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Common Patterns of Glomerular Gene Expression Profiles in Different Murine Models of Early Nephropathy

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From the Institute of Veterinary Pathology Chair of General Pathology and Pathological Anatomy Head: Prof. Dr. W. Hermanns Ludwig-Maximilians-University, Munich, Germany and the Department of Internal Medicine Division of Nephrology Head: Prof. Dr. F. Brosius III University of Michigan, Ann Arbor, United States of America Under the supervision of Prof. Dr. R. Wanke and Prof. Dr. M. Kretzler Common patterns of glomerular gene expression profiles in different murine models of early nephropathy Inaugural - Dissertation to achieve the doctor title of veterinary medicine at the Faculty of Veterinary Medicine of the Ludwig-Maximilians-University, Munich Andreas Falko Blutke from Stuttgart Munich 2007 - 1 - Gedruckt mit Genehmigung der Tierärztlichen Fakultät der Ludwig-Maximilians-Universität München Dekan: Univ.-Prof. Dr. Braun Berichterstatter: Univ.-Prof. Dr. Wanke Korreferent/en: Univ.-Prof. Dr. Hartmann Univ.-Prof. Dr. Kaspers Univ.-Prof. Dr. Müller Univ.-Prof. Dr. Poulsen-Nautrup Tag der Promotion: 8. Februar 2008 - 2 - Meinem Bruder - 3 - Table of contents Page 1. Introduction 1 2. Scientific background 3 2.1 Importance of chronic kidney diseases 3 2.1.2 Common pathological features of chronic kidney diseases 4 2.2 Glomerulosclerosis 5 2.3 Pathogenesis of progressive glomerulosclerosis 7 2.3.1 Glomerular hypertrophy 7 2.3.2 Podocyte function and the importance of podocyte damage for 12 the development of glomerulosclerosis 2.3.3 Proteinuria 16 2.4.1 Animal models of glomerulosclerosis 19 2.4.2 GIPRdn-transgenic mice: a novel mouse model of diabetes 21 mellitus 2.4.3 Growth hormone-transgenic mice 23 2.5.1 Transcript profiling analysis in nephrology 25 2.5.2 Real-time polymerase chain reaction 27 2.5.3 Microarray analysis 29 2.6 Methods of glomerulus isolation 33 3. Research design and methods 36 3.1 Experimental design 38 3.2 Animals 41 3.2.1 Breeding, animal husbandry and numbers of mice used for 41 analyses 3.2.2 Identification of transgenic mice by Polymerase chain reaction 44 3.2.2.1 Primers 44 3.2.2.2 DNA isolation 45 3.2.2.3 Polymerase chain reaction 45 3.2.2.4 Gel electrophoresis 46 3.3 Urine analysis 48 3.3.1 Definition of stages, time points and intervals of investigation 48 3.3.2 Detection of glucosuria in GIPRdn-transgenic mice 50 3.3.3 Urine protein analysis 50 - 4 - 3.3.3.1 Detection of absence or onset of albuminuria (SDS-PAGE) 50 3.3.3.2 Western-blot analysis 53 3.3.3.3 Determination of urine albumin concentrations by Enzyme- 54 Linked Immunosorbent Assay (ELISA) 3.4.1 Generation of sample materials and acquisition of additional 56 data 3.4.2 Kidney perfusion and glomerulus isolation 57 3.4.3 Tissue preparation for histology and electron microscopy 63 3.4.3.1 Plastic histology 63 3.4.3.2 Tissue preparation for transmission electron microscopy 65 3.5.1 Estimation of the mean glomerular volume 67 3.5.2 Estimation of numbers of glomerular cells 68 3.5.3 Determination of the filtration slit frequency 72 3.5.4 Measurement of the true harmonic mean thickness of the 73 glomerular basement membrane 3.6 Microarray analyses of samples of isolated glomeruli 75 3.6.1 Work-flow of microarray analysis 75 3.6.2 RNA preparation 77 3.6.3 Determination of quantity and quality of isolated total RNA by 78 microfluid electrophoresis 3.6.4 Preparation of amplified, biotin-labeled cDNA from total RNA 79 3.6.5.1 DNA microarray experiments 80 3.6.5.2 Quality controls of microarrays and cluster analyses 80 3.7 Statistical analysis of microarray data 81 3.7.1 Nomenclature of “differentially expressed transcripts” and 81 “genes” 3.7.2 Identification