A comprehensive genome‐scale reconstruction of Escherichia coli metabolism—2011

The initial genome‐scale reconstruction of the metabolic network of Escherichia coli K‐12 MG1655 was assembled in 2000. It has been updated and periodically released since then based on new and curated genomic and biochemical knowledge. An update has now been built, named iJO1366, which accounts for 1366 genes, 2251 metabolic reactions, and 1136 unique metabolites. iJO1366 was (1) updated in part using a new experimental screen of 1075 gene knockout strains, illuminating cases where alternative pathways and isozymes are yet to be discovered, (2) compared with its predecessor and to experimental data sets to confirm that it continues to make accurate phenotypic predictions of growth on different substrates and for gene knockout strains, and (3) mapped to the genomes of all available sequenced E. coli strains, including pathogens, leading to the identification of hundreds of unannotated genes in these organisms. Like its predecessors, the iJO1366 reconstruction is expected to be widely deployed for studying the systems biology of E. coli and for metabolic engineering applications.     

CONSTRICTOR: Constraint Modification Provides Insight into Design of Biochemical Networks

Advances in computational methods that allow for exploration of the combinatorial mutation space are needed to realize the potential of synthetic biology based strain engineering efforts. Here, we present Constrictor, a computational framework that uses flux balance analysis (FBA) to analyze inhibitory effects of genetic mutations on the performance of biochemical networks. Constrictor identifies engineering interventions by classifying the reactions in the metabolic model depending on the extent to which their flux must be decreased to achieve the overproduction target. The optimal inhibition of various reaction pathways is determined by restricting the flux through targeted reactions below the steady state levels of a baseline strain. Constrictor generates unique in silico strains, each representing an “expression state”, or a combination of gene expression levels required to achieve the overproduction target. The Constrictor framework is demonstrated by studying overproduction of ethylene in Escherichia coli network models iAF1260 and iJO1366 through the addition of the heterologous ethylene-forming enzyme from Pseudomonas syringae. Targeting individual reactions as well as combinations of reactions reveals in silico mutants that are predicted to have as high as 25% greater theoretical ethylene yields than the baseline strain during simulated exponential growth. Altering the degree of restriction reveals a large distribution of ethylene yields, while analysis of the expression states that return lower yields provides insight into system bottlenecks. Finally, we demonstrate the ability of Constrictor to scan networks and provide targets for a range of possible products. Constrictor is an adaptable technique that can be used to generate and analyze disparate populations of in silico mutants, select gene expression levels and provide non-intuitive strategies for metabolic engineering.     

Segregated flux balance analysis constrained by population structure/function data: The case of PHA production by mixed microbial cultures

In this study we developed a segregated flux balance analysis (FBA) method to calculate metabolic flux distributions of the individual populations present in a mixed microbial culture (MMC). Population specific flux data constraints were derived from the raw data typically obtained by the fluorescence in situ hybridization (FISH) and microautoradiography (MAR)‐FISH techniques. This method was applied to study the metabolic heterogeneity of a MMC that produces polyhydroxyalkanoates (PHA) from fermented sugar cane molasses. Three populations were identified by FISH, namely Paracoccus sp., Thauera sp., and Azoarcus sp. The segregated FBA method predicts a flux distribution for each of the identified populations. The method is shown to predict with high accuracy the average PHA storage flux and the respective monomeric composition for 16 independent experiments. Moreover, flux predictions by segregated FBA were slightly better than those obtained by nonsegregated FBA, and also highly concordant with metabolic flux analysis (MFA) estimated fluxes. The segregated FBA method can be of high value to assess metabolic heterogeneity in MMC systems and to derive more efficient eco‐engineering strategies. For the case of PHA‐producing MMC considered in this work, it becomes apparent that the PHA average monomeric composition might be controlled not only by the volatile fatty acids (VFA) feeding profile but also by the population composition present in the MMC. Biotechnol. Bioeng. 2013; 110: 2267–2276. © 2013 Wiley Periodicals, Inc.     

