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.     

Description and interpretation of adaptive evolution of Escherichia coli K-12 MG1655 by using a genome-scale in silico metabolic model

Genome-scale in silico metabolic networks of Escherichia coli have been reconstructed. By using a constraint-based in silico model of a reconstructed network, the range of phenotypes exhibited by E. coli under different growth conditions can be computed, and optimal growth phenotypes can be predicted. We hypothesized that the end point of adaptive evolution of E. coli could be accurately described a priori by our in silico model since adaptive evolution should lead to an optimal phenotype. Adaptive evolution of E. coli during prolonged exponential growth was performed with M9 minimal medium supplemented with 2 g of alpha-ketoglutarate per liter, 2 g of lactate per liter, or 2 g of pyruvate per liter at both 30 and 37 degrees C, which produced seven distinct strains. The growth rates, substrate uptake rates, oxygen uptake rates, by-product secretion patterns, and growth rates on alternative substrates were measured for each strain as a function of evolutionary time. Three major conclusions were drawn from the experimental results. First, adaptive evolution leads to a phenotype characterized by maximized growth rates that may not correspond to the highest biomass yield. Second, metabolic phenotypes resulting from adaptive evolution can be described and predicted computationally. Third, adaptive evolution on a single substrate leads to changes in growth characteristics on other substrates that could signify parallel or opposing growth objectives. Together, the results show that genome-scale in silico metabolic models can describe the end point of adaptive evolution a priori and can be used to gain insight into the adaptive evolutionary process for E. coli.     

The Virtual Cell: a software environment for computational cell biology

The newly emerging field of computational cell biology requires software tools that address the needs of a broad community of scientists. Cell biological processes are controlled by an interacting set of biochemical and electrophysiological events that are distributed within complex cellular structures. Computational modeling is familiar to researchers in fields such as molecular structure, neurobiology and metabolic pathway engineering, and is rapidly emerging in the area of gene expression. Although some of these established modeling approaches can be adapted to address problems of interest to cell biologists, relatively few software development efforts have been directed at the field as a whole. The Virtual Cell is a computational environment designed for cell biologists as well as for mathematical biologists and bioengineers. It serves to aid the construction of cell biological models and the generation of simulations from them. The system enables the formulation of both compartmental and spatial models, the latter with either idealized or experimentally derived geometries of one, two or three dimensions.     

Further developments towards a genome-scale metabolic model of yeast

Background

To date, several genome-scale network reconstructions have been used to describe the metabolism of the yeast Saccharomyces cerevisiae, each differing in scope and content. The recent community-driven reconstruction, while rigorously evidenced and well annotated, under-represented metabolite transport, lipid metabolism and other pathways, and was not amenable to constraint-based analyses because of lack of pathway connectivity.

Results

We have expanded the yeast network reconstruction to incorporate many new reactions from the literature and represented these in a well-annotated and standards-compliant manner. The new reconstruction comprises 1102 unique metabolic reactions involving 924 unique metabolites - significantly larger in scope than any previous reconstruction. The representation of lipid metabolism in particular has improved, with 234 out of 268 enzymes linked to lipid metabolism now present in at least one reaction. Connectivity is emphatically improved, with more than 90% of metabolites now reachable from the growth medium constituents. The present updates allow constraint-based analyses to be performed; viability predictions of single knockouts are comparable to results from in vivo experiments and to those of previous reconstructions.

Conclusions

We report the development of the most complete reconstruction of yeast metabolism to date that is based upon reliable literature evidence and richly annotated according to MIRIAM standards. The reconstruction is available in the Systems Biology Markup Language (SBML) and via a publicly accessible database http://www.comp-sys-bio.org/yeastnet/.     

Sympatric metabolic diversification of experimentally evolved Escherichia coli in a complex environment

Sympatric diversification in bacteria has been found to contravene initial evolutionary theories affirming the selection of the fittest type by competition for the same resource. Studies in unstructured (well-mixed) environments have discovered divergence of an ancestor strain into genomically and phenotypically divergent types growing both on single and mixed energy sources. This study addresses the metabolic diversification in an Escherichia coli population that evolved over ~1,000 generations under aerobic conditions in the nutritional complexity offered by Luria–Bertani (LB) broth. The medium lacked glucose but contained a variety of other resources. Two distinct metabolically-diverged types, coinciding with colony morphologies, were found to dominate the populations. One type was an avid carbohydrate consumer, which could quickly utilize the available (alternative) substrates feeding into glycolysis. The second type was a slow grower, which was able to specifically consume acetate. The capacity to utilize acetate might be providing an advantage to this second type, suggesting an increased capability to deal with adverse conditions that occur in the later stages of growth. The diverged metabolic preferences of the two forms suggested differential and interactive ecological roles within the population. We postulate that these types used different alternative metabolic strategies occupying different niches in a sympatric manner as an outcome of adaptation to the complex environment.     

