Production Of Target Metabolites Research Articles

Light-Exposed Metabolic Responses of Cordyceps militaris through Transcriptome-Integrated Genome-Scale Modeling.

The genome-scale metabolic model (GSMM) of Cordyceps militaris provides a comprehensive basis of carbon assimilation for cell growth and metabolite production. However, the model with a simple mass balance concept shows limited capability to probe the metabolic responses of C. militaris under light exposure. This study, therefore, employed the transcriptome-integrated GSMM approach to extend the investigation of C. militaris's metabolism under light conditions. Through the gene inactivity moderated by metabolism and expression (GIMME) framework, the iPS1474-tiGSMM model was furnished with the transcriptome data, thus providing a simulation that described reasonably well the metabolic responses underlying the phenotypic observation of C. militaris under the particular light conditions. The iPS1474-tiGSMM obviously showed an improved prediction of metabolic fluxes in correlation with the expressed genes involved in the cordycepin and carotenoid biosynthetic pathways under the sucrose culturing conditions. Further analysis of reporter metabolites suggested that the central carbon, purine, and fatty acid metabolisms towards carotenoid biosynthesis were the predominant metabolic processes responsible in light conditions. This finding highlights the key responsive processes enabling the acclimatization of C. militaris metabolism in varying light conditions. This study provides a valuable perspective on manipulating metabolic genes and fluxes towards the target metabolite production of C. militaris.

MetNetComp: Database for Minimal and Maximal Gene-Deletion Strategies for Growth-Coupled Production of Genome-Scale Metabolic Networks.

Growth-coupled production, in which cell growth forces the production of target metabolites, plays an essential role in the production of substances by microorganisms. The strains are first designed using computational simulation and then validated by biological experiments. In the simulations, gene-deletion strategies are often necessary because many metabolites are not produced in the natural state of the microorganisms. However, such information is not available for many metabolites owing to the requirement of heavy computation, especially when many gene deletions are required for genome-scale models. A database for such information will be helpful. However, developing such a database is not straightforward because heavy computation and the existence of replaceable genes render difficulty in efficient enumeration. In this study, the author developed efficient methods for enumerating minimal and maximal gene-deletion strategies and a web-based database system. MetNetComp provides information on 1) a total of 85,611 gene-deletion strategies excluding apparent duplicate counting for replaceable genes for 1,735 target metabolites, 11 constraint-based models, and 10 species; 2) necessary substrates and products in the process; and 3) reaction rates that can be used for visualization. MetNetComp is helpful for strain design and for new research paradigms using machine learning.

Modern Approaches to the Genome Editing of Antibiotic Biosynthetic Clusters in Actinomycetes.

Representatives of the phylum Actinomycetota are one of the main sources of secondary metabolites, including antibiotics of various classes. Modern studies using high-throughput sequencing techniques enable the detection of dozens of potential antibiotic biosynthetic genome clusters in many actinomycetes; however, under laboratory conditions, production of secondary metabolites amounts to less than 5% of the total coding potential of producer strains. However, many of these antibiotics have already been described. There is a continuous "rediscovery" of known antibiotics, and new molecules become almost invisible against the general background. The established approaches aimed at increasing the production of novel antibiotics include: selection of optimal cultivation conditions by modifying the composition of nutrient media; co-cultivation methods; microfluidics, and the use of various transcription factors to activate silent genes. Unfortunately, these tools are non-universal for various actinomycete strains, stochastic in nature, and therefore do not always lead to success. The use of genetic engineering technologies is much more efficient, because they allow for a directed and controlled change in the production of target metabolites. One example of such technologies is mutagenesis-based genome editing of antibiotic biosynthetic clusters. This targeted approach allows one to alter gene expression, suppressing the production of previously characterized molecules, and thereby promoting the synthesis of other unknown antibiotic variants. In addition, mutagenesis techniques can be successfully applied both to new producer strains and to the genes of known isolates to identify new compounds.

