不同时间序列COVID-19基因表达与基因调控网络构建  

Expression of COVID-19 gene in different time series and construction of gene regulatory network

作　　者：王琪 乔军[4] 王灿 张一凡 卢学春 张升校 孙翔飞 于琦 贺培凤 WANG Qi;QIAO Jun;WANG Can;ZHANG Yi-fan;LU Xue-chun;ZHANG Sheng-xiao;SUN Xiang-fei;YU Qi;HE Pei-feng(Shanxi Medical University,Taiyuan,Shanxi 030000,China;不详)

机构地区：[1]山西医科大学基础医学院,山西太原030000 [2]山西医科大学细胞生理学教育部重点实验室,山西太原030000 [3]山西省临床决策大数据重点实验室,山西太原030000 [4]山西医科大学第二医院风湿免疫科,山西太原030000 [5]山西医科大学管理学院,山西太原030000 [6]山西医科大学第一临床医学院,山西太原030000 [7]中国人民解放军总医院第二医学中心血液病科,北京100853 [8]国家老年疾病临床医学研究中心,北京100853

出　　处：《中华医院感染学杂志》2023年第12期1769-1776,共8页Chinese Journal of Nosocomiology

基　　金：山西省重点实验室基金资助项目(202104010910030);太原市新型冠状病毒(2019-nCoV)防治专项基金资助项目(XG2020-5-06)。

摘　　要：目的探讨新型冠状病毒肺炎(COVID-19)疾病进展相关的潜在调控因素。方法从GEO数据库中获得COVID-19的24个全血转录组样本。使用R包“DSEeq2”识别差异表达的mRNA和LncRNA。利用R包“MaSigPro”和短时间序列表达软件(STEM)识别mRNA和lncRNA的时间表达模式。共表达分析用于发现mRNA-LncRNA的共表达模式。利用TRRUST数据库获取转录因子(TFs)。miRcode、TargetScan、miRTarBase和miRDB构建ceRNA调控网络。利用R包“CIBERSORT”表征免疫细胞浸润情况。结果与健康对照组相比,COVID-19患者在3个伪时间内有3639个差异的mRNA和304个差异的LncRNA。主要功能富集在细胞分裂进展、细胞对干扰素-γ、干扰素-α的反应。CIBERSOR分析显示,CD_(4)^(+)、CD_(8)^(+)和浆细胞在COVID-19发病轨迹中发挥重要作用。随着疾病的进展,识别了1750个具有显著差异表达的mRNA和31个lncRNA。在ceRNA网络中,ALDH1A2/MORC3-FGD5-AS1-miR-144-3p,MORC3/SNTB2-FGD5-AS1-miR-153-3p,YOD1-FGD5-AS1-miR-140-5p和ZBTB44-FGD5-AS1-miR-144被确定为COVID-19的潜在治疗靶点。结论时间序列分析发现的关键基因和调控网络为探索COVID-19的疾病轨迹提供了新的线索。OBJECTIVE To explore the potential regulatory factors associated with disease progression in novel coronavirus pneumonia(COVID-19).METHODS A total of 24 whole-blood transcriptome samples of COVID-19 were obtained from the Gene Expression Omnibus database.DSEeq2 R package was used to identify differentially expressed mRNAs and lncRNAs.MaSigPro and short time-series expression miner(STEM) was used to identify temporal expression patterns of the mRNAs and lncRNAs.Co-expression analysis was used to identify relative pairwise expression of mRNAs-lncRNAs.TRRUST database was used to acquire transcription factors(TFs).miRcode,TargetScan,miRTarBase,and miRDB were used to construct the competing endogenous RNA(ceRNA) regulatory network.The R package "CIBERSORT" was utilized to determine immune cell infiltration.RESULTS Compared with healthy controls,patients with COVID-19 patients had 3639 differentially expressed mRNAs and 304 differentially expressed lncRNAs in three sham times,which were mainly involved in cell division progression,cellular response to interferon-gamma,interferon-alpha.CIBERSOR analysis showed that CD_(4)^(+),CD_(8)^(+),and plasma cells played important roles in the COVID-19 disease trajectory.As the disease progressed,1750 mRNAs and 31 lncRNAs with significant differential expression were identified.In the ceRNA network,ALDH1A2/MORC3-FGD5-AS1-miR-144-3p,MORC3/SNTB2-FGD5-AS1-miR-153-3p,YOD1-FGD5-AS1-miR-140-5p,and ZBTB44-FGD5-AS1-miR-144 were identified as potential therapeutic targets for COVID-19.CONCLUSION The key genes and regulatory networks identified by the time series analysis provide novel clues to explore the disease trajectories of COVID-19.

关 键 词：新冠肺炎 SARS-CoV-2 时间序列分析 ceRNA调控网络 免疫细胞 

分 类 号：R563.1[医药卫生—呼吸系统]