In silico models derived from such reconstructions can then be queried or interrogated through mathematical simulations. Proteomic profiling studies of the mature human erythrocyte have shown more proteins present related to metabolic function than previously thought; however the significance and the causal consequences of these findings have not been explored.The development of high-throughput technologies capable of whole cell measurements of genes, proteins, and metabolites has led to the emergence of systems biology. Integrated analysis of the resulting omic data sets has proved to be hard to achieve. Metabolic network reconstructions enable complex relationships amongst molecular components to be represented formally in a biologically relevant manner while respecting physical constraints. Erythrocyte proteomic data was used to reconstruct the most expansive description of erythrocyte metabolism to date, following extensive manual curation, assessment of the literature, and functional testing. The reconstruction contains 281 enzymes representing functions from glycolysis to cofactor and amino acid metabolism. Such a comprehensive view of erythrocyte metabolism implicates the erythrocyte as a potential biomarker for different diseases as well as a 'cell-based' drug-screening tool. The analysis shows that 94 erythrocyte enzymes are implicated in morbid single nucleotide polymorphisms, representing 142 pathologies. In addition, over 230 FDA-approved and experimental pharmaceuticals have enzymatic targets in the erythrocyte.The advancement of proteomic technologies and increased generation of high-throughput proteomic data have created the need for a means to analyze these data in a coherent manner. Network reconstructions provide a systematic means to integrate and analyze proteomic data in a biologically meaning manner. Analysis of the red cell proteome has revealed an unexpected level of complexity in the functional capabilities of human erythrocyte metabolism. The advancement of high-throughput data generation has ushered a new era of "omic" sciences. Whole-cell measurements can elucidate the genome sequence (genomics) as well as detect mRNA (transcriptomics), proteins (proteomics), and small metabolites (metabolomics) under a specific condition. Though these methods provide a broad coverage in determining cellular activities, little integrated functional analysis has been performed to date.Genome-scale network reconstructions are a common denominator for computational analysis in systems biology as well as an integrative platform for experimental data analysis ,2. ThereThough many cell and tissue-specific models have been reconstructed from Recon 1, the human erythrocyte has undeservedly received less attention as the cell has been largely assumed to be simple. Historically, red cell metabolic models began with simple glycolytic models ,14. In ain silico model of metabolism of the human red cell to date. Though comprehensive proteomic data provides an overview of red cell proteins, we believe it does not provide a full functional assessment of erythrocyte metabolism. Thus, we have also gathered 50+ years of erythrocyte experimental studies in the form of 60+ peer-reviewed articles and books to manually curate the final model. In order to objectively test physiological functionality, we have put the final model through rigorous simulation.Thus, we use available proteomics to develop the largest  iAB-RBC-283 is a proteomic based metabolic reconstruction and a biochemical knowledge-base, a functional integration of high-throughput biological data and existing experimentally verified biochemical erythrocyte knowledge that can be queried through simulations and calculations. We first describe the process and characterize the new erythrocyte reconstruction and determine the metabolic functionality. Then, we analyze the results by mapping genetic polymorphisms and drug target information onto the network.Thermotoga maritima [Proteomic data has been successfully used for reconstructions of maritima  and the maritima  and provmaritima . In addiThus, in this study, we construct a full bottom-up reconstruction of erythrocyte metabolism with rigorous manual curation in which reactions inferred from proteomically detected enzymes were cross-referenced with existing experimental studies and metabolomic data as part of the quality control measures to validate and gap fill metabolic pathways and reactions Figure .The final reconstruction, iAB-RBC-283, contains a metabolic network that is much more expansive than red blood cell models presented to date  was utilized to determine the functional metabolic pathways of the erythrocyte network . FVA determines the minimum and maximum allowable flux through each metabolic reaction [In order to ascertain the functional capabilities of the expanded erythrocyte reconstruction, iAB-RBC-283 was converted into a mathematical model. The expanded erythrocyte network was topologically and functionally compared to a previous constraint-based model of erythrocyte metabolism  see add. PredictNetwork level metabolic functional assessment showed that iAB-RBC-283 accounts for additional pathways into glycolysis through galactose, fructose, mannose, glucosamine, and amino sugars (N-Acetylneuraminate). Galactose can also be shuttled to the pentose phosphate pathway through glucuronate interconversions. Citric acid cycle enzymes  are present, but we were unable to fully understand their roles as full metabolic pathways were not present. However, fumarate can be shuttled into the model and exported as pyruvate through conversions by fumarase and malic enzyme.In addition, nucleotide metabolism and salvage pathways have been expanded from previous metabolic models to account for XMP, UMP, GMP, cAMP, and cGMP metabolism. In particular, ATP and GTP can be converted into cAMP and cGMP respectively as adenylate and guanylate cyclases were found to be present in the proteomic data.The erythrocyte uses amino acids to produce glutathione for redox balancing, converts arginine to polyamines and a byproduct of urea, and utilizes homocysteine for methylation. It has been proposed that Band III is the major methylated protein, particularly for timing cell death . This haAnother major expansion in metabolic capabilities represented in iAB-RBC-283 is lipid metabolism. Though the mature erythrocyte is unable to produce or degrade fatty acids, the cell can uptake fatty acids from the blood plasma to produce and incorporate diacylglyercol into phospholipids for upkeep of its membrane . A pseudFinally, FVA showed that the erythrocyte plays an important role in cofactor metabolism. The reconstruction accounts for uptake, modification, and secretion of multiple cofactors including vitamin B6, vitamin C, riboflavin, thiamine, heme, and NAD. In addition, human erythrocytes play a role in deactivating catecholamines , hydrolyin silico erythrocyte versus other similar metabolic networks, we calculated the connectivity of each metabolite [In order to compare the network structure of the tabolite . Simply,in silico erythrocyte to the global human metabolic network, Recon 1, as well as the separate human organelles  the erythrocyte biology or 2) the lack of complete proteomic profiling. First, as the erythrocyte circulates in blood, it has access to many types of metabolites. As the erythrocyte is relatively simple, it is possible that the reconstructed metabolic network is complete as different types of metabolites do not have to be created and can easily be transported into or out of the cell (e.g. amino and fatty acids). However, the network simplicity of the In silico simulations show a greater physiological functionality of erythrocyte metabolism. The additional functionality is not evident from the proteomic data alone. However, metabolite connectivity analysis shows that additional targeted proteomics are of interest. During the manual curation steps we noted that portions of the TCA cycle were detected but not functional. Further studies for cysteine, folate, and phospholipid metabolism are also of interest as some enzymes were detected in the proteomic data but little or no conclusive experimental literature was found.Decades of models have described erythrocyte metabolism to include principally glycolysis, the Rapoport-Luebering shunt, the pentose phosphate pathway, and nucleotide salvage pathways. Integration and compilation of proteomic data, however, has surprisingly shown evidence of a much richer metabolic role for the erythrocyte. Erythrocytes make contact with most portions of the body and are one of the most abundant cells . With such a varied metabolic capacity, the erythrocyte can act as a sink for and source of metabolites throughout the body.Erythrocytes have been previously studied as potential biomarkers for riboflavin deficiency , thiaminin silico erythrocyte as a potential biomarker, we cross-referenced morbid SNPs from the OMIM and nearly 4800 drugs from the DrugBank database with the 281 enzymes accounted for in iAB-RBC-283. 