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Integrated Gene Expression Analysis of Controls Across Datasets Available of the Genes Implicated in Type 2 Diabetes Mellitus Complications

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Integrated Gene Expression Analysis of Controls Across Datasets Available of the Genes Implicated in Type 2 Diabetes Mellitus Complications Integrated Gene Expression Analysis of Controls Across Datasets Available of The Genes Implicated In Type 2 Diabetes Mellitus Complications Thesis Submitted for Partial Fulfillment of the Master of Technology in Computational Biology By- Richa Rashmi MT15131 Supervised by- Dr. S Ramachandran CSIR- Institute of Genomics and Integrative Biology Acknowledgement With an overwhelming sense of pride and genuine obligation, I express my deepest regards to my supervisor Dr. Srinivasan Ramachandran. He explored my research compatibility and provided his valuable guidance throughout my work. He made me understand biological concepts, which was an unknown territory for me. He explored and nurtured my statistical knowledge. Without his perspicuous comments, suggestions and erudite guidance, I would not be able to accomplish this work. His patience to go through the draft is meticulously incredible. He has helped me a lot to improve my presentation and scientific writing skill through he comments and suggestions on this thesis. I would also like to acknowledge Director of our Institute for motivating and providing us the best facilities for studies. The faculty of Centre of Computational Biology have been very supportive and helped me whenever I approached them with a problem. The love and support given by all the lab members from CSIR-IGIB have been a great source of strength. They were always there to clear my smallest doubts and answer my queries promptly. I would also like to thank my batch mates from IIIT Delhi for their help and positive support. The IIIT Delhi and CSIR-IGIB staff has been most cooperative and have been very helpful during past two years. A million thanks to my family for being very supportive and giving freedom to pursue the M.Tech. in Computational Biology. They have always encouraged me and given opportunity to fulfil my dreams. Above all I am very grateful to almighty God who brought me to this educational platform. i Contents 1. Introduction 1.1. Signs and symptoms 1.2. Complications 1.3. Causes of development of type 2 diabetes 1.4. Medical conditions 1.5. Pathophysiology 1.6. Diagnosis 1.7. Prevention 1.8. Management 1.9. Lifestyle 1.10. Genetic causes of diabetes mellitus type 2 1.10.1. Diabetes Genetic Factors in Type 2 Diabetes all factors in detail 1.10.2. The Sulfonylurea Receptor (ABCC8) 1.10.3. The Calpain 10 Enzyme (CAPN10) 1.10.4. The Glucagon Receptor (GCGR) 1.10.5. Gly40Ser mutation 1.10.6. The Enzyme Glucosidase (GCK) 1.10.7. The Glucose Transporter GLUT2 1.10.8. The Transcription Factor HNF4A 1.10.9. The Insulin Hormone (INS) 2. Materials and Methods 2.1. Data collection 2.1.1. Gene Expression Omnibus 2.1.2. T2DiACoD 2.2. Cross Platform Normalization 2.2.1. Loading and storage of raw data 2.2.2. Transformations of raw data 2.2.3. Annotation of raw data 2.2.4. Collapsing of redundant probe sets 2.2.5. Removal of batch effects and merging datasets 2.2.6. COMBAT for batch effect removal 2.3. Filtering dataset for Type 2 Diabetes and getting the normal range 2.3.1. Confidence interval 3. T2DiGER –Type 2 diabetes Gene Expression Report 3.1. T2DiGER User Interface 3.2. T2DiGER User Input 3.3. T2DiGER Background Operation 3.1 Generating Gene Expression Report 3.1 Quantile Normalization 3.1 Matching Probe Ids to Gene Names 3.4. T2DiGER output ii 4. Results 4.1. Evaluation of normalized datasets for tissue wise normal samples 4.2. Tissue tables 5. Summary and Conclusion Appendix Bibliography iii List of Figures 1.1. Scheme of the steps performed for cross platform normalization. 3.1. User input for function 1 3.2. User input for function 2 4.1. Boxplot to show comparison of raw and LS transformed samples from pancreas 4.2. Boxplot to show comparison of raw and LS transformed samples from heart 4.3. Boxplot to show comparison of raw and LS transformed samples from blood 4.4. Boxplot to show comparison of raw and LS transformed samples from muscle 4.5. Boxplot to show comparison of raw and LS transformed samples from adipose 4.6. Boxplot to show comparison of raw and LS transformed samples from liver 4.7. Bar graph showing gene expression values of control samples from liver tissue after batch effect removal. 4.8. Bar graph showing gene expression values of control samples from blood tissue after batch effect removal. 4.9. Bar graph showing gene expression values of control samples from heart tissue after batch effect removal. 4.10. Bar graph showing gene expression values of control samples from liver tissue after batch effect removal. 4.11. Bar graph showing gene expression values of control samples from muscle tissue after batch effect removal. 