Metabolic flux analysis for appcmutantEscherichia colibased on13C-labelling experiments together with enzyme activity assays and intracellular metabolite measurements

Strategies to Enhance l-Isoleucine Synthesis by Modifying the Threonine Metabolism Pathway in Escherichia coli

l-threonine as an important precursor substance of l-isoleucine and improving its accumulation in Escherichia coli became an important idea to construct a chassis strain with high l-isoleucine production. Meanwhile, the effect of l-threonine metabolic pathway disruption in E. coli for the improved production of l-isoleucine remains unrevealed. In the present study, a mutant strain of E. coli was engineered by inactivating specific metabolic pathways (e.g., Δtdh, ΔltaE, and ΔyiaY) that were associated with l-threonine metabolism but unrelated to l-isoleucine synthesis. This was done with the aim to reduce the breakdown of l-threonine and, thereby, increase the production of l-isoleucine. The results obtained demonstrated a 72.3% increment in l-isoleucine production from 4.34 to 7.48 g·L-1 in the mutant strain compared with the original strain, with an unexpected 10.3% increment in bacterial growth as measured at OD600. Transcriptome analysis was also conducted on both the mutant strain NXU102 and the original strain NXU101 in the present study to gain a comprehensive understanding of their physiological attributes. The findings revealed a notable disparity in 1294 genes between the two strains, with 658 genes exhibiting up-regulation and 636 genes displaying down-regulation. The activity of tricarboxylic acid (TCA) cycle-related genes was found to decrease, but oxidative phosphorylation-related genes were highly up-regulated, which explained the increased activity of the mutant strain. For instance, l-lysine catabolism-related genes were found to be up-regulated, which reconfigured the carbon flow into the TCA cycle. The augmentation of acetic acid degradation pathway-related genes assisted in the reduction in acetic acid accumulation that could retard cell growth. Notably, substantial up-regulation of the majority of genes within the aspartate pathway could potentially account for the increased production of l-isoleucine in the present study. In this paper, a chassis strain with an l-isoleucine yield of 7.48 g·L-1 was successfully constructed by cutting off the threonine metabolic pathway. Meanwhile, transcriptomic analysis revealed that the cutting off of the threonine metabolic pathway induced perturbation of genes related to the pathways associated with the synthesis of l-isoleucine, such as the tricarboxylic acid cycle, glycolysis, and aspartic acid pathway.

The Construction of the Self-Induced Sal System and Its Application in Salicylic Acid Production

The design and construction of more complex and delicate genetic control circuits suffer from poor orthogonality in quorum sensing (QS) systems. The Sal system, which relies on salicylic acid as a signaling molecule, is an artificially engineered regulatory system with a structure that differs significantly from that of natural QS signaling molecules. Salicylic acid is an important drug precursor, mainly used in the production of drugs such as aspirin and anti-HIV drugs. However, there have been no reports on the construction of a self-induced Sal system in single cells. In this study, a high-copy plasmid backbone was used to construct the regulatory proteins and a self-induced promoter of salicylic acid in E. coli by adjusting the precise regulation of key gene expression; the sensitivity and induction range of this system were improved. Subsequently, the exogenous gene pchBA was introduced in E. coli to extend the shikimate pathway and synthesize salicylic acid, resulting in the construction of the first complete self-induced Sal system. Finally, the self-induced Sal System was combined with artificial trans-encoded sRNAs (atsRNAs) to repress the growth-essential gene ppc and accumulate the precursor substance PEP, thereby increasing the titer of salicylic acid by 151%. This construction of a self-induced artificial system introduces a new tool for selecting communication tools and induction systems in synthetic biology and metabolic engineering, but also demonstrates a self-inducible pathway design strategy for salicylic acid biosynthesis.

Dynamic Metabolic Control: From the Perspective of Regulation Logic

Establishing microbial cell factories has become a sustainable and increasingly promising approach for the synthesis of valuable chemicals. However, introducing heterologous pathways into these cell factories can disrupt the endogenous cellular metabolism, leading to suboptimal production performance. To address this challenge, dynamic pathway regulation has been developed and proven effective in improving microbial biosynthesis. In this review, we summarized typical dynamic regulation strategies based on their control logic. The applicable scenarios for each control logic were highlighted and perspectives for future research direction in this area were discussed.

Redesigning regulatory components of quorum-sensing system for diverse metabolic control

Quorum sensing (QS) is a ubiquitous cell–cell communication mechanism that can be employed to autonomously and dynamically control metabolic fluxes. However, since the functions of genetic components in the circuits are not fully understood, the developed QS circuits are still less sophisticated for regulating multiple sets of genes or operons in metabolic engineering applications. Here, we discover the regulatory roles of a CRP-binding site and the lux box to −10 region within luxR-luxI intergenic sequence in controlling the lux-type QS promoters. By varying the numbers of the CRP-binding site and redesigning the lux box to −10 site sequence, we create a library of QS variants that possess both high dynamic ranges and low leakiness. These circuits are successfully applied to achieve diverse metabolic control in salicylic acid and 4-hydroxycoumarin biosynthetic pathways in Escherichia coli. This work expands the toolbox for dynamic control of multiple metabolic fluxes under complex metabolic background and presents paradigms to engineer metabolic pathways for high-level synthesis of target products.

Existing quorum sensing (QS) circuits are less sophisticated for regulating multiple sets of genes or operons. Here, the authors redesign the luxR-luxI intergenic sequence of the lux-type QS system and apply it to achieve diverse metabolic control in salicylic acid and 4-hydroxycoumarin biosynthesis in E. coli.

OptDesign: Identifying Optimum Design Strategies in Strain Engineering for Biochemical Production

Computational tools have been widely adopted for strain optimization in metabolic engineering, contributing to numerous success stories of producing industrially relevant biochemicals. However, most of these tools focus on single metabolic intervention strategies (either gene/reaction knockout or amplification alone) and rely on hypothetical optimality principles (e.g., maximization of growth) and precise gene expression (e.g., fold changes) for phenotype prediction. This paper introduces OptDesign, a new two-step strain design strategy. In the first step, OptDesign selects regulation candidates that have a noticeable flux difference between the wild type and production strains. In the second step, it computes optimal design strategies with limited manipulations (combining regulation and knockout), leading to high biochemical production. The usefulness and capabilities of OptDesign are demonstrated for the production of three biochemicals in Escherichia coli using the latest genome-scale metabolic model iML1515, showing highly consistent results with previous studies while suggesting new manipulations to boost strain performance. The source code is available at https://github.com/chang88ye/OptDesign.

