Systematic phenome analysis of Escherichia coli multiple‐knockout mutants reveals hidden reactions in central carbon metabolism
2009; Springer Nature; Volume: 5; Issue: 1 Linguagem: Inglês
10.1038/msb.2009.65
ISSN1744-4292
AutoresKenji Nakahigashi, Yoshihiro Toya, Nobuyoshi Ishii, Tomoyoshi Soga, Miki Hasegawa, Hisami Watanabe, Yuki Takai, Masayuki Honma, Hirotada Mori, Masaru Tomita,
Tópico(s)Microbial Community Ecology and Physiology
ResumoArticle15 September 2009Open Access Systematic phenome analysis of Escherichia coli multiple-knockout mutants reveals hidden reactions in central carbon metabolism Kenji Nakahigashi Corresponding Author Kenji Nakahigashi Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan Search for more papers by this author Yoshihiro Toya Yoshihiro Toya Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, Japan Search for more papers by this author Nobuyoshi Ishii Nobuyoshi Ishii Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan Search for more papers by this author Tomoyoshi Soga Tomoyoshi Soga Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, Japan Search for more papers by this author Miki Hasegawa Miki Hasegawa Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan Search for more papers by this author Hisami Watanabe Hisami Watanabe Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan Search for more papers by this author Yuki Takai Yuki Takai Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan Search for more papers by this author Masayuki Honma Masayuki Honma Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan Search for more papers by this author Hirotada Mori Hirotada Mori Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan Graduate School of Biological Sciences, Nara Institute of Science and Technology, Nara, Japan Search for more papers by this author Masaru Tomita Corresponding Author Masaru Tomita Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, Japan Search for more papers by this author Kenji Nakahigashi Corresponding Author Kenji Nakahigashi Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan Search for more papers by this author Yoshihiro Toya Yoshihiro Toya Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, Japan Search for more papers by this author Nobuyoshi Ishii Nobuyoshi Ishii Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan Search for more papers by this author Tomoyoshi Soga Tomoyoshi Soga Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, Japan Search for more papers by this author Miki Hasegawa Miki Hasegawa Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan Search for more papers by this author Hisami Watanabe Hisami Watanabe Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan Search for more papers by this author Yuki Takai Yuki Takai Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan Search for more papers by this author Masayuki Honma Masayuki Honma Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan Search for more papers by this author Hirotada Mori Hirotada Mori Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan Graduate School of Biological Sciences, Nara Institute of Science and Technology, Nara, Japan Search for more papers by this author Masaru Tomita Corresponding Author Masaru Tomita Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, Japan Search for more papers by this author Author Information Kenji Nakahigashi 1, Yoshihiro Toya1,2, Nobuyoshi Ishii1, Tomoyoshi Soga1,2, Miki Hasegawa1, Hisami Watanabe1, Yuki Takai1, Masayuki Honma1, Hirotada Mori1,3 and Masaru Tomita 1,2 1Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan 2Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, Japan 3Graduate School of Biological Sciences, Nara Institute of Science and Technology, Nara, Japan *Corresponding authors. Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0017, Japan. Tel.: +81 235 29 0521; Fax: +81 235 29 0536; E-mail: [email protected] or Tel.: +81 235 29 0534; Fax: +81 235 29 0536; E-mail: [email protected] Molecular Systems Biology (2009)5:306https://doi.org/10.1038/msb.2009.65 PDFDownload PDF of article text and main figures. