Escherichia coli, bioconversion efficiency is determined by not only metabolic flux but also the turnover efficiency of relevant pathways. Methyl-d-erythritol 4-phosphate (MEP)-dependent carotenoid biosynthesis in E. coli requires efficient turnover of precursors and balanced flux among precursors, cofactors, and cellular energy. However, the imbalanced supply of glyceraldehyde 3-phosphate (G3P) and pyruvate precursors remains the major metabolic bottleneck. To address this problem, we manipulated various genetic targets related to the Entner–Doudoroff (ED)/pentose phosphate (PP) pathways. Systematic target modification was conducted to improve G3P and pyruvate use and rebalance the precursor and redox fluxes.In engineered strains of pgi deletion background further improved carotenoid synthesis. The fine-tuning of flux at the branch point between the ED and PP pathways was important for carotenoid biosynthesis. Deletion of pfkAB instead of pgi reduced the carotenoid yield. This suggested that anaplerotic flux of G3P and pyruvate might be necessary for carotenoid biosynthesis. Improved carotenoid yields were accompanied by increased biomass and decreased acetate overflow. Therefore, efficient use of G3P and pyruvate precursors resulted in a balance among carotenoid biosynthesis, cell growth, and by-product metabolism.Carotenoid production was improved to different degrees by modifying various targets in the Embden–Meyerhof–Parnas (EMP) and ED pathways, which directed metabolic flux from the EMP pathway towards the ED pathway. The improvements in yield were much greater when the MEP pathway was enhanced. The coordinated modification of ED and MEP pathway targets using gene expression enhancement and protein coupling strategies in the An efficient and balanced MEP-dependent carotenoid bioconversion strategy involving both the ED and PP pathways was implemented by the coordinated modification of diverse central metabolic pathway targets. In this strategy, enhancement of the ED pathway for efficient G3P and pyruvate turnover was crucial for carotenoid production. The anaplerotic role of the PP pathway was important to supply precursors for the ED pathway. A balanced metabolic flux distribution among precursor supply, NADPH generation, and by-product pathways was established.The online version of this article (doi:10.1186/s12934-015-0301-x) contains supplementary material, which is available to authorized users. Bioconversion by engineered microbes in cell factories is a promising alternative strategy to produce valuable chemicals, many of which are expensive to produce in their native hosts or by chemical processes , 2. To cEscherichia coli is a well-studied example of an engineered microbial cell factory in which there has been coordinated modification of multiple targets . Combining these targets, the resulting strain (PTXIGK-AEGG-SXID10RK) produced 17.8 ± 0.2 mg lycopene/g DCW in flask culture  availability. Disruption of the EMP pathway target pyruvate . In anotpyruvate . Researcate [pgi , 18. Howpgi [zwf . This ingnd expression. Weak gnd expression was better for carotenoid synthesis, indicating the importance of the proper flux ratio between the ED and PP pathways. Although the ED pathway generated precursors with an exactly balanced ratio for MEP-dependent biosynthesis, the precursor balance still might be perturbed as both G3P and pyruvate could be metabolized in other cellular pathways. The results of this study implied that the anaplerotic role of the PP pathway might be important for precursor supply. The first clue was that the lower part of the MEP pathway was still necessary for ED/PP pathway route-dependent carotenoid biosynthesis. In addition, the carotenoid yields of Δpgi strains were greater than those of ΔpfkAB strains. The PP pathway routes achieved by Δpgi and ΔpfkAB might differ in their theoretical flux distribution [pgi would block the recycling of F6P and G3P into the oxidative PP pathway, and increase flux into the lower part of the EMP pathway  was more favorable for MEP-dependent biosynthesis. This suggested that the major role of the PP pathway was to sustain the G3P pool, although NADPH generated by the PP pathway was also important because the ED pathway was redox imbalanced for MEP-pathway reactions. However, detailed flux and metabolite analyses are required to determine the exact metabolic profile.As expected, the ED/PP pathway route created by deleting EMP targets and/or improving ED/PP targets enhanced carotenoid biosynthesis. Eda and Dxs seemed to be key targets for improving carotenoid biosynthesis. This was most likely because of their roles in the balance and efficient use of G3P and pyruvate precursors supplied by the ED pathway. Further metabolic engineering work will focus on coordinated modifications of Eda and Dxs. The flux balance between the ED and PP pathways was optimized by fine tuning ribution . Deletiopgi not only provided a balanced precursor ratio for efficient turnover in the MEP pathway, but also created a well-distributed metabolic flux between carotenoid biosynthesis and other pathways. Acetate overflow is always a major metabolic burden in E. coli K-12 strains because of the imbalance of central metabolic flux in high-glucose culture conditions. Surprisingly, the improvement of carotenoid biosynthesis in the redistribution route led to both greater cell growth and less acetate accumulation. This indicated that MEP-dependent carotenoid biosynthesis might relieve the metabolic burden by balancing the level of redox or metabolites, e.g., pyruvate. Indeed, redistribution of central metabolic flux always has complex effects. A global flux response could occur when MEP precursors are used efficiently or when redox conditions are balanced, resulting in flexible adaptation of flux distribution. Additional factors in central metabolic pathways, such as the level of the regulator intermediate fructose 6-bisphosphate, might be affected in the redistribution route, resulting in various responses and effects on the carotenoid biosynthesis pathway. Further research should focus on the effects of global regulation of central metabolic networks and their flux in carotenoid biosynthesis.The bioconversion efficiency of a given chemical is determined by both the stoichiometric yield of the pathway and the kinetic efficiency of pathway reactions. The metabolic route established by deleting Escherichia coli BW25113 was used as the parental strain for genetic modification and carotenoid production. Gene knock-out strains were obtained from the KEIO collection [Escherichia coli DH5α was used for standard plasmid construction. The plasmids used for MEP gene expression and as reporter vectors were derived from expression vectors previously developed in our laboratory (unpublished). These vectors have the following features: a promoter (araBAD), multiple cloning sites, the rrnB terminator, origin of replication , and an antibiotic resistance gene . The plasmids pUKM (derivative of pKD4), pKD46, and pCP20 were used for gene integration. All E. coli strains and plasmids used in this study are described in Table llection . EscheriPlasmids were constructed using standard molecular biological protocols . For chrLB medium was used for all molecular construction experiments and strain cultures. Auto-inducing medium  was usedAll strains were stored at −80°C until use. Strains were pre-cultured in LB medium supplemented with appropriate antibiotics at 37°C overnight and then transferred into auto-inducing medium to induce expression of recombinant proteins. For neurosporene and lycopene production, cells were harvested by centrifugation after protein induction. Cells were then transferred to bioconversion medium at a starting biomass of 4 OD/L. For bioconversion experiments, cells were cultured in a flask at 37°C with shaking at 220 rpm for indicated times, and then harvested to quantify products and metabolites.g for 5 min. The cell pellet was washed and then extracted in 1 mL acetone at 4°C for 1 h. The mixture was centrifuged at 12,000g, and the absorbance of the supernatant was measured at 470 nm. Neurosporene was quantified as described elsewhere [600). The acetate concentration was determined by HPLC using a C18 column (Agilent), and the glucose concentration was measured using a glucose monitor (SDBI).Cells were harvested by centrifugation at 12,000lsewhere . LycopenFor batch fermentation of carotenoids, the strains were pre-cultured in auto-inducing medium to induce the synthesis of recombinant proteins. Then, cells were transferred into bioconversion medium in a bioreactor (BioLector), with a beginning cell density of 4 OD. The cells were cultured at 37°C with shaking at 800 rpm.


