Ho Sin Ka Janice. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 105-119). / Abstracts in English and Chinese. / Chapter CHAPTER 1. --- Introduction / Chapter 1.1. --- Epidemiology of Type 2 Diabetes --- p.1 / Chapter 1.2. --- Risk factors contributing to Type 2 Diabetes --- p.3 / Chapter 1.2.1. --- Environmental and physiological factors --- p.3 / Chapter 1.2.2. --- Genetic factors --- p.3 / Chapter 1.3. --- Disruption of energy homeostasis in the pathogenesis of type 2 diabetes --- p.6 / Chapter 1.3.1. --- Clinical spectrum of diabetes --- p.6 / Chapter 1.3.2. --- Insulin as a key regulator of energy homeostasis --- p.7 / Chapter 1.3.3. --- Insulin secretion and glucose metabolism --- p.8 / Chapter 1.3.4. --- Insulin action and lipid metabolism --- p.9 / Chapter 1.3.5. --- Lipotoxicity and glucotoxicity --- p.12 / Chapter 1.3.6. --- Role of transcription factors as metabolic switch --- p.13 / Chapter 1.4. --- Candidate genes implicated in type 2 diabetes susceptibility --- p.15 / Chapter 1.4.1. --- Candidate genes involved in insulin secretion pathway --- p.15 / Chapter 1.4.1.1. --- HNF4A --- p.15 / Chapter 1.4.1.2. --- HNF1A --- p.16 / Chapter 1.4.1.3. --- PDX1/PBX1 --- p.17 / Chapter 1.4.1.4. --- NEUROD1 --- p.17 / Chapter 1.4.1.5. --- GCK --- p.17 / Chapter 1.4.1.6. --- KCNJ11/ABCC8 --- p.18 / Chapter 1.4.2 --- Candidate genes involved in insulin action pathway --- p.19 / Chapter 1.4.2.1. --- PPARG --- p.19 / Chapter 1.4.2.2. --- PPARA --- p.20 / Chapter 1.4.2.3. --- PPARGC1A --- p.20 / Chapter 1.4.2.4. --- ADIP0Q --- p.21 / Chapter 1.4.2.5. --- LPL --- p.21 / Chapter 1.4.2.6. --- UPC --- p.22 / Chapter 1.5. --- Hypothesis and objectives of the study --- p.23 / Chapter CHAPTER 2. --- Materials and methods / Chapter 2.1. --- Study design --- p.25 / Chapter 2.1.1. --- Two-stage candidate gene association design --- p.25 / Chapter 2.1.2. --- Power calculation --- p.27 / Chapter 2.2. --- Study cohort --- p.29 / Chapter 2.2.1. --- Subject recruitment --- p.29 / Chapter 2.2.2. --- Clinical and biochemical measurements --- p.30 / Chapter 2.2.3. --- Clinical definitions --- p.31 / Chapter 2.3. --- Genetic study --- p.32 / Chapter 2.3.1. --- Candidate gene selection --- p.32 / Chapter 2.3.2. --- SNP selection --- p.32 / Chapter 2.3.3. --- DNA sample preparation --- p.35 / Chapter 2.3.4. --- Genotyping methods --- p.36 / Chapter 2.3.4.1. --- Allele specific Tm shift assay --- p.36 / Chapter 2.3.4.2. --- Mass spectrometry assay --- p.40 / Chapter 2.4. --- Data quality control --- p.42 / Chapter 2.4.1. --- Stage 1 --- p.42 / Chapter 2.4.2. --- Stage 2 --- p.42 / Chapter 2.5. --- Statistical analysis --- p.45 / Chapter 2.5.1. --- Stage 1 analysis --- p.45 / Chapter 2.5.2. --- Stage 2 analysis --- p.45 / Chapter 2.5.3. --- Stage 1 and 2 combined analysis --- p.46 / Chapter CHAPTER 3. --- Results / Chapter 3.1. --- Clinical characteristics of subjects in stages 1 and 2 studies --- p.48 / Chapter 3.2. --- Case-control associations in stage 1 --- p.51 / Chapter 3.2.1. --- Association with T2D --- p.51 / Chapter 3.2.2. --- Association with T2D subset by metabolic syndrome --- p.54 / Chapter 3.3. --- Case-control associations in stage 2 --- p.60 / Chapter 3.3.1. --- SNP selection for genotyping --- p.60 / Chapter 3.3.2. --- Association with T2D --- p.63 / Chapter 3.3.3. --- Association with T2D subset by metabolic syndrome --- p.64 / Chapter 3.4. --- Case-control associations in combined stages 1 and 2 --- p.66 / Chapter 3.4.1. --- Association with T2D --- p.66 / Chapter 3.4.2. --- Association with T2D subset by metabolic syndrome --- p.70 / Chapter 3.4.3. --- Association with T2D subset by age at diagnosis --- p.74 / Chapter 3.4.4. --- Association with T2D subset by gender --- p.76 / Chapter 3.4.5. --- Genetic epistasis for T2D association --- p.79 / Chapter 3.5. --- Metabolic traits associations in control subjects in combined stages 1 and 2 studies --- p.83 / Chapter CHAPTER 4. --- Discussion --- p.86 / Chapter 4.1. --- Role of insulin secretion genes in type 2 diabetes --- p.87 / Chapter 4.2. --- Role of insulin action genes in type 2 diabetes --- p.92 / Chapter 4.3. --- Combined genetic effects on risk for type 2 diabetes --- p.97 / Chapter 4.4. --- Summary --- p.98 / Chapter 4.5. --- Limitation of this study and future direction --- p.101 / REFERENCES --- p.104 / APPENDICES --- p.119 / Chapter Appendix 1: --- Gene structure and linkage disequilibrium of genotyped SNPs of candidate genes --- p.119 / Chapter Appendix 2: --- Information of SNPs genotyped in stage 1 --- p.130 / Chapter Appendix 3: --- T2D association results (additive model) of 152 SNPs for stage 1 case- control samples --- p.137 / Chapter Appendix 4: --- T2D association results (additive model) of 152 SNPs for stage 1 case- control samples subset by metabolic syndrome status in cases --- p.144 / Chapter Appendix 5: --- T2D association results (additive model) of 22 SNPs for stage 2 case- control samples --- p.151 / Chapter Appendix 6: --- T2D association results (additive model) of 22 SNPs for stage 2 case- control samples subset by metabolic syndrome status in cases --- p.153

