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

• Published in: Journal of diabetes Vol. 9; no. 8; pp. 764 - 777
• Main Authors: Safari‐Alighiarloo, Nahid, Taghizadeh, Mohammad, Tabatabaei, Seyyed Mohammad, Shahsavari, Soodeh, Namaki, Saeed, Khodakarim, Soheila, Rezaei‐Tavirani, Mostafa
• Format: Journal Article
• Language: English
• Published: Melbourne Wiley Publishing Asia Pty Ltd 01.08.2017; John Wiley & Sons, Inc
• Subjects: 1型糖尿病; Diabetes; Diabetes Mellitus, Type 1 - blood; Diabetes Mellitus, Type 1 - genetics; Gene Expression Profiling; Humans; Islets of Langerhans - metabolism; Kinases; Protein Interaction Maps; Proteins; protein–protein interaction network; topology; Transcriptome; type 1 diabetes; 拓扑; 蛋白质‐蛋白质相互作用网络; 转录组; type 1 diabetes; 拓扑; 蛋白质-蛋白质相互作用网络; topology; transcriptome; protein-protein interaction network; 1型糖尿病; 转录组
• ISSN: 1753-0393; 1753-0407
• DOI: 10.1111/1753-0407.12483

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. 背景 1型糖尿病(T1D)是一种自身免疫性疾病,其特征是胰腺β细胞被浸润的免疫细胞所破坏。有观点认为在T1D的进展过程中胰岛β细胞与免疫细胞具有双向的协同作用,但是迄今为止还没有一项系统性的研究对这种可能性进行分析研究。这项研究的目的是使用一种网络生物学方法来解释T1D潜在的分子学机制,并且鉴定出与T1D风险相关的关键基因。 方法 将相互作用组(蛋白质‐蛋白质相互作用[protein–protein interaction,PPI])与转录组数据整合后构建出在外周血单核细胞(peripheral blood mononuclear cells,PBMCs)与胰腺β细胞中差异表达的基因网络。为了获得更有意义的生物信息对网络进行了中心性、模块性与聚类分析。 结果 基因表达谱分析结果表明,β细胞以及PBMCs相关受体中有一些细胞因子与趋化因子,它们在这两种组织之间以PPIs的方式相互作用。功能模块与复合物分析可以解释在这些组织中的大部分的重要生物学途径,例如免疫反应、细胞凋亡、剪接体、蛋白酶体以及蛋白质合成途径。最后,经过鉴定发现Y‐盒结合蛋白1(YBX1)、SRSF蛋白激酶1(SRPK1)、蛋白酶体α1/3亚基(PSMA1/3)、 X‐射线修复交叉补体6(XRCC6)、Cbl原癌基因(CBL)、SRC原癌基因、非受体酪氨酸激酶(SRC)、磷脂酰肌醇‐3激酶调节亚基1(PIK3R1)、磷脂酶Cγ1(PLCG1)、SHC适配蛋白1(SHC1)以及泛素结合酶E2N(UBE2N)是关键的标志物,它们是功能模块与复合物中涉及到的hub‐bottleneck基因。 结论 这项研究针对网络生物标记物提出了新的见解,认为它们可能是潜在的治疗靶点。 Bilateral cooperation between immune cells and pancreatic β‐cells in early type 1 diabetes. For a definition of all gene symbols, refer to Table S4. PBMCs, peripheral blood mononuclear cells. Highlights This is the first study describing the concept of a dialogue between pancreatic islets and the immune system in type 1 diabetes (T1D) from a systems biology point of view. Interactome–transcriptome analysis revealed high centrality genes in the protein–protein interaction networks that are differentially expressed in peripheral blood mononuclear cells and pancreatic β‐cells in T1D. This study delineates potential underlying mechanisms of T1D and identifies key markers for further experimental validation using network‐based biological analysis in two tissues involved in T1D pathogenesis.