E. coli and S. cerevisiae with available experimental concentrations were utilized to develop a SVM model of the negative logarithm of the concentration (-logC). In this statistic model, in addition to common descriptors of molecular properties, two special types of descriptors including metabolic network topologic descriptors and metabolic pathway descriptors were included. All 1997 descriptors were finally reduced into 14 by variable selections including genetic algorithm (GA). The model was evaluated through internal validations by 10-fold and leave-one-out (LOO) cross-validation, as well as external validations by predicting -logC values of the test set. The developed SVM model is robust and has a strong predictive potential . An effective tool could be provided by this analysis for the large-batch prediction of the intracellular concentrations of the micro-organisms’ metabolites.With the rapid growth of micro-organism metabolic networks, acquiring the intracellular concentration of microorganisms’ metabolites accurately in large-batch is critical to the development of metabolic engineering and synthetic biology. Complementary to the experimental methods, computational methods were used as effective assessing tools for the studies of intracellular concentrations of metabolites. In this study, the dataset of 130 metabolites from  There are a large amount of experimental methods for identifying both metabolite concentrations and flux direction with the rapid development of high-throughput LC-MS technology and other analytical technologies. However, as the intracellular concentration of most metabolites is quite low (at the level of micro-mole), it is difficult to determine them by current precise analytical instruments2, especially in mass determination. It is still a huge challenge for experimental measurements in microenvironment, especially in microorganism cells. On the other hand, to enrich the intracellular metabolites is still quite difficult and may decrease the accuracy of measurement. Recently, there are an increasing number of computational and mathematical models for simulations of the cellular metabolism in metabolic engineering and synthetic biology6, which require the metabolites’ concentrations as basic parameters. In addition, metabolite concentration can also be used as a criterion for antibacterial discovery7, which further increases the demand for concentration determination.Metabolic engineering has been widely used to raise the outputs of many significant small chemicals in different industrial and daily products, such as food, beverages, medicine and even enzymes. No matter these molecules are natural or synthesized, in order to increase the metabolic flux of the target molecules9. Commonly, based on the second law of thermodynamics, establishing a theoretical model requires quantitative information like metabolite concentration or metabolic fluxes, which are always interrelated. According to thermodynamics laws, a chemical reaction follows the Van’t Hoff equation as below  method to predict intracellular metabolite concentration, but the NET method needs Gibbs free energy of the metabolic reaction as the prior condition, which greatly limits its application scope because of the difficulty in Gibbs free energy measurement10. Hamilton et al. developed a method named thermodynamics-based metabolic flux analysis (TMFA), which is the developed from the general flux balance analysis and thermodynamic constraints analysis11. The TMFA method can predict an approximate range of metabolite concentrations based on relatively few information, which is more suitable for qualitative analysis rather than accurately quantitative analysis.However, few theoretical methods have been developed by far for prediction of the intracellular concentrations. As well known, absolute metabolite concentration is a bridge to a quantitative understanding of cellular metabolism, as concentrations affect both the free energies and rates of metabolic reactionsEquation ,1\documeet al. with combination of bioinformatics and cheminformatics8. It is possible to develop theoretical methods for predicting the intracellular concentration. Bar-Even et al. found that the hydrophobicity and charge of metabolites has great influence on the intracellular concentration of metabolites, and initially established a rough linear model for metabolites concentration prediction based on physical and chemical properties for the first time12. In the following researches, Zhu et al. found that there is a certain correlation between biological phenotype and metabolic network topology, in addition to physical and chemical properties, based on which a support vector machine model was established to predict the intracellular metabolite concentrations8. However, while the squared cross-validation correlation coefficients (Q2) of this model reached 0.59 in the internal validation in E. coli, it had no external validation with independent test set.At the same time, prediction of metabolites’ concentrations was also attempted by statistical methods. With the rapid development in microorganism metabolic network, the relationship between metabolic network and chemical reactions was explored after reconstructing metabolic network. Since most biological metabolites are small molecules and metabolic processes are basically chemical reactions, to some extent, the metabolic network organization has chemical basis, which was discussed by Zhu 12, less biological information was employed to predict metabolite concentrations. In this paper, in addition to molecular descriptors of structural and physicochemical properties, topological parameters in organism specific metabolic network and a novel type of parameters describing the involvement of a metabolite in specific pathways were utilized to improve the predictive performance, with discussion on their biological meaning. All these variables were applied to variable selection using genetic algorithm (GA)15 and an additional optimization procedure. Incorporated with the support vector machine algorithm20, the variable set was iteratively optimized to build the final prediction model21 which has the best performance in internal validation. The final model was then applied to the external validation in the test set, which was randomly selected from a dataset containing the experimental concentrations of 130 metabolites from two kinds of microorganisms, 93 metabolites from E. coli9 and 37 metabolites from S. cerevisiae22.In addition to molecular properties, it might be helpful to utilize information of metabolites’ biological functions in the concentration prediction, since the functions are generally affected by the biological characters like the intracellular concentrations. However, in the earlier studies2) between computational and experimental values is increasing. Until in the fifth round, when the number of selected variables m is lowered to 14, the best performances are obtained both in 10-fold cross-validation with RMSE = 0.741, Q2 = 0.55 and in LOO method with RMSE = 0.730, Q2 = 0.57. In the next three rounds, the performances of the models are not improved, but drop a little bit. And in the ninth round, the dramatic increase in RMSE to 0.853 and decrease in Q2 to 0.41 in 10-fold cross-validation, as well as to 0.828 and 0.45 in LOO respectively, convinced us to stop further attempts.After a preprocessing, the variable selection is performed on 1669 variables step by step, including three rounds of GA selection and a further optimization procedure. The results are evaluated by both 10-fold cross-validation and leave-one-out (LOO) validation, as shown in Table Generally the variables’ number is fewer in a model, the model quality is much better. However, taking into account the number of training set samples, 91 is much bigger than the number of variables, 14, finally 14 descriptors are picked out as the variable set for the final SVM model described below. Detailed information about the 14 variables is shown in Table In Table et al.8 or Bar-Even et al.12.As we can see from Tables 8, in addition to the parameter Degree, this optimal variable set contains a metabolic network topology variable named as Clustering-Coefficient, which represents the density of the network around the specific nodes. In graph theory, Clustering-Coefficient reflects the tendency of nodes to cluster together. If one node is connected with n other nodes, Clustering-Coefficient is the ratio of the number of edges between n nodes and the maximum number of potential maximum edges . In both figures, the average metabolite concentrations are positively correlated with the average Clustering-Coefficient of the group of metabolites. Combining with the correlation coefficients in Table As in Fig. The third type of variable is metabolic pathway variable. As in Table 24, although not strictly linear. This positive correlation was shown in Fig. 2 as high as 0.838, suggesting that metabolites more polar and thus more water-soluble may have higher intercellular concentrations. Therefore, the metabolite concentration could be predicted by chemical basis, at least partially.The reason why the pathway variables of these five non-core pathways are retained in the final model might be explained by the deviation of the concentrations and polarities of the 14 metabolites in these pathways from the average level. In microbe metabolic pathways, the concentration and the polarity of the metabolites are two key points. In intracellular micro-environment, the concentration and polarity of the metabolites are generally positively correlatede of the 14 metabolites in E. coli and S. cerevisiae are 2.77 and 2.32, respectively, much smaller than the overall average value as 3.57 of all the 130 metabolites in both organisms. It means that these 14 metabolites may have a lower polarity, but a higher concentration. This is a good example that the positive correlation between polarity and concentration of metabolites may vary in certain pathways of metabolites, such as the 14 metabolites in the five pathways of MPF descriptor. Therefore, this MPF descriptor can provide a contribution to complement the concentration prediction as an important variable.In Table 25 based on 91 samples in training set, and then it was tested by the independent test set containing 39 samples. The confidence interval of 20 randomized trials is: R2 = 0.75 ± 0.02, RMSE = 0.746 ± 0.020, Q2 = 0.54 ± 0.05; R2p = 0.56 ± 0.05, RMSEp = 0.744 ± 0.041, Q2p = 0.53 ± 0.05. The performance of the model is evaluated by randomly choosing one result as in Table Q2 = 0.55 and RMSE = 0.74 in the 10-fold cross-validation and Q2 = 0.57, RMSE = 0.730 in the LOO cross-validation, which both suggest a good stability. Moreover, in the external validation by the independent test set, the predicted vs experimental –logC values have Q2p = 0.58, R2p = 0.586, RMSE = 0.702, which also proved the good reliability of this model. In order to further demonstrate the robustness of the model, the variance of prediction error in the LOO cross-validation and the independent test was calculated as 0.53 and 0.52, respectively. Compared with previous studies, our model was further testified by external validations and showed good reliability.The prediction model of metabolite concentration employing 14 descriptors was built by SVM regressionBacillus subtilis26, which did not exist in our training set, were used to test the generalization ability in other micro-organisms, getting a good result of RMSE = 0.71. However, this data set comes from a different experimental protocol that may lead to some systematic error in the concentration prediction.In addition, 11 metabolites from The plot of the predicted vs. experimental –logC values is shown in Fig. The distribution of prediction errors is shown in Figure 27, which was shown as Williams plot in Fig. h* of 3 × 14/91 = 0.46. From Fig. In this paper, leverage method was used to define the application domain of the prediction model28. Therefore, if the prediction error of –logC is less than 1, it may be accepted