Thesis (MTech (Biomedical Technology))--Cape Peninsula University of Technology, 2016. / The early detection of individuals who are at risk of developing type 2 diabetes mellitus (T2DM) would decrease the morbidity and mortality associated with this disease. DNA methylation, the most widely studied epigenetic mechanism, offers unique opportunities in this regard. Aberrant DNA methylation is associated with disease pathogenesis and is observed during the asymptomatic stage of disease. DNA methylation has therefore attracted increasing attention as a potential biomarker for identifying individuals who have an increased risk of developing T2DM. The identification of high risk biomarkers for T2DM could facilitate risk stratification and lifestyle interventions, which could ultimately lead to better ways to prevent, manage and control the T2DM epidemic that is rampant worldwide. The aim of the study was to investigate global DNA methylation as a potential risk factor for T2DM by studying the association between the global DNA methylation levels and hyperglycaemic states. A cross-sectional, quantitative study design, involving 564 individuals of mixed ancestry descent, residing in Bellville South, South Africa was used. Participants were classified as normal, pre-diabetic (impaired fasting glucose (IFG) and/or impaired glucose tolerance (IGT)) or diabetic (screen detected diabetic and known diabetics) according to WHO criteria of 1998. DNA was extracted from whole blood using the salt extraction method. The percentage global DNA methylation was measured by an enzyme-linked immunosorbent assay (ELISA). The association between global DNA methylation and hyperglycaemia, as well as other biochemical markers of T2DM was tested in a robust linear regression analysis adjusted for age, gender and smoking.

published_or_final_version / Biochemistry / Master / Master of Philosophy

This research explicated the use of exploratory data analysis in describing type II diabetes mellitus among the Navajo Indians. A sample of 98 diagnosed diabetics was obtained from a retrospective chart review and had a 1.3:1 female to male ratio, a median age of 58.6 years, and a mean duration for diabetes of 7.66 years. Other characteristics included a median age at diagnosis of 50 years, a median weight prior to diagnosis (expressed in percent desired weight) of 140%, and a median blood glucose value at time of diagnosis of 241 mg/dl. The distribution patterns for age, weight, and blood glucose revealed several asymmetry problems which had implications for the appropriateness of using parametric statistics in numerical summarizations. Bivariate analyses revealed a negative association between age at diagnosis and percent desired weight prior to diagnosis. This finding identifies the risk that obesity brings to the young and that aging brings to the non-obese, Navajo Indian.

廣泛的區域內和跨民族的轉錄變化反映了人類的適應和自然選擇。基因表達是轉化基因組信息為功能基因產品 - 蛋白質的主要機制。異常基因的表達和疾病的發病機制有關。基因組革命提供了獨特的機會為複雜的人類轉錄組進行全面的研究。轉錄分析需要複雜的生物信息學方法。在技術角度，一個實證模型用了哺乳動物基因組中內含子長度幾何尾分佈的定律準確地確定剪接交界處和非唯一映射讀取的位置。這種方法在處理非唯一映射讀取比BWA更好。這方法還比其他工具檢測出更多已經實驗證實的剪接交界處。核糖核酸測序首先用於北京漢人和西歐之間的表達表型與的轉錄變化的詳盡研究。民族的具體剪接交界處被發現。此外，民族的具體特點體現在相對異構體的豐度差。最後，這分子表型剪接頻譜的變化在不同種族之間的不同表明了另一個描繪種族多樣性的方法，核糖核酸測序還被用於探索的一種複雜的疾病：二型糖尿病的分子異常。二型糖尿病表現在廣泛不同的基因表達。（1）這研究證實先前公佈的全基因組關聯研究;（2）改善策劃不佳的位點和（3）發現新型2型糖尿病相關的基因。本研究通過整合各種改變的信號，並在一個高度可信的基因 - 基因相互作用網絡進行解釋，增強表達異常在2型糖尿病的認識。在更廣泛的69×79的情況下，對照組的結果進行了驗證。本研究增強表達異常在2型糖尿病的認識。 / Extensive intra- and inter- ethnic transcriptome variation reflects human adaptation and natural selection. Gene expression is the primary mechanism that translates genome information into functional gene product that lead to physiological phenotypes. Aberrant gene expression has been associated to the pathogenesis of diseases. The genome revolution has offered unique opportunity for a comprehensive interrogation of the complexity of human transcriptome. Analysis of transcriptome using RNA-Seq requires sophisticated bioinformatics approach. In a technical perspective, an empirical model based on the geometric-tail distribution of intron