Table_1_Dysregulation of Signaling Pathways Due to Differentially Expressed Genes From the B-Cell Transcriptomes of Systemic Lupus Erythematosus Patients – A Bioinformatics Approach.docx

Systemic lupus erythematosus (SLE) is an autoimmune inflammatory disorder that is clinically complex and has increased production of autoantibodies. Via emerging technologies, researchers have identified genetic variants, expression profiling of genes, animal models, and epigenetic findings that have paved the way for a better understanding of the molecular and genetic mechanisms of SLE. Our current study aimed to illustrate the essential genes and molecular pathways that are potentially involved in the pathogenesis of SLE. This study incorporates the gene expression profiling data of the microarray dataset GSE30153 from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) between the B-cell transcriptomes of SLE patients and healthy controls were screened using the GEO2R web tool. The identified DEGs were subjected to STRING analysis and Cytoscape to explore the protein–protein interaction (PPI) networks between them. The MCODE (Molecular Complex Detection) plugin of Cytoscape was used to screen the cluster subnetworks that are highly interlinked between the DEGs. Subsequently, the clustered DEGs were subjected to functional annotation with ClueGO/CluePedia to identify the significant pathways that were enriched. For integrative analysis, we used GeneGo MetacoreTM, a Cortellis Solution software, to exhibit the Gene Ontology (GO) and enriched pathways between the datasets. Our study identified 4 upregulated and 13 downregulated genes. Analysis of GO and functional enrichment using ClueGO revealed the pathways that were statistically significant, including pathways involving T-cell costimulation, lymphocyte costimulation, negative regulation of vascular permeability, and B-cell receptor signaling. The DEGs were mainly enriched in metabolic networks such as the phosphatidylinositol-3,4,5-triphosphate pathway and the carnitine pathway. Additionally, potentially enriched pathways, such as the signaling pathways induced by oxidative stress and reactive oxygen species (ROS), chemotaxis and lysophosphatidic acid signaling induced via G protein-coupled receptors (GPCRs), and the androgen receptor activation pathway, were identified from the DEGs that were mainly associated with the immune system. Four genes (EGR1, CD38, CAV1, and AKT1) were identified to be strongly associated with SLE. Our integrative analysis using a multitude of bioinformatics tools might promote an understanding of the dysregulated pathways that are associated with SLE development and progression. The four DEGs in SLE patients might shed light on the pathogenesis of SLE and might serve as potential biomarkers in early diagnosis and as therapeutic targets for SLE.

系统性红斑狼疮（systemic lupus erythematosus, SLE）是一种临床复杂的自身免疫性炎症性疾病，患者体内自身抗体产生增多。借助新兴技术，研究人员已明确遗传变异、基因表达谱特征、动物模型及表观遗传学研究发现，为深入理解SLE的分子与遗传机制奠定了基础。本研究旨在阐明参与SLE发病机制的关键基因及分子通路。本研究整合了源自基因表达综合数据库（Gene Expression Omnibus, GEO）的微阵列数据集GSE30153的基因表达谱数据，并通过GEO2R在线工具，筛选出SLE患者B细胞转录组与健康对照之间的差异表达基因（differentially expressed genes, DEGs）。将筛选得到的DEGs提交至STRING分析与Cytoscape软件，以探究彼此间的蛋白质相互作用（protein–protein interaction, PPI）网络。利用Cytoscape的MCODE（分子复合体检测，Molecular Complex Detection）插件，筛选出DEGs间高度互联的聚类子网络。随后，借助ClueGO/CluePedia对聚类得到的DEGs进行功能注释，以鉴定显著富集的通路。为开展整合分析，本研究采用Cortellis解决方案软件旗下的GeneGo Metacore™，以展示数据集间的基因本体（Gene Ontology, GO）术语及富集通路。本研究共鉴定得到4个上调基因与13个下调基因。通过ClueGO开展GO与功能富集分析，得到了具有统计学意义的通路，包括T细胞共刺激通路、淋巴细胞共刺激通路、血管通透性负调控通路以及B细胞受体信号通路。DEGs主要富集于磷脂酰肌醇-3,4,5-三磷酸通路、肉碱通路等代谢相关网络。此外，从主要与免疫系统相关的DEGs中，还鉴定得到潜在富集通路，包括氧化应激与活性氧（reactive oxygen species, ROS）诱导的信号通路、G蛋白偶联受体（G protein-coupled receptors, GPCRs）介导的趋化性及溶血磷脂酸信号通路，以及雄激素受体激活通路。最终明确EGR1、CD38、CAV1及AKT1这4个基因与SLE密切相关。本研究通过多种生物信息学工具开展整合分析，有助于加深对与SLE发生发展相关的失调通路的认知。本研究鉴定得到的4个DEGs可为阐明SLE的发病机制提供新的研究视角，同时有望成为该病早期诊断的潜在生物标志物及治疗靶点。