Salmonella nutrition and growth in a mouse typhoid fever model. The data revealed that Salmonella accessed an unexpectedly diverse set of at least 31 different host nutrients in infected tissues but the individual nutrients were available in only scarce amounts. Salmonella adapted to this situation by expressing versatile catabolic pathways to simultaneously exploit multiple host nutrients. A genome-scale computational model of Salmonella in vivo metabolism based on these data was fully consistent with independent large-scale experimental data on Salmonella enzyme quantities, and correctly predicted 92% of 738 reported experimental mutant virulence phenotypes, suggesting that our analysis provided a comprehensive overview of host nutrient supply, Salmonella metabolism, and Salmonella growth during infection. Comparison of metabolic networks of other pathogens suggested that complex host/pathogen nutritional interfaces are a common feature underlying many infectious diseases.Pathogen access to host nutrients in infected tissues is fundamental for pathogen growth and virulence, disease progression, and infection control. However, our understanding of this crucial process is still rather limited because of experimental and conceptual challenges. Here, we used proteomics, microbial genetics, competitive infections, and computational approaches to obtain a comprehensive overview of  Salmonella nutrition and growth in a well-characterized mouse model of human typhoid fever. We used a panel of Salmonella mutants with metabolic defects to assess the importance of various nutrient utilization pathways for Salmonella growth. We derived from these experimental data a computational model that predicts nutrient uptake rates, activity of metabolic pathways, and the effects of Salmonella enzyme defects on in vivo growth. The vast majority of these predictions were in close agreement with independent experimental data suggesting the model provided a consistent overview of Salmonella metabolism during infection. The data showed that Salmonella depend on a highly complex diet with many different host nutrients, but each of these nutrients is available in only scarce amounts. To grow and cause disease, Salmonella must simultaneously exploit these various nutrients with versatile degradation pathways. Similar complex pathogen diets might also drive many other infectious diseases.Infectious diseases are a major health problem worldwide. To cause disease, pathogens need to acquire host nutrients for growth in infected tissues and for the expression of virulence factors. In this study, we investigated  Infectious diseases are a major worldwide threat to human health Salmonella nutrition and growth in a mouse infection model mimicking human enteric fever. Enteric fever is caused by ingestion of food or water contaminated with Salmonella enterica serovars Typhi and Paratyphi (“typhoid/paratyphoid fever”) Salmonella invade intestinal tissues and disseminate to inner organs including spleen, liver, kidney, bone marrow, and brain, where they proliferate and cause tissue damages that can result in strong inflammation and organ failure. Enteric fever causes tremendous morbidity and mortality worldwide. Current control strategies become increasingly inefficient as a result of increasing antimicrobial resistance Salmonella serovars that are not covered by currently available safe vaccines In this study, we investigated Salmonella enterica serovars that cause human enteric fever usually do not cause any disease Slc11a1  coding for a Fe2+/Mn2+/Zn2+transporter, and such mice can successfully control systemic salmonellosis Slc11a1 allele which makes them highly susceptible to lethal systemic Salmonella infections. Salmonella infections in these genetically susceptible mice thus represent an excellent model for severe human typhoid (and paratyphoid) fever Salmonella genetics, availability of genome-scale in silico metabolic reconstructions Salmonella and the prime model organism E. coli.In mice, Salmonella nutrition and growth in this mouse typhoid fever model. Our data revealed an unexpectedly complex Salmonella nutritional landscape in infected host tissues, where many chemically diverse nutrients were available in scarce amounts. Salmonella adapted to this situation by simultaneously employing versatile nutrient utilization pathways.In this study, we used proteomics, mutant phenotyping, and computational approaches to investigate Salmonella metabolic capabilities during infection, we sorted Salmonella from infected mouse spleen and determined copy numbers of 477 metabolic enzymes (among 1182 identified proteins) using the well-established proteomics iBAQ label-free quantification approach Salmonella enzymes in the same disease model To characterize Salmonella metabolic reactions for which catalyzing enzymes have been annotated Salmonella in vivo proteome as limited material availability and mass spectrometry detection thresholds likely prevented identification of weakly expressed enzymes. These data suggested that during infection, Salmonella mobilized a large part of their diverse metabolic capabilities. In comparison, closely related E. coli requires only 293 reactions for optimal growth in a minimal in vitro medium E. coli expresses more than 200 apparently not required enzymes suggesting that enzyme expression alone is not indicative of metabolic relevance The detected enzymes are known to catalyze 925 metabolic reactions, a remarkably high proportion of all known/inferred 2023 Salmonella enzymes and associated metabolic reactions, our proteome data also provided quantitative data on Salmonella metabolic capabilities. We combined enzyme copy numbers with available turnover numbers to calculate maximal reaction rates for 469 reactions  has vmax = 22 µmol min−1 mg−1−1. Based on these data, a single Salmonella cell would have the catalytic capacity to phosphorylate up to 420’000 glycerol molecules s−1. Such results should be taken as approximate only since turnover numbers are usually determined for somewhat non-physiological in vitro conditions . Moreover, these data were incomplete because of undetected enzymes with abundance below the proteomics detection threshold and missing kinetic data. Nevertheless, the data yielded an unprecedented large-scale overview of Salmonella catalytic capacities in an infected host tissue, and provided a unique quantitative basis for in-depth analysis of metabolic activities involved in Salmonella virulence .In addition to the qualitative identification of expressed eactions . As an eSalmonella maintained versatile catabolic capabilities for diverse nutrients during infection.As expected Salmonella host tissue colonization, we inactivated defined utilization pathways. We preferentially deleted transporters to prevent high-affinity nutrient uptake instead of inactivating degradation enzymes that could result in accumulation of toxic upstream metabolites such as phosphorylated carbohydrates, which can cause pleiotropic effects To determine the actual relevance of specific nutrients for supporting E. coli growth in the intestinal lumen Utilization defects have previously been used in several studies, for example to determine the relevance of several carbohydrates for Salmonella mutants, we inactivated well-characterized biosynthesis pathways for essential biomass components. The resulting Salmonella auxotrophs were unable to grow unless the missing biomass components were provided externally  = 0) indicated that a mutant had equal colonization capabilities as wildtype Salmonella. Complementation of mutant alleles to verify mutation phenotypes was often difficult because most strains contained multiple mutations. However, we independently reconstructed the most attenuated mutant and confirmed the resulting colonization defect . Other utilization mutants had non-significant colonization phenotypes suggesting limited contributions of the corresponding nutrients.All seven identified nutrients can serve as a sole carbon source for Slc11a1 allele (see Introduction). Such mice are highly susceptible for systemic salmonellosis providing a useful model for severe human typhoid fever. For comparison, we also did some small-scale experiments in 129/Sv mice that carry a functional Slc11a1 allele and are therefore resistant to lethal salmonellosis. Competitive infections confirmed the importance of glycerol (or glycerol-3-phosphate) and N-acetyl-glucosamine for Salmonella growth vitamins and all tested amino acids (except proline). Similar colonization phenotypes were obtained for Salmonella mutants with utilization or biosynthesis defects in infected liver , glucose metabolization by host cells  and a non-preferred carbon source (mannitol) when available thus supporting our nutrient limitation hypothesis.To further test the hypothesis of parallel utilization of different available nutrients, we used a cell culture infection model where ar niche . Interesa growth . This grSalmonella growth being dependent on diverse scarce nutrients during infection.Taken together, both mutant colonization defects and cell culture experiments were consistent with Salmonella growth, we were interested to obtain quantitative nutrient supply rates as a basis for comprehensive understanding and computational modeling of Salmonella nutrition, metabolism and growth. Quantitative nutrient supply rates have not yet been reported for any infection model, but the severity of mutant colonization defects could provide some hints. As an example, the strong colonization defect of Salmonella glpFK gldA glpT ugpB defective for glycerol utilization, compared to Salmonella manX nagE defective for GlcNAc utilization, could suggest that more glycerol was available as compared to GlcNAc. This rationale has previously been used to assess the relative relevance of various carbohydrates for E. coli gut colonization In addition to these qualitative results on nutrient-limited Salmonella, we therefore used a computational approach called Flux-Balance Analysis (FBA) Salmonella, we recently established together with more than 20 Salmonella experts an in silico reconstruction of the entire Salmonella metabolic network that contains all experimentally determined Salmonella metabolic activities, all enzymes with annotated metabolic activity encoded in the Salmonella genome, and their catalyzed reactions with all participating metabolites, stoichiometries, and information on reaction reversibility Salmonella metabolic reconstruction has been extensively documented and is continuously being updated by manual curation of newly available literature for Salmonella and closely related E. coli enzyme orthologs  and in the Supporting Information . We adjusted nutrient uptake rates to reproduce our experimental Salmonella mutant phenotypes .We also explored scenarios of excess nutrient supply see . The resSalmonella that could serve as a basis for subsequent improvements  and in the Supporting Information . On the other hand, the resulting model provided a first comprehensive quantitative approximation to the host nutritional landscape and its exploitation by ormation .Salmonella nutrition and growth during infection, we compared model predictions with large-scale experimental data sets on Salmonella mutant phenotypes, enzyme expression, and metabolic capabilities.To assess how well the current computational model captured relevant aspects of Salmonella mutant colonization phenotypes .To validate functional aspects of the computational model, we systematically predicted in vivo growth phenotypes for all 1279 model enzymes, and compared these predictions to reported experimental Salmonella virulence (predicted mutant growth rate below 60% of wildtype ), while some genes contributed to virulence (predicted mutant growth between 60% and 98% of wildtype), and the vast majority of enzymes had non-detectable effects (mutant growth rate higher than 98% of wildtype) in agreement with our previous experimental data E. coli in vitro cultures Salmonella genes during infection. On the other hand, there were 61 