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. Due to its mucoid forming characteristics, UV-random mutagenesis was carried out to obtain a mucoid-free strain, 4071-15. As a result, capabilities of 4071-15 strain in terms of transformation efficiency of bacillus plasmids (pC194, pUB110, and pUCB129) and fermentation performance were highly upgraded compared to those of the parent strain. In particular, 4071-15 strain could produce 123 g/L of 2,3-BDO in a fed-batch fermentation in which the ratio of (2R,3S)- to (2R,3R)-form isomers was 1:1. To increase the selectivity of (2R,3R)-BDO, budC gene was deleted by using temperature-sensitive gene deletion process via homologous recombination. The 4071-15 △budC mutant strain dramatically increased selectivity of (2R,3R)-BDO to 91% [96.3 g/L of (2R,3R)-BDO and 9.33 g/L of (2R,3S)-BDO], which was 43% higher than that obtained by the parent strain. This study has shown the potential of an isolate for 2,3-BDO production, and that the ratio of 2,3-BDO can be controlled by genetic engineering depending on its industrial usage.

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. In particular, many 2,3-BD-producing microorganisms including bacteria and yeast have been isolated and metabolically engineered for efficient production of 2,3-BD. In addition, several fermentation processes have been tested using feedstocks such as starch, sugar, glycerol, and even lignocellulose as raw materials. Since separation and purification of 2,3-BD from fermentation broth account for the majority of its production cost, cost-effective processes have been simultaneously developed. The construction of a demonstration plant that can annually produce around 300 tons of 2,3-BD is scheduled to be mechanically completed in Korea in 2019. In this paper, core technologies for bio-based 2,3-BD production are reviewed and their potentials for use in the commercial sector are discussed.

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. In addition, the agitation speed, which is known to strongly affect 1,3-PD and 2,3-BD production in Klebsiella strains, was optimized. The K. pneumoniae ΔldhA Δmdh strain produced 125 g/L of diols (1,3-PD and 2,3-BD) with a productivity of 2.0 g/L/h in the lab-scale (5-L bioreactor) fed-batch fermentation using high-quality guaranteed reagent grade glycerol. To evaluate the industrial capacity of the constructed K. pneumoniae ΔldhA Δmdh strain, a pilot-scale (5000-L bioreactor) fed-batch fermentation was carried out using crude glycerol obtained from the industrial biodiesel plant. The pilot-scale fed-batch fermentation of the K. pneumoniae ΔldhA Δmdh strain produced 114 g/L of diols (70 g/L of 1,3-PD and 44 g/L of 2,3-BD), with a yield of 0.60 g diols per gram glycerol and a productivity of 2.2 g/L/h of diols, which should be suitable for the industrial co-production of 1,3-PD and 2,3-BD.

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 0.45 g per g glucose and a productivity of 1.62 g/l.h in fed-batch fermentation. The metabolic characteristics of the mutants were consistent with the results of in silico simulation. CONCLUSIONS: K. pneumoniae knockout mutants developed with an aid of in silico investigation could produce higher amounts of 2,3-BD with increased titer, yield, and productivity.

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. Engineered E. coli strains capable of producing homo-acetic and homo-lactic acids could be developed by taking this systems approach for the minimal identification of gene knockout targets. Also, failure to predict effective gene knockout targets for the homo-succinic acid production suggests that the multi-objective optimization is useful in assessing the suitability of a microorganism as a host strain for the production of a homo-organic acid. The systems metabolic engineering-based approach reported here should be applicable to the production of other industrially important organic acids.

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. A budC gene encoding 2,3-BD dehydrogenase in the K. oxytoca ΔldhA ΔpflB strain was replaced with an exogenous gene encoding (R,R)-2,3-BD dehydrogenase from Paenibacillus polymyxa (K. oxytoca ΔldhA ΔpflB ΔbudC::PBDH strain), and then its expression level was further amplified with using a pBBR1MCS plasmid. The fed-batch fermentation of the K. oxytoca ΔldhA ΔpflB ΔbudC::PBDH (pBBR-PBDH) strain with intermittent glucose feeding allowed the production of 106.7 g/L of (R,R)-2,3-BD [meso-2,3-BD, 9.3 g/L], with a yield of 0.40 g/g and a productivity of 3.1 g/L/h, which should be useful for the industrial application of 2,3-BD.