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Central carbon metabolism is a basic and exhaustively analyzed pathway. However, the intrinsic robustness of the pathway might still conceal uncharacterized reactions. To test this hypothesis, we constructed systematic multiple-knockout mutants involved in central carbon catabolism in Escherichia coli and tested their growth under 12 different nutrient conditions. Differences between in silico predictions and experimental growth indicated that unreported reactions existed within this extensively analyzed metabolic network. These putative reactions were then confirmed by metabolome analysis and in vitro enzymatic assays. Novel reactions regarding the breakdown of sedoheptulose-7-phosphate to erythrose-4-phosphate and dihydroxyacetone phosphate were observed in transaldolase-deficient mutants, without any noticeable changes in gene expression. These reactions, triggered by an accumulation of sedoheptulose-7-phosphate, were catalyzed by the universally conserved glycolytic enzymes ATP-dependent phosphofructokinase and aldolase. The emergence of an alternative pathway not requiring any changes in gene expression, but rather relying on the accumulation of an intermediate metabolite may be a novel mechanism mediating the robustness of these metabolic networks. Synopsis Systematic phenotype analysis of gene-deletion mutants, combined with in silico predictions from genome-scale metabolic network models, has been used to identify new genetic interactions and previously unknown gene functions in model microorganisms. As this approach depends on a predicted or observed phenotype, genetic robustness limits the availability of gene candidates showing some phenotype under the conditions tested. Such robustness could, in part, originate from redundancy such as the presence of an isozyme or another pathway with a duplicate function. In addition, the specialized functions of many genes for specific growth conditions, such as the availability of different carbon sources, could contribute to overall robustness. Systematic deletion of two or more genes, and fitness tests of the mutants under many conditions, would be powerful systems for the discovery of new gene functions. Using a new method employing a P1 phage derivative, we created systematic double-deletion mutants in the central carbon metabolism of E. coli. The mutants were created by combining 31 single-gene deletions (first deletion) with deletions in seven key reactions (second deletion). The seven key reactions were selected to represent each of the following pathways: glycolysis (two reactions), the pentose phosphate pathway (two reactions), the anaplerotic pathway (two reactions), and the glyoxylate shunt (one reaction). The resulting strains were then tested for growth capabilities under various nutrient conditions, including rich medium, minimal medium with 10 different carbon sources, and medium containing a combination of two carbon sources (Figure 1). At the same time, we performed model-based prediction of the growth phenotypes of these mutants using genome-scale metabolic models. By contrasting the simulation result with the experimental result, we aimed to elucidate previously unknown reactions within this exhaustively analyzed pathway in one of the best-studied organisms. Among 2177 double mutant experiments from which we obtained both experimental and predicted growth phenotypes, we found 39 cases in which model-based analysis predicted double mutant-specific slow-growth phenotypes, although experimental results indicated growth comparable with that of the parental single-knockout mutants. Out of the 39 cases, we were most interested in eight cases that carried one of their deletions in transaldolase (talA talB). Xylose was used as a carbon source in five of these eight cases. Further examination of metabolic pathways indicated that transaldolase must be essential for xylose utilization through currently known reactions in central carbon metabolism (Figure 4A and B). Although one known pathway could potentially serve as a bypass for utilizing xylose in transaldolase mutants, this bypass could not explain the normal growth of several double-knockout strains such as fbp-talAB, tpiA-talAB, deoC-talAB, and deoB-talAB (Figure 4C). Thus, we decided to focus on this discrepancy to find new reaction(s). First, we performed microarray analysis to find specifically induced genes in the transaldolase mutant growing on xylose minimal medium. However, it revealed no notable changes in the mRNA levels of genes involved in related metabolic pathways and did not suggest interesting candidates for the novel pathway. Next, we performed metabolome analysis by CE-MS, which revealed greater than 40-fold accumulation of S7P in the talAB-knockout cells and several-fold accumulation of pentose phosphates, but otherwise less than twofold change in the levels of metabolites in related pathways (Figure 4E). We also discovered accumulation of an unidentified metabolite, postulated to be S1,7P, which was previously not considered to be present in E. coli. Combined with another experimental result from the phenotype analysis that pfkA is necessary for the growth of transaldolase mutants on xylose, we hypothesized that S7P is utilized through S1,7P and degraded to DHAP and E4P in transaldolase mutants (red reactions in Figure 4F). To test this hypothesis, we performed MFA of wild-type and