discrepancies between computational predictions and experimental data that could help to identify remaining errors and knowledge gaps  essential enzymes, (ii) enzymes required for optimal growth with minimal overall flux (“optima”), (iii) enzymatically less efficient enzymes , (iv) metabolically less efficient enzymes , and (v) genes with no contribution to Salmonella growth (zero flux in the associated reactions). Comparison with our proteome data for ex vivo sorted Salmonella revealed that there was a statistically highly significant relationship between relevance and the proportion and abundance of detected Salmonella enzymes in the various classes   classes , similarSalmonella virulence that were undetectable with current in vivo methods. Alternatively, Salmonella might have prepared themselves for subsequent environments in their life cycle where these pathways would offer fitness benefits. Finally, Salmonella might be unable to optimally regulate its enzyme expression to shut down all dispensable enzymes (as it is likely the case in E. coli in vitro cultures). Further research is required to test these and other hypotheses.On the other hand, we detected several enzymes that were predicted to mediate no flux, again similar to observations for in vitro cultures Salmonella growth in minimal medium with glucose as sole source of carbon and energy. Interestingly, there was a large overlap between enzymes that were important for optimal growth of Salmonella under these two conditions. We detected 30 proteins that were predicted to be specifically required in vivo but not in vitro, providing some support for our in vivo model. On the other hand, we also detected 15 proteins that should be required only in the in vitro minimal medium but not in vivo. Interestingly, eleven of these 15 proteins were involved in amino acid biosynthesis suggesting that Salmonella maintained such biosynthetic capabilities in vivo despite access to host amino acids (see above). It is possible that the amino acid supply was just marginally sufficient and Salmonella prepared itself for future amino acid starvation. Further work is required to clarify this issue.We also compared our in vivo model with a model for In addition to predicting enzyme relevance, the model also provided quantitative predictions for fluxes through hundreds of metabolic reactions. For some reactions, a large range of reaction rates was possible whereas others had more restricted rates  as previSalmonella catalytic capacities calculated from experimentally determined enzyme concentrations and turnover numbers . Interestingly, 459 out of 469 analyzed reactions had feasible predicted rates . Interestingly, all three reactions were aminoacyl tRNA ligations . Possible causes for these discrepancies included inaccurate biomass assumptions for proline, alanine, and threonine protein content, experimental errors in protein quantification, and/or suboptimal assay conditions for tRNA ligase turnover number measurements. Moreover, the computational model disregards important processes outside metabolism such as macromolecular expression Salmonella had sufficient in vivo enzyme amounts and catalytic power to mediate nutrient utilization, metabolization, and biomass generation as predicted by the computational model.Despite these three minor discrepancies, the overwhelming feasibility of reaction rates indicated that Salmonella in silico growth, but resulted in a dramatically reduced flux solution space . Interestingly, these large-scale constraints still allowed normal on space . Specifion space . This enSalmonella nutrition, metabolism, and growth in infected host tissues.Together, these data revealed that the model predicted (i) largely correct mutant virulence phenotypes, (ii) predicted enzyme relevance that correlated with experimental protein detection, and (iii) predicted reaction rates that were biologically plausible. This large-scale consistency with experimental data suggested that the computational model captured major aspects of Salmonella conditions during mouse typhoid fever might be generally representative for pathogen nutrition in infected host tissues, we compared pathogen metabolic networks based on genome pathway annotations Salmonella used in the mouse typhoid fever model. Additional genome comparisons for 316 non-pathogenic species revealed that they might also preferentially utilize similar nutrients vitamins. Surprisingly, this included facile availability of all three aromatic amino acids based on full virulence of auxotrophic Salmonella pheA tyrA trpA. This was also consistent with common tryptophan auxotrophy of Salmonella enterica serovar Typhi clinical isolates from human typhoid fever patients Salmonella virulence ListeriaVirulence phenotypes of nutrient utilization mutants and auxotrophic mutants revealed that Salmonella metabolism but, in addition, also relieved Salmonella dependence on any particular nutrient and its corresponding utilization pathway, thus enabling Salmonella to maintain high virulence even when biosynthesis pathways for important biomass components such as amino acids were defective. This buffering capacity of the complex nutritional landscape significantly contributed to the remarkable robustness of Salmonella metabolism against internal perturbations during infection The large diversity of accessible host nutrients posed complex challenges to Salmonella purified from infected tissues revealed in vivo expression of enzymes involved in degradation of the major nutrients glycerol, fatty acids, and N-acetylglucosamine, glucose, lactate, and arginine suggesting that Salmonella allocated major enzyme resources to relevant pathways in agreement with earlier observations Salmonella virulence. Together, the proteome data revealed versatile Salmonella adaptation to a complex nutritional landscape.Proteome analysis of Salmonella mutant colonization phenotypes. Specifically, we updated a genome-scale reconstruction of the Salmonella metabolic network and established a modified in vivo biomass composition. We then determined which nutrient uptake rates would support Salmonella biomass production consistent with experimental colonization data for wildtype and mutant Salmonella. This approach yielded uptake rates for 31 organic and 13 inorganic nutrients. For consistency with the experimental wildtype Salmonella in vivo generation time, we had to increase the non-growth associated ATP maintenance requirements to some 145% of their original value for axenic in vitro cultures To deduce quantitative in vivo supply rates for the various nutrients, we used a genome-scale computational approach based on Salmonella genes  We disregarded nutrient utilization for purposes other than biomass generation or maintenance/virulence. (ii) We disregarded additional non-metabolic functions of the various mutated Salmonella that could serve as a basis for subsequent improvements.Because of all these caveats, the predicted nutrient supply rates and maintenance costs should be regarded only as rough estimates providing order-of-magnitude information as an approximation to the actual in vivo situation. On the other hand, the resulting model provided a first comprehensive quantitative approximation to the host nutritional landscape and its exploitation by Salmonella nutrition and metabolism during infection, we extensively validated model predictions with large-scale experimental data. Interestingly, enzymes with predicted high relevance for optimal Salmonella growth were experimentally detected at higher rates compared to non-functional enzymes. Moreover, rate predictions for hundreds of reactions were consistent with experimentally determined enzyme levels. This indicated that the simulated metabolic flux distribution was fully feasible with the amounts of enzymes that are actually present in Salmonella during infection.To assess how well the current stage of this model reflected nhaA defect in contrast to model predictions; low affinity zinc uptake through YgiE, which might be insufficient to compensate a defective ZnuABC zinc high-affinity transporter), (ii) incomplete biomass/maintenance functions that neglect signaling and detoxification needs , (iii) inappropriate treatment of biomass components that contribute to virulence but are not absolutely essential , (iv) neglect of continuous uptake of nutrients despite accumulation of toxic downstream intermediates  overestimated redundancies due to neglected regulation of isozyme/alternative pathway expression and/or differential substrate affinities  while resolving only one discrepancy (detectable growth defect of zwf) as compared to the unrestrained model. The enzyme capacities that we used as constraints in this model were based on protein profiles of wildtype Salmonella. Mutant Salmonella might have somewhat different protein profiles and enzyme capacities, and this might explain why the restrained model was not superior to the unrestrained model in predicting mutant colonization phenotypes.We also tested the predictive power of an enzyme capacity-restrained model that might more closely reproduce the wildtype flux state. Single gene deletion analysis of this model had very similar accuracy with three additional discrepancies . However, this is probably an oversimplification as some colonization defects might be caused by increased Salmonella killing by host antibacterial defenses, instead of differential Salmonella proliferation rates. In such cases, a simple metabolic interpretation in terms of diminished biomass production might be misleading. Future studies using methods such as Fluorescence Dilution SalmonellaSalmonella subpopulations with different growth characteristics exist in vivo Salmonella. An impressive study on tuberculosis already revealed some aspects of the interplay between host and pathogen metabolic networks in Mycobacterium tuberculosis-infected macrophages Salmonella as well. For Salmonella infections, analysis is complicated by the fact that the Salmonella-containing vacuole (SCV) communicates with late endosomes, from where it receives some incoming endocytosis cargo from the extracellular environment Salmonella might access some metabolites of the infected cell but additional experimental data will be needed to clarify the relative importance of the various nutrient supply routes. Another important aspect of metabolic host/Salmonella interactions is the question how Salmonella metabolism might influence host cell physiology. As an example, the capture by Salmonella of various host amino acids and nucleosides, as observed in this study, could modulate host cell functions that depend on these metabolites including antibacterial defense such as generation of nitric oxide Salmonella-infected macrophages was already obtained in recent transcriptome and proteome studies Salmonella metabolic interactions that enable Salmonella growth and enteric fever disease progression.However, there still remain important issues that should be addressed in future studies. (i) We interpreted net In addition to salmonellosis, the findings of this study also have some implications for infectious diseases in general. In particular, metabolic network comparisons suggested that many mammalian pathogens might share access to similar complex host nutrients that reflect general biochemical features of mammalian tissues. These results might provide a basis to establish in vitro culture conditions that closely mimic relevant in vivo conditions, helping to avoid drug development failures and to facilitate successful development of novel control strategies.Salmonella in inflamed intestine Mycobacterium tuberculosis access fatty acids and proline , but glycerol is not a major nutrient, and lysine, tryptophan, and leucine are apparently available in insufficient amounts to meet mycobacterial biomass needs On the other hand, the actual relevance of individual nutrients can vary. As an example, ethanolamine is an important nutrient for Interestingly, some of the commonly encountered nutrients are predominantly present as part of high