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The role of p300 transcriptional coactivators in pancreatic beta cells Wong, Chi Kin 2018

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THE ROLE OF P300 TRANSCRIPTIONAL COACTIVATORS IN PANCREATIC BETA CELLS  by CHI KIN WONG  B.Sc., The University of Hong Kong, 2010 M.MedSc., The University of Hong Kong, 2012  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Medical Genetics)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  October 2018  © Chi Kin Wong, 2018  ii  The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled:  The Role of p300 Transcriptional Coactivators in Pancreatic β Cells  submitted by Chi Kin Wong  in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Medical Genetics  Examining Committee: William T Gibson Supervisor  Francis C Lynn Supervisory Committee Member  Matthew C Lorincz  Supervisory Committee Member Stefan Taubert Supervisory Committee Member Bruce C Verchere University Examiner Angela M Devlin University Examiner   iii  Abstract Studies on genetic forms of diabetes have been pivotal in understanding how genetic mutations impair pancreatic β cell function. We identified a patient who presented with early-onset diabetes similar to known monogenic forms of diabetes. The patient carries a microdeletion that removes a copy of EP300, a gene that encodes the transcriptional coactivator p300. EP300 mutations cause Rubinstein-Taybi syndrome, a rare genetic condition that has been associated with early-onset glucose dysregulation. Whether and how p300 loss may affect β cell function in vivo was not clear. Here, we show that expression of p300 regulates β cell development and function in vivo. By deleting p300 at different developmental stages in mouse β cells, we demonstrate that p300 is required for the proliferation and proper maturation of developing β cells. β cell development requires p300 to acetylate histone H3K27 across the genome and to coactivate transcription. In mature β cells, p300 maintains insulin granule biosynthesis and secretion. To regulate these processes transcriptionally, p300 and NeuroD1/Nkx6.1/Pdx1 co-occupy loci that are critical for β cell function, including insulin. In addition to the mouse studies, we have identified three additional probands who developed hyperinsulinism associated with their rare, potentially gain-of-function EP300 variants. Our data demonstrate a critical role of p300 as a transcriptional coactivator and a histone acetyltransferase in β cells. Taken together, our human data highlight the relevance of p300 to the pathogenesis of genetic forms of diabetes, and our mouse data provide mechanistic insights on how p300 deficiency may lead to glucose dysregulation.  iv  Lay Summary We discovered a patient with a rare mutation in a gene called EP300, which makes the p300 protein. This patient developed diabetes at 23 years of age but did not require exogenous insulin. She likely has an intrinsic problem with her insulin-producing cells. We wondered if her EP300 mutation might cause diabetes by affecting her insulin-producing cells. To study this, we made mice that have no p300 in their insulin-producing cells. Similar to the patient’s condition, these mice were glucose intolerant at a young age. The insulin-producing cells in these mice made and secreted less insulin, because they could not fully establish their epigenetic marks, a mechanism that enables the cells to develop and function properly. We also report three other patients who had an opposite type of EP300 mutations and had high blood insulin levels that can lower their blood glucose to a dangerous level. We conclude that the EP300 mutations in either mice or humans can affect the organism’s capacity to metabolize glucose.  v  Preface Animal studies were approved by the University of British Columbia Animal Care Committee under protocol numbers A14-186, A14-187, A17-104, and A17-237.  Human genetics studies were approved by the University of British Columbia Human Research Ethics Board under protocol numbers H08-00784 and H15-00092.   All studies in this thesis were conceived and designed by CK Wong and WT Gibson. All experiments and data analysis were conducted by CK Wong with technical assistance as follows:  Chapter 3: AK Wade-Vallance helped with qPCR experiments. PK Brindle provided Ep300 flox/flox mice and FC Lynn provided Neurog3-Cre mice. DS Luciani provided guidance and equipment for calcium imaging experiments.  Chapter 4: A Lee helped with mouse metabolic phenotyping and islet perifusion experiments. KE Louie helped with immunofluorescence staining and imaging. A Li helped with the transfection experiments of p300 mutants. P Eydoux, JD Wasserman, K Hussain, M Steinraths, L Armstrong, A Lehman, AM Elliott, E Lopez-Rangel, JM Friedman, JC Mwenifumbo, MI Van Allen and TN Nelson contributed to the human genetics studies. FC Lynn provided mTmG animals and helped with the reprogramming of our patient’s blood cells into iPSCs. BG Hoffman provided guidance on ChIP-seq data analysis.  Chapter 5: J Rello Rivera helped with the p300/CBP ChIP-seq experiments. FC Lynn provided Pdx1-CreER animals. BG Hoffman provided guidance on ChIP-seq data analysis.    vi  Studies described in Chapter 3 have been published as Wong CK, Wade-Vallance AK, Luciani DS, Brindle PK, Lynn FC, Gibson WT. The p300 and CBP Transcriptional Coactivators are Required for β-Cell and α-Cell Proliferation. Diabetes 67, 412–422 (2018). CK Wong conceived the study, performed experiments, analyzed data, and wrote the manuscript (75% contribution). AK Wade-Vallance performed experiments and analyzed data (5% contribution). DS Luciani provided guidance and equipment for calcium imaging experiments (5% contribution). PK Brindle and FC Lynn provided reagents and animals necessary for the experiments (5% contribution). WT Gibson conceived the study and edited the manuscript (10% contribution). Copyright permission for using the published work in Chapter 3 was granted by the American Diabetes Association who holds the copyright on the published content.  Studies described in Chapter 4 and Chapter 5 are under review as Wong CK, Lee A, Louie KE, Li A, Rello Rivera J, Eydoux P, Wasserman JD, Hussain K, Steinraths M, Lehman A, Elliott AM, Lopez-Rangel E, Friedman JM, Mwenifumbo JC, Van Allen MI, Nelson TN, CAUSES Study, Brindle PK, Hoffman BG, Lynn FC, Gibson WT. (2018). p300 acetylates H3K27 and occupies NeuroD1/Nkx6.1/Pdx1-binding sites to maintain β cell development and function. CK Wong conceived the studies, performed experiments, analyzed data, and wrote the manuscript (75% contribution). A Lee, KE Louie, A Li, and J Rello Rivera performed experiments and analyzed data (5% contribution). P Eydoux, JD Wasserman, K Hussain, M Steinraths, A Lehman, AM Elliott, E Lopez-Rangel, JM Friedman, JC Mwenifumbo, MI Van Allen, TN Nelson, and CAUSES Study contributed to the human genetic study (5% contribution). PK Brindle and FC Lynn provided reagents and animals necessary for the experiments (5% contribution). BG Hoffman provided guidance on ChIP-seq data analysis (5% contribution). WT Gibson conceived the study and edited the manuscript (10% contribution).  vii  Table of Contents Abstract ............................................................................................................................. iii Lay Summary .................................................................................................................... iv Preface ................................................................................................................................ v Table of Contents ............................................................................................................ vii List of Tables ..................................................................................................................... xi List of Figures .................................................................................................................. xii List of Abbreviations ....................................................................................................... xv Acknowledgements ......................................................................................................... xx Chapter 1: Introduction..................................................................................................... 1 1.1 Diabetes Mellitus.................................................................................................. 1 1.1.1 Classification of Diabetes ............................................................................. 1 1.1.2 Type 1 Diabetes ........................................................................................... 1 1.1.3 Type 2 Diabetes ........................................................................................... 2 1.1.4 Monogenic Forms of Diabetes ..................................................................... 4 1.2 Pancreas as an Exocrine and Endocrine Organ ................................................ 6 1.2.1 Pancreatic Islets ........................................................................................... 6 Pancreatic Endocrine Development ......................................................... 7 1.2.2 β Cells ........................................................................................................... 9 β Cell Differentiation ................................................................................. 9 β Cell Proliferation during Development ................................................ 10 β Cell Maturation and Function .............................................................. 12 1.2.3 α Cells ......................................................................................................... 14 1.2.4 δ Cells, PP Cells and ε Cells ...................................................................... 15 1.3 Histone Modifications......................................................................................... 16 1.3.1 Histone Modification and Transcriptional Regulation in β Cells ................ 20 1.4 p300/CBP Transcriptional Coactivators ............................................................ 23 1.4.1 Molecular Function of p300/CBP ............................................................... 25 1.4.2 Cellular Function of p300/CBP................................................................... 29 1.4.3 p300/CBP in β Cells ................................................................................... 31  viii  1.4.4 p300/CBP in Human Diseases .................................................................. 32 1.5 Thesis Objectives .............................................................................................. 34 Chapter 2: Methods and Materials ................................................................................ 36 2.1 Animals .............................................................................................................. 36 2.2 Cell Culture ........................................................................................................ 36 2.3 Human Genetic Studies ..................................................................................... 37 2.4 5-Ethynyl-2’-Deoxyuridine Labeling .................................................................. 38 2.5 Tamoxifen Treatment......................................................................................... 38 2.6 Metabolic Phenotyping ...................................................................................... 38 2.6.1 Intraperitoneal Glucose Tolerance Test .................................................... 38 2.6.2 Measurement of In vivo Insulin Secretion.................................................. 38 2.6.3 Insulin Tolerance Test ................................................................................ 39 2.6.4 Body Composition Analysis and Metabolic Cages .................................... 39 2.7 Enzyme-Linked Immunosorbent Assay ............................................................ 39 2.8 Pancreas Dissection and Dispersion ................................................................ 39 2.9 Islet Isolation and Dispersion ............................................................................ 40 2.10 Fluorescence-Activated Cell Sorting ................................................................. 41 2.11 Histology Analysis .............................................................................................. 41 2.11.1 Formalin Fixed Paraffin Embedding .......................................................... 41 2.11.2 Immunofluorescence Staining .................................................................... 41 2.11.3 5-Ethynyl-2’-Deoxyuridine Staining ............................................................ 43 2.11.4 Apoptosis Assay ......................................................................................... 43 2.11.5 Image Acquisition and Quantification ........................................................ 43 2.12 Islet Assay .......................................................................................................... 44 2.12.1 Static Incubation Assay for Glucose-Stimulated Insulin Secretion ........... 44 2.12.2 Perifusion Assay ......................................................................................... 44 2.12.3 Calcium Imaging ......................................................................................... 45 2.12.4 Islet Insulin Content .................................................................................... 45 2.12.5 Transmission Electron Microscopy ............................................................ 45  ix  2.13 RNA Preparation and Reverse Transcription-Quantitative Polymerase Chain Reaction ........................................................................................................................ 46 2.14 Plasmid Preparation and Site-Directed Mutagenesis ....................................... 47 2.15 Transfection ....................................................................................................... 48 2.16 Western Blotting................................................................................................. 48 2.17 Immunoprecipitation .......................................................................................... 49 2.17.1 Co-immunoprecipitation ............................................................................. 49 2.17.2 Low-Input Native-Chromatin Immunoprecipitation .................................... 49 2.17.3 Dual-Crosslink Chromatin Immunoprecipitation ........................................ 50 2.18 High-Throughput Sequencing and Analysis ..................................................... 51 2.18.1 Bulk RNA-seq ............................................................................................. 51 2.18.2 Single Cell RNA-seq................................................................................... 52 2.18.3 ChIP-seq ..................................................................................................... 52 H3K27ac ChIP-seq ............................................................................. 53 p300/CBP ChIP-seq ............................................................................ 53 2.19 Statistics ............................................................................................................. 54 Chapter 3: Expression of p300/CBP is required for β cell and α cell development ........................................................................................................................................... 55 3.1 Introduction ........................................................................................................ 55 3.2 Results ............................................................................................................... 56 3.3 Discussion .......................................................................................................... 82 Chapter 4: Deficiency in human and mouse p300 is associated with glucose dysregulation ................................................................................................................... 85 4.1 Introduction ........................................................................................................ 85 4.2 Results ............................................................................................................... 86 4.3 Discussion ........................................................................................................ 109 Chapter 5: p300, Nkx6.1, NeuroD1, and Pdx1 co-occupies loci critical for mature β cell function ....................................................................................................................111 5.1 Introduction ...................................................................................................... 111 5.2 Results ............................................................................................................. 112  x  5.3 Discussion ........................................................................................................ 130 Chapter 6: Conclusions ................................................................................................132 References .....................................................................................................................143   xi  List of Tables Table 1. A summary of patients with Rubinstein-Taybi syndrome and glucose dysregulation reported in the literature. ........................................................................... 34 Table 2. A list of all primary antibodies used for immunofluorescence staining in this thesis. ................................................................................................................................ 42 Table 3. A list of all secondary antibodies used for immunofluorescence staining in this thesis. ................................................................................................................................ 43 Table 4. Sequences of quantitative PCR primers used in this thesis. ............................ 47 Table 5. A list of all primary and secondary antibodies used for Western Blotting in this thesis. ................................................................................................................................ 49 Table 6. Mendelian ratios of islet-specific p300/CBP double KO mice at the weaning age. ................................................................................................................................... 71 Table 7. Down-regulated genes in p300PβKO mouse islets associated with the GO term insulin secretion. ............................................................................................................. 126     xii  List of Figures Figure 1. The pancreatic endocrine differentiation program in mice. ............................... 8 Figure 2. The structure of a nucleosome and lysine modifications on histone tails. ..... 17 Figure 3. The structural domains in human p300 proteins. ............................................ 25 Figure 4. p300IsletHet mice, mice bearing Neurog3-Cre and mice bearing Ep300fl/fl are comparably glucose tolerant. ........................................................................................... 56 Figure 5. p300 is effectively removed in the pancreatic Ngn3 lineage during islet development. .................................................................................................................... 58 Figure 6. Mice lacking p300 in islets are glucose intolerant due to impaired insulin secretion. .......................................................................................................................... 59 Figure 7. p300IsletKO mice have normal body composition and energy metabolism. ..... 60 Figure 8. Total plasma GLP1 levels are comparable between WT and p300IsletKO mice. .......................................................................................................................................... 61 Figure 9. p300IsletKO mice have reduced β cell and α cell area. ..................................... 62 Figure 10. p300-null islets show elevated responses to potassium. .............................. 63 Figure 11. p300-null islets have reduced insulin content but normal glucagon content and secretion. ................................................................................................................... 64 Figure 12. CBP is deleted in CBPIsletKO mouse islets. .................................................... 65 Figure 13. Mice lacking CBP in islets are glucose intolerant due to impaired insulin secretion. .......................................................................................................................... 66 Figure 14. CBP-null islets exhibit reduced insulin content. ............................................ 67 Figure 15. CBPHet; p300KO mice are severely glucose intolerant. .................................. 68 Figure 16. CBPKO; p300Het mice are phenocopies of CBPHet; p300KO mice. ................. 69 Figure 17. CBPHet; p300KO mice exhibit reduced β cell area and islet insulin content. . 70 Figure 18. Mice lacking p300 and CBP in endocrine progenitors die shortly after birth due to failure to form sufficient β cell and α cell mass. ................................................... 72 Figure 19. ε cells are present in adult islets lacking p300. ............................................. 73 Figure 20. p300IsletKO mice exhibit normal pancreatic endocrine differentiation. ........... 74 Figure 21. p300-null islets display impaired neonatal β cell and α cell proliferation. .... 75  xiii  Figure 22. Islets lacking p300 or CBP display impaired expression of genes down-regulated in Hnf1α-null islets. ........................................................................................... 78 Figure 23. A significant portion of β cells in p300IsletKO mice express Npy. ................... 79 Figure 24. Loss of p300/CBP impairs H3K27 acetylation at loci down-regulated in Hnf1α-null islets. ............................................................................................................... 82 Figure 25. Rare pathogenic variants in human EP300 are associated with glucose dysregulation. ................................................................................................................... 86 Figure 26. Proband 1 is haploinsufficient for p300. ........................................................ 87 Figure 27. The p300 p.H1255R and p.F1595V variants are likely gain-of-function. ..... 89 Figure 28. Expression of p300 and CBP in p300βKO mouse islets. ................................ 90 Figure 29. p300βKO mice are glucose intolerant due to impaired insulin secretion in vivo. .......................................................................................................................................... 91 Figure 30. p300βKO mice have normal energy metabolism. ........................................... 92 Figure 31. p300βKO mice have reduced β cell area. ....................................................... 93 Figure 32. Phenotyping of p300βKO mouse islets............................................................ 94 Figure 33. H3K27ac ChIP-seq experiments reveal significant loss of H3K27ac in p300-null β cells. ........................................................................................................................ 96 Figure 34. Reduced H3K27ac enrichment in p300-null β cells correlate with impaired gene expression. .............................................................................................................. 98 Figure 35. Deletion of p300 impairs neonatal β cell proliferation. ................................ 100 Figure 36. The clustering of single cell RNA-seq data sets from control and p300βKO neonatal pancreatic cells. ............................................................................................... 101 Figure 37. Neonatal p300-null β cells express high levels of Npy. .............................. 102 Figure 38. Neonatal p300-null β cells show reduced expression in genes important for glucose homeostasis. ..................................................................................................... 104 Figure 39. Mice lacking CBP in β cells are glucose intolerant due to impaired insulin secretion. ........................................................................................................................ 105 Figure 40. CBPβKO mice have reduced β cell area throughout development. ............. 106 Figure 41. p300/CBPβdKO mice die between six and eight weeks of age due to failure to produce insulin and a complete loss of H3K27ac in β cells. ......................................... 108  xiv  Figure 42. Tamoxifen treatment induces p300 deletion in postnatal p300PβKO mouse β cells. ................................................................................................................................ 112 Figure 43. p300PβKO mice gradually develop glucose intolerance after tamoxifen administration. ................................................................................................................ 114 Figure 44. p300PβKO mice have normal insulin tolerance but impaired insulin secretion. ........................................................................................................................................ 115 Figure 45. p300PβKO mice have normal body composition and energy balance. ......... 116 Figure 46. p300PβKO mice have normal pancreas weight, islet cell mass and islet size. ........................................................................................................................................ 117 Figure 47. p300PβKO mouse islets secrete less insulin and exhibit reduced insulin granule size. ................................................................................................................... 118 Figure 48. The MIN6 p300 and CBP genome occupancy are located within the active promoters/enhancers in islets/β cells............................................................................. 119 Figure 49. De novo motif analysis of the p300 peak set in β cell genome. ................. 121 Figure 50. p300 co-occupies islet active enhancers with NeuroD1, Nkx6.1, and Pdx1. ........................................................................................................................................ 123 Figure 51. p300/NeuroD1/Nkx6.1/Pdx1-co-occupied sites are associated with genes critical for β cell development and function.................................................................... 124 Figure 52. Ablating p300 in mature β cells reduces the expression of p300-occupied genes significantly. ......................................................................................................... 127 Figure 53. Ablating p300 in mature β cells impairs both H3K27ac enrichment and gene expression at p300-occupied loci. .................................................................................. 129 Figure 54. A summary of the phenotypes of the three different p300 knockout mouse models studied in this thesis. ......................................................................................... 136    xv  List of Abbreviations AcK  Acetyl-lysine ADP  Adenosine diphosphate AMPK  5’-adenosine monophosphate-activated protein kinase ANOVA Analysis of variance Arx  Aristaless related homeobox ATP  Adenosine triphosphate BCA  Bicinchoninic acid Bmi1  B lymphoma Mo-MLV insertion region 1 homolog BWA  Burrows-Wheeler Aligner cAMP  Cyclin adenosine monophosphate CREB  cAMP-responsive element-binding protein CBP  CREB-binding protein CH  Cysteine/histidine-rich domain ChIP  Chromatin immunoprecipitation ChREBP Carbohydrate response element binding protein CITED2 CBP/p300 interacting transactivator with glutamic acid/aspartic acid rich carboxy-terminal domain 2 CoA  Coenzyme A CtBP2  Corepressor E1A C-terminal binding protein 2 DAPI  4’,6-diamidino-2-phenylindole DE  Differentially expressed DMEM Dulbecco’s Modified Eagle Medium DNA  Deoxyribonucleic acid DPPIV Dipeptidyl peptidase-4 EDTA  Ethylenediaminetetraacetic acid EdU  5-ethynyl-2’-deoxyuridine Eed  Embryonic ectoderm development EGS  Ethylene glycol-bis(succinimidyl succinate) EGTA  Ethylene glycol-bis(β-aminoethyl ether)-N,N,N’,N’-tetraacetic acid  xvi  ELISA  Enzyme-linked immunosorbent assay Erk  Extracellular signal-regulated kinase Ezh2  Enhancer of zeste homolog 2 FACS  Fluorescence-activated cell sorting FBS  Fetal bovine serum FDR  False discovery rate Foxa2  Forkhead box A2 FoxO  Forkhead box O Foxm1 Forkhead box M1 GFP  Green fluorescent protein GLP1  Glucagon-like peptide 1 Glut2  Glucose transporter 2 GNAT  Gcn5-related N-acetyltransferase GO  Gene ontology GWAS Genome-wide association study HBSS  Hank’s Balanced Salt Solution HDAC  Histone deacetylase HEK  Human embryonic kidney HEPES 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid Hhex  Haematopoietically-expressed homeobox protein HIF-1α Hypoxia-inducible factor 1-α HLA  Human leukocyte antigen Hnf1α  Hepatocyte nuclear factor 1A Hnf1β  Hepatocyte nuclear factor 1B Hnf4α  Hepatocyte nuclear factor 4A HOMA-β  Homeostatic model assessment-β HRP  Horseradish peroxidase IBiD  IRF3-binding domain IDR  Irreproducible discovery rate IGF1  Insulin-like growth factor 1  xvii  IgG  Immunoglobulin G Insm1  Insulinoma-associated 1 iPSCs  Induced pluripotent stem cells IRF  Interferon regulatory transcription factor Isl-1  Insulin gene enhancer protein KAT  Lysine acetyltransferase KIX  Kinase-inducible domain-interacting domain KLF11 Kruppel like factor 11 KO  Knockout KRB  Krebs-Ringer buffer MACS  Model-based an analysis of ChIP-seq MafA/B V-maf avian musculoaponeurotic fibrosarcoma oncogene A/B MIN6  Mouse insulinoma 6 MLL1  Mixed-lineage leukaemia 1 MNase Micrococcal nuclease MODY Maturity-onset diabetes of the young MOZ  Monocytic leukaemia zinc finger mRNA Messenger ribonucleic acid mTOR Mechanistic target of rapamycin MyoD  Myogenic differentiation MYST  MOZ, Ybf2, Sas2 and Tip60 NADH  Nicotinamide adenine dinucleotide NeuroD1 Neuronal differentiation 1 NFAT  Nuclear factor activated T-cells Ngn3  Neurogenin-3 Nkx2.2 NK2 homeobox 2 Nkx6.1 NK6 homeobox 1 NLS  Nuclear localization sequence Npy  Neuropeptide Y Pax4  Paired box gene 4  xviii  Pax6  Paired box gene 6 PBS  Phosphate-buffered saline PC  Prohormone convertase PCAF  p300/CBP-associated factor PDGF  Platelet-derived growth factor PDX1  Pancreatic and duodenal homeobox 1 PHD  Plant homeodomain PKA  Protein kinase A PRC  Polycomb repressive complex PVDF  Polyvinylidene fluoride qPCR  Quantitative polymerase chain reaction Rfx6  Regulatory factor X6 RING  Really interesting gene RIPA  Radioimmunoprecipitation assay RNA  Ribonucleic acid RPMI  Roswell Park Memorial Institute RTS  Rubinstein-Taybi syndrome SDS  Sodium dodecyl sulfate  SSEA4 Stage-specific embryonic antigen 4  SGLT2 Sodium/glucose cotransporter 2 TBP  TATA-binding protein TBST  Tris-buffered saline-tween TE  Tris-EDTA TEM  Transmission electron microscopy Tmem27 Transmembrane protein 27 TMX  Tamoxifen TRA-1-60 Podocalyxin TSS  Transcription start site TUNEL  Terminal deoxynucleotidyl transferase 2’-deoxyuridine 5’-triphosphate nick end labeling  xix  Txnip  Thioredoxin-interacting protein WT  Wildtype    xx  Acknowledgements I am grateful that I can pursue my PhD studies with Bill. It is always a leap of faith to place your trust in someone you barely know from the other side of the planet. I appreciate the freedom and mentorship Bill gave me – they shaped me into a critical and independent researcher. I have tried my best to be as conscientious as I can for the past five years – not only for myself, but also for the lab.  