Genome-scale reconstruction and in silico analysis of the Ralstonia eutropha H16 for polyhydroxyalkanoate synthesis, lithoautotrophic growth, and 2-methyl citric acid production

Abstract

Background: Ralstonia eutropha H16, found in both soil and water, is a Gram-negative lithoautotrophic bacterium that can utillize CO2 and H2 as its sources of carbon and energy in the absence of organic substrates. R. eutropha H16 can reach high cell densities either under lithoautotrophic or heterotrophic conditions, which makes it suitable for a number of biotechnological applications. It is the best known and most promising producer of polyhydroxyalkanoates (PHAs) from various carbon substrates and is an environmentally important bacterium that can degrade aromatic compounds. In order to make R. eutropha H16 a more efficient and robust biofactory, system-wide metabolic engineering to improve its metabolic performance is essential. Thus, it is necessary to analyze its metabolic characteristics systematically and optimize the entire metabolic network at systems level.

Results: We present the lithoautotrophic genome-scale metabolic model of R. eutropha H16 based on the annotated genome with biochemical and physiological information. The stoichiometic model, RehMBEL1391, is composed of 1391 reactions including 229 transport reactions and 1171 metabolites. Constraints-based flux analyses were performed to refine and validate the genome-scale metabolic model under environmental and genetic perturbations. First, the lithoautotrophic growth characteristics of R. eutropha H16 were investigated under varying feeding ratios of gas mixture. Second, the genome-scale metabolic model was used to design the strategies for the production of poly[R-(-)-3hydroxybutyrate] (PHB) under different pH values and carbon/nitrogen source uptake ratios. It was also used to analyze the metabolic characteristics of R. eutropha when the phosphofructokinase gene was expressed. Finally, in silico gene knockout simulations were performed to identify targets for metabolic engineering essential for the production of 2-methylcitric acid in R. eutropha H16.

Conclusion: The genome-scale metabolic model, RehMBEL1391, successfully represented metabolic characteristics of R. eutropha H16 at systems level. The reconstructed genome-scale metabolic model can be employed as an useful tool for understanding its metabolic capabilities, predicting its physiological consequences in response to various environmental and genetic changes, and developing strategies for systems metabolic engineering to improve its metabolic performance.

Figure 1

Sensitivity for gaseous uptake rates of H₂ and CO₂ on lithoautotrophic growth of R. eutropha. (A) Overview of the CBB cycle for carbon fixation and its overall reaction. (B) Mesh plots graph 3D data as a continuous surface for H₂ uptake rate, CO₂ uptake rate, and growth rate when the O₂ uptake rate is 10 mmol gDCW^-1 h^-1. For simulation of lithoautotrophic growth, the D-fructose uptake rate was constrained to zero and reactions related with CBB cycle were activated.

Figure 2

Effects on poly[R-(-)-3hydroxybutyrate] (PHB) production for different pH values and carbon/nitrogen source uptake ratios. (A) Genes, enzymes, and reactions for the biosynthesis of polyhydroxyalkanoate (PHA). (B) PHB production yield for different pH values, which are pH 6, 7, and 8. The PHB content (%wt) is defined as the percentage of PHB concentration (g∙L^-1) to cell concentration (g∙L^-1). The culture medium is the MR minimal medium with D-fructose. (C) Mesh plots graph 3D data as a continuous surface for nitrogen uptake rate, PHB production rate, and growth rate. (D) Predicted PHB production rate by limiting nitrogen uptake rate for the maximization of growth rate.

Figure 3

Effects of expression of phosphofructokinate reaction in R. eutropha. It was investigated for wild-type strain (I), eda- deficient strain (II), eda- deficient and pfk-expressing strain (III), and both edd- and pfk-expressing strain (IV) of R. eutropha. (A) Pathway and flux distribution of the central metabolism for each strain. The flux distribution was investigated by flux variability analysis (FVA). The y axis in each graph indicates the relative flux (%) that is normalized to the carbon source uptake rate, which is D-fructose uptake rate. The upper and lower values in each graph indicate the maximum and minimum values, respectively, in flux variability. (B) Predicted growth rate and maximum ATP production rate for each strain. The black bar denotes the predicted growth rate and the grey bar denotes the predicted maximum ATP production rate.

Figure 4

Flux solution space of wild-type and mutants (ΔprpB, ΔprpD, or ΔacnM) of R. eutropha. Based on the flux solution space of wild-type, 2-methylcitric acid is non-growth associated metabolite when the cell growth rate is maximized. After the in silico single knockout of each gene, 2-methylcitric acid is changed to the growth-associated metabolite according to the flux solution space of the mutants. The x and y axes indicate the relative flux (%) of 2-methylcitric acid production rate and cell growth rate that are normalized to the maximum values of 2-methylcitric acid production rate and cell growth rate in the wild-type of R. eutropha, respectively.