Escherichia coli. We demonstrate that the approach can be used with genome-scale metabolic models to arrive at predictions concordant with measurements from Escherichia coli under different growth scenarios. By application to 14 genome-scale metabolic models from diverse species, our approach specifies the cellular determinants of concentration ranges that can be effectively employed to make predictions for a variety of biotechnological and medical applications.Cellular functions are shaped by reaction networks whose dynamics are determined by the concentrations of underlying components. However, cellular mechanisms ensuring that a component’s concentration resides in a given range remain elusive. We present network properties which suffice to identify components whose concentration ranges can be efficiently computed in mass-action metabolic networks. We show that the derived ranges are in excellent agreement with simulations from a detailed kinetic metabolic model of  E. coli with real-world data sets, we further demonstrate the prediction power and limitations of the approach.We present a computational approach for inferring concentration ranges from genome-scale metabolic models. The approach specifies a determinant and molecular mechanism underling facile control of concentration ranges for components in large-scale cellular networks. Most importantly, the predictions about concentration ranges do not require knowledge of kinetic parameters , provided measurements of concentrations in a reference state. The approach assumes that reaction rates follow the mass action law used in the derivations of other types of kinetics. We apply the approach with large-scale kinetic and stoichiometric metabolic models of organisms from different kingdoms of life to show that we can identify a proportion of metabolites to which our approach is applicable. By challenging the predictions of concentration ranges in the genome-scale metabolic network of  Advances in systems biology studies have been propelled by the availability of high-quality genome-scale metabolic reconstructions for many organisms across all kingdoms of life . MetabolA naïve approach to determine a concentration range for a given component is to assume that it is present with a single molecule or that the entire cell dry weight under an investigated scenario is composed solely of that component. This derivation requires only knowledge of the component’s molecular weight, which is readily available. However, the derived ranges are vast and largely invariant under different scenarios; therefore, they are not informative. Here we ask whether large-scale metabolic models can be used for accurate prediction of concentration ranges. Resolving this question is tantamount to identifying a cellular mechanism underlying the control of concentration range for given cellular component.v(t) = (v1(t), ⋯, vn(t))T denotes reaction rates and x(t) = (x1(t), ⋯, xm(t)) the metabolite concentrations at time t, and N represents the stoichiometric matrix. The rows of the stoichiometric matrix correspond to metabolites, columns stand for reactions, and its entries denote the stoichiometric coefficients with which metabolites participate in reactions as substrates or products [v(t) = f(x(t), θ), which often leads to nonlinearities and involves multiple parameters, denoted by θ [Nf(x(t), θ) = 0 is intractable for large-scale networks even when the equations have a simplified mass action form often used in metabolic modeling [The change in concentration of metabolites can be described by a system of coupled ordinary differential equations (ODEs), products . Reactioted by θ . As a reted by θ , 9 and tted by θ . In addimodeling .v, for which Nv = 0, can be predicted based solely on the structure of the network with computational approaches from the constraint-based modeling framework [Feasible steady-state reaction rates, ramework . Howeverramework . By neglramework .The existing constraint-based approaches that can make predictions of metabolite concentrations and their ranges are based on consideration of thermodynamics constraints. Thermodynamics-based metabolic flux analysis (TMFA) produces flux distributions that do not contain any thermodynamically infeasible reactions or pathways, and it provides information about the free energy change of reactions and the range of metabolite concentrations in addition to reaction fluxes . HoweverHere we provide an approach which relies on the structure of the network, encoded in the stoichiometric matrix, to provide simulation-free prediction of steady-state concentration ranges by employing mass action kinetics. We focus on mass action kinetics since it underlies the derivation of more involved types of kinetics for different reaction mechanisms , allows m metabolites that participate in n reactions. The (m × n) stoichiometric matrix, N, can be written as a difference of two non-negative matrices, N = N+ − N−, where N+ includes the stoichiometry of the products and N− comprises the stoichiometry of the substrates of each reaction. For instance, the stoichiometry of substrates and products given in Ri is modeled according to mass action kinetic, whereby θi > 0 is the reaction constant and the concentration of each substrate molecule appears in vi as a multiplicative factor.Consider a metabolic network composed of Rk lacks one substrate molecule of Xi in comparison to reaction Rl, if i′ ≠ i, R7 lacks one substrate molecule of component B in comparison to reaction R2. Under the assumption of mass action kinetic, if a reaction lacks one substrate molecule in comparison to another, the reactions differ in their orders by one. As a result, the ratio of fluxes for such reactions at any state of the system depends only on the rate constants and the concentration of the substrate in which the reactions differ.To state our main result, we require some concepts and terminology which we next introduce and illustrate. We will say that a reaction Rk and Rl are fully coupled if there exists λ > 0, such that vl = λvk for any positive steady-state reaction rate v, i.e., Nv = 0 [R3 is fully coupled to R4 and R5 is fully coupled to R6. Such reactions, which are fully coupled irrespective of the kinetic law, can be efficiently determined based on the stoichiometry of large-scale networks by linear programming [Furthermore, two reactions , Nv = 0 . Therefogramming , 22 , if the following conditions hold: (i) for each reaction Rl in Sj, there exists a non-empty subset Xi in comparison to Rl; the union of all ii) all reactions in iii) all reactions in Pj are mutually fully coupled. A similar argument can be made with respect to condition (i) in terms of reactions in the set Pj , above, the sum Pj are fully coupled irrespective of the kinetic rate law, depends only on the network structure. In addition, due to condition (ii), above, the value of σp for any relevant rate constants, while the flux ratio relevant flux ratio.At any positive steady state, it then holds that v, a set Rp ∈ Pj and xi over any set, F, of steady-state flux distributions and subset Q (per definition above), yielding the following:Therefore, given a steady-state flux distribution, B in A, whereby the relevant flux ratio is θ3 and θ7  and upper bounds  of the SCC metabolites  a value of one, simulating a scenario in which all relevant rate constants are of the same value, (ii) the mean, or (iii) the median of the ratios of relevant rate constants that are present  in the model equation from which the conditions for SCC are established. We note that the units of the rate constants are not relevant since rate constants enter While the full reaction couplings considered by our approach can be readily obtained given the structure of the network and flux ratios are increasingly available from labeling approaches , the resi) and (iii), we obtain a median Pearson correlation coefficient between the predicted and simulated ranges of at least 0.6 , but its steady-state flux distributions result from the trade-off between tasks of optimizing growth, ATP synthesis, and total flux [h−1 (Supplementary h−1).We use the measurements of steady-state concentrations of 182 metabolites from cenarios . This dacenarios , which wFor the objective of optimizing ATP synthesis and total flux, our results demonstrate that measurements for 9, 10, and 6 of the 15 SCC metabolites fall in the predicted concentration range for the three growth rates, respectively . Neverthoutliers . In contoutliers . Moreoveoutliers  at cost E. coli grown in eight different carbon sources [E. coli. Since growth rate is fixed from measurements, as above, we optimize the weighted average of ATP synthesis and total flux, with weighting factors 0.001 for ATP synthesis and 1000 for total flux to reduce the effect of the order difference and make the comparison to optimization of ATP synthesis. Different weighting factors are used in comparison to the analysis of the data set from Ishii et al., above, since different constraints are used that affect the optimal values of the individual objectives. Here, too, at the obtained optimum we can efficiently estimate ranges for the relevant flux ratios (We use the measurements of steady-state concentrations of 43 metabolites from  sources . This dax ratios . To obtavp and −4), while it is only 0.33  when ATP synthesis and total flux are optimized. In addition, the Spearman correlation between the measured and predicted upper and lower bounds when maximization of ATP is used results in higher correlation values , lower bounds 0.85 ) than those when optimization of ATP synthesis and total flux are employed , lower bounds 0.54 ). These findings imply that the usage of different objectives to estimate flux ratios and through them concentrations of metabolites can also be used to discern importance of optimized objectives in a particular experiment.In Supplementary E. coli can be used to test the applicability of the approach to predict changes in metabolite concentrations in metabolic engineering scenarios. Here, we test the performance of the approach with knock-out mutants based on the following procedure: We make use of the model parameterization to simulate a steady-state concentration and flux distribution from initial physiologically reasonable values for metabolite concentrations. The resulting steady-state concentrations and fluxes yield a wild type reference. We then knock-out each reaction and predict positive steady state flux distribution closest to the wild type reference, following the Minimization of Metabolic Adjustment (MOMA) approach [In contrast to our findings, application of TMFA (briefly reviewed in the introduction) resulted in unconstrained ranges for concentrations (see); therefore, no correlation between upper / lower simulated and predicted bounds could be observed.The fully parameterized kinetic model of approach . The resions see; therefoWe next apply N. pharaonis and C. reinhardtii to 33.66% and 36.53% in the models of A. thaliana and Y. pestis (R2 = 0.82) and the number of reactions in the examined networks (R2 = 0.76). This finding indicates that the proposed approach is not limited to networks of a particular size.We find that the percentage of metabolites with SCC ranges from 7.74% and 8.02% in the models of . pestis . Interesi) metabolites which are synthesized and used by one reaction, respectively (in support of the linear pathway explanation), (ii) fully coupled reactions only due to structure, (iii) coupled reactions due to mass action (in support of the network density explanation), (iv) the combination of (ii) and (iii), to assess the couplings due to both structure and kinetics , are maintained within certain concentration ranges that can be readily controlled, as is the case for SCC metabolites. Despite the many biochemical reactions in which these ubiquitous metabolites participate . This can be achieved by augmenting the network to include reactions which model substrate-enzyme complex formation as well as the synthesis and degradation of enzymes. However, these extensions come at a cost of substantially larger data sets which are not yet readily available. In addition, our analyses demonstrate that the casting of a kinetic rate law in terms of mass action mechanisms may affect the findings regarding the SCC metabolites. For instance, we find that there are many more SCC metabolites in comparison to other SCC components  in each of the analyzed models  for the component Xj (not necessarily different from Xi) under the assumption that the reaction rates, v(t), satisfy mass action kinetics, whereby Pj is the set of reactions with Xj as one of their products and Sj is the set of reactions which have metabolite Xj as one of their substrates.A metabolic network can be represented by the stoichiometric matrix, i) the concentration of Xi appears in every vk(t) for which vk(t) there exist a set ii) the concentration of Xi appears in every vl(t) for which vl(t) there exist a set of reactions We consider the following two cases: (Rk and a reaction from the set The rates of a reaction j ≠ i and vk(t) such thatFrom rewriting the equation of Xj revealing structurally constrained concentration of component Xi is then given by:The ODE for component p and s bet two reaction indices such that v(t), we have thatLet In a steady state thenRl and Rs are fully coupled or share the same substrates, then Sj are fully coupled irrespective of the kinetic rate law, only depends on the network structure. Therefore,If for every Rk in Sj, there exists a non-empty subset Xi in comparison to Rk; the union of all Q be a subset of p and s are arbitrarily chosen, the concentration range of metabolite Xi for a given subset Q over a given set of flux distributions, F, is given asFor each reaction Rl and a reaction from the set The rates of a reaction j ≠ i and vl(t) such thatFrom rewriting the equation of Xj revealing structurally constrained concentration of component Xi is then given by:The ODE for component p and s bet two reaction indices such that v(t), we have thatLet In a steady state thenRk and Rp are fully coupled or share the same substrates, then Pj are fully coupled irrespective of the kinetic rate law, depends only on the network structure. Moreover, if for every If for every Rl in Pj, there exists a non-empty subset Xi in comparison to Rl; the union of all Q be a subset of p and s are arbitrarily chosen, the concentration range of metabolite Xi for a given subset Q over a given set of flux distributions, F, is given asFor each