Topological analysis of blood differentially expressed genes in protein-protein interaction network in type 1 diabetes

authors:

avatar Nahid Safari-Alighiarloo , avatar Mohammad Taghizadeh , avatar Mohammad Tabatabaei , * , avatar Soodeh Shahsavari , avatar Saeed Namaki , avatar Soheila Khoda karim , avatar Mostafa Rezaei-Tavirani ORCID , avatar A. li Rashidy-Pour

Koomesh: Vol.18, issue 1; 86-94
published online: September 28, 2016
article type: issue_documents_importer
received: December 04, 2015
accepted: May 02, 2016

how to cite: Safari-Alighiarloo N, Taghizadeh M, Tabatabaei M, Shahsavari S, Namaki S, et al. Topological analysis of blood differentially expressed genes in protein-protein interaction network in type 1 diabetes. koomesh. 2016;18(1):e151149. 

Abstract

Introduction: Type 1 diabetes (T1D) results from autoimmune destruction of insulin-producing beta cells in pancreatic islets of Langerhans. To develop efficient treatments for T1D, it is required to identify suitable therapeutic markers. Systems biology offers approaches to better understanding of functional elements in the disease. Our aim was to investigate larger number of candidate markers in T1D by topological analysis of constructed PPI based on gene expression. Materials and Methods: In this study, gene expression profile of peripheral blood mononuclear cells from newly diagnosed type 1 diabetic children was prepared from Gene Expression Omnibus and analyzed to get differentially expressed genes. Then, these genes were mapped to PPIs data to construct related subnetwork. Five topological features were calculated by Cytoscape software.  Finally, degree, betweenness and closeness centrality features were utilized to identify candidate markers. Results: 2467 differentially expression genes were obtained by statistical analyzing of gene expression profile in which 1024 were upregulated and 1443 were downregulated. After mapping these genes on PPI network, there was constructed subnetwork with 949 nodes and 1776 edges. By topological analysis of the subnetwork, we determined high degree nodes (hub) and high betweenness nodes (bottleneck). Then, 9 hub-bottleneck proteins that were more central (high closeness centrality) in the subnetwork were identified and introduced as candidate markers. Conclusion: The obtained markers from network via systemic view can be considered as new diagnostic markers and potential therapeutic targets for T1D

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