Network rigidity and metabolic engineering in metabolite overproduction

Improved Polysaccharide Production by Homologous Co-overexpression of Phosphoglucomutase and UDP Glucose Pyrophosphorylase Genes in the Mushroom Coprinopsis cinerea

Coprinopsis polysaccharides exhibit hypoglycemic and antioxidant activities. In this report, increases in polysaccharide production by homologous co-overexpression or individual homologous overexpression of phosphoglucomutase and UDP glucose pyrophosphorylase gene in Coprinopsis cinerea, which participate in polysaccharide biosynthesis. The transcription levels of the target genes were upregulated significantly in the oePGM-UGP strain when compared with the oePGM or oeUGP strain. The maximum intracellular polysaccharide content obtained in the oePGM-UGP strain was 1.49-fold higher than that of the WT strain, whereas a slight improvement in polysaccharide production was obtained in the oePGM and oeUGP strains. Extracellular polysaccharide production was enhanced by 75% in the oePGM-UGP strain when compared with that of the WT strain, whereas improvements of 30% and 16% were observed for the oePGM and oeUGP strains, respectively. These results show that multiple interventions in polysaccharide biosynthesis pathways of Basidiomycetes might improve polysaccharide yields when compared with that of single interventions.

Incorporating comparative genomics into the design-test-learn cycle of microbial strain engineering

Engineering microbes with new properties is an important goal in industrial engineering, to establish biological factories for production of biofuels, commodity chemicals and pharmaceutics. But engineering microbes to produce new compounds with high yield remains a major challenge toward economically viable production. Incorporating several modern approaches, including synthetic and systems biology, metabolic modeling and regulatory rewiring, has proven to significantly advance industrial strain engineering. This review highlights how comparative genomics can also facilitate strain engineering, by identifying novel genes and pathways, regulatory mechanisms and genetic background effects for engineering. We discuss how incorporating comparative genomics into the design-test-learn cycle of strain engineering can provide novel information that complements other engineering strategies.

The plasticity of cyanobacterial carbon metabolism

This opinion article aims to raise awareness of a fundamental issue which governs sustainable production of biofuels and bio-chemicals from photosynthetic cyanobacteria. Discussed is the plasticity of carbon metabolism, by which the cyanobacterial cells flexibly distribute intracellular carbon fluxes towards target products and adapt to environmental/genetic alterations. This intrinsic feature in cyanobacterial metabolism is being understood through recent identification of new biochemical reactions and engineering on low-throughput pathways. We focus our discussion on new insights into the nature of metabolic plasticity in cyanobacteria and its impact on hydrocarbons (e.g. ethylene and isoprene) production. We discuss approaches that need to be developed to rationally rewire photosynthetic carbon fluxes throughout primary metabolism. We outline open questions about the regulatory mechanisms of the metabolic network that remain to be answered, which might shed light on photosynthetic carbon metabolism and help optimize design principles in order to improve the production of fuels and chemicals in cyanobacteria.

Systems Biology of Metabolism

Metabolism is highly complex and involves thousands of different connected reactions; it is therefore necessary to use mathematical models for holistic studies. The use of mathematical models in biology is referred to as systems biology. In this review, the principles of systems biology are described, and two different types of mathematical models used for studying metabolism are discussed: kinetic models and genome-scale metabolic models. The use of different omics technologies, including transcriptomics, proteomics, metabolomics, and fluxomics, for studying metabolism is presented. Finally, the application of systems biology for analyzing global regulatory structures, engineering the metabolism of cell factories, and analyzing human diseases is discussed.

