New vessel formation can be characterized as NVD (new vessels on disc) ones ..... Institute of Ophthalmology (Rawalpindi), Shifa International Hospital.
Association of TCF7L2 Polymorphisms with Retinopathy in Diabetic Patients
By
Mahmood Hassan Dalhat CIIT/FA14-RBM-011/ISB
MS Thesis
COMSATS Institute of Information Technology, Islamabad – Pakistan Spring, 2016
COMSATS Institute of Information Technology
Association of TCF7L2 Polymorphisms with Retinopathy in Diabetic Patients A Thesis Presented to
COMSATS Institute of Information Technology, Islamabad
In Partial Fulfilment Of the requirement for the degree of
MS (Biochemistry and Molecular Biology) By
Mahmood Hassan Dalhat CIIT/FA14-RBM-011/ISB Spring, 2016
ii
Association of TCF7L2 Polymorphisms with Retinopathy in Diabetic Patients A postgraduate thesis submitted to the Department of Bioscience as partial fulfilment of the requirement for the award of the Degree of MS (Biochemistry and Molecular Biology).
Name
Registration Number
Mahmood Hassan Dalhat
CIIT/FA14-RBM-011/ISB
Supervisor
Prof. Dr. Raheel Qamar, T.I HOD Department of Biosciences/Dean ORIC CIIT Islamabad Campus
Co-Supervisor
Dr. Maleeha Azam Assistant Professor Department of Biosciences Islamabad Campus June, 2016.
iii
Final Approval This thesis titled
Association of TCF7L2 Polymorphisms with Retinopathy in Diabetic Patients By
Mahmood Hassan Dalhat CIIT/FA14-RBM-011/ISB
Has been approved For the COMSATS Institute of Information Technology, Islamabad
External Examiner: ______________________________________________________ Dr. Faheem Tahir Director Technical/Head of Chemical Pathology and Endocrinology NIH, Islamabad Supervisor: ________________________________________________ Prof. Dr. Raheel Qamar, T.I HoD Department of Biosciences/Dean ORIC CIIT, Islamabad
Co-Supervisor: ________________________________________________ Dr. Maleeha Azam. Assistant Professor Department of Biosciences, CIIT, Islamabad
HoD: ________________________________________________ Prof. Dr. Raheel Qamar, T.I HoD Department of Biosciences/Dean ORIC CIIT, Islamabad
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Declaration I, Mahmood Hassan Dalhat, CIIT/FA14-RBM-011/ISB, hereby declare that I have produced the work presented in this thesis, during the scheduled period of study. I also declare that I have not taken any material from any source, except referred to wherever due and that the amount of plagiarism is within acceptable range. If a violation of HEC rules on research has occurred in this thesis, I shall be liable to punishable action under the plagiarism rules of the HEC.
Signature of the student: Date: _______________ __________________ (Mahmood Hassan Dalhat) CIIT/FA14-RBM-011/ISB
v
Certificate It is certified that Mahmood Hassan Dalhat, CIIT/FA14-RBM-011/ISB, has carried out all the work related to this thesis under my supervision at the Department of Biosciences, COMSATS Institute of Information Technology, Islamabad and the work fulfills the requirement for the award of MS degree.
Date: _____________
Supervisor:
______________________ Prof. Dr. Raheel Qamar, T.I Tenured Professor of Biochemistry & Molecular Biology Dean of Research, Innovation & Commercialization, Islamabad
Head of Department:
_________________ Prof. Dr. Raheel Qamar, T.I Head of Department Department of Biosciences CIIT, Islamabad
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DEDICATION This thesis is dedicated to my late Uncle Engr. M.M Dalhat may his soul rest in peace.
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ACKNOWLEDGEMENTS All praises is to the omnipotent; Allah (SWT) for making my studies a success, He alone we worship and He alone we seek for help, may His eternal blessings be upon His noble prophet; Muhammad (SAW), members of his household and his companions. I am grateful to my supervisor Prof. Dr. Raheel Qamar, T.I, in charge campus CIIT Islamabad, Dean, Research, Innovation & Commercialization, and HoD Biosciences CIIT Islamabad, for his immeasurable guidance and support toward my research progress. I am also thankful to my co-supervisor Dr. Maleeha Azam, Assistant Professor Biosciences CIIT, whose inspirational personality drove me to develop an inquisition for scientific knowledge, and have cultivated my interest in research. Special gratitude goes to Dr. Beenish Ali Shah and Dr. Muhammad Ajmal for their inexhaustible guidance. I am also indebted to all my lecturers for molding me into what I am today. Special gratitude and acknowledgement to Commonwealth Scholarship for granting me all the financial support I needed from the beginning to the end of my study. My gratitude goes endlessly to my father Alhaji Hassan Dalhat, my mother Hajiya Maimuna Dalhat, Rear Admiral Sulaiman Sai’du (rtd), Malama Rabi’at Dalhat, Dr.D Bashir, Malam Ibrahim Mahmood, all my siblings and rest of the Dalhat family for their prayers and financial support. Finally, I would like to thank my friends who have acted as a constant support throughout my study; Ms. Faryal Gohar Noshahi, Mr. Aftab Malik, Ms. Saher Abbas, Ms. Muneeza Arbab, Mr. Muhammad Murad, Ms. Eyza Koreshe and Ms. Nimra Riaz, I would also like to thank my lab fellows; Ms. Amna Umar Khayyam, Ms. Mahnoor Ejaz , Ms. Maleeha Maria, Ms. Mobeen Khan, Ms. Saira Tahir, Ms. Minhal Nadeem, Ms. Natasha Khan, and Mr. Sharjeel Ahmad for stimulating discussions in research and for all the fun we had over the year. Mahmood Hassan Dalhat CIIT/FA14-RBM-011/ISB
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ABSTRACT Association of TCF7L2 Polymorphisms with Retinopathy in Diabetic Patients Diabetic Retinopathy (DR) is a microvascular complication of Diabetes Mellitus (DM) and it is known to be one of the major causes of blindness worldwide. In the present study we assess the role of TCF7L2 (Transcription factor 7 like 2) polymorphisms in type 2 diabetes mellitus (T2DM) patients with retinopathy in Pakistani cohort. TCF7L2 is a transcription factor of the Wnt signaling pathway, which is known to be involved in the determination of cell fate, cell migration and beta cell proliferation and development. TCF7L2 genetic polymorphisms are among the few verified genetic variants with large effects on the risk of T2DM in different ethnic groups worldwide. In the present case-control association study the two single nucleotide polymorphisms (SNPs) that we screened include rs7903146 (c.38241435C>T) in 322 T2DM cases (206 diabetic non-retinopathy (DNR), 116 diabetic retinopathy (DR) (61 non-proliferative diabetic retinopathy (NPDR) and 55 proliferative diabetic retinopathy (PDR) ) subjects) and 226 control individuals and rs12255372 (c.482+9017G>T) in 333 T2DM cases (196 DNR, 137 DR (67 NPDR and 70 PDR) subjects) and 234 control individuals. The samples of rs7903146 and rs12255372 were genotyped using amplification refractory mutation system-polymerase chain reaction (ARMS-PCR) and polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) methods respectively. Statistical analysis of genotyped data revealed association in both dominant model (DM) and recessive model (RM) of rs7903146 are associated with T2DM, DNR, DR and PDR whereas only DM was found to be associated with T2DM, DNR, DR, NPDR and DR in rs12255372. Combined genotype analysis was also performed which revealed protective role of CC-GG in T2DM, DR and NPDR,CT-GT showed risk association with the development of DNR and DR. A significant association was found between TT-GT with the progression of DR. whereas; TT-TT showed a strong association with the development of PDR. In conclusion, our study, in addition to confirming the association of TCF7L2 gene variants to T2DM, has also shown that TCF7L2 polymorphism may play a role in the development of diabetic complications such as DR.
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TABLE OF CONTENTS 1. Introduction ................................................................................................................... 1 1.1 Diabetes Mellitus ........................................................................................................... 2 1.1.1
History of DM ................................................................................................. 2
1.2 Types of DM .................................................................................................................. 3 1.2.1
Type 1 Diabetes Mellitus ................................................................................ 3
1.2.2
Type 2 Diabetes Mellitus ................................................................................ 3
1.3 Prevalence of DM .......................................................................................................... 4 1.4 Complications of DM .................................................................................................... 4 1.4.1
Macrovascular Complications ......................................................................... 6
1.4.2
Microvascular Complications ......................................................................... 6
1.5 Diabetic Retinopathy ..................................................................................................... 7 1.5.1 Signs and Symptoms of DR .................................................................................... 7 1.6 Classification of DR ...................................................................................................... 8 1.6.1 Non-Proliferative Diabetic Retinopathy ................................................................. 8 1.6.2 Proliferative Diabetic Retinopathy.......................................................................... 8 1.7 Prevalence of DR ......................................................................................................... 10 1.8 Cellular and Molecular Mechanism of DR ................................................................. 12 1.9 Transcription Factor 7 Like 2 ...................................................................................... 14 1.9.1 TCF7L2 and Wnt Signaling Pathway ................................................................... 14 1.9.2 Genetics of TCF7L2 .............................................................................................. 15 1.9.3 Association between DM and TCF7L2 ................................................................. 15 1.9.4 TCF7L2 Associated to Beta Cell Development .................................................... 18 1.9.5 Association between DR and TCF7L2.................................................................. 18 1.10 Objectives of the Study ............................................................................................. 19 2. Materials & Methods................................................................................................... 20 2.1 Sampling ...................................................................................................................... 21 2.1.1 Patient Selection .................................................................................................... 21 2.2 Reagents preparation for DNA Extraction .................................................................. 21 2.2.1 Phenol Preparation ................................................................................................ 22 2.3 DNA extraction protocol ............................................................................................. 22 2.3.1 Cell Lysis .............................................................................................................. 22 2.3.2 Phenol-Chloroform Phase Separation ................................................................... 25 x
2.4 Storage of genomic DNA ............................................................................................ 26 2.5 Quantification of Genomic DNA ................................................................................ 26 2.5.2 Horizontal Gel Electrophoresis ............................................................................. 26 2.6 Amplification of TCF7L2 ............................................................................................ 26 2.6.1 Genotyping of rs7903146 (ARMS-PCR) .............................................................. 28 2.6.1.1 Thermal Profile of rs7903146 ......................................................................... 28 2.6.1.2 Visualization of rs7903146 Amplified Product .............................................. 29 2.6.2 Genotyping of rs12255372 (PCR-RFLP) ............................................................. 29 2.6.2.1 Thermal profile for rs12255372...................................................................... 29 2.6.2.2 Visualization of rs12255372 Amplified Product ............................................ 31 2.6.2.3 RFLP for rs12255372 ..................................................................................... 31 2.7 Statistical analysis........................................................................................................ 31 3. Results ......................................................................................................................... 354 3.1
Analysis of Genotyped Data ................................................................................ 35