talAB-knockout strains using 1-13C-xylose as the sole carbon source and measured the isotopomer distribution of intermediate metabolites by CE-MS. The wild-type and talAB-knockout strains clearly showed distinct 13C isotopomer distributions for many metabolites, and the differences were explained by the presence of the hypothesized new reactions in the talAB knockout, but not in the wild type (Figure 4F). Finally, we validated these novel reactions at the level of enzymatic activity. Using purified recombinant PfkA, the candidate enzyme for converting S7P to S1,7P, and FbaA, the candidate enzyme for converting S1,7P to DHAP and E4P, we confirmed the conversion from S7P and ATP to (putative) S1,7P by PfkA and then to DHAP and E4P by addition of FbaA. Thus, consistent with our hypothesis, S7P must be converted to S1,7P and then to DHAP and E4P by sequential action of the glycolytic enzyme PfkA (phosphofructokinase) and FbpA (fructose-bisphosphate-aldorase) in transaldolase-deficient cells. The discovery of new reactions, in addition to proving the potency of a strategy combining experimental and computational phenotype analysis of large-scale multiple-knockout mutants, has two substantially important implications. First, although the novel reactions seemed to be present only in transaldolase mutants in E. coli, other organisms might also possess these reactions. The most probable candidate organism might be another bacterium, L. lactis, which does not seem to encode transaldolase in its genome, but is known to utilize xylose through glycolysis and the pentose phosphate pathway. In higher eukaryote, some mammalian tissues known to lack transaldolase, and associated with liver cirrhosis, represent another possible candidate having the novel reactions. Second, emergence of these alternative reactions does not require any change in gene expression, but rather relies on the accumulation of an intermediate metabolite, S7P. The emergence of an alternative pathway that does not require any change in gene expression, but rather relies on the accumulation of an intermediate metabolite, may be a novel mechanism that mediates the robustness of metabolic networks. Introduction Systematic phenome analysis of gene-deletion mutants combined with in silico predictions from genome-scale metabolic network models has been used to identify new genetic interactions and previously unknown gene functions in model microorganisms (Duarte et al, 2004; Joyce et al, 2006; Reed et al, 2006; Ohara et al, 2007). As this approach depends on a predicted or observed phenotype, genetic robustness, the phenomenon by which a majority of genes do not show a detectable phenotype when deleted (Giaever et al, 2002; Baba et al, 2006; Kato and Hashimoto, 2007), limits the availability of gene candidates. Such robustness could, in part, originate from redundancy such as the presence of an isozyme or other pathway with a duplicate function (Gu et al, 2003; Papp et al, 2004). In many cases, more than two redundant systems are present (Deutscher et al, 2006). Furthermore, the specialized functions of many genes for specific growth conditions, such as the availability of different carbon sources, could contribute to overall robustness. Systematic deletion of two or more genes, and fitness tests of the mutants under many conditions, would be powerful systems for the discovery of novel gene functions (Boone et al, 2007). The central carbon metabolism of Escherichia coli is a model example of a very robust system. Despite the essential functions of both catabolism and anabolism, only four genes (out of >70 genes in the pathways shown in Figure 1) are essential for growth using glucose as the sole carbon source. Through the use of a novel method to systematically create multiple-gene knockouts, we tested for the presence of unknown metabolic reactions in the extensively examined network of one of the most thoroughly analyzed model organisms. By combining experimental and computational phenome analyses of systematic double and triple knockouts, grown on various carbon sources, we demonstrated the emergence of an unreported pathway formed by previously unknown activities of well-characterized glycolytic enzymes that allow transaldolase-deficient mutants to unexpectedly grow on some carbon sources. Figure 1.Central carbon metabolism pathways examined in this study. Genes used for the first deletion are shown in red. Genes for the second deletion are boxed and abbreviations used in the text for the second deletion are indicated in boldface red if different from the deleted gene name. The reactions deleted by second deletion are shown with red arrows. For pckA and ppc, direction of the reaction catalyzing is shown by small arrows. The utilized carbon sources (blue) and