An improved kinetic model of the cyanobacterial Calvin cycle employing random sampling identifies probabilistic conditions for metabolome stability and metabolic flux control, agreeing with experimental data and aiding engineering efforts. 2 via the Calvin–Benson–Bassham cycle has massive ecological impact and offers potential for industrial exploitation, either by improving carbon fixation in plants and autotrophic bacteria, or by installation into new hosts. A kinetic model of the Calvin–Benson–Bassham cycle embedded in the central carbon metabolism of the cyanobacterium Synechocystis sp. PCC 6803 was developed to investigate its stability and underlying control mechanisms. To reduce the uncertainty associated with a single parameter set, random sampling of the steady-state metabolite concentrations and the enzyme kinetic parameters was employed, resulting in millions of parameterized models which were analyzed for flux control and stability against perturbation. Our results show that the Calvin cycle had an overall high intrinsic stability, but a high concentration of ribulose 1,5-bisphosphate was associated with unstable states. Low substrate saturation and high product saturation of enzymes involved in highly interconnected reactions correlated with increased network stability. Flux control, that is the effect that a change in one reaction rate has on the other reactions in the network, was distributed and mostly exerted by energy supply (ATP), but also by cofactor supply (NADPH). Sedoheptulose 1,7-bisphosphatase/fructose 1,6-bisphosphatase, fructose-bisphosphate aldolase, and transketolase had a weak but positive effect on overall network flux, in agreement with published observations. The identified flux control and relationships between metabolite concentrations and system stability can guide metabolic engineering. The kinetic model structure and parameterizing framework can be expanded for analysis of metabolic systems beyond the Calvin cycle.Biological fixation of atmospheric CO Furthermore, these kinetic models provide valuable information about the dynamic behavior of the system and potential for modification  (Existing kinetic models of the CBB cycle are either specific for chloroplasts (n rates) . AlthougSynechocystis sp. PCC 6803 (hereafter Synechocystis), accounting for metabolic regulations in cyanobacteria reported in the literature as well as enzyme promiscuity. To mitigate uncertainties associated with the metabolic state due to variability in metabolomics data sets  from an abstract summation of all phosphate sources in the cell, termed the phosphate pool (PPool). The Pi and PPool interconversion reaction follows mass action kinetics with equal forward and reverse rate constants. The reaction rate is therefore only dependent on the rate constant and the difference in concentration between Pi and PPool .A model of the central carbon metabolism of structed . The modWith the exception of fructose 1,6-bisphosphatase (FBPase), sedoheptulose 1,7-bisphosphatase (SBPase), the sink reactions, and the phosphate supply, the rate equations for each reaction follow reversible Michaelis–Menten-like kinetics, including equilibrium constants according to the general scheme:A↔B. Rate equation formulations are listed in for a simple conversion between two compounds 2 acting as a competitive inhibitor for CO2 binding.The metabolic regulations of enzymes were taken from the literature and are included in the rate equations as competitive inhibition and/or allosteric modifiers . ModelinKM in the reaction of SBP to sedoheptulose 7-phosphate (S7P) (see rate equations in KMSBP).In the case of enzyme promiscuity, where one enzyme catalyzes two reactions, the substrates and products of the second reaction were included as competitive inhibitors for enzyme-binding sites. For example, the reactions FBPase and SBPase are catalyzed by the same enzyme. Sedoheptulose 1,7-bisphosphate (SBP) acts as an inhibitor for the FBPase reaction from fructose 1,6-bisphosphate (FBP) to fructose 6-phosphate (F6P). The value of the inhibition constant in the FBP to F6P reaction was set to be the same as the SBP binding constant The local dynamic properties of the system, such as stability and flux control, were determined at specific metabolic (steady) states. Here, a metabolic state is defined by the sizes of the metabolite pools and the reaction rates (fluxes) connecting these pools at steady state. At steady state, the different fluxes acting on a metabolite must be balanced so that each metabolite pool stays constant over time.Synechocystis is scarce due to the challenging traceability of autotrophic metabolism   in the model, a value was randomly picked from a range of 0.01× to 100× around the corresponding metabolite concentration (translating to 1–99% enzyme saturation).Specifically, for each Michaelis–Menten, inhibition, and activation constant  with the steady-state input flux v0 of the corresponding reaction, viaThe This process was repeated 1000 times for each of the metabolite concentration sets, leading to sets of kinetic parameters describing a possible metabolic state of the system that covered most of the allowable parameter space.X0), where a negative real-part for all eigenvalues indicated a stable steady state:The stability of each combination of sampled sets was analyzed mathematically by determining the eigenvalues of the reduced Jacobian matrix at the metabolic state .The parameter sampling was performed in MATLAB v. R2015b on an Ubuntu 16.04 Linux system using 16 cores at 2.4 GHz, taking ~1 h.Parameterizing the model at a certain stable metabolic state allowed determination of control coefficients  for the which in turn were used to calculate the flux control coefficients viawith ectively .–6 and then inverse hyperbolic sine transformed (function asinh). A tenth of the effector and target FCC patterns, selected by random sampling (sample), were used in the subsequent steps due to limited computational capacity. To reduce the complexity of the data set further and extract the most prominent patterns, which, due to covariance, might not be reflected in the FCC medians, principal component analysis (PCA) was conducted. The data were scaled , reduced to patterns with finite values, subjected to PCA (prcomp), and rotated to the PCs (x variable of the prcomp output), yielding 29 by 29 matrices representing the patterns of control exertion or experience for the 29 reactions of the model. The matrices containing a reduced representation of the full effector and target pattern data sets were hierarchically clustered with multiscale bootstrap resampling (10 000 bootstraps) using the pvclust function  as well as target reactions . To prepare the data for analysis, the FCCs were rounded to zero if the absolute value was <1 × 10function  at defauhttps://github.com/MJanasch/CBB_Kinetics, allowing for recreating every sampling and analysis step [e.g. the >3 million parameter sets generated here (~28 GB)]. The sampling processes in MATLAB were parallelized using GNU Parallel v. 20141022  and distribution of flux control . The reactions and metabolites covered by the model are depicted in Synechocystis are associated with a large uncertainty  was employed to obtain 1000 parameter sets for each fMCS, with the constraint that computed flux distributions for each parameter set match those obtained by FBA.Then, random sampling of kinetic parameters , and FCCs were determined.While we focus our analysis on metabolome stability, enzyme saturation, and flux control, we emphasize that by providing the data sets and scripts to recreate every step, the model and the data sets can be further analyzed by the scientific community.Identifying conditions enabling a stable steady state is crucial for engineering modifications of the system. The stability conditions are also indicators of possible real metabolic states of the system, as an unstable steady state would not be sustained in nature due to accumulation or depletion of essential metabolites upon perturbation  or repression of Rubisco, may render the metabolic network less robust. Similar conclusions could be drawn for all distinctive patterns, such as for PEP, Xu5P, and SBP mentioned above.The main function of the CBB cycle is to recycle RuBP for another round of COEscherichia coli , again emphasizing the overall high predisposition for stability of the CBB cycle . HoweverKM. Saturation >50% was associated with lower stability. This case could mark a compromise in the trade-off between flux sensitivity and efficient enzyme usage, as low saturation would necessitate high enzyme abundance to enable the flux. This high protein burden is associated with metabolic costs and poses possible molecular crowding issues  followed the same tendency of low substrate saturation associated with stability . Low satThere are several reports of genetic instability in metabolically engineered cyanobacteria , where pEngineering improvements to the Calvin cycle is aided by knowledge of which enzymes control flux. An FCC is a quantitative measure of how a change in the rate of a selected reaction (‘effector reaction’) affects the flux through any reaction in the system (‘target reaction’). Positive or negative FCCs refer to an increase or decrease of the flux following a change in enzyme activity, respectively. A linear approximation method based on infinitesimal changes in activity (not probing activity changes of large magnitudes) was used to determine the FCCs for each stable parameter set. This approach resulted in distributions of FCCs for each enzyme and target reaction pair , prk, phosphoglycerate kinase (pgk), SBPase, and FBPase. Weaker effector reactions were glyceraldehyde 3-phosphate dehydrogenase (gapd), triosephosphate isomerase (tpi), tkt1 and fba.i underlined the dependency on free inorganic phosphate supply.The positive control of ATPSyn emphasized the direct dependence of the CBB cycle on reactions of the photosystems, which generate ATP and NADPH. Similarly, the positive control of Supply_PSynechocystis  exerted virtually no control over any other reaction but themselves. This emphasizes the parallel character of the subnetwork formed by the xfpk reactions towards ACCOA , bypassiThe three reactions in the xfpk subnetwork were, however, affected by many reactions. The negative control of tkt1 over xfpk1 could result from a competition for the same substrate (i.e. F6P), whereas the positive control on xfpk2 could result from the negative effect on xfpk1 combined with the supply of substrate for xfpk2 (i.e. Xu5P). Reasons for the reversed control of ald on xfpk1 and xfpk2 were not as obvious as simple substrate competition, and the relationships between the reactions were probably caused by the aforementioned non-trivial network effects in combination with the enzyme promiscuity interconnecting the two reactions, as they are catalyzed by the same enzyme. Further investigations will be necessary to understand this relationship fully.The model reproduced known and reasonable behavior and predicted trends that are in good agreement with published experimental data, displaying the strength of the random sampling approach and the subsequent probabilistic analysis. The capability to explore the whole metabolic and parameter space without constraining the search to finding a single data set