3.2 Comparison between T2DM and Controls in rs7903146 ........................................ 35 3.2.3 Comparison between DNR and Controls .............................................................. 37 3.2.4 Analysis between DR and Controls ...................................................................... 37 3.2.5 Analysis of DR Sub-classes .................................................................................. 39 3.2.5.1 Analysis of NPDR, PDR and Controls ........................................................... 39 3.2.5.2 NPDR and Controls ........................................................................................ 39 3.2.5.3 PDR and Controls ........................................................................................... 39 3.3 Comparison between T2DM and Controls in rs12255372 ...................................... 41 3.3.1 Comparison between DNR and Controls .............................................................. 43 3.3.2 Analysis between DR and Controls ...................................................................... 43 3.3.3 Analysis of DR Sub-classes .................................................................................. 45 3.3.3.1 Analysis of NPDR, PDR and Controls ........................................................... 45 3.3.3.2 NPDR and Controls ........................................................................................ 45 3.3.3.3 PDR and Controls ........................................................................................... 47 3.4 Combined Genotype Analysis ..................................................................................... 47 3.4.1 Comparison between T2DM and Controls ........................................................... 47 3.4.2 Analysis of DNR, DR and Controls ...................................................................... 49 3.4.2.1 Comparison between DNR and Controls ....................................................... 49 3.4.2.2 Comparison between DR and Controls .......................................................... 49 xi
3.4.3 Analysis of NPDR, PDR and Controls ................................................................. 53 3.4.3.1 Comparison between NPDR and Controls ..................................................... 53 3.4.3.2 Comparison between PDR and Controls ........................................................ 53 4. Discussion ..................................................................................................................... 53 5. References..................................................................................................................... 59
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LIST OF FIGURES
Figure 1.1 Fundus images of DR progression…………………………………………...9 Figure 1.2 Neurovascular hypothesis for the pathogenesis of proliferative diabetic retinopathy (PDR) ……………………………………………………………………....11 Figure 1.3 Pathogenesis of diabetic retinopathy………………………………………..13 Figure 1.4 Canonical Wnt signaling pathway…………………………………………..16 Figure 1.5 TCF7L2 gene structure.………………………………………………..........17 Figure 2.1 ARMS-PCR amplification of rs7903146 (c.382-41435C>T)……………….31 Figure 2.2 The PCR-RFLP detection of TCF7L2 rs12255372 (c.482+9017G>T)……..33
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LIST OF TABLES Table 1.1 Number of individuals with DM by World Health Organization (WHO) Region (WHO | Global report on diabetes, 2016)………………………………………..4 Table 2.1 Reagents required for preparation of stock solutions for DNA extraction…..23 Table 2.2 Working solution reagents and their concentrations required for DNA extraction………………………………………………………………………………...24 Table 2.3 Reagents and preparation required for gel electrophoresis……………….......27 Table 3.1 Statistical analysis of TCF7L2 (rs7903146) in controls and T2DM cases…...36 Table 3.2. Statistical analysis of TCF7L2 (rs7903146) in controls, DNR and DR cases……………………………………………………………………………………...38 Table 3.3 Statistical analysis of TCF7L2 (rs7903146) in controls PDR, and NPDR cases …………………………………………………………………………………...............40 Table 3.4 Statistical analysis of TCF7L2 (rs12255372) in controls and T2DM cases.....42 Table 3.5 Statistical analysis of TCF7L2 (rs12255372) in controls, DNR, and DR cases……………………………………………………………………………………...44 Table 3.6 Statistical analysis of TCF7L2 (rs12255372) in controls, PDR, and NPDR case…………………………………………………………………………………….…46 Table 3.7 Statistical analysis of TCF7L2 (rs7903146+rs12255372) in controls and T2DM cases……………………………………………………………………………………...48 Table 3.8 Statistical analysis of TCF7L2 (rs7903146+rs12255372) in controls, DNR, and DR……………………………………………………………………………………….50 Table 3.9 Statistical analysis of TCF7L2 (rs7903146+rs12255372) in controls, NPDR, and PDR…………………………………………………………………………………52
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LIST OF ABBREVIATIONS ADA ADR AGEs ARMS ARMS2 AR AS α bp β CAD CA xg χ2 CD36 CFH CI CVD CSME CWS dL DKA DM DM DNA DME DNR DR ECM EDTA FA GF GLP-1 gm ICAM-1 IDDM ICF ICR
American Diabetes Association Advanced Diabetic Retinopathy Advanced Glycation end products Allele Refractory Mutation System Allele Refractory Mutation System 2 Aldol reductase Allele Specific Alpha Base pair Beta Coronary Artery Disease Carbonic Anhydrase Centrifugal Force in Gravities Chi-Square Cluster Differentiation 36 Complement factor H Confidence Interval Cardiovascular Diseases Clinically Significant Macular Edema Cotton Wool Spots Decilitre Diabetic ketoacidosis Dominant model Diabetes mellitus Deoxyribose nucleic acid Diabetic macular edema Diabetic non-retinopathy Diabetic Retinopathy Extracellular Matrix Ethylene Diamine Tetraacetic Acid Fatty Acid G-specific Forward Glucagon like peptide-1 Grams Intracellular adhesion molecule-1 Insulin Dependent Diabetes Mellitus Internal Control Forward Internal Control Reverse xv
λ µl µM M MAs MAPK MENA MI mg ml mM ng NLB NIDDM NPDR NVD NVE OR Ox-LDL PAD PCR PDR PKC PPARγ RAGE RBC R RAS RD RM ROS rpm SDS SNP T1DM T2DM TBE TCF7L2 TE TF
Lambda Microlitre Micromolar Molar Microaneurysms Mitogen activated protein kinase Middle East North Asia Mayocardial Infarction Milligram Millilitre Millimolar Nanograms Nuclear Lysis Buffer Noninsulin Dependent Diabetes Mellitus Non-proliferative Diabetic Retinopathy New Vessels on Disc New Vessels Elsewhere Odds Ratio Oxidized Low density Lipoproteins Peripheral Arterial Diseases Polymerase Chain Reaction Proliferative Diabetic Retinopathy Protein Kinase C Peroxisome proliferator activator receptor gamma Receptor Advanced Glycation Endproducts Red Blood Cells Reverse Renin angiotensin system Retinal detachment Recessive Model Reactive Oxygen Species Revolutions Per Minute Sodium Dodecyl Sulphate Single Nucleotide Polymorphism Type-1 diabetes Type-2 diabetes Tris BoricAcid EDTA Trancription factor 7 like 2 Tris EDTA T-specific Forward xvi
T1DM T2DM U UV V VEGF VH VTDR WBC WHO
Type 1 diabetes mellitus Type 2 diabetes mellitus Unit Ultravoilet Volts Vascular Endothelial Growth Factor Vitreous hemorrhage Vision Threatening Diabetic Retinopathy White Blood Cells World Health Organization
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Chapter 1 Introduction
1.1 Diabetes Mellitus Diabetes mellitus (DM) is a group of metabolic disorders characterized by hyperglycemia (increased levels of blood sugar) due to insulin resistance or inadequate insulin secretion (Berg et al., 2002; Pickup & Williams, 2003). Numerous pathogenic processes play a pivotal role in the development of DM, these ranges from autoimmune pancreatic β cells destruction with consequent insulin deficiency to abnormalities that cause insulin resistance which subsequently affects tissue metabolisms (Olefsky et al., 2004). Long-term complications of diabetes as a result of hyperglycemia can lead to retinopathy leading to vision loss, nephropathy associated to renal failure and neuropathy with risk of neural damage (Alghadyan, 2011).
1.1.1 History of DM DM was first described in an Egyptian book called Ebers Papyrus; written around 1550 BC, the relic of this ancient book was excavated in 1862 AD from a grave in Thebes, Egypt, and later published by Egyptologist and Archeologist George Ebers in 1874. The book described DM as “too great emptying of the urine”, and described these medical conditions as continuous urination, excessive thirst, and severe weight loss. Until the early 20th century the prognosis for a diabetic patient with this condition was no better than it was over 3000 years ago (Papaspyros, 1964). Physicians in South Asia at around the same time developed what was described as the first clinical test for DM. They observed that the urine from people with DM attracted flies and ants. They named the clinical condition “madhumeha” or “honey urine.” They also noted that patients with “madhumeha” suffered from extreme thirst (polydypsia) and foul breath (probably because of ketoacidosis) (Papaspyros, 1964). Around 230 BC Apollonius of Memphis was the first to use the term “diabetes,” which in Greek means “to pass through” (dia – through, betes – to go) (Nwaneri, 2015; Papaspyros, 1964). The first complete clinical description of DM was then made by Aulus Cornelius Celsus (30BC–50 AD), in his monumental eight-volume book entitled De medicina (Medvei, 2012).
2
Aretaeus of Cappadocia, a Greek physician in the 2nd century AD, was the first to distinguish between what we now call DM and diabetes insipidus (Papaspyros, 1964). Avicenna generally known as Ibn-Sina (980–1037 AD), a court physician to Caliphs of Baghdad, compiled a medical medical text “Canon Avicennae” included a detailed description of DM characterized by clinical features, such as increased appetite, sweet urine, and complications, such as diabetic gangrene and sexual dysfunction (Medvei, 2012; Poretsky, 2010).
1.2 Types of DM DM clinically is classified into two major classes; type-1 diabetes mellitus (T1DM) and type-2 diabetes mellitus (T2DM). The T1DM usually occurs in the early years of life whereas T2DM manifests in later years (Berg et al., 2002). Hyperglycemia is the hallmark of both T1DM and T2DM.
1.2.1 Type 1 Diabetes Mellitus Among the subclinical classes of DM; T1DM accounts for only 5-10% of DM cases, it is also referred to as insulin dependent diabetes mellitus (IDDM) which manifests as a result of cellular mediated autoimmune destruction of pancreatic β cells leading to total insulin deficiency (Pirot et al., 2008; Pickup & Williams, 2003). The disease is usually predominant in infants and children because it is hereditary; however, rare cases are also seen in adults (Poretsky, 2010).
1.2.2 Type 2 Diabetes Mellitus Type 2 Diabetes (T2DM), also known as non-insulin dependent diabetes mellitus (NIDDM) account for 90-95% of DM; T2DM occurrence is more common in middle and aged people (Pickup & Williams, 2003). In T2DM the insulin is available but the cell fails to respond to its action, this phenomena is known as insulin resistance (Pickup & Williams, 2003). Insulin resistance significantly contributes to hyperglycemia in T2DM and thereby leads to changes in the body metabolism (Berg et al., 2002). T2DM is often related with obesity, hypertension, high level cholesterol, elevated triglycerides, abnormalities in lipoprotein metabolism and dyslipidemia (Pickup & Williams, 2003). 3
1.3 Prevalence of DM DM is the 8th leading cause of death in the world with mortality rate of 2.2 million and subsequently the 5th cause of death in women because of the incidence of polycystic ovary syndrome (PCOS) and gestational diabetes. The prevalence of DM increased at geometric rate from 171 million, 382 million, and 422 million in 2000, 2013, and 2014 respectively (WHO | Global report on diabetes, 2016). The number of people with DM has raised steadily due to population growth in the past decades. The highest number of DM individuals in 2014 was from SouthEast Asia and West Pacific Region (Table 1.1) thus accounting for approximately half DM cases in the world. Pakistan is the 8th largest country in the world with DM with approximately 14.5 million people (Guariguata et al., 2014). The reason for the rise of DM in these regions may be as a result of genetic susceptibility, social, behavioral or environmental risk factors. These factors contribute to risk susceptibility of individuals to DM.
1.4 Complications of DM The DM complications of are severe conditions which lead to deleterious health problems that are in diabetic patients. The complication usually manifest in the form of damage to vascular system of the body and are less common in DM patients with controlled hyperglycemia (Maji, 2004). Uncontrolled high blood sugar causes impaired metabolism which results in oxidative stress, increased lipolysis (breakdown of lipids), elevated ketone bodies and increased gluconeogenesis (production of energy from non-carbohydrate source) (Cade, 2008; Cunha-Vaz, 1978). These factors affect the body tissues and subsequently they can cause both morphological and functional defects in organs such as the eyes, heart, kidneys and liver (Fowler, 2008). These effects are categorized as macrovascular complications. i.e. acute complication that affect the arteries and microvascular complication i.e. chronic complication
that
damage
microvasculature
Chakravarthy, 2003).
4
(Maji,
2004;
Warpeha
&
Table 1.1 Number of individuals with DM by World Health Organization (WHO) Region (WHO | Global report on diabetes, 2016). Region (s) African region Eastern Mediterranean Region of the Americas European Southeast Asia Western Pacific Total
Number (millions) 2000 2014 7
25
15
43
33 33 46 35 171
5
62 64 96 131 422
1.4.1 Macrovascular Complications Macrovascular complications involve damage to the large vessels of the body, this phenomena is referred to as macroangiopathy. The pathological mechanism of macrovascular defect is the atherosclerosis; hardening of the arteries. Atherosclerosis manifests due to chronic inflammation and injury to walls of the arteries which implicates damage to coronary vascular system (Cade, 2008). In response to endothelial damage by atherosclerosis; angiotensin II; a vasoconstriction hormone, promotes the oxidation of lipids from low density lipoprotein (LDL) particles to oxidized lipid which accumulates at the endothelial walls of the arteries, this recruits immune cells such as monocytes which subsequently triggers an immunological response which cause morphological and functional impairment of large vessels causing cardiovascular disease (CVD), myocardial infarction (MI) and peripheral artery disease (PAD) (Maji, 2004; Maple-Brown et al., 2012; Warpeha & Chakravarthy, 2003).
