Metabolic investigation of host/pathogen interaction using MS2-infected Escherichia coli.
Metabolic investigation of host/pathogen interaction using MS2-infected Escherichia coli.
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DOI：
10.1186/1752-0509-3-121
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发表时间：
2009-12-30
影响因子：
--
通讯作者：
Srivastava R
中科院分区：
文献类型：
--
作者：
Jain R;Srivastava R
RNA病毒是导致人类多种疾病的原因，包括但不限于普通感冒、流感、艾滋病毒和埃博拉病毒。开发治疗这些病毒引起的疾病的新药和新策略可能是一个昂贵且耗时的过程。数学建模可用于阐明宿主-病原体相互作用并突出药物开发的潜在靶点，以及为优化患者治疗策略提供基础。这项工作的目的是确定是否可以使用基因组规模的建模方法来了解在病毒感染期间宿主-病原体相互作用如何影响代谢。大肠杆菌/MS 2被用作宿主-病原体模型系统，因为MS 2易于使用，对人类无害，但与真核病毒有许多共同特征。此外，还建立了E. coli是目前最全面的模型。采用称为“通量平衡分析”的代谢建模策略与实验研究相结合，我们能够预测病毒感染如何改变细菌代谢。根据我们的模拟，我们预测细胞生长和细胞壁的生物合成将停止。此外，我们预测了戊糖磷酸途径的代谢活性的大幅增加，作为增强病毒生物合成的一种手段，同时预测了柠檬酸循环的破坏。此外，预测糖酵解途径无变化。通过我们的方法，我们已经开发出一种病毒感染宿主代谢的建模技术，并研究了病毒感染的代谢效应。这些研究可能为如何设计更好的药物提供见解。它们还说明了将这种代谢分析扩展到包括人类在内的高级生物体的潜力。
RNA viruses are responsible for a variety of illnesses among people, including but not limited to the common cold, the flu, HIV, and ebola. Developing new drugs and new strategies for treating diseases caused by these viruses can be an expensive and time-consuming process. Mathematical modeling may be used to elucidate host-pathogen interactions and highlight potential targets for drug development, as well providing the basis for optimizing patient treatment strategies. The purpose of this work was to determine whether a genome-scale modeling approach could be used to understand how metabolism is impacted by the host-pathogen interaction during a viral infection. Escherichia coli/MS2 was used as the host-pathogen model system as MS2 is easy to work with, harmless to humans, but shares many features with eukaryotic viruses. In addition, the genome-scale metabolic model of E. coli is the most comprehensive model at this time. Employing a metabolic modeling strategy known as "flux balance analysis" coupled with experimental studies, we were able to predict how viral infection would alter bacterial metabolism. Based on our simulations, we predicted that cell growth and biosynthesis of the cell wall would be halted. Furthermore, we predicted a substantial increase in metabolic activity of the pentose phosphate pathway as a means to enhance viral biosynthesis, while a break down in the citric acid cycle was predicted. Also, no changes were predicted in the glycolytic pathway. Through our approach, we have developed a technique of modeling virus-infected host metabolism and have investigated the metabolic effects of viral infection. These studies may provide insight into how to design better drugs. They also illustrate the potential of extending such metabolic analysis to higher order organisms, including humans.
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