Essentiality and damage in metabolic networks

Understanding the architecture of physiological functions from annotated genome sequences is a major task for postgenomic biology. From the annotated genome sequence of the microbe Escherichia coli, we propose a general quantitative definition of enzyme importance in a metabolic network. Using a graph analysis of its metabolism, we relate the extent of the topological damage generated in the metabolic network by the deletion of an enzyme to the experimentally determined viability of the organism in the absence of that enzyme. We show that the network is robust and that the extent of the damage relates to enzyme importance. We predict that a large fraction (91%) of enzymes causes little damage when removed, while a small group (9%) can cause serious damage. Experimental results confirm that this group contains the majority of essential enzymes. The results may reveal a universal property of metabolic networks.     

Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters

Identifying the factors that determine microbial growth rate under various environmental and genetic conditions is a major challenge of systems biology. While current genome-scale metabolic modeling approaches enable us to successfully predict a variety of metabolic phenotypes, including maximal biomass yield, the prediction of actual growth rate is a long standing goal. This gap stems from strictly relying on data regarding reaction stoichiometry and directionality, without accounting for enzyme kinetic considerations. Here we present a novel metabolic network-based approach, MetabOlic Modeling with ENzyme kineTics (MOMENT), which predicts metabolic flux rate and growth rate by utilizing prior data on enzyme turnover rates and enzyme molecular weights, without requiring measurements of nutrient uptake rates. The method is based on an identified design principle of metabolism in which enzymes catalyzing high flux reactions across different media tend to be more efficient in terms of having higher turnover numbers. Extending upon previous attempts to utilize kinetic data in genome-scale metabolic modeling, our approach takes into account the requirement for specific enzyme concentrations for catalyzing predicted metabolic flux rates, considering isozymes, protein complexes, and multi-functional enzymes. MOMENT is shown to significantly improve the prediction accuracy of various metabolic phenotypes in E. coli, including intracellular flux rates and changes in gene expression levels under different growth rates. Most importantly, MOMENT is shown to predict growth rates of E. coli under a diverse set of media that are correlated with experimental measurements, markedly improving upon existing state-of-the art stoichiometric modeling approaches. These results support the view that a physiological bound on cellular enzyme concentrations is a key factor that determines microbial growth rate.     

Characterizing the metabolic phenotype: a phenotype phase plane analysis.

Genome-scale metabolic maps can be reconstructed from annotated genome sequence data, biochemical literature, bioinformatic analysis, and strain-specific information. Flux-balance analysis has been useful for qualitative and quantitative analysis of metabolic reconstructions. In the past, FBA has typically been performed in one growth condition at a time, thus giving a limited view of the metabolic capabilities of a metabolic network. We have broadened the use of FBA to map the optimal metabolic flux distribution onto a single plane, which is defined by the availability of two key substrates. A finite number of qualitatively distinct patterns of metabolic pathway utilization were identified in this plane, dividing it into discrete phases. The characteristics of these distinct phases are interpreted using ratios of shadow prices in the form of isoclines. The isoclines can be used to classify the state of the metabolic network. This methodology gives rise to a "phase plane" analysis of the metabolic genotype-phenotype relation relevant for a range of growth conditions. Phenotype phase planes (PhPPs) were generated for Escherichia coli growth on two carbon sources (acetate and glucose) at all levels of oxygenation, and the resulting optimal metabolic phenotypes were studied. Supplementary information can be downloaded from our website (http://epicurus.che.udel.edu).     

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.     

A metabolic model of cellular energetics and carbon flux during aerobic Escherichia coli fermentation

An integrated metabolic model for the production of acetate by Escherichia coli growing on glucose under aerobic conditions was presented previously (Ko et al., 1993). The resulting model equations can be used to explain phenomena often observed with industrial fermentations, i.e., increased acetate production which follows from high glucose uptake rate, a low dissolved oxygen concentration, a high specific growth rate, or a combination of these conditions. However, several questions still need to be addressed. First, cell composition is growth rate and media dependent. Second, the macromolecular composition varied between E. coli strains. And finally, a model that represents the carbon fluxes between the Embden-Meyerhof-Parnas (EMP) and the hexose monophosphate (HMP) pathways when cells are subject to internal and/or external stresses is still not well defined. In the present work, we have made an effort to account for these effects, and the resulting model equations show good agreement for wild-type and recombinant E. coli experimental data for the acetate concentration, the onset of acetate secretion, and cell yield based on glucose. These results are useful for optimizing aerobic E. coli fermentation processes. More specifically, we have determined the EMP pathway carbon flux profiles required by the integrated metabolic model for an accurate fit of the acetic acid profile data from a wild-type E. coli strain ML308. These EMP carbon flux profiles were correlated with a dimensionless measurement of biomass and then used to predict the acetic acid profiles for E. coli strain F-122 expressing human immunodeficiency virus-(HIV(528)) beta-galactosidase fusion protein. The effect of different macromolecular compositions and growth rates between these two E. coli strains required a constant scaling factor for improved quantitative predictions.     

