Shewanella bacteria makes species from this genus a promising platform for investigating the evolution of carbon metabolism and energy conservation. The Shewanella phylogeny is diverged into two major branches, referred to as group 1 and group 2. While the genotype-phenotype connections of group 2 species have been extensively studied with metabolic modeling, a genome-scale model has been missing for the group 1 species. The metabolic reconstruction of Shewanella piezotolerans strain WP3 represented the first model for Shewanella group 1 and the first model among piezotolerant and psychrotolerant deep-sea bacteria. The model brought insights into the mechanisms of energy conservation in WP3 under anaerobic conditions and highlighted its metabolic flexibility in using diverse carbon sources. Overall, the model opens up new opportunities for investigating energy conservation and metabolic adaptation, and it provides a prototype for systems-level modeling of other deep-sea microorganisms.The well-studied nature of the metabolic diversity of  Shewanella piezotolerans strain WP3 belongs to the group 1 branch of the Shewanella genus and is a piezotolerant and psychrotolerant species isolated from the deep sea. In this study, a genome-scale model was constructed for WP3 using a combination of genome annotation, ortholog mapping, and physiological verification. The metabolic reconstruction contained 806 genes, 653 metabolites, and 922 reactions, including central metabolic functions that represented nonhomologous replacements between the group 1 and group 2 Shewanella species. Metabolic simulations with the WP3 model demonstrated consistency with existing knowledge about the physiology of the organism. A comparison of model simulations with experimental measurements verified the predicted growth profiles under increasing concentrations of carbon sources. The WP3 model was applied to study mechanisms of anaerobic respiration through investigating energy conservation, redox balancing, and the generation of proton motive force. Despite being an obligate respiratory organism, WP3 was predicted to use substrate-level phosphorylation as the primary source of energy conservation under anaerobic conditions, a trait previously identified in other Shewanella species. Further investigation of the ATP synthase activity revealed a positive correlation between the availability of reducing equivalents in the cell and the directionality of the ATP synthase reaction flux. Comparison of the WP3 model with an existing model of a group 2 species, Shewanella oneidensis MR-1, revealed that the WP3 model demonstrated greater flexibility in ATP production under the anaerobic conditions. Such flexibility could be advantageous to WP3 for its adaptation to fluctuating availability of organic carbon sources in the deep sea.IMPORTANCE The well-studied nature of the metabolic diversity of Shewanella bacteria makes species from this genus a promising platform for investigating the evolution of carbon metabolism and energy conservation. The Shewanella phylogeny is diverged into two major branches, referred to as group 1 and group 2. While the genotype-phenotype connections of group 2 species have been extensively studied with metabolic modeling, a genome-scale model has been missing for the group 1 species. The metabolic reconstruction of Shewanella piezotolerans strain WP3 represented the first model for Shewanella group 1 and the first model among piezotolerant and psychrotolerant deep-sea bacteria. The model brought insights into the mechanisms of energy conservation in WP3 under anaerobic conditions and highlighted its metabolic flexibility in using diverse carbon sources. Overall, the model opens up new opportunities for investigating energy conservation and metabolic adaptation, and it provides a prototype for systems-level modeling of other deep-sea microorganisms. Shewanella genus are present in a wide range of environments, including fresh and salt waters, food products, sewage systems, and deep-sea sediments , and dimethyl sulfoxide (DMSO)  of metabolic networks have broad applications in phenotype prediction, evolutionary reconstruction, functional analysis, and metabolic engineering  and is a well-studied species in this group. Previous studies have provided detailed evidence related to the function and annotation of multiple key metabolic pathways in WP3, including nitrate utilization , revealed similarities and differences between the two organisms in their aerobic growth and anaerobic energy conservation.In this study, a GEM of WP3 was constructed and applied in simulating the carbon metabolism and energy conservation under both aerobic and anaerobic conditions. The model was verified based on the known physiology of the organism and new experimental data. Evolutionary analysis of the central metabolic genes revealed nonhomologous replacements between WP3 (and other group 1 species) and the group 2 S. piezotolerans WP3 was confirmed following a phylogenomic analysis using the protein sequences of 661 conserved single-copy genes (CSCGs) in the full genomes of 24 Shewanella species and 5 closely related Gammaproteobacteria species that served as the outgroup to the Shewanella genus . The phylogenomic reconstruction demonstrated the differentiation of the group 1 and group 2 Shewanella species into distinct evolutionary branches  associations were incorporated through mapping orthologous genes to the existing Shewanella reconstructions  that were conserved between WP3 and some  of the four previously modeled species, leading to the inclusion of 726 genes associated with 750 reactions in the WP3 reconstruction.The complete metabolic reconstruction of WP3, GEM-iWP3, was released in a public Git repository at ructions , 25 .–45.Shewa species –55. Putaproteins , as wellruginosa , 58. Thiruginosa –63.Shewanella reconstructions and experimental measurements performed on WP3. Specifically, the stoichiometry of the lipid biosynthesis equation . “Compound ID/Name” lists the identifiers/names of extracellular compounds with defined exchange reactions, which were used to simulate the availability of nutrients and the removal of metabolic by-products. The compound identifiers are shown for both the WP3 and MR-1 models. “Lower/Upper Bound” lists basal constraints for the lower and upper bounds of exchange reaction fluxes. Negative lower bounds indicate compounds provided as nutrient sources to the model, and a lower bound of zero indicates a compound that could only be released as a metabolic by-product but not acquired from the environment. “Type” lists the classification of the exchange compounds. “Growth supporting in WP3” lists the growth-supporting carbon sources, and terminal electron acceptors are marked as TRUE in this column. Download Copyright © 2017 Dufault-Thompson et al.2017Dufault-Thompson et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Shewanella species were identified. These included acetylornithine deacetylase (argE), which is essential for the biosynthesis of arginine, and glucosamine-6-phosphate deaminase (nagB), which is essential for utilizing N-acetyl-d-glucosamine (GlcNac). Both genes were experimentally identified in MR-1 and were found to be nonhomologous to the canonical genes in E. coli  and group 2 (B and D) Copyright © 2017 Dufault-Thompson et al.2017Dufault-Thompson et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Simulations of biomass production with the WP3 metabolic model were consistent with the known physiology of this organism. This included utilizing glucose, lactate, maltose, and GlcNac as carbon sources and using Fe(III), nitrate, nitrite, thiosulfate, TMAO, and DMSO as terminal electron acceptors for anaerobic respiration . From thTo quantitatively evaluate the prediction of biomass concentrations by the WP3 metabolic model, batch cultures were set up using a minimal medium developed in this study to experimentally measure the growth of WP3 with sole carbon sources see , the exc10.1128/mSystems.00165-16.9TEXT S1 Shewanella piezotolerans WP3. Medium components were adapted from a previously described defined marine medium . Download TEXT S1, PDF file, 0.1 MB.Components of the LMO-812 minimal medium used for the experimental culture of Copyright © 2017 Dufault-Thompson et al.2017Dufault-Thompson et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 4+ as the limiting factor of biomass production. This was because the uptake of these carbon sources was limited by the uptake bound of the NH4+ exchange flux, which corresponded to its concentration in the experimental medium. Allowing for higher uptake of NH4+ by the model led to higher biomass production and higher uptake of these carbon sources. In contrast, the model was not limited by the availability of NH4+ when an amino sugar, GlcNac, was used as a sole carbon source. This was because each molecule of GlcNac produced one molecule of NH4+ during its utilization, providing additional nitrogen that could be used during growth. As a result, higher biomass was observed with GlcNac as a carbon source, and this trend was seen in both the experimental measurements and the model simulations , based 10.1128/mSystems.00165-16.2FIG S2 FIG S2, PDF file, 0.1 MB.Biomass yields for WP3 and MR-1 simulated across 28 carbon sources under aerobic conditions. Download Copyright © 2017 Dufault-Thompson et al.2017Dufault-Thompson et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Δatp mutant) were simulated using flux balance analysis (FBA) under both aerobic (O2) and anaerobic (fumarate) conditions using either GlcNac or lactate as the sole carbon source. Under aerobic conditions, the Δatp mutant produced less than half of the WT biomass, indicating that oxidative phosphorylation played an important role in the aerobic growth of WP3. Under anaerobic conditions, however, the biomass production levels were comparable between the WT and Δatp models, demonstrating that oxidative phosphorylation had only a minor role in supporting anaerobic growth (The relative roles of oxidative and substrate-level phosphorylation were examined by simulating mutant models with reactions from each of the two pathways blocked (c growth .