Network Analysis Reveals Abberant Cell Signaling In
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NETWORK ANALYSIS REVEALS ABBERANT CELL SIGNALING IN MURINE DIABETIC KIDNEY By PRIYANKA GOPAL Submitted in partial fulfillment of the requirements Master of Science Thesis Advisor: Michael Simonson PhD Department of Physiology and Biophysics CASE WESTERN RESERVE UNIVERSITY May, 2015 CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We hereby approve the thesis/dissertation of Priyanka Gopal Candidate for the degree of Master of Science Committee Chair Dr. William P. Schilling, PhD Committee Member Dr. Christopher P. Ford, PhD Committee Member Dr. Jeffrey L. Garvin, PhD Committee Member Dr. Michael S. Simonson, PhD Date of Defense 03/16/2015 *We also certify that written approval has been obtained for any proprietary material contained therein. Table of Contents Table of Contents………………………………………………………………………...iii List of Tables……………………………………………………………………………..iv List of Figures…………………………………………………………………………….v Acknowledgements………………………………………………………………………vi List of Abbreviations…………………………………………………………………....vii Abstract…………………………………………………………………..........................x Introduction…………….……………………………………………...............................1 Research Objectives and Specific Aims………………………………………………….5 Materials and Methods……………………………………………………………………6 Results……………………………………………………………………………………11 Discussion………………………………………………………………………………..16 Summary and Future Directions…………………………………………………………21 Bibliography……………………………………………………………………………..37 iii List of Tables Table 1 Quantitative PCR measurements of mRNA for putative first messengers altered in 16 week db/db kidney as inferred from integrated analysis of the signal transduction protein interaction network……...............................................................24 Table 2 Top ranked biological processes in the Gene Ontology (GO) database that is statistically associated with the genes/proteins in subnetwork…………………………………..25 Table 3 Activated signal transduction pathways in db/db mouse kidney inferred by bioinformatics enrichment of the db/db gene set in the Reactome database…………………………………………….26 iv List of Figures Figure 1 Schematic showing the differences in modelling approaches…………..27 Figure 2 Schematic illustrating approach for inferring signal transduction………28 Figure 3 Schematic showing the Elements and types of subnetworks……………29 Figure 4 Inferred 8 week signal transduction network…………………………...30 Figure 5 Inferred 16 week signal transduction network………………………….31 Figure 6 Power law analysis …………………………………………...…………33 Figure 7 Subnetworks…………………………………………………………….34 Figure 8 Western blot of Traf6 in 16 week mouse kidney……………………….36 v Acknowledgements I would like to extend my appreciation to my thesis adviser Dr. Michael Simonson for his help and support throughout this process. vi List of Abbreviations BC Betweenness centrality BMP6 Bone morphogenetic protein 6 CA California CCL15 Chemokine (c-c motif) ligand 15 CCL2 Chemokine (C-C motif) ligand 2 CCL9 Chemokine (c-c motif) ligand 9 CDF Cumulative Differential Function cDNA Complimentary Deoxyribonucleic acid CTF1 Cardiotrophin-1 CWRU Case Western Reserve University CXCL2 Chemokine (C-X-C motif) ligand 2 CXCR2 Chemokine (C-X-C motif) receptor 2 DC Detergent compatible EDN1 Endothelin 1 EDN3 Endothelin 3 ET-1 Endothelin-1 ET-3 Endothelin-3 GDF15 Growth Differentiation Factor 15 GEO Gene Expression Omnibus GFR Glomerular Filtration Rate GRO-β Growth-regulated protein Beta HCl Hydrochloric acid vii HPPI2 Human PPI Network 2 HRAS Harvey rat sarcoma viral oncogene homolog HRP Horseradish peroxidase HSN High-scoring network IL-1 Interleukin-1 IL-1β Interleukin-1β IL-6 Interleukin-6 IL-8 Interleukin-8 INBB Inhibin beta b LCN2 Lipocalin-2 MAPK14 Mitogen-activated protein kinase 14 MAPKAPK2 Mitogen activated protein kinase activated protein kinase 2 MCP1 Monocyte chemotactic protein 1 MIP2α Macrophage inflammatory protein 2-Alpha MMP-9 Metalloproteinase-9 mRNA Messenger Ribonucleic acid NaCl Sodium Chloride NF-κB Nuclear factor kappa-light-chain-enhancer of activated B cells NGAL Neutrophil gelatinase-associated lipocalin P38MAPK P38 mitogen-activated protein kinases PAGE Polyacrylamide gel electrophoresis PI3K Phosphoinositide-3-kinase PIK3R1 Phosphoinositide-3-kinase, regulatory subunit 1 viii PPI Protein-protein interaction qPCR Quantitative Polymerase Chain Reaction RNA Ribonucleic acid SCGB1A1 Secretoglobin 1A SD Standard deviation of the mean SDS Sodium dodecyl sulfate TBS-T Tris-Buffered Saline and Tween 20 TGF Tumor Growth Factor TGFβ Tumor Growth Factor beta TLR Troll like receptor TNF Tumor Necrosis Factor TNFR Tumor Necrosis Factor Receptor TNFSF11 Tumor necrosis factor 11 TRAF6 Tumor Necrosis Factor Receptor Associated Factor 6 WNT10A Wingless 10A WNT7B Wingless 7B ix