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      Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models

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          Abstract

          • Proteomic and transcriptomic data from wild-type and laboratory-evolved strains of Escherichia coli are consistent with predicted pathway usage from optimal growth rate solutions.

          • In laboratory-evolved strains, there is an upregulation of the pathways in the computed optimal growth states, and downregulation of non-functional pathways.

          • Known regulatory mechanisms are only partially responsible for altered metabolic pathway activity.

          Abstract

          When prokaryotes are maintained at early- to mid-log phase growth through serial passaging for hundreds of generations, the strains improve fitness and evolve a higher growth rate ( Lenski and Travisano, 1994; Ibarra et al, 2002). This increased growth rate is the result of the appearance of a few causal mutations ( Herring et al, 2006; Conrad et al, 2009). In Escherichia coli, these altered growth phenotypes are consistent with predictions from genome-scale models of metabolism (GEMs) ( Ibarra et al, 2002; Fong and Palsson, 2004). However, it is still not known (1) whether absolute gene and protein expression levels and expression changes are consistent with optimal growth predictions from in silico GEMs or (2) whether measured expression changes can be linked to physiological changes that are based on known mechanisms or pathways. In this study, we begin to address these questions using constraint-based modeling of E. coli K-12 metabolism ( Feist and Palsson, 2008) to analyze omic data that document the expression changes in E. coli under adaptive evolution in three different growth conditions.

          Mapping high-throughput data to a network can be useful for interpretation. However, it does not account for upstream and downstream effects of gene and protein expression changes. The analysis of data in the context of GEMs can suggest if predicted activity is consistent with the data. For this work, we used a variant of flux balance analysis (FBA), called Parsimonious enzyme usage FBA (pFBA) ( Figure 1), to classify all genes according to whether they are used in the optimal growth solutions. Results from these models were compared with the data to assess whether the data were consistent with genes and proteins within the predicted optimal solutions, and whether the expression changes were consistent with measured physiology. Through this analysis, we find that the data provide a high coverage of genes that contribute to the optimal growth solutions ( Figure 1B). In fact, the union of the proteomic and transcriptomic data for non-essential genes provides support for 97.7% of all non-essential gene-associated reactions within the optimal growth predictions. Thus, the spectrum of expressed genes and proteins is consistent with the pathway utilization that is predicted for these optimal growth phenotypes.

          Laboratory-evolved strains attain a higher growth rate. This higher growth rate is usually associated with an increased substrate uptake rate ( Ibarra et al, 2002; Fong et al, 2005) and in some cases more efficient metabolism ( Ibarra et al, 2002). Both of these properties are also witnessed in the strains studied here. It has been reported that in most cases, evolved strain growth phenotype is consistent with GEM predictions ( Ibarra et al, 2002; Teusink et al, 2009). Here, we evaluate whether the laboratory-evolved strains adjust the gene and protein expression levels in accordance with pathway usage in the optimal growth predictions. Essential and non-essential genes and proteins within the optimal growth solutions are significantly upregulated ( Figure 1B). This suggests that these proteins may be acting as bottlenecks that are relieved through the adaptive process, thereby allowing for a higher substrate uptake rate and growth rate. However, genes and proteins associated with reactions that cannot carry a flux in the given growth conditions are downregulated in the evolved strains ( Figure 1B). Furthermore, there is downregulation of genes associated with less efficient pathways ( Figure 5C). Thus, the omic data support the emergence of the predicted optimal growth states, consistent with the increased substrate uptake upstream and the increased biomass production downstream of these internal pathways.

          Regulatory mechanisms, both known and unknown, are responsible for the changes seen here. Across all data sets, several metabolic regulons are significantly downregulated. However, no known regulons were enriched among upregulated genes or proteins for all but one data set. Aside from just regulating the metabolic pathways directly, these mechanisms lead to additional physiological changes. For example, in the minimal media growth conditions used here, the stringent response normally represses growth while upregulating amino-acid biosynthetic processes. However, evolved strain gene expression shows a suppression of the stringent response, as evolved strain gene expression shows either no expression change or changes opposite to the normal stringent response.

          The implications of this work are as follows: (1) genome-scale gene and protein expression data are consistent with FBA computed optimal growth states, and evolved strains reinforce these optimal states; (2) genome-scale models will have an important function bridging the gap between genotype and phenotype; and (3) the development of additional genome-scale models of other growth-related processes such as transcription and translation ( Thiele et al, 2009) will have an important function in elucidating the mechanisms that contribute the most to altered phenotypes ( Lewis et al, 2009a). In addition, reconstruction of the transcriptional regulation network will aid in identifying the control of expression changes seen in the other systems.

          Abstract

          After hundreds of generations of adaptive evolution at exponential growth, Escherichia coli grows as predicted using flux balance analysis (FBA) on genome-scale metabolic models (GEMs). However, it is not known whether the predicted pathway usage in FBA solutions is consistent with gene and protein expression in the wild-type and evolved strains. Here, we report that >98% of active reactions from FBA optimal growth solutions are supported by transcriptomic and proteomic data. Moreover, when E. coli adapts to growth rate selective pressure, the evolved strains upregulate genes within the optimal growth predictions, and downregulate genes outside of the optimal growth solutions. In addition, bottlenecks from dosage limitations of computationally predicted essential genes are overcome in the evolved strains. We also identify regulatory processes that may contribute to the development of the optimal growth phenotype in the evolved strains, such as the downregulation of known regulons and stringent response suppression. Thus, differential gene and protein expression from wild-type and adaptively evolved strains supports observed growth phenotype changes, and is consistent with GEM-computed optimal growth states.

