Integrating metabolomics into a systems biology framework to exploit metabolic complexity: strategies and applications in microorganisms

Analysis of differential metabolites and metabolic pathways in adipose tissue of tree shrews (Tupaia belangeri) under gradient cooling acclimation

In order to investigate the influence of gradient cooling acclimation on body mass regulation in tree shrews (Tupaia belangeri), white adipose tissue (WAT) and brown adipose tissue (BAT) in T. belangeri between the control group and gradient cooling acclimation group on day 56 were collected, body mass, food intake, thermogenic capacity, differential metabolites, and related metabolic pathways in WAT and BAT were measured, the changes of differential metabolites were analyzed by non-targeted metabolomics method based on liquid chromatography-mass spectrometry. The results shown that gradient cooling acclimation significantly increased body mass, food intake, resting metabolic rate (RMR), non-shivering thermogenesis (NST), and masses of WAT and BAT. 23 significant differential metabolites in WAT between the gradient cooling acclimation group and the control group, of which the relative contents of 13 differential metabolites were up-regulated and 10 differential metabolites were down-regulated. 27 significant differential metabolites in BAT, of which 18 differential metabolites decreased and 9 differential metabolites increased. 15 differential metabolic pathways in WAT, 8 differential metabolic pathways in BAT, and 4 differential metabolic pathways involved in both WAT and BAT, including Purine metabolism, Pyrimidine metabolism, Glycerol phosphate metabolism, Arginine and proline metabolism, respectively. All of the above results suggested that T. belangeri could use different metabolites of adipose tissue to withstand low temperature environments and enhance their survival.

Comprehensive optimization of the metabolomic methodology for metabolite profiling of Corynebacterium glutamicum

Metabolomics has been a potential tool for strain improvement through analyzing metabolite changes in the context of different conditions. However, the availability of a universal metabolite profiling analysis is still a big challenge. In this study, we presented an optimized liquid chromatography-tandem mass spectrometry-based metabolomics methodology for Corynebacterium glutamicum, an important industrial workhorse. It was found that quenching the cellular metabolism with 5-fold volume of - 20 °C 40% methanol was highly recommended due to its lower cell damage rate and higher intracellular metabolite recovery rate. For extracting intracellular metabolites, ethanol/water (3:1, v/v) at 100 °C combined with acidic acetonitrile/water (1:1, v/v, with 0.1% formic acid) at - 20 °C achieved the unbiased metabolite profiling of C. glutamicum. The established methodology was then applied to investigate the intracellular metabolite differences between C. glutamicum ATCC 13032 and an mscCG-deleted mutant under biotin limitation condition. It was observed that in the presence of the functional L-glutamate exporter MscCG, biotin limitation led to accumulation of intracellular 2-oxoglutarate but not L-glutamate. Deletion of mscCG severely inhibited L-glutamate excretion and resulted in a dramatical increase of intracellular L-glutamate, which in turn affected the metabolite profile. The optimized metabolomics methodology holds promise for promoting studies on metabolic mechanism of C. glutamicum.

Integrating omics to unravel the stress-response mechanisms in probiotic bacteria: Approaches, challenges, and prospects

Identifying the stress-response mechanism of probiotic bacteria has always captivated the interest of food producers. It is crucial to identify probiotic bacteria that have increased stress tolerance to survive during production, processing, and storage of food products. However, in order to achieve high resistance to environmental factors, there is a need to better understand stress-induced responses and adaptive mechanisms. With advances in bacterial genomics, there has been an upsurge in the application of other omic platforms such as transcriptomics, proteomics, metabolomics, and some more recent ones such as interactomics, fluxomics, and phenomics. These omic technologies have revolutionized the functional genomics and their application. There have been several studies implementing various omic technologies to investigate the stress responses of probiotic bacteria. Integrated omics has the potential to provide in-depth information about the mechanisms of stress-induced responses in bacteria. However, there remain challenges in integrating information from different omic platforms. This review discusses current omic techniques and challenges faced in integrating various omic platforms with focus on their use in stress-response studies.