Network Analysis Reveals Aberrant Cell Signaling in Murine Diabetic Kidney Abstract By PRIYANKA GOPAL Diabetes alters cell signaling in the kidney, and some of these changes contribute to fibrosis and nephron loss in experimental models of diabetic nephropathy. However, little is known about how signaling pathways in the diabetic kidney function in the context of networks. Here, diabetes-induced changes in signaling were inferred before and during incipient renal injury in db/db mice (i.e., 8 and 16 weeks) by overlaying kidney mRNA expression levels on a directed protein interaction network. The inferred network at 8 weeks consisted of 31 nodes and 38 edges (i.e., proteins and interactions) whereas the network at 16 weeks was larger with 84 nodes and 113 edges. This integrated network approach predicted previously reported changes including activation of Tgfβ/Smad. Interestingly, the 16-week network predicted new signaling pathways in the diabetic kidney including increased Traf6 and Mapkapk2 signaling to proinflammatory cytokines, and subnetworks derived from the 16-week network were highly enriched for biological processes related to apoptosis, wound healing and inflammation. One of the novel predictions was increased expression of the proinflammatory mediator Traf6 , which was validated by Western blotting in the renal cortex of 16 week db/db versus non-diabetic db/m mice (P<0.01). Taken together, these results demonstrate that diabetes induces multiple changes in the renal signal transduction network and suggests a possible role for Traf6--dependent proinflammatory signals in diabetic nephropathy. x 1. Introduction Diabetic nephropathy is the most prevalent cause of progressive kidney disease leading to end-stage renal failure (de Boer, Rue et al. 2011). Signal transduction is altered in the diabetic kidney, and some of these changes (i.e., activation of Tumor Growth Factor β (TGFβ)/Smad) have been causally linked in animal models to inflammation, fibrosis and loss of functioning nephrons (Ziyadeh, Hoffman et al. 2000, Chow, Nikolic-Paterson et al. 2007, Brosius, Khoury et al. 2010, Darisipudi, Kulkarni et al. 2011). However, studies of isolated pathways in diabetes may have limitations as kidney cells in vivo are exposed to multiple growth factors and inflammatory mediators, resulting in activation of many signals. Thus an emerging challenge is to understand how signals in the diabetic kidney function in networks, as it is increasingly recognized that phenotypes in chronic disease result from multi-pathway signals (Bhalla and Iyengar 1999, Jorgensen and Linding 2010). 1.1 Signal Transduction Networks A general approach to understand complex biological systems is to identify patterns of local and global interconnection, or networks. In a mathematical formalism, biological networks can be represented as graphs and analyzed using principles of graph theory (Barabasi and Oltvai 2004, Albert 2007). The simplest network graph reduces the system's elements to nodes (“vertices”) and their pairwise relationships to links (“edges”) connecting pairs of nodes. In signal transduction networks, links represent functional interactions between the nodes, such as “activate”, “inhibit”, “catalyze”, 1 “binds to” etc (Newman 2006, Barabasi 2009). The network's inference and analysis relates the state of the elements in a system to their functional relationships, thereby enhancing extraction of biological insight and predictions. Increasingly, studies have shown that network analysis helps reveal meaningful biological properties that cannot be understood from studies of simple linear pathways (Barabasi 2009, Janes and Lauffenburger 2013). Network formalism and analysis has been widely applied in areas such as genetics, molecular biology, microbiology, and epidemiology, often revealing surprising and unanticipated functional insights. In theory, this approach aims to define and analyze the regulatory relationships of nodes and edges to understand how a system works in ever changing conditions (i.e., “Systems Biology”) (Kitano 2002). To date, these approaches have not been applied widely to kidney disease or more specifically to diabetic nephropathy (Bhavnani, Eichinger et al. 2009, Ju and Brosius 2010). 1.2 Modeling Signal Transduction Networks Systems biology provides a framework for reconstructing signal transduction networks in chronic disease (Ideker and Sharan 2008, Barabasi, Gulbahce et al. 2011) (Figure 1). One approach for inferring signaling networks involves measuring the level and post-translational modification of proteins by high-throughput methods such as mass spectrometry (Choudhary and Mann 2010). High-throughput characterization of the proteome has identified complex signaling networks that confer biological robustness in cellular models of apoptosis and proliferation