1.4.2 Microvascular Complications The microvascular complications of DM encompass DM induced damage to small blood vessels (capillaries) and it is also known as microangiopathy. Microvascular complications in DM are induced by oxidative stress (excessive free radicals production), polyol accumulation (production of reducing sugar molecules) and advanced glycation end products (AGEs) (Fowler, 2008). These metabolic abnormalities in the affected tissues cause change in endothelial permeability and blood flow and leads to complications such as diabetic induced nephropathy, neuropathy and retinopathy (DR) (Bos & Agyemang, 2013). 1.4.2.1 DM Induced Microvascular Complication in Eye The eye is an organ for vision which detects light and subsequently convert it into electrochemical impulse (Miao et al., 2013). The eye is made of three layers: The outer layer containing the connective tissue that form the cornea (domed shaped transparent layer) and sclera, middle layer composed of iris which control light intensity entering the eye, ciliary body and choroid and the inner layer contains the retina. 6
The retina transfers photochemical signal to the brain. The retina contains varieties of cells that interact with the optical nerve which generates impulse for vision. Various retina cells have different functions, these include photoreceptor cells (cones and rods); ganglion cells which collect and transmit visual information from photoreceptors to the brain, pericytes which are contractile cells that are found in the retina, bipolar cells and amacrine cells mediate the communication between ganglion cells and photoreceptor cells (Malhotra et al., 2011; Ng, 2012; Robinett & Kahn, 2008). Microvascular complication damages to the eyes vessels leading to manifestation of change in retinal vasculature, impairment of any part of the eye can lead to partial or complete blindness (Malhotra et al., 2011). Visual loss as a result of microvascular complication of DM leads to DR (Tang & Kern, 2011).
1.5 Diabetic Retinopathy DR is one of the major complications of DM in which the retina becomes severely damaged in a progressive manner, leading to vision loss and hence blindness (Saleem et al., 2015). The DM induced retinal damage is either due to macular degeneration or increased microvascular permeability (Qazi et al., 2009). The potential risk factors associated with DR include hyperglycemia, hypertension (high blood pressure), age and dyslipidemia (elevated lipid profile). The clinical diagnosis of DR is based on the microvascular deformities, hemorrhages, cotton wool spots, proliferative retinal vessels and microaneuryms. 1.5.1 Signs and Symptoms of DR The clinical symptoms of DR include vessel occlusions, hard exudates; yellowwhite intra-retinal deposits of variable size made up of extracellular lipid leaked from abnormal retinal capillaries (Moreno, Lozano, & Salinas, 2013), and microaneuryms (MAs); a deep red dot that varies from 15-60μm in diameter usually found in the posterior poles of the eye due outpouching caused by pericyte degeneration (Scanlon et al., 2013). Prolonged MAs accumulate over time leading to retinal ischemia (inadequate blood flow in the retina). 7
The blockage of the retinal vessels induces angiogenesis and therefore, diabetic macular edema (DME); accumulation of fluid and protein in the macular region therefore results in blurred vision (Lee et al., 2015; Olefsky et al., 2004). In addition formation of new vessels from tissues that are avascular by either vasculogenesis or angiogenesis also occur (Qazi et al., 2009). However, the signs and symptoms observed in DR patients depend on the type of DR.
1.6 Classification of DR DR is clinically categorized into two conditions: non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR) (Duh, 2008). The early symptoms of DR involving intra-retinal microvascular changes induced by MAs, hard exudates, retinal ischemia and DME are predominant in NPDR, whereas, later symptoms triggered by angiogenesis in DR leads to NV usually found in PDR(Qazi et al., 2009).
1.6.1 Non-Proliferative Diabetic Retinopathy NPDR is the early stage of DR which is characterized by intra-retinal microvascular changes such as altered retinal vascular permeability and sudden retinal vessel and capillary closure. NPDR is sub-categorized into mild, moderate and severe conditions. In mild and moderate NPDR; relatively few levels of MAs and intra-retinal hemorrhage are noticed, whereas in severe NPDR the presence of intra-retinal microvascular abnormalities (IRMA), retinal ischemia, severe intraretinal hemorrhage and DME are seen. NPDR gradually progresses to PDR (Figure 1.1).
1.6.2 Proliferative Diabetic Retinopathy PDR is the advanced stage of DR characterized by neovascularization due to retinal ischemia. The vessels originate from the retina and optic disk, as a consequence of induced DM complication. New vessels grow along the vitreoretinal region, penetrating the internal limiting membrane, thus bleeding into sub-hyaloid space and vitreous cavity.
8
Normal
Severe NPDR
Background DR
Moderate NPDR
PDR neovascularization
Fibrovascular membranes
Figure 1.1 Fundus images of DR progression. This progression develops from background DR (mild DR) a form of NPDR to Fibrovascular membrane a form of PDR, as the DR progresses new vessels are formed. Gradual loss in the red colour and change in vein size occur with progression of DR (Wu, 2012).
9
New vessel formation can be characterized as NVD (new vessels on disc) ones that appear on optic disk or NVE (new vessels elsewhere) the ones that arise on other parts of the retina, inducing ischemic changes in retina (Alghadyan, 2011). The pathogenesis of PDR is explained by the neurovascular hypothesis. Several retinal cells are involved in neurovascular hypothesis. Figure 1.2 shows neurovascular hypothesis explanation of PDR pathogenesis, the exposure of retina cell components and structure to stress of hyperglycemia causes damage to blood vessels, glial cells, and retinal neurons there resulting in VEGF induced angiogenesis. The concept of neurovascular unit is crosstalk between retinal blood vessel and its surrounding cells. The hyperglycemia also induces vascular damage, thereafter destroying the microenvironment and the crosstalk between pericytes, retinal cells and endothelial cells and neuron. Destruction in retinal cells crosstalk may implicate in glial proliferation, neural degeneration, and vascular degeneration which causes PDR (Mi et al., 2014).
1.7 Prevalence of DR DR accounts for nearly 5% of the world’s 39 million people suffering from blindness and has now become a major cause of vision loss in people of working age (20-65) in developed countries (Kovarik et al., 2016). Currently more than 422 million people have DM worldwide and it is predicted that nearly half of them will develop some degree of DR during their lifetime. After years of living with diabetes, approximately 10% of the individuals usually develop severe visual impairment and about 2% become legally blind (“WHO | Diabetes, 2015). Despite of improved living standards and increased life expectancy, there is still an alarming increase in prevalence of T2DM in Asia and it has become a public health and economic threat. The highest numbers of cases of DM in recent years have been reported to be from South-East Asia, and Arab countries (Al-Maskari & El-Sadig, 2007; Javadi et al., 2009; Shera et al., 2007; Yau et al., 2012).
10
Figure 1.2 Neurovascular hypothesis for the pathogenesis of proliferative diabetic retinopathy (PDR) (Mi et al., 2014). Legends: BRB, blood-retinal barrier; BM, basement membrane; NPDR, nonproliferative diabetic retinopathy; PDR, proliferative diabetic retinopathy.
11
The prevalence in Pakistan is estimated to be 27% of the world’s DR population and has become a common cause of blindness; however, limited studies are done on the risk factors identification of DR in Pakistan. Therefore, little is known on its disease pathogenesis (Hussain et al., 2013).
1.8 Cellular and Molecular Mechanism of DR Hyperglycemia induced elevated metabolites in patients with DM have been shown to induce several related and interrelated biochemical pathways which are associated with the progression of DR (Figure 1.3) (Ola et al., 2013). Several studies have demonstrated that prolonged hyperglycemia, hyperlipidemia and hypertension contribute to the DR pathogenesis, however, the exact mechanisms by which elevated glucose initiates the vascular disruption in retinopathy remain poorly understood (Cumbie & Hermayer, 2007; Tarr, 2010). However, several pathways have been shown to have a correlation between hyperglycemia and microvascular complications of retinopathy (Ola, 2012). Among these pathways are polyol pathway, oxidative stress, production of protein kinase C (PKC), formation of advanced glycation end products (AGEs), activation of Reninangiotensin system (RAS), impaired hemodynamic factors, increased VEGF (vascular endothelial growth factor) activities, which eventually causes chronic inflammation and apoptosis (Wu, 2012) . Prolonged hyperglycemia induces the accumulation of sorbitol in the polyol pathway, causing degradation of the pericytes and thickening of the basement endothelial membrane of retinal capillaries (Cade, 2008). Destruction of pericytes leads to blood leakage (hemorrhage). Polyol pathway promotes the activation of aldol reductase (AR) enzyme which is present in retina, leading to increased sorbitol production in the intracellular region of retina causing imbalance in osmotic pressure in the retinal vasculature. The increased byproduct of polyol pathway in blood triggers AGEs production (Shah, 2008).
12
Growth factors
Hormones
Diabetes
Hypertension
Dyslipidemia
Hyperglycemia Elevated Mitochondrial ROS
Hexosamine AGEs
PKC
Polyol
oxidative stress
Apoptosis
Inflammation
Diabetic Retinopathy
Figure 1.3 Pathogenesis of diabetic retinopathy (Chang & Chuang, 2010).
13
Chronic hyperglycemia is also associated to oxidative stress which in turn creates vicious cycle of damage to macromolecules by increased the production of more reactive oxygen species (ROS), which also triggers other metabolic pathways that are detrimental and promote the development of DR (Figure 1.3). Some of the genes related to the above mentioned pathways include AR, VEGF, IGF, AGE, RAGE, IL-1, IL-6, IL-12, TNFα, TGFβ, EPO, ANG ½, PIEF, PDGF, PPARγ, and eNOS etc. (Radha, Rema, & Mohan, 2002). Transcription factor 7 like 2 (TCF7L2) is associated with the pathogenesis of T2DM is hypothesized to play a significant role in the pathophysiology of DR (Bodhini et al., 2007; Buraczynska et al., 2014; Ciccacci et al., 2013; Sudchada & Scarpace, 2014). This will be the subject of investigation in the current study.
1.9 Transcription Factor 7 Like 2 Transcription factor 7 like 2 (TCF7L2) also called transcription factor 4 (Tcf4) is a member of T-cell factor (Tcf)/Lymphoid enhancer factor (Lef) transcription factor family (Jin & Liu, 2008). The TCF7L2 spans 215,869 bp region on chromosome 10q25.3 and encodes for a transcription factor involved in the Wnt signaling pathway (Buraczynska et al., 2014; Yu et al., 2015). TCF7L2 protein transcribe several genes including the glucagon like peptide-1 (GLP-1); an incretin hormone responsible for insulin secretion. Moreover, TCF7L2 protein is also involved pancreatic β cell proliferation and differentiation. The expression and function of TCF7L2 depends on the activity of Wnt signaling pathway.
1.9.1 TCF7L2 and Wnt Signaling Pathway Wnt signaling pathway are signal transduction cascade involving the transport of signals into the cells through the cell surface receptors. The Wnt pathway plays a vital role in embryonic development, bone formation, cell proliferation, and cell migration (Chiang, Ip, & Jin, 2012). This signaling pathway is divided into canonical and non-canonical pathway. The canonical Wnt signaling pathway has a key downstream effector, bipartite transcription factor β-cat/TCF, formed by βcatenin and a member of the transcription factor (TCF) family. 14
The TCF family consists of TCF7, LEF-1, TCF7L1, and TCF7L2. In the absence of Wnt ligand induced stimulation, the β catenin resides in the destruction complex; containing proteins such as axin, adenomatosis polyposis coli (APC), protein phosphatase 2A (PP2A), casein kinase 1α (CK 1α) and glycogen synthase kinase 3 (GSK-3). The β catenin is phosphorylated by GSK-3 at serine position33 (Ser-33) which marks it as substrate for proteasome mediated degradation and hence no gene expression (Figure 1.4 A) (Zhou et al., 2015). However, if the Wnt pathway is stimulated, the destruction complex dissembles, leading to β catenin accumulation. The β catenin then enters the nucleus and form bipartite transcription factor β catenin/TCF, this results in stimulation of Wnt target gene expression (Figure 1.4 B). The stimulation of Wnt pathway subsequent induces the expression of TCF gene.
1.9.2 Genetics of TCF7L2 TCF7L2 plays a crucial role in different metabolic regulations including blood glucose regulation, and has a crucial role in β cell development (Grant, 2012). Several single nucleotide polymorphisms (SNPs) including rs7903146 and rs12255372 in the intron region of the TCF7L2 were identified and found to have association with metabolic disorders including T2DM. The rs7903146 is a nucleotide change from C to T at position 112998590 in fourth intron of TCF7L2, whereas rs12255372 is a change in nucleotide at position 113049143 in the fifth intron from G to T (Figure 1.5).
1.9.3 Association between DM and TCF7L2 TCF7L2 is among the major gene with the largest effect on disease susceptibility discovered to date (Chang et al., 2007), Grant et al. in 2006, reported TCF7L2 gene was mapped to a region of genetic linkage to T2DM in the Icelandic population on chromosome 10q25.3 (Grant et al., 2006a; Jensen et al., 2013; Norton et al., 2014; Yu et al., 2015). The mechanism through which TCF7L2 exerts its effect on T2DM is still unclear (Bradfield et al., 2011). The investigation of association of TCF7L2 variants and T2DM was replicated in different ethnic groups across the world.