My project has benefited from the help of some of the most amazing undergraduate trainees – Adam, Ann, Kristine, Jésica and Leo. It is truly my honour to be their mentors, and I have learnt more than I could have ever imagined during the process.  I am also grateful for the help from many others outside of the lab. I would like to thank my thesis advisory committee including Francis, Stefan and Matt, who have been helping me throughout my PhD by providing insights from different angles. My work also benefited tremendously from everyone who has been directly or indirectly involved in the work, including the clinical geneticists, the collaborators, the patients we were so fortunate to have the opportunity to study, all staff who provided technical support, and everyone on the 4th floor of the institute who has participated in the discussion of the work. I could not have asked for a better environment to study for my PhD.  Last but not least, my gratitude goes to my family in Hong Kong, and to Carey who made the sacrifice to move here. I thank them for bearing with my selfishness to pursue what my heart desires, and for their unreserved support on my decision to move far away from home. I hope they will be proud of what I have achieved here.        xxi              Aut inveniam viam aut faciam          1  Chapter 1: Introduction 1.1 Diabetes Mellitus Diabetes mellitus, or simply diabetes, is a metabolic disorder where hyperglycemia persists for an extended period of time when the body cannot produce or respond to insulin (DeFronzo et al., 2015). Currently, diabetes affects more than 400 million people around the world (International Diabetes Federation, 2017), with almost half of them being undiagnosed. By 2040, the prevalence of diabetes is projected to rise beyond 600 million (Ogurtsova et al., 2017). Many of these individuals will suffer from various severe complications, such as ketoacidosis, blindness, cardiovascular diseases and kidney diseases (International Diabetes Federation, 2017). In fact, 1.6 million people died in 2016 due to diabetes (International Diabetes Federation, 2017). The clinical management of diabetes and diabetes-related conditions was estimated to cost US$1.31 trillion globally in 2015 (Bommer et al., 2017). In Canada, diabetes is projected to cost the health system a total of CAD$15.4 billion between 2012 and 2022 (Bilandzic and Rosella, 2017). These highlight an unmet need to prevent, diagnose and manage diabetes.  1.1.1 Classification of Diabetes In general, there are four types of diabetes: type 1 diabetes, type 2 diabetes, gestational diabetes, and monogenic forms of diabetes. Type 2 diabetes comprises about 90% of all diabetes cases globally (Aschner, 2017). Between 5% and 10% of all diabetes cases are type 1 diabetes (Daneman, 2006), and fewer than 5% of all diabetes cases are gestational or monogenic diabetes. Each type of diabetes has different etiologies and develops by distinct mechanisms.  1.1.2 Type 1 Diabetes Type 1 diabetes is known as insulin-dependent diabetes. It is an autoimmune disease where the immune system destroys some of the body’s own pancreatic insulin producing β cells and impairs insulin production (Katsarou et al., 2017). As the destruction persists, the body eventually has to depend on exogenous insulin. Type 1  2  diabetes often develops in children or teenagers, and rarely it can also develop in older individuals.  It is not clear what directly triggers the onset of type 1 diabetes. Twin studies have established that between 30% and 50% of type 1 diabetes cases can be attributed to genetic risk factors (Redondo et al., 2008). Studies have investigated numerous locus-specific alleles that contribute to the genetic risk of type 1 diabetes. These alleles include variants of proteins important for immune responses, such as human leukocyte antigen (HLA) loci and cytotoxic T-lymphocyte associated protein 4 (Gregersen and Behrens, 2006; Onengut-Gumuscu et al., 2015). Certain alleles at the insulin locus can contribute to the disease (Bennett and Todd, 1996). In addition, environmental factors including infection, gut microbiota, dietary components and exposure to chemicals can contribute to the progress of type 1 diabetes (Rewers and Ludvigsson, 2016). Thus, both genetic and environmental factors contribute to the onset of type 1 diabetes.  Currently, there is no cure for type 1 diabetes. The primary treatment for the disease requires exogenous insulin administered through injection or a pump. Lifestyle interventions such as exercise and diet can help improve glucose control in these patients. For more severe cases, islet transplantation is a plausible option (Bruni et al., 2014). However, the procedure entails many difficulties, such as the availability of HLA-matched islets and low long-term success rates for transplantation (Robertson, 2004). The recent breakthrough in the generation of β-like cells from embryonic stem cells or induced pluripotent stem cells (iPSCs) may hold promises as an effective cure for type 1 diabetes (Kieffer et al., 2017), but the goal to restore a functional mass of glucose-responsive, insulin-secreting mature β cells is not within reach yet.  1.1.3 Type 2 Diabetes Type 2 diabetes is a common, complex disease which arises when β cells fail to cope with the insulin resistant state in the body. Insulin resistance occurs when the peripheral tissues in the body fail to respond to insulin normally. In the early stage of insulin  3  resistance, β cells can respond by producing and secreting more insulin (Prentki and Nolan, 2006). However, long-term insulin resistance can overstress the β cells, leading to β cell dysfunction and apoptosis. These events eventually interfere with the normal function of insulin and result in hyperglycemia. Type 2 diabetes mostly affects peoples older than 40 years of age. In Canada, about 10% of type 2 diabetes cases are younger than 40 years of age (Pelletier et al., 2012).  Obesity is one of the primary drivers of insulin resistance (Kahn and Flier, 2000). Obesity can raise plasma free fatty acid levels and increase the accumulation of triglycerides in muscle, where both events can contribute to insulin resistance (Borkman et al., 1993; Griffin et al., 1999). In addition to obesity, lifestyle factors and genetic predisposition also contribute significantly to the development of type 2 diabetes. A sedentary lifestyle contributes to type 2 diabetes (Risérus et al., 2009). The excessive intake of foods with high sugar also promotes the development of type 2 diabetes (Malik et al., 2010b, 2010a). As for genetic predisposition, genome-wide association studies (GWAS) have revealed numerous risk and protective alleles for type 2 diabetes (Ayub et al., 2014). However, these alleles generally have small effect sizes (Morris et al., 2012). Also, many of them reside in non-coding regions, and we do not completely understand how these alleles may mediate their risks or protective effects through the cis-regulatory elements (Gaulton et al., 2015). Overall, the interplay of complex environmental and polygenic factors modifies the risk of type 2 diabetes.  Although there is no cure for type 2 diabetes, lifestyle intervention and medication can manage the disease effectively. Lifestyle intervention comprising a modified diet and exercise can reduce the risk of developing type 2 diabetes and even hinder disease progress once it has begun (Li et al., 2008). Current antidiabetic medications mostly attempt to mitigate the hyperglycemia by enhancing β cell insulin secretion, by improving insulin sensitivity in insulin-responsive organs, or by enhancing glucose clearance in kidneys. The first line medication for type 2 diabetes is metformin (Rojas and Gomes, 2013), which primarily works by activating 5’-adenosine monophosphate- 4  activated protein kinase (AMPK) to reduce hepatic glucose production (Zhou et al., 2001), in addition to other secondary metabolic benefits. Other second or third line medications for type 2 diabetes include sulphonylureas, thiazolidinediones, dipeptidyl peptidase-4 (DPPIV) inhibitors, sodium/glucose cotransporter 2 (SGLT2) inhibitors, glucagon-like peptide 1 (GLP1) analogs and GLP1 receptor agonists (Inzucchi et al., 2015). In more severe cases of type 2 diabetes, insulin may also be a therapeutic option. Ongoing research aims at identifying medications that can effectively reverse the underlying metabolic defects in type 2 diabetes.  1.1.4 Monogenic Forms of Diabetes A small number of type 2-like diabetes cases distinguish themselves from the typical cases of type 1 or type 2 diabetes, because these type 2-like cases develop the disease in their adolescence or young adulthood, usually before age 25 years (Gardner and Shyong Tai, 2012). At the time of diagnosis, these early-onset cases do not show the typical features or type 1 or type 2 diabetes, such as obesity or the presence of auto-antibodies. Moreover, this subtype of diabetes is highly heritable. This unique, early-onset, and inheritable type of diabetes is known as maturity-onset diabetes of the young (MODY).   Pedigree-based studies have suggested that MODY is an autosomal dominant disorder. Most MODY cases can be traced to a rare, highly penetrant variant, although homozygous variants in some MODY genes can cause more severe diabetes phenotypes (Rubio-Cabezas et al., 2010; Thomas et al., 2009). A subset of MODY has an even earlier-onset and is known as permanent neonatal diabetes. Permanent neonatal diabetes can be caused by abnormal gain-of-function variants in genes that regulate depolarization of β cells. Other variants also affect whole-body insulin sensitivity, causing rare forms of insulin resistance syndromes. Here, the discussion focuses on the classical forms of monogenic diabetes with an onset of adolescence or young adulthood.   5  Currently, there are 13 subtypes of MODY listed in the Online Mendelian Inheritance in Man (OMIM) database (Hamosh et al., 2000). The most common subtypes of MODY are caused by mutations in GCK or HNF1A. GCK was among one of the first genetic causes found to cause MODY (Vionnet et al., 1992). Mutations in GCK account for between 30 – 60% of all MODY cases (Chakera et al., 2014). The gene encodes glucokinase, a critical glycolytic enzyme in β cells and hepatocytes. Mutations in GCK decrease the enzymatic activity of glucokinase (Aukrust et al., 2013). In rare cases, homozygous loss-of-function variants in GCK cause permanent neonatal diabetes (Njølstad et al., 2001). In contrast, gain-of-function variants in GCK can cause hyperinsulinism (Glaser et al., 1998). On the other hand, heterozygous variants in HNF1A account for between 30 – 60% of all MODY cases (Pihoker et al., 2013; Yamagata et al., 1996a). The gene encodes the transcription factor hepatocyte nuclear factor homeobox 1A (Hnf1α). Hnf1α is important for early islet and liver development, and later on for β cell insulin secretion (Servitja et al., 2009). Activating HNF1A variants can also cause hyperinsulinism (Stanescu et al., 2012). Together, the GCK and HNF1A variants represent 70% of all MODY cases.  The rest of the MODY genes tend to harbour rarer variants. Many of these MODY genes encode transcription factors of major importance in pancreas and β cell development, including PDX1, HNF4A, HNF1B, NEUROD1, KLF11 and PAX4. PDX1 encodes pancreatic and duodenal homeobox 1 (PDX1). PDX1 is a master regulator for pancreas development, and later it becomes critical for insulin biosynthesis in β cells (Ohlsson et al., 1993). The discovery of an individual carrying homozygous pathogenic PDX1 variants who developed pancreatic agenesis also supports a critical role of PDX1 in human pancreas development (Thomas et al., 2009). HNF4A and HNF1B, together with HNF1A, comprise an autoregulatory transcriptional network in β cells and livers (Hatzis and Talianidis, 2002; Kyrmizi et al., 2006). Pathogenic loss-of-function variants in HNF4A or HNF1B cause MODY (Horikawa et al., 1997; Yamagata et al., 1996b), whereas gain-of-function variants in HNF4A can cause hyperinsulinemic hypoglycemia (Flanagan et al., 2010). Variants in NEUROD1, KLF11 and PAX4 cause MODY likely by  6  affecting β cell transcription (Fajans et al., 2001). Although transgenic mouse studies have established the functions of these transcription factors in β cells, human genetic studies only revealed very few probands and families for each of the genes (Malecki et al., 1999; Neve et al., 2005; Shimajiri et al., 2001). In general, MODY genes encode proteins critical for β cell development and function, and mutations in these genes predispose the β cells to defects and failure. In addition, transcription factors are over-presented in the MODY gene set. This observation implies that β cell transcription factors plays a central role in β cell development and function.  1.2 Pancreas as an Exocrine and Endocrine Organ The pancreas is a digestive organ specialized for secretion. The organ manufactures and delivers exocrine and endocrine molecules. The majority of the pancreas volume contains clusters of acinar cells and pancreatic ducts which make up the exocrine pancreas. Acinar cells secrete peptidases and lipases into the duodenum, and pancreatic ducts are made up by ductal cells for transporting these enzymes. Only about 1% of the pancreas volume contains pancreatic islets or islets of Langerhans (Ionescu-Tirgoviste et al., 2015; Langerhans, 1869), which make up the endocrine pancreas. These islets are innervated by sympathetic and parasympathetic nerves and are highly vascularized to facilitate the transport of endocrine signals. Besides these primary pancreatic cell types, pancreatic stellate cells (Watari et al., 1982), immune cells and neurons can be found scattered across the pancreas. These cells regulate the overall functions of the exocrine and endocrine pancreas.  1.2.1 Pancreatic Islets Islets are spherical organoids that are scattered throughout the pancreas. Each islet can contain up to five types of endocrine cells specialized in hormone secretion. The most abundant endocrine cells in islets are insulin-secreting β cells, which make up 70% and 50% of the islet endocrine cells in mice and humans, respectively (Cabrera et al., 2006; Kim et al., 2009). Glucagon-secreting α cells make up 30% and 40% of the islet endocrine cells in mice and humans, respectively. Somatostatin-secreting δ cells make  7  up 10% of the islet endocrine cells. Pancreatic polypeptide cells, or PP cells, make up less than 10% of the islet endocrine cells. Lastly, a very small number of ghrelin-secreting ε cells are present in adult human islets and in developing mouse islets (Wierup et al., 2002), but are completely absent in adult mouse islets (Heller et al., 2005). In addition to the difference in the endocrine cell proportion, human and mouse islets display distinct cytoarchitectures. Alpha (α) cells are scattered across human islets, whereas in mouse islets α cells form a mantle surrounding the core of β cells. Pancreatic Endocrine Development At embryonic day 8.5 (E8.5), a subset of cells in the posterior foregut endoderm gives rise to the dorsal and ventral pancreas (Wessells and Cohen, 1967). At E10.5, the primordial pancreas buds out into the surrounding mesenchyme near the stomach and intestine. The primordial pancreas comprises Pdx1-positive progenitors which can differentiate into all types of pancreatic cells including the endocrine, acinar and ductal lineages (Guz et al., 1995). A subset of the pancreatic progenitors becomes Sox9/Nkx6.1-double positive trunk cells, which can in turn differentiate into either ductal cells or islet endocrine cells (Schaffer et al., 2010; Solar et al., 2009; Zhou et al., 2007). During the second transition between E13.5 and E15.5 (Pictet et al., 1972), some trunk cells commits to the endocrine cell fate by expressing neurogenin-3 (Ngn3) transiently (Gradwohl et al., 2000). These Ngn3-positive progenitors eventually differentiate into mature hormone-secreting cells, appearing in the order of glucagon, insulin, pancreatic peptide, somatostatin and ghrelin (Heller et al., 2005; Johansson et al., 2007).  After committing to the endocrine lineage, Ngn3-positive cells express a core set of transcription factors to initiate the pan-endocrine gene expression program. These transcription factors include insulin gene enhancer protein (Isl-1), insulinoma-associated 1 (Insm1), neuronal differentiation 1 (NeuroD1), paired box gene 4 (Pax4), paired box gene 6 (Pax6), and regulatory factor X6 (Rfx6) (Ahlgren et al., 1997; Gao et al., 2008; Gierl et al., 2006; Naya et al., 1997; Oliver-Krasinski et al., 2009; Smith et al., 2010) (Figure 1). These factors act immediately downstream of Ngn3 and are critical for the  8  differentiation of all pancreatic endocrine cells. They regulate pancreatic endocrine differentiation and maturation, including initiating endocrine secretory functions (Gierl et al., 2006), activating downstream targets to reinforce lineages (Du et al., 2009), and repressing undesirable targets (Itkin-Ansari et al., 2005). Furthermore, these factors work together with the lineage-determining transcription factors to guide the endocrine progenitors towards terminal differentiation and maturation.    Figure 1. The pancreatic endocrine differentiation program in mice. Transcription factors depicted in the figure have been shown to be critical for the differentiation of the lineage in mouse models.   9  In neonates, the differentiated endocrine cells proliferate and form long, ribbon-like structures (Jo et al., 2011; Seymour et al., 2004). These cells gradually divide into islets and continue to proliferate throughout the early period of life.  1.2.2 β Cells β cells secrete insulin, which is a short peptide that promotes glucose uptake in tissues to exert hypoglycemic effects in the body. The insulin-dependent hypoglycemic effect is mediated through multiple metabolically-active organs, primarily the liver, skeletal muscle, and adipose tissue (Rask-Madsen and Kahn, 2012). Canonically, insulin binds to insulin receptors to activate insulin receptor dimerization. The receptor dimer auto-phosphorylates, which further activates its kinase activity (Kahn and White, 1988). The receptor tyrosine kinase domain on the insulin receptors, once activated, can phosphorylate their downstream targets including insulin receptor substrate 1/2, protein kinase B, and extracellular signal-regulated kinase (Erk). In skeletal muscle and adipose tissue, insulin signaling promotes the translocation of glucose transporters to the cell surface to facilitate glucose uptake. In the liver, insulin signaling suppresses gluconeogenesis. These effects restore the elevated blood glucose levels to the homeostatic range. β Cell Differentiation Ngn3-positive cells differentiate towards endocrine cells by first expressing Pax4 and Pax6; Pax4 directs the cells away from the α cell lineage and Pax6 directs the cells away from the ε cell lineage (Heller et al., 2005; Sander et al., 1997; Sosa-Pineda et al., 1997) (Figure 1). Pax4 antagonizes the action of the α cell fate determinant aristaless related homeobox (Arx) (Collombat et al., 2003). Pax4 and Nkx2.2 co-operatively initiate the β cell gene expression program (Wang et al., 2004). Nkx2.2 dictates β cell differentiation in part by activating its downstream target NK6 homeobox 1 (Nkx6.1) (Papizan et al., 2011; Sander et al., 2000). Both Nkx2.2 and Nkx6.1 can activate v-maf avian musculoaponeurotic fibrosarcoma oncogene A (MafA) for the final steps of β cell differentiation (Raum et al., 2006; Schaffer et al., 2013). The expression of MafA also  10  requires Pdx1 when Pdx1 becomes restricted to mature β cells (Sachdeva et al., 2009). This cascade of events directs the expression of genes required for the insulin secretory function in β cells. Notably, transgenic mouse studies have shown that mature β cells that fail to express these transcription factors either become dysfunctional (Gu et al., 2010; Taylor et al., 2013), or revert to the α cell lineage (Gao et al., 2014; Gutiérrez et al., 2017; Nishimura et al., 2014; Swisa et al., 2017). These findings indicate that expression of β cell transcription factors is required not only for β cell differentiation, but also for stabilizing the mature, differentiated state. β Cell Proliferation during Development Although the starting pool of β cells in the body originates from differentiation, most adult β cells form from perinatal proliferation. In perinates, a number of the newly-differentiated β cells enter the cell cycle and proliferate. The process generates 10% of new β cells per day in rodents (Bernard-Kargar and Ktorza, 2001; Teta et al., 2007). The rate of proliferation gradually slows down postnatally (Scaglia et al., 1997). In adult, β cell mass remains largely constant in both mice and in humans. However, β cell mass can expand postnatally under metabolic stress, such as in pregnancy, in obesity and after pancreatic injury in rodent, and to a certain extent in humans (Wahid et al., 2017). These β cell mass expansion events are primarily driven by β cell proliferation, but how β cells proliferate under these conditions remains unclear and controversial (Kushner et al., 2010).  A number of signals and transcription factors regulate perinatal β cell proliferation. Arguably, nutrients, particularly glucose, are the most important external cues for β cell proliferation (Garofano et al., 1998; Reusens and Remacle, 2006). The exposure to fluctuating glucose levels in neonatal β cells is critical for initiating β cell maturation and proliferation (Pechhold et al., 2009; Porat et al., 2011). In addition, paracrine and endocrine signals such as GLP1 (Dai et al., 2017; Tschen et al., 2009), and platelet-derived growth factor (PDGF) (Chen et al., 2011), can positively regulate perinatal β cell proliferation. Other factors such as growth hormone, prolactin and insulin-like growth  11  factor 1 (IGF1) have been shown to simulate β cell proliferation in vitro (Nielsen, 1982; Swenne et al., 1988), although it is difficult to determine whether these hormones also simulate β cell proliferation in vivo. Furthermore, mice lacking MODY genes, such as Hnf1a (Yang et al., 2002), and Hnf4a (Gupta et al., 2007), exhibit reduced β cell proliferation. Other transcription factors such as forkhead box M1 (Foxm1) (Zhang et al., 2006) and nuclear factor activated T-cells (NFAT) (Goodyer et al., 2012) have also been implicated in neonatal β cell proliferation. These stimulatory factors can mediate perinatal β cell proliferation in either a transcription factor-dependent or a transcription factor-independent manner. For example, glucose induces proliferation by activating the mechanistic target of rapamycin (mTOR) pathway (Stamateris et al., 2016). GLP1-mediated β cell proliferation requires NFAT (Dai et al., 2017). Both PDGF and Pdx1 require an intact Erk signalling pathway to elicit their proliferative effects (Chen et al., 2011; Hayes et al., 2013). Hnf1α is thought to mediate β cell proliferation by activating the expression of transmembrane protein 27 (Tmem27), a membrane-bound protein that is cleaved and shed from the plasma membrane of β cells (Akpinar et al., 2005; Shih et al., 2001). In summary, multiple pathways exist to drive perinatal β cell proliferation.  The factors that activate perinatal β cell proliferation all converge on cell cycle regulatory components, including cyclins D 1, 2 and 3 (Georgia and Bhushan, 2004), cyclin A2 (Song et al., 2008), and cyclin-dependent kinase 4 (CDK4) (Rane et al., 1999). These cell cycle proteins are required for β cell proliferation. Overexpression of CDK inhibitor 1B, also known as p27, impairs perinatal β cell proliferation (Georgia and Bhushan, 2006; Rachdi et al., 2006), whereas its paralog CDK inhibitor 1A, also known as p27, does not appear to have a clear perinatal role (Cozar-Castellano et al., 2006; Yang et al., 2009). Interestingly, human and mouse β cells exhibit a distinct expression pattern of D-type cyclins (Fiaschi-Taesch et al., 2010). For example, cyclin D2 mediates glucose-induced proliferation in mouse β cells, but human β cells express very low levels of cyclin D2. The species-specific difference illustrates that human and mouse β  12  cells may enter the cell cycle by distinct mechanisms. It is also unclear whether perinatal and postnatal β cells rely on different cell cycle components for proliferation. Previous studies have established a key role of β cell dysfunction in type 2 diabetes and MODY. In contrast, the contribution of defective β cell mass to these diseases remains unclear. Transgenic moues studies have unveiled a mechanistic link between β cell proliferation and MODY genes HNF1A, HNF4A and PDX1 (Gupta et al., 2007; Hayes et al., 2013; Yang et al., 2002). In addition, GWAS has identified risk alleles within the loci of cell cycle regulators CDKN2A/CDKN2B (Pal et al., 2016). These data suggest that deficient perinatal β cell proliferation may contribute to type 2 diabetes and MODY. In the future, more robust methods that allow human β cell mass quantification in vivo will help ascertain the relative contribution of defects in β cell mass to type 2 diabetes and MODY. β Cell Maturation and Function Newly divided perinatal β cells are functionally immature. Immature β cells respond weakly to high glucose levels and secrete high levels of basal insulin (Blum et al., 2012). Interesting, immature β cells respond relatively normally to other nutrients such as amino acids (Hellerström and Swenne, 1991). The blunted glucose response in immature β cells is due to a higher rate of glycolysis and an immature nicotinamide adenine dinucleotide (NADH) shuttle system (Boschero et al., 1990; Gu et al., 2010; Tan et al., 2002). In addition, immature β cells express several characteristic genes, including Npy (Rodnoi et al., 2017; Teitelman et al., 1993), Mafb (Artner et al., 2006), and Ldha (Gu et al., 2010). These genes are gradually switched off as β cells become mature.  The proliferation capacity of β cells diminishes as the cells become functionally mature (Puri et al., 2018). β cells achieve functional maturity when they demonstrate proper glucose-stimulated insulin secretion responses. To manifest such responses, β cells have to be able to 1) sense an physiological increase in blood glucose levels, 2) secrete insulin in response to glucose in a concentration-dependent manner, and 3) switch off  13  insulin secretion when the glucose level is low (Blum et al., 2012). To acquire these features during maturation, β cells require both intrinsic and extrinsic inputs, such as glucose (Freinkel et al., 1984), transcription factors (Artner et al., 2007; Gu et al., 2010; Schaffer et al., 2013), microRNAs (Jacovetti et al., 2015), and epigenetic regulators (Dhawan et al., 2015). These factors activate gene targets that are associated with the mature state of β cells. Some of these genes encode protein markers with no clear function in β cells, such as Fltp (Bader et al., 2016), whereas other gene targets are functional in mature β cells, such as Ucn3 (Blum et al., 2012; Van Der Meulen et al., 2015). Importantly, stable expression of transcription factors and epigenetic regulators is required to activate these maturity-related genes in β cells (Szabat et al., 2012). Impaired activity of transcription factors or epigenetic regulators can disturb transcription in mature β cells, leading to β cell dysfunction and diabetes (Talchai et al., 2012).  Glucose-stimulated insulin secretion in β cells results from a complex interaction between transcriptional regulation, energy metabolism, electrophysiology and a protein secretory system. Transcriptionally, glucose itself can regulate insulin gene expression (Andrali et al., 2008). Glucose stimulates the recruitment of Pdx1, NeuroD1 and MafA to the insulin promoter (Melloul et al., 1993; Sharma and Stein, 1994), and enhances the transactivation ability of Pdx1 (Petersen et al., 1994). In addition, glucose can stabilize preproinsulin messenger ribonucleic acid (mRNA) directly (Welsh et al., 1985), although the mechanism underlying this effect is unclear. Translationally, preproinsulin mRNA is translated to the preproinsulin peptide. The peptide then enters the endoplasmic reticulum, where the signal peptide on preproinsulin is removed. Then, the cleaved preproinsulin undergoes folding and forms disulphide bonds (Röder et al., 2016). The folded peptide, known as proinsulin, is translocated to the Golgi apparatus. The Golgi apparatus packages proinsulin together with processing enzymes, chromogranin, secretogranin, zinc ions, and calcium ions into granules. These newly-packaged granules are referred to as immature insulin granules or light core granules, because they do not reflect electrons and appear light coloured under transmission electron microscopy (TEM). Within the light core granules, the enzymes prohormone convertase  14  1/3 (PC1/3), PC2 and carboxypeptidase E process the proinsulin peptide into mature insulin and C-peptide. Six insulin peptides chelate zinc to form a single insulin crystal. These insulin granules are referred to as mature insulin granules or dense core granules, because the insulin crystals reflect electron under TEM and appear dark. The dense core granules are translocated to and docked at the plasma membrane. These docked granules are ready to be released when β cells sense a rise in blood glucose levels. To sense changes in blood glucose levels, β cells import glucose by glucose transporters on the cell membrane. Mouse β cells depend on glucose transporter 2 (Glut2) for glucose import, whereas human β cells import glucose using both Glut1 and Glut2. β cells then convert the imported glucose into adenosine triphosphate (ATP) through glycolysis, Kreb’s cycle and oxidative phosphorylation (Fu et al., 2013). The resulting increase in the ATP to adenosine diphosphate (ADP) ratio closes ATP-sensitive potassium channels. The closure increases the membrane potential, leading to membrane depolarization. The depolarization open voltage-dependent calcium channels on the membrane, which causes an influx of extracellular calcium into the cytosol. The increase in intracellular calcium levels triggers insulin granule exocytosis. Hence, β cells are transcriptionally and physiologically primed to fulfil their role in releasing insulin under proper glucose stimulation, thereby maintaining glucose homeostasis.  1.2.3 α Cells α cells secrete glucagon to antagonize the hypoglycemic effects of insulin. Glucagon is a 29-amino acid peptide derived from the preproglucagon gene. α cells express PC2, which cleaves the proglucagon peptide into mature glucagon (Whalley et al., 2011). α cells secrete glucagon when blood glucose drops below 4 mM (Le Marchand and Piston, 2010). Circulating glucagon binds to glucagon receptors in the liver, increases intracellular cyclic adenosine monophosphate (cAMP) levels and activates protein kinase A (PKA). PKA can phosphorylate cAMP-responsive element binding protein (CREB), which can activate the expression of genes for glycogenolysis and gluconeogenesis (Han et al., 2016). The activation of glycogenolysis and  15  gluconeogenesis mobilizes the glucose storage and promotes glucose production in the liver to raise blood glucose levels. Glucagon also serves as a paracrine to stimulate β cell insulin secretion (Kawai et al., 1995). The paracrine effect promotes glucose uptake and prevents glucose deficiency in peripheral tissues.  