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

OptSSeq: High-Throughput Sequencing Readout of Growth Enrichment Defines Optimal Gene Expression Elements for Homoethanologenesis

The optimization of synthetic pathways is a central challenge in metabolic engineering. OptSSeq (Optimization by Selection and Sequencing) is one approach to this challenge. OptSSeq couples selection of optimal enzyme expression levels linked to cell growth rate with high-throughput sequencing to track enrichment of gene expression elements (promoters and ribosome-binding sites) from a combinatorial library. OptSSeq yields information on both optimal and suboptimal enzyme levels, and helps identify constraints that limit maximal product formation. Here we report a proof-of-concept implementation of OptSSeq using homoethanologenesis, a two-step pathway consisting of pyruvate decarboxylase (Pdc) and alcohol dehydrogenase (Adh) that converts pyruvate to ethanol and is naturally optimized in the bacterium Zymomonas mobilis. We used OptSSeq to determine optimal gene expression elements and enzyme levels for Z. mobilis Pdc, AdhA, and AdhB expressed in Escherichia coli. By varying both expression signals and gene order, we identified an optimal solution using only Pdc and AdhB. We resolved current uncertainty about the functions of the Fe²⁺-dependent AdhB and Zn²⁺-dependent AdhA by showing that AdhB is preferred over AdhA for rapid growth in both E. coli and Z. mobilis. Finally, by comparing predictions of growth-linked metabolic flux to enzyme synthesis costs, we established that optimal E. coli homoethanologenesis was achieved by our best pdc-adhB expression cassette and that the remaining constraints lie in the E. coli metabolic network or inefficient Pdc or AdhB function in E. coli. OptSSeq is a general tool for synthetic biology to tune enzyme levels in any pathway whose optimal function can be linked to cell growth or survival.

Recent applications of metabolomics to advance microbial biofuel production

Biofuel production from plant biomass is a promising source of renewable energy [1]. However, efficient biofuel production involves the complex task of engineering high-performance microorganisms, which requires detailed knowledge of metabolic function and regulation. This review highlights the potential of mass-spectrometry-based metabolomic analysis to guide rational engineering of biofuel-producing microbes. We discuss recent studies that apply knowledge gained from metabolomic analyses to increase the productivity of engineered pathways, characterize the metabolism of emerging biofuel producers, generate novel bioproducts, enable utilization of lignocellulosic feedstock, and improve the stress tolerance of biofuel producers.

WUFlux: an open-source platform for 13C metabolic flux analysis of bacterial metabolism

Background

Flux analyses, including flux balance analysis (FBA) and ¹³C-metabolic flux analysis (¹³C-MFA), offer direct insights into cell metabolism, and have been widely used to characterize model and non-model microbial species. Nonetheless, constructing the ¹³C-MFA model and performing flux calculation are demanding for new learners, because they require knowledge of metabolic networks, carbon transitions, and computer programming. To facilitate and standardize the ¹³C-MFA modeling work, we set out to publish a user-friendly and programming-free platform (WUFlux) for flux calculations in MATLAB^®.

Results

We constructed an open-source platform for steady-state ¹³C-MFA. Using GUIDE (graphical user interface design environment) in MATLAB, we built a user interface that allows users to modify models based on their own experimental conditions. WUFlux is capable of directly correcting mass spectrum data of TBDMS (N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide)-derivatized proteinogenic amino acids by removing background noise. To simplify ¹³C-MFA of different prokaryotic species, the software provides several metabolic network templates, including those for chemoheterotrophic bacteria and mixotrophic cyanobacteria. Users can modify the network and constraints, and then analyze the microbial carbon and energy metabolisms of various carbon substrates (e.g., glucose, pyruvate/lactate, acetate, xylose, and glycerol). WUFlux also offers several ways of visualizing the flux results with respect to the constructed network. To validate our model’s applicability, we have compared and discussed the flux results obtained from WUFlux and other MFA software. We have also illustrated how model constraints of cofactor and ATP balances influence fluxome results.

Conclusion

Open-source software for ¹³C-MFA, WUFlux, with a user-friendly interface and easy-to-modify templates, is now available at http://www.13cmfa.org/or (http://tang.eece.wustl.edu/ToolDevelopment.htm). We will continue documenting curated models of non-model microbial species and improving WUFlux performance.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1314-0) contains supplementary material, which is available to authorized users.

Activation of the Glutamic Acid-Dependent Acid Resistance System in Escherichia coli BL21(DE3) Leads to Increase of the Fatty Acid Biotransformation Activity

The biosynthesis of carboxylic acids including fatty acids from biomass is central in envisaged biorefinery concepts. The productivities are often, however, low due to product toxicity that hamper whole-cell biocatalyst performance. Here, we have investigated factors that influence the tolerance of Escherichia coli to medium chain carboxylic acid (i.e., n-heptanoic acid)-induced stress. The metabolic and genomic responses of E. coli BL21(DE3) and MG1655 grown in the presence of n-heptanoic acid indicated that the GadA/B-based glutamic acid-dependent acid resistance (GDAR) system might be critical for cellular tolerance. The GDAR system, which is responsible for scavenging intracellular protons by catalyzing decarboxylation of glutamic acid, was inactive in E. coli BL21(DE3). Activation of the GDAR system in this strain by overexpressing the rcsB and dsrA genes, of which the gene products are involved in the activation of GadE and RpoS, respectively, resulted in acid tolerance not only to HCl but also to n-heptanoic acid. Furthermore, activation of the GDAR system allowed the recombinant E. coli BL21(DE3) expressing the alcohol dehydrogenase of Micrococcus luteus and the Baeyer-Villiger monooxygenase of Pseudomonas putida to reach 60% greater product concentration in the biotransformation of ricinoleic acid (i.e., 12-hydroxyoctadec-9-enoic acid (1)) into n-heptanoic acid (5) and 11-hydroxyundec-9-enoic acid (4). This study may contribute to engineering E. coli-based biocatalysts for the production of carboxylic acids from renewable biomass.