reaction xi can be expressed as a function of a set of given flux distributions, ratios of specific fluxes and constants that depend only on the structure of the network and values for a subset of rate constants. Since fluxes are the integrated outcome of transcription, translation, and post-translational modifications and their interplay with the environment and nutrient availability, our derivation provides a direct relation between concentration ranges, flux ratios, and rate constants.As a result, the ranges for steady-state concentration N with n reactions, under the assumption that every reaction is irreversible. Here, we restrict our analysis to the subspace F ⊂ C(N) by bounding the fluxes: lb and ub are lower and upper flux bounds. We will refer to v ∈ F as the feasible flux distributions. A reaction Ri is called blocked if for every v ∈ F, vi = 0. A pair of reactions Ri and Rj is called fully coupled, if there exists λ > 0, such that for every v ∈ F, vi = λvj.Let F can be determined by the linear-fractional programming:The minimum and maximum value for the ratio ormation  to the fRi and Rj are fully coupled. Such reactions can be efficiently computed for large-scale networks [If the minimum and maximum values for the linear program are the same, then the reactions networks , 21.In addition, under the mass action kinetics, two reactions are fully coupled in any state of the system if they share the same substrates with the same stoichiometry. This leads to additional full couplings due to the transitivity of the relations, as demonstrated in the main text.In the following, we present an algorithm determining SCC metabolites under the assumption of mass action kinetics:Input: metabolic network, list of fully coupled reactionsOutput: metabolites with structurally constrained concentrationfor eachmetabolite xiin the networkdo: Si ← set of reactions having xias substrate ← set of all products of the reactions in Sifor eachmetabolitedo:   Sj ← set of reactions having xjas substrate  Pj ← set of reactions having xjas productset of reactions lacking one substrate molecule of xiin comparison to a reaction Rp ∈ Pjif for each reaction in Pj there is a reaction in and all reactions in and all reactions in Sj are fully coupled:  xi has SCC   end if  end for set of all substrates of the reactions in Si for eachmetabolitedo:   Sj ← set of reactions having xjas substrateset of reactions lacking one substrate molecule of xiin comparison to a reaction Rs ∈ Sj  Pj ← set of reactions having xjas productif for each reaction in Sj there is a reaction in and all reactions in and all reactions in Pj are fully coupled:  xi has SCC   end if  end for end forE. coli we simulate 100 steady-state flux distribution and steady-state concentrations from different initial concentrations. Initial concentrations were obtained by perturbation of the original initial concentration of a metabolite by 1, 5, 10 or 20%. We run the model until a steady-state was reached. Using the simulated steady-state flux distributions we can predict concentration ranges for 23 metabolites using (i) simulated and predicted upper bounds, (ii) simulated and predicted lower bounds, and (iii) the absolute range over simulated and predicted concentrations. In addition, we also determined the correlation between shadow price for the respective metabolites and the simulated range, as well as, to the coefficient of variation obtained over simulated concentrations  together with flux ratios obtained from constraint-based modelling to estimate the ratio Using the most recent genome-scale metabolic network of E. coli  togethercenarios  we prediFor each replicate we solve the following linear programs in order to obtain ranges for the relevant flux ratios βi,j  available from measurements [z*:The linear program above constrains rates of glucose and oxygen uptakes, carbon dioxide release as well as growth by values urements . We optiurements  and Gerourements , respecturements  and Gerourements , respectE. coli cells with growth rates of 0.4, 0.5, and 0.7h−1 using the previously obtained estimates for ranges of E. coli cells grown at rates of 0.4, 0.5, and 0.7h−1.From E. coli [We use a large-scale kinetic model of E. coli  to simulE. coli  for eachE. coli [https://github.com/EPFL-LCSB/matTFA [E. coli model are obtain by thermodynamics-based variability analysis for solutions ensuring biomass to be at least 95% of the optimum obtained without thermodynamic constrains. In addition, minimum and maximum bounds on metabolic activities of 10−10 and 1, as well as minimum and maximum bounds of 0 and 1000 on reaction net flux are used. Thermodynamic information are taken from the database provided within the TMFA implementation.We use the genome-scale kinetic model of E. coli  and the B/matTFA . ConcentS1 Figa) lower and (b) upper concentration bounds for 23 SCC metabolites in the large-scale kinetic model of E. coli. The very small discrepancies are due to numerical instabilities.The simulated and predicted ((EPS)Click here for additional data file.S2 Figa) the relevant rate constants and (b) their ratios for reactions coupled due to mass action kinetics; log-log distribution of (c) the relevant rate constants and (d) their ratios for reactions coupled due to mass action kinetics.Distribution of ((EPS)Click here for additional data file.S3 Figi) equality , (ii) the mean, or (iii) the median of the ratios of relevant rate constants that are still present in the model. Shown are the boxplots (red lines inside each box denote the corresponding medians) of the resulting Spearman correlation coefficients between the predicted and simulated (a) lower bound vectors and (b) upper bound vectors of concentrations over the SCC metabolites in the kinetic model of E. coli.We consider 10–90% of the relevant rate constants to be unknown by random removal. We consider three scenarios for the substitution of missing ratios of rate constants: ((EPS)Click here for additional data file.S4 Figi) equality , (ii) the mean, or (iii) the median of the ratios of relevant rate constants that are still present in the model. Shown are the boxplots (red lines inside each box denote the corresponding medians) of the average Euclidean distance between the predicted and simulated (a) lower bound vectors and (b) upper bound vectors of concentrations over the SCC metabolites in the kinetic model of E. coli.We consider 10–90% of the relevant rate constants to be unknown by random removal. We consider three scenarios for the substitution of missing ratios of rate constants: ((EPS)Click here for additional data file.S5 Figi) equality , (ii) the mean, or (iii) the median of the ratios of relevant rate constants that are still present in the model. Shown are the boxplots (red lines inside each box denote the corresponding medians) of the Euclidean distance between the log-transformed predicted and log-transformed simulated (a) lower bound vectors and (b) upper bound vectors of concentrations over the SCC metabolites in the kinetic model of E. coli.We consider 10–90% of the relevant rate constants to be unknown by random removal. We consider three scenarios for the substitution of missing ratios of rate constants: ((EPS)Click here for additional data file.S6 Figi) equality , (ii) the mean, or (iii) the median of the ratios of relevant rate constants that are still present in the model. Shown are the boxplots (red lines inside each box denote the corresponding medians) of the Euclidean distance between the predicted and simulated (a) lower bound vectors of concentrations normalized by the respective maximum value and (b) upper bound vectors of concentrations normalized by the respective maximum value over the SCC metabolites in the kinetic model of E. coli.We consider 10–90% of the relevant rate constants to be unknown by random removal. We consider three scenarios for the substitution of missing ratios of rate constants: ((EPS)Click here for additional data file.S7 Figa) over all three replicates and (b) over replicates with not more than one magnitude difference in estimated range for the ratio of The bars denote the predicted ranges from each of the three different scenarios ((EPS)Click here for additional data file.S8 FigFrom each of the 100 simulated steady-state flux distributions we predict concentrations for the SCC metabolites and calculate the average Euclidean distance between the simulated and predicted concentrations.(EPS)Click here for additional data file.S9 FigE. coli with absolute concentrations measured at growth rates (GR) of (a) 0.4, (b) 0.5 and (c) 0.7h−1. The colored bars denote the predicted ranges from each of the three different replicates, while the black bar represents the prediction over all replicates. For some metabolites no value could be predicted due to numerical instabilities. The red cross denotes the measured value at the respective GR. For metabolites with missing red cross, there is no access to measurements. The nomenclature of the metabolites is provided in Supplementary Comparison of the predicted concentration ranges for 15 intracellular metabolites in (EPS)Click here for additional data file.S10 FigE. coli with absolute concentrations measured at seven different carbon sources. The red bars denote the measured ranges over three different replicates, while the black bar represents the predicted concentration. For some metabolites no value could be predicted due to numerical instabilities. For the model simulating growth on succinate no steady-state solution could be obtained without further model adaptation, therefore, no SCC concentration could be predicted.Comparison of the predicted concentration ranges for 10 intracellular metabolites in (EPS)Click here for additional data file.S11 FigE. coli with absolute concentrations measured at seven different carbon sources. The red bars denote the measured ranges over three different replicates, while the black bar represents the predicted concentration. For some metabolites no value could be predicted due to numerical instabilities. For the model simulating growth on succinate no steady-state solution could be obtained without further model adaptation, therefore, no SCC concentration could be predicted.Comparison of the predicted concentration ranges for 10 intracellular metabolites in (EPS)Click here for additional data file.S12 FigDistributions of predicted and simulated fold change in concentration for the 23 SCC metabolites over 929 single knock-out mutants, for which a steady-state flux distribution could be simulated.(EPS)Click here for additional data file.S1 TableA) Initial conditions sampled for simulations of the large-scale kinetic model of E. coli. The initial concentration is given in units mmol/gDW. (B) Steady-state concentrations obtained from simulations of the large-scale kinetic model of E. coli starting from the respective initial conditions presented in Table S1A. The first two columns show the respective minimum and maximum steady-state concentration over all 100 simulations. The concentration is given in units mmol/gDW. (C) Steady-state flux distributions obtained from simulations of the large-scale kinetic model of E. coli starting from the respective initial conditions presented in Table S1A. The flux is given in units mmol/gDW/hr. (D) Simulated and predicted concentration ranges for 23 SCC metabolites in a kinetic metabolic model of E. coli.((XLSX)Click here for additional data file.S2 TableA) Correlation between predicted concentration range and shadow price for 23 structurally constrained metabolites to the corresponding metabolic concentrations obtained from 100 simulations of a kinetic model of E. coli core metabolism. (B) Euclidean distance between simulated and predicted concentration bounds for 23 SCC metabolites in large-scale kinetic model of E. coli. In addition the table provides simulated and predicted concentration bounds in mmol/gDW.((XLSX)Click here for additional data file.S3 TableIn addition to the used rate constants and the related organism in BRENDA, the table reports the reaction abbreviation used in the model and the enzyme EC number related to each reaction. In case more than one rate constant is known per reaction we consider the average value.(XLSX)Click here for additional data file.S4 TableA) Measured concentrations of SCC metabolites in E. coli under different growth scenarios. The three replicates at growth rate 0.2h-1 are used as reference state. Measured volumetric concentrations1 were converted to mmol/gDW by using a ratio of aqueous E. coli cell volume to dry weight of 0.0023L/g2. (B) Specific flux rates for E. coli grown under different scenarios.((XLSX)Click here for additional data file.S5 TableA) Predicted concentration ranges for the 15 SCC metabolites in a genome-scale metabolic model of E. coli with available data on concentration. (B) In addition correlation values between predicted and simulated bounds are provided.((XLSX)Click here for additional data file.S6 TableNumber of metabolites with structurally constrained concentrations for each of the metabolic networks analyzed. The numbers of reactions and metabolites correspond to the number after reaction splitting into irreversible reactions and removal of blocked reactions. The latter is needed to satisfy the prerequisite for a positive steady state.(XLSX)Click here for additional data file.S7 TableFraction of fully coupled reactions and reactions coupled due to mass action kinetics in 14 analyzed genome-scale metabolic networks.(XLSX)Click here for additional data file.S8 TableStructurally constrained metabolites across the 14 analyzed metabolic networks. In addition, the in- and out-degree for these metabolites are provided. Metabolites marked in red correspond to energy metabolism (see (XLSX)Click here for additional data file.S9 TableNumber of metabolites with structurally constrained concentrations metabolic networks analyzed including enzyme information. The numbers of reactions and metabolites correspond to the number after rewriting in Michaelis-Menten format, reaction splitting into irreversible reactions and removal of blocked reactions. Model components correspond to metabolites, enzymes and enzyme-substrate-complexes.(XLSX)Click here for additional data file.S10 TableA) Measured concentrations of SCC metabolites in E. coli under growth on different carbon sources. Replicates for growth on acetate are used as reference state. (B) Specific flux rates for E. coli under growth on different carbon sources.((XLSX)Click here for additional data file.


