CRISPR-Cas9 mediated engineering of Bacillus licheniformis for industrial production of (2R,3S)-butanediol.

Bacillus lichenformis is an industrially promising generally recognized as safe (GRAS) strain that can be used for the production of a valuable chemical, 2,3-butanediol (BDO). Conventional gene deletion vectors and/or methods are time-consuming and have poor efficiency. Therefore, clustered regularly interspaced short palindromic repeat (CRISPR)-Cas9 mediated homologous recombination was used to engineer a newly isolated and UV-mutagenized B.

Engineering a newly isolated Bacillus licheniformis strain for the production of (2R,3R)-butanediol.

Several microorganisms can produce 2,3-butanediol (BDO), an industrially promising chemical. In this study, a Bacillus licheniformis named as 4071, was isolated from soil sample. It is a GRAS (generally recognized as safe) strain and could over-produce 2,3-BDO.

Microbial production of 2,3-butanediol for industrial applications.

2,3-Butanediol (2,3-BD) has great potential for diverse industries, including chemical, cosmetics, agriculture, and pharmaceutical areas. However, its industrial production and usage are limited by the fairly high cost of its petro-based production. Several bio-based 2,3-BD production processes have been developed and their economic advantages over petro-based production process have been reported.

Isolation and Evaluation of Strains for Industrial Production of 2,3-Butanediol.

Biologically produced 2,3-butanediol (2,3-BDO) has diverse industrial applications. In this study, schematic isolation and screening procedures were designed to obtain generally regarded as safe (GRAS) and efficient 2,3-BDO producers. Over 4,000 candidate strains were isolated by pretreatment and enrichment, and the isolated strains were further screened by morphological, biochemical, and genomic analyses.

Metabolic engineering of Klebsiella pneumoniae based on in silico analysis and its pilot-scale application for 1,3-propanediol and 2,3-butanediol co-production.

Klebsiella pneumoniae naturally produces relatively large amounts of 1,3-propanediol (1,3-PD) and 2,3-butanediol (2,3-BD) along with various byproducts using glycerol as a carbon source. The ldhA and mdh genes in K. pneumoniae were deleted based on its in silico gene knockout simulation with the criteria of maximizing 1,3-PD and 2,3-BD production and minimizing byproducts formation and cell growth retardation.

Metabolic engineering of Klebsiella pneumoniae and in silico investigation for enhanced 2,3-butanediol production.

Objectives: To improve the production of 2,3-butanediol (2,3-BD) in Klebsiella pneumoniae, the genes related to the formation of lactic acid, ethanol, and acetic acid were eliminated.

Results: Although the cell growth and 2,3-BD production rates of the K. pneumoniae ΔldhA ΔadhE Δpta-ackA strain were lower than those of the wild-type strain, the mutant produced a higher titer of 2,3-BD and a higher yield in batch fermentation: 91 g 2,3-BD/l with a yield of 0.

Enhanced production of (R,R)-2,3-butanediol by metabolically engineered Klebsiella oxytoca.

Microbial fermentation produces a racemic mixture of 2,3-butanediol ((R,R)-BD, (S,S)-BD, and meso-BD), and the compositions and physiochemical properties vary from microorganism to microorganism. Although the meso form is much more difficult to transport and store because of its higher freezing point than those of the optically active forms, most microorganisms capable of producing 2,3-BD mainly yield meso-2,3-BD. Thus, we developed a metabolically engineered (R,R)-2,3-BD overproducing strain using a Klebsiella oxytoca ΔldhA ΔpflB strain, which shows an outstanding 2,3-BD production performance with more than 90 % of the meso form.

Design of homo-organic acid producing strains using multi-objective optimization.

Production of homo-organic acids without byproducts is an important challenge in bioprocess engineering to minimize operation cost for separation processes. In this study, we used multi-objective optimization to design Escherichia coli strains with the goals of maximally producing target organic acids, while maintaining sufficiently high growth rate and minimizing the secretion of undesired byproducts. Homo-productions of acetic, lactic and succinic acids were targeted as examples.

In silico aided metabolic engineering of Klebsiella oxytoca and fermentation optimization for enhanced 2,3-butanediol production.

Klebsiella oxytoca naturally produces a large amount of 2,3-butanediol (2,3-BD), a promising bulk chemical with wide industrial applications, along with various byproducts. In this study, the in silico gene knockout simulation of K. oxytoca was carried out for 2,3-BD overproduction by inhibiting the formation of byproducts.

Genome-scale reconstruction and in silico analysis of Klebsiella oxytoca for 2,3-butanediol production.

Background: Klebsiella oxytoca, a Gram-negative, rod-shaped, and facultative anaerobic bacterium, is one of the most promising 2,3-butanediol (2,3-BD) producers. In order to improve the metabolic performance of K. oxytoca as an efficient biofactory, it is necessary to assess its metabolic characteristics with a system-wide scope, and to optimize the metabolic pathways at a systems level.

Flux variability scanning based on enforced objective flux for identifying gene amplification targets.

Background: In order to reduce time and efforts to develop microbial strains with better capability of producing desired bioproducts, genome-scale metabolic simulations have proven useful in identifying gene knockout and amplification targets. Constraints-based flux analysis has successfully been employed for such simulation, but is limited in its ability to properly describe the complex nature of biological systems. Gene knockout simulations are relatively straightforward to implement, simply by constraining the flux values of the target reaction to zero, but the identification of reliable gene amplification targets is rather difficult.

