Fluxer (https://fluxer.umbc.edu), a free and open-access novel web application for the computation and visualization of genome-scale metabolic flux networks. Any genome-scale model based on the Systems Biology Markup Language can be uploaded to the tool, which automatically performs Flux Balance Analysis and computes different flux graphs for visualization and analysis. The major metabolic pathways for biomass growth or for biosynthesis of any metabolite can be interactively knocked-out, analyzed and visualized as a spanning tree, dendrogram or complete graph using different layouts. In addition, Fluxer can compute and visualize the k-shortest metabolic paths between any two metabolites or reactions to identify the main metabolic routes between two compounds of interest. The web application includes >80 whole-genome metabolic reconstructions of diverse organisms from bacteria to human, readily available for exploration. Fluxer enables the efficient analysis and visualization of genome-scale metabolic models toward the discovery of key metabolic pathways.Next-generation sequencing has paved the way for the reconstruction of genome-scale metabolic networks as a powerful tool for understanding metabolic circuits in any organism. However, the visualization and extraction of knowledge from these large networks comprising thousands of reactions and metabolites is a current challenge in need of user-friendly tools. Here we present  Metabolic reconstructions from whole-genome sequencing and biochemical data aim to determine all metabolic processes occurring within a cell or whole organism, which can then be integrated into genome-scale metabolic models (GEMs) able to predict cellular phenotypes ,2. ConstSeveral tools currently exist for the analysis and visualization of GEMs. The Systems Biology Markup Language (SBML)  is the sk-shortest metabolic paths between two reactions or metabolites, using a user-customizable metric for the link weights based on the computed metabolic fluxes. The graph visualizations are interactive, and the user can select any metabolite or reaction as the root for the flux tree, obtain information about the reactions and metabolites such as their chemical structures, and easily adjust the node labels, weight calculations, and inclusion or not of cofactors, zero-flux reactions or cellular localizations in the visualized graph. Fluxer allows any user with no programming experience to exploit the potential of genome-scale metabolic models in their ability to assist in the understanding of the whole metabolic network of an organism and predict specific phenotypes. Fluxer’s combined use of FBA and graph theory for metabolic network visualization and analysis in a user-friendly web-application can pave the way for advancements in applications for metabolic engineering.Here we present Fluxer, a web-application for the automated computation of GEM graphs based on flux and their interactive visualization with a user-friendly interface. The application is able to take as input an arbitrary GEM specified in SBML format and automatically compute and efficiently visualize the complete network and their metabolic fluxes. The tool performs FBA of the model and includes several algorithms to compute optimized visualizations of complete GEMs, including spanning trees, dendrograms and physics-based force layouts. The application can also compute and display the k-shortest paths and complete graphs with an interactive and user-friendly web interface. Any GEMs in SBML format can be uploaded to the tool, which then performs FBA optimization and calculates and renders the complete model with different interactive graph visualizations. The metabolic networks are encoded as bipartite graphs, where nodes represent metabolites or reactions and edges the involvement of a metabolite in a reaction as a product or a reactant. Nodes can be selected to display detailed information including metabolite structures and reaction fluxes. Furthermore, any number of reactions can be knocked-out through the interface to simulate enzyme gene deletions and their phenotypic effects in terms of growth rates and fluxes. The different available graph representations make use of the computed steady-state reaction fluxes, which together with the stoichiometric coefficients and molecular weights constitute the weight of each edge. The spanning tree graph calculates the input flux pathways toward the root node—the biomass growth or any other reaction or metabolite of interest—visualizing the most important pathways contributing to the root node. The k-shortest paths graph calculates the shortest pathways with highest fluxes between two metabolites or reactions of interest, easily visualizing each separate path in an interactive graph. Finally, the complete graph includes all the reactions and metabolites of the model, which can be visualized as a reaction network or as a tree rooted with a particular metabolite or reaction of interest. The models uploaded to the application can be privately shared with an automatically generated unique web link, and >80 genome-scale metabolic reconstructions from the BiGG Models knowledge base  or any other reaction or metabolite of interest. The spanning tree is recursively connected with the upstream most important reactions and metabolites leading to the root to neatly visualize the whole network. Each metabolic input and output for each reaction (an edge in the graph) is assigned a weight, which is the product of a user-selectable combination of the reaction metabolic flux , stoichiometry coefficient and metabolite molecular weight. The tree includes every node (metabolite or reaction) in the model once, without duplication. Starting from the root node, the algorithm builds the tree by iteratively adding the edge with the highest weight that connects a current node in the tree with a node not yet present in the tree; in case of more than one candidate edge having the same weight, the edge with the shortest path to the root node is selected; and in case of similar depth, the edges are selected according to the alphabetical order of their labels. In addition, precedence is always given to the edges with fluxes toward the root node, so the elements participating in the production of the metabolite or reaction of interest are always added first to the tree. In this way, the selected edge weights determine the layout of the tree. By default, the weights are calculated as the product of the reaction metabolic flux and metabolite stoichiometry coefficient, so the metabolites selected first are those that contribute the most amount  to the root metabolite or reaction. If none of the weight options are selected, all edges would have a default weight of 1 unit, and the spanning tree algorithm is equivalent to an alphabetically sorted breadth-first search, showing the shortest metabolic paths to the root node from every metabolite and reaction in the model.Five different layouts can be used to visualize the resulting spanning tree. A tree layout based on Reingold and Tilford’s tidy drawing algorithm  shows thEscherichia coli BL21  positions the nodes of the acyclic graph using layered techniques and cross-minimization algorithms . Reactions from both the glycolysis and the pentose phosphate pathways are involved in the shortest paths between the two metabolites selected, as it was predicted previously  for the backend, HTML5 and JavaScript for the frontend, and SQLite for the database. FBA optimization is performed with COBRApy  and the k-shortest paths and complete graphs. The different networks can be visualized with tree, dendrogram, radial, dagre and force-based layouts. The web application allows any user to load models in SBML format and interact and customize the different metabolic networks generated. Users require no special training or software installations to access and use the web application. The ability of Fluxer to perform visual comparisons of reaction fluxes makes it a powerful tool for understanding metabolic phenotypes and discovering pathways for metabolic engineering applications.We presented here Fluxer, a user-friendly web application for the computation, analysis and visualization of flux graphs for GEMs. The tool can perform FBA including specific reaction knockouts and compute metabolic network representations based on spanning trees, While Fluxer computes graph visualizations centered on a particular root metabolite or reaction of interest, other approaches have been proposed for the global analysis of GEMs. Extreme pathways  represenFurther future work will extend the current functionality of Fluxer. The capabilities to perform FBA customizations beyond the knockout of reactions will be improved to include different optimization objectives and the ability to change the flux boundaries of any reaction—particularly to specify different uptake rates for different growth media. New graph layouts will be implemented, such as maps to highlight the metabolic composition of a particular phenotype state. Finally, the web server will include the possibility for the user to make public any model uploaded to the application, and then list them in a dynamically updated repository of genome-wide metabolic models.Fluxer provides not only a topological solution for visualizing genome-scale metabolic models, but also specific computational methods to analyze reaction networks and fluxes. The comparison of metabolic network connectivity and motifs across organisms can provide evolutionary insights , an appr