System wide cofactor turnovers can propagate metabolic stability between pathways

Metabolic homeostasis, or low-level metabolic steady state, has long been taken for granted in metabolic engineering, and research priority has always been given to understand metabolic flux control and regulation of the reaction network. In the past, this has not caused concerns because the metabolic networks studied were invariably associated with living cells. Nowadays, there are needs to reconstruct metabolic networks, and so metabolic homeostasis cannot be taken for granted. For metabolic steady state, enzyme feedback control has been known to explain why metabolites in metabolic pathways can avoid accumulation. However, we reasoned that there are further contributing mechanisms. As a new methodology developed, we separated cofactor intermediates (CIs) from non-cofactor intermediates, and identified an appropriate type of open systems for operating putative reaction topologies. Furthermore, we elaborated the criteria to tell if a multi-enzyme over-all reaction path is of in vivo nature or not at the metabolic level. As new findings, we discovered that there are interactions between the enzyme feedback inhibition and the CI turnover, and such interactions may well lead to metabolic homeostasis, an emergent property of the system. To conclude, this work offers a new perspective for understanding the role of CIs and the presence of metabolic homeostasis in the living cell. In perspective, this work might provide clues for constructing non-natural metabolic networks using multi-enzyme reactions or by degenerating metabolic reaction networks from the living cell.

The Landscape of Evolution: Reconciling Structural and Dynamic Properties of Metabolic Networks in Adaptive Diversifications

The network of the interactions among genes, proteins, and metabolites delineates a range of potential phenotypic diversifications in a lineage, and realized phenotypic changes are the result of differences in the dynamics of the expression of the elements and interactions in this deterministic network. Regulatory mechanisms, such as hormones, mediate the relationship between the structural and dynamic properties of networks by determining how and when the elements are expressed and form a functional unit or state. Changes in regulatory mechanisms lead to variable expression of functional states of a network within and among generations. Functional properties of network elements, and the magnitude and direction of evolutionary change they determine, depend on their location within a network. Here, we examine the relationship between network structure and the dynamic mechanisms that regulate flux through a metabolic network. We review the mechanisms that control metabolic flux in enzymatic reactions and examine structural properties of the network locations that are targets of flux control. We aim to establish a predictive framework to test the contributions of structural and dynamic properties of deterministic networks to evolutionary diversifications.

Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming

13C metabolic flux analysis (13C-MFA) has been widely used to measure in vivo enzyme reaction rates (i.e., metabolic flux) in microorganisms. Mining the relationship between environmental and genetic factors and metabolic fluxes hidden in existing fluxomic data will lead to predictive models that can significantly accelerate flux quantification. In this paper, we present a web-based platform MFlux (http://mflux.org) that predicts the bacterial central metabolism via machine learning, leveraging data from approximately 100 13C-MFA papers on heterotrophic bacterial metabolisms. Three machine learning methods, namely Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Decision Tree, were employed to study the sophisticated relationship between influential factors and metabolic fluxes. We performed a grid search of the best parameter set for each algorithm and verified their performance through 10-fold cross validations. SVM yields the highest accuracy among all three algorithms. Further, we employed quadratic programming to adjust flux profiles to satisfy stoichiometric constraints. Multiple case studies have shown that MFlux can reasonably predict fluxomes as a function of bacterial species, substrate types, growth rate, oxygen conditions, and cultivation methods. Due to the interest of studying model organism under particular carbon sources, bias of fluxome in the dataset may limit the applicability of machine learning models. This problem can be resolved after more papers on 13C-MFA are published for non-model species.

S-system-based analysis of the robust properties common to many biochemical network models

Robustness is a key feature to characterize the adaptation of organisms to changes in their internal and external environments. A broad range of kinetic or dynamic models of biochemical systems have been developed. Robustness analyses are attractive for exploring some common properties of many biochemical models. To reveal such features, we transform different types of mathematical equations into a standard or intelligible formula and use the multiple parameter sensitivity (MPS) to identify some factors critically responsible for the total robustness to many perturbations. The MPS would be determined by the top quarter of the highly sensitive parameters rather than the single parameter with the maximum sensitivity. The MPS did not show any correlation to the network size. The MPS is closely related to the standard deviation of the sensitivity profile. A decrease in the standard deviation enhanced the total robustness, which shows the hallmark of distributed robustness that many factors (pathways) involve the total robustness.

