【摘要】 目的 预测并初步验证甲型流感病毒（influenza A virus,IAV）和严重急性呼吸综合征冠状病毒2型（severe acute respiratory syndrome coronavirus 2,SARS-CoV-2）感染引发心脏并发症的共同潜在关键基因。方法 基于GEO(gene expression omnibus)数据库获取心脏并发症差异表达基因（differentially expressed genes,DEGs），采用分层交集策略，首先分别将心脏并发症相关DEGs与两个独立来源的病毒相关基因集（包括GeneCards中与IAV感染相关的3 454个人类基因，以及Human Protein Atlas中与SARS-CoV-2相互作用的333个人类蛋白质编码基因）进行交集分析，随后再将这两个交集的结果进行第二轮交集，进一步确定枢纽基因。采用Lasso回归（lasso regression）、随机森林算法（random forest,RF）和支持向量机算法（support vector machine,SVM）3种机器学习算法对枢纽基因进行筛选。本研究主要采用H1N1病毒感染人心肌细胞系（AC16）和IFITM3-/-基因敲除小鼠模型，对预测基因的表达变化进行体外和体内验证。结果 对3个数据库进行生物信息学分析，筛选出22个枢纽基因。使用3种机器学习算法对枢纽基因进行评估，最终筛选出5个共同的关键基因。随后，通过H1N1感染体外培养的人心肌细胞系（AC16），观察到5种基因的转录水平均呈现出动态变化趋势（P <0.05）。而利用H1N1感染IFITM3-/-基因敲除小鼠的体内实验结果与体外实验结果一致，亦证实这5种基因转录水平均发生动态变化（P <0.05）。结论 通过结合生物信息学分析与机器学习算法，本研究筛选出5个与IAV和SARS-CoV-2感染引起心脏并发症相关的共同关键基因，分别为ACE2、TBK1、NUP210、PUSL1和MEPCE。进一步通过H1N1感染模型的体内和体外实验验证，确认了这些基因均与IAV感染引起的心脏并发症相关。更多还原

【Abstract】 Objective To predict and preliminarily validate potential shared key genes involved in cardiac complications caused by influenza A virus(IAV) and severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) infections. Methods Differentially expressed genes( DEGs) associated with cardiac complications were obtained from the Gene Expression Omnibus(GEO) database. A hierarchical intersection strategy was applied. First,cardiac complication related DEGs were overlapped with 2 independent virus related gene sets: 3 454 human genes linked to IAV infection in GeneCards and 333 human protein-coding genes interacting with SARS-CoV-2 in the Human Protein Atlas. The 2 overlap results were then intersected to yield 22 hub genes. Lasso regression,random forest(RF) and support vector machine algorithms(SVM) were employed to refine this list. Predicted genes were validated in vitro in H1N1-infected human cardiomyocyte AC16 cells and in vivo in IFITM3 knockout mice challenged with H1N1,assessing transcriptional changes. Results A total of 22 hub genes were identified through integrative bioinformatics analysis. Application of the 3 machine learning algorithms resulted in 5 common key genes:ACE2,TBK1,NUP210,PUSL1,and MEPCE. In vitro infection of AC16 cells with H1N1 revealed dynamic transcriptional changes in all 5 genes post-infection(P < 0.05). In vivo experiments using H1N1-infected IFITM3knockout mice confirmed the dynamic m RNA expression changes of these 5 genes, consistent with the in vitro results( P < 0.05). Conclusion By combining multilayered bioinformatics analysis with 3 machine learning approaches,5 common key genes are identified: ACE2,TBK1,NUP210,PUSL1 and MEPCE. Validation in H1N1 infection models confirms their relevance to IAV-induced cardiac complications.更多还原