Temporal changes of gene expression are a well-known regulatory feature of all cells, which is commonly perceived as a strategy to adapt the proteome to varying external conditions. However, temporal (rhythmic and non-rhythmic) changes of gene expression are also observed under virtually constant external conditions. Here we hypothesize that such changes are a means to render the synthesis of the metabolic output more efficient than under conditions of constant gene activities. In order to substantiate this hypothesis, we used a flux-balance model of the cellular metabolism. The total time span spent on the production of a given set of target metabolites was split into a series of shorter time intervals (metabolic phases) during which only selected groups of metabolic genes are active. The related flux distributions were calculated under the constraint that genes can be either active or inactive whereby the amount of protein related to an active gene is only controlled by the number of active genes: the lower the number of active genes the more protein can be allocated to the enzymes carrying non-zero fluxes. This concept of a predominantly protein-limited efficiency of gene expression clearly differs from other concepts resting on the assumption of an optimal gene regulation capable of allocating to all enzymes and transporters just that fraction of protein necessary to prevent rate limitation. Applying this concept to a simplified metabolic network of the central carbon metabolism with glucose or lactate as alternative substrates, we demonstrate that switching between optimally chosen stationary flux modes comprising different sets of active genes allows producing a demanded amount of target metabolites in a significantly shorter time than by a single optimal flux mode at fixed gene activities. Our model-based findings suggest that temporal expression of metabolic genes can be advantageous even under conditions of constant external substrate supply. The cellular metabolism represents a network of thousands of chemical reactions and transport processes, most of them catalyzed by specific enzymes and transport proteins. The function of this network consists basically in the conversion of chemical compounds that are taken up by the cell from the extracellular space (input) into a large variety of chemical compounds (output) serving either as building blocks for the formation and maintenance of cellular structures , extracellular structures in case of tissue cells , donors of chemical energy needed to drive otherwise endergonic reactions  or signaling molecules released from the cell . As the term ‘biomass’ is commonly reserved to designate all chemical compounds constituting a cell, tissue or organism, we will use the term ‘metabolic output’ to indicate all kinds of chemical compounds required to maintain the structure and physiological functions.The most important feature of the cellular metabolic network is its ability to adjust the metabolic output to varying external conditions as, for example, an increase in the concentration of pro-inflammatory cytokines or growth factors, depletion of specific substrates (in particular oxygen) or challenges by toxic compounds. This adjustment of the metabolic network to alterations in the external conditions is achieved by selectively increasing or decreasing the capacity of enzymes and membrane transporters. Short-term changes of enzyme activities on a time scale of a few seconds are mainly accomplished by allosteric effects, reversible phosphorylation and de-phosphorylation, and reversible protein-protein or protein-membrane association/dissociation. Long-term changes of enzyme activities on a time scale of minutes, hours and even days result from changes in the expression of enzymes, brought about by changes of the rate of transcription (DNA to mRNA), translation (mRNA to protein) and proteolysis (protein to amino acids).Long-term regulation of enzyme capacities by temporal gene expression is constrained by the condition that the total protein content of the cell has to be kept within rather narrow bounds  of 15–3Saccharomyces cerevisiae, when grown under nutrient-limited conditions 3 to be g0 = 0.12, g1 = 0.2 and g2 = 1. The resulting upper bounds are then ub = ⊤ = 4.545 ∙ ⊤ = v.Note that for this concrete example, the concept of perfect gene regulation  would alDW/h see . Perfect1 + Γ2 = 100 units, the optimal solution with strategy B always needs τ1 + τ2 = 6.67 h, whereas the time needed with a single flux mode (strategy A) varies between τ(1) = 5 h in the case Γ1 = Γ2 = 50 units to τ(1) = 9 h for Γ1 = 90 units, Γ2 = 10 units. When we fix Γ1 = Γ2 = 50 units and vary instead the catalytic constants kc1, kc2, strategy B becomes preferable when the constants are sufficiently different. For example, with kc1 = 0.5 h−1 and kc2 = 5 h−1 we get for strategy A ub1 = 5 h−1 and ub2 = 50 h−1 and therefore τ(1) = 10 h. For strategy B the bounds are reciprocally increased by the factor 3/2 or zero and they allow production of Γ in a time of τ1 + τ2 = 6.67 h + 0.67 h = 7.3 h only.For Γ1 of product P1 and Γ2 of product P2 in the four different gene activation strategies depends on the turnover number, molecular mass and expression efficiencies of the catalyzing enzymes. The minimal production times were computed by randomly varying the numerical values of these parameters in the range from 0.1 to 10 , which is mandatory active, compared with the amount of protein allocated to the enzymes of the two downstream pathways (v1 and v2) between which can be switched, the lower the benefit of strategy B , strategy B is not favorable for even 55% of the simulated cases. Conversely, if a significantly larger portion of available protein is spent on the downstream operating enzymes, strategy B enables a reduction of the production time in virtually all cases  with strategy A (simultaneous production of both products). For fixed cial see . General2 is not constrained; the rate of the membrane transporters for glucose and lactate is subject to the same constraints as all other enzymatic reactions. As already stated in the introduction, some of the metabolic objectives of a network have to be permanently fulfilled during the whole life cycle of a cell, and thus cannot be temporarily switched off. For the exemplary network in 1)  see  to minimn 1) see .vj ≥ mbj for j ∈ {GSHox, ATPase}. These 20 MGSs , each defining an optimal flux mode (MFMs) that produces the maximal amount of one of the four biomass components and the two maintenance metabolites ATP and GSH see . Operatikcj and molecular weights γj were taken from the BRENDA data base (http://www.brenda-enzymes.org/). Transport rates for glucose and lactate were taken from [s−1, according to [s−1, which is the minimum of the values for the individual reactions, as retrieved from BRENDA. For Glucose-6-P-dehydrogenase we assigned a turnover rate of 14 s−1 according to [Numerical values for the turnover rates ken from , 23, resrding to  and oxidrding to . The molAtot, was chosen such that the minimal time for the production of the demanded metabolic output given in Atot were obtained depending on the availability of the substrates glucose and lactate   with l = 1 allows inactivating parts of the network, whereas all genes are active in the reference case. As a result the times τ(1) are below 8h, see Having defined the reference case, we solved the minimization problem (𝒫1) see  to checkl = 2 to l = 4 are depicted in l = 4 optimal solutions). Intriguingly, the most significant drop of the minimal production time was already obtained by allowing two phases, i.e., switching the network once between two different steady states. A larger number of phases resulted only in a marginal further improvement. The best solution with a minimal production time τ = 4.78 h was obtained with three switches between l = 4 phases and glucose and lactate as allowed substrates. The relative proportions of the biomass components produced within the four phases and the corresponding flux modes are shown in de novo synthesis of mRNA and proteins consumes energy and other metabolic resources. Since the whole protein pool is distributed in every phase, degradation and synthesis are balanced and the cost for degradation is implicitly contained in the synthesis costs.The minimal production times obtained when the number of possible phases was increased stepwise from de novo synthesis of DNA) is predicted to occur only in short phases, whereas the production of the more abundant components (lipids and proteins) occurs in more than one phase. The optimal solutions depend critically on the availability of substrates. If, for example, only glucose is available, the total time interval during which the genes related to protein synthesis are active is longer than in a situation where both glucose and lactate can be used . However, expression efficiencies may considerably vary as meanwhile convincingly demonstrated by the generally poor correlation between mRNA and protein levels . As in the preceding sections, we determined for each sample the minimal value of Atot with which the demanded metabolic output can be accomplished within 8h in the reference state . Severatate see . As expel = 1, 2, 3, 4 different phases and different combinations of glucose and lactate as available substrates. τ(l), l = 1, 2, 3, 4. Remarkably, for all random samples the strategy of alternating gene switches resulted in a reduction of the total biomass-production-time. Thus, the findings of the preceding section, according to which a variable use of selected gene sets in different metabolic phases allows a reduction of the biomass production time, turn out to be robust against variation of enzyme expression efficiencies.Then, for each sample, we solved the optimization problem for We have presented a theoretical framework to study the impact of variable usage of genes and related metabolic enzymes on the efficiency of cells to accomplish their metabolic output. The main conclusion of this study is that even in a constant metabolic environment, a temporary switch between different metabolic phases could be a regulatory mechanism that allows a faster and thus more efficient production of the metabolic output. We deliberately emphasize the constancy of the metabolic environment, i.e., the constant availability of nutrients and oxygen, as our approach does not consider that switches between different metabolic states of the cell can be accomplished by other factors such as, for example, hormones, circadian variations of substrate supply, or changes of internal metabolites that upon accumulation or depletion may invoke transcriptional networks. From the evolutionary point of view, exploiting consecutively only certain parts of the whole metabolic network in order to minimize the time required for the production of the metabolic output at limited amount of investable protein could have been one relevant factor that has led to the establishment of temporary gene expression. In this light, temporal gene expression does not only serve as a means to adapt the metabolic output of different cells, tissues and organs to the varying needs of the organism, but also as a strategy to accomplish this output with high efficiency. This view holds in particular for a rapidly