In the post-genome era, it has been recognized that analyzing the behavior of the complex intracellular biological networks consisting of the genome, mRNA, proteins and metabolites is important for understanding cellular systems . Such analysis has been called “systems biology.” In particular, the metabolic engineering field is acutely aware of the importance of systems biology research . To breed industrially useful microorganisms such as those able to produce useful chemicals, genetic modifications, including overexpressions and deletions, have been conducted in host cells. Unfortunately, these modifications do not always result in the desired microorganism due to the complexity of biological networks. The lack of current knowledge regarding the complexity of biological networks limits the applicability of these modifications to the design of useful microorganism. Therefore analysis of the effects of genetic modifications on the complex biological networks will benefit future work aimed at breeding industrially useful microorganisms. In particular, detailed analysis of metabolic reaction network is required to produce useful chemicals, as the biosynthesis of target chemicals is connected to the metabolic network of the host microorganism .
Metabolic flux analysis (MFA) has become a powerful tool for analyzing the changes in the behavior of the intracellular metabolic network . MFA involving 13 C isotope-labeling experiments (13 C-MFA) is widely used to quantitatively determine the metabolic flux distribution. In these 13 C-MFA experiments, the cells are cultivated on 13 C-labeled carbon source(s), and then, the 13 C-labeling information of proteinogenic amino acids is measured by mass spectrometry and nuclear magnetic resonance spectroscopy [5, 6].
Previous studies have utilized 13 C-MFA to assess the effects of perturbations to the central carbon metabolism network on intracellular metabolism. For example, 13 C-MFA studies utilizing an Escherichia coli strain lacking the pykF gene, which encodes pyruvate kinase converting phosphoenolpyruvate to pyruvate, demonstrated [7, 8]. In the chemostat culture of the pykF-knockout strain, the glycolytic flux is decreased and the fluxes for anaplerotic reactions catalyzed by phosphoenolpyruvate carboxylase and malic enzyme are increased, in comparison with the parent strain. Moreover, the expression of zwf
gnd, and ppc encoding glucose-6-phosphate dehydrogenase, 6-phosphogluconate dehydrogenase, and phosphoenolpyruvate carboxylase, respectively, is increased, and the expression of glk
pgi, and tpi genes encoding glucokinase, phosphoglucose isomerase and triosephosphate isomerase, respectively, is decreased. Further research on the pgi-knockout strain demonstrated that the flux through the glyoxylate shunt is increased, and acetate secretion is decreased in comparison with the parent strain [9–11]. Recently, Ishii et al. used 24 single-gene knockout mutant strains of E. coli in the glycolysis and the pentose phosphate pathway to analyze their effect on central carbon metabolism by 13C-MFA as well as transcriptomic, proteomic, and metabolimic techniques . In addition, Nicolas et al. reported the difference in metabolic flux distributions between the wild-type, zwf knockout, and zwf-overexpressing strains of E. coli.
The majority of studies analyzing the effect of gene expression perturbations on biological networks utilize gene knockout strains as described above. However, because of the dramatic magnitude of the perturbation induced by gene knockout, unexpected, and perhaps, artificial phenomena may be observed. Moreover, these knockout strains have already adapted to the culture condition(s) during their construction, and therefore, the resulting analyses may be confounded. Therefore, analyzing the cellular states with native and perturbed gene expressions is important to understand the mechanism of the transition from native cellular state to the adapted state with complete loss of the target gene. For this purpose, other experimental system than utilization of knockout strains would be highly required.
In this study, we constructed a simple experimental system for analyzing the effect of perturbed gene expression on the behavior of intracellular biological networks in E. coli. Briefly, the target gene for analysis was cloned onto the single-copy plasmid under an inducible promoter and operator, and then, expressed; this disrupted the target gene on the chromosome. Subsequently, the expression levels of the target gene on the single-copy plasmid can be altered by changing the concentration of its inducer, and the effect of this change on the behavior of the intracellular biological networks can be analyzed. In addition, the transition from the initial cellular state to the complete loss of function of the target gene can also be analyzed.
In the present study, we reported the construction of expression-controllable strain for the pgi gene of E. coli. Subsequently, we analyzed the effect of perturbations to pgi expression levels on carbon metabolism via 13C-MFA. The pgi gene encodes the glycolytic enzyme phosphoglucose isomerase, which is an important member of the glycolysis pathway and links to the oxidative pentose phosphate pathway via its catalysis of the conversion from glucose-6-phosphate to fructose-6-phoshate . In addition, our method is expected to allow the analysis of essential genes. Therefore, we constructed eno expression-controllable strain of E. coli, and analyzed the effect of perturbations to eno expression levels on carbon metabolism by 13 C-MFA. The eno gene also encodes a glycolytic enzyme, enolase, which catalyzes the conversion from 2-phosphoglycerate to phosphoenolpyruvate, and its knockout is lethal when grown on glucose as a carbon source .
MFA helps us to understand the metabolic relationship between different metabolic pathways and to quantify the distribution of fluxes in the metabolic reaction networks. However, any quantitative measures of the control of the flux in metabolic networks cannot be obtained from the results of MFA only. To evaluate the sensitivity of the metabolic fluxes to the change in activities of metabolic reactions, metabolic control analysis (MCA) has been performed . In MCA, perturbation experiment to target enzyme activity has been carried out and sensitivity of metabolic fluxes to change in activity of metabolic enzymes was evaluated by estimating the flux control coefficients (FCCs). In this study, metabolic sensitivity analysis (MSA) was conducted, and the sensitivity of fluxes when changing the expression of pgi and eno genes was quantitatively evaluated. As a result, sensitivities of fluxes for the branch point between the glycolysis and pentose phosphate pathway, isocitrate dehydrogenase reaction, anaplerotic pathways and Entner-Doudoroff pathway to the change in pgi expression levels were large. In contrast, sensitivity of fluxes in E. coli metabolic network was small when changing the eno expression levels.