α cell differentiation occurs during a similar time frame as β cell differentiation. As discussed above, α cell differentiation requires the expression of Pax6 and Nkx2.2 (St-Onge et al., 1997; Sussel et al., 1998). Arx directs islet endocrine cells towards the α cell lineage and represses Pax4 to direct the cells away from the β cell lineage (Collombat et al., 2003) (Figure 1). Although the molecular mechanism of α cell maturation is not well understood, it is likely similar to the mechanism of β cell maturation (Qiu et al., 2017). The activation of glucagon gene in developing α cells involves MafB (Artner et al., 2006). Furthermore, Pax6, MafB, Arx, and Foxa2 are all important for maintaining glucagon gene expression (Kaestner et al., 1999). Therefore, α cells require ongoing transcriptional regulation to maintain glucagon gene expression and α cell identity.  1.2.4 δ Cells, PP Cells and ε Cells About 10% of all islet endocrine cells are δ cells, PP cells, and ε cells. δ cells secrete somatostatin, PP cells secrete pancreatic polypeptide and ε cells secrete ghrelin. In islets, somatostatin functions as a paracrine molecule to inhibit the secretion from both β cells and α cells (Hauge-Evans et al., 2009; Van Der Meulen et al., 2015; Strowski et al., 2000). Ghrelin may also be an islet paracrine signal likely by activating somatostatin secretion in δ cells (Dezaki et al., 2008; DiGruccio et al., 2016). The quantitative importance of islet paracrine crosstalk in glucose homeostasis remains to be determined. In addition, pancreatic polypeptide and ghrelin are appetite-regulating hormones: pancreatic polypeptide is anorexigenic (Batterham et al., 2003), whereas ghrelin is orexigenic (De Vriese and Delporte, 2007). However, the contribution of islet-derived pancreatic polypeptide and ghrelin to appetite regulation is not clear.   16  The mechanisms underlying δ cell, PP cell, and ε cell differentiation are incompletely understood. Similar to β cells, δ cell differentiation requires Pax4 (Sosa-Pineda et al., 1997). In contrast, Nkx2.2 and Nkx6.1, which are the downstream targets of Pax4 expression, are dispensable for δ cell differentiation (Mastracci et al., 2011). Pdx1 is expressed in some adult δ cells (Wilson et al., 2003). However, whether Pdx1 contributes to δ cell differentiation is not known. Thus far, haematopoietically-expressed homeobox protein (Hhex) is the only transcription factor found to be specific for δ cell differentiation (Zhang et al., 2014), perhaps by being a downstream target of Pax4. Novel δ cell lineage-determining factors downstream of Pax4 remain to be identified. Studies on PP cell differentiation are severely lacking. Although a subset of PP cells requires Nkx2.2 (Sussel et al., 1998), no transcription factor specific for the PP cell lineage has been identified. Similarly, no transcription factor has yet been implicated in ε cell differentiation. Interestingly, ε cells replace β cells in mice lacking Nkx2.2 during pancreatic endocrine development (Prado et al., 2004). The observation suggests that ε cells may be an intermediate endocrine cell type. Recent studies have used single cell RNA-seq technologies to reveal novel pathways and transcription factors involved in islet endocrine differentiation (Byrnes et al., 2018; Scavuzzo et al., 2018), although the molecular pathways that drive δ, PP, and ε cell differentiation remain to be clarified.  1.3 Histone Modifications Transcription occurs using genomic DNA as a template. In addition to protein-coding genes, cis-regulatory elements can be found along the entire DNA molecule. The regulatory elements include promoters, enhancers and silencers, and each serves a specific role in controlling transcription. Transcription factors recognize and bind to the specific motifs within cis-regulatory elements, and the sequence context of these motifs determines the specificity and affinity of the binding (Morozov et al., 2005). Furthermore, DNA methylation at CpG islands can influence the binding of transcription factors to DNA and DNA hydrophobicity (Kaur et al., 2012). Together, cis-regulatory elements and DNA methylation govern gene regulation at the DNA level.   17  Gene regulation can also be controlled beyond the DNA level. Within the nucleus, DNA is packaged into condensed structures called nucleosomes. A 147-bp segment of DNA molecule wraps around a histone octamer to form nucleosomes (Figure 2). A histone octamer is assembled from two units of each of the core histones: H2A, H2B, H3 and H4. The amino-(N-) terminal of each histone protein is referred to as its histone tail. Histone tails have a high proportion of lysine and arginine residues, which confer an overall net positive charge to histones and provides numerous reaction sites for post-translational modifications (Erler et al., 2014). Moreover, histone tails protrude outward from nucleosomes, allowing the access of proteins that “write” or “read” chemical moieties to the histone tails. The covalent modifications on histone tails are known as histone marks. Collectively, the marks constitute a “histone code” that contains chemical information in a similar way as do DNA and RNA molecules (Cosgrove and Wolberger, 2005).    Figure 2. The structure of a nucleosome and lysine modifications on histone tails. Left panel: the acetylation and methylation reactions on the ε amine group of the lysine residues on histone tails. The amine group can carry one acetyl group or three methyl groups. Right panel: the structure of a mono-nucleosome where a DNA molecule wraps around a histone octamer comprising two H2A-H2B dimers, two H3 and two H4 monomers. H1 is not present in the core histone octamer, but rather it binds to the linker DNA for stabilizing the chromatin structure.   18  Some of the most well-studied histone modifications include lysine acetylation, lysine/arginine methylation and serine phosphorylation (Bannister and Kouzarides, 2011). Different combinations of histone marks can indicate either an open or a closed structure of chromatin, known as euchromatin and heterochromatin, respectively. Chromatin remodeling switches the structure of chromatin between euchromatin and heterochromatin. The process controls the access of transcriptional machinery, transcription factors and cofactors to cis regulatory elements, leading to changes in gene expression. The study of how cellular factors extrinsic to the DNA may contribute to heritable changes in transcription has been termed “epigenetics”. The term “epigenomics” is the study of how transcription is controlled at the genome level based on the analyses of high-complexity data on transcription factor binding sites and histone modifications in particular cell types (Berger et al., 2009).  Histone lysine acetylation and methylation are associated with characteristic chromatin states. Generally, histone lysine acetylation destabilizes the chromatin structure. The negatively-charged acetyl group neutralizes the positive charge on the lysine residue, which allows the DNA to repel from the now neutrally-charged histone (Struhl, 1998) (Figure 2). A diffuse pattern of acetylation spreading across many histones can broadly relax the chromatin structure, and is generally associated with gene activation. On the other hand, the deposition of neutrally-charged methyl groups on histone lysine residues does not induce a strong electrostatic effect on histones. Therefore, methylation does not appear to influence transcription sterically. Instead, methylation-recognizing proteins can bind to each or a combination of methyl-lysine residues (Strahl and Allis, 2000). Then, the methylation-recognizing proteins can recruit additional factors and cofactors to modulate transcription. Each lysine residue can carry a maximum of three methyl groups, namely mono- (me1), di- (me2) and tri-methyl (me3) marks. The gradation from one to three methyl groups potentially adds a quantitative dimension to methyl marking that can be interpreted by methyl lysine readers. Overall, histone lysine acetylation and methylation represent two distinct mechanisms to control chromatin structures and gene expression.  19  Studies have investigated a number of acetyl histone H3 lysine residues that are associated with characteristic chromatin states. Acetylation at H3K4 marks active promoters and enhancers (Eissenberg and Shilatifard, 2010; Guillemette et al., 2011). Acetylation at H3K9 can promote transcription from initiation to elongation (Gates et al., 2017). Acetylation at H3K14 is associated with some regions of active chromatin (Karmodiya et al., 2012). Acetylation at H3K18/H3K27 flags active promoters and enhancers (Creyghton et al., 2010; Wang et al., 2008b). The role of H3K36ac is unclear in mammalian cells (Morris et al., 2007), although the mark is associated with active genes in plants (Mahrez et al., 2016). Furthermore, some acetyl-H3 marks are associated with responses to DNA damage, such as H3K14ac and H3K56ac (Wang et al., 2012; Yuan et al., 2009). Overall, histone H3 acetylation is associated with distinct euchromatin states.  Studies have also investigated individual methyl-lysine residues on histone H3 tails. H3K4 can harbour H3K4me1, which is associated with primed enhancers (Calo and Wysocka, 2013); H3K4me2, which is associated with active and primed promoters and enhancers (Orford et al., 2008); or H3K4me3, which is associated with active promoters and active transcriptional start sites (Liang et al., 2004). H3K9me1 can be found at active transcription start sites (Barski et al., 2007). In contrast, H3K9me2 is associated with the inactive X chromosome and late-replicating regions (Rougeulle et al., 2004; Yokochi et al., 2009), and H3K9me3 marks both X-chromosomal and autosomal heterochromatin (Becker et al., 2016). It is unclear whether H3K14 methylation has a role in normal physiology, although intracellular pathogens can hijack H3K14 methylation to modulate host cell gene expression (Rolando et al., 2013). The role of H3K18 methylation is also unclear. H3K27me1 is associated with active promoters (Barski et al., 2007). H3K27me2 can be found scattered across the majority of recoverable histone H3 and appears to block the activation of non-cell type-specific enhancers (Ferrari et al., 2014). H3K27me3 is a repressive mark that can be found at bivalent domains that can be switched on and off as cells differentiate (Bernstein et al., 2005). Lastly, H3K36me3 can be found throughout the active gene body (Barski et al.,  20  2007). The modification may suppress intragenic transcription initiation after transcription elongation by signalling histone deacetylation (Carrozza et al., 2005; Joshi and Struhl, 2005). H3K36me3 is also associated with DNA repair at some DNA double strand breaks (Fnu et al., 2011). Overall, each of these methylation marks can serve as a tag of distinct chromatin domains, and together they can serve as mutually reinforcing histone codes to shape the epigenome.  A histone lysine residue cannot be acetylated and methylated simultaneously. Once occupied by a covalent moiety, the lysine residue becomes unavailable for further modification. In addition, the modified lysine residue may recruit additional epigenetic regulators to occupy the residue. The occupancy prevents other epigenetic regulators from binding to and altering the modified lysine residue. A well-studied pair of opposing marks is H3K27ac and H3K27me3. Both marks can be deposited at H3K27 but exert opposite effects on chromatin. At H3K4me1-marked primed enhancers, the presence of H3K27ac marks the enhancers as active, whereas the presence of H3K27me3 marks the enhancers as repressed. Polycomb repressive complex 2 (PRC2) is the primary protein complex that methylates H3K27 (Kuzmichev et al., 2002), and loss of PRC2 activity leads to a reciprocal increase in H3K27ac (Pasini et al., 2010). Conversely, when loss of H3K27 acetyltransferase CREB-binding protein (CBP) in Drosophila melanogaster can lead to an increase in H3K27me3 (Tie et al., 2009). Notably, these studies have not determined whether the aberrant increase in the opposite mark is caused by the loss of the enzyme, by the loss of the antagonistic mark deposited by the enzyme, or by both. Studies using catalytically inactive but structurally stable enzymes may help deconvolute these possibilities.  1.3.1 Histone Modification and Transcriptional Regulation in β Cells Conrad Waddington defined epigenetics as the study of how cells differentiate from a plastic, pluripotent state towards a terminal, stable state (Waddington, 2012). Cell differentiation is associated with dynamic changes in histone modifications and chromatin structure. In some cases, epigenetic changes can be causative of cell  21  differentiation (Li, 2002). During development, each of the cellular states harbours a unique pattern of histone modifications that orchestrates the gene expression program towards the desired terminal stage (Lennartsson and Ekwall, 2009). In addition to histone modifications, lineage-determining transcription factors bind at a distinct set of enhancers to drive cell type-specific gene expression. Therefore, identifying the β cell-specific patterns of histone modifications and transcription factor occupancy is critical for appreciating the underlying gene regulatory network that converts stem cells into β cells (Spitz and Furlong, 2012),  Some studies have examined histone marks and the genome-wide occupancy of transcription factors in adult human and mouse islets or β cells (Bhandare et al., 2010; Gaulton et al., 2010; Hoffman et al., 2010; Pasquali et al., 2014). These data sets unveil complex transcriptional regulation networks within the adult β cells. In contrast, genome-wide studies of epigenetic modifications on developing islets or β cells are lacking. The difficulty perhaps lies in the scarcity of human islets from young cadaveric donors, and in the limited material from embryonic and perinatal mouse islet tissues. These obstacles prevent us from appreciating the dynamic changes of histone modifications throughout β cell development. The use of iPSCs has proven fruitful to unveil the epigenomic processes during pancreatic differentiation (Wang et al., 2015; Xie et al., 2013), and likely becomes instrumental for studying human β cell development in the future.  Most studies on histone acetylation in β cells focused on insulin promoters. Histone acetylation at insulin promoters is associated with the recruitment of E1A-assoicated protein p300, CBP, p300/CBP-associated factor (PCAF) or Gcn5 (Chakrabarti et al., 2003; Sampley and Özcan, 2012). However, it is unclear whether these proteins play a causal role in activating insulin gene expression by acetylating histones. Deleting PCAF in mouse β cells impairs the unfolded protein response (Rabhi et al., 2016), but whether PCAF deletion affects β cell histone acetylation is not known. Histone deacetylase 3 (HDAC3) exerts anti-apoptotic effects on β cells (Chou et al., 2012; Dahllöf et al., 2015;  22  Larsen et al., 2007; Meier and Wagner, 2014). Deleting β cell HDAC3 in vivo enhances basal insulin secretion (Remsberg et al., 2017). The most well-studied members of the HDAC family are sirtuins, and many in vitro studies have examined the role of sirtuins in β cells (Bordone et al., 2006; Caton et al., 2013; Luu et al., 2013; Wu et al., 2012). In contrast, in vivo studies on the role of sirtuins in β cells are lacking. It remains unclear how sirtuins may modulate β cell chromatin (Pinho et al., 2015; Xiong et al., 2016). Overall, more studies are needed to determine the role of histone acetylation in β cell development and function.  Histone methylation is critical for repressing undesired genes in β cells. Enhancer of zeste homolog 2 (Ezh2) is the subunit of PRC2 that methylates H3K27 (Czermin et al., 2002). In mouse β cells, Ezh2 maintains β cell proliferation in part by methylating H3K27 at the loci of CDK inhibitors p16INK4a and p19ARF (Chen et al., 2009). Furthermore, decreased binding of the PRC1 subunit B lymphoma Mo-MLV insertion region 1 homolog (Bmi1) and increased binding of the trithorax complex H3K4 methyltransferase mixed-lineage leukaemia 1 (MLL1) at the p16INK4a and p19ARF loci have been associated with reduced β cell proliferation (Dhawan et al., 2009). Being a regulatory subunit of PRC2, embryonic ectoderm development (Eed) is critical for silencing disallowed loci in β cells (Lu et al., 2018). In human, modulating histone methylation can reprogram human β cells into α cells directly (Bramswig et al., 2013). Although studies have explored the role of histone methylation in β cells, how histone modifications and histone-modifying enzymes mediate β cell function and development in general remains largely unclear.  Apart from histone modifications, studies have catalogued the occupancy of β cell transcription factors in mouse islet or insulinoma genomes. These transcription factors include Pdx1 (Gao et al., 2014; Tennant et al., 2013), Nkx6.1 (Taylor et al., 2013), Nkx2.2 (Gutiérrez et al., 2017), Pax6 (Swisa et al., 2017), Rfx6 (Piccand et al., 2014), NeuroD1/MafA/Foxa2 (Tennant et al., 2013), and Insm1 (Jia et al., 2015). Studies have also explored the transcription factor occupancy in human β cells (Pasquali et al., 2014).  23  These data sets allow the identification of islet/β cell-specific cis-regulatory elements and the target genes these regulatory elements control.  The mammalian insulin promoter serves as a prototypical model for investigating how combinatorial transcription factors regulate β cell gene expression (Ohneda et al., 2000). The coordinated binding of Pdx1, NeuroD1 and MafA at the insulin promoter synergistically activates insulin transcription in part by recruiting coactivators such as p300 (Babu et al., 2008; Qiu et al., 2002). In fact, each of these transcription factors activates a number of β cell genes to maintain a stable β cell identity (Gao et al., 2014; Gu et al., 2010; Matsuoka et al., 2007). Furthermore, these transcription factors likely function together by co-occupying a common set of β cell loci (Pasquali et al., 2014). These transcription factors likely require coactivators to function properly. Studying how coactivators modulate transcription in β cells may identify novel targets that maintain β cell development and function (Mouchiroud et al., 2014).  1.4 p300/CBP Transcriptional Coactivators Transcription factors bind to specific DNA sequences, or sequence motifs, within cis-regulatory elements to regulate transcriptions. These proteins can be classified based on their regulatory functions. Transcriptional activators promote the recruitment of RNA polymerases for gene activation, whereas repressors inhibit RNA polymerase recruitment and gene expression (Lee and Young, 2000). Frequently, transcription factors can recruit additional proteins called transcription cofactors. Transcription cofactors can directly bind to transcription factors, although most cofactors cannot bind to DNA directly. Similar to transcription factors, cofactors can also be classified by their functions. Coactivators enhance gene expression, whereas corepressors help silence gene expression. In addition, cofactors can be classified by their molecular functions. The two main classes of cofactors are histone modifiers and ATP-dependent chromatin modifiers (Kingston and Narlikar, 1999). New studies continue to expand the functional definitions of cofactors. For example, some cofactors can function as both a coactivator and a corepressor (Gurevich et al., 2007). Many histone modifiers can modify a wide  24  spectrum of non-histone proteins beyond histones (Chen and Zhu, 2016). Overall, the unique molecular functions of cofactors provide transcription factors with additional control on transcription.  Some transcriptional coactivators possess lysine acetyltransferase (KAT) activity. KATs catalyze lysine acetylation by transferring the acetyl group from acetyl coenzyme A (CoA) onto the lysine residues of histones and non-histone proteins (Roth et al., 2001). There are three main families of KATs represented in mammals: the MOZ, Ybf2, Sas2 and Tip60 (MYST) family, the Gcn5-related N-acetyltransferase (GNAT) family, and the p300 and CBP (p300/CBP) family. Some coactivators outside of these families also possess KAT activity, such as NCOA1, NCOA3, and CLOCK (Chen et al., 1997; Doi et al., 2006; Spencer et al., 1997), although they are typically known for their role as nuclear receptor coactivators or circadian regulators.  All members of the MYST, GNAT and p300/CBP KAT families can acetylate multiple core histone units. However, only p300/CBP can acetylate all four core histone units (McManus and Hendzel, 2003). Structurally, MYST family proteins contain chromodomains that can bind to methyl-lysine residues, whereas GNAT and p300/CBP family proteins contain bromodomains that can bind to acetyl-lysine residues (Bottomley, 2004). Because of these unique structures and substrate preferences, each KAT family is responsible for acetylating distinct protein targets.  p300 and CBP are the only members in the p300/CBP KAT family. These proteins exhibit highly similar structures based on their amino acid sequences. For example, human p300/CBP share 58% and 68% amino acid sequence identity and similarity respectively (Roth et al., 2001). p300/CBP may have evolved from the structural ortholog Rtt109 in fungi (Marmorstein and Trievel, 2009; Wang et al., 2008a). Both Drosophila melanogaster and Caenorhabditis elegans possess only one CBP ortholog, whereas the CBP gene diverges into p300/CBP in vertebrates (Yuan and Marmorstein,  25  2013). The increase in p300/CBP dosage in vertebrates may offer additional layers of transcriptional regulation during development.  1.4.1 Molecular Function of p300/CBP p300/CBP coactivate gene expression primarily through three mechanisms: by interacting with transcription factors as an adaptor molecule, by acetylating histone and non-histone substrates, and by recruiting transcriptional machinery to target loci (Boija et al., 2017; Goodman and Smolik, 2000). The various domains of p300/CBP contribute to these functions (Figure 3).    Figure 3. The structural domains in human p300 proteins.  The blue parts illustrate the catalytic domain of p300. The red parts highlight the domains responsible for the non-catalytic functions of p300. The nuclear localization sequence (NLS) allows p300 to be translocated into the nucleus for histone acetylation. Each of the CH1, KIX, CH3 and IBiD domains allows p300 to interact with different transcription factors.  p300/CBP depend on four domains to interact with transcription factors: cysteine/histidine-rich domain 1 (CH1), kinase-inducible domain-interacting domain (KIX), CH3, and interferon regulating transcription factor 3-binding domain (IBiD). Each of these domains binds to a different repertoire of transcription factors. For example, hypoxia-inducible factor 1-α (HIF-1α) can bind to CH1 (Kasper et al., 2005). CREB can bind to the KIX domain (Kasper et al., 2002). CBP/p300 interacting transactivator with glutamic acid/aspartic acid rich carboxy-terminal domain 2 (CITED2) can bind to CH3 to repress the HIF-1α pathway (Freedman et al., 2003). Interferon regulatory transcription factor 3 (IRF3) can bind to IBiD (Lin et al., 2001). Interestingly, p53 can bind to CH1, KIX, CH3 and IBiD simultaneously (Teufel et al., 2007). Through these domains,  26  p300/CBP can bind to at least four hundred different transcription factors (Bedford et al., 2010).  To acetylate lysine residues, the p300/CBP catalytic core binds to the target protein and to acetyl CoA. The catalytic core then transfers the acetyl group from the bound acetyl CoA to the protein substrate. The p300/CBP catalytic core contains three domains: a bromodomain, a plant homeodomain (PHD) finger, and a KAT domain (Thompson et al., 2004). In human p300, the catalytic core spans between the amino acid residues 1195 and 1673. The bromodomain in p300/CBP can bind to acetyl-lysine residues preferentially over non-acetylated lysine residues, and even target certain acetyl-lysine residues over others (Filippakopoulos et al., 2012). The plant homeodomain (PHD) finger in p300/CBP is structurally interrupted by a really interesting gene (RING) domain that inhibits the p300/CBP KAT domain (Delvecchio et al., 2013). The PHD finger can bind to nucleosomes (Ragvin et al., 2004), but appears to be dispensable for the acetylation of non-histone targets (Rack et al., 2015). The p300/CBP KAT domain harbours an auto-inhibitory loop that inhibits p300/CBP KAT activity. Through the unique structure of their KAT domains, p300/CBP catalyze the acetylation reaction on their ubiquitous substrates by a Theorell-Chance mechanism, also known as a “hit-and-run” mechanism (Liu et al., 2008). The mechanism is facilitated by the relatively weak binding affinity of p300/CBP for lysine residues within its target peptides, which allows the peptides to slide through the enzyme such that the acetylation reaction proceeds rapidly. Therefore, the acetylation mechanism of p300/CBP is significantly different from the acetylation mechanism of other KAT families.  Because of their unique acetylation mechanism, p300/CBP have broad substrate specificity for acetylation (Liu et al., 2008). In fact, a recent proteomic study has identified thousands of lysine sites that can be acetylated by p300/CBP (Weinert et al., 2018). p300/CBP are able to acetylate all core histones at multiple lysine residues (Ogryzko et al., 1996); such ability is crucial for establishing euchromatin across the genome (Yuan and Gambee, 2001; Yuan and Marmorstein, 2013; Zheng et al., 2013).  27  In addition to broad histone acetylation, p300/CBP are indispensable for the acetylation of H3K18 and H3K27 (Jin et al., 2011), which mark active promoters and enhancers for gene activation (Bose et al., 2017). Indeed, a landmark study has used a dCas9-p300 KAT fusion protein to show that directed p300-dependent H3K27 acetylation is sufficient to activate gene expression (Hilton et al., 2015). In summary, p300/CBP-mediated histone acetylation is instrumental in gene coactivation.  In addition to histones, transcription factors can be acetylated by p300/CBP. The acetylation of transcription factors can exert differential effects on their activity. For example, p300-mediated acetylation partially increases p53 transcriptional activity by altering its conformation (Gu and Roeder, 1997; Gu et al., 1997). In contrast, p300/CBP-mediated acetylation suppresses the activity of forkhead box O (FoxO) transcription factors (Fukuoka et al., 2003), perhaps by reducing their DNA binding affinity (Matsuzaki et al., 2005). p300-mediated acetylation stabilizes estrogen receptor α (Kim et al., 2010). Furthermore, p300 acetylates corepressor E1A C-terminal binding protein 2 (CtBP2) and promotes the translocation of CtBP2 to the nucleus (Zhao et al., 2006). These acetylation processes can be reversed by HDACs. Sirtuin 1 and HDAC1 appear to be the primary antagonists of p300-mediated acetylation (Li et al., 2014; Zaini et al., 2018). Overall, non-histone acetylation by p300/CBP is also an important mechanism of transcriptional regulation.  Studies continue to identify novel functions of p300/CBP beyond protein acetylation. For example, the N-terminal of p300/CBP possesses ubiquitin ligase activity that can promote p53 degradation (Grossman et al., 2003; Shi et al., 2009). In addition, p300 can catalyze various lysine acylation reactions, including crotonylation, propionylation, and 2-hydroxyisobutyrylation (Huang et al., 2018; Liu et al., 2009; Sabari et al., 2015). These additional catalytic activities of p300/CBP augment their transcription coactivation potential and allow the proteins to function beyond transcription. The in vivo relevance of these novel functions merit further investigation.   28  The catalytic activity of p300/CBP is subject to both auto-regulation and regulation by external factors at the post-translational level. The auto-inhibitory loop of p300/CBP sterically blocks the KAT domain and reduces its activity (Thompson et al., 2004). The loop contains multiple lysine residues, which can be auto-acetylated by p300/CBP (Karanam et al., 2006). Apart from its effects on the auto-inhibitory loop, p300/CBP auto-acetylation can alter the KAT domain structure (Arif et al., 2007), facilitate the KAT reaction (Stiehl et al., 2007), and stabilize the protein (Jain et al., 2012). Thus far, the only HDAC found to directly deacetylate the p300 KAT domain is sirtuin 2; the deacetylation appears to improve the binding of p300 to transcriptional machinery (Black et al., 2008).  In addition to acetylation, protein kinases from different signalling pathways can phosphorylate p300/CBP to modulate gene expression. Protein kinase C and adenosine monophosphate kinase can phosphorylate serine 89 of p300; the phosphorylation represses p300 activity and impairs the interaction between p300 and nuclear receptors (Yang et al., 2001; Yuan and Gambee, 2000). Protein kinase B phosphorylates p300 at S1834 to disrupt the interaction between p300 and CAAT/enhancer-binding protein β (Guo et al., 2004; Huang and Chen, 2005). Although most phosphorylation events inhibit the activity of p300/CBP, there are also exceptions. mTOR1 phosphorylates p300 at multiple serine residues to enhance its KAT activity sterically (Wan et al., 2017). Epidermal growth factor can activate Erk signaling which phosphorylates S2279, S2315, and S2366 of p300 (Chen et al., 2007). These phosphorylation events enhance the KAT activity of p300 and the interaction of p300 with specificity protein 1. Phosphorylation of CBP S436 enhances the recruitment of CBP by CREB in a growth factor-dependent manner (Zanger et al., 2001), whereas p300 lacks an analogous serine site for phosphorylation. In addition to phosphorylation, studies have implicated other modifications such as methylation, sumoylation, and ubiquitylation in the negative regulation of p300/CBP functions (Chevillard-Briet et al., 2002; Kuo et al., 2005; Lee et al., 2005; Zou and Mallampalli, 2014). Overall, post-translational modifications are crucial for controlling p300/CBP functions.  29  1.4.2 Cellular Function of p300/CBP p300/CBP are ubiquitously expressed because they are required for fundamental cellular functions. Generally, p300/CBP expression is important for cellular proliferation (Kawasaki et al., 1998; Liu et al., 2015; Sandberg et al., 2005). p300/CBP are critical for G1/S phase transition (Ait-Si-Ali et al., 2000). They prevent cells in the G1 phase from entering the S phase prematurely by inhibiting the phosphorylation of retinoblastoma protein and repressing c-myc expression (Baluchamy et al., 2003; Iyer et al., 2007; Kolli et al., 2001). In addition to guarding the G1/S checkpoint, p300/CBP facilitate the apoptosis responses induced by DNA damage through acetylating p53 (Gu and Roeder, 1997), and by stabilizing p53 (Yuan et al., 1999). p300, but not CBP, is required for the retinoic acid-mediated differentiation of embryonal carcinoma F9 cells (Kawasaki et al., 1998). Although these studies have demonstrated a key role of p300/CBP in proliferation, apoptosis, and differentiation, how p300/CBP contribute to these cellular processes in vivo is less clear.  The first transgenic mouse study on p300/CBP has identified an essential role of p300/CBP in mammalian embryonic development (Yao et al., 1998). Germ line loss of all p300 in mice caused embryonic arrest between E9 and E11.5 due to severe developmental defects. Almost half of the p300 heterozygotes also died in utero, although the lethality depended on the mouse strain background. Interestingly, p300/CBP double heterozygotes showed embryonic lethality identical to the p300 homozygotes. These data conclude that normal embryonic development requires sufficient p300/CBP dosage, and that some levels of redundancy between p300 and CBP exist. Whether each tissue requires different dosage of p300/CBP for development, and whether p300/CBP function differently in each tissue, remain unanswered.  