Streptomyces clavuligerus is a filamentous Gram-positive bacterial producer of the β-lactamase inhibitor clavulanic acid. Antibiotics biosynthesis in the Streptomyces genus is usually triggered by nutritional and environmental perturbations. In this work, a new genome scale metabolic network of Streptomyces clavuligerus was reconstructed and used to study the experimentally observed effect of oxygen and phosphate concentrations on clavulanic acid biosynthesis under high and low shear stress. A flux balance analysis based on experimental evidence revealed that clavulanic acid biosynthetic reaction fluxes are favored in conditions of phosphate limitation, and this is correlated with enhanced activity of central and amino acid metabolism, as well as with enhanced oxygen uptake. In silico and experimental results show a possible slowing down of tricarboxylic acid (TCA) due to reduced oxygen availability in low shear stress conditions. In contrast, high shear stress conditions are connected with high intracellular oxygen availability favoring TCA activity, precursors availability and clavulanic acid (CA) production. Streptomyces clavuligerus (S. clavuligerus) is a biotechnologically important Gram-positive filamentous bacterium for the production of β-lactam antibiotic compounds such as clavulanic acid (CA) and cephamycin C. In the current emergence of extended multidrug and antibiotic resistance phenomena, the β-lactam inhibitors remain as one of the main drugs used against bacterial resistant infections [S. clavuligerus [S. clavuligerus to different environmental and nutritional factors and its relationship with CA biosynthesis is not completely understood. In this regard, a strong correlation between macromorphology and CA production under low and high shear stress conditions in submerged cultures of S. clavuligerus was recently reported [S. clavuligerus cells, as comparably high specific growth and CA production rates were achieved. Additionally, it was assumed that CA production might be affected by a lower surface-to-volume ratio at thicker mycelia evolving under low-shear cultivation conditions, which would impact negatively upon the diffusion capacity and, therefore, the transport of nutrients, oxygen and product secretion.fections . CA has fections ,3. Severfections . It is luligerus ,6. Neverreported . The stuN2-(2-carboxy-ethyl) L-arginine to -clavaminic acid, and the late steps, comprising the reactions up to CA and the clavam 5S compounds [2-(2-carboxy-ethyl) L-arginine in a reaction catalyzed by the N2-(2-carboxyethyl) arginine synthase (CEAS). Glycerol is the preferred substrate for production of CA because of its rather direct conversion into glyceraldehyde 3-phosphate (GAP). GAP can be metabolized through three different pathways: (i) the gluconeogenesis and pentose phosphate pathway, (ii) the glycolysis and tricarboxylic acid (TCA) cycle and (iii) the clavam and CA biosynthesis pathway [CA is a product of the so-called clavam pathway. The clavam pathway is usually divided into the early steps, including the reactions from ompounds ,9. The f pathway .N2-(2-carboxy-ethyl) L-arginine by action of β-lactam synthetase (BLS) [Subsequently, deoxyguanidinoproclavaminic acid, a β-lactam compound, is formed by intramolecular reaction of se (BLS) ,12. Enzyse (BLS) . The amise (BLS) ,15. Procse (BLS) . At thisse (BLS) . Clavulase (BLS) . The thrse (BLS) ,20. CA bS. clavuligerus, metabolic network models have been reconstructed and/or updated for this purpose [A common approach for studying the effect of nutritional, genetic and environmental perturbations on metabolism is the genome-scale metabolic modeling using flux balance analysis (FBA) . In the  purpose ,22,23,24S. clavuligerus metabolism has been developed in this work. A novel top-down automated reconstruction tool proposed by Machado et al. [S. clavuligerus by Cao et al. [S. clavuligerus metabolism. This model was used in a combined approach of experimental and modeling work for investigating the connection between the different nutritional conditions observed in S. clavuligerus cultivations and CA biosynthesis. Defined media was used to study the influence of nutrients on carbon flux distribution during CA biosynthesis [S. clavuligerus given their connections with amino acid biosynthesis and degradation pathways [S. clavuligerus in a stirred tank and single-use 2-D rocking-motion bioreactors. A medium rich in glycerol but limited in phosphate and glutamate was used as feed in order to explore the effects of their depletion on the CA biosynthesis rate. Subsequently, the experimentally observed metabolic scenarios were simulated using the constructed and validated genome-scale model of S. clavuligerus in order to identify putative connections between the central carbon and amino acid metabolism, nutrient limiting conditions and CA production.Starting from recent advances in genome-scale model reconstruction and available genetic information, a new and improved genome-scale model of the o et al.  was usedo et al. . The iniynthesis ,27. Prevpathways . In thisS. clavuligerus representative genomes range between 7.6 and 8.5 Mb with a GC content of 72.6% and a median protein count of 6654. An initial Genome-Scale Metabolic Model (GEM) was generated from the representative genome of S. clavuligerus, sequenced by Cao et al. [S. clavuligerus, the script of CarveMe was executed in the Anaconda Python distribution for Linux; externally, the Diamond sequence aligner [o et al.  and curro et al. . The reco et al. . For the aligner  and IBM The initial GEM was manually curated by following a systematic procedure in order to improve the in silico representation of the physiology of the organism. The missing reactions and metabolites in the secondary metabolism, which correspond to penicillin–cephalosporin biosynthesis, clavulanic acid biosynthesis and 5S-clavams bifurcation were manually added and associated to the corresponding genes, while maintaining the connectivity of the model, and the mass and charge balances, respectively.® implementation of the Flux Variability Analysis (FVA) methodology proposed by Schellenberger et al. [−1.The flux distribution, obtained by solving a standard FBA optimization, frequently include thermodynamic infeasible cycles (TICs) that represent slopes of reactions like a perpetual motion machine that violates the Second Law of Thermodynamics. This heavily distorts the reaction flux predictions. It has been demonstrated that the number of infeasible loops grows rapidly with the network size . Therefor et al.  for TICsThe set of unbounded reactions which are part of the TICs were used to define a stoichiometric matrix. Its null space was used for the individual identification of TICs composed by two or more reactions. The manual curation of the TICs was performed by (i) elimination of linearly dependent reversible reactions resulting in redundancy due to erroneous identification of GPR during the automated reconstruction, and (ii) restriction of the reaction directionality by calculation of the correspondent Standard Gibbs free energy of reaction (∆Gr) using the NExT  algorithm . The scrS. clavuligerus is available in SBML (xml) and MS Excel (xlsx) file formats in the The final GEM, denoted as iDG1237, was reviewed with the MC3 Consistency Checker algorithm, which implements stoichiometric-based identification and FVA analysis to determine single connected and dead end metabolites in the network, as well as the consistent coupled and inconsistent coupled/zero-flux reactions . The iDGS. clavuligerus cultivations at high and low shear stress conditions. FBA solves a linear programming problem for calculating the steady-state reaction flux distributions along the metabolic network that maximizes (or minimizes) a pre-defined objective function, under specific constraints. The objective function shall reflect the achievement of a meaningful physiological state of the organism like a maximal ATP production, growth rate, or specific metabolite production [S is the stoichiometric matrix of coefficients for m metabolites and n reactions, v is the flux vector of dimension n, maxZ represents the biomass maximization, f is a vector of weights, and bl and bu are the lower and upper bounds, respectively. Thus, in the first stage the FBA standard problem for biomass maximization is solved, whereas the second stage deals with the minimization of the vector of fluxes. It is important to notice that the assumption underlying the objective function minimization, postulates that living organisms gain functional fitness by fulfilling their functions with maximal efficiency, and thus assuring a minimal energy requirement to accomplish a specific pattern of cellular functions [In the FBA studies the external/internal exchange reactions were constrained to represent, in silico, the environmental conditions observed in oduction . Some vaunctions . All FBAS. clavuligerus DSM 41826, cryo-preserved at −80 °C in glycerol solution (16.7% v/v), was inoculated for activation in seed medium as described by Roubos et al. [®  both operating at 5 L initial filling volume. Chemically defined media were used in all cultivations, prepared as follows (in g.L−1 deionized and distilled water): glycerol (9.3), K2HPO4 (0.8), (NH4)2SO4 (1.26), monosodium glutamate (9.8), FeSO4·7H2O (0.18), MgSO4·7H2O (0.72) and trace element solution (1.44 mL). The trace elements solution contained (in g.L−1 deionized and distilled water): H2SO4 (20.4), monosodium citrate·1H2O (50), ZnSO4·7H2O (16.75), CuSO4·5H2O (2.5), MnCl2·4H2O (1.5), H3BO3 (2), and Na2MoO4·2H2O (2) . Antifoam 204  was used at concentration of 1:1000 v/v. Bioreactors were inoculated (10% v/v) and operated at 28 °C; aeration at 0.6 VVM was provided and pH was maintained at 6.8 by addition of NaOH 3M and HCl 3M. Reactors were equipped with pH (Polylite Plus) dissolved oxygen (DO–VisiFerm) probes , O2, CO2 gas sensors and exhaust gas analyzer .s et al. . CultivaS. clavuligerus adapts differently to motion patterns and shear forces [−1 with a defined medium prepared as follows (in g.L−1 deionized and distilled water): glycerol (120.0), K2HPO4 (2.0), (NH4)2SO4 (8.0), until a final volume of 7.8 L was reached. During the batch operation DO was not controlled, only minimum DO was set at 20% to avoid anoxia. During fed-batch operation DO was maintained rather constant at 62 ± 5%. Culture samples (2 mL) were taken at 12 h intervals and centrifuged at 15,000 rpm and 4 °C for 10 min; wet biomass was washed with 0.9% NaCl, centrifuged and dried overnight at 75 °C for dry cell weight (DCW) determination.It has been reported that r forces . Therefo® GmbH, Aschaffenburg, Germany) operated with a flow rate of 1 mL.min−1 at 30 °C. The mobile phase consisted of H2PO4  and methanol (HPLC grade). Analyses were performed using the gradient method described by Ramirez-Malule [CA concentration in supernatant samples was determined in a HPLC  with a diode array detector (DAD), using a Zorbax Eclipse XDB-C-18 chromatographic column (Agilent Technologies) and a C-18 guard column  were used, and operated at a flow rate of 1 mL.min−1 and 40 °C. Mobile phase consisted of NaH2PO4  as polar eluent and a solution of methanol (45% vol.), acetonitrile (45% vol.) and water (10% vol.) as nonpolar eluent [Glycerol, glycolysis and TCA intermediates were quantified in a HPLC  with a refractive index detector (RID) and operated at 15 °C using a HyperREZ™ XP carbohydrate H+ column  at a constant flow rate of 0.5 mL.minle phase . Quantifr eluent . Semi-quS. clavuligerus in order to improve the in silico representation of its metabolism in comparison to previously reported GEMs. It considers the current state-of-the-art in genetics and biochemistry. The reconstruction process was based on an initial functional and high-quality universal model, in which infeasible reactions for S. clavuligerus were removed and constraints were added [Firstly, the aim of this study was to develop a novel GEM for re added . HoweverThe previous GEM share a common origin, the model iMM865 reported by Medema et al. . The derS. clavuligerus named iDG1237 consisted of 2177 reactions, including 543 internal/external metabolite exchange reactions and 1237 annotated genes. The metabolites were annotated according with their correspondent BiGG identifiers; similarly, the genes were annotated with their Refseq identifiers when available. The model consistency statistics for the iDG1237 model and the previous models for S. clavuligerus are presented in The resulting GEM of S. clavuligerus.The improved connectivity of the model is reflected in a lower number of dead-end metabolites. In the case of the iDG1237 GEM, the number of dead-end metabolites is considerably lower (3%) compared to previously reported GEMs (~27%). Additionally, the number of zero-flux reactions and inconsistent coupling was approximately 80% lower than in previous reconstructions. Therefore, the full consistent coupling allows better determination of relationships among reactions and pathways, connections between nutritional factors and desired metabolites, and analysis of potential knockouts, as well as further regulatory and expression studies ,44. The S. clavuligerus in a chemostat mode, using the experimental data published by Bushell et al. [ex,Glycv), glutamate , and phosphate  were constrained according to experimental data [expμ) and in silico (μ) growth rates, CA secretion fluxes  and cumulative mean square error (MSE), between the experimental and in silico data, are presented in 2 demand and CO2 excretion were also considered in the model evaluation , growth rate and CA accumulation were higher in HSS cultivations  than the one observed at HSS (11.9 g.L−1). The glycerol and glutamate concentration time courses are presented in −1, respectively. Similarly, the maximum uptake fluxes of glutamate were 1.34 and 1.29 mmol.