Saccharopolyspora spinosa with aerobic fermentation. However, the wild strain has a low productivity. In this article, a computational guided engineering approach was adopted in order to improve the yield of spinosad in S. spinosa.Spinosad is a macrolide antibiotic produced by S.spinosa based on its genome information, literature data and experimental data was extablished. The model was consists of 1,577 reactions, 1,726 metabolites, and 733 enzymes after manually refined. Then, amino acids supplying experiments were performed in order to test the capabilities of the model, and the results showed a high consistency. Subsequently, transhydrogenase  was chosen as the potential target for spinosad yield improvement based on the in silico metabolic network models. Furthermore, the target gene was manipulated in the parent strain in order to validate the model predictions. At last, shake flask fermentation was carried out which led to spinosad production of 75.32 mg/L, 86.5% higher than the parent strain (40.39 mg/L).Firstly, a genome-scale metabolic network reconstruction (GSMR) for S. spinosa for spinosad production. It is the first GSMM for S.spinosa, it has significance for a better understanding of the comprehensive metabolism and guiding strain designing of Saccharopolyspora spinosa in the future.Results confirmed the model had a high potential in engineering  Saccharopolyspora spinosa with aerobic fermentation. Due to its unique chemical structure and mechanism, it can control diamondback moth; beet armyworm and other lepidopteron pests effectively, while have non-toxic to mammals and birds [Spinosad is a macrolide antibiotic produced by nd birds . Therefond birds .S. spinosa fermentation,are defined as spinosad [SpnA, spnB, spnC, spnD, and spnE responsible for type I polyketide synthase; spnF,spnJ, spnL, and spnM for modifying the polyketide synthase product [Spinosyns A and D, the two major components in the spinosad . It mainspinosad . Waldronspinosad , In rece product ; spnG, s product ; spnP, s product  and besiSaccharopolyspora spinosa has low spinosad productivity, so lots of work has been done in strain modification. Three strategies have been mainly used in the spinosad improvement: (a) Traditional physical and chemical mutagenesis [gtt and gdh genes under the control of PermE* promoter. Tang [nP, spnQ, spnN, spnQ, spnR, spnS, spnK, gtt, gdh and kre genes under the control of PermE* promoter, spinosad production was a 5.0-fold enhancement compared with the wild-type S. spinosa. However, the spinosad is a secondary metabolite which has complex metabolic pathways. It is difficult to satisfy the industrial demands if we only pay attention to a few gene modifications.The wild stain of agenesis ; (b) Optagenesis ,11; (c) agenesis  got a over. Tang  increaseer. Tang  duplicatStreptomyces tsukubaensis to improve the FK506 production which led to a 1.47 fold increase [lactococcus lactis[In recent years, more and more whole genomes have been sequenced with the development of high-throughput sequencing technology and reduction of sequencing prices. It is a hot topic to study how to use such a large genome database. The genome-scale metabolic network (GSM) constructed based on genome annotation information provide an integral level to review organism metabolism. Now Genome-Scale-Metabolic-Model (GSMM) reconstruction technology has a great progress no matter in speed but quality ,16. At pincrease . The simus lactis. GSMM reSaccharopolyspora spinosa. In this work, the first manually refined GSMM for Saccharopolyspora spinosa was established. The amino acid supplementation experiments were conducted to verify the module. Then a potential target gene transhydrogenase was assessed. Overexpression of this gene led to 86.5% higher spinosad yield than that of the wild stain.There were no reports on the refined GSMM for Saccharopolyspora spinosa using a highthroughput protocol was constructed. The GSMM contained 1577 metabolites and 1736 reactions and 733 enzymes , tricarboxylic acid (TCA) cycle, and spinosad biosynthesis, are illustrated in Figure The first GSMM for in silico simulation results were showed respectively in Figures To validate the spinosad biosynthetic pathway, we used the GSMR model to predict the potential benefit of supplementing the media with individual amino acid (AA) in terms of spinosad production. Eight AAs  were individually added to study the effect on spinosad production. The experiments and GCN4 gene which encodes a positive regulator of unlinked amino acid biosynthetic genes in yeast is itself regulated by amino acid availability and that the regulation occurs at the translational level. Proline inhibit the spinosad synthesis may due to it or its metabolites participate in the regulation process. The GSMM in our work has no regulation systems, so the result was not consistent with the experiments.Amino acids play an important role in antibiotic synthesis and cell growth in the fermentation process as important biological small molecules. AA can directly participate in the biosynthesis of antibiotics as the precursor ,25; AA o+ and NADH/NAD+, has an important role in the metabolic regulation in the cell. Therefore the reaction R00112 (NADPH + NAD+ = NADP+ + NADH) was used as control variables, the spinosad synthesis reaction as a target reaction to study the effect of transhydrogenase activity on spinosad synthesis. The results were exhibited in Figure Spinosad is a secondary metabolite. Energy and reducing power NADPH were required in its synthesis process. Cellular reducing power level, such as NADPH/NADPpntAB was over expressed, the yield of spinosad in S.spinosa 261P increased to 37.9 mg/L which was little higher than the wild strain(34.4 mg/L). Then 0.1% rhamnose was added, the yield increased to 75.32 mg/L which was 0.86-fold higher than that in the wild-type S. spinosa (40.39 mg/L).In the Figure pntAB in E. coli got a 3.5-fold conversion of acetophenone to (R)-phenylethanol [Corynebacterium glutamicum to get a high yield of L-lysine [pntAB. In our work, two reasons may explain the results that the engineered strain has a low production in general fermentation medium: Firstly, spinosad is a secondary metabolite and it has a low synthesis rate; Secondly, NADPH was mainly used in biomass synthesis.Efficient regeneration of NADPH which mainly comes from PPP and TCA pathways plays an important role in biotransformation processes. The biotransformation is often limited by cofactor regeneration because cells would rather grow better than intend to sustain the biotransformation ,29. The lethanol . The simL-lysine . BastianL-lysine  obtainedSaccharopolyspora spinosa only have one set of rhamonose gene [gtt and gdh) in the previous study [pntAB, more NADPH was produced from NADH. When the rhamnose was added, more NADPH flowed into spinosad synthesis pathway, so the engineered strain had a high productivity in rhamnose medium.Rhamnose is both the precursor of spinosad and the cell wall ingredient, but the ose gene ,36. The us study . So the Saccharopolyspora spinosa was constructed following a systematic workflow. Then it was validated by amino acids supplementation experiments. Finally based on the GSMM, PntAB was identified as the target gene. The strain was engineered according to the predicted target. Fermentation characterization of the engineered strain showed the improved capacities of spinosad production. The final yield of spinosad is 75.32 mg/L which is increased by 86.5% than the parental strain. Our results show the genome-scale metabolic network model is a powerful tool to guide strain engineering towards improved bio-production in S.spinosa, as well as other microorganisms.In this work, the first Genome-scale metabolic network of Saccharopolyspora spinosa was sequenced and annotated in 2011 by Yuanlong Pan [Saccharopolyspora spinosa NRRL 18395 genome has a 67.94% GC content. The model reconstruction followed the standard protocols described previously [Saccharopolyspora spinosa can be described as follows:The genome of long Pan , the reslong Pan . The comeviously . The proFirstly, a draft reconstruction was created. The necessary information, containing genome annotation information and the biochemistry information of the enzymes, can be searched in KEGG and NCBI. The following factors were included in the draft: (1) the basic reaction information , (2) the enzymes and genes relevant to particular reaction. (3) the subsystems. All the information above was saved in an Excel which contains 1830 reactions and 1849 metabolites.Secondly, refine the draft model got above. Many reactions were redundant in the draft while some necessary ones were absent, so a lot of work was done in the refine process. The reactions and metabolites IDs may different in different database, only one ID was remained; The reactions which synthesize or modify the macromolecules were replaced by the biomass formulation reactions; Only one form of the multi-step reaction was remained in the model. For example, the overall reaction R004385 was kept while its sub-step reactions R00174 were removed; At last, reactions’ reversibility and cofactors were determined and verified.At the last stage, spontaneous reactions, exchange reactions, transport reactions, biomass reactions and spinosad synthesis reactions were added to get a complete GSMM. Biomass formation was mainly based on the chemicals of the cell. The reactions containing the metabolites which were verified to be taken up from the medium and the metabolite which can diffuse through the membranes were added as the transport reaction. Exchange reactions were added for all extracellular metabolites, which represented the system boundaries.Saccharopolyspora spinosa genome. Protein composition was determined based on open reading frames (ORFs). Other components were adapted from S. coelicolor[The biomass equation information was mainly got from available literature date. It is divided into proteins, RNA, DNA, lipid cell wall peptidoglycanpolysaccharide muramic acid. DNA and RNA composition were determined based on genomic data from the oelicolor. The bioConstraint-Based Flux Analysis (FBA) has a wildly use in flux distribution calculations ,41. The Escherichia coli DH5α was used for all plasmid constructions and amplification. E. coli ET-12567 was used as the door strain in biparental intergeneric conjugations. S. spinosa ATCC49460 was used as the parent strain. The pOJ261, which is pOJ260 with ermE* promoter, was used as expression plasmid. The ermE* promoter was digested from pIB139. Plasmids and stains used in this study are summarized in Table E. coli strains were cultured in Luria-Bertani (LB) medium at 37°C. The slant and seed medium contained (g/L): trypticase soy broth, 30; yeast extract, 3; MgSO4 · 7H2O, 2; glucose, 10; and maltose, 4, pH 7.2. In the slant medium 20 g/L agar was added. The fermentation medium contained (g/L): glucose, 68; cottonseed flour, 22; peptone C, 25; corn seed liquor, 14.5; methyloleate, 40; and CaCO3, 5, pH 7.2. ABB13 medium contained (g/L): soy peptone, 5; soluble starch; CaCO3, 3; MOPS, 2.1; agar 20.Strains were cultured on a 220 rpm rotary shaker at 29°C in a 250 mL flask containing 30 mL of seed medium for 3 days. Then 3 mL of seed medium was injected into 30 mL fermentation media in a 250 mL flask. Strains were cultured for 9 days at 29°C on a 220 rpm rotary shaker. The fermentations were run in triplicate .After 72 h fermentation, 0.05% of amino acids  Standards was added by 0.22 μ filter membrane filtration.S. spinosa using primer pairs of F-CCTATCTAGACGCCGGACAAGGACGl-XbaI,R-GGATCCATATGACCTCTCCGGAGAGC-NdeI. The PCR product of PntAB was digested by Ndel and Xbal. Then, it was cloned into pOJ261 which was also digested by Ndel and Xbal to get pOJ261P.General DNA manipulation was performed according to the standard protocols . PntAB wS.spinosa 261P was confirmed by PCR amplification with vector primers V-F:CCGTGATTTTGTAGCCCTGG and V-R: GGCCTACTTCA CCTATCCTGC, and the result was positive.The constructed plasmid was introduced into S. spinosa strains by conjugation from E. coli ET12567 and homologous recombination into the chromosome as described as .The recoSpinosad in the fermentation broth was determined as described . Every lThe authors declare that they have no competing interests.WYL and XYW conceived and designed the research. XYW and CBZ performed the model construction and calculation. Amino acids supplement experiments and strain engineering was conducted by MLW and XYW respectively. CBZ analyzed the date and drafted the manuscript. WYL supervised the research and revised the manuscript. All authors read and approved the final manuscript.The SPNV1.0 model content.Click here for fileBiomass composition of Saccharopolyspora spinosa.Click here for file


