Abstract
BackgroundZymomonas mobilis ZM4 is a Gram-negative bacterium that can efficiently produce ethanol from various carbon substrates, including glucose, fructose, and sucrose, via the Entner-Doudoroff pathway. However, systems metabolic engineering is required to further enhance its metabolic performance for industrial application. As an important step towards this goal, the genome-scale metabolic model of Z. mobilis is required to systematically analyze in silico the metabolic characteristics of this bacterium under a wide range of genotypic and environmental conditions.ResultsThe genome-scale metabolic model of Z. mobilis ZM4, ZmoMBEL601, was reconstructed based on its annotated genes, literature, physiological and biochemical databases. The metabolic model comprises 579 metabolites and 601 metabolic reactions (571 biochemical conversion and 30 transport reactions), built upon extensive search of existing knowledge. Physiological features of Z. mobilis were then examined using constraints-based flux analysis in detail as follows. First, the physiological changes of Z. mobilis as it shifts from anaerobic to aerobic environments (i.e. aerobic shift) were investigated. Then the intensities of flux-sum, which is the cluster of either all ingoing or outgoing fluxes through a metabolite, and the maximum in silico yields of ethanol for Z. mobilis and Escherichia coli were compared and analyzed. Furthermore, the substrate utilization range of Z. mobilis was expanded to include pentose sugar metabolism by introducing metabolic pathways to allow Z. mobilis to utilize pentose sugars. Finally, double gene knock-out simulations were performed to design a strategy for efficiently producing succinic acid as another example of application of the genome-scale metabolic model of Z. mobilis.ConclusionThe genome-scale metabolic model reconstructed in this study was able to successfully represent the metabolic characteristics of Z. mobilis under various conditions as validated by experiments and literature information. This reconstructed metabolic model will allow better understanding of Z. mobilis metabolism and consequently designing metabolic engineering strategies for various biotechnological applications.
Highlights
Zymomonas mobilis ZM4 is a Gram-negative bacterium that can efficiently produce ethanol from various carbon substrates, including glucose, fructose, and sucrose, via the Entner-Doudoroff pathway
Reactions, which are not assigned to a gene in Z. mobilis or for which no evidence is available for its presence in Z. mobilis, can be added to the metabolic model
Additional gene knockout simulations that contain grouping reaction constraints were performed, and obtained the same results. These analyses revealed that the inactivation of pyruvate decarboxylase essential for ethanol production redirected the metabolic fluxes towards lactic acid production, and the inactivation of D-lactate dehydrogenase redirected the metabolic fluxes toward succinic acid production in tricarboxylic acid (TCA) cycle
Summary
Zymomonas mobilis ZM4 is a Gram-negative bacterium that can efficiently produce ethanol from various carbon substrates, including glucose, fructose, and sucrose, via the Entner-Doudoroff pathway. As an important step towards this goal, the genome-scale metabolic model of Z. mobilis is required to systematically analyze in silico the metabolic characteristics of this bacterium under a wide range of genotypic and environmental conditions. In silico genome-scale metabolic modeling and simulation have proven to be useful in the field of systems metabolic engineering. This approach has successfully contributed to the design of strategies for engineering microorganisms for the production of amino acids, including L-valine [7] and L-threonine [8], lycopene [9], succinic acid [10], ethanol [11], and polylactic acid [12]. The strength of genome-scale modeling is that it predicts the effects of genetic and environmental perturbations on cellular metabolism from a holistic point of view, but can be used in combination with other highthroughput techniques, for instance gene expression data [7,13]
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