Compared to other omics techniques, quantitative metabolomics is still at its infancy. Complex sample preparation and analytical procedures render exact quantification extremely difficult. Furthermore, not only the actual measurement but also the subsequent interpretation of quantitative metabolome data to obtain mechanistic insights is still lacking behind the current expectations. Recently, the method of network-embedded thermodynamic (NET) analysis was introduced to address some of these open issues. Building upon principles of thermodynamics, this method allows for a quality check of measured metabolite concentrations and enables to spot metabolic reactions where active regulation potentially controls metabolic flux. So far, however, widespread application of NET analysis in metabolomics labs was hindered by the absence of suitable software.Escherichia coli metabolome data sets.We have developed in Matlab a generalized software called 'anNET' that affords a user-friendly implementation of the NET analysis algorithm. anNET supports the analysis of any metabolic network for which a stoichiometric model can be compiled. The model size can span from a single reaction to a complete genome-wide network reconstruction including compartments. anNET can (i) test quantitative data sets for thermodynamic consistency, (ii) predict metabolite concentrations beyond the actually measured data, (iii) identify putative sites of active regulation in the metabolic reaction network, and (iv) help in localizing errors in data sets that were found to be thermodynamically infeasible. We demonstrate the application of anNET with three published Our user-friendly and generalized implementation of the NET analysis method in the software anNET allows users to rapidly integrate quantitative metabolome data obtained from virtually any organism. We envision that use of anNET in labs working on quantitative metabolomics will provide the systems biology and metabolic engineering communities with a mean to proof the quality of metabolome data sets and with all further benefits of the NET analysis approach. Metabolomics, the technique to measure intra- and extracellular small molecules, was introduced a few years ago as the youngest child in the omics family. The technique provides data that, for example, can help us to complement our picture of metabolic pathways through identification of novel metabolites, or – by means of statistical analyses – to spot metabolic differences between strains or conditions ,2.in vivo reaction mechanisms and kinetic parameters. In addition, the continuing challenges in the area of computational analysis [Beyond these already valuable qualitative insights, however, further interpretation of metabolite data is difficult. This is due to the fact that the metabolome does not have a direct link to the genome such as mRNA or proteins. Furthermore, metabolite concentrations are the result of a multitude of interrelated molecular actions ranging from the gene expression level to the metabolic level and consequently the cause for an increased or decreased metabolite concentration is not intuitively accessible. Hence, in order to obtain mechanistic biological insights from metabolome data, we need rigorous integration in mathematical models ,4. Here,analysis  make it Apart from the lack of suitable methods for interpretation of metabolome data, also the quantitative measurement of intracellular metabolite concentrations still faces serious issues ,9. Specik, ΔrG'k,, using metabolite concentrations ci, reaction directionalities rj, the reaction stoichiometry of a metabolic network sij and predetermined standard formation energies ΔfG'°i:Recently, we presented a method called network-embedded thermodynamic (NET) analysis that can be utilized to address both aspects : NET anarj are not equivalent to enzyme reversibility. A non-zero reaction direction rj implies a non-zero net flux of reaction j. Lower and upper bounds for each reaction's Gibbs energy are determined by minimization and maximization of ΔrG'k in the non-linear system described Eq. (1), respectively. NET analysis can indicate, in which direction a reversible enzyme is operating under the experimental conditions: When the estimated upper bound of ΔrG'k is negative, the net flux of reaction k can only proceed in the forward direction. Analogously, when the estimated lower bound of ΔrG'k is positive, the net flux of reaction k can only proceed in the reverse direction.It should be emphasized that the reaction directionalities rG'k from zero reflects the distance, at which the particular reaction operates from equilibrium. Reactions that operate far from equilibrium (at least 10 kJ mol-1) are likely to be actively regulated. No conclusions can be drawn if lower and upper bounds are negative and positive, respectively, as estimated on the basis of the provided measurement data and constraints. Similarly, also the feasible ranges for metabolite concentrations cj can be determined by optimization. For detailed information on the method, the reader is referred to [In addition, a determined displacement of Δerred to .So far, the routine for network-embedded thermodynamic analysis was only available as a research tool that was not appropriate for non-expert users. In order to facilitate a more widespread use, we developed a Matlab-based software called anNET. anNET is a generalized implementation of NET analysis and thus can be applied (i) for consistency analysis of measured metabolite concentrations, (ii) for prediction of metabolite concentrations beyond the actually taken measurements and (iii) for identification of putative active sites of genetic or allosteric regulation. anNET is generalized to the extent that virtually any cellular reaction network  and any set of quantitative metabolome data can be integrated. Its user-friendly implementation does not sacrifice correctness of the analysis even in complex cases. The precondition is a stoichiometric model that describes the metabolic network of interest. Notably, the size of the model can span from one reaction to a comprehensive genome-wide reconstruction of metabolism. In large to genome-wide models, the resolving power of a NET analysis increases only when metabolome data or flux directions are available on peripheral pathways. Also when applied to a single reaction, NET analysis can verify whether measured concentrations are compatible with the expected flux direction.It is important to note that unknown pathways missing in the used metabolic network model as well as unknown and thus not specified flux directions, or unavailable thermodynamic data for certain metabolites will never render a data set infeasible, and thus NET analysis is rather conservative. Further, thermodynamic feasibility is only a necessary but not a sufficient condition for correct quantification of metabolite concentrations. However, NET analysis as an easy-to-apply tool can test for major experimental errors, while relying only on indisputable (i.e. thermodynamic) facts.We envisage that anNET will be used in labs working on quantitative metabolomics to check for thermodynamic consistency of metabolome data and, thus, pinpoint compound-specific flaws in the analysis procedure from sampling to quantification. We hope this will contribute to further improve the quality of metabolome measurements.In the following, we give a brief overview on possible utilizations of anNET, and then describe the algorithmic workflow, the user interface, the details of how cellular compartments are handled as well as depict two additional tools that support NET analysis to (i) identify errors in thermodynamically infeasible data sets and to (ii) identify minimal flux direction sets in metabolic reaction networks. Then, we demonstrate the validity of the generalized NET analysis implementation in anNET and compare different solvers, we apply anNET to recently published metabolome data sets and demonstrate its novel function to troubleshoot infeasible data sets.fG°, feasible ranges of ΔrG' for each reaction, or values for arbitrary non-linear terms such as the oxidation state of NADH/NAD+ or the energy charge of the adenylate pool.Examples for applications of anNET are listed in Table fG'°, for all metabolites and pseudoisomers in each compartment. From the complete list of reactions in the model, only those for which all reactants have a known ΔfG'° are selected to constitute the core model that is used for NET analysis. From this set of reactions, a system of linear constraints is built that describes Eq. (1). A set of routines is then called to handle the special cases of transporters (described below) and of reactions that only convert reactants in a specific charge state. The overall standard Gibbs formation energies of reactants that can occur in two or more charge states are calculated by combining the standard Gibbs formation energies of the pseudoisomer according to their molar fractions.The general workflow of anNET is depicted in Figure rG'. The provided metabolite data is handled according to whether the measurement specifies a concentration of a single intermediate, a sum of multiple concentrations, or a ratio of concentrations. Concentration data that relates to a single intermediate in a specific compartment directly translates into lower and upper bounds for that concentration. As a consequence, these kinds of constraints preserve linearity in Eq. 1 because we take the logarithm of the concentrations as variables in the optimization. In contrast, the provision of concentration sums and ratios result in non-linear constraints.Next, the provided information on known flux directions is added as linear constraints to constrain the respective reactions' Gibbs energies, ΔA first single optimization is performed to check whether the defined system is actually feasible, meaning that it is not contradictingly constrained by either flux directions or measured concentrations. If the system happens to be infeasible, a special routine is called to spot the conflicts in the dataset (see below). A successful feasibility test implies that the measured data is thermodynamically consistent. In this case, a complete NET analysis is performed by cycles of minimization and maximization that determine the feasible ranges of Gibbs reaction energies, of metabolite concentrations and of the non-linear terms that the user had specified to be estimated.A single main window Figure  acts as In the first panel, the user specifies