We show that, even though a large number of alternative pathways exist, the alternatives carry lower flux than the real pathway under typical physiological conditions. We also find that if physiological conditions were different, different pathways could outperform those found in nature. Together, our results demonstrate how thermodynamic and biophysical constraints restrict the biochemical alternatives that are open to evolution, and suggest that the existing trunk pathway of glycolysis and gluconeogenesis may represent a maximal flux solution.The universality of many pathways of core metabolism suggests a strong role for evolutionary selection, but it remains unclear whether existing pathways have been selected from a large or small set of biochemical possibilities. To address this question, we construct  The biochemical pathways of central carbon metabolism are highly conserved across all domains of life. Here, Court et al. use a computational approach to test all possible pathways of glycolysis and gluconeogenesis and find that the existing trunk pathways may represent a maximal flux solution selected for during evolution. The biochemical pathways of central carbon metabolism are highly conserved across all domains of life, and largely control the productivity of life on the Earth1356789et al.Previous studies have mainly addressed this question either by constructing simplified artificial metabolic networks5648911126510141516Here, we perform an exhaustive computational search for all possible biochemically feasible alternatives to one of the most ancient and most highly conserved sections of central carbon metabolism, the trunk pathway of glycolysis and gluconeogenesis. Glycolysis breaks down glucose to pyruvate, generating ATP, NADH and biosynthetic precursors, whereas gluconeogenesis uses ATP and NADH to generate glucose from pyruvate. The glycolytic-gluconeogenic pathway is almost linear and can be divided into two parts: an ‘upper' chain of reactions involving 6-carbon molecules, which connects glucose to glyceraldehyde-3-phosphate (G3P), and a ‘lower' chain of reactions involving 3-carbon molecules, which connects G3P to pyruvate . This loAlthough the upper part of the glycolytic/gluconeogenic pathway exists in several very distinct variants, most notably the Embden–Meyerhof–Parnas (EMP) and Entner–Doudoroff (ED) pathways 818, the 819We used a computer program to generate the network of all possible biochemically feasible pathways between G3P and pyruvate.fG for all our internal metabolites, using existing experimental data where possible252627Our program first systematically generates all possible relevant internal metabolites, that is, molecules intermediate between G3P and pyruvate, including those that are not found in nature (see Methods for details). Central carbon metabolism consists exclusively of reactions between ‘CHOPN' molecules: those composed of carbon, hydrogen, oxygen, phosphorus and nitrogen atoms, with phosphorus being present only in phosphate groups21+; we refer to these as external metabolites and we assume that they have fixed concentrations, which define the cellular  conditions. The full set of external metabolites in our reaction network is shown in +, the amino-group donor glutamate, as well as inorganic phosphates and CO2.Our program generates all possible reactions among our set of internal metabolites, based on the 20 Enzyme Commission (EC) reaction classesOur network reveals a huge number of alternative pathways connecting G3P and pyruvate, which are consistent with the rules of biochemistry. Enumerating these alternatives, we find 2, 29, 555 and 5,859 pathways of length 3, 4, 5 and 6, respectively, with the number of pathways increasing exponentially with path length . Some ofWe wish to investigate how many of these alternative trunk pathways are feasible under typical physiological conditions. First, in the glycolytic direction, we demand that candidate pathways produce at least two ATP molecules. This is required so that the full glycolytic pathway produces a net ATP yield, as the two dominant forms of glycolysis, the EMP and ED pathways, consume one ATP molecule per G3P in their upper halves. In addition, motivated by considerations of simplicity5We next evaluated the performance of the alternative trunk pathways generated by our network, by comparing their steady-state metabolic flux. For glycolytic pathways, the metabolic flux corresponds to the rate of ATP production, whereas for gluconeogenic pathways, it corresponds to the rate of production of G3P (and ultimately of glucose). This flux depends not only on the total free energy change across a given pathway, but also on the distribution of the individual reaction free energies . For each point in parameter space, we evaluated the flux of each candidate pathway.As a simple metric, we first compare the performance of our candidate pathways, averaged over the entire parameter space. To this end, we compute the comparative flux (CF), which is the flux of a given pathway, divided by the maximum flux obtained among all pathways, at a given point in parameter space. Averaging this quantity across the whole parameter space gives a measure of relative performance for each candidate path. Based on this metric, we find that the real glycolytic and gluconeogenic pathways perform remarkably well, compared with the many alternatives . For glyTo overcome this issue and to investigate in more detail, we analysed which of our candidate pathways achieved the highest flux at different points in the parameter space. This allows us to understand how the performance of a given pathway depends on the intracellular environment. Our results show that different candidate pathways perform best in different regions of the parameter space. In particular, pathway performance is very sensitive to the cellular energy state, as measured by the ratio of the ATP and ADP concentrations ([ATP]/[ADP]), and redox state, represented by the ratio [NAD][Pi]/[NADH] . FocusinThe flux calculation used above makes the simplifying assumption that all enzymes are perfect catalysts6730rG values, and the real glycolytic pathway falls to number 87 with respect to its CF averaged over parameter space  in Path C is similar to the real glycolytic trunk path, except that 1,3-bisphosphoglycerate  is first isomerized  and then dephosphorylated (to 2-phosphoglycerate (2-PG), with ATP generation), rather than being first dephosphorylated and then isomerized as in the natural pathway. Interestingly, a similar pathway exists in red blood cells, where 2,3-BPG is produced from 1,3-BPG via the Rapoport–Leubering shuntPath D differs from the natural pathway in that it converts G3P directly to 3-PG without the production of ATP, via EC 1.2.1.9. This large exorgonic initial step gives path D a favourable thermodynamic profile and results in the two ATP-generating steps being the final two reactions, catalysed by PEP carboxykinase EC 4.1.1.49 and pyruvate carboxylase EC 6.4.1.1. These two enzymes are usually associated with a gluconeogenic role in animals and their feasibility in the glycolytic direction is sensitive to the intracellular pH; it becomes increasingly thermodynamically favourable to produce two ATP molecules from PEP with decreasing pH  rather than from ADP and Pi—that is, it is the exact reverse of the natural ppdk gluconeogenic pathway. This may result in an effectively greater energetic yield than the natural glycolytic pathway, as an ATP is recovered from an AMP directly, rather than through the action of the adenylate kinase enzyme (EC 2.7.4.3), which involves the consumption of another ATP molecule. This pathway actually corresponds to a glycolytic variant observed in some anaerobic eukaryotes, in which glycolysis is the primary mode of ATP productionFinally, Path F represents a pathway of length 4 that produces two ATP molecules. It converts 3-PG to glycerate with the production of ATP, via glycerate 3-kinase, EC 2.7.1.31. Although the final reaction of this pathway is not present in the KEGG database, this putative reaction is chemically rather simple—involving the dehydration of glycerate to enolpyruvate followed by a spontaneous tautomerization to pyruvate—and so it seems plausible that it might be catalysed by the uncharacterized, non-primary action of an existing enzyme.Despite the huge variety and complexity of life on Earth, the biochemistry of core metabolism is remarkably universal. Our analysis shows that this universality does not arise from an absence of other possibilities. Using a systematic approach, we have identified many alternatives to perhaps the most highly conserved set of metabolic reactions, the glycolytic and gluconeogenic trunk pathways. Our alternative pathways obey the rules of biochemistry, carry positive flux under reasonable intracellular conditions, and satisfy reasonable constraints on metabolite concentrations. Remarkably, of all these alternatives, we find that the trunk pathway observed in nature carries the highest biochemical flux in both the glycolytic and gluconeogenic directions, for parameters that represent typical intracellular physiological conditions. Of the two variants of the prokaryotic gluconeogenic pathway that are found in nature, the pps route is the best performer across a wide parameter range, whereas the ppdk route is more sensitive to environmental conditions, requiring a low concentration of pyrophosphate . The facet al.Flux maximization is widely recognized as an important concept in the study of metabolism; both from the perspective of glycolysis and more broadly56Our analysis also reveals alternative trunk pathways that can perform better than the natural one under different physiological conditions. Although some of these alternatives involve compounds and reactions that are not found in biochemical databases, others use enzymes that are known to exist in nature. The latter pathways  are cleaIn this study, we have limited our analysis to pathways that start and end at G3P and pyruvate. Relaxing this requirement would certainly lead to many more alternative pathways for the generation of energy and for biosynthesis. Although it is also important to consider other factors, including the need for integration within a wider metabolic network, our analysis suggests that key principles underlying the structure of core metabolism may emerge from simple biochemical, thermodynamic and biophysical considerations.Finally, we briefly discuss broader evolutionary implications of our work. Assuming that the trunk pathway can be replaced by any of our alternative pathways without affecting the rest of the metabolic network too much, the ATP flux could be used as a proxy for fitness, that is, the ability of the organism to outcompete