142 morbid SNPs were found in 90 of the erythrocyte enzymes. In addition, 232 FDA-approved, FDA-withdrawn, and experimental drugs have known protein targets in the human erythrocyte , allergies (flunisolide), cancer (topotecan), HIV (saquinavir), and high cholesterol (gemfibrozil). Due to the availability of erythrocytes from any individual, drugs can be easily screened and optimized A comprehensive listing of all observed morbid SNPs and drugs are provided in the Supplementary Material  used to methylate cathecolamines. A morbid SNP of COMT gene has been implicated in susceptibility to schizophrenia . In siliIn silico simulations show that the erythrocyte can also be used as a diagnostic for drug treated conditions. For example, topimarate is a drug for treating seizures and migraines and inhibits carbonic anhydrase. Inhibition of this enzyme during simulations showed a drastic change in 54 exchange reaction fluxes pertaining to carbohydrate, cofactor , lipid, and nucleotide metabolism. As individuals react differently to pharmaceuticals and sometimes require different dosages and types of drugs, our analysis shows that the red blood cell can act as a readily available diagnostic for personalizing drug therapies.To further investigate the diagnostic capability of the red blood cell, we assessed the uniqueness of the metabolic signatures detected. We compared all the metabolite signatures to see if some were shared between different SNPs or drug treatments. In all, 67% of the metabolic signatures are unique with most of the remaining similar to only one other perturbed condition Table .in silico simulation results provide a method to focus biomarker discovery experiments in the human erythrocyte, as well as interpret global metabolomic profiling. The flux variability shows that a large number of morbid SNPs and drug effects can be detected in the erythrocyte, with most having a unique metabolic signature. The differential activity in exchanges for the perturbed conditions allow for focusing experiments to particular metabolites, exchanges, and associated pathways, allowing development of targeted assays. In addition, global metabolomic profiling of perturbed conditions can be interpreted using the calculated metabolic signatures and the erythrocyte reconstruction. A full listing of all detected morbid SNPs and drug treated conditions, as well as the corresponding exchange reactions with differential activity and fluxes is provided in the Supplementary Material  annotations. GPRs mechanistically connect the genome sequence with the proteome and the enzymatic reactions. GPRs provide a platform for integration of high-throughput data to model specific conditions. In this study, proteomic data was integrated with Recon 1 to build a comprehensive erythrocyte network.iAB-RBC-283 was reconstructed in the following manner. Proteomic data for erythrocytes from multiple sources -24 were Thus, possible remnant enzymes, such as glycogen synthase and aspartate aminotransferase, were properly removed when experimental validation was not available. The manual curation process yielded 60+ peer reviewed articles and books representing over 50 years of erythrocyte research. In addition, erythrocyte literature sources from the Human Metabolome Database  were usein silico), AB , RBC (red blood cell), 283 (number of open reading frames accounted for in the network). iAB-RBC-283 consists of all the known metabolites, reactions, thermodynamic directionality, and genetic information that the detected erythrocyte metabolic enzymes catalyze. The final reconstruction is provided in both an XLS and SBML format in the Supplementary Material  fatty acids, palmitic, linolenic, and linoleic  were used . The finS, that is formed from the stoichiometric coefficients of the biochemical transformations. Each column of the matrix represents a particular elementally and charge balanced reaction in the network, while each row corresponds to a particular metabolite [The network reconstruction can be represented as a stoichiometric matrix, tabolite . Thus, tm × n); where m is the number of compounds and n is the number of reactions. In the case of iAB-RBC-283, m is 342 and n is 469.As each reaction is comprised of only a few metabolites, but there are many metabolites in a network, each flux vector is quite sparse. The stoichiometric matrix is sparse, with 1.3% non-zero elements, and has dimensions ,As v) are bounded by thermodynamic constraints that limit the directionality of irreversible catalytic mechanisms (lb = 0 for irreversible reactions) as well as known vmax's.allowing for simulation without kinetic parameters. The network fluxes , based on existing literature of metabolite transport in the human erythrocyte  is a well-established optimization procedure  used to In this work, a variant of FBA, called flux variability analysis (FVA) , is usedThe stoichiometric matrices of the reconstructed erythrocyte network, Recon 1, and its constituent organelles were used to calculate metabolite connectivities  of everyThe Morbid Map from the Online Mendelian Inheritance in Man (OMIM)  and the Just as FVA can be used to assess the function of a network under a particular set of constraints, it can also be used to assess the changes in function and thus has applications for characterizing disease states  and idenAB reconstructed the network, performed the analyses, and drafted the manuscript. NJ and BOP conceived the study and revised the manuscript. All authors approved the content of the final manuscript.Comparison of iAB-RBC-283 and previous constraint-based model of human erythrocyte. iAB-RBC-283 was compared with the previous constraint-based model of the human erythrocyte. Topological analysis showed that iAB-RBC-283 is a much more expansive network that better describes human erythrocyte metabolism. In addition, we recapitulated the randomized sampling results of the previous network and showed that the new erythrocyte model is more accurate, capturing all the correct predictions of the previous model but also correcting its inaccurate predictions.Click here for fileDetected SNPs and FVA results for SNP perturbations. Tables containing the OMIM SNPs of enzymes that are in iAB-RBC-283 with known pathologies, symptoms, metabolic subsystem, and classification. In addition, exchange reactions determined by FVA to be different in SNP perturbations are provided.Click here for fileDetected drug targets and FVA results for drug effect perturbations. Tables containing the known drugs, from DrugBank, that have targets in enzymes that are in iAB-RBC-283. In addition, information is provided on the drug including name, description, and classification. The exchange reactions determined by FVA to be different in drug perturbations are also provided.Click here for fileiAB-RBC-283 Reconstruction (table format). The reconstruction is provided in a table format with reactions, metabolites, and gene-protein-reaction associations. In addition, information is provided on whether the reaction was detected in the proteomic or metabolomic data and citations are provided for reactions with existing experimental evidence, implicating the reactions presence in the human erythrocyte.Click here for fileiAB-RBC-283 Reconstruction (SBML format). The reconstruction is provided in the standardized SBML format. The XML file can be loaded in to COBRA toolbox to perform in silico simulations. A copy of the file is also available at the BioModels Database (id: MODEL1106080000).Click here for fileCitations for exchanges in the human erythrocyte. Table containing citations used to determine exchange rates of metabolites into the human erythrocyte.Click here for fileParameter sensitivity of threshold for FVA simulations. Figure showing the average number of FVA-detected exchange reactions for each perturbation and different thresholds. Thresholds were tested from 5-60% at intervals of 5%. The average detected reactions were quite stable from 15-40% for both the SNP and drug perturbations. A final 40% threshold was used in the study.Click here for file


Motivation: Metabolic engineering aims at modulating the capabilities of metabolic networks by changing the activity of biochemical reactions. The existing constraint-based approaches for metabolic engineering have proven useful, but are limited only to reactions catalogued in various pathway databases.Results: We consider the alternative of designing synthetic strategies which can be used not only to characterize the maximum theoretically possible product yield but also to engineer networks with optimal conversion capability by using a suitable biochemically feasible reaction called ‘stoichiometric capacitance’. In addition, we provide a theoretical solution for decomposing a given stoichiometric capacitance over a set of known enzymatic reactions. We determine the stoichiometric capacitance for genome-scale metabolic networks of 10 organisms from different kingdoms of life and examine its implications for the alterations in flux variability patterns. Our empirical findings suggest that the theoretical capacity of metabolic networks comes at a cost of dramatic system's changes.Contact:larhlimi@mpimp-golm.mpg.de, or nikoloski@mpimp-golm.mpg.deSupplementary Information:Supplementary tables are available at Bioinformatics online. Metabolic reactions play a fundamental role in sustaining cell growth through the import of nutrients from the environment and their conversion into molecules rendering a viable organism. Metabolic reactions do not operate in isolation; they are in fact related through shared metabolites and form large-scale metabolic networks . From thin silico verification of network modifications, including insertion and deletion of reactions from other species or under-and over-expression of gene products. All of these alternatives for network modifications rely on the assumption that gene expression is proportional to reaction flux. The findings of these optimization-based approaches suggest alternative pathways and/or flux redirections necessary to achieve the objective of interest. We note that the enumerated approaches, although very useful in designing strategies for metabolic engineering, are limited to the reactions already catalogued in various databases  has provided the basic framework for investigating reaction fluxes in a metabolic network . ExtensiHerein, we argue that quantifying the performance of metabolic engineering strategies in a given metabolic network should rely on the maximum ‘theoretically’ possible product yield in the network, given by the optimal inter-conversion of metabolites by using any biochemically feasible reaction. The notion of theoretical capacity refers to the estimated maximum production that can be delivered during a given period, provided a set of inputs. To emphasize the analogy, in the case of metabolic networks, the inputs are represented by the nutrients together with the set of metabolic intermediates and reactions, while the maximum production to be estimated is given by the suitably chosen objective function. Clearly, the inputs are given by the existing biochemical knowledge structured in a form of cellular networks together with the corresponding environmental constraints.Estimation of the theoretical capacity for large-scale metabolic networks is all the more pressing issue due to the incompleteness of biological knowledge. The gaps in the current genome-scale metabolic networks arising as a result of the uncharacterized function of 20–60% of gene products . This prIn addition, given the large fraction of unknown chemical reactions in biological systems and the immense space of macromolecules potentially catalyzing chemical reactions , the mosThe article is organized as follows: we first provide a brief overview of optimization-based approaches for metabolic network analysis. We then give a detailed and formal description of our novel concept, called stoichiometric