Here we present a comprehensive metabolic framework of methane and methanol utilization in M. alcaliphilum 20ZR. A set of novel metabolic reactions governing carbon distribution across central pathways in methanotrophic bacteria was predicted by in-silico simulations and confirmed by global non-targeted metabolomics and enzymatic evidences. Our data highlight the importance of substitution of ATP-linked steps with PPi-dependent reactions and support the presence of a carbon shunt from acetyl-CoA to the pentose-phosphate pathway and highly branched TCA cycle. The diverged TCA reactions promote balance between anabolic reactions and redox demands. The computational framework of C1-metabolism in methanotrophic bacteria can represent an efficient tool for metabolic engineering or ecosystem modeling.Biological methane utilization, one of the main sinks of the greenhouse gas in nature, represents an attractive platform for production of fuels and value-added chemicals. Despite the progress made in our understanding of the individual parts of methane utilization, our knowledge of how the whole-cell metabolic network is organized and coordinated is limited. Attractive growth and methane-conversion rates, a complete and expert-annotated genome sequence, as well as large enzymatic,  Methane-utilizing microbes  are becoming an attractive and efficient microbial platform for organic manufacturing of renewable fuels and chemicals6. Recent application of modern metabolic engineering tools has furthered methane-based biotechnology, leading to construction of novel biocatalyst traits with great biotechnological potential9. The recent progress in this area has also highlighted the need for a deeper understanding of the methane-conversion network organization.Climate change concerns linked to increasing concentrations of anthropogenic methane have spiked interest in biological methane utilization as a novel platform for improving ecological and human well-being12. The established network of methane utilization has been revisited through the discovery of high redundancy of core functional oxidation machinery and the incredible flexibility of the redox balancing pathways20. Access to complete genome sequences of methanotrophic bacteria22 enables whole-genome metabolic reconstruction and flux-balance modeling25. Several limitations of the top-down modeling of methylotrophy have been highlighted24, including limited knowledge of redox transfer reactions, flexibility of the primary methane oxidation pathways, and lack of data on energy maintenance. Herein, we applied a set of systems-level approaches for comprehensive investigation of the methane utilization network in Methylomicrobium alcaliphilum 20ZR (a rifampicin-resistant derivative of the strain 20Z16). Complete and expert-annotated genomic information, a large set of genetic tools, wide-ranging enzymatic studies, and the availability of whole-genome transcriptomic data make 20ZR an ideal model system for understanding the central metabolic pathways associated with methane oxidation34. M. alcaliphilum 20ZR possesses only particulate methane monooxygenase (pMMO) for the first step of methane conversion. The enzyme is a mixed function oxidase, in which one atom from O2 goes to methanol and the other to water, requiring the input of 2 electrons and 2 protons. To date the source of electrons is still unknown. The second step of the methane oxidation pathway is catalyzed by a periplasmic pyrroloquinoline quinone-linked methanol dehydrogenase. Two enzymes are predicted, a Ca-dependent MxaFI system and La-dependent XoxF15. Genome annotation for the strain 20Z predicts multiple pathways for formaldehyde oxidation, including tetrahydromethanopterin- and tetrahydrofolate-linked pathways, formaldehyde dehydrogenase, and the oxidative ribulose monophosphate cycle15. The genome has two clusters encoding NAD-dependent formate dehydrogenases: 1) NAD-linked, tungsten (W)-dependent formate dehydrogenase; and 2) a partial NAD-linked, molybdenum-dependent formate dehydrogenase. Only the first system is expressed under methane-growth conditions, even if W is not supplied. Since formate is typically accumulates in the growth media33, it could be suggested, that the enzyme is only partially active. The impact of tungsten addition on growth of Methylomicrobium alcaliphilum 20ZR has never been evaluated. Genome annotation indicates that metabolic pathways for carbon assimilation are highly branched and include the assimilatory ribulose monophosphate pathway (RuMP) and the partial serine cycle16. Two variants of the RuMP have been described in 20Z16. Furthermore, it has been demonstrated that 75% of intercellular pyruvate comes from the pyrophosphate (PPi)-mediated Embden-Meyerhof-Parnas pathway (EMP); and 25% from the Entner–Doudoroff pathway (EDD)16. Furthermore, more recent evidence suggested the presence of the phosphoketolase pathway, which has been proposed to contribute to acetyl-CoA production35. The genomic data predict a complete TCA cycle15. At least three possible pathways for 2-oxoglutarate to succinyl-Co-A/succinate conversion could be identified: 1) the traditional 2-oxoglutarate dehydrogenase complex; 2) 2-oxoglutarate:ferredoxin oxidoreductase; and 3) γ-aminobutyric acid shunt36. However, the exact arrangement of the TCA in the methanotroph remains to be established. Furthermore, while the functional significance of the PPi-phosphofructokinase, a key enzyme of the EMP has been well established, the role of other PPi-dependent enzymes involved in central metabolism and anaplerotic CO2 fixation has never been evaluated. In this study, we focus on mathematical modeling of methane and methanol utilization pathways in 20ZR taking into account updated metabolic networks, optimized growth parameters (growth rate and biomass yields), methane/oxygen consumption, and as well as enzymatic and global non-targeted metabolomics data.Our knowledge of aerobic methane utilization is founded on a number of remarkable discoveries in the biochemistry, physiology, genetics, and ecology of this unique biological function in ProteobacteriaM. alcaliphilum 20ZR will depend on availability of Cu, Ca (or La) and W. The M. alcaliphilum 20ZR does not grow without copper, and the optimal growth is observed between 5–10 µM of Cu (II) (data not shown). As tungsten is not a standard component of methanotrophic growth media, a set of batch cultivation experiments with/without tungsten (W) was performed to examine its effects on growth and formate excretion. The growth rate of the culture did not change, however the final biomass yield increased by 7.5 ± 2.5%. Concentrations of C1-C4 organic acids in the growth medium were measured using NMR as described previously16. We found that formate production in cells grown in the presence of tungsten is reduced significantly, suggesting that tungsten limitation is a major factor contributing to formate excretion , 8.1 ± 2.4% nitrogen (N) and 6.8 ± 0.1 hydrogen (H). The composition of main precursors  was measured and summarized in Table 37.Overall biomass of The model consists of three compartments  and 396 internal enzymatic reactions (represented by 407 genes), 16 transport reactions between cell compartments, 422 metabolites and 20 exchange rates for nutrients and excreted compounds , anaerobic respiratory pathways, and fermentation pathways16. As the source of electrons is still unknown, we incorporated a pMMO-specific electron carrier (e-donor), which could be reduced via three pathways: 1) a ubiquinone (UQH2)/e-carrier to represent UQ-linked methane oxidation; 2) a cytochrome cL (cytCL)/e-carrier to represent direct coupling between pMMO and methanol dehydrogenase; and 3) cytcL and cytbc1/2e-carriers to represent previously proposed reverse  electron transfer24. The electron transfer reactions were connected to the production of proton/ion motive force (PMF), with the following ratios: electron transfer from NADH/H+ to ubiquinone linked to the transfer of 4 H+ (or 0.4 PMF); electron transfer from ubiquinol to cytochrome bc1 (cytBc1) and to cytochrome c (cytC) linked to the transfer of 4 H+ (or 0.4 PMF); electron transfer from cytochrome c (cytC) or from cytochrome cL (cytcL) to cytochrome oxidase linked to the transfer of 2 H+ (or 0.2 PMF). The electron transfer from ubiquinol to ubiquinol oxidases was also linked to the translocation of 4 H+ (or 0.4 PMF). ATP production via oxidative phosphorylation was set as the following: 1 ATP produced per 3 H+ translocated. The initial value of non-growth and growth-associated ATP maintenance were adapted from Methylomicrobium buryatense 5 G(B1)24.Since the energetics of methane oxidation in 20Z2 are included as the expected excretion products; excreted formate, acetate, lactate, and exopolysaccharides were incorporated as part of the biomass equation and included in the biomass flux; 2) the methane consumption rate was set at 11.7 ± 0.14 mmol g−1 DCW h−1,  based on measurements taken during continuous culture growth (at observed μ = 0.12 ± 0.01 h−1); and 3) the oxygen consumption rate was set at >11.7 and <20.6 mmol gDCW−1 h−1. The lowest parameter of oxygen consumption is based on the stoichiometry of methane oxidation, as 1 mol of oxygen is needed for oxidation of 1 mol methane. The upper limit represents the highest oxygen consumption rate observed during methane-limited growth (unpublished data). The oxygen consumption rate during exponential growth in continuous culture was 14.7 ± 0.9 mmol g−1 DCW h−1.Additional constraints were added as follows: 1) only water and CO13C-carbon-labeling analyses, and transcriptomic data16. A number of central metabolic pathway reactions, including a set of reactions for the anaplerotic carbon fixation and PPi-linked reactions29 were validated and the following modifications were made: 1) both oxaloacetate and malate were set as key entry points for CO2 fixation, via pyruvate carboxykinase  or PPi-phosphoenolpyruvate carboxykinase  and malic enzyme reactions , respectively; 2) interconversions between pyruvate and phosphoenolpyruvate were via pyruvate kinase  and pyruvate phosphate dikinase ; (3) all variants of the RuMP and the serine cycle were incorporated into the model;35 (4) all possible variants of the TCA cycle were included36.The initial metabolic model was further refined using enzymatic data  experiments using the COBRA toolbox to validate the stoichiometric completeness of the proposed metabolic