other, similar to organisms. Endowed with a mutational graph of possible mutations between different pathways (different genotypes), this would create a fitness landscape in which the highest peaks correspond to local maxima of the ATP flux. An interesting question is whether the global maximum  is accessible, that is, can it be reached from anywhere in the landscape? Such fundamental evolutionary questions have been addressed recently using the full set of known biochemical reactions383940We created a list of chemical compounds with 2, 3 or 4 carbon atoms by generating all possible linear combinations of the 20 ‘building blocks' shown in fG were taken from the literature252627g1g2…gn, composed from building blocks {gi}, we calculated ΔfG usingFor those internal metabolites that are known biochemical species, standard free energies of formation ΔE0 is a constant, E1(gj) is the contribution of group gj and E2 is a small correction due to neighbouring group–group interactions. The values of E0, the vector E1 and matrix E2 are determined by performing a least-squares fit to a training set of molecules with known ΔfGs that correspond most closely to the linear CHOPN molecules of our network .Supplementary Figures 1-7, Supplementary Tables 1-4, Supplementary Notes 1-2, Supplementary Discussion, Supplementary Methods and Supplementary ReferencesGlycolytic pathways from Figure 2b in main text. Columns are: EC class, standard free energy, reaction equation.Gluconeogenic pathways from Figure 2d in main text. Columns are: EC class, standard free energy, reaction equation.Glycolytic pathways (top 100) from comparison in which no constraints are placed on the intermediate metabolites. Columns are: EC class, standard free energy, reaction equation.Glycolytic pathways from Supplementary Fig. 5. Analysis uses the extended range for feasible metabolite concentrations, i.e. 1 nM to 500 mM. Columns are: EC class, standard free energy, reaction equation.Gluconeogenic pathways from Supplementary Fig. 5. Analysis uses the extended range for feasible metabolite concentrations, i.e. 1 nM to 500 mM. Columns are: EC class, standard free energy, reaction equation.Glycolytic pathways from Supplementary Fig. 6, i.e. pathways contain only compounds found in the KEGG COMPOUND database. Columns are: EC class, standard free energy, reaction equation.


Exiguobacterium antarcticum B7 is extremophile Gram-positive bacteria able to survive in cold environments. A key factor to understanding cold adaptation processes is related to the modification of fatty acids composing the cell membranes of psychrotrophic bacteria. In our study we show the in silico reconstruction of the fatty acid biosynthesis pathway of E. antarcticum B7. To build the stoichiometric model, a semiautomatic procedure was applied, which integrates genome information using KEGG and RAST/SEED. Constraint-based methods, namely, Flux Balance Analysis (FBA) and elementary modes (EM), were applied. FBA was implemented in the sense of hexadecenoic acid production maximization. To evaluate the influence of the gene expression in the fluxome analysis, FBA was also calculated using the log2FC values obtained in the transcriptome analysis at 0°C and 37°C. The fatty acid biosynthesis pathway showed a total of 13 elementary flux modes, four of which showed routes for the production of hexadecenoic acid. The reconstructed pathway demonstrated the capacity of E. antarcticum B7 to de novo produce fatty acid molecules. Under the influence of the transcriptome, the fluxome was altered, promoting the production of short-chain fatty acids. The calculated models contribute to better understanding of the bacterial adaptation at cold environments. Corynebacterium glutamicum are rationally engineered to produce L-amino acids on an industrial scale [Bacteria are increasingly used in industrial processes to produce chemicals, foods, and drugs, among other products . The maial scale . To accoal scale  Cytoscapal scale , CellDesal scale , SBW . This lake was formed due to the warming in the region, which led to partial melting of ice caps [Psychrotrophic microorganisms have an optimal growth temperature higher than 15°C but are also able to grow and adapt to extremely cold environments, with temperatures of approximately 0°C . Thus, tice caps . The genice caps , and itsice caps . One of ice caps , 23. de novo synthesis of fatty acids is regulated by the protein FapR [ E. antarcticum B7. In cold, two of these operons are repressed (fabH1-fabF and fabI), and the expression levels of the other two remain unaltered [ in silico reconstruction of metabolic pathways [Bacterialein FapR , which inaltered . The chenaltered , 24. Biopathways . in silico reconstruction of the fatty acid biosynthesis pathway of the Exiguobacterium antarcticum B7, based on linear programming (FBA) and convex cone method (elementary modes). The influence of transcriptome in FBA calculation was also evaluated.In this work we present the E. antarcticum B7 in the formats  .gbk and  .fasta were collected from NCBI under accession number NC_018665. The metabolic pathway of the fatty acid biosynthesis was initially evaluated in the KEGG (Kyoto Encyclopedia of Genes and Genomes) database [The genomic data ofdatabase . When nedatabase . E. antarcticum B7 genome was submitted to the online tool KAAS (KEGG Automatic Annotation Server) [http://www.genome.jp/kaas-bin/kaas_main. The parameters chosen to run this software were as follows: (a) bidirectional Best Hit (BBH) Method, recommended for complete genomes, performs the search for orthologous genes between a specific group of organisms, and (b) prokaryote, the set of genes chosen, should be representative of the target organism, in this case, the bacterium E. antarcticum B7. Following the processing, a text file was generated (query.ko). Each line of this file is formed by two parameters: the first consists of the sequence identified (gene), and the second, when present, consists of the KO assignment, termed K number. This value indicates orthologous groups encoding the same enzymatic activity. Afterwards, the file generated is passed through a filter, an auxiliary computer software program (script) Python developed for the present study, which only selects K numbers and individually and increasingly commands per line into a new file (new_query.ko). This file was used as entry in the option User Data Mapping of the Pathway Mapping tool of the KEGG.This step was performed following two methods: one semiautomatic and the other automatic. The tools within the KEGG databases were used essentially in the semiautomatic method. The.fasta file with the Server) , availab E. antarcticum B7 genome to the online tool RAST (Rapid Annotation using Subsystems Technology) [http://rast.nmpdr.org/, to generate the drafts of the metabolic network and of the fatty acid biosynthesis pathway of the target microorganism. The final draft of this step was generated from the combination of the resulting pathways of the semiautomatic and automatic models. The common pathways were maintained, while surplus compounds, enzymes, and reactions, that is, present in some, but absent in others, were not directly excluded but were instead reserved for the curated step.The automatic method essentially consisted of submitting the  .fasta file of thehnology) , availabThe following steps were completed in this manual curation stage, following the protocol explained above. (i) Draft refinement: this phase began with the analysis of enzymes and reactions, components of the fatty acid biosynthesis pathway, by reading books and articles specifically on the subject. The objective was to diagnose the absence or presence of more than one element of the study pathway. The online databases KEGG, ENZYME , and SEE(ii) Assessment of the stoichiometry and reversibility of the reactions: in this step, all model reactions were assessed and stoichiometrically corrected, if necessary. The biochemical data on the organism are very important to determine the reversibility of the reaction. For this purpose, the databases  and the tool eQuilibrator  were use(iii) Addition of gene data and reaction location: the Artemis tool was used to identify the genes of the reactions (enzymes) from their locations in the genome assessed using the draft generated.(iv) Assessment of Gene-Protein-Reaction (GPR) associations: in this step, the function of each gene is indicated. GPR associations were identified using databases of the organism and specific literature.(v) Definition of constraints: some constraints were defined in the model in this manual curation step, including stoichiometric and thermodynamic constraints (through the reversibility and irreversibility of fluxes).The metabolic model designed and refined following the manual curation step was converted into a mathematical representation, termed a stoichiometric matrix, which encouraged the development of a wide variety of computational tools to analyze network properties.http://dx.doi.org/10.1155/2016/7863706).The constraints of capacity, which are the upper and lower limits defining the maximum and minimum fluxes allowed for the reactions, were added in this step. The inputs of the stoichiometric matrix are the coefficients of the metabolites in the reactions with negative values for consumed metabolites (substrates) and positive values when the metabolites are produced or secreted (products)  numbers were identified; only the locus tag Eab7_2235 has no added gene associated with it.The draft of the metabolic network ofdatabase . The KEG E. antarcticum B7. The RAST/SEED tool does not provide graphic display of the metabolic map draft as KEGG; for this purpose, it uses a standard table to list the 247 metabolic pathways that compose the network, regardless of whether they were identified in the genome of the microorganism. RAST identified 25 reactions, 40 compounds, and 20 EC numbers in the fatty acid biosynthesis pathway ofThe analysis of both fatty acid biosynthetic pathway drafts shows that the draft generated using KEGG apparently has the most complete flux, except for enzyme 6.3.4.14, which is exclusively present in the draft resulting from the RAST tool. The draft generated using RAST has a gap in which the enzymes FabA and FabB are not included in the pathway elongation process. The flux for the production of hexadecenoic acid is also absent from the pathway generated using RAST. E. antarcticum B7, except fabK gene, which was detected only by the automatic method.Artemis software was used to confirm the presence of all genes selected through the automatic and semiautomatic methods. The genes accABCD, fabD, fabH1, fabF, fabG, fabZ, and fabI and the locus tag Eab7_2235 were described in the genome ofThe KGML file produced by KEGG was submitted to the software KEGGtranslator  to be coThe data generated using the RAST/SEED tool were analyzed and added to the first step of the process, supplementing the data collected using KEGG. The files generated with both platforms were used to manage the manual curation data.The larger number of genes identified using KEGG (12) compared to those found using RAST/SEED (9) may be explained because the former uses orthology (KEGG Orthology (KO)) through protein homology to identify the so-called metabolite genes  in a gen fabF gene, the Eab7_2235 locus tag, and the extracellular metabolites the other processes are reversible.The reactions of the fatty acid synthesis pathway were annotated and refined. The metabolites were organized into two compartments (cytoplasm and extracellular compartment) based on the location of the enzymes associated with each pathway. The cofactors and the reversibility of the reactions were compiled from the data published in the literature and online tools (ENZYME