Computational design and analysis of modular cells for large libraries of exchangeable product synthesis modules

Microbial metabolism can be harnessed to produce a large library of useful chemicals from renewable resources such as plant biomass. However, it is laborious and expensive to create microbial biocatalysts to produce each new product. To tackle this challenge, we have recently developed modular cell (ModCell) design principles that enable rapid generation of production strains by assembling a modular (chassis) cell with exchangeable production modules to achieve overproduction of target molecules. Previous computational ModCell design methods are limited to analyze small libraries of around 20 products. In this study, we developed a new computational method, named ModCell-HPC, that can design modular cells for large libraries with hundreds of products with a highly-parallel and multi-objective evolutionary algorithm and enable us to elucidate modular design properties. We demonstrated ModCell-HPC to design Escherichia coli modular cells towards a library of 161 endogenous production modules. From these simulations, we identified E. coli modular cells with few genetic manipulations that can produce dozens of molecules in a growth-coupled manner with different types of fermentable sugars. These designs revealed key genetic manipulations at the chassis and module levels to accomplish versatile modular cells, involving not only in the removal of major by-products but also modification of branch points in the central metabolism. We further found that the effect of various sugar degradation on redox metabolism results in lower compatibility between a modular cell and production modules for growth on pentoses than hexoses. To better characterize the degree of compatibility, we developed a method to calculate the minimal set cover, identifying that only three modular cells are all needed to couple with all of 161 production modules. By determining the unknown compatibility contribution metric, we further elucidated the design features that allow an existing modular cell to be re-purposed towards production of new molecules. Overall, ModCell-HPC is a useful tool for understanding modularity of biological systems and guiding more efficient and generalizable design of modular cells that help reduce research and development cost in biocatalysis.

Flux redistribution of central carbon metabolism for efficient production of l-tryptophan in Escherichia coli

Microbial production of l-tryptophan (l-trp) has received considerable attention because of its diverse applications in food additives and pharmaceuticals. Overexpression of rate-limiting enzymes and blockage of competing pathways can effectively promote microbial production of l-trp. However, the biosynthetic process remains suboptimal due to imbalanced flux distribution between central carbon and tryptophan metabolism, presenting a major challenge to further improvement of l-trp yield. In this study, we redistributed central carbon metabolism to improve phosphoenolpyruvate (PEP) and erythrose-4-phosphate (E4P) pools in an l-trp producing strain of Escherichia coli for efficient l-trp synthesis. To do this, a phosphoketolase from Bifidobacterium adolescentis was introduced to strengthen E4P formation, and the l-trp titer and yield increased to 10.8 g/L and 0.148 g/g glucose, respectively. Next, the phosphotransferase system was substituted with PEP-independent glucose transport, meditated by a glucose facilitator from Zymomonas mobilis and native glucokinase. This modification improved l-trp yield to 0.164 g/g glucose, concomitant with 58% and 40% decreases of acetate and lactate accumulation, respectively. Then, to channel more central carbon flux to the tryptophan biosynthetic pathway, several metabolic engineering strategies were applied to rewire the PEP-pyruvate-oxaloacetate node. Finally, the constructed strain SX11 produced 41.7 g/L l-trp with an overall yield of 0.227 g/g glucose after 40 h fed-batch fermentation in 5-L bioreactor. This is the highest overall yield of l-trp ever reported from a rationally engineered strain. Our results suggest the flux redistribution of central carbon metabolism to maintain sufficient supply of PEP and E4P is a promising strategy for efficient l-trp biosynthesis, and this strategy would likely also increase the production of other aromatic amino acids and derivatives.

Metabolic flux configuration determination using information entropy

Constraint-based models use steady-state mass balances to define a solution space of flux configurations, which can be narrowed down by measuring as many fluxes as possible. Due to loops and redundant pathways, this process typically yields multiple alternative solutions. To address this ambiguity, flux sampling can estimate the probability distribution of each flux, or a flux configuration can be singled out by further minimizing the sum of fluxes according to the assumption that cellular metabolism favors states where enzyme-related costs are economized. However, flux sampling is susceptible to artifacts introduced by thermodynamically infeasible cycles and is it not clear if the economy of fluxes assumption (EFA) is universally valid. Here, we formulated a constraint-based approach, MaxEnt, based on the principle of maximum entropy, which in this context states that if more than one flux configuration is consistent with a set of experimentally measured fluxes, then the one with the minimum amount of unwarranted assumptions corresponds to the best estimation of the non-observed fluxes. We compared MaxEnt predictions to Escherichia coli and Saccharomyces cerevisiae publicly available flux data. We found that the mean square error (MSE) between experimental and predicted fluxes by MaxEnt and EFA-based methods are three orders of magnitude lower than the median of 1,350,000 MSE values obtained using flux sampling. However, only MaxEnt and flux sampling correctly predicted flux through E. coli’s glyoxylate cycle, whereas EFA-based methods, in general, predict no flux cycles. We also tested MaxEnt predictions at increasing levels of overflow metabolism. We found that MaxEnt accuracy is not affected by overflow metabolism levels, whereas the EFA-based methods show a decreasing performance. These results suggest that MaxEnt is less sensitive than flux sampling to artifacts introduced by thermodynamically infeasible cycles and that its predictions are less susceptible to overfitting than EFA-based methods.

