Redesigning Escherichia coli metabolism for anaerobic production of isobutanol

The bifunctional alcohol and aldehyde dehydrogenase gene, adhE, is necessary for ethanol production in Clostridium thermocellum and Thermoanaerobacterium saccharolyticum

Thermoanaerobacterium saccharolyticum and Clostridium thermocellum are anaerobic thermophilic bacteria being investigated for their ability to produce biofuels from plant biomass. The bifunctional alcohol and aldehyde dehydrogenase gene, adhE, is present in these bacteria and has been known to be important for ethanol formation in other anaerobic alcohol producers. This study explores the inactivation of the adhE gene in C. thermocellum and T. saccharolyticum. Deletion of adhE reduced ethanol production by >95% in both T. saccharolyticum and C. thermocellum, confirming that adhE is necessary for ethanol formation in both organisms. In both adhE deletion strains, fermentation products shifted from ethanol to lactate production and resulted in lower cell density and longer time to reach maximal cell density. In T. saccharolyticum, the adhE deletion strain lost >85% of alcohol dehydrogenase (ADH) activity. Aldehyde dehydrogenase (ALDH) activity did not appear to be affected, although ALDH activity was low in cell extracts. Adding ubiquinone-0 to the ALDH assay increased activity in the T. saccharolyticum parent strain but did not increase activity in the adhE deletion strain, suggesting that ALDH activity was inhibited. In C. thermocellum, the adhE deletion strain lost >90% of ALDH and ADH activity in cell extracts. The C. thermocellum adhE deletion strain contained a point mutation in the lactate dehydrogenase gene, which appears to deregulate its activation by fructose 1,6-bisphosphate, leading to constitutive activation of lactate dehydrogenase.

IMPORTANCE

Thermoanaerobacterium saccharolyticum and Clostridium thermocellum are bacteria that have been investigated for their ability to produce biofuels from plant biomass. They have been engineered to produce higher yields of ethanol, yet questions remain about the enzymes responsible for ethanol formation in these bacteria. The genomes of these bacteria encode multiple predicted aldehyde and alcohol dehydrogenases which could be responsible for alcohol formation. This study explores the inactivation of adhE, a gene encoding a bifunctional alcohol and aldehyde dehydrogenase. Deletion of adhE reduced ethanol production by >95% in both T. saccharolyticum and C. thermocellum, confirming that adhE is necessary for ethanol formation in both organisms. In strains without adhE, we note changes in biochemical activity, product formation, and growth.

Model-driven intracellular redox status modulation for increasing isobutanol production in Escherichia coli

BACKGROUND

Few strains have been found to produce isobutanol naturally. For building a high performance isobutanol-producing strain, rebalancing redox status of the cell was very crucial through systematic investigation of redox cofactors metabolism. Then, the metabolic model provided a powerful tool for the rational modulation of the redox status.

RESULTS

Firstly, a starting isobutanol-producing E. coli strain LA02 was engineered with only 2.7 g/L isobutanol produced. Then, the genome-scale metabolic modeling was specially carried out for the redox cofactor metabolism of the strain LA02 by combining flux balance analysis and minimization of metabolic adjustment, and the GAPD reaction catalyzed by the glyceraldehyde-3-phosphate dehydrogenase was predicted as the key target for redox status improvement. Under guidance of the metabolic model prediction, a gapN-encoding NADP(+) dependent glyceraldehyde-3-phosphate dehydrogenase pathway was constructed and then fine-tuned using five constitutive promoters. The best strain LA09 was obtained with the strongest promoter BBa_J23100. The NADPH/NADP + ratios of strain LA09 reached 0.67 at exponential phase and 0.64 at stationary phase. The redox modulations resulted in the decrease production of ethanol and lactate by 17.5 and 51.7% to 1.32 and 6.08 g/L, respectively. Therefore, the isobutanol titer was increased by 221% to 8.68 g/L.