lengths in mammalian genome was developed to accurately determine splice junctions from junction reads and locations of non-uniquely mapped reads. Such method handles non-uniquely mapped reads better than BWA. The method can also detect more experimentally confirmed splice junction than other tools. Expressional phenotyping was employed to explore global transcriptomic variation between Beijing Han Chinese and Western European. In addition to inter-ethnic variations in gene expression, ethnic specific splice juctions were found. Further, ethnic specific trait manifests in differential relative isoform abundance. Lastly, such spectrum of variations was different between different ethnic groups, suggesting alternative splicing as another molecular phenotype that delineates ethnic diversity. Expressional phenotyping was then used in a case-control study to explore the molecular abnormalities of a complex disease: Type 2 Diabetes (T2DM). T2DM manifested in wide-spread repression of gene expression. The study (1) confirmed previously reported Genome-wide Association Study (GWAS) loci; (2) curated poorly characteriezed GWAS loci and (3) discovered novel T2DM associated genes. By integrating various alteration signals and interpretation performed in a highly confident gene-gene interaction network, this study augmented the understanding of expressed abnormalities in T2DM. The results were validated in a broader 69 x 79 case-control group. / Detailed summary in vernacular field only. / Li, Jing Woei. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 118-130). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / Abstract --- p.v / 中文擇要 --- p.vi / Thesis/Assessment Committee --- p.ix / Acknowledgement --- p.ix / List of figures --- p.x / List of tables --- p.xii / List of Abbreviations --- p.xiii / Scientific contributions --- p.xv / List of Publication(s) related to this thesis --- p.xvi / Conference presentations --- p.xvii / Chapter Chapter 1: --- Introduction and Literature Reviews --- p.1 / Chapter 1.1 --- The variable human transcriptome --- p.1 / Chapter 1.2 --- Significance of variation in gene expression and transcript variants --- p.2 / Chapter 1.3 --- Transcriptomic study in a technological perspective --- p.8 / Chapter 1.3.1 --- Microarray: Probing what was designed to be probed --- p.8 / Chapter 1.3.2 --- RNA-Seq: the ab initio decoder of biological sequences --- p.9 / Chapter 1.4 --- Analysis of RNA-Seq data --- p.10 / Chapter 1.4.1 --- The bioinformatics challenges prevail --- p.10 / Chapter 1.4.2 --- Identifying changes in gene expression --- p.16 / Chapter 1.4.3 --- Identifying splice site, quantification of isoform level expression --- p.17 / Chapter 1.5 --- Conclusion --- p.19 / Chapter 1.6 --- Aims of this study --- p.20 / Chapter 1.6.1 --- Splice junction determination --- p.20 / Chapter 1.6.2 --- Expressional phenotyping in ethnical context --- p.20 / Chapter 1.6.3 --- Expressional phenotyping in a disease context --- p.20 / Chapter Chapter 2: --- Detection of splicing events --- p.21 / Chapter 2.1 --- Abstract --- p.21 / Chapter 2.2 --- Introduction --- p.22 / Chapter 2.3 --- Methods and workflow --- p.25 / Chapter 2.4 --- Algorithm --- p.29 / Chapter 2.5 --- Geometric-tail distribution --- p.32 / Chapter 2.6 --- Insert-size distribution --- p.33 / Chapter 2.7 --- Multiread analysis --- p.34 / Chapter 2.7.1 --- GT model probably places multiread more accurately than BWA --- p.35 / Chapter 2.8 --- Splice-site comparison --- p.37 / Chapter 2.8.1 --- GT model discovers more experimentally confirmed splice junction --- p.37 / Chapter 2.8.2 --- GT model is highly accurate --- p.39 / Chapter 2.9 --- Discussion --- p.40 / Chapter 2.10 --- Limitation --- p.40 / Chapter Chapter 3: --- Transcriptomic variation in a ethnicity context --- p.41 / Chapter 3.1 --- Abstract --- p.41 / Chapter 3.2 --- Introduction --- p.42 / Chapter 3.3 --- Materials and Methods --- p.46 / Chapter 3.3.1 --- HapMap lymphoblastoid cell-lines --- p.46 / Chapter 3.3.2 --- Sequenced samples --- p.48 / Chapter 3.3.3 --- Paired-end