molecular weight compounds such as glycans/glycoproteins (GlcNAc), proteins (most amino acids), or lipids  suggesting that macromolecule hydrolysis might be an important aspect of pathogen nutrition in infected tissues. Indeed, many pathogens express hydrolases that degrade macromolecules such lipases, proteases, carbohydratases, etc., as part of their virulence program.It might also be interesting to compare the common pathogen nutritional signature to the metabolism of commensal bacteria that inhabit body parts such as skin, genital mucosa, the oral cavity, or the intestine. Indeed, previous studies have already revealed commonalities among commensal gut bacteria such as the ability to digest complex carbohydrates Salmonella nutritional landscape during systemic salmonellosis and established a genome-scale model of Salmonella metabolism that explains major aspects of Salmonella infection biology. The results revealed an unexpectedly complex host/Salmonella nutritional interface that Salmonella exploited with versatile catabolic pathways. Similar complex host nutrients and versatile pathogen utilization pathways appear to be general features of many infectious diseases.In conclusion, this study provided a comprehensive quantitative description of the All animal experiments were approved by Kantonales Veterinäramt Basel-Stadt (license 2239) and performed according to local guidelines  and the Swiss animal protection law (Tierschutz-Gesetz).Salmonella mutants were constructed by lambda red-recombinase mediated allelic replacement int−1 streptomycin, 50 µg ml−1 kanamycin, 20 µg ml−1 chloramphenicol, and/or 100 µg ml−1 ampicillin. All auxotrophs required supplementation for growth as expected , mice were sacrificed and bacterial loads in spleen and liver were determined by plating of tissue homogenates treated with 0.3% Triton Tx-100. In competitive infections, wildtype and mutant Salmonella carrying different antibiotic resistance markers were mixed before administration. Individual strain tissue loads were determined by replica plating on selective media and competitive indices (CI = output ratio/input ratio) were calculated. Statistical significance was analyzed using t-test of log-transformed CI values  and sonicated for 2× 30 seconds. Released proteins were reduced and alkylated, and first digested for 4 hrs with sequencing grade LysC peptidase  before overnight trypsin digestion. The detergent was cleaved by adding 2 M HCL and 5% TFA to final concentrations of 50 mM and 0.5% respectively, and incubating for 45 min at 37°C. Prior to centrifugation to remove the cleaved detergent , a mixture containing 32 heavy labeled reference peptides were added to the samples , GFP, 204 frequently observed contaminants, all mouse entries from SwissProt (Version 57.12), and all sequences in reversed order  using the Mascot search algorithm. The search criteria were set as follows: full tryptic specificity was required (cleavage after lysine or arginine residues); 2 missed cleavages were allowed; carbamidomethylation (C) was set as fixed modification; oxidation (M) as variable modification. The mass tolerance was set to 10 ppm for precursor ions and 0.5 Da for fragment ions. The false discovery rate was set to 1% for protein and peptide identifications. In addition to Salmonella proteins a substantial number of contaminating mouse proteins were identified in the samples as previously noted Salmonella proteins that were detected along with a corresponding labeled AQUA peptide  to calculate maximal feasible reaction rates  with the COBRA toolbox enotypes , S5, S9 Salmonella access to excess nutrients using the new MATLAB function excess  for all single gene deletions using the deleteModelGenes function. Genes were defined as essential if predicted mutant growth rates were below 60% of wildtype  using the respective options in the optimize function. We determined flux variability in alternative solutions using the fluxVariability function. This flux variability analysis was performed without assuming lowest overall metabolic activity to obtain the full range of possible flux states compatible with optimal Salmonella colonization phenotypes and classified genes again as essential , contributing , or non-detectable (no significant difference to wildtype). We also used large-scale mutant phenotypes from two recent high-throughput studies Salmonella in vivo generation time of 6.4 h in susceptible mice To validate these predictions, we examined reported experimental Metabolic Pathway predictions for 909 genomes were generated by the MetaCyc consortium Figure S1Salmonella enterica serovar Typhimurium in infected mouse spleen. Symbols represent metabolites  and proteins (diamonds). The connecting lines present metabolic reactions. The brown lines represent the inner and outer membranes. Feasible reaction rates were calculated from in vivo enzyme abundance data and previously reported turnover numbers. An interactive version of this map with detailed descriptions for all reactions is available at http://www.biozentrum.unibas.ch/personal/bumann/steeb_et_al/index.html.Metabolic capabilities of (TIF)Click here for additional data file.Figure S2Salmonella mutant phenotypes in genetically resistant 129/Sv mice. Spleen colonization data are represented as competitive indices vs. wildtype Salmonella. A log2(CI) value of 0  indicates identical colonization of mutant and wildtype. Significance of attenuation was tested with t-test .(TIF)Click here for additional data file.Figure S3Determination of nutrient uptake rates which are consistent with corresponding mutant colonization phenotypes. Results for three mutants that were informative for access to proline, glycerol, and gluconate are shown.(TIF)Click here for additional data file.Figure S4Density plot of protein abundance for enzymes classified by parsimonious enzyme usage flux-balance analysis (pFBA) . Abundance levels of undetected proteins were set to an arbitrary value of 10 copies per cell. Statistical significance of differences between essential enzymes and other classes was determined using the Mann-Whitney test.(TIF)Click here for additional data file.Figure S5Simulated rates for active reactions for which we detected the catalyzing enzyme(s) or not. The lines represent the medians. Statistical significance was determined using the Mann-Whitney test.(TIF)Click here for additional data file.Figure S6Salmonella in infected mouse spleen are labeled with red crosses.Presence of degradation pathways for various nutrients in pathogenic and non-pathogenic microbes. Nutrients that were shown to be utilized by (TIF)Click here for additional data file.Table S1Salmonella sorted from infected mouse spleen as determined by quantitative proteomics.Enzyme abundance in (XLS)Click here for additional data file.Table S2Salmonella during infection based on enzyme quantities and previously reported turnover numbers.Feasible metabolic reaction rates in (XLS)Click here for additional data file.Table S3Salmonella mutants.Mouse spleen and liver colonization phenotypes of (XLS)Click here for additional data file.Table S4Salmonella auxotrophic mutants in chemically defined minimal M9 medium with or without supplementation.In vitro growth characteristics of (XLS)Click here for additional data file.Table S5Salmonella access to host nutrients based on mutant phenotypes.Evidence for (XLS)Click here for additional data file.Table S6Salmonella metabolic network reconstruction.Novel metabolites included in an updated genome-scale (XLS)Click here for additional data file.Table S7Salmonella metabolic network reconstruction.Changed reactions in the (XLS)Click here for additional data file.Table S8Salmonella metabolic network reconstruction.Changed biomass components in the (XLS)Click here for additional data file.Table S9Simulated nutrient uptake rates and maintenance costs.(XLS)Click here for additional data file.Table S10Comparison of predicted and experimental mutant colonization phenotypes.(XLS)Click here for additional data file.Table S11Lists of pathogenic and environmental organisms included in metabolic network comparisons.(XLS)Click here for additional data file.Table S12Isotope labeled AQUA peptides for calibration of absolute enzyme quantities.(XLS)Click here for additional data file.Model S1Salmonella in infected mouse spleen in SBML (Systems Biology Markup Language) format.Genome-scale metabolic in silico model of (TXT)Click here for additional data file.Script S1MATLAB function for determination of nutrient uptake rates and maintenance costs from mutant colonization data.(TXT)Click here for additional data file.Script S2MATLAB function for determination of maintenance costs that are consistent with experimental data and nutrient excess scenarios.(TXT)Click here for additional data file.


Escherichia coli cell physiology in terms of cell size, biomass composition, gene and protein expression. To understand the regulation behind these different phenotype properties, it is useful to know carbon flux patterns in the metabolic network which are generally calculated by metabolic flux analysis (MFA). However, rarely is biomass composition determined and carbon balance carefully measured in the same experiments which could possibly lead to distorted MFA results and questionable conclusions. Therefore, we carried out both detailed carbon balance and biomass composition analysis in the same experiments for more accurate quantitative analysis of metabolism and MFA.Growth substrates, aerobic/anaerobic conditions, specific growth rate (μ) etc. strongly influence E. coli K-12 MG1655 flux and energy metabolism dynamic responses to change of μ and glucose-acetate co-utilisation. Surprisingly, a 36% reduction of ATP spilling was detected with increasing μ and carbon wasting to non-CO2 by-products under constant biomass yield. The apparent discrepancy between constant biomass yield and decline of ATP spilling could be explained by the rise of carbon wasting from 3 to 11% in the carbon balance which was revealed by the discovered novel excretion profile of E. coli pyrimidine pathway intermediates carbamoyl-phosphate, dihydroorotate and orotate. We found that carbon wasting patterns are dependent not only on μ, but also on glucose-acetate co-utilisation capability. Accumulation of these compounds was coupled to the two-phase acetate accumulation profile. Acetate overflow was observed in parallel with the reduction of TCA cycle and glycolysis fluxes, and induction of pentose phosphate pathway.We applied advanced continuous cultivation methods (A-stat and D-stat) to continuously monitor It can be concluded that acetate metabolism is one of the major regulating factors of central carbon metabolism. More importantly, our model calculations with actual biomass composition and detailed carbon balance analysis in steady state conditions with -omics data comparison demonstrate the importance of a comprehensive systems biology approach for more advanced understanding of metabolism and carbon re-routing mechanisms potentially leading to more successful metabolic engineering. Escherichia coli exerts a very different gene and protein expression profile under different growth substrates [E. coli cell physiology as shown by studies of cell size [bstrates , aerobicbstrates  etc. Speell size ,4, biomaell size -7, energell size ,8, transell size -11 etc..2 and by-products. Flux distributions can also be calculated for batch cultures-however, the obtained values have to be considered with great care as the physiological state of cells is constantly changing during growth . Therefore, MFA is generally carried out with steady state input data from chemostat cultures which provide reproducible and strictly defined physiological state of cells [To gain insights into the regulation and control mechanisms behind these different phenotype properties, it is useful to know carbon flow patterns in the metabolic network. A widely used tool to calculate quantitative flux values and thereby describe the carbon flow is metabolic flux analysis (MFA). Essentially, MFA