where the information required to execute a NET analysis can be retrieved. Inputs are grouped into three categories: (i) defining the stoichiometric model and physicochemical properties of compartments (i.e. pH and ionic strength), (ii) specifying metabolite concentrations and flux directions, and (iii) providing the thermodynamic input data (i.e. the standard formation energies). All these input data are provided via flat files  or Excel spreadsheets using an intuitive syntax.More specifically, the stoichiometric model is entered as an array of reactions. Each reaction is defined with reactants of unique names, the stoichiometric coefficients, and the compartment, in which the reaction takes place. Transport processes between cellular compartments or between the intra- and extracellular environment are defined analogously and are automatically recognized as transport processes, when two or more compartments are defined for the participating reactants. For cases, where a participating reactant can only be transported in a specific charge state , the user can also define reactants' charge states in order to perform a mechanistically more correct NET analysis. Based on the provided list of reactions, a list with all metabolites occurring in the stoichiometric network is generated by anNET.Measured metabolite concentrations can be entered by the user as exact values, as ranges, as sum of concentrations of two or more metabolites, or as ratios between (sums of) concentrations of multiple metabolites. Hence, anNET seamlessly can process semi-quantitative metabolome data (i.e. ratios of metabolite concentrations) and pooled concentrations of e.g. structural isomers that could not be resolved by the employed separation or analytical technique. Known flux directions are defined in a separate worksheet with the flux sign. Thermodynamic properties are provided in a list that for each metabolite specifies the standard Gibbs energy of formation (if known), its charge, and the number of hydrogen atoms. These three quantities enable to accurately estimate the Gibbs energy of formation as a function of pH and ionic strength – specific for each compartment . Notablyfmincon from the Matlab Optimization Toolbox (The Mathworks) or the LINDO API library (LINDO Systems Inc.). The computational performance of the two solvers is compared in the Result section.The second panel of the graphical user interface affords a mean to rapidly set the constraints for the analysis. In the third panel, the user can decide what has to be estimated and which solver to use (if multiple are available), while in the fourth panel different options for reporting and the destination of the reported result can be defined. Currently, anNET can utilize two optimizers to solve the nonlinear optimization: To cope with biochemical reaction networks of virtually any organism, anNET has the capability to handle compartmentalized models and various kinds of transport reactions. For this purpose, metabolites present in different compartments are treated as independent entities and compartment-specific Gibbs energies of formation are computed . By default, the pools of a metabolite that is present in different compartments cannot exchange unless a respective transport process is defined in the model. Most metabolome platforms do not allow distinguishing between compartments and the measurements reflect the average over the entire cell. Therefore, a measured concentration typically translates into a constraint on the sum of the concentrations in all compartments weighted by the compartment volume. The context of the network operation that is specified by the user in terms of flux directions provides – amongst other things – also information about active transport processes across compartmental boundaries. These transport processes together with the network operation as a whole in several cases then enforce a distinct distribution of metabolite concentration in the different compartments. For example, if a metabolite has to cross a membrane separating two compartments by passive diffusion, then a fall in concentration must exist at steady state to sustain the flux.rG' of a transport process: First, proton transport is affected by the pH gradient between the two compartments. Second, when a charged molecule is transferred across a membrane with an electrical potential, the thermodynamics of acting upward or downward the membrane potential has to be considered. anNET automatically checks and corrects for these potential contributions , followed by metabolites, for which measurement values were specified in the input spreadsheet, and then followed by the non-linear expressions for concentration sums and ratios.Whenever inclusion of a metabolite leads to an infeasible system, the metabolite is blacklisted and the corresponding constraint is removed again. Once all concentration constraints and the non-linear constraints are tested, the blacklisted metabolites are taken to the second stage, where the flux direction constraints conflicting with the blacklisted metabolites are screened by a similar combinatorial approach. Overall, this procedure delivers a list of conflicting pairs of concentration/concentration constraints or of concentration/reaction direction constraints, which are useful in spotlighting infeasible subsystems.13C-flux analysis [Generally, the amount of information gathered from a (correctly quantified) metabolite data set scales with the number of constraints imposed on the system . As the analysis . For thiThe underlying idea is the following: Growing cells must synthesize certain biomass components. These components must either be taken up from the extracellular medium or they must be synthesized from the nutrients. Provided a certain medium composition, this fact can be used to predict a set of reactions whose flux must be non-zero and must be oriented in a certain direction to ensure that all biomass precursors can be synthesized. The only requisite for this analysis is a list of biomass components. Curated biomass models exist for most of the available manually reconstructed genome-scale metabolic network models ,17. In tE. coli dataset published by Schaub et al. [et al. consisted of 6 metabolite concentrations and 4 sums of concentrations that resulted from not fully analytically resolved analytes. Further 3 ratios were added to the system to assess the feasible range of the adenylate energy charge (AEC) and the redox state of the cofactors NAD+/NADH and NADP+/NADPH. The input concentration ranges of these three ratios were chosen very wide to avoid that they become active constraints. Notably, the two analyses delivered equivalent results for all ranges of concentrations and of ΔrG  were estimated in agreement with LINDO, while fmincon underestimated the feasible ratios between NADP/NADPH and NAD/NADH. Despite several modifications in the optimization settings, including the starting point and maximum duration, we were not able to find a universal configuration that lead to robust optimization of non-linear terms with fmincon. Furthermore, the LINDO solver consistently proved to complete the optimization 2–3 orders of magnitude faster than fmincon   and glyceraldehyde-3-phosphate (g3p) , the concentration of g6p has to be at least 3.1-fold larger than that of f6p, in contrast with the measured ratio of 2.3. The conflict is relieved when a relative error of at least 25% is allowed for both concentrations. In the second problem, g3p is incompatible with the concentrations of dihydroxyacetone-phosphate (dhap) and fructose-1,6-bisphosphate (fdp) and the connecting reactions catalyzed by the triosephosphate isomerase (tpi) and the fdp-aldolase (fba). The reaction directions imposed by the glycolytic flux dictate that the g3p concentration has to be in the range between 2–38 μM when the concentrations of fdp and dhap are assumed to be within 30% of the measured values. This range, however, is largely lower than the measured g3p value of 200 μM. Interestingly, no feasible system could be obtained when removing the experimental concentrations of either fdp or dhap from the dataset, because this resolved the infeasibility around either fba and tpi, respectively, but not both simultaneously. Overall, these examples demonstrate the usefulness of the troubleshooting function to identify the loci of thermodynamic infeasibility and to suggest potential error sources.In the data set by Hiller In general terms, it is important to emphasize two aspects. Firstly, apparent inconsistencies in metabolite concentrations may be linked to bad measurements but also reflect faulty thermodynamic data or local differences in reactant activity. The troubleshooting routines can not distinguish between these causes, but diagnoses all of them simultaneously by the requisite to further relax concentration constraints around specific nodes. Secondly, the fact that modification or removal of one constraint (or more) in an unfeasible system lead to a feasible one proves neither that the modified constraints were wrong, nor that the others were correct. It is a mere indication that requires experimental verification.anNET is the first tool publicly available for network-embedded thermodynamic analysis of metabolome data. The most immediate application of anNET is the consistency check of quantitative metabolome measurements . As outlIn this context, however, it is important to note that thermodynamic feasibility approved by NET analysis is not a sufficient condition to certify that the measured concentrations reflect the true state of a cell. Nevertheless, despite the rather conservative quality filter that is given by NET analysis, a previous study showed that out of seven published metabolite datasets, three were thermodynamically not consistent . A data The prerequisites for a consistency check by NET analysis is that (i) quantitative metabolomics data is available ; and (ii) flux directions can be defined. Hence, this precludes the application of NET analysis to the consistency check of for example serum metabolome, or to cells grown in rich media were flux directions are uncertain. We hope that anNET will soon be used for quality check of quantitative metabolome data and thus, in consequence, the quality of published quantitative metabolite data sets will rise.Project name: anNETOperating system: tested on Microsoft Windows XP and Linux Red Hat.Programming language: tested with Matlab 7.0 and later (The Mathworks).Other requirements: Matlab Optimization Toolbox (The Mathworks) or LINDO API, versions 2.0 – 5.0 (LINDO Systems Inc.)License: freely available from the authors for academic purposes.Any restriction to use by non-academics: license required.AK developed initial prototypes. NZ generalized and optimized the software. All authors participated to validation. NZ and MH wrote the manuscript. All authors read and approved the final manuscript.E. coli.Metabolic model used for Click here for fileComprehensive anNET analysis of the data set by Schaub.Click here for filefmincon results (Supporting material to Table Comparison of LINDO and Click here for fileDetailed anNET results for analysis of the three data sets reported in Table Click here for fileTroubleshooting of Ishii data set.Click here for fileTroubleshooting of Hiller data set.Click here for file