Anaplerotic Pathways in Halomonas elongata: The Role of the Sodium Gradient

Salt tolerance in the γ-proteobacterium Halomonas elongata is linked to its ability to produce the compatible solute ectoine. The metabolism of ectoine production is of great interest since it can shed light on the biochemical basis of halotolerance as well as pave the way for the improvement of the biotechnological production of such compatible solute. Ectoine belongs to the biosynthetic family of aspartate-derived amino-acids. Aspartate is formed from oxaloacetate, thereby connecting ectoine production to the anaplerotic reactions that refill carbon into the tricarboxylic acid cycle (TCA cycle). This places a high demand on these reactions and creates the need to regulate them not only in response to growth but also in response to extracellular salt concentration. In this work, we combine modeling and experiments to analyze how these different needs shape the anaplerotic reactions in H. elongata. First, the stoichiometric and thermodynamic factors that condition the flux distributions are analyzed, then the optimal patterns of operation for oxaloacetate production are calculated. Finally, the phenotype of two deletion mutants lacking potentially relevant anaplerotic enzymes: phosphoenolpyruvate carboxylase (Ppc) and oxaloacetate decarboxylase (Oad) are experimentally characterized. The results show that the anaplerotic reactions in H. elongata are indeed subject to evolutionary pressures that differ from those faced by other gram-negative bacteria. Ectoine producing halophiles must meet a higher metabolic demand for oxaloacetate and the reliance of many marine bacteria on the Entner-Doudoroff pathway compromises the anaplerotic efficiency of Ppc, which is usually one of the main enzymes fulfilling this role. The anaplerotic flux in H. elongata is contributed not only by Ppc but also by Oad, an enzyme that has not yet been shown to play this role in vivo. Ppc is necessary for H. elongata to grow normally at low salt concentrations but it is not required to achieve near maximal growth rates as long as there is a steep sodium gradient. On the other hand, the lack of Oad presents serious difficulties to grow at high salt concentrations. This points to a shared role of these two enzymes in guaranteeing the supply of oxaloacetate for biosynthetic reactions.

Harnessing Natural Modularity of Metabolism with Goal Attainment Optimization to Design a Modular Chassis Cell for Production of Diverse Chemicals

Modular design is key to achieve efficient and robust systems across engineering disciplines. Modular design potentially offers advantages to engineer microbial systems for biocatalysis, bioremediation, and biosensing, overcoming the slow and costly design-build-test-learn cycles in the conventional cell engineering approach. These systems consist of a modular (chassis) cell compatible with exchangeable modules that enable programmed functions such as overproduction of a desirable chemical. We previously proposed a multiobjective optimization framework coupled with metabolic flux models to design modular cells and solved it using multiobjective evolutionary algorithms. However, such approach might not achieve solution optimality and hence limits design options for experimental implementation. In this study, we developed the goal attainment formulation compatible with optimization algorithms that guarantee solution optimality. We applied goal attainment to design an Escherichia coli modular cell capable of synthesizing all molecules in a biochemically diverse library at high yields and rates with only a few genetic manipulations. To elucidate modular organization of the designed cells, we developed a flux variance clustering (FVC) method by identifying reactions with high flux variance and clustering them to identify metabolic modules. Using FVC, we identified reaction usage patterns for different modules in the modular cell, revealing that its broad pathway compatibility is enabled by the natural modularity and flexible flux capacity of endogenous core metabolism. Overall, this study not only sheds light on modularity in metabolic networks from their topology and metabolic functions but also presents a useful synthetic biology toolbox to design modular cells with broad applications.

Metabolic engineering of E. coli for improving mevalonate production to promote NADPH regeneration and enhance acetyl-CoA supply

Microbial production of mevalonate from renewable feedstock is a promising and sustainable approach for the production of value-added chemicals. We describe the metabolic engineering of Escherichia coli to enhance mevalonate production from glucose and cellobiose. First, the mevalonate-producing pathway was introduced into E. coli and the expression of the gene atoB, which encodes the gene for acetoacetyl-CoA synthetase, was increased. Then, the deletion of the pgi gene, which encodes phosphoglucose isomerase, increased the NADPH/NADP⁺ ratio in the cells but did not improve mevalonate production. Alternatively, to reduce flux toward the tricarboxylic acid cycle, gltA, which encodes citrate synthetase, was disrupted. The resultant strain, MGΔgltA-MV, increased levels of intracellular acetyl-CoA up to sevenfold higher than the wild-type strain. This strain produced 8.0 g/L of mevalonate from 20 g/L of glucose. We also engineered the sugar supply by displaying β-glucosidase (BGL) on the cell surface. When cellobiose was used as carbon source, the strain lacking gnd displaying BGL efficiently consumed cellobiose and produced mevalonate at 5.7 g/L. The yield of mevalonate was 0.25 g/g glucose (1 g of cellobiose corresponds to 1.1 g of glucose). These results demonstrate the feasibility of producing mevalonate from cellobiose or cellooligosaccharides using an engineered E. coli strain.

Metabolic engineering of Escherichia coli for shikimate pathway derivative production from glucose-xylose co-substrate

Glucose and xylose are the major components of lignocellulose. Effective utilization of both sugars can improve the efficiency of bioproduction. Here, we report a method termed parallel metabolic pathway engineering (PMPE) for producing shikimate pathway derivatives from glucose–xylose co-substrate. In this method, we seek to use glucose mainly for target chemical production, and xylose for supplying essential metabolites for cell growth. Glycolysis and the pentose phosphate pathway are completely separated from the tricarboxylic acid (TCA) cycle. To recover cell growth, we introduce a xylose catabolic pathway that directly flows into the TCA cycle. As a result, we can produce 4.09 g L⁻¹cis,cis-muconic acid using the PMPE Escherichia coli strain with high yield (0.31 g g⁻¹ of glucose) and produce l-tyrosine with 64% of the theoretical yield. The PMPE strategy can contribute to the development of clean processes for producing various valuable chemicals from lignocellulosic resources.

In lignocellulose biomass, microbes prefer consuming glucose over xylose, which affects target compound production. Here, the authors achieve simultaneous utilization of glucose and xylose for target chemical production and cell growth, respectively, and realize high-level production of shikimate pathway derivatives.

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.

Web application for genetic modification flux with database to estimate metabolic fluxes of genetic mutants

Computational analysis of metabolic fluxes is essential in understanding the structure and function of a metabolic network and in rationally designing genetically modified mutants for an engineering purpose. We had presented the genetic modification flux (GMF) that predicts the flux distribution of a broad range of genetically modified mutants. To enhance the feasibility and usability of GMF, we have developed a web application with a metabolic network database to predict a flux distribution of genetically modified mutants. One hundred and twelve data sets of Escherichia coli, Corynebacterium glutamicum, Saccharomyces cerevisiae, and Chinese hamster ovary were registered as standard models.

