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.


Computational modelling of metabolic networks has become an established procedure in the metabolic engineering of production strains. One key principle that is frequently used to guide the rational design of microbial cell factories is the stoichiometric coupling of growth and product synthesis, which makes production of the desired compound obligatory for growth. Here we show that the coupling of growth and production is feasible under appropriate conditions for almost all metabolites in genome-scale metabolic models of five major production organisms. These organisms comprise eukaryotes and prokaryotes as well as heterotrophic and photoautotrophic organisms, which shows that growth coupling as a strain design principle has a wide applicability. The feasibility of coupling is proven by calculating appropriate reaction knockouts, which enforce the coupling behaviour. The study presented here is the most comprehensive computational investigation of growth-coupled production so far and its results are of fundamental importance for rational metabolic engineering. Coupling of growth and product synthesis is an important principle in metabolic engineering, but its range of applicability is unclear. Here, the authors use a dedicated computational framework to study the feasibility of coupling the production of metabolites to growth in the genome-scale metabolic models of five production organisms, and show that coupling can be achieved for most metabolites. The shift from a petrochemical to a bio-based and sustainable production of chemicals and fuels remains as a major global challenge of humanity in the twenty-first century. Diverse commercial compounds are currently produced in fermentation processes including commodity chemicals, polymers, biofuels, pharmaceuticals, nutritional supplements and so on.1213One key design principle that has become particularly relevant for metabolic engineering and computational strain design over the past decade is to couple cellular growth with the production of a desired metabolite. The central goal is to make the desired metabolite a mandatory by-product of growth and its production thus an integral part of the organism’s metabolic function . In this891011E. coli are strains for the production of lactateSaccharomyces cerevisiae, mutant strains have been designed for the production of 2,3-butanediolOptKnock61415per se possible for every metabolite. Feasibility of growth-coupled product synthesis has recently been investigated from a theoretical point of viewE. coli (89 metabolites and 107 reactions) was taken to examine whether the production of each metabolite can be coupled to growthHowever, growth coupling is not E. coli  necessary for growth while uptake of the substrate glucose (photons in case of Synechocystis sp. PCC 6803) was limited to known maximal values. Outflows of typical organic  products for the given organism were left open and thus have to be accounted for when the cut sets were calculated. Importantly, to ensure that the calculated knockout strategies (cut sets) have a high degree of biological relevance, reactions were set to be irrepressible and can thus not be knocked out if they do not correspond to enzyme-catalysed biochemical reactions  or if no associated gene is known (in models where gene-reaction mappings were available). Overall, the percentage of irrepressible reactions in the respective models is significant and reaches up to 34.5% (in E. coli) of the operative reactions (The organisms and associated models for which the feasibility of strong coupling was examined are listed in eactions .In every model it was then tested for each organic metabolite producible from the substrate whether a suitable knockout strategy (cMCS) exists such that growth and production of the metabolite can be strongly coupled. For each candidate metabolite an exchange reaction for this compound was temporarily added to the model and calculations were done for three different levels of demanded minimal product yield, specifically 10, 30 and 50% of the maximum yield for the respective metabolite (see Methods section). The 10% level was chosen to check if strong coupling is in principle possible, whereas the 50% level provides information about whether the metabolite can be produced with a high yield under coupling. The intermediate level 30% was included to see in more detail how the feasibility of strong coupling changes with increasing minimum product yield. To test whether growth-coupled production is possible, it was attempted to calculate a cMCS for each metabolite and given minimum yield. The calculation of such a cMCS is a computationally hard problem. Based on earlier developments, we therefore built an algorithmic pipeline to determine effectively such cMCS by solving dedicated Linear Programming (LP) and Mixed Integer Linear Programming (MILP) problems (see Methods section). For a given metabolite and yield level, the algorithm seeks to either find a cMCS proving coupling or to disprove feasibility of coupling. If the MILP problem cannot be solved nor its