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

Rewiring the reductive TCA pathway and glyoxylate shunt of Escherichia coli for succinate production from corn stover hydrolysate using a two-phase fermentation strategy

Succinate was found extensive applications in the food additives, pharmaceutical, and biopolymers industries. However, the succinate biosynthesis in E. coli required IPTG, lacked NADH, and produced high yields only under anaerobic conditions, unsuitable for cell growth. To overcome these limitations, the glyoxylate shunt and reductive TCA pathway were simultaneously enhanced to produce succinate in both aerobic and anaerobic conditions and achieve a high cell growth meanwhile. On this basis, NADH availability and sugars uptake were increased. Furthermore, an oxygen-dependent promoter was used to dynamically regulate the expression level of key genes of reductive TCA pathway to avoid the usage of IPTG. The final strain E. coli Mgls7-32 could produce succinate from corn stover hydrolysate without an inducer, achieving a titer of 72.8 g/L in 5 L bioreactor (1.2 mol/mol of total sugars). Those findings will aid in the industrial production of succinate.

Precise metabolic modeling in post-omics era: accomplishments and perspectives

Microbes have been extensively utilized for their sustainable and scalable properties in synthesizing desired bio-products. However, insufficient knowledge about intracellular metabolism has impeded further microbial applications. The genome-scale metabolic models (GEMs) play a pivotal role in facilitating a global understanding of cellular metabolic mechanisms. These models enable rational modification by exploring metabolic pathways and predicting potential targets in microorganisms, enabling precise cell regulation without experimental costs. Nonetheless, simplified GEM only considers genome information and network stoichiometry while neglecting other important bio-information, such as enzyme functions, thermodynamic properties, and kinetic parameters. Consequently, uncertainties persist particularly when predicting microbial behaviors in complex and fluctuant systems. The advent of the omics era with its massive quantification of genes, proteins, and metabolites under various conditions has led to the flourishing of multi-constrained models and updated algorithms with improved predicting power and broadened dimension. Meanwhile, machine learning (ML) has demonstrated exceptional analytical and predictive capacities when applied to training sets of biological big data. Incorporating the discriminant strength of ML with GEM facilitates mechanistic modeling efficiency and improves predictive accuracy. This paper provides an overview of research innovations in the GEM, including multi-constrained modeling, analytical approaches, and the latest applications of ML, which may contribute comprehensive knowledge toward genetic refinement, strain development, and yield enhancement for a broad range of biomolecules.

Metabolomics and modelling approaches for systems metabolic engineering

Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.

Insights on the Advancements of In Silico Metabolic Studies of Succinic Acid Producing Microorganisms: A Review with Emphasis on Actinobacillus succinogenes

Succinic acid (SA) is one of the top candidate value-added chemicals that can be produced from biomass via microbial fermentation. A considerable number of cell factories have been proposed in the past two decades as native as well as non-native SA producers. Actinobacillus succinogenes is among the best and earliest known natural SA producers. However, its industrial application has not yet been realized due to various underlying challenges. Previous studies revealed that the optimization of environmental conditions alone could not entirely resolve these critical problems. On the other hand, microbial in silico metabolic modeling approaches have lately been the center of attention and have been applied for the efficient production of valuable commodities including SA. Then again, literature survey results indicated the absence of up-to-date reviews assessing this issue, specifically concerning SA production. Hence, this review was designed to discuss accomplishments and future perspectives of in silico studies on the metabolic capabilities of SA producers. Herein, research progress on SA and A. succinogenes, pathways involved in SA production, metabolic models of SA-producing microorganisms, and status, limitations and prospects on in silico studies of A. succinogenes were elaborated. All in all, this review is believed to provide insights to understand the current scenario and to develop efficient mathematical models for designing robust SA-producing microbial strains.

Reconstructing organisms in silico: genome-scale models and their emerging applications

Escherichia coli is considered to be the best-known microorganism given the large number of published studies detailing its genes, its genome and the biochemical functions of its molecular components. This vast literature has been systematically assembled into a reconstruction of the biochemical reaction networks that underlie E. coli's functions, a process which is now being applied to an increasing number of microorganisms. Genome-scale reconstructed networks are organized and systematized knowledge bases that have multiple uses, including conversion into computational models that interpret and predict phenotypic states and the consequences of environmental and genetic perturbations. These genome-scale models (GEMs) now enable us to develop pan-genome analyses that provide mechanistic insights, detail the selection pressures on proteome allocation and address stress phenotypes. In this Review, we first discuss the overall development of GEMs and their applications. Next, we review the evolution of the most complete GEM that has been developed to date: the E. coli GEM. Finally, we explore three emerging areas in genome-scale modelling of microbial phenotypes: collections of strain-specific models, metabolic and macromolecular expression models, and simulation of stress responses.