RNA-Seq, dataset and reads processing --- p.48 / Chapter 3.3.4 --- Genome reference and annotation --- p.49 / Chapter 3.3.5 --- Strategies for reads mapping --- p.49 / Chapter 3.3.6 --- Pathway and Gene Ontology analysis --- p.50 / Chapter 3.3.7 --- Differential gene expression analysis --- p.50 / Chapter 3.3.8 --- Ethnic specific splice junction --- p.51 / Chapter 3.3.9 --- Junction sites saturation analysis --- p.51 / Chapter 3.3.10 --- Ethnical novel transcribed regions --- p.52 / Chapter 3.3.11 --- Isoform dynamics and meta-analysis --- p.53 / Chapter 3.4 --- Result --- p.54 / Chapter 3.4.1 --- Paired-end RNA-Seq --- p.54 / Chapter 3.4.2 --- Differential gene expression and meta-analysis --- p.56 / Chapter 3.4.3 --- Ethnic specific splice junction is rare --- p.58 / Chapter 3.4.4 --- Saturation of discovery of highly confident annotated junctions --- p.59 / Chapter 3.4.5 --- Novel transcribed regions --- p.62 / Chapter 3.4.6 --- Isoform dynamics and meta-analysis --- p.63 / Chapter 3.5 --- Discussion --- p.66 / Chapter 3.6 --- Limitations --- p.67 / Chapter 3.6.1 --- HapMap LCLs may not reflect the entire spectrum of natural variation --- p.67 / Chapter 3.6.2 --- Sequencing depth and the usefulness of published dataset --- p.67 / Chapter 3.6.3 --- Knowledge gap in understanding of the human genome --- p.69 / Chapter Chapter 4: --- Transcriptomic investigation of complex disease: Type 2 Diabetes --- p.70 / Chapter 4.1 --- Abstract --- p.70 / Chapter 4.2 --- Introduction --- p.72 / Chapter 4.3 --- Materials and Methods --- p.75 / Chapter 4.3.1 --- Subjects --- p.75 / Chapter 4.3.2 --- Strand-specific RNA-Seq Library Construction --- p.77 / Chapter 4.3.3 --- Genome annotation sequencing reads processing --- p.81 / Chapter 4.3.4 --- Reads mapping for expression analysis --- p.82 / Chapter 4.3.5 --- Differential Gene expression analysis --- p.82 / Chapter 4.3.6 --- GWAS candidate genes --- p.83 / Chapter 4.3.7 --- Individual network, pathway and Gene Ontology analysis --- p.83 / Chapter 4.3.8 --- Alternative Splicing Variation --- p.83 / Chapter 4.3.9 --- Reads mapping and processing for expressed genomic variants discovery --- p.84 / Chapter 4.3.10 --- Expressed and functional genomic variants --- p.85 / Chapter 4.3.11 --- Screening for gene fusion --- p.86 / Chapter 4.3.12 --- Sense and Antisense analysis --- p.86 / Chapter 4.3.13 --- Integrated multi-level T2DM alternations gene interaction network --- p.87 / Chapter 4.3.14 --- Validation of selected genes --- p.87 / Chapter 4.4 --- Results --- p.88 / Chapter 4.4.1 --- High quality strand-specific pair-ended RNA-Seq facilitated downstream analyses --- p.88 / Chapter 4.4.2 --- Definition of significance --- p.91 / Chapter 4.4.3 --- Wide-spread repressed gene expression in T2DM --- p.91 / Chapter 4.4.4 --- Confirmation and curation of T2DM GWAS loci by RNA-Seq --- p.92 / Chapter 4.4.5 --- Global expression alteration on T2DM associated genes --- p.97 / Chapter 4.4.6 --- Alteration of relative splicing isoforms variations and T2DM specific isoforms --- p.100 / Chapter 4.4.7 --- Rare and deleterious SNPs --- p.100 / Chapter 4.4.8 --- Absence of alteration in Sense/Antisense ratio and expressed fusion gene --- p.101 / Chapter 4.4.9 --- T2DM manifests a broad spectrum of expressed abnormalities --- p.101 / Chapter 4.4.10 --- Pathway-based integration of multiple levels of alteration expanded the T2DM network --- p.103 / Chapter 4.4.11 --- Validation of selected genes --- p.107 / Chapter 4.5 --- Discussion --- p.108 / Chapter Chapter 5: --- Conclusions and future perspectives --- p.115 / Chapter 5.1 --- Conclusions --- p.115 / Chapter 5.2 --- Future perspective --- p.115 / Chapter 5.2.1 --- Splicing detection --- p.115 / Chapter 5.2.2 --- Studies related to ethnicity --- p.116 / Chapter 5.2.3 --- Complex diseases --- p.116 / References --- p.118 / Appendix --- p.131

