Xu Ye, Banerjee Ruma, Kasibhatla Sunitha, McFadden Johnjoe, Joshi Rajendra, Borah Slater Khushboo
Department of Clinical Laboratory, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; School of Biosciences, University of Surrey, Guildford, United Kingdom.
High Performance Computing: Medical & Bioinformatics Applications Group, Centre for Development of Advanced Computing (C-DAC), Pune, India.
J Biol Chem. 2025 Mar;301(3):108288. doi: 10.1016/j.jbc.2025.108288. Epub 2025 Feb 8.
Mycobacterium tuberculosis (Mtb) is one of the world's successful pathogens that flexibly adapts its metabolic nature during infection of the host, and in response to drugs. Here we used genome scale metabolic modelling coupled with differential producibility analysis (DPA) to translate RNA-seq datasets into metabolite signals and identified drug-associated metabolic response profiles. We tested four tuberculosis (TB) drugs bedaquiline (BDQ), isoniazid (INH), rifampicin (RIF), and clarithromycin (CLA); conducted RNA-seq experiments of Mtb exposed to the individual drugs at subinhibitory concentrations, followed by DPA of gene expression data to map up and downregulated metabolites. Here we highlight those metabolic pathways that were flexibly used by Mtb to tolerate stress generated upon drug exposure. BDQ and INH upregulated maximum number of central carbon metabolites in glycolysis, pentose phosphate pathway and tri-carboxylic acid cycle with concomitant downregulation of lipid and amino acid metabolite classes. Oxaloacetate was significantly upregulated in all four drug-treated Mtb cells highlighting it as an important metabolite in Mtb's metabolism. Amino acid metabolism was selectively induced by different drugs. We have enhanced our knowledge on Mtb's carbon and nitrogen metabolic adaptations in the presence of drugs and identify metabolic nodes for therapeutic development against TB. Our work also provides DPA omics platform to interrogate RNA-seq datasets of any organism that can be reconstructed as a genome scale metabolic network.
结核分枝杆菌（Mtb）是世界上成功的病原体之一，它在宿主感染期间以及对药物的反应中灵活地改变其代谢特性。在这里，我们使用基因组规模代谢建模结合差异可生产性分析（DPA）将RNA测序数据集转化为代谢物信号，并确定了与药物相关的代谢反应谱。我们测试了四种抗结核药物：贝达喹啉（BDQ）、异烟肼（INH）、利福平（RIF）和克拉霉素（CLA）；对处于亚抑制浓度下的Mtb进行了单个药物暴露的RNA测序实验，随后对基因表达数据进行DPA以绘制上调和下调的代谢物图谱。在这里，我们重点介绍了Mtb灵活利用的那些代谢途径，以耐受药物暴露时产生的应激。BDQ和INH上调了糖酵解、磷酸戊糖途径和三羧酸循环中最大数量的中心碳代谢物，同时下调了脂质和氨基酸代谢物类别。草酰乙酸在所有四种药物处理的Mtb细胞中均显著上调，突出了它作为Mtb代谢中一种重要代谢物的地位。氨基酸代谢由不同药物选择性诱导。我们增进了对Mtb在药物存在下碳和氮代谢适应性的了解，并确定了抗结核治疗开发的代谢节点。我们的工作还提供了DPA组学平台，以研究任何可以重建为基因组规模代谢网络的生物体的RNA测序数据集。
