Ganoderma lucidum strain CGMCC5.26, termed model iZBM1060, containing 1060 genes, 1202 metabolites, and 1404 reactions. Important findings based on model iZBM1060 and its predictions are as follows: (i) The extracellular polysaccharide (EPS) biosynthetic pathway was elucidated completely. (ii) A new fermentation strategy is proposed: addition of phenylalanine increased EPS production by 32.80% in simulations and by 38.00% in experiments. (iii) Eight genes for key enzymes were proposed for EPS overproduction. Model iZBM1060 provides a useful platform for regulating EPS production in terms of system metabolic engineering for G. lucidum, as well as a guide for future metabolic pathway construction of other high value-added edible/ medicinal mushroom species.In this study, we reconstructed for the first time a genome-scale metabolic model (GSMM) of  Ganoderma lucidum (lingzhi or reishi mushroom) is a species well known for its edible and medicinal properties, and has a long history of use for prevention and treatment of various human diseases. Extracellular polysaccharides (EPSs) from G. lucidum comprise a structurally diverse group of macromolecules that display immunomodulatory, antitumor, and a wide range of other biological activities , in which a systems biology approach is used to integrate genomic, transcriptomic, proteomic, and metabolomic data, are highly effective tools for metabolism research. GSMMs have been widely used for analysis of network properties, prediction of growth phenotypes, and interpretation of experimental data, particularly in e models .G. lucidum strain CGMCC5.26  as template frameworks to map the assigned genes. (b) The KEGG Automatic Annotation Server (KAAS) ((i) Sequenced database . Genes wsequence . An origer iMA871, Mortier iCY1106 , and Aspr (KAAS)  was used(ii) A draft model was developed and used as a starting point for subsequent network refinements. Biochemical information was acquired from public databases  was kept at 30°C on a rotary shaker (150 rpm). The minimal growth medium for functional tests was composed of carbon source 20 g/L, nitrogen source 10 g/L, KH2PO4 4.5 g/L, MgSO4⋅7 H2O 2 g/L, initial pH 6.0.Mycelia were harvested by centrifugation  for 10 min. The precipitate was washed three times with distilled water, and dried at 60°C to constant weight. Dry weight (DW) was determined by gravimetric method. Amount of residual sugar in medium was determined by 3, 5-dinitrosalicylic acid (DNS) method .For determination of EPS, centrifugal fluid as above was precipitated with adding 4 times of 95% (v/v) ethanol and left 8 h at 4°C to precipitate crude polysaccharides. Precipitate was collected by centrifugation  for 20 min, washed three times with 80% (v/v) ethanol, and dried at 60°C to remove residual ethanol. Total EPS content was assayed by phenol-sulfuric acid method .G. lucidum, we converted the reaction list to a standard SBML document that could be read by COBRA Toolbox , fPH being the product of the specific biomass overexpression and specific EPS overexpression rates.For identification of target genes, MOMA  frameworG. lucidum, termed model iZBM1060, contained 1060 genes, 1202 metabolites, and 1404 reactions  for cell growth was predicted qualitatively by FBA. Each of the carbon sources was used as sole carbon source in minimal growth medium. Results were compared to experimental data, and the growth phenotype matching rate was 94.4% for cell growth. When results were compared to experimental data, the matching rate was 95.5%  can be attributed to unclear transport pathways and the absence of regulatory mechanisms in this stoichiometric model.On the basis of carbon source metabolic pathways and the experimental results, we selected seven monosaccharides as single carbon sources for evaluation of effects of various carbon sources on biomass and EPS production .Fermentation data were used as constraints for simulation of cell growth, including specific growth rate and glucose uptake rate. Maximal specific growth rate was 0.076 hh Figure . For simin silico and in vivo indicated that model iZBM1060 successfully reflected G. lucidum cellular metabolism. Essential genes for cell growth were predicted by single-gene deletion in COBRA Toolbox (MATLAB package) with two media . Hundred and nineteen genes  were predicted to be essential in minimal growth medium, and 88 genes  were predicted to be essential in fermentation medium  biosynthesis of nucleoside sugar precursors; (ii) assembly of repeating units; (iii) process of polymerization . The moniZBM1060, we hereby propose a detailed nucleoside sugar biosynthetic pathway. Glucose, galactose, fucose, mannose, and arabinose reactions are catalyzed by monosaccharide kinase to produce corresponding phosphate monosaccharides, and UDP-glucose, UDP-galactose, GDP-mannose, and UDP-arabinose are then synthesized through action of pyrophosphorylase. E.g., hexokinase  and mannose phosphomutase (GL20742-R1 and GL21817-R1) participate respectively in synthesis of mannose-6-P and mannose-1-P. GDP-mannose is then synthesized by action of GDP-mannose pyrophosphorylase (GL25424-R1).Biosynthetic pathways of EPSs are poorly known because of our inadequate knowledge of related enzymes and their functions. On the basis of model Xylose enters the PPP to synthesize fructose-6-P, and then a nucleoside precursor. Fucose and rhamnose can also synthesize fructose-6-P via the gluconeogenesis pathway. Fructose-6-P has two pathways for synthesis of nucleoside sugar: (i) glucose-6-P isomerase (GL22245-R1) catalyzes conversion of fructose-6-P to glucose-6-P; (ii) fructose-6-P is converted to mannose-6-P by mannose-6-P isomerase (GL17878-R1 and GL22193-R1) and further synthesizes GDP-mannose and GDP-fucose  and 106 genes (10.00%), respectively, were identified as essential for EPS synthesis Figure .For minimal growth medium, predicted essential genes for EPS synthesis were involved primarily in amino acid metabolism (31.45%) and carbohydrate metabolism (21.77%) Figure . For ferG. lucidum EPSs and other important fungi.To further elucidate EPS metabolic mechanisms and pathways, we performed comparative genomics analysis of Antrodia cinnamomea  or tyrosine (25.00%) and no enhancement occurred when tryptophan or the other amino acids were applied in EPS production Figure . AdditioIn several recent studies, expression levels of EPS biosynthetic genes have been manipulated in order to increase EPS production. However, less is known regarding overexpression of target genes for this purpose. On the basis of GSMM, we simulated gene overexpression to guide metabolic engineering for enhancement of EPS production, using MOMA to reevaluate the fluxes and obtain an overexpression algorithm. Eight key enzymes were identified as potential targets for EPS production; i.e., overexpression of PGM gene , UGP gene , TSTA3 gene , GMDS gene , UXE gene (EC: 5.1.3.5), RFBC gene (EC: 5.1.3.13), TGDS gene (EC: 4.2.1.46), and RFFH gene (EC: 2.7.7.24) notably enhanced EPS production Figure .in silico caused an increase of EPS production rate to 0.0106 mmol/gDW/h. UGP is directly involved in synthesis of UDP-glucose, and EPSs contain a high proportion of glucose. UDP-glucose plays a key role in EPS production as a synthetic precursor. PGM catalyzed conversion of glucose-6-P to glucose-1-P; each of these compounds is an important intermediate in EPS biosynthetic pathway. Overexpression of PGM gene increased EPS production from 0.005 mmol/gDW/h in wild-type (WT) to 0.0133 mmol/gDW/h. Thus, increased PGM transcription level was directly correlated with increased EPS production. PGM was also implicated as the key enzyme for EPS biosynthesis in a previous study: maximal EPS production in a PGM gene-overexpressing strain was 1.76 g/L – 44.3% higher than in WT . OverexpUXE catalyzes interconversion of two EPS synthesis precursors: UDP-arabinose and UDP-xylose. When glucose is used as carbon source, UXE plays an essential role in production of UDP-arabinose. TSTA3 and GMDS are involved in synthesis of GDP-fucose. RFBC, TGDS, and RFFH participate in synthesis of dTDP-rhamnose.Results of the analysis described above indicate that EPS production can be effectively improved by overexpression of genes for eight key enzymes. Previous studies have demonstrated the usefulness of PGM and UGP genes in this regard. Future studies will focus on overexpression of the other six genes for improvement of EPS production.Ganoderma lucidum (lingzhi mushroom) is presented here for the first time. The GSMM (termed model iZBM1060) is focused on EPSs, and contains 1404 reactions, 1202 metabolites, and 1060 genes. The model was validated and shown to accurately simulate cell growth and EPS production under various conditions. The nucleoside sugar (EPS precursor) biosynthetic pathway in the model was elucidated completely. Essential genes for cell growth and EPS synthesis, and genes for eight key EPS production enzymes, were analyzed. Two strategies for improvement of EPS production, based on model iZBM1060, were proposed: (i) addition of phenylalanine; (ii) overexpression of the eight key enzyme genes. PGM and UGP genes have previously been shown to be useful targets for enhancement of EPS production, and future studies will focus on overexpression of the other six genes for this purpose. Model iZBM1060 provides a useful platform for regulating EPS production in terms of system metabolic engineering for G. lucidum, as well as a guide for future metabolic pathway construction of other high value-added edible/ medicinal mushroom species.A GSMM for ZM and ZD designed the experiments. ZM, WD, QW, and MX performed the experiments. ZM, CY, GL, FW, GS, ZD, LL, and ZX conceived the project, analyzed the data, and wrote 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.