dividing cell which may acquire an advantage if its biomass can be reproduced faster than that of its competitors.vmax). In contrast, all metabolic pathways studied so far have in common that only few enzymes (mostly only a single one) are rate limiting, while the others are not saturated with their ligands, and therefore possess a large overcapacity compared to the actual flux they carry. Second, an investigation of the control of metabolic flux in the model bacterium Bacillus subtilis by quantifying fluxes, transcripts and metabolites in eight metabolic states enforced by different environmental conditions revealed that for the majority of enzymes in central metabolism, enzyme concentrations were insufficient to explain the observed fluxes [de novo protein synthesis consumes some extra ATP. For a rough estimate of the additional costs associated with the de novo synthesis of enzymes in various metabolic phases, we added up the mass of protein allocated to those enzymes that are additionally expressed when switching from one phase to the next. This gave an additional ATP demand of 9% resp. 8% for the non-dividing resp. dividing cell, compared with the basal (maintenance) ATP production flux of 5 mmol/gDW/h. Taking into account this extra ATP demand when solving the optimization problem for varying metabolic phases, there was no significant change of the gain in biomass reproduction time. Regarding the time-delay caused by switching from one enzyme expression pattern to the next, experimental observations suggest this switch to occur in a smooth fashion in that the degradation of proteins (and related mRNAs) active in the preceding metabolic phase goes hand in hand with the synthesis of new proteins of the next metabolic phase. Such a tight coupling between protein degradation and synthesis assures the homeostasis of the cellular amino acid pool [de novo protein synthesis thus should only marginally prolong the total production time of biomass.One basic assumption made in our approach is that the amount of metabolic enzymes (as well as that of any other protein in the cell) is constrained by the general condition that the total protein mass cannot exceed a certain threshold value without reducing the aqueous phase to an extent that effective communication and catalysis inside the cell gets impaired. This condition was implemented into our approach by rendering the upper bound of fluxes dependent on the number of simultaneously expressed enzymes: The less enzymes are required, and the smaller their molecular masses, the more protein they can acquire. With this constraint on the upper bound of fluxes, the reduction of the biomass production time achieved by the strategy of alternating switches between different metabolic phases appears to be very robust against variations in the expression efficiency of the enzymes in the considered network. Importantly, compared to the approach in  also dead fluxes . In lined fluxes . Third, d fluxes . Moreoved fluxes . Contempd fluxes . Finallycid pool . The remRegarding the choice of metabolic targets for the concrete metabolic network studied in this work, one has to admit that they are not fully independent from each other. For example, a certain basic level of protein synthesis is indispensable at any time for the synthesis of those enzymes required in the current flux state. This, in turn, requires also a basic level of mRNA synthesis at any time. Therefore, the optimal solutions obtained by our approach can only represent rough approximations to the reality just indicating the general trend. Indeed, similar as in our computations, a clear phase shift between the peak activities of the pathways of carbohydrate, lipid, amino acid and nucleotide metabolism has been observed in liver cells . Generalγ) and lipid oxidation (PPAR-α). The cellular synthesis of proteins can be globally enhanced by the serine/threonine protein kinase mTOR. Hitherto, the gene regulatory mechanisms underlying the activation of metabolic enzymes by various TFs and the mutual interactions of different TFs at one and the same promotor are not exactly known. Hence, our concept of minimal gene sets appears to be at the moment a reasonable surrogate for the still not completely understood gene regulation of metabolic enzymes. It has to be critically noted, however, that the results of our optimization approach, i.e., the strategy used to sequentially activate metabolic pathways strongly depend on the assumptions made on the cistronic regulation of pathway enzymes [In our approach, the activation and inactivation of genes is accomplished by the regulation of so-called minimal gene sets. These are defined as sets of genes that can only be collectively turned on and off, because they are under the control of common transcription factors. This concept takes into account the existence of signaling pathways and related transcription factors (TFs) that specifically control metabolic pathways of the cell. For example, up-regulation of the transcription factor HIF-1 under hypoxic conditions leads to a higher expression of genes coding for glucose transporters and glycolytic enzymes. Transcription factors belonging to the group of peroxisome proliferator-activated receptors (PPAR) are known to specifically enhance the expression of genes related to lipid storage (PPAR- enzymes . In partε < 1 denote the fraction of biomass components that is lost in a given time and that needs to be restored within this time period (= homeostasis) to prevent severe cell damage or loss of cell functions. Then the solution of our optimization problem (𝒫1)  cell, the metabolic output does not lead to an accumulation of the total biomass but instead serves to replenish the permanent loss of biomass components by various degradation and damage processes . Let (𝒫1) see  predictscultures , 11. Indcultures . In mammcultures , 36. Ex cultures .v1, …, vl (in mol/gDW/h) that produces all required amounts of metabolic output while maintaining the indispensable target fluxes in all phases, and which minimizes the total time To find a sequence of flux modes s by the binary vector χs, with ws is the related MFM. In our model, we assume that an arbitrary gene is active if and only if it is member of at least one active MGS. Furthermore, we make the assumption that enzymes coded by inactive genes are also inactive, i.e., the transient phase between switching off genes and degrading the related proteins is not considered. Note also that one gene can be a member of different MGSs. In order to include the additional constraint of collectively activated and inactivated genes into the optimization problem, we introduce binary variables bs, s = 1, …, t, where t is the total number of MFMs, to indicate whether the gene group χs is active or inactive. It follows that gj = 1 if and only if there exists s ∈ {1, …, t} with bs = 1. To formulate this as a linear constraint, let j. Then we have to require thatWe denote the MGS associated with the accomplishment of a flux through the target reaction 0). We will call the resulting optimization problem (𝒫1), it performs the same optimization, but on a more restricted solution space.We can now add these constraints to the optimization problem (𝒫http://www.gurobi.com) via Matlab.All computations were implemented in Matlab. To solve the optimization problem we used the Gurobi Optimizer 5.6 (0) and (𝒫1) contain as an unknown the number l of allowed different metabolic phases. It can be shown that an optimal solution can always be achieved with some l bounded by the number of target metabolites. Moreover, the solution of the optimization problem does not depend on the order in which the differently activated MGSs are used, unless further constraints are added evaluating the costs of switching from one phase to the next.The optimization problems (𝒫l of metabolic phases in (𝒫0) resp. (𝒫1) is a priori not known. We show now that if the problem is feasible, there always exists an optimal solution using at most as many flux modes v1, …, vl as there are target metabolites in M*. Hence, in the optimisation problem (𝒫0) resp. (𝒫1), we can fix l = ∣M*∣.The number v1, …, vl and associated durations l > ∣M*∣. The constraints S* V)τ ≥ Γ, where V is the (n × l)-matrix whose columns are the flux modes v1, …, vl, and τ is the column vector ⊤ (where ∙⊤ denotes transposition). Consider the linear optimisation problem M*∣ (the number of rows of S* V). Such a solution M*∣ of the flux modes v1, …, vl. To find such an optimal solution, it is enough to solve (𝒫0) resp. (𝒫1) for the fixed value l = ∣M*∣.To prove this statement, assume there is an optimal solution consisting of flux modes see e.g.  Thm. 4.7S1 FigTo confirm the independence of the empirical results on the parameter range, the computations for (EPS)Click here for additional data file.S2 Fig(EPS)Click here for additional data file.S3 Fig(EPS)Click here for additional data file.S4 Fig(EPS)Click here for additional data file.S5 Fig(EPS)Click here for additional data file.S6 Fig(EPS)Click here for additional data file.S1 Text(PDF)Click here for additional data file.S1 Table(ODS)Click here for additional data file.S2 Table(ODS)Click here for additional data file.S1 Files(ZIP)Click here for additional data file.


Primary hyperoxaluria type I (PH1) is an autosomal-recessive inborn error of liver metabolism caused by alanine:glyoxylate aminotransferase (AGT) deficiency. In silico modeling of liver metabolism in PH1 recapitulated accumulation of known biomarkers as well as alteration of histidine and histamine levels, which we confirmed in vitro, in vivo, and in PH1 patients. AGT-deficient mice showed decreased vascular permeability, a readout of in vivo histamine activity. Histamine reduction is most likely caused by increased catabolism of the histamine precursor histidine, triggered by rerouting of alanine flux from AGT to the glutamic-pyruvate transaminase . Alanine administration reduces histamine levels in wild-type mice, while overexpression of GPT in PH1 mice increases plasma histidine, normalizes histamine levels, restores vascular permeability, and decreases urinary oxalate levels. Our work demonstrates that genome-scale metabolic models are clinically relevant and can link genotype to phenotype in metabolic disorders.  •In silico model of liver metabolism reveals global metabolic alterations in PH1•Changes in amino acid metabolism in PH1 result in a reduction of histidine and histamine•GPT overexpression normalizes histamine levels and reduces oxalate in PH1 mice Pagliarini et al. use a computational model of liver metabolism to predict alterations caused by the loss of alanine:glyoxylate aminotransferase (AGT), resulting in primary hyperoxaluria type I (PH1). In addition to known disease biomarkers, the model predicts a reduction in histidine and histamine levels. GPT overexpression in PH1 mice normalizes histamine and oxalate levels. Metabolism is primarily or secondarily affected in several acquired and inherited human diseases. Characterization of the metabolic changes occurring in health and disease states has a wide range of implications, from elucidation of pathogenetic mechanisms to development of new biomarkers and drug discovery.Inborn errors of metabolism (IEMs) are a group of Mendelian disorders resulting from genetic disruption of single metabolic enzymes. A large number of these reactions occurs in the liver. The study of these disorders has been instrumental to understanding the physiological consequences of metabolic reactions and pathogenesis of more common multifactorial diseases. In contrast to Mendelian diseases, which are due to severe impairment of single-enzyme reactions, common multifactorial diseases may result from mild impairment of several metabolic reactions . NeverthTissue-specific genome-scale metabolic models, which have only recently become available through the efforts of the modeling community, allow in silico prediction of the effects of genetic or chemical perturbations on human metabolism . These cAGXT gene and mutated in primary hyperoxaluria type 1 (PH1).Here we applied a computational approach to predict and analyze the metabolic alterations occurring in hepatocytes lacking alanine:glyoxylate aminotransferase (AGT), a peroxisomal enzyme encoded by the PH1 is an autosomal recessive disease that presents with hyperoxaluria, progressive renal involvement, and systemic deposition of calcium oxalate in multiple organs and tissues. Although the enzyme is only expressed in hepatocytes, lack of AGT results in excessive production of oxalate by the liver, leading to oxalate-induced damage in several tissues, particularly in kidneys. PH1 is a severe disease that results in high morbidity, pain, disability, poor quality of life, and early death if treated late or left untreated. Effective treatments for PH1 are still lacking and combined liver-kidney transplantation is the only available therapeutic option for patients with severe forms .PH1 was chosen for our computational metabolic modeling for the following reasons: (1) the defective enzyme is expressed only in the liver, (2) enzyme deficiency does not result in structural changes of the liver that could secondarily affect metabolic functions, and (3) a mouse model is available to readily investigate alterations predicted by the computational approach . We demoWe applied a computational systems-level approach to simulate in silico metabolic alterations in hepatocytes due to the gain of function (GoF) or loss of function (LoF) of a single enzyme starting from a recently published genome-scale metabolic network model of human hepatocytes (HepatoNet1). The in silico model comprises 777 metabolites and 2,539 reactions across eight compartments .cytosolic alanine-glyoxylate transamination carried out by the glutamic-pyruvate transaminase (GPT), also known as alanine aminotransferase (ALT)  . Hence, We next applied an algorithm we recently proposed, differential flux-balance analysis (DFA), to predict differences in metabolic fluxes and metabolite levels between wild-type (WT) and single-enzyme-defective hepatocytes across 442 different metabolic objectives  Pagliar. Of thesDFA is based on flux balance analysis (FBA), a standard mathematical procedure to semiquantitatively estimate the metabolic flux of each metabolic reaction in a genome-scale model at steady state  when satisfying a given metabolic objective  . For eacWe applied DFA and FVA on the extended HepatoNet1 model to simulate the effect of AGT enzyme LoF, causative of PH1. We thus obtained a ranked list of 590 metabolites , obtaineThe significance of model predications was assessed by metabolite set enrichment analysis (MSEA) . MSEA isThe metabolites predicted to change the most by the in silico analysis  were unexpected, but the most surprising and interesting metabolites were histamine and its precursor histidine, whose levels were predicted to be altered in the AGT LoF model. Histamine is a biogenic amine with central roles in allergic responses and gastric acid secretion, involved in regulation of immune response, inflammation-associated carcinogenesis, and neurotransmission. Histamine is endogenously generated from its precursor histidine by the histidine-decarboxylase in several tissues , includiAgxt−/− mouse model of PH1 that recapitulates the biochemical features of PH1 patients  . In addition, compared to WT controls, Agxt−/− mice had decreased vascular permeability, a functional readout of histamine-mediated response, which was restored by liver-directed AGXT gene transfer    A. Histamtransfer C and 1D.ic cells  and in putations .Agxt−/− mouse liver . Specifically, according to the model, the increase in glutamate production results in increased flux through the urea cycle, allowing more histidine to be degraded to glutamate . These rAgxt−/− mice, which was found to be increased compared to WT mice , we cannot rule out an additional contribution of these reactions to the observed phenotype. Therefore, we measured N-methyl-histamine in  WT mice B.To further probe the role of GPT in PH1, we also simulated GPT GoF in silico in the context of AGT LoF. As GPT carries out two reactions in the model , we simulated an increase in the flux of both reactions. As reported in In conclusion, as shown in According to the in silico model, histidine/histamine reduction in AGT LoF occurs because alanine is redirected from AGT to GPT and thus secondarily alters histidine metabolism . If thisAgxt−/− mice  are obviously the first choice for gene therapy. Nevertheless, for PH1 patients harboring null mutations, who are at an increased risk for an immune reaction against the transgene product, gene therapy with the gene encoding for GPT would be an alternative option. Moreover, GPT expression can be combined within the gene therapy vector expressing AGT to further improve therapeutic efficacy.In addition, we demonstrated both in silico and in vivo that the increase in oxalate caused by AGT LoF is partly rescued by GPT overexpression. Hence, we confirmed GPT as a modifier gene or potential drug target for oxalate detoxification in patients with hyperoxalurias either primary or secondary. There have been great efforts to develop liver-directed gene therapy for IEMs . VectorsGene therapy with viral vectors has proven to be more difficult than initially anticipated, and immune system reactions against viral vectors and risks of insertional carcinogenesis remain important obstacles for clinical translation. Therefore, development of pharmacologic approaches based on small molecule drugs remains attractive. Toward this goal, the data generated in this study also could be exploited for therapy of PH1 by searching for human-approved drugs able to increase GPT expression. Besides applications for PH1 therapy, drugs reducing hyperoxaluria by GPT overexpression might be effective also for treatment of other forms of primary hyperoxalurias  and more common forms of secondary hyperoxaluria . Our comOur work has implications for PH1 patients as well as for patients affected by other metabolic diseases. Genome-scale metabolic models painstakingly built up from scratch by the modeling research community  might beIn summary, our results show that a single-enzyme LoF results in dysregulation of several metabolic reactions, going beyond the classic view on the pathogenesis of a Mendelian disease, which is narrowly focused on the affected pathway in which the enzyme operates.More than a century ago, Archibald Garrod proposed the concept of “chemical individuality,” suggesting that even healthy individuals are biochemically unique due to inherited differences in enzymes, which are reflected in a predisposition to multifactorial diseases . In a moThe in silico metabolic network was described by a stoichiometric matrix  Supplem. A stoicTo validate the extended in silico model, we performed FBA to establish a flux distribution for each of the different metabolic objectives listed in To simulate the effect of an LoF of an enzyme ectively .To simulate a GoF of WT model .DFA is described in http://dibernardo.tigem.it).DFA allows us to compute the change in the concentration of a metabolite as result of an enzyme LoF or GoF, but not its sign. To establish if this concentration is expected to be elevated, reduced, or unchanged, we applied FVA . In FVA,http://www.metaboanalyst.ca/)  . MSEA woAgxt−/− mice were described previously  as described previously .Agxt−/− mice were injected intraperitoneally with L-Histidine (1 g/kg of body weight) and placed in metabolic cages for 24-hr urine collection. Blood samples for serum histamine levels were collected by retro-orbital bleeding 24 hr after histidine injections.3-month-old male Agxt−/− 3-month-old male mice were injected intraperitoneally with L-Alanine (1 g/kg of body weight). Blood samples for serum histamine levels were collected by retro-orbital bleeding 2 hr after the alanine injections.WT and AGXT or murine Gpt, respectively. HDAd was produced in 116 cells with the helper virus AdNG163 as described elsewhere . For histamine determination on liver lysates, organs were homogenized in PBS using a Tissue Lyser (QIAGEN) and histamine levels were normalized for protein concentrations that were determined using Bradford Reagent (Bio-Rad).Histamine in human plasma samples from PH1 patients and age-matched controls were analyzed by ELISA . Methylhistamine was measured by ELISA  on 24-hr urine collection according to the manufacturer’s protocol. Amino acids were measured by high-performance liquid chromatography (HPLC) at the Biochemical Laboratories at Baylor College of Medicine.For western blotting, liver specimens were homogenized in radio-immunoprecipitation assay (RIPA) buffer and complete protease inhibitor cocktail (Sigma), incubated for 20 min at 4°C and centrifuged at 13,200 rpm for 10 min. Pellets were discarded and cell lysates were used for western blots. Proteins were loaded on a 12% SDS-PAGE, and, after transfer to a polyvinylidene fluoride (PVDF) membrane, blots were blocked with TBS-Tween-20 containing 5% non-fat milk for 1 hr at room temperature, followed by incubation with primary antibody overnight at 4°C. The primary antibodies used were a rabbit anti-human AGT (Sigma) and rabbit anti-calnexin (Enzo Life Sciences).2 in DMEM supplemented with 10% v/v fetal bovine serum and penicillin/streptomicin. Cells were transfected with a plasmid containing a small hairpin RNA (shRNA) for AGXT gene (OriGene) using TransIT-LT1 transfection reagent according to the manufacturer’s instructions (Mirus). Selection of transfected clones was performed starting at 24 hr after transfection with 1 μg/ml puromycin, and single stably transformed cell line clones were isolated. Downregulation of AGT was shown by western blotting with primary rabbit polyclonal anti-AGXT (Sigma) and primary monoclonal anti-calnexin antibody (Enzo Life Sciences). Horseradish peroxidase (HRP)-conjugated antibodies were used as secondary antibody .Huh-7 cells were cultured at 37°C with 5% COExperimental data are presented as mean ± SD. Statistical significance was computed using the Student’s two-sided t test and p values < 0.05 were considered significant.R.P., N.B.-P., and D.d.B. conceived the idea. R.P. designed the computational approach and performed simulations. F.N. helped with software coding, debugging, and testing. R.C. and N.B.-P. conceived and designed the experiments. R.C. performed the experiments. G.M. and M.D.M. provided human PH1 samples. R.B. generated the AGT-deficient cell line. P.A. generated the HDAd vectors. R.P., N.B.-P., and D.d.B. wrote the manuscript.