Δpta mutant) or the acetate kinase (ΔackA mutant) gene or the double deletion of both genes (Δpta ΔackA mutant). When lactate was used as a sole carbon source, the WT model was able to produce nonzero biomass flux, while the Δpta, ΔackA, and Δpta ΔackA models had a maximum biomass flux of zero, indicating that these mutants are not viable in the lactate medium. When GlcNac was used as a sole carbon source, both the WT and mutant models were viable in the anaerobic medium. Compared to the WT, the Δpta mutant had a slight decrease in biomass production (97% of the WT flux), and the ΔackA and Δpta ΔackA mutants resulted in greater reductions of the biomass, to less than 50% of the WT level  from the pentose phosphate pathway so that substrate-level phosphorylation through AckA was maintained. This redirection resulted in reduced Fdh flux, potentially due to decreases in formate production, and increased Ndh flux, potentially for maintaining the redox activities in the electron transport chain. In the ΔackA and Δpta ΔackA models, more significant shifts were observed in the distribution of metabolic fluxes. The inhibition of AckA led to blockage of the upstream fluxes through Pta and Xpk and increased flux through pyruvate kinase (Pyk) to partially compensate for the loss of AckA-mediated ATP production.Additional examination of the internal fluxes obtained from FBA revealed changes in ATP production, PMF generation, and redox functions in the WT and mutant models . In the Δpta models and for the ATPase, AckA, Pta, and Xpk reactions in the ΔackA and Δpta ΔackA models. However, the Pyk, Fdh, and Ndh reactions had various flux values in the ΔackA and Δpta ΔackA mutants, indicating that these mutants had alternative strategies for balancing the ATP production, PMF generation, and redox activities in the cell.The variability of the internal fluxes was further examined using flux variability analysis (FVA) with biomass production constrained to its maximum under each simulation condition . Numbers in this table indicate raw values of the minimum and maximum fluxes before they were normalized by the biomass flux. Download TABLE S4, PDF file, 0.02 MB.Maximum and minimum flux values obtained from flux variability analysis (FVA), corresponding to the simulation conditions of the experiments whose results are shown in Copyright © 2017 Dufault-Thompson et al.2017Dufault-Thompson et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Shewanella anaerobic growth was the lack of oxidative phosphorylation via ATPase despite the obligate requirement for respiration through terminal electron acceptors  and the activity of ATPase in both the WP3 and the MR-1 model . The simulation demonstrated a positive correlation between the availability of reducing equivalents and the flux of the ATPase reaction for both WP3 and MR-1 . Fluxes of the ATPase reaction (black dots) were plotted based on a robustness simulation across varied fluxes of the EQ1 reaction. Linear models (red lines) were fitted to the observed correlations between EQ1 and ATPase fluxes and used to calculate the differences in NAD+ and NADH concentrations where the ATPase flux approached zero. Download FIG S3, PDF file, 0.04 MB.Linear models for the prediction of NADCopyright © 2017 Dufault-Thompson et al.2017Dufault-Thompson et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00165-16.4FIG S4 +/NADH homeostasis in the MR-1 model . Fluxes of the ATPase reaction (black dots) were plotted based on a robustness simulation across varied fluxes of the EQ1 reaction. Linear models (red lines) were fitted to the observed correlations between EQ1 and ATPase fluxes and used to calculate the differences in NAD+ and NADH concentrations where the ATPase flux approached zero. Download FIG S4, PDF file, 0.04 MB.Linear models for the prediction of NADCopyright © 2017 Dufault-Thompson et al.2017Dufault-Thompson et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Shewanella species, WP3 and MR-1, demonstrated complex interactions of ATP generation, PMF generation, and redox-balancing processes under anaerobic growth conditions. The WP3 model displayed higher capacity than MR-1 in producing excess reducing equivalents with most of the examined carbon sources. This may provide additional advantages to WP3 in its natural environment by enabling additional ATP production when using a diverse range of carbon sources.The comparison of redox states in the WP3 and MR-1 models when the ATPase reaction flux approached zero revealed metabolic differences between these two organisms across diverse carbon sources when using fumarate as the sole electron acceptor . The WP3Shewanella species  and substrate-level  phosphorylation . A data set of 24 Shewanella genomes was downloaded from the KEGG database . Gene-protein-reaction (GPR) associations in the initial WP3 reconstruction were mapped from conserved genes in the modeled species, following logic expressions that represent the “AND” and “OR” relationships of enzyme-coding genes. The “AND” logic was used to indicate multiple subunits of an enzyme complex, and the “OR” logic was used to indicate alternative enzymes. A GPR association was introduced from existing reconstructions only if orthologs were identified in the WP3 genome for all subunits of at least one alternative enzyme. The WP3 reconstruction was further expanded through manual curation by referencing existing annotations in the KEGG , macromolecular synthesis equations , and reactions involving the acyl carrier protein (ACP) or its apo form  were excluded from formula and charge checks due to the presence of undefined R or X groups in the metabolites.The WP3 metabolic reconstruction was developed using version 0.27 of the PSAMM software package . The rec W3-18-1 . The ortthe KEGG , SEED . The growth measurements at early stationary phase were converted to gDW/liter of biomass concentration using a previously determined correlation between OD600 and dry weight in Shewanella species .The growth of WP3 when utilizing a variety of sole carbon sources was examined in aerobic batch cultures using 50 ml of LMO-812 minimal medium . The carbon source was constrained to a maximum uptake of 10 mmol/liter, and the electron acceptor was unlimited. For simulations with fumarate as the electron acceptor, the succinate/fumarate antiporter reaction, SUCFUMtdc, was blocked, as it has been noted to be able to form artificial loops with other transporters , was introduced to the model to account for differences in the concentrations of NAD+ and NADH. First, a robustness analysis was performed by varying the flux value of EQ1 while optimizing the biomass production. This was performed using the robustness function in PSAMM  and the redox state of a cell, the intersection of the linear model with the EQ1 axis was used to determine the difference in NAD+ and NADH concentrations when the ATPase reaction flux approached zero. A negative intersection of the linear model on the EQ1 axis would indicate (NAD+) − (NADH) < 0, suggesting that the homeostasis was pushed toward generating more NADH; a positive intersection would indicate (NAD+) − (NADH) > 0, suggesting that the homeostasis was pushed toward generating more NAD+. Both the WP3 and MR-1 models were simulated using basal constraints, with the addition of fumarate as the anaerobic electron acceptor paired with one of 12 sole carbon sources that are growth supporting in both models (The NADiMR1_799 . To simuin PSAMM , where fh models  and S4. h models , 24, 25.


The heterotrimer Snf1 kinase complex plays a key role in the preference Saccharomyces cerevisiae exhibits for glucose over galactose, a phenomenon known as glucose repression or carbon catabolite repression. The SIP1 gene, encoding a part of this complex, has received little attention, presumably, because its knockout lacks a growth phenotype. We present a fluxomic investigation of the relative effects of the presence of galactose in classically glucose-repressing media and/or knockout of SIP1 using a multi-scale variant of 13C MFA known as 2-Scale 13C metabolic flux analysis (2S-13C MFA). In this study, all strains have the galactose metabolism deactivated  so as to be able to separate the metabolic effects purely related to glucose repression from those arising from galactose metabolism. The resulting flux profiles reveal that the presence of galactose in classically glucose-repressing conditions, for a CEN.PK113-7D gal1Δ background, results in a substantial decrease in pentose phosphate pathway (PPP) flux and increased flow from cytosolic pyruvate and malate through the mitochondria toward cytosolic branched-chain amino acid biosynthesis. These fluxomic redistributions are accompanied by a higher maximum specific growth rate, both seemingly in violation of glucose repression. Deletion of SIP1 in the CEN.PK113-7D gal1Δ cells grown in mixed glucose/galactose medium results in a further increase. Knockout of this gene in cells grown in glucose-only medium results in no change in growth rate and a corresponding decrease in glucose and ethanol exchange fluxes and flux through pathways involved in aspartate/threonine biosynthesis. Glucose repression appears to be violated at a 1/10 ratio of galactose-to-glucose. Based on the scientific literature, we may have conducted our experiments near a critical sugar ratio that is known to allow galactose to enter the cell. Additionally, we report a number of fluxomic changes associated with these growth rate increases and unexpected flux profile redistributions resulting from deletion of SIP1 in glucose-only medium. This phenomenon, known as glucose repression, involves the repression of genes and pathways involved in respiration , the use of alternative fermentable  and non-fermentable  carbon sources, and gluconeogenesis , a well studied and highly conserved eukaryotic regulator of cellular uptake of glucose, energy homeostasis, beta-oxidation of fatty acids, etc. . The isoform bound to Gal83 localizes in the nucleus and activates genes responsible for alternate carbon source utilization, the isoform bound to Sip2 remains in the cytosol, and the isoform bound to Sip1 is sequestered in the vacuole  kinase complex, which is central to glucose repression in sip1Δ mutants . We were curious about what shifts in the cell’s metabolic flux profile might be associated with this increase in growth rate. Additionally, we wanted to know how the presence of galactose interacted with this genetic perturbation from a fluxomic perspective.In spite of a lack of reported phenotypic difference upon knockout of SIP1 by measuring and comparing internal metabolic fluxes, key determinants of microbial physiology. All strains have galactose metabolism deactivated, so as to be able to distinguish glucose repression effects from the impact of galactose metabolism on overall metabolism. Internal metabolic fluxes represent the biomass-normalized activity of metabolic reactions in an organism per hour , combines the strengths of both FBA and 13C MFA: comprehensive genome-scale models constrained by 13C labeling data without the recourse to a biological objective. This approach models metabolism at two different scales of resolution: the lower scale of resolution constrains fluxes using only stoichiometry for the whole genome-scale model, while a higher resolution scale uses carbon labeling patterns on top of stoichiometry to constrain fluxes for a limited core set of reactions anticipated to carry most of the flux. A critical assumption is that most core reaction’s metabolites are not heavily affected by peripheral metabolism, an assumption that is routinely used in 13C MFA and can describe experimental data satisfactorily  and without (sip1Δ knockout) an intact SIP1 gene, similar to those from our previous work .Media used in this study, along with their component concentrations, are listed in Table 2.2S. cerevisiae strains were constructed in a haploid CEN.PK113-7D  . All knockouts were constructed via a near-markerless loxP/Cre recombinase strategy  and mutant sip1Δ) Δ S. cere2.3Cell and extracellular metabolite concentrations were monitored during exponential phase in strain characterization batch experiments. These data were necessary to calculate extracellular fluxes and specific