Minimum Network Enrichment Analysis (MiNEA) that is applied to GEMs to generate all possible alternative minimal networks (MiNs), which are possible and feasible networks composed of all the reactions pertaining to various metabolic subsystems that can synthesize a target metabolite. We applied MiNEA to investigate deregulated MiNs and to identify key regulators in different NAFLD phenotypes, such as a fatty liver and liver inflammation, in both humans and mice by integrating condition-specific transcriptomics data from liver samples. We identified key deregulations in the synthesis of cholesteryl esters, cholesterol, and hexadecanoate in both humans and mice, and we found that key regulators of the hydrogen peroxide synthesis network were regulated differently in humans and mice. We further identified which MiNs demonstrate the general and specific characteristics of the different NAFLD phenotypes. MiNEA is applicable to any GEM and to any desired target metabolite, making MiNEA flexible enough to study condition-specific metabolism for any given disease or organism.Nonalcoholic fatty liver disease (NAFLD) is associated with metabolic syndromes spanning a wide spectrum of diseases, from simple steatosis to the more complex nonalcoholic steatohepatitis. To identify the deregulation that occurs in metabolic processes at the molecular level that give rise to these various NAFLD phenotypes, algorithms such as pathway enrichment analysis (PEA) can be used. These analyses require the use of predefined pathway maps, which are composed of reactions describing metabolic processes/subsystems. Unfortunately, the annotation of the metabolic subsystems can differ depending on the pathway database used, making these approaches subject to biases associated with different pathway annotations, and these methods cannot capture the balancing of cofactors and byproducts through the complex nature and interactions of genome-scale metabolic networks (GEMs). Here, we introduce a framework entitled  This work aims to introduce a network-based enrichment analysis using metabolic networks and transcriptomics data. Previous pathways/subsystems enrichment methods use predefined gene annotations of metabolic processes and gene annotations can differ based on different resources and can produce bias in pathway definitions. Thus, we introduce a framework, Minimum Network Enrichment Analysis (MiNEA), which first finds all possible minimal-size networks for a given metabolic process/task and then identifies deregulated minimal networks using deregulated genes between two conditions. MiNEA also identifies the deregulation in key reactions that are overlapped across all possible minimal-size networks. We applied MiNEA to identify deregulated metabolic tasks and their synthesis networks in the steatosis and the nonalcoholic steatohepatitis (NASH) diseases using a metabolic network and transcriptomics data of mouse and human liver samples. We identified key regulators of NASH for the synthesis networks of hydrogen peroxide and ceramide in both humans and mice. We also identified opposite deregulation in NASH for the phosphatidylserine synthesis network between humans and mice. MiNEA finds synthesis networks for a given target metabolite and due to this it is flexible to study deregulation in different phenotypes. MiNEA can be widely applicable for studying context-specific metabolism for any organism because the metabolic networks and context-specific gene expression data are now available for many organisms. Nonalcoholic fatty liver disease (NAFLD) is the most common cause of liver disease in western countries , affectiGene deregulation occurs when the cell no longer maintains precise control over certain genes, and therefore expressed proteins, and is associated with various diseases such as NAFLD. Determining which genes are deregulated in disease and how these genes are deregulated can increase the understanding of any disease as well as provide new pathways for therapeutic treatments. To study gene deregulation, pathway enrichment analysis (PEA) uses maps of metabolic processes and subsystem reactions to determine if a given list of genes (gene set) is associated with a certain biochemical pathway, shedding important conceptual insight into gene deregulation. Many PEA algorithms, such as ConsensusPathDB  and PianTo overcome this, a method has been developed based on elementary flux modes (EFMs), which are non-decomposable flux distributions in metabolic networks .These flModeling methods of biological networks, such as metabolic, signaling, and protein-protein interaction can be used as a framework to study biological functions of the liver for the prevention and treatment of liver-associated disease . For exaMinimal Network Enrichment Analysis (MiNEA) that compares two conditions using transcriptomics, proteomics, and metabolomics to identify deregulated minimal networks (MiNs), which are reactions pertaining to various metabolic subsystems that can synthesize a target metabolite. The MiNEA algorithm works by formulating metabolic tasks (MTs) to mimic the various NAFLD phenotypes, such as lipid droplet formation, lipoapoptosis, liver inflammation, and oxidative stress. These MTs include the known set of metabolites necessary to form the modeled phenotype. The cell can use different pathways to form the same metabolites and create the same phenotypes, and the purpose of MiNEA is to identify deregulated alternative routes for a given MT between two conditions, such as a control and treatment. Additionally, MiNEA uses these MTs to compute alternative thermodynamically feasible MiNs applying thermodynamic constraints [Here, we propose a method called straints , 13 to astraints .We used mouse and human liver sample expression data , 15 and MiNEA can generate MiNs for any target metabolite, e.g. a metabolite produced under specific phenotypes or a biomass building block  that is human expression data and mouse expression data throughout this section and integrate it into a mouse model iMM1415 [Mus musculus) . We compared iMM1415 and Recon1 and found that the iMM1415 shares 98% of reactions with Recon1 and in the remaining 2% reactions more than 75%  were associated with the extracellular transport mechanism. This suggests that Recon1 and iMM1415 have a very similar metabolism. Computed minimal networks do not change between Recon1 and IMM1415 due to less variability between the two models. Thus, we use iMM1415 as a generic model. If we impose tissue specific constraints or organism specific external medium, we expect some changes in the minimal networks, mainly in the number of alternative networks. However, such constraints involve a lot of uncertainty and we chose to use a generic model and our results here can be viewed as an upper bound on the possible solutions. We analyzed both human and mouse MTs using the generic model (iMM1415) with the assumption that both types of mammalian cells have a similar metabolism  has been defined as a metabolic disease associated with insulin-resistance syndrome . To stud iMM1415 . Sigurdslism See . In the GEMs represent an entire cellular metabolic network in the form of mathematical constraints . These nTo examine the data that can be derived from MiNEA in terms of NAFLD, we first wanted to compute MiNs for given MTs see  that wer2O2) (network size = 37), and the longest were for the synthesis of cholesteryl ester  synthesis, 62% and 89% of the reactions were HFRs, respectively, which suggests that the alternative MiNs for the SOA synthesis were more divergent compared to the H2O2 synthesis.A summary of the MiNs calculated for MTs see  are show2O2 synthesis comprises many reactions from various metabolic subsystems (2O2 synthesis are from the pentose phosphate pathway (PPP) and the glycolysis/gluconeogenesis pathway. PEA differs in that it identifies a marked deregulation in a pathway as compared to MiNEA, which identifies deregulation in a MiN that could include multiple pathways.As an expanded example, a MiN from the Hbsystems . Each MiWe further analyzed the HFRs within each phenotype and found that the most represented metabolic pathway in the apoptosis and inflammation (AI) phenotype was related to amino acid metabolism  phenotype, but for the LD and AI phenotypes, we observed similar patterns of up- and downregulated HFRs to humans  and H2O2 synthesis networks in NS vs S  and B6 DDC vs N comparisons, we found that the percentage of downregulated HFRs in the SOA and H2O2 synthesis networks was higher than for upregulated ones , phosphatidylinositol (PAIL), diacylglycerol (DAG), and phosphatidic acid (PA) metabolites; a high percentage of downregulated HFRs was found for the alanine (ALA) and glutamine (GLU) synthesis networks , we analyzed B6 separately. For mice, in NS ted ones . This innetworks . This is2O2, the human NS vs N case presented with a high percentage of downregulated HFRs, while the same case in mice presented with a high percentage of upregulated HFRs  that prevents oxidative stress. Thus, the different patterns of regulation observed for these reactions can unbalance both NADPH concentration and H2O2 synthesis. Interestingly it has been reported that the unbalance in NADPH production through PPP by either an under or over-production of NADPH may induce oxidative stress [de novo glutathione synthesis and reported that redox imbalance is associated to metabolic dysfunction and the development of NAFLD.For the synthesis of ROS, such as Hted HFRs . The reae stress , 28. Inte stress  found anvs S and NS vs N in humans along with the NS vs N in mice. Thus, perturbation in the genes associated with these reactions can affect the ceramide level in NASH in both mice and humans. In line with this finding, a study suggested that deregulated ceramide production promotes liver injury and the development of NASH through disruption of endoplasmic calcium homeostasis, as well as through the inhibition of autophagy [The acetyl CoA C acetyltransferase (ACACT1rm) and phosphoglycerate mutase reactions of the ceramide synthesis networks were identified as downregulated HFRs for the NS utophagy . Furtherutophagy .p value based on hypergeometic test.We want to identify deregulation at network level based on an enrichment analysis with deregulated enzymes or genes. A Minimal network (MiN) of a metabolic task (MT) contains metabolites, active reactions and their associated enzymes in order to fulfill the MT. To identify deregulated MTs and their associated MiNs see  section vs N human case, we identified deregulated MiNs which corresponded to the synthesis of H2O2, PA, and TAG , which represents the percent of MiNs with enrichment in deregulated genes. For the NS vs N case in humans, H2O2 synthesis had the highest AMiNF (AMiNF = 0.143) compared to all other deregulated MTs . Interestingly, TAG synthesis was also deregulated in NS vs N, suggesting that lipid droplet formation can be perturbed in the NASH state but through different components.When we applied MiNEA to NS vs N and S vs N, but we observed significant deregulation in NS vs S for the synthesis of ALA, GLU, ceramide (CRM), PS, and phosphatidylethanolamine (PE)  , suggestde novo lipogenesis in the patients with high amount of liver fat, indicating an alteration in lipid droplet formation.In humans, MiNEA identified deregulation in the CHOL and TAG synthesis network, which are lipid droplet constituents. Similar deregulations were observed for the lipid droplet constituents in mice through the PS and PE synthesis networks. Thus, because MiNEA was applied with information on deregulated genes, we find that in both humans and mice, lipid droplet formation is perturbed but through different lipid constituents. Interestingly, a study  using flTo identify whether a specific up- or downregulated MiN was associated with the NAFLD phenotypes, we performed MiNEA while enriching for up- and downregulated reactions separately .vs S  and five downregulated  synthesis networks. Within the alternative MiNs that are used for the synthesis of PS, we found some networks that were upregulated and some that were downregulated. Since there was a higher frequency of downregulated MiNs (AMiNF = 0.5) compared to upregulated ones (AMiNF = 0.167), we could hypothesize that the downregulation of PS synthesis is an important molecular mechanism specific to NASH. Here, pathways enrichment methods [In mice, the MiNs associated with the synthesis of ALA, CRM, PE, PS, CHOL_ES, and GLU were identified as downregulated in NS vs N . Interesegulated . For the methods , 5 lack vs N case. In contrast, the PS synthesis was differently