It is a daunting task to identify all the metabolic pathways of brain energy metabolism and develop a dynamic simulation environment that will cover a time scale ranging from seconds to hours. To simplify this task and make it more practicable, we undertook stoichiometric modeling of brain energy metabolism with the major aim of including the main interacting pathways in and between astrocytes and neurons.The constructed model includes central metabolism , lipid metabolism, reactive oxygen species (ROS) detoxification, amino acid metabolism (synthesis and catabolism), the well-known glutamate-glutamine cycle, other coupling reactions between astrocytes and neurons, and neurotransmitter metabolism. This is, to our knowledge, the most comprehensive attempt at stoichiometric modeling of brain metabolism to date in terms of its coverage of a wide range of metabolic pathways. We then attempted to model the basal physiological behaviour and hypoxic behaviour of the brain cells where astrocytes and neurons are tightly coupled.in silico.The reconstructed stoichiometric reaction model included 217 reactions  and 216 metabolites  distributed in and between astrocytes and neurons. Flux balance analysis (FBA) techniques were applied to the reconstructed model to elucidate the underlying cellular principles of neuron-astrocyte coupling. Simulation of resting conditions under the constraints of maximization of glutamate/glutamine/GABA cycle fluxes between the two cell types with subsequent minimization of Euclidean norm of fluxes resulted in a flux distribution in accordance with literature-based findings. As a further validation of our model, the effect of oxygen deprivation (hypoxia) on fluxes was simulated using an FBA-derivative approach, known as minimization of metabolic adjustment (MOMA). The results show the power of the constructed model to simulate disease behaviour on the flux level, and its potential to analyze cellular metabolic behaviour The predictive power of the constructed model for the key flux distributions, especially central carbon metabolism and glutamate-glutamine cycle fluxes, and its application to hypoxia is promising. The resultant acceptable predictions strengthen the power of such stoichiometric models in the analysis of mammalian cell metabolism. Understanding of the biochemistry and energy metabolism of the brain is a prerequisite for evaluating the functioning of the central nervous system (CNS) as well as the physiology and pathology of the brain. The functions of the CNS are mainly excitation and conduction as reflected in the continuous electrical activity of the brain. The fact that this electrical energy is ultimately derived from chemical processes reveals the fundamental role of biochemistry in the operation of the brain.Developments in functional brain imaging techniques have led to better elucidation of the physiological and biochemical mechanisms of the brain -4. HowevBrain energy metabolism has been approached by the use of dynamic modeling ,8 where 2) localization in cytosol or mitochondria was reflected in the compiled reaction list. This is, to our knowledge, the first comprehensive attempt at stoichiometric modeling of brain metabolism in terms of its coverage of a wide range of metabolic pathways (214 reactions). Flux balance analysis (FBA), a steady-state metabolic modeling technique [We performed an extensive literature survey to obtain the catabolic, anabolic and exchange reactions in brain metabolism. Only about 100 references cited directly within the text are listed here. The ultimate goal was to develop a reliable stoichiometric model of the coupling mechanism, which will be compatible with physiological observations. The constructed model included central metabolism , amino acid metabolism (synthesis and catabolism), lipid metabolism, ROS detoxification pathway, neurotransmitter metabolism  as well as coupling reactions between astrocytes and neurons. The metabolic reactions were compartmentalized with respect to their localization in cells  to obtain a more realistic representation. Additionally, cofactor  or a release  mechanism. Additionally, synthesized lipids in both cell types were considered as released for the modeling purposes.A previous attempt for stoichiometric modeling of brain metabolism  covered 1–r37/r38–r73), except the pyruvate carboxylation reaction (r12), whose enzyme is known to be inactive in neurons [13, r49), is mitochondrial. Both NADH- and NADPH-dependent  isocitrate dehydrogenation reactions (r24–r26/r60–r62) were taken into account [33) whereas it is only mitochondrial in neurons (r69) [69) in the reverse direction was included in neurons in addition to the mitochondrial version, to mimick the shuttle. In astrocytes, however, cytosolic malate dehydrogenation was considered in the same direction as the mitochondrial one since it is known that the malate-aspartate shuttle is not active in astrocytes [37/r73) was also accounted for. Lactate release was assumed to be only from the astrocytes [As proposed -23, the  neurons ,24. That account . Malic eons r24–r/r60–r62 ns (r69) . The malns (r69) -33. Accotrocytes ,31, althtrocytes ,35. The trocytes  was also74–r79) [79) to convert astrocyte-derived glutamine into glutamate. One alternative for neuronal glutamate production is the transfer of TCA cycle intermediates from astrocytes to neurons. However, these exchange reactions were not added to the model since there is not sufficient evidence for such trafficking [+K+ATPase [75). Glutamine efflux from the astrocytes to the extracellular space [74, r76) [An extensive literature survey was performed to acquire the compartmentation of amino acid catabolism and synthesis between astrocytes and neurons. For the glutamate – glutamine cycle r74–r79) ,39, glut74–r79) . They hafficking ,16,40,41K+ATPase ,43, the 4–r79 ,39ar space  was take74, r76) .80) in neurons and then transferred into the neighboring glial cells where it is converted into glutamate and succinate irreversibly (r81–r83) [84–r85) [86, r88), and it can be transferred between the two cell types in both directions (r87) [89–r91).NMR studies indicate that the GABA, aspartate and alanine pathways are closely linked to the glutamate – glutamine cycle ,46. GABAr81–r83) ,48. Conv[84–r85) . Aspartans (r87) ,47. It hns (r87) ,51 that ns (r87)  that ala92) is cytosolic [92) [94), where it is converted to glycine (r95) [93) is also possible in astrocytes [96) [98), completing the cycle [97) is exclusively active in astrocytes [Serine and glycine are involved in a cycle between astrocytes and neurons analogous to the glutamate-glutamine cycle ,54. Therytosolic . Once folic [92) -57, serine (r95) ,58. Convtrocytes ,58,59. Ntes [96) , where ihe cycle . Additiotrocytes , and loc99), to supply amino nitrogen to the glial glutamate pool. Then KIC is transferred into the neuronal compartment (r104) and converted back to leucine (r105). The cycle is finalized by the conveyance of leucine to the astrocyte (r106) [100–r103). The other branched chain amino acids, valine and isoleucine, are associated with comparably lower uptake rates [107–r119) [100, r108, r115) take place in mitochondria [101, r109, r116) [Inclusion of branched chain amino acids (BCAA) in the model is crucial for the investigation of brain metabolism coupling and the glutamate – glutamine cycle because they serve as nitrogen donors for glutamate and transfer nitrogen from astrocytes to neurons -64. BCAAe (r106) . It is ake rates . Their m07–r119) . Branchechondria ,69,70, t9, r116) ,68,71.120–r121) and it was degraded to acetyl-CoA (r122–r124) [121), after which it takes place in mitochondria (r123) [Lysine catabolism via the saccharopine pathway has been shown to occur mostly in neurons . Hence, 22–r124) ,72. The a (r123) . No astr125) [126–r127) [128–r129). Dopamine can be released from neurons into the synaptic cleft or stored in vesicles [130–r131).Phenylalanine taken up from the extracellular space is catabolized to tyrosine (r125) ,73,74. T26–r127) ,75-77. Avesicles . Therefovesicles . This su132–r134) [135) [Tryptophan serves as a precursor for the synthesis of serotonin and melatonin in neurons following its uptake (r32–r134) . Since s4) [135) .Although they are essential amino acids for brain, the catabolism of threonine and methionine was ignored because of their very low uptake rates .136) [137), forming a cholesterol shuttle between the two cell types [136).The precursor for the synthesis of lipids is acetyl-CoA. The major lipid types are triacylglycerols, cholesterol, and phospholipids. Brain contains virtually no triacylglycerol ,80. Ther136) , with no136) . Necessall types ,83,84. T140–r149) in cytosol. Nonessential fatty acids  are synthesized de novo in both cell types  [141–r142), which can be provided externally, eg. through diet. Neurons are not capable of producing these two fatty acids, instead they take up the ones synthesized and released by astrocytes (r146–r147) [148–r149). The same composition was assumed for astrocytes and neurons since it has been reported that these two cell types have very similar fatty acid and lipid compositions [150, r158) [152–r157, r159–r164) were compiled from [165–r166).The building blocks for phospholipids are fatty acids, which are synthesized from acetyl-CoA (r45–r147) . Arachid46–r147) ,87. Thes46–r147) ,88, ther46–r147)   ,75. Here0, r158) ,80,89-91led from ,75,92. F150) [158) [151), allowing the transfer of cytosolic NADH to mitochondria in the form of FADH2.Glycerol-3-phosphate formation reaction is cytosolic in astrocytes (r150) , and mit0) [158) ,93. Sinc0) [158) , which i0) [158) . Therefo2O2. Glutathione is