calculations need a metabolic network with its stoichiometry, biomass amount and composition, measured steady state carbon influx and outflow-usually as COof cells ,9,12-14.E. coli mainly uses the consumed carbon for biomass formation and substantial amount of it goes to CO2 production. The flux (loss) of carbon to CO2 is closely associated with energy generation (spilling). Carbon usage for biomass synthesis and CO2 in the carbon balance can be directly measured in situ [E. coli strains in aerobic cultivations is acetic acid [e.g. pyrimidine pathway intermediates has been detected [et al. accounted a substantial amount of 'leftover carbon' in the carbon balance (7-13%) of E. coli continuous cultures to cells lysis which has not been observed before in the literature [ in situ ,13-15. Htic acid ,13,16. Itic acid ,13,17. Adetected ,18,19, nE. coli, is another step in addition to a non-comprehensive carbon balance analysis that could possibly lead to distorted MFA results and drawing incorrect conclusions in terms of metabolic regulation. Therefore, we carried out both detailed carbon balance and biomass composition analysis in the same experiments to produce more accurate metabolic flux calculations. What is more, important metabolic switch points and regulation dynamics can be missed when using chemostat cultures. To continuously monitor the flux and energy metabolism dynamic responses to change in μ, we applied the accelerostat (A-stat) [E. coli [Lactococcus lactis [E. coli overflow metabolism [Besides carbon inflow and outflow, biomass composition is another important input parameter in terms of MFA solutions also shown by sensitivity analysis . However(A-stat)  continuo[E. coli  and an is lactis . In addis lactis  was usedtabolism .E. coli continuous cultures with increasing μ and carbon wasting under constant biomass yield (YXS). We propose hypotheses about YXS maintenance mechanisms and maximal growth limitations for E. coli K-12 MG1655. Furthermore, our study revealed novel carbon wasting profiles into pyrimidine pathway intermediates and acetate metabolism governed metabolic flux dynamics that are dependent on μ and glucose-acetate co-utilisation capability.In short, we detected a 36% reduction of ATP spilling in E. coli K-12 MG1655 which growth characteristics are described in detail in Valgepea et al. [We carried out three replicate A-stat and four D-stat continuous cultivation experiments at various dilution rates with a et al. . Carbon -1 (average ± standard deviation)  Figure . Faster 1 Figure . Dihydro1 Figure  with the1 Figure .2 and NADH produced by TCA cycle  at the studied dilution rates under very different maximal glucose-acetate co-utilisation capability values  [E. coli transcriptome [A-stats have been shown to produce quantitatively comparable results with chemostats at the level of biomass) ,21 and Ecriptome . Our detE. coli metabolic flux dynamics and possible growth limiting factors. Detailed carbon balance and biomass composition analysis were carried out in A-stat and D-stat cultures to examine the dynamic responses of metabolic fluxes and energy metabolism to change of μ and glucose-acetate co-utilisation capability. A simplified MFA was conducted to map the carbon flow through central carbon metabolism.We applied a systems biology approach to study -1  was chosen which can be higher than the in vivo value. In any case, the P/O ratio per se does not affect the main conclusions of the manuscript since the ratio between ATP spilling and its overall production is independent from the P/O ratio value.Decrease in ATP spilling (32%) after overflow switch in A-stat was shown by MFA calculations Figure  and declXS, however, it remained constant in our experiments ) might indicate increase of YS Figure  as folloE. coli K-12 MG1655 into pyrimidine pathway intermediates orotate, DHO, CBASP, and NAA with rising μ  expression [-1 from the values calculated by our model  under very different maximal glucose-acetate co-utilisation capability values  co-utilisation has not been studied before. We found that carbon wasting patterns are dependent not only on μ as shown by A-stat, but also on glucose-acetate co-utilisation capability Figure . This fis Figure . The late.g. orotate, DHO, CBASP, NAA may be caused by an imbalance between the supply of precursors, intermediates of central metabolic network and insufficient use of them for the synthesis of biomass monomers and macromolecules which could be the result of energy limitations. Levels of metabolites from the upper part of energy generating glycolysis, fructose-1,6-bisphosphate and glyceraldehyde-3-phosphate, and TCA cycle components increased with the rise in μ [E. coli K-12 MG1655 (rph frameshift mutation leading to low pyrE expression) which leads to pyrimidine starvation as described above. The latter proposal is in agreement with RNA concentration measurements which showed that RNA amount increased together with μ until 0.40 h-1 after which it levelled off , DSM No.18039) was used in A-stat and D-stat experiments under the following conditions: temperature 37°C, pH 7, agitation speed of 800 rpm, and aerobic conditions (air flow rate 150 ml/min). Three A-stat cultivations were performed with acceleration rate 0.01 h-2 and four D-stat experiments at dilution rates 0.10; 0.24; 0.30; 0.45 h-1. A detailed description of medium and cultivation conditions in these experiments has been reported previously [eviously . In shoret al. [Biomass concentration was determined gravimetrically as dry cellular weight (expressed as DCW) described by Nahku et al. .g for 5 min, supernatant were collected and analysed immediately or stored at -20°C until analysis. Glucose and organic acids were analysed by HPLC  using a BioRad HPX-87H Aminex ion-exclusion column connected to RI and UV detectors . The column was eluted with 4.1 mM sulphuric acid for glucose, CBASP, lactate, orotate and with 26.5 mM formic acid for acetate, DHO and NAA analysis.Samples of culture medium for extracellular metabolome analysis were centrifuged at 14,000 × E. coli K-12 MG1655 for MFA was reconstructed taking into account main metabolic pathways-glycolysis, PPP, TCA cycle-, one pathway from pyrimidine metabolism  and NAA synthesis reaction ; Effect of specific growth rate dependent biomass composition on MFA results (Table S2); MFA results for triplicate E. coli K-12 MG1655 A-stat experiments (Table S3); MFA average results and standard deviations of triplicate E. coli K-12 MG1655 A-stat experiments (Table S4); MFA results of four E. coli K-12 MG1655 D-stat experiments (Table S5); Effect of taking novel carbon wasting routes into account in E. coli K-12 MG1655 A-stat experiments' MFA (Table S6).Click here for fileMetabolic flux analysis. Detailed description of model calculations with simplified metabolic flux analysis; Simplified metabolic network scheme of E. coli K-12 MG1655 .Click here for fileE. coli K-12 MG1655 proportion of CO2 and NADH production by TCA cycle with rising specific growth rate in three A-stat cultivations. μ, specific growth rate (h-1). CO2 production (blue square); NADH production (red triangle). Error bars represent standard deviation of triplicate A-stat experiments.Click here for fileE. coli K-12 MG1655 A-stat cultivationsSpecific growth rate dependent overall ATP production rate and proportion of ATP production by glycolysis in three . μ, specific growth rate (h-1). Overall ATP production (blue squares); ATP production by glycolysis (red triangle). Error bars represent standard deviation of triplicate A-stat experiments.Click here for fileE. coli K-12 MG1655 pyrimidine pathway rph frameshift mutation triggered accumulating precursor compounds. Carbamoyl-P, carbamoyl-phosphate; CBASP, carbamoyl-aspartate; DHO, dihydroorotate; Oro-5P, orotidine-5-phosphate; TCA cycle, tricarboxylic acid cycle; PPP, pentose phosphate pathway; pyrB, aspartate carbamoyltransferase; pyrC, dihydro-orotase; pyrD, dihydro-orotate oxidase; pyrE, orotate phosphoribosyltransferase. Gene names are italicised.Click here for fileE. coli acetyltransferase predictionHomology analysis for . BLAST results for homology analysis with Homo sapiens and Mus musculus aspartate N-acetyltransferases.Click here for file


Pseudomonas putida possesses two PTS branches, the C-branch (PTSFru) and a second branch (PTSNtr), which communicate with each other by phosphate exchange. Recent experimental results showed a cross talk between the two branches. However, the functional role of the crosstalk remains open.Signal transduction plays a fundamental role in the understanding of cellular physiology. The bacterial phosphotransferase system (PTS) together with the PEP/pyruvate node in central metabolism represents a signaling unit that acts as a sensory element and measures the activity of the central metabolism. A mathematical model was set up to describe the available data of the state of phosphorylation of PtsN, one of the PTS proteins, for different environmental conditions and different strain variants. Additionally, data from flux balance analysis was used to determine some of the kinetic parameters of the involved reactions. Based on the calculated and estimated parameters, the flux distribution during growth of the wild type strain on fructose could be determined.Our calculations show that during growth of the wild type strain on the PTS substrate fructose, the major part of the phosphoryl groups is provided by the second branch of the PTS. This theoretical finding indicates a new role of the second branch of the PTS and will serve as a basis for further experimental studies. Ntr of P. putida with the help of mathematical tools, in order to obtain a functional model that describes the system in detail and may provide new ideas on the physiological role of the PTSNtr and its cross-talk with the sugar PTS.Mathematical modelling of biological processes is a powerful tool towards the thorough understanding of a biological system. In the mathematical simulation, in the first step, experimental data is reproduced and subsequently, the model can be used to predict the behaviour of the system. This type of iterative model-based analysis is a hallmark of systems biology research that, in the future, is expected to be very helpful in enhancing the understanding of cellular systems in a better way. Here, we chose to analyse the PTSPseudomonas putida KT2440 is a ubiquitous Gram-negative, saprophytic soil bacterium, important in biotechnological and systems biological research. This organism is characterized by its metabolic versatility, which enables the strain to use a variety of natural and man-made compounds as carbon and energy source [y source , making y source .P. putida is able to degrade fructose [On one hand, it can degrade degrade a variety of toxic compounds including methoxylated or hydroxylated aromatic acids. On the other hand, fructose  and glucfructose -8, two ofructose . Glucosefructose .P. putida is different in comparison to e.g. E. coli. However, a common structural metabolic unit that is found in these two organisms, as well as in other bacteria, is the phosphoenolpyruvate-dependent: phosphotransferase system (PTS). In E. coli the PTS is the main uptake system for glucose and for other carbohydrates like mannitol, mannose, and N-acetyl-glucosamine. Besides transport properties, the PTS together with the reactions of the central metabolism represents an important signaling unit that measures the glycolytic and gluconeogenetic flux and maps it to the degree of phosphorylation of the PTS proteins [Central metabolism of proteins . These pproteins .P. putida KT2440 possesses also an alternative PTS, which is encoded by the genes ptsP, ptsN and ptsO. This unusual PTS preserves the phosphotransfer domains but lacks the permease component, ruling out its participation in sugar intake. Because ptsN and ptsO map adjacently to the rpoN gene that encodes nitrogen σ factor 54, it was generally believed that this alternative PTS was related to N metabolism. More recent data suggest that this PTS branch predominantly controls the