7, PIPING n=7, PUB n=4). * p <0.05 PIPING versus C57BL/6; # p <0.05 PUB versus C57BL/6.

124 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

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125 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

4.4.2. The effects of genetic susceptibility of hyperglycaemia on the metabolic response to high-fat diet feeding

Diet-induced obesity in hyperglycaemia susceptible mice

To investigate the impact of genetic susceptibility of hyperglycaemia on high-fat diet- induced obesity and glucose intolerance in the presence of advanced insulin resistance, hyperglycaemia susceptible strains (PIPING and PUB mice) and age-matched control C57BL/6 mice were challenged with a high fat diet containing 43% calories from fat or control chow diet (10% calories from fat) beginning at 6 weeks of age. As shown in Table 4.1, high-fat fed mice were significantly heavier in C57BL/6 as well as the hyperglycaemia susceptible strains, PIPING and PUB, as compared to their chow fed counterparts, despite a significantly lower initial body weight was observed in the high-fat fed PUB mice. In addition, an enhanced weight gain was observed in all three strains fed the high-fat diet compared to their chow fed groups (Figure 4.10A), which was associated with a significant increase in the fat mass of subcutaneous, infrarenal and epididymal adipose tissues (Figure 4.10B and 4.10C).

Table 4.1 Initial body weight, end body weight, average intake of food and energy, and fluid consumption in C57BL/6, PIPING and PUB fed chow or high-fat diet. Strain C57BL/6 PIPING PUB Diet Chow HF Chow HF Chow HF Number 5 7 5 10 3 9 BW initial (g) 24.5 ± 0.3 23.9 ± 0.4 17.3 ± 0.9 16.4 ± 0.5 19.3 ± 1.6 15.2 ± 0.7# BW end (g) 36.8 ± 1.1 43.1 ± 1.3# 21.6 ± 0.6 27.2 ± 1.2# 24.2 ± 1.3 29.2 ± 1.2# Average daily food 4.2 ± 0.1 3.3 ± 0.1# 4.0 ± 0.6 2.4 ± 0.05*# 4.7 ± 1.2 2.4 ± 0.1*# intake (g) Average daily energy 55.9 ± 1.2 63.0 ± 1.8# 52.3 ± 7.4 45.7 ± 0.9* 62.2 ± 16.4 45.4 ± 1.3*# intake (kJ) Average fat intake (g) 0.4 ± 0.008 1.4 ± 0.04# 0.4 ±0.05 1.0 ±0.02# 0.3 ± 0.02 1.0 ±0.02# HF: high-fat diet; Data presented as mean ± SEM. * p<0.05 versus age-matched C57BL/6 in the diet group; # p<0.05 HF versus chow fed group.

126 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

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127 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

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In terms of daily food intake, all three strains consumed significantly less of the high-fat diet than of the chow diet; moreover, the high-fat fed PIPING and PUB mice had reduced food intake compared to C57BL/6 mice. Analysis of average energy intake showed a significantly higher caloric intake with high-fat diet in C57BL/6 mice, whereas a decreased energy intake was observed in high-fat fed PIPING and PUB mice compared to C57BL/6 as well as their chow fed counterparts (Table 4.1). However, when fat intake was calculated, it was significantly higher in all strains fed a high-fat diet then their chow fed groups (2.5 to 3.5-fold increase). These results suggested that the enhanced weight gain in high-fat fed mice may be attributable to the increased fat content in the diet even though the total caloric intake did not increase.

128 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

The effect of high-fat diet feeding on glucose homeostasis and -cell function in the hyperglycaemia susceptible strains

The high-fat diet utilised in this study has been demonstrated for its rapid efficacy to induce glucose intolerance by deteriorating insulin resistance within three days (Wu et al., 2014). To determine the metabolic effects of high-fat feeding on mice which have genetic predisposition to hyperglycaemia, an insulin tolerance test (ITT) was performed to determine insulin resistance followed by 3-week of high-fat diet feeding. The ITT results showed that 3-weeks of high-fat feeding led to insulin resistance in both PIPING and PUB mice compared to the chow fed controls, while there was no appreciable difference being detected in C57BL/6 mice (Figure 4.11A). This result better reflected insulin resistance as presented by percentage of baseline glucose and demonstrated by significant differences at the time point of 60-minute and beyond in PIPING and PUB mice (Figure 4.11B). At this stage (3 weeks on chow or high fat diet), high-fat feeding has led to advanced hyperglycaemia in PIPING and PUB compared with the chow fed mice, while the non- fasted blood glucose was significantly lower in high-fat fed C57BL/6 compared to the chow fed mice (Table 4.2), suggesting that compensatory insulin secretion may have developed.

Table 4.2 Blood glucose and plasma insulin concentrations of mice fed chow or high- fat under various fasting conditions at different stage of diet study. Strain C57BL/6 PIPING PUB Diet Chow HF Chow HF Chow HF 9-week-old (3 weeks in diet study) Non-fasted blood glucose (mM) 12.3 ± 0.3 11.0 ± 0.4# 15.9 ± 0.9* 14.2 ± 0.9* 15.1 ± 0.6* 15.5 ± 0.9* 14-week-old (8 weeks in diet study) 6 h fasted glucose (mM) 10.2 ± 0.4 10.7 ± 0.5 11.7 ± 0.5* 15.4 ± 0.9*# 10.1 ± 1.0 16.2 ± 1.2*# 6 h fasted insulin (ng/ml) 0.61 ± 0.1 1.64 ± 0.3# 0.4 ± 0.05 0.83 ± 0.1* 0.27 ± 0.1 0.35 ± 0.06* 24-week-old (18 weeks in diet study) 6 h fasted glucose (mM) 9.6 ± 1.8 9.5 ± 1.1 21.9 ± 0.3* 23.0 ± 2.0*# 20 21.2 ± 0.6* 6 h fasted insulin (ng/ml) 1.86 ± 1.1 6.63 ± 0.8# 0.44 ± 0.02 0.61 ± 0.06*# 0.72 0.65 ± 0.2* O/N fasted glucose(mM) 8.4 ± 2.0 10.5 ± 2.1 11.2 ± 2.6 13.1 ± 3.2 14.9 ± 3.8* 13.0 ± 3.2 O/N fasted insulin (ng/ml) 0.64 ± 0.2 1.97 ± 0.3# 0.28 ± 0.04 0.76 ± 0.1*# 0.21 ± 0.01 0.98 ± 0.4# HF, high fat diet; O/N, overnight. Data presented as mean ± SEM (C57BL/6 chow n=3-7, HF n=3-6; PIPING chow n=3-7, HF n=5-13; PUB chow n=1-4, HF n=4-9). * p<0.05 versus age-matched C57BL/6 in the diet group; # p<0.05 HF versus chow fed group.

129 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

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Figure 4.11 ITT in C57BL/6, PIPING and PUB mice fed chow or high-fat diet for 3 weeks. Plasma glucose excursions (A) and percentage of baseline glucose (B) during an ITT in C57BL/6 (●), PIPING (■) and PUB (▲) mice fed chow (solid line) or high-fat diet (dash line). Data presented as mean ± SEM (C57BL/6 chow n=7, HF n=6; PIPING chow n=4, HF n=13; PUB chow n=3, HF n=9). *p<0.05 HF versus chow in C57BL/6; θ p<0.05 HF versus chow in PIPING; # p<0.05 HF versus chow PUB. HF, high-fat diet feeding.

130 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

Following the 8 weeks of high-fat diet feeding, there was no difference in 6-h fasted blood glucose in C57BL/6 mice between chow and high-fat fed groups, which was due to the compensatory increase (p<0.05) of plasma insulin in response to high-fat feeding (Table 4.2). Interestingly, the high-fat fed PIPING and PUB mice exhibited advanced hyperglycaemia (6-h fasted) compared with the chow fed counterparts as well as the high- fat fed C57BL/6 mice despite the baseline insulin levels trending higher (Table 4.2), indicative of the increased insulin demand as a result of deteriorating insulin resistance. Therefore, an oral glucose tolerance test (OGTT) was performed to determine the glucose tolerance and insulin response of the mice following the 8-weeks of high-fat diet feeding and the chow fed controls. As expected, high-fat feeding resulted in glucose intolerance in the C57BL/6 mice compared to the chow fed control (Figure 4.12A), and the overall glucose tolerance as measured by the area under the curve (AUC) and incremental AUC (iAUC) showed a significant increase in the high-fat fed C57BL/6 compared to the chow fed mice (Figure 4.12C and 4.12D). In addition, the high-fat diet further exacerbated the glucose intolerance in PUB mice compared with the chow fed control (Figure 4.12B), and this was also shown in the AUC of glucose (AUCglucose) during OGTTs (Figure 4.12C). However, a subtle increment was found in high-fat fed PIPING mice compared with the chow fed counterpart as the blood glucose measurements post the 30-minute time-point went beyond the measuring range of the hand-held glucose meter (0.6 - 33.3 mmol/L), thus a maximal value (33.3 mmol/L) was recorded even though higher measurements were anticipated (Figure 4.12A).

As shown in Table 4.2, following the 18 weeks high-fat feeding, fasted plasma glucose (6- h or overnight fasted) was not elevated in high-fat fed C57BL/6 mice compared with the chow fed counterpart. High-fat feeding did significantly raise the plasma insulin levels by as high as 3.5-fold in the C57BL/6 compared to the chow fed control. In contrast, high-fat feeding resulted in an advanced hyperglycaemia (6-h fasting) in PIPING mice, despite the significant (p<0.05) increase in basal insulin levels observed in response to the high-fat feeding. Overnight fasting normalised the hyperglycaemia in the high-fat fed PIPING mice, which was associated with a 2.7-fold increase in plasma insulin levels compared to the chow fed mice (Table 4.2). Similarly, high-fat feeding in the PUB mice showed no

131 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci difference in the fasted glucose levels compared to the chow fed mice, whereas the plasma insulin levels following overnight fasting was found to be increased by 4.6-fold than that of the chow-fed counterpart. Together, these results revealed that high-fat feeding may trigger an enhanced pancreatic insulin secretion in C57BL/6 mice as well as in the hyperglycaemia susceptible strains, PIPING and PUB mice.

Subsequently, mice were subjected to OGTTs to determine the effect of long-term high-fat feeding on glucose tolerance. The results showed no difference in glucose tolerance between the chow control and the high-fat fed C57BL/6 mice, and high-fat feeding did not cause further glucose intolerance in PIPING and PUB mice compared with their chow-fed counterparts (Figure 4.13A and 4.13B). When plasma insulin levels were examined, the data showed a pronounced augmentation in insulin secretion in the high-fat fed C57BL/6 compared to the chow fed group (Figure 4.13C and Figure 4.14). In contrast, chronic high- fat feeding did not cause enhanced insulin secretion in PIPING and PUB mice (Figure 4.13D and 4.13E), except for a small but significant increment in insulin secretion at the 15-min time point of the high-fat fed PIPING mice compared to the chow fed controls (Figure 4.13D). Although higher insulin levels were observed in both PIPING and PUB mice fed the high-fat diet during the tests, there was no significant increase in overall insulin secretion as measured by the total AUC during the OGTTs (Figure 4.14).

