Pseudomonas putida is the best studied pollutant degradative bacteria and is harnessed by industrial biotechnology to synthesize fine chemicals. Since the publication of P. putida KT2440's genome, some in silico analyses of its metabolic and biotechnology capacities have been published. However, global understanding of the capabilities of P. putida KT2440 requires the construction of a metabolic model that enables the integration of classical experimental data along with genomic and high-throughput data. The constraint-based reconstruction and analysis (COBRA) approach has been successfully used to build and analyze in silico genome-scale metabolic reconstructions.P. putida KT2440's metabolism, iJN746, which was constructed based on genomic, biochemical, and physiological information. This manually-curated reconstruction accounts for 746 genes, 950 reactions, and 911 metabolites. iJN746 captures biotechnologically relevant pathways, including polyhydroxyalkanoate synthesis and catabolic pathways of aromatic compounds , not described in other metabolic reconstructions or biochemical databases. The predictive potential of iJN746 was validated using experimental data including growth performance and gene deletion studies. Furthermore, in silico growth on toluene was found to be oxygen-limited, suggesting the existence of oxygen-efficient pathways not yet annotated in P. putida's genome. Moreover, we evaluated the production efficiency of polyhydroxyalkanoates from various carbon sources and found fatty acids as the most prominent candidates, as expected.We present a genome-scale reconstruction of P. putida, a biotechnologically interesting all-surrounder. Taken together, this work illustrates the utility of iJN746 as i) a knowledge-base, ii) a discovery tool, and iii) an engineering platform to explore P. putida's potential in bioremediation and bioplastic production.Here we presented the first genome-scale reconstruction of  Pseudomonas putida is a non-pathogenic member of rRNA group I of the genus Pseudomonas that colonizes many different environments and is well known for its broad metabolic versatility and genetic plasticity [P. putida KT2440 is a TOL plasmid cured, spontaneous restriction deficient derivative of P. putida mt-2 [asticity ,2. P. puida mt-2 ,4. This ida mt-2 -8. Conseida mt-2 ,10; ii) ida mt-2 ; iii) biida mt-2 ,12-14; iida mt-2 -17; and ida mt-2 ,19.P. putida KT2440's genome [in silico analyses of its metabolic and biotechnological capacities have been published [P. putida KT2440 requires the construction of a more comprehensive model enabling the integration of the canonical experimental data along with genomic and high-throughput data in a hierarchical and coherent fashion [Since the publication of s genome , our knos genome  and varis genome ,23, prots genome ,26. Subsublished ,28. Howe fashion .in silico. Many of the published mathematical tools have been reviewed [in silico reconstructions for representatives of archaea  approach is one possible modeling approach that uses stoichiometric information about biochemical transformation taking place in a target organism to construct the model. While a metabolic reconstruction is unique to the target organism one can derive many different condition-specific models from a single reconstruction. This conversion of a metabolic reconstruction of an organism into models requires the imposition of physicochemical and environmental constraints to define systems boundaries -32. The reviewed  and encoreviewed . A largereviewed ,32 has b barkeri ), of bac[E. coli ; B. subtsubtilis ; H. pylo. pylori ; M. tuberculosis ,40; S. a. aureus ,42; L. l. lactis ), and of. lactis ). The nu. lactis ); ii) pr. lactis -48; iii). lactis ; and iv). lactis -53. A re methods .Pseudomonas putida KT2440. Based on the naming convention for metabolic networks [iJN746, where i stands for in silico, JN are the initials of the constructor, and 746 corresponds to the number of included metabolic genes. The reconstruction was built using the COBRA approach [in silico metabolic network was further evaluated by comparing i) predicted growth rate capacities in different carbon sources and ii) predicted essential genes with experimental data from P. putida KT2440 and P. aeruginosa. Finally, we show the utility of the P. putida reconstruction to analyze its biodegradative (i.e. toluene degradation) and biotechnological (i.e. bioplastic production) capacities.Here, we describe a highly detailed, genome-scale, metabolic reconstruction of networks , this geapproach ,32 and vapproach ). The inP. putida KT2440, iJN746, was constructed based on its annotated genome sequence [Pseudomonas-specific [The metabolic reconstruction of sequence , primarysequence , PSEUDOCsequence , and SYSsequence ), and bispecific  and biociJN746 accounts for 746 open reading frames (ORF), whose corresponding gene products are involved in 810 metabolic and transport reactions , which are lipid related polymers, from a wide range of carbon sources [The reactions included in e Figure . In gene. putida  and succproducts . Reactios genome . Moreoven source ,60. A thpacities . Finally sources ,61. This sources ,63) and P. putida metabolic network  some reactions had to be added to the network for which no genetic or experimental evidence could be found. Those reactions represent modeling hypotheses, which need further experimental validation and thus received a confidence score of 1. Upon completion, the reconstruction had an overall average confidence score of 2.83. In fact, two thirds of P. putida's metabolic pathways have been very well or well studied, while only a third of the subsystems were primarily based on the genome annotation  [P. putida compared to the other reconstructions. In summary, this comparison indicates that the overall context coverage in iJN746 is comparable with other high-quality network reconstructions when the amount of available literature is considered.The properties of iAF1260, ), B. sub (iYO844 ), M. tub (iNJ661 ), and P.. Despite being close relatives, these two representatives have significant differences in lifestyle and metabolic capabilities. Subsequently, the two metabolic reconstructions have significant differences, emphasizing the importance of organism-specific reconstructions. For instance, the P. aeruginosa reconstruction contains pathways necessary for growth and production of common virulence factors, including alginate, rhamnolipids, phenezines, and quorum-sensing molecules [P. putida's metabolic network. In contrast, P. aeruginosa's metabolic network does not account for pathways necessary to degrade aromatic compounds.A metabolic reconstruction for another representative of the recently . A compaed Table . In contda Table . As a coolecules , which ain silico growth performance with experimental data allows for the assessment of the predictive potential of the metabolic reconstruction and thus represents a valuable tool for network evaluation. Furthermore, in silico growth analysis may expand the known array of carbon-, nitrogen-, and energy sources of the reconstructed organism. In this study, the aerobic growth capabilities of iJN746 in iM9 medium substituted with different carbon sources were determined qualitatively .Flux balance analysis (FBA ) can proly Table  and quanly Table . The groum Table . Furtherzosphere , which izosphere ,67. This46 Table . Of partiJN746, as expected, as only exchange reactions were included in the reconstruction for metabolites, which have been reported to be taken up or secreted by P. putida KT2440. In contrast, some disagreements, such as false negatives, were observed despite a good overall agreement with the in vivo data [P. putida can use L-alanine as a carbon- and nitrogen-source [iJN746 cannot use this compound as a carbon or nitrogen source. This disagreement could not be resolved. In contrast, iJN746 was initially unable to use choline-O-sulphate, choline, or glycine betaine as carbon- and nitrogen-sources despite experimental evidence [iJN746 to use these metabolites as carbon- and nitrogen-sources through the glycine metabolism. In addition, choline-O-sulphate could also be used as sulfur source [see Additional file in silico predictions and physiological properties can be used to drive new discoveries, as was shown for E. coli [No false positive carbon, nitrogen, or sulfur sources were found in ivo data  [Additioivo data . For exan-source  but iJN7evidence . However E. coli .P. putida KT2440, like other Pseudomonas species and rhizosymbionts, has an incomplete glycolytic pathway because of a missing 6-phosphofructokinase [P. putida KT2440 has a complete Entner-Doudoroff pathway, which allows for the utilization of glucose and other sugars as carbon sources . Interestingly, iJN746 grew faster in glucose than experimental in vivo data suggested for P. putida KT2442   Table . In the E. coli corresponding to the normal oxygen diffusion rate under atmospheric oxygen conditions [in silico growth rate.Linear Programming (LP) problems have two parameters, shadow price and reduced cost, which can be used to characterize the optimal solution. While shadow prices are associated with each network metabolite, reduced costs are associated with each network reaction. The reduced cost signifies the amount by which the objective function (e.g. growth rate) would increase when the flux rate through a chosen reaction was increased by a single unit . Analysenditions . Mathema-1) [We performed a phase plane analysis to further elucidate the correlation between toluene uptake, OUR, and biomass production rate Figure . We anal-1)  was achiP. putida KT2440 under toluene-dependent growth conditions, we carried out in vivo experiments to determine the OUR of P. putida KT2440 harboring the TOL plasmid (see Methods). As expected, the OUR in toluene growing cells was higher than glucose or octanoate growing cells; 20.93 compared to 15.34 and 14.88 mmol oxygen/gDW/h, respectively . In fact, oxygen dependent growth of toluene grown cells has been described for other P. putida strains. For example, Alagappan and Cowan reported a 10× higher oxygen-half saturation of P. putida F1 grown on toluene than other aerobic organisms [P. putida when grown on octanoate [In order to better understand this situation and since no detailed information about OUR was found for rganisms . Furtherrganisms ,80; howerganisms , indicatctanoate .P. putida metabolic network is incomplete. In fact, the current information and results suggest that the network is missing one or more reactions enabling a more oxygen-efficient catabolism of toluene and other highly reduced carbon sources (e.g. other aromatic compounds or fatty acids). This analysis represents a nice example of the broad range of applications for which iJN746 can be used to evaluate the consistency of experimental data and in silico prediction. iJN746 can serve as a platform to derive hypotheses about metabolic capabilities or missing functions in the network which can be ultimately tested in the laboratory. Hence, the metabolic reconstruction can help to increase our understanding and knowledge about this biotechnologically important