In Silico Constraint-Based Strain Optimization Methods: the Quest for Optimal Cell Factories

Shifting from chemical to biotechnological processes is one of the cornerstones of 21st century industry. The production of a great range of chemicals via biotechnological means is a key challenge on the way toward a bio-based economy. However, this shift is occurring at a pace slower than initially expected. The development of efficient cell factories that allow for competitive production yields is of paramount importance for this leap to happen. Constraint-based models of metabolism, together with in silico strain design algorithms, promise to reveal insights into the best genetic design strategies, a step further toward achieving that goal. In this work, a thorough analysis of the main in silico constraint-based strain design strategies and algorithms is presented, their application in real-world case studies is analyzed, and a path for the future is discussed.

A synthetic growth switch based on controlled expression of RNA polymerase

The ability to control growth is essential for fundamental studies of bacterial physiology and biotechnological applications. We have engineered an Escherichia coli strain in which the transcription of a key component of the gene expression machinery, RNA polymerase, is under the control of an inducible promoter. By changing the inducer concentration in the medium, we can adjust the RNA polymerase concentration and thereby switch bacterial growth between zero and the maximal growth rate supported by the medium. We show that our synthetic growth switch functions in a medium-independent and reversible way, and we provide evidence that the switching phenotype arises from the ultrasensitive response of the growth rate to the concentration of RNA polymerase. We present an application of the growth switch in which both the wild-type E. coli strain and our modified strain are endowed with the capacity to produce glycerol when growing on glucose. Cells in which growth has been switched off continue to be metabolically active and harness the energy gain to produce glycerol at a twofold higher yield than in cells with natural control of RNA polymerase expression. Remarkably, without any further optimization, the improved yield is close to the theoretical maximum computed from a flux balance model of E. coli metabolism. The proposed synthetic growth switch is a promising tool for gaining a better understanding of bacterial physiology and for applications in synthetic biology and biotechnology.

Integrative Approaches to Enhance Understanding of Plant Metabolic Pathway Structure and Regulation

Huge insight into molecular mechanisms and biological network coordination have been achieved following the application of various profiling technologies. Our knowledge of how the different molecular entities of the cell interact with one another suggests that, nevertheless, integration of data from different techniques could drive a more comprehensive understanding of the data emanating from different techniques. Here, we provide an overview of how such data integration is being used to aid the understanding of metabolic pathway structure and regulation. We choose to focus on the pairwise integration of large-scale metabolite data with that of the transcriptomic, proteomics, whole-genome sequence, growth- and yield-associated phenotypes, and archival functional genomic data sets. In doing so, we attempt to provide an update on approaches that integrate data obtained at different levels to reach a better understanding of either single gene function or metabolic pathway structure and regulation within the context of a broader biological process.

Advancing metabolic engineering through systems biology of industrial microorganisms

Development of sustainable processes to produce bio-based compounds is necessary due to the severe environmental problems caused by the use of fossil resources. Metabolic engineering can facilitate the development of highly efficient cell factories to produce these compounds from renewable resources. The objective of systems biology is to gain a comprehensive and quantitative understanding of living cells and can hereby enhance our ability to characterize and predict cellular behavior. Systems biology of industrial microorganisms is therefore valuable for metabolic engineering. Here we review the application of systems biology tools for the identification of metabolic engineering targets which may lead to reduced development time for efficient cell factories. Finally, we present some perspectives of systems biology for advancing metabolic engineering further.

