| چکیده انگلیسی مقاله |
Objective and background Diabetic nephropathy is the leading cause of chronic kidney disease (Reutens and Atkins 2011). Even with enormous investigations, the exact mechanisms of the progression of this complex disorder are not yet fully discovered and the therapeutic options are not satisfying. Systems biology could assist to develop more effective therapies through providing quantitative and holistic insights. In this study, we have constructed a multi-layer network (DE genes, transcription factors, kinases) to identify critical genes and signaling pathways related to DN. Keywords: Diabetic nephropathy, Microarray, Systems biology, Protein interaction network, Signaling Pathways Methodology The expression profile of GSE1009 deposited by Baelde et al (Baelde, Eikmans et al. 2004) was downloaded from the Gene Expression Omnibus (GEO) database. The quality of microarray data was measured by principal component analysis (PCA) and hierarchical clustering using ggplot2 package of R (http://www.R-project.org.) and ClusterMaker application (Morris, Apeltsin et al. 2011) of Cytoscape 3.2.1 (Lopes, Franz et al. 2010), respectively. Using GEO2R tool of GEO, genes with adjusted p-value≤0.05 were assumed as differentially expressed. Transcription factors (TFs) and kinases were obtained from Enrichr database (Chen, Tan et al. 2013) using ChEA and KEA tools, respectively. TFs and kinases with p-value ≤ 0.05 were selected. Using CluePedia plugin version 2.1.7 (Bindea, Galon et al. 2013) of Cytoscape, a protein-protein interaction (PPI) network was constructed for the DE genes, enriched TFs and kinases. The topology of the network was analyzed by Cytoscape NetworkAnalyzer tool. Pathway enrichment analysis was performed using Cytoscape ClueGO plugin version 2.1.7 (Bindea, Mlecnik et al. 2009). Pathways with adjusted p-value≤0.05 were chosen. Results In this study, we re-analyzed the GSE1009 microarray dataset which compares the expression profile of glomeruli of DN patients to healthy individuals. To determine the suitability of the dataset, we performed PCA and unsupervised hierarchical clustering. In both, these analyses samples were segregated based on disease state (normal or DN), indicating the satisfactory quality of this dataset. Analysis by GEO2R revealed 51 genes were differentially expressed with adjusted p-value< 0.05. Next, to construct a multi-layer network of DN, TFs that regulate DE genes were collected and also as the third layer, kinases that could interact with enriched TFs were obtained (Figure 1). The graph theory parameters were employed to assess the topology of the network. The genes were sorted based on Degree and Betweenness centrality parameters and the top 10% genes with the highest rank were selected (Table 1). Furthermore, pathway enrichment analysis resulted in deeply connected pathways which most of them are known to be associated with DN pathogenesis. Figure 1- A PPI network was constructed with DE genes, TFs, and kinases in the cortex. Red nodes represent DE genes, green shows TFs and the blue displays kinases. Table 1-The top 10% genes with the highest degree and betweenness centrality in PPI network are shown. Node Degree Node Betweenness Centrality TP53 83 TP53 0.32 JUN 57 AKT1 0.20 AKT1 55 SMAD4 0.10 FOXO3 42 JUN 0.09 SMAD4 35 FOXO3 0.07 MAPK1 34 CDK1 0.04 MAPK8 32 SMAD2 0.04 MAPK3 32 PRKACA 0.04 SMAD3 32 AR 0.04 EP300 29 MAPK8 0.04 SMAD2 28 MAPK1 0.03 Conclusions In conclusion, we have here introduced a systems biology approach to DN as a complex biological state. Methods employed in this study may also be used for other chronic diseases to suggest novel therapies via generation of a holistic multi-layer map. References Baelde, H. J., M. Eikmans, P. P. Doran, D. W. Lappin, E. de Heer and J. A. Bruijn (2004). Gene expression profiling in glomeruli from human kidneys with diabetic nephropathy. Am J Kidney Dis 43(4): 636-650. Bindea, G., J. Galon and B. Mlecnik (2013). CluePedia Cytoscape plugin: pathway insights using integrated experimental and in silico data. Bioinformatics 29(5): 661-663. Bindea, G., B. Mlecnik, H. Hackl, P. Charoentong, M. Tosolini, A. Kirilovsky, W. H. Fridman, F. Pages, Z. Trajanoski and J. Galon (2009). ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25(8): 1091-1093. Chen, E. Y., C. M. Tan, Y. Kou, Q. Duan, Z. Wang, G. V. Meirelles, N. R. Clark and A. Ma'ayan (2013). Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14: 128. Lopes, C. T., M. Franz, F. Kazi, S. L. Donaldson, Q. Morris and G. D. Bader (2010). Cytoscape Web: an interactive web-based network browser. Bioinformatics 26(18): 2347-2348. Morris, J. H., L. Apeltsin, A. M. Newman, J. Baumbach, T. Wittkop, G. Su, G. D. Bader and T. E. Ferrin (2011). clusterMaker: a multi-algorithm clustering plugin for Cytoscape. BMC Bioinformatics 12: 436. Reutens, A. T. and R. C. Atkins (2011). Epidemiology of diabetic nephropathy. Contrib Nephrol 170: 1-7. |
| نویسندگان مقاله |
یوسف قیصری | yousof gheisari
department of genetics and molecular biology, isfahan university of medical sciences, isfahan, iran ; 2 regenerative medicine lab, isfahan kidney diseases research center, isfahan university of medical sciences, isfahan, iran
دانشگاه علوم پزشکی اصفهان - گروه ژنتیک و بیولوژی مولکولی مرکز تحقیقات بیماری های کلیه
سازمان اصلی تایید شده: دانشگاه علوم پزشکی اصفهان (Isfahan university of medical sciences)
مریم عابدی | maryam abedi
department of genetics and molecular biology, isfahan university of medical sciences, isfahan, iran
دانشگاه علوم پزشکی اصفهان - گروه ژنتیک و بیولوژی مولکولی
سازمان اصلی تایید شده: دانشگاه علوم پزشکی اصفهان (Isfahan university of medical sciences)
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