As the insulin secretory function appeared to be enhanced in the high-fat fed mice in OGTTs, an intravenous glucose tolerance test (IVGTT) was then conducted to confirm and better characterise insulin secretory capability in mice fed high-fat or chow diet for 18 weeks. As shown in Figure 4.15A, high-fat fed C57BL/6 exhibited a substantial rise in glucose-induced insulin secretion compared to the chow fed control, which was also demonstrated by significant increases in the total AUC (Figure 4.15D) and incremental AUC of plasma insulin levels (Figure 4.15E) during IVGTTs. Interestingly, the high-fat fed PIPING mice demonstrated higher insulin secretion than the chow fed mice which initiated with an elevated baseline insulin level (p=0.001 vs chow) (Figure 4.15B). However, the insulin secretion in high-fat fed PUB mice was characterised by an overt hyperinsulinemia at baseline with no increment where the first- and second-phase insulin secretory responses were absent following a glucose bolus (1 g/kg) (Figure 4.13C).

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133 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

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Figure 4.13 OGTTs in 24 weeks old C57BL/6, PIPING and PUB mice fed chow or high-fat diet for 18 weeks. Plasma glucose excursions (A and B) and insulin secretion (C, D and E) during an OGTT in C57BL/6 (●), PIPING (■) and PUB (▲) mice fed chow (Chow, solid line) or high-fat diet (HF, dash line). Data presented as mean ± SEM (C57BL/6 chow n=3, HF n=5; PIPING chow n=2, HF n=4; PUB chow n=1, HF n=3). *p<0.05 HF versus chow in C57BL/6; θ p<0.05 HF versus chow PIPING.

134 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

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Figure 4.14 Glucose-stimulated insulin secretion of C57BL/6, PIPING and PUB mice fed chow or high-fat diet for 18 weeks. Total AUC of plasma insulin during OGTTs in C57BL/6 (□), PIPING (■) and PUB (■) mice. Data presented as mean ± SEM (C57BL/6 chow n=3, HF n=5; PIPING chow n=2, HF n=4; PUB chow n=2, HF n=3). *p<0.05 versus C57BL/6 in each diet group; # p<0.05 HF versus chow fed group.

135 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

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Figure 4.15 IVGTTs in 24 weeks old C57BL/6, PIPING and PUB mice fed chow or high-fat diet for 18 weeks. Plasma insulin secretion in C57BL/6 (A), PIPING (B) and PUB (C) fed chow (Chow, solid line) or high-fat diet (HF, dash line) during an IVGTT. Total AUC (D) and incremental AUC (E) of plasma insulin in C57BL/6 (□), PIPING (■) and PUB (■) mice. Data presented as mean ± SEM (C57BL/6 chow n=3, HF n=3; PIPING chow n=5, HF n=5; PUB chow n=2, HF n=5). In (A), *p<0.05 HF versus chow in C57BL/6. In (B) and (C) *p<0.05 versus C57BL/6 in the diet group; # p<0.05 HF versus chow fed group.

136 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

Histological examination of pancreatic islets

To assess the impact of chronic high-fat feeding on pancreatic islets in hyperglycaemia susceptible mice, histological analysis was conducted on pancreata from PIPING and PUB mice compared with C57BL/6 following 18 weeks of either high-fat or chow feeding. On a chow diet, a smaller pancreas was observed in PUB mice compared with the C57BL/6 mice, while there was no difference between PIPING and C57BL/6 mice. In addition, the high-fat fed PIPING and PUB mice had reduced pancreas weight compared to their chow fed counterparts (Figure 4.16). These pancreata were stained for nuclei (H&E staining) and counter stained with insulin to visualise -cells, the representative pancreatic sections of C57BL/6, PIPING and PUB mice fed high-fat or chow are showed in Figure 4.19. The immunohistological analyses on pancreatic islets from C57BL/6 mice revealed that high- fat feeding resulted in a significant reduction in pancreatic islet number compared with the chow fed control (Figure 4.17A). However, the data also indicated an increased islet size in high-fat fed C57BL/6 mice and, this was associated with a significant rise in the proportion of large islets compared with the mice in the chow fed group (Figure 4.17B and 4.17C and Table 4.3). Furthermore, this enlargement of islet size and proportion was translated into an increase in -cell mass in high-fat fed C57BL/6 as shown in Figure 4.18A. In contrast to the C57BL/6 mice on the chow diet, PIPING mice had comparable islet number but significant increase in islet size, islet proportion as well as -cell mass. High- fat feeding resulted in a further increment in islet proportion, which was independent of the alteration in islet number but due to the increase in the proportion of large islets (area >20,000 m2, hypertrophy) compared to the chow fed PIPING mice (Figure 4.17A, 4.17B and 4.17C and Table 4.3). However, this change did not lead to an increase in -cell mass because of the smaller pancreas mass in the high-fat fed PIPING mice. The PUB mice on a chow diet had an increased islet size and islet proportion compared to the C57BL/6 mice, and there was no difference in islet number. High-fat feeding resulted in an increase in the number of total islets (hyperplasia) in the PUB mice (Figure 4.17A), and mostly the small- sized islets (area <5,000 m2) (Table 4.3). In addition, there was no difference in islet

137 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci proportion and -cell mass of PUB mice between high-fat or chow diet groups (Figure 4.17C and Figure 4.18A).

Lastly, pancreatic insulin content was also assessed in the C57BL/6, PIPING and PUB mice fed either a high-fat or chow diet for 18 weeks. The results showed a significant reduction in high-fat fed PIPING mice compared to the chow fed counterpart, while there was no difference in C57BL/6 and PUB mice between high-fat and chow fed mice (Figure 4.18B).

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138 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

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Figure 4.17 Pancreas islets number, size and proportion of C57BL/6, PIPING and PUB mice fed chow or high-fat diet for 18 weeks. Pancreatic insulin immunochemistry of pancreas from C57BL/6 (□), PIPING (■) and PUB (■) mice fed chow or high-fat diet. Islets number (A), islet size (B) and proportion of insulin positive staining (C) were presented as mean ± SEM (C57BL/6 chow n= 6, HF n= 8; PIPING chow n= 7, HF n= 9; PUB chow n= 3, HF n= 9). Islet proportion was determined as the proportion of insulin positive area over the total area of pancreas tissue on sections. * p<0.05 versus C57BL/6 in each diet group; # p<0.05 HF versus chow fed group. HF, high-fat.

139 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

Table 4.3 Distribution of pancreatic islet size (%) of mice fed chow or high-fat diet for 18 weeks.

% Small islets % Medium islets % Large islets Strain Diet group < 5,000 m2 5,000 – 20,000m2 m2

Chow 18.3 ± 4.6 56.6 ± 2.1 23.7 ± 2.7 C57BL/6 HFD 9.7 ± 3.1 50.8 ± 2.2# 36.1 ± 4.4#

Chow 11.8 ± 2.0 54.3 ± 1.2 33.8 ± 1.9* PIPING HFD 6.3 ± 1.4# 52.6 ± 2.3 40.9 ± 2.2#

Chow 3.1 ± 1.8 52.8 ± 2.1 43.9 ± 3.7* PUB HFD 12.1 ± 2.1# 52.0 ± 1.3 35.7 ± 2.0#

Data presented as mean ± SEM (C57BL/6 chow n= 6, HF n= 8; PIPING chow n= 7, HF n= 9; PUB chow n= 3, HF n= 9). *p<0.05 versus C57BL/6 in each diet group; # p<0.05 HF versus chow fed group. HFD, high-fat diet.

140 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

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141 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

Figure 4.19 Pancreatic insulin staining of C57BL/6, PIPING and PUB mice fed chow or high-fat diet for 18 weeks. Representative images of insulin immunohistochemistry in pancreata harvested from 24 week old C57BL/6 (A, D), PIPING (B, E) and PUB (C, F) mice fed chow (top panel, A-C) or high-fat diet (bottom panel, D-F). Scale bar, 50 m; HFD, high-fat diet.

142 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

4.5. Discussion It has become apparent that a substantial decline in pancreatic insulin secretion is central to the pathogenesis of hyperglycaemia that is usually accompanied by varying degrees of insulin resistance in type 2 diabetes (T2D) (Elder, Prigeon, Wadwa, Dolan, & D'Alessio, 2006; Gungor, Bacha, Saad, Janosky, & Arslanian, 2005; S. E. Kahn, 2001; Matthews et al., 1998). Therefore, -cell dysfunction is recognised as a prerequisite for the development of hyperglycaemia (S. E. Kahn, 2001; Mitrakou et al., 1992; Porte, 1991) and the major challenge in glycaemic control (S. E. Kahn, 2001). Indeed, pancreatic -cell function and its susceptibility to failure appears to be genetically predetermined, which plays a critical part in determining an individual’s predisposition to develop T2D. This notion is supported by prevailing evidence that the vast majority of diabetogenic genes were found to be involved in human pancreatic islets mediating insulin secretion (Florez, 2008; Imamura & Maeda, 2011; McCarthy & Zeggini, 2009), which informs genetic regulation of diabetes susceptibility towards the relative importance of -cell function rather than insulin action.

In Chapter 3 of this thesis, the GWAS for hyperglycaemia identified two susceptibility loci (the E2F8 locus, chr7: 52.6-56.7 Mbp; the Dlg2 locus, chr7: 98.5-101.5 Mbp) that are significantly associated with elevated blood glucose. The mice which carry the deleterious allele(s) manifested elevated blood glucose and overt glucose intolerance, and this was not due to insulin resistance nor obesity. There are many factors that could affect the overall glucose homeostasis, including hyperglucagonemia (Miuchi, Miyagawa, & Namba, 2015; Wewer Albrechtsen, Kuhre, Pedersen, Knop, & Holst, 2016), incretin deficiency or incretin resistance (Srinivasan et al., 2018), increased renal glucose reabsorption (DeFronzo, Davidson, & Del Prato, 2012), brain insulin resistance (Arnold et al., 2018), uncontrolled hepatic glucose production (Sharabi, Tavares, Rines, & Puigserver, 2015), nonetheless it is readily evident that hyperglycaemia and T2D would not occur unless the present of pancreatic -cell dysfunction. Therefore, the current study aim was to test the hypothesis that in the presence of normal insulin sensitivity, hyperglycaemia is the consequence of impaired insulin secretion. Therefore, pancreatic -cell function was assessed in the hyperglycaemic CC strains, PIPING and PUB mice, to investigate how these hyperglycaemia-associated loci affect glucose homeostasis. Our physiological

143 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci characterisation showed that the fundamental cause of hyperglycaemia and overt glucose intolerance was reduced pancreatic insulin secretion. Furthermore, the insufficient insulin levels were independent of -cell deficits, reductions in-cell mass or insulin content in the pancreatic islets. These findings suggested that these hyperglycaemia susceptible loci resulted in the functional but not morphological defect in the pancreatic islets. As demonstrated in the previous chapter the hyperglycaemia in the PIPING and PUB mice was attributable to the susceptibility allele(s) originating from the NZO founder strain. Published data from our laboratory demonstrated that the impaired insulin secretion in the NZO mice was specific to glucose and tolbutamide, while insulin hypersecretion in response to arginine. In addition, the Abcc8 gene (locates in the E2F8 locus) was identified to be one of the causal genes of impaired early-phase insulin secretion in the NZO mice (Andrikopoulos et al., 2016). In contrast, the PIPING and PUB mice displayed profound defects in both phases of insulin secretion with glucose and arginine; moreover, impairment of tolbutamide-induced insulin secretion is also expected. This finding revealed that multiple genes (in addition to Abcc8 gene) in the susceptibility loci may lead to defective insulin secretion and likely to take part in the late-phase of insulin secretion. In addition, this also suggested the substantial and diverse effects of these detrimental NZO alleles on insulin secretion as the mice with susceptible alleles manifested various degree of defect in insulin secretion among three divergent inbred strains. Furthermore, the effect of the NZO-Dlg2 allele on insulin secretion was also supported by the published data from our laboratory, in which the NZO-derived E2F8 locus and Dlg2 locus were characterised using the C57BL/6 congenic strains in comparison with C57BL/6 and NZO mice (Andrikopoulos et al., 2016). As shown in Figure 4.20, the NZO-derived E2F8 and Dlg2 alleles gave rise to a substantial decline in insulin secretion in C57BL/6 mice (B6.NZO-7A), while the mice with functional E2F8 allele but not at the Dlg2 locus (B6.NZO-7C) showed significant improvement in the first-phase insulin secretion but not the late-phase insulin secretion. Importantly the insulin secretory defects persisted in the congenic strain with the NZO- Dlg2 allele (B6.NZO-7C), which was demonstrated by a greater than 5-fold reduction in tolbutamide-induced early-phase insulin secretion when compared with wild type C57BL/6 mice, suggesting an appreciable effect of the NZO-Dlg2 allele on pancreatic insulin secretion. Overall, these data support the finding that the E2F8 and Dlg2 loci have

144 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci substantial effects on insulin secretory function in vivo, reflecting that potent genetic determinants predispose the susceptible individuals to -cell dysfunction therefore contributing to the ensuing hyperglycaemia.