in predicting intracellular metabolite concentration. In the earlier study by Zhu and his coworkers8, the model has sample size n = 80, R2 = 0.683, Q2 = 0.59, RMSE = 0.729, as in Table 2 = 0.740, Q2 = 0.57, RMSE = 0.730. Furthermore, our model was validated by predicting an independent test set containing 39 metabolites, and resulted in R2p = 0.586, Q2p = 0.58, RMSEp = 0.702.According to the results shown above, based on GA variable selection, 14 variables were picked out to build the final prediction model. The metabolite concentrations oscillate during different phases of life. For example, during the life cycle of a yeast cell the amplitude of metabolite oscillation is usually within 10-fold, with a median of 2.4-foldE. coli and S. cerevisiae. In order to evaluate the possible influence from same metabolites in both training and test sets, we reconstructed the models with a non-overlap strategy that the same molecules from E. coli and S. cerevisiae were divided both into the training set, or both into the test set, so as to prevent possible similarity between the training set and the test set. As shown in the Table When randomly separated the metabolites into the training sets and test sets, there may be some overlaps of metabolites in both E. coli, so their model may only fit well for this species. When the 130 compounds in the data sets including both E. coli and S. cerevisiae were put into one model together using Zhu’s modeling method, the Q2 of the reconstructed model could only achieve 0.46 in training set and 0.44 in test set, both less than 0.50. Our SVM model results in Q2 as 0.57 for the training set and 0.58 for predicting the test set, that is, greater power in fitness and reduced predictive error.In addition, compared with Zhu’s model, our model has a strong advantage in generalizability to different organisms. It is well known that the distributions of metabolite concentrations in different species are quite different, so they are difficult to be completely determined by the degree of metabolic networks only. Zhu’s model was only based on the dataset of Obviously, the performance of Zhu’s model could be explained well mainly by one network topological parameter, Degree. Beside Degree, Clustering-Coefficient is another important topological variable, which represents the density of metabolic reactions around the specific metabolites. In our predictive model, Clustering-Coefficient was found to also give some effective information of the differences between the distributions of metabolites’ concentrations in different organisms, and may help to improve the performance of the predictive model.30. Therefore, as shown in Fig. Furthermore, in this paper metabolic pathway variables were employed to predict metabolite concentration and to explain the complex relationship between the intracellular metabolite concentrations and molecular physicochemical properties. In the evolution, energy metabolism, synthesis and decomposition pathways are most critical. The metabolites in these core pathways generally have higher polarity to reach relative higher concentrations in the hydrophilic environment of plasma. However, some other intracellular metabolic pathways involving different hydrophilic environment, such as signal transduction pathways and the pathways of degradation of harmful substances in the environment, are also important for organisms to adjust the metabolism to external environment. In these non-core pathways, metabolites still need a relatively high concentration to maintain their biological function, despite their lower polarity. For example, in signal transduction, metabolites need lower polarity to pass through the phospholipid bilayerThe procedure to develop the models between the metabolite intracellular concentrations and the descriptors includes data collection, metabolic network reconstruction, descriptor calculations, variable selections, model development and validation, and test on application domain, as shown in Fig. 32. In the network reconstruction, the metabolic network was automatically reconstructed based on the reactant pairs from KEGG reaction database33, which were screened by two criteria. Firstly, reactant pairs which did not appear in the metabolic pathway of the organism should be deleted. Secondly, reactant pairs containing metabolites with unspecified residues were deleted32. In the reconstructed metabolic network, each node represents a metabolite while each edge represents a substrate-product relation, which was introduced in details by Ma et al.32. The reconstructed metabolic network of E. coli was consisted of 1002 nodes (metabolites) and 1424 undirectional edges (substrate-product relations), while the one of S. cerevisiae was consisted of 2563 nodes (metabolites) and 3123 undirectional edges (substrate-product relations).Metabolic network of organisms was reconstructed by Python-2.7 scripts35. And 1850 molecular descriptors were calculated by E-DRAGON38. These molecular descriptors involve different categories including topological, geometrical, electrostatic and other physicochemical molecular properties of the metabolites.The metabolic network topology parameters were calculated by Network Analyzer Plugin in Cytoscape-3.3.0E. coli and S. cerevisiae in the KEGG pathway database33. If one metabolite presents in one of these metabolic pathways, its variable for this pathway is defined as 1, otherwise it is recorded as 0. After removing pathway variables that have the same values for almost all metabolites in our dataset described below, the remaining variable set consists of 81 pathway variables which are subject to further variable selection as below. The metabolic pathway variables remaining after variable selection were used to define a new variable, named as MPF (Metabolite Pathways’ Feature) descriptor. If one metabolite participated in any one reaction among the metabolic pathways corresponding to the selected pathway variables, the MPF descriptor of this compound was defined as 1, otherwise it is defined as 0.Pathway variables were chosen as another type of descriptors work to describe the involvement of the metabolites in specific metabolic pathways. There are 116 pathways in 39. Due to the absence of some type of atoms in all the metabolites of our dataset, some molecular descriptors’ values might be zero or a constant for all molecules, and these descriptors were abandoned. Then the descriptors with a standard deviation of <0.001 should also be abandoned because of too little statistical meaning. After the preprocessing procedures, 1669 descriptors were retained from the 1977 descriptors.Variable selection was performed prior to the construction of the predictive model in order to eliminate redundant variables and pick out the optimal ones40. The confidence intervals of model results are calculated with 20 times.The 130 metabolites are randomly separated into a training set of 91 samples and a test set of 39 samples13 is used for variable selection for the training set. Three rounds of selections by GA were executed. In each round of variable selections, the population size of each generation is 100, the maximum generation is 100, and the mutation rate is 0.01, and the optimization objective is RMSE 41 is applied to filter out irrelevant variables from the 70 descriptors for the training set. In this method, one descriptor which had been deleted from the original 1669 descriptor set was added back into the 70-variables set to construct a new (70 + 1)-variables set, or one descriptor was deleted from these 70 descriptors to obtain a new (70 − 1)-variables set. When one descriptor was added to or deleted from the variable set, the effect of the change was evaluated according to the RMSE  and the optimal set of descriptors for the 91 samples in the training set was developed by the support vector machine (SVM) regression method, using the kernlab package in RQ2)  and RMSE (RMSEp) are employed to evaluate the predictive power of the model.The model developed from the training set was then externally validate by predicting the –logC values of the 39 metabolites in the test set. And in addition, 11 metabolites from 47, hi (Equation X is the n × k matrix of k variable values for n training set metabolites, and xi is the ith row vector of X. The superscript “T” refers to the matrix/vector transpose. The control leverage h* is set as 3k/n.The Williams plot for the SVM model is defined by leverageEquation , to illu2 and RMSE values by the GA variable selection procedures. These descriptors were significant in construction of a SVM regression model, based on a data set of 130 metabolites of the E. coli and S. cerevisiae, which was randomly separated into a training set of 91 samples and a test set of 39 samples. The results of internal LOO and 10-fold cross-validation indicated that the model is robust, while the external validations on the test set showed good prediction powers. Therefore, this SVM model might be useful for prediction of the intracellular metabolites concentration with a well-defined application domain when experimental values are difficult to be acquired.Combining chemical descriptors, topological parameters and metabolic pathways descriptors, a machine learning model can be constructed to predict the metabolite concentration with relative reliability. 14 optimal descriptors are effectively derived from a great amount of DRAGON descriptors and metabolic pathways descriptors according to QSupplementary information


In this article, we present a protocol for generating a complete  metabolic resource allocation model, as well as a proposal for how to represent such models in the systems biology markup language (SBML). Such models are used to investigate enzyme levels and achievable growth rates in large-scale metabolic networks. Although the idea of metabolic resource allocation studies has been present in the field of systems biology for some years, no guidelines for generating such a model have been published up to now. This paper presents step-by-step instructions for building a (dynamic) resource allocation model, starting with prerequisites such as a genome-scale metabolic reconstruction, through building protein and noncatalytic biomass synthesis reactions and assigning turnover rates for each reaction. In addition, we explain how one can use SBML level 3 in combination with the flux balance constraints and our resource allocation modeling annotation to represent such models. Escherichia coli ” as main id . Of course, the user can choose these ids freely, but following these suggestions can help with easier evaluation of the model in deFBA implementations.If the enzyme is acting in only one compartment we choose its fbc:id, fbc:label, and fbc:associatedSpecies. The fbc:id must be unique among the fbc:geneProduct elements. Hence, we suggest using the same naming conventions as with the macromolecules. Following the Flux Balance Constraints package version 2 specifications, the fbc:label field is currently unused. Hence, we suggest to insert the recipe for the creation of the enzyme(-complex) in this attribute. Lastly, the fbc:associatedSpecies attribute contains the id of the biomass species associated with this gene product and used for bounding the flux. An example is ETrans2 from the toy model in Each catalytic macromolecule is not only present as a species, but also as a gene product, with the fields fbc:geneProduct fbc:id="Etrans2" fbc:label="1*GTRANS2 AND 1*GTRANS3"fbc:associatedSpecies="Etrans2"/>		<idreversible fast listOfReactants (may be empty)listOfProducts (may be empty)In the SBML specification, all reaction objects must contain the fields:fbc:geneProductAssociation and a ram:reaction annotation. Additionally, we recommend adding the EC number, if known, to the reactions in the form of a MIRIAM annotation [Depending on their type, reactions also include a notation :rdf:RDF xmlns:bqbiol="http://biomodels.net/biology-qualifiers/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">  <rdf:Description rdf:about="#thx1138">    <bqbiol:isVersionOf>       <rdf:Bag>         <rdf:li rdf:resource="http://identifiers.org/ec-code/2.7.1.17"/>       </rdf:Bag>    </bqbiol:isVersionOf>  </rdf:Description></rdf:RDF><id must be unique for each reaction. For reactions producing biomass we recommend starting the id with “synth_” for easier reading and reduced chance of assigning the same id multiple times. Furthermore, this makes it easier to distinguish between the reactions from the original metabolic network model and the deFBA additions. The fast attribute will be removed in SBML Level 3 Version 2 (L3V2). Hence, we set it to “false” for now and we will delete it all together in an upcoming version once SBML L3V2 is released.The fbc:geneProductAssociation is used to map catalysis relationships between the enzymes  and the reactions. The fbc:geneProductAssociation supports the inclusion of multiple fbc:geneProductRef elements connected by fbc:and and fbc:or nodes. While this can certainly be useful for certain model types, we decided to create the complex enzymes as stated above to eliminate the fbc:and connections and save the gene codes in the fbc:labels. Instead of including fbc:or elements for isoenzymes, we copy the catalyzed reaction until each reaction is catalyzed by exactly one fbc:geneProductRef , and hence their rates are not constrained by any enzyme.The tRef cf. . This waram:reaction annotation, in the attributes ram:kcatForward and ram:kcatBackward respectively. All irreversible reactions must have their reversible flag set to “false” and thus their ram:kcatBackward must be set to “zero”. Typically, the values in the kcatForward and kcatBackward are defined as parameters, and in these fields the ids of the respective parameters are stored, as in the examples below.The forward and reverse ram:maintenanceScaling inside the ram:reaction field, which specifies the coefficient Lastly, we explain how to handle maintenance in the SBML representation. Maintenance reactions are typically part of metabolic resource allocation models, since these models do not account for all energy expenditures of the cell. The maintenance reactions we consider are all biomass-associated, i.e., the flux forced through them is dependent on the total biomass at each time point using a fixed coefficient equality . Thus, wreaction id="Metab1_2" reversible="false" fast="false">  <annotation>    <ram:RAM xmlns:ram="https://www.fairdomhub.org/sops/304">      <ram:reaction ram:kcatForward="kcat2" ram:kcatBackward="zero"      ram:maintenanceScaling="zero"/>    </ram:RAM>  </annotation>  <fbc:geneProductAssociation fbc:id="Emetab2">    <fbc:geneProductRef fbc:geneProduct="Emetab2" />  </fbc:geneProductAssociation>  <listOfReactants>    <speciesReference species="N" stoichiometry="1" constant="true"/>  </listOfReactants>  <listOfProducts>    <speciesReference species="AA" stoichiometry="1" constant="true"/>    <speciesReference species="ATP" stoichiometry="1" constant="true"/>  </listOfProducts></reaction><For the maintenance reaction we have the representationreaction id="Maintenance" reversible="false" fast="false">  <annotation>    <ram:RAM xmlns:ram="https://www.fairdomhub.org/sops/304">      <ram:reaction ram:kcatForward="zero" ram:kcatBackward="zero"      ram:maintenanceScaling="main"/>    </ram:RAM>  </annotation>  <listOfReactants>    <speciesReference species="AA" stoichiometry="50" constant="true"/>    <speciesReference species="ATP" stoichiometry="60" constant="true"/>  </listOfReactants>  <listOfProducts>  </listOfProducts></reaction><Dynamic resource allocation models have emerged in recent years as a means of extending the predictive capabilities of constraint-based models. Such resource allocation models allow investigating how dynamics of the extracellular environment are reflected inside the metabolism in the form of cost-benefit tradeoffs of active pathways. This is achieved by extending FBA models in two directions: (i) accounting for the costs of producing enzymes before these can be used to catalyze their corresponding reactions; and (ii) taking a dynamic perspective where the levels of these enzymes change over time in response to changes in the environment.Such models have a larger predictive power and can predict complex biological behaviors such as the catabolite repression as demonstrated in . MoreoveAlthough their large predictive power has already been demonstrated, it is not possible to use resource allocation models for studying all biological processes connected to the metabolism. For instance, such models always assume optimality of growth. A scenario where this may not be an accurate assumption is starvation, when the objective changes more towards maximizing survival rather than growth. Moreover, starvation triggers different kinetics of nutrient uptake, where stochastic effects and substrate affinity may play a role. It is, therefore, no longer sufficient to consider only upper bounds on uptake rates based on enzyme amount and turnover rate, as done in resource allocation models.Another limitation of such models may also lie in the fact that, so far, they do not incorporate any kind of regulatory logic that may also have an impact on the metabolic strategies, in addition to the resource tradeoffs. Some resource allocation models such as ME models indeed go a step beyond modeling translation costs and also model transcription. Others, like deFBA or RBA, include such costs in the form of biomass composition constraints and maintenance.E. coli [T. maritima [B. subtilis [S. elongatus [https://bitbucket.org/hlindhor/defba-python-package; MATLAB implementation is available at https://github.com/alexandra-m-reimers/deFBA). We therefore felt there is a need in the community for a guideline for building such models as well as a means to exchange them without losing information.So far, there exist ME models of E. coli  and T. mmaritima , an RBA subtilis ,57, and longatus . Moreovelongatus  and deFBTherefore, we have provided in this article a list of ingredients of such models, together with links to the relevant databases where parameters can be found. Every step of this guideline can be automated except for (i) production of enzyme complexes for which the subunit stoichiometries cannot yet be automatically retrieved from online databases; (ii) adjustment of protein quota requirements, which needs a quantitative proteomics dataset; and (iii) maintenance requirements for adjusting growth rate.With respect to exchanging metabolic resource allocation models, we have provided here a specification that is based on SBML and the flux balance constraints package. This specification covers deFBA and RBA models and we believe that it can be extended to also incorporate ME models.We believe that our contribution will help extend the use and number of metabolic resource allocation models as well as exchange of these models among researchers.


Escherichia coli and relative concentrations of > 7,000 intracellular metabolite ions. Beyond expected metabolic changes in the proximity to abolished enzyme activities, the association map reveals a largely unknown landscape of gene–metabolite interactions that are not represented in metabolic models. Therefore, the map provides a unique resource for assessing the genetic basis of metabolic changes and conversely hypothesizing metabolic consequences of genetic alterations. We illustrate this by predicting metabolism‐related functions of 72 so far not annotated genes and by identifying key genes mediating the cellular response to environmental perturbations.Metabolism is one of the best‐understood cellular processes whose network topology of enzymatic reactions is determined by an organism's genome. The influence of genes on metabolite levels, however, remains largely unknown, particularly for the many genes encoding non‐enzymatic proteins. Serendipitously, genomewide association studies explore the relationship between genetic variants and metabolite levels, but a comprehensive interaction network has remained elusive even for the simplest single‐celled organisms. Here, we systematically mapped the association between > 3,800 single‐gene deletions in the bacterium  To enable analysis of more than 34,000 injections, we used high‐throughput, flow‐injection analysis on an high‐resolution, time‐of‐flight (TOF) instrument  overrepresentation of enzyme deletions yielding the largest metabolic changes up to two enzymatic steps distance. Local metabolic changes are reabsorbed already at a distance of three, after which the reduced probability of finding the large metabolic changes remains constant.Generally, metabolites are expected to change in proximity of reactions that were directly affected by gene deletions because of perturbed metabolic fluxes  and membrane transport  and citrate/aconitate in the TCA cycle, highlighting the dichotomous role of iron as an essential modulator of aconitase activity  or between the NADH:ubiquinone oxidoreductase (nuo) and the fumarate reductase (frd) complexes. Thus, similar metabolic profiles indeed reflect functional dependencies between genes.The observed occurrence of widespread metabolic responses to specific genetic perturbations complicates the interpretation of metabolomics data, in particular from mutants lacking a gene with unknown function. We wondered whether the metabolome profiles are sufficient to characterize the genetic lesion regardless of prior information on network structure or metabolic proximity. We therefore determined the metabolome similarities between mutants lacking genes encoding for (i) complementary partners of protein complexes or (ii) isoenzymes. Although not all genes are expected to be relevant under the tested condition, we were able to recover significant fractions of both types of functional relationships using the context likelihood of relatedness (CLR) algorithm  even in the best‐characterized species , with several differential metabolites in the coenzyme A biosynthesis pathway, including (R)‐pantoate and (R)‐pantothenate. Consistent with our data, YhhK was found to be an acetyl‐transferase activating PanD and recently annotated as PanZ . In addition, the top ranked differential ions observed in the ygfY mutant are dominated by succinate annotated ions . Furthermore, most strongly affected metabolites include various sugar derivates including UDP‐galactose, 3‐deoxy‐D‐manno‐2‐octulosonate, or tagatose 6‐phosphate , and we consistently observed changes in different metabolites related to carbohydrate metabolism, such as sedoheptulose‐7‐phosphate or d‐sorbitol 6‐phosphate. Additionally, four genes of the d‐galactonate degradation pathway  were among the top ten correlating genes in the M3D database  and stresses (osmotic and deoxycholate). By comparing the metabolic response to an external perturbing agent to the metabolome profiles of individual gene deletions, we tested the ability to recover genes mediating the adaptive response of E. coli to sudden environmental changes  or the release of iron into the cytosol (fes). While the function of these genes might suggest a direct role of iron, growth experiments under iron limitation and deoxycholate stress demonstrated that iron depletion per se is not sufficient to confer deoxycholate resistance  were surprisingly insensitive to deoxycholate stress in minimal medium containing exogenous enterobactin  in 3,807 An important result of our analysis of the gene–metabolite map is that marked metabolite changes can occur distant from the genetic lesion, even when enzymes with known catalytic functions were deleted and no global growth defect was detected. Hence, the topology and connectivity defined by the metabolic network are not sufficient to explain and predict the impact of gene deletions on the overall cell/metabolic phenotype. While these distal gene–metabolite interactions remain largely elusive to explain at this point, they can be interpreted as metabolic fingerprints of gene function and in our study were used to (i) predict the enzymatic functions of y‐genes even in the absence of growth phenotypes and (ii) establish a metabolic link between gene and their functional