CONCLUSIONS

This research has achieved rational redox status improvement of isobutanol-producing strain under guidance of the prediction and modeling of the genome-scale metabolic model of isobutanol-producing E. coli strain with the aid of synthetic promoters. Therefore, the production of isobutanol was dramatically increased by 2.21-fold from 2.7 to 8.68 g/L. Moreover, the developed model-driven method special for redox cofactor metabolism was of very helpful to the redox status modulation of other bio-products.

Construction of Escherichia Coli Cell Factories for Production of Organic Acids and Alcohols

Production of bulk chemicals from renewable biomass has been proved to be sustainable and environmentally friendly. Escherichia coli is the most commonly used host strain for constructing cell factories for production of bulk chemicals since it has clear physiological and genetic characteristics, grows fast in minimal salts medium, uses a wide range of substrates, and can be genetically modified easily. With the development of metabolic engineering, systems biology, and synthetic biology, a technology platform has been established to construct E. coli cell factories for bulk chemicals production. In this chapter, we will introduce this technology platform, as well as E. coli cell factories successfully constructed for production of organic acids and alcohols.

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.

Engineering modular ester fermentative pathways in Escherichia coli

Sensation profiles are observed all around us and are made up of many different molecules, such as esters. These profiles can be mimicked in everyday items for their uses in foods, beverages, cosmetics, perfumes, solvents, and biofuels. Here, we developed a systematic 'natural' way to derive these products via fermentative biosynthesis. Each ester fermentative pathway was designed as an exchangeable ester production module for generating two precursors- alcohols and acyl-CoAs that were condensed by an alcohol acyltransferase to produce a combinatorial library of unique esters. As a proof-of-principle, we coupled these ester modules with an engineered, modular, Escherichia coli chassis in a plug-and-play fashion to create microbial cell factories for enhanced anaerobic production of a butyrate ester library. We demonstrated tight coupling between the modular chassis and ester modules for enhanced product biosynthesis, an engineered phenotype useful for directed metabolic pathway evolution. Compared to the wildtype, the engineered cell factories yielded up to 48 fold increase in butyrate ester production from glucose.

Model-driven redox pathway manipulation for improved isobutanol production in Bacillus subtilis complemented with experimental validation and metabolic profiling analysis

To rationally guide the improvement of isobutanol production, metabolic network and metabolic profiling analysis were performed to provide global and profound insights into cell metabolism of isobutanol-producing Bacillus subtilis. The metabolic flux distribution of strains with different isobutanol production capacity (BSUL03, BSUL04 and BSUL05) drops a hint of the importance of NADPH on isobutanol biosynthesis. Therefore, the redox pathways were redesigned in this study. To increase NADPH concentration, glucose-6-phosphate isomerase was inactivated (BSUL06) and glucose-6-phosphate dehydrogenase was overexpressed (BSUL07) successively. As expected, NADPH pool size in BSUL07 was 4.4-fold higher than that in parental strain BSUL05. However, cell growth, isobutanol yield and production were decreased by 46%, 22%, and 80%, respectively. Metabolic profiling analysis suggested that the severely imbalanced redox status might be the primary reason. To solve this problem, gene udhA of Escherichia coli encoding transhydrogenase was further overexpressed (BSUL08), which not only well balanced the cellular ratio of NAD(P)H/NAD(P)⁺, but also increased NADH and ATP concentration. In addition, a straightforward engineering approach for improving NADPH concentrations was employed in BSUL05 by overexpressing exogenous gene pntAB and obtained BSUL09. The performance for isobutanol production by BSUL09 was poorer than BSUL08 but better than other engineered strains. Furthermore, in fed-batch fermentation the isobutanol production and yield of BSUL08 increased by 11% and 19%, up to the value of 6.12 g/L and 0.37 C-mol isobutanol/C-mol glucose (63% of the theoretical value), respectively, compared with parental strain BSUL05. These results demonstrated that model-driven complemented with metabolic profiling analysis could serve as a useful approach in the strain improvement for higher bio-productivity in further application.

