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Dissertation DISSERTATION Titel der Dissertation Kidney Disease Interactome: Systems Biology for analyzing Kidney Diseases Verfasserin Mag.rer.nat. Irmgard Mühlberger angestrebter akademischer Grad Doktorin der Naturwissenschaften (Dr.rer.nat.) Wien, 2011 Studienkennzahl lt. Studienblatt: A 091 490 Dissertationsgebiet lt. Molekulare Biologie Studienblatt: Betreuerin / Betreuer: Univ.Doz.Dr. Bernd Mayer 2 “Whereas the beautiful is limited, the sublime is limitless, so that the mind in the presence of the sublime, attempting to imagine what it cannot, has pain in the failure but pleasure in contemplating the immensity of the attempt.” Immanuel Kant 3 4 Acknowledgements I would like to take this opportunity to acknowledge and thank a number of people who got involved in this thesis in one way or another. First and foremost, I wish to express my special gratitude to my family and friends for giving me assistance in any respect, particularly during the completion of this project. I am furthermore heartily thankful to my supervisor, Bernd Mayer, whose encouragement, guidance and support from the initial to the final level of the working process, enabled this thesis. Many thanks go to Rainer Oberbauer and Gert Mayer. I greatly benefited from their clinical expertise. Moreover, it is my great pleasure to acknowledge the valuable support and help of Paul Perco, whose suggestions, comments and ideas guided me throughout this working process. His manners of providing orientation always helped me to get back on track during challenging times. Also, my overall gratefulness is devoted to all of my colleagues. I want to seize this opportunity to express my true gratitude for the experience of being part of the emergentec team. Finally, my deepest appreciation goes to Flo for his patience, love and endless support. He has been my anchor, throughout this work and beyond. 5 6 Index 1. Introduction .........................................................................................................................9 1.1 Systems Biology ...........................................................................................................9 1.1.1 The Evolution of High-throughput Technologies .................................................9 1.1.2 Computational Systems Biology ......................................................................... 11 1.1.3 Network Biology ................................................................................................... 12 1.1.4 Systems Biology in Disease ............................................................................... 13 1.2 Data Sources .............................................................................................................. 15 1.2.1 Omics Technologies ............................................................................................ 15 1.2.2 Literature Mining .................................................................................................. 21 1.3 Analysis Workflows .................................................................................................... 23 1.3.1 Sequential Workflows .......................................................................................... 23 1.3.2 Integrated Workflows ........................................................................................... 25 1.4 Applications ................................................................................................................. 26 1.4.1 Acute Renal Failure/Transplantation .................................................................. 26 1.4.2 Chronic Kidney Disease ...................................................................................... 28 1.4.3 Cardiorenal Syndrome ........................................................................................ 29 2. Publications ...................................................................................................................... 31 2.1 Computational analysis workflows for Omics data interpretation. Bioinformatics for Omics Data: Methods and Protocols. 2011 .............................................................. 31 2.1.1 The Thesis Author‘s Contribution ....................................................................... 55 2.2 Biomarkers in renal transplantation ischemia reperfusion injury. Transplantation. 2009 ................................................................................................................................... 57 2.2.1 The Thesis Author‘s Contribution ....................................................................... 73 2.3 Impaired metabolism in donor kidney grafts after steroid pretreatment. Transpl Int. 2010 ............................................................................................................................. 75 2.3.1 The Thesis Author‘s Contribution ..................................................................... 103 2.4 Linking transcriptomic and proteomic data on the level of protein interaction networks. Electrophoresis. 2010 ................................................................................... 105 2.4.1 The Thesis Author‘s Contribution ..................................................................... 129 7 2.5 Integrative bioinformatics analysis of proteins associated with the cardiorenal syndrome. Int J Nephrol. 2010 ...................................................................................... 131 2.5.1 The Thesis Author‘s Contribution ..................................................................... 154 2.6 Molecular pathways and crosstalk characterizing the cardiorenal syndrome. submitted to J Cell Mol Med. ......................................................................................... 155 2.6.1 The Thesis Author‘s Contribution ..................................................................... 176 3. Discussion ...................................................................................................................... 177 3.1 Major Findings .......................................................................................................... 177 3.1.1 Omics Workflows ............................................................................................... 177 3.1.2 Acute Renal Failure/Transplantation ............................................................... 177 3.1.3 Chronic Kidney Disease .................................................................................... 179 3.1.4 Cardiorenal Syndrome ...................................................................................... 180 3.2 Outlook ...................................................................................................................... 182 4. Appendix ......................................................................................................................... 185 References ...................................................................................................................... 185 Abstract ........................................................................................................................... 191 Zusammenfassung ......................................................................................................... 193 Curriculum Vitae ............................................................................................................. 195 8 1. Introduction 1.1 Systems Biology Systems Biology refers to a field in molecular biosciences that aims to understand molecular mechanisms of cells, tissues, or organisms by integrative analysis of multiple molecular and cellular components. However, since a system is not only the mere assembly of its components, a system-level understanding cannot be achieved by the study of singular molecules one by one and the focus of research has shifted from single elements to networks, from matters to states, and from structures to dynamics [1]. The following sections provide an overview of the different aspects and concepts of Systems Biology that build the basis for the concepts and methods used in the studies presented in this thesis. 1.1.1 The Evolution of High-throughput Technologies The idea of understanding biological entities as dynamic systems is not new, but the availability of methods for investigating them as such led to a tremendous increase of research in this field as seen over the last decades. Along with the technical progress, the advance of high-throughput technologies opened up possibilities for a more global view on cellular processes. Large-scale data generation triggered the advent of a new domain which can be embraced by the term ―omics‖, and refers to the comprehensive analysis of a biological system on the respective level of observation, including genomics, transcriptomics, proteomics and many more (an overview of the different omics technologies is given in section 1.2.1). The enormous increase in data amounts is illustrated in figure 1 referencing the number of available sequences provided in the NCBI RefSeq database between 2004 and 2011 (available at ftp://ftp.ncbi.nih.gov/refseq/release/release-statistics/). 9 Figure 1: The histograms show the increase in number of accessions available in the NCBI RefSeq database between 2004 and 2011 for genomic, RNA, and protein sequences respectively. The progress of high-throughput technologies has also implicated a shift from a traditional hypothesis-driven to a data-driven research. Data-driven or ―top-down‖ approaches make use of an iterative cycle that
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