Abstract：
UNASSIGNED: Circular RNAs (circRNAs) have been shown to play critical roles in the initiation and progression of chronic glomerulonephritis (CGN), while their role from mesangial cells in contributing to the pathogenesis of CGN is rarely understood. Our study aims to explore the potential functions of mesangial cell-derived circRNAs using RNA sequencing (RNA-seq) and bioinformatics analysis.
UNASSIGNED: Mouse mesangial cells (MMCs) were stimulated by lipopolysaccharide (LPS) to establish an in vitro model of CGN. Pro-inflammatory cytokines and cell cycle stages were detected by Enzyme-linked immunosorbent assay (ELISA) and Flow Cytometry experiment, respectively. Subsequently, differentially expressed circRNAs (DE-circRNAs) were identified by RNA-seq. GEO microarrays were used to identify differentially expressed mRNAs (DE-mRNAs) between CGN and healthy populations. Weighted co-expression network analysis (WGCNA) was utilized to explore clinically significant modules of CGN. CircRNA-associated CeRNA networks were constructed by bioinformatics analysis. The hub mRNAs from CeRNA network were identified using LASSO algorithms. Furthermore, utilizing protein-protein interaction (PPI), gene ontology (GO), pathway enrichment (KEGG), and GSEA analyses to explore the potential biological function of target genes from CeRNA network. In addition, we investigated the relationships between immune cells and hub mRNAs from CeRNA network using CIBERSORT.
UNASSIGNED: The expression of pro-inflammatory cytokines IL-1β, IL-6, and TNF-α was drastically increased in LPS-induced MMCs. The number of cells decreased significantly in the G1 phase but increased significantly in the S/G2 phase. A total of 6 DE-mRNAs were determined by RNA-seq, including 4 up-regulated circRNAs and 2 down-regulated circRNAs. WGCNA analysis identified 1747 DE-mRNAs of the turquoise module from CGN people in the GEO database. Then, the CeRNA networks, including 6 circRNAs, 38 miRNAs, and 80 mRNAs, were successfully constructed. The results of GO and KEGG analyses revealed that the target mRNAs were mainly enriched in immune, infection, and inflammation-related pathways. Furthermore, three hub mRNAs (BOC, MLST8, and HMGCS2) from the CeRNA network were screened using LASSO algorithms. GSEA analysis revealed that hub mRNAs were implicated in a great deal of immune system responses and inflammatory pathways, including IL-5 production, MAPK signaling pathway, and JAK-STAT signaling pathway. Moreover, according to an evaluation of immune infiltration, hub mRNAs have statistical correlations with neutrophils, plasma cells, monocytes, and follicular helper T cells.
UNASSIGNED: Our findings provide fundamental and novel insights for further investigations into the role of mesangial cell-derived circRNAs in CGN pathogenesis.
UNASSIGNED: Mouse mesangial cells (MMCs) were stimulated by lipopolysaccharide (LPS) to establish an in vitro model of CGN. Pro-inflammatory cytokines and cell cycle stages were detected by Enzyme-linked immunosorbent assay (ELISA) and Flow Cytometry experiment, respectively. Subsequently, differentially expressed circRNAs (DE-circRNAs) were identified by RNA-seq. GEO microarrays were used to identify differentially expressed mRNAs (DE-mRNAs) between CGN and healthy populations. Weighted co-expression network analysis (WGCNA) was utilized to explore clinically significant modules of CGN. CircRNA-associated CeRNA networks were constructed by bioinformatics analysis. The hub mRNAs from CeRNA network were identified using LASSO algorithms. Furthermore, utilizing protein-protein interaction (PPI), gene ontology (GO), pathway enrichment (KEGG), and GSEA analyses to explore the potential biological function of target genes from CeRNA network. In addition, we investigated the relationships between immune cells and hub mRNAs from CeRNA network using CIBERSORT.
UNASSIGNED: The expression of pro-inflammatory cytokines IL-1β, IL-6, and TNF-α was drastically increased in LPS-induced MMCs. The number of cells decreased significantly in the G1 phase but increased significantly in the S/G2 phase. A total of 6 DE-mRNAs were determined by RNA-seq, including 4 up-regulated circRNAs and 2 down-regulated circRNAs. WGCNA analysis identified 1747 DE-mRNAs of the turquoise module from CGN people in the GEO database. Then, the CeRNA networks, including 6 circRNAs, 38 miRNAs, and 80 mRNAs, were successfully constructed. The results of GO and KEGG analyses revealed that the target mRNAs were mainly enriched in immune, infection, and inflammation-related pathways. Furthermore, three hub mRNAs (BOC, MLST8, and HMGCS2) from the CeRNA network were screened using LASSO algorithms. GSEA analysis revealed that hub mRNAs were implicated in a great deal of immune system responses and inflammatory pathways, including IL-5 production, MAPK signaling pathway, and JAK-STAT signaling pathway. Moreover, according to an evaluation of immune infiltration, hub mRNAs have statistical correlations with neutrophils, plasma cells, monocytes, and follicular helper T cells.
