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


Escherichia coli MG1655:: tetA grown in the presence of ½ MIC (14 mg/L) of OTC, and for comparison WT MG1655 strain grown with 1//2 MIC of OTC (0.25 mg/L OTC). 1646 genes changed expression significantly (FDR > 0.05) in the resistant strain, the majority of which (1246) were also regulated in WT strain. Genes involved in purine synthesis and ribosome structure and function were top-enriched among up-regulated genes, and anaerobic respiration, nitrate metabolism and aromatic amino acid biosynthesis genes among down-regulated genes. Blocking of the purine-synthesis- did not affect resistance phenotypes (MIC and growth rate with OTC), while blocking of protein synthesis using low concentrations of chloramphenicol or gentamicin, lowered MIC towards OTC. Metabolic-modeling, using a novel model for MG1655 and continuous weighing factor that reflected the degree of up or down regulation of genes encoding a reaction, identified 102 metabolic reactions with significant change in flux in MG1655:: tetA when grown in the presence of OTC compared to growth without OTC. These pathways could not have been predicted by simply analyzing functions of the up and down regulated genes, and thus this work has provided a novel method for identification of reactions which are essential in the adaptation to growth in the presence of antimicrobials.We characterized the global transcriptome of  The prevalence of resistance to this class has steadily increased in clinical isolates from both humans3 and animals4, roughly by 0.5% per year from 1950 to 2001 in isolates from humans5. While the use of tetracycline in human medicine has gradually decreased, the drug class remains among the most used in livestock production worldwide6.Tetracycline drugs are bacteriostatic antimicrobials which bind reversibly to the bacterial ribosome and interfere with protein translation7, which selectively pumps tetracycline from the cytosol to the periplasm in exchange for a proton8. In E. coli, TetA and TetB are the most frequently observed tetracycline efflux pumps9. We have shown that a TetA-producing E. coli strain shows prolonged lag phase proportionally to the concentration of tetracycline in the growth medium, and that this concurs with increased expression of tetA10. This suggests that expression of the resistance mechanism or incorporation of the efflux pump into the cell-membrane constitute a burden to the bacterium. Similar observations of cellular adaptation in antimicrobial resistant bacteria grown in the presence of the drug to which they are resistant have been described for Extended Spectrum Beta-Lactamase producing (ESBL) E. coli12, methicillin resistant Staphylococcus aureus12 and colistin-resistant Klebsiella pneumoniae13, and interfering with these adaptive responses in certain cases re-sensitize the bacteria to the drugs13. It is therefore highly relevant to increase our understanding of the way that resistant bacteria adapt to growth in the presence of therapeutic concentrations of antimicrobials.The majority of the more than 40 genes encoding tetracycline resistance encode membrane-associated efflux proteinset al.11, a high number of genes encoding metabolic enzymes were shown to be significantly regulated. Understanding the importance of this for the metabolism is not straight forward, since the metabolic network contains a high number of redundant, alternative pathways15. Metabolic modeling has been a central tool for solving this problem16, but traditionally genome scale metabolic models (GSMs) only describe the network of enzyme-catalyzed and spontaneous reactions proceeding in the organism18, and not adaptation to changing environments19. Recently, however, several approaches to incorporate omic-data into modeling have been published (see for example20).In the study of ESBL adaptation to cephalosporin treatment by Møller tetA encoding E. coli strain to treatment with OTC. We show that despite being highly resistant to oxytetracycline, adaptation to growth in the presence of the drug involves a high number of genes, and using GSM with incorporation of transcriptome data, we identify metabolic adaptations that could not have been identified simply from looking at the list of significantly regulated genes.The aim of this study was to improve the understanding of the metabolic adaptations in an OTC-resistant, E. coli MG1655, where tetA, together with its natural regulator, tetR, had been cloned into the chromosome. RNA-sequencing was used to identify significantly up and down regulated genes, when the strain was grown in MH-2-broth with 14 mg/L OTC in comparison to growth without OTC. The chosen OTC concentration corresponds to ½ MIC of the strain to OTC, and is above the peak serum concentration that is obtained in humans following treatment21.In order to study the adaptation to OTC treatment, we selected a variant of 2FC) > 2.0 alternatively Abs(Log2FC) > 1.5 were used, 149 and 333 genes were up-regulated and 119 and 211 genes were down regulated. The changes were generally moderate in up-regulated genes with a maximum of Log2FC = 3.94 (14.58 fold). The most down regulated genes showed up to Log2FC = −7.46 (73.78 fold) change in expression  < 0.05, was demonstrated for 1646 genes, showing that either the presence of the drug in the cell cytosol or the production and incorporation of an increasing number of TetA-pumps caused dramatic changes in cell homeostasis. Of the significantly regulated genes, 946 genes were up-regulated and 700 were down-regulated . A total of 400 genes were only regulated in the resistant strain, suggesting that this was the number of genes associated with the TetA expression  > 1.5. None of the genes reported