Streptomyces xiamenensis 318, with a relatively compact genome for secondary metabolism, is used as a model organism in our investigation. Integrated studies via kinetic metabolic modelling, transcriptase measurements and metabolic profiling were performed on this strain. Our results demonstrate that the secondary metabolites increase the metabolic fitness of the organism via stabilizing the underlying metabolic network. And the fluxes directing to NADH, NADPH, acetyl-CoA and glutamate provide the key switches for the overall and secondary metabolism. The information may be helpful for improving the xiamenmycin production on the strain.The production of secondary metabolites, while important for bioengineering purposes, presents a paradox in itself. Though widely existing in plants and bacteria, they have no definite physiological roles. Yet in both native habitats and laboratories, their production appears robust and follows apparent metabolic switches. We show in this work that the enzyme-catalysed process may improve the metabolic stability of the cells. The latter can be responsible for the overall metabolic behaviours such as dynamic metabolic landscape, metabolic switches and robustness, which can in turn affect the genetic formation of the organism in question. Mangrove-derived  Many of these compounds have been actively explored for pharmaceutical and economic purposes . NeverthStreptomyces sp. is well known for producing novel secondary metabolites. The dynamic transcriptional and translational landscape on the strain S. coelicolor A3(2) was obtained previously /[jP] refers to the concentration of the substrate/product with f1 and f2 satisfyTake a general chemical reactionthe form ,174.2u([N metabolites and M reactions whose dynamics can be described byx(t) is an N × 1 vector for the metabolite concentrations, u is an M × 1 vector for the reactions, S is the stoichiometric matrix connecting the reactions to the metabolites and V denotes the parameter set for all the reactions .Accordingly, a metabolic network with A dynamical system under equation (4.6) is usually unstable as some metabolites will quickly accumulate and some others diminish. We therefore need to incorporate some biological aspects into our model. A living cell can adjust its parameters and maintains the metabolic stability under fluctuations of concentrations in both metabolites and enzymes (reaction rates). To find out how the enzymatic parameters should be regulated in order to achieve stability across the network, we can add random noises to equation (4.6) and study the stochastic processes that can help in achieving network stability against the fluctuations.T denotes the matrix transpose and N × N positive-definite matrix. Therefore, equation (4.6) may be be stabilized by a regulation process that minimizes x), which can take the generic formFollowing the formal mathematical analysis developed previously ,34–37, okV ∈ {VF, VB} which reads,kN number of metabolites affected by kV, and nc is the number of carbon atoms carried by the n-th metabolite. The thermodynamic constraint for each reactionThe postulation that equation (4.8) can resemble real, albeit complex biological regulations as well as approximations and choices for ous work . One canThe number of non-zero eigenvalues is determined by the rank of the stoichiometric matrix in equation (4.6). The stability of a solution is justified when all eigenvalues have negative real part. The RT values as a measure of the robustness of a given state are frequently referred to throughout this work. Furthermore, the efficiency of the state can be computed by the ratio of total carbon consumption to the biomass output.nb can be set to/from the ‘External’ by modifying the corresponding Since only a subset of the complete metabolic reactions in a cell is selected for study, one can use a fictitious ‘External’ metabolite to represent the rest of a cell. A number of fictitious reactions connecting to the ‘External’ can be introduced. To specify a desired boundary condition, a bias flux VF, VB} can be chosen in such a way that the reaction fluxes are kept small and uniformly distributed across the entire metabolic network. One then executes numerically the set of ordinary differential equations (ODEs) in equation (4.9) until a steady state of {VF, VB} is found. Once it is achieved, equation (4.11) is called to characterize the stability of the newly found state. The number of steady states so obtained is not unique. By varying the initial values of {VF, VB} for equation (4.9) a range of steady-state fluxes can be obtained and their properties can be analysed. Finally, simulations in the present study were carried out on Matlab platform with ode23t package as the core differential equation solver. Symbolic manipulation package was used in evaluating the eigenvalues in equation (4.11) on the steady states.Note that equations (4.9)–(4.11), together with the boundary set-up contain the full set of mathematical equations necessary to carry out the numerical simulations. In a typical programming set-up, one will have a collection of metabolites and a large set of reactions between them, their numbers run into hundreds. A suitable initial set of {4.5.http://www.shbio.com/). After sequencing, clean RNA-Seq data were produced by eliminating the low-quality raw reads and mapping reads to the reference genome and gene sequences using SOAP2. The levels of gene expression were calculated using the RPKM method and those genes with FDR ≤ 0.001 and a log2 ratio greater than 1 were considered differentially expressed. Raw sequencing data have been deposited in the Arrayexpress database with accession number E-MTAB-7314.For total RNA extraction, cells in the early and late stationary phase at 16, 24, 36 and 72 h were rapidly cooled using chilled ethanol and harvested by centrifugation . Total RNA was extracted using RNeasy Mini Kit  following the manufacturer's instructions and checked for an RIN number to inspect RNA integrity by an Agilent Bioanalyzer 2100 . Qualified total RNA was further purified by RNeasy micro kit  and RNase-Free DNase Set . RNA-sequencing was carried out on Illumina HiSeq 2500 by Shanghai Biotechnology Corporation (


Human muscles are tailored towards ATP synthesis. When exercising at high work rates muscles convert glucose to lactate, which is less nutrient efficient than respiration. There is hence a trade-off between endurance and power. Metabolic models have been developed to study how limited catalytic capacity of enzymes affects ATP synthesis. Here we integrate an enzyme-constrained metabolic model with proteomics data from muscle fibers. We find that ATP synthesis is constrained by several enzymes. A metabolic bypass of mitochondrial complex I is found to increase the ATP synthesis rate per gram of protein compared to full respiration. To test if this metabolic mode occurs in vivo, we conduct a high resolved incremental exercise tests for five subjects. Their gas exchange at different work rates is accurately reproduced by a whole-body metabolic model incorporating complex I bypass. The study therefore shows how proteome allocation influences metabolism during high intensity exercise. During high-intensity exercise, muscles convert glucose to lactate, in a process that is energetically less efficient than respiration. Here the authors develop a computational model based on muscle proteomic data showing that bypassing mitochondrial complex I increases ATP production rates, and validate these model predictions in an exercise test on 5 subjects. The phenomenon is known as overflow metabolism. Human muscles can deploy the maximum-power strategy by producing lactate3, but in contrast to microorganisms the human body is a closed system with regards to lactate and it must be metabolized elsewhere in the body under steady-state conditions.In the context of human endurance performance, high sustained ATP synthesis is one of the most important competitive advantages. It is increasingly recognized4, enabling high specific growth rates. Similarly, physical exercise increases the levels of metabolic enzymes in human muscles5. However, few adjustments can be made to a muscle’s protein composition during the course of an exercise bout, and it must be allocated so that it supports a wide range of ATP synthesis rates using different carbon sources, e.g., glycogen, glucose, and fat. The phenotypes of multicellular organisms do not only depend on the metabolic capacities of individual cells; while the maximal oxygen uptake rate in humans (VO2max) is proportional to the oxidative capacity of the muscle fibers6, it may also be limited by cardiopulmonary oxygen supply7. A number of potential limiting factors have been proposed, including the uptake capacity of oxygen by the lungs, the oxygen delivery due to the rate of blood flow, and the diffusion rate of oxygen into the cell8. To avoid overcapacity, most interacting components are expected to align, assuming that they are under selective pressure.A cells capacity to produce ATP depends on its protein composition. Microbes optimize their allocation of protein to their particular growth condition9. During exercise at varying work rates, the concentrations of key metabolites such as ATP and NADH barely change, despite large differences in ATP synthesis rates9. Some researchers argue that subtle changes in substrate concentrations are sufficient to regulate metabolism10, others find them insufficient and propose that both supply and demand of ATP must be under the regulatory control9. Regardless of which regulatory mechanism may be at play, the metabolic states of the muscle are ultimately constrained by the catalytic capacity of the metabolic enzymes. The range of possible phenotypes that can be attained in muscle through regulation may therefore be investigated using constraint-based modeling.Cells must regulate the rate of ATP synthesis to match the demand. To avoid exhaustion of the ATP pool, ATP must be synthesized at the same rate as it is consumed, and metabolism therefore operates at steady-state during physical activity that lasts longer than a few minutes11. Kinetic models are well suited to study the interactions between metabolite concentrations and fluxes and the roles of different regulatory elements. They have been used to study the biophysical mechanisms underlying experimental data in cells and isolated mitochondria, as well as the theoretical requirements on the regulatory system12. A kinetic model has also been developed on the whole-body level to simulate dynamics in substrate utilization13. Stoichiometric models are, on the other hand, well suited to study the metabolic steady states that