Yuen, Yee Lok. / On t.p. "alpha" appears as the Greek letter. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 165-177). / Abstracts in English and Chinese. / Abstract --- p.i / 摘要 --- p.iv / Acknowledgement --- p.vii / Table of Contents --- p.viii / List of Abbreviations --- p.xiii / List of Figures --- p.xv / List of Tables --- p.xx / Chapter Chapter 1 --- Literature review --- p.1 / Chapter 1.1 --- Peroxisome proliferator-activated receptor (PPAR) --- p.1 / Chapter 1.1.1 --- PPARα isoforms --- p.1 / Chapter 1.2 --- PPARα ligands --- p.2 / Chapter 1.3 --- Biological roles of PPARα --- p.3 / Chapter 1.3.1 --- Lipid metabolism --- p.3 / Chapter 1.3.2 --- Bile acid metabolism --- p.4 / Chapter 1.3.3 --- Biotransformation --- p.6 / Chapter 1.4 --- Roles of PPARα in hepatocarcinogenesis --- p.7 / Chapter 1.4.1 --- Cell proliferation and apoptosis --- p.7 / Chapter 1.4.2 --- Oxidative stress --- p.8 / Chapter 1.5 --- Discovery of novel PPARα target genes --- p.9 / Chapter 1.5.1 --- Identification of a novel PPARα-regulated gene L5#55 by fluorescent differential mRNA display (FDD) analysis --- p.9 / Chapter 1.6 --- Sulfotransferase (SULT) --- p.15 / Chapter 1.7 --- Objective of the present study --- p.16 / Chapter Chapter 2 --- Molecular cloning and characterization of mouse liver sulfotransferase-like (mL-STL) gene --- p.17 / Chapter 2.1 --- Introduction --- p.17 / Chapter 2.2 --- Materials and methods --- p.17 / Chapter 2.2.1 --- Animals --- p.17 / Chapter 2.2.2 --- Treatments --- p.18 / Chapter 2.2.3 --- Total RNA extraction --- p.18 / Chapter 2.2.3.1 --- Materials --- p.18 / Chapter 2.2.3.2 --- Methods --- p.19 / Chapter 2.2.4 --- Rapid amplification of cDNA ends (RACE) --- p.19 / Chapter 2.2.4.1 --- Materials --- p.19 / Chapter 2.2.4.2 --- Methods --- p.20 / Chapter 2.2.4.2.1 --- Primer design --- p.20 / Chapter 2.2.4.2.2 --- Rapid amplification of 5'- and 3'-cDNA ends --- p.20 / Chapter 2.2.5 --- Cloning of the 5'- and 3' RACE products --- p.25 / Chapter 2.2.5.1 --- Materials --- p.25 / Chapter 2.2.5.2 --- Methods --- p.25 / Chapter 2.2.6 --- Northern blot analysis --- p.28 / Chapter 2.2.6.1 --- Materials --- p.28 / Chapter 2.2.6.2 --- Methods --- p.28 / Chapter 2.2.6.2.1 --- Formaldehyde-agarose gel electrophoresis and blotting of RNA --- p.31 / Chapter 2.2.6.2.2 --- PCR DIG-labeling --- p.31 / Chapter 2.2.6.2.3 --- Hybridization and signal detection --- p.32 / Chapter 2.2.7 --- Reverse transcription (RT)-PCR --- p.34 / Chapter 2.2.7.1 --- Materials --- p.34 / Chapter 2.2.7.2 --- Methods --- p.34 / Chapter 2.3 --- Results and discussion --- p.37 / Chapter 2.3.1 --- Cloning of the full-length mL-STL cDNA --- p.37 / Chapter 2.3.2 --- In silico analysis of the mL-STL cDNAs --- p.50 / Chapter 2.3.3 --- Genomic organization of the mL-STL gene --- p.61 / Chapter 2.3.4 --- Tissue distribution of mL-STL mRNA transcript --- p.68 / Chapter 2.3.5 --- "PPARα-dependent regulation of mL-STL mRNA expression by fasting and Wy-14,643 treatment" --- p.74 / Chapter Chapter 3 --- Identification of the native mL-STL protein in mouse liver --- p.86 / Chapter 3.1 --- Introduction --- p.86 / Chapter 3.2 --- Materials and methods --- p.87 / Chapter 3.2.1 --- Animal and treatments --- p.87 / Chapter 3.2.2 --- Cloning of the mL-STL cDNA into a modified pRSET (mpRSET) expression vector --- p.88 / Chapter 3.2.2.1 --- Materials --- p.88 / Chapter 3.2.2.2 --- Methods --- p.88 / Chapter 3.2.2.2.1 --- Amplification of mL-STL cDNA fragments --- p.88 / Chapter 3.2.2.2.2 --- Preparation of mpRSET expression vector --- p.92 / Chapter 3.2.2.2.3 --- "Ligation, transformation, and screening of recombinants" --- p.92 / Chapter 3.2.3 --- Over-expression of the mL-STL recombinant proteins in E coli strains --- p.94 / Chapter 3.2.3.1 --- Materials --- p.94 / Chapter 3.2.3.2 --- Methods --- p.94 / Chapter 3.2.4 --- Mass spectrometry analysis of the mL-STL recombinant proteins --- p.95 / Chapter 3.2.4.1 --- Materials --- p.96 / Chapter 3.2.4.2 --- Methods --- p.96 / Chapter 3.2.4.2.1 --- Trypsin digestion and peptide extraction --- p.96 / Chapter 3.2.4.2.2 --- Matrix-assisted laser desorption/ionization time-of- flight (MALDI-TOF) mass spectrometry --- p.97 / Chapter 3.2.5 --- Purification of the mL-STL recombinant proteins --- p.98 / Chapter 3.2.5.1 --- Materials --- p.98 / Chapter 3.2.5.2 --- Methods --- p.98 / Chapter 3.2.5.2.1 --- Semi-purification of the mL-STL recombinant proteins by preparative SDS-PAGE --- p.98 / Chapter 3.2.5.2.2 --- Purification of mL-STL recombinant proteins by column chromatography --- p.99 / Chapter 3.2.6 --- Rabbit immunization using purified mL-STL recombinant proteins --- p.101 / Chapter 3.2.7 --- Subcellular fractionation of mouse liver by ultracentrifugation --- p.101 / Chapter 3.2.7.1 --- Materials --- p.101 / Chapter 3.2.7.2 --- Methods --- p.102 / Chapter 3.2.8 --- Western blot analysis of the native mL-STL protein --- p.104 / Chapter 3.2.8.1 --- Materials --- p.104 / Chapter 3.2.8.2 --- Methods --- p.104 / Chapter 3.2.8.2.1 --- SDS-PAGE and electro-blotting of proteins --- p.104 / Chapter 3.2.8.2.2 --- Immunostaining and signal detection --- p.105 / Chapter 3.3 --- Results and discussion --- p.106 / Chapter 3.3.1 --- Cloning of the mL-STLl and mL-STL2 cDNAs into a modified pRSET (mpRSET) vector --- p.106 / Chapter 3.3.2 --- IPTG induction of the mpRSET-mL-STL protein expression --- p.106 / Chapter 3.3.3 --- Confirmation of mL-STL recombinant proteins by mass spectrometry --- p.118 / Chapter 3.3.4 --- Purification of mL-STL recombinant proteins for rabbit immunization and polyclonal antisera production --- p.130 / Chapter 3.3.5 --- Antigenicity of mL-STL antisera --- p.134 / Chapter 3.3.6 --- Identification of mL-STL native protein and its induction pattern in mouse liver --- p.139 / Chapter 3.3.7 --- "Time-course of fasting and Wy-14,643 treatment on the mL- STLl native protein expression" --- p.147 / Chapter Chapter 4 --- Overall discussion --- p.153 / Future study --- p.163 / References --- p.165 / "Appendix A. Alignment of nucleotide sequences of mouse chromosome 7，Riken2810007J24, mL-STLl, and mL-STL2 cDNA sequences" --- p.178 / Appendix Bl. Theoretical tryptic peptide masses of mpRSET- mL-STLl protein --- p.217 / Appendix B2. Raw data from mass spectrometry analysis of mpRSET-mL-STLl protein --- p.218 / Appendix C1. Residue molecular mass of amino acids --- p.219 / Appendix C2. Di-peptide table --- p.220 / Appendix D1. Theoretical tryptic peptide masses of mpRSET- mL-STL2 protein --- p.221 / Appendix D2. Raw data from mass spectrometry analysis of mpRSET-mL-STL2 protein --- p.222