Large‐scale identification of genetic design strategies using local search

In the past decade, computational methods have been shown to be well suited to unraveling the complex web of metabolic reactions in biological systems. Methods based on flux–balance analysis (FBA) and bi‐level optimization have been used to great effect in aiding metabolic engineering. These methods predict the result of genetic manipulations and allow for the best set of manipulations to be found computationally. Bi‐level FBA is, however, limited in applicability because the required computational time and resources scale poorly as the size of the metabolic system and the number of genetic manipulations increase. To overcome these limitations, we have developed Genetic Design through Local Search (GDLS), a scalable, heuristic, algorithmic method that employs an approach based on local search with multiple search paths, which results in effective, low‐complexity search of the space of genetic manipulations. Thus, GDLS is able to find genetic designs with greater in silico production of desired metabolites than can feasibly be found using a globally optimal search and performs favorably in comparison with heuristic searches based on evolutionary algorithms and simulated annealing.     

Thermodynamics-based Metabolite Sensitivity Analysis in metabolic networks

The increasing availability of large metabolomics datasets enhances the need for computational methodologies that can organize the data in a way that can lead to the inference of meaningful relationships. Knowledge of the metabolic state of a cell and how it responds to various stimuli and extracellular conditions can offer significant insight in the regulatory functions and how to manipulate them. Constraint based methods, such as Flux Balance Analysis (FBA) and Thermodynamics-based flux analysis (TFA), are commonly used to estimate the flow of metabolites through genome-wide metabolic networks, making it possible to identify the ranges of flux values that are consistent with the studied physiological and thermodynamic conditions. However, unless key intracellular fluxes and metabolite concentrations are known, constraint-based models lead to underdetermined problem formulations. This lack of information propagates as uncertainty in the estimation of fluxes and basic reaction properties such as the determination of reaction directionalities. Therefore, knowledge of which metabolites, if measured, would contribute the most to reducing this uncertainty can significantly improve our ability to define the internal state of the cell. In the present work we combine constraint based modeling, Design of Experiments (DoE) and Global Sensitivity Analysis (GSA) into the Thermodynamics-based Metabolite Sensitivity Analysis (TMSA) method. TMSA ranks metabolites comprising a metabolic network based on their ability to constrain the gamut of possible solutions to a limited, thermodynamically consistent set of internal states. TMSA is modular and can be applied to a single reaction, a metabolic pathway or an entire metabolic network. This is, to our knowledge, the first attempt to use metabolic modeling in order to provide a significance ranking of metabolites to guide experimental measurements.     

A regulatory network modeled from wild-type gene expression data guides functional predictions in Caenorhabditis elegans development

Background

The iJO1366 reconstruction of the metabolic network of Escherichia coli is one of the most complete and accurate metabolic reconstructions available for any organism. Still, because our knowledge of even well-studied model organisms such as this one is incomplete, this network reconstruction contains gaps and possible errors. There are a total of 208 blocked metabolites in iJO1366, representing gaps in the network.

Results

A new model improvement workflow was developed to compare model based phenotypic predictions to experimental data to fill gaps and correct errors. A Keio Collection based dataset of E. coli gene essentiality was obtained from literature data and compared to model predictions. The SMILEY algorithm was then used to predict the most likely missing reactions in the reconstructed network, adding reactions from a KEGG based universal set of metabolic reactions. The feasibility of these putative reactions was determined by comparing updated versions of the model to the experimental dataset, and genes were predicted for the most feasible reactions.

Conclusions

Numerous improvements to the iJO1366 metabolic reconstruction were suggested by these analyses. Experiments were performed to verify several computational predictions, including a new mechanism for growth on myo-inositol. The other predictions made in this study should be experimentally verifiable by similar means. Validating all of the predictions made here represents a substantial but important undertaking.     