growth rates used to mathematically constrain flux profile inference. Exponentially growing cells, obtained as described in the Media and culturing conditions section, were used to inoculate, in quadruplicate, the final 40-mL shake flask cultures to achieve exponential growth the following morning. Optical density was monitored at 600 nm via UV–VIS, and 200 µL samples were spin-filtered and kept at −20°C for subsequent HPLC analysis.600) samples were taken, spun down , immediately quenched with 300 µL ice-cold methanol, and kept at −80°C.Exponentially growing cells (obtained as described above) were used to inoculate 40 mL labeled shake flask cultures in quadruplicate and monitored via UV–VIS. To prevent changes in intracellular metabolite labeling patterns, 1 mL mid-log (~0.75 OD2.4d-glycerate (3 pg), alanine (Ala), arginine (Arg), asparagine (Asp), glutamine (Gln), glutamate (Glu), isoleucine (Ile), leucine (Leu), lysine (Lys), phenylalanine (Phe), threonine (Thr), tyrosine (Tyr), valine , citrate (cit_m), fructose 1,6-bisphosphate (fdp), and succinate (succ_m). Succinate and citrate were assumed to be mitochondrial, while the rest are assumed to be cytosolic as has been done in previous studies  into a 2-mL collection tube. The aqueous layer was filtered (3 k MW cut-off (Amicon), 1.5 h, 13,000 g, 4°C), mixed with 1 mL ice-cold H2O, and snap frozen in liquid nitrogen. Three holes were punched in the tube cap, and the samples were lyophilized for 24 h. Lyophilized samples were resuspended in 40 µL 50/50 MeOH/H2O, and stored at −80°C. Samples were analyzed to obtain intracellular amino acid and non-amino acid labeling data via LC-MS as previously described  outfitted with UV and refraction index detectors and an Organic Acid Analysis Column  at a rate of 0.6 mL/min. Standards were used to identify metabolite retention times and sample concentrations.Extracellular concentrations for glucose, galactose, ethanol, glycerol, succinate, lactate, acetate, and formate were measured via HPLC. These concentrations, along with corresponding culture specific growth rates, were necessary to calculate extracellular fluxes. The 4 mM H2.6pp, ν, is give by equation , and p versus OD600. The value of α was taken to be 0.7742 based on multiple in-house experiments (data not shown). The average plus and minus the corresponding standard deviation was used to constrain all extracellular fluxes and specific growth rates.Extracellular fluxes and specific growth rates were derived from extracellular concentration and optical density time curves. Their means and standard deviations were used to constrain exchange fluxes for consumed and excreted metabolites and biomass fluxes during flux profile inference. Flask-specific maximum specific growth rates were determined from the slope of lnOD versus time data in manually determined linear ranges. The same time points were used with corresponding concentration data to calculate extracellular fluxes. The extracellular flux of metabolite equation . Mp is t2.713C metabolic flux analysis (2S-13C MFA)  was used to find the maximum and minimum values of each flux compatibles with the experimental data [see  was the maximum of the instrument error and the difference of the best fit computational labeling with the experimental labeling [equation (23) in García Martín et al. . The sip1Δ mutant grew ~8% faster than the base strain in mixed glucose/galactose medium . Consistent with the literature .The presence of galactose in the medium increased the maximum specific growth rate for both base and 3.2SIP1 resulted in any clear pattern in the extracellular fluxes. The means and standard deviations for all monitored extracellular metabolites for all strain/condition pairs can be found in Table sip1Δ mutant consumed glucose and excreted ethanol at ~44% and ~72% lower rates, respectively, than the base strain in glucose-only medium (base in 2% glucose versus sip1Δ in 2% glucose). The sip1Δ mutant in glucose/galactose medium relative to the parental strain  exhibited a ~98% increase in absolute glucose flux, a ~70% increase in absolute ethanol flux, and a ~54% increase in acetate. Addition of 0.2% galactose to the medium of the base strain  resulted in a decrease in absolute glucose flux of ~51% and an increase of ~55% for acetate flux. Addition of galactose to the medium of the mutant strain  resulted in a ~75% increase in absolute glucose flux, a ~250% increase in ethanol flux, a ~130% increase in glycerol flux, and a ~175% increase in acetate flux. Lactate, formate, and succinate were not detected in any strain/condition pair, hence their extracellular fluxes were considered zero. All other absolute fluxes were the same for all strain/condition pairs within error. The presence of galactose in the medium of the sip1Δ mutant appeared to restore the ethanol flux to its value before the gene knockout , within error.Neither the presence of galactose nor knockout of 13C labeling information in the context of the genome-scale model results in noticeable patterns for the intracellular fluxes.In spite of this lack of clear patterns, we will see below that the addition of 3.3d-glycerate (3 pg), alanine (Ala), arginine (Arg), asparagine (Asp), glutamine (Gln), glutamate (Glu), isoleucine (Ile), leucine (Leu), lysine (Lys), phenylalanine (Phe), threonine (Thr), tyrosine (Tyr), valine , citrate (cit_m), fructose 1,6-bisphosphate (fdp), and succinate (succ_m) are displayed in Figure 2, AMP, and CoASH. Arrows pointing toward the main black reaction arrow indicate the cofactor is a reactant and vice versa.Detailed fits between simulated and measured LC-MS data for metabolites 3-phospho-3.4SIP1 null mutant decreased a similar ~93% when galactose was present in the medium .The presence of galactose appeared to greatly reduce pentose phosphate pathway (PPP) activity. The split between glycolysis and the PPP for strain/condition pairs base in 2% glucose, sip1Δ in 2% glucose, base in 2% glucose + 0.2% galactose, and base in 2% glucose + 0.2% galactose are displayed in Figure 3.513C MFA literature, which indicates a small amount of activity  in glucose-repressing conditions for CEN.PK113-7D . This is mostly consistent with the 3.613C FVA  resulted in a ~35% decrease in pyruvate carboxylase and a ~41% decrease in flux toward aspartate/threonine biosynthesis. The presence of 0.2% galactose in the medium of the base strain  resulted in a ~10% increase in pyruvate carboxylase activity and a ~80% decrease in flux toward aspartate/threonine biosynthesis. Similarly, adding galactose to the medium of the sip1Δ mutant  resulted in a ~32% increase in pyruvate carboxylase flux and a ~66% decrease in flow toward aspartate/threonine biosynthesis. As before, the flux confidence intervals for SG are quite wide, limiting the quality of the inferences for this particular strain/condition.In the absence of galactose, both strains appear to direct pyruvate flux mainly toward ethanol and aspartate/threonine biosynthesis. The split of pyruvate carboxylase flux toward aspartate/threonine biosynthesis and production of cytosolic malate is displayed in Figure 4SIP1 in medium containing both glucose and galactose in a background similar to this study . This increase was unreported in the literature and, thus, attracted our attention for further investigation. We chose to perform an exploratory analysis of this unreported phenotype from a fluxomic perspective in a similar set of base and sip1Δ mutant strains constructed in a CEN.PK113-7D ura3Δ gal1Δ background. Exponential-phase intracellular flux profiles were inferred from 13C tracer experiments using 2S-13C MFA for all four strain/condition pairs and compared. Our hope was to compare the redistribution of fluxes, if any, resulting from deletion of SIP1 and/or inclusion of galactose in glucose medium and identify any resulting patterns.We previously  and they monitored growth in microwell plates (instead of shake flasks), it is possible that a similar effect might be occurring, even if CEN.PK113-7D is known to exhibit phenotypic differences relative to S288c  appeared in all four strain/condition pairs. Unfortunately, our flux confidence intervals are too wide to meaningfully compare the flux profile of the sip1Δ mutant, other than the PPP patterns, in mixed glucose/galactose medium with the remaining three strain/condition pairs. Thus, our analysis regarding it is limited to its higher growth rate and this section of the network. While this may seem like a disadvantage, it highlights a strength of our analysis. The combination of the ELVA and 13C FVA allows us to judge the consistency of our model, data, inferred flux profiles, and simulated labeling and when it is or is not appropriate to derive further conclusions.Our results are consistent with our mechanistic understanding that Sip1 is a negative regulator of the 13C labeling data. In particular, the use of the 2-scale version of 13C MFA, notably expanding the core set of reactions until acceptable simulated labeling ranges were obtained, led to our insights in mitochondrial transport. Most 13C MFA studies do not include these mitochondrial transport reactions. In fact, the initial carbon transition model in this study did not include them. It was only through the process of adding them to tighten the computational error in the ELVA plots and then visualizing the fluxes that it became apparent that this cycle was occurring.In this study, we have shown how to go from gross phenotypic changes  to mechanistic metabolic insights by using modeling techniques based on constraining comprehensive genome-scale models by SIP1 in normally carbon repressing conditions from a fluxomic perspective. We also encountered increases in growth rate when galactose was present in normally glucose-repressing medium not found in the scientific literature. It is also one of the first to apply 2S-13C MFA to model yeast. This model (as every modeling endeavor) needs to rely on a variety of assumptions . Some of the assumptions the model is based on may fail, so it is advisable that these insights be confirmed through further experiments . However, the model is able to take a profusion of disconnected quantitative data  and convert them into insights of what types of metabolic changes the SIP1 knockout  are likely to produce in the cell for further interrogation, similarly to what has been demonstrated before in terms of biofuel production increases (Ghosh et al., To our knowledge, this is the first published study to investigate the relative effects of the presence of galactose and knockout of CS conceived of the project, did the experiments, analyzed the data, and wrote the paper. GW and EB produced the metabolomics data and helped write the paper. JG helped performed experiments and wrote the paper. AA and AM helped write the paper. HM conceived of the project and helped analyze the data and write the paper. JK conceived of the project and helped write the paper.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