network at all steps of the model development. Simulations for unconstrained network supported the direct coupling (electrons for methane oxidation comes from cytCL) as the most optimal solution for methane oxidation, however the predicted O2/CH4 consumption was low . Two possible explanations were explored further: 1) ATP maintenance in methanotrophic bacteria is higher than assumed38; and 2) ubiquinol (UQH2) serve as supplemental sources of electrons for methane oxidation. The ATP requirements in methane grown cells of M. alcaliphilum 20ZR were measured, and the contribution of UQH2 was investigated by further constraining metabolic networks to observed oxygen consumption rate.An overview of the central metabolic pathways is shown in Fig. 39. Cells of 20ZR were supplied with various concentrations of methane from 0 to 2 mmol and the growth rate and methane consumption rate were measured. We found that 20ZR cells require 3.6 ± 0.3 mmol CH4 gDCW−1 h−1 to sustain metabolic activity but not grow. Methane concentrations below this parameter led to decreases in cell density, while higher methane concentrations supported cell growth. Using the assumed ATP yield YATP, max = 6 mol ATP per one mol CH4 consumed, the non-growth-associated ATP requirement was calculated as 21.6 mmol ATP g DCW−1 hr−1.Methane-limitation experiments were carried out, in order to estimate non-growth-associated ATP maintenanceR was found by fitting the reconstructed model to the experimentally determined biomass yield. We plotted the CH4-uptake rate against the growth rate from continuous culture experiments  gradually increased, ranging from 30% at a O2:CH4 consumption ratio of 1.3 to 75% at a O2/CH4 consumption ratio of 1.4, and completely replaced direct coupling at a O2:CH4 consumption ratio of 1.5. The switch to the redox arm leads to a decrease in biomass production. As the majority of the experimental data lay in the range of 1.25–1.4, optimal biomass production could not be modeled by any single mode, whether it was UQ-mediated or direct coupling. In summary, the most realistic behavior of the system could be modeled by dual simultaneous constraints on oxygen and methane consumption.A set of additional ios Fig. . The opt16. We ran an initial unconstrained model with only a constraint on oxygen uptake . This modification resulted in significantly reduced bacterial growth (0.06 h−1) and an extremely elevated excretion of acetate (3.83 mmol gDCW−1 h−1). According to the in-silico simulations, the phosphoketolase pathway is the most compelling mode of methane fermentation, with the majority of cellular ATP produced from an acetate kinase reaction. Both the growth rate and the increase in acetate excretion have been previously observed as main outcomes of O2-limiting growth40. Furthermore, the kinetic properties of purified acetate kinase from M. alcaliphilum 20Z support the in-silico prediction (Table 331).To further validate this prediction, we estimated the parameters for growth at low oxygen tension. It has been shown that upon oxygen limitation the strain switches to a fermentation modeThe validity of the metabolic model was further assessed by metabolomics. Metabolomic profiles were obtained for cells grown in continuous culture on methane, as well as for exponentially grown cells from batch cultures on methane and methanol. Two hundred and fifty-three compounds were identified in bacterial cell pellets. The complete list of the non-targeted metabolites is presented in Table 16.We conducted statistical comparisons between methane-grown cells  and methanol culture condition to reveal key differences in metabolic signatures  improved model predictions for intermediates of the pentose phosphate pathway. We also considered a complete TCA cycle for methanol and methane growth, which reduced the Spearman’s index between metabolomics profiling and flux ratios to 0.9 . Simulations with a complete TCA cycle for methanol growth and an incomplete (knockout of 2-oxogluterate dehydrogenase) TCA cycle for methane growth led to the Spearman’s index of 0.94 . However, changes in fumarate and succinate levels did not correlate with predicted flux changes, so the metabolic reactions producing or consuming those compounds were investigated further. Simulations with an unusual TCA cycle41—in which oxoglutarate is converted to glutamate and then to succinate via succinate-semialdehyde dehydrogenase and glutamate carboxylase—and, additionally, oxaloacetate converted to aspartate and then to fumarate via aminotransferase and aspartate lyase resulted in the Spearman’s index of 0.99 .The comparison also highlighted a set of metabolites whose changes contradict the model predictions, including intermediates of the TCA cycle, pentose phosphate pathway, and pyruvate Fig. . AdditioR cells demonstrating that the pathway is active upon growth on methane and methanol , and thus make accurate predictions.The model analysis has also identified the crucial role of an additional constraint on the Oin-silico reconstruction. The model simulations suggest that PPi-dependent reactions in the central metabolic pathways might contribute to energy preservation upon oxygen limitation, as well as improve carbon conversion efficiency. Global, non-targeted, metabolomic profiling combined with in-silico simulations uncovered a set of highly interconnected nodes at the level of acetyl-CoA, fumarate, and glutamate. Further enzymatic studies confirmed the carbon flux from acetyl-CoA to xulylose-5-phosphate via phosphoketolase, from glutamate to succinate via succinate-semialdehyde dehydrogenase and glutamate carboxylase, and from aspartate to fumarate via aspartate lyase. The model will serve to inform hypothesis-driven strain engineering strategies, targeting enhanced methane conversion rate and efficiency.Furthermore, we show that the integration of non-targeted metabolomics data with metabolic flux predictions can help to refine the central metabolic pathways, by highlighting nodes poorly resolved by R we used the whole genome sequence of Methylomicrobium alcaliphilum 20Z (NC_016112.1)15 and the previously published metabolic reconstruction for the closely related species, Methylomicrobium buryatense 5 G(B1)24. Each reaction in these metabolic pathways was checked for mass and charge balances except reactions coupled with electron transfer system. The model was developed in a format compatible for FBA44. Moreover, to standardize reaction and metabolite IDs in our model according to BiGG Models ID Specification and Guidelines45, we used iAF692 model data46 for almost all of entities and KEGG abbreviations for enzymatic reactions, for which we could not find corresponding BiGG reactions. To link genes with reactions, the NCBI Reference Sequence annotation, NC_016112.1, was applied. To mathematically model the methane utilization network and to solve FBA optimization problems we employed GNU Linear Programming Kit (GLPK) (http://www.gnu.org/software/glpk/) solver in MATLAB using the well-known COBRA toolbox designed for flux balance model reconstruction and analyses47. To visualize metabolic networks and obtained fluxes we employed the Escher web-tool .To build the metabolic framework for 20Zl Figure 48.M. alcaliphilum 20ZR cells were grown using modified P media (g/L): KNO3, 1; MgSO4 x 7H2O, 0.2; CaCl2 x 2H2O, 0.02; NaCl, 30; trace solution, 1 ml/L of trace solution9; and supplemented with 20 ml/L of phosphate solution  and 20 ml/L of 1 M carbonate buffer. The following methane mixtures were used for bioreactor studies: (i) 5% CH4: 3.5% O2 to represent oxygen-limiting conditions; (ii) 5% CH4: 5%O2, to represent optimal growth; and (iii) 2.5%CH4:10% O2 to represent methane-limiting conditions. Methane mixtures were directly purged into bioreactor culture at 0.1 sL h−1 rate. In batch cultures, methane  was injected into vials to represent 10–20% of the headspace. For methanol growth cultures were supplemented with 0.5% methanol.4, O2, and CO2); or 2) a 2.7 L bench top BioFlo 110 modular bioreactor .Culturing was carried out in either closed vials  with shaking at 200 r.p.m. or bioreactor cultures (fed-batch or turbidostat). Two types of bioreactors were used: 1) a DASbox mini bioreactor  with two individual bioreactor units, each having automatic temperature, pH, and DO controls, a sample port for measuring OD, and a coupling to a BlueSens sensor system for simultaneous measuring off-gases  of the corresponding compounds over time. The data were analyzed to assess yield (Y), growth rate, and O2/substrate ratios. In addition, samples (15 ml) were taken for metabolomics.Batch cultures were grown in 125 ml, 250 ml, or 1.2 L bottles. The headspace:medium was set at 4:1 ratio. CHR were pre-grown to OD = 0.36 ± 0.05 and transferred to new vials supplemented with various amounts of methane added to headspace . Each experiment was represented by 3–6 biological replicates. Vials with cultures were placed back onto shaking platform. OD and CH4, O2 and CO2 composition were measured every 30 min – 1 h over 12 hours. The growth rates of methanotrophic bacteria correlated with the initial concentrations of substrate added. No growth was observed in cultures supplemented with methane below 3.6 mmol g−1 CDW h−1.To estimate growth-dependent and non-growth associated ATP maintenance cells of 20ZMethane utilization for maintenance and growth is mathematically expressed as:Ymax is the maximum biomass yield including growth–associated maintenance, and μ is the growth rate39. These ATP requirements were determined from a plot of methane uptake versus optimal computed growth rate, given the experimentally determined values for Ymax. 4 g DCW−1 h−1, while Ymax has a value 0.6 gDCW per g of CH4. Using the assumed ATP yield YATP, max = 6 mol ATP per one mol CH4 consumed and the y-intercept (3.6 mmol CH4 g DCW−1 hr−1), the non-growth-associated ATP requirement was calculated as 21.6 mmol ATP g DCW−1 hr−1. To estimate growth-associated ATP maintenance the model was simulated by varying only the CH4 consumption rate upon different values of growth-associated ATP maintenance as a stoichiometric coefficient in the biomass equation  from bioreactors were centrifuged to collect the biomass. After careful removal of the liquid phase, tubes of known weight with biomass were weighed (to obtain wet-cell biomass weight), lyophilized overnight using a Labconco freeze-dry system and weighed again. The observed DCW parameters were as follows: 1 L of cell culture with OD = 1 corresponds to 0.336 ± 0.025 g DCW).http://matrixgenetics.com) for FAME derivatization and GC-MS analysis. The intracellular concentration of glycogen was determined using anthron reagent49; ectoine concentration was determined using an HPLC assay50. For glycogen measuremnts 20zR biomass (dry) was lysed by incubation of with potassium hydroxide (10% KOH) at 95 °C for 1 h. After neutralization and centrifugation , ethanol was added to the supernatants (two volumes) and samples were refrigerated overnight. The precipitated glycogen was resuspended in 1 ml of DI H2O and centrifuged again . 