Department of Infectious Diseases, Tianjin Second People's Hospital, Tianjin, China.
Tianjin Institute of Hepatology, Tianjin Second People's Hospital, Tianjin, China.
Front Cell Infect Microbiol. 2023 Jul 13;13:1216176. doi: 10.3389/fcimb.2023.1216176. eCollection 2023.
This study aimed to identify biomarkers for acute and chronic brucellosis using advanced proteomic and bioinformatic methods.
Blood samples from individuals with acute brucellosis, chronic brucellosis, and healthy controls were analyzed. Proteomic techniques and differential expression analysis were used to identify differentially expressed proteins. Co-expression modules associated with brucellosis traits were identified using weighted gene co-expression network analysis (WGCNA).
763 differentially expressed proteins were identified, and two co-expression modules were found to be significantly associated with brucellosis traits. 25 proteins were differentially expressed in all three comparisons, and 20 hub proteins were identified. Nine proteins were found to be both differentially expressed and hub proteins, indicating their potential significance. A random forest model based on these nine proteins showed good classification performance.
The identified proteins are involved in processes such as inflammation, coagulation, extracellular matrix regulation, and immune response. They provide insights into potential therapeutic targets and diagnostic biomarkers for brucellosis. This study improves our understanding of brucellosis at the molecular level and paves the way for further research in targeted therapies and diagnostics.
本研究旨在使用先进的蛋白质组学和生物信息学方法鉴定急性和慢性布氏菌病的生物标志物。
分析了急性布氏菌病、慢性布氏菌病和健康对照个体的血液样本。使用蛋白质组学技术和差异表达分析来鉴定差异表达蛋白。使用加权基因共表达网络分析（WGCNA）鉴定与布氏菌病特征相关的共表达模块。
鉴定出 763 个差异表达蛋白，发现两个共表达模块与布氏菌病特征显著相关。在所有三个比较中，有 25 个蛋白差异表达，有 20 个枢纽蛋白被鉴定出来。有 9 个蛋白既差异表达又枢纽蛋白，表明它们具有潜在的重要性。基于这 9 个蛋白的随机森林模型表现出良好的分类性能。
鉴定出的蛋白参与炎症、凝血、细胞外基质调节和免疫反应等过程。它们为布氏菌病的潜在治疗靶点和诊断生物标志物提供了新的思路。本研究提高了我们对布氏菌病的分子水平的理解，并为靶向治疗和诊断的进一步研究铺平了道路。