White biotechnology: State of the art strategies for the development of biocatalysts for biorefining

White biotechnology is a term that is now often used to describe the implementation of biotechnology in the industrial sphere. Biocatalysts (enzymes and microorganisms) are the key tools of white biotechnology, which is considered to be one of the key technological drivers for the growing bioeconomy. Biocatalysts are already present in sectors such as the chemical and agro-food industries, and are used to manufacture products as diverse as antibiotics, paper pulp, bread or advanced polymers. This review proposes an original and global overview of highly complementary fields of biotechnology at both enzyme and microorganism level. A certain number of state of the art approaches that are now being used to improve the industrial fitness of biocatalysts particularly focused on the biorefinery sector are presented. The first part deals with the technologies that underpin the development of industrial biocatalysts, notably the discovery of new enzymes and enzyme improvement using directed evolution techniques. The second part describes the toolbox available by the cell engineer to shape the metabolism of microorganisms. And finally the last part focuses on the 'omic' technologies that are vital for understanding and guide microbial engineering toward more efficient microbial biocatalysts. Altogether, these techniques and strategies will undoubtedly help to achieve the challenging task of developing consolidated bioprocessing (i.e. CBP) readily available for industrial purpose.

Alkaline conditions in hydrophilic interaction liquid chromatography for intracellular metabolite quantification using tandem mass spectrometry

Modeling of metabolic networks as part of systems metabolic engineering requires reliable quantitative experimental data of intracellular concentrations. The hydrophilic interaction liquid chromatography-electrospray ionization-tandem mass spectrometry (HILIC-ESI-MS/MS) method was used for quantitative profiling of more than 50 hydrophilic key metabolites of cellular metabolism. Without prior derivatization, sugar phosphates, organic acids, nucleotides, and amino acids were measured under alkaline and acidic mobile phase conditions with pre-optimized multiple reaction monitoring (MRM) transitions. Irrespective of the polarity mode of the acquisition method used, alkaline conditions achieved the best quantification limits and linear dynamic ranges. Fully 90% of the analyzed metabolites presented detection limits better than 0.5pmol (on column), and 70% presented 1.5-fold higher signal intensities under alkaline mobile phase conditions. The quality of the method was further demonstrated by absolute quantification of selected metabolites in intracellular extracts of Escherichia coli. In addition, quantification bias caused by matrix effects was investigated by comparison of calibration strategies: standard-based external calibration, isotope dilution, and standard addition with internal standards. Here, we recommend the use of alkaline mobile phase with polymer-based zwitterionic hydrophilic interaction chromatography (ZIC-pHILIC) as the most sensitive scenario for absolute quantification for a broad range of metabolites.

Metabolomic studies of human gastric cancer: Review

Metabolomics is a field of study in systems biology that involves the identification and quantification of metabolites present in a biological system. Analyzing metabolic differences between unperturbed and perturbed networks, such as cancerous and non-cancerous samples, can provide insight into underlying disease pathology, disease prognosis and diagnosis. Despite the large number of review articles concerning metabolomics and its application in cancer research, biomarker and drug discovery, these reviews do not focus on a specific type of cancer. Metabolomics may provide biomarkers useful for identification of early stage gastric cancer, potentially addressing an important clinical need. Here, we present a short review on metabolomics as a tool for biomarker discovery in human gastric cancer, with a primary focus on its use as a predictor of anticancer drug chemosensitivity, diagnosis, prognosis, and metastasis.

Xanthan Gum Removal for 1H-NMR Analysis of the Intracellular Metabolome of the Bacteria Xanthomonas axonopodis pv. citri 306

Xanthomonas is a genus of phytopathogenic bacteria, which produces a slimy, polysaccharide matrix known as xanthan gum, which involves, protects and helps the bacteria during host colonization. Although broadly used as a stabilizer and thickener in the cosmetic and food industries, xanthan gum can be a troubling artifact in molecular investigations due to its rheological properties. In particular, a cross-reaction between reference compounds and the xanthan gum could compromise metabolic quantification by NMR spectroscopy. Aiming at an efficient gum extraction protocol, for a ¹H-NMR-based metabolic profiling study of Xanthomonas, we tested four different interventions on the broadly used methanol-chloroform extraction protocol for the intracellular metabolic contents observation. Lower limits for bacterial pellet volumes for extraction were also probed, and a strategy is illustrated with an initial analysis of X. citri’s metabolism by ¹H-NMR spectroscopy.