Dynamic Flux Balance Analysis to Evaluate the Strain Production Performance on Shikimic Acid Production in Escherichia coli

Flux balance analysis (FBA) is used to improve the microbial production of useful compounds. However, a large gap often exists between the FBA solution and the experimental yield, because of growth and byproducts. FBA has been extended to dynamic FBA (dFBA), which is applicable to time-varying processes, such as batch or fed-batch cultures, and has significantly contributed to metabolic and cultural engineering applications. On the other hand, the performance of the experimental strains has not been fully evaluated. In this study, we applied dFBA to the production of shikimic acid from glucose in Escherichia coli, to evaluate the production performance of the strain as a case study. The experimental data of glucose consumption and cell growth were used as FBA constraints. Bi-level FBA optimization with maximized growth and shikimic acid production were the objective functions. Results suggest that the shikimic acid concentration in the high-shikimic-acid-producing strain constructed in the experiment reached up to 84% of the maximum value by simulation. Thus, this method can be used to evaluate the performance of strains and estimate the milestones of strain improvement.

Rediscovering Acetate Metabolism: Its Potential Sources and Utilization for Biobased Transformation into Value-Added Chemicals

One of the great advantages of microbial fermentation is the capacity to convert various carbon compounds into value-added chemicals. In this regard, there have been many efforts to engineer microorganisms to facilitate utilization of abundant carbon sources. Recently, the potential of acetate as a feedstock has been discovered; efforts have been made to produce various biochemicals from acetate based on understanding of its metabolism. In this review, we discuss the potential sources of acetate and summarized the recent progress to improve acetate utilization with microorganisms. Furthermore, we also describe representative studies that engineered microorganisms for the production of biochemicals from acetate.

Application of theoretical methods to increase succinate production in engineered strains

Computational methods have enabled the discovery of non-intuitive strategies to enhance the production of a variety of target molecules. In the case of succinate production, reviews covering the topic have not yet analyzed the impact and future potential that such methods may have. In this work, we review the application of computational methods to the production of succinic acid. We found that while a total of 26 theoretical studies were published between 2002 and 2016, only 10 studies reported the successful experimental implementation of any kind of theoretical knowledge. None of the experimental studies reported an exact application of the computational predictions. However, the combination of computational analysis with complementary strategies, such as directed evolution and comparative genome analysis, serves as a proof of concept and demonstrates that successful metabolic engineering can be guided by rational computational methods.

Model-guided metabolic gene knockout of gnd for enhanced succinate production in Escherichia coli from glucose and glycerol substrates

The metabolic role of 6-phosphogluconate dehydrogenase (gnd) under anaerobic conditions with respect to succinate production in Escherichia coli remained largely unspecified. Herein we report what are to our knowledge the first metabolic gene knockout of gnd to have increased succinic acid production using both glucose and glycerol substrates in E. coli. Guided by a genome scale metabolic model, we engineered the E. coli host metabolism to enhance anaerobic production of succinic acid by deleting the gnd gene, considering its location in the boundary of oxidative and non-oxidative pentose phosphate pathway. This strategy induced either the activation of malic enzyme, causing up-regulation of phosphoenolpyruvate carboxylase (ppc) and down regulation of phosphoenolpyruvate carboxykinase (ppck) and/or prevents the decarboxylation of 6 phosphogluconate to increase the pool of glyceraldehyde-3-phosphate (GAP) that is required for the formation of phosphoenolpyruvate (PEP). This approach produced a mutant strain BMS2 with succinic acid production titers of 0.35 g l(-1) and 1.40 g l(-1) from glucose and glycerol substrates respectively. This work further clearly elucidates and informs other studies that the gnd gene, is a novel deletion target for increasing succinate production in E. coli under anaerobic condition using glucose and glycerol carbon sources. The knowledge gained in this study would help in E. coli and other microbial strains development for increasing succinate production and/or other industrial chemicals.

Systems Biology of Microbial Exopolysaccharides Production

Exopolysaccharides (EPSs) produced by diverse group of microbial systems are rapidly emerging as new and industrially important biomaterials. Due to their unique and complex chemical structures and many interesting physicochemical and rheological properties with novel functionality, the microbial EPSs find wide range of commercial applications in various fields of the economy such as food, feed, packaging, chemical, textile, cosmetics and pharmaceutical industry, agriculture, and medicine. EPSs are mainly associated with high-value applications, and they have received considerable research attention over recent decades with their biocompatibility, biodegradability, and both environmental and human compatibility. However, only a few microbial EPSs have achieved to be used commercially due to their high production costs. The emerging need to overcome economic hurdles and the increasing significance of microbial EPSs in industrial and medical biotechnology call for the elucidation of the interrelations between metabolic pathways and EPS biosynthesis mechanism in order to control and hence enhance its microbial productivity. Moreover, a better understanding of biosynthesis mechanism is a significant issue for improvement of product quality and properties and also for the design of novel strains. Therefore, a systems-based approach constitutes an important step toward understanding the interplay between metabolism and EPS biosynthesis and further enhances its metabolic performance for industrial application. In this review, primarily the microbial EPSs, their biosynthesis mechanism, and important factors for their production will be discussed. After this brief introduction, recent literature on the application of omics technologies and systems biology tools for the improvement of production yields will be critically evaluated. Special focus will be given to EPSs with high market value such as xanthan, levan, pullulan, and dextran.