Other studies have developed models to understand the consequences of inactivating the specific domains of p300/CBP in vivo. A study has generated mice that harbour three point mutations, p.Y650A, p.A654Q and p.Y658A, in the KIX domain of p300 or  30  CBP (Kasper et al., 2002). Mice homozygous for the p300 KIX mutant exhibited defective hematopoiesis phenotypes, including anemia, B cell deficiency, thymic hypoplasia, megakaryocytosis, and thrombocytosis. In contrast, mice homozygous for the CBP KIX mutant exhibited normal hematopoiesis. This is one of the first in vivo studies that identifies a functional difference between p300 and CBP and specifies a critical role of p300 KIX domains in hematopoiesis. Another study introduced inactivating mutations p.W1466A and p.Y1467S into the p300 KAT domain, and p.W1503A and p.Y1504S into the CBP KAT domain (Roth et al., 2003). Interestingly, Mice heterozygous for the p300 KAT mutations died in utero between E12.5 and E16.5, whereas mice heterozygous for the CBP KAT mutations died shortly after birth. These data suggest that the p300 and CBP KAT activity are partially but not entirely fungible and could be differentially required during mouse embryonic development.  To gain further insights into how p300/CBP operate in each tissue, studies have taken full advantage of the Cre-loxP system (Orban et al., 1992), which allows an unprecedented control of tissue-specific genetic recombination in mouse models. The use of Cre-loxP system allows researchers to bypass the lethality of germ line p300 homozygotes, enabling the interrogation of p300/CBP functions in different tissues without cofounding pathogenesis elsewhere in the animal. Both p300 floxed and CBP floxed mice were first generated for studying the role of p300/CBP in T cell development (Kang-Decker et al., 2004; Kasper et al., 2006). Thus far, few studies have investigated the tissue-specific role of p300/CBP using these floxed mice. Expression of p300 at certain sub-regions of the forebrain is required for long-term memory formation (Oliveira et al., 2011). In rod and cone cells, p300/CBP maintain their nucleosome structures (Hennig et al., 2013). p300/CBP regulate the metabolic remodeling during sperm differentiation (Boussouar et al., 2014). Surprisingly, despite the well-established interaction between myogenic differentiation (MyoD) and p300 in myogenesis in vitro (Sartorelli et al., 1997), mice lacking p300 in skeletal muscle did not exhibit any apparent muscle phenotypes (LaBarge et al., 2016). Unexpectedly, many of these in vivo models of tissue-specific p300 loss did not exhibit impaired proliferation or  31  exacerbated apoptosis, which might have been expected based on the role of p300 in vitro. These studies raise the questions of why tissues appear to tolerate p300/CBP loss to different extents, how p300/CBP may regulate distinct functions in each cell type, and how p300/CBP may operate in other tissues that have yet been studied.  1.4.3 p300/CBP in β Cells Initially, studies on p300/CBP in β cells have focused on the identification of β cell transcription factors that interact with p300/CBP. One of the first studies reveals the recruitment of p300 by NeuroD1 for insulin gene coactivation (Qiu et al., 1998). A follow-up study has identified Pdx1 and E47 as additional factors that recruit p300 for insulin gene coactivation (Qiu et al., 2002). In addition, p300 interacts with Kruppel like factor 11 (KLF11) to regulate Pdx1 activity (Fernandez-Zapico et al., 2009). Notably, these transcription factors have been implicated in MODY. In fact, other transcription factors implicated in MODY such as Hnf1α (Ban et al., 2002), hepatocyte nuclear factor 1B (Hnf1β) (Barbacci et al., 2004), and hepatocyte nuclear factor 4A (Hnf4α) (Eeckhoute et al., 2004), can also recruit p300/CBP as coactivators. Most importantly, mutations found within the p300/CBP-interacting domain of Pdx1 (Hani et al., 1999), NeuroD1 (Malecki et al., 1999), Hnf1β (Barbacci et al., 2004), and Hnf4α (Eeckhoute et al., 2004), can cause MODY. These findings indicate that numerous β cell transcription factors may depend on p300/CBP for transcriptional coactivation.   Although studies have implicated that a number of β cell transcription factors may depend on p300/CBP for coactivation, the significance of these interactions in β cells in vivo is unclear. Most studies on p300/CBP in β cells are limited to in vitro investigation. High concentrations of glucose stimulate Pdx1 to recruit p300 to the insulin promoter, resulting in H4 hyperacetylation (Mosley et al., 2004). One of the only studies on p300/CBP in β cells in vivo has demonstrated that mice harbouring the CBP S436A mutant, which is unresponsive to insulin, exhibited increased β cell proliferation but impaired islet glucose-stimulated insulin secretion (Hussain et al., 2006). Interestingly, most of the subsequent studies on p300/CBP in β cells focused on thioredoxin- 32  interacting protein (Txnip), which is associated with glucolipotoxicity and may have therapeutic potential (Chen et al., 2008). Under high glucose condition, carbohydrate response element binding protein (ChREBP) binds to and recruits p300 to the Txnip promoter in β cells (Cha-Molstad et al., 2009). These events are associated with H4 hyperacetylation and the recruitment of RNA polymerase II. Deleting p300 in an INS-1 cell line enhanced glucose-stimulated insulin secretion, impaired histone acetylation at the Txnip promoter, and reduced Txnip expression (Bompada et al., 2016). A recent study has shown that human islets stimulated by high glucose or inflammatory cytokines can induce the proteasomal degradation of p300 in β cells (Ruiz et al., 2018). However, the reduction in p300 levels was very modest in type 2 diabetic islets. Overall, these studies provide limited insights on the role of p300/CBP in β cells in vivo.  1.4.4 p300/CBP in Human Diseases Early studies have identified that the tumor suppressor p53 can recruit p300/CBP (Lill et al., 1997). Consequently, studies to date on p300/CBP in human diseases have largely focused on cancer. In addition to the role of p300/CBP in p53 biology, studies have implicated p300/CBP in the formation of chimeric enzymes in leukaemia. Monocytic leukemia zine finger (MOZ) can fuse with CBP in acute myeloid leukaemia (Borrow et al., 1996), and with p300 in acute monocytic leukemia (Chaffanet et al., 2000). The p300/CBP-MOZ fusion protein contains the DNA-binding domain from MOZ and the KAT domain from p300/CBP. Apart from MOZ, the lysine methyltransferase MLL has also been shown to fuse with p300/CBP in acute myeloid leukaemia (Ohnishi et al., 2008; Sobulo et al., 1997). How these chimeric proteins may contribute to leukaemia pathogenesis is not clear. Currently, p300/CBP are classified as tumor suppressors due to their role in maintaining proper G1/S phase transition. This is evident from the discovery of truncating EP300 and CREBBP mutations in epithelial cancers (Gayther et al., 2000; Ozdağ et al., 2002). In contrast, some subtypes of cancer, such as castration-resistant prostate cancer and lung adenocarcinoma, depend on p300/CBP for tumour growth (Lasko et al., 2017; Tang et al., 2016). These studies justify targeting p300/CBP as a potential therapeutic approach in cancer.  33   Human subjects with germline mutations in EP300 (which encodes p300) or CREBBP (which encodes CBP) have provided important insights on the consequences of disrupted p300/CBP functions in humans. Mutations in EP300 or CREBBP cause Rubinstein-Taybi syndrome (RTS) (Petrij et al., 1995; Roelfsema et al., 2005), a rare autosomal dominant disease that was named after Jack Rubinstein and Hooshang Taybi (Rubinstein and Taybi, 1963). Individuals with RTS often present with intellectual disability, growth retardation, microcephaly, broad thumbs and facial dysmorphism. Most RTS cases are caused by a de novo mutation.  An interesting RTS phenotype that has been largely forgotten is defective glucose metabolism. In his review article, Rubinstein described that glucose dysregulation is relevant to RTS (Rubinstein, 1990). Four case reports published between the 1960s to 1970s described a total of eight RTS patients who developed defective glucose metabolism (Table 1). The first study reported a cohort of five RTS patients (Bartok, 1968). Three developed “frank diabetes”, and the remaining two were glucose intolerant upon cortisone administration. However, this study did not document the age at diagnosis of the glucose phenotypes. The second study reported a 26-year-old male with RTS who had impaired glucose tolerance (Rohlfing et al., 1971). The third study described a six-year-old female with RTS who also had impaired glucose tolerance (Völcker and Haase, 1975). The fourth study reported a male newborn who had RTS and transient hyperinsulinemic hypoglycemia (Wyatt, 1990). Because the onset of the glucose phenotypes was early in a number of these cases, these phenotypes may have a genetic origin similar to MODY, perhaps related to the genetic cause of RTS.        34  Table 1. A summary of patients with Rubinstein-Taybi syndrome and glucose dysregulation reported in the literature.    Since the discovery of the causal genes for RTS, no study has reported RTS patients with early-onset glucose dysregulation. A questionnaire-based study on 61 adult RTS cases, with an average age of 28.5 years old, has reported that 5% of the interviewed cases had diabetes and the other 5% had hypoglycemia (Stevens et al., 2011). The percentage of RTS cases with diabetes appears higher than the global prevalence of diabetes among the age-matched non-RTS individuals, which is approximately 2% as of 2018 (Cho et al., 2018). However, a number of these RTS cases, together with cases reported by other studies (Milani et al., 2015; Panigrahi et al., 2012), were overweight or obese, which can confound the comparison. The underlying mechanisms by which RTS may increase the risk of metabolic diseases, such as obesity and diabetes, warrant further investigation.  1.5 Thesis Objectives We identified a patient with RTS, who has a microdeletion spanning one of her EP300 genes that likely causes p300 haploinsufficiency. She presented with metabolic phenotypes, including hyperphagia and type 2 diabetes. Her diabetes condition was unusual, considering that 1) she was non-obese and physically active; 2) she was diagnosed with diabetes at 23 years of age, which fits the classification of MODY; 3) her diabetes was non-insulin-dependent and well-managed by metformin and the DPPIV inhibitor sitagliptin for more than 15 years; and 4) there have been reports on RTS patients who developed early-onset glucose dysregulation, including diabetes. Thus far,  35  the glucose phenotypes of RTS have not been associated with EP300 or CREBBP mutations. Identifying additional patients with EP300 mutations and examining their glucose phenotypes may help establish such an association.   The unusual phenotypes of the patient prompted us to investigate the role of p300 in β cells. Notably, no study yet has directly addressed the role of p300/CBP in β cells in vivo. It is not clear whether and how p300/CBP maintain islet and β cell development and function in vivo. The molecular targets of p300/CBP in β cells in vivo have not been determined. Furthermore, how histone acetylation, and particularly p300/CBP-mediated H3K27 acetylation, contributes to β cell development and function has been unclear. The use of mouse models with genetically inactivated p300 and CBP in islets and β cells are crucial to modelling these phenotypes and to answering these questions.   In this thesis, my overall hypothesis is that expression of p300 in β cells is required to maintain glucose homeostasis. My overall objective is to comprehensively determine the role of p300, and also CBP in some models, in β cell development and function. To achieve this, I have three primary objectives:  1) to determine the role of p300 in pancreatic islet development, as in Chapter 3, 2) to determine the role of p300 in β cell development, as in Chapter 4, and 3) to determine the role of p300 in postnatal β cells, as in Chapter 5.  These studies will clarify whether p300 is required in β cells to maintain glucose homeostasis, and how p300 functions at the cellular and molecular level. In addition, these studies will provide important insights on the mechanisms that may underlie the glucose phenotypes in the patient.   36  Chapter 2: Methods and Materials 2.1 Animals All mice used in this thesis were on the C57/BL6J background. Mice were housed under a 12-hour day-night cycle with ad libitum access to water and standard chow (Teklad 2918; Envigo, UK). Timed matings were set up to study mice at E15.5, E18.5, postnatal day 0 (P0), P6 and P7; the morning when a vaginal plug was found on the dam was designated as E0.5. The mouse lines used in this thesis include: Neurog3-Cre (obtained from Dr. Francis Lynn) (Schonhoff et al., 2004), Ep300fl/fl (a gift from Dr. Paul Brindle) (Kasper et al., 2006), Crebbpfl/fl (Jackson Laboratory, ME, USA) (Kang-Decker et al., 2004), mTmG (obtained from Dr. Francis Lynn) (Muzumdar et al., 2007), Ins1-Cre (Jackson Laboratory) (Thorens et al., 2015), and Pdx1-CreER (obtained from Dr. Francis Lynn) (Gu et al., 2003). These mice were crossed to generate the following experimental mice: Neurog3-Cre, Neurog3-Cre; Ep300fl/wt, Ep300fl/fl, Neurog3-Cre; Ep300fl/fl, Neurog3-Cre; Crebbpfl/fl, Neurog3-Cre; Crebbpfl/wt; Ep300fl/fl, Neurog3-Cre; Crebbpfl/fl; Ep300fl/wt and Neurog3-Cre; Crebbpfl/fl; Ep300fl/fl were used in Chapter 3. Ins1-Cre, Ins1-Cre; Ep300fl/wt, Ins1-Cre; Ep300fl/fl, Ins1-Cre; Crebbpfl/fl and Ins1-Cre; Ep300fl/fl; Crebbpfl/fl were used in Chapter 4. Pdx1-CreER and Pdx1-CreER; Ep300fl/fl were used in Chapter 5. For all Ins1-Cre and Pdx1-CreER studies that required FACS experiments, the mice were also heterozygous or homozygous for mTmG transgenes. Cre-negative flox-positive littermates were used as controls unless otherwise specified. The loxP alleles of Ep300fl/fl and Crebbpfl/fl flank the exon 9 of Ep300 and Crebbp genes respectively. Recombination of the loxP alleles produces a premature stop codon in EP300 or CREBBP immediately downstream of its exon 9 and results in a null mutation.  2.2 Cell Culture Passage 28 – 33 mouse insulinoma 6 (MIN6) cells were a gift from Dr. Francis Lynn (Miyazaki et al., 1990). Passage 8 – 11 human embryonic kidney (HEK) 293 cells were obtained from American Type Culture Collection (VA, USA). Both MIN6 and HEK293 cells were maintained in high glucose Dulbecco’s Modified Eagle Medium (DMEM) (Thermo Fisher Scientific, MA, USA) supplemented with 10% heat inactivated fetal  37  bovine serum (FBS) (Thermo Fisher Scientific) and 100 U/mL penicillin/streptomycin (Thermo Fisher Scientific). When the cells reached 70% confluency, they were passaged using 0.25% trypsin-ethylenediaminetetraacetic acid (EDTA) (Thermo Fisher Scientific). The control iPSC line 12 was obtained from Takara (Japan). The iPSCs of proband 1 were reprogrammed from the patient’s peripheral blood mononuclear cells as described before (Krentz et al., 2017), using the CytoTune-iPSC 2.0 Sendai Reprogramming Kit (Thermo Fisher Scientific) per manufacturer’s instruction. More than 98% of the control and proband iPSCs were positive for stage-specific embryonic antigen 4 (SSEA4) and podocalyxin (TRA-1-60) as assessed by flow cytometry. Both iPSC lines showed normal karyotypes of chromosomes 22.  2.3 Human Genetic Studies The clinical grade chromosomal microarray was performed on the blood sample of Proband 1 at BC Children’s Hospital (Vancouver, BC, Canada). The microdeletion in proband 1 was validated by fluorescence in situ hybridization. The rare variant in Proband 2 was detected by research grade whole exome sequencing at SickKids Genome Diagnostic Lab (Toronto, ON, Canada). Proband 3 was diagnosed by whole exome sequencing at Great Ormond Street Hospital for Children (London, UK). Proband 4 was diagnosed by whole exome sequencing under the Canadian Clinical Assessment of the Utility of Sequencing and Evaluation as a Service (CAUSES) study (Vancouver, BC, Canada). The EP300 variants were confirmed de novo in probands 1, 2, 3 and 4 and absent from their parents by Sanger sequencing. The pathogenicity of the variants was tested by SIFT and PolyPhen 2 in Ensembl (Adzhubei et al., 2013; Ng and Henikoff, 2003). For examining any potential variants in genes implicated in MODY or hyperinsulinemic hypoglycemia, the blood sample of Proband 1 was submitted to Exeter Laboratory (UK) for screening a panel of 22 MODY genes using targeted next generation sequencing. These 22 genes include GCK, HNF1A, HNF4A, HNF1B, NEUROD1, INS, INSR, KCNJ11, ABCC8, PDX1, CEL, PAX6, GATA6, TRMT10A, WFS1, ZFP57, PCBD1, LMNA, PPARG, PLIN1, POLD1 and MIDD. The exome data of proband 2, 3 and 4 were examined to rule out the presence of variants in these MODY  38  genes or in genes implicated in hyperinsulinemic hypoglycemia. The other four probands who carry EP300 mutations but do not have any apparent glucose phenotypes were from the CAUSES study.  2.4 5-Ethynyl-2’-Deoxyuridine Labeling In vivo 5-ethynyl-2’-deoxyuridine (EdU) labeling for quantification of β cell proliferation was performed as previously described (Teta et al., 2007). P6 pups were injected intraperitoneally with EdU (100 mg/kg; Thermo Fisher Scientific) in phosphate-buffered saline (PBS) and sacrificed after 24 hours. Pancreata were extracted and fixed for histology experiments.  2.5 Tamoxifen Treatment To induce recombination, six-week-old male mice were gavaged with an 8-mg dose of tamoxifen (Sigma-Aldrich, MS, USA) dissolved in 100 µL of corn oil every other day for a total of three doses.  2.6 Metabolic Phenotyping 2.6.1 Intraperitoneal Glucose Tolerance Test Mice were fasted for five hours from 10 a.m. to 3 p.m. prior to glucose injection. The mice were then injected intraperitoneally with glucose (2 g/kg; Sigma-Aldrich) in PBS. Blood glucose levels were measured immediately before and 15, 30, 60 and 90 minutes after glucose injection using a Onetouch UltraMini glucometer (Johnson & Johnson, NJ, USA).  2.6.2 Measurement of In vivo Insulin Secretion Mice were fasted for five hours followed by intraperitoneal glucose injection. Blood samples were collected immediately before and 10 minutes after glucose injection by puncturing the saphenous vein and collecting the blood with heparinized capillary tubes (Thermo Fisher Scientific). The blood sample collected per time point was no more than 75 µL. The samples were kept on ice until ready to be processed. Blood samples were  39  spun at 2,000 g for 15 minutes, and the top phase of each sample was recovered and frozen at -80 oC.  2.6.3 Insulin Tolerance Test Mice were fasted for five hours from 10 a.m. to 3 p.m. prior to insulin injection. The mice were then injected intraperitoneally with Humulin R (0.7 unit/kg; Eli Lily, IN, USA) in PBS. Blood glucose levels were measured immediately before and 15, 30, 60 and 90 minutes after insulin injection.  2.6.4 Body Composition Analysis and Metabolic Cages Conscious mice were restrained and placed in an EchoMRI-100 (EchoMRI, TX, USA) for non-invasive measurement of body composition including whole body lean mass and fat mass. For metabolic cage experiments, mice were singly-housed in the TSE LabMaster metabolic cage system (TSE, Germany) for measuring metabolic parameters including energy expenditure, respiratory exchange ratios, food intake, water intake and locomotor activity. Mice were allowed to acclimatize in the metabolic cages for 24 hours prior to data collection. Data were then collected for 48 to 72 hours for analysis.  2.7 Enzyme-Linked Immunosorbent Assay All enzyme-linked immunosorbent assay (ELISA) experiments were performed per manufacturer’s instruction. ELISA kits used include STELLUX Chemiluminescent Rodent Insulin ELISA (for insulin; ALPCO, NH, USA), Mercodia Glucagon ELISA (for glucagon; Mercodia, Sweden), Somatostatin EIA kit (for somatostatin; Phoenix Pharmaceuticals, CA, USA) and Multi Species GLP-1 Total ELISA (for total GLP1; Merck Millipore, MA, USA).  2.8 Pancreas Dissection and Dispersion For E15.5 and E18.5 embryonic pancreata, embryos were removed from the uterus of euthanized dams, decapitated and dissected to recover their pancreata. For P0 and P7  40  pups, the pups were anesthetized with isoflurane, euthanized by decapitation and their pancreata were recovered. To disperse P7 pancreatic cells for FACS, minced pancreata were incubated in collagenase XI (1 mg/mL; Sigma-Aldrich) in PBS at 37 oC with shaking at 1,100 rpm for 25 minutes. For adult pancreas histology studies, the mice were anesthetized with isoflurane and euthanized by carbon dioxide. Cardiac perfusion was performed on the carcasses with PBS followed by 10% formalin. The pancreata were then removed for fixation.   2.9 Islet Isolation and Dispersion Mouse pancreatic islets were isolated as described previously (Li et al., 2009). Mice were anesthetized with isoflurane and euthanized by cervical dislocation. The common bile duct was injected with 3 mL collagenase XI (1 mg/mL) in Hank’s Balanced Salt Solution (HBSS) supplemented with 1 mM CaCl2 to distend the pancreas. The pancreas was then transferred into 2 mL collagenase XI solution on ice. After each pancreas was processed, digestion was initiated by incubating the samples at 37 oC for 15 minutes. The pancreata were then shaken manually for 5 minutes to further digest the exocrine tissues. The reactions were quenched with 15 mL cold HBSS without calcium and then spun at 300 g for 1 minute to remove digested exocrine tissues. Each pellet, which contains pancreatic islets, was washed twice with HBSS and then passed through a 70-µm cell strainer to recover islets and to remove smaller exocrine fragments. Islets captured on the cell strainer were gently flushed into a 10-cm plate with 10 mL of pre-warmed RPMI 1640 medium supplemented with 10% FBS and 100 U/mL penicillin/streptomycin. The islets were allowed to recover overnight in a tissue culture incubator at 37 oC and 5% CO2. For islet dispersion, islets were washed twice in PBS. TrypLE (Thermo Fisher Scientific) was then added to the islets, and the islets were incubated at 37 oC for 5 minutes. After the incubation, the islets were triturated into single cell suspension. The dispersion was quenched with 800 µL of prewarmed Roswell Park Memorial Institute (RPMI) medium and spun at 300 g for 5 minutes. The cell pellet was resuspended in an appropriate volume of RPMI medium, PBS with FBS or a lysis buffer for subsequent experiments.  41  2.10 Fluorescence-Activated Cell Sorting Dispersed islet cells or pancreatic cells were passed through a 40-µm cell strainer. The cells were washed once with 2% FBS in PBS and resuspended in 350 µL of 2% FBS in PBS. Fluorescence-activated cell sorting (FACS) was performed on a BD FACSAria II (BD Biosciences, NJ, USA) for all experiments except the single cell RNA-seq experiments. For single cell RNA-seq experiments, 10% FBS in PBS was used instead and the samples were sorted on a MoFlo Astrios Cell Sorter (Beckman Coulter, CA, USA).  2.11 Histology Analysis 2.11.1 Formalin Fixed Paraffin Embedding Pancreata were sandwiched between sponges, placed in histology cassettes and incubated in 10% formalin in PBS for 24 hours at 4 oC. The fixed pancreata were then dehydrated as follows: 30 minutes in 50% ethanol twice, overnight in 70% ethanol, 30 minutes in 95% ethanol twice, 30 minutes in 100% ethanol thrice, 30 minutes in xylene twice, and 1 hour in paraffin twice. The processed pancreata were embedded in paraffin and sectioned on a microtome to a thickness of 5 µm. For E15.5 and E18.5 pancreata, all sections were collected. For P0 and P7 pancreata, sections separated by 60 µm were collected. For adult pancreata, sections separated by 150 µm were collected. The sections were dried at 37 oC overnight prior to immunofluorescence staining.  2.11.2 Immunofluorescence Staining The slides were rehydrated as follows: 5 minutes in xylene thrice, 5 minutes in 95% ethanol thrice, 5 minutes in 70% ethanol thrice, and 5 minutes in water. Antigen retrieval was performed by steaming the slides in a TE antigen retrieval buffer (10 mM Tris pH 9, 1 mM EDTA, 0.1% Tween) for 25 minutes. The slides were then washed thrice in water for 5 minutes. Sections were blocked and permeabilized with 10% donkey serum and 0.1% Triton X-100 in tris-buffered saline-tween (TBST) (50 mM Tris, 150 mM NaCl, 0.1% Tween 20) for 30 minutes. Primary antibody incubation was performed by diluting the antibodies in TBST and incubated at 4 oC overnight. The slides were then washed  42  with TBST for 5 minutes thrice and incubated with secondary antibodies dissolved in PBS supplemented with 4’,6-diamidino-2-phenylindole (DAPI) (5 µg/mL; Thermo Fisher Scientific) at room temperature for 1 hour. The slides were washed with TBST for 5 minutes thrice, rinsed with PBS and mounted with coverslips using Prolong Gold Antifade (Thermo Fisher Scientific) as a mounting medium. Slides were cured at room temperature for 24 hours prior to image acquisition. The full lists of primary and secondary antibodies used for the immunofluorescence staining in this thesis were shown as follows.  Table 2. A list of all primary antibodies used for immunofluorescence staining in this thesis.  Primary Antibody Host Vendor Catalogue Number Dilution Used Anti-CBP Rabbit CST 7389 1/200 Anti-Chromogranin A Rabbit Abcam ab15160 1/200 Anti-GFP Rabbit CST 2956 1/500 Anti-Ghrelin Goat Santa Cruz sc-10368 1/200 Anti-Glucagon Mouse Abcam K79bB10 1/1000 Anti-H3K27ac Rabbit CST 8137 1/200 Anti-H3K27me3 Rabbit CST 9733 1/200 Anti-Insulin Guinea Pig Abcam ab7842 1/100 Anti-Ki67 Rabbit CST 12202 1/200 Anti-Ngn3 Mouse DSHB F25A1B3 1/50 Anti-Npy Rabbit CST 11976 1/400 Anti-p300 (C-20) Rabbit Santa Cruz sc-585 1/50 (N:15 and C:20 in 1:1 ratio) Anti-p300 (N-15) Rabbit Santa Cruz sc-584 1/50 (N:15 and C:20 in 1:1 ratio) Anti-Somatostatin Goat Santa Cruz sc-7819 1/200 Anti-Somatostatin Rabbit Abcam ab22682 1/400 Anti-Tmem27 Goat R&D Systems AF4965 1/50        43  Table 3. A list of all secondary antibodies used for immunofluorescence staining in this thesis.  Primary Antibody Host Vendor Catalogue Number Dilution  Used Anti-Goat IgG,  Alexa Fluor 488 Donkey Jackson ImmunoResearch 705-545-003 1/400 Anti-Guinea Pig IgG, TRITC Donkey Jackson ImmunoResearch 706-025-148 1/400 Anti-Mouse IgG, Alexa Fluor 488 Donkey Jackson ImmunoResearch 715-545-150 1/400 Anti-Mouse IgG, Alexa Fluor 647 Donkey Jackson ImmunoResearch 715-605-151 1/400 Anti-Rabbit IgG, Alexa Fluor 488 Donkey Jackson ImmunoResearch 711-545-152 1/400 Anti-Rabbit IgG, Alexa Fluor 647 Donkey Jackson ImmunoResearch 711-605-152 1/400  2.11.3 5-Ethynyl-2’-Deoxyuridine Staining After antigen retrieval and washes, EdU staining was performed on EdU-labelled sections using the Click-iT EdU Alexa Fluor 647 Imaging Kit (Thermo Fisher Scientific) per manufacturer’s instruction. The slides were washed thrice with water prior to blocking.  2.11.4 Apoptosis Assay After blocking, terminal deoxynucleotidyl transferase 2’-deoxyuridine 5’-triphosphate nick end labeling (TUNEL) assays were performed on sections using an In Situ Cell Death Detection Kit (Sigma-Aldrich) per manufacturer’s instruction. The slides were washed with water thrice prior to primary antibody incubation. The assay was validated using stomach sections, where multiple TUNEL-positive cells can be detected in stomach epithelia.  2.11.5 Image Acquisition and Quantification For the quantification of pancreatic endocrine cell area, stained whole sections were tile-imaged on a BX61 microscope (Olympus, Japan) at 5x or 10x magnification. For adult and neonatal pancreata, four sections each separated by 150 µm and 60 µm were  44  imaged, respectively. For embryonic pancreata, six sections each separated by 30 µm were imaged. For representative images and the quantification of staining signal intensity, random sections were stained and imaged on an SP5 confocal microscope (Leica, Germany). Image analysis was performed in Fiji (Schindelin et al., 2012). The endocrine cell area was quantified by dividing total endocrine-positive area against total pancreas area.  2.12 Islet Assay 2.12.1 Static Incubation Assay for Glucose-Stimulated Insulin Secretion Overnight recovered islets were placed in a 12-well plate containing a low glucose Krebs-Ringer Buffer (LG-KRB) at 37 oC for 1 hour. After the pre-incubation, islets were transferred and incubated in a fresh LG-KRB, in high glucose KRB (HG-KRB) or in KCl-KRB at 37 oC for 1 hour, using 30 islets per condition. Supernatants were then collected and frozen at -80 oC. The composition of LG-KRB used was as follows: 137 mM NaCl, 4.7 mM KCl, 1.2 mM KH2PO4, 1.2 mM MgSO4-7H2O, 2.5 mM CaCl2-2H2O, 25 mM NaHCO3, 0.5% BSA and 2.8 mM glucose. For HG-KRB, the glucose concentration in LG-KRB was brought up to 16.7 mM. For KCl-KRB, the KCl concentration in LG-KRB was brought up to 30 mM. All buffers were adjusted to a pH 7.3 with 37% HCl and pre-warmed to 37 oC prior to use.  2.12.2 Perifusion Assay Perifusion assays were performed on a BioRep Perifusion system (BioRep, FL, USA) per manufacturer’s instruction. 100 islets were placed in a perifusion chamber connected to the perifusion pump. The perifusion rate was set at 100 µL/minute. Islets were first perifused with LG-KRB for 1 hour. 100 µL of perifusate was then collected every minute for the rest of the experiment. The islets were perifused with LG-KRB for 10 minutes, HG-KRB for 30 minutes, LG-KRB for 20 minutes, KCl-KRB for 10 minutes and LG-KRB for 5 minutes. Samples were frozen at -80 oC prior to ELISA measurement.  45  2.12.3 Calcium Imaging Calcium imaging experiments were carried out as previously described (Luciani et al., 2013). Ten whole islets were seeded and allowed to adhere onto the centre of a coverslip for 48 hours. The coverslips were then treated with 5 µM Fura-2 (Thermo Fisher Scientific) in RPMI medium at 37 oC for 30 minutes. Two coverslips, one with control islets and one with the knockout islets, were placed onto a perifusion chamber mounted on an SP8 confocal microscope (Leica). The perifusion rate was set at 2.5 mL/minute. The islets were perifused with LG-KRB for 1 hour as pre-incubation. Then, images were taken as the islets were perifused with LG-KRB for 10 minutes, HG-KRB for 20 minutes, LG-KRB for 20 minutes, KCl-KRB for 5 minutes and LG-KRB for 10 minutes. All KRB used in the calcium imaging experiments contained no BSA. The coverslips were excited at 340 nm and 380 nm, and images were taken every 0.167 seconds. At least 5 islets were imaged per coverslip. The 340/380 ratio for each time point, which positively correlates with the intracellular calcium concentration, was calculated and plotted.  2.12.4 Islet Insulin Content Islets were lysed in a radioimmunoprecipitation assay (RIPA) buffer (50 mM Tris pH 8, 1% NP-40, 0.5% sodium deoxycholate, 0.1% SDS, 150 mM NaCl) supplemented with 1/100 protease and phosphatase inhibitor cocktails (Thermo Fisher Scientific). The islet protein concentration was measured using a Pierce bicinchoninic acid (BCA) Protein Assay Kit (Thermo Fisher Scientific). Islet lysates were diluted 1000 times in PBS immediately before ELISA measurement.  