15
A) Wnt ligand absent
B) Wnt ligand present
Figure 1.4 Canonical Wnt signaling pathway A) Shows β catenin regulation in the absence of wnt ligands. The destruction complex containing CK 1α, axin, APC, and GSK-3 phosphorylates β catenin and mark it for proteosomal degradation B) Shows the β catenin stimulation in presence of wnt ligands which prevents interaction between the destruction complex and β catenin and subsequently promote wnt targeted gene expression (Chiang et al., 2012).
16
Figure 1.5 TCF7L2 gene structure. The TCF7L2 is located in chromosome 10q25.3. The blue coloured bar-shape are exons whereas the green coloured barshape indicate exons that undergo alternative splicing, STOP is the region where transcription of TCF7L2 terminate, whereas The two single nucleotide polymorphisms (SNPs) in red were studied in the current work (Chiang et al., 2012).
17
Lyssenko et al. found that the CT/TT genotypes of SNP rs7903146 (c.38241435C>T) are strongly associated with the risk of T2DM in two different cohorts. They also observed that the pancreatic islets in T2DM patients showed increased TCF7L2-mRNA levels (Chiang et al., 2012; Norton et al., 2014). Moreover, the T allele carriers show a significant elevation of TCF7L2 mRNA expression in their pancreatic islets, and associated with impaired insulin secretion and incretin effects (Chiang et al., 2012). The role of TCF7L2 in pancreatic β-cells development suggested potential deleterious effects of TCF7L2 (Migliorini & Lickert, 2015).
1.9.4 TCF7L2 Associated to Beta Cell Development Migliorini and Lickert (2015) explained the role of TCF7L2 in the development of β cells which is hypothesize as the mechanism linking of DM to TCF7L2 (Migliorini & Lickert, 2015). The TCF7L2 regulates proglucagon expression; a precursor of glucagon derived hormone such as glucagon like peptide-1 (GLP-1) inducing β cell proliferation and stromal derived factor-1(SDF-1) which mediated β cell survival (Gloyn et al., 2009). GLP-1 induces β cells function by via a receptor referred to as Glucagon like peptide-1receptor (GLP-1R). The GLP-1R is expressed in many tissues such as brain; it inhibit food intake by activation of GLP-1R in the hypothalamus, stomach; it inhibits emptying of gastric intestine and reduces plasma glucose levels, GLP-1R also promotes cardio-protective effect and in pancreas; it induce insulin secretion, β cell development and survival. The variation in TCF7L2 gene result in dysfunction of GLP-1 and hence causes inadequate insulin secretion (Chiang et al., 2012; Loos et al., 2007; Migliorini & Lickert, 2015).
1.9.5 Association between DR and TCF7L2 Genetic
variation
of
rs7903146
(c.382-41435C>T)
and
rs12255372
(c.482+9017G>T) were successfully linked to T2DM in various ethnic groups (Bodhini et al., 2007; Ciccacci et al., 2013; Grant et al., 2006; Chiang et al., 2012; Javadi et al., 2009; Nanfa et al., 2015). But it is still not clear if TCF7L2
18
genetic variant is related to DR (Jing Luo et al., 2013; Sudchada & Scarpace, 2014). Although the exact role of TCF7L2 in DR is not known, but there are several links between Wnt signaling, insulin secretion and β cells proliferation (Groop, 2010). Moreover, an investigation on PDR shows the relationship between TCF7L2 and VEGFα. The expression of VEGFα increases with increase in expression of TCF7L2. Therefore, the genetic polymorphism condition associated to TCF7L2 which leads to over expression of TCF7L2 will subsequently cause over expression of VEGFα; which may lead to dearrangement of retinal vessels and neovascularization (Luo et al., 2013).
1.10 Objectives of the Study TCF7L2 variant has been reported to be associated with T2DM, but it is unclear whether it is associated with its complications, such as DR. Therefore, the objective of this study was to screen TCF7L2 genetic variants (rs7903146 and rs12255372) and their role in the development of DR in diabetic patients.
19
Chapter 2 Materials & Methods
The current case-control association study was approved by Ethics Review Board of the Department of Biosciences; COMSATS Institute of Information Technology (CIIT), Islamabad Pakistan. Prior to blood sampling, the objectives of the study were clearly explained to the participants and written consents were taken from them.
2.1 Sampling 2.1.1 Patient Selection The cases were clinically diagnosed at the hospitals which were Armed Forces Institute
of
Ophthalmology
(Rawalpindi),
Shifa
International
Hospital
(Islamabad), and Myo Hospital, Lahore; and the Eye Outdoor Patient Department of the Railway Hospital. The current study is case-control association analysis. All cases were diagnosed for T2DM by a professional endocrinologist and had T2DM for over 10 years. Patients were diagnosed based on the American Diabetic Association criteria for the diagnosis of T2DM i.e. age 18–75years, fasting plasma glucose level ≥ 126mg/dl, random plasma glucose concentration ≥ 200mg/dl, and serum creatinine concentration ≤ 2.0 mg/dl. The DR patients were subjected to exclusive ocular examinations based on the Early Treatment of Diabetic Retinopathy Study (ETDRS). These tests included Funduscopy, visual acuity, and split lamp examination. Furthermore, DR patients were classified as NPDR based on microaneuryms, hard exudates, and hemorrhage and PDR based on the presence of neovascularization. The controls were sampled from the same general population as the cases.
2.1.2 Collection of Blood Samples A total 8cc (cubic centimeter) venous blood was drawn from both cases and controls using 10cc sterile syringe (Terumo® Corporation, Philippines). The blood was collected in Ethylene-Diamine-Tetra acetic acid (EDTA) tubes (Becton Dickinson, Plymouth, UK) and was stored at 4 °C prior to use.
2.2 Reagents preparation for DNA Extraction The reagents and materials required for the DNA extraction are mentioned in Table 2.1 and Table 2.2. 21
Working solutions were prepared from the stock solutions using the procedure in the Table 2.1. The pH of the each reagent was adjusted by either adding 5% HCl or 5M NaOH, depending on the required pH.
2.2.1 Phenol Preparation Phenol crystals were melted at 55º C in a water bath for 3 hours and an equal quantity of 1 M TE Buffer (pH 8.2) was added. A few pellets of 8hydroxyquinoline were added to the mixture, which serves as oxidation inhibitor. The solution was repeatedly shaken and the upper aqueous phase of the resultant mixture was removed and an equal volume of TE buffer was added. The solution was mixed thoroughly and the process was repeated until a pH of 8.4 was obtained.
2.3 DNA extraction protocol DNA extraction involved the extraction of genomic DNA from the whole blood sample via organic method (Sambrook & Russell, 2001). The steps involved are as follows:
2.3.1 Cell Lysis During the first step of the DNA extraction, all the enucleated red blood cells (RBC) were removed. About 2 ml of whole blood was added to the 15 ml of Falcon tube(s) (Corning Incorporation, NY, USA) and 6 ml of RBC lysis buffer was added. The mixture was then incubated for 5 mins at room temperature and the tubes were inverted several times to completely lyse the RBCs. The tubes were then centrifuged for 5 min at 4400 revolutions per minute (rpm) (5,200xg (gravitational force)) at room temperature. After centrifugation the supernatant of the lysed RBC was carefully discarded.
22
Table 2.1 Reagents required for preparation of stock solutions for DNA extraction.
Stocks Solutions Reagents
Stocks Solutions (1M, VOLUME = 1L)
Used in preparation of
Ammonium chloride (NH4Cl)
53.5 grams was dissolved in deionized RBC lysis buffer water
Sodium Bicarbonate (NaHCO3)
84 grams was dissolved in deionized RBC lysis buffer water
Ethylene Diamine Tetra Acetic Acid 58.448 mg was dissolved in deionized RBC lysis buffer, Tris-EDTA (TE) (EDTA)
water
buffer , Nuclear lysis buffer
Tris-HCL (pH = 8.2)
121.1 grams was dissolved in deionized Nuclear lysis buffer, TE buffer water
Sodium Chloride (NaCl)
58.44 grams dissolved in deionized water
23
Nuclear lysis buffer
Table 2.2 Working solution reagents and their concentrations required for DNA extraction. Working Solutions Reagent RBC lysis buffer
Nuclear lysis buffer
Tris-EDTA (TE) buffer
Solution Mole/Liter 155 ml 1M NH4Cl, 10 ml 1M NaHCO3, 0.5 ml 0.2M EDTA mixed in 834.5ml of autoclaved deionized water (pH 7.4)
Lysis of white blood cells’
906ml of autoclaved deionized water
nuclei
10ml 1M Tris-HCL (pH 8.2), 5ml EDTA were mixed in 985ml of autoclaved deionized water 200 gm SDS in 500ml deionized water and final volume was adjusted to
(SDS)
1 liter to make 20% working solution
Chloroform: Isoamyl Alcohol (24:1)
Lysis of red blood cells
10ml 1M Tris-HCL (pH 8.2), 80ml 1M NaCl, 4ml 0.2M EDTA mixed in
Sodium dodecyl sulfate
Proteinase K solution
Function
Ready-to-use working solution (Ambion®, Waltham, Massaachusetts, USA) 960 ml CHCl3 and 40 ml C5HO to make 24:1 working solution
DNA purification
Protein denaturation
Protein digestion Facilitates separation of aqueous and organic phase
Sodium Acetate
246 gm (3M working solution) dissolved in 500ml of autoclaved
(CH3COONa)
deionized water and final volume was adjusted to 1 liter
Iso-propanol
Ice-cold propanol (C3H8O), stored at -20°C
DNA precipitation
70 ml absolute ethanol, 30 ml deionized water (final volume = 100ml).
Washing of precipitated
Stored at -20°C prior to use
DNA
Ethanol (70%)
24
DNA precipitation
The pellet was then re-suspended in 2 ml of RBC lysis buffer followed by centrifugation for 5 mins at 4400 rpm (5,200xg). The supernatant was discarded again. The pellet was suspended in 200 µl of nuclear lysis buffer (pH 8.2), 15 µl of SDS (20 %) and 10 µl of Proteinase K, the suspension was carefully mixed until the pellet became a homogenous mixture. The mixture was incubated overnight at 55º C in a shaking water bath (Memmert Schwabach, Germany) to facilitate nuclear lysis.
2.3.2 Phenol-Chloroform Phase Separation The following day, the mixture of the lysed nuclei was incubated at room temperature for 15 min after which an equal amount of 1.5 ml of phenol and TE buffer (pH 8.0) were added to the mixture. The solution was gently mixed by inverting the tubes several times followed by incubation for 5min at room temperature. After incubation, the mixture was then centrifuged for 10 mins at 4400 rpm (5,200xg). This resulted in the separation of two phases, the upper aqueous phase (DNA) and the lower phase (cellular debris and digested proteins). DNA being in the upper phase (aqueous phase) was gently transferred into a new 15 ml falcon tube using micropipette (Eppendorf, Hambury, Germany) with cut tips, followed by addition of equal volume of Chloroform: Isoamylalcohol (24:1) mixture, the solution was centrifuged again at 10 mins at 4400 rpm (5,200xg). The upper aqueous phase was transferred to new tubes; approximately 1/10th volume of 3 M sodium acetate and double volume of chilled isopropanol were added, then tubes containing the solution were gently inverted several times to precipitate the DNA. The precipitated DNA was then centrifuged at 4400 rpm (5,200xg) for 10 min to get the DNA pellet after the isopropanol was discarded.
2.3.3 DNA Washing and Resuspension The DNA pellet obtained was washed with 1.5 ml of 70 % chilled ethanol and centrifuged for 10 mins at 4400 rpm (5,200xg). The supernatant was discarded and the DNA pellet was air dried in incubator (Memmert, Schwabach, Germany) at 37 oC for 4 hours to remove all traces of ethanol.
25
The DNA was then re-suspended in 200 µl of PCR RNAse-DNAse free Diethylpyrocarbonate (DEPC) treated with molecular biology grade water (SERVA®, Heidelberg, Germany).
2.4 Storage of genomic DNA The DNA samples were then transferred to 1.8ml screw cap tubes and stored at 20º C in a refrigerator till further use.
2.5 Quantification of Genomic DNA The stock DNA was quantified by the help of horizontal gel electrophoresis on 1% agarose. The reagents required for gel electrophoresis are given in Table 2.3.