synthesized from cysteine , which is derived from cystine (r167). Because only astrocytes can take cystine up from the blood vessel and convert it to cysteine, neurons are dependent on astrocytes for protection against oxidative stress [169) are removed by glutathione (r170). The resulting oxidized glutathione is converted back to the reduced form by glutathione reductase , which requires NADPH and is located in both cytosol and mitochondria [173) [174), which is used as the cysteine supply to the neurons . Then cysteine acts as precursor for neuronal glutathione (r177). The following protection mechanism is the same as in astrocytes (r178–r182).The brain requires glutathione for the removal of reactive oxygen species (ROS) such as He stress ,94,95. Ichondria . Alternaia [173) . Reduced183–r184).Brain has a high glycogen content , and astOther pathways such as nucleotide metabolism were not taken into account since there is no detailed information on the compartmentation of those pathways between the two cell types, and no significant fluxes have been reported through such pathways. One should also note that an individual neuron may not have all the reactions detailed above since individual neurons are specialized to synthesize specific neurotransmitters. Here we consider a population of neurons rather than individuals, thereby aiming at the overall picture in the brain.A hypothesis called ANLSH (astrocyte-neuron lactate shuttle hypothesis) proposes the use of astrocyte-derived lactate as energy substrate by neurons under activated conditions  where th75, r78, r81) with subsequent minimization of the Euclidean norm of fluxes result in a flux distribution in accordance with literature data. The following results and discussions are, therefore, based on this flux distribution. The deficits for other employed objective functions (the points where they contradict the used criteria) are given in Additional File The constructed model was first utilized to simulate the neuron-astrocyte flux distribution under resting conditions based on the constraints Table  detailedAn additional table is provided Table  which sh22/(r22 + r58), is calculated as 0.35 by our approach, which is in good agreement with the literature-reported value of 30% [37 + r75) to the total ATP generation rate is 0.27, indicating the degree of relative ATP production in both cells, as consistent with the above-stated fraction of oxidative metabolism. Additionally, the percentage of model-based pyruvate carboxylase flux (r12) with respect to CMRglc (11.7%) matches very well with reported results of around 10% [de novo synthesis of TCA cycle intermediates in this cell type. The flux through reaction, which represents the activity of the malate-aspartate shuttle in neurons by transferring NADH from cytosol to mitochondria (r68), is calculated as 0.34 μmole/g/min. The magnitude of this flux is reported to be similar to that of the flux through neuronal pyruvate dehydrogenase (r49) [22 + r58)/CMRglc, is calculated as 1.51 by our approach, which is lower than the reported values of approximately 2 [136) and fatty acid  syntheses. That is, some portion of glycolytic Acetyl-CoA is diverted to lipid metabolism leading to lower TCA fluxes. Therefore, our simulation result is in accordance with the expectation that the ratio rTCA,total/CMRglc must be lower than 2.The ratio of neuronal TCA cycle flux to the total TCA cyle flux, re of 30% ,97,100. e of 30% ,102, ratound 10% . This flse (r49) , contribse (r49) ,63. The mately 2 . The rea14 and r50, is at the specified boundaries for both cell types. Regarding the fluxes through the ROS pathway; the model calculates astrocytic peroxide formation rates as zero, implying that the pathway is inactive in this cell type. This is in accordance with the relatively lower oxidative metabolism in astrocytes. For neurons, however, there is significant peroxide formation, and hence glutathione is oxidized and then reduced to remove oxidative stress. NADPH used for oxidative stress reduction is 0.18 and 0.24 μmole/g/min in cytosol (r180) and in mitochondria (r181) respectively.The present model results suggest that NADPH production through the pentose phosphate pathway, rThe lactate release rate was calculated as 8.9% of glucose flux. In terms of the carbon-mole, this stands for 4.5% of glucose carbon through the lactate route, which is in the vicinity of the reported values at rest -108. ThiThe neuronal and glial compartments are known to be the two major compartments of brain metabolism, and they are metabolically linked with the glutamate-glutamine cycle. This has led to detailed investigations of the flux through this cycle, because it represents the hallmark of cerebral metabolic compartmentation and it is closely linked to the Krebs cycle ,104,109.78/CMRglc, was calculated by FBA as 0.68, which is in the range of reported values (0.41–0.80) [The ratio between the glutamate-glutamine cycle and the glucose consumption rate, r41–0.80) ,104. The41–0.80) ,111. Add41–0.80) . The val99) than the decarboxylation rate of KIC (r100) has been reported [99/r100) obtained by the present model is more than 5, consistent with physiological expectation. The directions of the aspartate and alanine cycle were from neurons to astrocytes, contributing to the astrocytic glutamate pool, with fluxes of 0.092 and 0.025 μmol/g/min respectively. Unlike the alanine cycle, the aspartate cycle acquires a relatively higher flux, which needs to be confirmed by experimental studies.The cycles other than the glutamate-glutamine cycle were calculated to have lower flux values. Serine-glycine cycling operates with a flux of 0.01 μmol/g/min. The flux through each of the BCAA cycles, which are directly linked to the glutamate pool, is about 33% of the glutamate-glutamine cycle flux. In this way, they contribute to the glutamate-glutamine cycle flux. This contribution for leucine alone was reported as 25–30%  in paralreported . The ratThe above-discussed FBA results show which metabolic interactions were active between astrocytes and neurons under resting states, and the redistribution of corresponding fluxes in both cell types is indicative of the relative activity of the interactions.Inclusion of lipid metabolism is especially important for ATP, NADPH and Acetyl-CoA balances to be closed. The model-based fluxes indicate that lipid synthesis under steady state conditions is possible in astrocytes, with a rate corresponding to 2.8% of glucose flux. This is in accordance with the literature value , which rin silico predictions.To identify the maximum production capabilities of the brain cells for the major neurotransmitters, FBA was applied to the constructed stoichiometric model using the maximization of each of these neurotransmitters as the objective function. The resultant fluxes were compared with those obtained in the simulation of the resting condition analyzed above. Since neurotransmitters are produced in neurons and released to synaptic clefts, the flux values of the reactions that carry them from neurons to the extracellular space, or to astrocytes to clear them from synaptic clefts, were used in the analysis. Figure Many diseases of the brain have been reported to result from neurovascular coupling disorders, where mainly oxygen deficiency leads to a cascade of events. A decrease in cerebral perfusion due to arterial obstruction  leads to the formation of hypoxic regions in the brain as encountered in the pathophysiology of aging and several psychiatric disorders as well as headache. Hypoxic regions in the brain have been known to cause major disturbances in the electrical activity of the brain (as in epilepsy) or lead to progressive diseases such as dementia, Alzheimer's and even emotional disturbances. Hence, as a good predictor of our model, we chose to simulate the effects of hypoxia in hope that it can be explained by stoichometric modeling approaches.It has been reported that deficient cells exhibit a flux profile closest to the healthy (non-deficient) flux distribution ,114. Thi68) in response to lower oxygen uptake flux, as shown in Figure 75) and the concurrent significant decrease in the return part of the cycle flux (r78). Such an impairment has already been described [Simulation of cerebral hypoxia (up to zero CMRO2) reveals more than tripling of astrocytic lactate production as well as significant neuronal production, implying the sharp activation of anaerobic metabolism Figure . That isescribed . The sigescribed . The abrin silico prediction since a significant increase, rather than a decrease, in glutamate uptake rates by astrocytes was calculated in this case. In addition, results suggest relative uncoupling of ATP production mechanisms of the two cells since the neuronal ATP production rate and TCA cycle rate are found to be almost unaffected .The present results demonstrate the power of the constructed model to simulate disease behaviour on the flux level, and its potential to analyze cellular metabolic behaviour Stoichiometric flux analysis techniques have been successfully applied to the analysis of mammalian cells ,125-128.The stoichiometric coefficients of the compiled reactions and their reversibility information were used to constrain the flux solution space;S is an m by n stoichiometric matrix with m being the number of metabolites (213) and n being the number of reactions covering the pathway and the exchange reactions (214), v is the flux vector to be identified, and vmin &vmax are the lower and upper bounds of the fluxes based on the reversibility information. The uptake rates of extracellular metabolites  and number of unknowns (i.e. reaction rates) in the linear equation system defined by equation (1), and it is calculated as 47. Literature values of 15 fluxes (Table where bolites 23 and n bbolites 23 and n bbolites 23 and n bsubject to equations (1) and (2), with all entries of the row vector, f, being zero except the entries corresponding to the fluxes to be maximized. FBA has so far been mainly applied to microbial cells, for which biomass growth was used as standard objective function in the optimization problem. For mammalian metabolism, however, there is no standard objective function. Therefore, the optimum solution for this problem was sought under different candidate objective functions that are listed in Additional File On the other hand, in the FBA approach, there is the possibility of multiple optima, different flux distributions with the same optimal objective