Decoding how a soil bacterium extracts building blocks and metabolic energy from ligninolysis provides road map for lignin valorization

Sphingobium sp. SYK-6 is a soil bacterium boasting a well-studied ligninolytic pathway and the potential for development into a microbial chassis for lignin valorization. An improved understanding of its metabolism will help researchers in the engineering of SYK-6 for the production of value-added chemicals through lignin valorization. We used ¹³C-fingerprinting, ¹³C metabolic flux analysis (¹³C-MFA), and RNA-sequencing differential expression analysis to uncover the following metabolic traits: (i) SYK-6 prefers alkaline conditions, making it an efficient host for the consolidated bioprocessing of lignin, and it also lacks the ability to metabolize sugars or organic acids; (ii) the CO₂ release (i.e., carbon loss) from the ligninolysis-based metabolism of SYK-6 is significantly greater than the CO₂ release from the sugar-based metabolism of Escherichia coli; (iii) the vanillin catabolic pathway (which is the converging point of majority of the lignin catabolic pathways) is coupled with the tetrahydrofolate-dependent C1 pathway that is essential for the biosynthesis of serine, histidine, and methionine; (iv) catabolic end products of lignin (pyruvate and oxaloacetate) must enter the tricarboxylic acid (TCA) cycle first and then use phosphoenolpyruvate carboxykinase to initiate gluconeogenesis; and (v) ¹³C-MFA together with RNA-sequencing differential expression analysis establishes the vanillin catabolic pathway as the major contributor of NAD(P)H synthesis. Therefore, the vanillin catabolic pathway is essential for SYK-6 to obtain sufficient reducing equivalents for its healthy growth; cosubstrate experiments support this finding. This unique energy feature of SYK-6 is particularly interesting because most heterotrophs rely on the transhydrogenase, the TCA cycle, and the oxidative pentose phosphate pathway to obtain NADPH.

13C-metabolic flux analysis for mevalonate-producing strain of Escherichia coli

Mevalonate (MVA) is used to produce various useful products such as drugs, cosmetics and food additives. An MVA-producing strain of Escherichia coli (engineered) was constructed by introducing mvaES genes from Enterococcus faecalis. The engineered strain produced 1.84 mmol/gDCW/h yielding 22% (C-mol/C-mol) of MVA from glucose in the aerobic exponential growth phase. The mass balance analysis revealed that the MVA yield of the engineered strain was close to the upper limit at the biomass yield. Since MVA is synthesized from acetyl-CoA using NADPH as a cofactor, the production of MVA affects central metabolism in terms of carbon utilization and NADPH requirements. The reason for this highly efficient MVA production was investigated based on ¹³C-metabolic flux analysis. The estimated flux distributions revealed that the fluxes of acetate formation and the TCA cycle in the engineered strain were lower than those in the control strain. Although the oxidative pentose phosphate pathway is considered as the NADPH generating pathway in E. coli, no difference of the flux was observed between the control and engineered strains. The production/consumption balance of NADPH suggested that additional requirement of NADPH for MVA synthesis was obtained from the transhydrogenase reaction in the engineered strain. Comparison between the measured flux distribution and the ideal values for MVA production proposes a strategy for further engineering to improve the MVA production in E. coli.

Investigation of useful carbon tracers for 13C-metabolic flux analysis of Escherichia coli by considering five experimentally determined flux distributions

The ¹³C-MFA experiments require an optimal design since the precision or confidence intervals of the estimated flux levels depends on factors such as the composition of ¹³C-labeled carbon sources, as well as the metabolic flux distribution of interest. In this study, useful compositions of ¹³C-labeled glucose for ¹³C-metabolic flux analysis (¹³C-MFA) of Escherichia coli are investigated using a computer simulation of the stable isotope labeling experiment. Following the generation of artificial mass spectra datasets of amino acid fragments using five literature-reported flux distributions of E. coli, the best fitted flux distribution and the 95% confidence interval were estimated by the ¹³C-MFA procedure. A comparison of the precision scores showed that [1, 2-¹³C]glucose and a mixture of [1-¹³C] and [U-¹³C]glucose at 8:2 are one of the best carbon sources for a precise estimation of flux levels of the pentose phosphate pathway, glycolysis and the TCA cycle. Although the precision scores of the anaplerotic and glyoxylate pathway reactions were affected by both the carbon source and flux distribution, it was also shown that the mixture of non-labeled, [1-¹³C], and [U-¹³C]glucose at 4:1:5 was specifically effective for the flux estimation of the glyoxylate pathway reaction. These findings were confirmed by wet ¹³C-MFA experiments.

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Useful compositions of ¹³C-labeled glucose are investigated for ¹³C-MFA of E. coli.

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Computer simulations revealed that [1,2-¹³C] was one of the best first choices.

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Mixture of non-labeled, [1-¹³C] and [U-¹³C] at 0:8:2 was also suitable for ¹³C-MFA.

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Mixture at 4:1:5 was specifically effective for estimation of glyoxylate pathway.

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The wet ¹³C-MFA experiments of E. coli confirmed the findings.

Co-utilization of glucose and xylose by evolved Thermus thermophilus LC113 strain elucidated by (13)C metabolic flux analysis and whole genome sequencing

We evolved Thermus thermophilus to efficiently co-utilize glucose and xylose, the two most abundant sugars in lignocellulosic biomass, at high temperatures without carbon catabolite repression. To generate the strain, T. thermophilus HB8 was first evolved on glucose to improve its growth characteristics, followed by evolution on xylose. The resulting strain, T. thermophilus LC113, was characterized in growth studies, by whole genome sequencing, and (13)C-metabolic flux analysis ((13)C-MFA) with [1,6-(13)C]glucose, [5-(13)C]xylose, and [1,6-(13)C]glucose+[5-(13)C]xylose as isotopic tracers. Compared to the starting strain, the evolved strain had an increased growth rate (~2-fold), increased biomass yield, increased tolerance to high temperatures up to 90°C, and gained the ability to grow on xylose in minimal medium. At the optimal growth temperature of 81°C, the maximum growth rate on glucose and xylose was 0.44 and 0.46h(-1), respectively. In medium containing glucose and xylose the strain efficiently co-utilized the two sugars. (13)C-MFA results provided insights into the metabolism of T. thermophilus LC113 that allows efficient co-utilization of glucose and xylose. Specifically, (13)C-MFA revealed that metabolic fluxes in the upper part of metabolism adjust flexibly to sugar availability, while fluxes in the lower part of metabolism remain relatively constant. Whole genome sequence analysis revealed two large structural changes that can help explain the physiology of the evolved strain: a duplication of a chromosome region that contains many sugar transporters, and a 5x multiplication of a region on the pVV8 plasmid that contains xylose isomerase and xylulokinase genes, the first two enzymes of xylose catabolism. Taken together, (13)C-MFA and genome sequence analysis provided complementary insights into the physiology of the evolved strain.