fitted to the input data expands the potential of the obtained results and avoids uncertainty-related overfitting issues.To improve the predictive strength of this framework, advanced omics technologies can be employed to constrain further the broad sampling boundaries employed here to capture the physiological metabolic state more accurately.in vivo fluxes and the role of enzyme abundance in the network context (In addition to the previously discussed benefits of more targeted metabolomics studies (Computational studies by While the model is formulated with kinetic rate equations describing the enzymatic reactions, to the best of our knowledge, complex reaction mechanisms and possible allosteric and competitive effectors might not yet have been discovered. By randomly assigning different types of regulators, the modeling approach used in this study could provide guidance in finding possible regulatory mechanisms influencing state stability. Furthermore, the activities of several enzymes of the Calvin cycle are regulated by protein interaction via thioredoxin  or CP12 Finally, expanding the model to cover larger parts could further increase the predictive strength, since it would reveal more complex dynamics between the parts of the highly interconnected central carbon metabolism.Kinetic relationships are crucial in uncovering controlling steps in core metabolic networks such as the Calvin cycle. This study aids to reduce the uncertainty related to possible metabolic states of the Calvin cycle by employing a kinetic model and random sampling, which allowed investigation of dynamic properties such as stability and the distribution of control. Stability correlated with low abundances of the metabolite RuBP, suggesting that accumulation of this metabolite is not tenable. Furthermore, saturation levels of some promiscuous enzymes were also associated with instability. In general, flux control over the Calvin cycle is distributed over several reactions. The results of this study can guide further investigations into the dynamics of the Calvin cycle as well as its modifications by metabolic engineering for improved performance or transplantation into other hosts. The combination of thermodynamic feasibility and steady-state stability presented here constrains the solution space of metabolic models towards a more realistic description of reaction networks in nature.JXB online.Supplementary data are available at Protocol S1. Model structure file and scripts for reproducing the sampling and the plots.Protocol S2. List of rate equations for all reactions in the model.Protocol S3. Detailed description of flux balance analysis conditions for calculating the input fluxes and thermodynamic analysis of the sampled metabolite concentration sets.Table S1. Metabolic regulators of the rate equations.Table S2. Input fluxes for every reaction used in the parameter sampling, derived from flux balance analysis.Table S3. Input ranges for metabolite concentration sampling, with minimum and maximum values for each metabolite.Table S4. All feasible metabolite concentration sets resulting from random metabolite sampling and their associated percentage stability from the parameter sampling.Fig. S1. Distribution of sampled metabolite concentrations for all thermodynamically feasible sets.Fig. S2. Tendencies of all metabolite concentrations towards more or fewer stable steady states based on the top and bottom deciles of all fMCSs for 1000 parameter sampling rounds.Fig. S3. Tendencies of saturation states of all enzymes towards association with stability and instability in the system.Fig. S4. Clustering of reactions by their FCCs according to their property of exerting control.Fig. S5. Clustering of reactions by their FCCs according to their property of being controlled.Supplementary TableClick here for additional data file.Supplements Protocols FiguresClick here for additional data file.Supplementary MaterialClick here for additional data file.


Niehaus et al. published in this issue illustrates a number of the problems that still arise when attempting to translate genotypes to phenotypes, such as for interpreting mutant phenotypes or building genome-scale metabolic models. In this case, the mutation concerned appears to map to an enzyme in one of the little-known but essential metabolite repair pathways that have been discovered in recent years. However, the bioinformatic and experimental evidence presented suggests that the annotated enzyme activity of the mutated gene product, whilst correct, accounts neither for the phenotype nor for the chromosomal and transcriptional associations of the gene. The bioinformatic and metabolomic evidence presented points to an additional but important role for the gene product in pyridoxal phosphate homoeostasis, thus adding the enzyme to the expanding list of those with a ‘moonlighting function’.The article ‘Evidence that the metabolite repair enzyme NAD(P)HX epimerase has a moonlighting function’ by  The article ‘Evidence that the metabolite repair enzyme NAD(P)HX epimerase has a moonlighting function’ by Niehaus et al.  in this One is that the accuracy of gene–protein–function assignments is central to the interpretation of organisms’ genome sequences and relating them to biological properties. The gene–protein–enzyme relationship was established by Tatum and Beadle  as the bEscherichia coli with a point mutation did not lead to abnormally high NAD(P)HX levels.The paper alludes to one of these other sources of uncertainty: the completeness of our knowledge of metabolism, even in biochemically well-studied organisms. All genome sequences contain coding regions that cannot be assigned a function by bioinformatic methods, and it is likely that there are metabolic enzymes amongst these unannotated genes, especially as genome-scale metabolic models frequently have network gaps, where a reaction needed to ensure necessary connectedness of the network cannot be linked to a gene. However, in recent years there has been increasing awareness of new metabolic reactions associated with metabolite damage repair . The repE. coli did not have a significantly defective phenotype whereas deletion of the protein did. The nature of the defective phenotype pointed to a role for the epimerase protein in pyridoxal-associated metabolism, though whether that was via a different enzyme activity or some other process was not established.Another confounding issue in the mapping of genome sequence to a metabolic phenotype is the main topic of the paper: enzyme moonlighting, that is, an enzyme participating in another process within a cell that is distinct from its catalytic activity and often not even a direct metabolic function. There are now several hundred known examples . The exiIn conclusion, Niehaus et al.  illustra


Staphylococcus aureus is a metabolically versatile pathogen that colonizes nearly all organs of the human body. A detailed and comprehensive knowledge of staphylococcal metabolism is essential to understand its pathogenesis. To this end, we have reconstructed and experimentally validated an updated and enhanced genome-scale metabolic model of S. aureus USA300_FPR3757. The model combined genome annotation data, reaction stoichiometry, and regulation information from biochemical databases and previous strain-specific models. Reactions in the model were checked and fixed to ensure chemical balance and thermodynamic consistency. To further refine the model, growth assessment of 1920 nonessential mutants from the Nebraska Transposon Mutant Library was performed, and metabolite excretion profiles of important mutants in carbon and nitrogen metabolism were determined. The growth and no-growth inconsistencies between the model predictions and in vivo essentiality data were resolved using extensive manual curation based on optimization-based reconciliation algorithms. Upon intensive curation and refinements, the model contains 863 metabolic genes, 1379 metabolites (including 1159 unique metabolites), and 1545 reactions including transport and exchange reactions. To improve the accuracy and predictability of the model to environmental changes, condition-specific regulation information curated from the existing knowledgebase was incorporated. These critical additions improved the model performance significantly in capturing gene essentiality, substrate utilization, and metabolite production capabilities and increased the ability to generate model-based discoveries of therapeutic significance. Use of this highly curated model will enhance the functional utility of omics data, and therefore, serve as a resource to support future investigations of S. aureus and to augment staphylococcal research worldwide. Staphylococcus aureus metabolism helps explore its metabolic versatility. A multidisciplinary team led by Rajib Saha at the University of Nebraska developed a new genome-scale metabolic model of the multi-drug resistant pathogen S. aureus by combining genome annotation data, reaction stoichiometry, and condition- and mutant-specific regulations from biochemical databases and previous strain-specific models. Extensive manual curation and incorporation of newly generated experimental data on growth and metabolite production improved the accuracy and predictability of the model and increased its ability to generate model-based discoveries of therapeutic significance. Use of this highly curated model will enhance the functional utility of omics data and, therefore, serve as a resource to support future investigations of S. aureus and to augment staphylococcal research worldwide.Integration of in vivo experiment with a newly developed model of  S. aureus is a versatile human pathogen that has emerged as one of the most successful infectious agents of recent times, affecting approximately 20% of the world’s population.3 The incidence of methicillin resistance at low fitness cost has significantly contributed to the rise in community-associated methicillin resistant S. aureus (CA-MRSA) infections, which significantly limit therapeutic options and increase rates of mortality, morbidity and costs associated with its treatment.5 This threat to human health has resulted in a steady interest and focus on understanding how staphylococcal metabolism relates to antibiotic resistance and pathogenesis. A number of studies have attempted to explore the metabolic aspects of antimicrobial functionality of MRSA, including nitric oxide metabolism, oxidative stress, carbon overflow metabolism, redox imbalance etc.11 However, a complete mechanistic understanding of staphylococcal metabolism is still missing, making the identification of therapeutic targets challenging.17 There have been a number of attempts to reconstruct the metabolism of multiple strains of S. aureus using semi-automated methods.22 However, the absence of organism-specific metabolic functions and the inclusion of genes without any specified reactions still limit the utility of these models. These models need to be continually refined and updated to accurately predict biological phenotypes by addressing these issues, as well as by reducing metabolic network gaps, elemental imbalance, and missing physiological information. Since the predictive genome-scale metabolic models of several microorganisms were useful in performing in silico gene essentiality and synthetic lethality