flux ,135. To subject to the constraints by equations (1), (2), and additionally to the constraint through the first optimization:objfun is the optimal value of the objective function obtained in the linear optimization problem of (3).where The underlying hypothesis is that cells aim to fulfill their functions with minimal effort since increasing the flux through any reaction will require an extra investment such as increasing enzyme levels . DerivatOptimizations were performed in the MATLAB 7.0 environment with a MATLAB interface to CLP solver developedLiterature-based uptake fluxes of glucose, oxygen, ammonia, cystine and essential amino acids as well as carbon dioxide release flux Table  were useO2/CMRglc in normal resting brain as about 5.5 [2/rO2) of brain is very close to one with values reported between 0.91–1.00 [2 production rate is constrained so as to give the reported RQ range for both cell types.Glucose and oxygen are the main substrates fueling both astrocytes and neurons, and their cerebral metabolic rates (CMR) are closely related. The arithmetic average of literature-reported cerebral glucose utilization rates for human brain under resting conditions (0.32 μmol/g tissue/min) ,141,142 bout 5.5 ,74,143. bout 5.5 ,144. Thebout 5.5 . Individ.91–1.00 ,145, the82 as 25% of r78. This enabled the GABA pathway to be more tightly coupled with the overall network.Amino acid uptake rates  were comAll the reported rates compiled from the literature Table  are usedThe rates were compiled from different sources since it was not possible to obtain all measurements from a single source. Studies reporting analysis of these rates in the same system, which will be facilitated by novel high throughput measurement techniques, will lead to more accurate results, strengthening the role of such models in medical modeling.FBA: Flux balance analysis, MOMA: Minimization of metabolic adjustment, CNS: Central nervous system, GABA: γ-aminobutyric acid, TCA: Tricarboxylic acid, CoA: Coenzyme-A, DOPA: Dihydroxyphenylalanine, CMR: Cerebral metabolic rate, BCAA: Branched chain amino acids, KIC: α-ketoisocapriate, KIV: α-ketoisovalerate, KMV: α-keto-β-methylvalerate, ROS: Reactive oxygen species, PPP: Pentose phosphate pathway, RQ: Respiratory Quotient, OX-PHOS: Oxidative Phosphorylation.The author(s) declare that they have no competing interests.KOU and AA designed the research. TC and SA reconstructed the model and performed the simulations. TC, SA, HS, AA and KOU analyzed the results. TC, SA, AA and KOU wrote the manuscript. All authors read and approved the final manuscript.Reaction Set of Metabolic Reconstruction for Astrocytes and Neurons.Click here for fileSupplementary Table 1 – Metabolic differences in two cell types reflected in our model.Click here for fileEmployed objective functions with corresponding reasonings.Click here for fileSupplementary Table 3 – Flux distributions for all reactions in resting and anoxic states.Click here for fileMatlab routines used in the simulations.Click here for file


The identification of genetic target genes is a key step for rational engineering of production strains towards bio-based chemicals, fuels or therapeutics. This is often a difficult task, because superior production performance typically requires a combination of multiple targets, whereby the complex metabolic networks complicate straightforward identification. Recent attempts towards target prediction mainly focus on the prediction of gene deletion targets and therefore can cover only a part of genetic modifications proven valuable in metabolic engineering. Efficient in silico methods for simultaneous genome-scale identification of targets to be amplified or deleted are still lacking.Corynebacterium glutamicum and Aspergillus niger, up to more than 20,000 modes were obtained for each case, differing strongly in production performance and intracellular fluxes. For lysine production in C. glutamicum the identified targets nicely matched with reported successful metabolic engineering strategies. In addition, simulations revealed insights, e.g. into the flexibility of energy metabolism. For enzyme production in A.niger flux correlation analysis suggested a number of targets, including non-obvious ones. Hereby, the relevance of most targets depended on the metabolic state of the cell and also on the carbon source.Here we propose the identification of targets via flux correlation to a chosen objective flux as approach towards improved biotechnological production strains with optimally designed fluxes. The approach, we name Flux Design, computes elementary modes and, by search through the modes, identifies targets to be amplified (positive correlation) or down-regulated (negative correlation). Supported by statistical evaluation, a target potential is attributed to the identified reactions in a quantitative manner. Based on systems-wide models of the industrial microorganisms Objective flux correlation analysis provided a detailed insight into the metabolic networks of industrially relevant prokaryotic and eukaryotic microorganisms. It was shown that capacity, pathway usage, and relevant genetic targets for optimal production partly depend on the network structure and the metabolic state of the cell which should be considered in future metabolic engineering strategies. The presented strategy can be generally used to identify priority sorted amplification and deletion targets for metabolic engineering purposes under various conditions and thus displays a useful strategy to be incorporated into efficient strain and bioprocess optimization. They do, however, not provide a prediction of genes to be amplified for superior performance. This rather important information on potential amplification targets can be extracted on basis of experimental 13C metabolic flux data including comparative 13C flux studies of mutants with different properties [13C flux data, has been successfully demonstrated e. g. for lysine producing C. glutamicum [in vivo kinetic data of the enzymes involved [The identification of genetic target genes is a key step in rational engineering of production strains towards bio-based chemicals, fuels or therapeutics. To fully account for the high complexity of metabolic networks and select promising genes out of many possible candidates, systems-wide approaches have recently emerged from the rapidly increasing amount of genome-scale models . As examoperties  or a bi-operties . The valutamicum ,9. They,involved . Thus, ein silico prediction of desirable cell phenotypes that result either from the variation of process parameters or from the perturbation of genotypes [E. coli cell for high yield ethanol production was enabled by prediction of gene deletion targets using elementary flux mode analysis [C. glutamicum is one of the dominating bacteria in biotechnology and applied to produce more than 2.000.000 tons of amino acids per year [C. glutamicum has been extensively investigated including the construction of a genome-scale model [A. niger is widely exploited for the production higher-value enzyme products [A. niger illustrates its complex metabolism located in different intracellular compartments [Among the available genome-scale modelling approaches, elementary flux mode analysis constitutes an important tool for the efficient study of cellular systems, since it allows the enotypes . In compenotypes . Elementenotypes , the ideenotypes  or the canalysis . Here weanalysis . Howeverper year . Its valle model  and diffle model . The filproducts . The recartments . Here weartments , glucoamartments , and epoartments .Computation of elementary flux modes allows the calculation of a solution space of all possible independent metabolic pathways in a steady-state . ElementS with the dimension dim S = (m × q), where m is the number of internal metabolites and q is the number of reactions, and r represents a flux distribution and is consequently a q × 1 vector. Any biochemical reaction network should fulfil the thermodynamic feasibility constraint, i.e. the following inequality should be valid for all irreversible reaction rates:Here, the metabolic network is expressed as stoichiometric matrix http://csb.inf.ethz.ch and was applied in this work. On basis of the determined elementary modes, a detailed investigation of metabolic network properties was carried out. This included the estimation of theoretical (maximum) yield, relative fluxes through intracellular metabolic pathways, and target prediction for strain engineering. Calculations were partially automated and implemented into MatLab  and evaluated in Excel™ .In the present work, elementary flux mode calculation was performed using the double description method (null space approach) introduced by Wagner  and extej the relative metabolic flux  of each metabolic reaction i, normalized to the substrate uptake flux, was determined. The symbol ξ refers to the molar carbon content expressed in c-mol per mol. To facilitate direct comparison between different carbon sources, all relative fluxes were normalized to one unit of hexose . The variable q refers to the number of metabolic reactions in the metabolic network. The variable n refers to the numbers of elementary modes respectively.For a given elementary mode jP/C, ) and biomass yield  was calculated for each elementary mode j according to Eq. 5. Basically it displays the relative flux towards the product or the biomass. The variable s refers to the stoichiometric coefficient of the product (P) and carbon source (C)The theoretical product  and the corresponding flux (νi) was determined. This was carried out for each reaction, so that the entire network could be screened for potential targets. Only statistically valid correlations were considered further. For this purpose a cut-off value of r2 = 0.7 was set for the regression coefficient of each linear correlation. Such a cut-off has proven valid in previous studies processing correlated data [To investigate, whether a reaction ted data ,28. Addi2 the regression coefficient. If, TS > t, then there exists a statistically significant relationship. Accordingly, statistical significance was a quality criterion to classify the corresponding reaction as a potential target. Subsequently, the potential of a metabolic reaction as genetic target was expressed as target potential coefficient , by the slope of the corresponding linear regression αi, obj = /νobj, whereby βi, obj is the intercept of the ordinate (Eq. 7).Here, the variable n is the