Improving the accuracy of flux balance analysis through the implementation of carbon availability constraints for intracellular reactions

Constraint-based modeling methods, such as Flux Balance Analysis (FBA), have been extensively used to decipher complex, information rich -omics datasets to elicit system-wide behavioral patterns of cellular metabolism. FBA has been successfully used to gain insight in a wide range of applications, such as range of substrate utilization, product yields and to design metabolic engineering strategies to improve bioprocess performance. A well-known challenge associated with large genome-scale metabolic networks is that they result in underdetermined problem formulations. Consequently, rather than unique solutions, FBA and related methods examine ranges of reaction flux values that are consistent with the studied physiological conditions. The wider the reported flux ranges, the higher the uncertainty in the determination of basic reaction properties, limiting interpretability of and confidence in the results. Herein, we propose a new, computationally efficient approach that refines flux range predictions by constraining reaction fluxes on the basis of the elemental balance of carbon. We compared carbon constraint FBA (ccFBA) against experimentally-measured intracellular fluxes using the latest CHO GEM (iCHO1766) and were able to substantially improve the accuracy of predicted flux values compared with FBA. ccFBA can be used as a stand-alone method but is also compatible with and complimentary to other constraint-based approaches.     

Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints

Genome‐scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics, which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance and turnover number. We applied GECKO to a Saccharomyces cerevisiae GEM and demonstrated that the new model could correctly describe phenotypes that the previous model could not, particularly under high enzymatic pressure conditions, such as yeast growing on different carbon sources in excess, coping with stress, or overexpressing a specific pathway. GECKO also allows to directly integrate quantitative proteomics data; by doing so, we significantly reduced flux variability of the model, in over 60% of metabolic reactions. Additionally, the model gives insight into the distribution of enzyme usage between and within metabolic pathways. The developed method and model are expected to increase the use of model‐based design in metabolic engineering.     

In silico and in vivo stability analysis of a heterologous biosynthetic pathway for 1,4-butanediol production in metabolically engineered E. coli

Recently, several approaches have been published in order to develop a functional biosynthesis route for the non-natural compound 1,4-butanediol (BDO) in E. coli using glucose as a sole carbon source or starting from xylose. Among these studies, there was reported as high as 18 g/L product concentration achieved by industrial strains, however BDO production varies greatly in case of the reviewed studies. Our motivation was to build a simple heterologous pathway for this compound in E. coli and to design an appropriate cellular chassis based on a systemic biology approach, using constraint-based flux balance analysis and bi-level optimization for gene knock-out prediction. Thus, the present study reports, at the “proof-of concept” level, our findings related to model-driven development of a metabolically engineered E. coli strain lacking key genes for ethanol, lactate and formate production (ΔpflB, ΔldhA and ΔadhE), with a three-step biosynthetic pathway. We found this strain to produce a limited quantity of 1,4-BDO (.89 mg/L BDO under microaerobic conditions and .82 mg/L under anaerobic conditions). Using glycerol as carbon source, an approach, which to our knowledge has not been tackled before, our results suggest that further metabolic optimization is needed (gene-introductions or knock-outs, promoter fine-tuning) to address the redox potential imbalance problem and to achieve development of an industrially sustainable strain. Our experimental data on culture conditions, growth dynamics and fermentation parameters can consist a base for ongoing research on gene expression profiles and genetic stability of such metabolically engineered E. coli strains.     

Prediction of Rate‐Limiting Reactions for Growth‐Associated Production Using a Constraint‐Based Approach

Identification of a rate‐limiting step in pathways is a key challenge in metabolic engineering. Although the prediction of rate‐limiting steps using a kinetic model is a powerful approach, there are several technical hurdles for developing a kinetic model. In this study, an in silico screening algorithm of key enzyme for metabolic engineering is developed to identify the possible rate‐limiting reactions for the growth‐coupled target production using a stoichiometric model without any experimental data and kinetic parameters. In this method, for each reaction, an upper‐bound flux constraint is imposed and the target production is predicted by linear programming. When the constraint decreases the target production at the optimal growth state, the reaction is thought to be a possible rate‐limiting step. For validation, this method is applied to the production of succinate or 1,4‐butanediol (1,4‐BDO) in Escherichia coli, in which the experimental engineering for eliminating rate‐limiting steps has been previously reported. In succinate production from glycerol, nine reactions including phosphoenolpyruvate carboxylase are predicted as the rate‐limiting steps. In 1,4‐BDO production from glucose, eight reactions including pyruvate dehydrogenase are predicted as the rate‐limiting steps. These predictions include experimentally identified rate‐limiting steps, which would contribute to metabolic engineering as a practical tool for screening candidates of rate‐limiting reactions.     