Engineering biological systems toward a sustainable bioeconomy

The nature of our major global risks calls for sustainable innovations to decouple economic growth from greenhouse gases emission. The development of sustainable technologies has been negatively impacted by several factors including sugar production costs, production scale, economic crises, hydraulic fracking development and the market inability to capture externality costs. However, advances in engineering of biological systems allow bridging the gap between exponential growth of knowledge about biology and the creation of sustainable value chains for a broad range of economic sectors. Additionally, industrial symbiosis of different biobased technologies can increase competitiveness and sustainability, leading to the development of eco-industrial parks. Reliable policies for carbon pricing and revenue reinvestments in disruptive technologies and in the deployment of eco-industrial parks could boost the welfare while addressing our major global risks toward the transition from a fossil to a biobased economy.

The oxidative pentose phosphate pathway is the primary source of NADPH for lipid overproduction from glucose in Yarrowia lipolytica

Oleaginous microbes represent an attractive means of converting a diverse range of feedstocks into oils that can be transesterified to biodiesel. However, the mechanism of lipid overproduction in these organisms is incompletely understood, hindering the development of strategies for engineering superior biocatalysts for "single-cell oil" production. In particular, it is unclear which pathways are used to generate the large quantities of NADPH required for overproduction of the highly reduced fatty acid species. While early studies implicated malic enzyme as having a key role in production of lipogenic NADPH in oleaginous fungi, several recent reports have cast doubts as to whether malic enzyme may contribute to production of lipogenic NADPH in the model oleaginous yeast Yarrowia lipolytica. To address this problem we have used (13)C-Metabolic Flux Analysis to estimate the metabolic flux distributions during lipid accumulation in two Y. lipolytica strains; a control strain and a previously published engineered strain capable of producing lipids at roughly twice the yield. We observe a dramatic rearrangement of the metabolic flux distribution in the engineered strain which supports lipid overproduction. The NADPH-producing flux through the oxidative Pentose Phosphate Pathway is approximately doubled in the engineered strain in response to the roughly two-fold increase in fatty acid biosynthesis, while the flux through malic enzyme does not differ significantly between the two strains. Moreover, the estimated rate of NADPH production in the oxidative Pentose Phosphate Pathway is in good agreement with the estimated rate of NADPH consumption in fatty acid biosynthesis in both strains. These results suggest the oxidative Pentose Phosphate Pathway is the primary source of lipogenic NADPH in Y. lipolytica.

A modular modulation method for achieving increases in metabolite production

Increasing the production of overproducing strains represents a great challenge. Here, we develop a modular modulation method to determine the key steps for genetic manipulation to increase metabolite production. The method consists of three steps: (i) modularization of the metabolic network into two modules connected by linking metabolites, (ii) change in the activity of the modules using auxiliary rates producing or consuming the linking metabolites in appropriate proportions and (iii) determination of the key modules and steps to increase production. The mathematical formulation of the method in matrix form shows that it may be applied to metabolic networks of any structure and size, with reactions showing any kind of rate laws. The results are valid for any type of conservation relationships in the metabolite concentrations or interactions between modules. The activity of the module may, in principle, be changed by any large factor. The method may be applied recursively or combined with other methods devised to perform fine searches in smaller regions. In practice, it is implemented by integrating to the producer strain heterologous reactions or synthetic pathways producing or consuming the linking metabolites. The new procedure may contribute to develop metabolic engineering into a more systematic practice.

A systems biology approach toward understanding seed composition in soybean

BACKGROUND

The molecular, biochemical, and genetic mechanisms that regulate the complex metabolic network of soybean seed development determine the ultimate balance of protein, lipid, and carbohydrate stored in the mature seed. Many of the genes and metabolites that participate in seed metabolism are unknown or poorly defined; even more remains to be understood about the regulation of their metabolic networks. A global omics analysis can provide insights into the regulation of seed metabolism, even without a priori assumptions about the structure of these networks.