(g DCW.h)−1 during a short time span in the batch stage.Experimental results of growth, CA, phosphate and metabolite secretion under high shear stress (HSS) and low shear stress (LSS) conditions are shown in ivations a,b. The S. clavuligerus, potentially limiting the oxygen diffusion from the media to the intracellular environment [The phosphate limitation was expected to start between 30 and 40 h and the specific CA production typically increased linked to phosphate depletion as observed in ironment . In LSS ironment . When inStreptomyces species like S. coelicolor and S. clavuligerus [Organic acid secretion in streptomycetes b–g is reuligerus ,47. In tL-arginine h is deriDuring growth, some accumulation of oxaloacetate is expected f due to S. clavuligerus cultivations in HSS and LSS conditions, six metabolic scenarios corresponding to different phases observed during the cultivation were considered for the in silico metabolic flux analysis. The complete results of the FBA simulations are available in the Based on the experimental results for Experimental results showed that growth, nutrient uptake and metabolite secretion were essentially the same in both bioreactors, during the first 37 h of cultivation. Two different growth scenarios (SC1 and SC2) were observed in absence of nutrient limitation during the initial batch phase (0–37 h). From 37 h onwards, the effect of nutritional and environmental conditions on growth rate and CA biosynthesis was more pronounced. Then, two metabolic scenarios (SC3 and SC5) observed under HSS conditions, and two under LSS (SC4 and SC6) were retained as representative of the fed-batch cultivation stages covering the time interval 37–93 h. SC3 and SC4 represent growth in the presence of high glycerol and low glutamate availabilities under phosphate limitation and different RQ, as observed in the HSS and LSS cultivations. SC5 and SC6 simulate the growth on glycerol after glutamate and phosphate depletions at different RQ values. Although extracellular concentrations of phosphate and its uptake are never strictly zero given the utilization of phosphate storages during phosphate scarcity, the scenarios of phosphate limitation are characterized by a very low phosphate uptake that likely approaches to zero and consequently decreases the growth rate. Thus, scenarios of phosphate limitation SC3–SC6 were simulated by approximating the exchange flux to zero.The scenarios with RQ = 1 (SC 4 and SC6) were observed under LSS conditions linked to lower oxygen uptake compared to carbon dioxide generation. The lower and upper bounds of the exchange fluxes for medium components were constrained between −1 and 1, respectively. The main nutrient uptakes, i.e., glycerol, glutamate, phosphate and ammonium, were set as equality constraints according to the experimentally observed fluxes, since these are the main exchange fluxes in the network. In the case of the simulations with RQ = 1, the carbon dioxide exchange flux was also constrained to satisfy the RQ constraint. The constraints of exchange fluxes for glycerol, glutamate, ammonium and phosphate, as well as simulation results for growth rate, CA and oxygen exchange fluxes are summarized in The simulation of SC1 showed that flux ratio between the second phase of the glycolysis and first oxidation in the TCA cycle was close to 1.0, indicating a high activity of the TCA cycle and balanced oxidative metabolism. This is consistent with the high nitrogen and carbon uptakes during the early stages of cultivation. In those scenarios, the oxidative direction of TCA cycle was preferred to promote anabolism and growth. FBA indicates that anaplerotic reactions catalyzed by PPC, citrate lyase (CITL) and the glyoxylate shunt are not activated under such conditions.2 generation. Previous reports suggested that RQ values between 1 and 1.2 are characteristic of growth without nutrient limitation, while values out of this range are associated to nitrogen, oxygen or phosphate limitations affecting also the growth rate [−1).As expected, SC2 reflects the highest metabolic activity and growth rates in silico, which is consistent with the experimental results. The experimentally verified increase of glycerol and ammonium uptakes, used as constraints in silico, leads to an RQ of 1.2 due to the increase in the COwth rate ,50. Anab−1) was observed in SC3, that is, growth under phosphate limitation and low glutamate uptake. This scenario was macroscopically characterized by a decrease in growth rate due to the low phosphate availability and hence, lower carbon and oxygen uptakes, causing a decline in the RQ down to 0.94 [CA production varies because of a nutrient-specific effect in each metabolic scenario. The highest reaction flux towards the CA pathway (0.03 mmol (g DCW.h) to 0.94 . Oxygen The calculated fluxes of reactions dependent on glutamate and related to the urea cycle in SC3 were up to 1.7-fold higher than those calculated for SC4. The carbon flux towards asparagine synthase (ASNS) decreased as consequence of the high fluxes in L-aspartate and L-glutamine synthesis via the urea cycle. These FBA results are consistent with the experimentally observed low accumulations of L-arginine, L-aspartate, L-glutamine and L-asparagine under HSS conditions in stirred tank bioreactor h–j, sincIn scenarios of growth under glutamate and phosphate depletions (SC5 and SC6), the flux decrease in TCA and urea cycles was higher in the oxygen restricted scenario (SC6). In contrast, in SC3 and SC4 scenarios the constrained exchange flux of L-glutamate fueled the fluxes in the urea cycle. In SC5 and SC6 these fluxes were more dependent on glycolysis and TCA, leading to a drop in the fluxes of amino acids metabolism and hence, the CA pathway. Thus, in the oxygen constrained scenarios (SC4 and SC6), the slowdown of TCA and urea cycles, as well as amino acid metabolism and CA biosynthesis, predicted in silico, suggested that intracellular limitation of oxygen in LSS cultivations causes a general drop in fluxes along central metabolism, which correlates with the experimentally observed accumulation of pyruvate, oxaloacetate, malate, and amino acids. Moreover, as a physiological objective of growth, the use of oxygen is prioritized to sustain biomass synthesis and anabolism rather than specialized metabolite synthesis. The main carbon fluxes along central metabolism are summarized in S. clavuligerus [Some differences between phosphate limited and non-limited scenarios were predicted in silico. High phosphate conditions repress both, CA and cephamycin C biosynthesis, in uligerus . Since puligerus . They woIn the case of SC3 and SC4 scenarios, the biosynthesis of specialized metabolites might constitute a metabolic sink playing a role in growth regulation under high carbon and nitrogen availability, coexisting with a phosphate limitation. FBA simulations of the SC3 scenario demonstrated that, besides stimulation of TCA and CA biosynthesis, fluxes through ATP synthase and NADH dehydrogenase also increase compared to the oxygen constrained scenario SC4. Interestingly, the lowest fluxes through ATP synthase and NADH dehydrogenase were observed in silico in the scenario of glutamate and phosphate exhaustion (SC5), coinciding with moderate CA production in HSS conditions. In contrast, SC6 did not show reaction fluxes toward the CA pathway while the fluxes of respiratory chain dehydrogenases and ATP production were almost 10% higher than in SC5. Under these conditions CA biosynthesis seems to have a similar metabolic scenario to the synthesis of Type II antibiotics. CA biosynthesis  involvesIn silico, FBA showed that phosphate limitation causes a stoichiometric increase in the reaction fluxes in several glutamate-dependent reactions, contributing to supply fluxes of Pi and/or PPi to other reactions in the network. Additionally, the reactions in the CA pathway catalyzed by CEAS and BLS involve Pi and PPi as by-products. PPi is further hydrolyzed by the inorganic diphosphatase (PPA), whose flux increases up to 43% under P-limitation in silico. FBA results suggest that L-glutamate availability promotes the carbon flux towards CA biosynthesis in a scenario of high oxygen uptake, since L-arginine as C-5 precursor of CA is derived from L-glutamate via the urea cycle. Indeed, the highest biosynthesis rate of CA was observed in the HSS cultivations when phosphate limitation and glutamate availability coexisted with high oxygen uptake.S. clavuligerus might induce this kind of cycle. The toxic impact of cephamycin C on the energetic metabolism could be counteracted by the action of a β-lactamase. Simultaneously, the β-lactamase levels would be regulated by CA as a β-lactamase inhibitor. These three systems with opposite effects would contribute to tightly regulate intracellular levels of CA and cephamycin C and, indirectly, the energetic metabolism of the bacteria. Thus, the high antibiotic synthesis rate observed in HSS cultivations is consistent with the high ATP generation calculated in silico in conditions of high intracellular oxygen and nutrients availability.Furthermore, phosphate limitation is known to trigger important adaptive responses that contribute to maintain the phosphate supply to the cells in phosphate limited scenarios. Cell growth in absence or at very low concentrations of extracellular phosphate proceeds via reutilization of intracellular phosphate. Under phosphate deficiency, mother and daughter cells share intracellular resources of phosphate from RNA, polyphosphate stores, phospholipids and teichoic acids accumulated during fast growth under high phosphate availability. During the fed-batch phase