cells may attain. They have been used to study the mechanisms behind overflow metabolism in microorganisms17, as well as humans19. Unlike the microbial models that are constrained on the level of individual enzymes, the models19 of human muscles have so far only been constrained by an estimated crowding constraint on the level of each cellular compartment. Metabolic interactions between multiple tissues have also been studied using a stochiometric model20, but was not applied to study fluxes during exercise.Metabolic models are useful tools to study biological systems. They may largely be divided into kinetic models, which use ordinary differential equations, and stoichiometric models, which use linear equations and constraints14. Here, we develop two metabolic models of human muscles, a small-scale enzyme-constrained model and a genome-scale, multi-tissue model. We use the small-scale model to simulate the optimal metabolic strategies at different ATP synthesis rates for different carbon sources. We find that metabolic bypass of complex I is an optimal strategy at high ATP synthesis rates. We integrate protein abundance measurements with the model to generate condition-specific constraints, and find that the capacity of complex I is constraining ATP synthesis. We incorporate these constraints into the multi-tissue model and fit the gas exchange of the model to experimental data from five subjects. A metabolic model without complex I bypass is unable to fit the fluxes. We use the model to study metabolic interactions between different muscle fiber types and peripheral tissue. We use the model to predict the maximum sustainable ATP synthesis rates at different exercise durations, and find that they correspond well to the data on world-record running speeds. Using these model systems, we were able to recapitulate known metabolic phenomena as well as predict previously unknown metabolic strategies in the working muscle during high-intensity exercise.We have previously shown that an enzyme-constrained model of yeast is able to capture shifts in metabolism across different metabolic regimes14. The model relies on estimating the mass of enzyme required for each biochemical reaction based on the specific activities of the enzymes (µmol [mg purified protein]−1 min−1). We reconstructed a small-scale model of intermediary metabolism (including 66 enzyme-mediated reactions) and parameterized it with specific activity values from literature  that enter the electron transport chain at complex III. Fatty acids have a higher energy density than glycogen, and it has been hypothesized that muscles attempt to minimize glycogen utilization (glycogen sparing) by utilizing fatty acid synthesis when possible22. We configured the model to minimize the amount of carbon required for the synthesis of ATP (per cmol), to simulate the optimal utilization of nutrients.Different metabolic strategies will be optimal depending on the type of physical activity, some strategies make efficient use of nutrients, whilst others favor rapid ATP synthesis. The optimal allocation of a finite pool of enzymes can be formulated as a linear optimization problem and studied using an enzyme-constrained model19. The concept of a partially coupled mode, through the bypass of complex I was less expected. However, this fits well with observations in rat23, where differences in oxidative coupling, P/O ratio (ATP produced per molecular oxygen consumed) have been observed among different tissues respiring on substrates of complex I. It is also in agreement with the observed shift to isoforms of complex I with lower proton stoichiometry in Escherichia coli at high growth rates24, and the absence of a proton-pumping complex I in Saccharomyces cerevisiae, as we noted in a previous study14.The optimal enzyme allocation was simulated at different ATP synthesis rates. It showed four distinct metabolic modes that differed in catalytic capacity and substrate efficiency that together formed a Pareto front with the maximum attainable substrate efficiency at each ATP synthesis rate Fig. . A large25. The main advantage over the fully oxidative mode is its high catalytic capacity; to synthesize equal amounts of ATP the fully oxidative mode would require a larger investment in proteins, with direct ATP costs for production and maintenance or opportunity costs in the form of reduced concentrations of other proteins if the total protein content is conserved. The main advantage over fermentation is the high substrate efficiency, which allows sustained ATP synthesis, additionally it does not result in the production of lactate, which cannot be sustained indefinitely. Complex I bypass thus enables a balance between ATP synthesis, protein allocation, and substrate efficiency, and constitutes a middle ground between the fully fermentative and the fully aerobic mode.Since reducing the P/O ratio is expected to decrease the total amount of ATP synthesized, we were interested in how this became an optimal mode. We analyzed the model and found that the effect was driven by the low specific activity of complex I  transfers electrons from mitochondrial NADH to QH2 without pumping protons with a specific activity hundred times higher than for complex I14. To test if the model would favor a non-proton-pumping NADH Dehydrogenase, we temporarily added NDI to the model and found that complex I bypass was even more accentuated in the hybrid model  of the different reactions. We investigated the published proteome of skeletal muscles  from eight healthy subjects 28. The proteome was dominated by motor proteins and metabolic enzymes . The reactions were constrained to half max capacity to account for different effects, e.g., incomplete enzyme saturation, thermodynamically driven backward fluxes29, effectively scaling the maximum flux to the expected biological range.Unlike microbes, human muscles cannot adjust their proteome during ATP synthesis. They must therefore be disposed to handle a range of different metabolic tasks. We evaluated if all four metabolic strategies would be reflected in the protein concentrations in muscle, as the protein concentrations together with their specific activities determine the flux capacities  maximum work rate on an exercise bike (1130 W) compared with the maximum aerobic steady-state rate (350 W)31.We simulated the optimal fluxes at various ATP synthesis rates using the constrained model. The carbon utilization was minimized for each ATP synthesis rate, which is equivalent to maximizing the amount of ATP per carbon consumed. The simulations showed that the proteome of muscle is allocated in such a way that all four metabolic modes could be used, depending on the ATP synthesis rate Fig. . The prep = 0.88). It is possible that enzymes from the beta-oxidation cycle require unusually high capacity to overcome backward fluxes, since the cycle is found to be close to the equilibrium32. Fatty acid reactions aside, most reactions had an effective enzyme usage of between 10 and 100%  was found between the flux capacity and maximum predicted fluxes among enzymes from glycolysis, TCA, and OXPHOS Fig. , suggesti = 0.5[vmax]i). By setting up simulations so that these constraints could not be overcome, it was possible to predict the maximum oxygen uptake rates in different metabolic context that were in good agreement with the corresponding literature data33 from permeabilized muscle fibers  by adjusting the resistance40, the median increase in oxygen expenditure is ~30% , where the excess increase in oxygen expenditure would begin  or glycogen (RER = 1) or a mixture (0.7 < RER < 1) is used as carbon source. The exchange of CO2 and O2 were quantified, and the RER was significantly higher at high work rates  was fitted for each subject. Beyond a critical work rate, the RER exceeds 1, which is driven by the release of CO2 from bicarbonate 44. Under steady-state conditions, this effect is zero, but during lactate accumulation there is a 1:1 relation between the CO2 and the lactate flux. To accommodate RER values exceeding 1, we allowed a manually fitted lactate accumulation flux.A number of metabolic factors influence the oxygen expenditure at the whole-body level. The rate depends on the carbon source as well as the mixture of oxidative and fermentative ATP. Prolonged lactate production at high work rates is unsustainable, it is, however, actively metabolized during exercisesody Fig.  that was2 and O2 as well as the RER fitted the experimental data well  and metabolism of lactate by m1 (type I fibers). This occurred when complex IV attained its maximum capacity in m2; it then became optimal for the model to abandon fatty acid consumption and replace it with glycogen consumption (since only glycogen can be fermented). Simultaneously, m1 was forced to switch from palmitate as a carbon source, to act as a sink for the excess lactate. Since our model assumes steady-state, it cannot capture dynamic effects when lactate begins to accumulate. This may explain why our predicted RER values are lower than observed around the lactate threshold. This may also explain the slow component phenomenonThe maximum work rate of a muscle depends on the carbon source. We used the model to investigate the maximum steady-state ATP synthesis rate on different carbon sources Fig. . The rat46. We simulated the maximum work rate that could be sustained for different durations of exercises by constraining the uptake rate of different carbon sources by the pool size divided by the duration of the exercise. For lactate, we assumed that it accumulated in blood and tissues  with a final lactate concentration of around 15 mmol per l43, allowing high lactate efflux at short durations. With these constraints, we arrived at a map of the metabolic capacities in exercise bouts of different durations . This corroborates our model predictions and suggests that much of the decrease in running speed at longer distances may be understood from the optimal utilization of different carbon sources with limited pool sizes. Other factors, including air drag, and differences in protein allocation and body composition between runners are expected to attenuate the effect, and may explain the larger dynamic range for the predicted values compared with the observed.To verify our predictions, we compared them with the world-record running speeds. The average running speed in exercising humans decreases as a function of the distance (and hence duration) of the race, which is related to the decreasing power outputons Fig.  and foun21 gly-phos shuttle, it is, however, not impossible that it is bypassed through another mechanism in vivo, e.g., disengagement of the proton pumps in complex I27. This seems