1 ml of 0.2% (w/v) anthrone reagent (95% sulfuric acid) was added to 0.2 ml resuspended glycogen and incubated for 15 minutes, after which the sample was measured using a Janeway 632OD spectrophotometer and 620 nm. The concentrations of glycogen were estimated by comparing to glycogen standards prepared in the same manner as cell samples. Extracellular polysaccharides were measured in samples of broth using a sulfuric acid assay 51. Extracellular concentrations of formate, acetate, and lactate were measured as previously described 16.Samples of lyophylized biomass were submitted to AminoAcids (aminoacids.com) for complete amino acid analys, and to Matrix Genetics , then imputing any null values with the minimum detected value. Thus, the scaled data are centered around 1.0 (median) for all compounds, but the variation among the samples is not affected54. Statistical analyses were performed on natural log-transformed data.Metabolomic analysis of cells and spent supernatant from cultures of the 55. 3D plots reflected metabolomics and model ranks taking into account metabolic pathways for measured substances were generated with assistance of Plotly tool .Statistical comparison between methane-grown cells (methane batch) and methanol culture condition was initially conducted to reveal key differences in metabolic signatures. The magnitudes of changes between two groups were compared to predicted flux changes between the same two conditions. An enzymatic reaction with the highest value of flux for biosynthesis of corresponding metabolite was used to calculate flux ratio between two growth conditions. In order to estimate Spearman’s correlation index, predicted flux ratios and metabolic signatures were ranked according to the methodg and culture supernatants were discarded. Cell pellets were quenched in liquid nitrogen, thawed and solubilized on ice in 1 mL of lysis buffer ). The cells were then sonicated on ice at 4 °C, at 90% power setting for 30 seconds × 3 cycles, with a one minute cool-down period between sonication cycles using a Braun-Sonic-L ultrasonicator. Lysates were cleared via centrifugation at 16,000 × g at 4 °C for 5 minutes, and the supernatants were isolated for use in subsequent enzymatic analysis.Cell lysates were generated from biomass cultivated under above described conditions. Cells were pelleted via centrifugation for 1 min at 13,000 Cell lysates were generated from biomass cultivated under above described conditions. Cells were pelleted via centrifugation for 1 min at 13,000 g and culture supernatants were discarded. Cell pellets were quenched in liquid nitrogen, thawed and solubilized on ice in 1 mL of lysis buffer ). The cells were then sonicated on ice at 4 °C, at 90% power setting for 30 seconds × 3 cycles, with a one minute cool-down period between sonication cycles using a Braun-Sonic-L ultrasonicator. Lysates were cleared via centrifugation at 16,000 g at 4 °C for 5 minutes, and the supernatants were isolated for use in subsequent enzymatic analysis.56. Briefly, enzymatic activity was quantitated via continuous spectrophotometric rate determination at 340 nm following enzyme incubation with substrate, Coenzyme A, and beta-nicotinamide adenine dinucleotide (or oxidized nicotinamide adenine dinucleotide for SSADH) at 30 °C. Final assay reaction concentrations for PDH, AKGDH, and GC were as follows: 50.8 mM MOPS, 0.2 mM MgCl2, 0.01 mM CaCl2, 0.3 mM cocarboxylase (thiamine pyrophosphate), 0.12 mM coenzyme A, 2.0 mM beta-nicotinamide adenine dinucleotide, 2.6mM L-cysteine, 5 mM substrate, and 25ug total protein. Final reaction concentrations for SSADH were as follows: 87 mM potassium pyrophosphate, 3 mM 2-mercaptoethanol, 1.3 mM ß-nicotinamide adenine dinucleotide phosphate, 5.0 mM succinic semialdehyde, 0.83% (v/v) glycerol, 2.5 mM potassium phosphate, and 25ug total protein.Enzymatic assays for pyruvate dehydrogenase (PDH), alpha ketoglutarate dehydrogenase (AKGDH), succinate semialdehyde dehydrogenase (SSADH), and glutamate carboxylase (GC) were conducted using methodology adapted from58. Briefly, 100 μL reactions in reaction buffer  were initiated by the addition of 25 μg total protein. Reactions were incubated at 37 °C for 1 h. After incubation, 50 μL of 2 M hydroxylamine hydro- chloride (pH 7.0) was added to the reaction and incubated at room temperature for 10 min. Reactions were then terminated by adding 300 μL of a 1:1 mixture of 2.5% ferric chloride in 2 M hydrochloric acid and 10% trichloroacetic acid. A color change due to ferric-hydroxamate formation was measured at A505nm, and the concentration of acetyl-P formed during the initial reaction was calculated by regression analysis compared to known standards.Phosphoketolase activity was measured using a hydroxymate assay that detects acetyl phosphateAll absorbance measurements were conducted using a FLUOstar Omega microplate reader (BMG Labtech) in 96-well, round-bottom plates (Corning Costar), 300-μL reaction volumes.http://sci.sdsu.edu/kalyuzhlab/.Developed genome-scale model (xls) is available on the web-site: Table S1Table S3Supplementary material


Clostridial acetone-butanol-ethanol (ABE) fermentation features a remarkable shift in the cellular metabolic activity from acid formation, acidogenesis, to the production of industrial-relevant solvents, solventogensis. In recent decades, mathematical models have been employed to elucidate the complex interlinked regulation and conditions that determine these two distinct metabolic states and govern the transition between them. In this review, we discuss these models with a focus on the mechanisms controlling intra- and extracellular changes between acidogenesis and solventogenesis. In particular, we critically evaluate underlying model assumptions and predictions in the light of current experimental knowledge. Towards this end, we briefly introduce key ideas and assumptions applied in the discussed modelling approaches, but waive a comprehensive mathematical presentation. We distinguish between structural and dynamical models, which will be discussed in their chronological order to illustrate how new biological information facilitates the ‘evolution’ of mathematical models. Mathematical models and their analysis have significantly contributed to our knowledge of ABE fermentation and the underlying regulatory network which spans all levels of biological organization. However, the ties between the different levels of cellular regulation are not well understood. Furthermore, contradictory experimental and theoretical results challenge our current notion of ABE metabolic network structure. Thus, clostridial ABE fermentation still poses theoretical as well as experimental challenges which are best approached in close collaboration between modellers and experimentalists. Acetate-butanol-ethanol (ABE) fermentation carried out by saccharolytic clostridia has attracted academic and industrial interest for over a hundred years. After the initial discovery of butanol formation by Louis Pasteur in 1862 Pasteur , it quicNatural ABE fermentation is exclusively performed by solventogenic clostridia. These strictly Gram-positive endospore-forming anaerobes possess a fermentative metabolism and utilize a wide variety of sugars, oligosaccharides and polysaccharides. Typically, their fermentation metabolism is characterized by two phases, exhibiting a distinct shift in the product spectrum. During acidogenesis, the main (liquid) products are acetic acid and butyric acid, whereas neutral solvents, i.e. butanol, acetone and ethanol, are the main fermentation products during the solventogenic phase. In both phases, hydrogen and carbon dioxide are formed as by-products.Clostridium acetobutylicum  We then summarize and discuss published structural and dynamic models in their chronological order. Finally, we address open and emerging questions regarding experimental and theoretical investigations.Two different setups are widely used to investigate the reversible shift from acidogenesis to solventogenesis in both experiment and theory: batch culture and continuous culture.During a standard batch fermentation as shown in Fig. C. acetobutylicum ATCC 824, the pH limits for this phase shift have recently been established . The latter can serve as a substrate for hydrogenase, thus liberating hydrogen.The principal metabolic network of the ABE fermentation see Fig. , is wellThree key branch points in the ABE network direct the metabolic flux towards either acids or solvents. First, acetyl-CoA serves as the starting point for the formation of acetate (+1 ATP), ethanol and acetoacetyl-CoA (via condensation). Second, acetoacetyl-CoA is a precursor for acetone and butyryl-CoA. Third, butyryl-CoA can be converted to butyrate (+1 ATP) and butanol. As mentioned above, after the initiation of solventogenesis, the formerly excreted acids are re-assimilated and converted to their respective CoA derivatives in reactions that involve CtfAB CoA transferase . Insight into the biological principles causing these changes can be gained through the generation of testable hypotheses using mathematical models. A discussion of various approaches towards the mathematical description of clostridial ABE fermentation is given in the following sections.Existing models of ABE fermentation can be classified according to different schemes. In this review, we group them according to the employed theoretical framework and, where applicable, the experimental setup, i.e. batch or continuous culture. This approach allows us to trace the development of different models from their first implementation, including the latest developments. However, we do not aim to provide a detailed description of each model and its theoretical basis; instead, we only sketch major features and refer to the respective publications for more information.Importantly, most