role in mediating the cell response to environmental perturbations.Altogether, we hypothesized metabolic function for 72 y‐genes, of which YidK and YidR were experimentally validated. Our metabolome compendium also provides a key to interpret metabolic changes induced upon perturbations other than gene deletions. By establishing an indirect link between common metabolic changes induced by gene deletions and external perturbations, we predicted non‐obvious mediators of the cellular response to deoxycholate treatment, which involved genes in iron metabolism.et al, et al, The two above examples of functional gene annotation and predicting genes involved in cellular responses demonstrate the utility of the association map for generating novel lead hypotheses on the functional roles of genes in metabolism. Because of the large number of tested genetic lesions and covered metabolites, the map constitutes a unique resource to inspire new and less conventional approaches to predict the mode of action of genetic and environmental perturbations. Moreover, the large number of newly revealed gene–metabolite associations paves the road to explore so far unknown functional and regulatory interactions beyond those represented in current genome‐scale metabolic models .Escherichia coli wild‐type and 4,320 deletion mutants 2SO4, 14.7 mg CaCl2·2H2O, 246.5 mg MgSO4·7H2O, 16.2 mg FeCl3·6H2O, 180 μg ZnSO4·7H2O, 120 μg CuCl2·2H2O, 120 μg MnSO4·H2O, 180 μg CoCl2·6H2O, 1 mg thiamine·HCl. Culture volumes of 1 ml were incubated in 96‐deep well plates at 37°C with shaking at 300 rpm. Growth was followed via absorbance at 600 nm measured at four time‐points, and all samples were harvested during mid‐exponential growth phase by centrifugation for 10 min at 0°C and 2,200 g. Cell pellets were immediately extracted with 150 μl preheated water containing 2 μM reserpine and 2 μM taurocholic acid for 10 min at 80°C and occasional vortexing. This extraction broth was centrifuged for 10 min at 0°C and 2,200 g, and supernatants were stored at −80°C until further analysis. mutants  from thes et al,  were groet al, m/z 50 to 1,000 with a frequency of 1.4 s for 2 × 0.48 min (double injection) using the highest resolving power (4 GHz HiRes). Source temperature was set to 325°C, with 5 l/min drying gas and a nebulizer pressure of 30 psig. Fragmentor, skimmer, and octopole voltages were set to 175 V, 65 V, and 750 V, respectively.The analysis was performed on a platform consisting of an Agilent Series 1100 LC pump coupled to a Gerstel MPS2 autosampler and an Agilent 6520 Series Quadrupole TOF mass spectrometer  as described previously . The resulting matrix lists the intensity of each mass peak in each analyzed sample. An accurate common m/z was recalculated with a weighted average of the values obtained from independent centroiding. Because mass axis calibration is applied online during acquisition, no m/z correction was applied during processing to correct for potential drifts. After merging, 3,169 and 4,365 common ions were obtained for negative and positive mode, respectively, which were annotated based on accurate mass using 3 mDa tolerance 2, +(H2PO4K)2, +(H2PO4)2NaH, and +(H2PO4)2KH. Putative coverage of E. coli metabolism is shown in Fig All steps of mass spectrometry data processing and analysis were performed with MATLAB  using functions embedded in the Bioinformatics, Statistics, Database, and Parallel Computing toolboxes as described previously  correction of intensity drift throughout sequential injections within one plate by a low pass filter over a moving window of five injections, (ii) correction of intensity drifts across plates due to regular instrument cleaning procedures, extraction effects or long‐term ionization drifts by normalizing the mean of every ion to be equal over all plates, (iii) correction of intensities for harvest OD dependencies using a locally weighted scatterplot smoothing (LOWESS). This procedure was used to perform a model‐free estimation of the ion‐specific dependency with harvest ODs. The local least square regression was employed to normalize for OD effects as follows:z‐score is then used to select for significantly and specifically affected ions according to:i and j denote ion and samples, respectively, and median as well as standard deviation (std) refers to all intensities of ion i in the entire dataset. Modified z‐scores referring to technical and biological replicates are summarized into a unique median modified z‐score, and three additional mutants were removed from the dataset having inconsistent z‐score among the replicates , resulting in a final data set with 3,807 mutants. Notably, no internal standards were used because they are not suited to normalize thousands of mostly unknown chemical entities.z‐scores  was compared with a permuted one (Sperm) obtained by randomizing 1,000 times the original distance matrix. P‐value was empirically estimated as follows:A genome‐scale network model of ymes Fig , we usedg is the selected gene and d is the distance (from 1 to 5).This first statistical analysis revealed a tendency of enzyme deletions, often within amino acids biosynthetic pathways, to exhibit larger metabolic changes within one to two enzymatic steps Figs  and EV4.et al, et al, For transcription factors (TFs), we augmented the metabolic network with the connections between TFs and their known metabolic enzyme targets extracted from RegulonDB  is computed. Weights are a function of the inverse of the squared distance between the gene i and the metabolite m and the z‐test P‐value associated with the ion measurements (measure of confidence of metabolite measurements). We performed a permutation test with randomly shuffling of the distance matrix D (Drand) K times  to assign a significance value to each gene.et al (z‐score) > 5 is counted. Significance of the observed statistics was assessed by counting how many times randomly assigned metabolites exceed the originally observed metabolic changes in each metabolic pathway.Enzyme deletions and annotated metabolites were grouped according to the corresponding metabolic pathways as defined in the genome‐scale model of Orth et al . Only thet al  was assez‐scores to select significantly differential ions. Choosing a typical P‐value cutoff of 0.05 for 0.1 percentile cutoff leads to four distinct scenarios: 0 hits for silent mutations, a region where we get less hits than expected (1–4 hits), a region with significant probability to get hits (5–10 hits), and a region where we get more hits than expected (> 10 hits)  for of Cluster of Orthologous Groups  cutoff to the distribution of the its) Fig B. Based its) Fig C. Enrichz‐score was relaxed to 0.5%  were then weighted by corresponding AUC indices ‐inducible lac‐promoter as well as chloramphenicol‐acetyl‐transferase providing chloramphenicol resistance as dominant selection marker. The selected expression strains were grown in 500 ml shake flasks (50 ml culture volume) on an orbital shaker for 16 h at 37°C and 300 rpm. Overexpression was performed in LB medium  supplemented with 5 g/l glucose, 20 μg/ml chloramphenicol, and 100 μg/ml IPTG. Cells were harvested by centrifugation at 2,200 g and 4°C for 10 min. The supernatant was discarded, and the pellet was washed once with 500 μl (2 ml cultures) or 10 ml (50 ml cultures) 0.9% NaCl + 1 mM MgCl2. For the 50 ml cultures, cells were resuspended in 4 ml of 100 mM Tris‐buffer at pH 7.5 supplemented with 5 mM MgCl2, 20 mM imidazole, 2 mM DTT, and 4 mM PMSF and lysed by three passages through a French pressure cell (Thermo Fisher) at 1,000 psig. Proteins were purified by immobilized metal‐ion affinity chromatography using a sepharose resin with nitriotriacetic acid groups chelating Ni2+ ions. Imidazole and salts from the elution buffer were removed by ultrafiltration using 10 kDa MWCO centrifugal filters in 4 ml tube (50 ml cultures) or 96‐well plate format (2 ml cultures) (Millipore). Proteins were resuspended in 2 mM Tris pH 7.4 and 1 mM MgCl2 and stored at 4°C until further usage. Expression and purity was checked by standard Bradford assays and SDS–PAGE analysis.A genomewide 50 (concentration inhibiting growth by 50%) of 10 mg/l was determined by automated microtiter‐scale cultivations with a gradient of 0–20 mg/l deoxycholate. Limiting concentrations of 35 μM sulfate (MgSO4), 100 nM ferric iron (FeCl3), and 120 μM phosphate (KH2PO4) was determined analogously. Perturbation experiments were performed in 5 ml cultures in glucose‐M9 minimal medium without casein hydrolysate at 37°C and 300 rpm. Anaerobic cultures were performed at 37°C under N2 atmosphere in N2 sparged septum flasks. 1 ml of cells at OD 0.5 was harvested in triplicates by fast filtration and extracted in 4 ml 40:40:20 acetonitrile:MeOH:ddH2O (vol %) for 2 h at −20°C. Extracts were dried under vacuum and resuspended in 150 μl ddH2O. Metabolomics measurements were performed by flow‐injection TOF‐MS as described previously  was calculated according to:Metabolites undergoing significant changes upon an environmental perturbations compared to mock treated cells were defined as passing an absolute fold‐change cutoff of 2 at a false‐discovery rate of 0.01 relative to unperturbed cells. Subsequently, the overlap between metabolites significantly affected by an environmental perturbation and metabolites influenced by single‐gene deletions (considering only the largest 0.1% of observed metabolic changes) was systematically determined. For each gene deletion, the sum of relative changes among the set of metabolites (Ω) commonly affected by each environmental perturbation . Randomization was performed 1,000 times for P‐value estimation.Significance of the overlap was estimated by means of Escherichia coli wild‐type strain and single‐gene deletion mutants with a metabolic phenotype consistent with that of cells under deoxycholate stress  with varying enterobactin concentration  in glucose‐M9 minimal medium  with varying iron concentration  in glucose minimal medium  can be downloaded from https://www.ebi.ac.uk/biostudies/, accession code: S‐BSST5.The following data are available as separate files online: Profile data for > 34,000 mass spectrometric analysis can be downloaded from TF, DCS, and MZ conceived and designed the study, performed the experimental work and computational analysis, and wrote the manuscript. NZ performed computational analysis, supervised the study, and wrote the manuscript. US supervised the study and wrote the manuscript. All authors read and approved the final paper.The authors declare that they have no conflict of interest.Expanded View Figures PDFClick here for additional data file.Table EV1Click here for additional data file.Table EV2Click here for additional data file.Table EV3Click here for additional data file.Table EV4Click here for additional data file.Review Process FileClick here for additional data file.