particularly probable for NADH of mitochondrial origin. Regardless of mechanism, a whole-body model that included complex I bypass showed a good fit to exchange fluxes of O2 and CO2 from human subjects, while the fit for a model without complex I bypass was poor. Furthermore, taking the pool sizes of different carbon sources into account, the model was able to predict a decreasing trend in power output with increasing durations, that was consistent with the trend for world-record running speeds.We investigated the metabolic fluxes in muscle fibers at different work rates using metabolic modeling. A metabolic model constrained with protein concentrations from muscle predicted that complex I is bypassed at high ATP synthesis rates. The oxygen fluxes predicted by the model were in good agreement with the literature data for muscle fibers. The bypass of complex I is predicted to increase the catalytic capacity of the muscle at the expense of substrate efficiency, an effect that emerges from the low specific activity of complex I. While the model predicted that complex I is bypassed through the well-established5. From this perspective, it is interesting to note how different forms of doping may improve performance, e.g., the maximum work rate and VO2max can be increased in response to drug erythropoeitin EPO, which is intended to increase oxygen delivery. In a recent randomized controlled trial49, the placebo group experienced a 2% increase in both maximum work rate and VO2max (12.5 dVO2 per dW), while the increase was 5% in maximum work rate and 9% in VO2max for the EPO-group (20.8 dVO2 per dW). Again suggesting that a linear relation between oxygen expenditure and work rate may be a too simple model.To maximize performance, the muscle must allocate the protein composition for the tasks it is likely to encounter. A coarse-grained adaptation appears to occur through the composition of muscle fibers, by tuning the ratio of glycolytic type II fibers and the more oxidative type I fibers. But the body is also capable of adjusting the catalytic capacity of individual fibers13, as well as experimental values at ~66% and 70%, respectivly50. The low empirical utilization of fat as a substrate may be due to an impaired ability to deliver fatty acids to the muscle, as was previously proposed50. The composition may also reflect an adaptation to a high carbohydrate diet, as the utilization of fat is shown to increase on a high-fat diet51, however, this also reduced their maximum work rate. Furthermore, postprandial effects may influence the disposition toward carbohydrate metabolism. It is also interesting to notice that glucose is predicted to be a suboptimal fuel compared with glycogen since glycogen bypasses the first ATP-demanding step of glycolysis. This emphasizes the value of glycogen loading52 in favor of carbohydrates from exogenous sources. The later should, however, be optimal at longer distances, as the uptake rate has been observed at a maximum of 100 g per h using a mixture of glucose and fructose53. At high work rates, lactate produced by one muscle fiber may be consumed by another, allowing a division of labor, but since lactate is a less oxygen efficient substrate than glycogen, it will increase the overall oxygen expenditure by the muscle. At the highest work rates, lactate accumulates, and the associated physiological disturbance quickly results in exhaustion.Regarding optimal substrate utilization, the model predicts a switch from mixed oxidation of fatty acids, glucose, and glycogen to exclusive glycogen oxidation at high work rates. This is due to the extra ATP gained from glycolysis compared with beta-oxidation. This is in agreements with dynamic models of muscle tissues that predicts that most (70–90%) of ATP originates from carbohydrates at high work rates, out of which most (~80%) originate from muscle glycogen54. This conflict may potentially be resolved by the concept of complex I bypass, assuming that it occurs through the disengagement of the proton pumps; mutations may induce complex I bypass and increase the catalytic activity of the cancer, increasing its proliferation, inhibitors of complex I may on the other hand also inhibit the NADH to QH2 reaction, effectively blocking complex I bypass. The relative expression of glycolytic and OXPHOS proteins shift under many paraphysiological conditions56. Here, complex I bypass may play an important role, given its intermediary location between oxidative and fermentative metabolism (Supplementary Discussion).Complex I bypass may play an important role in many pathological and paraphysiological conditions. Complex I is implicated in tumorigenesis, but there are conflicting observations with regards to its role. Many studies suggest that several subunits of complex I act as tumor suppressors and that mutations promote cancer progression, yet inhibition of complex I is the proposed mechanism of action for many anticancer compounds57. We have been able to overcome these limitations to some degree through the use of duration-dependent uptake rates and a simple dynamic model. But most of the results depend on the steady-state assumption. In our simulations, we also assume that all enzymes operate at half their maximum rate, which is an apparent simplification and new types of models are under development that will allow the saturation of each enzyme to be estimated58. Nevertheless, steady-state modeling of metabolism may, thanks to the low number of parameters required and its intuitive application, serve as a framework to further our understanding on metabolic adaptations to exercise and limitations to human performance.While the model has demonstrated merits in predicting phenotypes and providing systems-level insight, the phenomena that have been described herein, e.g., complex I bypass, require additional experiments and dynamic modeling to elucidate their mechanisms and regulation. Steady-state modeling is a useful paradigm, but many biological phenotypes are transient or rely on finite resources, e.g., the transient peaks in lactate concentration observed during moderate exerciseIn conclusion, the model developed here recapitulates long known metabolic strategies during high-intensity exercise, such as lactate production and a switch toward glycogen as carbon source. It also makes several predictions of metabolic strategies during high-intensity exercise, including complex I bypass and lactate accumulation. These metabolic strategies explain several observations relating to altered oxygen expenditure during exercise.59 was used with some additional manual curation (as specified in Supplementary Methods). The model originates from a generic GEM60 that has been reduced to a muscle model by removing reactions without experimental support in the proteomics and transcriptomics data. Simulations using flux balance analysis (FBA) were carried out using the RAVEN Toolbox61.A genome-scale metabolic model of the muscleA reduced stoichiometric model was constructed by simulating ATP production in the genome-scale model and removing the reactions that did not carry flux. To ensure that multiple metabolic strategies would be available also in the reduced model, multiple simulations were conducted where flux through important enzymes were blocked, e.g., enzymes from the respiration chain (Supplementary Methods).62 and literature  or binned as “other”. Myoglobin and albumin were given their own category due to their high abundance.The mean spectral count from eight subjects was calculated for each protein in a published data setmax of each enzyme-catalyzed reaction of the reduced model was calculated from the relationship vmax = [protein mass concentration] × [specific activity]. The flux of each reaction was constrained to an estimated effective vmax as v ≤ η × vmax, were the factor η (0 ≤ η ≤ 1), which represents incomplete enzyme usage, was taken as η = 0.5 for all reactions. The uptake of oxygen, glycogen, and octanoic acid was left unconstrained. The ATP synthesis rate was fixed at incrementally increasing values, and the optimal flux distribution was calculated using FBA by minimizing the influx of carbon, subject to the effective vmax constraints. To simulate oxygen expenditure in muscle fibers in different metabolic contexts, the ATP synthesis was maximized. A reaction that provided glutamate and removed aspartate was added to simulate the complex I substrates, and a reaction that provided succinate and removed fumarate was added to simulate complex II substrates. To simulate the oxygen flux in absence of complex I bypass, the gly-phos reaction was blocked. For the enzymes TPI and PFK, no measurements were found in the proteomics data, and these reactions were therefore not constrained. For TPI, the specific activity was sufficiently high to not be limiting even if protein levels were below the limit of detection. For PFK, there was a “PFK like protein” detected, but this was not used in the simulations (Pseudo code in Supplementary Methods).The v2 per dW metric at high and low work rates was calculated from a published data set (21 subjects) from an incremental exercise protocol on bike40. For low work rates (work rates below the lactate threshold), the reported slope of regression was used. No slope was reported for high work rates so there dVO2 per dW was calculated from the difference in reported VO2 and W at the lactate threshold (LT) and at max. The p-value was calculated using Wilcoxon rank-sum test.The dVO2max 62 ± 11 ml kg−1min−1, Wmax 5.2 ± 1 W per kg, ± s.d.) completed a high-resolution incremental test to measure VO2 and CO2 gas exchange in expired air at a variety of work rates. The subjects were all experienced cyclists, and refrained from the training (36 h), caffeine (6 h), and food (3 h) preceding test and received information and gave written consent before test. All cycling during the submaximal test was performed at 70 rpm on an ergometer  with continuously breath by breath measurement of VO2 and CO2 . Due to technical limitations of the bike and the need for steady-state O2 consumption, low work rates (rest to 100 W) was measured at durations of 2–5 min at increasing work rates of 10–20 W per step. At higher work rates (80 W up to a power output eliciting ≤ 1.00 RER), the work rate was increased in steps of 5 W per 90 s. The power- and gas-exchange data were time aligned and 15 s means were calculated. After the submaximal test, a short incremental test to exhaustion was performed for determining VO2max. On a separate occasion lactate was sampled  at 5 min long intervals for one of the subjects during a standard LT-test with increments of 30 W (rest to 330 W) used at the laboratory. It is assumed that