Digital and analog gene circuits for biotechnology

Biotechnology offers the promise of valuable chemical production via microbial processing of renewable and inexpensive substrates. Thus far, static metabolic engineering strategies have enabled this field to advance industrial applications. However, the industrial scaling of statically engineered microbes inevitably creates inefficiencies due to variable conditions present in large-scale microbial cultures. Synthetic gene circuits that dynamically sense and regulate different molecules can resolve this issue by enabling cells to continuously adapt to variable conditions. These circuits also have the potential to enable next-generation production programs capable of autonomous transitioning between steps in a bioprocess. Here, we review the design and application of two main classes of dynamic gene circuits, digital and analog, for biotechnology. Within the context of these classes, we also discuss the potential benefits of digital-analog interconversion, memory, and multi-signal integration. Though synthetic gene circuits have largely been applied for cellular computation to date, we envision that utilizing them in biotechnology will enhance the efficiency and scope of biochemical production with living cells.

Biochemical and structural studies of NADH-dependent FabG used to increase the bacterial production of fatty acids under anaerobic conditions

Major efforts in bioenergy research have focused on producing fuels that can directly replace petroleum-derived gasoline and diesel fuel through metabolic engineering of microbial fatty acid biosynthetic pathways. Typically, growth and pathway induction are conducted under aerobic conditions, but for operational efficiency in an industrial context, anaerobic culture conditions would be preferred to obviate the need to maintain specific dissolved oxygen concentrations and to maximize the proportion of reducing equivalents directed to biofuel biosynthesis rather than ATP production. A major concern with fermentative growth conditions is elevated NADH levels, which can adversely affect cell physiology. The purpose of this study was to identify homologs of Escherichia coli FabG, an essential reductase involved in fatty acid biosynthesis, that display a higher preference for NADH than for NADPH as a cofactor. Four potential NADH-dependent FabG variants were identified through bioinformatic analyses supported by crystallographic structure determination (1.3- to 2.0-Å resolution). In vitro assays of cofactor (NADH/NADPH) preference in the four variants showed up to ≈ 35-fold preference for NADH, which was observed with the Cupriavidus taiwanensis FabG variant. In addition, FabG homologs were overexpressed in fatty acid- and methyl ketone-overproducing E. coli host strains under anaerobic conditions, and the C. taiwanensis variant led to a 60% higher free fatty acid titer and 75% higher methyl ketone titer relative to the titers of the control strains. With further engineering, this work could serve as a starting point for establishing a microbial host strain for production of fatty acid-derived biofuels (e.g., methyl ketones) under anaerobic conditions.

Comparison and improvement of algorithms for computing minimal cut sets

Background

Constrained minimal cut sets (cMCSs) have recently been introduced as a framework to enumerate minimal genetic intervention strategies for targeted optimization of metabolic networks. Two different algorithmic schemes (adapted Berge algorithm and binary integer programming) have been proposed to compute cMCSs from elementary modes. However, in their original formulation both algorithms are not fully comparable.

Results

Here we show that by a small extension to the integer program both methods become equivalent. Furthermore, based on well-known preprocessing procedures for integer programming we present efficient preprocessing steps which can be used for both algorithms. We then benchmark the numerical performance of the algorithms in several realistic medium-scale metabolic models. The benchmark calculations reveal (i) that these preprocessing steps can lead to an enormous speed-up under both algorithms, and (ii) that the adapted Berge algorithm outperforms the binary integer approach.

Conclusions

Generally, both of our new implementations are by at least one order of magnitude faster than other currently available implementations.

Elementary flux modes in a nutshell: properties, calculation and applications

Elementary flux mode (EFM) analysis allows the unbiased decomposition of a metabolic network into minimal functional units, making it a powerful tool for metabolic engineering. While the use of EFM analysis (EFMA) is still limited by the size of the models it can handle, EFMA has been successfully applied to solve real-world metabolic engineering problems. Here we provide a user-oriented introduction to EFMA, provide examples of recent applications, analyze current research strategies to overcome the computational restrictions and give an overview over current approaches, which aim to identify and calculate only biologically relevant EFMs.