The use of functional genomics in conjunction with metabolomics for Mycobacterium tuberculosis research

Tuberculosis (TB), caused by Mycobacterium tuberculosis, is a fatal infectious disease, resulting in 1.4 million deaths globally per annum. Over the past three decades, genomic studies have been conducted in an attempt to elucidate the functionality of the genome of the pathogen. However, many aspects of this complex genome remain largely unexplored, as approaches like genomics, proteomics, and transcriptomics have failed to characterize them successfully. In turn, metabolomics, which is relatively new to the “omics” revolution, has shown great potential for investigating biological systems or their modifications. Furthermore, when these data are interpreted in combination with previously acquired genomics, proteomics and transcriptomics data, using what is termed a systems biology approach, a more holistic understanding of these systems can be achieved. In this review we discuss how metabolomics has contributed so far to characterizing TB, with emphasis on the resulting improved elucidation of M. tuberculosis in terms of (1) metabolism, (2) growth and replication, (3) pathogenicity, and (4) drug resistance, from the perspective of systems biology.

Analytical platform for metabolome analysis of microbial cells using methyl chloroformate derivatization followed by gas chromatography-mass spectrometry

This protocol describes an analytical platform for the analysis of intra- and extracellular metabolites of microbial cells (yeast, filamentous fungi and bacteria) using gas chromatography-mass spectrometry (GC-MS). The protocol is subdivided into sampling, sample preparation, chemical derivatization of metabolites, GC-MS analysis and data processing and analysis. This protocol uses two robust quenching methods for microbial cultures, the first of which, cold glycerol-saline quenching, causes reduced leakage of intracellular metabolites, thus allowing a more reliable separation of intra- and extracellular metabolites with simultaneous stopping of cell metabolism. The second, fast filtration, is specifically designed for quenching filamentous micro-organisms. These sampling techniques are combined with an easy sample-preparation procedure and a fast chemical derivatization reaction using methyl chloroformate. This reaction takes place at room temperature, in aqueous medium, and is less prone to matrix effect compared with other derivatizations. This protocol takes an average of 10 d to complete and enables the simultaneous analysis of hundreds of metabolites from the central carbon metabolism (amino and nonamino organic acids, phosphorylated organic acids and fatty acid intermediates) using an in-house MS library and a data analysis pipeline consisting of two free software programs (Automated Mass Deconvolution and Identification System (AMDIS) and R).

Sampling methods for NMR-based metabolomics of Staphylococcus aureus

To select an appropriate sampling method for comparison of metabolite profiles between planktonic and biofilm Staphylococcus aureus using NMR techniques, we evaluated three methods: quenching-centrifugation (QC), filtration-quenching (FQ) and filtration-quenching-lyophilization (FQL). We found differences in metabolite loss, yield, reproducibility and metabolite profile. QC caused severe metabolite leakage and possible decomposition of nucleotides. FQ achieved high yields and reproducibility, although it had the disadvantages of long filtration and rinse times before quenching. FQL resulted in a loss of a few metabolites and a lower yield due to lyophilization. Although the biomarkers discovered by each method were nearly the same and seemed insensitive to technical variances, we conclude that FQ is the most appropriate sampling method because of its high yield and reproducibility.

High-throughput quantitative metabolomics: workflow for cultivation, quenching, and analysis of yeast in a multiwell format

Metabolomics is a founding pillar of quantitative biology and a valuable tool for studying metabolism and its regulation. Here we present a workflow for metabolomics in microplate format which affords high-throughput and yet quantitative monitoring of primary metabolism in microorganisms and in particular yeast. First, the most critical step of rapid sampling was adapted to a multiplex format by using fritted 96-well plates for cultivation, which ensure fast sample transfer and permit us to use well-established quenching in cold solvents. Second, extensive optimization of large-volume injection on a GC/TOF instrument provided the sensitivity necessary for robust quantification of 30 primary metabolites in 0.6 mg of yeast biomass. The metabolome profiles of baker's yeast cultivated in fritted well plates or in shake flasks were equivalent. Standard deviations of measured metabolites were between 10% and 50% within one plate. As a proof of principle we compared the metabolome of wild-type Saccharomyces cerevisiae and the single-deletion mutant Delta sdh1, which were clearly distinguishable by a 10-fold increase of the intracellular succinate concentration in the mutant. The described workflow allows the production of large amounts of metabolome samples within a day, is compatible with virtually all liquid extraction protocols, and paves the road to quantitative screens.