一种基于代谢网络分析最小化基因组的方法及其在大肠杆菌中的应用

最小生命体的合成是合成生物学研究的重要方向。最小化基因组的同时而又不对细胞生长产生影响是代谢工程研究的一个重要目标。文中提出了一种从基因组尺度代谢网络模型出发,通过零通量反应删除及对非必需基因组合删除计算获得基因组最小化代谢网络模型的方法,利用该方法简化了大肠杆菌经典代谢网络模型iAF1260,由起始的1 260个基因简化得到了312个基因,而最优生物质生成速率保持不变。基因组最小化代谢网络模型预测了在细胞正常生长的前提下包含最少基因的代谢途径,为大肠杆菌获得最小基因组的湿实验设计提供了重要参考。     

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.     

Minimal reaction sets for Escherichia coli metabolism under different growth requirements and uptake environments

A computational procedure for identifying the minimal set of metabolic reactions capable of supporting various growth rates on different substrates is introduced and applied to a flux balance model of the Escherichia coli metabolic network. This task is posed mathematically as a generalized network optimization problem. The minimal reaction sets capable of supporting specified growth rates are determined for two different uptake conditions: (i) limiting the uptake of organic material to a single organic component (e.g., glucose or acetate) and (ii) allowing the importation of any metabolite with available cellular transport reactions. We find that minimal reaction network sets are highly dependent on the uptake environment and the growth requirements imposed on the network. Specifically, we predict that the E. coli network, as described by the flux balance model, requires 224 metabolic reactions to support growth on a glucose-only medium and 229 for an acetate-only medium, while only 122 reactions enable growth on a specially engineered growth medium.     

Comparative studies of Escherichia coli strains using different glucose uptake systems: Metabolism and energetics

Modifying substrate uptake systems is a potentially powerful tool in metabolic engineering. This research investigates energetic and metabolic changes brought about by the genetic modification of the glucose uptake and phosphorylation system of Escherichia coli. The engineered strain PPA316, which lacks the E. coli phosphotransferase system (PTS) and uses instead the galactose-proton symport system for glucose uptake, exhibited significantly altered metabolic patterns relative to the parent strain PPA305 which retains PTS activity. Replacement of a PTS uptake system by the galactose-proton symport system is expected to lower the carbon flux to pyruvate in both aerobic and anaerobic cultivations. The extra energy cost in substrate uptake for the non-PTS strain PPA 316 had a greater effect on anaerobic specific growth rate, which was reduced by a factor of five relative to PPA 305, while PPA 316 reached a specific growth rate of 60% of that of the PTS strain under aerobic conditions. The maximal cell densities obtained with PPA 316 were approximately 8% higher than those of the PTS strain under aerobic conditions and 14% lower under anaerobic conditions. In vivo NMR results showed that the non-PTS strain possesses a dramatically different intracellular environment, as evidenced by lower levels of total sugar phosphate, NAD(H), nucleoside triphosphates and phosphoenolpyruvate, and higher levels of nucleoside diphosphates. The sugar phosphate compositions, as measured by extract NMR, were considerably different between these two strains. Data suggest that limitations in the rates of steps catalyzed by glucokinase, glyceraldehyde-3-phosphate dehydrogenase, phosphofructokinase, and pyruvate kinase may be responsible for the low overall rate of glucose metabolism in PPA316. (c) 1997 John Wiley & Sons, Inc. Biotechnol Bioeng 56: 583-590, 1997.     

Only one of the two annotated Lactococcus lactis fabG genes encodes a functional beta-ketoacyl-acyl carrier protein reductase

