Metabolic investigation of host-pathogen interaction using MS2-infected Escherichia coliReport as inadecuate




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BMC Systems Biology

, 3:121

First Online: 30 December 2009Received: 27 March 2009Accepted: 30 December 2009

Abstract

BackgroundRNA 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.

ResultsEmploying 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.

ConclusionsThrough 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.

Electronic supplementary materialThe online version of this article doi:10.1186-1752-0509-3-121 contains supplementary material, which is available to authorized users.

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Author: Rishi Jain - Ranjan Srivastava

Source: https://link.springer.com/







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