There are an increasing number of studies regarding genetic manipulation of cyanobacteria to produce commercially interesting compounds. The majority of these works study the expression and optimization of a selected heterologous pathway, largely ignoring the wholeness and complexity of cellular metabolism. Regulation and response mechanisms are largely unknown, and even the metabolic pathways themselves are not fully elucidated. This poses a clear limitation in exploiting the rich biosynthetic potential of cyanobacteria.R-manoyl oxide in Synechocystis PCC 6803. We used genome-scale metabolic modelling to study fluxes in individual reactions and pathways, and we determined the concentrations of key metabolites, such as amino acids, carotenoids, and chlorophylls. This allowed us to identify metabolic crosstalk between the native and the introduced metabolic pathways. Most results and simulations highlight the metabolic robustness of cyanobacteria, suggesting that the host organism tends to keep metabolic fluxes and metabolite concentrations steady, counteracting the effects of the heterologous pathway. However, the amino acid concentrations of the dhurrin-producing strain show an unexpected profile, where the perturbation levels were high in seemingly unrelated metabolites.In this work, we focused on the production of two different compounds, the cyanogenic glucoside dhurrin and the diterpenoid 13There is a wealth of information that can be derived by combining targeted metabolite identification and computer modelling as a frame of understanding. Here we present an example of how strain engineering approaches can be coupled to ‘traditional’ metabolic engineering with systems biology, resulting in novel and more efficient manipulation strategies.The online version of this article (doi:10.1186/s12934-017-0757-y) contains supplementary material, which is available to authorized users. Escherichia coli and Saccharomyces cerevisiae; however, this limited choice of organisms restricts the application potentials, thus highlighting the need of expanding towards other organisms  and CfTPS3 [GenBank: KF444508], encoding the diterpene synthases that convert geranylgeranyl diphosphate (GGPP) to 13R-manoyl oxide  buffer, preheated at 80 °C. The samples were incubated for 20 min at 80 °C with the addition of 30 U thermostable α-amylase (Megazyme), and were subsequently incubated overnight with the addition of 7 U pullulanase M1 and amyloglucosidase (Megazyme). The extracts were spun down, the supernatant filtered, and analyzed by High Pressure Anion-Exchange Chromatography using a Pulsed Amperometric Detection (HPAEC-PAD) BioLC system  equipped with an AS50 autosampler, GS50 gradient pump and ED50 electrochemical detector. Samples (10 μL) were injected and separation of glucose from other sample components was performed using a CarboPac™ SA10 column with a flow rate of 0.35 mL/min and isocratic 1 mM NaOH as eluent. Peaks were evaluated using Chromeleon V.6.70 and glucose concentrations calculated by external authentic glucose standards.Glycogen concentration was determined by enzymatic hydrolysis of the cellular glycogen polysaccharide and quantifying the derived glucose. Pelleted cells were homogenized in a Bullet Blender3 was added to the samples to a final concentration of 10 mM. Oxygen evolution was measured using a Clark-type electrode . Oxygen evolution was measured at increasing light intensities  provided with a Schott lamp (HLX 64634 EFR 150 W Osram® Xenophot) with a 0.6 neutral density optic filter.Cyanobacterial samples were collected 48 h after inoculation. Samples were diluted with fresh BG11 media to a concentration of ~4 μg chlorophyll/mL and dark-adapted for 5 min. Prior to measurements, NaHCOSynechocystis [The iJN678 metabolic network reconstruction of hocystis  version hocystis , modifichocystis  and corrhocystis . The reahocystis , 24. Prihocystis  and the p-hydroxymandelonitrile and a UDP-glycosyltransferase that transfers a glucose from UDP-glucose  and a nickel-inducible promoter [Dhurrin is a cyanogenic glucoside derived from tyrosine, and its biosynthetic pathway involves two cytochrome P450s that convert tyrosine into ose Fig. a. Dhurrie vector   and the manoyl oxide (MO) producing strains exhibited slower growth rates , produced in cyanobacteria via the methylerythritol phosphate (MEP) pathway Fig. . InteracDhurrin derives from tyrosine, which in turn originates from the shikimate pathway Fig. . CorrelaComparison of the predicted flux values in the modified strains with the predicted fluxes in the control strain Fig.  shows thThe role of aromatic amino acids in dhurrin formation and the computational predictions described above prompted us to determine the amino acid concentration of the both the manoyl oxide and dhurrin strains and compare these to the empty vector control  of the control, dhurrin, and 13-R manoyl oxide producing strains. Table S5. Constraints applied in the three metabolic models used in this work. Figure S1. Glycogen content in the dhurrin (DH) and 13R-manoyl oxide (MO) producing strains compared to control (empty).Additional file 2: Table S6. Average fluxes of reactions models, determined with random sampling.Additional file 3. Updated metabolic models.


Mitochondrial fatty-acid beta-oxidation (mFAO) plays a central role in mammalian energy metabolism. Multiple severe diseases are associated with defects in this pathway. Its kinetic structure is characterized by a complex wiring of which the functional implications have hardly been explored. Repetitive cycles of reversible reactions, each cycle shortening the fatty acid by two carbon atoms, evoke competition between intermediates of different chain lengths for a common set of ‘promiscuous’ enzymes (enzymes with activity towards multiple substrates). In our validated kinetic model of the pathway, substrate overload causes a steep and detrimental flux decline. Here, we unravel the underlying mechanism and the role of enzyme promiscuity in it. Comparison of alternative model versions elucidated the role of promiscuity of individual enzymes. Promiscuity of the last enzyme of the pathway, medium-chain ketoacyl-CoA thiolase (MCKAT), was both necessary and sufficient to elicit the flux decline. Subsequently, Metabolic Control Analysis revealed that MCKAT had insufficient capacity to cope with high substrate influx. Next, we quantified the internal metabolic regulation, revealing a vicious cycle around MCKAT. Upon substrate overload, MCKAT’s ketoacyl-CoA substrates started to accumulate. The unfavourable equilibrium constant of the preceding enzyme, medium/short-chain hydroxyacyl-CoA dehydrogenase, worked as an amplifier, leading to accumulation of upstream CoA esters, including acyl-CoA esters. These acyl-CoA esters are at the same time products of MCKAT and inhibited its already low activity further. Finally, the accumulation of CoA esters led to a sequestration of free CoA. CoA being a cofactor for MCKAT, its sequestration limited the MCKAT activity even further, thus completing the vicious cycle. Since CoA is also a substrate for distant enzymes, it efficiently communicated the ‘traffic jam’ at MCKAT to the entire pathway. This novel mechanism provides a basis to explore the role of mFAO in disease and elucidate similar principles in other pathways of lipid metabolism. Burning of fat is essential to provide energy to our bodies. However, fat metabolism is also involved in well-known diseases, such as metabolic syndrome and type 2 diabetes. In severe, inherited deficiencies of enzymes in the fatty-acid oxidation pathway, children run the risk of life-threatening low glucose levels or ‘hypoglycemia’. This typically occurs during overnight fasting, when the body depends on fat stores for its energy supply. To understand the sequence of events leading to hypoglycemia and eventually provide novel therapies, we simulated the process with a validated computer model. We found that when the supply of fat to the liver was increased beyond a critical value, the rate of fatty-acid oxidation declined. Under these circumstances, intermediate metabolites accumulated, very much like the situation in a traffic jam. The risk of a metabolic traffic jam was enhanced in fatty-acid oxidation disorders. In the body this would lead to insufficient energy to maintain blood glucose levels. In this paper we unravel the vicious cycle that leads to the detrimental flux decline and identify a critical role for the last enzyme of the pathway, MCKAT. Our results suggests that MCKAT or its regulators may be promising drug targets. The mitochondrial fatty-acid oxidation (mFAO) in the liver is of key importance for the production of ATP and ketone bodies during fasting. It allows glucose to be spared for utilization in the periphery, can supply the energy for de novo glucose production, and provides precursors for ketone-body synthesis. Thus, mFAO is intricately connected with other pathways of energy metabolism. For almost all enzymes of the pathway, inborn deficiencies associated with human disease have been identified ,2. All tRecently, we constructed and experimentally validated a dynamic computational model of mFAO pathway in rat liver . Model sA special feature of the mFAO biochemistry is the promiscuity of its enzymes, meaning that each enzyme catalyses the conversion of fatty-acid derivatives of a range of chain lengths ,8. OxidaKm serving as competitive inhibition constant. An example of such a rate equation, for the conversion of C16-acyl-CoA by the enzyme VLCAD, is:In the construction of the model , the totin silico by taking out all competitive inhibition terms except direct product inhibition , the flux exhibited normal saturation behaviour. This suggests that the enzyme promiscuity and the resulting metabolic competition cause the flux decline at high substrate concentrations /[NADH] ratio in the mitochondrial matrix and the equilibrium constant of M/SCHAD (shown by the pink bars) and CROT (shown by the light blue bar) were in the top 4 of highest response coefficients (lCoACYT) , and theficients .