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Figure 4.20 The NZO-derived Dlg2 allele in C57BL/6 mice resulted in impaired insulin secretion. (A) Schematic diagram depicting the sequence identity for B6.NZO-7A and B6.NZO-7C congenic strains on chromosome 7. Boundary markers and genes are labeled on the corresponding position. Colour coding is used to indicate the sequence deriving from C57BL/6 (black), NZO (red) or the undefined regions (blue). (B) Insulin secretion in response to an intravenous tolbutamide stimulation in B6.NZO-7A (▲) and 7C (▼) compared with C57BL/6 (○) and NZO mice (■). (C) The AUC of insulin from 0-5 mins. Data presented as mean ± SEM. Hash (#) and asterisk (*) denote p <0.05 in NZO and B6.NZO-7C, respectively, compared with C57BL/6. a p <0.05 B6.NZO-7A vs 7C congenic strain. Data adapted from Andrikopoulos, S. et al. (Andrikopoulos et al., 2016).

145 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

Despite the strong effect of genetic components on pancreatic insulin secretion, it is certain that PIPING and PUB mice have not yet developed full-blown T2D at 10-12 weeks of age. T2D often develops when nutritional excess in association with obesity and combines genetic susceptibility to cause insulin deficiency in the setting of insulin resistance (S. E. Kahn, Cooper, & Del Prato, 2014). Here, we sought to investigate whether the genetic predisposition to impaired insulin secretion accelerate diabetes progression when exposed to chronic high-fat feeding. High-fat fed C57BL/6 mice are the most widely used model for diet-induced obesity because of their high susceptibility to develop obesity and insulin resistance when consuming a high-fat composite diet (Aston-Mourney et al., 2007; Burcelin, Crivelli, Dacosta, Roy-Tirelli, & Thorens, 2002; Surwit et al., 1988; Winzell & Ahren, 2004). Our results showed C57BL/6 mice gained approximately 84% of their initial body weight following the 18 weeks of high-fat feeding (compared to 26% in the chow fed control). This excess weight gain induced by consumption of a high-fat diet was attributable to a greater calorie intake in the C57BL/6 mice. However, we and others have shown that greater calorie intake is not necessarily the case in diet-induced weigh gain. In many instances laboratory animals increased weight gain on the high-fat diet despite an equivalent or even lower calorie intake compared to that of the control mice on chow (Bray, Lee, & Bray, 1980; Jen, 1988; Lamont, Waters, & Andrikopoulos, 2016; Oscai, Miller, & Arnall, 1987; Uhley & Jen, 1989). This discordance could be due to the individual differences in response to the varying dietary composition which leads to the alteration in energy expenditure or because of the divergent postabsorptive calorie efficiency of fat relative to carbohydrate. Our previous studies suggested that increased weight gain in mice fed a high-fat diet was due to reduced energy expenditure (Funkat, Massa, Jovanovska, Proietto, & Andrikopoulos, 2004; Lamont et al., 2016). Indeed, a high-fat diet elicits less postprandial thermogenesis than the chow diet which can therefore account for approximately 10% reduction in energy expenditure (Nair, Halliday, & Garrow, 1983; Welle, Lilavivat, & Campbell, 1981) (R. S. Schwartz, Ravussin, Massari, O'Connell, & Robbins, 1985). On the other hand, dietary fat is recognised as a more efficient macronutrient for fat deposition since less energy is required for storing fat from dietary fat than carbohydrate (Devlin, 1990). It was also supported by Mullen, B. J., et al. that the composition of dietary fat is important since they showed a high-fat diet containing tallow

146 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci promoted greater weight gain than low-fat diet of equivalent energy density (Mullen & Martin, 1990). In addition, in a human study lowering dietary fat content was found to be associated with weight loss despite an increase in total calorie intake (Prewitt et al., 1991). In the present study, we observed significant increases in fat intake in all three strains on the high-fat diet (3.5-fold in C57BL/6 mice, 2.5-fold in PIPING and 3.3-fold in PUB mice, p<0.05 versus their chow fed counterparts), which were accompanied by substantial expansion of fat pad mass. This result suggested that the excessive fat intake may have exceeded the proportion being oxidized in the mice fed a high-fat diet, leading to an increase in adipose tissue mass deposition and the ensuing weight gain, and this was independent of higher caloric intake. Overall, our results highlighted the individual differences in metabolic component responding to changes in dietary composition and weight gain among three divergent strains of mice. Moreover, the inherent predisposition to hyperglycaemia appears to also predispose individuals to become more sensitive to dietary fat-induced weight gain.

Concomitant with obesity, excessive nutrition leads to systemic insulin resistance as a result of increased exposure of insulin-responsive tissues to the by-products of nutritional overload, such as increased plasma levels of free fatty acids in obese subjects (Boden, 2011; Boucher et al., 2014; DeFronzo, Bonadonna, & Ferrannini, 1992; S. H. Kim & Reaven, 2008; Parker, Savage, O'Rahilly, & Semple, 2011). Our results demonstrated a marked degeneration of insulin sensitivity in hyperglycaemia-susceptible strains (PIPING and PUB mice) fed a high-fat diet for 3 weeks. However, a very subtle change in insulin resistance was observed in high-fat fed C57BL/6 mice, which indicated that insulin resistance has yet to become prominent by the time of examination. It is well established that insulin resistance and hyperinsulinemia are tightly linked which evolves as a compensatory mechanism for the increased demand of insulin as a result of enhanced -cell function (S. H. Kim & Reaven, 2008). Therefore, it is common to see hyperinsulinaemia in the obese and insulin-resistance humans and animals in which glycaemia remains normal as in a state of -cell compensation that protects them from diabetes for long periods of time before a subset of such prone individuals ultimately succumb to -cell failure. In the present study, our data showed that C57BL/6 mice developed prominent fasting hyperinsulinemia (2.7-

147 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci to 3.5-fold increase) in response to high-fat feeding, indicative of insulin resistance. In addition, the elevated plasma insulin levels ensured normal glycaemia in the high-fat fed C57BL/6 at all time during the whole period of dietary study. As expected, our results showed the normal glycaemia in the high-fat fed C57BL/6 mice was maintained via a compensatory enhancement of glucose-stimulated insulin secretion, suggesting an adequate metabolic adaption in response to nutrient overload. By contrast, PIPING and PUB mice developed an advanced hyperglycaemia despite the significant elevation in plasma insulin levels compared to the chow fed counterparts. Indeed, the worsening hyperglycaemia was the consequence of insufficient insulin levels. Although the plasma insulin levels were significantly elevated in PIPING and PUB mice in response to high-fat feeding, the absolute insulin levels were merely comparable to the degree of the chow fed C57BL/6 whereas they were still markedly lower than that of the high-fat fed C57BL/6. In addition, this finding is in line with the insulin secretion data that the plasma insulin excursions during GTTs in the PIPING and PUB mice were trending higher in the high-fat fed group. Nevertheless, the defective insulin secretion remains in the high-fat fed PIPING and PUB mice which was characterised by profound glucose intolerance and reduced insulin secretion when compared with C57BL/6 mice. These data revealed that in the face of insulin resistance, the hyperglycaemia-susceptible strains, PIPING and PUB mice, exhibited an incompetence of islet compensation to meet the increased insulin demand, and the increased plasma insulin levels do not necessarily indicate adequate -cell function. Our findings are also in concordance with the current concept that when -cell compensation fails, T2D occurs (Cnop et al., 2007; Weyer et al., 1999).

In the state of nutrient overload, the elevated pancreatic insulin secretion is believed to be the result of raised plasma levels of FFAs (Crespin, Greenough, & Steinberg, 1973; Felber & Vannotti, 1964; Pelkonen et al., 1968; van Citters et al., 2002). Elevated circulating FFA levels are prevailing in most obese people with insulin resistance (Y. Lee et al., 1994). It has been shown that an increase in plasma NEFA concentrations in healthy subjects through infusion of lipid emulsions caused insulin resistance and were associated with changes in post-receptor insulin signalling (Boden, 1997; Griffin et al., 1999; Roden et al., 1996). It has been shown that FFAs can potentiate insulin secretion in healthy subjects

148 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci

(Boden, Chen, Rosner, & Barton, 1995; Jensen et al., 2003), this mechanism may be applicable to those who are obese, insulin resistant but never develop diabetes. Conversely, ameliorating the chronically elevated plasma FFA levels in obese nondiabetic and diabetic subjects has been shown to reduce basal insulin secretion by 30% to 50% (Boden, Chen, & Iqbal, 1998; Dobbins, Chester, Daniels, McGarry, & Stein, 1998; Santomauro et al., 1999). It is intriguing that Kashyap et al. reported that FFA-triggered insulin secretion can precisely compensate for the increased insulin demand in the healthy subjects as well as in the glucose-tolerant obese individuals who do not have a family history of diabetes. However, this effect has failed in pre-diabetic individuals with first-degree relatives with T2D (S. Kashyap et al., 2003). These studies together supported our results that individuals with genetic predisposition to -cell dysfunction in which failure of insulin secretion to compensate for insulin resistance leads to the resultant hyperglycaemia in a state of obesity. Furthermore, the reduction in insulin release can be anticipated to result in advanced perturbations such as increased glucose production, reduced efficiency of glucose uptake and increased lipolysis which in turn accelerates the progression to T2D.

A sustained -cell compensation is often accompanied by increasing -cell mass which involves a sequence of events such as -cell replication, neogenesis, hypertrophy, and promote survival. Our results showed that the metabolic stress from high-fat feeding triggered islet -cell hypertrophy in the C57BL/6 mice, manifesting a significant increase in the proportion of large islets. This effect was translated to an appreciable rise in -cell mass, whereas there was no concomitant increment in pancreatic insulin content. This discordance may be a sign preceding -cell exhaustion (insulin depletion) in the high-fat fed C57BL/6 mice, suggesting progressive -cell failure. On the other hand, the hyperglycaemia susceptible PIPING and PUB mice displayed modest islet hypertrophy and hyperplasia in response to high-fat feeding, respectively. However, these modest changes did not cause significant -cell expansion in total mass or increase in pancreatic insulin content. These data demonstrated a metabolic decompensation in the pancreatic -cells of PIPING and PUB mice, which is postulated to develop T2D shortly since the acquired insulin resistance had exceeded the ability of the -cell to compensate for it. Moreover, it

149 Chapter Four Characterisation of Hyperglycaemia Susceptibility Loci appears that the metabolic stress triggered by consumption of a high-fat diet precipitates metabolic adaptation and compensatory mechanisms in susceptible individuals.