models available to date share a common starting point assuming that changes in population-wide metabolite concentrations are determined by the network structure, the speed of the biochemical conversions and transport processes and the population size, but differ in how they proceed further. This kinetic approach is commonly expressed in matrix form  and inhibitors. All these factors are subject to changes by cellular regulation and experimental setup. Furthermore, the frequency is restricted by physicochemical and biological constraints, which can be used to define the space of solutions but also to validate the found solution.The stoichiometric matrix  into the above matrix equation to emphasize that the population-wide metabolite concentrations are a function of the cellular activity and the number of cells in the population. The population size can be taken into account using either any observable quantity that is proportional to the number of cells, such as dry mass and optical density, or a growth law, e.g. exponential growth.In addition, we explicitly include the population size xi in continuous culture. For the sake of simplicity, we have assumed that the dilution rate D is constant in time and for every metabolite. However, if retention or product-specific extraction systems, e.g. membranes, are applied, the dilution rate transforms into a matrix with metabolite-specific elements.The second term in Eq.  considerMajor differences exist between the various models as to how the matrix equation is solved for a given biological system or problem. In very general terms, existing approaches focus either on the left or right hand side of Eq.  by simplS.Dynamic models intend to explain temporal changes, i.e. the matrix on the left hand side, based on assumptions about reaction rates, including reaction mechanism and cellular regulation. Consequently, these models are well suited to test hypotheses with respect to putative reactions and their mechanisms as well as their regulation. However, their high demand on dynamic data and additional information restrict the feasible network size, i.e. matrix ν that fulfils the resulting equationIn contrast, structural models use information about the network structure to investigate the metabolic capabilities of the cells. Towards this end, a (quasi-)steady assumption is applied that transforms the system of differential equations into a system of algebraic equations. Then, flux balance analysis (FBA)  assumes that the metabolism approaches a steady state that is determined by the metabolic network (stoichiometry) including enzymatic reactions, transport processes, physicochemical properties and biological constraints such as toxicity and ATP maintenance costs  for solventogenic pH values only.Finally, the authors concluded from the calculated production and reutilization rates of acetate, butyrate and acetone that acetate re-assimilation primarily accounted for acetone production, while butyrate was re-assimilated through both the reverse butyrate and acetone pathways see Fig. . Furtherbuk gene. Interestingly, analysis of the calculated metabolic fluxes suggested that butyrate re-utilization is decoupled from acetone formation, again implying the existence of a CtfAB-independent route for its re-assimilation.In a follow-up study  models use information from annotated genome sequences to reconstruct a first draft metabolic network. Then, missing and incorrect reactions are identified resulting from incomplete, inaccurate and ambiguous annotation. Thermodynamic information is used to categorize the metabolic reactions with respect to their direction and to determine the overall feasibility of the network. Furthermore, cellular information, e.g. biomass composition, cellular maintenance costs and biochemical information such as metabolite fluxes, constrain possible steady-state solutions . Contrary to previous models, it contains a full TCA cycle considering recent fluxomics data  bifunctional AdhE knockdowns and overexpressions were analysed with respect to changes in product formation.Starting from the model by Senger and Papoutsakis , b, McAnC. acetobutylicum (referred to as iCac802). Taking into account new results from labelling experiments  results in the experimentally found metabolic fluxes  of the network is determined by the abundance of participating or affecting molecules  and kinetic parameters describing molecular details of the processes . Furthermore, the population size is an important parameter as it governs the observed time courses of acids and solvents.As a mechanistic approach, dynamic models rely heavily on estimation of their parameters from experimental data. This is a non-trivial numerical problem , which results in formation of acetone and carbon dioxide formation, illustrates the requirement for improved consideration of both kinetic and genetic regulation. Its catalytic ‘constant’, In the absence of such detailed information, the measurement of the ratio of catalytic constant and Michaelis-Menten constant would provide valuable information, because enzyme kinetic reactions can also be represented as apparent bimolecular reactions that are only determined by this ratio  and butyrate (pKa = 4.82), it is generally assumed that their conversion into solvents is a protective countermeasure against undissociated acids diffusing back into the cells. In an updated physiological state model  could be affected simultaneously.C. saccharoperbutylacetonicum N1-4. Interestingly, the study concluded that slower substrate utilization gives rise to higher butanol yields and, thus, slower xylose utilization resulted in higher product yields. However, any regulatory effects of butyrate and butanol on butyrate re-assimilation and butanol formation were not considered in this model, in contrast to the preceding studies discussed above.Similar modelling approaches were applied in two subsequent publications. In the first  is converted following the shift to solvent formation and that sugar-limited batch cultures do not exhibit the classical ABE fermentation profile  are barely considered. While this information was not available when the first series of batch models was developed during the mid-1980s, it has not been taken advantage of even in the most recent batch culture models. As a consequence, despite yielding some interesting results, currently available batch models ultimately fail to provide a mechanistic, biological explanation for the metabolic shift from acid to solvent formation.C. acetobutylicum.Continuous culture allows bacteria to be grown in a constant, well-defined and highly reproducible environment. Important factors such as the concentration of nutrients and fermentation products, pH value, growth rate and population density, which inevitably change during batch culture growth, are maintained at a steady state and may even be varied independently  originating from the Rostock strain collection. ABE fermentation was studied using phosphate-limited continuous culture which offered superior reproducibility over traditional batch cultures  and the major non-gaseous fermentation products. Assuming a quasi-steady state, intermediary steps between those metabolites were merged into a single conversion step. In contrast to the dynamic batch culture models discussed above, additional biological data based on proteome analyses were taken into consideration.The first model published by Haus et al.  studied bdhA/B (butanol dehydrogenase) genes. Interestingly, considerable upregulation of adhE1, believed to be involved in the formation of both ethanol and butanol, was predicted to have a detrimental effect on formation of the latter, because formation of ethanol was preferred instead. However, a slight overexpression was predicted to have a positive effect on butanol formation. This result indicated that traditional sensitivity analysis focussing only on small changes might be misleading. Moreover, it suggested that successful metabolic engineering of the ABE fermentation network might rely on tightly controlled overexpression and underexpression of the genes involved.Furthermore, the authors used their model to investigate the effects of targeted single gene induction, including upregulation and downregulation, on solvent formation at steady state. This numerical analysis found that the regulation of a single solvent-forming gene is insufficient to significantly increase butanol formation. For instance, the model predicted that butanol production cannot be markedly improved by simply overexpressing the However, while this first model could explain the observed differences between acidogenic and solventogenic cells, it failed to reproduce the experimentally observed transition dynamics between the two metabolic steady states.Thorn et al.  thereforMotivated by the first model’s H offers challenges for future models. Stoichiometric considerations already indicate that balancing substrate and products results in different product yield depending on how many atoms of carbon, hydrogen and oxygen (often described as state of reduction) a substrate is able to provide  phenotypes represent different stable states of the metabolic network  phenotypes might offer an interesting alternative to the traditional engineering of metabolic networks.Considering ABE fermentation as a multistable system may also reveal an interesting feature not commonly associated with metabolic processes. In a multistable system, the system’s history can affect or even determine its future developmental options and capabilities. Thus, a sequence of metabolic states might be interpreted as developmental process (e.g., Veening et al. The existence of a solventogenic phenotype (or, more likely, phenotypes (Schuster et al. Despite the substantial contribution of mathematical models to our understanding of clostridial acetone-butanol-ethanol formation, they are yet unable to pinpoint definitive causes and mechanisms. It is evident that an integrated multi-level approach, including transcriptomics, proteomics and metabolomics, as well as information regarding the environoment, kinetic regulation and population heterogeneity, will be required to shed light on the process. A focus on the individual ‘objectives’ of cells and populations might also prove beneficial. Doing so will bring about new experimental and theoretical approaches, hopefully leading to a sustainable biotechnological process in the future.