organism.Taken together, our analysis suggests that the current iJN746 was used as a framework to analyze candidate essential genes in P. putida KT2440 in LB rich medium. Therefore, the network reaction(s) associated with each gene was individually "deleted" by setting the flux to 0 and optimizing for the biomass function [in silico essentiality predictions with experimental data to assess the predictive potential of the model. However, no large-scale, experimental gene essentiality data are available for P. putida; the information can only be found for its phylogenetic relative P. aeruginosa PAO1 and P. aeruginosa PA14 [P. putida and P. aeruginosa PAO1 genomes identified 3,143 potential orthologous pairs corresponding to 60% of P. putida's total ORFs, as well as large sections of conserved gene order (synteny) [in silico single gene deletion results with the 335 essential metabolic and non-metabolic genes of P. aeruginosa [iJN746 were predicted to be essential in iLB medium [see Additional file iJN746 agreed with essential genes of P. aeruginosa [see Additional file iJN746 [see Additional file E. coli [function . We wishosa PA14 ,83. A resynteny) . Thereforuginosa ,83. Abou E. coli .fabB gene was predicted to be only essential in iJN746; however, after carrying out a detailed search on Pseudomona's genomes using "The Pseudomonas Genome Database V2"  we found putative ORFs in the KT2440 and PA01 genome. These ORFs were annotated as alternative loci that could substitute a fabB deletion. Both, P. putida and P. aeruginosa have one copy of the fabB gene encoding for the 3-oxoacyl-(acyl-carrier-protein)synthase I . In addition, both strains have a copy of the fabF gene encoding for the 3-oxoacyl-(acyl-carrier-protein) synthase II (PP_1916 (40.92% identity with fabB-KT gene) and PA2965 (42.34% identity with fabB-PAO1 gene). Moreover, in the P. putida and P. aeruginosa genome, some ORFs were annotated putatively to encode for a 3-oxoacyl-(acyl-carrier-protein) synthase II (PP_3303 (35.94% identity) and PP_2780 (27.32% identity) in KT2440, and PA_1373 (36.17% identity) in PAO1 strain. These putative ORFs were not included in iJN746 due to the lack of supporting evidence for their metabolic function, but this analysis showed that i) PAO1 has an isozyme present in its genome, and ii) KT2440 is very likely to have at least one other ORF encoding this or a similar function. In a similar way, the discrepancy between in silico essentiality prediction and in vitro observation for msbA gene could be explained. The gene product of msbA encodes for a transporter of phosphatidylethanolamine, which is known to have a genetic redundancy in Pseudomonas sp. taking into account the Pseudomonas annotation present in "The Pseudomonas Genome Database V2". However, the supporting evidence for alternative ORFs was not strong enough to be included into iJN746.The disagreement between the experimental and computational results can reveal possible errors in the experimental data as well as in the reconstructed network. The disagreements might be caused by low experimental or sequence evidences, each of which would have hindered the inclusion of the information into the reconstruction. For example, the iJN746 but they were reported as essential genes in P. aeruginosa [glyA (PP_0322 or PP_0671), fold (PP_1945 or PP_2265), fabZ (PP_4174 or PP_1602), and pyrH (PP_1771 or 1593), have at least one isozyme in KT2440 which were also accounted for in iJN746. For many remaining incorrectly predicted non-essential genes, the in silico deletion had a significant effect on the growth rate, reflecting their important roles in iJN746 metabolism [see Additional file Finally, 37 genes were not predicted to be essential in ruginosa  [see Addruginosa  but are in vivo or in vitro procedure to enable the usage of the generated data for in silico predictions and comparison.In general, many of these discrepancies suggest that metabolites enabling growth in the knock-outs might be imported from the external rich media since the exact composition of LB medium is not known ,38. Thiset al. reported a detailed amino acid auxotroph study in P. aeruginosa PA01 using a minimal medium [iM9 medium and compared the results with this PA01 study. Here, we found an absolute agreement between in vivo and in silico gene essentiality for six amino acids, namely arginine, histidine, isoleucine, valine, leucine, and tryptophan  the basic msc-PHA production processes in KT2440 are well known [In the previous section, we used the metabolic reconstruction to assess the current knowledge of gn tools  in additgn tools . Here, wgn tools . The biogn tools . These pgn tools . The medll known ,61, ii) iJN746 accounts for msc-PHAs ranging from C6 to C14, including two unsaturated msc-PHAs and a mixed msc-PHA polymer consisting of C8 to C12 chains. We tested the msc-PHA production capability of iJN746 from the different carbon- and energy sources listed in Table -1). Many of these metabolites have been reported to yield in PHA production in Pseudomonas [see Additional file iJN746 as well  precursors includes i) L-branched-chain amino acids , ii) some aromatic compounds metabolized via β-ketoadipate pathway , and iii) other (phenylacetic acid or glycerol)  Figure . Intereslacetate .et al. used inhibitors of fatty acid metabolism and demonstrated that, depending on the nature of the substrate, precursors for PHA synthesis could be derived from either beta-oxidation or fatty acid biosynthesis, and interestingly, when hexanoate was used as carbon source for msc-PHA accumulation, both routes can operate simultaneously [8 and C10 monomers. The model, in contrast, is able to produce the full range of msc-PHAs from carbohydrates  for production of the different msc-PHA monomers or msc-PHA mixtures. Furthermore, computational tools such as OptKnock[E. coli [M. succiniciproducens [Taken together, this example illustrates how  OptKnock or OptSt OptKnock could he[E. coli  or succiroducens .P. putida, a biotechnologically interesting all-surrounder. iJN746 is a highly detailed reconstruction of the P. putida KT2440 metabolic network that captures the important biotechnological capabilities, such as biodegradation of aromatic compounds, of this paradigmatic bacterium. Moreover, iJN746 represents a comprehensive knowledge base summarizing and categorizing the information currently available for P. putida KT2440. This study evaluated the metabolic network content and showed some examples of how iJN746 could be used for biotechnological purposes. Taken together, our results underlined the value of iJN746 as a suitable tool to study of P. putida's metabolism and its biotechnical applications by the P. putida community.Here, we presented the first genome-scale reconstruction of P. putida KT2440 harboring the TOL plasmid was used for in vivo determination of oxygen consumption experiments. The bacterium was grown at 30°C in M9 minimal medium [P. putida KT2440 strain grown in each carbon source was diluted until the turbidity at 600 nm (OD600) was 0.05 in fresh M9 minimal medium with the appropriate carbon source, samples were then incubated until the culture reached a turbidity at 600 nm of 0.6 for glucose or octanoate growing cells and 0.45 in toluene growing cells. Aliquots of 2 ml were taken for OUR determination; the cells were harvested by centrifugation, washed twice and re-suspended in 1 ml of fresh medium containing the appropriate carbon source using the above concentrations. The OUR was measured by monitoring the substrate-dependent oxygen consumption rate at 30°C using an oxygen electrode  in 1-ml assay mixture. Cellular dry weight (CDW) was determined using previously published methods [g. The pellets were washed with 0.9% NaCl and then dried at 105°C for 24 h to a constant weight using pre-dried and weighed 2-ml Eppendorf cups.l medium  with oct methods , using aP. putida KT2440 was obtained from TIGR  and was used as the framework of the network reconstruction. P. putida-specific primary and review literature and books were used to retrieve information about every network reaction: i) substrate specificity, ii) coenzyme specificity, iii) reaction directionality, iv) enzyme and reaction localization, and v) gene-protein-reaction (GPR) association. Relevant references were associated with every network reaction [see Additional files +), water (H2O), and various co-factors ). No gene-associated reactions were included when no corresponding gene was annotated in P. putida's genome but physiological or experimental data supported the presence of the biochemical transformation being part of P. putida's metabolism. Finally the reversibility was determined from primary literature data for each particular enzyme/reaction, if available. This literature search resulted in a first manually-curated reconstruction specific to P. putida's metabolism based on genome annotation and available biochemical evidence. However, this list is normally incomplete and will contain network gaps that may need to be filled depending on supporting evidence. This step requires manual effort again by searching the scientific literature for supporting information. If no P. putida-specific experimental evidence could be found for a transport reaction or biochemical transformation of a metabolite, no reaction or transporter was added to the network. Finally, the network capabilities were evaluated and compared with experimental data as described in Reed et al. [The reconstruction process was done as described previously . BrieflyGR , 06/2/2007 andd et al. . DetaileSimPheny  software was used for the reconstruction and gap evaluation process.v =  to a vector of time derivatives of the concentration vector x =  as vi,min≤ vi ≤ vi,max for reaction i. In fact, for every irreversible network reaction i, the lower bound was defined as vi,min ≥ 0 and the upper bound was defined as vi,max ≥ 0.The reconstructed metabolic network is often represented in a tabular format, listing all network reactions and metabolites in a human-readable manner along with confidence scores and comments  reactions define the presence of media components as if one would add metabolites into an in silico flask.Exchange reactions, which supply the network with nutrients or remove secretion products from the medium, were defined for all known medium components  or different genetic background (e.g. enzyme-deficient mutant) allow the transition from metabolic network reconstruction to condition-specific model. Note that the metabolic network reconstruction is unique to the target organism (and defined by its genome) while it can give rise to many different models by applying condition-specific constraints. All flux rates, P. putida's biomass composition are available, the biomass composition from E. coli [iJN746's biomass function. However, data from P. putida were added,  -39. Sinc E. coli ,93 was uposition ), when ain silico M9 minimal medium (iM9) and in silico Luria-Bertani medium (iLB) [iM9 simulation, and according to the well described M9 minimal medium [2, Co2 +, Fe2 +, H+, H2O, Na2 +, Ni2 +, NH4, Pi and SO4 were allowed to enter and leave the network by setting the constraints on the