RESULTS

With the future goal of predictive biology in mind, we have combined metabolomics, transcriptomics, and metabolic flux technologies to reveal the global developmental and metabolic networks that determine the structure and composition of the mature soybean seed. We have coupled this global approach with interactive bioinformatics and statistical analyses to gain insights into the biochemical programs that determine soybean seed composition. For this purpose, we used Plant/Eukaryotic and Microbial Metabolomics Systems Resource (PMR, http://www.metnetdb.org/pmr, a platform that incorporates metabolomics data to develop hypotheses concerning the organization and regulation of metabolic networks, and MetNet systems biology tools http://www.metnetdb.org for plant omics data, a framework to enable interactive visualization of metabolic and regulatory networks.

CONCLUSIONS

This combination of high-throughput experimental data and bioinformatics analyses has revealed sets of specific genes, genetic perturbations and mechanisms, and metabolic changes that are associated with the developmental variation in soybean seed composition. Researchers can explore these metabolomics and transcriptomics data interactively at PMR.

Redirecting metabolic flux in Saccharomyces cerevisiae through regulation of cofactors in UMP production

Although it is generally known that cofactors play a major role in the production of different fermentation products, their role has not been thoroughly and systematically studied. To understand the impact of cofactors on physiological functions, a systematic approach was applied, which involved redox state analysis, energy charge analysis, and metabolite analysis. Using uridine 5'-monophosphate metabolism in Saccharomyces cerevisiae as a model, we demonstrated that regulation of intracellular the ratio of NADPH to NADP(+) not only redistributed the carbon flux between the glycolytic and pentose phosphate pathways, but also regulated the redox state of NAD(H), resulting in a significant change of ATP, and a significantly altered spectrum of metabolic products.

Central metabolic responses to the overproduction of fatty acids in Escherichia coli based on 13C-metabolic flux analysis

We engineered a fatty acid overproducing Escherichia coli strain through overexpressing tesA (“pull”) and fadR (“push”) and knocking out fadE (“block”). This “pull-push-block” strategy yielded 0.17 g of fatty acids (C12–C18) per gram of glucose (equivalent to 48% of the maximum theoretical yield) in batch cultures during the exponential growth phase under aerobic conditions. Metabolic fluxes were determined for the engineered E. coli and its control strain using tracer ([1,2-13C]glucose) experiments and 13C-metabolic flux analysis. Cofactor (NADPH) and energy (ATP) balances were also investigated for both strains based on estimated fluxes. Compared to the control strain, fatty acid overproduction led to significant metabolic responses in the central metabolism: (1) Acetic acid secretion flux decreased 10-fold; (2) Pentose phosphate pathway and Entner–Doudoroff pathway fluxes increased 1.5- and 2.0-fold, respectively; (3) Biomass synthesis flux was reduced 1.9-fold; (4) Anaplerotic phosphoenolpyruvate carboxylation flux decreased 1.7-fold; (5) Transhydrogenation flux converting NADH to NADPH increased by 1.7-fold. Real-time quantitative RT-PCR analysis revealed the engineered strain increased the transcription levels of pntA (encoding the membrane-bound transhydrogenase) by 2.1-fold and udhA (encoding the soluble transhydrogenase) by 1.4-fold, which is in agreement with the increased transhydrogenation flux. Cofactor and energy balances analyses showed that the fatty acid overproducing E. coli consumed significantly higher cellular maintenance energy than the control strain. We discussed the strategies to future strain development and process improvements for fatty acid production in E. coli.

Improving industrial yeast strains: exploiting natural and artificial diversity

Yeasts have been used for thousands of years to make fermented foods and beverages, such as beer, wine, sake, and bread. However, the choice for a particular yeast strain or species for a specific industrial application is often based on historical, rather than scientific grounds. Moreover, new biotechnological yeast applications, such as the production of second-generation biofuels, confront yeast with environments and challenges that differ from those encountered in traditional food fermentations. Together, this implies that there are interesting opportunities to isolate or generate yeast variants that perform better than the currently used strains. Here, we discuss the different strategies of strain selection and improvement available for both conventional and nonconventional yeasts. Exploiting the existing natural diversity and using techniques such as mutagenesis, protoplast fusion, breeding, genome shuffling and directed evolution to generate artificial diversity, or the use of genetic modification strategies to alter traits in a more targeted way, have led to the selection of superior industrial yeasts. Furthermore, recent technological advances allowed the development of high-throughput techniques, such as 'global transcription machinery engineering' (gTME), to induce genetic variation, providing a new source of yeast genetic diversity.