and BRENDA). The EC number was noted, and the genes were identified. A summary of those results is shown in It is very important to assess the quality of the annotated genome submitted to the online tools during curation. The literature categorically states that the quality of the reconstructed network directly depends on the annotated genome of the organism. The rule is to use the latest updated version of the annotated genome –38.The previous step added constraints regarding the stoichiometry , thermodynamics (reversibility of reactions), and physiology (cofactors used) of the study model. The result was a system of equations that describes the cell metabolism according to the metabolites of interest. The mathematical representation of this model essentially consisted of describing the performance of the fatty acid biosynthesis pathway using a stoichiometric matrix. This data structure consists of 54 metabolites and 59 reactions, resulting in a 54 × 59 stoichiometric matrix . Additional File 2 shows the list of biochemical reactions identified in the curated model.The reconstructed pathway model was converted into SBML in the used MATLAB toolbox. The SBML file was validated using the tool SBML validator and was then submitted to the tool Cytoscape, which generated the network of The FBA was coded in MATLAB implementing a constrained linear program using the GLPK (GNU Linear Programming Kit) linear optimization library . All flu2FC) obtained in vitro by Dall'Agnol and colleagues [2FC of genes that composes the fatty acid biosynthesis pathway is shown in Log base 2-fold change values  is converted to malonyl-CoA, which binds to the acyl carrier protein (ACP) at 0°C  at 0°C . As stat fabF, fabG, fabZ, and fabI genes. At each round, two carbons are added to the growing chain of fatty acid. In these reactions, the flow of metabolites remains unchanged until the fatty acid molecule reaches a size of eight carbons (octadecanoyl-ACP in reaction 38) where the percentage of the flow amount decreases . A total of 13 elementary flux modes were found for the fatty acid biosynthesis pathway of E. antarcticum B7 . Of these, only 4 elementary modes  have a positive nonzero coefficient for reaction 41, which indicates that the target product hexadecenoic acid may only be generated by one of these four elementary modes. The routes identified in EM2 and EM5 begin at the second reaction (R2) which is catalyzed by the enzyme FabD. The value of R2 for both elementary modes indicates a considerable production of malonyl-CoA. The remaining reactions of EM2 and EM5 are presented in Additional File 4. In EM8 and EM11, the routes begin from R1. The value 1 for this reaction indicates a lower activity, reflecting in a lower production of hexadecenoic acid. Regarding reaction 41, the values of elementary modes 2 and 5, in this case 1, are higher than the values of 8 and 11 (0.142857 each), indicating that both elementary modes 2 and 5 produce 1 mol hexadecenoic acid when they are active, while elementary modes 8 and 11 produce 0.142857 moles.The calculation of elementary modes was performed in MATLAB using the Metatool toolbox  , the biochemical reactions identified in the draft model and included in the stoichiometric matrix (File 2), and the calculated FBA rates (File 3). All these documents are in .xls format.


E. coli and yeast, among others, to develop a novel integrative network model to prioritize and identify candidate drug targets in neglected pathogen proteomes, and bioactive drug-like molecules. We modeled genomic (proteins) and chemical (bioactive compounds) data as a multilayer weighted network graph that takes advantage of bioactivity data across 221 species, chemical similarities between 1.7 105 compounds and several functional relations among 1.67 105 proteins. These relations comprised orthology, sharing of protein domains, and shared participation in defined biochemical pathways. We showcase the application of this network graph to the problem of prioritization of new candidate targets, based on the information available in the graph for known compound-target associations. We validated this strategy by performing a cross validation procedure for known mouse and Trypanosoma cruzi targets and showed that our approach outperforms classic alignment-based approaches. Moreover, our model provides additional flexibility as two different network definitions could be considered, finding in both cases qualitatively different but sensible candidate targets. We also showcase the application of the network to suggest targets for orphan compounds that are active against Plasmodium falciparum in high-throughput screens. In this case our approach provided a reduced prioritization list of target proteins for the query molecules and showed the ability to propose new testable hypotheses for each compound. Moreover, we found that some predictions highlighted by our network model were supported by independent experimental validations as found post-facto in the literature.Drug development for neglected diseases has been historically hampered due to lack of market incentives. The advent of public domain resources containing chemical information from high throughput screenings is changing the landscape of drug discovery for these diseases. In this work we took advantage of data from extensively studied organisms like human, mouse,  E. coli and yeast, among others, to prioritize and identify candidate drug targets in neglected pathogen proteomes, and drug-like bioactive molecules to foster drug development against neglected diseases. Our approach to the problem relied on applying bioinformatics and computational biology strategies to model large datasets spanning complete proteomes and extensive chemical information from publicly available sources. As a result, we were able to prioritize drug targets and identify potential targets for orphan bioactive drugs.Neglected tropical diseases are human infectious diseases that are often associated with poverty. Historically, lack of interest from the pharmaceutical industry resulted in the lack of good drugs to combat the majority of the pathogens that cause these diseases. Recently, the availability of open chemical information has increased with the advent of public domain chemical resources and the release of data from high throughput screening assays. Our aim in this work was to make use of data from extensively studied organisms like human, mouse,  Neglected tropical diseases (NTDs) devastate the lives of approximately 1 billion people, with a further 1 billion at risk . These dThe situation for human diseases that affect the developed world is radically different. In this case, many important contributions to drug discovery are made every year from academic and government laboratories, leading to the approval of ~20 new drugs per year on average . As partDrug repositioning  is the process of finding new indications for existing drugs . The benTarget prioritization, and drug repositioning are particularly amenable to the use of computational data mining techniques, which offer high-level integration of available knowledge . These sIt is also worthwhile mentioning that particularly in the case of neglected diseases, drug repositioning need not be taken in a strict sense to include only drugs approved for clinical use in humans. Widening the criteria to reposition drugs for veterinary use, or further, any bioactive compound (hits/leads) may significantly increase the chances of success by helping to guide efforts in academia and pharma. These will ultimately feed the pipeline of drug discovery for these important diseases.TDRtargets.org database pyridines [T. cruzi, where it was found to bind the compound purvalanol B [T. cruzi protein obtained in 10th place of the ranked list, TcMAPK2, has been studied and characterized. Interestingly, this MAP kinase could not be inhibited by the mammalian ERK2 inhibitor FR180204, raising the possibility of a differential inhibition profile, which would open the door to the development of selective inhibitors of the trypanosome vs mammalian proteins [The first protein in the ranking obtained for proteins , includie family . Transfee family . The 5thL. major . These cyridines . This taalanol B ,69. Finaproteins .As shown in T. brucei (listed 6th for T. cruzi) is an inositol 1,4,5-trisphosphate receptor. Inositol triphosphate receptors are intracellular calcium release channels that play a key role in Ca2+ signaling in cells [T. brucei and T. cruzi show that this target is essential for growth and establishment of infection [T. cruzi is a phosphatidyl inositol 3-kinase (PI3K). This protein has orthologs in several species and has 4 paralogs in humans. The PI3Ks can be divided into 3 classes (I-III). The protein prioritized by our method is a class I PI3K [T. cruzi the treatment with wortmannin, a PI3K inhibitor, prevented the entry of parasites to the cells [th for L. major, 9th for T. cruzi) is the carbamoyl-phosphate synthetase II (CPSII), a key regulatory enzyme of the de novo pyrimidine synthesis. This enzyme, which generates carbamoyl-phosphate from L-glutamine, bicarbonate, and two ATP molecules, is the first in the 6-enzyme cascade that catalyzes the formation of uridine 5'-monophosphate. In a recent study, a CPSII knock out strain of T. cruzi displayed significantly reduced growth (in epimastigotes) [de novo pyrimidine biosynthesis pathway and in particular this enzyme could be important targets to block parasite replication [The first protein in the prioritized list of in cells . Recent nfection ,73. The s I PI3K . These es I PI3K ,75. Givehe cells ,77. A clhe cells . Anothertigotes) . Also, ilication .rdin L. major, 5thin T. brucei). This finding represents a special case that serves both to validate the strategy and to highlight a number of gaps in the data curation process . But a number of studies have already reported on the inhibition of the T. brucei and Leishmania enzymes with CYP51 inhibitors [Another target suggested by this method is a lanosterol 14α demethylase  of the compound, to gain an understanding of the mechanism of action of the drug.m) have no links to the PP-layer but have bioactivities that meet the different specified cutoffs in m. For this, we only report here results obtained considering the G’rk network-based strategy, as the already observed bias for the G’r network-model affects the sensitivity of the corresponding prioritization results as shown in previous sections.In this part of the work we took advantage of the information contained in the constructed network to obtain candidate targets for a given orphan compound, defined as a node in the D-layer of our network with no links to the PP-layer. We assume that these compounds have been selected based on one of the case scenarios described above (i.e. from high-throughput phenotypic screenings). Such compounds , taking into account molecules directly linked to m through Edd edges. Next, we considered the set of target proteins in the PP-layer that were associated to the CSN(m) through bioactivity annotations. These protein nodes were used as seeds for the prioritization procedure described in the previous sections. Each seed protein, sj, was associated to an initial score, wj (see Eq(CSN(m) and the considered orphan compound of interest m  proportst m see .5) with exactly one known protein target in our dataset, and assessed our ability to recover these targets in the prioritized lists after removing the corresponding bioactivity links.To validate this strategy, bioactive molecules with known targets were artificially “orphaned” by removing the bioactivity links that associated these drugs with their cognate targets. We considered a random set of 1,000 molecules .Under this cross-validation exercise, we