大负荷运动后骨骼肌炎症反应的关键基因时序分析及信号通路鉴定  

Time series analysis of key genes and identification of signaling pathways in skeletal muscle inflammation after high-load exercise

作　　者：张燕 梁隆钰 夏雨 钱艳 丁海丽[1,2] Zhang Yan;Liang Longyu;Xia Yu;Qian Yan;Ding Haili(School of Sports Medicine and Health,Chengdu Sport University,Chengdu 610041,China;Institute of Sports Medicine and Health,Chengdu Sport University,Chengdu 610041,China)

机构地区：[1]成都体育学院运动医学与健康学院,四川成都610041 [2]成都体育学院运动医学与健康研究所,四川成都610041

出　　处：《中国运动医学杂志》2025年第1期29-43,共15页Chinese Journal of Sports Medicine

基　　金：国家自然科学青年基金(82305430);国家自然科学青年基金(81904318);四川省科技厅科技创新创业人才和苗子工程(2023JDRC0100);成都体育学院运动医学与健康学院/运动医学与健康研究所2024-2025年“卓越科研计划”项目(ZYRC2403)。

摘　　要：目的:基于生物信息学探讨大负荷运动后骨骼肌炎症反应基因的时间窗效应,鉴定运动性骨骼肌损伤(exercise-induced muscle damage,EIMD)炎症反应的关键基因与信号通路。方法:48只SD大鼠随机分为对照组(C组,n=8)和运动组(E组,n=40),分别于运动后即刻(E0)、12小时(E12)、24小时(E24)、48小时(E48)、72小时(E72)取腓肠肌,通过转录组测序,筛选差异表达基因,对其基因本体论(gene ontology,GO)和京都基因与基因组百科全书代谢通路数据库(kyotoencyclopedia of genes and genomes,KEGG)的注释进行富集分析。从数据库提取炎症相关基因,与差异表达基因(differentially expressed genes,DEGs)相交识别获取差异表达炎症相关基因(differentially expressed inflammationrelated genes,DEIRGs),基于Mfuzz算法对DEIRGs进行时间序列聚类获得具有相似表达特征的子集,并进行GO和KEGG分析以及蛋白互作网络分析,筛选获得关键DEIRGs,二次功能富集,并进行关键基因表达时间变化分析,确定关键信号通路。结果:大负荷运动后的骨骼肌炎症反应基因经Mfuzz时序聚类获得7个DEIRGs子集(cluster),cluster 5的差异基因表达总体下调,cluster 7的差异基因表达总体上调,cluster 3和4的差异基因表达在E0表达上调后在E12快速下调,cluster 2和6的差异基因表达在E0表达下调后在E12快速上调,cluster 1的差异基因表达在E0上调后在E12快速下调,E24再次上调,筛选鉴定出关键差异表达炎症基因TP53、STAT3、CD44、AKT1、KDR、GJA1、CYCS、HIF1A、IQGAP3、FASN、TFRC,富集在低氧诱导因子1(hypoxia-inducible factor-1,HIF-1)、细胞凋亡、铁死亡、丝裂原活化蛋白激酶(mitogen-activated protein kinase,MAPK)、血管内皮生长因子(vascular endothelial-derived growth factor,VEGF)、磷脂酰肌醇3-激酶-蛋白激酶B(phosphatidylinositol 3-kinase-protein kinaseB,PI3KAkt)、胰岛素抵抗、叉头盒转录因子O(forkhead box O,FoxO)、腺苷酸激活酶(AMP-activated prObjective To examine the time window effect of high-load exerciseon skeletal muscle inflammation genes,and identify the key genes and signaling pathways involved in this process.Methods Forty-eight Sprague-Dawley rats were randomly assigned to a control group(group C,n=8)and an exercise group(group E,n=40).Gastrocnemius muscles were collected immediately(group E0),12h(group E12),24h(group E24),48h(group E48),and 72 h(group E72)after exercise for transcriptome sequencing.Differentially expressed genes(DEGs)were identified,and enrichment analysis was carried out using the Gene Ontology(GO)and the Kyoto Encyclopedia of Genes and Genomes(KEGG)annotations.Meanwhile,inflammation-related genes were obtained from databases,and differentially expressed inflammation-related genes(DEIRGs)were done through identifying their intersection with DEGs.Moreover,the Mfuzz algorithm was used for time series clustering to obtain subsets with similar characteristics.GO and KEGG analyses,along with protein interaction network analysis,were performed to obtain key DEIRGs,followed by secondary functional enrichment to analyze changes in expression of key genes over time and identify key signaling pathways.Results Seven DEIRG clusters were obtained through Mfuzz time series clustering of skeletal muscle inflammation genes after high-load exercise.Overall,the expression of DEGs in cluster 5 was downregulated,while that in cluster 7 was upregulated.The expression of DEGs in clusters 3 and 4 was upregulated at E0 and rapidly downregulated at E12.In contrast,the expression of DEGs in cluster 2 and 6 were downregulated at E0 and rapidly upregulated at E12.The expression of DEGs in cluster 1 was upregulated at E0,rapidly downregulated at E12,and remained upregulated at E24.Screening identified TP53,STAT3,CD44,AKT1,KDR,GJA1,CYCS,HIF1A,IQGAP3,FASN,and TFRC as key DEIRGswhich were enriched in apoptosis,HIF-1,apoptosis,ferroptosis,MAPK,VEGF,PI3K-Akt,insulin resistance,FoxO,AMPK and the JAK-STAT signaling pathway.Conclusion Inflammation-related gene

关 键 词：运动 骨骼肌损伤 差异表达基因 炎症反应 时序分析 

分 类 号：R873[医药卫生—运动医学]