the experimentally determined fluxes were at steady-state, since the increments in work rate were small and each work rate target was maintained for a long time.The experiments on human subjects were approved by the Swedish Ethical Review Authority and conformed to the declaration of Helsinki. Five male subjects  assuming a ΔG of 60 KJ per mol ATP37. This is in good agreement with an experimentally estimated value of 27 J per mmol ATP found for active muscle performing high-intensity aerobic exercise39, which was used for the simulation. The gas exchange was converted assuming a concentration of 44.66 mmol oxygen per liter.Three genome-scale models of the muscle were combined to a multi-tissue model by a transport compartment that joined the exchange fluxes from all sub-models, allowing exchange of metabolites between the sub-models .The accumulation of lactate and uptake of each substrate was constrained to 0. The uptake of each carbon source was then permitted in turn, and the ATP synthesis was maximized using FBA. With lactate and fat as carbon sources, the peripheral tissue was allowed to consume the substrate to support maintenance (pseudo code in Supplementary Methods).64 for the m1 and m2 tissue were divided by the duration of the exercise to calculate the maximum steady-state influx at each duration. Glucose flux was calculated from an assumed liver glycogen store of 100 g, corresponding to 0.56 mol. Fat uptake was left unconstrained. The lactate concentration in blood was constrained to not exceed 15 mmol43. For each investigated duration, the work rate was maximized using FBA (pseudo code in Supplementary Methods).The reserves of glycogen of ~80 mmol per kg wet weightFurther information on research design is available in the Supplementary InformationSupplementary Dataset 1Supplementary Dataset 2Reporting SummaryDescription of Additional Supplementary FilesPeer Review File


Understanding the interactions between microbial communities and their environment sufficiently to predict diversity on the basis of physicochemical parameters is a fundamental pursuit of microbial ecology that still eludes us. However, modeling microbial communities is problematic, because (i) communities are complex, (ii) most descriptions are qualitative, and (iii) quantitative understanding of the way communities interact with their surroundings remains incomplete. One approach to overcoming such complications is the integration of partial qualitative and quantitative descriptions into more complex networks. Here we outline the development of a probabilistic framework, based on Event Transition Graph (ETG) theory, to predict microbial community structure across observed chemical data. Using reverse engineering, we derive probabilities from the ETG that accurately represent observations from experiments and predict putative constraints on communities within dynamic environments. These predictions can feedback into the future development of field experiments by emphasizing the most important functional reactions, and associated microbial strains, required to characterize microbial ecosystems. This knowledge is a mechanistic description of an event and is necessary to estimate the “cost,” or effect when one event occurs over another. Here we derive a nitrogen network composed of a hypothetical series of reactions , as laid out in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database map00919  and their consequences are the respective production and consumption of metabolites  when the product of one reaction becomes the substrate for another reaction (directed edge). Thus, the above 14 reactions result in a graph of 14 nodes and 32 edges see , a multiper se. This cost is necessary to maintain every compound at a stationary amount when every transition is equiprobable . For each compound, this initial cost will be assigned to events that do not mention them explicitly. For instance, for all reactions that do not consider ammonia, nitrite or nitrate as metabolites, one must compute a cost for these metabolites. Thus, following a computational procedure described in In addition to the overall definition of an event  and description of the interactions within events (through the construction of a graph), the cost of considering one event over another must also be defined. As a mechanistic description, each event consumes and produces compounds, which will point to the cost of using events. For instance, each reaction within the nitrogen cycle can be described by its stoichiometry . However, when randomly crossed, the graph could promote an artificial increase or decrease of a given compound, solely due to the graph topology and chemical stoichiometry. Such a result would not represent a correct output of the modeling approach, but rather a prospective flaw. To avoid this, one must compute the cost  for all compounds for each event, that is not the stoichiometry ETG modeling estimates probabilities associated with interactions between events (herein reactions) such that the succession of events reproduce quantitative experimental data. For illustration, we use chemical variables from Bouskill et al. , which dExperimental variation in rates for each season  for the years 2001, 2002, 2003, and 2004, and for each nutrient was thus estimated from Once the ETG model considers (i) a set of events and their putative interactions (section 2.1.1); (ii) a cost for each event  a quantitative rate that depicts an experimentally observed quantitative variation impacted by at least one event (section 2.1.3), one seeks then to learn probabilities to prioritize interactions between events in order to reproduce the above computed rates as they resume the environmental conditions to reproduce. The overall parameterized model will herein reproduce variations of ammonia, nitrite, and nitrate by weighting the succession of metabolic reactions . An optimization process , expressed in percentage, was also computed. The S score associated with a transition expresses the fact that the Euclidean distance between the expected rates  and their predictions is modified by S % when its probability value is changed by 1%. Such a sensitivity score permits ranking the transitions according to their respective sensitivities . In practice, sensitivities between two-time points depict in Along with probability estimates for transitions between each event, a sensitivity score  initial quantities  that one fixed in our study to 2 h.here, including a docker container. See The Event Transition Graph (ETG) modeling was performed via a Python package called POGG. This package is the first python implementation of the ETG modeling, as proposed in Bourdon et al. . POGG pa3 → NO2), a rate-limiting step in the Nitrogen cycle  mediate the rate-limiting step of nitrification  between 2001 and 2004  are of interest. Beyond the probabilistic simulations, the analysis of probabilities between reactions  behavior and to test it regarding experimental data. For an illustration of the interest of ETG specificity, we propose to build a counterexample by randomizing the model and training it on the same dataset.The randomized model consists of building a graph similar to the nitrogen cycle graph for which all edges have been shuffled by permutation. The randomized model is then similar to the ETG nitrogen cycle model regarding the numbers of nodes and edges. We then applied a similar modeling and training procedure to that described above. As pictured in The goal of this study is to demonstrate the interest of the ETG modeling framework. In this purpose, one uses a reduction of the nitrogen metabolic network. From the biological viewpoint, despite partial promising outcomes, several modeling results do not reproduce the experiments. First, the probabilistic model does not accurately simulate the variation of nitrate, while reproducing ammonia and nitrite quantity variations. Second, and not presented in this study, the model is not able to simulate ammonia and nitrite quantities as taken from anaerobic samples . Among them, once learned, probabilities allow considering a few to reproduce a given quantitative behavior. Compared to general Bayesian modelings, this combination of qualitative and quantitative knowledge makes our probabilistic modeling sensitive to mechanistic descriptions that take the form of scrupulous accumulations or consumptions of quantities over time  most studies focus on community changes, which is itself a transient behavior. Modeling such transient behaviors is the aim of state-of-the-art continuous modelings. However, contrary to these techniques, ETG promotes the use of simple mechanistic descriptions that do not consider kinetic parameters and initial conditions per se to simulate quantity variations over time, but respectively the probabilistic combination of simple additive laws and quantitative rates to reproduce.This ETG framework is ideal for investigating the dynamic and transient nature of microbial ecosystems when few quantitative knowledge is available. ETG does not begin with an assumption of a community at steady state, unlike Flux Balance Analysis techniques to model metabolic networks  necessary to satisfy for reproducing variations of quantities emerging from the biological system. One advocates that these constraints could impact as well the microbial communities that are providing the constrained metabolic reactions. To validate this assumption, one must consider further biological knowledge such as a systematic description of the microbial ecosystem over time. For instance via 16S rRNA sequencing, one could associate the patterns of microbial diversity with the metabolic constraints as highlighted by the ETG  and further compared them to co-occurrence patterns  or the analysis of exchanges of interest between microbial strains (Borenstein et al., Similarly, the same dynamical property should be a benefit to decipher subsets of metabolites that are of interest in a given ecosystem. In this purpose, one must associate this result with genomic descriptions of prokaryotic organisms, for instance via metatranscriptomic or metagenomic studies. Such association between modeling outcomes and meta-omics knowledge could drive future definitions of the keystone species (i.e., Finally, this study considered an hypothetical metabolic network as a qualitative description, but ETG is a modeling paradigm that could consider other qualitative descriptions of the microbial ecosystem such as co-occurrence networks (Patel et al., DE, NJB, BW, and JB designed the study. DE, DV, JG, and JB performed the study. DE and NJB analyzed the data. DE, NJB, DV, BW, and JB wrote 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.


