Integration of Metabolomics and Transcriptomics Reveals a Complex Diet of Mycobacterium tuberculosis during Early Macrophage Infection.
Integration of Metabolomics and Transcriptomics Reveals a Complex Diet of Mycobacterium tuberculosis during Early Macrophage Infection.
复制标题
代谢组学和转录组学的整合揭示了早期巨噬细胞感染期间结核分枝杆菌的复杂饮食。
DOI：
10.1128/msystems.00057-17
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发表时间：
2017-07
期刊：
影响因子：
6.4
通讯作者：
Sauer U
中科院分区：
文献类型：
--
作者：
Zimmermann M;Kogadeeva M;Gengenbacher M;McEwen G;Mollenkopf HJ;Zamboni N;Kaufmann SHE;Sauer U
细胞内病原体消耗的营养物质大多是未知的。这主要是由于解开共享大多数代谢途径和代谢物的宿主和病原体代谢的挑战。在这里，我们研究了结核病病原体结核分枝杆菌及其人类宿主细胞在早期感染过程中的代谢变化。为此，我们通过整合到全基因组代谢网络中，将感染过程中生物体的基因表达数据和代谢物变化结合起来。这导致了感染特异性代谢改变的识别，我们进一步利用它通过通量平衡分析定量模拟宿主-病原体相互作用。这些计算机数据表明，结核杆菌在早期巨噬细胞感染期间消耗多达 33 种不同的营养物质，细菌利用这些营养物质产生能量和生物量以建立细胞内生长。这种多底物供给策略使病原体的代谢对扰动（例如先天免疫反应或抗生素治疗）具有稳健性。从宿主环境中获取营养对于细胞内病原体的生存至关重要，但概念和技术挑战限制了我们对病原体饮食的了解。为了克服其中一些技术障碍，我们利用了一种实验上可访问的模型，用于结核病病原体结核分枝杆菌早期感染人类巨噬细胞，并通过多组学方法研究宿主与病原体的相互作用。我们收集了受感染巨噬细胞的代谢组学和完整转录组 RNA 测序（双 RNA-seq）数据，将它们整合到全基因组反应对网络中，并确定了宿主细胞和结核分枝杆菌中在感染过程中受到模块化调节的代谢子网络。这些代谢子网络的上调和下调表明病原体利用了多种源自宿主的化合物，同时测量了细菌和宿主的代谢和转录变化。为了量化宿主和细胞内病原体之间的代谢相互作用，我们使用了受双 RNA-seq 数据约束的巨噬细胞和结核分枝杆菌代谢的组合基因组规模模型。代谢通量平衡分析预测了总共 33 种不同碳源的共利用，使我们能够区分直接用作生物质前体的病原体底物和进一步代谢以获得能量或合成构件的底物。这种多底物燃料赋予了病原体代谢干预的高度鲁棒性。所提出的方法结合多组学数据作为模拟全系统宿主-病原体代谢相互作用的起点，是更好地了解病原体的细胞内生活方式及其代谢稳健性和对代谢干预的抵抗力的有用工具。重要性 细胞内病原体消耗的营养物质大多是未知的。这主要是由于解开共享大多数代谢途径和代谢物的宿主和病原体代谢的挑战。在这里，我们研究了结核病病原体结核分枝杆菌及其人类宿主细胞在早期感染过程中的代谢变化。为此，我们通过整合到全基因组代谢网络中，将感染过程中生物体的基因表达数据和代谢物变化结合起来。这导致了感染特异性代谢改变的识别，我们进一步利用它通过通量平衡分析定量模拟宿主-病原体相互作用。这些计算机数据表明，结核杆菌在早期巨噬细胞感染期间消耗多达 33 种不同的营养物质，细菌利用这些营养物质产生能量和生物量以建立细胞内生长。这种多底物供给策略使病原体的代谢对扰动（例如先天免疫反应或抗生素治疗）具有稳健性。
The nutrients consumed by intracellular pathogens are mostly unknown. This is mainly due to the challenge of disentangling host and pathogen metabolism sharing the majority of metabolic pathways and hence metabolites. Here, we investigated the metabolic changes of Mycobacterium tuberculosis, the causative agent of tuberculosis, and its human host cell during early infection. To this aim, we combined gene expression data of both organisms and metabolite changes during the course of infection through integration into a genome-wide metabolic network. This led to the identification of infection-specific metabolic alterations, which we further exploited to model host-pathogen interactions quantitatively by flux balance analysis. These in silico data suggested that tubercle bacilli consume up to 33 different nutrients during early macrophage infection, which the bacteria utilize to generate energy and biomass to establish intracellular growth. Such multisubstrate fueling strategy renders the pathogen’s metabolism robust toward perturbations, such as innate immune responses or antibiotic treatments. Nutrient acquisition from the host environment is crucial for the survival of intracellular pathogens, but conceptual and technical challenges limit our knowledge of pathogen diets. To overcome some of these technical roadblocks, we exploited an experimentally accessible model for early infection of human macrophages by Mycobacterium tuberculosis, the etiological agent of tuberculosis, to study host-pathogen interactions with a multi-omics approach. We collected metabolomics and complete transcriptome RNA sequencing (dual RNA-seq) data of the infected macrophages, integrated them in a genome-wide reaction pair network, and identified metabolic subnetworks in host cells and M. tuberculosis that are modularly regulated during infection. Up- and downregulation of these metabolic subnetworks suggested that the pathogen utilizes a wide range of host-derived compounds, concomitant with the measured metabolic and transcriptional changes in both bacteria and host. To quantify metabolic interactions between the host and intracellular pathogen, we used a combined genome-scale model of macrophage and M. tuberculosis metabolism constrained by the dual RNA-seq data. Metabolic flux balance analysis predicted coutilization of a total of 33 different carbon sources and enabled us to distinguish between the pathogen’s substrates directly used as biomass precursors and the ones further metabolized to gain energy or to synthesize building blocks. This multiple-substrate fueling confers high robustness to interventions with the pathogen’s metabolism. The presented approach combining multi-omics data as a starting point to simulate system-wide host-pathogen metabolic interactions is a useful tool to better understand the intracellular lifestyle of pathogens and their metabolic robustness and resistance to metabolic interventions. IMPORTANCE The nutrients consumed by intracellular pathogens are mostly unknown. This is mainly due to the challenge of disentangling host and pathogen metabolism sharing the majority of metabolic pathways and hence metabolites. Here, we investigated the metabolic changes of Mycobacterium tuberculosis, the causative agent of tuberculosis, and its human host cell during early infection. To this aim, we combined gene expression data of both organisms and metabolite changes during the course of infection through integration into a genome-wide metabolic network. This led to the identification of infection-specific metabolic alterations, which we further exploited to model host-pathogen interactions quantitatively by flux balance analysis. These in silico data suggested that tubercle bacilli consume up to 33 different nutrients during early macrophage infection, which the bacteria utilize to generate energy and biomass to establish intracellular growth. Such multisubstrate fueling strategy renders the pathogen’s metabolism robust toward perturbations, such as innate immune responses or antibiotic treatments.