Lau, Siu Him. / "November 2009." / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 46). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.7 / Chapter 2 --- Support Vector Machine --- p.9 / Chapter 2.1 --- Two-Class Support Vector Machine --- p.9 / Chapter 2.2 --- Kernel Tricks --- p.12 / Chapter 2.3 --- Weighted Support Vector Machine --- p.14 / Chapter 2.4 --- Parameter Selection in Support Vector Machine --- p.16 / Chapter 3 --- Feature Selection Methods --- p.17 / Chapter 3.1 --- Principle Component Analysis --- p.17 / Chapter 3.1.1 --- Maximizing Variance --- p.17 / Chapter 3.1.2 --- Relation with Singular Value Decomposition --- p.19 / Chapter 3.1.3 --- Feature Selection by Singular Value Decomposition --- p.20 / Chapter 3.1.4 --- Disadvantage of Unsupervised Learning --- p.21 / Chapter 3.2 --- Linear Discriminant Analysis --- p.21 / Chapter 3.2.1 --- Between-class Distance and Within-class Variance --- p.22 / Chapter 3.2.2 --- Generalized Eigenvalue Problem --- p.24 / Chapter 3.2.3 --- Feature Selection by Linear Discriminant Analysis --- p.25 / Chapter 4 --- Application on a Real Problem --- p.27 / Chapter 4.1 --- Problem and Goals --- p.27 / Chapter 4.2 --- Diabetes Data Set --- p.27 / Chapter 4.3 --- Data Processing --- p.28 / Chapter 4.3.1 --- Pre-processing for Categorical Data --- p.28 / Chapter 4.3.2 --- Handling Uneven Data Set --- p.31 / Chapter 5 --- Results on Simulated and Real Data --- p.33 / Chapter 5.1 --- Evaluation --- p.33 / Chapter 5.1.1 --- Training and Testing --- p.33 / Chapter 5.1.2 --- Evaluation Method --- p.34 / Chapter 5.2 --- Classification Procedure --- p.35 / Chapter 5.3 --- Performance on Simulated Data --- p.36 / Chapter 5.4 --- Results on a Real Data Set --- p.39 / Chapter 5.4.1 --- Features Selection --- p.39 / Chapter 5.4.2 --- Performance on the Real Data Set --- p.41 / Chapter 5.4.3 --- Analysis on Risk Factors --- p.42 / Chapter 6 --- Conclusion --- p.44 / Bibliography --- p.46

No description available.

研究背景和目的 / 高血壓和糖尿病是人群中常見的疾病，兩者常共同存在，其共存的病理生理機制非常複雜，其中腎素血管景張素系統功能紊亂起重要作用。多個研究表明血管緊張素轉化晦抑制劑和血管緊張素II 1 型受體阻滯劑通過調節不同基因的表達，發揮其保護心血管和腎臟功能的效用。然而，目前仍缺乏遠兩類藥物影響全基因表達譜的全面調查。因此，本研究應用全基因表達譜晶片技術，檢測分析了高血壓和糖尿病並發的病人在服用安慰劑、雷米普利(ramipril)和替米沙坦(telmisartan)後的全基因表達譜的變化，從而全面評估了血管緊張素轉化臨抑制劑和血管繁張素II 1 型受體阻滯劑對相關基因的轉錄調控作用。 / 方法 / 11 名患有高血壓和糖尿病的病人(男性5 名)在服用安慰劑最少2 星期后，以隨機吹序接受為期各6 星期的雷米普利和替米沙坦治療，並分別在安慰劑期和2 個藥物治療期結束后提取心A 進行全基因表達譜分析。 / 結果 / 與服用安慰劑時的全基因表達譜相比，雷米普利治療后有267 個基因的表達降低， 99 個基因的表達增強。表達差異幅度為-2.0 至1.3 (P < 0.05) 。表達下降的基因主要與血管平滑肌收縮、炎症反應和氧化壓力相關。表達增強的基因主要與心血管炎症反應負調節相關。基因共表達網絡分析表明， 2 個共表達基因組與雷米普利的降血壓作用相闕， 3 個共表達基因組與肥胖相關。 / 與服用安慰劑時的全基因表達譜相比， 替米拉)、坦治療后有55 個基因表達降低， 158 個基因的表達增強。表達差異幅度為-1. 9 至1.3 (P < 0.05) 。表達增強的基因主要與脂質代謝、糖代謝和抗炎症因子作用相關。基因共表達網絡分析表明， 2 個共表達基因組與替米沙坦對24 小時舒張壓負荷量的作用相關， 2 個共表達基因組則與總膽固醇， 低密度脂蛋白膽固醇和C 反應蛋白相關。 / 結論 / 本論文描述了高血壓和2 型糖尿病病患全基因組表達的總體模式及經藥物治療後表達譜的相應改變， 為今後進一步研究腎素血管緊張素系統抑制劑和高血壓、糖尿病發展進程的相互作用提供了方向。 / Background and aim: Pathophysiological mechanisms underpinning the coexistence of hypertension and type 2 diabetes are complex systemic responses involving dysregulation of the renin-angiotensin system (RAS). We conducted this study to investigate the genome wide gene expression changes in patients with both hypertension and diabetes at three treatment stages, including placebo, ramipril and telmisartan. This study aimed to obtain a panoramic view of interactions between gene transcription and antihypertensive therapy by RAS inhibition. / Methods: 11 diabetic patients (S men) with hypertension were treated with placebo for at least 2 weeks followed by 6 weeks randomised crossover treatment with ramipril Smg daily and telmisartan 40mg daily, respectively. Total RNA were extracted from leukocytes at the end of placebo and each treatment period, and were hybridized to the whole transcript microarray. The limma package for R was used to identify differentially expressed genes between placebo and the 2 active treatments. The weighted gene coexpression network analysis (WGCNA) was applied to identify groups of genes (modules) highly correlated to a common biological function in pathogenesis and progression of hypertension and diabetes. / Results: There were 267 genes down-regulated and 99 genes up-regulated with ramipril. Fold changes of gene expression were ranged from -2.0 to 1.3 (P < 0.05). The