Taken together, this work provided evidence demonstrating that the hyperglycaemia susceptibility loci, E2F8 and Dlg2 loci, have substantial influence on pancreatic -cell function. More importantly, the susceptible strains with the deleterious allele(s) at the E2F8 and/or Dlg2 loci presented -cell decompensation when exposed to chronic high-fat feeding. Our study clearly highlights that understanding the effect of inherent metabolic susceptibility on islet compensatory response is of importance to elucidate the pathophysiology of T2D and has implications for the treatment of the disease.

150

CHAPTER FIVE

IDENTIFICATION OF E2F8 AND DLG2 AS NOVEL DIABETES SUSCEPTIBILITY GENES CAUSING IMPAIRED INSULIN SECRETION

Chapter Five Identification of E2F8 and Dlg2 as Diabetes Susceptibility Genes

5. Chapter 5 Identification of E2F8 and Dlg2 as novel diabetes susceptibility genes causing impaired insulin secretion

5.1. Introduction Pancreatic islet dysfunction is the hallmark of type 2 diabetes resulting in uncontrolled elevations in blood glucose levels due to insufficient insulin secretion. Although environmental factors are expected to be involved, permissive genetic predispositions have been imputed as major contributors to the pathogenesis of diabetes. As described in Chapter 3 of this thesis, our GWAS for blood glucose concentration has led to the identification of two creditble loci on chromosome 7, the E2F8 locus (chr7: 52,631,000-56,700,000) and Dlg2 locus (chr7: 98,500,000-101,550,000), in which sequence variants derived from a polygenic model of diabetes, the NZO mouse, were ascribed to be detrimental. Thereby, candidate genes containing sequence polymorphisms specific to the NZO genome were prioritised to be studied for the E2F8 locus as follows: Gfy, Hsd17b14, Sphk2, E2F8, Ntn5, Abcc8 and Kcnj11. In addition, all 7 genes that are harboured in the Dlg2 region were studied as follows: Dlg2, Ccdc90b, Andkrd42, Pcf11, Ddias, Rab30 and Prcp.

Previous studies have strived to understand the genetic components underlying diabetes using the NZO mouse, thus the diabetes susceptible NZO mouse which manifests features of human diabetes have been studies in a number of genome-wide scans since 1998 (Andrikopoulos et al., 2016; Kluge et al., 2000; Leiter et al., 1998; Plum et al., 2002; Plum et al., 2000; Reifsnyder et al., 2000; Reifsnyder & Leiter, 2002; B. A. Taylor et al., 2001). Despite all that is known about the metabolic disturbances in the NZO mice, knowledge of the genetic basis of its physiological abnormalities have yet to be fully defined. However, a previous study from Gates et al. have demonstrated that the genetic lesion in the NZO mouse appears predominantly to be situated within the islets of Langerhans (Gates, Hunt, Smith, & Lazarus, 1972). In addition, as characterised in the previous chapter, the CC mice carrying the detrimental copy of genes at the susceptible regions (the E2F8 and/or Dlg2 locus) exhibited hyperglycaemia and profound glucose intolerance as a result of reduced

152 Chapter Five Identification of E2F8 and Dlg2 as Diabetes Susceptibility Genes insulin secretion. Moreover, the genetic defects appear to specifically affect insulin secretory function without histological destructions of pancreatic islets as revealed by pancreatic immunohistochemistry.

Furthermore, it has become evident that the majority of findings from GWAS for T2D and the related traits have enriched variants in islet-specific regulatory elements and -cell failure susceptibility genes, suggesting a common mechanism underlying the molecular genetics of islet dysfunction and T2DM (Florez, 2008; Imamura & Maeda, 2011; J. R. Perry & Frayling, 2008). Given the above observations, we reasoned that these NZO- derived variants in the susceptibility genes may directly alter insulin secretory function in the pancreatic -cells.

5.2. Aim The aim of this chapter was to determine whether the hyperglycaemia susceptibility genes contribute to diabetes predisposition through influencing insulin secretory function in the pancreatic islets.

Specific aims were as follows:

1. To determine whether these hyperglycaemia susceptibility genes are associated with diabetes predisposition in pancreatic islets. 2. To investigate the role of E2F8 and Dlg2 in pancreatic insulin secretion in an insulin secreting MIN6 cell line.

5.3. Methods Methods used in this chapter are outlined in Chapter 2.

153 Chapter Five Identification of E2F8 and Dlg2 as Diabetes Susceptibility Genes

5.4. Results 5.4.1. Identification of hyperglycaemia susceptibility genes in primary islets

Determination of differential gene expression between the islets from diabetes susceptible and non-diabetes susceptible subjects

Expression of candidate genes at E2F8 and Dlg2 locus were examined in primary islets from diabetes susceptible NZO mice compared with diabetes resistant C57BL/6 mice. At the E2F8 locus, Real-Time PCR analysis revealed differential gene expression in Abcc8, Kcnj11 and E2F8 between NZO and C57BL/6 islets (p <0.05), whilst there was no difference in Snrnp70 and Sphk2 at mRNA levels (Figure 5.1A). The expression of Gfy, Hsd17b14 and Ntn5 were not detectable in mouse pancreatic islets. The Dlg2 locus harboured seven known genes, their gene expression were also determined in isolated mouse islets. The results showed significant reductions in Ddias and Ccdc90b gene expression in NZO islets compared to C57BL/6. In addition, it was interesting to note that Dlg2 gene expression in diabetic NZO islets was trending lower (p =0.065) when compared with C57BL/6 (Figure 5.1B). Furthermore, no difference in Ankrd42, Pcf11, Rab30 and Prcp gene expression was observed between NZO and C57BL/6 islets. The differential expression identified in mouse islets sheds light on the importance of these candidate genes in pancreatic -cell function in related to the pathogenesis of diabetes.

To further determine the presence of these candidate genes in human islets and its correlation to diabetes, a quantitative real-time PCR was performed in islets isolated from eight diabetic and seven age-matched (53.5 ± 4.1 vs. 50.7 ± 4.3 years) and BMI-matched (29.7 ± 2.3 vs. 29.1 ± 1.95 kg/m2) control subjects (Table 5.1). Among the candidate genes, ABCC8 and KCJ11 are well known for their association with increased risk of type 2 diabetes in humans, which have already been developed as therapeutic targets to treat impaired insulin secretion (Y. Feng et al., 2008; Florez et al., 2004; Gloyn et al., 2003; Hamming et al., 2009). Thus the gene expression of ABCC8 and KCNJ11 were not measured in the present study as limited amount of primary human islet samples. As shown

154 Chapter Five Identification of E2F8 and Dlg2 as Diabetes Susceptibility Genes in Figure 5.2, no difference in any of the genes examined except that a significant reduction in Rab30 gene expression was found in diabetic islets compare with that of the non-diabetic subjects. In addition, there was a notable trend for an upregulation (3.5-fold increase) of E2F8 in islets from diabetic subjects when compared with non-diabetic control (P=0.08).

155 Chapter Five Identification of E2F8 and Dlg2 as Diabetes Susceptibility Genes

E2F8 and Dlg2 are identified as novel candidates for impaired insulin secretion

As described in section 3.4.2 in chapter 3, using the CC mouse resource, three important SNPs (rs253243259, p= 2.2e-15 at E2F8 locus; rs32123098, p= 3.8e-8 and rs243982980, p= 2.0e-08 at Dlg2 locus) were identified to be robustly associated with hyperglycaemia in repeat genome-wide association analysis. Among these, rs253243259 was located in the coding region of the E2F8 gene. Furthermore, other than Abcc8 and Kcnj11, two of the most well-established diabetic genes, E2F8 appears to be a novel gene candidate which was differentially expressed between diabetic and non-diabetic islets (Figure 5.1A and Figure 5.2). It was interestingly to also find that E2F8 gene expression in the human pancreatic islets exhibited a correlation (r= 0.4828) with increased BMI in non-diabetic subjects (Figure 5.3A), whilst this relationship was abrogated (r= -0.1526) in diabetic subjects (Figure 5.3 B). This result suggested that enhanced E2F8 gene expression in pancreatic islets appears to be correlated with obesity in non-diabetic subjects who presumably have normal or even enhanced insulin secretion (Polonsky et al., 1988). Thus, E2F8 was prioritised as one of the novel candidates to be studied in this thesis.

At the Dlg2 locus, rs32123098 and rs243982980 were both positioned in one gene, Dlg2, indicating Dlg2 as an important candidate in this region. Although the differential Dlg2 gene expression between the diabetes susceptible and resistant islets did not reach statistical significance, a prominent reduction in diabetes susceptible islets was observed and deemed to be a critical indication for further investigation (Figure 5.1B).

In addition to the result of genome-wide association analysis in Chapter 3 and expression analysis in islets from the diabetes susceptible NZO mice, the physiological characterisation in hyperglycaemia susceptible mice in Chapter 4 revealed the significant influence of these genetic defects on pancreatic insulin secretion. Overall, we identified E2F8 and Dlg2 as promising candidate genes which may be contributory to impaired insulin secretion in the pathogenesis of hyperglycaemia. However, the role of E2F8 and Dlg2 in pancreatic -cells and insulin secretory function are yet to be defined.

156 Chapter Five Identification of E2F8 and Dlg2 as Diabetes Susceptibility Genes

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e 0 .0 R D d ia s D lg 2 C c d c 9 0 b A n k rd 4 2 P c f1 1 R a b 3 0 P r c p Figure 5.1 Real-Time PCR analysis of candidate gene expressions in isolated mouse islets from C57BL/6 and NZO mice. (A) Islet mRNA expression of candidate genes at chr7: 52.6-56.7 Mbp in male C57BL/6 (□) and NZO (■) mice. (B) Islet mRNA expression of all genes at chr7: 98.0-101.5 Mbp in isolated islets from male C57BL/6 (□) and NZO (■) mice. Relative mRNA expression was calculated relative to an islet housekeeping gene, Ins2. Data are shown as fold-change (2ΔΔCt) ± (2ΔΔCt±SEM) and presented as mean ± SEM (n=5-6). Asterisk (*) indicates significant p <0.05 compared to C57BL/6.

157 Chapter Five Identification of E2F8 and Dlg2 as Diabetes Susceptibility Genes

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Figure 5.2 Gene expression in primary human islets from diabetic and non-diabetic subjects. Real-Time PCR analysis of candidate genes within the human orthologue regions (chr11: 17.3-19.7 Mbp and chr11: 81.4-84.6 Mbp) in non-diabetic (□) and diabetic (■) islets. Relative mRNA expression was calculated relative to the housekeeping gene, RPLPO. Results are shown as fold-change (2ΔΔCt) ± (2ΔΔCt ± SEM). Data presented as mean ± SEM (non-diabetic group, n=7; diabetic group, n=8). Asterisk (*) indicates significant p<0.05 compared to non-diabetic group.

158 Chapter Five Identification of E2F8 and Dlg2 as Diabetes Susceptibility Genes

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Figure 5.3 Correlation between islet E2F8 gene expression and BMI in non-diabetic and diabetic subjects. (A)Correlation of BMI with islet E2F8 gene expression in non-diabetic subjects. (B) Correlation of BMI with islet E2F8 gene expression in diabetic subjects. Results presented as raw values, non-diabetic group, n=6; diabetic group, n=7). Regression line, red dash line; r, correlation coefficient.