Plasmodium falciparum, a malaria pathogen, has shown substantial resistance to treatment coupled with poor response to some vaccines thereby requiring urgent, holistic, and broad approach to prevent this endemic disease. Understanding the biology of the malaria parasite has been identified as a vital approach to overcome the threat of malaria. This study is aimed at identifying essential proteins unique to malaria parasites using a reconstructed iPfa genome-scale metabolic model (GEM) of the 3D7 strain of Plasmodium falciparum by filling gaps in the model with nineteen (19) metabolites and twenty-three (23) reactions obtained from the MetaCyc database. Twenty (20) currency metabolites were removed from the network because they have been identified to produce shortcuts that are biologically infeasible. The resulting modified iPfa GEM was a model using the k-shortest path algorithm to identify possible alternative metabolic pathways in glycolysis and pentose phosphate pathways of Plasmodium falciparum. Heuristic function was introduced for the optimal performance of the algorithm. To validate the prediction, the essentiality of the reactions in the reconstructed network was evaluated using betweenness centrality measure, which was applied to every reaction within the pathways considered in this study. Thirty-two (32) essential reactions were predicted among which our method validated fourteen (14) enzymes already predicted in the literature. The enzymatic proteins that catalyze these essential reactions were checked for homology with the host genome, and two (2) showed insignificant similarity, making them possible drug targets. In conclusion, the application of the intelligent search technique to the metabolic network of P. falciparum predicts potential biologically relevant alternative pathways using graph theory-based approach. Plasmodium falciparum accounted for the majority of the cases. P. falciparum has developed resistance to all antimalarial medications including the most potent one—artemisinin  = ∅, then P[i] = ⋃j∈A−(i)⋃PϵP∣j∣ P ⋃ {}  due to the nature of the dataset where several reactions within the dataset could be on the same level in the topological order thereby varying the results from each run of the algorithm. For instance, if reactions A, B, and C have the same topological order, then it implies that at different runs of the algorithm, reaction A could come first or reaction B or reaction C, which varies the result of the algorithm at different runs. The metabolic network of T∗Therefore, the modified k-shortest path algorithm to improve the quality of biologically relevant alternative path prediction. Reward shaping is the addition of an extra reward signal that encodes some heuristic knowledge of the system designer or domain expert, thus encouraging the learning agent to explore parts of the state space believed to contain good solutions [The concept of reward shaping was also introduced to the olutions –28. In polutions  and thisolutions , 31.Rk of each feasible path between a source reaction and target reaction is computed using the number of reactions within the path that can be found in the set of annotated reactions RA. An annotated pathway was obtained from the KEGG database. The justification for this model is based on the assumption that the annotated pathway for a metabolic process represents the shortest path; hence, the next shortest alternative path is most likely to have branched from the annotated path thereby containing most of the reactions in annotation. The reactions in the feasible path were compared to the annotated path to determine the number of intersecting reactions. However, the total cost of a path is a function of its length and the number of annotated reaction contained in the path. Mk represents the initial reward for path k, c(ri) represents the coefficient for reaction (ri) which has a constant value of 1. xi will assume the value of 1 if ri is present in the set of annotated reaction for that pathway and 0 if otherwise.The reward t is a “balancing factor” which is an arbitrary value that is suitable to provide a penalty value that balances the positiveness of annotated reactions present in the path and the disadvantages of the path length.In addition to the objective, obtaining biologically relevant paths is also to obtain the shortest path; hence, a penalty function is introduced which is meant to reduce the reward of paths based on their length; this is represented ink is given as the difference between its initial reward and penalty score as shown inThe reward for a path t to be 0.5 for a fair penalty score and the adjusted cost is the difference between the number of reaction in the path and the reward. We then applied the modified T∗ algorithm to the glycolysis and pentose phosphate pathway of P. falciparum to obtain the k-shortest paths for k = 5.In this study, we chose T∗ algorithm to the glycolysis and pentose phosphate pathway of P. falciparum to obtain the k-shortest paths for k = 5. We then applied the modified For the glycolysis pathway, we predicted a pathway, which could provide an alternative path for the generation of pyruvate. This metabolic reaction involves alpha-D-glucose-1-phosphate uridylyltransferase. The predicted alternative paths for the glycolysis pathway are presented in The predicted alternative paths for the pentose phosphate pathway are presented in bik(pj) is the proportion of the shortest path linking pi to pk that contain pj, gik(pj) is the number of the shortest path that contain point pj as an intermediary in the shortest path from pi to pk, and gik is the number of the shortest path from pi to pk. dij is the pair dependency which represents the degree to which a point, pi, must depend upon another, pj. CB(pj) is the betweenness point centrality.To verify the predicted pathways, a test of the essentiality of these genes was done on the predicted pathways. Betweenness centrality measure was applied to the reconstructed metabolic network to each reaction in the network to determine the essentiality of a particular metabolic pathway that is critical for the production of a target compound identified. The betweenness centrality method is stated as follows:The genes that coded the enzymes responsible for the essential reactions discovered were obtained and blasted against the human genome to evaluate the existence of homology in the host. We used a centrality score of 0.25 as the level of significance because the centrality score of some of our predicted reactions that validated the gold standard set of essential enzymes and other predicted ones in literature has a centrality score of approximately 0.3. Figures P. falciparum metabolic network reconstructions have been done previously, but this study is aimed at predicting alternative metabolic pathways for energy generation in the malaria parasite via the pentose phosphate and glycolytic reactions using the k-shortest path algorithm. The essentiality of these reactions to the survival of these parasites was also evaluated in the predicted models , which catalyzes energy-generating reactions such as galactose metabolism and glycogen synthesis, has been reported [P. falciparum in vitro; however, it may not completely kill the parasite [Similarly, different stages of malaria parasites have been reported to have different energy requirements while isolates . Malariaisolates , 37; it reported  thereby parasite . This reparasite , 39.For the cysteine and methionine pathways, hemoglobin digestion is essential for the malaria parasite; L-methionine transport reaction and L-cysteine:2-oxoglutarate aminotransferase were also predicted as essential reactions. The altered transport of nutrients across the membrane may also vary in sensitive and resistant parasites ; hence, P. falciparum essentially synthesize nucleotides de novo. Hence, it cannot salvage pyrimidines from an extracellular environment. This biosynthetic pathway has been identified as a good target for malaria control [P. falciparum will further elucidate survival and alternate energy generation pathways in the parasite. The hypothesized potential pharmacological enzyme targets essential for the parasite can be targeted to control malaria infection globally.Similarly, our study predicted a metabolite—sedoheptulose-1,7-diphosphate—from the breakdown of xylulose, in the pentose phosphate pathway, which may be utilized by the parasite to scavenge for energy. Only sedoheptulose-7-phosphate:D-glyceraldehyde-3-phosphate glycolaldehyde transferase was predicted as an essential reaction within the pentose phosphate pathway.  control . For theP. falciparum. We predicted two (2) essential proteins in the glycerophospholipid, purine, pyrimidine, and glycolysis metabolic pathways (k-shortest path algorithm, we have been able to identify potential biologically relevant paths using a computational graph-based technique which hitherto is less utilized due to its very high false positive result. Biologically targeting these candidate proteins in Plasmodium is recommended to improve understanding of the predicted alternative pathways and the precision of the graph theory-based method.In this work, we have been able to predict alternative metabolic paths in the glycolysis and pentose phosphate pathways of pathways  without


