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中华肾病研究电子杂志 ›› 2024, Vol. 13 ›› Issue (03) : 134 -144. doi: 10.3877/cma.j.issn.2095-3216.2024.03.003

论著

基于生物信息学分析小鼠急性肾损伤和急性肺损伤的枢纽基因
林玲1, 李京儒2, 沈瑞华1, 林惠1, 乔晞1,()   
  1. 1. 030001 太原,山西医科大学第二医院肾内科;030001 太原,山西医科大学;030001 太原,山西省肾脏病研究所;030001 太原,山西医科大学肾脏疾病研究中心
    2. 030001 太原,山西医科大学
  • 收稿日期:2024-03-21 出版日期:2024-06-28
  • 通信作者: 乔晞
  • 基金资助:
    国家自然科学基金资助项目(81970643); 山西省海外归国人员科学活动基金项目(2017-29); 中国山西省留学基金委资助课题(2020-186)

Bioinformatics analysis of hub genes of acute kidney injury and acute lung injury in mice

Ling Lin1, Jingru Li2, Ruihua Shen1, Hui Lin1, Xi Qiao1,()   

  1. 1. Department of Nephrology, Second Hospital of Shanxi Medical University; Shanxi Medical University; Shanxi Kidney Disease Institute; Kidney Research Center of Shanxi Medical University; Taiyuan 030001, Shanxi Province, China
    2. Shanxi Medical University
  • Received:2024-03-21 Published:2024-06-28
  • Corresponding author: Xi Qiao
引用本文:

林玲, 李京儒, 沈瑞华, 林惠, 乔晞. 基于生物信息学分析小鼠急性肾损伤和急性肺损伤的枢纽基因[J]. 中华肾病研究电子杂志, 2024, 13(03): 134-144.

Ling Lin, Jingru Li, Ruihua Shen, Hui Lin, Xi Qiao. Bioinformatics analysis of hub genes of acute kidney injury and acute lung injury in mice[J]. Chinese Journal of Kidney Disease Investigation(Electronic Edition), 2024, 13(03): 134-144.

目的

基于生物信息学方法对小鼠急性肾损伤(AKI)和急性肺损伤(ALI)进行基因表达谱数据挖掘,旨在探讨AKI和ALI之间关系。

方法

从基因表达综合数据库(GEO)中分别下载AKI和ALI基因表达谱,去除批次效应后进行差异分析。利用基因本体(GO)和京都基因和基因组百科全书(KEGG)数据库对AKI和ALI的差异表达基因(DEGs)进行富集分析。使用STRING数据库构建蛋白-蛋白相互作用(PPI)网络后,检测网络的核心模块和枢纽基因。通过使用Cytoscape插件的算法获得最佳枢纽基因后,进行免疫细胞浸润分析。

结果

AKI和ALI建模后4~6 h、24 h分别筛选出交集DEGs为114个、62个,GO和KEGG通路富集分析提示其与免疫应答和炎症信号通路有关。进一步分析,在AKI和ALI建模后4~6 h筛选出5个最佳枢纽基因,包括IL-6IL-1βCCL2TLR2CXCL10;AKI和ALI建模后24 h筛选出5个最佳枢纽基因,包括IL-6IL-1βTNFTLR2CXCL10。在AKI建模后4~6 h,枢纽基因表达与中性粒细胞和M1巨噬细胞呈正相关,而与记忆CD4 T细胞、记忆CD8 T细胞呈负相关。在ALI建模后4~6 h,枢纽基因表达与树突状细胞和单核细胞呈正相关,而与未成熟树突状细胞和嗜酸性粒细胞呈负相关。在AKI建模后24 h,枢纽基因表达与中性粒细胞呈正相关,而与初始B细胞和调节性T细胞呈负相关。在ALI建模后24 h,枢纽基因表达与M1、M2巨噬细胞和中性粒细胞等呈正相关,而与初始CD8 T细胞和嗜酸性粒细胞呈负相关。

结论

小鼠AKI和ALI建模后4~6 h和24 h具有相似的枢纽基因和信号通路,两种疾病分子机制密切相关,尚需深入研究以探讨潜在的治疗靶点。

Objective

Data mining of gene expression profiles of acute kidney injury (AKI) and acute lung injury (ALI) in mice was conducted based on bioinformatics analysis in order to study the relationship between AKI and ALI.