Metabolic engineering of enhanced glycerol-3-phosphate synthesis to increase lipid production in Synechocystis sp. PCC 6803

With the growing attention to global warming and energy sustainability, biosynthesis of lipids by photosynthetic microorganisms has attracted more interest for the production of renewable transportation fuels. Recently, the cyanobacterium Synechocystis sp. PCC 6803 has been widely used for biofuel production through metabolic engineering because of its efficient photosynthesis and well-developed genetic tools. In lipid biosynthesis, glycerol-3-phosphate (G3P) is a key node for both CO2 fixation and lipid metabolism in cyanobacteria. However, few studies have explored the use of G3P synthesis to improve photosynthetic lipid production. In this study, metabolic engineering combined with flux balance analysis (FBA) was conducted to reveal the effect of G3P synthesis on lipid production. Heterologous genes that encoded glycerol-3-phosphate dehydrogenase (GPD) and diacylglycerol acyltransferase (DGAT) were engineered into Synechocystis sp. PCC 6803 to enhance G3P supply and lipid production. The resultant recombinant Synechocystis produced higher levels of lipids without a significant reduction in cell growth. Compared with the wild-type strain, lipid content and productivity of the engineered cyanobacteria increased by up to 36 and 31 %, respectively, under autotrophic conditions. Lipid production under mixotrophic conditions of the engineered cyanobacteria was also investigated. This work demonstrated that enhanced G3P synthesis was an important factor in photosynthetic lipid production and that introducing heterologous GPD and DGAT genes was an effective strategy to increase lipid production in Synechocystis sp. PCC 6803.

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.

Engineering Escherichia coli for Conversion of Glucose to Medium-Chain ω-Hydroxy Fatty Acids and α,ω-Dicarboxylic Acids

In search of sustainable approaches to plastics production, many efforts have been made to engineer microbial conversions of renewable feedstock to short-chain (C2-C8) bifunctional polymer precursors (e.g., succinic acid, cadaverine, 1,4-butanediol). Less attention has been given to medium-chain (C12-C14) monomers such as ω-hydroxy fatty acids (ω-OHFAs) and α,ω-dicarboxylic acids (α,ω-DCAs), which are precursors to high performance polyesters and polyamides. Here we engineer a complete microbial conversion of glucose to C12 and C14 ω-OHFAs and α,ω-DCAs, with precise control of product chain length. Using an expanded bioinformatics approach, we screen a wide range of enzymes across phyla to identify combinations that yield complete conversion of intermediates to product α,ω-DCAs. Finally, through optimization of culture conditions, we enhance production titer of C12 α,ω-DCA to nearly 600 mg/L. Our results indicate potential for this microbial factory to enable commercially relevant, renewable production of C12 α,ω-DCA-a valuable precursor to the high-performance plastic, nylon-6,12.

Evolution of E. coli on [U-13C]Glucose Reveals a Negligible Isotopic Influence on Metabolism and Physiology

¹³C-Metabolic flux analysis (¹³C-MFA) traditionally assumes that kinetic isotope effects from isotopically labeled compounds do not appreciably alter cellular growth or metabolism, despite indications that some biochemical reactions can be non-negligibly impacted. Here, populations of Escherichia coli were adaptively evolved for ~1000 generations on uniformly labeled ¹³C-glucose, a commonly used isotope for ¹³C-MFA. Phenotypic characterization of these evolved strains revealed ~40% increases in growth rate, with no significant difference in fitness when grown on either labeled (¹³C) or unlabeled (¹²C) glucose. The evolved strains displayed decreased biomass yields, increased glucose and oxygen uptake, and increased acetate production, mimicking what is observed after adaptive evolution on unlabeled glucose. Furthermore, full genome re-sequencing revealed that the key genetic changes underlying these phenotypic alterations were essentially the same as those acquired during adaptive evolution on unlabeled glucose. Additionally, glucose competition experiments demonstrated that the wild-type exhibits no isotopic preference for unlabeled glucose, and the evolved strains have no preference for labeled glucose. Overall, the results of this study indicate that there are no significant differences between ¹²C and ¹³C-glucose as a carbon source for E. coli growth.

Elucidation of the co-metabolism of glycerol and glucose in Escherichia coli by genetic engineering, transcription profiling, and (13)C metabolic flux analysis

BACKGROUND

Glycerol, a byproduct of biodiesel, has become a readily available and inexpensive carbon source for the production of high-value products. However, the main drawback of glycerol utilization is the low consumption rate and shortage of NADPH formation, which may limit the production of NADPH-requiring products. To overcome these problems, we constructed a carbon catabolite repression-negative ΔptsGglpK* mutant by both blocking a key glucose PTS transporter and enhancing the glycerol conversion. The mutant can recover normal growth by co-utilization of glycerol and glucose after loss of glucose PTS transporter. To reveal the metabolic potential of the ΔptsGglpK* mutant, this study examined the flux distributions and regulation of the co-metabolism of glycerol and glucose in the mutant.

RESULTS

By labeling experiments using [1,3-(13)C]glycerol and [1-(13)C]glucose, (13)C metabolic flux analysis was employed to decipher the metabolisms of both the wild-type strain and the ΔptsGglpK* mutant in chemostat cultures. When cells were maintained at a low dilution rate (0.1 h(-1)), the two strains showed similar fluxome profiles. When the dilution rate was increased, both strains upgraded their pentose phosphate pathway, glycolysis and anaplerotic reactions, while the ΔptsGglpK* mutant was able to catabolize much more glycerol than glucose (more than tenfold higher). Compared with the wild-type strain, the mutant repressed its flux through the TCA cycle, resulting in higher acetate overflow. The regulation of fluxomes was consistent with transcriptional profiling of several key genes relevant to the TCA cycle and transhydrogenase, namely gltA, icdA, sdhA and pntA. In addition, cofactor fluxes and their pool sizes were determined. The ΔptsGglpK* mutant affected the redox NADPH/NADH state and reduced the ATP level. Redox signaling activated the ArcA regulatory system, which was responsible for TCA cycle repression.

CONCLUSIONS

This work employs both (13)C-MFA and transcription/metabolite analysis for quantitative investigation of the co-metabolism of glycerol and glucose in the ΔptsGglpK* mutant. The ArcA regulatory system dominates the control of flux redistribution. The ΔptsGglpK* mutant can be used as a platform for microbial cell factories for the production of biofuels and biochemicals, since most of fuel molecule (e.g., alcohols) synthesis requires excess reducing equivalents.