Systems metabolic engineering of microorganisms for natural and non-natural chemicals.

Growing concerns over limited fossil resources and associated environmental problems are motivating the development of sustainable processes for the production of chemicals, fuels and materials from renewable resources. Metabolic engineering is a key enabling technology for transforming microorganisms into efficient cell factories for these compounds. Systems metabolic engineering, which incorporates the concepts and techniques of systems biology, synthetic biology and evolutionary engineering at the systems level, offers a conceptual and technological framework to speed the creation of new metabolic enzymes and pathways or the modification of existing pathways for the optimal production of desired products.

Engineering of microorganisms for the production of biofuels and perspectives based on systems metabolic engineering approaches.

The increasing oil price and environmental concerns caused by the use of fossil fuel have renewed our interest in utilizing biomass as a sustainable resource for the production of biofuel. It is however essential to develop high performance microbes that are capable of producing biofuels with very high efficiency in order to compete with the fossil fuel. Recently, the strategies for developing microbial strains by systems metabolic engineering, which can be considered as metabolic engineering integrated with systems biology and synthetic biology, have been developed.

Arrays of Lucius microprisms for directional allocation of light and autostereoscopic three-dimensional displays.

Directional and asymmetric properties are attractive features in nature that have proven useful for directional wetting, directional flow of liquids and artificial dry adhesion. Here we demonstrate that an optically asymmetric structure can be exploited to guide light with directionality. The Lucius prism array presented here has two distinct properties: the directional transmission of light and the disproportionation of light intensity.

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

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.

Application of metabolic flux analysis in metabolic engineering.

Metabolic flux analysis (MFA) is an important analytical technique to quantify intracellular metabolic fluxes as a consequence of all catalytic and transcriptional interactions. In systems metabolic engineering, MFA has played important role to understand cellular physiology under particular conditions and predict its metabolic capability after genetic or environmental perturbations. Two methods using optimization procedure, ¹³C-based flux analysis and constraints-based flux analysis, have been used generally on the basis of stoichiometry of metabolic reactions and mass balances around intracellular metabolites under pseudo-steady state assumption.

The genome-scale metabolic network analysis of Zymomonas mobilis ZM4 explains physiological features and suggests ethanol and succinic acid production strategies.

Background: 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. 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.

Prediction of metabolic fluxes by incorporating genomic context and flux-converging pattern analyses.

Flux balance analysis (FBA) of a genome-scale metabolic model allows calculation of intracellular fluxes by optimizing an objective function, such as maximization of cell growth, under given constraints, and has found numerous applications in the field of systems biology and biotechnology. Due to the underdetermined nature of the system, however, it has limitations such as inaccurate prediction of fluxes and existence of multiple solutions for an optimal objective value. Here, we report a strategy for accurate prediction of metabolic fluxes by FBA combined with systematic and condition-independent constraints that restrict the achievable flux ranges of grouped reactions by genomic context and flux-converging pattern analyses.

Development of a gene knockout system for Ralstonia eutropha H16 based on the broad-host-range vector expressing a mobile group II intron.

Ralstonia eutropha H16 is a Gram-negative lithoautotrophic bacterium and is one of the best biopolymer-producing bacteria. It can grow to high cell densities either under lithoautotrophic or under heterotrophic conditions, which makes it suitable for a number of biotechnological applications. Also, R.

In silico genome-scale metabolic analysis of Pseudomonas putida KT2440 for polyhydroxyalkanoate synthesis, degradation of aromatics and anaerobic survival.

Genome-scale metabolic models have been appearing with increasing frequency and have been employed in a wide range of biotechnological applications as well as in biological studies. With the metabolic model as a platform, engineering strategies have become more systematic and focused, unlike the random shotgun approach used in the past. Here we present the genome-scale metabolic model of the versatile Gram-negative bacterium Pseudomonas putida, which has gained widespread interest for various biotechnological applications.

Constraints-based genome-scale metabolic simulation for systems metabolic engineering.

Random mutagenesis and selection approaches used traditionally for the development of industrial strains have largely been complemented by metabolic engineering, which allows purposeful modification of metabolic and cellular characteristics by using recombinant DNA and other molecular biological techniques. As systems biology advances as a new paradigm of research thanks to the development of genome-scale computational tools and high-throughput experimental technologies including omics, systems metabolic engineering allowing modification of metabolic, regulatory and signaling networks of the cell at the systems-level is becoming possible. In silico genome-scale metabolic model and its simulation play increasingly important role in providing systematic strategies for metabolic engineering.

EcoProDB: the Escherichia coli protein database.

Unlabelled: EcoProDB is a web-based database for comparative proteomics of Escherichia coli. The database contains information on E. coli proteins identified on 2D gels along with other resources collected from various databases and published literature, with a special feature of showing the expression levels of E.

Genome-scale analysis of Mannheimia succiniciproducens metabolism.

Mannheimia succiniciproducens MBEL55E isolated from bovine rumen is a capnophilic gram-negative bacterium that efficiently produces succinic acid, an industrially important four carbon dicarboxylic acid. In order to design a metabolically engineered strain which is capable of producing succinic acid with high yield and productivity, it is essential to optimize the whole metabolism at the systems level. Consequently, in silico modeling and simulation of the genome-scale metabolic network was employed for genome-scale analysis and efficient design of metabolic engineering experiments.