Abstract
In this report, a genome-scale reconstruction of Bacillus subtilis metabolism and its iterative development based on the combination of genomic, biochemical, and physiological information and high-throughput phenotyping experiments is presented. The initial reconstruction was converted into an in silico model and expanded in a four-step iterative fashion. First, network gap analysis was used to identify 48 missing reactions that are needed for growth but were not found in the genome annotation. Second, the computed growth rates under aerobic conditions were compared with high-throughput phenotypic screen data, and the initial in silico model could predict the outcomes qualitatively in 140 of 271 cases considered. Detailed analysis of the incorrect predictions resulted in the addition of 75 reactions to the initial reconstruction, and 200 of 271 cases were correctly computed. Third, in silico computations of the growth phenotypes of knock-out strains were found to be consistent with experimental observations in 720 of 766 cases evaluated. Fourth, the integrated analysis of the large-scale substrate utilization and gene essentiality data with the genome-scale metabolic model revealed the requirement of 80 specific enzymes (transport, 53; intracellular reactions, 27) that were not in the genome annotation. Subsequent sequence analysis resulted in the identification of genes that could be putatively assigned to 13 intracellular enzymes. The final reconstruction accounted for 844 open reading frames and consisted of 1020 metabolic reactions and 988 metabolites. Hence, the in silico model can be used to obtain experimentally verifiable hypothesis on the metabolic functions of various genes.
Highlights
To date, constraint-based reconstruction and analysis (COBRA)4 of cellular metabolism has been employed successfully to develop organism-specific genome-scale in silico models that have enabled numerous applications [6]
Unlike other modeling strategies such as kinetic [7], stochastic [8], and cybernetic [9] methods, the COBRA approach does not attempt to compute precisely what a biochemical network does; rather, it seeks to distinguish between the network states that are achievable from those that are not, based on a detailed reconstruction of metabolism and incorporation of physiological parameters that are consistent with known experimental information
Metabolic Reconstruction—The overall iterative model development procedure of genome-scale metabolic reconstruction for B. subtilis was based on established reconstruction methods augmented with high-throughput substrate utilization experiments and large-scale gene essentiality data sets (Fig. 1)
Summary
Metabolic Network Reconstruction—The reconstruction procedure followed the approach outlined in Edwards and Palsson [17]. Proteins, and reactions, corresponding literature information, definitions of the metabolite abbreviations, and a list of the exchange fluxes are described in the supplemental material (Bacillus subtilis Model Excel file). For simulation of aerobic growth on minimal media, the following external metabolites were allowed to freely enter and leave the network: Kϩ, Naϩ, Mg2ϩ, Ca2ϩ, Fe3ϩ, CO2, H2O, and Hϩ (exchanges fluxes, Ϫ1 ϫ 106 to 1 ϫ 106 mmol/g cell/h). Except for the substrates tested, were only allowed to leave the system (exchanges fluxes, 0 to 1 ϫ 106 mmol/g cell/h). Growth on different substrates was simulated by allowing corresponding external metabolites to enter the system with a maximum uptake rate of 5 mmol/g cell/h if not specified (exchange flux, Ϫ5 to 1 ϫ 106 mmol/g cell/h). Confidence level on data, predicted growth rates, and corresponding literature information are detailed in the supplemental Table S1
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