A novel, genetically encoded whole-cell biosensor for directed evolution of myrcene synthase in Escherichia coli

β-myrcene is a high-value acyclic monoterpene. The low activity of myrcene synthase resulted to low biosynthetic titer of it. Biosensor is a promising tool applied for enzyme directed evolution. In this work, a novel genetically encoded biosensor responding to myrcene was established based on the MyrR regulator from Pseudomonas sp. Through sensing promoter characterization and engineering, the biosensor exhibiting excellent specificity and dynamic range was developed, and applied for directed evolution of myrcene synthase. After high-throughput screening of the myrcene synthase random mutation library, the best mutant R89G/N152S/D517N was obtained. Its catalytic efficiency was 1.47-fold than that of parent. Based on the mutants, the final production of myrcene reached 510.38 mg/L, which is the highest myrcene titer reported to date. This work demonstrates the great potential of whole-cell biosensor for improving enzymatic activity and the production of target metabolite.

Metabolic profiling of bacterial co-cultures reveals intermicrobiome interactions and dominant species

In animal production, the use of probiotic microorganisms has increased since the ban on antibiotic growth promoters in 2006. The added microorganisms interact with the microbiome of the animals, whereby the probiotic activity is not fully understood. Several microorganisms of the genus Bacillus are already known for their probiotic activity and are applied as feed supplements to increase the health status of the animals. They are thought to interact with Escherichia coli, one of the most abundant bacteria in the animal gut. In biotechnological applications, co-culturing enables the regulation of bacterial interaction or the production of target metabolites. The basic principles of multi-imaging high-performance thin-layer chromatography (HPTLC) with upstream cultivation were further developed to analyze the metabolic profiles of three axenic bacilli cultures compared to their co-cultures with E. coli DSM 18039 (K12). The comparative profiling visualized bacteria's metabolic interactions and showed how the presence of E. coli affects the metabolite formation of bacilli. The characteristic metabolic profile images showed not only the influence of microbiomes but also of inoculation, cultivation and nutrients on the commercial probiotic. The formation of antimicrobially active metabolites, detected via three different planar bioassays, was influenced by the presence of other microorganisms, especially in the probiotic. This first application of multi-imaging HPTLC in the field of co-culturing enabled visualization of metabolic interactions of bacteria via their produced chemical as well as bioactive metabolite profiles. The metabolic profiling provided evidence of bacterial interactions, intermicrobiome influences and dominant species in the co-culture.

Gene Deletion Algorithms for Minimum Reaction Network Design by Mixed-Integer Linear Programming for Metabolite Production in Constraint-Based Models: gDel_minRN.

Genome-scale constraint-based metabolic networks play an important role in the simulation of growth-coupled production, which means that cell growth and target metabolite production are simultaneously achieved. For growth-coupled production, a minimal reaction-network-based design is known to be effective. However, the obtained reaction networks often fail to be realized by gene deletions due to conflicts with gene-protein-reaction (GPR) relations. Here, we developed gDel_minRN that determines gene deletion strategies using mixed-integer linear programming to achieve growth-coupled production by repressing the maximum number of reactions via GPR relations. The results of computational experiments showed that gDel_minRN could determine the core parts, which include only 30% to 55% of whole genes, for stoichiometrically feasible growth-coupled production for many target metabolites, which include useful vitamins such as biotin (vitamin B7), riboflavin (vitamin B2), and pantothenate (vitamin B5). Since gDel_minRN calculates a constraint-based model of the minimum number of gene-associated reactions without conflict with GPR relations, it helps biological analysis of the core parts essential for growth-coupled production for each target metabolite. The source codes, implemented in MATLAB using CPLEX and COBRA Toolbox, are available on https://github.com/MetNetComp/gDel-minRN.

Recent Advances in Chitin Biosynthesis Associated with the Morphology and Secondary Metabolite Synthesis of Filamentous Fungi in Submerged Fermentation.