with phosphate limitation (from 60 h onwards), cells do not need these constituents and are subjected to self-degradation by means of nucleases, phosphatases and related hydrolytic enzymes, releasing inorganic phosphate, pyrophosphate, and P-containing monomers into the cytoplasm. This response is controlled by the two-component system PhoR/PhoP, which is able to induce the high-affinity phosphate transport system, phosphatases, down-regulation of phosphate consuming processes and upregulation of genes involving phosphate mediated reactions ,54. In pS. clavuligerus, using a state-of-the-art top-down approach during the network reconstruction. The resultant curated iDG1237 metabolic model showed better predictive capabilities compared with its predecessors in relation to biomass and product biosynthesis, network topology, thermodynamic consistency and simulation of metabolic scenarios of nutritional limitation. The present model constitutes a new basis for computer-aided analysis and design of CA production scenarios as well as strain engineering of S. clavuligerus.In this work, we generated an improved genome-scale metabolic model of S. clavuligerus metabolism, when cultivated at high and low shear stress conditions. Under identical scenarios of phosphate limitation, the decrease of oxygen uptake halved the carbon fluxes in TCA and urea cycles negatively impacting CA biosynthesis. Low oxygen availability in low shear stress conditions likely leads to a slowdown of TCA cycle causing overflow metabolism (secretion of acids of the acetyl-CoA node) and accumulation of TCA intermediates due to reduction of anabolism and inhibition of antibiotic production. Experimental evidence showed that the stoichiometric effect of phosphate depletion alone does not enhance CA production, since oxygen diffusion determines the activity of oxidative and energetic metabolism. In this regard, antibiotic biosynthesis might have an important role in regulating the energetic balance of the bacteria under phosphate limitation.The in silico and experimental results of this study provide insights regarding the nutritional regulation of CA biosynthesis and the effects of intracellular oxygen availability in S. clavuligerus. Further exploration of metabolic scenarios considering nutritional and genetic regulation of metabolism could contribute to the design of suitable conditions to optimize CA production and construct genetically engineered CA-overproducing strains.The present combined experimental and modelling approach highlighted the importance of the metabolic relationship between the TCA cycle, amino acid biosynthesis and oxygen uptake under phosphate limitation during antibiotic production in


Brugia malayi represents a leading cause of disability in the developing world, causing lymphatic filariasis in nearly 40 million people. Currently available drugs are not well-suited to mass drug administration efforts, so new treatments are urgently required. One potential vulnerability is the endosymbiotic bacteria Wolbachia—present in many filariae—which is vital to the worm. Genome scale metabolic networks have been used to study prokaryotes and protists and have proven valuable in identifying therapeutic targets, but have only been applied to multicellular eukaryotic organisms more recently. Here, we present iDC625, the first compartmentalized metabolic model of a parasitic worm. We used this model to show how metabolic pathway usage allows the worm to adapt to different environments, and predict a set of 102 reactions essential to the survival of B. malayi. We validated three of those reactions with drug tests and demonstrated novel antifilarial properties for all three compounds.The filarial nematode  Brugia malayi, Brugia timori, and Wuchereria bancrofti. As of 2015, an estimated 38.8 million people had lymphatic filariasis with an estimated 1 billion people at risk in 72 endemic countries —also called elephantiasis—which is caused by ountries . Transmiountries .Onchocerca volvulus or Loa loa—other filarial nematodes—are endemic , an optimization method that is applied to a metabolic network , we assembled a set of required biomass metabolites together with their relative abundances. This collection represents the biomass objective function and is used in constraints-based modeling to calculate flux distributions for each reaction in the model. Here we based the objective function on a previously defined function generated for O. volvulus . After filtering compartmental duplicates, the 1011 enzymatic reactions represent 761 unique KEGG reactions. For context, the C. elegans metabolic reconstruction iCEL1273  are shared with iCEL1273. Of the 1252 metabolites, 661 are associated with the cytosol, 202 with the mitochondrion, 362 with the Wolbachia, and 27 are used in the artificial conversions of biomass components. These represent 1025 unique KEGG metabolites, of which 602 (59%) are unique to B. malayi and 423 (41%) are shared with iCEL1273.The final reconstruction, designated olbachia . Of the iCEL1273  containsiDC625. In total, we obtained relative expression data for 11,840 B. malayi and 823 Wolbachia genes across ten different life-stages; 87.6% of the B. malayi genes and 96.4% of the Wolbachia genes were expressed in at least one stage. This yielded 11 total models: unconstrained (open), L3, L3 6 days post-infection (L3D6), L3 9 days post-infection (L3D9), L4, adult female 30 days post-infection (F30), adult female 42 days post-infection (F42), adult female 120 days post-infection (F120), adult male 30 days post-infection (M30), adult male 42 days post-infection (M42), and adult male 120 days post-infection (M120).We also used life stage-specific gene expression data to constrain the reactions of Wolbachia directly impacts model dynamics, both through the production of metabolites that contribute to the biomass objective function as well as the consumption of metabolites to maintain its own growth, the relative weight between bacteria and worm must be considered in the model. This weight is implemented by weighting the Wolbachia contribution to the biomass objective function , as well as constraining reactions assigned to the endosymbiont. Using these constraints in a pFBA framework, we examined how changes in Wolbachia weights affect the maximum flux through the objective function of the model. As the availability of a carbon source and of oxygen are two of the most important determinants of the model’s activities, we examined the model under four different nutrient conditions: high oxygen (580 units) and high glucose (250 units) (HOHG), high oxygen and low glucose (45 units) (HOLG), low oxygen (90 units) and high glucose (LOHG), and low oxygen and low glucose (LOLG). These units are not calibrated to any real-world quantities, so our choices were inherently arbitrary. The low values were as low as possible where metabolic pathways were functional and relatively stable; these pathways were utilized in different ways as levels of both nutrients were increased, so our high values were chosen after the point where pathway utilization became stable again.Since the presence of Wolbachia, we found that the maximum production of the biomass objective function occurs at Wolbachia weights of 0.04, 0.02, 0.02, and 0.01 under HOHG, HOLG, LOHG, and LOLG, respectively , and as the Wolbachia weight increased the objective function peaked rapidly followed by a steady decline. Since the negative impact to the objective function was minimal until 0.18, we assigned a weight of 0.1 under all nutrient conditions. This was chosen to minimize the impact on the objective function, while still being conservative in allowing reasonable flux through Wolbachia reactions.The behavior of the life stage models under changing weights was relatively similar, except for F120 and M120, which had by far the least biomass production. In all cases the models were more sensitive to increasing the  to HOLG . InteresWolbachia population dynamics have been well-studied in B. malayi . The increase was largest in the three adult male models (10.3%), compared to adult females (6.16%), the larval stages (7.20%), or the unconstrained model (5.88%). This effect was not simply an artifact of our choice of Wolbachia weight, as the same trend was observed when measuring the maximum flux achieved under any Wolbachia weight with and without the addition of excess pyruvate (Wolbachia weight').One approximation is to compare the model performance at a high pyruvate . At a WoB. malayi encounters a range of different nutrient conditions and likely regulates enzyme expression to alter metabolic flux to optimize growth in each condition. For example, adult worms are found in the lymphatic system where the expectation is that the parasite is exposed to a substantially lower oxygen environment compared to earlier life stages like the L3. We therefore performed a series of pFBA simulations in which we examined the impact of changes in two key metabolites on worm growth, oxygen and glucose, in the different life stage models , there is a switch to the aerobic pathway in the unconstrained and L3 models. Interestingly, the acetate and propanoate waste transporters are no longer used above this threshold; the predicted presence of these waste products is consistent with previous studies of helminth anaerobic metabolism , amino acids (100 units), and several cofactors: ammonia, phosphate, H+, adenine, pyridoxal phosphate, heme, folate, cholesterol, oleic acid, pantothenate, choline, riboflavin, putrescine, nicotinate, UTP, CTP, Fe2+, and N-acetyl-D-glucosamine; in all simulations, the maximum usage of fatty acids, amino acids, and cofactors was less than provided.To further explore the impact of glucose and oxygen availability on Wolbachia.We observe that the model produces the most biomass under HOHG conditions, as expected. Instead of using the full TCA cycle, the model predicts a reliance on the glyoxylate shunt to produce malate and succinate . A large2 subsequently exported as waste. Similar to HOHG, 71.2% of the oxygen is used for oxidative phosphorylation in the mitochondria, while 6.0% (35 units) is used by Wolbachia.Under HOLG conditions, growth is predicted to fall to 82% of optimal biomass production. While the glyoxylate shunt is still used, succinate is no longer exported, but instead is processed by the TCA cycle to produce energy, with COWolbachia.Under LOHG conditions, biomass production falls to 69%. Here, the glycolytic pathway is used to generate energy, with glucose metabolized to phosphoenolpyruvate (PEP) producing approximately half of the total NADH used by the model, which is then transported into the mitochondria via the malate-aspartate shuttle. The other half of the NADH is produced by the classical anaerobic pathway involving the conversion of some PEP (37.3%) to pyruvate, which is subsequently transported into the mitochondria, metabolized to acetate, and excreted as waste. However, most of the PEP (59.0%) is processed by the nematode-specific