capacitance. Our approach is designed to determine the theoretical capacity of a given metabolic network and relies on determining a feasible chemical reaction whose addition to the network results in maximum product yield. Therefore, it can be used in designing synthetic metabolic engineering strategies aimed at improving the yield of any product of interest. We illustrate the proposed concept on the Krebs cycle with the idea of verifying if glucose can be synthesized from fatty acids  in the sum formula of the l-th metabolite and Sj* stands for the j-th column in the stoichiometric matrix corresponding to the internal reaction j. Interestingly, this fundamental physical constraint is rarely considered in metabolic network analyses, although it provides a necessary precondition for the flux balance In addition, the law of mass conservation implies that all internal reactions must be stoichiometrically ‘mass balanced’, i.e. each stoichiometric column corresponding to an internal reaction must lie in the kernel of the mass matrix, τ defined by a vector c∈ℝn, such that cTv ≥ 0 for all v∈P. As in FBA, the optimal value, τ*, for a metabolic network with stoichiometric matrix, S, is obtained by solving the following linear programming (LP) problem:The linear constraints given in τ.Clearly, the set of feasible fluxes for the problem in τ* > 0, an unblocked reaction i is termed essential for τ if there exists αi> 0 such that cTv ≤ αivi for all v∈P. Let the essential reactions for τ be denoted by Ψ. If we have i∈Ψ, then the optimal (i.e. maximum) capability τ* is given byτ* requires the relaxation of the upper bound imposed by the essential reactions on the objective τ. Such a relaxation either allows essential reactions to carry more flux or reduces the set of essential reactions, i.e. certain reactions are no longer essential for τ.In analogy to essential reactions for the growth of a micro-organism , if τ* >In the following, we introduce the concept of stoichiometric capacitance, which inherently depends on the aforementioned relaxation, and illustrate its applications in genome-scale metabolic networks as well as well-studied pathways.τ* in the original network. This can be achieved by placing the living system in an environment which is in favor of the maximization of the objective τ; for instance, the living system can import more nutrients. The change in the objective value τ due to the relaxation of environmental conditions can be assessed using shadow prices in linear programming (τ can be obtained by determining a sparse vector r∈ℝm such that τ *(r)> τ* withI(r)= {i∈{1,...,m} | ri ≠ 0}. The vector r can be chosen to simulate a boundary reaction exporting (respectively importing) the accumulated (respectively depleted) internal metabolites in I(r). More importantly, if the vector r lies in the null space of the mass matrix M, defined in r represents the stoichiometry of a feasible chemical conversion of the internal metabolites I(r), up to multiplication by scalars. This may include any set of feasible chemical reactions with net conversion given by r. In the following, we refer to r as stoichiometric capacitance. We note that if the net conversion of the stoichiometric capacitance is thermodynamically feasible, its inclusion in the considered metabolic model, specified by τ, given the same amount of nutrients.The relaxation of some of the linear inequalities (2) may lead to an increase of the maximal objective gramming . AlternaIn physics and engineering, the term capacitance is classically used to denote the change that must be added to a system to raise the electrical potential by one unit. Analogously, the concept of stoichiometric capacitance will denote the change in the metabolic structure given by the feasible chemical reaction whose inclusion in the network increases the objective of interest. The reference point for the increase is provided by the maximum value of the objective in the original metabolic network.τ by adding a stoichiometric capacitance r to an investigated metabolic network, for each internal metabolite i∈{1,...,m}, we first introduce a binary variable xi such that i∈I(r) implies xi = 1. Let μ denote a given maximum number of non-zero components of r and λ be a predefined upper bound on the flux through the added reaction. The maximum capability τ*(r) and the corresponding stoichiometric capacitance r can be obtained by solving the following mixed integer linear programming (MILP) problemr represents a mass-balanced net conversion that preserves the number of consumed and produced atoms. The inequality in (ii) guarantees that the stoichiometric capacitance is thermodynamically feasible, with T denoting the vector of the standard Gibbs free energy of formation of the internal metabolites. Constraints (iii) and (iv) ensure that r involves a limited number of metabolites from I(r), i.e. |I(r)| ≤ μ. Finally, the optional condition (v) allows modeling a stoichiometric capacitance that involves only a predefined subset E ⊆ {1,...,m} of metabolites. For instance, E may be the set of metabolites in a particular subcellular compartment or tissue. It is clear that further constraints can be included in the above MILP problem to determine a stoichiometric capacitance corresponding to a net conversion of only reactions that are more likely to occur in nature.To investigate the extent to which we can improve the objective ℛ of known enzymatic reactions. The set ℛ can be defined by using the reactions catalogued in several existing databases of biochemical reactions  of involved metabolites consists only of the following six metabolites: acetyl coenzyme A (AcCoA), coenzyme A (CoA), isocitrate (Isocit), (S)-malate , succinate (Succ) and water (H2O). The rows of the matrices M and T corresponding to these metabolites are included in Supplementary Table S1.To illustrate our novel concept, we first consider the Krebs cycle whose mass-balanced reactions are depicted in S given by the stoichiometric matrix of the glycolysis/gluconeogenesis and Krebs cycle  and the malate synthase (MAS) from the glyoxylate cycle:Supplementary Table S2.Solving the proposed MILP problem, with Saccharomyces cerevisiae, iND750  which provide this capacity, thus facilitating the design of metabolic engineering strategies. The used optimization-based formulation takes into account physico-chemical constraints to restrict the search space in a biochemically meaningful manner. To this end, we used a mixed integer linear program that can be efficiently resolved with existing optimization software. It becomes apparent that the proposed approach can easily be extended to consider non-linear objective functions discussed in Funding: A.L. is supported by the OPTIMAL project funded by the Federal Ministry of Education and Research, 0315958F. S.G. is supported by the ColoNET project funded by the Federal Ministry of Education and Research, 0315417F. Z.N. is supported by the Max-Planck Society.Conflict of Interest: none declared.


Pseudomonas aeruginosa is a human pathogen that frequently causes urinary tract and catheter-associated urinary tract infections. Here, using 13C-metabolic flux analysis, we conducted quantitative analysis of metabolic fluxes in the model strain P. aeruginosa PAO1 and 17 clinical isolates. All P. aeruginosa strains catabolized glucose through the Entner-Doudoroff pathway with fully respiratory metabolism and no overflow. Together with other NADPH supplying reactions, this high-flux pathway provided by far more NADPH than needed for anabolism: a benefit for the pathogen to counteract oxidative stress imposed by the host. P. aeruginosa recruited the pentose phosphate pathway exclusively for biosynthesis. In contrast to glycolytic metabolism, which was conserved among all isolates, the flux through pyruvate metabolism, the tricarboxylic acid cycle, and the glyoxylate shunt was highly variable, likely caused by adaptive processes in individual strains during infection. This aspect of metabolism was niche-specific with respect to the corresponding flux because strains isolated from the urinary tract clustered separately from those originating from catheter-associated infections. Interestingly, most glucose-grown strains exhibited significant flux through the glyoxylate shunt. Projection into the theoretical flux space, which was computed using elementary flux-mode analysis, indicated that P. aeruginosa metabolism is optimized for efficient growth and exhibits significant potential for increasing NADPH supply to drive oxidative stress response. Pseudomonas aeruginosa is a metabolically versatile bacterium that resides in a wide range of biotic and abiotic habitats and is a human pathogen that causes numerous acute and opportunistic infections P. aeruginosa infections includes wound and urinary tract infections, meningitis, and necrotizing pneumonia P. aeruginosa has focused its virulence P. aeruginosa is important for devising specific control strategies 13C-metabolic flux analysis (fluxomics) detects common and specific pathways employed by pathogens and identifies candidate pathways as targets for therapy Research on P. aeruginosa have not been published. Here, we investigated the laboratory strain P. aeruginosa PAO1 at the level of carbon fluxes by using 13C-metabolic flux analysis that combined isotopic tracer experiments with mass spectrometric labeling analysis and stoichiometric and isotopomer balancing for flux calculation P. aeruginosa clinical isolates from patients with urinary tract infections and catheter-associated urinary tract infections. These strains are genetically diverse, differ from strains that cause chronic lung infections in patients with cystic fibrosis, and exhibit heterogeneous production of virulence factors in vitroThis network-wide approach provides information on the activities of central enzymes and pathways most directly linked to phenotype P. aeruginosa PAO1 served as a reference P. aeruginosa isolates from patients with direct urinary tract infections included the strains MH06u, MH09u, RN12u, RN13u, MH16u, MH17u, MH26u, and MH29u. Isolates from patients with catheter-associated urinary tract infections included the strains MH15c, MH25c, MH33c, MH34c, MH36c, MH37c, MH39c, MH56c, and MH57c Corynebacterium glutamicum ATCC 13032 and P. putida KT2440 were acquired from the German Collection of Strains and Cell Cultures . All strains were stored in 10% (w/v) glycerol at −80°C.The model strain, −1 yeast extract , 10 g·L−1 peptone  and 10 g·L−1 NaCl. For