deregulated between the two species  rules should be able to resolve the regulation of reaction’s rates, but only if the GPR is well curated and validated with experimental data during the reconstruction process. Based on GPR rules, if a reaction is associated with a mixture of upregulated and downregulated genes, we did not consider the reaction as regulated. Here, we chose a rather conservative threshold to resolve these uncertainties, and provide an We should acknowledge that gene regulation based on mRNA levels is not sufficient to determine reaction rates (fluxes) because post-translational modifications, protein translation alone, and kinetics of the enzymes are really important in determining the mapping between mRNA levels and reaction rates. Depending on the availability of proteomics (and phosphoproteomics) data, and even kinetics (such V_max), they can be integrated within the MiNEA method.We applied MiNEA for the study of deregulated metabolic processes rather signaling and regulatory processes because metabolism is better characterized, as is shown with the increased availability of GEMS for many organisms . DespiteIn contrast to graph-based methods (GBM) and pathway enrichment analysis (PEA), the methodology introduced here (MiNEA) expands the notion of pathway into a set of mass balanced subnetworks that can be used to understand the carbon, energy, and redox flows from precursor metabolites to target metabolites and complex metabolic tasks. One of the main advantages of MiNEA compared to PEA is that MiNEA attempts an enumeration of alternative minimal networks for each metabolic task (MT), which helps us understand and study the MT flexibility. Although a large number of alternative enumerations for a complex metabolic network can be time-consuming, once the enumeration is completed, MiNEA applies a statistical analysis that is fast and extracts additional information, such as the deregulation of MTs or the deregulation in reaction hubs. As an example of the utility of MiNEA, we identified deregulation in key metabolic network of the ceramide and hydrogen peroxide synthesis for NASH in both humans and mice. We also identified similar deregulation in NASH for the cholesterol synthesis networks in humans and mice, and we found opposite deregulation for the phosphatidylserine synthesis network between humans and mice. It has been reported that significant differences between mouse and human metabolism exist mainly in genes for cytochrome P450 based metabolic reactions, which are key reactions in lipid and cholesterol metabolism . TherefoThe microarray gene expression data pertaining to the three diagnostic groups  were collected from the ArrayExpress public repository for microarray data under the accession number E-MEXP-3291 . We perfNASH phenotypes can be reproduced in mouse models by treatment of chronic intoxication of with 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC)-supplemented diet [vs N represents the AJ mouse strain treated with the DDC diet vs the AJ mouse strain fed the control diet, S vs N represents the PWD mouse strain treated with the DDC diet vs the PWD mouse strain fed the control diet, and NS vs S represents the AJ mouse strain treated with the DDC diet vs the PWD mouse strain fed the DDC diet.The normal (N) feeding group is associated with mice fed the control diet. All mice strains, AJ, B6 and PWD, were fed this diet and are labeled as N≈AJ, N≈PWD or N≈B6 to indicate the control diet and mouse strain. After feeding the DDC-supplemented diet, AJ and PWD showed high NASH phenotypes and high steatosis phenotypes, respectively . Thus, uMetabolite concentration data and RNA-seq gene expression data from mouse liver samples of the three mouse strains  measured under the control and DDC-supplemented diet conditions were collected from the work of Pandey et al. . The R-pSteatosis can occur due to the accumulation of lipid droplets in the liver, which can subsequently lead to NASH upon liver inflammation . Lipid dThe cell can use various different pathways depending on the current state and conditions of the environment to fulfill its immediate metabolic needs. In order for MiNEA to identify deregulated alternative routes for a given MT between two conditions , the inpThe MiNEA calculations begin by applying thermodynamic constraints to the model as described previously  to elimiRGEM is the number of reactions in a GEM and rxn,iz is a binary variable associated with reaction i. Reversible reactions were split into two forward reactions. MT,maxV is the maximum yield to produce a metabolite associated with a MT. The parameter c is generates MiNs that allow for flexibility on the yield from the MT. We chose c = 0.8 to allow for a yield of at least 80% of the maximum yield for the associated MT.All alternative MiNs that are the minimum size (msize) for the synthesis of the metabolites in k and K are the numbers of deregulated genes and the number of total genes in a given MiN, respectively, and n and N are the total number of deregulated genes and the total number of genes of a metabolic network, respectively. In this context, the term “deregulated genes” is used for both up- or downregulated genes.The significance of the MiN based on the deregulated genes was calculated using the hypergeometric probability density function (P),A reaction can have three states: upregulated, downregulated, or unregulated. The regulation of a reaction was determined based on its associated differentially expressed genes. According to this metric, a reaction was identified as upregulated or downregulated if the corresponding genes are only upregulated or only downregulated, respectively. A reaction that is associated with a mixture of up- and downregulated genes is not characterized as regulated due to the inconsistency of gene expression. Up- and downregulated MiNs, which could contain various combination of up- or downregulated reactions, were identified in Step 4 of MiNEA  based onThe significance of a MiN based on upregulated or downregulated reactions was calculated using multivariate Fisher's hypergeometric distribution. This method has been previously used for the selection of tissue-specific elementary modes using gene expression data . To idenAssume R and T are the total numbers of reactions in a GEM and a MiN, respectively, which can be decomposed as follows:Rup, Rdown, and Rno represent the number of upregulated, downregulated, and unregulated reactions in a GEM, respectively. In the context of a MiN, T is the total number of reactions, and Tup, Tdown, and Tno are the number of upregulated, downregulated, and unregulated reactions, respectively.Tup, Tdown) in the MiN of T reactions cannot arise by chance in the context of the whole network. To obtain a better upregulation by chance, the p value was computed using Eq  satisfies two conditions: (i) the number of downregulated reactions is smaller than or equal to Tdown, and (ii) the number of upregulated reactions is greater than or equal to Tup, whereas the number of reactions in the MiN remains unchanged.To consider a MiN as upregulated, we need to ensure that the pair  for each MiN, which indicates the percentage of up- or downregulated genes in a MiN. For example, if a given MiN comprises 20 genes, and 5 genes of the MiN are deregulated, the then DRP is equal to 0.4. Since the active reactions in a MiN were classified as either up- or downregulated, we calculated both upregulated percentage (UPR) and downregulated percentage (DnRP) for each MiN.To extend the degree of confidence of the results from MiNEA, we introduced the alternative minimal network frequency (AMiNF) metric that determines the significantly deregulated percentage of MiNs for a given MT. If all the alternative MiNs for a MT are significantly deregulated, AMiNF takes the value 1. The AMiNF is 0 when none of the MiNs for a MT are significantly deregulated.MiNEA was implemented using Matlab r2016a , and MILP problems were solved using CPLEX solver . The MILP gaps for all problems were converged to less than 0.05% in less than 2,400 s. We also plan to make the MiNEA algorithm available as a tool for distribution to the community.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 TableDescription of the table heading is illustrated in ‘Symbol’ sheet of the excel file. Please see the Symbol sheet for the detail description for the tables: S5-S9.(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S1 FigUpper and lower panels represent human and mouse, respectively.(EPS)Click here for additional data file.S2 FigThe reaction regulation metric was used to identify up- and downregulated reactions.(EPS)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.


Metabolomic analysis combined to metabolic network reconstruction highlighted different mechanisms at lower doses of exposure. At the highest dose, our results evidence that BPA shares with E2 the capability to modulate several major metabolic routes that ensure cellular functions and detoxification processes, although the effects of the model xeno-estrogen and of the natural hormone can still be distinguished.The model xeno-estrogen bisphenol A (BPA) has been extensively studied over the past two decades, contributing to major advances in the field of endocrine disrupting chemicals research. Besides its well documented adverse effects on reproduction and development observed in rodents, latest studies strongly suggest that BPA disrupts several endogenous metabolic pathways, with suspected steatogenic and obesogenic effects. BPA's adverse effects on reproduction are attributed to its ability to activate estrogen receptors (ERs), but its effects on metabolism and its mechanism(s) of action at low doses are so far only marginally understood. Metabolomics based approaches are increasingly used in toxicology to investigate the biological changes induced by model toxicants and chemical mixtures, to identify markers of toxicity and biological effects. In this study, we used proton nuclear magnetic resonance ( In the field of endocrine disrupting chemicals (EDCs), bisphenol A (BPA) is among the xeno-estrogens that have been the subject of the most extensive studies over the past two decades. In addition to its adverse effects on reproduction and development observed in rodents, BPA was found to disturb several metabolic pathways, resulting in steatogenic and obesogenic effects . EffectsAlthough part of BPA adverse effects are attributed to its ability to activate estrogen receptors (ERs), its mechanism(s) of action at low doses remain(s) incompletely known . In partin vitro studies become a priority in toxicity testing. High throughput assays that use cells or cell lines, preferably of human origin, are required to assess relevant disruptions in key toxicity pathways  of selected chemicals . We prevpathways , 14. Thepathways , 16.1H-NMR) based untargeted metabolite profiling followed by multivariate statistics and computational analysis of metabolic networks to examine the metabolic modulation produced in human hepatic cells (HepG2) by an exposure to low and very low doses of BPA, vs. the female reference hormone 17β-estradiol (E2). The HepG2 cell line is derived from a human hepatoblastoma. This cell line expresses biotransformation capacities for numerous xenobiotics, notably BPA , 17β-estradiol (E2) and dimethyl sulfoxide (DMSO)  were obtained from Sigma-Aldrich . Penicillin, streptomycin, trypsin, and PBS were also purchased from Sigma-Aldrich. The concentration of the stock solutions was 50 mM in DMSO.