entry points into central carbon metabolism are shown. See Supplementary Table I for gene and product names and Supplementary Table III for the abbreviations of metabolites not defined in the text. Download figure Download PowerPoint Results Systematic construction of multiple-knockout mutants and phenotype analysis To efficiently analyze the phenotypes of multiple-knockout mutations in central carbon metabolism, seven key reactions were selected and a deletion of each reaction (second deletion) was combined with each of 31 single-gene deletions (first deletion). The names of the genes and their products are listed in Supplementary Table I, and the locations of the genes on the metabolic map are shown in Figure 1. Seven key reactions were selected to represent each of the following pathways: glycolysis (two reactions), the pentose phosphate pathway (two reactions), the anaplerotic pathway (two reactions), and the glyoxylate shunt (one reaction). As four of the selected reactions can be catalyzed by two isozymes, the deletion of two genes was required to attain the second deletion, and the resulting strain bore a triple deletion. To construct these multiple-deletion strains systematically, a derivative of P1 phage (P1dl) enabling multiple rounds of transduction in the liquid phase was constructed and used to transduce the second deletion into each single-gene-deletion strain. Two independently isolated single-deletion strains carrying the first deletion (31 metabolic-gene deletions and a control rrnH deletion) were used for duplicate analysis. We hereafter use the term 'double' knockout even if the second deletion consists of two genes and the strain bears three-gene knockouts, and refer to the strain in which a first deletion X and a second deletion Y were combined as X–Y strain, for example, pgi-Tal or pgi-ppc_pckA. The growth phenotypes of the resulting double-deletion cells were tested under various nutrient conditions, including rich medium, minimal media containing one of 10 different carbon sources, and a minimal medium with a combination of two carbon sources. The carbon sources were selected as they connect to central carbon metabolism at points distributed across most of the central pathways investigated (Figure 1). Cell growth was monitored by evaluating the OD600 at 24 and 48 h after inoculation (Supplementary Table II). Since we used an incubation method without forced aeration, the oxygenation level of the cultures is not known. However, growth of a wild-type strain on non-fermentative carbon sources, succinate and acetate (OD600 of 0.26 and 0.39 at 24 h, and 0.27 and 0.51 at 48 h, case numbers 5 and 6, respectively; Supplementary Table II), indicated that the oxygen supply was sufficient to support aerobic growth at least to this cell density. To eliminate the absolute growth difference on different media, relative growth (W) was calculated for each strain as the growth ratio of the test strain (single or double mutant) over the wild-type strain grown on the same medium (see Materials and methods for calculation of W). A heat map of the relative growth (W) is shown in Figure 2A. Figure 2.Heat maps showing global relative growth phenotype and comparison between measured and predicted values. (A) Growth rates of the multiple knockouts compared with wild type are shown in a heat map. Each row indicates the first gene deletion. Columns group indicates the second gene deletion, as well as the time point (24 or 48 h) and medium. The results for second mutations in talAB are enlarged in the lower panel. (B) RGIe and RGIs score are shown side by side for comparison. Tiles shown in black indicate conditions under which RGI scores were not calculated. The numbers at the bottom indicate the medium, as shown in panel A. The conditions mentioned in the text are highlighted by red boxes. Download figure Download PowerPoint When compared with the parental single-deletion strains bearing only the first or second deletion, many double-knockout strains exhibited a slow-growth phenotype displayed as a shift of the relative growth distribution (the averages ±s.d. was −0.22±0.36; Supplementary Figure 1A and B). By contrast, when the comparison was made with the slower growing of the two parental strains, the distribution of the relative growth was centred around zero (average ±s.d. was −0.03±0.22; Supplementary Figure 1C), indicating that the single mutation that caused the larger growth defect generally determined the growth of the double mutants in most of the experiments. However, some double mutants exhibited growth phenotypes that were very different from those of both corresponding single mutants. For example, rpe-pgi and rpe-zwf mutants exhibited very limited ability to utilize most carbon sources, whereas both of