the second and main cultures, a minimal medium developed during the study ensured balanced growth of all strains, an important prerequisite for the 13C-flux approach −1, which reflects that of human urine 2PO4×2 H2O, 20.6 g K2HPO4, 0.49 g MgSO4×7 H2O, 2.5 g NH4Cl, 1.41 g Na2SO4, 0.085 g CaCl2×2 H2O, 1.2 mg FeSO4×7 H2O, and 25 mg 3,4-dihydroxybenzoate. For 13C-flux experiments, naturally labeled glucose was replaced with 99% [1-13C] glucose .Starter cultures were grown in complex LB medium containing 5 g·L13C-tracer, the inoculum level was always kept below 1% of the final sampled cell concentration to exclude potential interference of a differently labeled inoculum on subsequent calculation of flux Cultures were shaken at 1,050 rpm and 37°C in 10 mL deep-well plates  with a working volume of 1.4 mL . Starter cultures were prepared by inoculating LB medium with 20 µL of a glycerol stock. After 6 h of incubation, 50 µL of cell suspension was transferred to a second culture in minimal medium. Subsequently, exponentially growing cells (50 µL) were used as inoculum for main cultures, and strains were grown in 30 mL of minimal medium in 300 mL baffled shake flasks. These were incubated at 37°C with shaking at 200 rpm on an orbital shaker . Dissolved oxygen was monitored online using shake flasks with integrated sensor spots . The sensor device was installed in the orbital shaker as described previously 578) . Cell dry weight (CDW) was determined as follows: Dried plastic tubes were filled with 20 mL cell suspension, and cells were harvested by centrifugation ; subsequently, pellets were washed twice with deionized water, and then dried to a constant weight.Cell growth was monitored spectrophotometrically at 578 nm  using a YSI 2700 Select biochemical analyzer . The analysis of organic acids  and amino acids in the culture supernatant was conducted using high-pressure liquid chromatography g, 4°C, 5 min), washed with 100 mM Tris-HCl (pH 7.5) containing 10 mM MgCl2 and 0.75 mM dithiothreitol, and then resuspended in the same buffer. The cells were then mechanically disrupted . Total protein concentration of the obtained cell extract was determined using Roti-Quant . Phosphofructokinase activity was determined spectrophotometrically at 340 nm −1 aldolase, 1 U·mL−1 triosephosphate isomerase, 0.5 U·ml−1 glycerol-phosphate dehydrogenase, 5 mM MgCl2, 0.25 mM NADH, 0.1 mM ATP, and 4 mM fructose 6-phosphate. The activity of isocitrate lyase was determined as described previously Cultures (10 mL) were harvested by centrifugation , washed twice with deionized water, and incubated with 30 µL 6 M HCl for 24 h at 100°C. The obtained hydrolysate was filtered  and then lyophilized. The proteinogenic amino acids in the lyophilisate were dissolved in 50 µL N, N-dimethylformamide containing 1% (v/v) pyridine and then derivatized at 80°C for 30 min with 50 µL N-methyl-t-butyldimethylsilyl-trifluoroacetamide . Mass isotopomer distributions of selected ion clusters of the analytes were then quantified using gas chromatography-mass spectrometry (GC-MS)  as described earlier Cells (0.5 mg CDW) were harvested in the mid-exponential growth phase by centrifugation , glycine (m/z 246), valine (m/z 288), serine (m/z 390), threonine (m/z 404), phenylalanine (m/z 336), aspartate (m/z 418), glutamate (m/z 432), tyrosine (m/z 466), and the [M-85] fragment of serine (m/z 362) were used as input after correction for natural isotopes A large-scale model of the central metabolism of P. aeruginosa was investigated using in silico pathway analysis involving the computation of elementary flux modes. The network topology agreed with that of the related species P. putidaP. aeruginosa PAO1 The metabolic network of 13C-labeling data to a set of linearly uncorrelated variables, i.e., the principal components Hierarchical clustering analysis (HCA) and principal component analysis (PCA) were conducted using MATLAB . PCA was used to convert the set of P. aeruginosa PAO1 consumed glucose from an early time point  and catheter-associated urinary tract infections (9 strains). All individual growth profiles are presented in the Supporting Information , a value derived by in silico pathway simulation. Briefly, the corresponding simulation for the carbon core network of P. aeruginosa provided 13,138 elementary modes, each representing a theoretical metabolic state of the cell. Together, all modes span the feasible flux space of the organism and contain the optimum growth mode with maximum possible yield The efficiency for recruiting glucose for anabolism, i.e., the biomass yield, differed by more than a factor of 1.5. Strain MH15c produced the lowest biomass yield, which was approximately 65% of the value of the two most efficient strains, MH34c and MH29u, which yielded a biomass 0.53 g·gP. aeruginosa PAO1 was grown on [1-13C] glucose to estimate metabolic flux and exhibited metabolic and isotopic steady state growth (see Supporting Information). This fulfilled an important prerequisite for the 13C-flux approach −1). Accordingly, P. aeruginosa PAO1 catabolized glucose only through the EDP to subsequent glycolytic steps and further to pyruvate.13C] glucose, the combination of 13C enrichment in pyruvate and oxaloacetate can be used to discriminative between PEP and pyruvate carboxylase  could not be confirmed. During growth on  amino acid fragments (C). The growth behavior in shake flasks and deep-well plates was identical regarding growth stoichiometry (B) and 13C labeling data (D) justifying the joint use of data. Accordingly, cell dry weight measurements from shake flask cultures could be integrated with growth and labeling data from deep-well plates for flux calculations, when needed. Experiments were performed in three replicates.(PDF)Click here for additional data file.Figure S2Dissolved oxygen during cultivation of P. aeruginosa on minimal medium in shake flask culture. To ensure sufficient aeration during cultivation, the level of dissolved oxygen was monitored on-line. As exemplified for P. aeruginosa PAO1, the oxygen level was above 80% of saturation so that fully aerobic conditions were given (one of three replicates shown). The excellent agreement of growth kinetics and stoichiometry and of the 13C labeling fingerprint Click here for additional data file.Figure S3Growth characteristics of uropathogenic P. aeruginosa isolates. Cultivation profiles on minimal glucose medium . For none of the strains, extracellular by-products were detected.(PDF)Click here for additional data file.Figure S4Enzymatic analysis of isocitrate lyase as key enzyme of the glyoxylate shunt.C. glutamicum and P. aeruginosa were cultivated in minimal medium, supplemented either with 40 mM acetate or with 14 mM glucose as sole carbon source, respectively.(PDF)Click here for additional data file.Figure S5Metabolic profiles of consumed glucose concentration and formed cell dry weight of clinical P. aeruginosa isolates on minimal glucose medium . Metabolic steady-state is inferred from the constant yield for biomass, derived as slope from the profiles.(PDF)Click here for additional data file.Table S1P. aeruginosa by quantification of the alginate capsule and the corresponding anabolic demand for F6P for its biosynthesis. For quantification, alginate was detached from the cells, and then analyzed by the sulfamate-biphenyl method −1, 25°C). The supernatant (200 µL), obtained by centrifugation , was then amended with 20 µL 4 M sulfamate and with 1.2 mL 0.075 M tetraborate, dissolved in concentrated H2SO4, and then incubated for 20 min at 99°C. Afterwards, the suspension was transferred to an ice bath (5 min), followed by addition of 40 µL 0.15% 3-hydroxybiphenyl, dissolved in 0.5% NaOH. The mixture was incubated for 10 min at room temperature. Alginate was quantified by photometry at 525 nm, using isolated alginate from mucoid P. aeruginosa FRD1 as external standard Refinement of the cellular composition of (PDF)Click here for additional data file.Table S213C labelling analysis of proteinogenic amino acids by GC-MS for all strains, the entire set of estimated metabolic fluxes, and information on the goodness of fit, i.e. the comparison of experimental and simulated labelling data, corresponding to the optimized fit.Supporting information on metabolic flux analysis. The data include the (XLSX)Click here for additional data file.


Saccharomyces cerevisiae physiology during the fermentation of must with these altered characteristics. The present study aims to determine the distribution of metabolic fluxes during the yeast exponential growth phase, when both carbon and nitrogen sources are in excess, using continuous cultures. Two different sugar concentrations were studied under two different winemaking temperature conditions. Although consumption and production rates for key metabolites were severely affected by the different experimental conditions studied, the general distribution of fluxes in central carbon metabolism was basically conserved in all cases. It was also observed that temperature and sugar concentration exerted a higher effect on the pentose phosphate pathway and glycerol formation than on glycolysis and ethanol production. Additionally, nitrogen uptake, both quantitatively and qualitatively, was strongly influenced by environmental conditions. This work provides the most complete stoichiometric model used for Metabolic Flux Analysis of S. cerevisiae in wine fermentations employed so far, including the synthesis and release of relevant aroma compounds and could be used in the design of optimal nitrogen supplementation of wine fermentations.As a consequence of the increase in global average temperature, grapes with the adequate phenolic and aromatic maturity tend to be overripe by the time of harvest, resulting in increased sugar concentrations and imbalanced C/N ratios in fermenting musts. This fact sets obvious additional hurdles in the challenge of obtaining wines with reduced alcohols levels, a new trend in consumer demands. It would therefore be interesting to understand  Over the past few decades, the intensified emissions of greenhouse gases and aerosol particles mainly derived from industrial activity and transport have led to one of the major challenges in the history of mankind: global warming . Apart fVitis vinifera, changes in plagues and microbiological diseases and drastic modifications in grape physiology. With respect to the latter, changes in acidity, phenolic maturation, tannin content and sugar concentration have been proven