to belong to these systems were significantly regulated. pspE of the phage shock proteins was boarder line down-regulated (log2FC −1.44), however, this gene is believed to encode a thiosulphate sulfotransferase, and even though it is named phage shock protein E, is not considered a stress associated protein27. Together, the patterns of gene regulation did not indicate severe membrane stress due to incorporation of TetA pumps.es Table . This ge2FC) > 2.0 to focus on the most highly regulated genes. MG1655::tetA was found to up-regulate biosynthesis processes related to ribonucleoside and ribonucleotide production, in particular purine biosynthesis, and molecular processes and cellular components related to ribosome function  were up-regulated between Log2FC 3.62 and Log2FC 1.59. To analyze the significance of this up-regulation, single and double mutants of purN and purT were constructed. These genes encode the two redundant, phosphoribosylglycinamide transformylases acting at step three in the purine biosynthesis28. Blockage of this step results in accumulation of glycinamide ribonucleotide (GAR) and depletion of all down-stream products, and is therefore considered essential for the purine synthesis28. MIC of OTC was 28–32 mg/L for MG1655::tetA, MG1655::tetA∆purN and MG1655::tetA∆purT, while it increased to 64 mg/L for MG1655::tetA∆purN∆purT  were significantly regulated by treatment with ½ MIC of OTC in MG1655::tetA as well as in WT MG1655 .In total, 14 purine synthesis genes  was not significantly changed compared to MIC of the WT (28 mg/L) .Fifty-one ribosomal and translational genes were significantly up-regulated with LogtyrA and pheA, which both catalyzes the first step in the formation of the amino acids from chorismate. Though several optimizations were tested, it was impossible to construct this mutant, and we concluded that tyrA and/or pheA are essential for growth in LB media which we used for construction of the mutants. It should be noted, however, that others have succeeded in constructing combined tyrA/phe mutants in E. coli33. Instead, we knocked out aroB and aroC, which are the last enzyme in the Shimikate pathway before it branches to the aromatic amino acid biosynthesis pathways. As shown in Fig. tetAΔaroB only) the mutant grew at comparable growth rate as the wild type strain both with and without ½ MIC of OTC. MIC towards OTC did not differ significantly between MG1655::tetA and the two mutants .Genes involved in aromatic amino acid biosynthesis, as mentioned above, were highly down-regulated. In common, these amino acids are synthesized with chorismate as substrate. Chorismate is the end product of the Shikimate pathway. In order to analyse the importance of down-regulation of the amino acid biosynthesis genes, we first tried to knock-out ts Table . As expetetA (49.5% of up regulated genes), and 319 genes were down regulated (44.3% of down regulated genes). This suggested that changes in the metabolism were a major component in the adaptation to growth in the presence of high concentrations of OTC. In order to investigate the metabolic significance of this we used a modeling approach. We built a genome scale model of E. coli K-12 MG1655 from the ecocyc database. It had a total of 1335 reactions encoded by 880 unique genes, 1335 internal and 64 external metabolites. The model was curated to be fully balanced to all atomic species, including protons and was demonstrated to have no violations of conservation of energy or mass. The model was able to produce all biomass components of growing E. coli individually and in combination in proportions corresponding to published experimental data34 (data not shown). Generating this biomass composition, using FBA to minimize total flux in the network and assuming growth on M9 media, the model utilized 331 of the 1351 reactions in the model.In total 468 genes encoding metabolic enzymes were significantly up-regulated in MG1655::2 excretion systems, all pointing to a substantial increased energy demand resulting from OTC exposure. Other regulated reactions were primarily associated with central carbon metabolism in addition to reactions involved with amino acid and nucleotide metabolism. These reactions were then extracted to form a regulated sub-model for further analysis.We used the change in the expression of a metabolic gene to calculate a weighting factor for the reaction(s) associated with that gene, and when these weighting factors were applied, Flux Balance Analysis showed that 59 reaction fluxes increased significantly and 43 decreased. Inspection of this set of reactions showed a 50% increase in the flux of the proton-translocating ATP synthase reaction and concomitant increases in reactions of the electron transport chain (ETC), Oxygen and glucose uptake systems and COet al.35, the flux distributions in both the weighted and un-weighted sets could be accounted for by 8 of these elementary modes as shown in Table The sub-model formed from the 102 up and down regulated reactions contained a total of 50 metabolites that were internal to the sub-model, and 75 metabolites that were exchanged with the rest of the model (i.e. those reactions whose computed fluxes did not vary in response to the weighting associated with OTC exposure). It proved infeasible to calculate elementary modes of the sub model, presumably because of the large number of exchange reactions. However, as described in the methods section, it was possible to further reduce this sub-model to a network of 33 reactions and 50 metabolites of which 20 were exchanged with the whole model. This reduced sub-model had a total 94 elementary modes, and using the method described by Schuster The reactions used by the elementary modes comprised