the parents could use a broad spectrum of carbon sources (Figure 2A, boxed). Here, we refer to such specific slow- or fast-growth phenotypes that are specific to the double-knockout strain, but are not found in either of the parental single mutants as synthetic slow-growth phenotypes or synthetic fast-growth phenotypes, respectively. In general, the emergence of a synthetic slow- or fast-growth phenotype suggests functional interaction between the genes mutated or redundancy in their function. In the above case, the inability of the rpe-zwf mutant to grow on most carbon sources is due to its inability to produce the essential metabolite ribose-5-phosphate (R5P), as fluxes leading to this metabolite are blocked in both directions of the pentose phosphate pathway (see Supplementary Text 1 for details). To evaluate the presence of such synthetic slow- or fast-growth phenotypes, we defined a Relative Growth Index from experiment (RGIe) score that weighs the growth of a mutant strain against that of its parental strain(s) (see Materials and methods). Within a threshold of two standard deviations (s.d.s), we observed 229 cases (9.3%) that exhibited synthetic slow-growth phenotypes and 20 cases (0.8%) exhibiting synthetic fast-growth phenotypes out of the 2465 cases in which we could calculate the RGIe score. Similarly, using the RGIe score of single-knockout strains, we observed 30 cases (5.9%) with slow-growth and 6 (1.2%) with fast-growth phenotypes from a total of 493 experiments. Assuming normal distribution, the number of fast-growth phenotypes was within the range of experimental variation in both single- and double-knockout experiments; thus, most of them could result from experimental variation (see section Discussion), so we examined only synthetic slow-growth phenotypes. The rate of emergence of synthetic slow-growth phenotypes in double knockouts was slightly higher than the rate of emergence of slow-growth phenotypes by single deletions. However, controlling for the fact that four glycolysis genes (fbaA, gapA, pgk, and eno) could not be tested as first deletions because they are essential (Baba et al, 2006; Kato and Hashimoto, 2007), the rate of emergence of the slow-growth phenotype was not higher in the double mutants. This suggests that the first mutation did not cause drastic loss of the robustness of central carbon metabolism against the additional loss of the single gene. Examining the 229 cases of synthetic slow-growth phenotypes, we found that most of them, except for five cases, were restricted to strains carrying a first deletion in rpe, pckA, aceE, aceF, lpdA, or rpiA. All of the rpe-related cases were rpe-pgi and rpe-zwf mutants, like those already mentioned. All four pckA-related synthetic slow-growth phenotypes were observed in the pckA-ME mutant, which was deleted of all the reactions of the central carbon metabolism, connecting tricarboxylic acid (TCA) cycle to glycolysis. Thus, it is likely to result in a strain that cannot utilize gluconeogenic carbon sources entering metabolism from the TCA cycle intermediates. Three of the four synthetic slow-growth phenotypes were of such carbon sources: acetate, succinate, and glutamate. Additionally, pckA-ME mutants displayed synthetic slow-growth on minimal media supplemented with casamino acid, indicating that limited growth of many strains on casamino acid was dependent on some amino acid(s) feeding into the TCA cycle. In the cases of aceE, aceF, and lpdA, all of which encode subunits of the pyruvate dehydrogenase (PDH) complex, single mutants of any of these genes exhibited growth on most fermentative carbon sources, but growth on these carbon sources was abolished in many of the double mutants. This resulted in 53, 45, and 49 aceE aceF-, and lpdA-based synthetic slow-growth phenotypes, respectively. Similarly, while a single mutation in rpiA (encoding the major isozyme of ribose-5-phosphate isomerase (Rpi)) did not exhibited a drastic slow-growth phenotype, most of the secondary deletions affected its growth on many carbon sources, resulting in 52 cases of synthetic slow-growth phenotypes. The effect of secondary mutations of enzymes in the pentose phosphate pathway (zwf or Tal) was less pronounced compared with secondary mutations in other pathways. Such extreme bias, as observed for the first deletion, was neither observed for the second mutations nor in the medium conditions employed. Comparison of synthetic slow-growth phenotypes with those predicted by simulation To determine the extent to which the observed phenotypes can be explained based on current knowledge of central carbon metabolism, and thereby possibly identifying missing elements of the current knowledge, we performed metabolic model-based predictions of growth rates and contrasted