2.5.2 Horizontal Gel Electrophoresis For horizontal gel electrophoresis, 1 % agarose gel was prepared as mentioned in Table 2.3. The gel tank contained 0.5X TBE running buffer. 2μl of bromophenol blue was added to 1-2μl of stock of stock DNA and loaded on to the wells of gel (1% agrose). The gel electrophoresis was carried out at (120V, 160MA, 50W) for approximately 45 min which was later visualized under UV light using the transilluminator and the images were saved with the help of Gel Documentation System (Alpha-Imager Mini Bucher Biotech, Basel, Switzerland). The concentration of stock DNA was analyzed based on intensity, after it was compared with lambda HindIII DNA ladder. DNA working dilutions were prepared and the dilutions were then stored at 4ºC used for genotyping.
2.6 Amplification of TCF7L2 The genotyping of TCF7L2 rs7903146 (c.382-41435C>T) was performed by Multiplex Allele Refractory Mutation System (ARMS-PCR), which required two allele specific primers, with a common reverse primer and a pair of internal control primer of exon 8 RB1 gene.
26
Table 2.3 Reagents and preparation required for gel electrophoresis.
Reagents TBE
Buffer
Preparation
Use in agarose gel electrophoresis
(Tris- 10X Stock solution: 108 gm Tris, 58 gm boric acid, 9.31 gm Agarose is dissolved in 0.5X TBE
Boric Acid-EDTA)
EDTA dissolved in deionized water (final volume = 1L)
buffer. Running buffer is also 0.5X
0.5X Working solution: 10X TBE buffer and deionized water TBE buffer. Provides ionic medium mixed in a 1:19 ratio (5 ml TBE buffer)
for movement of DNA form –ve to +ve electrode.
Agarose
1% agarose gel (for DNA quantification): 1 gm agarose
Sample loaded into wells of agarose
dissolved in 100 ml 0.5X TBE buffer, heated for 1-2 mins, until gel, which is placed in gel tank for dissolved completely after which 3 µl of ethidium bromide was electrophoretic separation of DNA added.
content. Provides a matrix for sample
2% agarose gel (for PCR visualization): 2 gm agarose dissolved to separate according to size. in 100 ml 0.5X TBE buffer, heated for 1-2 mins, until completely 3.5% agarose gel (for RFLP visualization): 3.5 gm agarose dissolved in 100 ml 0.5X TBE buffer, heated for 1-2 mins, until completely
27
To determine the genotype of each sample, PCR amplification was carried out separately for both the alleles (C/T) and rs12255372 (c.482+9017G>T) was genotyped
by
polymerase
chain
reaction-restriction
fragment
length
polymorphism (PCR-RFLP), a pair of primer was used for PCR and subsequent digestion of PCR product by a restriction enzyme. The primer sequences used during ARMS-PCR and PCR-RFLP methods were described previously by Dutra and col. (Dutra et al., 2008) and Nanfa and col. (Nanfa et al., 2015) respectively. 2.6.1 Genotyping of rs7903146 (ARMS-PCR) The rs7903146 (c.382-41435C>T) polymorphism of TCF7L2 was genotyped by ARMS-PCR using the following primers: forward rs7903146-C: GAA CAA TTA GAG AGC TAA GCA CTT TTT AGA AAC and rs7903146-T: GAA CAA TTA GAG AGC TAA GCA CTT TTT AGA GAT and a common reverse primer: AGA TGA AAT GTA GCA GTG AAG TGC (Integrated DNA Technologies, Inc., Iowa, USA) and the internal control forward primer (RB1-F Exon 8): GAA TGT TAC CAA GAT TAT TTT TGA CC and reverse primer (RB1-R Exon 8 ): TGC TAC TGC AAA AGA GTT AGCAC. A final volume of 25 µl for the Polymerase Chain Reaction (PCR) was constituted, which contained 2 µl (40-50 ng/µl) of diluted genomic DNA for amplification of the target gene. Each reaction mixture
contained
0.5
mM
deoxyribonucleotide
triphosphate
(dNTPs)
(Invitrogen®, Grand Island, NY), 1.25 X ammonium sulphate Taq Buffer (Invitrogen®), 3.0 mM MgCl2 (Invitrogen®), Taq DNA Polymerase 2.5U/ reaction (Invitrogen®), DNase/RNase free water (Invitrogen®) and 0.24 μM of two allele specific primer forward and a common reverse primer each and 2.0 μM for the internal control. 2.6.1.1 Thermal Profile of rs7903146 The PCR reaction for rs7903146 (c.382-41435C>T) was carried out on a thermal cycler (Thermo Electron Corporation) under the following conditions: 94°C for 3 min, followed by 32 cycles of 94°C for 1 min (denaturation), 50°C for 1 min (annealing), 72°C for 1 min (extension), and a final extension of 72°C for 5 min.
28
2.6.1.2 Visualization of rs7903146 Amplified Product PCR product containing 205 bp and 376 bp of TCF7L2 rs7903146 and RB1-Exon 8 was separated on 2% agarose gel electrophoresis. 14 µl of the PCR product and 4 µl of loading dye were mixed and loaded into the gel wells. 2.5 µl of 100 bp DNA ladder was used adjacent to the loaded product. Electrophoresis was carried out for 45 min at a voltage of 120V, 120mA, 8W) followed by visualization under UV light using the trans-illuminator, band sizes of the products were compared with the DNA leader and the images were saved with the help of Gel Documentation System (Alpha-Imager Mini Bucher Biotech, Basel, Switzerland). Figure 2.1 illustrates the saved gel image.
2.6.2 Genotyping of rs12255372 (PCR-RFLP) The rs12255372 (c.482+9017G>T) polymorphism of TCF7L2 was genotyped by Polymerase Chain Reaction Chain-Restriction Fragment Length Polymorphism (PCR-RFLP) using the following primers, forward rs12255372-F: CTG GAA ACT AAG GCG TGA GG and reverse rs12255372-R: GGG TCG ATG TTG TTG AGC TT (Integrated DNA Technologies, Inc., Iowa, USA). A total volume of 25 µl was formed for PCR amplification of rs12255372, which contained 2 µl (40-50 ng/µl) of diluted genomic DNA for amplification of the target gene. Each reaction mixture contained 0.25 mM dNTPs (Invitrogen®, Grand Island, NY), 1.25 X ammonium sulphate ((NH4)2SO4) Taq Buffer (Invitrogen®), 2.0 mM MgCl2 (Invitrogen®), Taq DNA Polymerase 3.3U/ reaction (Invitrogen®), DNase/RNase free water (Invitrogen®) and 0.3 mM of forward and reverse primer each. 2.6.2.1 Thermal profile for rs12255372 The PCR reaction for rs12255372 (c.482+9017G>T) was carried out on a thermal cycler (Thermo Electron Corporation) under the following conditions: 95°C for 2 min, followed by 35 cycles of 95°C for 30 sec (denaturation), 56°C for 30 sec (annealing), 72°C for 30 sec (extension), and a final extension of 72°C for 5 min.
29
Figure 2.1 ARMS-PCR amplification of rs7903146 (c.382-41435C>T). The Cspecific and T-specific allele reaction performed on 6 samples with a negative control (NC; negative control for C allele and NT; negative control for T allele) on 2% gel image. Presence or absence of allele-specific (AS) band at 205 bp confirms genotype, while internal control (IC) at 376 bp is present in all. The size of each fragment is compared with DNA ladder (L) at the center.
30
2.6.2.2 Visualization of rs12255372 Amplified Product The PCR product 346 bp amplified products of rs12255372 was separated on 2% agarose gel electrophoresis. 5 µl of rs12255372 PCR product and 2 µl of the loading dye were mixed and loaded into the well. 2.5 µl of 100 bp DNA ladder was used adjacent to the PCR product. Gel electrophoresis was carried out for 45 min at a voltage of 120V, 120mA, 8W) followed by visualization under UV light using the trans-illuminator, band sizes of the products were compared with the DNA leader and the images were saved in Gel Documentation System (AlphaImager Mini Bucher Biotech, Basel, Switzerland). 2.6.2.3 RFLP for rs12255372 Genotyping of rs12255372 was performed by RFLP of the PCR amplified product (346 bp) by digestion with Thermus species (Tsp509I) restriction enzyme using the protocol as follows: The reaction volume was set to 15 μl, containing 7 μl of amplicons, 1× buffer B (Invitrogen®, Grand Island, NY), 1Unit of Tsp509I, and 6.3 μl of nuclease free water. Tools, such as Vector NTI® Advanced V 11.5 (Thermo Fisher Scientific) and NEBcutter V2.0 (New England Biolabs) were used for designing the restriction site. The digested products were then separated by electrophoresis on a 3.5% agarose gel (given in Table 2.3.) in the presence of ethidium bromide (10 mg/mL) and visualized using Gel Documentation System (Alpha-Imager Mini Bucher Biotech, Basel, Switzerland). The expected size of digested product was for the ancestral homozygous allele GG, heterozygous allele GT, and risk homozygous allele TT, were illustrated in Figure 2.2.
2.7 Statistical analysis In the present case-control association studies, statistical analysis test and calculations including Chi-square (χ²) test, Z test and Odds ratio (OR) were performed on the genotyped data. The genotyped data of rs7903146 (c.382-41435 C>T) and rs12255372 (c.482+9017 G>T) TCF7L2 polymorphism were analyzed using R software (version 3.2.2); Copyright (C) 2015 The R Foundation for Statistical
Computing
and
an
online
(http://www.socscistatistics.com/tests/chisquare2/Default2.aspx).
31
software
Figure 2.2 The PCR-RFLP detection of TCF7L2 rs12255372 (c.482+9017G>T) polymorphism PCR followed by digestion with Tsp509I- 3.5% agarose gel electrophoresis followed by ethidium staining and UV trans-illminator performed. The expected product sizes after digestion were: normal homozygote GG, 143 bp, 104 bp; mutant homozygote TT, 126 bp, 104 bp; and heterozygote GT, 143, 126, and 104 bp, respectively. The digested product fragments smaller than 100 bp were not visualized.
32
Graphpad InStat® version 3.10 (GraphPad Software Inc, Califonia coperation, USA). The Z-test was performed to compare the differences in individual’s genotypes
of
the
cases
and
controls,
using
online
software
(http://www.socscistatistics.com/tests/ztest/Default2.aspx). To determine the association of risk genotype and allele with the disease, OR were calculated by performing Logistic regression analysis using JavaStat online tool (http://statpages.org/ctab2x2.html). Hardy-Weinberg equilibrium (HWE) was tested
using
the
goodness-of-fit
chi-square
(http://www.had2know.com/academics/hardy-weinberg-equilibrium-calculator-2alleles.html). A p-value of ≤ 0.05 (95% confidence interval (95%CI)) were considered
as
statistically
33
significant.
Chapter 3 Results
3. Results The current case-control association study was aimed to assess the role of TCF7L2 SNP; rs7903146 (c.382-41435C>T) and rs12255372 (c.482+9017G>T) in 322 T2DM cases, 226 controls and 333 cases and 234 controls of the former and latter SNPs in Pakistani population.
3.1 Analysis of Genotyped Data The Hardy Weinberg equilibrium (HWE) of rs7903146 and rs12255372 were 0.978 and 0.014 respectively.
3.2 Comparison between T2DM and Controls in rs7903146 Table 3.1 shows the statistical analysis for the genotype and allele frequencies for rs7903146 (c.382-41435C>T) in T2DM cases and control individuals. Out of 226 control individuals 69.91% were CC homozygous, 2.66% were TT homozygous and the remaining 27.43% were heterozygous CT. The genotype distribution in T2DM population was as follows: Out of 322 T2DM, 41.62 % were homozygous for the ancestral (non-risk) CC allele, 6.61% were homozygous for TT (risk) allele, while 47.20% subjects were heterozygous CT. The allele frequency for T2DM cases was 65.22% for C and 34.78% for T whereas in healthy controls it was 83.63% and 16.37% respectively. χ² test was performed to determine differences in genotype frequency distribution among T2DM cases and healthy controls, a significant difference (χ2=45.84, p=1.11×10-10) was observed between the two groups. Moreover, linear comparison of the genotyped data also showed significant difference (z=6.54, p=6.34×10-11; z=-4.67, p=3.02×10-6; z=-3.69, p=2.2×10-4) between T2DM cases and controls for homozygous CC, heterozygous CT and homozygous risk TT genotypes respectively. Logistic regression analysis of the genotyped data under the dominant model (DM) and recessive model (RM) revealed association of the CT and TT genotypes (Odds Ratio (OR) =3.26, 95% confidence interval (CI) =2.26-4.75, p =5.40×1011
), and (OR=4.61, 95% CI=1.88-13.61, p =1.35×10-4) respectively with T2DM. 35
Table 3.1 Statistical analysis of TCF7L2 (rs7903146) in Controls and T2DM cases. Controls
T2DM
n (%)= 226
n (%)=322
CC
158(69.91)
134(41.62)
6.54(6.34×10-11)
CT
62(27.43)
152(47.20)
-4.67(3.02×10-6)
Genotype
T2DM vs. Controls χ² (p-value)
z-test (p-value)
45.84(1.11×10-10) TT
6(2.66)
36(6.61)
Allele
Controls
T2DM
Frequency
n (%)=452
n (%)=644
C
378(83.63)
420(65.22)
T
74(16.37)
OR (95% CI) p-value
DM: 3.26(2.24-4.75) 5.40×10-11 -3.69(2.2×10-4)
RM: 4.61(1.88-13.61) 1.35×10-4
T2DM vs. Controls χ² (p-value)
OR (95% CI) p-value
45.47(2.48×10-11)
2.72(2.00-3.71) 6.65×10-12
224(34.78)
Legends: χ²: Chi-square of independence; OR (95%CI): Odds Ratio (95% Confidence Interval); DM: Dominant Model (CT+TT versus CC); RM: Recessive Model (CC+CT versus TT).