Systematic analysis of conservation relations in Escherichia coli genome-scale metabolic network reveals novel growth media

A biochemical species is called producible in a constraints-based metabolic model if a feasible steady-state flux configuration exists that sustains its nonzero concentration during growth. Extreme semipositive conservation relations (ESCRs) are the simplest semipositive linear combinations of species concentrations that are invariant to all metabolic flux configurations. In this article, we outline a fundamental relationship between the ESCRs of a metabolic network and the producibility of a biochemical species under a nutrient media. We exploit this relationship in an algorithm that systematically enumerates all minimal nutrient sets that render an objective species weakly producible (i.e., producible in the absence of thermodynamic constraints) through a simple traversal of ESCRs. We apply our results to a recent genome scale model of Escherichia coli metabolism, in which we traverse the 51 anhydrous ESCRs of the metabolic network to determine all 928 minimal aqueous nutrient media that render biomass weakly producible. Applying irreversibility constraints, we find 287 of these 928 nutrient sets to be thermodynamically feasible. We also find that an additional 365 of these nutrient sets are thermodynamically feasible in the presence of oxygen. Since biomass producibility is commonly used as a surrogate for growth in genome scale metabolic models, our results represent testable hypotheses of alternate growth media derived from in silico analysis of the E. coli genome scale metabolic network.     

Reconstruction and analysis of genome-scale metabolic model of a photosynthetic bacterium

Background  

Synechocystis sp. PCC6803 is a cyanobacterium considered as a candidate photo-biological production platform - an attractive cell factory capable of using CO₂ and light as carbon and energy source, respectively. In order to enable efficient use of metabolic potential of Synechocystis sp. PCC6803, it is of importance to develop tools for uncovering stoichiometric and regulatory principles in the Synechocystis metabolic network.     

Biofuel production in Escherichia coli: the role of metabolic engineering and synthetic biology

The microbial production of biofuels is a promising avenue for the development of viable processes for the generation of fuels from sustainable resources. In order to become cost and energy effective, these processes must utilize organisms that can be optimized to efficiently produce candidate fuels from a variety of feedstocks. Escherichia coli has become a promising host organism for the microbial production of biofuels in part due to the ease at which this organism can be manipulated. Advancements in metabolic engineering and synthetic biology have led to the ability to efficiently engineer E. coli as a biocatalyst for the production of a wide variety of potential biofuels from several biomass constituents. This review focuses on recent efforts devoted to engineering E. coli for the production of biofuels, with emphasis on the key aspects of both the utilization of a variety of substrates as well as the synthesis of several promising biofuels. Strategies for the efficient utilization of carbohydrates, carbohydrate mixtures, and noncarbohydrate carbon sources will be discussed along with engineering efforts for the exploitation of both fermentative and nonfermentative pathways for the production of candidate biofuels such as alcohols and higher carbon biofuels derived from fatty acid and isoprenoid pathways. Continued advancements in metabolic engineering and synthetic biology will help improve not only the titers, yields, and productivities of biofuels discussed herein, but also increase the potential range of compounds that can be produced.     

Development and experimental verification of a genome-scale metabolic model for Corynebacterium glutamicum

Background  

In silico genome-scale metabolic models enable the analysis of the characteristics of metabolic systems of organisms. In this study, we reconstructed a genome-scale metabolic model of Corynebacterium glutamicum on the basis of genome sequence annotation and physiological data. The metabolic characteristics were analyzed using flux balance analysis (FBA), and the results of FBA were validated using data from culture experiments performed at different oxygen uptake rates.     