Strain engineering for high‐level 5‐aminolevulinic acid production in Escherichia coli

Herein, we report the development of a microbial bioprocess for high‐level production of 5‐aminolevulinic acid (5‐ALA), a valuable non‐proteinogenic amino acid with multiple applications in medical, agricultural, and food industries, using Escherichia coli as a cell factory. We first implemented the Shemin (i.e., C4) pathway for heterologous 5‐ALA biosynthesis in E. coli. To reduce, but not to abolish, the carbon flux toward essential tetrapyrrole/porphyrin biosynthesis, we applied clustered regularly interspersed short palindromic repeats interference (CRISPRi) to repress hemB expression, leading to extracellular 5‐ALA accumulation. We then applied metabolic engineering strategies to direct more dissimilated carbon flux toward the key precursor of succinyl‐CoA for enhanced 5‐ALA biosynthesis. Using these engineered E. coli strains for bioreactor cultivation, we successfully demonstrated high‐level 5‐ALA biosynthesis from glycerol (~30 g L^?1) under both microaerobic and aerobic conditions, achieving up to 5.95 g L^?1 (36.9% of the theoretical maximum yield) and 6.93 g L^?1 (50.9% of the theoretical maximum yield) 5‐ALA, respectively. This study represents one of the most effective bio‐based production of 5‐ALA from a structurally unrelated carbon to date, highlighting the importance of integrated strain engineering and bioprocessing strategies to enhance bio‐based production.     

Metabolic constraints on the evolution of antibiotic resistance

Despite our continuous improvement in understanding antibiotic resistance, the interplay between natural selection of resistance mutations and the environment remains unclear. To investigate the role of bacterial metabolism in constraining the evolution of antibiotic resistance, we evolved Escherichia coli growing on glycolytic or gluconeogenic carbon sources to the selective pressure of three different antibiotics. Profiling more than 500 intracellular and extracellular putative metabolites in 190 evolved populations revealed that carbon and energy metabolism strongly constrained the evolutionary trajectories, both in terms of speed and mode of resistance acquisition. To interpret and explore the space of metabolome changes, we developed a novel constraint‐based modeling approach using the concept of shadow prices. This analysis, together with genome resequencing of resistant populations, identified condition‐dependent compensatory mechanisms of antibiotic resistance, such as the shift from respiratory to fermentative metabolism of glucose upon overexpression of efflux pumps. Moreover, metabolome‐based predictions revealed emerging weaknesses in resistant strains, such as the hypersensitivity to fosfomycin of ampicillin‐resistant strains. Overall, resolving metabolic adaptation throughout antibiotic‐driven evolutionary trajectories opens new perspectives in the fight against emerging antibiotic resistance.     

Structural analysis of metabolic networks based on flux centrality

Metabolic reactions are fundamental to living organisms, and a large number of reactions simultaneously occur at a given time in living cells transforming diverse metabolites into each other. There has been an ongoing debate on how to classify metabolites with respect to their importance for metabolic performance, usually based on the analysis of topological properties of genome scale metabolic networks. However, none of these studies have accounted quantitatively for flux in metabolic networks, thus lacking an important component of a cell’s biochemistry.We therefore analyzed a genome scale metabolic network of Escherichia coli by comparing growth under 19 different growth conditions, using flux balance analysis and weighted network centrality investigation. With this novel concept of flux centrality we generated metabolite rankings for each particular growth condition. In contrast to the results of conventional analysis of genome scale metabolic networks, different metabolites were top-ranking dependent on the growth condition. At the same time, several metabolites were consistently among the high ranking ones. Those are associated with pathways that have been described by biochemists as the most central part of metabolism, such as glycolysis, tricarboxylic acid cycle and pentose phosphate pathway. The values for the average path length of the analyzed metabolite networks were between 10.5 and 12.6, supporting recent findings that the metabolic network of E. coli is not a small-world network.     