Saccharomyces xylose utilizing yeasts.Efficient xylose fermentation still demands knowledge regarding xylose catabolism. In this study, metabolic flux analysis (MFA) and metabolomics were used to improve our understanding of xylose metabolism. Thus, a stoichiometric model was constructed to simulate the intracellular carbon flux and used to validate the metabolome data collected within xylose catabolic pathways of non-Scheffersomyces stipitis, Spathaspora arborariae, and Spathaspora passalidarum. In total, 39 intracellular metabolic reactions rates were utilized validating the measurements of 11 intracellular metabolites, acquired by mass spectrometry. Among them, 80% of total metabolites were confirmed with a correlation above 90% when compared to the stoichiometric model. Among the intracellular metabolites, fructose-6-phosphate, glucose-6-phosphate, ribulose-5-phosphate, and malate are validated in the three studied yeasts. However, the metabolites phosphoenolpyruvate and pyruvate could not be confirmed in any yeast. Finally, the three yeasts had the metabolic fluxes from xylose to ethanol compared. Xylose catabolism occurs at twice-higher flux rates in S. stipitis than S. passalidarum and S. arborariae. Besides, S. passalidarum present 1.5 times high flux rate in the xylose reductase reaction NADH-dependent than other two yeasts.A metabolic flux model was constructed using xylose fermentation data from yeasts S. stipitis and S. passalidarum showed respectively three and twice higher flux rates of XR with NADH cofactor, reducing the xylitol production when compared to S. arborariae. Besides then, the higher flux rates directed to pentose phosphate pathway (PPP) and glycolysis pathways resulted in better ethanol production in S. stipitis and S. passalidarum when compared to S. arborariae.This study demonstrated a novel strategy for metabolome data validation and brought insights about naturally xylose-fermenting yeasts. The online version of this article (10.1186/s12896-019-0548-0) contains supplementary material, which is available to authorized users. Saccharomyces yeasts capable of naturally utilize xylose as carbon source have been identified [Scheffersomyces stipitis is one of the most studied species and the Spathaspora ssp. has attracted attention in recent years [Several non-entified –7. Amongnt years –10. The nt years , 11–13. nt years . TherefoMetabolic flux analysis (MFA) is used to estimate the intracellular fluxes under a defined metabolic network , 16. It The understating of a real state of a cell depends on a set of analyzes that provides dataset about the genome, transcriptome, proteome, and metabolome . Among tNevertheless, the utilization of data from intracellular metabolite quantification is not routinely incorporated into MFA due to various technical challenges , 25. AmoScheffersomyces stipitis, Spathaspora passalidarum, and Spathaspora arborariae.Thereby, the purpose of this work was to validate a dataset of 11 intracellular metabolites of naturally xylose-fermenting yeasts utilizing MFA. Thus, for the first time, a comparative evaluation of metabolic flux analysis with addition metabolome data was performed for S. stipitis than S. passalidarum and S. arborariae. In yeasts S. stipitis and S. passalidarum is observed that after the xylose assimilation reactions, approximately 50% of the carbon flux rates are directed to pentose phosphate pathway (PPP) and 50% to glycolysis. Different from S. arborariae, where first, carbon flux is directed to reaction into oxidative-PPP. Besides, S. passalidarum present 1.5 times high flux rate in the xylose reductase reaction NADH-dependent than other two yeasts.Among them, 80% of total metabolites were confirmed with a correlation above 90% when compared to the stoichiometric model. Nevertheless, the metabolites phosphoenolpyruvate and pyruvate could not be validated in any studied yeasts. Finally, the three yeasts had the metabolic fluxes from xylose to ethanol compared. Xylose catabolism occurs at twice-higher flux rates in S. stipitis, S. arborariae, and S. passalidarum containing the xylose catabolism, pentose phosphate pathway, glycolysis, and tricarboxylic acid cycle. The model has 39 reactions and 35 metabolites, including the cofactors NAD(P) H, NAD(P)+, and ATP  than observed in S. stipitis (R09 = 0.66) and S. passalidarum (R09 = 0.42), respectively. Also, it was found that ethanol production rates are inversely proportional to glycerol and xylitol metabolites formation.In our analysis of MFA, we can identify that some metabolites influence the flux distribution. For example, the reactions for conversion the metabolites erythrose-4-phosphate and D-Ribulose-5-phosphate are 2.8 and 1.8 times lower in S. stipitis but was detected in S. arborariae and S. passalidarum, indicating that in S. stipitis less carbon was directed into respiratory metabolism. The RU5P (0.04 mM) is twice concentrated than R5P (0.02 mM) in both S. stipitis and S. passalidarum. While in S. arborariae, the R5P concentration (0.01 mM) was three times lower than RU5P (0.04 mM). The concentration of S7P is seven times higher in S. passalidarum than observed in S. stipitis and S. arborariae. The metabolite E4P was not detected only S arborariae but was detected in S. stipitis and S. passalidarum, indicating that E4P may rapidly be converted to G3P and F6P. The DHAP was about four times lower in S. arborariae (0.005 mM) than observed in S. stipitis (0.020 mM) and S. passalidarum (0.015 mM). The metabolites G6P (0.05 mM), F6P (0.06 mM) and PEP (0.06 mM) were at least twice concentrated in S. passalidarum than observed in S. stipitis  and S. arborariae , respectively. The metabolite PYR in S. stipitis (0.10 mM) is five times higher than observed in S. arborariae (0.02 mM) and S. passalidarum (0.02 mM). Finally, the concentration of MAL in S. arborariae (0.10 mM) was twice and three times lower than observed in S. stipitis (0.20 mM) and S. passalidarum (0.30 mM), respectively  in all studied yeasts is shown in Fig. + dependent. The NADPH/NADP+ ratio is twice higher in S. stipitis, while in S. arborariae and S. passalidarum we did not observe differences. The high NADPH released can influence the higher biomass formation observed in S. stipitis in contrast with S. arborariae and S. passalidarum. Already the NAD+/NADH ratio is 2.5 times high in S. arborariae, showing high NAD+ released, which characterize an unbalance cofactor in this yeast. Among the simulations of intracellular carbon flux distribution, some reaction rates caught our attention  in S. arborariae was four times higher than observed in S. stipitis and S. passalidarum. Showing that in the yeast S. arborariae, there is a higher necessity of regeneration the NADPH cofactor. Corroborating the high reaction rate (R01) that use NADPH in reaction conversion. Besides, the yeast S. passalidarum showed the reaction rate DHAP + NADH → GROL + NAD+ (R15) negative, contrasting with S. stipitis and S. arborariae. This characteristic in S. passalidarum agrees with the absence of glycerol formation and better cofactors balance. The reactions XYL + NADH → XOL + NAD+ (R02), XOL + NAD+→ XYLO + NADH (R03), GAP + NAD+→ PEP + NADH (R16), and ACDH + NADH → ETOH + NAD+ (R19) presented the highest rates. Indicating the importance of cofactor balance. The reaction rates with negative values indicate reversible reactions.The metabolic model constructed contains specific reaction rates cofactors NAD(P) H – NAD(P)ion Fig. . For exaS. stipitis and S. arborariae, ten intracellular metabolites were quantified and, consequently, ten simulations performed. While in S. passalidarum, 11 metabolites quantified, which resulted in 11 simulations of carbon flux distribution.The metabolite quantification was validated using MFA analysis. For that, the entire metabolome dataset was added to the MFA-calculated Fig. . The metS. stipitis, it was observed that from ten metabolites, eight  showed a correlation higher than 0.90 between calculated and measured fluxes  showed a correlation higher than 0.90 between calculated and measured fluxes  showed a correlation higher than 0.90 between calculated and measured fluxes   and PYR (69%) has a weak correlation in three tested xylose-fermenting yeasts.The consistency of intracellular metabolites measurements were verified using the Pearson correlation test (R2) Fig. . The corS. stipitis and S. arborariae in the xylose assimilation pathway showed that the reaction XYL + NADPH → XOL + NADP (R01) have an error of 85 and 60%, respectively , responsible for regenerating NADPH in oxidative pentose phosphate pathway and the reversible reaction G6P ← → F6P, also showed error higher than 60%. In contrast, in S. passalidarum, the reaction rate G6P → F6P (R11) showed at least 2.5 times less error when compared to S. stipitis and S. arborariae. In general, our analysis was able to predict 80% of intracellular carbon flux rates with an accuracy above 90% in relationship to calculated and measured flux rates from xylose until ethanol formation.The reaction rate G6P Metabolome data increase the precision of the actual state of cell metabolism . Its mea. Nevertheless, here, both isomers could be detected and quantified in the three yeasts S. stipitis, S. arborariae, and S. passalidarum. Also, the method based on UHPLC-MS/MS employed in this study enabled the separation and quantification of RU5P and R5P [S. passalidarum [Previous metabolomic studies did not succeed in quantifying the sugar-phosphate as such as G6P and F6P , 32. Nev and R5P , overcomalidarum .S. arborariae. Since the flux rate formation for E4P in this yeast is at least twice slowly (reaction S7P + GAP → E4P + F6P = 0.23) when compared to S. stipitis and S. passalidarum (reaction S7P + GAP → E4P + F6P = 0.65 and 0.42). Therefore this metabolite was below the detection limit. Also, the absence of E4P is associated with low carbon flux rate into the pentose phosphate pathway in S. arborariae, when compared to S. stipitis and S. passalidarum. The ACCOA could not be detected in S. stipitis since it is observed that the carbon flux is preferably directed to ethanol formation.Some metabolites could not be detected in particular yeasts; for example, E4P could not be detected only in Consequently, less carbon flux is directed to respiratory metabolism in the tricarboxylic acid cycle. Also, the difficulty of accurately quantifying metabolic flux influenced the detection of the ACCOA metabolite. Possibly, this limitation can be solved with the addition of more metabolic reactions in the stoichiometric model. Moreover, the ACCOA is involved in 34 compartmentalized metabolic reactions and used for acetylation of macromolecules [S. passalidarum in xylose fermentation encountered similar concentrations that were quantified here [The metabolites PEP and PYR could not be validated using MFA in any studied yeast. However, a previous study that assessed the metabolome of ied here . Possiblied here . Moreoveied here .The changes in metabolite concentration do not readily allow conclusions on metabolic fluxes or the direction of the flux changes. An increase in metabolite concentration can both be indicative of the increased activity of metabolite producing enzymes, but also decreased the activity of metabolite consuming enzymes . Neverth→ XOL + NADP. Second reaction is XYL + NADH → XOL + NAD+. Xylose reduction reaction NADH-dependent showed approximately twice higher flux rate in S. stipitis (1.97) and S. passalidarum (1.28) that is observed in S. arborariae (0.68). In S. arborariae, the flux rate of conversion xylose to xylitol has employed the cofactor NADPH preferentially [S. arborariae is associated with xylitol accumulation [S. arborariae present xylitol production (Table S. arborariae have a flux rate for oxidative-PPP (reaction G6P → RU5P) in 3.0 times higher than observed in S. passalidarum. This observation indicates the need to regenerate the NADPH cofactor in S. arborariae. Also, it noted that S. arborariae has a slower flux rate to consume xylose and a smaller growth rate. Due to the decreased cell growth, the requirement of NADPH has been reduced and caused the down-regulation of fluxes through the pentose phosphate pathway [The first step of xylose reduction to xylitol realized by xylose reductase (XR) enzyme using both NADPH and NADH as cofactors , 8, 38. pitis 1.9 and S. ppitis 1.9 and S. p pathway , 25.S. passalidarum already showed that it has two XR (genes XIL1.1 and XIL1.2), and one of them uses NADH preferentially as a cofactor [→ RU5P in S. passalidarum was 3.0 times slower than S. stipitis and S. arborariae. Demonstrating less need for carbon flux to oxidative-PPP and carbon flux directed more to PPP and glycolysis pathways.On the contrary, for cofactor . Its enzcofactor . BesidesS. stipitis and S. arborariae cell extract are twice higher using NADPH as cofactor when compared to NADH-dependent activity [Saccharomyces cerevisiae [S. stipitis and S. passalidarum).The enzymatic activities for xylose reductase (XR) of activity , 8, 23. S. arborariae), confirming the literature data and our previous study [S. arborariae and prior studies [On the other hand, the XR NADPH preference showed xylitol accumulation  is 4.5 times faster than S. arborariae, and 2.7 times faster than observed in S. passalidarum. The faster metabolic fluxes in the glycolytic pathway observed in S. stipitis resulted in better ethanol production, and it was the main characteristic that differentiated it from the other evaluated yeasts.Our results demonstrate a positive correlation between glycolytic flux rate and ethanol production. The low glycolytic flux seems to limit xylose utilization. These results are in agreement with a previous study that also applied metabolic flux analysis in genetic engineered revisiae , 44. It revisiae . TherefoSpathaspora yeasts for the first time. The metabolic flux model validated the quantification of 11 metabolites, where up to 80% of intracellular carbon flux rates could be correlated with an accuracy above 90%. The flux analysis corroborated that S. stipitis and S. passalidarum are the two yeasts with better metabolic characteristics towards xylose fermentation. These characteristics include higher xylose consumption rates, a higher reaction rate of XR with NADH preference, higher flux rates directed to PPP and glycolysis pathways, and less need to directed carbon flux to oxidative-PPP for the regeneration of NADPH. Characteristics that would enable better NADH/NAD+ balance, thus allowing improves ethanol production from xylose.The present study elucidated for the first time a stoichiometric model from xylose until ethanol to estimate the carbon flux distribution in Scheffersomyces stipitis (NRRL Y-7124), Spathaspora arborariae (NRRL Y-48658), and Spathaspora passalidarum (NRRL Y-27907). These were kindly provided by the ARS (NRRL) culture collection . All are preserved in 30% glycerol at − 80 °C. As described previously, were performed all cultivations in the bioreactors [− 1 xylose as a carbon source. The fermentation started with an optical density of 600 nm (OD600) equals 0.5. The temperature set up at 28 °C and stirred was kept at 400 rpm, pH was maintained at 5.5 by addition 3 M KOH. Oxygen was supplied at limited conditions. At those conditions, dissolved oxygen (DO) was kept below 10% with airflow of only 0.05 L/min throughout the cultivation in the bioreactors. All fermentations were carried out in biological triplicates. Samples were withdrawn to determine xylose consumption and product formation at regular intervals of time (approximately every 8 h) of cultivation. Extra- and intracellular quantification of metabolites was done using samples in the middle exponential growth phase where a pseudo-steady state is assumed, and therefore, all rates at that time-points were considered as constant [S. stipitis, S. arborariae, and S. passalidarum, respectively.The yeasts used in this study were s NRRL Y-124, Spatreactors , supplemconstant . The tim− 1 of three biological triplicates . For each collected point, cell dry weight (CDW) was performed through 5 mL of pre-inoculum and stationary growth phase of all yeasts in fermentative processes. Samples were withdrawn and centrifuged . Before weighing, the cells were placed in a glass tube and incubated to dry at 60 °C at least 48 h. Therefore, it established a correlation between OD600 values and CDW. Approximately, for each OD600 = 1.0 were obtained 0.5 g L− 1 of cells dry weight.The extracellular metabolites such as xylose, ethanol, xylitol, glycerol, acetate, pyruvate, and succinate concentrations were determined by High-Performance Liquid Chromatography (HPLC) as previously described . BrieflyAcetate (ACE), acetyl coenzyme A (ACCOA), alpha-ketoglutaric acid (AKG), dihydroxyacetone phosphate (DHAP), erythrose-4-phosphate (E4P), ethanol (EtOH), fructose-6-phosphate (F6P), glucose (GLU), glucose-6-phosphate (G6P), glyceraldehyde-3-phosphate (GAP), glycerol (GOL), malate (MAL), phosphoenolpyruvate (PEP), pyruvate (PYR), ribose-5-phosphate (R5P), ribulose-5-phosphate (RU5P), sedoheptulose-7-phosphate (S7P), succinate (SUC), xylitol (XOL), xylose (XYL), xylulose (XYLU) and all solvents as such as sulphuric acid, tributylamine, acetonitrile and methanol used in HPLC and UHPLC-MS/MS analysis were purchased from Sigma-Aldrich  in their highest purity. Ultrapure water (18.2 MΩ) was obtained from a Direct 16 Milli-Q purification system .tm UPLC system  coupled to a triple quadrupole mass spectrometer  equipped with an electrospray ionization source. UPLC it performed on a Hydrophilic Interaction Liquid Chromatography (HILIC) with a BEH amide column (2.1 × 150 mm × 1.7 μm)  and Ion-Pairing Chromatography (IPC) with a reverse phase column, HSS-T3 (2.1 × 150 mm × 1.8 μm) . The metabolites detected in both environment extra and intracellular, for example, xylose, xylitol, and glycerol were not included into the metabolome dataset since the quantification of these compounds presents high variance, and it was not possible to define how much was intra- and extracellular.The experimental setup for determination and quantification of metabolome data is in Additional file http://www.rstudio.com, https://www.rstudio.com/products/rstudio/download/) was used to construct the ANOVA model. The following mathematical equation represents how the ANOVA model test was performed.It performed in biological triplicates all cultivations in bioreactors. For each replicate, it extracted the intracellular metabolites in three-time points within the exponential growth phase of yeasts. The intracellular extraction of each time point was analyzed by UHPLC-MS/MS method in three technical replicates, giving a total of 27 samples measurements. Therefore, to compile all resulting data into a single value, the metabolome dataset was statistically processed through a measured repeated ANOVA design. RStudio software (ijk = μ + αi + βj(i) + τk + (ατ)ik + eijkUsing this linear model, it was assumed that the data for class (i) for yeast (j) at time (k) is equal to an overall mean (μ) plus the treatment effect (αi), the effect of the yeast within that class (βj(i)) the effect of time (τk), the effect of the interaction between time and class ((ατ)ik) and the error (eijk).yμ = overall meani = effect of class iαj(i) = random effect of yeast j receiving class iβk = effect of time kτik = class by time interaction(ατ)ijk = experimental errorεSuch that:The Additional file S. stipitis (Entry T01023). The genes encoding for the enzymes on the carbohydrate metabolism present in the respective genome could be determined using KEGG pathway.An overview of the metabolic model is shown in Additional file The model uploaded into OptFlux from an Excel file  and used to limit the initial metabolic model resolution. Considering the principle of mass conservation and molarity, this ensures that the total amount of compounds produced must be equal to the total amount being consumed , 36, 52.− 1), whereas biomass production rates were fixed with 1.45, 1.27, and 1.01 (g. L− 1) for S. stipitis, S. arborariae, and S. passalidarum, respectively. The extracellular ethanol flux rates were used to validate the carbon flux distribution in the calculated model. First, the simulations were performed without the ethanol flux rate, and then, the flux rate obtained through this simulation was compared with the ethanol rate obtained experimentally.Between them, for each yeast, xylose consumption, and biomass production rates were maintained fixed during all simulations. Xylose consumption rates were fixed with following values − 2.15, − 0.90, and − 1.37 (mM/gCDW.h2). The correlation coefficient is useful to find the relationship between the calculated and measured fluxes distributions. The metabolites, whose correlation was above 90%, were used in the stoichiometric model for a new round of carbon flux simulations. Thus it was possible to estimate the percentage of error, and consequently, identify hits between calculated and measured fluxes for each measured metabolite.The statistical analysis resulted in a single value of intracellular metabolites concentration. Those were added to the stoichiometric model. After that, a simulation was performed to determine the flux distribution within the metabolic network. Initially, the simulations performed with the addition of one metabolite by time. Thus, 11 measured fluxes distribution were obtained, one for each measured metabolite. Then, the fluxes derived from stoichiometric calculations and the ones with the addition of metabolome data were compared using the Pearson correlation coefficient  utilized in some reactions. (PDF 169 kb) Overview of metabolic network from xylose to ethanol. The metabolic model showns the directions of intracellular metabolic reactions (continuos arrows), xylose consumption and products formation (dashed arrows), and cofactors Additional file 3: List of metabolites. The intracellular and extracellular metabolites added to the OptFlux. (PDF 39 kb)Additional file 4: Intracellular metabolites concentrations (μg/mL). Average and standart desviation of metabolites concentrations obtained after statistical analysis (ANOVA) from the metabolome data. (PDF 52 kb)Additional file 5:a Correlation (R2) between calculated and measured fluxes - S. stipitis. Acetyl-CoA (ACCOA), dihydroxy-acetone-phosphate (DHAP), erythrose-4-phosphate (E4P), fructose-6-phosphate (F6P), glucose-6-phosphate (G6P), malate (MAL), phosphoenolpyruvate (PEP), pyruvate (PEP), ribose-5-phosphate (R5P), ribulose-5-phosphate (RU5P), and sedoheptulose-7-phosphate (S7P) were the metabolites measured. (X-axis) show the calculated fluxes using the constrained values of products formation. (Y-axis) show measured fluxes with respectively metabolites concentrations. Graphics in square presents a correlation higher than 0.9. Flux rates are in mmol/gCDW.h− 1. b Correlation (R2) between calculated and measured fluxes - S. arborariae. Acetyl-CoA (ACCOA), dihydroxy-acetone-phosphate (DHAP), erythrose-4-phosphate (E4P), fructose-6-phosphate (F6P), glucose-6-phosphate (G6P), malate (MAL), phosphoenolpyruvate (PEP), pyruvate (PYR), ribose-5-phosphate (R5P), ribulose-5-phosphate (RU5P), and sedoheptulose-7-phosphate (S7P) were the metabolites measured. (X-axis) show the calculated fluxes using the constrained values of products formation. (Y-axis) show measured fluxes with respectively metabolites concentrations. Graphics in square presents a correlation higher than 0.9. Flux rates are in mmol/gCDW.h− 1. c Correlation (R2) between calculated and measured fluxes - S. passalidarum. Acetyl-CoA (ACCOA), dihydroxy-acetone-phosphate (DHAP), erythrose-4-phosphate (E4P), fructose-6-phosphate (F6P), glucose-6-phosphate (G6P), malate (MAL), phosphoenolpyruvate (PEP), pyruvate (PYR), ribose-5-phosphate (R5P), ribulose-5-phosphate (RU5P), and sedoheptulose-7-phosphate (S7P) were the metabolites measured. (X-axis) show the calculated fluxes using the constrained values of products formation. (Y-axis) show measured fluxes with respectively metabolites concentrations. Graphics in square presents a correlation higher than 0.9. Flux rates are in mmol/gCDW.h− 1. (ZIP 183 kb)Additional file 6:S. stipitis, S. arborariae, and S. passalidarum. The times of replicates  and the technical replicates  are repeated for each biological replicate into an oxygen-limited condition . (PDF 135 kb) Experimental design for metabolomics data. Three species of xylose-fermenting yeasts