−1), and streptomycin (100 μg mL−1) (Fisher Scientific), in a humidified atmosphere of 5% CO2 at 37°C. Continuous cultures were maintained by sub-culturing flasks every 3–5 days.HepG2 human hepatoblastoma cells (ATCC N° HB-8065) were cultured in monolayer culture in phenol red-free αMEM  supplemented with 10% fetal calf serum v/v (PAN biotech), penicillin  DMSO in the culture medium (controls) and supplemented with BPA  or E2  or DMSO . At the end of the 24-h treatment period, cells were washed in ice cold PBS and were recovered by scraping each well twice with 1 mL ice cold water/acetonitrile . After each water/acetonitrile addition, cells were agitated with a vortex for 1 min. Samples were then centrifuged at 7000 g for 15 min at 4°C. The supernatant was then evaporated to dryness. The lyophilisates were reconstituted in 600 μL of D2O containing 0.25 mM TMSP . The reconstituted solutions were transferred to NMR tubes.HepG2 cells, 1 × 101H-NMR spectra were obtained on a Bruker DRX-600-Avance NMR spectrometer operating at 600.13 MHz for 1H resonance frequency using an inverse detection 5 mm 1H-13C-15N cryoprobe attached to a CryoPlatform (the preamplifier cooling unit).All 1H-NMR spectra were acquired at 300 K using the Carr-Purcell-Meiboom-Gill (CPMG) spin-echo pulse sequence with pre-saturation, with a total spin-echo delay (2nτ) of 320 ms to attenuate broad signals from proteins and lipoproteins. A total of 128 transients were collected into 32 k data points using a spectral width of 12 ppm, a relaxation delay of 2.5 s and an acquisition time of 2.28 s. Prior to Fourier Transformation, an exponential line broadening function of 0.3 Hz was applied to the FID.The 1H-1H COSY (Correlation Spectroscopy) and 2D 1H-13C-HSQC (Heteronuclear Single Quantum Coherence Spectroscopy) NMR experiments were performed on selected samples.To confirm the chemical structure of metabolites of interest, 2D 2). This supervised method maximizes the separation between treatment groups. The number of components in the PLS models was chosen by cross validation (7-fold). The R2 parameter represents the explained variance. The predictive performance of the model was evaluated using the Q2 parameter (predictive capacity), calculated by cross-validation. Typically a robust model is characterized by a R2 >50% and a Q2 >0.4 . Principal Component Analysis (PCA) was first used to detect intrinsic clusters and eventual outliers. Then Partial Least Squares—Discriminant Analysis (PLS–DA) was used to study the effect of the treatment on the cell metabolome  not linked to the studied factor (treatment), OSC filtering was applied to the data, as such variations may overshadow the variability due to the factor under study . The treIn addition, the statistical significance and validity of the PLS-DA models were assessed using a permutation test (200 permutations). This test determines whether the specific classification of individuals in the designated groups is significantly better than any other random classification in two arbitrary groups .−6M) vs. control conditions is considered as a “metabolic fingerprint” of the impact of this treatment exposure.VIP (Variable Importance in the Projection) was used to determine the most important NMR variables for the separation observed between experimental groups. VIP is a global measure of the influence of each variable on the PLS components. An arbitrary threshold of VIP >1.5 was chosen to select the variables. The Kruskal–Wallis test, a non-parametric version of Analysis of Variance, was then used to determine variables that differed significantly between groups , with 0.05 being chosen as the level of significance. The set of discriminant metabolites identified from the comparison of a given treatment condition  aim at gathering in a single formalism all metabolic reactions which can occur in an organism . Each re−6M metabolic subnetwork” for the higher dose of BPA) and E2 .We performed metabolic sub-network extraction from the discriminant metabolites identified from the statistical analyses. Metabolic sub-network extraction consists in computationally identifying among the 4,210 reactions, the ones that are more likely to be related to the metabolic fingerprint of each exposure. The algorithm computes the lightest path between each pair of metabolites in the fingerprint. The lightest path is a sequence of reactions and metabolites connecting two metabolites and minimizing a topological criterion in the network . For one−6 exposure by subtracting, from the BPA 10−6M metabolic subnetwork, the metabolites and reactions belonging to the E2 10−9M metabolic sub-network. All computational and visualization tasks were performed within MetExplore web server based on the modified Recon2 metabolic network (biosource id 3223)  , 31.−6M, BPA 10−9M, and BPA 10−12M). This model explained 62.2% of the variability (R2) with a predictive ability (Q2 = 0.561) validating the robustness of this model (−6M) group was discriminated from the BPA 10−9M and BPA 10−12M groups, respectively, along the 2nd axis . However, no significant discrimination between these two lowest BPA exposure groups (10−9M and 10−12M) was evidenced in this 4-group comparison. Twenty metabolites were found to be responsible for the separation between BPA exposed groups and the control group  and demonstrated a high predictive ability (Q2 = 0.635). The score plot showed a clear separation between the DMSO and E2 doses groups (E2 10−12M and E2 10−15M) and the E2 10−9M group along axis 1 . Figure −9M, BPA 10−12M, E2 10−12M, and E2 10−15M) and that of higher doses (BPA 10−6M and E2 10−9M), along axis 1. In addition, a marked discrimination between the effects of the xeno-estrogenic model compound BPA and that of the natural hormone E2 was observed along axis 2. All these groups were clearly separated from the DMSO (control) group. This 7-group comparison model was further validated by the permutation test step. As detailed in Table The 7-group comparison generated a 4-component PLS-DA model, explaining 59.5% of the treatment variability and having a Q1H-NMR data, to further investigate the effects shared by the two molecules.Finally, we proceeded to network analysis based on −6M and E2 10−9M appear to share some metabolic effects (axis 2) but also have specific effects (axis 1), suggesting both common and molecule-specific mechanisms of action. In order to further distinguish between shared mechanisms (which likely reflect an estrogenic effect) from the effects specific to BPA 10−6M, we performed a network analysis. Two networks were reconstructed based on metabolic fingerprints obtained in the 7-group analysis . We extracted the common part for these two subnetworks, resulting in a common BPA-E2 sub-network , these two amino acids were highlighted as major endogenous metabolites modulated by BPA in the BPA-specific subnetwork. Network reconstruction also identified some intermediates in the metabolism of leucine and isoleucine, namely isovaleryl CoA and methylbutanoyl-CoA, that are therefore likely to be specifically modulated by BPA exposure.a priori, to compare the effects of a range of concentrations of E2 and BPA on a broadly used human hepatic cell line (HepG2). Then, we used bioinformatics (Metabolic Network modeling) methods to further investigate the biological pathways modulated by these estrogens: E2 as the reference hormone, and BPA as a model xeno-estrogen with well-documented endocrine disrupting properties, but also strongly suspected metabolic effects. Many EDCs with xeno-estrogenic properties can act as metabolic modulators  guiding principles for more ethical use of animals in testing, require developing alternative in vitro bioassays, preferably based on human models. The implementation of metabolomics and bioinformatics on the bases of in vitro bioassays first needs to be validated with model molecules. It also opens the road for a better understanding of the mechanisms of action of EDCs. These approaches should be as sensitive and relevant as possible to further examine the reliability of biomarkers of effects of EDCs. Experiments carried out to seek for these biomarkers should also link to low, environmentally relevant, exposure levels of EDCs in human body, as mentioned for xeno-estrogens by Wang et al. regarding BPA   were in accordance with the bibliography regarding their respective estrogenic potency, as it is estimated that the estrogenic potency of E2 is approximately 1 000 to 10 000 time greater that BPA' s estrogenic potency in vitro  , 22. Alton (MoA) , 36, 37.on (MoA) , 38. Likon (MoA) –21, 38. in vitro , 40.1H-NMR metabolomics approach to HepG2 cells extracts to evidence the impact of BPA on the whole metabolism, in order to gain a more “global picture” and with no a priori hypothesis regarding the effect of this endocrine disruptor. The same methodology was applied to estradiol as the reference estrogenic compound, to identify common and specific metabolic pathways modulated by both molecules, with the benefit of bioinformatics and network reconstruction to help identifying metabolites not detected directly during analyses, but yet playing a role in the metabolic pathways modulated by BPA and/or E2.Although few papers already reported the effects of low doses of BPA exposure on HepG2 cells, these studies mainly used targeted approaches, providing results on hepatotoxic endpoints, such as serum aspartate aminotransferase (AST) and alanine transferase (ALT) measurements, or inflammatory genes analyzed by RT-qPCR , 42. In in vitro. For instance, BPA and E2 were both shown to promote HepG2 cell proliferation by inhibition of apoptosis and stimulation of telomerase activity via an estrogen receptor-dependent pathway . This specific concentration of BPA was reported to be estrogenic in vitro in several models such as MCF-7 cells and ZELH-zfERs cell lines (−9M). According to Gould et al., BPA interacts with the ERα in a distinct manner from estradiol. BPA is not merely a weak estrogen mimic but exhibits a distinct mechanism of action at the level of ERα. The distinct activity of BPA is most likely due to an induction of a conformation of the activated ERα by BPA that differs from these other known classes of ER ligands. Generally, BPA is considered as a weak estrogenic compound , which is not the case for the two other BPA doses tested. Interestingly, reduced glutathione was modulated in the lowest BPA dose group only, which may suggest a change in the capacity of detoxification of the cells through this specific biochemical pathway. More importantly, differences in reduced glutathione levels also determine the expressed mode of cell death, being either apoptosis or cell necrosis. Lower levels of reduced glutathione may result in the systematic breakage of the cell which may lead to cell death  and in vitro (HepG2 and Caco cells)  and apolipoprotein C3 (ApoC3) pathways in the shared mechanism between E2 and BPA. These components of high density lipoproteins (HDL) and very low density lipoproteins (VLDL) respectively, are in charge of the uptake, transport and catabolism of lipids. It has already been reported that ApoC3 was affected by BPA exposure, as is the expression of the APOC3 gene, which was found to be decreased in the liver of BPA-exposed C57BL/6 mice , 57. It o cells) , 58. In s of BPA , 12, 35.Network analysis allows identifying reactions that are likely to be modulated by the various exposure conditions. One of the key challenge in metabolic network analysis is the quality of the metabolic network reconstruction used. In fact, since these networks are built based on genomic information they may contain false positive reactions (reactions which should not be included) and false negatives (missing reactions). Recon2 is a highly curated reconstruction for the human metabolic network applied in many studies, but improved versions are regularly released, such as Recon3D, which has been recently introduced but has still not been used in the field of metabolomics –62. The To go further with these findings, it would be interesting to perform non-targeted lipidomics to identify which family of lipids are modulated by BPA and to complete these identifications using targeted lipidomics for specific lipid families. To complete our metabolomics set of data, and to improve the quality of the metabolic networks, mass spectrometry metabolomics could be performed in order to get access to a larger set of discriminant metabolites in complement of the NMR generated list.in vivo results, provide first proofs of evidence that metabolomics combined with network reconstruction can be used in vitro (here on the human HepG2 model) as relevant approaches to investigate commonalities and differences in MoA. An even greater added value of metabolic network will come with a metabolite list as complete and specific as possible to identify the most impacted pathways associated with BPA exposure. Connecting these biochemical pathways with MoA will help to identify AOP and to facilitate the hazard characterization of other compounds . MA contributed to supervising the cell work to generate the data, and contributed to revising the manuscript. CC was responsible of the NMR analyses and contributed in the writing of this specific section. MT-F was in charge of the statistical analyses and contributed to the writing of this section. J-PC contributed to the writing of this MS, AR contributed in supervising the experiments and revising this MS, FJ contributed in performing the metabolic networks and the writing of the MS. DZ contributed in supervising all this work, including the writing of this MS.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.