down-regulated genes were involved in vascular signalling pathways responsible for vascular smooth muscle contraction, inflammation and oxidative stress. The up-regulated genes were associated with negative regulation of cardiovascular inflammation. The WGCNA identified 17 coexpression gene modules related to ramipril. The midnight blue (57 genes, r < -0.44, P < 0.05) and magenta (190 genes, r < -0.44, P < 0.05) modules were significantly correlated to blood pressure differences between placebo and ramipril. / There were 55 genes down-regulated and 158 genes up-regulated with telmisartan. Fold changes of gene expression were ranged from -1.9 to 1.3 (P < 0.05). The down-regulated genes were mainly associated with cardiovascular inflammation and oxidative stress. The up-regulated genes were associated with lipid and glucose metabolism and anti-inflammatory actions. The WGCNA identified 8 coexpression gene modules related to telmisartan. The black (56 genes, r = 0.46, P = 0.03) and turquoise (1340 genes, r = -0.48, P = 0.02) modules were correlated with diastolic blood pressure load. The blue (1027 genes) module was enriched with genes correlated with total cholesterol (r = - 0.52, P = 0.01), LDL-C (r = - 0.58, P = 0.004), and hsCRP (r = -0.57, P = 0.006). The green module (272 genes) was significantly correlated with LDL-C (r = - 0.44, P = 0.04) and hsCRP (r = - 0.59, P = 0.004). / Conclusion: Genome wide gene expression profiling in this study describes the general pattern and treatment responses in patients with hypertension and type 2 diabetes, which suggests future directions for further investigations on the interaction between actions of the RAS blockers and disease progression. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Deng, Hanbing. / "December 2011." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 198-256). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / Declaration --- p.i / Publications --- p.ii / Abstract --- p.iv / 論文摘要 --- p.vi / Acknowledgements --- p.viii / Table of Contents --- p.x / List of tables --- p.xiv / List of figures --- p.xv / List of appendices --- p.xvii / List of abbreviations --- p.xviii / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.2 --- Epidemiology --- p.6 / Chapter 1.2.1 --- Epidemiology of hypertension --- p.9 / Chapter 1.2.2 --- Epidemiology of type 2 diabetes --- p.10 / Chapter 1.3 --- Aetiology --- p.13 / Chapter 1.3.1 --- Ageing --- p.13 / Chapter 1.3.1.1 --- Age-induced artery stiffness --- p.14 / Chapter 1.3.1.2 --- Age-related endothelial dysfunction --- p.14 / Chapter 1.3.2 --- The renin-angiotensin system (RAS) --- p.16 / Chapter 1.3.2.1 --- The local RAS --- p.20 / Chapter 1.3.2.2 --- The RAS and insulin resistance --- p.22 / Chapter 1.3.2.3 --- The RAS and inflammation --- p.26 / Chapter 1.3.2.4 --- The RAS and oxidative stress --- p.28 / Chapter 1.3.3 --- Obesity --- p.31 / Chapter 1.3.3.1 --- Obesity and renin-angiotensin system (RAS) --- p.33 / Chapter 1.3.3.2 --- Obesity and insulin resistance --- p.36 / Chapter 1.3.3.3 --- Obesity and oxidative stress --- p.38 / Chapter 1.3.3.4 --- Obesity and sympathetic nervous system (SNS) --- p.38 / Chapter 1.4 --- Pharmacogenomics of antihypertensive therapy --- p.39 / Chapter 1.4.1 --- Angiotensin-converting enzyme inhibitors (ACEIs) --- p.41 / Chapter 1.4.2 --- Angiotensin II type 1 receptor blockers (ARBs) --- p.43 / Chapter Chapter 2 --- Aim --- p.59 / Chapter Chapter 3 --- Methods --- p.60 / Chapter 3.1 --- Subjects --- p.60 / Chapter 3.1.1 --- Subject recruitment protocol --- p.60 / Chapter 3.1.2 --- Definition of type 2 diabetes --- p.62 / Chapter 3.1.3 --- Definition of obesity --- p.62 / Chapter 3.1.4 --- Definition of dyslipidaemia --- p.63 / Chapter 3.2 --- Study design and procedure --- p.64 / Chapter 3.2.1 --- Blood pressure assessments --- p.65 / Chapter 3.2.2 --- Anthropometric measurements --- p.68 / Chapter 3.2.3 --- Medical history, life style and side effect evaluation --- p.68 / Chapter 3.2.4 --- RNA isolation --- p.68 / Chapter 3.2.5 --- RNA quality assessment --- p.70 / Chapter 