Thermus thermophilus, an extremely thermophilic bacterium, has been widely recognized as a model organism for studying how microbes can survive and adapt under high temperature environment. However, the thermotolerant mechanisms and cellular metabolism still remains mostly unravelled. Thus, it is highly required to consider systems biological approaches where T. thermophilus metabolic network model can be employed together with high throughput experimental data for elucidating its physiological characteristics under such harsh conditions.T. thermophilus, iTT548, the first ever large-scale network of a thermophilic bacterium, accounting for 548 unique genes, 796 reactions and 635 unique metabolites. Our initial comparative analysis of the model with Escherichia coli has revealed several distinctive metabolic reactions, mainly in amino acid metabolism and carotenoid biosynthesis, producing relevant compounds to retain the cellular membrane for withstanding high temperature. Constraints-based flux analysis was, then, applied to simulate the metabolic state in glucose minimal and amino acid rich media. Remarkably, resulting growth predictions were highly consistent with the experimental observations. The subsequent comparative flux analysis under different environmental conditions highlighted that the cells consumed branched chain amino acids preferably and utilized them directly in the relevant anabolic pathways for the fatty acid synthesis. Finally, gene essentiality study was also conducted via single gene deletion analysis, to identify the conditional essential genes in glucose minimal and complex media.We reconstructed a genome-scale metabolic model of T. thermophilus, thus allowing us to gain valuable insights into its cellular metabolism through in silico simulations. The information obtained from such analysis would not only shed light on the understanding of physiology of thermophiles but also helps us to devise metabolic engineering strategies to develop T. thermophilus as a thermostable microbial cell factory.The reconstructed genome-scale metabolic model elucidates the phenotypes of  Thermus thermophilus is a gram-negative, obligate aerobic bacterium, representing one of the best-studied thermophiles. It usually colonizes the terrestrial volcanic hot springs  and was originally isolated from a Japanese thermal spa [T. thermophilus is resistant to other stress such as harsh chemical conditions [T. thermophilus, making it as a model organism in structural genomics with significant industrial potential [T. thermophilus is being recognized as a potential microbial cell factory for the low cost ethanol fermentation from lignocellulosic waste materials since it can grow by utilizing most of the C5/C6 carbon sources at relatively high temperatures, i.e. 70–80°C, thus reducing the energy costs: no cooling step is required following enzymatic hydrolysis, rendering it easier to distil subsequent fermentations [rmal spa . In addiotential -6. For eotential . Moreoventations .T. thermophilus is very limited; to date, the production of distinctive carotenoid molecules [T. thermophilus cellular behaviour. In this regard, constraints-based in silico metabolic modeling and analysis can be considered as one of the promising techniques to characterize the physiological behaviour and metabolic states of an organism upon various environmental/genetic changes as they systematically capture the genotype-phenotype relationships from the entire genome information [Escherichia coli[Bacillus subtilis[Saccharomyces cerevisiae[Pichia pastoris[Corynebacterium glutamicum[Ralstonia eutropha[Pseudomonas aeruginosa[Mus musculus[Homo sapiens[T. thermophilus GSMM based on the currently available biochemical and genomic information and its subsequent in silico analysis enables us to elucidate its unique metabolic behaviour.Despite enormous potentials for biotechnological applications, the current knowledge on the unique cellular physiology of olecules  and the olecules  are onlyormation ,13. As achia coli, Bacillu subtilis, Saccharerevisiae, Pichia  pastoris, Coryneblutamicum, Ralston eutropha, Pseudomeruginosa, and eve musculus and Homoo sapiens. Moreoveo sapiens, these mo sapiens-19,24,25in silico model of Thermotoga maritima was presented, covering its central metabolism along with the 3D structures of all the enzymes accounted in the network [Sulfolobus solfataricus, was also developed, and used to describe its autotrophic growth in bicarbonate via hydroxypropionate-hydroxybutyrate cycle under aerobic conditions [T. thermophilus based on the genome annotation of HB27 wild-type strain [In thermophilic microbes regard, there have been only a few initial attempts to model their cellular metabolisms. First, an  network . Recentlnditions . Howevere strain  for inveT. thermophilus HB27 was reconstructed through a three step procedure: (1) construction of draft network via compilation of genes, reactions and pathway information from biochemical databases based on the genome annotation of T. thermophilus HB27, (2) manual curation of the draft model by verifying the elemental balances in reactions and assigning proper gene-reaction relationships, and (3) gap filling using organism specific knowledge (see Methods). During the reconstruction process, significant efforts were highly required to identify and resolve the network gaps across various metabolic pathways. Such gaps exist due to the incomplete genome annotations which result in missing biochemical reactions and dead ends. These gaps can be appropriately filled by the addition of new reactions based on information obtained from the literature or inferred by the genome annotation of other organisms. For example, the initial model contained several metabolic gaps in the synthetic pathway of thermozeaxanthin and thermobiszeaxanthin, the unique type of carotenoids that are found only in thermophiles, enabling their cellular membrane to retain its fluidity even at very high temperatures [Staphylococcus aureus subsp. aureus 11819–97 and Halobacillus halophilus DSM 2266 for the sequential glycosylation and esterification of zeaxanthin with glucose and branched-chain fatty acids, producing thermozeaxanthin. Similarly, the draft model also had several gaps in the carbon assimilatory pathways, and thus was unable to consume six carbon sources, namely, trehalose, palatinose, isomaltose, cellobiose, glutamate and mannose. However, earlier studies have reported that T. thermophilus can grow on all these carbon sources [T. thermophilus HB27 genome. In such a way, of the 63 enzymes added, we could assign a putative locus for 10 of them, thus providing possible new annotations , 796 reactions and 635 unique metabolites. In iTT548, all the 796 reactions were classified into seven major metabolic subsystems: carbohydrates, amino acid, energy and cofactors, lipids, nucleotides, carotenoids and transport. Among them, amino acid metabolism has largest number of reactions and genes, followed by carbohydrates and energy and cofactors metabolism   [T. thermophilus with other thermophiles since the T. maritima model is only limited to the central metabolism while S. solfataricus GSMM was not accessible and no link was provided. From the comparison, there are 363 enzymes/genes are common between E. coli and T. thermophilus, mostly belonging to the central metabolic pathways such as glycolysis, pentose phosphate pathway and the TCA cycle. However, certain notable differences were observed in the amino acid synthetic pathways of T. thermophilus. For instance, lysine is synthesized via the alpha-aminoadipate pathway instead of diaminopimelate pathway. Similarly, the upstream of methionine synthetic pathway is not conserved with E. coli: the precursor molecule, homocysteine, is produced from O-acetyl-L-homoserine and hydrogen sulphide via O-acetyl-L-(homo) serine sulfhydrylase in T. thermophilus and from O-succinyl-L-homoserine and cysteine through O-succinyl-homoserine lyase in E. coli. The comparison between transport reactions further revealed that T. thermophilus lacks the phosphoenolpyruvate-dependent phosphotransferase system (PTS), a typical bacterial transport system, and thus consumes most of the carbohydrates including glucose via ATP-binding cassette (ABC) transporters.Figure iAF1260)  in termsT. thermophilus, iTT548 contains the necessary biosynthetic machinery for synthesizing several molecules which help them in habituating high temperatures. Unlike many other gram-negative bacteria, T. thermophilus does not contain lipopolysaccharides in cell outer membrane [T. thermophilus synthesizes several unique polyamines such as thermine, spermine, thermospermine and caldopentamine using a distinct pathway from L-arginine via aminopropyl agmatine [T. thermophilus also synthesizes branched chain fatty acids from amino acids such as valine, leucine and isoleucine via ketoisovalerate oxidoreductase (vorA) as in gram-positive bacteria such as Bacillus. Table iTT548; Figure T. thermophilus where the branched chain fatty acids and carotenoid synthetic pathways are highlighted.Interestingly, as a unique feature of membrane . Insteadmembrane ,35. Furtagmatine . These piTT548, we have also included a biomass equation based on our amino acid compositional analysis and the data obtained from literature. Importantly, such biomass composition must be carefully formulated to avoid any erroneous conclusions from the flux balance analysis [T. maritima and S. solfataricus, adopted the biomass equation from E. coli and M. bakeri, respectively, both grow at 37°C which is well below the optimal growing temperature range of thermophiles (50 ~ 80°C). In this regard, the compositions of some amino acids, valine, lysine, threonine, lysine, glutamine and isoleucine, are very distinctive between T. thermophilus and E. coli  glucose at 70°C. The residual concentration of glucose and the cell density were monitored  possibly due to the differences in H+ permeability of their cytoplasmic membrane at optimal growth temperature; T. thermophilus cytoplasmic membrane is highly permeable to H+, and thus, leaks protons without ATP synthesis via ATPase [in silico predicted cell growth of 0.66 h-1 was very close (within the acceptable error range of 10%) to the experimentally observed specific growth rate of 0.64 h-1, thus clearly indicating the high predictive ability of iTT548 even in complex media.To simulate the cellular growth in the minimal medium, the biomass equation was maximized in the flux analysis simulations while simultaneously constraining the glucose uptake rates measured during the exponential phase based on the assumption that wild-type organisms typically evolve towards the maximization of cellular growth during exponential phase . The exclication . Additioa ATPase . NotablyT. thermophilus in minimal and complex media, not surprisingly, it was significantly higher in the latter owing to the availability of rich carbon and nitrogen sources in the form of various amino acids. Since the microbe can take up some of the amino acids directly from the medium, it could be possible that the protein biosynthetic demand can be partially fulfilled. In order to confirm such hypothesis, we compared the simulated metabolic fluxes between minimal (phase P1) and complex media, and observed much lower fluxes through the relevant biosynthetic reactions of amino acids in the complex medium . The genes were then categorized into three classes: (i) completely essential – genes which are required for cellular growth in both the media, (ii) conditionally essential – required only in one of the media and (iii) non-essential – dispensable in both the media. The gene essentiality study revealed that 23.5% and 19.5% of the total 548 genes in iTT548 are essential for cell growth in minimal and complex media, respectively . On the other hand, examination of conditionally essential genes, i.e. genes which are essential only in minimal media, revealed that amino acid metabolism contains almost all such genes , possibly due to the smaller Open Reading Frames (ORF) content  despite possessing all the necessary modules for the cell to be viable at high temperatures. Furthermore, this observation also highlights the fact that since T. thermophilus thrives at higher temperatures than most other microbes, its fitness might be relatively less competitive with more rigid network organization. Interestingly, the gene essentiality analyses also indicated that the carotenoids metabolism is functionally quite fragile since almost all of its genes are essential for cellular growth. However, it should be noted that the gene deletion analysis results are sensitive to several parameters such as in silico medium setup and biomass composition. In this regard, the current biomass composition is obtain from T. thermophilus at optimum growth temperatures, i.e. 70°C, and thus the gene deletion results of the current study are only applicable to this condition.The gene deletion analyses of T. thermophilus, iTT548, the first ever representing thermophiles, containing 548 unique genes, 796 reactions and 635 unique metabolites. As a unique feature of