Pichia pastoris is one of the most important cell factories for production of industrial enzymes and heterogenous proteins. The genome-scale metabolic model of high quality is crucial for comprehensive understanding of the P. pastoris metabolism.P. pastoris genome-scale metabolic model based on the combination of latest genome annotations and literatures. Then the performance of the new model was evaluated using the Cobra Toolbox v2.0.In this paper, we upgraded P. pastoris genome-scale metabolic model, the biosynthesis pathways of vitamins and cofactors were carefully added. iRY1243 showed good performances when predicting the growth capability on most of the reported carbon and nitrogen sources, the metabolic flux distribution with glucose as a sole carbon source, the essential and partially essential genes, and the effects of gene deletion or overexpression on cell growth and S-adenosyl-l-methionine production.Compared with the recently published model iMT1026, the reaction number in the new model iRY1243 was increased from 2035 to 2407 and the metabolite number was increased from 1018 to 1094. Accordingly, the unique ORF number was increased from 1026 to 1243. To improve the metabolic functions of P. pastoris genome-scale metabolic model with significant improvements in the metabolic coverage and prediction ability, and thus it will be a potential platform for further systematic investigation of P. pastoris metabolism.iRY1243 is an upgraded The online version of this article (doi:10.1186/s40643-017-0152-x) contains supplementary material, which is available to authorized users. Pichia pastoris (P. pastoris), one important cell factory, has been widely used to express more than 200 kinds of industrial enzymes and heterogenous proteins  has been one of the most widely used tools in system biology  was gathered and merged  in the model was adjusted from 3.7 to 2.52 mmol ATP/gDCW h.Pichia pastoris strains GS115 and G1HL were used to evaluate the model performance.The In this study, 50 mL YPG medium as the seed medium was taken in 500-mL flask . The composition of the medium used in chemostat cultivations was as follows: 10 g/L glucose, 9.1 g/L K2SO4, 0.46 g/L CaSO4, 7.5 g/L MgSO4·7H2O, 2.06 g/L KOH, 13.4 mL/L H3PO4, and 6 mL/L PTM1. The components of PTM1 were described by Baumann et al. .2 and CO2 concentrations in the off-gas were measured online using a process mass spectrometer . Pressure, pH, and temperature were controlled at 0.04 MPa, pH 5.5 (with adding NH3·H2O), and 30 °C, respectively. The specific growth rate of the cells was controlled by the dilution ratio (D).The culture conditions for seed culture and chemostat culture were described by Nie et al. . ChemostThe cell concentration was measured from the optical density at 600 nm. For dry cell weight (DCW) measurement, 10 mL broth was filtered using a pre-dried and weighted microporous membrane . After being washed 3 times, the filter paper was dried at 105 °C for 3 h. The glucose kits  were used for residual glucose concentration analysis. Extracellular metabolite measurements  were performed as described in Nie et al. .During the sensitivity analysis of the GSMM, the specific glucose consumption rate was changed from 0 to 2.0 mmol/gDCW h. In each simulation, only one of the following model parameters was evaluated, including the lipid composition (6.2–16.2%), protein composition (37–47%), RNA composition (6.6–16.6%), DNA composition (0.1–1.0%), carbohydrate composition (26.9–36.9%), and NGAM value (1.26–3.78 mmol ATP/gDCW h). The specific biomass growth rate and oxygen uptake rate were simulated to evaluate the effects of the changes of parameters on the model prediction ability.The ATP required for cell survival can be calculated by chemostat experiments. The generated ATP can be divided into two forms: non-growth-associated ATP maintenance (NGAM) and growth-associated ATP maintenance (GAM). GAM required for biomass synthesis  can be obtained from literature. NGAM, used for cell maintenance, is an independent reaction in the model, which can be calculated from chemostat data.YxATP and mATP represent the GAM and NGAM, respectively, μ is the specific growth rate, and rATP represents the total amount of ATP consumed by P. pastoris.NGAM and GAM are added to calculate the total amount of ATP required for cell growth, as shown in the following equation:P. pastoris. The biomass is composed of macromolecular substances . The details on the biomass components of P. pastoris were referred from Verduyn et al.  , the exchange rates of glucose were set at −0.478, −0.692, −0.942, −1.294, and −1.846 mmol/gDCW h, respectively. The exchange rates of other substrates and by-products were set at the corresponding values at each dilution rate.During the simulation with iRY1243, NGAM and GAM were set at 2.52 mmol ATP/gDCW h and 20.4 mmolATP/gDCW, respectively , and non-essential genes . The synthetic medium, which was made up of glucose, oxygen, ammonia, sulfur, and phosphorus, was used to predict the essential genes for P. pastoris growth.The prediction of essential genes and partially essential genes was conducted using the singleGeneDeletion function based on Cobra Toolbox. During the essential gene analysis, according to the matrix of GPRs and the Boolean rule definition, the fluxes of reactions containing the relating genes were set at zero, while the bounds of other reactions were maintained constant. Based on the simulated specific growth rate, the genes were classified into essential genes , partially essential genes .During the stable phase of P. pastoris GSMM was based on the iPP668, PpaMBEL1254, iLC915, and iMT1026 models. The procedure for reconstruction of P. pastoris is shown in Fig. P. pastoris gene annotation information from three common databases  was sorted and compared. Among them, KEGG was the largest metabolic network database.The update of the P. pastoris GSMM. The ORF number in iRY1243 is 1243, which is also more than that in iMT1026. Based on the latest gene annotations, the model iRY1243 contains more genes compared to the previous model iMT1026, such as the genes relating to the metabolism of porphyrin and chlorophyll, biotin, the membrane transport reactions, etc. The biomass contains the products of these pathways, which are crucial for the prediction of essential genes. iRY1243 is an upgraded P. pastoris GSMM with significant improvements in the metabolic coverage. In conclusion, the P. pastoris GSMM was comprehensively improved based on gene annotations from different databases and literatures  could be verified according to the results of RNA-seq  and the specific oxygen uptake rate (qo2) were hardly affected by the changes of the protein, RNA, DNA, and carbohydrate contents. However, as the content of lipid increased, µ decreased. The reason may be that the synthesis of fatty acids requires more ATP and NADPH. Different from protein, RNA, DNA, carbohydrate, and the predicted µ and qo2 were more obviously affected by NGAM changes. As NGAM increased, the predicted µ decreased rapidly, while the qo2 increased accordingly as shown in Fig. During sensitivity analysis, the influence of different biomass components  and the energy parameters (NGAM) on the prediction accuracy of the strain phenotype was comprehensively investigated. As shown in Fig. P. pastoris phenotype data reported from 1998 to 2017 were collected . As shown in Fig. μ, qo2, qco2, and RQ under a range of the specific glucose consumption rate inputs.The maximization of cell growth rate usually served as the objective function in FBA. Thus, a comparison between the measured and predicted P. pastoris specific growth rate and CO2 specific consumption rate. However, the model iMT1026 predicted some unreasonable by-products .To compare the function of each model, the chemostat data published by Carnicer et al.  were use13C labeled experiments  were collected from our lab and previously published studies. Then iRY1243 was used to simulate the effects of the corresponding gene insertion, deletion, or overexpression. The high consistency between the experimental and simulated results  requirement.Additional file 4. Detailed comparison of Pichia pastoris GSMMs prediction performance.Additional file 5. Comparison between 13C fluxes and simulated fluxes by iRY1078.Additional file 6. Essential genes and partially essential genes predicted by iRY1078.Additional file 7. Measured and simulated influences of gene deletion or overexpression on the growth and S-adenosyl-l-methionine production by P. pastoris.


Escherichia coli is critical for the proper functioning of the central metabolism and for the cellular response to carbon nutrition changes, either directly or indirectly via the control of the heat-shock response. We identified carbon sources whose utilization was positively or negatively affected by DnaK and isolated several central metabolism genes (among other genes identified in this work) that compensate for the lack of DnaK and/or DnaK/Trigger Factor chaperone functions in vivo. Using carbon sources with specific entry points coupled to NMR analyses of real-time carbon assimilation, metabolic coproducts production and flux rearrangements, we demonstrate that DnaK significantly impacts the hierarchical order of carbon sources utilization, the excretion of main coproducts and the distribution of metabolic fluxes, thus revealing a multilevel interaction of DnaK with the central metabolism.Networks of molecular chaperones maintain cellular protein homeostasis by acting at nearly every step in the biogenesis of proteins and protein complexes. Herein, we demonstrate that the major chaperone DnaK/HSP70 of the model bacterium  Heat Shock Protein of 70 kDa) family of ATP-dependent molecular chaperones have been associated with almost all known chaperone functions2Escherichia coli, the multifunctional HSP70, named DnaK, acts in concert with its cochaperones DnaJ and GrpE  to efficiently assist de novo protein folding, protein disaggregation, protein targeting and translocation through biological membranes, as well as the remodeling of protein complexes456E. coli proteins, more than 700dnaK induces a plethora of cellular defects that result in various phenotypes such as a cold- and a temperature-sensitive growth, a susceptibility to nutrient starvation and to various antibiotics3Ubiquitous molecular chaperone machines control cellular protein homeostasis, both under lenient and more stressful growth conditions32 to facilitate its degradation by the membrane-anchored FtsH protease32 and the subsequent induction of more than hundred HSPsIt is known that DnaKJE downregulates the entire heat shock response (HSR) by interacting directly with heat shock transcription factor σE. coli proteins, a significant fraction of these proteins, over 30%, necessitates assistance by the downstream DnaKJE and GroESL chaperones to complete their folding. Deletion of the tig gene encoding TF enables DnaK to interact with shorter nascent chains and increases the number of DnaK interactors by more than 35%, indicating that substrates of both chaperones overlap significantly. In addition, TF and DnaK can sequentially interact with large multi-domain proteins and actively cooperate to promote their foldingtig and dnaK genes is synthetic lethal at temperatures ≥30 °C and induces a strong accumulation of cytosolic protein aggregates containing more than one thousand different proteins, both pre-existing and newly synthesized81415Remarkably, previous works showed that DnaKJE acts in a concerted and cooperative manner with other major cytosolic chaperones, namely Trigger Factor (TF) and GroESL, especially during the biogenesis of cytosolic proteins8in vivo interactome of DnaK in E. coli revealed that at least 50% of the central metabolism (CM) enzymes interact with DnaKdnaK mutants cannot grow on mannose or sorbitol as sole carbon sourcesackA, ldhA, lpd, pykF, talB and csrC, that partially suppress the growth defect of the sensitive Δtig ΔdnaKJ chaperone mutant, with the three first also suppressing the growth defect of the single ΔdnaKJ mutation at high temperature, thus strongly suggesting a major role of DnaK in this process. Using a combination of growth assays on carbon sources with specific entry points in the CM coupled to a functional analysis of the metabolism, we next demonstrate that DnaK significantly impacts the responsiveness of the CM by acting either directly at the level of the CM or along the first steps of carbon assimilation. How does the multifunctional DnaK chaperone modulate carbon consumption in response to proteostasis failure or in response to nutrient starvation is discussed.The recently described tig ΔdnaKJ chaperone-deficient strain is a sensitive genetic tool to study proteostasis networks in bacteria and identify novel factors involved1519E. coli genes that partially suppress the bacterial growth defect observed in the absence of these main chaperones led to the identification of several new genes involved in multiple cellular processes including transcription , protein synthesis , tRNA maturation (rnt), oxidative stress (tpx), metabolism  and a gene with unknown cellular functions (ypaB). This indicates that cells can use multiple pathways to counteract the severe proteostasis collapse induced by the lack of chaperones, as