Flux-sum analysis identifies metabolite targets for strain improvement

Background

Rational design of microbial strains for enhanced cellular physiology through in silico analysis has been reported in many metabolic engineering studies. Such in silico techniques typically involve the analysis of a metabolic model describing the metabolic and physiological states under various perturbed conditions, thereby identifying genetic targets to be manipulated for strain improvement. More often than not, the activation/inhibition of multiple reactions is necessary to produce a predicted change for improvement of cellular properties or states. However, as it is more computationally cumbersome to simulate all possible combinations of reaction perturbations, it is desirable to consider alternative techniques for identifying such metabolic engineering targets.

Results

In this study, we present the modified version of previously developed metabolite-centric approach, also known as flux-sum analysis (FSA), for identifying metabolic engineering targets. Utility of FSA was demonstrated by applying it to Escherichia coli, as case studies, for enhancing ethanol and succinate production, and reducing acetate formation. Interestingly, most of the identified metabolites correspond to gene targets that have been experimentally validated in previous works on E. coli strain improvement. A notable example is that pyruvate, the metabolite target for enhancing succinate production, was found to be associated with multiple reaction targets that were only identifiable through more computationally expensive means. In addition, detailed analysis of the flux-sum perturbed conditions also provided valuable insights into how previous metabolic engineering strategies have been successful in enhancing cellular physiology.

Conclusions

The application of FSA under the flux balance framework can identify novel metabolic engineering targets from the metabolite-centric perspective. Therefore, the current approach opens up a new research avenue for rational design and engineering of industrial microbes in the field of systems metabolic engineering.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0198-3) contains supplementary material, which is available to authorized users.

Computing autocatalytic sets to unravel inconsistencies in metabolic network reconstructions

MOTIVATION

Genome-scale metabolic network reconstructions have been established as a powerful tool for the prediction of cellular phenotypes and metabolic capabilities of organisms. In recent years, the number of network reconstructions has been constantly increasing, mostly because of the availability of novel (semi-)automated procedures, which enabled the reconstruction of metabolic models based on individual genomes and their annotation. The resulting models are widely used in numerous applications. However, the accuracy and predictive power of network reconstructions are commonly limited by inherent inconsistencies and gaps.

RESULTS

Here we present a novel method to validate metabolic network reconstructions based on the concept of autocatalytic sets. Autocatalytic sets correspond to collections of metabolites that, besides enzymes and a growth medium, are required to produce all biomass components in a metabolic model. These autocatalytic sets are well-conserved across all domains of life, and their identification in specific genome-scale reconstructions allows us to draw conclusions about potential inconsistencies in these models. The method is capable of detecting inconsistencies, which are neglected by other gap-finding methods. We tested our method on the Model SEED, which is the largest repository for automatically generated genome-scale network reconstructions. In this way, we were able to identify a significant number of missing pathways in several of these reconstructions. Hence, the method we report represents a powerful tool to identify inconsistencies in large-scale metabolic networks.

AVAILABILITY AND IMPLEMENTATION

The method is available as source code on http://users.minet.uni-jena.de/∼m3kach/ASBIG/ASBIG.zip.

CONTACT

christoph.kaleta@uni-jena.de

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Metabolomic analysis of the food-borne pathogen Campylobacter jejuni: application of direct injection mass spectrometry for mutant characterisation

Campylobacter jejuni is the most frequent cause of human food-borne bacterial gastroenteritis but its physiology and biochemistry are poorly understood. Only a few amino-acids can be catabolised and these are known to be important for host colonization. Here we have established methods for rapid high throughput analyses of global metabolism in C. jejuni using direct injection mass spectrometry (DIMS) to compare metabolite fingerprints of wild-type and mutant strains. Principal component analyses show that the metabolic fingerprint of mutants that have a genomic deletion in genes for key amino-acid catabolic enzymes (either sdaA, serine dehydratase; aspA, aspartase or aspB, aspartate:glutamate transaminase) can easily be distinguished from the isogenic parental strain. Assignment of putative metabolites showed predictable changes directly associated with the particular metabolic lesion in these mutants as well as more extensive changes in the aspA mutant compared to the sdaA or aspB strains. Further analyses of a cj0150c mutant strain, which has no obvious phenotype, suggested a role for Cj0150 in the conversion of cystathionine to homocysteine. Our results show that DIMS is a useful technique for probing the metabolism of this important pathogen and may help in assigning function to genes encoding novel enzymes with currently unknown metabolic roles.