Methods

AKI and ALI gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. After the batch effect was removed, difference analysis was performed. Enrichment analysis of differentially expressed genes (DEGs) in AKI and ALI with the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. The STRING database was used to build a protein-protein interaction (PPI) network in which the core modules and hub genes were detected. The best hub genes were obtained by using six algorithms from Cytoscape′s plugin cytoHubba. Finally, Immune cell infiltration analysis was conducted to analyze the relationship between optimal hub genes and immune cells.

Results

A total of 114 overlapping DEGs were screened out at 4-6 h after establishment of AKI and ALI. A total of 62 overlapping DEGs were screened out at 24 h after establishment of AKI and ALI. The GO and KEGG enrichment analysis suggested that the overlapping DEGs were mainly related to immune and inflammatory signaling pathways. Finally, five optimal hub genes including IL-6, IL-1β, CCL2, TLR2, and CXCL10 were screened out at 4-6 h after establishment of AKI and ALI. Besides, five optimal hub genes including IL-6, IL-1β, TNF, TLR2, and CXCL10 were also screened out at 24 h after establishment of AKI and ALI. At 4-6 h after establishment of AKI, the hub genes expression were positively correlated with neutrophils and M1 macrophages, but negatively correlated with memory CD4+ T cells and memory CD8+ T cells. At 4-6 h after establishment of ALI, expression of the hub genes were positively correlated with dendritic cells and monocytes, but negatively correlated with immature dendritic cells and eosinophils. At 24 h after establishment of AKI, expression of the hub genes were positively correlated with neutrophils, and negatively correlated with naive B cells and regulatory T cells. At 24 h after establishment of ALI, expression of the hub genes were positively correlated with M1 and M2 macrophages and neutrophils, but negatively correlated with naive CD8+ T cells and eosinophils.

Conclusion

The AKI and ALI models showed similar hub genes and signaling pathways at 4-6 h and 24 h after models establishemnt. The molecular mechanisms of the two diseases were closely related, and further studies are needed to further explore the potential therapeutic targets.

