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Letter

Genome-scale analysis of Streptomyces coelicolor A3(2) metabolism

¹ Center for Microbial Biotechnology, BioCentrum-DTU, Technical University of Denmark, DK-2800 Lyngby, Denmark ² Department of Biochemical Engineering, University College London, WC1E7JE London, United Kingdom

	ABSTRACT

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Streptomyces are filamentous soil bacteria that produce more than half of the known microbial antibiotics. We present the first genome-scale metabolic model of a representative of this group—Streptomyces coelicolor A3(2). The metabolism reconstruction was based on annotated genes, physiological and biochemical information. The stoichiometric model includes 819 biochemical conversions and 152 transport reactions, accounting for a total of 971 reactions. Of the reactions in the network, 700 are unique, while the rest are iso-reactions. The network comprises 500 metabolites. A total of 711 open reading frames (ORFs) were included in the model, which corresponds to 13% of the ORFs with assigned function in the S. coelicolor A3(2) genome. In a comparative analysis with the Streptomyces avermitilis genome, we showed that the metabolic genes are highly conserved between these species and therefore the model is suitable for use with other Streptomycetes. Flux balance analysis was applied for studies of the reconstructed metabolic network and to assess its metabolic capabilities for growth and polyketides production. The model predictions of wild-type and mutants' growth on different carbon and nitrogen sources agreed with the experimental data in most cases. We estimated the impact of each reaction knockout on the growth of the in silico strain on 62 carbon sources and two nitrogen sources, thereby identifying the "core" of the essential reactions. We also illustrated how reconstruction of a metabolic network at the genome level can be used to fill gaps in genome annotation.

Streptomyces coelicolor A3(2) is by far the best genetically studied Streptomyces strain and has become a model organism for Streptomyces species (Hopwood 1999). Release of the S. coelicolor A3(2) genome sequence (Bentley et al. 2002) has further expanded the knowledge of this organism and enabled large-scale analyses of transcriptome (Huang et al. 2001; Bucca et al. 2003) and proteome (Hesketh et al. 2002; Novotna et al. 2003). The rapidly growing amount of genome-wide data represents a valuable database for gaining more fundamental insight into the molecular mechanisms governing different processes in S. coelicolor A3(2), but particularly integration of these data through the use of mathematical models would enable extraction of more information from this database. In this context it has recently been shown particularly valuable to use genome-scale models for the metabolism (Ideker et al. 2001; Stelling et al. 2002; Covert et al. 2004; Kell 2004).

At the present, metabolic networks have been reconstructed for several bacteria (e.g., Escherichia coli) (Edwards and Palsson 2000b; Reed et al. 2003) and for the yeast Saccharomyces cerevisiae (Förster et al. 2003a; Duarte et al. 2004). The models have been successfully used to predict organism phenotype for a modified genotype (Edwards and Palsson 2000a; Förster et al. 2003b; Covert et al. 2004), and to predict cellular behavior under different physiological conditions (Edwards et al. 2001; Famili et al. 2003). Furthermore, these models may form the basis for functional characterization (Papp et al. 2004), for extraction of information of coordinated transcriptional responses that cannot be identified through classical statistical methods (Ihmels et al. 2004), and in design of improved strains for production of specific metabolites (Burgard et al. 2003).

Here, the reconstructed metabolic network of S. coelicolor A3(2) is applied for detailed study of the organism's metabolism.

	Results and Discussion

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Reconstruction and characteristics of the metabolic network
Reconstruction of high-quality and well-annotated metabolic networks is laborious as information needs to be acquired from many different sources. The reconstruction can be facilitated by software, for example, PathoLogic (Karp et al. 2002), that assigns reactions to the annotated genes according to E.C. number or enzyme name. We used the S. coelicolor A3(2) pathway database in KEGG (ftp://ftp.genome.ad.jp/pub/kegg/pathways/sco/) for extraction of the draft model. Automatically created models need to be manually curated using books, literature, and other available information sources. The most problematic aspects are wrongly or insufficiently defined substrate specificity, reaction reversibility, protein complexes, cofactor specificity, and the missing enzymes. Examples of these problems and the ways of dealing with them are listed in Table 1.

