Systems-Based Approaches to Probing Metabolic Variation within the Mycobacterium tuberculosis Complex

The Mycobacterium tuberculosis complex includes bovine and human strains of the tuberculosis bacillus, including Mycobacterium tuberculosis, Mycobacterium bovis and the Mycobacterium bovis BCG vaccine strain. M. bovis has evolved from a M. tuberculosis-like ancestor and is the ancestor of the BCG vaccine. The pathogens demonstrate distinct differences in virulence, host range and metabolism, but the role of metabolic differences in pathogenicity is poorly understood. Systems biology approaches have been used to investigate the metabolism of M. tuberculosis, but not to probe differences between tuberculosis strains. In this study genome scale metabolic networks of M. bovis and M. bovis BCG were constructed and interrogated, along with a M. tuberculosis network, to predict substrate utilisation, gene essentiality and growth rates. The models correctly predicted 87-88% of high-throughput phenotype data, 75-76% of gene essentiality data and in silico-predicted growth rates matched measured rates. However, analysis of the metabolic networks identified discrepancies between in silico predictions and in vitro data, highlighting areas of incomplete metabolic knowledge. Additional experimental studies carried out to probe these inconsistencies revealed novel insights into the metabolism of these strains. For instance, that the reduction in metabolic capability observed in bovine tuberculosis strains, as compared to M. tuberculosis, is not reflected by current genetic or enzymatic knowledge. Hence, the in silico networks not only successfully simulate many aspects of the growth and physiology of these mycobacteria, but also provide an invaluable tool for future metabolic studies.结核分枝杆菌复合群（Mycobacterium tuberculosis complex）包含结核杆菌的人源与牛源菌株，涵盖结核分枝杆菌（Mycobacterium tuberculosis）、牛分枝杆菌（Mycobacterium bovis）以及牛分枝杆菌卡介苗（BCG）疫苗株（Mycobacterium bovis BCG vaccine strain）。牛分枝杆菌由类结核分枝杆菌祖先进化而来，同时也是卡介苗疫苗的始祖菌株。这类病原体在毒力、宿主范围与代谢特征上存在显著差异，但代谢差异在致病性中的作用仍未得到充分阐明。 此前已有系统生物学（systems biology）方法被用于探究结核分枝杆菌的代谢特性，但尚未有研究针对不同结核分枝杆菌菌株间的代谢差异进行系统性解析。本研究构建了牛分枝杆菌、牛分枝杆菌卡介苗以及结核分枝杆菌的基因组规模代谢网络（genome scale metabolic networks），并结合这三类网络开展分析，以预测底物利用、基因必需性与生长速率。 该模型可准确预测87%~88%的高通量表型数据、75%~76%的基因必需性数据，且计算机模拟（in silico）得到的生长速率与实验实测值吻合度较高。不过，对代谢网络的分析发现，计算机模拟预测结果与体外实验数据之间存在偏差，这凸显出当前代谢认知存在诸多空白领域。 针对这些不一致之处开展的补充实验研究，为解析这类菌株的代谢机制提供了全新的研究视角。例如，相较于结核分枝杆菌，牛分枝杆菌菌株所呈现的代谢能力衰减现象，无法通过现有遗传或酶学知识得到合理解释。综上，本研究构建的计算机模拟代谢网络不仅可成功模拟这类分枝杆菌生长与生理活动的诸多关键特征，还可为未来的代谢研究提供极具价值的研究工具。