Hebei Province Center for Disease Control and Prevention, Shijiazhuang, China.
Hebei Medical University, Shijiazhuang, China.
Viral Immunol. 2024 May;37(4):194-201. doi: 10.1089/vim.2023.0094.
COVID-19 is a highly infectious respiratory disease whose progression has been associated with multiple factors. From SARS-CoV-2 infection to death, biomarkers capable of predicting different disease processes are needed to help us further understand the molecular progression of COVID-19 disease. The aim is to find differentially expressed proteins that are associated with the progression of COVID-19 disease or can be potential biomarkers, and to provide a reference for further understanding of the molecular mechanisms of COVID-19 occurrence, progression, and treatment. Data-independent Acquisition (DIA) proteomics to obtain sample protein expression data, using R language screening differentially expressed proteins. Gene Ontology and Kyoto Encyclopedia for Genes and Genomes analysis was performed on differential proteins and protein-protein interaction (PPI) network was constructed to screen key proteins. A total of 47 differentially expressed proteins were obtained from COVID-19 incubation patients and healthy population (L/H), mainly enriched in platelet-related functions, and complement and coagulation cascade reaction pathways, such as platelet degranulation and platelet aggregation. A total of 42 differential proteins were obtained in clinical and latent phase patients (C/L), also mainly enriched in platelet-related functions and in complement and coagulation cascade reactions, platelet activation pathways. A total of 10 differential proteins were screened in recovery and clinical phase patients (R/C), mostly immune-related proteins. The differentially expressed proteins in different stages of COVID-19 are mostly closely associated with coagulation, and key differential proteins, such as FGA, FGB, FGG, ACTB, PFN1, VCL, SERPZNCL, APOC3, LTF, and DEFA1, have the potential to be used as early diagnostic markers.
新型冠状病毒肺炎（COVID-19）是一种高传染性的呼吸道疾病，其进展与多种因素有关。从 SARS-CoV-2 感染到死亡，需要能够预测不同疾病过程的生物标志物，以帮助我们进一步了解 COVID-19 疾病的分子进展。目的是找到与 COVID-19 疾病进展相关或可能成为潜在生物标志物的差异表达蛋白，为进一步了解 COVID-19 的发生、进展和治疗的分子机制提供参考。采用数据非依赖性采集（DIA）蛋白质组学获取样本蛋白表达数据，使用 R 语言筛选差异表达蛋白。对差异蛋白进行基因本体论和京都基因与基因组百科全书分析，并构建蛋白-蛋白相互作用（PPI）网络筛选关键蛋白。从 COVID-19 潜伏期和健康人群（L/H）中获得 47 个差异表达蛋白，主要富集在血小板相关功能和补体与凝血级联反应途径，如血小板脱颗粒和血小板聚集。从临床潜伏期和潜伏期末期患者（C/L）中获得 42 个差异表达蛋白，主要富集在血小板相关功能和补体与凝血级联反应途径，如血小板激活途径。从恢复期和临床期患者（R/C）中筛选出 10 个差异表达蛋白，主要是免疫相关蛋白。COVID-19 不同阶段的差异表达蛋白主要与凝血密切相关，关键差异蛋白如 FGA、FGB、FGG、ACTB、PFN1、VCL、SERPZNCL、APOC3、LTF 和 DEFA1，具有作为早期诊断标志物的潜力。