analyses and yielded promising results in pinpointing metabolic bottlenecks and potential drug targets,26 the potential for accurately modeling S. aureus metabolism is immense. To this end, Seif et al. recently developed an updated genome-scale model of S. aureus strain JE2, incorporated 3D protein structures, evaluated gene essentiality predictions against experimental physiological data, and assessed flux distributions in different media types.21 Their model was informed by multilevel omics data and a significant step toward deciphering the metabolic differences of this organism under different environmental conditions. Given the vast knowledgebase of experimental data, incorporation of the latest strain-specific annotation information, addition of condition-specific and mutant-specific regulations, and removal of spurious functions could result in a refined and more useful metabolic model for S. aureus USA300_FPR3757.The increase in knowledge of macromolecular structures, availability of numerous biochemical database resources, advances in high-throughput genome sequencing, and increase in computational efficiency have accelerated the use of in silico methods for metabolic model development and analysis, strain design, therapeutic target discovery, and drug development.32 However, a majority of these studies were focused on specific segments of staphylococcal metabolism and overlooked a system-wide interdependence that drives fitness, metabolic robustness, virulence, and antimicrobial resistance. Hence, a holistic approach of in silico genome-scale modeling and in vivo experimentation is crucial for gaining an improved mechanistic understanding of staphylococcal metabolism, and thereby, facilitating the development of novel therapeutic strategies to combat staphylococcal infections.Several other studies have been dedicated to elucidating the metabolic aspects of staphylococcal virulence and to pinpoint the key metabolic “hubs” in carbon and nitrogen metabolism.S. aureus USA300_FPR3757, namely iSA863, was reconstructed using annotation information from biochemical databases34 and previous strain-specific models,34 and validated through experimental observations and published phenotypic data. Strain USA300 FPR3757 is one of the common MRSA strains with available genome annotation (GenBank accession number NC_007793.1) and is closely related to the strain JE2 (with only 11 SNPs between these strains).35 The Nebraska Transposon Mutant Library (NTML)36 was developed for JE2; however, the S. aureus USA300 FPR3757 chromosomal genome sequence was used to map transpositions of bursa aurealis into the genome of S. aureus JE2, since the annotated genome sequence of strain JE2 was not available at that time. Therefore, the modeling framework took advantage of the existing knowledgebase. The model underwent extensive manual curation to ensure chemical and charge balance, thermodynamic consistency, and biomass precursor production. To test and inform the model, the fitness level of 1920 mutants from NTML36 was assessed, and the metabolite excretion profiles of eight important mutants distributed across several pathways of the carbon and nitrogen metabolism were measured. The growth-phenotyping results of the NTML mutants were utilized via GrowMatch procedure37 to reconcile in silico versus in vivo growth inconsistencies. Upon incorporating conditional regulations in the model gleaned from existing “omics” datasets,39 the predictive capability of the model in terms of gene essentiality and metabolite excretions in different environmental conditions was further improved. Furthermore, the growth predictions from the model on 69 different carbon sources were validated against the existing growth experiment.21 Overall, this model is extensively tested by multiple available and newly developed experimental datasets on staphylococcal metabolism and subsequently refined to pave a way forward to advance system-wide analysis of fitness and virulence.In this study, a comprehensive genome-scale metabolic model of 20 was assembled into a preliminary model of S. aureus. Out of 842 genes in the latest strain-specific USA300_FPR3757_uid58555 model by Bosi et al.,19 109 did not have any reactions associated with them, which were not included in our model at this stage. Checking reactions from the S. aureus N315 model iSB61920 against the annotations of strain USA300_FPR3757 in the KEGG database40 resulted in the inclusion of seven unique reactions to the preliminary model. In addition, every reaction in the model was verified for correct gene annotations in the NCBI, KEGG, and UniProt databases and published resources43 to amend the model with 90 metabolic reactions and annotate 75 reactions with correct Gene-Protein-Reaction (GPR) rules.A collection of 1511 metabolic reactions obtained from a consensus of recently published strain-specific models44 was followed when developing the biomass equation according to experimental measurements of macromolecular composition22 and transcriptomic data45 and the biomass compositions by previous models.21 Biomass precursors that do not have either experimental measurements or any literature evidence of synthesis in S. aureus were excluded. For example, S. aureus lacks an identifiable polyamine biosynthetic pathway and therefore cannot produce putrescine.46 In addition, phosphatidylethanolamine is not produced in S. aureus.48 Therefore, these components are not included in our biomass equation  were found to be imbalanced in terms of proton, carbon, nitrogen, oxygen, or sulfur. Most of these reactions  were fixed for proton imbalance and four reactions were fixed for imbalance in other elements .50 To resolve these cycles, 42 reactions were made irreversible, and four reactions were reversed in directionality either when thermodynamic information and literature evidence were available, or the restrictions assumed did not conflict with any literature evidence but resolved an infeasible cycle , glycerol-3-phosphate dehydrogenase (NAD), guanylate kinase (GMP:dATP), and homoserine dehydrogenase (NADH) were turned off to ensure correct cofactor usage in these reactions. Reactions involved in polyamine synthesis and degradation were removed due to the lack of convincing evidence of polyamine metabolism in S. aureus USA300_FPR3757.46 After these manual curation steps, the number of unbounded reactions (reaction fluxes hitting either the upper or the lower bound without any nutrient uptake) was reduced to seven. At this step, the model was checked for erroneous generation of energetic cofactors and confirmed that it could not produce unlimited amount of them without any nutrient input, as described by Zomorrodi and Maranas53 and followed in previous modeling studies by us58 and other groups.60In addition to charge and elemental imbalances, the preliminary model had 291 reaction fluxes unnecessarily hitting the upper or lower bounds during a Flux Variability Analysis (FVA) when no nutrients were provided (see the Methods section). Also, the inconsistent dissipation of ATP and other cofactors, which was persistent in earlier models,S. aureus USA300_FPR3757 genome in the KEGG database was next used to bridge several network gaps in the model. At this stage, the model contained 528 blocked reactions compared with 784 in the preliminary reconstruction. While this was a significant improvement, the model still contained a greater number of blocked reactions than other similar-sized models.21 The blocked reactions were not removed at the current stage because they either contained proper gene annotation information and their terminal dead-end metabolite was beyond the scope of the model or no convincing evidence  for filling the gap was available. A detailed list of the corrections and additions/removals made is given in Supplementary Data S. aureus models are shown in Fig. The annotation of 65 For example, our growth evaluation study of the viable S. aureus mutants from NTML36 found varying degrees of growth inhibition  were excluded from the essential gene list. Finally, for the modeling purpose, only the 167 metabolic genes (excluding 74 non-metabolic genes) present in the model were considered to be the core set of essential genes in the current study  or proper gene annotations, were removed from the model, resulting in an 8% reduction in GNGs. In total, 81 of the GrowMatch predicted resolution strategies were not accepted because they resulted in conflicts with correct growth (GG) and no-growth (NGNG) predictions in the model. The details of the GrowMatch results are presented in Supplementary Data Comparison of essential and nonessential genes between the experimental (in vivo) and model-based (in silico) gene essentiality analysis (see Methods section for details) showed some disagreements Fig. . CorrectS. aureus-specific and/or incorrectly annotated reactions might be present in the network. Figure S. aureus, while they were predicted to be nonessential by the model. The reason was the presence of an alternate pathway to convert glucose-6-phosphate (G6P) to ribulose-5-phosphate (Ru5P) in the model. Since literature and database searches failed to identify the presence of phospho-glucono lactonase in S. aureus, it was removed, and the model was made consistent with experimental essentiality prediction of glucose-6-phosphate isomerase and ribulose phosphate-3-epimerase genes. On the other hand, amino acid synthases, ATP-binding cassettes, and phosphoribosyltransferases are found among the NGG inconsistencies, which indicate possible cases for missing annotations. The details of NGG fixes made in this work can be found in Supplementary Data It was observed that the majority of the GNG inconsistencies fall in the category of metal ion/proton antiporters and amino-acid-tRNA ligases, which indicates that non-72 (see the Methods section for details), were incorporated into the model to achieve biologically meaningful distribution of fluxes that sharpened the model predictions of mutant growth phenotype and metabolite excretion behavior. The full list of regulations can be found in Supplementary Data 39 In addition, SrrAB and Rex-dependent transcriptional regulation are prominent driving forces of metabolic flux through respiratory metabolism that was integrated into the model.75 Furthermore, mutant-specific repression of respiration, histidine and ornithine metabolism, and pyruvate metabolism was imposed on the model for the menD mutant.38 In addition to the repressions mentioned above, reactions were added and constrained in flux values and directionalities as part of the refinement process.An automated procedure like GrowMatch can significantly improve the gene essentiality predictions in the model. However, without extensive validation against experimental data and manual curation, it is difficult to obtain biologically significant and meaningful prediction capability from the model. Hence, the model was validated against multiple experimental observations from previous studies and results obtained in the current work for further refinements. In this step, conditional regulations, via a valve approach29 The mutants were grown in a chemically defined medium (CDM) supplemented with 18 amino acids but lacking glucose. That study29 found that 11 of the mutations did not cause any growth defect, while 