2.12.5 Transmission Electron Microscopy Islets were fixed in 2% glutaraldehyde in PBS and submitted to the Electron Microscopy Facility at McMaster University (Hamilton, ON, Canada). Samples were processed for TEM as previously described (Bruin et al., 2008). The samples were rinsed twice with PBS and post-fixed in 1% osmium tetroxide for 1 hour followed by ethanol dehydration. The fixed islets were slowly infiltrated and then embedded with Spurr's  46  resin, and thin sections were cut on a Leica UCT ultramicrotome and picked up onto Cu grids. The sections were post-stained with uranyl acetate and lead citrate. Images were taken using a JEOL JEM 1200 EX TEMSCAN transmission electron microscope (JEOL, Peabody, MA, USA) mounted with an AMT 4-megapixel digital camera (Advanced Microscopy Techniques, MA, USA). The size and density of dense core granules were quantified on five randomly-taken TEM images per mouse. At least 4,500 dense core granules were quantified per sample. The dense core granule density was calculated by dividing the total number of dense core granules by the total β cell area.  2.13 RNA Preparation and Reverse Transcription-Quantitative Polymerase Chain Reaction Whole islets or sorted cells were lysed in TRIzol (Thermo Fisher Scientific). Chloroform was added to separate the aqueous phase, which contains RNA, from the organic phenol phase. RNA was extracted from the aqueous phase using an RNeasy Micro Kit (QIAgen, Germany) per manufacturer’s instruction. Reverse transcription was performed using an Omniscript RT kit (QIAgen) or a Superscript III Reverse Transcriptase kit (Thermo Fisher Scientific). Quantitative polymerase chain reactions (qPCR) were set up using a GoTaq qPCR Master Mix (Promega, WI, USA). The reactions were run on ViiA 7 Real-Time PCR System (Thermo Fisher Scientific). The primer sequences used were shown as follows.          47  Table 4. Sequences of quantitative PCR primers used in this thesis.  Target Forward Sequence Reverse Sequence Tmem27 mRNA TACAGTCGGCCATAAGAAAG ATACAATAATCCAGACGGGC Pklr mRNA TCATTGTGCTGACAAAGACT TAGAGCAAGGGGAAGACTC Hnf4a mRNA GGCTGGCATGAAGAAGGAAG GGAGAGGTGATCTGCTGGG G6pc2 mRNA CTGGTCCTTTCTGTGGAGTGT TCCAAGAATGACCTGATGGGG Slc2a2 mRNA CAGTTTCTTTGAGATTGGGC GATGACAAAATTGCAGACCC 18s rRNA GTAACCCGTTGAACCCCATT CCATCCAATCGGTAGTAGCG Tmem27 Promoter AAGTGAAAGCCTACAAGCAA AAAGAGCTGACCTCATTTCA Pklr Promoter CAGGAAAAACGGATGACCTA CCCTAACTGCTGGTCTTATC Hnf4a Promoter TTAAGATTCCCCTAACCCCA TGGGTGGATACGTTAAACAG G6pc2 Promoter TTGTGACTATTAAGTTATGTGTTG ATGTTTTTCCTCATCACCTG Slc2a2 Promoter TGAAAATGGGTCTGTCTCTG AGAAGCTGAGTATTGATGGT Iapp Promoter AAA CTC TAA ACG CCT ACG G GGC CAT CAA CAC ATT AAC AC Ins1 Promoter TACCTTGCTGCCTGAGTTCTGC GCATTTTCCACATCATTCCCC Iapp Enhancer GGGGAGGAAGAGAAGCTCAC AAAACCAGCCTTTTGCAGAC Ins1 Enhancer CACACACACACACGTTCACC CCTGCCTCTTCCTCTGTAGTG Lep Promoter TAGAATGGAGCACTAGGTTG CTCTTATAACTGCCCCAGTG Chr 5 MER20 TTCCTTTCATAGCTTTCAGTACCA GGAAGGACCATCAAGAAGAGTT  2.14 Plasmid Preparation and Site-Directed Mutagenesis pCMV-GFP was a gift from Connie Cepko (Addgene plasmid # 11153). pCMVβ-p300-myc and pCMVβ-p300-D1399Y-myc were gifts from Tso-Pang Yao (Addgene plasmid # 30489). Plasmids harboring EP300 variants p.H1255R or p.F1595V were generated from pCMVβ-p300-myc using a Q5 site-directed mutagenesis kit (NEB, MA, USA) per manufacturer’s instruction. DH5α cells (Thermo Fisher Scientific) were transformed with the plasmids by shocking the cells at 42 oC for 30 seconds. Cells were selected and expanded in lysogeny broth with 100 µg/mL ampicillin (Sigma-Aldrich). Plasmids were  48  purified from the cells using a QIAprep Spin Miniprep kit (QIAgen), and the mutations introduced in the p300 construct were confirmed by Sanger sequencing.   2.15 Transfection HEK293 cells at 90 – 95% confluency were transfected with the plasmids using Lipofectamine 2000 (Thermo Fisher Scientific) in a 24-well plate (1 µg of plasmids used), a 12-well plate (2 µg of plasmids used), or a 10-cm plate format (24 µg of plasmids used). Cells were lysed in an NP-40 lysis buffer (20 mM Tris pH 7.6, 150 mM NaCl, 0.5% NP-40) after 48 hours, sonicated thrice (30 seconds on, 30 seconds off) and subjected to Western blotting.  2.16 Western Blotting Cell lysates boiled in a Laemmli buffer were resolved on a 4% stacking and an 8% or a 15% separating polyacrylamide gel. Gels were transferred onto polyvinylidene fluoride (PVDF) membranes at 350 mA at 4 oC for 90 minutes, blocked in 5% milk in TBST at room temperature for 30 minutes and then incubated with primary antibodies at 4oC overnight. The blots were washed thrice in tris-buffered saline-tween (TBST) and then incubated with secondary antibodies at room temperature for 1 hour. The blots were washed thrice in TBST, incubated with Clarity enhanced chemiluminescence substrates (Bio-Rad, CA, USA) and exposed on films.            49  Table 5. A list of all primary and secondary antibodies used for Western Blotting in this thesis. Primary Antibody Host Vendor Catalogue Number Dilution Used Anti-Acetyl-p300/CBP Rabbit CST 4771 1/5,000 Anti-β tubulin Rabbit CST 2128 1/10,000 Anti-CBP Rabbit CST 7389 1/2,000 Anti-Myc tag Mouse CST 2276 1/2,000 Anti-Nkx6.1 Mouse  F55A12 1/2,000 Anti-p300 Mouse Thermo Fisher MA1-16608 1/2,000 Anti-TBP Mouse abcam ab818 1/20,000 Secondary Antibody Host Vendor Catalogue Number Dilution Used Anti-Mouse IgG, HRP-linked Donkey CST 7076 1/5,000 Anti-Rabbit IgG, HRP-linked Donkey CST 7074 1/5,000  2.17 Immunoprecipitation  2.17.1 Co-immunoprecipitation MIN6 cells were lysed in a nuclei EZ lysis buffer (Sigma-Aldrich) followed by an NP-40 lysis buffer supplemented with 0.1% Triton X-100 and 0.1% sodium deoxycholate. Antibody-conjugated beads were prepared by incubating the antibodies, including rabbit anti-Nkx6.1 (10 µL per reaction; CST, 54551), rabbit anti-acetyl-lysine (5 µL per reaction; CST, 9441), rabbit anti-CBP (10 µL per reaction; CST, 7389) and mouse anti-p300 (10 µL per reaction; Thermo Fisher MA1-16608), with Dynabeads Protein G (Thermo Fisher Scientific) at 4 oC for 3 hours. Lysates were precleared in Dynabeads Protein G at 4 oC for 2 hours. The precleared lysates were mixed with antibody-conjugated beads and rotated at 4 oC overnight. The beads were washed thrice with the NP-40 lysis buffer, and then boiled in a Laemmli buffer followed by Western blotting.  2.17.2 Low-Input Native-Chromatin Immunoprecipitation Cell pellets were processed for native-chromatin immunoprecipitation (ChIP) as previously described (Brind’Amour et al., 2015). Cells were lysed in a nuclei EZ lysis  50  buffer and digested with micrococcal nuclease (MNase) (NEB) at 37 oC for 5 minutes. The lysates were diluted in a native-ChIP buffer (20 mM Tris pH 8, 2 mM EDTA, 150 mM NaCl, 0.1% Triton X-100). One-tenth of each sample was retained as input controls, and the rest of the sample was aliquoted into 20,000-cell portions. Each aliquot was subjected to one ChIP reaction. Rabbit anti-H3K27ac (2 µL per ChIP reaction; CST, 8173) antibodies were conjugated to Dynabeads Protein A (Thermo Fisher Scientific) at 4oC for 3 hours. The MNase-digested lysates were precleared with Dynabeads Protein A at 4 oC for 2 hours, followed by mixing and rotating with the antibody-conjugated beads at 4oC overnight. The beads were washed twice with a low salt native-ChIP buffer (20 mM Tris pH 8, 2 mM EDTA, 150 mM NaCl, 1% Triton X-100, 0.1% SDS) and twice with a high salt native-ChIP buffer (20 mM Tris pH 8, 2 mM EDTA, 500 mM NaCl, 1% Triton X-100, 0.1% SDS). Bound chromatin was eluted and purified by phenol-chloroform extraction. For phenol-chloroform extraction, samples were mixed with an equal volume of phenol:chloroform:isoamyl alcohol (25:24:1) (Thermo Fisher Scientific) and spun at 12,000 g in a MaXtract High Density tubes (QIAgen) at 4 oC for 5 minutes. The aqueous phase was recovered and supplemented with 75 mM sodium acetate (Thermo Fisher Scientific), 50 µg/mL glycogen (Thermo Fisher Scientific) and 70% ethanol. Samples were incubated at -80 oC for 30 minutes followed by spinning at 13,000 g for 30 minutes. Pellets were washed once with 70% ethanol, dried at 75 oC until all liquids evaporated and resuspended in an elution buffer.  2.17.3 Dual-Crosslink Chromatin Immunoprecipitation A dual-crosslinking method was used to preserve the indirect interaction between p300/CBP and genomic DNA (Zeng et al., 2006). The experiments were performed on MIN6 cells from two different passages. MIN6 cells at 95% confluency in a 15-cm plate were harvested and treated with 2 mM ethylene glycol bis(succinimidyl succinate) (EGS) for 45 minutes. Then, they were washed thrice with PBS and fixed with 1% formaldehyde at room temperature for 10 minutes. After quenching with 2.5 M glycine and washing with PBS thrice, crosslinked cells were lysed in a modified Farnham buffer (50 mM HEPES pH 7.5, 2 mM EDTA, 140 mM NaCl, 0.75% NP-40, 0.25% Triton X-100,  51  10% glycerol) on ice for 10 minutes. Nuclei were recovered by spinning at 1,700 g for 10 minutes, followed by washing once with a wash buffer (50 mM Tris pH 8, 200 mM NaCl, 1 mM EDTA, 0.5 mM EGTA) and once with a sonication buffer (10 mM Tris pH 7.6, 1 mM EDTA, 0.1% SDS). Nuclei were resuspended in the sonication buffer and sonicated on a Covaris M220 Focus-Ultrasonicator (Covaris) for 20 minutes with the following condition: 10% Duty Factor, 75W Peak Incidence and 200 Cycles per Burst. The fragment size of the sonicated chromatin, ranging between 200 and 500 bp, was confirmed on a 2% agarose gel. One-tenth of each sample was retained as input, and the lysates were then split into three aliquots, with one aliquot used for p300 ChIP and one used for CBP ChIP. Each aliquot equals to approximately 6 million cells used. The chromatin was processed with standard immunoprecipitation steps similar to the low-input native ChIP experiment. The antibody-bound beads were prepared with 2 µg of rabbit anti-p300 (N-15 and C-20 in 1:1 ratio; CST, sc-584/sc-585) or 10 µL of rabbit anti-CBP (CST, 7389) and Dynabeads Protein A. Lysates were mixed with the antibody-bound beads at 4oC overnight, followed by washing once in a low salt native-ChIP buffer, once in a high salt native-ChIP buffer, once in a LiCl ChIP buffer (50 mM Tris pH 8, 0.5 mM EDTA, 250 mM LiCl, 1% NP-40, 1% sodium deoxycholate), and twice in TE pH 8. Washed beads were heated at 65oC for 30 minutes in an elution buffer (0.1 M NaHCO3, 1% SDS). Samples were reverse-crosslinked with 200 mM NaCl at 65 oC for 4 hours, followed by incubating with 100 µg of RNase A at 37 oC for 30 minutes and with 500 µg of proteinase K at 37 oC for 2 hours. The DNA yielded from the immunoprecipitation reactions was purified by the phenol-chloroform method.  2.18 High-Throughput Sequencing and Analysis 2.18.1 Bulk RNA-seq The integrity of RNA samples was checked on an Agilent 2100 Bioanalyzer (Agilent, CA, USA). Samples with an RNA integrity number above 8 were prepared into poly-A-positive sequencing libraries using an NEBNext Ultra RNA Library Prep Kit (NEB) and sequenced on a NextSeq 500 (Illumina) to obtain at least 20 millions read pairs; each read is 100-bp long. Reads were aligned to mm10 using the Burrow-Wheeler Aligner  52  (BWA) and then subjected to DEseq2 for differentially expressed gene calling using with a cutoff of adjusted p value < 0.05 (Li and Durbin, 2010; Love et al., 2014). Down-regulated gene lists were submitted to Webgestalt for gene ontology (GO) term assignment (Wang et al., 2013).  2.18.2 Single Cell RNA-seq For single cell RNA-seq experiments, sorted cells were captured into single cell droplets on a Chromium Controller (10X Genomics, CA, USA) and prepared into sequencing libraries per manufacturer’s instruction. The libraries were sequenced on a NextSeq 500. Reads were aligned to mm10 using the BWA and converted into single cell expression matrices using CellRanger. The mean number of reads was 106,000 per cell and the median of number of genes detected was 2,400 per cell; these parameters were almost identical between the genotypes. Low quality cells with a mitochondrial read percentage higher than 5% or with more than 4000 genes detected were filtered out in Seurat (Satija et al., 2015). Mitochondrial genes and ribosomal genes were then removed from analysis, and the processed Seurat object was passed to Monocle for unsupervised clustering and differentially expressed gene calling (Trapnell et al., 2014). Insulin-positive clusters were selected based on the expression of Ins1 and Ins2 > 200. Cells that have no Ins1 (< 200), no green fluorescent protein (GFP) gene expression (< 0.5), or express hemoglobin genes in the clusters, were filtered out.  2.18.3 ChIP-seq DNAs recovered from ChIP reactions were prepared into sequencing libraries using an NEB Ultra II DNA Library Prep Kit (NEB). ChIP-seq libraries were sequenced on a NextSeq 500. Raw reads were aligned to mm10 using the BWA. Duplicate reads, unpaired reads, and reads aligned to mitochondrial or unknown chromosomes were removed using BEDtools (Quinlan and Hall, 2010). At least 20 million quality-filtered read pairs were obtained per sample for peak calling.   53 H3K27ac ChIP-seq H3K27ac peaks were called using the irreproducible discovery rate (IDR) pipeline described by the ENCODE consortium (Landt et al., 2012), with model-based analysis of ChIP-seq 2 (MACS2) used for default narrow peak calling (Zhang et al., 2008). Conserved peak sets were used for all subsequent analyses. After applying IDR, the peak sets generated from each sample were subjected to DiffBind for the differential peak analysis between control and knockout samples (Stark and Brown, 2011). For chromatin segmentation, wildtype mouse islet H3K4me1 and H3K4me3 ChIP-seq data (Hoffman et al., 2010), islet H3K9me3 and H3K27me3 ChIP-seq data (Tennant et al., 2013), and the control β cell H3K27ac ChIP-seq data generated in Chapter 4 were subjected to ChromHMM to generate a 10-state model (Ernst and Kellis, 2017). ChromHMM is based on a multivariate hidden Marhov model to learn chromatin state signatures by modeling the presence and absence of different chromatin marks. The overlapping chromatin states were collapsed to generate a final 8-state model used for this thesis. Each H3K27ac peak was annotated with the segmentation data, assigned to the closest transcription start site (TSS) within 100 kb, and mapped with the expression of each β cell gene. All genes containing at least a single significantly reduced H3K27ac peak were considered as genes with reduced H3K27ac. Profile plots for all promoter and enhancer H3K27ac enrichment, and H3K27ac ChIP-seq and RNA-seq signal tracks of control and knockout samples were generated from BAM files using deepTools (Ramírez et al., 2014). The signal tracks were visualized on IGV (Robinson et al., 2011). All violin plots were generated using ggplots2 in R. p300/CBP ChIP-seq For p300/CBP ChIP-seq, at least 20 million quality-filtered read pairs were pooled from the two replicates per mark for peak calling using MACS2 with default narrow peak settings. De novo p300 motif analysis was performed in HOMER (Heinz et al., 2010). Raw sequences from all previously published islet transcription factor data sets were re-aligned to mm10 using Bowtie 2 (Langmead and Salzberg, 2012). Peaks were called on these data using MACS2 with the following q value cutoffs: MafA and Pdx1: q < 0.05,  54  Foxa2 and NeuroD1: q < 0.01, Nkx6.1: q < 1 x 10-15. For co-occupancy analysis, the z scores for each pair of co-occupancy were calculated with regioneR by computing and comparing against 1000 random permutations generated with a seed number of 62 (Gel et al., 2015). Overlapping peak sets were retrieved using BEDtools. The cataloged p300 peak sets were annotated with the chromatin segmentation and transcriptome data as described above. All H3K27ac ChIP-seq, p300/CBP ChIP-seq and RNA-seq signal tracks of control and knockout samples were generated from the respective BAM files using deepTools. The sequencing data generated in each chapter have been deposited into the GEO database under the following accession numbers: Chapter 3 (GSE101537), Chapter 4 (GSE111987), and Chapter 5 (GSE116368).  2.19 Statistics All data are shown as mean ± standard deviation. Statistical significance was tested using the Student’s t test, one-way analysis of variance (ANOVA), two-way ANOVA, Chi-squared test, Mann-Whitney U test, or Krukal-Wallis H test, as appropriate, with p < 0.05 considered statistically significant. For RNA-seq and ChIP-seq differential expression or binding analysis, the p values were corrected for multiple testing and the corrected values were expressed as adjusted p values or false discovery rate (FDR).  55  Chapter 3: Expression of p300/CBP is required for β cell and α cell development 3.1 Introduction The expression of specific transcription factors determines and maintains the identities of pancreatic endocrine cells by activating the endocrine genes. For example, Pdx1, MafA and NeuroD1 form a transcriptional complex at insulin promoters and enhancers to synergistically activate insulin transcription (Melloul et al., 2002). These transcription factors also recruit transcription cofactors to fine-tune gene expression.   p300/CBP are structurally similar transcriptional coactivators that function similarly. p300/CBP acetylate lysine residues on histones to modulate chromatin structures or functions (Bedford and Brindle, 2012). While p300/CBP can acetylate most histones, they are the only proteins that can acetylate histone H3 lysine 27. Acetylation of H3K27 marks tissue-specific promoters and enhancers to signal transcription of the target genes (Bose et al., 2017; Heinz et al., 2015).  p300/CBP appear to regulate important β cell functions in vitro. For instance, Pdx1 and NeuroD1/E47 recruit p300/CBP to coactivate insulin transcription in vitro (Qiu et al., 2004). Knocking down p300/CBP in INS-1 cells using small interfering RNA reduced glucose-stimulated insulin gene expression (Sampley and Özcan, 2012). In contrast, knocking out p300 in INS-1 832/13 cells by CRISPR/Cas9 induced a subtle increase in glucose-stimulated insulin secretion and reduced high glucose-mediated apoptosis (Bompada et al., 2016). Mice homozygous for the CBP S436A variant, a mutation which renders CBP unresponsive to insulin-triggered phosphorylation, had increased islet mass but relatively normal β cell function (Hussain et al., 2006). These data have yet to resolve whether p300/CBP expression in pancreatic islets is necessary for establishing glucose homeostasis in vivo. Furthermore, little is known about the role of p300/CBP in other islet endocrine cell types. In this Chapter, we hypothesized that the removal of p300/CBP from pancreatic endocrine progenitors would lead to postnatal glucose intolerance due to defects in islet mass and function. We generated and phenotyped  56  pancreatic islet-specific p300 and CBP knockout mice to study the roles of these coactivators in pancreatic islets in vivo.  3.2 Results Mice lacking p300 in pancreatic islets develop glucose intolerance due to hypoinsulinemia We first characterized Neurog3-Cre; Ep300fl/WT (p300IsletHet) mice which were heterozygous for p300. We predicted that these mice would model the genetic lesion present in our patient’s islets, but would not necessarily to recapitulate all of the patient’s phenotypes because the patient also lacks one copy of the EP300 gene in other diabetes-relevant tissues, including the brain, skeletal muscle, cardiomyocytes, the liver and adipose tissue. By 16 weeks of age, p300IsletHet mice showed normal glucose tolerance and appeared phenotypically identical to Cre-negative Ep300fl/fl mice and to mice bearing the Neurog3-Cre transgene alone (Figure 4).    Figure 4. p300IsletHet mice, mice bearing Neurog3-Cre and mice bearing Ep300fl/fl are comparably glucose tolerant. Intraperitoneal glucose tolerance tests in eight-week-old male Ep300fl/fl (n = 11), Neurog3-Cre (n = 7) and p300IsletHet (n = 7) mice.   0 20 40 60 800102030Time (minute)Blood Glucose (mmol/L)Ep300fl/flp300IsletHetNeurog3-Cre 57  Based on the phenotypes of the heterozygous mice, we expected that homozygous deletion of p300 is required to induce a quantifiable defect on glucose metabolism. We then generated Neurog3-Cre; Ep300fl/fl (p300IsletKO) mice. At E15.5, p300 was removed in 95.5% ± 1.51% (n = 3) chromogranin A-positive cells in p300IsletKO mice, while their Ngn3-positive progenitors remained p300-positive (Figure 5A). This is likely due to the time lag between the onset of Cre expression and the onset of recombination. In adult p300IsletKO mouse pancreata, p300 was present in their exocrine tissues but not in their islets (Figure 5B and Figure 5C). The protein levels of CBP were similar between wildtype (WT) and p300-null islets, indicating that the loss of p300 was not compensated by the overexpression of the p300 paralog CBP.    58    Figure 5. p300 is effectively removed in the pancreatic Ngn3 lineage during islet development. (A) Representative immunofluorescence images of p300 in E15.5 p300IsletKO (n = 3) mouse pancreata. Scale bar = 50 µm. (B) Western blotting for p300 and CBP in isolated  59  WT and p300-null islet nuclear extracts. TBP was used as a loading control. The experiment was replicated once. (C) Representative immunofluorescence images of p300 and CBP in ten-week-old male WT and p300IsletKO mouse islets. Scale bar = 50 µm.  p300IsletKO mice were glucose intolerant at eight weeks of age (Figure 6A). p300IsletKO mice had normal insulin tolerance (Figure 6B), but their plasma insulin levels were 60% lower than that of WT both before and after glucose injection (Figure 6C).     Figure 6. Mice lacking p300 in islets are glucose intolerant due to impaired insulin secretion. (A) Intraperitoneal glucose tolerance tests in eight-week-old WT (n = 8) and p300IsletKO (n = 9) mice. (B) Insulin tolerance tests in nine-week-old WT (n = 10) and p300IsletKO (n = 6) mice. (C) Plasma insulin measurement before and fifteen minutes after glucose injection in ten-week-old WT (n = 4) and p300IsletKO (n = 5) mice. Two-way ANOVA for Figures A and C. * p < 0.05, ** p < 0.01, *** p < 0.001  Theoretically, defective glucose metabolism in p300IsletKO mice could be partly due to the Neurog3-Cre mediated recombination outside of islets such as in ventromedial hypothalamus and enteroendocrine cells (Schonhoff et al., 2004; Song et al., 2010). However, p300IsletKO mice exhibited normal food intake, energy expenditure, body composition and locomotor activity (Figure 7A to Figure 7D). Also, plasma total GLP1 0 30 60 90010203040Time (minute)Blood Glucose (mmol/L)WT p300IsletKO****0 30 60 90050100150Time (minute)% of Baseline Blood Glucose0 (minute)Plasma Insulin (ng/mL)*****A B C 60  levels were normal in p300IsletKO mice (Figure 8). In the absence of observable extra-pancreatic phenotypes, the glucose intolerance and hypoinsulinemia can be attributed to the recombination within pancreatic islets rather than within other Neurog3-expressing tissues.    Figure 7. p300IsletKO mice have normal body composition and energy metabolism. (A) Body composition of ten-week-old male WT (n = 4) and p300IsletKO (n = 5) mice as measured by an EchoMRI body composition analyzer. (B) Energy expenditure, (C) food intake and (D) locomotor activity of ten-week-old male WT (n = 7) and p300IsletKO (n = 8) mice as measured by TSE metabolic cages.  61    Figure 8. Total plasma GLP1 levels are comparable between WT and p300IsletKO mice. Plasma total GLP1 levels in ten-week-old male WT (n = 5) and p300IsletKO (n = 5) mice as measured by a total GLP1 ELISA.  p300IsletKO mice have reduced islet area and islet insulin content To examine how p300IsletKO mice develop glucose intolerance and hypoinsulinemia, we first quantified islet cell area in adult p300IsletKO mice. Pancreas weight in p300IsletKO mice was comparable to that in WT mice (Figure 9A). However, p300IsletKO mice displayed 25% reductions in both β cell and α cell area (Figure 9B), which did not affect β-to-α cell ratios (Figure 9C). δ cell area was normal in p300IsletKO mice. WTp300IsletKO05101520Plasma GLP-1 (pM) 62    Figure 9. p300IsletKO mice have reduced β cell and α cell area. (A) Pancreas weight in ten-week-old male WT (n = 4) and p300IsletKO (n = 5) mice. (B) The β cell, α cell and δ cell area in ten-week-old male WT and p300IsletKO mouse pancreata as quantified by immunofluorescence staining. For β cell area, WT, n = 7; p300IsletKO, n = 8. For α cell and δ cell area, WT, n = 4; p300IsletKO, n = 5. (C) Β-to-α ratios in WT (n = 4) and p300IsletKO (n = 5) mouse pancreata. Student’s t test for Figure B. * p < 0.05.  We performed static incubation assays to assess the glucose-stimulated insulin secretion in p300-null islets ex vivo. p300-null islets had elevated KCl-stimulated insulin secretion and calcium responses, although these islets responded normally to glucose (Figure 10A and Figure 10B).   63    Figure 10. p300-null islets show elevated responses to potassium.  (A) Glucose-stimulated insulin secretion in ten-week-old WT (n = 9) and p300IsletKO (n = 5) mouse islets as measured by static incubation assays. Each dot represents islets from one mouse. (B) Calcium flux in ten-week-old WT (n = 3) and p300-null (n = 3) islets upon glucose stimulation as measured by calcium imaging. Student’s t test for Figure B. Two-way ANOVA for Figure C. * p < 0.05. ** p < 0.01.  p300-null islets had 19% lower insulin content and nearly two-fold higher somatostatin content (Figure 11A). Islet glucagon content was normal in p300IsletKO mice, which agreed with the normal fasting plasma glucagon levels in these mice (Figure 11B). Similarly, p300-null islets secreted a normal amount of glucagon under a low glucose condition (Figure 11C), suggesting that the α cell phenotypes were independent of the impaired glucose tolerance in p300IsletKO mice. Because p300IsletKO mouse islets had reduced insulin content, we examined the ultrastructure of p300-null β cells by TEM. The dense core granules in p300-null β cells were visibly smaller (Figure 11D). Thus, the combined defects in islet mass and islet insulin content resulted in glucose intolerance in p300IsletKO mice. 2.8 mM glucose16.7 mM glucose30 mM KCl051015Insulin Release (% of Insulin Content)WT p300IsletKO**0 10 20 30 40 50 600. (min.)Fura-2 340/380 RatioWT p300IsletKO2.8 mMglucose16.7 mMglucose2.8 mMglucose2.8 mM glucose + 30 mM KCl2.8 mMglucoseA B 64    Figure 11. p300-null islets have reduced insulin content but normal glucagon content and secretion. (A) Islet insulin, glucagon and somatostatin content in ten-week-old male WT and p300IsletKO mice. For insulin, WT, n = 5; p300IsletKO = 6. For glucagon, WT, n = 6; p300IsletKO = 6. For somatostatin, WT, n = 5; p300IsletKO = 5. (B) Plasma glucagon levels in five hour-fasted ten-week-old male WT (n = 6) and p300IsletKO (n = 6) mice. (C) Glucagon secretion upon 2.8 mM glucose stimulation in ten-week-old WT (n = 4) and p300-null (n = 4) islets. (D) Representative transmission electron micrographs of β cells in ten-week-old male WT and p300IsletKO mouse islets. Scale bar = 2 µm. Student’s t test for Figure A. * p < 0.05   InsGcg Sst01234200300400500Islet Hormone Content (ng/µg of Total Protein)WT p300IsletKO**WTp300IsletKO05101520Plasma Glucagon (pM)WTp300IsletKO0. Release  (% of Glucagon Content)A B CD WT p300IsletKO 65  CBPIsletKO mice share similar phenotypes with p300IsletKO mice In addition to p300IsletKO mice, we generated and studied Neurog3-Cre; Crebbpfl/fl (CBPIsletKO) mice to understand whether CBP functions similarly as p300 in islets. CBP was deleted specifically in the islets in these mice, with no consequence on p300 expression (Figure 12).   Figure 12. CBP is deleted in CBPIsletKO mouse islets.  Representative immunofluorescence images of p300, CBP, insulin and glucagon in ten-week-old male WT and CBPIsletKO mouse islets. Scale bar = 50 µm.  CBPIsletKO mice were glucose intolerant at eight weeks of age (Figure 13A). They were insulin tolerant but exhibited impaired insulin release upon glucose injection (Figure 13B and Figure 13C). CBPIsletKO mouse pancreata had 40% less α cell area, 30% less β cell area, and unchanged δ cell area (Figure 14A). In contrast to p300-null islets, CBP-null islets had reduced glucagon content but normal somatostatin content (Figure 14B). CBP-null islets had lower insulin content, although their β cell secretory functions  66  appeared normal (Figure 14C). Overall, both p300IsletKO and CBPIsletKO mice developed glucose intolerance and displayed reduced islet area and insulin content.    Figure 13. Mice lacking CBP in islets are glucose intolerant due to impaired insulin secretion. (A) Intraperitoneal glucose tolerance tests in eight-week-old male WT (n = 4) and CBPIsletKO (n = 5) mice. (B) Insulin tolerance tests in nine-week-old male WT (n = 5) and CBPIsletKO (n = 6) mice. (C) Plasma insulin measurement in ten-week-old male WT (n = 7) and CBPIsletKO (n = 5) mice before and fifteen minutes after glucose injection. Two-way ANOVA for Figures A and C. * p < 0.05, *** p < 0.001     67    Figure 14. CBP-null islets exhibit reduced insulin content.  (A) The quantification of β cell, α cell and δ cell area in ten-week-old male WT and CBPIsletKO mice pancreata as % of total pancreas area. For β cell and α cell area, WT, n = 4; CBPIsletKO, n = 5. For δ cell area; WT, n = 6; CBPIsletKO, n = 5. (B) Islet insulin, glucagon and somatostatin content in ten-week-old WT and CBP-null islets as quantified by ELISAs. WT, n = 4; CBPIsletKO, n = 4. (C) Perifusion assays for insulin secretion in ten-week-old WT (n = 3) and CBP-null (n = 3) islets. Student’s t test for Figures A and B. * p < 0.05, ** p < 0.01     68  Mice with only one copy of p300 or CBP in islets develop more severe phenotypes than mice lacking p300 or CBP alone in islets We next asked whether deleting an additional copy of islet p300/CBP in p300IsletKO or CBPIsletKO mice would lead to similar yet more severe phenotypes. To test this, we generated Neurog3-Cre; Crebbpfl/WT; Ep300fl/fl (CBPHet; p300KO) mice and Neurog3-Cre; Crebbpfl/fl; Ep300fl/WT mice (CBPKO; p300Het mice). Because these mice lack three of the four functioning p300/CBP alleles, we refer to them as two different “triallelic” genotypes. The CBPHet; p300KO triallelic mice developed severe glucose intolerance by eight weeks of age with normal insulin tolerance (Figure 15A and Figure 15B). Unlike p300IsletKO or CBPIsletKO mice, the triallelic p300/CBP mice of either genotype failed to secrete insulin upon glucose injection (Figure 15C; CBPKO; p300Het mouse data in Figure 16).     Figure 15. CBPHet; p300KO mice are severely glucose intolerant.  (A) Intraperitoneal glucose tolerance tests in eight-week-old male WT (n = 6) and CBPHet; p300KO (n = 6) mice. (B) Insulin tolerance tests in nine-week-old male WT (n = 6) and CBPHet; p300KO (n = 7) mice. (C) Plasma insulin measurement in ten-week-old male WT (n = 7) and CBPHet; p300KO (n = 7) mice before and fifteen minutes after glucose injection. Two-way ANOVA for Figure C. ** p < 0.01, *** p < 0.001  69    Figure 16. CBPKO; p300Het mice are phenocopies of CBPHet; p300KO mice. (A) Intraperitoneal glucose tolerance tests in eight-week-old WT (n = 7) and CBPKO; p300Het (n = 5) mice. (B) Insulin tolerance tests in nine-week-old male WT (n = 7) and CBPKO; p300Het (n = 6) mice. (C) Plasma insulin measurement in ten-week-old male WT (n = 4) and CBPKO; p300Het (n = 4) mice before and fifteen minutes after glucose injection. Two-way ANOVA test for Figures A and C. * p < 0.05, *** p < 0.001  As expected, CBPHet; p300KO mice had 58% less α cell area and 45% less β cell area (Figure 17A). CBPHet; p300KO mouse islets contained 72% less insulin content (Figure 17B). These phenotypes recapitulated those seen in p300IsletKO or CBPIsletKO mice but were more severe.    70    Figure 17. CBPHet; p300KO mice exhibit reduced β cell area and islet insulin content.  (A) The quantification of β cell, α cell and δ cell area in ten-week-old male WT (n = 4 – 6) and CBPHet; p300KO (n = 4) mouse pancreata. (B) Insulin content in ten-week-old WT (n = 4) and CBPHet; p300KO (n = 4) islets as quantified by an ELISA. Student’s t test for Figures A and B. * p < 0.05, ** p < 0.01, *** p < 0.001  Mice lacking all copies of p300/CBP die after birth due to the absence of β cell mass We attempted to generate Neurog3-Cre; Crebbpfl/fl; Ep300fl/fl mice (p300/CBP double knockout mice). However, in a cohort of 59 pups, we did not observe any double knockout mice at the weaning age, in contrast to the triallelic p300/CBP mice which were observed at the expected Mendelian ratio (Table 6; chi-squared = 40.684, p < 0.0001).        71  Table 6. Mendelian ratios of islet-specific p300/CBP double KO mice at the weaning age.   