infeasibility be determined within the given time limit (see Methods section), then it is not possible to decide if strong coupling is feasible. This happened only in relatively few of the considered cases (see below).E. coli and S. cerevisiae; these two models were simulated under aerobic and anaerobic conditions  in The mean cMCS sizes calculated for the organisms are partially quite large because genome-scale metabolic networks are used and, to keep the overall computation time acceptable, only limited time resources could be invested to minimize the size of each cMCS (compare steps 7 and 10 in Methods section). To analyse whether smaller  cMCS exist for the 50% minimum product yield level under aerobic conditions in E. coli and yeast, we also analysed feasibility of strong coupling under anaerobic conditions. Here it should first be noted that, in our simulations, the degree of feasibility of coupling under anaerobic conditions can never be greater than for aerobic conditions since a deactivation of respiratory reactions in the cut sets for aerobic conditions can always mimic an anaerobic regime . In E. coli, we found that the production of 77.4% of the metabolites producible from glucose under anaerobic conditions can still be coupled to growth at the 10% minimum yield level. With increasing demand for product yield, this fraction drops more strongly than in the aerobic case. Interestingly, because of the reduced solution space of flux vectors under anaerobic conditions, infeasibility of coupling can now be proven by the solver for a larger number of metabolites. For example, at the 50% minimum yield level, strong coupling was proven to be feasible for 30.9% and to be infeasible for 61.4% of the substrate-producible metabolites; hence, the percentage of feasible couplings is between 30.9 and 38.6%. The situation in yeast changes much more drastically when moving from an aerobic to an anaerobic growth regime since coupling becomes almost impossible for all metabolites. Even at the 10% minimum yield level, the fraction of substrate-producible metabolites that can be coupled drops to 3.9% and infeasibility can already be proven for more than 94%. We analysed in detail what structural properties of the yeast metabolism induce these sharp differences compared to E. coli. In contrast to the latter, in the chosen yeast model with standard outflows we found that excretion of ethanol is essential for anaerobic growth on glucose confirming experimental resultsD-glycerate, 3-phospho-D-glycerate, 2-phospho-D-glycerate, phosphoenolpyruvate (PEP), pyruvate and acetaldehyde in a series of reaction steps that become essential under anaerobic conditions and are thus unavailable as knockout targets. In contrast, under anaerobic conditions in E. coli only the two reactions from glyceraldehyde 3-phosphate along 1,3-bisphospho-D-glycerate to 3-phospho-D-glycerate are essential. Since in yeast ethanol synthesis along the path listed above must be kept active in the model, strong coupling is lost for almost all metabolites: no suitable knockout sets can then exist that would guarantee a minimum yield of the respective metabolite because the substrate glucose could, in principle, be completely converted to ethanol. In fact, it has been reported that formation of ethanol as an undesired by-product is one disadvantage when establishing new fermentation processes based on yeastFor To illustrate how the fluxes in a metabolic network are affected by a cMCS, we describe the effects of the found cMCS inducing growth-coupled production of shikimate in yeast under aerobic conditions. The cMCS, which ensures a shikimate yield above 50% of its maximum yield, consists of three targets compare : {{PGCD E. coli model sorted with respect to the frequency of their occurrence in the found cMCS. As can be expected, reactions lying on pathways to standard (fermentation) products of E. coli  are frequently used targets.A. niger and C. glutamicum on glucose and for photoautotrophic growth of Synechocystis sp. PCC 6803 lead to very similar findings as for aerobic growth in E. coli and yeast  exchange reactions for organic metabolites, which simplifies the induction of coupling.The results of the computations for aerobic growth of nd yeast . Growth-E. coli iJO1366 , enzyme complexes  or multifunctional enzymes , suitable cut sets with gene knockouts may differ from the reaction cut sets and one may ask whether the results on the feasibility of coupling holds also true with gene knockouts as relevant interventions. If the association between genes, enzymes and reactions is known and provided in the model, then the cMCS can also be calculated with gene knockouts by integrating the gene association into the model366 ref.  model co366 ref. . For a fThe central goal of this study was to investigate systematically for five major production organisms frequently used in biotechnological applications how far suitable intervention strategies exist by which stoichiometric coupling of growth and synthesis of native metabolites can be achieved at genome scale. The results of our study are highly encouraging as