number of pairs of values, r is the correlation coefficient and robj and νi divided by the square of the standard deviation δ of the corresponding objective flux νobj (Eq. 8).The calculation was carried out by determining the covariance (cov) of the variables of νi, obj account for amplification targets, whereas negative values denote deletion or attenuation targets.Positive values of αThe major characteristics of the models used in the present work were as follows. A detailed description of the biochemical reactions in the different networks is given in the supplement files.E. coli, which was previously used for the discussion of the concept of elementary flux mode analysis [The principle of the developed approach is elucidated using a simple metabolic network from analysis . It inclanalysis .C. glutamicum considered the actual knowledge from the genome scale model recently created [The metabolic reaction model of  created . It incl created . For ATP created . The sto created .A. niger contained was constructed on basis of the genome scale model recently published [A.niger using the program IdentiCS [2Man8(GlcNAc) as average composition of the glycosylation residues in filamentous fungi [6Man24(GlcNAc)3 per protein. For the calculation of the exact demand it was assumed that on average 64% of all proteins are glycosylated [http://www.cbs.dtu.dk/). Hereby, the glycosylation pattern Galf18Man308(GlcNAc)8.5, as previously determined for this enzyme, was considered [The metabolic reaction model of the central metabolism of ublished . The modublished . The stoublished . The amiIdentiCS . Glycosyus fungi  and an aus fungi  into accosylated . The celosylated , wherebynsidered . The aminsidered . Similarnsidered  was takeE. coli serves as example to introduce the principle of the developed approach for target identification  and non-growth-associated ones  prior to analysis. Hereby, only modes with zero by-product formation were considered. This increased the hit rate of the approach substantially. The results for production on glucose, xylose, glycerol and oleic acid under growth-associated (+) and non-growth-associated conditions (-) are visualized as heat map Figure . FructofOther target enzymes studied, including glucoamylase or epoxide hydrolase which differ in amino acid composition and glycosylation degree yielded rather similar targets for all metabolic scenarios studied.C. glutamicum. From the targets predicted, various reactions have been successfully implemented towards superior production of lysine. This includes amplification of glucose 6-phosphate dehydrogenase [C. glutamicum for improved performance. Moreover, not yet validated targets such as the amplification of ammonium metabolism or reactions of the non-oxidative PPP or deletion/attenuation of TCA cycle reactions are predicted. For enzyme production in A. niger, much less metabolic engineering progress of central carbon metabolism is reported. The few studies available, however, illustrate that targets predicted here have proven valuable. As example, the amplification of the synthesis of glycosylation residues increased protein over-production [A. niger [A. niger [A. oryzae [Elementary mode analysis provides a rigorous basis to systematically characterize cellular phenotypes, metabolic flexibility and robustness which facilitates the understanding of cell physiology ,40. In trogenase , 6-phosprogenase , reactiorogenase  as well rogenase , all shorogenase  or pyruvrogenase , have beoduction ,47. SimiA. niger . Beyond,A. niger ,21 and A. oryzae  whereby 13C flux analysis as major targets for improved lysine production in C. glutamicum, was not predicted here. Still, the presented approach can be generally used to identify priority sorted amplification and deletion targets for metabolic engineering purposes under various conditions and thus displays a useful strategy to be combined with existing in silico tools [The present approach did not reveal all relevant targets previously reported to redirect carbon flux. As example, the amplification of fructose bisphosphatase  or the dA. niger) or the regeneration of ATP (C. glutamicum). A closer inspection showed that this characteristic mainly originates from a small sub set of reactions, adding flexibility and robustness to the networks. The possibility to recruit different pathway modes for high production appears advantageous when approaching metabolic engineering strategies. Since it can be expected that certain genetic engineering strategies might not work for reasons of growth deficiency or undesired regulatory behaviour, the possibility to choose among different promising directions seems useful. Interestingly, the prediction of genetic targets depended on the metabolic state of the cell ; FADH2: flavin adenine dinucleotide (reduced); FBP: fructose 1,6-bisphosphate; FFase: fructofuranosidase; FMM: fructose mannose metabolism; FOR: formate; FRC: fructose; FUM: fumarate; G6P: glucose 6-phosphate; Galf: galactofuranose; GAP: glyceraldehyde 3-phosphate; GLC: glucose; GlcNAc: N-acetyl-glucosamine; GLN: gluconate; GLP: glycerol 3-phosphate; GLY: glycerol; GLYN: glycerone; GOX: glyoxylate; H2S: hydrogen sulphide; ICT: isocitrate; LCoA: lenoate-CoA; LEN: lenoate; M6P: mannose 6-phosphate; MAL: malate; MAN: mannitol; MAP: mannitol 1-phosphate; MFA: metabolic flux analysis; NAD: nicotinamide adenine dinucleotide (oxidized); NADH: nicotinamide adenine dinucleotide (reduced); NADP: nicotinamide adenine dinucleotide phosphate (oxidized); NADPH: nicotinamide adenine dinucleotide phosphate (reduced); NH3: ammonia; NO3: nitrate; O2: oxygen; OAA: oxaloacetate; OCoA: oleate-CoA; OEL: oleate; OXA: oxalate; PEP: phosphoenolpyruvate; PPP: pentose phosphate pathway; PYR: pyruvate; R5P: ribose 5-phosphate; RUP: ribulose 5-phosphate; S7P: sedoheptulose 7-phosphate; SO4: sulphate; SUC: succinate; TCA: tricarboxylic acid; Q: ubiquinone ox.; QH2: ubiquinone red.; XOL: xylitol; XUP: xylulose 5-phosphate; XYL: xylose.GM created the metabolic models, designed the simulation experiments, performed the simulation studies, analysed the results, drafted all figures and assisted in drafting of the manuscript. ME programmed the post-processing toolbox for data processing. EFL contributed by discussions on the manuscript. CW supervised the work, designed the simulation experiments and drafted the paper. All authors read and approved the final manuscript.Escherichia coliScenario . Small example network model of E. coli for succinyl-CoA production, results of the target validity calculation and statistical evaluation.Click here for fileCorynebacterium glutamicumScenario . Metabolic network model of C. glutamicum, results of the target validity calculation and statistical evaluation.Click here for fileAspergillus nigerScenario . Metabolic network model of A. niger, results of the target validity calculation and statistical evaluation.Click here for file


Human disease is heterogeneous, with similar disease phenotypes resulting from distinct combinations of genetic and environmental factors. Small-molecule profiling can address disease heterogeneity by evaluating the underlying biologic state of individuals through non-invasive interrogation of plasma metabolite levels. We analyzed metabolite profiles from an oral glucose tolerance test (OGTT) in 50 individuals, 25 with normal (NGT) and 25 with impaired glucose tolerance (IGT). Our focus was to elucidate underlying biologic processes. Although we initially found little overlap between changed metabolites and preconceived definitions of metabolic pathways, the use of unbiased network approaches identified significant concerted changes. Specifically, we derived a metabolic network with edges drawn between reactant and product nodes in individual reactions and between all substrates of individual enzymes and transporters. We searched for “active modules”—regions of the metabolic network enriched for changes in metabolite levels. Active modules identified relationships among changed metabolites and highlighted the importance of specific solute carriers in metabolite profiles. Furthermore, hierarchical clustering and principal component analysis demonstrated that changed metabolites in OGTT naturally grouped according to the activities of the System A and L amino acid transporters, the osmolyte carrier SLC6A12, and the mitochondrial aspartate-glutamate transporter SLC25A13. Comparison between NGT and IGT groups supported blunted glucose- and/or insulin-stimulated activities in the IGT group. Using unbiased pathway models, we offer evidence supporting the important role of solute carriers in the physiologic response to glucose challenge and conclude that carrier activities are reflected in individual metabolite profiles of perturbation experiments. Given the involvement of transporters in human disease, metabolite profiling may contribute to improved disease classification via the interrogation of specific transporter activities. Human disease is complex, arising from the interaction of many genetic and environmental factors. Efforts to personalize treatment have been thwarted by “phenotypic heterogeneity”, the apparent similarity of disease states with diverse underlying causes. One approach to resolve this heterogeneity is to redefine diseases on the basis of abnormal physiologic activities, which should allow grouping patients into categories with similar treatment response and prognosis. Physiologic activities can be identified and assessed through quantitative measurements of biomolecules—proteins, mRNAs, metabolites—in individual patient samples. The field of metabolomics involves the analysis of a broad array of metabolite levels from clinical fluid samples such as blood or urine and can be used to evaluate disease states. Because metabolic profiles are complex, we have taken an integrative network-based approach to understand them in terms of abnormal activities of enzymes and small molecule transporters. We have focused on the oral glucose tolerance test, used to diagnose diabetes, and have found that multiple transporters play an important role in the normal response to ingesting sugar. Many of these transporter activities are abnormal in individuals with impaired glucose tolerance and differing activities among them may reflect the diverse underlying causes and variable clinical courses of such patients. Disease heterogeneity has challenged the practice of medicine. Individuals with the same apparent disease at our current diagnostic resolution