Seed germination is inherently related to seed metabolism, which changes throughout its maturation, desiccation and germination processes. The metabolite content of a seed and its ability to germinate are determined by underlying genetic architecture and environmental effects during development.This study aimed to assess an integrative approach to explore genetics modulating seed metabolism in different developmental stages and the link between seed metabolic- and germination traits.Solanum lycopersicum and Solanum pimpinellifolium.We have utilized gas chromatography-time-of-flight/mass spectrometry (GC-TOF/MS) metabolite profiling to characterize tomato seeds during dry and imbibed stages. We describe, for the first time in tomato, the use of a so-called generalized genetical genomics (GGG) model to study the interaction between genetics, environment and seed metabolism using 100 tomato recombinant inbred lines (RILs) derived from a cross between QTLs were found for over two-thirds of the metabolites within several QTL hotspots. The transition from dry to 6 h imbibed seeds was associated with programmed metabolic switches. Significant correlations varied among individual metabolites and the obtained clusters were significantly enriched for metabolites involved in specific biochemical pathways.Extensive genetic variation in metabolite abundance was uncovered. Numerous identified genetic regions that coordinate groups of metabolites were detected and these will contain plausible candidate genes. The combined analysis of germination phenotypes and metabolite profiles provides a strong indication for the hypothesis that metabolic composition is related to germination phenotypes and thus to seed performance.The online version of this article (doi:10.1007/s11306-017-1284-x) contains supplementary material, which is available to authorized users. Genomic approaches have accelerated the study of the quantitative genetics that underlie phenotypic variation. The mutualistic relationship between metabolomics and genetics goes back to Mendel’s reliance upon metabolic phenotypes (anthocyanins and starch) to develop his basic genetic theory  approach for metabolic profiling using GC-TOF/MS on 100 recombinant inbred lines (RILs) of tomato to describe the genetic regulation of variation in the tomato seed metabolome. This new GGG model may prove to be useful in tomato seeds and allows the investigation of the mechanisms that contribute to complex variations in the tomato seed metabolome during germination by analyzing two different developmental stages in one study and it offers unique reduction of experimental load with minimal compromise in statistical power as has been shown for Arabidopsis seed germination and associated metabolites  in combination with the analysis of the genetic variation present in the studied RILs, to study the multiple environments and to identify genotype-by-environment interactions, offering a unique reduction of experimental load with minimal compromise of statistical power. Thus, the present approach reveals the plasticity of molecular networks in tomato for seed performance traits and forms a crucial step toward understanding different influences of genetic and developmental responses in tomato seeds. The present study attempts to link seed traits to metabolic signatures. Furthermore, it supports previous findings in other crops and provides additional evidence that relationships between a seed trait and a single metabolite is generally absent but that strong canonical correlation with a specific combination of metabolites illustrates the complexity of such quantitative traits  for 1 h. Finally, the seeds were dried on clean filter paper at room temperature and were brushed to remove impurities with a seed brusher . The cleaned seeds were dried for 3 days at 20 °C and were stored in a cool, dry storage room (13 °C and 30% RH) in paper bags until further use.A In this study the population of 100 RILs was intelligently allocated to two sub-populations optimized for the distribution of parental alleles in such a way that the allele distribution in the two sub-populations is as similar to each other and to the total population as possible using the R-procedure DesignGG . 12.5 µL of O-methylhydroxylamine hydrochloride (20 mg ml− 1 pyridine) was added to the dried samples an incubated for 30 min. Then the samples were derivatized with 17.5 µL N-methyl-N-trimethylsilyltrifluoroaceticamide  for 60 min. An alkane mixture of C10–C34 was added to calculate the retention indices of metabolites. 2 µL of the derivatized samples were injected into an Optic three high-performance injector (ATAS) at 70 °C at a spilt ratio of 19:1 and the injector was heated to 240 °C at 6 °C/s− 1. Chromatography was performed on a Agilent 6890 gas chromatograph (Agilent Technologies) coupled to a Pegasus III time-of-flight mass spectrometer (Leco Instruments) using a VF-5 ms capillary column  including a 10 m guardian column with helium as carrier gas with a flow rate of 1 ml/min− 1. The oven was isothermal for 2 min at 70 °C followed by a 10 °C/min− 1 ramp to 310 °C. The transfer line was set at 270 °C and the column effluent was ionized by electron impact at 70 eV. Solvent delay was set at 300 s. Detector voltage at 1600 V.In December 2009, a bulk of approximately 70–100 seeds (30 mg) were either immediately frozen in liquid nitrogen or imbibed for 6 h in the dark on pre-wetted filter paper  with demineralized water, after which seeds were frozen in liquid nitrogen. The extraction method is modified from the method previously described by Roessner et al. . A bulk http://www.nist.gov/srd/mslist.htm) and Golm libraries (http://csbdb.mpimp-golm.mpg.de/csbdb/gmd/gmd.html)]. This identification is based on similarity of spectra and comparison with retention indices calculated by using a third order polynomial function  with 5% FDR correction.Metabolomics data were log2 transformed and then statistically analyzed using the rank product method  of this linear model will be used henceforward. Separate likelihood estimates  are generated for the E, G, and G:E effects.For QTL analysis a previously developed R script  on the two conditions separately. This was followed by a combined mapping allowing for a developmental co-variate and interaction term between the genetic marker and the developmental stage (yi = β0 + β1ei + β2gi + β3ei:gi + εi) . With the open source statistical package R (version 2.14.1) we fitted a basic linear model . All procedures were applied after the missing value estimation followed by normalization of the metabolic matrix.Canonical correlation analysis calculates the highest possible correlation between linear combinations of the columns from two matrices with the same number of rows. The R function “cancor” was used to calculate the canonical correlation between metabolites and seed performance phenotypes. For cross validation a partial least square (PLS) regression was performed. To carry out the procedure the R package “pls” implementing partial least squares regression (PLSR) was used (in) and a minimum value for cluster property that determines the nature of periphery tracking (cpin). The values for density and cluster properties should be within the following range: 0 < din ≤1, and 0 < cpin ≤1  as an alternative experimental setup using balanced fractions of a RIL population has been used recently for genetic and environmental perturbation  × Solanum pimpinellifolium (‘Pimp’) RIL population  and Golm libraries (http://csbdb.mpimp-golm.mpg.de/csbdb/gmd/gmd.html)]. In total, 167 metabolites were detected in this study and the chemical nature was identified for 66 of these metabolites. The known metabolites included central metabolism derived compounds, such as glucose-6-phosphate, members of the tricarboxylic acid (TCA) cycle, such as succinate, citrate and malate, members of the membrane/phospholipid biosynthesis, such as glycerol-3-phosphate, ethanolamine, amino acids and precursors thereof, sugars, and some other common metabolic end products . This list was compiled to encompass the different classes of intermediates in primary metabolism. These metabolites are ubiquitously present in living organisms and are at the core of the biochemical reaction networks with the largest fluxes and largest number of regulatory circuits.We utilized an in-house gas chromatography–time of flight–mass spectrometry (GC-TOF/MS) metabolomics platform to measure metabolite accumulation in the seeds of a S. lycopersicum and S. pimpinellifolium possess significant genetic variation for metabolite accumulation.The majority of the metabolites were detected in both parents and in more than 90% of the RILs. Transgressive segregation for metabolite presence was manifested in a significant fraction of the metabolites found in the RIL population. Analysis of the RILs for 167 metabolites identified positive and negative transgressive segregation for metabolite accumulation . Thus, The data set obtained by GC-TOF/MS for the RIL population was examined by principal component analysis Fig. . PrincipQuantitative changes in the amounts of major metabolites in the two different stages are presented in Fig. Concentration of organic acids, namely, galactonate, glycolate, glycerate, erythronic acid, phosphoric acid, quinate and threonate, decreased dramatically upon imbibition. The levels of amino acid and their precursors were invariant between dry and 6 h imbibed seeds. The levels of alpha-hydroxybutyrate and the sugars xylofuranose and sucrose also exhibited considerable decrease upon imbibition. The levels of the TCA-cycle intermediate oxalate showed significant decrease while the other TCA-cycle metabolites declined even further on imbibition. The