Optimizing metabolite production using periodic oscillations

Methods for improving microbial strains for metabolite production remain the subject of constant research. Traditionally, metabolic tuning has been mostly limited to knockouts or overexpression of pathway genes and regulators. In this paper, we establish a new method to control metabolism by inducing optimally tuned time-oscillations in the levels of selected clusters of enzymes, as an alternative strategy to increase the production of a desired metabolite. Using an established kinetic model of the central carbon metabolism of Escherichia coli, we formulate this concept as a dynamic optimization problem over an extended, but finite time horizon. Total production of a metabolite of interest (in this case, phosphoenolpyruvate, PEP) is established as the objective function and time-varying concentrations of the cellular enzymes are used as decision variables. We observe that by varying, in an optimal fashion, levels of key enzymes in time, PEP production increases significantly compared to the unoptimized system. We demonstrate that oscillations can improve metabolic output in experimentally feasible synthetic circuits.

Comprehensive detection of genes causing a phenotype using phenotype sequencing and pathway analysis

Discovering all the genetic causes of a phenotype is an important goal in functional genomics. We combine an experimental design for detecting independent genetic causes of a phenotype with a high-throughput sequencing analysis that maximizes sensitivity for comprehensively identifying them. Testing this approach on a set of 24 mutant strains generated for a metabolic phenotype with many known genetic causes, we show that this pathway-based phenotype sequencing analysis greatly improves sensitivity of detection compared with previous methods, and reveals a wide range of pathways that can cause this phenotype. We demonstrate our approach on a metabolic re-engineering phenotype, the PEP/OAA metabolic node in E. coli, which is crucial to a substantial number of metabolic pathways and under renewed interest for biofuel research. Out of 2157 mutations in these strains, pathway-phenoseq discriminated just five gene groups (12 genes) as statistically significant causes of the phenotype. Experimentally, these five gene groups, and the next two high-scoring pathway-phenoseq groups, either have a clear connection to the PEP metabolite level or offer an alternative path of producing oxaloacetate (OAA), and thus clearly explain the phenotype. These high-scoring gene groups also show strong evidence of positive selection pressure, compared with strictly neutral selection in the rest of the genome.

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.

¹³C-metabolic flux analysis for Escherichia coli

(13)C-Metabolic flux analysis ((13)C-MFA) is used here to study the effects of the knockout of such genes as pgi, zwf, gnd, ppc, pck, pyk, and lpdA on the metabolic changes in Escherichia coli cultivated under aerobic condition. The metabolic regulation mechanisms were clarified by integrating such information as fermentation data, gene expression, enzyme activities, and metabolite concentrations as well the result of (13)C-MFA.

Directed pathway evolution of the glyoxylate shunt in Escherichia coli for improved aerobic succinate production from glycerol

α-Ketoglutarate is accumulated as the main byproduct during the aerobic succinate production from glycerol by Escherichia coli BL21(DE3) in minimal medium. To address this issue, here a strategy of directed pathway evolution was developed to enhance the alternative succinate production route-the glyoxylate shunt. Via the directed pathway evolution, the glyoxylate shunt was recruited as the primary anaplerotic pathway in a ppc mutant, which restored its viability in glycerol minimal medium. Subsequently, the operon sdhCDAB was deleted and the gene ppc was reverted in the evolved strain for succinate production. The resulting strain E2-Δsdh-ppc produced 30 % more succinate and 46 % less α-ketoglutarate than the control strain. A G583T mutation in gene icdA, which significantly decreased the activity of isocitrate dehydrogenase, was identified in the evolved strain as the main mutation responsible for the observed phenotype. Overexpression of α-ketoglutarate dehydrogenase complex in E2-Δsdh-ppc further reduced the amount of byproduct and improved succinate production. The final strain E2-Δsdh-ppc-sucAB produced 366 mM succinate from 1.3 M glycerol in minimal medium in fed-batch fermentation. The maximum and average succinate volumetric productivities were 19.2 and 6.55 mM h(-1), respectively, exhibiting potential industrial production capacity from the low-priced substrate.

Low mass MS/MS fragments of protonated amino acids used for distinction of their 13C-isotopomers in metabolic studies

Glu, Gln, Pro, and Ala are the main amino acids involved in ammonia detoxification in mosquitoes. In order to develop a tandem mass spectrometry method (MS(2)) to monitor each carbon of the above isotopically-labeled (13)C-amino acids for metabolic studies, the compositions and origins of atoms in fragments of the protonated amino acid should be first elucidated. Thus, various electrospray (ESI)-based MS(2) tools were employed to study the fragmentation of these unlabeled and isotopically-labeled amino acids and better understand their dissociation pathways. A broad range of fragments, including previously-undescribed low m/z fragments was revealed. The formulae of the fragments (from m/z 130 down to m/z 27) were confirmed by their accurate masses. The structures and conformations of the larger fragments of Glu were also explored by ion mobility mass spectrometry (IM-MS) and gas-phase hydrogen/deuterium exchange (HDX) experiments. It was found that some low m/z fragments (m/z 27-30) are common to Glu, Gln, Pro, and Ala. The origins of carbons in these small fragments are discussed and additional collision induced dissociation (CID) MS(2) fragmentation pathways are proposed for them. It was also found that small fragments (≤m/z 84) of protonated, methylated Glu, and methylated Gln are the same as those of the underivatized Glu and Gln. Taken together, the new approach of utilizing low m/z fragments can be applied to distinguish, identify, and quantify (13)C-amino acids labeled at various positions, either in the backbone or side chain.