to occur, especially in warm climates [Saccharomyces cerevisiae, the yeast species mainly responsible for the alcoholic fermentation that takes place during winemaking. High sugar concentrations induce an osmotic stress response in yeast, which can result in stuck or sluggish fermentations and lead to increased formation of fermentation by-products such as glycerol and acetic acid [Global warming has been shown to cause lower yields in climates . Among ttic acid ,5.S. cerevisiae has been thoroughly studied and characterized over the last century due to the status of this yeast as model organism, the distribution of metabolic fluxes occurring during wine fermentations has not been tackled in depth. Due to the intrinsic dynamic nature of this process, yeast metabolism undergoes a series of physiological adaptive changes in response to the continuous environmental variations that take place, clearly hampering its understanding.Although the physiology of During the last decade, several laboratories have aimed to study yeast physiology under winemaking conditions. Varela et al.  describeS. cerevisiae EC1118 growing under phase I conditions at fermentation temperatures typical for red (28 °C) and white (16 °C) wines. Emulating an extreme increase in sugar concentration motivated by global warming, we have used a synthetic must with 280 g L-1 glucose and compared the obtained results with a control must containing 240 g L-1 glucose. A control must with 240 g L-1 glucose and another, emulating an extreme increase in sugar concentration motivated by global warming with 280 g L-1 glucose, have been used. In all cases, media were supplemented with potassium metabisulphite in order to mimic cellar conditions. During this first phase, yeast reaches its maximum growth rate (μmax) that depends on fermentation temperature, organic and inorganic nitrogen sources are available and not growth-limiting, and high sugar and low ethanol concentrations are present in the fermenting must.In the present study, we investigated the impact of growth temperature and sugar content on the distribution of metabolic fluxes of S. cerevisiae under winemaking conditions using continuous cultures in bioreactors.The proposed and validated model includes reactions from central carbon and nitrogen metabolism. With regard to previously used models, the latter has been expanded to incorporate transport reactions for ammonium and 19 different amino acids, their anabolism and/or catabolism and, for the first time, pathways involved in the synthesis of aroma precursors. Moreover, the present work combines the application of a stoichiometric model for analyzing the metabolic flux distribution of Saccharomyces cerevisiae LALVIN EC1118, the strain used in this study, is a commercial wine yeast strain isolated from the Champagne region (France) produced and commercialized by Lallemand Inc. . The strain was grown at 28 °C and routinely maintained at 4 °C on YPD plates containing 2% glucose (w/v), 2% peptone, 1% yeast extract and 2% agar, and in glycerol stocks at -80 °C.TM Yeast Nitrogen Base w/o amino acids and ammonium sulphate , 1,7 g; ammonium chloride, 306 mg; alanine, 97 mg; arginine, 245 mg; aspartic acid, 29 mg; cysteine, 14 mg; glutamic acid, 80 mg; glutamine, 333 mg; glycine, 12 mg; histidine, 23 mg; isoleucine, 22 mg; leucine, 32 mg; lysine, 11 mg; methionine, 21 mg; phenylalanine, 25 mg; proline, 400 mg; serine, 52 mg; threonine, 50 mg; tryptophan, 116 mg; tyrosine, 13 mg; valine, 29 mg; ergosterol, 15 mg; sodium oleate, 5 mg; Tween 80, 0.5 mL and potassium metabisulphite, 60 mg. All cultures were run in a DASGIP parallel fermentation platform  equipped with four SR0400SS vessels. Batch cultures were performed in duplicate using 300 mL as the initial working volume and were used to establish the specific production and consumption rates of the main metabolites  throughout the whole fermentative process. Continuous cultures were performed in duplicate at a constant volume of 200 mL at near μmax dilution rate (D): 0.25 h-1 for those fermentations performed at 28 °C and 0.1 h-1 for those performed at 16 °C. These D values had been experimentally determined previously in the aforementioned batch cultures. Agitation was maintained at 250 rpm and the temperature kept at 28 or 16 °C using a water bath and a 1:1 water/ethylenglycol-cooled chiller. During batch and continuous cultures, the pH of the medium was kept at 3.5 by the automated addition of 2N NaOH. Anaerobic conditions were maintained by gassing the headspace of the bioreactors with pure nitrogen gas (3.5 sL h-1). The use of spargers was avoided in order to reduce ethanol stripping. The effluent fermentation gas measured every 30 s for determination of CO2 concentration with a GA4 gas analyzer (DASGIP AG). For the inoculation of both batch and continuous cultures, EC1118 was grown in Falcon tubes containing 25 mL YPD and incubated at 28 °C and 150 rpm orbital shaking for 48h in order to reach stationary phase. Cells were then washed twice in sterile deionized water, resuspended in 5 mL of the synthetic must and inoculated to an initial OD600 0.2. Steady states were sampled only after all continuous cultures had been running for at least five residence times and the CO2 production rate, the biomass values and the concentration of the main metabolites were constant.A modification of a chemically defined synthetic must previously described  was usedTM XP Carbohydrate H+ (8 µm particle size) column and guard . The column was maintained at 50 °C and 1.5 mM H2SO4 was used as the mobile phase at a flow rate of 0.6 mL min-1. Prior to injection in duplicate, samples were filtered through 0.22 µm pore size nylon filters  and diluted when necessary 10 or 20-fold.The concentration of glucose, glycerol, ethanol and lactic, acetic and succinic acid was determined following the protocol described by Quirós et al.  using a The concentration of each amino acid was analyzed in duplicate according to the method of Gomez-Alonso et al.  using anThe concentration of ammonium was determined enzymatically using the R-Biopharm assay kit Cat. No. 11 112 732 035 .The volatile fraction of steady states was analyzed using headspace solid-phase micro extraction coupled with gas chromatography-mass spectrometry (HS-SPME/GCMS) by a modification of the protocol described by Pozo-Bayon et al. . A Therm-1 up to 200 °C and 15 °C min-1 up to 240 °C (10 min hold), while the 0.75 mm I.D. SPME liner was held at 180 °C. Helium was used as the carrier gas at a flow rate of 1 mL min-1, operating in split mode (ratio 30). For the MS detector, the temperatures of transfer line and ion source were both 250 °C, ionization mode was electron impact at a voltage of 70 eV and acquisitions were performed in SIM mode (dwell time 50 ms). Instrument control, data analysis and quantification results were carried out with Xcalibur 2.1 software. Volatile compounds were identified and quantified by comparison with standards, and analyses were carried out in duplicate.Five fusel alcohols  were quantified using 2 ml of fermentation broth. One gram of sodium chloride was added to the sample placed in a 20 mL headspace vial, followed by 10 µL of 2000 ppm internal standard solution (4-methyl-2-pentanol and 1-nonanol in 0.5% ethanol). Briefly, the vial was tightly capped with a PTFE/Silicone cap and then heated for 10 min at 70 °C. Then, a Supelco 100 µm PDMS fiber was exposed to the headspace of the sample vials for 30 min and desorbed in the GC inlet for 4 min. The instrument was fitted with a 30 m × 0.25 mm TG-WAXMS A fused-silica capillary column, 0.25 mm film thickness . The GC temperature program was as follows: 40 °C (5 min hold), 3 °C min600 measurements in the linear range of 0.1-0.4 units. OD data were then transformed to dry cell weight values using a calibration curve for S. cerevisiae EC1118 in the synthetic must described above. When steady states were reached, biomass dry weight was determined in triplicate by filtering 5 ml (for fermentations run at 28 °C) or 15 mL (for fermentations run at 16 °C) of the cultures followed by 10 mL distilled water through a 25 mm, 0.45 µm pore size pre-dried and pre-weighed nitrocellulose filter . Filters were then heat dried in an oven at 70 °C until weight was constant (12-24 h).To determine cell growth during the course of batch fermentations, the optical density (OD) was monitored using a Shimadzu UV-1800 Spectrophotometer . When necessary, samples were diluted with deionized water to obtain ODSamples of cultivation broth from all steady states were centrifuged at 10,000 rpm for 5 min and the cell pellet was washed three times in sterile distilled water. The recovered pellet was immediately frozen by immersion in liquid nitrogen and lyophilized during 72 h. Once lyophilized, cell pellets were additionally dried in an oven at 65 °C for 24 h.-1 and subjected in triplicate to the phenol-sulphuric acid method as described by Segarra et al. [To determine the biomass total carbohydrate content, an aqueous solution of lyophilized biomass was prepared at a concentration of 0.1 g La et al.  using a Total protein content and amino acid composition of the biomass was determined following the protocol described by Carnicer et al. . This alBiomass recovered from 50 mL of each steady state was washed three times with deionized water, dried in an oven at 70 °C for 72 hours and crushed thoroughly using a porcelain mortar and pestle. The fine powdered biomass was dried again in the same conditions for 24 h and 1 mg analyzed in an EA 1110 CHNS-O elemental analyzer (CE-Instruments/Thermo Fisher Scientific) coupled to an E3200 autosampler and a thermoconductivity detector.In order to determine the biomass ash content, the powdered biomass was placed in a pre-weighted crucible and calcinated in a muffle furnace at 800 °C for 24 h. The difference in weight was measured with an AB204-S electronic precision scale .2-test (p≤0.05) as proposed by Wang and Stephanopoulos [Data obtained during continuous cultures were checked for consistency and reconciled based on a χnopoulos . The tesnopoulos  was usedThe physiological parameters and metabolic flux values obtained in all conditions were compared by means of Student’s t-test at 95% confidence level. Principal Components Analysis (PCA) was performed in order to identify relevant experimental condition effects using IBM SPSS win 19.0 software .S. cerevisiae EC1118 was adapted from the model of Varela et al. [-1 h-1/mol gDW-1 h-1) of a specific amino acid was ≥ 1, the biosynthetic pathway for that amino acid was included. In contrast, when this ratio was < 1, it has been assumed that there is an excess of such compound in the cell and, therefore, the corresponding degradation pathway was included. The total cell content of each amino acid residue was estimated from the molar fraction of each amino acid in the total protein.The stoichiometric model used to represent the metabolic network of a et al.  . On theAdditionally, some alternative amino acid degradation routes, such as the Ehrlich pathway, which involves the synthesis of isoamyl alcohol, active amyl alcohol and isobutanol, among others, have also been included in the proposed stoichiometric model.m=88 metabolites and n=82 reactions (www.yeastgenome.org) and the Kyoto Encyclopedia of Genes and Genomes .The final metabolic network consists of eactions . The exiAll numerical calculations were performed using Matlab 2010b . CellDesigner 4.2  was used for metabolic network design and Cell-Net-Analyzer 9.4 [S. cerevisiae has been extensively described and studied under different laboratory growth conditions, data regarding key physiological parameters in the different stages of wine fermentation are not easily found in the literature. One of the main objectives of the present work is to contribute to the understanding of yeast physiology by providing accurate production and consumption rates of the main metabolites during the initial stages of wine fermentation in different enological conditions. Since there are important physiological and transcriptional differences between laboratory and industrial wine yeast strains [Although the metabolism of  strains , the broResults for consumption/production rates  were exp-1 at both sugar concentrations than at 16 °C and D = 0.1 h-1  were approximately three times higher at 28 °C and D = 0.25 hrevisiae ,24,25. Orevisiae ,24,26. Hrevisiae , ethanol studies . This fa2). Sugar concentration exerted a slight (not significant) effect on ethanol and CO2 yields on glucose, with yields always being higher at 240 g L-1 glucose. On the other hand, the highest acetic acid yields were found at 280 g L-1 glucose (-1) while higher glycerol yields were calculated at 28 °C (µ=0.25 h-1)  .2 yields were observed between different growth conditions as previously reported by other authors [Pathways involved in energy production are strongly regulated, regardless of culture conditions ,33. As a authors . On the  authors ,35 and tS. cerevisiae biomass has been determined in multiple studies, none of the culture conditions employed resembled the composition of a grape must. We were therefore motivated to perform these analyses in the four conditions studied. Furthermore, the precise knowledge of these values is essential for energetic and metabolic calculations [Although the elemental and macromolecular composition for ulations . If the ulations .S. cerevisiae grown at 0.1 h-1 using measurements of dried molecular biomass composition. This finding could be due to differences in the nitrogen sources and concentrations employed in both studies. Albers and co-workers [The biomass unit C formulae determined were very similar in the four conditions studied  and are -workers  had prevThe molar fraction of each amino acid did not significantly vary between the four conditions studied , resultiProteins were the main constituent of the biomass, normally representing around 50% of the cell dry weight and not showing significant differences between growth conditions. The same trend was also observed for the carbohydrate content, in all conditions close to 30% . These tThe stoichiometric model used in this work is described in the Materials and methods section. Besides the given details, additional particularities should be noted. Although a grape must is being mimicked, glucose has been used as the only carbon source. A previous characterization of batch cultures using equimolar amounts of glucose and fructose showed that fructose uptake was much lower than glucose consumption during yeast exponential growth phase (data not shown). For this reason, and to improve the accuracy in the determination of sugar uptake, we decided to use glucose as the sole carbon source.-1 h-1/mol gDW-1 h-1) for each amino acid, allowed us to define whether anabolic or catabolic reactions had to be included in each case . As shown in On the other hand, regarding nitrogen metabolism, the ratio “incorporation rate into the biomass/uptake rate”  in all cases. The flux towards the tri-carboxylic acid cycle (TCA) remained at levels below 1%, which should be enough to maintain the intracellular levels of key metabolic intermediates [-1 h-1 and normalized to the specific glucose uptake. The reproducibility of the experimental set-up was tested and confirmed using PCA  and carbohydrate biosynthesis. On the other hand, the flux directed towards glycolysis at 240 g L-1 glucose compared to 240 g L-1.The flux in the remaining branch of this node is directed towards carbohydrate biosynthesis. Following the same trend observed for the PPP, higher fluxes were calculated at 16 °C compared to 28 °C and at 280 g LPichiapastoris [S. cerevisiae [-1 of glucose compared to 240 g L-1, which would therefore require a higher demand for structural components.As our work focuses on exponentially growing cells and despite the osmotic pressure imposed by high sugar concentrations, the carbon flux directed towards this branch is expected to be destined to structural (mainly glucans and mannoproteins) rather than to reserve carbohydrates  ,21,41, aastoris  and S. crevisiae . Higher -1 glucose while that directed to glycerol biosynthesis was higher at 280 g L-1  was higher at 16 °C than at 28 °C while the flux towards glycerol was higher at 28 °C. Regarding the sugar concentration effect, the glycolytic flux was higher at 240 g L80 g L-1 , panel BS. cerevisiae strains at 12 °C compared to 28 °C.Glycerol has been often referred to as a “secondary metabolite” in wine fermentation due to its lower concentration in wine when compared to ethanol. However, glycerol is not “secondary” from a metabolic point of view. Synthesis of this compound plays a major role during anaerobic growth, providing precursors for the synthesis of phospholipids, protecting yeast from high osmotic pressure (caused by high sugar concentrations found in winemaking) and maintaining cell redox balance ,43. Howe-1 glucose and was always accompanied by higher fluxes towards glycerol. This fact could be explained through redox balance, as the excretion of 1 mol of acetate involves the synthesis of 2 moles of NADH [-1 glucose, irrespective of temperature, could be due to the increase in osmotic pressure.The C flux towards acetic acid production was also higher at 280 g L of NADH . Additio-1, for 240 and 280 g L-1 of glucose, respectively) and concentrations below the detection limit (5 mg L-1) in the cultures performed at 16 °C. Low levels of lactic acid are still likely to be produced at 16 °C as all the conditions studied implied overflow metabolism through glycolysis , amino acids could be differentially grouped according to the percentage of consumption in the steady states , and by temperature and dilution rate, with all cases higher at 28 °C than at 16 °C  compared to 16 °C (D = 0.1 h-1). This data is in accordance with a previous study performed in nitrogen limited anaerobic cultures of S. cerevisiae, which demonstrated the existence of a linear relationship between dilution rate and ammonium uptake rate [-1) than at 28 °C  observed by other authors [Temperature and dilution rate exerted a clear effect on nitrogen metabolism while that of sugar concentration was negligible. ake rate . Howeverat 28 °C , panel B 0.1 h-1 , panel C authors .Considering amino acids individually, it is worth mentioning that arginine, alanine and glutamine account for approximately 60% of the total organic nitrogen incorporated . Aspartic acid, serine and threonine represented approximately 19%, individually accounting for around 6-7%. Another group formed by glutamic acid, isoleucine, leucine, lysine, methionine, phenylalanine, tryptophan and valine accounted for around 19% of the total organic nitrogen incorporated, each of them never representing more than 4%. The individual contribution of the remaining amino acids never exceeded 1%. Detailed data are shown in -1) was analyzed, significantly higher values were obtained for the continuous cultures performed at 16 °C at both sugar concentrations. This implies that the biomass yield on nitrogen (gDW N-mmol-1) in a synthetic medium mimicking grape must decreases at low temperatures as had previously been reported for the industrial strain CEN.PK 113-7D and EC1118 in a medium that only presented inorganic nitrogen sources [It should be highlighted that the individual contribution of amino acids to the total nitrogen uptake differed between conditions. While the contribution of glutamine increased at low temperature, that of glutamic acid decreased  was significantly higher in all continuous cultures performed at 28 °C (D = 0.25 h-1) compared to those performed at 16 °C. An effect of sugar concentration was also observed, with higher effects at 240 g L-1 glucose than at 280 g L-1 at the two temperatures assayed. These findings could also be partly explained as a result of the redox balance in the cell. As in the case of glycerol, the biosynthesis of these compounds could contribute to the regeneration of the NAD+.The production of fusel alcohols normalized per gram of biomass Click here for additional data file.Appendix S1Biochemical reactions included in the stoichiometric model.(DOCX)Click here for additional data file.Table S1Measured consumption/production rates of the different metabolites analyzed in the study. sd values correspond to the standard deviation of the average value measured in two independent biological replicates. Consumption rates are indicated with a minus sign. ND: Not detected in the analysis.(DOC)Click here for additional data file.Table S2-1 in the steady state/ mg L-1 in the feed)·100). Code: colour values given according to the classification proposed by Crépin et al. [Percentage of consumption of each amino acid in the steady states of each condition Click here for additional data file.Table S3aNot properly quantified in the analysis. Data from taken from Lange and Heijnen [Molar fraction of amino acids present in proteins.  Heijnen . Asx: As(DOCX)Click here for additional data file.


