Nobuyoshi Ishii

Systematic phenome analysis of Escherichia coli multiple-knockout mutants reveals hidden reactions in central carbon metabolism

Central carbon metabolism is a basic and exhaustively analyzed pathway. However, the intrinsic robustness of the pathway might still conceal uncharacterized reactions. To test this hypothesis, we constructed systematic multiple‐knockout mutants involved in central carbon catabolism in Escherichia coli and tested their growth under 12 different nutrient conditions. Differences between in silico predictions and experimental growth indicated that unreported reactions existed within this extensively analyzed metabolic network. These putative reactions were then confirmed by metabolome analysis and in vitro enzymatic assays. Novel reactions regarding the breakdown of sedoheptulose‐7‐phosphate to erythrose‐4‐phosphate and dihydroxyacetone phosphate were observed in transaldolase‐deficient mutants, without any noticeable changes in gene expression. These reactions, triggered by an accumulation of sedoheptulose‐7‐phosphate, were catalyzed by the universally conserved glycolytic enzymes ATP‐dependent phosphofructokinase and aldolase. The emergence of an alternative pathway not requiring any changes in gene expression, but rather relying on the accumulation of an intermediate metabolite may be a novel mechanism mediating the robustness of these metabolic networks.

13C‐metabolic flux analysis for batch culture of Escherichia coli and its pyk and pgi gene knockout mutants based on mass isotopomer distribution of intracellular metabolites

Since most bio‐production processes are conducted in a batch or fed‐batch manner, the evaluation of metabolism with respect to time is highly desirable. Toward this aim, we applied 13C‐metabolic flux analysis to nonstationary conditions by measuring the mass isotopomer distribution of intracellular metabolites. We performed our analysis on batch cultures of wild‐type Escherichia coli, as well as on Pyk and Pgi mutants, obtained the fluxes and metabolite concentrations as a function of time. Our results for the wild‐type indicated that the TCA cycle flux tended to increase during growth on glucose. Following glucose exhaustion, cells controlled the branch ratio between the glyoxylate pathway and the TCA cycle, depending on the availability of acetate. In the Pyk mutant, the concentrations of glycolytic intermediates changed drastically over time due to the dumping and feedback inhibition caused by PEP accumulation. Nevertheless, the flux distribution and free amino acid concentrations changed little. The growth rate and the fluxes remained constant in the Pgi mutant and the glucose‐6‐phosphate dehydrogenase reaction was the rate‐limiting step. The measured fluxes were compared with those predicted by flux balance analysis using maximization of biomass yield or ATP production. Our findings indicate that the objective function of biosynthesis became less important as time proceeds on glucose in the wild‐type, while it remained highly important in the Pyk mutant. Furthermore, ATP production was the primary objective function in the Pgi mutant. This study demonstrates how cells adjust their metabolism in response to environmental changes and/or genetic perturbations in the batch cultivation.

Dynamic simulation of an in vitro multi‐enzyme system

Parameters often are tuned with metabolite concentration time series data to build a dynamic model of metabolism. However, such tuning may reduce the extrapolation ability (generalization capability) of the model. In this study, we determined detailed kinetic parameters of three purified Escherichia coli glycolytic enzymes using the initial velocity method for individual enzymes; i.e., the parameters were determined independently from metabolite concentration time series data. The metabolite concentration time series calculated by the model using the parameters matched the experimental data obtained in an actual multi‐enzyme system consisting of the three purified E. coli glycolytic enzymes. Thus, the results indicate that kinetic parameters can be determined without using an undesirable tuning process.

Multi-Omics Data-Driven Systems Biology of E. coli

The omics, which means comprehensive analysis of a specific layer in a cellular system, are emerging as essential methodological approaches for molecular biology and systems biology. However, single omics analysis does not always provide enough information to understand the behaviors of a cellular system. Therefore, a combination of multiple omics analyses, the multi-omics approach, is required to acquire a precise picture of living organisms. In this chapter, basic concepts of omics studies, and recent technologies in the omics of metabolism and published multi-omics analyses of Escherichia coli, are reviewed. Subsequently, a large-scale multi-omics analysis of E. coli K-12, including transcriptomics, proteomics, metabolomics and fluxomics, is presented. This study uncovered the complementary strategies of E. coli that result in a metabolic network robust against various types of perturbations, therefore demonstrating the power of a multi-omics, data-driven approach for understanding the functional principles of total cellular systems.

Distinguishing enzymes using metabolome data for the hybrid dynamic/static method

BackgroundIn the process of constructing a dynamic model of a metabolic pathway, a large number of parameters such as kinetic constants and initial metabolite concentrations are required. However, in many cases, experimental determination of these parameters is time-consuming. Therefore, for large-scale modelling, it is essential to develop a method that requires few experimental parameters. The hybrid dynamic/static (HDS) method is a combination of the conventional kinetic representation and metabolic flux analysis (MFA). Since no kinetic information is required in the static module, which consists of MFA, the HDS method may dramatically reduce the number of required parameters. However, no adequate method for developing a hybrid model from experimental data has been proposed.ResultsIn this study, we develop a method for constructing hybrid models based on metabolome data. The method discriminates enzymes into static modules and dynamic modules using metabolite concentration time series data. Enzyme reaction rate time series were estimated from the metabolite concentration time series data and used to distinguish enzymes optimally for the dynamic and static modules. The method was applied to build hybrid models of two microbial central-carbon metabolism systems using simulation results from their dynamic models.ConclusionA protocol to build a hybrid model using metabolome data and a minimal number of kinetic parameters has been developed. The proposed method was successfully applied to the strictly regulated central-carbon metabolism system, demonstrating the practical use of the HDS method, which is designed for computer modelling of metabolic systems.

Metabolome analysis and metabolic simulation

For many decades microorganisms have been used for industrial purposes; traditional fermentations such as brewing and production of food additives, aroma molecules, organic acids and pharmaceutical-like antibiotics or recombinant proteins are instances of the industrial microorganism utilization. Therefore, microorganism modeling and simulation have been required for engineering purposes, because of demands for design, optimization and quality control of large-scale fermentation plants. Modeling has recently become more highly developed, aided by the deciphering of microorganism genomes, the completion of metabolic databases, the development of analytical methodologies and improvements in the performance of computers. This paper reviews past and recent metabolic simulation of microorganisms, and also discusses the metabolome analytical techniques and the construction of large-scale microorganism models which are now being developed in our group.