Reconciling a Salmonella enterica metabolic model with experimental data confirms that overexpression of the glyoxylate shunt can rescue a lethal ppc deletion mutant

The in silico reconstruction of metabolic networks has become an effective and useful systems biology approach to predict and explain many different cellular phenotypes. When simulation outputs do not match experimental data, the source of the inconsistency can often be traced to incomplete biological information that is consequently not captured in the model. To address this problem, general approaches continue to be needed that can suggest experimentally testable hypotheses to reconcile inconsistencies between simulation and experimental data. Here, we present such an approach that focuses specifically on correcting cases in which experimental data show a particular gene to be essential but model simulations do not. We use metabolic models to predict efficient compensatory pathways, after which cloning and overexpression of these pathways are performed to investigate whether they restore growth and to help determine why these compensatory pathways are not active in mutant cells. We demonstrate this technique for a ppc knockout of Salmonella enterica serovar Typhimurium; the inability of cells to route flux through the glyoxylate shunt when ppc is removed was correctly identified by our approach as the cause of the discrepancy. These results demonstrate the feasibility of our approach to drive biological discovery while simultaneously refining metabolic network reconstructions.

Importance of understanding the main metabolic regulation in response to the specific pathway mutation for metabolic engineering of Escherichia coli

Recent metabolic engineering practice was briefly reviewed in particular for the useful metabolite production such as natural products and biofuel productions. With the emphasis on systems biology approach, the metabolic regulation of the main metabolic pathways in E. coli was discussed from the points of view of enzyme level (allosteric and phosphorylation/ dephosphorylation) regulation, and gene level (transcriptional) regulation. Then the effects of the specific pathway gene knockout such as pts, pgi, zwf, gnd, pyk, ppc, pckA, lpdA, pfl gene knockout on the metabolism in E. coli were overviewed from the systems biology point of view with possible application for strain improvement point.

Metabolic flux ratio analysis and multi-objective optimization revealed a globally conserved and coordinated metabolic response of E. coli to paraquat-induced oxidative stress

The ability of a microorganism to adapt to changes in the environment, such as in nutrient or oxygen availability, is essential for its competitive fitness and survival. The cellular objective and the strategy of the metabolic response to an extreme environment are therefore of tremendous interest and, thus, have been increasingly explored. However, the cellular objective of the complex regulatory structure of the metabolic changes has not yet been fully elucidated and more details regarding the quantitative behaviour of the metabolic flux redistribution are required to understand the systems-wide biological significance of this response. In this study, the intracellular metabolic flux ratios involved in the central carbon metabolism were determined by fractional (13)C-labeling and metabolic flux ratio analysis (MetaFoR) of the wild-type E. coli strain JM101 at an oxidative environment in a chemostat. We observed a significant increase in the flux through phosphoenolpyruvate carboxykinase (PEPCK), phosphoenolpyruvate carboxylase (PEPC), malic enzyme (MEZ) and serine hydroxymethyltransferase (SHMT). We applied an ε-constraint based multi-objective optimization to investigate the trade-off relationships between the biomass yield and the generation of reductive power using the in silico iJR904 genome-scale model of E. coli K-12. The theoretical metabolic redistribution supports that the trans-hydrogenase pathway should not play a direct role in the defence mounted by E. coli against oxidative stress. The agreement between the measured ratio and the theoretical redistribution established the significance of NADPH synthesis as the goal of the metabolic reprogramming that occurs in response to oxidative stress. Our work presents a framework that combines metabolic flux ratio analysis and multi-objective optimization to investigate the metabolic trade-offs that occur under varied environmental conditions. Our results led to the proposal that the metabolic response of E. coli to paraquat-induced oxidative stress is globally conserved and coordinated.

Metabolic networks of Sodalis glossinidius: a systems biology approach to reductive evolution

BACKGROUND

Genome reduction is a common evolutionary process affecting bacterial lineages that establish symbiotic or pathogenic associations with eukaryotic hosts. Such associations yield highly reduced genomes with greatly streamlined metabolic abilities shaped by the type of ecological association with the host. Sodalis glossinidius, the secondary endosymbiont of tsetse flies, represents one of the few complete genomes available of a bacterium at the initial stages of this process. In the present study, genome reduction is studied from a systems biology perspective through the reconstruction and functional analysis of genome-scale metabolic networks of S. glossinidius.

RESULTS

The functional profile of ancestral and extant metabolic networks sheds light on the evolutionary events underlying transition to a host-dependent lifestyle. Meanwhile, reductive evolution simulations on the extant metabolic network can predict possible future evolution of S. glossinidius in the context of genome reduction. Finally, knockout simulations in different metabolic systems reveal a gradual decrease in network robustness to different mutational events for bacterial endosymbionts at different stages of the symbiotic association.

CONCLUSIONS

Stoichiometric analysis reveals few gene inactivation events whose effects on the functionality of S. glossinidius metabolic systems are drastic enough to account for the ecological transition from a free-living to host-dependent lifestyle. The decrease in network robustness across different metabolic systems may be associated with the progressive integration in the more stable environment provided by the insect host. Finally, reductive evolution simulations reveal the strong influence that external conditions exert on the evolvability of metabolic systems.

Construction and elementary mode analysis of a metabolic model for Shewanella oneidensis MR-1

A stoichiometric model describing the central metabolism of Shewanella oneidensis MR-1 wild-type and derivative strains was developed and used in elementary mode analysis (EMA). Shewanella oneidensis MR-1 can anaerobically respire a diverse pool of electron acceptors, and may be applied in several biotechnology settings, including bioremediation of toxic metals, electricity generation in microbial fuel cells, and whole-cell biocatalysis. The metabolic model presented here was adapted and verified by comparing the growth phenotypes of 13 single- and 1 double-knockout strains, while considering respiration via aerobic, anaerobic fumarate, and anaerobic metal reduction (Mtr) pathways, and utilizing acetate, n-acetylglucosamine (NAG), or lactate as carbon sources. The gene ppc, which encodes phosphoenolpyruvate carboxylase (Ppc), was determined to be necessary for aerobic growth on NAG and lactate, while not essential for growth on acetate. This suggests that Ppc is the only active anaplerotic enzyme when cultivated on lactate and NAG. The application of regulatory and substrate limitations to EMA has enabled creation of metabolic models that better reflect biological conditions, and significantly reduce the solution space for each condition, facilitating rapid strain optimization. This wild-type model can be easily adapted to include utilization of different carbon sources or secretion of different metabolic products, and allows the prediction of single- and multiple-knockout strains that are expected to operate under defined conditions with increased efficiency when compared to wild type cells.