corresponding exchange reactions (i) to vi,min≥ -106 mmol/gDW/h and to vi,max≤ 106 mmol/gDW/h. The uptake rate for each carbon source was constrained to vi,min≥ -10 mmol/gDW/h and vi,max≤ 0 mmol/gDW/h. The oxygen uptake rate (OUR) was limited to vi,min≥ -18.5 mmol/gDW/h  . For iM9l medium , the fololi data ), if not methods .iM9 minimal medium (as described above) and setting the bounds of toluene uptake between vi,min≥ -11.9 mmol/gDW/h  was carried out using SimPheny . The underlying algorithm was described elsewhere ,97. The ement by  and vi,mvi) and represents the amount by which the objective function (e.g. growth rate) could be increased when the flux rate through this reaction was increased by a single unit [iM9 medium with toluene as carbon source. The constraints were set as described above and linear programming was employed to solve the optimization problem (maximizing growth).Reduced cost is a parameter of linear programming (LP) problems which is associated with each network reaction  iLB rich medium for general gene essentially experiment and ii) glucose-iM9 minimal medium for auxotrophy experiments (See above). The glucose uptake rate was fixed to vi,min = vi,max = -6.3 mmol/gDW/h in the latter study. OUR was set to be vi,min≥ -18.5 mmol/gDW/h in both cases.In order to determine the effect of a single gene deletion, all the reactions associated with each gene in function . A lethaiM9 medium was determined by setting the growth rate to vgrowth,min = vgrowth,max 0.2 gDW/gDW/h. The lower bound of each carbon uptake reaction was set to vi,min≥ -10 mmol/gDW/h and the upper bound was set to be vi,max≤ 0 mmol/gDW/h. The lower bound of the oxygen uptake rate was set to vi,min≥ -20 mmol/gDW/h for all simulations. In iJN746, six types of msc-PHAs are defined as well as msc-PHA compounds consisting of four different carbon chains [see Figure The msc-PHA production from each possible carbon source Table  in iM9 mAll computational simulations were performed using Matlab  if not stated otherwise. TomLab  was used as linear programming solver. Optimization formulations and the gene deletion studies employed the Matlab-based COBRA toolbox .Pseudomonas putida KT2440. JN and IT performed the analyses. JN, IT, BOP designed the study and wrote manuscript. All authors read and approved the final manuscript.All authors conceived the study. JN carried out the reconstruction of Table S1. Carbon, nitrogen, and sulfur sources, which enabled growth of iJN746.Click here for fileTable S2. Essentials genes predicted correctly in iJN746 compared with experimental data of P. aeruginosa.Click here for fileFigure S1. Schematic representation of in silico gene essentiality in iJN746 (iLB medium) compared experimental data of gene essentiality in P. aeruginosa [ruginosa .Click here for fileTable S3. False-positive essential genes in iJN746 when compared with P. aeruginosa's experimental data [tal data .Click here for fileTable S4. False-negative essential genes in iJN746. Genes that were not predicted to be essential in iJN746 but were reported as essential genes in P. aeruginosa [ruginosa .Click here for fileTable S5. Predicted essential genes in Glucose-iM9 minimal medium. Not shown are genes that were also predicted to be essential in iLB rich medium.Click here for fileTable S6. PHA polymer composition found in different Pseudomonas strains sorted by carbon sources.Click here for fileTable S7. List of metabolites in iJN746. The file contains a detail list of metabolites present in the metabolic reconstruction. The molecular formulae, the charge as well as the KeggID are shown.Click here for fileTable S8. List of the reactions contain in iJN746. The file details the reactions account in the metabolic reconstruction. The official name, the equation of the reaction, the subsystem, the EC number and de GPR association is shown.Click here for fileTable S9. List of biomass components in iJN746. This file contains the complete list of compounds which are part of Pseudomonas putida biomass.Click here for file


An investigation of metabolic networks in E. coli and S. cerevisiae reveals that asymmetric protein interactions affect gene expression, the relative effect of gene-knockouts and genome evolution. The relationships between proteins are often asymmetric: one protein (A) depends for its function on another protein (B), but the second protein does not depend on the first. In metabolic networks there are multiple pathways that converge into one central pathway. The enzymes in the converging pathways depend on the enzymes in the central pathway, but the enzymes in the latter do not depend on any specific enzyme in the converging pathways. Asymmetric relations are analogous to the “if->then” logical relation where A implies B, but B does not imply A (A->B).Escherichia coli and Saccharomyces cerevisiae are asymmetric. We show furthermore that these asymmetric relationships are reflected in the expression of the genes encoding those enzymes, the effect of gene knockouts and the evolution of genomes. From the asymmetric relative dependency, one would expect that the gene that is relatively independent (B) can occur without the other dependent gene (A), but not the reverse. Indeed, when only one gene of an A->B pair is expressed, is essential, is present in a genome after an evolutionary gain or loss, it tends to be the independent gene (B). This bias is strongest for genes encoding proteins whose asymmetric relationship is evolutionarily conserved.We show that the majority of relationships between enzymes in metabolic flux models of metabolism in The asymmetric relations between proteins that arise from the system properties of metabolic networks affect gene expression, the relative effect of gene knockouts and genome evolution in a predictable manner. The relationship between two proteins can be symmetric - for example, when they mutually depend upon each other for their function within a protein complex. Proteins can also be asymmetrically related. This occurs when the function of one protein (A) depends on another protein (B), but the function of protein B does not depend on A: A→B. For example, in regulatory interactions, the function of the regulator depends on the presence of its target, but the target can often function without the regulator. Examples of asymmetrical relationships also exist in metabolism. For instance, multiple enzymes may produce the same substance Figure , creatinymmetry) , and arehia coli  and Saccrevisiae  with genrevisiae , gene esrevisiae ,7, growtrevisiae , and phyrevisiae .Saccharomyces cerevisiae [Escherichia coli [E. coli and S. cerevisiae with four main types of genome scale data in which genes can be 'present' or 'absent'.Most coupled reaction pairs have an asymmetric dependency : 82% in revisiae  and 67% hia coli  . The result for the condition-specific growth defects of non-essential A→B pairs is less pronounced, but still for 64% of the conditions, the loss of B causes a greater growth defect than the loss of gene A .We first assessed the asymmetry in the lethality ,7 and coS. cerevisiae, n = 573; E. coli, n = 1,166; p < 10-6). In conclusion, these analyses show that asymmetric relations between metabolic enzymes are reflected in system properties of the specific organisms.We also find a consistency of the asymmetric relations with gene expression patterns. Because gene A depends for its function on gene B, there should be few conditions where A is expressed without B, relative to situations where B is expressed without A. As expected, the B gene is expressed in 61% of the conditions where only one of two asymmetrically related genes is expressed . Besides asymmetry in the occurrence of genes in present day species, we also expect asymmetry in the gains and losses across evolutionary history. We inferred the occurrence of A and B in their ancestors by maximum parsimony [E. coli, n = 1,215; S. cerevisiae, n = 1,423; p < 10-7). Gene B is more often gained (60%) in cases where an absence of both A and B in the ancestor was followed by a gain of either A or B . It is also expected that a gain of A depends on the presence of B  than when B is absent . Finally, there are also situations where a presence of only one gene in the ancestor is maintained along the evolutionary lineage . As expected, maintenance of A absent and B present was found more frequently than the reverse .Next, we asked whether the asymmetric relations between enzymes are also reflected in evolution. Generally, functionally interacting proteins tend to co-occur across genomes ,11. This species . Indeed,arsimony . In linevolution ). IndeedE. coli or S. cerevisiae, exceptions remain where gene A is present while gene B is not. How can this be explained? For phylogenetic presence/absence patterns, one explanation for these irregularities is species-specific differences in metabolism. For example, the large scale replacement of amino-acid biosynthetic pathways by amino acid importers in Thermofilum pendens [S. cerevisiae, depends on the tryptophan biosynthesis pathway, while in Homo sapiens tryptophane is part of the diet and tryptophan biosynthesis has been lost but ubiquinone synthesis has been conserved. In most cases of unexpected loss, however, B has been replaced by a non-orthologous functional equivalent. Thus, the metabolic dependency of reaction A on B as identified in our reference metabolism may have remained intact, but the protein catalyzing B has changed. We also found cases of multiple functional specificities in orthologous group A, corresponding to a different substrate specificity of A in the species where B was lost, relative to the reference species E. coli or S. cerevisiae .Although the various genomic and phylogenetic properties correlate significantly with the asymmetric relationships in the metabolic networks of  pendens  has led E. coli and S. cerevisiae  network distances (d ≥ 4), with a fraction ranging from 56% to 99% . This shows that the asymmetric dependencies are not simply an effect of local network topology.We show here that the relationships between proteins that arise from their functional dependencies can have an important influence on other elements of the biological system. The analysis of relationships between genes has so far focused on symmetric relations, including correlated and anticorrelated phylogenetic distributions of genes, and on higher order logic ,11,15,16E. coli K12 (iJR904 GSM/GPR) [S. cerevisiae iLL672 [min = lowest possible vA/vB ratio and Rmax = highest possible vA/vB) to determine the dependency between reaction A and B within the network (at steady-state [Flux coupling  between GSM/GPR)  and S. ce iLL672  was basee iLL672 ,4. Flux dy-state ), given min = 0 and Rmax = finite value). These reactions are coupled, but may not always operate together. In contrast, in fully coupled pairs (symmetric dependency) the activity of one reaction implies the activity of the other and vice versa . Calculations were done without assuming a constant biomass composition to avoid coupling of a large set of fluxes to the biomass reaction. All biomass components were allowed to be drained independently of one another : the activity (flux) of one reaction (A) implies the activity of the other (B), but not necessarily the reverse  were calculated by representing the network as a directed graph consisting of nodes (metabolites) and edges (reactions), and applying a shortest path algorithm. Distances correspond to the minimal number of nodes that separate any two reactions in the network. To increase the functional relevance of network distance, we removed the most highly connected nodes, including ATP, ADP, AMP, COhosphate .We grouped directionally coupled pairs (A→B) into two network distance groups - close network distance (d < 4) and non-trivial distance (d ≤ 4) - to investigate whether the identified asymmetric relations are independent of network distance. Our conclusions are not affected by the exact distance cutoff between small and large network distance .S. cerevisiae was obtained from the MIPS (Munich Information Center for Protein Sequences) database [E. coli, we used the gene essentiality determined by Gerdes et al. [Essentiality data for database  . Subsequently, for each reaction pair A→B with a corresponding pair of growth effect profiles we calculated the fraction (f0/1) of conditions in which reaction A showed no growth effect while reaction B did (n0/1), relative to the total number of conditions in which only one of the reactions showed a growth effect (n0/1 + n1/0). We tested the distribution of these fractions against the null-hypothesis that there is no bias, that is, no asymmetry (H0: f0/1 = 0.5), with the two-sided one-sample Wilcoxon test as implemented in R [0/1 > 0.5.We used the condition-specific growth data of Hillenmeyer et al.  restrict-workers  to derivted in R . We averS. cerevisiae and 12 studies with 420 conditions for E. coli . These data were obtained from the Gene Expression Omnibus (GEO) [0/1) of conditions in which reaction A is absent while reaction B is present (n0/1) relative to the total number of conditions in which only one of the reactions is present (n0/1 + n1/0). We tested the distribution of these fractions against the null-hypothesis that there is no bias - that is, no asymmetry (H0: f0/1 = 0.5) - with the two-sided one-sample Wilcoxon test as implemented in R [The expression data were based on 13 studies with 327 conditions for us (GEO)  at the Nus (GEO) . For eacted in R .0/1) of genomes in which reaction A is absent while reaction B is present (n0/1) relative to the total number of genomes in which exactly one of the reactions is present (n0/1 + n1/0). We tested the distribution of these fractions against the null-hypothesis that there is no bias - that is, no asymmetry (H0: f0/1 = 0.5) - with the two-sided one-sample Wilcoxon test as implemented in R [We constructed phylogenetic profiles that denote the presence and absence of enzymes across 373 species according to the STRING 7.0 orthologous groups . To explted in R .We inferred the most parsimonious ancestral presence/absence states of A and B using a phylogenetic tree of all 373 species included in this analysis (this tree contained some multifurcations to account for uncertainties ) and PAU0/1) where B was gained  and where A was lost  or maintained  relative to the total number of instances of that type . We tested the distribution of these fractions  against the null-hypothesis as mentioned above.We examined for each reaction pair A→B the following situations: type i, both reactions are absent in the ancestor and one is gained in the descendent; type ii, both reactions are present in the ancestor and one is lost in the descendent; type iii, the presence of exactly one of the reactions is maintained, that is, no change of state occurs. We calculated the fraction (f0: fgain of A in presence of B = 0.5).To analyze contingent gain of A, we determined for all gain events of A whether B was already present in the ancestor or not. The fraction of gains in presence of B  was tested against the null hypothesis that a gain of A is independent of the presence of B  to be conserved.The absolute number of conserved directionally coupled pairs is limited (n = 16) because conservation of directional coupling required: both genes of a pair to be present in BD, RN, PK, MH conceived and designed the study. RN, BD and PK performed the analyses. RN, PK, MH and BD wrote the manuscript.Saccharomyces cerevisisae [Escherichia coli [The following additional data are available with the online version of this paper. Additional data file evisisae -34 and Ehia coli -44 exprevice versa ('AB = 01'), both in at least five species. In this table, R is the smallest possible partition in the species tree  and RING 7.0 ) that coClick here for fileE. coli and S. cerevisiae because the gluconeogenesis contains an alternative flux that converges into fructose bisphosphatase. This asymmetry is, however, not reflected in evolution because fructose-bisphosphate aldolase occurs, as part of glycolysis, in a number of species in which gluconeogenesis and its specific enzyme fructose bisphosphatase are not present. This exception shows that the predicted asymmetry is not trivial, and depends on the conservation of the metabolism between species.The relation between fructose-bisphosphate aldolase (A) and the fructose bisphosphatase (B) is asymmetric in Click here for file0/1 = n0/1/(n0/1 + n1/0)) where only B is essential in rich medium  or has an effect on the growth across conditions (growth), where only B is expressed across conditions (expression), where only B is present across species (occurrence), where only B is present after gain, loss or maintenance over evolutionary lineages, and where A is contingently gained over evolutionary lineages (contingent gain A) is averaged over all reaction pairs . Asterisk indicates p < 0.01.The fraction (fClick here for fileSaccharomyces cerevisisae [Escherichia coli [evisisae -34 and Ehia coli -44 expreClick here for file


Metabolic networks show great evolutionary plasticity, because they can differ substantially even among closely related prokaryotes. Any one metabolic network can also effectively compensate for the blockage of individual reactions by rerouting metabolic flux through other pathways. These observations, together with the continual discovery of new microbial metabolic pathways and enzymes, raise the possibility that metabolic networks are only weakly constrained in changing their complement of enzymatic reactions.To ask whether this is the case, I characterized pairwise and higher-order associations in the co-occurrence of genes encoding metabolic enzymes in more than 200 completely sequenced representatives of prokaryotic genera. The majority of reactions show constrained evolution. Specifically, genes encoding most reactions tend to co-occur with genes encoding other reaction(s). Constrained reaction pairs occur in small sets whose number is substantially greater than expected by chance alone. Most such sets are associated with single biochemical pathways. The respective genes are not always tightly linked, which renders horizontal co-transfer of constrained reaction sets an unlikely sole cause for these patterns of association.Even a limited number of available genomes suffices to show that metabolic network evolution is highly constrained by reaction combinations that are favored by natural selection. With increasing numbers of completely sequenced genomes, an evolutionary constraint-based approach may enable a detailed characterization of co-evolving metabolic modules. Evolutionary history can constrain future evolution. It can constrain both the production and the preservation of phenotypic variation -6. For iThe best-studied cases of constrained variation regard macroscopic and readily observable organismal traits . HoweverThis problem suggests that more tractable genetic systems, where genotypic information is readily available, may be a useful starting point to learn more about the extent and pervasiveness of evolutionary constraints. Molecules such as proteins and RNA are the best-studied such candidate systems, but regulatory and metabolic networks are increasingly accessible with the available of genome-scale sequence and functional data -34. TakeStudies of evolutionary constraints as applied to DNA, RNA, or protein sequences have a long history ,39. They3 different biochemical reactions [I will here ask this and related questions with data from more than 200 prokaryotic genome-scale metabolic networks. Such networks are central to all life. They sustain it by producing metabolic energy and biosynthetic precursors. The metabolic network of typical free-living heterotrophic organisms comprises of the order of 10eactions ,45,46. Teactions ,37,47 thCan individual reactions in a metabolic network vary independently from other such reactions? If so, what fraction of reactions can vary independently? If co-variation among reactions occurs, does it affect pairs of enzymes or larger groupings? Only a few years ago, these and similar questions could not have been addressed, because the number of completely sequenced genomes required for at least a coarse metabolic annotation  was too ; [The elementary unit of evolutionary change in metabolic networks is the individual chemical reaction catalyzed by an enzyme that is encoded by a metabolic gene. Except where mentioned otherwise, I here represent each such reaction on the level of the gene, as represented by orthologs of metabolic enzyme-coding genes in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database . This rD of reactions that occurs in only one but not both of the two networks. (D = 1 for networks that share no reactions.) Figure D. Its large mean of D = 0.68 suggests that two networks share on average only about one third of their reactions. Superimposed on the distribution of Figure D for prokaryotic taxa that share a similar, broadly defined habitat. Metabolic networks are not much less diverse in these habitats than in the whole data set of metabolic networks. Even 13 different strains of E. coli show a mean D = 0.36 .Before studying constrained variation in individual reactions, it is useful to ask how diverse the composition of different metabolic networks really is. Previous work focused on different questions has assessed different aspects of this diversity ,48-51, bThese observations suggest that metabolic networks are very diverse in their complement of metabolic reactions, even for organisms living in similar habitats. Together with the resilience of such networks to elimination of reactions ,26,40-447 possible pairs of the 5188 reactions found in the 222 networks I studied. This test determines whether two reactions occur jointly in these networks more often than expected by chance alone. Among several approaches to account for multiple testing [7) = 3.7 × 10-9. Figure -35. It is important to note, however, that the association of two reactions, that is, their co-occurrence in the same genome, is rarely perfect. This is illustrated in Figure On this level, evolution would be unconstrained for any one reaction, if the occurrence of the reaction in a metabolic network can vary independently of other reactions. The most-straightforward way to assess