Metabolomics is an emerging branch of “omics” and it involves identification and quantification of metabolites and chemical footprints of cellular regulatory processes in different biological species. The metabolome is the total metabolite pool in an organism, which can be measured to characterize genetic or environmental variations. Metabolomics plays a significant role in exploring environment–gene interactions, mutant characterization, phenotyping, identification of biomarkers, and drug discovery. Metabolomics is a promising approach to decipher various metabolic networks that are linked with biotic and abiotic stress tolerance in plants. In this context, metabolomics-assisted breeding enables efficient screening for yield and stress tolerance of crops at the metabolic level. Advanced metabolomics analytical tools, like non-destructive nuclear magnetic resonance spectroscopy (NMR), liquid chromatography mass-spectroscopy (LC-MS), gas chromatography-mass spectrometry (GC-MS), high performance liquid chromatography (HPLC), and direct flow injection (DFI) mass spectrometry, have sped up metabolic profiling. Presently, integrating metabolomics with post-genomics tools has enabled efficient dissection of genetic and phenotypic association in crop plants. This review provides insight into the state-of-the-art plant metabolomics tools for crop improvement. Here, we describe the workflow of plant metabolomics research focusing on the elucidation of biotic and abiotic stress tolerance mechanisms in plants. Furthermore, the potential of metabolomics-assisted breeding for crop improvement and its future applications in speed breeding are also discussed. Mention has also been made of possible bottlenecks and future prospects of plant metabolomics. Metabolomics is one of the emerging and fascinating approaches of omics tools, which has now been extensively applied for crop improvement. Metabolomics is crucial to studying the abiotic stress tolerance, pathogen resistance, robust ecotypes, and metabolic assisted-breeding of crops. To date, enormous progress has been made to develop modern metabolomics tools for crop improvement . The plaBiotic and abiotic stresses adversely affect crop productivity and cause a massive reduction in the global annual crop yield. Metabolomics tools can be integrated with other omics tools, such as genomics, transcriptomics, and proteomics, to tackle abiotic/biotic stresses in plants . It helpPrimary metabolites are important for the biosynthesis of lipids, sugars, and amino acids in plants. They mediate the tricarboxylic acid and glycolysis cycle during photosynthesis and thereby affect plant growth and development. Variations in the synthesis of primary metabolites may lead to photosynthesis malfunctioning and imbalanced osmotic adjustment in plants. Primary metabolism results in the production of secondary metabolites, like flavonoids, atropine, carotenoids, and phytic acid. These are not essential for plant survival and are produced in response to different stress conditions, such as high temperature, chilling, drought, salinity, and insect/pest attack. Additionally, secondary metabolites also include reactive oxygen species (ROS), antioxidants, and co-enzymes . The plaBased on the previous research knowledge, numerous methods have been devised for the detection and identification of specific metabolites . HoweverMetabolomics tools are used for metabolic profiling of biofluids and various cell tissues, which are involved in different cellular processes, thus portraying the whole physiological composition of a cell . These cThis review describes the advantages and disadvantages of advanced analytical tools and adopted workflow in plant metabolomics research. Here, we also discuss plant genetics and biochemistry implications, which trigger the extraordinary diversity of plant metabolites and provide an overview of the stress tolerance mechanism in plants under extreme climatic conditions. We further focus on the fast-growing era of metabolomics-assisted breeding for crop improvement and the pitfalls in current metabolomics studies are also outlined.The elucidation of plant metabolites in metabolic profiling is considerably challenging due to an insufficient connection between the proteome and metabolome. Additionally, it is hard to detect some metabolites during the whole metabolome analysis due to technical hurdles, like the incompatibility of instruments, lack of standardized protocols, and volatility of the desired metabolites . In metaThe choice of a metabolomics approach depends upon the accuracy, selectivity, speed, precision, and sensitivity of any analytical tool. For a comprehensive study of metabolites, the NMR technique can be used conveniently in many organisms, including plants. NMR-based metabolic profiling is quick, expedient, and efficient technology for the screening and identification of similar biological samples. It is non-destructive, selective, and very proficient at mapping metabolic pathways . MoreoveThe mass spectrometry technique provides the benefit of quick sample preparation and examination in their natural state . Ultra-hIn contrast to the GC-MS approach, LC-MS uses an electrospray ionization (ESI) source to analyze high molecular weight metabolites, which are polar and thermo-labile. It is executed largely for secondary metabolite profiling, including vitamins, glucosinolates, flavonoids, and carotenoids, but can also be used for primary metabolites’ detection. LC-MS has unique features that permit direct probing of metabolites in any sample with no need for derivatization . MoreoveThe core steps involved in the metabolomics experimental design are sample preparation, data acquisition through analytical strategies, and utilization of suitable chemo-metric techniques for data mining . These sSample preparation is one of the most crucial parts of metabolomics as it has a tremendous impact on the final results . AbovegrThe main objective of sample preparation is to separate metabolites from unwanted elements and enrich the desired metabolites. The best sample preparation technique should be quick, economical, simple, easy, and uphold the sample integrity . Plant sMany extraction protocols have been developed in the last few years for metabolomics analysis ,50. ConvThe modern era of metabolomics provides deep insights into the molecular complexity of downstream of the genome, transcriptome, and proteome of plants either in normal growth conditions or in response to various stresses. Whole metabolome analysis has established an enormous amount of data sets because of the huge number of metabolites present in different parts of plant cells or tissues. The complicated nature and composition of metabolites in different plant samples has made metabolomics data analysis more complex. The basic goal for whole metabolome analysis is to categorize the different metabolites of various plant samples induced by numerous factors . A consiMetabolomics measures the metabolite abundance as a predictive biomarker for disease diagnosis. It also scores the genetic as well as environmental-induced changes in metabolites’ concentration. The identification of biomarkers relies on data analysis using different statistical methods. Metabolic marker probing is connected to the concept of linking response variables, such as the desired phenotype, to explanatory variables representing biomarkers. An appropriate, multi-dimensional statistical platform is mandatory for fast forward analysis in order to estimate the relationship among metabolites and phenotypic variables. A pairwise Pearson’s correlation can be used to discover a specific biomarker, where only one metabolite is connected to the desired phenotype. Although, more than one metabolite analysis is required to design a predictive model, and canonical correlation analysis (CCA) is often applied to study the maximize correlation between variables ,65.Statistical tools can be used to handle the high-throughput metabolomics data that are mainly adapted from already existing omics technologies. Many statistical tools originally developed for transcriptomic analysis can also be used for metabolomics data analysis. Traditionally, in any metabolomics data, we aim to see the groupwise differences either in a univariate, i.e., parameter-by-parameter fashion using univariate techniques , and Mann–Whitney U-test). Univariate analysis is generally performed for biomarker discovery at initial levels of systems biology, which studies one variable at a specific time. In addition, it can also verify the performance and legitimacy of a presumed metabolic marker . On the In order to study high-throughput metabolomics data, multivariate statistical strategies are generally categorized into two approaches : Unsuperhttps://www.mathworks.com) and SIMCA-P (http://umetrics.com/products/simca), which propose several types of procedures. Several other packages established for metabolic profiling [http://metabr.r-forge.r-project.org/) [https://github.com/xialab/MetaboAnalystR) [https://github.com/lanagarmire/lilikoi) [http://github.com/andreasmock/MetaboDiff/a) [PCA is recognized as one of the most important unsupervised multivariate statistical tools and is being extensively used for the multi-dimensional reduction approach . Furtherrofiling ,75,76,77rofiling . For unict.org/) , MetaboAnalystR) , Lilikoililikoi) , and MetoDiff/a)  are somehttps://xcmsonline.scripps.edu), which allows raw data to be uploaded directly and assist the user in statistical analysis and data processing [Time is no longer a limiting factor for data mining in metabolomics due to the rapid advancements in modern analytical and technical tools. Computational informatics is a prerequisite of metabolomic experiments . During ocessing . XCMS seocessing . It can ocessing . In addiocessing .http://metlin.scripps.edu) is another online database applied in numerous studies related to stress response metabolic profiling