Flux analysis and metabolomics for systematic metabolic engineering of microorganisms

Rational engineering of metabolism is important for bio-production using microorganisms. Metabolic design based on in silico simulations and experimental validation of the metabolic state in the engineered strain helps in accomplishing systematic metabolic engineering. Flux balance analysis (FBA) is a method for the prediction of metabolic phenotype, and many applications have been developed using FBA to design metabolic networks. Elementary mode analysis (EMA) and ensemble modeling techniques are also useful tools for in silico strain design. The metabolome and flux distribution of the metabolic pathways enable us to evaluate the metabolic state and provide useful clues to improve target productivity. Here, we reviewed several computational applications for metabolic engineering by using genome-scale metabolic models of microorganisms. We also discussed the recent progress made in the field of metabolomics and (13)C-metabolic flux analysis techniques, and reviewed these applications pertaining to bio-production development. Because these in silico or experimental approaches have their respective advantages and disadvantages, the combined usage of these methods is complementary and effective for metabolic engineering.

From the first drop to the first truckload: commercialization of microbial processes for renewable chemicals

Fermentation of carbohydrate substrates by microorganisms represents an attractive route for the manufacture of industrial chemicals from renewable resources. The technology to manipulate metabolism of bacteria and yeast, including the introduction of heterologous chemical pathways, has accelerated research in this field. However, the public literature contains very few examples of strains achieving the production metrics required for commercialization. This article presents the challenges in reaching commercial titer, yield, and productivity targets, along with other necessary strain and process characteristics. It then reviews various methods in systems biology, synthetic biology, enzyme engineering, and fermentation engineering which can be applied to strain improvement, and presents a strategy for using these tools to overcome the major hurdles on the path to commercialization.

The role of aldehyde/alcohol dehydrogenase (AdhE) in ethanol production from glycerol by Klebsiella pneumoniae

Transcriptome analysis of a K. pneumoniae GEM167 mutant strain derived by irradiation with gamma rays, which exhibited high-level production of ethanol from glycerol, showed that the mutant expressed AdhE at a high level. Ethanol production decreased significantly, from 8.8 to 0.5 g l−1, when an adhE-deficient derivative of that strain was grown on glycerol. Bacterial growth was also reduced under such conditions, showing that AdhE plays a critical role in maintenance of redox balance by catalyzing ethanol production. Overexpression of AdhE enhanced ethanol production, from pure or crude glycerol, to a maximal level of 31.9 g l−1 under fed-batch fermentation conditions; this is the highest level of ethanol production from glycerol reported to date.

Linking genome content to biofuel production yields: a meta-analysis of major catabolic pathways among select H2 and ethanol-producing bacteria

Background

Fermentative bacteria offer the potential to convert lignocellulosic waste-streams into biofuels such as hydrogen (H₂) and ethanol. Current fermentative H₂ and ethanol yields, however, are below theoretical maxima, vary greatly among organisms, and depend on the extent of metabolic pathways utilized. For fermentative H₂ and/or ethanol production to become practical, biofuel yields must be increased. We performed a comparative meta-analysis of (i) reported end-product yields, and (ii) genes encoding pyruvate metabolism and end-product synthesis pathways to identify suitable biomarkers for screening a microorganism’s potential of H₂ and/or ethanol production, and to identify targets for metabolic engineering to improve biofuel yields. Our interest in H₂ and/or ethanol optimization restricted our meta-analysis to organisms with sequenced genomes and limited branched end-product pathways. These included members of the Firmicutes, Euryarchaeota, and Thermotogae.

Results

Bioinformatic analysis revealed that the absence of genes encoding acetaldehyde dehydrogenase and bifunctional acetaldehyde/alcohol dehydrogenase (AdhE) in Caldicellulosiruptor, Thermococcus, Pyrococcus, and Thermotoga species coincide with high H₂ yields and low ethanol production. Organisms containing genes (or activities) for both ethanol and H₂ synthesis pathways (i.e. Caldanaerobacter subterraneus subsp. tengcongensis, Ethanoligenens harbinense, and Clostridium species) had relatively uniform mixed product patterns. The absence of hydrogenases in Geobacillus and Bacillus species did not confer high ethanol production, but rather high lactate production. Only Thermoanaerobacter pseudethanolicus produced relatively high ethanol and low H₂ yields. This may be attributed to the presence of genes encoding proteins that promote NADH production. Lactate dehydrogenase and pyruvate:formate lyase are not conducive for ethanol and/or H₂ production. While the type(s) of encoded hydrogenases appear to have little impact on H₂ production in organisms that do not encode ethanol producing pathways, they do influence reduced end-product yields in those that do.