Background: Healthcare-associated infections (HAIs) are a serious public health problem. They can be associated with morbidity and mortality and are responsible for the increase in patient hospitalization. Antimicrobial resistance among pathogens causing HAI has increased at alarming levels. In this paper, a robust method for analyzing genome-scale metabolic networks of bacteria is proposed in order to identify potential therapeutic targets, along with its corresponding web implementation, dubbed FindTargetsWEB. The proposed method assumes that every metabolic network presents fragile genes whose blockade will impair one or more metabolic functions, such as biomass accumulation. FindTargetsWEB automates the process of identification of such fragile genes using flux balance analysis (FBA), flux variability analysis (FVA), extended Systems Biology Markup Language (SBML) file parsing, and queries to three public repositories, i.e., KEGG, UniProt, and DrugBank. The web application was developed in Python using COBRApy and Django.Results: The proposed method was demonstrated to be robust enough to process even non-curated, incomplete, or imprecise metabolic networks, in addition to integrated host-pathogen models. A list of potential therapeutic targets and their putative inhibitors was generated as a result of the analysis of Pseudomonas aeruginosa metabolic networks available in the literature and a curated version of the metabolic network of a multidrug-resistant P. aeruginosa strain belonging to a clone endemic in Brazil (P. aeruginosa ST277). Genome-scale metabolic networks of other gram-positive and gram-negative bacteria, such as Staphylococcus aureus, Klebsiella pneumoniae, and Haemophilus influenzae, were also analyzed using FindTargetsWEB. Multiple potential targets have been found using the proposed method in all metabolic networks, including some overlapping between two or more pathogens. Among the potential targets, several have been previously reported in the literature as targets for antimicrobial development, and many targets have approved drugs. Despite similarities in the metabolic network structure for closely related bacteria, we show that the method is able to selectively identify targets in pathogenic versus non-pathogenic organisms.Conclusions: This new computational system can give insights into the identification of new candidate therapeutic targets for pathogenic bacteria and discovery of new antimicrobial drugs through genome-scale metabolic network analysis and heterogeneous data integration, even for non-curated or incomplete networks. Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacteriaceae family), and R&D on new antibiotics against these is considered to be of critical priority , previously called hospital infections, are a serious public health problem and can develop either as a direct result of medical or surgical treatment or from being in contact with a healthcare setting. These infections include central line-associated bloodstream infections, catheter-associated urinary tract infections, ventilator-associated pneumonia (VAP), and surgical site infections. Among the pathogens related to HAI, the group of bacteria is the one that stands out. More than 2 million HAIs occur each year in the USA , with 50priority . In humafibrosis  and is aGiven the potential severity of multidrug-resistant bacteria and the lack of treatment options, the identification and implementation of effective strategies to prevent such infections are urgent priorities.The integration of mathematical, statistical, and computational methods for biological data analysis to enable the discovery of new therapeutic targets for any bacteria is extremely relevant. The combination of bioinformatics, system modeling, and heterogeneous data integration can be a powerful tool for this purpose.Several strategies have been proposed to search for drug targets from genome-scale models of bacterial metabolism. More often, essential genes are identified from single virtual knockouts where flux balance analysis (FBA)  is used The construction of genome-scale metabolic network is a laborious endeavor. It combines automated steps with manual curation. The most used protocol, proposed by Tier 1 corresponds to PGDBs that have received at least 1 year of manual curation and are updated continuously. Tier 2 includes PGDBs that have received moderate (less than a year) amounts of review and are usually not updated on an ongoing basis. Finally, Tier 3 refers to PGDBs that were created computationally and received no subsequent manual review or updating.The BioCyc database  classifidraft metabolic reconstructions are considered Tier 3 models. Published curated metabolic models are classified as Tier 2, unless the model is identified in the literature as Tier 1.In this work, the same classification for genome-scale metabolic network models is adopted. The focus here is on metabolic network models that can be classified as Tier 2 and Tier 3, according to the BioCyc database classification. In this manuscript, FindTargetsWEB. The proposed method is computationally efficient, user-friendly, and robust to errors in reconstructed genome-scale metabolic networks, which are more frequent in Tier 3 (draft) metabolic networks. The web interface of the application is straightforward, and results are sent directly to an email address informed by the user. To demonstrate the flexibility of FindTargetsWEB, 10 genomic-scale metabolic networks of bacterial strains are analyzed in this paper. Nine of the 10 networks are available in the literature, all classified as Tier 2 models in this work: P. aeruginosa PAO1—version 2008   . Both reThe web application proposed in this work combines FBA, flux variability analysis (FVA) , extendeCOnstraint-Based Reconstruction and Analysis Toolbox (COBRA)   methods  (COBRA)  is a Pyt (COBRA) Figure 1Target identification is carried out through a computational workflow that runs the metabolic network analysis and pinpoints genes whose virtual knockout interrupts the generation of biomass. Therefore, the minimum level of curation required for a metabolic network model to be processed by FindTargetsWEB is to have a biomass reaction flux greater than zero. The list of potential targets is filtered using FVA (if the user decides to do so), and the workflow retrieves possible inhibitors for the identified genes, verify if such inhibitors are available as approved drugs, and evaluate their toxicity to humans by querying several repositories.https://www.gnu.org/software/glpk/), which is the COBRApy default solver that is easily deployable on Linux platforms. The system is deployed in an Ubuntu v18.04 server with 64GB RAM. Prior to processing, when needed, SBML files were converted to the SBML level 3 format using the command cobra.io.sbml3.write_sbml_model from COBRA. The SBML files processed in this manuscript were retrieved from the BioModels repository  relations. In the same way as the previous step, if the value of the generation of biomass has zeroed, the corresponding gene information is stored in a second list. It is worth noting that one gene can be associated with more than one reaction, and one reaction may require the expression of several genes.5. Consolidation/unification of knockouts results—In this step, both lists generated in the previous steps are unified, i.e., the list of reactions generated in step 3 and the gene list generated in step 4. In order to a gene to be included in the final list, it should be included in the list of step 4 and be associated with at least one reaction stored in step 3 (see Algorithm 1). These are the candidate genes that the workflow is going to consider in the following steps. It should be highlighted that the final list is filtered according to the FVA processing performed in step 2, if the option FBA+FVA is selected by the user.Algorithm 1: Consolidation of knockout results (SBML with mapped genes)Input: List of genes from knocked-out reactions/list of knocked-out genesOutput: Unified list of target genes in a text fileprocedure UnificationTargetsList	2:			read targetGeneListFromReact	3:			read targetGeneList	4:			open file “targetgenes.txt”	5:			for all targetgene in targetGeneListFromReact do	6:				if targetgene in targetGeneList then	7:				write targetgene in file “targetgenes.txt”	8:				end if	9:			end for	10:			close file “targetgenes.txt”	11:	end procedure	1:	6a. Search for EC numbers of consolidated genes. In this step, the system queries the KEGG repository to obtain the EC number of each gene included in the final gene list obtained in the previous step (file “targetgenes.txt”). KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information  metabolic network models, EC numbers are retrieved from KEGG based on reaction information. This step is particularly important for incomplete metabolic reconstructions that do not include GPR relations and is directly related to the application’s requirement of robustness to incompleteness on metabolic network data. The KEGG search is performed using all the compounds involved in the corresponding reaction. See Algorithm 2 for a detailed description of the processing related to this step. It is worth emphasizing that this step is executed only for incomplete descriptions of genome-scale metabolic networks. The complexity of Algorithm 2 is O(C), where C is the number of compounds included in the SBML file.Algorithm 2: Search for EC numbers using reaction information (SBML without mapped genes)Input: List of chemical compounds of reactionOutput: List of EC numbers foundread listCompoundFromSBML	4:	5:	# Instance of biomodels python module	6:	k <- KEGG instance	7:	8:	# setting timeout in seconds	9:	k.timeout <- 200000	10:	11:	# All compounds in SBML file. Ex.: 2 A –> B	12:	for all compound in listCompoundFromSBML do	13:	14:			# find all stoichiometric values with regex method in compound. Ex.: A –> B	15:			compound_no_stoich <- remove all stoichiometric values in compound	16:			param_splt <- empty	17:	18:			# Verify if reaction is reversible or irreversible	19:			if “< = >“ in compound_no_stoich then	20:				param_splt <- “< = >“	21:			else	22:				param_splt <- “–>“	23:			end if	24:	25:			# Separate compounds in reactant and product. Ex.: compound_splt = 	26:			compound_splt <- compound_no_stoich.split(param_splt)	27:	28:			# If compound belongs to a transport reaction (influx or eflux), jump to next iteration	29:			if compound_splt.length < 2 then	30:				continue	31:			end if	32:	33:			list_ec_number_0 <- initialize empty list	34:			list_ec_number_1 <- initialize empty list	35:		36:			# Start iterating compound_splt list with reactant and product	37:			for  do	38:	39:				# Get reactant or product in this variable	40:				item_compound <- compound_splt[x] without spaces	41:			list_id_cpd_KEGG <- initialize empty list	42:			43:			# If reactant or product contains “+”, find ID in KEGG for components.	44:			# Else, find ID in KEGG for only one component.	45:			if “ + “ in item_compound then	46:				item_compound_splt <- item_compound.split(“ + “)	47:				for all cpd_item in item_compound_splt do	48:					# find id compound in KEGG for cpd_item	49:					# and insert in ids list	50:					result_id_cpd <- k.find	51:					insert result_id_cpd in list_id_cpd_KEGG	52:				end for	53:			else	54:				result_id_cpd <- k.find	55:				insert item_compound in list_id_cpd_KEGG	56:			end if	57:			58:			# Here, all list_id_cpd_KEGG are concatenated	59:			# found to search the reaction in KEGG.	60:			# In Python, if list_id_cpd_KEGG length is less than 2,	61:			# don’t put the “+” in end of string.	