Bacterial toxicity of cyclodextrins: Luminuous Escherichia coli as a model

The effect of various concentrations of natural and chemically modified cyclodextrins on the luminescence of an Escherichia coli suspension was investigated. All cyclodextrins were found to reduce, albeit to a varying degree, the luminescence level of the bacterial cells, thus suggesting a direct interaction between the cyclodextrins and cells. The inhibitory concentrations IC₂₀ and IC₅₀ of the various cyclodextrins were determined and taken to represent their toxicity effects upon the bacterial cells. Among the natural cyclodextrins, - and -CD interfered minimally with the bacterial luminescence and consequently were essentially non-toxic. The following descending order of toxicity was observed: -CD -CD > -CD. Among the chemically modified cyclodextrins, Dimeb was clearly toxic while Trimeb and the hydroxylated derivatives (hydroxypropyl-ga-CD, HPACD;--CD, HPBCD;--CD, HPGCD) were essentially non-toxic. The following descending order of toxicity was observed: Dimeb HPBCD > Trimeb > HPACD > HPGCD.     

Uropathogenic Escherichia coli as a model of host-parasite interaction

Resistance to mucosal infection varies greatly in the population, but the molecular basis of disease susceptibility is often unknown. Studies of host-pathogen infections are helpful to identify virulence factors, which characterise disease isolates, and successful defence strategies of hosts that resist infection. In the urinary tract infection (UTI) model, we have identified crucial steps in the pathogen-activated innate host response, and studied the genetic control of these activation steps. Furthermore, genetic variation in the innate host-response defence is investigated as a basis of disease susceptibility. The Toll-like receptor 4 (TLR4) controls initial mucosal response to uropathogenic Escherichia coli (UPEC). Bacterial TLR4 activation in epithelial cells leads to chemokine secretion and neutrophil recruitment and TLR4 mutant mice develop an asymptomatic carrier state. The chemokine receptor CXCR1 determines the efficiency of neutrophil migration and activation, and thus of bacterial clearance. CXCR1 mutant mice become bacteremic and develop renal scars and studies in UTI prone children have detected low CXCR1 expression, suggesting that CXCR1 is also essential for human disease susceptibility.     

iBsu1103: a new genome-scale metabolic model of Bacillus subtilis based on SEED annotations

Background  

Bacillus subtilis is an organism of interest because of its extensive industrial applications, its similarity to pathogenic organisms, and its role as the model organism for Gram-positive, sporulating bacteria. In this work, we introduce a new genome-scale metabolic model of B. subtilis 168 called iBsu1103. This new model is based on the annotated B. subtilis 168 genome generated by the SEED, one of the most up-to-date and accurate annotations of B. subtilis 168 available.     

Modeling hybridoma cell metabolism using a generic genome-scale metabolic model of Mus musculus

The reconstructed cellular metabolic network of Mus musculus, based on annotated genomic data, pathway databases, and currently available biochemical and physiological information, is presented. Although incomplete, it represents the first attempt to collect and characterize the metabolic network of a mammalian cell on the basis of genomic data. The reaction network is generic in nature and attempts to capture the carbon, energy, and nitrogen metabolism of the cell. The metabolic reactions were compartmentalized between the cytosol and the mitochondria, including transport reactions between the compartments and the extracellular medium. The reaction list consists of 872 internal metabolites involved in a total of 1220 reactions, whereof 473 relate to known open reading frames. Initial in silico analysis of the reconstructed model is presented.     

A genome-scale metabolic model of potato late blight suggests a photosynthesis suppression mechanism

Background

Phytophthora infestans is a plant pathogen that causes an important plant disease known as late blight in potato plants (Solanum tuberosum) and several other solanaceous hosts. This disease is the main factor affecting potato crop production worldwide. In spite of the importance of the disease, the molecular mechanisms underlying the compatibility between the pathogen and its hosts are still unknown.

Results

To explain the metabolic response of late blight, specifically photosynthesis inhibition in infected plants, we reconstructed a genome-scale metabolic network of the S. tuberosum leaf, PstM1. This metabolic network simulates the effect of this disease in the leaf metabolism. PstM1 accounts for 2751 genes, 1113 metabolic functions, 1773 gene-protein-reaction associations and 1938 metabolites involved in 2072 reactions. The optimization of the model for biomass synthesis maximization in three infection time points suggested a suppression of the photosynthetic capacity related to the decrease of metabolic flux in light reactions and carbon fixation reactions. In addition, a variation pattern in the flux of carboxylation to oxygenation reactions catalyzed by RuBisCO was also identified, likely to be associated to a defense response in the compatible interaction between P. infestans and S. tuberosum.

Conclusions

In this work, we introduced simultaneously the first metabolic network of S. tuberosum and the first genome-scale metabolic model of the compatible interaction of a plant with P. infestans.