Xu, Jian. / Thesis Ph.D. Chinese University of Hong Kong 2015. / Includes bibliographical references (leaves 238-255). / Abstracts also in Chinese. / Title from PDF title page (viewed on 24, October, 2016).

Thesis (Ph.D.)--Memorial University of Newfoundland, 2002. / Bibliography: leaves 251-271.

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (leaves 122-140).

Diss. (sammanfattning) Linköping : Linköpings universitet, 2004. / Härtill 4 uppsatser.

Diss. (sammanfattning) Stockholm : Karol. inst., 2005. / Härtill 4 uppsatser.

Thesis (Ph.D.) -- University of Texas Southwestern Medical Center at Dallas, 2006. / Embargoed. Vita. Bibliography: 121-142.

It has become increasingly clear that transcription factors (TFs) play crucial roles in the development and day-to-day homeostasis that all biological systems experience. TFs target particular genes in a genome, at the appropriate place and time, to regulate their expression so as to elicit the most appropriate biological response from a cell or multicellular organism. TFs can often be grouped into families based on the presence of similar DNA binding domains, and these families are believed to have expanded and diverged throughout evolution by several rounds of gene duplication and mutation. The extent to which TFs within a family have functionally diverged, however, has remained unclear. We propose that systematic analysis of multiple aspects, or parameters, of TF functionality for entire families of TFs could provide clues as to how divergent paralogous TFs really are. We present here a multiparameter integrated network of the activity of the basic helix-loop-helix (bHLH) TFs from the nematode Caenorhabditis elegans. Our data, and the resulting network, indicate that several parameters of bHLH function contribute to their divergence and that many bHLH TFs and their associated parameters exhibit a wide range of connectivity in the network, some being uniquely associated to one another, whereas others are highly connected to multiple parameter associations. We find that 34 bHLH proteins dimerize to form 30 bHLH dimers, which are expressed in a wide range of tissues and cell types, particularly during the development of the nematode. These dimers bind to E-Box DNA sequences and E-Box-like sequences with specificity for nucleotides central to and flanking those E-Boxes and related sequences. Our integrated network is the first such network for a multicellular organism, describing the dimerization specificity, spatiotemporal expression patterns, and DNA binding specificities of an entire family of TFs. The network elucidates the state of bHLH TF divergence in C. elegans with respect to multiple functional parameters and suggests that each bHLH TF, despite many molecular similarities, is distinct from its family members. This functional distinction may indeed explain how TFs from a single family can acquire different biological functions despite descending from common genetic ancestry.