62:			str_item_compound_in_cpd <- list_id_cpd_KEGG concat with “+”	63:			64:			# find all reactions in KEGG with IDs of compounds	65:			result_link_reactions_cpd <- k.link	66:			67:			# All results of result_link_reactions_cpd are inserted here	68:			set_id_reaction_KEGG <- insert all reactions found in KEGG.	69:			70:			# find all EC numbers in KEGG with reactions IDs	71:			# in set_id_reaction_KEGG and insert in result_list_ec	72:			result_list_ec = k.link	73:			if x = 0 then	74:				insert result_list_ec in list_ec_number_0	75:			else	76:				insert result_list_ec in list_ec_number_1	77:			end if	78:	79:		end for	80:	81:		list_ec_number_intersect <- initialize empty list	82:		txt_file <- initialize txt file	83:		84:		# Starts to iterate the list of ECs to identify intersections	85:		# If found, related EC numbers are written in a text file	86:		for all ec_number_0 in list_ec_number_0 do	87:			if ec_number_0 in list_ec_number_1 then	88:				record ec_number_0 in a txt_file	89:			end if	90:		end for	91:		92:	end for	93:	94: end procedure	1:	procedure alternativeStepToGetECNumberWithoutGenes(listCompoundFromSBML)	2:	# file with all compounds in SBML.	3:	7. Search for EC numbers on DrugBank—With the EC numbers obtained in the previous steps, the system queries the DrugBank repository to verify if this database has any record of the listed EC numbers. The DrugBank database is a repository that combines detailed drug data with comprehensive drug target information  is necessary to confirm whether the protein retrieved has a homologue in the target bacterium. Clearly, exact matches are also possible. In any case, the retrieved data is validated in the next step.8. Search for homologues on UniProt—Finally, the system searches for sequence similarity between the proteins described by UniProt IDs retrieved in the last step and the proteins encoded by the genome of the target bacterium using the BLAST  (ch tool)  applicatIn this step, the homology between the target protein and human proteins is also considered. If the sequence similarity with a human protein is greater than the similarity with the target bacterium, the protein under analysis is discarded, since the inhibition of that protein could be harmful to the host. Otherwise, several data are stored, such as metabolic pathway, function, and catalytic activity, among others. This step of the workflow is the most time-consuming, since BLAST is executed for all proteins identified in the previous step.9. Search for existing inhibitors—The last step is to query the DrugBank repository, using the stored UniProt IDs, in order to retrieve known inhibitors, if available. After this last step, the system generates spreadsheets containing all results that are sent to the user in a compressed file.This method presents as results candidate genes that, when knocked-out, will cease the biomass production of the microorganism. Candidate genes must be associated with potential drug targets in DrugBank, and their sequence similarity to human proteins is also checked. The application then identifies available ligands, most often inhibitors, to the selected genes.Results of FindTargetsWEB’s analysis are sent to the user as a compressed file, to the e-mail address informed at the start of execution. Five spreadsheets are included in the compressed file:08-filter_ECNumbers_DrugBank—This spreadsheet contains the EC number of putative targets, along with product, organism name, UniProt ID, and DrugBank ID- 11- hits_Uniprot—This spreadsheet contains additional information related to UniProt queries, such as percentage of sequence similarity, BLAST e-value, gene name, pathway, function, and catalytic activity.- 13-list_inhibitors_per_target—This spreadsheet lists all inhibitors found for all targets. Included information are drug name, drug group , and drug action.- 14-list_inhibitors_approved—This spreadsheet lists all inhibitors with approved drugs found for all targets. Included information are drug name, drug group (approved), and drug action.- model_data—This spreadsheet lists data related to the input SBML file, such as gene IDs and associated reactions. The complete information of which reactions are associated with each gene in the metabolic network model is included in this file.- summary_results—This spreadsheet contains a summary of data included in the previous files. Included fields are EC numbers, product, organism name, gene name, pathway, function, catalytic activity, drug name, drug group, and drug action.- P. aeruginosa, K. pneumoniae, H. influenzae, S. aureus, P. putida, and a host-pathogen genome-scale reconstruction based on the M. tuberculosis metabolic network are presented. It should be highlighted that FindTargetsWEB can carry out analysis for other bacterial species, as indicated by the list box on the initial web page of the application. Indeed, even this list can be easily expanded to include additional species of interest, through a user request to FindTargetsWEB support team.In this section, analysis results for several strains of P. aeruginosa is discussed. A survey of the literature is also presented to confirm the feasibility of the candidate genes as antibacterial drug targets. Gene function and related pathways are also considered in the evaluation of results.To evaluate the accuracy of results for several metabolic networks, initially, the analysis of four metabolic networks of P. aeruginosa strains analyzed by FindTargetWEB were: PAO1 version 2008—iMO1056 (BioModels ID 1507180020)  , PAO1 ve7180020) , PA14—iP7180020) , and a cCCBH4851 . The SBMP. aeruginosa spp., which may vary between 0.3 and 0.8 h-1, depending on cultivation conditions  and murB (EC 1.3.1.98) genes encode enzymes involved in bacterial cell wall synthesis and have been identified as essential in both Pseudomonas spp. and Escherichia coli (folP gene product (EC 2.5.1.15) is important for folic acid biosynthesis, which is fundamental for bacterial growth and reproduction (folA gene product (EC 1.5.1.3) is related to the biosynthesis of cofactors, being an important intermediary of folate metabolism. It is considered the key enzyme of this process and essential for microbial growth (aroE gene (EC 1.1.1.25), which has been described as a potential therapeutic target of both P. putida and E. coli  and lysine. Lysine is utilized for protein synthesis in bacteria and forms part of the peptidoglycan cross-link structure in the cell wall of most gram-positive species, whilst meso-DAP is the peptidoglycan cross-linking moiety in the cell wall of gram-negative bacteria   are favorable candidate drug targets and several inhibitors have been already reported  . Becausereported . SpecififabA participates in fatty acid synthesis (FAS) processes, which includes also fabB, fabD, fabI, and fabH. The proteins encoded by these genes have an essential role during the synthesis of bacterial phospholipid membranes, lipopolysaccharide (LPS), and lipoproteins, thus representing attractive targets due to the structural differences between the human and bacterial proteins and the essentiality of FAS  of the overall method. The growth rate of the Tier 3 version of the P. aeruginosa CCBH4851 network is 1.757 h-1, which is less consistent to the biology of P. aeruginosa spp. than the growth rate obtained by the Tier 2 version of the network. The processing of this Tier 3 network generated 32 targets in the FBA+FVA analysis, and 48 targets using the FBA-only method. It is remarkable that this less curated version of P. aeruginosa CCBH4851 network generated more potential targets in the FBA+FVA analysis than the corresponding Tier 2 network.To evaluate the robustness of FindTargetsWEB regarding Tier 3 networks, which generally are networks generated automatically without manual curation, FindTargetsWEB processed a preliminary version of the metabolic network model of dapA is present as a potential target in the Tier 3 network.Among targets identified using the FVA+FBA method, 10 targets are common between the Tier 2 and Tier 3 networks. For the FBA-only analysis, 21 targets are common between the two versions. It is worth mentioning that many targets found in Tier 2 networks are present in the analysis of the CCBH4851 Tier 3 network, which corroborates the relevance of the targets found even in draft versions of metabolic networks. This comparison also highlights the importance of careful curation of automatically generated metabolic networks. For instance, from the targets discussed in the previous subsection, only P. aeruginosa were also processed using FindTargetsWEB. In the previous subsections, results for P. aeruginosa metabolic network models were presented, but it is also possible to analyze networks of other species of bacteria. In this subsection, FindTargetsWEB results for a metabolic network reconstruction of K. pneumoniae MGH78578—iYL1228 (BioModels ID 1507180054) (H. influenzae—iCS400 (BioModels ID 1507180053) (Metabolic networks of bacteria other than 7180054)  and H. i7180053)  are pres7180053) Table 4.K. pneumoniae, a total of 45 unique potential targets were found using the FBA+FVA method and also 45 for the FBA-only method. Some of the more representative targets are listed in For lpxA gene is involved in the initial steps of lipid A production through the Raetz pathway. As stated in the previous subsection, fabA, fabB, and fabF participate in FAS processes and represent attractive targets due to the structural differences between the human and bacterial proteins and the essentiality of FAS. The cytoplasmic protein N-acetylglutamate (NAG) kinase (encoded by argB), which promotes phosphorylation of NAG in a rate-limiting step of bacterial L-arginine production, occurs through acetylated intermediates, unlike mammals which use non-acetylated intermediates, and for this reason, it was previously considered a candidate drug target  and P. aeruginosa (folA). Both methods, FBA+FVA and FBA-only, generate exactly the same results. Therefore, the FVA ranges for all targets in It is worth mentioning that the genes M. tuberculosis H37Rv model, iNJ661 , which integrates a cell-specific alveolar macrophage model, iAB-AMØ-1410, from the global human metabolic reconstruction, with an , iNJ661 . The intM. tuberculosis survival   , reconstmurB, aroC), while others such as mvaA , tkt , and dfrA  are defined as essential for S. aureus in both minimal and rich medias  networks, pointing out that such metabolic network models are very common and are the only models available for some organisms. The system is deployed as a web application and is asynchronous: the user is notified when results are available. The performance of the system is optimized, since the COBRApy framework can make use of multiple cores available in the host machine, and it is able to process the metabolic network of various bacteria, as described in the previous section. The only requirement is the availability of an SBML level 3 file describing the corresponding genome-scale metabolic network. The user interface is straightforward see 2, and tE. coli and Bacillus subtilis, without pinpointing any potential drug. K. pneumoniae. However, their method is not automated, and it was not applied to other species of bacteria. None of these works go as far as FindTargetsWEB, which can process metabolic network models of several species of bacteria, identify potential targets, confirm homology with the analyzed gene, and identify all available drugs for the potential target in a fully automated manner.Other proposals for the analysis of metabolic networks at genomic scale are available in the literature. FBA+FVA and FBA-only, one can conclude that the FBA+FVA method represents a way to prioritize the targets identified by the FBA-only method, since the set of targets identified by FBA+FVA is a proper subset of the set of targets identified by FBA-only. However, as stated in the detailed description of the targets of the Results section, potential targets that are associated with the FBA-only method and do not appear as results of the FBA+FVA method should not be disconsidered. Many important targets described in the literature have a FVA range greater than zero, and a careful analysis of both sets of potential targets is advised.Regarding the options to identify potential targets, i.e., P. aeruginosa and other bacteria and can be effective against non multidrug-resistant strains. As expected, for the multidrug-resistant strain, most of the approved drugs are not effective. For instance, it is known that P. aeruginosa can be resistant to both to trimethoprim and sulfamethoxazole  is both essential to metabolic growth and biofilm formation, according to the FUNCTION field returned by FindTargetsWEB and literature sources License: Not ApplicableAny restriction to use by non-academics: Not ApplicableThe user must provide a SBML level 3 file describing the metabolic network reconstruction and an e-mail address to which the results will be forwarded.All datasets generated for this study are included in the manuscript and the supplementary files.TCM, FPSJ, and FABS designed the system. TCM was the main programmer. MWC and ADC-A tested the system and evaluated its correctness. All authors have equally participated in the writing of this paper.The authors would like to thank CAPES, FAPERJ, CNPq and FIOCRUZ (INOVA-FIOCRUZ VPPCB-007-FIO-18-2-29) for financial support.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.


