Tian Zhengyun, Wang Weiwei, Hao Hao, Kong Li, Li Guochen
Emergency Intensive Care Medicine Center, Shandong University of Traditional Chinese Medicine Affiliated Hospital, Jinan, China.
Special Inspection Department, Shandong University of Traditional Chinese Medicine Affiliated Hospital, Jinan, China.
Biotechnol Appl Biochem. 2025 Sep 9. doi: 10.1002/bab.70049.
Differentially expressed genes (DEGs) have been known to provide important information on disease mechanisms and potential therapeutic targets. The traditional Chinese medicine (TCM) offers a large reservoir of bioactive compounds that could modulate at these targets. This study is an attempt to investigate the biomarkers in Sepsis and COVID-19 using gene expression analysis and molecular modeling validation of TCM-derived candidate compounds targeting key DEGs associated with sepsis.
Gene expression data were obtained from NCBI, and limma package in R Studio was used to identify DEGs. Functional annotation was followed by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Protein-protein interaction (PPI) networks were created using STRING, and key hub proteins identified utilizing Cytoscape. Molecular docking was conducted using 216 bioactive compounds obtained from TCM databases against target proteins. To study binding stability, molecular dynamics (MD) simulations of 100 ns were performed using GROMACS on top ranked protein-ligand complexes.
A total of 432 key DEGs were functionally enriched in disease related pathways. Bioinformatics analysis identified the RRM2, AURKB, and CDK1 as hub proteins that could serve as promising therapeutic agents. Salvianolic Acid C, Hesperidin, and Gallocatechin Gallate were lead TCM compounds which showed strong binding affinity to these targets on the basis of molecular docking. Selected protein-ligand complexes were stable according to MD simulations.
The current study indicates the possibility of TCM compounds to target DEGs crucial in sepsis pathology. The integrated bioinformatics approach establishes an approach to identify novel drug candidates, which need further experimental validation.
已知差异表达基因（DEGs）能提供有关疾病机制和潜在治疗靶点的重要信息。中药提供了大量可作用于这些靶点的生物活性化合物。本研究旨在通过基因表达分析以及对靶向与脓毒症相关关键DEGs的中药衍生候选化合物进行分子模拟验证，来探究脓毒症和新冠肺炎中的生物标志物。
从NCBI获取基因表达数据，并使用R Studio中的limma软件包来识别DEGs。接着通过基因本体论（GO）和京都基因与基因组百科全书（KEGG）通路富集进行功能注释。使用STRING创建蛋白质 - 蛋白质相互作用（PPI）网络，并利用Cytoscape识别关键枢纽蛋白。针对靶蛋白，使用从中药数据库中获取的216种生物活性化合物进行分子对接。为研究结合稳定性，使用GROMACS对排名靠前的蛋白质 - 配体复合物进行100纳秒的分子动力学（MD）模拟。
共有432个关键DEGs在疾病相关通路中功能富集。生物信息学分析确定RRM2、AURKB和CDK1为枢纽蛋白，可作为有前景的治疗药物。丹酚酸C、橙皮苷和表没食子儿没食子酸酯是主要的中药化合物，基于分子对接显示它们与这些靶点具有很强的结合亲和力。根据MD模拟结果选定的蛋白质 - 配体复合物是稳定的。
当前研究表明中药化合物有可能靶向脓毒症病理中关键的DEGs。综合生物信息学方法建立了一种识别新型候选药物的途径，这需要进一步的实验验证。
