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Rabieian R, Abedi M, Gheisari Y. Central Nodes in Protein Interaction Networks Drive Critical Functions in Transforming Growth Factor Beta-1 Stimulated Kidney Cells. Cell J. 2016;18(4):514-531doi: 10.22074/cellj.2016.4718.
Rabieian, R., Abedi, M., & Gheisari, Y. (2017). Central Nodes in Protein Interaction Networks Drive Critical Functions in Transforming Growth Factor Beta-1 Stimulated Kidney Cells. Cell journal, 18(4), 514-531. https://doi.org/10.22074/cellj.2016.4718
Rabieian, Reyhaneh, et al. "Central Nodes in Protein Interaction Networks Drive Critical Functions in Transforming Growth Factor Beta-1 Stimulated Kidney Cells." Cell journal vol. 18,4 (2017): 514-531. doi: https://doi.org/10.22074/cellj.2016.4718
Rabieian R, Abedi M, Gheisari Y. Central Nodes in Protein Interaction Networks Drive Critical Functions in Transforming Growth Factor Beta-1 Stimulated Kidney Cells. Cell J. 2017;18(4):514-531. doi: 10.22074/cellj.2016.4718. Epub 2016 Sep 26. PMID: 28042536; PMCID: PMC5086330.
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Cell J. 2017;18(4):514-531. doi: 10.22074/cellj.2016.4718. Epub 2016 Sep 26.

Central Nodes in Protein Interaction Networks Drive Critical Functions in Transforming Growth Factor Beta-1 Stimulated Kidney Cells.

Cell journal

Reyhaneh Rabieian, Maryam Abedi, Yousof Gheisari

Affiliations

  1. 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.

PMID: 28042536 PMCID: PMC5086330 DOI: 10.22074/cellj.2016.4718

Abstract

OBJECTIVE: Despite the huge efforts, chronic kidney disease (CKD) remains as an unsolved problem in medicine. Many studies have shown a central role for transforming growth factor beta-1 (TGFβ-1) and its downstream signaling cascades in the pathogenesis of CKD. In this study, we have reanalyzed a microarray dataset to recognize critical signaling pathways controlled by TGFβ-1.

MATERIALS AND METHODS: This study is a bioinformatics reanalysis for a microarray data. The GSE23338 dataset was downloaded from the gene expression omnibus (GEO) database which assesses the mRNA expression profile of TGFβ-1 treated human kidney cells after 24 and 48 hours incubation. The protein interaction networks for differentially expressed (DE) genes in both time points were constructed and enriched. In addition, by network topology analysis, genes with high centrality were identified and then pathway enrichment analysis was performed with either the total network genes or with the central nodes.

RESULTS: We found 110 and 170 genes differentially expressed in the time points 24 and 48 hours, respectively. As the genes in each time point had few interactions, the networks were enriched by adding previously known genes interacting with the differentially expressed ones. In terms of degree, betweenness, and closeness centrality parameters 62 and 60 nodes were considered to be central in the enriched networks of 24 hours and 48 hours treatment, respectively. Pathway enrichment analysis with the central nodes was more informative than those with all network nodes or even initial DE genes, revealing key signaling pathways.

CONCLUSION: We here introduced a method for the analysis of microarray data that integrates the expression pattern of genes with their topological properties in protein interaction networks. This holistic novel approach allows extracting knowledge from raw bulk

Keywords: Chronic Kidney Disease; Microarray Analysis; Protein Interaction Maps; Systems Biology; Transforming Growth Factor Beta-1

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