Examining Escherichia coli glycolytic pathways, catabolite repression, and metabolite channeling using Δpfk mutants

BACKGROUND

Glycolysis breakdowns glucose into essential building blocks and ATP/NAD(P)H for the cell, occupying a central role in its growth and bio-production. Among glycolytic pathways, the Entner Doudoroff pathway (EDP) is a more thermodynamically favorable pathway with fewer enzymatic steps than either the Embden-Meyerhof-Parnas pathway (EMPP) or the oxidative pentose phosphate pathway (OPPP). However, Escherichia coli do not use their native EDP for glucose metabolism.

RESULTS

Overexpression of edd and eda in E. coli to enhance EDP activity resulted in only a small shift in the flux directed through the EDP (~20 % of glycolysis flux). Disrupting the EMPP by phosphofructokinase I (pfkA) knockout increased flux through OPPP (~60 % of glycolysis flux) and the native EDP (~14 % of glycolysis flux), while overexpressing edd and eda in this ΔpfkA mutant directed ~70 % of glycolytic flux through the EDP. The downregulation of EMPP via the pfkA deletion significantly decreased the growth rate, while EDP overexpression in the ΔpfkA mutant failed to improve its growth rates due to metabolic burden. However, the reorganization of E. coli glycolytic strategies did reduce glucose catabolite repression. The ΔpfkA mutant in glucose medium was able to cometabolize acetate via the citric acid cycle and gluconeogenesis, while EDP overexpression in the ΔpfkA mutant repressed acetate flux toward gluconeogenesis. Moreover, ¹³C-pulse experiments in the ΔpfkA mutants showed unsequential labeling dynamics in glycolysis intermediates, possibly suggesting metabolite channeling (metabolites in glycolysis are pass from enzyme to enzyme without fully equilibrating within the cytosol medium).

CONCLUSIONS

We engineered E. coli to redistribute its native glycolytic flux. The replacement of EMPP by EDP did not improve E. coli glucose utilization or biomass growth, but alleviated catabolite repression. More importantly, our results supported the hypothesis of channeling in the glycolytic pathways, a potentially overlooked mechanism for regulating glucose catabolism and coutilization of other substrates. The presence of channeling in native pathways, if proven true, would affect synthetic biology applications and metabolic modeling.

Targeted cofactor quantification in metabolically engineered E. coli using solid phase extraction and hydrophilic interaction liquid chromatography-mass spectrometry

Quantification of energy and redox cofactors is of great value to synthetic biologists to infer the balance of energy metabolism in engineered microbial strains and assess each strain's potential for further improvement. Most currently used methods for intracellular cofactor measurement suffer from incomplete coverage, low reproducibility, suboptimal sensitivity or specificity. In this study, we described an SPE-HILIC/MS approach for simultaneous determination of six cofactor targets (ATP, ADP, NAD, NADH, NADP, NADPH) in Escherichia coli cells. Sufficient linearity, precision and metabolite recoveries of this new approach justified its reliability in targeted cofactor quantification. Our approach was then compared with conventional enzymatic assays to demonstrate its superior performance. We applied the SPE-HILIC/MS approach to profile shift of cofactor balances in several engineered E. coli strains with varying isobutanol production. Our cofactor analysis clearly revealed that optimal energy fitness was achieved in the highest-yield strain through combined modulation of a transhydrogenase and a NAD(+) kinase. Apart from the targeted cofactors, the SPE enrichment procedure also allowed for confident identification of 39 groups of polar metabolites mainly involved in central carbon metabolism in E. coli cells.

13C-Metabolic Flux Analysis: An Accurate Approach to Demystify Microbial Metabolism for Biochemical Production

Metabolic engineering of various industrial microorganisms to produce chemicals, fuels, and drugs has raised interest since it is environmentally friendly, sustainable, and independent of nonrenewable resources. However, microbial metabolism is so complex that only a few metabolic engineering efforts have been able to achieve a satisfactory yield, titer or productivity of the target chemicals for industrial commercialization. In order to overcome this challenge, ¹³C Metabolic Flux Analysis (¹³C-MFA) has been continuously developed and widely applied to rigorously investigate cell metabolism and quantify the carbon flux distribution in central metabolic pathways. In the past decade, many ¹³C-MFA studies have been performed in academic labs and biotechnology industries to pinpoint key issues related to microbe-based chemical production. Insightful information about the metabolic rewiring has been provided to guide the development of the appropriate metabolic engineering strategies for improving the biochemical production. In this review, we will introduce the basics of ¹³C-MFA and illustrate how ¹³C-MFA has been applied via integration with metabolic engineering to identify and tackle the rate-limiting steps in biochemical production for various host microorganisms

(13)C-metabolic flux analysis of lipid accumulation in the oleaginous fungus Mucor circinelloides

The oleaginous fungus Mucor circinelloides is of industrial interest because it can produce high levels of polyunsaturated fatty acid γ-linolenic acid. M. circinelloides CBS 277.49 is able to accumulate less than 15% of cell dry weight as lipids, while M. circinelloides WJ11 can accumulate lipid up to 36%. In order to better understand the mechanisms behind the differential lipid accumulation in these two strains, tracer experiments with (13)C-glucose were performed with the growth of M. circinelloides and subsequent gas chromatography-mass spectrometric detection of (13)C-patterns in proteinogenic amino acids was carried out to identify the metabolic network topology and estimate intracellular fluxes. Our results showed that the high oleaginous strain WJ11 had higher flux of pentose phosphate pathway and malic enzyme, lower flux in tricarboxylic acid cycle, higher flux in glyoxylate cycle and ATP: citrate lyase, together, it might provide more NADPH and substrate acetyl-CoA for fatty acid synthesis.