36
Furthermore, the differences in the allele frequency distribution were also determined between the two groups for ancestral (non-risk) C allele and risk T allele, and the number of T allele was significantly higher (χ²=45.47, p=2.48×1011
; OR=2.72, 95% CI=2.00-3.71, p=6.65×10-12) in T2DM cases as compared to
controls. 3.2.3 Comparison between DNR and Controls Table 3.2 represented statistic comparison between DNR cases and unaffected controls of 206 cases which 48.06% were homozygous CC, 9.22% were homozygous TT, and the remaining 42.72% of the cases were heterozygous CT. Significant difference (χ²=23.93, p=6.34×10-6) was found between the genotype frequency distribution among both the studied groups. Linear comparison of the genotyped data revealed a significant difference (z=4.62, p=3.82×10-6; z=-3.33, p=3.33×10-4; z=-2.92, p=3.50×10-3); for homozygous CC genotype, heterozygous CT genotype, and homozygous TT respectively when DNR patients were compared to the control individuals. Logistic regression analysis of genotyped data revealed under DM showed significant association with the disease for CT and TT genotypes (OR=2.51, 95% CI=1.66-3.80, p=3.98×10-6), and RM (OR=3.73, 95% CI=1.37-10.66, p=4.00×103
). The allele frequencies of C and T alleles were compared among the DNR cases
and controls, and revealed significant difference (OR=2.25, 95% CI=1.61-3.16, p=7.74×10-7). 3.2.4 Analysis between DR and Controls Statistical comparison between the DR cases and controls is represented in Table 3.2. Out of the 116 DR cases, 27.59% were homozygous for CC genotype, 57.76% were heterozygous CT while 14.65% were homozygous TT. Significant difference (χ²=59.82, p=1.02×10-13) was observed in the genotype distribution among the DR cases and healthy controls. Linear comparison analysis of genotyped data revealed a significant difference (z=7.46, p=8.86×10-14; z=-5.48, p=4.00×10-8; z=-4.20, p=4.5×10-2); for homozygous CC, heterozygous CT and homozygous TT respectively. 37
Table 3.2. Statistical analysis of TCF7L2 (rs7903146) in Controls, DNR and DR cases. Controls Genotype
CC
CT
TT
n (%) = 226
158(69.91)
62(27.43)
6(2.66) Controls
DNR n (%) =
DNR vs. Controls
206
DR n (%) = 116
99(48.06)
32(27.59)
88(42.72)
67(57.76)
19(9.22)
17(14.65)
DR n (%)=232
Allele Frequency
n (%)= 452
DNR n (%)=412
C
378(83.63)
286(69.42)
131(56.47)
T
74(16.37)
126(30.58)
101(43.53)
χ2 (p-value)
z-test (p-value) 4.62 (3.82× 10-6)
23.94 (6.34×10-6)
-3.33 (8.60× 10-4) -2.92 (3.50× 10-3)
χ2 (p-value) 24.47 (1.14× 10-6)
DR vs. Controls OR (95% CI) p-value
RM: 3.73 (1.3710.66) 4.00× 10-3
z-test (p-value) 7.46 (8.86× 10-14)
DM: 2.51 (1.66-3.80) 3.98× 10-6
DNR vs. Controls OR (95% CI) p-value 2.25(1.61-3.16) 7.74× 10-7
38
χ2 (p-value)
59.82 (1.02× 10-13)
-5.48 (4.00× 10-8) -4.20 (4.50× 10-2)
χ2 (p-value) 59.41 (2.63× 10-14)
OR (95% CI) p-value
DM:6.10 (3.61-10.35) 7.30× 10-14 RM: 6.296 (2.25-18.48) 1.08× 10-4
DR vs. Controls OR (95% CI) p-value 3.94(2.71-5.74) 4.77× 10-14
Logistic regression analysis showed significant association of CT and TT genotype with the disease under DM (OR=6.10, 95% CI=3.61-10.35, p=7.30×1014
), and RM (OR=6.30, 95% CI=2.25-18.48, p=1.08×10-4). Allele frequencies of
C and T alleles were found to be statistically different (χ²=59.41, p=2.63×10-14; OR=3.94, 95% CI=2.71-5.74, p=4.77×10-14) for the TCF7L2 SNP between the DR subjects and controls.
3.2.5 Analysis of DR Sub-classes 3.2.5.1 Analysis of NPDR, PDR and Controls Table 3.3 displays the genotype frequencies of TCF7L2 gene (rs7903146) in patients with NPDR and PDR and controls. 3.2.5.2 NPDR and Controls Statistical analysis in Table 3.3 showed a total of 61 NPDR subjects, 22.95% had CC genotype, 8.20% had TT and 68.85% were heterozygous with CT genotype. A significant difference (χ²=44.27, p=2.44×10-10) was found in the genotype frequency distribution of the NPDR cases and healthy controls. Linear comparison analysis of genotyped data revealed a significant difference (z=-5.97, p=3.24×10-9; z=6.64, p=3.10×10-11; z=-2.00, p=4.55×10-2); for homozygous CT genotype, heterozygous CC genotype, and homozygous TT respectively was found. Logistic regression analysis revealed significant association for the CT genotype under DM (OR =7.80, 95% CI=3.86-15.99, p=4.51×10-11) and no association was found for TT genotype under RM (OR=3.27, 95% CI=0.83-12.69, p=6.00×10-2). C and T allele frequencies were found to be significantly different (χ²=38.64, p=1.11×10-9; OR=3.795, 95% CI=2.395-6.013, p=5.26×10-9). 3.2.5.3 PDR and Controls Genotype frequencies of the PDR subjects and healthy controls were statistically analyzed.
39
Table 3.3 Statistical analysis of TCF7L2 (rs7903146) in Controls, PDR, and NPDR cases.
Controls n(%) =226
NPDR n(%) = 61
158(69.91)
14(22.95)
Genotype
PDR n (%)=55
NPDR vs. Controls χ (p-value) 2
z-test (p-value)
PDR vs. Controls OR (95% CI) (p-value)
z-test (p-value)
χ2 (p-value)
6.64 CC
44.27 62(27.43)
42(68.85)
-5.97
25(45.46) (2.44× 10-10)
(3.24× 10-9) -2.00
TT
6(2.66)
5(8.20)
5.11
18(32.72) (3.10× 10-11)
CT
12(21.82)
(3.20× 10-7)
DM: 7.80 (3.86-15.99) 4.51× 10-11
-2.59 -9
39.76(2.32× 10 ) (9.60× 10-3)
RM:3.27 (0.83-12.69) 6.00× 10-2
-5.21
(4.55× 10-2) Allele frequency
C
Controls n(%) = 468
NPDR n(%) = 122
PDR n(%) = 110
378(83.63)
70(57.38)
61(55.45)
χ (p-value)
OR (95% CI) p-value
7.36× 10-7 RM:10.23 (3.33-32.65) 7.79× 10-6
PDR vs. Controls χ (p-value)
OR (95% CI) p-value
41.08 3.80(2.40-6.01)5.26× 10-9
4.10(2.55-6.61)2.06× 10-9 -10
(1.11× 10 )
52(42.62)
(2.44-9.44)
2
-9
74(16.37)
DM:4.78
(2.00× 10-7)
NPDR vs. Controls 2
38.64
T
OR (95% CI) (p-value)
(3.38× 10 )
49(44.55)
40
In 55 PDR cases, 32.72% had CC genotype, 45.46% had the CT genotype, and 21.82% had TT genotype. There was a statistically significant difference (χ²=36.76, p=2.32×10-9) between the genotype frequency of rs7903146. Also significant difference (z=5.11, p=3.20×10-7; z=-2.59, p=9.60×10-3; z=-5.21, p=2.00×10-7) was observed under the linear comparison of the genotype frequency distribution of homozygous CC, heterozygous CT, and homozygous risk genotype TT respectively. Logistic regression analysis revealed significant association for CT and TT genotype under DM, as well as RM (OR=4.78, 95% CI=2.44-9.44, p=7.36×10-7; OR=10.23, 95% CI=3.33-32.65, p=7.79×10-6) respectively. C and T allele frequencies show significant difference (χ²=41.08, p=3.38×10-10; OR=4.10, 95% CI=2.55-6.61, p=2.06×10-9) between PDR subjects and controls.
3.3 Comparison between T2DM and Controls in rs12255372 Table 3.4 represents the genotype and allele frequency of rs12255372 (c.482+9017G>T) in T2DM cases and control individuals. Out of 234 control individuals 74.36% were GG homozygous, 4.27% were TT homozygous, and the remaining 21.37% were heterozygous GT. Furthermore, allele distribution in T2DM population was as follows: Out of 333 T2DM subjects, 48.95% were homozygous for the ancestral (non-risk) GG allele, were 6.61% homozygous for TT (risk) allele, and 44.44% subjects were heterozygous GT. The allele frequencies for T2DM cases are 71.17% for G and 28.83% for T whereas in healthy controls it is 85.04% and 14.96% respectively. χ² test was performed to determine the genotype distribution differences among T2DM cases and healthy controls, which resulted in a significant difference (χ²=37.21, p=8.31×10-9) between the two groups. Furthermore, linear comparison of the genotyped data revealed significant difference (z=-6.07, p=1.31×10-9; z=5.67, p=1.39×10-9; z=1.19, p=23.40×10-2) for GG, GT and TT genotypes respectively was observed when T2DM cases were compared to controls.
41
Table 3.4 Statistical analysis of TCF7L2 (rs12255372) in Controls and T2DM cases. Controls
T2DM
n (%)= 234
n (%)=333
GG
174(74.36)
163(48.95)
6.07(1.31× 10-9)
GT
50(21.37)
148(44.44)
-5.67(1.39× 10-9)
Genotype
T2DM vs. Controls χ² (p-value)
37.21(8.31× 10-9) TT
10(4.27)
22(6.61)
Allele
Controls
T2DM
Frequency
n(%) =468
n (%)=666
G
398(85.04)
474(71.17)
T
70(14.96)
192(28.83)
z-test (p-value)
-1.19(23.40 × 10-2)
OR (95% CI) p-value
DM:3.03(2.07-4.43) 1.00× 10-9 RM:1.59(0.70-3.66) 27.10× 10-2
T2DM vs. Controls χ² (p-value)
OR (95% CI) p-value
29.77(7.26× 10-8)
2.30(1.68-3.16) 3.14× 10-8
Legends: χ²: Chi-square of independence; OR (95%CI): Odds Ratio (95% Confidence Interval); DM: Dominant Model (CT+TT versus CC); RM: Recessive Model (CC+CT versus TT).
42
Logistic regression analysis of the genotyped data under DM showed significant association (OR=3.03, 95% CI=2.025-4.43, p=1.00×10-9) for the TT risk genotype, whereas under RM no association was found (OR =1.59, 95% CI=0.703.66, p=27.10×10-2). Furthermore, allele frequency distribution were performed for both ancestral (non-risk) G allele and T risk allele, and a significant difference (χ²=29.77, p=7.26×10-8; OR=2.30, 95% CI=1.68-3.16, p=3.14×10-8) was observed when T2DM cases were compared to controls. 3.3.1 Comparison between DNR and Controls Statistical comparison between DNR cases and unaffected controls is represented in Table 3.5 from a total of 196 DNR subjects, 53.06% were homozygous GG, 4.59% were homozygous TT, and 42.35% of the cases were heterozygous GT. A significant difference (χ²=22.69, p=1.19×10-5) was found between the genotype frequencies among two the studied groups. Linear comparison analysis of genotyped data revealed significant difference (z=4.60, p=4.20×10-6; z=-4.69, p=2.76×10-6) for homozygous GG and heterozygous GT genotypes respectively. Whereas no significant difference (z=-0.16, p=87.29×10-2) was found in TT genotype when DNR cases were compared with controls. Logistic regression analysis under DM revealed significant association for GT genotype (OR=2.57, 95% CI=1.68-3.93, p=4.88×10-6). However, there was no association observed for TT genotype under RM (OR =1.08, 95% CI=0.39-2.94, p=1.000). Allele frequencies of G and T alleles were compared between DNR cases and controls, and significant differences (χ2=15.64, p=1.09×10-4; OR=1.97, 95% CI=1.39-2.81, p=1.06×10-4) was observed. 3.3.2 Analysis between DR and Controls Statistical comparison was performed between the DR cases and controls are represented in Table 3.5. Out of total of 137 cases, 43.07% were homozygous GG,
47.47%,
heterozygous
GT
while
43
9.48%
were
TT
genotype.