previously proposed8151920ackA encoding the acetate kinase AckA that performs the reversible conversion of acetyl-phosphate (AcP) into acetate, ldhA encoding the heat shock protein lactate dehydrogenase LdhA, mainly responsible for the production of D-lactate from pyruvate, lpd encoding the lipoamide dehydrogenase Lpd that is part of the glycine cleavage, the pyruvate dehydrogenase and α-ketoglutarate dehydrogenase complexes, pykF encoding the pyruvate kinase PykF, which converts phosphoenolpyruvate into pyruvate, talB encoding the transladolase B enzyme of the non-oxidative branch of the pentose phosphate pathway, and csrC, a small non coding RNA regulating negatively CsrA, a central metabolism regulatortig ΔdnaKJ double mutant growth defect at high temperature was confirmed  phenotype  in the absence of the chaperones, and whose overexpression restores a sufficient pool of active proteins. Some of these gene products might also have a high propensity to be unfolded and to aggregate, which could induce another stress response that potentially facilitates growth in the absence of TF and DnaK. In addition, with respect to the nature of the suppressors, their overexpression might also reroute metabolic reactions to bypass the need for TF and DnaK functions. This could occur either through their enzymatic properties or through transcriptional or post-transcriptional remodeling, as it might be the case for csrB/C via a shutdown of CsrA regulatory mechanismtig ΔdnaKJ double mutant suggests that the TF/DnaK chaperone pathway might contribute at the level of the pyruvate node, which involves the assembly of the large pyruvate dehydrogenase multimeric complex composed of 60 subunits of AceE, AceF and LpddnaK strain and, more severely, in the double Δtig ΔdnaK mutant8Partial suppression of the growth defect by genes of the CM suggests that some suppressors could possess chaperone function or be E. coli wild type revealed that DnaK interacts with a large number of CM enzymes , including the suppressors LdhA, Lpd, PykF and TalB, and that endogenous expression of at least ten CM enzymes, including TalB and LdhA, increases significantly in the ΔdnaKJ mutant , known to escape the DnaK-dependent targeting to FtsH32 mutant harbors the isoleucine 54 to asparagine amino acid substitution in the first part of the second region defined as a small patch with a crucial function in σ32 degradation32 binding to RNA polymerasednaKJ null mutant. Accordingly, steady state levels of σ32 and other HSPs were similar both in MG1655 rpoH(I54N) and MG1655ΔdnaKJ mutant strains  mutants compared to wild type. Class I is represented by carbon sources that do not support growth of both mutants, thus suggesting that the HSR is detrimental in this case; class II carbon sources support higher growth rates for both mutants, which suggests a positive contribution of the HSR; class III carbon sources only support growth of both wild type and rpoH(I54N), and class IV contains carbon sources on which the ΔdnaKJ mutant exhibits a reduced growth rate when compared to both wild type and rpoH(I54N). Both class III and IV thus contain carbon sources for which DnaK likely plays a specific role independently of the HSR. Finally, class V represents carbon sources for which no significant difference could be observed between the strains  with selected entry points in the CM , and gro strains . A repreependent .dnaKJ and/or rpoH(I54N) mutations severely affected growth on carbon sources belonging to class I and III but not on carbon sources belonging the class V, although some of these carbon sources share the same entry point in the CM, i.e., D-galactose (class I) and D-glucose (class V) enter at the level of glucose-6-phosphate; D-ribose (class III) and D-xylose (class V) at the level of X5P/R5P; D-mannose (class I), D-sorbitol (class III) and NAG (class V) at the level of fructose-6-phosphate  or via the fine tuning of HSP synthesis (class I and II). We found that the Δhosphate . This sui.e., the carbon substrates and the metabolic by-products, present in culture supernatants using an untargeted quantitative NMR approach. Analyses were performed for each time point on culture supernatants collected during growth of the three strains on each of the 21 carbon sources. Representative sets of substrate consumption and bacterial growth experiments obtained on lactate, malate and glucose are shown as examples in s) for each strain on each carbon source are summarized in s vary accordingly to growth rates, leading to similar biomass yields for the three strains. Yet, in the case of pyruvate and lactate, qs are lower for both mutants although growth rates are significantly higher. This suggests that an increased level of HSPs induces an increased biomass yield on these carbon sources. Analysis of metabolic by-products revealed that pyruvate accumulates in the supernatant when wild type was grown on lactate dnaKJ mutant accumulates significantly less pyruvate when compared to the wild type, suggesting that pyruvate is more efficiently consumed in the absence of DnaK. Interestingly, three of the Δtig ΔdnaKJ multicopy suppressors, namely LdhA, Lpd and PykF play a role in pyruvate production and utilization, thus suggesting that DnaK could regulate pyruvate utilization within the cell. Yet a direct involvement of DnaK at the pyruvate node remains to be determined.To further characterize the impact of DnaK in the CM, we next analyzed the different metabolites, E. coli growthrphE that is present in E. coli MG1655, which generates a bottleneck within the pyrimidine biosynthesis pathwayi.e., acetate, orotate, pyruvate, succinate and fumarate) for both mutants relative to the wild type grown on lactate, malate and glucose, respectively members of classes II, III and V, are shown in dnaK and rpoH mutants. Indeed, excretion of acetate and orotate is significantly reduced in both mutants when compared to the wild type, while their growth rates are similar or even higher on class V and II carbon sources, respectively. These data reveal a major role of DnaK and the HSR in the metabolism of acetate and orotate.More generally, analysis of metabolic by-products revealed that acetate and orotate were both efficiently detected in culture supernatants , except dnaKJ mutation can have a profound effect on E. coli growth according to the carbon source available. Yet, in its main ecological niche i.e., the mammalian intestine, E. coli generally has access to several carbon sources available to support bacterial growth and colonization of the intestine. Accordingly, it has been shown that E. coli can efficiently co-metabolize several sugars and that its survival and colonization abilities are defined by nutrient availability and consumptioni.e., NAG, gluconate, D-galactose, N-acetyl-neuraminate (NANA), D-galacturonate, D-glucuronate, D-mannose, D-ribose, L-arabinose, glucosamine, maltose, L-fucose and acetate , as described by Fabich and colleaguesdnaKJ mutant when present as sole carbon sources , the preference order of the two mutants  is significantly different than the one observed for the wild type, with NANA, glucuronate and galacturonate being consumed earlier by both mutants  mutant in the presence of galactose. Similarly, the two mutants are now capable of consuming mannose and glucosamine, both carbon sources that normally do not support growth of the mutants as sole carbon sources, albeit less efficiently than the wild type strain  mutants (tig ΔdnaKJ and ΔdnaKJ mutants by AckA (dnaKJ strain is severely reduced when compared to the wild type and the rpoH(I54N) strains, and is directly correlated with a lower acetate consumption rate .The flux map obtained for the wild type strain  showed ttig ΔdnaKJ and ΔdnaKJ mutants, is involved in the PP pathway. Whether TalB is a limiting factor in this case is not known. In the case of rpoH(I54N), the relative flux through the EMP pathway is higher than the ones observed for the wild type and the ΔdnaKJ mutant, thus reflecting the reduced flux in the PP pathway for this mutant. The split ratio between the PP and the ED pathways for the ΔdnaKJ mutant is significantly higher when compared to the two other strains, with a flux through ED of 9%, thus leading to a corresponding decrease of the relative flux in the PP pathway (from 24% for the wild type to 11% for the mutant). Therefore, in the presence of glucose, the contribution of the PP pathway is significantly lower in the two mutants compared to the wild type strain due to a higher contribution of the EMP pathway for the rpoH(I54N) mutant, and of the ED pathway in the case of the ΔdnaKJ mutant. Intriguingly, a comparable decrease of fluxes in the PP pathway was found for the lpd mutant in E. coli, further supporting the link between DnaK and the suppressor Lpd described abovednaKJ and rpoH(I54N) mutants. In this case, fluxes that enter the TCA cycle were about 30% and 40% higher for the rpoH(I54N) and ΔdnaKJ mutants, respectively. Correspondingly, the flux increase in the TCA cycle correlates with a major decrease in acetate production (about 50%).Remarkably, metabolic fluxes through the PP pathway are significantly reduced for both mutants when compared to the wild type . Of noterpoH(I54N) mutant, except for the ED pathway, suggesting that DnaK’s impact on fluxes remodeling predominantly occurs via the control of HSP synthesis.In sum, these flux data show that in the absence of DnaK there is significant carbon flux redistribution in the PP and ED pathways, in the TCA cycle and in the acetate production. A similar rearrangement is observed in the dnaKJ mutant  mutants  or limits (class I carbon sources) the utilization of certain carbon sources , and sig mutants . This stdnaKJ operon further supports such an adaptive model involving DnaKdnaKJ and rpoH(I54N) mutants 25 have been previously described. Mutations described in this study were moved into the MG1655 genetic background by bacteriophage P1-mediated transduction. The W3110 ΔdnaK52::CmRthr::Tn10 has been previously describedackA::KanR, ΔldhA::KanR, Δlpd::KanR, ΔpykF::KanR and ΔtalB::KanR mutant alleles were obtained from strain JWK2293, JWK1375, JWK0112, JWK1666 and JWK0007, respectively (Keio collection). The ΔcsrBΔcsrC::KanR is a kind gift of Catherine Turlan and Agamemnon Carpousis (Toulouse). The MG1655 sidB1 derivatives were constructed as follows. The zhf-50::Tn10 (TetR) allele from CAG18450sidB1 strain using P1 transduction and selection at 30 °C on LB agar plates supplemented tetracycline  into MG1655 following selection at 30 °C on LB agar plates supplemented tetracycline. Finally, the ΔdnaKJ::KanR mutant allele was then moved into MG1655 sidB+zhf-50::Tn10 (TetR) and MG1655 sidB1 zhf-50::Tn10 (TetR), thus resulting in a set of isogenic MG1655 derivatives. Bacteria were grown in LB or M9 based medium with specific carbon sources (see below) supplemented when necessary with chloramphenicol (10 μg/ml), kanamycin (50 μg/ml), ampicillin (50 μg/ml) and tetracycline (15 μg/ml).The MC4100 mutant derivatives Δwww.addgene.org/vector-database/4063/) in which a small region of the multi-cloning site (nt 268 to 306 that includes the NcoI to BstB1 restriction sites) has been deleted, leaving an EcoRI site directly downstream the original ribosome-binding site. This leads to the following sequence: CACAGGAGAATTCAGCA (ribosome-binding site underlined and EcoRI site bold)ackA, 990-bp ldhA, 1425-bp lpd, 1413-bp pykF, 954-bp talB,369-bp csrB, 245-bp csrC, 2145-bp pta and 1959-bp acs coding sequences were PCR-amplified using appropriate primers listed in Plasmid pSE380ΔNcoI is a derivative of pSE380 from Invitrogen (tig ΔdnaKJ double mutant were isolated as follows. A pMPMA2–based E. coli multicopy genomic library (laboratory collection) was transformed into MG1655 Δtig ΔdnaKJ and selection was performed at 35 °C on LB agar plates supplemented in ampicillin. From approximately 70 000 transformants tested, ninety two plasmids capable of suppressing the growth defect were first extracted and retransformed into the same strain in order to confirm suppression. The thirty two