Microbiomes are complex aggregates of organisms, each of which has its own extensive metabolic network. A variety of metabolites are exchanged between the microbes. The challenge we address is understanding the overall metabolic capabilities of a microbiome: through what series of metabolic transformations can a microbiome convert a starting compound to an ending compound?We developed an efficient software tool to search for metabolic routes that include metabolic reactions from multiple organisms. The metabolic network for each organism is obtained from BioCyc, where the network was inferred from the annotated genome. The tool searches for optimal metabolic routes that minimize the number of reactions in each route, maximize the number of atoms conserved between the starting and ending compounds, and minimize the number of organism switches. The tool pre-computes the reaction sets found in each organism from BioCyc to facilitate fast computation of the reactions defined in a researcher-specified organism set. The generated routes are depicted graphically, and for each reaction in a route, the tool lists the organisms that can catalyze that reaction.We present solutions for three route-finding problems in the human gut microbiome: (1) production of indoxyl sulfate, (2) production of trimethylamine N-oxide (TMAO), and (3) synthesis and degradation of autoinducers. The optimal routes computed by our multi-organism route-search (MORS) tool for indoxyl sulfate and TMAO were the same as routes reported in the literature.Our tool quickly found plausible routes for the discussed multi-organism route-finding problems. The routes shed light on how diverse organisms cooperate to perform multi-step metabolic transformations. Our tool enables scientists to consider multiple alternative routes and identifies the organisms responsible for each reaction.The online version of this article (10.1186/s40168-019-0706-6) contains supplementary material, which is available to authorized users. Microbiomes harbor a multitude of different microbes that are living together in close contact. These microbes are interacting with each other synergistically, competitively, and antagonistically, by various mechanisms. One key interaction is the exchange of metabolites. To understand the functional capabilities and dynamics of a microbiome, knowing how exchanged metabolites hold together the microbiome’s overall metabolic network is necessary.For example, indoxyl sulfate is derived from the breakdown of L-tryptophan by colon microbes, involving also the human host. It is an extensively studied uremic solute in humans, which has been implicated in toxicity among patients with kidney disease . ClearanMulti Organism Route Search (MORS) to propose plausible biosynthetic routes between researcher-supplied starting and ending  metabolites, where such routes can span multiple microbes and other organisms, including the human host. MORS finds linear reaction sequences that convert the specified start metabolite to the specified final metabolite. Such a series of consecutive biosynthetic reactions is called a route. A key use case for MORS is exploratory searching for implicated organisms and reactions, when a goal metabolite is given, which may have been found in a metabolomics experiment.We have developed a software tool called During more than two decades, we have developed the BioCyc website  and its We introduced a single-organism Metabolic Route Search tool  in 2014  between MORS extends the single-organism RouteSearch to enable route searches that utilize reactions from an arbitrary number of organism databases in the BioCyc collection. Thus, one new feature is to enable the user to select a set of organisms to consider in a particular search. An organism set can be selected in several ways: by searching for individual organism by name, by browsing alphabetical lists of organism names, by selecting organisms from the NCBI taxonomy , and by R in MetaCyc is assigned to R in every other BioCyc database in which it occurs.To make MORS practical, finding an efficient way to perform route searches across an arbitrary subset of the 14,560 organism databases in BioCyc was essential. Our solution exploits the special role that our MetaCyc  databaseThe exceptions are transport reactions that are inferred by our Transport Inference Parser  based onTo speed the execution of MORS, we pre-compute a 2D binary array whose rows are reaction IDs in MetaRoute and whose columns are BioCyc organism IDs. At the intersection of a particular reaction ID and a particular organism ID, the bit is either set if this reaction is in that organism or unset otherwise. This array enables quickly determining all the reactions that a given organism in BioCyc contains, without even having to open and load that particular database. Constructing this array requires opening all BioCyc organism databases and takes several hours of processing time. A separate, pre-computed database contains genomic metadata for each BioCyc organism, so we can rapidly obtain the list of organisms for a specific HMP body site, for example. Combined, these pre-computed data enable efficiently finding the union of all reaction IDs expected for the set of organisms the researcher has selected.R1 leads to a reaction R2 where the set of organisms containing R1 shares no organism with the set of organisms containing R2. In other words, the sets of organisms containing R1 and R2 have an empty intersectionMORS provides an additional property that is minimized during route searches, in addition to the RouteSearch costs of lost atoms and of the number of reactions in the route. MORS also minimizes switching of organisms in a route. A switching of organisms occurs in a route when a reaction S of organisms as it proceeds to find a route from the source compound to the target compound. The initial value of S is the subset of organisms, from the user-specified set of organisms, which contains the first reaction of the route. S is updated when expanding the route with a reaction R by the intersection of S and the set of organisms containing R. Switching of organisms in a route is detected when S becomes empty, at which point, the set of organisms S is reinitialized to the user-specified set of organisms that contains the most recently added reaction R, and the user-selected cost of switching is added to the cost of the route. Note that during the search of a route, S is a set of organisms that contains all the preceding reactions up to the last switching. That algorithm computes not only the cost of switching, but also a series of maximal sets of organisms that maintains a minimum switching strategyThe search algorithm was modified to minimize organism switching given a user-provided cost for one switching of organisms. The costs of all organisms switches are added to the other costs, such as the cost of lost atoms, in evaluating a route. As before, the overall minimum cost route is considered the best route. The algorithm to detect switching of organisms maintains a set R in the route, a set SR is computed based on the intersection operation, which is the maximal set of organisms that we can have, to not force a switching to other organisms. Note that maximal means that no organism can be added to that set, such that this organism would contain all the reactions up to the last switching of organisms. Naturally, if the set SR becomes empty, a switching occurs and that set is reinitialized to the set of organisms that contains R, which, by definition, is also the maximal set of organisms that contain R. By induction, the maximal property is true for all sets SR of the route.Indeed, as just discussed, for each reaction For efficiency, the intersection of organism sets is implemented by bit-vector operations. We have considered using a sparse representation of sets when the user selected a large set of organisms (> 200), but that does not appear useful, because many reactions belong to many different organisms resulting in large non-sparse bit vectors.https://biocyc.org/PToolsWebsiteHowto.shtml#smarttables.Commonly, single start and goal metabolites will be specified. But consider that in some cases, the start  metabolite may be unknown. In a case where the start metabolite is not known, the researcher may want to direct the tool to explore routes from any of a set of start metabolites to determine which is the most plausible candidate. With an additional set of selectors, the researcher can specify a starting metabolite set, or a goal metabolite set, as the set of metabolites that are recorded in a BioCyc SmartTable. A SmartTable is a stored collection of BioCyc objects . SmartTaFor convenience, we have pre-defined a SmartTable containing a useful set of start or goal metabolites consisting of 13 common intermediate metabolites of central metabolism. This SmartTable can be chosen within the MORS input dialog, and its metabolites are listed in Table The researcher can also specify the number of routes to compute, the maximum allowed time for the computation, and the maximum route length. Longer routes can take substantially more time to find, because a deeper tree of choices must be traversed, which, in the worst case, could lead to an exponential time increase. Therefore, a search may possibly time out, before truly optimal routes may have been discovered, based on the search criteria entered. In this case, it might be best to experiment with increasing the allotted time or changing start and/or goal metabolites to similar candidates that could lead to shorter routes.MORS presents computed routes at the bottom of the results page as SVG graphics, sorted by increasing computed cost. Each route is a linear succession of reactions, connecting a start to a goal metabolite and showing the intermediates. Mouse-over tooltips on reaction arrows show the full reaction equation and state the count of the organisms that contain the reaction. If there are few organisms, they will be listed by name. The full table of organisms can be captured in a SmartTable and subsequently exported.The following results were obtained with version 22.6 of BioCyc. The size of the pre-computed binary reaction array is 17,342 reactions versus 14,562 organisms.Table Homo sapiens. In total, this amounted to 675 organisms, which we call the GI-Human set. The organism count refers to individual strains. We like the GI-Human set, because it is one of the most studied microbiomes, such that example routes can be found that also have been described in the literature, to verify the results produced by MORS.Below, we show several example routes computed by MORS. For all examples, the same set of organisms was used, namely all organisms in the human microbiome body site called “gastrointestinal-tract” plus Metabolism →Metabolic Route Search and then clicking on the checkbox next to “Routes across Multiple Organisms?”