mainly of the Entner-Doudoroff (E-D) pathway, the oxidative pentose phosphate pathway (OPPP) and oxidative phosphorylation (OP). In addition to these reactions, the network also utilized fructose-6-phosphate aldolase, PEP-glycerone phosphotransferase and glutamate synthase  approach to characterize adaptive changes in pan-resistant K. pneumoniae13. Together, the data has shown that even for resistant bacteria, fighting antibiotics is not just a matter of expressing the resistance mechanisms, but involves regulation of genes that are not part of the resistance mechanism, per se. The work by Brochmann et al.12 also showed that there are strains differences in this response, and the results of the current study should be viewed with some caution, as only one strain was analyzed.Global expression analysis was performed to characterize adaptation to growth in the presence of OTC in an OTC resistant E. coli, belonging to the Extra Intestinal Pathogenic E. coli group APEC and grown in the presence of 32 mg/L tetracycline, was published36. Our study confirms the observation from that study of a general up regulation of ribosome associated genes and the purine metabolism and the down regulation of anaerobic respiration and nitrate reduction, when grown with tetracycline. Biosynthesis of aromatic amino acids was not down regulated in that study, confirming strain specific differences.OTC treatment of the resistant strain caused major changes in gene regulation, and genes belonging to 44 different biological functions were found to be significantly regulated. The majority of the significantly regulated genes were also significantly regulated when WT MG1655 was treated with ½ MIC of OTC, showing that the main part of the response was not directly related to expression of the TetA pump. In parallel to our study, another transcriptome study of a tetracycline resistant 10. In the current study, we did not observe indications of membrane stress. It has proved technically impossible to determine the number of TetA pumps in E. coli by proteomics10, and thus it is difficult to estimate whether there is a fixed upper number of TetA pumps that can be incorporated into the membrane, thus avoiding membrane stress.TetA strains grown in the presence of high concentrations of OTC show a significant increase in lag phase, and this has been proposed to indicate stress due to incorporation of TetA pumps in the membranede novo synthesis is an important part of the adaptive response to OTC treatment. Similar up-regulation of purine synthesis was observed in ESBL E. coli in the presence of cefotaxime11, and was also shown in the recent expression study with the APEC strain36. Despite this, an increase in MIC in the presence of OTC was observed when purine synthesis was blocked. Purines can be obtained in other ways, either by uptake from the medium or by purine salvage pathways38. In the current study, important genes in the purine salvage pathway were up-regulated in the TetA strains as well as in the WT MG1655 in the presence of ½ MIC of OTC, and it is likely that this system can fully compensate for lack of purine biosynthesis under these conditions. In minimal medium, lack of the purine synthesis pathway was costlier to the strain judged from growth responses, and it may be that the effect of blocking purine biosynthesis in vivo (in the patient) is more pronounced than the growth study in MH-2 media suggests.We tested whether significantly regulated pathways were essential for expression of the resistant phenotype. The purine synthesis was highly up-regulated indicating that purine tetA towards OTC. The MIC towards tetracycline decreased below the epidemiology cut-off for OTC (≤8 mg/L39) when low concentrations of the ribosome inhibitors chloramphenicol or gentamicin were present. MIC in the WT MG1655 strain did not decrease in the same way. This suggests that the mechanism was related to operating the efflux pump in the MG1655::tetA strain, putting high metabolic demands (presumably energy) on the resistant strain, and that this is then overloaded by the challenge from the second antibiotic. However, an additive effect of OTC and chloramphenicol on E. coli has previously been observed by Ciak and Hahn40, and confirmed by Garrett and Brown41 based on studies of antibiotic sensitive E. coli strains, and it may also be that the synergistic effect is too small to create a significant effect on MIC at the low OTC concentrations tolerated by the WT strain. To our knowledge, the promising effect of combining OTC and chloramphenicol with the goal of reducing OTC resistance in a highly resistant strain has not been reported previously. The combination of tetracycline and chloramphenicol has previously been used to treat against Streptococcus viridans infections, however, it only resulted in a partial treatment effect42. Furthermore, the two antibiotics showed no synergy when tested on several strains of Staphylococcus aureus42. Gentamicin, like OTC, targets the 30S ribosomal subunit, but with a different mode of action compared to OTC43. This could be the reason for the observed synergy. Combining gentamicin and doxycycline has been used as an effective combination for treatment of human brucellosis44.Protein synthesis was shown to be essential for the response of MG1655::aroB encodes the 3-dehydroquinate synthase, which is the second step in the synthesis of chorismate, while aroC encodes the chorismate synthase, which is the last step in the Shikimate pathway. After this step, chorismate is used in the biosynthesis of the aromatic amino acids46. All the enzymes of the Shikimate pathway are attractive targets for the development of anti-metabolites and antimicrobials, as the pathway is not present in mammals48. 