Development of a capillary electrophoresis-mass spectrometry method using polymer capillaries for metabolomic analysis of yeast

Metabolomics is an emerging field in analytical biochemistry, and the development of such a method for comprehensive and quantitative analysis of organic acids, carbohydrates, and nucleotides is a necessity in the era of functional genomics. When a concentrated yeast extract was analyzed by CE-MS using a successive multiple ionic-polymer layer (SMIL)-coated capillary, the adsorption of the contaminants on the capillary wall caused severe problems such as no elution, band-broadening, and asymmetric peaks. Therefore, an analytical method for the analysis of anionic metabolites in yeast was developed by pressure-assisted CE using an inert polymer capillary made from poly(ether etherketone) (PEEK) and PTFE. We preferred to use the PEEK over the PTFE capillary in CE-MS due to the easy-to-use PEEK capillary and its high durability. The separation of anionic metabolites was successfully achieved with ammonium hydrogencarbonate/formate buffer (pH 6.0) as the electrolyte solution. The use of 2-propanol washing after every electrophoresis run not only eliminated wall-adsorption phenomena, but allowed for good repeatability to be obtained for migration times in the metabolomic analysis.

Affinity-capture tandem mass spectrometric characterization of polyprenyl-linked oligosaccharides: tool to study protein N-glycosylation pathways

N-glycosylation of proteins is recognized as one of the most common post-translational modifications. Until recently it was believed that N-glycosylation occurred exclusively in eukaryotes before the discovery of the general protein glycosylation pathway (Pgl) in Campylobacter jejuni. To date, most techniques to analyze lipid-linked oligosaccharides (LLOs) of these pathways involve the use of radiolabels and chromatographic separation. Technologies capable of characterizing eukaryotic and the newly described bacterial N-glycosylation systems from biologically relevant samples in a quick, accurate, and cost-effective manner are needed. In this paper a new glycomics strategy based on lectin-affinity capture was devised and validated on the C. jejuni N-glycan pathway and the engineered Escherichia coli strains expressing the functional C. jejuni pathway. The lipid-linked oligosaccharide intermediates of the Pgl pathway were then enriched using SBA-agarose affinity-capture and examined by capillary electrophoresis-mass spectrometry (CE-MS). We demonstrate that this method is capable of detecting low levels of LLOs, the sugars are indeed assembled on undecaprenylpyrophosphate, and structural information for expected and unexpected LLOs can be obtained without further sample manipulation. Furthermore, CE-MS analyses of C. jejuni and the E. coli "glyco-factories" showed striking differences in the assembly and control of N-glycan biosynthesis.

Identification of biomarkers for genotyping Aspergilli using non-linear methods for clustering and classification

BACKGROUND

In the present investigation, we have used an exhaustive metabolite profiling approach to search for biomarkers in recombinant Aspergillus nidulans (mutants that produce the 6- methyl salicylic acid polyketide molecule) for application in metabolic engineering.

RESULTS

More than 450 metabolites were detected and subsequently used in the analysis. Our approach consists of two analytical steps of the metabolic profiling data, an initial non-linear unsupervised analysis with Self-Organizing Maps (SOM) to identify similarities and differences among the metabolic profiles of the studied strains, followed by a second, supervised analysis for training a classifier based on the selected biomarkers. Our analysis identified seven putative biomarkers that were able to cluster the samples according to their genotype. A Support Vector Machine was subsequently employed to construct a predictive model based on the seven biomarkers, capable of distinguishing correctly 14 out of the 16 samples of the different A. nidulans strains.

CONCLUSION

Our study demonstrates that it is possible to use metabolite profiling for the classification of filamentous fungi as well as for the identification of metabolic engineering targets and draws the attention towards the development of a common database for storage of metabolomics data.

