Analysis of gene-expression in kidney diseases

The number of Austrian residents, who develop end stage renal disease (ESRD) and therefore need renal replacement therapy (RRT, i.e. dialysis and/or kidney transplantation) is increasing dramatically. Costs of therapy have become a major burden for the society. It is estimated, that 1-2 % of the total health care budget have to be spent to take care for ESRD patients, which represent only about 0.05% of the total population (in total 80 million/ATS 1.1 billion are spent per year for RRT in Austria). Although treatment options for progressive kidney diseases have been improved over the last years, the response to potentially toxic therapy is characterized by a significant interindividual variability. Thus, the analysis of gene expression patterns represents a new and very powerful method not only to better understand the pathology of renal diseases but also to recognize response to specific treatment at an early stage. Our goal is the analysis of genome-wide gene expression patterns in renal tubular epithelial cells (RTEC) in correlation to the clinical course of various diseases and response to treatment. It has been shown in a large number of studies that progression of kidney diseases is more dependent upon the number and function of RTEC than on the degree of glomerular injury. Furthermore, it has been increasingly recognized that RTEC actively contribute to kidney injury (fibrosis, inflammation) by direct interaction with interstitial cells, e.g. fibroblasts, macrophages. We will isolate RTEC by Laser Capture Microdissection from kidney biopsies of patients obtained at our Department. RNA is then isolated, amplified and fluorescently labeled. This probe is competitively hybridized with a reference RNA to a cDNA microarray (so called "gene-chip"). This arrays contain about 43.000 spots of cDNAs representing about 20.000 known genes and approximately 24.000 sequences of unknown function (so called expressed sequence tags or ESTs). The enormous amount of image and numeric data which is generated by this method will be analyzed by bioinformatical statistical methods (cluster-analysis). Finally, the results will be displayed graphically to show potential relations between individual renal diseases as well as correlation in expression between single genes and ESTs. The aim of this study is (1) to find and analyze gene-expression patterns of kidney diseases, (2) to examine gene- expression in RTEC in correlation to response to treatment and to progression of disease and (3) to find and characterize expressed sequence tags with previously unknown function and formulate new hypothesis on the pathophysiology of renal diseases. The future goal of our project is to create an own "kidney-chip" and to use the microarray technology as a new tool for diagnosis and therapy of kidney diseases.

The key objective of this proposal was to study the genome wide gene expression profiles in renal tubular epithelial cells (RTEC) obtained from biopsies of patients with proteinuric and progressive native kidney diseases. Disorders of kidney function are of considerable public interest as their prevalence is increasing dramatically. They currently affect about 11 % of the total population. The terminal phase of the disease is end stage renal failure requiring treatment with either transplantation or dialysis. Even though progress in these areas has been made both options are extremely costly and furthermore do not return the life expectancy or the quality of life of affected individuals to normal. Renal tubular epithelial cells are the primary target of proteinuria and also directly involved in renal disease progression. Therefore, data on the changes in RPTEC function as reflected by gene expression induced by proteinuria or associated with progressive renal disease will provide important insights into the pathophysiology. This will open the way for the development of new and potentially better biomarkers to identify patients at high risk for progression of kidney disease. Furthermore, the results will also enable the identification of new therapeutic interventions. Several other authors have used microarrays to study genome wide gene expression in renal disease. However, they have either used animal models and studied total renal tissue rather than a specific population of cells. Both approaches have considerable shortcomings which have been avoided by our work. In an initial study we optimized the methodology used, which was published in a first paper. The microarray studies have been completed by now, the bioinformatical analysis however is partly still ongoing. In a second paper we report the alterations in genome wide gene expression in RTEC obtained from patients with proteinuric renal diseases when compared to healthy controls (tissue obtained from the unaffected part of tumor nephrectomy specimen). Besides providing a extensive map of genes and pathways distinguishing both groups under study the most important finding of the project was that proteinuria not only activates damaging but also intrinsic protective pathways. These have to be taken into account if new treatment targets are considered. In a third paper - which is currently in preperation - we report the alterations of gene expression profiles in progressive renal disease (defined by a loss of renal excretory function during a follow up period of at least two years after the biopsy) using a similar bioinformatical approach.