Metabolites produced by filamentous fungi are used extensively in the food and drug industries. With the development of the morphological engineering of filamentous fungi, numerous biotechnologies have been applied to alter the morphology of fungal mycelia and enhance the yields and productivity of target metabolites during submerged fermentation. Disruption of chitin biosynthesis can modify the cell growth and mycelial morphology of filamentous fungi and regulate the biosynthesis of metabolites during submerged fermentation. In this review, we present a comprehensive coverage of the categories and structures of the enzyme chitin synthase, chitin biosynthetic pathways, and the association between chitin biosynthesis and cell growth and metabolism in filamentous fungi. Through this review, we hope to increase awareness of the metabolic engineering of filamentous fungal morphology, provide insights into the molecular mechanisms of morphological control via chitin biosynthesis, and describe strategies for the application of morphological engineering to enhance the production of target metabolites in filamentous fungi during submerged fermentation.

Dynamic Solution Space Division-Based Methods for Calculating Reaction Deletion Strategies for Constraint-Based Metabolic Networks for Substance Production: DynCubeProd.

Flux balance analysis (FBA) is a crucial method to analyze large-scale constraint-based metabolic networks and computing design strategies for strain production in metabolic engineering. However, as it is often non-straightforward to obtain such design strategies to produce valuable metabolites, many tools have been proposed based on FBA. Among them, GridProd, which divides the solution space into small squares by focusing on the cell growth rate and the target metabolite production rate to efficiently find the reaction deletion strategies, was extended to CubeProd, which divides the solution space into small cubes. However, as GridProd and CubeProd naively divide the solution space into equal sizes, even places where solutions are unlikely to exist are examined. To address this issue, we introduce dynamic solution space division methods based on CubeProd for faster computing by avoiding searching in places where the solutions do not exist. We applied the proposed method DynCubeProd to iJO1366, which is a genome-scale constraint-based model of Escherichia coli. Compared with CubeProd, DynCubeProd significantly accelerated the calculation of the reaction deletion strategy for each target metabolite production. In addition, under the anaerobic condition of iJO1366, DynCubeProd could obtain the reaction deletion strategies for almost 40% of the target metabolites that the elementary flux vector-based method, which is one of the most effective methods in existence, could not. The developed software is available on https://github.com/Ma-Yier/DynCubeProd.

L1 Norm Minimal Mode-Based Methods for Listing Reaction Network Designs for Metabolite Production

Metabolic networks represent the relationship between chemical reactions and compounds in cells. In useful metabolite production using microorganisms, it is often required to calculate reaction deletion strategies from the original network to result in growth coupling, which means the target metabolite production and cell growth are simultaneously achieved. Although simple elementary flux mode (EFM)-based methods are useful for listing such reaction deletions strategies, the number of cases to be considered is often proportional to the exponential function of the size of the network. Therefore, it is desirable to develop methods of narrowing down the number of reaction deletion strategy candidates. In this study, the author introduces the idea of L1 norm minimal modes to consider metabolic flows whose L1 norms are minimal to satisfy certain criteria on growth and production, and developed a fast metabolic design listing algorithm based on it (minL1-FMDL), which works in polynomial time. Computational experiments were conducted for (1) a relatively small network to compare the performance of minL1-FMDL with that of the simple EFM-based method and (2) a genome-scale network to verify the scalability of minL1-FMDL. In the computational experiments, it was seen that the average value of the target metabolite production rates of minL1-FMDL was higher than that of the simple EFM-based method, and the computation time of minL1-FMDL was fast enough even for genome-scale networks. The developed software, minL1-FMDL, implemented in MATLAB, is available on https://sunflower.kuicr.kyoto-u.ac.jp/~tamura/software, and can be used for genome-scale metabolic network design for metabolite production.

Heterologous Metabolic Pathways: Strategies for Optimal Expression in Eukaryotic Hosts

Heterologous pathways are linked series of biochemical reactions occurring in a host organism after the introduction of foreign genes. Incorporation of metabolic pathways into host organisms is a major strategy used to increase the production of valuable secondary metabolites. Unfortunately, simple introduction of the pathway genes into the heterologous host in most cases does not result in successful heterologous expression. Extensive modification of heterologous genes and the corresponding enzymes on many different levels is required to achieve high target metabolite production rates. This review summarizes the essential techniques used tocreate heterologous biochemical pathways, with a focus on the key challenges arising in the process and the major strategies for overcoming them.