Complete genome sequence, metabolic model construction and phenotypic characterization of Geobacillus LC300, an extremely thermophilic, fast growing, xylose-utilizing bacterium

We have isolated a new extremely thermophilic fast-growing Geobacillus strain that can efficiently utilize xylose, glucose, mannose and galactose for cell growth. When grown aerobically at 72 °C, Geobacillus LC300 has a growth rate of 2.15 h(-1) on glucose and 1.52 h(-1) on xylose (doubling time less than 30 min). The corresponding specific glucose and xylose utilization rates are 5.55 g/g/h and 5.24 g/g/h, respectively. As such, Geobacillus LC300 grows 3-times faster than E. coli on glucose and xylose, and has a specific xylose utilization rate that is 3-times higher than the best metabolically engineered organism to date. To gain more insight into the metabolism of Geobacillus LC300 its genome was sequenced using PacBio's RS II single-molecule real-time (SMRT) sequencing platform and annotated using the RAST server. Based on the genome annotation and the measured biomass composition a core metabolic network model was constructed. To further demonstrate the biotechnological potential of this organism, Geobacillus LC300 was grown to high cell-densities in a fed-batch culture, where cells maintained a high xylose utilization rate under low dissolved oxygen concentrations. All of these characteristics make Geobacillus LC300 an attractive host for future metabolic engineering and biotechnology applications.

A Method to Constrain Genome-Scale Models with 13C Labeling Data

Current limitations in quantitatively predicting biological behavior hinder our efforts to engineer biological systems to produce biofuels and other desired chemicals. Here, we present a new method for calculating metabolic fluxes, key targets in metabolic engineering, that incorporates data from ¹³C labeling experiments and genome-scale models. The data from ¹³C labeling experiments provide strong flux constraints that eliminate the need to assume an evolutionary optimization principle such as the growth rate optimization assumption used in Flux Balance Analysis (FBA). This effective constraining is achieved by making the simple but biologically relevant assumption that flux flows from core to peripheral metabolism and does not flow back. The new method is significantly more robust than FBA with respect to errors in genome-scale model reconstruction. Furthermore, it can provide a comprehensive picture of metabolite balancing and predictions for unmeasured extracellular fluxes as constrained by ¹³C labeling data. A comparison shows that the results of this new method are similar to those found through ¹³C Metabolic Flux Analysis (¹³C MFA) for central carbon metabolism but, additionally, it provides flux estimates for peripheral metabolism. The extra validation gained by matching 48 relative labeling measurements is used to identify where and why several existing COnstraint Based Reconstruction and Analysis (COBRA) flux prediction algorithms fail. We demonstrate how to use this knowledge to refine these methods and improve their predictive capabilities. This method provides a reliable base upon which to improve the design of biological systems.

While metabolic fluxes constitute the most direct window into a cell’s metabolism, their accurate measurement is non trivial. The gold standard for flux measurement involves providing a labeled feed where some of the carbon atoms have been substituted by isotopes with higher atomic mass (¹³C instead of ¹²C). The ensuing labeling found in intracellular metabolites is then used to computationally infer the metabolic fluxes that produced the observed pattern. However, this procedure is typically performed with small metabolic models encompassing only central carbon metabolism. The genomic revolution has afforded us easily available genomes and, with them, comprehensive genome-scale models of cellular metabolism. It would be desirable to use the ¹³C labeling experimental data to constrain genome-scale models: these data constrain fluxes very effectively and provide in the labeling data fit an obvious proof that the underlying model correctly explains measured quantities. Here, we introduce a rigorous, self-consistent method that uses the full amount of information contained in ¹³C labeling data to constrain fluxes for a genome-scale model where underlying assumptions are explicitly stated.

Integrated isotope-assisted metabolomics and (13)C metabolic flux analysis reveals metabolic flux redistribution for high glucoamylase production by Aspergillus niger

Background

Aspergillus niger is widely used for enzyme production and achievement of high enzyme production depends on the comprehensive understanding of cell’s metabolic regulation mechanisms.

Results

In this paper, we investigate the metabolic differences and regulation mechanisms between a high glucoamylase-producing strain A. niger DS03043 and its wild-type parent strain A. niger CBS513.88 via an integrated isotope-assisted metabolomics and ¹³C metabolic flux analysis approach. We found that A. niger DS03043 had higher cell growth, glucose uptake, and glucoamylase production rates but lower oxalic acid and citric acid secretion rates. In response to above phenotype changes, A. niger DS03043 was characterized by an increased carbon flux directed to the oxidative pentose phosphate pathway in contrast to reduced flux through TCA cycle, which were confirmed by consistent changes in pool sizes of metabolites. A higher ratio of ATP over AMP in the high producing strain might contribute to the increase in the PP pathway flux as glucosephosphate isomerase was inhibited at higher ATP concentrations. A. niger CBS513.88, however, was in a higher redox state due to the imbalance of NADH regeneration and consumption, resulting in the secretion of oxalic acid and citric acid, as well as the accumulation of intracellular OAA and PEP, which may in turn result in the decrease in the glucose uptake rate.

Conclusions

The application of integrated metabolomics and ¹³C metabolic flux analysis highlights the regulation mechanisms of energy and redox metabolism on flux redistribution in A. niger. Graphical abstract

Metabolic network capacity of Escherichia coli for Krebs cycle-dependent proline hydroxylation

Background

Understanding the metabolism of the microbial host is essential for the development and optimization of whole-cell based biocatalytic processes, as it dictates production efficiency. This is especially true for redox biocatalysis where metabolically active cells are employed because of the cofactor/cosubstrate regenerative capacity endogenous in the host. Recombinant Escherichia coli was used for overproducing proline-4-hydroxylase (P4H), a dioxygenase catalyzing the hydroxylation of free l-proline into trans-4-hydroxy-l-proline with a-ketoglutarate (a-KG) as cosubstrate. In this whole-cell biocatalyst, central carbon metabolism provides the required cosubstrate a-KG, coupling P4H biocatalytic performance directly to carbon metabolism and metabolic activity. By applying both experimental and computational biology tools, such as metabolic engineering and ¹³C-metabolic flux analysis (¹³C-MFA), we investigated and quantitatively described the physiological, metabolic, and bioenergetic response of the whole-cell biocatalyst to the targeted bioconversion and identified possible metabolic bottlenecks for further rational pathway engineering.

Results

A proline degradation-deficient E. coli strain was constructed by deleting the putA gene encoding proline dehydrogenase. Whole-cell biotransformations with this mutant strain led not only to quantitative proline hydroxylation but also to a doubling of the specific trans-4-l-hydroxyproline (hyp) formation rate, compared to the wild type. Analysis of carbon flux through central metabolism of the mutant strain revealed that the increased a-KG demand for P4H activity did not enhance the a-KG generating flux, indicating a tightly regulated TCA cycle operation under the conditions studied. In the wild type strain, P4H synthesis and catalysis caused a reduction in biomass yield. Interestingly, the ΔputA strain additionally compensated the associated ATP and NADH loss by reducing maintenance energy demands at comparably low glucose uptake rates, instead of increasing the TCA activity.