imbibed seed stage was associated with a general increase in concentrations of monomethyl phosphate, the organic acids, parbanic acid and pentonic acid, and the TCA-cycle intermediates citrate and fumarate. In contrast, the levels of gluconate, quinate, shikimate and succinate were significantly reduced. While the levels of most amino acids and their precursors were reduced to different extents, the levels of Gly, Asp, Asn and hypotaurine significantly increased. Similarly, the levels of most sugars declined but the levels of the sugar phosphates, glucose-6-phosphate and glycerol-3-phosphate were elevated significantly. This general observation suggests that the transition from dry to 6 h imbibed is associated with the activation of initially important metabolic processes needed for seed germination. It is also likely that germination is associated with a follow up of additional metabolic processes, which occur later during germination and therefore were not observed by our metabolic profiling. The major metabolic changes observed after 6 h of imbibition were significant reductions in the levels of the majority of different metabolites, which had accumulated in the dry seeds Fig. . In addiBH; Benjamini-Hochberg) are provided in Supplemental Table 4. Unsupervised hierarchical clustering revealed several ‘‘hot spots’’ of highly correlated metabolites . In another cluster, glycine clustered most highly with pyroglutamate , but also with glycerol-3-phosphate and urea. Glucose correlated most highly with myo-inositol , and GABA with glutamate . Also, in the same cluster ethanolamine, glutamine, shikimate and its precursor quinate, as well as TCA intermediates malate and succinate, grouped together.We created a correlation matrix of all pairwise comparisons among individual metabolites by performing Spearman rank correlation analysis for all pairs of measured traits across the whole population. Spearman’s rank correlation coefficients (Rs) and accompanying false discovery rate (FDR)-corrected P values  are listed in Supplemental Table 7 as an example, whereas details concerning connection between metabolites and different germination traits are provided in Supplemental Table 5. The highest absolute correlation found was for an unknown metabolite (RI_2442), which yielded a value of 0.406. Although the correlation is statistically highly significant , it can only explain 16.48% of the variance. Other significantly correlated compounds are allantoin, alpha-hydroxybutyrate, C16:1, fructose, gluconate, glucuronate, guanosine, hexonic acid, hypotaurine, shikimate, xylofuranose and a number of unknown metabolites . Their individual contribution to the explained variance ranges from 5 to 15%. In contrast to the aforementioned pairwise correlation analysis, CCA yielded a much stronger correlation of 0.60. This value corresponds to 36% of variance explained by the linear combination of metabolites, almost 1–5 times more than explained by any individual metabolite. Comparing the results obtained from a combination of different metabolites and germination traits which showed significant associations, it can be seen that strongly represented metabolites are compounds of central metabolism, such as glucose and fructose, members of the tricarboxylic acid (TCA) cycle, such as succinate, citrate and fumarate, amino acid and precursors, members of the membrane/phospholipid biosynthetic pathways, such as glycerol-3-phosphate, ethanolamine and myo-insitol, or sucrose . Interestingly, with regard to different germination traits , in particular under stress conditions, the predominantly represented metabolites are well known for abiotic stress responses. The single most remarkable observation to emerge from the data comparison was that of those stress-related metabolites, most were ominously present in the correlation with salt- and osmotically-stressed seeds for Gmax as well as unprecedentedly for t10 − 1, t50 − 1, MGR and AUC, including myo-insitol, Pro, fumarate and succinate. High temperature stress was associated with Ile, Leu and Val; again their response to abiotic and biotic stresses and heat is well known, whereas strong representation of sugars , organic acids  and some bases and alcohols was evident in the case of Gmax and some additional metabolites in the case of AUC under oxidative stress conditions. The overall association of metabolites with phenotypic traits across different conditions provides an indication of possible cross-talk between different abiotic stresses . It also provides insight into the nature and consequences of the genetic variation of metabolic function and its relationship with seed performance phenotypes.Using an analogous approach to that taken previously for metabolites  display up to two QTLs co-localizing with any of the phQTL .Rs ≥ 0.5) and visualizing them using DPClus  optimized for the distribution of parental alleles using the R package DesignGG, aiming at the most accurate estimate of G and G:E effects (TIF 133 KB)Supplemental figure 2 A. Histogram of metabolite detection in the 100 RILs dependent upon detection in the MM and/or Pimp parental genotypes. The axis presents the number of metabolites found in a given number of RILs (on the axis). The axis separates the metabolites into four detection classes dependent upon whether the metabolites were found in the Money ‘MM’ and /or Pimp parents. ND means that the metabolite was detected in the given parental genotype. There were 90 metabolites detected in both parents, 7 metabolites detected in only Money ‘MM’, 10 metabolites detected in only Pimp and 53 metabolites detected in neither parent for dry seeds while 76 metabolites detected in both parents, 22 metabolites detected in only Money ‘MM’, 3 metabolites detected in only Pimp and 59 metabolites detected in neither parent for 6h imbibed seeds (TIF 90 KB)Supplemental figure 2 B. Positive and negative transgressive segregation. Shown are distributions of glycerol-3-phosphate (top) and Hydroxylamine (bottom) within S. lycopersicum × S. pimpinellifolium RILs as examples of positive and negative transgressive segregation, respectively. Labels ‘MM’ and ‘Pimp’ show the average accumulation of these metabolites in the parental genotypes (PDF 1811 KB)Supplemental figure 3. Heat map of correlations between all 167 metabolites. Each square represents the Spearman correlation coefficient between the metabolic phenotypes of the column with that of the row. Metabolic phenotype order is determined as in hierarchical clustering using the distance function 1-correlation (TIF 3042 KB)Supplemental figure 4. A- Genomic locations of Genetic mQTLs identified for all 167 metabolites. B- Genomic locations of G × E mQTLs identified for all 167 metabolites. Tomato chromosomes are identified by arabic numerals (1–12), with centimorgans ascending from left to right; chromosomes are separated by white lines. Colored cells indicate QTL significant. Significant thresholds were defined with permutation analysis  by randomizing the genotypes over each metabolite and was set to LOD > 3 accordingly. The LOD color scale is indicated, showing blue and light blue when the Solanum pimpinellifolium (‘Pimp’) allele, and yellow and red when the Solanum lycopersicum (Money ‘MM’) allele, at that marker results in an elevated level of metabolic phenotype (TIF 195 KB)Supplementary material 6 (TIF 202 KB)Supplemental figure 5. Overview of distribution of overlapping metabolic and phenotypic QTLs. See legend to Supplemental figure 4A and 4B for description (TIF 394 KB)Supplemental figure 6. Correlation network properties of the 66 metabolites in the tomato seeds across a range of correlation coefficients. Networks were constructed for a range of correlation thresholds from 0 to 1.0 by 0.01 increments, and each resulting network was calculated for: (A) the graph density - the ratio of the number of edges and the number of possible edges, (B) the clustering coefficient, (C) the average degree of all nodes, (D) the average path length, (E) the number of connected components, and (F) the number of metabolite-metabolite correlations (edges) in the network. Within each plot, black solid circles represent the observed data points; black dots represent 100 randomized data (TIF 280 KB)Supplemental table 1. List of nutrients with their concentrations which were used as fertilizer for the tomato plants (XLSX 41 KB)Supplemental table 2A. List of analyzed known and unknown metabolites and their distribution among the two parents and RIL lines of the studied RIL population. Abundancies are based on the quantification mass. Known metabolites are matched against several libraries. RI, MS match scores and probability are noted for each identified metabolite. 2B. Level of identification according to L.W. Sumner et al. 2007 and category for known metabolites (XLSX 325 KB)Supplemental table 3. EI fragments of each known and unknown metabolite that are used for identification of compounds (XLSX 10 KB)Supplemental table 4. Spearman Rsvalues and associated P Values for all pairwise correlations between known metabolites  and between all 167 metabolites  (XLSX 11 KB)Supplemental table 5A. List of significantly correlated metabolites resulting from pairwise correlations (ordered by correlation). Supplemental table 5B. List of all relevant metabolites determined by the correlation between them and the canonical variate (ordered by absolute correlation) and ranked according to the strength of the canonical correlation (XLSX 41 KB)Supplemental table 6. List of KEGG pathways used in this study (XLSX 1243 KB)Supplemental table 7. List of top 20 signature metabolites ranked according to the strength of the canonical correlation  (DOCX 13 KB)