+ is a substrate of M/SCHAD, it seems likely that the high flux response coefficients of [NAD+]/[NADH] and the equilibrium constant are mutually related. We therefore set out to elucidate the mechanism via which they affected the flux. The role of NAD+ is complex, since it is also a substrate for the MTP reaction and both M/SCHAD and MTP catalyse reactions of substrates with a range of fatty-acid chain lengths. Therefore, we dissected the response coefficient of [NAD+]/[NADH] into contributions of all the different reactions that are influenced by this ratio, based on the combined response theorem +]/[NADH] on the flux can be attributed exclusively to the short-chain reactions (C4 and C6) of M/SCHAD, while MTP even exerted a small negative contribution. The M/SCHAD reaction is close to thermodynamic equilibrium when the pathway is at steady state , but hardly any parameters related to the C6 reaction, except for the affinity toward its product C4-acyl-CoA , since the latter have lower response coefficients. This may be attributed to differences in the catalytic constants between the C4 and C6 reactions, notably (i) the C4-specific MCKAT catalytic capacity  being 2-fold lower than its C6-specific counterpart (sfMCKATC6) (0.49 versus 1) and (ii) the almost 2-fold lower affinity of MCKAT for its C4-ketoacyl-CoA substrate than for its C6-ketoacyl-CoA substrate, quantified by a 2-fold higher corresponding Michaelis-Menten constant (Km) (12.4 versus 6.7 μM).Finally, in To understand the sequence of events leading to flux decline and the potential role of competitive inhibition therein, we inspected time courses of metabolite concentrations and enzyme rates. Starting from a steady state at 0.1 μM, the concentration of the substrate palmitoyl-CoA was increased at time point zero to 25 or 60 μM and the response of the pathway was followed until the system relaxed into a new steady state. At both substrate concentrations metabolites accumulated or declined gradually, with hardly any detectable over- or undershoots  the high flux control of MCKAT and product inhibition of this enzyme by a competing set of acyl-CoA esters, leading to ketoacyl-CoA accumulation; (ii) the amplification of CoA-ester accumulation by M/SCHAD and the role of the [NAD+]/[NADH] ratio therein; and (iii) the resulting depletion of the free CoA concentration which further limits the MCKAT activity. We will structure our discussion around these three elements and their connection to the surrounding metabolic network. This will give insight into how perturbations in the surrounding metabolic network may stabilize or destabilize the pathway and thereby attenuate or aggravate the risk of metabolic failure.In this section we will use our new insights to better understand how the pathway structure may play a role in disease, particularly regarding the lack of robustness of glucose and ketone-body homeostasis in patients with inherited mFAO defects. Products of the mFAO are required as energy source for MCKAT–The elucidated vicious cycle started at MCKAT. At low palmitoyl-CoA substrate concentrations the first enzyme of the pathway, CPT1 exerted a high flux control, in agreement with measurements in rat liver /[NADH] ratio connects the mFAO pathway to the respiratory chain and to other NADH producing pathways. We showed that the [NAD+]/[NADH] ratio affects the accumulation of CoA esters via the close-to-equilibrium reaction M/SCHAD. It is plausible, therefore, that individual differences in the activity of the respiratory chain modulate the risk of substrate overload.A link to CoA metabolism–The depletion of free CoA closed the vicious cycle. The relatively low capacity of MCKAT, accumulation of its ketoacyl-CoA substrates, and amplification of CoA-ester accumulation by the low equilibrium constant of M/SCHAD led to a sequestration of CoA in these esters. This limited MCKAT further, leading to even more sequestration of CoA. Yet, we never observed a complete depletion of CoA in the model. In fact, complete depletion would be incompatible with mFAO flux, since both MCKAT and CPT2 require CoA as a substrate. This crucial role of CoA suggests that CoA-releasing reactions may protect the mFAO pathway. Examples of CoA releasing processes are the acyl-CoA hydrolysis by thioesterases CYT in the published model, the model without promiscuity and the 9 other models with each one enzyme without promiscuity. (B) Juptake versus [palmitoyl-CoA]CYT in the published model and the model without promiscuity at MCKAT for a concentration range from 0 till 500 μM.(TIF)Click here for additional data file.S2 Fig(A) Flux control coefficients of all enzymes in the model on Juptake. (B) Flux control of chain-length specific MCKAT-catalysed reactions on Juptake. The sum of these chain-length specific control coefficients equals the overall (TIF)Click here for additional data file.S3 Fig(A), enoyl-CoA (B), hydroxyacyl-CoA (C) and ketoacyl-CoA esters (D) as a function of [palmitoyl-CoA]CYT, specified per chain length.Steady-state concentrations of mitochondrial acyl-CoA (TIF)Click here for additional data file.S4 FigCYT from 0.1 to 25 μM (A) or 60 μM (B).Time course of mitochondrial concentrations of acyl-CoA, enoyl-CoA, hydroxyacyl-CoA and ketoacyl-CoA after an upshift of [palmitoyl-CoA](TIF)Click here for additional data file.S5 Fig(TIF)Click here for additional data file.S6 Fig(TIF)Click here for additional data file.S7 Fig(TIF)Click here for additional data file.S8 FigCYT.Fraction of the active site occupied by each of the indicated metabolites, calculated for MCKAT (A), MCAD (B), and SCAD (C) as a function of [palmitoyl-CoA] The occupancy (Occ) of an enzyme (E) by a metabolite Xj was calculated by:k denotes a metabolite that the enzyme can also bind in the same pocket as Xj.(TIF)Click here for additional data file.S9 Fig(TIF)Click here for additional data file.S1 Appendix(NB)Click here for additional data file.S2 Appendix(NB)Click here for additional data file.


Brassica, one of the economically important crops of India, is seriously affected by the disease, Alternaria blight. The disease is mainly caused by two major necrotrophic fungi, Alternaria brassicae and Alternaria brassicicola which are responsible for significant yield losses. Till date, no resistant source is available against Alternaria blight, hence plant breeding methods can not be used to develop disease resistant varieties. Jasmonate mediated signalling pathway, which is known to play crucial role during defense response against necrotrophs, could be strengthened in Brassica plants to combat the disease. Since scanty information is available in Brassica-Alternaria pathosystems at molecular level therefore, in the present study efforts have been made to model jasmonic acid pathway in Arabidopsis thaliana to simulate the dynamic behaviour of molecular species in the model. Besides, the developed model was also analyzed topologically for investigation of the hubs node. COI1 is identified as one of the promising candidate genes in response to Alternaria and other linked components of plant defense mechanisms against the pathogens. The findings from present study are therefore informative for understanding the molecular basis of pathophysiology and rational management of Alternaria blight for securing food and nutritional security.The productivity of Oilseed  Brassica species include major group of oilseed crops being grown in 53 countries across the six continents in world, with India being the second largest grower after China2. Despite that, India has to import large amount of vegetable oils to meet the annual edible oil needs. In future, the demand for oilseeds production is likely to go up significantly due to increase in population4. One of the ways to increase productivity of mustard crops is to avoid the losses caused by various biotic and abiotic stresses6. Fungi and Oomycete are the main pathogens causing major yield losses in oil seed crops; more than thirty diseases are incurred in Brassica crops in India7. Alternaria blight, white rust, downy mildew and powdery mildew hold major importance on the basis of their wide distribution and yield losses9. Of these, Alternaria blight disease caused by Alternaria brassicae and Alternaria brassicicola is responsible for significant yield losses of the Brassica species all over the world. Depending upon severity, the yield losses have been reported to range from 35% to 46% in India and up to 70% all over the world with no proven source of transferable resistance in any of the hosts12. Disease management strategies utilizing fungicidal chemicals are practically insufficient in addition to being environmentally hazardous. Rapid evolution of the new pathogenic strains has further complicated breeding for resistance in crop plants. Alternaria brassicae is a necrotrophic pathogen which produces lesions on leaves, stem and siliquae affecting quantity as well as quality of seed by reducing oil content, size and colour13.via Mitogen Activated Protein Kinases (MAPKs)17. In addition, immunity can be induced in tissues remote from the sites of infection by systemic acquired resistance, initiated after gene-for-gene recognition between plant resistance proteins and pathogen effectors14. Jasmonates and its functional analogs are known to play key roles for systemic defense, possibly acting as the initiating signal for systemic acquired resistance14. Jasmonic acid accumulates rapidly in phloem exudates of the leaves treated with an avirulent strain of Pseudomonas syringae in Arabidopsis thaliana, the transcripts associated with biosynthesis of jasmonate are up-regulated within 4 h, and increases transiently. The systemic defense can be mimicked by accumulation of JA during interaction of plant systems with unknown molecules; such activity was not shown in JA mutant plants.Plants respond locally to biotic stresses using inducible basal defense networks triggered through recognition and response to conserved pathogen-associated molecular patterns (PAMP) 18. JA synthesis is initiated in the chloroplasts, while most MAPKs are cytosolic; thus, MAPKs are likely to transmit the extracellular elicitor signal for biosynthesis of JA in plants19. The biosynthesis of jasmonic acid takes place in three subcellular compartments: it is initiated in the chloroplast, followed by the peroxisome, and finally the cytoplasm. It starts with the 18C fatty acid α-linolenic acid 18:3 (α-LA). The first step involves release of the α-LA from galacto- and phospholipids situated at the chloroplast membrane by the enzymatic action of phospholipases (PLAs), which contain DEFECTIVE IN ANTHER DEHISCENCE 1 (DAD1) in Arabidopsis thaliana20. After that, the polyunsaturated fatty acids α-LA is oxidized by 13-LIPOXYGENASE (LOX) leading to the formation of 13-hydroperoxy-9,11,15-octadecatrienoicacid (13-HPOT)23. Subsequently, 13-HPOT is converted into the stable cis(+)-oxophytodienoic acid (cis-OPDA) intermediate by two different enzyme families called as ALLENE OXYDE SYNTHASE (AOS) and ALLENE OXIDE CYCLASE (AOC)27. After that the cis(+)-oxophytodienoic acid (cis-OPDA) is transported from chloroplast to peroxisome. However, how cis-OPDA changes its subcellular compartment is largely unidentified. To date, only one gene, a peroxisome-localized protein of the ATP binding cassette (ABC) transporter class, termed as COMATOSE has been linked with subcellular transport of JA29. On the other hand, as loss of function mutants  can still produce some JA, it suggests that most probably other transporters are also involved30. The cis-OPDA is reduced by an enzyme called OPDA REDUCTASE (OPR) and subsequently undergoes three rounds of β-oxidation by the action of ACYL-CoA OXIDASE (ACX) leading to the manufacture of jasmonic acid (JA)32. JA is then transported from peroxisome to cytoplasm via unknown route where it can be transformed by several enzymes33. The best example is GRETCHEN HAGEN 3s (GH3s), which conjugates JA with various amino acids mostly isoleucine, leading to the production of bioactive JA-Ile molecule37, which activate the expression of JA responsive genes.Jasmonates play tremendous role in protecting plants from pathogen attack by receiving signals through MAPKsArabidopsis thaliana at molecular level, which may be utilized for deciphering the complexity of jasmonic acid signalling triggered during pathogenesis of Alternaria species of Brassica that enables us to identify possible molecular targets. These targets will further be exploited to develop strategies for induction of de novo defense in crop plants during pathogenesis38. It is a demand of time to