this kind of constraint is to study statistical associations among all pairs of reactions. To this end, I applied an exact binomial test to the 1.35 × 10 testing ,53, I he-17  Such connected components can be thought of as sets of constrained or co-occurring reactions, and their identification is the target of this part of my analysis. The size and number of components of the reaction constraint graph may vary, depending on which P-value threshold is used to define the graph. At a high threshold , the graph may consist only of one or few large components that comprise most reactions, whereas at lower thresholds, the graph may fragment into multiple components of decreasing size that indicate increasingly strongly associated sets of reactions. To have a frame of reference, I compared the structure of this graph at any given threshold to that of a randomized graph. This randomized graph was generated from the original reaction association graph through swapping of edge pairs (see Methods), which leaves the number of edges, and the number of edges per node constant, but randomizes the graph in other respects. More specifically, I generated 20 such randomized graphs and characterized the component size distributions of each of these graphs.In any metabolic network, the production of important metabolites for biomass production requires the cooperation of multiple reactions. This observation calls for an extension of the above reaction-centered approach to larger units. Co-occurrence of reaction sets would suggest joint constrained evolution and joint requirement for key metabolic processes. A substantial technical problem to identifying such sets is the astronomical number of possible combinations  of reactions, which renders an exhaustive evaluation infeasible. To circumvent this problem, I take the following graph-theoretic approach. I define a -10.5) and then declines steadily. Figure -10.5). At other P-values, this distribution is qualitatively similar. Clearly, the overwhelming majority of reactions are associated in small sets of two, three, or four reactions, and there are only few larger reaction sets. The mean and maximal component sizes are generally smaller for the actual randomized reaction constraint graph than for randomized graphs  and 96.6% (85/88) constrained reaction sets belong in one pathway. In sum, individual reactions for most constrained reaction sets can be allocated to the same pathway.In sum, sets of evolutionary constrained reactions are typically small, much smaller than would be expected if reaction associations were distributed randomly across metabolic networks. These observations raise the question whether the constrained sets of reactions are largely congruent with traditional classifications of metabolic pathways. To address this question, I took advantage of the biochemical pathway classification of reactions in KEGG . For any-16. Column 2 shows the number of reactions in each set. In keeping with the skewed distribution of reaction set sizes ; biotin (vitamin H or B7), a cofactor in fatty acid and leucine biosynthesis, and pyrroloquinoline quinone (PQQ), a redox cofactor. Such coenzymes have a complex molecular structure and complex biosynthetic pathways that are not situated at the center of an organism's metabolic network, but rather at the periphery. For these reasons they are often less reticulate than the most central parts of metabolism, with fewer alternative routes between metabolic intermediates. This may be the very reason why they are highly constrained: Absence of an individual reaction in such peripheral, complex pathways may not be as easily compensated through reactions in alternative pathways [Table s Figure , most sepathways . The proThe same causes may also explain the conspicuous absence of reactions in the most central parts of metabolism in the set of Table -23 for each of the three possible reaction pairs), whose congruence in genomic association is nearly perfect. Figure -13, for each of the three reaction pairs).I will next focus on those reaction sets in Table I next asked whether constrained gene pairs in this set are always tightly linked, which might indicate that they are always transferred jointly. Figure 12), one of the most complex biogenic small molecules. Its biosynthesis is restricted to prokaryotes [-17 for each pair). The genes encoding these four reactions occur in between 92 and 98 of the examined genomes, and every pair of genes co-occurs in at least 82 genomes. Figure -13). As for histidine biosynthesis, the distance distributions of genes encoding cobalamin biosynthesis genes are bimodal, as shown in Figures A second example (row 7 in Table karyotes . The laskaryotes . These r-18), whose reactions are poorly characterized, and in the catabolism of myo-inositol . As opposed to eukaryotes, where inositol derivatives have signaling roles, in prokaryotes they serve structural roles as membrane anchors of proteins and glycolipods, and they can aid the infectivity of pathogens [The remaining most highly constrained reaction sets of size greater than two occur in PQQ synthesis : Only four genomes contain orthologs for both reactions  and two prephenate dehydrogenases (P < 10-11). There are also several strong negative associations of unknown biological significance, such as that of the DNA mismatch repair protein MutS2 (KEGG ortholog identifier K02339) and the DNA polymerase holoenzyme subunit χ (K07456), which occur in 171 genomes but in only one of them jointly (P < 10-15).A final class of examples comes from the  set of negative pairwise associations, where the occurrence of one reaction in a genome implies that the other reaction is absent. One might think that such associations might reflect alternative metabolic routes, where one route might exclude the presence of the other route in an organism, but this is not so. Earlier steps of cobalamin biosynthesis than those shown in Figure s Figure . Similarure -13, ,63), an Both experimental and computational work shows that metabolic networks vary greatly in their organization. For example, earlier work on the organization of the citric acid cycle in 19 completely sequenced microbes showed that almost every organism encodes a different subset of the cycle's reactions . Given tIn contrast to this scenario, the merely 222 genomes studied here suffice to show that the majority of reactions are indeed highly constrained in their evolution. This is indicated by their statistically significant co-occurrence with other reactions in constrained reaction pairs. The genes in such a pair are not always tightly linked, which renders horizontal co-transfer of constrained reaction sets an unlikely sole cause for these patterns of association. Constrained reaction pairs can be grouped into small sets whose number is substantially greater than would be expected if the same number of associations occurred among randomly chosen reaction pairs in a metabolic network. The reactions in a set typically belong to the same biochemical pathway(s). Despite the distributed nature of metabolic networks, where perturbations in one part of a large network can be compensated through changes in other, superficially remote parts, clearly identifiable constrained reaction sets exist, and are usually highly localized. These observations are consistent with earlier observations of modular structures in metabolic (and other) networks ,51,65-73In the bioinformatics community, questions regarding the modular organization of biological networks have attracted significant attention, unlike the topic of evolutionary constraints. In contrast, whether evolutionary constraints exist, and how pervasive they are has been an important and controversial topic in evolutionary biology. Standard textbooks  would diIn the evolutionary biology literature, constraints are hardly ever absolute. Classical examples include the tetrapod limb, which has mostly five digits, although ichthyosaurs had more; the lower jaw of frogs, which generally lacks teeth, except for the genus Gastrotheca . ConstraE. coli species showed that the majority of transfer events involved fewer than 15 kilo base pairs of DNA [The tightly constrained evolution of many reaction sets raises a question about the causes of these constraints. Specifically, is it caused merely by natural selection favoring certain reaction combinations, or do metabolic reactions also co-vary in their transfer from bacterium to bacterium? An important source of variation in metabolic and other networks is horizontal gene transfer ,74-82. Is of DNA . Tight ls of DNA -88. In tI asked how important gene linkage is in constraining reaction evolution by examining the most highly constrained reaction pairs and how closely together their encoding genes are linked in a genome. Not surprisingly, the results show clear evidence that some such linkage occurs. However, there are also many cases where linkage is not likely to be solely responsible for constrained variation. The reason is that covarying metabolic genes are often unlinked in many genomes in which they occur. This observation is in line with previous work , which sHorizontal gene transfer is perhaps the most rapidly acting form of change in prokaryotic metabolic networks, because it can introduce multiple new genes into a genome on short evolutionary time scales. However, metabolic networks can change also without such horizontal transfer, albeit on longer time scales. Aside from the mutational loss of reactions (which may be slow for reactions in a highly constrained reaction set), new enzymes can be created through gene duplication and subsequent sequence divergence, as well as through recombination and shuffling of domains and exons -94. SuchCurrent limitations of any statistical approach to analyze constrained evolution of metabolic networks include the limited number of available genomes. With hundreds of genomes at hand, such statistical characterization is beginning to be meaningful, but it is unlikely to resolve the fine-structure of such associations, for example by distinguishing more conserved from less conserved pathways with any confidence. The observation that some 20 percent of reactions appear to be unconstrained Figure  may be eA second limitation comes from a particular class of potential network misannotation. A reaction can be catalyzed by an enzyme that is unrelated in sequence to any other known enzyme catalyzing this reaction. If so, then identification of metabolic network composition based on genome-sequence alone would miss the reaction. This is an example of convergent or parallel protein evolution, and has also been called orthologous gene replacement. For example, 5 out of 14 reactions in the citric acid cycle were reported to be subject to non-orthologous displacement . . One 16S rDNA sequence was extracted from the genbank file for each genome. 