of differentially expressed transcripts 82 3.7.3 Identification of commonly differentially expressed genes 84 3.7.3.1 Identification of commonly differentially expressed genes in the 84 single stages of investigation 3.7.3.2 Identification of commonly differentially expressed genes in both 85 stages of investigation 3.7.4 Estimation of statistical enrichment of commonly differentially 85 expressed genes by Monte Carlo simulation - 5 - 3.8 Cluster analyses 86 3.9.1 Confirmation of array data by quantitative real-time PCR 86 3.9.2 Reverse transcription of RNA into cDNA 87 3.9.3 Performance of real-time PCR 87 3.10 Bioinformatical analyses 90 3.11 Statistical analysis and data presentation 90 4. Results 91 4.1 Characterization of nephropathy stages 91 4.1.1 Age of animals 91 4.1.2 Body weight 92 4.1.3 Kidney weight and relative kidney weight 92 4.1.4 Glomerular histology 94 4.1.5 Morphometric analysis and quantitative stereology 95 4.1.5.1 Mean glomerular volume 95 4.1.5.2 Numerical density of glomerular cells 97 4.1.5.3 Number of cells per glomerulus 98 4.1.5.4 Filtration slit frequency 99 4.1.5.5 Thickness of the glomerular basement membrane 99 4.1.6 Results of urine analyses 100 4.1.6.1 Detection of glucosuria in GIPRdn -transgenic mice 100 4.1.6.2 Urine creatinine concentrations 100 4.1.6.3 Urinary protein excretion patterns (SDS-PAGE) 101 4.1.4.4 Western blot analysis 104 4.1.4.5 Quantification of urine albumin concentrations by ELISA 104 4.2 Magnetic large scale isolation of kidney glomeruli 105 4.2.1 Pilot experiments 105 4.2.2 Quantity and purity of generated glomerulus isolates 107 4.3 Results of transcript profiling analyses 109 4.3.1.1 Quantities and quality of total glomerular RNA 109 4.3.1.2 Quality of target cDNA 109 4.3.2.1 Control of quality and comparability of microarray data 110 4.3.2.2 Cluster analysis of normalized microarray data 111 4.3.3.1 Identification of differentially expressed genes in the specific 113 groups and stages of investigation - 6 - 4.3.3.2 Commonly differentially expressed genes in stage I 114 4.3.3.3 Commonly differentially expressed genes in stage II 114 4.3.3.4 Commonly differentially expressed genes in both stages of 115 investigation 4.3.4 Estimation of statistical enrichment of commonly differentially 117 expressed genes by Monte Carlo simulation 4.3.5 Cluster analyses of common expression profiles 118 4.3.6 Confirmation of array data by real time polymerase chain 120 reaction 4.3.7 Bioinformatical analysis 124 4.3.7.1 Molecular functions of gene products of commonly differentially 124 expressed genes in the single stages of investigation 4.3.7.2 Molecular functions and subcellular distributions of gene 126 products of commonly differentially expressed genes in all investigated stages and groups 4.3.7.3 Identification of known interactions of single gene products 127 corresponding to commonly differentially expressed genes in all groups and stages of investigation 5. Discussion 128 5.1 General aspects 128 5.2 Experimental design 129 5.3 Quality of glomerulus isolates 131 5.4 Morphological and functional investigations 133 5.4.1 Histology 133 5.4.2 Glomerular hypertrophy and numbers of cells per glomerulus 133 5.4.3 Thickness of the glomerular basement membrane 136 5.4.4 Filtration slit frequency 137 5.4.5 Urine analyses 138 5.4.6 Ages of animals and further parameters 141 5.5 Transcript profiling analyses 142 5.5.1 Microarray analyses: Patterns of glomerular gene expression 142 5.5.2 Microarray analyses: Differential glomerular gene expression 144 profiles - 7 - 5.5.3 Microarray analyses: Common differential gene expression 145 profiles 5.5.4 Confirmation of array data by real-time PCR 146 5.5.5 Relative depletion of podocytic RNA in samles of hypertrophied 147 glomeruli: A potential pitfall of interpretation