they show that, under appropriate conditions, it is possible to strongly couple the production of the large majority of metabolites to growth for the organisms investigated here. Our work thus proves that growth-coupled product synthesis is indeed a widely usable design principle for metabolic engineering applicable to diverse organisms for enhancing the production of a large variety of metabolic products.E. coli) only38E. coli as proven herein could not be concluded with any of these methodsThe presented exhaustive and genome-scale study on feasibility of growth-coupled strain designs is by far the largest and most comprehensive of its kind and our developed algorithmic pipeline turned out to be a very efficient and fast procedure for this purpose. So far, studies on feasibility of growth-coupled product synthesis focused on single products or/and on a single organism  must be possible but must be restricted by reaction cuts in such a manner that not too much carbon is lost through fermentation because otherwise it will not be possible to keep up the minimum product yield. Therefore, coupling can be expected to be more difficult to realize  than under aerobic conditions where a possible NADH excess can be removed through respiration. This is related to the second possible reason why coupling is easier under aerobic conditions. Owing to respiration a much higher amount of ATP is available, which can support production of metabolites with high energy demand. However, for cases where no suitable knockout strategy for coupling could be found, it has to be noted that all calculations for the heterotrophic organisms were made based on glucose as substrate. We expect that, at least for some products, an infeasible coupling might become feasible with other substrates or if heterologous reactions or pathways are added.For The cMCS calculated here are primarily intended to test whether coupling is, in principle, possible or not. Existence of a suitable cMCS proves stoichiometric feasibility of coupling but does not consider regulatory  or capacitive constraints . Furthermore, although measures have been taken to make sure that the cMCS are biologically plausible , not all calculated cMCS will represent suitable candidates for the construction of real production strains. When determining cMCS for experimental implementation, the time for minimization of the cut sets should be extended (see below) and a variety of cMCS can be calculated from which some might be potentially more promising  than the others. In addition, higher minimum yields can be tested for production strains to determine what maximal product yields under coupling can be achieved with a limited number of knockouts.E. coli . In those cases, feasibility of coupling would increase again, if we allow knockouts also for at least those exchange reactions where corresponding genes of the involved transporters are definitely known  are excreted by E. coli. On the other hand, if a production strain constructed from a cut set excretes a metabolite whose exchange reaction was not open in the model, then this cut set might not work as expected. For those cases, a practical solution can be found as follows: when the situation arises that, after experimental implementation of some knockouts of a calculated cut set, a metabolite is excreted whose outflow was not considered in the model before, it is possible to modify the model accordingly and then to recalculate and adapt the current cut set(s) to get intervention strategies which additionally suppress the unwanted excretion. In this manner, a production strain can be designed through an iterative cycle of calculation and experiment as was recently demonstrated for high-yield itaconic acid synthesis in E. coliAn important factor when setting up the model for cMCS calculation for growth-coupled product synthesis is the selection of active organic metabolite outflows. In the model configurations used herein, we allowed standard (fermentation) products to be excreted by the cells. If outflows for other organic metabolites, that are unlikely to be excreted, are left open, then the number of required cuts will increase making the calculation and experimental implementation of the cut sets more difficult than necessary. Moreover, feasibility of coupling can then even be completely lost for some metabolites because no knockout strategy can be found that can prevent synthesis of undesired by-products while still allowing growth. For instance, when all 285 organic metabolite outflows contained in the iJO1366 model are open in 366 ref.  model haIn summary, our results underline the great potential of growth-coupled designs for the rational engineering of cell factories. We have shown that such designs are, in principle, widely realizable in all production organisms investigated. Several microbial strains that implement coupling have already been developed81012171819202122E. coli and S. cerevisiae aerobic as well as anaerobic growth was considered. Simulation of anaerobic growth of E. coli was implemented by removing the exchange reaction for oxygen, while for S. cerevisiae