5-enolpyruvylshikimate-3-phosphate (EPSP) synthase, encoded by aroA, has been studied extensively and is the target by the active ingredient of the commercially available broad-spectrum herbicide RoundUp™ 49. In the current study, no effect on growth with OTC was seen from knocking out genes of the Shikimate pathway, which is in accordance with the observation that biosynthesis was downregulated. For tryptophan, we saw indication that the down regulation of biosynthesis was counter-acted by tryptophan uptake systems being highly upregulated.Biosynthesis genes for aromatic amino acids were significantly down-regulated. To investigate the importance of this, and the Shikimate pathway in general, we eliminated two essential enzymes in the Shikimate pathway. E. coli MG1655 genome scale metabolic model based on information available in the publicly available and standardized biochemical database, BioCyc. There is already an existing E. coli model, EcoCyc–18.0–GEM50, constructed from the EcoCyc database. There are important differences between these two models; EcoCyc–18.0–GEM is a larger model with 2286 reactions and 1453 metabolites. Most of these extra reactions were removed from the model presented here, owing to more stringent criteria for reaction inclusion in terms of metabolic relevance and atomic balance. Furthermore, a small number of reactions in our model had no equivalent in EcoCyc–18.0–GEM. These reactions were manually defined in the module of extra reactions (see methods section). The EcoCyc–18.0–GEM model has a greater number of un-conserved metabolites (1170), than our model. This includes compounds consisting of carbon, nitrogen, phosphate and sulfur. The model presented here had no un-conserved metabolites.The largest group of regulated genes was metabolic enzymes. In order to investigate the metabolic network on a global scale, we used a novel approach where we incorporated the transcriptome date into global metabolic modeling. We first build an et al.51. Different approaches have been used. Covert et al. used Boolean logic to evaluate whether sufficient expression had occurred for a given reaction to proceed in order to further constrain the FBA problem52. This may not be fully accurate, as many regulatory mechanisms cannot be described with Boolean logic19. Other methods, e.g. GIMME53 and iMAT55, are based on systematic removal of reactions from the model based on low transcriptomic levels. A more realistic approach was recently introduced be Yang et al.20, who simulated growth with incorporation of data on protein abundances. The advantage of the weighting system used in the current study was that it favours reactions that are associated with high levels of gene expression and select against those with low expression levels, and thus the method (in contrast to other approaches55) does not depend on setting arbitrary threshold values of expression that lead to the inclusion or exclusion of a given reaction. One problem with the integration of transcriptomic data into metabolic models consists of multi-gene and multi-reactions situations19 and that there is not necessarily a straight correlation between gene and protein expression.Methods for the integration of transcriptomic data with genome scale metabolic models have been described previously. The aim with all the integration methods is to obtain improved flux predictions using transcriptomic data, and they are based upon the assumption that the expression rate of a given gene, obtained from the experimental data, is correlated with the flux through the reaction catalysed by the enzyme encoded by the gene. This assumption is not necessarily correct as illustrated by Yang E. coli MG1655::tetA grows in the presence of high concentrations of OCT. This demand is being met by changes in flux in both the Entner-Doudoroff (E-D) and oxidative pentose pathways. However, these flux rearrangements do not follow a conventional view of the functioning of the E-D pathway; although the first steps, R3-R5 increase in flux, the subsequent steps, R6-R9, decrease. This is compensated for by additional consumption of GAP, by increased activity in the OPPP, whose fluxes also increase. The result of these changes is an increased flux in reactions of the electron transport chain (ETC) and oxidative phosphorylation (OP) utilising the increased production of reducing equivalents.The modelling analysis resulted in 102 reactions whose flux changed in response to the added weighting. This sub-network was reduced to a more tractable size of 25 reactions. The calculated flux changes are consistent with an increased demand for ATP when 57 and whose metabolic role is as yet not clearly understood.It is also of note that in addition to reactions that might be expected to be associated with increased metabolic stress, the network also contains fructose-6-phosphate aldolase (R19) and dihydroxyacetone kinase (R11). Both of the enzymes responsible for these reactions have been relatively recently discoveredpur-genes, only one, phosphoribosyl pyrophosphate synthetase (prs) showed an increase in flux in the model analysis, and this response was common to both the WT and the oxytetracycling resistant MG1655::tetA strain. The fact that increases in expression were not reflected in changes in calculated flux are not surprising for two reasons: Firstly, even if an increase in transcript level does lead to an increase in enzyme activity, the flux catalysed by that enzyme is a function of the activity of all enzymes in the network and not that of the enzyme alone; a major motivation in the design of the model analysis was to reflect this interdependency. Secondly these reactions are ultimately contributing to nucleotide and amino