Enhanced production of bacitracin via energy metabolism engineering in Bacillus licheniformis DW2

Bacitracin is a broad-spectrum cyclic peptide antibiotic, and mainly produced by Bacillus. Energy metabolism plays as a critical role in high-level production of target metabolites. In this study, Bacillus licheniformis DW2, an industrial strain for bacitracin production, was served as the original strain. First, our results confirmed that elimination of cytochrome bd oxidase branch via deleting gene cydB benefited bacitracin synthesis. Bacitracin titer and ATP content were increased by 10.97% and 22.96%, compared with those of original strain, respectively. Then, strengthening cytochrome aa3 oxidase branch via overexpressing gene qoxA was conducive to bacitracin production. Bacitracin titer and ATP content were increased by 18.97% and 34.00%, respectively. In addition, strengthening ADP synthesis supply is also proven as an effective strategy to promote intracellular ATP accumulation, overexpression of adenosine kinase DcK and adenylate kinase AdK could all improve bacitracin titers, among which, dck overexpression strain showed the better performance, and bacitracin titer was increased by 16.78%. Based on the above individual methods, a method of combining the deletion of gene cydB and overexpression of genes qoxA, dck were used to enhance ATP content of cells to 39.54 nmol/L, increased by 49.32% compared to original strain, and bacitracin titer produced by the final strain DW2-CQD (DW2ΔcydB::qoxA::dck) was 954.25 U/mL, increased by 21.66%. The bacitracin titer produced per cell was 2.11 U/CFU, increased by 11.05%. Collectively, this study demonstrates that improving ATP content was an efficient strategy to improve bacitracin production, and a promising strain B. licheniformis DW2-CQD was attained for industrial production of bacitracin.

Heterologous Metabolic Pathways: Strategies for Optimal Expression in Eukaryotic Hosts.

Heterologous pathways are linked series of biochemical reactions occurring in a host organism after the introduction of foreign genes. Incorporation of metabolic pathways into host organisms is a major strategy used to increase the production of valuable secondary metabolites. Unfortunately, simple introduction of the pathway genes into the heterologous host in most cases does not result in successful heterologous expression. Extensive modification of heterologous genes and the corresponding enzymes on many different levels is required to achieve high target metabolite production rates. This review summarizes the essential techniques used to create heterologous biochemical pathways, with a focus on the key challenges arising in the process and the major strategies for overcoming them.

Enhanced production of heterologous proteins via engineering the cell surface of Bacillus licheniformis.

Cell surface engineering was proven as the efficient strategy for enhanced production of target metabolites. In this study, we want to improve the yield of target protein by engineering cell surface in Bacillus licheniformis. First, our results confirmed that deletions of D-alanyl-lipoteichoic acid synthetase gene dltD, cardiolipin synthase gene clsA and CDP-diacylglycerol-serine O-phosphatidyltransferase gene pssA were not conducive to cell growth, and the biomass of gene deletion strains were, respectively, decreased by 10.54 ± 1.43%, 14.17 ± 1.51%, and 17.55 ± 1.28%, while the concentrations of total extracellular proteins were improved, due to the increases of cell surface net negative charge and cell membrane permeability. In addition, the activities of target proteins, nattokinase, and α-amylase were also improved significantly in gene deletion strains. Furthermore, the triplicate gene (dltD, clsA, and pssA) deletion strain was constructed, which further led to the 45.71 ± 2.43% increase of cell surface net negative charge and 26.45 ± 2.31% increase of cell membrane permeability, and the activities of nattokinase and α-amylase reached 37.15 ± 0.89FU/mL and 305.3 ± 8.4U/mL, increased by 46.09 ± 3.51% and 96.34 ± 7.24%, respectively. Taken together, our results confirmed that cell surface engineering via deleting dltD, clsA, and pssA is an efficient strategy for enhanced production of target proteins, and this research provided a promising host strain of B. licheniformis for efficient protein expression.