this was achieved by removing the cytochrome c oxidase which is part of the respiratory chain . The other two heterotrophic organisms, A. niger and C. glutamicum, are obligate aerobes; hence, only aerobic growth was considered. The fifth organism, Synechocystis sp. PCC 6803, is photoautotrophic and was simulated with light as limited ‘substrate’ together with an unlimited supply of CO2. All models allow an unlimited uptake of inorganic compounds necessary for growth while uptake of the substrate (photons in case of Synechocystis sp. PCC 6803) is limited to known maximal values. Furthermore, organism-specific ATP requirements for non-growth-associated maintenance processes were taken into account if provided by the original models  outflows of organic metabolites were restricted to typical (fermentation) products for the given organism when the cut sets were calculated (no restrictions were set for inorganic compounds). Concretely, in E. coli we considered ethanol, lactate, formate, acetate, succinate and hydrogen as possible outflows . For S. cerevisiae we allowed ethanol, glycerol, pyruvate, acetate and succinate to leave the cell. For A. niger the open outflows are gluconate, citrate, oxalate, malate, succinate and erythritol; for C. glutamicum they are glutamate, succinate, lysine, lactate, acetate, alanine, isoleucine and glycine. In the Synechocystis sp. PCC 6803 model only three organic metabolites can be excreted from the cell and because these outflows are essential for growth they were left open.The models of the four heterotrophic organisms contain many exchange reactions for organic metabolites allowing the outflow of the associated metabolites from the cell. For example, the E. coli model, all reactions that do not have an associated gene were also considered irrepressible. For the other two models that include some gene-reaction mappings (A. niger and C. glutamicum), the cases of missing associations were investigated in more detail to decide whether or not to make such reactions irrepressible. The number and percentage of irrepressible reactions in the respective models is shown in To ensure that the calculated knockout strategies (cut sets) have a high degree of biological relevance, reactions were set to be irrepressible in the models if they do not correspond to enzyme-catalysed biochemical reactions in which substrates are converted to products. This pertains to pseudo reactions representing transport processes, for example, between different cellular compartments and the exchange of substances to/from the extracellular space . Furthermore, other pseudo reactions (including the consumption of ATP for maintenance processes) and non-enzymatic spontaneous reactions are contained in the models. All these reactions mentioned above were considered as irrepressible, that is, they cannot be knocked out in the cut sets . In the m internal metabolites and n reactions is represented by its m × n stoichiometric matrix N together with the sets Irr and Rev containing the indices of the irreversible and reversible reactions, respectively. The network is assumed to be in steady state implying that the net reaction rates r=T fulfilEach metabolic network with αi) or/and upper (βi) flux bounds might be known further constraining the reaction rates:For some reactions, lower (YP/S) can be specified by linear inequalitiesMetabolic flux distributions in a cell that are unfavourable for the efficient production of a certain chemical :with T consists here of a single row containing zeros except a ‘+1’ for rP and rS, while vector t has only one (row) element being zero.Hence, matrix Similarly, the inequalitiesd × n matrix D and d × 1 vector d can be used to represent desired (wanted) metabolic behaviours. We used the following inequalities to describe desired flux distributions with a product yield above μ is the growth rate):with D consists here of two rows: the first contains zeros except a ‘−1’ for rP and rS, while the second row contains non-zero values only for the growth rate (−1) and again for the substrate uptake rate rS  strong coupling, a reaction knockout set (a cMCS) has to be found that disables all undesired flux vectors (fulfilling (1)) while keeping at least one desired flux vector fulfilling (2). Note that the desired behaviour described in (2) is a subset of the complement of the undesired behaviour in (1) as the latter does not contain a constraint for biomass yield.Given these specifications, the MILP problem that is used to calculate a cMCS to check if growth-coupled production is possible takes the following form34N, the identity matrix I and the matrix T are split into two submatrices containing the reversible  and irreversible  reactions (columns), respectively. The zpi and zni variables are Boolean indicator variables that distinguish whether the corresponding vpi and vni variables are equal or unequal to zero . In case an indicator variable is unequal to zero, then its associated reaction is in the cut set and can carry no flux as demanded by the constraints for ri. For this MILP problem it is essential that finite lower (αi) and upper (βi) bounds for all fluxes are provided (see below). Finally, for the irrepressible reactions that are not allowed to be knocked out  is connected to one of the open standard outflow pathways of the model (which may go through different compartments), then it is assumed that the metabolite to be coupled is excreted along this outflow. Hence, in those cases, coupling is only considered for the (excreted) metabolite in the extracellular space and different instances of the same metabolite in other compartments are not taken as candidates for coupling. The respective numbers of candidate metabolites for coupling are shown in The MILP explained above is the central element of the procedure for testing whether a suitable knockout strategy (cMCS) exists that induces growth-coupled production of a metabolite with a demanded minimum product yield. In each model, the feasibility of growth coupling is checked for all organic metabolites that can be produced from the substrate, with two exceptions: First, for the models that use glucose as substrate the possibility of coupling the production of unphosphorylated glucose (which may occur in other compartments beside the extracellular space) is not considered as it is the same compound as the substrate. Second, if a metabolite  of the metabolite is possible.The flux through this export reaction is maximized via solving an appropriate linear optimization (LP) problem . If the result is zero, then the metabolite cannot be produced at all, if it its unbounded then its production is not bounded by the limited substrate (those metabolites are not part of the list of candidate metabolites for coupling). When the result is greater than zero and bounded it is divided by the substrate uptake and taken as maximum product yield.−1 glucose for the heterotrophic organisms and to 10−4 gDW mmol−1 photons for the cyanobacterium Synechocystis sp. PCC 6803 (which requires 51 photons to produce one molecule glucose).The minimum demanded product yield The network is compressed by merging sets of fully coupled reactions and by removing conservation relations.In case a cut set for a lower minimum product yield is known, it is checked whether this cut is also applicable for the current minimum yield. If this is the case steps 6 to 8 are skipped.−1 h−1. Compared to the LP in step 2, minimum biomass yield has been added as additional restriction for the FVA. Consequently, the LPs of the FVA may now be infeasible in which case growth-coupled production is not possible for this metabolite (where the following steps can be skipped).A flux variability analysis (FVA) with substrate uptake limit, ATP maintenance and minimum biomass yield The MILP is run with a given time limit (1–10 min.). The MILP minimizes the number of knockouts, but, to reduce the computation time, the solver is configured to stop as soon as the relative gap between the current objective and the best bound drops below 98% (which is still large but sufficient for our purpose). The solver stops when a solution is found, the time limit is reached or when the problem is determined by the solver to be infeasible (meaning that coupling is not possible).In case a solution (and thus a cut set) has been found by the solver for the MILP problem it is verified with separate LPs, testing whether, under application of the reaction knockouts contained in the cut set, coupling is achieved, that is, the undesired behaviour becomes infeasible and the desired behaviour remains feasible.If neither a solution was found nor the infeasibility of the problem has been determined, then the procedure is repeated up to 10 times from step 7 using a different solver seed which leads to a different exploration of the search space.non-minimal cut set; hence, a superset of one or more cMCS. A cMCS is then extracted from the cut set by iteratively checking the necessity of each knockout with a LP .If a cut set has been found this will typically be a For each candidate metabolite for coupling, the following steps are performed:C1 was found that required less interventions than a cut set C2 found for the same metabolite for a lower coupling yield , then cut set C2 was replaced by C1 , a cut set E. coli model, we adapted a recently proposed approachFor the calculation of gene cut sets in the All calculations were carried out on a computer with two Intel Xeon X5650 (2.67 GHz) hexacore CPUs using API functions of CellNetAnalyzerhttp://www2.mpi-magdeburg.mpg.de/projects/cna/cna.html. An example script to calculate cMCS for growth-coupled product synthesis can be found at: http://www2.mpi-magdeburg.mpg.de/projects/cna/etcdownloads.html.CellNetAnalyzer can be downloaded at: The authors declare that the data supporting the findings of this study are available within the paper and its How to cite this article: von Kamp, A. & Klamt, S. Growth-coupled overproduction is feasible for almost all metabolites in five major production organisms. Nat. Commun.8, 15956 doi: 10.1038/ncomms15956 (2017).Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Results of calculations.


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.