first proceeded to analyze the global sensitivity of our recovery strategy. For each artificially orphaned drug l of the global rankings rG, the number of recovered targets, ρ(rG), and the corresponding recovery rate, λ(rG), defined as the ratio between the incremental gain in ρ, per ranking interval (i.e. λ(rG).The plot in λ∞, as the mean As can be appreciated in r*G-top ranked molecules) where reasonable predictions can be anticipated, in the sense that a high rate of success is expected to occur. In our cross-validation study we found that r*G = 38. Considering this threshold level, the sought target proteins were globally ranked before r*G for ~70% of the 1,000 tested molecules. rSS). We observed that 70% of these predicted target proteins appeared at the top three positions of the corresponding rSS ranking, and ~97% were ranked within the top 10 suggested targets. On the other hand, we observed that top-ranked target proteins for 297 out of the 1,000 tested molecules were globally ranked after the r*is found within a sharp change of regime (i.e. an elbow) of the recovery rate curve.This parameter serves to identify a global ranking range  is added to the N-terminus of a glycine residue in specific target proteins [a posteriori analysis of the literature. First, several studies show that this protein is indeed a promising target for development of new antimalarials [Plasmodium enzyme [C. albicans enzyme are different compounds.One example of this drug-target prediction is shown in proteins ,85. We valarials –88. FurtP. falciparum strain 3D7 and the multidrug-resistant strain Dd2 . In our network model this compound is connected with other active compounds, with varying levels of similarity, as shown in the figure. All these compounds are hydroxamic acid derivatives, some of which are known to inhibit bacterial peptide deformylases [P. falciparum [P. falciparum for this orphan compound was ranked in the prediction zone, under 3σ (r*G < 38).Another interesting case is shown in rmylases . The moslciparum , as welllciparum . Althouglciparum ), this ePlasmodium falciparum M1 alanyl aminopeptidase (PfA-M1). This enzyme has been shown to be an essential hemoglobinase, catalyzing the final stages of hemoglobin break-down within intra-erythrocytic parasites [Other orphan compounds with antimalarial activity  were conarasites ,94. A nuarasites –98, and arasites . In our E. coli, M. tuberculosis, S. aureus and P. falciparum [P. falciparum [Five orphan compounds  where prlciparum ,101. Sevlciparum ,102–104 lciparum ; howeverP. falciparum. This is the case of a putative 3-demethylubiquinone-9 3-methyltransferase (PF3D7_0724300), a putative 3-oxo-5-alpha-steroid 4-dehydrogenase (PF3D7_1135900), and a putative polyprenol reductase  [In some other cases, the compounds had proposed targets that, to our knowledge, have not yet been characterized experimentally as potential drug targets in 1455900) . An exce1455900) . The bioIn this work we show a novel multilayer network strategy that addresses a number of important problems in the field of drug discovery as applied to neglected tropical diseases. First, we show how the information integrated in a multilayer network containing complete proteomes from pathogen and non-pathogen organisms allow the identification of relevant candidate drug targets, even in the presence of scarce target inhibition data for the pathogen of interest. This is particularly important in this field as this provides a mean to leverage data from other, more studied organisms to guide drug repositioning exercises for diseases that usually lack experimental, high-volume, chemogenomic datasets.a priori defined sets of criteria by different users and different ad-hoc scoring systems for target features. In contrast, in this work we show how the availability of target-drug associations in our network model  can be used to guide the scoring of targets (weighting of graph edges) through a simple statistical assessment of enrichment of seed proteins (known targets of bioactive compounds) for functional annotation classes (target features), followed by prioritization of first-neighbors using a voting algorithm. As a result, we are now able to prioritize targets without resorting to ad-hoc hypotheses about desirable or undesirable target features.We and others have previously devised a number of target-centric prioritization strategies that were focused on target features with only minor integration of chemical information ,108,109.The network model  showed an increased performance when compared to a simple (naïve) sequence similarity search against known druggable targets, .G’r and G’rk, can be derived from the original network to perform prioritization tasks. Differences in the respective ranking lists could be understood in terms of the observed prioritization dependency on the strength of target nodes in the G’r graph. The strength of a node in a weighted graph takes into account not only its degree (i.e. the number of connections to other adjacent nodes) but also the weighted values of these connections. As discussed in detail in Supp. Text S1, prioritizations based on uncorrected scores (G’r network) were a priori heavily driven by strong nodes. A bias towards these high-strength nodes may not be necessarily bad, as the strength reflects embedded information on functional categories enriched in links to active compounds  with numerous examples of inhibitors targeting several kinases at low micromolar concentrations [Moreover, our methodology provides additional flexibility as two different graphs, trations .Additionally, when considering the prospects of testing the compounds associated with these targets in whole-cell assays against other organisms, it is worth considering that perhaps because of this demonstrated promiscuity, there have been many cases of success in the identification of non-kinase targets of kinase inhibitors –114. ThiFinally, as shown in a priori reliable species-specific rankings, given observed global ranks of protein targets along the entire network. Some of the connections highlighted by our model were supported by independent experimental validations as found post-facto in the literature. However, further experimentation should be carried out to test the activities of other orphan compounds . In this context it is appropriate to bear in mind the high attrition rate that is usually associated with confirmatory assays, even when performing these on the very same pathogen species [Prediction of candidate targets for orphan compounds is not straightforward. Several approaches rely on chemical similarity to relate ligands to candidate targets ,18. Howe species .The utility of this approach lies not only in the search for new chemical leads for drug discovery, but also to identify and map tool/probe compounds ,119. Altpost-facto validation of our findings, we noticed a number of publications with relevant information but that pre-dated the initial data gathering exercise for this paper . However, upon detailed scrutiny, the disparate ways and units in which bioactivities are reported  demanded a serious and very time consuming curation effort. This is the main reason limiting the number of links between the D-layer and the PP-layer in our network model. Adding more proteomes , or more compounds , is just a matter of throwing more computational resources at the problem. However, increasing the number of links between targets and compounds still requires a heavy investment in data curation.Construction of our network model also required some manual curation, which represents a huge bottleneck in terms of time invested at this task. The single most laborious step in our approach has been the manual curation required to classify compounds into active vs inactive. This was necessary because bioactivity databases such as ChEMBL include negative data as well  of a defined outcome. We have decided to use 80% activity as a cutoff see , but we Echinococcus spp. (Hydatid disease) [Loa loa (loiasis) [Trichomonas vaginalis [Giardia [The network model developed in this work can certainly be expanded further, connecting more targets from other proteomes of interest, and connecting more compounds. We have already identified recent datasets listing bioactivities of new and existing compounds . These are already in the public domain ,120. We disease) , Loa loaloiasis) , Fasciolloiasis) , and othaginalis  and Giar[Giardia ,126. Thiis the target essential for the cell?’). These functional genomics data are mostly derived from genome-wide experiments (knockouts or knockdowns). However, it takes a sustained curation effort to identify, and integrate these data for all the genomes of interest.Finally, although theoretically the model can also be expanded to include other types of affiliation-type annotations, or relations, these would have to be amenable to obtain from scalable computational analyses, in order to avoid the curation bottleneck. For example, one of the most valuable query types supported by TDR Targets is based on integration of phenotypic annotations Click here for additional data file.S2 FigM. musculus, and T. cruzi.Overlap between top ranked proteins (1%) according to G’r, G’rk and FASTA  strategies is shown using Venn diagrams for two genomes: (TIFF)Click here for additional data file.S3 FigThe plot shows the number of compounds involved in substructure similarity relationships that can be filtered out as a function of molecule size (MW) for different promiscuity threshold levels (different curves). PS = parental structures (those that contain a compound as part of its structure).(PDF)Click here for additional data file.S4 FigT. cruzi and M. musculus). Network projections were calculated using different values of the free parameter α. Overall we observed that differences in AUC-0.1 values were within 5% of tolerance.The figure shows the performance of 10-fold cross-validation target prioritization exercises in which all target-compound bioactivity links were removed for two query species ((TIFF)Click here for additional data file.S1 TableWe list the name of the organism and a brief summary of the taxonomic classification or grouping for each species.(PDF)Click here for additional data file.S2 TableEPF edges in the network (see main text); K = degree of each VP node; S/K = normalized score over the degree. In both cases, S/K network outperform performances.Voting Scheme (VS) and Functional Flow (FF) network prioritization strategies were compared in terms of its AUC-0.1 performance . In spi(PDF)Click here for additional data file.S3 TableTargets were prioritized using either non-normalized scores (Sheet "NN Prioritization"), or after applying a degree-normalizing scoring function (Sheet "DN Prioritization"). Three rankings are shown in a single table in each spreadsheet . The first column therefore shows the corresponding position of the target in each ranking. For simplicity, the affiliation of targets to metabolic pathways is summarized in an EC number. Ortholog groups (OG) are OrthoMCL IDs, Target IDs are either from TriTrypDB (prioritized targets) or ChEMBL (druggable homologs). For clarity, a single representative druggable homolog is shown. In the non-normalized prioritization, because the list is composed mostly exclusively by protein kinases, two additional columns are used to provide information on their classification, according to Bahia et al. .(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S1 TextG’rk and G’r based prioritizations. We showed how the performance discrepancy happened to be related to a strong correlation that existed between G’r -prioritization scores and network´s connectivity topological features.We analyzed in detail the discrepancies among (PDF)Click here for additional data file.