Escherichia coli to continuously changing conditions during batch growth in complex medium. Unexpectedly, model-based analysis of time resolved exo-metabolome data revealed that fastest growth coincides with preferred catabolism of amino acids, which, in turn, reduces glucose uptake and increases acetate overflow. We show that high intracellular levels of the amino acid degradation metabolites pyruvate and oxaloacetate can directly inhibit the phosphotransferase system (PTS), and reveal their functional role in mediating regulatory decisions for uptake and catabolism of alternative carbon sources. Overall, the proposed methodology expands the spectrum of possible applications of flux balance analysis to decipher metabolic adaptation mechanisms in naturally occurring habitats and diverse organisms.How microbes dynamically coordinate uptake and simultaneous utilization of nutrients in complex nutritional ecosystems is still an open question. Here, we develop a constraint-based modeling approach that exploits non-targeted exo-metabolomics data to unravel adaptive decision-making processes in dynamic nutritional environments. We thereby investigate metabolic adaptation of  Bacteria must adapt their metabolism in the face of dynamically changing nutrient availability. Here, using their constraint-based modeling approach the authors analyze E. coli exometabolome data during growth in complex medium, revealing temporal coordination of glucose and amino acid catabolism. To ensure growth and survival, bacteria constantly sense metabolic changes and rapidly adapt to the appearance or depletion of nutrients3. To this end, cells integrate complex and dynamic external inputs to decide, which nutrients to consume and when to switch to consumption of alternative available substrates5. Monitoring dynamic changes in intracellular metabolism is key to understand regulatory mechanisms underlying adaptation to fluctuating environments7. However, resolving dynamic flux rearrangements is challenging, in particular in complex media with parallel consumption of multiple nutrients9.In natural environments, bacteria are frequently exposed to continuous changes in the flow of nutrients12. Alternatively, by assuming optimality functions of cellular behavior, fluxes can be estimated from constraint-based modeling of the metabolic network stoichiometry, such as in flux balance analysis (FBA)15. While FBA-like methods provide network-wide flux solutions, simplistic objective functions, such as maximization of growth, have been met with limited success for complex media conditions17. In such complex and dynamic environments without a defined limiting substrate, predicting metabolic fluxes using FBA remains a major challenge18. New analytical methodologies capable of tackling the chemical complexity of natural environments and of directly measuring rapid changes in the abundance of multiple substrates and byproducts in parallel, can provide additional constraints to model intracellular metabolism and open new opportunities to shed light on the mechanisms underlying decision-making processes of bacteria dealing with complex and dynamic nutritional conditions.The most rigorous approaches infer intracellular fluxes from isotopic tracer experiments that are typically limited to steady state conditions and consumption of a single growth-limiting nutrient19 and developed a novel computational approach to resolve dynamic metabolic adaptation of bacteria in complex chemical environments featuring continuous changes in available nutrients. Specifically, we applied this methodology to Escherichia coli growing in glucose minimal medium supplemented with casamino acids, an undefined mixture of amino acids and oligopeptides, as a proxy for undefined environmental conditions20. Continuous changes in nutrients availability are monitored by mass-spectrometry profiling of relative nutrient and by-product abundance in the supernatant. To meet the time-resolution and coverage demands for fluctuating environments with large chemical diversity, we monitored relative time-dependent changes in extracellular metabolite concentrations using a high-throughput non-targeted mass-spectrometry method22. Constraint-based modeling of metabolome dynamics reveals temporal coordination of glucose and amino acid catabolism, in which degradation of low-cost amino acids, into oxaloacetate and pyruvate, is responsible for a reduced glucose uptake and increased acetate secretion.Here, we exploit non-targeted mass-spectrometryE. coli wild-type BW25113 in M9 minimal medium with glucose (5 g/L) and casamino acids (CAA) (2 g/L), an undefined mixture of amino acids and oligopeptides from digested milk protein23. The culture exhibited a classical batch growth curve with 4 h of growth fluctuating around maximum exponential growth rate, followed by a continuous decline in growth rate 21. 427 of these ions could be putatively annotated as deprotonated metabolites, which corresponded to nearly 40% of the metabolites represented in a genome-scale model of E. coli25, and 61% when considering only metabolites with a known active or passive transport mechanism  and multiplied by the instantaneous growth rate  and fluxes were assumed to be constant within these intervals. Absolute and relative consumption/secretion rates are used as soft constraints to reduce the space of feasible flux solutions. To account for the variability in estimated rates, lower and upper bounds for each exchange flux  are included in the model. As changes in OD600 can be routinely measured at high time-resolution and accuracy using standard plate readers, experimentally measured growth rates are used as hard constraint in the model. Hence, maximization of the biomass objective does not need to be invoked. For metabolites without external calibration, we introduce an auxiliary time-independent variable representing the proportional scaling factor (c) between measured MS intensities and actual concentrations. Instead of solving fluxes (v(t)) at each time point independently33, our method is formulated as a one-step global linear optimization problem to generate time dependent flux maps. The single constraint-based model contains genome-scale networks (S) for each time point, time-specific bounds on uptake and secretion fluxes estimated from absolute measurements of glucose, acetate, and amino acids in the supernatant (u(t)), the vector of time-independent scaling factors c.While our metabolomics platform revealed unexpectedly complex exo-metabolome dynamics, suggesting widespread metabolite secretion as a homeostatic mechanism, the obtained relative concentration changes do not allow for a direct inference of intracellular fluxes within a classical FBA frameworkv(t) and c, we sequentially minimized three objectives: (i) the L1-norm distance between predicted (v(t)) and estimated (v(t) and estimated relative exchange rates (u(t)), and (iii) the sum of absolute fluxes along the entire time course (c) and time-dependent fluxes (v(t)) at once, such that the flux solutions for each time interval depend on each other.To infer the unknowns To test uncertainty in flux estimates we performed flux variability analysis (FVA) of the optimal flux solution: for each reaction, we calculated the maximum and minimum sum of fluxes over time. We found that for the vast majority (81%) of metabolic reactions, flux variability was within 10% of the reported flux solution   Fig. . Since mons Fig. . This ex600 of 1. The first phase is characterized by catabolism of amino acids such as aspartate, glycine, glutamate and serine, that provided a large portion of carbon (~40%) and nitrogen (70-80%) for rapid  growth during the first ~3 h , in particular serine, glycine, threonine, aspartate, and glutamate, were consumed at rates much higher than required solely for protein biosynthesis. Consistent with previous evidence40, we found that the average amount of amino acid consumed per unit change in OD600  is inversely related to its metabolic cost, defined as the number of high-energy phosphate bonds required for biosynthesis41 , EIIAGlc, leads to the transport inhibition of several non-PTS carbon sources. According to the current model, a rapid increase of the ratio between phosphoenolpyruvate and pyruvate levels would correspond to increased phosphorylation of EIIAGlc49, and hence a reduced glucose uptake. We monitored immediate changes in the intracellular ratio between phosphoenolpyruvate and pyruvate after supplementation of CAA, using a targeted Liquid Chromatography–Mass Spectrometry (LC–MS) method50 . While in CAA supplemented cultures the concentration of most of these metabolites increased steadily over the course of 90 min  found here between pyruvate levels and glucose uptake. Hence, pyruvate changes are unlikely to be a mere indirect consequence of changes in glucose uptake.The rapid accumulation and subsequent depletion of intracellular pyruvate levels upon CAA supplementation were compatible with the initial growth phase during which glucose consumption was reduced, and the subsequent phase, after depletion of low-cost amino acids, characterized by increased glucose uptake and decreased acetate secretion Figs. a and 5c.54. Furthermore, E. coli grows relatively slow on pyruvate, but with very high acetate secretion of >30% of the consumed carbon34 and similar overflow metabolism has been observed in other bacteria55. Moreover, pyruvate represses the activity of PdhR, a transcriptional regulator that negatively regulates formation of pyruvate dehydrogenase complex (PDHc). We found that while deletion of pdhR doesn’t affect growth in M9 + CAA, ΔpdhR growth rate is mildly (9%) but significantly reduced in glucose M9 , while other intermediates of central metabolism such as glutamate, alanine, aspartate, fructose bisphosphate and succinate did not exhibit any inhibitory activity , hinting at the possibility that pyruvate interferes not only with PtsI, but also with the stability of the entire protein complex  as phosphate donor. We determined PtsI in vitro activity in the presence of nine different intermediates of central metabolism, using the known inhibitor α-ketoglutarate as a positive controlate Fig. . Consistity Fig. . BesidesE. coli growing with either the PTS carbon source glucose or the non-PTS carbon source succinate. Since Crp-mediated catabolic repression would prevent pyruvate uptake during exponential growth on glucose, we first grew E. coli in glucose M9 media overnight to stationary phase when Crp activity is high, enabling uptake of alternative carbon sources. Overnight cultures from M9 glucose or M9 succinate media were then diluted 1 to 100 in the same minimal media supplemented with 20 or 40 mM pyruvate. Consistent with the pyruvate inhibition of the PTS system hypothesis, we observed that pyruvate addition caused lower growth rates during adaptation to the reappearance of glucose  medium, and subsequently overnight in M9 minimal medium. As carbon sources 5 g/L glucose and 2 g/L casamino acids (CAA) (Sigma-Aldrich) were used, if not stated otherwise. The M9 medium contained, per liter of deionized water: 7.5 g of Na2HPO4 2H2O, 3.0 g KH2PO4, 1.5 g (NH4)2SO4, and 0.5 g NaCl and was adjusted to pH 7 before autoclaving. The following components were filter-sterilized separately and then added : 1 mL of 1 M MgSO4, 1 mL of 0.1 M CaCl2, 1 mL 0.1 M FeCl3, and 10 mL of a trace element solution containing (per liter) 180 mg ZnSO4 7H2O, 120 mg MnSO4 H2O, 180 mg CoCl2 6H2O, and 120 mg CuCl2 2H2O. Carbon source solutions were filter-sterilized and added separately to the medium.For all growth experiments, E. coli in M9 minimal medium with glucose and CAA was monitored in a 48-well plate with a Tecan Infinite M200 plate reader (Tecan) , by measuring the