Production of 2,3-butanediol in Saccharomyces cerevisiae by in silico aided metabolic engineering

Department of Chemical & Biological Engineering, Korea University, Seoul, 136-701, Republic of Korea. .
Microbial Cell Factories (Impact Factor: 4.22). 05/2012; 11(1):68. DOI: 10.1186/1475-2859-11-68
Source: PubMed


2,3-Butanediol is a chemical compound of increasing interest due to its wide applications. It can be synthesized via mixed acid fermentation of pathogenic bacteria such as Enterobacter aerogenes and Klebsiella oxytoca. The non-pathogenic Saccharomyces cerevisiae possesses three different 2,3-butanediol biosynthetic pathways, but produces minute amount of 2,3-butanediol. Hence, we attempted to engineer S. cerevisiae strain to enhance 2,3-butanediol production.
We first identified gene deletion strategy by performing in silico genome-scale metabolic analysis. Based on the best in silico strategy, in which disruption of alcohol dehydrogenase (ADH) pathway is required, we then constructed gene deletion mutant strains and performed batch cultivation of the strains. Deletion of three ADH genes, ADH1, ADH3 and ADH5, increased 2,3-butanediol production by 55-fold under microaerobic condition. However, overproduction of glycerol was observed in this triple deletion strain. Additional rational design to reduce glycerol production by GPD2 deletion altered the carbon fluxes back to ethanol and significantly reduced 2,3-butanediol production. Deletion of ALD6 reduced acetate production in strains lacking major ADH isozymes, but it did not favor 2,3-butanediol production. Finally, we introduced 2,3-butanediol biosynthetic pathway from Bacillus subtilis and E. aerogenes to the engineered strain and successfully increased titer and yield. Highest 2,3-butanediol titer (2.29 g·l-1) and yield (0.113 g·g-1) were achieved by Δadh1 Δadh3 Δadh5 strain under anaerobic condition.
With the aid of in silico metabolic engineering, we have successfully designed and constructed S. cerevisiae strains with improved 2,3-butanediol production.

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    • "Therefore, both PDC and ADH have been attractive disruption targets for 2,3-butanediol production in S. cerevisiae. In a previous study, deletion of ADH1, ADH3, and ADH5 genes resulted in increased 2,3-butanediol production (2.29 g/L) with a yield of 0.113 g/g glucose under anaerobic condition (Ng et al., 2012). Although ethanol production can be completely eliminated by deleting PDC1 and PDC5 genes or all PDC genes (PDC1, PDC5, and PDC6), the resulting PDC-deficient strains have severe growth defects on glucose as a sole carbon source and "
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    ABSTRACT: 2,3-Butanediol is a promising valuable chemical that can be used in various areas as a liquid fuel and a platform chemical. Here, 2,3-butanediol production in Saccharomyces cerevisiae was improved stepwise by eliminating byproduct formation and redox rebalancing. By introducing heterologous 2,3-butanediol biosynthetic pathway and deleting competing pathways producing ethanol and glycerol, metabolic flux was successfully redirected to 2,3-butanediol. In addition, the resulting redox cofactor imbalance was restored by overexpressing water-forming NADH oxidase (NoxE) from Lactococcus lactis. In a flask fed-batch fermentation with optimized conditions, the engineered adh1Δadh2Δadh3Δadh4Δadh5Δgpd1Δgpd2Δ strain overexpressing Bacillus subtilis α-acetolactate synthase (AlsS) and α-acetolactate decarboxylase (AlsD), S. cerevisiae 2,3-butanediol dehydrogenase (Bdh1), and L. lactis NoxE from a single multigene-expression vector, produced 72.9g/L 2,3-butanediol with the highest yield (0.41g/g glucose) and productivity (1.43g/(L·h)) ever reported in S. cerevisiae. Copyright © 2015. Published by Elsevier Inc.
    Metabolic Engineering 07/2015; 31. DOI:10.1016/j.ymben.2015.07.006 · 6.77 Impact Factor
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    • "A combination of gene overexpression and deletions resulted in a 4-fold increase in malonyl-CoA concentration relative to the wild-type strain (Xu et al., 2011). This method was also used for fatty acid production in E. coli, in a study where 39% of the maximum theoretical yield was reached (Ranganathan et al., 2012). FSEOF and FVSEOF were respectively used to find gene upregulation targets for lycopene (Choi et al., 2010) and putrescine (Park et al., 2012) production in E. coli. "
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    ABSTRACT: More than a decade ago, the first genome-scale metabolic models for two of the most relevant microbes for biotechnology applications, Escherichia coli and Saccaromyces cerevisiae, were published. Shortly after followed the publication of OptKnock, the first strain design method using bilevel optimization to couple cellular growth with the production of a target product. This initiated the development of a family of strain design methods based on the concept of flux balance analysis. Another family of strain design methods, based on the concept of elementary mode analysis, has also been growing. Although the computation of elementary modes is hindered by computational complexity, recent breakthroughs have allowed applying elementary mode analysis at the genome scale. Here we review and compare strain design methods and look back at the last ten years of in silico strain design with constraint-based models. We highlight some features of the different approaches and discuss the utilization of these methods in successful in vivo metabolic engineering applications.
    05/2015; 7. DOI:10.1016/j.meteno.2015.04.001
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    • "Furthermore, over-expression or inhibition of host genes can either increase or dampen metabolic flux through the reactions that their expressed proteins catalyze. Successful application of such strategies can be used to overproduce host-native targets (Ng et al., 2012; Li et al., 2014) or produce non-host-native targets (Atsumi et al., 2009; Angermayr et al., 2014; Yuan et al., 2014). Identifying successful gene manipulation combinations has traditionally relied on static network inspection, and experimental trial and error to test the strategies (Varman et al., 2011). "
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    ABSTRACT: Sustainable production of target compounds such as biofuels and high-value chemicals for pharmaceutical, agrochemical, and chemical industries is becoming an increasing priority given their current dependency upon diminishing petrochemical resources. Designing these strains is difficult, with current methods focusing primarily on knocking-out genes, dismissing other vital steps of strain design including the overexpression and dampening of genes. The design predictions from current methods also do not translate well-into successful strains in the laboratory. Here, we introduce RobOKoD (Robust, Overexpression, Knockout and Dampening), a method for predicting strain designs for overproduction of targets. The method uses flux variability analysis to profile each reaction within the system under differing production percentages of target-compound and biomass. Using these profiles, reactions are identified as potential knockout, overexpression, or dampening targets. The identified reactions are ranked according to their suitability, providing flexibility in strain design for users. The software was tested by designing a butanol-producing Escherichia coli strain, and was compared against the popular OptKnock and RobustKnock methods. RobOKoD shows favorable design predictions, when predictions from these methods are compared to a successful butanol-producing experimentally-validated strain. Overall RobOKoD provides users with rankings of predicted beneficial genetic interventions with which to support optimized strain design.
    Frontiers in Cell and Developmental Biology 03/2015; 3(17). DOI:10.3389/fcell.2015.00017
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