16S rDNA sequences were aligned using the NAST (Nearest Alignment Space Termination) algorithm, as implemented in a web server specifically designed to align 16S rDNA sequences . Pairwiences , where n12 is the observed number of networks that harbor both R1 and R2. If P(X ≥ n12) is smaller than a pre-determined significance threshold  then the association of the two reactions is deemed significant. If n1 ≤ n2, then the test is carried out with reversed roles of the two reactions R1 and R2. This test essentially asks if the less frequent of two reactions is significantly associated with the more frequent one. An exactly analogous test can be carried out to determine whether two reactions co-occur significantly less often than expected by chance alone. Specifically, values of 1- P(X ≥ n12) that are smaller than a predetermined significance threshold would indicate reaction pairs with a tendency to not occur in the same metabolic network. In applications of these tests, I restricted myself to reaction pairs R1 and R2 where genes encoding these reactions occur in at least 5 genomes.If two reactions e1 and e2 at random that do not share any nodes. It then reconnects e1 such that its source node becomes linked to the target node of e2, and e2 such that its source node becomes linked to the target node of e1. I iterate this algorithm 2E times, where E is the total number of edges in the graph, to yield one randomized reaction graph. All results for random reaction graphs reported here are based on 20 randomized graphs for each significance threshold. To evaluate whether members of a constrained reaction set can be assigned to the same pathway, I first determined for each pair of members of this set whether they share at least one KEGG pathway annotation. I then calculated the fraction of pairs in the set for which this was the case.A reaction constraint graph is a graph whose nodes are reactions, and where two reactions are connected by an edge if the statistical significance of their pairwise association falls below a given significance threshold. To randomize such a graph, I used an edge swapping algorithm  that pre;[. In those cases where one genome contains more than one ortholog encoding the same reaction, I calculated pairwise distances for each of these orthologs separately, and include these distances in the distributions reported here.For any two gene pairs that show a statistically significant association, I identified the names of all known orthologs of genes encoding these reactions from the KEGG "ko" file . I then


Sulcia muelleri and Baumannia cicadellinicola, that live inside specific cells of the sharpshooter Homalodisca coagulata and studied the metabolic exchanges involving transfers of carbon atoms between the three. We automatically determined the set of metabolites potentially exogenously acquired (seeds) for both metabolic networks. We show that the number of seeds needed by both bacteria in the carbon metabolism is extremely reduced. Moreover, only three seeds are common to both metabolic networks, indicating that the complementarity of the two metabolisms is not only manifested in the metabolic capabilities of each bacterium, but also by their different use of the same environment. Furthermore, our results show that the carbon metabolism of S. muelleri may be completely independent of the metabolic network of B. cicadellinicola. On the contrary, the carbon metabolism of the latter appears dependent on the metabolism of S. muelleri, at least for two essential amino acids, threonine and lysine. Next, in order to define which subsets of seeds (precursor sets) are sufficient to produce the metabolites involved in a symbiotic function, we used a graph-based method, PITUFO, that we recently developed. Our results highly refine our knowledge about the complementarity between the metabolisms of the two bacteria and their host. We thus indicate seeds that appear obligatory in the synthesis of metabolites are involved in the symbiotic function. Our results suggest both B. cicadellinicola and S. muelleri may be completely independent of the metabolites provided by the co-resident endocytobiont to produce the carbon backbone of the metabolites provided to the symbiotic system (B. cicadellinicola to produce its proteins).Endosymbiotic bacteria from different species can live inside cells of the same eukaryotic organism. Metabolic exchanges occur between host and bacteria but also between different endocytobionts. Since a complete genome annotation is available for both, we built the metabolic network of two endosymbiotic bacteria,  Some bacteria, called endocytobionts, permanently live inside the cells of a pluricellular organism and often bring an adaptative advantage to their host by providing compounds that the latter cannot produce or find in its diet. The association may involve several species of bacteria within the same host. The sap-feeding insect called glassy-winged sharpshooter thus maintains a permanent metabolic association with two different species of bacteria that it hosts within specialised cells. Complete genome annotations of the two endocytobionts allowed to reconstruction of their metabolism. By manually inspecting those annotations and comparing them to reference metabolic functions, earlier studies revealed a great complementarity between the metabolisms of the two endocytobionts and indicated potential metabolic exchanges between them. However, the metabolism of an organism is complex enough that such an approach could only give a partial description of the metabolic exchanges in the symbiotic system. We therefore determined all the metabolic exchanges in the symbiotic system by a systematic and automatic exploration of the full metabolism of the two endocytobionts in order to detail those leading to the biosynthesis of compounds involved in the symbiotic function of each bacterium. Our results highly refine our knowledge about the complementarity and the connections between the metabolisms of the two bacteria and their host. Intracellular symbiosis involves a unicellular organism (the endocytobiont) which durably lives inside the cells of the other partner (the host). In the last century, the crucial role of intracellular symbiosis in the ecology and evolution of many eukaryotes was many times demonstrated Homalodisca coagulata) which hosts two bacteria: the Baumannia cicadellinicola and the Bacteroidetes Sulcia muelleri. The complete genome annotation of the two endocytobionts revealed that their metabolic capacities are broadly complementary B. cicadellinicola is globally devoted to cofactor and vitamin biosynthesis whereas the metabolism of S. muelleri is specialised in the essential amino acid biosynthesis that the sharpshooter cannot produce nor find in its diet, the xylem sap. Nevertheless, the partition of these metabolic roles is not so perfect: B. cicadellinicola produces two essential amino acids, methionine and histidine, that S. muelleri cannot produce while the latter appears to be able to synthesise menaquinone, a vitamin. Moreover, the complementarity between the two metabolisms also concerns the biosynthesis of some metabolites not needed by the insect host, such as the fatty acid biosynthesis pathway, supplied by B. cicadellinicola, except for one step which is provided by S. muelleriIntracellular mutualism (where the presence of the endocytobiont increases the fitness of both host and endocytobiont) was particularly well described in several associations between insects and bacteria However, these previous analyses were essentially manually performed by comparing the lists of annotated metabolic genes using as reference the metabolic pathways available in metabolic databases such as KEGG The aim of this study was therefore to determine the possible metabolic exchanges in the symbiotic system by a systematic and automatic exploration of the full metabolic networks of the two endocytobionts in order to detail those leading to the biosynthesis of metabolites involved in the symbiotic function of each bacterium.Defining in an exhaustive way the metabolic exchanges in a symbiotic system implies to be able to indicate all the metabolites needed by one partner and produced by another partner. Our first task was thus to identify for each endocytobiont the metabolites potentially imported from the host cell  and produced by another partner.Focusing on the biosynthesis of the specific compounds that each bacterium produces and provides to the symbiotic system  then necessitates to determine which sets of exchanged metabolites lead to their production. Our second task was thus to identify for each endocytobiont the subsets of seeds  that are sufficient to produce the targets, and to identify them all, that is all alternative precursor sets for each target.These two tasks, and particularly the second one, are hardly feasible by just manually inspecting the metabolic pathways inferred from genomic annotations. Such broad and systematic analyses require working with the full metabolic network to consider it in a systemic way.et al. extended the seed definition in a metabolic network by decomposing the metabolic graph into strongly connected components and then detecting those without incoming edge An intuitive way to define the seeds of a metabolic network is to consider as nutrient a metabolite not produced by any reaction but consumed by one or several ones. However, in particular because of reversible reactions that produce nutrients, this definition is not sufficient. Borenstein et al. (2001) proposed a method returning alternative precursor sets for a set of target compounds Whatever way is adopted to define the sources of a metabolic network, the next question is to determine which precursor sets are able to produce the targets. Romero et al. to find precursor sets is based on a forward traversing of the metabolic graph et al. is then to test the reachability of several sets of seeds heuristically defined Another method proposed by Handorf Recently, we proposed the first definition of minimal precursor sets that explicitly addresses the problem of cycles and the first exact method to find them B. cicadellinicola and S. muelleri, we first defined the set of seeds for each bacterium thanks to the method developed by Borenstein et al.To explore the metabolic exchanges occurring between Our study offers the first detailed and systematic description of the metabolic exchanges occurring in a symbiotic system. Our results also demonstrate the usefulness of graph-based dedicated methods in the metabolic analysis of multi-species systems.Baumannia cicadellinicola and Sulcia muelleri were downloaded from the MaGe annotation system Draft metabolic reconstructions for the bacterial genomes of S. muelleri and 37 reactions from the metabolic network of B. cicadellinicola , or that are connected to the network only by cofactors. The topology of the network provides thus a clue to remove 14 reactions from the metabolic network of cola see  and S2. B. cicadellinicola. If specific reactions do not exist, the reaction has to be duplicated into several reactions so that they involve compounds already existing in the metabolic network. This is the case of the reaction RXN-8972 for which a substrate is “lysine or meso-diaminopimelate”. This reaction was thus splitted into two reactions  in the draft metabolic networks. When the same reaction existed with specific compounds, the generic reaction was