11 mutations caused intermediate growth defect and seven mutations were lethal. The model failed to recapitulate growth phenotype for nine  out of the 29 mutants, which warranted further investigation and refinements in the relevant pathways in the model. The gudB mutant did not appear to be an essential gene in the model simulation because other genes including d-alanine transaminase (dat) and aspartate transaminase (aspA) could convert glutamate to alpha-ketoglutarate. Based on information about kinetic limitation on alanine uptake76 and the experimentally measured uptake values reported by Seif et al.,21 a tighter constraint on alanine uptake of 0.4 mmol/gDW.h was imposed in the model, which resulted in a correct prediction of the essentiality of the gudB gene. The essentiality of sucC and sucA genes was ensured in the model by rectifying the direction of the alternate pathway consisting of succinyldiaminopimelate transaminase (dapE) and tetrahydrodipicolinate succinylase (dapD). In addition to that, the TCA cycle reactions converting citrate to succinyl-CoA were constrained to allow flux toward the forward direction only. Two of the gaps in the histidine transport pathway and proline catabolism were filled during the refinement process to allow for utilization of these alternate carbon sources in the absence of glucose. Ornithine–putrescine antiport, lactate dehydrogenase (ferricytochrome), malic enzyme (NADP), and succinyldiaminopimelate transaminase were removed from the model due to the lack of evidence in S. aureus. Upon these refinements, the model was able to correctly predict 24 (out of 29) of the mutant phenotypes, except gltA, acnA, icd, fumC, and rocF mutants. In comparison, the previous S. aureus model iYS854 failed to predict the growth phenotype for gudB, ald1, ald2, pyc, argD, and gltA mutants.21 The model refinements in the central metabolic pathway in terms of correction of reaction directionality, additions, and deletions are shown in Fig. The essentiality predictions for 29 amino acid catabolic pathway genes in the model were validated against the mutant growth phenotypes evaluated in a previous study.pyc (pyruvate carboxylase), citZ (citrate synthase), sucA (2-oxoglutarate dehydrogenase), ackA (acetate kinase), gudB (glutamate dehydrogenase), ndhA (NADH dehydrogenase), menD (menaquinone biosynthesis protein), and atpA (a subunit of ATPase). These mutants were selected for their potential in affecting glycolysis, TCA cycle, gluconeogenesis, electron transport chain (ETC), cellular redox potential, overflow metabolism, and fitness, as was evident by the growth inhibition of these mutants in our experiment. In general, supplementation of glucose (CDMG) as the primary carbon source resulted in the excretion of acetate as the major by-product in all mutants  of S. aureus resulted in increased levels of lactate production to maintain cellular redox when grown in CDMG . These correct predictions can be attributed to the deamination of the amino acids consumed in CDM media when the cell adapts to amino acids due to CcpA-mediated control of amino acid metabolism. The Rex and SrrAB repression on central carbon metabolism allowed the model to correctly simulate the oxygen deprivation in the model, which, in turn, resulted in correct predictions of decreased acetate excretion by the ndhA mutant in both CDM and CDMG media. Rex and SrrAB-mediated repression of pyruvate formate lyase (PFLr), alcohol dehydrogenase , and other pathways downstream of pyruvate shifted carbon flux away from the acetate production. At the same time, the flux space for lactate dehydrogenase (LDH) widened, which allowed for more lactate excretion in the CDMG media. Mutant-specific regulations and refinements also improved the model’s predictive capacity for menD and pyc mutants, which are discussed in detail in Supplementary Information Each of the mutants exhibited a deviation of the metabolic flux space  compared with the wild-type strain, as illustrated in Fig. S. aureus by the model, it should be noted that there are still gaps in our knowledge about the quantitative repression effect on the reaction fluxes in the presence of these regulators. To explore the quantitative effect of repression on the mutant phenotypes, different levels of repression  were imposed on the model, and metabolite excretion behavior was observed (data not shown). The different levels of repression showed varied degrees of agreement with experimental observation, with the 50% of the wild-type cutoff preforming the best overall. However, there were specific cases when the 50% cutoff was not highly predictive. For example, in CDMG media, ammonia production was not predicted in the menD, atpA, and sucA mutants by the model with a 50% cutoff, which was observed experimentally. Upon further investigation, it was observed that relaxing the repressions (to 90% of the wild-type flux space), the discrepancies were removed. In addition, a stronger repression effect (10–25% of wild type) on the reactions downstream of pyruvate redirected a portion of the carbon flux to acetolactate and resulted in acetoin excretion, which was not observed with a more relaxed (50–90% of wild-type) repression effect. In CDMG media, the citZ mutant correctly predicted the excretion pattern of acetate, because with the reduced flux space for the TCA cycle reactions, more carbon could be directed to the Pta–AckA pathway. However, in the CDM media, when amino acids were the primary source of carbon, deletion of the citZ gene did change the model-predicted flux space in the Pta–AckA pathway, and hence could not capture the decrease in acetate excretion rate. The reason for these inconsistencies could be either the lack of a complete understanding of the regulatory processes that affects the relationship between amino acid catabolism, urea cycle, TCA cycle, and pyruvate metabolism, or the inherent nonlinearity that exists between gene expressions and flux levels in some cases , and therefore, warrants further investigation.While the incorporation of the CcpA, Rex, and SrrAB regulations was critical in capturing the physiological behavior of S. aureus strain USA300-TCH1516 by Seif et al.21 Out of the 69 carbon sources tested, the authors observed growth on 53 metabolites and no growth on 16 metabolites in their BIOLOG experiment. Our model correctly predicted growth on 41 and no growth on 12 of the carbon sources, and falsely predicted growth on four and no growth on 12 carbon sources  and is closely related to the strain JE2.35 While the Nebraska Transposon Mutant Library36 was developed for JE2, the S. aureus USA300 FPR3757 chromosomal genome sequence was used to map transpositions of bursa aurealis into the genome of S. aureus JE2, since the annotated genome sequence of strain JE2 was not available at that time. Therefore, we chose to utilize the existing knowledgebase. Reactions were examined and fixed to ensure chemical and charge balance and thermodynamic consistencies. The extensive manual curation performed on the preliminary reconstruction resulted in improved prediction capabilities and successful capture of experimentally observed metabolic traits. All these demonstrate the necessity of exhaustive manual scrutiny and rectification of automated reconstructions. Further experimental results from gene essentiality, mutant growth, and metabolite excretion studies enabled high-resolution model refinements to further enhance the predictive capabilities of the model. The final genome-scale metabolic reconstruction (iSA863) is therefore a product of the series of automated and manual curation steps.In the current study, an updated and comprehensive genome-scale metabolic model of the methicillin-resistant human pathogen 65 Therefore, the true set of essential genes required further scrutiny, which is why, as a conservative estimate, we used a consensus set of essential genes by utilizing the existing knowledgebase and our own experimental findings  or potential redundancies/missing regulations in the model .The growth-phenotyping studies of mutations in the amino acid catabolic pathwayS. aureus remains a significant threat to human health, which drives a growing number of studies toward understanding how staphylococcal metabolism relates to antibiotic resistance and pathogenesis. Very few studies have addressed these interrelationships from a systems-biology perspective, which requires a predictive in silico metabolic model capable of capturing the biochemical features of the pathogen. This work addresses these gaps through the development of a detailed metabolic model informed not only from existing resources, such as the NTML, in silico genome sequences, annotation databases, and theoretical metabolic stoichiometry but also from our own experimental studies on mutant fitness, gene essentiality, and metabolite excretion profile. The results presented in this work demonstrate the predictive capacity of the new genome-scale metabolic reconstruction of S. aureus USA300_FPR3757, iSA863, in different environments, utilizing different substrates, and with perturbed genetic contents, which paves the way for a mechanistic understanding of S. aureus metabolism. This latest genome-scale model of S. aureus demonstrates high performance in capturing gene essentiality, mutant phenotype, and substrate utilization behavior observed in experiments. However, the accuracy and prediction capability, as well as the ability to generate model-based drug-target discoveries, can be further enhanced by incorporating extensively vetted flux measurements, quantitative proteomics, and kinetic measurements of metabolic intermediates. The development of a more accurate systems-level metabolic model for S. aureus will have a tremendous impact on future scientific discoveries and will be a valuable resource shared among the staphylococcal research community for the identification and implementation of intervention strategies that are successful against a wide range of pathogenic strains.S. aureus USA300_FPR3757 by Bosi et al.19 Reactions from the S. aureus N315 model iSB61920 were checked against annotations of S. aureus USA300_FPR3757 based on the KEGG database40 and merged with the reaction set to get the preliminary model. Flux balance analysis (FBA)80 was employed during model testing, validation, and analyzing flux distributions at different stages of the study. For performing FBA, the reconstruction was represented in a mathematical form of stoichiometric coefficients (known as stoichiometric matrix or S-matrix), where each column represents a metabolite and each row signifies a reaction. In addition to the mass balance constraints,81 environmental constraints based on nutrient availability, the relational constraint of reaction rates with concentrations of metabolites, and thermodynamic constraints were imposed as necessary. The effects of gene expressions were incorporated as regulatory constraints on the model as the cell adapted to a change in media or gene knockouts.82 The non-growth-associated ATP maintenance demand was estimated to be 5.00 mmol/gDCW.h in CDM media and 7.91 mmol/gDCW.h in CDMG media in this study, according to the established protocol in the absence of chemostat growth data.44 In CDMG media, glucose uptake rate was limited to 10 mmol/gDW.h with other nutrients set to be in excess  