anaerobic pathway. Through this pathway, PEP is metabolized into malate and transported to the mitochondria where it is processed to succinate by the reverse Complex II, and ultimately converted to propanoate and exported as waste. Under these conditions, most of the oxygen in the model (61.0%) is used to generate ATP through oxidative phosphorylation, while 37.8% (34 units) is used by Wolbachia.Under LOLG conditions biomass production is reduced to 37% of the optimal. Glucose is converted to malate and transported into the mitochondria as observed under LOHG conditions. Approximately half is processed to succinate by the reverse Complex II, while the other half is used to generate NADH via conversion to oxaloacetate, α-ketoglutarate (AKG), succinyl-CoA, and then succinate. All of the succinate is then converted to propanoate and exported. Under these conditions 69.8% of the oxygen is being consumed by oxidative phosphorylation, and 29.6% (27 units) is used by 2, along with the production of key energy metabolites: GTP, 2 x NADH, and ubiquinone. This is almost equivalent to the energy produced by the catabolism of a single molecule of acetyl CoA by the TCA cycle: 2 x CO2, GTP, 3 x NADH, and ubiquinone. Given that the glutamate/aspartate transporter is driven by the proton-motive force, it is not known if this pathway would be energetically favourable in vivo  into the B. malayi cytosol. It was also suggested that Wolbachia uses pyruvate as its primary carbon source , and the model uses both the TCA cycle and the glutamate metabolic pathway to generate energy. The Wolbachia metabolic pathways appear much the same under the other conditions, except that ATP export drops to 71%, and 44% of LOLG for HOLG and HOHG, respectively.It has previously been suggested that ematodes ; our modn source , but undB. malayi life cycle, pFBA was performed under the four different nutrient conditions for each of the ten life cycle stage models . This indicates that the different reaction constraints are playing a minor role under these conditions, and that the concentrations of glucose and oxygen are the limiting factors.While the L3 model experiences a large benefit under LOHG or HOLG conditions compared to LOLG, there is no increased benefit when moving from either to HOHG conditions. This indicates that the reaction constraints imposed for this stage limit the model’s ability to exploit increases in both nutrients, in contrast to the open model which sees biomass increasing when moving from either LOHG or HOLG to HOHG. The M120 model appears to be saturated for these nutrients even under LOLG conditions, as its biomass production never increases when more of either nutrient is made available. The F120 model appears to be nearly saturated and receives only a modest benefit from additional glucose or oxygen.Wolbachia compartment changes the least among the different models and conditions, while the mitochondrial compartment shows the most variation. This also allows us to identify ‘enzymatically constrained’ models, which is a term that relates to the number of possible ways a model is able to achieve its maximum objective flux; if there are multiple alternate metabolic pathways that can be used to satisfy the objective function, a model would be considered enzymatically unconstrained. Models generally become less constrained as they are provided with more nutrients.Besides measuring the biomass production for each model under different nutrient conditions, we also quantified the number of reactions used by the models . We founB. malayi is molting, specifically during the apolysis stage of molting where a new cuticle is being synthesized to replace the old cuticle. These reduced usage numbers, combined with reduced biomass production ; of these, 103 and 316 metabolites were found in at least three samples, respectively. Directly relating these data to the model predictions is unfortunately non-trivial, as the metabolomics analyses detect the size of the pool of some metabolite at one point in time, while our pFBA predictions are steady-state rates.Most of the TCA cycle intermediates  were detWe were able to detect several fatty acid degradation intermediates as well, in particular carnitine-conjugated fatty acids. Carnitine and acetylcarnitine were detected in all samples in both analyses and were found to be highest in males, followed by females, microfilaria, and finally L3. Other short-chain acylcarnitines, butyrylcarnitine and valerylcarnitine were detected in all samples except L3 in the hybrid analysis. Consistent with above, these metabolites were found to be highest in males, followed by females, and then microfilaria. Propionylcarnitine was detected only in the adult samples in the hybrid analysis. The only long-chain fatty acid intermediate detected was palmitoylcarnitine, and only in the microfilaria and one male sample in the predicted analysis.C. elegans .If we accept that higher levels of these intermediates imply increased metabolic activity, then we observe a general trend of the highest activity in the adults , followed by the microfilaria, with L3 having the lowest levels if any were detected. This fits with the observation that L3 is a non-feeding stage, which has been likened to the dauer stage of  elegans . Though in silico knockouts in which each reaction constraint was set to zero . Of the 1011 enzymatic reactions in iDC625, 815 had no impact on the biomass objective function when knocked out . The only change under LOLG conditions was to reclassify this same reaction as non-essential. Under HOLG conditions three new reactions were classified as essential , though these same reactions from the Wolbachia compartment were already classified as essential. Four new essential reactions were identified under LOHG conditions , though again these same reactions from the Wolbachia compartment were already classified as essential.This set of essential reactions was generated under HOHG conditions, but proved to be quite robust to the model conditions. Increasing the iCEL1273 C. elegans model, as well as experimentally determined essential reactions .Our set of essential reactions was compared to those predicted in the eactions . The autd iDC625 , but thiIn addition to single reactions, we also investigated reaction pairs exhibiting synthetic lethal relationships. Such relationships may exist, for example, when two reactions operate in alternative pathways that can each lead to production of the same key metabolite. This ‘two hit’ strategy may offer greater long-term potential through the development of combination therapies that ultimately reduce the risk of emergence of resistance, both through requiring the pathogen to simultaneously acquire resistance to two independent targets, and through the use of lower drug dosages that can result from increased efficacy .Wolbachia TCA cycle is functional , of which 77 resulted in no biomass production when knocked out in silico . This subset was chosen because they were considered less likely to be model artifacts. Reactions were first ranked by the number of inhibitors to their cognate protein identified in the ChEBML database , 80 were associated with one or more genes  has moderate expression that is consistent across most stages, high in early adult males but dropping off over time. The target gene of MDL-29951, Bm13850 (fructose biphosphatase), has moderate expression that is highest in L3 but relatively consistent across stages. Wolbachia also has a homolog of this gene, Wbm0158, that is expressed at a higher level especially in the late adult stages. Tenofovir was identified as a ChEMBL hit of B. malayi Bm9070 (an adenylate kinase), which has very low expression in most stages, but moderate in adult males. There is a Wolbachia homolog of this gene as well, Wbm0321, with low expression throughout the life stages. There are two other B. malayi genes that are associated with this reaction: Bm3965 (a UMP-CMP kinase) has moderate expression, highest in adult females but quite consistent across stages; and Bm14014 (adenylate kinase isoenzyme 1) has very high expression, highest in L3 but still high in male and female adults.As mentioned above, reactions were only prioritized as potential targets if their cognate genes were expressed across the e stages . The prein vitro. Fosmidomycin, MDL-29951, and Tenofovir were found to reduce the number of Wolbachia per worm to 53% , 24% , and 30%  of control, respectively .These three predicted drug targets were subsequently validated by testing their effects on worms ectively . We alsoB. malayi, which we term iDC625. While the model captures many known features of nematode metabolism, simulations under a variety of different conditions yielded a number of emergent behaviours, including switching between aerobic and anaerobic metabolic pathways, a predicted Crabtree effect under high oxygen and glucose, and a novel pathway that relies on the catabolism of glutamate to aspartate to generate energy. This suggests that in addition to being robust, the model is capable of generating novel biological hypotheses.We present the first constraints based metabolic model for A high quality compartmentalized metabolic model allows us to study the metabolic processes of the cell in detail, including pathways that have been poorly studied in the past; in particular, the anaerobic metabolic pathways used by parasitic nematodes are unlike those studied in most other eukaryotes . Our modin vitro studies of worms exposed to different oxygen and glucose concentrations.An emergent behaviour predicted by the model was the exhibition of the Crabtree effect, a previously described phenomenon observed in yeast where anaerobic fermentation pathways are activated in aerobic conditions, but only in the presence of high levels of glucose . It is iThe glyoxylate pathway is primarily discussed in the literature as a way for plants to process fatty acids into glucose and starches, but it is also found in fungi, some protists, and bacteria . The relC. elegans metabolism, iCEL1273, were broadly consistent with in vitro gene essentiality screens  and aldo-2 (Bm3135). Knocking out the cognate reactions led to unrecoverable states in the model, both genes have several druggable homologs in ChEMBL, and the expression of both genes was consistent across the adult life stages . These were not pursued in this work, as in a previous study we examined the effects of knocking down these genes with RNAi in adult female B. malayi  for Tenofivir; Wbm0158  for MDL-29951)—that the drugs may be acting against.We validated our predictions of essential reactions by observing the effects of three existing drugs on ial drug . It actsatitis B . It is udiabetes . Interes species . That st species . Both TeB. malayi worms, via reduction in Wolbachia populations and/or microfilaria production. The successful validation of these effects suggests that our metabolic model is a useful approximation of the worm. Consistent with metabolic reconstructions generated