The small genome of the Gram-positive bacterium Lactococcus lactis ssp. lactis IL1403 contains two genes that encode proteins annotated as homologues of Escherichia coli beta-hydroxyacyl-acyl carrier protein (ACP) reductase. E. coli fabG encodes beta-ketoacyl-acyl carrier protein (ACP) reductase, the enzyme responsible for the first reductive step of the fatty acid synthetic cycle. Both of the L. lactis genes are adjacent to (and predicted to be cotranscribed with) other genes that encode proteins having homology to known fatty acid synthetic enzymes. Such relationships have often been used to strengthen annotations based on sequence alignments. Annotation in the case of beta-ketoacyl-ACP reductase is particularly problematic because the protein is a member of a vast protein family, the short-chain alcohol dehydrogenase/reductase (SDR) family. The recent isolation of an E. coli fabG mutant strain encoding a conditionally active beta-ketoacyl-ACP reductase allowed physiological and biochemical testing of the putative L. lactishomologues. We report that expression of only one of the two L. lactis proteins (that annotated as FabG1) allows growth of the E. coli fabG strain under nonpermissive conditions and restores in vitro fatty acid synthetic ability to extracts of the mutant strain. Therefore, like E. coli, L. lactis has a single beta-ketoacyl-ACP reductase active with substrates of all fatty acid chain lengths. The second protein (annotated as FabG2), although inactive in fatty acid synthesis both in vivo and in vitro, was highly active in reduction of the model substrate, beta-ketobutyryl-CoA. As expected from work on the E. coli enzyme, the FabG1 beta-ketobutyryl-CoA reductase activity was inhibited by ACP (which blocks access to the active site) whereas the activity of FabG2 was unaffected by the presence of ACP. These results seem to be an example of a gene duplication event followed by divergence of one copy of the gene to encode a protein having a new function.     

Toward metabolic phenomics: analysis of genomic data using flux balances.

Small genome sequencing and annotations are leading to the definition of metabolic genotypes in an increasing number of organisms. Proteomics is beginning to give insights into the use of the metabolic genotype under given growth conditions. These data sets give the basis for systemically studying the genotype-phenotype relationship. Methods of systems science need to be employed to analyze, interpret, and predict this complex relationship. These endeavors will lead to the development of a new field, tentatively named phenomics. This article illustrates how the metabolic characteristics of annotated small genomes can be analyzed using flux balance analysis (FBA). A general algorithm for the formulation of in silico metabolic genotypes is described. Illustrative analyses of the in silico Escherichia coli K-12 metabolic genotypes are used to show how FBA can be used to study the capabilities of this strain.     

Aerobic fermentation of D-glucose by an evolved cytochrome oxidase-deficient Escherichia coli strain

Fermentation of glucose to D-lactic acid under aerobic growth conditions by an evolved Escherichia coli mutant deficient in three terminal oxidases is reported in this work. Cytochrome oxidases (cydAB, cyoABCD, and cbdAB) were removed from the E. coli K12 MG1655 genome, resulting in the ECOM3 (E. coli cytochrome oxidase mutant) strain. Removal of cytochrome oxidases reduced the oxygen uptake rate of the knockout strain by nearly 85%. Moreover, the knockout strain was initially incapable of growing on M9 minimal medium. After the ECOM3 strain was subjected to adaptive evolution on glucose M9 medium for 60 days, a growth rate equivalent to that of anaerobic wild-type E. coli was achieved. Our findings demonstrate that three independently adaptively evolved ECOM3 populations acquired different phenotypes: one produced lactate as a sole fermentation product, while the other two strains exhibited a mixed-acid fermentation under oxic growth conditions with lactate remaining as the major product. The homofermenting strain showed a D-lactate yield of 0.8 g/g from glucose. Gene expression and in silico model-based analyses were employed to identify perturbed pathways and explain phenotypic behavior. Significant upregulation of ygiN and sodAB explains the remaining oxygen uptake that was observed in evolved ECOM3 strains. E. coli strains produced in this study showed the ability to produce lactate as a fermentation product from glucose and to undergo mixed-acid fermentation during aerobic growth.     

Fundamental Escherichia coli biochemical pathways for biomass and energy production: identification of reactions

Cells grow by oxidizing nutrients using a complex network of biochemical reactions. During this process new biological material is produced along with energy used for maintaining cellular organization. Because the metabolic network is highly branched, these tasks can be accomplished using a wide variety of unique reaction sequences. However, evolutionary pressures under carbon-limited growth conditions likely select organisms that utilize highly efficient pathways. Using elementary-mode analysis, we demonstrate that the metabolism of the bacterium Escherichia coli contains four unique pathways that most efficiently convert glucose and oxygen into new cells and maintenance energy under any level of oxygen limitation. Observed regulatory patterns and experimental findings suggest growing cells use these highly efficient pathways. It is predicted that five knockout mutations generate a strain that supports growth using only the most efficient reaction sequence. The analysis approach should be generally useful for predicting metabolic capabilities and efficient network designs based on only genomic information.     

Systems properties of the Haemophilus influenzae Rd metabolic genotype.