acid components of the biomass, and as the proportion of these was fixed, this places additional constraints on the values that these reaction fluxes can take.Of the reactions associated with the E. coli MG1655::tetA. This suggests that pumping out of OCT using the tetracycline specific pump TetA requires highly adaptive responses, and this may render the bacterium vulnerable in other parts of the cell than those targeted by the OTC. Indeed, we identified combinations of antibiotics (chloramphenicol and gentamycin), where even sub-therapeutic concentrations of the other drug were shown to increase the OTC susceptibility. The study investigated a novel approach to characterize the metabolic adaptation in antimicrobial resistant bacteria. It demonstrated that metabolic modeling with incorporated transcription data identified adaptive reactions that were not obvious from just looking at up and down regulated genes. On the contrary, the pathways which were identified using metabolic modeling were generally not controlled by enzymes encoded from genes which were among the most highly regulated genes. We suggest that this approach is generally applicable to studies of adaptation to antimicrobial treatment in antimicrobial resistant bacteria.In summary, the results of the study showed that growth in the presence of ½ MIC of OTC resulted in profound changes in gene expression in the OTC resistant 58. It has tetA gene and its repressor gene, tetR, cloned into the pseudo gene ybeM. Strains were maintained in DifcoTM Lysogeny broth (LB), Lennox  and on LB agar plates  supplemented with OTC (10 mg/L)  when appropriate.The strains used in this study are listed in Table 59, resulting in exchange of the gene of interest with chloramphenicol or kanamycin gene cassettes. During strain construction, the medium was supplemented with kanamycin (30 mg/L) or chloramphenicol (10 mg/L)  when appropriate. Primer sequences used for Lambda Red mediated mutagenesis and PCR verifications are listed in Table Site-specific mutants were produced by Lambda Red recombination11.The MICs for OTC, chloramphenicol , and gentamicin , were determined using the broth-micro-dilution method in the range from 0 to 512 mg/L by 2-fold dilutions increase following the CLSI guidelines, When necessary, more detailed determination with more concentration steps were included, as previous described11. The panel was incubated aerobically at 37 °C for 18–22 hours, without shaking. The fractional inhibitory concentration (FIC) index was defined as; ∑FIC = FIC A + FIC B, where FIC A is the MIC of drug A in the combination divided by the MIC of drug A alone, and FIC B is the MIC of drug B in the combination divided by the MIC of drug B alone60. The antibiotic combinations were interpreted as synergetic when the ∑FIC ≤ 0.5, indifferent when the ∑FIC > 0.5–4, and antagonistic when the ∑FIC > 432.Checkerboard assays were performed in Mueller-Hinton II (MH-2)  broth with two different antibiotic combinations: OTC and chloramphenicol, and OTC and gentamicin, as previously described61). Growth was at 37 °C and with shaking (225 rpm). The flasks were inoculated with pre-cultures grown for 2 hours at 37 °C and 225 rpm to a final cell density of 105 CFU/mL, as determined by use of Sensititre™ Nephelometer (Thermo Scientific™) with a McFarland 0.5 standard. The cultures were monitored by measuring optical density until samples reached the logarithmic phase (OD600nm = 0.5–0.6), where aliquots were taken for RNA extraction. Biological duplicates were performed.In order to obtain RNA for expression analysis, the strains were grown in 250 mL flasks containing 100 mL of MH-2 broth either without OTC or with OTC corresponding to ½ MIC of the OTC-resistant strain (14 mg/L) or ½ MIC of the wild type strain MG1655  representing ½ MIC of the corresponding strain when appropriate. Strains were also grown in M9 minimal media . Lag phase of growth curves was defined as the time necessary to reach an OD600 of 0.1. Statistical comparison of lag phase was performed by one-way ANOVA using GraphPad Prism. Growth characterization experiments were performed with three biological replicates and with three technical repeats for each of these.Growth for characterization of growth-responses to treatment were performed in Mueller-Hinton II (MH-2) on a BioScreen C11. Briefly, 3 mL cell samples were mixed with RNAlater . Total RNA was extracted from two independently grown samples using a FastPrep system  and an RNeasy Mini kit , and quantity and quality of RNA was determined using a NanoDrop 1000 spectrophotometer . After DNase treatment , rRNAs (23S and 16S) were removed by subtractive hybridization using the MICROBExpress kit . Removal of rRNAs was confirmed with an Agilent 2100 Bioanalyzer .RNA extraction for RNA seq was performed essentially as previously describedgapA and nusG were used as reference genes. RT-qPCR was performed twice by two separate biological samples and the results were caiculated by the 2−∆∆CT method as described by Pfallf62. Primer sequences can be found in the Table Purified RNA was reverse-transcribed into cDNA using the High Capacity cDNA Reverse Transcription Kit . RT-qPCR was performed using FastStart Essential DNA Green Master . The library for RNA-Seq was prepared using the TruSeq RNA Sample Preparation kit , following the manufacturer’s instructions. The samples were validated after each step using an Agilent 2100 Bioanalyzer , and the final concentration of mRNA was measured using a Qubit 2.0 Fluorometer . The libraries were sequenced using the Illumina HiSeq. 