for other organisms such as Yeast and E. coli, we expect future iterations of our metabolic reconstruction will undergo further modifications involving, for example, the inclusion of new reactions or refinement of existing reactions as new experimental data is generated. Further research on this model may therefore yield even more therapeutic targets.All three of the drugs tested in this study appear to possess activity against adult B. malayi gene models using DETECT V2 .A draft metabolic reconstruction was generated as described previously . BrieflyETECT V2 , BLASTP ETECT V2  searchesETECT V2 , PRIAM , ubiquinol, and folate (Wolbachia compartment from the cytosol. The integrity of the model was tested using FBA to ensure that it was able to produce biomass (as defined below), and the major metabolite fluxes were manually traced to ensure they were not the product of futile cycles or other biologically unfeasible artifacts.Metabolites are confined to a single compartment and can only participate in reactions in other compartments if shuttled there by an explicit transport reaction. Most of these transporters were taken from the d folate , reactioAll network analysis methods, including FBA, were performed using the cobrapy package version 0.5.10 , which iB and BW are the B. malayi- and Wolbachia-specific biomass objective functions, and the coefficient on BW controls the Wolbachia load . Infective third-stage larvae (iL3) were recovered from mosquitoes (Aedes aegypti) and mammalian stage larvae were recovered from gerbils (Meriones unguiculatus) at 6 and 9 days post infection (dpi). Total RNA was prepared from B. malayi worms and Wolbachia as previously described  or mosquitoes (Aedes aegypti). Worms were flash-frozen and shipped to the New York Blood Center for processing. Stages used for metabolomics analysis included L3 larvae from mosquitoes, adult male and female worms at 120 dpi, and microfilaria. The number of worms per sample were 20 adult female worms, 40 adult males, 2 × 106 microfilariae, and 200 L3 larvae per biological replicate. Samples were washed in 1x PBS and run in triplicate. Adult male and female worms were picked individually from PBS and each biological was weighed. The microfilaria and L3 samples were spun down, the PBS pipetted off, and weighed directly into a metabolomics 2 mL screw cap vial with total amounts ranging from 1.3 mg  to 15.8 mg (microfilaria). Metabolites were extracted and the data analyzed as described below.All parasites were obtained from FR3  where they were isolated and separated by sex from infected gerbils  and homogenized for 3 min at 4°C in a BeadBlasterTM with a 30 s on, 30 s off pattern. The resulting lysate was centrifuged at 21,000 x g for 3 min, and 90% of the supernatant volume was transferred to a 1.5 mL microfuge tube for speed vacuum concentration, no heating. The dry extracts were resolublized in a volume of LCMS grade water 1/10th of that used for the homogenization step, sonicated in a water bath for 3 min, and transferred to a glass insert for analysis.All data were acquired by liquid chromatography coupled to high resolution tandem mass spectrometry (LC-MS/MS). The LC column was a MilliporeTM ZIC-pHILIC  coupled to a Dionex Ultimate 3000TM system and the column oven temperature was set to 25°C for the gradient elution. A flow rate of 100 μL/min was used with the following buffers; A) 10 mM ammonium carbonate in water, pH 9.0, and B) neat acetonitrile. The gradient profile was as follows; 80–20%B (0–30 min), 20–80%B (30–31 min), 80–80%B (31–42 min). Injection volume was set to 2 μL for all analyses . MS analyses were carried out by coupling the LC system to a Thermo Q Exactive HFTM mass spectrometer operating in heated electrospray ionization mode (HESI). Method duration was 30 min with a polarity switching data-dependent Top five method for both positive and negative modes. Spray voltage for both positive and negative modes was 3.5kV and capillary temperature was set to 320°C with a sheath gas rate of 35, aux gas of 10, and max spray current of 100 μA. The full MS scan for both polarities utilized 120,000 resolution with an AGC target of 3e6 and a maximum IT of 100 ms, and the scan range was from 67 to 1000 m/z. Tandem MS spectra for both positive and negative mode used a resolution of 15,000, AGC target of 1e5, maximum IT of 50 ms, isolation window of 0.4 m/z, isolation offset of 0.1 m/z, fixed first mass of 50 m/z, and 3-way multiplexed normalized collision energies (nCE) of 10, 35, 80. The minimum AGC target was 1e4 with an intensity threshold of 2e5. All data were acquired in profile mode.Metabolomics data were processed with two approaches called the hybrid analysis and predictive analysis. The resulting relative metabolite intensities for both methods were then processed with an in-house pipeline for statistical analyses and plot generation using a variety of custom Python code and R libraries including: pheatmap, MetaboAnalystR, manhattanly. For the hybrid analysis, peak height intensities were extracted based on the established accurate mass and retention time for each metabolite as adapted from the Whitehead Institute , and verin vitro for each drug treatment or control group. Worms were cultured for six days in a 12-well plate with two worms per well, in complete culture medium  at 37°C under 5% CO2. Three drugs were assayed: Fosmidomycin, MDL-29951, and Tenofovir, each at a concentration of 12 µM. Media was changed every other day.For validation, 15 adult female and 15 adult male worms (120 dpi) were cultured th day of treatment and collection of adult worms. Two technical replicates and 10 biological replicates were used to determine microfilaria production. Averages are presented with their standard deviations. Significance was determined by a single factor ANOVA, followed by t-tests between each treatment group and the control using a Bonferroni correction.Microfilarial release by female worms was determined by quantifying the number present in the media on the 6Wolbachia per worm, DNA was extracted from adult male worms using the QIAmp DNA Mini Kit (QIAGEN) according to the manufacturer’s protocol. Wolbachia per individual worm was calculated by genomic qPCR using primers for a single-copy Wolbachia gene . Each treatment or control group had 8 (or 9) biological replicates, and each replicate contained three male worms. Averages are presented with their standard deviations. Significance was determined by a single factor ANOVA, followed by t-tests between each treatment group and the control using a Bonferroni correction.To analyze the number of The Worminator system  was usedhttps://github.com/ParkinsonLab/Brugia_metabolic_network.All of our metabolic models–the unconstrained and life stage-specific variants–are freely available at B. malayi molt (life stages L3 to L3D6 to L3D9) are available through the Sequence Read Archive (PRJNA557263).Our transcriptomics data for the https://github.com/ParkinsonLab/Brugia_metabolic_network (https://github.com/elifesciences-publications/Brugia_metabolic_network).All of our metabolomics data – from both the hybrid and predictive analyses – are freely available at  In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:Brugia malayi to predict metabolic pathways used by the parasite at different lifecycle stages and within different environments. The work represents one of the first comprehensive investigations using metabolic modelling to predict essential reactions in a parasitic nematode and offers a potentially useful strategy for the development of new drugs targeting these important pathogens. The authors validate their work by predicting several druggable targets based on the model, and proceed to validate three of these reactions using already available drugs.The manuscript employs a metabolic model of the filarial nematode Decision letter after peer review:[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]eLife. Your article has been reviewed by three peer reviewers, including Nicola L Harris as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by a Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: James B Lok (Reviewer #3).Thank you for submitting your work entitled "Modeling the metabolic interplay between a parasitic worm and its bacterial endosymbiont identifies novel drug targets" for consideration by eLife.Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in C. elegans metabolic Reconstruction : e1373939) The modelling of host conditions or the bacterium Wolbachia was felt to be insufficient in the current form.Although reviewers agree the idea of using metabolic modelling approaches to identify novel drug targets for fillarial infection is of high interest, a number of deficiencies in the experimental rationale, modelling approach employed and communication of the rationale and outcomes were identified. In particular it was not clear why the authors did not choose the most recent work regarding Reviewer #1:Brugia malayi to predict metabolic pathways used by the worm at different lifecycle stages and within different environments. The authors then use the model to predict essential reactions useful for drug targeting and validated three of these reactions using already available drugs.The manuscripts by Curran et al. employs a metabolic model of the filarial nematode The work is both interesting and novel in that 1) new drug targets for Brugia are needed and valuable and 2) large scale metabolic modelling of a parasitic nematode has not been reported previously.However as it stands a large amount of data is given and the rationale behind the experimental design is not always clear, making it difficult for the reader to determine how meaningful each dataset is. The manuscript could be greatly improved if more effort was made to restrict the data shown in the main figures related to environmental conditions  to the those in that each lifecycle stage would be expected to actually encounter within the host. The relationship of each dataset to one another also needs to be commented on more clearly .One of the most important figures in the manuscript is Figure 8 in which the ability of the model to predict drug targets was determined using adult worms. Given that the preceding sections of the manuscript focused heavily on determining the environmental factors influencing the model it would important to determine whether these same factors influence in the activity of the drugs. This could be achieved by performing the assays in cultures containing medium with different levels of glucose and under low or high oxygen conditions. This is particularly important given emerging data indicating that drug screening for anti-helminthics yields different outcomes when drugs are tested under high oxygen conditions  versus low oxygen conditions). Drugs should also be compared side by side in these assays to an “in use” anti-helminthic such as ivermectin.Reviewer #2:The paper presents the metabolic network model of a parasitic worm and its bacterial endosymbiont, and it uses this model to study the metabolic adaptations that allow the survival of the parasite. Metabolic models can be indispensable for the study of such complex systems, the interpretation of experimental data and the identification of drug targets.The modeling effort presented in this manuscript suffers from major issues which make the present model(s) and their presentation unacceptable for final publication. When one uses a metabolic model, and any model, it is of fundamental importance to first assess the quality of modeling framework, before using the model for the analysis of experimental data. Otherwise, the results of the model-based analysis of the data are simply artifacts of the errors in the low-quality models.The following are some of the major issues in the development and quality of the models in the manuscript.