Conclusions

The putA knockout in recombinant E. coli BL21(DE3)(pLysS) was found to be promising for productive P4H catalysis not only in terms of biotransformation yield, but also regarding the rates for biotransformation and proline uptake and the yield of hyp on the energy source. The results indicate that, upon a putA knockout, the coupling of the TCA-cycle to proline hydroxylation via the cosubstrate a-KG becomes a key factor constraining and a target to further improve the efficiency of a-KG-dependent biotransformations.

Electronic supplementary material

The online version of this article (doi:10.1186/s12934-015-0298-1) contains supplementary material, which is available to authorized users.

NADPH-generating systems in bacteria and archaea

Reduced nicotinamide adenine dinucleotide phosphate (NADPH) is an essential electron donor in all organisms. It provides the reducing power that drives numerous anabolic reactions, including those responsible for the biosynthesis of all major cell components and many products in biotechnology. The efficient synthesis of many of these products, however, is limited by the rate of NADPH regeneration. Hence, a thorough understanding of the reactions involved in the generation of NADPH is required to increase its turnover through rational strain improvement. Traditionally, the main engineering targets for increasing NADPH availability have included the dehydrogenase reactions of the oxidative pentose phosphate pathway and the isocitrate dehydrogenase step of the tricarboxylic acid (TCA) cycle. However, the importance of alternative NADPH-generating reactions has recently become evident. In the current review, the major canonical and non-canonical reactions involved in the production and regeneration of NADPH in prokaryotes are described, and their key enzymes are discussed. In addition, an overview of how different enzymes have been applied to increase NADPH availability and thereby enhance productivity is provided.

(13)C-metabolic flux analysis of co-cultures: A novel approach

In this work, we present a novel approach for performing (13)C metabolic flux analysis ((13)C-MFA) of co-culture systems. We demonstrate for the first time that it is possible to determine metabolic flux distributions in multiple species simultaneously without the need for physical separation of cells or proteins, or overexpression of species-specific products. Instead, metabolic fluxes for each species in a co-culture are estimated directly from isotopic labeling of total biomass obtained using conventional mass spectrometry approaches such as GC-MS. In addition to determining metabolic fluxes, this approach estimates the relative population size of each species in a mixed culture and inter-species metabolite exchange. As such, it enables detailed studies of microbial communities including species dynamics and interactions between community members. The methodology is experimentally validated here using a co-culture of two E. coli knockout strains. Taken together, this work greatly extends the scope of (13)C-MFA to a large number of multi-cellular systems that are of significant importance in biotechnology and medicine.

(13)C metabolic flux analysis of the extremely thermophilic, fast growing, xylose-utilizing Geobacillus strain LC300

Thermophiles are increasingly used as versatile hosts in the biotechnology industry. One of the key advantages of thermophiles is the potential to achieve high rates of feedstock conversion at elevated temperatures. The recently isolated Geobacillus strain LC300 grows extremely fast on xylose, with a doubling time of less than 30 min. In the accompanying paper, the genome of Geobacillus LC300 was sequenced and annotated. In this work, we have experimentally validated the metabolic network model using parallel (13)C-labeling experiments and applied (13)C-metabolic flux analysis to quantify precise metabolic fluxes. Specifically, the complete set of singly labeled xylose tracers, [1-(13)C], [2-(13)C], [3-(13)C], [4-(13)C], and [5-(13)C]xylose, was used for the first time. Isotopic labeling of biomass amino acids was measured by gas chromatography mass spectrometry (GC-MS). Isotopic labeling of carbon dioxide in the off-gas was also measured by an on-line mass spectrometer. The (13)C-labeling data was then rigorously integrated for flux elucidation using the COMPLETE-MFA approach. The results provided important new insights into the metabolism of Geobacillus LC300, its efficient xylose utilization pathways, and the balance between carbon, redox and energy fluxes. The pentose phosphate pathway, glycolysis and TCA cycle were found to be highly active in Geobacillus LC300. The oxidative pentose phosphate pathway was also active and contributed significantly to NADPH production. No transhydrogenase activity was detected. Results from this work provide a solid foundation for future studies of this strain and its metabolic engineering and biotechnological applications.

An ancient Chinese wisdom for metabolic engineering: Yin-Yang

In ancient Chinese philosophy, Yin-Yang describes two contrary forces that are interconnected and interdependent. This concept also holds true in microbial cell factories, where Yin represents energy metabolism in the form of ATP, and Yang represents carbon metabolism. Current biotechnology can effectively edit the microbial genome or introduce novel enzymes to redirect carbon fluxes. On the other hand, microbial metabolism loses significant free energy as heat when converting sugar into ATP; while maintenance energy expenditures further aggravate ATP shortage. The limitation of cell "powerhouse" prevents hosts from achieving high carbon yields and rates. Via an Escherichia coli flux balance analysis model, we further demonstrate the penalty of ATP cost on biofuel synthesis. To ensure cell powerhouse being sufficient in microbial cell factories, we propose five principles: 1. Take advantage of native pathways for product synthesis. 2. Pursue biosynthesis relying only on pathways or genetic parts without significant ATP burden. 3. Combine microbial production with chemical conversions (semi-biosynthesis) to reduce biosynthesis steps. 4. Create "minimal cells" or use non-model microbial hosts with higher energy fitness. 5. Develop a photosynthesis chassis that can utilize light energy and cheap carbon feedstocks. Meanwhile, metabolic flux analysis can be used to quantify both carbon and energy metabolisms. The fluxomics results are essential to evaluate the industrial potential of laboratory strains, avoiding false starts and dead ends during metabolic engineering.

Methods and advances in metabolic flux analysis: a mini-review

Metabolic flux analysis (MFA) is one of the pillars of metabolic engineering. Over the past three decades, it has been widely used to quantify intracellular metabolic fluxes in both native (wild type) and engineered biological systems. Through MFA, changes in metabolic pathway fluxes are quantified that result from genetic and/or environmental interventions. This information, in turn, provides insights into the regulation of metabolic pathways and may suggest new targets for further metabolic engineering of the strains. In this mini-review, we discuss and classify the various methods of MFA that have been developed, which include stoichiometric MFA, (13)C metabolic flux analysis, isotopic non-stationary (13)C metabolic flux analysis, dynamic metabolic flux analysis, and (13)C dynamic metabolic flux analysis. For each method, we discuss key advantages and limitations and conclude by highlighting important recent advances in flux analysis approaches.