In this work we aim to show how Genome Scale Metabolic Models (GSMMs) can be used as tools for drug design. By comparing the chemical structures of human metabolites (obtained using their KEGG indexes) and the compounds contained in the DrugBank database, we have observed that compounds showing Tanimoto scores higher than 0.9 with a metabolite, are 29.5 times more likely to bind the enzymes metabolizing the considered metabolite, than ligands chosen randomly. By using RNA-seq data to constrain a human GSMM it is possible to obtain an estimation of its distribution of metabolic fluxes and to quantify the effects of restraining the rate of chosen metabolic reactions . This method allowed us to predict the differential effects of lipoamide analogs on the proliferation of MCF7 (a breast cancer cell line) and ASM (airway smooth muscle) cells respectively. These differential effects were confirmed experimentally, which provides a proof of concept of how human GSMMs could be used to find therapeutic windows against cancer. By using RNA-seq data of 34 different cancer cell lines and 26 healthy tissues, we assessed the putative anticancer effects of the compounds in DrugBank which are structurally similar to human metabolites. Among other results it was predicted that the mevalonate pathway might constitute a good therapeutic window against cancer proliferation, due to the fact that most cancer cell lines do not express the cholesterol transporter NPC1L1 and the lipoprotein lipase LPL, which makes them rely on the mevalonate pathway to obtain cholesterol. Predicting which ligands bind a particular protein and modify its activity is a fundamental step in drug-design, which is typically solved using molecular docking . The strThe effects of a drug on a particular patient and disease, are not just reduced to its interaction with its targets, but have to be seen within the context of the whole cell. The effect of inhibiting the activity of a particular enzyme on a certain disease phenotype, depends on the activities of many other enzymes that form a complex network of metabolic reactions in which the products of a reaction are the substrates of others. Therefore, the response to a certain drug can be very different in different patients (or in different cell types), which leads to the emerging field of personalized medicine and personalized drug-choice . DiffereGSMMs , 9 are cThis approach has been used to identify drugs contained in the DrugBank database  that areAn updated human GSMM  was usedFor example, 7,8-dihydrobiopterin is described in DrugBank as an inhibitor of the enzyme dihydroneopterin aldolase, which catalyzes the conversion of its analog 7,8-dihydroneopterin to 6-hydroxymethyl-7,8-dihydropterin and glycolaldehyde .-1h-1 for an expression level of 10 RPKM. This constant was chosen in order to fit the experimental growth rate of the cell line A549. Before setting each constraint, the expression levels (in RPKM) were rounded up to the next multiple of 10. This proved to be an efficient way to avoid numerical problems while performing linear optimization. For reactions with several enzymes, the expression level was chosen to be the one of the most abundant enzyme. After constraining each reaction in the model based on the gene expression of its associated enzymes, the maximal value of the biomass production reaction (or any other reaction associated to a certain phenotype) can be computed by linear optimization . Relative growth rate is the ratio of the maximal growth rate with inhibition to the maximal growth rate without inhibition. A value of 1 would mean that the cell is able to fully compensate the effects of the drug by using alternative metabolic pathways to produce all its biomass components. A relative growth equal to one minus the relative inhibition means that the cell cannot use any alternative pathway to compensate the effects of the drug. Values in between mean that there are alternative metabolic pathways that the cell can use, but they are not as efficient as the pathways targeted by the drug. The actual relative inhibition of a drug on a certain target would depend both on the concentration of the drug and its binding strength to the target. The problem of predicting this relative inhibition is out of the scope of this article. Here it is modeled only the impact that a certain relative inhibition would have on the global cell metabolism.GSMMs can be used to predict any phenotype that could be linked to the rate of production or consumption of one or several metabolites. In particular, cell growth can be described as the rate of assembly of biomass building blocks into macromolecules (amino acids into proteins for example). This is described in the models by a so called “biomass equation”, which reflects the relative proportions in which different biomass building blocks are added to biomass. Predicting actual metabolic flux distributions would require knowing the kinetics of each metabolic reaction as well as other parameters outside metabolism, such as ribosome concentrations, protein synthesis rates, etc. The number of parameters to be adjusted from experimental data is too large for any realistic attempt to describe kinetically whole cell metabolic flux distributions, which makes necessary some rough assumptions. We assume that the maximal reaction rate of a metabolic reaction is proportional to the abundance of the enzyme catalyzing this reaction, which is itself proportional to the abundance of the transcript coding the enzyme. The proportionality constants might be very different for each reaction, however we will consider them to be equal. This assumption is far from reality but it is still able to capture the fact that increases in the expression of a gene are likely to result in a higher maximal flux through the reactions catalyzed by its gene products. The proportionality constant was set to be 0.027 mmol g-DWtion see . In orde-1h-1, we cannot expect the model to predict fluxes below 0.027 mmol g-DW-1h-1 . Remarkably, the model is able to reproduce the Warburg effect (aerobic production of lactic acid) as well as the secretion of substantial amounts of citric acid, which are common features of cancer cells.In order to check the ability of our Flux Balance Analysis approach to recapitulate , the actual uptake and secretion profiles of cell lines, we compared our predictions see  with avahttps://discover.nci.nih.gov/cellminer/) corresponding to the NCI-60 cell lines. A false discovery rate of 0.05 was used. The selected reactions are reported in the In order to assess if our constraint-based modeling approach (pyTARG) can provide realistic estimations of metabolic fluxes we compare it with the existing method called PRIME . PRIME wStructural similarity with natural metabolic intermediates is a well-known drug design principle, which is at the basis of the mechanism of anti-metabolites that interfere with DNA synthesis due to its structural similarity to pholates. Here we used the described modeling approach to assess the effects of lipoamide analogs on the growth capabilities of MCF7 and ASM cells. http://www.ambinter.com/reference/19149142) based on its similarity with lipoamide. The tested compound significantly decreased the growth of MCF7 cells while did not have statistically significant effects on ASM cells (We observe that at high relative inhibitions (around 0.9), the relative growth capability of ASM cells is 30% higher than the relative growth capability of MCF7 cells, which suggests the existence of a therapeutic window. We proceeded by looking for compounds with structural similarity to lipoamide. A compound with a pentagonal ring and an aliphatic chain that finishes with a polar group was tested (SM cells . The reswww.proteinatlas.com). The results for each DrugBank entry and each tissue or cell type are reported in the We have tested the relative effects of each of the compounds contained in DrugBank (see in silico) cancer cell lines are sensitive to a disruption of the mevalonate pathway, it was observed that these cells fail to express the cholesterol transporter NPC1L1 and the lipoprotein lipase LPL, involved in the assimilation of lipoproteins from the blood stream. This makes these cells unable to incorporate external cholesterol and dependent on the mevalonate pathway to synthesize it, while most of the healthy tissues are able to uptake external cholesterol. Inhibition of the mevalonate pathway has been already described as a way to target cancer stem cells [First of all we noticed that many of the DrugBank entries correspond to actual human metabolites, which excludes their utilization as competitive inhibitors, however the obtained results still provide many valuable indications. The most significant putative therapeutic windows were associated to intermediates of the mevalonate pathway or ligands putatively binding enzymes in the mevalonate pathway. This pathway is responsible for the synthesis of cholesterol and its derivatives. This observation is particularly interesting because there are drugs in the market (such as statins or bisphosphonates) targeting this pathway. Looking closer on the reason why many of the tested  is a good predictor of the ability of a ligand to bind the chosen metabolic enzymes. One of the best known human biological network is metabolism, due to the fact that the accumulative knowledge of more than a century of biochemical research allows defining with precision the metabolic reactions catalyzed by each enzyme and these reactions have well known stoichiometry. All this stoichiometric information can be condensed in a GSMM which defines the metabolic capabilities of the cell. GSMMs can be used to predict the effects of constraining the rate of these metabolic reactions. Therefore, GSMMs