3.2.6 --- Oligonucleotide microarrays --- p.71 / Chapter 3.2.7 --- DNA extraction --- p.75 / Chapter 3.2.8 --- Biomedical measurements --- p.76 / Chapter 3.2.8.1 --- Glycosylated haemoglobin Alc (HbA₁c) --- p.77 / Chapter 3.2.8.2 --- Fasting plasma glucose (FP G) --- p.77 / Chapter 3.2.8.3 --- Fasting insulin --- p.77 / Chapter 3.2.8.4 --- Plasma urate --- p.77 / Chapter 3.2.8.5 --- High sensitive C-reactive protein (hsCRP) --- p.78 / Chapter 3.2.8.6 --- Fasting plasma triglycerides (TG) --- p.78 / Chapter 3.2.8.7 --- Fasting plasma cholesterols --- p.78 / Chapter 3.2.8.8 --- Renal and liver functions --- p.78 / Chapter 3.2.8.9 --- Urinary parameters --- p.79 / Chapter 3.3 --- Statistical Analysis --- p.79 / Chapter 3.3.1 --- Statistical analysis of clinical and biomedical data --- p.79 / Chapter 3.3.2 --- Analysis of microarray data --- p.80 / Chapter 3.3.2.1 --- Raw data assessment --- p.80 / Chapter 3.3.2.2 --- Data normalisation --- p.92 / Chapter 3.3.2.3 --- Data filtering --- p.96 / Chapter 3.3.2.4 --- Linear models for assessment of differential expression --- p.96 / Chapter 3.3.2.5 --- Weighted gene coexpression network analysis --- p.101 / Chapter 3.3.2.6 --- Network visualisation and gene ontology analysis --- p.102 / Chapter 3.3.3 --- Sample size calculation --- p.103 / Chapter Chapter 4 --- Results --- p.104 / Chapter 4.1 --- Demographic and biomedical characteristics at baseline --- p.104 / Chapter 4.1.1 --- Hypertension and diabetes status at baseline --- p.108 / Chapter 4.1.2 --- Prevalence of dyslipidaemia --- p.108 / Chapter 4.1.3 --- Prevalence of obesity --- p.109 / Chapter 4.1.4 --- Prevalence of metabolic syndrome --- p.109 / Chapter 4.1.5 --- Inflammation markers --- p.110 / Chapter 4.2 --- Blood pressure response to the RAS blockers --- p.110 / Chapter 4.2.1 --- Clinic blood pressure --- p.110 / Chapter 4.2.2 --- 24-hour ambulatory blood pressure --- p.112 / Chapter 4.3 --- Biomedical characteristics --- p.118 / Chapter 4.4 --- Compliance, side effects and adverse events --- p.120 / Chapter 4.5 --- Gene expression differences between treatments --- p.121 / Chapter 4.5.1 --- Gene expression differences between placebo and ramipril --- p.121 / Chapter 4.5.1.1 --- Expression changes in genes related to regulation of transcription with ramipril --- p.122 / Chapter 4.5.1.2 --- Expression changes with ramipril in genes related to molecular mechanism of cardiovascular changes in hypertension --- p.125 / Chapter 4.5.1.3 --- Expression changes in genes related to blood pressure with ramipril --- p.128 / Chapter 4.5.1.4 --- Expression changes in genes related to fatty acid metabolism with ramipril --- p.130 / Chapter 4.5.1.5 --- Expression changes in genes related to inflammation with ramipril --- p.130 / Chapter 4.5.1.6 --- Expression changes in genes related to oxidative stress with ramipril --- p.133 / Chapter 4.5.1.7 --- Power estimation --- p.133 / Chapter 4.5.2 --- Gene expression differences between placebo and telmisartan --- p.135 / Chapter 4.5.2.1 --- Changes in regulation oftranscription with telmisartan --- p.137 / Chapter 4.5.2.2 --- Expression changes in genes related to glucose metabolism with telmisartan --- p.141 / Chapter 4.5.2.3 --- Expression changes in genes related to lipid metabolism with telmisartan --- p.143 / Chapter 4.5.2.4 --- Expression changes in genes related to inflammation with telmisartan --- p.143 / Chapter 4.5.2.5 --- Power estimation --- p.145 / Chapter 4.5.3 --- WGCNA for comparison between placebo and ramipriI --- p.147 / Chapter 4.5.3.1 --- Midnight blue module and clinical responses to ramipril --- p.152 / Chapter 4.5.3.2 --- Magenta module and blood pressure responses to ramipril --- p.154 / Chapter 4.5.3.3 --- Yellow module and clinical responses to ramipril --- p.158 / Chapter 4.5.3.4 --- Red module and clinical responses to ramipril --- p.161 / Chapter 4.5.3.5 --- Salmon module and clinical responses to ramipril --- p.163 / Chapter 4.5.4 --- WGCNA for comparison between placebo and telmisaItan --- p.168 / Chapter 4.5.4.1 --- Diastolic blood pressure load and