     

Zebrafish as a model for systems biology

Zebrafish offer a unique vertebrate model for research areas such as drug development, disease modeling and other biological exploration. There is significant conservation of genetics and other cellular networks among zebrafish and other vertebrate models, including humans. Here we discuss the recent work and efforts made in different fields of biology to explore the potential of zebrafish. Along with this, we also reviewed the concept of systems biology. A biological system is made up of a large number of components that interact in a huge variety of combinations. To understand completely the behavior of a system, it is important to know its components and interactions, and this can be achieved through a systems biology approach. At the end of the paper we present a concept of integrating zebrafish into the systems biology approach.     

A genome-scale metabolic reconstruction of Pseudomonas putida KT2440: iJN746 as a cell factory

Background

The study of biological interaction networks is a central theme of systems biology. Here, we investigate the relationships between two distinct types of interaction networks: the metabolic pathway map and the protein-protein interaction network (PIN). It has long been established that successive enzymatic steps are often catalyzed by physically interacting proteins forming permanent or transient multi-enzymes complexes. Inspecting high-throughput PIN data, it was shown recently that, indeed, enzymes involved in successive reactions are generally more likely to interact than other protein pairs. In our study, we expanded this line of research to include comparisons of the underlying respective network topologies as well as to investigate whether the spatial organization of enzyme interactions correlates with metabolic efficiency.

Results

Analyzing yeast data, we detected long-range correlations between shortest paths between proteins in both network types suggesting a mutual correspondence of both network architectures. We discovered that the organizing principles of physical interactions between metabolic enzymes differ from the general PIN of all proteins. While physical interactions between proteins are generally dissortative, enzyme interactions were observed to be assortative. Thus, enzymes frequently interact with other enzymes of similar rather than different degree. Enzymes carrying high flux loads are more likely to physically interact than enzymes with lower metabolic throughput. In particular, enzymes associated with catabolic pathways as well as enzymes involved in the biosynthesis of complex molecules were found to exhibit high degrees of physical clustering. Single proteins were identified that connect major components of the cellular metabolism and may thus be essential for the structural integrity of several biosynthetic systems.

Conclusion

Our results reveal topological equivalences between the protein interaction network and the metabolic pathway network. Evolved protein interactions may contribute significantly towards increasing the efficiency of metabolic processes by permitting higher metabolic fluxes. Thus, our results shed further light on the unifying principles shaping the evolution of both the functional (metabolic) as well as the physical interaction network.     

Construction of an l-serine producing Escherichia coli via metabolic engineering

l-Serine is a nonessential amino acid, but plays a crucial role as a building block for cell growth. Currently, l-serine production is mainly dependent on enzymatic or cellular conversion. In this study, we constructed a recombinant Escherichia coli that can fermentatively produce l-serine from glucose. To accumulate l-serine, sdaA encoding the l-serine dehydratase, iclR encoding the isocitrate lyase regulator, and arcA encoding the aerobic respiration control protein were deleted in turn. In batch fermentation, the engineered E. coli strain YF-5 exhibited obvious l-serine accumulation but poor cell growth. To restore cell growth, aceB encoding the malate synthase was knocked out, and the engineered strain was then transformed with plasmid that overexpressed serA ^FR , serB, and serC genes. The resulting strain YF-7 produced 4.5 g/L l-serine in batch cultivation and 8.34 g/L l-serine in fed-batch cultivation.     

Foundations of metabolic organization: coherence as a basis of computational properties in metabolic networks

Biological organization is based on the coherent energy transfer allowing for macromolecules to operate with high efficiency and realize computation. Computation is executed with virtually 100% efficiency via the coherent operation of molecular machines in which low-energy recognitions trigger energy-driven non-equilibrium dynamic processes. The recognition process is of quantum mechanical nature being a non-demolition measurement. It underlies the enzymatic conversion of a substrate into the product (an elementary metabolic phenomenon); the switching via separation of the direct and reverse routes in futile cycles provides the generation and complication of metabolic networks (coherence within cycles is maintained by the supramolecular organization of enzymes); the genetic level corresponding to the appearance of digital information is based on reflective arrows (catalysts realize their own self-reproduction) and operation of hypercycles. Every metabolic cycle via reciprocal regulation of both its halves can generate rhythms and spatial structures (resulting from the temporally organized depositions from the cycles). Via coherent events which percolate from the elementary submolecular level to organismic entities, self-assembly based on the molecular complementarity is realized and the dynamic informational field operating within the metabolic network is generated.