suppressors that passed the second selection were then sequenced. At the end we have identified 19 suppressor fragments encoding proteins that belong to diverse cell pathways  or have unknown functions. Interestingly, six of the suppressor fragments contain genes involved in the central metabolism of the cell  at 30 °C, washed with M9 salts and inoculated at OD600 0,1 and grown aerobically in baffled Erlenmeyer flask containing M9 based medium containing independently L-arabinose (1.2 g/l), D-glucose (1.2 g/l), malate (1.2 g/l), succinate (1.2 g/l), maltose (1.2 g/l), N-acetyl-glucosamine (1.2 g/l), glycerol (1.2 g/l), L-fucose (0.7 g/l), pyruvate (0.7 g/l), D-xylose (0.7 g/l), D-galactose (0.7 g/l), D-glucosamine (0.7 g/l), D-mannose (0.7 g/l), D-ribose (0.7 g/l), D-sorbitol (0.7 g/l), L-rhamnose (0.7 g/l), N-acetyl-neuraminate (0.7 g/l), L-lactate (0.7 g/l), D-glucuronate (0.7 g/l), galacturonate (0.7 g/l), fumarate (0.7 g/l) or acetate (0.35 g/l or 2.5 g/l). Carbon source references are listed in 600 was monitored, 500 μl of culture was filtered  and stored at −20 °C before NMR analysis. In the case of the multiple carbon source assay the second inoculation was performed to an OD600 of 0,05 and grown in M9 containing N-Acetyl glucosamine, gluconate, galactose N-acetyl-neuraminate, galacturonate, glucuronate, mannose, ribose, arabinose, glucosamine, maltose, fucose and acetate . Samples were taken every 30 min for the first 6 hours then every 20 min until 15 hours and every 10 min for the last two hours. Each time OD600 was monitored and sample for NMR analysis were taken.Overnight cultures in LB at 30 °C were diluted 1/50, grown in M9 based medium with glucose (2 g/l) until mid-log phase . A total of 64 scans were accumulated after 8 dummy scans. Molar yields have been calculated at the time of exhaustion of the primary carbon source. Spectra collected from samples of the 13 carbon source experiments contained at least one isolated peak for each carbon source from which the quantification of the compound could be performed unambiguously. This analysis was performed at 280 K.The main metabolic products which accumulated during growth in the extracellular medium were measured by NMR spectroscopy. Samples were analyzed by quantitative 2: total production of each reduced cofactor was calculated by adding fluxes of all reactions which produced the reduced cofactor. ATP: production of ATP via substrate-level phosphorylation was calculated by adding all the fluxes measured for all ATP producing reactions in carbon metabolism, and subtracting the fluxes of all ATP-consuming reactions. The ATP produced from NADH and FADH2 via oxidative phosphorylation was estimated assuming a P:O ratio of 1.75 for both redox cofactorsNADH, NADPH and FADHdnaKJ and rpoH(I54N) strains grown in LB at 30 °C were diluted 1/50 and grown in baffled Erlenmeyer flask containing M9 based medium with a mixture of 80% [1-13C] glucose and 20% [U-13C] glucose  at 30 °C. Cells were washed once with M9 salts inoculated to an OD600 of 0,05 and incubated at 30 °C a second time in baffled Erlenmeyer flask containing M9 based medium with the same glucose labeled mixture at 30 °C to minimize the effect of unlabeled carbon. Note that the ratio of (1-13C) and (U-13C) glucose used in this experiment was determined and optimized using the software IsodesignOvernight cultures of MG1655 wild type, Δ glucose  until mi600 of 1 to ensure both isotopic and metabolic steady-state conditions. In this case, 9 ml of solvent H2O/Methanol/Acetonitrile (20:40:40) was added to 1 ml of cell culture to quench the metabolism and samples were stored at −20 °C for 15 min. The mixture was then centrifuged at 12,000 g at −20 °C for 5 min. Then, the 400 mg of harvested cells were incubated in 5 ml of 6 M HCl at 105 °C for 24 h for metabolite extraction. The acid was removed by evaporation (SC110A SpeedVac Plus), and labile protons were exchanged three times with deuterium by successive resuspension in 2 ml of D2O 99.8% (EurisoTop), and the hydrolysate was finally resuspended in 600 ml of D2O before analysis.Culture samples were collected for NMR analyses at an ODet al., 491H NMR using the 800 MHz spectrometer. Flux calculations were performed with influx_s . The metabolic network implemented in the FTBL model included all major reactions of the central carbon metabolism: glucose uptake, EMP, ED and PP pathways, TCA cycle, acetate production and amino acid biosynthesis pathways. Precursor requirements for biomass formation were determined according to the molecular composition of E. coli.Analyses of samples and flux calculation were performed as described by Millard and coworkers  stress-responsive chaperone machine with the central metabolism. Sci. Rep.7, 41341; doi: 10.1038/srep41341 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Vitis vinifera L. cultivar Gamay Teinturier  cell suspension line as a model system to study the regulation of anthocyanin accumulation in response to nitrogen supply. The cells were sub-cultured in the presence of either control (25 mM) or low (5 mM) nitrate concentration. Targeted metabolomics and enzyme activity determinations were used to parametrize a constraint-based model describing both the central carbon and nitrogen metabolisms and the flavonoid (phenylpropanoid) pathway connected by the energy (ATP) and reducing power equivalents (NADPH and NADH) cofactors. The flux analysis  clearly showed that in low nitrogen-fed cells all the metabolic fluxes of central metabolism were decreased, whereas fluxes that consume energy and reducing power, were either increased  or maintained (pentose phosphate pathway). Also, fluxes of flavanone 3β-hydroxylase, flavonol synthase, and anthocyanidin synthase were strongly increased, advocating for a regulation of the flavonoid pathway by alpha-ketoglutarate levels. These results strongly support the hypothesis of anthocyanin biosynthesis acting as an energy escape valve in plant cells, and they open new possibilities to manipulate flavonoid production in plant cells. They do not, however, support a role of anthocyanins as an effective mechanism for coping with carbon excess in high carbon to nitrogen ratio situations in grape cells. Instead, constraint-based modeling output and biomass analysis indicate that carbon excess is dealt with by vacuolar storage of soluble sugars.Anthocyanin biosynthesis is regulated by environmental factors  and nutrient status . Previous reports show that low nitrogen availability strongly enhances anthocyanin accumulation in non carbon-limited plant organs or cell suspensions. It has been hypothesized that high carbon-to-nitrogen ratio would lead to an energy excess in plant cells, and that an increase in flavonoid pathway metabolic fluxes would act as an “energy escape valve,” helping plant cells to cope with energy and carbon excess. However, this hypothesis has never been tested directly. To this end, we used the grapevine  Flavonoids are naturally occurring secondary metabolites belonging to the group of polyphenols, which are ubiquitous in all land plants, with currently over 9,000 compounds identified . Among pFigure 1; Anthocyanins, which are key compounds for premium red wine making, are present in the skin (epicarp) of the red grape berries, and sometimes, in the case of the so-called “teinturier” cultivars also in the pulp (mesocarp) . Hence, Figure 1).In several crops like tomato or grapevine and model plants such as Arabidopsis, nitrogen depletion increases the concentration of phenolics in general and of anthocyanins in particular . The traConstraint-based modeling can be used in order to test that hypothesis by comparing maps of metabolic fluxes in the two contrasted situations, i.e., in nitrogen limiting condition compared to the control condition. Mathematical modeling of metabolism is a particularly promising tool as it offers a systems approach to analyze the structure, dynamics, and behavior of complex metabolic networks. In plant research, the issue of modeling metabolism is increasingly gaining attention, and several mathematical modeling approaches applied to plant metabolism exist  or 25 mM  KNO3, in 1 L Erlenmeyer flasks. The ammonium concentration was identical in both N- and N treatments (2 mM). For each sampling point, three replicate flasks of cell culture were harvested at 0 (or 1 for experiment 1), 4, 6, 8, and 11 days post-inoculation by vacuum filtration, quickly washed twice with ice-cold distillated water, weighed and quick-frozen in liquid nitrogen. Frozen cells were then reduced to fine powder in a liquid nitrogen-cooled MM200 ball grinder , and stored at -80°C until further analysis. kinetin . Cells wAnthocyanins and flavonols were analyzed on powdered freeze-dried cells, which were extracted and analyzed according to g for 10 min. Two milliliters of supernatant were vacuum-dried using a SpeedVac SC 110 plus  for the analysis of stilbenes and 50 μL were used for the analysis of the total phenolic content. Then, 100 μL MeOH (100%) and 1 mL H2O were added to the vacuum-dried samples and filtered through an Ion Exchange Resin (Dowex 50 WX 4-400) to remove anthocyanins. Extracts were vacuum-dried using a SpeedVac SC 110 plus and the dry pellet was re-suspended in 800 μL of MeOH/H20 50/50 (v/v) for the HPLC analysis. Extracts were then filtered through a 0.45 μm polypropylene syringe filter . Stilbenes analysis was performed with a Summit HPLC System consisting of P680 pump, ASI-100TTM autosampler and UVD 340U UV-Vis detector operating at 320 nm  (Tannins and stilbenes were extracted from 40 mg of freeze-dried cells with 4 mL of methanol (100%) overnight at +4°C. The samples were centrifuged at 6,000 ×  States) . After i2O , 250 μL Folin–Ciocalteu reagent and 2.25 mL of ultrapure water. After 3 min, 2 mL Na2CO3 (75g L-1) were added and the samples were incubated at 50°C for 5 min and absorbance was read at 760 nm. Calculation of phenolics was based on a standard curve prepared using gallic acid, and the results were expressed as mg gallic acid equivalents per liter (mg GAE L-1).Total phenolic content was assessed by the Folin–Ciocalteu method . The assFive hundred milligrams of cell powder (FW) were extracted from cell suspension samples using decreasing concentrations of ethanol: ethanol 80%, ethanol 50% (v/v) and ultrapure water. All three supernatants were pooled, vacuum-dried using a Speed Vac SC 110 plus . The dry pellet was re-suspended in 2 mL of ultrapure water and stored at -20°C before further analysis.