absorbance at 600 nm. Cell dry-weight was inferred from a predetermined conversion factor of 0.48 g cells/OD60064. For exo-metabolome profiling, 50 µL of culture broth were sampled at the time points indicated in Fig. Growth of 21 to profile relative changes of metabolite concentrations in the supernatant. Specifically, culture supernatants were injected into an Agilent 6550 Series Quadrupole Time-of-flight mass spectrometer , operated in negative mode. The experiment was performed in triplicates and each sample of the ten time points was injected twice. From the spectrum of detected ions, amino acids and other metabolites were annotated as mono-deprotonated ions with the genome-scale model of E. coli by Orth et al.25. Absolute amounts of amino acids were estimated by comparing peak intensities measured in the culture supernatant with intensities of a dilution series of pure amino acids in M9 minimal medium . The validity of the estimated concentrations was confirmed using an alternative standard method for amino acid quantification   . In parallel, we used a direct flow-injection mass-spectrometry method (FIA-Q-TOF)51. Aliquots of cell culture were extracted without cell separation using cold solvent extraction and then directly injected into a time-of-flight mass spectrometer21. We supplemented exponentially growing E.coli cells with synthetic amino acid (SAA) mixtures, containing the same concentrations of individual amino acids as measured in CAA, but deprived of groups of amino acids catabolized into pyruvate  or threonine, glycine, serine, tryptophan, cysteine and alanine (SAA-TGSTCA)) and other α-keto acids such as α-ketoglutarate ) and oxaloacetate ). Pure M9 glucose medium containing 0.125 g/L of glutamate (SAA-GLT) was used as a control.To monitor rapid metabolic changes in response to diverse mixes of amino acids and to facilitate dynamic sampling at high-throughput, reducing the risk of sample processing artifacts, we used a 96-well plate whole-culture broth extraction protocol4 to exclusively monitor intracellular changes upon CAA and SAA perturbations and showed that most of the patterns we obtained in WCB samples resembled measured intracellular changes  measurements were representative for intracellular metabolome changes, we also used a fast filtration protocolE. coli was cultivated in 150 mL M9 minimal medium with glucose in 1 L shake flasks  to exponential phase (OD600 of 0.2–0.3) before splitting the culture into equal portions for further cultivation . At an OD600 of 0.5, the cultures were spiked with amino acid mixtures to a final concentration of 2 g/L. Subsequently, cell growth was monitored photometrically and a culture volume equivalent to 2 mL at OD600 = 1 was sampled for intracellular metabolite measurements using the fast filtration technique4.For the spiking experiments, 50. Only for targeted analysis, 100 μL of an E. coli13C extract were added as internal standard to the extraction solution66, immediately after filter addition. After incubation at −20 °C for 1 h, samples were immediately dried under vacuum at 30 °C for targeted analysis by LC–MS62 or directly used without drying for untargeted analysis by FIA-Q-TOF51, respectively. For targeted analysis by LC–MS, dried samples were resuspended in 100 μL of water of which ten μL were injected on a Waters Acquity UPLC with a Waters Acquity T3 end-capped reverse phase column . Metabolites were detected on a tandem mass spectrometer .For extraction of intracellular metabolites, the filter with cells was transferred into precooled (−20 °C) extraction solution  supplemented with 25 µM phenylhydrazine for derivatization of α-keto acidsvgrowth), are selected with the aid of linear programming solvers:v represents the vector of fluxes, S the stoichiometric MxR matrix , Sv = 0 is the mass balance constraint and U/L are the thermodynamic and capacity constraints. Typically, only one substrate is limiting and maximum growth corresponds to maximum yield. The in-silico medium is defined by allowing the import of a metabolite from the external compartment to the intracellular ones  by adapting the lower/upper bound of the corresponding reaction. Often, the flux distribution that optimizes biomass production and is the most parsimonious—i.e., minimum sum of absolute values, is selected67.Standard FBA couples mass balance, thermodynamic  and capacity constraints to stoichiometric metabolic models. Linear constraints define a space of feasible steady state flux solutions, and the flux distributions that maximize an objective function, typically biomass production , the complexity of the in-silico medium is reflected in undetermined systems, where the sparsity of constraints inevitably cause predictions to be inaccurate and unreliable69. To overcome these limitations, uptake rates can be experimentally determined and used as constraints in the model to reduce the space of feasible solutions. Here, we used non-targeted mass-spectrometry, which allows fast, accurate, and sensitive measurements of nearly 60% of metabolites that can be exchanged between the cell and its environment . The L1-norm distance between absolute measured fluxes (v) throughout all time intervals is minimized. Subsequently, we minimize the L1-norm distance between the relative estimates of exchange rates for metabolites without a calibration curve, multiplied by a scaling factor c. To this end we introduce an auxiliary variable D. For metabolites detected to be part of casamino acids a maximum initial amount of 50 µmol was considered, consistently with the overall estimates of most abundant metabolites in casamino acids like GMP, uridine and citrate (see Supplementary Text). Results are robust to small changes of this parameter. For each exchangeable metabolite in the model, the uptake at time t cannot exceed the sum of produced and consumed metabolite up to that time. For those compounds found to be secreted in the medium a lower bound for the corresponding c factor was estimated by assuming that if the metabolite was detected in medium at any point during growth, then the maximum concentration has to be >50 µmol. It is worth noting that all measured rates (absolute and relative) are used as soft constraints, besides growth rate, which is here imposed as a hard constraint:The genome-scale model of ent Fig. . Howevert is the time interval in hours between two contiguous instances, OD(t) is the optical density measured at time t and ɣ = 0.413 is the conversion factor used to transform OD readouts in gram of dry biomass (gDW). Solutions were calculated using the CPLEX IBM optimization software. The three minimizations were carried out one after the other in the order described above. Specifically, after solving the first optimization, the minimum sum of absolute differences between absolute estimated and predicted fluxes (obj1) is used as a constraint in the second optimization. In the second optimization, we minimize the L1-norm distance between relative estimates of metabolite secretion/uptake and predicted fluxes. Finally the objective value (obj2) is used as constraint to find the flux solution that minimizes the sum of absolute fluxes. To find the full range of possible flux values (v(t)) we used flux variability analysis70. Specifically, we minimize and maximize the flux through each metabolic reaction under the constraint that solutions has to lie in the 99.9% of the optimal solution  containing an N-terminal histidine tag was purified from ASKA library strain b241673 and its identity verified by SDS page. Purified PtsI was pre-incubated for 5 min at 37 °C with a 1:4 mixture of 2 mM sodium phosphoenolpyruvate  and sodium pyruvate , in a buffer containing 25 mM sodium phosphate adjusted to pH 7 and 2.5 mM MgCl2. The reaction was started by addition of an amount of 13C-labeled sodium pyruvate  equivalent to the amount included in the pre-incubation mixture, yielding a final PEP/pyruvate ratio of 1:8. Approximately 1 μg of purified protein was used in each reaction, and the total reaction volume was 300 μl. Putative inhibitors were included in the pre-incubation mixture at a concentration of 2 mM. At each time point of interest, 20-μl samples were simultaneously quenched and diluted by pipetting into 980 μl of 80% v/v methanol in water at −70 °C. After drying the samples and resuspension in water, the fraction of 13C-labeled PEP in the total PEP pool was determined by LC–MS analysis. Similarly to5, for each time course weighted nonlinear least-squares regression was used to fit the following equation:L is the 13C-labeled fraction of the PEP pool at time t, and k is the fitted parameter representing the rate constant of the labeling reaction; 0.45 is the 13C-labeled fraction of total carbon. The estimated k-values of each assay were then compared using t-test analysis to identify compounds with a significant inhibitory effect (Fig. PtsI enzyme assays were performed as previously described by Doucette et al.ect Fig. .Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting SummarySource Data


Alternaria sp. MG1, an endophytic fungus isolated from grape, is a native producer of resveratrol, which has important application potential. However, the metabolic characteristics and physiological behavior of MG1 still remains mostly unraveled. In addition, the resveratrol production of the strain is low. Thus, the whole-genome sequencing is highly required for elucidating the resveratrol biosynthesis pathway. Furthermore, the metabolic network model of MG1 was constructed to provide a computational guided approach for improving the yield of resveratrol.Alternaria sp. MG1 (named iYL1539) was reconstructed, accounting for 1539 genes, 2231 metabolites, and 2255 reactions. The model was validated qualitatively and quantitatively by comparing the in silico simulation with experimental data, and the results showed a high consistency. In iYL1539, 56 genes were identified as growth essential in rich medium. According to constraint-based analysis, the importance of cofactors for the resveratrol biosynthesis was successfully demonstrated. Ethanol addition was predicted in silico to be an effective method to improve resveratrol production by strengthening acetyl-CoA synthesis and pentose phosphate pathway, and was verified experimentally with a 26.31% increase of resveratrol. Finally, 6 genes were identified as potential targets for resveratrol over-production by the recently developed methodology. The target-genes were validated using salicylic acid as elicitor, leading to an increase of resveratrol yield by 33.32% and the expression of gene 4CL and CHS by 1.8- and 1.6-fold, respectively.Firstly, a draft genomic sequence of MG1 was generated with a size of 34.7 Mbp and a GC content of 50.96%. Genome annotation indicated that MG1 possessed complete biosynthesis pathways for stilbenoids, flavonoids, and lignins. Eight secondary metabolites involved in these pathways were detected by GC–MS analysis, confirming the metabolic diversity of MG1. Furthermore, the first genome-scale metabolic network of Alternaria sp. MG1 to produce multiple secondary metabolites. The first model of the species Alternaria was constructed, providing an overall understanding of the physiological behavior and metabolic characteristics of MG1. The model is a highly useful tool for enhancing productivity by rational design of the metabolic pathway for resveratrol and other secondary metabolites.This study details the diverse capability and key genes of The online version of this article (10.1186/s12934-019-1063-7) contains supplementary material, which is available to authorized users. Taxus brevifolia  + coa[c] + atp[c] → h[c] + accoa[c] + ppi[c] + amp[c]) from 0 to 0.038 mmol/gDW/h, thus synthesizing more malonyl-CoA  algorithm was used to identify