* While many metabolites and nutrients are present in the worm cytosol, not all of them are available to bacterium. The conditional availability of the nutrients and metabolites will impact the conclusions about the physiology  of the bacterium.* The model appears to be built based on a semi-manual curation. There are currently established methods and workflows that should be used (such as RAVEN). The authors should consider all these, discuss their capabilities and provide a justification why they are not using one of them. I think that couple of these methods have been optimized and any other procedure such the one followed here is at the best suboptimal.* Within the established metabolic modeling workflows there is also the concept of "metabolic tasks" which defines the metabolic capabilities of the organisms and the ability of the model to fulfill these tasks. The authors must follow such workflow, define the metabolic tasks and assess the performance of their models with respect to these tasks. Otherwise, we cannot systematically evaluate the quality of the models.* The model is using only two compartments of the host organism. This is already a major inefficiency of the models here. For every eukaryote that has been model with high quality additional other compartments have been considered (such as ER and nucleus) and their consideration has been shown to be important for capturing the physiology of the organism.* It is not clear what is the information used for modeling transporters . It is important to carefully consider which transporters across compartments and across cytosol and the bacterium are feasible. Careful consideration, curation, modeling and justification of the transporters is extremely important for the quality and the performance of the model.* The authors ignore the most important "compartment" which is the environment of the human host. It has been shown in the past that modeling and properly contraining these interactions are essential for the evaluation of the models of parasites and on the use of these models for the analysis and interpretations of experimental data : e1005397 and PLoS Comput Biol. 2017 Mar; 13(3): e1005397.) For example the studies here should first identify what are the essential nutrients and what are the commonly and most probably used nutrients. The same applies also for modeling the nutrients available to bacterium (as mentioned earlier.)C. elegans metabolic Reconstruction : e1373939) which should be consider. The authors should use this work as reference or provide a strong justification why they choose exclusive use iCEL1273, which is not of good quality.* The authors use as a reference model the previously published model iCEL1273 for the worm E. elegans. However, the quality of this model is low and there is a later work for a consensus * There is not a good analysis on the modeling of the bacterium Wolbachia. The choice of reactions is not justified, and again, there is not a clear procedure outlined for the reconstruction and modeling of the bacterium metabolic modeling.* It also appears that important information on model development and statistics is missing. The only Supplementary File is an excel file with "Single knockout" data.* The authors do not discuss the reversibility of the reactions and their thermodynamic feasibility. This is an essential procedure in model reconstruction and development, and it is well known and appreciated fact that reaction directionality has a significant impact in the performance of the model for flux estimation and gene essentiality analysis.* The model simulations do not really offer a lot in our understanding of the physiology of the organisms. The authors could try to extract the import lessons from the simulations and communicate them in a more clear and informative way. However, and without a proper constraining and classification of the nutrient uptake fluxes, it is not possible to gain a clear understanding of the results presented here.* It is very hard to assign significance in the essentiality analysis since the model reconstruction and the assignment of constraints has not been done properly and it has been justified in a convincing manner (as discussed above).* The predictions discussed in the manuscript do not provide a sufficient validation of the model. Some of them could have been predicted without the use of the model. The small number of predictions does not provide enough evidence and confidence to support the claim the "metabolic model (in this manuscript) is a useful approximation of the worm”.* It is not clear what are the methods used by the authors for the integration of transcriptomics and metabolomics data. There are exist many well established methods that are used for such integration and such method-based integration is the current standard in model reconstruction and analysis.Reviewer #3:Brugia malayi. This model predicts metabolic flux through catabolic and biosynthetic pathways under varying conditions of oxygen tension and glucose availability in key life cycle stages of the parasite that infect the definitive host .This paper reports the development of a new computational model of intermediary metabolism in the lymphatic dwelling filarial nematode The paper has many strengths of a substantive nature, which are enumerated below. The paper is also very well written, and only a few minor changes are recommended in the text. The authors should give some attention to specific points about the presentation of Figures 1 and 2.Substantive strengthsOnchocerca and C. elegans. This is a logical approach and one that undoubtedly streamlined construction of the Brugia model.1) The metabolic model builds on others developed for 2) Confidence in the fidelity of the model is engendered because it captures salient features of nematode metabolism, such as the switching between aerobic and anaerobic pathways, a predicted Crabtree effect under high oxygen and glucose, and use of glutamate to aspartate conversion to generate energy.3) Great that the model predicts ability of the parasite to utilize aerobic and anerobic metabolism as it proceeds through various body compartments in its migration within the host.B. malayi is an interesting and worthwhile finding that could direct discovery of new compounds acting on energy metabolism.4) The model prediction that glucose and oxygen are limiting factors in fitness of adult 5) Identification of scenarios where multiple redundant pathways are available to the parasite to achieve the same metabolic output also seems a very astute finding, as such pathways would presumably be less advantageous likely drug targets. ie. they are less likely to be essential.6) Concordance between model predictions and actual metabolomics on extracts of staged parasites bolsters confidence in the model.7) Through a process of in silico knockouts, the authors have predicted 99 enzymatic reactions that are essential. This demonstrates a crucial practical application of the model.8) The authors definition of essential reactions as being ones whose disruption wouldreduce predicted parasite biomass by 50% or more seems valid to this reviewer given that such a reduction in biomass would likely compromise the worms' ability to survive in the hostile environment of an immunocompetent host.9) The "two hit" discovery from model simulations that two different reactions participate in pathways that lead to the same metabolite could facilitate the design of combination therapies that would greatly decrease the likelihood of drug resistance.Wolbachia loads, and/or fecundity in cultured adult B. malayi. These results bolster confidence in the model's utility in identifying drug targets in Brugia spp, and perhaps in Wuchereria bancrofti as well.10) The authors used a rational and very practical scheme for identifying potential target pathways from among the 99 essential reactions predicted by the model. Criteria in this scheme included expression across multiple life stages, number and availability of existing inhibitors in the ChEMBL database and similarity to human homologs (presumably a detracting factor). Selected compounds from this list, targeting diverse pathways such as isoprenoid precursor biosynthesis, gluconeogenesis and purine metabolism, namely fosmidomycin, mdl-29951 and tenofovir, respectively, either decreased eLife. Your letter of appeal has been considered by a Senior Editor and a Reviewing editor, and we are prepared to consider a revised submission with no guarantees of acceptance.Thank you for choosing to send your work entitled "Modeling the metabolic interplay between a parasitic worm and its bacterial endosymbiont identifies novel drug targets" for consideration at SummaryBrugia malayi to predict metabolic pathways used by the worm at different lifecycle stages and within different environments. The authors then use the model to predict essential reactions useful for drug targeting and validated three of these reactions using already available drugs.The manuscripts employs a metabolic model of the filarial nematode The work is both interesting and novel in large scale metabolic modelling of a parasitic nematode has not been reported previously and the reviewers agree that it offers information will be useful for the community, keeping in mind some major challenges when working with parasitic nematodes.However it was felt that the demonstration of anti-filarial activity as shown for the three drugs is not sufficient validation of a genome scale model. The authors should detail under which model assumptions these validations will fail and state these as a calibration rather than a validation. Further validation should be provided as outlined under essential revisions below.Essential revisions:1) A combination of the following things should be provided as further validation of the model:a) Predicted drugs being effective. At the current time it isn't possible to judge whether this is significant in absence of the knowledge regarding how the drugs tested were selected from the prioritized list? Details of how the three drugs were selected from the prioritized list should be provided C. elegans. A proper comparison