Wu Fan-Lin, Liu Yin, Jiang He-Wei, Luan Yi-Zhao, Zhang Hai-Nan, He Xiang, Xu Zhao-Wei, Hou Jing-Li, Ji Li-Yun, Xie Zhi, Czajkowsky Daniel M, Yan Wei, Deng Jiao-Yu, Bi Li-Jun, Zhang Xian-En, Tao Sheng-Ce
From the ‡Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China.
§State Key Laboratory of Oncogenes and Related Genes, Shanghai Jiao Tong University, Shanghai 200240, China.
Mol Cell Proteomics. 2017 Aug;16(8):1491-1506. doi: 10.1074/mcp.M116.065771. Epub 2017 Jun 1.
(Mtb) is the causative agent of tuberculosis, the leading cause of death among all infectious diseases. There are 11 eukaryotic-like serine/threonine protein kinases (STPKs) in Mtb, which are thought to play pivotal roles in cell growth, signal transduction and pathogenesis. However, their underlying mechanisms of action remain largely uncharacterized. In this study, using a Mtb proteome microarray, we have globally identified the binding proteins in Mtb for all of the STPKs, and constructed the first STPK protein interaction (KPI) map that includes 492 binding proteins and 1,027 interactions. Bioinformatics analysis showed that the interacting proteins reflect diverse functions, including roles in two-component system, transcription, protein degradation, and cell wall integrity. Functional investigations confirmed that PknG regulates cell wall integrity through key components of peptidoglycan (PG) biosynthesis, MurC. The global STPK-KPIs network constructed here is expected to serve as a rich resource for understanding the key signaling pathways in Mtb, thus facilitating drug development and effective control of Mtb.
结核分枝杆菌(Mtb)是结核病的病原体,结核病是所有传染病中主要的致死原因。Mtb中有11种类真核丝氨酸/苏氨酸蛋白激酶(STPKs),它们被认为在细胞生长、信号转导和发病机制中起关键作用。然而,它们潜在的作用机制在很大程度上仍未明确。在本研究中,我们使用Mtb蛋白质组芯片,全面鉴定了Mtb中所有STPKs的结合蛋白,并构建了首个包含492个结合蛋白和1027个相互作用的STPK蛋白质相互作用(KPI)图谱。生物信息学分析表明,相互作用蛋白反映了多种功能,包括双组分系统、转录、蛋白质降解和细胞壁完整性方面的作用。功能研究证实,PknG通过肽聚糖(PG)生物合成的关键成分MurC调节细胞壁完整性。此处构建的全局STPK-KPIs网络有望成为理解Mtb关键信号通路的丰富资源,从而促进药物开发和对Mtb的有效控制。