| چکیده انگلیسی مقاله |
Objective and background Chronic diseases are the main cause of death and disability in human society (Drawz and Rahman, 2015). Despite different investigations, the molecular mechanisms are not fully discovered. Systems biology approaches can enhance our knowledge about these complex diseases (Wang et al., 2015). Diabetic nephropathy (DN), as a chronic disease, is one of the major complication of diabetes mellitus patients (Tziomalos and Athyros, 2015). In spite of huge investigations, a holistic view of this complex disorder has rarely been done. In this study, we have reanalyzed two microarray datasets of DN patients to identify key genes and functions in this complex condition. Keywords Diabetic nephropathy, Differential expressed gene, Microarray analysis, Pathway enrichment analysis Methodology In this study, two microarray datasets GSE30528 (kidney glomeruli) and GSE30529 (kidney tubule) which are the expression profile of diabetic nephropathy (DN) patients and healthy individuals were downloaded from Gene Expression Omnibus (GEO) database. The quality of each dataset was evaluated by unsupervised hieratical clustering and principal component analysis (PCA) using cluster maker application of cytoscape 3.5.1 and GGplot2 package of R software, respectively (Morris et al., 2011; Walter et al., 2015). Using GEO2R and considering adjusted p-value< 0.05, differentially expressed (DE) genes were identified (Barrett et al., 2013). In addition, using CluePedia application of Cytoscape software version 3.5.1, the networks of DE genes were constructed (Bindea et al., 2013). NetworkAnalyser tool of Cytoscape was used for networks analyses. The pathways of DE genes were collected using ClueGo application of Cytoscape (Bindea et al., 2009). MCODE application of Cytoscape was used for finding modules in the constructed networks and the modules with more than five score were collected. (Bader and Hogue, 2003). Results The quality of both glomeruli and tubule datasets was acceptable as PCA and unsupervised hieratical clustering could separate disease and normal samples (Figure 1a and 1b). In glomeruli dataset, 2517 genes and in tubule dataset 4958 genes were differentially expressed with adjusted p-value< 0.05. The topology analysis of constructed networks with DE genes revealed central nodes in DN. In addition, pathway enrichment analysis identified some well-known role player in DN such as FGFR, WNT and MAPK signaling. Furthermore, modules analysis showed 5 modules in glomeruli and 11 ones in tubule networks. These modules were associated with ECM organization, apoptosis, signaling by VEGF, signaling by PDGF, and adherence junction. Conclusions In conclusion, here we have shown the central genes, key signaling pathways, and interactions in DN. These findings deepen our knowledge about the pathogenesis of this complex disease. References Argani, P., Lewin, J. R., Edmonds, P., Netto, G. J., Prieto-Granada, C., Zhang, L., Jungbluth, A. A., Antonescu, C. R., 2015. Primary renal sclerosing epithelioid fibrosarcoma: report of 2 cases with EWSR1-CREB3L1 gene fusion. Am J Surg Pathol. 39, 365-73. Bader, G. D., Hogue, C. W., 2003. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 4, 2. Barrett, T., et al., 2013. NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res. 41, D991-5. Bindea, G., Galon, J., Mlecnik, B., 2013. CluePedia Cytoscape plugin: pathway insights using integrated experimental and in silico data. Bioinformatics. 29, 661-3. Bindea, G., et al., 2009. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics. 25, 1091-3. Drawz, P., Rahman, M., 2015. Chronic kidney disease. Ann Intern Med. 162, ITC1-16. Johnson, F. L., et al., 2017. Inhibition of IkappaB Kinase at 24 Hours After Acute Kidney Injury Improves Recovery of Renal Function and Attenuates Fibrosis. J Am Heart Assoc. 6. Morris, J. H., Apeltsin, L., Newman, A. M., Baumbach, J., Wittkop, T., Su, G., Bader, G. D., Ferrin, T. E., 2011. clusterMaker: a multi-algorithm clustering plugin for Cytoscape. BMC Bioinformatics. 12, 436. Tziomalos, K., Athyros, V. G., 2015. Diabetic Nephropathy: New Risk Factors and Improvements in Diagnosis. Rev Diabet Stud. 12, 110-8. Walter, W., Sanchez-Cabo, F., Ricote, M., 2015. GOplot: an R package for visually combining expression data with functional analysis. Bioinformatics. 31, 2912-4. Wang, R. S., Maron, B. A., Loscalzo, J., 2015. Systems medicine: evolution of systems biology from bench to bedside. Wiley Interdiscip Rev Syst Biol Med. 7, 141-61. |