View this table: [in this window] [in a new window]   Table 1. Typical problems arising from automatic metabolic networks reconstruction and examples of dealing with them

View this table: [in this window] [in a new window]   Table 2. Comparison of the genome characteristics and the in silico metabolic networks of prokaryotic and eukaryotic species

Connectivity
A frequency plot for all the metabolites in the metabolic networks of S. coelicolor A3(2), S. cerevisiae (Förster et al. 2003a), and E. coli (Edwards and Palsson 2000b) is shown in Figure 1. With the exception of proton, which is the most frequent metabolite in yeast because of proton-driven transport reactions, the most connected metabolites are involved in energy metabolism: ATP, ADP, and phosphate. The reducing equivalent NADH is more common for the metabolic network of S. coelicolor A3(2), which also has the highest proportion of oxidoreductases. The higher occurrence of glutamate and -ketoglutarate in S. coelicolor A3(2) reflects a higher frequency of the use of aminotransferases. Coenzyme A and acetyl-carrier protein occur more often in the S. coelicolor A3(2) model, which directly correlates with extensive propanoate, butanoate metabolism, and the production of polyketide actinorhodin.

Growth energetics
Besides differences in some reactions and the preferred cofactors for some reactions, the core part of microbial metabolism, that is, the central carbon metabolism (glycolysis, pentose phosphate pathway, and TCA cycle), biosynthesis of the biomass building blocks, is highly conserved. The biomass composition and the growth energetics are, however, unique properties of each organism. Here, the biomass composition was calculated based on the measurements performed in S. coelicolor A3(2) and related species (Supplemental Data Set 5). The growth energetics are described by Stouthamer equation 1:

(1)

which states that for a pseudo-steady state, the rate of ATP production r_ATP is equal to the ATP consumption for growth Y_xATP · µ and maintenance m_ATP, where µ is the specific growth rate and Y_xATP is the ATP yield coefficient (Stouthamer and Bettenhaussen 1973). The main source of ATP in aerobically growing organisms is oxidative phosphorylation. Streptomyces are known to use menaquinone as an electron transporter in the membrane (Pandya and King 1966; Yassin et al. 1988), even though surprisingly enough the genes for menaquinone synthesis were not found in the sequenced genomes of S. coelicolor A3(2) and S. avermitilis. The stoichiometry of the electron transport chain was assumed to be the same as recently described in the actinomycete Corynebacterium glutamicum (Bott and Niebisch 2003). The values of the ATP yield coefficient Y_xATP and the maintenance flux m_ATP were estimated from the chemostate cultivations data (Melzoch et al. 1997) as described in Methods (Figs. 2, 3).

Reactions activity
In order to investigate the topology of the metabolic network, we created a list of unique reactions, and in this way eliminated the redundancy in the network resulting from the large number of isoenzymes (Supplemental Data Set 7). This model was used for growth simulation in five different media: (1) glucose as carbon source (C-source) and nitrate as nitrogen source (N-source), (2) mixture of carbohydrates, (3) mixture of organic acids and alcohols, (4) mixture of amino acids, and (5) mixture of nucleotides (Supplemental Data Set 8). Except for the first scenario, unlimited ammonia uptake was allowed. Additionally, we simulated secondary metabolites production at slow growth rate on glucose and ammonia. The analysis of the fluxes that computationally provided optimal growth rate or optimal antibiotics production rate at the given conditions (also considering alternative optimal solutions) showed that 88% of reactions were active under some or all of the tested conditions, 6% of reactions were inactive because of the formation of dead ends (that means that one of the reaction's products or reactants cannot be used or produced in the network) (Fig. 4). In this way there was only a very small part (6%) of the non-dead-end reactions that never got involved in the cellular metabolism, perhaps because the conditions that evoke these reactions' activity have not been considered in the simulations.