the gene candidates responsible for increased resveratrol production with the lower bound of the resveratrol exchange reaction constrained to 0.001 mmol/gDW/h. As a result, six genes were identified by computationally overexpressing all reactions with non-zero flux value in a FBA simulation  or CHS (EC: 2.3.1.74) resulted in a twofold increase of resveratrol production in silico.To find the key genes related to the enhancement of resveratrol production by Alternaria sp. MG1 because they had been reported as elicitors of the phenylpropane pathway genes toward accumulation of stilbenes and flavonoids. Xu et al. [V. vinifera cell cultures with SA and MeJA and successfully upregulated the expression of 4CL/CHS and resveratrol production. In this study, 100 μM SA and MeJA were added to the PDB medium used for Alternaria sp. MG1 cultivation. As a result, SA induction resulted in a 33.32% increase of resveratrol production  and methyl jasmonate (MeJA) to verify whether the above identified genes were related to the enhancement of resveratrol production by u et al.  treated ion Fig. c, and siely Fig. d. HoweveAlternaria species; (3) verifying the occurrence of the resveratrol biosynthesis pathway in a nongenetically modified microorganism at the gene level. We previously found that Alternaria sp. MG1 could produce resveratrol stably, although the achieved level of resveratrol production was low compared to genetically modified E. coli and yeast [Alternaria sp. MG1 showed potential in this field because it possesses diverse biosynthesis pathways to produce multiple metabolites with high value. The development of the gene editing tool CRISPR-Cas9 in fungal model organisms would also greatly promote the exploration of the fungal metabolism [CHS/4CL is highly expected to lead to a significant increase in resveratrol production compared to the induction of the expression of CHS/4CL by using an elicitor indirectly. Therefore, it is reasonable to anticipate that the production of secondary metabolites could be greatly improved by using genetically modified Alternaria sp. MG1. Furthermore, key genes and pathways identified for the synthesis of stilbenes, flavonoids, and lignins in this study provide a rich gene resource for the production of these natural compounds using genetically engineered microorganisms. Finally, the study would also provide useful information and reference for similar studies on the exploration of other endophytic fungi.Overall, this study obtained novel results with regard to three aspects: (1) revealing the occurrence of multiple biosynthesis pathways for the production of high value secondary metabolites that were commonly found in plants; (2) constructing a genome scale of the metabolic network model (GSMM) for nd yeast , 49. Witnd yeast , 54, Alttabolism , such astabolism . In addiAlternaria sp. MG1, was sequenced and annotated for the verification of the occurrence of phenylpropanoid—resveratrol biosynthesis pathways in a nongenetically modified microorganism at the gene level. It also details the diverse capability and key genes of Alternaria sp. MG1 to produce multiple phenylpropanoid metabolites, such as stilbenes, flavonoids, and lignins. In this study, a genome scale metabolic network model (GSMM) for the species Alternaria was constructed, providing an overall understanding of the physiological behavior and metabolic characteristics of the tested strain. Both biochemical and genetic strategies were proposed for the improvement of resveratrol production in silico and verified with wet experiments. Furthermore, important information was obtained for the exploration of key genes in this strain toward producing high value secondary metabolites. In summary, the constructed model provided a reliable platform for future studies aimed at the exploitation of other secondary metabolites in Alternaria sp. MG1.A resveratrol-producing endophytic fungus, Alternaria sp. MG1 (code: CCTCC M 2011348), currently preserved in the China Centre for Type Culture Collection , was used throughout this study. Potato-dextrose broth (PDB), containing 200 g potato, 20 g dextrose, and 1 L distilled water, was used to cultivate the strain at 28 °C and 160 rpm for 10 days.g and 4 °C and stored at − 80 °C until further use. Genomic DNA was extracted using the EasyPure Genomic DNA Kit . For analysis, the whole-genome shotgun sequencing strategy was used and subsequent short-insert libraries (600 bp) and long-insert libraries (3 kb) were constructed using the standard protocol provided by Illumina . Paired-end sequencing was performed via the Illumina HiSeq 2500 system. Genome assembly, prediction of encoding genes, and genome annotation were conducted according to previously published methods [V. vinifera was downloaded from the NCBI and used as a reference to identify the resveratrol biosynthesis pathway of Alternaria sp. MG1.Cells at the mid-exponential stage were collected after centrifugation for 10 min at 5000× methods –61. The V methanol:V chloroform = 3:1) containing 20 μL of l-2-chlorophenylalanine (1 mg/mL stock in dH2O) as internal standard. The mixture was homogenized in a ball mill for 4 min at 45 Hz, then ultrasonically treated for 5 min (incubated in ice water), which was repeated twice, and finally centrifuged for 15 min at 13,000 rpm, 4 °C. The supernatant (0.4 mL) was transferred into a fresh 2 mL GC/MS glass vial and dried in a vacuum concentrator without heating. Methoxy amination hydrochloride  was then added and incubated for 30 min at 80 °C. 100 μL of the BSTFA regent  was added to sample aliquots, incubated for 1.5 h at 70 °C, and mixed well for GC–MS analysis. GC–TOFMS analysis was performed using an Agilent 7890 gas chromatograph system coupled with a Pegasus HT time-of-flight mass spectrometer. The system utilized a DB-5MS capillary column coated with 5% diphenyl cross-linked with 95% dimethylpolysiloxane . A 1 μL aliquot of the analyte was injected in splitless mode. Helium was used as carrier gas, the front inlet purge flow was 3 mL/min, and the gas flow rate through the column was 1 mL/min. The initial temperature was kept at 50 °C for 1 min, then increased to 310 °C at a rate of 10 °C/min, then kept for 8 min at 310 °C. The injection, transfer line, and ion source temperatures were 280, 270, and 220 °C, respectively. The energy was − 70 eV in electron impact mode. The mass spectrometry data were acquired in full-scan mode with the m/z range of 50–500 at a rate of 20 spectra per second after a solvent delay of 455 s. Chroma TOF 4.3X software of LECO Corporation and LECO-Fiehn Rtx5 database were used for raw peak exaction, data baselines filtering, calibration of the baseline, peak alignment, deconvolution analysis, peak identification, and integration of the peak area [The cells were separated from the culture by vacuum filtration. The cell-free culture was then freeze-dried into powder before use. The prepared sample was extracted with 0.5 mL extraction liquid  coupled to VION IMS QTOF mass spectrometer . The mobile phase consisted of 0.1% formic acid in water (A) and acetonitrile (B) was carried with elution gradient as follows: 0 min, 5% B; 3 min, 100% B; 4 min, 100% B; 4.1 min, 5% B; 6 min, 5% B, which was delivered at 0.4 mL/min. The column and autosampler were maintained at 35 and 10 °C, respectively. The injection volume was 2 μL. The ion source was operated in positive electrospray ionization (ESI) mode under the following specific conditions: capillary voltage, 1.0 kV; source temperature, 100 °C; desolvation gas temperature, 500 °C; desolvation gas flow, 800 L/h, and cone gas flow, 50 L/h. Nitrogen (> 99.5%) was employed as desolvation and cone gas. The scan range was from 50 to 1000 m/z. The scan time for each function was set to 0.2 s. The low collision energy was set at 6 eV, and the high collision energy was ramped from 20 to 45 eV. The data were acquired and processed using the MassLynx 4.1 software  that was incorporated with the instrument.Aspergillus terreus iJL1454 [Penicillium chrysogenum iRA1006 [Saccharomyces cerevisiae iTO977 [A. terreus NIH2624, P. chrysogenum Wisconsin 54-1255, and S. cerevisiae Sc288 were downloaded from UniProt [Alternaria sp. MG1 was uploaded to NCBI. To verify the specific metabolic capabilities, both KEGG database and literature evidences were used to assemble unique reactions that were non-existent in reference strains. Transport reactions were obtained by referring to the Transporter Classification Database (TCDB) [An initial model was firstly drafted by amassing reactions from the established GSMM of related organisms  iJL1454 , Penicil iRA1006 , and Sace iTO977 . Reactioe iTO977 . Protein UniProt . Open ree (TCDB) . Exchange (TCDB)  and Metae (TCDB)  were usee (TCDB) .Alternaria alternate [A. infectoria [A. terreus [Nucleic acids (DNA and RNA), proteins, lipids, carbohydrates, and ash were considered in the biomass equation. Total DNA and RNA were extracted  and theilternate  and A. ifectoria , respect terreus .Flux balance analysis (FBA) was conducted using the Cobra Toolbox 2.05 in MATLAB R2016a to analyze the flow of metabolites through a metabolic network . Based o2O/H+, O2, NH3/NH4+, sulfite, and phosphate) were set as − 1000 mmol/gDW/h to mimic minimal medium in silico [To validate carbon utilization, the uptake rate of the target carbon source was set to 2.0 mmol/gDW/h, while those of other carbon sources were set to zero. The same algorithm was applied to validate the nitrogen source. The lower bounds of the exchange reactions of basic nutrient components . To retain high biomass and enzymes activity, ethanol addition was conducted at day 4. The cells were collected from the culture after 10 days (in triplicate) to determine the cell dry weight (g/L) . The resSalicylic acid and methyl jasmonate  were dissolved in ethanol and added to the PDB medium at a final concentration of 100 μM at day 4. Each treatment was performed in triplicate and samples without elicitor treatment were always run in parallel as control. After cultivation for 10 days, cells were collected and used for RNA extraction. Cell-free culture broth was used for the quantitative analysis of resveratrol production.4CL and CHS) were designed according to the transcriptome data of Alternaria sp. MG1 [EF1 [Total RNA was extracted and cDNA was synthesized via TransScript One-Step gDNA Removal and cDNA Synthesis SuperMix Kit . Primers of target genes  and a pterostilbene standard (B). Mass spectrum of a sample (C) and pterostilbene standard (D). The suspected pterostilbene detected in the sample (2.16 min) and pterostilbene standard (2.16 min) showed a similar retention time, molecular ion of m/z = 257.1, verifying the production of pterostilbene. Figure S2. Growth of Alternaria sp. MG1 for control (A) and cerulenin treatment (B) after cultured for 7 days on potato dextrose agar (PDA) plate and the rate of colony expanding changes with cultivation time (C). Table S1. Identified ATP-binding cassette (ABC) transport proteins in Alternaria sp. MG1. Table S2. Potential targets identified by MOMA which could enhance resveratrol production.Additional file 2.Dataset S1. Metabolites detected inside cells and in the cell-free culture by setting the identification standard of similarity value greater than 200.Additional file 3.Dataset S2. Biomass composition of Alternaria sp. MG1.Additional file 4.Dataset S3. Detailed information about model iYL1539.Additional file 5.Dataset S4. Result of essential genes identified by FBA using PDB medium.Additional file 6.Dataset S5. Flux distribution with the resveratrol production rate increased from 0.0073 to 0.02 mmol/gDW/h.Additional file 7.Dataset S6. Flux distribution involved in resveratrol synthesis with the ethanol uptake rate perturbed from 0 to 0.05 mmol/gDW/h.