Determination of growth-coupling strategies and their underlying principles

BackgroundMetabolic coupling of product synthesis and microbial growth is a prominent approach for maximizing production performance. Growth-coupling (GC) also helps stabilizing target production and allows the selection of superior production strains by adaptive laboratory evolution. To support the implementation of growth-coupling strain designs, we seek to identify biologically relevant, metabolic principles that enforce strong growth-coupling on the basis of reaction knockouts.ResultsWe adapted an established bilevel programming framework to maximize the minimally guaranteed production rate at a fixed, medium growth rate. Using this revised formulation, we identified various GC intervention strategies for metabolites of the central carbon metabolism, which were examined for GC generating principles under diverse conditions. Curtailing the metabolism to render product formation an essential carbon drain was identified as one major strategy generating strong coupling of metabolic activity and target synthesis. Impeding the balancing of cofactors and protons in the absence of target production was the underlying principle of all other strategies and further increased the GC strength of the aforementioned strategies.ConclusionMaximizing the minimally guaranteed production rate at a medium growth rate is an attractive principle for the identification of strain designs that couple growth to target metabolite production. Moreover, it allows for controlling the inevitable compromise between growth coupling strength and the retaining of microbial viability. With regard to the corresponding metabolic principles, generating a dependency between the supply of global metabolic cofactors and product synthesis appears to be advantageous in enforcing strong GC for any metabolite. Deriving such strategies manually, is a hard task, due to which we suggest incorporating computational metabolic network analyses in metabolic engineering projects seeking to determine GC strain designs.

Magnesium starvation improves production of malonyl-CoA-derived metabolites in Escherichia coli

Starvation of essential nutrients, such as nitrogen, sulfur, magnesium, and phosphorus, leads cells into stationary phase and potentially enhances target metabolite production because cells do not consume carbon for the biomass synthesis. The overall metabolic behavior changes depend on the type of nutrient starvation in Escherichia coli. In the present study, we determined the optimum nutrient starvation type for producing malonyl-CoA-derived metabolites such as 3-hydroxypropionic acid (3HP) and naringenin in E. coli. For 3HP production, high production titer (2.3 or 2.0 mM) and high specific production rate (0.14 or 0.28 mmol gCDW−1 h−1) was observed under sulfur or magnesium starvation, whereas almost no 3HP production was detected under nitrogen or phosphorus starvation. Metabolic profiling analysis revealed that the intracellular malonyl-CoA concentration was significantly increased under the 3HP producing conditions. This accumulation should contribute to the 3HP production because malonyl-CoA is a precursor of 3HP. Strong positive correlation (r = 0.95) between intracellular concentrations of ATP and malonyl-CoA indicates that the ATP level is important for malonyl-CoA synthesis due to the ATP requirement by acetyl-CoA carboxylase. For naringenin production, magnesium starvation led to the highest production titer (144 ± 15 μM) and specific productivity (127 ± 21 μmol gCDW−1). These results demonstrated that magnesium starvation is a useful approach to improve the metabolic state of strains engineered for the production of malonyl-CoA derivatives.

Homolactic Acid Fermentation by the Genetically Engineered Thermophilic Homoacetogen Moorella thermoacetica ATCC 39073.