View larger version (22K): [in this window] [in a new window]   Figure 1. Frequency of the most connected metabolites in the reconstructed metabolic networks of S. coelicolor A3(2), S. cerevisiae (Förster et al. 2003a), and E. coli (Edwards and Palsson 2000b). Frequency is calculated as the number of times a certain metabolite appears in the metabolic network divided by the sum of all metabolite occurrences. S. coelicolor A3(2) (), S. cerevisiae (), E. coli (). (H) External proton; (GLU) glutamate; (PPI) pyrophosphate; (COA) coenzyme A; (AKG) -ketoglutarate; (PYR) pyruvate; (ACCOA) acetyl-coenzyme A; (ACP) acetyl-carrier protein.

View larger version (15K): [in this window] [in a new window]   Figure 2. The ATP yield coefficient YxATP () and maintenance energy mATP () as a function of the maximal P/O ratio in S. coelicolor A3(2).

(2)

that is, the reaction essentiality is 1 for an essential reaction, whereas it is 0 for a reaction that upon removal has no growth-retarding effect. The reaction essentialities for the growth on glucose and ammonia are shown in Figure 5A, and Figure 5B illustrates the summed reaction essentialities for growth on the 63 different media (Supplemental Data Set 9). Reactions with a summed reaction essentiality of 63 are required for growth under all the defined conditions, and hence are true essential reactions. The reactions that have a summed reaction essentiality lower than 63 are either necessary only under certain conditions or their deletion leads to growth retardation. The comparison of Figure 5A and Figure 5B shows that the choice of conditions is important for defining the dispensability of reactions. While during growth on glucose and ammonia 64% of the reactions could be eliminated without any consequences for cellular growth, several of these reactions turned out to be essential for growth on other C-sources than glucose, and the number of nonessential deletions was reduced from 64% to 39% when all the growth conditions were considered. The minimal metabolic net, necessary for growth under all the conditions, consists of 146 reactions. Considering that 17% of them have an isoenzyme, there are basically 121 metabolic genes that are truly essential (e.g., essential on the complete medium), which corresponds to 12% of the original metabolic network.

Essential and nonessential reactions were found to have almost equal occurrence of isoenzymes (17% and 15%, respectively), whereas a higher fraction of the growth-retarding or conditionally essential reactions were catalyzed by isoenzymes (26%). This undermines a hypothesis that isoenzymes exist to increase the robustness of the metabolic network to mutations.

View larger version (18K): [in this window] [in a new window]   Figure 3. Simulation of experimental chemostates data (Melzoch et al. 1997). Specific glucose uptake rate (experimental , model ----), specific carbon dioxide production rate (experimental , model ), specific oxygen uptake rate (experimental , model ---), and actinorhodin production rate (experimental , model — - — - — -).

View this table: [in this window] [in a new window]   Table 3. Comparison of experimentally determined and in silico predicted growth phenotypes

View larger version (45K): [in this window] [in a new window]   Figure 4. Activity of the reactions in the model of S. coelicolor A3(2). All the unique reactions in the model are divided into several categories based on their predicted activity under different growth conditions. The proportion of reactions for which the corresponding enzymes were detected on a 2D gel is shown by the dashed areas.

The described inconsistencies can be resolved in the future by expanding the model to include regulatory constraints.

Biomass yield
The biomass yields of in silico S. coelicolor A3(2) on various C-sources were similar to that of in silico yeast (Fig. 6A; Förster et al. 2003a). The energy requirements were considered for the simulations. The exception was growth on C-2 compounds like acetate and ethanol, where S. coelicolor A3(2) was more efficient at using these substrates. This is due to the action of the acetate kinase (encoded by ackA) and the phosphate acetyltransferase (encoded by pta), which catalyze the conversion of acetate into acetyl-phosphate and further into acetyl-CoA at the expense of only one high-energy phosphate bond hydrolysis in ATP. In eukaryotes these enzymes are not present and acetate is activated by acetyl-CoA synthase (encoded by acs) with the cocurrent hydrolysis of ATP to AMP, which is energetically more costly. Comparison of the growth capabilities of in silico S. coelicolor and in silico E. coli (Edwards and Palsson 2000b) showed that the S. coelicolor had higher biomass yields, which was primarily because of higher maintenance energy requirements in E. coli (Fig. 6B).