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          Most cited references45

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          A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information

          An updated genome-scale reconstruction of the metabolic network in Escherichia coli K-12 MG1655 is presented. This updated metabolic reconstruction includes: (1) an alignment with the latest genome annotation and the metabolic content of EcoCyc leading to the inclusion of the activities of 1260 ORFs, (2) characterization and quantification of the biomass components and maintenance requirements associated with growth of E. coli and (3) thermodynamic information for the included chemical reactions. The conversion of this metabolic network reconstruction into an in silico model is detailed. A new step in the metabolic reconstruction process, termed thermodynamic consistency analysis, is introduced, in which reactions were checked for consistency with thermodynamic reversibility estimates. Applications demonstrating the capabilities of the genome-scale metabolic model to predict high-throughput experimental growth and gene deletion phenotypic screens are presented. The increased scope and computational capability using this new reconstruction is expected to broaden the spectrum of both basic biology and applied systems biology studies of E. coli metabolism.
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            The effects of alternate optimal solutions in constraint-based genome-scale metabolic models.

            Genome-scale constraint-based models of several organisms have now been constructed and are being used for model driven research. A key issue that may arise in the use of such models is the existence of alternate optimal solutions wherein the same maximal objective (e.g., growth rate) can be achieved through different flux distributions. Herein, we investigate the effects that alternate optimal solutions may have on the predicted range of flux values calculated using currently practiced linear (LP) and quadratic programming (QP) methods. An efficient LP-based strategy is described to calculate the range of flux variability that can be present in order to achieve optimal as well as suboptimal objective states. Sample results are provided for growth predictions of E. coli using glucose, acetate, and lactate as carbon substrates. These results demonstrate the extent of flux variability to be highly dependent on environmental conditions and network composition. In addition we examined the impact of alternate optima for growth under gene knockout conditions as calculated using QP-based methods. It was observed that calculations using QP-based methods can show significant variation in growth rate if the flux variability among alternate optima is high. The underlying biological significance and general source of such flux variability is further investigated through the identification of redundancies in the network (equivalent reaction sets) that lead to alternate solutions. Collectively, these results illustrate the variability inherent in metabolic flux distributions and the possible implications of this heterogeneity for constraint-based modeling approaches. These methods also provide an efficient and robust method to calculate the range of flux distributions that can be derived from quantitative fermentation data.
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              Analysis of optimality in natural and perturbed metabolic networks.

              An important goal of whole-cell computational modeling is to integrate detailed biochemical information with biological intuition to produce testable predictions. Based on the premise that prokaryotes such as Escherichia coli have maximized their growth performance along evolution, flux balance analysis (FBA) predicts metabolic flux distributions at steady state by using linear programming. Corroborating earlier results, we show that recent intracellular flux data for wild-type E. coli JM101 display excellent agreement with FBA predictions. Although the assumption of optimality for a wild-type bacterium is justifiable, the same argument may not be valid for genetically engineered knockouts or other bacterial strains that were not exposed to long-term evolutionary pressure. We address this point by introducing the method of minimization of metabolic adjustment (MOMA), whereby we test the hypothesis that knockout metabolic fluxes undergo a minimal redistribution with respect to the flux configuration of the wild type. MOMA employs quadratic programming to identify a point in flux space, which is closest to the wild-type point, compatibly with the gene deletion constraint. Comparing MOMA and FBA predictions to experimental flux data for E. coli pyruvate kinase mutant PB25, we find that MOMA displays a significantly higher correlation than FBA. Our method is further supported by experimental data for E. coli knockout growth rates. It can therefore be used for predicting the behavior of perturbed metabolic networks, whose growth performance is in general suboptimal. MOMA and its possible future extensions may be useful in understanding the evolutionary optimization of metabolism.
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                Author and article information

                Journal
                Mol Syst Biol
                Molecular Systems Biology
                Nature Publishing Group
                1744-4292
                2010
                27 July 2010
                27 July 2010
                : 6
                : 390
                Affiliations
                [1 ]simpleDepartment of Bioengineering, University of California San Diego , La Jolla, CA, USA
                [2 ]simpleBiological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory , Richland, WA, USA
                [3 ]simpleDepartment of Chemistry and Biochemistry, University of California San Diego , La Jolla, CA, USA
                [4 ]simpleSystems Biology and Bioinformatics Program, University of California San Diego , La Jolla, CA, USA
                [5 ]simpleDepartment of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, and Bioquant, University of Heidelberg , Heidelberg, Germany
                [6 ]simpleDepartment of Theoretical Bioinformatics, German Cancer Research Center (DKFZ) , Heidelberg, Germany
                Author notes
                [a ]Department of Bioengineering, University of California San Diego, 417 Powell-Focht Bioengineering Hall, 9500 Gilman Drive, La Jolla, CA 92093-0412, USA. Tel.: +1 858 534 5668; Fax: +1 858 822 3120; palsson@ 123456ucsd.edu
                Article
                msb201047
                10.1038/msb.2010.47
                2925526
                20664636
                7fd7f345-cadb-465b-9da1-63adcdef1a6b
                Copyright © 2010, EMBO and Macmillan Publishers Limited

                This is an open-access article distributed under the terms of the Creative Commons Attribution Noncommercial Share Alike 3.0 Unported License, which allows readers to alter, transform, or build upon the article and then distribute the resulting work under the same or similar license to this one. The work must be attributed back to the original author and commercial use is not permitted without specific permission.

                History
                : 18 March 2010
                : 01 June 2010
                Categories
                Article

                Quantitative & Systems biology
                proteomics,genome-scale models,microarray,escherichia coli,optimality

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