The gut microbiota plays an important role in human health and disease by acting as a metabolic organ. Metagenomic sequencing has shown how dysbiosis in the gut microbiota is associated with human metabolic diseases such as obesity and diabetes. Modeling may assist to gain insight into the metabolic implication of an altered microbiota. Fast and accurate reconstruction of metabolic models for members of the gut microbiota, as well as methods to simulate a community of microorganisms, are therefore needed. The Integrated Microbial Genomes (IMG) database contains functional annotation for nearly 4,650 bacterial genomes. This tremendous new genomic information adds new opportunities for systems biology to reconstruct accurate genome scale metabolic models (GEMs).Bifidobacterium adolescentis L2-32, which produces acetate during fermentation, and Faecalibacterium prausnitzii A2-165, which consumes acetate and produces butyrate. F. prausnitzii is less abundant in patients with Crohn’s disease and has been suggested to play an anti-inflammatory role in the gut ecosystem. The B. adolescentis model, iBif452, comprises 699 reactions and 611 unique metabolites. The F. prausnitzii model, iFap484, comprises 713 reactions and 621 unique metabolites. Each model was validated with in vivo data. We used OptCom and Flux Balance Analysis to simulate how both organisms interact.Here we assembled a reaction data set containing 2,340 reactions obtained from existing genome-scale metabolic models, where each reaction is assigned with KEGG Orthology. The reaction data set was then used to reconstruct two genome scale metabolic models for gut microorganisms available in the IMG database F. prausnitzii’s demand for acetate and production of butyrate which plays an essential role in colonic homeostasis and cancer prevention. The assembled reaction set is a useful tool to generate bacterial draft models from KEGG Orthology.The consortium of iBif452 and iFap484 was applied to predict  Metagenomic sequencing facilitates the study of a large number of microorganisms in environmental samples . This tePichia stipitis and Pichia pastoris using iIN800, a GEM of Saccharomyces cerevisiae).Click here for fileMatlab funtions used in the paper.Click here for fileGenes with annotations: TIGRFAMs, KOs, SEED, TC number.Click here for fileFlux variability analysis for prediction fluxes in the study.Click here for fileThe structure of community as XML format.Click here for fileReference reaction data set.Click here for fileMapping the assembled reaction set to KEGG metabolic pathways maps.Click here for fileThe two models in SBML and MS Excel format. The two models are also available through the Human Metabolic Atlass http://www.metabolicatlas.com/downloads/micro.Click here for fileBifidobacterium adolescentis adolescentis L2-32.Main carbon metabolism in Click here for fileFaecalibacterium prausnitzii A2-165.Main carbon metabolism in Click here for file


Arthrospira (Spirulina) platensis is a promising feedstock and host strain for bioproduction because of its high accumulation of glycogen and superior characteristics for industrial production. Metabolic simulation using a genome-scale metabolic model and flux balance analysis is a powerful method that can be used to design metabolic engineering strategies for the improvement of target molecule production. In this study, we constructed a genome-scale metabolic model of A. platensis NIES-39 including 746 metabolic reactions and 673 metabolites, and developed novel strategies to improve the production of valuable metabolites, such as glycogen and ethanol. The simulation results obtained using the metabolic model showed high consistency with experimental results for growth rates under several trophic conditions and growth capabilities on various organic substrates. The metabolic model was further applied to design a metabolic network to improve the autotrophic production of glycogen and ethanol. Decreased flux of reactions related to the TCA cycle and phosphoenolpyruvate reaction were found to improve glycogen production. Furthermore, in silico knockout simulation indicated that deletion of genes related to the respiratory chain, such as NAD(P)H dehydrogenase and cytochrome-c oxidase, could enhance ethanol production by using ammonium as a nitrogen source. Cyanobacteria have also attracted increasing attention as biomass feedstock and host organisms for bioproduction because they can produce biomass and biofuels from COynthesis . Moreoveynthesis , lactateynthesis , isobutaynthesis , glycogeynthesis , and fatynthesis .Arthrospira (Spirulina) platensis, a filamentous non-N2-fixing cyanobacterium, is promising for use as a biomass feedstock because of its capability to accumulate a large amount of glycogen, which is an excellent feedstock for biofuel production [A. platensis [A. platensis has been produced as a superior nutrient because of its high content of protein and carotenes [oduction , 9. Undelatensis , 10. Morarotenes , and it arotenes . Its suparotenes  could prin silico metabolic model that includes most of the metabolic reactions and metabolites of the target strain [Synechocystis sp. PCC 6803 [Synechococcus sp. PCC 7002 [Cyanothece sp. ATCC 51142 [A. platensis, two small metabolic models were constructed that include 22 metabolic reactions [A. platensis C1 (PCC9438) including 875 reactions was constructed, and metabolic phenotypes and essential genes under various culture conditions, such as autotrophic and mixotrophic conditions, were analyzed [Recently, metabolic simulation using a genome-scale metabolic model has been widely used for rational metabolic design for the improvement of target molecule production , 14. A gt strain , 16. Flut strain , 18. Thit strain , 14. In PCC 6803 –22, SynePCC 7002 , and CyaCC 51142 , thus preactions  and 121 eactions . Recentlanalyzed . HoweverA. platensis NIES-39 and developed metabolic engineering strategies to improve production of valuable materials, such as glycogen and ethanol. A. platensis NIES-39 is a well-studied A. platensis strain, as the whole genome sequence [In this study, we constructed a genome-scale metabolic model of sequence , glycogesequence , and metsequence  have beeA. platensis NIES-39 was constructed based on various information sources such as databases, the literature, and genome sequences. The annotation data were collected from CyanoBase [A. platensis NIES-39 by comparison with the protein sequence of related cyanobacteria species such as Synechocystis sp. PCC 6803 and Synechococcus sp. PCC 7002 using BLASTP in KEGG  (http://www.genome.jp/tools/blast/). When the candidate genes for the synthesis reactions for the biomass components were not identified in the genome sequence, the reactions were added to the draft model based on the KEGG database. The cofactors of the reactions, such as NADPH and NADH, and transport reactions between the cytosol and periplasm and the periplasm and extracellular environment were added to the metabolic model based on previous genome-scale metabolic models of Synechocystis sp. PCC 6803 [Escherichia coli [A genome-scale metabolic model of yanoBase , Kyoto EyanoBase , and theyanoBase , and a dPCC 6803  and Eschhia coli .A. platensis were collected from various previous reports. The macromolecular portion of the biomass was composed of 68% proteins, 16% carbohydrates, 0.88% DNA, 3.12% RNA, 11% lipids, and 1% chlorophyll, based on a previous study [A. platensis NIES-39 was unknown, that of Synechocystis sp. PCC 6803 under autotrophic condition [The biomass components of us study . The amius study . The carus study , lipopolus study . Carotenus study , which aus study , followius study . Becauseondition  was usedondition , 37.A. platensis NIES-39 was constructed, which includes 673 unique metabolites and 746 metabolic reactions including 652 intracellular reactions, 60 transport reactions and 34 exchange reactions. The details of all reactions, metabolites, and biomass compositions are summarized in Finally, the metabolic model of A. platensis NIES-39 was calculated using FBA [S represents the stoichiometric matrix of metabolites in metabolic reactions, and v indicates a vector of the flux for each metabolic reaction. The values vmin and vmax represent the minimum and maximum constraints of the flux for each reaction, and c is a vector that represents the objective function to be maximized or minimized. In this study, biomass production was used as the objective function to be maximized, with the assumption that cellular metabolism is self-organized to maximize growth rate.The metabolic flux distribution on the genome-scale metabolic model of sing FBA , 32. BriBecause linear programming problems can present multiple solutions with an identical value for the objective function, the flux variability of each reaction was calculated . In briehttp://glpkmex.sourceforge.net/).All simulations were performed using Matlab  with a linear programming solver, GLPK .For FBA simulation, the following external metabolites were assumed to be freely transported through the cell membrane: COTo simulate the metabolic flux distribution under autotrophic conditions, the photon uptake rate was set to a given value, and the glucose uptake rate was set to zero. For heterotrophic conditions, the uptake rate of one of the organic substrates, such as glucose, was set to a given value, and the photon uptake rate was set to zero. For simplification, we assumed that all photons are used for photosynthesis and neglected the effect of excess light energy to dissipate.A. platensis NIES-39 were described in a previous study [3, 0.5 g/L K2HPO4, 2.5 g/L NaNO3, 1.0 g/L K2SO4, 1.0 g/L NaCl, 0.2 g/L MgSO4 7H2O, 0.04 g/L CaCl2 2H2O, 0.01 g/L FeSO4 7H2O, 0.08 g/L Na2 EDTA, and 1.0 mL of A5 solution . Pre-cultivated cells were grown under 50 μmol photons/m2/s at 30°C. In the log-growth phase, the pre-cultivated cells were