Haemophilus influenzae Rd was the first free-living organism for which the complete genomic sequence was established. The annotated sequence and known biochemical information was used to define the H. influenzae Rd metabolic genotype. This genotype contains 488 metabolic reactions operating on 343 metabolites. The stoichiometric matrix was used to determine the systems characteristics of the metabolic genotype and to assess the metabolic capabilities of H. influenzae. The need to balance cofactor and biosynthetic precursor production during growth on mixed substrates led to the definition of six different optimal metabolic phenotypes arising from the same metabolic genotype, each with different constraining features. The effects of variations in the metabolic genotype were also studied, and it was shown that the H. influenzae Rd metabolic genotype contains redundant functions under defined conditions. We thus show that the synthesis of in silico metabolic genotypes from annotated genome sequences is possible and that systems analysis methods are available that can be used to analyze and interpret phenotypic behavior of such genotypes.     

Genome-scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data

In this report, a genome-scale reconstruction of Bacillus subtilis metabolism and its iterative development based on the combination of genomic, biochemical, and physiological information and high-throughput phenotyping experiments is presented. The initial reconstruction was converted into an in silico model and expanded in a four-step iterative fashion. First, network gap analysis was used to identify 48 missing reactions that are needed for growth but were not found in the genome annotation. Second, the computed growth rates under aerobic conditions were compared with high-throughput phenotypic screen data, and the initial in silico model could predict the outcomes qualitatively in 140 of 271 cases considered. Detailed analysis of the incorrect predictions resulted in the addition of 75 reactions to the initial reconstruction, and 200 of 271 cases were correctly computed. Third, in silico computations of the growth phenotypes of knock-out strains were found to be consistent with experimental observations in 720 of 766 cases evaluated. Fourth, the integrated analysis of the large-scale substrate utilization and gene essentiality data with the genome-scale metabolic model revealed the requirement of 80 specific enzymes (transport, 53; intracellular reactions, 27) that were not in the genome annotation. Subsequent sequence analysis resulted in the identification of genes that could be putatively assigned to 13 intracellular enzymes. The final reconstruction accounted for 844 open reading frames and consisted of 1020 metabolic reactions and 988 metabolites. Hence, the in silico model can be used to obtain experimentally verifiable hypothesis on the metabolic functions of various genes.     

Development of a software tool for in silico simulation of Escherichia coli using a visual programming environment

This study describes the development of a software tool, EcoSim, to assist users in implementing quantitative in silico simulation easily. It consists of four parts: extracellular environment and constraints setting mode, table for optimal metabolic flux distribution and chart for changes of substrate concentration, dynamic flux distribution viewer and dynamic hierarchical regulatory network viewer. Representation of a hierarchical regulatory network was constructed with defined modeling symbols and weight in the central Escherichia coli metabolism. All programming procedures for EcoSim were accomplished in a visual programming environment (LabVIEW). To illustrate quantitative in silico simulation with EcoSim, this program was performed on E. coli using glucose and acetate as carbon sources. The simulation results were in agreement with the experimental data obtained from the literature. EcoSim can be used to assist biologists and engineers in predicting and interpreting dynamic behaviors of E. coli under a variety of environmental conditions.     

Genomic peculiarity of coding sequences and metabolic potential of probiotic Escherichia coli strain Nissle 1917 inferred from raw genome data

Probiotic Escherichia coli strain Nissle 1917 (O6:K5:H1) is a commensal E. coli isolate that has a long tradition in medicine for the treatment of various intestinal disorders in humans. To elucidate the molecular basis of its probiotic nature, we started sequencing the genome of this organism with a whole-genome shotgun approach. A 7.8-fold coverage of the genomic sequence has been generated and is now in the finishing stage. To exploit the genome data as early as possible and to generate hypotheses for functional studies, the unfinished sequencing data were analyzed in this work using a new method [Sun, J., Zeng, A.P., 2004. IdentiCS--identification of coding sequence and in silico reconstruction of the metabolic network directly from unannotated low-coverage bacterial genome sequence. BMC Bioinformatics 5, 112] which is particularly suitable for the prediction of coding sequences (CDSs) from unannotated genome sequence. The CDSs predicted for E. coli Nissle 1917 were compared with those of all five other sequenced E. coli strains (E. coli K-12 MG1655, E. coli K-12 W3110, E. coli CFT073, EHEC O157:H7 EDL933 and EHEC O157:H7 Sakai) published to date. Five thousand one hundred and ninety-two CDSs were predicted for E. coli Nissle 1917, of which 1065 were assigned with enzyme EC numbers. The comparison of all predicted CDSs of E. coli Nissle 1917 to the other E. coli strains revealed 108 CDSs specific for this isolate. They are organized as four big genome islands and many other smaller gene clusters. Based on CDSs with EC numbers for enzymes, the potential metabolic network of Nissle 1917 was reconstructed and compared to those of the other five E. coli strains. Overall, the comparative genomic analysis sheds light on the genomic peculiarity of the probiotic E. coli strain Nissle 1917 and is helpful for designing further functional studies long before the sequencing project is completely finished.