During the course of an immune response, CD4+ T helper cells differentiate into a number of subsets including: T helper 1 (TH1), TH2, TH17, and T follicular helper (TFH) populations. The functional diversity of CD4+ T effector cells results in a coordinated, pathogen-specific immune response. For example, the production of IFNγ by TH1 cells is vital for the clearance of intracellular pathogens, while TFH cell engagement with cognate B cells is required for germinal center (GC) formation and the generation of pathogen- and vaccine- induced antibody production. The development of CD4+ subsets is contingent on extracellular signals, in the form of cytokines, and downstream transcriptional networks responsible for promoting the unique gene expression profile for each subset while simultaneously suppressing alternative cell fates. However, the exact composition of, and stage-specific requirements for, these environmental cytokines and transcription factor networks in the governance of TFH cell differentiation remain incompletely understood. The work in this dissertation seeks to understand how cell-extrinsic cytokine signals and cell-intrinsic transcription factor activities are integrated to properly regulate TFH cell development. Here, we demonstrate that in response to decreased IL-2 and constant IL-12 signaling, T helper 1 (TH1) cells upregulate a TFH-like phenotype, including expression of the TFH lineage defining transcription factor Bcl-6. Intriguingly, our work established that signals from IL-12 were required for both the differentiation and function of this TFH-like population. Mechanistically, IL-12 signals are propagated through both STAT3 and STAT4, leading to the upregulation of the TFH associated genes Bcl6, Il21, and Icos, correlating with increased B cell helper activity. Conversely, exposure of these TFH-like cells to IL-7 results in the STAT5-dependent repression of Bcl-6 and subsequent inhibition of the TFH phenotype. Finally, we describe a novel regulatory mechanism wherein STAT3 and the Ikaros zinc finger transcription factors Ikaros and Aiolos cooperate to regulate Bcl-6 expression in these TFH-like cells. Collectively, the work in this dissertation significantly advances our understanding of the regulatory mechanisms that govern TFH cell differentiation, setting the basis for the rational design of novel immunotherapeutic strategies and increasingly effective vaccines. / Ph. D. / Specialized cells called T helper cells serve as a critical interface between the innate (first line of defense) and adaptive (specialized and long-term) immune systems. During the course of an infection, T helper cells are responsible for orchestrating the immune-mediated elimination of invading viruses, bacteria, and parasites. This wide breadth of functionality is achieved through the formation of distinct T helper subsets including T helper 1 (TH1), TH2, TH17, and T follicular helper (TFH) populations. Individual subsets have distinct developmental requirements and have unique functions within the immune system. For example, TFH cells are required for the production of effective antibodies that recognize invading pathogens, leading to their subsequent elimination. This naturally occurring process is the basis for a number of modern medical therapies including vaccination. Conversely, aberrant generation of antibodies that recognize host tissues can result in the onset of various autoimmune diseases including lupus, multiple sclerosis, and crohn’s disease. Due to the importance of TFH cells to human health, there is intense interest in understanding how these cells are formed. It is recognized that the generation of these therapeutically important immune cells is mediated by numerous cell-extrinsic andintrinsic influences, including proteins in their cellular environment called cytokines, and important proteins inside of the cell called transcription factors. However, as this is a complicated and multi-step process, many questions remain regarding the identity of these cytokines and transcription factors. The work in this dissertation seeks to understand how cellextrinsic cytokine signals and cell-intrinsic transcription factor activities are integrated to properly regulate TFH cell development. Collectively, this body of work significantly advances our understanding of the regulatory mechanisms that govern TFH cell differentiation, setting the basis for the rational design of novel immunotherapeutic strategies and increasingly effective vaccines.