influx of potassium ions [E. coli could be artifactual [Ntr. This was performed in order to get an idea of how the phosphate flow is distributed between the two PTS (PTSFru and PTSNtr) and to shed some light on the role of both systems in the process of fructose uptake.Like many other eubacteria, ium ions ,13, whicium ions . Furtherifactual , therebyifactual . While wifactual  for the E. coli PTS has progressed in the last decade and several models are currently available  constant specific growth rate) contain little information on kinetic parameters, experimental data from a perturbation experiment was used. In these experiments exponential growth of P. putida on casamino acids (CAA) was compared with growth on CAA plus fructose. Since uptake of fructose takes place in the late exponential phase [Mathematical modeling of the iewed in ,11). The [E. coli. These ral phase , this caal phase . CombiniPseudomonas putida possesses two distinct phosphoenolpyruvate (PEP) phosphotransferase systems. One is responsible for fructose uptake  and the other is suggested to be involved in signal transduction. In both branches, a high-energy phosphate from PEP is transferred by a number of proteins either to the incoming fructose or to PtsN the last protein in the second branch. As depicted in Figure pts genes of P. putida in minimal media with different C sources (casamino acids (CAA), CAA plus fructose and CAA plus glucose) revealed different degree of phosphorylation of PtsN. The figure also shows the reactions considered in the mathematical model.in vivo phosphorylation studies using dielectric breakdown and subsequent Western blotting, the direction of the flow of the high-energy phosphate within the PTSNtr was recently confirmed to succeed in the direction PEP → PtsP → PtsO → PtsN in analogy to the sugar PTS [Ntr and the PTSFru communicate with each other by phosphate exchange under specific metabolic conditions [Fru was restricted to conditions in which the PTSFru was active [ptsO mutant strain [P. putida is slightly different from E. coli, e.g. glucose is metabolized a different way. Figure By ugar PTS ,21. Furtnditions . Phosphos active , i.e. itt strain . Thus, vt strain . Monitort strain . The catFor the involved PTS proteins shown in Figure r1to r3describe the phosphoryl transfer from PEP via PtsP and PtsO to PtsN, reaction r4describes the phosphoryl transfer needed for fructose uptake, and reaction r5describes the cross talk between the two branches. A general form of mass action kinetics for the reaction rate is used. The riread:Reactions X (e.g. PtsP) is the phosphoryl donor, XPthe phosphorylated form (e.g. PtsPP) and Y , YPare the unphosphorylated and the phosphorylated recipient . Conservation relations can be taken into account for the four proteins:where Ki, five velocity reactions kiand constant entities representing the overall concentration of the proteins.The system can be described with five equilibrium constants Xi,0 of the respective compound and on a chosen time constant leads to a set of equations with seven velocity constants, five equilibrium constants Ki and a scaled uptake rate Scaling is a well known tool to reduce the number of parameters. Scaling on the overall concentration P. putida metabolism iJP815 [The flux distributions were computed by applying Flux Balance Analysis (FBA) on the genome-scale metabolic reconstruction of m iJP815 . Cellulam iJP815 . The folNu) and known rates (Nkn). Introducing kinetic rate laws allows the calculation of the respective elasticities ∈;ij=∂ri/∂cj. If it is assumed that the two input fluxes are not independent, but are related by a factor (an assumption that is reasonable while taking into account that for higher uptake rates (e.g. ra) the demands of the cell are changing (e.g. rb)), the following condition holds true for the concentration control coefficients dcj/dri:Based on the experimental data ,22, fluxu, and ∈; is the matrix with all elasticities. To determine the concentration control coefficients matrix, matrix ∈; has to be invertible. This requires at least two entries different from zero. Since specific entries of matrix ∈; are usually not known, an inversion of the matrix ∈;is only possible if it has the maximal structural rank. To determine the structural rank, we refer to the following definition [ra and rd are the measured rates, and assuming that all remaining reactions rb(Pyr), rc(Pyr) are irreversible, matrix ∈;is given by:with finition : “The stra and rb are measured, the respective matrix can be inverted in (nearly) all cases since it is of the formwhere stars mark the respective dependency. The matrix has structural rank one. In case of Having the concentration control coefficients at hand, they are used to determine the kinetic properties of the system.Pseudomonas strains used in this work were derived from strain Pseudomonas putida MAD2. P. putida MAD2 variants bear directed chromosomal insertions of each gene , with either a kanamycin (Km) resistance gene or the xylE marker and all mutants used  in this work have been described previously [All eviously ,26. For eviously  with 0.2600) and fructose/ glucose concentrations were determined enzymatically with the fructose/ glucose assay kit (Sigma-Aldrich) according to the supplier’s manual. The following physiological parameters were determined by regression analysis during the exponential growth phase in batch culture, as described elsewhere [rc) between cellular dry weight (CDW) and OD600 were determined from batch cultures of each mutant. Therefore, CDW was measured from at least three parallel 10 ml cell suspensions by harvesting the cells by fast filtration through pre-weight nitrocellulose filters (0.45 μm), which were subsequently washed with 0.9% NaCl and dried at 105°C for 24 h to a constant weight. We have followed the procedures described in [Cell growth was monitored spectrophotometrically at 600 nm  are not sufficient to determine all kinetic parameters directly. However, using the data for the mutant strains allows to decompose the system in a smaller set of equations. Table providing an additional constraint for the parameters.Figure r5). In contrast, for growth on CAA, it is assumed that the fru operon is not induced and the levels of phosphorylation for both mutants are low. The PEP pyruvate ratio was estimated to:The degree of phosphorylation of PtsN of the mutant strains PtsP and PtsO on fructose is characterized by the cross talk between the C and the second branch of the PTS. Although the phosphoryl flux in these mutants is interrupted in the second branch, a certain degree of phosphorylation of PtsN is detected. This can only be explained with a transfer of the phosphoryl group from FruB to PtsN . Howeved values .FBA can be used to estimate flux distributions given a stoichiometric network, uptake and production rates for compounds in the medium and an objective function. Here, the network was analyzed for growth on CAA and growth on CAA plus fructose. The complete flux distribution can be found in the Additional file rc and rd, since in this case matrix ∈; has full rank. To perform the calculation for different input fluxes ra, the values for rb had to be interpolated as shown in Figure k . A linear interpolation was used and the values for rbare given as follows:Having the results from the previous sections at hand, a mathematical model taking into account the kinetics of reactions where PEP and pyruvate are involved was set up. The focus is on the steady-state values of intracellular metabolites PEP and pyruvate in dependence on the input flux. Therefore, fluxes that produce PEP or pyruvate or have PEP or pyruvate as substrate were summed up. This resulted in a network with 5 fluxes and two nodes as shown in Figure k is the slope as shown in Figure with The elasticity matrix is in the form:and the respective concentration control coefficient are:rcand rdpower law kinetics are applied:For the unknown reaction rates and the respective elasticities arerathe PEP/pyruvate ratio is also increasing, a necessary condition for the positive slope can be calculated from the following condition:From the observation that for increasing input flux and thereforeInserting Equations (18) and (20) result in:rc=rd, αkd=kc, and n′=1/nd−1/nc the PEP/pyruvate ratio is:which give first insights for the kinetic parameters. Since for the (relative) simple model, the steady-state values for PEP and pyruvate can be calculated, an estimation of the kinetic parameters is possible. With αand n′, corresponding to kdand nd) could be calculated when kc=1and nc=1.Having two measurements available, two values  have the same value. From the experimental data, cross talk occurs with a rather low value of the equilibrium constant K5. This leads to a rather small value for K4and corresponding to a high affinity of PEP to FruB. The high affinity leads to a high phosphorylation of FruB, 99%, hence, the substrate, unphosphorylated FruB, is rare and consequently, the flux r4 is low. This result is rather unexpected and poses new questions on the function of cross talk.Having both parts of the model available, an estimation of the complete flux distribution, considering fluxes through both parts of the PTS branches, is possible. Table E. coli[E. coli are published [Pseudomonas putida experimental data on the phsophorylation state of the PTS protein PtsN is available from a perturbation experiment and this data was used to set up and validate a mathematical model.The PTS is a key player in the coordination of catabolic reactions in E. coli. The secE. coli since itE. coli. Severalublished ,18 that Keq=K1K2K3=K4K5=0.02. This value is a factor 35 smaller than reported for the E. coli PTS [in vitro and therefore a fair comparison is not possible. The equilibrium constant for the cross talk between the two branches, parameter K5, is 654.6, indicating a very weak connection between the two branches.The model comprises two parts: The first one relates the PEP/pyruvate ratio for different growth conditions of a wild type strain and mutant strains  to the degree of phosphorylation of the PTS protein PtsN. Based on the information for the different mutant strains, the complete set of equations could be simplified for every growth condition/strain and the equilibrium constants were calculated or chosen to describe the experimental data at best. Moreover, it was possible to estimate the PEP/pyruvate ratio for growth on CAA plus fructose if the PEP/pyruvate ratio for growth on CAA alone was given. The experimental results are in good agreement with the simulated data; this is reflected by the fact that the mean of the residuals is in the range of 10% of the measured values . The experimental data revealed differences in the phosphorylation state of PtsN between the wild type and the FruB mutant strain for growth on CAA as well as for the PtsP and PtsO mutant for growth on fructose. These differences could not be reproduced by the model, since in both cases the mathematical equations are the same. However, it was assumed in the model that the PEP/pyruvate ratio is the same for one substrate. So, the differences could be explained with slightly different values of the PEP/pyruvate ratio for growth on the same substrate. The overall equilibrium constant for the PTS was estimated to be coli PTS . Howevercoli PTS  is basedP. putida strain and the mutant strains, the PEP/pyruvate ratio for growth in CAA plus glucose was adjusted to describe the data. Since the degree of phosphorylation of PtsN under this condition is much lower than for growth on CAA or CAA plus fructose, the PEP/pyruvate ratio is an order of magnitude smaller ppCAA + Glc=0.05. Based on the data, a prediction of the degree of phosphorylation of PtsN was performed for different mutant strains . The experimental data could be reproduced very well.Based on the kinetic parameters obtained from the experiments with the wild type rc and rdand using the other fluxes as input fluxes, conditions for the hill coefficient for both enzymes could be calculated. Furthermore, setting the two parameters for enzyme PEP synthase (rc) to 1 the ratio of the parameters of the enzyme for rd(representing gluconeogenetic reactions) is calculated. As a result, the hill coefficient for the gluconeogenetic reactions is smaller then the one for PEP synthase (factor 0.30) while the reaction constant k is higher (factor 2.73). Taking into account the values from flux variability analysis a confidence region for the kinetics shown in Figure Having PEP/pyruvate ratios for growth on CAA and CAA plus fructose available, in a second step, kinetic properties of the system were analyzed based on the respective flux distributions. As expected the flux pattern was different for the two conditions. The main fluxes from or to the both nodes PEP and pyruvate are shown in Figure An interesting observation was seen when calculating all reaction rates in case of growth on CAA plus fructose. In this case, the individual reactions are not in equilibrium since fructose as additional input enhances the PTS reaction. Approx. 