2000 platform with a single-end protocol, multiplexing and read lengths of 100 nt.E. coli MG1655 genome (RefSeq Accession No. NC_000913) as previously described11. Briefly, hits of reads to annotated genes were counted and all rRNA read counts were removed to obtain a proper normalization. The data was normalized using the conditional quantile normalization method, while also taking into account difference in replicate library sizes and gene GC content using edgeR bioconductor software package for R63. The differential expression of each annotated mRNA transcript was calculated using EdgeR and significantly regulated genes were selected based on the false discovery rate (FDR) (Benjamini–Hochberg multiple testing correction)64 using a threshold of 0.05. The resulting datasets from analysis of MG1655::tetA grown with and without 14 mg/L OTC and MG1655 growin with 0.25 mg/L OTC are included as supplementary material, Tables 65. EcoCyC database66 was used to further analyse the functions of differentially expressed genes.The analysis was made on libraries consisting of 2.5–3 million unique reads with a length of 100 bp. A total of 4482 genes were considered in the analysis. Reads were trimmed for adaptor sequences using the CLC Genomics Workbench . The trimmed reads were mapped onto the E. coli K-12 MG1655 genome scale model was constructed in a modular way using already described techniques68. The final model consisted of (1) A top-level module which contain information about the other model modules and its prime function was to import other modules; (2) A module containing all the automatically generated reactions (see below); (3) A module containing all transport reactions importing nutrients and exporting biomass components and metabolic by-products; (4) A module containing the electron transport chain, and (5) A module containing additional and modified reactions.The E. coli biomass components from a rich medium (Mueller Hinton broth 2 (MH-2)), consisting of glucose, amino acids 70, phosphate and O2.The final GSM describes a metabolic network which can generate all Details on model construction can be seen in Supplementary Material, Table The weighting factor wr, for a given reaction, r, was calculated as:Defining the linear program:i is the weighting factor described above and subject to three constrains: The first constraint specified stead-state, the second ensured that irreversible reactions carry positive flux and the third constraint represents the export of biomass components. The problem was solved twice, with and without wi, and the solutions compared to identify up or down regulated reactions. This set of reaction was then used to construct a regulated sub-model.Stating to minimize flux, where wa priori be guaranteed to represent a balanced network as a result of constant fluxes of metabolites exchanged between the regulated and non-regulated sub-networks. However, these could be identified by constructing a stoichiometry matrix, N’ constructed from the reactions in the regulated sub-network. We could then calculate:The set of reactions identified above could not r represents the stoichiometry matrix of the regulated sub-network with the additional transporters included, and vr the corresponding flux vector. In this way we generated not only fully a balanced sub-model, but also a corresponding steady-state flux vector.The non-zero elements of s′ corresponded to unbalanced metabolites, and hence required additional transport reactions to be added to N′. Their values corresponded to the negative flux to be carried by these transporters in order to generate a regulated sub-network, such that:targ to the minimum (absolute) flux in vobs, and minimise the total absolute flux in the system subject to the constraint that no flux in the solution can take an absolute value greater than that in vobs, and that the sign of the reaction flux in the solution is the same as that in the observed. We can now proceed as shown in Fig. The sub-models proved hard to analyse, presumably due to large numbers of additional transporters included, and correlation trees with standard algorithms for flux balance analysis were inconclusive. To solve this problem, we generated a new algorithm, taking advantage of the fact that our analysis had created a valid steady-state flux vector, and based on the assumption that any Linear Programming solution in which only a single reaction is fixed will represent an elementary mode (EM). The novel algorithm also generated a set of fluxes corresponding to reactions in the newly determined EM, so as well as identifying the EMs we could assign fluxes to them. The calculations were as follows: Given an initial (observed) steady-state flux vector, vobs, define an LP problem:Supplementary Dataset 1.


Streptococcus oralis SK141. We validated the model using gene essentiality and amino acid auxotrophy data from closely related species. iCJ415 has 71-76% accuracy in predicting gene essentiality and 85% accuracy in predicting amino acid auxotrophy. Further, the phenotype of S. oralis was tested using the Biolog Phenotype microarrays, giving iCJ415 a 82% accuracy in predicting carbon sources. iCJ415 can be used to explore the metabolic differences within the MGS, and to explore the complicated metabolic interactions between different species in the human oral cavity.The mitis group of streptococci (MGS) is a member of the healthy human microbiome in the oral cavity and upper respiratory tract. Troublingly, some MGS are able to escape this niche and cause infective endocarditis, a severe and devastating disease. Genome-scale models have been shown to be valuable in investigating metabolism of bacteria. Here we present the first genome-scale model, iCJ415, for  Streptococcus, Enterococcus, and Aerococcus is composed of a wide variety of bacteria important for the food industry (The mitis group of streptococci (MGS) consists of 20 different species, all gram positive cocci arranged as pairs or in chains . MGS is industry .Streptococcus oralis is the most common  of metabolism have proven to be valuable in investigating the metabolism in a single bacterial strain . GEMs haLactococcus, Lactobacillus, and Streptococcus for the food industry, GEMs have been created and published . The genome sequence was annotated using the RAST server on default settings . For gapfilling, pathways were visualized using Escher  version 0.13.4 .S. agalactiae and other Streptococcus species (version: 16-May-09) provided by the manufacturer with the following exception: Due to a high degree of background noise in the negative control well, phenotype microarrays 3 was tested using only 1/10 of the carbon sources reported.Biolog experiments were performed using Biolog’s phenotype microarrays . All experiments were carried out according to the phenotype microarray protocol for The Biolog results were analyzed with the “OPM” package in R, and statistical analysis was done with the “Dunnett-type comparison: one-against-all” function . All welS. oralis we used the macromolecular composition of a Lactobacillus plantarum previously published  (Streptococcus sanguinis (Strain SK36)   and Strein SK36) . OrtholoS. pneumoniae strain D39  as a carbon source. We sequentially opened and closed the exchange for all nitrogens sources tested. If the opening resulted in an increase in Biomass yield, it was considered a positive result.All simulations on gene essentiality, amino acid auxotrophy and carbon and nitrogen sources were done using Flux Balance Analysis (FBA) while optimizing for the Biomass reaction. For all metabolites present in the media, the upper and lower bounds were set to -1000 and 1000 mmol/g/h, respectively. When doing Biolog validations the exchange reactions for the carbon- and nitrogen sources tested were also set to -1000 and 1000 mmol/g/h for lower and upper bounds, respectively. -8 as a growth/no growth cutoff.For all FBA simulations we used a value of 10S. pneumoniae strain D39 . We used the same media conditions to simulate growth of iCJ415  categorized as discordant.There are 61 genes where there are discordant results. In 36 of those genes, iCJ415 shows essentiality while the essentiality datasets show non-essentiality. An explanation is that not much In the remaining 25 genes where there are discordant results, iCJ415 shows genes to be non-essential, while the essentiality data shows the genes to be essential. This can be explained in part by the use of rich medium in both gene essentiality datasets. Both of these studies use a broth-based, non-defined media, where it is not possible to get the exact composition of the different metabolites. When we simulate gene deletions in rich media we open all exchange reactions in the model, which corresponds to an experimental setup where all metabolites are present in the growth media. But if a metabolite is absent from the experimental growth media, a gene could be deemed essential, however the gene would not be essential if the metabolite was present.The two gene essentiality studies use either Todd Hewitt or BHI media for the experiments . Both ofWhen using glucose as a sole carbon source, seven “discordant” genes become essential; six of those are part of the carbon metabolism. These genes are becoming essential due to the lack of essential metabolite synthesis when only glucose is used as a carbon source, e.g. teichoic acid cannot be produced when the gene SK141_RS03865 is knocked out due to the inability of the model to synthesize ribulose 5-phosphate. When retesting all genes that were “concordant” in the full media, only one of the 169 genes were essential when testing with glucose as the sole carbon source. Furthermore, only 10 gene deletions had a lower growth rate when glucose was the only carbon source, and no deletion gave more than a 20% reduction in growth rate.S. oralis. Further, S. oralis has one of the smallest genomes in the MGS, having a genome size of 1.9 mio basepairs  were not able to promote growth in iCJ415. Four of them had transport reactions in iCJ415 but were still not able to use the metabolites as carbon sources . The remaining four metabolites were not present in the model and we could not find any information on the transport or metabolism in the genome annotation or in the literature .Knowledge about the differences in carbon source utilization within the MGS is limited. The published data is mainly about eumoniae . TherefoS. oralis and S. pneumoniae resides in two different ecological niches, different nutritional sources are available. While the oral cavity contains multiple different carbon sources, partly degraded by oral enzymes, pharynx has a low content of readily available carbon sources S. pneumoniae R6 and S. oralis SK141 this is reflected by the content of transport systems specific for breakdown products of human tissue. S. pneumoniae R6 contains a transporter for hyaluronate, which is not present in S. oralis SK141, and together with lyases this transporter can make part of the human extracellular matrix available as carbon sources . S. pneumoniae mutants deficient of these lyases are attenuated in their ability to colonization and infection making them important virulence factors These metabolic associated genes are increasingly considered crucial to the pathogenesis since they determine in which ecological niche an organism can grow, both during asymptomatic carriage and in disease . TherefoS. pneumoniae and S. pyogenes GEMs previously published all contains several carbohydrate transporters, but the hyaluronate transporter was not included in any of them. The GEM for S. pneumoniae R6, iDS372, contains