often show remarkable variation in prognosis and treatment responsiveness, presumably because a superficially similar disease state can arise from diverse combinations of genetic and environmental factors Using tumor biopsy samples, oncologists are now exploring the incorporation of genomewide expression profiling into therapy Metabolomics data analysis may be facilitated by techniques applied to other high-throughput ‘omic data types. For microarray data, the integration of network information from protein-protein interaction data or predefined biologic pathways has greatly assisted elucidation of underlying processes and led to the development of increasingly robust and accurate gene-based classifiers for disease We use data derived from oral glucose tolerance tests (OGTT) in 25 individuals with normal (NGT) and 25 with impaired (IGT) glucose tolerance We examined metabolite profiles from a previously descibed oral glucose tolerance experiment (OGTT) We were thus interested in further elucidating the underlying biologic processes leading to the observed pattern of changes. Analyzing the OGTT metabolite profiles of the 25 NGT and 25 IGT Framingham Heart Study participants see , we idenp-value <0.001.We evaluated NGT and IGT individually  and found enrichment solely in NGT for Bile Acid Biosynthesis at an adjusted The low yield of pathway enrichment could arise in part from the sparseness of our metabolome coverage or from the fact that most metabolites are implicated in multiple pathways. Furthermore, even if a pathway has uniformly increased flux, this will not generally lead to uniform increases in metabolite abundance. The relationship between enzymatic activity and metabolite concentration can be understood in terms of the relative contribution of “metabolic regulation” and “hierarchical regulation”. Metabolic regulation involves control of reaction flux through the interaction of enzymes with the rest of the metabolic network, such as changing substrate, product or modifier concentrations We based our analysis on the fact that metabolites are linked via chemical reactions. We hypothesized that OGTT is a physiologic stimulus that alters flux through specific metabolic reactions. Since products from one reaction may serve as reactants for and drive other reactions, we sought groups of metabolites that are connected through metabolic reactions and collectively show a high degree of change. Furthermore we hypothesized that a perturbation such as OGTT would increase the activity of enzymes and transporters, many of which have multiple substrates. Thus, we were also interested in groups of changed metabolites linked by virtue of being substrates of a common enzyme or transporter.We framed the search for functionally-linked, highly changed metabolites in OGTT in terms of the discovery of active modules (or subnetworks). Active module approaches have previously been applied in bioinformatics analysis to elucidate underlying biologic processes in gene expression data. In such analyses, the investigators typically overlaid gene scores based on differential expression in microarray experiments onto protein-protein interaction We first built a Metabolic Reaction Network (MRN) using the 3338 metabolic reactions in Recon 1. Although Recon 1 includes most known transport reactions, the specific transporters were not always explicitly mentioned. Thus we expanded this list with 737 additional reactions explicitly modeling transport processes for the metabolites measured in this experiment see , highligp-values) for metabolites to scores  indicating that the clustering of metabolite changes in the network is highly non-random.Distributions of active module scores were evaluated for statistical significance relative to those obtained from random networks, where metabolite scores were permuted randomly amongst measured nodes. At an FDR threshold of 0.01, all of the solutions were highly significant (padj = 0.029) for the NGT-EMRN AMG and more convincing enrichment for Glycine, Serine, and Threonine Metabolism (padj = 0.008) within the NGT-CMRN AMG. However, these enriched predefined pathways encompass very few of the AMG metabolites.We selected all metabolites that appeared with sufficient frequency see  across tpadj<0.05) for metabolites predicted to be active in kidney and/or liver, suggest that OGTT responses primarily involve metabolites produced in and/or consumed in these organs. Both tissues are established targets of insulin action, with liver demonstrating increased glycogen storage and fatty acid production and decreased gluconeogenesis We next sought to characterize whether the AMG metabolites are active in any particular human tissue. To do so, we exploited recent predictions of which metabolic reactions in the Recon 1 network were likely to be active in ten specific human tissues, using constraint-based flux modeling and altered amino acid transporter activity. In fact, it is known that certain amino acid transporter activities are activated in response to insulin (see below).An inspection of the AMGs for NGT samples  revealedAmino acid transport activities have historically been grouped into “Systems” that describe the chemical properties of the transported molecules  and the response to specific inhibitors de novo purine biosynthesis. Interestingly, many of the other peripheral changed metabolites linked to PRPP in the NGT-CMRN AMG are also involved in nucleotide biosynthesis. Ribose-5-phosphate, which is interconverted with ribose-1-phosphate , is phosphorylated to form PRPP. R1P combines with xanthine (and hypoxanthine) as part of the purine salvage pathway of nucleic acid biosynthesis, in a reaction catalyzed by purine nucleotide phosphorylase (PNP). PNP also catalyzes the reaction of nicotinamide with R1P to ultimately form nicotinamide adenine dinucleotide (NAD). Thus the peripheral metabolite cluster shown in NGT-CMRN captures the interrelationship of the various metabolites involved in insulin-stimulated purine nucleotide biosynthesis In addition to the core cluster of amino acids, the AMGs include additional changed metabolites on their periphery. These peripheral metabolites are connected to the amino acid core via unmeasured metabolites, which represent potential functional links. For example, in the NGT-CMRN AMG , glutamaThe other peripheral metabolite clusters in the NGT-CMRN and NGT-EMRN AMGs capture other insulin-stimulated activities including glycolysis, triglyceride biosynthesis, and an increase in bile salt plasma levels (by unknown mechanisms). Although these were commented upon previously For the NGT group, there is a significant drop in L-Proline and N,N-dimethylglycine levels and an increase in glycine betaine levels. All three amino acids appear in the NGT-EMRN and NGT-CMRN AMGs with the edges between them representing shared transport by the SLC6A12 carrier The IGT-EMRN and IGT-CMRN  consist Although the AMGs convincingly illustrate that changed metabolites are common substrates of small molecule transporters, they cannot establish coordinated activity of these cotransported substrates. To explore whether metabolite substrates of individual transporters are in fact coregulated, we performed hierarchical clustering across the 25 individuals from the NGT and IGT groups, looking to identify metabolites that show a similar absolute percentage of change across individuals.Heatmaps of the results of hierarchical clustering ,5 demonsCluster II in NGT includes all 3 of the measured SLC6A12 substrates , which demonstrate absolute pairwise correlation coefficients ranging from 0.23 (for dimethylglycine and glycine betaine) to 0.57 (for glycine betaine and proline). Proline and glycine betaine also are strongly correlated and co-cluster in IGT (Spearman correlation coefficient = 0.60). The proline-betaine correlation likely reflects the fact that these metabolites are cotransported by at least three carriers . By contrast, these two metabolites are not known to participate in any common metabolic pathways, supporting the hypothesis that coordination of measured plasma levels of proline and glycine betaine is via regulation of their common transporters.http://www.ncbi.nlm.nih.gov/pubmed/) to find a connection between these metabolites and, interestingly, found that deficiency in the hepatic splice variant of the SLC25A13 protein (citrin), a component of the mitochondrial aspartate-malate shuttle involved in liver NAD+/NADH shuttling, leads to a buildup of both hepatic bile salts and citrulline In Cluster I, bile salts are found with citrulline in both NGT and IGT. In IGT, malate also clusters closely with citrulline. We searched PubMed . This analytic technique attempts to find linear combination of metabolites that best explain the interindividual variation seen in metabolite profiles. PCA revealed that the top two eigenvectors for NGT coincided with SLC25A13 and amino acid transport activities, respectively, explaining a total of 39% of interindividual variance in metabolite changes see . The dishttp://www.ncbi.nlm.nih.gov/omim), and found 179 human disease phenotypes associated with transporter mutations. These include some of the transporters whose activity is reflected in OGTT metabolite profiles, such as SLC25A13, described above. In addition, the SLC6A14 amino acid/acyl-carnitine transporter, which primarily carries large hydrophobic and cationic amino acids, was both identified in our analysis as relevant to OGTT and previously been found to be associated with metabolic disease. Mutations in SLC6A14 have been shown to be associated with obesity in three independent populations p-value∼10−4–10−5) with waist circumference and weight in type 2 diabetes patients studied in the Diabetes Genomics Initiative genome-wide association study Given that metabolite profiling of perturbation experiments can interrogate specific underlying transporter activities, we investigated to what extent transporters are involved in human disease. We consulted the OMIM database of Mendelian diseases . A similar approach used gene expression profiles One motivation for this approach is to redefine human disease in terms of aberrant metabolic activities. Given groups of affected and control individuals, active module analysis can achieve this in a number of ways. Baseline differences in each metabolite's abundance can be scored between affected and unaffected individuals As the relationship of metabolite abundance with disease incidence and outcomes is better understood, we