78% of the required phosphoryl groups for the fructose uptake are provided by the second branch of the PTS by cross talk.Ntr, at least in P. putida. It might not only be involved in the regulation of various processes, as the activity of central enzymes [Ntr, they provide a storage system of rapidly available phosphate. This system comes into action when fructose is provided to the cells. Fructose in P. putida is degraded by both pathways, the EMP and the ED pathway [P. putida as the fructose-6-phosphate kinase, able to catalyze the conversion of fructose-6-phsophate to fructose-1,6-biphosphate and thereby making the connection to the EMP pathway, is missing [Ntr might serve as a “pre”-adaptation to the potential presence of fructose, enabling the cell to rapidly and efficiently metabolize fructose, when it is available. This hypothesis is currently under investigation in our laboratory.This result allows to speculate on a complete new and unexpected role for the PTS enzymes , the acc enzymes , or the  enzymes , but ser pathway , whereas missing . Thus, tModeling of signal transduction units together with genome scale stoichiometric models will help for a better understanding of the cellular system. Especially the PTS is an important system that is involved in the coordination of catabolic reactions. The proposed model is good starting point to extend research in direction of the coordination between the carbon and other networks.The authors declare that they have no competing interests.AK performed the modeling of the PTS, KPG and MC performed the experiments, JP and VMdS performed the flux balance analysis, AK and VdL designed the study, AK and KPG wrote the manuscript. All authors read and approved the final manuscript.The files describes the model equations and the kinetic parameters.Click here for fileThe xls-sheet gives all values for the flux distributions based on a FBA analysis for different growth conditions. The xls-sheet is subdivided into two parts.In the first part, the values for the growth conditions are summarized; afterwards the flux for each reaction for the different conditions is provided. For all conditions, three columns are shown: given are the nominal value, and minimal/maximal values based on FVA. Reactions with PEP and pyruvate as substrate (S) or product (P) are highlighted.Click here for file


Synechocystis sp. PCC 6803. Building upon several recent reconstructions of cyanobacterial metabolism, unclear reaction steps are experimentally validated and the functional consequences of unknown or dissenting pathway topologies are discussed. The updated model integrates novel results with respect to the cyanobacterial TCA cycle, an alleged glyoxylate shunt, and the role of photorespiration in cellular growth. Going beyond conventional flux-balance analysis, we extend the computational analysis to diurnal light/dark cycles of cyanobacterial metabolism.Cyanobacteria are versatile unicellular phototrophic microorganisms that are highly abundant in many environments. Owing to their capability to utilize solar energy and atmospheric carbon dioxide for growth, cyanobacteria are increasingly recognized as a prolific resource for the synthesis of valuable chemicals and various biofuels. To fully harness the metabolic capabilities of cyanobacteria necessitates an in-depth understanding of the metabolic interconversions taking place during phototrophic growth, as provided by genome-scale reconstructions of microbial organisms. Here we present an extended reconstruction and analysis of the metabolic network of the unicellular cyanobacterium  Synechocystis sp. PCC 6803. Taking into account several recent reconstructions, the functional consequences of unclear and dissenting pathway annotations are discussed. The model is supplemented with experimental data to validate specific reactions steps. As a specific feature of phototrophic organisms, the re-organization of metabolism in alternating diurnal light/dark cycles is studied.Phototrophic microorganisms hold great promises as a resource to generate high-value products and biofuels using only atmospheric carbon dioxide, light, and some minerals. In particular cyanobacteria, the only known prokaryotes capable of oxygen-evolving photosynthesis, have attracted recent attention as a possible chassis for the generation of third generation biofuels. Rational engineering of microorganisms is increasingly guided by large-scale reconstructions of the metabolic network of the respective organism. Such reconstructions then serve as an integrated knowledge base for all metabolic interconversions taking place during cellular growth. Here, we present and analyze such a genome-scale reconstruction for the unicellular cyanobacterium  Almost all life on Earth ultimately depends on oxygenic photosynthesis to capture solar energy and convert atmospheric carbon into organic compounds that serve as nutrients for heterotrophic organisms. Photosynthesis and the assimilation of inorganic carbon are evolutionarily old processes, with signatures RuBisCO activity, the major enzyme of carbon fixation, tracing back more than 3 billion years Synechocystis sp. PCC 6803, became available Synechocystis sp. PCC 6803.Today, cyanobacteria are the only known prokaryotes capable of oxygen-evolving photosynthesis and remain to have major impact on almost all geochemical cycles, including the global carbon cycle, global oxygen recycling and nitrogen fixation. From a metabolic perspective, cyanobacteria are highly versatile organisms and occupy diverse ecological niches where light is available. Renewed attention on cyanobacterial metabolism was triggered by the prospect to utilize their light-driven capability of Synechocystis sp. PCC 6803. The updated model integrates novel results with respect to the cyanobacterial TCA cycle, an alleged glyoxylate shunt, the role of photorespiration in cellular growth, as well peculiarities of photosynthetic reactions such as light-dependent oxidative stress. In this paper, we seek to explore the implications of alternative network topologies for phototrophic flux patterns and optimal growth. Closing the iterative cycle of Systems Biology, our computational analysis is supplemented with specifically acquired experimental data to validate unclear reaction steps and growth conditions. Furthermore, we seek to improve the applicability of FBA on phototrophic conditions by implementing a full diurnal cycle, guided by the diurnal transcription of key enzymes. The manuscript is organized as follows: First, we provide a brief overview on the current status of our reconstruction of the metabolic network of the cyanobacterium Synechocystis sp. PCC 6803, including several computational validation steps. Subsequently, we discuss a reference condition for phototrophic growth that allows us to compare different network topologies with respect to optimal biomass yield using flux-balance analysis. Based on this reference condition, details of alternative flux solutions with respect to the TCA cycle, the glyoxylate bypass, the RuBisCO oxygenase and photorespiration are explored. In the final section, we discuss the diurnal cycles of phototrophic metabolism using a time-varying objective function.In this work, we present and interrogate an updated representation of the metabolic network of Synechocystis sp. PCC 6803. The reconstruction is based on a previously published network of the organism Synechocystis sp. PCC 6803, the network was extended to include lipid and fatty-acid metabolism, biosynthesis of peptidoglycan, chlorophylls, carotenoids, terpenoids, quinones and tocopheroles, thiamine-diphosphates, as well as the synthesis of several co-factors, vitamins and several stress related-pathways. The description of photosynthetic light reactions and transport processes was significantly improved. The reconstruction process itself followed standard procedures described in the literature The starting point of our analysis is an extended and updated metabolic reconstruction of the cyanobacterium Synechocystis sp. PCC 6803. During the reconstruction process, completeness of all synthesis pathways was validated with respect to biomass components, co-factors and dilution of metabolites by growth. The core reconstruction encompasses 677 genes, that encode for 495 enzymes or enzyme-complexes. The annotated enzymes give rise to 759 metabolic reactions among 601 metabolites. In addition, the core reconstruction contains 6 spontaneous interconversions and 61 transport reactions, including diffusion. The reaction network distinguishes between six cellular compartments, the cytosol, the thylakoid membrane, the thylakoid lumen, the plasma membrane, the periplasmic space, carboxysomes, as well as the extracellular space. Several key properties of the network are summarized in The core network encompasses a connected set of all known metabolic pathways for the synthesis of main biomass and co-factors known to occur within the cyanobacterium Large scale metabolic reconstructions offer the possibility to investigate physiological properties of the respective organism using constraint-based analysis In addition to cellular growth, the reference solution must accommodate additional processes known to affect cyanobacterial metabolism. We assume a basal growth-independent ATP utlilization for cellular maintenance. Likewise, cyanobacteria are assumed to exhibit residual respiratory activity also during light al.al.In order to constrain and parametrize the flux-balance solution under constant light, we assume an average doubling time of 24 h under constant illumination, corresponding to an average growth rate of Given the constraints and conditions defined above, a solution for the flux-optimization problem was obtained using the COBRA toolbox Saccharomyces cerevisiaeCorynebacterium glutamicumTo discuss the validity of the computational flux-optimization, a comparison with experimentally obtained flux values is crucial. To this end, Young et al. A further discrepancy between the two flux maps is the role of the malic enzyme (ME) during phototrophic growth. In accordance with earlier studies on heterotrophic growth Finally, the experimental study of Young et al. Synechocystis sp. PCC 6803 has to survive extended periods of darkness, usually relying on endogenous storage compounds that are accumulated when light is available. Unfortunately, data on experimental flux patterns under prolonged darkness are scarce. To nonetheless approximate metabolic flux under periods of darkness, we assume that, unlike for some diazotrophic cyanobacteria, dark metabolism in Synechocystis sp. PCC 6803 is dominated by a low level of cellular maintenance and hence utilization of ATP. Growth is assumed to be minimal, which is in good agreement with experimental observations. As a constraint for the flux-optimization problem, we therefore allow for a maximal rate of glycogen consumption that only slightly exceeds the requirements for respiratory metabolism and the demand of ATP. In addition to ATP utilization, we assume residual