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. Here we discuss the general strategies of systems metabolic engineering and examples of its application and offer insights as to when and how each of the different strategies should be used. Finally, we highlight the limitations and challenges to be overcome for the systems metabolic engineering of microorganisms at more advanced levels.

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. Provision of the complete genome sequence of K. oxytoca enabled the construction of genome-scale metabolic model of K. oxytoca and its in silico analyses. RESULTS: The genome-scale metabolic model of K. oxytoca was constructed using the annotated genome with biochemical and physiological information. The stoichiometric model, KoxGSC1457, is composed of 1,457 reactions and 1,099 metabolites. The model was further refined by applying biomass composition equations and comparing in silico results with experimental data based on constraints-based flux analyses. Then, the model was applied to in silico analyses to understand the properties of K. oxytoca and also to improve its capabilities for 2,3-BD production according to genetic and environmental perturbations. Firstly, in silico analysis, which tested the effect of augmenting the metabolic flux pool of 2,3-BD precursors, elucidated that increasing the pyruvate pool is primarily important for 2,3-BD synthesis. Secondly, we performed in silico single gene knockout simulation for 2,3-BD overproduction, and investigated the changes of the in silico flux solution space of a ldhA gene knockout mutant in comparison with that of the wild-type strain. Finally, the KoxGSC1457 model was used to optimize the oxygen levels during fermentation for 2,3-BD production. CONCLUSIONS: The genome-scale metabolic model, KoxGSC1457, constructed in this study successfully investigated metabolic characteristics of K. oxytoca at systems level. The KoxGSC1457 model could be employed as an useful tool to analyze its metabolic capabilities, to predict its physiological responses according to environmental and genetic perturbations, and to design metabolic engineering strategies to improve its metabolic performance.

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. The knockouts of ldhA and pflB genes were targeted with the criteria of maximization of 2,3-BD production and minimization of byproducts formation. The constructed K. oxytoca ΔldhA ΔpflB strain showed higher 2,3-BD yields and higher final concentrations than those obtained from the wild-type and ΔldhA strains. However, the simultaneous deletion of both genes caused about a 50 % reduction in 2,3-BD productivity compared with K. oxytoca ΔldhA strain. Based on previous studies and in silico investigation that the agitation speed during 2,3-BD fermentation strongly affected cell growth and 2,3-BD synthesis, the effect of agitation speed on 2,3-BD production was investigated from 150 to 450 rpm in 5-L bioreactors containing 3-L culture media. The highest 2,3-BD productivity (2.7 g/L/h) was obtained at 450 rpm in batch fermentation. Considering the inhibition of acetoin for 2,3-BD production, fed-batch fermentations were performed using K. oxytoca ΔldhA ΔpflB strain to enhance 2,3-BD production. Altering the agitation speed from 450 to 350 rpm at nearly 10 g/L of acetoin during the fed-batch fermentation allowed for the production of 113 g/L 2,3-BD, with a yield of 0.45 g/g, and for the production of 2.1 g/L/h of 2,3-BD.

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. Analyses of three types of genomic contexts, conserved genomic neighborhood, gene fusion events, and co-occurrence of genes across multiple organisms, were performed to suggest a group of fluxes that are likely on or off simultaneously. The flux ranges of these grouped reactions were constrained by flux-converging pattern analysis. FBA of the Escherichia coli genome-scale metabolic model was carried out under several different genotypic (pykF, zwf, ppc, and sucA mutants) and environmental (altered carbon source) conditions by applying these constraints, which resulted in flux values that were in good agreement with the experimentally measured (13)C-based fluxes. Thus, this strategy will be useful for accurately predicting the intracellular fluxes of large metabolic networks when their experimental determination is difficult.

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. licheniformis 4071-15 strain. With the help of a CRISPR-Cas9 system, this one-step process could be used for the deletion of ldh gene within 4 days with high-efficiency exceeding 60%. In addition, the sequential deletion of target genes for engineering studies was evaluated, and it was confirmed that a triple mutant strain (ldh, dgp, and acoR) could be obtained by repeated one-step cycles. Furthermore, a practical metabolic engineering study was carried out using a CRISPR-Cas9 system for the stereospecific production of (2R,3S)-BDO. The predicted (2R,3R)-butanediol dehydrogenase encoded by the gdh gene was selected as a target for the production of (2R,3S)-BDO, and the mutant was successfully obtained. The results show that the stereospecific production of (2R,3S)-BDO was possible with the gdh deletion mutant, while the 4071-15 host strain still generated 26% of (2R,3R)-BDO. It was also shown that the 4071-15 Δgdh mutant could produce 115 g/L of (2R,3S)-BDO in 64 hr by two-stage fed-batch fermentation. This study has shown the efficient development of a (2R,3S)-BDO producing B. licheniformis strain based on CRISPR-Cas9 and fermentation technologies.