in plants. It is beneficial for metabolic profiling of unique metabolites and the workflow of data mining, annotation, and processing is not time-consuming. Furthermore, it permits immediate retrieval of LC/MS, MS/MS, and FTMS analysis outcomes by allowing its operator to put a query in the database through a programmed framework [http://metagenealyse.mpimp-golm.mpg.de/) is a web-based tool that implements regular clustering techniques, like independent component analysis (ICA) and k-means. This web-tool also offers many ways for statistical analysis, such as pathway enrichment analysis, PLS-DA, and t-test [http://metpa.metabolomics.ca) [http://www.msea.ca) [https://meltdb.cebitec.uni-bielefeld.de) [http://ms.iis.sinica.edu.tw/comics/Software_iMet-Q.html), MS-Dial (http://prime.psc.riken.jp/Metabolomics_Software/MS-DIAL/), and MetAlign , are operated by windows GUIs  and do not need any local installation [http://www.genome.jp/kegg/) have been extensively applied [http://www.babelomics.org/) [http://software.broadinstitute.org/cancer/software/genepattern/) [METLIN (ramework . MetaGend t-test . During mics.ca)  and MSEAmsea.ca) . A signifeld.de) . Other dallation ,95,96. T applied ,98. Rece applied . Meta bo applied . Babelomcs.org/)  and Geneattern/)  are two attern/) ,122,123.Metabolomics has emerged as the most promising tool to decipher abiotic stress tolerance in plant species. Recently, metabolomics has been applied to probe for unique metabolites during the life cycle of plants. Biotic/abiotic stresses have a significant role in the reduction of the crop yield . Plants Abiotic stresses are major limiting factors of agriculture production and can be described as any change in plants’ growth conditions that adversely affect plant metabolism, normal plant development, and plant physiology. The main abiotic stresses that negatively regulate the plants’ growth are drought, salinity, temperature extremes, waterlogging, heavy metal, and chilling . MetabolMany advanced tools, like NMR, LC-MS, and GC-MS, are extensively employed in metabolomics studies to elucidate abiotic stress tolerance in plants . GC-MC c2 concentrations and stomatal closure [Drought is a major limiting factor for agricultural production around the world. In drought stress, plants adopt several physiological modifications, such as leaf abscission, lead area reduction, and greater nutrient uptake by plant roots. Furthermore, stomatal closure affects transpiration activity and reduces water loss. These physiological alterations improve water use efficiency (WUE) that ultimately hinders photosynthesis activity due to reduced CO closure . Plants  closure . Metabol closure . Compara closure . Similar closure .Recently, GC-MS-based metabolic profiling of eight wheat cultivars was performed to observe the drought tolerance mechanism. Data regarding the chlorophyll content, stomatal conductance, relative water content, and metabolites of different cultivars was recorded. An increased concentration of glutamine, serine, methionine, lysine, and asparagine was recorded under drought stress . In 2018A higher level of soil salinity results in ion toxicity and disturbance of the ion uptake mechanism, causing metabolic syndrome and osmotic imbalance that leads to stunted growth and the capture of several physiological activities . ImbalanThe GC-MS-based metabolic profiling of rice seedlings was performed under salt stress that proved hyperaccumulation of important amino acids, like leucine, isoleucine, valine, and proline . Gupta a2 and oxygen, which ultimately hampers the photosynthesis process. Waterlogging for a longer time causes hypoxia that directly affects roots and prevents CO2 assimilation. Signal transduction, metabolic alteration, and morphological changes are three adaptations to waterlogging stress [Waterlogging is another type of abiotic stress that hinders crop growth and yield. Waterlogging causes extreme injuries to plants due to the limited supply of COg stress . NMR-basg stress . In anotg stress . Herzog g stress . In anotg stress .Plants need optimum temperatures for their normal growth and development. Fluctuations in temperature can cause severe damage and cease the developmental processes. High temperatures disturb the homeostasis and other physiological mechanisms ,155. PlaBrassica rapa, as indicated by 1H-NMR, 2D-J-based metabolomic analysis [Heavy metal stress is another abiotic stress that has become a very important factor that influences crop yield. It prompts several signals in plants, heading towards variations in complex biochemical, molecular, and physiological mechanisms. Trace elements, such as cobalt Co W, V, Cr, Zn, Ni, and Cu, are considered lethal to plants at their higher concentrations . Metals,analysis . Similaranalysis .Solanum lycopersicum L.) under N and phosphorus (P) deficiencies. It was observed that N stress reduces the concentration of organic acids and amino acids while triggering the synthesis of soluble sugars [For normal growth and development, plants require essential nutrients in optimum concentrations. Organic molecules and metabolites are built of structural units, like carbon, phosphorus, sulfur, and nitrogen, in plant cells. Deficiencies of these nutrients directly affect the growth, metabolism, and physiology of plants. Nitrogen (N) is considered as the most vital element in nature as it is the basic structural unit for cellular metabolites, such as nucleic acid, amino acids, and proteins, as well as for many secondary metabolites . Plants e sugars . The sece sugars . Recentle sugars . P is ese sugars . Metabole sugars . Similare sugars  and low e sugars .Arabidopsis thaliana, and investigated for their resistant ability in barley. The result showed an improved disease resistance in barley as compared to wild cultivars [Fusarium graminearum [Metabolomics profiling determines important changes in plant primary and secondary metabolites due to any pathogen attack . Plants ultivars . Comparaultivars . Phenylpminearum .Zymoseptoria tritici. Microbiome and comparative metabolomic profiling were conducted via FT-ICR-MS to detect immune and defense-related metabolites in resistant and susceptible wheat cultivars [Fusarium graminearum. NMR-based metabolic analysis indicated the higher accumulation of some disease-resistant biomarkers, such as trehalose, asparagine, phenylalanine, myoinositol, 3-hydroxybutarate, and L-alanine [Fusarium graminearum, metabolic analysis was conducted via NMR and revealed the enhanced production of spermine, putrescine, GABA, inositols, galactose, and lactic acid [Recently, Seybold and colleagues analyzed the wheat metabolome to elucidate the stress responsive mechanism against ultivars . The whe-alanine . Likewistic acid . Comparatic acid .Rhizoctonia solani attack. Jasmonic acid, mucic acid, and glyceric acid were synthesized in greater concentrations in response to fungal infection [Xanthomonas oryzae pv. oryzae (Xoo)-resistant rice cultivars [Magnaporthe grisea, which is one of the most damaging pests of rice. The metabolic analysis demonstrated that significant variation has been induced by M. grisea in the rice metabolic profile [Fusarium graminearum and identified two metabolites, such as smiglaside and smilaside, that might be linked with resistance against fungal attack [In 2016, Suharti and colleagues performed metabolomics analysis via the CE/TOF-MS platform to examine the resistance response of rice cultivars against nfection . GC-MS tnfection . Anotherultivars . LC-MS,  profile . The mail attack . In anotl attack .Ostrinia furnacalis is one of the most damaging insects of maize and causes massive yield reduction of commercial maize on a large scale. Metabolomics analysis revealed the higher production of volatiles, phtohormones, and benzoxzinoids, and upregulation of many metabolic pathways related to disease resistance in maize [Nilaparvata lugens is a destructive insect pest of rice and offers an ideal system to study plant–insect interactions. Metabolic profiling via GC/MS was performed to study the metabolic fluxes in response to insect attack and showed an enhanced synthesis of GABA and glyoxylate, which offers resistance against brown planthopper [Chilo suppressalis and aphid resistance in wheat [in maize . The intnthopper . In anotin wheat ,188. Weein wheat . Targetein wheat ,191. Simin wheat .Rhizoctonia solani-suppressive soil [Soil metabolomics is another important application of metabolomics for crop improvement. Soil consists of organic/inorganic compounds as well as a wide range of microbial populations, the connection of which is significantly elaborated as soil metabolomics. The relationship among crop production and the soil metabolome is an area of great interest because soil microbe diversity and composition affect crop production. A precise metabolic analysis of soil microbe populations in the soil metabolome and their connection to diverse metabolite synthesis is crucial to increase our understanding and allow the exploration of new avenues in soil microbiome investigations . Metabolive soil . Similarive soil .During the last decade, metabolomics has perceived remarkable developments in both software tools’ design and instrumentation advancement, providing an excellent opportunity to scan the whole metabolome of various plant species in a high-throughput way. The applications of metabolomics have assisted numerous research areas, especially biotechnology, like precision plant breeding, functional genomics, and disease diagnostics . FurtherKnowledge about metabolic networks controlling the complex mechanisms in metabolomics have a potential role in metabolomics-assisted breeding to develop elite cultivars for better quality and yield. Furthermore, data obtained from mQTL investigations lead to more comprehensive knowledge about quantitative genetics . MetabolAdvancement in next-generation sequencing (NGS) has allowed mQTLs identifications for candidate genes through ultra-high-density maps . CandidaBrassica napus, metabolomic profiling and genetic analysis were conducted based on glucosinolate synthesis. Results showed 105 mQTLs linked with glucosinolate production in seeds and leaves [Recombinant inbred lines (RILs) of barley were subjected to mQTL analysis and identified 98 different metabolites under drought stress. These stress-responsive metabolites, including sinapic acid, ferulic, and flavones, serve as antioxidants and exert control over gene expression regulation and modulate protein function under stress . Templerd leaves .The dissection of genomic regions linked with the synthesis of secondary metabolites in wild and introgression lines (ILs) of tomato led to the identification of 679 secondary mQTLs, which are linked with environmental stress tolerance . In the Moreover, numerous studies identified mQTLs controlling the biotic interactions in plants. With the progress in sequencing technology, more plant genomes have been sequenced with frequent use of mQTL analysis in crop plants. For example, genes for phenylpropanoid synthesis in maize , phenolaThe mGWAS has emerged as a powerful tool to describe the natural genetic basis of various metabolic changes in the plant metabolome. Recent studies revealed the global view of secondary plant metabolites associated with a specific trait . The parThe characteristics of primary and secondary metabolites can be used as metabolic markers to facilitate crop breeding for genetic improvement. In 2014, Wen and coworkers integrated