图1 研究设计流程图注:AKI:急性肾损伤;ALI:急性肺损伤;DEGs:差异表达基因;GO:基因本体论;KEGG:京都基因和基因组百科全书;PPI:蛋白-蛋白相互作用
表1 数据集详细信息
图2 急性肾损伤和急性肺损伤后4~6 h的差异基因表达注:A:AKI后4~6 h差异基因表达火山图,红色代表上调基因、蓝色代表下调基因;B:ALI后4~6 h差异基因表达火山图,红色代表上调基因、蓝色代表下调基因;C:AKI后4~6 h差异基因表达热图,显示DEGs表达AKI实验组与对照组相比情况,红色为高表达、蓝色为低表达,颜色越深代表程度越高;D:ALI后4~6 h差异基因表达热图,显示DEGs表达ALI实验组与对照组相比情况,红色为高表达、蓝色为低表达,颜色越深代表程度越高;E:AKI与ALI后4~6 h的交集差异表达基因(有114个交集DEGs)
图3 急性肾损伤和急性肺损伤后24 h的差异基因表达注:A:AKI后24 h差异基因表达火山图,红色代表上调基因、蓝色代表下调基因;B:ALI后24 h差异基因表达火山图,红色代表上调基因、蓝色代表下调基因;C:AKI后24 h差异基因表达热图,显示DEGs表达AKI实验组与对照组相比情况,红色为高表达、蓝色为低表达,颜色越深代表程度越高;D:ALI后24 h差异基因表达热图,显示DEGs表达ALI实验组与对照组相比情况,红色为高表达、蓝色为低表达,颜色越深代表程度越高;E:AKI与ALI后24 h的交集差异表达基因(62个交集DEGs)
图4 基因本体论与京都基因和基因组百科全书富集分析注:A:AKI和ALI后4~6 h交集DEGs的GO功能富集分析,其中:白细胞迁移(P=5.03×10-30)、中性粒细胞迁移(P=1.11×10-27)、白细胞趋化作用(P=1.40×10-28);B:AKI和ALI后4~6 h交集DEGs的KEGG通路富集分析,其中:肿瘤坏死因子(TNF)信号通路(P=8.22×10-19)、细胞因子与细胞因子受体的相互作用(P=1.17×10-14)、趋化因子信号通路(P=4.57×10-8);C:AKI和ALI后24 h交集DEGs的GO功能富集分析,其中:粒细胞的趋化作用(P=1.92×10-18)、白细胞迁移(P=6.93×10-18)、趋化因子的产生(P=7.13×10-12);D:AKI和ALI后24 h交集DEGs的KEGG通路富集分析,其中:TNF信号通路(P=3.93×10-12)、Toll样受体(TLR)信号通路(P=5.50×10-7)和细胞因子与细胞因子受体的相互作用(P=4.83×10-11),红色表示P值越小,蓝色表示P值越大
图5 蛋白-蛋白相互作用网络及重要的基因聚类模块和枢纽基因共表达网络(4~6 h)注:A:AKI和ALI后4~6 h交集DEGs的PPI网络通过Cytoscape软件中MCODE插件分析(获得7个核心模块);B和C:AKI和ALI后4~6 h核心模块的GO功能富集分析[白细胞迁移(P=2.80×10-25)、趋化因子介导的信号通路(P=1.34×10-18)和细胞因子介导的信号通路(P=2.65×10-18)]和KEGG富集分析[肿瘤坏死因子信号通路(P=5.48×10-11)、细胞因子与细胞因子受体的相互作用(P=1.24×10-13)和趋化因子信号通路(P=1.01×10-10)];D:Cytoscape软件中CytoHubba插件的6种算法所有交点的矩阵布局,每个条形图上方的数字表示每列的基因数量,左下横条图为各个集合的大小,右下点线图为各个集合的交集情况(黑点表示有、灰色点表示无),第2列为6种算法的重叠区即表示获得5个最佳枢纽基因(IL-6IL-1βCCL2TLR2CXCL10);E:通过GeneMANIA对交集枢纽基因及其共表达基因进行分析,图中圆点直径大小表示相关性的强度,圆点内不同颜色代表不同的功能、连线颜色代表不同的网络(图例详示)
图6 蛋白-蛋白相互作用网络及重要的基因聚类模块和枢纽基因共表达网络(24 h)注:AKI和ALI后24 h交集DEGs的PPI网络通过Cytoscape软件中MCODE插件分析(获得1个核心模块);B和C:AKI和ALI后24 h核心模块的GO功能富集分析[中性粒细胞迁移(P=1.95×10-18)、细胞因子介导的信号通路(P=9.31×10-14)和趋化因子介导的信号通路(P=4.80×10-13)]和KEGG富集分析[肿瘤坏死因子信号通路(P=9.03×10-11)、TLR信号通路(P=3.98×10-9)和细胞因子与细胞因子受体的相互作用(P=5.76×10-13)];D:Cytoscape软件中CytoHubba插件的6种算法所有交点的矩阵布局,每个条形图上方的数字表示每列的基因数量,左下横条图为各个集合的大小,右下点线图为各个集合的交集情况(黑点表示有、灰色点表示无),第2列为6种算法的重叠区即表示获得5个最佳枢纽基因(IL-6IL-1βTNFTLR2CXCL10);E:通过GeneMANIA对交集枢纽基因及其共表达基因进行分析,图中圆点直径大小表示相关性的强度,圆点内不同颜色代表不同的功能、连线颜色代表不同的网络(图例详示)
表2 CytoHubba中6种算法前10个枢纽基因(4~6 h)
表3 CytoHubba中6种算法前10个枢纽基因(24 h)
图7 免疫细胞浸润分析(4~6 h)注:A:AKI中各样本免疫细胞浸润情况;B:ALI中各样本免疫细胞浸润情况;C:与对照组相比,AKI中免疫细胞浸润差异分析;D:与对照组相比,ALI中免疫细胞浸润差异分析;E:AKI中最佳枢纽基因与免疫细胞相关性分析;F:ALI中最佳枢纽基因与免疫细胞相关性分析。与对照组相比,aP< 0.05,bP< 0.01,cP< 0.001
图8 免疫细胞浸润分析(24 h)注:A:AKI中各样本免疫细胞浸润情况;B:ALI中各样本免疫细胞浸润情况;C:与对照组相比,AKI中免疫细胞浸润差异分析;D:与对照组相比,ALI中免疫细胞浸润差异分析;E:AKI中最佳枢纽基因与免疫细胞相关性分析;F:ALI中最佳枢纽基因与免疫细胞相关性分析;与对照组相比,aP< 0.05,bP< 0.01,cP< 0.001
图9 枢纽基因的验证注:A:最佳枢纽基因在GSE998622(4~6 h AKI)中的表达情况;B:最佳枢纽基因在GSE9368(4~6 h ALI)中的表达情况;C:最佳枢纽基因在GSE998622(24 h AKI)中的表达情况;D:最佳枢纽基因在GSE109913(24 h ALI)中的表达情况
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