Integrated 13C-metabolic flux analysis of 14 parallel labeling experiments in Escherichia coli

The use of parallel labeling experiments for (13)C metabolic flux analysis ((13)C-MFA) has emerged in recent years as the new gold standard in fluxomics. The methodology has been termed COMPLETE-MFA, short for complementary parallel labeling experiments technique for metabolic flux analysis. In this contribution, we have tested the limits of COMPLETE-MFA by demonstrating integrated analysis of 14 parallel labeling experiments with Escherichia coli. An effort on such a massive scale has never been attempted before. In addition to several widely used isotopic tracers such as [1,2-(13)C]glucose and mixtures of [1-(13)C]glucose and [U-(13)C]glucose, four novel tracers were applied in this study: [2,3-(13)C]glucose, [4,5,6-(13)C]glucose, [2,3,4,5,6-(13)C]glucose and a mixture of [1-(13)C]glucose and [4,5,6-(13)C]glucose. This allowed us for the first time to compare the performance of a large number of isotopic tracers. Overall, there was no single best tracer for the entire E. coli metabolic network model. Tracers that produced well-resolved fluxes in the upper part of metabolism (glycolysis and pentose phosphate pathways) showed poor performance for fluxes in the lower part of metabolism (TCA cycle and anaplerotic reactions), and vice versa. The best tracer for upper metabolism was 80% [1-(13)C]glucose+20% [U-(13)C]glucose, while [4,5,6-(13)C]glucose and [5-(13)C]glucose both produced optimal flux resolution in the lower part of metabolism. COMPLETE-MFA improved both flux precision and flux observability, i.e. more independent fluxes were resolved with smaller confidence intervals, especially exchange fluxes. Overall, this study demonstrates that COMPLETE-MFA is a powerful approach for improving flux measurements and that this methodology should be considered in future studies that require very high flux resolution.

OpenFLUX2: (13)C-MFA modeling software package adjusted for the comprehensive analysis of single and parallel labeling experiments

BACKGROUND

Steady-state (13)C-based metabolic flux analysis ((13)C-MFA) is the most powerful method available for the quantification of intracellular fluxes. These analyses include concertedly linked experimental and computational stages: (i) assuming the metabolic model and optimizing the experimental design; (ii) feeding the investigated organism using a chosen (13)C-labeled substrate (tracer); (iii) measuring the extracellular effluxes and detecting the (13)C-patterns of intracellular metabolites; and (iv) computing flux parameters that minimize the differences between observed and simulated measurements, followed by evaluating flux statistics. In its early stages, (13)C-MFA was performed on the basis of data obtained in a single labeling experiment (SLE) followed by exploiting the developed high-performance computational software. Recently, the advantages of parallel labeling experiments (PLEs), where several LEs are conducted under the conditions differing only by the tracer(s) choice, were demonstrated, particularly with regard to improving flux precision due to the synergy of complementary information. The availability of an open-source software adjusted for PLE-based (13)C-MFA is an important factor for PLE implementation.

RESULTS

The open-source software OpenFLUX, initially developed for the analysis of SLEs, was extended for the computation of PLE data. Using the OpenFLUX2, in silico simulation confirmed that flux precision is improved when (13)C-MFA is implemented by fitting PLE data to the common model compared with SLE-based analysis. Efficient flux resolution could be achieved in the PLE-mediated analysis when the choice of tracer was based on an experimental design computed to minimize the flux variances from different parts of the metabolic network. The analysis provided by OpenFLUX2 mainly includes (i) the optimization of the experimental design, (ii) the computation of the flux parameters from LEs data, (iii) goodness-of-fit testing of the model's adequacy, (iv) drawing conclusions concerning the identifiability of fluxes and construction of a contribution matrix reflecting the relative contribution of the measurement variances to the flux variances, and (v) precise determination of flux confidence intervals using a fine-tunable and convergence-controlled Monte Carlo-based method.

CONCLUSIONS

The developed open-source OpenFLUX2 provides a friendly software environment that facilitates beginners and existing OpenFLUX users to implement LEs for steady-state (13)C-MFA including experimental design, quantitative evaluation of flux parameters and statistics.

Quantifying biomass composition by gas chromatography/mass spectrometry

We developed a set of methods for the quantification of four major components of microbial biomass using gas chromatography/mass spectrometry (GC/MS). Specifically, methods are described to quantify amino acids, RNA, fatty acids, and glycogen, which comprise an estimated 88% of the dry weight of Escherichia coli. Quantification is performed by isotope ratio analysis with fully (13)C-labeled biomass as internal standard, which is generated by growing E. coli on [U-(13)C]glucose. This convenient, reliable, and accurate single-platform (GC/MS) workflow for measuring biomass composition offers significant advantages over existing methods. We demonstrate the consistency, accuracy, precision, and utility of this procedure by applying it to three metabolically unique E. coli strains. The presented methods will have widespread applicability in systems microbiology and bioengineering.

Impacts of high β-galactosidase expression on central metabolism of recombinant Pichia pastoris GS115 using glucose as sole carbon source via (13)C metabolic flux analysis

The yeast Pichia pastoris GS115 is a widely used microbial cell factory for the production of heterologous protein. In order to reveal the impacts of high heterologous protein expression on the central metabolism of Pichia pastoris GS115 using glucose as sole carbon source, we engineered a high β-galactosidase expression strain P. pastoris G1HL and a low expression control strain P. pastoris GHL through controlling the initiation strength of constitutive promoter pGAP. The carbon flux distributions in these two strains were quantified via (13)C metabolic flux analysis. Compared to the control strain, G1HL showed a lower growth rate, a higher flux through glycolysis pathway, a higher flux through pentose phosphate pathway, and a lower flux through by-products secretion pathway. The metabolic flux redistribution in G1HL was thought to compensate the increased redox cofactors and energy demands caused by the high protein expression. Although the fluxes through Krebs cycle in two engineered strains were almost the same, they were significantly lower than those in wild strain. The enhanced expression of β-galactosidase by glutamate supplementation demonstrated the potential of P. pastoris GS115 to catabolize more carbon through the Krebs cycle for even higher protein expression. In conclusion, our work indicates that P. pastoris GS115 can readjusts the central metabolism for higher heterologous protein expression and provides strategies for strain development or process optimization for enhancing production of heterologous protein.