159 Chapter Five Identification of E2F8 and Dlg2 as Diabetes Susceptibility Genes

Table 5.1. Basic characteristics for donors of pancreatic islets. Identity number, body mass index (BMI), age and gender of islets donors in the non- diabetic group and diabetic group.

Islets Donor BMI Age (yr) Sex

Non-diabetic Group SVI-010-11 37.9 50 M SVI-020-11 22.3 58 F SVI-015-14 29.9 41 M SVI-030-14 16 7 M SVI-001-15 33.2 67 F SVI-012-15 28.7 48 M SVI-025-15 25.5 58 M SVI-048-15 26.7 33 M

Type 2 Diabetes SVI-005-10* 28.4 43 M SVI-007-11 30.9 47 M SVI-001-12* 44.2 44 M SVI-004-13 26.4 62 F SVI-005-14 24.8 57 F SVI-004-15 31.1 73 M SVI-014-15* 30.5 40 M SVI-023-15 21.5 62 F

* Undiagnosed cases diagnosed as HbA1c> 6; M, male; F, female; Sample excluded.

160 Chapter Five Identification of E2F8 and Dlg2 as Diabetes Susceptibility Genes

5.4.2. The effect of E2F8 or Dlg2 knockdowns on pancreatic insulin secretion

To determine whether E2F8 has a role in insulin secretion, E2F8 knockdown was achieved utilising RNA interference (RNAi) approach in a mouse insulinoma MIN6 cell line. Gene specific silencing RNA (siRNA) was delivered using a cationic lipid reagent Lipofectamine RNAiMAX (Invitrogen), experiments were conducted as the procedure outlined in section 2.2.2.1.1 of chapter 2. A siRNA concentration of 50 nM was used to transfect a pre- designed, gene specific siRNA against E2F8 (ID: s99361, Ambion, Life Technologies) in the MIN6 cells. The real-time PCR results showed that we were able to knockdown E2F8 gene expression by more than 50% in the MIN6 cells (Figure 5.4).

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Figure 5.4 Knockdown efficiency of lipofectamine-mediated siRNA transfection targeting E2F8 in MIN6 cells. Real-Time PCR analysis of E2F8 gene expression in MIN6 cells four days post transfection with 50 nM scramble (□) or E2F8 (■) siRNA. Relative gene expression was calculated relative to the housekeeping gene, Ppia. Data presented as fold change from the scramble siRNA-treated group (n=5 each treatment, three replicates in each experiment). Asterisk (***) indicates significant P<0.005 compared to scramble control.

161 Chapter Five Identification of E2F8 and Dlg2 as Diabetes Susceptibility Genes

To determine the role of E2F8 on insulin secretory function, the impact of E2F8 inhibition on glucose-stimulated insulin secretion (GSIS) was examined by insulin secretion assay in response to glucose and a number of non-glucose secretagogues. The results showed that reduction in E2F8 led to a 1.5-fold increase in basal insulin secretion at 2 mM glucose, while insulin secretion in response to 20 mM glucose was abolished compared with the scramble control (Figure 5.5). To further define the molecular basis of the defects mediated by E2F8 suppression, we investigated the response to a series of non-glucose secretagogues. + Tolbutamide binds to the SUR1 subunit of the K ATP channel as an antagonist on the -cell membrane to trigger insulin secretion. Whilst arginine causes depolarisation of the -cell + membrane downstream and bypassing the K ATP channel. The results showed tolbutamide induced a two-fold increase in insulin secretion in response to 20 mM glucose in scramble siRNA treated cells, but the enhancement was absent in E2F8 knockdown cells (Figure 5.6A). Arginine led to a greater insulin secretion (> 4-fold) in response to 20 mM glucose compared with the basal (2 mM) in the scramble control, however this effect was significantly suppressed in E2F8 siRNA-treated cells (Figure 5.6B).

+ On the other hand, the K ATP -independent signalling pathway was also examined. Incretins augment GSIS through increasing intracellular cAMP and PKA activity, thereby facilitating exocytosis of insulin granules. Therefore, it was interesting to find that in response to the incretin, GLP-1, insulin secretion was preserved in the E2F8 knockdown cells (Figure 5.6C). KCl acts directly on the -cell membrane triggering membrane depolarization and the following insulin exocytosis. KCl-induced insulin secretion from E2F8 knockdown cells was comparable with that of the scramble control (Figure 5.6D). These results suggest that inhibition of E2F8 gene expression resulted in a substantial reduction in insulin secretion, and the mechanisms into E2F8–regulated insulin secretion + appears to be dependent on K ATP channel-dependent signalling pathway.

162 Chapter Five Identification of E2F8 and Dlg2 as Diabetes Susceptibility Genes

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Figure 5.5 Glucose-stimulated insulin secretion in MIN6 cells transfected with scramble siRNA or E2F8 siRNA. Insulin release in response to low (2 mM) and high glucose (20 mM) in MIN6 cells with scramble siRNA (■) or E2F8 siRNA (□) transfection. Insulin secretion was calculated as percentage of insulin content in MIN6 cells and presented as fold-change from the low glucose (2 mM) in scramble siRNA treated group. Values are presented as mean ± SEM, n=5 per group, three replicates in each group. *P<0.05 compared to 2 mM glucose stimulation. Glucose concentration used in insulin secretory assays are indicated with 2 (basal, 2 mM) or 20 (high glucose, 20 mM) as column insert.

163 Chapter Five Identification of E2F8 and Dlg2 as Diabetes Susceptibility Genes

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Figure 5.6 Insulin secretion in response to non-glucose secretagogues in MIN6 cells transfected with scramble siRNA or E2F8 siRNA. (A) Insulin release in response to 275 M Tolbutamide, (B) 20 mM arginine and (C) 10 nM GLP-1 at low or high glucose. (D) Insulin secretion in response to 30 mM KCl or 2 mM glucose in MIN6 cells transfected with scramble (■) or E2F8 siRNA (□). Insulin secretion was calculated as percentage of insulin content in MIN6 cells and presented as fold-change from the low glucose (2 mM) in scramble siRNA treated group. Values are presented as mean ± SEM, n=5 per group, three replicates in each group. *p<0.05 compared to 2 mM glucose stimulation. Glucose concentration used in insulin secretory assays are indicated with 2 (basal, 2 mM) or 20 (high glucose, 20 mM) as column insert.

164 Chapter Five Identification of E2F8 and Dlg2 as Diabetes Susceptibility Genes

To investigate the role of Dlg2 in pancreatic insulin secretion, a stable cell line expressing shRNA against Dlg2 was generated using lentiviral transduction in the MIN6 cells. To generate a pool of MIN6 cells constantly expressing shRNA against Dlg2, antibiotic selection was performed as the lentiviral vector contained a puromycin resistance gene (puroR) for mammalian selection. Experiments were performed as the procedure outlined in section 2.2.2.1.2 of Chapter 2. To test if Dlg2 has a role in insulin secretion, the Dlg2 shRNA stable transductants which has a greater than 60% reduction in Dlg2 mRNA was studied. Glucose-stimulated insulin secretion rose 3-fold in response to 20 mM glucose in the hexadimethrine bromide treated cells (control) when compared to the basal (2 mM glucose). However, this response was abrogated in the Dlg2 knockdown cells (Figure 5.8), indicating an effect of Dlg2 on glucose-stimulated insulin secretion.

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165 Chapter Five Identification of E2F8 and Dlg2 as Diabetes Susceptibility Genes

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Figure 5.8 Glucose-stimulated insulin secretion in Dlg2 knockdown MIN6 cells. Insulin release in response to low (2 mM) and high glucose (20 mM) in lentiviral Dlg2- shRNA stable transductant (Dlg2 shRNA, ■) and hexadimethrine bromide-treated (control, □) MIN6 cells. Insulin secretion was calculated as percentage of insulin content in MIN6 cells and presented as fold-change from the low glucose (2 mM) in control group. Values are presented as mean ± SEM, n=5 per group, three replicates in each group. *p<0.05 compared to 2 mM glucose stimulation. Glucose concentration used in insulin secretory assays are indicated with 2 (basal, 2 mM) or 20 (high glucose, 20 mM) as column insert.

166 Chapter Five Identification of E2F8 and Dlg2 as Diabetes Susceptibility Genes

In addition, insulin secretion in response to a number of non-glucose secretagogues was also determined in Dlg2 knockdown cells. As revealed in Figure 5.9, the basal (2 mM) insulin secretion in Dlg2 knockdown cells was observed to be reduced in all secretagogue treatment groups when compared with the basal in the control group. In response to tolbutamide, high glucose (20 mM) triggered a further elevation of insulin secretion in control treated cells, but this response was markedly diminished in Dlg2 knockdown cells (Figure 5.9A). Arginine triggered a four-fold rise in insulin secretion in response to 20mM glucose in the control, while no significant increase was observed in Dlg2 knockdown cells (Figure 5.9B). In terms of incretin-mediated insulin response, GLP-1 elicited a significant increased (2.66-fold, p=0.041) insulin secretion in control group when compared to the basal (2 mM) and to the high glucose (20 mM) (Figure 5.9C). In addition, KCl-induced insulin secretion was markedly reduced in the Dlg2 knockdown cells (Figure 5.9D). Together, the results suggested that inhibition of Dlg2 gene expression resulted in a generalised impairment in insulin secretion and also highlighted the potent role of Dlg2 in the pancreatic insulin secretory function.

167 Chapter Five Identification of E2F8 and Dlg2 as Diabetes Susceptibility Genes

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Figure 5.9 Insulin secretion in response to non-glucose secretagogues in Dlg2 knockdown MIN6 cells. (A) Insulin release in response to 275 M Tolbutamide, (B) 20 mM arginine and (C) 10 nM GLP-1 at low or high glucose. (D) Insulin secretion in response to 30 mM KCl or 2 mM glucose in lentiviral Dlg2-shRNA stable transductant (Dlg2 shRNA, ■) and hexadimethrine bromide-treated (control, □) MIN6 cells. Insulin secretion was calculated as percentage of insulin content in MIN6 cells and presented as fold-change from the low glucose (2 mM) in scramble siRNA treated group. Values are presented as mean ± SEM, n=5 per group, three replicates in each group. *p<0.05 compared to 2 mM glucose stimulation. Glucose concentration used in insulin secretory assays are indicated with 2 (basal, 2 mM) or 20 (high glucose, 20 mM) as column insert.

168 Chapter Five Identification of E2F8 and Dlg2 as Diabetes Susceptibility Genes

5.5. Discussion Pancreatic -cell dysfunction underlies much of the pathology of type 2 diabetes due to reduced insulin secretion which contributes to hyperglycaemia and subsequent complications. In order to understand the genetic causes of impaired insulin secretion, there has been considerable effort made in identifying genes associated with T2D and in particular those associated with -cell dysfunction. As discussed in Chapter 3, we identified two credible loci on chromosome 7 to have significant association with elevated blood glucose. Among these, significant evidence is available to support the finding that the E2F8 locus (chr7: 52,631,000-56,700,000) is highly associated with diabetes-related abnormalities, particularly in relation to pancreatic insulin secretion. By contrast, little is known about the Dlg2 locus (chr7: 98,500,000-101,550,000) as only one study in African Americans has suggested an association with the disposition index and -cell function (N. D. Palmer et al., 2010).