For the efficient production of target metabolites from carbohydrates, syngas, or H2-CO2 by genetically engineered Moorella thermoacetica, the control of acetate production (a main metabolite of M. thermoacetica) is desired. Although propanediol utilization protein (PduL) was predicted to be a phosphotransacetylase (PTA) involved in acetate production in M. thermoacetica, this has not been confirmed. Our findings described herein directly demonstrate that two putative PduL proteins, encoded by Moth_0864 (pduL1) and Moth_1181 (pduL2), are involved in acetate formation as PTAs. To disrupt these genes, we replaced each gene with a lactate dehydrogenase gene from Thermoanaerobacter pseudethanolicus ATCC 33223 (T-ldh). The acetate production from fructose as the sole carbon source by the pduL1 deletion mutant was not deficient, whereas the disruption of pduL2 significantly decreased the acetate yield to approximately one-third that of the wild-type strain. The double-deletion (both pduL genes) mutant did not produce acetate but produced only lactate as the end product from fructose. These results suggest that both pduL genes are associated with acetate formation via acetyl-coenzyme A (acetyl-CoA) and that their disruption enables a shift in the homoacetic pathway to the genetically synthesized homolactic pathway via pyruvate.IMPORTANCE This is the first report, to our knowledge, on the experimental identification of PTA genes in M. thermoacetica and the shift of the native homoacetic pathway to the genetically synthesized homolactic pathway by their disruption on a sugar platform.

Metabolome analysis and pathway abundance profiling of Yarrowia lipolytica cultivated on different carbon sources

Yarrowia lipolytica, a model microorganism of oleaginous yeasts with developed sophisticated genetic tools, is able to metabolize a wide range of substrates and accumulate large amounts of lipids. However, there is a lack of literature reporting the metabolic characteristics of Y. lipolytica metabolizing these substrates in a systematic view. In this study, Y. lipolytica was cultivated on a variety of carbon sources, among which cell growth and production characteristics on two representative substrates (glucose and oleic acid) were investigated in detail at metabolomic level. Metabolic pathway abundance was computed to interpret the metabolome data in a straightforward way. The results showed that most pathway abundances decreased in the shift from growth to production phase. Specifically, when cultivated on glucose, abundances of twelve pathways decreased markedly between the growth and lipid production phases, while thirteen pathways reduced and only three pathways increased significantly in abundances on oleic acid. In comparison, for the same cultivation phase only a few pathways exhibited significant changes between glucose-grown and oleic acid-grown cells. This study revealed that the pathway abundance could be used to effectively show the activity changes of pathways, providing a new perspective to employ metabolomics data for understanding cell metabolism and enhancing the production of target metabolites.

Methylation and subsequent glycosylation of 7,8-dihydroxyflavone

An O-methyltransferase SpOMT2884, originating from Streptomyces peucetius ATCC 27952, was cloned, expressed, and applied for the production of target metabolite from Escherichia coli. Biochemical characterization of the 25kDa recombinant protein by in vitro and in vivo experiments showed that SpOMT2884 was an S-adenosyl-l-methionine-dependent O-methyltransferase. SpOMT2884 catalyzed O-methylation of different classes of flavonoids such as flavones (7,8-dihydroxyflavone (7,8-DHF), luteolin), flavonols (quercetin, rutin), flavanone (naringenin), and isoflavonoids (daidzein, formononetin). Biotransformation of 7,8-DHF, a preferred substrate of SpOMT2884, in a grown-induced culture of E. coli BL21 (DE3) harboring the recombinant pET-28a-SpOMT2884 stoichiometrically converted 7,8-DHF into 7-hydroxy-8-methoxyflavone, which was confirmed by liquid chromatography, mass spectrometry and various nuclear magnetic resonance (NMR) spectroscopy analyses. In order to improve the biotransformation substrate, time and media parameters were optimized and the production was scaled up using a 3-L fermentor. The maximum yield of 7-hydroxy-8-methoxyflavone was 192μM (52.57mg/L), representing almost 96% bioconversion within 12h, when 200μM of 7,8-DHF was supplemented in the culture. Further, the 7-hydroxy-8-methoxyflavone was purified in large scale and was used as a substrate separately for in vitro glycosylation to produce glucose, galactose and 2-deoxyglucose conjugated at 7th hydroxyl position of 7-hydroxy-8-methoxyflavone. Biological activity showed that 7-hydroxy-8-methoxyflavone had long term cytoprotective and antioxidant effects compared to 7,8-DHF suggesting that methylation enhances the stability of substrate and glycosylation has proved to increase the water solubility.