Fueling industrial biotechnology growth with bioethanol

Industrial biotechnology is the conversion of biomass via biocatalysis, microbial fermentation, or cell culture to produce chemicals, materials, and/or energy. Industrial biotechnology processes aim to be cost-competitive, environmentally favorable, and self-sustaining compared to their petrochemical equivalents. Common to all processes for the production of energy, commodity, added value, or fine chemicals is that raw materials comprise the most significant cost fraction, particularly as operating efficiencies increase through practice and improving technologies. Today, crude petroleum represents the dominant raw material for the energy and chemical sectors worldwide. Within the last 5 years petroleum prices, stability, and supply have increased, decreased, and been threatened, respectively, driving a renewed interest across academic, government, and corporate centers to utilize biomass as an alternative raw material. Specifically, bio-based ethanol as an alternative biofuel has emerged as the single largest biotechnology commodity, with close to 46 billion L produced worldwide in 2005. Bioethanol is a leading example of how systems biology tools have significantly enhanced metabolic engineering, inverse metabolic engineering, and protein and enzyme engineering strategies. This enhancement stems from method development for measurement, analysis, and data integration of functional genomics, including the transcriptome, proteome, metabolome, and fluxome. This review will show that future industrial biotechnology process development will benefit tremendously from the precedent set by bioethanol - that enabling technologies (e.g., systems biology tools) coupled with favorable economic and socio-political driving forces do yield profitable, sustainable, and environmentally responsible processes. Biofuel will continue to be the keystone of any industrial biotechnology-based economy whereby biorefineries leverage common raw materials and unit operations to integrate diverse processes to produce demand-driven product portfolios.

Cold glycerol–saline: The promising quenching solution for accurate intracellular metabolite analysis of microbial cells

Microbial metabolomics has been seriously limited by our inability to perform a reliable separation of intra- and extracellular metabolites with efficient quenching of cell metabolism. Microbial cells are sensitive to most (if not all) quenching agents developed to date, resulting in leakage of intracellular metabolites to the extracellular medium during quenching. Therefore, as yet we are unable to obtain an accurate concentration of intracellular metabolites from microbial cell cultures. However, knowledge of the in vivo concentrations of intermediary metabolites is of fundamental importance for the characterization of microbial metabolism so as to integrate meaningful metabolomics data with other levels of functional genomics analysis. In this article, we report a novel and robust quenching method for microbial cell cultures based on cold glycerol-saline solution as the quenching agent that prevents significant leakage of intracellular metabolites and, therefore, permits more accurate measurement of intracellular metabolite concentrations in microbial cells.

Towards dynamic metabolic network measurements by multi-dimensional NMR-based fluxomics

Novel technologies for measuring biological systems and methods for visualizing data have led to a revolution in the life sciences. Nuclear magnetic resonance (NMR) techniques can provide information on metabolite structure and metabolic dynamics at the atomic level. We have been developing a new method for measuring the dynamic metabolic network of crude extracts that combines [(13)C(6)]glucose stable isotope labeling of Arabidopsis thaliana and multi-dimensional heteronuclear NMR analysis, whereas most conventional metabolic flux analyses examine proteinogenic amino acids that are specifically labeled with partially labeled substrates such as [2-(13)C(1)]glucose or 10% [(13)C(6)]glucose. To show the validity of our method, we investigated how to obtain information about biochemical reactions, C-C bond formation, and the cleavage of the main metabolites, such as free amino acids, in crude extracts based on the analysis of the (13)C-(13)C coupling pattern in 2D-NMR spectra. For example, the combination of different extraction solvents allows one to distinguish complicated (13)C-(13)C fine couplings at the C2 position of amino acids. As another approach, f1-f3 projection of the HCACO spectrum also helps in the analysis of (13)C-(13)C connectivities. Using these new methods, we present an example that involves monitoring the incorporation profile of [(13)C(6)]glucose into A. thaliana and its metabolic dynamics, which change in a time-dependent manner with atmospheric (12)CO(2) assimilation.

Metabolomics: current state and evolving methodologies and tools

In recent years, metabolomics developed to an accepted and valuable tool in life sciences. Substantial improvements of analytical hardware allow metabolomics to run routinely now. Data are successfully used to investigate genotype-phenotype relations of strains and mutants. Metabolomics facilitates metabolic engineering to optimise mircoorganisms for white biotechnology and spreads to the investigation of biotransformations and cell culture. Metabolomics serves not only as a source of qualitative but also quantitative data of intra-cellular metabolites essential for the model-based description of the metabolic network operating under in vivo conditions. To collect reliable metabolome data sets, culture and sampling conditions, as well as the cells' metabolic state, are crucial. Hence, application of biochemical engineering principles and method standardisation efforts become important. Together with the other more established omics technologies, metabolomics will strengthen its claim to contribute to the detailed understanding of the in vivo function of gene products, biochemical and regulatory networks and, even more ambitious, the mathematical description and simulation of the whole cell in the systems biology approach. This knowledge will allow the construction of designer organisms for process application using biotransformation and fermentative approaches making effective use of single enzymes, whole microbial and even higher cells.