to achieve sensible and realistic metabolic flux distributions.One of the limitations of constraint-based genome-scale models is that the mass balance constraints only describe the net accumulation or consumption of metabolites, without restricting the individual reaction fluxes. Therefore, they have an inherent tendency to ignore the loop low for electric circuits, which states that there can be no flow through a closed loop in any network at steady state.85 FVA maximizes and minimizes each of the reaction fluxes subject to mass balance, environmental, and any artificial  constraints.85 The reaction fluxes which hit either the lower or upper bounds, are defined as unbounded reactions, and were grouped as a linear combination of the null basis of their stoichiometric matrix. These groups are indicative of possible thermodynamically infeasible cycles.53 To eliminate/destroy the cycles, duplicate reactions were removed, lumped reactions were turned off, or reactions were selectively turned on/off based on available cofactor specificity information  was used.The General Algebraic Modeling System (GAMS) version 24.7.4 with IBM CPLEX solver was used to run FBA and FVA, estimate gene essentiality, calculate metabolite excretion, and run Growmatch algorithm on the model. For each of the algorithms, the required optimization algorithm was scripted in GAMS and then run on a Linux-based high-performance cluster computing system at the University of Nebraska-Lincoln. The model was parsed from Systems-Biology Markup Language (SBML) level 3 version 1 document using standard programming languages  to generate the input files required by GAMS.600) of cultures for 24 h at 30-min intervals. The area under the growth curve (AUC) was calculated as a measure of growth for each strain and used for comparative analyses.Pre-cultures of wild-type and isogenic transposon mutant strains were grown overnight aerobically in 384-well plates containing 100 μL of Tryptic Soy Broth (TSB)/well with 14 mM glucose. The overnight cultures (1 μL) were seeded into a fresh 384-well plate containing TSB (100 μL/well) using a solid 384-pin tool (V & P Scientific) and cultured for 24 h at 37 °C under maximum agitation in a TECAN microplate reader. Preculture ODs were not specifically standardized due to the large number of mutants in this collection. Growth was monitored by recording the optical density  catalyzed by the gene following the Boolean logic of the Gene-Protein-Reaction (GPR) relationships and estimating growth as a result of the deletion. Isozymes  for essential reactions are not considered as essential, and for reactions catalyzed by proteins with multiple subunits , each gene responsible for each subunit is considered essential. A mutant was classified as lethal if its growth rate is below a preset threshold. Essential genes with the threshold values of 1, 10, 25, and 50% of the wild-type growth rate were estimated. A 1 or 10% threshold did not have any difference in the number of essential genes, and following conventions56 used in the community, the 10% threshold was used in this study.Metabolic robustness of an organism in the event of genetic manipulations is attributed to the essentiality of the respective gene(s) under a specific nutrient medium or regulatory condition.65 as explained in detail in Supplementary Information S. aureus and excluding mutations that remained after growing the cells.64 An adaptation of data from multiple sources using antisense RNA was also used to determine essential enzymes and thus essential genes through the Boolean relationships.65 Genes reported to be essential in any source were considered essential unless there was evidence suggesting otherwise.65 There were three types of positive evidence. First, mutants obtained from Nebraska’s Transposon Mutant Library86 were not considered essential unless it was found to be domain-essential.61 This is because the transposon may have inserted in a nonessential part of the gene, allowing a partially functional protein to be formed. Second, if the gene was found to be essential at only 43 °C, then it is evident that the gene was incorrectly found to be essential in literature because of a high-temperature plasmid-curing step in the processes used in the other literature sources.61 Third, if the gene was found to be essential using a promoterless transposon insert, but not with promoter-containing methodologies, then the gene is upstream of an essential gene, and other sources found it to be essential due to polar effects that disrupt expression.61 The step-by-step methodology used in determining core essential gene set is illustrated in Supplementary Fig. In vivo essential genes were curated from multiple sources,iSA863 metabolic model were considered for further model refinements. The results of the in silico growth estimation were compared with these experimental evidences, and the genes were classified based on the matches and mismatches between in silico and in vivo results. Correct model predictions for nonessential and essential genes are denoted by GG and NGNG, while wrong model predictions for nonessential and essential genes are denoted by NGG and GNG, respectively. GNG inconsistencies imply that the metabolic model erroneously contains reactions that complement for the lost gene function. In contrast, NGG inconsistencies are generally indicative of missing or poor annotations in the model.Out of the consensus set of the essential genes, 167 metabolic genes that are present in the 37 GrowMatch tries to reconcile GNG predictions by suppressing spurious functionalities that were mistakenly included in the model and NGG predictions by adding missing functionalities to the model while maintaining the already-identified correct growth and no-growth predictions.37 Every suggested GrowMatch modification was filtered for the resolution of conflict following the procedure of Henry et al. in 2009.67 Details of these cases can be found in Supplementary Data To resolve the growth and no-growth inconsistencies in the model, an automated procedure called GrowMatch was used.87 with minor modifications to amino acid content. Amino acids were diluted to final concentrations in the media from working stocks, as described by Vitko and Richardson.88 Briefly, the media contained the following components: Na2HPO4.2H2O, 10 g/L; KH2PO4, 3 g/L; MgSO4.7H2O, 0.5 g/L biotin, 0.1 mg/L; nicotinic acid, 2 mg/L; D-pantothenic acid Ca salt, 2 mg/L; pyridoxal, 4 mg/L; pyridoxamine dihydrochloride, 4 mg/L; riboflavin, 2 mg/L; thiamin hydrochloride, 2 mg/L; adenine sulfate, 20 mg/L; guanine hydrochloride, 20 mg/L; CaCl2.6H2O, 10 mg/L; MnSO4, 5 mg/L; (NH4)2SO4.FeSO4.6H2O, 6 mg/L. The individual amino acids were diluted 100-fold into CDM from stock solutions prepared as follows: L-aspartic acid, 15 g/L in 1 N HCl; L-alanine, 10 g/L in dH2O; L-arginine, 10 g/L in 1 N HCl; L-cystine, 5 g/L in 1 N HCl; glycine, 10 g/L in dH2O; L-glutamic acid, 15 g/L in 1 N HCl; L-histidine, 10 g/L in 1 N HCl; L-isoleucine, 15 g/L in 1 M NH4OH; L-lysine, 10 g/L in 1 N HCl; L-leucine, 15 g/L in 1 N HCl; L-methionine, 10 g/L in 1 N HCl; L-phenylalanine, 10 g/L in 1 M NH4OH; L-proline, 15 g/L in dH2O; L-serine, 10 g/L in dH2O; L-threonine, 15 g/L in dH2O; L-tryptophan, 10 g/L in 1 N HCl; L-tyrosine, 10 g/L in 1 N HCl; L-valine, 15 g/L in dH2O. In all, 2.5 g/L glucose was added for CDMG media. Cultures were cultivated in 250-ml flasks with a 10:1 flask:volume ratio and aerated at 250 rpm at 37 °C. To determine the metabolite excretion profile of various strains, cell-free culture supernatants were analyzed by HPLC for multiple weak acids, acetoin, and sugars as previously described. Briefly, the analysis was performed isocratically at 0.5 mL/min and 65 °C using a Biorad Aminex HPX-87H cation exchange column with 0.13 N H2SO4 as the mobile phase. The peaks corresponding to various metabolites were identified by their retention time obtained by using genuine standards. Absolute concentrations were determined from calibration curves specific to each metabolite. The excretion rates were calculated from the concentration values at two time points (0 and 3 h), and normalizing the slope against the difference in optical densities corresponding to those time points (data not shown). Ammonia and urea were measured using a kit (R-biopharm) according to the manufacturer’s protocol. Since the metabolite excretion rates are semiquantitative due to only two data points being considered, a qualitative comparison approach between model predictions and experimental measurements was employed in this work.Chemically defined media (CDM) was prepared as previously described by Hussain, Hastings, and WhiteS. aureus in terms of differential expression of genes or high/low abundance of the corresponding proteins was accumulated from multiple sources as listed in Supplementary Data 72 our condition-specific and mutant-specific repressions were incorporated using a “valve” approach similar to several other researchers.90 Gene-Protein-Reaction (GPR) Boolean relationships for each of the genes were used to determine the corresponding reactions to be regulated in model simulations in different conditions. If a reaction in catalyzed by multiple isozymes, the reaction was only suppressed if all the isozymes were downregulated in a certain condition. For a reaction catalyzed by multiple subunit proteins, it was suppressed if any of the genes responsible for a subunit was downregulated. To simulate the condition- and mutant-specific repressions, the allowable flux ranges were limited to a fraction of their maximum wild-type flux range. To assess the effect of the level of repression, we performed a sensitivity analysis using the repression effect simulating 10, 25, 50, and 90% of maximum wild-type flux space.Regulation information for Further information on research design is available in the Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Information 1Supplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6reporting summaryDataset 1