Table 3.5 Statistical analysis of TCF7L2 (rs12255372) in Control, DNR, and DR cases. DNR vs. Controls
Controls Genotype n (%)= 234
GG GT
174(74.36) 50(21.37)
DNR n (%)= 196
DR n (%)= 137
104(53.06)
59(43.07)
83(42.35)
χ2 (p-value)
4.60 (4.20× 10-6)
65(47.47) 22.69 (1.19× 10-5)
TT
10(4.27)
Controls
9(4.59)
13(9.48)
DR n (%)=274
Allele Frequency
n (%)= 468
DNR n (%)= 392
G
398(85.04)
291(74.23)
183(66.79)
T
70(14.96)
101(25.77)
91(33.21)
z-test (p-value)
χ2 (p-value)
DR vs. Controls OR (95% CI) p-value
-4.69 (2.76× 10-6)
DM:2.57 (1.68-3.93) 4.88× 10-6
-0.16 (87.29× 10-2)
RM:1.078 (0.39-2.94) 1.000
DNR vs. Controls OR (95% CI) p-value
15.64 (1.09× 10-4)
1.97(1.39-2.81) 1.06× 10-4
44
χ2 (p-value)
z-test (p-value) 6.02 (1.76× 10-9)
36.22 (1.36× 10-8)
-5.24 (1.60× 10-9) -2.01 (4.50× 10-2)
χ2 (p-value) 33.90 (1.01× 10-8)
OR (95% CI) p-value
DM:3.83 (2.39-6.15) 2.59× 10-9 RM: 2.35 (0.93-5.97) 7.20×10-2
DR vs. Controls OR (95% CI) p-value 2.83(1.95-4.10) 1.25× 10-8
Significant difference (χ²=36.22, p=1.36×10-8) was observed in the genotype distribution among the DR cases and healthy controls. Linear comparison of the genotyped data revealed significant difference (z=-6.02, p=1.76×10-9; z=5.24, p=1.60×10-9; z=2.01, p=4.50×10-2) for homozygous GG, heterozygous GT, and homozygous risk TT genotypes respectively was observed. The logistic regression analysis showed significant association for GT genotype under DM (OR=3.83, 95% CI=2.39-6.16, p=2.59×10-9), whereas a non-significant association was observed for TT genotype under RM (OR=2.35, 95% CI=0.935.97, p=7.20×10-2). Allele frequencies of G and T alleles were found to be statistically different (χ²=33.90, p=1.01×10-8; OR=2.83, 95% CI=1.95-4.11, p=1.25×10-8) for the TCF7L2 rs12255372 SNP.
3.3.3 Analysis of DR Sub-classes 3.3.3.1 Analysis of NPDR, PDR and Controls Table 3.6 displays the genotype frequencies of TCF7L2 gene (rs12255372) in patients with NPDR and PDR and healthy controls. 3.3.3.2 NPDR and Controls In a total of 67 NPDR subjects, 43.28% had GG genotype, 7.47% had TT and 49.25% were heterozygous GT genotype. A significant difference was found in the genotype frequency distribution of the NPDR cases and controls (χ²=23.21, p=9.12×10-6). The Linear comparison of genotyped data revealed no significance (z=-1.06, p=28.9×10-2) for TT genotype whereas significant difference (z=-4.50, p=6.66×10-6; z=4.79, p=1.70×10-6) was observed for GT and GG genotypes respectively. Logistic regression analysis revealed significant association under DM (OR=3.80, 95% CI=2.08-6.96, p=3.48×10-6), whereas no association was found under RM (OR=1.81, 95% CI=0.52-6.03, p=33.80×10-2). 45
Table 3.6 Statistical analysis of TCF7L2 (rs12255372) in Control, PDR, and NPDR cases.
Controls Genotype n (%)= 234
NPDR vs. Controls NPDR n(%) = 67
PDR n (%) = 70
29(43.28)
30(42.86)
χ2 (p-value)
PDR vs. Controls OR (95% CI) p-value
z-test (p-value)
χ (p-value) 2
4.79 GG
174(74.36)
GT
50(21.37)
33(49.25)
32(45.71)
(9.12× 10 )
(6.66× 10-6)
10(4.27)
5(7.47)
3.48× 10-6
-6
(4.86× 10 )
8(11.43)
Allele frequency
G
T
Controls n (%)= 468
NPDR n (%)= 134
PDR n (%) = 140
398(85.04)
91(67.91)
92(65.71)
70(14.96)
43(32.09)
(5.60× 10-5)
2.21× 10-6 RM:2.89 (1.00-8.37) 4.00×10-2
-2.24
(28.90× 10-2)
DM: 3.87 (2.14-7.02)
-4.03
24.47
RM:1.81 (0.52-6.03) 33.80×10-2
-1.06 TT
(8.60× 10-7)
DM:3.80 (2.080-6.961)
-4.50 -6
OR (95% CI) p-value
4.92
(1.70× 10-6)
23.210
z-test (p-value)
(28.90× 10-2)
NPDR vs. Controls χ2 OR (95% CI) (p-value) p-value
χ2 (p-value)
PDR vs. Controls OR (95% CI) p-value
20.05
2.69(1.68-4.28)
25.74
2.97(1.88-4.67)
(1.35× 10-5)
2.60× 10-5
(7.36× 10-7)
1.41× 10-6
48(34.29)
46
However, G and T allele frequencies were found to be significantly different (χ²=20.05, p=1.35×10-5; OR=2.69, 95% CI=1.68-4.29, p=2.60×10-5). 3.3.3.3 PDR and Controls Genotype frequencies of the PDR cases and healthy controls were statistically analyzed, and are represented in Table 3.6. In total of 70 PDR cases, 42.86% were GG genotype, 45.71% were GT genotype, while 11.43% were TT genotype. There was a statistically significant difference (χ²=24.47, p=4.86×10-6) between genotype frequency of rs12255372. Also significant difference (z=4.92, p=8.60×10-7; z=-4.03, p=5.60×10-5) was observed when PDR cases were compared to controls under the linear comparison of the genotype frequency distribution of homozygous GG, and heterozygous GT respectively, except homozygous risk genotype TT (z=-2.24, p=28.90×10-2). Logistic regression analysis revealed significant association for GT genotype under DM (OR=3.87, 95% CI=2.14-7.02, p=2.21×10-6), association was also found for TT genotype under RM (OR=2.89, 95% CI=1.00-8.37, p=4.00×10-2). The G and T allele frequencies show significant difference (χ²=25.74, p=7.36×107
; OR=2.97, 95% CI=1.88-4.67, p=1.41×10-6).
3.4 Combined Genotype Analysis 3.4.1 Comparison between T2DM and Controls Table 3.7 shows statistical comparison of combined genotyped (rs7903146 and rs12255372) data of T2DM and controls. Out of the total of 215 control individuals 53.95% were CC-GG, 14.42% were CC-GT, 1.86% were CC-TT, 16.60% were CT-GG, 6.98% were CT-GT, 0.93% were CT-TT, 2.33% were TTGG and 0.47% were TT-GT and 0.47% TT-TT. Whereas, out of 238 T2DM cases 26.05% were CC-GG, 15.13% were CC-GT, 1.26% were CC-TT, 23.95% were CT-GG, 2.52% were CT-TT, 1.26% were TT-GG, 4.20% were TT-GT and 2.52% were TT-TT. CC-GT was used as reference genotype combination for both cases and control individuals.
47
Table 3.7 Statistical analysis of TCF7L2 (rs7903146+rs12255372) in Controls and T2DM.
Genotype
Controls n(%)=215
T2DM n(%)=238
OR(95% CI)p-value
CC-GG
116(53.95)
62 (26.05)
0.46(0.25-0.85)0.009
CC-TT
4(1.86)
3 (1.26)
0.65(0.10-3.80)0.701
CT-GG
40(16.60)
57(23.95)
1.23(0.62-2.41)0.527
CT-GT
15(6.98)
55 (23.11)
3.16(1.41-7.15)0.004
CT-TT
2(0.93)
6(2.52)
2.58(0.42-20.10)0.453
TT-GG
5(2.33)
3(1.26)
0.52(0.09-2.78)0.469
TT-GT
1(0.47)
10(4.20)
8.61(1.02-189.93)0.023
TT-TT
1(0.47)
6(2.52)
2.58(0.42-20.10)0.453
48
A protective effect was observed in the combined genotyped data of CC-GG (OR=0.46, 95%CI=0.25-0.85, p=0.009). However, CT-GT (OR=3.16, 95% CI=1.41-7.15, p=0.004) and TT-GT (OR=8.61, 95% CI=1.02-189.93, p=0.023) showed disease association. No significant association was found in the rest of the genotype data. 3.4.2 Analysis of DNR, DR and Controls 3.4.2.1 Comparison between DNR and Controls Statistical comparison of combined genotyped (rs7903146 and rs12255372) data of DNR and controls was represented in Table 3.8. whose total of 215 control individuals 53.95% were CC-GG, 14.42% were CC-GT, 1.86% were CC-TT, 16.60% were CT-GG, 6.98% were CT-GT, 0.93% were CT-TT, 2.33% were TTGG and 0.47% were TT-GT and 0.47% TT-TT whereas out of 151 T2DM cases 33.11% were CC-GG, 15.89% were CC-GT, 1.99% were CC-TT, 23.18% were CT-GG, 19.87% were CT-GT, 1.32% were CT-TT, 0.66% were TT-GG and 2.65% were TT-GT while 1.32% were TT-TT. The reference genotype combination of DNR case and control was CC-GT. A significant risk association was found in CT-GT (OR=2.583, 95% CI=1.057-6.373, p=0.02) with disease. However, no significant association with disease was found in other combined genotyped data. 3.4.2.2 Comparison between DR and Controls Statistical comparison of combined genotyped (rs7903146 and rs12255372) data of DR and control individuals is represented in Table 3.8. Out of 87 T2DM cases 13.79% were CC-GG, 13.79% were CC-GT, 0% were CC-TT, 25.29% were CTGG, 28.74% were CT-GT, 4.60% were CT-TT, 2.30% were TT-GG and 6.90% were TT-GT while 4.60% were TT-TT , A protective effect was observed in GGCC (OR=0.27 95% CI=0.10-0.71, p=0.005), and disease association CT-GT (OR=2.58, 95% CI=1.06-6.37, p=0.027), TT-GT (OR=15.50; 95% CI=1.52380.36; p=0.006) and TT-TT (OR=10.33; 95% CI=1.00-270.18; p=0.028).