harness the potential of systems biology for decoding the resistance machinery in Brassica through modeling of jasmonate signalling pathway in Arabidopsis thaliana for sustainable agriculture.Recent advances in omics science and technology have produced wealth of information about plant-pathogen interactions in the model plant Alternaria blight. The integrated pathways help to reduce the effects of external perturbation to whole plant systems due to its robust properties39. Each species in the pathway is considered as node and linked to other nodes at defined rate kinetics40. The aim of metabolic modelling of Jasmonate signaling pathway is to provide a hypothesis based on computational simulation at various time extents and network analysis of Arabidopsis-Alternaria interaction for identification of key regulatory element present in the pathway15. The integrated pathways will prove useful in understanding the pathophysiology of Alternaria blight disease in Brassica5. It is important to determine the key properties and parameters which are used to predict the dynamics of model40. Compatibility of quantity and type of data utilized in the model is essential to meet the model parameters41. It is wonderfully established that the functional molecules of the living systems such as DNA, RNAs, receptor, enzymes, hormones, metabolites etc., are necessary for the integration of network to modulate behavior of the metabolic pathways42. Various In silico comprehensive maps48 have been constructed for dynamic analysis, pathophysiology and control mechanism as well as identification of biomarker for disease management. In the present study attempts were made to develop jasmonate mediated pathway model for functional characterization of components involved in induction of defense against Alternaria blight in Brassica.In view of above fact, a model of JA signalling pathway with respect to plant-pathogen interaction has been developed to predict the pathophysiology of 49. The goal of this study is to decipher the complexity of Arabidopsis-Alternaria pathosystem for understanding the pathophysiology of Alternaria blight in Brassica through quantitative and qualitative analysis of jasmonate biosynthesis pathway in terms of upstream signal and downstream response produced during pathogenesis process. These molecular mechanisms were modelled based on system biology approach to determine the duration of JA responses as well as try to identify key components involved in regulation of pathway during pathogenesis for achieving resistance against the disease. SBGN was utilized to assemble the relationship among different molecular species in the model based upon previous studies mined from scientific literatures and databases  has not been constructedses Fig. . The modses Fig. .Figure 1(Table ). This could be useful for understanding the pathophysiology of Alternaria blight in Brassica for developing an efficient disease management strategy. The simulation jobs were run on hardware configuration comprises of intel corei3 processor of 2.40 GHz and 4GB RAM on 64 bit windows operating system laptop.Present study explains the outcome of integrated systems based approach that explored the intricate nature of jasmonate signalling pathway during pathogenesis at molecular level. The rate laws produced by SBMLsqueezer were utilized to predict the dynamic behavior of the pathway Table . This cAlternaria blight in Arabidopsis thaliana.The real values for each molecular species in the model have not been used due to un-availability of experimental data for an individual cell. Simulation can decipher the behaviour of key molecular species in the presence and absence of pathogen elicitor for a particular disease. Defense responses through jasmonate signalling were predicted during pathogenesis of 40. The values for each molecular species in the model ranged from 0.25 to 2.5. The value of pathogen is set at 0.25; receptor is set to equal of 0.5 due to its basal amount. The amounts of species are set at 0.5, 1 and 2, according to their activation and inactivation states, for example non-phosphorylated MAPKs are set at 0.5 and phosphorylated are set at 1; all the metabolites are set at 0.5 and all the enzymes involved in biosynthetic pathway are set at 1. The value of JA, MeJA and JA-IIe are considered as variable species and its values range from 0.5 to 1 to observe the dynamic behaviour of system. The value of COI1 is set in the range from 0.0 to 2 and that of SCF-COI1 complex is equal to 1.5; the amount of transcription factors is set to 1 and proteins is set to 2 as well as the amount of phenotypic response is set equal to 2.5.In the absence of quantitative molecular data of an individual cell, it is difficult to estimate the relationship between species. Therefore, quantity is occupied in terms of amount to confirm their existence15. Here, the dynamic behaviour of the different species in the model was visualized during simulation with course of time. The variation in defense response with respect to COI1 is revealed in Fig. Alternaria species and its effect on expression level of COI1 was shown in Fig. Previous studies clearly demonstrated that when the intensity of abiotic and biotic stresses increases, the defense responses decrease rapidly in time dependent mannerArabidopsis thaliana through interaction with jasmonate signal and its expression in balanced way is essential to crop plants for defending pest and pathogen attack6. Present study decoded the fluctuation of COI1 at different amount with respect to time that can useful to maintaining its expression level in the balanced way during pathogenesis in future. Jasmonic acid and its structural analogs are known natural substrates that initiates the activity of COI1; Me-JA is one of the functional analogs of jasmonic acid which is known to promote COI1 interaction with JAZ1, which is responsible for defense responses, thus, we perturbed the model with different amount of Me-JA to predict the dynamic behaviour of COI1 , mechanical wounding and heat stress could significantly induce its expression level62. Network analysis has been publicized to be one of the powerful tools for decoding the key regulatory elements (hubs), which is responsible for controlling the complex biological machinery during diseases63. Hub is a concept originated from the field of network science that refers to a node having number of links. Identification of hub nodes in complex networks has attracted a growing attention over the last decade65. In biology, a hub is metabolite, gene, or protein which is highly connected to the other nodes  in a network and that is known to regulates several other pathways associated with the main pathway. Recent advances in plant disease resistance research have provided exciting novel insights into decoding of defense signaling network. Diverse number of components plays essential roles in the regulation of whole network through cross-communication in antagonistic or synergistic manners. Biological properties of hubs in JA signalling pathway are very significant as these are closely related to the well characterized components linked to the defense responses. Our finding demonstrated the highly connected nodes (hubs) such as JAZ1, JAZ3 and MYC2 but it was predicted that the expression of these hubs is managed by COI1. These hub nodes are involved in the regulation of defense machinery through interaction of COI1 with jasmonate signal. For that reason, COI1 is considered as one of the key components of JA pathway. Previous study suggested that COI1 has the structural traits for binding to jasmonyl-isoleucine (JA-IIe/Coronatine)66 and determined as an intracellular signal with a hormone binding site67. Therefore, it may serve as a potential molecular target for triggering de novo resistance in crop plant systems during plant-pathogen interaction via engineering of jasmonic acid pathway for sustainable production of jasmonic acid and its functional analogs during pathogenesis or providing exogenous compounds as defense induces that can mimick the expression of COI1.The aim of modeling and simulation is to describe and understand the pathophysiology at molecular level where key regulatory element and its complex mechanisms that happen at several time scales were predicted. During simulation analysis at variable amount of jasmonic acid and its functional analogs, Me-JA; COI1 is predicted as one of the potential molecular target because it controls the expression of various defense related genes during pathogenesis. It was also predicted that the up and down regulation of COI1 during plant-pathogen interaction is totally linked to amount of jasmonic acid and its functional analogs. Besides, we have manually increased and decreased the amount of COI1 to determine the behaviour of system during pathogenesisde novo resistance in crop plants for robust agricultural productivity and sustainability6. Biologically active small molecules have been verified previously for crop plant protection by producing de novo resistance69. In our knowledge, we have modeled JA signalling pathway first time to analyze the whole systems during plant-pathogen interaction. This integrated approach shows that computational demonstration can construct and explain precisely the complete observable fact of JA signalling during pathogenesis at molecular level. The constructed technical model describes the biochemical relationship of different molecular species and effect of pathogen in different unit time. These types of In silico finding provides a valuable hypothesis and mimics by the simulation analysis as in vitro test. The study exhibits that cellular reaction depends on the effective quantity of jasmonic acid and its functional analogs inside the cell70. As a result, the understanding of the integrated behaviour of model can be valuable to develop novel plant health and disease management strategies to fulfilling the demand of Brassica oilseed for rapidly growing world population.It is evident that small molecule targeting COI1 leads to the production of appropriate defense whereas engineering of jasmonic acid pathway for proper production of jasmonic acid during diseases has a very complex and time taking process, therefore, we have concluded that there is a need of novel defense inducer molecule more efficient and stable as compared to jasmonic acid and its structural analogs to tackle today’s problem of pathogen resistance by developing de novo resistance during pathogenesis. During analysis, COI1 was found to control the expression of many defense related component in the model. Besides, other identified hubs node may also utilized for further investigation with respect to plant-pathogen interaction. A number of undiscovered effects of JA with their perturbed amount to its respective target can be predicted. This finding can be useful for experimental design and interpretation by using diseased and healthy plants. Nevertheless, it seems to be essential to examine more conditions that imitate realistic stress situation in an attempt to find out which methods need to be developed to cope with their usual environment. In term of agricultural significance, it is assumed that the availability of a