growth, or, equivalently, a residual cellular turnover that is likewise approximated by the biomass objective function Synechocystis sp. PCC 6803 is likely to utilize other substrates as well. As shown recently, mutants impaired in glycogen synthesis have strongly reduced viability in dark-night cycles – however the reduction in viability is not strong enough to confirm glycogen as only storage compound In addition to phototrophic growth, slr1239 and slr1434, EC 1.6.1.2) is assumed to be active and allowed to carry reversible flux, redox potential (NADH) for respiration is generated via cyclic flux through the TCA cycle and subsequently converted into NADPH. NADPH is mainly fed into the NADPH dehydrogenase complexes (NDH-1). NDH-1 was reported to be specific for NADPH The resulting optimal flux patterns shows considerable variability in dependence of detailed assumptions about enzyme specificity and directionality. When the annotated transhydrogenase reaction , the product of photorespiration. A non-zero rate of photorespiration therefore emerges as a result of the flux optimization problem. See The situation is similar within the current reconstruction. Optimization with respect to biomass yield again results in a nonzero rate of photorespiration. We therefore investigated three scenarios related to the possible production of the amino acids glycine and serine. The first scenario assumes that the current annotation is not incomplete and the enzymatic activity of a phosphoserine transaminase and phosphoserine phosphatase are indeed absent in Reverting to a simulation of dark metabolism, as defined above, photorespiratory flux is no longer part of the optimal solution. Instead, the residual demand for glycine, serine and cysteine is met by degradation of proline, resulting in a slightly higher yield than photorespiration under non-phototrophic conditions. As argued previously As our second scenario, we assumed that as yet unidentified genes encode for a phosphoserine transaminase and phosphoserine phosphatase. When the respective enzymatic steps are introduced into the reconstruction, photorespiration ceases under phototrophic conditions and the RuBisCO oxygenase is no longer part of the optimized solution. Instead serine, and subsequently glycine and cysteine, are produced by the newly introduced phosphoserine pathway. This solution is also in agreement with the results of Young et al. Synechocystis sp. PCC 6803. The gene was recognized as a hydroxypyruvate reductase (EC 1.1.1.81) in the work of Eisenhut et al. Within the third scenario, taking into account that photorespiration is presumably an inevitable process under current atmospheric conditions, we investigated the optimal flux distribution in the presence of the phosphoserine pathway while simultaneously forcing a non-zero flux of the RuBisCO oxygenase. Specifically, the flux through RuBisCO oxygenase is constrained with a lower bound of 3% of the carboxylase flux. In this case, flux variability analysis shows that several equivalent flux solutions exists. The photorespiratory intermediate glyoxylate may either be used for the synthesis of glycine, or, stoichiometrically equivalent in terms of biomass yield, may be recycled into the CBB cycle via glycerate. In the latter case, serine is synthesized via the phosphoserine pathway. The corresponding pathway maps are shown in As a preliminary conclusion, we therefore favor a scenario where the phosphoserine pathway is present, albeit encoded with as yet unidentified genes. In the following, all simulations correspond to the third scenario studied above, with RuBisCO oxygenase activity forced as 3% of it carboxylase activity. The phosphoserine pathway then functions as an auxiliary supply of serine that allows to cope with varying levels of photorespiration.Synechocystis sp. PCC 6803 include the metabolic reactions isocitrate lyase  and malate synthase (EC 2.3.3.9), encoding a bacterial glyoxylate shunt Synechocystis sp. PCC 6803. However, as argued previously Several recent reconstructions of the metabolic network of Synechocystis sp. PCC 6803, we applied a refined methodology to determinate isocitrate lyase activity in cell-free extracts of Synechocystis sp. PCC 6803. The method was adopted from Dixon and Kornberg To experimentally resolve the possibility of enzymatic activity of the isocitrate lyase in Synechocystis cells, passed over a PD-10 gel filtration column to remove small reactive metabolites and NADP. There is no significant isocitrate lyase activity detectable in filtered crude extracts of Synechocystis sp. PCC 6803 (Synechocystis sp. PCC 6803 lacks the capability to utilize acetate as sole carbon source in the presence of the photosystem II inhibitor DCMU (photoheterotrophic growth). Model simulations clearly show that in such a case, in the presence of the glyoxylate, photoheterotrophic growth is possible. Experimental results are shown in Measurements were therefore performed with crude extracts of PCC 6803 , Trace BPCC 6803 , Trace ASynechocystis sp. PCC 6803, have genes encoding for two enzymes that replace the lacking OGDH complex: A 2-oxoglutarate decarboxylase  and a succinate semialdehyde dehydrogenase . Together these two enzymes constitute a shortcut that closes the incomplete TCA cycle. See As one of the most iconic pathways in central metabolism, the TCA cycle has a dual role of oxidizing respiratory substrates for ATP synthesis and providing precursor metabolites, such as oxaloacetate and 2-oxoglutarate, for biosynthesis Synechococcus sp. PCC 7002, mutants lacking the OGDH complex and SSADH grow at a slower rate than the wildtype also during constant illumination, as well as in light-dark cycles The recent discovery of a closed cyanobacterial TCA cycle calls to reconsider the role of the cycle under different growth conditions. As outlined above, the photoautotrophic flux map obtained from FBA shows that during phototrophic growth the TCA cycle carries non-cyclic flux. In this case, the TCA cycle mainly provides oxaloacetate and 2-oxoglutarate for growth and incorporates fumarate originating in purine synthesis back into metabolism. According to the computational flux map, the two newly discovered enzymes carry no flux – consistent with the autotrophic lifestyle and highlighting the fact that the conventional closed TCA cycle is only one way how that flux through the component reactions can be organized The predicted optimal flux pattern changes in periods of darkness. Apart from special conditions, respiratory metabolism in the dark phase typically requires cyclic flux through the TCA cycle to drive ATP synthesis. To evaluate the role of cyclic flux during periods of darkness, we distinguish between four putative scenarios to close the cyanobacterial TCA cycle: (i) a conventional bacterial TCA cycle, involving an OGDH complex that is not annotated in cyanobacteria; (ii) the actual cyanobacterial TCA cycle using a 2-oxoglutarate decarboxylase and a succinic semialdehyde dehydrogenase; (iii) an incomplete TCA cycle that is closed by the GABA shunt to establish cyclic flux through the cycle; as well as (iv) a glyoxylate shunt, via isocitrate lyase activity, to establish cyclic flux through the cycle.Within our previous reconstruction E. coli, the complex consists of a 24-mer core of its E2 component, encoded by the gene sucB, with an as yet unclear stoichiometry of its two other components Therefore, in the context of FBA, both cycles result in identical yield. Interestingly, this yield is below the yield of the conventional cycle using a OGDH complex, therefore representing a seemingly sub-optimal solution for respiratory metabolism. To solve this evolutionary conundrum, Nogales et al. In most natural habitats cyanobacterial metabolism is subject to a diurnal cycle of light availability, resulting in significant change and re-organization within the metabolic network. Correspondingly, cyanobacteria are the only known prokaryotes with an endogenous circadian clock that acts as an intracellular zeitgeber Prochlorococcus MED4 showed only poor correlation between mRNA and protein abundance Here, we seek to augment the picture by an analysis of the temporal coordination of cyanobacterial metabolism, by simulating the diurnal cycle of phototrophic metabolism. To incorporate circadian changes into a large-scale model of metabolism is not trivial. In general two approaches are available: Following a bottom-up approach, large-scale data on transcript or protein expression may be used to constrain the availability of certain enzymatic interconversions. However, transcript or protein abundance must not necessarily correspond to metabolic flux and often contradicting expression values for single pathways or isoenzymes are observed. Indeed, a recent analysis of paired mRNA-protein abundance in light-dark synchronized cultures of the cyanobacterium Synechocystis sp. PCC 6803 Synechocystis or other cyanobacterial strains. For example, for Cyanothece sp. ATCC 51142 measurements of biomass and chlorophyll concentration, both by optical density proxy, indicate that chlorophyll concentration rises sharply from early morning to well before noon, and remains constant afterwards Synechococcus PCC 7942 it was reported that, while cells are dividing rhythmically, DNA synthesis proceeds at a constant rate, effectively uncoupling DNA synthesis and cell division Instead, we follow a top-down approach, such that the cellular objectives are defined as a function of time and change in accordance with light availability. Specifically, we use a recently obtained dataset on cyclic transcript behavior in Synechocystis sp. PCC 6803. Instead of a single BOF, the biomass components are synthesized according to the following rules: We assume constant rate of uptake of micronutrients (inorganic ions). Likewise, DNA is assumed to be synthesized at a constant rate. All other biomass components are represented by a factor in a BOF that is optimized according to light availability. To mimic results on pigment fluorescence, the factor for pigments increases two hours before sunrise and decreases again after noon. In contrast, the factor corresponding to storage synthesis only increases after noon. In addition, ATP requirements for protein synthesis are included in the BOF, corresponding to an increased demand of ATP during growth. Similar to the dark simulation discussed above, the solution assumes a residual respiration also during periods of light availability, implemented as a lower bound for the corresponding flux. Formation of superoxide and the Mehler reaction is light dependent. During night glycogen is used to drive cellular respiration and maintenance. Biomass accumulation makes use of dynamic FBA Based on these empirical observations, we constructed a putative time-resolved biomass objective function, shown in The results of a full diurnal simulations are shown in Synechocystis sp. PCC 6803. The updated reconstruction of the metabolic network is based on several existing reconstructions and incorporates novel results with respect to the cyanobacterial TCA cycle, an alleged glyoxylate shunt, as well as the role of photorespiration in cellular growth. The model includes various aspects specific for phototrophic metabolism, such as a light-dependent generation of reactive oxygen species. In addition to the model itself, which is encoded and made available in SBML format, we prepared a detailed graphical overview to facilitate discussion also among non-experts. The focus of our analysis was phototrophic growth of the organism, in particular the functional consequences of dissenting or unclear pathway topologies.Phototrophic microorganisms hold great promises as a resource to generate high-value products and biofuels using only atmospheric carbon dioxide, sunlight, and some minerals. In this respect, cyanobacteria have attracted recent attention as a possible chassis for the generation of third generation biofuels. We presented an updated and extended genome-scale reconstruction for the unicellular cyanobacterium Synechocystis sp. PCC 6803, several