them with the experimental results. Several methods for growth rate prediction using genome-scale models have been proposed (Edwards and Palsson, 2000; Segre et al, 2002; Reed et al, 2003; Shlomi et al, 2005), but considering that one of our main objectives was to discover unknown reactions, we mainly used flux balance analysis (FBA), which attempts to use every non-constrained reaction in the model to obtain the best objective results. Additionally, prediction by Minimization of Metabolic Adjustment (MoMA) was also performed. This method was developed for more accurate predictions of growth phenotypes in non-evolved, knockout bacterial strains where the assumption of growth optimality used for FBA may not hold (Segre et al, 2002). The results of MoMA analysis were mentioned when differing from those made by FBA. Both predictions were made using the COBRA toolbox (Becker et al, 2007) to obtain the maximum rate of biomass production (μ) as the objective function. We used E. coli genome-scale metabolic reconstruction, iAF1260 (Feist et al, 2007), considering gene expression levels observed for aerobic growth on glucose as a base model, and incorporated several changes. In the initial model iAF1260, 16 of the 31 genes for the first deletions were annotated with reactions harboring two or three isozymes (Table I). While deleting a gene associated with an isozyme from the in silico model does not affect the prediction, deleting the same gene in vivo could result in graded effects anywhere from no effective loss of the reaction to complete loss. Since all of the targeted reactions are well studied and the accepted major or minor roles of most isozymes are known (Sprenger, 1995; Romeo and Snoep, 2005; Keseler et al, 2009), we removed all minor isozymes from the model and included only the genes encoding the major isozymes. In this modified model, deleting the gene for any major isozyme (pfkA, fbp, gpmA, pykF, rpe, rpiA, tktA, or talB) resulted in complete loss of the corresponding reaction, whereas deleting the gene for a minor isozyme, which had already been removed, had no effect on the growth phenotype; thus, we could examine both extreme results. Table 1. Genes encoding enzyme having two or more isozyme, and used for the deletion study Reactiona Majorb Unknownc Minord PFK pfkA pfkB FBP fbp (glpx)e FBA (fbaA)e fbaB PGM gpmA gpmI gpmB PYK pykF pykA RPI rpiA rpiB RPE rpe (sgcE)e TKTf tktA tktB TALA talB talA a Name of the reaction in the iAF1260 model. b Gene encoding the major isozyme of the reaction. c Gene encoding the isozyme of uncharacterized contribution. d Gene encoding the minor or lesser role of the reaction. e Not selected for deletion. f TKT1 and TKT2. Since we used several different carbon sources, the necessary changes reflecting the expression of source-specific gene induction and flux direction constraints were considered. Comparison of the growth phenotype and the predicted growth of single-gene knockout strains was used to further tune the model by restricting the use of several reactions (see section Materials and methods). Finally, several conditions (varying maximum oxygen consumption limits, and the presence or absence of anaerobic gene expression) were used for the growth rate prediction. To test consistency with the experimental data, correlation coefficients between the cell density at 24 h and the predicted growth (μ) were compared for each medium (Supplementary Figure 2A). For most carbon sources, predictions assuming a nearly unlimited supply of oxygen without anaerobic gene expression showed the best correlation with the experimental results, and therefore we selected these conditions for further comparisons. The prediction results obtained under these conditions are shown in Supplementary Table II. To evaluate the predicted growth of a mutant strain against its parental strain, we defined RGIs score similarly to the RGIe score used to evaluate the experimental data. Growth predicted by FBA was used instead of the OD600 for RGIe score calculation, and the RGIs scores were calculated for 2177 cases among 2465 for which we calculated the RGIe score of the double-knockout strains. The results obtained in complete medium (205) were not used for the prediction. In addition, the RGIs scores of the double mutants were not calculated when one of the parental single deletions was predicted to show no growth (83). Within the 2177 cases, 221 of the 229 cases that showed synthetic slow-growth phenotype in experiment were included, excluding the eight cases in complete medium. Using RGI scores and by applying a cut-off of ±2 s.d., 66 cases of slow-growth phenotypes were predicted whereas no cases of synthetic fast-growth phenotypes were predicted
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