Systems metabolic engineering for the production of bio‐nylon precursor

Systematic approaches for in silico design of metabolic pathways and experimental evaluation of cellular performance are important for the development of industrially useful organisms. In this issue of Biotechnology Journal, Buschke et al. [1] report the production of 1,5-diaminopentane using xylose produced by Corynebacterium glutamicum by employing integration methodologies of rational design for genetic improvement, experimental analysis of metabolic fluxes, and comprehensive gene expression. Recent progress in 'omics sciences has facilitated the analysis of microorganisms for molecular breeding. There has been increasing interest for the integration of in silico and experimental approaches for the development of microbial cells for industrial applications. In silico simulations aid the rational design of metabolic pathways for the production of target compounds. On the basis of the in silico simulation data, the host organism is genetically manipulated to improve target metabolite production [2, 3]. The 13C-metabolic flux analysis method is useful for determining the flux distribution; this method requires experimental data procured from genetically manipulated strains [4, 5]. Identification of the genes that confer useful phenotypes for cells on the basis of DNA microarray analysis is also important [6]. The integration method, which combines in silico and experimental approaches for the development of cell factories is shown in Fig. 1. Many applications of the integration method for the bioproduction of chemicals and fuels have been reported [7, 8]. Concept of systems metabolic engineering. Production of 1,5-diaminopentane, a bio-nylon precursor, is reported by Buschke et al. [1], in this Special Issue “Systems Metabolic Engineering” of Biotechnology Journal. Buschke et al.'s study [1] is a good example of the successful application of bioengineering that contributes to the recent progress in systems metabolic engineering. Diaminopentane is produced by a one-step reaction from lysine by C. glutamicum, which is a well-known amino acid-producing bacterium and has recently used for chemical production as well [9, 10]. The authors have previously developed a high-producing strain that converts glucose to diaminopentane [11]. In the current study [1], a novel strain which produces diaminopentane from xylose was developed. Xylose utilization is particularly useful because xylose is a main component of lignocellulose waste, which is widely available. The previously developed diaminopentane-producing strain, DAP-Xyl1, produce diaminopentane less efficiently from xylose than from glucose. Thus, Buschke et al. [1] apply a systematic approach to improve diaminopentane production. In silico elementary flux mode analysis predicts an ideal metabolic state for diaminopentane production. The theoretical maximum yield from xylose is predicted, and the analysis reveal that the high oxidative pentose phosphate pathway (PPP) flux and the zero flux of the tricarboxylic acid (TCA) cycle are important for diaminopentane production; however, the metabolic flux of these pathways in the strain DAP-Xyl1 does not show good agreement with ideal metabolic flux for diaminopentane production. The small flux of PPP and the large flux of the TCA cycle cause low production of diaminopentane by the strain DAP-Xyl1. Additionally, gene selection is performed on the basis of the transcriptome analysis results. The novel, rationally designed strain DAP-Xyl2 successfully produce more than 100 g/L of diaminopentane after 75 h of cultivation. This study demonstrates the development process of a high performance strain of C. glutamicum and the importance of systems metabolic engineering approach for developing an industrially useful microorganism [7, 12]. The author declares no conflict of interest.

Metabolic Engineering of Bacteria

Yield and productivity are critical for the economics and viability of a bioprocess. In metabolic engineering the main objective is the increase of a target metabolite production through genetic engineering. Metabolic engineering is the practice of optimizing genetic and regulatory processes within cells to increase the production of a certain substance. In the last years, the development of recombinant DNA technology and other related technologies has provided new tools for approaching yields improvement by means of genetic manipulation of biosynthetic pathway. Industrial microorganisms like Escherichia coli, Actinomycetes, etc. have been developed as biocatalysts to provide new or to optimize existing processes for the biotechnological production of chemicals from renewable plant biomass. The factors like oxygenation, temperature and pH have been traditionally controlled and optimized in industrial fermentation in order to enhance metabolite production. Metabolic engineering of bacteria shows a great scope in industrial application as well as such technique may also have good potential to solve certain metabolic disease and environmental problems in near future.