Escherichia coli populations that are grown in a homogeneous environment with the single carbon source glucose. In such experiments, stable communities of genetically diverse cross-feeding E. coli cells readily emerge. Some cells that consume the primary carbon source glucose excrete a secondary carbon source, such as acetate, that sustains other community members. Few such cross-feeding polymorphisms are known experimentally, because they are difficult to screen for. We studied the potential of bacterial metabolism to create new ecological niches based on cross-feeding. To do so, we used genome scale models of the metabolism of E. coli and metabolisms of similar complexity, to identify unique pairs of primary and secondary carbon sources in these metabolisms. We then combined dynamic flux balance analysis with analytical calculations to identify which pair of carbon sources can sustain a polymorphic cross-feeding community. We identified almost 10,000 such pairs of carbon sources, each of them corresponding to a unique ecological niche. Bacterial metabolism shows an immense potential for the construction of new ecological niches through cross feeding.Microorganisms modify their environment by excreting by-products of metabolism, which can create new ecological niches that can help microbial populations diversify. A striking example comes from experimental evolution of genetically identical  Escherichia coli can construct, if a novel niche must be able to sustain a stable community of microbes that differ in the nutrients they consume. We identify thousands of such niches. They differ in their primary carbon source and a secondary carbon source that is excreted by some microbes and used by others. Because we restricted ourselves to chemically simple environments, we may even have underestimated the enormous potential of microbes for niche construction.Biodiversity can emerge in a completely homogeneous environment from populations with initially genetically identical individuals. This striking observation comes from experimental evolution of bacteria, which create new ecological niches when they excrete nutrient-rich waste products that can sustain the life of other bacteria. It is difficult to estimate the potential of any one organism for such metabolic niche construction experimentally, because it is challenging to screen for novel metabolic abilities on a large scale. We therefore used experimentally validated models of bacterial metabolism to predict how many novel niches organisms like  With as many as one trillion predicted species, microbial diversity on our planet is enormous . To undePseudomonas fluorescens [Escherichia coli expressing different membrane proteins [Sympatric diversification is easiest in heterogeneous environments ,17. Becaorescens ; spatialorescens ; the divorescens ; as wellproteins ,9.E. coli populations cultured in the homogeneous and well-mixed environment of a batch culture or a chemostat, a device in which a cell culture is kept in a constant nutrient environment by continually supplying it with nutrient medium [E. coli can diversify into multiple genetically different strains [E. coli may readily diversify genetically and metabolically in a completely homogeneous environment.In apparent contradiction to the niche exclusion principle, sympatric diversification can also occur in homogeneous environments ,13,14,20t medium . For exa strains –23. Thes strains .That is, strains . ExperimPseudomonas fluorescens become dependent on their own modifications of their chemical environment [The emergence of cross-feeding is an example of niche construction, a process where organisms change their environment in ways that can affect the evolutionary dynamics of themselves and of other organisms –29. Promironment ,34.E. coli central carbon metabolism, and show that glucose-acetate cross-feeding can originate in such a population. Most recently, a genome scale metabolic network of E. coli was used to study cross-feeding and other metabolic dependencies that emerge as a result of evolution under gene loss [The origin of new niches associated with bacterial cross-feeding is not easy to detect experimentally: Except for differences in colony morphology, cross-feeding polymorphisms generally lack phenotypes that are both macroscopically visible and highly specific. However, computational analysis can help predict the conditions under which cross-feeding polymorphisms can originate and persist. Some authors use small biochemical networks to search for the conditions that promote genetic diversification through cross-feeding interactions –40. Otheene loss  or aminoene loss .E. coli and to complex metabolic systems in general. That is, we ask how many different kinds of ecologically stable cross-feeding interactions can emerge in an initially homogeneous population, where one bacterial strain feeds on a primary carbon source and produces a secondary carbon source that sustains the other strain. To answer this question, we take advantage of a well-studied and experimentally validated [E. coli metabolism. We use Flux Balance Analysis (FBA), an experimentally validated computational technique [E. coli and other metabolic reaction networks of similar complexity. Our work demonstrates the great potential of metabolic systems to construct new ecological niches.Here we go beyond this work and evaluate the general potential for the construction of new niches associated with cross-feeding that is inherent to the metabolism of alidated  genome-sechnique , to charechnique ,47, a vaechnique , we thenE. coli strains. We studied this specific polymorphism, because it is experimentally well documented [in silico. Specifically, we simulated the dynamic of a community composed of two cross-feeding E. coli strains , a prod iJO1366  to modelary time . The metary time .pscs,P for this carbon source via the exchange reaction that transports the secondary carbon source out of the cell. And we model the secondary carbon source consumption of strain C by limiting the strain’s primary carbon source consumption. This modeling decision is motivated by the experimental observation that when cross-feeding emerges in E. coli [We model these differences phenomenologically, through differences in the flux through two specific reactions in strains P and C. Specifically, we model the secondary carbon source production of strain P by imposing a non-zero production flux  E. coli , the conFor the well-studied cross-feeding interaction of acetate producer and consumer strains, regulatory mutations in specific genes are known to bring forth the metabolic behavior of producer and consumer strains ,23,50. Spac,P, and consumed by the consumer strain C , an assumption that we relax below . To ensure survival of the producer strain P, we simulated chemostat dynamics at an acetate production rate pac,P that is equal to 5% of this maximum (2.6 mmol gDW-1 h-1). We initialized the chemostat in the presence of only the acetate producing strain P, and once this strain had reached steady-state, which occurred after no more than 50 hours, we introduced the acetate consuming strain C. We then monitored the joint dynamics of both strains until they had reached steady-state or until one strain had gone extinct.To mimic typical experimental conditions, we performed all simulations with a dilution rate = 0.2 h-1. At thisDW-1 h-1 . , but also on the amount of producer biomass. As acetate production pac,P increases, the amount of acetate produced per unit biomass increases but the amount of producer biomass decreases. The joint effect of these opposing patterns is a unimodal distribution of consumer biomass.We next wanted to find out how the population’s behavior changes if the amount of acetate excreted by the producer strain P varies. We thus varied the acetate production rate ncreases , blue, bThe total (community's) biomass , black dRegardless of the acetate production of the producer strain P, the producer and consumer strains stably coexist, as has also been found experimentally . Additiocglc,C > 0), the two strains compete for this carbon source, and coexistence is no longer guaranteed. However, analytical calculations supplemented by simulations show that the two strains can stably coexist under a broad range of glucose consumption and acetate production rates . We used FBA to identify which of these molecules can sustain E. coli growth when present as the sole carbon source, which is a prerequisite for a molecule’s usefulness in the cross-feeding interactions we study. FBA predicts 180 metabolites (whose acronyms are given in the circle of E. coli when used as sole carbon sources (Methods). . There are 58 such secondary carbon sources, acetate (black arrow) being one of them. In other words, E. coli cells growing on glucose can modify the environment by producing 58 alternative nutrients, each of which can sustain the life of other E. coli individuals.For each of these 180 metabolites, we determined whether urce See . Fig 2A -1 h-1 for N-Acetyl-D-glucosamine(anhydrous)N-Acetylmuramic acid to 178 mmol gDW-1 h-1 for formate. Acetate’s maximal synthesis rate is 50.3 mmol gDW-1 h-1, about twice the mean production rate of 24.7 mmol gDW-1 h-1, and second highest among all secondary carbon sources (together with glycolate). This maximum production rate reflects the cost of producing a carbon source: The costlier the production of a secondary carbon source is, the smaller is its maximum production rate . Acetate’s biomass yield equals 0.025 and is thus low, less than half of the mean value of 0.068. Thus, while acetate is not costly to produce, its low biomass yield also does not allow for much biomass production in strains that consume it.  and the consumption rate of glucose  that lead to a stable cross-feeding polymorphism? We found that all secondary carbon sources other than formate can sustain a stable community of two strains, even though these communities vary greatly in the amount of total biomass that they contain  to support a growth rate greater than the dilution rate D = 0.2 h-1. This level of consumption is impossible under our assumed transport limit (Vmax = 20 mmol gDW-1 h-1). Higher transport limits or lower dilution rates would, however, permit the existence of a stable community.The absence of a stable community in the glucose-formate niche space is a consequence of formate’s low biomass yield, which requires high formate consumption (112 mmol gDWpmax), which affects the carbon source’s availability for C’s consumption. The second is the biomass yield α of the secondary carbon source, which affects the growth rate achieved per unit flux of consumed carbon source. The product of production and yield . About half of the secondary carbon sources (29 out of the 58) have a higher maximal growth rate The product of production and yield cglc,C = 0). However, relaxing this assumption to cglc,C > 0 also allows for stable coexistence of P and C  and the iMM904, ).  