The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan, China.
Department of Nephrology, Shanxi Provincial People's Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan, China.
Front Immunol. 2022 Aug 17;13:929138. doi: 10.3389/fimmu.2022.929138. eCollection 2022.
IgA nephropathy (IgAN) is an autoimmune disease that affects people of any age and is an important cause of end-stage renal disease. However, the pathogenesis and pathophysiology of IgAN is not clear. This article aimed to explore the immune-mediated inflammation and genetic mechanisms in IgAN.
The transcriptome sequencing data of IgAN glomeruli in the Gene Expression Omnibus database were downloaded. Single-sample gene set enrichment analysis was used to estimate the immune microenvironment of the merged microarray data and GSE141295. IgAN samples were divided into two clusters by cluster analysis. "limma" and "DEseq2" package in R were used to identify differentially expressed genes (DEGs). The weighted gene co-expression network analysis (WGCNA) was used to identify the co-expression modules related to inflammation in IgAN. R software package "clusterProfiler" was used for enrichment analysis, whereas Short Time-Series Expression Miner (STEM) analysis was used to identify the trend of gene expression. Machine-learn (ML) was performed using the shiny app. Finally, Drug Signatures Database (DSigDB) was used to identify potential molecules for treating IgAN.
The infiltration of macrophages in IgAN glomeruli was increased, whereas CD4+ T cells, especially inducedregulatory T cells (iTregs) were decreased. A total of 1,104 common DEGs were identified from the merged data and GSE141295. Brown module was identified to have the highest inflammatory correlation with IgAN using WGCNA, and 15 hub genes were screened from this module. Among these 15 hub genes, 14 increased with the severity of IgAN inflammation based on STEM analysis. Neural network (nnet) is considered as the best model to predict the severity of IgAN. Fucose identified from DSigDB has a potential biological activity to treat IgAN.
The increase of macrophages and the decrease of iTregs in glomeruli represent the immune-mediated inflammation of IgAN, and fucose may be a potential therapeutic molecule against IgAN because it affects genes involved in the severe inflammation of IgAN.
IgA 肾病（IgAN）是一种影响任何年龄段人群的自身免疫性疾病，也是导致终末期肾病的重要原因。然而，IgAN 的发病机制和病理生理学尚不清楚。本文旨在探讨 IgAN 中的免疫介导炎症和遗传机制。
从基因表达综合数据库（GEO）中下载 IgAN 肾小球的转录组测序数据。采用单样本基因集富集分析（ssGSEA）来估计合并微阵列数据和 GSE141295 的免疫微环境。通过聚类分析将 IgAN 样本分为两个簇。使用 R 语言中的“limma”和“DEseq2”包识别差异表达基因（DEGs）。采用加权基因共表达网络分析（WGCNA）识别与 IgAN 炎症相关的共表达模块。使用 R 软件包“clusterProfiler”进行富集分析，而短时间序列表达挖掘（STEM）分析则用于识别基因表达的趋势。使用 shiny app 进行机器学习（ML）。最后，使用药物特征数据库（DSigDB）来识别潜在的 IgAN 治疗分子。
IgAN 肾小球中巨噬细胞的浸润增加，而 CD4+T 细胞，特别是诱导调节性 T 细胞（iTregs）减少。通过合并数据和 GSE141295 共鉴定出 1104 个共同的 DEGs。通过 WGCNA 发现棕色模块与 IgAN 的炎症相关性最高，并从该模块中筛选出 15 个枢纽基因。在这 15 个枢纽基因中，根据 STEM 分析，有 14 个基因随着 IgAN 炎症的严重程度而增加。神经网络（nnet）被认为是预测 IgAN 严重程度的最佳模型。从 DSigDB 中鉴定出的岩藻糖具有治疗 IgAN 的潜在生物学活性。
肾小球中巨噬细胞的增加和 iTregs 的减少代表了 IgAN 的免疫介导炎症，岩藻糖可能是一种潜在的治疗 IgAN 的治疗分子，因为它影响了参与 IgAN 严重炎症的基因。