Anaerobic growth
The inability of Streptomyces to grow anaerobically has been a puzzle for quite some time (Hodgson 2000). Generally the microbes are capable of growing anaerobically, as they may obtain their Gibbs free energy by substrate-level phosphorylation resulting in fermentative metabolism. The presence of lactate dehydrogenase in the sequenced genome of S. coelicolor A3(2) indicates that the organism should be capable of having a fully operational fermentative metabolism with lactate as a key fermentation product. Indeed, excretion of lactate has been observed for Streptomyces griseus grown under microaerophilic conditions (Hockenhull et al. 1954). Two reasons were suggested to explain the anaerobic Streptomyces paradox: either the organisms are sensitive to the fermentation products or/and there are some essential reactions that require oxygen.

View larger version (24K): [in this window] [in a new window]   Figure 5. Essentiality of the reactions in the model of S. coelicolor A3(2) during growth on glucose (A) and during growth under 63 various conditions (B). All the reactions were sorted into three categories: essential reactions, conditionally essential reactions, and nonessential reactions depending on their influence on biomass synthesis. The percentage of reactions with isoenzymes among all the reactions in the given category is shown.

Actinorhodin production
Several recent publications are dedicated to expressing bacterial and fungal polyketide synthase (PKS) genes in heterologous hosts. (Kealey et al. 1998; Kennedy et al. 2003; Kinoshita et al. 2003). As an example, we addressed the issue of metabolic capabilities of yeast and S. coelicolor A3(2) to produce reducing cofactors and the most common polyketides precursors (Fig. 7). The capabilities of the metabolic networks of yeast and S. coelicolor A3(2) to produce acetyl-CoA, malonyl-CoA, and reducing equivalents NADH and NADPH turned out to be very similar. However, Streptomyces have a clear advantage in the form of more extensive polyols metabolism, which allows them to make such typical building blocks for polyketides production as methylmalonyl-CoA, propionyl-CoA, and butanoyl-CoA. Development of heterologous production of certain polyketides from E. coli or yeast is tedious as it requires not only addition of PKS genes, but also the genes necessary for biosynthesis of precursors.

Filling the gaps in genome annotation
We assumed that the following processes had to be included in order to produce a "viable" model cell:

• transport reactions (nutrients and oxygen uptake, metabolites excretion);
  
• biosynthesis of the 12 biomass precursors;
  
• biosynthesis of each biomass component (amino acids, vitamins, cofactors, etc.);
  
• degradation of the experimentally utilizable substrates.
  

If any of these conditions were not fulfilled, a missing reaction(s) was added according to the pathway structure (organism specific or general if the former was not available). A total of 205 reactions without assigned ORFs were added to the model: 79 enzymatic reactions, 117 transport reactions, five spontaneous, and four artificial reactions like maintenance and biomass assembly (Supplemental Data Set 10). Out of the 79 enzymatic reactions, 27 were identified as being essential for growth on glucose and salts. In the context of functional genomics, the reconstructed metabolic network hence serves as physiological evidence for the presence of these genes and allows directed search for the ORFs with the necessary function. Each of these 27 reactions was subsequently analyzed on an individual basis. The genome was searched for genes with specific protein motifs (http://www.sanger.ac.uk/Software/Pfam/), and the obtained hits were evaluated using the functions of the neighboring genes and their possible organization in an operon. The previously described Bayesian method (Green and Karp 2004) has essentially the same logics, but it does not account for the protein motifs and uses homology search instead; besides, it requires model implementation in Pathway Tools (Karp et al. 2002).

