Modeling phenotypic metabolic adaptations of Mycobacterium tuberculosis H37Rv under hypoxia.
Modeling phenotypic metabolic adaptations of Mycobacterium tuberculosis H37Rv under hypoxia.
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DOI：
10.1371/journal.pcbi.1002688
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发表时间：
2012
影响因子：
4.3
通讯作者：
Reifman J
中科院分区：
文献类型：
--
作者：
Fang X;Wallqvist A;Reifman J
适应不同条件的能力是结核分枝杆菌成功感染人类宿主的关键。结核分枝杆菌是结核病的病原体。适应使生物体在急性感染期间逃避宿主免疫反应，并在潜伏感染阶段持续较长时间。在潜伏感染的个体中，估计包括三分之一的人口，这种有机体以各种代谢状态存在，这阻碍了控制或根除这种疾病的简单策略的发展。直接了解患者体内结核分枝杆菌的代谢状态将有助于疾病的管理，并为开发新药和设计更有效的药物鸡尾酒奠定基础。在这里，我们提出了一种基于现成基因表达数据的计算机方法来创建特定状态的模型。差异基因表达数据与代谢网络模型的耦合使我们能够表征结核分枝杆菌H37Rv对缺氧的代谢适应。基于基因表达改变的微阵列数据，我们的模型预测了氧摄取减少，ATP产生变化，以及从氧化到还原性三羧酸（TCA）程序的全球变化。生物量组成的变化表明，细胞壁生长所需的细胞壁代谢物增加，以及甘油三酯的积累增加，为低营养、低代谢活动的生活方式做准备。相比之下，编码即时缺氧反应调控因子的dosR缺失突变体的基因表达程序未能适应低氧胁迫。我们的预测与最近在缺氧和厌氧条件下结核分枝杆菌活性的实验观察相一致。重要的是，特定代谢物的流动和积累的改变不一定与催化相关代谢反应的酶的差异基因表达直接相关。结核分枝杆菌潜伏感染三分之一的人口，每年在全世界造成数百万人死亡。病原体在人群中持续存在的能力源于其适应宿主诱导的应激和调整其代谢以适应不同宿主环境的能力。我们将基因转录数据与基因组尺度的代谢网络模型相结合，建立了一个新的模型来解释结核分枝杆菌H37Rv的代谢调节。利用我们的模型，我们能够识别与缺氧相关的代谢程序的变化，预测表型变化，并确定病原体生存所需的关键代谢酶和途径。特别是，我们预测了三羧酸循环从氧化路径到还原路径的转换。缺氧条件下不同代谢物和途径的重要性的改变可能为设计新的辅助药物治疗提供指导，以清除持续和潜伏的感染。
The ability to adapt to different conditions is key for Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), to successfully infect human hosts. Adaptations allow the organism to evade the host immune responses during acute infections and persist for an extended period of time during the latent infectious stage. In latently infected individuals, estimated to include one-third of the human population, the organism exists in a variety of metabolic states, which impedes the development of a simple strategy for controlling or eradicating this disease. Direct knowledge of the metabolic states of M. tuberculosis in patients would aid in the management of the disease as well as in forming the basis for developing new drugs and designing more efficacious drug cocktails. Here, we propose an in silico approach to create state-specific models based on readily available gene expression data. The coupling of differential gene expression data with a metabolic network model allowed us to characterize the metabolic adaptations of M. tuberculosis H37Rv to hypoxia. Given the microarray data for the alterations in gene expression, our model predicted reduced oxygen uptake, ATP production changes, and a global change from an oxidative to a reductive tricarboxylic acid (TCA) program. Alterations in the biomass composition indicated an increase in the cell wall metabolites required for cell-wall growth, as well as heightened accumulation of triacylglycerol in preparation for a low-nutrient, low metabolic activity life style. In contrast, the gene expression program in the deletion mutant of dosR, which encodes the immediate hypoxic response regulator, failed to adapt to low-oxygen stress. Our predictions were compatible with recent experimental observations of M. tuberculosis activity under hypoxic and anaerobic conditions. Importantly, alterations in the flow and accumulation of a particular metabolite were not necessarily directly linked to differential gene expression of the enzymes catalyzing the related metabolic reactions. Mycobacterium tuberculosis latently infects one-third of the human population and is responsible for millions of deaths worldwide every year. The ability of the pathogen to persist in the human population stems from its capacity to adapt to host-induced stresses and adjust its metabolism to different host environments. We have developed a novel model to interpret M. tuberculosis H37Rv metabolic adjustment by combining gene transcription data with a genome-scale metabolic network model. Using our model, we were able to identify the changes in the metabolic program associated with hypoxia, predict phenotypic change, and determine the critical metabolic enzymes and pathways that are required for pathogen survival. In particular, we predicted the switch in the tricarboxylic acid cycle from an oxidative to a reductive path. The altered importance of different metabolites and pathways under hypoxic conditions may provide guidance for designing novel, adjuvant drug therapies for clearing persistent and latent infections.
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DOI：
10.1038/nrmicro1949
发表时间：
2009-02
期刊：
Nature reviews. Microbiology
影响因子：
--
作者：
通讯作者：
--
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
--
作者：
Fang, Xin;Wallqvist, Anders;Reifman, Jaques
通讯作者：
Reifman, Jaques
影响因子：
7
作者：
Griffin, TJ;Gygi, SP;Aebersold, R
通讯作者：
Aebersold, R
影响因子：
3.2
作者：
Gomez, JE;McKinney, JD
通讯作者：
McKinney, JD