A growth‐rate composition formula for the growth of E. coli on co‐utilized carbon substrates

When bacteria are cultured in medium with multiple carbon substrates, they frequently consume these substrates simultaneously. Building on recent advances in the understanding of metabolic coordination exhibited by Escherichia coli cells through cAMP‐Crp signaling, we show that this signaling system responds to the total carbon‐uptake flux when substrates are co‐utilized and derive a mathematical formula that accurately predicts the resulting growth rate, based only on the growth rates on individual substrates.     

High-level heterologous production of propionate in engineered Escherichia coli

A propanologenic (i.e., 1-propanol-producing) bacterium Escherichia coli strain was previously derived by activating the genomic sleeping beauty mutase (Sbm) operon. The activated Sbm pathway branches out of the tricarboxylic acid (TCA) cycle at the succinyl-CoA node to form propionyl-CoA and its derived metabolites of 1-propanol and propionate. In this study, we targeted several TCA cycle genes encoding enzymes near the succinyl-CoA node for genetic manipulation to identify the individual contribution of the carbon flux into the Sbm pathway from the three TCA metabolic routes, that is, oxidative TCA cycle, reductive TCA branch, and glyoxylate shunt. For the control strain CPC-Sbm, in which propionate biosynthesis occurred under relatively anaerobic conditions, the carbon flux into the Sbm pathway was primarily derived from the reductive TCA branch, and both succinate availability and the SucCD-mediated interconversion of succinate/succinyl-CoA were critical for such carbon flux redirection. Although the oxidative TCA cycle normally had a minimal contribution to the carbon flux redirection, the glyoxylate shunt could be an alternative and effective carbon flux contributor under aerobic conditions. With mechanistic understanding of such carbon flux redirection, metabolic strategies based on blocking the oxidative TCA cycle (via ∆sdhA mutation) and deregulating the glyoxylate shunt (via ∆iclR mutation) were developed to enhance the carbon flux redirection and therefore propionate biosynthesis, achieving a high propionate titer of 30.9 g/L with an overall propionate yield of 49.7% upon fed-batch cultivation of the double mutant strain CPC-Sbm∆sdhA∆iclR under aerobic conditions. The results also suggest that the Sbm pathway could be metabolically active under both aerobic and anaerobic conditions.     

A systematic analysis of TCA Escherichia coli mutants reveals suitable genetic backgrounds for enhanced hydrogen and ethanol production using glycerol as main carbon source

Biodiesel has emerged as an environmentally friendly alternative to fossil fuels; however, the low price of glycerol feed‐stocks generated from the biodiesel industry has become a burden to this industry. A feasible alternative is the microbial biotransformation of waste glycerol to hydrogen and ethanol. Escherichia coli, a microorganism commonly used for metabolic engineering, is able to biotransform glycerol into these products. Nevertheless, the wild type strain yields can be improved by rewiring the carbon flux to the desired products by genetic engineering. Due to the importance of the central carbon metabolism in hydrogen and ethanol synthesis, E. coli single null mutant strains for enzymes of the TCA cycle and other related reactions were studied in this work. These strains were grown anaerobically in a glycerol‐based medium and the concentrations of ethanol, glycerol, succinate and hydrogen were analysed by HPLC and GC. It was found that the reductive branch is the more relevant pathway for the aim of this work, with malate playing a central role. It was also found that the putative C4‐transporter dcuD mutant improved the target product yields. These results will contribute to reveal novel metabolic engineering strategies for improving hydrogen and ethanol production by E. coli.