Expected Fraction Observed Number (Total = 59) Observed Fraction Neurog3-Cre; p300fl/fl 0.0625 4 0.0678 Neurog3-Cre; CBPfl/WT; p300fl/fl 0.25 16 0.27 Neurog3-Cre; CBPfl/fl; p300fl/fl 0.1875 0 0 p300fl/fl 0.0625 13 0.22 CBPfl/WT; p300fl/fl 0.25 21 0.356 CBPfl/fl; p300fl/fl 0.1875 5 0.085  Based on the biallelic and triallelic mouse data, we speculated that p300/CBP double knockout mice might die shortly after birth due to failure to establish sufficient β cell mass. At P0, some double KO pups survived, but their pancreata completely lacked α cells and β cells (Figure 18). Unexpectedly, deleting all p300/CBP in pancreatic endocrine progenitors had no effect on δ cell and ε cell populations. Furthermore, some ε cells persisted in the biallelic and triallelic mouse pancreata (Figure 19), although ε cells are normally absent in adult mouse pancreata. Thus, at least one allele of p300 or CBP is necessary for the development of α cells and β cells, but not for δ cells and ε cells.  72    Figure 18. Mice lacking p300 and CBP in endocrine progenitors die shortly after birth due to failure to form sufficient β cell and α cell mass. (A) The quantification of β cell, α cell, δ cell, ε cell and chromogranin A-positive pan-endocrine cell area in P0 WT (n = 3) and p300/CBP double knockout (dKO) (n = 3) mouse pancreata. (B) Representative immunofluorescence images of insulin, glucagon, somatostatin, ghrelin, chromogranin A and DAPI in P0 WT and p300/CBP double knockout mouse pancreata. Scale bar = 50 µm. Student’s t test for Figure A. * p < 0.05, ** p < 0.01 β cellα cellδ cellε cellChromogranin A0. Total Pancreas AreaWT dKO*****AB 73    Figure 19. ε cells are present in adult islets lacking p300.  Representative immunofluorescence images of ghrelin in ten-week-old WT, p300IsletKO and CBPHet; p300KO mouse islets. Scale bar = 50 µm.  Deleting p300 impairs neonatal β cell and α cell proliferation  All islet-specific p300/CBP models shared the common phenotypes of reduced β cell and α cell area, which could be caused by defects in differentiation, proliferation and/or apoptosis. To determine the cause of the defects, we examined these processes throughout pancreatic endocrine development in WT and p300IsletKO mice. We first excluded apoptosis as a possible cause of the islet cell loss by performing TUNEL assays. In E18.5 and adult p300IsletKO mouse pancreata, apoptotic events in the knockout islets were as rare as in WT islets. Moreover, at E15.5, deleting p300 did not affect the number of the newly differentiated endocrine cells and Ngn3-positive endocrine progenitors (Figure 20A). At E18.5, p300IsletKO mouse pancreata also showed normal β cell, α cell, and pan-endocrine cell area (Figure 20B). These data suggest that the reduced β cell and α cell mass in adult p300IsletKO mouse pancreata originate after E18.5.   74    Figure 20. p300IsletKO mice exhibit normal pancreatic endocrine differentiation.  (A) Quantification of β cell, α cell, δ cell, ε cell, chromogranin A-positive and Ngn3-positive cells in E15.5 WT (n = 5) and p300IsletKO (n = 3) mouse pancreata as % of total pancreatic cells. (B) Quantification of β cell, α cell and chromogranin A-positive pan-endocrine cell area of E18.5 WT (n = 6) and p300IsletKO (n = 7) mouse pancreata as % of total pancreas area.  At P7, p300IsletKO mouse pancreata showed reduced β cell and α cell area (Figure 21A). Correspondingly, p300IsletKO mouse pancreata had lower percentages of α and β cells positive for Ki67, which is a marker for proliferating, non-G0 phase cells (Gerdes et al., 1983). The percentages of Ki67-positive pancreatic cells were similar between WT and p300IsletKO mice (Figure 21B and Figure 21C). These data indicate that loss of p300 specifically impairs β cell and α cell proliferation. Furthermore, we speculate that severe defects in β cell and α cell proliferation may account for the islet cell loss in p300/CBP triallelic and double knockout mice.   75     Figure 21. p300-null islets display impaired neonatal β cell and α cell proliferation.  (A) The quantification of β cell, α cell and δ cell area in P7 WT (n = 4) and p300IsletKO (n = 6) mouse pancreata as % of total pancreas area. (B) The quantification of Ki67-positive β cells, α cells and all pancreatic cells in P7 WT (n = 8) and p300IsletKO (n = 8) mice. (C) Representative immunofluorescence images of Ki67 in P7 WT and p300IsletKO mouse islets. Scale bar = 50 µm. Student’s t test for Figures A and C. * p < 0.05, ** p < 0.01 β cellα cellδ cell0. of Pancreas AreaWT p300IsletKO****β cellα cellTotal pancreatic cell010203040% Ki67+**ABC 76  Deleting p300/CBP impairs the expression of genes regulated by multiple islet/β cell transcription factors and impairs the coactivation of genes regulated by Hnf1α in vivo As p300/CBP are transcriptional coactivators, loss of p300/CBP expression likely impairs transcription. Based on the islet phenotypes in the p300IsletKO mice, we hypothesized that loss of p300/CBP expression would disproportionately affect genetic cascades that are important for islet function or development. We performed RNA-seq to profile the transcriptomic changes in p300IsletKO, CBPIsletKO, and CBPHet; p300KO mice. We identified 761 (477 down, 284 up), 923 (513 down, 410 up) and 5,589 (2,411 down, 3,178 up) differentially expressed genes in p300-null, CBP-null and the triallelic islets relative to WT islets, respectively.  We focused our analyses on the down-regulated genes to identify potential p300/CBP coactivation targets. Superimposing the three down-regulated gene sets revealed 230 down-regulated genes overlapped between p300-null islets (48.2%) and CBP-null islets (44.8%) (Figure 22A). The genes down-regulated in CBPHet; p300KO islets overlapped with 436 (91.4%) genes down-regulated in p300-null islets, and 437 (85.2%) genes down-regulated in CBP-null islets. Enrichment analyses of the Biological Process GO terms on the three down-regulated gene sets revealed three common themes of the genes: lipid metabolic processes, regulation of hormone levels, and ion transport (Figure 22B) (Ashburner et al., 2000). Prediction of transcription factor targets by Webgestalt showed that all three gene sets were significantly enriched for the predicted transcription factor Hnf1α (Figure 22C) (Zhang et al., 2005). We also performed Gene Set Enrichment Analysis by comparing our gene sets to the sets of genes down-regulated in mouse islets lacking factors important for β cell development and function, including Pdx1, NeuroD1, Hnf1α, Pax6, MafA, Nkx6.1, and Nkx2.2 (Gao et al., 2014; Gu et al., 2010; Gutiérrez et al., 2017; Hang et al., 2014; Servitja et al., 2009; Subramanian et al., 2005; Swisa et al., 2017; Taylor et al., 2013). Our gene sets overlapped with the gene sets of Hnf1α and Nkx2.2 more significantly, followed by MafA, Nkx6.1, Pdx1, and NeuroD1 (Figure 22D).   77  Because Hnf1α can recruit p300/CBP for coactivation (Soutoglou et al., 2000), we further examined the genes that overlapped between the gene set down-regulated in Hnf1α-null islets and the three sets of genes down-regulated by loss of p300/CBP. All three models exhibited reduced Tmem27 levels, a downstream target of Hnf1α that can mediate β cell proliferation (Akpinar et al., 2005). In addition, other genes down-regulated in Hnf1α-null islets, including Pklr, Slc2a2, and G6pc2, were expressed at lower levels in the triallelic islets (Figure 22E) (Servitja et al., 2009). Loss of p300 or CBP alone in islets did not affect the mRNA levels of β cell transcription factors, whereas the levels of Hnf4a, Hnf1b and Neurod1 decreased in the triallelic islets. Similarly, p300-null and CBP-null islets expressed normal levels of Ins1, Ins2, and other insulin processing genes, whereas triallelic islets expressed 50% less Ins1 and Ins2. A particularly interesting gene highly upregulated in the p300-null islets was Npy. Npy encodes the orexigenic peptide neuropeptide Y (Npy), which appears to be a marker of partially differentiated β cells (Rodnoi et al., 2017). Both P7 and adult p300-null β cells expressed high levels of Npy (Figure 23). The abnormal presence of ghrelin and Npy suggests that p300-null islets may exhibit some differentiation or maturation defects.   78    Figure 22. Islets lacking p300 or CBP display impaired expression of genes down-regulated in Hnf1α-null islets. (A) The Venn diagram of down-regulated genes overlapping among p300IsletKO, CBPIsletKO and CBPHet; p300KO mouse islets as compared with WT islets. (B) The three ABCDE 79  Biological Processes GO terms commonly overrepresented in the down-regulated genes of p300IsletKO, CBPIsletKO and CBPHet; p300KO mouse islets. (C) Transcription factor target analysis by Webgestalt on the sets of down-regulated gene in p300IsletKO, CBPIsletKO and CBPHet; p300KO mouse islets. Hnf1α was commonly over-represented in all three gene sets. (D) Gene set enrichment analysis comparing between the down-regulated gene sets of p300IsletKO, CBPIsletKO and CBPHet; p300KO mouse islets, and the down-regulated gene sets derived from the microarray or RNA-seq data of islets or β cells lacking β cell transcription factors. A higher enrichment score indicates a more significant overlapping between the gene sets. Random 1 and 2 were control gene lists generated randomly from the 15,999 genes in the WT reference list. (E) The expression levels of Hnf1α-null binding targets in WT (n = 5) and CBPHet; p300KO (n = 6) mouse islets as quantified by qPCRs. Student’s t test for Figure E. * p < 0.05    Figure 23. A significant portion of β cells in p300IsletKO mice express Npy. (A) Representative immunofluorescence images of Npy in P7 and adult WT and p300IsletKO mouse islets. Scale bar = 50 µm. (B) The quantification of Npy-positive β cells in P7 WT (n = 4) and p300IsletKO (n = 7) mice. Student’s t test for Figure B. * p < 0.05  Because p300/CBP coactivate gene expression in part by acetylating H3K27 at target promoters and enhancers, we hypothesized that the loss of p300/CBP would  80  disproportionately reduce H3K27 acetylation at the loci down-regulated in Hnf1α-null islets. We assessed the acetylation and methylation levels of H3K27 at various loci using low-input native ChIP (Brind’Amour et al., 2015). In triallelic islets, the H3K27ac levels were significantly reduced at the promoters of G6pc2, Hnf4a, Pklr, and Tmem27 (Figure 24A; negative controls shown in Figure 24C). Triallelic islets also showed reduced H3K27ac levels at the promoters and enhancers of Pdx1-associated genes (Figure 24B). CBP-null islets displayed reduced H3K27ac levels at these loci, although the reductions did not reach statistical significance. These loci-specific H3K27ac levels correlated with the total p300/CBP dosage in the cells. We confirmed a 60% reduction in H3K27ac staining globally in the triallelic islet nuclei (Figure 24D). The total and locus-specific H3K27me3 levels in triallelic islets remained normal (Figure 24D and Figure 24E). Overall, the reduced dosage of p300/CBP impairs expression of genes down-regulated in Hnf1α-null islets that is attributable to reductions in global and locus-specific H3K27ac levels.   81    G6pc2 PromoterHnf4a PromoterPklr PromoterTmem27 PromoterSlc2a2 Promoter051015% InputH3K27ac ChIPCBPHet; p300KOWT CBPIsletKO*******Lep PromoterChr 5 MER20Chr 7 Ccdc179 -73 kb010203040% InputH3K27ac ChIPIapp PromoterIns1 PromoterIapp EnhancerIns1 Enhancer0102030% InputH3K27ac ChIP***G6pc2 PromoterHnf4a PromoterPklr PromoterSlc2a2 PromoterTmem27 PromoterIapp PromoterIns1 PromoterIapp EnhancerIns1 EnhancerLep PromoterChr 5 MER20Chr 7 Ccdc179 -73 kb010203040% InputH3K27me3 ChIPCBPHet; p300KOWTACEDB 82  Figure 24. Loss of p300/CBP impairs H3K27 acetylation at loci down-regulated in Hnf1α-null islets. (A) Low input native H3K27ac ChIP-qPCRs at Hnf1α-associated loci in WT (n = 5), CBPIsletKO (n = 3) and CBPHet; p300KO (n = 3) mouse islets. (B) Low input native H3K27ac ChIP-qPCRs at Pdx1-associated loci in WT (n = 5), CBPIsletKO (n = 3) and CBPHet; p300KO (n = 3) mouse islets. (C) Low input native H3K27ac ChIP-qPCRs at negative control loci in WT (n = 5), CBPIsletKO (n = 3) and CBPHet; p300KO (n = 3) mouse islets. (D) Representative immunofluorescence images of H3K27ac and H3K27me3 in WT and CBPHet; p300KO mouse islets. (E) Low input native H3K27me3 ChIP-qPCRs at all loci tested for H3K27ac in Figure A, B and C in WT (n = 5), CBPIsletKO (n = 3) and CBPHet; p300KO (n = 3) mouse islets. Scale bar = 50 µm. One-way ANOVA for Figures A and B. * p < 0.05, ** p < 0.01, *** p < 0.001  3.3 Discussion In this Chapter, we have generated and phenotyped mice lacking various copies of p300/CBP in pancreatic islets. Deleting either p300 or CBP alone in islets was sufficient to perturb whole body glucose homeostasis. Mice lacking p300 or CBP in islets developed similar β cell phenotypes including reduced β cell area and insulin content. Mechanistically, p300/CBP can coactivate the expression of Pdx1, NeuroD1, Hnf4α, and Hnf1α/β downstream targets in vitro (Ban et al., 2002; Dell and Hadzopoulou-Cladaras, 1999; Qiu et al., 2002). Our RNA-seq data suggest that reduced dosage of p300/CBP in islets can reduce the expression of genes down-regulated in Hnf1α-null islets. Hnf1α/β are homeobox transcription factors critical for pancreas and β cell development (Haumaitre et al., 2005; Pontoglio et al., 1998). In particular, deleting p300/CBP may reduce β cell proliferation through the Hnf1α target gene Tmem27 (Akpinar et al., 2005). The role of Hnf1α in α cells remains unclear. Notably, FACS-enriched human α cells express high levels of HNF1α mRNA (Bramswig et al., 2013), thereby implying that p300/CBP may also regulate aspects of α cell biology, such as proliferation, through HNF1α. p300/CBP bind to Hnf1α/β through their transactivation domains and coactivate their downstream targets, at least partly by acetylating the histones bound to the regulatory elements affiliated with these targets (Barbacci et al., 2004). The observed H3K27ac loss in the triallelic islets at loci down-regulated in Hnf1α-null islets appears to be in line with such a mechanism.  83  Although Hnf1α may interact with p300/CBP in islets, other transcription factors may also play a part in the phenotypes of p300/CBP-null islets. NeuroD1 may contribute to the developmental phenotypes in p300-null islets. Because β cell-specific NeuroD1 knockout mice also show a high proportion of Npy-positive β cells (Gu et al., 2010), NeuroD1 may work with p300 to inhibit Npy as β cells mature (Sharma et al., 1999). Surprisingly, p300/CBP are dispensable for the development of δ cells or ε cells. The p300/CBP double knockout mice phenotypically resemble Nkx2.2-null mice (Mastracci et al., 2011; Prado et al., 2004). Nkx2.2 has not been shown to interact with p300/CBP. Due to the significant overlap between the sets of genes down-regulated in Nkx2.2-null islets and the sets of genes down-regulated in p300/CBP-null islets, we speculate that Nkx2.2 may mediate some of the phenotypes seen in the p300/CBP mutant mice. Intriguingly, Nkx2.2 is mainly known as a transcriptional repressor, so Nkx2.2 may recruit p300/CBP to initiate its activator function instead (Doyle and Sussel, 2007; Gutiérrez et al., 2017). Future studies will determine whether p300/CBP interact physically with Nkx2.2 and acetylate H3K27 at Nkx2.2-associated loci, and whether the genomic occupancy of Nkx2.2, NeuroD1, or Hnf1α is affected by p300 deletion in islets.  Both Ins1 and Ins2 mRNAs were reduced in the triallelic islets, but not in p300-null islets nor in CBP-null islets. In addition, the acetylation of H3K27 at the Ins1 promoter correlated with the dosage of p300/CBP present in the islets. Although it is unclear whether MafA and Nkx6.1 can recruit p300/CBP for coactivation, reduced transcriptional activities of MafA and Nkx6.1 may contribute indirectly to the reduced insulin transcription seen in the triallelic mice. Alternatively, p300/CBP may regulate insulin transcription by binding to Pdx1 and NeuroD1 (Qiu et al., 2002; Sampley and Özcan, 2012). In either case, the potential defect in coactivation may be a consequence of the reduced acetylation of H3K27 at insulin promoters. Taken together, p300/CBP may coordinate transcriptional networks in β cells by coactivating various key β cell transcription factors in addition to Hnf1α/β and/or Nkx2.2, in part by acetylating H3K27.   84  To conclude this Chapter, mice lacking p300 or CBP alone in islets developed glucose intolerance and hypoinsulinemia associated with reduced islet area and insulin content. Mice lacking three copies of p300/CBP in islets developed similar yet exacerbated phenotypes. Mice lacking all copies of p300/CBP died postnatally due to their failure to establish sufficient β cell mass. Islet genes coactivated by p300/CBP overlapped significantly with genes found to be down-regulated among islets lacking transcription factors such as Hnf1α and Nkx2.2. p300/CBP expression was required to acetylate H3K27 at specific loci that are also down-regulated in Hnf1α-null islets, including Slc2a2, Pklr, Hnf4a, and particularly Tmem27. p300/CBP may coactivate these genes to regulate β cell proliferation. We argue that the expression of p300/CBP in islets is critical to drive β cell genesis and to maintain β cell proliferation and insulin content. In the pancreatic endocrine lineage, p300 and CBP are limiting coactivators that function similarly to maintain whole body glucose homeostasis.  85  Chapter 4: Deficiency in human and mouse p300 is associated with glucose dysregulation 4.1 Introduction Pathogenic coding variants in pancreatic β cell transcription factors can cause MODY and, in some cases, hyperinsulinemic hypoglycemia (Fajans et al., 2001; Stanescu et al., 2012). These transcription factors are important for β cell development and function.  These factors can recruit the acetyltransferases p300/CBP for coactivation. Mutations located within the p300/CBP-interacting domain of Pdx1, NeuroD1, Hnf1β and Hnf4α have been shown to cause MODY (Barbacci et al., 2004; Eeckhoute et al., 2004; Hani et al., 1999; Malecki et al., 1999). Thus, loss of p300-mediated coactivation in β cells may cause glucose dysregulation.  Mutation in CREBBP or EP300 cause RTS, a rare autosomal dominant disorder that presents with characteristic facial dysmorphism, intellectual disability and developmental delay (Rubinstein and Taybi, 1963). Although glucose dysregulation is not considered a typical RTS phenotype, earlier case reports have documented glucose dysregulation among patients with RTS. These include seven patients with mild to “frank” diabetes (Bartok, 1968; Rohlfing et al., 1971; Völcker and Haase, 1975), and one patient with hyperinsulinemic hypoglycemia (Wyatt, 1990). However, these reports predated identification of the causal genes for RTS. Recently, we identified a patient who has a microdeletion spanning one copy of the EP300 genes. The patient developed non-immune diabetes in her early 20s, with no evidence of insulin resistance. Based on this patient, we hypothesized that mutations in EP300 cause glucose dysregulation. In this Chapter, we report the glucose phenotypes of two patients with EP300 mutations and characterize the phenotypic and molecular features of p300-null β cells in a mouse model.     86  4.2 Results Three patients with rare pathogenic variants in EP300 presented with early-onset glucose dysregulation We have gathered eight patients who are heterozygous for rare coding variants in EP300. Among them, three developed early-onset glucose dysregulation (Figure 25A and Figure 25B). Proband 1 was diagnosed with non-immune, MODY-like diabetes at 23 years of age. The prescription of metformin and sitagliptin has been sufficient to manage her diabetes since her diagnosis. A chromosomal microarray study detected a heterozygous microdeletion spanning the first 21 exons of an EP300 gene and a microRNA mir-1281 with unknown functions. We derived iPSCs from the patient’s blood cells and confirmed that the patient is haploinsufficient for p300 (Figure 26).    Figure 25. Rare pathogenic variants in human EP300 are associated with glucose dysregulation. (A) A table describing the rare pathogenic EP300 variants and the phenotypes of the four probands in our cohort. (B) A schematic showing the location of the EP300 variants found in the four probands. The blue regions denote the catalytic core of p300. All missense variants described here are within the p300 catalytic core.   87    Figure 26. Proband 1 is haploinsufficient for p300. (A) Western blotting for p300 and TBP (a loading control) in iPSCs from a control and proband 1. (B) The quantification of the p300 band density normalized against the TBP band density in the control and proband 1 from three independent Western blotting experiments.  Probands 2 and 3 developed hyperinsulinemic hypoglycemia at 18 and 19 months of age, respectively. Both patients harbour heterozygous missense variants within the catalytic core of p300 in proband 2 (p.F1595V) and 3 (p.H1255R). Notably, proband 4 displayed random elevated plasma insulin at 13 years of age, although the proband did not exhibit pathological hypoglycemia. The proband carries a missense EP300 variant identical to the variant found in the unrelated proband 2 (p.F1595V). These probands do not carry any mutations in genes implicated in MODY or hyperinsulinemic hypoglycemia, and none of them has a first-degree relative history of early-onset glucose dysregulation. We performed Sanger sequencing and proved that the variants are de novo in the probands and are absent from their parents. According to the American College of Medical Genetics and Genomics guidelines for the interpretation of sequence variants (Richards et al., 2015), the microdeletion, the p.H1255R variant and the p.F1595V variant are “likely pathogenic” for glucose dysregulation. This is based on the fact that the variants are 1) located within the well-established domains of p300 (PM1), 2) confirmed de novo in the patients and not in their parents (PS2), and 3) predicted to exert deleterious effects on the protein by SIFT/PolyPhen (PP3). ControlProband Intensity of p300 Relative to TBP (A.U.)**A B 88  It is puzzling that the glucose phenotypes appear reciprocal to each other: proband 1 appears to have insufficient insulin secretion that has been well controlled, whereas probands 2 and 3 had hyperinsulinemic hypoglycemia in infancy. Proband 4 is difficult to classify, other than to note her relatively elevated plasma insulin. Possible explanations for the reciprocal phenotypes include ascertainment bias (the phenomenon whereby the inherently nonrandom process of case selection itself disproportionately recruits patients with apparently similar biochemical features but that in fact have different aetiologies), and different physiological consequences for glucose metabolism from loss-of-function variants in EP300 compared with gain-of-function variants in EP300. However, reciprocal glucose phenotypes have been observed in patients with mutations in some of the MODY genes that have opposing functional effects on their cognate proteins. For example, gain-of-function mutations in ABCC8 cause neonatal diabetes (de Wet et al., 2007), whereas loss-of-function mutations in the same gene cause neonatal hyperinsulinemic hypoglycemia (Kapoor et al., 2011).  Because p300 can auto-acetylate, we investigated the effects of the missense EP300 variants on p300 auto-acetylation. Cells over-expressing the p300 p.H1255R or p.F1595V mutants exhibited increased p300 auto-acetylation (Figure 27), which is thought to enhance p300 KAT activity (Delvecchio et al., 2013; Rack et al., 2015). The auto-acetylation was weak in cells over-expressing WT p300, and was completely absent in cells over-expressing the p300 p.D1399Y mutant that has no KAT activity (Hilton et al., 2015). These data suggest that the p.H1255R and p.F1595V variants are likely gain-of-function mutations, in contrast to the p300 haploinsufficiency that is presumably a loss-of-function mutation. These data further suggest that the rare pathogenic EP300 variants are associated causally, rather than coincidentally, with the distinct glucose phenotypes.  89    Figure 27. The p300 p.H1255R and p.F1595V variants are likely gain-of-function.  Western blotting for acetyl-p300, myc-tag and β tubulin (a loading control) in HEK293 cells transfected with GFP plasmids, WT p300 plasmids, or plasmids of p300 mutants including p.D1399Y (which lacks KAT activity), p.H1255R or p.F1595V.  β cell-specific p300 knockout mice develop glucose intolerance due to reduced β cell area and insulin granule size. The MODY-like phenotype of proband 1 led us to hypothesize that p300 deficiency in β cells causes glucose intolerance. To test this, we generated β cell-specific p300 knockout mice by crossing Ins1-Cre mice with Ep300fl/fl mice (Kasper et al., 2006; Thorens et al., 2015). Immuno-staining experiments confirmed the deletion of p300 specifically in the β cells of adult Cre-positive, Ep300fl/fl (p300βKO) mice (Figure 28).   90    Figure 28. Expression of p300 and CBP in p300βKO mouse islets. Representative immunofluorescence images of p300 and CBP in ten-week-old WT and p300βKO mouse islets. Scale bar = 50 µm.  p300βKO mice were glucose intolerant by eight weeks of age, whereas Cre-negative Ep300fl/fl (WT) mice and Cre-positive, Ep300fl/wt (p300βHet) mice were glucose tolerant (Figure 29A). p300βKO mice failed to secrete a sufficient amount of insulin when they were challenged by glucose (Figure 29B), although these mice had normal insulin tolerance (Figure 29C). Other metabolic parameters of p300βKO mice were normal (Figure 30).   91    Figure 29. p300βKO mice are glucose intolerant due to impaired insulin secretion in vivo. (A) Intraperitoneal glucose tolerance tests in eight-week-old male WT (n = 16), p300βHet (n = 4) and p300βKO (n = 9) mice. (B) Plasma insulin measurement in ten-week-old male WT (n = 12) and p300βKO (n = 6) mice before and fifteen minutes after glucose injection. (C) Insulin tolerance tests in nine-week-old male WT (n = 7) and p300βKO (n = 4) mice. Two-way ANOVA for Figures A and B. * p < 0.05, ** p < 0.01, *** p < 0.001 0 30 60 90010203040Time (minute)Blood Glucose (mmol/L)WT p300βHet p300βKO******0 (minute)Plasma Insulin (ng/mL)WT p300βKO**0 30 60 90050100150Time (minute)% of Fasting Blood GlucoseWT p300βKOA B C 92    Figure 30. p300βKO mice have normal energy metabolism. (A) Body composition of ten-week-old male WT (n = 4), and p300βKO (n = 4) mice. (B) Energy expenditure of ten-week-old male WT (n = 4) and p300βKO (n = 4) mice. (C) Food intake of ten-week-old male WT (n = 4) and p300βKO (n = 4) mice. (D) Locomotor activity of ten-week-old male WT (n = 4) and p300βKO (n = 4) mice.  Adult p300βKO mouse pancreata had reduced β cell area but unchanged α cell area. (Figure 31). Pancreas weight in the knockout mice was normal. Glucose-stimulated insulin secretion of p300βKO mouse islets was comparable to control islets (Figure 32A). However, the knockout islets had reduced insulin content (Figure 32B). Dense core Fat MassLean MassBody Weight010203040Mass (g)WT p300βKODark PhaseLight PhaseTotal012345Food Intake (g)0 12 24 36 480. (hour)Energy Expenditure (kcal/hour)0 12 24 36 4805000100001500020000Time (hour)Locomotor Activity (Count)A BC D 93  granules in p300-null β cells were visibly smaller, but the density of the dense core granules in these cells was normal (Figure 32C and Figure 32D). The density of immature granules, which appear pale on the electron micrographs, in p300-null β cells was also normal (Figure 32E). The granule phenotypes agreed with the normal plasma proinsulin-to-C-peptide ratio in p300βKO mice (Figure 32F). In summary, deleting p300 in β cells alone is sufficient to cause glucose intolerance associated with defective β cell area and insulin granule size. The glucose phenotypes of p300βKO mice are largely similar to those of the islet-specific p300 knockout mice (Wong et al., 2018), but the p300βKO mice show no apparent phenotypes in α cells at the histological level.    Figure 31. p300βKO mice have reduced β cell area. (A) The quantification of β cell and α cell area in ten-week-old male WT (n = 5) and p300βKO (n = 4) mice. (B) Pancreas weight in ten-week-old male WT (n = 5) and p300βKO (n = 3) mice. (C) β-to-α cell ratios in ten-week-old male WT (n = 5) and p300βKO (n = 4) mouse pancreata. Student’s t test for Figures A and C. * p < 0.05    β cellα cell0. of Pancreas AreaWT p300βKO*WTp300BetaKO0. Weight (g)WTp300BetaKO02468β-to-α ratio*A B C 94    Figure 32. Phenotyping of p300βKO mouse islets. (A) Perifusion assays on islets isolated from ten-week-old male WT (n = 3) and p300βKO (n = 3) mice. (B) Insulin content in islets isolated from ten-week-old male WT (n = 7) and p300βKO (n = 7) mice. (C) Representative transmission electron micrographs of β cells in islets isolated from ten-week-old male WT and p300βKO mice. Scale bar = 2 µm. (D) The mean dense core granule size of β cells in islets isolated from ten-week-old male WT (n = 3) and p300βKO (n = 3) mice. (E) The dense core granule density of β cells in islets isolated from ten-week-old male WT (n = 3) and p300βKO (n = 3) mice. (F) Plasma proinsulin-to-C-peptide ratios in ten-week-old male WT (n = 8) and p300βKO (n = 6) mice. Student’s t test for Figure B and D. * p < 0.05, *** p < 0.001  Loss of p300 impairs transcription correlated with a global loss of H3K27ac in mature β cells. Based on the observed β cell defects in p300βKO mice, we hypothesized that loss of p300 impairs transcription and H3K27 acetylation, a p300/CBP-dependent histone modification thought to be important for establishing active enhancers (Creyghton et al., 2010; Jin et al., 2011), in β cells. We bred mTmG transgenes, which express GFP 0 20 40 60 8001020304050Time (minute)Insulin Secreted (ng/mL/100 islets)WT p300βKO16.7 mM Glucose30 mMKClWTp300βKO0. Dense CoreGranule Size (µm2)*WTp300βKO0. Core Granule Density (Count/µm2)WTp300βKO0100200300400Islet Insulin Content (ng/µg of Total Protein) ***WTp300βKO0510152025Proinsulin/C-Peptide Ratio (%)A BD E FC 95  following exposure to the Cre recombinase, onto Cre-positive, Ep300wt/wt (Cre-positive controls) and p300βKO mice to track the recombined β cells (Muzumdar et al., 2007). Using 10-week old animals, we isolated and dispersed islets from these mice, and sorted for GFP-positive cells, followed by bulk RNA-seq and H3K27ac ChIP-seq on the sorted cells.  RNA-seq data revealed 418 differentially expressed (DE) genes in p300-null β cells, of which 317 (76%) were down-regulated. We performed GO term analysis on the down-regulated gene set. The gene set was significantly associated with the GO term implicated in insulin secretion. Some notable genes associated with the term include Slc30a8, Tmem27, and Glud1. Slc30a8 encodes zinc transporter 8 that is implicated in insulin granule biogenesis (Lemaire et al., 2009). As discussed previously, Tmem27 encodes a plasma membrane protein that regulates β cell proliferation (Akpinar et al., 2005). Glud1 encodes a glutamate dehydrogenase that can cause hyperinsulinemic hypoglycemia when a gain-of-function variant is present in the gene (Stanley et al., 1998). These data suggest that β cell-specific loss of p300 impairs the expression of genes important for β cell function.  From our ChIP-seq data, we identified 22,826 H3K27ac peaks in control β cells, but only 17,060 peaks in p300-null β cells (Figure 33A). Deleting p300 in β cells significantly reduced the enrichment of most H3K27ac peaks as shown by the differential binding analysis (Figure 33B). Immuno-staining confirmed the H3K27ac reductions, which excluded the possibility that the reductions were an artefact from immunoprecipitation or sequencing (Figure 33C and Figure 33D). In contrast, H3K27me3 levels in p300-null β cells were normal (Figure 33E and Figure 33F). Next, we performed islet chromatin segmentation on our control H3K27ac data and the previously published islet H3K4me1, H3K4me3, H3K27me3, and H3K9me3 ChIP-seq data using ChromHMM (Ernst and Kellis, 2017; Hoffman et al., 2010; Tennant et al., 2013) (Figure 33G). We used the chromatin state data to divide the H3K27ac peak sets into active promoter  96  H3K27ac and active enhancer H3K27ac, followed by the analysis of each peak set separately.   