Conclusions

Here we show that composition of genes encoding pathways involved in pyruvate catabolism and end-product synthesis pathways can be used to approximate potential end-product distribution patterns. We have identified a number of genetic biomarkers for streamlining ethanol and H₂ producing capabilities. By linking genome content, reaction thermodynamics, and end-product yields, we offer potential targets for optimization of either ethanol or H₂ yields through metabolic engineering.

Systematic applications of metabolomics in metabolic engineering

The goals of metabolic engineering are well-served by the biological information provided by metabolomics: information on how the cell is currently using its biochemical resources is perhaps one of the best ways to inform strategies to engineer a cell to produce a target compound. Using the analysis of extracellular or intracellular levels of the target compound (or a few closely related molecules) to drive metabolic engineering is quite common. However, there is surprisingly little systematic use of metabolomics datasets, which simultaneously measure hundreds of metabolites rather than just a few, for that same purpose. Here, we review the most common systematic approaches to integrating metabolite data with metabolic engineering, with emphasis on existing efforts to use whole-metabolome datasets. We then review some of the most common approaches for computational modeling of cell-wide metabolism, including constraint-based models, and discuss current computational approaches that explicitly use metabolomics data. We conclude with discussion of the broader potential of computational approaches that systematically use metabolomics data to drive metabolic engineering.

Systems-level characterization and engineering of oxidative stress tolerance in Escherichia coli under anaerobic conditions

Despite many prior studies on microbial response to oxidative stress, our understanding of microbial tolerance against oxidative stress is currently limited to aerobic conditions, and few engineering strategies have been devised to resolve toxicity issues of oxidative stress under anaerobic conditions. Since biological processes, such as anaerobic fermentation, are frequently hampered by toxicity arising from oxidative stress, increased microbial tolerance against oxidative stress improves the overall productivity and yield of biological processes. Here, we show a systems-level analysis of oxidative stress response of Escherichia coli under anaerobic conditions, and present an engineering strategy to improve oxidative stress tolerance. First, we identified essential cellular mechanisms and regulatory factors underlying oxidative stress response under anaerobic conditions using a transcriptome analysis. In particular, we showed that nitrogen metabolisms and respiratory pathways were differentially regulated in response to oxidative stress under anaerobic and aerobic conditions. Further, we demonstrated that among transcription factors with oxidative stress-derived perturbed activity, the deletion of arcA and arcB significantly improved oxidative stress tolerance under aerobic and anaerobic conditions, respectively, whereas fnr was identified as an essential transcription factor for oxidative stress tolerance under anaerobic conditions. Moreover, we showed that oxidative stress increased the intracellular NADH : NAD(+) ratio under aerobic and anaerobic conditions, which indicates a regulatory role of NADH in oxidative stress tolerance. Based on this finding, we demonstrated that increased NADH availability through fdh1 overexpression significantly improved oxidative stress tolerance under aerobic conditions. Our results here provide novel insight into better understanding of cellular mechanisms underlying oxidative stress tolerance under anaerobic conditions, and into developing strain engineering strategies to enhance microbial tolerance against oxidative stress towards improved biological processes.

Designing optimal cell factories: integer programming couples elementary mode analysis with regulation

BACKGROUND

Elementary mode (EM) analysis is ideally suited for metabolic engineering as it allows for an unbiased decomposition of metabolic networks in biologically meaningful pathways. Recently, constrained minimal cut sets (cMCS) have been introduced to derive optimal design strategies for strain improvement by using the full potential of EM analysis. However, this approach does not allow for the inclusion of regulatory information.

RESULTS

Here we present an alternative, novel and simple method for the prediction of cMCS, which allows to account for boolean transcriptional regulation. We use binary linear programming and show that the design of a regulated, optimal metabolic network of minimal functionality can be formulated as a standard optimization problem, where EM and regulation show up as constraints. We validated our tool by optimizing ethanol production in E. coli. Our study showed that up to 70% of the predicted cMCS contained non-enzymatic, non-annotated reactions, which are difficult to engineer. These cMCS are automatically excluded by our approach utilizing simple weight functions. Finally, due to efficient preprocessing, the binary program remains computationally feasible.

