Identification of New Key Genes for Type 1 Diabetes Through Construction and Analysis of Protein–Protein Interaction Networks Based on Blood and Pancreatic Islet Transcriptomes Publisher Pubmed



Safarialighiarloo N¹ ; Taghizadeh M⁶ ; Tabatabaei SM² ; Shahsavari S³ ; Namaki S⁴ ; Khodakarim S⁵ ; Rezaeitavirani M¹ Show Affiliations
Source: Journal of Diabetes Published:2017

Abstract

Background: Type 1 diabetes (T1D) is an autoimmune disease in which pancreatic β-cells are destroyed by infiltrating immune cells. Bilateral cooperation of pancreatic β-cells and immune cells has been proposed in the progression of T1D, but as yet no systems study has investigated this possibility. The aims of the study were to elucidate the underlying molecular mechanisms and identify key genes associated with T1D risk using a network biology approach. Methods: Interactome (protein–protein interaction [PPI]) and transcriptome data were integrated to construct networks of differentially expressed genes in peripheral blood mononuclear cells (PBMCs) and pancreatic β-cells. Centrality, modularity, and clique analyses of networks were used to get more meaningful biological information. Results: Analysis of genes expression profiles revealed several cytokines and chemokines in β-cells and their receptors in PBMCs, which is supports the dialogue between these two tissues in terms of PPIs. Functional modules and complexes analysis unraveled most significant biological pathways such as immune response, apoptosis, spliceosome, proteasome, and pathways of protein synthesis in the tissues. Finally, Y-box binding protein 1 (YBX1), SRSF protein kinase 1 (SRPK1), proteasome subunit alpha1/ 3, (PSMA1/3), X-ray repair cross complementing 6 (XRCC6), Cbl proto-oncogene (CBL), SRC proto-oncogene, non-receptor tyrosine kinase (SRC), phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1), phospholipase C gamma 1 (PLCG1), SHC adaptor protein1 (SHC1) and ubiquitin conjugating enzyme E2 N (UBE2N) were identified as key markers that were hub-bottleneck genes involved in functional modules and complexes. Conclusions: This study provide new insights into network biomarkers that may be considered potential therapeutic targets. © 2016 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and John Wiley & Sons Australia, Ltd