UNASSIGNED: Our findings provide fundamental and novel insights for further investigations into the role of mesangial cell-derived circRNAs in CGN pathogenesis.
摘要：
■环状RNA（circularRNAs，circRNAs）已被证明在慢性肾小球肾炎（CGN）的发生和发展中起关键作用，而肾小球系膜细胞在CGN发病机制中的作用却鲜为人知。我们的研究旨在使用RNA测序（RNA-seq）和生物信息学分析探索肾小球系膜细胞来源的circRNAs的潜在功能。
■用脂多糖（LPS）刺激小鼠肾小球系膜细胞（MMC），以建立CGN的体外模型。酶联免疫吸附试验(ELISA)和流式细胞术实验检测促炎细胞因子和细胞周期,分别。随后，通过RNA-seq鉴定差异表达的circRNAs(DE-circRNAs)。GEO微阵列用于鉴定CGN和健康群体之间差异表达的mRNA(DE-mRNA)。加权共表达网络分析（WGCNA）用于探索CGN的临床重要模块。通过生物信息学分析构建CircRNA相关CeRNA网络。使用LASSO算法鉴定来自CeRNA网络的hubmRNA。此外，利用蛋白质-蛋白质相互作用(PPI)，基因本体论(GO)，途径富集（KEGG），和GSEA分析从CeRNA网络中探索靶基因的潜在生物学功能。此外,我们使用CIBERSORT研究了免疫细胞与CeRNA网络中hubmRNA之间的关系。
■促炎细胞因子IL-1β的表达，IL-6和TNF-α在LPS诱导的MMC中急剧增加。细胞数在G1期明显减少，在S/G2期明显增多。通过RNA-seq确定了总共6个DE-mRNA，包括4个上调的circRNAs和2个下调的circRNAs。WGCNA分析确定了GEO数据库中CGN人的绿松石模块的1747个DE-mRNA。然后，CeRNA网络，包括6个circRNAs，38个miRNAs,和80个mRNA，成功建造。GO和KEGG分析结果表明，靶mRNA主要富集在免疫、感染,和炎症相关途径。此外，三个中心mRNA(BOC，使用LASSO算法筛选来自CeRNA网络的MLST8和HMGCS2）。GSEA分析显示hubmRNAs参与了大量的免疫系统反应和炎症通路,包括IL-5的生产，MAPK信号通路,和JAK-STAT信号通路。此外，根据对免疫浸润的评估，hubmRNA与中性粒细胞有统计相关性，浆细胞,单核细胞,和滤泡辅助性T细胞。
■我们的发现为进一步研究肾小球系膜细胞来源的circRNAs在CGN发病机制中的作用提供了基础和新颖的见解。
■用脂多糖（LPS）刺激小鼠肾小球系膜细胞（MMC），以建立CGN的体外模型。酶联免疫吸附试验(ELISA)和流式细胞术实验检测促炎细胞因子和细胞周期,分别。随后，通过RNA-seq鉴定差异表达的circRNAs(DE-circRNAs)。GEO微阵列用于鉴定CGN和健康群体之间差异表达的mRNA(DE-mRNA)。加权共表达网络分析（WGCNA）用于探索CGN的临床重要模块。通过生物信息学分析构建CircRNA相关CeRNA网络。使用LASSO算法鉴定来自CeRNA网络的hubmRNA。此外，利用蛋白质-蛋白质相互作用(PPI)，基因本体论(GO)，途径富集（KEGG），和GSEA分析从CeRNA网络中探索靶基因的潜在生物学功能。此外,我们使用CIBERSORT研究了免疫细胞与CeRNA网络中hubmRNA之间的关系。
■促炎细胞因子IL-1β的表达，IL-6和TNF-α在LPS诱导的MMC中急剧增加。细胞数在G1期明显减少，在S/G2期明显增多。通过RNA-seq确定了总共6个DE-mRNA，包括4个上调的circRNAs和2个下调的circRNAs。WGCNA分析确定了GEO数据库中CGN人的绿松石模块的1747个DE-mRNA。然后，CeRNA网络，包括6个circRNAs，38个miRNAs,和80个mRNA，成功建造。GO和KEGG分析结果表明，靶mRNA主要富集在免疫、感染,和炎症相关途径。此外，三个中心mRNA(BOC，使用LASSO算法筛选来自CeRNA网络的MLST8和HMGCS2）。GSEA分析显示hubmRNAs参与了大量的免疫系统反应和炎症通路,包括IL-5的生产，MAPK信号通路,和JAK-STAT信号通路。此外，根据对免疫浸润的评估，hubmRNA与中性粒细胞有统计相关性，浆细胞,单核细胞,和滤泡辅助性T细胞。
■我们的发现为进一步研究肾小球系膜细胞来源的circRNAs在CGN发病机制中的作用提供了基础和新颖的见解。
