Systems biology approaches toward autosomal dominant polycystic kidney disease (ADPKD)
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 GSE78...
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doaj-bb593f340d1a42eb871a6c78dd1415762020-11-25T03:43:55ZengWileyClinical and Translational Medicine2001-13262020-01-01911810.1186/s40169-019-0254-5Systems biology approaches toward autosomal dominant polycystic kidney disease (ADPKD)Ilnaz Rahimmanesh0Razieh Fatehi1Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical SciencesDepartment of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical SciencesAbstract 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.http://link.springer.com/article/10.1186/s40169-019-0254-5Autosomal dominant polycystic kidney diseaseMicroarrayProtein interaction networkSignal pathway |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ilnaz Rahimmanesh Razieh Fatehi |
spellingShingle |
Ilnaz Rahimmanesh Razieh Fatehi Systems biology approaches toward autosomal dominant polycystic kidney disease (ADPKD) Clinical and Translational Medicine Autosomal dominant polycystic kidney disease Microarray Protein interaction network Signal pathway |
author_facet |
Ilnaz Rahimmanesh Razieh Fatehi |
author_sort |
Ilnaz Rahimmanesh |
title |
Systems biology approaches toward autosomal dominant polycystic kidney disease (ADPKD) |
title_short |
Systems biology approaches toward autosomal dominant polycystic kidney disease (ADPKD) |
title_full |
Systems biology approaches toward autosomal dominant polycystic kidney disease (ADPKD) |
title_fullStr |
Systems biology approaches toward autosomal dominant polycystic kidney disease (ADPKD) |
title_full_unstemmed |
Systems biology approaches toward autosomal dominant polycystic kidney disease (ADPKD) |
title_sort |
systems biology approaches toward autosomal dominant polycystic kidney disease (adpkd) |
publisher |
Wiley |
series |
Clinical and Translational Medicine |
issn |
2001-1326 |
publishDate |
2020-01-01 |
description |
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. |
topic |
Autosomal dominant polycystic kidney disease Microarray Protein interaction network Signal pathway |
url |
http://link.springer.com/article/10.1186/s40169-019-0254-5 |
work_keys_str_mv |
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