T. thermophilus, iTT548 contains necessary metabolic pathways for synthesizing several unique carotenoids and polyamines which help them in habituating high temperatures. The reconstructed metabolic model was subsequently validated with the batch culture experiments on glucose minimal and complex medium where the in silico growth predictions of the iTT548 were in good agreement with the observed experimental results. The comparative flux analysis between minimal and complex media highlighted that the consumption and utilization of branched chain amino acids directly in the relevant fatty acids anabolic pathways, thus resulting in higher growth rates in the rich medium. A gene essentiality study was also conducted through in silico simulation studies in both minimal and complex media, highlighting a very high percentage of lethal genes in comparison with E. coli, suggesting that the metabolic backbone of T. thermophilus could to be quite rigid. Overall, the metabolic network presented in the current study is expected to be a significant contribution towards systems analysis of thermophiles where the metabolic model can be utilized along with high throughput datasets for the better understanding of organism.We presented the genome-scale metabolic network of T. thermophilus HB27 strain was used as a model organism. For fermentation in complex medium, a single colony was cultivated overnight at 70°C with 150 rpm in 5 mL of the TM medium [M medium , and theg and 4°C for 5 min and the supernatant was analyzed with the column using 5 mM sulfuric acid as a mobile phase at 0.6 mL-1 min. Concentrations of amino acids were determined by gas chromatograph/mass spectrophotometer (GC/MS)  equipped with an HP-5MS column (Agilent), as previously reported [Concentrations of glucose, organic acids and ethanol in the culture broth were measured by high performance liquid chromatography (HPLC)  equipped with an HPX-87H column , a dual λ absorbance detector. The collected samples were centrifuged at 14,000 reported . In brieT. thermophilus HB27 was reconstructed using the published genome annotation [The genome-scale metabolic network of notation  and the notation . First, notation  and Metanotation . Then, tnotation . The idein silico flux analysis, especially during the exponential growth phase, where the primary cellular objective is to maximize growth. Amino acid composition of T. thermophilus HB27 was estimated by hydrolyzing the cell pellets with 6 N HCl for 24 h at 130°C, and subsequently analysing the hydrolysates using HPLC equipped with UV-detector and C18 column. Cell wall and lipid compositions were obtained from previous publications on Thermus sp. [E. coli[Thermus sp. The individual weights of nucleotides in the DNA and RNA were calculated based on the reported G + C content of 69.4% [Cellular biomass composition is an important prerequisite for the rmus sp. ,35,52. T [E. coli since noof 69.4% . DetaileT. thermophilus metabolism under varying environmental conditions. The biomass reaction was maximized to simulate the exponential growth phase as described elsewhere [We implemented constraints-based flux analysis to simulate the lsewhere -55. MathSij refers to the stoichiometric coefficient of metabolite i involved in reaction j, vj denotes to the flux or specific rate of metabolic reaction j, j, respectively; and Z corresponds to the cellular objective as a linear function of all the metabolic reactions where the relative weights are determined by the coefficient cj. In this study, the constraints-based flux analysis problems were solved using COBRA toolbox [where  toolbox .As constraints-based flux analysis is an optimization based technique, it is often possible to have multiple flux distributions attaining the same physiological state. Therefore, in order to confirm the plausibility of internal metabolic fluxes simulated in minimal and complex media by flux analysis, we performed the flux variability analysis (FVA) to identify the possible range of all fluxes while simulating a particular phenotypic state. Mathematically, the optimization problem specific to FVA can be represented as follows:Zobj denotes the value of objective calculated by flux analysis and n is the number of fluxes. The upper range of fluxes is identified by maximizing the objective whereas the lower range is obtained by minimizing the same. In this study, the FVA was implemented using COBRA toolbox.where Gene deletion simulations were performed by maximizing the cellular biomass while constraining flux through the corresponding reaction(s) to be zero via the GPR relationships under defined nutrient uptake rates. In case of glucose minimal medium, only glucose was fueled as carbon source. On the other hand, glucose, trehalose and amino acids such as valine, leucine and isoleucine were supplied as carbon source based on the complex media based on nutrient consumption profile. The simulation results were subsequently analyzed to identify the essential genes where a gene is classified to be essential if the resulting cell growth prediction for the corresponding mutant is less than or equal to 5% of wild-type. Note that all the gene deletion analysis in this study was performed using COBRA toolbox.The authors declare that they have no competing interests.NRL, ML, JBP and DYL conceived and designed the study. NRL and JWS performed the batch culture experiments. NRL and SA created the draft model. ML refined the model and performed simulations. NRL, ML, IAK, JBP and DYL wrote the manuscript. JBP and DYL coordinated and directed the project. All authors have read and approved the final manuscript.Details of iTT548 containing all genes, reactions, metabolites. A list of reactions added during gap-filling and possible new annotations identified in this study are also provided.Click here for fileSBML file of iTT548.Click here for fileBiomass composition of T. thermophilus HB27 and NGAM calculations.Click here for fileList of essential genes in glucose minimal and complex media.Click here for file


Clostridium cellulovorans has been one of promising microorganisms to use biomass efficiently; however the basic metabolic pathways have not been completely known. We carried out 13C-isotopomer-based target metabolome analysis, or carbohydrate conversion process analysis, for more profound understanding of metabolic pathways of the bacterium. Our findings that pyruvate + oxaloacetate, fumarate, and malate inside and outside cells exhibited 13C incorporation suggest that C. cellulovorans exactly fixed CO2 and partly operated the TCA cycle in a reductive manner. Accompanied with CO2 fixation, the microorganism was also found to produce and secrete lactate. Overall, our study demonstrates that a part of C. cellulovorans metabolic pathways related to glycolysis and the TCA cycle are involved in CO2 fixation. C. cellulovorans, an anaerobic mesophilic bacterium, can degrade and assimilate not only various kinds of carbohydrates  and but also actual biomass (rice straw and corn waste) ; 5% CO2 (C. acetobutylicum and C. kluyveri); 10% CO2 (C. thermocellum and C. difficile)  and phosphoenolpyruvic acid (PEP) carboxylase (PEPC) were annotated. Notably, PFOR of glycolysis and PEPC of the TCA cycle are both in the node of main metabolic pathways in C. cellulovorans. Therefore, the study of CO2 fixation by metabolome analysis would help to clarify the complete metabolic pathway of C. cellulovorans. In particular, 13C-labeling studies of metabolic products are useful for understanding the in vivo metabolism since 13-carbon isotope can distinguish fluxes through different pathways when these fluxes result in different positional isotopic enrichments in metabolic intermediates  was grown anaerobically at 37°C in an atmosphere of ‘100%’ CO2 unless otherwise noted. Liquid cultivation media contained the following reagents: 0.45 g/l KH2PO4 · H2O, 0.45 g/l K2HPO4, 0.9 g/l NaCl, 0.3675 g/l NH4Cl, 0.1575 g/l MgCl2 · 6H2O, 0.12 g/l CaCl2 · 2H2O, 5.2 mg/l Na2-EDTA, 1.5 mg/l FeCl2 · 4H2O, 0.942 mg/l CoCl2 · 6H2O, 0.85 mg/l MnCl2 · 4H2O, 0.07 mg/l ZnCl2 · 6H2O, 0.062 mg/l H3BO4, 0.036 mg/l Na2MoO4 · 2H2O, 0.024 mg/l NiCl2 · 6H2O, 0.017 mg/l CuCl2 · 6H2O, 5g/l NaHCO3, 4 g/l Bacto™ Yeast Extract (Becton and Dickinson Company), 3 g/l glucose, and 1 g/l L-cysteine. For labeling experiments, NaHCO3 and glucose were replaced by NaH13CO3 and [U-13C]-glucose, respectively .g at −9°C for 10 min for quenching were removed rapidly, and washed with 1 ml of 60% aqueous methanol (−40°C), followed by centrifugation at 3000 × g at −9°C for 10 min. Subsequently, supernatants were thoroughly removed, and cell pellets were frozen in liquid nitrogen and kept at −80°C until the following extraction procedures. Cell pellets were suspended in 500 μl of 100% methanol (−40°C), frozen in liquid nitrogen, and allowed to thaw on dry ice. After, the freeze-thaw cycle was performed 3 times in total, the suspensions were centrifuged at 16000 × g, at −9°C, for 5 min. Supernatants were retained and stored on dry ice, and another aliquot (500 μl) of 100% methanol (−40°C) was added to each pellet. The procedure was repeated twice, and the second aliquot of methanol was combined with the first one.Quenching and metabolite extraction were carried out as previously described trifluoroacetamide  was added and incubated at 37°C for 30 min. Insoluble residues were removed by centrifugation at 12000 × g at 4°C for 5 min, and cultivation supernatants were transferred to clean vials.Extract aliquots and cultivation medium supernatants (20 μl each), as well as dilution series of standard mixtures of target metabolites Table , were spDerivatized metabolites were analyzed using GCMS-QP2010 Ultra  equipped with a 30 m × 0.25 mm i.d. fused silica capillary column coated with 0.25-μm CP-SIL 8 CB low bleed . Aliquots (1 μl) were injected in the split mode  at 230°C, using helium as carrier gas at a flow rate of 1.12 ml/min. The column temperature was held at 80°C for 2 min isothermally, raised to 130°C (4°C/min) and then to 330°C (25°C/min), and maintained for 6 min isothermally. The interface and MS source temperatures were 250°C and 200°C, respectively, and the ion voltage was 1 kV. Data were collected by GCMS solution (Shimadzu), and identified metabolites are shown in Table 2 was required for culturing some Clostridium species, we speculate that C. cellulovorans also has the activity of CO2 fixation. Our speculation is further supported by the fact that C. cellulovorans, whose genes related to CO2 fixation were also annotated in the genome of C. cellulovorans , lactate, fumarate, and malate inside  could be the preservation of redox balance in the cell. That is, the reactions of lactate and malate production  might be accompanied with the regeneration of 1 molecule of NAD(P)+. In addition, the reaction of CO2 fixation by PFOR produces oxidized ferredoxin, and a molecule of oxidized ferredoxin subsequently produces 2 molecules of NAD+. These reactions may help oxidizing agents to be used in glycolysis. We also examined the existence of CO2 fixation enzymes (PFOR and PEPC) by the proteome analysis (data not shown). Compared to the flux from glycolysis to the TCA cycle, the flux to lactate could be dominant, since our results show that higher amounts of 13C atoms were incorporated into lactate, but not malate and fumarate  to make other metabolites. If C. cellulovorans produces citrate from glutamate, redox balance would be better maintained because glutamate dehydrogenase or glutamate synthase uses NADP+ and isocitrate dehydrogenase uses NADPH. In particular, when C. cellulovorans lives under a reductive condition, such a pathway is more reasonable than the pathway of citrate production from acetyl-CoA in an oxidative manner. To examine this hypothesis in the future investigation, it will be a promising approach to study how 13C atoms are incorporated into metabolites when using media containing 13C-labeled glutamate. Some other amino acids may be needed to maintain the metabolic pathway in C. cellulovorans, because the bacterium cannot be cultivated in media without yeast extract, which has glutamate  is valuable for the condition which was absent from O2. C. cellulovorans lives under anaerobic conditions because photosynthesis is not operated under the natural growth condition of C. cellulovorans (wood chip). It has been reported that CO2 fixation is useful to maintain redox balance in microorganisms that have the Calvin cycle or TCA cycle , which is about twice of that reported in the previous study (Sleat et al. 2 fixation, C. cellulovorans produced several kinds of organic acids and that the TCA cycle was partly operated in a reductive manner at the metabolite level. These presented results would provide important information for the application of C. cellulovorans as industrial cellulosome-producing bacteria.Here, we demonstrated that, accompanied with COThe authors declare that they have no competing interests.MS, HM, HM, YT, EF, KK and MU conceived and designed the study. MS, HS, HM and EF analyzed the data. MS, HS, HM, KK and MU drafted the manuscript. All authors read and approved the final manuscript.