simply removed. This is the case of nine reactions in the metabolic network of S. muelleri and of 19 reactions in the metabolic network of B. cicadellinicola. We removed also six reactions in the metabolic network of S. muelleri and seven reactions in the metabolic network of B. cicadellinicola that were classified into the small molecule metabolism whereas they clearly involve macromolecules  that catalyses the production of erythronate-phosphate from erythrose-phosphate appears as absent in the genome of B. cicadellinicola. Wu et al. made the assumption that this role could be carried by glyceraldehyde 3-phosphate dehydrogenase B. cicadellinicola ” In order to describe the metabolic exchanges between the endocytobionts, the first step consisted in identifying which metabolites each bacterium potentially acquires from its environment. For this, we based ourselves on the definition of Borenstein To apply the Borenstein method to identify the seed sets, the metabolic network of each bacterium was modelled as a directed compound graph. In such a graph, nodes represent compounds and there is an arc between two compound nodes if at least one reaction produces one of the compounds (possibly more) from the other (possibly more). A reversible reaction between two metabolites is modelled by two arcs with opposite directions linking them. Since the side compounds were previously filtered (see previous Section), we avoid paths between metabolites that are biologically meaningless for our study.The seeds identification is based on the detection of the strongly connected components (SCC) in the compound graph. An SCC is a subgraph et al. for more precisions This definition of seeds allows to take into account the uncertainty about the direction of some peripheral reactions and about the presence of reactions producing metabolites that are actually exogenously acquired. We invite the reader to refer to the paper of Borenstein For each symbiont, we further inspect the collection of seed sets identified in order to classify these seeds as potentially provided by the insect or by the other co-symbiont. This is done by analysing the feeding source of the host as described in the literature We implemented a version of the Borenstein's method using the Igraph package et al.B. cicadellinicola because of their presence both in its metabolic network and in the precursor sets identified for S. muelleri. These additional targets are particularly interesting since they could directly correspond to metabolic pathways shared between the two metabolic networks. For the same reasons, we added homoserine and 2-ketovaline to the list of target compounds for S. muelleri.Once the set of seeds was defined for each metabolic network, the next step was to identify which subsets of the seeds, henceforward called “precursor sets”, are sufficient to produce the metabolites known to be involved in the symbiotic metabolic association, that from now on we call the “targets”. Those are metabolites output by one bacterium that may then be used by the other co-symbiont or the host. We first put as targets the metabolites reported as involved in the symbiotic association by McCutcheon To identify the precursor sets, we used the PITUFO method that we recently developed et al.The strength of PITUFO comes from the fact that it takes into account cycles in the definition of precursor sets in a fully formalised manner. This allows to find paths from the precursor sets to the targets that pass through cycles in the network but are still feasible. Previous methods, such as those that compute the scope of a subset of the seeds as defined by Handorf The strategy of PITUFO to deal with cycles is to allow the use of metabolites involved in cycles if they are also produced (regenerated) in the forward propagation from the seeds to the targets. Indeed, in The metabolites The following definitions allowed us then to formally establish what is a precursor set of a given target compound: a subset In the above definition and in , a metabet al.The use of hypergraph modelling allows the formalisation of hyperpaths between precursor sets and targets since when a reaction is modelled as an hyperarc, it already explicitly establishes that all of its substrates are needed. On the other hand, an hypergraph could lead to some confusion for the method previously applied to identify the seed sets and proposed by Borenstein Since PITUFO is an exact method, it is enough to describe its input and output without recalling how the second is produced from the first. For those interested in the method itself, the algorithm is described in detail in The current version of the algorithm was implemented in Java and takes as parameter an SBML file describing a metabolic network http://sites.google.com/site/pitufosoftware/.The reconstructed metabolic networks complete or filtered are available in the Supplementary material. The method used is available at this address: B. cicadellinicola is less than half the size of the metabolic network of the free bacterium Escherichia coli. The reduction is even more important in S. muelleri since, with only 64 reactions, its metabolic network is less than ten percent the size of the network of E. coli. In both cases, the extensive manual curation allowed to highly reduce the number of reversible reactions as we succeeded to assign a direction to most of the reactions in the two metabolic networks.B. cicadellinicola. Second, this also means that an amino acid identified as a seed is involved in the small molecule metabolism and not only in the production of proteins.We recall that the reactions involving big molecules are not taken into account in this analysis. For instance, the reactions charging amino acids onto their corresponding tRNAs do not appear in the metabolic network we built. This means two things. First, an amino acid involved only in the production of proteins does not appear in the seeds that we identified, which explains the absence of some essential amino acids in the set of seeds identified in B. cicadellinicola. This metabolite is thus a seed in the original metabolic network of the bacterium but not in the filtered one.Moreover, we focused on the transfers of carbon atoms. There are then no inorganic metabolites in the sets of seeds. Furthermore, the organic compounds not involved in carbon atom transfers do not appear in this list. This is the case for instance of glutamine that appears as a source of nitrogen but not of carbon in the metabolism of B. cicadellinicola and 10 seeds in the metabolic graph of S. muelleri. Only three seeds are common to the two sets: serine, aspartate and bicarbonate ion.We identified 19 seeds in the metabolic graph of S. muelleri could be linked to a transport reaction, four seeds  correspond to transport reactions annotated in B. cicadellinicola. Furthermore, a general amino acid ABC transporter annotated in its genome enables B. cicadellinicola to import also other amino acids identified as seeds: threonine, glycine, tyrosine and alanine.Whereas none of the seeds in S. muelleri, three are amino acids  and three are sugars: erythrose-4-phosphate and ribose-5-phosphate are classically produced by the pentose phosphate pathway and ribose-5-phosphate by the glycolysis pathway.Among the 10 seeds identified in B. cicadellinicola, we found 13 amino acids or related metabolites  and only one sugar, glucose.Among the 19 seeds identified in B. cicadellinicola, serine belongs to the same source component : erythrose-4-phosphate and phosphoenolpyruvate. Both are potentially provided by B. cicadellinicola. We found oxaloacetate and aspartate as alternative precursors for the synthesis of isoleucine and lysine. Indeed, each one can produce the other by the same reversible reaction in the metabolic network of S. muelleri  For both bacteria, the number of seeds that we identified is very reduced, even considering that our study is limited to the carbon metabolism of small molecules. This means that the global reduction of the metabolism in the symbionts comes with a reduction in the number of metabolites imported from the host cell. The identification by PITUFO of a unique precursor set for most of the selected target compounds shows that there are almost no alternative sources to produce essential compounds. Indeed, the mutualistic association of the symbionts with their host is very ancient  or in S. muelleri  is interesting in the sense that they are not only provided as essential building blocks of proteins but also as starting points of the biosynthesis of other metabolites. This clarifies the role of the exchanged amino acids as reported in earlier studies The set of seeds identified in the metabolic network of B. cicadellinicola genome. Other metabolites such as non-essential amino acids and glucose, the only sugar identified as seed in the metabolic network of B. cicadellinicola, were also mentioned as components of the xylem sap and then available for the two symbionts Some seeds automatically defined in our study were already mentioned in earlier studies B. cicadellinicola and appears only as output in the metabolic network of S. muelleri. Glutamine in both metabolic networks is only used as a nitrogen source and thus does not appear in the filtered metabolic networks. Malate is completely absent from the metabolic network of S. muelleri. It is produced from fumarate in the metabolic network of B. cicadellinicola and then does not need to be imported.However, some metabolites mentioned as highly present in the xylem sap were not identified in our set of seeds. For instance, arginine, an amino acid which is abundant in proteins, is absent from the metabolic network of S. muelleri are glycolytic products. Erythrose-4-phosphate and ribose-5-phosphate are commonly produced in the pentose phosphate pathway. Phosphoenolpyruvate is commonly produced during glycolysis. PITUFO returned erythrose-4-phosphate and phosphoenolpyruvate as obligatory precursors in the synthesis of the metabolites that S. muelleri provides to the symbiotic system, except homoserine and menaquinone. Ribose-5-phosphate was identified as obligatory precursor for tryptophan. These three metabolites, as well as oxaloacetate and octaprenyl diphosphate, are produced by the metabolic network of B. cicadellinicola. However, it is likely that these metabolites could be made available by the insect host. This means that the carbon metabolism of S. muelleri may be completely independent of the metabolic network of B. cicadellinicola. On the contrary, the two essential amino acids (threonine and lysine) identified as seeds for B. cicadellinicola are certainly not produced by the insect host nor present in its diet and must be provided by S. muelleri. The carbon metabolism of B. cicadellinicola therefore appears as dependent on the metabolism of S. muelleri, at least for these two amino acids. This dependence is added to the obligatory supply of other essential amino acids by S. muelleri that are required for protein biosynthesis in B. cicadellinicolaThree seeds identified in the metabolic network of B. cicadellinicola passes on to the symbiotic system, only methionine and