Metabolic reconstruction and flux analysis of industrial Pichia yeasts

Pichia yeasts have been recognized as important microbial cell factories in the biotechnological industry. Notably, the Pichia pastoris and Pichia stipitis species have attracted much research interest due to their unique cellular physiology and metabolic capability: P. pastoris has the ability to utilize methanol for cell growth and recombinant protein production, while P. stipitis is capable of assimilating xylose to produce ethanol under oxygen-limited conditions. To harness these characteristics for biotechnological applications, it is highly required to characterize their metabolic behavior. Recently, following the genome sequencing of these two Pichia species, genome-scale metabolic networks have been reconstructed to model the yeasts' metabolism from a systems perspective. To date, there are three genome-scale models available for each of P. pastoris and P. stipitis. In this mini-review, we provide an overview of the models, discuss certain limitations of previous studies, and propose potential future works that can be conducted to better understand and engineer Pichia yeasts for industrial applications.

Metabolic engineering of biocatalysts for carboxylic acids production

Fermentation of renewable feedstocks by microbes to produce sustainable fuels and chemicals has the potential to replace petrochemical-based production. For example, carboxylic acids produced by microbial fermentation can be used to generate primary building blocks of industrial chemicals by either enzymatic or chemical catalysis. In order to achieve the titer, yield and productivity values required for economically viable processes, the carboxylic acid-producing microbes need to be robust and well-performing. Traditional strain development methods based on mutagenesis have proven useful in the selection of desirable microbial behavior, such as robustness and carboxylic acid production. On the other hand, rationally-based metabolic engineering, like genetic manipulation for pathway design, has becoming increasingly important to this field and has been used for the production of several organic acids, such as succinic acid, malic acid and lactic acid. This review investigates recent works on Saccharomyces cerevisiae and Escherichia coli, as well as the strategies to improve tolerance towards these chemicals.

Comparative reaction engineering studies for succinic acid production from sucrose by metabolically engineered Escherichia coli in fed-batch-operated stirred tank bioreactors

This study presents a comparative reaction engineering analysis of metabolically engineered sucrose-utilizing Escherichia coli derived from E. coli K12 MG1655 for the anaerobic production of succinic acid. Production capacities of 16 different recombinant strains were evaluated in 48 parallel fed-batch-operated milliliter-scale stirred tank bioreactors (10 mL) with continuous CO₂ sparging. The effects of recombinant sucrose-utilization systems (csc-operon or scr-operon), enhancements of anaplerotic reactions (pck, ppc, maeA, maeB or heterologous pyc) and gene deletions (ldhA, adhE, ack-pta and ptsG) were studied with respect to the overall process performances of the respective recombinant strains. Both sucrose-utilization systems enabled the production of succinic acid from sucrose in E. coli K12 MG1655. Maximum succinate production was observed by overexpressing the pyruvate carboxylase from Corynebacterium glutamicum resulting in a succinate concentration of 26.8 g L⁻¹ after 48 h and a cell-specific productivity of 0.14 g g⁻¹ h⁻¹. Further experiments in a fed-batch-operated laboratory-scale stirred tank bioreactor (2 L) showed that micro-aerobic conditions preceding the anaerobic phase enhance succinic acid production of E. coli K12 MG1655-derived strains. The work demonstrates the importance of parallel approaches within the scope of applied metabolic engineering studies.

On the function of the various quinone species in Escherichia coli

The respiratory chain of Escherichia coli contains three quinones. Menaquinone and demethylmenaquinone have low midpoint potentials and are involved in anaerobic respiration, while ubiquinone, which has a high midpoint potential, is involved in aerobic and nitrate respiration. Here, we report that demethylmenaquinone plays a role not only in trimethylaminooxide-, dimethylsulfoxide- and fumarate-dependent respiration, but also in aerobic respiration. Furthermore, we demonstrate that demethylmenaquinone serves as an electron acceptor for oxidation of succinate to fumarate, and that all three quinol oxidases of E. coli accept electrons from this naphtoquinone derivative.

Applications of system-level models of metabolism for analysis of bacterial physiology and identification of new drug targets

For nearly all of the 20th century, biologists gained considerable insights into the fundamental principles of cellular dynamics by examining select modules of biochemical processes. This form of analysis provides detailed information about the workings of the examined pathways. However, any attempt to alter the normal function of bacteria (perhaps for industrial or medicinal goals) requires a detailed global understanding of cellular mechanisms. The reductionist mode of analysis cannot provide the required information for developing the needed perspective on the complex interactions of biochemical pathways. Thankfully, the increasing availability of microbial genomic, transcriptomic, proteomic and other high-throughput data permits system-level analyses of microbiology. During the past two decades, systems biologists have developed constraint-based genome-scale models (GSM) of metabolism for a variety of pathogens. These models are important tools for assessing the metabolic capabilities of various genotypes. Simultaneously, new computational methods have been developed that use these network reconstructions to answer an array of important immunological questions. The objective of this article is to briefly review some of the uses of GSMs for studying bacterial metabolism under different conditions and to discuss how the calculated solutions can be used for rational design of drugs.