gene coexpression modules --- p.168 / Chapter 4.5.4.2 --- Lipids, hsCRP and gene coexpression modules --- p.172 / Chapter Chapter 5 --- Discussion --- p.176 / Chapter 5.1 --- Gene expression changes related to ramipril --- p.177 / Chapter 5.1.1 --- Gene expression changes and blood pressure reduction by ramipri1 --- p.177 / Chapter 5.1.2 --- Gene expression changes and vascular protection by ramipri1 --- p.181 / Chapter 5.1.3 --- Obesity and gene expression changes by ramipril --- p.183 / Chapter 5.2 --- Gene expression changes related to telmisartan --- p.185 / Chapter 5.2.1 --- Blood pressure and coexpressed gene modules with telmisartan --- p.185 / Chapter 5.2.2 --- Lipid metabolism and gene expression changes by telmisartan --- p.187 / Chapter 5.2.3 --- Glucose metabolism and gene expression changes by telmisartan --- p.189 / Chapter 5.2.4 --- hsCRP and gene expression changes by telmisartan --- p.190 / Chapter 5.3 --- Limitations of this study and future directions of research --- p.191 / Chapter Chapter 6 --- Conclusion --- p.194 / References --- p.198 / Appendices --- p.257

BACKGROUND AND OBJECTIVES: Type 2 diabetes mellitus (T2DM) is a complex disease with genetic predisposition and histopathological characterization. Pancreatic islet amyloidosis, hyaline arteriolosclerosis, and diabetic glomerulopathy are histopathological hallmarks of T2DM at autopsy examination. The associations of genetic variants with diabetic amyloidosis, arteriosclerosis and glomerulopathy have not been fully elucidated. Several candidate genes including apolipoprotein E (ApoE), insulin degrading-enzyme (IDE) and glucose transporter-1 ( GLUT1) have been reported to increase risk of T2DM in human studies although results are not always consistent. Capitalizing on the pathological hallmarks of T2DM, I used autopsy specimens to investigate the risk associations of polymorphisms of ApoE (rs429358 and rs7412), IDE (rs6583813) and GLUT1 (rs710218) genes with clinical features and specific pathological changes in diabetic kidney and pancreas. I further explored the mechanisms of these associations by evaluating the histopathological changes and protein expression in pancreas and kidney. / CONCLUSIONS: These findings suggest that genetic factors have important effects in the development of tissue-specific changes and chronic complications in T2DM. Islet amyloidosis, arteriosclerosis and glomerulosclerosis in T2DM may share common pathogenetic processes as suggested by the coexistence of chaperone proteins, amyloid P and ApoE. Genetic--pathologic correlation studies are useful in advancing our understanding of the mechanisms of complex diseases such as T2DM. / METHODS AND MATERIALS: Genomic DNA was extracted from white blood cell-concentrated paraffin embedded formalin fixed spleen tissues. Genotyping for ApoE, IDE and GLUT1 polymorphisms was determined by polymerase chain reaction (PCR) and ligase detection reaction (LDR). The pathological changes were blindly assessed in pancreatic and kidney tissues of autopsy specimens. Protein expression of these genes was examined by immunostaining and quantified by using Metamorph image analysis system. / RESULTS: In a consecutive study population of 3693 autopsy specimens containing 328 T2DM and 209 control cases, the respective frequencies of genotypes were as follows: 1) TT of GLUT1 rs710218: 11.2% vs. 11.3%; 2) ApoE epsilon2: 19.4% vs. 10.9%; 3) ApoE epsilon4: 12.1% vs. 9.1% and 4) C carriers of IDE rs6583813: 51.2% vs. 47.9%. The key genotype-phenotype correlations were as follows. 1) In the T2DM cases, GLUT1 rs710218 IT genotype carriers (0% in TT genotype vs. 59.1% in AA genotype, P=0.0407) were less likely but ApoE epsilon 2 allele carriers (57.1% in epsilon2 allele carriers vs. 23.5% in epsilon3 allele carriers P=0.0382) were more likely to have diabetic glomerular hypertrophy than referential group. ApoE epsilon2 carriers showed increased glomerular ApoE protein expression with the immunoreactivity found mainly in the mesangial regions and nodular lesions. On the other hand, ApoE epsilon 3/epsilon4 cases had diffuse ApoE expression in glomerular capillaries. 