may ultimately be able to use integrated analyses of metabolic profiles to subclassify disease on the basis of distinct enzymatic/transporter activities, thus allowing a more individualized approach to clinical medicine.Metabolite abundance measurements from an oral glucose tolerance test have been described Metabolite peak intensities were determined as described previously p-values from the Wilcoxon test to scores. Although a variety of methods can be selected for this purpose, we used the beta-uniform distribution method p-values was modeled as a mixture of a beta distribution for the signal and a uniform distribution for the noise as shown below:x is a given p-value, f(x) is the probability density at x, π represents the mixture parameter, and a represents a shape parameter for the beta distribution. The values of π and a were fit using the optim function in R. The parameter τ was chosen to achieve an FDR of 0.01. p-values were converted into scores as described in FDR(x), is computed as follows:In order to identify significantly changed clusters of functionally connected metabolites, we converted experimental p-value>τ, will have a negative score.Using this statistic, nodes for which the A Metabolic Reaction Network (MRN) was constructed based on the 3338 metabolic reactions in Recon 1. We treated all 1500 reactant and product metabolites in Recon 1 as nodes. Cellular locations were assigned to each metabolite as specified in Recon 1, and metabolites were split into multiple nodes (each corresponding to a different location). This process resulted in 2779 total nodes.For each metabolic reaction, edges in the MRN were drawn between each reactant and product nodes and between all common metabolites substrates (reactants and products) of enzymes and transporters. Furthermore, since transporter annotation was not complete, for each of the measured metabolites we manually searched the literature to identify transporter-substrate relationships and identified all substrates for any transporter found. Finally, we expanded the list of reactions so that each transporter-reaction relationship was reflected in an independent entry. This approach resulted in an additional 737 reactions see .2, Na+, K+, SO4, NH4, CO2, Phosphate, O2, Pyrophosphate, H2O, H+, OH−, ATP, ADP, AMP, CTP, CDP, CMP, NAD, NADH, NADP, NADPH, H2O2, and HCO3−. As many of these metabolites serve as cofactors, their inclusion would contribute to non-specific bridges between metabolites in active modules and would thus be of limited use for biological processes. We did however include nodes and edges for reactions where nucleotides serve as primary reactants and products, such as those involved in nucleotide biosynthesis or catabolism; NAD metabolism; and Riboflavin metabolism.To improve the specificity of the active module discovery process, nodes and edges involving the following 28 ‘promiscuous’ and/or buffered metabolites were also eliminated: UMP, UDP, UTP, FAD, FADHBecause abundance measurements were only available for a small fraction of the metabolic network, we limited the MRN to the union of measured metabolites and all nodes found on paths (up to path-length three) between two measured nodes. This filtering, used to reduce computing cost, did not alter downstream results because to be included in an active module, a metabolite must either be measured or lie on a path between measured metabolites.Scores generated above were assigned to measured nodes in the MRN. We built both a Scored Extracellular MRN (EMRN) and a separate Scored Cytoplasmic MRN (CMRN). For the EMRN, if a metabolite had two cellular locations, the metabolite score was assigned to the extracellular metabolite, modeling extracellular levels; for the CMRN, scores, in such a situation, were assigned to the cytoplasmic metabolite. The EMRN and CMRN networks had 297 nodes and 5515 edges and 344 nodes and 6089 edges, respectively, after applying the above steps.p-values for unmeasured nodes were sampled randomly from the uniform distribution expected of unchanged metabolites, thus sampling the joint distribution of unmeasured metabolite abundance under the null hypothesis. Such an approach is common practice in Bayesian data analysis to estimate posterior probability distributions and accounts for our uncertainty about unmeasured metabolites more accurately than would asserting that we are sure that the metabolite has not changed. It also permits an unmeasured metabolite to be included in a cluster if there is sufficient support, in that the unmeasured metabolite bridges multiple high-scoring measured metabolites. Scores were then determined for the unmeasured nodes as described in π, a, and τ determined using the distribution of measured p-values. We repeated the random sampling 100 times for each scored network and identified one optimal solution per network. We explored searching also for suboptimal solutions but found that these primarily consisted of a subset of the metabolites of the optimal solution rather than other distinct areas of the graph.To identify active modules in our MRN, we used a recently developed method p-values for unmeasured nodes were sampled uniformly at random. The distribution of scores for the original and permuted data were compared by a one-sided Mann-Whitney-Wilcoxon test using the R package (www.Rproject.org).For the purpose of evaluating statistical significance of observed active modules, we generated random solutions by repeating the active module discovery process for 100 random scored MRNs. Although the topology was preserved, the scores for each random MRN were randomly permuted among measured nodes and p-values is generated by repeating the test for 1000 randomly generated query lists of the same size from the same universe of metabolites. For each random query list, the minimum p-value observed for any pathway is retained. The adjusted p-value (padj) is then the fraction of random query lists that yield a minimum p-value equal to or lower than the minimum p-value observed.For each scored MRN, the frequency of appearance in the corresponding 100 solutions was measured for all nodes. Nodes with ≥0.20 relative frequency were grouped together to form an Active Module Group (AMG), which was examined for significant overlap with metabolite sets corresponding to predefined pathways. To identify predefined pathway enrichment in the AMGs, we used a modified version of the FuncAssociate program http://llama.med.harvard.edu/cgi/func1/ funcassociate_advanced), which tests a ranked list of metabolites and finds the rank cutoff that optimizes significance (using resampling to adjust for multiple testing). For this analysis, the metabolite list was ranked in both increasing and decreasing order of magnitude of change.We generated pathway and enzyme/transporter-to-metabolite mappings for Recon 1, limiting our analysis to pathways, enzymes or transporters that included at least three metabolites in the metabolite universe. We were interested in enrichment for analysis for both AMGs and for our ranked list of changed metabolite. To look for pathway enrichment in the ranked list of changed metabolites, we used the “ordered” setting in FuncAssociate . L-Alanine and L-Cysteine, which were the only measured standard amino acids that failed to change significantly in either group, were also included. Input data corresponded to fractional change in metabolite abundance across individuals. For clustering, the absolute value of the Spearman coefficient was used to compute the dissimilarity matrix. Heatmaps and principal component analyses were performed using the heatmap and prcomp functions, respectively, in the R package.Hierarchical clustering and PCA was performed on the subset of metabolites that changed significantly in either NGT or IGT at an FDR<0.05, determined using the In our analysis, three parameters determined the balance between measured and unmeasured metabolites in our active module solutions: 1) the false-discovery rate (FDR) threshold used in the determination of scores for measured metabolites; 2) the path-length threshold between measured metabolites used in filtering unmeasured metabolites from the MRN; and 3) the frequency threshold used in selecting active module solution metabolites for inclusion into active module groups. Although unmeasured metabolites are useful for hypothesis generation in terms of identifying potentially novel markers of insulin function, such hypotheses should not be so numerous as to dominate the analysis. At the stringent FDR of 0.01 selected, the active module discovery process was relatively insensitive to changes in the other two parameters, with little difference in the observed results at higher or lower path lengths. In fact, all unmeasured metabolites in active module groups were directly connected to one or more measured metabolites. For the frequency cutoff for active module group metabolites, our primary conclusions were robust to all thresholds from 0.10 to 0.50.Table S1p-value) of change in abundance after glucose challenge. Metabolite abundances pre- and post- glucose challenge are also provided in separate tabs for the NGT and IGT groups.Metabolite profiles for NGT and IGT groups. Recon 1 symbols and names for measured metabolites are listed alongside magnitude (fraction of baseline) and significance ((0.13 MB XLS)Click here for additional data file.Table S2Manually curated transport reactions used in addition to Recon 1 reactions to derive Metabolite Reaction networks. The first tab corresponds to 190 reactions already described in Recon 1 - a column was added that makes explicit the mapping to a single solute transporter. The second tab corresponds to 737 additional transport reactions identified by manual literature curation - these have also each been mapped to a single transporter.(0.22 MB XLS)Click here for additional data file.Table S3Relative frequencies for metabolites in Active Module Solutions. A frequency threshold of 0.20 for appearance of metabolites was used for subsequent analysis and figure construction.(0.07 MB XLS)Click here for additional data file.Table S4Metabolic reactions and/or enzymes/transporters corresponding to edges in AMGs. Edges correspond to reactant-product pairs in individual reactions and/or substrates of common enzymes/transporters.(0.12 MB XLS)Click here for additional data file.Figure S1Active Module Group from a) IGT-EMRN and b) IGT-CMRN. For details see legend for (0.46 MB EPS)Click here for additional data file.