In this chapter, candidate genes were investigated for association with diabetes predisposition and pancreatic -cell function by determining islet gene expression in pre- clinical models of diabetes susceptible (NZO) compared with resistant (C57BL/6) mice. In the E2F8 locus, causal genes often discussed are Abcc8 (SUR1) and Kcnj11 (Kir6.2) which + + encode key components of ATP-sensitive K channel (K ATP channel) that modulates insulin secretion. As expected, differential gene expression of Abcc8 and Kcnj11 were identified to be reduced in diabetes susceptible islets. However, accumulating evidence has suggested this region contains multiple genes other than Abcc8 and Kcnj11 that can determine diabetes predisposition. Our group previously reported that the NZO Abcc8/Kcnj11 allele (within the E2F8 locus) contains a number of insertion/deletion events which contributed to impairment of insulin secretion (Andrikopoulos et al., 2016). Furthermore, replacement with a functional Abcc8/Kcnj11 allele was not able to fully restore insulin secretion in NZO mice, suggesting that other genes within the candidate region may be involved.

In the present study, we identified E2F8 as a high-confident candidate which is located ~2.7 Mbp downstream of Abcc8 and Kcnj11 on chromosome 7. As described in Chapter 3,

169 Chapter Five Identification of E2F8 and Dlg2 as Diabetes Susceptibility Genes a sequence variant (rs253243295) on E2F8 exhibited strong association with hyperglycaemia in our GWAS. In addition, gene expression analysis revealed a significant reduction in islet E2F8 mRNA in diabetes susceptible mice, which was first characterised herein to be associated with diabetes susceptibility, suggesting that islet E2F8 level may predict diabetes susceptibility and reduced insulin secretory function in pancreatic islets.

E2F8 is a member of the E2F transcription factor family, which has a well-established role in mediating cell cycle regulation and tumorigenesis (Christensen et al., 2005; Maiti et al., 2005). However, the clinical significance and biological function of E2F8 in glucose homeostasis remains to be explored. There is evidence showing an increase in E2F8 mRNA expression in subcutaneous adipose tissue was associated with impaired glucose tolerance in the obese subjects as compared to obese individuals with normal glucose tolerance as well as lean control subjects (O.H. Minchenko, 2016). In addition, Suwa et al. found that E2F8 mRNA was strongly induced by high-fat feeding and glucose treatment in mice and adipocyte-derived cell lines (Suwa, Kurama, & Shimokawa, 2011; Suwa et al., 2010). These studies suggested E2F8 as a significant effector associated with the development of impaired glucose tolerance and obesity.

However, the role of E2F8 with respect to insulin secretion has not previously been investigated. A study on islet gene profiling by Keller et al. (Keller et al., 2008) showed that changes in cell cycle-related genes including E2F8 in islets predicted diabetes susceptibility through modulating -cell proliferation. Although studies have suggested a number of cell cycle-related genes may modulate -cell fate, we showed a lower E2F8 mRNA expression in diabetes susceptible NZO islets, and this reduction can lead to defective insulin secretion without affecting cell proliferation as revealed by RNA interference in MIN6 cells. Our results suggested a novel role of E2F8 in -cell insulin secretion independent of cell cycle proliferation. In support of this finding, increasing evidence has suggested non-canonical functions of cell cycle genes in metabolic processes, such as insulin secretion (Annicotte et al., 2009), hepatic glucose production (Y. Lee et al., 2014) and adipogenesis (Abella et al., 2005; Hanse et al., 2012; Hydbring, Malumbres, & Sicinski, 2016). For example, E2F1, a pivotal activator and regulatory target of E2F8 (Li

170 Chapter Five Identification of E2F8 and Dlg2 as Diabetes Susceptibility Genes et al., 2008), has been shown to control insulin secretion through transcriptional regulation of Kir6.2 gene expression (Annicotte et al., 2009). It is worth noting that E2F8 levels were found to be down-regulated by 28.6-fold in E2F1-/- mouse islets, suggesting a potential role of E2F8 in an insulin deficient mouse model. More importantly, our results demonstrated that reduced insulin secretion in E2F8 knockdown cells is specific to glucose, tolbutamide and arginine but not GLP-1 or KCl, indicating that E2F8 may regulate insulin secretion + through a K ATP-dependent signalling pathway. In addition, E2F8 was reported as an activator of heterotrimeric G proteins that acted specifically on Gi (Hagemann et al., 2007), which may therefore regulate insulin release via G protein-coupled receptor-mediated signalling pathways (Oh & Olefsky, 2016). Our results provide the first direct evidence of the functional significance of E2F8 in pancreatic insulin secretion, the extent to which the mechanism of E2F8 action is of therapeutic significance remains unclear and, in any case, + it is dependent on K ATP-mediated signalling pathway. Furthermore, based on these findings the inferred mechanism by which E2F8 involves in pancreatic insulin secretion is illustrated in Figure 5.10.

Importantly, we showed reasonable expression of E2F8 gene in human primary islets, suggesting the potential involvement of E2F8 in islet biology in humans. In addition, a strong association (r= 0.4828) of E2F8 gene expression in human pancreatic islets with increased BMI in the non-diabetic subjects reinforced the idea that E2F8 may have a role in regulating pancreatic insulin secretion as concomitants of weight gain. Interestingly, our results showed that E2F8 was differentially expressed in both murine and human diabetic islets compared to their non-diabetic counterparts, although the trend was opposite. The significant role of E2F8 in the pathogenesis of diabetes in humans was first addressed in this study, as we found the positive correlation of islet E2F8 expression with BMI was abolished in diabetic subjects, suggesting E2F8 expression profile was altered when diabetes occurs. Moreover, the upregulated E2F8 levels in diabetic patients is postulated as a concomitant effect of the medication for enhancing insulin secretion since the present diabetic patients received anti-diabetic interventions. Furthermore, this was a small number of human samples (n=7-8 per group), the heterogeneous pathogenesis of diabetes in human population could also contribute to this discordance in E2F8 gene expression.

171 Chapter Five Identification of E2F8 and Dlg2 as Diabetes Susceptibility Genes

At the Dlg2 locus, Ddias (DNA Damage Induced Apoptosis Suppressor) and Ccdc90b showed differential gene expression in isolated islets owing to divergent diabetes predisposition. Ddias is known to function as an anti-apoptotic protein in response to DNA repair preventing cell death (Nakaya et al., 2007), but the link to pancreatic islet survival and function is not known. This reduction in Ddias expression in diabetic susceptible islets may be an indication of defective response to cellular stresses, such as ER stress, oxidative stress and DNA damage in pancreatic islets (Hatanaka et al., 2017), thereby fragile or dysfunctional islets can also be expected. On the other hand, Ccdc90b was also identified to be downregulated in diabetic susceptible islets. Ccdc90b is part of mitochondrial calcium uniporter (MCU) complex which serves as a critical component mediating Ca2+ uptake at the inner mitochondrial membrane (Kirichok, Krapivinsky, & Clapham, 2004). Although the function of Ccdc90b per se in mitochondrial Ca2+ influx has yet to be understood, a 2+ study suggested a nominal role of Ccdc90b in MCU activity has shown no effect on [Ca ]m uptake when silencing of Ccdc90b (Tomar et al., 2016).

Another potential candidate identified was the Dlg2 gene. However, the mechanism by which Dlg2 participates in insulin secretion has yet to be addressed. Dlg2 encodes postsynaptic density protein (PSD)-93, which forms scaffold complexes for clustering and anchoring membrane proteins, such as NMDA receptors (NMDARs) (E. Kim, Cho, Rothschild, & Sheng, 1996) and K+ channel clusters (Leyland & Dart, 2004), to mediate signalling transduction primarily in neurons. A recent study reported NMDAR in pancreatic islets was involved in the regulation of insulin release and blood glucose control (Marquard et al., 2015). In addition, NMDARs and K+ channels have been suggested to be drug targets to regulate pancreatic -cell function for treating insulin-dependent diabetes mellitus.

In contrast, the knowledge of Dlg2 in islets has yet to be addressed. In the present study, we propose a role for islet Dlg2 in -cell function as we found a reduced Dlg2 mRNA expression in diabetes susceptible islets from NZO mice, and genetic ablation of Dlg2 resulted in a generalized defect in insulin secretion in MIN6 cells. These results support the hypothesis that Dlg2 may directly participate in insulin secretion in pancreatic-cells.

172 Chapter Five Identification of E2F8 and Dlg2 as Diabetes Susceptibility Genes

On the other hand, Dlg2 is also a component of the scribble complex which functions as a major regulator to determine cell polarity (S. Roberts, Delury, & Marsh, 2012). Although the knowledge of -cell polarity is limited, the functional significance of appropriate cell polarity has been emphasised in the control of insulin secretion (Gan et al., 2017; Low et al., 2013; Low et al., 2014). Therefore, the role of Dlg2 in cell polarity may also be a mechanism for the alteration of insulin secretory function. Here we highlight the importance and potential function of Dlg2 in insulin secretion, and more work needs to be done towards the understanding of the mechanism by which it regulates -cell function.

Our results showed these human homologous of mouse candidate genes have decent expression in primary human islets indicative of potential roles in human islet biology, although most of them did not exhibit differential expression in mRNA level between diabetic and non-diabetic subjects. Indeed, alteration in transcripts level is not the only case of genetic regulation, to a large extent, posttranslational regulations may affect protein property causing dysregulation, however which is yet to be investigated here. Except for E2F8, Rab30 gene expression was found to be significantly decreased (30% reduction) in islets from diabetic subjects. Rab30, Rab GTPase protein, a member of Ras-related protein family. Rab proteins have been implicated in various stages of granule trafficking, including budding, transportation along microtubules, and docking with the membrane (Pfeffer & Aivazian, 2004; Stenmark, 2009). For instance, Rab3A and Rab27A have been demonstrated to function in insulin-granule docking and priming during exocytosis (Regazzi et al., 1996; Yi et al., 2002).

In summary, the findings in this chapter provide experimental evidence to support that multiple gene candidates positioned in our previously identified hyperglycaemia susceptibility loci were associated with diabetes susceptibility and pancreatic insulin secretory function in murine and human studies. Among these, we validated E2F8 and Dlg2 as novel hyperglycaemia susceptibility genes which have substantial influences on pancreatic insulin secretion as revealed by gene knockdown experiments. Taken together, we link the hyperglycaemia susceptibility loci to molecular genomic regulation of -cell dysfunction in pancreatic islets.

173 Chapter Five Identification of E2F8 and Dlg2 as Diabetes Susceptibility Genes

Figure 5.10 Putative roles of E2F8 and Dlg2 in glucose-stimulated insulin secretion in the pancreatic -cells. Glucose-stimulated insulin secretion initiates from glucose uptake by pancreatic -cells through GLUT2 glucose transporter; subsequently the intracellular glucose is phosphorylated by glucokinase (GK) then oxidized to generate ATP. The increased + ATP/ADP ratio results in closure of K ATP channel which triggers membrane depolarisation, and in turn activating the voltage-dependent Ca2+ channel (VDCC) that elicits the exocytosis of insulin granules. E2F8 may involves in this machinery through + alteration of the K ATP channel by regulating E2F1 at the transcriptional level, the reduced E2F1 lead to decreased expression of Kcnj11 (Kir6.2). On the other hand, Dlg2 acts on islet NMDAR to ensure proper function leading to -cell membrane repolarisation + whereby opens K ATP channel. In addition, Dlg2 may involve insulin secretion via modulating cell polarity that can assemble a number of cell adhesion molecules leading to cytoskeleton remodeling, vesicle trafficking and insulin release. NMDAR, ionotropic N- methyl-D-aspartate receptor; VDCC, voltage-dependent Ca2+ channel.