the genetic expression and metabolic profiling approaches to understand the genetic diversity of various metabolites in maize kernels. In another experiment, mGWAS analysis was applied to study 289 inbred lines of maize in order to examine complex metabolic traits. About 26 metabolites were identified that are linked with SNPs and control the major objective of cinnamoyl-CoA reductase in enhancing the lignocellulosic quality in maize . SimilarRecently, metabolic profiling of winter wheat revealed the association among 18372 SNPs and identified 76 metabolites. The correlation between metabolites showed their functional association, representing the close linkage of several pathways of the citric acid cycle. The mGWAS detected a strong correlation for six metabolic features, such as sugar oligomer, L-tyrosine, pentose alcohol III, L-arginine, ornithine, and oxalic acid, among 1 and 17 SNPs. The results provide a baseline for anticipating the impact of genetic interventions on similar metabolic characters and ultimately on a particular metabolic phenotype . LikewisIn recent years, tremendous progress has been made in the field of metabolomics. However, some bottlenecks need to be adequately addressed to exploit metabolomics to its full potential. The removal of these bottlenecks will assist in exploring new platforms for crop improvement, which in turn will guarantee global food security. Current analytical tools for plant metabolomics analysis are unable to detect all the metabolites in sample tissues. This drawback is directly associated with biological modification in each cell, the complex chemical nature of metabolites in the plant metabolome, and vibrant coverage of analytical tools.Technical bottlenecks for broad-range coverage and biological bottlenecks to draw efficient knowledge and improve understanding of whole metabolome profile are the major pitfalls in metabolomics research. Due to the wide range, multiple dynamics, and diverse chemical composition, it is hard to identify metabolites using current analytical techniques as compared to RNA sequencing. Advancements in analytical instruments may lead to precision, such as improved NMR has been exploited for a whole metabolite coverage. The coverage is expected to be progressively improved with the modernization of technology that we have at this time. This would need a holistic integrated approach, whereby several tissues from diversified species can be examined by executing numerous ways for sample extraction and evaluating them on available metabolomics stages.Still, the identification and elucidation of a large number of unexplored plant metabolites is the major hurdle in metabolomics. The coordination of different enzymes in a metabolic process together with the complexity of metabolic pathways makes it a challenging task to change the features of a single metabolite in a biological process. Hence, these metabolic characteristics hinder direct evaluation of any specific metabolite through conventional approaches of reverse genetics. To cope with this issue, highly sophisticated approaches are needed for efficient modifications in enzymes’ kinetic features. While designing advanced tools to overcome these hurdles is expected involve more sophisticated approaches, we hope that they will be beneficial in assisting the exploration of the accurate features of any metabolite and elucidate the biological function to study a particular metabolite successfully. In the end, we would propose that deciphering the function of a specific metabolite represents the big hurdle remaining in the third decade of metabolomics.Metabolomics has attained a prominent place in plant biology research. It has vast applications in plant sciences, ranging from exploring various climatic stresses, probing the functions of candidate genes for analyzing the whole biological mechanism in cells, and dissecting thee genotype–phenotype relationship in response to various stresses. However, plant metabolomics still demands extensive research and proper attention for data mining, data annotation, assessment, processing, and evaluation. The integration of advanced bioinformatics tools with omics approaches proficiently dissects novel metabolic networks for crop improvement.The integration of metabolomics with post-genomics tools and genetic approaches has offered an exciting way forward to study the genetic regulations of plants in the context to their metabolism. Metabolomics has immense potential in the field of genetic breeding. Exploitation of high-throughput genome sequencing, reverse genetics, and metabolomics tools has significantly reduced the varied development time via metabolomics-assisted breeding. The combination of omics approaches, such as genomics, transcriptomics, and metabolomics, has huge potential for exploring complex metabolic pathways that govern important regulatory processes in plant metabolism. Unblocking certain bottlenecks, like decoding the structure of metabolic networks, the effect of artificial screening on crop metabolomes, and the relationship among metabolism and phenotyping, needs more dedicated efforts.Future applications of metabolomics may include identifying metabolic markers to study plant metabolism and to predict the nature and scale of biotic/abiotic stress. Approaches like metabolomics-assisted breeding will find wide applications in crop improvement programs to develop high-yielding, stress-tolerant germplasm and create climate-smart crop varieties. Furthermore, metabolomics approaches can be used for metabolic profiling of genome-edited plants using a modern genome-editing toolkit like the CRISPR/Cas9 system  for risk


Pectobacterium parmentieri SCC3193 with the use of KBase. The model was curated by using inParanoind and phenotypic data generated with the use of the OmniLog system. Metabolic modeling was performed through COBRApy Toolbox v. 0.10.1. The curated metabolic model of P. parmentieri SCC3193 is highly reliable, as in silico obtained results overlapped up to 91% with experimental data on carbon utilization phenotypes. By mean of flux balance analysis (FBA), we predicted the metabolic adaptation of P. parmentieri SCC3193 to two different ecological niches, relevant for the persistence and plant colonization by this bacterium: soil and the rhizosphere. We performed in silico gene deletions to predict the set of essential core genes for this bacterium to grow in such environments. We anticipate that our metabolic model will be a valuable element for defining a set of metabolic targets to control infection and spreading of this plant pathogen.Understanding plant–microbe interactions is crucial for improving plants’ productivity and protection. Constraint-based metabolic modeling is one of the possible ways to investigate the bacterial adaptation to different ecological niches and may give insights into the metabolic versatility of plant pathogenic bacteria. We reconstructed a raw metabolic model of the emerging plant pathogenic bacterium  Pseudomonas syringae pv. tomato explicitly employs amino acid and sugar transporters to gain access to nutrients present in the environment [Plant–bacteria interplays have been studied for a long time, mainly regarding pathogenic and beneficial (symbiotic) interactions. Various details are now known concerning the molecular basis of all these interactions . For exaironment . Subsequironment .To access the nutrients present in the plant tissues, pathogenic bacteria colonize, invade, and, later on, establish chronic infections within host plants. During the infection process, they enter plant tissues, through either wounds or natural openings, and occupy the apoplast of tissues or the xylem, where they multiply and spread . PhytopaPectobacterium parmentieri, combine numerous strategies to accomplish that goal; e.g., they rely on the quorum sensing system [To achieve compatible interaction, microorganisms at first need to overcome the plant’s defenses that could abort the infection. Plant pathogenic bacteria, like g system .P. parmentieri are pectinolytic bacteria belonging to Pectobacteriaceae family  [Pectobacterium have been included among the ten most significant bacterial plant pathogens based on their economic impact [ae, SRP) ,9, whichae, SRP) . Also, tae, SRP) . These Gae, SRP) ,13. Inteae, SRP) . Howeverae, SRP) . On the ae, SRP) . It is wc impact  since crc impact .P. parmentieri. It was previously reported that initialization of infection progress is controlled by quorum sensing in closely related Pectobacterium atrosepticum [Dickeya and Pectobacterium was reported [P. parmentieri have received little attention so far.There is still limited knowledge regarding the cascade of genes expressed before and during the infection process in septicum . Moreovereported . HoweverRalstonia solanacearum showed that the quorum-sensing-dependent regulatory protein PhcA32 controls the trade-off between virulence factor production and bacterial proliferation [P. carotovorum subsp. carotovorum PC1 revealed 19 potential bactericide targets through a comprehensive in silico gene-deletion study [Given the complexity of bacteria–host relationships, they cannot be adequately investigated only using classical microbiological and molecular methods; instead, the coupled use of Phenotype Microarrays, computational, large-scale, and systematic frameworks are advisable. Constraint-based metabolic modeling is now a promising way to interpret puzzling, heterogeneous bacterial phenotypes , especiaferation . Also, mon study .P. parmentieri SCC3193 and the exploitation of this model to putatively identify the metabolic pathways relevant for P. parmentieri fitness in two different ecological niches, i.e., soil and rhizosphere. We show that niche changes may lead to a metabolic reconstitution of carbon-related pathways in P. parmentieri SCC3193 and we spot the core-set of essential genes in the two examined conditions. Moreover, we anticipate the model itself will represent a valuable element, which will pave the way to both the knowledge-base of the strain’s biology and novel, applied technologies, such as genetic engineering and synthetic biology experiments.The aim of this work was the reconstruction of a highly curated metabolic model of the plant pathogenic bacterium www.kbase.com) which supports the reconstruction of metabolic models in microbes based on functional protein annotations. The closed genome of Pectobacterium sp. SCC3193 (NCBI Reference Sequence: NC_017845.1) [A draft metabolic model was built using the KBase Narrative Interface (17845.1)  was usedEscherichia coli K-12 MG1655 [This process allowed adding 44 new reactions and made two already existing reactions reversible  Experiment), where we observed that all the added gene allowed the model to grow on a different carbon source correctly.Since the majority of d-arabinose, N-acetyl-d-galactosamine, stachyose, and sucrose, we added gap-filling transporters for such compounds, and we named them after the missing metabolite, even if the gene–protein relationship for that function was absent. These gap-fillings are fictitious reactions enabling the model to replicate a known physiological property of the strain when gene annotations for such a function are missing or when the metabolites enter the cell through one or more nonspecific transporter. The qualitative results of the comparison are reported in Finally, during this test phase, the metabolic reconstruction was further enriched with