Variability of metabolite levels is linked to differential metabolic pathways in Arabidopsis's responses to abiotic stresses

Constraint-based approaches have been used for integrating data in large-scale metabolic networks to obtain insights into metabolism of various organisms. Due to the underlying steady-state assumption, these approaches are usually not suited for making predictions about metabolite levels. Here, we ask whether we can make inferences about the variability of metabolite levels from a constraint-based analysis based on the integration of transcriptomics data. To this end, we analyze time-resolved transcriptomics and metabolomics data from Arabidopsis thaliana under a set of eight different light and temperature conditions. In a previous study, the gene expression data have already been integrated in a genome-scale metabolic network to predict pathways, termed modulators and sustainers, which are differentially regulated with respect to a biochemically meaningful data-driven null model. Here, we present a follow-up analysis which bridges the gap between flux- and metabolite-centric methods. One of our main findings demonstrates that under certain environmental conditions, the levels of metabolites acting as substrates in modulators or sustainers show significantly lower temporal variations with respect to the remaining measured metabolites. This observation is discussed within the context of a systems-view of plasticity and robustness of metabolite contents and pathway fluxes. Our study paves the way for investigating the existence of similar principles in other species for which both genome-scale networks and high-throughput metabolomics data of high quality are becoming increasingly available.

Engineering synergy in biotechnology

Biotechnology is a central focus in efforts to provide sustainable solutions for the provision of fuels, chemicals and materials. On the basis of a recent open discussion, we summarize the development of this field, highlighting the distinct but complementary approaches provided by metabolic engineering and synthetic biology for the creation of efficient cell factories to convert biomass and other feedstocks to desired chemicals.

Metabolic engineering of Escherichia coli for production of butyric acid

The butyrate production by engineered Escherichia coli is afflicted by both low titer and low selectivity (defined as the butyrate/acetate (B/A) ratio). To address this issue, a strategy for metabolic engineering of E. coli was implemented including (1) elimination of all major NADH-dependent reactions in the fermentation metabolism, (2) reconstruction of a heterologous pathway leading to butyryl-CoA, (3) recruitment of endogenous atoDA for conversion of butyryl-CoA to butyrate with acetate as a CoA acceptor, and (4) removal of the acetate-synthesis pathway. Grown on glucose (20 g/L) plus acetate (8 g/L), the engineered strain consumed almost all glucose and acetate and produced 10 g/L butyrate as a predominant product within 48 h. It leads to high butyrate selectivity with the B/A ratio reaching 143. The result shows that our proposed approach may open a new avenue in biotechnology for production of butyrate in E. coli.

The EcoCyc Database

EcoCyc is a bioinformatics database available at EcoCyc.org that describes the genome and the biochemical machinery of Escherichia coli K-12 MG1655. The long-term goal of the project is to describe the complete molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli. EcoCyc is an electronic reference source for E. coli biologists and for biologists who work with related microorganisms. The database includes information pages on each E. coli gene, metabolite, reaction, operon, and metabolic pathway. The database also includes information on E. coli gene essentiality and on nutrient conditions that do or do not support the growth of E. coli. The website and downloadable software contain tools for analysis of high-throughput data sets. In addition, a steady-state metabolic flux model is generated from each new version of EcoCyc. The model can predict metabolic flux rates, nutrient uptake rates, and growth rates for different gene knockouts and nutrient conditions. This review provides a detailed description of the data content of EcoCyc and of the procedures by which this content is generated.