Pseudomonas is generally attributed to downregulated expression of Fe‐requiring proteins accompanied by an increase in siderophore biosynthesis. Here, we applied a stable isotope‐assisted metabolomics approach to explore the underlying carbon metabolism in glucose‐grown Pseudomonas putida KT2440. Compared to Fe‐replete cells, Fe‐limited cells exhibited a sixfold reduction in growth rate but the glucose uptake rate was only halved, implying an imbalance between glucose uptake and biomass growth. This imbalance could not be explained by carbon loss via siderophore production, which accounted for only 10% of the carbon‐equivalent glucose uptake. In lieu of the classic glycolytic pathway, the Entner–Doudoroff (ED) pathway in Pseudomonas is the principal route for glucose catabolism following glucose oxidation to gluconate. Remarkably, gluconate secretion represented 44% of the glucose uptake in Fe‐limited cells but only 2% in Fe‐replete cells. Metabolic 13C flux analysis and intracellular metabolite levels under Fe limitation indicated a decrease in carbon fluxes through the ED pathway and through Fe‐containing metabolic enzymes. The secreted siderophore was found to promote dissolution of Fe‐bearing minerals to a greater extent than the high extracellular gluconate. In sum, bypasses in the Fe‐limited glucose metabolism were achieved to promote Fe availability via siderophore secretion and to reroute excess carbon influx via enhanced gluconate secretion.Decreased biomass growth in iron (Fe)‐limited  In aerobic matrices, Fe is present predominantly within Fe hydroxides and oxides, which have very low solubility (less than 10Pseudomonas are well known to secrete high‐affinity Fe‐binding molecules (or siderophores), which are metabolite expensive , a TCA cycle metabolite  pathway by splitting 6‐phosphogluconate (6P‐Glucn), instead of the classic Embden–Meyerhof–Parnas (EMP) glycolytic pathway that splits fructose‐1,6‐bisphosphate (FBP)  in the cytosol or oxidized to gluconate (and to a lesser extent 2‐ketogluconate) in the periplasm  a decrease in carbon uptake and subsequent influx toward the ED pathway, (2) a decrease in metabolic flux through Fe‐demanding pathways, (3) overflow secretion of partially oxidized metabolites, and (4) a high metabolic expense for siderophore biosynthesis. Using liquid chromatography–mass spectrometry (LC‐MS), we applied a stable isotope‐assisted metabolomics approach to explore these hypotheses. We found that glucose uptake did not agree with the significant reduction in the growth rate of Fe‐limited cells. And, the siderophore secretion rate did not represent a significant carbon loss. Our metabolic analysis revealed that glucose uptake was routed away from entry into the ED pathway through a significant increase in gluconate secretion. Monitoring of in vivo metabolic enzyme activity through kinetic flux profiling (KFP) demonstrated a decrease in the metabolic flux through the ED pathway as well as through Fe‐containing enzymes; this was confirmed via quantitative metabolic flux analysis (MFA).Based on the aforementioned studies, we hypothesized that the decreased metabolic investment toward biomass growth in glucose‐grown Pseudomonas putida cells (strain KT2440) were obtained from the American Type Culture Collection . Stable isotope‐labeled glucose was obtained from Cambridge Isotopes. All other chemicals were obtained analytical grade from Fisher Scientific  or Sigma‐Aldrich . All solutions were prepared with ultrapure water  or LC‐MS water. All containers used for media storage, culturing, and mineral dissolution were acid washed (15% and HNO3) overnight, washed three times with ultrapure water, and autoclaved. Nylon filters (0.2 μm) were used for all filtration. Absorbance measurements were conducted using an Agilent Cary UV–visible spectrophotometer. The LC‐MS analysis was performed with ultra‐high performance LC [Thermo Scientific  DionexUltimate 3000] coupled with a high‐resolution MS (Thermo Scientific Q Exactive quadrupole‐Orbitrap hybrid MS). Total dissolved Fe in solution was determined via inductively coupled plasma atomic emission spectroscopy .Pseudomonas syringae  kept at 30°C. Liquid cultures, in either 250‐mL Erlenmeyer flasks or 20‐mL glass test tubes, were agitated vigorously on a rotary shaker (200 rpm) in order to obtain well‐mixed aerated cultures. Cells were first grown in Luria–Bertani medium and subsequently transferred to minimal growth medium containing glucose as the sole organic carbon source. The growth medium, which was pH‐adjusted (7.0) and filter‐sterilized, contained the following: 20 mmol/L K2HPO4, 5 mmol/L NaH2PO4, 0.8 mmol/L MgSO4·7H2O, 37 mmol/L NH4Cl, 34 μmol/L CaCl2·2H2O, 13 μmol/L CuSO4·5H2O, 0.49 μmol/L H3BO3, 35 μmol/L ZnSO4·5H2O, 2.9 μmol/L MnSO4·5H2O, 0.11 μmol/L NiCl2·5H2O, 0.6 μmol/L Na2MoO4·5H2O, and 51.3 mmol/L (or 9.25 g/L) of glucose. For the Fe‐replete condition, the growth medium was supplemented with 20 μmol/L FeSO4·7H2O. For the Fe‐limiting condition, the growth medium was not supplemented with Fe of any forms; the background Fe concentration of the minimal medium  was below the Fe detection limit as measured by ICP‐AES. Subjecting the cells to at least two transfers into fresh minimal growth medium (with or without Fe supplementation) ensured that the cells were acclimated to the respective growth medium. As was done previously  . Measurements of cell dry weight in grams (gCDW) as a function of OD600 were obtained following lyophilization of sample aliquots (1.5 mL) using a Labconco  Freeze‐Dryer System. A conversion factor of 0.58 ± 0.08 gCDW L−1 per OD600 was determined from sampling during the exponential growth phase. This conversion factor is consistent with a previous value, 0.55 gCDW L−1 per OD660, obtained with batch cultures of glucose‐grown P. putida mt‐2  was monitored by measuring the optical density at 600 nm  and storing the filtrate at 4°C until NMR analysis. NMR samples were prepared as previously described  were harvested during the exponential growth of m/z range 70–900) for the detection of metabolites based on accurate masses, following established methods  were analyzed by reversed‐phase ion‐pairing LC coupled with electrospray ionization high‐resolution MS. The MS was operated in full scan negative mode  for about two doubling times during early to mid‐exponential growth phase (at least 3 h for Fe‐replete cells and 12 h for Fe‐limiting cells) . In previous metabolic labeling studies on Pseudomonas, the labeling of these metabolites were deduced from the labeling patterns of derivatized amino acids detected via gas chromatography–mass spectrometry  can be assumed , metabolism was quenched using the aforementioned 40:40:20 solvent mixture and the extracts were prepared and analyzed as mentioned above.We used kinetic isotopic incorporation of , or magnetite (Fe3O4). These experiments (two to six independent replicates) were conducted with cell‐free extracts obtained when the growth of cells reached the end of exponential phase under both Fe conditions. Cells were removed by centrifugation  followed by filtering. Control experiments were conducted with the minimal growth medium (without Fe supplementation) alone or amended with glucose (100 mmol/L), gluconate (10 mmol/L), or PVD standard (100 μmol/L). Prior to the mineral addition, all solutions were adjusted to pH 7.5. Experiments were conducted in an incubator shaker as described above for the cell culturing. The tubes were wrapped in aluminum foil to exclude light. Samples were taken after 100 h reaction time, centrifuged, and filtered. Total Fe was measured via ICP‐AES analysis. Prior to analysis, sample pH was adjusted to pH 4.5 with acetate buffer. Metal concentrations were blank‐corrected with appropriate blank experiments: minerals without cell extracts or minimal medium without minerals.Dissolution experiments (10 mL) were conducted in 50 mL propylene tubes with the following minerals at 1 g LP. putida under both Fe‐replete and Fe‐limited conditions   to the extracellular medium, growth of the Fe‐limited cells was quickly recovered to similar growth kinetics as the Fe‐replete cells . This confirmed that the reduced growth rate of the cells was due to Fe limitation.To investigate the link between biomass growth and carbon uptake under Fe limitation, we obtained the exponential growth rate and the glucose uptake rate in CDW−1 h−1, consistent with previously reported uptake rates in Fe‐replete P. fluorescens   used in this study does not secrete other specialized siderophores in addition to PVD . We also determined that a total of 62 moles of carbons was consumed from the central carbon metabolism to produce each mole of PVD, based on the PVD structure produced by P. putida KT2440 (succinate–chromophore–aspartate–ornithine–hydroxyaspartate–diaminobutyrate–glycine–serine–cyclic ornithine) and the additional fatty acid chain needed to form the pre‐PVD molecule . Excluding gluconate, the sum of the metabolite secretion rates was 22.59 ± 6.63 μmol C gCDW−1 h−1 and 30.28 ± 13.27 μmol C gCDW−1 h−1 in the Fe‐replete and Fe‐limited cells, respectively; these rates represent less than 0.3% of the glucose uptake rate (Table S2). However, the gluconate secretion rate was at one to two orders of magnitude above the rates of the other metabolites, respectively, 0.57 ± 0.08 mmol C gCDW−1 h−1 and 6.44 ± 1.33 mmol C gCDW−1 h−1 in the Fe‐replete and Fe‐limited cells, respectively (Table S2). Thus, 43.6 ± 0.03% of the glucose taken up by Fe‐limited cells was secreted as gluconate, whereas the corresponding value in the Fe‐replete cells was only 2.01 ± 0.16%  