Figure 33. H3K27ac ChIP-seq experiments reveal significant loss of H3K27ac in p300-null β cells. (A) IDR analysis on H3K27ac ChIP-seq data generated from GFP-positive cells enriched from ten-week-old male WT and p300βKO mouse islets. (B) A histogram plot showing the signal strength difference at each H3K27ac peak derived from GFP-positive cells from WT and p300βKO mouse islets. (C) Representative immunofluorescence images of H3K27ac in ten-week-old male WT and p300βKO mouse islets. Scale bar = 50 µm. (D) The quantification of β cell H3K27ac levels normalized to pancreatic exocrine cell H3K27ac levels in ten-week-old male WT (n = 5) and p300βKO (n = 4) mice. (E) Representative immunofluorescence images of H3K27me3 in ten-week-old male WT and p300βKO mouse islets. Scale bar = 50 µm. (F) The quantification of β cell H3K27me3 levels normalized to pancreatic exocrine cell H3K27ac levels in ten- 97  week-old male WT (n = 3) and p300βKO (n = 3) mice. (G) An eight-state chromatin segmentation data generated from previously published islet histone mark data (H3K4me1, H3K4me3, H3K27me3, H3K9me3) and from our β cell H3K27ac data using ChromHMM. Student’s t test for Figure D. * p < 0.05  We examined how loss of p300 may affect active promoter and enhancer H3K27 marks and the expression of their affiliated loci. In p300-null β cells, the H3K27ac signal at active enhancers was lower than at active promoters (Figure 34A and Figure 34B), which indicates that loss of p300 appears to reduce active enhancer H3K27ac preferentially over active promoter H3K27ac signals. In general, the loci with reduced H3K27ac signals were down-regulated (Figure 34C). Furthermore, the down-regulation was the strongest at loci with altered enhancer H3K27ac signals, followed by the loci with altered promoter H3K27ac signals. A specific example of a down-regulated gene with significant H3K27ac loss at its enhancer was the Kcnj12 locus (Figure 34D). Based on the correlation between H3K27ac enrichment and gene expression, and the association between reduced enhancer H3K27ac enrichment and DE genes (Figure 34E and Figure 34F), the gene down-regulation in p300-null β cells can be attributed, at least in part, to the loss of active enhancer H3K27ac. Overall, these data indicate that β cell enhancers require p300-mediated H3K27 acetylation to fully activate β cell transcription.   98    Figure 34. Reduced H3K27ac enrichment in p300-null β cells correlates with impaired gene expression. (A) A violin plot showing the reduced enhancer and promoter H3K27ac enrichment in p300-null β cells. (B) Profile plots showing the distribution of the enrichment scores of the aggregated promoter and enhancer H3K27ac enrichment in control and p300-null β cells. (C) A violin plot showing the gene expression changes at loci harbouring promoters or enhancers with or without differential H3K27ac enrichment. Differential: A B CDE F 99  H3K27ac signals were significantly different between control and p300-null β cells. (D) Signal tracks for RNA-seq and H3K27ac ChIP-seq between control and p300-null β cells at the Kcnj12 locus. (E) A correlation plot between gene expression changes and all H3K27ac signal changes at loci that are differentially expressed between control and p300-null β cells. (F) A violin plot showing the changes in active enhancer H3K27ac enrichment at loci that are differentially expressed or non-differentially expressed. Mean values for each group were shown at the bottom of the violin plots. Mann-Whitney U test for Figures A and F. Kruskal-Wallis H test for Figure C.   β cell-specific p300 deletion impairs neonatal β cell proliferation. We investigated the developmental phenotypes of p300βKO mice at P7. p300βKO pups had reduced β cell area but normal α cell area (Figure 35A). These pups had fewer β cells positive for the proliferation marker Ki67 as shown by immuno-staining (Figure 35B and Figure 35C). Similarly, there were less neonatal p300-null β cells that incorporated EdU, suggesting a reduced proportion of p300-null β cells in the S phase (Figure 35D and Figure 35E) (Buck et al., 2008). We did not detect any apoptotic events in either WT or p300βKO mouse islets at P7 as assessed by TUNEL assays. Thus, the reduced β cell area in p300βKO mice is a consequence of impaired proliferation.      100    Figure 35. Deletion of p300 impairs neonatal β cell proliferation. (A) The quantification of β cell and α cell area in P7 WT (n = 9) and p300βKO (n = 8) mice. (B) The quantification of Ki67-positive β cells and α cells in P7 WT (n = 7) and p300βKO (n = 7) mice. (C) Representative immunofluorescence images of Ki67 in P7 WT and p300βKO mouse β cells and α cells. Scale bar = 50 µm. (D) The quantification of EdU-positive β cells and α cells in P7 WT (n = 6) and p300βKO (n = 6) mice. (E) Representative immunofluorescence images of EdU in P7 WT and p300βKO mouse β cells and α cells. Scale bar = 50 µm.  Neonatal p300-null β cells remain neuropeptide Y-positive and exhibit impaired expression of genes associated with glucose response and homeostasis. To identify the molecular changes underlying the proliferation defect, we sorted for GFP+ cells from P7 Cre-positive control and p300βKO mouse pancreata, and subjected the sorted cells to single cell RNA-seq. However, because we only performed the sorting once, the sorted cells subjected to single cell RNA-seq were not highly purified for only GFP+ cells. To circumvent this bioinformatically, we identified the β cell clusters β cellα cell0. of Pancreas Area*β cellα cell0102030% Ki67+*β cellα cell051015% EdU+*ABDCE 101  from the control and p300βKO mouse pancreata based on the expression of Ins1, Ins2, and GFP (Figure 36).    Figure 36. The clustering of single cell RNA-seq data sets from control and p300βKO neonatal pancreatic cells. The unsupervised clustering of P7 pancreatic cells from control (n = 635) and p300βKO (n = 631) mice by (A) genotypes, (B) unsupervised clusters, (C) Ins1 expression, (D) Ins2 expression, and (E) GFP expression. (F) Filtered control (n = 125) and p300-null (n = 156) neonatal β cell clusters. Clusters 2, 5, 9 and 14 were used to identify the β cell clusters as in Figure F.  There were 134 differentially expressed genes in neonatal p300-null β cells. The gene with the greatest change in expression was Npy (Figure 37A), which was also upregulated in p300IsletKO mice (see Chapter 3). The significant increase in Npy expression was due to an increased proportion of β cells positive for Npy (Figure 37B). The immuno-staining data confirmed the phenotype, where 25% of the P7 p300-null β  102  cells expressed Npy, as opposed to 5% in controls (Figure 37C and Figure 37D). These data suggest a role of p300 in down-regulating Npy during β cell development, perhaps indirectly by binding to NeuroD1.    Figure 37. Neonatal p300-null β cells express high levels of Npy. (A) Gene expression levels of Npy in control and p300-null β cell clusters. (B) The percentages of Npy-positive control and p300-null β cells in the β cell clusters. (C) The WTp300βKO010203040% of Npy+ β Cells at P7***A B CD 103  quantification of Npy-positive β cells in P7 WT (n = 5) and p300βKO (n = 4) pancreata. (D) Representative immunofluorescence images of Npy in P7 WT and p300βKO mouse islets. Scale bar = 50 µm. Student’s t-test for Figure D. *** p < 0.001.  Several genes down-regulated in neonatal p300-null β cells may account for the proliferation defects. These genes included Tmem27, which was also down-regulated in adult p300-null β cells, Mlxipl, and Ccnd2. Mlxipl encodes ChREBP, a glucose-responsive transcription factor that mediates β cell proliferation (Metukuri et al., 2012). Ccnd2 encodes cyclin D2, which is important for neonatal β cell proliferation (Georgia and Bhushan, 2004). The gene down-regulation in neonatal p300-null β cells was associated with reduced H3K27 acetylation at P7 (Figure 38A and Figure 38B). Lastly, we superimposed the P7 single cell RNA-seq data on the 10-week-old adult RNA-seq data. There were 17 DE genes overlapping between P7 and adult p300-null β cells (Figure 38A). These 17 genes were enriched for GO terms implicated in glucose homeostasis (Figure 38B). Among them, Tmem27 was consistently down-regulated throughout the development of p300-null β cells. At the protein level, neonatal p300-null β cells also expressed less Tmem27 (Figure 38C). Overall, p300 maintains expression of genes critical for glucose homeostasis throughout development.   104    Figure 38. Neonatal p300-null β cells show reduced expression in genes important for glucose homeostasis.  (A) Representative immunofluorescence images of H3K27ac in P7 WT and p300βKO mouse islets. Scale bar = 50 µm. (B) The quantification of β cell H3K27ac levels normalized to pancreatic exocrine cell H3K27ac levels between P7 WT (n = 9) and p300βKO (n = 9) mice. (C) The 17 differentially-expressed genes overlapping between p300-null β cells at P7 and adult p300-null β cells. (D) GO analysis on the 17 overlapping DE genes between P7 and adult p300-null β cells. (E) Representative immunofluorescence images of Tmem27 in β cells from P7 WT and p300βKO mice. Scale bar = 50 µm.  105  CBPβKO mice develop similar glucose and β cell phenotypes to p300βKO mice We hypothesized that Ins1-Cre-positive β cell-specific p300/CBP double knockout mice could survive past weaning, unlike their Neurog3-Cre counterparts that die shortly after birth. During the breeding process, we generated and phenotyped mice lacking CBP in β cells (CBPβKO). These mice exhibited glucose intolerance at eight weeks of age (Figure 39A), although they were insulin tolerant (Figure 39B). CBPβKO mice secreted less insulin when challenged by a glucose bolus (Figure 39C). At ten weeks of age, CBPβKO mice displayed reduced β cell area but normal α cell area (Figure 40A). The specific reduction in β cell area decreased the β-to-α ratios in CBPβKO mice (Figure 40B). At P7, we also observed reduced β cell area in CBPβKO mice (Figure 40C). These data indicate that CBPβKO mice share a similar β cell area phenotype, and perhaps a similar proliferation defect, with p300ΒKO mice.    Figure 39. Mice lacking CBP in β cells are glucose intolerant due to impaired insulin secretion. (A) Intraperitoneal glucose tolerance tests in eight-week-old male WT (n = 6) and CBPβKO (n = 4) mice. (B) Insulin tolerance tests in nine-week-old male WT (n = 6) and CBPβKO (n = 4) mice. (C) Plasma insulin measurement in ten-week-old male WT (n = 5) and CBPβKO (n = 4) mice before and fifteen minutes after glucose injection. Two-way ANOVA for Figures A and C. * p < 0.05, ** p < 0.01, *** p < 0.001  0 30 60 90010203040Time (minute)Blood Glucose (mmol/L)***WT CBPβKO0 30 60 90050100150Time (minute)% of Fasting Blood Glucose0 (minute)Plasma Insulin (ng/mL)***A B C 106    Figure 40. CBPβKO mice have reduced β cell area throughout development. (A) The quantification of β cell and α cell area in ten-week-old male WT (n = 5) and CBPβKO (n = 5) mice. (B) β-to-α ratios in ten-week-old male WT (n = 5) and CBPβKO (n = 5) mice. (C) The quantification of β cell and α cell area in P7 WT (n = 6) and CBPβKO (n = 6) mice. Student’s t test for Figures A, B and C. * p < 0.05  The Ins1-Cre-positive mice without any p300 or CBP in their β cells (p300/CBPβdKO) survived past weaning. However, all these mice died between six and eight weeks of age. Plasma insulin measurement after a five-hour fast indicated that p300/CBPβdKO mice failed to produce detectable amounts of plasma insulin (0.028 ± 0.008 ng/mL; n = 3; the sensitivity of the Stellux insulin ELISA is 0.089 ng/mL). At the histological level, p300/CBPβdKO mouse islets harboured very few β cells and were interspersed by α cells (Figure 41A). These islets also appeared smaller in general. Using mTmG transgenes to label the Ins1 lineage, a significant portion of GFP-positive cells failed to express insulin (Figure 41A). Very few insulin-positive β cells did not express GFP. We speculated that these GFP-negative cells had either escaped recombination entirely, or had not yet undergone recombination, such that this population of β cells may have β cellα cell0. of Pancreas AreaWT CBPβKO*WTCBPβKO 0246810Beta-to-alpha ratio*β cellα cell0. of Pancreas Area*A B C 107  allowed some of the p300/CBPβdKO mice to survive past the perinatal stage. The α cells in the double knockout islets were not GFP-positive. Therefore, it was unlikely that the rest of the recombined Ins1 lineage transdifferentiated into α cells. Because p300/CBP are essential for H3K27 acetylation (Jin et al., 2011), we expected H3K27ac to be absent in the double knockout β cells. As expected, the nuclei of the GFP-positive cells stained negative for H3K27ac (Figure 41B). Taken together, we have functionally validated the complete loss of p300/CBP in the double knockout β cells and have found no evidence of redundant KAT activity for H3K27ac outside of p300/CBP. These findings also suggest that expression of p300/CBP is necessary for β cells to develop and function properly.   108    Figure 41. p300/CBPβdKO mice die between six and eight weeks of age due to failure to produce insulin and a complete loss of H3K27ac in β cells. (A) Representative immunofluorescence images of GFP in ten-week-old p300βKO and eight-week-old p300/CBPβdKO mouse islets. The white arrows indicate the β cells that still expressed insulin, or β cells that had not recombined. Scale bar = 50 µm. (B) Representative immunofluorescence images of H3K27ac in ten-week-old p300βKO and eight-week-old p300/CBPβdKO mouse islets. Scale bar = 20 µm.   109  4.3 Discussion Our human and mouse studies highlight an important role of p300 in regulating β cell-mediated glucose homeostasis. Interestingly, the functional consequences of the EP300 missense variants in our small patient cohort appear to be opposite to the functional consequence of the microdeletion. The missense variants may operate as gain-of-function variants that can enhance p300 auto-acetylation, whereas the microdeletion results in p300 haploinsufficiency, which is likely to reduce the acetylation of p300 and H3K27. The early-onset diabetes in proband 1 with p300 haploinsufficiency is consistent with the early-onset glucose intolerance in p300βKO mice. Although the heterozygous mice did not exhibit any apparent glucose phenotypes, they may harbour subtler defects in β cells that may only result in glucose intolerance under metabolic stress, such as a high-fat diet challenge. Our findings indicate that the mutations reported here likely alter the structures and functions of p300 proteins, which in turn contribute to the differential glucose phenotypes in the probands.   Notably, the EP300-associated glucose phenotypes are highly similar to the phenotypes of MODY and/or hyperinsulinemic hypoglycemia associated with mutations in HNF1A or HNF4A (Stanescu et al., 2012). Because Hnf1α and Hnf4α can recruit p300 (Ban et al., 2002; Eeckhoute et al., 2004), the EP300-associated glucose phenotypes may involve Hnf1α/Hnf4α. In p300-null β cells, the reduction in Tmem27, a gene down-regulated in Hnf1α KO mice (Akpinar et al., 2005), also indicates a potential role of Hnf1α in maintaining β cell development in mice, and perhaps also in humans. Interestingly, previous studies have tied CBP to the pathogenesis of type 2 diabetes as a result of the interaction among type 2 diabetes susceptibility loci, including HNF1A and HNF4A (Morris et al., 2012). Future studies are warranted to test whether CREBBP mutations are associated with early-onset glucose dysregulation.  The similar phenotypes between islet/β cell-specific p300 and CBP knockout mice suggest that CBP is likely a functionally redundant paralog of p300 in β cells.   110  Deleting p300/CBP specifically in β cells allows p300/CBPβdKO mice to survive past weaning for a short period of time. The undisturbed α cells in newborn p300/CBPβdKO mice likely secrete enough glucagon to maintain blood glucose levels during fasting. In contrast, loss of p300/CBP in α cells in the Neurog3-Cre model may lead to severe hypoglycemia shortly after birth and therefore accounts for the early lethality. Alternatively, the later onset of recombination in the Ins1-Cre model may allow insulin secretion to last for a short period of time after birth. This is evident from the presence of a small, but detectable, number of β cells in p300/CBPβdKO mouse islets. As the mice develop past weaning, the minimal insulin secretion in p300/CBPβdKO mice is no longer sufficient for maintaining normoglycemia. This is because the complete loss of p300/CBP abolishes insulin production in most of the recombined β cells, perhaps due to the complete loss of H3K27ac.   To conclude this Chapter, our data on mice lacking p300 specifically in β cells provide mechanistic explanations on how defective p300 functions can cause glucose dysregulation at the cellular level. The p300-mediated glucose dysregulation in mice may arise from the defective insulin granule synthesis that can impair whole body insulin secretion capacity; from the impaired β cell proliferation and reduced functional β cell mass; and from the impaired β cell maturation such that many developing β cells still expressed Npy. At the molecular level, p300 maintains the optimal expression of genes important for glucose homeostasis and β cell function, likely by acetylating H3K27 at active enhancers and promoters. Our human data indicate that glucose dysregulation, including early-onset diabetes and hyperinsulinemic hypoglycemia, may be a relevant complication in patients with EP300 mutations. Taken together, our human data justify caregiver vigilance regarding the risk of hypoglycemic episodes and early-onset diabetes for patients with EP300-related RTS, and our mouse data inform on how defective p300 function may result in human glucose dysregulation.   111  Chapter 5: p300, Nkx6.1, NeuroD1, and Pdx1 co-occupies loci critical for mature β cell function 5.1 Introduction Differentiated β cells secrete insulin upon glucose stimulation, and the mature state requires constant maintenance. The maintenance of β cell maturity depends on transcription factors such as MafA, Pdx1, NeuroD1 and Nkx6.1. These factors dock at and activate loci required by mature β cells (Gao et al., 2014; Gu et al., 2010; Nishimura et al., 2015; Taylor et al., 2013). Notably, studies have documented that β cells can dedifferentiate in the setting of type 2 diabetes (Talchai et al., 2012). β cell dedifferentiation involves the loss of insulin expression, together with the aberrant expression of various pluripotent markers and the endocrine progenitor marker Ngn3. Identifying the mechanisms that establish and maintain β cell maturity could be informative on the ways to prevent β cell dedifferentiation.  Transcription factors can recruit transcription cofactors for additional fine-tuning to synergistically activate or silence gene expression (Morgunova and Taipale, 2017). In β cells, one of these cofactors is p300, a transcriptional coactivator with KAT activity. In Chapters 3 and 4, we have demonstrated that p300 is required for β cell proliferation and insulin granule biosynthesis (Wong et al., 2018). However, whether p300 directly regulates mature β cell function remains unsolved. In this Chapter, we report the phenotypes of mice lacking p300 specifically in mature β cells, and the p300 occupancy in mature β cells, and the consequences of postnatal p300 deletion on β cell H3K27 acetylomes and transcriptomes. These studies will clarify the specific role of p300 in mature β cells.       112  5.2 Results The tamoxifen-induced deletion of p300 in mature β cells leads to glucose intolerance We generated mice carrying a Pdx1-CreER transgene and Ep300 flox/flox alleles (p300PβKO). To induce recombination, we administered tamoxifen to these animals at six weeks of age. The tamoxifen treatment effectively induced recombination in the β cells of p300PβKO mice as shown by the absence of p300 in these cells (Figure 42).    Figure 42. Tamoxifen treatment induces p300 deletion in postnatal p300PβKO mouse β cells. Representative immunofluorescence images of p300 and CBP in male WT and p300PβKO mouse islets three weeks after tamoxifen treatment. Scale bar = 50 µm.   113  After tamoxifen treatment, we monitored the glucose tolerance of control and p300PβKO mice for nine weeks. p300PβKO mice gradually became glucose intolerant between five and seven weeks after tamoxifen treatment (Figure 43A to Figure 43C). All p300PβKO mice became glucose intolerant nine weeks after tamoxifen treatment, whereas Cre-negative, flox-positive mice and Cre-positive, flox-negative mice were glucose tolerant at the same time point (Figure 43D). We performed all subsequent experiments on mice nine weeks after tamoxifen treatment, which was considered as the experimental end point.  p300PβKO mice were insulin tolerant by the time they developed glucose intolerance (Figure 44A). However, these mice exhibited impaired glucose-induced insulin secretion in vivo (Figure 44B). Other metabolic parameters including body composition, energy expenditure, food intake and locomotor activities were normal in p300PβKO mice (Figure 45), suggesting that postnatal p300 deletion in the Pdx1-Cre lineage did not impact overall energy balance significantly through an extra-pancreatic mechanism. Overall, postnatal expression of p300 in the Pdx1 lineage maintains glucose homeostasis and insulin secretion in vivo.  114    Figure 43. p300PβKO mice gradually develop glucose intolerance after tamoxifen administration. Intraperitoneal glucose tolerance tests in male WT (n = 5), Pdx1-CreER (n = 7) and p300PβKO (n = 4) mice (A) three weeks, (B) five weeks, (C) seven weeks and (D) nine weeks after tamoxifen (TMX) administration. Two-way ANOVA for comparison between WT and p300PβKO mice for Figures C and D. * p < 0.05, *** p < 0.001. 0 30 60 90010203040Time (minute)Blood Glucose (mmol/L)WT + TMX Pdx1-CreER + TMXp300PBetaKO + TMX0 30 60 90010203040Time (minute)Blood Glucose (mmol/L)*0 30 60 90010203040Time (minute)Blood Glucose (mmol/L)0 30 60 90010203040Time (minute)Blood Glucose (mmol/L)* ***3 weeks post-TMX 5 weeks post-TMX7 weeks post-TMX 9 weeks post-TMXA BC D 115    Figure 44. p300PβKO mice have normal insulin tolerance but impaired insulin secretion.  (A) Insulin tolerance tests in male WT (n = 3) and p300PβKO (n = 4) mice nine weeks after tamoxifen treatment. (B) Plasma insulin measurement in male tamoxifen-treated WT (n = 5) and p300PβKO (n = 5) mice before and fifteen minutes after glucose injection. Two-way ANOVA for Figures A and B. * p < 0.05, ** p < 0.01, *** p < 0.001  0 30 60 90050100150Time (minute)% of Fasting Blood Glucose WT + TMX p300PβKO + TMX0 (minute)Plasma Insulin (ng/mL)*A B 116    Figure 45. p300PβKO mice have normal body composition and energy balance. (A) Body composition of male WT (n = 4) and p300PβKO (n = 5) mice nine weeks after tamoxifen administration. The (B) energy expenditure, (C) food intake and (D) locomotor activity of male WT (n = 4) and p300PβKO (n = 4) mice nine weeks after tamoxifen administration as measured by metabolic cages.     Postnatal deletion of p300 in β cells impairs islet glucose-stimulated insulin secretion At the end point, p300PβKO mouse pancreata showed normal pancreas weight, β cell area or islet size (Figure 46). Therefore, defects in β cell mass do not account for the Fat MassLean MassBody Weight010203040Mass (g)WT + TMX p300PβKO + TMXDark PhaseLight PhaseTotal01234Food Intake (g)Dark PhaseLight PhaseTotal0. Expenditure (kcal/hour)Dark PhaseLight PhaseTotal020000400006000080000100000Locomotor Activity (Count)A BC D 117  glucose intolerance in p300PβKO mice. Next, we assessed the β cell function of p300PβKO mouse islets by performing perifusion assays. p300PβKO mouse islets secreted less insulin when stimulated by glucose, for both first-phase and second-phase responses, or KCl (Figure 47A). The overall blunting of islet insulin secretion was associated with lower islet insulin content (Figure 47B), which was due to the smaller dense core granules as shown by TEM (Figure 47C to Figure 47E). Therefore, the underlying cause of the glucose intolerance in p300PβKO mice is specifically due to β cell dysfunction rather than to defective β cell development. Furthermore, p300βKO and p300PβKO mice harbour a similar insulin granule phenotype, indicating that the insulin granule defect in these mice arises independent of developmental insults.    Figure 46. p300PβKO mice have normal pancreas weight, islet cell mass and islet size.  (A) Pancreas weight in male WT (n = 6) and p300PβKO (n = 6) mice nine weeks after tamoxifen treatment. (B) β cell, α cell and δ cell area in male WT (n = 4) and p300PβKO (n = 5) mice nine weeks after tamoxifen treatment. (C) Mean islet area in male WT (n = 4) and p300PβKO (n = 4) mice nine weeks after tamoxifen treatment. WT + TMXp300PβKO  + TMX0. Weight (g)β cellα cellδ cell of Total Pancreas AreaWT + TMX p300PβKO + TMXWT + TMXp300PβKO  + TMX0100200300400Mean Islet Size (µm2)A B C 118    Figure 47. p300PβKO mouse islets secrete less insulin and exhibit reduced insulin granule size. (A) Perifusion assays on islets isolated from male WT (n = 3) and p300PβKO (n = 3) mice nine weeks after tamoxifen treatment. (B) Islet insulin content in male WT (n = 4) and p300PβKO (n = 4) mice nine weeks after tamoxifen treatment. (C) The mean size of dense core granules in the β cells of male WT (n = 3) and p300PβKO (n = 3) mice nine weeks after tamoxifen treatment. (D) Representative transmission electron micrographs of the β cells from male WT and p300PβKO mice nine weeks after tamoxifen treatment. Scale bar = 2 µm. (E) The mean density of dense core granules in the β cells of male WT (n = 3) and p300PβKO (n = 3) mice nine weeks after tamoxifen treatment. Two-way ANOVA for Figure A. Student’s t test for Figures B and C. * p < 0.05  0 20 40 60 800510152025Time (minute)Insulin Secreted (ng/mL/100 islets)WT + TMX p300PβKO + TMX**16.7 mM Glucose30 mMKClWT + TMXp300PβKO  + TMX020406080100Islet Insulin Content (ng/mL/islet)*WT + TMXp300PβKO  + TMX0. Core Granule Desnity (Count/µm2)WT + TMXp300PβKO  + TMX0. Dense Core Granule Size (µm2)*A B CED 119  p300, Pdx1, NeuroD1, and Nkx6.1 co-occupy critical β cell loci The co-occupancy of key transcription factors and cofactors at the active enhancers of β cell genes can dictate β cell identity (Ediger et al., 2017; Gutiérrez et al., 2017; Swisa et al., 2017). p300 is known to co-occupy cell type-specific enhancers with multiple transcription factors in other cell types (Creyghton et al., 2010; McCord et al., 2011). To gain insights on the p300 occupancy in β cells, we performed p300 ChIP-seq on the MIN6 cell line. We identified a total of 1,944 peaks. Annotation of the p300 peak sets with our islet chromatin state data indicated that the majority (88%) of the p300 peaks resided in H3K4me3-positive/H3K27ac-positive active promoters, in H3K4me1-positive/H3K27ac-positive active enhancers or in H3K4me1-positive primed enhancers (Figure 48). These data agree with the role of p300 as a transcription coactivator and its association with active promoters or enhancers in islets or β cells.    Figure 48. The MIN6 p300 and CBP genome occupancy are located within the active promoters/enhancers in islets/β cells. Left panel: functional annotation of the histone mark signatures based on the islet chromatin state data as described in Chapter 4. Right panel: the fraction of p300 and CBP peak sets that was annotated with a particular islet chromatin state. The numbers on top indicate the number of peaks found in each ChIP-seq data set. The numbers in each box indicate the fraction of the p300 or CBP peaks annotated to that particular feature. The colour scale reflects the fraction of peaks belonging to the category, with deeper purple representing a fraction close to 1.  120  To understand the context of p300 occupancy with regards to other β cell transcription factors, we first performed de novo motif analysis on the p300 peak set. We used this method to predict the factors that may associate with p300 in islet or β cell genome. The p300 peak set was significantly enriched for four motifs (Figure 49A). These motifs corresponded to NeuroD1, Foxm1/Foxa2, Nkx6.1, and Rfx6, all of which are critical for pancreas or β cell development and function (Gao et al., 2007; Gu et al., 2010; Shirakawa et al., 2017; Smith et al., 2010; Taylor et al., 2013). In addition, we mapped the p300 peaks to nearby transcription start sites. p300 occupied numerous highly important β cell loci, including β cell hormones (Ins1, Ins2, Iapp), insulin processing proteins (Cpe, Pcsk1), insulin granule components (Slc30a8, Ero1lb), and β cell transcription factors (Nkx6-1, Mafa) (Figure 49B).     121    Figure 49. De novo motif analysis of the p300 peak set in β cell genome. (A) De novo motif analysis showing that the p300 peak set significantly enriched for motifs recognized by the bHLH family, the Forkhead family, the Homeobox family and the HTH family transcription factors. The transcription factors in the parentheses were the one most significantly enriched in the family, and all of them have known roles in β cell biology. (B) Signal tracks showing the regulatory sites at mouse Ins2 and Mafa loci. p300 and CBP peaks could be found at the active promoter of Ins2 and at the active enhancer of Mafa as denoted by the red arrows.  With the results from the motif analysis, we next asked whether the p300 occupancy overlaps with the occupancy of different β cell transcription factors in the β cell genome. We performed a permutation test-based analysis to ask whether the p300 peak set significantly overlapped with the published islet NeuroD1, Foxa2, Nkx6.1, Pdx1, and MafA ChIP-seq data sets (Hoffman et al., 2010; Taylor et al., 2013; Tennant et al., 2013). Interestingly, the p300 peak set clustered more significantly with the binding sites  122  of NeuroD1, Nkx6.1, and Pdx1, than with the binding sites of MafA and Foxa2 (Figure 50A). The co-occupancy among p300, NeuroD1 and Pdx1 agreed with the known interaction among these three factors at the insulin promoter (Qiu et al., 2002). Because the interaction between Nkx6.1 and p300 was not determined, we also performed co-immunoprecipitation experiments and showed that p300 could interact with Nkx6.1 (Figure 50B). These data suggest that p300 may occupy the binding sites of NeuroD1, Nkx6.1, and Pdx1 in β cells.  Because β cell transcription factors can work synergistically, we next examined the loci that could be co-occupied by p300 and the three transcription factors. Among the 1,059 p300 peaks that were occupied by either Pdx1, NeuroD1 or Nkx6.1, there were 153 peaks that could be co-occupied by all four factors (Figure 50C). More than 90% of these peaks were located within the H3K4me1-positive or H3K4me1-positive/H3K27ac-positive regions, suggesting that p300/NeuroD1/Nkx6.1/Pdx1 may co-occupy in β cell active enhancers specifically. Importantly, most of the 153 peaks were not occupied by MafA, Foxa2, or both (Figure 50D), which implied the specificity of the p300/NeuroD1/Nkx6.1/Pdx1 co-occupancy.   123    Figure 50. p300 co-occupies islet active enhancers with NeuroD1, Nkx6.1, and Pdx1. (A) A heatmap showing the significance of the overlap among the peak sets from different islet transcription factors and p300/CBP. A higher z score indicates a significant similarity between the two peak sets based on overlap. (B) Top panel: Western blotting for p300 in MIN6 cell lysates immunoprecipitated for IgG, Nkx6.1 or p300 (a positive control). Bottom panel: Western blotting for Nkx6.1 in MIN6 cell lysates immunoprecipitated for IgG, acetyl-lysine (AcK; a positive control) or CBP. (C) A Venn diagram showing the overlap among the p300-transcription factor peaks. The four-factor-co-occupied (NeuroD1/Nkx6.1/Pdx1/p300) sites were annotated with the islet chromatin state data. The number shown in the boxes indicates the fraction of the four-factor-occupied sites belonging to the annotation. (D) A Venn diagram showing the overlap between the four-factor-co-occupied sites and Foxa2 or MafA peaks.  