as the In our sampling procedure, we only required these networks to be viable on glucose, but as a result of complex correlations between metabolic phenotypes ,60, theyE. coli). Most secondary carbon sources are produced by more than one random viable network. E. coli on glucose are among the top-ranked carbon sources, with respective ranks of 19 and 13.When exposed to glucose as the primary carbon source, these networks produce between 0 and 22 secondary carbon sources (mean 11±5) that can sustain a two-strain community . Taking When exposed to not just glucose but to each of its primary carbon sources in turn, a random viable metabolic network can produce on average 14±6 secondary carbon sources (ranging from 0 to 26). The number of primary-secondary carbon source pairs varied greatly between networks, ranging from 0 to 1065 (mean 404±236). In total we observe 15685 different primary-secondary carbon source pairs that could serve as the foundation of a stable community in at least one random viable network.In sum, because even random viable metabolisms show high niche construction and cross-feeding potential, this potential is likely an intrinsic property of metabolic systems.E. coli. This focuses our analysis on novel biosynthetic abilities rather than on novel transport as a reason for the production or consumption of novel carbon sources. It also implies that we may underestimate the numbers of primary and secondary carbon sources, and perhaps dramatically so.Our last analysis complements the previous analysis by constructing a pan-metabolic network that contains all metabolic reactions from a known and curated “universe” of metabolic reactions (Methods). For various reasons, such a network could never be realized in any one organism, but it provides another way to inform us which kind of cross feeding interactions are metabolically possible. . See The pan-metabolic network harbors 221 metabolites that can be used as primary carbon sources, 41 more than ce is 85 , compare is a computational method to predict metabolic fluxes–the rate at which chemical reactions convert substrates into products–of all reactions in a genome-scale metabolic network . FBA reqS is the stoichiometric matrix, a matrix of size m × r that mathematically describes the stoichiometry of the network’s metabolic reactions. The integer m denotes the number of metabolites and r denotes the number of biochemical reactions in the network. These reactions include all known metabolic reactions that take place in an organism, which are called internal reactions. They also include reactions that represent the exchange (import or export) of metabolites with the external environment. Furthermore, they include a biomass growth reaction, which is a “virtual” reaction that reflects in which proportion biomass precursors are incorporated into the biomass of the modeled organism [Sij of the stoichiometric matrix contains the stoichiometric coefficient with which metabolite i participates in reaction j. The vector v is a vector (of size r) that harbors the metabolic flux through each reaction in the network. vgrowth specifies the flux through the biomass growth reaction. Fluxes through biochemical reactions are restricted by lower and upper bounds that constrain the flux through each reaction in the network. These bounds are given by the variables l and u, respectively, which are vectors of size r. We performed FBA optimization with the GNU Linear Programming Kit .Here, organism –46. EachμP and μC, a process that will increase their respective biomasses XP and XC. In a chemostat, fresh medium is continuously added and culture is continuously removed at a dilution rate D. Such dilution leads to a decrease in biomass inside the chemostat. Overall, the change in biomass for P and C can be expressed by the following system of ordinary differential equations:Strains P and C will grow at rates M) in a chemostat also varies. If a metabolite is present in the fresh medium at concentration M0, the metabolite's concentration will increase at a rate DM0 as a result of fresh medium continually being added to the chemostat. In addition, the metabolite’s concentration will also increase if it is produced by strain P  in h-1), as well as consumption and excretion fluxes of each metabolite by strains P and C . We used the values thus computed in dynamic FBA [gDW/l) of our microbial strains, as well as the abundance of all metabolites (in mM), nutrients, and waste products in our simulated chemostat.We performed FBA to compute instantaneous growth rates (amic FBA  to deterE. coli in batch and fed-batch cultures [Dynamic FBA (dFBA)  is an FBcultures , to studcultures  and to scultures .Briefly, dFBA starts from some initial time point and performs Flux Balance Analysis (FBA) iteratively at each time point during a given time interval to compute the maximally possible growth rate for each strain in the chemostat environment. As microbial strains grow, they consume nutrients and excrete waste products  and thus change their growth environments. Dynamic FBA takes these changes into account by computing the chemical composition of the environment at each time point. In doing so, dynamic FBA predicts how the biomass of bacterial strains and the chemical composition of the environment can change over time.We used dFBA to predict the temporal behavior of a microbial population composed of acetate producer strain P and consumer strains C in a chemostat. We next describe in detail how we used dFBA to simulate population growth in a glucose-limited minimal medium. We note that our procedure can be applied to any other carbon source by substituting glucose with the desired carbon source.-1 to mirror conditions from previous experiments that had identified cross-feeding interactions [7 cells/ml [Our simulations used the following parameters and initial conditions. We chose a dilution rate of D = 0.2 hractions . We set ractions . We assucells/ml –87. We cAfter initializing our simulations, using these parameters, we discretized time into short intervals of 0.1 h and performed dynamic FBA  by itera1. Calculation of maximum uptake rates. The uptake of a nutrient by an organism is limited by two factors: the capacity to transport the nutrient across the cell wall (transport limitation) and the availability of the nutrient in the environment (nutrient availability limitation). To determine the nutrient transport limit (for a nutrient at concentration M), we assumed Michaelis-Menten kinetics (VmaxM/(kM + M)) with parameter values set to Vmax = 20 mmol gDW-1 h-1 and kM = 0.05 mM. These parameters are based on data in the Brenda enzyme database [database ,89 and hdatabase .Vmax and kM can vary over a wide range without affecting the steady state. In Vmax and kM to exemplify how these parameters modify the transient biomass dynamics, but not the steady state.At the beginning of a simulation, the nutrient concentration is high and the biomasses of P and C are low. Therefore, nutrient consumption is initially limited by transport. As biomass grows the nutrient begins to be scarce and nutrient availability rather than transport become limiting for nutrient consumption. In other words, the transport limit shapes the transient biomass dynamics but the availability limit determines the steady-state biomass. This also means that M by the nutrient consuming biomass. This biomass depends on how much nutrients the strains consumed in the immediately past   M by theg to Eqs . We descg to Eqs .Once we had determined a strain’s transport limit for a nutrient and the nutrient’s availability limit, we set the uptake rate of the nutrient to the minimum of both, which ensures that organisms do not consume more of a nutrient than is physiologically feasible and available to them.2. FBA. Once we had calculated maximum uptake rates of nutrients as just described, we performed FBA for each strain independently. The calculation yielded growth rate values (μP and μC) for both strains, as well as consumption or excretion rates of each metabolite M for both strains .3. Calculation of environmental composition. With the results of FBA in hand, we used Euler’s method [s method  to deter4 time steps) had elapsed or until the chemostat had reached steady state. We assumed that steady state had been reached if the standard deviation of growth rates determined over 50 consecutive time steps was smaller than 10−5 for both strains. We carried out these simulations using MATLAB (Mathworks Inc.).We repeated these three steps until at most 1000 h . Third, if any one carbon source was able to sustain non-zero biomass production, we considered it an actual primary carbon source. Here and below, we viewed only biomass production fluxes above 10−5 mmol gDW-1 h-1 as being different from zero.We searched for all metabolites that could serve as carbon sources in the following way. To identify primary carbon sources, we first considered all metabolites in the  iJO1366  a candidE. coli has been demonstrated experimentally [E. coli, they may be a minor cause compared to regulatory constraints that are not incorporated by most genome-scale models analyzed with FBA [E. coli that we model, we assume that regulatory constraints can be by-passed, and thus use all 180 primary carbon sources on which FBA predicts growth in our analyses.Our approach identified 180 primary carbon sources . On aboumentally ,92. No ewith FBA . Becausepotential secondary carbon sources, but only some of them may be produced as metabolic by-products in a given environment. Most importantly, whether a carbon source is produced depends on the available primary carbon source. To identify actual secondary carbon sources and distinguish them from potential ones, we iterated through all pairs of primary carbon sources and potential secondary carbon sources, and performed FBA. More specifically, we used the primary carbon source as the sole carbon source (uptake rate: 10 mmol gDW-1h-1), maximized the production of the potential secondary carbon source, and constrained biomass production in FBA to be greater than zero. If the potential secondary carbon source could be produced at a rate greater than zero under this constraint, we considered the carbon source an actual secondary carbon source.We considered a metabolite a secondary carbon source if (i) it can serve as a primary carbon source and (ii) if it can be produced as a metabolic by-product when another metabolite serves as a primary carbon source. The first condition ensures that the metabolite can sustain growth of a strain consuming it, and the second condition ensures that the metabolite can be produced. Note that all primary carbon sources are B. subtilis  from the BiGG Database[We used the same method described in the previous paragraphs to search for primary and secondary carbon sources in the genome scale metabolic networks of l iYO844 ) and S.  iMM904, ), modify Database.E. coli iJO1366 that were not already present in this network. This amended pan-metabolic network includes 5625 metabolites and 7222 reactions.The pan-metabolic network is a network containing all metabolic reactions with well-defined stoichiometry that are known to take place in some organism. For our analysis, we extended a previously used pan-metabolic network comprising 5484 metabolites and 6892 reactions  by addinE. coli is permeable can freely enter and leave the cell. We note that if more metabolites where allowed to enter and leave the pan-metabolic network the number of blocked reactions would decrease. For reference, in the well-curated iJO1366 model 227 reactions (9%) are unconditionally blocked. In terms of absolute numbers, the pan-metabolic network contains 1569 more reactions that can carry nonzero flux than the iJO1366 model.We found that 3070 reactions (43%) in the pan-metabolic network are unconditionally blocked . That isGenome scale metabolic network reconstructions often contain spurious energy producing cycles that violate the second low of thermodynamic –77. UnleE. coli, but of multiple networks of similar complexity that do not share E. coli’s or any other organism’s evolutionary history. Any one metabolic network can be thought of as a subset of reactions drawn from the set of all metabolic reactions feasible in a living organism, i.e., the pan-metabolic network. In the enormous space of all possible metabolic networks only a tiny fraction is viable on any one carbon source, i.e., they can produce biomass when this carbon source is the sole carbon source. We focused on such viable networks, and to sample them from the space of such networks, we used a technique based on Markov Chain Monte Carlo (MCMC) sampling [We wanted to study the niche construction capacity not only of sampling ,57, whicsampling  shows thsampling . One reqsampling , we deteE. coli. To create this initial network, we first performed Flux Balance Analysis on the pan-metabolic network, with glucose as the only source of carbon. Of all reactions in the pan-metabolic network, 1263 reactions showed non-zero