Phospholipids biosynthesis
One of the enzymes missing in the phospholipids biosynthesis is cardiolipin synthase. No homologs of bacterial cardiopin synthase, which catalyzes the condensation of two phosphatidylglycerols into cardiolipin and glycerol, were found in the genome of S. coelicolor A3(2). Presumably the cardiolipin synthesis occurs in the same way as in eukaryotes in the reaction of phosphatidylglycerol with CDP-glycerol accompanied by formation of CMP. In this case, the enzyme should possess a CDP-alcohol phosphatidyltransferase domain. With a Pfam motif search eight ORFs were found to contain such a domain in the S. coelicolor A3(2) genome. Three of these ORFs (SCO1527, SCO1389, and SCO5753) were synteny conserved with Mycobacterium tuberculosis enzymes coding for phosphatidylinositol synthase (pgsA), cardiolipin synthase (pgsA2), and phosphatidylglycerol synthase (pgsA3), respectively (Jackson et al. 2000). Once a link has been established between gene and enzymatic function, it is possible to transfer the knowledge between different species by homology and synteny information, and we hereby predict that SCO5753 encodes phosphatididylglycerol synthase, SCO1527—a phosphatidylinositol synthase and SCO1389—a cardiolipin synthase.

View larger version (27K): [in this window] [in a new window]   Figure 6. Maximal simulated biomass yield of S. coelicolor A3(2) as compared to (A) S. cerevisiae (Förster et al. 2003a) and (B) E. coli (Edwards and Palsson 2000b) on different carbon sources (gram/gram substrate). All the calculations were made for substrate uptake rate of 6 C-mmol/g DW/h. The maintenance energy requirement was considered.

	Conclusions

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We have reconstructed the metabolic network of S. coelicolor A3(2). Besides resulting in improved annotation of several genes and suggestions for annotation of other genes, the reconstructed network may be used as a model of the metabolism in Streptomyces. Earlier attempts to model Streptomyces spp. (Avignone et al. 2002; Bruheim et al. 2002; Roubos 2002) show that there is a demand for a high-quality metabolic model of Streptomyces both in academic and industrial research. The model presented in this paper has been reconstructed based on the up-to-date knowledge about Streptomyces and can readily (or with small adjustments for species specificity) be used for simulations. We therefore expect that the model will be a useful tool for genome-wide analysis of the transcriptome and the proteome as well as for designing strategies for improvement of antibiotic yields in Streptomyces.

View larger version (19K): [in this window] [in a new window]   Figure 7. Predicted polyketides production capabilities of S. coelicolor A3(2) and S. cerevisiae (Förster et al. 2003a) (in moles per mole glucose). All the calculations were made for the glucose uptake rate of 1 mmol/g DW/h.

	Methods

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Modeling
Flux balance analysis (FBA) was used for quantification of metabolic fluxes (Varma and Palsson 1994; Schilling et al. 1999). The reactions set composed a stoichiometric matrix S with dimensions m x n, where m was the number of metabolites and n was the number of reactions. Assuming pseudo-steady state over intracellular metabolite concentrations, the metabolic model could be defined by the following matrix equation:

(3)

where represented all the metabolic fluxes.

As the number of metabolic fluxes exceeded the number of mass balance constraints, there existed a set of feasible metabolic fluxes distributions. A solution was found using linear programming by introducing an optimization problem: MAXIMIZE Z = c · , where c was a row vector showing the influence of individual fluxes on the objective function. Additional constraints were imposed on fluxes: a_{i i} b_i, indicating the reaction irreversibility (0 _i < ) or measured flux. The transport fluxes for phosphate, sulfate, ammonia, and oxygen were not restrained (- _i < ). The uptake rates of metabolites that were not available in the medium were set to zero (_i = 0). The excretion of the major metabolic products (carbon dioxide, pyruvate, acetate, etc.) was allowed (0 _i < ).

For simulations of growth, the flux to biomass was set to a certain value and the substrate uptake rate was minimized. For simulation of antibiotic production, the substrate uptake rate was constrained and the flux toward the antibiotic was maximized.

In order to find fluxes that were active during alternative optimal solutions, the following algorithm was used:

1. Substrate uptake rates were constrained and the biomass growth rate was maximized.
   
2. The growth rate was fixed to the found maximal value, and thus an additional constraint was introduced.
   
3. By changing vector c, each reaction in the network was maximized and then minimized. If the flux through the reaction was different from zero in at least one of these optimizations, the reaction was considered to be active in alternative optimal solutions.
   