. Please see Additional file The researcher invokes MORS by the BioCyc website menu command Indoxyl sulfate is implicated in toxicity among patients with kidney disease . DietaryTo see whether MORS can find this route, we selected the GI-Human set described previously. The start compound was L-tryptophan and the goal compound was indoxyl sulfate. We set the “number of routes” to 3 and the “maximum route length” to 9.Homo sapiens only. The single organism switch is clearly indicated in the pathway diagram in Fig. MORS was found as the two top routes the expected route discussed above, consisting of three reaction steps, retaining nine atoms, shown in Fig. A third route of nine reactions is also found, retaining seven atoms and involving one organism switch  by gut microbes; the liver further converts TMA to trimethylamine N-oxide (TMAO) . TMAO isTo see whether MORS can find this known route, we again began with the GI-Human organism set. The start compound was L-carnitine and the goal compound was TMAO. We set the “number of routes” to 3 and the “maximum route length” to 7.Homo sapiens only. The single organism switch is depicted in the figure.MORS found the expected route, discussed above, as the top route, consisting of two reaction steps, retaining four atoms, shown in Fig. γ-butyrobetaine, which then is converted to TMA by a reaction that is very similar to the first step in the known route. Thereafter, TMA is converted to TMAO by the same reaction step as in the known route. This last reaction is catalyzed by Homo sapiens only, whereas the second to last is catalyzed by 44 different microbes. Therefore, the first five steps all were found to be catalyzable by at least 44 microbes. The routes differ in their first step, which utilizes a carnitine-CoA ligase in the second route, whereas it is a γ-butyrobetaine-CoA:carnitine CoA transferase in the third route. An interesting paper made the case that dietary L-carnitine is first primarily converted to γ-butyrobetaine, at a rate three orders of magnitude higher than the formation of TMA [γ-butyrobetaine is converted to TMA in a downstream part of the gut, by a somewhat different microbial community. MORS was able to explore such longer and apparently more physiologically relevant routes, despite their higher cost.A second and third route, both containing six reactions, are also found. Both retain four atoms and involve one organism switch . So we picked L-homoserine lactone as the goal compound. As the start compound, we chose S-adenosyl-L-methionine (SAM), the known source for the biosynthesis of this class of autoinducers.We again selected the GI-Human organism set. We set the “number of routes” to 4 and the “maximum route length” to 4, because a large value such as 10 led to a time-out. One possible cause could be that S-adenosyl-L-methionine is used in a very large number of reactions, so the search tree is very large and time consuming to traverse.MORS found several related routes, which all have two reactions, retaining seven atoms and involving zero organism switches, shown in Fig. Ralstonia pickettii 5_7_47FAA, does not occur in the organism set for the first reaction, indicating an example of a microbe that solely degrades a signaling compound synthesized by other microbes, which could lead to an asymmetrical interference with quorum sensing.However, by setting the “switching organisms cost” parameter to 0 (zero) and re-running the search, a slightly different result is obtained. One microbe from the set of organisms for the second step, The complexities of organism diversity in microbiomes and of the metabolic networks in each of these organisms present challenges to our understanding of what exactly are the metabolic capabilities of a microbiome. We have developed an extension of our RouteSearch tool , such thThe accuracy of MORS predictions depends on several factors. One is the quality of the metabolic reconstructions in BioCyc. The vast majority of organism databases in BioCyc have been computationally predicted from annotated genomes; the quality of the resulting databases depends on the thoroughness and correctness of the original genome annotation. Their quality also depends on the state of curation in MetaCyc, our master database that is used as a template for metabolic reconstruction. Additionally, the reconstructions depend on the correctness of the PathoLogic reactome prediction algorithm .Cellular compartments are currently not taken into account by MORS. All metabolic reactions among the selected organisms are considered by the search to be fully accessible. In an actual collection of microbes, cell compartments will segregate many reactions, which will thus be unavailable, unless specific transporters are also added to the metabolic network. Generally, it appears that correctly predicting transport reactions computationally is difficult. The compartment-agnostic operation of MORS reduces false negative predictions at the expense of increasing false positives, which seems the better trade off for exploration. However, extending MORS to optionally take transporters and compartments into account could increase the realism of the routes found.We developed a software tool called MORS, which efficiently finds plausible metabolic routes within researcher-specified subsets of the BioCyc collection of 14,560 organism databases. MORS searches for optimal metabolic routes that minimize the number of reactions in each route and maximize the number of atoms conserved between the starting compound and the ending compound. Multiple routes can be found after computing for a few seconds to a few minutes, depending on the details of the search. MORS pre-computes a bit array describing the presence of metabolic reactions in the BioCyc databases; that array facilitates fast computation of the full set of reactions present in a researcher-specified set of organisms. MORS calls the previously developed RouteSearch algorithm to search for optimal metabolic routes. MORS enables the researcher to search between individual start and goal metabolites. In addition, when the researcher is unsure of the start or goal metabolite, the researcher can specify a set of compounds for either the start or the goal.We have demonstrated the utility of MORS by finding routes to several microbiome-related metabolites that affect human health, which have been previously reported in the experimental literature. In the first two cases, where routes were previously known in the literature, the optimal solution found by MORS matched the route in the literature. In our third example, MORS generated hypothetical routes for the synthesis and degradation of several autoinducer compounds.For future work, we would like to work with experimental collaborators to generate routes to additional microbiome-relevant metabolites, to help determine the metabolic origins of these metabolites. Such collaborations could lead to implementing additional features and support for more complex and targeted route searches.https://biocyc.org/ via the menu commandMetabolism →Metabolic Route Search.Operating systems: MacOS, Windows, Linux.Programming languages: Common LISP, JavaScript, SVG. The MORS source code is freely available to academic institutions upon request, as part of version 22. 5 (September 2018) of the Pathway Tools software.Other requirements: A modern Web browser is recommended. MORS needs SVG graphics, which does not work correctly on Safari, Chrome, and Internet Explorer. Please choose Firefox as the browser. We tested on Firefox 56.0.2 on MacOS High Sierra (10.13.6) and on Firefox 65.0.1 on MacOS 10.11.6. Because of the problems with SVG, we are looking into changing the display code to use HTML 5 technology, instead of SVG. Once this is done, hopefully the full range of modern browsers will be supported in a future BioCyc release.License: Access to organism databases in BioCyc, other than MetaCyc and EcoCyc, requires a subscription. However, BioCyc provides a level of free accesses per month to each researcher. The results reported here were obtained using version 22.6 of BioCyc. Subsequent versions of BioCyc are likely to show differing numbers for organism sets and may show variations in the exact routes found. The route searching directly depends on the available data in our collection of PGDBs, which over time increases in size, amount of curation, and automated inferences that we might apply.Project name: Multi Organism Route Search (MORS)Project home page: Additional file 1Walkthrough of the MORS User Interface. We provide a step by step guide, with screen snapshots, for setting up the parameters in the MORS Graphical User Interface (GUI), to run the route search for example 1 from the main paper. The steps easily generalize to the other examples. (PDF 2722 kb)Additional file 2Excel worksheet of organism tables for example routes. This Excel workbook of spreadsheets provides organism tables for several example routes, which were exported from the corresponding SmartTables. Such tables describe the organisms that can catalyze each reaction step in a MORS route. The format of the tables is described in Additional file Additional file 3L-tryptophan to Indoxyl Sulfate, two routes. The MORS interface controls and their selections are shown, followed by the resulting two routes computed by MORS. (PNG 756 kb)Additional file 4L-carnitine to TMAO, three routes. The MORS interface controls and their selections are shown, followed by the resulting three routes computed by MORS. (PNG 594 kb)