exchange reactions . At this stage, the model still presented six discrepancies that we decided to fix with manual gap-filling. Precisely, since PM showed evidence of strain growth on amylotriose, dihydroxyacetone, glucose-6-phosphate, P. parmentieri biomass is available in the literature. However, such data are available for E. coli [Parmentieri genus, belongs to the Enterobacteriaceae family. Therefore, the generic Gram-negative biomass reaction embedded in the model by KBase was substituted with the more accurate and similar one of E. coli model [No comprehensive description of the macromolecular composition of the  E. coli  which, lli model  and set The complete biomass composition is given in www.gurobi.com) with libSBML library v5.10, in Python 2.7 framework . The scripts for running all the in silico analysis performed in this work are available in Metabolic modeling was performed using COBRApy Toolbox version 0.10.1  and the P. parmentieri reference strain SCC3193, isolated from potato tuber in Finland [The bacterial strain used in this study is P. parmentieri SCC3193 Biolog Plates for Carbon, Nitrogen, and Phosphorus-Sulfur utilization assay were used . Overnight bacterial culture was transferred from TSA medium to 5 mL of 0.85% NaCl, and bacterial suspension was adjusted to OD600 equaling 0.1 Later, 1 mL of bacterial suspension was transferred to 11 mL of Minimal Salts medium  supplemented with 120 µL of Biolog A dye. To inoculate wells in PM plates 100 µL of described bacterial suspension was used. The measurement was carried out in OmniLog™ for 46 h. Each reaction was tested in triplicate, and the experiment was performed twice. Mean results were analyzed with DuctApe [For high-throughput phenotypic characterization of  DuctApe . All theTM plate reader was performed to cross-check high-throughput phenotypic characterization. M9 media supplemented with 20% of selected carbon sources: α-d-glucose, d-xylose, and d-mannitol, d-fructose, and citric acid were prepared. P. parmentieri SCC3193 was grown in LB medium overnight at 28 °C with constant agitation (120 RPM). Afterward, overnight bacterial cultures were centrifuged and washed twice in sterile Ringer Buffer and later OD600 of inoculum was adjusted to 0.1. Fifty µL of inoculum was transferred to 450 µL of M9 supplemented with different carbon sources to establish growth curves. Bacteria were cultured with agitation set at 120 RPM in 28 °C in 24-well plate in EnVisionTM plate reader. Optical density measurement at 600 nm was performed every 30 min. The quantification of wet bacterial biomass was performed after bacterial growth in 300 mL of the media mentioned above after 48 h. Bacterial cultures were centrifuged at 10 °C with 10,000 RPM for 10 min  and frozen for 24 h. Afterward, the wet bacterial biomass was weighed.Nutrients metabolic assay with the use of EnVisionPM1 and PM2A obtained data analysis was performed with DuctApe . ActivitIn silico representations of the nutritional composition of the soil, and rhizosphere were derived from a previously published paper . The comFlux distribution predictions were assessed by performing FBA in M9, soil, and rhizosphere media. We performed loopless-FBA to avoid net fluxes around a closed cycle in a network at steady state, known as loops.Moreover, since FBA only predicts one flux distribution among all possible solutions, we also performed loopless-FVA . Since in some cases, the range of reaction flux was unbounded (going from −1000 to 1000) we decided to filter out those reactions whose FVA predicted range was larger than the predicted FBA flux the following criteria:FBA < 0(1) If fFBA > 0(2) If fLoopless FBA and FVA predictions for each reaction can be retrieved in M9 growth medium was simulated in silico by constraining the lower bound of import reactions for each of the compounds present in the medium (as reported in The reliability of the obtained predictions was then estimated according to the following parameters:μKO) and the one of the wild-type (μWT) as:Using genome-scale metabolic networks (GEMs), gene knockouts can be simulated to identify those genes whose removal is likely to impair the organism’s growth. Specifically, it is possible to simulate mutants by deleting each gene included in the metabolic reconstruction and testing the predicted effects on the microbe’s growth. Through this strategy, it is possible to estimate the contribution of each gene to the fitness of the strain by calculating the growth ratio (GRratio) between the growth rate of the mutant model  algoritp-value cutoff of 0.001) to each gene in the model. The COG annotation for each gene associated with variable reactions during the transition between two niches was extracted from the WebMGA server. Biases were determined after standardizing by the number of genes in each class of variable genes. Statistical significance was determined using Pearson’s Chi-squared tests. The complete list of COG annotations is available as The WebMGA web server  was usedP. parmentieri to produce biomass on utilized carbon sources. In particular, after verifying their presence in the metabolic reconstruction, we tested 91 compounds previously used in PM experiment. Details on the predictions of metabolites usage (before and after refinements) are given in FBA was employed to test if the reconstructed model could accurately predict the ability of P. parmentieri model was termed iLP1245, by the nomenclature standard [At the end of that refinement round, the final standard . It inclF-score , and is available as Systems Biology, providing a unique, unambiguous, perennial, standard-compliant, and directly resolvable Markup Language (SBML) file of the model was validated by the online SBML validator tool )  together with xanthine  and E. coli (71.06% of agreement). Also, these findings support the biomass of P. parmentieri being approximated to that of E. coli . We have also established of growth curves of P. parmentieri SCC3193 with randomly chosen carbon sources also analyzed in PM Microarray experiment, namely M9 medium supplemented with α-d-glucose, d-fructose, and d-xylose. In general, the obtained results agree with the qualitative model’s predictions ) . The bacxanthine . Khayi eeriments . Besideseriments . Those reviously . A compadictions .By analyzing flux changes in response to simulated environmental conditions , it is possible to observe whether any significant metabolic rewiring occurs. Loopless FBA and loopless FVA see  were useThe total amount of reactions taken into account was 208, corresponding to ~10% of those embedded in the reconstruction. These were further classified in reactions with increased/decreased flux, and reactions turned on/off because of the environmental change . NotablyP. parmentieri, where these bacteria can thrive.Based on that criterion, it appears that several pathways remain active in both examined conditions, though recruiting a different set of reactions. The primary distinction seems to occur in sugar metabolic pathways , trackinP. parmentieri SCC3193 to the disparity of environmental  setting. These compounds can either be precursors for volatiles compounds (VOCs) production or enter in VOC metabolic pathways; e.g., butanoate is an ester of butyric acid, which is among the most frequently secreted compounds [Also, some peculiar systems are turned on  in concoompounds ,40. VOCsompounds ,40. If sSinorhizobium meliloti model [P. parmentieri SCC3193, only 10% of reactions undergo a 50% flux change when comparing soil vs. rhizosphere; however, in S. meliloti, in the same conditions, more than the 20% of flux reactions was affected (including reactions which reversed flux direction). Moreover, in S. meliloti, ~13% of active reactions were specific to just one of the environments, while only 5.3% of active reactions were environment-specific in P. parmentieri.iLP1245 results in soil and rhizosphere were compared to those of the ti model , a plantP. parmentieri compared to the S. meliloti genome  allows a reduced metabolic redundancy for the first compared to the second [P. parmentieri SCC3193 possesses an essential gene core composed of 241 metabolic genes, allowing growth in both soil and rhizosphere. Here, a set of eight genes was found to be essential in soil but not in the rhizosphere. This is in vast contrast to S. meliloti, where 66 genes were found as essential for growth in the same simulated rhizosphere environment [P. parmentieri SCC3193, which may allow the strain to rapidly accommodate relevant changes in environmental nutrient sources. Additionally, we can assume that the dense core of essential genes important for P. parmentieri allows bacteria of this species to quickly and effectively adapt to changing environmental conditions. This might explain why bacteria from this species can persist on plant residuals without interacting with host plant (potato) for extended periods, and subsequently, also the cosmopolitan lifestyle of this bacteria in the environment [We can hypothesize that the smaller and more compact genome of e second  and a moironment , and supironment .Simulated single gene deletion is a very powerful in silico method for estimating a gene’s knock-out fatality in as many settings as desired. Simulations on iLP1245 were made by using MOMA algorithm in three different media.The number of essential genes found is 251 in M9 medium, 250 in soil, and 245 in the rhizosphere. The locus tag of such genes, alongside with their corresponding encoded protein, are reported in Three condition-specific genes have been predicted, one during growth in M9 medium, W5S_RS19605, encoding for class II fructose-bisphosphate aldolase (WP_014701539.1) and two during growth in the rhizosphere, W5S_RS19610 and W5S_RS06525, respectively encoding for a phosphoglycerate kinase (WP_005973111.1) and a long-chain fatty acid transporter (WP_014699104.1).Pectobacterium carotovorum PC1 [P. parmentieri in all the simulated conditions, while seven are specifically essential in M9.Moreover, we evaluated how the predicted EGs in iLP1245 compared to those predicted by the GEM reconstruction of orum PC1 , a closeImportantly, we found out that nine genes, predicted as essentials by both models  of this plant pathogenic bacterium (iLP1245).Plant–microbe interactions have been under intensive investigation in recent years, regardless of the nature of this communication: pathogenic or symbiotic. The ability to understand this interplay is connected with the complexity of the environment in which bacteria persist: soil, rhizosphere, or plant tissues. Metabolic modeling allows predicting and examining biochemical reactions involved in adaptation to the above-mentioned ecological niches as well as predicting the phenotypic outcomes of gene deletions. In this paper, for the first time, we report a high throughput experimental validation on the metabolic capability of Pectobacterium carotovorum PC1 GEM showed an agreement of 80.4% with Phenotype Microarray (Biolog) experiments [iLP1245 is highly reliable, obtaining 91% overlap in silico with large scale experimental data on carbon utilization phenotypes. This value perfectly fits with the currently accepted standard for GEMs ,44; for eriments .Moreover, we presented a broad set of EGs to be experimentally tested, nine of which are very likely to be used for bactericide design. This last result is significant since, at the moment, there is no available, efficient method for plant pathogenic bacteria eradication.