Graph-based methods have been widely used for the analysis of biological networks. Their application to metabolic networks has been much discussed, in particular noting that an important weakness in such methods is that reaction stoichiometry is neglected. In this study, we show that reaction stoichiometry can be incorporated into path-finding approaches via mixed-integer linear programming. This major advance at the modeling level results in improved prediction of topological and functional properties in metabolic networks. The use of graph theory in the analysis of biological networks has been extensive in the past decade . Particuet al. [Graph-based methods start by converting the metabolic network into an appropriate graph. Different representations are possible here: i) metabolite graphs, where nodes are metabolites and arcs represent reactions linking an input and output metabolite; ii) reaction graphs, in which nodes are reactions and arcs represent intermediate metabolites shared by reactions; iii) bipartite graphs, where nodes are reactions and metabolites, while arcs link metabolites to reactions (for substrates) and reactions to metabolites (for products). Note here that each type of graph can be either directed or undirected. A deeper introduction to such graphs can be found in Deville et al. .Importantly, graph-based methods rely on the definition of connectivity based on paths, that is, two nodes in the graph are connected (or not) depending upon whether (or not) we have a path linking them. This definition of connectivity is debatable, however, particularly when it is claimed that such a path is a competent metabolic pathway, as recently discussed -27. In tThe steady-state condition requires the definition of the boundary of the metabolic network under study. Metabolites inside the boundary of the network, typically called internal metabolites , must beet al. [et al. [The steady-state condition and its underlying boundary definition are critical for the performance of any method for analyzing a metabolic network and ignoring it may provide misleading insights. A nice illustration of this is the one presented in the work of de Figueiredo et al. , which  represent a more general and elegant concept for metabolic pathways than paths ,30. TheiGiven the limitations discussed above, a novel theoretical concept termed flux paths is introduced here. A flux path is a simple path  from a source metabolite to a target metabolite able to operate in sustained steady-state. In essence, flux paths incorporate reaction stoichiometry into traditional path-finding methods ,7,34,35.cr be the stoichiometric coefficient associated with metabolite c  in reaction r . As usual in the literature [Assume we have a metabolic network that comprises R reactions and C metabolites. Note here that reversible reactions contribute two different reactions to the metabolic network. For this reason we can regard all fluxes as taking positive values. Let Sterature , input mWe here used a metabolite (directed) graph representation of the network where nodes are metabolites and arcs link the input and output metabolites of each reaction. Figure Suppose that we are concerned with finding a flux path from a source metabolite α to a target metabolite β. As mentioned above, a flux path is a simple path from the source metabolite α to the target metabolite β able to operate in steady-state. We present below our mathematical optimization model for flux paths.ij, where uij = 1 if the arc i→j linking metabolite i  to metabolite j  is active in the flux path, 0 otherwise.We need to decide the arcs involved in the flux path from the source metabolite α to the target metabolite β. This fact is represented with a zero-one (binary) variable uDeletion of arcs from the metabolic graph is standard practice in path-finding methods ,7,34,35.Note here that a similar criterion has been used in . In thisijr be a binary (0/1) coefficient establishing whether (or not) there exists an effective carbon exchange between input metabolite i (Sir < 0) and output metabolite j (Sjr > 0) in reaction r. If ij is also fixed to zero.Let dIn the following lines we present constraints needed to obtain an appropriate directed path from the source metabolite (α) to the target metabolite (β). Equation 1 ensures that one arc leaves α and one arc enters β; equation 2 that no arc enters α and no arc leaves β:Equation 3 ensures that the number of arcs entering a metabolite k is equal to the number leaving; Equation 4 ensures that a metabolite cannot be revisited in the path:Equations 1 to 4 define a simple path that preserves carbon exchange in each of its intermediate steps. We need to guarantee that this path can work in sustained steady-state. As will be shown below, to do this, it is required to find a steady-state flux distribution able to involve the path. We here introduce variables and constraints needed to define the steady-state flux space.r the non-negative (continuous) flux associated with each reaction, r = 1,...,R:Any steady-state flux distribution satisfies Equation 5 for the set of internal metabolites (I). We denote vm) is introduced, however, metabolites not involved in such a medium can be produced, but cannot be consumed, as observed in Equation 6:External metabolites (E) are not subject to balancing constraints. If a specific growth medium , which defines the reactions involved in a steady-flux distribution, namely zr = 1 if reaction r has a non-zero flux, 0 otherwise . We need constraints relating the reaction variables zr and the flux variables vr. Equation 7 ensures that no flux traverses a reaction r if zr = 0:For convenience, we introduced a zero-one (binary) variable zr is non-zero if zr = 1. Here we have scaled fluxes so that the maximum flux is M and the minimum (non-zero) flux is 1. This does not constitute an issue if we consider M sufficiently large.In addition, it guarantees that vAs we split reversible reactions into two irreversible steps, we need to prevent a reaction and its reverse from appearing together in any steady-state flux distribution, as observed in Equation 8, where the set B = {| reaction λ and reaction μ are the reverse of each other}:Current path-finding approaches deal with this situation indirectly, namely by removing computed paths involving a reaction and its reverse.Equations 5 to 8 define the steady-state flux space for a particular metabolic network.ijr = 1, that is, involving effective carbon exchange between i and j, is contained in the steady-state flux distribution. This is a critical point in our formulation, which makes it different from previous path-finding methods. With this condition we naturally link the topological and (steady-state) flux planes. This linking constraint is reflected in Equation 9:As noted above, it is required that the path defined by constraints 1 to 4 can operate in a steady-state flux distribution. For this purpose, we need to guarantee that if we use an arc i→j in a path, then some reaction r with dij = 1), then at least one reaction r containing this arc in carbon exchange (so dijr = 1) is forced to be active. By forcing zr to be 1 there will be a non-zero flux associated with the reaction due to Equation 7. An important point to note from Equation 9 is that it allows reactions to be active even if they are not involved in the CFP. In other words reactions can be active with non-zero flux  but without any of their input/output metabolites being involved in the CFP.Equation 9 ensures that if an arc i→j is active in the CFP (so uAB = uBC = uCE = uEF = 1). Due to Equation 9, reactions 2, 3, 4 and 5 are active, that is, z2 = z3 = z4 = z5 = 1 and, therefore, via Equation 7, their flux will be non-zero. To balance such a path and satisfy the steady-state condition, Equation 5, we require three additional reactions off-path: reaction 1 for the production of A, reaction 7 to consume F and reaction 6 to produce D. If these off-path reactions are active, the path from A to F is able to work in sustained steady-state and, therefore, it is a flux path, as denoted in the Background section. We are obviously considering that Aext and Dext are in the growth medium. If we remove one of these metabolites from the medium, though we still have a path from A to F at the graph-theoretical level, no flux path will exist, since the path cannot work in sustained steady-state.To illustrate constraint 9, consider the example metabolic network in Figure Equations 1 to 9 define the set of constraints for the determination of any CFP between metabolite α and metabolite β. However, our purpose here is to find the shortest CFP, as observed in Equation 10:ij variable in the k-shortest CFP:As in other path-finding approaches, we may be interested in computing not only the shortest CFP, but the k-shortest CFPs . Since we have an objective relating to finding the shortest CFP, we need to add constraints eliminating previously found CFPs, as shown in Equation 11. In that constraint This section is organized as follows. By means of several well-documented examples, we first illustrate the biochemical relevance of particular constraints in our CFP approach. We then carry out a side-by-side comparison of our CFP approach with current path-finding approaches.As shown in the 'Mathematical model' section, the path-finding strategy used in our CFP approach is based on using arcs involving effective carbon exchange and imposing the reversibility constraint, Equation 8. In this sub-section we illustrate the importance of these factors and show that a path-finding approach incorporating them outperforms existing methods in the literature. For this analysis, the effect of stoichiometry is not considered, as is common in existing approaches. Its effect will be separately considered in detail in the next sub-section ('Effect of stoichiometry'). Therefore, for this analysis, Equations 5 and 6 were ignored.Escherichia coli. The long path is a well-known  metabolic pathway for de novo pyrimidine biosynthesis. The short path is a shortcut via ADP, which has no biological relevance. The removal of arcs not involving carbon exchange, as done in our CFP approach, considerably reduces the appearance of such non-meaningful paths. Indeed, when we applied our approach to find a CFP from HCO3 to CDP to the genome-scale metabolic network of E. coli [et al. [et al. [Figure  E. coli , the lon [et al. . The res [et al. , under dE. coli, which is the Entner-Doudoroff pathway, as shown in the left-hand side of Figure Path-finding methods typically split reversible reactions into two irreversible steps. In contrast to current approaches , in our E. coli (metabolic) pathways previously discussed in Planes and Beasley [In order to analyze the performance of any path-finding method, it is usual in the literature to evaluate its ability in recovering well-known metabolic pathways. For this purpose, we used a database of 40 reference  Beasley   cannot perform in steady-state and therefore are not biologically meaningful. We then repeat the side-by-side comparison presented in Figure et al. [et al. [Figure et al. , which c [et al. , this faE. coli metabolic pathways discussed in Planes and Beasely [E. coli metabolic network in Feist et al. [We repeated the side-by-side comparison previously presented in Figure  Beasely , and thet et al. .As we previously showed above Figure  that ourE. coli due to stoichiometric constraints. For example, the tricarboxylic acid (TCA) cycle cannot work in anoxic conditions in E. coli. The ability to detect these false positives cannot be accomplished without the use of stoichiometry. In light of this, the definition of recovery  prevents some known metabolic pathways from functioning in E. coli [Figure E. coli . This loE. coli. OAA plays an important role in the regulation of carbon flux in most organisms. Again, for this study, we used the metabolic network presented in Feist et al. [To emphasize the effect of stoichiometry, we examined the connectivity structure of oxaloacetate (OAA) in t et al.  and a miWe determined CFPs from OAA to all reachable metabolites (obviously some metabolites may not be reachable via a CFP from OAA). In order to organize and compare the obtained results, we plotted a connectivity curve that shows the total number of connected metabolites when we move a specified number of reaction steps away from the source metabolite. To show the effect of stoichiometry, we plot the connectivity curves when stoichiometry is included (so including Equations 5 and 6) and when it is not included (so excluding Equations 5 and 6).Figure It is usual to find K paths between a pair of key metabolites/reactions in, for example, the interpretation of 'omics' data ,20. CurrE. coli in two different scenarios, namely in oxic and anoxic conditions. Pyr and OAA are