should be made inclusive of statistics. If the model predictions have significant enrichment of such genes, this would be helpful.b) Gene essentiality comparison with wolbachia load in different stages, this could be another validation if it is consistent with what the model predicts in terms of optimal wolbachia load under different stage transcriptome-based constraints.c) If there is data out there about 2) A few more cases of FBA's (and pFBA's) demonstrated utility in uncovering real biologically relevant insights should be included in the Introduction along with corresponding references. [Editors’ note: The authors appealed the original decision. What follows is the authors’ response to the first round of review.]C. elegans Metabolic Reconstruction and Metabolomics Community and Workshop Series. Worm. 2017; 6(2): e1373939) The modelling of host conditions or the bacterium Wolbachia was felt to be insufficient in the current form.Although reviewers agree the idea of using metabolic modelling approaches to identify novel drug targets for fillarial infection is of high interest, a number of deficiencies in the experimental rationale, modelling approach employed and communication of the rationale and outcomes were identified. In particular it was not clear why the authors did not choose the most recent work regarding C. elegans metabolic Reconstruction  we consider to be well off base for current expectations in the field. We note that the other two reviewers were very positive about the study, highlighting, in the case of reviewer 3, ten substantive strengths.Brugia malayi remains an extremely challenging organism to work with. Consequently, unlike model organisms, or the experimentally tractable Plasmodium parasite, biochemical data for Brugia is greatly limited, an issue that perhaps is not apparent to reviewer 2. By attempting to crystalize current knowledge of Brugia metabolism, this study provides a valuable resource for the community that has been demonstrated to generate new hypotheses, including drug targets that we have successfully validated. We therefore politely suggest that the points raised by reviewer 2 do not reflect the current state of the field and, as we show below, we believe that each of their comments are readily addressable. We are therefore writing to request whether you would be willing to consider a revised version of our study which addresses each and every one of the comments raised by the reviewers.As you can appreciate, despite its global health importance, Reviewer 2 expressed the following concerns:1) It is not clear which metabolites are available to the worm and its endosymbiont which impact predictionsThis is a common criticism of any metabolic model and reflects an incomplete knowledge of transport reactions between cellular compartments, particularly for non-model organisms. Such reactions are challenging to infer computationally and typically identified through biochemical studies. Here we chose to be conservative and included transport reactions as required by reactions assigned to the compartments. This is a standard approach used in the field has the potential to result in false negatives, but importantly, will not impact false positives. Hence any essential reactions we predict in such a model will remain essential irrespective of additional transport constraints.2) The authors do not use established methods and workflows such as RAVEN or concepts such as metabolic tasksWe would politely point out that, as outlined by Machado et al. NAR 2018, our approach is equivalent to the many tools that use a bottom-up approach including: (i) annotate genes with metabolic functions; (ii) retrieve the respective biochemical reactions from a reaction database, such as KEGG (26); (iii) assemble a draft metabolic network; (iv) manually curate the draft model. Our approach has been applied to several reconstructions including: Song et al., 2013; Blazejewski et al., 2015; Cotton et al., 2016; International Helminths Genome Consortium Nature Genetics 2019). We would add that a key advance of our approach over other approaches is the use of more sensitive enzyme annotation tools to complete the first step. We can of course provide additional details concerning the construction of our model.3) Unlike other eukaryotic models, only two host compartments are considered in the Brugia model and that, unlike other studies of parasites, the human host is ignored.C. elegans model (iCEL1273) also features only two compartments. Regarding the comment concerning the human host, the reviewer cites a study  to illustrate how additional data is used to improve a previously published model of Plasmodium metabolism. In the same way, we would expect that our initial model will be refined as new data becomes available.As noted above, Brugia is a challenging organism to work with and lacks the depth of knowledge available for other organisms to accurately assign metabolic reactions to specific compartments. We further note that the 4) The model simulations do not offer a lot of our understanding of the physiology of the organismsThis seems to be entirely contradicted by the ten substantive strengths outlined by reviewer 3 who appears to be more knowledgeable concerning Brugia physiology.5) We compare our model to iCEL1273 which is deemed low quality and should compare to a later workC. elegans Metabolic Reconstruction and Metabolomics Community and Workshop Series. Worm. 2017; 6(2): e1373939. This latter reference appears to report on a community effort to develop a metabolic reconstruction for C. elegans, but provides no link to any actual reconstruction. After some digging we found Witting et al. Frontiers in Molecular Biosciences 2018 which describes the integration of four previously published models including iCEL1273. We can of course compare our model to this new model in any revision. But perhaps more importantly this illustrates exactly the process of how initial metabolic reconstructions can be further embellished as new data comes in. However, this process cannot work if the initial models do not get published!We were puzzled by this comment as the reviewer does not provide any evidence to support their claim that the quality of iCEL1273 is low. We were also initially puzzled by the suggestion to use the reconstruction referred to in WormJam: A consensus 6) It is hard to assign significance to the essentiality analysisAs noted above, our model is conservative and we expect that we may have missed some essential reactions. Nonetheless we stand by our predictions of essential reactions: due to limitations in obtaining worms for these experiments, we were only able to target 3 reactions; satisfyingly we validated all three. This raises a key point, due challenges in obtaining worms for such experiments, resources such as the one presented are critical to help prioritize experiments for in vitroexperiments that rely on such a rare resource.[Editors’ note: what follows is the authors’ response to the second round of review.]Essential revisions:1) A combination of the following things should be provided as further validation of the model:a) Predicted drugs being effective. At the current time it isn't possible to judge whether this is significant in absence of the knowledge regarding how the drugs tested were selected from the prioritized list? Details of how the three drugs were selected from the prioritized list should be provided As requested we now provide more details concerning the prioritization strategy together with a full table .“To validate the performance of our model, we selected a subset of reactions for targeted inhibition using known drugs. Of the 102 reactions predicted to be essential, 77 were associated with one or more genes . This subset was chosen because they were considered less likely to be model artifacts. Reactions were prioritized by considering their expression across different life stages, the number of inhibitors identified in the ChEMBL database , and the similarity to human homologs (see Supplementary file 2 for details). From this list we selected three inhibitors to validate our predictions through in vitro assays (Table 1), primarily based on their cost and availability from suppliers.”C. elegans. A proper comparison should be made inclusive of statistics. If the model predictions have significant enrichment of such genes, this would be helpful.b) Gene essentiality comparison with As recommended we performed a one-tailed Fisher's Exact Test comparing these sets, and calculated a p-value of 1.9E-11 (meaning the probability of finding 52 or more hits out of 71 choices by random chance is 1.9E-11).C. elegans .”“73% of the predicted iDC625 essential reactions overlap with the experimentally determined essential reactions of c) If there is data out there about wolbachia load in different stages, this could be another validation if it is consistent with what the model predicts in terms of optimal wolbachia load under different stage transcriptome-based constraints.Wolbachia population dynamics have been well-studied in B. malayi , we agree with the reviewer that one source of additional validation might be to calibrate our model to life stage-specific population sizes. However, it should be appreciated that our model is concerned with the total metabolic capacity of the bacterium which does not directly correlate with population size. Further, there is very little information available on the nutrients available in the environments occupied by many of the life stages, rendering it highly challenging to accurately model metabolically inert stages like L3 . We include the above as new text in the manuscript:As Wolbachia population dynamics have been well-studied in B. malayi , it is tempting to attempt to calibrate our model to life stage-specific population sizes. However, our model is concerned with the total metabolic capacity of the bacterium which may not correlate directly with population size, and there is very little known about the nutrients available in the environments occupied by many of the life stages, especially metabolically inert stages like L3 .”“As 2) A few more cases of FBA's (and pFBA's) demonstrated utility in uncovering real biologically relevant insights should be included in the Introduction along with corresponding references.We now provide more background and references on the application of FBA in the Introduction:Pseudomonasaeruginosa  and Leishmania major , improve bioreactor yields of non-vital compounds in Pseudomonasputida , explain the complex observed substrate specificities of Desulfovibrio vulgaris , explain observed metabolic changes in the brains of patients with Parkinson’s disease , and even demonstrate the non-biomass related factors affecting tissues growing by cell expansion in tomato plants .”“Beyond the identification of essential genes and potential therapeutic targets, the analyses of metabolic reconstructions with FBA have been used to identify knowledge gaps and improve annotations in pathogens like