2) ApoE epsilon4 carriers were more likely to have islet amyloidosis than non-carriers (62.5% in epsilon4 allele carriers vs. 23.6% in epsilon 3 allele carriers P=0.0232). There was immunolocalization of the chaperone proteins, amyloid P and ApoE in both islet amyloid deposits and arterial walls with hyaline arteriolosclerosis. 3) In T2DM cases, IDE rs6583813 C allele carriers had higher prevalence of vascular disorders [hypertension (67.4% vs. 43.6%, P=0.0332), death due to cardiovascular disease (58.1% vs. 25.6%, P=0.0479) and cerebral vascular accident (CVA) (20.9% vs. 2.4%, P=0.0412)1 than T allele carriers. / Guan, Jing. / Adviser: Chan Chung Ngor Juliana. / Source: Dissertation Abstracts International, Volume: 73-02, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 175-192). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

Zhang, Ji. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 100-104). / Abstracts in English and Chinese. / Chapter CHAPTER 1. --- Introduction / Chapter 1.1. --- Introduction of genetic association studies --- p.1 / Chapter 1.1.1. --- Application of genetic association studies in complex diseases --- p.3 / Chapter 1.1.2. --- Application of genetic association studies in type-2 diabetes --- p.4 / Chapter 1.2. --- Study design of genetic association studies --- p.7 / Chapter 1.3. --- Overview of statistical approaches in association studies --- p.10 / Chapter 1.3.1. --- Preliminary analyses --- p.10 / Chapter 1.3.1.1. --- HardýؤWeinberg equilibrium testing --- p.10 / Chapter 1.3.1.2. --- Inference of missing genotype data --- p.12 / Chapter 1.3.1.3. --- SNP tagging --- p.14 / Chapter 1.3.2. --- Single-point and multipoint tests for association --- p.15 / Chapter 1.4. --- Other relevant methods employed in this study --- p.20 / Chapter 1.4.1. --- Self-Organizing Maps (SOM) with further classification by K-means clustering --- p.20 / Chapter 1.4.2. --- Random forests --- p.27 / Chapter 1.5. --- Main objectives of this study --- p.31 / Chapter CHAPTER 2. --- Materials and methods / Chapter 2.1. --- Study cohort --- p.32 / Chapter 2.2. --- Study design --- p.34 / Chapter 2.2.1. --- Construction of sample sets for each stage using SOM and K-means clustering --- p.34 / Chapter 2.2.2. --- Stage 1 analysis by random forests --- p.37 / Chapter 2.2.3. --- Stage 2 analysis by chi-square test --- p.42 / Chapter 2.2.4. --- Two-stage genetic association study by chi-square test --- p.43 / Chapter 2.2.5. --- Comparison of results: random forests plus chi-square test versus chi-square test --- p.43 / Chapter 2.2.6. --- Validation of results in the whole sample set by allelic chi-square test --- p.44 / Chapter 2.2.7. --- Extensions of the study: cumulative effects of candidate SNPs on risk of type-2 diabetes --- p.45 / Chapter CHAPTER 3. --- Results / Chapter 3.1. --- Effects of sample classification by SOM and K-means clustering --- p.50 / Chapter 3.2. --- Genetic associations in stage 1 --- p.64 / Chapter 3.3. --- Genetic associations in stage 2 and validation of results --- p.69 / Chapter 3.4. --- Cumulative effects of candidate SNPs on risk of type-2 diabetes --- p.76 / Chapter CHAPTER 4. --- Discussion / Chapter 4.1. --- Overall strategy --- p.81 / Chapter 4.1.1. --- Effects of SOM and K-means clustering --- p.82 / Chapter 4.1.2. --- Effects of random forests in the first stage of association study --- p.83 / Chapter 4.1.3. --- Comparison of our method with traditional chi-square test --- p.84 / Chapter 4.1.4. --- Joint effects of candidate SNPs selected by the hybrid method --- p.86 / Chapter 4.2. --- Biological significance of candidate SNPs --- p.88 / Chapter 4.2.1. --- Gene CDKAL1 --- p.89 / Chapter 4.2.2. --- Gene KIAA1305 --- p.90 / Chapter 4.2.3. --- Gene DACH1 --- p.91 / Chapter 4.2.4. --- Gene FUCA1 --- p.92 / Chapter 4.2.5. --- Gene KCNQ1 --- p.93 / Chapter 4.2.6. --- Gene SLC27A1 --- p.94 / Chapter 4.3. --- Limits and improvement of this study --- p.96 / Chapter 4.4. --- Conclusion --- p.99 / REFERENCES --- p.100