The calculations were performed using the commercially available linear programming package LINDO (Lindo Systems Inc.). The algorithm for finding fluxes that are active during alternative optimal solutions was implemented in Matlab (The MathWorks Inc.), but the LINDO package was used as the solver for the linear programming problems.

Estimation of energetic parameters
Knowing one of the energetic parameters (P/O ratio, Y_xATP or m_ATP) allows calculation of the other two parameters from continuous cultivation data. Because none of the parameters was known, they were estimated for P/O ratios in the range of 0.5 to 2 (Fig. 2).

The Y_xATP is composed of three parts:

• ATP costs for the synthesis of biomass building blocks (amino acids, nucleotides, etc.), which were accounted for through reactions stoichiometry;
  
• ATP costs for polymerization of monomers into biological polymers (proteins, DNA, etc.), which were assumed to be the same as for E. coli (Ingraham et al. 1983) and were included in the reactions of macromolecules biosynthesis; and
  
• ATP costs for growth-associated maintenance Y_{xATP_growth_maintenance}, added to the growth equation.
  

The last composite Y_{xATP_growth_maintenance} as well as maintenance ATP (m_ATP) were not known and were estimated from the experimental chemostate data (Melzoch et al. 1997).

Normally the glucose uptake rate q_gluc, carbon dioxide production rate q_CO2 and oxygen uptake rate q_O2 are linearly dependent on the specific growth rate (Fig. 3). The Y_{xATP_growth_maintenance} and m_ATP were set up to arbitrary values, and the simulations were run for each of the experimentally investigated dilution rates by fixing the specific growth rate and actinorhodin production rate to the experimental values and performing linear optimization for glucose uptake rate minimization (Supplemental Data Set 6). The obtained q_gluc, q_CO2 and q_O2 dependence on dilution rate was compared to the experimental rate. The Y_{xATP_growth_maintenance} and m_ATP were changed until a good prediction was obtained. For the further simulations, the maximal P/O ratio was fixed to 1.5 and Y_xATP and m_ATP were set to the corresponding values.

	Acknowledgements

 
We thank D.A. Hodgson for providing his Ph.D. thesis and for the excellent review on the primary metabolism of Streptomyces. We are grateful to Jochen Förster for sharing his experience in genome-scale modeling.

	Footnotes

 
³ Corresponding author.
E-mail jn{at}biocentrum.dtu.dk; fax 45 4588 4148.

[Supplemental material is available online at www.genome.org.]

Article and publication are at http://www.genome.org/cgi/doi/10.1101/gr.3364705.

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	WEB SITE REFERENCES

Top ABSTRACT Results and Discussion Conclusions Methods REFERENCES WEB SITE REFERENCES

 

ftp://ftp.genome.ad.jp/pub/kegg/pathways/sco/; Streptomyces coelicolor KEGG pathway database (on ftp server).

http://dbkweb.ch.umist.ac.uk/StreptoBASE/s_coeli/referencegel/; Streptomyces coelicolor 2D gel protein database.

http://www.expasy.org/cgi-bin/search-biochem-index; ExPASy Biochemical Pathways database.

http://www.expasy.org/enzyme; ExPASy Enzymes nomenclature database.

http://www.expasy.org/sprot/sprot-top.html; SWISS-PROT protein knowledgebase.

http://www.genome.ad.jp/dbget-bin/get_htext?S.coelicolor.kegg+-f+T+w+C; Streptomyces coelicolor KEGG Pathways database.

http://www.genome.ad.jp/kegg/ligand.html; KEGG Ligand database.

http://www.sanger.ac.uk/Projects/S_coelicolor/scheme.shtml; The Wellcome Trust Sanger Institute database of Streptomyces coelicolor.

http://www.sanger.ac.uk/Software/Pfam/; Protein families database of alignments and HMMs.

Received October 15, 2004; accepted in revised format March 1, 2005.

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