can be also used to predict the putative effects of drugs targeting metabolic enzymes on the metabolic capabilities of the cell. This has an important practical application, which is finding therapeutic windows, that’s drugs that affect the metabolic capabilities of a malignant cell type while having milder effects on healthy cell types. As a prove of concept it was shown experimentally that exposure to a lipoamide structural analog can reduce the proliferation rate of the cancer cell line MCF7 while having lower effects on healthy ASM cells. In order to further explore the potential of structural analogs to natural metabolites as selective anticancer agents, a statistical analysis was performed comparing the predicted effects of different metabolite-analogs on cancer cell lines and healthy mesenchymal stem cells. Mesenchymal stem cells were chosen because they share with metastatic cells the property of having undergone epithelial to mesenchymal transition. The compounds with the highest predicted selective effects included the analogs of intermediates of the mevalonate pathway as well as analogs to xanthine, such as caffeine, theobromine and theophylline. These findings seem to agree with previous evidence from the literature.DrugBank and KEGG databases were parsed using the Perl library libwww-perl. OpenBabel  was usedwww.proteinatlas.com in the form of a comma separated file that contains the gene expression of each gene in each cell line (given as RMPK). This file was parsed using a customized python script (available upon request). The gene expression profile of ASM cells was obtained from the GEO database (accession number GSE52778) [Gene expression data for the 34 cancer cell lines (BioProject accession number PRJNA183192) as well as healthy tissues included in the Human Proteome Atlas were downloaded from SE52778) .GSMMs are compilations of all the metabolic reactions that can be catalyzed in a particular organism. This information can be condensed in the so called stoichiometric matrix, which contains the stoichiometric coefficients of each metabolite in each reaction. If we consider the internal metabolites in steady state, the feasible metabolic flux distributions become constrained to the null space of the stoichiometric matrix S. The steady state condition for internal metabolites is expressed in Each reaction rate can be further constrained by imposing upper and lower boundaries. For irreversible reactions, the lower boundaries are set to zero. Upper boundaries and lower boundaries for reversible reactions are set as functions of the expression levels of their associated genes as described below.The constraints represented by https://github.com/SergioBordel/pyTARG. The updated version of the HMR model is available in SBML format in the BioModels database (with the identifier MODEL1707250000) [https://github.com/SergioBordel/pyTARG (Human.xml). To model a particular cell line the upper bounds were constrained to be 0.0027 mmol g-DW-1h-1 times the expression level of the most abundant enzyme associated to each reaction (measured in RPKM). The boundaries were set in a discrete way (by rounding up the expression levels to their upper multiple of 10), this helped to avoid numerical problems while performing linear optimization. Numerical problems were observed trying to use a continuous spectrum of values for the reaction upper bounds. Other authors [The prediction of metabolic flux distributions was done using the python library COBRApy (version 0.9.0 and solver glpk)  and the 7250000)  and at h authors  have pre authors . The bouThe error bars in 2 humidified incubator at 37°C. Unless indicated otherwise, all chemicals were purchased from Sigma Aldrich Corp. .MCF-7  were cultivated in Dulbecco’s Modified Eagle Medium: Ham’s F-12   medium with 10% fetal bovine serum (FBS) and antibiotics . Immortalized human airway smooth muscle cells (ASM)  were obtained as described elsewhere . ASM cel4 cells per well. Cell proliferation was observed 120 h after the treatment with Amb19149142 compound  , as well as solvent control (0.1% ethanol). The total cell number was estimated using 0.4% Trypan Blue stain  and Neubauer improved cell counting chamber .Cells were seeded in 6-well plates at a density 1.5×10P < 0.05. The error bars in Averaged data are reported as means ± standard error of the mean. Statistical analysis was performed using two-tailed Student’s t-test. Differences were considered statistically significant at S1 TableA file in Excel format with information about computationally investigated drugs containing 3 sheets:Sheet Metabolite drug contains structural analogies between DrugBank entries and metabolites in the HMR model.Sheet Drug effects contains predicted effect of drugs at 90% target inhibition.Sheet Therapeutic windows contains drugs that tend to have larger effects on cancer cell lines compared to healthy tissues determined by a statistical t-test.(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S1 FigThe percentages were computed by extracting 4000 random metabolite-drug pairs. The process was repeated 1000 times.(TIF)Click here for additional data file.


Rhodococcus jostii RHA1 was investigated for steady-state feast-famine cycles on glucose and acetate as the sole carbon sources. R. jostii RHA1 is capable of accumulating the three storage compounds  simultaneously. According to the experimental observations, when glucose was the substrate, feast phase chemical oxygen demand (COD) accumulation was similar for the three storage compounds; when acetate was the substrate, however, PHB accumulation was 3 times higher than TAG accumulation and essentially no glycogen was accumulated. These results were simulated using the genome-scale metabolic model of R. jostii RHA1 (iMT1174) by means of flux balance analysis (FBA) to determine the objective functions capable of predicting these behaviours. Maximization of the growth rate was set as the main objective function, while minimization of total reaction fluxes and minimization of metabolic adjustment  were considered as the sub-objective functions. The environmental MOMA sub-objective performed better than the minimization of total reaction fluxes sub-objective function at predicting the mixture of storage compounds accumulated. Additional experiments with 13C-labelled bicarbonate (HCO3−) found that the fluxes through the central metabolism reactions during the feast phases were similar to the ones during the famine phases on acetate due to similarity in the carbon sources in the feast and famine phases ; this suggests that the environmental MOMA sub-objective function could be used to analyze successive environmental conditions such as the feast and famine cycles while the metabolically similar carbon sources are taken up by microorganisms.Feast-famine cycles in biological wastewater resource recovery systems select for bacterial species that accumulate intracellular storage compounds such as poly-β-hydroxybutyrate (PHB), glycogen, and triacylglycerols (TAG). These species survive better the famine phase and resume rapid substrate uptake at the beginning of the feast phase faster than microorganisms unable to accumulate storage. However, ecophysiological conditions favouring the accumulation of either storage compounds remain to be clarified, and predictive capabilities need to be developed to eventually rationally design reactors producing these compounds. Using a genome-scale metabolic modelling approach, the storage metabolism of  Biological wastewater resource recovery systems are often highly dynamic with regard to availability of external electron donors . Thus, tRhodococcus jostii RHA1 under feast-famine cycles.Genome-scale metabolic models are mathematical representations of the stoichiometry of the biochemical networks based on genomic information. It is one of the tools to make the metabolic predictions on the behaviour of microorganisms in different environmental conditions. Predicting the composition mixture under various growth conditions can be accomplished by solving the genome-scale metabolic model at steady-state using flux balance analysis (FBA). This approach typically yields a set of underdetermined mass balance equations. The solution space of this equation system can be investigated by finding solutions optimally satisfying biologically relevant linear objective functions implemented in a linear-programming approach , 5. DefiR. jostii RHA1 was isolated from lindane contaminated soil . Br. Br26]. 2SO4. Trinonadecanoin  was used as quantitative internal standard  as a function of the growth- and non-growth- associated maintenance energy  for the simulated feast growth with glucose  or acetate  as carbon substrates. Fig C. Contour plots of flux balance results obtained with the minimization of metabolic fluxes sub-objective function in conjunction with the maximization of growth rate for weighted average maximum growth rates  in function of pairs of glycogen and PHB (a and d), PHB and TAG (b and e), and TAG and glycogen (c and f) storage fluxes at experimentally observed glucose—panels a, b, and c: 14.2 g-COD/(g-COD-Biomass·d)—and acetate uptake rates—panels d, e, and f: 4.9 g-COD/(g-COD-Biomass·d). Fig D. Contour plots of flux balance results obtained with the environmental MOMA sub-objective function in conjunction with the maximization of growth rate for weighted average maximum growth rates  in function of pairs of glycogen and PHB (a and d), PHB and TAG (b and e), and TAG and glycogen (c and f) storage fluxes at experimentally observed glucose—panels a, b, and c: 14.2 g-COD/(g-COD-Biomass·d)—and acetate uptake rates—panels d, e, and f: 4.9 g-COD/(g-COD-Biomass·d).Including: (DOCX)Click here for additional data file.