Metabolic flux analysis for Escherichia coli by flux balance analysis

Conventional metabolic flux analysis (MFA) of Escherichia coli wild type and of pathway gene knockout mutants cultivated under anaerobic condition is explained in detail in this chapter. To place the MFA results into the context of the literature, the regulation of central carbon metabolism in terms of catabolite regulation by the phosphotransferase system (PTS) and the response to oxygen limitations via global regulators is reviewed. The effects of gene deletions such as pflA, pta, ppc, pykF, adhE, and ldhA on the metabolic network are presented. Moreover, for the pflA mutant the effects of various carbon sources were quantified. The chapter thereby contributes to the discussion of metabolic network function and the design of microbial cell factories.

Isotopomer measurement techniques in metabolic flux analysis I: nuclear magnetic resonance

Two-dimensional [(1)H, (13)C] heteronuclear single quantum correlation (HSQC) spectroscopy nuclear magnetic resonance (NMR) is a comprehensive tool in metabolic flux analysis using (13)C-labeling experiments. NMR is particularly relevant when extensive isotopomer measurements are required, such as for plant cells and tissues, which contain multiple cellular compartments. Several isotope isomers (isotopomers) can be detected and their distribution extracted quantitatively from a single 2-D HSQC NMR spectrum. For example, 2-D HSQC detects the labeling patterns of adjacent carbon atoms and provides the enrichment of individual carbon atoms of the amino acids and glucosyl and mannosyl units present in hydrolysates of glycosylated protein. The HSQC analysis can quantitatively distinguish differences between the glucosyl units in the starch hydrolysate and a protein hydrolysate of plant biomass: this specifies crucial information about compartmentalization in the plant system. The peak structures obtained from the HSQC experiment show multiplet patterns that are directly related to the isotopomer abundances. These abundances have a nonlinear relationship to the fluxes via isotopomer balancing. Fluxes are obtained from the numerical solution of these balances and a stoichiometric model that includes biomass composition data as well as consumption rates of carbohydrate and nitrogen sources. Herein, we describe the methods for the experimental measurements for flux analysis, i.e., determination of the biomass composition (lipid, protein, soluble sugar, and starch) as well as detailed procedures of acid hydrolysis of protein and starch samples and NMR sample preparation, using soybean embryo culture as the model plant system. Techniques to obtain the relative intensity of 16 amino acids and glucosyl units for protein hydrolysate and the glucosyl units of starch hydrolysate of soybean embryos in 2-D HSQC NMR spectra also are provided.

Analysis of proteinogenic amino acid and starch labeling by 2D NMR

Comprehensive analysis of isotopic labeling patterns of metabolites in proteinogenic amino acids and starch for plant systems lay in the powerful tool of 2-Dimensional [(1)H, (13)C] Nuclear Magnetic Resonance (2D NMR) spectroscopy. From (13)C-labeling experiments, 2D NMR provides information on the labeling of particular carbon positions, which contributes to the quantification of positional isotope isomers (isotopomer). 2D Heteronuclear Single Quantum Correlation (HSQC) NMR distinguishes particularly between the labeling patterns of adjacent carbon atoms, and leads to a characteristic enrichment of each carbon atom of amino acids and glucosyl and mannosyl units present in hydrolysates of glycosylated protein. Furthermore, this technique can quantitatively classify differences in glucosyl units of starch hydrolysate and of protein hydrolysate of plant biomass. Therefore, the 2D HSQC NMR method uses proteinogenic amino acids and starch to provide an understanding of carbon distribution of compartmentalization in the plant system. NMR has the advantage of minimal sample handle without separate individual compounds prior to analysis, for example multiple isotopomers can be detected, and their distribution extracted quantitatively from a single 2D HSQC NMR spectrum. The peak structure obtained from the HSQC experiment show multiplet patterns, which are directly related to isotopomer balancing. These abundances can be translated to maximum information on the metabolic flux analysis. Detailed methods for the extractions of protein, oil, soluble sugars, and starch, hydrolysis of proteinogenic amino acid and starch, and NMR preparation using soybean embryos cultured in vitro as a model plant systems are reported in this text. In addition, this chapter includes procedures to obtain the relative intensity of 16 amino acids and glucosyl units from protein hydrolysate and the glucosyl units of starch hydrolysate of soybean embryos in 2D HSQC NMR spectra.

