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Open Access Research

Investigating the effects of perturbations to pgi and eno gene expression on central carbon metabolism in Escherichia coli using 13 C metabolic flux analysis

Yuki Usui1, Takashi Hirasawa1*, Chikara Furusawa12, Tomokazu Shirai35, Natsuko Yamamoto46, Hirotada Mori4 and Hiroshi Shimizu1*

Author Affiliations

1 Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan

2 Quantitative Biology Center, RIKEN, 6-2-3 Furuedai, Suita, Osaka, 565-0874, Japan

3 Catalysis Science Laboratory, Mitsui Chemicals, Inc, 1900 Togo, Mobara, Chiba, 297-8666, Japan

4 Graduate School of Biological Sciences, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, 630-0192, Japan

5 Present address: RIKEN Biomass Engineering Program, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan

6 Present address: Institute for Research in cities, Kyoto University, Yoshidahonmachi, Sakyo-ku, Kyoto, 606-8501, Japan

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Microbial Cell Factories 2012, 11:87  doi:10.1186/1475-2859-11-87

Published: 21 June 2012

Abstract

Background

It has long been recognized that analyzing the behaviour of the complex intracellular biological networks is important for breeding industrially useful microorganisms. However, because of the complexity of these biological networks, it is currently not possible to obtain all the desired microorganisms. In this study, we constructed a system for analyzing the effect of gene expression perturbations on the behavior of biological networks in Escherichia coli. Specifically, we utilized 13 C metabolic flux analysis (13 C-MFA) to analyze the effect of perturbations to the expression levels of pgi and eno genes encoding phosphoglucose isomerase and enolase, respectively on metabolic fluxes.

Results

We constructed gene expression-controllable E. coli strains using a single-copy mini F plasmid. Using the pgi expression-controllable strain, we found that the specific growth rate correlated with the pgi expression level. 13 C-MFA of this strain revealed that the fluxes for the pentose phosphate pathway and Entner-Doudoroff pathway decreased, as the pgi expression lelvel increased. In addition, the glyoxylate shunt became active when the pgi expression level was almost zero. Moreover, the flux for the glyoxylate shunt increased when the pgi expression level decreased, but was significantly reduced in the pgi-knockout cells. Comparatively, eno expression could not be decreased compared to the parent strain, but we found that increased eno expression resulted in a decreased specific growth rate. 13 C-MFA revealed that the metabolic flux distribution was not altered by an increased eno expression level, but the overall metabolic activity of the central metabolism decreased. Furthermore, to evaluate the impact of perturbed expression of pgi and eno genes on changes in metabolic fluxes in E. coli quantitatively, metabolic sensitivity analysis was performed. As a result, the perturbed expression of pgi gene had a great impact to the metabolic flux changes in the branch point between the glycolysis and pentose phosphate pathway, isocitrate dehydrogenase reaction, anaplerotic pathways and Entner-Doudoroff pathway. In contrast, the impact of perturbed eno expression to the flux changes in E. coli metabolic network was small.

Conclusions

Our results indicate that the response of metabolic fluxes to perturbation to pgi expression was different from that to eno expression; perturbations to pgi expression affect the reaction related to the Pgi protein function, the isocitrate dehydrogenase reaction, anaplerotic reactions and Entner-Doudoroff pathway. Meanwhile, eno expression seems to affect the overall metabolic activity, and the impact of perturbed eno expression on metabolic flux change is small. Using the gene expression control system reported here, it is expected that we can analyze the response and adaptation process of complex biological networks to gene expression perturbations.

Keywords:
Escherichia coli; Gene expression perturbations; 13 C Metabolic flux analysis; Metabolic sensitivity analysis; pgi; eno