Synthetic biology: an emerging research field in China

Synthetic biology is considered as an emerging research field that will bring new opportunities to biotechnology. There is an expectation that synthetic biology will not only enhance knowledge in basic science, but will also have great potential for practical applications. Synthetic biology is still in an early developmental stage in China. We provide here a review of current Chinese research activities in synthetic biology and its different subfields, such as research on genetic circuits, minimal genomes, chemical synthetic biology, protocells and DNA synthesis, using literature reviews and personal communications with Chinese researchers. To meet the increasing demand for a sustainable development, research on genetic circuits to harness biomass is the most pursed research within Chinese researchers. The environmental concerns are driven force of research on the genetic circuits for bioremediation. The research on minimal genomes is carried on identifying the smallest number of genomes needed for engineering minimal cell factories and research on chemical synthetic biology is focused on artificial proteins and expanded genetic code. The research on protocells is more in combination with the research on molecular-scale motors. The research on DNA synthesis and its commercialisation are also reviewed. As for the perspective on potential future Chinese R&D activities, it will be discussed based on the research capacity and governmental policy.

Succinate production in Escherichia coli

Succinate has been recognized as an important platform chemical that can be produced from biomass. While a number of organisms are capable of succinate production naturally, this review focuses on the engineering of Escherichia coli for the production of four-carbon dicarboxylic acid. Important features of a succinate production system are to achieve an optimal balance of reducing equivalents generated by consumption of the feedstock, while maximizing the amount of carbon channeled into the product. Aerobic and anaerobic production strains have been developed and applied to production from glucose and other abundant carbon sources. Metabolic engineering methods and strain evolution have been used and supplemented by the recent application of systems biology and in silico modeling tools to construct optimal production strains. The metabolic capacity of the production strain, the requirement for efficient recovery of succinate, and the reliability of the performance under scaleup are important in the overall process. The costs of the overall biorefinery-compatible process will determine the economic commercialization of succinate and its impact in larger chemical markets.

Isolation, characterization and evolution of a new thermophilic Bacillus licheniformis for lactic acid production in mineral salts medium

The high fermentation cost of lactic acid is a barrier for polylactic acid (PLA) to compete with the petrochemical derived plastics. In order to lower the cost of lactic acid, the industry needs a microorganism that can ferment various sugars at high temperature (50°C) and at the same time using low cost mineral salts (MS) medium. One such bacterium, BL1, was isolated at 50°C and identified as Bacillus licheniformis. BL1 can ferment glucose to optically pure l-lactate with a maximum specific productivity of 7.8 g/hl in LB medium and 0.7 g/hl in MS medium at 50°C. BL1 can also consume 10% and 15% glucose in 20 and 48 h, respectively. After serial transfer of BL1 and BL2 in different concentrations of xylose and MS medium respectively, the final mutant BL3 could efficiently ferment glucose and xylose with specific productivity of 1.9 g/hl and 1.2g/hl in strict MS medium.

Expanding a dynamic flux balance model of yeast fermentation to genome-scale

Background

Yeast is considered to be a workhorse of the biotechnology industry for the production of many value-added chemicals, alcoholic beverages and biofuels. Optimization of the fermentation is a challenging task that greatly benefits from dynamic models able to accurately describe and predict the fermentation profile and resulting products under different genetic and environmental conditions. In this article, we developed and validated a genome-scale dynamic flux balance model, using experimentally determined kinetic constraints.

Results

Appropriate equations for maintenance, biomass composition, anaerobic metabolism and nutrient uptake are key to improve model performance, especially for predicting glycerol and ethanol synthesis. Prediction profiles of synthesis and consumption of the main metabolites involved in alcoholic fermentation closely agreed with experimental data obtained from numerous lab and industrial fermentations under different environmental conditions. Finally, fermentation simulations of genetically engineered yeasts closely reproduced previously reported experimental results regarding final concentrations of the main fermentation products such as ethanol and glycerol.

Conclusion

A useful tool to describe, understand and predict metabolite production in batch yeast cultures was developed. The resulting model, if used wisely, could help to search for new metabolic engineering strategies to manage ethanol content in batch fermentations.

Microarray data can predict diurnal changes of starch content in the picoalga Ostreococcus

Background

The storage of photosynthetic carbohydrate products such as starch is subject to complex regulation, effected at both transcriptional and post-translational levels. The relevant genes in plants show pronounced daily regulation. Their temporal RNA expression profiles, however, do not predict the dynamics of metabolite levels, due to the divergence of enzyme activity from the RNA profiles.