key reactions steps remain unclear. We evaluated several alternative possibilities to close the cyanobacterial TCA cycle, and compared biomass yield for different scenarios. An evolutionary advantage of the GABA shunt over the traditional OGDH, as proposed by Nogales et al.Synechocystis sp. PCC 6803 able to grow on acetate in the presence of DCMU to inhibit PSII and water oxidation and thus linear electron transport, but not PSI cyclic electron transport and ATP generation. Both facts strongly suggest the absence of a functional glyoxylate shunt.Indeed, despite several recent reconstructions, and the extensive biochemical literature available for As a feature that is specific to phototrophic organisms, the re-organization of metabolism in alternating diurnal light/dark cycles was demonstrated. As yet, almost all existing reconstructions have focussed on an evaluation of hetero-, mixo-, or phototrophic growth under constant light, with little reference to the actual environmental conditions the organism experiences. While such a simulation is clearly in its infancy, data on gene expression and physiological properties allow to describe a basic diurnal metabolic cycle of the organism. We are confident that similar computational approaches are required to obtain a better understanding of principles and trade-offs during phototrophic growth. In addition to the evaluation of a diurnal cycle, our reconstruction highlights several open questions with respect to cyanobacterial metabolism that deserve future attention. In particular, dark metabolism, as well as the interplay between oxygenic photosynthesis and aerobic respiration taking place in a single compartment are still insufficiently understood. We also conjecture that large-scale metabolic network modelling has to move beyond the stoichiometric reconstruction process itself and increasingly has to take into account additional biophysical constraints, such as photorespiration and the generation of reactive oxygen species, as well as suboptimal flux distributions to elucidate and explain observed metabolic behavior.Synechocystis sp. PCC 6803 have recently become available, only few attempts have been made to systematize the missing metabolic knowledge. Indeed, several aspects of the metabolic network and its main synthesis pathways are still insufficiently understood. For example, within the current reconstruction, the amino acids methionine and asparagine lack a complete synthesis pathway. To ensure viability in silico, the synthesis steps from Microcystis aeruginosa have been adopted for the synthesis of methionine. Asparagine is assumed to be synthesized from aspartate via an asparagine synthetase (EC 6.3.5.4). Synthesis pathways for all remaining amino acids are annotated, the putative enzymatic steps for serine and glycine are discussed in more detail below. Cyanobacteria utilize glycogen, cyanophycin and polyhydroxybutyrate (PHB) as storage compounds. However, the enzymatic steps necessary for breakdown of internal PHB are not known, even though the compound is detected Synechocystis sp. PCC 6803, a knock-out showed that its absence had no effect on photosynthetic function Although several reconstructions of the cyanobacterium Synechocystis sp. PCC 6803 changes under diurnal conditions, from approx Prior to conversion into final simulation files, the network was tested for elemental and charge balances using the COBRA toolbox Cyanothece sp. ATCC 51142 Synechococcus sp. PCC 7002 Cyanothece sp. ATCC 51142 and Synechocystis sp. PCC 6803 was performed While metabolic reconstruction of phototrophic organisms are still underrepresented as compared to heterotrophic microorganisms, recently a number of cyanobacterial reconstructions have become available. The first application of FBA on cyanobacterial metabolism was performed by Shastri and Morgan Nogales et al. −1 gDW−1, we integrated the flux values over 24 h to get an estimate of the total amount of each component during one diurnal cycle. Based on these given amounts, we postulated a scenario for a full diurnal cycle. We assume a continuously synthesis of the biomass components ‘DNA’ and ‘inorganic ions’. The components ‘Protein’, ‘RNA’, ‘Cell wall’ and ‘lipids’ are only synthesized during the light phase of the simulation. The synthesis of pigments starts two hours before the beginning of the light phase and ends two hours before the start of the dark phase. To account for the demand for energy during the dark phase, when no light is available, we assume that glycogen, synthesized and accumulated from the middle of the light phase on, is used as storage compound. During the light phase a steady respiration rate of the cytochrome c oxidase of For the modelling of a full diurnal cycle under usage of the flux balance analysis we assume a doubling time of about 24 h. Since FBA estimates the flux towards biomass components, given in mmol hSynechocystis PCC 6803 was cultivated in liquid BG11 medium buffered with HEPES/KOH Click here for additional data file.Dataset S2The reconstructed network suitable for simulation with the COBRA toolbox.(XML)Click here for additional data file.Figure S1A detailed graphical overview of the metabolic network. Best printed in A0 format.(PDF)Click here for additional data file.Figure S2Synechocystis sp. PCC 6803 as a function of circadian time (CT). Two independent replicates are shown consecutively. The majority of transcripts peaks during day. A list of phase-sorted transcripts is provided as Supplemental Phase-sorted expression profiles of metabolic genes in (PNG)Click here for additional data file.Figure S3Clustergram of metabolic fluxes over a full diurnal cycle. Shown are reactions that carry a flux along the diurnal cycle. Isoreactions are combined into a single reaction.(EPS)Click here for additional data file.Figure S4E. coli was measured in phenylhydrazin reaction buffer. Increase in A324 nm after adding of the substrate isocitrate (IC) with an end concentration of 1 mM shows the formation of glyoxylate phenylhydrazon.Positive control of ICL activity. Isocitrate lyase activity in cell free extracts of (TIFF)Click here for additional data file.Table S1Synechocystis sp. PCC 6803.An excel sheet containing the metabolic network of (XLS)Click here for additional data file.Table S2A list of annotated enzymes that are not part of the core network.(XLS)Click here for additional data file.Table S3Results of Flux Balance Analysis, including simulated flux values for light and dark metabolism, flux variability and diurnal variation.(XLS)Click here for additional data file.Table S4A list of phase-sorted transcripts.(XLS)Click here for additional data file.Table S5Amino acid requirements for the TCA cycle and its bypass.(XLS)Click here for additional data file.Text S1Positive control of ICL activity.(PDF)Click here for additional data file.Text S2Summary of existing reconstructions.(PDF)Click here for additional data file.


Saccharomyces cerevisiae for efficient xylose utilization, a fungal pathway consisting of xylose reductase, xylitol dehydrogenase, and xylulose kinase is often introduced to the host strain. Despite extensive in vitro studies on the xylose pathway, the intracellular metabolism rewiring in response to the heterologous xylose pathway remains largely unknown. In this study, we applied 13C metabolic flux analysis and stoichiometric modeling to systemically investigate the flux distributions in a series of xylose utilizing S. cerevisiae strains.To engineer 13C metabolic flux analysis, the oxidative pentose phosphate pathway was actively used for producing NADPH required by the fungal xylose pathway during xylose utilization of recombinant S. cerevisiae strains. The TCA cycle activity was found to be tightly correlated with the requirements of maintenance energy and biomass yield. Based on in silico simulations of metabolic fluxes, reducing the cell maintenance energy was found crucial to achieve the optimal xylose-based ethanol production. The stoichiometric modeling also suggested that both the cofactor-imbalanced and cofactor-balanced pathways could lead to optimal ethanol production, by flexibly adjusting the metabolic fluxes in futile cycle. However, compared to the cofactor-imbalanced pathway, the cofactor-balanced xylose pathway can lead to optimal ethanol production in a wider range of fermentation conditions.As revealed by 13C-MFA and in silico flux balance analysis to a series of recombinant xylose-utilizing S. cerevisiae strains, this work brings new knowledge about xylose utilization in two aspects. First, the interplays between the fungal xylose pathway and the native host metabolism were uncovered. Specifically, we found that the high cell maintenance energy was one of the key factors involved in xylose utilization. Potential strategies to reduce the cell maintenance energy, such as adding exogenous nutrients and evolutionary adaptation, were suggested based on the in vivo and in silico flux analysis in this study. In addition, the impacts of cofactor balance issues on xylose utilization were systemically investigated. The futile pathways were identified as the key factor to adapt to different degrees of cofactor imbalances and suggested as the targets for further engineering to tackle cofactor-balance issues.By applying  Escherichia coli xylose (Cambridge Isotope Laboratories). For 13C tracer experiments in YPAX medium , 0.1% inoculum was transferred from the seed culture to YPAX medium with 40 g/L [1-13C] xylose as the carbon source. For all 13C tracer experiments, S. cerevisiae strains were grown in duplicates, and the cells (i.e. biomass) were harvested at the mid-log phase based on the growth curve previously determined using non-labeled xylose in defined minimal medium and YPAX medium + group (containing unfragmented amino acids); and the [M-159]+ or [M-85]+ group (containing amino acids that had lost an α-carboxyl group). For each type of fragments, the labeling patterns were represented by M0, M1, M2, etc., which were fractions of unlabeled, singly labeled, and doubly labeled amino acids . The amino acids were derivatized in 50 μl of tetrahydrofuran and 50 μl of 00°C. TheS. cerevisiae strains was generated based on genome annotation from the KEGG database (Kyoto Encyclopedia of Genes and Genomes) and previous fluxomics studies  xylose into the YPAX medium. The 13C enrichments of all the amino acids detected were only 20 ~ 50% of their counterparts from the minimal medium  is more advantageous for ethanol production. As suggested by the 13C-MFA to a series of recombinant xylose-utilizing S. cerevisiae strains, the interplays between the fungal xylose pathway and the native host metabolism were uncovered. The oxidative pentose phosphate pathway of S. cerevisiae strains was activated to provide NADPH for the fungal xylose pathway. The TCA cycle was found to be tightly regulated in response to the requirements of maintenance energy caused by heterologous protein expression. Based on in silico simulations, reducing the maintenance energy can be a crucial strategy to improve ethanol production. The addition of exogenous nutrients such as amino acids to reduce the de novo synthesis of biomass building blocks can be an efficient strategy to reduce the maintenance energy. Also, metabolic engineering strategies, such as directed evolution, can be potentially helpful to reduce the maintenance requirement and improve the xylose based ethanol production.In summary, by systemically applying The authors declare that they have no competing interests.XF and HZ conceived this study. XF performed the experiments; XF and HZ interpreted and analyzed data. XF and HZ wrote the manuscript and prepared a revised version. All authors approved the final version.Text S2. Metabolic flux calculation by 13C-MFA. Figure S1. Measured and simulated isotopomer labeling patterns of proteinogenic amino acids in enzyme-based-library of S. cerevisiae strains.Isotopomer labeling patterns of proteinogenic amino acids. The variations between the biological replicates were less than 2%. Click here for file