constructed model further speed up the research for crop protection. Such type of models can be utilize in identification of molecular target and its dynamics at cellular and system level for management of Alternaria blight: a recalcitrant disease of Brassica through designing of defense inducer molecules that can able to trigger defense related pathway or application of genetic engineering approaches for sustainable agriculture.COI1 was found as the most important molecular target. It can be utilizing to mimick jasmonic acid mediated immunity through engineering of the plant system or designing of the cost effective molecule, more efficient than naturally produced jasmonic acid for triggering Arabidospis thaliana. A Systems Biology Graphical Notation (SBGN) for building of biological networks to express adequate information in a clearly visible and precise way was used49  representation of all components of network. SBMLsqueezer judges the Systems Biology Ontology (SBO) annotations to pull out this information77. The rate laws that can be produced by SBMLsqueezer consist of numerous types of generalized mass action, and comprehensive as well as generalized enzyme kinetics (Table ). Kinetic rate equations for metabolic reactions include uni–uni Michaelis–Menten kinetics, generalized hill equation for uni–uni reactions, Irreversible non-modulated non-interacting reactant enzymes, bi-uni enzyme reactions, bi–bi enzyme reactions, thermodynamics and convenience kinetics modular rate laws for enzymatic reactions78.SBMLsqueezer_v2.1 was applied to generate kinetic rate equations for each reaction of the constructed model through CellDesigner. This approach assists the modeling steps 79, which enables us to run ordinary differential equations (ODE) based simulations. This is a frequently used method for quantitative analysis of biological systems in term of computational efficiency. SOSlib is a programming library for symbolic and numerical analysis of biochemical reaction network models encoded in the SBML79. CellDesigner is an integrated modeling and simulation platform that provides several third-party tools for interactive model simulation that is SOSlib79, COPASI80 and Simulation Core Library81. We have employed deterministic algorithm for our constructed model. In studying model of biological systems, we need solution for a given set of parameter values with their assessment for dependency of other species in the model. The simulation engine itself was executed by the native library, and the results of simulation analysis were shown in a Graphical User Interface (GUI) window written in JAVA programming language82.To simulate the dynamic behavior of constructed model, SBML ODE Solver Library (SOSlib) was used through CellDesigner83. BiNoM is a Cytoscape plug in84 to support the operation on biological networks represented in standard systems biology formats  and to carry out studies on the complex network Structure85. Cytoscape is an open source software platform for integration, analysis and visualization of biological network86. Different types of plugins are available for various kinds of analysis, which facilitate us for decoding the complexity of biological networks. NetworkAnalyzer plugin87 was applied for investigating and visualizing the important components of JA signaling pathway triggered during plant-pathogen interaction for devising the strategies to develop effective management system against Alternaria blight of Brassica88.Jasmonate signaling model generated in CellDesigner was exported in SBML format, which was imported in the Cytoscape 2.8.3 using Biological Network Manager (BiNoM)


Escherichia coli can arise via changing nutrient conditions. Second, using phylogenetic approaches, we demonstrate that the acquisition patterns of complex metabolic pathways during the evolutionary history of bacterial genomes support the hypothesis. Third, we show how adaptation of laboratory populations of E. coli to one carbon source facilitates the later adaptation to another carbon source. Our work demonstrates how complex innovations can evolve through series of adaptive steps without the need to invoke non-adaptive processes.A central challenge in evolutionary biology concerns the mechanisms by which complex metabolic innovations requiring multiple mutations arise. Here, we propose that metabolic innovations accessible through the addition of a single reaction serve as stepping stones towards the later establishment of complex metabolic features in another environment. We demonstrate the feasibility of this hypothesis through three complementary analyses. First, using genome-scale metabolic modelling, we show that complex metabolic innovations in  A fundamental question in evolutionary biology is how complex innovations requiring multiple genetic changes arise. Here the authors provide lines of evidence that changing environments facilitate the adaptive evolution of complex metabolic innovations via stepwise acquisition of single reactions. Evolutionary novelties frequently depend on the fixation of multiple, highly specific mutations, where intermediate stages of evolution seemingly provide little or no benefit236Previous population genetic modelsIn this work, we focus on bacterial metabolic networks to examine how novel nutrient utilization phenotypes can be acquired via the addition of new reactions to an organism's enzyme repertoire. While not all complexity at the level of molecular systems are expected to provide a functional advantage1416de novo laboratory evolution of nutrient utilizations in Escherichia coli (E. coli) provide clear support for the hypothesis.Specifically, we employ detailed simulations on a pan-genome scale to demonstrate that complex metabolic innovations can evolve via the successive acquisition of single biochemical reactions that each confers a benefit to utilize specific nutrients. Thus, temporal changes in nutrient availability or complex environments (where multiple nutrients are available) can facilitate adaptive evolution of metabolic pathways through the step-by-step expansion of metabolic niches. Gene acquisition patterns across bacterial genomes and E. coli K-12, arguably the best studied and most reliable reconstruction of a genome-scale metabolic system, composed of 2,077 unique reactions, including transport processesE. coli network. We compiled a data set of 2,566 known enzymatic and 159 transport reactions across the three kingdoms of life  absent from the E. coli modelE. coli network in each of these environments using flux balance analysis (FBA)E. coli across nutrient conditionsIn this work, we systematically studied the expansion of metabolic networks. We specifically asked whether metabolic innovations can evolve in a purely Darwinian manner through series of adaptive steps. Our starting point was the previously reconstructed metabolic network of E. coli metabolic network was unable to grow  in 321 environments in which the network expanded by the complete universal reaction set was able to grow  environment. To this end, for each environment, we enumerated all possible minimal reaction sets that can support growth when added to the E. coli through horizontal gene transfer confer condition-specific advantages and contribute to growth only in specific environmentsin silico tend to be gained on an earlier branch of the phylogenetic tree than their partner enzyme . We note that this pattern holds for different parameter values of the gene-gain reconstruction procedure  whether the mutations conferring these two distinct growth phenotypes exhibit epistasis on EG, as predicted by the hypothesis.New metabolic pathways can evolve not only through the acquisition of full-blown enzymes from other organisms but also through the enhancement of weak side activities of existing enzymes311 cells with wild-type mutation rate  or propylene glycol (PG+) from large populations of bacteria . No EG+ ion rate , demonstre found . As discound  and absent from E. coli. First, we mapped the metabolites of the manually curated E. coli genome-scale metabolic modelhttp://blog.theseed.org/model_seed/), a comprehensive resource for automatically generated genome-scale metabolic network reconstructions. Because Model SEED does not contain the most recent version  model  of the 260 ref. ) that isents see . FinallyL) model .For more details on the reconstruction of the universal reaction set, see E. coli. For each environment, we replaced the carbon (C), nitrogen (N), phosphate (P) or sulfur (S) source by an alternative one. To obtain a list of environments that is both representative of novel nutrient compounds and computationally tractable, we focused on only those growth media that differed from glucose minimal medium by one compound instead of enumerating all possible combinations of C, N, P and S-sources, as in previous works24We first defined a comprehensive set of nutrient environments by starting from a glucose minimal medium for E. coli network and the universal network across these conditions using FBAE. coli metabolic network was unable to grow in 321 environments in which the network expanded by the universal reaction set allowed growth . The lower bound of the biomass production reaction was then constrained to 10−4 as the minimal growth requisite:Next, for each environment in which the universal network was viable but the wild-type N′ be the number of non-coli reactions. We assigned a binary variable to each non-coli reaction, bi, which tells whether the non-coli reaction r′i is active (bi=1) or not (bi=0). The following equations ensure these rules:The reversible non-coli reactions of the universal network were decomposed into two opposing irreversible reactions. This way the fluxes of the non-coli reactions can only take positive values. Let v′i is the flux and u′i is the maximal possible flux of reaction r′i, while ɛ is the minimal flux value . Also to avoid having two opposing reactions derived from the same reversible reaction being active simultaneously we introduced the following constraint:Where Finally, the objective of the MILP problem was to minimize the active non-coli reactions:The result of this minimization is the minimum number of non-coli reactions need to be added to the coli model to allow growth in a particular environment.The MILP optimization model described above not only provide the minimal number of reactions that support growth in new environments but also the list of the non-coli reactions involved in this solution: one of the minimal reaction sets. However, multiple equivalent minimal sets might exist for any given environment. To identify another minimal reaction set we extended the MILP problem with a new constraint which prevents the algorithm to find the same solution again:Bi is the binary solution of the first minimal reaction set, and Bi equals to 1 or 0 if reaction r′i was active or inactive in the first solution, respectively. This constraint is fulfilled only if the two solutions differ in at least one active reaction. We can harvest more minimal reaction sets in an iterative way where after each solution we add a new constraint and we run the algorithm again. Our algorithm stopped when the new solution had more active