Unicellular phytoplankton retains the complexity of plant carbohydrate metabolism, and recent transcriptomic profiling suggests a major input of transcriptional regulation.

Results

We used a quasi-steady-state, constraint-based modelling approach to infer the dynamics of starch content during the 12 h light/12 h dark cycle in the model alga Ostreococcus tauri. Measured RNA expression datasets from microarray analysis were integrated with a detailed stoichiometric reconstruction of starch metabolism in O. tauri in order to predict the optimal flux distribution and the dynamics of the starch content in the light/dark cycle. The predicted starch profile was validated by experimental data over the 24 h cycle. The main genetic regulatory targets within the pathway were predicted by in silico analysis.

Conclusions

A single-reaction description of starch production is not able to account for the observed variability of diurnal activity profiles of starch-related enzymes. We developed a detailed reaction model of starch metabolism, which, to our knowledge, is the first attempt to describe this polysaccharide polymerization while preserving the mass balance relationships. Our model and method demonstrate the utility of a quasi-steady-state approach for inferring dynamic metabolic information in O. tauri directly from time-series gene expression data.

Bridging the gap between fluxomics and industrial biotechnology

Metabolic flux analysis is a vital tool used to determine the ultimate output of cellular metabolism and thus detect biotechnologically relevant bottlenecks in productivity. ¹³C-based metabolic flux analysis (¹³C-MFA) and flux balance analysis (FBA) have many potential applications in biotechnology. However, noteworthy hurdles in fluxomics study are still present. First, several technical difficulties in both ¹³C-MFA and FBA severely limit the scope of fluxomics findings and the applicability of obtained metabolic information. Second, the complexity of metabolic regulation poses a great challenge for precise prediction and analysis of metabolic networks, as there are gaps between fluxomics results and other omics studies. Third, despite identified metabolic bottlenecks or sources of host stress from product synthesis, it remains difficult to overcome inherent metabolic robustness or to efficiently import and express nonnative pathways. Fourth, product yields often decrease as the number of enzymatic steps increases. Such decrease in yield may not be caused by rate-limiting enzymes, but rather is accumulated through each enzymatic reaction. Fifth, a high-throughput fluxomics tool hasnot been developed for characterizing nonmodel microorganisms and maximizing their application in industrial biotechnology. Refining fluxomics tools and understanding these obstacles will improve our ability to engineer highlyefficient metabolic pathways in microbial hosts.

Metabolic pathways and fermentative production of L-aspartate family amino acids

The L-aspartate family amino acids (AFAAs), L-threonine, L-lysine, L-methionine and L-isoleucine have recently been of much interest due to their wide spectrum of applications including food additives, components of cosmetics and therapeutic agents, and animal feed additives. Among them, L-threonine, L-lysine and L-methionine are three major amino acids produced currently throughout the world. Recent advances in systems metabolic engineering, which combine various high-throughput omics technologies and computational analysis, are now facilitating development of microbial strains efficiently producing AFAAs. Thus, a thorough understanding of the metabolic and regulatory mechanisms of the biosynthesis of these amino acids is urgently needed for designing system-wide metabolic engineering strategies. Here we review the details of AFAA biosynthetic pathways, regulations involved, and export and transport systems, and provide general strategies for successful metabolic engineering along with relevant examples. Finally, perspectives of systems metabolic engineering for developing AFAA overproducers are suggested with selected exemplary studies.

DETECT--a density estimation tool for enzyme classification and its application to Plasmodium falciparum

MOTIVATION

A major challenge in genomics is the accurate annotation of component genes. Enzymes are typically predicted using homology-based search methods, where the membership of a protein to an enzyme family is based on single-sequence comparisons. As such, these methods are often error-prone and lack useful measures of reliability for the prediction.

RESULTS

Here, we present DETECT, a probabilistic method for enzyme prediction that accounts for the sequence diversity across enzyme families. By comparing the global alignment scores of an unknown protein to those of all known enzymes, an integrated likelihood score can be readily calculated, ranking the reaction classes relevant for that protein. Comparisons to BLAST reveal significant improvements in enzyme annotation accuracy. Applied to Plasmodium falciparum, we identify potential annotation errors and predict novel enzymes of therapeutic interest.