of glomerular gene expression profiles 5.6.1 Bioinformatical analyses 149 5.6.2 Commonly differentially expressed genes involved in lipid 150 metabolism 5.6.3 Commonly differentially expressed genes involved in oxidative 154 stress 5.6.4 Commonly differentially expressed genes involved in cytokine 156 and chemokine signaling pathways 5.6.5 Commonly differentially expressed genes involved in 159 extracellular matrix turnover 5.6.6 Commonly differentially expressed genes involved in mediation 160 of cell-matrix contacts 5.6.7 Commonly differentially expressed genes involved in 164 cytoskeletal functions 5.6.8 Commonly differentially expressed genes involved in G-protein 167 dependent signaling processes 5.6.9 Commonly differentially expressed genes involved in 168 immunological events 5.7 Conclusions and future prospects 170 6. Summary 172 7. Zusammenfassung 174 8. References 177 - 8 - 9. Appendix 216 9.1 Protocol for silver staining of SDS-PAGE gels 216 9.2 Drying of SDS-PAGE gels 217 9.3 Preparation of murine albumin standard dilutions for 217 quantification of urine albumin concentrations by ELISA 9.4 Pattern of photography of glomerular peripheral capillary loops 217 9.5.1 Dimensions of the logarithmic ruler for measurement of the 219 thickness of the glomerular basement membrane 9.5.2 Example for the calculation of the true harmonic mean GBM- 220 thickness 9.6 Preparation of amplified, biotin-labeled cDNA from total RNA 221 9.7 Principle of relative quantification of real-time PCR results using 223 the ΔCT method 9.8 Performance of Monte Carlo simulation (example) 225 9.9.1 Commonly differentially expressed genes in stage I 226 (GIPRdn vs. bGH) 9.9.2 Commonly differentially expressed genes in stage II 230 (GIPRdn vs. bGH) Acknowledgements 242 Curriculum vitae 244 - 9 - 1 Introduction Development of glomerulosclerotic alterations is a common feature of various chronic kidney diseases leading to a progressive loss of functioning kidney parenchyma and finally resulting in terminal renal failure (Klahr et al.
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  • Qt38n028mr Nosplash A3e1d84

    Qt38n028mr Nosplash A3e1d84

    ! ""! ACKNOWLEDGEMENTS I dedicate this thesis to my parents who inspired me to become a scientist through invigorating scientific discussions at the dinner table even when I was too young to understand what the hippocampus was. They also prepared me for the ups and downs of science and supported me through all of these experiences. I would like to thank my advisor Dr. Elizabeth Blackburn and my thesis committee members Dr. Eric Verdin, and Dr. Emmanuelle Passegue. Liz created a nurturing and supportive environment for me to explore my own ideas, while at the same time teaching me how to love science, test my questions, and of course provide endless ways to think about telomeres and telomerase. Eric and Emmanuelle both gave specific critical advice about the proper experiments for T cells and both volunteered their lab members for further critical advice. I always felt inspired with a sense of direction after thesis committee meetings. The Blackburn lab is full of smart and dedicated scientists whom I am thankful for their support. Specifically Dr. Shang Li and Dr. Brad Stohr for their stimulating scientific debates and “arguments.” Dr. Jue Lin, Dana Smith, Kyle Lapham, Dr. Tet Matsuguchi, and Kyle Jay for their friendships and discussions about what my data could possibly mean. Dr. Eva Samal for teaching me molecular biology techniques and putting up with my late night lab exercises. Beth Cimini for her expertise with microscopy, FACs, singing, and most of all for being a caring and supportive friend. Finally, I would like to thank Dr. Imke Listerman, my scientific partner for most of the breast cancer experiments.