harvested by filtration and inoculated into fresh SOT medium without nitrate at 0.15 g dry cell weight (DW)/L. The main culture was performed under 250 μmol photons/m2/s at 30°C. Before inoculation, cells were washed three times with nitrate-free SOT medium to prevent a change in nitrate concentration.The detailed culture conditions of us study . The cel750) by using a UVmini-1240 spectrophotometer .The cell concentration was measured according to the optical density of the culture medium at 750 nm  equipped with an Aminex HPX-87H column . During HPLC analysis, the column temperature was kept at 65°C; the mobile phase, 2 mM HThe ethanol concentration was measured using a gas chromatograph (GC) system with a hydrogen flame ionization detector  and a Stabiliwax column .A. platensis NIES-39 was constructed based on information from genome sequences, databases such as KEGG [A. platensis NIES-39. However, a recent study reported that a TCA bypass pathway complements the lack of the reaction [A. platensis NIES-39, the gene encoding succinic semialdehyde dehydrogenase has been already annotated (NIES39_L06120), but the gene encoding α-ketoglutarate decarboxylase has not been identified. The candidate gene for α-ketoglutarate decarboxylase was searched for in the A. platensis NIES-39 genome sequence by BLASTP (http://www.genome.jp/tools/blast/) using the amino acid sequence of SynPCC7002_A2770, which was identified as α-ketoglutarate decarboxylase in Synechococcus sp. PCC 7002 [A. platensis NIES-39 but that are required for synthesis of biomass components, e.g., methionine, serine, and fatty acid biosynthesis reactions, were added based on the information of the KEGG database. Because A. platensis has been reported to produce some metabolites such as ethanol, formate, lactate, and acetate [A genome-scale metabolic model of  as KEGG  and Cyan as KEGG , and pre as KEGG , 33, 34. as KEGG , 41, andreaction  that conPCC 7002 , as a qu acetate , 37, theA. platensis NIES-39, which included 746 reactions and 673 metabolites, was constructed  from culture results under various light intensity conditions [2 uptake rate was estimated to be 3.1 mmol/gDW/h from the carbon composition in the biomass [2 uptake rate, 3.1 mmol/gDW/h; photon uptake rate, free; glucose uptake rate, zero. The simulated specific growth rate (0.069/h) agreed well with the experimental results (0.074/h). The specific growth rate under heterotrophic conditions was also analyzed. The specific growth rate and specific glucose uptake rate were calculated from a previous experiment under heterotrophic conditions generated with glucose and without light illumination [To evaluate the simulation results using the constructed model, the simulated specific growth rates under various conditions were compared with the experimental results. To analyze the growth rate under autotrophic conditions, the maximum specific growth rate of nditions . The max biomass  and the mination . Metabolmination ; photon A. platensis NIES-39 was grown on 14 organic substrates, such as sugars, metabolites of the central metabolism, and amino acids, under heterotrophic conditions [A. platensis NIES-39 was described with its synonymous name, Spirulina platensis IAM M-135, in the previous study [A. platensis could grow on all substrates and were consistent with the experimental results in 11 of 14 cases  in A. platensis NIES-39. In fact, metabolic simulation without the α-ketoglutarate transport reaction predicted non-growth on α-ketoglutarate.For further evaluation of the simulation results, growth capabilities on various organic substrates under heterotrophic conditions were analyzed. In a previous study, nditions . Note thus study . To simu14 cases . Inconsibacteria . A. platlutamate , which abacteria . AnotherA. platensis NIES-39 H production or consumption and requires ATP consumption, which reduces the growth rate.The metabolic model enabled us to develop metabolic engineering strategies for improvement of glycogen production. We performed an Next, we analyzed the effect of increasing or decreasing the flux of each reaction on glycogen production by flux response analysis (FRA) , 46. In zwf encoding glucose-6-phosphate dehydrogenase was deleted, glycogen production was predicted to be increased by activation of the phosphoglucose isomerase reaction (data not shown). Under the nitrate depletion condition in Synechocystis sp. PCC 6803, in which the flux toward glycogen was increased via phosphoglucose isomerase, the intracellular concentration of 6-phosphogluconate belonging to oxidative PPP was increased, suggesting that the flux through PPP was increased [zwf, is a possible strategy to improve glycogen production under nitrogen limitation conditions as predicted by metabolic simulation.An increase of the precursor supply is an important strategy to improve target molecule production. However, FRA estimated that glycogen production was not enhanced by increased flux of the phosphoglucose isomerase reaction (F6P → G6P). When the flux of phosphoglucose isomerase was increased, the flux into oxidative PPP was increased because increase of oxidative PPP flux contributes to biomass production rather than increase of glycogen production. When ncreased . TherefoA. platensis NIES-39 is a natural producer of ethanol [2 as a carbon source and light as an energy source, for bio-engineering purposes. As results of this screening, two sets of knockouts were identified . When nitrate was used as a nitrogen source, a large amount of excess NAD(P)H could not be produced because a great amount of NADPH was used to reduce the oxidized ferredoxin generated by ferredoxin-nitrate reductase, which reduced nitrate to ammonium. Therefore, we speculated that using ammonium rather than nitrate could reduce the oxidization of NADPH by nitrate reduction and generate excess NADPH.The metabolic model was further applied to design the metabolic network to improve the production of another useful product, ethanol, which has the potential to replace fossil fuels.  ethanol . Both NAd Tables . The detc oxidase, which belong to the respiratory chain, were commonly included among the candidates. NADH dehydrogenase and NADPH dehydrogenase are encoded by genes from NIES39_E02860, NIES39_A01530, NIES39_A08120, NIES39_K04850, NIES39_O01600, NIES39_O01620, NIES39_A06520, NIES39_K04840, NIES39_A06510, NIES39_O01610, NIES39_E02840, NIES39_O07110, NIES39_E02850, NIES39_A08140, NIES39_A08130, NIES39_F00440, NIES39_J01870, NIES39_H00330, NIES39_L04070, NIES39_L04060, and NIES39_L04040. Cytochrome-c oxidase is encoded by NIES39_O03120, NIES39_O03080, NIES39_O03090, NIES39_O03100, NIES39_A01920, and NIES39_A01910. The ethanol yield was estimated to be zero in the original metabolic model and 43 C-mol% in this knockout model. Because NAD(P)H was oxidized through the respiratory chain, excess NADPH was generated in the deletion model of these reactions. Generally, in non-photosynthetic microorganisms, ethanol production is performed under anaerobic conditions to generate excess intracellular NADH by reducing respiratory chain activity. However, in cyanobacteria, oxygen is produced by photosynthesis under autotrophic conditions, which makes it difficult to generate anaerobic conditions. Thus, disruption of the respiratory chain may be effective to increase ethanol production rather than anaerobic cultivation in cyanobacteria. In this case, the specificity of alcohol dehydrogenase for NADH and NADPH did not affect the results because transhydrogenase converts NADPH to NADH in our metabolic model. The disruption of respiratory chain also resulted in increasing flux on Photosystem I (PSI) and decreasing flux on Photosystem II (PSII). This change in PSI and PSII fluxes was probably due to compensating the decrease in proton pumping flux by the disruption of respiratory chain. Although the biomass production significantly decreased by the disruption of respiratory chain, still the cells can produce biomass. In the disrupted network, the proton pumping activity is maintained by the process of photosynthesis, which in turn results in producing ATP.The triple gene knockout simulation was performed using ammonium as a nitrogen source by changing the nitrate uptake rate to zero and the ammonium uptake rate to free. The knockout simulations identified several sets of knockout candidates whose deletion could improve ethanol production . The reaMoreover, additional deletions of some genes in the knockout of the respiratory chain were predicted to further increase ethanol production , i.e. geA. platensis NIES-39 and developed novel metabolic engineering strategies for glycogen and ethanol production. The metabolic model was constructed based on information from databases and the literature, and the simulation results using the metabolic model were evaluated by comparing them with the experimental results for growth rates and growth capabilities on various organic carbon sources. The high consistency between the simulation and experimental results indicates the availability of the constructed metabolic model for metabolic simulation. The metabolic simulation was further applied to demonstrate glycogen production under nitrogen limitation conditions and to design metabolic engineering strategies to increase glycogen production by FRA. Moreover, in silico knockout simulation successfully identified candidate genes to be deleted for improvement of ethanol production.In this study, we constructed a genome-scale metabolic model of A. platensis is expected to be a suitable feedstock and host strain for bioproduction because of its superior characteristics for industrial bioproduction processes. The genome-scale metabolic model constructed in this study will provide a detailed metabolic understanding and valuable information to achieve industrial bioproduction using A. platensis.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.