coenzyme A need metabolites produced by S. muelleri: homoserine and 2-ketovaline. The first one may be produced by the plant and then be present in the diet of the insect, and the second one may be produced by the insect via the degradation of valine. This suggests that B. cicadellinicola, as well as S. muelleri, may be only dependent on the metabolites obtained from the insect to produce the metabolites provided to the symbiotic system. Indeed, threonine and lysine which are supplied by S. muelleri to B. cicadellinicola, are only exploited by the latter to produce its proteins.However, among the precursors identified for the synthesis of metabolites that The reconstruction of the metabolic network of the host and a better knowledge about the metabolome of the plants that the insect feeds upon will inform us whether some seeds, such as intermediates in the biosynthesis of essential amino acids, are actually produced by the insect or present in its diet.B. cicadellinicolaS. muelleri, and none corresponds to the seeds that we identified B. cicadellinicola often appear to adhere to the surface of the much larger cells of S. muelleriB. cicadellinicola.One challenging issue concerns how the metabolites are exchanged between the three partners. The annotation of transporters for amino acids and sugars in S. muelleri (SMDSEM) and by Hodgkinia cicadicola, found in the cells of cicadas S. muelleri exhibit almost identical metabolic capabilities. They suggested also that, although phylogenetically distant, H. cicadicola and B. cicadellinicola have converged on similar metabolic functions, especially those that are complementary to the metabolism preserved in S. muelleri.One other remaining interesting question is evolutionary: how did the reductions of the metabolism of the two symbionts get organised during evolution to reach their current complementarity? A recent study compared the metabolic gene sets of two pairs of co-resident endocytobionts. One pair was formed by the two endocytobionts of the sharpshooter studied in this paper and the other pair was formed by another strain of The application of such methods as we used in this study to this other pair of co-resident endosymbionts should allow to identify some common patterns in the sharing of a set of nutrients and their use in the metabolic networks of the different partners. The extension to other endosymbiotic systems could provide us with crucial information to understand the establishment of such nutritional associations.Dataset S1S. muelleri in SBML format.Metabolic network of (0.08 MB XML)Click here for additional data file.Dataset S2B. cicadellinicola in SBML format.Metabolic network of (0.27 MB XML)Click here for additional data file.Dataset S3S. muelleri in SBML format.Filtered metabolic network of (0.06 MB XML)Click here for additional data file.Dataset S4B. cicadellinicola in SBML format.Filtered metabolic network of (0.25 MB XML)Click here for additional data file.Figure S1Sulcia muelleri. Squares correspond to reactions and circles to metabolites. The colour of the edges differentiates the two sides of a reaction.Sub-network corresponding to the production of leucine and valine from erythrose-4-phosphate and phosphoenolpyruvate in (0.15 MB PDF)Click here for additional data file.Figure S2Sulcia muelleri. Squares correspond to reactions and circles to metabolites. The colour of the edges differentiates the two sides of a reaction.Sub-network corresponding to the production of lysine, homoserine and isoleucine from erythrose-4-phosphate, oxaloacetate, aspartate and phosphoenolpyruvate in (0.16 MB PDF)Click here for additional data file.Figure S3Sulcia muelleri. Squares correspond to reactions and circles to metabolites. The colour of the edges differentiates the two sides of a reaction.Sub-network corresponding to the production of phenylalanine from erythrose-4-phosphate and phosphoenolpyruvate in (0.11 MB PDF)Click here for additional data file.Figure S4Sulcia muelleri. Squares correspond to reactions and circles to metabolites. The colour of the edges differentiates the two sides of a reaction.Sub-network corresponding to the production of tryptophane from erythrose-4-phosphate, ribose-5-phosphate, serine and phosphoenolpyruvate in (0.12 MB PDF)Click here for additional data file.Figure S5Sulcia muelleri. Squares correspond to reactions and circles to metabolites. The colour of the edges differentiates the two sides of a reaction.Sub-network corresponding to the production of menaquinone from octaprenyl-diphosphate and 1,4-dihydroxy-2-naphthoate in (0.06 MB PDF)Click here for additional data file.Figure S6B. cicadellinicola. Squares correspond to reactions and circles to metabolites. The colour of the edges differentiates the two sides of a reaction.Sub-network corresponding to the production of histidine from glucose, and aspartate in (0.13 MB PDF)Click here for additional data file.Figure S7B. cicadellinicola. Squares correspond to reactions and circles to metabolites. The colour of the edges differentiates the two sides of a reaction.Sub-network corresponding to the production of methionine from serine, glycine, threonine and homoserine in (0.09 MB PDF)Click here for additional data file.Figure S8B. cicadellinicola. Squares correspond to reactions and circles to metabolites. The colour of the edges differentiates the two sides of a reaction.Sub-network corresponding to the production of co-enzyme A from β-alanine, glucose, aspartate, glycine, serine, threonine and keto-isovalerate in (0.13 MB PDF)Click here for additional data file.Figure S9B. cicadellinicola. Squares correspond to reactions and circles to metabolites. The colour of the edges differentiates the two sides of a reaction.Sub-network corresponding to the production of biotin from alanine in (0.08 MB PDF)Click here for additional data file.Figure S10B. cicadellinicola. Squares correspond to reactions and circles to metabolites. The colour of the edges differentiates the two sides of a reaction.Sub-network corresponding to the production of pyridoxal-5′-phosphate from glucose in (0.14 MB PDF)Click here for additional data file.Figure S11B. cicadellinicola. Squares correspond to reactions and circles to metabolites. The colour of the edges differentiates the two sides of a reaction.Sub-network corresponding to the production of riboflavin from glucose in (0.10 MB PDF)Click here for additional data file.Figure S12B. cicadellinicola. Squares correspond to reactions and circles to metabolites. The colour of the edges differentiates the two sides of a reaction.Sub-network corresponding to the production of glutathione from glutamate, glycine, serine and threonine in (0.12 MB PDF)Click here for additional data file.Figure S13B. cicadellinicola. Squares correspond to reactions and circles to metabolites. The colour of the edges differentiates the two sides of a reaction.Sub-network corresponding to the production of thiamine from glucose, serine, glycine, tyrosine and threonine in (0.19 MB PDF)Click here for additional data file.Figure S14B. cicadellinicola. Squares correspond to reactions and circles to metabolites. The colour of the edges differentiates the two sides of a reaction.Sub-network corresponding to the production of tetrahydrofolate from dihydroneopterin, glucose and glutamate in (0.15 MB PDF)Click here for additional data file.Figure S15B. cicadellinicola. Squares correspond to reactions and circles to metabolites. The colour of the edges differentiates the two sides of a reaction.Sub-network corresponding to the production of BH4 precursor from glucose and aspartate in (0.16 MB PDF)Click here for additional data file.Figure S16B. cicadellinicola. Squares correspond to reactions and circles to metabolites. The colour of the edges differentiates the two sides of a reaction.Sub-network corresponding to the production of heme from glucose and protoheme in (0.13 MB PDF)Click here for additional data file.Figure S17B. cicadellinicola. Squares correspond to reactions and circles to metabolites. The colour of the edges differentiates the two sides of a reaction.Sub-network corresponding to the production of erythrose-4-phosphate and of ribose-5-phosphate from glucose in (0.11 MB PDF)Click here for additional data file.Figure S18B. cicadellinicola. Squares correspond to reactions and circles to metabolites. The colour of the edges differentiates the two sides of a reaction.Sub-network corresponding to the production of oxaloacetate from aspartate in (0.06 MB PDF)Click here for additional data file.Figure S19B. cicadellinicola. Squares correspond to reactions and circles to metabolites. The colour of the edges differentiates the two sides of a reaction.Sub-network corresponding to the production of phosphoenolpyruvate from glucose or aspartate in (0.10 MB PDF)Click here for additional data file.Figure S20B. cicadellinicola. Squares correspond to reactions and circles to metabolites. The colour of the edges differentiates the two sides of a reaction.Sub-network corresponding to the production of octaprenyl-diphosphate from glucose in (0.15 MB PDF)Click here for additional data file.Table S1S. muelleri. IR: isolated reaction; ES: enzyme specificity misannotation; MM: reaction involving macromolecules out of the scope of the study.Reactions manually removed from the automatic recontruction of the small-molecule metabolism of (0.04 MB PDF)Click here for additional data file.Table S2B. cicadellinicola. GR: generic reaction; IR: isolated reactions; ES: enzyme specificity misannotations; MM: reactions involving macromolecules out of the scope of the study; NC: reactions not corresponding to carbon atom transferts.Reactions manually removed from the automatic recontruction of the small-molecule metabolism of (0.05 MB PDF)Click here for additional data file.Table S3S. muelleri and the evidences used to assign the direction of the reactions. The metabolites kept in the reactions after filtering appear in bold. M: MetaCyc pathways evidence; T: topological evidence.List of reactions in the metabolic network we build for (0.06 MB PDF)Click here for additional data file.Table S4B. cicadellinicola and the evidences used to assign the direction of the reactions. The metabolites kept in the reactions after filtering appear in bold. M: MetaCyc pathways evidence; T: topological evidence.List of reactions in the metabolic network we build for (0.11 MB PDF)Click here for additional data file.Table S5B. cicadellinicola and of S. muelleri. Each line corresponds to a transformation of cofactors. If the metabolites written in bold in the first column appear in a side of a reaction and the metabolites written in bold in the second column appear in the other side, they and the corresponding subproducts  are removed in the reaction. The third column and the fourth column indicate the number of affected reactions in the metabolic networks of S. muelleri and B. cicadellinicola.List of transformations cofactors used to automatically filter the metabolic networks of (0.05 MB PDF)Click here for additional data file.Text S1Sub-networks linking each target metabolite to their precursor sets.(2.11 MB PDF)Click here for additional data file.