ReacKnock: identifying reaction deletion strategies for microbial strain optimization based on genome-scale metabolic network

Gene knockout has been used as a common strategy to improve microbial strains for producing chemicals. Several algorithms are available to predict the target reactions to be deleted. Most of them apply mixed integer bi-level linear programming (MIBLP) based on metabolic networks, and use duality theory to transform bi-level optimization problem of large-scale MIBLP to single-level programming. However, the validity of the transformation was not proved. Solution of MIBLP depends on the structure of inner problem. If the inner problem is continuous, Karush-Kuhn-Tucker (KKT) method can be used to reformulate the MIBLP to a single-level one. We adopt KKT technique in our algorithm ReacKnock to attack the intractable problem of the solution of MIBLP, demonstrated with the genome-scale metabolic network model of E. coli for producing various chemicals such as succinate, ethanol, threonine and etc. Compared to the previous methods, our algorithm is fast, stable and reliable to find the optimal solutions for all the chemical products tested, and able to provide all the alternative deletion strategies which lead to the same industrial objective.

Coregulation of Terpenoid Pathway Genes and Prediction of Isoprene Production in Bacillus subtilis Using Transcriptomics

The isoprenoid pathway converts pyruvate to isoprene and related isoprenoid compounds in plants and some bacteria. Currently, this pathway is of great interest because of the critical role that isoprenoids play in basic cellular processes, as well as the industrial value of metabolites such as isoprene. Although the regulation of several pathway genes has been described, there is a paucity of information regarding system level regulation and control of the pathway. To address these limitations, we examined Bacillus subtilis grown under multiple conditions and determined the relationship between altered isoprene production and gene expression patterns. We found that with respect to the amount of isoprene produced, terpenoid genes fall into two distinct subsets with opposing correlations. The group whose expression levels positively correlated with isoprene production included dxs, which is responsible for the commitment step in the pathway, ispD, and two genes that participate in the mevalonate pathway, yhfS and pksG. The subset of terpenoid genes that inversely correlated with isoprene production included ispH, ispF, hepS, uppS, ispE, and dxr. A genome-wide partial least squares regression model was created to identify other genes or pathways that contribute to isoprene production. These analyses showed that a subset of 213 regulated genes was sufficient to create a predictive model of isoprene production under different conditions and showed correlations at the transcriptional level. We conclude that gene expression levels alone are sufficiently informative about the metabolic state of a cell that produces increased isoprene and can be used to build a model that accurately predicts production of this secondary metabolite across many simulated environmental conditions.

Exploring antibiotic biosynthesis: Leo Vining's insights lead to new strategies in the quest for 'The 10 × '20 Initiative'

The late Professor Leo Vining began his antibiotics research career as a visiting scientist in the laboratory of Selman Waksman at Rutgers University during the golden age of antibiotics. Through six decades of his distinguished career, Vining explored the biosynthesis of dozens of antibacterial and antifungal compounds produced by microorganisms. A number of underlying mechanisms of antibiotic biosynthesis were unraveled through his holistic approach and the findings laid the foundation to our understanding of regulation of antibiotic biosynthesis. In this paper, we reflect on Professor Vining's antibiotic research philosophy from a personal perspective and connect this philosophy to new approaches for rapid development of the next generation of antibiotics, which is urgently needed to combat the threat of escalating antimicrobial resistance. Facing the urgency, The Infectious Disease Society of America launched 'The 10 × '20 Initiative' in 2010 and called for a global commitment to develop 10 new, safe and effective antibiotics by the year 2020.(1.)