CONCLUSIONS

We used integer programming to predict efficient deletion strategies to metabolically engineer a production organism. Our formulation utilizes the full potential of cMCS but adds additional flexibility to the design process. In particular our method allows to integrate regulatory information into the metabolic design process and explicitly favors experimentally feasible deletions. Our method remains manageable even if millions or potentially billions of EM enter the analysis. We demonstrated that our approach is able to correctly predict the most efficient designs for ethanol production in E. coli.

Designing optimal cell factories: integer programming couples elementary mode analysis with regulation

BACKGROUND

Elementary mode (EM) analysis is ideally suited for metabolic engineering as it allows for an unbiased decomposition of metabolic networks in biologically meaningful pathways. Recently, constrained minimal cut sets (cMCS) have been introduced to derive optimal design strategies for strain improvement by using the full potential of EM analysis. However, this approach does not allow for the inclusion of regulatory information.

RESULTS

Here we present an alternative, novel and simple method for the prediction of cMCS, which allows to account for boolean transcriptional regulation. We use binary linear programming and show that the design of a regulated, optimal metabolic network of minimal functionality can be formulated as a standard optimization problem, where EM and regulation show up as constraints. We validated our tool by optimizing ethanol production in E. coli. Our study showed that up to 70% of the predicted cMCS contained non-enzymatic, non-annotated reactions, which are difficult to engineer. These cMCS are automatically excluded by our approach utilizing simple weight functions. Finally, due to efficient preprocessing, the binary program remains computationally feasible.

CONCLUSIONS

We used integer programming to predict efficient deletion strategies to metabolically engineer a production organism. Our formulation utilizes the full potential of cMCS but adds additional flexibility to the design process. In particular our method allows to integrate regulatory information into the metabolic design process and explicitly favors experimentally feasible deletions. Our method remains manageable even if millions or potentially billions of EM enter the analysis. We demonstrated that our approach is able to correctly predict the most efficient designs for ethanol production in E. coli.

Rational improvement of the engineered isobutanol-producing Bacillus subtilis by elementary mode analysis

Background

Isobutanol is considered as a leading candidate for the replacement of current fossil fuels, and expected to be produced biotechnologically. Owing to the valuable features, Bacillus subtilis has been engineered as an isobutanol producer, whereas it needs to be further optimized for more efficient production. Since elementary mode analysis (EMA) is a powerful tool for systematical analysis of metabolic network structures and cell metabolism, it might be of great importance in the rational strain improvement.

Results

Metabolic network of the isobutanol-producing B. subtilis BSUL03 was first constructed for EMA. Considering the actual cellular physiological state, 239 elementary modes (EMs) were screened from total 11,342 EMs for potential target prediction. On this basis, lactate dehydrogenase (LDH) and pyruvate dehydrogenase complex (PDHC) were predicted as the most promising inactivation candidates according to flux flexibility analysis and intracellular flux distribution simulation. Then, the in silico designed mutants were experimentally constructed. The maximal isobutanol yield of the LDH- and PDHC-deficient strain BSUL05 reached 61% of the theoretical value to 0.36 ± 0.02 C-mol isobutanol/C-mol glucose, which was 2.3-fold of BSUL03. Moreover, this mutant produced approximately 70 % more isobutanol to the maximal titer of 5.5 ± 0.3 g/L in fed-batch fermentations.

Conclusions

EMA was employed as a guiding tool to direct rational improvement of the engineered isobutanol-producing B. subtilis. The consistency between model prediction and experimental results demonstrates the rationality and accuracy of this EMA-based approach for target identification. This network-based rational strain improvement strategy could serve as a promising concept to engineer efficient B. subtilis hosts for isobutanol, as well as other valuable products.