13C-metabolic flux analysis for batch culture of Escherichia coli and its Pyk and Pgi gene knockout mutants based on mass isotopomer distribution of intracellular metabolites

Since most bio-production processes are conducted in a batch or fed-batch manner, the evaluation of metabolism with respect to time is highly desirable. Toward this aim, we applied (13)C-metabolic flux analysis to nonstationary conditions by measuring the mass isotopomer distribution of intracellular metabolites. We performed our analysis on batch cultures of wild-type Escherichia coli, as well as on Pyk and Pgi mutants, obtained the fluxes and metabolite concentrations as a function of time. Our results for the wild-type indicated that the TCA cycle flux tended to increase during growth on glucose. Following glucose exhaustion, cells controlled the branch ratio between the glyoxylate pathway and the TCA cycle, depending on the availability of acetate. In the Pyk mutant, the concentrations of glycolytic intermediates changed drastically over time due to the dumping and feedback inhibition caused by PEP accumulation. Nevertheless, the flux distribution and free amino acid concentrations changed little. The growth rate and the fluxes remained constant in the Pgi mutant and the glucose-6-phosphate dehydrogenase reaction was the rate-limiting step. The measured fluxes were compared with those predicted by flux balance analysis using maximization of biomass yield or ATP production. Our findings indicate that the objective function of biosynthesis became less important as time proceeds on glucose in the wild-type, while it remained highly important in the Pyk mutant. Furthermore, ATP production was the primary objective function in the Pgi mutant. This study demonstrates how cells adjust their metabolism in response to environmental changes and/or genetic perturbations in the batch cultivation.

Synergy between (13)C-metabolic flux analysis and flux balance analysis for understanding metabolic adaptation to anaerobiosis in E. coli

Genome-based Flux Balance Analysis (FBA) and steady-state isotopic-labeling-based Metabolic Flux Analysis (MFA) are complimentary approaches to predicting and measuring the operation and regulation of metabolic networks. Here, genome-derived models of Escherichia coli (E. coli) metabolism were used for FBA and ¹³C-MFA analyses of aerobic and anaerobic growths of wild-type E. coli (K-12 MG1655) cells. Validated MFA flux maps reveal that the fraction of maintenance ATP consumption in total ATP production is about 14% higher under anaerobic (51.1%) than aerobic conditions (37.2%). FBA revealed that an increased ATP utilization is consumed by ATP synthase to secrete protons from fermentation. The TCA cycle is shown to be incomplete in aerobically growing cells and submaximal growth is due to limited oxidative phosphorylation. An FBA was successful in predicting product secretion rates in aerobic culture if both glucose and oxygen uptake measurement were constrained, but the most-frequently predicted values of internal fluxes yielded from sampling the feasible space differ substantially from MFA-derived fluxes.

Modeling and simulation of the main metabolism in Escherichia coli and its several single-gene knockout mutants with experimental verification

BACKGROUND

It is quite important to simulate the metabolic changes of a cell in response to the change in culture environment and/or specific gene knockouts particularly for the purpose of application in industry. If this could be done, the cell design can be made without conducting exhaustive experiments, and one can screen out the promising candidates, proceeded by experimental verification of a select few of particular interest. Although several models have so far been proposed, most of them focus on the specific metabolic pathways. It is preferred to model the whole of the main metabolic pathways in Escherichia coli, allowing for the estimation of energy generation and cell synthesis, based on intracellular fluxes and that may be used to characterize phenotypic growth.

RESULTS

In the present study, we considered the simulation of the main metabolic pathways such as glycolysis, TCA cycle, pentose phosphate (PP) pathway, and the anapleorotic pathways using enzymatic reaction models of E. coli. Once intracellular fluxes were computed by this model, the specific ATP production rate, the specific CO₂ production rate, and the specific NADPH production rate could be estimated. The specific ATP production rate thus computed was used for the estimation of the specific growth rate. The CO₂ production rate could be used to estimate cell yield, and the specific NADPH production rate could be used to determine the flux of the oxidative PP pathway. The batch and continuous cultivations were simulated where the changing patterns of extracellular and intra-cellular metabolite concentrations were compared with experimental data. Moreover, the effects of the knockout of such pathways as Ppc, Pck and Pyk on the metabolism were simulated. It was shown to be difficult for the cell to grow in Ppc mutant due to low concentration of OAA, while Pck mutant does not necessarily show this phenomenon. The slower growth rate of the Ppc mutant was properly estimated by taking into account the lower specific ATP production rate. In the case of Pyk mutant, the enzyme level regulation was made clear such that Pyk knockout caused PEP concentration to be up-regulated and activated Ppc, which caused the increase in MAL concentration and backed up reduced PYR through Mez, resulting in the phenotypic growth characteristics similar to the wild type.

CONCLUSIONS

It was shown to be useful to simulate the main metabolism of E. coli for understanding metabolic changes inside the cell in response to specific pathway gene knockouts, considering the whole main metabolic pathways. The comparison of the simulation result with the experimental data indicates that the present model could simulate the effect of the specific gene knockouts to the changes in the metabolisms to some extent.

Prediction of metabolic fluxes by incorporating genomic context and flux-converging pattern analyses

Flux balance analysis (FBA) of a genome-scale metabolic model allows calculation of intracellular fluxes by optimizing an objective function, such as maximization of cell growth, under given constraints, and has found numerous applications in the field of systems biology and biotechnology. Due to the underdetermined nature of the system, however, it has limitations such as inaccurate prediction of fluxes and existence of multiple solutions for an optimal objective value. Here, we report a strategy for accurate prediction of metabolic fluxes by FBA combined with systematic and condition-independent constraints that restrict the achievable flux ranges of grouped reactions by genomic context and flux-converging pattern analyses. Analyses of three types of genomic contexts, conserved genomic neighborhood, gene fusion events, and co-occurrence of genes across multiple organisms, were performed to suggest a group of fluxes that are likely on or off simultaneously. The flux ranges of these grouped reactions were constrained by flux-converging pattern analysis. FBA of the Escherichia coli genome-scale metabolic model was carried out under several different genotypic (pykF, zwf, ppc, and sucA mutants) and environmental (altered carbon source) conditions by applying these constraints, which resulted in flux values that were in good agreement with the experimentally measured (13)C-based fluxes. Thus, this strategy will be useful for accurately predicting the intracellular fluxes of large metabolic networks when their experimental determination is difficult.