flux and were included in the initial network, which ensured viability on glucose. We chose the remaining (1320) reactions needed to arrive at an equal number of reactions as E. coli at random from the pan-metabolic network. From this initial network the MCMC method creates a long sequence of modified networks. In our implementation of the method, a new network is created by a reaction swap, in which a reaction from the existing network is randomly chosen for deletion, while a randomly chosen reaction (that is not yet present in the network) from the pan-metabolic network is added to the network. If the network remains viable after this reaction swap, the swap is accepted, and the network is modified with a further reaction swap. In contrast, if the reaction swap disrupts viability on glucose, the swap is rejected and a new swap is tried. Modifying metabolic networks through reaction swaps ensures that the number of reactions in the network remains constant . As the number of reaction swaps increases, the number of reactions that the altered networks share with the initial network becomes smaller and smaller, until the complement of reactions has become effectively randomized after 5000 successful swaps [The method starts from an initial network, which we chose as a network that is viable on glucose as a sole source of carbon and that has the same number of reactions (2583) as ul swaps . We storE. coli does. Each of them is the end point of a sequence of 5000 successful reaction swaps. This procedure is very time consuming, and to accelerate it, we first determined the reactions that are essential for growth on glucose in the pan-metabolic network, and did not subject these (169) reactions to deletions.We performed 500 independent such random walks, thus creating 500 metabolic networks all viable on glucose and containing as many reactions as the network of E. coli. We also note that random networks contain a large number of reactions (1197±36) that cannot carry non-zero flux in any of the environments we consider, i.e., they are unconditionally blocked. Even though all networks analyzed in this work contain unconditionally blocked reactions, the numbers observed for the random viable networks are especially large.We note that we did not alter the exchange reactions of the starting network, which ensures that in the randomized networks the same metabolites can be exchanged with the environment as in Because we created random networks by sampling reactions from the pan-metabolic network, these networks may also contain spurious cycles –77. For We implemented our sampling procedure in python, using the cobrapy package  to perfoS1 Text(DOCX)Click here for additional data file.S2 Text(DOCX)Click here for additional data file.S3 Text(DOCX)Click here for additional data file.S4 Text(DOCX)Click here for additional data file.S1 FileE. coli can produce when growing on a glucose minimal medium.The table contains the names, biomass yield, maximal production rate, and cost of each of the 58 secondary carbon sources that (ODS)Click here for additional data file.S2 File(XML)Click here for additional data file.S1 DatasetThe folder contains 500 files, each corresponding to a random network obtained sampling  from the pan-metabolic network. Each network is represented with a sequence of zeros and ones where one and zero indicates presence or absence of the reaction in the random network respectively.(ZIP)Click here for additional data file.S1 FigHorizontal axes in all panels indicate time in hours. Panels (A) and (B) show the biomass values of P and C respectively , as a function of time, while changing the initial biomass ratio of P and C (see color legends in both panels). Panels (C) and (D) show the biomass values of P and C respectively, as a function of time when changing the total initial biomass, while maintaining the ratio of P to C biomass at a constant value of 0.5 (see color legends in both panels). Panels (E) and (F) show the biomass values of P and C, respectively, as a function of time for two scenarios. In the first, the chemostat is initiated with equal amounts of P and C biomass at time zero (red line). In the second, the chemostat is initiated just with P, and C is introduced at various times in an amount equal to that of P at time zero (see color legend).The chemostat composition and all other parameters used in these simulations are identical to those described in the main text (Section “Simulating chemostat dynamics with dynamic FBA” in methods).(PDF)Click here for additional data file.S2 FigSteady state biomass of P (left panel) and of C (right panel) in the chemostat. The amounts of biomass are shown as a function of P’s acetate production rate  and of C’s glucose consumption rate , which are expressed as percentages of the maximal acetate production rates and glucose consumption rates that permit coexistence of metabolically distinguishable strains P and C. Note the nonlinear scale used at low values of pac,P . The amount of biomass is indicated by a color gradient (see color legend) in the region where the two strains can coexist. Note that for parameter combinations where no acetate is produced and where glucose consumption is higher than 99% of cglc,C, both strains coexist but are metabolically indistinguishable, because both completely respire glucose to carbon dioxide. Areas (parameter combinations) where coexistence is not possible are shown in light grey (only P persists), dark grey (only C persists) and black (neither strain persists).(PDF)Click here for additional data file.S3 Figpac,P and glucose consumption rates cglc,C that permit coexistence of metabolically distinguishable strains P and C. As in cglc,C and pac,P, the chemostat in steady state may either contain no biomass (black region), only strain P (light grey), only strain C (dark grey), or both P and C (color). Colors from blue to yellow (see color bar) indicate the ratio of C’s glucose consumption rate and P’s glucose consumption rate when both strains are present in the chemostat, which varies between zero (when C does not consume glucose) and one (when C and P consume glucose at the same rate). The glucose consumption ratio is large  when the producer strain produces little acetate  while the consumer strain C consumes a lot of glucose . The maximally possible glucose consumption ratio of one (bright yellow) is observed only when both strains are metabolically indistinguishable, i.e., when P produces no acetate and C uses glucose as the only source of carbon. The lower panel magnifies part of the upper panel for low values of acetate production  to appreciate how glucose consumption changes at these values.The figure shows the ratio of glucose consumption rates of strains C and P, as a function of P’s acetate production rate  and of C’s glucose consumption rate , expressed as percentages of the maximal acetate production rates (PDF)Click here for additional data file.S4 FigE. coli can produce when growing on glucose. The black arrow indicates the data for acetate. See The figure shows the maximal production rate and cost for each of 58 secondary carbon source (grey circles) that (PDF)Click here for additional data file.S5 Figαm) of the secondary carbon sources considered here . Each circle in the plot corresponds to a secondary carbon source that E. coli can produce when glucose is the primary carbon source. Circle colors indicate the product of maximal production (αm) of a secondary carbon source. This product equals 1.26 h-1 for acetate. The biomass ratio is shown for 1, 10 and 30% of the maximally possible secondary carbon source production flux, as indicated by the numbers in the panel.The figure is a different representation of the data shown in (PDF)Click here for additional data file.S6 Fig2 by the producer strain, without synthesis of any secondary carbon source. The dashed line indicates where both strains have identical biomass. Circle colors indicate the product of maximal production (αm) of a secondary carbon source. This product equals 1.26 h-1 for acetate (black line superposed with blue circles).The consumer strain has a consumption rate of glucose cglc,C that is equal to 0% (A), 54% (B), and 99% (C) of the rate it needs to persist on glucose alone in the chemostat. Panel A is identical to Steady state biomass of producer strain P  and consumer strain C  at different percentages of the maximum synthesis rate ((PDF)Click here for additional data file.S7 FigE. coli's carbon sources  indicate from how many primary carbon sources this carbon source can be produced as a secondary carbon source . Most secondary carbon sources can be produced from all primary carbon sources (ln(179) = 5.2 on the red scale), but some can be produced only from few primary carbon sources. Among them is glucose (grey arrow), which can be produced only from four primary carbon sources.The outermost circle lists all of es as in , ordered(PDF)Click here for additional data file.S8 FigE. coli  Histogram of the number of secondary carbon sources that can be produced per primary carbon source in (PDF)Click here for additional data file.S9 FigVmax and kM (Methods). Horizontal axes in all panels indicate time in hours. Panels (A) and (B) show the biomass values of P and C, respectively , as a function of time, while changing kM . The range of kM values simulated covers 60% of all kM values present in Brenda database [kM in the database is approximately 0.1 mM, with 60% of kM values between 0.001 and 1 mM [Vmax . If, for any one consumed metabolite, Vmax is not high enough to permit growth at the dilution rate, the population will go extinct. Values of Vmax above this minimalvalue that permits growth at the dilution rate can alter the transient biomass dynamics but does not affect the steady state biomass. The chemostat composition and all other parameters used in these simulations are identical to those described in the main text (Section “Simulating chemostat dynamics with dynamic FBA” in methods). We ended any one simulation when a population had reached steady state, which is the reason why the lines end at different time points.We model transport limitation with Michaelis-Menten kinetics with parameters database  (The medand 1 mM ).Panels (PDF)Click here for additional data file.S10 FigPanel (A) shows a hypothetical pan-metabolic network comprising 10 internal and 5 transport reactions. Panel (B) shows four networks created by randomly selecting 4 internal reactions (in black) from the pan-metabolic network. In this hypothetical example, ATP must be produced from at least one of the environmental nutrients  for a network to be viable. All four networks are viable on A if nutrient H is available in the environment. The random networks 2, 3 and 4 are also viable on metabolites D, F and F and G respectively. If a metabolite on which a network is viable can be produced when nutrient A is consumed, the metabolite is a secondary carbon source (green). (C) Histogram of the number of secondary carbon sources per random network, for the four random networks considered here. (D) Rank plot of secondary carbon sources  that can be produced by at least one random network when A is used as a primary carbon source, ranked by the fraction of random viable networks  by which the secondary carbon source can be produced.(PDF)Click here for additional data file.