174

CHAPTER SIX

SUMMARY AND FUTURE DIRECTIONS

Chapter Six Thesis Summary, Conclusions and Future Directions

6. Chapter 6 Summary, Conclusions and Future Directions

6.1. Summary and Conclusions Type 2 diabetes (T2D) is manifested by hyperglycaemia arising from insufficient insulin secretion due to -cell failure (Holman, 1998; Weyer et al., 1999). The complex pathogenesis of T2D is known to result in the observation that the inter-individual response to therapy and slope of disease progression varies substantially among diabetic patients. Gene-gene, gene-environment, and gene-treatment interactions may be able to explain the variation in T2D progression. Indeed, several genetic variants have been demonstrated to be associated with diabetes susceptibility and the individual’s response to antidiabetic drugs. Therefore, it is believed that precision medicine in diabetes will be a hope for a more effective strategy than conventional treatment algorithms (McCarthy, 2017b). Therefore, advances in the fields of genomic sciences and understanding of the genetic basis of -cell dysfunction is critical.

This thesis aimed to identify and characterise genetic determinants of hyperglycaemia and diabetes susceptibility using a genetic reference mouse panel, the Collaborative Cross (CC). In Chapter 3 of this thesis the CC mouse population, exhibiting a great metabolic diversity in blood glucose levels, degree of insulin sensitivity and body weights, was employed to investigate the genetic causes of hyperglycaemia. An unbiased genome-wide association study (GWAS) for elevated blood glucose was conducted using the blood glucose data from over 1,000 mice comprising of males from 53 strains and females from 48 strains of the CC mouse. This GWAS led to the identification of four single nucleotide polymorphisms (SNPs) to be significantly (p < 5x10-8) associated with hyperglycaemia. The linkage QTL analysis showed that these SNPs fell into two major regions on chromosome 7 as the E2F8 locus (Chr7:52,631,000-56,700,000; LOD=15.4) and the Dlg2 locus (Chr7:98,500,000-101,550,000; LOD=8.7). Moreover, the founder haplotype analysis indicated that the CC strains which carry the NZO-derived allele(s) at the implicated loci have elevated blood glucose levels in the presence of normal insulin

176 Chapter Six Thesis Summary, Conclusions and Future Directions sensitivity, suggesting the substantial influence of these hyperglycaemia susceptibility E2F8 and Dlg2 loci on pancreatic insulin secretory function. Interestingly, this finding is in accordance with the prior observation that most of the diabetic associated genetic variants seem to affect -cell function rather than insulin sensitivity (Florez, 2008).

Our findings of the E2F8 locus in association with hyperglycaemia susceptibility are in line with previous evidence that a proximal region (chr7: 56,255,370-62,555,370) was identified for plasma glucose in the Hybrid Mouse Diversity Panel (Parks et al., 2015). In fact, the position of the E2F8 locus overlapped with a well-established diabetes susceptibility region in which causal genes often discussed are Abcc8 (SUR1) and Kcnj11 (Kir6.2) that have been known to modulate insulin secretion (Ashcroft et al., 1984; Gloyn et al., 2004; Koster et al., 2000; Miki et al., 1998). In addition, the human orthologue region has been reported in human GWAS to be associated with T2D (Diabetes Genetics Initiative of Broad Institute of et al., 2007; Replication et al., 2014; Scott et al., 2007; Timpson et al., 2009; Zeggini et al., 2008; Zeggini et al., 2007), cholesterol (Global Lipids Genetics et al., 2013; Teslovich et al., 2010), and obesity-related traits (Comuzzie et al., 2012; Locke et al., 2015). In contrast, the association of Dlg2 locus with diabetes related traits is relatively novel and there is one study in a small African American cohort which supported the association with disposition index (N. D. Palmer et al., 2010).

The physiological implications of these susceptible loci in the context of glucose homeostasis was investigated by characterising the affected mouse strains which have the deleterious NZO allele(s) at the hyperglycaemia susceptibility loci (Chapter 4). It is interesting to show that these hyperglycaemia susceptible mice were not obese nor insulin resistance, thus one possible defect underlying the hyperglycaemia is likely to be -cell dysfunction. The hyperglycaemia susceptible strains were shown to have a diabetes susceptible phenotype. This included hyperglycaemia under non-fasting and six-hour fasting states associated with reduced plasma insulin concentrations and a profound defect of first- and second-phase insulin secretion in response to glucose and arginine stimulation. This severe impairment of insulin secretion was not due to reduced -cell mass as revealed by pancreatic histological analysis. Thus, these results suggested that the fundamental

177 Chapter Six Thesis Summary, Conclusions and Future Directions defect arises from the detrimental copy at the E2F8 and Dlg2 loci is reduced insulin secretory function in the pancreatic -cells.

It is believed that hyperglycaemia will not develop unless -cells fail to meet the increased insulin demand due to insulin resistance (B. B. Kahn, 1998). Energy dense or high fat diets in combination with physical inactivity lead to obesity which is associated with systemic insulin resistance (Zimmet et al., 2001). In response to insulin resistance, insulin hypersecretion from pancreatic -cells compensates for worsening insulin resistance, therefore, diabetes does not develop (Benjamin et al., 2003; Warram et al., 1990). Conversely, in a subset of susceptible individuals who are more genetically prone to diabetes, this compensatory mechanism fails over time due to β-cell dysfunction, eventually T2D occurs (Bergman, Phillips, & Cobelli, 1981; S. E. Kahn et al., 1993). Given the predisposition to -cell dysfunction in the hyperglycaemia susceptible models, we sought to study the effect of the deleterious E2F8 and Dlg2 alleles on individual’s response to high-fat diet induced metabolic abnormalities, focusing mainly on the capability of - cells to develop compensatory insulin secretion (Chapter 4). The results showed that high- fat fed hyperglycaemia susceptible PIPING and PUB mice developed features of diabetes and -cell decompensation at the early stage of dietary challenge. These included insulin resistance, deteriorating fasting hyperglycaemia, impaired biphasic insulin secretion, worsening glucose intolerance with modest islet hypertrophy and hyperplasia. However, the morphological changes in pancreatic islets did not lead to significant -cell expansion or increase in pancreatic insulin content. These results together demonstrated an inherent metabolic decompensation in the pancreatic -cells of PIPING and PUB mice, which is postulated to be at great risk of T2D as the -cells were not capable of adaptation.

Given the prominent influence of the E2F8 and Dlg2 loci on -cell insulin secretory function, the next aim attempted to identify the contributory genes accounting for the deleterious effects in pancreatic -cells (Chapter 5). It has previously been reported by our laboratory that Abcc8 is a contributory gene within the E2F8 locus that impaired early- phase insulin secretion in the NZO mice; however, the in vivo data of Abcc8 transgenic mice suggested that multiple genes in this area (in addition to Abcc8) may account for -

178 Chapter Six Thesis Summary, Conclusions and Future Directions cell dysfunction (Andrikopoulos et al., 2016). Candidate genes identification was enabled by sequence analysis and examining differential gene expression between diabetes and non-diabetes in mouse and human islets. Initially, the E2F8 and Dlg2 genes, which have the SNPs significantly associated with hyperglycaemia in our GWAS, were prioritised to be characterised in the -cell insulin secretory function. E2F8 is a transcription factor involved in the cell cycle regulation while there is no evidence to suggest its implication in -cell function. Our results showed downregulation of E2F8 gene expression is associated with diabetes susceptibility in the NZO mouse islets, and indeed knocking down E2F8 + resulted in impaired insulin secretion due to defective K ATP channel-dependent signalling pathway. Of interest, a compensatory enhancement of E2F8 gene expression (3.5-fold) was identified in human islets from diabetic donors, suggesting a compensatory effect. In addition, E2F8 gene expression was found to be positively correlated with BMI in non- diabetic subjects while this relationship was abrogated in islets from T2D patients, indicating a role of E2F8 gene expression during the progression of T2D. However, the mechanism by which E2F8 regulates pancreatic insulin secretion is unknown. It is established that E2F8 can modulate E2F1-activated transcription (Christensen et al., 2005; Maiti et al., 2005), most interestingly that E2F1 has been shown to control insulin secretion via direct regulation of Kir6.2 gene expression (Annicotte et al., 2009). Based on this a potential mechanism was proposed that E2F8 regulates insulin secretion through the inhibition of E2F1-mediated transactivation of Kir6.2 expression in the pancreatic -cells.

At the Dlg2 locus all eight genes were considered as promising candidates for further investigation for diabetes susceptibility. Among these, the Dlg2 gene has been identified in a human study to be associated with the disposition index, in addition, our results showed that the Dlg2 gene expression was trending lower (p=0.065) in the diabetes susceptible NZO islets. More importantly, our in vitro study revealed that reduction of the Dlg2 gene expression caused a generalised secretory defect of insulin in the MIN6 -cell line. Collectively, these results highlighted the novelty and importance of E2F8 and Dlg2 in the pancreatic insulin secretion that appears to be critical in determining diabetes susceptibility.

179 Chapter Six Thesis Summary, Conclusions and Future Directions

Taken together, using the CC mouse resource led to the identification of two hyperglycaemia susceptibility loci on chromosome 7 which were associated with diabetes susceptibility and reduced -cell function in pancreatic islets. Of importance, a novel gene candidate for elevated blood glucose, E2F8, is first recognised in a well-established diabetes-associated (Abcc8/Kcnj11) locus to be implicated in diabetes susceptibility and - cell dysfunction in both mouse and human studies as well as in the in vitro knockdown experiments. Our results provide evidence that links previous unexplained findings of E2F8 in metabolic disorders to a novel function in glucose-stimulated insulin secretion and glucose homeostasis. Likewise, the functional significance of the association of Dlg2 with hyperglycaemia was first established and characterised to be implicated in pancreatic insulin secretory function. This thesis provided evidence demonstrating the strong effect of E2F8 and Dlg2 loci on diabetes susceptibility particularly on -cell function, therefore understanding this mechanism appears to be critical as it may inform new therapeutic strategies to delay or perhaps prevent -cell dysfunction in T2D.

180 Chapter Six Thesis Summary, Conclusions and Future Directions

6.2. Future Directions The role of E2F8 and Dlg2 in pancreatic insulin secretion.

This thesis has generated data which provides evidence from the perspective of genomic association analysis, physiology and molecular biology suggested that E2F8 and Dlg2 are promising gene candidates for impaired insulin secretion in pancreatic -cells. Of interest, using the RNA interference oligonucleotides indicate that reduction of E2F8 or Dlg2 gene expression indeed result in impaired insulin secretion in the insulin secreting MIN6 cells. However, the effect of E2F8 and Dlg2 genes in pancreatic -cells on insulin secretion and whole body glucose homeostasis has not yet been evaluated. Generation of a β-cell specific knockout mouse model of E2F8 or Dlg2 will enable us to ascertain whether these genetic manipulations can lead to reduced insulin release in vivo and its effect on global glucose homeostasis. More importantly, the underlying molecular mechanism will be examined by performing RNA-sequencing and proteomics to evaluate the differences at transcriptome and proteome levels in multiple tissues, including pancreatic islets as well as liver, muscle, adipose tissue and brain. The results from characterising the knockout models will decisively show whether E2F8 and Dlg2 have important roles in -cell function. Furthermore, this -cell specific E2F8 knockout model will be employed to further study the hyperglycaemia susceptible variant (rs253243259) by expressing either the mutant or wild type E2F8 in the pancreatic islets of E2F8 knockout mice using an in vivo adeno- associated virus or lentiviral expression system. This study will enable us to further explore the functional effects of this missense mutation (Lys401Asn) on -cell function in vivo and understand how this variant influence E2F8 function and its associated physiology.