Biofuel production: an odyssey from metabolic engineering to fermentation scale-up

Metabolic engineering has developed microbial cell factories that can convert renewable carbon sources into biofuels. Current molecular biology tools can efficiently alter enzyme levels to redirect carbon fluxes toward biofuel production, but low product yield and titer in large bioreactors prevent the fulfillment of cheap biofuels. There are three major roadblocks preventing economical biofuel production. First, carbon fluxes from the substrate dissipate into a complex metabolic network. Besides the desired product, microbial hosts direct carbon flux to synthesize biomass, overflow metabolites, and heterologous enzymes. Second, microbial hosts need to oxidize a large portion of the substrate to generate both ATP and NAD(P)H to power biofuel synthesis. High cell maintenance, triggered by the metabolic burdens from genetic modifications, can significantly affect the ATP supply. Thereby, fermentation of advanced biofuels (such as biodiesel and hydrocarbons) often requires aerobic respiration to resolve the ATP shortage. Third, mass transfer limitations in large bioreactors create heterogeneous growth conditions and micro-environmental fluctuations (such as suboptimal O₂ level and pH) that induce metabolic stresses and genetic instability. To overcome these limitations, fermentation engineering should merge with systems metabolic engineering. Modern fermentation engineers need to adopt new metabolic flux analysis tools that integrate kinetics, hydrodynamics, and ¹³C-proteomics, to reveal the dynamic physiologies of the microbial host under large bioreactor conditions. Based on metabolic analyses, fermentation engineers may employ rational pathway modifications, synthetic biology circuits, and bioreactor control algorithms to optimize large-scale biofuel production.

Metabolic flux analysis of Escherichia coli knockouts: lessons from the Keio collection and future outlook

Cellular metabolic and regulatory systems are of fundamental interest to biologists and engineers. Incomplete understanding of these complex systems remains an obstacle to progress in biotechnology and metabolic engineering. An established method for obtaining new information on network structure, regulation and dynamics is to study the cellular system following a perturbation such as a genetic knockout. The Keio collection of all viable Escherichia coli single-gene knockouts is facilitating a systematic investigation of the regulation and metabolism of E. coli. Of all omics measurements available, the metabolic flux profile (the fluxome) provides the most direct and relevant representation of the cellular phenotype. Recent advances in (13)C-metabolic flux analysis are now permitting highly precise and accurate flux measurements for investigating cellular systems and guiding metabolic engineering efforts.

Elucidation of intrinsic biosynthesis yields using 13C-based metabolism analysis

This paper discusses the use of 13C-based metabolism analysis for the assessment of intrinsic product yields - the actual carbon contribution from a single carbon substrate to the final product via a specific biosynthesis route - in the following four cases. First, undefined nutrients (such as yeast extract) in fermentation may contribute significantly to product synthesis, which can be quantified through an isotopic dilution method. Second, product and biomass synthesis may be dependent on the co-metabolism of multiple-carbon sources. 13C labeling experiments can track the fate of each carbon substrate in the cell metabolism and identify which substrate plays a main role in product synthesis. Third, 13C labeling can validate and quantify the contribution of the engineered pathway (versus the native pathway) to the product synthesis. Fourth, the loss of catabolic energy due to cell maintenance (energy used for functions other than production of new cell components) and low P/O ratio (Phosphate/Oxygen Ratio) significantly reduces product yields. Therefore, 13C-metabolic flux analysis is needed to assess the influence of suboptimal energy metabolism on microbial productivity, and determine how ATP/NAD(P)H are partitioned among various cellular functions. Since product yield is a major determining factor in the commercialization of a microbial cell factory, we foresee that 13C-isotopic labeling experiments, even without performing extensive flux calculations, can play a valuable role in the development and verification of microbial cell factories.

Systems metabolic engineering design: fatty acid production as an emerging case study

Increasing demand for petroleum has stimulated industry to develop sustainable production of chemicals and biofuels using microbial cell factories. Fatty acids of chain lengths from C₆ to C₁₆ are propitious intermediates for the catalytic synthesis of industrial chemicals and diesel-like biofuels. The abundance of genetic information available for Escherichia coli and specifically, fatty acid metabolism in E. coli, supports this bacterium as a promising host for engineering a biocatalyst for the microbial production of fatty acids. Recent successes rooted in different features of systems metabolic engineering in the strain design of high-yielding medium chain fatty acid producing E. coli strains provide an emerging case study of design methods for effective strain design. Classical metabolic engineering and synthetic biology approaches enabled different and distinct design paths towards a high-yielding strain. Here we highlight a rational strain design process in systems biology, an integrated computational and experimental approach for carboxylic acid production, as an alternative method. Additional challenges inherent in achieving an optimal strain for commercialization of medium chain-length fatty acids will likely require a collection of strategies from systems metabolic engineering. Not only will the continued advancement in systems metabolic engineering result in these highly productive strains more quickly, this knowledge will extend more rapidly the carboxylic acid platform to the microbial production of carboxylic acids with alternate chain-lengths and functionalities.

In vitro reconstitution of mevalonate pathway and targeted engineering of farnesene overproduction in Escherichia coli

Approaches using metabolic engineering and synthetic biology to overproduce terpenoids, such as the precursors of taxol and artemisinin, in microbial systems have achieved initial success. However, due to the lack of steady-state kinetic information and incomplete understanding of the terpenoid biosynthetic pathway, it has been difficult to build a highly efficient, universal system. Here, we reconstituted the mevalonate pathway to produce farnesene (a precursor of new jet fuel) in vitro using purified protein components. The information from this in vitro reconstituted system guided us to rationally optimize farnesene production in E. coli by quantitatively overexpressing each component. Targeted proteomic assays and intermediate assays were used to determine the metabolic status of each mutant. Through targeted engineering, farnesene production could be increased predictably step by step, up to 1.1 g/L (∼ 2,000 fold) 96 h after induction at the shake-flask scale. The strategy developed to release the potential of the mevalonate pathway for terpenoid overproduction should also work in other multistep synthetic pathways.

Metabolic flux pattern of glucose utilization by Xanthomonas campestris pv. campestris: prevalent role of the Entner–Doudoroff pathway and minor fluxes through the pentose phosphate pathway and glycolysis

Complex metabolic flux pattern ofX. campestris.