Metabolome analysis by capillary electrophoresis-mass spectrometry

Capillary electrophoresis (CE)-mass spectrometry (MS), as an analytical platform, has made significant contributions in advancing metabolomics research, if still limited up to this time. This review, covering reports published between 1998 and 2006, describes how CE-MS has been used thus far in this field, with the majority of the works dealing with targeted metabolite analyses and only a small fraction using it in the comprehensive context. It also discusses how some of the key features of CE-MS were exploited in selected metabolomic applications.

Comprehensive analysis of yeast metabolite GC×GC–TOFMS data: combining discovery-mode and deconvolution chemometric software

The first extensive study of yeast metabolite GC x GC-TOFMS data from cells grown under fermenting, R, and respiring, DR, conditions is reported. In this study, recently developed chemometric software for use with three-dimensional instrumentation data was implemented, using a statistically-based Fisher ratio method. The Fisher ratio method is fully automated and will rapidly reduce the data to pinpoint two-dimensional chromatographic peaks differentiating sample types while utilizing all the mass channels. The effect of lowering the Fisher ratio threshold on peak identification was studied. At the lowest threshold (just above the noise level), 73 metabolite peaks were identified, nearly three-fold greater than the number of previously reported metabolite peaks identified (26). In addition to the 73 identified metabolites, 81 unknown metabolites were also located. A Parallel Factor Analysis graphical user interface (PARAFAC GUI) was applied to selected mass channels to obtain a concentration ratio, for each metabolite under the two growth conditions. Of the 73 known metabolites identified by the Fisher ratio method, 54 were statistically changing to the 95% confidence limit between the DR and R conditions according to the rigorous Student's t-test. PARAFAC determined the concentration ratio and provided a fully-deconvoluted (i.e. mathematically resolved) mass spectrum for each of the metabolites. The combination of the Fisher ratio method with the PARAFAC GUI provides high-throughput software for discovery-based metabolomics research, and is novel for GC x GC-TOFMS data due to the use of the entire data set in the analysis (640 MB x 70 runs, double precision floating point).

Integration of omics data: how well does it work for bacteria?

In the current omics era, innovative high-throughput technologies allow measuring temporal and conditional changes at various cellular levels. Although individual analysis of each of these omics data undoubtedly results into interesting findings, it is only by integrating them that gaining a global insight into cellular behaviour can be aimed at. A systems approach thus is predicated on data integration. However, because of the complexity of biological systems and the specificities of the data-generating technologies (noisiness, heterogeneity, etc.), integrating omics data in an attempt to reconstruct signalling networks is not trivial. Developing its methodologies constitutes a major research challenge. Besides for their intrinsic value towards health care, environment and industry, prokaryotes are ideal model systems to further develop these methods because of their lower regulatory complexity compared with eukaryotes, and the ease with which they can be manipulated. Several successful examples outlined in this review already show the potential of the systems approach for both fundamental and industrial applications, which would be time-consuming or impossible to develop solely through traditional reductionist approaches.

Genome-scale in silico aided metabolic analysis and flux comparisons of Escherichia coli to improve succinate production

In the post-genome era, it is one challenge to understand the cellular metabolism at the systematic levels. Mathematical modeling of microorganisms and subsequent computer simulation are effective tools for systems biology. In this paper, based on the genome-scale Escherichia coli stoichiometric model iJR904, through the GAMS linear programming package, the in silico maximal succinate yield was estimated to be 1.714 mol/mol glucose. When another two constraints were added, the maximal succinate yield dropped to 1.60 mol/mol glucose. Further analysis substantiated the uniqueness of the flux distribution under such constraints. After comparisons with the metabolic flux analysis (MFA) results computed from the wet experimental data of the three kinds of E. coli, three potential improvement target sites, the glucose phosphotransferase transport system, the pyruvate carboxylase, and the glyoxylate shunt, were identified and selected for the genetic modifications. All the three genetic modified strains showed increased succinate yield. The final strain TUQ19/pQZ6 had a high yield of 1.29 mol succinate/mol glucose and high productivity. The success of the above experiments proved that this in silico optimal succinate production pathway is reasonable and practical. This method may also be used as a general strategy to help enhance the yields of other favorable metabolites in E. coli.