AVAILABILITY

A standalone application is available from the website: http://www.compsysbio.org/projects/DETECT/

OptForce: an optimization procedure for identifying all genetic manipulations leading to targeted overproductions

Computational procedures for predicting metabolic interventions leading to the overproduction of biochemicals in microbial strains are widely in use. However, these methods rely on surrogate biological objectives (e.g., maximize growth rate or minimize metabolic adjustments) and do not make use of flux measurements often available for the wild-type strain. In this work, we introduce the OptForce procedure that identifies all possible engineering interventions by classifying reactions in the metabolic model depending upon whether their flux values must increase, decrease or become equal to zero to meet a pre-specified overproduction target. We hierarchically apply this classification rule for pairs, triples, quadruples, etc. of reactions. This leads to the identification of a sufficient and non-redundant set of fluxes that must change (i.e., MUST set) to meet a pre-specified overproduction target. Starting with this set we subsequently extract a minimal set of fluxes that must actively be forced through genetic manipulations (i.e., FORCE set) to ensure that all fluxes in the network are consistent with the overproduction objective. We demonstrate our OptForce framework for succinate production in Escherichia coli using the most recent in silico E. coli model, iAF1260. The method not only recapitulates existing engineering strategies but also reveals non-intuitive ones that boost succinate production by performing coordinated changes on pathways distant from the last steps of succinate synthesis.

Over the past few years, there has been an unprecedented increase in the use of microorganisms for the production of biofuels, industrial chemicals and pharmaceutical precursors. In this regard, biotechnologists are confronted with the challenge to efficiently convert biomass and other renewable resources into useful biochemicals. With the advent of organism-specific mathematical models of metabolism, scientists have used computations to identify genetic modifications that maximize the yield of a desired product. In this paper, we introduce OptForce, an algorithm that identifies all possible metabolic interventions that lead to the overproduction of a biochemical of interest. Unlike existing techniques, OptForce does not rely on the maximization of a fitness function to predict metabolic fluxes. Instead, OptForce contrasts the metabolic flux patterns observed in an initial strain and a strain overproducing the chemical at the target yield. The essence of this procedure is the identification of all coordinated reaction modifications that force the network towards the overproduction target. We used OptForce to predict metabolic interventions for succinate overproduction in Escherichia coli. The results described in this paper not only uncover existing strain designs for succinate production but also elucidate new ones that can be experimentally explored.

Accomplishments in genome-scale in silico modeling for industrial and medical biotechnology

Driven by advancements in high-throughput biological technologies and the growing number of sequenced genomes, the construction of in silico models at the genome scale has provided powerful tools to investigate a vast array of biological systems and applications. Here, we review comprehensively the uses of such models in industrial and medical biotechnology, including biofuel generation, food production, and drug development. While the use of in silico models is still in its early stages for delivering to industry, significant initial successes have been achieved. For the cases presented here, genome-scale models predict engineering strategies to enhance properties of interest in an organism or to inhibit harmful mechanisms of pathogens. Going forward, genome-scale in silico models promise to extend their application and analysis scope to become a trans-formative tool in biotechnology.

Metabolic flux control at the pyruvate node in an anaerobic Escherichia coli strain with an active pyruvate dehydrogenase

During anaerobic growth of Escherichia coli, pyruvate formate-lyase (PFL) and lactate dehydrogenase (LDH) channel pyruvate toward a mixture of fermentation products. We have introduced a third branch at the pyruvate node in a mutant of E. coli with a mutation in pyruvate dehydrogenase (PDH*) that renders the enzyme less sensitive to inhibition by NADH. The key starting enzymes of the three branches at the pyruvate node in such a mutant, PDH*, PFL, and LDH, have different metabolic potentials and kinetic properties. In such a mutant (strain QZ2), pyruvate flux through LDH was about 30%, with the remainder of the flux occurring through PFL, indicating that LDH is a preferred route of pyruvate conversion over PDH*. In a pfl mutant (strain YK167) with both PDH* and LDH activities, flux through PDH* was about 33% of the total, confirming the ability of LDH to outcompete the PDH pathway for pyruvate in vivo. Only in the absence of LDH (strain QZ3) was pyruvate carbon equally distributed between the PDH* and PFL pathways. A pfl mutant with LDH and PDH* activities, as well as a pfl ldh double mutant with PDH* activity, had a surprisingly low cell yield per mole of ATP (Y(ATP)) (about 7.0 g of cells per mol of ATP) compared to 10.9 g of cells per mol of ATP for the wild type. The lower Y(ATP) suggests the operation of a futile energy cycle in the absence of PFL in this strain. An understanding of the controls at the pyruvate node during anaerobic growth is expected to provide unique insights into rational metabolic engineering of E. coli and related bacteria for the production of various biobased products at high rates and yields.

Integration of systems biology with bioprocess engineering: L: -threonine production by systems metabolic engineering of Escherichia coli

Random mutation and selection or targeted metabolic engineering without consideration of its impact on the entire metabolic and regulatory networks can unintentionally cause genetic alterations in the region, which is not directly related to the target metabolite. This is one of the reasons why strategies for developing industrial strains are now shifted towards targeted metabolic engineering based on systems biology, which is termed systems metabolic engineering. Using systems metabolic engineering strategies, all the metabolic engineering works are conducted in systems biology framework, whereby entire metabolic and regulatory networks are thoroughly considered in an integrated manner. The targets for purposeful engineering are selected after all possible effects on the entire metabolic and regulatory networks are thoroughly considered. Finally, the strain, which is capable of producing the target metabolite to a high level close to the theoretical maximum value, can be constructed. Here we review strategies and applications of systems biology successfully implemented on bioprocess engineering, with particular focus on developing L: -threonine production strains of Escherichia coli.