To understand whether the co-occupancy has functional relevance, we annotated the NeuroD1/Nkx6.1/Pdx1/p300-co-occupied sites based on their proximity to transcription start sites, followed by GO and KEGG pathway analyses. These peaks were associated  124  with GO terms and KEGG pathways implicated in pancreas endocrine development, monogenic forms of diabetes and insulin secretion (Figure 51A and Figure 51B). These data reveal a putative set of p300/NeuroD1/Nkx6.1/Pdx1-co-occupied loci that are critical for β cell function.     Figure 51. p300/NeuroD1/Nkx6.1/Pdx1-co-occupied sites are associated with genes critical for β cell development and function. (A) Tables showing the GO term and KEGG pathway enrichment for the NeuroD1/Nkx6.1/Pdx1/p300-co-occupied sites. (B) Signal tracks showing the co-occupancy of NeuroD1/Nkx6.1/Pdx1/p300 at the active enhancers of the Nkx6.1 locus.  We also performed CBP ChIP-seq on MIN6 cells to determine whether CBP occupies p300 sites in β cell chromatin. We detected 1844 CBP peaks, and 89% of the peaks were located within active promoters, active enhancers or primed enhancers in islets or β cells (Figure 48). As expected, the CBP peak set clustered significantly with the p300  125  peak set, and also with the Pdx1, NeuroD1, and Nkx6.1 peak sets (Figure 50A and Figure 51B). Taken together, p300/CBP and other β cell transcription factors appear to co-occupy loci critical for β cell function.  Ablating p300 in mature β cells disrupts H3K27 acetylation and gene expression at p300-occupied loci In Chapter 4, we have shown that deleting p300 in developing β cells impairs global H3K27 acetylation. Because the cooperative binding of transcription factors may recruit p300/CBP for establishing genome-wide H3K27ac, we hypothesized that postnatal p300 expression disrupts the H3K27 acetylation and transcription at p300/NeuroD1/Nkx6.1/Pdx1-co-occupied loci in mature β cells.   To test this hypothesis, we performed RNA-seq and H3K27ac ChIP-seq experiments on islets isolated from control and p300PβKO mice. RNA-seq analysis identified 469 (358 down, 111 up) differentially expressed (DE) genes in p300PβKO mouse islets. The down-regulated genes were significantly enriched for the GO term associated with insulin secretion (Table 7). Importantly, p300PβKO mouse islets expressed 38% less Ins1 and 26% less Ins2. These data suggest that the impaired expression of genes involved in insulin synthesis may contribute to the smaller dense core granules in the p300-null β cells.                 126  Table 7. Down-regulated genes in p300PβKO mouse islets associated with the GO term insulin secretion.    We segregated the 13,556 genes expressed in mouse islets into three groups based on their proximal p300 occupancy: genes that were not occupied by p300 (p300-unoccupied), genes that were co-occupied by Pdx1/NeuroD1/Nkx6.1/p300 (four-factor-co-occupied) and genes that were occupied by p300 but did not fit the four-factor-co-occupied category. In general, p300-occupied loci was significantly down-regulated in p300PβKO mouse islets. Specifically, the four-factor-co-occupied loci were more strongly down-regulated than the other p300-occupied loci that were not co-occupied by all four  127  factors (Figure 52). These data indicate that expression of p300 is necessary to maintain the transcription of p300-occupied loci in mature β cells. In fact, the stable expression of loci that are critical for β cell development and function requires maximum co-occupancy by p300 and transcription factors.   Figure 52. Ablating p300 in mature β cells reduces the expression of p300-occupied genes significantly. A violin plot showing the expression level changes of loci that are unoccupied by p300, genes that are occupied by NeuroD1/Nkx6.1/Pdx1/p300 and genes that are occupied by p300 but do not fit the other categories between WT and p300PβKO mouse islets. Kruskal-Wallis H test for the data depicted in the Figure.   128  Surprisingly, the ChIP-seq experiments revealed a relatively undisturbed H3K27ac landscape in p300PβKO mouse islets (Figure 53A). Instead of observing global loss of H3K27ac, only 620 peaks displayed altered H3K27ac enrichment in p300PβKO mouse islet. 90% of these H3K27ac peaks were reduced, including the ones at Ins1 and Ins2. Next, we asked whether the p300-occupied loci may exhibit lower H3K27ac levels in p300PβKO mouse islets. In general, the H3K27ac enrichment at p300-occupied loci was reduced in these islets. Furthermore, the four-factor-co-occupied loci showed stronger reductions in H3K27ac enrichment than did the other p300-occupied loci (Figure 53B). Therefore, the four-factor-co-occupied loci in p300PβKO mouse islets showed significant down-regulation and reductions in H3K27ac enrichment. We also ask whether p300-occupied loci or loci with reduced H3K27ac more frequently harbour down-regulated genes in p300PβKO mouse islets. Indeed, down-regulated genes were more frequently found at p300-occupied loci or loci with reduced H3K27ac enrichment in these islets (Figure 53C and Figure 53D), as opposed to loci not occupied by p300 or loci without reductions in H3K27ac enrichment. Taken together, deleting p300 in postnatal β cells disrupts the occupancy of p300 at β cell loci, including insulin, and consequently impairs H3K27ac acetylation and transcription at these loci. These molecular defects may account for the defects in insulin granule size, insulin secretion, and glucose homeostasis in p300PβKO mice.  129     Figure 53. Ablating p300 in mature β cells impairs both H3K27ac enrichment and gene expression at p300-occupied loci. (A) A histogram showing the comparison of H3K27ac signals at each H3K27ac peak detected between WT and p300PβKO mouse islets. (B) A violin plot showing the changes in H3K27ac enrichment between WT and p300PβKO mouse islets at loci associated with different states of p300 occupancy. (C) A bar chart comparing the expression changes at loci that were or were not occupied by p300. (D) A bar chart comparing the expression changes at loci that showed significant reductions in H3K27ac or no reduction in H3K27ac. The number at the top of each bar in Figures C and D shows the total number of genes in the category. Numbers corresponding to the up-regulated  130  (green) or down-regulated (red) genes are also shown in the bar charts. Kruskal-Wallis H test for Figure B. Fisher’s exact test for Figures C and D.  5.3 Discussion In this Chapter, we demonstrate that expression of p300 in mature β cells is required to maintain glucose homeostasis. Loss of p300 in mature β cells impaired insulin secretion in vivo and ex vivo by perturbing islet insulin granule biosynthesis. These cellular phenotypes likely arise from the perturbation of p300-dependent coactivation of β cell genes, such as insulin. Our data reveal that p300 may coactivate β cell gene expression by two inter-dependent mechanisms: by interacting with transcription factors important for β cell function, such as NeuroD1, Nkx6.1, and Pdx1, and by acetylating H3K27 at the appropriate loci.  Our p300/CBP ChIP-seq data echo with our findings on islet/β cell-specific p300/CBP knockout mice described in Chapters 3 and 4 (Wong et al., 2018), and provide further evidence that p300/CBP function similarly in β cells. The apparent specificity of co-occupancy among p300/CBP, NeuroD1, and Pdx1, but not with MafA, agrees with previous studies which have shown that NeuroD1 and Pdx1, but not MafA, recruit p300 to the insulin promoters for coactivation (Zhao et al., 2005).   Based on the findings that the p300 and Nkx6.1 ChIP-seq data overlapped significantly, and that p300 can be co-immunoprecipitated with Nkx6.1, we propose that p300 is likely a coactivator of Nkx6.1. Importantly, β cells lacking Nkx6.1 produce smaller insulin granules (Taylor et al., 2013), a phenotype that is also present in p300-null β cells. We speculate that loss of p300 in mature β cells may disrupt the interaction between p300 and Nkx6.1. This is also evident from the many genes associated with insulin secretion and biosynthesis that are down-regulated in both p300-null β cells and Nkx6.1-null β cells, such as Ins1, Ins2, Slc30a8, Gpr119, and Ucn3. p300 and Nkx6.1 may together activate molecular targets that modulate insulin granule biosynthesis.   131  Interactions among transcription factors can facilitate the recruitment of p300 for histone acetylation at the nearby cis-regulatory elements (Vo and Goodman, 2001). In Chapter 4, we have shown that the levels of H3K27ac correlate with gene activation in p300βKO mouse β cells, and the correlation we describe here agrees with observations in the literature (Creyghton et al., 2010). In contrast, the reductions in H3K27 signals in p300PβKO mouse islets were modest. It is possible that developing β cells, as opposed to mature β cells, require more p300 to fully establish H3K27 acetylation, whereas mature β cells have a lower requirement for p300 to maintain H3K27 acetylation. Such requirement for H3K27 acetylation may be essential for establishing a proper transcription program for β cell proliferation. The neonatal-onset H3K27 hypoacetylation may therefore account for the defects of β cell proliferation that were specific to p300IsletKO and p300βKO mice. In summary, p300 may fulfil two distinct roles in β cells temporally: one that establishes the H3K27ac landscape as β cells develop, and one that sustains β cell gene activation as the cells mature.   Overall, loss of p300 in postnatal β cells led to glucose intolerance and impaired insulin secretion in vivo due to reduced capacities to synthesize and secrete insulin. These phenotypes can be attributed to the impaired interaction between p300 and other β cell transcription factors, which reduced H3K27 acetylation and transcription at loci implicated in β cell function, such as insulin. Our data suggest that p300 functions as a transcriptional coactivator, and an acetyltransferase for H3K27, in mature β cells to maintain glucose homeostasis. These mechanisms may be relevant to type 2 diabetes in humans, because the disruption of p300/CBP-regulated gene networks may contribute to this late-onset disease (Morris et al., 2012).       132  Chapter 6: Conclusions Because genetic and environmental risk factors are known to influence the risk of β cell failure and type 2 diabetes in the general population (Keating et al., 2016), the hypothesis that diabetes would be influenced by rare, highly-penetrant mutations in major epigenetic regulators was attractive. Our identification of the first patient, who has early-onset diabetes and is haploinsufficient for p300, led us to hypothesize that p300 is relevant to genetic forms of diabetes, likely via effects on β cells rather than on peripheral insulin sensitivity. Furthermore, few studies have investigated the role of transcriptional coactivators or epigenetic regulators in β cell development and function in vivo, and neither transcriptional coactivators nor epigenetic regulators have been directly implicated in the genetic forms of diabetes. These gaps in knowledge prompted us to further investigate the functions of p300 in β cell development and function.  Based on the data presented in Chapters 2 through 5, including the identification of additional historical cases and our collection of novel cases (Bartok, 1968; Rohlfing et al., 1971; Völcker and Haase, 1975), we propose that human EP300 mutations are associated with defective glucose metabolism. Our patient with a microdeletion spanning EP300 has early-onset diabetes, whereas two probands with missense variants in EP300 developed hyperinsulinemic hypoglycemia. Our findings with respect to p300 are similar to those described for mutations in other MODY genes such as HNF1A, HNF4A, ABCC8, KCNJ11 and GCK (Glaser et al., 1998; Pearson et al., 2007; Stanescu et al., 2012; Thomas et al., 1996, 1995), where both diabetes and hyperinsulinemic hypoglycemia can develop depending on the effects of the variants on their cognate proteins. This observation suggests that EP300 mutations and mutations in other MODY genes may share a similar pathogenic mechanism to cause glucose dysregulation. In addition, the case reports describing seven patients with RTS and glucose dysregulation support our hypothesis, even though these cases predated the identification of the causal genes for RTS (Petrij et al., 1995; Roelfsema et al., 2005). Taken together, we speculate that mutations in EP300 are associated with MODY-like glucose dysregulation in human.  133  Based on the functions of the protein domains in p300, we can predict the effects of the EP300 missense variants observed in the patients on p300 . Our transfection experiment showed that cells overexpressing the EP300 variant p.H1255R or p.F1595V had elevated levels of acetyl-p300. A study has demonstrated that the p.H1255R variant affects zinc ligation in the PHD finger (Delvecchio et al., 2013), which likely affects the  ability of the PHD finger to inhibit p300 auto-acetylation (Rack et al., 2015). The p.F1595V variant is located within the KAT domain immediately downstream of the auto inhibitory loop. This variant may directly enhance the KAT activity of p300 and increase p300 auto-acetylation. Because both p.H1255R and p.F1595V are located within the catalytic domains of p300, enhanced p300 KAT activity is a potential mechanism that causes hyperinsulinism in the patients with these variants.  We would expect studies on larger cohorts of RTS patients with EP300 mutations to identify additional patients who have glucose dysregulation. However, a recent study reported 52 RTS patients with EP300 mutations, and none of them were reported to have glucose dysregulation (Fergelot et al., 2016). Four potential explanations may account for the apparent discrepancy. Firstly, a significant number of pre-diabetes and type 2 diabetes cases are asymptomatic (American Diabetes Association, 2003), and existing guidelines only recommend screening high-risk populations, such as obese individuals or individuals with cardiovascular problems (Pottie et al., 2012), for hyperglycemia. Because RTS patients are not considered at risk for developing diabetes, it is likely that Fergelot et al. did not specifically screen the RTS patients for hyperglycemia. Secondly, out of the 51 variants reported by Fergelot et al., there were only two EP300 interstitial deletions (c.1529-1622, 93 aa; hg19 g.41556074_41563532, 7,458 bp) that have unknown consequences on p300 dosage, and three EP300 missense mutations (p.N1286S, p.H1334R, and p.V1413D). Comparing these variants with the mutations described in the thesis, the variants we found appear to be relatively rare in EP300-associated RTS patients. We speculate that perhaps only certain EP300 variants may cause glucose dysregulation. Thirdly, most of the patients reported in the study by Fergelot et al. were children, and they may have been too young to manifest  134  glucose intolerance. Lastly, our study on the patients with EP300 variants and glucose dysregulation may reflect ascertainment bias, which resulted from our preference to identify patients with glucose dysregulation over normoglycemic patients.  Previous studies have reported more than 300 RTS patients with pathogenic CREBBP variants (Fergelot et al., 2016). If glucose dysregulation is truly associated with defective p300/CBP expression, the glucose phenotype will be more likely observed in cases with CREBBP mutations, as opposed to cases with EP300 mutations. Intriguingly, no report has yet to associate pathogenic CREBBP variants with glucose phenotypes. We expect that blood glucose levels are not actively screened in patients with pathogenic CREBBP variants. In the future, case-control studies will be necessary for ascertaining the prevalence for glucose dysregulation in patients with CREBBP or EP300 mutations.  The diverse glucose phenotypes of the patients with EP300 mutations parallel the phenotypes of patients with mutations in MODY genes. The EP300-associated phenotypes may originate from β cells, because all MODY genes identified to date encode proteins critical for β cell development and function. Different approaches allow direct or indirect assessment of β cell function in patients with EP300 mutations. Although it is theoretically possible to assess β cell function using islets harvested from cadaveric donors with EP300 mutations, such patients are extremely scarce, and the harvest of cadaveric islets must take place shortly after death for them to remain viable. Instead, patient-derived iPSCs showed significant promise for studying the effects of EP300 mutations on human β cell development, and to a certain extent on human β cell function. We have generated iPSCs from the patient with the microdeletion spanning EP300, assessed the protein levels in these cells, and demonstrated that the patient is haploinsufficient for p300. Future studies using these patient-derived iPSCs will clarify the role of β cells in EP300-associated glucose dysregulation.     135  The calculation of homeostatic model assessment-β (HOMA-β) or clamping studies can indirectly assess the β cell function in patients with EP300 mutations. The HOMA-β values generally correlate with results from hyperglycemic clamping (Wallace et al., 2004). HOMA-β can be calculated from fasting blood glucose and insulin levels, and therefore the approach is relatively less invasive. Hyperglycemic clamping assesses insulin secretion, and hyperinsulinemic euglycemic clamping assesses peripheral insulin sensitivity (DeFronzo et al., 1979). These methods are some of the most robust for assessing glucose metabolism in human. Studies have applied clamping techniques to study glucose metabolism in patients with mutations in PTEN (Pal et al., 2012). However, both clamping techniques are invasive and require close monitoring during the procedures. Because patients with EP300 mutations typically have intellectual disability, obtaining informed consent from these patients to pursue invasive clinical studies was not possible.  Because of the limitations in pursuing detailed clinical studies in our cohort, we selected mouse models to test our hypotheses. These living systems allow us to interrogate the molecular function of p300 in β cells in considerable detail. Here, we summarize the phenotypes found in all three mouse models lacking p300 at various stages of β cell development (Figure 54). Our data pinpoint some key features shared by these models. All three mouse models exhibited glucose intolerance due to impaired insulin secretion in vivo, and they also showed reduced insulin content and dense core insulin granule size. Deleting p300 early during β cell development impaired β cell proliferation and increased Npy expression, whereas a postnatal-onset of p300 deletion in β cells bypasses these developmental defects. To summarize, our transgenic knockout mouse studies have established that p300 governs two important aspects of β cell biology: 1) β cell development, where p300 is required for optimal β cell proliferation and perhaps maturation; and 2) β cell function, where p300 is required to maintain optimal insulin biosynthesis and secretion.    136    Figure 54. A summary of the phenotypes of the three different p300 knockout mouse models studied in this thesis.  Although Neurog3-Cre, Ins1-Cre, and Pdx1-CreER models recapitulated some aspects of the glucose phenotypes seen in the patient with p300 haploinsufficiency, these models also gave rise to some conflicting observations, which can be explained as follows. Although the p300 hemizygous mice did not develop glucose intolerance, they could be more susceptible to glucose intolerance induced by a high-fat diet. Alternatively, the p300 hemizygous mice may harbour subtle defects in proliferation and/or insulin granule biosynthesis, and such defects have negligible effects on glucose homeostasis that cannot be detected by our assays.  The Pdx1-CreER model exhibited impaired islet insulin secretion while maintaining normal β cell mass. In contrast, mice lacking p300 in pancreatic endocrine progenitors or developing β cells exhibited impaired β cell proliferation, but their islets showed no  137  apparent defects in glucose-stimulated insulin secretion. The discrepancy can be explained as follows. Firstly, the reduced β cell proliferation in the latter models may result in functional compensation in their β cells. The compensation is evident in prediabetic humans who exhibit elevated insulin secretion prior to β cell failure (Meyer et al., 2015). Loss of p300 in the Neurog3-Cre and Ins1-Cre model may lead to a similar compensation in their β cell function, which may mask any potential secretion defects. Because we performed the static incubation and perifusion assays on islets isolated from young mice (10 – 15 weeks old), we cannot exclude the possibility that these islets may become dysfunctional as the mice grow older. Alternatively, disrupted paracrine signalling due to an altered proportion of endocrine cells in these islets may rescue the secretion phenotype. Our calcium imaging data suggest that Neurog3-Cre p300-null islets elicited a stronger KCl-stimulated calcium response than controls. Because KCl can stimulate an intracellular calcium increase in δ cells (DiGruccio et al., 2016), the p300-null islets may exhibit elevated somatostatin secretion, which can suppress glucagon secretion and indirectly enhance insulin secretion (Ravier and Rutter, 2005; Watts et al., 2016). However, perfusion assays on whole pancreata, which can be technically challenging, will be required to investigate such paracrine effects. The precise mechanism that may explain the phenotypic differences among the Neurog3-Cre, Ins1-Cre, and Pdx1-CreER models remains to be determined.  Loss of p300 significantly impacted the H3K27 acetylomes in both neonatal and adult β cells. In Chapter 4 and Chapter 5, we showed that perinatal and mature β cells in the Ins1-Cre model manifested a global loss of H3K27ac, whereas islets in the Pdx1-CreER model did not. From the H3K27 ChIP-seq data, we conclude that p300 expression is required to fully acetylate H3K27 across the β cell genome during development, but not necessarily after maturation. Our conclusion is in line with a requirement for continuous establishment of active enhancers throughout cell differentiation (Creyghton et al., 2010). Although the mechanisms underlying the temporal requirement remain to be determined, we can provide two potential explanations. Firstly, p300 is limiting during β cell development, perhaps due to a low amount of p300 in cells (Hottiger et al., 1998),  138  or due to a high demand for p300 by transcription factors during β cell differentiation that may effectively sequester and reduce the functional dosage of p300. Therefore, loss of p300 may exert a stronger impact early in development as opposed to later. Alternatively, p300/CBP bromodomains have higher affinities towards acetyl-lysine residues over the non-acetylated ones, which may favour the maintenance of H3K27ac rather than the de novo establishment of the mark (Mujtaba et al., 2004). Perinatal loss of p300 in the Ins1-Cre model disrupts the establishment of H3K27ac during β cell development. In contrast, the β cells in the Pdx1-CreER model have fully established H3K27 acetylation by the time p300 is lost, which may minimize the impact of p300 loss on H3K27 acetylation. In both cases, these p300-null β cells have to rely on the remaining copies of CBP to establish H3K27ac incompletely and later on to maintain the mark. To summarize, our studies on the effect of p300 loss on H3K27ac identify the correlation and dependence between p300-mediated H3K27 acetylation and gene expression in β cells. In addition, we gain valuable insights on how faulty histone acetylation, and consequently transcription, may compromise β cell function and glucose homeostasis.  In this thesis, we also attempted to address the mechanistic question: which protein(s) would be the key p300-interacting partners that mediate the phenotypes we observed in the p300-null β cells? Although we have explored in detail how p300 can in part acetylate H3K27 for optimal gene expression, we would like to discover what non-histone targets p300 may interact with in β cells. We hypothesized that the β cell and H3K27ac phenotypes upon loss of p300 are caused by the loss of the interaction between p300 and certain β cell transcription factors. Furthermore, the phenotypes of the p300 knockout mice may resemble the phenotypes of mice lacking such transcription factors in their β cells. The most relevant transcription factors that may interact with p300 in β cells are those encoded by MODY genes, namely Hnf1α, Hnf1β, NeuroD1, and Pdx1, because they are critical for β cell development and function and have been shown to bind p300 in vitro.   139  To identify the interacting partners of p300, we need to know which transcription factor families p300 can interact with. However, p300 can interact with hundreds of different partners (Bedford et al., 2010; Weinert et al., 2018), and the availability of these p300 partners are likely regulated spatially and temporally. To examine the potential interaction between p300/CBP and other DNA-binding targets in β cells relatively unbiasedly, we performed p300/CBP ChIP-seq on MIN6 cells. The experiments reveal a surprisingly small number of loci (fewer than 2,000 peaks) occupied by p300/CBP in the β-like cell genome. We mapped the p300/CBP peaks to our mouse islet chromatin state data for functional annotation, because we expected p300/CBP to localize within tissue-specific enhancers (Visel et al., 2009). Our de novo motif analysis and peak set overlapping analysis arrive to a similar conclusion: p300 and NeuroD1/Nkx6.1/Pdx1, which are transcription factors important for β cell development and function, co-occupy active enhancers and promoters in islets/β cells. Therefore, our p300/CBP ChIP data analysis have unveiled how p300 may maintain mature β cell function interacting with selected transcription factors cooperatively. The data also help us identify Nkx6.1 as a potentially novel target of p300-mediated coactivation.   Based on the potential interaction among p300, NeuroD1, Nkx6.1, and Pdx1, we can predict the p300 targets that may mediate the phenotypes seen in our mouse models. These phenotypes bear some similarities to mice lacking Nkx6.1 or NeuroD1 in β cells. The abnormal presence of Npy is one of the unique features in β cell-specific NeuroD1-null mice (Gu et al., 2010). The dense core granule phenotype in p300-null β cells also resembles the phenotype in the Nkx6.1-null β cells (Taylor et al., 2013), although the Nkx6.1-null β cells also display other defects in insulin processing, such as an increased proportion of immature granules. Furthermore, the β cell- and α cell-specific phenotypes of Neurog3-Cre-positive p300/CBP dKO islets resemble the phenotypes of Nkx2.2-null mice (Prado et al., 2004). In addition, the p300-mediated proliferation defects may involve Hnf1α and its downstream target Tmem27 (Servitja et al., 2009). These observations collectively point to a mechanism where these transcription factors may each contribute partially to the phenotypes of p300-null β cells. Most importantly, Hnf1α,  140  NeuroD1, and Pdx1 have been implicated in MODY (Malecki et al., 1999; Stoffers et al., 1997; Yamagata et al., 1996a), and the p300-dependent functions of these factors may be affected in the patient with p300 haploinsufficiency. Taken together, various β cell transcription factors may recruit p300 temporally for coactivation throughout β cell development.  Several methods may be suitable for further examining the role of p300 in β cells. ChIP experiments can examine how p300 loss may affect the binding of β cell transcription factors to the islet/β cell genome in control and knockout islet samples. In addition, mass spectrometry can provide a relatively biased-free way to identify novel targets of p300-mediated acetylation in β cells. Although these methods require a substantial amount of protein as starting material, they will undoubtedly broaden our understanding on the other underappreciated functions of p300, such as non-histone acetylation, in β cells. Combining with the use of iPSCs that carry EP300 mutations, these experiments will provide further insights on the role of p300 in the human β cell epigenome throughout development.  Understanding how p300 mediates glucose homeostasis through β cells may shed new light on the possibility of using HDAC inhibitors to reverse defective glucose metabolism. This will be relevant not only to the patient with a microdeletion of EP300, but also to the more general population who have the common, complex version of type 2 diabetes. HDAC inhibitors are currently in clinical trials for the treatment of cancer, and several studies have proposed exploiting HDAC inhibitors to treat type 2 diabetes (Christensen et al., 2011; Meier and Wagner, 2014). Our data suggest a positive role of p300-mediated H3K27 acetylation in β cell development and function. The HDAC inhibitors may provide some advantages for treating diabetes in the patient with p300 haploinsufficiency. However, HDAC inhibitors may not reverse the pre-existing defect in β cell mass if such defect is present. Furthermore, other studies have suggested that locus-specific changes in H3K27 acetylation in β cells may precipitate type 2 diabetes (Bompada et al., 2016; Lu et al., 2018). Also, HDAC inhibitors exert complex effects by  141  enhancing protein acetylation non-specifically. We only have very limited understanding on how HDACs may function differently in the metabolically-relevant tissues, and how histone acetylation functions dynamically under healthy and diabetic conditions. Examining the epigenomic and transcriptomic signatures in various metabolic tissues after HDAC inhibitor treatment, together with studies of mice with tissue-specific knockout of lysine modifying enzymes, will clarify the molecular mechanisms that dictate the tissue- and locus-specific changes in histone and non-histone acetylation in the setting of diabetes.  To conclude, our study began with a patient who developed MODY-like early-onset diabetes and harbours a microdeletion spanning EP300. We found three other probands who exhibited hyperinsulinism and carry potentially activating EP300 variants. We interrogated the role of p300 in β cells at the organismal level, at the tissue level, at the cellular level, and at the molecular level, using three mouse models that lack p300 in islet endocrine progenitors, in developing β cells, or in mature β cells. Our findings collectively indicate that p300 is required for the proliferation of developing β cells, perhaps in a Tmem27-dependent mechanism; for the proper maturation of β cells, likely by down-regulating Npy expression; and for the optimal insulin secretion in mature β cells, which depends on insulin gene expression and insulin granule biosynthesis. These aspects of β cell biology are fulfilled in part by the ability of p300 to acetylate H3K27 at the appropriate loci throughout β cell development, and to interact preferentially with transcription factors important for mature β cell function, including NeuroD1, Nkx6.1, and Pdx1.   Our mouse data highlight the necessity of p300 expression to maintain β cell development and function through the diverse and complex interaction between p300, β cell transcription factors, the H3K27 acetylome and the transcriptome. 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