To determine the mechanism underlying E2F8 regulates pancreatic insulin secretion.

The molecular mechanism underlying E2F8 mediated insulin secretion will be investigated. E2F8 is an atypical E2F transcription factor that has been discovered to be involved in a range of cellular processes such as angiogenesis (Weijts et al., 2017) and polyploidization + (Pandit et al., 2012). Our results showed that knockdown E2F8 resulted in impaired K ATP- + dependent insulin secretion in MIN6 cells, implying a functional failure on K ATP channel

181 Chapter Six Thesis Summary, Conclusions and Future Directions or before this point. It has been demonstrated that E2F8 can act as a transcription repressor (Chen, Tsai, & Leone, 2009; Lammens, Li, Leone, & De Veylder, 2009; Logan et al., 2005) as well as an activator (Hagemann et al., 2007) depending on the type of tissue. E2F8 can regulate typical E2F-regulatory elements and gene expression of other E2Fs, particularly serves as a potent regulator of E2F1 (Christensen et al., 2005; Li et al., 2008; Maiti et al., 2005). Interestingly, recent findings from Annicotte J. et al. showed that E2F1-/- mice have impaired insulin secretion and glucose intolerance due to the loss the E2F1-mediated transactivation of Kir6.2 gene expression in islets (Annicotte et al., 2009). Of note, E2F8 levels were found markedly decreased by 28.6-fold in the E2F1-/- mouse islets, suggesting a potential link of E2F8 to the E2F1-mediated pathway in pancreatic insulin secretion. Based on these findings, the hypothesis will be tested is that E2F8 modulates insulin secretion through manipulating the E2F1-Kir6.2 signalling pathway. In addition, E2F8 and E2F1 were found to regulate the same subset of target promoters, therefore, it will be interesting to also test whether E2F8 can modulate Kir6.2 gene expression through altering promoter activity.

The regulation between E2F8 and E2F1 has been established in many cell types but yet to be studied in pancreatic islets or -cells. Firstly, we would like to determine whether E2F1 and Kir6.2 gene expressions are associated with E2F8 in pancreatic islets, gene expression of E2F1 and Kir6.2 will be determined in isolated islets from the E2F8 knockout mice and their control littermates. If E2F1 and Kir6.2 expression are altered in islets of the E2F8 knockout mice, suggesting that E2F8 has a role in determining the gene expression of E2F1 and Kir6.2. In addition, a chromatin-immunoprecipitation (CHIP) will be performed to determine whether E2F8 modulates E2F1 and Kir6.2 gene expression by directly bindingto their promotor region in isolated islets. Insulin secretion in response to glucose and non- glucose secretagogue, sulphonylureas, will be examined in isolated islets of E2F8-/- mouse, E2F1-/- E2F8-/- mouse, E2F1-/- mouse and wild type mouse. Compared to the wild type mice, reduced insulin secretion is postulated in all three type of knockout mice (E2F8-/- mouse, E2F1-/- E2F8-/- mouse and E2F1-/- mouse) albeit the degree of insulin secretory defect may not be different, supporting the possibility that E2F8 acts through the E2F1-Kir6.2 axis in insulin secretory pathway.

182 Chapter Six Thesis Summary, Conclusions and Future Directions

Examination of allelic frequency of hyperglycaemia-associated SNPs (rs253243259 and rs32123098) and determine whether these SNPs are associated with human Type 2 Diabetes.

Two SNPs (rs253243259 and rs32123098) were identified to be significantly associated with hyperglycaemia in the CC mouse population as described in the Chapter 3 of this thesis. The preliminary data examining the mRNA expression of candidate genes in human islet cDNA between non-diabetic and diabetic donors was demonstrated and presented in the Chapter 5. The clinical application of our findings will be realised by determining whether these SNPs associated with T2D in humans. Sequence alignment (BLASTN 2.6.1+, blastn suite-2sequences) between mouse and human at the E2F8 and Dlg2 loci showed 85% and 88% sequence identity, respectively. In terms of E2F8 and Dlg2 gene sequence, there was 80% and 76% identy between mouse and human. Due to the E2F8 and Dlg2 being highly conserved between species, the allelic frequency of hyperglycaemia- associated polymorphisms in human T2D will be performed in DNA samples from diabetic and non-diabetic subjects. In addition, the regulation of these SNPs in -cell function can be achieved through further characterisation in the population that is affected. Understanding the presence and regulation of these variants will provide more comprehensive insight of our findings in relation to diabetes susceptibility and insulin secretory function in pancreatic islets.

Interrogation of other candidate genes in the hyperglycaemia susceptibility E2F8 and Dlg2 loci.

It would be interesting to determine whether other genes at the E2F8 and Dlg2 loci have effects on insulin secretion. Candidate genes at the E2F8 locus which will be examined are Sphk2, Snrnp70, Ddias and Grin2d; and all genes (Ankrd42, Pcf11, Rab30, Prcp and Ccdc90b) at the Dlg2 locus are considered as candidates. This could be tested by knocking down each gene individually or in combination using RNAi approach in MIN6 cells and determining the effect on glucose-stimulated insulin secretion. It is particularly interesting to study Grin2d and Dlg2 gene in combination. Since Grin2d encodes a subunit of the NMDA receptor which has been reported to be involved in insulin secretion (Marquard et

183 Chapter Six Thesis Summary, Conclusions and Future Directions al., 2015), and Dlg2 is known as a scaffold protein acts to stabilise NMDA receptor in neurons (E. Kim et al., 1996). Therefore, it is postulated that knockdown Grin2d and Dlg2 in the MIN6 cells would inform the role and regulation of Dlg2 in pancreatic -cell function.

Development of precision medicine using the Collaborative Cross (CC) mice as preclinical models of reduced insulin secretion for anti-diabetes drug screening.

A complementary application of the CC mouse resource as a source of new mouse models of human diseases for studying pathogenesis, disease progression and development of therapy. As shown in the Chapter 4 of this thesis the PIPING and PUB mice are identified susceptible to hyperglycaemia manifesting overt glucose intolerance due to profound reduction in glucose-stimulated insulin secretion. Of note, these hyperglycaemic mice displayed the insulin secretory defects with preserved -cell mass, indicating the qualitative defects in insulin secretory function independent of reduced quantity of -cells. The PIPING and PUB mice emerge as invaluable models of impaired insulin secretion which more accurately model -cell dysfunction with preserved -cell mass in the setting of an inherent susceptibility to develop hyperglycaemia. These strains are employed to provide a mechanistic understanding of hyperglycaemia associated with -cell dysfunction and gene-by-environment interaction in the present Ph.D thesis. Since at this time there is no known pharmacological intervention that is capable of preventing this inexorable decline in -cell function in patients with T2D, these inbred mouse strains will be useful in studying pharmacogenomics to treat progressive -cell dysfunction. Currently many antidiabetic drugs are available which achieve glucose lowing in patients with T2D through various aspects: enhancing pancreatic insulin secretion (i.e. Sulphonylureas, GLP-1 analogues), improving whole-body insulin sensitivity (i.e. Metformin and Thiazolidinedione) or facilitating glucose disposal (i.e. Sodium-glucose cotransporter 2 inhibitors). Therefore, in a future study, the hyperglycaemic PIPING and PUB mice will be treated with a number of oral antidiabetic drugs, including metformin, tolbutamide , pioglitazone, DPP4-inhibitor and dapagliflozin. Glucose tolerance and pancreatic insulin secretion will be determined at various stages of drug treatment. The results from this experiment will inform specific treatment algorithms for reverting -cell function in the susceptible subjects, and likely,

184 Chapter Six Thesis Summary, Conclusions and Future Directions provide suggestions for early intervention in subjects at risk which may prevent the onset of diabetes for treating prediabetes.

185

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APPENDICES

Appendices

APPENDICES

6.3. Appendix I: Standard Laboratory Chow Diet

IRRADIATED WEHI MICE CUBES FEATURES:  A complete and balanced diet to support growth and health of mice and rats in a laboratory environment.  Contains a high level of fat (9% fat).  Product is Gamma Irradiated at a rate of 25 kilo gray.  Pelleted product 12mm in diameter and an average of 20mm in length.

TYPICAL ANALYSIS (AS-FED): Crude Protein 22% Vitamin K3 59mg/kg Crude Fat 9% Vitamin B1 69mg/kg Crude Fibre 3.2% Vitamin B2 52mg/kg ADF 4.4% Vitamin B6 34mg/kg NDF 10.4% Vitamin B12 0.08mg/kg DE_HORSE 13.2 MJ/kg Niacin 437mg/kg Calcium 1.2% Panto 235mg/kg Phosphorous 0.96% Biotin 1.68mg/kg Sodium 0.35% Folic 11.82mg/kg Potassium 0.89% Iron 180mg/kg Chlorine 0.57% Zinc 103mg/kg Magnesium 0.25% Manganese 156mg/kg Lysine 1.22% Copper 20mg/kg Methionine 0.38% Selenium 0.33mg/kg Linoleic Acid 3.11% Molybdenum 1.19mg/kg Added Vitamins and Trace Minerals Cobalt 0.67mg/kg Vitamin A 15 IU/g Iodine 1.67mg/kg Vitamin D3 2 IU/g Starch 25% Vitamin E 270mg/kg

INGREDIENTS: PRESENTATION:  Wheat, Wheat Byproducts, Groats  10kg net weight (Dehulled Oats), Meat Meal, Canola Oil,  Packaged in a double walled paper bag, then Full Fat Soyabean Meal, Skim Milk placed in a 200 micron plastic bag, which is Powder, Molasses, Salt, Vitamins, vacuum packed and heat-sealed. This sealed Minerals. package is then sewn in a single walled paper IMPORTANT NOTES: bag and then packed in a cardboard outer carton for shipping.  Product code 8720610  Product is packed 48 per pallet.

225 Appendices

6.4. Appendix II: Composition of High Fat Diet

226 Appendices

Continue

227 Appendices

6.5. Appendix III: The Collaborative Cross Strain and Number Used in Phenotypic Screening

Strain Male Female Strain Male Female BALIN 6 - NOD 13 - BEM 10 14 NUK 14 9 BEW 3 - NZO 8 20 BOM 9 12 PAT 12 5 BOON 13 10 PEF 10 10 C57BL/6 10 10 PEF2 10 9 CIS 4 7 PER2 - 3 CIS2 5 - PIPING 17 17 CIV2 3 3 POH 11 12 DAVIS 10 10 POT 4 3 DET3 7 6 PUB 15 11 DOD 11 10 ROGAN 12 11 FEW 19 9 SAT 11 10 FIM 10 10 SEH 11 9 FIV 11 10 STUCKY 10 5 GALASUPREME 5 - TAS 17 10 GAV 10 11 TOFU 10 10 GEK2 12 3 TOP 10 14 GET 10 3 TUY 12 6 GIG 12 9 VOY 12 5 GIT 14 8 WAB2 14 16 HAZ 14 11 WAD 5 8 HIP 12 13 WOB2 3 4 JUD 11 9 YID 16 14 JUNIOR 10 11 YOX 9 10 LAM2 5 - ZIE2 - 5 MEE 11 8 ZIF2 11 9 MOP 9 -

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Minerva Access is the Institutional Repository of The University of Melbourne

Author/s: Yang, Chieh-Hsin

Title: Identification and characterisation of genes associated with hyperglycaemia susceptibility and reduced insulin secretion

Date: 2017

Persistent Link: http://hdl.handle.net/11343/216215

File Description: Complete thesis

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