efflux during glucose metabolism through the central carbon metabolism  was consistent with the assimilation of the doubly 13C‐labeled glucose  reflected the formation of pyruvate both from the splitting of doubly 13C‐labeled 6P‐Glucn through the ED pathway and from nonlabeled metabolites downstream of the nonlabeled GAP . Nearly 80% of the PPP metabolites  were in nonlabeled forms under both Fe conditions, indicating that the nonoxidative PPP was the primary PPP route and that the flux through the oxidative PPP was small  indicated that these metabolites were formed primarily from the nonoxidative PPP, rather than from the oxidative PPP Fig. B. Nonlaball Fig. B. A smal13C‐labeled acetyl‐CoA  was in agreement with the combination of acetyl‐CoA (nonlabeled and singly 13C labeled) and oxaloacetate (OAA)  in the TCA cycle  downstream of the ED pathway and from the TCA cycle downstream of succinate . Minimal to no involvement of this pathway was determined during flux analysis of Fe‐replete glucose‐grown In sum, examination of the labeling patterns of intracellular metabolites allowed us to map out the metabolic network structure. However, it would be challenging to determine the relative contributions of the different metabolic reactions based only on the metabolite labeling schemes. Therefore, we applied a MFA approach to quantitate the fluxes through the different metabolic pathways.The MFA was constrained on the glucose uptake rate, siderophore secretion, metabolite secretion, and biomass growth. The agreement between model‐estimated and experimentally determined labeling patterns demonstrated the good quality of our optimization fits under both Fe conditions . Using the metabolic network structure elucidated in the previous section, we were able to quantitate a total of 28 metabolic reaction rates in the central carbon metabolism (Table S3).Pseudomonas wherein metabolite labeling was deduced from amino acid labeling determined via gas chromatography‐MS, some metabolic reactions could not be determined  that are required for many enzymes and, on the other hand, to the increased expression of genes and proteins that promote Fe acquisition  in the periplasmic oxidation of glucose to gluconate accompanied by a decrease (by up to twofold) in the metabolic fluxes throughout the intracellular metabolism under Fe limitation Fig. A. This d2 efflux, and metabolite secretions  that was invested toward biomass growth, siderophore secretion, COons Fig. B. The moons Fig. B, the inons Fig. B and 3B.ons Fig. C, the glons Fig. .2 efflux, this value was only slightly higher in Fe‐replete cells (37.8 ± 0.7% of glucose) than in Fe‐limited cells (31.0 ± 2.0%)  under Fe limitation was not proportional to the significant difference in the amount of carbon dedicated to biomass production (about 81% less under Fe limitation). It appears that the biosynthetic demands for biomass growth in the Fe‐limited cells were replaced by those for siderophore biosynthesis such that the Fe‐limited metabolism of the assimilated glucose still resulted in a significant CO2 efflux.Regarding the fractional amount of the glucose uptake lost as COin vivo  and citrate in the ons Fig. A. Howeveons Fig. B. Accumuion Fig. A.α‐KG, there was a decrease in both labeling kinetics and metabolite levels in the Fe‐limited cells . Therefore, the decrease in the succinate pool under Fe limitation may be a result of depletion of α‐KG due to siderophore biosynthesis, a decrease in the activity of the enzymes involved in the conversion of α‐KG to succinate, and an increase in the consumption of succinate. The relative contribution of these factors cannot be resolved by our experimental data. However, the MFA demonstrated that effluxes out of α‐KG (for biomass and siderophore) led to a decrease in the metabolic reaction rates downstream of α‐KG  and downstream  of the ED pathway Fig. A. HoweveIn sum, changes in the KFP and the intracellular metabolite pool provided in vivo experimental evidence for a decrease in the fluxes responsible for the initial catabolism of glucose and gluconate upstream and downstream the ED pathway, in addition to the expected decrease in fluxes through Fe‐containing metabolic enzymes. These changes were confirmed by the MFA modeling results. These findings suggest that, in addition to gluconate secretion, there was a tight metabolic regulation of gluconate catabolism through the ED pathway under Fe limitation.P. putida in promoting Fe availability in the soil environment, we reacted the bacterial secretions  with three Fe‐bearing minerals common in soils: hematite, goethite, and magnetite . The gluconate solution facilitated dissolution of goethite and magnetite but not hematite  Fe] and Fe‐limited [(−)Fe] conditions. Data are from four biological replicates of batch cultures. The red square indicates the sampling time during early to mid‐exponential growth phase when data were taken to determine excretion rates and growth rate. Red arrows indicate sampling points for steady state, kinetic and intracellular quantitation experiments.Figure S2. Effects of iron (Fe) addition (30 μmol/L) on the growth of Fe‐limited cells; the time when Fe was added is shown. The data obtained with Fe‐limited cells before and after Fe addition are shown with the white‐filled and black‐filled squares, respectively. Data points are from independent biological replicates (n = 2–5).Figure S3. Estimation of acetyl‐CoA (Ac‐CoA) labeling. (A) Biosynthesis of Ac‐CoA from pyruvate and (B) Biosynthesis of citrate from the combination of aspartate and Ac‐CoA. The Ac‐CoA labeling deduced from pyruvate was confirmed by the difference in the labeling patterns of aspartate and citrate.Figure S4. Estimation of CO2 labeling biosynthesis of N‐carbamoyl‐aspartate from aspartate. N‐carbamoyl‐aspartate is formed from aspartate following the incorporation of dissolved CO2 and ammonia from the extracellular medium. Addition of 13C‐labeled carbons from aspartate to N‐carbamoyl‐aspartate is taken as the addition of labeled dissolved CO2.Figure S5. Carbon mapping of intracellular metabolite labeling in the traditional tricarboxylic acid cycle and the glyoxylate shunt. Filled circles  indicate 13C‐labeled carbons. Red‐filled and red‐lined circles represent metabolites generated from the glyoxylate shunt; gray‐filled circles in OAA represent incorporation of labeled carbon dioxide (CO2) in solution. Legend for metabolite names are the same as reported in Figure Figure S6. Model‐estimated (white bars) and experimentally determined (black bars) of isotopomer distributions in the carbon labeling patterns of selected metabolites in Fe‐replete Pseudomonas putida cells. Data presented are the average of two independent optimizations of experimental data with two model predictions, both shown with standard deviation error bars. Legend for metabolite names: G6P, glucose‐6‐phosphate; F6P, fructose‐6‐phosphate; FBP, fructose‐1,6‐bisphosphate; 3PG, 3‐phosphoglycerate; Xu5P, xylulose‐5‐phosphate; S7P, sedoheptulose‐7‐phosphate.Figure S7. Model‐estimated (white bars) and experimentally determined (black bars) of isotopomer distributions in the carbon labeling patterns of selected metabolites in Fe‐limited Pseudomonas putida cells. Data presented are the average of two independent optimizations of experimental data with two model predictions, both shown with standard deviation error bars. Legend for metabolite names: G6P, glucose‐6‐phosphate; F6P, fructose‐6‐phosphate; FBP, fructose‐1,6‐bisphosphate; 3PG, 3‐phosphoglycerate; Xu5P, xylulose‐5‐phosphate; S7P, sedoheptulose‐7‐phosphate.Figure S8. Changes in metabolic reactions rates in response to 100% decrease  and 50% increase  in metabolite contribution toward biomass compared to the rates obtained from the reference biomass composition (as detailed Table S4). Model‐estimated values (averages ± standard deviations) are shown.Figure S9. Estimation of 2‐ketogluconate production. The production of 2‐ketogluconate was estimated by taking into account the measured flux (r4) from our experiments and the estimated flux (r4 + r6) from our metabolic flux analysis for gluconate excretion. The estimated flux was only greater than the measured flux in the Fe‐replete cells (see Table S4 below). The difference is taken as a flux out of gluconate to form 2‐ketogluconate.Figure S10. Effects of Fe on pyoverdine (PVD) quantification via UV–vis absorbance (400 nm). Measurements of PVD concentrations in bacterial supernatants in the absence (control) and in the presence of 50 nm or 50 μmol/L of either Fe2+ (FeSO4) or Fe3+ (FeCl3). Within a precision of two standard deviation values, the quantitation of PVD was not affected by the addition of Fe higher than the concentration in the starting solution in the Fe‐replete growth medium (Fe2+ = 20 μmol/L).Table S1. Specific rates (μmol gCDW−1 h−1) of siderophore and metabolite secretions in exponentially growing Pseudomonas putida KT2440 under Fe‐replete [(+)Fe] and Fe‐limiting [(‐)Fe] conditions.Table S2. Carbon requirement for pyoverdine biosynthesis.Table S3. Intracellular metabolic rates determined from quantitative flux modeling of the metabolism in Fe‐replete [(+)Fe] and Fe‐limited [(−)Fe] Pseudomonas putida using the 13CFLUX2 software. These metabolic fluxes are illustrated Figure Table S4. Normalized reaction rates (%)1 from metabolic flux balance analysis in Fe‐replete [(+)Fe] Pseudomonas putida KT2440.Table S5. Estimation of gluconate flux toward 2‐ketogluconate.Table S6. Metabolite effluxes toward biomass growth determined from the biomass composition of Pseudomonas putida.Click here for additional data file.