transporters for nine carbon sources also present in iCJ415. While iDS371 can grow on six of these carbon sources, iCJ415 can grow on 8. Neither of the models can use glycerol as a carbon despite having a transporter. In contrast to iCJ415, iDS372 cannot grow on either stachyose or raffinose, which is due to lack of a raffinose degrading reaction (Stachyose is degraded to raffinose). Raffinose utilization by S. pneumoniae has been shown to be an independent predictor for disease spectrum The S. oralis SK141 was able to use some nitrogen sources which could be mapped to iCJ415 , we were not able to distinguish single nitrogen capabilities in iCJ415 with the used in silico media, revealing that there are still knowledge gaps in iCJ415.Though, Biolog data showed that S. oralis is able to grow on multiple carbon sources, but the transport and metabolism of some of these metabolites are unknown or only poorly understood.iCJ415 reflects that S. pneumoniae D39. We opened the exchange reactions for all the amino acids in iCJ415 and all the metabolites present in the media used by Härtel et al. To test amino acid auxotrophy we consecutively closed each of the amino acid exchange reactions in iCJ415 . To simulate the impact of the carbon catabolite repressor on the genes in the model, fluxes in reactions catalyzed by repressed genes were set to a maximum flux defined by the flux in the unconstrained media, see materials and methods.For validating Biomass yield and byproduct formation of iCJ415 we used previously published experimental growth data from niae D39 . The catS. pneumoniae D39. The experimental data was obtained when grown in glucose, galactose, N-acetyl-D-glucosamine, and mannose. When compared with iDS372, iCJ415 has a lower growth rate than iDS372 except when N-acetyl-D-glucosamine is used as a carbon source. Especially when using glucose as a carbon source, the growth rate is only 0.44 h-1 in iCJ415, with growth rates in iDS372 and in the experimental of 0.85 and 0.82 h-1 respectively. When grown on glucose the 0% activity of pfl causes severe growth retardation in iCJ415, but only a small increase to 1% pfl activity causes an increase in growth rate to 0.76 h-1. The reason for decrease in growth rate is due to lack of formate production when the pfl gene is knocked out. Formate is used for the synthesis of folates in the reaction “FTHLFI.” This reveals differences in the synthesis of folates, which in part can be explained by a folate transporter in iDS372. A folate transporter has earlier been identified in Streptococcus mutans, but we could not find any evidence for the folate transporter being present in the annotation of S. oralis or S. pneumoniae and it has been postulated earlier that such a transporter is not present in S. pneumoniaeS. oralis.Interestingly, iCJ415, like iDS372, has a much higher rate of acetate production, than the experimental data, when N-acetyl-D-glucosamine is used as a carbon source. The high acetate production I iCJ415 is explained by a production of acetate in the breakdown of N-acetyl-D-glucosamine 6-phosphate. This anomaly reveals a knowledge gap in the metabolism of N-acetyl-glucosamine in iCJ415 was tested using the Memote web application with all exchanges open. iCJ415 scores >99% on all consistency scores except “unbounded flux in default medium” where it scores 95% .S. oralis, the first GEM of a bacterium that is a major constituent of the human oral microbiome and an IE causing bacterium. We used an annotated genome together with strain, species or genus specific data obtained from the literature. To get information in carbon source utilization in S. oralis SK141 we used the Biolog phenotypic microarray.The aim of this study was to construct a GEM of a S. oralis are able to grow on at least 28 carbon sources, including commonly known carbohydrates as glucose and galactose, but also rarely encountered ones like lyxose. iCJ415 were able to grow on 21 of the 28 carbon sources tested. In the remaining seven carbon sources we did not have enough information to simulate growth on these carbon sources.When comparing gene essentiality between iCJ415 and published experimental data, we found concordance in 71–76% of the genes tested. When testing carbon sources and amino acid auxotrophies we found concordance with experimental data in 82% and 85% of metabolites tested, respectively. Especially the carbon source utilization data obtained in this study are valuable. It is, beside the genomic data, the only strain specific data used. Furthermore, carbon source utilization capacities are increasingly being considered as possible virulence factors in bacteria . We founiCJ415 should be used as a starting point for exploring the complicated metabolic interactions between different strains of the same species and between different species in the human mouth. Further it can be used to compare metabolic capabilities between non-pathogenic and known pathogenic strains, finding metabolic traits important for virulence.iCJ415 was tested using memote and scored >99% on all consistency scores except “unbounded flux in default medium” where it scores 95%.All datasets generated for this study are included in the article/BP, XN, JC, and CJ conceived the project. XF, CN, JM, and CJ performed the metabolic network reconstruction. CN and CJ performed the model simulations. CJ prepared the draft manuscript. All authors have read, commented and approved the final manuscript.This work was supported by the Region Zealand Foundation for Health Research, The Knud Højgård Foundation (Grant no: 18-02-0767), and the Ovesen Foundation. None of the funders had any influence on the study or the conclusions.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.