Tuberculosis (TB) is still a leading cause of death worldwide. Treatments remain unsatisfactory due to an incomplete understanding of the underlying host–pathogen interactions during infection. In the present study, weighted gene co-expression network analysis (WGCNA) was conducted to identify key macrophage modules and hub genes associated with mycobacterial infection. WGCNA was performed combining our own transcriptomic results using Mycobacterium aurum-infected human monocytic macrophages (THP1) with publicly accessible datasets obtained from three types of macrophages infected with seven different mycobacterial strains in various one-to-one combinations. A hierarchical clustering tree of 11,533 genes was built from 198 samples, and 47 distinct modules were revealed. We identified a module, consisting of 226 genes, which represented the common response of host macrophages to different mycobacterial infections that showed significant enrichment in innate immune stimulation, bacterial pattern recognition, and leukocyte chemotaxis. Moreover, by network analysis applied to the 74 genes with the best correlation with mycobacteria infection, we identified the top 10 hub-connecting genes: NAMPT, IRAK2, SOCS3, PTGS2, CCL20, IL1B, ZC3H12A, ABTB2, GFPT2, and ELOVL7. Interestingly, apart from the well-known Toll-like receptor and inflammation-associated genes, other genes may serve as novel TB diagnosis markers and potential therapeutic targets.

中文翻译：

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加权基因共表达网络分析确定与人类巨噬细胞分枝杆菌感染相关的关键模块和集线器基因

结核病（TB）仍然是全球范围内的主要死亡原因。由于对感染期间潜在的宿主-病原体相互作用的不完全了解，治疗仍不能令人满意。在本研究中，进行了加权基因共表达网络分析（WGCNA）以鉴定与分枝杆菌感染相关的关键巨噬细胞模块和中枢基因。结合我们自己的转录组结果，使用金黄色分枝杆菌进行了WGCNA感染人类单核细胞巨噬细胞（THP1），其公开数据集是从三种类型的巨噬细胞中以七种不同的一对一组合感染了三种不同的分枝杆菌菌株。从198个样本中构建了11,533个基因的层次聚类树，揭示了47个不同的模块。我们确定了一个模块，该模块由226个基因组成，代表宿主巨噬细胞对不同分枝杆菌感染的常见反应，这些分枝杆菌在先天免疫刺激，细菌模式识别和白细胞趋化性上显示出明显的富集。此外，通过将网络分析应用于与分枝杆菌感染最相关的74个基因，我们确定了排名前10位的枢纽连接基因：NAMPT，IRAK2，SOCS3，PTGS2，CCL20，IL1B，ZC3H12A，ABTB2，GFPT2和ELOVL7。有趣的是，除了众所周知的Toll样受体和炎症相关基因外，其他基因还可以作为新型TB诊断标记和潜在的治疗靶标。