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. mobilis is required to systematically analyze in silico the metabolic characteristics of this bacterium under a wide range of genotypic and environmental conditions. RESULTS: The 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. CONCLUSION: The 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.

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. coli proteins under different genetic and environmental conditions. It also provides comparative information of subcellular localization, theoretical 2D map, experimental 2D map and integrated protein information via an interactive web interface and application such as the Map Browser. Users can also upload their own 2D gels, extract core information associated with the proteins and 2D gel results from different experiments and consequently generate new knowledge and hypotheses for further studies. AVAILABILITY: EcoProDB database system is accessible at http://eecoli.kaist.ac.kr.

Isolation and Evaluation of Bacillus 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 Bacillus strains were further screened by morphological, biochemical, and genomic analyses. The screened strains were then used to test the utilization of the most common carbon (glucose, xylose, fructose, sucrose) and nitrogen (yeast extract, corn steep liquor) sources for the economical production of 2,3-BDO. Two-stage fed-batch fermentation was finally carried out to enhance 2,3-BDO production. In consequence, a newly isolated Bacillus licheniformis GSC3102 strain produced 92.0 g/l of total 2,3-BDO with an overall productivity and yield of 1.40 g/l/h and 0.423 g/g glucose, respectively, using a cheap and abundant nitrogen source. These results strongly suggest that B. licheniformis, which is found widely in nature, can be used as a host strain for the industrial fermentative production of 2,3-BDO.

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. Systems metabolic engineering allows successful development of microbes that are capable of producing several different biofuels including bioethanol, biobutanol, alkane, and biodiesel, and even hydrogen. In this review, the approaches employed to develop efficient biofuel producers by metabolic engineering and systems metabolic engineering approaches are reviewed with relevant example cases. It is expected that systems metabolic engineering will be employed as an essential strategy for the development of microbial strains for industrial applications.

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. Here, we report a new algorithm which incorporates physiological data into a model to improve the model's prediction capabilities and to capitalize on the relationships between genes and metabolic fluxes. RESULTS: We developed an algorithm, flux variability scanning based on enforced objective flux (FVSEOF) with grouping reaction (GR) constraints, in an effort to identify gene amplification targets by considering reactions that co-carry flux values based on physiological omics data via "GR constraints". This method scans changes in the variabilities of metabolic fluxes in response to an artificially enforced objective flux of product formation. The gene amplification targets predicted using this method were validated by comparing the predicted effects with the previous experimental results obtained for the production of shikimic acid and putrescine in Escherichia coli. Moreover, new gene amplification targets for further enhancing putrescine production were validated through experiments involving the overexpression of each identified targeted gene under condition-controlled batch cultivation. CONCLUSIONS: FVSEOF with GR constraints allows identification of gene amplification targets for metabolic engineering of microbial strains in order to enhance the production of desired bioproducts. The algorithm was validated through the experiments on the enhanced production of putrescine in E. coli, in addition to the comparison with the previously reported experimental data. The FVSEOF strategy with GR constraints will be generally useful for developing industrially important microbial strains having enhanced capabilities of producing chemicals of interest.

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. 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.

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. Practically, MFA has been applied to generate new knowledge on the biological system, analyze cellular physiology system-wide, and consequently design metabolic engineering strategies at a systems-level. In this chapter, we study the basic principle of MFA (more particularly constraints-based flux analysis), inspect the characteristics of several in silico algorithms developed for system-wide analysis of cellular metabolic fluxes, and discuss their applications.

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. These allow the illumination of objects only in desired directions. Set up as an array, the Lucius prism can function as an autostereoscopic three-dimensional display.

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. With the construction of the genome-scale metabolic model of P. putida KT2440, PpuMBEL1071, we investigated various characteristics of P. putida, such as its capacity for synthesizing polyhydroxyalkanoates (PHA) and degrading aromatics. Although P. putida has been characterized as a strict aerobic bacterium, the physiological characteristics required to achieve anaerobic survival were investigated. Through analysis of PpuMBEL1071, extended survival of P. putida under anaerobic stress was achieved by introducing the ackA gene from Pseudomonas aeruginosa and Escherichia coli.