4.12. Bar graph showing gene expression values of control samples from pancreatic tissue after batch effect removal. iv List of Tables 2.1. Details of datasets collected 2.2. Annotation files for the datasets collected A.1 Table for adipose tissue A.2 Table for blood tissue A.3 Table for heart tissue A.4 Table for liver tissue A.5 Table for muscle tissue A.6 Table for pancreas tissue v Chapter – 1 Introduction India is the Diabetes capital of the world. 1As per a recent study approximately11 % of Indians may be victims. It may even be more as many cases go untested.2 Type 2 Diabetes mellitus is a chronic metabolic disease that is described by high glucose, insulin resistance, and the relative absence of insulin. Normal manifestations incorporate expanded thirst, frequent urination, and weight reduction. Side effects may likewise incorporate more craving, tiredness, and injuries that do not heal. Often symptoms come on slowly. Long term complications from high blood sugar include heart disease, strokes, diabetic retinopathy which can result in blindness, kidney failure, and poor blood flow in the limbs which may lead to amputations. The sudden onset of hyperosmolar hyperglycaemic state may happen however, ketoacidosis is remarkable. Diabetes Mellitus type 2 is majorly because of obesity and insufficient exercise in individuals who are hereditarily inclined. It makes up around 90% of instances of diabetes, with the other 10% due principally to Type 1 diabetes mellitus and gestational diabetes. In the case of Type 1 diabetes mellitus, there is an absence of insulin because of the breakdown of islet cells in the pancreas. The diagnosis of diabetes is by blood tests, for example, fasting plasma glucose, oral glucose resilience test, or A1C. Diabetes Mellitus type 2 can be prevented by maintaining a healthy weight, regular exercise, and eating appropriately. Treatment includes exercise and dietary changes. In the event that glucose levels are not satisfactorily brought down, the drug metformin is often prescribed. Many individuals may in the end likewise require insulin injections. A routine check of glucose levels is prescribed for those on insulin. This may not be required in those taking pills. Bariatric surgery frequently enhances diabetes in the individuals who are obese. 3Rates of Type 2 diabetes Mellitus have increased since 1960 in parallel with obesity. Starting at 2013, there were roughly 368 million individuals determined to have the ailment contrasted with around 30 million of every 1985. Generally, it happens in middle or old age. 1.1 Signs and symptoms The 4most important symptoms of Type 2 diabetes mellitus are polyuria, polydipsia, polyphagia, and reduced weight. Moreover, obscured vision, irritation, fringe neuropathy, intermittent vaginal contaminations, and weakness are some of the symptoms that are normally present in individuals with Type 2 diabetes mellitus. Many individuals have no manifestations amid an initial couple of years and are diagnosed on routine testing. Individuals with Type 2 diabetes mellitus may seldom give the hyperosmolar hyperglycaemic express (a state of high glucose related with a diminished level of awareness and low blood pressure). 1.2 Complications Diabetes Mellitus Type 2 is a chronic ailment resulting to a ten-year reduced life expectancy. Partly this is because of various complexities with which it is related, including 5 the increased risk of the cardiovascular diseases, including ischemic coronary illness and stroke; 20 times increased the risk of lower limb amputations, and 1 increased rates of hospitalizations. Diabetes Mellitus Type 2 is the biggest reason for kidney failure and visual impairments. Moreover, it has been related to the risk of vascular dementia, dementia because of Alzheimer's disease and cognitive dysfunction. 6Complications such as acanthosis nigricans, sexual dysfunction, and frequent infections are also related to Type 2 Diabetes Mellitus. Hence the focus of this study is to devise a tool to detect if patients of Diabetes are at risk of developing complications. In addition, the tool can also predict if a normal individual without Diabetes is at risk of developing complications if at a later stage he/she develops Diabetes, prompting them to adopt healthy life style measures so that they either do not suffer from Diabetes later or at least delay its onset. 1.3 Causes of development of type 2 diabetes The development of type 2 diabetes is caused by a combination of lifestyle and genetic factors. While some of these factors are under personal control, such as diet and obesity, other factors are not, such as increasing age, female gender, and genetics. A lack of sleep has been linked to type 2 diabetes. 7This is believed to act through its effect on metabolism. The nutritional status of a mother during fetal development may also play a role, with one proposed mechanism being that of altered DNA methylation. Various life factors are known to be vital to the advancement of T2DM, including being overweight (characterized by a body mass index of more prominent than 25), the absence of physical movement, horrible eating routine, stress, and smoking additionally seems to build the danger of type 2 diabetes mellitus.
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