Yarrowia lipolytica has considerable potential for producing single cell oil, which can be converted to biodiesel, a sustainable alternative to fossil fuels. However, extensive fundamental and engineering efforts must be carried out before commercialized production become cost-effective. Therefore, in this study, metabolic flux analysis of Y. lipolytica was performed using 13C-labeled glucose as a sole carbon source in nitrogen sufficient and insufficient media. The nitrogen limited medium inhibited cell growth while promoting lipid accumulation (from 8.7% of their biomass to 14.3%). Metabolic flux analysis showed that flux through the pentose phosphate pathway was not significantly regulated by nitrogen concentration, suggesting that NADPH generation is not the limiting factor for lipid accumulation in Y. lipolytica. Furthermore, metabolic flux through malic enzyme was undetectable, confirming its non-regulatory role in lipid accumulation in this yeast. Nitrogen limitation significantly increased flux through ATP:citrate lyase (ACL), implying that ACL plays a key role in providing acetyl-CoA for lipid accumulation in Y. lipolytica.The oleaginous yeast  Due to recent petroleum crises and the worldwide accumulation of greenhouse gases in recent years, alternative fuels have attracted more and more interest. Biodiesel, which is produced via the esterification of vegetable oils or animal fats with lower alcohols, is an alternative to fossil fuel-derived diesel and is commercially available in Europe, the United States, and many other countries , 2. The +-dependent malic enzyme (NADP-ME)  glucose : unlabeled glucose or 100% [1-13C] glucose . Cultures were incubated at 28°C and shaken at 200 rpm. Each culture was inoculated from a fresh preculture with a starting optical density (OD600) at 0.01. Each culture was performed in triplicate.Labeling experiments were performed in a 1 L shake flask with 100 mL of medium, which consisted of a 20%: 80% mixture of  glucose: unlabeled glucose or 100% [1-13C] glucose to facilitate 13C-labeled metabolic flux analysis. Metabolic and isotopic steady states are prerequisites for 13C-labeled metabolic flux analyses. These prerequisites mean that all intermediate concentrations and fluxes remain constant throughout the 13C-labeled experiment. In batch cultures, metabolic and isotopic steady states exist during exponential growth, where growth rates and isotopomer fractions are constant. Therefore, 20 h was chosen as the time point for biomass composition analysis and 13C-labeled flux experiments.Y. lipolytica was analyzed . Second, the macromolecular composition of Y. lipolytica was also measured for the prediction of intracellular fluxes. This macromolecular composition consists of the compositions of proteinogenic amino acids, fatty acids, DNA and RNA. The nucleotide composition of the DNA was obtained from the genome sequence, and the same composition was assumed for RNA. The DNA and RNA compositions calculated from the genome sequence of Y. lipolytica are shown in To calculate absolute fluxes, the amount of metabolites withdrawn to fulfill biosynthetic demands must be known. First, the macromolecular composition of analyzed . This ma13C-labeled metabolic flux analysis method. For this study, a model of the central carbon metabolism network is required for flux analysis. This network was constructed based on the genome sequence of Y. lipolytica. The genomic information contained within the sequence database was organized into various essential pathways, including the glycolysis/gluconeogenesis, the pentose phosphate pathway, the TCA cycle, fatty acid biosynthesis, amino acid metabolic pathways and nucleotide metabolism. Metabolic pathway information for Y. lipolytica was also available from previous studies. This information was used to identify the metabolic networks in Y. lipolytica. All biochemical reactions are listed in the Supporting Information.A model of the metabolic networks to be studied is a prerequisite for 13C-labeled proteinogenic amino acid carbon skeletons must be determined via GC-MS or Nuclear Magnetic Resonance (NMR). In recent years, GC-MS has become a sensitive and useful tool for the estimation of 13C-labeled fluxes. To track the mass isotopomer patterns of proteinogenic amino acids in Y. lipolytica, cells were harvested from mid-exponential growth phase cultures grown with 13C-labeled glucose under nitrogen-rich and nitrogen-limited conditions. Samples were then subjected to hydrolysis and derivatization for GC-MS analysis. Because 13C-labeled metabolic flux analysis requires exclusive mass distributions of carbon skeletons, raw amino acid fragment mass data were corrected for natural isotope abundances  values of approximately 0.2. FL values for proteinogenic amino acid fragments were close to this theoretical FL  and exhibited decreased protein content under nitrogen-limited conditions, indicating that the pentose phosphate pathway supplies sufficient NADPH for lipid biosynthesis. Excess NADPH generated under nitrogen-rich conditions could be used by the strain to increase higher protein content and to synthesize other metabolites. Indeed, overexpression of glucose-6-phosphate dehydrogenase or 6-phosphogluconate dehydrogenase did not significantly increase lipid accumulation in this yeast (unpublished data). These results suggest that the pentose phosphate pathway produces sufficient NADPH for lipid biosynthesis in this low-oleaginous wild type strain and does not limit lipid accumulation. In a recent report, 13C-labeled metabolic flux analysis showed that in a high-oleaginous yeast strain engineered to overexpress ACC and DGAT, pentose phosphate pathway activity was upregulated compared to a the control strain. This result demonstrates that the pentose phosphate pathway is the primary source of NADPH for lipid accumulation in Y. lipolytica [Apart from malic enzyme, other major NADPH generating enzymes that are involved in lipid biosynthesis include glucose-6-phosphate dehydrogenase and 6-phosphogluconate dehydrogenase from the pentose phosphate pathway. In some cases, cytoplasmic NADPynthesis . The invynthesis . In thisY. lipolytica was approximately 20% lower than compared to in the nitrogen-rich conditions. This finding is consistent with early biochemical studies on lipid accumulation in the oleaginous fungi M. alpina and Mucor circinelloides, which found that nitrogen limitation leads to a decrease in the activity of TCA cycle enzymes and to the accumulation of cytosolic citrate. This cytosolic citrate is then converted to acetyl-CoA by ACL for fatty acid synthesis [13C-labeled metabolic flux analysis has indicated that ACL is the primary source for excess lipid accumulation in the oleaginous yeast T. cutaneum [Mus musculus ACL, which has a low Km (0.05 mM) for citrate, increased lipid production in Y. lipolytica by 200% [Y. lipolytica [Y. lipolytica.The two carbon unit acetyl-CoA molecule, which is primarily generated by the TCA cycle, is the building block for fatty acid biosynthesis. In nitrogen-limited condition, the flux through the TCA cycle in ynthesis . We founcutaneum . In addi by 200% . A 30% kpolytica . Therefo13C-labeled glucose as a sole carbon source was performed on Y. lipolytica grown in nitrogen-rich and nitrogen-limited mediums. Nitrogen limitation led to increased lipid accumulation. The results of metabolic flux analysis showed that malic enzyme is inactivity in Y. lipolytica, indicating that the enzyme does not regulate lipid accumulation in this yeast strain. Furthermore, nitrogen concentration in the medium was found to have little influence on the metabolic flux through the pentose phosphate pathway, suggesting that the pentose phosphate pathway yields sufficient NADPH for lipid biosynthesis and is not a limiting factor for lipid accumulation. Meanwhile, nitrogen limitation significantly increased flux through ACL, indicating that this enzyme plays a key role in providing acetyl-CoA for lipid accumulation in Y. lipolytica. Taken together, these results provide some insights into the roles of key enzymes for lipid accumulation in Y. lipolytica.In conclusion, metabolic flux analysis using S1 FigEach experiment was performed in triplicate, and all data are reported as means ± standard deviations.(TIF)Click here for additional data file.S2 FigY. lipolytica cultivated in high nitrogen medium (A) and low nitrogen medium (B). A value of 0 in the metabolic flux map indicates a very low flux (below 0.5).Metabolic flux distribution of (TIF)Click here for additional data file.S1 File(DOCX)Click here for additional data file.S1 TableThe theoretical FL value is 0.2, equal to the labeling content of the input glucose.(DOCX)Click here for additional data file.