Constraint-based analysis of metabolic capacity of Salmonella typhimurium during host-pathogen interaction.
Constraint-based analysis of metabolic capacity of Salmonella typhimurium during host-pathogen interaction.
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DOI：
10.1186/1752-0509-3-38
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发表时间：
2009-04-08
影响因子：
--
通讯作者：
Daefler S
中科院分区：
文献类型：
--
作者：
Raghunathan A;Reed J;Shin S;Palsson B;Daefler S
沙门氏菌感染在全球范围内导致严重的发病率和死亡率。鼠伤寒沙门氏菌在其宿主细胞内的复制是研究细胞内细菌感染发病机制的模型系统。细菌代谢网络的基因组规模建模提供了一个强大的工具，以确定和分析所需的宿主-病原体相互作用过程中成功的细胞内复制的途径。我们已经开发并验证了鼠伤寒沙门氏菌LT 2（iRR 1083）的基因组规模的代谢网络。这个模型解释了1,083个基因编码的蛋白质催化细菌中1,087个独特的代谢和运输反应。我们采用通量平衡分析和计算机基因必要性分析来研究在模拟体外和宿主细胞环境的广泛条件下的生长。S.从巨噬细胞系中分离的鼠伤寒沙门氏菌用于约束模型以预测在感染期间可能起作用的代谢途径。我们的分析表明，有一个强大的最小的代谢途径，这是成功复制宿主细胞内的沙门氏菌所需的。该模型还可作为高通量数据集成的平台。它的计算能力允许识别网络化的代谢途径，并产生关于感染期间代谢的假设，这可能用于合理设计新型抗生素或疫苗株。
Infections with Salmonella cause significant morbidity and mortality worldwide. Replication of Salmonella typhimurium inside its host cell is a model system for studying the pathogenesis of intracellular bacterial infections. Genome-scale modeling of bacterial metabolic networks provides a powerful tool to identify and analyze pathways required for successful intracellular replication during host-pathogen interaction. We have developed and validated a genome-scale metabolic network of Salmonella typhimurium LT2 (iRR1083). This model accounts for 1,083 genes that encode proteins catalyzing 1,087 unique metabolic and transport reactions in the bacterium. We employed flux balance analysis and in silico gene essentiality analysis to investigate growth under a wide range of conditions that mimic in vitro and host cell environments. Gene expression profiling of S. typhimurium isolated from macrophage cell lines was used to constrain the model to predict metabolic pathways that are likely to be operational during infection. Our analysis suggests that there is a robust minimal set of metabolic pathways that is required for successful replication of Salmonella inside the host cell. This model also serves as platform for the integration of high-throughput data. Its computational power allows identification of networked metabolic pathways and generation of hypotheses about metabolism during infection, which might be used for the rational design of novel antibiotics or vaccine strains.
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影响因子：
12.3
作者：
Baart GJ;Zomer B;de Haan A;van der Pol LA;Beuvery EC;Tramper J;Martens DE
通讯作者：
Martens DE
影响因子：
--
作者：
Jamshidi, Neema;Palsson, Bernhard O
通讯作者：
Palsson, Bernhard O
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：
15.9
作者：
BUCHMEIER, NA;LIBBY, SJ;FANG, FC
通讯作者：
FANG, FC
影响因子：
5.2
作者：
Coldham, Nick G.;Randall, Luke P.;Woodward, Martin J.
通讯作者：
Woodward, Martin J.