-1 with a 67 min linear gradient ). Chromatograms corresponding to excitation at 250 nm and emission at 395 nm were recorded and quantified with chemical standards purchased from Sigma .Amino-acid content was analyzed by the method described by Soluble sugars  were measured enzymatically with a microplate reader  as described by Malate, starch, and protein content were measured as described in 1, using 100 mg of freeze-dried cell powder, as described in Total cell wall polysaccharides quantification was subcontracted to the BIBS platform of INRA-Nantes2SO4 , supplemented with 2 μg of heptadecanoic acid  and incubated 60 min at 80°C in screw-capped tubes. Then, 400 μL hexane and 1.5 mL of ultrapure water were added, vigorously mixed and centrifuged at 3,000 g for 5 min. The organic phase was collected and transferred to injection vial to analyze fatty acids by GC-FID (Gas Chromatography coupled to Flame Ionization Detection), as described by Five hundred milligrams of cell powder (FW) were extracted by 1 mL of MeOH:HCell total carbon and nitrogen contents were determined by Dumas’ combustion method, with a Flash EA 112 auto-analyzer , following the manufacturer’s instructions and using 8 mg of freeze-dried cell powder. In the case of culture medium analysis, 250 μL of freeze-dried medium were used instead.The total DNA content was measured using the deoxyribose-specific diphenylamine reaction, using 15 mg of freeze-dried cell powder as starting material, and salmon sperm DNA for calibration .g at 4°C. The protein extract was desalted on a PD-10 column  equilibrated with 25 mL of 0.1 mM Tris–HCl (pH 8.8). Aliquots of desalted proteins were frozen in liquid nitrogen and stored at -80°C. Spectrophotometric assays contained 300 μL of protein extract in 30 mM L-phenylalanine in 0.1 M Tris–HCl (pH 8.8) and 150 μL of 30 mM L-phenylalanine in 0.1 M Tris–HCl (pH 8.8). Reactions were incubated for 15 to 180 min at 37°C. The amount of trans-cinnamic acid formed in the assay was measured spectrophotometrically at 290 nm. PAL activity was expressed as μg of cinnamic acid formed per μg FW-1.Phenylalanine ammonia-lyase (PAL) activity was measured according to For all other enzyme measurements , aliquots of 20 mg frozen FW cell powder were extracted by vigorous mixing with extraction buffer . FK, GK,g for 5 min) and the resulting pellet gently re-suspended in 1 mL of cell culture medium. Respiration rates were initially expressed in nmol O2 min-1 g-1 FW.Oxygen consumption rates of cells were measured with Clark’s electrode at 25°C in a 1 mL thermostatically controlled chamber. Respiration assays of growing cells were performed in the GT3 cell suspension medium under stirring. Seven hundred and fifty microliters of cell suspension were centrifuged  was added and the extract was neutralized to a final pH in the range from 5.5 to 6.5 with 0.2 M NaOH. To quantify NADH and NADPH, other aliquots of frozen cells were extracted as for NAD+ and NADP+ except that the extraction medium was 0.2 M NaOH and the heated sample was neutralized with 0.2 N HCl to a final pH in the range from 7.5 to 8.5.All extractions were performed at 4°C with 200 mg of frozen powder cell. For the assays of NAD+ and NADH assay, the reaction was started by adding 3.5 U of alcohol dehydrogenase  and 10 μL ethanol (99%). For the NADP+ and NADPH assay, 1.6 U of glucose-6-phosphate dehydrogenase (Roche) and 0.5 M glucose-6-phosphate were added to the assay. Absorbance was read at 570 nm for 10 min, and the results were expressed in nmol g-1 FW.Coenzyme content was quantified by adapting methods described by sbml format, Supplementary Presentation Concentrations of accumulated metabolites and biomass components were converted from gram-to mole-basis and then multiplied by the specific growth rate calculated at day 4 and 6 in order to calculate the corresponding fluxes used as constraints in the flux balance model. Stoichiometric network reconstruction encompassing central and polyphenol metabolism . Conversely, total anthocyanin accumulation patterns were strikingly different in control and low nitrogen condition cultures . In control cells , total anthocyanin content was fairly stable during the culture (around 2–3 mg g DW-1), whereas in cells cultivated at low nitrogen  total anthocyanin cell content strongly increased from the 4th day of culture to the 12th day, reaching a maximum of about 20 mg g DW-1 at the end of the culture.Dry biomass accumulation kinetics were nearly identical for cells cultured in either control or low nitrogen conditions, increasing from about 1.6 g DW-1 of cells per L at the 11th day of culture  was determined as the growth rate (g DW day-1) related to the biomass (g DW) and the average was: 0.288 ± 0.013 day-1 and 0.160 ± 0.045 day-1 at day 4 and 6 of the culture, respectively.This result has been reproduced with three cell cultures , that were conducted in the same conditions. Similar growth profiles were observed with about 10 g DWBoth total nitrogen and total carbon have been determined in the medium  was the carbon source supplied in the culture medium, but it is generally cleaved to form hexoses by cell wall invertase activity . The inorganic nitrogen source was nitrate (Vno3-up) involving enzymes of the nitrogen assimilation pathway . Ammonium, with a low concentration (2 mM), was neglected as nitrogen source. For the phenolic pathway, three reversible reactions involving naringenin, dihydroquercetin, and leucocyanidin  and two irreversible reactions involving cinnamate and coumaroyl coenzyme A  were connected to central metabolism via phenylalanine. The fluxes directed toward the main phenolic compounds, i.e., anthocyanins, flavonols, tannins, and stilbenes were described by four overall reactions . The main biosynthetic processes were described with overall reactions: (1) cell wall polysaccharides from UDP-glucose (Vcw), (2) protein synthesis (Vprotein) according to the measured amino acid composition of proteins (Supplementary Table Vdag) from pyruvate and trioses phosphate according to total fatty acid biomass measurement (Supplementary Table Vnucleotides) from ribose-5-phosphate by using plant metabolic pathway databases2. All other accumulated compounds were described as a simple accumulation: (1) malate , (2) soluble sugars, i.e., glucose (Vac-glc), fructose (Vac-fru), and sucrose (Vac-suc), and (3) four groups of free amino acids, glutamate (Vac-Glu), aspartate (Vac-Asp), alanine (Vac-Ala), and phenylalanine (Vac-Phe). It has been checked that no metabolites were excreted in the medium (data not shown).The flux-balance model was constructed by integrating biochemical and physiological knowledge about the stoichiometry of reactions and the boundary conditions, i.e., the definition of external compounds. The model describes one cell and assumes that the suspension is homogeneous. The model combines the central metabolism previously described  dedicatentration  mM, was trate Vno-up involVnrj1 and Vnrj2), which are associated to the mitochondrial respiration. Recycling of AMP by adenylate kinase is described by Vadk. The portion of synthesized ATP that is not used for growth has been balanced by the model as an ATP hydrolyzing reaction (Vnga-ATPm) that physiologically represents cellular maintenance H, were explicitly taken into account. The cofactors NADP/NADPH were linked to biomass and the phenolic compounds production, and the cofactors NAD/NADH and FAD/FADH were linked to ATP synthesis via two essential reactions of oxidative phosphorylation  int, j=1…ni=1…m the stoichiometry matrix where nij is the stoichiometric coefficient of metabolite xint,i in reaction j. To solve the system, a lower and an upper bound constrained each flux.With The first type of constraints applied to limit the flux space to flux directions was inferred from thermodynamic properties of reversibility or irreversibility. Thus, among the internal reactions of the metabolic network, 33 were irreversible as indicated by unidirectional arrows on Supplementary Figure -1 day-1, Supplementary Table The second type of constraints was the maximal enzyme capacities. Experimentally determined activities of enzymes of central metabolism and flavonoid pathway, considered as maximal enzyme capacities (converted in mmol g DWVnrj1 and Vnrj2) was constrained by the respiration measurements: 3.54 ± 0.18 and 2.86 ± 0.24 mmol g DW-1 day-1 in control and low nitrogen conditions at day 4 of culture, respectively.The third type of constraint concerned the respiration rate . The sum of the two reactions of ATP synthesis by oxidative phosphorylation  were calculated from experimental data .The mass balance of accumulated metabolites and biomass components covered an average of 81 and 91% of the dry biomass in control and nitrogen-limiting conditions, respectively . Unsurprisingly, flux maps obtained with low nitrogen cultured cells compared to control condition showed higher fluxes in primary than in secondary metabolism both at 4 and 6 days of culture. Also a lower flux of ATP synthesis is pointed in low nitrogen condition.Flux minimization, which leads to a unique solution , was useFigure 5). Concerning external fluxes (4th day) the main changes in nitrogen-limiting condition were the increase in accumulation of phenolic compounds (except flavonols), hexoses and starch . Conversely protein synthesis, sucrose accumulation, and respiration were decreased. Consequently, the calculated fluxes in the main pathways  were decreased of about 20 to 30%, except the fluxes of the phenolic pathway which were increased: Vmacl, Vshik, Vpal, and Vcoum by 38% and, Vnar, Vdhq, and Vlcc by 26%. More surprisingly, two internal fluxes, the PPi-dependent phosphofructokinase (Vpfp) and the pyruvate kinase (Vpk) were strongly increased (80 and 63%), and also glucose and fructose uptakes, were also slightly increased (7 and 6%) at day 4. Finally, fluxes corresponding to enzymatic steps of the flavonoid biosynthesis that use α-ketoglutarate (α-KG) as a reducing agent and convert it to succinate, namely Vdhq and Vanthoc were increased by 25 and 104%, also at day 4. Conversely, α-KG conversion to succinate the TCA cycle (Vkgdh) was decreased by 25% in low nitrogen culture conditions.More than the absolute values of the calculated fluxes, we were interested in the relative changes in the fluxes to look for cell metabolism reprograming under low nitrogen condition  was reduced by 50%.The global behavior of external fluxes was exacerbated at day 6 and resulted in similar observations than at day 4, i.e., a global diminution of all fluxes (40–50%) but here without significant change in +, NADH, NADP+, and NADPH). While NADH was slightly affected, NADPH significantly increased in low nitrogen condition compared to the control . The same trend was observed at day 6, but was not deemed statistically significant according to Student’s t-test . These results clearly showed an excess of NADPH, concomitant with the accumulation of anthocyanin (at day 4 and 6) and stilbene compounds (at day 4).The internal metabolite concentrations were not accessible with the flux-balance model. Then complementary analyses have been done to determine the total contents of redox metabolism coenzymes  biosynthesis in grapevine GT3 cells. In the same review from The question of the existence of a metabolic driver that would fuel the increase in anthocyanin  biosynthesis in such situation remains open. One emerging property of the FBA-generated flux maps is the fact that in low nitrogen conditions (N-), several enzymatic steps that consume ATP and reducing power (NADPH or NADH) have their metabolic flux either maintained or increased . Conversely, most of the metabolic fluxes that lead to ATP, NADH, or NADPH formation were decreased by low nitrogen conditions . This strongly advocates for a link between cell energy status  and secondary metabolism, confirming an hypothesis made by Besides the fact that low nitrogen culture conditions might lead to an altered cell energy status , another output of the FBA-generated flux maps is that three fluxes of the flavonoid pathway that use α-KG were strongly up-regulated in low nitrogen cultured cells. α-KG has emerged in the past decade as a signal molecule in plants, linking TCA cycle to secondary metabolism, including the flavonoid pathway . Indeed,Flux balance analysis modeling was used to investigate metabolic flux reprogramming in grapevine cells in response to low nitrogen culture conditions and revise the well-known up-regulation of anthocyanin biosynthesis in response to low nitrogen availability. Model outputs unambiguously point toward cell energy excess and increased α-KG availability as the metabolic drivers of anthocyanin synthesis (and more broadly flavonoid synthesis) under high carbon-to-nitrogen ratio conditions. This work was conducted in a cell suspension culture, and the next obvious question is whether such a metabolic driver effect is also occurring in ripening berries of red grape varieties, which accumulate anthocyanins to high levels in their exocarp cells, a key feature for high quality red wine making. Further modeling and biochemical work is needed to address that question.ES, GH, CR, StC, LM-P, and BB performed the experiments and the analytical work. SoC performed model construction and calculations, participated to data analysis and manuscript writing. MD-N generated the flux maps. YG, J-MM, ZD, and SD discussed the results and performed manuscript critical reading. EG led the project and designed the experimental flowchart, discussed the results and coordinated the manuscript writing and critical reading.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.