Systems biology approaches toward autosomal dominant polycystic kidney disease (ADPKD). Issue 1 (6th January 2020)

• Record Type: Journal Article
• Authors: Rahimmanesh, Ilnaz; Fatehi, Razieh
• Is Part Of: Clinical and translational medicine. Volume 9:Issue 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-01-06
• Subjects: Autosomal dominant polycystic kidney disease -- Microarray -- Protein interaction network -- Signal pathway; Clinical medicine -- Periodicals; Medicine, Experimental -- Periodicals; Medical innovations -- Periodicals; Molecular biology -- Periodicals; Pathology, Molecular -- Periodicals; 616.027
• DOI: 10.1186/s40169-019-0254-5
• Languages: English
• ISSNs: 2001-1326

Abstract

Abstract: Background: Autosomal dominant polycystic kidney disease (ADPKD), a common of monogenetic disorder caused by the polycystic kidney disease‐1 (PKD1) or PKD2 genes deficiency. In this study, we have re‐analyzed a microarray dataset to generate a holistic view of this disease. Methodology: GSE7869, an expression profiling dataset was downloaded from the Gene Expression Omnibus (GEO) database. After quality control assessment, using GEO2R tool of GEO, genes with adjusted p‐value ≤ 0.05 were determined as differentially expressed (DE). The expression profiles from ADPKD samples in different sizes were compared. Using CluePedia plugin of Cytoscape software, the protein–protein interaction (PPI) networks were constructed and analyzed by Cytoscape NetworkAnalyzer tool and MCODE application. Pathway enrichment analysis of clustered genes by MCODE with the high centrality parameters in PPI networks was performed using Cytoscape ClueGO plugin. Moreover, by Enrichr database, microRNAs (miRNAs) and transcription factors (TFs) targeted DE genes were identified. Results: In this study to explore the molecular pathogenesis of kidney in ADPKD, mRNA expression profiles of cysts from patients in different sizes were re‐analyzed. The comparisons were performed between normal with minimally cystic tissue (MCT) samples, MCTs with small cysts, and small cysts with large cysts. 512, 7024, and 655 DE genes were determined, respectively. The top central genes, e.g. END1, EGFR, and FOXO1 were identified with topology and clustering analysis. DE genes that were significantly enriched in PPI networks are critical genes and their roles in ADPKD remain to be assessed in future experimental studies beside miRNAs and TFs predicted. Furthermore, the functional analysis resulted in which most of them are expected to be associated with ADPKD pathogenesis, such as signal pathways that involved in cell growth, inflammation, and cell polarity. Conclusion: We have here explored systematic approaches for molecular mechanisms assay of ADPKD as a monogenic disease, which may also be used for other monogenetic diseases beside complex diseases to provide suitable therapeutic targets.