Elucidating and reprogramming Escherichia coli metabolisms for obligate anaerobic n-butanol and isobutanol production

Elementary mode (EM) analysis based on the constraint-based metabolic network modeling was applied to elucidate and compare complex fermentative metabolisms of Escherichia coli for obligate anaerobic production of n-butanol and isobutanol. The result shows that the n-butanol fermentative metabolism was NADH-deficient, while the isobutanol fermentative metabolism was NADH redundant. E. coli could grow and produce n-butanol anaerobically as the sole fermentative product but not achieve the maximum theoretical n-butanol yield. In contrast, for the isobutanol fermentative metabolism, E. coli was required to couple with either ethanol- or succinate-producing pathway to recycle NADH. To overcome these "defective" metabolisms, EM analysis was implemented to reprogram the native fermentative metabolism of E. coli for optimized anaerobic production of n-butanol and isobutanol through multiple gene deletion (~8-9 genes), addition (~6-7 genes), up- and downexpression (~6-7 genes), and cofactor engineering (e.g., NADH, NADPH). The designed strains were forced to couple both growth and anaerobic production of n-butanol and isobutanol, which is a useful characteristic to enhance biofuel production and tolerance through metabolic pathway evolution. Even though the n-butanol and isobutanol fermentative metabolisms were quite different, the designed strains could be engineered to have identical metabolic flux distribution in "core" metabolic pathways mainly supporting cell growth and maintenance. Finally, the model prediction in elucidating and reprogramming the native fermentative metabolism of E. coli for obligate anaerobic production of n-butanol and isobutanol was validated with published experimental data.

Visualizing evolution in real time to determine the molecular mechanisms of n-butanol tolerance in Escherichia coli

Toxicity of products or feedstock components poses a challenge in the biocatalyst-based production of fuels and chemicals. The genetic determinants that are involved in increased resistance to an inhibitor form the adaptive landscape for the phenotype; so in order to engineer more robust biocatalysts, a better understanding of the adaptive landscape is required. Here, we used an adaptive laboratory evolution method called visualizing evolution in real time (VERT) to help map out part of the adaptive landscape of Escherichia coli tolerance to the biofuel n-butanol. VERT enables identification of adaptive events (population expansions triggered by adaptive mutants) via visualization of the relative proportions of different fluorescently-labeled cells. Knowledge of the occurrence of adaptive events allows for a more systematic isolation of adaptive mutants while simultaneously reducing the number of missed adaptive mutants (and the underlying adaptive mechanisms) that result from clonal interference during the course of in vitro evolution. Based on the evolutionary dynamics observed, clonal interference was found to play a significant role in shaping the population structure of E. coli during exposure to n-butanol, and VERT helped to facilitate the isolation of adaptive mutants from the population. We further combined adaptive laboratory evolution with genome shuffling to significantly enhance the desired n-butanol tolerance phenotype. Subsequent transcriptome analysis of the isolated adaptive mutants revealed different mechanisms of n-butanol resistance in different lineages. In one fluorescently-marked subpopulation, members of the Fur regulon were upregulated; which was not observed in the other subpopulation. In addition, genome sequencing of several adaptive mutants revealed the genetic basis for some of the observed transcriptome profiles. We further elucidated the potential role of the iron-related gene in n-butanol tolerance via overexpression and deletion studies and hypothesized that the upregulation of the iron-related genes indirectly led to modifications in the outer membrane, which contributed to enhanced n-butanol tolerance.

Elementary mode analysis: a useful metabolic pathway analysis tool for reprograming microbial metabolic pathways

Elementary mode analysis is a useful metabolic pathway analysis tool to characterize cellular metabolism. It can identify all feasible metabolic pathways known as elementary modes that are inherent to a metabolic network. Each elementary mode contains a minimal and unique set of enzymatic reactions that can support cellular functions at steady state. Knowledge of all these pathway options enables systematic characterization of cellular phenotypes, analysis of metabolic network properties (e.g. structure, regulation, robustness, and fragility), phenotypic behavior discovery, and rational strain design for metabolic engineering application. This chapter focuses on the application of elementary mode analysis to reprogram microbial metabolic pathways for rational strain design and the metabolic pathway evolution of designed strains.