linked by two fundamental metabolic processes. Firstly, Pyr (via PEP) can be carboxylated to OAA for the replenishment of TCA cycle intermediates or for anabolic purposes . This process is typically referred to as anaplerosis. In addition, Pyr and OAA are strongly related via the TCA cycle, which oxidizes carbon of Pyr to CO2 and requires OAA to operate.PEP, Pyr and OAA are important metabolites whose underlying inter-conversions control the carbon flux distribution in bacteria . The peret al. [We calculated the 100 shortest CFPs in both scenarios using the metabolic network presented in Feist et al. . Again, Figure Though the number of non-meaningful paths has been substantially reduced, it can be observed in Figure 2 + NADPH), 2-oxogluterate dehydrogenase (AKG + CoA + NAD → CO2 + NADH + SUCCoA) and SUCOAS (ATP + CoA + SUCC ↔ ADP + Pi + SUCCoA) are not in Figure We repeated the same analysis in anoxic conditions Figure . In thisFinally, as observed in Figures Graph-based methods have been widely used for the analysis of metabolic networks, but suffer from the important weakness that reaction stoichiometry is neglected. In this paper we show that, using the novel concept of CFPs, reaction stoichiometry can be incorporated into path-finding approaches, which constitute a clear progress over the state of the art at the methodological level.Our results show that, when stoichiometry is incorporated into path-finding methods, the resulting set of functional pathways is substantially altered, as observed in the analysis of the 40 reference pathways. This idea is also reflected in the analysis of aerobic and anaerobic Pyr-OAA metabolism, which emphasizes the importance of the steady-state condition and its underlying boundary definition for the analysis of metabolic networks. In addition, connectivity analysis revealed important differences when stoichiometry was considered, as we illustrated with regard to a number of metabolites. In summary, CFPs open new avenues for analyzing metabolic networks at the topological and functional levels and constitute a major advance.E. coli database incorporating effective carbon exchange information  represeIt is important to mention that our CFP approach is formulated as a mixed-integer linear program, which cannot be solved using classical algorithms from graph theory and requires a branch and bound approach. Computational experience shows that the determination of CFPs is not expensive, namely in the order of milliseconds. This fact makes our approach an effective tool for addressing other relevant questions previously addressed by path-finding approaches.Our analysis of CFPs in aerobic Pyr-OAA metabolism allowed us to detect several bypasses to the TCA cycle. Some of these bypasses have been recently reported using a different pathway analysis technique, namely elementary flux patterns for the bypass via the GABA shunt  and geneFinally, despite much debate in the field comparing the performance of path-finding methods and stoichiometric methods ,27,49, tEquations 1 to 11 presented in the 'Mathematical model' sub-section define a mixed-integer linear problem and, algorithmically, such problems are solved by linear programming-based tree search. Modern software packages to perform this task, such as ILOG CPLEX, which we used, are well developed and highly sophisticated. ILOG CPLEX was run in a Matlab environment version 7.5 (R2007b).1.4. This implies that the computation time of CFPs grows only as a low power of the number of paths (K) sought.The computation of the shortest CFP and the 100 shortest CFPs took us (on average) 300 ms and 2.5 minutes, respectively, on a 64-bit, 2.00 GHz PC with 12 Gb RAM. Analysis using regression indicated that, over the range of K values examined (up to K = 250), the total time for computing the K shortest CFPs was (approximately) proportional to KAcCoA: acetyl-CoA; AcGlc-D: 6-acetyl-D-glucose; AKG: 2-oxoglutarate; AST: Arginine succinyltransferase; BiGG: Biochemical Genetic and Genomic; CDP: cytidine-diphosphate; CFP: carbon flux path; CoA: coenzyme A; CoQ: coenzyme Q; D-Glc: glucose; Fum: fumarate; G6P: glucose 6-phosphate; GABA: gamma-aminobutyric acid; GLCATr: D-glucose O-acetyltransferase; GLX: Glyoxylate; PA120: phosphatidic acid; HCO3: bicarbonate; ICit: isocitrate; L-Arg: arginine; L-Mal: acetyl-maltose; OAA: oxaloacetate; PEP: phosphoenolpyruvate; PPCoA: propionyl-CoA; Pyr: pyruvate; SUCArg: Succinyl-L-arginine; SUCC: Succinate; SUCCoA: succinyl-coenzyme A; SUCOAS: succinyl-CoA synthetase; TCA: tricarboxylic acid.JPe developed and implemented the method, wrote the manuscript and performed analyses; JPr developed the method and carbon exchange database; JEB developed the method and wrote the manuscript; FJP conceived the study, developed the method and wrote the manuscript. All authors discussed the results, and read, commented and approved the final manuscript.Database of carbon exchange arcs. PDF document containing a list of arcs involving effective carbon flux in the metabolic network of Feist et al. [t et al. .Click here for fileSupporting data for side-by-side comparison. Word document containing a list of 40 reference pathways used in the side-by-side comparison, a side-by-side comparison using accuracy rate, and a discussion on infeasible pathways in Figure 6 [Figure 6 ,44,50-56Click here for fileSupporting data for Figures 8 and 9. Details of the 100 shortest CFPs in oxic and anoxic conditions from Pyr to OAA.Click here for file


Mycobacterium tuberculosis (Mtb) is a pathogenic bacteria species in the genus Mycobacterium and the causative agent of most cases of tuberculosis. Tuberculosis (TB) is the leading cause of death in the world from a bacterial infectious disease. This antibiotic resistance strain lead to development of the new antibiotics or drug molecules which can kill or suppress the growth of Mycobacterium tuberculosis. We have performed an in silico comparative analysis of metabolic pathways of the host Homo sapiens and the pathogen Mycobacterium tuberculosis (H37Rv). Novel efforts in developing drugs that target the intracellular metabolism of M. tuberculosis often focus on metabolic pathways that are specific to M. tuberculosis. We have identified five unique pathways for Mycobacterium tuberculosis having a number of 60 enzymes, which are nonhomologous to Homo sapiens protein sequences, and among them there were 55 enzymes, which are nonhomologous to Homo sapiens protein sequences. These enzymes were also found to be essential for survival of the Mycobacterium tuberculosis according to the DEG database. Further, the functional analysis using Uniprot showed involvement of all the unique enzymes in the different cellular components. Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), remains a major health threat. Each year, 8 million new TB cases appear and 2 million individuals die of TB [ie of TB . Furtherie of TB .Escherichia coli, Helicobacter pylori, and Chromohalobacter salexigens, eukaryotic microorganisms, mice, and even humans [http://gcrg.ucsd.edu/), provides a continuously updated list of genome-scale metabolic network reconstructions. Analysis of metabolic networks can provide insights into an organism's ability to grow under specific conditions. For example, given a specific set of nutrient conditions, flux balance analysis (FBA) of metabolic networks can accurately predict microbial cellular growth rates. In a recent work, a group of researchers used an approximate representation of in-host nutrient availability inferred from the literature to simulate the in-host metabolism of Salmonella typhimurium [Recently, genome-scale metabolic network reconstructions for different organisms have enabled systematic analyses of metabolic functions and predictions of metabolism-related phenotypes. By collecting all possible biochemical reactions for specific organisms, different groups have reconstructed metabolic networks for bacteria, for example, n humans –6. The whimurium . Moreovehimurium –10.Pseudomonas aeruginosa and in Helicobacter pylori [The computational approach has been used to investigate novel drug targets in other pathogenic organisms such as r pylori , 11.M. tuberculosis (H37Rv). Only those enzymes which show unique properties than the host were selected as the target. Metabolic genes that are essential for pathogen growth but are not present in humans constitute actual and potential drug targets.As most currently known, antibacterials are essentially inhibitors of certain bacterial enzymes; all enzymes specific to bacteria can be considered as potential drug targets . In thishttp://www.genome.jp/pathways.html) [H. sapiens and the pathogen M. tuberculosis (H37Rv) were extracted from the KEGG database. Pathways which do not appear in the host but are present in the pathogen according to KEGG database have been identified as pathways unique to M. tuberculosis as in comparison to the host H. sapiens. Enzymes in these unique pathways as well as enzymes involved in other metabolic pathways under carbohydrate metabolism, energy metabolism, lipid metabolism, nucleotide metabolism, amino acid metabolism, metabolism of other amino acids, and glycan biosynthesis were identified from the KEGG database. The corresponding protein sequences of enzymes involved in unique pathways were identified and their protein sequences were retrieved in FASTA format from KEGG database.KEGG (Kyoto Encyclopedia of Gene and Genome) (ys.html)  pathway http://tubic.tju.edu.cn/deg/) [M. tuberculosis.The unique enzymes were further analyzed for essentiality to pathogen by DEG  database (cn/deg/) , and conhttp://www.uniprot.org/) database to find out their functions. This is required to find out the surface membrane proteins which could be probable vaccine targets.The obtained targets genes were further analyzed by UniProt  (Tuberculosis (TB) is a major cause of illness and death worldwide, especially in Asia and Africa. Globally, 9.2 million new cases and 1.7 million deaths from TB occurred in 2006, of which 0.7 million cases and 0.2 million deaths were in HIV-positive people [Mtb, can produce a list of reliable targets very rapidly. These methods have the advantage of speed and low cost and, even more importantly, provide a systems view of the whole microbe at a time. Since it is generally believed that the genomes of bacteria contain genes both with and without homologues to the human host. Using computational approach for target identification it is very quick to produce a desirable list.No new anti-Mtb drugs have been developed for well over 20 years. In view of the increasing development of resistance to the current leading anti-Mtb drugs, novel strategies are desperately needed to avert the “global catastrophe” forecast by the WHO . Therefore, computational approach for drug targets identification, specifically for in silico comparative metabolic pathway analysis of Homo sapiens and M. tuberculosis H37Rv using KEGG database. Pathways which are not present in the Homo sapiens but present in the Mycobacterium are designated as unique pathways. Design and targeting inhibitors against these nonhomologous sequences could be the better approach for generation of new drugs. Thus total 5 unique metabolic pathways have been taken in M. tuberculosis (In the present study, 5 unique pathways, C5-branched dibasic acid metabolism, carbon fixation pathways in prokaryotes, methane metabolism, lipopolysaccharide biosynthesis, and peptidoglycan biosynthesis with 60 new nonhomologous targets were identified through rculosis .M. tuberculosis life cycle (Essential genes are those indispensable for the survival of an organism, and their functions are, therefore, considered a foundation of life. Total 55 enzymes out of all were found to be essential for fe cycle . These tM. tuberculosis were evaluated by UniProt server. As it was suggested that, membrane associated protein could be the better target for developing vaccines. After functional analysis unique enzymes involved in cellular components like cell wall, cytoplasm, extracellular region, plasma membrane, and so forth, their biological processes and their functions have been retrieved (The subcellular localization analysis of all supposed essential and unique enzymes of etrieved .M. tuberculosis. Use of the DEG database is more efficient than conventional methods for identification of essential genes and it facilitates the exploratory identification of the most relevant drug targets in the pathogen. The current study can be carried forward to design a drug that can block these drug targets. The microorganisms are fast in gaining resistance to the existing drugs, so designing better and effective drugs needs a faster method.In conclusion, the computational genomic approach has facilitated the search for potential drug targets against