Engineering the Escherichia coli fermentative metabolism

Fermentative metabolism constitutes a fundamental cellular capacity for industrial biocatalysis. Escherichia coli is an important microorganism in the field of metabolic engineering for its well-known molecular characteristics and its rapid growth. It can adapt to different growth conditions and is able to grow in the presence or absence of oxygen. Through the use of metabolic pathway engineering and bioprocessing techniques, it is possible to explore the fundamental cellular properties and to exploit its capacity to be applied as industrial biocatalysts to produce a wide array of chemicals. The objective of this chapter is to review the metabolic engineering efforts carried out with E. coli by manipulating the central carbon metabolism and fermentative pathways to obtain strains that produce metabolites with high titers, such as ethanol, alanine, lactate and succinate.

Systematic phenome analysis of Escherichia coli multiple-knockout mutants reveals hidden reactions in central carbon metabolism

Central carbon metabolism is a basic and exhaustively analyzed pathway. However, the intrinsic robustness of the pathway might still conceal uncharacterized reactions. To test this hypothesis, we constructed systematic multiple-knockout mutants involved in central carbon catabolism in Escherichia coli and tested their growth under 12 different nutrient conditions. Differences between in silico predictions and experimental growth indicated that unreported reactions existed within this extensively analyzed metabolic network. These putative reactions were then confirmed by metabolome analysis and in vitro enzymatic assays. Novel reactions regarding the breakdown of sedoheptulose-7-phosphate to erythrose-4-phosphate and dihydroxyacetone phosphate were observed in transaldolase-deficient mutants, without any noticeable changes in gene expression. These reactions, triggered by an accumulation of sedoheptulose-7-phosphate, were catalyzed by the universally conserved glycolytic enzymes ATP-dependent phosphofructokinase and aldolase. The emergence of an alternative pathway not requiring any changes in gene expression, but rather relying on the accumulation of an intermediate metabolite may be a novel mechanism mediating the robustness of these metabolic networks.

Influence of uncertainties in pH, pMg, activity coefficients, metabolite concentrations, and other factors on the analysis of the thermodynamic feasibility of metabolic pathways

Thermodynamic feasibility analysis (TFA) has been used as a tool capable of providing additional constraints to the mass balance-based methods of analysis of metabolic networks (e.g., flux balance analysis). Several publications have recently appeared in which TFA of different metabolic pathways from relatively simple to the genome-scale networks was described as a means of detecting the possible metabolic control steps. However, in order to perform TFA, many simplifying assumptions were necessary. On the other hand, it has been shown by applying TFA to the well-known pathway of glycolysis that erroneous simplifying assumptions may seriously bias the results of the analysis. A quantitative analysis of the influence of non-ideality of the biochemical system, pH, temperature, and complexation of the metabolites with Mg(2+) ions as well as a number of other factors on the TFA is reported. It is shown that the feasibility of glycolysis is very seriously limited by the reaction of oxidative phosphorylation of glyceraldehyde phosphate, and that the intracellular concentration of the main product of this reaction, biphosphoglycerate, must be anywhere from 10 to 100 times lower than published values. In addition, the driving force for this reaction, and consequently the feasibility of the entire pathway depend strongly on the intracellular pH and ionic strength and to a lesser extent on pMg and temperature. The analysis may also be influenced by uncertainties of the dissociation and magnesium complexation constants of glyceraldehyde phosphate. The analysis demonstrates the crucial importance of taking such factors into account when performing TFA. It also suggests an urgent need for experimental determinations of such factors as a prerequisite for sensible thermodynamic analysis of metabolism on a genome-wide scale.

In silico strategy to rationally engineer metabolite production: A case study for threonine in Escherichia coli

Genetic engineering of metabolic pathways is a standard strategy to increase the production of metabolites of economic interest. However, such flux increases could very likely lead to undesirable changes in metabolite concentrations, producing deleterious perturbations on other cellular processes. These negative effects could be avoided by implementing a balanced increase of enzyme concentrations according to the Universal Method [Kacser and Acerenza (1993) Eur J Biochem 216:361-367]. Exact application of the method usually requires modification of many reactions, which is difficult to achieve in practice. Here, improvement of threonine production via pyruvate kinase deletion in Escherichia coli is used as a case study to demonstrate a partial application of the Universal Method, which includes performing sensitivity analysis. Our analysis predicts that manipulating a few reactions is sufficient to obtain an important increase in threonine production without major perturbations of metabolite concentrations.

Maximum entropy decomposition of flux distribution at steady state to elementary modes

Enzyme Control Flux (ECF) is a method of correlating enzyme activity and flux distribution. The advantage of ECF is that the measurement integrates proteome data with metabolic flux analysis through Elementary Modes (EMs). But there are a few methods of effectively determining the Elementary Mode Coefficient (EMC) in cases where no objective biological function is available. Therefore, we proposed a new algorithm implementing the maximum entropy principle (MEP) as an objective function for estimating the EMC. To demonstrate the feasibility of using the MEP in this way, we compared it with Linear Programming and Quadratic Programming for modeling the metabolic networks of Chinese Hamster Ovary, Escherichia coli, and Saccharomyces cerevisiae cells. The use of the MEP presents the most plausible distribution of EMCs in the absence of any biological hypotheses describing the physiological state of cells, thereby enhancing the prediction accuracy of the flux distribution in various mutants.