reactions than the size of the minimal reaction sets, that is, when we collected all minimal reaction sets. This algorithm is based on the method of finding the k-shortest elementary flux modesWhere E. coli model cannot grow. By repeating this procedure we probed all possible reaction pairs in the universal reaction set and identified those that provide growth in at least one environmental condition . As a next step, we determined if the identified two-reaction adaptations can be accessed by the consecutive addition of single beneficial reactions to the network, that is, whether at least one of the two reactions provide a fitness benefit on its own in any of the environments. For this purpose, we repeated the above procedure but instead of activating reaction pairs we activated single reactions and evaluated their fitness effect across environments using FBA. The list of 538 reaction pairs and corresponding environments can be found in To systematically test the dynamic environment model, we investigated all possible two-step adaptation scenarios. First, we inactivated all non-coli reactions in the universal reaction network. Next, we activated two non-coli reactions at a time and applied FBA to calculate the fitness of the model in each environment where the native All simulations were implemented in GNU R ref.  using thTo investigate contingent gain and co-gain in the evolutionary history of genes, we first generated the phylogenetic presence and absence profile across the present-day species for each reaction by mapping the profiles from gene to reaction level. Presence and absence profiles of orthologous genes across 943 bacterial species were obtained from EggNOG v3.0 ref. . Reactiohttp://string905.embl.de/newstring_download/species.tree.v9.05.txt)  ref. . Reactioref. 1949 and a deref. 1949.f), defined as f=p1/(p1+p2), where p1 is calculated by dividing the number of evolutionary events where B is gained in the descendent (d) when A is already present in the ancestor (a) (a10_d11) by the total number of all possible gain and loss scenarios taking place in the descendant when A is present but B is absent in the ancestor , and p2 is calculated by dividing the number of evolutionary events where B is gained in the descendant when A is absent in the ancestor (a00_d01) by the total number of all possible gain and loss scenarios taking place in the descendant when both A and B are absent in the ancestor . The observed distribution of fractions was then compared with the null-hypothesis that a gain of B is independent of the presence of A, that is, f=0.5, using a one-tailed one-sample Wilcoxon signed-rank test.For each stepping-stone reaction pair A–B, A is defined as the reaction that is beneficial in a given nutrient environment without B, while a gain of B is only beneficial in another environment when A is already present. For each A–B pair we calculated the phylogenetic contingent gain fraction (f=n1/(n1+n2), where n1 is the number of evolutionary events where both reactions were absent in the ancestor (a) and both were gained in the descendent (d) (a00_d11), and n2 is the number of evolutionary events where both reactions were absent in the ancestor and only one was gained in the descendent (a00_d10 or a00_d01). We compared the fractions (f) from reaction pairs that are predicted to be beneficial for growth only when they are simultaneously gained, referred to as ‘beneficial without individual effect', with the fractions from reaction pairs that are beneficial for growth in a specific environment when co-gained, but at least one of the reactions is also beneficial on its own in a different environment  /1,001. The randomization analysis was also carried out for reaction pairs that are beneficial for growth in a specific environment when co-gained, but at least one of the reactions is also beneficial on its own in a different environment ).For the phylogenetic co-gain analysis we calculated for reaction pairs the co-gain fraction, defined as E. coli K-12 MG1655 was considered as the wild-type strain in our experiments. MG1655 mutD5 was constructed using a suicide plasmid-based genome engineering method incorporating a C->T mutation at position 236,110 on the genome (within the dnaQ gene) resulting in a T15I mutation of the encoded enzyme described previouslyfucO gene was selected from the ASKA libraryFor the overexpression of FucO, the pCA24N plasmid containing the fucO and aldA, the chloramphenicol resistance cassette (CmR) of the pCA24N-aldA plasmid from the ASKA library was exchanged to the kanamycin resistance marker (KmR), resulting in pCA24N-aldA-Km. The pCA24N-aldA plasmid was first linearized by inverse PCR amplification using the pCA24N_frame_1 and pCA24N_frame_2 primer pair flanking the CmR cassette. The PCR product was treated with DpnI for 60 min at 37 °C and purified using the DNA Clean & Concentrator-5 Kit (Zymo Research #D4004). The KmR marker was PCR amplified from a pSTKST template using the ASKA-Gibson_Kan_Fw and ASKA-Gibson_Kan_Rev primers. The PCR fragment was then isolated from 1% agarose gel using the GeneJET Gel Extraction Kit (Thermo Scientific #K0691). The resulting DNA fragments were assembled using Gibson assembly cloning , according to the manufacturer's protocol, then electroporated into electrocompetent E. coli DH10B cells. Correct assemblies were verified by colony PCR using the ASKA-S2 and aldA-1 primer pair. Sequences of primers used in this construction are listed in For the simultaneous overexpression of −1 ampicillin (Ap), 25 μg ml−1 chloramphenicol (Cm) and 25 μg ml−1 kanamycin (Km).Minimal salts (MS) medium was used as described previouslyfucO overexpression plasmid was present) at 30 °C. An MG1655 strain carrying the pCA24N-fucO plasmid was previously found to grow at 30 °C in 2 ml MS media supplemented with 30 mM PG (with 25 μg ml−1 Cm and 50 μM IPTG added). This culture was subsequently plated onto MS+0.4% glycerol (+Cm) agar plates, from which the PG+colonies, starters for selection for EG-utilization, were isolated. We opted for glycerol as a base carbon source to avoid catabolite repression  as in ref. fucO overexpression plasmid was present). Plates were then incubated at 30 °C for 40 days after which adapted colonies were counted and isolated. The plates were placed in plastic bags for the duration of the incubation to prevent significant drying of the agar media. Rates of adaptive mutations were calculated based on three replicate experiments as follows. When adapted colonies were observed, we simply calculated the average ratio of the number of adapted colonies per total cell number. In cases where no growing colonies were obtained, we calculated an upper limit to the adaptive mutation rate following the approach presented in ref. Three replicates of each individual strain were started from single colonies grown on MS+0.4% glycerol agar plates , and placed in a PowerWave XS2 (BioTek) microplate spectrophotometer and grown at 30 °C. The edges of the plate were sealed with Breathe-Easy gas permeable sealing membrane (Diversified Biotech) to prevent evaporation.Individual colonies of strains MG1655, MG1655+pCA24N-rpoB (ref. −1 chloramphenicol in the case of pCA24N_fucO carrying samples) were inoculated with approximately 104 cells each. FucO overexpression was induced by adding 50 μM IPTG after 2 h of growth, and cultures were grown to early stationary phase, all at 37 °C. Appropriate dilutions were spread onto non-selective LB agar plates and LB agar plates containing rifampicin (100 μg ml−1). The samples were incubated at 37 °C and colony counts were performed after 24 or 48 h, respectively. Mutation rates were calculated with the Ma–Sandri–Sarkar maximum-likelihood methodWe estimated mutation frequencies of BW25113 (wild-type) and BW25113 overexpressing the FucO protein from the pCA24N_fucO plasmid. Briefly, cells resistant to rifampicin (carrying mutations in poB ref. ) were seFragment libraries were constructed from purified genomic DNA using NEBNext Fast DNA Fragmentation & Library Prep Set for Ion Torrent (New England Biolabs) according to manufacturer's instructions. Briefly, genomic DNA was enzymatically digested and the fragments were end-repaired. Ion Xpress Barcode Adaptors (Life Technologies) were than ligated and the template fragments size-selected using AmPure beads (Agencourt). Adaptor ligated fragments were then PCR amplified, cleaned-up using AmPure beads, quality checked on D1000 ScreenTape and Reagents using TapeStation instrument (Agilent) and finally quantitated using Ion Library TaqMan Quantitation Kit (Life Technologies). The library templates were prepared for sequencing using the Life Technologies Ion OneTouch protocols and reagents. Briefly, library fragments were clonally amplified onto Ion Sphere Particles (ISPs) through emulsion PCR and then enriched for template-positive ISPs. More specifically, PGM emulsion PCR reactions utilized the Ion OneTouch 200 Template Kit (Life Technologies), and as specified in the accompanying protocol, emulsions and amplification were generated using the Ion OneTouch System (Life Technologies). Enrichment was completed by selectively binding the ISPs containing amplified library fragments to streptavidin-coated magnetic beads, removing empty ISPs through washing steps, and denaturing the library strands to allow for collection of the template-positive ISPs. For all reactions, these steps were accomplished using the Life Technologies ES module of the Ion OneTouch System. Template-positive ISPs were deposited onto the Ion 318 chips (Life Technologies); finally, sequencing was performed with the Ion PGM Sequencing Kit (Life Technologies).E. coli K12 MG1655 genome sequence (U00096.3). Torrent Variant caller (tvc) module of Torrent Suite was subsequently applied to detect single nucleotide mutations as well as small in/del variants. Variant caller was programmed to run in high stringency mode requesting at least 12 × read coverage and at least 66% mutation frequency. Only those variants were taken into account that were supported by sequencing on both strands. BAM alignment files were imported in CLC Genomics Workbench v7.5.1 (CLCBio) and variant regions were manually inspected in all strains. Large genomic rearrangements (deletions or amplifications with lengths above 10 kb) were manually identified using CLC Genomics Workbench Tool.The PGM sequencing data was processed using Ion Torrent Suite v4.2.1 in order to perform signal processing and base calling. Read mapper module of Torrent Suite (tmap) was used to align raw reads to the SRX1167076 and SRX1167031).Sequencing data of the ancestral and evolved strains are deposited in the NCBI SRA database .Supplementary figures 1-3, supplementary tables 1-7, supplementary methods, supplementary referenceslists the set of 1776 environmental conditions defined in the present study and the in silico biomass flux for both the E. coli and the universal reaction networks.lists the reactions which enable in silico growth in a condition where the E. coli metabolic network cannot growlists all reaction pairs which can confer a novel nutrient utilization phenotype when added to the E. coli metabolic networkthe universal metabolic network in SBML format