49
Table 3.8 Statistical analysis of TCF7L2 (rs7903146+rs12255372) in Controls, DNR, and DR. Genotype
Controls n(%)=215
DNR n(%)=151
DR n(%)=87
DNR vs. Controls
DR vs. Controls
OR(95%CI)p-value
OR(95% CI)p-value
CC-GG
116(53.95)
50 (33.11)
12(13.79)
0.58(0.07-1.09)0.072
0.27(0.10-0.71)0.005
CC-TT
4(1.86)
3 (1.99)
0(0)
0.97(0.15-5.85)1.000
0.00(0.00-4.75)0.560
CT-GG
40(16.60)
35(23.18)
22(25.29)
1.13(0.53-2.42)0.859
1.42(0.56-3.61)0.53
CT-GT
15(6.98)
30 (19.87)
25(28.74)
2.58(1.06-6.37)0.027
4.31(1.56-12.15)0.002
CT-TT
2(0.93)
2(1.32)
4(4.60)
1.29(0.12-14.15)1.000
5.17(0.67-47.89)0.080
TT-GG
5(2.33)
1(0.66)
2(2.30)
0.26(0.01-2.57)0.386
1.03(0.12-7.47)1.000
TT-GT
1(0.47)
4(2.65)
6(6.90)
5.17(0.48-129.81)0.175
15.50(1.52-380.36)0.006
TT-TT
1(0.47)
2(1.32)
4(4.60)
1.29(0.12-14.15)1.000
10.33(1.00-270.18)0.028
50
No significant association with disease was found in other combined genotyped data. 3.4.3 Analysis of NPDR, PDR and Controls Statistical analysis in Table 3.9 showed combined association of NPDR, PDR and control groups, and CC-GT was used as reference genotype. 3.4.3.1 Comparison between NPDR and Controls Statistical comparison of combined genotyped (rs7903146 and rs12255372) data of NPDR, PDR and control individuals is represented in Table 3.9. Out of the total of 215 control individuals 53.95% were CC-GG, 14.42% were CC-GT, 1.86% were CC-TT, 16.60% were CT-GG, 6.98% were CT-GT, 0.93% were CTTT, 42.33% were TT-GG and 0.47% were TT-GT and 0.47% TT-TT. Whereas in 42 NPDR cases 9.52% were CC-GG, 14.29% were CC-GT, 0% were CC-TT, 35.71% were CT-GG, 2.19% were CT-GT, 4.76% were CT-TT, 0% were TT-GG and 7.14% were TT-GT while 2.38% were TT-TT. The reference genotype combination of case and control was CC-GT. A significant protective association was observed in CC-GG (OR=0.18; 95% CI=0.04-0.77; p=0.012). Disease association was observed in combined genotyped data of CT-GT (OR=3.79; 95% CI=1.04-14.48; p=0.042), TT-GT (OR=15.50; 95% CI=1.09-468.27; p=0.028). No significant association with disease was found in other combined genotype data. 3.4.3.2 Comparison between PDR and Controls Statistical comparison of combined genotyped (rs7903146 and rs12255372) data of NPDR, PDR and control individuals is represented in Table 3.12. Out of 45 PDR cases 17.78% were CC-GG, 13.33% were CC-GT, 0% were CC-TT, 15.56% were CT-GG, 31.11% were CT-GT, 4.44% were CT-TT, 4.44% were TT-GG and 6.67% were TT-GT while 6.67% were TT-TT. Disease association was observed in combined genotyped data of CT-GT (OR=4.822; 95% CI=1.364-17.762; p=0.007), TT-GT (OR=15.50; 95% CI=1.087-468.27; p=0.028) and TT-TT (OR=15.50; 95%CI=1.087-468.27; p=0.028). No significant association with disease was found in the rest of the genotyped combined genotype data. 53
Table 3.9 Statistical analysis of TCF7L2 (rs7903146+rs12255372) in Controls, NPDR and PDR. Genotype
Controls
NPDR
PDR
NPDR vs. Control
PDR vs. Controls
n(%)=215
n(%)= 42
n(%) = 45
OR(95%CI)p-value
OR(95% CI)p-value
CC-GG
116(53.95)
4 (9.52)
8(17.78)
0.18(0.04-0.77)0.012
0.36(0.10-1.27)0.092
CC-TT
4(1.86)
0(0)
0(0)
0.00(0.00-10.89)1.000
0.00(0.00-10.89)1.000
CT-GG
40(16.60)
15(35.71)
7(15.56)
1.94(0.61-6.39)0.311
0.90(0.24-3.44)1.000
CT-GT
15(6.98)
11(26.19)
14(31.11)
3.79(1.03-14.48)0.042
4.82(1.36-17.76)0.007
CT-TT
2(0.93)
2(4.76)
2(4.44)
5.17(0.406-69.080)0.165
5.17(0.41-69.08)0.17
TT-GG
5(2.33)
0(0)
2(4.44)
0.00(0.00-8.22)1.000
2.07(0.22-17.55)0.59
TT-GT
1(0.47)
3(7.14)
3(6.67)
15.50(1.09-468.28)0.028
15.50(1.09-468.28)0.028
TT-TT
1(0.47)
1(2.38)
3(6.67)
5.18(0.12-228.30)0.331
15.50(1.087-468.28)0.028
52
Chapter 4 Discussion
The TCF7L2 is reported to harbor various single nucleotide polymorphisms (SNPs) that are associated with different diseases including T2DM and its microvascular complications such as DR. In the current case-control study the association of TCF7L2 genetic polymorphisms (rs7903146 and rs12255372) has been assessed with retinopathy in T2DM patients in Pakistani population, and according to our knowledge this is the first study in Pakistani diabetic cohort. DR is a microvascular complication which affects the retina leading to vision loss; it is the 5th leading cause of blindness worldwide. The DR accounts for 34.5% of the world population, and 27% of people in Pakistan were estimated to have DR. Several studies have investigated the increased risk of T2DM and its microvascular complications were associated with the risk allele of TCF7L2 polymorphism (rs7903146 and rs12255372). TCF7L2 gene spans 215,869 bp region on the chromosome 10q25.3 and encodes for a transcription factor of the Wnt signaling pathway, it is considered as one of the major genes that plays significant role in β cell development, blood-glucose homeostasis, cell survival, cell migration and cell proliferation (Assmann et al., 2014). The TCF7L2 gene has seventeen (17) exons, of which five (5) undergo alternative splicing. The highest overall TCF7L2 gene expression was detected in pancreas, followed by other tissues such as colon, small intestine, brain, monocytes and lungs. Lower expression of TCF7L2 was observed in T and B lymphocytes. Alternative splicing in TCF7L2 is predicted to either activate or repress the Wnt signaling pathway. The transcription factor TCF7L2 encoded by TCF7L2 belongs to TCF family (TCF7, TCF7L1 and TCF7L2) of the Wnt signaling pathway. The interaction of TCF7L2 protein with beta catenin enables the translocation of TCF7L2 from the cytosol to the nucleus; and subsequent binding to promoter region thereby induce gene expression. The TCF7L2 protein mediates the expression of many genes including glucagon like peptide-1 (GLP-1), vascular endothelial growth factor (VEGF) and intercellular adhesion molecule-1 (ICAM1). The GLP-1 is an incretin (sugar regulating) hormone that mediates insulin secretion.
54
It has been observed that the DNA variations in the TCF7L2 cause over expression of the protein product which results in the GLP-1 dysfunction and hence impair insulin secretion. This malfunction in insulin secretion leads to insulin resistance which is the hallmark of T2DM. T2DM is a complex metabolic disorder of environmental risk factors and genetic susceptibility; it manifests when insulin insufficiency is accompanied by insulin resistance. Recent reports indicated an increase in T2DM cases with its associated complications worldwide, particularly in developing countries. Several studies have investigated the increased risk of T2DM was associated with TCF7L2 polymorphism and these studies hypothesized the association was due to β cell dysfunction and insulin resistance (Migliorini & Lickert, 2015). A study conducted in Canada revealed the role of TCF7L2 expression in β cell development and impaired glucose homeostasis in mouse models using functional knockdown approach. The study concluded that TCF7L2 plays a significant role in pancreatic β cells biogenesis and development (Shao et al., 2015). Similarly another study reported the role of TCF7L2 in GLP-1 and stromal-derived factor-1 (SDF-1) relating the overexpression of these hormones to polymorphisms in TCF7L2 which lead to insulin resistance (Jin & Liu, 2008; Migliorini & Lickert, 2015). The first study on TCF7L2 polymorphism association with T2DM was conducted in 2006 in Icelandic population, the study reported the comparison of non-risk controls with heterozygous and homozygous cases of the risk alleles for rs12255373 and rs7903146 TCF7L2 polymorphisms association with risks of T2DM, of which the relative risk of 1.45 and 2.41, respectively were found using Mantel-Haenszel statistical test (Grant et al., 2006). Our study supported the association of TCF7L2 SNPs with T2DM as proposed by Grant et al., (Table 3.1 and Table 3.4) except for the rs12255372 polymorphism which showed departure from Hardy-Weinberg equilibrium (HWE) observed in the control groups for this SNP. Similarly this deviation from HWE was also reported in UK-resident South Asian population, which was due to the chance sampling after re-genotyping the control groups.
55
Fresh reagents and a different method of genotyping were used (Rees et al., 2008). Cameroonian population reported that the deviation might be as a result of genotyping error (Nanfa et al., 2015). In our study control samples were selected at random and re-genotyped using fresh reagents to rule out the possibilities of genotyping errors, but the same results were obtained. Large number of ethnic groups from different cohorts reported the association of TCF7L2 gene polymorphism with T2DM i.e. Bodhini et al., in 2007 and Uma Jyothi et al., in 2013 (Bodhini et al., 2007; Uma Jyothi et al., 2013) independently reported association of TCF7L2 gene variants with T2DM in Indian population. Rees et al., also (Rees et al., 2008) in 2008 also reported association between TCF7L2 gene variants and T2DM in UK-resident South Asian population. Similarly, association was found in Iranian cohort (Alami et al., 2012; Javadi et al., 2009), Lebanon (Ghassibe-Sabbagh et al., 2014) and Japanese cohorts (Horikoshi et al., 2007). Moreover, In Africa; association was reported in Cameroonian population (Nanfa et al., 2015; Ngwa et al., 2015) and Ghanaian population (Danquah et al., 2013). Our study revealed the association of TCF7L2 polymorphisms with T2DM in Pakistani population. Several researches (Cauchi et al., 2007; Grant et al., 2006) identified rs7903146 SNP as the single genetic variant with the strongest association, as observed in our data (Table 3.1 and Table 3.2). In contrast to our study, association of T2DM with TCF7L2 polymorphisms were not found in some countries i.e. Alsmadi et al., (Alsmadi et al., 2008) in 2008 did not detect any association of TCF7L2 polymorphism with T2DM in Saudi Arabian population, and United Arab Emirates (Saadi et al., 2008). Also no significant association of rs7903146 and rs12255372 of TCF7L2 gene in Han Chinese population were reported but instead identified a novel SNP, rs290487, associated with T2DM using GenomeLab SNPstream genotyping platform. The study reported that lack of association was attributed to low frequency of the established risk alleles (rs7903146 T and rs12255372 T) in the Chinese population (Chang et al., 2007).
56
Although association of TCF7L2 polymorphism to some diabetic complications such diabetic nephropathy was reported but the association of TCF7L2 with DR is unclear (Sudchada & Scarpace, 2014). However various studies reported association between TCF7L2 and microvascular complication of T2DM (Buraczynska et al., 2011; Ciccacci et al., 2013; Fu et al., 2012). Buraczynska et al., (Buraczynska et al., 2011) in 2011 found the association between TCF7L2 rs7903146 and DR in Polish T2DM; the study revealed a significant association between the risk T allele and DR. A study was reported on TCF7L2 polymorphism and DR in Asian population. This analysis was limited as the definitions used for T2DM, DR, as well as the inclusion criteria were unclear, and the authors did not report any specific OR (odds ratio) (Fu et al., 2012). On a contrary, in our study the inclusion criteria were well defined and OR for all comparisons were clearly reported. Luo et al., (Luo et al., 2013) reported the association between rs7903146 TCF7L2 polymorphism and PDR; using gene expression analysis studies with tunicamycin-treated lymphoblastic cells and mouse models, the discordant in the authors’ study was due to lack of distinction between NPDR and PDR. However, in our studies there was proper separation between PDR and NPDR. But as reported by Luo et al., (Luo et al., 2013) our result showed significant association between rs7903146 TCF7L2 and PDR, we also discovered a novel association between rs12255372 TCF7L2 with PDR. Furthermore, we also for the first time worldwide discovered association between rs12255372 and rs7903146 variants with NPDR in T2DM subjects. Our study also provided a new evidence that TCF7L2 gene polymorphism association is higher in DR subjects compared to DNR for both rs7903146 and rs12255372 (Table 3.2 and Table 3.5), with the most significant association observed in rs7903146 TCF7L2 polymorphism in DR subjects. Furthermore, we also observed the highest association in PDR subjects for both rs7903146 and rs12255372 compared to NPDR and DNR. Therefore, we assumed that TCF7L2 polymorphism may contribute to the risk of DR. To confirm the association of DR to TCF7L2 polymorphisms (rs7903146 and rs12255372); which were in linkage disequlibrium (LD).
57
A combined genotyping was performed which revealed CC-TT has protective effect, CT-GT was associated to T2DM, TT-GT was associated with progression of DR and TT-TT associated with the risk of PDR progression. In conclusion, our results confirm the association of TCF7L2 polymorphism with risk of T2DM in Pakistanis, also evidence of association between TCF7L2 variants confirmed its role as risk factor for the development of DR.
58
Chapter 5 References
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stage
causes
impaired
glucose
homeostasis.
Molecular
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