Flux balance analysis of barley seeds: a computational approach to study systemic properties of central metabolism

The accumulation of storage compounds is an important aspect of cereal seed metabolism. Due to the agronomical importance of the storage reserves of starch, protein, and oil, the understanding of storage metabolism is of scientific interest, with practical applications in agronomy and plant breeding. To get insight into storage patterning in developing cereal seed in response to environmental and genetic perturbation, a computational analysis of seed metabolism was performed. A metabolic network of primary metabolism in the developing endosperm of barley (Hordeum vulgare), a model plant for temperate cereals, was constructed that includes 257 biochemical and transport reactions across four different compartments. The model was subjected to flux balance analysis to study grain yield and metabolic flux distributions in response to oxygen depletion and enzyme deletion. In general, the simulation results were found to be in good agreement with the main biochemical properties of barley seed storage metabolism. The predicted growth rate and the active metabolic pathway patterns under anoxic, hypoxic, and aerobic conditions predicted by the model were in accordance with published experimental results. In addition, the model predictions gave insight into the potential role of inorganic pyrophosphate metabolism to maintain seed metabolism under oxygen deprivation.

Development and application of a differential method for reliable metabolome analysis in Escherichia coli

Quantitative metabolomics of microbial cultures requires well-designed sampling and quenching procedures. We successfully developed and applied a differential method to obtain a reliable set of metabolome data for Escherichia coli K12 MG1655 grown in steady-state, aerobic, glucose-limited chemostat cultures. From a rigorous analysis of the commonly applied quenching procedure based on cold aqueous methanol, it was concluded that it was not applicable because of release of a major part of the metabolites from the cells. No positive effect of buffering or increasing the ionic strength of the quenching solution was observed. Application of a differential method in principle requires metabolite measurements in total broth and filtrate for each measurement. Different methods for sampling of culture filtrate were examined, and it was found that direct filtration without cooling of the sample was the most appropriate. Analysis of culture filtrates revealed that most of the central metabolites and amino acids were present in significant amounts outside the cells. Because the turnover time of the pools of extracellular metabolites is much larger than that of the intracellular pools, the differential method should also be applicable to short-term pulse response experiments without requiring measurement of metabolites in the supernatant during the dynamic period.

Metabolic flux analysis in plants: from intelligent design to rational engineering

Metabolic flux analysis (MFA) is a rapidly developing field concerned with the quantification and understanding of metabolism at the systems level. The application of MFA has produced detailed maps of flow through metabolic networks of a range of plant systems. These maps represent detailed metabolic phenotypes, contribute significantly to our understanding of metabolism in plants, and have led to the discovery of new metabolic routes. The presentation of thorough statistical evaluation with current flux maps has set a new standard for the quality of quantitative flux studies. In microbial systems, powerful methods have been developed for the reconstruction of metabolic networks from genomic and transcriptomic data, pathway analysis, and predictive modeling. This review brings together the recent developments in quantitative MFA and predictive modeling. The application of predictive tools to high quality flux maps in particular promises to be important in the rational metabolic engineering of plants.

Minimizing acetate formation in E. coli fermentations

Escherichia coli remains the best-established production organism in industrial biotechnology. However, when aerobic fermentation runs at high growth rates, considerable amounts of acetate are accumulated as by-product. This by-product has negative effects on growth and protein production. Over the last 20 years, substantial research efforts have been expended on reducing acetate accumulation during aerobic growth of E. coli on glucose. From the onset it was clear that this quest would not be a simple or uncomplicated one. Simple deletion of the acetate pathway reduced the acetate accumulation, but other by-products were formed. This mini review gives a clear outline of these research efforts and their outcome, including bioprocess level approaches and genetic approaches. Recently, the latter seems to have some promising results.

Integration of enzyme activities into metabolic flux distributions by elementary mode analysis

Background

In systems biology, network-based pathway analysis facilitates understanding or designing metabolic systems and enables prediction of metabolic flux distributions. Network-based flux analysis requires considering not only pathway architectures but also the proteome or transcriptome to predict flux distributions, because recombinant microbes significantly change the distribution of gene expressions. The current problem is how to integrate such heterogeneous data to build a network-based model.

Results

To link enzyme activity data to flux distributions of metabolic networks, we have proposed Enzyme Control Flux (ECF), a novel model that integrates enzyme activity into elementary mode analysis (EMA). ECF presents the power-law formula describing how changes in enzyme activities between wild-type and a mutant are related to changes in the elementary mode coefficients (EMCs). To validate the feasibility of ECF, we integrated enzyme activity data into the EMCs of Escherichia coli and Bacillus subtilis wild-type. The ECF model effectively uses an enzyme activity profile to estimate the flux distribution of the mutants and the increase in the number of incorporated enzyme activities decreases the model error of ECF.

Conclusion

The ECF model is a non-mechanistic and static model to link an enzyme activity profile to a metabolic flux distribution by introducing the power-law formula into EMA, suggesting that the change in an enzyme profile rather reflects the change in the flux distribution. The ECF model is highly applicable to the central metabolism in knockout mutants of E. coli and B. subtilis.

Response of fluxome and metabolome to temperature-induced recombinant protein synthesis in Escherichia coli

The response of the central carbon metabolism of Escherichia coli to temperature-induced recombinant production of human fibroblast growth factor was studied on the level of metabolic fluxes and intracellular metabolite levels. During production, E. coli TG1:plambdaFGFB, carrying a plasmid encoded gene for the recombinant product, revealed stress related characteristics such as decreased growth rate and biomass yield and enhanced by-product excretion (acetate, pyruvate, lactate). With the onset of production, the adenylate energy charge dropped from 0.85 to 0.60, indicating the occurrence of a severe energy limitation. This triggered an increase of the glycolytic flux which, however, was not sufficient to compensate for the increased ATP demand. The activation of the glycolytic flux was also indicated by the readjustment of glycolytic pool sizes leading to an increased driving force for the reaction catalyzed by phosphofructokinase. Moreover, fluxes through the TCA cycle, into the pentose phosphate pathway and into anabolic pathways decreased significantly. The strong increase of flux into overflow pathways, especially towards acetate was most likely caused by a flux redirection from pyruvate dehydrogenase to pyruvate oxidase. The glyoxylate shunt, not active during growth, was the dominating anaplerotic pathway during production. Together with pyruvate oxidase and acetyl CoA synthase this pathway could function as a metabolic by-pass to overcome the limitation in the junction between glycolysis and TCA cycle and partly recycle the acetate formed back into the metabolism.