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.

pH and base counterion affect succinate production in dual-phase Escherichia coli fermentations

Succinate production was studied in Escherichia coli AFP111, which contains mutations in pyruvate formate lyase (pfl), lactate dehydrogenase (ldhA) and the phosphotransferase system glucosephosphotransferase enzyme II (ptsG). Two-phase fermentations using a defined medium at several controlled levels of pH were conducted in which an aerobic cell growth phase was followed by an anaerobic succinate production phase using 100% (v/v) CO(2). A pH of 6.4 yielded the highest specific succinate productivity. A metabolic flux analysis at a pH of 6.4 using (13)C-labeled glucose showed that 61% of the PEP partitioned to oxaloacetate and 39% partitioned to pyruvate, while 93% of the succinate was formed via the reductive arm of the TCA cycle. The flux distribution at a pH of 6.8 was also analyzed and was not significantly different compared to that at a pH of 6.4. Ca(OH)(2) was superior to NaOH or KOH as the base for controlling the pH. By maintaining the pH at 6.4 using 25% (w/v) Ca(OH)(2), the process achieved an average succinate productivity of 1.42 g/l h with a yield of 0.61 g/g.

Compensatory ability to null mutation in metabolic networks

Robustness is an inherent property of biological system. It is still a limited understanding of how it is accomplished at the cellular or molecular level. To this end, this article analyzes the impact degree of each reaction to others, which is defined as the number of cascading failures of following and/or forward reactions when an initial reaction is deleted. By analyzing more than 800 organism's metabolic networks, it suggests that the reactions with larger impact degrees are likely essential and the universal reactions should also be essential. Alternative metabolic pathways compensate null mutations, which represents that average impact degrees for all organisms are small. Interestingly, average impact degrees of archaea organisms are smaller than other two categories of organisms, eukayote and bacteria, indicating that archaea organisms have strong robustness to resist the various perturbations during the evolution process. The results show that scale-free feature and reaction reversibility contribute to the robustness in metabolic networks. The optimal growth temperature of organism also relates the robust structure of metabolic network.

The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli

The number and scope of methods developed to interrogate and use metabolic network reconstructions has significantly expanded over the past 15 years. In particular, Escherichia coli metabolic network reconstruction has reached the genome scale and been utilized to address a broad spectrum of basic and practical applications in five main categories: metabolic engineering, model-directed discovery, interpretations of phenotypic screens, analysis of network properties and studies of evolutionary processes. Spurred on by these accomplishments, the field is expected to move forward and further broaden the scope and content of network reconstructions, develop new and novel in silico analysis tools, and expand in adaptation to uses of proximal and distal causation in biology. Taken together, these efforts will solidify a mechanistic genotype-phenotype relationship for microbial metabolism.

Insights into the biology of Escherichia coli through structural proteomics

Escherichia coli has historically been an important organism for understanding a multitude of biological processes, and represents a model system as we attempt to simulate the workings of living cells. Many E. coli strains are also important human and animal pathogens for which new therapeutic strategies are required. For both reasons, a more complete and comprehensive understanding of the protein structure complement of E. coli is needed at the genome level. Here, we provide examples of insights into the mechanism and function of bacterial proteins that we have gained through the Bacterial Structural Genomics Initiative (BSGI), focused on medium-throughput structure determination of proteins from E. coli. We describe the structural characterization of several enzymes from the histidine biosynthetic pathway, the structures of three pseudouridine synthases, enzymes that synthesize one of the most abundant modified bases in RNA, as well as the combined use of protein structure and focused functional analysis to decipher functions for hypothetical proteins. Together, these results illustrate the power of structural genomics to contribute to a deeper biological understanding of bacterial processes.

Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox

The manner in which microorganisms utilize their metabolic processes can be predicted using constraint-based analysis of genome-scale metabolic networks. Herein, we present the constraint-based reconstruction and analysis toolbox, a software package running in the Matlab environment, which allows for quantitative prediction of cellular behavior using a constraint-based approach. Specifically, this software allows predictive computations of both steady-state and dynamic optimal growth behavior, the effects of gene deletions, comprehensive robustness analyses, sampling the range of possible cellular metabolic states and the determination of network modules. Functions enabling these calculations are included in the toolbox, allowing a user to input a genome-scale metabolic model distributed in Systems Biology Markup Language format and perform these calculations with just a few lines of code. The results are predictions of cellular behavior that have been verified as accurate in a growing body of research. After software installation, calculation time is minimal, allowing the user to focus on the interpretation of the computational results.